= self.image.shape[0]) and (self.i + self.step >= self.image.shape[1]):\n self.no_more_crops = True\n elif self.i + self.step >= self.image.shape[1]:\n self.i = 0\n self.j += self.step\n else:\n self.i += self.step","def update(self, dt):","def update(self, dt):","def set_up_orbit_correctors(ps_beg, delay, id_slice1, ds_slice, zplot, id_slices, U_core, lambdaref):\n SXSS = Chicane(3.2716, 0.362, 0.830399, delay[0])\n HXSS = Chicane(3.2, 0.3636, 0.5828, delay[1])\n\n OC2 = [CORR08, D1_SXSS, SXSS, D2_SXSS, QUAD09, CORR09]\n OC3 = [CORR15, D1_HXSS, HXSS, D2_HXSS, QUAD16, CORR16]\n\n ps_end1 = beam_transportation(ps_beg, U_core[0])\n\n # ps_end1 is a 4-by-N array. N is the number of macro-particles. It is the full\n # 4D phase space distribution at the end of the first undulator section.\n\n # The id of the slice on the axis in the second undulator section\n on_axis_id_U2 = int(id_slice1+delay[0]/ds_slice+ (8*110)*lambdaref/ds_slice) # The last part is slippage\n\n print(on_axis_id_U2)\n\n ps_end_slice1 = beam_property_along_s(ps_end1, id_slices)[0:4, :]\n ps_on_axis_2 = np.ravel(ps_end_slice1[:, on_axis_id_U2])\n\n # print(ps_on_axis_2)\n\n OC2_optimized = analyze_orbit_corrector(OC2[0], OC2[-1], OC2[1:-1], ps_on_axis_2)\n print(OC2_optimized)\n CORR08_new = Orbit_Corrector(OC2[0].length, OC2_optimized[0], OC2_optimized[2])\n CORR09_new = Orbit_Corrector(OC2[-1].length, OC2_optimized[1], OC2_optimized[3])\n\n # The whole U2 with optimized orbit correctors\n U2_new = [CORR08_new] + OC2[1:-1] + [CORR09_new] + U_core[1]\n ps_end2 = beam_transportation(ps_end1, U2_new)\n\n # ps_end2 is a 4-by-N array. N is the number of macro-particles. It is the full\n # 4D phase space distribution at the end of the second undulator section.\n\n # The id of the slice on the axis in the third undulator section\n on_axis_id_U3 = int(id_slice1+(delay[0]+delay[1])/ds_slice +(14*110*lambdaref)/ds_slice) # The last term is the slipage\n\n print(on_axis_id_U3)\n\n ps_end_slice2 = beam_property_along_s(ps_end2, id_slices)[0:4, :]\n ps_on_axis_3 = np.ravel(ps_end_slice2[ :, on_axis_id_U3])\n\n # print(ps_on_axis_3)\n\n OC3_optimized = analyze_orbit_corrector(OC3[0], OC3[-1], OC3[1:-1], ps_on_axis_3)\n print(OC3_optimized)\n CORR15_new = Orbit_Corrector(OC3[0].length, OC3_optimized[0], OC3_optimized[2])\n CORR16_new = Orbit_Corrector(OC3[-1].length, OC3_optimized[1], OC3_optimized[3])\n\n U3_new = [CORR15_new] + OC3[1:-1] + [CORR16_new] + U_core[2]\n\n Undulator_Beamline = U_core[0]+U2_new+U3_new\n\n return Undulator_Beamline","def update_apc22(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n if len(self._old) >= 1:\n\n\n try:\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\n\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n pos = self.pos+delta_t/2.0*(self.vel+vel)\n vel = self.vel+delta_t/2.0*(self.force(pos, vel, self.time+delta_t,\n drag=False)+kap[1])\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n\n self.time += delta_t\n\n else:\n ## reduced to using the Adams first order method for the first timestep:\n\n kap = update_apc1(self)\n\n self.set_old(kap, 1)\n\n return kap","def _update_active_rides_fast(self, time: datetime) -> None:\n pass","def adjust_image_data(self):\r\n\r\n print('Adjusting image data: ')\r\n\r\n if self.removeFirstSequence: # used to remove the first trial from the sequence\r\n\r\n frames_per_rep = self.nFrames/self.nrepetitions\r\n\r\n self.imageData = self.imageData[frames_per_rep:, :, :]\r\n\r\n self.nFrames = self.imageData.shape[0]\r\n\r\n self.nrepetitions = int(self.nFrames/(self.period * self.framerate))\r\n\r\n self.times = np.arange(0, self.nFrames/self.framerate, 1.0/self.framerate)\r\n\r\n \r\n\r\n # first squeeze the image to 3d if it is 4d\r\n\r\n maxt = np.max(self.times) # find last image time\r\n\r\n sh = self.imageData.shape\r\n\r\n if len(sh) == 4:\r\n\r\n self.imageData = self.imageData.squeeze()\r\n\r\n sh = self.imageData.shape\r\n\r\n dt = np.mean(np.diff(self.times)) # get the mean dt\r\n\r\n n_Periods = int((maxt+dt)/self.period) # how many full periods in the image set - include the first?\r\n\r\n if self.nrepetitions > 0 and self.nrepetitions < n_Periods:\r\n\r\n n_Periods = self.nrepetitions\r\n\r\n n_PtsPerCycle = int(np.floor(self.period/dt)); # estimate image points in a stimulus cycle\r\n\r\n ndt = self.period/n_PtsPerCycle\r\n\r\n self.imageData = self.imageData[range(0, n_Periods*n_PtsPerCycle),:,:] # reduce to only what we need\r\n\r\n print (' Adjusted image info')\r\n\r\n print (\" # Periods: %d Pts/cycle: %d Cycle dt %8.4fs (%8.3fHz) Cycle: %7.4fs\" %(n_Periods, n_PtsPerCycle, ndt, 1.0/ndt, self.period))\r\n\r\n self.print_image_info()","def on_correct_utterance(self, event):\n if not self.quitting:\n number = event.utterance_number\n instance = event.instance_name\n self.the_mediator.correct_utterance(instance, number)","def update(self):\n super().update()\n self.decelerate()\n #check for a collisison with all rock types\n self.checkForRockCollisions()\n #when the ship gets hit by a rock, it enters a period of invulnerability. we need to make sure that period ends at the proper time\n self.checkGracePeriodDuration()\n #movement stuff\n if (self.isAcceleratingForward):\n self.accelerateForwards()\n if (self.isRotatingLeft):\n self.rotateLeft()\n if (self.isRotatingRight):\n self.rotateRight()","def motion_correct(mov, max_iters=5, shift_threshold=1., reslice=slice(None,None), in_place=True, verbose=True, compute_kwargs={}, apply_kwargs={}):\n if not in_place:\n mov = mov.copy()\n mov = mov[reslice]\n \n all_vals = []\n for it in range(max_iters):\n if verbose:\n print('Iteration {}'.format(it)); sys.stdout.flush()\n template,vals = compute_motion(mov, **compute_kwargs)\n mov = apply_motion_correction(mov, vals, **apply_kwargs)\n maxshifts = np.abs(vals[:,[0,1]]).max(axis=0)\n all_vals.append(vals)\n if verbose:\n print('Shifts: {}'.format(str(maxshifts))); sys.stdout.flush()\n if np.all(maxshifts < shift_threshold):\n break\n\n # combine values from iterations\n all_vals = np.array(all_vals)\n return_vals = np.empty([all_vals.shape[1],all_vals.shape[2]])\n return_vals[:,[0,1]] = all_vals[:,:,[0,1]].sum(axis=0)\n return_vals[:,2] = all_vals[-1,:,2]\n\n return mov,template,return_vals","def update(self):\n self.syncSpriteCoordinates()\n self.moveBasedOnCurrentMomentum()\n #self.decelerate()\n self.checkCanvasBoundsAndWrap()","def routine(self):\n # Read in time table data\n\n _, t1, t2, t3, t4, _, _ = self.times\n _, p1, p2, p3, p4, _, _ = self.positions\n \n # Carry out routine\n self.throttle_valve_set(p1)\n self.valve_pulse(1, t1)\n if self.interrupt_measurement_called:\n self.shutoff()\n return\n \n self.throttle_valve_set(p2) \n self.purge(t2)\n if self.interrupt_measurement_called:\n self.shutoff()\n return\n \n \n ## Check selected method for t3 in main recipe table,\n ## Carry out an operation based on the selection\n mode = self.settings['t3_method']\n self.throttle_valve_set(p3)\n \n if mode == 'Shutter':\n self.shutter_pulse(t3)\n if self.interrupt_measurement_called:\n self.shutoff()\n return\n elif mode == 'PV':\n self.valve_pulse(2, t3)\n if self.interrupt_measurement_called:\n self.shutoff()\n return \n elif mode == 'RF':\n power = self.seren.settings['recipe_power']\n self.plasma_dose(t3, power)\n if self.interrupt_measurement_called:\n self.shutoff()\n return\n# elif mode == 'PV/Purge':\n# '''Run sub_cyc number of subroutine cycles.\n# Subroutine consists of a valve pulse and a purge period.'''\n# for _ in range(sub_cyc):\n# self.valve_pulse(2, sub_t0)\n# self.purge(sub_t1)\n \n \n self.throttle_valve_set(p4)\n self.purge(t4)\n if self.interrupt_measurement_called:\n self.shutoff()\n return","def target_velocity(self, time):\n pass","def target_velocity(self, time):\n pass","def on_mouse_move(self, event):\n\n # self.view = 1 * np.eye(4, dtype=np.float32)\n # self.model = 1 * np.eye(4, dtype=np.float32)\n\n # self.translate -= event.delta[1]\n # self.translate = max(-1, self.translate)\n # print(event.delta[1])\n # print(self.translate)\n # self.view = translate((0, 0, -self.translate))\n # self.game_program['u_view'] = self.view\n # self.game_program['u_size'] = 5 / self.translate\n # self.view = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.view\n # self.model = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.model\n # print(self.view)\n\n # self.game_program['u_model'] = self.model\n # self.game_program['u_view'] = self.view\n\n x, y = event.pos\n #print(x, y)\n self.x_offset, self.y_offset = x - self.last_x, - (y - self.last_y)\n self.last_x, self.last_y = x, y\n self.x_offset *= self.sensitivity\n self.y_offset *= self.sensitivity\n\n self.yaw, self.pitch = self.yaw - self.x_offset, self.pitch + self.y_offset\n self.rot_y(self.yaw * np.pi / 180)\n self.rot_x(self.pitch * np.pi / 180)\n\n self.view = np.dot(self.rot_mat_y, self.rot_mat_x)\n self.game_program['u_view'] = self.view\n\n self.update()","def _correct_overflow(timestamps, detectors, overflow_ch, overflow):\n index_overflows = np.where((detectors == overflow_ch))[0]\n for n, (idx1, idx2) in enumerate(zip(index_overflows[:-1],\n index_overflows[1:])):\n timestamps[idx1:idx2] += (n + 1)*overflow\n timestamps[idx2:] += (n + 2)*overflow","def on_timer(self, event):\n \n o = Unicorn()\n data = o.get_data(rt)\n k = len(data[0])\n y[:, :-k] = y[:, k:]\n y[:, -k:] = remap((data), -40, 40, -1, 1 ) \n t2 = _thread.start_new_thread(printT, ())\n #y2 = np.array([lfilter(b, a, y[i]) for i in range(17)])\n self.program['a_position'].set_data(y.ravel().astype(np.float32))\n self.update()","def stabilize(td):\n\n def correct_saccade1(data):\n data.x -= np.rint(3.5 * data.ts / 105e3).astype(np.uint16)\n data.y -= np.rint(7 * data.ts / 105e3).astype(np.uint16)\n return data\n\n def correct_saccade2(data):\n data.x -= np.rint(3.5 + 3.5 * (data.ts - 105e3) / 105e3).astype(\n np.uint16)\n data.y -= np.rint(7 - 7 * (data.ts - 105e3) / 105e3).astype(np.uint16)\n return data\n\n def correct_saccade3(data):\n data.x -= np.rint(7 - 7 * (data.ts - 210e3) / 105e3).astype(np.uint16)\n return data\n\n copy = np.piecewise(td.data,\n [td.data.ts <= 105e3,\n (td.data.ts > 105e3) & (td.data.ts <= 210e3),\n (td.data.ts > 210e3)],\n [correct_saccade1, correct_saccade2,\n correct_saccade3]).view(np.recarray)\n\n # after saccades, we might end up with invalid x and y values, have to\n # correct these\n x_vals = copy.x\n y_vals = copy.y\n copy.x = np.piecewise(x_vals,\n [x_vals >= 65000,\n (x_vals < 65000) & (x_vals >= td.width),\n x_vals < td.width],\n [0, td.width - 1, lambda x: x])\n copy.y = np.piecewise(y_vals,\n [y_vals >= 65000,\n (y_vals < 65000) & (y_vals >= td.height),\n y_vals < td.height],\n [0, td.height - 1, lambda y: y])\n\n return copy","def arm_calibration(self):\n self.arm_motor.run_forever(speed_sp=900)\n while not self.touch_sensor.is_pressed:\n time.sleep(0.01)\n self.arm_motor.stop(stop_action=\"brake\")\n ev3.Sound.beep()\n self.arm_motor.run_to_rel_pos(\n speed_sp=900, position_sp=-5100)\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\n print('motor is no longer running')\n ev3.Sound.beep()\n self.arm_motor.position = 0","def cool(self):\n self.t = self.t - 1","def timerange_change():\n global transformer_top\n assert transformer_top is not None\n global transformer_bottom\n assert transformer_bottom is not None\n global label_encoders_per_modality\n assert label_encoders_per_modality is not None\n global DEVICE\n assert DEVICE is not None\n global USE_LOCAL_CONDITIONING\n assert USE_LOCAL_CONDITIONING is not None\n global partial_sample_model\n assert partial_sample_model is not None\n\n layer = str(request.args.get('layer'))\n temperature = request.args.get('temperature', type=float)\n start_index_top = request.args.get('start_index_top', type=int)\n uniform_sampling = bool(strtobool(\n request.args.get('uniform_sampling', type=str,\n default=\"False\")))\n\n # try to retrieve local conditioning map in the request's JSON payload\n (class_conditioning_top_map, class_conditioning_bottom_map,\n input_conditioning_top, input_conditioning_bottom) = (\n parse_conditioning(request)\n )\n global_instrument_family_str = str(\n request.args.get('instrument_family_str'))\n global_pitch = request.args.get('pitch', type=int)\n global_class_conditioning = {\n 'pitch': global_pitch,\n 'instrument_family_str': global_instrument_family_str\n }\n if (not USE_LOCAL_CONDITIONING\n or not transformer_bottom.local_class_conditioning):\n class_conditioning_bottom = global_class_conditioning.copy()\n class_conditioning_tensors_bottom = make_conditioning_tensors(\n class_conditioning_bottom,\n label_encoders_per_modality)\n class_conditioning_bottom_map = None\n else:\n class_conditioning_bottom = class_conditioning_tensors_bottom = None\n\n top_code, bottom_code = parse_codes(request)\n\n # extract frame to operate on\n end_index_top = start_index_top + transformer_top.shape[1]\n top_code_frame = top_code[..., start_index_top:end_index_top]\n\n upsampling_ratio_time = (transformer_bottom.shape[1]\n // transformer_top.shape[1])\n start_index_bottom = upsampling_ratio_time * start_index_top\n end_index_bottom = start_index_bottom + transformer_bottom.shape[1]\n bottom_code_frame = bottom_code[..., start_index_bottom:end_index_bottom]\n generation_mask_batched = parse_mask(request).to(DEVICE)\n\n time_indexes_top = make_time_indexes(start_index_top,\n top_code.shape[-1],\n transformer_top.shape[-1])\n time_indexes_bottom = make_time_indexes(start_index_bottom,\n bottom_code.shape[-1],\n transformer_bottom.shape[-1])\n\n if layer == 'bottom':\n if not uniform_sampling:\n bottom_code_resampled_frame = partial_sample_model(\n model=transformer_bottom,\n condition=top_code_frame,\n batch_size=1,\n codemap_size=transformer_bottom.shape,\n temperature=temperature,\n class_conditioning=class_conditioning_tensors_bottom,\n local_class_conditioning_map=class_conditioning_bottom_map,\n initial_code=bottom_code_frame,\n mask=generation_mask_batched,\n time_indexes_source=time_indexes_top,\n time_indexes_target=time_indexes_bottom,\n )\n else:\n bottom_code_resampled_frame = bottom_code_frame.masked_scatter(\n generation_mask_batched,\n torch.randint_like(bottom_code_frame,\n high=transformer_bottom.n_class_target)\n )\n\n bottom_code_resampled = bottom_code\n bottom_code_resampled[..., start_index_bottom:end_index_bottom] = (\n bottom_code_resampled_frame)\n\n # create JSON response\n response = make_response(top_code, bottom_code_resampled,\n input_conditioning_top,\n input_conditioning_bottom)\n elif layer == 'top':\n if (not USE_LOCAL_CONDITIONING\n or not transformer_top.local_class_conditioning):\n # try to retrieve conditioning from http arguments\n class_conditioning_top = global_class_conditioning.copy()\n class_conditioning_tensors_top = make_conditioning_tensors(\n class_conditioning_top,\n label_encoders_per_modality)\n class_conditioning_top_map = None\n else:\n class_conditioning_top = class_conditioning_tensors_top = None\n\n if not uniform_sampling:\n if transformer_top.self_conditional_model:\n condition = top_code_frame\n else:\n condition = None\n top_code_resampled_frame = partial_sample_model(\n model=transformer_top,\n condition=condition,\n device=DEVICE,\n batch_size=1,\n codemap_size=transformer_top.shape,\n temperature=temperature,\n class_conditioning=class_conditioning_tensors_top,\n local_class_conditioning_map=class_conditioning_top_map,\n initial_code=top_code_frame,\n mask=generation_mask_batched,\n time_indexes_source=time_indexes_top,\n time_indexes_target=time_indexes_top,\n )\n else:\n top_code_resampled_frame = top_code_frame.masked_scatter(\n generation_mask_batched,\n torch.randint_like(top_code_frame,\n high=transformer_top.n_class_target)\n )\n\n top_code_resampled = top_code\n top_code_resampled[..., start_index_top:end_index_top] = (\n top_code_resampled_frame)\n\n upsampling_ratio_frequency = (transformer_bottom.shape[0]\n // transformer_top.shape[0])\n generation_mask_bottom_batched = (\n generation_mask_batched\n .repeat_interleave(upsampling_ratio_frequency, -2)\n .repeat_interleave(upsampling_ratio_time, -1)\n )\n bottom_code_resampled_frame = partial_sample_model(\n model=transformer_bottom,\n condition=top_code_resampled_frame,\n device=DEVICE,\n batch_size=1,\n codemap_size=transformer_bottom.shape,\n temperature=temperature,\n class_conditioning=class_conditioning_tensors_bottom,\n local_class_conditioning_map=class_conditioning_bottom_map,\n initial_code=bottom_code_frame,\n mask=generation_mask_bottom_batched,\n time_indexes_source=time_indexes_top,\n time_indexes_target=time_indexes_bottom,\n )\n\n # update conditioning map\n bottom_mask = generation_mask_bottom_batched[0]\n new_conditioning_map_bottom = {\n modality: masked_fill(modality_conditioning,\n bottom_mask,\n class_conditioning_bottom[modality])\n for modality, modality_conditioning\n in input_conditioning_bottom.items()\n }\n\n bottom_code_resampled = bottom_code\n bottom_code_resampled[..., start_index_bottom:end_index_bottom] = (\n bottom_code_resampled_frame)\n\n # create JSON response\n response = make_response(top_code_resampled, bottom_code_resampled,\n input_conditioning_top,\n new_conditioning_map_bottom)\n\n return response","def test_fix_metadata_correct_time(self):\n fixed_cube = self.fix.fix_metadata([self.cube])[0]\n time_coord = fixed_cube.coord('time')\n np.testing.assert_allclose(time_coord.points, [0, 1])\n assert time_coord.bounds is None","def photometric_calibration():\n pass","def arm_calibration(self):\n # DONE: 3. Implement the arm calibration movement by fixing the code below (it has many bugs). It should to this:\n # Command the arm_motor to run forever in the positive direction at max speed.\n # Create an infinite while loop that will block code execution until the touch sensor's is_pressed value is True.\n # Within that loop sleep for 0.01 to avoid running code too fast.\n # Once past the loop the touch sensor must be pressed. So stop the arm motor quickly using the brake stop action.\n # Make a beep sound\n # Now move the arm_motor 14.2 revolutions in the negative direction relative to the current location\n # Note the stop action and speed are already set correctly so we don't need to specify them again\n # Block code execution by waiting for the arm to finish running\n # Make a beep sound\n # Set the arm encoder position to 0 (the last line below is correct to do that, it's new so no bug there)\n\n # Code that attempts to do this task but has MANY bugs (nearly 1 on every line). Fix them!\n self.arm_motor.run_forever(speed_sp=900)\n while not self.touch_sensor.is_pressed:\n time.sleep(0.01)\n self.arm_motor.stop(stop_action='brake')\n ev3.Sound.beep().wait()\n # time.sleep(2)\n # arm_motor.stop(stop_action='brake')\n\n arm_revolutions_for_full_range = 14.2 * 360\n self.arm_motor.run_to_rel_pos(position_sp=-arm_revolutions_for_full_range, speed_sp=900)\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\n ev3.Sound.beep()\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this line is correct as is).","def test_rolling_before_analysis(self):\n cheese = TomoCheese.from_demo_images()\n cheese.analyze()\n original_roi_1 = copy.copy(cheese.module.rois[\"1\"].pixel_value)\n for img in cheese.dicom_stack:\n img.roll(direction=\"x\", amount=20)\n cheese.analyze()\n new_roi_1 = cheese.module.rois[\"1\"].pixel_value\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)","def post_process(self):\n\t\ti_s = 0\n\t\ti_e = 0\n\t\tif self.trans_t_dict[0][1] == 0:\n\t\t\tif len(self.noise_itv) == 0:\n\t\t\t\tself.trans_t_dict[0][1] = self.fake_start_offset\n\t\t\telse:\n\t\t\t\tself.trans_t_dict[0][1] = self.noise_itv[0][1] # start_offset\n\t\t\tself.trans_t_dict[0][2] = 0.1\n\t\tif self.trans_t_dict[len(self.trans_t_dict)-1][1] == 0:\n\t\t\tif len(self.noise_itv) == 0:\n\t\t\t\tself.trans_t_dict[len(self.trans_t_dict)-1][1] = self.fake_end_offset\n\t\t\telse:\n\t\t\t\tself.trans_t_dict[len(self.trans_t_dict)-1][1] = self.noise_itv[-1][0] # end_offset\n\t\t\tself.trans_t_dict[len(self.trans_t_dict)-1][2] = 0.1\n\n\t\twhile i_s < len(self.trans_t_dict):\n\t\t\twhile i_s < len(self.trans_t_dict) and self.trans_t_dict[i_s][1] != 0:\n\t\t\t\ti_s += 1\n\t\t\tif i_s == len(self.trans_t_dict):\n\t\t\t\ti_e = len(self.trans_t_dict)\n\t\t\tif i_s < len(self.trans_t_dict):\n\t\t\t\ti_s -= 1\n\t\t\t\ti_e = i_s + 1\n\t\t\t\twhile i_e < len(self.trans_t_dict) and self.trans_t_dict[i_e][1] == 0:\n\t\t\t\t\ti_e += 1\n\t\t\t\tif i_e == len(self.trans_t_dict):\n\t\t\t\t\tbreak\n\n\t\t\t\t# incorperate the noise inverval\n\t\t\t\ts_time = self.trans_t_dict[i_s][1]\n\t\t\t\te_time = self.trans_t_dict[i_e][1]\n\t\t\t\t\"\"\"\n\t\t\t\tfor ts in self.noise_itv:\n\t\t\t\t\tif len(ts) == 2:\t\t\t\t\t\t\n\t\t\t\t\t\ttime1 = ts[0]\n\t\t\t\t\t\ttime2 = ts[1]\n\t\t\t\t\t\tif s_time < time1 and time2 < e_time:\n\t\t\t\t\t\t\te_time = min(e_time, time1)\n\t\t\t\t\telse:\n\t\t\t\t\t\ttime0 = ts[0]\n\t\t\t\t\t\tif s_time < time0 and time0 < e_time:\n\t\t\t\t\t\t\te_time = min(e_time, time0)\n\t\t\t\t\"\"\"\n\t\t\t\tchar_len = 0\n\t\t\t\tfor i in range(i_s, i_e):\n\t\t\t\t\tchar_len += len(self.trans_t_dict[i][0])\n\t\t\t\t# ratio = float(self.trans_t_dict[i_e][1]-self.trans_t_dict[i_s][1]) / float(char_len)\n\t\t\t\tratio = float(e_time - s_time) / float(char_len)\n\t\t\t\tchar_len = 0\n\t\t\t\t# s_time = self.trans_t_dict[i_s][1]\n\t\t\t\tfor i in range(i_s+1, i_e):\n\t\t\t\t\tchar_len += len(self.trans_t_dict[i-1][0])\n\t\t\t\t\tself.trans_t_dict[i][1] = s_time + char_len * ratio\n\t\t\t\t\tself.trans_t_dict[i][2] = len(self.trans_t_dict[i][0]) * ratio\n\t\t\ti_s = i_e","def auto_correction(self):\n\n if self.mi_standard_fb is not None:\n try: \n is_st_fb_running = self.mi_standard_fb.is_running()\n except Exception as e:\n logger.warning(\"error during status of st. FB reading: \" + str(e))\n is_st_fb_running = False\n \n if self.mi_standard_fb is not None and is_st_fb_running:\n logger.info(\"auto_correction: St.FB is running. Stop Ad. FB\")\n self.stop_feedback()\n return 0\n \n is_nan = self.ref_orbit_calc()\n if is_nan:\n logger.warning(\"auto_correction: nan in the ref orbit. Pause 1 sec\")\n time.sleep(1)\n return\n\n start = time.time()\n stop_flag = self.correct()\n print(\"correct: \", time.time() - start)\n time.sleep(0.01)\n if not stop_flag:\n start = time.time()\n self.apply_kicks()\n print(\"apply_kicks: \", time.time() - start)\n self.sase_hist.append(self.target_filtered[-1])\n self.le_warn.clear()\n else:\n logger.warning(\"auto_correction: stop_flag = True. Pause 1 sec\")\n self.le_warn.clear()\n self.le_warn.setText(\"Stop flag. Kicks are not applied\")\n self.le_warn.setStyleSheet(\"color: red\")\n\n time.sleep(1)","def shifter(self):\n #self.BA_shift = self.timeshift_latitude(self.latB, self.latA)\n #self.BC_shift = self.timeshift_latitude(self.latB, self.latC)\n\n\n self.shifted = True #changing boolean to True when function is called.\n\n secondsA = self.secondsA\n secondsB = self.secondsB\n secondsC = self.secondsC\n\n NeA = self.holefill(self.NeA, secondsA)\n NeB = self.holefill(self.NeB, secondsB)\n NeC = self.holefill(self.NeC, secondsC)\n\n start = 0\n stop = len(NeA) - np.max(np.array([self.BA_shift, self.BC_shift]))\n\n startA = start + self.BA_shift\n stopA = stop + self.BA_shift\n\n startC = start + self.BC_shift\n stopC = stop + self.BC_shift\n\n NeA = NeA[startA:stopA]\n NeB = NeB[start:stop]\n NeC = NeC[startC:stopC]\n\n longA = self.holefill(self.longA, secondsA)\n longB = self.holefill(self.longB, secondsB)\n longC = self.holefill(self.longC, secondsC)\n longA = longA[startA:stopA]\n longB = longB[start:stop]\n longC = longC[startC:stopC]\n\n latA = self.holefill(self.latA, secondsA)\n latB = self.holefill(self.latB, secondsB)\n latC = self.holefill(self.latC, secondsC)\n latA = latA[startA:stopA]\n latB = latB[start:stop]\n latC = latC[startC:stopC]\n\n radA = self.holefill(self.radA, secondsA)\n radB = self.holefill(self.radB, secondsB)\n radC = self.holefill(self.radC, secondsC)\n radA = radA[startA:stopA]\n radB = radB[start:stop]\n radC = radC[startC:stopC]\n\n velA = self.holefill(self.velA, secondsA)\n velB = self.holefill(self.velB, secondsB)\n velC = self.holefill(self.velC, secondsC)\n velA = velA[startA:stopA]\n velB = velB[start:stop]\n velC = velC[start:stop]\n\n altA = self.holefill(self.altA, secondsA)\n altB = self.holefill(self.altB, secondsB)\n altC = self.holefill(self.altC, secondsC)\n altA = altA[startA:stopA]\n altB = altB[start:stop]\n altC = altC[startC:stopC]\n\n\n mlatA = self.holefill(self.mlatA, secondsA)\n mlatB = self.holefill(self.mlatB, secondsB)\n mlatC = self.holefill(self.mlatC, secondsC)\n mlatA = mlatA[startA:stopA]\n mlatB = mlatB[start:stop]\n mlatC = mlatC[startC:stopC]\n\n mlongA = self.holefill(self.mlongA, secondsA)\n mlongB = self.holefill(self.mlongB, secondsB)\n mlongC = self.holefill(self.mlongC, secondsC)\n mlongA = mlongA[startA:stopA]\n mlongB = mlongB[start:stop]\n mlongC = mlongC[startC:stopC]\n\n mltA = self.holefill(self.mltA, secondsA)\n mltB = self.holefill(self.mltB, secondsB)\n mltC = self.holefill(self.mltC, secondsC)\n mltA = mltA[startA:stopA]\n mltB = mltB[start:stop]\n mltC = mltC[startC:stopC]\n\n secondsA = self.holefill(secondsA, secondsA)\n secondsB = self.holefill(secondsB, secondsB)\n secondsC = self.holefill(secondsC, secondsC)\n secondsA = secondsA[startA:stopA]\n secondsB = secondsB[start:stop]\n secondsC = secondsC[startC:stopC]\n\n indsA = np.nonzero(secondsA)[0]\n indsB = np.nonzero(secondsB)[0]\n indsC = np.nonzero(secondsC)[0]\n\n inds = np.intersect1d(indsA, indsB)\n inds = np.intersect1d(inds, indsC)\n\n self.NeA = NeA[inds]\n self.NeB = NeB[inds]\n self.NeC = NeC[inds]\n\n self.longA = longA[inds]\n self.longB = longB[inds]\n self.longC = longC[inds]\n\n self.latA = latA[inds]\n self.latB = latB[inds]\n self.latC = latC[inds]\n\n self.radA = radA[inds]\n self.radB = radB[inds]\n self.radC = radC[inds]\n\n self.velA = velA[inds]\n self.velB = velB[inds]\n self.velC = velC[inds]\n\n self.altA = altA[inds]\n self.altB = altB[inds]\n self.altC = altC[inds]\n\n self.mlatA = mlatA[inds]\n self.mlatB = mlatB[inds]\n self.mlatC = mlatC[inds]\n\n self.mlongA = mlongA[inds]\n self.mlongB = mlongB[inds]\n self.mlongC = mlongC[inds]\n\n self.mltA = mltA[inds]\n self.mltB = mltB[inds]\n self.mltC = mltC[inds]\n\n self.secondsA = secondsA[inds]\n self.secondsB = secondsB[inds]\n self.secondsC = secondsC[inds]","def _step(self, board, elapsedTime):\n\t\tpass","def auto_resting(self):\n self.image_list = self.animation_dict[self.direction]\n self.image = self.image_list[self.index]\n\n if self.rect.y % 32 != 0:\n self.correct_position(self.rect.y)\n if self.rect.x % 32 != 0:\n self.correct_position(self.rect.x)\n\n if (self.current_time - self.move_timer) > 2000:\n direction_list = ['up', 'down', 'left', 'right']\n random.shuffle(direction_list)\n direction = direction_list[0]\n self.begin_auto_moving(direction)\n self.move_timer = self.current_time","def step(self, time_delta: float, game_data):\n if self.get_invincible(): # if become invincible\n if time.time() - self._invincible_time > 10: # if the time past 10 seconds\n self.set_invincible(False) # remove the buff\n\n if self.get_velocity()[0] != 0: # if moved horizontally\n self._image_time += time_delta # count time while move\n # set the order of image id while moving, generally it's 3-2-1-2-3-...\n if self._image_time <= 0.1: # if time past 0.1 second\n self._image = '3' # set image id to '3'\n elif 0.1 < self._image_time <= 0.2 or 0.3 < self._image_time <= 0.4:\n # if time past 0.2 or 0.4 second\n self._image = '2' # set image id to '2'\n elif 0.2 < self._image_time <= 0.3: # if time past 0.3 second\n self._image = '1' # set image id to '1'\n elif self._image_time > 0.4: # reset the cycle\n self._image = '3'\n self._image_time = 0\n else: # if not moving\n self._image = 'default' # set it with default image","def calibrate(self):\n\t\tLTOGRIGHT = []\n\t\tLTOGUP = []\n\t\tRTOGRIGHT = []\n\t\tRTOGUP = []\n\t\tstart = time.time()\n\t\tcalibration_time = 5.0\n\t\twhile time.time() - start < calibration_time:\n\t\t\tevents = pygame.event.get()\n\t\t\tfor event in events:\n\t\t\t\tif event.type == pygame.JOYAXISMOTION:\n\t\t\t\t\tLTOGRIGHT.append(self.joystick.get_axis(self.LTOGRIGHT))\n\t\t\t\t\tLTOGUP.append(-self.joystick.get_axis(self.LTOGUP))\n\t\t\t\t\tRTOGRIGHT.append(self.joystick.get_axis(self.RTOGRIGHT))\n\t\t\t\t\tRTOGUP.append(-self.joystick.get_axis(self.RTOGUP))\n\n\t\t# calibration sets highest value equal to 1.0\n\t\tself.calibration[0] = 1.0/max(LTOGRIGHT)\n\t\tself.calibration[1] = -1.0/min(LTOGRIGHT)\n\t\tself.calibration[2] = -1.0/min(LTOGUP)\n\t\tself.calibration[3] = 1.0/max(LTOGUP)\n\t\tself.calibration[4] = 1.0/max(RTOGRIGHT)\n\t\tself.calibration[5] = -1.0/min(RTOGRIGHT)\n\t\tself.calibration[6] = -1.0/min(RTOGUP)\n\t\tself.calibration[7] = 1.0/max(RTOGUP)","def apply_motion_correction(mov, shifts, interpolation=None, crop=None, in_place=False, verbose=True):\n if interpolation is None and cv2 is not None:\n interpolation = cv2.INTER_LINEAR\n\n if not in_place:\n mov=mov.copy()\n\n if mov.ndim==2:\n mov = mov[None,...]\n\n if type(shifts) in [str] and mov.filename:\n shifts,crop_ = retrieve_motion_correction_data(shifts, mov.filename)\n if crop is None:\n crop = crop_\n\n if shifts.ndim==1:\n shifts = shifts[None,...]\n\n if shifts.ndim==2 and shifts.shape[1]==3:\n shifts = shifts[:,:2]\n\n assert shifts.ndim==2 and shifts.shape[1]==2\n\n t,h,w=mov.shape\n if verbose:\n print('Applying shifts:')\n pbar = ProgressBar(maxval=len(mov)).start()\n for i,frame in enumerate(mov):\n sh_x_n, sh_y_n = shifts[i]\n if cv2 is not None:\n M = np.float32([[1,0,sh_x_n],[0,1,sh_y_n]]) \n mov[i] = cv2.warpAffine(frame,M,(w,h),flags=interpolation)\n elif cv2 is None:\n M = np.float32([[1,0,sh_y_n],[0,1,sh_x_n],[0,0,1]]) \n transform = sktf.AffineTransform(matrix=M)\n mov[i] = sktf.warp(frame, transform)\n if verbose:\n pbar.update(i)\n\n if verbose:\n pbar.finish()\n\n if crop:\n if crop == True:\n ymax = int(min([0, min(shifts[:,0])]) or None)\n xmax = int(min([0, min(shifts[:,1])]) or None)\n ymin = int(max(shifts[:,0]))\n xmin = int(max(shifts[:,1]))\n elif isinstance(crop, PF_numeric_types):\n crop = int(crop)\n ymax,xmax = -crop,-crop\n ymin,xmin = crop,crop\n mov = mov[:, ymin:ymax, xmin:xmax]\n\n return mov.squeeze()","def always_touching(self):\n assert int(self.snake[0].real - self.snake[1].real) in [1, 0, -1] and int(\n self.snake[0].real - self.snake[1].real) in [1, 0, -1]","def _doCalibration(self):\n self._cmdCalibration(2)","def chacha(self):\n self.right()\n time.sleep(2)\n self.stop()\n self.back()\n time.sleep(1)\n self.servo(1000)\n time.sleep(1)\n self.stop()\n self.fwd()\n time.sleep(1)\n self.stop()\n self.servo(2000)\n time.sleep(1)\n self.stop()\n self.left()\n time.sleep(2)\n self.stop()","def run_go_to_star(self):\n if self.altitude != self.tele_altitude or self.azimuth != self.tele_azimuth:\n self.azimuth_update()\n self.altitude_update()","def punched(self):\n if not self.dizzy:\n self.dizzy = 1\n self.original = self.image\n Chimp.count_punch += 1","def CorrectStokes(self, t, fluid):\n\n\t\tself.SetTime(t + fluid.dt)\n\t\t\n\t\tself.F = (self.Tether - self.X)\n\t\tf = self.F.sum(axis=0) / self.Nb\n\n\t\tself.X += f\n\n\t\tfluid.u += f / fluid.dt","def compute_tide_corrections(\n x: np.ndarray, y: np.ndarray, delta_time: np.ndarray,\n DIRECTORY: str | pathlib.Path | None = None,\n MODEL: str | None = None,\n ATLAS_FORMAT: str = 'netcdf',\n GZIP: bool = False,\n DEFINITION_FILE: str | pathlib.Path | None = None,\n EPSG: str | int = 3031,\n EPOCH: list | tuple = (2000, 1, 1, 0, 0, 0),\n TYPE: str or None = 'drift',\n TIME: str = 'UTC',\n METHOD: str = 'spline',\n EXTRAPOLATE: bool = False,\n CUTOFF: int | float=10.0,\n APPLY_FLEXURE: bool = False,\n FILL_VALUE: float = np.nan,\n **kwargs\n ):\n\n # check that tide directory is accessible\n if DIRECTORY is not None:\n DIRECTORY = pathlib.Path(DIRECTORY).expanduser()\n if not DIRECTORY.exists():\n raise FileNotFoundError(\"Invalid tide directory\")\n\n # validate input arguments\n assert TIME in ('GPS', 'LORAN', 'TAI', 'UTC', 'datetime')\n assert METHOD in ('bilinear', 'spline', 'linear', 'nearest')\n\n # get parameters for tide model\n if DEFINITION_FILE is not None:\n model = pyTMD.io.model(DIRECTORY).from_file(\n pathlib.Path(DEFINITION_FILE).expanduser())\n else:\n model = pyTMD.io.model(DIRECTORY, format=ATLAS_FORMAT,\n compressed=GZIP).elevation(MODEL)\n\n # determine input data type based on variable dimensions\n if not TYPE:\n TYPE = pyTMD.spatial.data_type(x, y, delta_time)\n # reform coordinate dimensions for input grids\n # or verify coordinate dimension shapes\n if (TYPE.lower() == 'grid') and (np.size(x) != np.size(y)):\n x,y = np.meshgrid(np.copy(x),np.copy(y))\n elif (TYPE.lower() == 'grid'):\n x = np.atleast_2d(x)\n y = np.atleast_2d(y)\n elif TYPE.lower() in ('time series', 'drift'):\n x = np.atleast_1d(x)\n y = np.atleast_1d(y)\n\n # converting x,y from EPSG to latitude/longitude\n try:\n # EPSG projection code string or int\n crs1 = pyproj.CRS.from_epsg(int(EPSG))\n except (ValueError,pyproj.exceptions.CRSError):\n # Projection SRS string\n crs1 = pyproj.CRS.from_string(EPSG)\n # output coordinate reference system\n crs2 = pyproj.CRS.from_epsg(4326)\n transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)\n lon, lat = transformer.transform(x.flatten(), y.flatten())\n\n # assert delta time is an array\n delta_time = np.atleast_1d(delta_time)\n # convert delta times or datetimes objects to timescale\n if (TIME.lower() == 'datetime'):\n timescale = pyTMD.time.timescale().from_datetime(\n delta_time.flatten())\n else:\n timescale = pyTMD.time.timescale().from_deltatime(delta_time,\n epoch=EPOCH, standard=TIME)\n # number of time points\n nt = len(timescale)\n\n # read tidal constants and interpolate to grid points\n if model.format in ('OTIS','ATLAS','TMD3'):\n amp,ph,D,c = pyTMD.io.OTIS.extract_constants(lon, lat, model.grid_file,\n model.model_file, model.projection, type=model.type,\n method=METHOD, extrapolate=EXTRAPOLATE, cutoff=CUTOFF,\n grid=model.format, apply_flexure=APPLY_FLEXURE)\n # use delta time at 2000.0 to match TMD outputs\n deltat = np.zeros((nt), dtype=np.float64)\n elif (model.format == 'netcdf'):\n amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(lon, lat, model.grid_file,\n model.model_file, type=model.type, method=METHOD,\n extrapolate=EXTRAPOLATE, cutoff=CUTOFF, scale=model.scale,\n compressed=model.compressed)\n # use delta time at 2000.0 to match TMD outputs\n deltat = np.zeros((nt), dtype=np.float64)\n elif (model.format == 'GOT'):\n amp,ph,c = pyTMD.io.GOT.extract_constants(lon, lat, model.model_file,\n method=METHOD, extrapolate=EXTRAPOLATE, cutoff=CUTOFF,\n scale=model.scale, compressed=model.compressed)\n # delta time (TT - UT1)\n deltat = timescale.tt_ut1\n elif (model.format == 'FES'):\n amp,ph = pyTMD.io.FES.extract_constants(lon, lat, model.model_file,\n type=model.type, version=model.version, method=METHOD,\n extrapolate=EXTRAPOLATE, cutoff=CUTOFF, scale=model.scale,\n compressed=model.compressed)\n # available model constituents\n c = model.constituents\n # delta time (TT - UT1)\n deltat = timescale.tt_ut1\n\n # calculate complex phase in radians for Euler's\n cph = -1j*ph*np.pi/180.0\n # calculate constituent oscillation\n hc = amp*np.exp(cph)\n\n # predict tidal elevations at time and infer minor corrections\n if (TYPE.lower() == 'grid'):\n ny,nx = np.shape(x)\n tide = np.ma.zeros((ny,nx,nt),fill_value=FILL_VALUE)\n tide.mask = np.zeros((ny,nx,nt),dtype=bool)\n for i in range(nt):\n TIDE = pyTMD.predict.map(timescale.tide[i], hc, c,\n deltat=deltat[i], corrections=model.format)\n MINOR = pyTMD.predict.infer_minor(timescale.tide[i], hc, c,\n deltat=deltat[i], corrections=model.format)\n # add major and minor components and reform grid\n tide[:,:,i] = np.reshape((TIDE+MINOR), (ny,nx))\n tide.mask[:,:,i] = np.reshape((TIDE.mask | MINOR.mask), (ny,nx))\n elif (TYPE.lower() == 'drift'):\n tide = np.ma.zeros((nt), fill_value=FILL_VALUE)\n tide.mask = np.any(hc.mask,axis=1)\n tide.data[:] = pyTMD.predict.drift(timescale.tide, hc, c,\n deltat=deltat, corrections=model.format)\n minor = pyTMD.predict.infer_minor(timescale.tide, hc, c,\n deltat=deltat, corrections=model.format)\n tide.data[:] += minor.data[:]\n elif (TYPE.lower() == 'time series'):\n nstation = len(x)\n tide = np.ma.zeros((nstation,nt), fill_value=FILL_VALUE)\n tide.mask = np.zeros((nstation,nt),dtype=bool)\n for s in range(nstation):\n TIDE = pyTMD.predict.time_series(timescale.tide, hc[s,None,:], c,\n deltat=deltat, corrections=model.format)\n MINOR = pyTMD.predict.infer_minor(timescale.tide, hc[s,None,:], c,\n deltat=deltat, corrections=model.format)\n tide.data[s,:] = TIDE.data[:] + MINOR.data[:]\n tide.mask[s,:] = (TIDE.mask | MINOR.mask)\n # replace invalid values with fill value\n tide.data[tide.mask] = tide.fill_value\n\n # return the ocean or load tide correction\n return tide","def move(self):\n \"\"\" Responsible for transformations \"\"\"\n pos, com, success = self.perception \n if self.destination is None:\n return array([0,0])\n\n if not self.awake:\n return array([0,0])\n\n\n if self.phase == 4 and self.proper_formation is not None:\n no_go = []\n for i in range(0,len(self.proper_formation)):\n if i != self.order and self.proper_formation[i][0] == self.proper_formation[self.order][0]:\n no_go.append(self.transform(self.proper_formation[i][1] - self.position))\n pos = merge_array_lists(pos, no_go)\n\n if self.phase == 2:\n point = self.destination.copy() - self.position\n elif self.phase > 2:\n point = self.transform(self.destination.copy() - self.position)\n else:\n point = self.destination.copy()\n\n if not array_equal(point, array([0,0])):\n reachable, path = findpathtoclosest(array([0,0]), point, pos)\n \n if len(path) == 0:\n move = array([0,0]) \n else:\n move = path[0]\n if not reachable and not array_equal(move,array([0,0])):\n if self.phase == 2:\n self.closest_i_could_get = path[-1] + self.position\n elif self.phase > 2:\n self.closest_i_could_get = self.transform2(path[-1]) + self.position\n else:\n self.closest_i_could_get = path[-1]\n elif not reachable:\n if self.phase > 1:\n self.closest_i_could_get = self.position\n else:\n self.closest_i_could_get = array([0,0])\n else:\n self.closest_i_could_get = None\n\n if reachable and self.phase == 4 and array_equal(move,array([0,0])):\n move = self.randomStep()\n self.closest_i_could_get = None\n\n else:\n move = array([0,0])\n self.closest_i_could_get = None\n\n return move","def _step(self) -> None:\n self._update_gradient_at(DimerPoint.left)\n\n self._optimise_rotation()\n trns_result = self._translate()\n\n if trns_result == _StepResult.skipped_translation:\n self._converged_translation = True\n\n return None","def set_flow_corrected(self):\n self.exh.temp_v_press_fit = (\n np.polyfit(self.exh.pressure_drop[0:4],\n self.exh.T_array[0:4], 2) ) \n self.flow_data.T_hx = np.polyval(self.exh.temp_v_press_fit,\n self.flow_data.pressure_drop) \n self.flow_data.flow = ( self.flow_data.flow_trash *\n self.flow_data.T_hx / self.flow_data.T )","def _update_moved(self):\n self._RAS_textbox.setPlainText('{:.2f}, {:.2f}, {:.2f}'.format(\n *self._ras))\n self._VOX_textbox.setPlainText('{:3d}, {:3d}, {:3d}'.format(\n *self._current_slice))\n self._intensity_label.setText('intensity = {:.2f}'.format(\n self._base_data[tuple(self._current_slice)]))","def before_tick(self, time):\n pass","def evaluate(self, time, view) -> ControlPoint:\n ...","def dst(self, dt):","def turn_until_clear(self):\n print(\"Rotating until lane is clear\")\n #make sure robot is looking straight\n self.servo(self.MIDPOINT)\n while self.read_distance() < self.SAFE_DISTANCE:\n self.left(primary=40,counter=-40)\n time.sleep(.05)\n #stop motion before ending method\n self.stop()","def compute_tide_corrections(x, y, delta_time, DIRECTORY=None, MODEL=None,\n EPSG=3031, EPOCH=(2000,1,1,0,0,0), TYPE='drift', TIME='UTC',\n METHOD='spline', EXTRAPOLATE=False, FILL_VALUE=np.nan):\n\n #-- select between tide models\n if (MODEL == 'CATS0201'):\n grid_file = os.path.join(DIRECTORY,'cats0201_tmd','grid_CATS')\n model_file = os.path.join(DIRECTORY,'cats0201_tmd','h0_CATS02_01')\n model_format = 'OTIS'\n model_EPSG = '4326'\n model_type = 'z'\n elif (MODEL == 'CATS2008'):\n grid_file = os.path.join(DIRECTORY,'CATS2008','grid_CATS2008')\n model_file = os.path.join(DIRECTORY,'CATS2008','hf.CATS2008.out')\n model_format = 'OTIS'\n model_EPSG = 'CATS2008'\n model_type = 'z'\n elif (MODEL == 'CATS2008_load'):\n grid_file = os.path.join(DIRECTORY,'CATS2008a_SPOTL_Load','grid_CATS2008a_opt')\n model_file = os.path.join(DIRECTORY,'CATS2008a_SPOTL_Load','h_CATS2008a_SPOTL_load')\n model_format = 'OTIS'\n model_EPSG = 'CATS2008'\n model_type = 'z'\n elif (MODEL == 'TPXO9-atlas'):\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas')\n grid_file = 'grid_tpxo9_atlas.nc.gz'\n model_files = ['h_q1_tpxo9_atlas_30.nc.gz','h_o1_tpxo9_atlas_30.nc.gz',\n 'h_p1_tpxo9_atlas_30.nc.gz','h_k1_tpxo9_atlas_30.nc.gz',\n 'h_n2_tpxo9_atlas_30.nc.gz','h_m2_tpxo9_atlas_30.nc.gz',\n 'h_s2_tpxo9_atlas_30.nc.gz','h_k2_tpxo9_atlas_30.nc.gz',\n 'h_m4_tpxo9_atlas_30.nc.gz','h_ms4_tpxo9_atlas_30.nc.gz',\n 'h_mn4_tpxo9_atlas_30.nc.gz','h_2n2_tpxo9_atlas_30.nc.gz']\n model_format = 'netcdf'\n model_type = 'z'\n SCALE = 1.0/1000.0\n elif (MODEL == 'TPXO9-atlas-v2'):\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas_v2')\n grid_file = 'grid_tpxo9_atlas_30_v2.nc.gz'\n model_files = ['h_q1_tpxo9_atlas_30_v2.nc.gz','h_o1_tpxo9_atlas_30_v2.nc.gz',\n 'h_p1_tpxo9_atlas_30_v2.nc.gz','h_k1_tpxo9_atlas_30_v2.nc.gz',\n 'h_n2_tpxo9_atlas_30_v2.nc.gz','h_m2_tpxo9_atlas_30_v2.nc.gz',\n 'h_s2_tpxo9_atlas_30_v2.nc.gz','h_k2_tpxo9_atlas_30_v2.nc.gz',\n 'h_m4_tpxo9_atlas_30_v2.nc.gz','h_ms4_tpxo9_atlas_30_v2.nc.gz',\n 'h_mn4_tpxo9_atlas_30_v2.nc.gz','h_2n2_tpxo9_atlas_30_v2.nc.gz']\n model_format = 'netcdf'\n model_type = 'z'\n SCALE = 1.0/1000.0\n elif (MODEL == 'TPXO9-atlas-v3'):\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas_v3')\n grid_file = 'grid_tpxo9_atlas_30_v3.nc.gz'\n model_files = ['h_q1_tpxo9_atlas_30_v3.nc.gz','h_o1_tpxo9_atlas_30_v3.nc.gz',\n 'h_p1_tpxo9_atlas_30_v3.nc.gz','h_k1_tpxo9_atlas_30_v3.nc.gz',\n 'h_n2_tpxo9_atlas_30_v3.nc.gz','h_m2_tpxo9_atlas_30_v3.nc.gz',\n 'h_s2_tpxo9_atlas_30_v3.nc.gz','h_k2_tpxo9_atlas_30_v3.nc.gz',\n 'h_m4_tpxo9_atlas_30_v3.nc.gz','h_ms4_tpxo9_atlas_30_v3.nc.gz',\n 'h_mn4_tpxo9_atlas_30_v3.nc.gz','h_2n2_tpxo9_atlas_30_v3.nc.gz',\n 'h_mf_tpxo9_atlas_30_v3.nc.gz','h_mm_tpxo9_atlas_30_v3.nc.gz']\n model_format = 'netcdf'\n TYPE = 'z'\n SCALE = 1.0/1000.0\n elif (MODEL == 'TPXO9.1'):\n grid_file = os.path.join(DIRECTORY,'TPXO9.1','DATA','grid_tpxo9')\n model_file = os.path.join(DIRECTORY,'TPXO9.1','DATA','h_tpxo9.v1')\n model_format = 'OTIS'\n model_EPSG = '4326'\n model_type = 'z'\n elif (MODEL == 'TPXO8-atlas'):\n grid_file = os.path.join(DIRECTORY,'tpxo8_atlas','grid_tpxo8atlas_30_v1')\n model_file = os.path.join(DIRECTORY,'tpxo8_atlas','hf.tpxo8_atlas_30_v1')\n model_format = 'ATLAS'\n model_EPSG = '4326'\n model_type = 'z'\n elif (MODEL == 'TPXO7.2'):\n grid_file = os.path.join(DIRECTORY,'TPXO7.2_tmd','grid_tpxo7.2')\n model_file = os.path.join(DIRECTORY,'TPXO7.2_tmd','h_tpxo7.2')\n model_format = 'OTIS'\n model_EPSG = '4326'\n model_type = 'z'\n elif (MODEL == 'TPXO7.2_load'):\n grid_file = os.path.join(DIRECTORY,'TPXO7.2_load','grid_tpxo6.2')\n model_file = os.path.join(DIRECTORY,'TPXO7.2_load','h_tpxo7.2_load')\n model_format = 'OTIS'\n model_EPSG = '4326'\n model_type = 'z'\n elif (MODEL == 'AODTM-5'):\n grid_file = os.path.join(DIRECTORY,'aodtm5_tmd','grid_Arc5km')\n model_file = os.path.join(DIRECTORY,'aodtm5_tmd','h0_Arc5km.oce')\n model_format = 'OTIS'\n model_EPSG = 'PSNorth'\n model_type = 'z'\n elif (MODEL == 'AOTIM-5'):\n grid_file = os.path.join(DIRECTORY,'aotim5_tmd','grid_Arc5km')\n model_file = os.path.join(DIRECTORY,'aotim5_tmd','h_Arc5km.oce')\n model_format = 'OTIS'\n model_EPSG = 'PSNorth'\n model_type = 'z'\n elif (MODEL == 'AOTIM-5-2018'):\n grid_file = os.path.join(DIRECTORY,'Arc5km2018','grid_Arc5km2018')\n model_file = os.path.join(DIRECTORY,'Arc5km2018','h_Arc5km2018')\n model_format = 'OTIS'\n model_EPSG = 'PSNorth'\n model_type = 'z'\n elif (MODEL == 'GOT4.7'):\n model_directory = os.path.join(DIRECTORY,'GOT4.7','grids_oceantide')\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/100.0\n elif (MODEL == 'GOT4.7_load'):\n model_directory = os.path.join(DIRECTORY,'GOT4.7','grids_loadtide')\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\n 's1load.d.gz','m4load.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/1000.0\n elif (MODEL == 'GOT4.8'):\n model_directory = os.path.join(DIRECTORY,'got4.8','grids_oceantide')\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/100.0\n elif (MODEL == 'GOT4.8_load'):\n model_directory = os.path.join(DIRECTORY,'got4.8','grids_loadtide')\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\n 's1load.d.gz','m4load.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/1000.0\n elif (MODEL == 'GOT4.10'):\n model_directory = os.path.join(DIRECTORY,'GOT4.10c','grids_oceantide')\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/100.0\n elif (MODEL == 'GOT4.10_load'):\n model_directory = os.path.join(DIRECTORY,'GOT4.10c','grids_loadtide')\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\n 's1load.d.gz','m4load.d.gz']\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\n model_format = 'GOT'\n SCALE = 1.0/1000.0\n elif (MODEL == 'FES2014'):\n model_directory = os.path.join(DIRECTORY,'fes2014','ocean_tide')\n model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',\n 'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',\n 'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',\n 'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',\n 'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',\n 'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',\n 'ssa.nc.gz','t2.nc.gz']\n c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6','m8',\n 'mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2','n4',\n 'nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']\n model_format = 'FES'\n TYPE = 'z'\n SCALE = 1.0/100.0\n elif (MODEL == 'FES2014_load'):\n model_directory = os.path.join(DIRECTORY,'fes2014','load_tide')\n model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',\n 'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',\n 'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',\n 'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',\n 'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',\n 'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',\n 'ssa.nc.gz','t2.nc.gz']\n c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',\n 'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',\n 'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']\n model_format = 'FES'\n model_type = 'z'\n SCALE = 1.0/100.0\n else:\n raise Exception(\"Unlisted tide model\")\n\n #-- converting x,y from EPSG to latitude/longitude\n crs1 = pyproj.CRS.from_string(\"epsg:{0:d}\".format(EPSG))\n crs2 = pyproj.CRS.from_string(\"epsg:{0:d}\".format(4326))\n transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)\n lon,lat = transformer.transform(x.flatten(), y.flatten())\n\n #-- assert delta time is an array\n delta_time = np.atleast_1d(delta_time)\n #-- calculate leap seconds if specified\n if (TIME.upper() == 'GPS'):\n GPS_Epoch_Time = pyTMD.time.convert_delta_time(0, epoch1=EPOCH,\n epoch2=(1980,1,6,0,0,0), scale=1.0)\n GPS_Time = pyTMD.time.convert_delta_time(delta_time, epoch1=EPOCH,\n epoch2=(1980,1,6,0,0,0), scale=1.0)\n #-- calculate difference in leap seconds from start of epoch\n leap_seconds = pyTMD.time.count_leap_seconds(GPS_Time) - \\\n pyTMD.time.count_leap_seconds(np.atleast_1d(GPS_Epoch_Time))\n elif (TIME.upper() == 'TAI'):\n #-- TAI time is ahead of GPS time by 19 seconds\n GPS_Epoch_Time = pyTMD.time.convert_delta_time(-19.0, epoch1=EPOCH,\n epoch2=(1980,1,6,0,0,0), scale=1.0)\n GPS_Time = pyTMD.time.convert_delta_time(delta_time-19.0, epoch1=EPOCH,\n epoch2=(1980,1,6,0,0,0), scale=1.0)\n #-- calculate difference in leap seconds from start of epoch\n leap_seconds = pyTMD.time.count_leap_seconds(GPS_Time) - \\\n pyTMD.time.count_leap_seconds(np.atleast_1d(GPS_Epoch_Time))\n else:\n leap_seconds = 0.0\n\n #-- convert time to days relative to Jan 1, 1992 (48622mjd)\n t = pyTMD.time.convert_delta_time(delta_time - leap_seconds, epoch1=EPOCH,\n epoch2=(1992,1,1,0,0,0), scale=(1.0/86400.0))\n\n #-- read tidal constants and interpolate to grid points\n if model_format in ('OTIS','ATLAS'):\n amp,ph,D,c = extract_tidal_constants(lon, lat, grid_file, model_file,\n model_EPSG, TYPE=model_type, METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE,\n GRID=model_format)\n deltat = np.zeros_like(t)\n elif (model_format == 'netcdf'):\n amp,ph,D,c = extract_netcdf_constants(lon, lat, model_directory,\n grid_file, model_files, TYPE=model_type, METHOD=METHOD, \n EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\n deltat = np.zeros_like(t)\n elif (model_format == 'GOT'):\n amp,ph = extract_GOT_constants(lon, lat, model_directory, model_files,\n METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\n #-- interpolate delta times from calendar dates to tide time\n delta_file = pyTMD.utilities.get_data_path(['data','merged_deltat.data'])\n deltat = calc_delta_time(delta_file, t)\n elif (model_format == 'FES'):\n amp,ph = extract_FES_constants(lon, lat, model_directory, model_files,\n TYPE=model_type, VERSION=MODEL, METHOD=METHOD,\n EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\n #-- interpolate delta times from calendar dates to tide time\n delta_file = pyTMD.utilities.get_data_path(['data','merged_deltat.data'])\n deltat = calc_delta_time(delta_file, t)\n\n #-- calculate complex phase in radians for Euler's\n cph = -1j*ph*np.pi/180.0\n #-- calculate constituent oscillation\n hc = amp*np.exp(cph)\n\n #-- predict tidal elevations at time and infer minor corrections\n if (TYPE.lower() == 'grid'):\n ny,nx = np.shape(x); nt = len(t)\n tide = np.ma.zeros((ny,nx,nt),fill_value=FILL_VALUE)\n tide.mask = np.zeros((ny,nx,nt),dtype=np.bool)\n for i in range(nt):\n TIDE = predict_tide(t[i], hc, c,\n DELTAT=deltat[i], CORRECTIONS=model_format)\n MINOR = infer_minor_corrections(t[i], hc, c,\n DELTAT=deltat[i], CORRECTIONS=model_format)\n #-- add major and minor components and reform grid\n tide[:,:,i] = np.reshape((TIDE+MINOR), (ny,nx))\n tide.mask[:,:,i] = np.reshape((TIDE.mask | MINOR.mask), (ny,nx))\n else:\n npts = len(t)\n tide = np.ma.zeros((npts), fill_value=FILL_VALUE)\n tide.mask = np.any(hc.mask,axis=1)\n tide.data[:] = predict_tide_drift(t, hc, c,\n DELTAT=deltat, CORRECTIONS=model_format)\n minor = infer_minor_corrections(t, hc, c,\n DELTAT=deltat, CORRECTIONS=model_format)\n tide.data[:] += minor.data[:]\n #-- replace invalid values with fill value\n tide.data[tide.mask] = tide.fill_value\n\n #-- return the tide correction\n return tide","def correct_proper_motion(self, invert=False, mjd=None):\n\n # If mjd not set directly, check that it was set from FITS headers in get_cutout method\n if self.mjd is None:\n if mjd is None:\n raise FITSException(\"Date could not be inferred from header, supply with epoch keyword.\")\n else:\n self.mjd = mjd\n\n obstime = Time(self.mjd, format='mjd')\n\n simbad = Simbad.query_region(self.position, radius=180 * u.arcsec)\n\n # Catch SIMBAD failure either from None return of query or no stellar type matches in region\n try:\n simbad = simbad.to_pandas()\n pm_types = ['*', '**', 'PM*', 'EB*', 'Star', 'PSR', 'Pulsar', 'Flare*']\n simbad = simbad[(simbad['OTYPE'].isin(pm_types)) | (simbad['SP_TYPE'].str.len() > 0)]\n\n assert len(simbad) > 0\n\n except (ValueError, AssertionError):\n logger.debug(\"No high proper-motion objects within 180 arcsec.\")\n self.correct_pm = False\n\n return\n\n # Treat non-existent proper motion parameters as extremely distant objects\n simbad['PMRA'].fillna(0, inplace=True)\n simbad['PMDEC'].fillna(0, inplace=True)\n simbad['PLX_VALUE'].fillna(0.01, inplace=True)\n\n newtime = Time(self.radio.mjd, format='mjd')\n pmra = simbad['PMRA'].values * u.mas / u.yr\n pmdec = simbad['PMDEC'].values * u.mas / u.yr\n\n dist = Distance(parallax=simbad['PLX_VALUE'].values * u.mas)\n\n simbad['j2000pos'] = SkyCoord(\n ra=simbad['RA_d'].values * u.deg,\n dec=simbad['DEC_d'].values * u.deg,\n frame='icrs',\n distance=dist,\n pm_ra_cosdec=pmra,\n pm_dec=pmdec,\n obstime='J2000',\n )\n\n datapos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(obstime))\n newpos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(newtime))\n\n simbad_cols = {\n 'MAIN_ID': 'Object',\n 'OTYPE': 'Type',\n 'SP_TYPE': 'Spectral Type',\n 'DISTANCE_RESULT': 'Separation (arcsec)',\n }\n simbad = simbad.rename(columns=simbad_cols)\n simbad = simbad[simbad_cols.values()].copy()\n simbad['PM Corrected Separation (arcsec)'] = np.round(newpos.apply(\n lambda x: x.separation(self.position).arcsec), 3)\n\n # Only display PM results if object within 15 arcsec\n if simbad['PM Corrected Separation (arcsec)'].min() > 15:\n logger.debug(\"No PM corrected objects within 15 arcsec\")\n self.correct_pm = False\n\n return\n\n self.simbad = simbad.sort_values('PM Corrected Separation (arcsec)')\n logger.info(f'SIMBAD results:\\n {self.simbad.head()}')\n\n nearest = self.simbad['PM Corrected Separation (arcsec)'].idxmin()\n\n self.oldpos = datapos[nearest]\n self.pm_coord = newpos[nearest]\n\n near_object = self.simbad.loc[nearest].Object\n msg = f'{near_object} proper motion corrected to <{self.pm_coord.ra:.4f}, {self.pm_coord.dec:.4f}>'\n logger.info(msg)\n\n missing = simbad[simbad['PM Corrected Separation (arcsec)'].isna()]\n if len(missing) > 0:\n msg = f\"Some objects missing PM data, and may be a closer match than presented:\\n {missing}\"\n logger.warning(msg)\n\n return","def _perturbInPlaceHard(self):\n die","def timerCallback(self,evprent):\n self._odom_list.waitForTransform('map', 'base_footprint', rospy.Time(0), rospy.Duration(1.0))\n (position, orientation) = self._odom_list.lookupTransform('map','base_footprint', rospy.Time(0)) #finds the position and oriention of two objects relative to each other (hint: this returns arrays, while Pose uses lists)\n self._current.position.x = position[0]\n self._current.position.y = position[1]\n\n self._current.orientation.x = orientation[0]\n self._current.orientation.y = orientation[1]\n self._current.orientation.z = orientation[2]\n self._current.orientation.w = orientation[3]\n q = [self._current.orientation.x,\n self._current.orientation.y,\n self._current.orientation.z,\n self._current.orientation.w] # quaternion nonsense\n\n (roll, pitch, yaw) = euler_from_quaternion(q)","def update(self, dayHour, comicId):\n #TODO test\n self.loadComic(comicId)\n\n #Sprint \"Predictor.update(\", comicId, \")\"\n\n\n for i in range(1):\n # Stop weeding?\n stopWeeding = False\n\n if self.__predictorData == None:\n print \"Line 252 (NONE)\"\n\n if (self.__predictorData.isWeeding()):\n tgm = time.gmtime()\n sec = time.mktime(time.strptime(str(tgm.tm_year) +' '+ str(tgm.tm_mon) +' '+ str(tgm.tm_mday) +' '+ str(tgm.tm_hour) +' '+ str(tgm.tm_min) +' '+ str(tgm.tm_sec), '%Y %m %d %H %M %S'))\n # print \"weeding times:\", self.__predictorData.getWeedingStart(), sec\n if (sec >= self.__predictorData.getWeedingStart() + 7*24*self.hourScale):\n self.stopWeeding(dayHour)\n stopWeeding = True\n\n if (self.__predictorData.isWeeding() or stopWeeding):\n # Weeding\n uRanges = self.__predictorData.getUpdateRanges()\n if (len(uRanges) > 0):\n if (self.__predictorData.addDayHour(dayHour)):\n break\n # if (self.inURange(self.incDayHour(dayHour, -self.rangeWidth), uRanges[-1])):\n # uRangeLast = self.incDayHour(uRanges[-1]['position'], uRanges[-1]['width'])\n # #self.__predictorData.addUpdateRange(self.addUpdateRange(incDayHour(uRangeLast, Predictor.rangeWidth + 1)))\n # self.__predictorData.addUpdateRange(self.incDayHour(uRangeLast, Predictor.rangeWidth + 1))\n # break \n self.__predictorData.addUpdateRange(self.blankUpdateRange(dayHour))\n else:\n # Regular update\n if (not self.__predictorData.addDayHour(dayHour)):\n # Add dayHour to nearest range\n updateRanges = self.__predictorData.getUpdateRanges()\n dist = {}\n dh = self.dayHourToHours(dayHour)\n for i, uRange in enumerate(updateRanges):\n ur_pos_hours = self.dayHourToHours(uRange['position'])\n ur_width = uRange['width']\n hours = self.dayHourToHours(dayHour)\n dist[min(abs(ur_pos_hours - ur_width - hours), abs(ur_pos_hours + ur_width - hours))] = i\n self.__predictorData.addDayHourToURange(dayHour, dist[min(dist.keys())])\n\n \n self.__predictorData.setUpdateRanges(self.calculateURPositions(self.__predictorData.getUpdateRanges()))\n self.__predictorData.setSchedule(self.calculateScheduleUR(self.__predictorData.getUpdateRanges()))\n \n self.updatePredictorList(self.__predictorData.getSchedule(), comicId)\n self.saveComic(comicId)","def act_func_part4_v2_t(action_raw, action_raw_idx, raw_state_limits, stptLmt, ob_this_raw, logger, is_show_debug):\n IAT_STPT_IDX = 4;\n OCP_IDX = 7;\n PMV_IDX = 6;\n ACTIONS = [[-2.0],\n [-1.0],\n [-0.5],\n [0.0],\n [0.5],\n [1.0],\n [2.0],];\n\n ocp = ob_this_raw[OCP_IDX];\n pmv = ob_this_raw[PMV_IDX];\n iat_stpt_ob = ob_this_raw[IAT_STPT_IDX]; \n act_num = len(ACTIONS);\n # Process based on PMV\n action_prcd = action_raw;\n action_prcd_idx = action_raw_idx;\n if ocp == 1:\n if pmv < -0.5 and action_raw[0] < 0:\n # If pmv < -0.5 and the action is to reduce setpoint\n # Choose one action to increase the setpoint\n action_prcd_idx = np.random.randint(3, act_num);\n action_prcd = ACTIONS[action_prcd_idx];\n elif pmv > 0.5 and action_raw[0] > 0:\n # If pmv > 0.5 and the action is to increase setpoint\n # Choose one action to reduce the setpoint\n action_prcd_idx = np.random.randint(0, 4);\n action_prcd = ACTIONS[action_prcd_idx];\n # Get final action\n iat_stpt_act_raw = iat_stpt_ob + action_prcd[0];\n action_ret = [max(min(iat_stpt_act_raw, stptLmt[0][1]), stptLmt[0][0])];\n action_ret_idx = action_prcd_idx;\n # Log\n if action_raw_idx != action_ret_idx:\n if is_show_debug:\n logger.debug('Action function: raw action %s has been changed to %s for '\n 'the PMV %s.'%(action_raw_idx, action_ret_idx, pmv));\n return (action_ret, action_ret_idx);","def update(self, dt):\n event = super(Kick, self).update(dt)\n if event is not None:\n return event\n\n if not self._motion.is_running():\n if not self._motion_completed:\n effectors = \"LLeg\"\n delta_pos1 = np.asarray([0.15, 0.0, 0.0, 0.0, -0.03, 0.0])\n\n if self._support_leg == \"left\":\n effectors = \"RLeg\"\n delta_pos1[4] *= -1\n\n delta_pos = [[delta_pos1,\n np.asarray([0.09, 0.0, 0.00, 0.0, 0.0, 0.0])]]\n times = [[0.2, 0.3]]\n self._motion.interpolate_positions(effectors, delta_pos, times, NaoMotionController.FRAME_TORSO)\n\n self._motion_completed = True\n return\n\n return \"done\"","def update_for_odor_detection(self, dt, odor, wind_uvecs, masks):\n x_wind_unit = wind_uvecs['x']\n y_wind_unit = wind_uvecs['y']\n mask_startmode = masks['startmode']\n mask_castfor = masks['castfor']\n\n mask_gt_upper = odor >= self.param['odor_thresholds']['upper']\n mask_candidates = mask_gt_upper & (mask_startmode | mask_castfor)\n dice_roll = scipy.full((self.size,),scipy.inf)\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\n\n # Convert probabilty/sec to probabilty for time step interval dt\n odor_probability_upper = 1.0 - (1.0 - self.param['odor_probabilities']['upper'])**dt\n mask_change = dice_roll < odor_probability_upper\n self.mode[mask_change] = self.Mode_FlyUpWind\n\n # Compute new heading error for flies which change mode\n heading_error_std = self.param['heading_error_std']\n self.heading_error[mask_change] = heading_error_std*scipy.randn(mask_change.sum())\n\n # Set x and y velocities for the flies which just changed to FlyUpWind.\n x_unit_change, y_unit_change = rotate_vecs(\n x_wind_unit[mask_change],\n y_wind_unit[mask_change],\n self.heading_error[mask_change]\n )\n speed = self.param['flight_speed'][mask_change]\n self.x_velocity[mask_change] = -speed*x_unit_change\n self.y_velocity[mask_change] = -speed*y_unit_change","def _reset(self) -> ts.TimeStep:","def update_apc23(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n if len(self._old) >= 1:\n\n\n try:\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\n\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n beta1 = (3.0*(self.time-self.get_old(0,2))+delta_t)/(6.0*self.time-self.get_old(0,2))\n beta2 = -delta_t**2/(6.0*(self.time+delta_t-self.get_old(0,2))*(self.time-self.get_old(0,2)))\n\n print self.force(pos, vel, self.time+delta_t), kap[1], self.get_old(0,1)\n\n pos = self.pos+delta_t*((1.0-beta1-beta2)*vel+beta1*self.vel+beta2*self.get_old(0, 0))\n vel = self.vel+delta_t*((1.0-beta1-beta2)*self.force(pos, vel, self.time+delta_t,\n drag=False)+beta1*kap[1]+beta2*self.get_old(0,1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n\n self.time += delta_t\n\n else:\n ## reduced to using the Adams first order method for the first timestep:\n\n kap = update_apc1(self)\n\n self.set_old(kap, 1)\n\n return kap","def target_position(self, time):\n \"\"\"\n start_pos = self.points[self.cur_start]\n seg_time = time - self.last_checkpoint_time\n\n #The arguement of target-velocity dosent matter\n cur_pos = self.target_velocity(time)*seg_time + start_pos\n\n \n # or time > (self.total_time / 4)*(self.cur_start + 1)\n cur_pos_norm = length(cur_pos - start_pos)\n\n next_corner = self.points[(self.cur_start + 1)%4]\n \n seg_norm = length(next_corner - start_pos)\n print(\"cur_pos : \", cur_pos, \"segment: \", self.cur_start, seg_norm - cur_pos_norm)\n\n if cur_pos_norm >= seg_norm:\n self.cur_start = (self.cur_start + 1) % 4\n self.last_checkpoint_time = time\n return cur_pos\n \"\"\"\n\n #Possibly use rospy.sleep()\n total_time = self.total_time\n\n\n if time < total_time/4:\n return self.path1.target_position(time)\n\n elif time - total_time/4 == 0:\n rospy.sleep(0.5)\n\n elif time < total_time/2:\n return self.path2.target_position(time - (total_time/4 + 0.5))\n # return self.path2.target_position(time - (total_time/4 ))\n\n\n elif time - total_time/2 == 0:\n rospy.sleep(0.5)\n\n elif time <= total_time/4*3:\n return self.path3.target_position(time - (total_time/2 + 1))\n # return self.path3.target_position(time - (total_time/2))\n\n\n elif time - total_time/4*3 == 0:\n rospy.sleep(0.5)\n\n else:\n return self.path4.target_position(time - (total_time/4*3 + 1.5))\n # return self.path4.target_position(time - (total_time/4*3))","def __update_current_motion_start(self) -> None:\n if len(self.past_movements) < 2:\n return\n if self.past_movements[-1] * self.past_movements[-2] < 0:\n self.current_motion_start_element = self.counterpoint[-2]","def sweep(self):\n position = 0\n countdown = False\n\n for i in range(14):\n\n if position == 14:\n countdown = True\n if countdown:\n position -= 2\n else:\n position += 2\n\n self.servo.ChangeDutyCycle(position)\n GPIO.output(self.pin, False)\n sleep(0.1)","def update_for_odor_loss(self, t, dt, odor, wind_uvecs, masks):\n\n x_wind_unit = wind_uvecs['x']\n y_wind_unit = wind_uvecs['y']\n mask_flyupwd = masks['flyupwd']\n mask_castfor = masks['castfor']\n\n mask_lt_lower = odor <= self.param['odor_thresholds']['lower']\n mask_candidates = mask_lt_lower & mask_flyupwd\n dice_roll = scipy.full((self.size,),scipy.inf)\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\n\n # Convert probabilty/sec to probabilty for time step interval dt\n odor_probability_lower = 1.0 - (1.0 - self.param['odor_probabilities']['lower'])**dt\n mask_change = dice_roll < odor_probability_lower\n self.mode[mask_change] = self.Mode_CastForOdor\n\n # Lump together flies changing to CastForOdor mode with casting flies which are\n # changing direction (e.g. time to make cast direction change)\n mask_change |= mask_castfor & (t > (self.t_last_cast + self.dt_next_cast))\n\n # Computer new heading errors for flies which change mode\n self.heading_error[mask_change] = self.param['heading_error_std']*scipy.randn(mask_change.sum())\n\n # Set new cast intervals and directions for flies chaning to CastForOdor or starting a new cast\n cast_interval = self.param['cast_interval']\n self.dt_next_cast[mask_change] = scipy.random.uniform(\n cast_interval[0],\n cast_interval[0],\n (mask_change.sum(),)\n )\n self.t_last_cast[mask_change] = t\n self.cast_sign[mask_change] = scipy.random.choice([-1,1],(mask_change.sum(),))\n\n # Set x and y velocities for new CastForOdor flies\n x_unit_change, y_unit_change = rotate_vecs(\n x_wind_unit[mask_change],\n -y_wind_unit[mask_change],\n self.heading_error[mask_change]\n )\n speed = self.param['flight_speed'][mask_change]\n self.x_velocity[mask_change] = self.cast_sign[mask_change]*speed*x_unit_change\n self.y_velocity[mask_change] = self.cast_sign[mask_change]*speed*y_unit_change","def data_move_case_zero(tik_instance, ub_ori, ub_trans,\n is_last, num_outer_axis, num_loop_time,\n loop_time, loop_col, loop_len):\n with tik_instance.if_scope(tik.all(loop_time ==\n self.dst_shape[-4] //\n loop_col - 1,\n self.dst_shape[-4] % loop_col ==\n 0)):\n is_last.set_as(1)\n num_data_one_loop = self.dst_shape[-4] * self.dst_shape[-3] * \\\n self.dst_shape[-2] * self.dst_shape[-1]\n src_ub_index = 0\n if self.src_shape[-1] % CUBE_SIZE != 0 or \\\n (self.src_shape[-1] - loop_len * CUBE_SIZE) // \\\n self.num_data > MAX_STRIDE_BLK:\n if self.src_shape[-2] % CUBE_SIZE != 0:\n with tik_instance.if_scope(num_loop_time ==\n self.dst_shape[-3] - 1):\n with tik_instance.for_range(0,\n self.src_shape[-2] %\n CUBE_SIZE) as num_cube_col:\n src_gm_index = num_outer_axis *\\\n self.src_shape[-1] * \\\n self.src_shape[-2] + \\\n (num_loop_time * CUBE_SIZE +\n num_cube_col) *\\\n self.src_shape[-1] + loop_time *\\\n loop_col * CUBE_SIZE\n tik_instance.data_move(ub_ori[loop_len *\n CUBE_SIZE *\n num_cube_col],\n self.src_gm[src_gm_index],\n 0, 1,\n loop_len * self.num_byte //\n 2, 0, 0)\n with tik_instance.else_scope():\n with tik_instance.for_range(0, CUBE_SIZE) \\\n as num_cube_col:\n src_gm_index = num_outer_axis * \\\n self.src_shape[-1] * \\\n self.src_shape[-2] + \\\n (num_loop_time * CUBE_SIZE +\n num_cube_col) *\\\n self.src_shape[-1] + loop_time *\\\n loop_col * CUBE_SIZE\n tik_instance.data_move(ub_ori[loop_len *\n CUBE_SIZE *\n num_cube_col],\n self.src_gm[src_gm_index],\n 0, 1,\n loop_len * self.num_byte //\n 2, 0, 0)\n else:\n with tik_instance.for_range(0, CUBE_SIZE) as num_cube_col:\n src_gm_index = num_outer_axis * self.src_shape[-1] * \\\n self.src_shape[-2] + \\\n (num_loop_time * CUBE_SIZE +\n num_cube_col) * self.src_shape[-1] + \\\n loop_time * loop_col * CUBE_SIZE\n tik_instance.data_move(ub_ori[loop_len * CUBE_SIZE *\n num_cube_col],\n self.src_gm[src_gm_index],\n 0, 1,\n loop_len * self.num_byte //\n 2, 0, 0)\n else:\n src_gm_index = num_outer_axis * self.src_shape[-1] * \\\n self.src_shape[-2] + num_loop_time *\\\n CUBE_SIZE * self.src_shape[-1] +\\\n loop_time * loop_col * CUBE_SIZE\n if self.src_shape[-2] % CUBE_SIZE != 0:\n with tik_instance.if_scope(num_loop_time ==\n self.dst_shape[-3] - 1):\n tik_instance.data_move(ub_ori[src_ub_index],\n self.src_gm[src_gm_index], 0,\n self.src_shape[-2] % CUBE_SIZE,\n loop_len * self.num_byte // 2,\n (self.src_shape[-1] -\n loop_len * CUBE_SIZE) //\n self.num_data,\n 0)\n with tik_instance.else_scope():\n tik_instance.data_move(ub_ori[src_ub_index],\n self.src_gm[src_gm_index],\n 0, CUBE_SIZE,\n loop_len * self.num_byte // 2,\n (self.src_shape[-1] -\n loop_len * CUBE_SIZE) //\n self.num_data,\n 0)\n else:\n tik_instance.data_move(ub_ori[src_ub_index],\n self.src_gm[src_gm_index],\n 0, CUBE_SIZE,\n loop_len * self.num_byte // 2,\n (self.src_shape[-1] - loop_len *\n CUBE_SIZE) // self.num_data, 0)\n self.data_rearrange_case_four(tik_instance, ub_ori,\n ub_trans, num_loop_time,\n loop_len, is_last)\n\n if((self.dst_shape[-3] - 1) * self.dst_shape[-1] *\n self.dst_shape[-2] // self.num_data > MAX_STRIDE_BLK):\n with tik_instance.for_range(0, loop_len) as \\\n num_col_cube:\n dst_gm_index = num_outer_axis * num_data_one_loop + \\\n num_loop_time * self.dst_shape[-1] * \\\n self.dst_shape[-2] + \\\n (loop_time * loop_col + num_col_cube) * \\\n self.dst_shape[-1] * self.dst_shape[-2] * \\\n self.dst_shape[-3]\n tik_instance.data_move(self.dst_gm[dst_gm_index],\n ub_trans[num_col_cube *\n CUBE_SIZE *\n (CUBE_SIZE + 1)],\n 0, 1,\n self.dst_shape[-1] *\n self.dst_shape[-2] //\n self.num_data,\n 0, 0)\n else:\n dst_gm_index = num_outer_axis * num_data_one_loop + \\\n num_loop_time * self.dst_shape[-1] * \\\n self.dst_shape[-2] + loop_time * \\\n loop_col * self.dst_shape[-1] * \\\n self.dst_shape[-2] * \\\n self.dst_shape[-3]\n tik_instance.data_move(self.dst_gm[dst_gm_index],\n ub_trans[0],\n 0, loop_len,\n self.dst_shape[-1] *\n self.dst_shape[-2] // self.num_data,\n self.num_byte // 2,\n (self.dst_shape[-3] - 1) *\n self.dst_shape[-1] *\n self.dst_shape[-2] // self.num_data)","def calibrate(self):\n self.mode = Mode.calibrating\n yaw_sensor = yaw_button()\n while not yaw_sensor.is_pressed():\n self.move_left()\n for _ in range(75):\n self.move_right()\n\n pitch_sensor = pitch_button()\n while not pitch_sensor.is_pressed():\n self.move_up()\n for _ in range(21):\n self.move_down()\n\n self.pitch = 0.0\n self.yaw = 0.0\n self.mode = Mode.waiting","def update(self):\n if self.dizzy:\n self._spin()\n else:\n self._walk()","def quarter_frame(self) -> None:\n if self._linear_ctr_reload_flag:\n self._linear_ctr = self.linear_ctr_reload_value\n elif self._linear_ctr > 0:\n self._linear_ctr -= 1\n\n if not self.control_flag:\n self._linear_ctr_reload_flag = False","def update(self, time):\n raise NotImplementedError","def update(self, time):\n raise NotImplementedError","def calibrateAutoGuider(self, exposureLengthSeconds):\n\n print \"Calibrating autoguider orientation...\"\n self.camera.GuiderCalibrate(exposureLengthSeconds)\n while self.camera.GuiderRunning:\n sys.stdout.write(\".\")\n time.sleep(1)\n print\n print \"Calibration finished\"","def undo_calibration_using_diagnostics(x,y,z,cd):\n undo_calibration(x, y, z, [cd[\"x_offset\"],cd[\"x_scale\"],cd[\"y_offset\"],cd[\"y_scale\"],cd[\"z_offset\"],cd[\"z_scale\"]])","def _twist_callback(self, cmd):\n self.set_velocity(cmd.linear.x, cmd.angular.z)","def right_twist(self):\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()\n self.turn_by_deg(180)\n #time.sleep(.1)\n self.stop()","def run_update_step(self, time, pids, hole_rating, observations):\n\t\treturn NotImplemented","def reduce_velocity(self):\n if self.controls[\"make_velocity_0\"]:\n # print(self.controls[\"bar_move_velocity\"])\n self.controls[\"bar_move_velocity\"] = 0","def update_apc11(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n pos = self.pos+delta_t*self.vel\n vel = self.vel+delta_t*kap[1]\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n force = self.force(pos,\n vel,\n self.time+delta_t, drag=False)\n\n pos = self.pos+delta_t*vel\n vel = self.vel+delta_t*force\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n try:\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n vel = self.vel+col.delta_t*kap[1]\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n \n self.time += delta_t\n\n return (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)","def update(self, delta_time):\r\n self.check_keys()\r\n\r\n \r\n # TODO: Tell everything to advance or move forward one step in time\r\n \r\n for Large_asteriod in self.rocks:\r\n Large_asteriod.advance()\r\n Large_asteriod.wrap()\r\n #this is where I call my functions\r\n \r\n # TODO: Check for collisions\r\n for bullet in self.bullets:\r\n bullet.advance()\r\n bullet.wrap()\r\n \r\n \r\n \r\n self.check_collisions() \r\n self.cleanup_deadstuff() \r\n self.ship.advance()\r\n self.ship.wrap()\r\n self.frame_count += 1","def apply_model(self, original, t1, t2, resolution_scaling_factor=1):\n img = Image()\n img.time_stamp = t2\n\n if t1 == t2:\n img.initialize_with_image(original)\n return img\n\n calc_shift_fnc = self.calculate_shift\n orig_get_fnc = original.get\n interp_fnc = my_math.linear_interpolation\n\n def generate(y, x):\n \"\"\"Function describing the transformed image\"\"\"\n realy = y / resolution_scaling_factor\n realx = x / resolution_scaling_factor\n\n # move to time t2\n posy = y + calc_shift_fnc(realy, realx, t2, 0) - \\\n calc_shift_fnc(realy, realx, t1, 0)\n posx = x + calc_shift_fnc(realy, realx, t2, 1) - \\\n calc_shift_fnc(realy, realx, t1, 1)\n\n x_left = int(posx) # math.floor(pos[0])\n x_right = x_left + 1 # math.ceil(pos[0])\n y_down = int(posy) # math.floor(pos[1])\n y_up = y_down + 1 # math.ceil(pos[1])\n\n v11 = orig_get_fnc(y_down, x_left, resolution_scaling_factor)\n v12 = orig_get_fnc(y_down, x_right, resolution_scaling_factor)\n v21 = orig_get_fnc(y_up, x_left, resolution_scaling_factor)\n v22 = orig_get_fnc(y_up, x_right, resolution_scaling_factor)\n\n return interp_fnc(y_down, x_left, y_up, x_right, v11, v12, v21, v22,\n posy, posx)\n\n img.image_data = np.fromfunction(np.vectorize(generate),\n (original.shape()[0]*resolution_scaling_factor,\n original.shape()[1]*resolution_scaling_factor))\n\n if resolution_scaling_factor != 1:\n img.image_data = skimage.transform.resize(img.image_data,\n original.shape(),\n preserve_range=True)\n\n return img","def autonomousPeriodic(self):\n '''\n for i in self.dataSet:\n if i[4][0] < self.timer.get() and self.timer.get() <= i[4][1]:\n self.drive.arcadeDrive(i[0],i[1])\n self.SV1.set(i[3])\n self.sd.putValue(\"Camera\",i[5])\n else:\n self.drive.arcadeDrive(0,0)\n '''\n\n #self.auto = self.sd.getNumber(\"auto\",0)\n #test\n #if(self.auto != 1):\n if self.auto == 6:\n if self.autoState == 0:\n if self.gyro.getAngle() >= -41 and self.gyro.getAngle() <= 0:\n self.drive.arcadeDrive(0.5,-0.4)\n else:\n self.autoState = 1\n self.EC1.reset()\n if self.autoState == 1:\n if self.EC1.getDistance() <= 282 and self.EC1.getDistance() >= 0:\n self.drive.arcadeDrive(0.6,0)\n else:\n self.autoState = 2\n if self.autoState == 2:\n if self.gyro.getAngle() >= -41 and self.gyro.getAngle() <= 0:\n self.drive.arcadeDrive(0.5,0.4)\n else:\n self.autoState = 3\n self.EC1.reset()\n if self.autoState == 3:\n if self.EC1.getDistance() <= 120 and self.EC1.getDistance() >= 0:\n self.drive.arcadeDrive(0,6,0)\n else:\n self.autoState = 4\n if self.autoState == 4:\n if self.EC2.getDistance() <= 831 and self.EC2.getDistance() >= 0: #shoulder\n self.S1.set(-0.25)\n self.S2.set(-0.25)\n else:\n self.autoState = 5\n if self.autoState == 5:\n if self.EC2.getDistance() >= 831 and self.EC2.getDistance() <= 887:\n self.goldenArrowhead.set(False)\n self.S1.set(-0.25)\n self.S2.set(-0.25)\n else:\n self.autoState = 6","def update(self, deltatime):\n pass","def update(self, index, ttime):\n if self.dead:\n return 1\n self.pos[0] += self.vector[0]\n self.pos[1] += self.vector[1]\n self.rect.x += self.vector[0]\n self.rect.y += self.vector[1]\n if sum(self.vector):\n if self.vector[0] < 0:\n self.vector[0] += 1\n elif self.vector[0] > 0:\n self.vector[0] -= 1\n if self.vector[1] < 0:\n self.vector[1] += 1\n elif self.vector[1] > 0:\n self.vector[1] -= 1\n if self.game.Player.collides(self.rect) and not sum(self.vector):\n self.game.Invent.add(self.name)\n self.game.Player.headDraw(self.name, self.game.Player.player_r)\n self.dead = 1","def update(self, seconds):\n # Gravity\n self.calcGravity(seconds)\n \n # Move left/right\n self.rect.x += self.change_x\n \n # See if we hit anything\n block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n for block in block_hit_list:\n # If we are moving right,\n # set our right side to the left side of the item we hit\n if self.change_x > 0:\n self.rect.right = block.rect.left\n elif self.change_x < 0:\n # Otherwise if we are moving left, do the opposite.\n self.rect.left = block.rect.right\n \n # Move up/down\n self.rect.y += self.change_y\n \n # update arm position\n self.arm.update(seconds)\n \n # Check and see if we hit anything\n block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n for block in block_hit_list:\n \n # Reset our position based on the top/bottom of the object.\n if self.change_y > 0:\n self.rect.bottom = block.rect.top\n elif self.change_y < 0:\n self.rect.top = block.rect.bottom\n \n # Stop our vertical movement\n self.change_y = 0","def updatePos(self):\n self.timeDriving +=1\n self.pos[0] += self.vx\n self.pos[1] += self.vy","def moveBasedOnRetreatAction(self, time_passed):\n cpos = self.toScreenCoordinate()\n mpos = pygame.mouse.get_pos()\n toMouse = Vector2.from_points(cpos,mpos)\n toMouse.normalize()\n rheading = -toMouse\n \n heading = self.heading\n angle_between = heading.angle_between(rheading)\n if angle_between>=-30 and angle_between<=30:\n return\n \n distance = time_passed * self.speed\n movement = rheading * distance\n x = movement.get_x()\n y = movement.get_y()\n if not self.checkCollision(x, y) and self.checkValidCoord(x, y):\n self.move(x, y)","def update(self, current):\n\n self.times[self.index] = time.time()\n\n self.current = current\n\n # for circular PID, keep values within [-pi, pi]\n if self.circular:\n while self.current > 2*math.pi:\n self.current = self.current - 2*math.pi\n while self.current < 0:\n self.current = self.current + 2*math.pi\n\n # COMPUTE PROPORTIONAL\n\n self.error_p = self.__target - self.current\n\n # for circular PID, keep error values within [-pi, pi]\n if self.circular: \n while self.error_p > math.pi:\n self.error_p = self.error_p - 2*math.pi\n while self.error_p < -math.pi:\n self.error_p = self.error_p + 2*math.pi\n\n self.errors[self.index] = self.error_p\n if callable(self.debug_callback):\n self.debug_callback(self.errors)\n\n # COMPUTE INTEGRAL\n\n # time step here is only the diff between current and past sample\n time_step = self.times[self.index] - self.times[(self.index - 1) % self.samples]\n # impose upper bound on time step (to avoid jump from 0 to unix time)\n time_step = min(time_step, 0.1)\n self.error_i += self.error_p * time_step\n self.error_i = max(min(self.error_i, self.I_LIMIT), -self.I_LIMIT)\n\n # COMPUTE DIFFERENTIAL\n\n # time_step here is over all self.samples=5 samples\n time_step = self.times[self.index] - self.times[(self.index + 1) % self.samples]\n # impose lower bound on time step (to avoid divide by zero error)\n time_step = max(time_step, 0.001)\n self.error_d = (self.errors[self.index] \\\n - self.errors[(self.index + 1) % self.samples]) \\\n / (time_step)\n\n # increment index for next irritation\n self.index = (self.index + 1) % self.samples\n\n # COMPUTE CORRECTION\n\n correction = self.KP * self.error_p \\\n + self.KI * self.error_i \\\n + self.KD * self.error_d\n\n # safety feature in case update() is not called frequently enough\n if time_step > 0.2:\n if callable(self.debug_callback):\n self.debug_callback(\"infrequent updates, returning 0\")\n return 0\n\n if callable(self.debug_callback):\n self.debug_callback(\"target = {:2.4f} current = {:2.4f}\".format( \\\n self.__target, self.current))\n self.debug_callback(\"errors = \" + str(self.errors))\n self.debug_callback(\"e = {:2.4f} e_i = {:2.4f} e_d = {:2.4f} corr = {:2.4f}\".format( \\\n self.error_p, self.error_i, self.error_d, correction))\n\n return correction"],"string":"[\n \"def prepare_drift_correction(self, pos):\\n\\n\\t\\tprint(\\\"function not supported yet\\\")\",\n \"def refframe_correct(self, ra, dec, obstime, sobjs=None):\\n # Correct Telescope's motion\\n refframe = self.par['calibrations']['wavelengths']['refframe']\\n if refframe in ['heliocentric', 'barycentric'] \\\\\\n and self.par['calibrations']['wavelengths']['reference'] != 'pixel':\\n msgs.info(\\\"Performing a {0} correction\\\".format(self.par['calibrations']['wavelengths']['refframe']))\\n # Calculate correction\\n radec = ltu.radec_to_coord((ra, dec))\\n vel, vel_corr = wave.geomotion_correct(radec, obstime,\\n self.spectrograph.telescope['longitude'],\\n self.spectrograph.telescope['latitude'],\\n self.spectrograph.telescope['elevation'],\\n refframe)\\n # Apply correction to objects\\n msgs.info('Applying {0} correction = {1:0.5f} km/s'.format(refframe, vel))\\n if (sobjs is not None) and (sobjs.nobj != 0):\\n # Loop on slits to apply\\n gd_slitord = self.slits.slitord_id[np.logical_not(self.extract_bpm)]\\n for slitord in gd_slitord:\\n indx = sobjs.slitorder_indices(slitord)\\n this_specobjs = sobjs[indx]\\n # Loop on objects\\n for specobj in this_specobjs:\\n if specobj is None:\\n continue\\n specobj.apply_helio(vel_corr, refframe)\\n\\n # Apply correction to wavelength image\\n self.vel_corr = vel_corr\\n self.waveimg *= vel_corr\\n\\n else:\\n msgs.info('A wavelength reference frame correction will not be performed.')\",\n \"def _do_updates(self):\\n is_right = self._puzzle.is_guess_right()\\n if is_right:\\n self._puzzle.reveal_puzzle()\\n else:\\n self._jumper.cut_line()\",\n \"def drift_correction(self, pos=None, fix_triggered=False):\\n\\t\\t\\n\\t\\tif pos == None:\\n\\t\\t\\tpos = self.dispsize[0] / 2, self.dispsize[1] / 2\\n\\t\\tif fix_triggered:\\n\\t\\t\\treturn self.fix_triggered_drift_correction(pos)\\t\\t\\n\\t\\tself.draw_drift_correction_target(pos[0], pos[1])\\n\\t\\tpressed = False\\n\\t\\twhile not pressed:\\n\\t\\t\\tpressed, presstime = self.kb.get_key()\\n\\t\\t\\tif pressed:\\n\\t\\t\\t\\tif pressed == 'escape' or pressed == 'q':\\n\\t\\t\\t\\t\\tprint(\\\"libeyetribe.EyeTribeTracker.drift_correction: 'q' or 'escape' pressed\\\")\\n\\t\\t\\t\\t\\treturn self.calibrate()\\n\\t\\t\\t\\tgazepos = self.sample()\\n\\t\\t\\t\\tif ((gazepos[0]-pos[0])**2 + (gazepos[1]-pos[1])**2)**0.5 < self.pxerrdist:\\n\\t\\t\\t\\t\\treturn True\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.errorbeep.play()\\n\\t\\treturn False\",\n \"def update_dead_reckoning(self):\\n now = time.time()\\n time_diff_s = now - self._last_observation_s\\n self._last_observation_s = now\\n\\n self._prediction_step(time_diff_s)\",\n \"def non_causal_timecrop(self, length):\\n assert length < self.time_length\\n\\n cut = (self.time_length - length) / 2\\n\\n _, i_start = _find_nearest(self.times, cut)\\n _, i_end = _find_nearest(self.times, self.time_length - cut)\\n\\n h = np.fft.ifftshift(np.fft.fftshift(self.in_time)[..., i_start:i_end])\\n\\n new_response = self.from_time(self.fs, h)\\n\\n if new_response.time_length != length:\\n w = f\\\"Could not precisely shrink to {length}s with fs = {self.fs}\\\"\\n warnings.warn(w)\\n\\n return new_response\",\n \"def arm_calibration(self):\\n self.arm_motor.run_forever(speed_sp=self.MAX_SPEED)\\n while not self.touch_sensor.is_pressed:\\n time.sleep(0.01)\\n self.arm_motor.stop()\\n ev3.Sound.beep().wait()\\n arm_revolutions_for_full_range = 14.2 * 360\\n self.arm_motor.run_to_rel_pos(\\n position_sp=-arm_revolutions_for_full_range,\\n speed_sp=self.MAX_SPEED,\\n stop_action=ev3.Motor.STOP_ACTION_BRAKE)\\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\\n\\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this\\n # line is correct as is).\",\n \"def recalc_spd(*args):\\n return _ida_frame.recalc_spd(*args)\",\n \"def acoustic_lasting(self):\\n for i in range(self._number_of_events):\\n if self._number_of_events == 1:\\n self._events[i].corrected_pitch_classes += self._events[i].pitch_classes_octave\\n else:\\n if i == 0:\\n self._events[i].corrected_pitch_classes += self._events[i].pitch_classes_octave\\n self._events[i].corrected_pitch_classes += self._events[i+1].pitch_classes_octave\\n\\n elif i == self._number_of_events - 1:\\n self._events[i].corrected_pitch_classes += self._events[i].pitch_classes_octave\\n self._events[i].corrected_pitch_classes += self._events[i-1].pitch_classes_octave\\n else:\\n self._events[i].corrected_pitch_classes += self._events[i+1].pitch_classes_octave\\n self._events[i].corrected_pitch_classes += self._events[i].pitch_classes_octave\\n self._events[i].corrected_pitch_classes += self._events[i-1].pitch_classes_octave\\n\\n self._events[i].corrected_pitch_classes = self.sort_single_event_notes(self._events[i].corrected_pitch_classes)\\n self._events[i].corrected_pitch_classes = remove_identical(self._events[i].corrected_pitch_classes)\",\n \"def _update_transition(self, dt, time, direction): #pylint:disable-msg=C0103,C0301\\r\\n pass\",\n \"def sos_correction(self, ratio):\\n\\n # Correct velocities\\n self.u_mps = self.u_mps * ratio\\n self.v_mps = self.v_mps * ratio\",\n \"def handleUpdateTimer(self):\\n self.mustRun(task = self.position,\\n ret_signal = self.positionUpdate)\",\n \"def runTask1(self):\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.initialize()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.sweepDuckie()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieSuctionOn()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLiftEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppSweep()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppSuctionOff()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.reset()\\r\\n\\t\\tself._motion.terminate()\",\n \"def revolver(self):\\r\\n\\t\\tself.__revuelto=True\",\n \"def test2(self):\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.initialize()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.sweepDuckie()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieSuctionOn()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLiftEE()\\r\\n\\t\\tself._motion.terminate()\",\n \"def _calibrate(self, t_send, t_recv, server_timestamp):\\n pass\",\n \"def update(self):\\n if (self.j + self.step >= self.image.shape[0]) and (self.i + self.step >= self.image.shape[1]):\\n self.no_more_crops = True\\n elif self.i + self.step >= self.image.shape[1]:\\n self.i = 0\\n self.j += self.step\\n else:\\n self.i += self.step\",\n \"def update(self, dt):\",\n \"def update(self, dt):\",\n \"def set_up_orbit_correctors(ps_beg, delay, id_slice1, ds_slice, zplot, id_slices, U_core, lambdaref):\\n SXSS = Chicane(3.2716, 0.362, 0.830399, delay[0])\\n HXSS = Chicane(3.2, 0.3636, 0.5828, delay[1])\\n\\n OC2 = [CORR08, D1_SXSS, SXSS, D2_SXSS, QUAD09, CORR09]\\n OC3 = [CORR15, D1_HXSS, HXSS, D2_HXSS, QUAD16, CORR16]\\n\\n ps_end1 = beam_transportation(ps_beg, U_core[0])\\n\\n # ps_end1 is a 4-by-N array. N is the number of macro-particles. It is the full\\n # 4D phase space distribution at the end of the first undulator section.\\n\\n # The id of the slice on the axis in the second undulator section\\n on_axis_id_U2 = int(id_slice1+delay[0]/ds_slice+ (8*110)*lambdaref/ds_slice) # The last part is slippage\\n\\n print(on_axis_id_U2)\\n\\n ps_end_slice1 = beam_property_along_s(ps_end1, id_slices)[0:4, :]\\n ps_on_axis_2 = np.ravel(ps_end_slice1[:, on_axis_id_U2])\\n\\n # print(ps_on_axis_2)\\n\\n OC2_optimized = analyze_orbit_corrector(OC2[0], OC2[-1], OC2[1:-1], ps_on_axis_2)\\n print(OC2_optimized)\\n CORR08_new = Orbit_Corrector(OC2[0].length, OC2_optimized[0], OC2_optimized[2])\\n CORR09_new = Orbit_Corrector(OC2[-1].length, OC2_optimized[1], OC2_optimized[3])\\n\\n # The whole U2 with optimized orbit correctors\\n U2_new = [CORR08_new] + OC2[1:-1] + [CORR09_new] + U_core[1]\\n ps_end2 = beam_transportation(ps_end1, U2_new)\\n\\n # ps_end2 is a 4-by-N array. N is the number of macro-particles. It is the full\\n # 4D phase space distribution at the end of the second undulator section.\\n\\n # The id of the slice on the axis in the third undulator section\\n on_axis_id_U3 = int(id_slice1+(delay[0]+delay[1])/ds_slice +(14*110*lambdaref)/ds_slice) # The last term is the slipage\\n\\n print(on_axis_id_U3)\\n\\n ps_end_slice2 = beam_property_along_s(ps_end2, id_slices)[0:4, :]\\n ps_on_axis_3 = np.ravel(ps_end_slice2[ :, on_axis_id_U3])\\n\\n # print(ps_on_axis_3)\\n\\n OC3_optimized = analyze_orbit_corrector(OC3[0], OC3[-1], OC3[1:-1], ps_on_axis_3)\\n print(OC3_optimized)\\n CORR15_new = Orbit_Corrector(OC3[0].length, OC3_optimized[0], OC3_optimized[2])\\n CORR16_new = Orbit_Corrector(OC3[-1].length, OC3_optimized[1], OC3_optimized[3])\\n\\n U3_new = [CORR15_new] + OC3[1:-1] + [CORR16_new] + U_core[2]\\n\\n Undulator_Beamline = U_core[0]+U2_new+U3_new\\n\\n return Undulator_Beamline\",\n \"def update_apc22(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n if len(self._old) >= 1:\\n\\n\\n try:\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\\n\\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n pos = self.pos+delta_t/2.0*(self.vel+vel)\\n vel = self.vel+delta_t/2.0*(self.force(pos, vel, self.time+delta_t,\\n drag=False)+kap[1])\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n\\n self.time += delta_t\\n\\n else:\\n ## reduced to using the Adams first order method for the first timestep:\\n\\n kap = update_apc1(self)\\n\\n self.set_old(kap, 1)\\n\\n return kap\",\n \"def _update_active_rides_fast(self, time: datetime) -> None:\\n pass\",\n \"def adjust_image_data(self):\\r\\n\\r\\n print('Adjusting image data: ')\\r\\n\\r\\n if self.removeFirstSequence: # used to remove the first trial from the sequence\\r\\n\\r\\n frames_per_rep = self.nFrames/self.nrepetitions\\r\\n\\r\\n self.imageData = self.imageData[frames_per_rep:, :, :]\\r\\n\\r\\n self.nFrames = self.imageData.shape[0]\\r\\n\\r\\n self.nrepetitions = int(self.nFrames/(self.period * self.framerate))\\r\\n\\r\\n self.times = np.arange(0, self.nFrames/self.framerate, 1.0/self.framerate)\\r\\n\\r\\n \\r\\n\\r\\n # first squeeze the image to 3d if it is 4d\\r\\n\\r\\n maxt = np.max(self.times) # find last image time\\r\\n\\r\\n sh = self.imageData.shape\\r\\n\\r\\n if len(sh) == 4:\\r\\n\\r\\n self.imageData = self.imageData.squeeze()\\r\\n\\r\\n sh = self.imageData.shape\\r\\n\\r\\n dt = np.mean(np.diff(self.times)) # get the mean dt\\r\\n\\r\\n n_Periods = int((maxt+dt)/self.period) # how many full periods in the image set - include the first?\\r\\n\\r\\n if self.nrepetitions > 0 and self.nrepetitions < n_Periods:\\r\\n\\r\\n n_Periods = self.nrepetitions\\r\\n\\r\\n n_PtsPerCycle = int(np.floor(self.period/dt)); # estimate image points in a stimulus cycle\\r\\n\\r\\n ndt = self.period/n_PtsPerCycle\\r\\n\\r\\n self.imageData = self.imageData[range(0, n_Periods*n_PtsPerCycle),:,:] # reduce to only what we need\\r\\n\\r\\n print (' Adjusted image info')\\r\\n\\r\\n print (\\\" # Periods: %d Pts/cycle: %d Cycle dt %8.4fs (%8.3fHz) Cycle: %7.4fs\\\" %(n_Periods, n_PtsPerCycle, ndt, 1.0/ndt, self.period))\\r\\n\\r\\n self.print_image_info()\",\n \"def on_correct_utterance(self, event):\\n if not self.quitting:\\n number = event.utterance_number\\n instance = event.instance_name\\n self.the_mediator.correct_utterance(instance, number)\",\n \"def update(self):\\n super().update()\\n self.decelerate()\\n #check for a collisison with all rock types\\n self.checkForRockCollisions()\\n #when the ship gets hit by a rock, it enters a period of invulnerability. we need to make sure that period ends at the proper time\\n self.checkGracePeriodDuration()\\n #movement stuff\\n if (self.isAcceleratingForward):\\n self.accelerateForwards()\\n if (self.isRotatingLeft):\\n self.rotateLeft()\\n if (self.isRotatingRight):\\n self.rotateRight()\",\n \"def motion_correct(mov, max_iters=5, shift_threshold=1., reslice=slice(None,None), in_place=True, verbose=True, compute_kwargs={}, apply_kwargs={}):\\n if not in_place:\\n mov = mov.copy()\\n mov = mov[reslice]\\n \\n all_vals = []\\n for it in range(max_iters):\\n if verbose:\\n print('Iteration {}'.format(it)); sys.stdout.flush()\\n template,vals = compute_motion(mov, **compute_kwargs)\\n mov = apply_motion_correction(mov, vals, **apply_kwargs)\\n maxshifts = np.abs(vals[:,[0,1]]).max(axis=0)\\n all_vals.append(vals)\\n if verbose:\\n print('Shifts: {}'.format(str(maxshifts))); sys.stdout.flush()\\n if np.all(maxshifts < shift_threshold):\\n break\\n\\n # combine values from iterations\\n all_vals = np.array(all_vals)\\n return_vals = np.empty([all_vals.shape[1],all_vals.shape[2]])\\n return_vals[:,[0,1]] = all_vals[:,:,[0,1]].sum(axis=0)\\n return_vals[:,2] = all_vals[-1,:,2]\\n\\n return mov,template,return_vals\",\n \"def update(self):\\n self.syncSpriteCoordinates()\\n self.moveBasedOnCurrentMomentum()\\n #self.decelerate()\\n self.checkCanvasBoundsAndWrap()\",\n \"def routine(self):\\n # Read in time table data\\n\\n _, t1, t2, t3, t4, _, _ = self.times\\n _, p1, p2, p3, p4, _, _ = self.positions\\n \\n # Carry out routine\\n self.throttle_valve_set(p1)\\n self.valve_pulse(1, t1)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return\\n \\n self.throttle_valve_set(p2) \\n self.purge(t2)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return\\n \\n \\n ## Check selected method for t3 in main recipe table,\\n ## Carry out an operation based on the selection\\n mode = self.settings['t3_method']\\n self.throttle_valve_set(p3)\\n \\n if mode == 'Shutter':\\n self.shutter_pulse(t3)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return\\n elif mode == 'PV':\\n self.valve_pulse(2, t3)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return \\n elif mode == 'RF':\\n power = self.seren.settings['recipe_power']\\n self.plasma_dose(t3, power)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return\\n# elif mode == 'PV/Purge':\\n# '''Run sub_cyc number of subroutine cycles.\\n# Subroutine consists of a valve pulse and a purge period.'''\\n# for _ in range(sub_cyc):\\n# self.valve_pulse(2, sub_t0)\\n# self.purge(sub_t1)\\n \\n \\n self.throttle_valve_set(p4)\\n self.purge(t4)\\n if self.interrupt_measurement_called:\\n self.shutoff()\\n return\",\n \"def target_velocity(self, time):\\n pass\",\n \"def target_velocity(self, time):\\n pass\",\n \"def on_mouse_move(self, event):\\n\\n # self.view = 1 * np.eye(4, dtype=np.float32)\\n # self.model = 1 * np.eye(4, dtype=np.float32)\\n\\n # self.translate -= event.delta[1]\\n # self.translate = max(-1, self.translate)\\n # print(event.delta[1])\\n # print(self.translate)\\n # self.view = translate((0, 0, -self.translate))\\n # self.game_program['u_view'] = self.view\\n # self.game_program['u_size'] = 5 / self.translate\\n # self.view = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.view\\n # self.model = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.model\\n # print(self.view)\\n\\n # self.game_program['u_model'] = self.model\\n # self.game_program['u_view'] = self.view\\n\\n x, y = event.pos\\n #print(x, y)\\n self.x_offset, self.y_offset = x - self.last_x, - (y - self.last_y)\\n self.last_x, self.last_y = x, y\\n self.x_offset *= self.sensitivity\\n self.y_offset *= self.sensitivity\\n\\n self.yaw, self.pitch = self.yaw - self.x_offset, self.pitch + self.y_offset\\n self.rot_y(self.yaw * np.pi / 180)\\n self.rot_x(self.pitch * np.pi / 180)\\n\\n self.view = np.dot(self.rot_mat_y, self.rot_mat_x)\\n self.game_program['u_view'] = self.view\\n\\n self.update()\",\n \"def _correct_overflow(timestamps, detectors, overflow_ch, overflow):\\n index_overflows = np.where((detectors == overflow_ch))[0]\\n for n, (idx1, idx2) in enumerate(zip(index_overflows[:-1],\\n index_overflows[1:])):\\n timestamps[idx1:idx2] += (n + 1)*overflow\\n timestamps[idx2:] += (n + 2)*overflow\",\n \"def on_timer(self, event):\\n \\n o = Unicorn()\\n data = o.get_data(rt)\\n k = len(data[0])\\n y[:, :-k] = y[:, k:]\\n y[:, -k:] = remap((data), -40, 40, -1, 1 ) \\n t2 = _thread.start_new_thread(printT, ())\\n #y2 = np.array([lfilter(b, a, y[i]) for i in range(17)])\\n self.program['a_position'].set_data(y.ravel().astype(np.float32))\\n self.update()\",\n \"def stabilize(td):\\n\\n def correct_saccade1(data):\\n data.x -= np.rint(3.5 * data.ts / 105e3).astype(np.uint16)\\n data.y -= np.rint(7 * data.ts / 105e3).astype(np.uint16)\\n return data\\n\\n def correct_saccade2(data):\\n data.x -= np.rint(3.5 + 3.5 * (data.ts - 105e3) / 105e3).astype(\\n np.uint16)\\n data.y -= np.rint(7 - 7 * (data.ts - 105e3) / 105e3).astype(np.uint16)\\n return data\\n\\n def correct_saccade3(data):\\n data.x -= np.rint(7 - 7 * (data.ts - 210e3) / 105e3).astype(np.uint16)\\n return data\\n\\n copy = np.piecewise(td.data,\\n [td.data.ts <= 105e3,\\n (td.data.ts > 105e3) & (td.data.ts <= 210e3),\\n (td.data.ts > 210e3)],\\n [correct_saccade1, correct_saccade2,\\n correct_saccade3]).view(np.recarray)\\n\\n # after saccades, we might end up with invalid x and y values, have to\\n # correct these\\n x_vals = copy.x\\n y_vals = copy.y\\n copy.x = np.piecewise(x_vals,\\n [x_vals >= 65000,\\n (x_vals < 65000) & (x_vals >= td.width),\\n x_vals < td.width],\\n [0, td.width - 1, lambda x: x])\\n copy.y = np.piecewise(y_vals,\\n [y_vals >= 65000,\\n (y_vals < 65000) & (y_vals >= td.height),\\n y_vals < td.height],\\n [0, td.height - 1, lambda y: y])\\n\\n return copy\",\n \"def arm_calibration(self):\\n self.arm_motor.run_forever(speed_sp=900)\\n while not self.touch_sensor.is_pressed:\\n time.sleep(0.01)\\n self.arm_motor.stop(stop_action=\\\"brake\\\")\\n ev3.Sound.beep()\\n self.arm_motor.run_to_rel_pos(\\n speed_sp=900, position_sp=-5100)\\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\\n print('motor is no longer running')\\n ev3.Sound.beep()\\n self.arm_motor.position = 0\",\n \"def cool(self):\\n self.t = self.t - 1\",\n \"def timerange_change():\\n global transformer_top\\n assert transformer_top is not None\\n global transformer_bottom\\n assert transformer_bottom is not None\\n global label_encoders_per_modality\\n assert label_encoders_per_modality is not None\\n global DEVICE\\n assert DEVICE is not None\\n global USE_LOCAL_CONDITIONING\\n assert USE_LOCAL_CONDITIONING is not None\\n global partial_sample_model\\n assert partial_sample_model is not None\\n\\n layer = str(request.args.get('layer'))\\n temperature = request.args.get('temperature', type=float)\\n start_index_top = request.args.get('start_index_top', type=int)\\n uniform_sampling = bool(strtobool(\\n request.args.get('uniform_sampling', type=str,\\n default=\\\"False\\\")))\\n\\n # try to retrieve local conditioning map in the request's JSON payload\\n (class_conditioning_top_map, class_conditioning_bottom_map,\\n input_conditioning_top, input_conditioning_bottom) = (\\n parse_conditioning(request)\\n )\\n global_instrument_family_str = str(\\n request.args.get('instrument_family_str'))\\n global_pitch = request.args.get('pitch', type=int)\\n global_class_conditioning = {\\n 'pitch': global_pitch,\\n 'instrument_family_str': global_instrument_family_str\\n }\\n if (not USE_LOCAL_CONDITIONING\\n or not transformer_bottom.local_class_conditioning):\\n class_conditioning_bottom = global_class_conditioning.copy()\\n class_conditioning_tensors_bottom = make_conditioning_tensors(\\n class_conditioning_bottom,\\n label_encoders_per_modality)\\n class_conditioning_bottom_map = None\\n else:\\n class_conditioning_bottom = class_conditioning_tensors_bottom = None\\n\\n top_code, bottom_code = parse_codes(request)\\n\\n # extract frame to operate on\\n end_index_top = start_index_top + transformer_top.shape[1]\\n top_code_frame = top_code[..., start_index_top:end_index_top]\\n\\n upsampling_ratio_time = (transformer_bottom.shape[1]\\n // transformer_top.shape[1])\\n start_index_bottom = upsampling_ratio_time * start_index_top\\n end_index_bottom = start_index_bottom + transformer_bottom.shape[1]\\n bottom_code_frame = bottom_code[..., start_index_bottom:end_index_bottom]\\n generation_mask_batched = parse_mask(request).to(DEVICE)\\n\\n time_indexes_top = make_time_indexes(start_index_top,\\n top_code.shape[-1],\\n transformer_top.shape[-1])\\n time_indexes_bottom = make_time_indexes(start_index_bottom,\\n bottom_code.shape[-1],\\n transformer_bottom.shape[-1])\\n\\n if layer == 'bottom':\\n if not uniform_sampling:\\n bottom_code_resampled_frame = partial_sample_model(\\n model=transformer_bottom,\\n condition=top_code_frame,\\n batch_size=1,\\n codemap_size=transformer_bottom.shape,\\n temperature=temperature,\\n class_conditioning=class_conditioning_tensors_bottom,\\n local_class_conditioning_map=class_conditioning_bottom_map,\\n initial_code=bottom_code_frame,\\n mask=generation_mask_batched,\\n time_indexes_source=time_indexes_top,\\n time_indexes_target=time_indexes_bottom,\\n )\\n else:\\n bottom_code_resampled_frame = bottom_code_frame.masked_scatter(\\n generation_mask_batched,\\n torch.randint_like(bottom_code_frame,\\n high=transformer_bottom.n_class_target)\\n )\\n\\n bottom_code_resampled = bottom_code\\n bottom_code_resampled[..., start_index_bottom:end_index_bottom] = (\\n bottom_code_resampled_frame)\\n\\n # create JSON response\\n response = make_response(top_code, bottom_code_resampled,\\n input_conditioning_top,\\n input_conditioning_bottom)\\n elif layer == 'top':\\n if (not USE_LOCAL_CONDITIONING\\n or not transformer_top.local_class_conditioning):\\n # try to retrieve conditioning from http arguments\\n class_conditioning_top = global_class_conditioning.copy()\\n class_conditioning_tensors_top = make_conditioning_tensors(\\n class_conditioning_top,\\n label_encoders_per_modality)\\n class_conditioning_top_map = None\\n else:\\n class_conditioning_top = class_conditioning_tensors_top = None\\n\\n if not uniform_sampling:\\n if transformer_top.self_conditional_model:\\n condition = top_code_frame\\n else:\\n condition = None\\n top_code_resampled_frame = partial_sample_model(\\n model=transformer_top,\\n condition=condition,\\n device=DEVICE,\\n batch_size=1,\\n codemap_size=transformer_top.shape,\\n temperature=temperature,\\n class_conditioning=class_conditioning_tensors_top,\\n local_class_conditioning_map=class_conditioning_top_map,\\n initial_code=top_code_frame,\\n mask=generation_mask_batched,\\n time_indexes_source=time_indexes_top,\\n time_indexes_target=time_indexes_top,\\n )\\n else:\\n top_code_resampled_frame = top_code_frame.masked_scatter(\\n generation_mask_batched,\\n torch.randint_like(top_code_frame,\\n high=transformer_top.n_class_target)\\n )\\n\\n top_code_resampled = top_code\\n top_code_resampled[..., start_index_top:end_index_top] = (\\n top_code_resampled_frame)\\n\\n upsampling_ratio_frequency = (transformer_bottom.shape[0]\\n // transformer_top.shape[0])\\n generation_mask_bottom_batched = (\\n generation_mask_batched\\n .repeat_interleave(upsampling_ratio_frequency, -2)\\n .repeat_interleave(upsampling_ratio_time, -1)\\n )\\n bottom_code_resampled_frame = partial_sample_model(\\n model=transformer_bottom,\\n condition=top_code_resampled_frame,\\n device=DEVICE,\\n batch_size=1,\\n codemap_size=transformer_bottom.shape,\\n temperature=temperature,\\n class_conditioning=class_conditioning_tensors_bottom,\\n local_class_conditioning_map=class_conditioning_bottom_map,\\n initial_code=bottom_code_frame,\\n mask=generation_mask_bottom_batched,\\n time_indexes_source=time_indexes_top,\\n time_indexes_target=time_indexes_bottom,\\n )\\n\\n # update conditioning map\\n bottom_mask = generation_mask_bottom_batched[0]\\n new_conditioning_map_bottom = {\\n modality: masked_fill(modality_conditioning,\\n bottom_mask,\\n class_conditioning_bottom[modality])\\n for modality, modality_conditioning\\n in input_conditioning_bottom.items()\\n }\\n\\n bottom_code_resampled = bottom_code\\n bottom_code_resampled[..., start_index_bottom:end_index_bottom] = (\\n bottom_code_resampled_frame)\\n\\n # create JSON response\\n response = make_response(top_code_resampled, bottom_code_resampled,\\n input_conditioning_top,\\n new_conditioning_map_bottom)\\n\\n return response\",\n \"def test_fix_metadata_correct_time(self):\\n fixed_cube = self.fix.fix_metadata([self.cube])[0]\\n time_coord = fixed_cube.coord('time')\\n np.testing.assert_allclose(time_coord.points, [0, 1])\\n assert time_coord.bounds is None\",\n \"def photometric_calibration():\\n pass\",\n \"def arm_calibration(self):\\n # DONE: 3. Implement the arm calibration movement by fixing the code below (it has many bugs). It should to this:\\n # Command the arm_motor to run forever in the positive direction at max speed.\\n # Create an infinite while loop that will block code execution until the touch sensor's is_pressed value is True.\\n # Within that loop sleep for 0.01 to avoid running code too fast.\\n # Once past the loop the touch sensor must be pressed. So stop the arm motor quickly using the brake stop action.\\n # Make a beep sound\\n # Now move the arm_motor 14.2 revolutions in the negative direction relative to the current location\\n # Note the stop action and speed are already set correctly so we don't need to specify them again\\n # Block code execution by waiting for the arm to finish running\\n # Make a beep sound\\n # Set the arm encoder position to 0 (the last line below is correct to do that, it's new so no bug there)\\n\\n # Code that attempts to do this task but has MANY bugs (nearly 1 on every line). Fix them!\\n self.arm_motor.run_forever(speed_sp=900)\\n while not self.touch_sensor.is_pressed:\\n time.sleep(0.01)\\n self.arm_motor.stop(stop_action='brake')\\n ev3.Sound.beep().wait()\\n # time.sleep(2)\\n # arm_motor.stop(stop_action='brake')\\n\\n arm_revolutions_for_full_range = 14.2 * 360\\n self.arm_motor.run_to_rel_pos(position_sp=-arm_revolutions_for_full_range, speed_sp=900)\\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\\n ev3.Sound.beep()\\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this line is correct as is).\",\n \"def test_rolling_before_analysis(self):\\n cheese = TomoCheese.from_demo_images()\\n cheese.analyze()\\n original_roi_1 = copy.copy(cheese.module.rois[\\\"1\\\"].pixel_value)\\n for img in cheese.dicom_stack:\\n img.roll(direction=\\\"x\\\", amount=20)\\n cheese.analyze()\\n new_roi_1 = cheese.module.rois[\\\"1\\\"].pixel_value\\n assert math.isclose(original_roi_1, new_roi_1, abs_tol=3)\",\n \"def post_process(self):\\n\\t\\ti_s = 0\\n\\t\\ti_e = 0\\n\\t\\tif self.trans_t_dict[0][1] == 0:\\n\\t\\t\\tif len(self.noise_itv) == 0:\\n\\t\\t\\t\\tself.trans_t_dict[0][1] = self.fake_start_offset\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.trans_t_dict[0][1] = self.noise_itv[0][1] # start_offset\\n\\t\\t\\tself.trans_t_dict[0][2] = 0.1\\n\\t\\tif self.trans_t_dict[len(self.trans_t_dict)-1][1] == 0:\\n\\t\\t\\tif len(self.noise_itv) == 0:\\n\\t\\t\\t\\tself.trans_t_dict[len(self.trans_t_dict)-1][1] = self.fake_end_offset\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.trans_t_dict[len(self.trans_t_dict)-1][1] = self.noise_itv[-1][0] # end_offset\\n\\t\\t\\tself.trans_t_dict[len(self.trans_t_dict)-1][2] = 0.1\\n\\n\\t\\twhile i_s < len(self.trans_t_dict):\\n\\t\\t\\twhile i_s < len(self.trans_t_dict) and self.trans_t_dict[i_s][1] != 0:\\n\\t\\t\\t\\ti_s += 1\\n\\t\\t\\tif i_s == len(self.trans_t_dict):\\n\\t\\t\\t\\ti_e = len(self.trans_t_dict)\\n\\t\\t\\tif i_s < len(self.trans_t_dict):\\n\\t\\t\\t\\ti_s -= 1\\n\\t\\t\\t\\ti_e = i_s + 1\\n\\t\\t\\t\\twhile i_e < len(self.trans_t_dict) and self.trans_t_dict[i_e][1] == 0:\\n\\t\\t\\t\\t\\ti_e += 1\\n\\t\\t\\t\\tif i_e == len(self.trans_t_dict):\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\t\\t# incorperate the noise inverval\\n\\t\\t\\t\\ts_time = self.trans_t_dict[i_s][1]\\n\\t\\t\\t\\te_time = self.trans_t_dict[i_e][1]\\n\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tfor ts in self.noise_itv:\\n\\t\\t\\t\\t\\tif len(ts) == 2:\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\ttime1 = ts[0]\\n\\t\\t\\t\\t\\t\\ttime2 = ts[1]\\n\\t\\t\\t\\t\\t\\tif s_time < time1 and time2 < e_time:\\n\\t\\t\\t\\t\\t\\t\\te_time = min(e_time, time1)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttime0 = ts[0]\\n\\t\\t\\t\\t\\t\\tif s_time < time0 and time0 < e_time:\\n\\t\\t\\t\\t\\t\\t\\te_time = min(e_time, time0)\\n\\t\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\t\\tchar_len = 0\\n\\t\\t\\t\\tfor i in range(i_s, i_e):\\n\\t\\t\\t\\t\\tchar_len += len(self.trans_t_dict[i][0])\\n\\t\\t\\t\\t# ratio = float(self.trans_t_dict[i_e][1]-self.trans_t_dict[i_s][1]) / float(char_len)\\n\\t\\t\\t\\tratio = float(e_time - s_time) / float(char_len)\\n\\t\\t\\t\\tchar_len = 0\\n\\t\\t\\t\\t# s_time = self.trans_t_dict[i_s][1]\\n\\t\\t\\t\\tfor i in range(i_s+1, i_e):\\n\\t\\t\\t\\t\\tchar_len += len(self.trans_t_dict[i-1][0])\\n\\t\\t\\t\\t\\tself.trans_t_dict[i][1] = s_time + char_len * ratio\\n\\t\\t\\t\\t\\tself.trans_t_dict[i][2] = len(self.trans_t_dict[i][0]) * ratio\\n\\t\\t\\ti_s = i_e\",\n \"def auto_correction(self):\\n\\n if self.mi_standard_fb is not None:\\n try: \\n is_st_fb_running = self.mi_standard_fb.is_running()\\n except Exception as e:\\n logger.warning(\\\"error during status of st. FB reading: \\\" + str(e))\\n is_st_fb_running = False\\n \\n if self.mi_standard_fb is not None and is_st_fb_running:\\n logger.info(\\\"auto_correction: St.FB is running. Stop Ad. FB\\\")\\n self.stop_feedback()\\n return 0\\n \\n is_nan = self.ref_orbit_calc()\\n if is_nan:\\n logger.warning(\\\"auto_correction: nan in the ref orbit. Pause 1 sec\\\")\\n time.sleep(1)\\n return\\n\\n start = time.time()\\n stop_flag = self.correct()\\n print(\\\"correct: \\\", time.time() - start)\\n time.sleep(0.01)\\n if not stop_flag:\\n start = time.time()\\n self.apply_kicks()\\n print(\\\"apply_kicks: \\\", time.time() - start)\\n self.sase_hist.append(self.target_filtered[-1])\\n self.le_warn.clear()\\n else:\\n logger.warning(\\\"auto_correction: stop_flag = True. Pause 1 sec\\\")\\n self.le_warn.clear()\\n self.le_warn.setText(\\\"Stop flag. Kicks are not applied\\\")\\n self.le_warn.setStyleSheet(\\\"color: red\\\")\\n\\n time.sleep(1)\",\n \"def shifter(self):\\n #self.BA_shift = self.timeshift_latitude(self.latB, self.latA)\\n #self.BC_shift = self.timeshift_latitude(self.latB, self.latC)\\n\\n\\n self.shifted = True #changing boolean to True when function is called.\\n\\n secondsA = self.secondsA\\n secondsB = self.secondsB\\n secondsC = self.secondsC\\n\\n NeA = self.holefill(self.NeA, secondsA)\\n NeB = self.holefill(self.NeB, secondsB)\\n NeC = self.holefill(self.NeC, secondsC)\\n\\n start = 0\\n stop = len(NeA) - np.max(np.array([self.BA_shift, self.BC_shift]))\\n\\n startA = start + self.BA_shift\\n stopA = stop + self.BA_shift\\n\\n startC = start + self.BC_shift\\n stopC = stop + self.BC_shift\\n\\n NeA = NeA[startA:stopA]\\n NeB = NeB[start:stop]\\n NeC = NeC[startC:stopC]\\n\\n longA = self.holefill(self.longA, secondsA)\\n longB = self.holefill(self.longB, secondsB)\\n longC = self.holefill(self.longC, secondsC)\\n longA = longA[startA:stopA]\\n longB = longB[start:stop]\\n longC = longC[startC:stopC]\\n\\n latA = self.holefill(self.latA, secondsA)\\n latB = self.holefill(self.latB, secondsB)\\n latC = self.holefill(self.latC, secondsC)\\n latA = latA[startA:stopA]\\n latB = latB[start:stop]\\n latC = latC[startC:stopC]\\n\\n radA = self.holefill(self.radA, secondsA)\\n radB = self.holefill(self.radB, secondsB)\\n radC = self.holefill(self.radC, secondsC)\\n radA = radA[startA:stopA]\\n radB = radB[start:stop]\\n radC = radC[startC:stopC]\\n\\n velA = self.holefill(self.velA, secondsA)\\n velB = self.holefill(self.velB, secondsB)\\n velC = self.holefill(self.velC, secondsC)\\n velA = velA[startA:stopA]\\n velB = velB[start:stop]\\n velC = velC[start:stop]\\n\\n altA = self.holefill(self.altA, secondsA)\\n altB = self.holefill(self.altB, secondsB)\\n altC = self.holefill(self.altC, secondsC)\\n altA = altA[startA:stopA]\\n altB = altB[start:stop]\\n altC = altC[startC:stopC]\\n\\n\\n mlatA = self.holefill(self.mlatA, secondsA)\\n mlatB = self.holefill(self.mlatB, secondsB)\\n mlatC = self.holefill(self.mlatC, secondsC)\\n mlatA = mlatA[startA:stopA]\\n mlatB = mlatB[start:stop]\\n mlatC = mlatC[startC:stopC]\\n\\n mlongA = self.holefill(self.mlongA, secondsA)\\n mlongB = self.holefill(self.mlongB, secondsB)\\n mlongC = self.holefill(self.mlongC, secondsC)\\n mlongA = mlongA[startA:stopA]\\n mlongB = mlongB[start:stop]\\n mlongC = mlongC[startC:stopC]\\n\\n mltA = self.holefill(self.mltA, secondsA)\\n mltB = self.holefill(self.mltB, secondsB)\\n mltC = self.holefill(self.mltC, secondsC)\\n mltA = mltA[startA:stopA]\\n mltB = mltB[start:stop]\\n mltC = mltC[startC:stopC]\\n\\n secondsA = self.holefill(secondsA, secondsA)\\n secondsB = self.holefill(secondsB, secondsB)\\n secondsC = self.holefill(secondsC, secondsC)\\n secondsA = secondsA[startA:stopA]\\n secondsB = secondsB[start:stop]\\n secondsC = secondsC[startC:stopC]\\n\\n indsA = np.nonzero(secondsA)[0]\\n indsB = np.nonzero(secondsB)[0]\\n indsC = np.nonzero(secondsC)[0]\\n\\n inds = np.intersect1d(indsA, indsB)\\n inds = np.intersect1d(inds, indsC)\\n\\n self.NeA = NeA[inds]\\n self.NeB = NeB[inds]\\n self.NeC = NeC[inds]\\n\\n self.longA = longA[inds]\\n self.longB = longB[inds]\\n self.longC = longC[inds]\\n\\n self.latA = latA[inds]\\n self.latB = latB[inds]\\n self.latC = latC[inds]\\n\\n self.radA = radA[inds]\\n self.radB = radB[inds]\\n self.radC = radC[inds]\\n\\n self.velA = velA[inds]\\n self.velB = velB[inds]\\n self.velC = velC[inds]\\n\\n self.altA = altA[inds]\\n self.altB = altB[inds]\\n self.altC = altC[inds]\\n\\n self.mlatA = mlatA[inds]\\n self.mlatB = mlatB[inds]\\n self.mlatC = mlatC[inds]\\n\\n self.mlongA = mlongA[inds]\\n self.mlongB = mlongB[inds]\\n self.mlongC = mlongC[inds]\\n\\n self.mltA = mltA[inds]\\n self.mltB = mltB[inds]\\n self.mltC = mltC[inds]\\n\\n self.secondsA = secondsA[inds]\\n self.secondsB = secondsB[inds]\\n self.secondsC = secondsC[inds]\",\n \"def _step(self, board, elapsedTime):\\n\\t\\tpass\",\n \"def auto_resting(self):\\n self.image_list = self.animation_dict[self.direction]\\n self.image = self.image_list[self.index]\\n\\n if self.rect.y % 32 != 0:\\n self.correct_position(self.rect.y)\\n if self.rect.x % 32 != 0:\\n self.correct_position(self.rect.x)\\n\\n if (self.current_time - self.move_timer) > 2000:\\n direction_list = ['up', 'down', 'left', 'right']\\n random.shuffle(direction_list)\\n direction = direction_list[0]\\n self.begin_auto_moving(direction)\\n self.move_timer = self.current_time\",\n \"def step(self, time_delta: float, game_data):\\n if self.get_invincible(): # if become invincible\\n if time.time() - self._invincible_time > 10: # if the time past 10 seconds\\n self.set_invincible(False) # remove the buff\\n\\n if self.get_velocity()[0] != 0: # if moved horizontally\\n self._image_time += time_delta # count time while move\\n # set the order of image id while moving, generally it's 3-2-1-2-3-...\\n if self._image_time <= 0.1: # if time past 0.1 second\\n self._image = '3' # set image id to '3'\\n elif 0.1 < self._image_time <= 0.2 or 0.3 < self._image_time <= 0.4:\\n # if time past 0.2 or 0.4 second\\n self._image = '2' # set image id to '2'\\n elif 0.2 < self._image_time <= 0.3: # if time past 0.3 second\\n self._image = '1' # set image id to '1'\\n elif self._image_time > 0.4: # reset the cycle\\n self._image = '3'\\n self._image_time = 0\\n else: # if not moving\\n self._image = 'default' # set it with default image\",\n \"def calibrate(self):\\n\\t\\tLTOGRIGHT = []\\n\\t\\tLTOGUP = []\\n\\t\\tRTOGRIGHT = []\\n\\t\\tRTOGUP = []\\n\\t\\tstart = time.time()\\n\\t\\tcalibration_time = 5.0\\n\\t\\twhile time.time() - start < calibration_time:\\n\\t\\t\\tevents = pygame.event.get()\\n\\t\\t\\tfor event in events:\\n\\t\\t\\t\\tif event.type == pygame.JOYAXISMOTION:\\n\\t\\t\\t\\t\\tLTOGRIGHT.append(self.joystick.get_axis(self.LTOGRIGHT))\\n\\t\\t\\t\\t\\tLTOGUP.append(-self.joystick.get_axis(self.LTOGUP))\\n\\t\\t\\t\\t\\tRTOGRIGHT.append(self.joystick.get_axis(self.RTOGRIGHT))\\n\\t\\t\\t\\t\\tRTOGUP.append(-self.joystick.get_axis(self.RTOGUP))\\n\\n\\t\\t# calibration sets highest value equal to 1.0\\n\\t\\tself.calibration[0] = 1.0/max(LTOGRIGHT)\\n\\t\\tself.calibration[1] = -1.0/min(LTOGRIGHT)\\n\\t\\tself.calibration[2] = -1.0/min(LTOGUP)\\n\\t\\tself.calibration[3] = 1.0/max(LTOGUP)\\n\\t\\tself.calibration[4] = 1.0/max(RTOGRIGHT)\\n\\t\\tself.calibration[5] = -1.0/min(RTOGRIGHT)\\n\\t\\tself.calibration[6] = -1.0/min(RTOGUP)\\n\\t\\tself.calibration[7] = 1.0/max(RTOGUP)\",\n \"def apply_motion_correction(mov, shifts, interpolation=None, crop=None, in_place=False, verbose=True):\\n if interpolation is None and cv2 is not None:\\n interpolation = cv2.INTER_LINEAR\\n\\n if not in_place:\\n mov=mov.copy()\\n\\n if mov.ndim==2:\\n mov = mov[None,...]\\n\\n if type(shifts) in [str] and mov.filename:\\n shifts,crop_ = retrieve_motion_correction_data(shifts, mov.filename)\\n if crop is None:\\n crop = crop_\\n\\n if shifts.ndim==1:\\n shifts = shifts[None,...]\\n\\n if shifts.ndim==2 and shifts.shape[1]==3:\\n shifts = shifts[:,:2]\\n\\n assert shifts.ndim==2 and shifts.shape[1]==2\\n\\n t,h,w=mov.shape\\n if verbose:\\n print('Applying shifts:')\\n pbar = ProgressBar(maxval=len(mov)).start()\\n for i,frame in enumerate(mov):\\n sh_x_n, sh_y_n = shifts[i]\\n if cv2 is not None:\\n M = np.float32([[1,0,sh_x_n],[0,1,sh_y_n]]) \\n mov[i] = cv2.warpAffine(frame,M,(w,h),flags=interpolation)\\n elif cv2 is None:\\n M = np.float32([[1,0,sh_y_n],[0,1,sh_x_n],[0,0,1]]) \\n transform = sktf.AffineTransform(matrix=M)\\n mov[i] = sktf.warp(frame, transform)\\n if verbose:\\n pbar.update(i)\\n\\n if verbose:\\n pbar.finish()\\n\\n if crop:\\n if crop == True:\\n ymax = int(min([0, min(shifts[:,0])]) or None)\\n xmax = int(min([0, min(shifts[:,1])]) or None)\\n ymin = int(max(shifts[:,0]))\\n xmin = int(max(shifts[:,1]))\\n elif isinstance(crop, PF_numeric_types):\\n crop = int(crop)\\n ymax,xmax = -crop,-crop\\n ymin,xmin = crop,crop\\n mov = mov[:, ymin:ymax, xmin:xmax]\\n\\n return mov.squeeze()\",\n \"def always_touching(self):\\n assert int(self.snake[0].real - self.snake[1].real) in [1, 0, -1] and int(\\n self.snake[0].real - self.snake[1].real) in [1, 0, -1]\",\n \"def _doCalibration(self):\\n self._cmdCalibration(2)\",\n \"def chacha(self):\\n self.right()\\n time.sleep(2)\\n self.stop()\\n self.back()\\n time.sleep(1)\\n self.servo(1000)\\n time.sleep(1)\\n self.stop()\\n self.fwd()\\n time.sleep(1)\\n self.stop()\\n self.servo(2000)\\n time.sleep(1)\\n self.stop()\\n self.left()\\n time.sleep(2)\\n self.stop()\",\n \"def run_go_to_star(self):\\n if self.altitude != self.tele_altitude or self.azimuth != self.tele_azimuth:\\n self.azimuth_update()\\n self.altitude_update()\",\n \"def punched(self):\\n if not self.dizzy:\\n self.dizzy = 1\\n self.original = self.image\\n Chimp.count_punch += 1\",\n \"def CorrectStokes(self, t, fluid):\\n\\n\\t\\tself.SetTime(t + fluid.dt)\\n\\t\\t\\n\\t\\tself.F = (self.Tether - self.X)\\n\\t\\tf = self.F.sum(axis=0) / self.Nb\\n\\n\\t\\tself.X += f\\n\\n\\t\\tfluid.u += f / fluid.dt\",\n \"def compute_tide_corrections(\\n x: np.ndarray, y: np.ndarray, delta_time: np.ndarray,\\n DIRECTORY: str | pathlib.Path | None = None,\\n MODEL: str | None = None,\\n ATLAS_FORMAT: str = 'netcdf',\\n GZIP: bool = False,\\n DEFINITION_FILE: str | pathlib.Path | None = None,\\n EPSG: str | int = 3031,\\n EPOCH: list | tuple = (2000, 1, 1, 0, 0, 0),\\n TYPE: str or None = 'drift',\\n TIME: str = 'UTC',\\n METHOD: str = 'spline',\\n EXTRAPOLATE: bool = False,\\n CUTOFF: int | float=10.0,\\n APPLY_FLEXURE: bool = False,\\n FILL_VALUE: float = np.nan,\\n **kwargs\\n ):\\n\\n # check that tide directory is accessible\\n if DIRECTORY is not None:\\n DIRECTORY = pathlib.Path(DIRECTORY).expanduser()\\n if not DIRECTORY.exists():\\n raise FileNotFoundError(\\\"Invalid tide directory\\\")\\n\\n # validate input arguments\\n assert TIME in ('GPS', 'LORAN', 'TAI', 'UTC', 'datetime')\\n assert METHOD in ('bilinear', 'spline', 'linear', 'nearest')\\n\\n # get parameters for tide model\\n if DEFINITION_FILE is not None:\\n model = pyTMD.io.model(DIRECTORY).from_file(\\n pathlib.Path(DEFINITION_FILE).expanduser())\\n else:\\n model = pyTMD.io.model(DIRECTORY, format=ATLAS_FORMAT,\\n compressed=GZIP).elevation(MODEL)\\n\\n # determine input data type based on variable dimensions\\n if not TYPE:\\n TYPE = pyTMD.spatial.data_type(x, y, delta_time)\\n # reform coordinate dimensions for input grids\\n # or verify coordinate dimension shapes\\n if (TYPE.lower() == 'grid') and (np.size(x) != np.size(y)):\\n x,y = np.meshgrid(np.copy(x),np.copy(y))\\n elif (TYPE.lower() == 'grid'):\\n x = np.atleast_2d(x)\\n y = np.atleast_2d(y)\\n elif TYPE.lower() in ('time series', 'drift'):\\n x = np.atleast_1d(x)\\n y = np.atleast_1d(y)\\n\\n # converting x,y from EPSG to latitude/longitude\\n try:\\n # EPSG projection code string or int\\n crs1 = pyproj.CRS.from_epsg(int(EPSG))\\n except (ValueError,pyproj.exceptions.CRSError):\\n # Projection SRS string\\n crs1 = pyproj.CRS.from_string(EPSG)\\n # output coordinate reference system\\n crs2 = pyproj.CRS.from_epsg(4326)\\n transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)\\n lon, lat = transformer.transform(x.flatten(), y.flatten())\\n\\n # assert delta time is an array\\n delta_time = np.atleast_1d(delta_time)\\n # convert delta times or datetimes objects to timescale\\n if (TIME.lower() == 'datetime'):\\n timescale = pyTMD.time.timescale().from_datetime(\\n delta_time.flatten())\\n else:\\n timescale = pyTMD.time.timescale().from_deltatime(delta_time,\\n epoch=EPOCH, standard=TIME)\\n # number of time points\\n nt = len(timescale)\\n\\n # read tidal constants and interpolate to grid points\\n if model.format in ('OTIS','ATLAS','TMD3'):\\n amp,ph,D,c = pyTMD.io.OTIS.extract_constants(lon, lat, model.grid_file,\\n model.model_file, model.projection, type=model.type,\\n method=METHOD, extrapolate=EXTRAPOLATE, cutoff=CUTOFF,\\n grid=model.format, apply_flexure=APPLY_FLEXURE)\\n # use delta time at 2000.0 to match TMD outputs\\n deltat = np.zeros((nt), dtype=np.float64)\\n elif (model.format == 'netcdf'):\\n amp,ph,D,c = pyTMD.io.ATLAS.extract_constants(lon, lat, model.grid_file,\\n model.model_file, type=model.type, method=METHOD,\\n extrapolate=EXTRAPOLATE, cutoff=CUTOFF, scale=model.scale,\\n compressed=model.compressed)\\n # use delta time at 2000.0 to match TMD outputs\\n deltat = np.zeros((nt), dtype=np.float64)\\n elif (model.format == 'GOT'):\\n amp,ph,c = pyTMD.io.GOT.extract_constants(lon, lat, model.model_file,\\n method=METHOD, extrapolate=EXTRAPOLATE, cutoff=CUTOFF,\\n scale=model.scale, compressed=model.compressed)\\n # delta time (TT - UT1)\\n deltat = timescale.tt_ut1\\n elif (model.format == 'FES'):\\n amp,ph = pyTMD.io.FES.extract_constants(lon, lat, model.model_file,\\n type=model.type, version=model.version, method=METHOD,\\n extrapolate=EXTRAPOLATE, cutoff=CUTOFF, scale=model.scale,\\n compressed=model.compressed)\\n # available model constituents\\n c = model.constituents\\n # delta time (TT - UT1)\\n deltat = timescale.tt_ut1\\n\\n # calculate complex phase in radians for Euler's\\n cph = -1j*ph*np.pi/180.0\\n # calculate constituent oscillation\\n hc = amp*np.exp(cph)\\n\\n # predict tidal elevations at time and infer minor corrections\\n if (TYPE.lower() == 'grid'):\\n ny,nx = np.shape(x)\\n tide = np.ma.zeros((ny,nx,nt),fill_value=FILL_VALUE)\\n tide.mask = np.zeros((ny,nx,nt),dtype=bool)\\n for i in range(nt):\\n TIDE = pyTMD.predict.map(timescale.tide[i], hc, c,\\n deltat=deltat[i], corrections=model.format)\\n MINOR = pyTMD.predict.infer_minor(timescale.tide[i], hc, c,\\n deltat=deltat[i], corrections=model.format)\\n # add major and minor components and reform grid\\n tide[:,:,i] = np.reshape((TIDE+MINOR), (ny,nx))\\n tide.mask[:,:,i] = np.reshape((TIDE.mask | MINOR.mask), (ny,nx))\\n elif (TYPE.lower() == 'drift'):\\n tide = np.ma.zeros((nt), fill_value=FILL_VALUE)\\n tide.mask = np.any(hc.mask,axis=1)\\n tide.data[:] = pyTMD.predict.drift(timescale.tide, hc, c,\\n deltat=deltat, corrections=model.format)\\n minor = pyTMD.predict.infer_minor(timescale.tide, hc, c,\\n deltat=deltat, corrections=model.format)\\n tide.data[:] += minor.data[:]\\n elif (TYPE.lower() == 'time series'):\\n nstation = len(x)\\n tide = np.ma.zeros((nstation,nt), fill_value=FILL_VALUE)\\n tide.mask = np.zeros((nstation,nt),dtype=bool)\\n for s in range(nstation):\\n TIDE = pyTMD.predict.time_series(timescale.tide, hc[s,None,:], c,\\n deltat=deltat, corrections=model.format)\\n MINOR = pyTMD.predict.infer_minor(timescale.tide, hc[s,None,:], c,\\n deltat=deltat, corrections=model.format)\\n tide.data[s,:] = TIDE.data[:] + MINOR.data[:]\\n tide.mask[s,:] = (TIDE.mask | MINOR.mask)\\n # replace invalid values with fill value\\n tide.data[tide.mask] = tide.fill_value\\n\\n # return the ocean or load tide correction\\n return tide\",\n \"def move(self):\\n \\\"\\\"\\\" Responsible for transformations \\\"\\\"\\\"\\n pos, com, success = self.perception \\n if self.destination is None:\\n return array([0,0])\\n\\n if not self.awake:\\n return array([0,0])\\n\\n\\n if self.phase == 4 and self.proper_formation is not None:\\n no_go = []\\n for i in range(0,len(self.proper_formation)):\\n if i != self.order and self.proper_formation[i][0] == self.proper_formation[self.order][0]:\\n no_go.append(self.transform(self.proper_formation[i][1] - self.position))\\n pos = merge_array_lists(pos, no_go)\\n\\n if self.phase == 2:\\n point = self.destination.copy() - self.position\\n elif self.phase > 2:\\n point = self.transform(self.destination.copy() - self.position)\\n else:\\n point = self.destination.copy()\\n\\n if not array_equal(point, array([0,0])):\\n reachable, path = findpathtoclosest(array([0,0]), point, pos)\\n \\n if len(path) == 0:\\n move = array([0,0]) \\n else:\\n move = path[0]\\n if not reachable and not array_equal(move,array([0,0])):\\n if self.phase == 2:\\n self.closest_i_could_get = path[-1] + self.position\\n elif self.phase > 2:\\n self.closest_i_could_get = self.transform2(path[-1]) + self.position\\n else:\\n self.closest_i_could_get = path[-1]\\n elif not reachable:\\n if self.phase > 1:\\n self.closest_i_could_get = self.position\\n else:\\n self.closest_i_could_get = array([0,0])\\n else:\\n self.closest_i_could_get = None\\n\\n if reachable and self.phase == 4 and array_equal(move,array([0,0])):\\n move = self.randomStep()\\n self.closest_i_could_get = None\\n\\n else:\\n move = array([0,0])\\n self.closest_i_could_get = None\\n\\n return move\",\n \"def _step(self) -> None:\\n self._update_gradient_at(DimerPoint.left)\\n\\n self._optimise_rotation()\\n trns_result = self._translate()\\n\\n if trns_result == _StepResult.skipped_translation:\\n self._converged_translation = True\\n\\n return None\",\n \"def set_flow_corrected(self):\\n self.exh.temp_v_press_fit = (\\n np.polyfit(self.exh.pressure_drop[0:4],\\n self.exh.T_array[0:4], 2) ) \\n self.flow_data.T_hx = np.polyval(self.exh.temp_v_press_fit,\\n self.flow_data.pressure_drop) \\n self.flow_data.flow = ( self.flow_data.flow_trash *\\n self.flow_data.T_hx / self.flow_data.T )\",\n \"def _update_moved(self):\\n self._RAS_textbox.setPlainText('{:.2f}, {:.2f}, {:.2f}'.format(\\n *self._ras))\\n self._VOX_textbox.setPlainText('{:3d}, {:3d}, {:3d}'.format(\\n *self._current_slice))\\n self._intensity_label.setText('intensity = {:.2f}'.format(\\n self._base_data[tuple(self._current_slice)]))\",\n \"def before_tick(self, time):\\n pass\",\n \"def evaluate(self, time, view) -> ControlPoint:\\n ...\",\n \"def dst(self, dt):\",\n \"def turn_until_clear(self):\\n print(\\\"Rotating until lane is clear\\\")\\n #make sure robot is looking straight\\n self.servo(self.MIDPOINT)\\n while self.read_distance() < self.SAFE_DISTANCE:\\n self.left(primary=40,counter=-40)\\n time.sleep(.05)\\n #stop motion before ending method\\n self.stop()\",\n \"def compute_tide_corrections(x, y, delta_time, DIRECTORY=None, MODEL=None,\\n EPSG=3031, EPOCH=(2000,1,1,0,0,0), TYPE='drift', TIME='UTC',\\n METHOD='spline', EXTRAPOLATE=False, FILL_VALUE=np.nan):\\n\\n #-- select between tide models\\n if (MODEL == 'CATS0201'):\\n grid_file = os.path.join(DIRECTORY,'cats0201_tmd','grid_CATS')\\n model_file = os.path.join(DIRECTORY,'cats0201_tmd','h0_CATS02_01')\\n model_format = 'OTIS'\\n model_EPSG = '4326'\\n model_type = 'z'\\n elif (MODEL == 'CATS2008'):\\n grid_file = os.path.join(DIRECTORY,'CATS2008','grid_CATS2008')\\n model_file = os.path.join(DIRECTORY,'CATS2008','hf.CATS2008.out')\\n model_format = 'OTIS'\\n model_EPSG = 'CATS2008'\\n model_type = 'z'\\n elif (MODEL == 'CATS2008_load'):\\n grid_file = os.path.join(DIRECTORY,'CATS2008a_SPOTL_Load','grid_CATS2008a_opt')\\n model_file = os.path.join(DIRECTORY,'CATS2008a_SPOTL_Load','h_CATS2008a_SPOTL_load')\\n model_format = 'OTIS'\\n model_EPSG = 'CATS2008'\\n model_type = 'z'\\n elif (MODEL == 'TPXO9-atlas'):\\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas')\\n grid_file = 'grid_tpxo9_atlas.nc.gz'\\n model_files = ['h_q1_tpxo9_atlas_30.nc.gz','h_o1_tpxo9_atlas_30.nc.gz',\\n 'h_p1_tpxo9_atlas_30.nc.gz','h_k1_tpxo9_atlas_30.nc.gz',\\n 'h_n2_tpxo9_atlas_30.nc.gz','h_m2_tpxo9_atlas_30.nc.gz',\\n 'h_s2_tpxo9_atlas_30.nc.gz','h_k2_tpxo9_atlas_30.nc.gz',\\n 'h_m4_tpxo9_atlas_30.nc.gz','h_ms4_tpxo9_atlas_30.nc.gz',\\n 'h_mn4_tpxo9_atlas_30.nc.gz','h_2n2_tpxo9_atlas_30.nc.gz']\\n model_format = 'netcdf'\\n model_type = 'z'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'TPXO9-atlas-v2'):\\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas_v2')\\n grid_file = 'grid_tpxo9_atlas_30_v2.nc.gz'\\n model_files = ['h_q1_tpxo9_atlas_30_v2.nc.gz','h_o1_tpxo9_atlas_30_v2.nc.gz',\\n 'h_p1_tpxo9_atlas_30_v2.nc.gz','h_k1_tpxo9_atlas_30_v2.nc.gz',\\n 'h_n2_tpxo9_atlas_30_v2.nc.gz','h_m2_tpxo9_atlas_30_v2.nc.gz',\\n 'h_s2_tpxo9_atlas_30_v2.nc.gz','h_k2_tpxo9_atlas_30_v2.nc.gz',\\n 'h_m4_tpxo9_atlas_30_v2.nc.gz','h_ms4_tpxo9_atlas_30_v2.nc.gz',\\n 'h_mn4_tpxo9_atlas_30_v2.nc.gz','h_2n2_tpxo9_atlas_30_v2.nc.gz']\\n model_format = 'netcdf'\\n model_type = 'z'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'TPXO9-atlas-v3'):\\n model_directory = os.path.join(DIRECTORY,'TPXO9_atlas_v3')\\n grid_file = 'grid_tpxo9_atlas_30_v3.nc.gz'\\n model_files = ['h_q1_tpxo9_atlas_30_v3.nc.gz','h_o1_tpxo9_atlas_30_v3.nc.gz',\\n 'h_p1_tpxo9_atlas_30_v3.nc.gz','h_k1_tpxo9_atlas_30_v3.nc.gz',\\n 'h_n2_tpxo9_atlas_30_v3.nc.gz','h_m2_tpxo9_atlas_30_v3.nc.gz',\\n 'h_s2_tpxo9_atlas_30_v3.nc.gz','h_k2_tpxo9_atlas_30_v3.nc.gz',\\n 'h_m4_tpxo9_atlas_30_v3.nc.gz','h_ms4_tpxo9_atlas_30_v3.nc.gz',\\n 'h_mn4_tpxo9_atlas_30_v3.nc.gz','h_2n2_tpxo9_atlas_30_v3.nc.gz',\\n 'h_mf_tpxo9_atlas_30_v3.nc.gz','h_mm_tpxo9_atlas_30_v3.nc.gz']\\n model_format = 'netcdf'\\n TYPE = 'z'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'TPXO9.1'):\\n grid_file = os.path.join(DIRECTORY,'TPXO9.1','DATA','grid_tpxo9')\\n model_file = os.path.join(DIRECTORY,'TPXO9.1','DATA','h_tpxo9.v1')\\n model_format = 'OTIS'\\n model_EPSG = '4326'\\n model_type = 'z'\\n elif (MODEL == 'TPXO8-atlas'):\\n grid_file = os.path.join(DIRECTORY,'tpxo8_atlas','grid_tpxo8atlas_30_v1')\\n model_file = os.path.join(DIRECTORY,'tpxo8_atlas','hf.tpxo8_atlas_30_v1')\\n model_format = 'ATLAS'\\n model_EPSG = '4326'\\n model_type = 'z'\\n elif (MODEL == 'TPXO7.2'):\\n grid_file = os.path.join(DIRECTORY,'TPXO7.2_tmd','grid_tpxo7.2')\\n model_file = os.path.join(DIRECTORY,'TPXO7.2_tmd','h_tpxo7.2')\\n model_format = 'OTIS'\\n model_EPSG = '4326'\\n model_type = 'z'\\n elif (MODEL == 'TPXO7.2_load'):\\n grid_file = os.path.join(DIRECTORY,'TPXO7.2_load','grid_tpxo6.2')\\n model_file = os.path.join(DIRECTORY,'TPXO7.2_load','h_tpxo7.2_load')\\n model_format = 'OTIS'\\n model_EPSG = '4326'\\n model_type = 'z'\\n elif (MODEL == 'AODTM-5'):\\n grid_file = os.path.join(DIRECTORY,'aodtm5_tmd','grid_Arc5km')\\n model_file = os.path.join(DIRECTORY,'aodtm5_tmd','h0_Arc5km.oce')\\n model_format = 'OTIS'\\n model_EPSG = 'PSNorth'\\n model_type = 'z'\\n elif (MODEL == 'AOTIM-5'):\\n grid_file = os.path.join(DIRECTORY,'aotim5_tmd','grid_Arc5km')\\n model_file = os.path.join(DIRECTORY,'aotim5_tmd','h_Arc5km.oce')\\n model_format = 'OTIS'\\n model_EPSG = 'PSNorth'\\n model_type = 'z'\\n elif (MODEL == 'AOTIM-5-2018'):\\n grid_file = os.path.join(DIRECTORY,'Arc5km2018','grid_Arc5km2018')\\n model_file = os.path.join(DIRECTORY,'Arc5km2018','h_Arc5km2018')\\n model_format = 'OTIS'\\n model_EPSG = 'PSNorth'\\n model_type = 'z'\\n elif (MODEL == 'GOT4.7'):\\n model_directory = os.path.join(DIRECTORY,'GOT4.7','grids_oceantide')\\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/100.0\\n elif (MODEL == 'GOT4.7_load'):\\n model_directory = os.path.join(DIRECTORY,'GOT4.7','grids_loadtide')\\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\\n 's1load.d.gz','m4load.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'GOT4.8'):\\n model_directory = os.path.join(DIRECTORY,'got4.8','grids_oceantide')\\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/100.0\\n elif (MODEL == 'GOT4.8_load'):\\n model_directory = os.path.join(DIRECTORY,'got4.8','grids_loadtide')\\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\\n 's1load.d.gz','m4load.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'GOT4.10'):\\n model_directory = os.path.join(DIRECTORY,'GOT4.10c','grids_oceantide')\\n model_files = ['q1.d.gz','o1.d.gz','p1.d.gz','k1.d.gz','n2.d.gz',\\n 'm2.d.gz','s2.d.gz','k2.d.gz','s1.d.gz','m4.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/100.0\\n elif (MODEL == 'GOT4.10_load'):\\n model_directory = os.path.join(DIRECTORY,'GOT4.10c','grids_loadtide')\\n model_files = ['q1load.d.gz','o1load.d.gz','p1load.d.gz','k1load.d.gz',\\n 'n2load.d.gz','m2load.d.gz','s2load.d.gz','k2load.d.gz',\\n 's1load.d.gz','m4load.d.gz']\\n c = ['q1','o1','p1','k1','n2','m2','s2','k2','s1','m4']\\n model_format = 'GOT'\\n SCALE = 1.0/1000.0\\n elif (MODEL == 'FES2014'):\\n model_directory = os.path.join(DIRECTORY,'fes2014','ocean_tide')\\n model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',\\n 'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',\\n 'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',\\n 'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',\\n 'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',\\n 'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',\\n 'ssa.nc.gz','t2.nc.gz']\\n c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6','m8',\\n 'mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2','n4',\\n 'nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']\\n model_format = 'FES'\\n TYPE = 'z'\\n SCALE = 1.0/100.0\\n elif (MODEL == 'FES2014_load'):\\n model_directory = os.path.join(DIRECTORY,'fes2014','load_tide')\\n model_files = ['2n2.nc.gz','eps2.nc.gz','j1.nc.gz','k1.nc.gz',\\n 'k2.nc.gz','l2.nc.gz','la2.nc.gz','m2.nc.gz','m3.nc.gz','m4.nc.gz',\\n 'm6.nc.gz','m8.nc.gz','mf.nc.gz','mks2.nc.gz','mm.nc.gz',\\n 'mn4.nc.gz','ms4.nc.gz','msf.nc.gz','msqm.nc.gz','mtm.nc.gz',\\n 'mu2.nc.gz','n2.nc.gz','n4.nc.gz','nu2.nc.gz','o1.nc.gz','p1.nc.gz',\\n 'q1.nc.gz','r2.nc.gz','s1.nc.gz','s2.nc.gz','s4.nc.gz','sa.nc.gz',\\n 'ssa.nc.gz','t2.nc.gz']\\n c = ['2n2','eps2','j1','k1','k2','l2','lambda2','m2','m3','m4','m6',\\n 'm8','mf','mks2','mm','mn4','ms4','msf','msqm','mtm','mu2','n2',\\n 'n4','nu2','o1','p1','q1','r2','s1','s2','s4','sa','ssa','t2']\\n model_format = 'FES'\\n model_type = 'z'\\n SCALE = 1.0/100.0\\n else:\\n raise Exception(\\\"Unlisted tide model\\\")\\n\\n #-- converting x,y from EPSG to latitude/longitude\\n crs1 = pyproj.CRS.from_string(\\\"epsg:{0:d}\\\".format(EPSG))\\n crs2 = pyproj.CRS.from_string(\\\"epsg:{0:d}\\\".format(4326))\\n transformer = pyproj.Transformer.from_crs(crs1, crs2, always_xy=True)\\n lon,lat = transformer.transform(x.flatten(), y.flatten())\\n\\n #-- assert delta time is an array\\n delta_time = np.atleast_1d(delta_time)\\n #-- calculate leap seconds if specified\\n if (TIME.upper() == 'GPS'):\\n GPS_Epoch_Time = pyTMD.time.convert_delta_time(0, epoch1=EPOCH,\\n epoch2=(1980,1,6,0,0,0), scale=1.0)\\n GPS_Time = pyTMD.time.convert_delta_time(delta_time, epoch1=EPOCH,\\n epoch2=(1980,1,6,0,0,0), scale=1.0)\\n #-- calculate difference in leap seconds from start of epoch\\n leap_seconds = pyTMD.time.count_leap_seconds(GPS_Time) - \\\\\\n pyTMD.time.count_leap_seconds(np.atleast_1d(GPS_Epoch_Time))\\n elif (TIME.upper() == 'TAI'):\\n #-- TAI time is ahead of GPS time by 19 seconds\\n GPS_Epoch_Time = pyTMD.time.convert_delta_time(-19.0, epoch1=EPOCH,\\n epoch2=(1980,1,6,0,0,0), scale=1.0)\\n GPS_Time = pyTMD.time.convert_delta_time(delta_time-19.0, epoch1=EPOCH,\\n epoch2=(1980,1,6,0,0,0), scale=1.0)\\n #-- calculate difference in leap seconds from start of epoch\\n leap_seconds = pyTMD.time.count_leap_seconds(GPS_Time) - \\\\\\n pyTMD.time.count_leap_seconds(np.atleast_1d(GPS_Epoch_Time))\\n else:\\n leap_seconds = 0.0\\n\\n #-- convert time to days relative to Jan 1, 1992 (48622mjd)\\n t = pyTMD.time.convert_delta_time(delta_time - leap_seconds, epoch1=EPOCH,\\n epoch2=(1992,1,1,0,0,0), scale=(1.0/86400.0))\\n\\n #-- read tidal constants and interpolate to grid points\\n if model_format in ('OTIS','ATLAS'):\\n amp,ph,D,c = extract_tidal_constants(lon, lat, grid_file, model_file,\\n model_EPSG, TYPE=model_type, METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE,\\n GRID=model_format)\\n deltat = np.zeros_like(t)\\n elif (model_format == 'netcdf'):\\n amp,ph,D,c = extract_netcdf_constants(lon, lat, model_directory,\\n grid_file, model_files, TYPE=model_type, METHOD=METHOD, \\n EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\\n deltat = np.zeros_like(t)\\n elif (model_format == 'GOT'):\\n amp,ph = extract_GOT_constants(lon, lat, model_directory, model_files,\\n METHOD=METHOD, EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\\n #-- interpolate delta times from calendar dates to tide time\\n delta_file = pyTMD.utilities.get_data_path(['data','merged_deltat.data'])\\n deltat = calc_delta_time(delta_file, t)\\n elif (model_format == 'FES'):\\n amp,ph = extract_FES_constants(lon, lat, model_directory, model_files,\\n TYPE=model_type, VERSION=MODEL, METHOD=METHOD,\\n EXTRAPOLATE=EXTRAPOLATE, SCALE=SCALE)\\n #-- interpolate delta times from calendar dates to tide time\\n delta_file = pyTMD.utilities.get_data_path(['data','merged_deltat.data'])\\n deltat = calc_delta_time(delta_file, t)\\n\\n #-- calculate complex phase in radians for Euler's\\n cph = -1j*ph*np.pi/180.0\\n #-- calculate constituent oscillation\\n hc = amp*np.exp(cph)\\n\\n #-- predict tidal elevations at time and infer minor corrections\\n if (TYPE.lower() == 'grid'):\\n ny,nx = np.shape(x); nt = len(t)\\n tide = np.ma.zeros((ny,nx,nt),fill_value=FILL_VALUE)\\n tide.mask = np.zeros((ny,nx,nt),dtype=np.bool)\\n for i in range(nt):\\n TIDE = predict_tide(t[i], hc, c,\\n DELTAT=deltat[i], CORRECTIONS=model_format)\\n MINOR = infer_minor_corrections(t[i], hc, c,\\n DELTAT=deltat[i], CORRECTIONS=model_format)\\n #-- add major and minor components and reform grid\\n tide[:,:,i] = np.reshape((TIDE+MINOR), (ny,nx))\\n tide.mask[:,:,i] = np.reshape((TIDE.mask | MINOR.mask), (ny,nx))\\n else:\\n npts = len(t)\\n tide = np.ma.zeros((npts), fill_value=FILL_VALUE)\\n tide.mask = np.any(hc.mask,axis=1)\\n tide.data[:] = predict_tide_drift(t, hc, c,\\n DELTAT=deltat, CORRECTIONS=model_format)\\n minor = infer_minor_corrections(t, hc, c,\\n DELTAT=deltat, CORRECTIONS=model_format)\\n tide.data[:] += minor.data[:]\\n #-- replace invalid values with fill value\\n tide.data[tide.mask] = tide.fill_value\\n\\n #-- return the tide correction\\n return tide\",\n \"def correct_proper_motion(self, invert=False, mjd=None):\\n\\n # If mjd not set directly, check that it was set from FITS headers in get_cutout method\\n if self.mjd is None:\\n if mjd is None:\\n raise FITSException(\\\"Date could not be inferred from header, supply with epoch keyword.\\\")\\n else:\\n self.mjd = mjd\\n\\n obstime = Time(self.mjd, format='mjd')\\n\\n simbad = Simbad.query_region(self.position, radius=180 * u.arcsec)\\n\\n # Catch SIMBAD failure either from None return of query or no stellar type matches in region\\n try:\\n simbad = simbad.to_pandas()\\n pm_types = ['*', '**', 'PM*', 'EB*', 'Star', 'PSR', 'Pulsar', 'Flare*']\\n simbad = simbad[(simbad['OTYPE'].isin(pm_types)) | (simbad['SP_TYPE'].str.len() > 0)]\\n\\n assert len(simbad) > 0\\n\\n except (ValueError, AssertionError):\\n logger.debug(\\\"No high proper-motion objects within 180 arcsec.\\\")\\n self.correct_pm = False\\n\\n return\\n\\n # Treat non-existent proper motion parameters as extremely distant objects\\n simbad['PMRA'].fillna(0, inplace=True)\\n simbad['PMDEC'].fillna(0, inplace=True)\\n simbad['PLX_VALUE'].fillna(0.01, inplace=True)\\n\\n newtime = Time(self.radio.mjd, format='mjd')\\n pmra = simbad['PMRA'].values * u.mas / u.yr\\n pmdec = simbad['PMDEC'].values * u.mas / u.yr\\n\\n dist = Distance(parallax=simbad['PLX_VALUE'].values * u.mas)\\n\\n simbad['j2000pos'] = SkyCoord(\\n ra=simbad['RA_d'].values * u.deg,\\n dec=simbad['DEC_d'].values * u.deg,\\n frame='icrs',\\n distance=dist,\\n pm_ra_cosdec=pmra,\\n pm_dec=pmdec,\\n obstime='J2000',\\n )\\n\\n datapos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(obstime))\\n newpos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(newtime))\\n\\n simbad_cols = {\\n 'MAIN_ID': 'Object',\\n 'OTYPE': 'Type',\\n 'SP_TYPE': 'Spectral Type',\\n 'DISTANCE_RESULT': 'Separation (arcsec)',\\n }\\n simbad = simbad.rename(columns=simbad_cols)\\n simbad = simbad[simbad_cols.values()].copy()\\n simbad['PM Corrected Separation (arcsec)'] = np.round(newpos.apply(\\n lambda x: x.separation(self.position).arcsec), 3)\\n\\n # Only display PM results if object within 15 arcsec\\n if simbad['PM Corrected Separation (arcsec)'].min() > 15:\\n logger.debug(\\\"No PM corrected objects within 15 arcsec\\\")\\n self.correct_pm = False\\n\\n return\\n\\n self.simbad = simbad.sort_values('PM Corrected Separation (arcsec)')\\n logger.info(f'SIMBAD results:\\\\n {self.simbad.head()}')\\n\\n nearest = self.simbad['PM Corrected Separation (arcsec)'].idxmin()\\n\\n self.oldpos = datapos[nearest]\\n self.pm_coord = newpos[nearest]\\n\\n near_object = self.simbad.loc[nearest].Object\\n msg = f'{near_object} proper motion corrected to <{self.pm_coord.ra:.4f}, {self.pm_coord.dec:.4f}>'\\n logger.info(msg)\\n\\n missing = simbad[simbad['PM Corrected Separation (arcsec)'].isna()]\\n if len(missing) > 0:\\n msg = f\\\"Some objects missing PM data, and may be a closer match than presented:\\\\n {missing}\\\"\\n logger.warning(msg)\\n\\n return\",\n \"def _perturbInPlaceHard(self):\\n die\",\n \"def timerCallback(self,evprent):\\n self._odom_list.waitForTransform('map', 'base_footprint', rospy.Time(0), rospy.Duration(1.0))\\n (position, orientation) = self._odom_list.lookupTransform('map','base_footprint', rospy.Time(0)) #finds the position and oriention of two objects relative to each other (hint: this returns arrays, while Pose uses lists)\\n self._current.position.x = position[0]\\n self._current.position.y = position[1]\\n\\n self._current.orientation.x = orientation[0]\\n self._current.orientation.y = orientation[1]\\n self._current.orientation.z = orientation[2]\\n self._current.orientation.w = orientation[3]\\n q = [self._current.orientation.x,\\n self._current.orientation.y,\\n self._current.orientation.z,\\n self._current.orientation.w] # quaternion nonsense\\n\\n (roll, pitch, yaw) = euler_from_quaternion(q)\",\n \"def update(self, dayHour, comicId):\\n #TODO test\\n self.loadComic(comicId)\\n\\n #Sprint \\\"Predictor.update(\\\", comicId, \\\")\\\"\\n\\n\\n for i in range(1):\\n # Stop weeding?\\n stopWeeding = False\\n\\n if self.__predictorData == None:\\n print \\\"Line 252 (NONE)\\\"\\n\\n if (self.__predictorData.isWeeding()):\\n tgm = time.gmtime()\\n sec = time.mktime(time.strptime(str(tgm.tm_year) +' '+ str(tgm.tm_mon) +' '+ str(tgm.tm_mday) +' '+ str(tgm.tm_hour) +' '+ str(tgm.tm_min) +' '+ str(tgm.tm_sec), '%Y %m %d %H %M %S'))\\n # print \\\"weeding times:\\\", self.__predictorData.getWeedingStart(), sec\\n if (sec >= self.__predictorData.getWeedingStart() + 7*24*self.hourScale):\\n self.stopWeeding(dayHour)\\n stopWeeding = True\\n\\n if (self.__predictorData.isWeeding() or stopWeeding):\\n # Weeding\\n uRanges = self.__predictorData.getUpdateRanges()\\n if (len(uRanges) > 0):\\n if (self.__predictorData.addDayHour(dayHour)):\\n break\\n # if (self.inURange(self.incDayHour(dayHour, -self.rangeWidth), uRanges[-1])):\\n # uRangeLast = self.incDayHour(uRanges[-1]['position'], uRanges[-1]['width'])\\n # #self.__predictorData.addUpdateRange(self.addUpdateRange(incDayHour(uRangeLast, Predictor.rangeWidth + 1)))\\n # self.__predictorData.addUpdateRange(self.incDayHour(uRangeLast, Predictor.rangeWidth + 1))\\n # break \\n self.__predictorData.addUpdateRange(self.blankUpdateRange(dayHour))\\n else:\\n # Regular update\\n if (not self.__predictorData.addDayHour(dayHour)):\\n # Add dayHour to nearest range\\n updateRanges = self.__predictorData.getUpdateRanges()\\n dist = {}\\n dh = self.dayHourToHours(dayHour)\\n for i, uRange in enumerate(updateRanges):\\n ur_pos_hours = self.dayHourToHours(uRange['position'])\\n ur_width = uRange['width']\\n hours = self.dayHourToHours(dayHour)\\n dist[min(abs(ur_pos_hours - ur_width - hours), abs(ur_pos_hours + ur_width - hours))] = i\\n self.__predictorData.addDayHourToURange(dayHour, dist[min(dist.keys())])\\n\\n \\n self.__predictorData.setUpdateRanges(self.calculateURPositions(self.__predictorData.getUpdateRanges()))\\n self.__predictorData.setSchedule(self.calculateScheduleUR(self.__predictorData.getUpdateRanges()))\\n \\n self.updatePredictorList(self.__predictorData.getSchedule(), comicId)\\n self.saveComic(comicId)\",\n \"def act_func_part4_v2_t(action_raw, action_raw_idx, raw_state_limits, stptLmt, ob_this_raw, logger, is_show_debug):\\n IAT_STPT_IDX = 4;\\n OCP_IDX = 7;\\n PMV_IDX = 6;\\n ACTIONS = [[-2.0],\\n [-1.0],\\n [-0.5],\\n [0.0],\\n [0.5],\\n [1.0],\\n [2.0],];\\n\\n ocp = ob_this_raw[OCP_IDX];\\n pmv = ob_this_raw[PMV_IDX];\\n iat_stpt_ob = ob_this_raw[IAT_STPT_IDX]; \\n act_num = len(ACTIONS);\\n # Process based on PMV\\n action_prcd = action_raw;\\n action_prcd_idx = action_raw_idx;\\n if ocp == 1:\\n if pmv < -0.5 and action_raw[0] < 0:\\n # If pmv < -0.5 and the action is to reduce setpoint\\n # Choose one action to increase the setpoint\\n action_prcd_idx = np.random.randint(3, act_num);\\n action_prcd = ACTIONS[action_prcd_idx];\\n elif pmv > 0.5 and action_raw[0] > 0:\\n # If pmv > 0.5 and the action is to increase setpoint\\n # Choose one action to reduce the setpoint\\n action_prcd_idx = np.random.randint(0, 4);\\n action_prcd = ACTIONS[action_prcd_idx];\\n # Get final action\\n iat_stpt_act_raw = iat_stpt_ob + action_prcd[0];\\n action_ret = [max(min(iat_stpt_act_raw, stptLmt[0][1]), stptLmt[0][0])];\\n action_ret_idx = action_prcd_idx;\\n # Log\\n if action_raw_idx != action_ret_idx:\\n if is_show_debug:\\n logger.debug('Action function: raw action %s has been changed to %s for '\\n 'the PMV %s.'%(action_raw_idx, action_ret_idx, pmv));\\n return (action_ret, action_ret_idx);\",\n \"def update(self, dt):\\n event = super(Kick, self).update(dt)\\n if event is not None:\\n return event\\n\\n if not self._motion.is_running():\\n if not self._motion_completed:\\n effectors = \\\"LLeg\\\"\\n delta_pos1 = np.asarray([0.15, 0.0, 0.0, 0.0, -0.03, 0.0])\\n\\n if self._support_leg == \\\"left\\\":\\n effectors = \\\"RLeg\\\"\\n delta_pos1[4] *= -1\\n\\n delta_pos = [[delta_pos1,\\n np.asarray([0.09, 0.0, 0.00, 0.0, 0.0, 0.0])]]\\n times = [[0.2, 0.3]]\\n self._motion.interpolate_positions(effectors, delta_pos, times, NaoMotionController.FRAME_TORSO)\\n\\n self._motion_completed = True\\n return\\n\\n return \\\"done\\\"\",\n \"def update_for_odor_detection(self, dt, odor, wind_uvecs, masks):\\n x_wind_unit = wind_uvecs['x']\\n y_wind_unit = wind_uvecs['y']\\n mask_startmode = masks['startmode']\\n mask_castfor = masks['castfor']\\n\\n mask_gt_upper = odor >= self.param['odor_thresholds']['upper']\\n mask_candidates = mask_gt_upper & (mask_startmode | mask_castfor)\\n dice_roll = scipy.full((self.size,),scipy.inf)\\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\\n\\n # Convert probabilty/sec to probabilty for time step interval dt\\n odor_probability_upper = 1.0 - (1.0 - self.param['odor_probabilities']['upper'])**dt\\n mask_change = dice_roll < odor_probability_upper\\n self.mode[mask_change] = self.Mode_FlyUpWind\\n\\n # Compute new heading error for flies which change mode\\n heading_error_std = self.param['heading_error_std']\\n self.heading_error[mask_change] = heading_error_std*scipy.randn(mask_change.sum())\\n\\n # Set x and y velocities for the flies which just changed to FlyUpWind.\\n x_unit_change, y_unit_change = rotate_vecs(\\n x_wind_unit[mask_change],\\n y_wind_unit[mask_change],\\n self.heading_error[mask_change]\\n )\\n speed = self.param['flight_speed'][mask_change]\\n self.x_velocity[mask_change] = -speed*x_unit_change\\n self.y_velocity[mask_change] = -speed*y_unit_change\",\n \"def _reset(self) -> ts.TimeStep:\",\n \"def update_apc23(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n if len(self._old) >= 1:\\n\\n\\n try:\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\\n\\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n beta1 = (3.0*(self.time-self.get_old(0,2))+delta_t)/(6.0*self.time-self.get_old(0,2))\\n beta2 = -delta_t**2/(6.0*(self.time+delta_t-self.get_old(0,2))*(self.time-self.get_old(0,2)))\\n\\n print self.force(pos, vel, self.time+delta_t), kap[1], self.get_old(0,1)\\n\\n pos = self.pos+delta_t*((1.0-beta1-beta2)*vel+beta1*self.vel+beta2*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1.0-beta1-beta2)*self.force(pos, vel, self.time+delta_t,\\n drag=False)+beta1*kap[1]+beta2*self.get_old(0,1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n\\n self.time += delta_t\\n\\n else:\\n ## reduced to using the Adams first order method for the first timestep:\\n\\n kap = update_apc1(self)\\n\\n self.set_old(kap, 1)\\n\\n return kap\",\n \"def target_position(self, time):\\n \\\"\\\"\\\"\\n start_pos = self.points[self.cur_start]\\n seg_time = time - self.last_checkpoint_time\\n\\n #The arguement of target-velocity dosent matter\\n cur_pos = self.target_velocity(time)*seg_time + start_pos\\n\\n \\n # or time > (self.total_time / 4)*(self.cur_start + 1)\\n cur_pos_norm = length(cur_pos - start_pos)\\n\\n next_corner = self.points[(self.cur_start + 1)%4]\\n \\n seg_norm = length(next_corner - start_pos)\\n print(\\\"cur_pos : \\\", cur_pos, \\\"segment: \\\", self.cur_start, seg_norm - cur_pos_norm)\\n\\n if cur_pos_norm >= seg_norm:\\n self.cur_start = (self.cur_start + 1) % 4\\n self.last_checkpoint_time = time\\n return cur_pos\\n \\\"\\\"\\\"\\n\\n #Possibly use rospy.sleep()\\n total_time = self.total_time\\n\\n\\n if time < total_time/4:\\n return self.path1.target_position(time)\\n\\n elif time - total_time/4 == 0:\\n rospy.sleep(0.5)\\n\\n elif time < total_time/2:\\n return self.path2.target_position(time - (total_time/4 + 0.5))\\n # return self.path2.target_position(time - (total_time/4 ))\\n\\n\\n elif time - total_time/2 == 0:\\n rospy.sleep(0.5)\\n\\n elif time <= total_time/4*3:\\n return self.path3.target_position(time - (total_time/2 + 1))\\n # return self.path3.target_position(time - (total_time/2))\\n\\n\\n elif time - total_time/4*3 == 0:\\n rospy.sleep(0.5)\\n\\n else:\\n return self.path4.target_position(time - (total_time/4*3 + 1.5))\\n # return self.path4.target_position(time - (total_time/4*3))\",\n \"def __update_current_motion_start(self) -> None:\\n if len(self.past_movements) < 2:\\n return\\n if self.past_movements[-1] * self.past_movements[-2] < 0:\\n self.current_motion_start_element = self.counterpoint[-2]\",\n \"def sweep(self):\\n position = 0\\n countdown = False\\n\\n for i in range(14):\\n\\n if position == 14:\\n countdown = True\\n if countdown:\\n position -= 2\\n else:\\n position += 2\\n\\n self.servo.ChangeDutyCycle(position)\\n GPIO.output(self.pin, False)\\n sleep(0.1)\",\n \"def update_for_odor_loss(self, t, dt, odor, wind_uvecs, masks):\\n\\n x_wind_unit = wind_uvecs['x']\\n y_wind_unit = wind_uvecs['y']\\n mask_flyupwd = masks['flyupwd']\\n mask_castfor = masks['castfor']\\n\\n mask_lt_lower = odor <= self.param['odor_thresholds']['lower']\\n mask_candidates = mask_lt_lower & mask_flyupwd\\n dice_roll = scipy.full((self.size,),scipy.inf)\\n dice_roll[mask_candidates] = scipy.rand(mask_candidates.sum())\\n\\n # Convert probabilty/sec to probabilty for time step interval dt\\n odor_probability_lower = 1.0 - (1.0 - self.param['odor_probabilities']['lower'])**dt\\n mask_change = dice_roll < odor_probability_lower\\n self.mode[mask_change] = self.Mode_CastForOdor\\n\\n # Lump together flies changing to CastForOdor mode with casting flies which are\\n # changing direction (e.g. time to make cast direction change)\\n mask_change |= mask_castfor & (t > (self.t_last_cast + self.dt_next_cast))\\n\\n # Computer new heading errors for flies which change mode\\n self.heading_error[mask_change] = self.param['heading_error_std']*scipy.randn(mask_change.sum())\\n\\n # Set new cast intervals and directions for flies chaning to CastForOdor or starting a new cast\\n cast_interval = self.param['cast_interval']\\n self.dt_next_cast[mask_change] = scipy.random.uniform(\\n cast_interval[0],\\n cast_interval[0],\\n (mask_change.sum(),)\\n )\\n self.t_last_cast[mask_change] = t\\n self.cast_sign[mask_change] = scipy.random.choice([-1,1],(mask_change.sum(),))\\n\\n # Set x and y velocities for new CastForOdor flies\\n x_unit_change, y_unit_change = rotate_vecs(\\n x_wind_unit[mask_change],\\n -y_wind_unit[mask_change],\\n self.heading_error[mask_change]\\n )\\n speed = self.param['flight_speed'][mask_change]\\n self.x_velocity[mask_change] = self.cast_sign[mask_change]*speed*x_unit_change\\n self.y_velocity[mask_change] = self.cast_sign[mask_change]*speed*y_unit_change\",\n \"def data_move_case_zero(tik_instance, ub_ori, ub_trans,\\n is_last, num_outer_axis, num_loop_time,\\n loop_time, loop_col, loop_len):\\n with tik_instance.if_scope(tik.all(loop_time ==\\n self.dst_shape[-4] //\\n loop_col - 1,\\n self.dst_shape[-4] % loop_col ==\\n 0)):\\n is_last.set_as(1)\\n num_data_one_loop = self.dst_shape[-4] * self.dst_shape[-3] * \\\\\\n self.dst_shape[-2] * self.dst_shape[-1]\\n src_ub_index = 0\\n if self.src_shape[-1] % CUBE_SIZE != 0 or \\\\\\n (self.src_shape[-1] - loop_len * CUBE_SIZE) // \\\\\\n self.num_data > MAX_STRIDE_BLK:\\n if self.src_shape[-2] % CUBE_SIZE != 0:\\n with tik_instance.if_scope(num_loop_time ==\\n self.dst_shape[-3] - 1):\\n with tik_instance.for_range(0,\\n self.src_shape[-2] %\\n CUBE_SIZE) as num_cube_col:\\n src_gm_index = num_outer_axis *\\\\\\n self.src_shape[-1] * \\\\\\n self.src_shape[-2] + \\\\\\n (num_loop_time * CUBE_SIZE +\\n num_cube_col) *\\\\\\n self.src_shape[-1] + loop_time *\\\\\\n loop_col * CUBE_SIZE\\n tik_instance.data_move(ub_ori[loop_len *\\n CUBE_SIZE *\\n num_cube_col],\\n self.src_gm[src_gm_index],\\n 0, 1,\\n loop_len * self.num_byte //\\n 2, 0, 0)\\n with tik_instance.else_scope():\\n with tik_instance.for_range(0, CUBE_SIZE) \\\\\\n as num_cube_col:\\n src_gm_index = num_outer_axis * \\\\\\n self.src_shape[-1] * \\\\\\n self.src_shape[-2] + \\\\\\n (num_loop_time * CUBE_SIZE +\\n num_cube_col) *\\\\\\n self.src_shape[-1] + loop_time *\\\\\\n loop_col * CUBE_SIZE\\n tik_instance.data_move(ub_ori[loop_len *\\n CUBE_SIZE *\\n num_cube_col],\\n self.src_gm[src_gm_index],\\n 0, 1,\\n loop_len * self.num_byte //\\n 2, 0, 0)\\n else:\\n with tik_instance.for_range(0, CUBE_SIZE) as num_cube_col:\\n src_gm_index = num_outer_axis * self.src_shape[-1] * \\\\\\n self.src_shape[-2] + \\\\\\n (num_loop_time * CUBE_SIZE +\\n num_cube_col) * self.src_shape[-1] + \\\\\\n loop_time * loop_col * CUBE_SIZE\\n tik_instance.data_move(ub_ori[loop_len * CUBE_SIZE *\\n num_cube_col],\\n self.src_gm[src_gm_index],\\n 0, 1,\\n loop_len * self.num_byte //\\n 2, 0, 0)\\n else:\\n src_gm_index = num_outer_axis * self.src_shape[-1] * \\\\\\n self.src_shape[-2] + num_loop_time *\\\\\\n CUBE_SIZE * self.src_shape[-1] +\\\\\\n loop_time * loop_col * CUBE_SIZE\\n if self.src_shape[-2] % CUBE_SIZE != 0:\\n with tik_instance.if_scope(num_loop_time ==\\n self.dst_shape[-3] - 1):\\n tik_instance.data_move(ub_ori[src_ub_index],\\n self.src_gm[src_gm_index], 0,\\n self.src_shape[-2] % CUBE_SIZE,\\n loop_len * self.num_byte // 2,\\n (self.src_shape[-1] -\\n loop_len * CUBE_SIZE) //\\n self.num_data,\\n 0)\\n with tik_instance.else_scope():\\n tik_instance.data_move(ub_ori[src_ub_index],\\n self.src_gm[src_gm_index],\\n 0, CUBE_SIZE,\\n loop_len * self.num_byte // 2,\\n (self.src_shape[-1] -\\n loop_len * CUBE_SIZE) //\\n self.num_data,\\n 0)\\n else:\\n tik_instance.data_move(ub_ori[src_ub_index],\\n self.src_gm[src_gm_index],\\n 0, CUBE_SIZE,\\n loop_len * self.num_byte // 2,\\n (self.src_shape[-1] - loop_len *\\n CUBE_SIZE) // self.num_data, 0)\\n self.data_rearrange_case_four(tik_instance, ub_ori,\\n ub_trans, num_loop_time,\\n loop_len, is_last)\\n\\n if((self.dst_shape[-3] - 1) * self.dst_shape[-1] *\\n self.dst_shape[-2] // self.num_data > MAX_STRIDE_BLK):\\n with tik_instance.for_range(0, loop_len) as \\\\\\n num_col_cube:\\n dst_gm_index = num_outer_axis * num_data_one_loop + \\\\\\n num_loop_time * self.dst_shape[-1] * \\\\\\n self.dst_shape[-2] + \\\\\\n (loop_time * loop_col + num_col_cube) * \\\\\\n self.dst_shape[-1] * self.dst_shape[-2] * \\\\\\n self.dst_shape[-3]\\n tik_instance.data_move(self.dst_gm[dst_gm_index],\\n ub_trans[num_col_cube *\\n CUBE_SIZE *\\n (CUBE_SIZE + 1)],\\n 0, 1,\\n self.dst_shape[-1] *\\n self.dst_shape[-2] //\\n self.num_data,\\n 0, 0)\\n else:\\n dst_gm_index = num_outer_axis * num_data_one_loop + \\\\\\n num_loop_time * self.dst_shape[-1] * \\\\\\n self.dst_shape[-2] + loop_time * \\\\\\n loop_col * self.dst_shape[-1] * \\\\\\n self.dst_shape[-2] * \\\\\\n self.dst_shape[-3]\\n tik_instance.data_move(self.dst_gm[dst_gm_index],\\n ub_trans[0],\\n 0, loop_len,\\n self.dst_shape[-1] *\\n self.dst_shape[-2] // self.num_data,\\n self.num_byte // 2,\\n (self.dst_shape[-3] - 1) *\\n self.dst_shape[-1] *\\n self.dst_shape[-2] // self.num_data)\",\n \"def calibrate(self):\\n self.mode = Mode.calibrating\\n yaw_sensor = yaw_button()\\n while not yaw_sensor.is_pressed():\\n self.move_left()\\n for _ in range(75):\\n self.move_right()\\n\\n pitch_sensor = pitch_button()\\n while not pitch_sensor.is_pressed():\\n self.move_up()\\n for _ in range(21):\\n self.move_down()\\n\\n self.pitch = 0.0\\n self.yaw = 0.0\\n self.mode = Mode.waiting\",\n \"def update(self):\\n if self.dizzy:\\n self._spin()\\n else:\\n self._walk()\",\n \"def quarter_frame(self) -> None:\\n if self._linear_ctr_reload_flag:\\n self._linear_ctr = self.linear_ctr_reload_value\\n elif self._linear_ctr > 0:\\n self._linear_ctr -= 1\\n\\n if not self.control_flag:\\n self._linear_ctr_reload_flag = False\",\n \"def update(self, time):\\n raise NotImplementedError\",\n \"def update(self, time):\\n raise NotImplementedError\",\n \"def calibrateAutoGuider(self, exposureLengthSeconds):\\n\\n print \\\"Calibrating autoguider orientation...\\\"\\n self.camera.GuiderCalibrate(exposureLengthSeconds)\\n while self.camera.GuiderRunning:\\n sys.stdout.write(\\\".\\\")\\n time.sleep(1)\\n print\\n print \\\"Calibration finished\\\"\",\n \"def undo_calibration_using_diagnostics(x,y,z,cd):\\n undo_calibration(x, y, z, [cd[\\\"x_offset\\\"],cd[\\\"x_scale\\\"],cd[\\\"y_offset\\\"],cd[\\\"y_scale\\\"],cd[\\\"z_offset\\\"],cd[\\\"z_scale\\\"]])\",\n \"def _twist_callback(self, cmd):\\n self.set_velocity(cmd.linear.x, cmd.angular.z)\",\n \"def right_twist(self):\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\\n self.turn_by_deg(180)\\n #time.sleep(.1)\\n self.stop()\",\n \"def run_update_step(self, time, pids, hole_rating, observations):\\n\\t\\treturn NotImplemented\",\n \"def reduce_velocity(self):\\n if self.controls[\\\"make_velocity_0\\\"]:\\n # print(self.controls[\\\"bar_move_velocity\\\"])\\n self.controls[\\\"bar_move_velocity\\\"] = 0\",\n \"def update_apc11(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n pos = self.pos+delta_t*self.vel\\n vel = self.vel+delta_t*kap[1]\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n force = self.force(pos,\\n vel,\\n self.time+delta_t, drag=False)\\n\\n pos = self.pos+delta_t*vel\\n vel = self.vel+delta_t*force\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n try:\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n vel = self.vel+col.delta_t*kap[1]\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n \\n self.time += delta_t\\n\\n return (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\",\n \"def update(self, delta_time):\\r\\n self.check_keys()\\r\\n\\r\\n \\r\\n # TODO: Tell everything to advance or move forward one step in time\\r\\n \\r\\n for Large_asteriod in self.rocks:\\r\\n Large_asteriod.advance()\\r\\n Large_asteriod.wrap()\\r\\n #this is where I call my functions\\r\\n \\r\\n # TODO: Check for collisions\\r\\n for bullet in self.bullets:\\r\\n bullet.advance()\\r\\n bullet.wrap()\\r\\n \\r\\n \\r\\n \\r\\n self.check_collisions() \\r\\n self.cleanup_deadstuff() \\r\\n self.ship.advance()\\r\\n self.ship.wrap()\\r\\n self.frame_count += 1\",\n \"def apply_model(self, original, t1, t2, resolution_scaling_factor=1):\\n img = Image()\\n img.time_stamp = t2\\n\\n if t1 == t2:\\n img.initialize_with_image(original)\\n return img\\n\\n calc_shift_fnc = self.calculate_shift\\n orig_get_fnc = original.get\\n interp_fnc = my_math.linear_interpolation\\n\\n def generate(y, x):\\n \\\"\\\"\\\"Function describing the transformed image\\\"\\\"\\\"\\n realy = y / resolution_scaling_factor\\n realx = x / resolution_scaling_factor\\n\\n # move to time t2\\n posy = y + calc_shift_fnc(realy, realx, t2, 0) - \\\\\\n calc_shift_fnc(realy, realx, t1, 0)\\n posx = x + calc_shift_fnc(realy, realx, t2, 1) - \\\\\\n calc_shift_fnc(realy, realx, t1, 1)\\n\\n x_left = int(posx) # math.floor(pos[0])\\n x_right = x_left + 1 # math.ceil(pos[0])\\n y_down = int(posy) # math.floor(pos[1])\\n y_up = y_down + 1 # math.ceil(pos[1])\\n\\n v11 = orig_get_fnc(y_down, x_left, resolution_scaling_factor)\\n v12 = orig_get_fnc(y_down, x_right, resolution_scaling_factor)\\n v21 = orig_get_fnc(y_up, x_left, resolution_scaling_factor)\\n v22 = orig_get_fnc(y_up, x_right, resolution_scaling_factor)\\n\\n return interp_fnc(y_down, x_left, y_up, x_right, v11, v12, v21, v22,\\n posy, posx)\\n\\n img.image_data = np.fromfunction(np.vectorize(generate),\\n (original.shape()[0]*resolution_scaling_factor,\\n original.shape()[1]*resolution_scaling_factor))\\n\\n if resolution_scaling_factor != 1:\\n img.image_data = skimage.transform.resize(img.image_data,\\n original.shape(),\\n preserve_range=True)\\n\\n return img\",\n \"def autonomousPeriodic(self):\\n '''\\n for i in self.dataSet:\\n if i[4][0] < self.timer.get() and self.timer.get() <= i[4][1]:\\n self.drive.arcadeDrive(i[0],i[1])\\n self.SV1.set(i[3])\\n self.sd.putValue(\\\"Camera\\\",i[5])\\n else:\\n self.drive.arcadeDrive(0,0)\\n '''\\n\\n #self.auto = self.sd.getNumber(\\\"auto\\\",0)\\n #test\\n #if(self.auto != 1):\\n if self.auto == 6:\\n if self.autoState == 0:\\n if self.gyro.getAngle() >= -41 and self.gyro.getAngle() <= 0:\\n self.drive.arcadeDrive(0.5,-0.4)\\n else:\\n self.autoState = 1\\n self.EC1.reset()\\n if self.autoState == 1:\\n if self.EC1.getDistance() <= 282 and self.EC1.getDistance() >= 0:\\n self.drive.arcadeDrive(0.6,0)\\n else:\\n self.autoState = 2\\n if self.autoState == 2:\\n if self.gyro.getAngle() >= -41 and self.gyro.getAngle() <= 0:\\n self.drive.arcadeDrive(0.5,0.4)\\n else:\\n self.autoState = 3\\n self.EC1.reset()\\n if self.autoState == 3:\\n if self.EC1.getDistance() <= 120 and self.EC1.getDistance() >= 0:\\n self.drive.arcadeDrive(0,6,0)\\n else:\\n self.autoState = 4\\n if self.autoState == 4:\\n if self.EC2.getDistance() <= 831 and self.EC2.getDistance() >= 0: #shoulder\\n self.S1.set(-0.25)\\n self.S2.set(-0.25)\\n else:\\n self.autoState = 5\\n if self.autoState == 5:\\n if self.EC2.getDistance() >= 831 and self.EC2.getDistance() <= 887:\\n self.goldenArrowhead.set(False)\\n self.S1.set(-0.25)\\n self.S2.set(-0.25)\\n else:\\n self.autoState = 6\",\n \"def update(self, deltatime):\\n pass\",\n \"def update(self, index, ttime):\\n if self.dead:\\n return 1\\n self.pos[0] += self.vector[0]\\n self.pos[1] += self.vector[1]\\n self.rect.x += self.vector[0]\\n self.rect.y += self.vector[1]\\n if sum(self.vector):\\n if self.vector[0] < 0:\\n self.vector[0] += 1\\n elif self.vector[0] > 0:\\n self.vector[0] -= 1\\n if self.vector[1] < 0:\\n self.vector[1] += 1\\n elif self.vector[1] > 0:\\n self.vector[1] -= 1\\n if self.game.Player.collides(self.rect) and not sum(self.vector):\\n self.game.Invent.add(self.name)\\n self.game.Player.headDraw(self.name, self.game.Player.player_r)\\n self.dead = 1\",\n \"def update(self, seconds):\\n # Gravity\\n self.calcGravity(seconds)\\n \\n # Move left/right\\n self.rect.x += self.change_x\\n \\n # See if we hit anything\\n block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\\n for block in block_hit_list:\\n # If we are moving right,\\n # set our right side to the left side of the item we hit\\n if self.change_x > 0:\\n self.rect.right = block.rect.left\\n elif self.change_x < 0:\\n # Otherwise if we are moving left, do the opposite.\\n self.rect.left = block.rect.right\\n \\n # Move up/down\\n self.rect.y += self.change_y\\n \\n # update arm position\\n self.arm.update(seconds)\\n \\n # Check and see if we hit anything\\n block_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\\n for block in block_hit_list:\\n \\n # Reset our position based on the top/bottom of the object.\\n if self.change_y > 0:\\n self.rect.bottom = block.rect.top\\n elif self.change_y < 0:\\n self.rect.top = block.rect.bottom\\n \\n # Stop our vertical movement\\n self.change_y = 0\",\n \"def updatePos(self):\\n self.timeDriving +=1\\n self.pos[0] += self.vx\\n self.pos[1] += self.vy\",\n \"def moveBasedOnRetreatAction(self, time_passed):\\n cpos = self.toScreenCoordinate()\\n mpos = pygame.mouse.get_pos()\\n toMouse = Vector2.from_points(cpos,mpos)\\n toMouse.normalize()\\n rheading = -toMouse\\n \\n heading = self.heading\\n angle_between = heading.angle_between(rheading)\\n if angle_between>=-30 and angle_between<=30:\\n return\\n \\n distance = time_passed * self.speed\\n movement = rheading * distance\\n x = movement.get_x()\\n y = movement.get_y()\\n if not self.checkCollision(x, y) and self.checkValidCoord(x, y):\\n self.move(x, y)\",\n \"def update(self, current):\\n\\n self.times[self.index] = time.time()\\n\\n self.current = current\\n\\n # for circular PID, keep values within [-pi, pi]\\n if self.circular:\\n while self.current > 2*math.pi:\\n self.current = self.current - 2*math.pi\\n while self.current < 0:\\n self.current = self.current + 2*math.pi\\n\\n # COMPUTE PROPORTIONAL\\n\\n self.error_p = self.__target - self.current\\n\\n # for circular PID, keep error values within [-pi, pi]\\n if self.circular: \\n while self.error_p > math.pi:\\n self.error_p = self.error_p - 2*math.pi\\n while self.error_p < -math.pi:\\n self.error_p = self.error_p + 2*math.pi\\n\\n self.errors[self.index] = self.error_p\\n if callable(self.debug_callback):\\n self.debug_callback(self.errors)\\n\\n # COMPUTE INTEGRAL\\n\\n # time step here is only the diff between current and past sample\\n time_step = self.times[self.index] - self.times[(self.index - 1) % self.samples]\\n # impose upper bound on time step (to avoid jump from 0 to unix time)\\n time_step = min(time_step, 0.1)\\n self.error_i += self.error_p * time_step\\n self.error_i = max(min(self.error_i, self.I_LIMIT), -self.I_LIMIT)\\n\\n # COMPUTE DIFFERENTIAL\\n\\n # time_step here is over all self.samples=5 samples\\n time_step = self.times[self.index] - self.times[(self.index + 1) % self.samples]\\n # impose lower bound on time step (to avoid divide by zero error)\\n time_step = max(time_step, 0.001)\\n self.error_d = (self.errors[self.index] \\\\\\n - self.errors[(self.index + 1) % self.samples]) \\\\\\n / (time_step)\\n\\n # increment index for next irritation\\n self.index = (self.index + 1) % self.samples\\n\\n # COMPUTE CORRECTION\\n\\n correction = self.KP * self.error_p \\\\\\n + self.KI * self.error_i \\\\\\n + self.KD * self.error_d\\n\\n # safety feature in case update() is not called frequently enough\\n if time_step > 0.2:\\n if callable(self.debug_callback):\\n self.debug_callback(\\\"infrequent updates, returning 0\\\")\\n return 0\\n\\n if callable(self.debug_callback):\\n self.debug_callback(\\\"target = {:2.4f} current = {:2.4f}\\\".format( \\\\\\n self.__target, self.current))\\n self.debug_callback(\\\"errors = \\\" + str(self.errors))\\n self.debug_callback(\\\"e = {:2.4f} e_i = {:2.4f} e_d = {:2.4f} corr = {:2.4f}\\\".format( \\\\\\n self.error_p, self.error_i, self.error_d, correction))\\n\\n return correction\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60318804","0.54270923","0.53947157","0.53792375","0.5303855","0.5262295","0.5260591","0.52238935","0.5190531","0.5141736","0.5131086","0.50906426","0.5085911","0.5074954","0.50716","0.506681","0.50448614","0.50436366","0.50436366","0.50377357","0.5031351","0.50200623","0.5010756","0.5002735","0.50016814","0.500161","0.5000479","0.4999179","0.4985841","0.4985841","0.49856302","0.49830484","0.4982839","0.4979517","0.49773633","0.4971263","0.49584177","0.4935071","0.4925788","0.49207863","0.49176532","0.4915776","0.49156523","0.4913673","0.49063012","0.48929578","0.48760146","0.48671046","0.48542184","0.4841125","0.48369187","0.4832591","0.48321497","0.48291305","0.48270628","0.48238102","0.48227936","0.48227596","0.4818937","0.4813212","0.48026145","0.48011157","0.47962642","0.47925806","0.47901633","0.4789329","0.47883862","0.47853282","0.47839934","0.4783918","0.4778102","0.47777617","0.47774047","0.47733536","0.47677723","0.47645885","0.47593936","0.47585133","0.47577548","0.47572333","0.4757021","0.4756637","0.47500563","0.47500563","0.4748517","0.47434902","0.47411945","0.47408488","0.47405225","0.4735711","0.4731181","0.47308704","0.47302568","0.47292036","0.4726586","0.4725529","0.4722268","0.47208422","0.47192377","0.4715402"],"string":"[\n \"0.60318804\",\n \"0.54270923\",\n \"0.53947157\",\n \"0.53792375\",\n \"0.5303855\",\n \"0.5262295\",\n \"0.5260591\",\n \"0.52238935\",\n \"0.5190531\",\n \"0.5141736\",\n \"0.5131086\",\n \"0.50906426\",\n \"0.5085911\",\n \"0.5074954\",\n \"0.50716\",\n \"0.506681\",\n \"0.50448614\",\n \"0.50436366\",\n \"0.50436366\",\n \"0.50377357\",\n \"0.5031351\",\n \"0.50200623\",\n \"0.5010756\",\n \"0.5002735\",\n \"0.50016814\",\n \"0.500161\",\n \"0.5000479\",\n \"0.4999179\",\n \"0.4985841\",\n \"0.4985841\",\n \"0.49856302\",\n \"0.49830484\",\n \"0.4982839\",\n \"0.4979517\",\n \"0.49773633\",\n \"0.4971263\",\n \"0.49584177\",\n \"0.4935071\",\n \"0.4925788\",\n \"0.49207863\",\n \"0.49176532\",\n \"0.4915776\",\n \"0.49156523\",\n \"0.4913673\",\n \"0.49063012\",\n \"0.48929578\",\n \"0.48760146\",\n \"0.48671046\",\n \"0.48542184\",\n \"0.4841125\",\n \"0.48369187\",\n \"0.4832591\",\n \"0.48321497\",\n \"0.48291305\",\n \"0.48270628\",\n \"0.48238102\",\n \"0.48227936\",\n \"0.48227596\",\n \"0.4818937\",\n \"0.4813212\",\n \"0.48026145\",\n \"0.48011157\",\n \"0.47962642\",\n \"0.47925806\",\n \"0.47901633\",\n \"0.4789329\",\n \"0.47883862\",\n \"0.47853282\",\n \"0.47839934\",\n \"0.4783918\",\n \"0.4778102\",\n \"0.47777617\",\n \"0.47774047\",\n \"0.47733536\",\n \"0.47677723\",\n \"0.47645885\",\n \"0.47593936\",\n \"0.47585133\",\n \"0.47577548\",\n \"0.47572333\",\n \"0.4757021\",\n \"0.4756637\",\n \"0.47500563\",\n \"0.47500563\",\n \"0.4748517\",\n \"0.47434902\",\n \"0.47411945\",\n \"0.47408488\",\n \"0.47405225\",\n \"0.4735711\",\n \"0.4731181\",\n \"0.47308704\",\n \"0.47302568\",\n \"0.47292036\",\n \"0.4726586\",\n \"0.4725529\",\n \"0.4722268\",\n \"0.47208422\",\n \"0.47192377\",\n \"0.4715402\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6062027"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8451,"cells":{"query":{"kind":"string","value":"Compute motioncorrection transformation matrices, catenate with transform from fieldmap to structural, then inteprolate the data to the final grid."},"document":{"kind":"string","value":"def MotcorCatenate(self, info, base, anat_tgt):\n# First compute the transformation matrices due to epi-to-epi motion.\n fmt = '3dvolreg -prefix NULL -1Dmatrix_save %s -twopass ' + \\\n '-verbose -base %s+orig[%s] -dfile %s %s+orig'\n cmd = fmt % (info['matfile_m'], info['basefile'], base, \\\n info['mot_file'], info['imgfile_t'])\n self.CheckExec(cmd, [info['matfile_m']])\n\n# Catenate with transformation from epi base image to the anatomical.\n cmd = 'cat_matvec -ONELINE %s -P %s -P > %s' % \\\n (self.info[anat_tgt]['matfile'], info['matfile_m'], \\\n info['matfile_mcat'])\n self.CheckExec(cmd, [info['matfile_mcat']])\n\n# Interpolate the data to the new grid.\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s ' + \\\n '-warp shift_rotate -base %s+orig[%s] %s+orig'\n cmd = fmt % (info['imgfile_m'], info['matfile_mcat'], info['basefile'], \\\n base, info['imgfile_t'])\n self.CheckExec(cmd, ['%s+orig.BRIK'%info['imgfile_m'], \\\n '%s+orig.HEAD'%info['imgfile_m']])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.\n suffix = '+orig'\n epifile = '%s%s' % (info['imgfile'], suffix)\n prefix = info['imgfile_m']\n base_entry = info['base_entry']\n if info['base'] == 'start':\n# Use the first frame specified in template file. Defaults\n# to zero.\n base = info['motion_ref_frame']\n else:\n# Use the last frame.\n base = self.info[base_entry]['tdim'] - info['skip']-1\n base = ('%d' % base).replace(' ','')\n\n# Correct for slice-timing.\n self.SliceTimeCorrect(info, epifile)\n\n plane = info['plane']\n anat_tgt = info['anat_tgt']\n# anat_entry = self.anat_entry[plane]\n\n if info['catmats']:\n# Include additonal transformation in motion correction such\n# that final image is in register with the fieldmap, which has\n# been registered to the structural image that will be used for\n# spatial normalization.\n self.MotcorCatenate(info, base, anat_tgt)\n else:\n# Assume fieldmap is in register with the structural.\n self.Motcor(info, base)\n\n if info.get('fmapname', None) is None:\n# No fieldmap correction.\n if self.fsl_flip:\n# Flip the way fslview likes it.\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\n elif info['suffix'] == '.nii':\n# Copy motion-corrected images from /tmp to output directory\n outfile = info['imgfile_final'] + info['suffix']\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\n self.CheckExec(cmd, [outfile], force=True)\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\n self.CheckExec(cmd, [], force=True)","def _calculate_transforms(self):\n\n self._logger.info(\"Generating transformations.\")\n\n # Calculate partial transforms - get partial transformation chain;\n partial_transformation_pairs = \\\n map(lambda idx: self._get_slice_pair(idx),\n self.options.slice_range)\n\n # Flatten the slices pairs\n partial_transformation_pairs =\\\n list(flatten(partial_transformation_pairs))\n\n # If user decided to prealign the images by their centre of gravity\n # an additional series of transformations has to be carried out.\n if self.options.enableMomentsAlignment:\n commands = map(lambda x: self._get_cog_alignment(*x),\n partial_transformation_pairs)\n commands = filter(None, commands)\n\n self._logger.info(\"Executing the centre of gravity transforms.\")\n self.execute(commands)\n\n # Calculate affine transformation for each slices pair\n commands = map(lambda x: self._get_partial_transform(*x),\n partial_transformation_pairs)\n commands = filter(None, commands)\n self._logger.info(\"Executing the transformation commands.\")\n self.execute(commands)","def AlignFieldmaps(self):\n for entry in self.entry_map['fmap']:\n info = self.info[entry]\n\n# Register the magnitude image at the shortest TR to the T1-IR\n# structural image.\n target = self.info[self.norm_src]['imgfile'] + \\\n self.info[self.norm_src]['suffix']\n source = info['magfile'] + info['suffix']\n matfile = info['matfile']\n fmt = '3dAllineate -prefix NULL -1Dmatrix_save %s -base %s ' + \\\n '-source %s -cost mi -warp shift_rotate'\n cmd = fmt % (info['matfile'], target, source)\n self.CheckExec(cmd, [info['matfile']])\n\n# Convert to unitary matrix (remove scaling component.)\n cmd = 'cat_matvec -ONELINE %s -P > %s' % \\\n (info['matfile'], info['matfile_unitary'])\n self.CheckExec(cmd, [info['matfile_unitary']])\n\n# Rotate the magnitude image to the new grid.\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\n cmd = fmt % (info['magfile_r']+info['suffix'], \\\n info['matfile_unitary'], info['magfile'] + info['suffix'])\n self.CheckExec(cmd, [info['magfile_r']+info['suffix']])\n\n# Rotate the fieldmap to the new grid.\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\n cmd = fmt % (info['imgfile_r']+info['suffix'], \\\n info['matfile_unitary'], info['imgfile'] + info['suffix'])\n self.CheckExec(cmd, [info['imgfile_r']+info['suffix']])","def preprocess(self):\n\n mm_magcoord.add_aacgm_coordinates(self)\n mm_magcoord.add_quasi_dipole_coordinates(self)\n mm_sc.calculate_ecef_velocity(self)\n mm_sc.add_ram_pointing_sc_attitude_vectors(self)\n\n return","def _derive_transformation_matrices(self):\n\n if hasattr(self, '_primaries') and hasattr(self, '_whitepoint'):\n if self._primaries is not None and self._whitepoint is not None:\n npm = normalised_primary_matrix(self._primaries,\n self._whitepoint)\n\n self._derived_RGB_to_XYZ_matrix = npm\n self._derived_XYZ_to_RGB_matrix = np.linalg.inv(npm)","def _apply_transformations(structure, rotations, translations):\n # Additional first dimension for 'structure.repeat()'\n assembly_coord = np.zeros((len(rotations),) + structure.coord.shape)\n\n # Apply corresponding transformation for each copy in the assembly\n for i, (rotation, translation) in enumerate(zip(rotations, translations)):\n coord = structure.coord\n # Rotate\n coord = matrix_rotate(coord, rotation)\n # Translate\n coord += translation\n assembly_coord[i] = coord\n\n return repeat(structure, assembly_coord)","def assemble_matrices(self):\n \n #Pointer reassignment for convenience\n N = self.ngrids\n\n #Begin with a linked-list data structure for the transmissibilities,\n #and one-dimenstional arrays for the diagonal of B and the flux vector\n T = lil_matrix((N, N), dtype=np.double)\n B = np.zeros(N, dtype=np.double)\n Q = np.zeros(N, dtype=np.double)\n\n #Read in boundary condition types and values\n bcs = self.input_data['boundary conditions']\n bc_type_1 = bcs['left']['type'].lower()\n bc_type_2 = bcs['right']['type'].lower()\n bc_value_1 = bcs['left']['value']\n bc_value_2 = bcs['right']['value']\n \n #Loop over all grid cells\n for i in range(N):\n\n #Apply left BC\n if i == 0:\n T[i, i+1] = -self.compute_transmissibility(i, i + 1)\n\n if bc_type_1 == 'neumann':\n T[i, i] = T[i,i] - T[i, i+1]\n elif bc_type_1 == 'dirichlet':\n #Computes the transmissibility of the ith block\n T0 = self.compute_transmissibility(i, i)\n T[i, i] = T[i,i] - T[i, i+1] + 2.0 * T0\n Q[i] = 2.0 * T0 * bc_value_1\n else:\n pass #TODO: Add error checking here if no bc is specified\n\n #Apply right BC\n elif i == (N - 1):\n T[i, i-1] = -self.compute_transmissibility(i, i - 1)\n\n if bc_type_2 == 'neumann':\n T[i, i] = T[i,i] - T[i, i-1]\n elif bc_type_2 == 'dirichlet':\n #Computes the transmissibility of the ith block\n T0 = self.compute_transmissibility(i, i)\n T[i, i] = T[i, i] - T[i, i-1] + 2.0 * T0\n Q[i] = 2.0 * T0 * bc_value_2\n else:\n pass #TODO:Add error checking here if no bc is specified\n\n #If there is no boundary condition compute interblock transmissibilties\n else:\n T[i, i-1] = -self.compute_transmissibility(i, i-1)\n T[i, i+1] = -self.compute_transmissibility(i, i+1)\n T[i, i] = (self.compute_transmissibility(i, i-1) +\n self.compute_transmissibility(i, i+1))\n\n #Compute accumulations\n B[i] = self.compute_accumulation(i)\n\n #If constant-rate wells are present, add them to the flux vector\n if self.rate_well_grids is not None:\n Q[self.rate_well_grids] += self.rate_well_values\n\n \n #Return sparse data-structures\n return (T.tocsr(), \n csr_matrix((B, (np.arange(N), np.arange(N))), shape=(N,N)), \n Q)","def transform(self, src):\n T, feature_dim = src.shape[0], self.Y_static_dim*3\n\n if feature_dim == self.Y_static_dim:\n return super(GMM_M, self).transform(src)\n\n # A suboptimum mixture sequence (eq.37)\n optimum_mix = self.px.predict(src)\n\n # Compute E eq.(40)\n E = np.empty((T, feature_dim))\n for t in range(T):\n m = optimum_mix[t] # estimated mixture index at time t\n xx = np.linalg.solve(self.covarXX[m], src[t] - self.src_means[m])\n #print(xx.shape,self.tgt_means[m].shape,self.covarYX[m].shape)\n # Eq. (22)\n E[t] = self.tgt_means[m] +np.dot(self.covarYX[m], xx)\n\n # Compute D eq.(23)\n # Approximated variances with diagonals so that we can do MLPG\n # efficiently in dimention-wise manner\n #print(E.shape)\n D = np.empty((T, feature_dim))\n #print(D.shape)\n for t in range(T):\n m = optimum_mix[t]\n # Eq. (23), with approximating covariances as diagonals\n #D[t] = np.diag(self.covarYY[m]) - np.diag(self.covarYX[m]) / \\\n # np.diag(self.covarXX[m]) * np.diag(self.covarXY[m])\n\n # Exact Inference\n dd = self.covarYY[m] - np.linalg.multi_dot([self.covarYX[m], np.linalg.pinv(self.covarXX[m]), self.covarXY[m]])\n #print(dd.shape)\n D[t] = np.diag(dd)\n\n # Once we have mean and variance over frames, then we can do MLPG\n return E, D, self.windows#mlpg(E, D, self.windows)","def __set_transform_matrices(self):\n self.tf_matrices_list = []\n\n transform_matrix = eye(4) # creates a unit matrix via passing argument.\n for i in range(self.joint_count):\n transform_matrix = transform_matrix * self.__create_tf_matrix(self.alpha[i], self.a[i], self.d[i], self.q[i]).subs(self.dh_params)\n self.tf_matrices_list.append(transform_matrix)","def rebuild_the_laplacians():\n local_matrix = InteractomeInterface()\n local_matrix.full_rebuild()\n\n annot_matrix = AnnotomeInterface()\n annot_matrix.full_rebuild()","def recalculate_transform(self):\n bias_mat = wireframe.translationMatrix(*self.bias)\n rot_mat = wireframe.rotateMatrix(*self.view_angle)\n scale_mat = wireframe.scaleMatrix(*self.scale)\n\n self.tf_wireframe.nodes = (self.wireframe.nodes - self._center_half) @ \\\n rot_mat @ scale_mat @ bias_mat","def mca_transformer(transform_data):\n M, dims, index, v0v1 = transform_data\n def transform(dfp):\n # dims, index, v0v1\n P = np.zeros((len(dfp), dims), dtype=float)\n print(\"transforming\")\n for i, (_, row) in (enumerate(dfp.iterrows())):\n ivec = np.zeros(M)\n for col, val in zip(row.index, row):\n if (col, val) in index:\n ivec[index[col, val]] = 1\n proj = ivec.dot(v0v1)\n assert(all(proj.imag == 0))\n P[i,:] = proj.real\n return P\n return transform","def test_transform_update():\n pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_answer_0)\n ncs_obj = ncs.input(hierarchy=pdb_inp.construct_hierarchy())\n pdb_inp = iotbx.pdb.input(lines=pdb_answer_0,source_info=None)\n nrgl = ncs_obj.get_ncs_restraints_group_list()\n asu_site_cart = pdb_inp.atoms().extract_xyz()\n # reference matrices\n r1 = nrgl[0].copies[0].r\n t1 = nrgl[0].copies[0].t\n r2 = nrgl[0].copies[1].r\n t2 = nrgl[0].copies[1].t\n # modify matrices in the ncs group list\n nrgl[0].copies[0].r = r1 + r2\n nrgl[0].copies[0].t = t1 + t2\n nrgl[0].copies[1].r = r1 + r2\n nrgl[0].copies[1].t = t1 + t2\n nrgl.recalculate_ncs_transforms(asu_site_cart)\n # Get the updated values\n r1_n = nrgl[0].copies[0].r\n t1_n = nrgl[0].copies[0].t\n r2_n = nrgl[0].copies[1].r\n t2_n = nrgl[0].copies[1].t\n #\n assert approx_equal(r1, r1_n, eps=0.001)\n assert approx_equal(t1, t1_n, eps=0.1)\n assert approx_equal(r2, r2_n, eps=0.001)\n assert approx_equal(t2, t2_n, eps=0.1)","def update_variables(self):\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\n # aquire transformation matrices and tips for segment 1 and 2\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\n # Frenet frames\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\n # Bishop frames\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]","def chain_corrections():\n \n #read the files\n sample_4m=read_sample(map_files('sample_4m'))\n empty_cell_4m=read_sample(map_files('empty_cell_4m'))\n empty_4m=read_sample(map_files('empty_4m'))\n transmission_sample_cell_4m=read_sample(map_files('trans_sample_4m'))\n transmission_empty_cell_4m=read_sample(map_files('trans_empty_cell_4m'))\n blocked_beam_4m=read_sample(map_files('blocked_4m'))\n sensitivity=read_div(map_files('div'))\n #mask=read_sample(map_files('mask'))\n \n #normalize the monitors\n \n sample_4m_norm=monitor_normalize(sample_4m)\n empty_cell_4m_norm=monitor_normalize(empty_cell_4m)\n transmission_sample_cell_4m_norm=monitor_normalize(transmission_sample_cell_4m)\n transmission_empty_cell_4m_norm=monitor_normalize(transmission_empty_cell_4m)\n empty_4m_norm=monitor_normalize(empty_4m)\n blocked_beam_4m_norm=monitor_normalize(blocked_beam_4m)\n \n #calculate q\n sample_4m_norm_q=convert_q(sample_4m_norm)\n empty_cell_4m_norm_q=convert_q(empty_cell_4m)\n blocked_beam_4m_norm_q=convert_q(blocked_beam_4m_norm)\n transmission_sample_cell_4m_norm_q=convert_q(transmission_sample_cell_4m_norm)\n transmission_empty_cell_4m_norm_q=convert_q(transmission_empty_cell_4m_norm)\n empty_4m_norm_q=convert_q(empty_4m_norm)\n \n \n print 'converted'\n #convert flatness\n sample_4m_solid=correct_solid_angle(sample_4m_norm_q)\n empty_cell_4m_solid=correct_solid_angle(empty_cell_4m_norm_q)\n blocked_beam_4m_solid=correct_solid_angle(blocked_beam_4m_norm_q)\n transmission_sample_cell_4m_solid=correct_solid_angle(transmission_sample_cell_4m_norm_q)\n transmission_empty_cell_4m_solid=correct_solid_angle(transmission_empty_cell_4m_norm_q)\n empty_4m_solid=correct_solid_angle(empty_4m_norm_q)\n \n \n #calculate transmission\n coord_left=(60,60)\n coord_right=(70,70)\n transmission_sample_cell_4m_rat=generate_transmission(transmission_sample_cell_4m_solid,empty_4m_solid,\n coord_left,coord_right)\n transmission_empty_cell_4m_rat=generate_transmission(transmission_empty_cell_4m_solid,empty_4m_solid,\n coord_left,coord_right)\n print 'Sample transmission= {} (IGOR Value = 0.724)'.format(transmission_sample_cell_4m_rat)\n print 'Empty Cell transmission= {} (IGOR Value = 0.929)'.format(transmission_empty_cell_4m_rat)\n print 'hi'\n \n #Initial Correction -- Not with the sub/mult tools,\n #SAM = sample_4m_solid.data\n #print SAM.x\n #EMP = empty_4m_solid.data\n #print \"EMP: \"\n #print EMP.x\n #BGD = blocked_beam_4m_solid.data\n #print \"BGD\"\n #print BGD.x\n #Tsam = transmission_sample_cell_4m_rat\n #Temp = transmission_empty_cell_4m_rat\n #COR1 = SAM.__sub__(BGD)\n #COR2 = (EMP.__sub__(BGD)).__mul__(Tsam/Temp)\n #COR = COR1.__sub__(COR2)\n #print \"after initial correction: \"\n #print COR.x\n \n SAM = sample_4m_solid\n print SAM.data.x\n EMP = empty_4m_solid\n print \"EMP: \"\n print EMP.data.x\n BGD = blocked_beam_4m_solid\n print \"BGD:\"\n print BGD.data.x\n Tsam = transmission_sample_cell_4m_rat\n Temp = transmission_empty_cell_4m_rat\n print \"COR1:\"\n COR1 = SAM.__sub1__(BGD)\n print COR1.data.x #check=works\n #-----Problems Here-------\n print \"COR2:\"\n COR2 = (EMP.__sub1__(BGD)) #check=works\n print COR2.data.x\n print \"COR3:\"\n #AJJ - __mul__ not working because Tsam and Temp are Measurement instances and not simply floats. See above.\n COR3 = COR2.__mul__(Tsam/Temp) #mul not working\n print COR3.data.x\n #COR = COR1.__sub1__(COR2)\n #print \"after initial correction: \"\n #print COR.x\n #COR2 = (EMP.__sub__(BGD)).__mul__(Tsam/Temp)\n #COR = COR1.__sub__(COR2)\n #print \"after initial correction: \"\n #print COR.data.x","def _calculate_composite_transforms(self):\n\n self._calculate_similarity()\n # Finally, calculate composite transforms\n commands = []\n for moving_slice_index in self.options.slice_range:\n commands.append(self._calculate_composite(moving_slice_index))\n self.execute(commands)\n\n self._logger.info(\"Done with calculating the transformations.\")","def motionCorrection(baseName, outDir, rawDataFile, granularity ='plane',\n maxDisplacement=[40, 40],\n nCpu=(multiprocessing.cpu_count() - 1), \n exportFrames=True):\n try:\n # instead of os.makedirs use os.mkdir\n # the former creates also intermediate dirs\n os.mkdir(outDir + '/' + baseName)\n except OSError:\n print('Either directory exists or \\\n the intermedite directories do not exist!')\n raise\n print(\"--Motion correction started with %s...\" % baseName)\n # Checking if there are two cycles of images. Important to know otherwise \n # you get merged motion corrected images.\n allImages = (glob.glob(rawDataFile))\n tseries_names = [os.path.basename(imageName).split('_')[0] for imageName in allImages]\n uniqueTSeries = Counter(tseries_names).keys()\n tseries_lengths = Counter(tseries_names).values()\n \n if len(uniqueTSeries) > 1:\n print('---More than 1 T-Series found. Aligning them together...')\n\n match = [int(re.search(r'Cycle(.*?)_',imageName).group(1)) for imageName in allImages]\n uniqueCycles = numpy.unique(numpy.asarray(match))\n \n if len(uniqueCycles) > 1:\n warnstr='More than 1 image cycle detected. Aborting alignment.'\n warnings.warn(warnstr)\n return \n sequence = [sima.Sequence.create('TIFFs', [[rawDataFile]])]\n print(\"Creating sima dataset of non-aligned images\")\n nonAlignedDatasetDir = outDir + '/' + baseName + '/' + 'TIFFs.sima'\n sima.ImagingDataset(sequence, nonAlignedDatasetDir)\n\n print(\"Running motion correction.\")\n \n mc_approach = sima.motion.HiddenMarkov2D(granularity=granularity,\n max_displacement=maxDisplacement,\n verbose=True,\n n_processes=nCpu)\n print(\"Creating sima dataset of aligned images\")\n motCorrDir = outDir + '/' + baseName + '/' + 'motCorr.sima'\n dataset = mc_approach.correct(sequence, motCorrDir)\n\n if exportFrames:\n print(\"Exporting motion-corrected movies.\")\n \n for iTSeries, curr_T_series in enumerate(uniqueTSeries):\n \n start_frame = tseries_names.index(curr_T_series)\n end_frame = start_frame+tseries_lengths[iTSeries]\n dataset[0,start_frame:end_frame].export_frames([[[os.path.join(motCorrDir,\n '{t}_motCorr.tif'.format(t = curr_T_series))]]],\n fill_gaps=True)\n \n\n print(\"--Motion correction done with %s...\" % baseName)\n\n return uniqueTSeries","def transform_calcualtion(height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag):\n # default coordination/location of transformed matrix according to source data(data map)\n coord_map = default_coord(height, width, h_offset, w_offset)\n\n for i in range(height):\n for j in range(width):\n # base calculations\n result = [(t_mat[0][0])*(coord_map[i, j, 0])+int((t_mat[0][1])*(coord_map[i, j, 1])),\n (t_mat[1][0])*(coord_map[i, j, 0])+(t_mat[1][1])*(coord_map[i, j, 1])]\n # since all coordinations must not be negative\n # if happened also apply a translation by offset\n coord_map[i, j, :] = [(result[0], result[0]+h_offset)[nh_flag],\n (result[1], result[1]+w_offset)[nw_flag]]\n return coord_map","def imageTransform(self):\n ims = self.imageShape\n acs = self.activeShape\n dx = self.colVector\n dy = self.rowVector\n\n p0 = self.activeOrigin\n p1 = p0 + acs[2] * dx\n p2 = p0 + acs[1] * dy\n\n # print p0, p1, p2\n # print acs, dx, dy\n\n localPts = list(map(pg.Vector, [[0,0], [ims[2],0], [0,ims[1]], [0,0,1]])) # w and h of data of image in pixels.\n globalPts = list(map(pg.Vector, [p0, p1, p2, [0,0,1]]))\n m = pg.solve3DTransform(localPts, globalPts)\n m[:,2] = m[:,3]\n m[2] = m[3]\n m[2,2] = 1\n tr = Qt.QTransform(*m[:3,:3].transpose().reshape(9))\n return tr","def gridalign(self):\n self.position.x = int(round(self.position.x))\n self.position.y = int(round(self.position.y))\n self.position.z = int(round(self.position.z))\n\n if self.fan:\n self.fan = (int(round(self.fan[0])),int(round(self.fan[1])),int(round(self.fan[2])))\n\n bestDist = 2*9\n bestMatrix = makeMatrix(0,0,0)\n\n for compass in [0, 90, 180, 270]:\n for pitch in [0, 90, 180, 270]:\n for roll in [0, 90, 180, 270]:\n m = makeMatrix(compass,pitch,roll)\n dist = matrixDistanceSquared(self.matrix, m)\n if dist < bestDist:\n bestMatrix = m\n bestDist = dist\n\n self.matrix = bestMatrix\n self.positionOut()\n self.directionOut()","def _preprocess(self):\n if self.gt_matrix_PCs is None:\n start = time.time()\n gt_matrix = self._process_vcf().get('gt_matrix')\n print('gt_matrix took {} secs'.format(time.time() - start))\n \n # Normalize gt_matrix by site\n gt_matrix_norm = gt_matrix - np.mean(gt_matrix, axis=1)[:, np.newaxis]\n \n # PCA\n start = time.time()\n u, s, vh = np.linalg.svd(gt_matrix_norm.T, full_matrices=False)\n print('SVD took {} secs'.format(time.time() - start))\n self.gt_matrix_PCs = -u @ np.diag(s)\n \n # Get relevant objects from result of ripser\n if self.ripser_result is None:\n start = time.time()\n print(\"Getting ripser object\")\n self.ripser_result = ripser(self.gt_matrix_PCs, coeff=2, maxdim=1, do_cocycles=True)\n print('Ripser took {} secs'.format(time.time() - start))","def get_all_coding_matrices(self):\n class tran_matrix():\n def __init__(self):\n self.S = None\n self.T = None\n self.H1 = None\n self.H2 = None\n self.G1 = None\n self.G2 = None\n self.H = None\n self.H2G2 = None\n\n def __init__(self, S, T, H1, H2, G1, G2, H, H2G2):\n self.S = S\n self.T = T\n self.H1 = H1\n self.H2 = H2\n self.G1 = G1\n self.G2 = G2\n self.H = H\n self.H2G2 = H2G2\n\n s = self.sources\n s_edges = [self.out_edges(x) for x in s]\n S = [list(set(self.get_edges_indices(src))) for src in s_edges]\n\n matrices = []\n for dsts in self.dst_evolution_rec[::-1]:\n T = [sorted(x) for x in dsts]\n H2_raw = self.coding_matrix[T[0]].ix[S[1]].values\n G2_raw = self.coding_matrix[T[1]].ix[S[1]].values\n H1 = numpy_to_generic(self.coding_matrix[T[0]].ix[S[0]].values)\n H2 = numpy_to_generic(H2_raw)\n G1 = numpy_to_generic(self.coding_matrix[T[1]].ix[S[0]].values)\n G2 = numpy_to_generic(G2_raw)\n H = numpy_to_generic(self.coding_matrix[T[0]].values)\n H2G2 = numpy_to_generic(np.hstack((H2_raw, G2_raw)))\n temp = tran_matrix(S, T, H1, H2, G1, G2, H, H2G2)\n matrices.append(temp)\n\n return matrices","def assembleMatrices(self):\n # All nodes informations\n self.aircraftNodesPoints = []\n self.aircraftMassPoints = []\n self.aircraftMassDistances = []\n self.aircraftSegmentsLengths = []\n self.aircraftNodesNames = []\n self.aircraftInitNodesAreas = []\n self.aircraftNodesA = []\n self.aircraftNodesIy = []\n self.aircraftNodesIz = []\n self.aircraftNodesJ = []\n # More general information\n self.aircraftConnectedNodes = []\n self.aircraftNonRotatingNodes = []\n\n self.computeProportionFuselage()\n self.computeProportionWings()\n\n # adds fulseage infos to matrices if there is one\n if self.nFuselage > 0:\n self.aircraftNodesPoints.append(self.fs_m_points[0])\n # Assumption is made that for a fuselage section, its cog is\n # aligned with the section center.\n self.aircraftMassPoints.append(self.fs_m_points[0])\n self.aircraftMassDistances.append(np.zeros((len(self.fs_m_points[0]),3)))\n self.aircraftSegmentsLengths.append(self.fs_m_distanceBetweenPoints[0])\n self.aircraftNodesNames.append(self.fs_m_pointsName[0])\n self.aircraftInitNodesAreas.append(self.fs_m_pointsInitArea[0])\n self.aircraftNodesA.append(self.fs_m_pointsA[0])\n self.aircraftNodesIy.append(self.fs_m_pointsIy[0])\n self.aircraftNodesIz.append(self.fs_m_pointsIz[0])\n self.aircraftNodesJ.append(self.fs_m_pointsJ[0])\n logger.debug('\\n')\n for i in range(self.nWings):\n self.aircraftNodesPoints.append(self.ws_me_points[i])\n self.aircraftMassPoints.append(self.ws_ma_points[i])\n self.aircraftMassDistances.append(self.ws_ma_distance[i])\n self.aircraftSegmentsLengths.append(self.ws_me_distances[i])\n # logger.debug(i)\n # logger.debug(self.ws_me_distances)\n # logger.debug(self.aircraftSegmentsLengths)\n self.aircraftNodesNames.append(self.ws_me_pointsName[i])\n self.aircraftInitNodesAreas.append(self.ws_me_pointsInitArea[i])\n self.aircraftNodesA.append(self.ws_me_pointsA[i])\n self.aircraftNodesIy.append(self.ws_me_pointsIy[i])\n self.aircraftNodesIz.append(self.ws_me_pointsIz[i])\n self.aircraftNodesJ.append(self.ws_me_pointsJ[i])\n self.computeMass()","def matrix_map(self, bkg_reduction=True, data_correction=True):\r\n\r\n if bkg_reduction is True:\r\n if data_correction is True:\r\n data = self.df4\r\n \r\n else:\r\n data = self.df2\r\n\r\n else:\r\n if data_correction is True:\r\n data = self.df3\r\n \r\n else:\r\n data = self.df1\r\n\r\n return data","def applyMat(my_map, linsys_setup):\n \n datamaps, ninvs, beams, freqs, power_2d, precond_2d, clumaps, g_nu, \\\n map_prop = linsys_setup\n \n \n\n nx, ny, pixScaleX, pixScaleY = map_prop\n nFreq = len(g_nu); nCluster = len(clumaps[0])\n\n #Always apply beam * precond\n beam_prec=[]\n\n for f in range(nFreq):\n beam_prec+=[beams[f][:,:ny/2+1]*precond_2d[:,:ny/2+1]]\n precond_2d=precond_2d[:,:ny/2+1]\n power_2d=power_2d[:,:ny/2+1]\n \n ksz = False\n if len(clumaps) == 2: ksz = True\n \n # Routines to perform block matrix multiplication defined in Eriksen Eq. 19\n \n def apply_cmb_cmb(d0):\n \"\"\"\n Apply (S^-1 + A N^-1 A) x\n \"\"\"\n d1 = d0.copy()\n d1 = numpy.reshape(d1,(nx,ny))\n a_l = fft.rfft(d1,axes=[-2,-1])\n \n c_l = 0\n for f in range(nFreq):\n\n b_l = a_l * beam_prec[f]\n d2 = fft.irfft(b_l,axes=[-2,-1],normalize=True)\n d2 *= ninvs[f]\n b_l = fft.rfft(d2,axes=[-2,-1])\n c_l += b_l * beam_prec[f]\n \n d2 = fft.irfft(c_l,axes=[-2,-1],normalize=True)\n d1 = fft.irfft(precond_2d**2 * a_l/power_2d,axes=[-2,-1],normalize=True)\n \n d2 += d1\n \n return d2.reshape((nx*ny,))\n \n \"\"\"\n def apply_tsz_tsz(d0): # DONE\n \\\"\"\"\n Apply (F^T A^T N^-1 A F) x\n \\\"\"\"\n d1 = numpy.zeros(nCluster)\n mat = numpy.zeros((nCluster, nCluster))\n # TODO: This could probably be more efficient (e.g. using np.outer)\n for freq in range(nFreq):\n for ic in range(nCluster):\n for jc in range(0, ic+1):\n mat[ic,jc] = numpy.sum( ninvs[freq] * g_nu[freq]**2. \\\n * clumaps[0][ic][freq] * clumaps[0][jc][freq] )\n if ic != jc: mat[jc,ic] = mat[ic,jc]\n d1 += numpy.dot(mat, d0)\n return d1\n \n def apply_ksz_ksz(d0): # DONE\n \\\"\"\"\n Apply (K^T A^T N^-1 A K) x\n \\\"\"\"\n # FIXME: Missing factor of ivcov\n d1 = numpy.zeros(nCluster)\n mat = numpy.zeros((nCluster, nCluster))\n # TODO: This could probably be more efficient (e.g. using np.outer)\n for freq in range(nFreq):\n for ic in range(nCluster):\n for jc in range(0, ic+1):\n mat[ic,jc] = numpy.sum( ninvs[freq] \\\n * clumaps[1][ic][freq] * clumaps[1][jc][freq] )\n if ic != jc: mat[jc,ic] = mat[ic,jc]\n d1 += numpy.dot(mat, d0)\n d1 += numpy.dot(ivcov, d0) # Add prior term\n return d1\n \"\"\"\n \n def apply_cmb_foreground_block(dc, dm, dt, dk=None):\n \"\"\"\n Apply the CMB x (Monopole + TSZ + KSZ) terms in one block:\n (A^T N^-1 A T) x_mono\n (A^T N^-1 A F) x_tsz\n (A^T N^-1 A K) x_ksz\n \n (T^T A^T N^-1 A) x_cmb\n (F^T A^T N^-1 A) x_cmb\n (K^T A^T N^-1 A) x_cmb\n \"\"\"\n ksz = False\n if dk is not None: ksz = True\n \n # (A^T N^-1 A T) x_mono; (A^T N^-1 A F) x_tsz; (A^T N^-1 A K) x_ksz\n b_lt = 0; b_lk = 0; b_lm = 0\n for f in range(nFreq):\n mct = 0; mck = 0\n for ic in range(nCluster):\n mct += dt[ic] * ninvs[f] * clumaps[0][ic][f] * g_nu[f]\n if ksz: mck += dk[ic] * ninvs[f] * clumaps[1][ic][f]\n \n b_lm += fft.rfft(dm * ninvs[f],axes=[-2,-1]) * beam_prec[f]\n b_lt += fft.rfft(mct,axes=[-2,-1]) * beam_prec[f]\n if ksz: b_lk += fft.rfft(mck,axes=[-2,-1]) * beam_prec[f]\n\n mcm = fft.irfft(b_lm,axes=[-2,-1],normalize=True).reshape((nx*ny,))\n mct = fft.irfft(b_lt,axes=[-2,-1],normalize=True).reshape((nx*ny,))\n if ksz: mck = fft.irfft(b_lk,axes=[-2,-1],normalize=True).reshape((nx*ny,))\n \n # (T^T A^T N^-1 A) x_cmb; (F^T A^T N^-1 A) x_cmb; (K^T A^T N^-1 A) x_cmb\n mc = dc.copy().reshape((nx,ny))\n a_l = fft.rfft(mc,axes=[-2,-1])\n mtc = numpy.zeros(nCluster)\n mkc = numpy.zeros(nCluster)\n mmc = 0\n for f in range(nFreq):\n b_l = a_l * beam_prec[f]\n mc = fft.irfft(b_l,axes=[-2,-1],normalize=True)\n mmc += numpy.sum(mc * ninvs[f])\n for ic in range(nCluster):\n mtc[ic] += numpy.sum(mc * ninvs[f] * clumaps[0][ic][f] * g_nu[f])\n if ksz: mkc[ic] += numpy.sum(mc * ninvs[f] * clumaps[1][ic][f])\n \n if ksz: return mct, mcm, mck, mtc, mmc, mkc\n return mct, mcm, mtc, mmc\n \n \n def apply_foreground_block(m0, t0, k0=None):\n \"\"\"\n Apply the TSZ + KSZ + Monopole terms in one block:\n [ (T^T A^T N^-1 A F) (T^T A^T N^-1 A K) (T^T A^T N^-1 A T) ] (x_mono)\n [ (F^T A^T N^-1 A F) (F^T A^T N^-1 A K) (F^T A^T N^-1 A T) ] (x_tsz)\n [ (K^T A^T N^-1 A F) (K^T A^T N^-1 A K) (K^T A^T N^-1 A T) ] (x_ksz)\n \"\"\"\n ksz = True if k0 is not None else False\n \n dtt, dkk, dtk, dkt = [numpy.zeros(nCluster) for i in range(4)]\n mtt, mkk, mtk, mkt = [numpy.zeros((nCluster, nCluster)) for i in range(4)]\n dmm, dmk, dmt = [0 for i in range(3)]\n dkm, dtm = [numpy.zeros(nCluster) for i in range(2)]\n \n # TODO: This could probably be more efficient (e.g. using np.outer)\n for f in range(nFreq):\n dmm += numpy.sum(ninvs[f]) * m0\n \n # Loop through clusters\n for ic in range(nCluster):\n dmt += numpy.sum( ninvs[f] * g_nu[f] * clumaps[0][ic][f] * t0[ic] )\n dtm[ic] += numpy.sum( ninvs[f] * g_nu[f] * clumaps[0][ic][f] * m0 )\n if ksz:\n dmk += numpy.sum( ninvs[f] * clumaps[1][ic][f] * k0[ic] )\n dkm[ic] += numpy.sum( ninvs[f] * clumaps[1][ic][f] * m0 )\n \n for jc in range(0, ic+1):\n mtt[ic,jc] = numpy.sum( ninvs[f] * g_nu[f]**2. \\\n * clumaps[0][ic][f] * clumaps[0][jc][f] )\n if ksz:\n mkk[ic,jc] = numpy.sum( ninvs[f] \\\n * clumaps[1][ic][f] * clumaps[1][jc][f] )\n mtk[ic,jc] = numpy.sum( ninvs[f] * g_nu[f] \\\n * clumaps[0][ic][f] * clumaps[1][jc][f] )\n mkt[ic,jc] = numpy.sum( ninvs[f] * g_nu[f] \\\n * clumaps[1][ic][f] * clumaps[0][jc][f] )\n # Mirror indices\n mtt[jc,ic] = mtt[ic,jc]\n if ksz:\n mkk[jc,ic] = mkk[ic,jc]\n mtk[jc,ic] = mtk[ic,jc]\n mkt[jc,ic] = mkt[ic,jc]\n \n # Add total contribs. for this band\n dtt += numpy.dot(mtt, t0)\n if ksz:\n dkk += numpy.dot(mkk, k0)\n dtk += numpy.dot(mtk, k0)\n dkt += numpy.dot(mkt, t0)\n \n if ksz: return dtt, dkk, dmm, dtk, dkt, dmk, dkm, dtm, dmt\n return dtt, dmm, dtm, dmt\n \n # Apply block matrix multiplications and return\n # FIXME: What if KSZ not used?\n x0 = my_map[:nx*ny]\n x1 = my_map[nx*ny:nx*ny+1]\n x2 = my_map[nx*ny+1:nx*ny+nCluster+1]\n if ksz: x3 = my_map[nx*ny+nCluster+1:nx*ny+2*nCluster+1]\n \n # Multiply input vector in blocks\n #t=time.time()\n dcc = apply_cmb_cmb(x0)\n #print 'CMB', time.time()-t\n if ksz:\n dct, dcm, dck, dtc, dmc, dkc = apply_cmb_foreground_block(x0, x1, x2, x3)\n dtt, dkk, dmm, dtk, dkt, dmk, dkm, dtm, dmt = apply_foreground_block(x1, x2, x3)\n x_new_0 = dcc + dct + dck + dcm\n x_new_1= dmc + dmt + dmk + dmm\n x_new_2 = dtc + dtt + dtk + dtm\n x_new_3 = dkc + dkt + dkk + dkm\n x_new = numpy.concatenate((x_new_0, x_new_1, x_new_2, x_new_3))\n else:\n #t=time.time()\n dct, dcm, dtc, dmc = apply_cmb_foreground_block(x0, x1, x2)\n #print 'CMB-F', time.time()-t\n \n #t=time.time()\n dtt, dmm, dtm, dmt = apply_foreground_block(x1, x2)\n #print 'F', time.time()-t\n \n x_new_0 = dcc + dct + dcm\n x_new_1 = dmc + dmt + dmm\n x_new_2 = dtc + dtt + dtm\n x_new = numpy.concatenate((x_new_0, x_new_1, x_new_2))\n\n\n#sys.exit()\n return x_new","def _compose_transforms(basis_transforms, source_basis, source_dag):\n example_gates = _get_example_gates(source_dag)\n mapped_instrs = {}\n\n for gate_name, gate_num_qubits in source_basis:\n # Need to grab a gate instance to find num_qubits and num_params.\n # Can be removed following https://github.com/Qiskit/qiskit-terra/pull/3947 .\n example_gate = example_gates[gate_name, gate_num_qubits]\n num_params = len(example_gate.params)\n\n placeholder_params = ParameterVector(gate_name, num_params)\n placeholder_gate = Gate(gate_name, gate_num_qubits, list(placeholder_params))\n placeholder_gate.params = list(placeholder_params)\n\n dag = DAGCircuit()\n qr = QuantumRegister(gate_num_qubits)\n dag.add_qreg(qr)\n dag.apply_operation_back(placeholder_gate, qr[:], [])\n mapped_instrs[gate_name, gate_num_qubits] = placeholder_params, dag\n\n for gate_name, gate_num_qubits, equiv_params, equiv in basis_transforms:\n logger.debug(\n \"Composing transform step: %s/%s %s =>\\n%s\",\n gate_name,\n gate_num_qubits,\n equiv_params,\n equiv,\n )\n\n for mapped_instr_name, (dag_params, dag) in mapped_instrs.items():\n doomed_nodes = [\n node\n for node in dag.op_nodes()\n if (node.op.name, node.op.num_qubits) == (gate_name, gate_num_qubits)\n ]\n\n if doomed_nodes and logger.isEnabledFor(logging.DEBUG):\n\n logger.debug(\n \"Updating transform for mapped instr %s %s from \\n%s\",\n mapped_instr_name,\n dag_params,\n dag_to_circuit(dag, copy_operations=False),\n )\n\n for node in doomed_nodes:\n\n replacement = equiv.assign_parameters(\n dict(zip_longest(equiv_params, node.op.params))\n )\n\n replacement_dag = circuit_to_dag(replacement)\n\n dag.substitute_node_with_dag(node, replacement_dag)\n\n if doomed_nodes and logger.isEnabledFor(logging.DEBUG):\n\n logger.debug(\n \"Updated transform for mapped instr %s %s to\\n%s\",\n mapped_instr_name,\n dag_params,\n dag_to_circuit(dag, copy_operations=False),\n )\n\n return mapped_instrs","def do_transform(self):\n # TODO: after unit tests are added switch to astropy fftconvolve here.\n from scipy.signal import fftconvolve\n total_background = self.model + self.background + self.approx\n excess = self.image - total_background\n for key, kern in self.kernbase.items():\n self.transform[key] = fftconvolve(excess, kern, mode='same')\n self.error[key] = np.sqrt(fftconvolve(total_background, kern ** 2, mode='same'))\n\n self.approx = fftconvolve(self.image - self.model - self.bkg,\n self.kern_approx, mode='same')\n self.approx_bkg = fftconvolve(self.bkg, self.kern_approx, mode='same')","def gonio_axis_align():\n \n # Invert camera image, so dark pin on light image becomes a peak\n cam_7.proc1.scale.put(-1)\n cam_8.proc1.scale.put(-1)\n \n # High threshold, so AD centroid doesn't interpret background\n cam_8ThresholdOld = cam_8.stats4.centroid_threshold.get()\n cam_8.stats4.centroid_threshold.put(150)\n cam_7ThresholdOld = cam_7.stats4.centroid_threshold.get()\n cam_7.stats4.centroid_threshold.put(150)\n \n # HiMag\n # Copy ROI2 geometry (HiMag Mag3) to ROI4 and use ROI4 centroid plugin\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get())\n cam_8.roi4.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get())\n cam_8.roi4.size.x.put(cam_8.roi2.size.x.get() * 0.20)\n cam_8.roi4.size.y.put(cam_8.roi2.size.y.get())\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get() + cam_8.roi2.size.x.get()/2 - cam_8.roi4.size.x.get()/2)\n \n # LoMag\n # Copy ROI2 geometry (LoMag Mag1) to ROI4 and use ROI4 centroid plugin\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get())\n cam_7.roi4.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get())\n cam_7.roi4.size.x.put(cam_7.roi2.size.x.get() * 0.05)\n cam_7.roi4.size.y.put(cam_7.roi2.size.y.get())\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get() + cam_7.roi2.size.x.get()/2 - cam_7.roi4.size.x.get()/2)\n \n centerPinYHiMag0 = centroid_avg(cam_8.stats4)[1]\n centerPinYLoMag0 = centroid_avg(cam_7.stats4)[1]\n yield from bps.mvr(gonio.o,180)\n time.sleep(2)\n centerPinYHiMag180 = centroid_avg(cam_8.stats4)[1]\n centerPinYLoMag180 = centroid_avg(cam_7.stats4)[1]\n centerPinYHiMag = (centerPinYHiMag0 + centerPinYHiMag180)/2\n centerPinYLoMag = (centerPinYLoMag0 + centerPinYLoMag180)/2\n\n centerPinOffsYHiMag = centerPinYHiMag - cam_8.roi4.size.y.get() / 2\n centerPinOffsYLoMag = centerPinYLoMag - cam_7.roi4.size.y.get() / 2\n \n # Correct Mag 3 (cam_8 ROI2)\n cam_8.roi2.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + centerPinOffsYHiMag)\n # Correct Mag 4 (cam_8 ROI1)\n cam_8.roi1.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + (cam_8.roi2.size.y.get()-cam_8.roi1.size.y.get())/2)\n \n # Correct Mag 1 (cam_7 ROI2)\n cam_7.roi2.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + centerPinOffsYLoMag)\n # Correct Mag 2 (cam_7 ROI3)\n cam_7.roi3.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + (cam_7.roi2.size.y.get()-cam_7.roi3.size.y.get())/2)\n\n # De-invert image\n cam_7.proc1.scale.put(-1)\n cam_8.proc1.scale.put(-1)\n \n # Set thresold to previous value\n cam_8.stats4.centroid_threshold.put(cam_8ThresholdOld)\n cam_7.stats4.centroid_threshold.put(cam_7ThresholdOld)\n \n return","def init_transformations(self):\n shifts = (self.img_shape[0] // 2, self.img_shape[1] // 2)\n unshifts = (-self.img_shape[0] // 2, -self.img_shape[1] // 2)\n self.preshift = skimage.transform.SimilarityTransform(\n translation = shifts)\n self.postshift = skimage.transform.SimilarityTransform(\n translation = unshifts)","def apply_dof_transformation(tdim, edge_count, face_count, entity_transformations, entity_dofs,\n data, cell_info, face_types):\n if tdim >= 2:\n if tdim == 3:\n face_start = 3 * face_count\n else:\n face_start = 0\n\n dofstart = 0\n for i in entity_dofs[0]:\n dofstart += i\n # NOTE: Copy array to make numba compilation faster (contiguous array assumption)\n edge_reflection = entity_transformations[\"interval\"][0].copy()\n for e in range(edge_count):\n edofs = entity_dofs[1][e]\n if edofs == 0:\n continue\n if cell_info >> (face_start + e) & 1:\n data[dofstart:dofstart+edofs] = numpy.dot(edge_reflection, data[dofstart:dofstart+edofs])\n dofstart += edofs\n\n if tdim == 3:\n for f in range(face_count):\n face_rotation = entity_transformations[face_types[f]][0].copy()\n face_reflection = entity_transformations[face_types[f]][1].copy()\n fdofs = entity_dofs[2][f]\n if fdofs == 0:\n continue\n if cell_info >> (3 * f) & 1:\n data[dofstart:dofstart+fdofs] = numpy.dot(face_reflection, data[dofstart:dofstart+fdofs])\n for _ in range(cell_info >> (3 * f + 1) & 3):\n data[dofstart:dofstart+fdofs] = numpy.dot(face_rotation, data[dofstart:dofstart+fdofs])\n dofstart += fdofs","def var_transform(self, do_data=False):\n\n empty_vars = ['leadJetEn', 'leadJetPt', 'leadJetPhi', 'leadJetEta', 'leadJetQGL',\n 'subleadJetEn', 'subleadJetPt', 'subleadJetPhi', 'subleadJetEta', 'subleadJetQGL',\n 'subsubleadJetEn', 'subsubleadJetPt', 'subsubleadJetPhi', 'subsubleadJetEta', 'subsubleadJetQGL',\n 'dijetMinDRJetEle', 'dijetDieleAbsDEta','dijetDieleAbsDPhiTrunc', 'dijetCentrality', 'dijetMass', \n 'dijetAbsDEta', 'dijetDPhi'] \n\n replacement_value = -10\n\n for empty_var in empty_vars:\n self.data_obj.mc_df_sig[empty_var] = self.data_obj.mc_df_sig[empty_var].replace(-999., replacement_value)\n self.data_obj.mc_df_bkg[empty_var] = self.data_obj.mc_df_bkg[empty_var].replace(-999., replacement_value)\n if do_data: self.data_obj.data_df[empty_var] = self.data_obj.data_df[empty_var].replace(-999., replacement_value)\n\n #print self.data_obj.mc_df_sig[empty_vars]\n #print np.isnan(self.data_obj.mc_df_sig[empty_vars]).any()\n\n for var in gev_vars:\n if var in (self.low_level_vars_flat+self.high_level_vars):\n self.data_obj.mc_df_sig[var] = self.data_obj.mc_df_sig.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\n self.data_obj.mc_df_bkg[var] = self.data_obj.mc_df_bkg.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\n if do_data: self.data_obj.data_df[var] = self.data_obj.data_df.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\n\n #print np.isnan(self.data_obj.mc_df_sig[empty_vars]).any()","def transform(self, image_matrix):\n\n # Centering the data\n mean = np.mean(image_matrix, axis=0)\n image_matrix = image_matrix - mean\n\n # Dimension reduction is done by multiplying the original matrix with the components\n transformed_matrix = np.dot(image_matrix, self.components.T)\n return transformed_matrix","def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\n import glob\n import os\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\n\n vcj_models=sorted(glob.glob(template_glob))\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\n\n n_ages=7\n n_zs=5\n n_imfs=5\n\n \n\n\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\n\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\n\n\n\n y=x35[t_mask]\n x=temp_lamdas[t_mask]\n\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x)\n\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\n\n\n\n for a, Z in enumerate(Zs): \n for b, age in enumerate(ages):\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\n if verbose:\n print 'Loading {}'.format(model)\n data=np.genfromtxt(model)\n\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\n \n #Interpolate templates onto a uniform wavelength grid and then log-rebin\n y=data[:, counter][t_mask] \n x=temp_lamdas[t_mask]\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\n\n interp=si.interp1d(x, y, fill_value='extrapolate')\n out=interp(new_x) \n\n templates[:, b, a, c]=out\n\n return templates, new_x","def transformation(self):\n for key in self.combination_dict.keys():\n if self.combination_dict[key]['column_count'] == 2:\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'tem' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'tem':\n self.temporal_transformation(self.combination_dict[key])\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'cat' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'cat':\n self.categorical_transformation(self.combination_dict[key])\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num' and self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\n self.numerical_transformation(self.combination_dict[key])\n\n elif self.combination_dict[key]['column_count'] == 3:\n num_count = 0\n num_column = []\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num':\n num_count += 1\n num_column.append(0)\n elif self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\n num_count += 1\n num_column.append(1)\n elif self.data_dict[self.combination_dict[key]['column3']]['data_type'] == 'num':\n num_count += 1\n num_column.append(2)\n\n if num_count == 1:\n self.three_column_groupby_logic(self.combination_dict[key], num_column)\n\n m_score_pie = []\n m_score_bar = []\n m_score_line = []\n m_score_scatter = []\n # for key in self.scenario_dict:\n # if self.scenario_dict\n for key in self.scenario_dict:\n if math.isnan(self.scenario_dict[key][\"Scatter_chart_score\"]):\n m_score_scatter.append(0)\n else:\n m_score_scatter.append(self.scenario_dict[key][\"Scatter_chart_score\"])\n m_score_pie.append(self.scenario_dict[key][\"Pie_chart_score\"])\n m_score_bar.append(self.scenario_dict[key][\"Bar_chart_score\"])\n m_score_line.append(self.scenario_dict[key][\"Line_chart_score\"])\n\n m_score_pie /= np.max(m_score_pie)\n m_score_bar /= np.max(m_score_bar)\n m_score_line /= np.max(m_score_line)\n m_score_scatter /= np.max(m_score_scatter)\n m_score = [m_score_pie, m_score_bar, m_score_line, m_score_scatter]\n match_index = np.argmax(m_score, axis = 0)\n i = 0\n for key in self.scenario_dict:\n if match_index[i] == 0:\n self.scenario_dict[key][\"Chart_Type\"] = \"pie\"\n if match_index[i] == 1:\n self.scenario_dict[key][\"Chart_Type\"] = \"bar\"\n if match_index[i] == 2:\n self.scenario_dict[key][\"Chart_Type\"] = \"line\"\n if match_index[i] == 3:\n self.scenario_dict[key][\"Chart_Type\"] = \"scatter\"\n self.scenario_dict[key][\"m_score\"] = m_score[match_index[i]][i]\n i += 1\n\n return self.scenario_dict","def postTransform(self,T: RigidTransform) -> None:\n for i,m in enumerate(self.milestones):\n assert len(m) == 24\n mq = self.to_se3(m[:12])\n mv = self.to_se3(m[12:])\n self.milestones[i] = self.from_se3(se3.mul(mq,T)) + self.from_se3((so3.mul(mv[0],T[0]),so3.apply(so3.inv(T[0]),mv[1])))","def _project_loops(self):\n\n self._create_projection_datasets()\n self._get_sho_chunk_sizes(10)\n\n '''\n Loop over the FORCs\n '''\n for forc_chunk_index in range(self._num_forcs):\n pos_chunk_index = 0\n\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\n self._current_met_spec_slice = slice(self.metrics_spec_inds_per_forc * self._current_forc,\n self.metrics_spec_inds_per_forc * (self._current_forc + 1))\n dc_vec = self._get_dc_offset()\n '''\n Loop over positions\n '''\n while self._current_pos_slice.stop < self._end_pos:\n loops_2d, nd_mat_shape_dc_first, order_dc_offset_reverse = self._get_projection_data(pos_chunk_index)\n\n # step 8: perform loop unfolding\n projected_loops_2d, loop_metrics_1d = self._project_loop_batch(dc_vec, np.transpose(loops_2d))\n\n # test the reshapes back\n projected_loops_2d = self._reshape_projected_loops_for_h5(projected_loops_2d,\n order_dc_offset_reverse,\n nd_mat_shape_dc_first)\n self.h5_projected_loops[self._current_pos_slice, self._current_sho_spec_slice] = projected_loops_2d\n\n metrics_2d = self._reshape_results_for_h5(loop_metrics_1d, nd_mat_shape_dc_first)\n\n self.h5_loop_metrics[self._current_pos_slice, self._current_met_spec_slice] = metrics_2d\n\n # Reset the position slice\n self._current_pos_slice = slice(None)\n\n pass","def cal_dist_and_mtx(self, calibration_patterns, pattern_size, retain_calibration_patterns):\n # prepare object points, like (0,0,0), (1,0,0), (2,0,0), ....,(8,5,0)\n # use numpy mgrid function to generate the coordinates values for a given grid size.\n grid_x, grid_y = pattern_size[0], pattern_size[1]\n objp = np.zeros((grid_y * grid_x, 3), np.float32)\n objp[:,:2] = np.mgrid[:grid_x, :grid_y].T.reshape(-1,2)\n\n # Arrays to store object points and image points from all the sample images.\n objpoints = [] # 3d points in real world space\n imgpoints = [] # 2d points in image plane.\n \n # Step through the list and search for chessboard corners in distorted calibration images.\n for path in calibration_patterns:\n img = mpimg.imread(path)\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n\n # Find the chessboard's inner corners\n found, corners = cv2.findChessboardCorners(gray, pattern_size, None)\n\n # If found, add object points, image points\n if found:\n objpoints.append(objp)\n imgpoints.append(corners)\n \n # Draw and display the corners to see what was detected.\n if retain_calibration_patterns:\n cv2.drawChessboardCorners(img, pattern_size, corners, found)\n self.calibration_patterns_success.append(img)\n else:\n if retain_calibration_patterns:\n self.calibration_patterns_error.append(img)\n\n img_size = (img.shape[1], img.shape[0])\n\n if objpoints and imgpoints:\n _, self.camera_matrix, self.distortion_coef, _, _ = cv2.calibrateCamera(\n objpoints, imgpoints, img_size, None, None)","def compute_controller(self):\n # region Input Info\n\n # here we implement an example for a consensus algorithm\n neig = self.get_neighbors()\n messages = self.get_messages()\n pos, rot = self.get_pos_and_orientation()\n \n #send message of positions to all neighbors indicating our position\n for n in neig:\n self.send_message(n, pos)\n \n # check if we received the position of our neighbors and compute desired change in position\n # as a function of the neighbors (message is composed of [neighbors id, position])\n dx = 0.\n dy = 0.\n # print(messages)\n # endregion\n if messages:\n # similar to laplacian but for each robot\n # for m in messages:\n # dx += m[1][0] - pos[0]\n # dy += m[1][1] - pos[1]\n\n # position of All robots\n Apos = np.zeros([6,2])\n Apos[self.id,:]=pos[0:2]\n for m in messages:\n Apos[m[0],:]=m[1][0:2]\n\n TarM = np.zeros([6,2])\n TarM[self.id, :] = self.TargetP[self.Tid,:]-pos[0:2]\n Cdiff = Apos-pos[0:2]\n Cdiff = np.sqrt(np.square(Cdiff[:,0])+np.square(Cdiff[:,1]))\n Cdiff = np.sum(Cdiff)\n Ddiff = self.P_Des-self.P_Des[self.id]\n Ddiff = np.sqrt(np.square(Ddiff[:, 0]) + np.square(Ddiff[:, 1]))\n Ddiff = np.sum(Ddiff)\n Tdiff = np.abs(Ddiff - Cdiff)\n\n\n\n\n # region Obstacles\n Obc = Apos\n # Obc = self.Obstacles\n # Obc = np.vstack([Obs,pos[0:2]])\n Diff = pos[0:2] - Obc\n for m in range(0, Diff.shape[0]):\n if (np.sqrt(np.square(Diff[m, 0]) + np.square(Diff[m, 1]))) > 0.35:\n Diff[m, :] = np.array([0, 0])\n\n DiffY = Diff[:, 1].reshape([1, -1])\n DiffX = Diff[:, 0].reshape([1, -1])\n x_odot = np.sum(np.exp(-np.square(DiffX) / self.Var) * DiffX)\n y_odot = np.sum(np.exp(-np.square(DiffY) / self.Var) * DiffY)\n\n ObsAv = np.array([x_odot, y_odot])\n # endregion\n\n\n NewGd = np.square(np.transpose(self.E) @ Apos)\n NewGd = (NewGd[:, 0] + NewGd[:, 1]).reshape([-1, 1])\n G = self.Gdsq - NewGd\n Rg = self.DistJacobian(Apos, self.Edges)\n p_ddot = np.zeros(([6, 2]))\n\n if (Tdiff < self.Thr):\n self.StartTimer = True\n\n\n if(self.StartTimer):\n self.Timer += 1\n if (self.Timer > 500+self.OffTimer):\n self.FormStable = True\n self.StartTimer = False\n self.Timer = 0\n\n if(self.Tid > 3 and np.sum(TarM[self.id, 0])<5):\n TarM[self.id, 0] = 5\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) < 5):\n TarM[self.id, 1] = 5\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) > -5):\n TarM[self.id, 1] = -5\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) > -5):\n TarM[self.id, 1] = -5\n\n if (self.Tid > 3 and np.sum(TarM[self.id, :]) < 0.01):\n self.Tid +=1\n\n if (self.FormStable):\n # Formation Done\n if self.Tid == 0 and self.Formation == \"square\":\n self.P_Des = self.Form_HRec(0.5)\n self.Reset_Form()\n # self.Tid += 1\n # self.FormStable = False\n print(self.P_Des, self.Formation, \" \", self.Tid)\n # self.K1 = 5\n # self.K2 = 50\n if (self.Tid < self.TargetP.shape[0]-1 and self.FormStable):\n self.Tid += 1\n if(self.Tid == 1):\n self.K1 = 2\n self.K3 = 10\n self.Thr = 0.001\n if (self.Tid == 2):\n self.K1 = 20\n self.K3 = 1\n self.P_Des = self.Form_HRec(0.5)\n self.Reset_Form()\n self.FormStable = False\n # Linear Control Law\n p_dot = np.zeros([6,2])\n p_dot = -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\n p_dot += self.dt * (self.OK / self.Var) * ObsAv\n # p_dot += self.K3 * TarM\n # Non - linear Control Law\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\n # p_dot += p_ddot*self.dt\n if(self.id == 0):\n # print(Tdiff,self.TargetP[self.Tid,:],np.sum(G),self.Tid,self.Timer)\n p_dot = self.K3 * TarM\n if (self.id == 0):\n print(Tdiff,self.TargetP[self.Tid,:],np.sum(G),self.Tid,self.Timer)\n # if(self.Tid == 1):\n # p_dot += -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\n\n dx = p_dot[self.id, 0]\n dy = p_dot[self.id, 1]\n\n # Non - linear Control\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\n # p_ddot += (self.OK / self.Var) * ObsAv\n # dx = self.dt * p_ddot[self.id, 0]\n # dy = self.dt * p_ddot[self.id, 1]\n #else:\n # TarM[self.id, :] = Tdiff\n # # Linear Control\n # p_dot = -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\n # p_dot += self.dt * (self.OK / self.Var) * ObsAv\n # p_dot += self.K3 * TarM\n # dx = p_dot[self.id, 0]\n # dy = p_dot[self.id, 1]\n\n # Non - linear Control\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\n # p_ddot += self.K3 * TarM\n # p_ddot += (self.OK / self.Var) * ObsAv\n # dx = self.dt * p_ddot[self.id, 0]\n # dy = self.dt * p_ddot[self.id, 1]\n\n # region Robot Wheel Control\n # integrate\n des_pos_x = pos[0] + self.dt * dx\n des_pos_y = pos[1] + self.dt * dy\n\n #compute velocity change for the wheels\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\n if vel_norm < 0.01:\n vel_norm = 0.01\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n self.set_wheel_velocity([left_wheel, right_wheel])\n # endregion","def run(prefix=\"tst_map_model_cc\"):\n # original (zero-origin) map and model\n pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)\n pdb_inp.write_pdb_file(file_name=\"%s_%s\"%(prefix,\"orig.pdb\"))\n ph = pdb_inp.construct_hierarchy()\n xrs = pdb_inp.xray_structure_simple()\n fc = xrs.structure_factors(d_min=1.5).f_calc()\n fft_map = fc.fft_map(resolution_factor = 0.25)\n fft_map.apply_sigma_scaling()\n map_data = fft_map.real_map_unpadded()\n assert map_data.all() == (40, 45, 54)\n assert map_data.origin() == (0, 0, 0)\n assert map_data.focus() == (40, 45, 54)\n write_ccp4_map(map_data=map_data, cs=xrs.crystal_symmetry(),\n file_name=\"%s_%s\"%(prefix,\"orig.ccp4\"))\n # shift origin of the map\n g = flex.grid((-20,-25,-27), (20,20,27))\n map_data.reshape(g)\n assert map_data.all() == (40, 45, 54)\n assert map_data.origin() == (-20, -25, -27)\n assert map_data.focus() == (20, 20, 27)\n write_ccp4_map(map_data=map_data, cs=xrs.crystal_symmetry(),\n file_name=\"%s_%s\"%(prefix,\"shifted.ccp4\"))\n # apply same shift to the model\n a,b,c = xrs.crystal_symmetry().unit_cell().parameters()[:3]\n N = map_data.all()\n O=map_data.origin()\n sites_cart = ph.atoms().extract_xyz()\n sx,sy,sz = a/N[0]*O[0], b/N[1]*O[1], c/N[2]*O[2]\n sites_cart_shifted = sites_cart+\\\n flex.vec3_double(sites_cart.size(), [sx,sy,sz])\n ph.atoms().set_xyz(sites_cart_shifted)\n ph.write_pdb_file(file_name=\"%s_%s\"%(prefix,\"shifted.pdb\"))\n # run phenix.real_space_refine\n checked = 0\n cmd = \" \".join([\n \"phenix.map_model_cc\",\n \"%s_shifted.pdb\"%prefix,\n \"%s_shifted.ccp4\"%prefix,\n \"resolution=1.5\",\n \"> %s.zlog\"%prefix\n ])\n print cmd\n easy_run.call(cmd)\n # check results\n fo = open(\"%s.zlog\"%prefix,\"r\")\n for l in fo.readlines():\n if(l.startswith(\" CC_mask :\")):\n cc = float(l.split()[2])\n assert cc>0.989\n checked+=1\n fo.close()\n assert checked==1\n # Exercise corresponding library function\n params = mmtbx.maps.map_model_cc.master_params().extract()\n params.map_model_cc.resolution=1.5\n task_obj = mmtbx.maps.map_model_cc.map_model_cc(\n map_data = map_data,\n pdb_hierarchy = ph,\n crystal_symmetry = xrs.crystal_symmetry(),\n params = params.map_model_cc)\n task_obj.validate()\n task_obj.run()\n result = task_obj.get_results()\n assert approx_equal(result.cc_mask , 1.0, 1.e-3)\n assert approx_equal(result.cc_peaks , 1.0, 1.e-3)\n assert approx_equal(result.cc_volume, 1.0, 1.e-3)","def compute_transform(self, image_pair, figure):\t\n\t\timage_pair = [int(image_pair[1]), int(image_pair[3])]\n\t\tindices = np.where(np.all(np.abs(self.Matches[\"image_pairs\"][0] - image_pair) == 0, axis=1)) \n\t\tfeature_pairs = self.Matches[\"image_match_pairs\"][0][indices[0][0]]\n\t\tfeature_pair_scores = self.Matches[\"image_feature_scores\"][0][indices[0][0]] \n\n\t\ti1_loc = self.Database[\"image_locs\"][np.where(np.array(self.Database[\"image_idx\"]) == image_pair[0])[0][0]]\n\t\ti2_loc = self.Database[\"image_locs\"][np.where(np.array(self.Database[\"image_idx\"]) == image_pair[1])[0][0]]\n\n\t\tself.image1 = cv2.cvtColor(extract_image(figure, i1_loc), cv2.COLOR_BGR2GRAY)\n\t\tself.image2 = cv2.cvtColor(extract_image(figure, i2_loc), cv2.COLOR_BGR2GRAY)\n\t\t# self.image1 = extract_image(figure, i1_loc)\n\t\t# self.image2 = extract_image(figure, i2_loc)\n\n\t\tself.match_alignment = {}; self.match_alignment_scores = {}\n\t\tfor pair, score in zip(feature_pairs, feature_pair_scores):\n\t\t\ti1 = self.Database[\"image_idx\"][pair[0]]\n\t\t\ti2 = self.Database[\"image_idx\"][pair[1]]\n\n\t\t\tif i1 == image_pair[0]:\n\t\t\t\ttarget = pair[0] \n\t\t\t\tstart = pair[1]\n\t\t\t\tif target not in self.match_alignment.keys():\n\t\t\t\t\tself.init_match_alignment(target)\n\t\t\telse:\n\t\t\t\ttarget = pair[1]\n\t\t\t\tstart = pair[0]\t\t\t\t\n\t\t\t\tif target not in self.match_alignment.keys():\n\t\t\t\t\tself.init_match_alignment(target)\n\n\t\t\t# get ellipse and convert to three cardinal points\n\t\t\ttarget_ellipse = self.Database[\"orientation\"][target]\n\t\t\tstart_ellipse = self.Database[\"orientation\"][start]\n\t\t\ttarget_points, target_params = utils.ellipse2points(target_ellipse)\n\t\t\tstart_points, start_params = utils.ellipse2points(start_ellipse)\n\n\t\t\t# compute angle between Major axes\n\t\t\trotation_angle = int(np.abs(target_params[0] - start_params[0]))\n\t\t\treflection_truth = target_params[1] * start_params[1]\n\t\t\tif reflection_truth > 0:\n\t\t\t\treflection_truth = \"No\"\n\t\t\telse:\n\t\t\t\treflection_truth = \"Yes\"\n\t\t\tresolution_perc = round(((target_params[2] / start_params[2]) - 1), 1)\n\n\t\t\t# extract features for histogram matching\n\t\t\ttarget_bloc = self.Database[\"blot_locs\"][target]\n\t\t\tstart_bloc = self.Database[\"blot_locs\"][start]\n\t\t\ttarget_feature = extract_image(self.image1, target_bloc, ex=5)\n\t\t\tfinal_feature = extract_image(self.image2, start_bloc, ex=5) \n\n\t\t\t#find affine transform to match\n\t\t\tif self.AFFINE:\n\t\t\t\tT = utils.compute_affine(start_points, target_points)\n\t\t\t\timage2_warped = cv2.warpAffine(self.image2.copy(), T, (self.image1.shape[1], self.image1.shape[0]))\n\t\t\t\tfinal_feature = extract_image(image2_warped, target_bloc, ex=5)\n\t\t\t\n\t\t\t# match hist\n\t\t\tif self.CONTRAST:\n\t\t\t\tfinal_feature = utils.histogram_match(final_feature, target_feature)\n\n\t\t\tself.match_alignment[target].append((final_feature, start, rotation_angle, reflection_truth, \\\n\t\t\t\t\t\t\t\t\t\t\t\t resolution_perc, score))\n\t\t\tself.match_alignment_scores[target].append(score)","def design_matrix_motor_df(meta):\n\n mouse = utils.meta_mouse(meta)\n\n # go\n new_meta = {}\n new_meta['go'] = np.zeros(len(meta))\n new_meta['go'][meta['trialerror'].isin([0, 3, 5, 7]).values] = 1\n assert np.sum(new_meta['go']) > 0\n meta_df_out = pd.DataFrame(data=new_meta, index=meta.index)\n\n # nogo\n new_meta = {}\n new_meta['nogo'] = np.zeros(len(meta))\n new_meta['nogo'][meta['trialerror'].isin([1, 2, 4, 6]).values] = 1\n assert np.sum(new_meta['nogo']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get cues defined by new terms\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[mm] = np.zeros(len(meta))\n new_meta[mm][meta.initial_condition.isin([inverted_lookup[mm]]).values] = 1\n assert np.sum(new_meta[mm]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get previous cue same\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[f'prev_same_{mm}'] = np.zeros(len(meta))\n new_meta[f'prev_same_{mm}'][meta[f'prev_same_{inverted_lookup[mm]}'].values] = 1\n assert np.sum(new_meta[f'prev_same_{mm}']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get previous cue diff\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[f'prev_diff_{mm}'] = np.zeros(len(meta))\n not_same = ~meta[f'prev_same_{inverted_lookup[mm]}'].values & meta.initial_condition.isin([inverted_lookup[mm]]).values\n new_meta[f'prev_diff_{mm}'][not_same] = 1\n assert np.sum(new_meta[f'prev_diff_{mm}']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get salient events\n new_meta = {}\n for col in ['prev_reward', 'prev_punish', 'prev_blank', 'hmm_engaged']:\n new_meta[col] = np.zeros(len(meta))\n new_meta[col][meta[col].values] = 1\n assert np.sum(new_meta[col]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get normalized running and baseline licking\n new_meta = {}\n for col in ['speed', 'pre_speed', 'post_speed', 'pre_licks', 'post_licks', 'anticipatory_licks']:\n new_meta[col] = meta[col].values / np.nanmax(meta[col].values)\n new_meta[col][np.isnan(new_meta[col])] = 0\n assert np.sum(new_meta[col]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get normalized dprime\n # new_meta = {}\n # for col in ['dprime_run']:\n # new_meta[col] = meta[col].values - np.nanmin(meta[col].values)\n # new_meta[col] = new_meta[col] / np.nanmax(new_meta[col])\n # new_meta[col][np.isnan(new_meta[col])] = 0\n # assert np.sum(new_meta[col]) > 0\n # new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n # meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n return meta_df_out","def __updateMatC(self):\n\t\tif self.regScheme == 2:\n\t\t\tfor id1 in range(self.nTemplates):\n\t\t\t\tself.C[id1,id1] = 1.0 / self.w0[id1]**2","def mounting_matrix(self):\n # fmt: off\n count = 0\n for x in range(self.ntheta):\n self.M[count][count] = 1\n self.f[count][0] = self.p_in\n count = count + self.nz - 1\n self.M[count][count] = 1\n self.f[count][0] = self.p_out\n count = count + 1\n count = 0\n for x in range(self.nz - 2):\n self.M[self.ntotal - self.nz + 1 + count][1 + count] = 1\n self.M[self.ntotal - self.nz + 1 + count][self.ntotal - self.nz + 1 + count] = -1\n count = count + 1\n count = 1\n j = 0\n for i in range(1, self.nz - 1):\n a = (1 / self.dtheta ** 2) * (self.c1[i][self.ntheta - 1])\n self.M[count][self.ntotal - 2 * self.nz + count] = a\n b = (1 / self.dz ** 2) * (self.c2[i - 1, j])\n self.M[count][count - 1] = b\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][self.ntheta - 1])\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\n self.M[count, count] = c\n d = (1 / self.dz ** 2) * (self.c2[i][j])\n self.M[count][count + 1] = d\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\n self.M[count][count + self.nz] = e\n count = count + 1\n count = self.nz + 1\n for j in range(1, self.ntheta - 1):\n for i in range(1, self.nz - 1):\n a = (1 / self.dtheta ** 2) * (self.c1[i, j - 1])\n self.M[count][count - self.nz] = a\n b = (1 / self.dz ** 2) * (self.c2[i - 1][j])\n self.M[count][count - 1] = b\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][j - 1])\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\n self.M[count, count] = c\n d = (1 / self.dz ** 2) * (self.c2[i][j])\n self.M[count][count + 1] = d\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\n self.M[count][count + self.nz] = e\n count = count + 1\n count = count + 2\n count = 1\n for j in range(self.ntheta - 1):\n for i in range(1, self.nz - 1):\n if j == 0:\n self.f[count][0] = (self.c0w[i][j] - self.c0w[i][self.ntheta - 1]) / self.dtheta\n else:\n self.f[count][0] = (self.c0w[i, j] - self.c0w[i, j - 1]) / self.dtheta\n count = count + 1\n count = count + 2\n # fmt: on","def _init_transformation_matrix(self):\n # Set up basic transformation matrix\n c_transform = np.zeros((self.n_beads, self.n_beads))\n\n # Get auxiliary array with bead indices\n n = np.arange(1, self.n_beads + 1)\n\n # for k = 0\n c_transform[0, :] = 1.0\n\n for k in range(1, self.n_beads // 2 + 1):\n c_transform[k, :] = np.sqrt(2) * np.cos(2 * np.pi * k * n / self.n_beads)\n\n for k in range(self.n_beads // 2 + 1, self.n_beads):\n c_transform[k, :] = np.sqrt(2) * np.sin(2 * np.pi * k * n / self.n_beads)\n\n if self.n_beads % 2 == 0:\n c_transform[self.n_beads // 2, :] = (-1) ** n\n\n # Since matrix is initialized as C(k,n) does not need to be transposed\n c_transform /= np.sqrt(self.n_beads)\n c_transform = torch.from_numpy(c_transform)\n\n return c_transform","def get_linear_discrete_matrices(self):\n\n vf_op, vb_op = get_operating_point_inputs(self.operating_point[1], self.model_type)\n if self.model_type == ModelType.EASY:\n At, Bt, Vf_op, Vd_op = getLinearizedMatrices(self.model_type, self.operating_point[0:6], vf_op, vb_op)\n elif self.model_type == ModelType.GYROMOMENT:\n At, Bt = get_gyro_matrices(self.operating_point, self.operating_point[6] / mc.K_f, vf_op, vb_op)\n if self.model_type == ModelType.EASY:\n Ct = np.array([[1, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0]])\n Dt = np.array([[0, 0],\n [0, 0],\n [0, 0]])\n elif self.model_type == ModelType.GYROMOMENT:\n if self.nOutputs == 3:\n Ct = np.array([[1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0]])\n Dt = np.array([[0, 0],\n [0, 0],\n [0, 0],\n [0, 0],\n [0, 0]])\n elif self.nOutputs == 5:\n Ct = np.array([[1, 0, 0, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0, 0, 0],\n [0, 0, 1, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 0, 0, 1]])\n Dt = np.array([[0, 0],\n [0, 0],\n [0, 0],\n [0, 0],\n [0, 0]])\n\n Ak, Bk, Ck, Dk = discretize_linear_state_space(At, Bt, Ct, Dt, self.timeStep)\n return Ak, Bk, Ck, Dk","def __compose_transformation(self):\n s = self.scale\n rotR = self.rotation\n t = self.translation\n T = np.eye(4)\n T[0:3, 3] = t\n R = np.eye(4)\n R[0:3, 0:3] = rotR\n M = T.dot(R)\n if s == 1:\n M = T.dot(R)\n else:\n S = np.eye(4)\n S[0:3, 0:3] = np.diag([s, s, s])\n M = T.dot(R).dot(S)\n return M","def align_images(self, translation_only=False):\n # Extract feature point locations and descriptors.\n # print('here')\n points_and_descriptors = []\n for file in self.files:\n # print('kaka')\n image = sol4_utils.read_image(file, 1)\n # print('bla',image)\n self.h, self.w = image.shape\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\n points_and_descriptors.append(find_features(pyramid))\n\n # Compute homographies between successive pairs of images.\n Hs = []\n\n for i in range(len(points_and_descriptors) - 1):\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\n\n # Find matching feature points.\n ind1, ind2 = match_features(desc1, desc2, .7)\n points1, points2 = points1[ind1, :], points2[ind2, :]\n\n # Compute homography using RANSAC.\n # print(points1)\n # print(points2)\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\n\n # Uncomment for debugging: display inliers and outliers among matching points.\n # In the submitted code this function should be commented out!\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\n\n Hs.append(H12)\n\n # Compute composite homographies from the central coordinate system.\n # print(Hs)\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\n self.homographies = np.stack(accumulated_homographies)\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\n self.homographies = self.homographies[self.frames_for_panoramas]","def rebuildMatrixCache(self):\n self.converterYUR = Mat4.convertMat(CSYupRight, self.lens.getCoordinateSystem()) * self.lens.getProjectionMat()","def _fp32_1_0_2_mc_on_1(loop_cnt, left_data):\n\n def _fp32_mte_process_1(axis_0_index, w_lp_index, sub_w_size):\n \"\"\"\n do transpose by mte for not last dim under multiple core on axis 1\n \"\"\"\n\n def _fp32_inner_mte_1(h_lp_index, sub_h_size):\n \"\"\"\n inner mte\n \"\"\"\n # move data in\n in_offset = ((block_idx * per_core_col_size + axis_0_index * axis_1 +\n h_lp_index * max_core_axis_size) * axis_2 +\n w_lp_index * max_no_core_axis_size)\n data_in_inf = (sub_h_size, sub_w_size, axis_1, axis_2, in_offset)\n _data_move_in_last_dim_be_one_block(tik_inst, ub_input, data_in, data_in_inf)\n\n # move data out\n out_offset = ((block_idx * per_core_col_size * axis_0 + axis_0_index +\n h_lp_index * max_core_axis_size * axis_0) * axis_2 +\n w_lp_index * max_no_core_axis_size)\n data_out_inf = (sub_h_size, sub_w_size, axis_0, axis_1, axis_2, out_offset)\n _data_move_out_last_dim_be_one_block(tik_inst, data_out, ub_input, data_out_inf)\n\n with tik_inst.for_range(0, loop_cnt) as h_lp_idx:\n _fp32_inner_mte_1(h_lp_idx, max_core_axis_size)\n with tik_inst.if_scope(left_data > 0):\n _fp32_inner_mte_1(loop_cnt, left_data)\n\n with tik_inst.for_range(0, axis_0) as axis_0_idx:\n with tik_inst.for_range(0, no_core_loop_cnt) as w_lp_idx:\n _fp32_mte_process_1(axis_0_idx, w_lp_idx, max_no_core_axis_size)\n with tik_inst.if_scope(no_core_left > 0):\n _fp32_mte_process_1(axis_0_idx, no_core_loop_cnt, no_core_left)","def preTransform(self, T: RigidTransform) -> None:\n for i,m in enumerate(self.milestones):\n assert len(m) == 24\n mq = self.to_se3(m[:12])\n mv = self.to_se3(m[12:])\n self.milestones[i] = self.from_se3(se3.mul(T,mq)) + self.from_se3((so3.mul(T[0],mv[0]),so3.apply(T[0],mv[1])))","def matrix_of_changes():\n\tdrivers = ['PC','WCD']\n\ttasks = ['WM','GAMBLING','RELATIONAL','MOTOR','LANGUAGE','SOCIAL','REST']\n\tproject='hcp'\n\tatlas = 'power'\n\tknown_membership,network_names,num_nodes,name_int_dict = network_labels(atlas)\n\tfor driver in drivers:\n\t\tall_matrices = []\n\t\tviolin_df = pd.DataFrame()\n\t\tfor task in tasks:\n\t\t\t# subjects = np.array(hcp_subjects).copy()\n\t\t\t# subjects = list(subjects)\n\t\t\t# subjects = remove_missing_subjects(subjects,task,atlas)\n\t\t\tsubjects = np.load('/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_subs_fz.npy' %('hcp',task,atlas))\n\t\t\tassert (subjects == np.load('/home/despoB/mb3152/dynamic_mod/results/hcp_%s_%s_subs_fz.npy'%(task,atlas))).all()\n\t\t\tstatic_results = graph_metrics(subjects,task,atlas)\n\t\t\tsubject_pcs = static_results['subject_pcs']\n\t\t\tsubject_mods = static_results['subject_mods']\n\t\t\tsubject_wmds = static_results['subject_wmds']\n\t\t\tmatrices = static_results['matrices']\n\t\t\ttask_perf = task_performance(subjects,task)\n\t\t\tassert subject_pcs.shape[0] == len(subjects)\n\t\t\tmean_pc = np.nanmean(subject_pcs,axis=0)\n\t\t\tmean_wmd = np.nanmean(subject_wmds,axis=0)\n\t\t\tmod_pc_corr = np.zeros(subject_pcs.shape[1])\n\t\t\tfor i in range(subject_pcs.shape[1]):\n\t\t\t\tmod_pc_corr[i] = nan_pearsonr(subject_mods,subject_pcs[:,i])[0]\n\t\t\tmod_wmd_corr = np.zeros(subject_wmds.shape[1])\n\t\t\tfor i in range(subject_wmds.shape[1]):\n\t\t\t\tmod_wmd_corr[i] = nan_pearsonr(subject_mods,subject_wmds[:,i])[0]\n\t\t\tif driver == 'PC':\n\t\t\t\tpredict_nodes = np.where(mod_pc_corr>0.0)[0]\n\t\t\t\tlocal_predict_nodes = np.where(mod_pc_corr<0.0)[0]\n\t\t\t\tpc_edge_corr = np.arctanh(pc_edge_correlation(subject_pcs,matrices,path='/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_pc_edge_corr_z.npy' %(project,task,atlas)))\n\t\t\telse:\t\t\n\t\t\t\tpredict_nodes = np.where(mod_wmd_corr>0.0)[0]\n\t\t\t\tlocal_predict_nodes = np.where(mod_wmd_corr<0.0)[0]\n\t\t\t\tpc_edge_corr = np.arctanh(pc_edge_correlation(subject_wmds,matrices,path='/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_wmd_edge_corr_z.npy' %(project,task,atlas)))\n\t\t\t# Plot matrix of changes\n\t\t\tedge_thresh = 75\n\t\t\tedge_thresh = np.percentile(np.nanmean(matrices,axis=0),edge_thresh)\n\t\t\tpc_edge_corr[:,np.nanmean(matrices,axis=0)<edge_thresh,] = np.nan\n\t\t\thigh_pc_edge_matrix = np.nanmean(pc_edge_corr[predict_nodes],axis=0)\n\t\t\tlow_pc_edge_matrix = np.nanmean(pc_edge_corr[local_predict_nodes],axis=0)\n\t\t\tmatrix = (np.tril(low_pc_edge_matrix) + np.triu(high_pc_edge_matrix)).reshape((264,264))\n\t\t\tplot_matrix = matrix.copy()\n\t\t\tplot_matrix_mask = np.isnan(plot_matrix)\n\t\t\tzscores = scipy.stats.zscore(plot_matrix[plot_matrix_mask==False].reshape(-1))\n\t\t\tplot_matrix[plot_matrix_mask==False] = zscores\n\t\t\tif task != 'REST':\n\t\t\t\tall_matrices.append(plot_matrix)\n\t\t\tplot_corr_matrix(plot_matrix,network_names.copy(),out_file='/home/despoB/mb3152/dynamic_mod/figures/%s_corr_matrix_%s.pdf'%(driver,task),plot_corr=False,return_array=False)\n\n\t\t\tpc_edge_corr[np.isnan(pc_edge_corr)] = 0.0\n\t\t\tconnector_within_network_mask = pc_edge_corr.copy().astype(bool)\n\t\t\tlocal_within_network_mask = pc_edge_corr.copy().astype(bool)\n\t\t\tconnector_between_network_mask = pc_edge_corr.copy().astype(bool)\n\t\t\tlocal_between_network_mask = pc_edge_corr.copy().astype(bool)\n\t\t\tconnector_within_network_mask[:,:,:] = False\n\t\t\tlocal_within_network_mask[:,:,:] = False\n\t\t\tconnector_between_network_mask[:,:,:] = False\n\t\t\tlocal_between_network_mask[:,:,:] = False\n\t\t\t\n\t\t\tfor n in predict_nodes:\n\t\t\t\tfor node1,node2 in combinations(range(264),2):\n\t\t\t\t\tif n == node1:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif n == node2:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node1] == 0:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node2] == 0:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node1] == known_membership[node2]:\n\t\t\t\t\t\tconnector_within_network_mask[n][node1,node2] = True\n\t\t\t\t\t\tconnector_within_network_mask[n][node2,node1] = True\n\t\t\t\t\telse:\n\t\t\t\t\t\tconnector_between_network_mask[n][node1,node2] = True\n\t\t\t\t\t\tconnector_between_network_mask[n][node2,node1] = True\n\n\t\t\tfor n in local_predict_nodes:\n\t\t\t\tfor node1,node2 in combinations(range(264),2):\n\t\t\t\t\tif n == node1:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif n == node2:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node1] == 0:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node2] == 0:\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif known_membership[node1] == known_membership[node2]:\n\t\t\t\t\t\tlocal_within_network_mask[n][node1,node2] = True\n\t\t\t\t\t\tlocal_within_network_mask[n][node2,node1] = True\n\t\t\t\t\telse:\n\t\t\t\t\t\tlocal_between_network_mask[n][node1,node2] = True\n\t\t\t\t\t\tlocal_between_network_mask[n][node2,node1] = True\n\n\t\t\tdef make_strs_for_df(array_to_add,str_to_add):\n\t\t\t\tarray_len = len(array_to_add)\n\t\t\t\tstr_array_ = np.chararray(array_len,itemsize=40)\n\t\t\t\tstr_array_[:] = str_to_add\n\t\t\t\treturn str_array_\n\t\t\t\n\t\t\tdef make_array_for_df(arrays_to_add):\n\t\t\t\tappend_array = np.zeros((len(arrays_to_add[0]),len(arrays_to_add))).astype(str)\n\t\t\t\tappend_array[:,0] = arrays_to_add[0]\n\t\t\t\tappend_array[:,1] = arrays_to_add[1]\n\t\t\t\tappend_array[:,2] = arrays_to_add[2]\n\t\t\t\treturn append_array\n\n\t\t\tviolin_columns = [\"r value, node i's PCs and j's edge weights\",\"Node Type\",\"Edge Type\"]\n\t\t\ttask_violin_df = pd.DataFrame(columns=violin_columns)\n\t\t\tresult_array_to_add = pc_edge_corr[connector_within_network_mask].reshape(-1)[pc_edge_corr[connector_within_network_mask].reshape(-1)!=0.0]\n\t\t\tedge_type_ = make_strs_for_df(result_array_to_add,'Within Community')\n\t\t\tnode_type_ = make_strs_for_df(result_array_to_add,'Q+')\n\t\t\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\n\t\t\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\n\n\t\t\tresult_array_to_add = pc_edge_corr[local_within_network_mask].reshape(-1)[pc_edge_corr[local_within_network_mask].reshape(-1)!=0.0]\n\t\t\tedge_type_ = make_strs_for_df(result_array_to_add,'Within Community')\n\t\t\tnode_type_ = make_strs_for_df(result_array_to_add,'Q-')\n\t\t\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\n\t\t\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\n\n\t\t\tresult_array_to_add = pc_edge_corr[connector_between_network_mask].reshape(-1)[pc_edge_corr[connector_between_network_mask].reshape(-1)!=0.0]\n\t\t\tedge_type_ = make_strs_for_df(result_array_to_add,'Between Community')\n\t\t\tnode_type_ = make_strs_for_df(result_array_to_add,'Q+')\n\t\t\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\n\t\t\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\n\n\t\t\tresult_array_to_add = pc_edge_corr[local_between_network_mask].reshape(-1)[pc_edge_corr[local_between_network_mask].reshape(-1)!=0.0]\n\t\t\tedge_type_ = make_strs_for_df(result_array_to_add,'Between Community')\n\t\t\tnode_type_ = make_strs_for_df(result_array_to_add,'Q-')\n\t\t\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\n\t\t\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\n\t\t\ttask_violin_df[\"r value, node i's PCs and j's edge weights\"] = task_violin_df[\"r value, node i's PCs and j's edge weights\"].astype(float)\n\t\t\tif driver == 'PC':\n\t\t\t\tprint task + ', Connector Hubs(Q+): ' + str(scipy.stats.ttest_ind(task_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Within Community'],\n\t\t\t\t\ttask_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Between Community']))\n\t\t\t\tprint task + ', Non-Connector Hubs(Q-): ' + str(scipy.stats.ttest_ind(task_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Within Community'],\n\t\t\t\t\ttask_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Between Community']))\n\t\t\telse:\n\t\t\t\tprint task + ', Local Hubs(Q+): ' + str(scipy.stats.ttest_ind(task_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Within Community'],\n\t\t\t\t\ttask_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Between Community']))\n\t\t\t\tprint task + ', Non Local Hubs (Q-): ' + str(scipy.stats.ttest_ind(task_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Within Community'],\n\t\t\t\t\ttask_violin_df[\"r value, node i's PCs and j's edge weights\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Between Community']))\n\t\t\t#append for average of all\n\t\t\tviolin_df = violin_df.append(pd.DataFrame(data=task_violin_df,columns=violin_columns),ignore_index=True)\n\t\t\t#Figure for single Task\n\t\t\tsns.set_style(\"white\")\n\t\t\tsns.set_style(\"ticks\")\n\t\t\tcolors = sns.color_palette(['#fdfd96','#C4D8E2'])\n\t\t\twith sns.plotting_context(\"paper\",font_scale=2):\n\t\t\t\tplt.figure(figsize=(24,16))\n\t\t\t\tsns.boxplot(x=\"Node Type\", y=\"r value, node i's PCs and j's edge weights\", hue=\"Edge Type\", order=['Q+','Q-'], data=task_violin_df)\n\t\t\t\tplt.savefig('/home/despoB/mb3152/dynamic_mod/figures/%s_edge_mod_%s.pdf'%(driver,task),dpi=4600)\n\t\t\t\tplt.close()\n\t\t# Average of All\n\t\tplot_corr_matrix(np.nanmean(all_matrices,axis=0),network_names.copy(),out_file='/home/despoB/mb3152/dynamic_mod/figures/%s_corr_matrix_avg.pdf'%(driver),plot_corr=False,return_array=False)\n\t\tif driver == 'PC':\n\t\t\tprint task + ',Connector Hubs(Q+): ' + str(scipy.stats.ttest_ind(violin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Within Community'],\n\t\t\t\tviolin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Between Community']))\n\t\t\tprint task + ', Non-Connector Hubs(Q-): ' + str(scipy.stats.ttest_ind(violin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Within Community'],\n\t\t\t\tviolin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Between Community']))\n\t\telse:\n\t\t\tprint task + ', Local Hubs(Q+): ' + str(scipy.stats.ttest_ind(violin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Within Community'],\n\t\t\t\tviolin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Between Community']))\n\t\t\tprint task + ', Non-Local Hubs(Q-): ' + str(scipy.stats.ttest_ind(violin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Within Community'],\n\t\t\t\tviolin_df[\"r value, node i's PCs and j's edge weights\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Between Community']))\n\t\tsns.set_style(\"white\")\n\t\tsns.set_style(\"ticks\")\n\t\tcolors = sns.color_palette(['#fdfd96','#C4D8E2'])\n\t\twith sns.plotting_context(\"paper\",font_scale=3):\n\t\t\tplt.figure(figsize=(24,16))\n\t\t\tsns.boxplot(x=\"Node Type\", y=\"r value, node i's PCs and j's edge weights\",hue=\"Edge Type\", palette=colors,order=['Q+','Q-'], data=violin_df)\n\t\t\tplt.savefig('/home/despoB/mb3152/dynamic_mod/figures/%s_edge_mod_avg.pdf'%(driver),dpi=4600)\n\t\t\tplt.close()","def tcs2(self):\n S = self.M.allocState({})\n self.M.propagate(S, 0, 1)\n\n # set initial beam data\n S.ref_IonZ = self.refIonZ\n S.IonZ = self.IonZ\n\n S.moment0 = self.BC0\n S.moment1 = self.ENV0\n\n S.ref_IonEk = self.refIonEk\n \n S.phis = S.moment0[PS_S,:]\n S.IonEk = S.moment0[PS_PS,:]*MeVtoeV + S.ref_IonEk\n\n ### Reconstract data\n U = collections.OrderedDict()\n T = collections.OrderedDict()\n \n U,T = self.save(U, T, S)\n\n\n #S.clng = self.clng\n\n fin = len(self.M)\n\n H = self.M.propagate(S, 1, fin, observe=range(fin))\n \n ### Reconstract data\n U = collections.OrderedDict()\n T = collections.OrderedDict()\n\n for i in range(fin-1):\n U,T = self.save(U, T, H[i][1])\n\n return U,T","def _transform(self, matrix):\n for x in list(self.keys()):\n ar = self[x]\n if len(ar.shape) == 2 and ar.shape[1] == 3:\n self[x] = np.dot(matrix, ar.transpose()).transpose()","def compute(self):\n if self.loaded():\n debug(\"Skipping {} computation due to transform data already loaded.\" .format(self.name))\n return\n\n # sanity checks\n error(\"Got transform dimension of {} but input dimension is {}.\".format(self.dimension, self.input_dimension), self.input_dimension < self.dimension)\n\n # output containers\n self.term_components = []\n\n # compute\n info(\"Applying {} {}-dimensional transform on the raw representation.\".\n format(self.base_name, self.dimension))\n\n # train\n train_data = self.input_vectors[self.train_index, :]\n\n info(\"Transforming training input data shape: {}\".format(train_data.shape))\n if self.is_supervised:\n ground_truth = np.reshape(match_labels_to_instances(self.train_epi, self.train_labels), (len(train_data), ))\n self.vectors = self.process_func_train(train_data, ground_truth)\n else:\n self.vectors = self.process_func_train(train_data)\n self.output_roles = (roles.train,)\n\n if self.test_index.size > 0:\n # make zero output matrix\n output_data = np.zeros((len(self.input_vectors), self.dimension), np.float32)\n output_data[self.train_index, :] = self.vectors\n\n test_data = self.input_vectors[self.test_index, :]\n info(\"Transforming test input data shape: {}\".format(test_data.shape))\n vecs = self.process_func_test(test_data)\n output_data[self.test_index, :] = vecs\n self.vectors = output_data\n\n self.output_roles = (roles.train, roles.test)\n else:\n info(f\"Skipping empty test indexes.\")\n\n self.term_components = self.get_term_representations()\n self.verify_transformed(self.vectors)\n info(f\"Output shape: {self.vectors.shape}\")\n # write the output data\n write_pickled(self.serialization_path_preprocessed, self.get_all_preprocessed())\n # write the trained transformer model\n self.save_model()","def mirrorTransformations(self):\n\n currentSelection = cmds.ls(sl=True)\n\n # get the mirror module\n networkNode = self.returnNetworkNode\n mirrorModule = cmds.getAttr(networkNode + \".mirrorModule\")\n moduleName = cmds.getAttr(networkNode + \".moduleName\")\n parent = cmds.getAttr(networkNode + \".parentModuleBone\")\n\n # get mirror module instance and information\n mirrorInst = self.returnMirrorModuleInst\n\n # turn off aim mode\n mirrorInst.aimMode_Setup(False)\n\n # turn off coplanar mode IF it exists on the module\n try:\n state = mirrorInst.coplanarBtn.isChecked()\n if state:\n mirrorInst.coplanarBtn.setChecked(False)\n mirrorInst.coplanarMode()\n except:\n pass\n\n moverTypes = self.returnJointMovers\n for moverType in moverTypes:\n for jointMover in moverType:\n attrs = cmds.listAttr(jointMover, keyable=True)\n\n for attr in attrs:\n value = cmds.getAttr(jointMover + \".\" + attr)\n\n mirrorMover = jointMover.partition(moduleName)[2]\n mirrorMover = mirrorModule + mirrorMover\n mirrorAttrs = [\"translateX\", \"translateY\", \"translateZ\"]\n\n if attr in mirrorAttrs:\n cmds.setAttr(mirrorMover + \".\" + attr, value * -1)\n else:\n cmds.setAttr(mirrorMover + \".\" + attr, value)\n\n cmds.select(clear=True)\n if len(currentSelection) > 0:\n cmds.select(currentSelection)\n\n # turn aim mode on\n mirrorInst.aimMode_Setup(True)\n\n # extend functionality\n self.mirrorTransformations_Custom()","def apply_dof_transformation_to_transpose_prism(\n entity_transformations, entity_dofs, data, cell_info\n):\n apply_dof_transformation_to_transpose(3, 9, 5, entity_transformations, entity_dofs,\n data, cell_info, List([\"triangle\"] + [\"quadrilateral\"] * 4 + [\"triangle\"]))","def cbindMatrices(hm, args):\n hm2 = heatmapper.heatmapper()\n\n # Make a dict of region name:row associations\n hm.read_matrix_file(args.matrixFile[0])\n d = dict({x: dict() for x in hm.parameters[\"group_labels\"]})\n for idx, group in enumerate(hm.parameters[\"group_labels\"]):\n s = hm.parameters[\"group_boundaries\"][idx]\n e = hm.parameters[\"group_boundaries\"][idx + 1]\n for idx2, reg in enumerate(hm.matrix.regions[s:e]):\n d[group][reg[2]] = idx2 + s\n\n # Iterate through the other matrices\n for idx in range(1, len(args.matrixFile)):\n hm2.read_matrix_file(args.matrixFile[idx])\n # Add the sample labels\n hm.parameters['sample_labels'].extend(hm2.parameters['sample_labels'])\n # Add the sample boundaries\n lens = [x + hm.parameters['sample_boundaries'][-1] for x in hm2.parameters['sample_boundaries']][1:]\n hm.parameters['sample_boundaries'].extend(lens)\n\n # Add on additional NA initialized columns\n ncol = hm.matrix.matrix.shape[1]\n hm.matrix.matrix = np.hstack((hm.matrix.matrix, np.empty(hm2.matrix.matrix.shape)))\n hm.matrix.matrix[:, ncol:] = np.NAN\n\n # Update the values\n for idx2, group in enumerate(hm2.parameters[\"group_labels\"]):\n if group not in d:\n continue\n s = hm2.parameters[\"group_boundaries\"][idx2]\n e = hm2.parameters[\"group_boundaries\"][idx2 + 1]\n for idx3, reg in enumerate(hm2.matrix.regions[s:e]):\n if reg[2] not in d[group]:\n continue\n hm.matrix.matrix[d[group][reg[2]], ncol:] = hm2.matrix.matrix[s + idx3, :]\n\n # Append the special params\n for s in hm.special_params:\n hm.parameters[s].extend(hm2.parameters[s])\n\n # Update the sample parameters\n hm.matrix.sample_labels = hm.parameters['sample_labels']\n hm.matrix.sample_boundaries = hm.parameters['sample_boundaries']","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_H2 = self.edp_par['rho_H2'].value\n Z_H2 = self.edp_par['Z_H2'].value\n sigma_H2 = self.edp_par['sigma_H2'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n \n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\n if Z_H1 > Z_H2:\n Z_H1, Z_H2 = Z_H2, Z_H1\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\n rho_H1, rho_H2 = rho_H2, rho_H1\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H2 + sigma_H2\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def _arrange_xfms(transforms, num_files, tmp_folder):\n base_xform = [\"#Insight Transform File V1.0\", \"#Transform 0\"]\n # Initialize the transforms matrix\n xfms_T = []\n for i, tf_file in enumerate(transforms):\n # If it is a deformation field, copy to the tfs_matrix directly\n if guess_type(tf_file)[0] != \"text/plain\":\n xfms_T.append([tf_file] * num_files)\n continue\n\n with open(tf_file) as tf_fh:\n tfdata = tf_fh.read().strip()\n\n # If it is not an ITK transform file, copy to the tfs_matrix directly\n if not tfdata.startswith(\"#Insight Transform File\"):\n xfms_T.append([tf_file] * num_files)\n continue\n\n # Count number of transforms in ITK transform file\n nxforms = tfdata.count(\"#Transform\")\n\n # Remove first line\n tfdata = tfdata.split(\"\\n\")[1:]\n\n # If it is a ITK transform file with only 1 xform, copy to the tfs_matrix directly\n if nxforms == 1:\n xfms_T.append([tf_file] * num_files)\n continue\n\n if nxforms != num_files:\n raise RuntimeError(\n \"Number of transforms (%d) found in the ITK file does not match\"\n \" the number of input image files (%d).\" % (nxforms, num_files)\n )\n\n # At this point splitting transforms will be necessary, generate a base name\n out_base = fname_presuffix(\n tf_file, suffix=\"_pos-%03d_xfm-{:05d}\" % i, newpath=tmp_folder.name\n ).format\n # Split combined ITK transforms file\n split_xfms = []\n for xform_i in range(nxforms):\n # Find start token to extract\n startidx = tfdata.index(\"#Transform %d\" % xform_i)\n next_xform = base_xform + tfdata[startidx + 1:startidx + 4] + [\"\"]\n xfm_file = out_base(xform_i)\n with open(xfm_file, \"w\") as out_xfm:\n out_xfm.write(\"\\n\".join(next_xform))\n split_xfms.append(xfm_file)\n xfms_T.append(split_xfms)\n\n # Transpose back (only Python 3)\n return list(map(list, zip(*xfms_T)))","def _transform_frame(self, src):\n D = len(src)\n\n # Eq.(11)\n E = np.zeros((self.num_mixtures, D))\n for m in range(self.num_mixtures):\n xx = np.linalg.solve(self.covarXX[m], src - self.src_means[m])\n E[m] = self.tgt_means[m] + self.covarYX[m].dot(xx)\n\n # Eq.(9) p(m|x)\n posterior = self.px.predict_proba(np.atleast_2d(src))\n\n # Eq.(13) conditinal mean E[p(y|x)]\n return posterior.dot(E).flatten()","def connector_mediation(task):\n\tatlas = 'power'\n\tproject='hcp'\n\tknown_membership,network_names,num_nodes,name_int_dict = network_labels(atlas)\n\tsubjects = np.load('%s/dynamic_mod/results/%s_%s_%s_subs_fz.npy' %(homedir,'hcp',task,atlas))\n\tstatic_results = graph_metrics(subjects,task,atlas,run_version='fz')\n\tmatrices = static_results['matrices']\n\tsubject_pcs = static_results['subject_pcs']\n\tsubject_mods = static_results['subject_mods']\n\tmod_pc_corr = np.zeros(subject_pcs.shape[1])\n\tfor i in range(subject_pcs.shape[1]):\n\t\tmod_pc_corr[i] = nan_pearsonr(subject_mods,subject_pcs[:,i])[0]\n\tmean_conn = np.nanmean(matrices,axis=0)\n\te_tresh = np.percentile(mean_conn,85)\n\tsubject_pcs[np.isnan(subject_pcs)] = 0.0\n\tm = np.zeros((264,264,264))\n\tpool = Pool(40)\n\tfor n in range(264):\n\t\tprint n\n\t\tsys.stdout.flush()\n\t\tvariables = []\n\t\tfor i,j in combinations(range(264),2):\n\t\t\tvariables.append(pd.DataFrame(data={'pc':subject_pcs[:,n],'weight':matrices[:,i,j],'q':subject_mods},index=range(len(subject_pcs))))\n\t\tresults = pool.map(multi_med,variables)\n\t\tfor r,i in zip(results,combinations(range(264),2)):\n\t\t\tm[n,i[0],i[1]] = r\n\t\t\tm[n,i[1],i[0]] = r\n\t\tnp.save('/home/despoB/mb3152/dynamic_mod/results/full_med_matrix_new_%s.npy'%(task),m)","def apply_alignment_matrix(job, matrix):\n # Get the SVG-style 6-element vector from the 3x3 matrix\n mat = [matrix[0][0], matrix[1][0], matrix[0][1], matrix[1][1], matrix[0][2], matrix[1][2]]\n\n # Only the 'defs' list contains coordinates that need to be transformed\n defs = job['defs']\n\n for one_def in defs:\n if one_def['kind'] == \"image\":\n # TODO: raster images are not supported!\n # The rest of the code assumes the rows of pixel data are aligned\n # with the X axis, so handling rotation will require transforming\n # the entire image and generating new data.\n pass\n elif one_def['kind'] == \"path\":\n for one_path in one_def['data']:\n for one_point in one_path:\n matrixApply(mat, one_point)","def __update_transformation_view(self):\n text = \"Computed transformation.\\n\\n\"\n text += \"Rotation:\\n\"\n for i in range(3):\n text += str(self.rotation[i, 0]) + \" \" + str(self.rotation[i, 1]) + \" \" + str(self.rotation[i, 2]) + \"\\n\"\n text += \"\\nTranslation:\\n\"\n text += str(self.translation[0]) + \" \" + str(self.translation[1]) + \" \" + str(self.translation[2])\n text += \"\\n\\nScale:\\n\"\n text += str(self.scale)\n text += \"\\n\\n4x4 Matrix:\\n\"\n mat = self.__compose_transformation()\n for i in range(4):\n text += f\"{str(mat[i, 0])}, {str(mat[i, 1])}, {str(mat[i, 2])}, {str(mat[i, 3])}\\n\"\n self.qt_transformation_textbox.setText(text)","def finalize_fields(self):\n \n # Commented out until the MPI implementation is ready\n self.comm.allreduce(self.delta_P_dc, op=mpi.SUM)\n self.comm.allreduce(self.delta_P_omega, op=mpi.SUM)\n\n self.dc_coords[:,:,0] += self.delta_P_dc[:,:]\n self.omega_coords[:,:,0] += self.delta_P_omega[:,:]\n\n self.delta_P_dc = np.zeros((self.n_modes_z,self.n_modes_r))\n self.delta_P_omega = np.zeros((self.n_modes_z,self.n_modes_r))","def compute_T_matrix(coordinates, p, reference=[[1,0,0],[0,1,0],[0,0,1]], origin=[0,0,0]):\n e_b_x = coordinates[0]\n e_b_y = coordinates[1]\n e_b_z = coordinates[2]\n \n e_a_x = reference[0] \n e_a_y = reference[1]\n e_a_z = reference[2]\n \n # Compute the rotation matrix\n x_b_a = [np.dot(e_b_x, e_a_x), np.dot(e_b_x, e_a_y), np.dot(e_b_x, e_a_z)]\n y_b_a = [np.dot(e_b_y, e_a_x), np.dot(e_b_y, e_a_y), np.dot(e_b_y, e_a_z)]\n z_b_a = [np.dot(e_b_z, e_a_x), np.dot(e_b_z, e_a_y), np.dot(e_b_z, e_a_z)]\n \n R_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0]],[x_b_a[1], y_b_a[1], z_b_a[1]],x_b_a[2], y_b_a[2], z_b_a[2]]\n \n # Compute the displacement \n displacement = [p[0]-origin[0], p[1]-origin[1], p[2]-origin[2]]\n \n # Make it into a transform matrix\n T_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0], displacement[0]],\n [x_b_a[1], y_b_a[1], z_b_a[1], displacement[1]],\n [x_b_a[2], y_b_a[2], z_b_a[2], displacement[2]],\n [0, 0, 0, 1]]\n \n T_a_b = np.linalg.inv(T_b_a).tolist()\n \n return T_b_a, T_a_b","def compute_T_matrix(coordinates, p, reference=[[1,0,0],[0,1,0],[0,0,1]], origin=[0,0,0]):\n e_b_x = coordinates[0]\n e_b_y = coordinates[1]\n e_b_z = coordinates[2]\n \n e_a_x = reference[0] \n e_a_y = reference[1]\n e_a_z = reference[2]\n \n # Compute the rotation matrix\n x_b_a = [np.dot(e_b_x, e_a_x), np.dot(e_b_x, e_a_y), np.dot(e_b_x, e_a_z)]\n y_b_a = [np.dot(e_b_y, e_a_x), np.dot(e_b_y, e_a_y), np.dot(e_b_y, e_a_z)]\n z_b_a = [np.dot(e_b_z, e_a_x), np.dot(e_b_z, e_a_y), np.dot(e_b_z, e_a_z)]\n \n R_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0]],[x_b_a[1], y_b_a[1], z_b_a[1]],x_b_a[2], y_b_a[2], z_b_a[2]]\n \n # Compute the displacement \n displacement = [p[0]-origin[0], p[1]-origin[1], p[2]-origin[2]]\n \n # Make it into a transform matrix\n T_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0], displacement[0]],\n [x_b_a[1], y_b_a[1], z_b_a[1], displacement[1]],\n [x_b_a[2], y_b_a[2], z_b_a[2], displacement[2]],\n [0, 0, 0, 1]]\n \n T_a_b = np.linalg.inv(T_b_a).tolist()\n \n return T_b_a, T_a_b","def apply_dof_transformation_to_transpose_interval(entity_transformations, entity_dofs,\n data, cell_info):\n return","def generateMatrix():\n num_probes = 16\n\n # print(position_vectors)\n # Create the (48x4) calibration matrix:\n calibration_lookup= [[0] * 3 for i in range(num_probes)]\n calibration_matrix = [[0] * 9 for i in range(num_probes)]\n counter = 0\n\n # first populate with x-direction:\n shot_range = [17, 20] #x-direction\n dir = 0 #the direction of the orentation of the probe array\n position_vectors = get_probeLocs_calib_setup(dir)\n for probe_num in range(num_probes):\n position_vector = position_vectors[probe_num]\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\n calibration_lookup[probe_num][dir] = ratio_Bx\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\n print(\"Progress: %d / %d\" %(counter+1,num_probes*3 ))\n counter +=1\n\n # Then populate with y-direction:\n shot_range = [21, 25] #y-direction\n dir = 1 #the direction of the orentation of the probe array\n position_vectors = get_probeLocs_calib_setup(dir)\n for probe_num in range(num_probes):\n position_vector = position_vectors[probe_num]\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\n calibration_lookup[probe_num][dir] = ratio_By\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\n print(\"Progress: %d / %d\" %(counter+1,num_probes*3 ))\n counter +=1\n\n # Then populate with z-direction:\n shot_range = [11, 15] #z-direction\n dir = 2\n position_vectors = get_probeLocs_calib_setup(dir)\n for probe_num in range(num_probes):\n position_vector = position_vectors[probe_num]\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\n calibration_lookup[probe_num][dir] = ratio_Bz\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\n print(\"Progress: %d / %d\" %(counter+1,num_probes*3 ))\n counter +=1\n\n pth = os.getcwd()\n date = '050119'\n print(\"Finished! File saved as calib-%s-4x4_lookup.txt and _3x3 in current working directory\" %(date))\n savetxt(os.path.join(pth, 'calib-%s-4x4_lookup_no_switch.txt' % (date)) , calibration_lookup)\n savetxt(os.path.join(pth, 'calib-%s-4x4_3x3_no_switch.txt' % (date)) , calibration_matrix)","def position_modules_interpolate(self, data):\n assert data.shape == (16, 512, 128)\n size_yx, centre = self._get_dimensions()\n tmp = np.empty((16 * 8,) + size_yx, dtype=data.dtype)\n\n for i, (module, mod_data) in enumerate(zip(self.modules, data)):\n tiles_data = np.split(mod_data, 8)\n for j, (tile, tile_data) in enumerate(zip(module, tiles_data)):\n # We store (x, y, z), but numpy indexing, and hence affine_transform,\n # work like [y, x]. Rearrange the numbers:\n fs_vec_yx = tile.fs_vec[:2][::-1]\n ss_vec_yx = tile.ss_vec[:2][::-1]\n\n # Offset by centre to make all coordinates positive\n corner_pos_yx = tile.corner_pos[:2][::-1] + centre\n\n # Make the rotation matrix\n rotn = np.stack((ss_vec_yx, fs_vec_yx), axis=-1)\n\n # affine_transform takes a mapping from *output* to *input*.\n # So we reverse the forward transformation.\n transform = np.linalg.inv(rotn)\n offset = np.dot(rotn, corner_pos_yx) # this seems to work, but is it right?\n\n affine_transform(\n tile_data,\n transform,\n offset=offset,\n cval=np.nan,\n output_shape=size_yx,\n output=tmp[i * 8 + j],\n )\n\n # Silence warnings about nans - we expect gaps in the result\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", category=RuntimeWarning)\n out = np.nanmax(tmp, axis=0)\n\n return out, centre","def combine_par(output_dir): \n #start time\n start_time = time.time()\n \n # set input/output file paths\n infile0 = output_dir + 'TransformParameters.0.txt'\n infile1 = output_dir + 'TransformParameters.1.txt'\n outfile0 = output_dir +'TransformParameters.fwd.txt'\n outfile1 = output_dir +'TransformParameters.inv.txt'\n \n # define reference frame for registration\n ref = 0\n spacing = 1\n \n # Open parameter file 0 and search for GridSpacing and GridOrigin line\n text_filein0 = open( infile0, \"r\" )\n for line in text_filein0:\n if line.find( \"(GridOrigin \" ) == 0:\n origin_str = line\n elif line.find( \"(GridSpacing \" ) == 0:\n spacing_str = line\n text_filein0.close()\n \n # Extract time point origin from line\n origin_split = origin_str.strip().split(' ')\n origin_split = origin_split[ len( origin_split ) - 1 ].split(')')\n old_origin = float( origin_split[ 0 ] )\n \n # Extract time point spacing from line\n spacing_split = spacing_str.strip().split(' ')\n spacing_split = spacing_split[ len( spacing_split ) - 1 ].split(')')\n old_spacing = float( spacing_split[ 0 ] )\n \n \n print(\"Original grid origin in time dimension: \" + str( old_origin ))\n print(\"Original grid spacing in time dimension: \" + str( old_spacing ))\n print(\"\")\n \n # Determine new grid origin\n new_origin = ref - ( spacing / old_spacing ) * ( ref - old_origin )\n print( \"New grid origin in time dimension: \" + str( new_origin ))\n \n # Recompose origin and spacing lines\n new_origin_string = origin_str.strip().split(' ')\n new_origin_string.pop()\n new_origin_string = \" \".join( new_origin_string ) + \" \" + str( new_origin ) + \")\\n\"\n new_spacing_string = spacing_str.strip().split(' ')\n new_spacing_string.pop()\n new_spacing_string = \" \".join( new_spacing_string ) + \" \" + str( spacing ) + \")\\n\"\n \n # Reopen text file, replace origin and spacing and write to output file 0\n text_filein0 = open( infile0, \"r\" )\n text_fileout0 = open( outfile0, \"w\" )\n for line in text_filein0:\n if line.find( \"(GridOrigin \" ) == 0:\n # Write new origin line\n text_fileout0.write( new_origin_string )\n elif line.find( \"(GridSpacing \" ) == 0:\n # Write new spacing line\n text_fileout0.write( new_spacing_string )\n elif line.find( \"(InitialTransformParametersFileName \" ) == 0:\n # Remove initial transform\n text_fileout0.write( \"(InitialTransformParametersFileName \\\"NoInitialTransform\\\")\\n\" )\n else:\n # Write line read from input file (no change)\n text_fileout0.write( line )\n text_filein0.close()\n text_fileout0.close()\n \n # Open parameter file 1 and search for GridSize\n text_filein1 = open( infile1, \"r\" )\n for line in text_filein1:\n if line.find(\"(GridSize\") == 0:\n grid_str = line\n grid_split = grid_str.strip().split(' ')\n grid_split[-1] = grid_split[-1].replace(')','')\n grid_split = grid_split[1:]\n grid_float = [float(s) for s in grid_split]\n grid_all = int(grid_float[0] * grid_float[1] * grid_float[2] * grid_float[3])\n num_phase = int(grid_float[3])\n text_filein1.close()\n \n # Replace initial transform parameter filename\n text_filein1 = open( infile1, \"r\" )\n text_fileout1 = open( outfile1, \"w\" )\n for line in text_filein1:\n if line.find( \"(InitialTransformParametersFileName \" ) == 0:\n # Set initial transform filename\n text_fileout1.write( \"(InitialTransformParametersFileName \\\"\" + outfile0 + \"\\\")\\n\" )\n elif line.find(\"(TransformParameters \") == 0:\n # extract b-spline parameters, arrangment : x (Px*Py*Pz), y(Px*Py*Pz), z(Px*Py*Pz), t(Px*Py*Pz)\n transPar_str = line\n transPar_split = transPar_str.strip().split(' ')\n transPar_split[-1] = transPar_split[-1].replace(')','')\n transPar_split = transPar_split[1:]\n num_grid3d = int(grid_all / num_phase) \n str_seg = transPar_split[(ref*num_grid3d):((ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all+(ref*num_grid3d)): (grid_all + (ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all*2+(ref*num_grid3d)): (grid_all*2 + (ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all*3+(ref*num_grid3d)): (grid_all*3 + (ref+1)*num_grid3d)] * num_phase\n #str_seg = \"\"\n #str_seg = [str_seg + transPar_split[((ref*num_grid3d)+grid*i):((ref+1)*num_grid3d+grid*i)] * num_phase for i in range(4)]\n str_joined = ' '.join(str_seg)\n text_fileout1.write(\"(TransformParameters \" + str_joined + \")\\n\")\n else:\n # Write line read from input file (no change)\n text_fileout1.write( line )\n text_filein1.close()\n text_fileout1.close()\n \n # caclulate elapsed time\n end_time = time.time()\n elapsed_time = end_time - start_time\n print('combine_par done. elapsed time:', elapsed_time, 's')","def run(self):\n #self.source_fixed = center_of_mass(self.source_mesh.vs)\n #self.destination_fixed = center_of_mass(self.destination_mesh.vs)\n\n source_high_curvature = get_mesh_of_high_curvature(self.source_mesh)\n destination_high_curvature = get_mesh_of_high_curvature(self.destination_mesh)\n\n self.matrix, error = icp(source_high_curvature.vs, \n destination_high_curvature.vs, \n self.source_fixed, \n self.destination_fixed,\n 300,\n verbose = self.verbose)\n\n self.inverse = np.linalg.inv(self.matrix)\n self.global_confidence = 1 - error\n print self.global_confidence","def apply_transformation_np(source, transformation):\n source_homog = np.ones((source.shape[0], 4))\n source_homog[:, :-1] = source\n # source_homog = np.hstack(\n # (source, np.ones(source.shape[0], 1))\n # )\n\n source_transformed = np.matmul(transformation, source_homog.T).T[:, :-1]\n return source_transformed","def align_images(self, translation_only=False):\n # Extract feature point locations and descriptors.\n points_and_descriptors = []\n for file in self.files:\n image = sol4_utils.read_image(file, 1)\n self.images.append(image)\n self.h, self.w = image.shape\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, PYR_FLT_LEVEL)\n points_and_descriptors.append(find_features(pyramid))\n\n # Compute homographies between successive pairs of images.\n Hs = []\n for i in range(len(points_and_descriptors) - 1):\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\n\n # Find matching feature points.\n ind1, ind2 = match_features(desc1, desc2, MATCH_MIN_SCORE)\n points1, points2 = points1[ind1, :], points2[ind2, :]\n\n # Compute homography using RANSAC.\n H12, inliers = ransac_homography(points1, points2,\n RANSAC_ITER_NUM, RANAC_ERR_TOL,\n translation_only)\n\n # Uncomment for debugging: display inliers and outliers among matching points.\n # In the submitted code this function should be commented out!\n if(self.display_match):\n display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\n\n Hs.append(H12)\n\n # Compute composite homographies from the central coordinate system.\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\n self.homographies = np.stack(accumulated_homographies)\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\n print((self.frames_for_panoramas.shape))\n self.homographies = self.homographies[self.frames_for_panoramas]","def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\n for technique in technique_data.technique.get_component_techniques():\n direct_calculator = DirectTechniqueCalculator(technique.definition)\n similarity_matrix = direct_calculator.calculate_technique_data(\n technique_data.dataset\n ).similarity_matrix\n technique_data.transitive_matrices.append(similarity_matrix)","def reconstruct(self):\n self.print_tee(\"starting reconstruction\", self.log_file)\n\n # #############################################\n # estimate SIM parameters\n # #############################################\n # estimate frequencies\n tstart = time.time() # since done using joblib, process_time() is not useful...\n\n if self.use_fixed_frq:\n self.frqs = self.frqs_guess\n self.print_tee(\"using fixed frequencies\", self.log_file)\n else:\n if self.find_frq_first:\n self.frqs = self.estimate_sim_frqs(self.imgs_ft, self.imgs_ft, self.frqs_guess)\n else:\n self.print_tee(\"doing phase demixing prior to frequency finding\", self.log_file)\n self.separated_components_ft = separate_components(self.imgs_ft, self.phases_guess, np.ones((self.nangles, self.nphases)))\n imgs1 = np.expand_dims(self.separated_components_ft[:, 0], axis=1)\n imgs2 = np.expand_dims(self.separated_components_ft[:, 1], axis=1)\n self.frqs = self.estimate_sim_frqs(imgs1, imgs2, self.frqs_guess)\n\n tend = time.time()\n self.print_tee(\"fitting frequencies took %0.2fs\" % (tend - tstart), self.log_file)\n\n # estimate phases\n tstart = time.process_time()\n if self.use_fixed_phase:\n self.phases = self.phases_guess\n self.amps = np.ones((self.nangles, self.nphases))\n self.print_tee(\"Using fixed phases\", self.log_file)\n else:\n self.phases, self.amps = self.estimate_sim_phases(self.frqs, self.phases_guess)\n\n tend = time.process_time()\n self.print_tee(\"estimated %d phases in %0.2fs\" % (self.nangles * self.nphases, tend - tstart), self.log_file)\n\n # separate components\n self.separated_components_ft = separate_components(self.imgs_ft, self.phases, self.amps)\n\n # estimate modulation depths and power spectrum fit parameters\n tstart = time.process_time()\n # for the moment, need to do this feet even if have fixed mod depth, because still need the\n # power spectrum parameters\n self.mod_depths, self.power_spectrum_params, self.pspec_masks = self.estimate_mod_depths()\n\n if self.use_fixed_mod_depths:\n self.mod_depths = np.zeros((self.nangles, self.nphases))\n self.mod_depths[:, 0] = 1\n for jj in range(1, self.nphases):\n self.mod_depths[:, jj] = self.mod_depths_guess\n\n # also correct power spectrum params\n self.power_spectrum_params[:, jj, 2] = self.mod_depths[:, jj]\n\n tend = time.process_time()\n self.print_tee('estimated %d modulation depths in %0.2fs' % (self.nangles, tend - tstart), self.log_file)\n\n # #############################################\n # estimate modulation contrast to noise ratio for raw images\n # #############################################\n mcnr = np.zeros((self.nangles, self.nphases))\n for ii in range(self.nangles):\n for jj in range(self.nphases):\n mcnr[ii, jj] = get_mcnr(self.imgs_ft[ii, jj], self.frqs[ii], self.fx, self.fy, self.fmax)\n\n # if mcnr is too low (typically < 1), use guess values instead\n if self.default_to_guess_on_low_mcnr and np.min(mcnr[ii]) < self.min_mcnr and self.frqs_guess is not None:\n self.frqs[ii] = self.frqs_guess[ii]\n self.print_tee(\"Angle %d/%d, minimum mcnr = %0.2f is less than the minimum value, %0.2f,\"\n \" so fit frequency will be replaced with guess\"\n % (ii + 1, self.nangles, np.min(mcnr[ii]), self.min_mcnr), self.log_file)\n\n for jj in range(self.nphases):\n mcnr[ii, jj] = get_mcnr(self.imgs_ft[ii, jj], self.frqs[ii], self.fx, self.fy, self.fmax)\n\n self.mcnr = mcnr\n # for convenience, also save periods and angles\n self.periods = 1 / np.sqrt(self.frqs[:, 0] ** 2 + self.frqs[:, 1] ** 2)\n self.angles = np.angle(self.frqs[:, 0] + 1j * self.frqs[:, 1])\n\n # #############################################\n # estimate noise in component images\n # #############################################\n self.noise_power = self.power_spectrum_params[:, :, -1]\n\n # #############################################\n # SIM reconstruction\n # #############################################\n tstart = time.process_time()\n self.img_sr, self.img_sr_ft, self.components_deconvolved_ft, self.components_shifted_ft, \\\n self.weights, self.weight_norm, self.snr, self.snr_shifted = self.combine_components()\n # self.img_sr, self.img_sr_ft, self.components_deconvolved_ft, self.components_shifted_ft, \\\n # self.weights, self.weight_norm, self.snr, self.snr_shifted = self.combine_components_fairSIM()\n tend = time.process_time()\n\n self.print_tee(\"combining components took %0.2fs\" % (tend - tstart), self.log_file)\n\n # #############################################\n # widefield deconvolution\n # #############################################\n # get signal to noise ratio\n wf_noise = get_noise_power(self.widefield_ft, self.fx, self.fy, self.fmax)\n fit_result, self.mask_wf = fit_power_spectrum(self.widefield_ft, self.otf, self.fx, self.fy, self.fmax, self.fbounds,\n init_params=[None, self.power_spectrum_params[0, 0, 1], 1, wf_noise],\n fixed_params=[False, True, True, True])\n\n self.pspec_params_wf = fit_result['fit_params']\n ff = np.sqrt(self.fx[None, :]**2 + self.fy[:, None]**2)\n sig = power_spectrum_fn([self.pspec_params_wf[0], self.pspec_params_wf[1], self.pspec_params_wf[2], 0], ff, 1)\n wf_snr = sig / wf_noise\n # deconvolution\n wf_decon_ft, wfilter = wiener_deconvolution(self.widefield_ft, self.otf, wf_snr, snr_includes_otf=False)\n\n # upsample to make fully comparable to reconstructed image\n self.widefield_deconvolution_ft = tools.expand_fourier_sp(wf_decon_ft, 2, 2, centered=True)\n self.widefield_deconvolution = fft.fftshift(fft.ifft2(fft.ifftshift(self.widefield_deconvolution_ft))).real\n\n # #############################################\n # print parameters\n # #############################################\n self.print_parameters()\n\n try:\n self.log_file.close()\n except AttributeError:\n pass","def apply_transform_matrix(self, img: np.ndarray, transform_matrix):\n h, w = img.shape[0], img.shape[1]\n transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)\n img = np.rollaxis(img, 2, 0)\n final_affine_matrix = transform_matrix[:2, :2]\n final_offset = transform_matrix[:2, 2]\n\n channel_images = [scipy.ndimage.interpolation.affine_transform(\n x_channel,\n final_affine_matrix,\n final_offset,\n order=1,\n mode=self.fill_mode,\n cval=self.cval) for x_channel in img]\n img = np.stack(channel_images, axis=0)\n img = np.rollaxis(img, 0, 2 + 1)\n # img = apply_affine_transform(img, transform_matrix, channel_axis=2, fill_mode=self.fill_mode, cval=self.cval) # apply_transform\n return img","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H1 + sigma_H1\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def _compute_correction(self, initial_state, final_state, a, b, c, s):\r\n pertub = self.pertub\r\n pertub_s = pertub *10\r\n \r\n pred_no_pertub = self._motion_update_one_shot(initial_state, a, b, c, s)\r\n pred_pertub_a = self._motion_update_one_shot(initial_state, a +pertub, b, c, s)\r\n pred_pertub_b = self._motion_update_one_shot(initial_state, a, b +pertub, c, s)\r\n # no need to correct C, C is constrained by kappa_final\r\n # # pred_pertub_c = self._motion_update_one_shot(initial_state, a, b, c +pertub, s)\r\n pred_pertub_s = self._motion_update_one_shot(initial_state, a, b, c, s +pertub_s)\r\n\r\n d_state = np.zeros((3,1))\r\n d_pertub_state = np.zeros((3,3))\r\n Jacobian = np.zeros((3,3))\r\n for i in range(0, 3):\r\n d_pertub_state[i][0] = (final_state[i] - pred_pertub_a[i]) # a\r\n d_pertub_state[i][1] = (final_state[i] - pred_pertub_b[i]) # b\r\n # d_pertub_state[i][2] = (final_state[i] - pred_pertub_c[i]) # c (no update)\r\n d_pertub_state[i][2] = (final_state[i] - pred_pertub_s[i]) # s\r\n \r\n d_state[i] = final_state[i] - pred_no_pertub[i]\r\n \r\n Jacobian[i][0] = (d_pertub_state[i][0] - d_state[i])/pertub # a\r\n Jacobian[i][1] = (d_pertub_state[i][1] - d_state[i])/pertub # b\r\n # Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub # c (no update)\r\n Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub_s # s\r\n\r\n # inv_Jacobian = np.linalg.inv(Jacobian)\r\n inv_Jacobian = np.linalg.pinv(Jacobian)\r\n correction = np.dot(inv_Jacobian, d_state)\r\n # pdb.set_trace()\r\n return correction","def shifts_projection(sc, clean):\n def shifts_projected(clean, axis):\n projected = clean.map(lambda x: x.mean(axis=axis)[:, :, np.newaxis])\n target = getTarget(projected, 30, 1)\n shifts = registerByPlane(sc, projected, target[:, :, np.newaxis], 10, False)\n return shifts[:, :, 0]\n\n # shifts_xy = shifts_projected(clean, 2)\n shifts_xz = shifts_projected(clean, 1)\n shifts_yz = shifts_projected(clean, 0)\n\n # x_shifts = np.mean(np.stack((shifts_xz[:, 0], shifts_xy[:, 0])), axis=0)\n z_shifts = np.mean(np.stack((shifts_xz[:, 1], shifts_yz[:, 1])), axis=0)\n # y_shifts = np.mean(np.stack((shifts_yz[:, 0], shifts_xy[:, 1])), axis=0)\n plt.figure()\n plt.plot(shifts_xz[:, 1])\n plt.plot(shifts_yz[:, 1])\n plt.plot(z_shifts)\n plt.title('Z')\n # plt.figure()\n # plt.plot(shifts_xz[:, 0])\n # plt.plot(shifts_xy[:, 0])\n # plt.plot(x_shifts)\n # plt.title('X')\n # plt.figure()\n # plt.plot(shifts_yz[:, 0])\n # plt.plot(shifts_xy[:, 1])\n # plt.plot(y_shifts)\n # plt.title('Y')\n # shifts_all = np.stack((x_shifts, y_shifts, z_shifts))\n\n def initReg(kv):\n from scipy.ndimage.interpolation import shift\n index, volume = kv\n current_shift = (0, 0, -1 * z_shifts[int(index[0])])\n shifted = shift(volume, current_shift)\n return shifted.astype(np.int16)\n\n reg = clean.map(initReg, with_keys=True, value_shape=clean.shape[1:], dtype=np.int16)\n reg.cache()\n reg.count()\n return reg","def _calc_matrix(self):\n\t\tz = self.zoom\n\t\talloc = self.allocation\n\t\tif self.image:\n\t\t\tiw, ih = self.image.get_width(), self.image.get_height()\n\t\telse:\n\t\t\tiw, ih = 0, 0\n#\t\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\n\t\t\n\t\ti2w = cairo.Matrix(\n\t\t\tz,0,\n\t\t\t0,z,\n\t\t\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \n\t\t\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\n\t\t\t)\n\t\t\n\t\tself._i2w_matrix = i2w\n\t\t\n\t\tw2i = cairo.Matrix(*i2w) #copy\n\t\tw2i.invert()\n\t\tself._w2i_matrix = w2i","def design_matrix_df(meta):\n\n mouse = utils.meta_mouse(meta)\n\n # go\n new_meta = {}\n new_meta['go'] = np.zeros(len(meta))\n new_meta['go'][meta['trialerror'].isin([0, 3, 5, 7]).values] = 1\n assert np.sum(new_meta['go']) > 0\n meta_df_out = pd.DataFrame(data=new_meta, index=meta.index)\n\n # nogo\n new_meta = {}\n new_meta['nogo'] = np.zeros(len(meta))\n new_meta['nogo'][meta['trialerror'].isin([1, 2, 4, 6]).values] = 1\n assert np.sum(new_meta['nogo']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get cues defined by new terms\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[mm] = np.zeros(len(meta))\n new_meta[mm][meta.initial_condition.isin([inverted_lookup[mm]]).values] = 1\n assert np.sum(new_meta[mm]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get previous cue same\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[f'prev_same_{mm}'] = np.zeros(len(meta))\n new_meta[f'prev_same_{mm}'][meta[f'prev_same_{inverted_lookup[mm]}'].values] = 1\n assert np.sum(new_meta[f'prev_same_{mm}']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get previous cue diff\n new_meta = {}\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\n for mm in mm_type:\n new_meta[f'prev_diff_{mm}'] = np.zeros(len(meta))\n not_same = ~meta[f'prev_same_{inverted_lookup[mm]}'].values & meta.initial_condition.isin([inverted_lookup[mm]]).values\n new_meta[f'prev_diff_{mm}'][not_same] = 1\n assert np.sum(new_meta[f'prev_diff_{mm}']) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get salient events\n new_meta = {}\n for col in ['prev_reward', 'prev_punish', 'prev_blank', 'hmm_engaged']:\n new_meta[col] = np.zeros(len(meta))\n new_meta[col][meta[col].values] = 1\n assert np.sum(new_meta[col]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get normalized running and baseline licking\n new_meta = {}\n for col in ['speed', 'pre_speed', 'post_speed', 'pre_licks']:\n new_meta[col] = meta[col].values / np.nanmax(meta[col].values)\n new_meta[col][np.isnan(new_meta[col])] = 0\n assert np.sum(new_meta[col]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n # get normalized dprime\n new_meta = {}\n for col in ['dprime_run']:\n new_meta[col] = meta[col].values - np.nanmin(meta[col].values)\n new_meta[col] = new_meta[col] / np.nanmax(new_meta[col])\n new_meta[col][np.isnan(new_meta[col])] = 0\n assert np.sum(new_meta[col]) > 0\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\n\n return meta_df_out","def build_mm_df(sralist):\n\n def convert_to_codon(nts_array):\n \"\"\"\n pysam output is in nucleotides resolution, but scikit_curated_df uses codon resolution.\n This function converts nucleotide arrays to codon length (nts to codon resolution):\n \"\"\"\n \n nts_array = np.array(nts_array)\n codon_array = np.sum( np.reshape(A, (int(np.floor(nts_array[1]/3)),3) ), 1)/3.\n\n return codon_array\n\n\n def compute_mm(mmdata):\n \"\"\"\n get per gene average multi-mapping score\n \"\"\"\n\n mm_df = pd.DataFrame(columns=['ORF', 'MM'])\n counter = 0\n\n for gene in mmdata.keys():\n current_matrix = mmdata[gene]\n current_avrg = np.mean( np.sum(current_matrix, 1) / current_matrix.shape[1] )\n mm_df.loc[counter] = [gene, current_avrg]\n counter += 1\n\n return mm_df\n\n\n mm_mat = {}\n mm_pct = {}\n\n N = len(sralist)\n\n for ix, dataset in enumerate(sralist):\n samfile = pysam.AlignmentFile(TMP_DIR+'/ambiguous_reads/'+dataset+'_STAR_transcriptome_multi_mapped_sorted.bam', 'rb')\n genes_list = list(samfile.references)\n print(ix, dataset)\n\n for geneID in genes_list:\n # count the coverage of genomic positions by reads in region.\n # Returns: four array.arrays of the same length in order A C G T\n # The coverage is computed per-base [ACGT]\n cov = samfile.count_coverage(geneID, read_callback='nofilter')\n # Summ all 4 arrays\n cov_sum = np.sum(cov, axis=0)\n #print(geneID, cov_sum)\n codon_cov = convert_to_codon(cov_sum)\n codon_bool = np.asarray([1 if i > 0 else 0 for i in codon_cov])\n \n M = len(codon_bool)\n\n if ix == 0:\n \tmm_mat[geneID] = np.zeros((N,M)) * np.nan\n \n current_matrix = mm_mat[geneID]\n current_matrix[ix,:] = np.copy(codon_bool)\n mm_mat[geneID] = current_matrix\n\n\n mm_avrg = compute_mm(mm_mat)\n #mm_avrg.to_json('yeast_mm.json')\n #mm_avrg.to_csv('yeast_mm.txt', header=True, index=False, sep='\\t')\n\n \n mm_profile = {}\n theta_mm = 5\n for orf in mm_mat.keys():\n current_mat = mm_mat[orf]\n current_bool = np.sum(current_mat, 0) <= theta_mm\n mm_profile[orf] = current_bool\n\n with open('../data/processed/mm_consensus.pkl', 'wb') as f_mm:\n pickle.dump(mm_profile, f_mm)\n\n\n return mm_mat, mm_avrg, mm_profile","def run_phot_normalization(setup, **params):\n\n log = logs.start_stage_log( setup.red_dir, 'postproc_phot_norm', version=VERSION )\n\n xmatch = crossmatch.CrossMatchTable()\n xmatch.load(params['crossmatch_file'],log=log)\n\n # Identify the datasets to be used as the primary reference in each\n # filter:\n xmatch.id_primary_datasets_per_filter()\n log.info('Identified datasets to be used as the primary references in each filter: '\\\n +repr(xmatch.reference_datasets))\n\n # Add columns to the dataset Table to hold the photometric calibration\n # parameters\n ndset = len(xmatch.datasets)\n ncol = len(xmatch.datasets.colnames)\n if 'norm_a0' not in xmatch.datasets.colnames:\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_a0', index=ncol+1)\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_a1', index=ncol+2)\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_0', index=ncol+3)\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_1', index=ncol+4)\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_2', index=ncol+5)\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_3', index=ncol+6)\n log.info('Expanded xmatch.datasets table for normalization parameters')\n\n # Extract list of filters from xmatch.images['filter'] column\n filter_list = np.unique(xmatch.images['filter'].data)\n log.info('Identified list of filters to process: '+repr(filter_list))\n\n # Read data from quadrant 1\n # Reading in the timeseries data for all four quadrants is at the very\n # edge of the memory limits on the machines available, so it is preferable\n # to calibrate the quadrant's data separately. However, there are sufficient\n # stars in each quantant to be able to determine the photometric calibration\n # from a single quadrant, and apply it to the rest of the image.\n log.info('Loading the timeseries photometry from quadrant 1')\n file_path = path.join(setup.red_dir, params['field_name']+'_quad1_photometry.hdf5')\n phot_data = hd5_utils.read_phot_from_hd5_file(file_path, return_type='array')\n log.info('-> Completed photometry load')\n\n # Identify constant stars in the dataset\n constant_stars = find_constant_stars(xmatch, phot_data, log)\n star = 1\n\n # Normalize the photometry of each dataset to that of the reference\n # image in the primary reference dataset in that filter\n #for filter in filter_list:\n for filter in filter_list:\n\n # Plot an RMS diagram of the lightcurves for all stars in this filter,\n # prior to normalization, for comparison\n image_index = np.where(xmatch.images['filter'] == filter)[0]\n phot_data_filter = phot_data[:,image_index,:]\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\n qc_col = 16\n\n plot_multisite_rms(params, phot_data_filter, mag_col, mag_err_col, qc_col,\n 'rms_prenorm_'+str(filter)+'.png', log)\n\n # Extract the reference image photometry for the primary-ref dataset\n # for this filter\n ref_datacode = xmatch.reference_datasets[filter]\n sitecode = get_site_code(ref_datacode)\n log.info('Reference dataset in '+filter+' is '+ref_datacode+', sitecode='+sitecode)\n\n ref_phot = np.zeros((len(xmatch.stars),2))\n ref_phot[:,0] = xmatch.stars['cal_'+filter.replace('p','')+'_mag_'+sitecode]\n ref_phot[:,1] = xmatch.stars['cal_'+filter.replace('p','')+'_magerr_'+sitecode]\n\n # Extract the lightcurves for all other datasets in this filter in turn\n dataset_index = np.where(xmatch.datasets['dataset_filter'] == filter)[0]\n\n for idset in dataset_index:\n dset_datacode = xmatch.datasets['dataset_code'][idset]\n dset_sitecode = get_site_code(dset_datacode)\n\n # If the dataset is the reference dataset, replicate the photometric\n # measurements from the corrected columns to the normalized columns,\n # since no normalization is required - this ensures the full\n # lightcurve can be accessed from the normalization columns.\n if dset_datacode == ref_datacode:\n log.info('Replicating primary reference photometry from dataset '\\\n +dset_datacode+' to the normalized photometry columns')\n image_index = np.where(xmatch.images['dataset_code'] == dset_datacode)[0]\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\n for i in image_index:\n phot_data[:,i,norm_mag_col] = phot_data[:,i,mag_col]\n phot_data[:,i,norm_mag_err_col] = phot_data[:,i,mag_err_col]\n\n # Normalize any dataset that isn't the same as the reference dataset\n else:\n log.info('Normalizing dataset '+dset_datacode+', sitecode='+dset_sitecode)\n image_index = np.where(xmatch.images['dataset_code'] == dset_datacode)[0]\n\n ## Dset created to hold all stars in field, not quadrant - \n # normalization is calculated from whole field.\n dset_phot = np.zeros((len(xmatch.stars),2))\n dset_phot[:,0] = xmatch.stars['cal_'+filter.replace('p','')+'_mag_'+dset_sitecode]\n dset_phot[:,1] = xmatch.stars['cal_'+filter.replace('p','')+'_magerr_'+dset_sitecode]\n\n # Calculate their weighted offset relative to the primary-ref\n # dataset for the filter\n (fit, covar_fit) = calc_phot_normalization(ref_phot, dset_phot,\n constant_stars, log,\n diagnostics=True, ref=sitecode,\n dset=dset_sitecode, f=filter)\n\n # Store the fit results for this dataset\n xmatch = store_dataset_phot_normalization(idset, xmatch, fit, covar_fit, log)\n\n # Apply the normalization calibration to the dataset's reference\n # image photometry, and store the results in the xmatch.stars table\n log.info('Applying normalization to the datasets reference image photometry')\n cal_phot = apply_phot_normalization_single_frame(fit, covar_fit, dset_phot,\n 0, 1, log,\n diagnostics=True, ref=sitecode,\n dset=dset_sitecode, f=filter)\n xmatch.stars['norm_'+filter.replace('p','')+'_mag_'+dset_sitecode] = cal_phot[:,0]\n xmatch.stars['norm_'+filter.replace('p','')+'_magerr_'+dset_sitecode] = cal_phot[:,1]\n\n # Apply the photometry calibration to the timeseries data\n # for this dataset\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\n phot_data = normalize_timeseries_photometry(phot_data, image_index,\n fit, covar_fit,\n mag_col, mag_err_col,\n norm_mag_col, norm_mag_err_col,\n log)\n\n # Plot a second RMS diagram of the lightcurves for all stars in this\n # filter, post normalization, for comparison\n image_index = np.where(xmatch.images['filter'] == filter)[0]\n phot_data_filter = phot_data[:,image_index,:]\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\n plot_multisite_rms(params, phot_data_filter, mag_col, mag_err_col, qc_col,\n 'rms_postnorm_'+str(filter)+'.png', log)\n\n\n fig = plt.figure(3,(10,10))\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\n idx = np.where(phot_data[star,:,norm_mag_col] > 0.0)[0]\n plt.errorbar(phot_data[star,idx,0], phot_data[star,idx,norm_mag_col],\n yerr=phot_data[star,idx,norm_mag_err_col], fmt='none', color='k')\n (xmin,xmax,ymin,ymax) = plt.axis()\n ymin = max(ymin,14.0)\n ymax = min(ymax,22.0)\n plt.axis([xmin,xmax,ymax,ymin])\n plt.xlabel('HJD')\n plt.ylabel('Mag')\n plt.savefig('Star_'+str(star)+'_lc_norm.png')\n plt.close(3)\n\n # Output updated crossmatch table\n xmatch.save(params['crossmatch_file'])\n\n # Output the photometry for quadrant 1:\n output_quadrant_photometry(params, setup, 1, phot_data, log)\n\n logs.close_log(log)\n\n status = 'OK'\n report = 'Completed successfully'\n return status, report","def apply(self, data):\n print(\"this is morphism '{}'\".format(self.name))\n data = np.array(data)\n transformed_data = self.transf(data)\n return pd.DataFrame.from_dict({\"transf\": transformed_data.flatten()})","def motions2hik(motions, device=0, cuda=True):\n\n nreps, njoints, nfeats, nframes = motions.shape\n j2s = joints2smpl(num_frames=nframes, device_id=device, cuda=cuda)\n\n thetas = []\n root_translation = []\n for rep_idx in range(nreps):\n rep_motions = motions[rep_idx].transpose(2, 0, 1) # [nframes, njoints, 3]\n\n if nfeats == 3:\n print(f'Running SMPLify for repetition [{rep_idx + 1}] of {nreps}, it may take a few minutes.')\n motion_tensor, opt_dict = j2s.joint2smpl(rep_motions) # [nframes, njoints, 3]\n motion = motion_tensor.cpu().numpy()\n\n elif nfeats == 6:\n motion = rep_motions\n thetas.append(rep_motions)\n\n # Convert 6D rotation representation to Euler angles\n thetas_6d = motion[0, :-1, :, :nframes].transpose(2, 0, 1) # [nframes, njoints, 6]\n thetas_deg = []\n for frame, d6 in enumerate(thetas_6d):\n thetas_deg.append([_rotation_6d_to_euler(d6)])\n\n thetas.append([np.concatenate(thetas_deg, axis=0)])\n root_translation.append([motion[0, -1, :3, :nframes].transpose(1, 0)]) # [nframes, 3]\n\n thetas = np.concatenate(thetas, axis=0)[:nframes]\n root_translation = np.concatenate(root_translation, axis=0)[:nframes]\n\n data_dict = {\n 'joint_map': JOINT_MAP,\n 'thetas': thetas.tolist(), # [nreps, nframes, njoints, 3 (deg)]\n 'root_translation': root_translation.tolist(), # [nreps, nframes, 3 (xyz)]\n }\n\n return data_dict","def computeCoarseAlignment(self, TiltSeries_, mute=True, outfile='', optimizeShift=True, logfile_residual=''):\n #print('ref index: ', numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0], TiltSeries_._TiltAlignmentParas.ireftilt )\n (psiindeg, shiftX, shiftY, x, y, z, distLine, diffX, diffY,\n shiftVarX, shiftVarY) = alignmentFixMagRot(\n Markers_=self._Markers, cTilt=self._cTilt, sTilt=self._sTilt,\n ireftilt=numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0],\n irefmark=TiltSeries_._TiltAlignmentParas.irefmark,\n r=TiltSeries_._TiltAlignmentParas.r, imdim=TiltSeries_._imdim,imdimX=TiltSeries_._imdimX, imdimY=TiltSeries_._imdimY,\n handflip=TiltSeries_._TiltAlignmentParas.handflip, mute=mute, writeResults=outfile,\n optimizeShift=optimizeShift, logfile_residual=logfile_residual)\n if not mute:\n print((\"Tilt Axis: %.2f\" % psiindeg))\n # copy parameters to TiltSeries\n ireftilt = numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0]\n self._alignmentRotations = numpy.array(self._ntilt * [psiindeg])\n self.setRotationsInTiltSeries(TiltSeries_)\n self._alignmentTransX = shiftX\n self._alignmentTransY = shiftY\n self.set_TranslationsInTiltSeries(TiltSeries_)\n self.Psi = psiindeg\n\n for (imark, Marker) in enumerate(self._Markers):\n Marker.set_r(numpy.array([x[imark], y[imark], z[imark]]))\n # if not optimizeShift:\n # Marker.set_r(numpy.array([x[imark] + 6.326546124766944 , y[imark] + 5.187672225662868, z[imark]]))","def set_correction(self, matrix=[[1, 0], [0, 1]], shift=[0, 0], meta=None,\n **kwargs):\n # compute the matrix for the scale and rotation correction\n shift = (np.asarray(shift) - np.dot(self._wcslin.wcs.crpix, matrix) +\n self._wcslin.wcs.crpix)\n\n matrix = inv(matrix).T\n\n cwcs = self._wcs.deepcopy()\n\n # estimate step for numerical differentiation. We need a step\n # large enough to avoid rounding errors and small enough to get a\n # better precision for numerical differentiation.\n # TODO: The logic below should be revised at a later time so that it\n # better takes into account the two competing requirements.\n crpix1, crpix2 = self._wcs.wcs.crpix\n hx = max(1.0, min(20.0, (crpix1 - 1.0) / 100.0,\n (self._wcs.pixel_shape[0] - crpix1) / 100.0))\n hy = max(1.0, min(20.0, (crpix2 - 1.0) / 100.0,\n (self._wcs.pixel_shape[1] - crpix2) / 100.0))\n\n # compute new CRVAL for the image WCS:\n crpixinref = self._wcslin.wcs_world2pix(\n self._wcs.wcs_pix2world([self._wcs.wcs.crpix], 1), 1)\n crpixinref = np.dot(crpixinref - shift, matrix.T).astype(np.float64)\n self._wcs.wcs.crval = self._wcslin.wcs_pix2world(crpixinref, 1)[0]\n self._wcs.wcs.set()\n\n # approximation for CD matrix of the image WCS:\n (U, u) = _linearize(cwcs, self._wcs, self._wcslin, self._wcs.wcs.crpix,\n matrix, shift, hx=hx, hy=hy)\n self._wcs.wcs.cd = np.dot(self._wcs.wcs.cd.astype(np.longdouble),\n U).astype(np.float64)\n self._wcs.wcs.set()\n\n # save linear transformation info to the meta attribute:\n super().set_correction(matrix=matrix, shift=shift, meta=meta, **kwargs)","def mirrorTransformations_Custom(self):\n\n pass","def preprocess_gene(gene_id,data_dict,t2g_mapping,out_paths,locks):\n \n # features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean','start_idx','end_idx'] # columns in the eventalign file per read.\n\n events = []\n condition_labels = []\n run_labels = []\n read_ids = []\n genomic_coordinates = []\n \n # Concatenate\n# if len(data_dict) == 0:\n# return\n\n\n for read_index,events_per_read in data_dict.items():\n# if len(events_per_read) > 0:\n # ===== transcript to gene coordinates ===== # TODO: to use gtf.\n# tx_ids = [tx_id.decode('UTF-8').split('.')[0] for tx_id in events_per_read['transcript_id']]\n tx_ids = [tx_id for tx_id in events_per_read['transcript_id']] \n tx_positions = events_per_read['transcriptomic_position']\n genomic_coordinate = list(itemgetter(*zip(tx_ids,tx_positions))(t2g_mapping)) # genomic_coordinates -- np structured array of 'chr','gene_id','genomic_position','kmer'\n genomic_coordinate = np.array(genomic_coordinate,dtype=np.dtype([('chr','<U2'),('gene_id','<U15'),('genomic_position','<i4'),('g_kmer','<U5')]))\n # ===== \n\n # Based on Ensembl, remove transcript version.\n\n events_per_read['transcript_id'] = tx_ids\n events_per_read = np.array(events_per_read,dtype=np.dtype([('transcript_id', 'S15'), ('transcriptomic_position', '<i8'), ('reference_kmer', 'S5'), ('norm_mean', '<f8')]))\n\n #\n\n events += [events_per_read]\n genomic_coordinates += [genomic_coordinate]\n n_events_per_read = len(events_per_read)\n# else:\n# print(read_index,len(events_per_read))\n\n events = np.concatenate(events)\n genomic_coordinates = np.concatenate(genomic_coordinates)\n \n # Sort and split # \n# idx_sorted = np.lexsort((events['reference_kmer'],genomic_coordinates['genomic_position'],genomic_coordinates['gene_id']))\n# key_tuples, index = np.unique(list(zip(genomic_coordinates['gene_id'][idx_sorted],genomic_coordinates['genomic_position'][idx_sorted],events['reference_kmer'][idx_sorted])),return_index = True,axis=0) #'chr',\n# y_arrays = np.split(events['norm_mean'][idx_sorted], index[1:])\n# # read_id_arrays = np.split(events['read_id'][idx_sorted], index[1:])\n# g_kmer_arrays = np.split(genomic_coordinates['g_kmer'][idx_sorted], index[1:])\n\n idx_sorted = np.argsort(genomic_coordinates['genomic_position'])\n unique_positions, index = np.unique(genomic_coordinates['genomic_position'][idx_sorted],return_index = True)\n y_arrays = np.split(events['norm_mean'][idx_sorted], index[1:])\n # read_id_arrays = np.split(events['read_id'][idx_sorted], index[1:])\n g_kmer_arrays = np.split(genomic_coordinates['g_kmer'][idx_sorted], index[1:])\n g_positions_arrays = np.split(genomic_coordinates['genomic_position'][idx_sorted], index[1:])\n\n # Prepare\n # print('Reformating the data for each genomic position ...')\n data = defaultdict(dict)\n # for each position, make it ready for json dump\n# data = dict(zip(key_tuples, y_arrays))\n\n asserted = True\n# for key_tuple,y_array,g_kmer_array in zip(key_tuples,y_arrays,g_kmer_arrays):\n for position,y_array,g_kmer_array,g_positions_array in zip(unique_positions,y_arrays,g_kmer_arrays,g_positions_arrays):\n# gene_id,position,kmer = key_tuple \n if (len(set(g_kmer_array)) == 1) and ('XXXXX' in set(g_kmer_array)) or (len(y_array) == 0):\n continue\n \n if 'XXXXX' in set(g_kmer_array):\n y_array = y_array[g_kmer_array != 'XXXXX'] \n assert len(y_array) == len(g_kmer_array) - (g_kmer_array=='XXXXX').sum()\n g_kmer_array = g_kmer_array[g_kmer_array != 'XXXXX'] \n \n try:\n assert len(set(g_kmer_array)) == 1\n assert {position} == set(g_positions_array)\n except:\n asserted = False\n break\n kmer = set(g_kmer_array).pop()\n\n data[position] = {kmer: list(y_array)} #,'read_ids': [read_id.decode('UTF-8') for read_id in read_id_array]}\n \n # write to file.\n log_str = '%s: %s' %(gene_id,asserted)\n\n with locks['json'], open(out_paths['json'],'a') as f:\n\n pos_start = f.tell()\n f.write('{')\n f.write('\"%s\":' %gene_id)\n ujson.dump(data, f)\n f.write('}\\n')\n pos_end = f.tell()\n\n with locks['index'], open(out_paths['index'],'a') as f:\n f.write('%s,%d,%d\\n' %(gene_id,pos_start,pos_end))\n \n with locks['readcount'], open(out_paths['readcount'],'a') as f: #todo: repeats no. of tx >> don't want it.\n n_reads = len(data_dict)\n f.write('%s,%d\\n' %(gene_id,n_reads))\n \n with locks['log'], open(out_paths['log'],'a') as f:\n f.write(log_str + '\\n')","def set_correction(self, matrix=[[1, 0], [0, 1]], shift=[0, 0], meta=None,\n **kwargs):\n frms = self._wcs.available_frames\n\n # if original WCS did not have tangent-plane corrections, create\n # new correction and add it to the WCs pipeline:\n if self._tpcorr is None:\n self._tpcorr = TPCorr(\n v2ref=self._wcsinfo['v2_ref'] / 3600.0,\n v3ref=self._wcsinfo['v3_ref'] / 3600.0,\n roll=self._wcsinfo['roll_ref'],\n matrix=matrix,\n shift=shift,\n name='tangent-plane linear correction'\n )\n idx_v2v3 = frms.index(self._v23name)\n pipeline = deepcopy(self._wcs.pipeline)\n pf, pt = pipeline[idx_v2v3]\n pipeline[idx_v2v3] = (pf, deepcopy(self._tpcorr))\n frm_v2v3corr = deepcopy(pf)\n frm_v2v3corr.name = 'v2v3corr'\n pipeline.insert(idx_v2v3 + 1, (frm_v2v3corr, pt))\n self._wcs = gwcs.WCS(pipeline, name=self._owcs.name)\n self._v23name = 'v2v3corr'\n\n else:\n # combine old and new corrections into a single one and replace\n # old transformation with the combined correction transformation:\n tpcorr2 = self._tpcorr.__class__(\n v2ref=self._tpcorr.v2ref, v3ref=self._tpcorr.v3ref,\n roll=self._tpcorr.roll, matrix=matrix, shift=shift,\n name='tangent-plane linear correction'\n )\n\n self._tpcorr = tpcorr2.combine(tpcorr2, self._tpcorr)\n\n idx_v2v3 = frms.index(self._v23name)\n pipeline = deepcopy(self._wcs.pipeline)\n pipeline[idx_v2v3 - 1] = (pipeline[idx_v2v3 - 1][0],\n deepcopy(self._tpcorr))\n self._wcs = gwcs.WCS(pipeline, name=self._owcs.name)\n\n # reset definitions of the transformations from detector/world\n # coordinates to the tangent plane:\n self._update_transformations()\n\n # save linear transformation info to the meta attribute:\n super().set_correction(matrix=matrix, shift=shift, meta=meta, **kwargs)","def align_reconstruction_no_numpy(reconstruction, anchor_points_dict):\n modified_shots_dict = {}\n all_anchor_shot_ids = sorted(anchor_points_dict.keys())\n for i in range(len(all_anchor_shot_ids) - 1):\n anchor_coords = []\n recon_coords = []\n\n for j in range(2):\n shot_id = all_anchor_shot_ids[i+j]\n anchor_coords.append(anchor_points_dict[shot_id])\n o = get_origin_no_numpy_opencv(reconstruction.shots[shot_id].pose.rotation,\n reconstruction.shots[shot_id].pose.translation)\n\n recon_coords.append(o)\n\n s, A, b = get_affine_transform_2d_no_numpy(anchor_coords, recon_coords)\n\n start_shot_id = all_anchor_shot_ids[i]\n end_shot_id = all_anchor_shot_ids[i+1]\n\n # in first iteration, we transform from first shot of recon\n # in last iteration, we transform until last shot of recon\n shot_ids = sorted(reconstruction.shots.keys())\n if i == 0:\n start_shot_id = shot_ids[0]\n\n if i == len(anchor_points_dict)-2:\n end_shot_id = shot_ids[-1]\n\n new_dict = {}\n\n start_index = _shot_id_to_int(start_shot_id)\n end_index = _shot_id_to_int(end_shot_id)\n\n # transform pdr shots\n for i in range(start_index, end_index + 1):\n shot_id = _int_to_shot_id(i)\n\n if shot_id in reconstruction.shots:\n X = get_origin_no_numpy_opencv(reconstruction.shots[shot_id].pose.rotation,\n reconstruction.shots[shot_id].pose.translation)\n A_dot_X = [A[0][0] * X[0] + A[0][1] * X[1] + A[0][2] * X[2],\n A[1][0] * X[0] + A[1][1] * X[1] + A[1][2] * X[2],\n A[2][0] * X[0] + A[2][1] * X[1] + A[2][2] * X[2]]\n Xp = [i * s + j for i, j in zip(A_dot_X, b)]\n new_dict[shot_id] = [Xp[0], Xp[1], Xp[2]]\n\n modified_shots_dict.update(new_dict)\n\n return modified_shots_dict","def pan_corr(file):\n\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\flatfield\\\\NHDBflat_1D'\n # # infile = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Jure_naloga_banje_raw_pyt\\\\NHDRGoreMorje_3D'\n #\n # # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\n # in_ref = in_path + in_pan_ref_file\n #\n # inreffil = gdal.Open(in_ref)\n # image_ref = inreffil.ReadAsArray()\n # # size_ref = image_ref.shape\n # # pix_count = size_ref[0]*size_ref[1]\n #\n # image_ref = image_ref[800:930, 1420:1640]\n # size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n #\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # raz_g1 = 1\n # raz_g2 = g1_avg/g2_avg\n # raz_r1 = g1_avg/r1_avg\n # raz_b1 = g1_avg/b1_avg\n #\n # avg = (g1+g2+r1+b1)/pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n raz_g1 = 1\n raz_g2 = 1.0245196396115988\n raz_r1 = 1.0131841989689434\n raz_b1 = 1.0517113199247086\n\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Peking_PAN\\\\'\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\Flat field images_new2020\\\\20201028 Vignetting\\\\flatfield\\\\'\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\n\n # in_path = 'd:\\Projekti\\Satelit\\CO\\Razpis\\_POSNETKI\\Slo_PAN\\_26_30\\\\'\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\"nhd\") and filename.lower().endswith(\"tif\")]\n\n \n\n \n\n # print('image', i)\n in_ref=file\n inreffil = gdal.Open(in_ref)\n image_ref = inreffil.ReadAsArray()\n size_ref = image_ref.shape\n # pix_count = size_ref[0] * size_ref[1]\n # pix_count = np.count_nonzero(image_ref)\n # pix_count = 3664*650\n\n # g1 = 0.\n # g2 = 0.\n # r1 = 0.\n # b1 = 0.\n #\n # for i in range(size_ref[0]):\n # for j in range(size_ref[1]):\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\n #\n # g1_avg = g1 / pix_count * 4\n # g2_avg = g2 / pix_count * 4\n # r1_avg = r1 / pix_count * 4\n # b1_avg = b1 / pix_count * 4\n #\n # avg = (g1 + g2 + r1 + b1) / pix_count\n #\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\n\n # popravek\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\n\n\n for i in range(size_ref[0]):\n for j in range(size_ref[1]):\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\n \n _,_,_,_,P=return_flatfield_set_path(2)\n P_flat=gdal_array.LoadFile(P)\n \n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \n \n # outout\n \n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\n \n out=os.path.abspath(file)+\"/corr/\"+os.path.basename(file)[:-4] + \"_pop_flat_corr.tif\"\n\n \n # out = in_ref[:-4] + \"_pop_flat_corr.tif\"\n\n driver = gdal.GetDriverByName('GTiff')\n\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\n\n outband = outRaster.GetRasterBand(1)\n outband.WriteArray(im_p_pop)\n outband.FlushCache()","def generate_transformations(self):\n if self.perform_aug:\n print(\"\\nAugmentation will be applied to the training images\")\n data_transforms = {\n \"train\": transforms.Compose([\n transforms.Resize((self.input_size, self.input_size)),\n transforms.RandomRotation(degrees=45),\n transforms.ColorJitter(),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\n \"val\": transforms.Compose([\n transforms.Resize((self.input_size, self.input_size)), # 256 used to be\n transforms.CenterCrop(self.input_size),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n }\n else:\n print(\"\\nNo augmentation will be applied to the training images\")\n data_transforms = {\n \"train\": transforms.Compose([\n transforms.Resize((self.input_size, self.input_size)),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\n \"val\": transforms.Compose([\n transforms.Resize((self.input_size, self.input_size)), # 256 used to be\n transforms.CenterCrop(self.input_size),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n }\n\n return data_transforms","def _project(self):\n ghosts_w = self.input_field.topology.ghosts()\n self.input_field.data[0], self.input_field.data[1], \\\n self.input_field.data[2] = \\\n fftw2py.projection_om_3d(self.input_field.data[0],\n self.input_field.data[1],\n self.input_field.data[2], ghosts_w)","def process_align(self):\n\t\tstm_t_dict = self._process_recog()\n\t\ttrans_t_dict = self._process_trans()\n\t\talign_obj = viterbi_align(stm_t_dict, trans_t_dict, self.label, self.pair_file_path)\n\t\tself.trans_t_dict = align_obj.viterbi(0, len(stm_t_dict)-1, 0, len(trans_t_dict)-1)","def align_images(self, translation_only=False):\n # Extract feature point locations and descriptors.\n points_and_descriptors = []\n for file in self.files:\n image = sol4_utils.read_image(file, 1)\n self.h, self.w = image.shape\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\n points_and_descriptors.append(find_features(pyramid))\n\n # Compute homographies between successive pairs of images.\n Hs = []\n for i in range(len(points_and_descriptors) - 1):\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\n\n # Find matching feature points.\n ind1, ind2 = match_features(desc1, desc2, .7)\n points1, points2 = points1[ind1, :], points2[ind2, :]\n\n # Compute homography using RANSAC.\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\n\n # Uncomment for debugging: display inliers and outliers among matching points.\n # In the submitted code this function should be commented out!\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\n\n Hs.append(H12)\n\n # Compute composite homographies from the central coordinate system.\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\n self.homographies = np.stack(accumulated_homographies)\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\n self.homographies = self.homographies[self.frames_for_panoramas]","def reiniciarMatrix(self):\n self.matrixMAPA = []\n self.rellenarMatrix()","def createMatrices(file, word2Idx, maxSentenceLen, extendMapping, labelsMapping, aspectMapping, typeMapping, tenseMapping, eventClassMapping, distanceMapping, sentenceLengthMapping):\n labels = []\n eventMatrix = []\n timeMatrix = []\n\n sentenceLengths = []\n sentenceMatrix = []\n positionMatrix_e = []\n positionMatrix_t = []\n\n aspectMatrix = []\n typeMatrix = []\n tenseMatrix = []\n eventClassMatrix = []\n featuresList = []\n \n \n minDistance = 0\n for distanceKey in distanceMapping.iterkeys():\n if isinstance(distanceKey, (int, long)):\n minDistance = min(minDistance, int(distanceKey))\n \n for line in open(file):\n features = eval(line.strip())\n featuresList.append(features)\n label = features['label']\n event = features[\"Token[0]\"]\n eventPosition = int(features[\"eventPosition\"])\n tokens = features[\"textInBetween\"] if \"textInBetween\" in features else features[\"sentence\"]\n \n aspect = features[\"aspect\"]\n tense = features[\"tense\"]\n eventClass = features[\"eventClass\"]\n \n labels.append(labelsMapping[label.lower()] if label.lower() in labelsMapping else -1)\n eventMatrix.append(getWordIdx(event, word2Idx))\n \n aspectMatrix.append(getMappingIdx(aspect, aspectMapping, extendMapping))\n tenseMatrix.append(getMappingIdx(tense, tenseMapping, extendMapping))\n eventClassMatrix.append(getMappingIdx(eventClass, eventClassMapping, extendMapping))\n \n if 'TimeTokenFirst' in features:\n time = features['TimeTokenFirst']\n timeMatrix.append(getWordIdx(time, word2Idx))\n \n if 'type' in features:\n type = features[\"type\"]\n typeMatrix.append(getMappingIdx(type, typeMapping, extendMapping))\n \n timePosition = int(features[\"timeFirstPosition\"]) if 'timeFirstPosition' in features else 0\n \n\n if len(tokens) in sentenceLengthMapping:\n sentenceLengths.append(sentenceLengthMapping[len(tokens)])\n else:\n sentenceLengths.append(sentenceLengthMapping['GreaterMax'])\n\n \n tokenIds = np.zeros(maxSentenceLen)\n positionValues_e = np.zeros(maxSentenceLen)\n positionValues_t = np.zeros(maxSentenceLen)\n for idx in xrange(0, min(maxSentenceLen, len(tokens))):\n tokenIds[idx] = getWordIdx(tokens[idx], word2Idx)\n \n distance_e = idx - eventPosition\n distance_t = idx - timePosition\n\n if distance_e in distanceMapping:\n positionValues_e[idx] = distanceMapping[distance_e]\n elif distance_e <= minDistance:\n positionValues_e[idx] = distanceMapping['LowerMin']\n else:\n positionValues_e[idx] = distanceMapping['GreaterMax']\n \n if distance_t in distanceMapping:\n positionValues_t[idx] = distanceMapping[distance_t]\n elif distance_t <= minDistance:\n positionValues_t[idx] = distanceMapping['LowerMin']\n else:\n positionValues_t[idx] = distanceMapping['GreaterMax']\n\n sentenceMatrix.append(tokenIds)\n positionMatrix_e.append(positionValues_e)\n positionMatrix_t.append(positionValues_t)\n\n \n labels = np.array(labels, dtype='int32')\n eventMatrix = np.expand_dims(np.array(eventMatrix, dtype='int32'), axis=1)\n timeMatrix = np.expand_dims(np.array(timeMatrix, dtype='int32'), axis=1)\n aspectMatrix = np.expand_dims(np.array(aspectMatrix, dtype='int32'), axis=1)\n typeMatrix = np.expand_dims(np.array(typeMatrix, dtype='int32'), axis=1)\n tenseMatrix = np.expand_dims(np.array(tenseMatrix, dtype='int32'), axis=1)\n eventClassMatrix = np.expand_dims(np.array(eventClassMatrix, dtype='int32'), axis=1)\n sentenceLengths = np.expand_dims(np.array(sentenceLengths, dtype='int32'), axis=1)\n \n \n sentenceMatrix = np.array(sentenceMatrix, dtype='int32')\n positionMatrix_e = np.array(positionMatrix_e, dtype='int32')\n positionMatrix_t = np.array(positionMatrix_t, dtype='int32')\n \n\n\n return {'labels': labels,\n 'event':eventMatrix, \n 'time':timeMatrix, \n 'sentence':sentenceMatrix, \n 'positions_e':positionMatrix_e, \n 'positions_t':positionMatrix_t, \n 'aspect':aspectMatrix, \n 'tense':tenseMatrix, \n 'eventClass':eventClassMatrix, \n 'type': typeMatrix,\n 'sentence_len': sentenceLengths,\n 'features': featuresList}","def transform(self, X):\n\n t0 = time.perf_counter()\n check_is_fitted(self)\n self.check_external_components_modified()#[WARN] in d3m, primitives can \"restore\" private class variables...\n X = self._validate_data(X, accept_sparse=[\"csr\", \"csc\"], reset=False)\n t1 = time.perf_counter()\n\n if X.shape[1] != self.components_af_.shape[1]:\n raise ValueError(\n \"Impossible to perform projection:\"\n \"X at fit stage had a different number of features. \"\n \"(%s != %s)\" % (X.shape[1], self.components_af_.shape[1])\n )\n\n #X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)\n #import pdb; pdb.set_trace()\n X_af = af.interop.from_ndarray(X).as_type(self.components_af_.dtype())\n X_new = af.matmulNT(X_af, self.components_af_)\n X_new = X_new.to_ndarray()\n t2 = time.perf_counter()\n return X_new","def preprocess(self):\n inputMatrix = pd.read_csv(self.input, index_col = 0)\n\n original_filename = self.input.split(\"/\")[-1]\n mapped_filename = \"mapped_\" + self.desiredFormat + \"_\" + original_filename\n output = self.input\n output_filepath = \"/\".join(self.input.split(\"/\")[0:-1])\n #as the DataFormatter always transposes the data before any further processing, we can expect all genes to be in the columns\n genesInColumn = \"true\"\n #only map genes if the current format is not the desired format\n if (self.currentFormat != self.desiredFormat):\n output = output_filepath + \"/\" + mapped_filename\n benchutils.mapDataMatrix(inputMatrix, genesInColumn, self.currentFormat, self.desiredFormat, output, self.labeled)\n\n return output"],"string":"[\n \"def CorrectMotion(self):\\n if self.verbose:\\n print \\\"Correct for motion\\\"\\n for entry in self.entry_map['epi']:\\n info = self.info[entry]\\n\\n if os.path.exists(info['imgfile_m'] + info['suffix']):\\n return\\n# Always use brik for 3dDeconvolve.\\n suffix = '+orig'\\n epifile = '%s%s' % (info['imgfile'], suffix)\\n prefix = info['imgfile_m']\\n base_entry = info['base_entry']\\n if info['base'] == 'start':\\n# Use the first frame specified in template file. Defaults\\n# to zero.\\n base = info['motion_ref_frame']\\n else:\\n# Use the last frame.\\n base = self.info[base_entry]['tdim'] - info['skip']-1\\n base = ('%d' % base).replace(' ','')\\n\\n# Correct for slice-timing.\\n self.SliceTimeCorrect(info, epifile)\\n\\n plane = info['plane']\\n anat_tgt = info['anat_tgt']\\n# anat_entry = self.anat_entry[plane]\\n\\n if info['catmats']:\\n# Include additonal transformation in motion correction such\\n# that final image is in register with the fieldmap, which has\\n# been registered to the structural image that will be used for\\n# spatial normalization.\\n self.MotcorCatenate(info, base, anat_tgt)\\n else:\\n# Assume fieldmap is in register with the structural.\\n self.Motcor(info, base)\\n\\n if info.get('fmapname', None) is None:\\n# No fieldmap correction.\\n if self.fsl_flip:\\n# Flip the way fslview likes it.\\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\\n elif info['suffix'] == '.nii':\\n# Copy motion-corrected images from /tmp to output directory\\n outfile = info['imgfile_final'] + info['suffix']\\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\\n self.CheckExec(cmd, [outfile], force=True)\\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\\n self.CheckExec(cmd, [], force=True)\",\n \"def _calculate_transforms(self):\\n\\n self._logger.info(\\\"Generating transformations.\\\")\\n\\n # Calculate partial transforms - get partial transformation chain;\\n partial_transformation_pairs = \\\\\\n map(lambda idx: self._get_slice_pair(idx),\\n self.options.slice_range)\\n\\n # Flatten the slices pairs\\n partial_transformation_pairs =\\\\\\n list(flatten(partial_transformation_pairs))\\n\\n # If user decided to prealign the images by their centre of gravity\\n # an additional series of transformations has to be carried out.\\n if self.options.enableMomentsAlignment:\\n commands = map(lambda x: self._get_cog_alignment(*x),\\n partial_transformation_pairs)\\n commands = filter(None, commands)\\n\\n self._logger.info(\\\"Executing the centre of gravity transforms.\\\")\\n self.execute(commands)\\n\\n # Calculate affine transformation for each slices pair\\n commands = map(lambda x: self._get_partial_transform(*x),\\n partial_transformation_pairs)\\n commands = filter(None, commands)\\n self._logger.info(\\\"Executing the transformation commands.\\\")\\n self.execute(commands)\",\n \"def AlignFieldmaps(self):\\n for entry in self.entry_map['fmap']:\\n info = self.info[entry]\\n\\n# Register the magnitude image at the shortest TR to the T1-IR\\n# structural image.\\n target = self.info[self.norm_src]['imgfile'] + \\\\\\n self.info[self.norm_src]['suffix']\\n source = info['magfile'] + info['suffix']\\n matfile = info['matfile']\\n fmt = '3dAllineate -prefix NULL -1Dmatrix_save %s -base %s ' + \\\\\\n '-source %s -cost mi -warp shift_rotate'\\n cmd = fmt % (info['matfile'], target, source)\\n self.CheckExec(cmd, [info['matfile']])\\n\\n# Convert to unitary matrix (remove scaling component.)\\n cmd = 'cat_matvec -ONELINE %s -P > %s' % \\\\\\n (info['matfile'], info['matfile_unitary'])\\n self.CheckExec(cmd, [info['matfile_unitary']])\\n\\n# Rotate the magnitude image to the new grid.\\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\\n cmd = fmt % (info['magfile_r']+info['suffix'], \\\\\\n info['matfile_unitary'], info['magfile'] + info['suffix'])\\n self.CheckExec(cmd, [info['magfile_r']+info['suffix']])\\n\\n# Rotate the fieldmap to the new grid.\\n fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'\\n cmd = fmt % (info['imgfile_r']+info['suffix'], \\\\\\n info['matfile_unitary'], info['imgfile'] + info['suffix'])\\n self.CheckExec(cmd, [info['imgfile_r']+info['suffix']])\",\n \"def preprocess(self):\\n\\n mm_magcoord.add_aacgm_coordinates(self)\\n mm_magcoord.add_quasi_dipole_coordinates(self)\\n mm_sc.calculate_ecef_velocity(self)\\n mm_sc.add_ram_pointing_sc_attitude_vectors(self)\\n\\n return\",\n \"def _derive_transformation_matrices(self):\\n\\n if hasattr(self, '_primaries') and hasattr(self, '_whitepoint'):\\n if self._primaries is not None and self._whitepoint is not None:\\n npm = normalised_primary_matrix(self._primaries,\\n self._whitepoint)\\n\\n self._derived_RGB_to_XYZ_matrix = npm\\n self._derived_XYZ_to_RGB_matrix = np.linalg.inv(npm)\",\n \"def _apply_transformations(structure, rotations, translations):\\n # Additional first dimension for 'structure.repeat()'\\n assembly_coord = np.zeros((len(rotations),) + structure.coord.shape)\\n\\n # Apply corresponding transformation for each copy in the assembly\\n for i, (rotation, translation) in enumerate(zip(rotations, translations)):\\n coord = structure.coord\\n # Rotate\\n coord = matrix_rotate(coord, rotation)\\n # Translate\\n coord += translation\\n assembly_coord[i] = coord\\n\\n return repeat(structure, assembly_coord)\",\n \"def assemble_matrices(self):\\n \\n #Pointer reassignment for convenience\\n N = self.ngrids\\n\\n #Begin with a linked-list data structure for the transmissibilities,\\n #and one-dimenstional arrays for the diagonal of B and the flux vector\\n T = lil_matrix((N, N), dtype=np.double)\\n B = np.zeros(N, dtype=np.double)\\n Q = np.zeros(N, dtype=np.double)\\n\\n #Read in boundary condition types and values\\n bcs = self.input_data['boundary conditions']\\n bc_type_1 = bcs['left']['type'].lower()\\n bc_type_2 = bcs['right']['type'].lower()\\n bc_value_1 = bcs['left']['value']\\n bc_value_2 = bcs['right']['value']\\n \\n #Loop over all grid cells\\n for i in range(N):\\n\\n #Apply left BC\\n if i == 0:\\n T[i, i+1] = -self.compute_transmissibility(i, i + 1)\\n\\n if bc_type_1 == 'neumann':\\n T[i, i] = T[i,i] - T[i, i+1]\\n elif bc_type_1 == 'dirichlet':\\n #Computes the transmissibility of the ith block\\n T0 = self.compute_transmissibility(i, i)\\n T[i, i] = T[i,i] - T[i, i+1] + 2.0 * T0\\n Q[i] = 2.0 * T0 * bc_value_1\\n else:\\n pass #TODO: Add error checking here if no bc is specified\\n\\n #Apply right BC\\n elif i == (N - 1):\\n T[i, i-1] = -self.compute_transmissibility(i, i - 1)\\n\\n if bc_type_2 == 'neumann':\\n T[i, i] = T[i,i] - T[i, i-1]\\n elif bc_type_2 == 'dirichlet':\\n #Computes the transmissibility of the ith block\\n T0 = self.compute_transmissibility(i, i)\\n T[i, i] = T[i, i] - T[i, i-1] + 2.0 * T0\\n Q[i] = 2.0 * T0 * bc_value_2\\n else:\\n pass #TODO:Add error checking here if no bc is specified\\n\\n #If there is no boundary condition compute interblock transmissibilties\\n else:\\n T[i, i-1] = -self.compute_transmissibility(i, i-1)\\n T[i, i+1] = -self.compute_transmissibility(i, i+1)\\n T[i, i] = (self.compute_transmissibility(i, i-1) +\\n self.compute_transmissibility(i, i+1))\\n\\n #Compute accumulations\\n B[i] = self.compute_accumulation(i)\\n\\n #If constant-rate wells are present, add them to the flux vector\\n if self.rate_well_grids is not None:\\n Q[self.rate_well_grids] += self.rate_well_values\\n\\n \\n #Return sparse data-structures\\n return (T.tocsr(), \\n csr_matrix((B, (np.arange(N), np.arange(N))), shape=(N,N)), \\n Q)\",\n \"def transform(self, src):\\n T, feature_dim = src.shape[0], self.Y_static_dim*3\\n\\n if feature_dim == self.Y_static_dim:\\n return super(GMM_M, self).transform(src)\\n\\n # A suboptimum mixture sequence (eq.37)\\n optimum_mix = self.px.predict(src)\\n\\n # Compute E eq.(40)\\n E = np.empty((T, feature_dim))\\n for t in range(T):\\n m = optimum_mix[t] # estimated mixture index at time t\\n xx = np.linalg.solve(self.covarXX[m], src[t] - self.src_means[m])\\n #print(xx.shape,self.tgt_means[m].shape,self.covarYX[m].shape)\\n # Eq. (22)\\n E[t] = self.tgt_means[m] +np.dot(self.covarYX[m], xx)\\n\\n # Compute D eq.(23)\\n # Approximated variances with diagonals so that we can do MLPG\\n # efficiently in dimention-wise manner\\n #print(E.shape)\\n D = np.empty((T, feature_dim))\\n #print(D.shape)\\n for t in range(T):\\n m = optimum_mix[t]\\n # Eq. (23), with approximating covariances as diagonals\\n #D[t] = np.diag(self.covarYY[m]) - np.diag(self.covarYX[m]) / \\\\\\n # np.diag(self.covarXX[m]) * np.diag(self.covarXY[m])\\n\\n # Exact Inference\\n dd = self.covarYY[m] - np.linalg.multi_dot([self.covarYX[m], np.linalg.pinv(self.covarXX[m]), self.covarXY[m]])\\n #print(dd.shape)\\n D[t] = np.diag(dd)\\n\\n # Once we have mean and variance over frames, then we can do MLPG\\n return E, D, self.windows#mlpg(E, D, self.windows)\",\n \"def __set_transform_matrices(self):\\n self.tf_matrices_list = []\\n\\n transform_matrix = eye(4) # creates a unit matrix via passing argument.\\n for i in range(self.joint_count):\\n transform_matrix = transform_matrix * self.__create_tf_matrix(self.alpha[i], self.a[i], self.d[i], self.q[i]).subs(self.dh_params)\\n self.tf_matrices_list.append(transform_matrix)\",\n \"def rebuild_the_laplacians():\\n local_matrix = InteractomeInterface()\\n local_matrix.full_rebuild()\\n\\n annot_matrix = AnnotomeInterface()\\n annot_matrix.full_rebuild()\",\n \"def recalculate_transform(self):\\n bias_mat = wireframe.translationMatrix(*self.bias)\\n rot_mat = wireframe.rotateMatrix(*self.view_angle)\\n scale_mat = wireframe.scaleMatrix(*self.scale)\\n\\n self.tf_wireframe.nodes = (self.wireframe.nodes - self._center_half) @ \\\\\\n rot_mat @ scale_mat @ bias_mat\",\n \"def mca_transformer(transform_data):\\n M, dims, index, v0v1 = transform_data\\n def transform(dfp):\\n # dims, index, v0v1\\n P = np.zeros((len(dfp), dims), dtype=float)\\n print(\\\"transforming\\\")\\n for i, (_, row) in (enumerate(dfp.iterrows())):\\n ivec = np.zeros(M)\\n for col, val in zip(row.index, row):\\n if (col, val) in index:\\n ivec[index[col, val]] = 1\\n proj = ivec.dot(v0v1)\\n assert(all(proj.imag == 0))\\n P[i,:] = proj.real\\n return P\\n return transform\",\n \"def test_transform_update():\\n pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_answer_0)\\n ncs_obj = ncs.input(hierarchy=pdb_inp.construct_hierarchy())\\n pdb_inp = iotbx.pdb.input(lines=pdb_answer_0,source_info=None)\\n nrgl = ncs_obj.get_ncs_restraints_group_list()\\n asu_site_cart = pdb_inp.atoms().extract_xyz()\\n # reference matrices\\n r1 = nrgl[0].copies[0].r\\n t1 = nrgl[0].copies[0].t\\n r2 = nrgl[0].copies[1].r\\n t2 = nrgl[0].copies[1].t\\n # modify matrices in the ncs group list\\n nrgl[0].copies[0].r = r1 + r2\\n nrgl[0].copies[0].t = t1 + t2\\n nrgl[0].copies[1].r = r1 + r2\\n nrgl[0].copies[1].t = t1 + t2\\n nrgl.recalculate_ncs_transforms(asu_site_cart)\\n # Get the updated values\\n r1_n = nrgl[0].copies[0].r\\n t1_n = nrgl[0].copies[0].t\\n r2_n = nrgl[0].copies[1].r\\n t2_n = nrgl[0].copies[1].t\\n #\\n assert approx_equal(r1, r1_n, eps=0.001)\\n assert approx_equal(t1, t1_n, eps=0.1)\\n assert approx_equal(r2, r2_n, eps=0.001)\\n assert approx_equal(t2, t2_n, eps=0.1)\",\n \"def update_variables(self):\\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\\n # aquire transformation matrices and tips for segment 1 and 2\\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\\n # Frenet frames\\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\\n # Bishop frames\\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]\",\n \"def chain_corrections():\\n \\n #read the files\\n sample_4m=read_sample(map_files('sample_4m'))\\n empty_cell_4m=read_sample(map_files('empty_cell_4m'))\\n empty_4m=read_sample(map_files('empty_4m'))\\n transmission_sample_cell_4m=read_sample(map_files('trans_sample_4m'))\\n transmission_empty_cell_4m=read_sample(map_files('trans_empty_cell_4m'))\\n blocked_beam_4m=read_sample(map_files('blocked_4m'))\\n sensitivity=read_div(map_files('div'))\\n #mask=read_sample(map_files('mask'))\\n \\n #normalize the monitors\\n \\n sample_4m_norm=monitor_normalize(sample_4m)\\n empty_cell_4m_norm=monitor_normalize(empty_cell_4m)\\n transmission_sample_cell_4m_norm=monitor_normalize(transmission_sample_cell_4m)\\n transmission_empty_cell_4m_norm=monitor_normalize(transmission_empty_cell_4m)\\n empty_4m_norm=monitor_normalize(empty_4m)\\n blocked_beam_4m_norm=monitor_normalize(blocked_beam_4m)\\n \\n #calculate q\\n sample_4m_norm_q=convert_q(sample_4m_norm)\\n empty_cell_4m_norm_q=convert_q(empty_cell_4m)\\n blocked_beam_4m_norm_q=convert_q(blocked_beam_4m_norm)\\n transmission_sample_cell_4m_norm_q=convert_q(transmission_sample_cell_4m_norm)\\n transmission_empty_cell_4m_norm_q=convert_q(transmission_empty_cell_4m_norm)\\n empty_4m_norm_q=convert_q(empty_4m_norm)\\n \\n \\n print 'converted'\\n #convert flatness\\n sample_4m_solid=correct_solid_angle(sample_4m_norm_q)\\n empty_cell_4m_solid=correct_solid_angle(empty_cell_4m_norm_q)\\n blocked_beam_4m_solid=correct_solid_angle(blocked_beam_4m_norm_q)\\n transmission_sample_cell_4m_solid=correct_solid_angle(transmission_sample_cell_4m_norm_q)\\n transmission_empty_cell_4m_solid=correct_solid_angle(transmission_empty_cell_4m_norm_q)\\n empty_4m_solid=correct_solid_angle(empty_4m_norm_q)\\n \\n \\n #calculate transmission\\n coord_left=(60,60)\\n coord_right=(70,70)\\n transmission_sample_cell_4m_rat=generate_transmission(transmission_sample_cell_4m_solid,empty_4m_solid,\\n coord_left,coord_right)\\n transmission_empty_cell_4m_rat=generate_transmission(transmission_empty_cell_4m_solid,empty_4m_solid,\\n coord_left,coord_right)\\n print 'Sample transmission= {} (IGOR Value = 0.724)'.format(transmission_sample_cell_4m_rat)\\n print 'Empty Cell transmission= {} (IGOR Value = 0.929)'.format(transmission_empty_cell_4m_rat)\\n print 'hi'\\n \\n #Initial Correction -- Not with the sub/mult tools,\\n #SAM = sample_4m_solid.data\\n #print SAM.x\\n #EMP = empty_4m_solid.data\\n #print \\\"EMP: \\\"\\n #print EMP.x\\n #BGD = blocked_beam_4m_solid.data\\n #print \\\"BGD\\\"\\n #print BGD.x\\n #Tsam = transmission_sample_cell_4m_rat\\n #Temp = transmission_empty_cell_4m_rat\\n #COR1 = SAM.__sub__(BGD)\\n #COR2 = (EMP.__sub__(BGD)).__mul__(Tsam/Temp)\\n #COR = COR1.__sub__(COR2)\\n #print \\\"after initial correction: \\\"\\n #print COR.x\\n \\n SAM = sample_4m_solid\\n print SAM.data.x\\n EMP = empty_4m_solid\\n print \\\"EMP: \\\"\\n print EMP.data.x\\n BGD = blocked_beam_4m_solid\\n print \\\"BGD:\\\"\\n print BGD.data.x\\n Tsam = transmission_sample_cell_4m_rat\\n Temp = transmission_empty_cell_4m_rat\\n print \\\"COR1:\\\"\\n COR1 = SAM.__sub1__(BGD)\\n print COR1.data.x #check=works\\n #-----Problems Here-------\\n print \\\"COR2:\\\"\\n COR2 = (EMP.__sub1__(BGD)) #check=works\\n print COR2.data.x\\n print \\\"COR3:\\\"\\n #AJJ - __mul__ not working because Tsam and Temp are Measurement instances and not simply floats. See above.\\n COR3 = COR2.__mul__(Tsam/Temp) #mul not working\\n print COR3.data.x\\n #COR = COR1.__sub1__(COR2)\\n #print \\\"after initial correction: \\\"\\n #print COR.x\\n #COR2 = (EMP.__sub__(BGD)).__mul__(Tsam/Temp)\\n #COR = COR1.__sub__(COR2)\\n #print \\\"after initial correction: \\\"\\n #print COR.data.x\",\n \"def _calculate_composite_transforms(self):\\n\\n self._calculate_similarity()\\n # Finally, calculate composite transforms\\n commands = []\\n for moving_slice_index in self.options.slice_range:\\n commands.append(self._calculate_composite(moving_slice_index))\\n self.execute(commands)\\n\\n self._logger.info(\\\"Done with calculating the transformations.\\\")\",\n \"def motionCorrection(baseName, outDir, rawDataFile, granularity ='plane',\\n maxDisplacement=[40, 40],\\n nCpu=(multiprocessing.cpu_count() - 1), \\n exportFrames=True):\\n try:\\n # instead of os.makedirs use os.mkdir\\n # the former creates also intermediate dirs\\n os.mkdir(outDir + '/' + baseName)\\n except OSError:\\n print('Either directory exists or \\\\\\n the intermedite directories do not exist!')\\n raise\\n print(\\\"--Motion correction started with %s...\\\" % baseName)\\n # Checking if there are two cycles of images. Important to know otherwise \\n # you get merged motion corrected images.\\n allImages = (glob.glob(rawDataFile))\\n tseries_names = [os.path.basename(imageName).split('_')[0] for imageName in allImages]\\n uniqueTSeries = Counter(tseries_names).keys()\\n tseries_lengths = Counter(tseries_names).values()\\n \\n if len(uniqueTSeries) > 1:\\n print('---More than 1 T-Series found. Aligning them together...')\\n\\n match = [int(re.search(r'Cycle(.*?)_',imageName).group(1)) for imageName in allImages]\\n uniqueCycles = numpy.unique(numpy.asarray(match))\\n \\n if len(uniqueCycles) > 1:\\n warnstr='More than 1 image cycle detected. Aborting alignment.'\\n warnings.warn(warnstr)\\n return \\n sequence = [sima.Sequence.create('TIFFs', [[rawDataFile]])]\\n print(\\\"Creating sima dataset of non-aligned images\\\")\\n nonAlignedDatasetDir = outDir + '/' + baseName + '/' + 'TIFFs.sima'\\n sima.ImagingDataset(sequence, nonAlignedDatasetDir)\\n\\n print(\\\"Running motion correction.\\\")\\n \\n mc_approach = sima.motion.HiddenMarkov2D(granularity=granularity,\\n max_displacement=maxDisplacement,\\n verbose=True,\\n n_processes=nCpu)\\n print(\\\"Creating sima dataset of aligned images\\\")\\n motCorrDir = outDir + '/' + baseName + '/' + 'motCorr.sima'\\n dataset = mc_approach.correct(sequence, motCorrDir)\\n\\n if exportFrames:\\n print(\\\"Exporting motion-corrected movies.\\\")\\n \\n for iTSeries, curr_T_series in enumerate(uniqueTSeries):\\n \\n start_frame = tseries_names.index(curr_T_series)\\n end_frame = start_frame+tseries_lengths[iTSeries]\\n dataset[0,start_frame:end_frame].export_frames([[[os.path.join(motCorrDir,\\n '{t}_motCorr.tif'.format(t = curr_T_series))]]],\\n fill_gaps=True)\\n \\n\\n print(\\\"--Motion correction done with %s...\\\" % baseName)\\n\\n return uniqueTSeries\",\n \"def transform_calcualtion(height, width, t_mat, h_offset, w_offset, nh_flag, nw_flag):\\n # default coordination/location of transformed matrix according to source data(data map)\\n coord_map = default_coord(height, width, h_offset, w_offset)\\n\\n for i in range(height):\\n for j in range(width):\\n # base calculations\\n result = [(t_mat[0][0])*(coord_map[i, j, 0])+int((t_mat[0][1])*(coord_map[i, j, 1])),\\n (t_mat[1][0])*(coord_map[i, j, 0])+(t_mat[1][1])*(coord_map[i, j, 1])]\\n # since all coordinations must not be negative\\n # if happened also apply a translation by offset\\n coord_map[i, j, :] = [(result[0], result[0]+h_offset)[nh_flag],\\n (result[1], result[1]+w_offset)[nw_flag]]\\n return coord_map\",\n \"def imageTransform(self):\\n ims = self.imageShape\\n acs = self.activeShape\\n dx = self.colVector\\n dy = self.rowVector\\n\\n p0 = self.activeOrigin\\n p1 = p0 + acs[2] * dx\\n p2 = p0 + acs[1] * dy\\n\\n # print p0, p1, p2\\n # print acs, dx, dy\\n\\n localPts = list(map(pg.Vector, [[0,0], [ims[2],0], [0,ims[1]], [0,0,1]])) # w and h of data of image in pixels.\\n globalPts = list(map(pg.Vector, [p0, p1, p2, [0,0,1]]))\\n m = pg.solve3DTransform(localPts, globalPts)\\n m[:,2] = m[:,3]\\n m[2] = m[3]\\n m[2,2] = 1\\n tr = Qt.QTransform(*m[:3,:3].transpose().reshape(9))\\n return tr\",\n \"def gridalign(self):\\n self.position.x = int(round(self.position.x))\\n self.position.y = int(round(self.position.y))\\n self.position.z = int(round(self.position.z))\\n\\n if self.fan:\\n self.fan = (int(round(self.fan[0])),int(round(self.fan[1])),int(round(self.fan[2])))\\n\\n bestDist = 2*9\\n bestMatrix = makeMatrix(0,0,0)\\n\\n for compass in [0, 90, 180, 270]:\\n for pitch in [0, 90, 180, 270]:\\n for roll in [0, 90, 180, 270]:\\n m = makeMatrix(compass,pitch,roll)\\n dist = matrixDistanceSquared(self.matrix, m)\\n if dist < bestDist:\\n bestMatrix = m\\n bestDist = dist\\n\\n self.matrix = bestMatrix\\n self.positionOut()\\n self.directionOut()\",\n \"def _preprocess(self):\\n if self.gt_matrix_PCs is None:\\n start = time.time()\\n gt_matrix = self._process_vcf().get('gt_matrix')\\n print('gt_matrix took {} secs'.format(time.time() - start))\\n \\n # Normalize gt_matrix by site\\n gt_matrix_norm = gt_matrix - np.mean(gt_matrix, axis=1)[:, np.newaxis]\\n \\n # PCA\\n start = time.time()\\n u, s, vh = np.linalg.svd(gt_matrix_norm.T, full_matrices=False)\\n print('SVD took {} secs'.format(time.time() - start))\\n self.gt_matrix_PCs = -u @ np.diag(s)\\n \\n # Get relevant objects from result of ripser\\n if self.ripser_result is None:\\n start = time.time()\\n print(\\\"Getting ripser object\\\")\\n self.ripser_result = ripser(self.gt_matrix_PCs, coeff=2, maxdim=1, do_cocycles=True)\\n print('Ripser took {} secs'.format(time.time() - start))\",\n \"def get_all_coding_matrices(self):\\n class tran_matrix():\\n def __init__(self):\\n self.S = None\\n self.T = None\\n self.H1 = None\\n self.H2 = None\\n self.G1 = None\\n self.G2 = None\\n self.H = None\\n self.H2G2 = None\\n\\n def __init__(self, S, T, H1, H2, G1, G2, H, H2G2):\\n self.S = S\\n self.T = T\\n self.H1 = H1\\n self.H2 = H2\\n self.G1 = G1\\n self.G2 = G2\\n self.H = H\\n self.H2G2 = H2G2\\n\\n s = self.sources\\n s_edges = [self.out_edges(x) for x in s]\\n S = [list(set(self.get_edges_indices(src))) for src in s_edges]\\n\\n matrices = []\\n for dsts in self.dst_evolution_rec[::-1]:\\n T = [sorted(x) for x in dsts]\\n H2_raw = self.coding_matrix[T[0]].ix[S[1]].values\\n G2_raw = self.coding_matrix[T[1]].ix[S[1]].values\\n H1 = numpy_to_generic(self.coding_matrix[T[0]].ix[S[0]].values)\\n H2 = numpy_to_generic(H2_raw)\\n G1 = numpy_to_generic(self.coding_matrix[T[1]].ix[S[0]].values)\\n G2 = numpy_to_generic(G2_raw)\\n H = numpy_to_generic(self.coding_matrix[T[0]].values)\\n H2G2 = numpy_to_generic(np.hstack((H2_raw, G2_raw)))\\n temp = tran_matrix(S, T, H1, H2, G1, G2, H, H2G2)\\n matrices.append(temp)\\n\\n return matrices\",\n \"def assembleMatrices(self):\\n # All nodes informations\\n self.aircraftNodesPoints = []\\n self.aircraftMassPoints = []\\n self.aircraftMassDistances = []\\n self.aircraftSegmentsLengths = []\\n self.aircraftNodesNames = []\\n self.aircraftInitNodesAreas = []\\n self.aircraftNodesA = []\\n self.aircraftNodesIy = []\\n self.aircraftNodesIz = []\\n self.aircraftNodesJ = []\\n # More general information\\n self.aircraftConnectedNodes = []\\n self.aircraftNonRotatingNodes = []\\n\\n self.computeProportionFuselage()\\n self.computeProportionWings()\\n\\n # adds fulseage infos to matrices if there is one\\n if self.nFuselage > 0:\\n self.aircraftNodesPoints.append(self.fs_m_points[0])\\n # Assumption is made that for a fuselage section, its cog is\\n # aligned with the section center.\\n self.aircraftMassPoints.append(self.fs_m_points[0])\\n self.aircraftMassDistances.append(np.zeros((len(self.fs_m_points[0]),3)))\\n self.aircraftSegmentsLengths.append(self.fs_m_distanceBetweenPoints[0])\\n self.aircraftNodesNames.append(self.fs_m_pointsName[0])\\n self.aircraftInitNodesAreas.append(self.fs_m_pointsInitArea[0])\\n self.aircraftNodesA.append(self.fs_m_pointsA[0])\\n self.aircraftNodesIy.append(self.fs_m_pointsIy[0])\\n self.aircraftNodesIz.append(self.fs_m_pointsIz[0])\\n self.aircraftNodesJ.append(self.fs_m_pointsJ[0])\\n logger.debug('\\\\n')\\n for i in range(self.nWings):\\n self.aircraftNodesPoints.append(self.ws_me_points[i])\\n self.aircraftMassPoints.append(self.ws_ma_points[i])\\n self.aircraftMassDistances.append(self.ws_ma_distance[i])\\n self.aircraftSegmentsLengths.append(self.ws_me_distances[i])\\n # logger.debug(i)\\n # logger.debug(self.ws_me_distances)\\n # logger.debug(self.aircraftSegmentsLengths)\\n self.aircraftNodesNames.append(self.ws_me_pointsName[i])\\n self.aircraftInitNodesAreas.append(self.ws_me_pointsInitArea[i])\\n self.aircraftNodesA.append(self.ws_me_pointsA[i])\\n self.aircraftNodesIy.append(self.ws_me_pointsIy[i])\\n self.aircraftNodesIz.append(self.ws_me_pointsIz[i])\\n self.aircraftNodesJ.append(self.ws_me_pointsJ[i])\\n self.computeMass()\",\n \"def matrix_map(self, bkg_reduction=True, data_correction=True):\\r\\n\\r\\n if bkg_reduction is True:\\r\\n if data_correction is True:\\r\\n data = self.df4\\r\\n \\r\\n else:\\r\\n data = self.df2\\r\\n\\r\\n else:\\r\\n if data_correction is True:\\r\\n data = self.df3\\r\\n \\r\\n else:\\r\\n data = self.df1\\r\\n\\r\\n return data\",\n \"def applyMat(my_map, linsys_setup):\\n \\n datamaps, ninvs, beams, freqs, power_2d, precond_2d, clumaps, g_nu, \\\\\\n map_prop = linsys_setup\\n \\n \\n\\n nx, ny, pixScaleX, pixScaleY = map_prop\\n nFreq = len(g_nu); nCluster = len(clumaps[0])\\n\\n #Always apply beam * precond\\n beam_prec=[]\\n\\n for f in range(nFreq):\\n beam_prec+=[beams[f][:,:ny/2+1]*precond_2d[:,:ny/2+1]]\\n precond_2d=precond_2d[:,:ny/2+1]\\n power_2d=power_2d[:,:ny/2+1]\\n \\n ksz = False\\n if len(clumaps) == 2: ksz = True\\n \\n # Routines to perform block matrix multiplication defined in Eriksen Eq. 19\\n \\n def apply_cmb_cmb(d0):\\n \\\"\\\"\\\"\\n Apply (S^-1 + A N^-1 A) x\\n \\\"\\\"\\\"\\n d1 = d0.copy()\\n d1 = numpy.reshape(d1,(nx,ny))\\n a_l = fft.rfft(d1,axes=[-2,-1])\\n \\n c_l = 0\\n for f in range(nFreq):\\n\\n b_l = a_l * beam_prec[f]\\n d2 = fft.irfft(b_l,axes=[-2,-1],normalize=True)\\n d2 *= ninvs[f]\\n b_l = fft.rfft(d2,axes=[-2,-1])\\n c_l += b_l * beam_prec[f]\\n \\n d2 = fft.irfft(c_l,axes=[-2,-1],normalize=True)\\n d1 = fft.irfft(precond_2d**2 * a_l/power_2d,axes=[-2,-1],normalize=True)\\n \\n d2 += d1\\n \\n return d2.reshape((nx*ny,))\\n \\n \\\"\\\"\\\"\\n def apply_tsz_tsz(d0): # DONE\\n \\\\\\\"\\\"\\\"\\n Apply (F^T A^T N^-1 A F) x\\n \\\\\\\"\\\"\\\"\\n d1 = numpy.zeros(nCluster)\\n mat = numpy.zeros((nCluster, nCluster))\\n # TODO: This could probably be more efficient (e.g. using np.outer)\\n for freq in range(nFreq):\\n for ic in range(nCluster):\\n for jc in range(0, ic+1):\\n mat[ic,jc] = numpy.sum( ninvs[freq] * g_nu[freq]**2. \\\\\\n * clumaps[0][ic][freq] * clumaps[0][jc][freq] )\\n if ic != jc: mat[jc,ic] = mat[ic,jc]\\n d1 += numpy.dot(mat, d0)\\n return d1\\n \\n def apply_ksz_ksz(d0): # DONE\\n \\\\\\\"\\\"\\\"\\n Apply (K^T A^T N^-1 A K) x\\n \\\\\\\"\\\"\\\"\\n # FIXME: Missing factor of ivcov\\n d1 = numpy.zeros(nCluster)\\n mat = numpy.zeros((nCluster, nCluster))\\n # TODO: This could probably be more efficient (e.g. using np.outer)\\n for freq in range(nFreq):\\n for ic in range(nCluster):\\n for jc in range(0, ic+1):\\n mat[ic,jc] = numpy.sum( ninvs[freq] \\\\\\n * clumaps[1][ic][freq] * clumaps[1][jc][freq] )\\n if ic != jc: mat[jc,ic] = mat[ic,jc]\\n d1 += numpy.dot(mat, d0)\\n d1 += numpy.dot(ivcov, d0) # Add prior term\\n return d1\\n \\\"\\\"\\\"\\n \\n def apply_cmb_foreground_block(dc, dm, dt, dk=None):\\n \\\"\\\"\\\"\\n Apply the CMB x (Monopole + TSZ + KSZ) terms in one block:\\n (A^T N^-1 A T) x_mono\\n (A^T N^-1 A F) x_tsz\\n (A^T N^-1 A K) x_ksz\\n \\n (T^T A^T N^-1 A) x_cmb\\n (F^T A^T N^-1 A) x_cmb\\n (K^T A^T N^-1 A) x_cmb\\n \\\"\\\"\\\"\\n ksz = False\\n if dk is not None: ksz = True\\n \\n # (A^T N^-1 A T) x_mono; (A^T N^-1 A F) x_tsz; (A^T N^-1 A K) x_ksz\\n b_lt = 0; b_lk = 0; b_lm = 0\\n for f in range(nFreq):\\n mct = 0; mck = 0\\n for ic in range(nCluster):\\n mct += dt[ic] * ninvs[f] * clumaps[0][ic][f] * g_nu[f]\\n if ksz: mck += dk[ic] * ninvs[f] * clumaps[1][ic][f]\\n \\n b_lm += fft.rfft(dm * ninvs[f],axes=[-2,-1]) * beam_prec[f]\\n b_lt += fft.rfft(mct,axes=[-2,-1]) * beam_prec[f]\\n if ksz: b_lk += fft.rfft(mck,axes=[-2,-1]) * beam_prec[f]\\n\\n mcm = fft.irfft(b_lm,axes=[-2,-1],normalize=True).reshape((nx*ny,))\\n mct = fft.irfft(b_lt,axes=[-2,-1],normalize=True).reshape((nx*ny,))\\n if ksz: mck = fft.irfft(b_lk,axes=[-2,-1],normalize=True).reshape((nx*ny,))\\n \\n # (T^T A^T N^-1 A) x_cmb; (F^T A^T N^-1 A) x_cmb; (K^T A^T N^-1 A) x_cmb\\n mc = dc.copy().reshape((nx,ny))\\n a_l = fft.rfft(mc,axes=[-2,-1])\\n mtc = numpy.zeros(nCluster)\\n mkc = numpy.zeros(nCluster)\\n mmc = 0\\n for f in range(nFreq):\\n b_l = a_l * beam_prec[f]\\n mc = fft.irfft(b_l,axes=[-2,-1],normalize=True)\\n mmc += numpy.sum(mc * ninvs[f])\\n for ic in range(nCluster):\\n mtc[ic] += numpy.sum(mc * ninvs[f] * clumaps[0][ic][f] * g_nu[f])\\n if ksz: mkc[ic] += numpy.sum(mc * ninvs[f] * clumaps[1][ic][f])\\n \\n if ksz: return mct, mcm, mck, mtc, mmc, mkc\\n return mct, mcm, mtc, mmc\\n \\n \\n def apply_foreground_block(m0, t0, k0=None):\\n \\\"\\\"\\\"\\n Apply the TSZ + KSZ + Monopole terms in one block:\\n [ (T^T A^T N^-1 A F) (T^T A^T N^-1 A K) (T^T A^T N^-1 A T) ] (x_mono)\\n [ (F^T A^T N^-1 A F) (F^T A^T N^-1 A K) (F^T A^T N^-1 A T) ] (x_tsz)\\n [ (K^T A^T N^-1 A F) (K^T A^T N^-1 A K) (K^T A^T N^-1 A T) ] (x_ksz)\\n \\\"\\\"\\\"\\n ksz = True if k0 is not None else False\\n \\n dtt, dkk, dtk, dkt = [numpy.zeros(nCluster) for i in range(4)]\\n mtt, mkk, mtk, mkt = [numpy.zeros((nCluster, nCluster)) for i in range(4)]\\n dmm, dmk, dmt = [0 for i in range(3)]\\n dkm, dtm = [numpy.zeros(nCluster) for i in range(2)]\\n \\n # TODO: This could probably be more efficient (e.g. using np.outer)\\n for f in range(nFreq):\\n dmm += numpy.sum(ninvs[f]) * m0\\n \\n # Loop through clusters\\n for ic in range(nCluster):\\n dmt += numpy.sum( ninvs[f] * g_nu[f] * clumaps[0][ic][f] * t0[ic] )\\n dtm[ic] += numpy.sum( ninvs[f] * g_nu[f] * clumaps[0][ic][f] * m0 )\\n if ksz:\\n dmk += numpy.sum( ninvs[f] * clumaps[1][ic][f] * k0[ic] )\\n dkm[ic] += numpy.sum( ninvs[f] * clumaps[1][ic][f] * m0 )\\n \\n for jc in range(0, ic+1):\\n mtt[ic,jc] = numpy.sum( ninvs[f] * g_nu[f]**2. \\\\\\n * clumaps[0][ic][f] * clumaps[0][jc][f] )\\n if ksz:\\n mkk[ic,jc] = numpy.sum( ninvs[f] \\\\\\n * clumaps[1][ic][f] * clumaps[1][jc][f] )\\n mtk[ic,jc] = numpy.sum( ninvs[f] * g_nu[f] \\\\\\n * clumaps[0][ic][f] * clumaps[1][jc][f] )\\n mkt[ic,jc] = numpy.sum( ninvs[f] * g_nu[f] \\\\\\n * clumaps[1][ic][f] * clumaps[0][jc][f] )\\n # Mirror indices\\n mtt[jc,ic] = mtt[ic,jc]\\n if ksz:\\n mkk[jc,ic] = mkk[ic,jc]\\n mtk[jc,ic] = mtk[ic,jc]\\n mkt[jc,ic] = mkt[ic,jc]\\n \\n # Add total contribs. for this band\\n dtt += numpy.dot(mtt, t0)\\n if ksz:\\n dkk += numpy.dot(mkk, k0)\\n dtk += numpy.dot(mtk, k0)\\n dkt += numpy.dot(mkt, t0)\\n \\n if ksz: return dtt, dkk, dmm, dtk, dkt, dmk, dkm, dtm, dmt\\n return dtt, dmm, dtm, dmt\\n \\n # Apply block matrix multiplications and return\\n # FIXME: What if KSZ not used?\\n x0 = my_map[:nx*ny]\\n x1 = my_map[nx*ny:nx*ny+1]\\n x2 = my_map[nx*ny+1:nx*ny+nCluster+1]\\n if ksz: x3 = my_map[nx*ny+nCluster+1:nx*ny+2*nCluster+1]\\n \\n # Multiply input vector in blocks\\n #t=time.time()\\n dcc = apply_cmb_cmb(x0)\\n #print 'CMB', time.time()-t\\n if ksz:\\n dct, dcm, dck, dtc, dmc, dkc = apply_cmb_foreground_block(x0, x1, x2, x3)\\n dtt, dkk, dmm, dtk, dkt, dmk, dkm, dtm, dmt = apply_foreground_block(x1, x2, x3)\\n x_new_0 = dcc + dct + dck + dcm\\n x_new_1= dmc + dmt + dmk + dmm\\n x_new_2 = dtc + dtt + dtk + dtm\\n x_new_3 = dkc + dkt + dkk + dkm\\n x_new = numpy.concatenate((x_new_0, x_new_1, x_new_2, x_new_3))\\n else:\\n #t=time.time()\\n dct, dcm, dtc, dmc = apply_cmb_foreground_block(x0, x1, x2)\\n #print 'CMB-F', time.time()-t\\n \\n #t=time.time()\\n dtt, dmm, dtm, dmt = apply_foreground_block(x1, x2)\\n #print 'F', time.time()-t\\n \\n x_new_0 = dcc + dct + dcm\\n x_new_1 = dmc + dmt + dmm\\n x_new_2 = dtc + dtt + dtm\\n x_new = numpy.concatenate((x_new_0, x_new_1, x_new_2))\\n\\n\\n#sys.exit()\\n return x_new\",\n \"def _compose_transforms(basis_transforms, source_basis, source_dag):\\n example_gates = _get_example_gates(source_dag)\\n mapped_instrs = {}\\n\\n for gate_name, gate_num_qubits in source_basis:\\n # Need to grab a gate instance to find num_qubits and num_params.\\n # Can be removed following https://github.com/Qiskit/qiskit-terra/pull/3947 .\\n example_gate = example_gates[gate_name, gate_num_qubits]\\n num_params = len(example_gate.params)\\n\\n placeholder_params = ParameterVector(gate_name, num_params)\\n placeholder_gate = Gate(gate_name, gate_num_qubits, list(placeholder_params))\\n placeholder_gate.params = list(placeholder_params)\\n\\n dag = DAGCircuit()\\n qr = QuantumRegister(gate_num_qubits)\\n dag.add_qreg(qr)\\n dag.apply_operation_back(placeholder_gate, qr[:], [])\\n mapped_instrs[gate_name, gate_num_qubits] = placeholder_params, dag\\n\\n for gate_name, gate_num_qubits, equiv_params, equiv in basis_transforms:\\n logger.debug(\\n \\\"Composing transform step: %s/%s %s =>\\\\n%s\\\",\\n gate_name,\\n gate_num_qubits,\\n equiv_params,\\n equiv,\\n )\\n\\n for mapped_instr_name, (dag_params, dag) in mapped_instrs.items():\\n doomed_nodes = [\\n node\\n for node in dag.op_nodes()\\n if (node.op.name, node.op.num_qubits) == (gate_name, gate_num_qubits)\\n ]\\n\\n if doomed_nodes and logger.isEnabledFor(logging.DEBUG):\\n\\n logger.debug(\\n \\\"Updating transform for mapped instr %s %s from \\\\n%s\\\",\\n mapped_instr_name,\\n dag_params,\\n dag_to_circuit(dag, copy_operations=False),\\n )\\n\\n for node in doomed_nodes:\\n\\n replacement = equiv.assign_parameters(\\n dict(zip_longest(equiv_params, node.op.params))\\n )\\n\\n replacement_dag = circuit_to_dag(replacement)\\n\\n dag.substitute_node_with_dag(node, replacement_dag)\\n\\n if doomed_nodes and logger.isEnabledFor(logging.DEBUG):\\n\\n logger.debug(\\n \\\"Updated transform for mapped instr %s %s to\\\\n%s\\\",\\n mapped_instr_name,\\n dag_params,\\n dag_to_circuit(dag, copy_operations=False),\\n )\\n\\n return mapped_instrs\",\n \"def do_transform(self):\\n # TODO: after unit tests are added switch to astropy fftconvolve here.\\n from scipy.signal import fftconvolve\\n total_background = self.model + self.background + self.approx\\n excess = self.image - total_background\\n for key, kern in self.kernbase.items():\\n self.transform[key] = fftconvolve(excess, kern, mode='same')\\n self.error[key] = np.sqrt(fftconvolve(total_background, kern ** 2, mode='same'))\\n\\n self.approx = fftconvolve(self.image - self.model - self.bkg,\\n self.kern_approx, mode='same')\\n self.approx_bkg = fftconvolve(self.bkg, self.kern_approx, mode='same')\",\n \"def gonio_axis_align():\\n \\n # Invert camera image, so dark pin on light image becomes a peak\\n cam_7.proc1.scale.put(-1)\\n cam_8.proc1.scale.put(-1)\\n \\n # High threshold, so AD centroid doesn't interpret background\\n cam_8ThresholdOld = cam_8.stats4.centroid_threshold.get()\\n cam_8.stats4.centroid_threshold.put(150)\\n cam_7ThresholdOld = cam_7.stats4.centroid_threshold.get()\\n cam_7.stats4.centroid_threshold.put(150)\\n \\n # HiMag\\n # Copy ROI2 geometry (HiMag Mag3) to ROI4 and use ROI4 centroid plugin\\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get())\\n cam_8.roi4.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get())\\n cam_8.roi4.size.x.put(cam_8.roi2.size.x.get() * 0.20)\\n cam_8.roi4.size.y.put(cam_8.roi2.size.y.get())\\n cam_8.roi4.min_xyz.min_x.put(cam_8.roi2.min_xyz.min_x.get() + cam_8.roi2.size.x.get()/2 - cam_8.roi4.size.x.get()/2)\\n \\n # LoMag\\n # Copy ROI2 geometry (LoMag Mag1) to ROI4 and use ROI4 centroid plugin\\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get())\\n cam_7.roi4.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get())\\n cam_7.roi4.size.x.put(cam_7.roi2.size.x.get() * 0.05)\\n cam_7.roi4.size.y.put(cam_7.roi2.size.y.get())\\n cam_7.roi4.min_xyz.min_x.put(cam_7.roi2.min_xyz.min_x.get() + cam_7.roi2.size.x.get()/2 - cam_7.roi4.size.x.get()/2)\\n \\n centerPinYHiMag0 = centroid_avg(cam_8.stats4)[1]\\n centerPinYLoMag0 = centroid_avg(cam_7.stats4)[1]\\n yield from bps.mvr(gonio.o,180)\\n time.sleep(2)\\n centerPinYHiMag180 = centroid_avg(cam_8.stats4)[1]\\n centerPinYLoMag180 = centroid_avg(cam_7.stats4)[1]\\n centerPinYHiMag = (centerPinYHiMag0 + centerPinYHiMag180)/2\\n centerPinYLoMag = (centerPinYLoMag0 + centerPinYLoMag180)/2\\n\\n centerPinOffsYHiMag = centerPinYHiMag - cam_8.roi4.size.y.get() / 2\\n centerPinOffsYLoMag = centerPinYLoMag - cam_7.roi4.size.y.get() / 2\\n \\n # Correct Mag 3 (cam_8 ROI2)\\n cam_8.roi2.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + centerPinOffsYHiMag)\\n # Correct Mag 4 (cam_8 ROI1)\\n cam_8.roi1.min_xyz.min_y.put(cam_8.roi2.min_xyz.min_y.get() + (cam_8.roi2.size.y.get()-cam_8.roi1.size.y.get())/2)\\n \\n # Correct Mag 1 (cam_7 ROI2)\\n cam_7.roi2.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + centerPinOffsYLoMag)\\n # Correct Mag 2 (cam_7 ROI3)\\n cam_7.roi3.min_xyz.min_y.put(cam_7.roi2.min_xyz.min_y.get() + (cam_7.roi2.size.y.get()-cam_7.roi3.size.y.get())/2)\\n\\n # De-invert image\\n cam_7.proc1.scale.put(-1)\\n cam_8.proc1.scale.put(-1)\\n \\n # Set thresold to previous value\\n cam_8.stats4.centroid_threshold.put(cam_8ThresholdOld)\\n cam_7.stats4.centroid_threshold.put(cam_7ThresholdOld)\\n \\n return\",\n \"def init_transformations(self):\\n shifts = (self.img_shape[0] // 2, self.img_shape[1] // 2)\\n unshifts = (-self.img_shape[0] // 2, -self.img_shape[1] // 2)\\n self.preshift = skimage.transform.SimilarityTransform(\\n translation = shifts)\\n self.postshift = skimage.transform.SimilarityTransform(\\n translation = unshifts)\",\n \"def apply_dof_transformation(tdim, edge_count, face_count, entity_transformations, entity_dofs,\\n data, cell_info, face_types):\\n if tdim >= 2:\\n if tdim == 3:\\n face_start = 3 * face_count\\n else:\\n face_start = 0\\n\\n dofstart = 0\\n for i in entity_dofs[0]:\\n dofstart += i\\n # NOTE: Copy array to make numba compilation faster (contiguous array assumption)\\n edge_reflection = entity_transformations[\\\"interval\\\"][0].copy()\\n for e in range(edge_count):\\n edofs = entity_dofs[1][e]\\n if edofs == 0:\\n continue\\n if cell_info >> (face_start + e) & 1:\\n data[dofstart:dofstart+edofs] = numpy.dot(edge_reflection, data[dofstart:dofstart+edofs])\\n dofstart += edofs\\n\\n if tdim == 3:\\n for f in range(face_count):\\n face_rotation = entity_transformations[face_types[f]][0].copy()\\n face_reflection = entity_transformations[face_types[f]][1].copy()\\n fdofs = entity_dofs[2][f]\\n if fdofs == 0:\\n continue\\n if cell_info >> (3 * f) & 1:\\n data[dofstart:dofstart+fdofs] = numpy.dot(face_reflection, data[dofstart:dofstart+fdofs])\\n for _ in range(cell_info >> (3 * f + 1) & 3):\\n data[dofstart:dofstart+fdofs] = numpy.dot(face_rotation, data[dofstart:dofstart+fdofs])\\n dofstart += fdofs\",\n \"def var_transform(self, do_data=False):\\n\\n empty_vars = ['leadJetEn', 'leadJetPt', 'leadJetPhi', 'leadJetEta', 'leadJetQGL',\\n 'subleadJetEn', 'subleadJetPt', 'subleadJetPhi', 'subleadJetEta', 'subleadJetQGL',\\n 'subsubleadJetEn', 'subsubleadJetPt', 'subsubleadJetPhi', 'subsubleadJetEta', 'subsubleadJetQGL',\\n 'dijetMinDRJetEle', 'dijetDieleAbsDEta','dijetDieleAbsDPhiTrunc', 'dijetCentrality', 'dijetMass', \\n 'dijetAbsDEta', 'dijetDPhi'] \\n\\n replacement_value = -10\\n\\n for empty_var in empty_vars:\\n self.data_obj.mc_df_sig[empty_var] = self.data_obj.mc_df_sig[empty_var].replace(-999., replacement_value)\\n self.data_obj.mc_df_bkg[empty_var] = self.data_obj.mc_df_bkg[empty_var].replace(-999., replacement_value)\\n if do_data: self.data_obj.data_df[empty_var] = self.data_obj.data_df[empty_var].replace(-999., replacement_value)\\n\\n #print self.data_obj.mc_df_sig[empty_vars]\\n #print np.isnan(self.data_obj.mc_df_sig[empty_vars]).any()\\n\\n for var in gev_vars:\\n if var in (self.low_level_vars_flat+self.high_level_vars):\\n self.data_obj.mc_df_sig[var] = self.data_obj.mc_df_sig.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\\n self.data_obj.mc_df_bkg[var] = self.data_obj.mc_df_bkg.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\\n if do_data: self.data_obj.data_df[var] = self.data_obj.data_df.apply(self.var_transform_helper, axis=1, args=[var, replacement_value])\\n\\n #print np.isnan(self.data_obj.mc_df_sig[empty_vars]).any()\",\n \"def transform(self, image_matrix):\\n\\n # Centering the data\\n mean = np.mean(image_matrix, axis=0)\\n image_matrix = image_matrix - mean\\n\\n # Dimension reduction is done by multiplying the original matrix with the components\\n transformed_matrix = np.dot(image_matrix, self.components.T)\\n return transformed_matrix\",\n \"def prepare_CvD16_for_M2L_calc(templates_lam_range, verbose=False):\\n import glob\\n import os\\n template_glob=os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*.s100')\\n\\n vcj_models=sorted(glob.glob(template_glob))\\n temp_lamdas, x35, x3, x23, kroupa, flat=np.genfromtxt(vcj_models[-1], unpack=True)\\n\\n n_ages=7\\n n_zs=5\\n n_imfs=5\\n\\n \\n\\n\\n Zs=['m1.5', 'm1.0', 'm0.5', 'p0.0', 'p0.2']\\n ages=[1.0, 3.0, 5.0, 7.0, 9.0, 11.0, 13.5]\\n model_imfs_order=['x35', 'x3', 'x23', 'kroupa', 'flat']\\n\\n t_mask = ((temp_lamdas > templates_lam_range[0]) & (temp_lamdas <templates_lam_range[1]))\\n\\n\\n\\n y=x35[t_mask]\\n x=temp_lamdas[t_mask]\\n\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x)\\n\\n templates=np.empty((len(out), n_ages, n_zs, n_imfs))\\n\\n\\n\\n for a, Z in enumerate(Zs): \\n for b, age in enumerate(ages):\\n model=glob.glob(os.path.expanduser('~/z/Data/stellarpops/CvD2/vcj_models/VCJ_*{}*{}.ssp.s100'.format(Z, age)))[0]\\n if verbose:\\n print 'Loading {}'.format(model)\\n data=np.genfromtxt(model)\\n\\n for c, counter in enumerate(reversed(range(1, data.shape[-1]))):\\n \\n #Interpolate templates onto a uniform wavelength grid and then log-rebin\\n y=data[:, counter][t_mask] \\n x=temp_lamdas[t_mask]\\n #Make a new lamda array, carrying on the delta lamdas of high resolution bit\\n new_x=temp_lamdas[t_mask][0]+0.9*(np.arange(np.ceil((temp_lamdas[t_mask][-1]-temp_lamdas[t_mask][0])/0.9))+1)\\n\\n interp=si.interp1d(x, y, fill_value='extrapolate')\\n out=interp(new_x) \\n\\n templates[:, b, a, c]=out\\n\\n return templates, new_x\",\n \"def transformation(self):\\n for key in self.combination_dict.keys():\\n if self.combination_dict[key]['column_count'] == 2:\\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'tem' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'tem':\\n self.temporal_transformation(self.combination_dict[key])\\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'cat' or self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'cat':\\n self.categorical_transformation(self.combination_dict[key])\\n elif self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num' and self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\\n self.numerical_transformation(self.combination_dict[key])\\n\\n elif self.combination_dict[key]['column_count'] == 3:\\n num_count = 0\\n num_column = []\\n if self.data_dict[self.combination_dict[key]['column1']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(0)\\n elif self.data_dict[self.combination_dict[key]['column2']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(1)\\n elif self.data_dict[self.combination_dict[key]['column3']]['data_type'] == 'num':\\n num_count += 1\\n num_column.append(2)\\n\\n if num_count == 1:\\n self.three_column_groupby_logic(self.combination_dict[key], num_column)\\n\\n m_score_pie = []\\n m_score_bar = []\\n m_score_line = []\\n m_score_scatter = []\\n # for key in self.scenario_dict:\\n # if self.scenario_dict\\n for key in self.scenario_dict:\\n if math.isnan(self.scenario_dict[key][\\\"Scatter_chart_score\\\"]):\\n m_score_scatter.append(0)\\n else:\\n m_score_scatter.append(self.scenario_dict[key][\\\"Scatter_chart_score\\\"])\\n m_score_pie.append(self.scenario_dict[key][\\\"Pie_chart_score\\\"])\\n m_score_bar.append(self.scenario_dict[key][\\\"Bar_chart_score\\\"])\\n m_score_line.append(self.scenario_dict[key][\\\"Line_chart_score\\\"])\\n\\n m_score_pie /= np.max(m_score_pie)\\n m_score_bar /= np.max(m_score_bar)\\n m_score_line /= np.max(m_score_line)\\n m_score_scatter /= np.max(m_score_scatter)\\n m_score = [m_score_pie, m_score_bar, m_score_line, m_score_scatter]\\n match_index = np.argmax(m_score, axis = 0)\\n i = 0\\n for key in self.scenario_dict:\\n if match_index[i] == 0:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"pie\\\"\\n if match_index[i] == 1:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"bar\\\"\\n if match_index[i] == 2:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"line\\\"\\n if match_index[i] == 3:\\n self.scenario_dict[key][\\\"Chart_Type\\\"] = \\\"scatter\\\"\\n self.scenario_dict[key][\\\"m_score\\\"] = m_score[match_index[i]][i]\\n i += 1\\n\\n return self.scenario_dict\",\n \"def postTransform(self,T: RigidTransform) -> None:\\n for i,m in enumerate(self.milestones):\\n assert len(m) == 24\\n mq = self.to_se3(m[:12])\\n mv = self.to_se3(m[12:])\\n self.milestones[i] = self.from_se3(se3.mul(mq,T)) + self.from_se3((so3.mul(mv[0],T[0]),so3.apply(so3.inv(T[0]),mv[1])))\",\n \"def _project_loops(self):\\n\\n self._create_projection_datasets()\\n self._get_sho_chunk_sizes(10)\\n\\n '''\\n Loop over the FORCs\\n '''\\n for forc_chunk_index in range(self._num_forcs):\\n pos_chunk_index = 0\\n\\n self._current_sho_spec_slice = slice(self.sho_spec_inds_per_forc * self._current_forc,\\n self.sho_spec_inds_per_forc * (self._current_forc + 1))\\n self._current_met_spec_slice = slice(self.metrics_spec_inds_per_forc * self._current_forc,\\n self.metrics_spec_inds_per_forc * (self._current_forc + 1))\\n dc_vec = self._get_dc_offset()\\n '''\\n Loop over positions\\n '''\\n while self._current_pos_slice.stop < self._end_pos:\\n loops_2d, nd_mat_shape_dc_first, order_dc_offset_reverse = self._get_projection_data(pos_chunk_index)\\n\\n # step 8: perform loop unfolding\\n projected_loops_2d, loop_metrics_1d = self._project_loop_batch(dc_vec, np.transpose(loops_2d))\\n\\n # test the reshapes back\\n projected_loops_2d = self._reshape_projected_loops_for_h5(projected_loops_2d,\\n order_dc_offset_reverse,\\n nd_mat_shape_dc_first)\\n self.h5_projected_loops[self._current_pos_slice, self._current_sho_spec_slice] = projected_loops_2d\\n\\n metrics_2d = self._reshape_results_for_h5(loop_metrics_1d, nd_mat_shape_dc_first)\\n\\n self.h5_loop_metrics[self._current_pos_slice, self._current_met_spec_slice] = metrics_2d\\n\\n # Reset the position slice\\n self._current_pos_slice = slice(None)\\n\\n pass\",\n \"def cal_dist_and_mtx(self, calibration_patterns, pattern_size, retain_calibration_patterns):\\n # prepare object points, like (0,0,0), (1,0,0), (2,0,0), ....,(8,5,0)\\n # use numpy mgrid function to generate the coordinates values for a given grid size.\\n grid_x, grid_y = pattern_size[0], pattern_size[1]\\n objp = np.zeros((grid_y * grid_x, 3), np.float32)\\n objp[:,:2] = np.mgrid[:grid_x, :grid_y].T.reshape(-1,2)\\n\\n # Arrays to store object points and image points from all the sample images.\\n objpoints = [] # 3d points in real world space\\n imgpoints = [] # 2d points in image plane.\\n \\n # Step through the list and search for chessboard corners in distorted calibration images.\\n for path in calibration_patterns:\\n img = mpimg.imread(path)\\n gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\\n\\n # Find the chessboard's inner corners\\n found, corners = cv2.findChessboardCorners(gray, pattern_size, None)\\n\\n # If found, add object points, image points\\n if found:\\n objpoints.append(objp)\\n imgpoints.append(corners)\\n \\n # Draw and display the corners to see what was detected.\\n if retain_calibration_patterns:\\n cv2.drawChessboardCorners(img, pattern_size, corners, found)\\n self.calibration_patterns_success.append(img)\\n else:\\n if retain_calibration_patterns:\\n self.calibration_patterns_error.append(img)\\n\\n img_size = (img.shape[1], img.shape[0])\\n\\n if objpoints and imgpoints:\\n _, self.camera_matrix, self.distortion_coef, _, _ = cv2.calibrateCamera(\\n objpoints, imgpoints, img_size, None, None)\",\n \"def compute_controller(self):\\n # region Input Info\\n\\n # here we implement an example for a consensus algorithm\\n neig = self.get_neighbors()\\n messages = self.get_messages()\\n pos, rot = self.get_pos_and_orientation()\\n \\n #send message of positions to all neighbors indicating our position\\n for n in neig:\\n self.send_message(n, pos)\\n \\n # check if we received the position of our neighbors and compute desired change in position\\n # as a function of the neighbors (message is composed of [neighbors id, position])\\n dx = 0.\\n dy = 0.\\n # print(messages)\\n # endregion\\n if messages:\\n # similar to laplacian but for each robot\\n # for m in messages:\\n # dx += m[1][0] - pos[0]\\n # dy += m[1][1] - pos[1]\\n\\n # position of All robots\\n Apos = np.zeros([6,2])\\n Apos[self.id,:]=pos[0:2]\\n for m in messages:\\n Apos[m[0],:]=m[1][0:2]\\n\\n TarM = np.zeros([6,2])\\n TarM[self.id, :] = self.TargetP[self.Tid,:]-pos[0:2]\\n Cdiff = Apos-pos[0:2]\\n Cdiff = np.sqrt(np.square(Cdiff[:,0])+np.square(Cdiff[:,1]))\\n Cdiff = np.sum(Cdiff)\\n Ddiff = self.P_Des-self.P_Des[self.id]\\n Ddiff = np.sqrt(np.square(Ddiff[:, 0]) + np.square(Ddiff[:, 1]))\\n Ddiff = np.sum(Ddiff)\\n Tdiff = np.abs(Ddiff - Cdiff)\\n\\n\\n\\n\\n # region Obstacles\\n Obc = Apos\\n # Obc = self.Obstacles\\n # Obc = np.vstack([Obs,pos[0:2]])\\n Diff = pos[0:2] - Obc\\n for m in range(0, Diff.shape[0]):\\n if (np.sqrt(np.square(Diff[m, 0]) + np.square(Diff[m, 1]))) > 0.35:\\n Diff[m, :] = np.array([0, 0])\\n\\n DiffY = Diff[:, 1].reshape([1, -1])\\n DiffX = Diff[:, 0].reshape([1, -1])\\n x_odot = np.sum(np.exp(-np.square(DiffX) / self.Var) * DiffX)\\n y_odot = np.sum(np.exp(-np.square(DiffY) / self.Var) * DiffY)\\n\\n ObsAv = np.array([x_odot, y_odot])\\n # endregion\\n\\n\\n NewGd = np.square(np.transpose(self.E) @ Apos)\\n NewGd = (NewGd[:, 0] + NewGd[:, 1]).reshape([-1, 1])\\n G = self.Gdsq - NewGd\\n Rg = self.DistJacobian(Apos, self.Edges)\\n p_ddot = np.zeros(([6, 2]))\\n\\n if (Tdiff < self.Thr):\\n self.StartTimer = True\\n\\n\\n if(self.StartTimer):\\n self.Timer += 1\\n if (self.Timer > 500+self.OffTimer):\\n self.FormStable = True\\n self.StartTimer = False\\n self.Timer = 0\\n\\n if(self.Tid > 3 and np.sum(TarM[self.id, 0])<5):\\n TarM[self.id, 0] = 5\\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) < 5):\\n TarM[self.id, 1] = 5\\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) > -5):\\n TarM[self.id, 1] = -5\\n if (self.Tid > 3 and np.sum(TarM[self.id, 1]) > -5):\\n TarM[self.id, 1] = -5\\n\\n if (self.Tid > 3 and np.sum(TarM[self.id, :]) < 0.01):\\n self.Tid +=1\\n\\n if (self.FormStable):\\n # Formation Done\\n if self.Tid == 0 and self.Formation == \\\"square\\\":\\n self.P_Des = self.Form_HRec(0.5)\\n self.Reset_Form()\\n # self.Tid += 1\\n # self.FormStable = False\\n print(self.P_Des, self.Formation, \\\" \\\", self.Tid)\\n # self.K1 = 5\\n # self.K2 = 50\\n if (self.Tid < self.TargetP.shape[0]-1 and self.FormStable):\\n self.Tid += 1\\n if(self.Tid == 1):\\n self.K1 = 2\\n self.K3 = 10\\n self.Thr = 0.001\\n if (self.Tid == 2):\\n self.K1 = 20\\n self.K3 = 1\\n self.P_Des = self.Form_HRec(0.5)\\n self.Reset_Form()\\n self.FormStable = False\\n # Linear Control Law\\n p_dot = np.zeros([6,2])\\n p_dot = -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\\n p_dot += self.dt * (self.OK / self.Var) * ObsAv\\n # p_dot += self.K3 * TarM\\n # Non - linear Control Law\\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\\n # p_dot += p_ddot*self.dt\\n if(self.id == 0):\\n # print(Tdiff,self.TargetP[self.Tid,:],np.sum(G),self.Tid,self.Timer)\\n p_dot = self.K3 * TarM\\n if (self.id == 0):\\n print(Tdiff,self.TargetP[self.Tid,:],np.sum(G),self.Tid,self.Timer)\\n # if(self.Tid == 1):\\n # p_dot += -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\\n\\n dx = p_dot[self.id, 0]\\n dy = p_dot[self.id, 1]\\n\\n # Non - linear Control\\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\\n # p_ddot += (self.OK / self.Var) * ObsAv\\n # dx = self.dt * p_ddot[self.id, 0]\\n # dy = self.dt * p_ddot[self.id, 1]\\n #else:\\n # TarM[self.id, :] = Tdiff\\n # # Linear Control\\n # p_dot = -self.K1 * np.matmul(self.L, Apos) + self.K1 * np.matmul(self.E, self.Z_Des)\\n # p_dot += self.dt * (self.OK / self.Var) * ObsAv\\n # p_dot += self.K3 * TarM\\n # dx = p_dot[self.id, 0]\\n # dy = p_dot[self.id, 1]\\n\\n # Non - linear Control\\n # p_ddot = self.K2 * (np.transpose(Rg) @ G).reshape([-1, 2])\\n # p_ddot += self.K3 * TarM\\n # p_ddot += (self.OK / self.Var) * ObsAv\\n # dx = self.dt * p_ddot[self.id, 0]\\n # dy = self.dt * p_ddot[self.id, 1]\\n\\n # region Robot Wheel Control\\n # integrate\\n des_pos_x = pos[0] + self.dt * dx\\n des_pos_y = pos[1] + self.dt * dy\\n\\n #compute velocity change for the wheels\\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\\n if vel_norm < 0.01:\\n vel_norm = 0.01\\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n self.set_wheel_velocity([left_wheel, right_wheel])\\n # endregion\",\n \"def run(prefix=\\\"tst_map_model_cc\\\"):\\n # original (zero-origin) map and model\\n pdb_inp = iotbx.pdb.input(source_info=None, lines=pdb_str)\\n pdb_inp.write_pdb_file(file_name=\\\"%s_%s\\\"%(prefix,\\\"orig.pdb\\\"))\\n ph = pdb_inp.construct_hierarchy()\\n xrs = pdb_inp.xray_structure_simple()\\n fc = xrs.structure_factors(d_min=1.5).f_calc()\\n fft_map = fc.fft_map(resolution_factor = 0.25)\\n fft_map.apply_sigma_scaling()\\n map_data = fft_map.real_map_unpadded()\\n assert map_data.all() == (40, 45, 54)\\n assert map_data.origin() == (0, 0, 0)\\n assert map_data.focus() == (40, 45, 54)\\n write_ccp4_map(map_data=map_data, cs=xrs.crystal_symmetry(),\\n file_name=\\\"%s_%s\\\"%(prefix,\\\"orig.ccp4\\\"))\\n # shift origin of the map\\n g = flex.grid((-20,-25,-27), (20,20,27))\\n map_data.reshape(g)\\n assert map_data.all() == (40, 45, 54)\\n assert map_data.origin() == (-20, -25, -27)\\n assert map_data.focus() == (20, 20, 27)\\n write_ccp4_map(map_data=map_data, cs=xrs.crystal_symmetry(),\\n file_name=\\\"%s_%s\\\"%(prefix,\\\"shifted.ccp4\\\"))\\n # apply same shift to the model\\n a,b,c = xrs.crystal_symmetry().unit_cell().parameters()[:3]\\n N = map_data.all()\\n O=map_data.origin()\\n sites_cart = ph.atoms().extract_xyz()\\n sx,sy,sz = a/N[0]*O[0], b/N[1]*O[1], c/N[2]*O[2]\\n sites_cart_shifted = sites_cart+\\\\\\n flex.vec3_double(sites_cart.size(), [sx,sy,sz])\\n ph.atoms().set_xyz(sites_cart_shifted)\\n ph.write_pdb_file(file_name=\\\"%s_%s\\\"%(prefix,\\\"shifted.pdb\\\"))\\n # run phenix.real_space_refine\\n checked = 0\\n cmd = \\\" \\\".join([\\n \\\"phenix.map_model_cc\\\",\\n \\\"%s_shifted.pdb\\\"%prefix,\\n \\\"%s_shifted.ccp4\\\"%prefix,\\n \\\"resolution=1.5\\\",\\n \\\"> %s.zlog\\\"%prefix\\n ])\\n print cmd\\n easy_run.call(cmd)\\n # check results\\n fo = open(\\\"%s.zlog\\\"%prefix,\\\"r\\\")\\n for l in fo.readlines():\\n if(l.startswith(\\\" CC_mask :\\\")):\\n cc = float(l.split()[2])\\n assert cc>0.989\\n checked+=1\\n fo.close()\\n assert checked==1\\n # Exercise corresponding library function\\n params = mmtbx.maps.map_model_cc.master_params().extract()\\n params.map_model_cc.resolution=1.5\\n task_obj = mmtbx.maps.map_model_cc.map_model_cc(\\n map_data = map_data,\\n pdb_hierarchy = ph,\\n crystal_symmetry = xrs.crystal_symmetry(),\\n params = params.map_model_cc)\\n task_obj.validate()\\n task_obj.run()\\n result = task_obj.get_results()\\n assert approx_equal(result.cc_mask , 1.0, 1.e-3)\\n assert approx_equal(result.cc_peaks , 1.0, 1.e-3)\\n assert approx_equal(result.cc_volume, 1.0, 1.e-3)\",\n \"def compute_transform(self, image_pair, figure):\\t\\n\\t\\timage_pair = [int(image_pair[1]), int(image_pair[3])]\\n\\t\\tindices = np.where(np.all(np.abs(self.Matches[\\\"image_pairs\\\"][0] - image_pair) == 0, axis=1)) \\n\\t\\tfeature_pairs = self.Matches[\\\"image_match_pairs\\\"][0][indices[0][0]]\\n\\t\\tfeature_pair_scores = self.Matches[\\\"image_feature_scores\\\"][0][indices[0][0]] \\n\\n\\t\\ti1_loc = self.Database[\\\"image_locs\\\"][np.where(np.array(self.Database[\\\"image_idx\\\"]) == image_pair[0])[0][0]]\\n\\t\\ti2_loc = self.Database[\\\"image_locs\\\"][np.where(np.array(self.Database[\\\"image_idx\\\"]) == image_pair[1])[0][0]]\\n\\n\\t\\tself.image1 = cv2.cvtColor(extract_image(figure, i1_loc), cv2.COLOR_BGR2GRAY)\\n\\t\\tself.image2 = cv2.cvtColor(extract_image(figure, i2_loc), cv2.COLOR_BGR2GRAY)\\n\\t\\t# self.image1 = extract_image(figure, i1_loc)\\n\\t\\t# self.image2 = extract_image(figure, i2_loc)\\n\\n\\t\\tself.match_alignment = {}; self.match_alignment_scores = {}\\n\\t\\tfor pair, score in zip(feature_pairs, feature_pair_scores):\\n\\t\\t\\ti1 = self.Database[\\\"image_idx\\\"][pair[0]]\\n\\t\\t\\ti2 = self.Database[\\\"image_idx\\\"][pair[1]]\\n\\n\\t\\t\\tif i1 == image_pair[0]:\\n\\t\\t\\t\\ttarget = pair[0] \\n\\t\\t\\t\\tstart = pair[1]\\n\\t\\t\\t\\tif target not in self.match_alignment.keys():\\n\\t\\t\\t\\t\\tself.init_match_alignment(target)\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttarget = pair[1]\\n\\t\\t\\t\\tstart = pair[0]\\t\\t\\t\\t\\n\\t\\t\\t\\tif target not in self.match_alignment.keys():\\n\\t\\t\\t\\t\\tself.init_match_alignment(target)\\n\\n\\t\\t\\t# get ellipse and convert to three cardinal points\\n\\t\\t\\ttarget_ellipse = self.Database[\\\"orientation\\\"][target]\\n\\t\\t\\tstart_ellipse = self.Database[\\\"orientation\\\"][start]\\n\\t\\t\\ttarget_points, target_params = utils.ellipse2points(target_ellipse)\\n\\t\\t\\tstart_points, start_params = utils.ellipse2points(start_ellipse)\\n\\n\\t\\t\\t# compute angle between Major axes\\n\\t\\t\\trotation_angle = int(np.abs(target_params[0] - start_params[0]))\\n\\t\\t\\treflection_truth = target_params[1] * start_params[1]\\n\\t\\t\\tif reflection_truth > 0:\\n\\t\\t\\t\\treflection_truth = \\\"No\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\treflection_truth = \\\"Yes\\\"\\n\\t\\t\\tresolution_perc = round(((target_params[2] / start_params[2]) - 1), 1)\\n\\n\\t\\t\\t# extract features for histogram matching\\n\\t\\t\\ttarget_bloc = self.Database[\\\"blot_locs\\\"][target]\\n\\t\\t\\tstart_bloc = self.Database[\\\"blot_locs\\\"][start]\\n\\t\\t\\ttarget_feature = extract_image(self.image1, target_bloc, ex=5)\\n\\t\\t\\tfinal_feature = extract_image(self.image2, start_bloc, ex=5) \\n\\n\\t\\t\\t#find affine transform to match\\n\\t\\t\\tif self.AFFINE:\\n\\t\\t\\t\\tT = utils.compute_affine(start_points, target_points)\\n\\t\\t\\t\\timage2_warped = cv2.warpAffine(self.image2.copy(), T, (self.image1.shape[1], self.image1.shape[0]))\\n\\t\\t\\t\\tfinal_feature = extract_image(image2_warped, target_bloc, ex=5)\\n\\t\\t\\t\\n\\t\\t\\t# match hist\\n\\t\\t\\tif self.CONTRAST:\\n\\t\\t\\t\\tfinal_feature = utils.histogram_match(final_feature, target_feature)\\n\\n\\t\\t\\tself.match_alignment[target].append((final_feature, start, rotation_angle, reflection_truth, \\\\\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t resolution_perc, score))\\n\\t\\t\\tself.match_alignment_scores[target].append(score)\",\n \"def design_matrix_motor_df(meta):\\n\\n mouse = utils.meta_mouse(meta)\\n\\n # go\\n new_meta = {}\\n new_meta['go'] = np.zeros(len(meta))\\n new_meta['go'][meta['trialerror'].isin([0, 3, 5, 7]).values] = 1\\n assert np.sum(new_meta['go']) > 0\\n meta_df_out = pd.DataFrame(data=new_meta, index=meta.index)\\n\\n # nogo\\n new_meta = {}\\n new_meta['nogo'] = np.zeros(len(meta))\\n new_meta['nogo'][meta['trialerror'].isin([1, 2, 4, 6]).values] = 1\\n assert np.sum(new_meta['nogo']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get cues defined by new terms\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[mm] = np.zeros(len(meta))\\n new_meta[mm][meta.initial_condition.isin([inverted_lookup[mm]]).values] = 1\\n assert np.sum(new_meta[mm]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get previous cue same\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[f'prev_same_{mm}'] = np.zeros(len(meta))\\n new_meta[f'prev_same_{mm}'][meta[f'prev_same_{inverted_lookup[mm]}'].values] = 1\\n assert np.sum(new_meta[f'prev_same_{mm}']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get previous cue diff\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[f'prev_diff_{mm}'] = np.zeros(len(meta))\\n not_same = ~meta[f'prev_same_{inverted_lookup[mm]}'].values & meta.initial_condition.isin([inverted_lookup[mm]]).values\\n new_meta[f'prev_diff_{mm}'][not_same] = 1\\n assert np.sum(new_meta[f'prev_diff_{mm}']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get salient events\\n new_meta = {}\\n for col in ['prev_reward', 'prev_punish', 'prev_blank', 'hmm_engaged']:\\n new_meta[col] = np.zeros(len(meta))\\n new_meta[col][meta[col].values] = 1\\n assert np.sum(new_meta[col]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get normalized running and baseline licking\\n new_meta = {}\\n for col in ['speed', 'pre_speed', 'post_speed', 'pre_licks', 'post_licks', 'anticipatory_licks']:\\n new_meta[col] = meta[col].values / np.nanmax(meta[col].values)\\n new_meta[col][np.isnan(new_meta[col])] = 0\\n assert np.sum(new_meta[col]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get normalized dprime\\n # new_meta = {}\\n # for col in ['dprime_run']:\\n # new_meta[col] = meta[col].values - np.nanmin(meta[col].values)\\n # new_meta[col] = new_meta[col] / np.nanmax(new_meta[col])\\n # new_meta[col][np.isnan(new_meta[col])] = 0\\n # assert np.sum(new_meta[col]) > 0\\n # new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n # meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n return meta_df_out\",\n \"def __updateMatC(self):\\n\\t\\tif self.regScheme == 2:\\n\\t\\t\\tfor id1 in range(self.nTemplates):\\n\\t\\t\\t\\tself.C[id1,id1] = 1.0 / self.w0[id1]**2\",\n \"def mounting_matrix(self):\\n # fmt: off\\n count = 0\\n for x in range(self.ntheta):\\n self.M[count][count] = 1\\n self.f[count][0] = self.p_in\\n count = count + self.nz - 1\\n self.M[count][count] = 1\\n self.f[count][0] = self.p_out\\n count = count + 1\\n count = 0\\n for x in range(self.nz - 2):\\n self.M[self.ntotal - self.nz + 1 + count][1 + count] = 1\\n self.M[self.ntotal - self.nz + 1 + count][self.ntotal - self.nz + 1 + count] = -1\\n count = count + 1\\n count = 1\\n j = 0\\n for i in range(1, self.nz - 1):\\n a = (1 / self.dtheta ** 2) * (self.c1[i][self.ntheta - 1])\\n self.M[count][self.ntotal - 2 * self.nz + count] = a\\n b = (1 / self.dz ** 2) * (self.c2[i - 1, j])\\n self.M[count][count - 1] = b\\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][self.ntheta - 1])\\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\\n self.M[count, count] = c\\n d = (1 / self.dz ** 2) * (self.c2[i][j])\\n self.M[count][count + 1] = d\\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\\n self.M[count][count + self.nz] = e\\n count = count + 1\\n count = self.nz + 1\\n for j in range(1, self.ntheta - 1):\\n for i in range(1, self.nz - 1):\\n a = (1 / self.dtheta ** 2) * (self.c1[i, j - 1])\\n self.M[count][count - self.nz] = a\\n b = (1 / self.dz ** 2) * (self.c2[i - 1][j])\\n self.M[count][count - 1] = b\\n c = -((1 / self.dtheta ** 2) * ((self.c1[i][j]) + self.c1[i][j - 1])\\n + (1 / self.dz ** 2) * (self.c2[i][j] + self.c2[i - 1][j]))\\n self.M[count, count] = c\\n d = (1 / self.dz ** 2) * (self.c2[i][j])\\n self.M[count][count + 1] = d\\n e = (1 / self.dtheta ** 2) * (self.c1[i][j])\\n self.M[count][count + self.nz] = e\\n count = count + 1\\n count = count + 2\\n count = 1\\n for j in range(self.ntheta - 1):\\n for i in range(1, self.nz - 1):\\n if j == 0:\\n self.f[count][0] = (self.c0w[i][j] - self.c0w[i][self.ntheta - 1]) / self.dtheta\\n else:\\n self.f[count][0] = (self.c0w[i, j] - self.c0w[i, j - 1]) / self.dtheta\\n count = count + 1\\n count = count + 2\\n # fmt: on\",\n \"def _init_transformation_matrix(self):\\n # Set up basic transformation matrix\\n c_transform = np.zeros((self.n_beads, self.n_beads))\\n\\n # Get auxiliary array with bead indices\\n n = np.arange(1, self.n_beads + 1)\\n\\n # for k = 0\\n c_transform[0, :] = 1.0\\n\\n for k in range(1, self.n_beads // 2 + 1):\\n c_transform[k, :] = np.sqrt(2) * np.cos(2 * np.pi * k * n / self.n_beads)\\n\\n for k in range(self.n_beads // 2 + 1, self.n_beads):\\n c_transform[k, :] = np.sqrt(2) * np.sin(2 * np.pi * k * n / self.n_beads)\\n\\n if self.n_beads % 2 == 0:\\n c_transform[self.n_beads // 2, :] = (-1) ** n\\n\\n # Since matrix is initialized as C(k,n) does not need to be transposed\\n c_transform /= np.sqrt(self.n_beads)\\n c_transform = torch.from_numpy(c_transform)\\n\\n return c_transform\",\n \"def get_linear_discrete_matrices(self):\\n\\n vf_op, vb_op = get_operating_point_inputs(self.operating_point[1], self.model_type)\\n if self.model_type == ModelType.EASY:\\n At, Bt, Vf_op, Vd_op = getLinearizedMatrices(self.model_type, self.operating_point[0:6], vf_op, vb_op)\\n elif self.model_type == ModelType.GYROMOMENT:\\n At, Bt = get_gyro_matrices(self.operating_point, self.operating_point[6] / mc.K_f, vf_op, vb_op)\\n if self.model_type == ModelType.EASY:\\n Ct = np.array([[1, 0, 0, 0, 0, 0],\\n [0, 1, 0, 0, 0, 0],\\n [0, 0, 1, 0, 0, 0]])\\n Dt = np.array([[0, 0],\\n [0, 0],\\n [0, 0]])\\n elif self.model_type == ModelType.GYROMOMENT:\\n if self.nOutputs == 3:\\n Ct = np.array([[1, 0, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 0, 0, 0, 0, 0],\\n [0, 0, 1, 0, 0, 0, 0, 0]])\\n Dt = np.array([[0, 0],\\n [0, 0],\\n [0, 0],\\n [0, 0],\\n [0, 0]])\\n elif self.nOutputs == 5:\\n Ct = np.array([[1, 0, 0, 0, 0, 0, 0, 0],\\n [0, 1, 0, 0, 0, 0, 0, 0],\\n [0, 0, 1, 0, 0, 0, 0, 0],\\n [0, 0, 0, 0, 0, 0, 1, 0],\\n [0, 0, 0, 0, 0, 0, 0, 1]])\\n Dt = np.array([[0, 0],\\n [0, 0],\\n [0, 0],\\n [0, 0],\\n [0, 0]])\\n\\n Ak, Bk, Ck, Dk = discretize_linear_state_space(At, Bt, Ct, Dt, self.timeStep)\\n return Ak, Bk, Ck, Dk\",\n \"def __compose_transformation(self):\\n s = self.scale\\n rotR = self.rotation\\n t = self.translation\\n T = np.eye(4)\\n T[0:3, 3] = t\\n R = np.eye(4)\\n R[0:3, 0:3] = rotR\\n M = T.dot(R)\\n if s == 1:\\n M = T.dot(R)\\n else:\\n S = np.eye(4)\\n S[0:3, 0:3] = np.diag([s, s, s])\\n M = T.dot(R).dot(S)\\n return M\",\n \"def align_images(self, translation_only=False):\\n # Extract feature point locations and descriptors.\\n # print('here')\\n points_and_descriptors = []\\n for file in self.files:\\n # print('kaka')\\n image = sol4_utils.read_image(file, 1)\\n # print('bla',image)\\n self.h, self.w = image.shape\\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\\n points_and_descriptors.append(find_features(pyramid))\\n\\n # Compute homographies between successive pairs of images.\\n Hs = []\\n\\n for i in range(len(points_and_descriptors) - 1):\\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\\n\\n # Find matching feature points.\\n ind1, ind2 = match_features(desc1, desc2, .7)\\n points1, points2 = points1[ind1, :], points2[ind2, :]\\n\\n # Compute homography using RANSAC.\\n # print(points1)\\n # print(points2)\\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\\n\\n # Uncomment for debugging: display inliers and outliers among matching points.\\n # In the submitted code this function should be commented out!\\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\\n\\n Hs.append(H12)\\n\\n # Compute composite homographies from the central coordinate system.\\n # print(Hs)\\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\\n self.homographies = np.stack(accumulated_homographies)\\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\\n self.homographies = self.homographies[self.frames_for_panoramas]\",\n \"def rebuildMatrixCache(self):\\n self.converterYUR = Mat4.convertMat(CSYupRight, self.lens.getCoordinateSystem()) * self.lens.getProjectionMat()\",\n \"def _fp32_1_0_2_mc_on_1(loop_cnt, left_data):\\n\\n def _fp32_mte_process_1(axis_0_index, w_lp_index, sub_w_size):\\n \\\"\\\"\\\"\\n do transpose by mte for not last dim under multiple core on axis 1\\n \\\"\\\"\\\"\\n\\n def _fp32_inner_mte_1(h_lp_index, sub_h_size):\\n \\\"\\\"\\\"\\n inner mte\\n \\\"\\\"\\\"\\n # move data in\\n in_offset = ((block_idx * per_core_col_size + axis_0_index * axis_1 +\\n h_lp_index * max_core_axis_size) * axis_2 +\\n w_lp_index * max_no_core_axis_size)\\n data_in_inf = (sub_h_size, sub_w_size, axis_1, axis_2, in_offset)\\n _data_move_in_last_dim_be_one_block(tik_inst, ub_input, data_in, data_in_inf)\\n\\n # move data out\\n out_offset = ((block_idx * per_core_col_size * axis_0 + axis_0_index +\\n h_lp_index * max_core_axis_size * axis_0) * axis_2 +\\n w_lp_index * max_no_core_axis_size)\\n data_out_inf = (sub_h_size, sub_w_size, axis_0, axis_1, axis_2, out_offset)\\n _data_move_out_last_dim_be_one_block(tik_inst, data_out, ub_input, data_out_inf)\\n\\n with tik_inst.for_range(0, loop_cnt) as h_lp_idx:\\n _fp32_inner_mte_1(h_lp_idx, max_core_axis_size)\\n with tik_inst.if_scope(left_data > 0):\\n _fp32_inner_mte_1(loop_cnt, left_data)\\n\\n with tik_inst.for_range(0, axis_0) as axis_0_idx:\\n with tik_inst.for_range(0, no_core_loop_cnt) as w_lp_idx:\\n _fp32_mte_process_1(axis_0_idx, w_lp_idx, max_no_core_axis_size)\\n with tik_inst.if_scope(no_core_left > 0):\\n _fp32_mte_process_1(axis_0_idx, no_core_loop_cnt, no_core_left)\",\n \"def preTransform(self, T: RigidTransform) -> None:\\n for i,m in enumerate(self.milestones):\\n assert len(m) == 24\\n mq = self.to_se3(m[:12])\\n mv = self.to_se3(m[12:])\\n self.milestones[i] = self.from_se3(se3.mul(T,mq)) + self.from_se3((so3.mul(T[0],mv[0]),so3.apply(T[0],mv[1])))\",\n \"def matrix_of_changes():\\n\\tdrivers = ['PC','WCD']\\n\\ttasks = ['WM','GAMBLING','RELATIONAL','MOTOR','LANGUAGE','SOCIAL','REST']\\n\\tproject='hcp'\\n\\tatlas = 'power'\\n\\tknown_membership,network_names,num_nodes,name_int_dict = network_labels(atlas)\\n\\tfor driver in drivers:\\n\\t\\tall_matrices = []\\n\\t\\tviolin_df = pd.DataFrame()\\n\\t\\tfor task in tasks:\\n\\t\\t\\t# subjects = np.array(hcp_subjects).copy()\\n\\t\\t\\t# subjects = list(subjects)\\n\\t\\t\\t# subjects = remove_missing_subjects(subjects,task,atlas)\\n\\t\\t\\tsubjects = np.load('/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_subs_fz.npy' %('hcp',task,atlas))\\n\\t\\t\\tassert (subjects == np.load('/home/despoB/mb3152/dynamic_mod/results/hcp_%s_%s_subs_fz.npy'%(task,atlas))).all()\\n\\t\\t\\tstatic_results = graph_metrics(subjects,task,atlas)\\n\\t\\t\\tsubject_pcs = static_results['subject_pcs']\\n\\t\\t\\tsubject_mods = static_results['subject_mods']\\n\\t\\t\\tsubject_wmds = static_results['subject_wmds']\\n\\t\\t\\tmatrices = static_results['matrices']\\n\\t\\t\\ttask_perf = task_performance(subjects,task)\\n\\t\\t\\tassert subject_pcs.shape[0] == len(subjects)\\n\\t\\t\\tmean_pc = np.nanmean(subject_pcs,axis=0)\\n\\t\\t\\tmean_wmd = np.nanmean(subject_wmds,axis=0)\\n\\t\\t\\tmod_pc_corr = np.zeros(subject_pcs.shape[1])\\n\\t\\t\\tfor i in range(subject_pcs.shape[1]):\\n\\t\\t\\t\\tmod_pc_corr[i] = nan_pearsonr(subject_mods,subject_pcs[:,i])[0]\\n\\t\\t\\tmod_wmd_corr = np.zeros(subject_wmds.shape[1])\\n\\t\\t\\tfor i in range(subject_wmds.shape[1]):\\n\\t\\t\\t\\tmod_wmd_corr[i] = nan_pearsonr(subject_mods,subject_wmds[:,i])[0]\\n\\t\\t\\tif driver == 'PC':\\n\\t\\t\\t\\tpredict_nodes = np.where(mod_pc_corr>0.0)[0]\\n\\t\\t\\t\\tlocal_predict_nodes = np.where(mod_pc_corr<0.0)[0]\\n\\t\\t\\t\\tpc_edge_corr = np.arctanh(pc_edge_correlation(subject_pcs,matrices,path='/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_pc_edge_corr_z.npy' %(project,task,atlas)))\\n\\t\\t\\telse:\\t\\t\\n\\t\\t\\t\\tpredict_nodes = np.where(mod_wmd_corr>0.0)[0]\\n\\t\\t\\t\\tlocal_predict_nodes = np.where(mod_wmd_corr<0.0)[0]\\n\\t\\t\\t\\tpc_edge_corr = np.arctanh(pc_edge_correlation(subject_wmds,matrices,path='/home/despoB/mb3152/dynamic_mod/results/%s_%s_%s_wmd_edge_corr_z.npy' %(project,task,atlas)))\\n\\t\\t\\t# Plot matrix of changes\\n\\t\\t\\tedge_thresh = 75\\n\\t\\t\\tedge_thresh = np.percentile(np.nanmean(matrices,axis=0),edge_thresh)\\n\\t\\t\\tpc_edge_corr[:,np.nanmean(matrices,axis=0)<edge_thresh,] = np.nan\\n\\t\\t\\thigh_pc_edge_matrix = np.nanmean(pc_edge_corr[predict_nodes],axis=0)\\n\\t\\t\\tlow_pc_edge_matrix = np.nanmean(pc_edge_corr[local_predict_nodes],axis=0)\\n\\t\\t\\tmatrix = (np.tril(low_pc_edge_matrix) + np.triu(high_pc_edge_matrix)).reshape((264,264))\\n\\t\\t\\tplot_matrix = matrix.copy()\\n\\t\\t\\tplot_matrix_mask = np.isnan(plot_matrix)\\n\\t\\t\\tzscores = scipy.stats.zscore(plot_matrix[plot_matrix_mask==False].reshape(-1))\\n\\t\\t\\tplot_matrix[plot_matrix_mask==False] = zscores\\n\\t\\t\\tif task != 'REST':\\n\\t\\t\\t\\tall_matrices.append(plot_matrix)\\n\\t\\t\\tplot_corr_matrix(plot_matrix,network_names.copy(),out_file='/home/despoB/mb3152/dynamic_mod/figures/%s_corr_matrix_%s.pdf'%(driver,task),plot_corr=False,return_array=False)\\n\\n\\t\\t\\tpc_edge_corr[np.isnan(pc_edge_corr)] = 0.0\\n\\t\\t\\tconnector_within_network_mask = pc_edge_corr.copy().astype(bool)\\n\\t\\t\\tlocal_within_network_mask = pc_edge_corr.copy().astype(bool)\\n\\t\\t\\tconnector_between_network_mask = pc_edge_corr.copy().astype(bool)\\n\\t\\t\\tlocal_between_network_mask = pc_edge_corr.copy().astype(bool)\\n\\t\\t\\tconnector_within_network_mask[:,:,:] = False\\n\\t\\t\\tlocal_within_network_mask[:,:,:] = False\\n\\t\\t\\tconnector_between_network_mask[:,:,:] = False\\n\\t\\t\\tlocal_between_network_mask[:,:,:] = False\\n\\t\\t\\t\\n\\t\\t\\tfor n in predict_nodes:\\n\\t\\t\\t\\tfor node1,node2 in combinations(range(264),2):\\n\\t\\t\\t\\t\\tif n == node1:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif n == node2:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node1] == 0:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node2] == 0:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node1] == known_membership[node2]:\\n\\t\\t\\t\\t\\t\\tconnector_within_network_mask[n][node1,node2] = True\\n\\t\\t\\t\\t\\t\\tconnector_within_network_mask[n][node2,node1] = True\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tconnector_between_network_mask[n][node1,node2] = True\\n\\t\\t\\t\\t\\t\\tconnector_between_network_mask[n][node2,node1] = True\\n\\n\\t\\t\\tfor n in local_predict_nodes:\\n\\t\\t\\t\\tfor node1,node2 in combinations(range(264),2):\\n\\t\\t\\t\\t\\tif n == node1:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif n == node2:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node1] == 0:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node2] == 0:\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\tif known_membership[node1] == known_membership[node2]:\\n\\t\\t\\t\\t\\t\\tlocal_within_network_mask[n][node1,node2] = True\\n\\t\\t\\t\\t\\t\\tlocal_within_network_mask[n][node2,node1] = True\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tlocal_between_network_mask[n][node1,node2] = True\\n\\t\\t\\t\\t\\t\\tlocal_between_network_mask[n][node2,node1] = True\\n\\n\\t\\t\\tdef make_strs_for_df(array_to_add,str_to_add):\\n\\t\\t\\t\\tarray_len = len(array_to_add)\\n\\t\\t\\t\\tstr_array_ = np.chararray(array_len,itemsize=40)\\n\\t\\t\\t\\tstr_array_[:] = str_to_add\\n\\t\\t\\t\\treturn str_array_\\n\\t\\t\\t\\n\\t\\t\\tdef make_array_for_df(arrays_to_add):\\n\\t\\t\\t\\tappend_array = np.zeros((len(arrays_to_add[0]),len(arrays_to_add))).astype(str)\\n\\t\\t\\t\\tappend_array[:,0] = arrays_to_add[0]\\n\\t\\t\\t\\tappend_array[:,1] = arrays_to_add[1]\\n\\t\\t\\t\\tappend_array[:,2] = arrays_to_add[2]\\n\\t\\t\\t\\treturn append_array\\n\\n\\t\\t\\tviolin_columns = [\\\"r value, node i's PCs and j's edge weights\\\",\\\"Node Type\\\",\\\"Edge Type\\\"]\\n\\t\\t\\ttask_violin_df = pd.DataFrame(columns=violin_columns)\\n\\t\\t\\tresult_array_to_add = pc_edge_corr[connector_within_network_mask].reshape(-1)[pc_edge_corr[connector_within_network_mask].reshape(-1)!=0.0]\\n\\t\\t\\tedge_type_ = make_strs_for_df(result_array_to_add,'Within Community')\\n\\t\\t\\tnode_type_ = make_strs_for_df(result_array_to_add,'Q+')\\n\\t\\t\\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\\n\\t\\t\\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\\n\\n\\t\\t\\tresult_array_to_add = pc_edge_corr[local_within_network_mask].reshape(-1)[pc_edge_corr[local_within_network_mask].reshape(-1)!=0.0]\\n\\t\\t\\tedge_type_ = make_strs_for_df(result_array_to_add,'Within Community')\\n\\t\\t\\tnode_type_ = make_strs_for_df(result_array_to_add,'Q-')\\n\\t\\t\\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\\n\\t\\t\\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\\n\\n\\t\\t\\tresult_array_to_add = pc_edge_corr[connector_between_network_mask].reshape(-1)[pc_edge_corr[connector_between_network_mask].reshape(-1)!=0.0]\\n\\t\\t\\tedge_type_ = make_strs_for_df(result_array_to_add,'Between Community')\\n\\t\\t\\tnode_type_ = make_strs_for_df(result_array_to_add,'Q+')\\n\\t\\t\\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\\n\\t\\t\\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\\n\\n\\t\\t\\tresult_array_to_add = pc_edge_corr[local_between_network_mask].reshape(-1)[pc_edge_corr[local_between_network_mask].reshape(-1)!=0.0]\\n\\t\\t\\tedge_type_ = make_strs_for_df(result_array_to_add,'Between Community')\\n\\t\\t\\tnode_type_ = make_strs_for_df(result_array_to_add,'Q-')\\n\\t\\t\\tdf_array_to_add = make_array_for_df([result_array_to_add,node_type_,edge_type_])\\n\\t\\t\\ttask_violin_df = task_violin_df.append(pd.DataFrame(data=df_array_to_add,columns=violin_columns),ignore_index=True)\\n\\t\\t\\ttask_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"] = task_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"].astype(float)\\n\\t\\t\\tif driver == 'PC':\\n\\t\\t\\t\\tprint task + ', Connector Hubs(Q+): ' + str(scipy.stats.ttest_ind(task_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\t\\ttask_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\t\\tprint task + ', Non-Connector Hubs(Q-): ' + str(scipy.stats.ttest_ind(task_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\t\\ttask_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint task + ', Local Hubs(Q+): ' + str(scipy.stats.ttest_ind(task_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\t\\ttask_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q+'][task_violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\t\\tprint task + ', Non Local Hubs (Q-): ' + str(scipy.stats.ttest_ind(task_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\t\\ttask_violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][task_violin_df['Node Type']=='Q-'][task_violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\t#append for average of all\\n\\t\\t\\tviolin_df = violin_df.append(pd.DataFrame(data=task_violin_df,columns=violin_columns),ignore_index=True)\\n\\t\\t\\t#Figure for single Task\\n\\t\\t\\tsns.set_style(\\\"white\\\")\\n\\t\\t\\tsns.set_style(\\\"ticks\\\")\\n\\t\\t\\tcolors = sns.color_palette(['#fdfd96','#C4D8E2'])\\n\\t\\t\\twith sns.plotting_context(\\\"paper\\\",font_scale=2):\\n\\t\\t\\t\\tplt.figure(figsize=(24,16))\\n\\t\\t\\t\\tsns.boxplot(x=\\\"Node Type\\\", y=\\\"r value, node i's PCs and j's edge weights\\\", hue=\\\"Edge Type\\\", order=['Q+','Q-'], data=task_violin_df)\\n\\t\\t\\t\\tplt.savefig('/home/despoB/mb3152/dynamic_mod/figures/%s_edge_mod_%s.pdf'%(driver,task),dpi=4600)\\n\\t\\t\\t\\tplt.close()\\n\\t\\t# Average of All\\n\\t\\tplot_corr_matrix(np.nanmean(all_matrices,axis=0),network_names.copy(),out_file='/home/despoB/mb3152/dynamic_mod/figures/%s_corr_matrix_avg.pdf'%(driver),plot_corr=False,return_array=False)\\n\\t\\tif driver == 'PC':\\n\\t\\t\\tprint task + ',Connector Hubs(Q+): ' + str(scipy.stats.ttest_ind(violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\tviolin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\tprint task + ', Non-Connector Hubs(Q-): ' + str(scipy.stats.ttest_ind(violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\tviolin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Between Community']))\\n\\t\\telse:\\n\\t\\t\\tprint task + ', Local Hubs(Q+): ' + str(scipy.stats.ttest_ind(violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\tviolin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q+'][violin_df['Edge Type']=='Between Community']))\\n\\t\\t\\tprint task + ', Non-Local Hubs(Q-): ' + str(scipy.stats.ttest_ind(violin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Within Community'],\\n\\t\\t\\t\\tviolin_df[\\\"r value, node i's PCs and j's edge weights\\\"][violin_df['Node Type']=='Q-'][violin_df['Edge Type']=='Between Community']))\\n\\t\\tsns.set_style(\\\"white\\\")\\n\\t\\tsns.set_style(\\\"ticks\\\")\\n\\t\\tcolors = sns.color_palette(['#fdfd96','#C4D8E2'])\\n\\t\\twith sns.plotting_context(\\\"paper\\\",font_scale=3):\\n\\t\\t\\tplt.figure(figsize=(24,16))\\n\\t\\t\\tsns.boxplot(x=\\\"Node Type\\\", y=\\\"r value, node i's PCs and j's edge weights\\\",hue=\\\"Edge Type\\\", palette=colors,order=['Q+','Q-'], data=violin_df)\\n\\t\\t\\tplt.savefig('/home/despoB/mb3152/dynamic_mod/figures/%s_edge_mod_avg.pdf'%(driver),dpi=4600)\\n\\t\\t\\tplt.close()\",\n \"def tcs2(self):\\n S = self.M.allocState({})\\n self.M.propagate(S, 0, 1)\\n\\n # set initial beam data\\n S.ref_IonZ = self.refIonZ\\n S.IonZ = self.IonZ\\n\\n S.moment0 = self.BC0\\n S.moment1 = self.ENV0\\n\\n S.ref_IonEk = self.refIonEk\\n \\n S.phis = S.moment0[PS_S,:]\\n S.IonEk = S.moment0[PS_PS,:]*MeVtoeV + S.ref_IonEk\\n\\n ### Reconstract data\\n U = collections.OrderedDict()\\n T = collections.OrderedDict()\\n \\n U,T = self.save(U, T, S)\\n\\n\\n #S.clng = self.clng\\n\\n fin = len(self.M)\\n\\n H = self.M.propagate(S, 1, fin, observe=range(fin))\\n \\n ### Reconstract data\\n U = collections.OrderedDict()\\n T = collections.OrderedDict()\\n\\n for i in range(fin-1):\\n U,T = self.save(U, T, H[i][1])\\n\\n return U,T\",\n \"def _transform(self, matrix):\\n for x in list(self.keys()):\\n ar = self[x]\\n if len(ar.shape) == 2 and ar.shape[1] == 3:\\n self[x] = np.dot(matrix, ar.transpose()).transpose()\",\n \"def compute(self):\\n if self.loaded():\\n debug(\\\"Skipping {} computation due to transform data already loaded.\\\" .format(self.name))\\n return\\n\\n # sanity checks\\n error(\\\"Got transform dimension of {} but input dimension is {}.\\\".format(self.dimension, self.input_dimension), self.input_dimension < self.dimension)\\n\\n # output containers\\n self.term_components = []\\n\\n # compute\\n info(\\\"Applying {} {}-dimensional transform on the raw representation.\\\".\\n format(self.base_name, self.dimension))\\n\\n # train\\n train_data = self.input_vectors[self.train_index, :]\\n\\n info(\\\"Transforming training input data shape: {}\\\".format(train_data.shape))\\n if self.is_supervised:\\n ground_truth = np.reshape(match_labels_to_instances(self.train_epi, self.train_labels), (len(train_data), ))\\n self.vectors = self.process_func_train(train_data, ground_truth)\\n else:\\n self.vectors = self.process_func_train(train_data)\\n self.output_roles = (roles.train,)\\n\\n if self.test_index.size > 0:\\n # make zero output matrix\\n output_data = np.zeros((len(self.input_vectors), self.dimension), np.float32)\\n output_data[self.train_index, :] = self.vectors\\n\\n test_data = self.input_vectors[self.test_index, :]\\n info(\\\"Transforming test input data shape: {}\\\".format(test_data.shape))\\n vecs = self.process_func_test(test_data)\\n output_data[self.test_index, :] = vecs\\n self.vectors = output_data\\n\\n self.output_roles = (roles.train, roles.test)\\n else:\\n info(f\\\"Skipping empty test indexes.\\\")\\n\\n self.term_components = self.get_term_representations()\\n self.verify_transformed(self.vectors)\\n info(f\\\"Output shape: {self.vectors.shape}\\\")\\n # write the output data\\n write_pickled(self.serialization_path_preprocessed, self.get_all_preprocessed())\\n # write the trained transformer model\\n self.save_model()\",\n \"def mirrorTransformations(self):\\n\\n currentSelection = cmds.ls(sl=True)\\n\\n # get the mirror module\\n networkNode = self.returnNetworkNode\\n mirrorModule = cmds.getAttr(networkNode + \\\".mirrorModule\\\")\\n moduleName = cmds.getAttr(networkNode + \\\".moduleName\\\")\\n parent = cmds.getAttr(networkNode + \\\".parentModuleBone\\\")\\n\\n # get mirror module instance and information\\n mirrorInst = self.returnMirrorModuleInst\\n\\n # turn off aim mode\\n mirrorInst.aimMode_Setup(False)\\n\\n # turn off coplanar mode IF it exists on the module\\n try:\\n state = mirrorInst.coplanarBtn.isChecked()\\n if state:\\n mirrorInst.coplanarBtn.setChecked(False)\\n mirrorInst.coplanarMode()\\n except:\\n pass\\n\\n moverTypes = self.returnJointMovers\\n for moverType in moverTypes:\\n for jointMover in moverType:\\n attrs = cmds.listAttr(jointMover, keyable=True)\\n\\n for attr in attrs:\\n value = cmds.getAttr(jointMover + \\\".\\\" + attr)\\n\\n mirrorMover = jointMover.partition(moduleName)[2]\\n mirrorMover = mirrorModule + mirrorMover\\n mirrorAttrs = [\\\"translateX\\\", \\\"translateY\\\", \\\"translateZ\\\"]\\n\\n if attr in mirrorAttrs:\\n cmds.setAttr(mirrorMover + \\\".\\\" + attr, value * -1)\\n else:\\n cmds.setAttr(mirrorMover + \\\".\\\" + attr, value)\\n\\n cmds.select(clear=True)\\n if len(currentSelection) > 0:\\n cmds.select(currentSelection)\\n\\n # turn aim mode on\\n mirrorInst.aimMode_Setup(True)\\n\\n # extend functionality\\n self.mirrorTransformations_Custom()\",\n \"def apply_dof_transformation_to_transpose_prism(\\n entity_transformations, entity_dofs, data, cell_info\\n):\\n apply_dof_transformation_to_transpose(3, 9, 5, entity_transformations, entity_dofs,\\n data, cell_info, List([\\\"triangle\\\"] + [\\\"quadrilateral\\\"] * 4 + [\\\"triangle\\\"]))\",\n \"def cbindMatrices(hm, args):\\n hm2 = heatmapper.heatmapper()\\n\\n # Make a dict of region name:row associations\\n hm.read_matrix_file(args.matrixFile[0])\\n d = dict({x: dict() for x in hm.parameters[\\\"group_labels\\\"]})\\n for idx, group in enumerate(hm.parameters[\\\"group_labels\\\"]):\\n s = hm.parameters[\\\"group_boundaries\\\"][idx]\\n e = hm.parameters[\\\"group_boundaries\\\"][idx + 1]\\n for idx2, reg in enumerate(hm.matrix.regions[s:e]):\\n d[group][reg[2]] = idx2 + s\\n\\n # Iterate through the other matrices\\n for idx in range(1, len(args.matrixFile)):\\n hm2.read_matrix_file(args.matrixFile[idx])\\n # Add the sample labels\\n hm.parameters['sample_labels'].extend(hm2.parameters['sample_labels'])\\n # Add the sample boundaries\\n lens = [x + hm.parameters['sample_boundaries'][-1] for x in hm2.parameters['sample_boundaries']][1:]\\n hm.parameters['sample_boundaries'].extend(lens)\\n\\n # Add on additional NA initialized columns\\n ncol = hm.matrix.matrix.shape[1]\\n hm.matrix.matrix = np.hstack((hm.matrix.matrix, np.empty(hm2.matrix.matrix.shape)))\\n hm.matrix.matrix[:, ncol:] = np.NAN\\n\\n # Update the values\\n for idx2, group in enumerate(hm2.parameters[\\\"group_labels\\\"]):\\n if group not in d:\\n continue\\n s = hm2.parameters[\\\"group_boundaries\\\"][idx2]\\n e = hm2.parameters[\\\"group_boundaries\\\"][idx2 + 1]\\n for idx3, reg in enumerate(hm2.matrix.regions[s:e]):\\n if reg[2] not in d[group]:\\n continue\\n hm.matrix.matrix[d[group][reg[2]], ncol:] = hm2.matrix.matrix[s + idx3, :]\\n\\n # Append the special params\\n for s in hm.special_params:\\n hm.parameters[s].extend(hm2.parameters[s])\\n\\n # Update the sample parameters\\n hm.matrix.sample_labels = hm.parameters['sample_labels']\\n hm.matrix.sample_boundaries = hm.parameters['sample_boundaries']\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_H2 = self.edp_par['rho_H2'].value\\n Z_H2 = self.edp_par['Z_H2'].value\\n sigma_H2 = self.edp_par['sigma_H2'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n \\n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\\n if Z_H1 > Z_H2:\\n Z_H1, Z_H2 = Z_H2, Z_H1\\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\\n rho_H1, rho_H2 = rho_H2, rho_H1\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H2 + sigma_H2\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def _arrange_xfms(transforms, num_files, tmp_folder):\\n base_xform = [\\\"#Insight Transform File V1.0\\\", \\\"#Transform 0\\\"]\\n # Initialize the transforms matrix\\n xfms_T = []\\n for i, tf_file in enumerate(transforms):\\n # If it is a deformation field, copy to the tfs_matrix directly\\n if guess_type(tf_file)[0] != \\\"text/plain\\\":\\n xfms_T.append([tf_file] * num_files)\\n continue\\n\\n with open(tf_file) as tf_fh:\\n tfdata = tf_fh.read().strip()\\n\\n # If it is not an ITK transform file, copy to the tfs_matrix directly\\n if not tfdata.startswith(\\\"#Insight Transform File\\\"):\\n xfms_T.append([tf_file] * num_files)\\n continue\\n\\n # Count number of transforms in ITK transform file\\n nxforms = tfdata.count(\\\"#Transform\\\")\\n\\n # Remove first line\\n tfdata = tfdata.split(\\\"\\\\n\\\")[1:]\\n\\n # If it is a ITK transform file with only 1 xform, copy to the tfs_matrix directly\\n if nxforms == 1:\\n xfms_T.append([tf_file] * num_files)\\n continue\\n\\n if nxforms != num_files:\\n raise RuntimeError(\\n \\\"Number of transforms (%d) found in the ITK file does not match\\\"\\n \\\" the number of input image files (%d).\\\" % (nxforms, num_files)\\n )\\n\\n # At this point splitting transforms will be necessary, generate a base name\\n out_base = fname_presuffix(\\n tf_file, suffix=\\\"_pos-%03d_xfm-{:05d}\\\" % i, newpath=tmp_folder.name\\n ).format\\n # Split combined ITK transforms file\\n split_xfms = []\\n for xform_i in range(nxforms):\\n # Find start token to extract\\n startidx = tfdata.index(\\\"#Transform %d\\\" % xform_i)\\n next_xform = base_xform + tfdata[startidx + 1:startidx + 4] + [\\\"\\\"]\\n xfm_file = out_base(xform_i)\\n with open(xfm_file, \\\"w\\\") as out_xfm:\\n out_xfm.write(\\\"\\\\n\\\".join(next_xform))\\n split_xfms.append(xfm_file)\\n xfms_T.append(split_xfms)\\n\\n # Transpose back (only Python 3)\\n return list(map(list, zip(*xfms_T)))\",\n \"def _transform_frame(self, src):\\n D = len(src)\\n\\n # Eq.(11)\\n E = np.zeros((self.num_mixtures, D))\\n for m in range(self.num_mixtures):\\n xx = np.linalg.solve(self.covarXX[m], src - self.src_means[m])\\n E[m] = self.tgt_means[m] + self.covarYX[m].dot(xx)\\n\\n # Eq.(9) p(m|x)\\n posterior = self.px.predict_proba(np.atleast_2d(src))\\n\\n # Eq.(13) conditinal mean E[p(y|x)]\\n return posterior.dot(E).flatten()\",\n \"def connector_mediation(task):\\n\\tatlas = 'power'\\n\\tproject='hcp'\\n\\tknown_membership,network_names,num_nodes,name_int_dict = network_labels(atlas)\\n\\tsubjects = np.load('%s/dynamic_mod/results/%s_%s_%s_subs_fz.npy' %(homedir,'hcp',task,atlas))\\n\\tstatic_results = graph_metrics(subjects,task,atlas,run_version='fz')\\n\\tmatrices = static_results['matrices']\\n\\tsubject_pcs = static_results['subject_pcs']\\n\\tsubject_mods = static_results['subject_mods']\\n\\tmod_pc_corr = np.zeros(subject_pcs.shape[1])\\n\\tfor i in range(subject_pcs.shape[1]):\\n\\t\\tmod_pc_corr[i] = nan_pearsonr(subject_mods,subject_pcs[:,i])[0]\\n\\tmean_conn = np.nanmean(matrices,axis=0)\\n\\te_tresh = np.percentile(mean_conn,85)\\n\\tsubject_pcs[np.isnan(subject_pcs)] = 0.0\\n\\tm = np.zeros((264,264,264))\\n\\tpool = Pool(40)\\n\\tfor n in range(264):\\n\\t\\tprint n\\n\\t\\tsys.stdout.flush()\\n\\t\\tvariables = []\\n\\t\\tfor i,j in combinations(range(264),2):\\n\\t\\t\\tvariables.append(pd.DataFrame(data={'pc':subject_pcs[:,n],'weight':matrices[:,i,j],'q':subject_mods},index=range(len(subject_pcs))))\\n\\t\\tresults = pool.map(multi_med,variables)\\n\\t\\tfor r,i in zip(results,combinations(range(264),2)):\\n\\t\\t\\tm[n,i[0],i[1]] = r\\n\\t\\t\\tm[n,i[1],i[0]] = r\\n\\t\\tnp.save('/home/despoB/mb3152/dynamic_mod/results/full_med_matrix_new_%s.npy'%(task),m)\",\n \"def apply_alignment_matrix(job, matrix):\\n # Get the SVG-style 6-element vector from the 3x3 matrix\\n mat = [matrix[0][0], matrix[1][0], matrix[0][1], matrix[1][1], matrix[0][2], matrix[1][2]]\\n\\n # Only the 'defs' list contains coordinates that need to be transformed\\n defs = job['defs']\\n\\n for one_def in defs:\\n if one_def['kind'] == \\\"image\\\":\\n # TODO: raster images are not supported!\\n # The rest of the code assumes the rows of pixel data are aligned\\n # with the X axis, so handling rotation will require transforming\\n # the entire image and generating new data.\\n pass\\n elif one_def['kind'] == \\\"path\\\":\\n for one_path in one_def['data']:\\n for one_point in one_path:\\n matrixApply(mat, one_point)\",\n \"def __update_transformation_view(self):\\n text = \\\"Computed transformation.\\\\n\\\\n\\\"\\n text += \\\"Rotation:\\\\n\\\"\\n for i in range(3):\\n text += str(self.rotation[i, 0]) + \\\" \\\" + str(self.rotation[i, 1]) + \\\" \\\" + str(self.rotation[i, 2]) + \\\"\\\\n\\\"\\n text += \\\"\\\\nTranslation:\\\\n\\\"\\n text += str(self.translation[0]) + \\\" \\\" + str(self.translation[1]) + \\\" \\\" + str(self.translation[2])\\n text += \\\"\\\\n\\\\nScale:\\\\n\\\"\\n text += str(self.scale)\\n text += \\\"\\\\n\\\\n4x4 Matrix:\\\\n\\\"\\n mat = self.__compose_transformation()\\n for i in range(4):\\n text += f\\\"{str(mat[i, 0])}, {str(mat[i, 1])}, {str(mat[i, 2])}, {str(mat[i, 3])}\\\\n\\\"\\n self.qt_transformation_textbox.setText(text)\",\n \"def finalize_fields(self):\\n \\n # Commented out until the MPI implementation is ready\\n self.comm.allreduce(self.delta_P_dc, op=mpi.SUM)\\n self.comm.allreduce(self.delta_P_omega, op=mpi.SUM)\\n\\n self.dc_coords[:,:,0] += self.delta_P_dc[:,:]\\n self.omega_coords[:,:,0] += self.delta_P_omega[:,:]\\n\\n self.delta_P_dc = np.zeros((self.n_modes_z,self.n_modes_r))\\n self.delta_P_omega = np.zeros((self.n_modes_z,self.n_modes_r))\",\n \"def compute_T_matrix(coordinates, p, reference=[[1,0,0],[0,1,0],[0,0,1]], origin=[0,0,0]):\\n e_b_x = coordinates[0]\\n e_b_y = coordinates[1]\\n e_b_z = coordinates[2]\\n \\n e_a_x = reference[0] \\n e_a_y = reference[1]\\n e_a_z = reference[2]\\n \\n # Compute the rotation matrix\\n x_b_a = [np.dot(e_b_x, e_a_x), np.dot(e_b_x, e_a_y), np.dot(e_b_x, e_a_z)]\\n y_b_a = [np.dot(e_b_y, e_a_x), np.dot(e_b_y, e_a_y), np.dot(e_b_y, e_a_z)]\\n z_b_a = [np.dot(e_b_z, e_a_x), np.dot(e_b_z, e_a_y), np.dot(e_b_z, e_a_z)]\\n \\n R_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0]],[x_b_a[1], y_b_a[1], z_b_a[1]],x_b_a[2], y_b_a[2], z_b_a[2]]\\n \\n # Compute the displacement \\n displacement = [p[0]-origin[0], p[1]-origin[1], p[2]-origin[2]]\\n \\n # Make it into a transform matrix\\n T_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0], displacement[0]],\\n [x_b_a[1], y_b_a[1], z_b_a[1], displacement[1]],\\n [x_b_a[2], y_b_a[2], z_b_a[2], displacement[2]],\\n [0, 0, 0, 1]]\\n \\n T_a_b = np.linalg.inv(T_b_a).tolist()\\n \\n return T_b_a, T_a_b\",\n \"def compute_T_matrix(coordinates, p, reference=[[1,0,0],[0,1,0],[0,0,1]], origin=[0,0,0]):\\n e_b_x = coordinates[0]\\n e_b_y = coordinates[1]\\n e_b_z = coordinates[2]\\n \\n e_a_x = reference[0] \\n e_a_y = reference[1]\\n e_a_z = reference[2]\\n \\n # Compute the rotation matrix\\n x_b_a = [np.dot(e_b_x, e_a_x), np.dot(e_b_x, e_a_y), np.dot(e_b_x, e_a_z)]\\n y_b_a = [np.dot(e_b_y, e_a_x), np.dot(e_b_y, e_a_y), np.dot(e_b_y, e_a_z)]\\n z_b_a = [np.dot(e_b_z, e_a_x), np.dot(e_b_z, e_a_y), np.dot(e_b_z, e_a_z)]\\n \\n R_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0]],[x_b_a[1], y_b_a[1], z_b_a[1]],x_b_a[2], y_b_a[2], z_b_a[2]]\\n \\n # Compute the displacement \\n displacement = [p[0]-origin[0], p[1]-origin[1], p[2]-origin[2]]\\n \\n # Make it into a transform matrix\\n T_b_a = [[x_b_a[0], y_b_a[0], z_b_a[0], displacement[0]],\\n [x_b_a[1], y_b_a[1], z_b_a[1], displacement[1]],\\n [x_b_a[2], y_b_a[2], z_b_a[2], displacement[2]],\\n [0, 0, 0, 1]]\\n \\n T_a_b = np.linalg.inv(T_b_a).tolist()\\n \\n return T_b_a, T_a_b\",\n \"def apply_dof_transformation_to_transpose_interval(entity_transformations, entity_dofs,\\n data, cell_info):\\n return\",\n \"def generateMatrix():\\n num_probes = 16\\n\\n # print(position_vectors)\\n # Create the (48x4) calibration matrix:\\n calibration_lookup= [[0] * 3 for i in range(num_probes)]\\n calibration_matrix = [[0] * 9 for i in range(num_probes)]\\n counter = 0\\n\\n # first populate with x-direction:\\n shot_range = [17, 20] #x-direction\\n dir = 0 #the direction of the orentation of the probe array\\n position_vectors = get_probeLocs_calib_setup(dir)\\n for probe_num in range(num_probes):\\n position_vector = position_vectors[probe_num]\\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\\n calibration_lookup[probe_num][dir] = ratio_Bx\\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\\n print(\\\"Progress: %d / %d\\\" %(counter+1,num_probes*3 ))\\n counter +=1\\n\\n # Then populate with y-direction:\\n shot_range = [21, 25] #y-direction\\n dir = 1 #the direction of the orentation of the probe array\\n position_vectors = get_probeLocs_calib_setup(dir)\\n for probe_num in range(num_probes):\\n position_vector = position_vectors[probe_num]\\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\\n calibration_lookup[probe_num][dir] = ratio_By\\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\\n print(\\\"Progress: %d / %d\\\" %(counter+1,num_probes*3 ))\\n counter +=1\\n\\n # Then populate with z-direction:\\n shot_range = [11, 15] #z-direction\\n dir = 2\\n position_vectors = get_probeLocs_calib_setup(dir)\\n for probe_num in range(num_probes):\\n position_vector = position_vectors[probe_num]\\n ratio_Bx, ratio_By, ratio_Bz, norm_factor= getRatio(probe_num, position_vector, shot_range, dir)\\n calibration_lookup[probe_num][dir] = ratio_Bz\\n calibration_matrix[probe_num][(dir*3):(dir*3+1)] = [ratio_Bx, ratio_By, ratio_Bz]\\n print(\\\"Progress: %d / %d\\\" %(counter+1,num_probes*3 ))\\n counter +=1\\n\\n pth = os.getcwd()\\n date = '050119'\\n print(\\\"Finished! File saved as calib-%s-4x4_lookup.txt and _3x3 in current working directory\\\" %(date))\\n savetxt(os.path.join(pth, 'calib-%s-4x4_lookup_no_switch.txt' % (date)) , calibration_lookup)\\n savetxt(os.path.join(pth, 'calib-%s-4x4_3x3_no_switch.txt' % (date)) , calibration_matrix)\",\n \"def position_modules_interpolate(self, data):\\n assert data.shape == (16, 512, 128)\\n size_yx, centre = self._get_dimensions()\\n tmp = np.empty((16 * 8,) + size_yx, dtype=data.dtype)\\n\\n for i, (module, mod_data) in enumerate(zip(self.modules, data)):\\n tiles_data = np.split(mod_data, 8)\\n for j, (tile, tile_data) in enumerate(zip(module, tiles_data)):\\n # We store (x, y, z), but numpy indexing, and hence affine_transform,\\n # work like [y, x]. Rearrange the numbers:\\n fs_vec_yx = tile.fs_vec[:2][::-1]\\n ss_vec_yx = tile.ss_vec[:2][::-1]\\n\\n # Offset by centre to make all coordinates positive\\n corner_pos_yx = tile.corner_pos[:2][::-1] + centre\\n\\n # Make the rotation matrix\\n rotn = np.stack((ss_vec_yx, fs_vec_yx), axis=-1)\\n\\n # affine_transform takes a mapping from *output* to *input*.\\n # So we reverse the forward transformation.\\n transform = np.linalg.inv(rotn)\\n offset = np.dot(rotn, corner_pos_yx) # this seems to work, but is it right?\\n\\n affine_transform(\\n tile_data,\\n transform,\\n offset=offset,\\n cval=np.nan,\\n output_shape=size_yx,\\n output=tmp[i * 8 + j],\\n )\\n\\n # Silence warnings about nans - we expect gaps in the result\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", category=RuntimeWarning)\\n out = np.nanmax(tmp, axis=0)\\n\\n return out, centre\",\n \"def combine_par(output_dir): \\n #start time\\n start_time = time.time()\\n \\n # set input/output file paths\\n infile0 = output_dir + 'TransformParameters.0.txt'\\n infile1 = output_dir + 'TransformParameters.1.txt'\\n outfile0 = output_dir +'TransformParameters.fwd.txt'\\n outfile1 = output_dir +'TransformParameters.inv.txt'\\n \\n # define reference frame for registration\\n ref = 0\\n spacing = 1\\n \\n # Open parameter file 0 and search for GridSpacing and GridOrigin line\\n text_filein0 = open( infile0, \\\"r\\\" )\\n for line in text_filein0:\\n if line.find( \\\"(GridOrigin \\\" ) == 0:\\n origin_str = line\\n elif line.find( \\\"(GridSpacing \\\" ) == 0:\\n spacing_str = line\\n text_filein0.close()\\n \\n # Extract time point origin from line\\n origin_split = origin_str.strip().split(' ')\\n origin_split = origin_split[ len( origin_split ) - 1 ].split(')')\\n old_origin = float( origin_split[ 0 ] )\\n \\n # Extract time point spacing from line\\n spacing_split = spacing_str.strip().split(' ')\\n spacing_split = spacing_split[ len( spacing_split ) - 1 ].split(')')\\n old_spacing = float( spacing_split[ 0 ] )\\n \\n \\n print(\\\"Original grid origin in time dimension: \\\" + str( old_origin ))\\n print(\\\"Original grid spacing in time dimension: \\\" + str( old_spacing ))\\n print(\\\"\\\")\\n \\n # Determine new grid origin\\n new_origin = ref - ( spacing / old_spacing ) * ( ref - old_origin )\\n print( \\\"New grid origin in time dimension: \\\" + str( new_origin ))\\n \\n # Recompose origin and spacing lines\\n new_origin_string = origin_str.strip().split(' ')\\n new_origin_string.pop()\\n new_origin_string = \\\" \\\".join( new_origin_string ) + \\\" \\\" + str( new_origin ) + \\\")\\\\n\\\"\\n new_spacing_string = spacing_str.strip().split(' ')\\n new_spacing_string.pop()\\n new_spacing_string = \\\" \\\".join( new_spacing_string ) + \\\" \\\" + str( spacing ) + \\\")\\\\n\\\"\\n \\n # Reopen text file, replace origin and spacing and write to output file 0\\n text_filein0 = open( infile0, \\\"r\\\" )\\n text_fileout0 = open( outfile0, \\\"w\\\" )\\n for line in text_filein0:\\n if line.find( \\\"(GridOrigin \\\" ) == 0:\\n # Write new origin line\\n text_fileout0.write( new_origin_string )\\n elif line.find( \\\"(GridSpacing \\\" ) == 0:\\n # Write new spacing line\\n text_fileout0.write( new_spacing_string )\\n elif line.find( \\\"(InitialTransformParametersFileName \\\" ) == 0:\\n # Remove initial transform\\n text_fileout0.write( \\\"(InitialTransformParametersFileName \\\\\\\"NoInitialTransform\\\\\\\")\\\\n\\\" )\\n else:\\n # Write line read from input file (no change)\\n text_fileout0.write( line )\\n text_filein0.close()\\n text_fileout0.close()\\n \\n # Open parameter file 1 and search for GridSize\\n text_filein1 = open( infile1, \\\"r\\\" )\\n for line in text_filein1:\\n if line.find(\\\"(GridSize\\\") == 0:\\n grid_str = line\\n grid_split = grid_str.strip().split(' ')\\n grid_split[-1] = grid_split[-1].replace(')','')\\n grid_split = grid_split[1:]\\n grid_float = [float(s) for s in grid_split]\\n grid_all = int(grid_float[0] * grid_float[1] * grid_float[2] * grid_float[3])\\n num_phase = int(grid_float[3])\\n text_filein1.close()\\n \\n # Replace initial transform parameter filename\\n text_filein1 = open( infile1, \\\"r\\\" )\\n text_fileout1 = open( outfile1, \\\"w\\\" )\\n for line in text_filein1:\\n if line.find( \\\"(InitialTransformParametersFileName \\\" ) == 0:\\n # Set initial transform filename\\n text_fileout1.write( \\\"(InitialTransformParametersFileName \\\\\\\"\\\" + outfile0 + \\\"\\\\\\\")\\\\n\\\" )\\n elif line.find(\\\"(TransformParameters \\\") == 0:\\n # extract b-spline parameters, arrangment : x (Px*Py*Pz), y(Px*Py*Pz), z(Px*Py*Pz), t(Px*Py*Pz)\\n transPar_str = line\\n transPar_split = transPar_str.strip().split(' ')\\n transPar_split[-1] = transPar_split[-1].replace(')','')\\n transPar_split = transPar_split[1:]\\n num_grid3d = int(grid_all / num_phase) \\n str_seg = transPar_split[(ref*num_grid3d):((ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all+(ref*num_grid3d)): (grid_all + (ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all*2+(ref*num_grid3d)): (grid_all*2 + (ref+1)*num_grid3d)] * num_phase + transPar_split[(grid_all*3+(ref*num_grid3d)): (grid_all*3 + (ref+1)*num_grid3d)] * num_phase\\n #str_seg = \\\"\\\"\\n #str_seg = [str_seg + transPar_split[((ref*num_grid3d)+grid*i):((ref+1)*num_grid3d+grid*i)] * num_phase for i in range(4)]\\n str_joined = ' '.join(str_seg)\\n text_fileout1.write(\\\"(TransformParameters \\\" + str_joined + \\\")\\\\n\\\")\\n else:\\n # Write line read from input file (no change)\\n text_fileout1.write( line )\\n text_filein1.close()\\n text_fileout1.close()\\n \\n # caclulate elapsed time\\n end_time = time.time()\\n elapsed_time = end_time - start_time\\n print('combine_par done. elapsed time:', elapsed_time, 's')\",\n \"def run(self):\\n #self.source_fixed = center_of_mass(self.source_mesh.vs)\\n #self.destination_fixed = center_of_mass(self.destination_mesh.vs)\\n\\n source_high_curvature = get_mesh_of_high_curvature(self.source_mesh)\\n destination_high_curvature = get_mesh_of_high_curvature(self.destination_mesh)\\n\\n self.matrix, error = icp(source_high_curvature.vs, \\n destination_high_curvature.vs, \\n self.source_fixed, \\n self.destination_fixed,\\n 300,\\n verbose = self.verbose)\\n\\n self.inverse = np.linalg.inv(self.matrix)\\n self.global_confidence = 1 - error\\n print self.global_confidence\",\n \"def apply_transformation_np(source, transformation):\\n source_homog = np.ones((source.shape[0], 4))\\n source_homog[:, :-1] = source\\n # source_homog = np.hstack(\\n # (source, np.ones(source.shape[0], 1))\\n # )\\n\\n source_transformed = np.matmul(transformation, source_homog.T).T[:, :-1]\\n return source_transformed\",\n \"def align_images(self, translation_only=False):\\n # Extract feature point locations and descriptors.\\n points_and_descriptors = []\\n for file in self.files:\\n image = sol4_utils.read_image(file, 1)\\n self.images.append(image)\\n self.h, self.w = image.shape\\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, PYR_FLT_LEVEL)\\n points_and_descriptors.append(find_features(pyramid))\\n\\n # Compute homographies between successive pairs of images.\\n Hs = []\\n for i in range(len(points_and_descriptors) - 1):\\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\\n\\n # Find matching feature points.\\n ind1, ind2 = match_features(desc1, desc2, MATCH_MIN_SCORE)\\n points1, points2 = points1[ind1, :], points2[ind2, :]\\n\\n # Compute homography using RANSAC.\\n H12, inliers = ransac_homography(points1, points2,\\n RANSAC_ITER_NUM, RANAC_ERR_TOL,\\n translation_only)\\n\\n # Uncomment for debugging: display inliers and outliers among matching points.\\n # In the submitted code this function should be commented out!\\n if(self.display_match):\\n display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\\n\\n Hs.append(H12)\\n\\n # Compute composite homographies from the central coordinate system.\\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\\n self.homographies = np.stack(accumulated_homographies)\\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\\n print((self.frames_for_panoramas.shape))\\n self.homographies = self.homographies[self.frames_for_panoramas]\",\n \"def append_direct_component_matrices(technique_data: TransitiveTechniqueData):\\n for technique in technique_data.technique.get_component_techniques():\\n direct_calculator = DirectTechniqueCalculator(technique.definition)\\n similarity_matrix = direct_calculator.calculate_technique_data(\\n technique_data.dataset\\n ).similarity_matrix\\n technique_data.transitive_matrices.append(similarity_matrix)\",\n \"def reconstruct(self):\\n self.print_tee(\\\"starting reconstruction\\\", self.log_file)\\n\\n # #############################################\\n # estimate SIM parameters\\n # #############################################\\n # estimate frequencies\\n tstart = time.time() # since done using joblib, process_time() is not useful...\\n\\n if self.use_fixed_frq:\\n self.frqs = self.frqs_guess\\n self.print_tee(\\\"using fixed frequencies\\\", self.log_file)\\n else:\\n if self.find_frq_first:\\n self.frqs = self.estimate_sim_frqs(self.imgs_ft, self.imgs_ft, self.frqs_guess)\\n else:\\n self.print_tee(\\\"doing phase demixing prior to frequency finding\\\", self.log_file)\\n self.separated_components_ft = separate_components(self.imgs_ft, self.phases_guess, np.ones((self.nangles, self.nphases)))\\n imgs1 = np.expand_dims(self.separated_components_ft[:, 0], axis=1)\\n imgs2 = np.expand_dims(self.separated_components_ft[:, 1], axis=1)\\n self.frqs = self.estimate_sim_frqs(imgs1, imgs2, self.frqs_guess)\\n\\n tend = time.time()\\n self.print_tee(\\\"fitting frequencies took %0.2fs\\\" % (tend - tstart), self.log_file)\\n\\n # estimate phases\\n tstart = time.process_time()\\n if self.use_fixed_phase:\\n self.phases = self.phases_guess\\n self.amps = np.ones((self.nangles, self.nphases))\\n self.print_tee(\\\"Using fixed phases\\\", self.log_file)\\n else:\\n self.phases, self.amps = self.estimate_sim_phases(self.frqs, self.phases_guess)\\n\\n tend = time.process_time()\\n self.print_tee(\\\"estimated %d phases in %0.2fs\\\" % (self.nangles * self.nphases, tend - tstart), self.log_file)\\n\\n # separate components\\n self.separated_components_ft = separate_components(self.imgs_ft, self.phases, self.amps)\\n\\n # estimate modulation depths and power spectrum fit parameters\\n tstart = time.process_time()\\n # for the moment, need to do this feet even if have fixed mod depth, because still need the\\n # power spectrum parameters\\n self.mod_depths, self.power_spectrum_params, self.pspec_masks = self.estimate_mod_depths()\\n\\n if self.use_fixed_mod_depths:\\n self.mod_depths = np.zeros((self.nangles, self.nphases))\\n self.mod_depths[:, 0] = 1\\n for jj in range(1, self.nphases):\\n self.mod_depths[:, jj] = self.mod_depths_guess\\n\\n # also correct power spectrum params\\n self.power_spectrum_params[:, jj, 2] = self.mod_depths[:, jj]\\n\\n tend = time.process_time()\\n self.print_tee('estimated %d modulation depths in %0.2fs' % (self.nangles, tend - tstart), self.log_file)\\n\\n # #############################################\\n # estimate modulation contrast to noise ratio for raw images\\n # #############################################\\n mcnr = np.zeros((self.nangles, self.nphases))\\n for ii in range(self.nangles):\\n for jj in range(self.nphases):\\n mcnr[ii, jj] = get_mcnr(self.imgs_ft[ii, jj], self.frqs[ii], self.fx, self.fy, self.fmax)\\n\\n # if mcnr is too low (typically < 1), use guess values instead\\n if self.default_to_guess_on_low_mcnr and np.min(mcnr[ii]) < self.min_mcnr and self.frqs_guess is not None:\\n self.frqs[ii] = self.frqs_guess[ii]\\n self.print_tee(\\\"Angle %d/%d, minimum mcnr = %0.2f is less than the minimum value, %0.2f,\\\"\\n \\\" so fit frequency will be replaced with guess\\\"\\n % (ii + 1, self.nangles, np.min(mcnr[ii]), self.min_mcnr), self.log_file)\\n\\n for jj in range(self.nphases):\\n mcnr[ii, jj] = get_mcnr(self.imgs_ft[ii, jj], self.frqs[ii], self.fx, self.fy, self.fmax)\\n\\n self.mcnr = mcnr\\n # for convenience, also save periods and angles\\n self.periods = 1 / np.sqrt(self.frqs[:, 0] ** 2 + self.frqs[:, 1] ** 2)\\n self.angles = np.angle(self.frqs[:, 0] + 1j * self.frqs[:, 1])\\n\\n # #############################################\\n # estimate noise in component images\\n # #############################################\\n self.noise_power = self.power_spectrum_params[:, :, -1]\\n\\n # #############################################\\n # SIM reconstruction\\n # #############################################\\n tstart = time.process_time()\\n self.img_sr, self.img_sr_ft, self.components_deconvolved_ft, self.components_shifted_ft, \\\\\\n self.weights, self.weight_norm, self.snr, self.snr_shifted = self.combine_components()\\n # self.img_sr, self.img_sr_ft, self.components_deconvolved_ft, self.components_shifted_ft, \\\\\\n # self.weights, self.weight_norm, self.snr, self.snr_shifted = self.combine_components_fairSIM()\\n tend = time.process_time()\\n\\n self.print_tee(\\\"combining components took %0.2fs\\\" % (tend - tstart), self.log_file)\\n\\n # #############################################\\n # widefield deconvolution\\n # #############################################\\n # get signal to noise ratio\\n wf_noise = get_noise_power(self.widefield_ft, self.fx, self.fy, self.fmax)\\n fit_result, self.mask_wf = fit_power_spectrum(self.widefield_ft, self.otf, self.fx, self.fy, self.fmax, self.fbounds,\\n init_params=[None, self.power_spectrum_params[0, 0, 1], 1, wf_noise],\\n fixed_params=[False, True, True, True])\\n\\n self.pspec_params_wf = fit_result['fit_params']\\n ff = np.sqrt(self.fx[None, :]**2 + self.fy[:, None]**2)\\n sig = power_spectrum_fn([self.pspec_params_wf[0], self.pspec_params_wf[1], self.pspec_params_wf[2], 0], ff, 1)\\n wf_snr = sig / wf_noise\\n # deconvolution\\n wf_decon_ft, wfilter = wiener_deconvolution(self.widefield_ft, self.otf, wf_snr, snr_includes_otf=False)\\n\\n # upsample to make fully comparable to reconstructed image\\n self.widefield_deconvolution_ft = tools.expand_fourier_sp(wf_decon_ft, 2, 2, centered=True)\\n self.widefield_deconvolution = fft.fftshift(fft.ifft2(fft.ifftshift(self.widefield_deconvolution_ft))).real\\n\\n # #############################################\\n # print parameters\\n # #############################################\\n self.print_parameters()\\n\\n try:\\n self.log_file.close()\\n except AttributeError:\\n pass\",\n \"def apply_transform_matrix(self, img: np.ndarray, transform_matrix):\\n h, w = img.shape[0], img.shape[1]\\n transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)\\n img = np.rollaxis(img, 2, 0)\\n final_affine_matrix = transform_matrix[:2, :2]\\n final_offset = transform_matrix[:2, 2]\\n\\n channel_images = [scipy.ndimage.interpolation.affine_transform(\\n x_channel,\\n final_affine_matrix,\\n final_offset,\\n order=1,\\n mode=self.fill_mode,\\n cval=self.cval) for x_channel in img]\\n img = np.stack(channel_images, axis=0)\\n img = np.rollaxis(img, 0, 2 + 1)\\n # img = apply_affine_transform(img, transform_matrix, channel_axis=2, fill_mode=self.fill_mode, cval=self.cval) # apply_transform\\n return img\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H1 + sigma_H1\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def _compute_correction(self, initial_state, final_state, a, b, c, s):\\r\\n pertub = self.pertub\\r\\n pertub_s = pertub *10\\r\\n \\r\\n pred_no_pertub = self._motion_update_one_shot(initial_state, a, b, c, s)\\r\\n pred_pertub_a = self._motion_update_one_shot(initial_state, a +pertub, b, c, s)\\r\\n pred_pertub_b = self._motion_update_one_shot(initial_state, a, b +pertub, c, s)\\r\\n # no need to correct C, C is constrained by kappa_final\\r\\n # # pred_pertub_c = self._motion_update_one_shot(initial_state, a, b, c +pertub, s)\\r\\n pred_pertub_s = self._motion_update_one_shot(initial_state, a, b, c, s +pertub_s)\\r\\n\\r\\n d_state = np.zeros((3,1))\\r\\n d_pertub_state = np.zeros((3,3))\\r\\n Jacobian = np.zeros((3,3))\\r\\n for i in range(0, 3):\\r\\n d_pertub_state[i][0] = (final_state[i] - pred_pertub_a[i]) # a\\r\\n d_pertub_state[i][1] = (final_state[i] - pred_pertub_b[i]) # b\\r\\n # d_pertub_state[i][2] = (final_state[i] - pred_pertub_c[i]) # c (no update)\\r\\n d_pertub_state[i][2] = (final_state[i] - pred_pertub_s[i]) # s\\r\\n \\r\\n d_state[i] = final_state[i] - pred_no_pertub[i]\\r\\n \\r\\n Jacobian[i][0] = (d_pertub_state[i][0] - d_state[i])/pertub # a\\r\\n Jacobian[i][1] = (d_pertub_state[i][1] - d_state[i])/pertub # b\\r\\n # Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub # c (no update)\\r\\n Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub_s # s\\r\\n\\r\\n # inv_Jacobian = np.linalg.inv(Jacobian)\\r\\n inv_Jacobian = np.linalg.pinv(Jacobian)\\r\\n correction = np.dot(inv_Jacobian, d_state)\\r\\n # pdb.set_trace()\\r\\n return correction\",\n \"def shifts_projection(sc, clean):\\n def shifts_projected(clean, axis):\\n projected = clean.map(lambda x: x.mean(axis=axis)[:, :, np.newaxis])\\n target = getTarget(projected, 30, 1)\\n shifts = registerByPlane(sc, projected, target[:, :, np.newaxis], 10, False)\\n return shifts[:, :, 0]\\n\\n # shifts_xy = shifts_projected(clean, 2)\\n shifts_xz = shifts_projected(clean, 1)\\n shifts_yz = shifts_projected(clean, 0)\\n\\n # x_shifts = np.mean(np.stack((shifts_xz[:, 0], shifts_xy[:, 0])), axis=0)\\n z_shifts = np.mean(np.stack((shifts_xz[:, 1], shifts_yz[:, 1])), axis=0)\\n # y_shifts = np.mean(np.stack((shifts_yz[:, 0], shifts_xy[:, 1])), axis=0)\\n plt.figure()\\n plt.plot(shifts_xz[:, 1])\\n plt.plot(shifts_yz[:, 1])\\n plt.plot(z_shifts)\\n plt.title('Z')\\n # plt.figure()\\n # plt.plot(shifts_xz[:, 0])\\n # plt.plot(shifts_xy[:, 0])\\n # plt.plot(x_shifts)\\n # plt.title('X')\\n # plt.figure()\\n # plt.plot(shifts_yz[:, 0])\\n # plt.plot(shifts_xy[:, 1])\\n # plt.plot(y_shifts)\\n # plt.title('Y')\\n # shifts_all = np.stack((x_shifts, y_shifts, z_shifts))\\n\\n def initReg(kv):\\n from scipy.ndimage.interpolation import shift\\n index, volume = kv\\n current_shift = (0, 0, -1 * z_shifts[int(index[0])])\\n shifted = shift(volume, current_shift)\\n return shifted.astype(np.int16)\\n\\n reg = clean.map(initReg, with_keys=True, value_shape=clean.shape[1:], dtype=np.int16)\\n reg.cache()\\n reg.count()\\n return reg\",\n \"def _calc_matrix(self):\\n\\t\\tz = self.zoom\\n\\t\\talloc = self.allocation\\n\\t\\tif self.image:\\n\\t\\t\\tiw, ih = self.image.get_width(), self.image.get_height()\\n\\t\\telse:\\n\\t\\t\\tiw, ih = 0, 0\\n#\\t\\tif __debug__: print self._vadj.lower, self._vadj.value, self._vadj.upper\\n\\t\\t\\n\\t\\ti2w = cairo.Matrix(\\n\\t\\t\\tz,0,\\n\\t\\t\\t0,z,\\n\\t\\t\\t-self._hadj.value if alloc.width < iw*z else (alloc.width - iw*z)/2, \\n\\t\\t\\t-self._vadj.value if alloc.height < ih*z else (alloc.height - ih*z)/2,\\n\\t\\t\\t)\\n\\t\\t\\n\\t\\tself._i2w_matrix = i2w\\n\\t\\t\\n\\t\\tw2i = cairo.Matrix(*i2w) #copy\\n\\t\\tw2i.invert()\\n\\t\\tself._w2i_matrix = w2i\",\n \"def design_matrix_df(meta):\\n\\n mouse = utils.meta_mouse(meta)\\n\\n # go\\n new_meta = {}\\n new_meta['go'] = np.zeros(len(meta))\\n new_meta['go'][meta['trialerror'].isin([0, 3, 5, 7]).values] = 1\\n assert np.sum(new_meta['go']) > 0\\n meta_df_out = pd.DataFrame(data=new_meta, index=meta.index)\\n\\n # nogo\\n new_meta = {}\\n new_meta['nogo'] = np.zeros(len(meta))\\n new_meta['nogo'][meta['trialerror'].isin([1, 2, 4, 6]).values] = 1\\n assert np.sum(new_meta['nogo']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get cues defined by new terms\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[mm] = np.zeros(len(meta))\\n new_meta[mm][meta.initial_condition.isin([inverted_lookup[mm]]).values] = 1\\n assert np.sum(new_meta[mm]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get previous cue same\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[f'prev_same_{mm}'] = np.zeros(len(meta))\\n new_meta[f'prev_same_{mm}'][meta[f'prev_same_{inverted_lookup[mm]}'].values] = 1\\n assert np.sum(new_meta[f'prev_same_{mm}']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get previous cue diff\\n new_meta = {}\\n mm_type = ['becomes_unrewarded', 'remains_unrewarded', 'becomes_rewarded']\\n inverted_lookup = {v:k for k, v in lookups.lookup_mm[mouse].items()}\\n for mm in mm_type:\\n new_meta[f'prev_diff_{mm}'] = np.zeros(len(meta))\\n not_same = ~meta[f'prev_same_{inverted_lookup[mm]}'].values & meta.initial_condition.isin([inverted_lookup[mm]]).values\\n new_meta[f'prev_diff_{mm}'][not_same] = 1\\n assert np.sum(new_meta[f'prev_diff_{mm}']) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get salient events\\n new_meta = {}\\n for col in ['prev_reward', 'prev_punish', 'prev_blank', 'hmm_engaged']:\\n new_meta[col] = np.zeros(len(meta))\\n new_meta[col][meta[col].values] = 1\\n assert np.sum(new_meta[col]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get normalized running and baseline licking\\n new_meta = {}\\n for col in ['speed', 'pre_speed', 'post_speed', 'pre_licks']:\\n new_meta[col] = meta[col].values / np.nanmax(meta[col].values)\\n new_meta[col][np.isnan(new_meta[col])] = 0\\n assert np.sum(new_meta[col]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n # get normalized dprime\\n new_meta = {}\\n for col in ['dprime_run']:\\n new_meta[col] = meta[col].values - np.nanmin(meta[col].values)\\n new_meta[col] = new_meta[col] / np.nanmax(new_meta[col])\\n new_meta[col][np.isnan(new_meta[col])] = 0\\n assert np.sum(new_meta[col]) > 0\\n new_meta_df = pd.DataFrame(data=new_meta, index=meta.index)\\n meta_df_out = pd.concat([meta_df_out, new_meta_df], axis=1)\\n\\n return meta_df_out\",\n \"def build_mm_df(sralist):\\n\\n def convert_to_codon(nts_array):\\n \\\"\\\"\\\"\\n pysam output is in nucleotides resolution, but scikit_curated_df uses codon resolution.\\n This function converts nucleotide arrays to codon length (nts to codon resolution):\\n \\\"\\\"\\\"\\n \\n nts_array = np.array(nts_array)\\n codon_array = np.sum( np.reshape(A, (int(np.floor(nts_array[1]/3)),3) ), 1)/3.\\n\\n return codon_array\\n\\n\\n def compute_mm(mmdata):\\n \\\"\\\"\\\"\\n get per gene average multi-mapping score\\n \\\"\\\"\\\"\\n\\n mm_df = pd.DataFrame(columns=['ORF', 'MM'])\\n counter = 0\\n\\n for gene in mmdata.keys():\\n current_matrix = mmdata[gene]\\n current_avrg = np.mean( np.sum(current_matrix, 1) / current_matrix.shape[1] )\\n mm_df.loc[counter] = [gene, current_avrg]\\n counter += 1\\n\\n return mm_df\\n\\n\\n mm_mat = {}\\n mm_pct = {}\\n\\n N = len(sralist)\\n\\n for ix, dataset in enumerate(sralist):\\n samfile = pysam.AlignmentFile(TMP_DIR+'/ambiguous_reads/'+dataset+'_STAR_transcriptome_multi_mapped_sorted.bam', 'rb')\\n genes_list = list(samfile.references)\\n print(ix, dataset)\\n\\n for geneID in genes_list:\\n # count the coverage of genomic positions by reads in region.\\n # Returns: four array.arrays of the same length in order A C G T\\n # The coverage is computed per-base [ACGT]\\n cov = samfile.count_coverage(geneID, read_callback='nofilter')\\n # Summ all 4 arrays\\n cov_sum = np.sum(cov, axis=0)\\n #print(geneID, cov_sum)\\n codon_cov = convert_to_codon(cov_sum)\\n codon_bool = np.asarray([1 if i > 0 else 0 for i in codon_cov])\\n \\n M = len(codon_bool)\\n\\n if ix == 0:\\n \\tmm_mat[geneID] = np.zeros((N,M)) * np.nan\\n \\n current_matrix = mm_mat[geneID]\\n current_matrix[ix,:] = np.copy(codon_bool)\\n mm_mat[geneID] = current_matrix\\n\\n\\n mm_avrg = compute_mm(mm_mat)\\n #mm_avrg.to_json('yeast_mm.json')\\n #mm_avrg.to_csv('yeast_mm.txt', header=True, index=False, sep='\\\\t')\\n\\n \\n mm_profile = {}\\n theta_mm = 5\\n for orf in mm_mat.keys():\\n current_mat = mm_mat[orf]\\n current_bool = np.sum(current_mat, 0) <= theta_mm\\n mm_profile[orf] = current_bool\\n\\n with open('../data/processed/mm_consensus.pkl', 'wb') as f_mm:\\n pickle.dump(mm_profile, f_mm)\\n\\n\\n return mm_mat, mm_avrg, mm_profile\",\n \"def run_phot_normalization(setup, **params):\\n\\n log = logs.start_stage_log( setup.red_dir, 'postproc_phot_norm', version=VERSION )\\n\\n xmatch = crossmatch.CrossMatchTable()\\n xmatch.load(params['crossmatch_file'],log=log)\\n\\n # Identify the datasets to be used as the primary reference in each\\n # filter:\\n xmatch.id_primary_datasets_per_filter()\\n log.info('Identified datasets to be used as the primary references in each filter: '\\\\\\n +repr(xmatch.reference_datasets))\\n\\n # Add columns to the dataset Table to hold the photometric calibration\\n # parameters\\n ndset = len(xmatch.datasets)\\n ncol = len(xmatch.datasets.colnames)\\n if 'norm_a0' not in xmatch.datasets.colnames:\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_a0', index=ncol+1)\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_a1', index=ncol+2)\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_0', index=ncol+3)\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_1', index=ncol+4)\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_2', index=ncol+5)\\n xmatch.datasets.add_column(np.zeros(ndset), name='norm_covar_3', index=ncol+6)\\n log.info('Expanded xmatch.datasets table for normalization parameters')\\n\\n # Extract list of filters from xmatch.images['filter'] column\\n filter_list = np.unique(xmatch.images['filter'].data)\\n log.info('Identified list of filters to process: '+repr(filter_list))\\n\\n # Read data from quadrant 1\\n # Reading in the timeseries data for all four quadrants is at the very\\n # edge of the memory limits on the machines available, so it is preferable\\n # to calibrate the quadrant's data separately. However, there are sufficient\\n # stars in each quantant to be able to determine the photometric calibration\\n # from a single quadrant, and apply it to the rest of the image.\\n log.info('Loading the timeseries photometry from quadrant 1')\\n file_path = path.join(setup.red_dir, params['field_name']+'_quad1_photometry.hdf5')\\n phot_data = hd5_utils.read_phot_from_hd5_file(file_path, return_type='array')\\n log.info('-> Completed photometry load')\\n\\n # Identify constant stars in the dataset\\n constant_stars = find_constant_stars(xmatch, phot_data, log)\\n star = 1\\n\\n # Normalize the photometry of each dataset to that of the reference\\n # image in the primary reference dataset in that filter\\n #for filter in filter_list:\\n for filter in filter_list:\\n\\n # Plot an RMS diagram of the lightcurves for all stars in this filter,\\n # prior to normalization, for comparison\\n image_index = np.where(xmatch.images['filter'] == filter)[0]\\n phot_data_filter = phot_data[:,image_index,:]\\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\\n qc_col = 16\\n\\n plot_multisite_rms(params, phot_data_filter, mag_col, mag_err_col, qc_col,\\n 'rms_prenorm_'+str(filter)+'.png', log)\\n\\n # Extract the reference image photometry for the primary-ref dataset\\n # for this filter\\n ref_datacode = xmatch.reference_datasets[filter]\\n sitecode = get_site_code(ref_datacode)\\n log.info('Reference dataset in '+filter+' is '+ref_datacode+', sitecode='+sitecode)\\n\\n ref_phot = np.zeros((len(xmatch.stars),2))\\n ref_phot[:,0] = xmatch.stars['cal_'+filter.replace('p','')+'_mag_'+sitecode]\\n ref_phot[:,1] = xmatch.stars['cal_'+filter.replace('p','')+'_magerr_'+sitecode]\\n\\n # Extract the lightcurves for all other datasets in this filter in turn\\n dataset_index = np.where(xmatch.datasets['dataset_filter'] == filter)[0]\\n\\n for idset in dataset_index:\\n dset_datacode = xmatch.datasets['dataset_code'][idset]\\n dset_sitecode = get_site_code(dset_datacode)\\n\\n # If the dataset is the reference dataset, replicate the photometric\\n # measurements from the corrected columns to the normalized columns,\\n # since no normalization is required - this ensures the full\\n # lightcurve can be accessed from the normalization columns.\\n if dset_datacode == ref_datacode:\\n log.info('Replicating primary reference photometry from dataset '\\\\\\n +dset_datacode+' to the normalized photometry columns')\\n image_index = np.where(xmatch.images['dataset_code'] == dset_datacode)[0]\\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\\n for i in image_index:\\n phot_data[:,i,norm_mag_col] = phot_data[:,i,mag_col]\\n phot_data[:,i,norm_mag_err_col] = phot_data[:,i,mag_err_col]\\n\\n # Normalize any dataset that isn't the same as the reference dataset\\n else:\\n log.info('Normalizing dataset '+dset_datacode+', sitecode='+dset_sitecode)\\n image_index = np.where(xmatch.images['dataset_code'] == dset_datacode)[0]\\n\\n ## Dset created to hold all stars in field, not quadrant - \\n # normalization is calculated from whole field.\\n dset_phot = np.zeros((len(xmatch.stars),2))\\n dset_phot[:,0] = xmatch.stars['cal_'+filter.replace('p','')+'_mag_'+dset_sitecode]\\n dset_phot[:,1] = xmatch.stars['cal_'+filter.replace('p','')+'_magerr_'+dset_sitecode]\\n\\n # Calculate their weighted offset relative to the primary-ref\\n # dataset for the filter\\n (fit, covar_fit) = calc_phot_normalization(ref_phot, dset_phot,\\n constant_stars, log,\\n diagnostics=True, ref=sitecode,\\n dset=dset_sitecode, f=filter)\\n\\n # Store the fit results for this dataset\\n xmatch = store_dataset_phot_normalization(idset, xmatch, fit, covar_fit, log)\\n\\n # Apply the normalization calibration to the dataset's reference\\n # image photometry, and store the results in the xmatch.stars table\\n log.info('Applying normalization to the datasets reference image photometry')\\n cal_phot = apply_phot_normalization_single_frame(fit, covar_fit, dset_phot,\\n 0, 1, log,\\n diagnostics=True, ref=sitecode,\\n dset=dset_sitecode, f=filter)\\n xmatch.stars['norm_'+filter.replace('p','')+'_mag_'+dset_sitecode] = cal_phot[:,0]\\n xmatch.stars['norm_'+filter.replace('p','')+'_magerr_'+dset_sitecode] = cal_phot[:,1]\\n\\n # Apply the photometry calibration to the timeseries data\\n # for this dataset\\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('corrected')\\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\\n phot_data = normalize_timeseries_photometry(phot_data, image_index,\\n fit, covar_fit,\\n mag_col, mag_err_col,\\n norm_mag_col, norm_mag_err_col,\\n log)\\n\\n # Plot a second RMS diagram of the lightcurves for all stars in this\\n # filter, post normalization, for comparison\\n image_index = np.where(xmatch.images['filter'] == filter)[0]\\n phot_data_filter = phot_data[:,image_index,:]\\n (mag_col, mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\\n plot_multisite_rms(params, phot_data_filter, mag_col, mag_err_col, qc_col,\\n 'rms_postnorm_'+str(filter)+'.png', log)\\n\\n\\n fig = plt.figure(3,(10,10))\\n (norm_mag_col, norm_mag_err_col) = field_photometry.get_field_photometry_columns('normalized')\\n idx = np.where(phot_data[star,:,norm_mag_col] > 0.0)[0]\\n plt.errorbar(phot_data[star,idx,0], phot_data[star,idx,norm_mag_col],\\n yerr=phot_data[star,idx,norm_mag_err_col], fmt='none', color='k')\\n (xmin,xmax,ymin,ymax) = plt.axis()\\n ymin = max(ymin,14.0)\\n ymax = min(ymax,22.0)\\n plt.axis([xmin,xmax,ymax,ymin])\\n plt.xlabel('HJD')\\n plt.ylabel('Mag')\\n plt.savefig('Star_'+str(star)+'_lc_norm.png')\\n plt.close(3)\\n\\n # Output updated crossmatch table\\n xmatch.save(params['crossmatch_file'])\\n\\n # Output the photometry for quadrant 1:\\n output_quadrant_photometry(params, setup, 1, phot_data, log)\\n\\n logs.close_log(log)\\n\\n status = 'OK'\\n report = 'Completed successfully'\\n return status, report\",\n \"def apply(self, data):\\n print(\\\"this is morphism '{}'\\\".format(self.name))\\n data = np.array(data)\\n transformed_data = self.transf(data)\\n return pd.DataFrame.from_dict({\\\"transf\\\": transformed_data.flatten()})\",\n \"def motions2hik(motions, device=0, cuda=True):\\n\\n nreps, njoints, nfeats, nframes = motions.shape\\n j2s = joints2smpl(num_frames=nframes, device_id=device, cuda=cuda)\\n\\n thetas = []\\n root_translation = []\\n for rep_idx in range(nreps):\\n rep_motions = motions[rep_idx].transpose(2, 0, 1) # [nframes, njoints, 3]\\n\\n if nfeats == 3:\\n print(f'Running SMPLify for repetition [{rep_idx + 1}] of {nreps}, it may take a few minutes.')\\n motion_tensor, opt_dict = j2s.joint2smpl(rep_motions) # [nframes, njoints, 3]\\n motion = motion_tensor.cpu().numpy()\\n\\n elif nfeats == 6:\\n motion = rep_motions\\n thetas.append(rep_motions)\\n\\n # Convert 6D rotation representation to Euler angles\\n thetas_6d = motion[0, :-1, :, :nframes].transpose(2, 0, 1) # [nframes, njoints, 6]\\n thetas_deg = []\\n for frame, d6 in enumerate(thetas_6d):\\n thetas_deg.append([_rotation_6d_to_euler(d6)])\\n\\n thetas.append([np.concatenate(thetas_deg, axis=0)])\\n root_translation.append([motion[0, -1, :3, :nframes].transpose(1, 0)]) # [nframes, 3]\\n\\n thetas = np.concatenate(thetas, axis=0)[:nframes]\\n root_translation = np.concatenate(root_translation, axis=0)[:nframes]\\n\\n data_dict = {\\n 'joint_map': JOINT_MAP,\\n 'thetas': thetas.tolist(), # [nreps, nframes, njoints, 3 (deg)]\\n 'root_translation': root_translation.tolist(), # [nreps, nframes, 3 (xyz)]\\n }\\n\\n return data_dict\",\n \"def computeCoarseAlignment(self, TiltSeries_, mute=True, outfile='', optimizeShift=True, logfile_residual=''):\\n #print('ref index: ', numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0], TiltSeries_._TiltAlignmentParas.ireftilt )\\n (psiindeg, shiftX, shiftY, x, y, z, distLine, diffX, diffY,\\n shiftVarX, shiftVarY) = alignmentFixMagRot(\\n Markers_=self._Markers, cTilt=self._cTilt, sTilt=self._sTilt,\\n ireftilt=numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0],\\n irefmark=TiltSeries_._TiltAlignmentParas.irefmark,\\n r=TiltSeries_._TiltAlignmentParas.r, imdim=TiltSeries_._imdim,imdimX=TiltSeries_._imdimX, imdimY=TiltSeries_._imdimY,\\n handflip=TiltSeries_._TiltAlignmentParas.handflip, mute=mute, writeResults=outfile,\\n optimizeShift=optimizeShift, logfile_residual=logfile_residual)\\n if not mute:\\n print((\\\"Tilt Axis: %.2f\\\" % psiindeg))\\n # copy parameters to TiltSeries\\n ireftilt = numpy.argwhere( self._projIndices.astype(int) == TiltSeries_._TiltAlignmentParas.ireftilt)[0][0]\\n self._alignmentRotations = numpy.array(self._ntilt * [psiindeg])\\n self.setRotationsInTiltSeries(TiltSeries_)\\n self._alignmentTransX = shiftX\\n self._alignmentTransY = shiftY\\n self.set_TranslationsInTiltSeries(TiltSeries_)\\n self.Psi = psiindeg\\n\\n for (imark, Marker) in enumerate(self._Markers):\\n Marker.set_r(numpy.array([x[imark], y[imark], z[imark]]))\\n # if not optimizeShift:\\n # Marker.set_r(numpy.array([x[imark] + 6.326546124766944 , y[imark] + 5.187672225662868, z[imark]]))\",\n \"def set_correction(self, matrix=[[1, 0], [0, 1]], shift=[0, 0], meta=None,\\n **kwargs):\\n # compute the matrix for the scale and rotation correction\\n shift = (np.asarray(shift) - np.dot(self._wcslin.wcs.crpix, matrix) +\\n self._wcslin.wcs.crpix)\\n\\n matrix = inv(matrix).T\\n\\n cwcs = self._wcs.deepcopy()\\n\\n # estimate step for numerical differentiation. We need a step\\n # large enough to avoid rounding errors and small enough to get a\\n # better precision for numerical differentiation.\\n # TODO: The logic below should be revised at a later time so that it\\n # better takes into account the two competing requirements.\\n crpix1, crpix2 = self._wcs.wcs.crpix\\n hx = max(1.0, min(20.0, (crpix1 - 1.0) / 100.0,\\n (self._wcs.pixel_shape[0] - crpix1) / 100.0))\\n hy = max(1.0, min(20.0, (crpix2 - 1.0) / 100.0,\\n (self._wcs.pixel_shape[1] - crpix2) / 100.0))\\n\\n # compute new CRVAL for the image WCS:\\n crpixinref = self._wcslin.wcs_world2pix(\\n self._wcs.wcs_pix2world([self._wcs.wcs.crpix], 1), 1)\\n crpixinref = np.dot(crpixinref - shift, matrix.T).astype(np.float64)\\n self._wcs.wcs.crval = self._wcslin.wcs_pix2world(crpixinref, 1)[0]\\n self._wcs.wcs.set()\\n\\n # approximation for CD matrix of the image WCS:\\n (U, u) = _linearize(cwcs, self._wcs, self._wcslin, self._wcs.wcs.crpix,\\n matrix, shift, hx=hx, hy=hy)\\n self._wcs.wcs.cd = np.dot(self._wcs.wcs.cd.astype(np.longdouble),\\n U).astype(np.float64)\\n self._wcs.wcs.set()\\n\\n # save linear transformation info to the meta attribute:\\n super().set_correction(matrix=matrix, shift=shift, meta=meta, **kwargs)\",\n \"def mirrorTransformations_Custom(self):\\n\\n pass\",\n \"def preprocess_gene(gene_id,data_dict,t2g_mapping,out_paths,locks):\\n \\n # features = ['read_id','transcript_id','transcriptomic_position','reference_kmer','norm_mean','start_idx','end_idx'] # columns in the eventalign file per read.\\n\\n events = []\\n condition_labels = []\\n run_labels = []\\n read_ids = []\\n genomic_coordinates = []\\n \\n # Concatenate\\n# if len(data_dict) == 0:\\n# return\\n\\n\\n for read_index,events_per_read in data_dict.items():\\n# if len(events_per_read) > 0:\\n # ===== transcript to gene coordinates ===== # TODO: to use gtf.\\n# tx_ids = [tx_id.decode('UTF-8').split('.')[0] for tx_id in events_per_read['transcript_id']]\\n tx_ids = [tx_id for tx_id in events_per_read['transcript_id']] \\n tx_positions = events_per_read['transcriptomic_position']\\n genomic_coordinate = list(itemgetter(*zip(tx_ids,tx_positions))(t2g_mapping)) # genomic_coordinates -- np structured array of 'chr','gene_id','genomic_position','kmer'\\n genomic_coordinate = np.array(genomic_coordinate,dtype=np.dtype([('chr','<U2'),('gene_id','<U15'),('genomic_position','<i4'),('g_kmer','<U5')]))\\n # ===== \\n\\n # Based on Ensembl, remove transcript version.\\n\\n events_per_read['transcript_id'] = tx_ids\\n events_per_read = np.array(events_per_read,dtype=np.dtype([('transcript_id', 'S15'), ('transcriptomic_position', '<i8'), ('reference_kmer', 'S5'), ('norm_mean', '<f8')]))\\n\\n #\\n\\n events += [events_per_read]\\n genomic_coordinates += [genomic_coordinate]\\n n_events_per_read = len(events_per_read)\\n# else:\\n# print(read_index,len(events_per_read))\\n\\n events = np.concatenate(events)\\n genomic_coordinates = np.concatenate(genomic_coordinates)\\n \\n # Sort and split # \\n# idx_sorted = np.lexsort((events['reference_kmer'],genomic_coordinates['genomic_position'],genomic_coordinates['gene_id']))\\n# key_tuples, index = np.unique(list(zip(genomic_coordinates['gene_id'][idx_sorted],genomic_coordinates['genomic_position'][idx_sorted],events['reference_kmer'][idx_sorted])),return_index = True,axis=0) #'chr',\\n# y_arrays = np.split(events['norm_mean'][idx_sorted], index[1:])\\n# # read_id_arrays = np.split(events['read_id'][idx_sorted], index[1:])\\n# g_kmer_arrays = np.split(genomic_coordinates['g_kmer'][idx_sorted], index[1:])\\n\\n idx_sorted = np.argsort(genomic_coordinates['genomic_position'])\\n unique_positions, index = np.unique(genomic_coordinates['genomic_position'][idx_sorted],return_index = True)\\n y_arrays = np.split(events['norm_mean'][idx_sorted], index[1:])\\n # read_id_arrays = np.split(events['read_id'][idx_sorted], index[1:])\\n g_kmer_arrays = np.split(genomic_coordinates['g_kmer'][idx_sorted], index[1:])\\n g_positions_arrays = np.split(genomic_coordinates['genomic_position'][idx_sorted], index[1:])\\n\\n # Prepare\\n # print('Reformating the data for each genomic position ...')\\n data = defaultdict(dict)\\n # for each position, make it ready for json dump\\n# data = dict(zip(key_tuples, y_arrays))\\n\\n asserted = True\\n# for key_tuple,y_array,g_kmer_array in zip(key_tuples,y_arrays,g_kmer_arrays):\\n for position,y_array,g_kmer_array,g_positions_array in zip(unique_positions,y_arrays,g_kmer_arrays,g_positions_arrays):\\n# gene_id,position,kmer = key_tuple \\n if (len(set(g_kmer_array)) == 1) and ('XXXXX' in set(g_kmer_array)) or (len(y_array) == 0):\\n continue\\n \\n if 'XXXXX' in set(g_kmer_array):\\n y_array = y_array[g_kmer_array != 'XXXXX'] \\n assert len(y_array) == len(g_kmer_array) - (g_kmer_array=='XXXXX').sum()\\n g_kmer_array = g_kmer_array[g_kmer_array != 'XXXXX'] \\n \\n try:\\n assert len(set(g_kmer_array)) == 1\\n assert {position} == set(g_positions_array)\\n except:\\n asserted = False\\n break\\n kmer = set(g_kmer_array).pop()\\n\\n data[position] = {kmer: list(y_array)} #,'read_ids': [read_id.decode('UTF-8') for read_id in read_id_array]}\\n \\n # write to file.\\n log_str = '%s: %s' %(gene_id,asserted)\\n\\n with locks['json'], open(out_paths['json'],'a') as f:\\n\\n pos_start = f.tell()\\n f.write('{')\\n f.write('\\\"%s\\\":' %gene_id)\\n ujson.dump(data, f)\\n f.write('}\\\\n')\\n pos_end = f.tell()\\n\\n with locks['index'], open(out_paths['index'],'a') as f:\\n f.write('%s,%d,%d\\\\n' %(gene_id,pos_start,pos_end))\\n \\n with locks['readcount'], open(out_paths['readcount'],'a') as f: #todo: repeats no. of tx >> don't want it.\\n n_reads = len(data_dict)\\n f.write('%s,%d\\\\n' %(gene_id,n_reads))\\n \\n with locks['log'], open(out_paths['log'],'a') as f:\\n f.write(log_str + '\\\\n')\",\n \"def set_correction(self, matrix=[[1, 0], [0, 1]], shift=[0, 0], meta=None,\\n **kwargs):\\n frms = self._wcs.available_frames\\n\\n # if original WCS did not have tangent-plane corrections, create\\n # new correction and add it to the WCs pipeline:\\n if self._tpcorr is None:\\n self._tpcorr = TPCorr(\\n v2ref=self._wcsinfo['v2_ref'] / 3600.0,\\n v3ref=self._wcsinfo['v3_ref'] / 3600.0,\\n roll=self._wcsinfo['roll_ref'],\\n matrix=matrix,\\n shift=shift,\\n name='tangent-plane linear correction'\\n )\\n idx_v2v3 = frms.index(self._v23name)\\n pipeline = deepcopy(self._wcs.pipeline)\\n pf, pt = pipeline[idx_v2v3]\\n pipeline[idx_v2v3] = (pf, deepcopy(self._tpcorr))\\n frm_v2v3corr = deepcopy(pf)\\n frm_v2v3corr.name = 'v2v3corr'\\n pipeline.insert(idx_v2v3 + 1, (frm_v2v3corr, pt))\\n self._wcs = gwcs.WCS(pipeline, name=self._owcs.name)\\n self._v23name = 'v2v3corr'\\n\\n else:\\n # combine old and new corrections into a single one and replace\\n # old transformation with the combined correction transformation:\\n tpcorr2 = self._tpcorr.__class__(\\n v2ref=self._tpcorr.v2ref, v3ref=self._tpcorr.v3ref,\\n roll=self._tpcorr.roll, matrix=matrix, shift=shift,\\n name='tangent-plane linear correction'\\n )\\n\\n self._tpcorr = tpcorr2.combine(tpcorr2, self._tpcorr)\\n\\n idx_v2v3 = frms.index(self._v23name)\\n pipeline = deepcopy(self._wcs.pipeline)\\n pipeline[idx_v2v3 - 1] = (pipeline[idx_v2v3 - 1][0],\\n deepcopy(self._tpcorr))\\n self._wcs = gwcs.WCS(pipeline, name=self._owcs.name)\\n\\n # reset definitions of the transformations from detector/world\\n # coordinates to the tangent plane:\\n self._update_transformations()\\n\\n # save linear transformation info to the meta attribute:\\n super().set_correction(matrix=matrix, shift=shift, meta=meta, **kwargs)\",\n \"def align_reconstruction_no_numpy(reconstruction, anchor_points_dict):\\n modified_shots_dict = {}\\n all_anchor_shot_ids = sorted(anchor_points_dict.keys())\\n for i in range(len(all_anchor_shot_ids) - 1):\\n anchor_coords = []\\n recon_coords = []\\n\\n for j in range(2):\\n shot_id = all_anchor_shot_ids[i+j]\\n anchor_coords.append(anchor_points_dict[shot_id])\\n o = get_origin_no_numpy_opencv(reconstruction.shots[shot_id].pose.rotation,\\n reconstruction.shots[shot_id].pose.translation)\\n\\n recon_coords.append(o)\\n\\n s, A, b = get_affine_transform_2d_no_numpy(anchor_coords, recon_coords)\\n\\n start_shot_id = all_anchor_shot_ids[i]\\n end_shot_id = all_anchor_shot_ids[i+1]\\n\\n # in first iteration, we transform from first shot of recon\\n # in last iteration, we transform until last shot of recon\\n shot_ids = sorted(reconstruction.shots.keys())\\n if i == 0:\\n start_shot_id = shot_ids[0]\\n\\n if i == len(anchor_points_dict)-2:\\n end_shot_id = shot_ids[-1]\\n\\n new_dict = {}\\n\\n start_index = _shot_id_to_int(start_shot_id)\\n end_index = _shot_id_to_int(end_shot_id)\\n\\n # transform pdr shots\\n for i in range(start_index, end_index + 1):\\n shot_id = _int_to_shot_id(i)\\n\\n if shot_id in reconstruction.shots:\\n X = get_origin_no_numpy_opencv(reconstruction.shots[shot_id].pose.rotation,\\n reconstruction.shots[shot_id].pose.translation)\\n A_dot_X = [A[0][0] * X[0] + A[0][1] * X[1] + A[0][2] * X[2],\\n A[1][0] * X[0] + A[1][1] * X[1] + A[1][2] * X[2],\\n A[2][0] * X[0] + A[2][1] * X[1] + A[2][2] * X[2]]\\n Xp = [i * s + j for i, j in zip(A_dot_X, b)]\\n new_dict[shot_id] = [Xp[0], Xp[1], Xp[2]]\\n\\n modified_shots_dict.update(new_dict)\\n\\n return modified_shots_dict\",\n \"def pan_corr(file):\\n\\n # # infile = 'd:\\\\\\\\Projekti\\\\\\\\Satelit\\\\\\\\CO\\\\\\\\Razpis\\\\\\\\Flat field images_new2020\\\\\\\\flatfield\\\\\\\\NHDBflat_1D'\\n # # infile = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Jure_naloga_banje_raw_pyt\\\\\\\\NHDRGoreMorje_3D'\\n #\\n # # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_1D_py.tif'\\n # in_ref = in_path + in_pan_ref_file\\n #\\n # inreffil = gdal.Open(in_ref)\\n # image_ref = inreffil.ReadAsArray()\\n # # size_ref = image_ref.shape\\n # # pix_count = size_ref[0]*size_ref[1]\\n #\\n # image_ref = image_ref[800:930, 1420:1640]\\n # size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n #\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # raz_g1 = 1\\n # raz_g2 = g1_avg/g2_avg\\n # raz_r1 = g1_avg/r1_avg\\n # raz_b1 = g1_avg/b1_avg\\n #\\n # avg = (g1+g2+r1+b1)/pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n raz_g1 = 1\\n raz_g2 = 1.0245196396115988\\n raz_r1 = 1.0131841989689434\\n raz_b1 = 1.0517113199247086\\n\\n print('razmerje:', raz_g1, raz_g2, raz_r1, raz_b1)\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Peking_PAN\\\\\\\\'\\n # in_pan_ref_file = 'NHDPfoc_swp6_4D_py.tif'\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\Flat field images_new2020\\\\\\\\20201028 Vignetting\\\\\\\\flatfield\\\\\\\\'\\n # in_pan_ref_file = 'NHDPflat_3D_py.tif'\\n\\n # in_path = 'd:\\\\Projekti\\\\Satelit\\\\CO\\\\Razpis\\\\_POSNETKI\\\\Slo_PAN\\\\_26_30\\\\\\\\'\\n # in_pan_ref_file = [filename for filename in os.listdir(in_path) if filename.lower().startswith(\\\"nhd\\\") and filename.lower().endswith(\\\"tif\\\")]\\n\\n \\n\\n \\n\\n # print('image', i)\\n in_ref=file\\n inreffil = gdal.Open(in_ref)\\n image_ref = inreffil.ReadAsArray()\\n size_ref = image_ref.shape\\n # pix_count = size_ref[0] * size_ref[1]\\n # pix_count = np.count_nonzero(image_ref)\\n # pix_count = 3664*650\\n\\n # g1 = 0.\\n # g2 = 0.\\n # r1 = 0.\\n # b1 = 0.\\n #\\n # for i in range(size_ref[0]):\\n # for j in range(size_ref[1]):\\n # if (i % 2) == 0 and (j % 2) == 0: g1 = g1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 1: g2 = g2 + image_ref[i, j]\\n # if (i % 2) == 0 and (j % 2) == 1: r1 = r1 + image_ref[i, j]\\n # if (i % 2) == 1 and (j % 2) == 0: b1 = b1 + image_ref[i, j]\\n #\\n # g1_avg = g1 / pix_count * 4\\n # g2_avg = g2 / pix_count * 4\\n # r1_avg = r1 / pix_count * 4\\n # b1_avg = b1 / pix_count * 4\\n #\\n # avg = (g1 + g2 + r1 + b1) / pix_count\\n #\\n # print(g1_avg, g2_avg, r1_avg, b1_avg, avg)\\n\\n # popravek\\n im_p_pop = np.zeros((size_ref[0], size_ref[1]), np.uint16)\\n\\n\\n for i in range(size_ref[0]):\\n for j in range(size_ref[1]):\\n if (i % 2) == 0 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g1\\n if (i % 2) == 1 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_g2\\n if (i % 2) == 0 and (j % 2) == 1 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_r1\\n if (i % 2) == 1 and (j % 2) == 0 and image_ref[i, j] != 0: im_p_pop[i, j] = image_ref[i, j] * raz_b1\\n \\n _,_,_,_,P=return_flatfield_set_path(2)\\n P_flat=gdal_array.LoadFile(P)\\n \\n # im_p_pop=simple_flatfield_corr(P_flat, im_p_pop, 2, 1) \\n \\n # outout\\n \\n im_p_pop=BLUE_simple_flatfield_corr(P_flat, im_p_pop)\\n \\n out=os.path.abspath(file)+\\\"/corr/\\\"+os.path.basename(file)[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n \\n # out = in_ref[:-4] + \\\"_pop_flat_corr.tif\\\"\\n\\n driver = gdal.GetDriverByName('GTiff')\\n\\n # outRaster = driver.Create(out, size[1], size[0], 3, gdal.GDT_UInt16)\\n outRaster = driver.Create(out, size_ref[1], size_ref[0], 1, gdal.GDT_UInt16)\\n\\n outband = outRaster.GetRasterBand(1)\\n outband.WriteArray(im_p_pop)\\n outband.FlushCache()\",\n \"def generate_transformations(self):\\n if self.perform_aug:\\n print(\\\"\\\\nAugmentation will be applied to the training images\\\")\\n data_transforms = {\\n \\\"train\\\": transforms.Compose([\\n transforms.Resize((self.input_size, self.input_size)),\\n transforms.RandomRotation(degrees=45),\\n transforms.ColorJitter(),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\\n \\\"val\\\": transforms.Compose([\\n transforms.Resize((self.input_size, self.input_size)), # 256 used to be\\n transforms.CenterCrop(self.input_size),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\\n }\\n else:\\n print(\\\"\\\\nNo augmentation will be applied to the training images\\\")\\n data_transforms = {\\n \\\"train\\\": transforms.Compose([\\n transforms.Resize((self.input_size, self.input_size)),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]),\\n \\\"val\\\": transforms.Compose([\\n transforms.Resize((self.input_size, self.input_size)), # 256 used to be\\n transforms.CenterCrop(self.input_size),\\n transforms.ToTensor(),\\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\\n }\\n\\n return data_transforms\",\n \"def _project(self):\\n ghosts_w = self.input_field.topology.ghosts()\\n self.input_field.data[0], self.input_field.data[1], \\\\\\n self.input_field.data[2] = \\\\\\n fftw2py.projection_om_3d(self.input_field.data[0],\\n self.input_field.data[1],\\n self.input_field.data[2], ghosts_w)\",\n \"def process_align(self):\\n\\t\\tstm_t_dict = self._process_recog()\\n\\t\\ttrans_t_dict = self._process_trans()\\n\\t\\talign_obj = viterbi_align(stm_t_dict, trans_t_dict, self.label, self.pair_file_path)\\n\\t\\tself.trans_t_dict = align_obj.viterbi(0, len(stm_t_dict)-1, 0, len(trans_t_dict)-1)\",\n \"def align_images(self, translation_only=False):\\n # Extract feature point locations and descriptors.\\n points_and_descriptors = []\\n for file in self.files:\\n image = sol4_utils.read_image(file, 1)\\n self.h, self.w = image.shape\\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\\n points_and_descriptors.append(find_features(pyramid))\\n\\n # Compute homographies between successive pairs of images.\\n Hs = []\\n for i in range(len(points_and_descriptors) - 1):\\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\\n\\n # Find matching feature points.\\n ind1, ind2 = match_features(desc1, desc2, .7)\\n points1, points2 = points1[ind1, :], points2[ind2, :]\\n\\n # Compute homography using RANSAC.\\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\\n\\n # Uncomment for debugging: display inliers and outliers among matching points.\\n # In the submitted code this function should be commented out!\\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\\n\\n Hs.append(H12)\\n\\n # Compute composite homographies from the central coordinate system.\\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\\n self.homographies = np.stack(accumulated_homographies)\\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\\n self.homographies = self.homographies[self.frames_for_panoramas]\",\n \"def reiniciarMatrix(self):\\n self.matrixMAPA = []\\n self.rellenarMatrix()\",\n \"def createMatrices(file, word2Idx, maxSentenceLen, extendMapping, labelsMapping, aspectMapping, typeMapping, tenseMapping, eventClassMapping, distanceMapping, sentenceLengthMapping):\\n labels = []\\n eventMatrix = []\\n timeMatrix = []\\n\\n sentenceLengths = []\\n sentenceMatrix = []\\n positionMatrix_e = []\\n positionMatrix_t = []\\n\\n aspectMatrix = []\\n typeMatrix = []\\n tenseMatrix = []\\n eventClassMatrix = []\\n featuresList = []\\n \\n \\n minDistance = 0\\n for distanceKey in distanceMapping.iterkeys():\\n if isinstance(distanceKey, (int, long)):\\n minDistance = min(minDistance, int(distanceKey))\\n \\n for line in open(file):\\n features = eval(line.strip())\\n featuresList.append(features)\\n label = features['label']\\n event = features[\\\"Token[0]\\\"]\\n eventPosition = int(features[\\\"eventPosition\\\"])\\n tokens = features[\\\"textInBetween\\\"] if \\\"textInBetween\\\" in features else features[\\\"sentence\\\"]\\n \\n aspect = features[\\\"aspect\\\"]\\n tense = features[\\\"tense\\\"]\\n eventClass = features[\\\"eventClass\\\"]\\n \\n labels.append(labelsMapping[label.lower()] if label.lower() in labelsMapping else -1)\\n eventMatrix.append(getWordIdx(event, word2Idx))\\n \\n aspectMatrix.append(getMappingIdx(aspect, aspectMapping, extendMapping))\\n tenseMatrix.append(getMappingIdx(tense, tenseMapping, extendMapping))\\n eventClassMatrix.append(getMappingIdx(eventClass, eventClassMapping, extendMapping))\\n \\n if 'TimeTokenFirst' in features:\\n time = features['TimeTokenFirst']\\n timeMatrix.append(getWordIdx(time, word2Idx))\\n \\n if 'type' in features:\\n type = features[\\\"type\\\"]\\n typeMatrix.append(getMappingIdx(type, typeMapping, extendMapping))\\n \\n timePosition = int(features[\\\"timeFirstPosition\\\"]) if 'timeFirstPosition' in features else 0\\n \\n\\n if len(tokens) in sentenceLengthMapping:\\n sentenceLengths.append(sentenceLengthMapping[len(tokens)])\\n else:\\n sentenceLengths.append(sentenceLengthMapping['GreaterMax'])\\n\\n \\n tokenIds = np.zeros(maxSentenceLen)\\n positionValues_e = np.zeros(maxSentenceLen)\\n positionValues_t = np.zeros(maxSentenceLen)\\n for idx in xrange(0, min(maxSentenceLen, len(tokens))):\\n tokenIds[idx] = getWordIdx(tokens[idx], word2Idx)\\n \\n distance_e = idx - eventPosition\\n distance_t = idx - timePosition\\n\\n if distance_e in distanceMapping:\\n positionValues_e[idx] = distanceMapping[distance_e]\\n elif distance_e <= minDistance:\\n positionValues_e[idx] = distanceMapping['LowerMin']\\n else:\\n positionValues_e[idx] = distanceMapping['GreaterMax']\\n \\n if distance_t in distanceMapping:\\n positionValues_t[idx] = distanceMapping[distance_t]\\n elif distance_t <= minDistance:\\n positionValues_t[idx] = distanceMapping['LowerMin']\\n else:\\n positionValues_t[idx] = distanceMapping['GreaterMax']\\n\\n sentenceMatrix.append(tokenIds)\\n positionMatrix_e.append(positionValues_e)\\n positionMatrix_t.append(positionValues_t)\\n\\n \\n labels = np.array(labels, dtype='int32')\\n eventMatrix = np.expand_dims(np.array(eventMatrix, dtype='int32'), axis=1)\\n timeMatrix = np.expand_dims(np.array(timeMatrix, dtype='int32'), axis=1)\\n aspectMatrix = np.expand_dims(np.array(aspectMatrix, dtype='int32'), axis=1)\\n typeMatrix = np.expand_dims(np.array(typeMatrix, dtype='int32'), axis=1)\\n tenseMatrix = np.expand_dims(np.array(tenseMatrix, dtype='int32'), axis=1)\\n eventClassMatrix = np.expand_dims(np.array(eventClassMatrix, dtype='int32'), axis=1)\\n sentenceLengths = np.expand_dims(np.array(sentenceLengths, dtype='int32'), axis=1)\\n \\n \\n sentenceMatrix = np.array(sentenceMatrix, dtype='int32')\\n positionMatrix_e = np.array(positionMatrix_e, dtype='int32')\\n positionMatrix_t = np.array(positionMatrix_t, dtype='int32')\\n \\n\\n\\n return {'labels': labels,\\n 'event':eventMatrix, \\n 'time':timeMatrix, \\n 'sentence':sentenceMatrix, \\n 'positions_e':positionMatrix_e, \\n 'positions_t':positionMatrix_t, \\n 'aspect':aspectMatrix, \\n 'tense':tenseMatrix, \\n 'eventClass':eventClassMatrix, \\n 'type': typeMatrix,\\n 'sentence_len': sentenceLengths,\\n 'features': featuresList}\",\n \"def transform(self, X):\\n\\n t0 = time.perf_counter()\\n check_is_fitted(self)\\n self.check_external_components_modified()#[WARN] in d3m, primitives can \\\"restore\\\" private class variables...\\n X = self._validate_data(X, accept_sparse=[\\\"csr\\\", \\\"csc\\\"], reset=False)\\n t1 = time.perf_counter()\\n\\n if X.shape[1] != self.components_af_.shape[1]:\\n raise ValueError(\\n \\\"Impossible to perform projection:\\\"\\n \\\"X at fit stage had a different number of features. \\\"\\n \\\"(%s != %s)\\\" % (X.shape[1], self.components_af_.shape[1])\\n )\\n\\n #X_new = safe_sparse_dot(X, self.components_.T, dense_output=self.dense_output)\\n #import pdb; pdb.set_trace()\\n X_af = af.interop.from_ndarray(X).as_type(self.components_af_.dtype())\\n X_new = af.matmulNT(X_af, self.components_af_)\\n X_new = X_new.to_ndarray()\\n t2 = time.perf_counter()\\n return X_new\",\n \"def preprocess(self):\\n inputMatrix = pd.read_csv(self.input, index_col = 0)\\n\\n original_filename = self.input.split(\\\"/\\\")[-1]\\n mapped_filename = \\\"mapped_\\\" + self.desiredFormat + \\\"_\\\" + original_filename\\n output = self.input\\n output_filepath = \\\"/\\\".join(self.input.split(\\\"/\\\")[0:-1])\\n #as the DataFormatter always transposes the data before any further processing, we can expect all genes to be in the columns\\n genesInColumn = \\\"true\\\"\\n #only map genes if the current format is not the desired format\\n if (self.currentFormat != self.desiredFormat):\\n output = output_filepath + \\\"/\\\" + mapped_filename\\n benchutils.mapDataMatrix(inputMatrix, genesInColumn, self.currentFormat, self.desiredFormat, output, self.labeled)\\n\\n return output\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62273926","0.6207363","0.6104549","0.5850489","0.58403313","0.58327806","0.5827466","0.5725734","0.5681582","0.56439","0.5603615","0.5602365","0.5596903","0.5577693","0.555407","0.5526825","0.5518143","0.5506637","0.5487023","0.5475069","0.54521763","0.54350317","0.54161745","0.53889203","0.5372838","0.536524","0.5362892","0.53603494","0.5355388","0.53502476","0.5342728","0.53392434","0.5332614","0.53318256","0.5330306","0.5314524","0.5300495","0.52994806","0.5297486","0.5285395","0.52770185","0.5269956","0.52256763","0.52124995","0.5207145","0.5197813","0.5195588","0.5192883","0.5188637","0.5187531","0.5186836","0.51834","0.5165309","0.5156512","0.51475817","0.51468784","0.51353276","0.5119405","0.5116644","0.51156557","0.5109496","0.51088977","0.51020604","0.50936353","0.5090827","0.5090827","0.5085803","0.5075744","0.5061394","0.50505054","0.50451446","0.5026819","0.50219977","0.50108826","0.5005841","0.5003727","0.4991218","0.49900836","0.49880436","0.49835762","0.49827647","0.49754396","0.49713415","0.49713227","0.49624228","0.49619067","0.49594814","0.49590448","0.4957838","0.49543044","0.4953803","0.4951752","0.49468637","0.494229","0.4941374","0.4940706","0.49354962","0.4935158","0.49322695","0.4932221"],"string":"[\n \"0.62273926\",\n \"0.6207363\",\n \"0.6104549\",\n \"0.5850489\",\n \"0.58403313\",\n \"0.58327806\",\n \"0.5827466\",\n \"0.5725734\",\n \"0.5681582\",\n \"0.56439\",\n \"0.5603615\",\n \"0.5602365\",\n \"0.5596903\",\n \"0.5577693\",\n \"0.555407\",\n \"0.5526825\",\n \"0.5518143\",\n \"0.5506637\",\n \"0.5487023\",\n \"0.5475069\",\n \"0.54521763\",\n \"0.54350317\",\n \"0.54161745\",\n \"0.53889203\",\n \"0.5372838\",\n \"0.536524\",\n \"0.5362892\",\n \"0.53603494\",\n \"0.5355388\",\n \"0.53502476\",\n \"0.5342728\",\n \"0.53392434\",\n \"0.5332614\",\n \"0.53318256\",\n \"0.5330306\",\n \"0.5314524\",\n \"0.5300495\",\n \"0.52994806\",\n \"0.5297486\",\n \"0.5285395\",\n \"0.52770185\",\n \"0.5269956\",\n \"0.52256763\",\n \"0.52124995\",\n \"0.5207145\",\n \"0.5197813\",\n \"0.5195588\",\n \"0.5192883\",\n \"0.5188637\",\n \"0.5187531\",\n \"0.5186836\",\n \"0.51834\",\n \"0.5165309\",\n \"0.5156512\",\n \"0.51475817\",\n \"0.51468784\",\n \"0.51353276\",\n \"0.5119405\",\n 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No slicetime correction."},"document":{"kind":"string","value":"def Motcor(self, info, base):\n fmt = '3dvolreg -prefix %s -twopass %s -verbose -base %s+orig[%s] ' + \\\n '-dfile %s %s+orig'\n cmd = fmt % (info['imgfile_m'], info['motion_interp'], \\\n info['basefile'], base, info['mot_file'], info['imgfile_t'])\n\n self.CheckExec(cmd, ['%s+orig.BRIK' % info['imgfile_m'], \\\n '%s+orig.HEAD' % info['imgfile_m']])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.\n suffix = '+orig'\n epifile = '%s%s' % (info['imgfile'], suffix)\n prefix = info['imgfile_m']\n base_entry = info['base_entry']\n if info['base'] == 'start':\n# Use the first frame specified in template file. Defaults\n# to zero.\n base = info['motion_ref_frame']\n else:\n# Use the last frame.\n base = self.info[base_entry]['tdim'] - info['skip']-1\n base = ('%d' % base).replace(' ','')\n\n# Correct for slice-timing.\n self.SliceTimeCorrect(info, epifile)\n\n plane = info['plane']\n anat_tgt = info['anat_tgt']\n# anat_entry = self.anat_entry[plane]\n\n if info['catmats']:\n# Include additonal transformation in motion correction such\n# that final image is in register with the fieldmap, which has\n# been registered to the structural image that will be used for\n# spatial normalization.\n self.MotcorCatenate(info, base, anat_tgt)\n else:\n# Assume fieldmap is in register with the structural.\n self.Motcor(info, base)\n\n if info.get('fmapname', None) is None:\n# No fieldmap correction.\n if self.fsl_flip:\n# Flip the way fslview likes it.\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\n elif info['suffix'] == '.nii':\n# Copy motion-corrected images from /tmp to output directory\n outfile = info['imgfile_final'] + info['suffix']\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\n self.CheckExec(cmd, [outfile], force=True)\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\n self.CheckExec(cmd, [], force=True)","def test_revolute(self):\n # Rotate around the z axis\n r = Joint.revolute(np.array([0, 0, 1]))\n t_mat = r(np.array([np.pi / 2]))\n rot_vec = np.dot(t_mat, np.array([1, 0, 0, 1]))[:3]\n self.assertTrue(np.allclose(\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))","def on_mouse_move(self, event):\n\n # self.view = 1 * np.eye(4, dtype=np.float32)\n # self.model = 1 * np.eye(4, dtype=np.float32)\n\n # self.translate -= event.delta[1]\n # self.translate = max(-1, self.translate)\n # print(event.delta[1])\n # print(self.translate)\n # self.view = translate((0, 0, -self.translate))\n # self.game_program['u_view'] = self.view\n # self.game_program['u_size'] = 5 / self.translate\n # self.view = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.view\n # self.model = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.model\n # print(self.view)\n\n # self.game_program['u_model'] = self.model\n # self.game_program['u_view'] = self.view\n\n x, y = event.pos\n #print(x, y)\n self.x_offset, self.y_offset = x - self.last_x, - (y - self.last_y)\n self.last_x, self.last_y = x, y\n self.x_offset *= self.sensitivity\n self.y_offset *= self.sensitivity\n\n self.yaw, self.pitch = self.yaw - self.x_offset, self.pitch + self.y_offset\n self.rot_y(self.yaw * np.pi / 180)\n self.rot_x(self.pitch * np.pi / 180)\n\n self.view = np.dot(self.rot_mat_y, self.rot_mat_x)\n self.game_program['u_view'] = self.view\n\n self.update()","def driftRHS_3D(field,drift_velocity,t,x):\n f = field.getValue(x)\n fs = np.sqrt(f[0]**2 + f[1]**2 + f[2]**2)\n f = f/fs\n return -f*drift_velocity(fs)","def kelvin_effect(pres, surft, temp, mw_ba, dcell):\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))","def testNonVarying(self):\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration)\n self._testPhotoCalibCenter(photoCalib, 0)\n\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointXShift))\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointXShift))\n result = photoCalib.instFluxToMaggies(self.instFlux, self.instFluxErr)\n self.assertEqual(1, result.value)\n\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration, self.calibrationErr)\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)\n\n # constant, with a bbox\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration, bbox=self.bbox)\n self._testPhotoCalibCenter(photoCalib, 0)","def test_velocity(self):\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\n # r must contain 2 elements, otherwise the density and pressure are nan\n r = np.array([0.7, 0.8])\n t = 6.25e-6\n solrt = sol(r, t)\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)","def update_rk3(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n try:\n\n kap1 = (self.vel, self.force(self.pos,\n self.vel,\n self.time))\n\n pos = self.pos+0.5*delta_t*kap1[0]\n vel = self.vel+0.5*delta_t*kap1[1]\n\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n 0.5*delta_t, drag=False)\n\n kap2 = (vel, self.force(pos, vel, self.time+0.5*delta_t))\n\n pos = self.pos+delta_t*(2.0*kap2[0]-kap1[0])\n vel = self.vel+delta_t*(2.0*kap2[1]-kap1[1])\n\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=False)\n\n kap3 = (vel, self.force(pos, vel, self.time+delta_t))\n\n pos = self.pos+delta_t*(kap1[0]+4.0*kap2[0]+kap3[0])/6.0\n vel = self.vel+delta_t*(kap1[1]+4.0*kap2[1]+kap3[1])/6.0\n\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=False)\n\n except Collision.CollisionException as col:\n col.vel = self.vel+col.delta_t*kap1[0]\n raise col\n\n self.time += self.delta_t","def radio3mmPointingConstants( az, el, sag, ant, subarray=DEFAULT):\n \n mproot = \"Control.Antenna%d.Aperture3mm.PointingConstants.\" %ant\n azValid = False\n elValid = False\n sagValid = False\n numInvalid = 0\n try :\n azDelta = az - queryDouble(mproot+\"azOffset\") \n azValid = True\n except:\n numInvalid += 1\n try :\n elDelta = el - queryDouble(mproot+\"elOffset\") \n elValid = True\n except:\n numInvalid += 1\n try :\n sagDelta = sag - queryDouble(mproot+\"sag\") \n sagValid = True\n except:\n numInvalid += 1\n allValid = azValid and elValid and sagValid\n #print \"Deltas:\", azDelta, elDelta, sagDelta\n\n R3MM = carma.control.APERTURE_RADIO3MM\n multiSubarray('aperturePointingConstants',subarray, R3MM, az, el, sag, ant)\n\n if ant > 15: return\n \n # Apply deltas to the 1mm aperture\n prefix = \"\"\n if not allValid:\n m = \"Could not get the initial value\"\n if numInvalid > 1: m += \"s\"\n m += \" for the 3mm\"\n if not azValid:\n m += prefix + \" az\"\n prefix = \",\"\n if not elValid:\n m += prefix + \" el\"\n prefix = \",\"\n if not sagValid:\n m += prefix + \" sag\"\n m += \" term\" \n if numInvalid > 1: m += \"s\"\n m += \".\"\n print m\n m = \"The 3mm offsets have been changed but the relative 1mm offsets\\n\"\n m += \"will not be changed because of the missing initial 3mm values.\\n\"\n m += \"This is a serious error \"\n m += \"that should be reported to the operations staff!!\"\n print m\n return\n \n mproot = \"Control.Antenna%d.Aperture1mm.PointingConstants.\" %ant \n az1mm = azDelta + queryDouble(mproot+\"azOffset\") \n el1mm = elDelta + queryDouble(mproot+\"elOffset\") \n sag1mm = sagDelta + queryDouble(mproot+\"sag\") \n R1MM = carma.control.APERTURE_RADIO1MM\n #print \"1mm constants:\", az1mm, el1mm, sag1mm\n multiSubarray('aperturePointingConstants', subarray, R1MM, \n az1mm, el1mm,sag1mm, ant)","def test_volume_3d(self):\n # generate voronoi mesh \n mesh = Mesh3d(self.particles, self.bound)\n print(\"building mesh...\")\n mesh.build_geometry()\n print(\"mesh complete\")\n\n # calculate voronoi volumes of all real particles \n real_indices = self.particles[\"tag\"] == ParticleTAGS.Real\n tot_vol = np.sum(self.particles[\"volume\"][real_indices])\n\n self.assertAlmostEqual(tot_vol, 1.0)","def test_backward_injection_in_3D(self):\n self.fom = ModeMatch(monitor_name = 'figure_of_merit',\n mode_number = 1,\n direction = 'Backward',\n multi_freq_src = True,\n target_T_fwd = lambda wl: np.ones(wl.size),\n norm_p = 1)\n Optimization.set_source_wavelength(self.sim, 'source', self.fom.multi_freq_src, len(self.wavelengths))\n self.sim.fdtd.setnamed('FDTD','dimension','3D')\n self.sim.fdtd.setnamed('source', 'x', -self.sim.fdtd.getnamed('source','x'))\n self.sim.fdtd.setnamed('source','direction','Backward')\n self.sim.fdtd.setnamed('figure_of_merit','x', -self.sim.fdtd.getnamed('figure_of_merit','x'))\n self.fom.initialize(self.sim)\n self.fom.make_forward_sim(self.sim)\n self.sim.run(name = 'modematch_backward_injection_in_3D', iter = 1)\n FOM = self.fom.get_fom(self.sim)\n self.assertAlmostEqual(FOM, self.ref_fom, 4)","def update_ab3(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n if len(self._old) >= 2:\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n beta = -(1.0/6.0)*delta_t*(delta_t*(5.0*delta_t+3.0*(self.time-self.get_old(0, 2)))\n /((self.time-self.get_old(0, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\n gamma = (1.0/6.0)*delta_t*(delta_t*(2.0*delta_t+3.0*(self.time-self.get_old(0, 2)))\n /((self.time-self.get_old(1, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\n\n pos = self.pos+(delta_t-beta-gamma)*self.vel+beta*self.get_old(0, 0)+gamma*self.get_old(1, 0)\n vel = self.vel+(delta_t-beta-gamma)*kap[1]+beta*self.get_old(0, 1)+gamma*self.get_old(1, 1)\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n try:\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n beta = -(1.0/6.0)*col.delta_t*(col.delta_t*(5.0*col.delta_t+3.0*(self.time-self.get_old(0, 2)))\n /((self.time-self.get_old(0, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\n gamma = (1.0/6.0)*col.delta_t*(col.delta_t*(2.0*col.delta_t+3.0*(self.time-self.get_old(0, 2)))\n /((self.time-self.get_old(1, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\n vel = self.vel+(col.delta_t-beta-gamma)*kap[1]+beta*self.get_old(0, 1)+gamma*self.get_old(0, 1)\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n\n self.set_old(kap, 2)\n\n self.time += delta_t\n\n else:\n ## reduced to using Adams Bashforth 2nd order method for the second timestep:\n\n try:\n tmp = [self.get_old(0)]\n except IndexError:\n tmp = []\n kap = update_ab2(self)\n if tmp:\n self._old = self._old + tmp\n\n return kap","def test_revolute_from_dh(self):\n x_offset = 1\n z_offset = 2\n # Rotate around the z axis\n r = Joint.revolute_from_dh(0, 0, x_offset, z_offset)\n t_mat = r(np.array([np.pi / 2]))\n rot_vec = np.dot(t_mat[:3, :3], np.array([1, 0, 0]))\n self.assertTrue(np.allclose(\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))\n self.assertTrue(np.allclose(t_mat[2, 3], z_offset))\n # x was rotated 90 degrees, and is now y\n self.assertTrue(np.allclose(t_mat[1, 3], x_offset))","def update_apc22(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n if len(self._old) >= 1:\n\n\n try:\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\n\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n pos = self.pos+delta_t/2.0*(self.vel+vel)\n vel = self.vel+delta_t/2.0*(self.force(pos, vel, self.time+delta_t,\n drag=False)+kap[1])\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n\n self.time += delta_t\n\n else:\n ## reduced to using the Adams first order method for the first timestep:\n\n kap = update_apc1(self)\n\n self.set_old(kap, 1)\n\n return kap","def tick(self):\n \n if self.state == ForceFeedbackState.NORMAL:\n # send desired position to IK directly\n # Actually publish if we have some publishers\n rospy.logerr('Im in NORMAL state')\n if self.pubs is not None:\n p0 = self.desired_pos\n # Record the current sent pos as element 0 of self.prev_pos\n # shuffling the other pos to the right. Then, truncate the\n # list to be a maximum of 2 pos long.\n self.prev_pos = [p0] + self.prev_pos\n self.prev_pos = self.prev_pos[:2]\n \n #Publish the desired position to /pos_for_IK\n p0_list = p0.tolist()\n msg = Float64MultiArray()\n msg.data = deepcopy(p0_list)\n self.pubs.publish(msg)\n \n elif self.state == ForceFeedbackState.FORCEFB:\n rospy.logerr('Im in FORCEFB state')\n # Calculate correction vector\n \n # Need to check whether desired_pos is nan. Can use one only because the function only allows scalar\n if math.isnan(self.desired_pos[0]) == False:\n self.stored_desired_pos = self.desired_pos\n \n correction = self.stored_desired_pos - self.camera_pos\n correction_mag = np.sqrt(np.sum(correction * correction))\n \n #separate out two terms for testing and simplicity (and also because error in camera measurements in 2 dimensions) \n correction_x_mag = np.sqrt(correction[0]*correction[0])\n correction_z_mag = np.sqrt(correction[2]*correction[2])\n rospy.logerr('correction_z_mag = ')\n rospy.logerr(correction_z_mag) \n \n #if the correction_vector bigger and smaller than some threshold, then do something\n #if math.isnan(correction_mag)==True or correction_mag <= epsilon or correction_mag >= 0.05:\n #need correction_z_mag in case we are using force feedback on the wrong pose\n if math.isnan(correction_x_mag)==False and math.isnan(correction_z_mag)==False and correction_z_mag <= 0.15:\n if correction_x_mag >= 0.04 or correction_z_mag >= 0.03:\n rospy.logerr('FORCE FEEDBACK with correction mag: ' + str(correction_mag))\n \n # Calculate new commanded pose\n p0 = self.stored_desired_pos\n p0_new = p0 + 1.0*correction #the constant can be changed depending on needs\n \n self.prev_pos = [p0_new] + self.prev_pos\n self.prev_pos = self.prev_pos[:2]\n \n #Publish the new p0 to /pos_for_IK\n p0_list = p0_new.tolist()\n msg = Float64MultiArray()\n msg.data = deepcopy(p0_list)\n \n rospy.logerr('Publishing msg: ' + str(msg))\n self.pubs.publish(msg) \n \n else:\n rospy.logerr('TRIED FORCE FB BUT NOT REQUIRED')\n p0 = self.desired_pos\n # Record the current sent pos as element 0 of self.prev_pos\n # shuffling the other pos to the right. Then, truncate the\n # list to be a maximum of 2 pos long.\n self.prev_pos = [p0] + self.prev_pos\n self.prev_pos = self.prev_pos[:2]\n \n #Publish the desired position to /pos_for_IK\n p0_list = p0.tolist()\n msg = Float64MultiArray()\n msg.data = deepcopy(p0_list)\n self.pubs.publish(msg)\n else:\n rospy.logerr('MATH IS NAN or wrong pose, equivalent to NORMAL state')\n p0 = self.desired_pos\n # Record the current sent pos as element 0 of self.prev_pos\n # shuffling the other pos to the right. Then, truncate the\n # list to be a maximum of 2 pos long.\n self.prev_pos = [p0] + self.prev_pos\n self.prev_pos = self.prev_pos[:2]\n \n #Publish the desired position to /pos_for_IK\n p0_list = p0.tolist()\n msg = Float64MultiArray()\n msg.data = deepcopy(p0_list)\n self.pubs.publish(msg)\n \n self.desired_pos = np.ones(3) * np.nan\n self.state = ForceFeedbackState.NORMAL","def define_ufl_convec_accel_diff(self):\n\n if hasattr(self, 'ufl_convec_accel_dv'):\n return None\n\n # Exit if problem is formulated with respect to Eulerian\n # coordinates and is not an elastic material.\n eulerian = self.config['formulation']['domain'] == 'eulerian'\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\n stokes = self.config['material']['const_eqn'] == 'stokes'\n if (not eulerian) or lin_elastic or stokes:\n self.ufl_convec_accel_dv = 0\n return None\n\n self.ufl_convec_accel_dv = dlf.derivative(self.ufl_convec_accel,\n self.velocity,\n self.trial_vector)\n\n return None","def update_apc11(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n pos = self.pos+delta_t*self.vel\n vel = self.vel+delta_t*kap[1]\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n force = self.force(pos,\n vel,\n self.time+delta_t, drag=False)\n\n pos = self.pos+delta_t*vel\n vel = self.vel+delta_t*force\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n try:\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n vel = self.vel+col.delta_t*kap[1]\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n \n self.time += delta_t\n\n return (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)","def update_rk4(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n try:\n\n kap1 = (self.vel, self.force(self.pos,\n self.vel,\n self.time))\n\n pos = self.pos+0.5*delta_t*kap1[0]\n vel = self.vel+0.5*delta_t*kap1[1]\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n 0.5*delta_t, drag=False)\n\n kap2 = (self.vel + 0.5*delta_t*kap1[1],\n self.force(pos, vel, self.time + 0.5*delta_t))\n\n pos = self.pos+0.5*delta_t*kap2[0]\n vel = self.vel+0.5*delta_t*kap2[1]\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n 0.5*delta_t, drag=False)\n\n\n kap3 = (self.vel+0.5*delta_t*kap2[1],\n self.force(pos, vel, self.time+0.5*delta_t))\n\n pos = self.pos+0.5*delta_t*kap3[0]\n vel = self.vel+0.5*delta_t*kap3[1]\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n 0.5*delta_t, drag=False)\n\n\n kap4 = (self.vel + delta_t * kap3[1],\n self.force(pos, vel, self.time + delta_t))\n\n pos = self.pos+delta_t*(kap1[0]+2.0*kap2[0]+2.0*kap3[0]+kap4[0])/6.0\n vel = self.vel+delta_t*(kap1[1]+2.0*kap2[1]+2.0*kap3[1]+kap4[1])/6.0\n self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=False)\n\n self.pos = pos\n self.vel = vel\n\n except Collision.CollisionException as col:\n col.vel = self.vel+col.delta_t*kap1[0]\n raise col\n\n self.time += delta_t","def test_forward_injection_in_3D(self):\n self.fom = ModeMatch(monitor_name = 'figure_of_merit',\n mode_number = 1,\n direction = 'Forward',\n multi_freq_src = True,\n target_T_fwd = lambda wl: np.ones(wl.size),\n norm_p = 1)\n Optimization.set_source_wavelength(self.sim, 'source', self.fom.multi_freq_src, len(self.wavelengths))\n self.sim.fdtd.setnamed('FDTD','dimension','3D')\n self.fom.initialize(self.sim)\n self.fom.make_forward_sim(self.sim)\n self.sim.run(name = 'modematch_forward_injection_in_3D', iter = 0)\n FOM = self.fom.get_fom(self.sim)\n self.assertAlmostEqual(FOM, self.ref_fom, 4)","def test_3D_m6_1k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L4_2,\n Support: 'gpu_1k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L4_2,\n Support: '',\n Splitting: 'o2'},\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def test_3D_m6_2k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L4_2,\n Support: 'gpu_2k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L4_2,\n Support: '',\n Splitting: 'o2'}\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def single_volume_inference(self, volume):\n self.model.eval()\n\n # Assuming volume is a numpy array of shape [X,Y,Z] and we need to slice X axis\n slices = []\n\n # Write code that will create mask for each slice across the X (0th) dimension. After \n # that, put all slices into a 3D Numpy array. You can verify if your method is \n # correct by running it on one of the volumes in your training set and comparing \n # with the label in 3D Slicer.\n \n # normalize\n image = (volume.astype(np.single) - np.min(volume))/(np.max(volume) - np.min(volume))\n \n new_image = med_reshape(image, new_shape=(self.patch_size, self.patch_size, image.shape[2]))\n mask3d = np.zeros(new_image.shape)\n \n for slc_ix in range(new_image.shape[2]):\n tsr_test = torch.from_numpy(new_image[:,:,slc_ix].astype(np.single)).unsqueeze(0).unsqueeze(0)\n #image = torch.from_numpy(self.data[slc[0]][\"image\"][:,:,slc[1]]).unsqueeze(0)\n #tsr_test = torch.from_numpy(slc.astype(np.single)).unsqueeze(0).unsqueeze(0)\n pred = self.model(tsr_test.to(self.device))\n pred = np.squeeze(pred.cpu().detach())\n mask3d[:,:,slc_ix] = torch.argmax(pred, dim=0)\n\n return mask3d","def test_3D_m4_1k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L2_1,\n Support: 'gpu_1k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L2_1,\n Support: '',\n Splitting: 'o2'},\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def set_voltages(): \n #0) set parameters\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\n import pickle\n with open(trapFile,'rb') as f:\n trap = pickle.load(f)\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\n tc=trap.configuration\n C = tc.multipoleControl\n el = []\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\n if tc.trap_knobs != True:\n return 'WARNING: You must run trap_knobs first!'\n #2a) determine electrode voltages directly\n elif multipoleControls: # note plurality to contrast from attribute\n el = np.dot(C,coefs.T) # these are the electrode voltages\n #2b) determine electrode volages indirectly\n else:\n charge = tc.charge\n mass = tc.mass\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\n U2 = az*V0/8\n U1 = U2+ax*V0/4\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\n U1p= np.sqrt(U1**2+U3**2/2)\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\n mCf = tc.multipoleCoefficients[1:9,:]\n el = np.dot(mCf.T,inp) # these are the electrode voltages\n el = np.real(el)\n #3) regularize if set to do so\n reg = 0\n if reg: \n C = el\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\n Lambda=Lambda[0]\n el = el-(np.dot(tc.multipoleKernel,Lambda))\n return el","def setCameraRotation3D(ang):\n dislin.vup3d(ang)","def match_det2cube(self, input_model,\n x, y, file_slice_no,\n this_par1, this_par2,\n spaxel,\n c1_offset, c2_offset):\n\n#________________________________________________________________________________\n if self.instrument == 'MIRI':\n\n det2ab_transform = input_model.meta.wcs.get_transform('detector', 'alpha_beta')\n detector2v23 = input_model.meta.wcs.get_transform('detector', 'v2v3')\n v23toworld = input_model.meta.wcs.get_transform(\"v2v3\", \"world\")\n worldtov23 = input_model.meta.wcs.get_transform(\"world\", \"v2v3\")\n v2ab_transform = input_model.meta.wcs.get_transform('v2v3',\n 'alpha_beta')\n\n alpha, beta, wave = det2ab_transform(x, y)\n v2, v3, lam23 = detector2v23(x, y)\n ra, dec, lam = v23toworld(v2, v3, lam23)\n\n valid1 = np.isfinite(v2)\n valid2 = np.isfinite(v3)\n\n if self.weighting == 'miripsf':\n wave_resol = self.instrument_info.Get_RP_ave_Wave(this_par1, this_par2)\n alpha_resol = self.instrument_info.Get_psf_alpha_parameters()\n beta_resol = self.instrument_info.Get_psf_beta_parameters()\n\n # transform Cube Spaxel centers to alpha,beta of exposure\n # for MIRI weighting parameters are based on distance in\n # alpha-beta coord system\n # transform the cube coordinate values to alpha and beta values\n # xi,eta -> ra,dec\n # world -> v2,v3\n # v2,v3 -> local alpha,beta\n\n elif self.instrument == 'NIRSPEC':\n islice = file_slice_no\n slice_wcs = nirspec.nrs_wcs_set_input(input_model, islice)\n\n x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box, \n step=(1, 1), center=True)\n ra, dec, lam = slice_wcs(x, y) # return v2,v3 are in degrees\n valid1 = np.isfinite(ra)\n valid2 = np.isfinite(dec)\n#________________________________________________________________________________\n#________________________________________________________________________________\n# Slices are curved on detector. A slice region is grabbed by corner regions so\n# the region returned may include pixels not value for slice. There are gaps\n# between the slices. Pixels not belonging to a slice are assigned NaN values.\n\n x = x.astype(np.int)\n y = y.astype(np.int)\n\n flux_all = input_model.data[y, x]\n# error_all = input_model.err[y, x]\n dq_all = input_model.dq[y, x]\n\n valid3 = np.isfinite(lam)\n valid4 = np.isfinite(flux_all)\n valid = valid1 & valid2 & valid3 & valid4\n#________________________________________________________________________________\n# using the DQFlags from the input_image find pixels that should be excluded\n# from the cube mapping\n all_flags = (dqflags.pixel['DO_NOT_USE'] + dqflags.pixel['DROPOUT'] +\n dqflags.pixel['NON_SCIENCE'] +\n dqflags.pixel['DEAD'] + dqflags.pixel['HOT'] +\n dqflags.pixel['RC'] + dqflags.pixel['NONLINEAR'])\n\n # find the location of all the values to reject in cube building\n good_data = np.where((np.bitwise_and(dq_all, all_flags) == 0) & (valid == True))\n\n # good data holds the location of pixels we want to map to cube\n flux = flux_all[good_data]\n# error = error_all[good_data]\n wave = lam[good_data]\n\n# xpix = x[good_data] # only used for testing\n# ypix = y[good_data] # only used for testing\n\n ra = ra - c1_offset / 3600.0\n dec = dec - c2_offset / 3600.0\n ra_use = ra[good_data]\n dec_use = dec[good_data]\n if self.instrument == 'MIRI':\n # need alpha,beta if weigthing is miripsf or cubes in alpha-beta space\n alpha_det = alpha[good_data]\n beta_det = beta[good_data]\n# MIRI can make cubes in alpha-beta:\n if self.coord_system == 'alpha-beta':\n coord1 = alpha[good_data]\n coord2 = beta[good_data]\n\n else:\n# xi,eta in arc seconds\n xi, eta = coord.radec2std(self.Crval1, self.Crval2, ra_use, dec_use)\n coord1 = xi\n coord2 = eta\n\n nplane = self.naxis1 * self.naxis2\n lower_limit = 0.01\n\n# iprint = 0\n# now loop over the pixel values for this region and find the spaxels that fall\n# withing the region of interest.\n nn = coord1.size\n\n# print('looping over n points mapping to cloud',nn)\n#________________________________________________________________________________\n for ipt in range(0, nn - 1):\n#________________________________________________________________________________\n # Cube.Xcenters, ycenters is a flattened 1-D array of the 2 X 2 xy plane\n # cube coordinates.\n # find the spaxels that fall withing ROI of point cloud defined by\n # coord1,coord2,wave\n# if(ipt > 2): sys.exit('STOP')\n# print('For point ',coord1[ipt],coord2[ipt],wave[ipt],ipt)\n\n# if(ipt == 0):\n# print('size of Xcenters',self.Xcenters.size)\n xdistance = (self.Xcenters - coord1[ipt])\n ydistance = (self.Ycenters - coord2[ipt])\n radius = np.sqrt(xdistance * xdistance + ydistance * ydistance)\n indexr = np.where(radius <= self.rois)\n indexz = np.where(abs(self.zcoord - wave[ipt]) <= self.roiw)\n\n# print('indexz',indexz)\n# print('indexr',indexr)\n zlam = self.zcoord[indexz] # z Cube values falling in wavelength roi\n xi_cube = self.Xcenters[indexr] # x Cube values within radius\n eta_cube = self.Ycenters[indexr] # y cube values with the radius\n\n# print('found xi_cube',xi_cube)\n# print('found eta_cube',eta_cube)\n\n#________________________________________________________________________________\n# loop over the points in the ROI\n for iz, zz in enumerate(indexz[0]):\n istart = zz * nplane\n for ir, rr in enumerate(indexr[0]):\n# yy_cube = int(rr / self.naxis1)\n# xx_cube = rr - yy_cube * self.naxis1\n# print('xx yy cube',rr,self.naxis1,xx_cube,yy_cube)\n#________________________________________________________________________________\n if self.weighting == 'msm':\n d1 = (xi_cube[ir] - coord1[ipt]) / self.Cdelt1\n d2 = (eta_cube[ir] - coord2[ipt]) / self.Cdelt2\n d3 = (zlam[iz] - wave[ipt]) / self.Cdelt3\n\n weight_distance = math.sqrt(d1 * d1 + d2 * d2 + d3 * d3)\n weight_distance = math.pow(weight_distance, self.weight_power)\n#________________________________________________________________________________\n# if weight is miripsf -distances determined in alpha-beta coordinate system\n elif self.weighting == 'miripsf':\n weights = FindNormalizationWeights(wave[ipt],\n wave_resol,\n alpha_resol,\n beta_resol)\n\n\n ra_spaxel, dec_spaxel = coord.std2radec(self.Crval1,\n self.Crval2,\n xi_cube[ir],\n eta_cube[ir])\n\n v2_spaxel, v3_spaxel, zl = worldtov23(ra_spaxel,\n dec_spaxel,\n zlam[iz])\n\n alpha_spaxel, beta_spaxel, wave_spaxel = v2ab_transform(v2_spaxel,\n v3_spaxel,\n zlam[iz])\n alpha_distance = alpha_det[ipt] - alpha_spaxel\n beta_distance = beta_det[ipt] - beta_spaxel\n wave_distance = abs(wave[ipt] - wave_spaxel)\n\n xn = alpha_distance / weights[0]\n yn = beta_distance / weights[1]\n wn = wave_distance / weights[2]\n\n # only included the spatial dimensions\n weight_distance = math.sqrt(xn * xn + yn * yn + wn * wn)\n weight_distance = math.pow(weight_distance, self.weight_power)\n#________________________________________________________________________________\n# We have found the weight_distance based on instrument type\n\n if weight_distance < lower_limit: weight_distance = lower_limit\n weight_distance = 1.0 / weight_distance\n\n cube_index = istart + rr\n spaxel[cube_index].flux = spaxel[cube_index].flux + weight_distance * flux[ipt]\n spaxel[cube_index].flux_weight = spaxel[cube_index].flux_weight + weight_distance\n spaxel[cube_index].iflux = spaxel[cube_index].iflux + 1","def quad_eq_of_motion2(self,state,time,force,moment):\n\n\t\t\tA = np.matrix([ [0.25,0, -0.5/self.arm_length],\n\t\t\t\t[0.25,0.5/self.arm_length,0.],\n\t\t\t\t[0.25,0,0.5/self.arm_length],\n\t\t\t\t[0.25,-0.5/self.arm_length,0]])\n\t\t\tT=A*np.asmatrix(np.hstack((force,moment[:2]))).transpose()\n\t\t\tT_clamped=np.maximum(np.minimum(T,self.max_force/4.0),self.min_force/4.0)\n\t\t\tB = np.matrix([[1.0,1.0,1.0,1.0],\n\t\t\t\t\t\t\t[0.0,self.arm_length,0.0,-self.arm_length],\n\t\t\t\t\t\t\t[-self.arm_length,0.0,self.arm_length,0.]])\n\t\t\tforce = B[[0],:]*T_clamped;\n\t\t\tforce = np.array(force).reshape(-1,).tolist()\n\t\t\tmoment = np.vstack( (B[[1,2],:]*np.asmatrix(T_clamped), moment[2]));\n\t\t\tmoment = np.array(moment).reshape(-1,).tolist()\n\t\t\t\n\t\t\t#Assign 13 states\n\t\t\t#x = state[0]\n\t\t\t#y = state[1]\n\t\t\t#z = state[2]\n\t\t\txdot = state[3];\n\t\t\tydot = state[4];\n\t\t\tzdot = state[5];\n\t\t\tqW = state[6];\n\t\t\tqX = state[7];\n\t\t\tqY = state[8];\n\t\t\tqZ = state[9];\n\t\t\tp = state[10];\n\t\t\tq = state[11];\n\t\t\tr = state[12];\n\n\t\t\tquat = np.vstack((qW,qX,qY,qZ)); #!! Attention to the order!!\n\t\t\tbRw=self.quat2mat(quat.transpose())\n\t\t\tbRw=bRw.reshape(3,3) #to remove the last dimension i.e., 3,3,1\n\t\t\twRb = bRw.transpose()\n\t\t\t\n\t\t\t# Acceleration\n\t\t\taccel = 1.0 / self.mass * (wRb * np.matrix([[0],[0],force]) - np.matrix([[0],[0],[self.mass * self.gravity]]))\n\t\t\taccel = np.array(accel).reshape(-1,).tolist()\n\t\t\t# Angular velocity\n\t\t\tK_quat = 2.0; #%this enforces the magnitude 1 constraint for the quaternion\n\t\t\tquaterror = 1 - (qW**2 + qX**2 + qY**2 + qZ**2);\n\t\t\tqdot = -1/2*np.matrix([ [0,-p,-q,-r],[p,0,-r,q],[q,r,0,-p],[r,-q,p,0]])*quat + K_quat*quaterror * quat\n\t\t\tqdot = np.array(qdot).reshape(-1,).tolist()\n\t\t\t# % Angular acceleration\n\t\t\tomega = np.matrix([[p],[q],[r]])\n\t\t\ttemp = np.squeeze(np.cross(omega.transpose(),(self.Inertia*omega).transpose()))\n\t\t\tpqrdot = self.invInertia * (moment - temp).reshape(-1,1)\n\t\t\tsdot=np.zeros(13) #default=float64\n\t\t\tsdot[0]=xdot#[]\n\t\t\tsdot[1]=ydot\n\t\t\tsdot[2]=zdot\n\t\t\tsdot[3]=accel[0]\n\t\t\tsdot[4]=accel[1]\n\t\t\tsdot[5]=accel[2]\n\t\t\tsdot[6]=qdot[0]\n\t\t\tsdot[7]=qdot[1]\n\t\t\tsdot[8]=qdot[2]\n\t\t\tsdot[9]=qdot[3]\n\t\t\tsdot[10]=pqrdot[0]\n\t\t\tsdot[11]=pqrdot[1]\n\t\t\tsdot[12]=pqrdot[2]\n\t\t\treturn sdot","def revolver(self):\r\n\t\tself.__revuelto=True","def control_OM(self, ang_vel, dt, t1):\n print(\"OM CONTROL\")\n for i in range(ang_vel.shape[0]):\n t1 = time.time()\n t2 = time.time()\n while t2 - t1 < dt:\n self.compare_OM_w_gyro(ang_vel[i])\n self.move_forward(ang_vel[i])\n t2 = time.time()","def test_3D_m4_2k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L2_1,\n Support: 'gpu_2k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: L2_1,\n Support: '',\n Splitting: 'o2'},\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def refframe_correct(self, ra, dec, obstime, sobjs=None):\n # Correct Telescope's motion\n refframe = self.par['calibrations']['wavelengths']['refframe']\n if refframe in ['heliocentric', 'barycentric'] \\\n and self.par['calibrations']['wavelengths']['reference'] != 'pixel':\n msgs.info(\"Performing a {0} correction\".format(self.par['calibrations']['wavelengths']['refframe']))\n # Calculate correction\n radec = ltu.radec_to_coord((ra, dec))\n vel, vel_corr = wave.geomotion_correct(radec, obstime,\n self.spectrograph.telescope['longitude'],\n self.spectrograph.telescope['latitude'],\n self.spectrograph.telescope['elevation'],\n refframe)\n # Apply correction to objects\n msgs.info('Applying {0} correction = {1:0.5f} km/s'.format(refframe, vel))\n if (sobjs is not None) and (sobjs.nobj != 0):\n # Loop on slits to apply\n gd_slitord = self.slits.slitord_id[np.logical_not(self.extract_bpm)]\n for slitord in gd_slitord:\n indx = sobjs.slitorder_indices(slitord)\n this_specobjs = sobjs[indx]\n # Loop on objects\n for specobj in this_specobjs:\n if specobj is None:\n continue\n specobj.apply_helio(vel_corr, refframe)\n\n # Apply correction to wavelength image\n self.vel_corr = vel_corr\n self.waveimg *= vel_corr\n\n else:\n msgs.info('A wavelength reference frame correction will not be performed.')","def test_vertical_velocity_dry_air():\n omega = 1 * units('microbar/second')\n w_truth = -0.7995291 * units('cm/s')\n w_test = vertical_velocity(omega, 1000. * units.mbar, 273.15 * units.K)\n assert_almost_equal(w_test, w_truth, 6)","def update_apc23(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n if len(self._old) >= 1:\n\n\n try:\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\n\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n beta1 = (3.0*(self.time-self.get_old(0,2))+delta_t)/(6.0*self.time-self.get_old(0,2))\n beta2 = -delta_t**2/(6.0*(self.time+delta_t-self.get_old(0,2))*(self.time-self.get_old(0,2)))\n\n print self.force(pos, vel, self.time+delta_t), kap[1], self.get_old(0,1)\n\n pos = self.pos+delta_t*((1.0-beta1-beta2)*vel+beta1*self.vel+beta2*self.get_old(0, 0))\n vel = self.vel+delta_t*((1.0-beta1-beta2)*self.force(pos, vel, self.time+delta_t,\n drag=False)+beta1*kap[1]+beta2*self.get_old(0,1))\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n\n self.time += delta_t\n\n else:\n ## reduced to using the Adams first order method for the first timestep:\n\n kap = update_apc1(self)\n\n self.set_old(kap, 1)\n\n return kap","def geocentricToApparentRadiantAndVelocity(ra_g, dec_g, vg, lat, lon, elev, jd, include_rotation=True):\n\n\n # Compute ECI coordinates of the meteor state vector\n state_vector = geo2Cartesian(lat, lon, elev, jd)\n\n eci_x, eci_y, eci_z = state_vector\n\n\n # Assume that the velocity at infinity corresponds to the initial velocity\n v_init = np.sqrt(vg**2 + (2*6.67408*5.9722)*1e13/vectMag(state_vector))\n\n\n # Calculate the geocentric latitude (latitude which considers the Earth as an elipsoid) of the reference \n # trajectory point\n lat_geocentric = np.degrees(math.atan2(eci_z, math.sqrt(eci_x**2 + eci_y**2)))\n\n\n\n\n ### Uncorrect for zenith attraction ###\n\n # Compute the radiant in the local coordinates\n azim, elev = raDec2AltAz(ra_g, dec_g, jd, lat_geocentric, lon)\n\n # Compute the zenith angle\n eta = np.radians(90.0 - elev)\n\n # Numerically correct for zenith attraction\n diff = 10e-5\n zc = eta\n while diff > 10e-6:\n \n # Update the zenith distance\n zc -= diff\n\n # Calculate the zenith attraction correction\n delta_zc = 2*math.atan((v_init - vg)*math.tan(zc/2.0)/(v_init + vg))\n diff = zc + delta_zc - eta\n\n\n # Compute the uncorrected geocentric radiant for zenith attraction\n ra, dec = altAz2RADec(azim, 90.0 - np.degrees(zc), jd, lat_geocentric, lon)\n\n ### ###\n\n\n\n # Apply the rotation correction\n if include_rotation:\n\n # Calculate the velocity of the Earth rotation at the position of the reference trajectory point (m/s)\n v_e = 2*math.pi*vectMag(state_vector)*math.cos(np.radians(lat_geocentric))/86164.09053\n\n\n # Calculate the equatorial coordinates of east from the reference position on the trajectory\n azimuth_east = 90.0\n altitude_east = 0\n ra_east, dec_east = altAz2RADec(azimuth_east, altitude_east, jd, lat, lon)\n\n # Compute the radiant vector in ECI coordinates of the apparent radiant\n v_ref_vect = v_init*np.array(raDec2Vector(ra, dec))\n\n\n v_ref_nocorr = np.zeros(3)\n\n # Calculate the derotated reference velocity vector/radiant\n v_ref_nocorr[0] = v_ref_vect[0] + v_e*np.cos(np.radians(ra_east))\n v_ref_nocorr[1] = v_ref_vect[1] + v_e*np.sin(np.radians(ra_east))\n v_ref_nocorr[2] = v_ref_vect[2]\n\n\n # Compute the radiant without Earth's rotation included\n ra_norot, dec_norot = vector2RaDec(vectNorm(v_ref_nocorr))\n v_init_norot = vectMag(v_ref_nocorr)\n\n ra = ra_norot\n dec = dec_norot\n v_init = v_init_norot\n\n\n\n return ra, dec, v_init","def test_3D_m8_1k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: M8Prime,\n Support: 'gpu_1k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: M8Prime,\n Support: '',\n Splitting: 'o2'},\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def angular_memory_effect_analysis(tilt_coef_range, input_field, TM, data_shape, sample_pitch, wavelength, plot_std = True):\n zernike_rho = np.linspace(-0.5, 0.5, data_shape[1])\n\n\n centered_outputs = np.zeros([tilt_coef_range.size, data_shape[1]]) # For storage of outputs that where tilted and then artificially shifted back to the center\n shift_idxs = np.zeros(tilt_coef_range.size) # Will store information on how much outputs needed to be shifted to be centered\n for idx, coef in enumerate(tilt_coef_range):\n output_intensity = np.abs( TM @ ( input_field * np.exp(2j * np.pi * coef * zernike_rho) ).ravel() )**2 # propagation via TM, only saving intensity\n centered_outputs[idx, :], shift_idxs[idx] = calc.center_tilted_output(output_intensity, coef, data_shape, sample_pitch, wavelength, return_shift_idx = True)\n\n centered_outputs = centered_outputs / np.linalg.norm(centered_outputs, 1) # Normalising all outputs\n\n\n input_field = input_field.ravel()\n reference_output = np.abs( TM @ input_field )**2 # untilted output field, which we will use for std\n reference_output = reference_output / np.linalg.norm(reference_output)\n\n # fig, axs = plt.subplots()\n # axs.plot(centered_outputs[-1,:])\n # plt.show()\n\n #Comparing the outputs with a standard deviation method\n subtracted_outputs = np.zeros(centered_outputs.shape)\n for idx in range(centered_outputs[:,0].size):\n subtracted_outputs[idx, :] = centered_outputs[idx, :] - centered_outputs[0, :] # subtracting the outputs with the reference\n\n std_of_outputs = np.std(subtracted_outputs, axis = 1) # Computing the standard deviation of subtracted outputs\n if plot_std:\n fig, axs = plt.subplots()\n img_1, = axs.plot(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, color = 'black')\n img_2 = axs.scatter(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, s = 40, color = 'crimson', edgecolors = 'black')\n axs.set_title('Angular memory effect decay')\n axs.set(xlabel = '$\\delta x, \\ \\mu m$', ylabel = 'Standard deviation, p.d.u.')\n\n\n #Fitting a logistic curve onto the results\n popt, pcov = curve_fit(logistic_curve, (shift_idxs * sample_pitch[1] * 1e6), std_of_outputs)\n smooth_range = np.linspace(-0.1, shift_idxs[-1] * sample_pitch[1] * 1e6, 1000)\n fitted_std = logistic_curve(smooth_range, *popt)\n img_fit, = axs.plot(smooth_range, fitted_std, color = 'blue')\n\n axs.text(0.65 * (shift_idxs[-1] * sample_pitch[1] * 1e6), 0.2 * std_of_outputs.max(), 'Fitting parameters: \\nL = {:.3g} \\nx_0 = {:.3g} \\nk = {:.3g}'.format(*popt))\n\n # img_3 = axs.scatter((shift_idxs * sample_pitch[1] * 1e6)[-8:], std_of_outputs[-8:], s = 70, marker = 'x', color = 'green')#excluded point\n plt.legend([(img_2, img_1), img_fit], ['STD($\\delta x $)', '$L / [1 + \\exp(-k (\\delta x - x_0)) ]$'])\n plt.plot(block = False)\n\n return centered_outputs","def correct_proper_motion(self, invert=False, mjd=None):\n\n # If mjd not set directly, check that it was set from FITS headers in get_cutout method\n if self.mjd is None:\n if mjd is None:\n raise FITSException(\"Date could not be inferred from header, supply with epoch keyword.\")\n else:\n self.mjd = mjd\n\n obstime = Time(self.mjd, format='mjd')\n\n simbad = Simbad.query_region(self.position, radius=180 * u.arcsec)\n\n # Catch SIMBAD failure either from None return of query or no stellar type matches in region\n try:\n simbad = simbad.to_pandas()\n pm_types = ['*', '**', 'PM*', 'EB*', 'Star', 'PSR', 'Pulsar', 'Flare*']\n simbad = simbad[(simbad['OTYPE'].isin(pm_types)) | (simbad['SP_TYPE'].str.len() > 0)]\n\n assert len(simbad) > 0\n\n except (ValueError, AssertionError):\n logger.debug(\"No high proper-motion objects within 180 arcsec.\")\n self.correct_pm = False\n\n return\n\n # Treat non-existent proper motion parameters as extremely distant objects\n simbad['PMRA'].fillna(0, inplace=True)\n simbad['PMDEC'].fillna(0, inplace=True)\n simbad['PLX_VALUE'].fillna(0.01, inplace=True)\n\n newtime = Time(self.radio.mjd, format='mjd')\n pmra = simbad['PMRA'].values * u.mas / u.yr\n pmdec = simbad['PMDEC'].values * u.mas / u.yr\n\n dist = Distance(parallax=simbad['PLX_VALUE'].values * u.mas)\n\n simbad['j2000pos'] = SkyCoord(\n ra=simbad['RA_d'].values * u.deg,\n dec=simbad['DEC_d'].values * u.deg,\n frame='icrs',\n distance=dist,\n pm_ra_cosdec=pmra,\n pm_dec=pmdec,\n obstime='J2000',\n )\n\n datapos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(obstime))\n newpos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(newtime))\n\n simbad_cols = {\n 'MAIN_ID': 'Object',\n 'OTYPE': 'Type',\n 'SP_TYPE': 'Spectral Type',\n 'DISTANCE_RESULT': 'Separation (arcsec)',\n }\n simbad = simbad.rename(columns=simbad_cols)\n simbad = simbad[simbad_cols.values()].copy()\n simbad['PM Corrected Separation (arcsec)'] = np.round(newpos.apply(\n lambda x: x.separation(self.position).arcsec), 3)\n\n # Only display PM results if object within 15 arcsec\n if simbad['PM Corrected Separation (arcsec)'].min() > 15:\n logger.debug(\"No PM corrected objects within 15 arcsec\")\n self.correct_pm = False\n\n return\n\n self.simbad = simbad.sort_values('PM Corrected Separation (arcsec)')\n logger.info(f'SIMBAD results:\\n {self.simbad.head()}')\n\n nearest = self.simbad['PM Corrected Separation (arcsec)'].idxmin()\n\n self.oldpos = datapos[nearest]\n self.pm_coord = newpos[nearest]\n\n near_object = self.simbad.loc[nearest].Object\n msg = f'{near_object} proper motion corrected to <{self.pm_coord.ra:.4f}, {self.pm_coord.dec:.4f}>'\n logger.info(msg)\n\n missing = simbad[simbad['PM Corrected Separation (arcsec)'].isna()]\n if len(missing) > 0:\n msg = f\"Some objects missing PM data, and may be a closer match than presented:\\n {missing}\"\n logger.warning(msg)\n\n return","def CreateMotionKernel(kernel):\r\n TrajSize = 64\r\n anxiety = 0.2* np.random.rand()\r\n numT = 10\r\n MaxTotalLength =10\r\n TotLength = 0\r\n #term determining, at each sample, the strengh of the component leating towards the previous position\r\n centripetal = 0.7 * np.random.rand()\r\n #term determining, at each sample, the random component of the new direction\r\n gaussianTerm =10 * np.random.rand()\r\n #probability of having a big shake, e.g. due to pressing camera button or abrupt hand movements\r\n freqBigShakes = 3 *np.random.rand()\r\n #v is the initial velocity vector, initialized at random direction\r\n init_angle = 360 * np.random.rand()\r\n #initial velocity vector having norm 1\r\n v0 = math.cos(init_angle / 180.0 * math.pi) + 1.0j * math.sin(init_angle/ 180.0 * math.pi)\r\n #the speed of the initial velocity vector\r\n v = v0* MaxTotalLength/(numT-1);\r\n\r\n if anxiety > 0:\r\n v = v0 * anxiety\r\n # initialize the trajectory vector\r\n x = np.zeros(numT,dtype = np.complex);\r\n\r\n abruptShakesCounter = 0\r\n for t in range(numT-1):\r\n # determine if there is an abrupt (impulsive) shake\r\n if np.random.rand() < freqBigShakes * anxiety:\r\n #if yes, determine the next direction which is likely to be opposite to the previous one\r\n nextDirection = 2 * v * (np.exp( 1.0j * (math.pi + (np.random.rand() - 0.5))))\r\n abruptShakesCounter = abruptShakesCounter + 1\r\n else:\r\n nextDirection=0\r\n\r\n #determine the random component motion vector at the next step\r\n dv = nextDirection + anxiety * (gaussianTerm * (np.random.randn()- + 1.0j * np.random.randn()) - centripetal * x[t]) * (MaxTotalLength / (numT - 1))\r\n v = v + dv\r\n # velocity vector normalization\r\n v = (v / np.abs(v)) * MaxTotalLength / (numT - 1)\r\n #print v\r\n x[t + 1] = x[t] + v\r\n # compute total length\r\n #TotLength=TotLength+np.abs(x([t+1]-x[t]))\r\n x_real = []\r\n x_imag = []\r\n for elem in x:\r\n x_real.append(elem.real)\r\n x_imag.append(elem.imag)\r\n x_real = np.round((x_real - np.min(x_real))/(np.max(x_real) - np.min(x_real)) * kernel-0.5)\r\n x_imag = np.round((x_imag - np.min(x_imag))/(np.max(x_imag) - np.min(x_imag)) * kernel-0.5)\r\n for idx in range(len(x_real)):\r\n if x_real[idx] < 0:\r\n x_real[idx] = 0\r\n if x_imag[idx] < 0:\r\n x_imag[idx] = 0\r\n if x_real[idx] > kernel -1:\r\n x_real[idx] = kernel -1\r\n if x_imag[idx] > kernel -1:\r\n x_imag[idx] = kernel -1\r\n\r\n ker = np.zeros((kernel, kernel))\r\n for idx in range(len(x_real)):\r\n ker[np.int(x_real[idx])][np.int(x_imag[idx])] = 1\r\n ker = ker/np.sum(np.sum(ker))\r\n return ker","def _CorrectMomentum(optimizer, param_keys, correction):\n logger.info('Scaling update history by %.6f (new lr / old lr)', correction)\n for p_key in param_keys:\n try:\n optimizer.state[p_key]['momentum_buffer'] *= correction\n except:\n continue","def photometric_calibration():\n pass","def LeapFrog(self,r,v,dt):\n\n rhalf = r + np.asarray(v)*(dt/2) #Taking a half step forward with positional vector\n # predict the final velocity at the next timestep using the acceleration field at the rhalf position \n vnew = v + self.M31Accel(rhalf)*dt\n # predict the final position using the average of the current velocity and the final velocity\n rnew = r + 0.5*(v+vnew)*dt\n \n return rnew,vnew","def test_3D_m8_2k():\n scal, velo = setup_3D()\n\n advec = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: M8Prime,\n Support: 'gpu_2k',\n Splitting: 'o2'}\n )\n advec_py = Advection(velo, scal, discretization=d3d,\n method={TimeIntegrator: RK2,\n Interpolation: Linear,\n Remesh: M8Prime,\n Support: '',\n Splitting: 'o2'},\n )\n assertion_3D_withPython(scal, velo, advec, advec_py)","def ccm_unred(wave, flux, ebv, r_v=\"\"):\n import numpy as np\n wave = np.array(wave, float)\n flux = np.array(flux, float)\n \n if wave.size != flux.size: raise TypeError, 'ERROR - wave and flux vectors must be the same size'\n \n if not bool(r_v): r_v = 3.1\n \n x = 10000.0/wave\n npts = wave.size\n a = np.zeros(npts, float)\n b = np.zeros(npts, float)\n \n ###############################\n #Infrared\n \n good = np.where( (x > 0.3) & (x < 1.1) )\n a[good] = 0.574 * x[good]**(1.61)\n b[good] = -0.527 * x[good]**(1.61)\n \n ###############################\n # Optical & Near IR\n \n good = np.where( (x >= 1.1) & (x < 3.3) )\n y = x[good] - 1.82\n \n c1 = np.array([ 1.0 , 0.104, -0.609, 0.701, 1.137, \\\n -1.718, -0.827, 1.647, -0.505 ])\n c2 = np.array([ 0.0, 1.952, 2.908, -3.989, -7.985, \\\n 11.102, 5.491, -10.805, 3.347 ] )\n \n a[good] = np.polyval(c1[::-1], y)\n b[good] = np.polyval(c2[::-1], y)\n \n ###############################\n # Mid-UV\n \n good = np.where( (x >= 3.3) & (x < 8) )\n y = x[good]\n F_a = np.zeros(np.size(good),float)\n F_b = np.zeros(np.size(good),float)\n good1 = np.where( y > 5.9 )\n \n if np.size(good1) > 0:\n y1 = y[good1] - 5.9\n F_a[ good1] = -0.04473 * y1**2 - 0.009779 * y1**3\n F_b[ good1] = 0.2130 * y1**2 + 0.1207 * y1**3\n \n a[good] = 1.752 - 0.316*y - (0.104 / ( (y-4.67)**2 + 0.341 )) + F_a\n b[good] = -3.090 + 1.825*y + (1.206 / ( (y-4.62)**2 + 0.263 )) + F_b\n \n ###############################\n # Far-UV\n \n good = np.where( (x >= 8) & (x <= 11) )\n y = x[good] - 8.0\n c1 = [ -1.073, -0.628, 0.137, -0.070 ]\n c2 = [ 13.670, 4.257, -0.420, 0.374 ]\n a[good] = np.polyval(c1[::-1], y)\n b[good] = np.polyval(c2[::-1], y)\n \n # Applying Extinction Correction\n \n a_v = r_v * ebv\n a_lambda = a_v * (a + b/r_v)\n \n funred = flux * 10.0**(0.4*a_lambda) \n \n return funred","def _update_vel(self) -> None:\n self.state[:, :, Boids.Attr.VEL] += self.state[:, :, Boids.Attr.ACC]\n self.state[:, :, Boids.Attr.VEL] = maglim(\n self.state[:, :, Boids.Attr.VEL], self.max_vel)","def mvr(self, delta, timeout=None, wait=False, log=True):\n\n self.mv(delta + self.wm(), timeout=timeout, wait=wait, log=log)","def process_3D(radar, velname=\"VEL\", dbzname=\"DBZ\", zdrname=\"ZDR\", rhohvname=\"RHOHV\",\n gatefilter=None, nyquist_velocity=None, two_passes=False, debug=False):\n # Filter\n if gatefilter is None:\n gatefilter = filtering.do_gatefilter(radar, \"VEL\", \"DBZ\", zdr_name=\"ZDR\", rho_name=\"RHOHV\")\n\n if nyquist_velocity is None:\n nyquist_velocity = radar.instrument_parameters['nyquist_velocity']['data'][0]\n\n # Start with first reference.\n slice_number = 0\n myslice = radar.get_slice(slice_number)\n\n r = radar.range['data'].copy()\n velocity = radar.fields[\"VEL\"]['data'].copy()\n azimuth_reference = radar.azimuth['data'][myslice]\n elevation_reference = radar.elevation['data'][myslice].mean()\n\n velocity_reference = np.ma.masked_where(gatefilter.gate_excluded, velocity)[myslice]\n\n # Dealiasing first sweep.\n final_vel, flag_vel, azi_s, azi_e = dealiasing_process_2D(r, azimuth_reference, velocity_reference,\n elevation_reference, nyquist_velocity, debug=False)\n\n velocity_reference = final_vel.copy()\n flag_reference = flag_vel.copy()\n\n ultimate_dealiased_velocity = np.zeros(radar.fields[\"VEL\"]['data'].shape)\n ultimate_dealiased_velocity[myslice] = final_vel.copy()\n\n for slice_number in range(1, radar.nsweeps):\n print(slice_number)\n myslice = radar.get_slice(slice_number)\n azimuth_slice = radar.azimuth['data'][myslice]\n elevation_slice = radar.elevation['data'][myslice].mean()\n\n if len(azimuth_slice) < 60:\n print(f\"Problem with slice #{slice_number}, only {len(azimuth_slice)} radials.\")\n continue\n\n vel = np.ma.masked_where(gatefilter.gate_excluded, velocity)[myslice]\n velocity_slice = vel.filled(np.NaN)\n\n flag_slice = np.zeros_like(velocity_slice) + 1\n flag_slice[np.isnan(velocity_slice)] = -3\n\n # 3D dealiasing\n velocity_slice, flag_slice = continuity.unfolding_3D(r, elevation_reference,\n azimuth_reference, elevation_slice,\n azimuth_slice, velocity_reference,\n flag_reference, velocity_slice,\n flag_slice, nyquist_velocity,\n loose=True)\n\n final_vel, flag_vel, azi_s, azi_e = dealiasing_process_2D(r, azimuth_slice, velocity_slice,\n elevation_slice, nyquist_velocity,\n debug=False, inherit_flag=flag_slice,\n inherit_azi_start=azi_s, inherit_azi_end=azi_e)\n\n if two_passes:\n velocity_slice, flag_slice = continuity.unfolding_3D(r, elevation_reference,\n azimuth_reference,\n elevation_slice,\n azimuth_slice,\n velocity_reference,\n flag_reference,\n final_vel, flag_vel,\n nyquist_velocity, loose=False)\n azimuth_reference = azimuth_slice.copy()\n velocity_reference = final_vel.copy()\n flag_reference = flag_vel.copy()\n elevation_reference = elevation_slice\n\n ultimate_dealiased_velocity[myslice] = final_vel.copy()\n # plot_radar(final_vel, flag_vel, slice_number)\n\n ultimate_dealiased_velocity = np.ma.masked_where(gatefilter.gate_excluded,\n ultimate_dealiased_velocity)\n\n return ultimate_dealiased_velocity","def wheel_center_disp_damper_movement(rocker_theta, dictionary, results_dictionary, heave):\n results_dictionary['Pushrod Rocker'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\n dictionary['Rocker Pivot Axis'],\n dictionary['Pushrod Rocker'],\n rocker_theta))\n results_dictionary['Damper Rocker'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\n dictionary['Rocker Pivot Axis'],\n dictionary['Damper Rocker'],\n rocker_theta))\n results_dictionary['Roll Damper a'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\n dictionary['Rocker Pivot Axis'],\n dictionary['Roll Damper a'],\n rocker_theta))\n results_dictionary['Pushrod Control Arm'][-1] = fsolve(three_point_method,\n dictionary['Pushrod Control Arm'],\n args=(dictionary['Upper Fore'],\n dictionary['Upper Aft'],\n results_dictionary['Pushrod Rocker'][-1],\n dictionary['Upper Fore'],\n dictionary['Upper Aft'],\n dictionary['Pushrod Rocker'],\n dictionary['Pushrod Control Arm']),\n )\n\n results_dictionary['Lower Out'][-1] = fsolve(three_point_method, dictionary['Lower Out'],\n args=(dictionary['Lower Fore'],\n dictionary['Lower Aft'],\n results_dictionary['Pushrod Control Arm'][-1],\n dictionary['Lower Fore'],\n dictionary['Lower Aft'],\n dictionary['Pushrod Control Arm'],\n dictionary['Lower Out']))\n\n results_dictionary['Upper Out'][-1] = fsolve(three_point_method, dictionary['Upper Out'],\n args=(dictionary['Upper Fore'],\n dictionary['Upper Aft'],\n results_dictionary['Lower Out'][-1],\n dictionary['Upper Fore'],\n dictionary['Upper Aft'],\n dictionary['Lower Out'],\n dictionary['Upper Out']))\n\n # Steering\n results_dictionary['Tie Rod Upright'][-1] = fsolve(three_point_method,\n dictionary['Tie Rod Upright'],\n args=(results_dictionary['Tie Rod Chassis'][-1],\n results_dictionary['Lower Out'][-1],\n results_dictionary['Upper Out'][-1],\n dictionary['Tie Rod Chassis'],\n dictionary['Lower Out'],\n dictionary['Upper Out'],\n dictionary['Tie Rod Upright']))\n\n results_dictionary['Wheel Center'][-1] = (fsolve(three_point_method, dictionary['Wheel Center'],\n args=(results_dictionary['Tie Rod Upright'][-1],\n results_dictionary['Upper Out'][-1],\n results_dictionary['Lower Out'][-1],\n dictionary['Tie Rod Upright'],\n dictionary['Upper Out'],\n dictionary['Lower Out'],\n dictionary['Wheel Center'])))\n\n # solved for wheel displacement - actual wheel displacement\n return (dictionary['Wheel Center'][2] - results_dictionary['Wheel Center'][-1][2] + heave)**2","def ev_controlleraxismotion(self, event: tcod.event.ControllerAxis) -> T | None:","def update_focal_axes(self):\n #self.update_sigma()\n self.updateGL()","def define_ufl_velocity_equation(self):\n\n if hasattr(self, 'f1'):\n return None\n\n if self.config['material']['type'] == 'viscous':\n self.f1 = 0\n return None\n\n if not self.config['formulation']['time']['unsteady']:\n self.f1 = 0\n return None\n\n theta = self.config['formulation']['time']['theta']\n dt = self.config['formulation']['time']['dt']\n f1 = self.displacement - self.displacement0 \\\n - dt*(theta*self.velocity + (1.0 - theta)*self.velocity0)\n\n self.f1 = dlf.dot(self.test_vector, f1)*dlf.dx\n\n return None","def current_update():\n # Compute the multiplier coefficient:\n ci = dt / (L * dx)\n for k in range(0, nx-1):\n I[k] = I[k] - (ci * (V[k + 1] - V[k]))","def update_volume(self):\r\n\r\n # for the first cell\r\n self.cells[0].volume = self.cells[0].volume + \\\r\n self.inflow - self.flows[0]\r\n # for the intermediate cells\r\n for i in range(1, self.cells_number-1):\r\n self.cells[i].volume = self.cells[i].volume + \\\r\n self.flows[i-1]-self.flows[i]\r\n # for the last cells\r\n self.cells[-1].volume = self.cells[-1].volume + \\\r\n self.flows[-1] - self.outflow","def illumination_correction(img, avg):\n corrected = (img / avg * img.mean() / (img / avg).mean()).astype('uint8')\n return corrected","def detect_velocity(image):\n nonlocal prev, v_last\n curr_bgr = cv.warpPerspective(image, M, (160, 120))\n curr = cv.cvtColor(curr_bgr, cv.COLOR_BGR2GRAY)\n\n if prev is None:\n prev = curr\n v_last = 0.0\n return v_last, curr_bgr, np.zeros_like(image)\n\n flow = cv.calcOpticalFlowFarneback(\n prev, # Previous image\n curr, # Current image\n None, # Computed flow image that has the same size oas prev and type CV_32FC2.\n 0.5, # Specifies the image scale (<1) to build pyramids for each image.\n 3, # Number of pyramid layers including the initial image.\n 15, # winsize, averaging windows size.\n 3, # iterations, number of iterations the algorithm does at each pyramid level.\n 5, # standard deviation of the Gaussian that is used to smooth derivative\n 1.5,\n 0)\n\n mag, ang = cv.cartToPolar(flow[..., 0], flow[..., 1])\n\n v = mag * np.sin(ang)\n\n ######################\n ## Histogram for mag\n ar = np.arange(-20.0, 20.0, 0.50, dtype=np.float)\n his = np.histogram(v, bins=ar)\n\n for i, n in enumerate(his[0]):\n bgr = (255, 255, 0)\n if his[1][i] < 0:\n bgr = (0, 255, 255)\n\n #print('[{}] {} - {}'.format(i, n, his[1][i]))\n cv.rectangle( image, #curr_bgr,\n (i*2, HEIGHT),\n (i*2, HEIGHT - int(n / 10)),\n bgr, #(0, 255, 255),\n cv.FILLED)\n\n hsv = np.zeros_like(image)\n hsv[..., 0] = ang * 180 / np.pi / 2\n hsv[..., 1] = 255\n hsv[..., 2] = cv.normalize(np.abs(v), None, 0, 255, cv.NORM_MINMAX)\n hsv_bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)\n ##\n ######################\n\n v_abs = np.absolute(v)\n v = v[v_abs >= np.percentile(v_abs, VELOCITY_CUTOFF_PCT)]\n\n v_max = v_last + MAX_ACC\n v_min = v_last - MAX_ACC\n v = np.clip(v, v_min, v_max)\n if v.size > 0:\n v_avg = v.mean()\n else:\n if v_last > 0:\n v_avg = max(v_last - MAX_ACC, 0)\n elif v_last < 0:\n v_avg = min(v_last + MAX_ACC, 0)\n else:\n v_avg = 0\n\n prev = curr\n v_last = v_avg\n return v_last, curr_bgr, hsv_bgr","def sos_correction(self, ratio):\n\n # Correct velocities\n self.u_mps = self.u_mps * ratio\n self.v_mps = self.v_mps * ratio","def test_sequential_update_mvar_missing_middle(ft_ar2_mvar_kw, theta_ar2_mvar, \n Yt_ar2_mvar, Xt_ar2_mvar):\n t = 1\n kf = Filter(ft_ar2_mvar_kw, Yt_ar2_mvar, Xt_ar2_mvar, for_smoother=True)\n kf.init_attr(theta_ar2_mvar)\n for t_ in range(t+1):\n kf._sequential_update(t_)\n Mt = kf.ft(kf.theta, kf.T, x_0=Xt_ar2_mvar[0])\n\n Ht = Mt['Ht'][t][[0, 2]]\n Bt = Mt['Bt'][t]\n Dt = Mt['Dt'][t][[0, 2]]\n Ft = Mt['Ft'][t]\n Qt = Mt['Qt'][t]\n Rt = Mt['Rt'][t][[0, 2]][:,[0, 2]]\n Upsilon = Ht.dot(kf.P_star_t[t][0]).dot(Ht.T) + Rt \n K = kf.P_star_t[t][0].dot(Ht.T).dot(linalg.pinvh(Upsilon))\n v = kf.Yt[t][[0, 1]] - Ht.dot(kf.xi_t[t][0]) - Dt.dot(kf.Xt[t])\n \n expected_xi_t1_0 = Ft.dot(kf.xi_t[t][0] + K.dot(v)) + Bt.dot(kf.Xt[t])\n P_t_0 = kf.P_star_t[t][0] \n P_t_t = P_t_0 - P_t_0.dot(Ht.T).dot(linalg.pinvh(Upsilon)).dot(\n Ht).dot(P_t_0)\n expected_P_t1_0 = Ft.dot(P_t_t).dot(Ft.T) + Qt\n np.testing.assert_array_almost_equal(expected_P_t1_0, \n kf.P_star_t[t+1][0])\n np.testing.assert_array_almost_equal(expected_xi_t1_0, \n kf.xi_t[t+1][0])","def edge_velocity(self):\n #reflext x values at x edges\n self.u[1,:,0] = -self.u[1,:,1]\n self.u[1,:,-1] = -self.u[1,:,-2]\n #mirror x values at y edges \n self.u[1,0,:] = self.u[1,1,:]\n self.u[1,-1,:] = self.u[1,-2,:]\n #mirror y values at x edges\n self.u[0,:,0] = self.u[0,:,1]\n self.u[0,:,-1] = self.u[0,:,-2]\n #mirror y values at y edges \n self.u[0,0,:] = -self.u[0,1,:]\n self.u[0,-1,:] = -self.u[0,-2,:]","def test_converts_to_photomodeler_and_back_exactly() -> None:\n # fmm must be equal, k* and p* must be zero\n cam = Camera(\n imgsz=(4288, 2848), fmm=(3200, 3200), cmm=(0.5, -0.4), sensorsz=(35.1, 24.2)\n )\n xcam = PhotoModeler.from_camera(cam)\n residuals = Converter(xcam, cam).residuals()\n np.testing.assert_allclose(residuals, 0, rtol=0, atol=1e-12)\n cam2 = xcam.to_camera()\n np.testing.assert_allclose(cam.to_array(), cam2.to_array(), rtol=0, atol=1e-13)","def radial_velocity(wv_obj, fx_obj, sig_obj, wv_std, fx_std, sig_std, obj_name, std_name, rv_std, rv_std_err, order,\n xcorr_width, cut, cutstart, cutend):\n\n # The more random iterations, the better... but it takes longer\n n_iter = 1000\n\n # Step 1: Fix the spectra:\n # * Select only the region in which they overlap\n # * Make a new stretched wavelength array (for sub-pixel precision work)\n # * Interpolate the data onto the new wavelength array\n # * Remove large scale slopes so we only compare line and band features\n\n # Find where standard and object overlap ---------------\n wv_min = max([min(wv_std), min(wv_obj)])\n wv_max = min([max(wv_std), max(wv_obj)])\n\n n_pix_std = len(wv_std)\n\n # Creates ln standard wavelength array ---------------------------------\n # AR 2013.0423 The wavelength array only covers the overlap region. Also, I'm folding the rebinning by 10 into this statement.\n acoef_std = (n_pix_std * 10 - 1) / (math.log(wv_max) - math.log(wv_min))\n bcoef_std = (n_pix_std * 10) - (acoef_std * math.log(wv_max))\n\n arr = np.arange(n_pix_std * 10) + 1\n wv_ln_std = np.exp((arr - bcoef_std) / acoef_std)\n\n # AR 2012.1018: Find the conversion between pixels and velocity. This will vary from instrument\n # to instrument and spectral order to spectral order, so we should preferentially calculate this\n # based on the actual input spectrum.\n # AR 2013.0422: Change the calculation to happen AFTER the corrected wavelength scale has been made\n # Find the average pixel/spectrum offset\n # Note: even though it's called micron_per_pix, it will still work if the wavelengths are\n # angstroms instead (it really converts <wavelength unit> to km/s)\n\n # Interpolate data onto same ln wavelength scale -------------------------------\n\n fx_interp_std = np.interp(wv_ln_std, wv_std, fx_std)\n fx_interp_obj = np.interp(wv_ln_std, wv_obj, fx_obj)\n sig_interp_std = np.interp(wv_ln_std, wv_std, sig_std) # AR 2012.1018 Also need to rebin sig\n sig_interp_obj = np.interp(wv_ln_std, wv_obj, sig_obj) # AR 2012.1018 Also need to rebin sig\n\n # Rebin Data ----------------------------\n\n wv_arr_std = np.asarray(wv_ln_std, dtype=float)\n fx_arr_obj = np.asarray(fx_interp_obj, dtype=float)\n fx_arr_std = np.asarray(fx_interp_std, dtype=float)\n sig_arr_obj = np.asarray(sig_interp_obj, dtype=float)\n sig_arr_std = np.asarray(sig_interp_std, dtype=float)\n\n datalen = len(fx_arr_obj)\n\n # Step 2: Measure vsini:\n # Note that as of 2015.0605, this doesn't actually work.\n\n # AR 2014.0922: For vsini:\n # In a loop:\n # Take the standard spectrum\n # broaden it to width X\n # autocorrelate,\n # measure width of gaussian Y (this is supposed to give you a means of translating between width-of-cross-correlation and vsini)\n # Fit function solving Y for X.\n # For each cross correlation of object and standard:\n # Determine vsini\n\n pix_scale = (2.99792458 * 10 ** 5) / acoef_std\n\n # vsinirange = [1,2,5,10,20,30,40,50,60,80,100,100]\n # widthrange = []\n # for v in vsinirange:\n # # Make convolution kernel for v km/s\n # kernel = lsf_rotate(pix_scale,v)\n # # Broaden the standard spectrum\n # fx_obj_wide = np.correlate(fx_arr_obj, kernel, mode='same')\n # # Rectify the spectrum\n # fx_obj_orig = (fx_arr_obj - np.mean(fx_arr_obj))/np.std(fx_arr_obj,ddof=1)\n # fx_obj_wide = (fx_obj_wide - np.mean(fx_obj_wide))/np.std(fx_obj_wide,ddof=1)\n #\n # # Remove a cubic (flatten the spectrum)\n # coeff,pcov = op.curve_fit(cubic,wv_arr_std,fx_obj_wide)\n # fx_obj_wide = fx_obj_wide - (coeff[0] + coeff[1]*wv_arr_std + coeff[2]*wv_arr_std**2 + coeff[3]*wv_arr_std**3)\n # coeff,pcov = op.curve_fit(cubic,wv_arr_std,fx_obj_orig)\n # fx_obj_orig = fx_obj_orig - (coeff[0] + coeff[1]*wv_arr_std + coeff[2]*wv_arr_std**2 + coeff[3]*wv_arr_std**3)\n #\n # # Cross-correlate the spectrum with its broadened self\n # ycorr = np.correlate(fx_obj_orig, fx_obj_wide, mode='full')\n # # Now determine where the peak is (should be near 0)\n # length = len(ycorr)\n # xcorr = np.arange(length) - length//2\n # xmid = np.argmax(ycorr)\n # ymax = np.max(ycorr)\n # # Chop out just the portion of the array near the peak\n # xcorr_min=xmid-xcorr_width\n # xcorr_max=xmid+xcorr_width\n # ycorr1=ycorr[xcorr_min:xcorr_max]\t#isolate section of array with gaussian\n # xcorr1=xcorr[xcorr_min:xcorr_max] #isolate the same section of the pixel range\n #\n # # set up initial values for gaussian fitting via chi2\n # sig = 10\n # sky = np.min(ycorr1)/1.2\n # # print ycorr1[-1],ycorr1[0],xcorr1[-1],xcorr1[0]\n # sky2 = (ycorr1[-1]-ycorr1[0])/(xcorr1[-1]-xcorr1[0])\n # lnamp = np.log(ymax/1.2-sky)\t# guess some values\n # mean = xcorr[xmid]\n #\n # amp = np.exp(lnamp)\n # sig2 = sig**2\n # # suggestion from D. Hogg 12/15/12: Add extra linear feature to fit.\n # # suggestion from D. Hogg 12/15/12: operate on ln(amp) so that the amplitude CANNOT be negative.\n # def chi2(p):\t#define gaussian function for fitting\n # sig2=p[2] ** 2\n # m = (np.exp(p[0]) * np.exp(-0.5 * (xcorr1 - p[1]) ** 2 / sig2)) + p[3] + p[4]*xcorr1\n # return (ycorr1 - m)\n #\n # # Fit the gaussian.\n # popt, ier = op.leastsq(chi2, [lnamp, mean, sig, sky, sky2])\n # lnamp, mean, sig, sky, sky2 = popt\n #\n # amp = np.exp(lnamp)\n # # record the width\n # widthrange.append(sig)\n #\n # # Plot all the widths to get a width-vsini curve\n # vsinicoeff,popt = op.curve_fit(quartic,np.asarray(widthrange),np.asarray(vsinirange))\n #\n # relationx = np.arange(50,200,1)\n # relationy = vsinicoeff[0]+vsinicoeff[1]*relationx+vsinicoeff[2]*relationx**2+vsinicoeff[3]*relationx**3+vsinicoeff[4]*relationx**4\n # figv = plt.figure(1)\n # axv = figv.add_subplot(211)\n # axv.scatter(widthrange,vsinirange)\n # axv.plot(relationx,relationy)\n # #ax.text(70,100,\"{0:} {1:} {2:} {3:} {4:}\".format(vsinicoeff))\n\n # 3. Cross-correlate the data, using n_iter trials:\n # * Generate two random gaussian noises scaled to the uncertainty on the fluxes\n # * Apply those gaussian noises to the standard and target stars\n # * Cross-correlate the standard and target stars\n # * Find and then cut out just the part of the cross-correlation curve near the maximum\n # * Set up gaussian\n # * Fit gaussian to that center part\n # * Save fitted parameters (pixel shift aka mean of gaussian, width aka stddev of gaussian)\n # * Repeat n_iter times\n\n # Cross correlation loop --------------------------------\n pix_shift = np.array([]) # initialize array for pixel shift values\n pix_width = np.zeros(n_iter) # initialize array for pixel width values\n l = 0\n\n # using the xrange generator rather than making a full list saves memory\n while len(pix_shift) < n_iter:\n # prepare the randomized data\n # GETTING ARRAYS READY FOR CROSS CORRELATION\n\n\n # Randomize noise:\n # create gaussian distribution of random numbers b/t 1 and -1, multiply err by numbers, add numbers to flux\n # I have drastically simplified the arrays here AR 2013.0319\n # AR 2013.0318: There was a problem, previously: noise was a fixed value, not linked to the known error values\n\n # AR 2013.0321: Speed fix. Rather than step through the array and generate one\n # normally-distributed error value scaled to the SNR at that point, I will generate an\n # array of normally-distributed error values scaled to 1, and then multiply by the SNR:\n # One array generation, one array multiplication.\n\n rand_dist = np.random.normal(loc=0.0, scale=1.0, size=datalen)\n rand_dist2 = np.random.normal(loc=0.0, scale=1.0, size=datalen)\n\n fx_temp_obj = np.asarray(fx_arr_obj + rand_dist * sig_arr_obj)\n fx_temp_std = np.asarray(fx_arr_std + rand_dist2 * sig_arr_std)\n mean_obj = np.mean(fx_temp_obj)\n mean_std = np.mean(fx_temp_std)\n stddev_obj = np.std(fx_temp_obj, ddof=1)\n stddev_std = np.std(fx_temp_std, ddof=1)\n\n # Regularize data (subtract mean, divide by std dev) (Should definitely be done AFTER noise was added)\n fx_reg_temp_obj = fx_temp_obj - mean_obj\n fx_reg_temp_obj = fx_reg_temp_obj / stddev_obj\n fx_reg_temp_std = fx_temp_std - mean_std\n fx_reg_temp_std = fx_reg_temp_std / stddev_std\n\n # curve fit - remove a cubic AR 2012.1113\n coeff, pcov = op.curve_fit(cubic, wv_arr_std, fx_reg_temp_obj)\n fx_reg_temp_obj = fx_reg_temp_obj - (\n coeff[0] + coeff[1] * wv_arr_std + coeff[2] * wv_arr_std ** 2 + coeff[3] * wv_arr_std ** 3)\n coeff, pcov = op.curve_fit(cubic, wv_arr_std, fx_reg_temp_std)\n fx_reg_temp_std = fx_reg_temp_std - (\n coeff[0] + coeff[1] * wv_arr_std + coeff[2] * wv_arr_std ** 2 + coeff[3] * wv_arr_std ** 3)\n\n # CROSS CORRELATION\n\n # compute the cross-correlation between the two spectra\n\n ycorr = np.correlate(fx_reg_temp_obj, fx_reg_temp_std, mode='full')\n # time required: 0.045 seconds average\n\n # http://stackoverflow.com/questions/12323959/fast-cross-correlation-method-in-python\n # conv1 = np.zeros(datalen * 2)\n # conv1[datalen/2:datalen/2+datalen] = fx_reg_temp_obj\n # conv2 = fx_reg_temp_std[::-1]\n # ycorr = signal.fftconvolve(conv1,conv2, mode='valid')\n # time required: 0.006 seconds average, but it segfaults by the third try.\n\n ## slight smoothing AR 2013.0315\n # ycorr = scipy.ndimage.filters.gaussian_filter1d(ycorr,11)\n\n # create the x offset axis (same length as ycorr, with 0 in the MIDDLE)\n length = len(ycorr)\n xcorr = np.arange(length) - length // 2\n # AR 2012.1126 Select a tiny piece around the maximum to fit with a gaussian.\n xmid = np.argmax(ycorr)\n ymax = np.max(ycorr)\n # now take just the portion of the array that matters\n xcorr_min = int(xmid - xcorr_width)\n xcorr_max = int(xmid + xcorr_width)\n ycorr1 = ycorr[xcorr_min:xcorr_max] # isolate section of array with gaussian\n xcorr1 = xcorr[xcorr_min:xcorr_max] # isolate the same section of the pixel range\n ycorr2 = ycorr[xcorr_min - 50:xcorr_max + 50]\n xcorr2 = xcorr[xcorr_min - 50:xcorr_max + 50]\n\n # suggestion from D. Hogg 12/15/12: Add extra linear feature to fit.\n # suggestion from D. Hogg 12/15/12: operate on ln(amp) so that the amplitude CANNOT be negative.\n def chi2(p): # define gaussian function for fitting\n sig2 = p[2] ** 2\n m = (np.exp(p[0]) * np.exp(-0.5 * (xcorr1 - p[1]) ** 2 / sig2)) + p[3] + p[4] * xcorr1\n return (ycorr1 - m)\n\n # set up initial values for chi2\n sig = 10\n sky = np.min(ycorr1) / 1.2\n # print ycorr1[-1],ycorr1[0],xcorr1[-1],xcorr1[0]\n sky2 = (ycorr1[-1] - ycorr1[0]) / (xcorr1[-1] - xcorr1[0])\n lnamp = np.log(ymax / 1.2 - sky) # guess some values\n mean = xcorr[xmid]\n\n amp = np.exp(lnamp)\n sig2 = sig ** 2\n\n popt, ier = op.leastsq(chi2, [lnamp, mean, sig, sky, sky2])\n lnamp, mean, sig, sky, sky2 = popt\n\n amp = np.exp(lnamp)\n\n # print_num=len(pix_shift)%100\n print_num = l % 100\n if print_num == 0:\n ## Uncomment the following to make a plot every 500 fits.\n # fig = plt.figure(l)\n # ax = fig.add_subplot(111)\n # my_gauss = (amp * (np.exp(-0.5 * ((xcorr1 - mean) ** 2) / sig**2))) + sky + sky2 * xcorr1\n # ax.plot(xcorr1,my_gauss,'r--')\n # ax.plot(xcorr2,ycorr2,'#000000')\n # ax.plot(xcorr1,ycorr1-my_gauss,'#00CC00')\n ##if abs(mean - xcorr[xmid]) > 5:\n ## print \"Mean is off\",mean,xcorr[xmid]\n # figname='rv_{0:}_{1:}_{2:}_{3:}.png'.format(std_name,obj_name,order,l)\n # ax.set_xlim(xcorr[xcorr_min-50],xcorr[xcorr_max+50])\n # fig.savefig(figname)\n # fig.clf()\n # plt.close()\n print\n \"amp={0: 12.4f} mu={1: 10.4f} sig={2: 9.4f} sky={3: 11.4f} sky2={4: 8.4f} n_entries={5:}\".format(amp,\n mean,\n sig,\n sky,\n sky2,\n len(\n pix_shift))\n\n l += 1\n if (cut == 0) | (mean > np.float(cutstart)) & (mean < np.float(cutend)):\n pix_shift = np.append(pix_shift, mean)\n # if ier < 5:\n # I'm calculating the vsini now because I need errors, and the vsini calculation is not linear.\n # pix_width[l] = vsinicoeff[0] + vsinicoeff[1] * sig + vsinicoeff[2] * sig**2 + vsinicoeff[3] * sig**3 + vsinicoeff[4] * sig**4\n\n # End cross correlation loop ---------------------------------\n\n # 4. Find the RV\n # All 5000 rv fits have been calculated and stored in arrays\n # 4a. Cut out outlier RVs. Useful if the cross-correlation produces occasional bad results. Use cutstart and cutend to force the code to only fit a gaussian to a certain region. Don't over-use this to force the result you want, though.\n # 4b. Compute the mean pixel shift and pixel shift uncertainty.\n # 4c. Convert pixel shift into RV\n # 4d. Shift the wavelength array appropriately - all lines should now line up.\n\n ## Uncomment this to print out an example cross-correlation diagram\n # fig = plt.figure(2)\n # ax = fig.add_subplot(111)\n # ax.plot(xcorr,ycorr,'k')\n # figname='rv_{0:}_{1:}_{2:}_xcorr.png'.format(std_name,obj_name,order)\n # fig.savefig(figname)\n # fig.clf()\n # plt.close()\n\n # Turn the list of pixel shifts into a numpy array\n pix_shift = np.asarray(pix_shift)\n\n # 4a. Cut out outliers from the pixel shift\n if cut == 1:\n pix_shift = pix_shift[np.where((pix_shift > np.float(cutstart)) & (pix_shift < np.float(cutend)))]\n\n # 4b. Compute the mean pixel shift (rv value) and pixel shift uncertainty (RV uncertainty).\n\n print\n l, len(pix_shift), np.float(len(pix_shift)) / np.float(n_iter) * 100.0\n\n mu = np.mean(pix_shift)\n sigma = np.std(pix_shift, ddof=1)\n\n # vsini = np.mean(pix_width)\n # vsini_err = np.std(pix_width,ddof=1)\n\n # axh = figv.add_subplot(212)\n # n, bins, patches=axh.hist(pix_width,bins=30,normed=1.0,facecolor='green',align='mid')\n # figv.savefig('vsiniplot.png')\n # plt.clf()\n # plt.close()\n\n # 4c. Transform pixel shift to shift in radial velocity\n\n # AR 2013.0423: The actually appropriate method requires a speed-of-light correction. This works for both angstroms and microns.\n rv_meas = (2.99792458 * 10 ** 5 * mu) / acoef_std\n rv_meas_err = (2.99792458 * 10 ** 5 * sigma) / acoef_std\n\n # 4d. Apply shift to arrays\n wv_rvcorr_obj = wv_arr_std * (1 - rv_meas / (2.99792458 * 10 ** 5))\n\n ## 5. Create plots ---------------------------------\n # The plots are the only reason find_rv.py needs to know the names of either star, or the RV of the standard.\n\n # Plot object and standard so you can clearly see that shift exists --------------------------------\n fig = plt.figure(1)\n\n # AR 2013.0703 Regularize the spectra for display purposes in the final graph\n # I'm using the mean and stddev of the last random-added attempt so it won't be perfect...\n fx_reg_obj = fx_arr_obj - mean_obj\n fx_reg_obj = fx_reg_obj / stddev_obj\n fx_reg_std = fx_arr_std - mean_std\n fx_reg_std = fx_arr_std / stddev_std\n\n # Plots target and standard with shift applied\n ax1 = fig.add_subplot(311)\n ax1.plot(wv_rvcorr_obj, fx_reg_obj, 'red')\n ax1.plot(wv_arr_std, fx_reg_std, 'blue')\n ax1.set_xlabel('wavelength (microns)')\n ax1.set_ylabel('normalized flux')\n target = 'Target: %s' % (obj_name)\n standard = 'Standard: %s' % (std_name)\n ax1.annotate(target, xy=(.7, .9), xycoords='axes fraction', xytext=(.6, .9), textcoords='axes fraction',\n color='red')\n ax1.annotate(standard, xy=(.7, .8), xycoords='axes fraction', xytext=(.6, .8), textcoords='axes fraction',\n color='blue')\n\n sig2 = sig ** 2\n my_gauss = (amp * (np.exp(-0.5 * ((xcorr1 - mu) ** 2) / sig2))) + sky + sky2 * xcorr1\n\n # Plots example of gaussian fit to cross correlation function\n ax2 = fig.add_subplot(312)\n ax2.plot(xcorr1, ycorr1, 'k.')\n ax2.plot(xcorr1, my_gauss, 'r--', linewidth=2)\n ax2.plot(xcorr1, ycorr1 - my_gauss, '#00CC00')\n ax2.set_xlabel('example of fit to cross correlation function')\n ax2.set_xlim(xcorr[xcorr_min - 50], xcorr[xcorr_max + 50])\n # print pix_shift\n\n\n ## Plot histogram of pixel shift values --------------------------------\n ax3 = fig.add_subplot(313)\n n, bins, patches = plt.hist(pix_shift, bins=30, normed=1.0, facecolor='green', align='mid')\n # Plot best fit gaussian over histogram\n y = mlab.normpdf(bins, mu, sigma)\n ax3.plot(bins, y, 'r--', linewidth=2)\n ax3.set_xlabel('radial velocity of target (pixels)')\n ax3.set_ylabel('frequency (normalized)')\n rad = 'RV = %.3f +/- %.3f' % (rv_meas, rv_meas_err)\n corr = 'RV (corr) = %.3f +/- %.3f' % (rv_std + rv_meas, (rv_std_err ** 2 + rv_meas_err ** 2) ** (0.5))\n # vsinistr = 'VsinI = %.3f +/- %.3f' % (vsini,vsini_err)\n ax3.annotate(rad, xy=(.66, .9), xycoords='axes fraction', xytext=(.66, .9), textcoords='axes fraction',\n color='black')\n ax3.annotate(corr, xy=(.6, .8), xycoords='axes fraction', xytext=(.60, .8), textcoords='axes fraction',\n color='black')\n # ax3.annotate(vsinistr,xy=(.6,.6),xycoords='axes fraction',xytext=(.60,.6),textcoords='axes fraction',color='black')\n ax3.annotate('{0:+5.2f} {1: 5.2f}'.format(mu, sigma), xy=(.05, .9), xycoords='axes fraction', xytext=(.05, .9),\n textcoords='axes fraction', color='black')\n ax3.annotate('{0:5.3f} km/s/pix'.format((2.99792458 * 10 ** 5) / acoef_std), xy=(.05, .8), xycoords='axes fraction',\n xytext=(.05, .8), textcoords='axes fraction', color='black')\n fig.subplots_adjust(hspace=.3)\n\n figname = 'rv_%s_%s_%d.png' % (std_name, obj_name, order)\n fig.savefig(figname)\n fig.clf()\n plt.close()\n\n # plt.figure(l+1)\n # plt.hist(pix_shift)\n\n # END RADIAL VELOCITY FUNCTION -----------------------------------------\n return rv_meas, rv_meas_err","def test_vertical_velocity_pressure_dry_air():\n w = 1 * units('cm/s')\n omega_truth = -1.25073619 * units('microbar/second')\n omega_test = vertical_velocity_pressure(w, 1000. * units.mbar, 273.15 * units.K)\n assert_almost_equal(omega_test, omega_truth, 6)","def test_interferometer(self, tol):\n # fmt:off\n U = np.array([[0.83645892-0.40533293j, -0.20215326+0.30850569j],\n [-0.23889780-0.28101519j, -0.88031770-0.29832709j]])\n # fmt:on\n\n S = symplectic.interferometer(U)\n expected = np.block([[U.real, -U.imag], [U.imag, U.real]])\n\n assert np.allclose(S, expected, atol=tol, rtol=0)","def arm_calibration(self):\n self.arm_motor.run_forever(speed_sp=self.MAX_SPEED)\n while not self.touch_sensor.is_pressed:\n time.sleep(0.01)\n self.arm_motor.stop()\n ev3.Sound.beep().wait()\n arm_revolutions_for_full_range = 14.2 * 360\n self.arm_motor.run_to_rel_pos(\n position_sp=-arm_revolutions_for_full_range,\n speed_sp=self.MAX_SPEED,\n stop_action=ev3.Motor.STOP_ACTION_BRAKE)\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\n\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this\n # line is correct as is).","def global_phase_correction(imgs_shifted_ft):\n nangles = imgs_shifted_ft.shape[0]\n phase_corrections = np.zeros((nangles))\n\n # todo: should weight by SNR, or something like this\n for ii in range(nangles):\n phase_corrections[ii] = np.angle(np.sum(imgs_shifted_ft[ii, 0] * imgs_shifted_ft[ii, 1].conj()))\n\n return phase_corrections","def trap_depth_old(V,X,Y,Z,Im,Jm,Km,debug=False): \n from project_parameters import debug\n #from all_functions import sum_of_e_field\n def a(a,N):\n \"\"\"Shortcut function to convert array x into a row vector.\"\"\" \n a=np.ravel(a, order='F') # Same order\n return a\n def index_sort(y,x):\n \"\"\"Takes in two lists of the same length and returns y sorted by the indexing of x sorted.\"\"\"\n xs=np.sort(x)\n ix=np.argsort(x)\n ys=np.ones(len(y)) #Sorted by the sorting defined by f being sorted. \n for i in range(len(y)):\n j=ix[i]\n ys[i]=y[j]\n return ys\n if len(V.shape)!=3:\n return('Problem with find_saddle.py dimensionalities.\\n')\n N1,N2,N3=V.shape\n N=N1*N2*N3\n f=V\n [Ex,Ey,Ez]=np.gradient(f,abs(X[1]-X[0]),abs(Y[1]-Y[0]),abs(Z[1]-Z[0]))\n E=np.sqrt(Ex**2+Ey**2+Ez**2)\n fs,Es=a(f,N),a(E,N) # Convert 3D to 1D array\n fs,Es=np.real(fs),np.real(Es)\n # identify the escape position and height by checking each point\n minElectricField=max(fs) # initialize as maximum E field magnitude\n distance=0\n escapeHeight=1\n escapePosition=[0,0,0]\n for i in range(N1):\n for j in range(N2):\n for k in range(N3):\n if [i,j,k]==[Im,Jm,Km]:\n Vm=V[i,j,k]\n elif E[i,j,k]<minElectricField:\n minElectricField=E[i,j,k]\n escapeHeight=V[i,j,k]\n escapePosition=[i,j,k]\n distance=abs(Im+Jm+Km-i-j-k) \n if debug.trap_depth: # plot sortings of potential and electric field to view escape position\n plt.plot(np.sort(fs)) \n plt.title('sorted potential field')\n plt.show()\n plt.plot(np.sort(Es)) \n plt.title('sorted electric field')\n plt.show()\n q1=index_sort(fs,Es) \n plt.title('potential field sorted by sorted indexing of electric field')\n plt.plot(q1)\n plt.show()\n q2=index_sort(Es,fs) \n plt.title('electric field sorted by sorted indexing of potential field')\n plt.plot(q2)\n plt.show() \n check=1 \n if debug.trap_depth: \n print minElectricField,escapeHeight,escapePosition,distance \n if distance<check:\n print('trap_depth.py: Escape point too close to trap minimum. Improve grid resolution or extend grid.')\n if escapeHeight>0.2:\n print('trap_depth.py: Escape point parameter too high. Improve grid resolution or extend grid.')\n D=escapeHeight-Vm\n [Ie,Je,Ke]=escapePosition\n [Xe,Ye,Ze]=[X[Ie],Y[Je],Z[Ke]] \n return [D,Xe,Ye,Ze]","def test_vw_controller(self):\n pass\n\n yarp.Network.init()\n\n pose_stream = yarp.BufferedPortBottle()\n pose_stream.open(\"/morse/test/pose/in\")\n yarp.Network.connect(\"/morse/robots/ATRV/Pose/out\", \"/morse/test/pose/in\")\n\n cmd_stream = yarp.BufferedPortBottle()\n cmd_stream.open(\"/morse/test/vw/out\")\n yarp.Network.connect(\"/morse/test/vw/out\", \"/morse/robots/ATRV/Motion_Controller/in\")\n \n # Read the start position, it must be (0.0, 0.0, 0.0)\n pose = pose_stream.read()\n for i in range(6):\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\n\n\n send_speed(cmd_stream, 1.0, 0.0, 2.0)\n\n pose = pose_stream.read()\n self.assertAlmostEqual(pose.get(0).asDouble(), 2.0, delta=0.1)\n self.assertAlmostEqual(pose.get(1).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(3).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\n\n send_speed(cmd_stream, -1.0, 0.0, 2.0)\n\n pose = pose_stream.read()\n for i in range(6):\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\n\n send_speed(cmd_stream, 1.0, -math.pi/4.0, 2.0)\n pose = pose_stream.read()\n self.assertAlmostEqual(pose.get(0).asDouble(), 4.0 / math.pi, delta=0.1)\n self.assertAlmostEqual(pose.get(1).asDouble(), -4.0 / math.pi , delta=0.1)\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(3).asDouble(), -math.pi/2.0, delta=0.1)\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\n\n send_speed(cmd_stream, 0.5, -math.pi/8.0, 12.0)\n pose = pose_stream.read()\n for i in range(6):\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\n\n send_speed(cmd_stream, -2.0, math.pi/2.0, 3.0)\n pose = pose_stream.read()\n self.assertAlmostEqual(pose.get(0).asDouble(), 4.0 / math.pi, delta=0.1)\n self.assertAlmostEqual(pose.get(1).asDouble(), -4.0 / math.pi , delta=0.1)\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(3).asDouble(), -math.pi/2.0, delta=0.1)\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\n\n yarp.Network.fini()","def test_invalid_events(subarray_and_event_gamma_off_axis_500_gev):\n\n # 4-LST bright event already calibrated\n # we'll clean it and parametrize it again in the TelescopeFrame\n subarray, event = subarray_and_event_gamma_off_axis_500_gev\n\n tel_azimuth = {}\n tel_altitude = {}\n\n #source = EventSource(filename, max_events=1)\n #subarray = source.subarray\n calib = CameraCalibrator(subarray)\n fit = HillasReconstructor(subarray)\n\n #for event in source:\n\n calib(event)\n\n hillas_dict = {}\n for tel_id, dl1 in event.dl1.tel.items():\n\n geom = subarray.tel[tel_id].camera.geometry\n tel_azimuth[tel_id] = event.pointing.tel[tel_id].azimuth\n tel_altitude[tel_id] = event.pointing.tel[tel_id].altitude\n\n mask = tailcuts_clean(\n geom, dl1.image, picture_thresh=10.0, boundary_thresh=5.0\n )\n\n dl1.parameters = ImageParametersContainer()\n\n try:\n moments = hillas_parameters(geom[mask], dl1.image[mask])\n hillas_dict[tel_id] = moments\n dl1.parameters.hillas = moments\n except HillasParameterizationError:\n dl1.parameters.hillas = HillasParametersContainer()\n continue\n\n # copy event container to modify it\n event_copy = deepcopy(event)\n # overwrite all image parameters but the last one with dummy ones\n for tel_id in list(event_copy.dl1.tel.keys())[:-1]:\n event_copy.dl1.tel[tel_id].parameters.hillas = HillasParametersContainer()\n fit(event_copy)\n assert event_copy.dl2.stereo.geometry[\"HillasReconstructor\"].is_valid is False\n\n # Now use the original event, but overwrite the last width to 0\n event.dl1.tel[tel_id].parameters.hillas.width = 0 * u.m\n fit(event)\n assert event.dl2.stereo.geometry[\"HillasReconstructor\"].is_valid is False\n\n # Now use the original event, but overwrite the last width to NaN\n event.dl1.tel[tel_id].parameters.hillas.width = np.nan * u.m\n fit(event)\n assert event.dl2.stereo.geometry[\"HillasReconstructor\"].is_valid is False","def predicted_data_vol(self):\n pass","def test_el3_vs_original():\n # store computations from original implementation\n # from florian_ell3_paper import el3 as el30\n # N = 10000\n # x11 = np.random.rand(N)*5\n # kc11 = (np.random.rand(N)-.5)*10\n # p11 = (np.random.rand(N)-.5)*10\n # result0 = np.array([el30(x, kc, p) for x,kc,p in zip(x11,kc11,p11)])\n # np.save('data_test_el3', np.array([result0,x11,kc11,p11]))\n\n # load data from orginal implementation\n data = np.load(\"tests/testdata/testdata_el3.npy\")\n res0, x11, kc11, p11 = data\n\n # compare to vectorized\n resv = el3v(x11, kc11, p11)\n assert np.allclose(res0, resv)\n\n # compare to modified original\n res1 = np.array([el30(x, kc, p) for x, kc, p in zip(x11, kc11, p11)])\n assert np.allclose(res0, res1)","def test_delta_in_diff(self):\n xk = 1 * self.ureg.kelvin\n yk = 2 * self.ureg.kelvin\n yf = yk.to('degF')\n yc = yk.to('degC')\n self.assertEqual(yk - xk, 1 * self.ureg.kelvin)\n self.assertEqual(yf - xk, 1 * self.ureg.kelvin)\n self.assertEqual(yc - xk, 1 * self.ureg.kelvin)","def runMT3D(self):\n \n # write mt3dms input\n self.__mt.write_input()\n # run mt3dms\n self.__mt.run_model()","def _diff_pot(a2,t2,d2,wair,temp,pres,ppot,airf,dhum):\n ph2 = _eq_pressure(0,0,0,a2,t2,d2)\n gi2 = _ice_g(0,0,t2,ppot)\n gv2 = _eq_vappot(0,0,0,a2,t2,d2)\n sh1 = -_air_f(0,1,0,airf,temp,dhum)\n si1 = -_ice_g(1,0,temp,pres)\n s1 = wair/airf*sh1 + (1-wair/airf)*si1\n sh2 = -_air_f(0,1,0,a2,t2,d2)\n si2 = -_ice_g(1,0,t2,ppot)\n s2 = wair/a2*sh2 + (1-wair/a2)*si2\n lhs = numpy.array([ppot, gi2, s1])\n rhs = numpy.array([ph2, gv2, s2])\n \n ph2_a = _eq_pressure(1,0,0,a2,t2,d2)\n ph2_t = _eq_pressure(0,1,0,a2,t2,d2)\n ph2_d = _eq_pressure(0,0,1,a2,t2,d2)\n gi2_t = _ice_g(1,0,t2,ppot)\n gv2_a = _eq_vappot(1,0,0,a2,t2,d2)\n gv2_t = _eq_vappot(0,1,0,a2,t2,d2)\n gv2_d = _eq_vappot(0,0,1,a2,t2,d2)\n sh2_a = -_air_f(1,1,0,a2,t2,d2)\n sh2_t = -_air_f(0,2,0,a2,t2,d2)\n sh2_d = -_air_f(0,1,1,a2,t2,d2)\n si2_t = -_ice_g(2,0,t2,ppot)\n s2_a = -wair/a2**2*(sh2 - a2*sh2_a - si2)\n s2_t = wair/a2*sh2_t + (1-wair/a2)*si2_t\n s2_d = wair/a2*sh2_d\n dlhs = numpy.array([[0.,0.,0.], [0.,gi2_t,0.], [0.,0.,0.]])\n drhs = numpy.array([[ph2_a,ph2_t,ph2_d], [gv2_a,gv2_t,gv2_d],\n [s2_a,s2_t,s2_d]])\n return lhs, rhs, dlhs, drhs","def __inverse_kinematics(self, guess, target_point):\n\n error = 1.0\n tolerance = 0.05\n\n # Initial Guess - Joint Angles\n thetas = np.matrix(guess) # thetas is list which is contain all axes theta angles.\n target_point = np.matrix(target_point) # X, Y, Z list to matrix for Target Position\n # print(target_point.shape)\n # Jacobian\n self.__calc_jacobian_matrix()\n tf_matrix_first_to_last = self.tf_matrices_list[-1]\n\n error_grad = []\n\n theta_dict = {}\n\n lr = 0.2\n while error > tolerance:\n for i in range(len(np.array(thetas)[0])):\n theta_dict[self.q[i]] = np.array(thetas)[0][i]\n\n theta_dict[self.q[-1]] = self.q[-1]\n\n calculated_target_point = np.matrix(self.get_coords_from_forward_kinematics(self.__forward_kinematics(np.array(thetas)[0])[-1]))\n logger.debug(f'calculated target point is \\n{calculated_target_point}')\n\n diff_wanted_calculated = target_point - calculated_target_point\n\n jacob_mat = np.matrix(self.jacobian_matrix.evalf(subs=theta_dict, chop=True, maxn=4)).astype(np.float64).T\n logger.debug(f'jacobian matrix is\\n{jacob_mat} \\n\\n diff is \\n {diff_wanted_calculated}')\n\n thetas = thetas + lr * (jacob_mat * diff_wanted_calculated.T)\n # thetas = np.array(thetas)[0] # this line's purpose is changing Q from matrix level to array level.\n\n prev_error = error\n\n error = linalg.norm(diff_wanted_calculated)\n\n if error > 10 * tolerance:\n lr = 0.3\n elif error < 10 * tolerance:\n lr = 0.2\n error_grad.append((error - prev_error))\n\n # print(error)\n return np.array(thetas)[0]","def _solve_3d_scalar(self, simu=None):\n # # Call fftw filter\n # self._output_field.data[0] = fftw2py.solve_poisson_3d_pressure(\n # self._input_field.data[0],\n # self._input_field.data[1],\n # self._input_field.data[2])\n pass","def test_deconvolve_to_motor_error(self):\n tau = 50.0\n mrate = 50.0\n Mrate = 100.0\n\n tmax = 50.0\n dt = 0.1\n\n self.rule.tau = tau\n self.rule.min_rate = mrate\n self.rule.max_rate = Mrate\n self.rule.compress_rates = False\n self.rule.gain = 1\n self.rule.tau_deconv1 = tau\n\n self.motor.error_fct = lambda _: np.ones(self.Nsrc)\n\n M = simulation.StateMonitor(self.rule, 'out')\n\n sim = simulation.Simulation(self.source, self.motor, self.rule, M, dt=dt)\n sim.run(tmax)\n \n # the output should be almost constant\n self.assertAlmostEqual(np.std(M.out)/np.mean(M.out), 0)","def testConstantBoundedField(self):\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration)\n self._testPhotoCalibCenter(photoCalib, 0)\n\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointYShift))\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointYShift))\n self.assertFloatsAlmostEqual(1e-9, photoCalib.instFluxToMaggies(self.instFlux*1e-9, self.pointXShift))\n self.assertFloatsAlmostEqual(22.5, photoCalib.instFluxToMagnitude(\n self.instFlux*1e-9, self.pointXShift))\n\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration, self.calibrationErr)\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)","def test_diff_analog_in_cal_5v_loop(self):\n for g in self.l.gains:\n for s,c,e in [(5, 11, .1), (2.5, 10, .03)]:\n v = self.l.input(channels=(c,c,c,c), gains=(g,g,g,g))\n r = v[0]\n if s*g > 20:\n if s*g > 25:\n self.assertTrue(v[3],\n \"%s should be overvoltage (%g, %g)\" % (v,s,g))\n continue\n for i in r:\n self.assertTrue(abs(s-i) < e,\n \"%g is not %g, channel %g, gain %g\" % (i,s,c,g))","def onApplyECVButton(self):\n\n NodeName = 'ECV Map'\n try :\n self.ECVMapNode = slicer.util.getNode(NodeName)\n except:\n slicer.mrmlScene.AddNewNodeByClass('vtkMRMLScalarVolumeNode', NodeName)\n self.ECVMapNode = slicer.util.getNode(NodeName)\n\n T1Native_Matrix,T1Enhanced_Matrix = self.MatchMatrixs(self.NativeT1_Selector.currentNode(),self.EnhancedT1_Selector.currentNode())\n\n Haematocrit = self.SB_Haematocrit.value\n NT1B = self.SB_NBlodd.value\n ET1B = self.SB_EBlodd.value\n Factor = (100-Haematocrit)*(NT1B*ET1B/(NT1B-ET1B))\n epsilon = 0.1\n\n T1Enhanced_Matrix = T1Enhanced_Matrix + epsilon\n T1Native_Matrix = T1Native_Matrix + epsilon\n self.ECV_Matrix = (1/T1Enhanced_Matrix-1/T1Native_Matrix)*Factor\n self.ECV_Matrix = np.nan_to_num(self.ECV_Matrix)\n self.ECV_Matrix[ np.logical_or(self.ECV_Matrix<0 , self.ECV_Matrix>100) ] = 0\n\n slicer.util.updateVolumeFromArray(self.ECVMapNode, self.ECV_Matrix)\n self.SetLayoutViewer(self.ECVMapNode, 'Slice4')\n self.SetScalarDisplay(self.ECVMapNode, 1, 100) ## Que onda el Auto WL\n self.ThSlider_ECV.SetNode(self.ECVMapNode)\n Max = np.nanmax(self.ECV_Matrix)\n self.updateThresholdValues(self.ThSlider_ECV, self.ECVMapNode, Max)","def detectMotion():\n global MotionDetected\n MotionDetected = False\n return MotionDetected","def updatePWM(self):\n v_dc = self.dcmotorSpeed * self.dcmotor_sgn # changed \"vr\" to \"v_dc\", \"rightSpeed\" to \"dcmotorSpeed\" and \"right_sgn\" to dcmotor_sgn\", RFMH_2019_02_26\n pwm_dc = self.PWMvalue(v_dc, self.DC_MOTOR_MIN_PWM,\n self.DC_MOTOR_MAX_PWM) # changed \"pwmr\" to \"pwm_dc\" and \"vr\" to \"v_dc\" and adjusted both orange constants to \"DC_MOTOR_MIN_PWM\" AND \"DC_MOTOR_MAX_PWM\", RFMH_2019_02_26\n\n # TODO: Fix this debug message. I am trying to port this code over from an old version, and I do not know\n # what v and u are supposed to be here. Timothy Scott, 5.11.2019\n # if self.debug: # where the duck does the \"u\" come from?!?, RFMH_2019_02_26\n # print(\"v = %5.3f, u = %5.3f, v_dc = %5.3f, pwm_dc = %3d\" % (\n # v, u, v_dc, pwm_dc)) # deleted \"vl\" and \"pwml\" and adjust \"vr\" to \"v_dc\" to \"pwm_dc\"\n\n if math.fabs(v_dc) < self.SPEED_TOLERANCE: # changed v_r to v_dc in if loop , RFMH_2019_02_28\n DcMotorMode = Adafruit_MotorHAT.RELEASE\n pwm_dc = 0\n elif v_dc > 0:\n DcMotorMode = Adafruit_MotorHAT.FORWARD\n elif v_dc < 0:\n DcMotorMode = Adafruit_MotorHAT.BACKWARD\n\n if not self.old_pwm_dc == pwm_dc:\n self.DcMotor.setSpeed(pwm_dc) # changed rightMotor to DcMotor and pwmr to pwm_dc , RFMH_2019_02_28\n self.DcMotor.run(DcMotorMode)\n\n self.old_pwm_dc = pwm_dc","def nzErr(xerr, yerr, vxerr, vyerr, year_x, year_y, mag, alnDir = '13_08_21/', chainsDir = 'efit/chains_S0-2_newRV2/'):\n\n #Read in values for error in position and velocity of sgr*\n origin_val = asciidata.open('/g/ghez/align/' + alnDir + chainsDir + 'efit_summary.txt')\n ori_x0e = origin_val[25][0]\n ori_y0e = origin_val[26][0]\n ori_vxe = origin_val[27][0]\n ori_vye = origin_val[28][0]\n t_0 = 2000.0 #hard coded t_0 of sgr*\n\n # magBins=np.array([9,11,12,13,14,15,16,17,18,19,20,21])\n # deltaArr=np.array([3.5,71.0,58.0,210.0,300.0,650.0,700.0,1100.0,1900.0,2200.0,3000.0])*1e-6\n\n# delta = mag*0.0\n# for i in range(len(mag)):\n# for j in range(len(deltaArr)):\n# if ((mag[i] > magBins[j]) & (mag[i] <= magBins[j+1])):\n# delta[i]=deltaArr[j]\n\n#pdb.set_trace()\n\n #Update errors\n xerr = np.sqrt(xerr**2 + ori_x0e**2 + ((year_x - t_0)*ori_vxe)**2)\n yerr = np.sqrt(yerr**2 + ori_y0e**2 + ((year_y - t_0)*ori_vye)**2)\n vxerr = np.sqrt(vxerr**2 + ori_vxe**2)\n vyerr = np.sqrt(vyerr**2 + ori_vye**2)\n\n return xerr, yerr, vxerr, vyerr","def adjust_u(self):\r\n # compute the volume integrals of the x,y, and z components of u\r\n ux = assemble(self.u.sub(0)*dx)\r\n uy = assemble(self.u.sub(1)*dx)\r\n uz = assemble(self.u.sub(2)*dx)\r\n\r\n # create a function of value 1, which can be integrated.\r\n try:\r\n self.unit\r\n except AttributeError:\r\n self.unit = Function(self.Q)\r\n self.unit.assign(Constant(1))\r\n\r\n # compute the volume of the body\r\n Vol = assemble(self.unit*dx)\r\n\r\n try:\r\n self.umean\r\n except AttributeError:\r\n self.umean = Function(self.Z)\r\n\r\n # compute the volume-averaged component means\r\n self.umean.assign(Constant((ux/Vol, uy/Vol, uz/Vol, 0)))\r\n\r\n # subtract the mean from the solution function\r\n self.up.assign(self.up-self.umean)","def compute_ground_truth_volume(self, display_opt):\n\n self.meshActor.GetProperty().SetOpacity(0.2)\n self.meshActor.GetProperty().SetColor(1, 0, 0)\n\n clean = vtk.vtkCleanPolyData()\n clean.SetInputData(self.endo_poly)\n\n d3 = vtk.vtkDelaunay3D()\n d3.SetInputConnection(clean.GetOutputPort())\n d3.SetTolerance(0.01)\n d3.SetAlpha(0.0)\n d3.Update()\n\n surfaceFilter = vtk.vtkDataSetSurfaceFilter() # output is triangular mesh\n surfaceFilter.SetInputConnection(d3.GetOutputPort())\n surfaceFilter.Update()\n\n Mass = vtk.vtkMassProperties()\n Mass.SetInputConnection(surfaceFilter.GetOutputPort())\n Mass.Update()\n\n self.ground_truth_vol = Mass.GetVolume()/1000.0\n\n if display_opt:\n\n m = vtk.vtkDataSetMapper()\n m.SetInputConnection(d3.GetOutputPort())\n\n a = vtk.vtkActor()\n a.SetMapper(m)\n\n # set mapper for epi for visualization\n m2 = vtk.vtkDataSetMapper()\n m2.SetInputData(self.epi_poly)\n\n epi_actor = vtk.vtkActor()\n epi_actor.SetMapper(m2)\n epi_actor.GetProperty().SetOpacity(0.3)\n epi_actor.GetProperty().SetColor(1,0,0)\n\n ren = vtk.vtkRenderer()\n ren.SetBackground(0.0, 0.0, 0.0)\n ren.AddActor(epi_actor)\n ren.AddActor(a)\n\n vtk_show(ren)","def update_theta(self, lhs_m, rhs_m):\n t = 0\n # FIXIME: fix the symbol\n self.P = self.E\n self.M = self.K\n self.T = self.G\n self.z_m = self.theta_m.T.copy()\n new_z_m = (lhs_m * rhs_m).T\n z_m_noLag = new_z_m.copy()\n if self.positive_em:\n # print('rhs_m', rhs_m)\n new_z_m = np.asmatrix(np.zeros((self.M, self.P))).T\n for p in range(self.P):\n old_z = self.z_m[p, :].T\n lag_lambda_m = np.asmatrix(np.zeros((1, self.M)))\n # get active set\n active = set()\n for _ in range(self.M):\n # print('[z]:before update\\n', old_z)\n # print('[z]:update without lag\\n', z_m_noLag[p, :])\n # print('[z]:lag term\\n', lag_lambda_m[p,:].T)\n new_z = lhs_m * (rhs_m[:,p] + 1/2*lag_lambda_m[0,:].T)\n # print('[z]:after lag\\n', new_z)\n\n # print('after round up\\n', new_z)\n v = new_z - old_z\n min_k = 1\n min_m = 0\n found = False\n for m in range(self.M):\n if new_z[m,0] < 0:\n if v[m, 0] < 0:\n k = old_z[m, 0] / (-v[m, 0])\n if k < min_k:\n min_k = k\n min_m = m\n found = True\n old_z += min_k*v\n for m in range(self.M):\n if -self.precision <= old_z[m,0] <= self.precision:\n old_z[m,0] = 0\n # print('[z]:updated z', old_z[:, 0])\n \n if found:\n active.add(min_m)\n # print(active)\n # print('[z]:active set', active)\n # set lag term to 0\n done = False\n while not done:\n lag_lambda_m = np.asmatrix(np.zeros((1, self.M)))\n \n if len(active) > 0:\n active_len = len(active)\n lag_lambda = np.asmatrix(np.zeros((1, active_len)))\n lag_lhs = np.asmatrix(np.zeros((active_len, active_len)))\n lag_lhs_bar = np.asmatrix(np.zeros((active_len, self.M - active_len)))\n lag_rhs = np.asmatrix(np.zeros((active_len, 1)))\n lag_rhs_bar = np.asmatrix(np.zeros((self.M - active_len, 1)))\n for r, val in enumerate(active):\n counter = 0\n bar_counter = 0\n for c in range(self.M):\n if c in active:\n lag_lhs[r, counter] = lhs_m[val, c]\n counter += 1\n else:\n lag_lhs_bar[r, bar_counter] = lhs_m[val, c]\n bar_counter += 1\n counter = 0\n bar_counter = 0\n for c in range(self.M):\n if c in active:\n lag_rhs[counter, 0] = rhs_m[c, p]\n counter += 1\n else:\n lag_rhs_bar[bar_counter, 0] = rhs_m[c, p]\n bar_counter += 1\n lag_lambda = (-2 * np.linalg.pinv(lag_lhs) * lag_lhs_bar * lag_rhs_bar - 2 * lag_rhs).T\n # print('lag_lambda\\n', lag_lambda)\n # print('lag_lhs\\n', lag_lhs)\n # print('lag_lhs_bar\\n', lag_lhs_bar)\n # print('lag_rhs\\n', lag_rhs)\n # print('lag_rhs_bar\\n', lag_rhs_bar)\n expected_theta = lag_lhs * (lag_rhs + 1/2*lag_lambda.T) + lag_lhs_bar * lag_rhs_bar\n # print('expected theta\\n', expected_theta) \n \n active_remove = set()\n # print(\"[Neg lag]\", lag_lambda)\n for c, val in enumerate(active):\n if lag_lambda[:, c] < 0:\n print(\"[Neg lag]\", lag_lambda[:, c])\n active_remove.add(val)\n active = active.difference(active_remove)\n if len(active_remove) == 0:\n done = True\n for c, val in enumerate(active):\n lag_lambda_m[:, val] = lag_lambda[:, c]\n else:\n break\n if not self.check_positive(old_z):\n print(\"[Warning]: get negative z value\")\n print(old_z)\n # raise Exception('[z]:Non positive state')\n new_z_m[p, :] = old_z[:, 0].T\n self.theta_m = new_z_m.T.copy()","def cmdVelCallback(self, req):\n x = req.linear.x # m/s\n th = req.angular.z # rad/s\n\n if x == 0:\n # Turn in place\n right = th * self.wheel_track * self.gear_reduction / 2.0\n left = -right\n elif th == 0: \n # Pure forward/backward motion\n left = right = x\n else:\n # Rotation about a point in space\n left = x - th * self.wheel_track * self.gear_reduction / 2.0\n right = x + th * self.wheel_track * self.gear_reduction / 2.0\n\n # Set motor speeds in meters per second.\n self.mySerializer.mogo_m_per_s([1, 2], [left, right])","def test3(self):\n sig1 = np.array([0, 1, 0])\n sig2 = np.array([0, 1, 0, 0])\n d, p = EventSync.estimate_delay(sig1, sig2)\n self.assertTrue(d == 0)","def test_force(self):\n group = hoomd.group.all()\n\n # compute forces\n f = azplugins.restrain.plane(group=group, point=(0,0,0), normal=(1,0,0), k=2.0)\n hoomd.run(1)\n np.testing.assert_array_almost_equal(f.forces[0].force, (-2.,0,0))\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 2.,0,0))\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 6.,0,0))\n self.assertAlmostEqual(f.forces[0].energy, 1.)\n self.assertAlmostEqual(f.forces[1].energy, 1.)\n self.assertAlmostEqual(f.forces[2].energy, 9.)\n np.testing.assert_array_almost_equal(f.forces[0].virial, (-2.,0,0,0,0,0))\n np.testing.assert_array_almost_equal(f.forces[1].virial, (-2.,0,4.,0,0,0))\n np.testing.assert_array_almost_equal(f.forces[2].virial, (12.,0,0,0,0,0))\n\n # change the spring constant\n f.set_params(k=1.0)\n hoomd.run(1)\n np.testing.assert_array_almost_equal(f.forces[0].force, (-1.,0,0))\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 1.,0,0))\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 3.,0,0))\n self.assertAlmostEqual(f.forces[0].energy, 0.5)\n self.assertAlmostEqual(f.forces[1].energy, 0.5)\n self.assertAlmostEqual(f.forces[2].energy, 4.5)\n\n # shift the plane down\n f.set_params(point=(-1,0,0))\n hoomd.run(1)\n np.testing.assert_array_almost_equal(f.forces[0].force, (-2.,0,0))\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 0.,0,0))\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 2.,0,0))\n self.assertAlmostEqual(f.forces[0].energy, 2.0)\n self.assertAlmostEqual(f.forces[1].energy, 0.0)\n self.assertAlmostEqual(f.forces[2].energy, 2.0)\n\n # rotate the plane so that only particle 1 is off the line\n f.set_params(point=(0,0,0), normal=(0,0,1))\n hoomd.run(1)\n np.testing.assert_array_almost_equal(f.forces[0].force, (0,0,0))\n np.testing.assert_array_almost_equal(f.forces[1].force, (0,0,-2))\n np.testing.assert_array_almost_equal(f.forces[2].force, (0,0,0))\n self.assertAlmostEqual(f.forces[0].energy, 0.0)\n self.assertAlmostEqual(f.forces[1].energy, 2.0)\n self.assertAlmostEqual(f.forces[2].energy, 0.0)","def _compute_correction(self, initial_state, final_state, a, b, c, s):\r\n pertub = self.pertub\r\n pertub_s = pertub *10\r\n \r\n pred_no_pertub = self._motion_update_one_shot(initial_state, a, b, c, s)\r\n pred_pertub_a = self._motion_update_one_shot(initial_state, a +pertub, b, c, s)\r\n pred_pertub_b = self._motion_update_one_shot(initial_state, a, b +pertub, c, s)\r\n # no need to correct C, C is constrained by kappa_final\r\n # # pred_pertub_c = self._motion_update_one_shot(initial_state, a, b, c +pertub, s)\r\n pred_pertub_s = self._motion_update_one_shot(initial_state, a, b, c, s +pertub_s)\r\n\r\n d_state = np.zeros((3,1))\r\n d_pertub_state = np.zeros((3,3))\r\n Jacobian = np.zeros((3,3))\r\n for i in range(0, 3):\r\n d_pertub_state[i][0] = (final_state[i] - pred_pertub_a[i]) # a\r\n d_pertub_state[i][1] = (final_state[i] - pred_pertub_b[i]) # b\r\n # d_pertub_state[i][2] = (final_state[i] - pred_pertub_c[i]) # c (no update)\r\n d_pertub_state[i][2] = (final_state[i] - pred_pertub_s[i]) # s\r\n \r\n d_state[i] = final_state[i] - pred_no_pertub[i]\r\n \r\n Jacobian[i][0] = (d_pertub_state[i][0] - d_state[i])/pertub # a\r\n Jacobian[i][1] = (d_pertub_state[i][1] - d_state[i])/pertub # b\r\n # Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub # c (no update)\r\n Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub_s # s\r\n\r\n # inv_Jacobian = np.linalg.inv(Jacobian)\r\n inv_Jacobian = np.linalg.pinv(Jacobian)\r\n correction = np.dot(inv_Jacobian, d_state)\r\n # pdb.set_trace()\r\n return correction","def test_vertical_velocity(self):\n L_x = self.x_edge[-1]\n np.testing.assert_array_equal(self.dVbox_dz(self.t, 0), 0)\n np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5 * L_x), 0)\n np.testing.assert_array_equal(self.dVbox_dz(self.t, L_x), 0)","def rk4(accel,m,r,h,v): \n k1v = accel(m,r) \n k1r = v \n k2v = accel(m,r + h*0.5*k1r) \n k2r = v+k1v*h*0.5 \n k3v = accel(m,r + h*0.5*k2r) \n k3r = v+k2v*h*0.5\n k4v = accel(m,r + h*k3r) \n k4r = v+k3v*h\n new_v = v + h*(k1v + 2*k2v + 2*k3v + k4v)/float(6)\n new_r = r + h*(k1r + 2*k2r + 2*k3r + k4r)/float(6)\n return new_v,new_r","def check_change_power_spectrum(test_knotpos, test_knotval, matpow):\n #Get the modified power spectrum\n kval = matpow[:,0]\n newpk = lyasimulation.change_power_spectrum_knots(test_knotpos, test_knotval, matpow)\n #Check the kvalues are still the same for comparison to the transfer function\n assert np.all([k in newpk[:,0] for k in kval])\n #Build interpolators for the new power spectrum\n #Only interpolate a subset of Pk for speed\n newpkint = build_restrict_interp(newpk, test_knotpos[0]/3., test_knotpos[-1]*3)\n #Build interpolator for old power spectrum\n pkint = build_restrict_interp(matpow, test_knotpos[0]/3., test_knotpos[-1]*3)\n #Build interpolator for knots\n ext_knotpos = np.concatenate([[kval[0],],test_knotpos, [kval[-1],]])\n ext_knotval = np.concatenate([[test_knotval[0],],test_knotval, [test_knotval[-1],]])\n knotint = interp.interp1d(ext_knotpos, ext_knotval, kind='linear')\n #Check that the interpolator works\n assert np.all(np.abs(knotint(test_knotpos) / test_knotval-1) < 1e-5)\n lg_knotpos = np.log(test_knotpos)\n #Check modification worked at the knot values\n assert np.all(np.abs(np.exp(newpkint(lg_knotpos)) / (np.exp(pkint(lg_knotpos)) * test_knotval) - 1) < 1e-3)\n #Pick some random k values distributed uniformly in log space\n krand = (lg_knotpos[-1]-lg_knotpos[0]+0.2)*np.random.random(250)+lg_knotpos[0]-0.1\n #Check that the modification was accurate at random positions\n #print(np.max(np.abs(np.exp(newpkint(krand)) / (np.exp(pkint(krand)) * knotint(np.exp(krand))) - 1)))\n assert np.all(np.abs(np.exp(newpkint(krand)) / (np.exp(pkint(krand)) * knotint(np.exp(krand))) - 1) < 0.01)","def get_correction(d, a, hfov, img_x):\n\n width = 2 * d*math.tan((hfov/2)*math.pi/180) # in meters\n one_meter = img_x / width\n return int(a*one_meter)","def loss_comparison(warp_fname, offpar_fname, moving_rmli_fname,\n fixed_rmli_fname, moved_fname, crop_center, crop_size,\n reg_weight, ncc_win, debug=False):\n rg_crop = crop_size[0]\n az_crop = crop_size[1]\n rg_cen = crop_center[0]\n az_cen = crop_center[1]\n\n # Import voxelmorph with pytorch backend\n os.environ['VXM_BACKEND'] = 'pytorch'\n import voxelmorph as vxm\n\n # Read the voxelmorph warp file\n warp_file = np.load(warp_fname)\n warp = warp_file['offs']\n warp = warp[np.newaxis, :, :, :]\n\n # Read moved scene\n moved_file = np.load(moved_fname)\n moved = moved_file['scene']\n moved = moved[np.newaxis, np.newaxis, :, :]\n\n # Read, crop, and scale the fixed RMLI\n fixed_rmli = gx.MLI(fixed_rmli_fname,\n par=gx.MLI_Par(fixed_rmli_fname + '.par'))\n rmli_dim = fixed_rmli.dim\n fixed_full = fixed_rmli.array\n fixed = fixed_full[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\n az_cen - az_crop // 2:az_cen + az_crop // 2]\n fixed = scale_rmli(fixed)\n fixed = fixed[np.newaxis, np.newaxis, :, :]\n\n # Read, crop, and scale the moving RMLI\n moving_rmli = gx.MLI(moving_rmli_fname,\n par=gx.MLI_Par(moving_rmli_fname + '.par'))\n moving_full = moving_rmli.array\n moving = moving_full[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\n az_cen - az_crop // 2:az_cen + az_crop // 2]\n moving = scale_rmli(moving)\n moving = moving[np.newaxis, np.newaxis, :, :]\n\n # Read in the Gamma offsets\n # Scale the Gamma offsets to be the same size as the original data that\n # was cropped to feed into voxelmorph.\n offs_basename, _ = os.path.splitext(os.path.basename(offpar_fname))\n offs_fname = os.path.join(os.path.dirname(offpar_fname), offs_basename + '.offs')\n offpar = gx.OFF_Par(offpar_fname)\n offs_dim = (offpar['offset_estimation_range_samples'],\n offpar['offset_estimation_azimuth_samples'])\n gx_offs = gx.readBin(offs_fname, offs_dim, _dtype='complex64')\n zoom_factor = (rmli_dim[0] / offs_dim[0], rmli_dim[1] / offs_dim[1])\n multilook = (fixed_rmli.par['range_looks'],\n fixed_rmli.par['azimuth_looks'])\n gamma_rg_offs = scipy.ndimage.zoom(np.real(gx_offs), zoom_factor)\n gamma_az_offs = scipy.ndimage.zoom(np.imag(gx_offs), zoom_factor)\n gamma_rg_offs = gamma_rg_offs[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\n az_cen - az_crop // 2:az_cen + az_crop // 2]\n gamma_rg_offs /= multilook[0]\n gamma_az_offs = gamma_az_offs[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\n az_cen - az_crop // 2:az_cen + az_crop // 2]\n gamma_az_offs /= multilook[1]\n gamma_warp = np.stack((gamma_rg_offs, gamma_az_offs), axis=0)\n gamma_warp = gamma_warp[np.newaxis, :, :, :]\n\n # Create a moved image with the gamma offsets\n transformer = vxm.layers.SpatialTransformer(crop_size)\n gamma_moved = transformer(torch.from_numpy(moving).float(),\n torch.from_numpy(gamma_warp).float())\n\n # Prepare ncc loss with square window\n ndims = len(list(fixed.shape)) - 2\n assert ndims in [1, 2, 3], \"volumes should be 1 to 3 dimensions. found: %d\" % ndims\n ncc_win_sq = [ncc_win] * ndims # Build a square window\n ncc = vxm.losses.NCC(ncc_win_sq, cuda=False)\n\n # Now we have all the data, compute the losses\n loss_sim_vxm = ncc.loss(torch.from_numpy(fixed).float(),\n torch.from_numpy(moved).float())\n loss_sim_gamma = ncc.loss(torch.from_numpy(fixed).float(), gamma_moved)\n\n grad = vxm.losses.Grad(penalty='l2')\n loss_smooth_vxm = grad.loss(None, torch.from_numpy(warp).float())\n loss_smooth_gamma = grad.loss(None, torch.from_numpy(gamma_warp).float())\n\n loss_total_vxm = loss_sim_vxm + (reg_weight * loss_smooth_vxm)\n loss_total_gamma = loss_sim_gamma + (reg_weight * loss_smooth_gamma)\n\n # Print everything\n print('Lambda: {}\\n'.format(reg_weight))\n print('Voxelmorph:\\nSimilarity loss: {}\\nSmoothness loss: {}\\n'\n 'Total: {}\\n'.format(loss_sim_vxm, loss_smooth_vxm, loss_total_vxm))\n print('Gamma:\\nSimilarity loss: {}\\nSmoothness loss: {}\\n'\n 'Total: {}\\n'.format(loss_sim_gamma, loss_smooth_gamma, loss_total_gamma))\n\n if debug:\n plt.figure()\n plt.imshow(moved[0, 0, :, :])\n plt.title('moved')\n plt.colorbar()\n plt.figure()\n plt.imshow(gamma_moved[0, 0, :, :])\n plt.title('gamma_moved')\n plt.colorbar()\n plt.figure()\n plt.imshow(fixed[0, 0, :, :])\n plt.title('fixed')\n plt.colorbar()\n plt.figure()\n plt.imshow(warp[0, 0, :, :])\n plt.title('warp_rg')\n plt.colorbar()\n plt.figure()\n plt.imshow(warp[0, 1, :, :])\n plt.title('warp_az')\n plt.colorbar()\n plt.figure()\n plt.imshow(gamma_warp[0, 0, :, :])\n plt.title('gamma_warp_rg')\n plt.colorbar()\n plt.figure()\n plt.imshow(gamma_warp[0, 1, :, :])\n plt.title('gamma_warp_az')\n plt.colorbar()\n plt.show()","def update_apc12(self, delta_t=None):\n\n delta_t = delta_t or self.delta_t\n\n kap = (self.vel, self.force(self.pos,\n self.vel,\n self.time, drag=False), self.time)\n\n pos = self.pos+delta_t*self.vel\n vel = self.vel+delta_t*kap[1]\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\n\n\n force = self.force(pos,\n vel,\n self.time+delta_t, drag=False)\n\n pos = self.pos+delta_t/2.0*(vel+self.vel)\n vel = self.vel+delta_t/2.0*(force+kap[1])\n\n for cback in self.pos_callbacks:\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n for cback in self.vel_callbacks:\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\n\n try:\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\n vel, self.vel,\n delta_t, drag=True)\n except Collision.CollisionException as col:\n vel = self.vel+col.delta_t*kap[1]\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\n raise col\n \n self.time += delta_t\n\n return kap","def test_compute_after_smooth_goddard_2013(\r\n PM_ds_initialized_3d_full, PM_ds_control_3d_full\r\n):\r\n PM_ds_control_3d_full = smooth_goddard_2013(\r\n PM_ds_control_3d_full,\r\n )\r\n PM_ds_initialized_3d_full = smooth_goddard_2013(\r\n PM_ds_initialized_3d_full,\r\n )\r\n actual = compute_perfect_model(PM_ds_initialized_3d_full, PM_ds_control_3d_full).tos\r\n\r\n north_atlantic = actual.sel(lat=slice(40, 50), lon=slice(-30, -20))\r\n assert not north_atlantic.isnull().any()","def calc(self):\n\n # the following if query ensures that volume- and interaction-terms\n # are only calculated if tau > 0.\n # (to avoid nan-values from invalid function-evaluations)\n\n if self.V.tau.shape == (1,):\n Isurf = self.surface()\n # differentiation for non-existing canopy, as otherwise NAN values\n if self.V.tau > 0.:\n Ivol = self.volume()\n if self.int_Q is True:\n Iint = self.interaction()\n else:\n Iint = np.array([0.])\n else:\n Ivol = np.array([0.])\n Iint = np.array([0.])\n else:\n # calculate surface-term (valid for any tau-value)\n Isurf = self.surface()\n\n # store initial parameter-values\n old_t_0 = self.t_0\n old_p_0 = self.p_0\n old_t_ex = self.t_ex\n old_p_ex = self.p_ex\n\n old_tau = self.V._get_tau()\n old_omega = self.V._get_omega()\n old_NN = self.SRF._get_NormBRDF()\n\n # set mask for tau > 0.\n mask = old_tau > 0.\n valid_index = np.where(mask)\n inval_index = np.where(~mask)\n\n # set parameter-values to valid values for calculation\n self.t_0 = old_t_0[valid_index[0]]\n self.p_0 = old_p_0[valid_index[0]]\n self.t_ex = old_t_ex[valid_index[0]]\n self.p_ex = old_p_ex[valid_index[0]]\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\n if np.array(self.V.omega).size != 1:\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\n if np.array(self.SRF.NormBRDF).size != 1:\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\n\n # calculate volume and interaction term where tau-values are valid\n _Ivol = self.volume()\n if self.int_Q is True:\n _Iint = self.interaction()\n else:\n _Iint = np.full_like(self.t_0, 0.)\n\n # reset parameter values to old values\n self.t_0 = old_t_0\n self.p_0 = old_p_0\n self.t_ex = old_t_ex\n self.p_ex = old_p_ex\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically add an axis to the arrays!\n self.V.tau = np.squeeze(old_tau)\n self.V.omega = np.squeeze(old_omega)\n self.SRF.NormBRDF = np.squeeze(old_NN)\n\n # combine calculated volume-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n Ivol = np.ones_like(self.t_0)\n Ivol[valid_index[0]] = _Ivol\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\n\n # combine calculated interaction-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n if self.int_Q is True:\n Iint = np.ones_like(self.t_0)\n Iint[valid_index[0]] = _Iint\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\n else:\n Iint = np.full_like(self.t_0, 0.)\n\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint","def single_volume_inference_unpadded(self, volume):\n \n # normalize the data volume \n image = (volume.astype(np.single) - np.min(volume))/(np.max(volume) - np.min(volume))\n # reshape the image volume to the same patch size used for training\n img_reshaped = med_reshape(image, new_shape=(self.patch_size, self.patch_size, image.shape[2]))\n # create a new 3d mask to store predicted results\n mask3d = np.zeros(img_reshaped.shape)\n # iterate over the image array and predict the all the slices\n for slc_idx in range(img_reshaped.shape[2]):\n # compute for each slice\n slc = torch.from_numpy(img_reshaped[:,:,slc_idx].astype(np.single)).unsqueeze(0).unsqueeze(0)\n # make prediction\n pred = self.model(slc.to(self.device))\n pred = np.squeeze(pred.cpu().detach())\n # store predicted data\n mask3d[:,:,slc_idx] = torch.argmax(pred, dim=0)\n # return the predicted volume\n return mask3d","def update_speed_input_step(self,curr_v):\n \n # update speed inputs \n self.speed_inputs_east*=0\n self.speed_inputs_west*=0\n self.speed_inputs_north*=0\n self.speed_inputs_south*=0\n\n if self.use_eight_directions is True: \n self.speed_inputs_north_east*=0\n self.speed_inputs_north_west*=0\n self.speed_inputs_south_east*=0\n self.speed_inputs_south_west*=0\n \n #speed_values=self.rr[:self.N_e,0] \n speed_values=np.ones((self.N_e,1))\n\n if curr_v[0]>0:\n \n # north-east\n if self.use_eight_directions is True and curr_v[1]>0:\n self.speed_inputs_north_east=speed_values \n \n # south-east \n elif self.use_eight_directions is True and curr_v[1]<0:\n self.speed_inputs_south_east=speed_values\n \n #east \n else:\n self.speed_inputs_east=speed_values\n\n\n elif curr_v[0]<0:\n\n # north-west \n if self.use_eight_directions is True and curr_v[1]>0:\n self.speed_inputs_north_west=speed_values\n\n # south-west \n elif self.use_eight_directions is True and curr_v[1]<0:\n self.speed_inputs_south_west=speed_values\n \n # west \n else:\n self.speed_inputs_west=speed_values\n\n else: \n # north\n if curr_v[1]>0:\n self.speed_inputs_north=speed_values\n\n # south\n elif curr_v[1]<0:\n self.speed_inputs_south=speed_values","def update_focal_axes(self):\n self.update_sigma()\n self.updateGL()","def compute_desired_velocity(self):\n mask_red = (self.image_red == 255) \\\n *(self.image_green == 0) \\\n *(self.image_blue == 0)\n ind_red = sp.where( mask_red )\n phi = sp.ones(self.image_red.shape)\n phi[ind_red] = 0\n phi = sp.ma.MaskedArray(phi, mask=self.mask)\n numpy.set_printoptions(threshold=sys.maxsize)\n self.door_distance = skfmm.distance(phi, dx=self.pixel_size)\n tmp_dist = self.door_distance.filled(9999)\n grad = sp.gradient(tmp_dist,edge_order=2)\n grad_X = -grad[1]/self.pixel_size\n grad_Y = -grad[0]/self.pixel_size\n norm = sp.sqrt(grad_X**2+grad_Y**2)\n norm = (norm>0)*norm+(norm==0)*0.001\n self.desired_velocity_X = self.vmax * (grad_X/norm)\n self.desired_velocity_Y = self.vmax * (grad_Y/norm)\n '''plt.subplot(1,2,1)\n plt.imshow(self.desired_velocity_X, cmap='hot', interpolation='nearest')\n plt.gca().invert_yaxis()\n plt.colorbar()\n plt.subplot(1,2,2)\n plt.imshow(self.desired_velocity_X, cmap='hot', interpolation='nearest')\n plt.gca().invert_yaxis()\n plt.colorbar()\n plt.show()'''\n return self.door_distance, self.desired_velocity_X, self.desired_velocity_Y","def _update_motion_data(self, msg):\n if self._auv_motion != msg.motion:\n self._target_euler[\"alpha\"] = self._actual_euler[\"alpha\"]\n self._target_euler[\"beta\"] = self._actual_euler[\"beta\"]\n self._target_euler[\"gamma\"] = self._actual_euler[\"gamma\"]\n self._auv_motion = msg.motion\n self._thrusters_actual_speed[\"1\"] = msg.thrusters_speed.thruster_id1_speed\n self._thrusters_actual_speed[\"2\"] = msg.thrusters_speed.thruster_id2_speed\n self._thrusters_actual_speed[\"3\"] = msg.thrusters_speed.thruster_id3_speed\n self._thrusters_actual_speed[\"4\"] = msg.thrusters_speed.thruster_id4_speed\n self._thrusters_actual_speed[\"5\"] = msg.thrusters_speed.thruster_id5_speed\n self._thrusters_actual_speed[\"6\"] = msg.thrusters_speed.thruster_id6_speed\n self._thrusters_actual_speed[\"7\"] = msg.thrusters_speed.thruster_id7_speed\n self._thrusters_actual_speed[\"8\"] = msg.thrusters_speed.thruster_id8_speed"],"string":"[\n \"def CorrectMotion(self):\\n if self.verbose:\\n print \\\"Correct for motion\\\"\\n for entry in self.entry_map['epi']:\\n info = self.info[entry]\\n\\n if os.path.exists(info['imgfile_m'] + info['suffix']):\\n return\\n# Always use brik for 3dDeconvolve.\\n suffix = '+orig'\\n epifile = '%s%s' % (info['imgfile'], suffix)\\n prefix = info['imgfile_m']\\n base_entry = info['base_entry']\\n if info['base'] == 'start':\\n# Use the first frame specified in template file. Defaults\\n# to zero.\\n base = info['motion_ref_frame']\\n else:\\n# Use the last frame.\\n base = self.info[base_entry]['tdim'] - info['skip']-1\\n base = ('%d' % base).replace(' ','')\\n\\n# Correct for slice-timing.\\n self.SliceTimeCorrect(info, epifile)\\n\\n plane = info['plane']\\n anat_tgt = info['anat_tgt']\\n# anat_entry = self.anat_entry[plane]\\n\\n if info['catmats']:\\n# Include additonal transformation in motion correction such\\n# that final image is in register with the fieldmap, which has\\n# been registered to the structural image that will be used for\\n# spatial normalization.\\n self.MotcorCatenate(info, base, anat_tgt)\\n else:\\n# Assume fieldmap is in register with the structural.\\n self.Motcor(info, base)\\n\\n if info.get('fmapname', None) is None:\\n# No fieldmap correction.\\n if self.fsl_flip:\\n# Flip the way fslview likes it.\\n self.FSLFlip(info['imgfile_m'], info['imgfile_final'])\\n elif info['suffix'] == '.nii':\\n# Copy motion-corrected images from /tmp to output directory\\n outfile = info['imgfile_final'] + info['suffix']\\n cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)\\n self.CheckExec(cmd, [outfile], force=True)\\n cmd = '/bin/rm %s+orig*' % info['imgfile_m']\\n self.CheckExec(cmd, [], force=True)\",\n \"def test_revolute(self):\\n # Rotate around the z axis\\n r = Joint.revolute(np.array([0, 0, 1]))\\n t_mat = r(np.array([np.pi / 2]))\\n rot_vec = np.dot(t_mat, np.array([1, 0, 0, 1]))[:3]\\n self.assertTrue(np.allclose(\\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))\",\n \"def on_mouse_move(self, event):\\n\\n # self.view = 1 * np.eye(4, dtype=np.float32)\\n # self.model = 1 * np.eye(4, dtype=np.float32)\\n\\n # self.translate -= event.delta[1]\\n # self.translate = max(-1, self.translate)\\n # print(event.delta[1])\\n # print(self.translate)\\n # self.view = translate((0, 0, -self.translate))\\n # self.game_program['u_view'] = self.view\\n # self.game_program['u_size'] = 5 / self.translate\\n # self.view = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.view\\n # self.model = (0.1*self.translate*np.eye(4, dtype=np.float32)) + self.model\\n # print(self.view)\\n\\n # self.game_program['u_model'] = self.model\\n # self.game_program['u_view'] = self.view\\n\\n x, y = event.pos\\n #print(x, y)\\n self.x_offset, self.y_offset = x - self.last_x, - (y - self.last_y)\\n self.last_x, self.last_y = x, y\\n self.x_offset *= self.sensitivity\\n self.y_offset *= self.sensitivity\\n\\n self.yaw, self.pitch = self.yaw - self.x_offset, self.pitch + self.y_offset\\n self.rot_y(self.yaw * np.pi / 180)\\n self.rot_x(self.pitch * np.pi / 180)\\n\\n self.view = np.dot(self.rot_mat_y, self.rot_mat_x)\\n self.game_program['u_view'] = self.view\\n\\n self.update()\",\n \"def driftRHS_3D(field,drift_velocity,t,x):\\n f = field.getValue(x)\\n fs = np.sqrt(f[0]**2 + f[1]**2 + f[2]**2)\\n f = f/fs\\n return -f*drift_velocity(fs)\",\n \"def kelvin_effect(pres, surft, temp, mw_ba, dcell):\\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))\",\n \"def testNonVarying(self):\\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration)\\n self._testPhotoCalibCenter(photoCalib, 0)\\n\\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointXShift))\\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointXShift))\\n result = photoCalib.instFluxToMaggies(self.instFlux, self.instFluxErr)\\n self.assertEqual(1, result.value)\\n\\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration, self.calibrationErr)\\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)\\n\\n # constant, with a bbox\\n photoCalib = lsst.afw.image.PhotoCalib(self.calibration, bbox=self.bbox)\\n self._testPhotoCalibCenter(photoCalib, 0)\",\n \"def test_velocity(self):\\n sol = Mader(p_cj=3.0e11, d_cj=8.0e5, gamma=3.0, u_piston=0.0)\\n # r must contain 2 elements, otherwise the density and pressure are nan\\n r = np.array([0.7, 0.8])\\n t = 6.25e-6\\n solrt = sol(r, t)\\n np.testing.assert_allclose(solrt.velocity[0], 144000.0)\",\n \"def update_rk3(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n try:\\n\\n kap1 = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time))\\n\\n pos = self.pos+0.5*delta_t*kap1[0]\\n vel = self.vel+0.5*delta_t*kap1[1]\\n\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n 0.5*delta_t, drag=False)\\n\\n kap2 = (vel, self.force(pos, vel, self.time+0.5*delta_t))\\n\\n pos = self.pos+delta_t*(2.0*kap2[0]-kap1[0])\\n vel = self.vel+delta_t*(2.0*kap2[1]-kap1[1])\\n\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=False)\\n\\n kap3 = (vel, self.force(pos, vel, self.time+delta_t))\\n\\n pos = self.pos+delta_t*(kap1[0]+4.0*kap2[0]+kap3[0])/6.0\\n vel = self.vel+delta_t*(kap1[1]+4.0*kap2[1]+kap3[1])/6.0\\n\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=False)\\n\\n except Collision.CollisionException as col:\\n col.vel = self.vel+col.delta_t*kap1[0]\\n raise col\\n\\n self.time += self.delta_t\",\n \"def radio3mmPointingConstants( az, el, sag, ant, subarray=DEFAULT):\\n \\n mproot = \\\"Control.Antenna%d.Aperture3mm.PointingConstants.\\\" %ant\\n azValid = False\\n elValid = False\\n sagValid = False\\n numInvalid = 0\\n try :\\n azDelta = az - queryDouble(mproot+\\\"azOffset\\\") \\n azValid = True\\n except:\\n numInvalid += 1\\n try :\\n elDelta = el - queryDouble(mproot+\\\"elOffset\\\") \\n elValid = True\\n except:\\n numInvalid += 1\\n try :\\n sagDelta = sag - queryDouble(mproot+\\\"sag\\\") \\n sagValid = True\\n except:\\n numInvalid += 1\\n allValid = azValid and elValid and sagValid\\n #print \\\"Deltas:\\\", azDelta, elDelta, sagDelta\\n\\n R3MM = carma.control.APERTURE_RADIO3MM\\n multiSubarray('aperturePointingConstants',subarray, R3MM, az, el, sag, ant)\\n\\n if ant > 15: return\\n \\n # Apply deltas to the 1mm aperture\\n prefix = \\\"\\\"\\n if not allValid:\\n m = \\\"Could not get the initial value\\\"\\n if numInvalid > 1: m += \\\"s\\\"\\n m += \\\" for the 3mm\\\"\\n if not azValid:\\n m += prefix + \\\" az\\\"\\n prefix = \\\",\\\"\\n if not elValid:\\n m += prefix + \\\" el\\\"\\n prefix = \\\",\\\"\\n if not sagValid:\\n m += prefix + \\\" sag\\\"\\n m += \\\" term\\\" \\n if numInvalid > 1: m += \\\"s\\\"\\n m += \\\".\\\"\\n print m\\n m = \\\"The 3mm offsets have been changed but the relative 1mm offsets\\\\n\\\"\\n m += \\\"will not be changed because of the missing initial 3mm values.\\\\n\\\"\\n m += \\\"This is a serious error \\\"\\n m += \\\"that should be reported to the operations staff!!\\\"\\n print m\\n return\\n \\n mproot = \\\"Control.Antenna%d.Aperture1mm.PointingConstants.\\\" %ant \\n az1mm = azDelta + queryDouble(mproot+\\\"azOffset\\\") \\n el1mm = elDelta + queryDouble(mproot+\\\"elOffset\\\") \\n sag1mm = sagDelta + queryDouble(mproot+\\\"sag\\\") \\n R1MM = carma.control.APERTURE_RADIO1MM\\n #print \\\"1mm constants:\\\", az1mm, el1mm, sag1mm\\n multiSubarray('aperturePointingConstants', subarray, R1MM, \\n az1mm, el1mm,sag1mm, ant)\",\n \"def test_volume_3d(self):\\n # generate voronoi mesh \\n mesh = Mesh3d(self.particles, self.bound)\\n print(\\\"building mesh...\\\")\\n mesh.build_geometry()\\n print(\\\"mesh complete\\\")\\n\\n # calculate voronoi volumes of all real particles \\n real_indices = self.particles[\\\"tag\\\"] == ParticleTAGS.Real\\n tot_vol = np.sum(self.particles[\\\"volume\\\"][real_indices])\\n\\n self.assertAlmostEqual(tot_vol, 1.0)\",\n \"def test_backward_injection_in_3D(self):\\n self.fom = ModeMatch(monitor_name = 'figure_of_merit',\\n mode_number = 1,\\n direction = 'Backward',\\n multi_freq_src = True,\\n target_T_fwd = lambda wl: np.ones(wl.size),\\n norm_p = 1)\\n Optimization.set_source_wavelength(self.sim, 'source', self.fom.multi_freq_src, len(self.wavelengths))\\n self.sim.fdtd.setnamed('FDTD','dimension','3D')\\n self.sim.fdtd.setnamed('source', 'x', -self.sim.fdtd.getnamed('source','x'))\\n self.sim.fdtd.setnamed('source','direction','Backward')\\n self.sim.fdtd.setnamed('figure_of_merit','x', -self.sim.fdtd.getnamed('figure_of_merit','x'))\\n self.fom.initialize(self.sim)\\n self.fom.make_forward_sim(self.sim)\\n self.sim.run(name = 'modematch_backward_injection_in_3D', iter = 1)\\n FOM = self.fom.get_fom(self.sim)\\n self.assertAlmostEqual(FOM, self.ref_fom, 4)\",\n \"def update_ab3(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n if len(self._old) >= 2:\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n beta = -(1.0/6.0)*delta_t*(delta_t*(5.0*delta_t+3.0*(self.time-self.get_old(0, 2)))\\n /((self.time-self.get_old(0, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\\n gamma = (1.0/6.0)*delta_t*(delta_t*(2.0*delta_t+3.0*(self.time-self.get_old(0, 2)))\\n /((self.time-self.get_old(1, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\\n\\n pos = self.pos+(delta_t-beta-gamma)*self.vel+beta*self.get_old(0, 0)+gamma*self.get_old(1, 0)\\n vel = self.vel+(delta_t-beta-gamma)*kap[1]+beta*self.get_old(0, 1)+gamma*self.get_old(1, 1)\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n try:\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n beta = -(1.0/6.0)*col.delta_t*(col.delta_t*(5.0*col.delta_t+3.0*(self.time-self.get_old(0, 2)))\\n /((self.time-self.get_old(0, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\\n gamma = (1.0/6.0)*col.delta_t*(col.delta_t*(2.0*col.delta_t+3.0*(self.time-self.get_old(0, 2)))\\n /((self.time-self.get_old(1, 2))*(self.get_old(0, 2)-self.get_old(1, 2))))\\n vel = self.vel+(col.delta_t-beta-gamma)*kap[1]+beta*self.get_old(0, 1)+gamma*self.get_old(0, 1)\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n\\n self.set_old(kap, 2)\\n\\n self.time += delta_t\\n\\n else:\\n ## reduced to using Adams Bashforth 2nd order method for the second timestep:\\n\\n try:\\n tmp = [self.get_old(0)]\\n except IndexError:\\n tmp = []\\n kap = update_ab2(self)\\n if tmp:\\n self._old = self._old + tmp\\n\\n return kap\",\n \"def test_revolute_from_dh(self):\\n x_offset = 1\\n z_offset = 2\\n # Rotate around the z axis\\n r = Joint.revolute_from_dh(0, 0, x_offset, z_offset)\\n t_mat = r(np.array([np.pi / 2]))\\n rot_vec = np.dot(t_mat[:3, :3], np.array([1, 0, 0]))\\n self.assertTrue(np.allclose(\\n rot_vec, np.array([0, 1, 0]), rtol=1e-5, atol=1e-5))\\n self.assertTrue(np.allclose(t_mat[2, 3], z_offset))\\n # x was rotated 90 degrees, and is now y\\n self.assertTrue(np.allclose(t_mat[1, 3], x_offset))\",\n \"def update_apc22(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n if len(self._old) >= 1:\\n\\n\\n try:\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\\n\\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n pos = self.pos+delta_t/2.0*(self.vel+vel)\\n vel = self.vel+delta_t/2.0*(self.force(pos, vel, self.time+delta_t,\\n drag=False)+kap[1])\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n\\n self.time += delta_t\\n\\n else:\\n ## reduced to using the Adams first order method for the first timestep:\\n\\n kap = update_apc1(self)\\n\\n self.set_old(kap, 1)\\n\\n return kap\",\n \"def tick(self):\\n \\n if self.state == ForceFeedbackState.NORMAL:\\n # send desired position to IK directly\\n # Actually publish if we have some publishers\\n rospy.logerr('Im in NORMAL state')\\n if self.pubs is not None:\\n p0 = self.desired_pos\\n # Record the current sent pos as element 0 of self.prev_pos\\n # shuffling the other pos to the right. Then, truncate the\\n # list to be a maximum of 2 pos long.\\n self.prev_pos = [p0] + self.prev_pos\\n self.prev_pos = self.prev_pos[:2]\\n \\n #Publish the desired position to /pos_for_IK\\n p0_list = p0.tolist()\\n msg = Float64MultiArray()\\n msg.data = deepcopy(p0_list)\\n self.pubs.publish(msg)\\n \\n elif self.state == ForceFeedbackState.FORCEFB:\\n rospy.logerr('Im in FORCEFB state')\\n # Calculate correction vector\\n \\n # Need to check whether desired_pos is nan. Can use one only because the function only allows scalar\\n if math.isnan(self.desired_pos[0]) == False:\\n self.stored_desired_pos = self.desired_pos\\n \\n correction = self.stored_desired_pos - self.camera_pos\\n correction_mag = np.sqrt(np.sum(correction * correction))\\n \\n #separate out two terms for testing and simplicity (and also because error in camera measurements in 2 dimensions) \\n correction_x_mag = np.sqrt(correction[0]*correction[0])\\n correction_z_mag = np.sqrt(correction[2]*correction[2])\\n rospy.logerr('correction_z_mag = ')\\n rospy.logerr(correction_z_mag) \\n \\n #if the correction_vector bigger and smaller than some threshold, then do something\\n #if math.isnan(correction_mag)==True or correction_mag <= epsilon or correction_mag >= 0.05:\\n #need correction_z_mag in case we are using force feedback on the wrong pose\\n if math.isnan(correction_x_mag)==False and math.isnan(correction_z_mag)==False and correction_z_mag <= 0.15:\\n if correction_x_mag >= 0.04 or correction_z_mag >= 0.03:\\n rospy.logerr('FORCE FEEDBACK with correction mag: ' + str(correction_mag))\\n \\n # Calculate new commanded pose\\n p0 = self.stored_desired_pos\\n p0_new = p0 + 1.0*correction #the constant can be changed depending on needs\\n \\n self.prev_pos = [p0_new] + self.prev_pos\\n self.prev_pos = self.prev_pos[:2]\\n \\n #Publish the new p0 to /pos_for_IK\\n p0_list = p0_new.tolist()\\n msg = Float64MultiArray()\\n msg.data = deepcopy(p0_list)\\n \\n rospy.logerr('Publishing msg: ' + str(msg))\\n self.pubs.publish(msg) \\n \\n else:\\n rospy.logerr('TRIED FORCE FB BUT NOT REQUIRED')\\n p0 = self.desired_pos\\n # Record the current sent pos as element 0 of self.prev_pos\\n # shuffling the other pos to the right. Then, truncate the\\n # list to be a maximum of 2 pos long.\\n self.prev_pos = [p0] + self.prev_pos\\n self.prev_pos = self.prev_pos[:2]\\n \\n #Publish the desired position to /pos_for_IK\\n p0_list = p0.tolist()\\n msg = Float64MultiArray()\\n msg.data = deepcopy(p0_list)\\n self.pubs.publish(msg)\\n else:\\n rospy.logerr('MATH IS NAN or wrong pose, equivalent to NORMAL state')\\n p0 = self.desired_pos\\n # Record the current sent pos as element 0 of self.prev_pos\\n # shuffling the other pos to the right. Then, truncate the\\n # list to be a maximum of 2 pos long.\\n self.prev_pos = [p0] + self.prev_pos\\n self.prev_pos = self.prev_pos[:2]\\n \\n #Publish the desired position to /pos_for_IK\\n p0_list = p0.tolist()\\n msg = Float64MultiArray()\\n msg.data = deepcopy(p0_list)\\n self.pubs.publish(msg)\\n \\n self.desired_pos = np.ones(3) * np.nan\\n self.state = ForceFeedbackState.NORMAL\",\n \"def define_ufl_convec_accel_diff(self):\\n\\n if hasattr(self, 'ufl_convec_accel_dv'):\\n return None\\n\\n # Exit if problem is formulated with respect to Eulerian\\n # coordinates and is not an elastic material.\\n eulerian = self.config['formulation']['domain'] == 'eulerian'\\n lin_elastic = self.config['material']['const_eqn'] == 'lin_elastic'\\n stokes = self.config['material']['const_eqn'] == 'stokes'\\n if (not eulerian) or lin_elastic or stokes:\\n self.ufl_convec_accel_dv = 0\\n return None\\n\\n self.ufl_convec_accel_dv = dlf.derivative(self.ufl_convec_accel,\\n self.velocity,\\n self.trial_vector)\\n\\n return None\",\n \"def update_apc11(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n pos = self.pos+delta_t*self.vel\\n vel = self.vel+delta_t*kap[1]\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n force = self.force(pos,\\n vel,\\n self.time+delta_t, drag=False)\\n\\n pos = self.pos+delta_t*vel\\n vel = self.vel+delta_t*force\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n try:\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n vel = self.vel+col.delta_t*kap[1]\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n \\n self.time += delta_t\\n\\n return (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\",\n \"def update_rk4(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n try:\\n\\n kap1 = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time))\\n\\n pos = self.pos+0.5*delta_t*kap1[0]\\n vel = self.vel+0.5*delta_t*kap1[1]\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n 0.5*delta_t, drag=False)\\n\\n kap2 = (self.vel + 0.5*delta_t*kap1[1],\\n self.force(pos, vel, self.time + 0.5*delta_t))\\n\\n pos = self.pos+0.5*delta_t*kap2[0]\\n vel = self.vel+0.5*delta_t*kap2[1]\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n 0.5*delta_t, drag=False)\\n\\n\\n kap3 = (self.vel+0.5*delta_t*kap2[1],\\n self.force(pos, vel, self.time+0.5*delta_t))\\n\\n pos = self.pos+0.5*delta_t*kap3[0]\\n vel = self.vel+0.5*delta_t*kap3[1]\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n 0.5*delta_t, drag=False)\\n\\n\\n kap4 = (self.vel + delta_t * kap3[1],\\n self.force(pos, vel, self.time + delta_t))\\n\\n pos = self.pos+delta_t*(kap1[0]+2.0*kap2[0]+2.0*kap3[0]+kap4[0])/6.0\\n vel = self.vel+delta_t*(kap1[1]+2.0*kap2[1]+2.0*kap3[1]+kap4[1])/6.0\\n self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=False)\\n\\n self.pos = pos\\n self.vel = vel\\n\\n except Collision.CollisionException as col:\\n col.vel = self.vel+col.delta_t*kap1[0]\\n raise col\\n\\n self.time += delta_t\",\n \"def test_forward_injection_in_3D(self):\\n self.fom = ModeMatch(monitor_name = 'figure_of_merit',\\n mode_number = 1,\\n direction = 'Forward',\\n multi_freq_src = True,\\n target_T_fwd = lambda wl: np.ones(wl.size),\\n norm_p = 1)\\n Optimization.set_source_wavelength(self.sim, 'source', self.fom.multi_freq_src, len(self.wavelengths))\\n self.sim.fdtd.setnamed('FDTD','dimension','3D')\\n self.fom.initialize(self.sim)\\n self.fom.make_forward_sim(self.sim)\\n self.sim.run(name = 'modematch_forward_injection_in_3D', iter = 0)\\n FOM = self.fom.get_fom(self.sim)\\n self.assertAlmostEqual(FOM, self.ref_fom, 4)\",\n \"def test_3D_m6_1k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L4_2,\\n Support: 'gpu_1k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L4_2,\\n Support: '',\\n Splitting: 'o2'},\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def test_3D_m6_2k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L4_2,\\n Support: 'gpu_2k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L4_2,\\n Support: '',\\n Splitting: 'o2'}\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def single_volume_inference(self, volume):\\n self.model.eval()\\n\\n # Assuming volume is a numpy array of shape [X,Y,Z] and we need to slice X axis\\n slices = []\\n\\n # Write code that will create mask for each slice across the X (0th) dimension. After \\n # that, put all slices into a 3D Numpy array. You can verify if your method is \\n # correct by running it on one of the volumes in your training set and comparing \\n # with the label in 3D Slicer.\\n \\n # normalize\\n image = (volume.astype(np.single) - np.min(volume))/(np.max(volume) - np.min(volume))\\n \\n new_image = med_reshape(image, new_shape=(self.patch_size, self.patch_size, image.shape[2]))\\n mask3d = np.zeros(new_image.shape)\\n \\n for slc_ix in range(new_image.shape[2]):\\n tsr_test = torch.from_numpy(new_image[:,:,slc_ix].astype(np.single)).unsqueeze(0).unsqueeze(0)\\n #image = torch.from_numpy(self.data[slc[0]][\\\"image\\\"][:,:,slc[1]]).unsqueeze(0)\\n #tsr_test = torch.from_numpy(slc.astype(np.single)).unsqueeze(0).unsqueeze(0)\\n pred = self.model(tsr_test.to(self.device))\\n pred = np.squeeze(pred.cpu().detach())\\n mask3d[:,:,slc_ix] = torch.argmax(pred, dim=0)\\n\\n return mask3d\",\n \"def test_3D_m4_1k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L2_1,\\n Support: 'gpu_1k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L2_1,\\n Support: '',\\n Splitting: 'o2'},\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def set_voltages(): \\n #0) set parameters\\n from project_parameters import trapFile,multipoleControls,reg,driveFrequency,ax,az,phi,coefs\\n import pickle\\n with open(trapFile,'rb') as f:\\n trap = pickle.load(f)\\n V,X,Y,Z=trap.instance.DC,trap.instance.X,trap.instance.Y,trap.instance.Z\\n tc=trap.configuration\\n C = tc.multipoleControl\\n el = []\\n #1) check if trap_knobs has been run yet, creating multipoleControl and multipoleKernel\\n if tc.trap_knobs != True:\\n return 'WARNING: You must run trap_knobs first!'\\n #2a) determine electrode voltages directly\\n elif multipoleControls: # note plurality to contrast from attribute\\n el = np.dot(C,coefs.T) # these are the electrode voltages\\n #2b) determine electrode volages indirectly\\n else:\\n charge = tc.charge\\n mass = tc.mass\\n V0 = mass*(2*np.pi*frequencyRF)**2/charge\\n U2 = az*V0/8\\n U1 = U2+ax*V0/4\\n U3 = 2*U1*np.tan(2*np.pi*(phi+tc.thetaRF)/180)\\n U1p= np.sqrt(U1**2+U3**2/2)\\n U4 = U1p*tc.Qrf[4]/tc.Qrf[1]\\n U5 = U1p*tc.Qrf[5]/tc.Qrf[1]\\n inp = np.array([E[0], E[1], E[2], U1, U2, U3, U4, U5]).T\\n mCf = tc.multipoleCoefficients[1:9,:]\\n el = np.dot(mCf.T,inp) # these are the electrode voltages\\n el = np.real(el)\\n #3) regularize if set to do so\\n reg = 0\\n if reg: \\n C = el\\n Lambda = np.linalg.lstsq(tc.multipoleKernel,C)\\n Lambda=Lambda[0]\\n el = el-(np.dot(tc.multipoleKernel,Lambda))\\n return el\",\n \"def setCameraRotation3D(ang):\\n dislin.vup3d(ang)\",\n \"def match_det2cube(self, input_model,\\n x, y, file_slice_no,\\n this_par1, this_par2,\\n spaxel,\\n c1_offset, c2_offset):\\n\\n#________________________________________________________________________________\\n if self.instrument == 'MIRI':\\n\\n det2ab_transform = input_model.meta.wcs.get_transform('detector', 'alpha_beta')\\n detector2v23 = input_model.meta.wcs.get_transform('detector', 'v2v3')\\n v23toworld = input_model.meta.wcs.get_transform(\\\"v2v3\\\", \\\"world\\\")\\n worldtov23 = input_model.meta.wcs.get_transform(\\\"world\\\", \\\"v2v3\\\")\\n v2ab_transform = input_model.meta.wcs.get_transform('v2v3',\\n 'alpha_beta')\\n\\n alpha, beta, wave = det2ab_transform(x, y)\\n v2, v3, lam23 = detector2v23(x, y)\\n ra, dec, lam = v23toworld(v2, v3, lam23)\\n\\n valid1 = np.isfinite(v2)\\n valid2 = np.isfinite(v3)\\n\\n if self.weighting == 'miripsf':\\n wave_resol = self.instrument_info.Get_RP_ave_Wave(this_par1, this_par2)\\n alpha_resol = self.instrument_info.Get_psf_alpha_parameters()\\n beta_resol = self.instrument_info.Get_psf_beta_parameters()\\n\\n # transform Cube Spaxel centers to alpha,beta of exposure\\n # for MIRI weighting parameters are based on distance in\\n # alpha-beta coord system\\n # transform the cube coordinate values to alpha and beta values\\n # xi,eta -> ra,dec\\n # world -> v2,v3\\n # v2,v3 -> local alpha,beta\\n\\n elif self.instrument == 'NIRSPEC':\\n islice = file_slice_no\\n slice_wcs = nirspec.nrs_wcs_set_input(input_model, islice)\\n\\n x, y = wcstools.grid_from_bounding_box(slice_wcs.bounding_box, \\n step=(1, 1), center=True)\\n ra, dec, lam = slice_wcs(x, y) # return v2,v3 are in degrees\\n valid1 = np.isfinite(ra)\\n valid2 = np.isfinite(dec)\\n#________________________________________________________________________________\\n#________________________________________________________________________________\\n# Slices are curved on detector. A slice region is grabbed by corner regions so\\n# the region returned may include pixels not value for slice. There are gaps\\n# between the slices. Pixels not belonging to a slice are assigned NaN values.\\n\\n x = x.astype(np.int)\\n y = y.astype(np.int)\\n\\n flux_all = input_model.data[y, x]\\n# error_all = input_model.err[y, x]\\n dq_all = input_model.dq[y, x]\\n\\n valid3 = np.isfinite(lam)\\n valid4 = np.isfinite(flux_all)\\n valid = valid1 & valid2 & valid3 & valid4\\n#________________________________________________________________________________\\n# using the DQFlags from the input_image find pixels that should be excluded\\n# from the cube mapping\\n all_flags = (dqflags.pixel['DO_NOT_USE'] + dqflags.pixel['DROPOUT'] +\\n dqflags.pixel['NON_SCIENCE'] +\\n dqflags.pixel['DEAD'] + dqflags.pixel['HOT'] +\\n dqflags.pixel['RC'] + dqflags.pixel['NONLINEAR'])\\n\\n # find the location of all the values to reject in cube building\\n good_data = np.where((np.bitwise_and(dq_all, all_flags) == 0) & (valid == True))\\n\\n # good data holds the location of pixels we want to map to cube\\n flux = flux_all[good_data]\\n# error = error_all[good_data]\\n wave = lam[good_data]\\n\\n# xpix = x[good_data] # only used for testing\\n# ypix = y[good_data] # only used for testing\\n\\n ra = ra - c1_offset / 3600.0\\n dec = dec - c2_offset / 3600.0\\n ra_use = ra[good_data]\\n dec_use = dec[good_data]\\n if self.instrument == 'MIRI':\\n # need alpha,beta if weigthing is miripsf or cubes in alpha-beta space\\n alpha_det = alpha[good_data]\\n beta_det = beta[good_data]\\n# MIRI can make cubes in alpha-beta:\\n if self.coord_system == 'alpha-beta':\\n coord1 = alpha[good_data]\\n coord2 = beta[good_data]\\n\\n else:\\n# xi,eta in arc seconds\\n xi, eta = coord.radec2std(self.Crval1, self.Crval2, ra_use, dec_use)\\n coord1 = xi\\n coord2 = eta\\n\\n nplane = self.naxis1 * self.naxis2\\n lower_limit = 0.01\\n\\n# iprint = 0\\n# now loop over the pixel values for this region and find the spaxels that fall\\n# withing the region of interest.\\n nn = coord1.size\\n\\n# print('looping over n points mapping to cloud',nn)\\n#________________________________________________________________________________\\n for ipt in range(0, nn - 1):\\n#________________________________________________________________________________\\n # Cube.Xcenters, ycenters is a flattened 1-D array of the 2 X 2 xy plane\\n # cube coordinates.\\n # find the spaxels that fall withing ROI of point cloud defined by\\n # coord1,coord2,wave\\n# if(ipt > 2): sys.exit('STOP')\\n# print('For point ',coord1[ipt],coord2[ipt],wave[ipt],ipt)\\n\\n# if(ipt == 0):\\n# print('size of Xcenters',self.Xcenters.size)\\n xdistance = (self.Xcenters - coord1[ipt])\\n ydistance = (self.Ycenters - coord2[ipt])\\n radius = np.sqrt(xdistance * xdistance + ydistance * ydistance)\\n indexr = np.where(radius <= self.rois)\\n indexz = np.where(abs(self.zcoord - wave[ipt]) <= self.roiw)\\n\\n# print('indexz',indexz)\\n# print('indexr',indexr)\\n zlam = self.zcoord[indexz] # z Cube values falling in wavelength roi\\n xi_cube = self.Xcenters[indexr] # x Cube values within radius\\n eta_cube = self.Ycenters[indexr] # y cube values with the radius\\n\\n# print('found xi_cube',xi_cube)\\n# print('found eta_cube',eta_cube)\\n\\n#________________________________________________________________________________\\n# loop over the points in the ROI\\n for iz, zz in enumerate(indexz[0]):\\n istart = zz * nplane\\n for ir, rr in enumerate(indexr[0]):\\n# yy_cube = int(rr / self.naxis1)\\n# xx_cube = rr - yy_cube * self.naxis1\\n# print('xx yy cube',rr,self.naxis1,xx_cube,yy_cube)\\n#________________________________________________________________________________\\n if self.weighting == 'msm':\\n d1 = (xi_cube[ir] - coord1[ipt]) / self.Cdelt1\\n d2 = (eta_cube[ir] - coord2[ipt]) / self.Cdelt2\\n d3 = (zlam[iz] - wave[ipt]) / self.Cdelt3\\n\\n weight_distance = math.sqrt(d1 * d1 + d2 * d2 + d3 * d3)\\n weight_distance = math.pow(weight_distance, self.weight_power)\\n#________________________________________________________________________________\\n# if weight is miripsf -distances determined in alpha-beta coordinate system\\n elif self.weighting == 'miripsf':\\n weights = FindNormalizationWeights(wave[ipt],\\n wave_resol,\\n alpha_resol,\\n beta_resol)\\n\\n\\n ra_spaxel, dec_spaxel = coord.std2radec(self.Crval1,\\n self.Crval2,\\n xi_cube[ir],\\n eta_cube[ir])\\n\\n v2_spaxel, v3_spaxel, zl = worldtov23(ra_spaxel,\\n dec_spaxel,\\n zlam[iz])\\n\\n alpha_spaxel, beta_spaxel, wave_spaxel = v2ab_transform(v2_spaxel,\\n v3_spaxel,\\n zlam[iz])\\n alpha_distance = alpha_det[ipt] - alpha_spaxel\\n beta_distance = beta_det[ipt] - beta_spaxel\\n wave_distance = abs(wave[ipt] - wave_spaxel)\\n\\n xn = alpha_distance / weights[0]\\n yn = beta_distance / weights[1]\\n wn = wave_distance / weights[2]\\n\\n # only included the spatial dimensions\\n weight_distance = math.sqrt(xn * xn + yn * yn + wn * wn)\\n weight_distance = math.pow(weight_distance, self.weight_power)\\n#________________________________________________________________________________\\n# We have found the weight_distance based on instrument type\\n\\n if weight_distance < lower_limit: weight_distance = lower_limit\\n weight_distance = 1.0 / weight_distance\\n\\n cube_index = istart + rr\\n spaxel[cube_index].flux = spaxel[cube_index].flux + weight_distance * flux[ipt]\\n spaxel[cube_index].flux_weight = spaxel[cube_index].flux_weight + weight_distance\\n spaxel[cube_index].iflux = spaxel[cube_index].iflux + 1\",\n \"def quad_eq_of_motion2(self,state,time,force,moment):\\n\\n\\t\\t\\tA = np.matrix([ [0.25,0, -0.5/self.arm_length],\\n\\t\\t\\t\\t[0.25,0.5/self.arm_length,0.],\\n\\t\\t\\t\\t[0.25,0,0.5/self.arm_length],\\n\\t\\t\\t\\t[0.25,-0.5/self.arm_length,0]])\\n\\t\\t\\tT=A*np.asmatrix(np.hstack((force,moment[:2]))).transpose()\\n\\t\\t\\tT_clamped=np.maximum(np.minimum(T,self.max_force/4.0),self.min_force/4.0)\\n\\t\\t\\tB = np.matrix([[1.0,1.0,1.0,1.0],\\n\\t\\t\\t\\t\\t\\t\\t[0.0,self.arm_length,0.0,-self.arm_length],\\n\\t\\t\\t\\t\\t\\t\\t[-self.arm_length,0.0,self.arm_length,0.]])\\n\\t\\t\\tforce = B[[0],:]*T_clamped;\\n\\t\\t\\tforce = np.array(force).reshape(-1,).tolist()\\n\\t\\t\\tmoment = np.vstack( (B[[1,2],:]*np.asmatrix(T_clamped), moment[2]));\\n\\t\\t\\tmoment = np.array(moment).reshape(-1,).tolist()\\n\\t\\t\\t\\n\\t\\t\\t#Assign 13 states\\n\\t\\t\\t#x = state[0]\\n\\t\\t\\t#y = state[1]\\n\\t\\t\\t#z = state[2]\\n\\t\\t\\txdot = state[3];\\n\\t\\t\\tydot = state[4];\\n\\t\\t\\tzdot = state[5];\\n\\t\\t\\tqW = state[6];\\n\\t\\t\\tqX = state[7];\\n\\t\\t\\tqY = state[8];\\n\\t\\t\\tqZ = state[9];\\n\\t\\t\\tp = state[10];\\n\\t\\t\\tq = state[11];\\n\\t\\t\\tr = state[12];\\n\\n\\t\\t\\tquat = np.vstack((qW,qX,qY,qZ)); #!! Attention to the order!!\\n\\t\\t\\tbRw=self.quat2mat(quat.transpose())\\n\\t\\t\\tbRw=bRw.reshape(3,3) #to remove the last dimension i.e., 3,3,1\\n\\t\\t\\twRb = bRw.transpose()\\n\\t\\t\\t\\n\\t\\t\\t# Acceleration\\n\\t\\t\\taccel = 1.0 / self.mass * (wRb * np.matrix([[0],[0],force]) - np.matrix([[0],[0],[self.mass * self.gravity]]))\\n\\t\\t\\taccel = np.array(accel).reshape(-1,).tolist()\\n\\t\\t\\t# Angular velocity\\n\\t\\t\\tK_quat = 2.0; #%this enforces the magnitude 1 constraint for the quaternion\\n\\t\\t\\tquaterror = 1 - (qW**2 + qX**2 + qY**2 + qZ**2);\\n\\t\\t\\tqdot = -1/2*np.matrix([ [0,-p,-q,-r],[p,0,-r,q],[q,r,0,-p],[r,-q,p,0]])*quat + K_quat*quaterror * quat\\n\\t\\t\\tqdot = np.array(qdot).reshape(-1,).tolist()\\n\\t\\t\\t# % Angular acceleration\\n\\t\\t\\tomega = np.matrix([[p],[q],[r]])\\n\\t\\t\\ttemp = np.squeeze(np.cross(omega.transpose(),(self.Inertia*omega).transpose()))\\n\\t\\t\\tpqrdot = self.invInertia * (moment - temp).reshape(-1,1)\\n\\t\\t\\tsdot=np.zeros(13) #default=float64\\n\\t\\t\\tsdot[0]=xdot#[]\\n\\t\\t\\tsdot[1]=ydot\\n\\t\\t\\tsdot[2]=zdot\\n\\t\\t\\tsdot[3]=accel[0]\\n\\t\\t\\tsdot[4]=accel[1]\\n\\t\\t\\tsdot[5]=accel[2]\\n\\t\\t\\tsdot[6]=qdot[0]\\n\\t\\t\\tsdot[7]=qdot[1]\\n\\t\\t\\tsdot[8]=qdot[2]\\n\\t\\t\\tsdot[9]=qdot[3]\\n\\t\\t\\tsdot[10]=pqrdot[0]\\n\\t\\t\\tsdot[11]=pqrdot[1]\\n\\t\\t\\tsdot[12]=pqrdot[2]\\n\\t\\t\\treturn sdot\",\n \"def revolver(self):\\r\\n\\t\\tself.__revuelto=True\",\n \"def control_OM(self, ang_vel, dt, t1):\\n print(\\\"OM CONTROL\\\")\\n for i in range(ang_vel.shape[0]):\\n t1 = time.time()\\n t2 = time.time()\\n while t2 - t1 < dt:\\n self.compare_OM_w_gyro(ang_vel[i])\\n self.move_forward(ang_vel[i])\\n t2 = time.time()\",\n \"def test_3D_m4_2k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L2_1,\\n Support: 'gpu_2k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: L2_1,\\n Support: '',\\n Splitting: 'o2'},\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def refframe_correct(self, ra, dec, obstime, sobjs=None):\\n # Correct Telescope's motion\\n refframe = self.par['calibrations']['wavelengths']['refframe']\\n if refframe in ['heliocentric', 'barycentric'] \\\\\\n and self.par['calibrations']['wavelengths']['reference'] != 'pixel':\\n msgs.info(\\\"Performing a {0} correction\\\".format(self.par['calibrations']['wavelengths']['refframe']))\\n # Calculate correction\\n radec = ltu.radec_to_coord((ra, dec))\\n vel, vel_corr = wave.geomotion_correct(radec, obstime,\\n self.spectrograph.telescope['longitude'],\\n self.spectrograph.telescope['latitude'],\\n self.spectrograph.telescope['elevation'],\\n refframe)\\n # Apply correction to objects\\n msgs.info('Applying {0} correction = {1:0.5f} km/s'.format(refframe, vel))\\n if (sobjs is not None) and (sobjs.nobj != 0):\\n # Loop on slits to apply\\n gd_slitord = self.slits.slitord_id[np.logical_not(self.extract_bpm)]\\n for slitord in gd_slitord:\\n indx = sobjs.slitorder_indices(slitord)\\n this_specobjs = sobjs[indx]\\n # Loop on objects\\n for specobj in this_specobjs:\\n if specobj is None:\\n continue\\n specobj.apply_helio(vel_corr, refframe)\\n\\n # Apply correction to wavelength image\\n self.vel_corr = vel_corr\\n self.waveimg *= vel_corr\\n\\n else:\\n msgs.info('A wavelength reference frame correction will not be performed.')\",\n \"def test_vertical_velocity_dry_air():\\n omega = 1 * units('microbar/second')\\n w_truth = -0.7995291 * units('cm/s')\\n w_test = vertical_velocity(omega, 1000. * units.mbar, 273.15 * units.K)\\n assert_almost_equal(w_test, w_truth, 6)\",\n \"def update_apc23(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n if len(self._old) >= 1:\\n\\n\\n try:\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n beta = 0.5*self.delta_t/(self.time-self.get_old(0, 2))\\n\\n pos = self.pos+delta_t*((1+beta)*self.vel-beta*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1+beta)*kap[1]-beta*self.get_old(0, 1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n beta1 = (3.0*(self.time-self.get_old(0,2))+delta_t)/(6.0*self.time-self.get_old(0,2))\\n beta2 = -delta_t**2/(6.0*(self.time+delta_t-self.get_old(0,2))*(self.time-self.get_old(0,2)))\\n\\n print self.force(pos, vel, self.time+delta_t), kap[1], self.get_old(0,1)\\n\\n pos = self.pos+delta_t*((1.0-beta1-beta2)*vel+beta1*self.vel+beta2*self.get_old(0, 0))\\n vel = self.vel+delta_t*((1.0-beta1-beta2)*self.force(pos, vel, self.time+delta_t,\\n drag=False)+beta1*kap[1]+beta2*self.get_old(0,1))\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n beta = 0.5*col.delta_t/(self.time-self.get_old(0, 2))\\n vel = self.vel+col.delta_t*(1+beta)*kap[1]-beta*self.get_old(0, 1)\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest=True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n\\n self.time += delta_t\\n\\n else:\\n ## reduced to using the Adams first order method for the first timestep:\\n\\n kap = update_apc1(self)\\n\\n self.set_old(kap, 1)\\n\\n return kap\",\n \"def geocentricToApparentRadiantAndVelocity(ra_g, dec_g, vg, lat, lon, elev, jd, include_rotation=True):\\n\\n\\n # Compute ECI coordinates of the meteor state vector\\n state_vector = geo2Cartesian(lat, lon, elev, jd)\\n\\n eci_x, eci_y, eci_z = state_vector\\n\\n\\n # Assume that the velocity at infinity corresponds to the initial velocity\\n v_init = np.sqrt(vg**2 + (2*6.67408*5.9722)*1e13/vectMag(state_vector))\\n\\n\\n # Calculate the geocentric latitude (latitude which considers the Earth as an elipsoid) of the reference \\n # trajectory point\\n lat_geocentric = np.degrees(math.atan2(eci_z, math.sqrt(eci_x**2 + eci_y**2)))\\n\\n\\n\\n\\n ### Uncorrect for zenith attraction ###\\n\\n # Compute the radiant in the local coordinates\\n azim, elev = raDec2AltAz(ra_g, dec_g, jd, lat_geocentric, lon)\\n\\n # Compute the zenith angle\\n eta = np.radians(90.0 - elev)\\n\\n # Numerically correct for zenith attraction\\n diff = 10e-5\\n zc = eta\\n while diff > 10e-6:\\n \\n # Update the zenith distance\\n zc -= diff\\n\\n # Calculate the zenith attraction correction\\n delta_zc = 2*math.atan((v_init - vg)*math.tan(zc/2.0)/(v_init + vg))\\n diff = zc + delta_zc - eta\\n\\n\\n # Compute the uncorrected geocentric radiant for zenith attraction\\n ra, dec = altAz2RADec(azim, 90.0 - np.degrees(zc), jd, lat_geocentric, lon)\\n\\n ### ###\\n\\n\\n\\n # Apply the rotation correction\\n if include_rotation:\\n\\n # Calculate the velocity of the Earth rotation at the position of the reference trajectory point (m/s)\\n v_e = 2*math.pi*vectMag(state_vector)*math.cos(np.radians(lat_geocentric))/86164.09053\\n\\n\\n # Calculate the equatorial coordinates of east from the reference position on the trajectory\\n azimuth_east = 90.0\\n altitude_east = 0\\n ra_east, dec_east = altAz2RADec(azimuth_east, altitude_east, jd, lat, lon)\\n\\n # Compute the radiant vector in ECI coordinates of the apparent radiant\\n v_ref_vect = v_init*np.array(raDec2Vector(ra, dec))\\n\\n\\n v_ref_nocorr = np.zeros(3)\\n\\n # Calculate the derotated reference velocity vector/radiant\\n v_ref_nocorr[0] = v_ref_vect[0] + v_e*np.cos(np.radians(ra_east))\\n v_ref_nocorr[1] = v_ref_vect[1] + v_e*np.sin(np.radians(ra_east))\\n v_ref_nocorr[2] = v_ref_vect[2]\\n\\n\\n # Compute the radiant without Earth's rotation included\\n ra_norot, dec_norot = vector2RaDec(vectNorm(v_ref_nocorr))\\n v_init_norot = vectMag(v_ref_nocorr)\\n\\n ra = ra_norot\\n dec = dec_norot\\n v_init = v_init_norot\\n\\n\\n\\n return ra, dec, v_init\",\n \"def test_3D_m8_1k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: M8Prime,\\n Support: 'gpu_1k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: M8Prime,\\n Support: '',\\n Splitting: 'o2'},\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def angular_memory_effect_analysis(tilt_coef_range, input_field, TM, data_shape, sample_pitch, wavelength, plot_std = True):\\n zernike_rho = np.linspace(-0.5, 0.5, data_shape[1])\\n\\n\\n centered_outputs = np.zeros([tilt_coef_range.size, data_shape[1]]) # For storage of outputs that where tilted and then artificially shifted back to the center\\n shift_idxs = np.zeros(tilt_coef_range.size) # Will store information on how much outputs needed to be shifted to be centered\\n for idx, coef in enumerate(tilt_coef_range):\\n output_intensity = np.abs( TM @ ( input_field * np.exp(2j * np.pi * coef * zernike_rho) ).ravel() )**2 # propagation via TM, only saving intensity\\n centered_outputs[idx, :], shift_idxs[idx] = calc.center_tilted_output(output_intensity, coef, data_shape, sample_pitch, wavelength, return_shift_idx = True)\\n\\n centered_outputs = centered_outputs / np.linalg.norm(centered_outputs, 1) # Normalising all outputs\\n\\n\\n input_field = input_field.ravel()\\n reference_output = np.abs( TM @ input_field )**2 # untilted output field, which we will use for std\\n reference_output = reference_output / np.linalg.norm(reference_output)\\n\\n # fig, axs = plt.subplots()\\n # axs.plot(centered_outputs[-1,:])\\n # plt.show()\\n\\n #Comparing the outputs with a standard deviation method\\n subtracted_outputs = np.zeros(centered_outputs.shape)\\n for idx in range(centered_outputs[:,0].size):\\n subtracted_outputs[idx, :] = centered_outputs[idx, :] - centered_outputs[0, :] # subtracting the outputs with the reference\\n\\n std_of_outputs = np.std(subtracted_outputs, axis = 1) # Computing the standard deviation of subtracted outputs\\n if plot_std:\\n fig, axs = plt.subplots()\\n img_1, = axs.plot(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, color = 'black')\\n img_2 = axs.scatter(shift_idxs * sample_pitch[1] * 1e6, std_of_outputs, s = 40, color = 'crimson', edgecolors = 'black')\\n axs.set_title('Angular memory effect decay')\\n axs.set(xlabel = '$\\\\delta x, \\\\ \\\\mu m$', ylabel = 'Standard deviation, p.d.u.')\\n\\n\\n #Fitting a logistic curve onto the results\\n popt, pcov = curve_fit(logistic_curve, (shift_idxs * sample_pitch[1] * 1e6), std_of_outputs)\\n smooth_range = np.linspace(-0.1, shift_idxs[-1] * sample_pitch[1] * 1e6, 1000)\\n fitted_std = logistic_curve(smooth_range, *popt)\\n img_fit, = axs.plot(smooth_range, fitted_std, color = 'blue')\\n\\n axs.text(0.65 * (shift_idxs[-1] * sample_pitch[1] * 1e6), 0.2 * std_of_outputs.max(), 'Fitting parameters: \\\\nL = {:.3g} \\\\nx_0 = {:.3g} \\\\nk = {:.3g}'.format(*popt))\\n\\n # img_3 = axs.scatter((shift_idxs * sample_pitch[1] * 1e6)[-8:], std_of_outputs[-8:], s = 70, marker = 'x', color = 'green')#excluded point\\n plt.legend([(img_2, img_1), img_fit], ['STD($\\\\delta x $)', '$L / [1 + \\\\exp(-k (\\\\delta x - x_0)) ]$'])\\n plt.plot(block = False)\\n\\n return centered_outputs\",\n \"def correct_proper_motion(self, invert=False, mjd=None):\\n\\n # If mjd not set directly, check that it was set from FITS headers in get_cutout method\\n if self.mjd is None:\\n if mjd is None:\\n raise FITSException(\\\"Date could not be inferred from header, supply with epoch keyword.\\\")\\n else:\\n self.mjd = mjd\\n\\n obstime = Time(self.mjd, format='mjd')\\n\\n simbad = Simbad.query_region(self.position, radius=180 * u.arcsec)\\n\\n # Catch SIMBAD failure either from None return of query or no stellar type matches in region\\n try:\\n simbad = simbad.to_pandas()\\n pm_types = ['*', '**', 'PM*', 'EB*', 'Star', 'PSR', 'Pulsar', 'Flare*']\\n simbad = simbad[(simbad['OTYPE'].isin(pm_types)) | (simbad['SP_TYPE'].str.len() > 0)]\\n\\n assert len(simbad) > 0\\n\\n except (ValueError, AssertionError):\\n logger.debug(\\\"No high proper-motion objects within 180 arcsec.\\\")\\n self.correct_pm = False\\n\\n return\\n\\n # Treat non-existent proper motion parameters as extremely distant objects\\n simbad['PMRA'].fillna(0, inplace=True)\\n simbad['PMDEC'].fillna(0, inplace=True)\\n simbad['PLX_VALUE'].fillna(0.01, inplace=True)\\n\\n newtime = Time(self.radio.mjd, format='mjd')\\n pmra = simbad['PMRA'].values * u.mas / u.yr\\n pmdec = simbad['PMDEC'].values * u.mas / u.yr\\n\\n dist = Distance(parallax=simbad['PLX_VALUE'].values * u.mas)\\n\\n simbad['j2000pos'] = SkyCoord(\\n ra=simbad['RA_d'].values * u.deg,\\n dec=simbad['DEC_d'].values * u.deg,\\n frame='icrs',\\n distance=dist,\\n pm_ra_cosdec=pmra,\\n pm_dec=pmdec,\\n obstime='J2000',\\n )\\n\\n datapos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(obstime))\\n newpos = simbad.j2000pos.apply(lambda x: x.apply_space_motion(newtime))\\n\\n simbad_cols = {\\n 'MAIN_ID': 'Object',\\n 'OTYPE': 'Type',\\n 'SP_TYPE': 'Spectral Type',\\n 'DISTANCE_RESULT': 'Separation (arcsec)',\\n }\\n simbad = simbad.rename(columns=simbad_cols)\\n simbad = simbad[simbad_cols.values()].copy()\\n simbad['PM Corrected Separation (arcsec)'] = np.round(newpos.apply(\\n lambda x: x.separation(self.position).arcsec), 3)\\n\\n # Only display PM results if object within 15 arcsec\\n if simbad['PM Corrected Separation (arcsec)'].min() > 15:\\n logger.debug(\\\"No PM corrected objects within 15 arcsec\\\")\\n self.correct_pm = False\\n\\n return\\n\\n self.simbad = simbad.sort_values('PM Corrected Separation (arcsec)')\\n logger.info(f'SIMBAD results:\\\\n {self.simbad.head()}')\\n\\n nearest = self.simbad['PM Corrected Separation (arcsec)'].idxmin()\\n\\n self.oldpos = datapos[nearest]\\n self.pm_coord = newpos[nearest]\\n\\n near_object = self.simbad.loc[nearest].Object\\n msg = f'{near_object} proper motion corrected to <{self.pm_coord.ra:.4f}, {self.pm_coord.dec:.4f}>'\\n logger.info(msg)\\n\\n missing = simbad[simbad['PM Corrected Separation (arcsec)'].isna()]\\n if len(missing) > 0:\\n msg = f\\\"Some objects missing PM data, and may be a closer match than presented:\\\\n {missing}\\\"\\n logger.warning(msg)\\n\\n return\",\n \"def CreateMotionKernel(kernel):\\r\\n TrajSize = 64\\r\\n anxiety = 0.2* np.random.rand()\\r\\n numT = 10\\r\\n MaxTotalLength =10\\r\\n TotLength = 0\\r\\n #term determining, at each sample, the strengh of the component leating towards the previous position\\r\\n centripetal = 0.7 * np.random.rand()\\r\\n #term determining, at each sample, the random component of the new direction\\r\\n gaussianTerm =10 * np.random.rand()\\r\\n #probability of having a big shake, e.g. due to pressing camera button or abrupt hand movements\\r\\n freqBigShakes = 3 *np.random.rand()\\r\\n #v is the initial velocity vector, initialized at random direction\\r\\n init_angle = 360 * np.random.rand()\\r\\n #initial velocity vector having norm 1\\r\\n v0 = math.cos(init_angle / 180.0 * math.pi) + 1.0j * math.sin(init_angle/ 180.0 * math.pi)\\r\\n #the speed of the initial velocity vector\\r\\n v = v0* MaxTotalLength/(numT-1);\\r\\n\\r\\n if anxiety > 0:\\r\\n v = v0 * anxiety\\r\\n # initialize the trajectory vector\\r\\n x = np.zeros(numT,dtype = np.complex);\\r\\n\\r\\n abruptShakesCounter = 0\\r\\n for t in range(numT-1):\\r\\n # determine if there is an abrupt (impulsive) shake\\r\\n if np.random.rand() < freqBigShakes * anxiety:\\r\\n #if yes, determine the next direction which is likely to be opposite to the previous one\\r\\n nextDirection = 2 * v * (np.exp( 1.0j * (math.pi + (np.random.rand() - 0.5))))\\r\\n abruptShakesCounter = abruptShakesCounter + 1\\r\\n else:\\r\\n nextDirection=0\\r\\n\\r\\n #determine the random component motion vector at the next step\\r\\n dv = nextDirection + anxiety * (gaussianTerm * (np.random.randn()- + 1.0j * np.random.randn()) - centripetal * x[t]) * (MaxTotalLength / (numT - 1))\\r\\n v = v + dv\\r\\n # velocity vector normalization\\r\\n v = (v / np.abs(v)) * MaxTotalLength / (numT - 1)\\r\\n #print v\\r\\n x[t + 1] = x[t] + v\\r\\n # compute total length\\r\\n #TotLength=TotLength+np.abs(x([t+1]-x[t]))\\r\\n x_real = []\\r\\n x_imag = []\\r\\n for elem in x:\\r\\n x_real.append(elem.real)\\r\\n x_imag.append(elem.imag)\\r\\n x_real = np.round((x_real - np.min(x_real))/(np.max(x_real) - np.min(x_real)) * kernel-0.5)\\r\\n x_imag = np.round((x_imag - np.min(x_imag))/(np.max(x_imag) - np.min(x_imag)) * kernel-0.5)\\r\\n for idx in range(len(x_real)):\\r\\n if x_real[idx] < 0:\\r\\n x_real[idx] = 0\\r\\n if x_imag[idx] < 0:\\r\\n x_imag[idx] = 0\\r\\n if x_real[idx] > kernel -1:\\r\\n x_real[idx] = kernel -1\\r\\n if x_imag[idx] > kernel -1:\\r\\n x_imag[idx] = kernel -1\\r\\n\\r\\n ker = np.zeros((kernel, kernel))\\r\\n for idx in range(len(x_real)):\\r\\n ker[np.int(x_real[idx])][np.int(x_imag[idx])] = 1\\r\\n ker = ker/np.sum(np.sum(ker))\\r\\n return ker\",\n \"def _CorrectMomentum(optimizer, param_keys, correction):\\n logger.info('Scaling update history by %.6f (new lr / old lr)', correction)\\n for p_key in param_keys:\\n try:\\n optimizer.state[p_key]['momentum_buffer'] *= correction\\n except:\\n continue\",\n \"def photometric_calibration():\\n pass\",\n \"def LeapFrog(self,r,v,dt):\\n\\n rhalf = r + np.asarray(v)*(dt/2) #Taking a half step forward with positional vector\\n # predict the final velocity at the next timestep using the acceleration field at the rhalf position \\n vnew = v + self.M31Accel(rhalf)*dt\\n # predict the final position using the average of the current velocity and the final velocity\\n rnew = r + 0.5*(v+vnew)*dt\\n \\n return rnew,vnew\",\n \"def test_3D_m8_2k():\\n scal, velo = setup_3D()\\n\\n advec = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: M8Prime,\\n Support: 'gpu_2k',\\n Splitting: 'o2'}\\n )\\n advec_py = Advection(velo, scal, discretization=d3d,\\n method={TimeIntegrator: RK2,\\n Interpolation: Linear,\\n Remesh: M8Prime,\\n Support: '',\\n Splitting: 'o2'},\\n )\\n assertion_3D_withPython(scal, velo, advec, advec_py)\",\n \"def ccm_unred(wave, flux, ebv, r_v=\\\"\\\"):\\n import numpy as np\\n wave = np.array(wave, float)\\n flux = np.array(flux, float)\\n \\n if wave.size != flux.size: raise TypeError, 'ERROR - wave and flux vectors must be the same size'\\n \\n if not bool(r_v): r_v = 3.1\\n \\n x = 10000.0/wave\\n npts = wave.size\\n a = np.zeros(npts, float)\\n b = np.zeros(npts, float)\\n \\n ###############################\\n #Infrared\\n \\n good = np.where( (x > 0.3) & (x < 1.1) )\\n a[good] = 0.574 * x[good]**(1.61)\\n b[good] = -0.527 * x[good]**(1.61)\\n \\n ###############################\\n # Optical & Near IR\\n \\n good = np.where( (x >= 1.1) & (x < 3.3) )\\n y = x[good] - 1.82\\n \\n c1 = np.array([ 1.0 , 0.104, -0.609, 0.701, 1.137, \\\\\\n -1.718, -0.827, 1.647, -0.505 ])\\n c2 = np.array([ 0.0, 1.952, 2.908, -3.989, -7.985, \\\\\\n 11.102, 5.491, -10.805, 3.347 ] )\\n \\n a[good] = np.polyval(c1[::-1], y)\\n b[good] = np.polyval(c2[::-1], y)\\n \\n ###############################\\n # Mid-UV\\n \\n good = np.where( (x >= 3.3) & (x < 8) )\\n y = x[good]\\n F_a = np.zeros(np.size(good),float)\\n F_b = np.zeros(np.size(good),float)\\n good1 = np.where( y > 5.9 )\\n \\n if np.size(good1) > 0:\\n y1 = y[good1] - 5.9\\n F_a[ good1] = -0.04473 * y1**2 - 0.009779 * y1**3\\n F_b[ good1] = 0.2130 * y1**2 + 0.1207 * y1**3\\n \\n a[good] = 1.752 - 0.316*y - (0.104 / ( (y-4.67)**2 + 0.341 )) + F_a\\n b[good] = -3.090 + 1.825*y + (1.206 / ( (y-4.62)**2 + 0.263 )) + F_b\\n \\n ###############################\\n # Far-UV\\n \\n good = np.where( (x >= 8) & (x <= 11) )\\n y = x[good] - 8.0\\n c1 = [ -1.073, -0.628, 0.137, -0.070 ]\\n c2 = [ 13.670, 4.257, -0.420, 0.374 ]\\n a[good] = np.polyval(c1[::-1], y)\\n b[good] = np.polyval(c2[::-1], y)\\n \\n # Applying Extinction Correction\\n \\n a_v = r_v * ebv\\n a_lambda = a_v * (a + b/r_v)\\n \\n funred = flux * 10.0**(0.4*a_lambda) \\n \\n return funred\",\n \"def _update_vel(self) -> None:\\n self.state[:, :, Boids.Attr.VEL] += self.state[:, :, Boids.Attr.ACC]\\n self.state[:, :, Boids.Attr.VEL] = maglim(\\n self.state[:, :, Boids.Attr.VEL], self.max_vel)\",\n \"def mvr(self, delta, timeout=None, wait=False, log=True):\\n\\n self.mv(delta + self.wm(), timeout=timeout, wait=wait, log=log)\",\n \"def process_3D(radar, velname=\\\"VEL\\\", dbzname=\\\"DBZ\\\", zdrname=\\\"ZDR\\\", rhohvname=\\\"RHOHV\\\",\\n gatefilter=None, nyquist_velocity=None, two_passes=False, debug=False):\\n # Filter\\n if gatefilter is None:\\n gatefilter = filtering.do_gatefilter(radar, \\\"VEL\\\", \\\"DBZ\\\", zdr_name=\\\"ZDR\\\", rho_name=\\\"RHOHV\\\")\\n\\n if nyquist_velocity is None:\\n nyquist_velocity = radar.instrument_parameters['nyquist_velocity']['data'][0]\\n\\n # Start with first reference.\\n slice_number = 0\\n myslice = radar.get_slice(slice_number)\\n\\n r = radar.range['data'].copy()\\n velocity = radar.fields[\\\"VEL\\\"]['data'].copy()\\n azimuth_reference = radar.azimuth['data'][myslice]\\n elevation_reference = radar.elevation['data'][myslice].mean()\\n\\n velocity_reference = np.ma.masked_where(gatefilter.gate_excluded, velocity)[myslice]\\n\\n # Dealiasing first sweep.\\n final_vel, flag_vel, azi_s, azi_e = dealiasing_process_2D(r, azimuth_reference, velocity_reference,\\n elevation_reference, nyquist_velocity, debug=False)\\n\\n velocity_reference = final_vel.copy()\\n flag_reference = flag_vel.copy()\\n\\n ultimate_dealiased_velocity = np.zeros(radar.fields[\\\"VEL\\\"]['data'].shape)\\n ultimate_dealiased_velocity[myslice] = final_vel.copy()\\n\\n for slice_number in range(1, radar.nsweeps):\\n print(slice_number)\\n myslice = radar.get_slice(slice_number)\\n azimuth_slice = radar.azimuth['data'][myslice]\\n elevation_slice = radar.elevation['data'][myslice].mean()\\n\\n if len(azimuth_slice) < 60:\\n print(f\\\"Problem with slice #{slice_number}, only {len(azimuth_slice)} radials.\\\")\\n continue\\n\\n vel = np.ma.masked_where(gatefilter.gate_excluded, velocity)[myslice]\\n velocity_slice = vel.filled(np.NaN)\\n\\n flag_slice = np.zeros_like(velocity_slice) + 1\\n flag_slice[np.isnan(velocity_slice)] = -3\\n\\n # 3D dealiasing\\n velocity_slice, flag_slice = continuity.unfolding_3D(r, elevation_reference,\\n azimuth_reference, elevation_slice,\\n azimuth_slice, velocity_reference,\\n flag_reference, velocity_slice,\\n flag_slice, nyquist_velocity,\\n loose=True)\\n\\n final_vel, flag_vel, azi_s, azi_e = dealiasing_process_2D(r, azimuth_slice, velocity_slice,\\n elevation_slice, nyquist_velocity,\\n debug=False, inherit_flag=flag_slice,\\n inherit_azi_start=azi_s, inherit_azi_end=azi_e)\\n\\n if two_passes:\\n velocity_slice, flag_slice = continuity.unfolding_3D(r, elevation_reference,\\n azimuth_reference,\\n elevation_slice,\\n azimuth_slice,\\n velocity_reference,\\n flag_reference,\\n final_vel, flag_vel,\\n nyquist_velocity, loose=False)\\n azimuth_reference = azimuth_slice.copy()\\n velocity_reference = final_vel.copy()\\n flag_reference = flag_vel.copy()\\n elevation_reference = elevation_slice\\n\\n ultimate_dealiased_velocity[myslice] = final_vel.copy()\\n # plot_radar(final_vel, flag_vel, slice_number)\\n\\n ultimate_dealiased_velocity = np.ma.masked_where(gatefilter.gate_excluded,\\n ultimate_dealiased_velocity)\\n\\n return ultimate_dealiased_velocity\",\n \"def wheel_center_disp_damper_movement(rocker_theta, dictionary, results_dictionary, heave):\\n results_dictionary['Pushrod Rocker'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\\n dictionary['Rocker Pivot Axis'],\\n dictionary['Pushrod Rocker'],\\n rocker_theta))\\n results_dictionary['Damper Rocker'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\\n dictionary['Rocker Pivot Axis'],\\n dictionary['Damper Rocker'],\\n rocker_theta))\\n results_dictionary['Roll Damper a'][-1] = (parametrized_circle(dictionary['Rocker Pivot'],\\n dictionary['Rocker Pivot Axis'],\\n dictionary['Roll Damper a'],\\n rocker_theta))\\n results_dictionary['Pushrod Control Arm'][-1] = fsolve(three_point_method,\\n dictionary['Pushrod Control Arm'],\\n args=(dictionary['Upper Fore'],\\n dictionary['Upper Aft'],\\n results_dictionary['Pushrod Rocker'][-1],\\n dictionary['Upper Fore'],\\n dictionary['Upper Aft'],\\n dictionary['Pushrod Rocker'],\\n dictionary['Pushrod Control Arm']),\\n )\\n\\n results_dictionary['Lower Out'][-1] = fsolve(three_point_method, dictionary['Lower Out'],\\n args=(dictionary['Lower Fore'],\\n dictionary['Lower Aft'],\\n results_dictionary['Pushrod Control Arm'][-1],\\n dictionary['Lower Fore'],\\n dictionary['Lower Aft'],\\n dictionary['Pushrod Control Arm'],\\n dictionary['Lower Out']))\\n\\n results_dictionary['Upper Out'][-1] = fsolve(three_point_method, dictionary['Upper Out'],\\n args=(dictionary['Upper Fore'],\\n dictionary['Upper Aft'],\\n results_dictionary['Lower Out'][-1],\\n dictionary['Upper Fore'],\\n dictionary['Upper Aft'],\\n dictionary['Lower Out'],\\n dictionary['Upper Out']))\\n\\n # Steering\\n results_dictionary['Tie Rod Upright'][-1] = fsolve(three_point_method,\\n dictionary['Tie Rod Upright'],\\n args=(results_dictionary['Tie Rod Chassis'][-1],\\n results_dictionary['Lower Out'][-1],\\n results_dictionary['Upper Out'][-1],\\n dictionary['Tie Rod Chassis'],\\n dictionary['Lower Out'],\\n dictionary['Upper Out'],\\n dictionary['Tie Rod Upright']))\\n\\n results_dictionary['Wheel Center'][-1] = (fsolve(three_point_method, dictionary['Wheel Center'],\\n args=(results_dictionary['Tie Rod Upright'][-1],\\n results_dictionary['Upper Out'][-1],\\n results_dictionary['Lower Out'][-1],\\n dictionary['Tie Rod Upright'],\\n dictionary['Upper Out'],\\n dictionary['Lower Out'],\\n dictionary['Wheel Center'])))\\n\\n # solved for wheel displacement - actual wheel displacement\\n return (dictionary['Wheel Center'][2] - results_dictionary['Wheel Center'][-1][2] + heave)**2\",\n \"def ev_controlleraxismotion(self, event: tcod.event.ControllerAxis) -> T | None:\",\n \"def update_focal_axes(self):\\n #self.update_sigma()\\n self.updateGL()\",\n \"def define_ufl_velocity_equation(self):\\n\\n if hasattr(self, 'f1'):\\n return None\\n\\n if self.config['material']['type'] == 'viscous':\\n self.f1 = 0\\n return None\\n\\n if not self.config['formulation']['time']['unsteady']:\\n self.f1 = 0\\n return None\\n\\n theta = self.config['formulation']['time']['theta']\\n dt = self.config['formulation']['time']['dt']\\n f1 = self.displacement - self.displacement0 \\\\\\n - dt*(theta*self.velocity + (1.0 - theta)*self.velocity0)\\n\\n self.f1 = dlf.dot(self.test_vector, f1)*dlf.dx\\n\\n return None\",\n \"def current_update():\\n # Compute the multiplier coefficient:\\n ci = dt / (L * dx)\\n for k in range(0, nx-1):\\n I[k] = I[k] - (ci * (V[k + 1] - V[k]))\",\n \"def update_volume(self):\\r\\n\\r\\n # for the first cell\\r\\n self.cells[0].volume = self.cells[0].volume + \\\\\\r\\n self.inflow - self.flows[0]\\r\\n # for the intermediate cells\\r\\n for i in range(1, self.cells_number-1):\\r\\n self.cells[i].volume = self.cells[i].volume + \\\\\\r\\n self.flows[i-1]-self.flows[i]\\r\\n # for the last cells\\r\\n self.cells[-1].volume = self.cells[-1].volume + \\\\\\r\\n self.flows[-1] - self.outflow\",\n \"def illumination_correction(img, avg):\\n corrected = (img / avg * img.mean() / (img / avg).mean()).astype('uint8')\\n return corrected\",\n \"def detect_velocity(image):\\n nonlocal prev, v_last\\n curr_bgr = cv.warpPerspective(image, M, (160, 120))\\n curr = cv.cvtColor(curr_bgr, cv.COLOR_BGR2GRAY)\\n\\n if prev is None:\\n prev = curr\\n v_last = 0.0\\n return v_last, curr_bgr, np.zeros_like(image)\\n\\n flow = cv.calcOpticalFlowFarneback(\\n prev, # Previous image\\n curr, # Current image\\n None, # Computed flow image that has the same size oas prev and type CV_32FC2.\\n 0.5, # Specifies the image scale (<1) to build pyramids for each image.\\n 3, # Number of pyramid layers including the initial image.\\n 15, # winsize, averaging windows size.\\n 3, # iterations, number of iterations the algorithm does at each pyramid level.\\n 5, # standard deviation of the Gaussian that is used to smooth derivative\\n 1.5,\\n 0)\\n\\n mag, ang = cv.cartToPolar(flow[..., 0], flow[..., 1])\\n\\n v = mag * np.sin(ang)\\n\\n ######################\\n ## Histogram for mag\\n ar = np.arange(-20.0, 20.0, 0.50, dtype=np.float)\\n his = np.histogram(v, bins=ar)\\n\\n for i, n in enumerate(his[0]):\\n bgr = (255, 255, 0)\\n if his[1][i] < 0:\\n bgr = (0, 255, 255)\\n\\n #print('[{}] {} - {}'.format(i, n, his[1][i]))\\n cv.rectangle( image, #curr_bgr,\\n (i*2, HEIGHT),\\n (i*2, HEIGHT - int(n / 10)),\\n bgr, #(0, 255, 255),\\n cv.FILLED)\\n\\n hsv = np.zeros_like(image)\\n hsv[..., 0] = ang * 180 / np.pi / 2\\n hsv[..., 1] = 255\\n hsv[..., 2] = cv.normalize(np.abs(v), None, 0, 255, cv.NORM_MINMAX)\\n hsv_bgr = cv.cvtColor(hsv, cv.COLOR_HSV2BGR)\\n ##\\n ######################\\n\\n v_abs = np.absolute(v)\\n v = v[v_abs >= np.percentile(v_abs, VELOCITY_CUTOFF_PCT)]\\n\\n v_max = v_last + MAX_ACC\\n v_min = v_last - MAX_ACC\\n v = np.clip(v, v_min, v_max)\\n if v.size > 0:\\n v_avg = v.mean()\\n else:\\n if v_last > 0:\\n v_avg = max(v_last - MAX_ACC, 0)\\n elif v_last < 0:\\n v_avg = min(v_last + MAX_ACC, 0)\\n else:\\n v_avg = 0\\n\\n prev = curr\\n v_last = v_avg\\n return v_last, curr_bgr, hsv_bgr\",\n \"def sos_correction(self, ratio):\\n\\n # Correct velocities\\n self.u_mps = self.u_mps * ratio\\n self.v_mps = self.v_mps * ratio\",\n \"def test_sequential_update_mvar_missing_middle(ft_ar2_mvar_kw, theta_ar2_mvar, \\n Yt_ar2_mvar, Xt_ar2_mvar):\\n t = 1\\n kf = Filter(ft_ar2_mvar_kw, Yt_ar2_mvar, Xt_ar2_mvar, for_smoother=True)\\n kf.init_attr(theta_ar2_mvar)\\n for t_ in range(t+1):\\n kf._sequential_update(t_)\\n Mt = kf.ft(kf.theta, kf.T, x_0=Xt_ar2_mvar[0])\\n\\n Ht = Mt['Ht'][t][[0, 2]]\\n Bt = Mt['Bt'][t]\\n Dt = Mt['Dt'][t][[0, 2]]\\n Ft = Mt['Ft'][t]\\n Qt = Mt['Qt'][t]\\n Rt = Mt['Rt'][t][[0, 2]][:,[0, 2]]\\n Upsilon = Ht.dot(kf.P_star_t[t][0]).dot(Ht.T) + Rt \\n K = kf.P_star_t[t][0].dot(Ht.T).dot(linalg.pinvh(Upsilon))\\n v = kf.Yt[t][[0, 1]] - Ht.dot(kf.xi_t[t][0]) - Dt.dot(kf.Xt[t])\\n \\n expected_xi_t1_0 = Ft.dot(kf.xi_t[t][0] + K.dot(v)) + Bt.dot(kf.Xt[t])\\n P_t_0 = kf.P_star_t[t][0] \\n P_t_t = P_t_0 - P_t_0.dot(Ht.T).dot(linalg.pinvh(Upsilon)).dot(\\n Ht).dot(P_t_0)\\n expected_P_t1_0 = Ft.dot(P_t_t).dot(Ft.T) + Qt\\n np.testing.assert_array_almost_equal(expected_P_t1_0, \\n kf.P_star_t[t+1][0])\\n np.testing.assert_array_almost_equal(expected_xi_t1_0, \\n kf.xi_t[t+1][0])\",\n \"def edge_velocity(self):\\n #reflext x values at x edges\\n self.u[1,:,0] = -self.u[1,:,1]\\n self.u[1,:,-1] = -self.u[1,:,-2]\\n #mirror x values at y edges \\n self.u[1,0,:] = self.u[1,1,:]\\n self.u[1,-1,:] = self.u[1,-2,:]\\n #mirror y values at x edges\\n self.u[0,:,0] = self.u[0,:,1]\\n self.u[0,:,-1] = self.u[0,:,-2]\\n #mirror y values at y edges \\n self.u[0,0,:] = -self.u[0,1,:]\\n self.u[0,-1,:] = -self.u[0,-2,:]\",\n \"def test_converts_to_photomodeler_and_back_exactly() -> None:\\n # fmm must be equal, k* and p* must be zero\\n cam = Camera(\\n imgsz=(4288, 2848), fmm=(3200, 3200), cmm=(0.5, -0.4), sensorsz=(35.1, 24.2)\\n )\\n xcam = PhotoModeler.from_camera(cam)\\n residuals = Converter(xcam, cam).residuals()\\n np.testing.assert_allclose(residuals, 0, rtol=0, atol=1e-12)\\n cam2 = xcam.to_camera()\\n np.testing.assert_allclose(cam.to_array(), cam2.to_array(), rtol=0, atol=1e-13)\",\n \"def radial_velocity(wv_obj, fx_obj, sig_obj, wv_std, fx_std, sig_std, obj_name, std_name, rv_std, rv_std_err, order,\\n xcorr_width, cut, cutstart, cutend):\\n\\n # The more random iterations, the better... but it takes longer\\n n_iter = 1000\\n\\n # Step 1: Fix the spectra:\\n # * Select only the region in which they overlap\\n # * Make a new stretched wavelength array (for sub-pixel precision work)\\n # * Interpolate the data onto the new wavelength array\\n # * Remove large scale slopes so we only compare line and band features\\n\\n # Find where standard and object overlap ---------------\\n wv_min = max([min(wv_std), min(wv_obj)])\\n wv_max = min([max(wv_std), max(wv_obj)])\\n\\n n_pix_std = len(wv_std)\\n\\n # Creates ln standard wavelength array ---------------------------------\\n # AR 2013.0423 The wavelength array only covers the overlap region. Also, I'm folding the rebinning by 10 into this statement.\\n acoef_std = (n_pix_std * 10 - 1) / (math.log(wv_max) - math.log(wv_min))\\n bcoef_std = (n_pix_std * 10) - (acoef_std * math.log(wv_max))\\n\\n arr = np.arange(n_pix_std * 10) + 1\\n wv_ln_std = np.exp((arr - bcoef_std) / acoef_std)\\n\\n # AR 2012.1018: Find the conversion between pixels and velocity. This will vary from instrument\\n # to instrument and spectral order to spectral order, so we should preferentially calculate this\\n # based on the actual input spectrum.\\n # AR 2013.0422: Change the calculation to happen AFTER the corrected wavelength scale has been made\\n # Find the average pixel/spectrum offset\\n # Note: even though it's called micron_per_pix, it will still work if the wavelengths are\\n # angstroms instead (it really converts <wavelength unit> to km/s)\\n\\n # Interpolate data onto same ln wavelength scale -------------------------------\\n\\n fx_interp_std = np.interp(wv_ln_std, wv_std, fx_std)\\n fx_interp_obj = np.interp(wv_ln_std, wv_obj, fx_obj)\\n sig_interp_std = np.interp(wv_ln_std, wv_std, sig_std) # AR 2012.1018 Also need to rebin sig\\n sig_interp_obj = np.interp(wv_ln_std, wv_obj, sig_obj) # AR 2012.1018 Also need to rebin sig\\n\\n # Rebin Data ----------------------------\\n\\n wv_arr_std = np.asarray(wv_ln_std, dtype=float)\\n fx_arr_obj = np.asarray(fx_interp_obj, dtype=float)\\n fx_arr_std = np.asarray(fx_interp_std, dtype=float)\\n sig_arr_obj = np.asarray(sig_interp_obj, dtype=float)\\n sig_arr_std = np.asarray(sig_interp_std, dtype=float)\\n\\n datalen = len(fx_arr_obj)\\n\\n # Step 2: Measure vsini:\\n # Note that as of 2015.0605, this doesn't actually work.\\n\\n # AR 2014.0922: For vsini:\\n # In a loop:\\n # Take the standard spectrum\\n # broaden it to width X\\n # autocorrelate,\\n # measure width of gaussian Y (this is supposed to give you a means of translating between width-of-cross-correlation and vsini)\\n # Fit function solving Y for X.\\n # For each cross correlation of object and standard:\\n # Determine vsini\\n\\n pix_scale = (2.99792458 * 10 ** 5) / acoef_std\\n\\n # vsinirange = [1,2,5,10,20,30,40,50,60,80,100,100]\\n # widthrange = []\\n # for v in vsinirange:\\n # # Make convolution kernel for v km/s\\n # kernel = lsf_rotate(pix_scale,v)\\n # # Broaden the standard spectrum\\n # fx_obj_wide = np.correlate(fx_arr_obj, kernel, mode='same')\\n # # Rectify the spectrum\\n # fx_obj_orig = (fx_arr_obj - np.mean(fx_arr_obj))/np.std(fx_arr_obj,ddof=1)\\n # fx_obj_wide = (fx_obj_wide - np.mean(fx_obj_wide))/np.std(fx_obj_wide,ddof=1)\\n #\\n # # Remove a cubic (flatten the spectrum)\\n # coeff,pcov = op.curve_fit(cubic,wv_arr_std,fx_obj_wide)\\n # fx_obj_wide = fx_obj_wide - (coeff[0] + coeff[1]*wv_arr_std + coeff[2]*wv_arr_std**2 + coeff[3]*wv_arr_std**3)\\n # coeff,pcov = op.curve_fit(cubic,wv_arr_std,fx_obj_orig)\\n # fx_obj_orig = fx_obj_orig - (coeff[0] + coeff[1]*wv_arr_std + coeff[2]*wv_arr_std**2 + coeff[3]*wv_arr_std**3)\\n #\\n # # Cross-correlate the spectrum with its broadened self\\n # ycorr = np.correlate(fx_obj_orig, fx_obj_wide, mode='full')\\n # # Now determine where the peak is (should be near 0)\\n # length = len(ycorr)\\n # xcorr = np.arange(length) - length//2\\n # xmid = np.argmax(ycorr)\\n # ymax = np.max(ycorr)\\n # # Chop out just the portion of the array near the peak\\n # xcorr_min=xmid-xcorr_width\\n # xcorr_max=xmid+xcorr_width\\n # ycorr1=ycorr[xcorr_min:xcorr_max]\\t#isolate section of array with gaussian\\n # xcorr1=xcorr[xcorr_min:xcorr_max] #isolate the same section of the pixel range\\n #\\n # # set up initial values for gaussian fitting via chi2\\n # sig = 10\\n # sky = np.min(ycorr1)/1.2\\n # # print ycorr1[-1],ycorr1[0],xcorr1[-1],xcorr1[0]\\n # sky2 = (ycorr1[-1]-ycorr1[0])/(xcorr1[-1]-xcorr1[0])\\n # lnamp = np.log(ymax/1.2-sky)\\t# guess some values\\n # mean = xcorr[xmid]\\n #\\n # amp = np.exp(lnamp)\\n # sig2 = sig**2\\n # # suggestion from D. Hogg 12/15/12: Add extra linear feature to fit.\\n # # suggestion from D. Hogg 12/15/12: operate on ln(amp) so that the amplitude CANNOT be negative.\\n # def chi2(p):\\t#define gaussian function for fitting\\n # sig2=p[2] ** 2\\n # m = (np.exp(p[0]) * np.exp(-0.5 * (xcorr1 - p[1]) ** 2 / sig2)) + p[3] + p[4]*xcorr1\\n # return (ycorr1 - m)\\n #\\n # # Fit the gaussian.\\n # popt, ier = op.leastsq(chi2, [lnamp, mean, sig, sky, sky2])\\n # lnamp, mean, sig, sky, sky2 = popt\\n #\\n # amp = np.exp(lnamp)\\n # # record the width\\n # widthrange.append(sig)\\n #\\n # # Plot all the widths to get a width-vsini curve\\n # vsinicoeff,popt = op.curve_fit(quartic,np.asarray(widthrange),np.asarray(vsinirange))\\n #\\n # relationx = np.arange(50,200,1)\\n # relationy = vsinicoeff[0]+vsinicoeff[1]*relationx+vsinicoeff[2]*relationx**2+vsinicoeff[3]*relationx**3+vsinicoeff[4]*relationx**4\\n # figv = plt.figure(1)\\n # axv = figv.add_subplot(211)\\n # axv.scatter(widthrange,vsinirange)\\n # axv.plot(relationx,relationy)\\n # #ax.text(70,100,\\\"{0:} {1:} {2:} {3:} {4:}\\\".format(vsinicoeff))\\n\\n # 3. Cross-correlate the data, using n_iter trials:\\n # * Generate two random gaussian noises scaled to the uncertainty on the fluxes\\n # * Apply those gaussian noises to the standard and target stars\\n # * Cross-correlate the standard and target stars\\n # * Find and then cut out just the part of the cross-correlation curve near the maximum\\n # * Set up gaussian\\n # * Fit gaussian to that center part\\n # * Save fitted parameters (pixel shift aka mean of gaussian, width aka stddev of gaussian)\\n # * Repeat n_iter times\\n\\n # Cross correlation loop --------------------------------\\n pix_shift = np.array([]) # initialize array for pixel shift values\\n pix_width = np.zeros(n_iter) # initialize array for pixel width values\\n l = 0\\n\\n # using the xrange generator rather than making a full list saves memory\\n while len(pix_shift) < n_iter:\\n # prepare the randomized data\\n # GETTING ARRAYS READY FOR CROSS CORRELATION\\n\\n\\n # Randomize noise:\\n # create gaussian distribution of random numbers b/t 1 and -1, multiply err by numbers, add numbers to flux\\n # I have drastically simplified the arrays here AR 2013.0319\\n # AR 2013.0318: There was a problem, previously: noise was a fixed value, not linked to the known error values\\n\\n # AR 2013.0321: Speed fix. Rather than step through the array and generate one\\n # normally-distributed error value scaled to the SNR at that point, I will generate an\\n # array of normally-distributed error values scaled to 1, and then multiply by the SNR:\\n # One array generation, one array multiplication.\\n\\n rand_dist = np.random.normal(loc=0.0, scale=1.0, size=datalen)\\n rand_dist2 = np.random.normal(loc=0.0, scale=1.0, size=datalen)\\n\\n fx_temp_obj = np.asarray(fx_arr_obj + rand_dist * sig_arr_obj)\\n fx_temp_std = np.asarray(fx_arr_std + rand_dist2 * sig_arr_std)\\n mean_obj = np.mean(fx_temp_obj)\\n mean_std = np.mean(fx_temp_std)\\n stddev_obj = np.std(fx_temp_obj, ddof=1)\\n stddev_std = np.std(fx_temp_std, ddof=1)\\n\\n # Regularize data (subtract mean, divide by std dev) (Should definitely be done AFTER noise was added)\\n fx_reg_temp_obj = fx_temp_obj - mean_obj\\n fx_reg_temp_obj = fx_reg_temp_obj / stddev_obj\\n fx_reg_temp_std = fx_temp_std - mean_std\\n fx_reg_temp_std = fx_reg_temp_std / stddev_std\\n\\n # curve fit - remove a cubic AR 2012.1113\\n coeff, pcov = op.curve_fit(cubic, wv_arr_std, fx_reg_temp_obj)\\n fx_reg_temp_obj = fx_reg_temp_obj - (\\n coeff[0] + coeff[1] * wv_arr_std + coeff[2] * wv_arr_std ** 2 + coeff[3] * wv_arr_std ** 3)\\n coeff, pcov = op.curve_fit(cubic, wv_arr_std, fx_reg_temp_std)\\n fx_reg_temp_std = fx_reg_temp_std - (\\n coeff[0] + coeff[1] * wv_arr_std + coeff[2] * wv_arr_std ** 2 + coeff[3] * wv_arr_std ** 3)\\n\\n # CROSS CORRELATION\\n\\n # compute the cross-correlation between the two spectra\\n\\n ycorr = np.correlate(fx_reg_temp_obj, fx_reg_temp_std, mode='full')\\n # time required: 0.045 seconds average\\n\\n # http://stackoverflow.com/questions/12323959/fast-cross-correlation-method-in-python\\n # conv1 = np.zeros(datalen * 2)\\n # conv1[datalen/2:datalen/2+datalen] = fx_reg_temp_obj\\n # conv2 = fx_reg_temp_std[::-1]\\n # ycorr = signal.fftconvolve(conv1,conv2, mode='valid')\\n # time required: 0.006 seconds average, but it segfaults by the third try.\\n\\n ## slight smoothing AR 2013.0315\\n # ycorr = scipy.ndimage.filters.gaussian_filter1d(ycorr,11)\\n\\n # create the x offset axis (same length as ycorr, with 0 in the MIDDLE)\\n length = len(ycorr)\\n xcorr = np.arange(length) - length // 2\\n # AR 2012.1126 Select a tiny piece around the maximum to fit with a gaussian.\\n xmid = np.argmax(ycorr)\\n ymax = np.max(ycorr)\\n # now take just the portion of the array that matters\\n xcorr_min = int(xmid - xcorr_width)\\n xcorr_max = int(xmid + xcorr_width)\\n ycorr1 = ycorr[xcorr_min:xcorr_max] # isolate section of array with gaussian\\n xcorr1 = xcorr[xcorr_min:xcorr_max] # isolate the same section of the pixel range\\n ycorr2 = ycorr[xcorr_min - 50:xcorr_max + 50]\\n xcorr2 = xcorr[xcorr_min - 50:xcorr_max + 50]\\n\\n # suggestion from D. Hogg 12/15/12: Add extra linear feature to fit.\\n # suggestion from D. Hogg 12/15/12: operate on ln(amp) so that the amplitude CANNOT be negative.\\n def chi2(p): # define gaussian function for fitting\\n sig2 = p[2] ** 2\\n m = (np.exp(p[0]) * np.exp(-0.5 * (xcorr1 - p[1]) ** 2 / sig2)) + p[3] + p[4] * xcorr1\\n return (ycorr1 - m)\\n\\n # set up initial values for chi2\\n sig = 10\\n sky = np.min(ycorr1) / 1.2\\n # print ycorr1[-1],ycorr1[0],xcorr1[-1],xcorr1[0]\\n sky2 = (ycorr1[-1] - ycorr1[0]) / (xcorr1[-1] - xcorr1[0])\\n lnamp = np.log(ymax / 1.2 - sky) # guess some values\\n mean = xcorr[xmid]\\n\\n amp = np.exp(lnamp)\\n sig2 = sig ** 2\\n\\n popt, ier = op.leastsq(chi2, [lnamp, mean, sig, sky, sky2])\\n lnamp, mean, sig, sky, sky2 = popt\\n\\n amp = np.exp(lnamp)\\n\\n # print_num=len(pix_shift)%100\\n print_num = l % 100\\n if print_num == 0:\\n ## Uncomment the following to make a plot every 500 fits.\\n # fig = plt.figure(l)\\n # ax = fig.add_subplot(111)\\n # my_gauss = (amp * (np.exp(-0.5 * ((xcorr1 - mean) ** 2) / sig**2))) + sky + sky2 * xcorr1\\n # ax.plot(xcorr1,my_gauss,'r--')\\n # ax.plot(xcorr2,ycorr2,'#000000')\\n # ax.plot(xcorr1,ycorr1-my_gauss,'#00CC00')\\n ##if abs(mean - xcorr[xmid]) > 5:\\n ## print \\\"Mean is off\\\",mean,xcorr[xmid]\\n # figname='rv_{0:}_{1:}_{2:}_{3:}.png'.format(std_name,obj_name,order,l)\\n # ax.set_xlim(xcorr[xcorr_min-50],xcorr[xcorr_max+50])\\n # fig.savefig(figname)\\n # fig.clf()\\n # plt.close()\\n print\\n \\\"amp={0: 12.4f} mu={1: 10.4f} sig={2: 9.4f} sky={3: 11.4f} sky2={4: 8.4f} n_entries={5:}\\\".format(amp,\\n mean,\\n sig,\\n sky,\\n sky2,\\n len(\\n pix_shift))\\n\\n l += 1\\n if (cut == 0) | (mean > np.float(cutstart)) & (mean < np.float(cutend)):\\n pix_shift = np.append(pix_shift, mean)\\n # if ier < 5:\\n # I'm calculating the vsini now because I need errors, and the vsini calculation is not linear.\\n # pix_width[l] = vsinicoeff[0] + vsinicoeff[1] * sig + vsinicoeff[2] * sig**2 + vsinicoeff[3] * sig**3 + vsinicoeff[4] * sig**4\\n\\n # End cross correlation loop ---------------------------------\\n\\n # 4. Find the RV\\n # All 5000 rv fits have been calculated and stored in arrays\\n # 4a. Cut out outlier RVs. Useful if the cross-correlation produces occasional bad results. Use cutstart and cutend to force the code to only fit a gaussian to a certain region. Don't over-use this to force the result you want, though.\\n # 4b. Compute the mean pixel shift and pixel shift uncertainty.\\n # 4c. Convert pixel shift into RV\\n # 4d. Shift the wavelength array appropriately - all lines should now line up.\\n\\n ## Uncomment this to print out an example cross-correlation diagram\\n # fig = plt.figure(2)\\n # ax = fig.add_subplot(111)\\n # ax.plot(xcorr,ycorr,'k')\\n # figname='rv_{0:}_{1:}_{2:}_xcorr.png'.format(std_name,obj_name,order)\\n # fig.savefig(figname)\\n # fig.clf()\\n # plt.close()\\n\\n # Turn the list of pixel shifts into a numpy array\\n pix_shift = np.asarray(pix_shift)\\n\\n # 4a. Cut out outliers from the pixel shift\\n if cut == 1:\\n pix_shift = pix_shift[np.where((pix_shift > np.float(cutstart)) & (pix_shift < np.float(cutend)))]\\n\\n # 4b. Compute the mean pixel shift (rv value) and pixel shift uncertainty (RV uncertainty).\\n\\n print\\n l, len(pix_shift), np.float(len(pix_shift)) / np.float(n_iter) * 100.0\\n\\n mu = np.mean(pix_shift)\\n sigma = np.std(pix_shift, ddof=1)\\n\\n # vsini = np.mean(pix_width)\\n # vsini_err = np.std(pix_width,ddof=1)\\n\\n # axh = figv.add_subplot(212)\\n # n, bins, patches=axh.hist(pix_width,bins=30,normed=1.0,facecolor='green',align='mid')\\n # figv.savefig('vsiniplot.png')\\n # plt.clf()\\n # plt.close()\\n\\n # 4c. Transform pixel shift to shift in radial velocity\\n\\n # AR 2013.0423: The actually appropriate method requires a speed-of-light correction. This works for both angstroms and microns.\\n rv_meas = (2.99792458 * 10 ** 5 * mu) / acoef_std\\n rv_meas_err = (2.99792458 * 10 ** 5 * sigma) / acoef_std\\n\\n # 4d. Apply shift to arrays\\n wv_rvcorr_obj = wv_arr_std * (1 - rv_meas / (2.99792458 * 10 ** 5))\\n\\n ## 5. Create plots ---------------------------------\\n # The plots are the only reason find_rv.py needs to know the names of either star, or the RV of the standard.\\n\\n # Plot object and standard so you can clearly see that shift exists --------------------------------\\n fig = plt.figure(1)\\n\\n # AR 2013.0703 Regularize the spectra for display purposes in the final graph\\n # I'm using the mean and stddev of the last random-added attempt so it won't be perfect...\\n fx_reg_obj = fx_arr_obj - mean_obj\\n fx_reg_obj = fx_reg_obj / stddev_obj\\n fx_reg_std = fx_arr_std - mean_std\\n fx_reg_std = fx_arr_std / stddev_std\\n\\n # Plots target and standard with shift applied\\n ax1 = fig.add_subplot(311)\\n ax1.plot(wv_rvcorr_obj, fx_reg_obj, 'red')\\n ax1.plot(wv_arr_std, fx_reg_std, 'blue')\\n ax1.set_xlabel('wavelength (microns)')\\n ax1.set_ylabel('normalized flux')\\n target = 'Target: %s' % (obj_name)\\n standard = 'Standard: %s' % (std_name)\\n ax1.annotate(target, xy=(.7, .9), xycoords='axes fraction', xytext=(.6, .9), textcoords='axes fraction',\\n color='red')\\n ax1.annotate(standard, xy=(.7, .8), xycoords='axes fraction', xytext=(.6, .8), textcoords='axes fraction',\\n color='blue')\\n\\n sig2 = sig ** 2\\n my_gauss = (amp * (np.exp(-0.5 * ((xcorr1 - mu) ** 2) / sig2))) + sky + sky2 * xcorr1\\n\\n # Plots example of gaussian fit to cross correlation function\\n ax2 = fig.add_subplot(312)\\n ax2.plot(xcorr1, ycorr1, 'k.')\\n ax2.plot(xcorr1, my_gauss, 'r--', linewidth=2)\\n ax2.plot(xcorr1, ycorr1 - my_gauss, '#00CC00')\\n ax2.set_xlabel('example of fit to cross correlation function')\\n ax2.set_xlim(xcorr[xcorr_min - 50], xcorr[xcorr_max + 50])\\n # print pix_shift\\n\\n\\n ## Plot histogram of pixel shift values --------------------------------\\n ax3 = fig.add_subplot(313)\\n n, bins, patches = plt.hist(pix_shift, bins=30, normed=1.0, facecolor='green', align='mid')\\n # Plot best fit gaussian over histogram\\n y = mlab.normpdf(bins, mu, sigma)\\n ax3.plot(bins, y, 'r--', linewidth=2)\\n ax3.set_xlabel('radial velocity of target (pixels)')\\n ax3.set_ylabel('frequency (normalized)')\\n rad = 'RV = %.3f +/- %.3f' % (rv_meas, rv_meas_err)\\n corr = 'RV (corr) = %.3f +/- %.3f' % (rv_std + rv_meas, (rv_std_err ** 2 + rv_meas_err ** 2) ** (0.5))\\n # vsinistr = 'VsinI = %.3f +/- %.3f' % (vsini,vsini_err)\\n ax3.annotate(rad, xy=(.66, .9), xycoords='axes fraction', xytext=(.66, .9), textcoords='axes fraction',\\n color='black')\\n ax3.annotate(corr, xy=(.6, .8), xycoords='axes fraction', xytext=(.60, .8), textcoords='axes fraction',\\n color='black')\\n # ax3.annotate(vsinistr,xy=(.6,.6),xycoords='axes fraction',xytext=(.60,.6),textcoords='axes fraction',color='black')\\n ax3.annotate('{0:+5.2f} {1: 5.2f}'.format(mu, sigma), xy=(.05, .9), xycoords='axes fraction', xytext=(.05, .9),\\n textcoords='axes fraction', color='black')\\n ax3.annotate('{0:5.3f} km/s/pix'.format((2.99792458 * 10 ** 5) / acoef_std), xy=(.05, .8), xycoords='axes fraction',\\n xytext=(.05, .8), textcoords='axes fraction', color='black')\\n fig.subplots_adjust(hspace=.3)\\n\\n figname = 'rv_%s_%s_%d.png' % (std_name, obj_name, order)\\n fig.savefig(figname)\\n fig.clf()\\n plt.close()\\n\\n # plt.figure(l+1)\\n # plt.hist(pix_shift)\\n\\n # END RADIAL VELOCITY FUNCTION -----------------------------------------\\n return rv_meas, rv_meas_err\",\n \"def test_vertical_velocity_pressure_dry_air():\\n w = 1 * units('cm/s')\\n omega_truth = -1.25073619 * units('microbar/second')\\n omega_test = vertical_velocity_pressure(w, 1000. * units.mbar, 273.15 * units.K)\\n assert_almost_equal(omega_test, omega_truth, 6)\",\n \"def test_interferometer(self, tol):\\n # fmt:off\\n U = np.array([[0.83645892-0.40533293j, -0.20215326+0.30850569j],\\n [-0.23889780-0.28101519j, -0.88031770-0.29832709j]])\\n # fmt:on\\n\\n S = symplectic.interferometer(U)\\n expected = np.block([[U.real, -U.imag], [U.imag, U.real]])\\n\\n assert np.allclose(S, expected, atol=tol, rtol=0)\",\n \"def arm_calibration(self):\\n self.arm_motor.run_forever(speed_sp=self.MAX_SPEED)\\n while not self.touch_sensor.is_pressed:\\n time.sleep(0.01)\\n self.arm_motor.stop()\\n ev3.Sound.beep().wait()\\n arm_revolutions_for_full_range = 14.2 * 360\\n self.arm_motor.run_to_rel_pos(\\n position_sp=-arm_revolutions_for_full_range,\\n speed_sp=self.MAX_SPEED,\\n stop_action=ev3.Motor.STOP_ACTION_BRAKE)\\n self.arm_motor.wait_while(ev3.Motor.STATE_RUNNING)\\n\\n self.arm_motor.position = 0 # Calibrate the down position as 0 (this\\n # line is correct as is).\",\n \"def global_phase_correction(imgs_shifted_ft):\\n nangles = imgs_shifted_ft.shape[0]\\n phase_corrections = np.zeros((nangles))\\n\\n # todo: should weight by SNR, or something like this\\n for ii in range(nangles):\\n phase_corrections[ii] = np.angle(np.sum(imgs_shifted_ft[ii, 0] * imgs_shifted_ft[ii, 1].conj()))\\n\\n return phase_corrections\",\n \"def trap_depth_old(V,X,Y,Z,Im,Jm,Km,debug=False): \\n from project_parameters import debug\\n #from all_functions import sum_of_e_field\\n def a(a,N):\\n \\\"\\\"\\\"Shortcut function to convert array x into a row vector.\\\"\\\"\\\" \\n a=np.ravel(a, order='F') # Same order\\n return a\\n def index_sort(y,x):\\n \\\"\\\"\\\"Takes in two lists of the same length and returns y sorted by the indexing of x sorted.\\\"\\\"\\\"\\n xs=np.sort(x)\\n ix=np.argsort(x)\\n ys=np.ones(len(y)) #Sorted by the sorting defined by f being sorted. \\n for i in range(len(y)):\\n j=ix[i]\\n ys[i]=y[j]\\n return ys\\n if len(V.shape)!=3:\\n return('Problem with find_saddle.py dimensionalities.\\\\n')\\n N1,N2,N3=V.shape\\n N=N1*N2*N3\\n f=V\\n [Ex,Ey,Ez]=np.gradient(f,abs(X[1]-X[0]),abs(Y[1]-Y[0]),abs(Z[1]-Z[0]))\\n E=np.sqrt(Ex**2+Ey**2+Ez**2)\\n fs,Es=a(f,N),a(E,N) # Convert 3D to 1D array\\n fs,Es=np.real(fs),np.real(Es)\\n # identify the escape position and height by checking each point\\n minElectricField=max(fs) # initialize as maximum E field magnitude\\n distance=0\\n escapeHeight=1\\n escapePosition=[0,0,0]\\n for i in range(N1):\\n for j in range(N2):\\n for k in range(N3):\\n if [i,j,k]==[Im,Jm,Km]:\\n Vm=V[i,j,k]\\n elif E[i,j,k]<minElectricField:\\n minElectricField=E[i,j,k]\\n escapeHeight=V[i,j,k]\\n escapePosition=[i,j,k]\\n distance=abs(Im+Jm+Km-i-j-k) \\n if debug.trap_depth: # plot sortings of potential and electric field to view escape position\\n plt.plot(np.sort(fs)) \\n plt.title('sorted potential field')\\n plt.show()\\n plt.plot(np.sort(Es)) \\n plt.title('sorted electric field')\\n plt.show()\\n q1=index_sort(fs,Es) \\n plt.title('potential field sorted by sorted indexing of electric field')\\n plt.plot(q1)\\n plt.show()\\n q2=index_sort(Es,fs) \\n plt.title('electric field sorted by sorted indexing of potential field')\\n plt.plot(q2)\\n plt.show() \\n check=1 \\n if debug.trap_depth: \\n print minElectricField,escapeHeight,escapePosition,distance \\n if distance<check:\\n print('trap_depth.py: Escape point too close to trap minimum. Improve grid resolution or extend grid.')\\n if escapeHeight>0.2:\\n print('trap_depth.py: Escape point parameter too high. Improve grid resolution or extend grid.')\\n D=escapeHeight-Vm\\n [Ie,Je,Ke]=escapePosition\\n [Xe,Ye,Ze]=[X[Ie],Y[Je],Z[Ke]] \\n return [D,Xe,Ye,Ze]\",\n \"def test_vw_controller(self):\\n pass\\n\\n yarp.Network.init()\\n\\n pose_stream = yarp.BufferedPortBottle()\\n pose_stream.open(\\\"/morse/test/pose/in\\\")\\n yarp.Network.connect(\\\"/morse/robots/ATRV/Pose/out\\\", \\\"/morse/test/pose/in\\\")\\n\\n cmd_stream = yarp.BufferedPortBottle()\\n cmd_stream.open(\\\"/morse/test/vw/out\\\")\\n yarp.Network.connect(\\\"/morse/test/vw/out\\\", \\\"/morse/robots/ATRV/Motion_Controller/in\\\")\\n \\n # Read the start position, it must be (0.0, 0.0, 0.0)\\n pose = pose_stream.read()\\n for i in range(6):\\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\\n\\n\\n send_speed(cmd_stream, 1.0, 0.0, 2.0)\\n\\n pose = pose_stream.read()\\n self.assertAlmostEqual(pose.get(0).asDouble(), 2.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(1).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(3).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\\n\\n send_speed(cmd_stream, -1.0, 0.0, 2.0)\\n\\n pose = pose_stream.read()\\n for i in range(6):\\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\\n\\n send_speed(cmd_stream, 1.0, -math.pi/4.0, 2.0)\\n pose = pose_stream.read()\\n self.assertAlmostEqual(pose.get(0).asDouble(), 4.0 / math.pi, delta=0.1)\\n self.assertAlmostEqual(pose.get(1).asDouble(), -4.0 / math.pi , delta=0.1)\\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(3).asDouble(), -math.pi/2.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\\n\\n send_speed(cmd_stream, 0.5, -math.pi/8.0, 12.0)\\n pose = pose_stream.read()\\n for i in range(6):\\n self.assertAlmostEqual(pose.get(i).asDouble(), 0.0, delta=0.1)\\n\\n send_speed(cmd_stream, -2.0, math.pi/2.0, 3.0)\\n pose = pose_stream.read()\\n self.assertAlmostEqual(pose.get(0).asDouble(), 4.0 / math.pi, delta=0.1)\\n self.assertAlmostEqual(pose.get(1).asDouble(), -4.0 / math.pi , delta=0.1)\\n self.assertAlmostEqual(pose.get(2).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(3).asDouble(), -math.pi/2.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(4).asDouble(), 0.0, delta=0.1)\\n self.assertAlmostEqual(pose.get(5).asDouble(), 0.0, delta=0.1)\\n\\n yarp.Network.fini()\",\n \"def test_invalid_events(subarray_and_event_gamma_off_axis_500_gev):\\n\\n # 4-LST bright event already calibrated\\n # we'll clean it and parametrize it again in the TelescopeFrame\\n subarray, event = subarray_and_event_gamma_off_axis_500_gev\\n\\n tel_azimuth = {}\\n tel_altitude = {}\\n\\n #source = EventSource(filename, max_events=1)\\n #subarray = source.subarray\\n calib = CameraCalibrator(subarray)\\n fit = HillasReconstructor(subarray)\\n\\n #for event in source:\\n\\n calib(event)\\n\\n hillas_dict = {}\\n for tel_id, dl1 in event.dl1.tel.items():\\n\\n geom = subarray.tel[tel_id].camera.geometry\\n tel_azimuth[tel_id] = event.pointing.tel[tel_id].azimuth\\n tel_altitude[tel_id] = event.pointing.tel[tel_id].altitude\\n\\n mask = tailcuts_clean(\\n geom, dl1.image, picture_thresh=10.0, boundary_thresh=5.0\\n )\\n\\n dl1.parameters = ImageParametersContainer()\\n\\n try:\\n moments = hillas_parameters(geom[mask], dl1.image[mask])\\n hillas_dict[tel_id] = moments\\n dl1.parameters.hillas = moments\\n except HillasParameterizationError:\\n dl1.parameters.hillas = HillasParametersContainer()\\n continue\\n\\n # copy event container to modify it\\n event_copy = deepcopy(event)\\n # overwrite all image parameters but the last one with dummy ones\\n for tel_id in list(event_copy.dl1.tel.keys())[:-1]:\\n event_copy.dl1.tel[tel_id].parameters.hillas = HillasParametersContainer()\\n fit(event_copy)\\n assert event_copy.dl2.stereo.geometry[\\\"HillasReconstructor\\\"].is_valid is False\\n\\n # Now use the original event, but overwrite the last width to 0\\n event.dl1.tel[tel_id].parameters.hillas.width = 0 * u.m\\n fit(event)\\n assert event.dl2.stereo.geometry[\\\"HillasReconstructor\\\"].is_valid is False\\n\\n # Now use the original event, but overwrite the last width to NaN\\n event.dl1.tel[tel_id].parameters.hillas.width = np.nan * u.m\\n fit(event)\\n assert event.dl2.stereo.geometry[\\\"HillasReconstructor\\\"].is_valid is False\",\n \"def predicted_data_vol(self):\\n pass\",\n \"def test_el3_vs_original():\\n # store computations from original implementation\\n # from florian_ell3_paper import el3 as el30\\n # N = 10000\\n # x11 = np.random.rand(N)*5\\n # kc11 = (np.random.rand(N)-.5)*10\\n # p11 = (np.random.rand(N)-.5)*10\\n # result0 = np.array([el30(x, kc, p) for x,kc,p in zip(x11,kc11,p11)])\\n # np.save('data_test_el3', np.array([result0,x11,kc11,p11]))\\n\\n # load data from orginal implementation\\n data = np.load(\\\"tests/testdata/testdata_el3.npy\\\")\\n res0, x11, kc11, p11 = data\\n\\n # compare to vectorized\\n resv = el3v(x11, kc11, p11)\\n assert np.allclose(res0, resv)\\n\\n # compare to modified original\\n res1 = np.array([el30(x, kc, p) for x, kc, p in zip(x11, kc11, p11)])\\n assert np.allclose(res0, res1)\",\n \"def test_delta_in_diff(self):\\n xk = 1 * self.ureg.kelvin\\n yk = 2 * self.ureg.kelvin\\n yf = yk.to('degF')\\n yc = yk.to('degC')\\n self.assertEqual(yk - xk, 1 * self.ureg.kelvin)\\n self.assertEqual(yf - xk, 1 * self.ureg.kelvin)\\n self.assertEqual(yc - xk, 1 * self.ureg.kelvin)\",\n \"def runMT3D(self):\\n \\n # write mt3dms input\\n self.__mt.write_input()\\n # run mt3dms\\n self.__mt.run_model()\",\n \"def _diff_pot(a2,t2,d2,wair,temp,pres,ppot,airf,dhum):\\n ph2 = _eq_pressure(0,0,0,a2,t2,d2)\\n gi2 = _ice_g(0,0,t2,ppot)\\n gv2 = _eq_vappot(0,0,0,a2,t2,d2)\\n sh1 = -_air_f(0,1,0,airf,temp,dhum)\\n si1 = -_ice_g(1,0,temp,pres)\\n s1 = wair/airf*sh1 + (1-wair/airf)*si1\\n sh2 = -_air_f(0,1,0,a2,t2,d2)\\n si2 = -_ice_g(1,0,t2,ppot)\\n s2 = wair/a2*sh2 + (1-wair/a2)*si2\\n lhs = numpy.array([ppot, gi2, s1])\\n rhs = numpy.array([ph2, gv2, s2])\\n \\n ph2_a = _eq_pressure(1,0,0,a2,t2,d2)\\n ph2_t = _eq_pressure(0,1,0,a2,t2,d2)\\n ph2_d = _eq_pressure(0,0,1,a2,t2,d2)\\n gi2_t = _ice_g(1,0,t2,ppot)\\n gv2_a = _eq_vappot(1,0,0,a2,t2,d2)\\n gv2_t = _eq_vappot(0,1,0,a2,t2,d2)\\n gv2_d = _eq_vappot(0,0,1,a2,t2,d2)\\n sh2_a = -_air_f(1,1,0,a2,t2,d2)\\n sh2_t = -_air_f(0,2,0,a2,t2,d2)\\n sh2_d = -_air_f(0,1,1,a2,t2,d2)\\n si2_t = -_ice_g(2,0,t2,ppot)\\n s2_a = -wair/a2**2*(sh2 - a2*sh2_a - si2)\\n s2_t = wair/a2*sh2_t + (1-wair/a2)*si2_t\\n s2_d = wair/a2*sh2_d\\n dlhs = numpy.array([[0.,0.,0.], [0.,gi2_t,0.], [0.,0.,0.]])\\n drhs = numpy.array([[ph2_a,ph2_t,ph2_d], [gv2_a,gv2_t,gv2_d],\\n [s2_a,s2_t,s2_d]])\\n return lhs, rhs, dlhs, drhs\",\n \"def __inverse_kinematics(self, guess, target_point):\\n\\n error = 1.0\\n tolerance = 0.05\\n\\n # Initial Guess - Joint Angles\\n thetas = np.matrix(guess) # thetas is list which is contain all axes theta angles.\\n target_point = np.matrix(target_point) # X, Y, Z list to matrix for Target Position\\n # print(target_point.shape)\\n # Jacobian\\n self.__calc_jacobian_matrix()\\n tf_matrix_first_to_last = self.tf_matrices_list[-1]\\n\\n error_grad = []\\n\\n theta_dict = {}\\n\\n lr = 0.2\\n while error > tolerance:\\n for i in range(len(np.array(thetas)[0])):\\n theta_dict[self.q[i]] = np.array(thetas)[0][i]\\n\\n theta_dict[self.q[-1]] = self.q[-1]\\n\\n calculated_target_point = np.matrix(self.get_coords_from_forward_kinematics(self.__forward_kinematics(np.array(thetas)[0])[-1]))\\n logger.debug(f'calculated target point is \\\\n{calculated_target_point}')\\n\\n diff_wanted_calculated = target_point - calculated_target_point\\n\\n jacob_mat = np.matrix(self.jacobian_matrix.evalf(subs=theta_dict, chop=True, maxn=4)).astype(np.float64).T\\n logger.debug(f'jacobian matrix is\\\\n{jacob_mat} \\\\n\\\\n diff is \\\\n {diff_wanted_calculated}')\\n\\n thetas = thetas + lr * (jacob_mat * diff_wanted_calculated.T)\\n # thetas = np.array(thetas)[0] # this line's purpose is changing Q from matrix level to array level.\\n\\n prev_error = error\\n\\n error = linalg.norm(diff_wanted_calculated)\\n\\n if error > 10 * tolerance:\\n lr = 0.3\\n elif error < 10 * tolerance:\\n lr = 0.2\\n error_grad.append((error - prev_error))\\n\\n # print(error)\\n return np.array(thetas)[0]\",\n \"def _solve_3d_scalar(self, simu=None):\\n # # Call fftw filter\\n # self._output_field.data[0] = fftw2py.solve_poisson_3d_pressure(\\n # self._input_field.data[0],\\n # self._input_field.data[1],\\n # self._input_field.data[2])\\n pass\",\n \"def test_deconvolve_to_motor_error(self):\\n tau = 50.0\\n mrate = 50.0\\n Mrate = 100.0\\n\\n tmax = 50.0\\n dt = 0.1\\n\\n self.rule.tau = tau\\n self.rule.min_rate = mrate\\n self.rule.max_rate = Mrate\\n self.rule.compress_rates = False\\n self.rule.gain = 1\\n self.rule.tau_deconv1 = tau\\n\\n self.motor.error_fct = lambda _: np.ones(self.Nsrc)\\n\\n M = simulation.StateMonitor(self.rule, 'out')\\n\\n sim = simulation.Simulation(self.source, self.motor, self.rule, M, dt=dt)\\n sim.run(tmax)\\n \\n # the output should be almost constant\\n self.assertAlmostEqual(np.std(M.out)/np.mean(M.out), 0)\",\n \"def testConstantBoundedField(self):\\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration)\\n self._testPhotoCalibCenter(photoCalib, 0)\\n\\n self.assertEqual(1, photoCalib.instFluxToMaggies(self.instFlux, self.pointYShift))\\n self.assertEqual(0, photoCalib.instFluxToMagnitude(self.instFlux, self.pointYShift))\\n self.assertFloatsAlmostEqual(1e-9, photoCalib.instFluxToMaggies(self.instFlux*1e-9, self.pointXShift))\\n self.assertFloatsAlmostEqual(22.5, photoCalib.instFluxToMagnitude(\\n self.instFlux*1e-9, self.pointXShift))\\n\\n photoCalib = lsst.afw.image.PhotoCalib(self.constantCalibration, self.calibrationErr)\\n self._testPhotoCalibCenter(photoCalib, self.calibrationErr)\",\n \"def test_diff_analog_in_cal_5v_loop(self):\\n for g in self.l.gains:\\n for s,c,e in [(5, 11, .1), (2.5, 10, .03)]:\\n v = self.l.input(channels=(c,c,c,c), gains=(g,g,g,g))\\n r = v[0]\\n if s*g > 20:\\n if s*g > 25:\\n self.assertTrue(v[3],\\n \\\"%s should be overvoltage (%g, %g)\\\" % (v,s,g))\\n continue\\n for i in r:\\n self.assertTrue(abs(s-i) < e,\\n \\\"%g is not %g, channel %g, gain %g\\\" % (i,s,c,g))\",\n \"def onApplyECVButton(self):\\n\\n NodeName = 'ECV Map'\\n try :\\n self.ECVMapNode = slicer.util.getNode(NodeName)\\n except:\\n slicer.mrmlScene.AddNewNodeByClass('vtkMRMLScalarVolumeNode', NodeName)\\n self.ECVMapNode = slicer.util.getNode(NodeName)\\n\\n T1Native_Matrix,T1Enhanced_Matrix = self.MatchMatrixs(self.NativeT1_Selector.currentNode(),self.EnhancedT1_Selector.currentNode())\\n\\n Haematocrit = self.SB_Haematocrit.value\\n NT1B = self.SB_NBlodd.value\\n ET1B = self.SB_EBlodd.value\\n Factor = (100-Haematocrit)*(NT1B*ET1B/(NT1B-ET1B))\\n epsilon = 0.1\\n\\n T1Enhanced_Matrix = T1Enhanced_Matrix + epsilon\\n T1Native_Matrix = T1Native_Matrix + epsilon\\n self.ECV_Matrix = (1/T1Enhanced_Matrix-1/T1Native_Matrix)*Factor\\n self.ECV_Matrix = np.nan_to_num(self.ECV_Matrix)\\n self.ECV_Matrix[ np.logical_or(self.ECV_Matrix<0 , self.ECV_Matrix>100) ] = 0\\n\\n slicer.util.updateVolumeFromArray(self.ECVMapNode, self.ECV_Matrix)\\n self.SetLayoutViewer(self.ECVMapNode, 'Slice4')\\n self.SetScalarDisplay(self.ECVMapNode, 1, 100) ## Que onda el Auto WL\\n self.ThSlider_ECV.SetNode(self.ECVMapNode)\\n Max = np.nanmax(self.ECV_Matrix)\\n self.updateThresholdValues(self.ThSlider_ECV, self.ECVMapNode, Max)\",\n \"def detectMotion():\\n global MotionDetected\\n MotionDetected = False\\n return MotionDetected\",\n \"def updatePWM(self):\\n v_dc = self.dcmotorSpeed * self.dcmotor_sgn # changed \\\"vr\\\" to \\\"v_dc\\\", \\\"rightSpeed\\\" to \\\"dcmotorSpeed\\\" and \\\"right_sgn\\\" to dcmotor_sgn\\\", RFMH_2019_02_26\\n pwm_dc = self.PWMvalue(v_dc, self.DC_MOTOR_MIN_PWM,\\n self.DC_MOTOR_MAX_PWM) # changed \\\"pwmr\\\" to \\\"pwm_dc\\\" and \\\"vr\\\" to \\\"v_dc\\\" and adjusted both orange constants to \\\"DC_MOTOR_MIN_PWM\\\" AND \\\"DC_MOTOR_MAX_PWM\\\", RFMH_2019_02_26\\n\\n # TODO: Fix this debug message. I am trying to port this code over from an old version, and I do not know\\n # what v and u are supposed to be here. Timothy Scott, 5.11.2019\\n # if self.debug: # where the duck does the \\\"u\\\" come from?!?, RFMH_2019_02_26\\n # print(\\\"v = %5.3f, u = %5.3f, v_dc = %5.3f, pwm_dc = %3d\\\" % (\\n # v, u, v_dc, pwm_dc)) # deleted \\\"vl\\\" and \\\"pwml\\\" and adjust \\\"vr\\\" to \\\"v_dc\\\" to \\\"pwm_dc\\\"\\n\\n if math.fabs(v_dc) < self.SPEED_TOLERANCE: # changed v_r to v_dc in if loop , RFMH_2019_02_28\\n DcMotorMode = Adafruit_MotorHAT.RELEASE\\n pwm_dc = 0\\n elif v_dc > 0:\\n DcMotorMode = Adafruit_MotorHAT.FORWARD\\n elif v_dc < 0:\\n DcMotorMode = Adafruit_MotorHAT.BACKWARD\\n\\n if not self.old_pwm_dc == pwm_dc:\\n self.DcMotor.setSpeed(pwm_dc) # changed rightMotor to DcMotor and pwmr to pwm_dc , RFMH_2019_02_28\\n self.DcMotor.run(DcMotorMode)\\n\\n self.old_pwm_dc = pwm_dc\",\n \"def nzErr(xerr, yerr, vxerr, vyerr, year_x, year_y, mag, alnDir = '13_08_21/', chainsDir = 'efit/chains_S0-2_newRV2/'):\\n\\n #Read in values for error in position and velocity of sgr*\\n origin_val = asciidata.open('/g/ghez/align/' + alnDir + chainsDir + 'efit_summary.txt')\\n ori_x0e = origin_val[25][0]\\n ori_y0e = origin_val[26][0]\\n ori_vxe = origin_val[27][0]\\n ori_vye = origin_val[28][0]\\n t_0 = 2000.0 #hard coded t_0 of sgr*\\n\\n # magBins=np.array([9,11,12,13,14,15,16,17,18,19,20,21])\\n # deltaArr=np.array([3.5,71.0,58.0,210.0,300.0,650.0,700.0,1100.0,1900.0,2200.0,3000.0])*1e-6\\n\\n# delta = mag*0.0\\n# for i in range(len(mag)):\\n# for j in range(len(deltaArr)):\\n# if ((mag[i] > magBins[j]) & (mag[i] <= magBins[j+1])):\\n# delta[i]=deltaArr[j]\\n\\n#pdb.set_trace()\\n\\n #Update errors\\n xerr = np.sqrt(xerr**2 + ori_x0e**2 + ((year_x - t_0)*ori_vxe)**2)\\n yerr = np.sqrt(yerr**2 + ori_y0e**2 + ((year_y - t_0)*ori_vye)**2)\\n vxerr = np.sqrt(vxerr**2 + ori_vxe**2)\\n vyerr = np.sqrt(vyerr**2 + ori_vye**2)\\n\\n return xerr, yerr, vxerr, vyerr\",\n \"def adjust_u(self):\\r\\n # compute the volume integrals of the x,y, and z components of u\\r\\n ux = assemble(self.u.sub(0)*dx)\\r\\n uy = assemble(self.u.sub(1)*dx)\\r\\n uz = assemble(self.u.sub(2)*dx)\\r\\n\\r\\n # create a function of value 1, which can be integrated.\\r\\n try:\\r\\n self.unit\\r\\n except AttributeError:\\r\\n self.unit = Function(self.Q)\\r\\n self.unit.assign(Constant(1))\\r\\n\\r\\n # compute the volume of the body\\r\\n Vol = assemble(self.unit*dx)\\r\\n\\r\\n try:\\r\\n self.umean\\r\\n except AttributeError:\\r\\n self.umean = Function(self.Z)\\r\\n\\r\\n # compute the volume-averaged component means\\r\\n self.umean.assign(Constant((ux/Vol, uy/Vol, uz/Vol, 0)))\\r\\n\\r\\n # subtract the mean from the solution function\\r\\n self.up.assign(self.up-self.umean)\",\n \"def compute_ground_truth_volume(self, display_opt):\\n\\n self.meshActor.GetProperty().SetOpacity(0.2)\\n self.meshActor.GetProperty().SetColor(1, 0, 0)\\n\\n clean = vtk.vtkCleanPolyData()\\n clean.SetInputData(self.endo_poly)\\n\\n d3 = vtk.vtkDelaunay3D()\\n d3.SetInputConnection(clean.GetOutputPort())\\n d3.SetTolerance(0.01)\\n d3.SetAlpha(0.0)\\n d3.Update()\\n\\n surfaceFilter = vtk.vtkDataSetSurfaceFilter() # output is triangular mesh\\n surfaceFilter.SetInputConnection(d3.GetOutputPort())\\n surfaceFilter.Update()\\n\\n Mass = vtk.vtkMassProperties()\\n Mass.SetInputConnection(surfaceFilter.GetOutputPort())\\n Mass.Update()\\n\\n self.ground_truth_vol = Mass.GetVolume()/1000.0\\n\\n if display_opt:\\n\\n m = vtk.vtkDataSetMapper()\\n m.SetInputConnection(d3.GetOutputPort())\\n\\n a = vtk.vtkActor()\\n a.SetMapper(m)\\n\\n # set mapper for epi for visualization\\n m2 = vtk.vtkDataSetMapper()\\n m2.SetInputData(self.epi_poly)\\n\\n epi_actor = vtk.vtkActor()\\n epi_actor.SetMapper(m2)\\n epi_actor.GetProperty().SetOpacity(0.3)\\n epi_actor.GetProperty().SetColor(1,0,0)\\n\\n ren = vtk.vtkRenderer()\\n ren.SetBackground(0.0, 0.0, 0.0)\\n ren.AddActor(epi_actor)\\n ren.AddActor(a)\\n\\n vtk_show(ren)\",\n \"def update_theta(self, lhs_m, rhs_m):\\n t = 0\\n # FIXIME: fix the symbol\\n self.P = self.E\\n self.M = self.K\\n self.T = self.G\\n self.z_m = self.theta_m.T.copy()\\n new_z_m = (lhs_m * rhs_m).T\\n z_m_noLag = new_z_m.copy()\\n if self.positive_em:\\n # print('rhs_m', rhs_m)\\n new_z_m = np.asmatrix(np.zeros((self.M, self.P))).T\\n for p in range(self.P):\\n old_z = self.z_m[p, :].T\\n lag_lambda_m = np.asmatrix(np.zeros((1, self.M)))\\n # get active set\\n active = set()\\n for _ in range(self.M):\\n # print('[z]:before update\\\\n', old_z)\\n # print('[z]:update without lag\\\\n', z_m_noLag[p, :])\\n # print('[z]:lag term\\\\n', lag_lambda_m[p,:].T)\\n new_z = lhs_m * (rhs_m[:,p] + 1/2*lag_lambda_m[0,:].T)\\n # print('[z]:after lag\\\\n', new_z)\\n\\n # print('after round up\\\\n', new_z)\\n v = new_z - old_z\\n min_k = 1\\n min_m = 0\\n found = False\\n for m in range(self.M):\\n if new_z[m,0] < 0:\\n if v[m, 0] < 0:\\n k = old_z[m, 0] / (-v[m, 0])\\n if k < min_k:\\n min_k = k\\n min_m = m\\n found = True\\n old_z += min_k*v\\n for m in range(self.M):\\n if -self.precision <= old_z[m,0] <= self.precision:\\n old_z[m,0] = 0\\n # print('[z]:updated z', old_z[:, 0])\\n \\n if found:\\n active.add(min_m)\\n # print(active)\\n # print('[z]:active set', active)\\n # set lag term to 0\\n done = False\\n while not done:\\n lag_lambda_m = np.asmatrix(np.zeros((1, self.M)))\\n \\n if len(active) > 0:\\n active_len = len(active)\\n lag_lambda = np.asmatrix(np.zeros((1, active_len)))\\n lag_lhs = np.asmatrix(np.zeros((active_len, active_len)))\\n lag_lhs_bar = np.asmatrix(np.zeros((active_len, self.M - active_len)))\\n lag_rhs = np.asmatrix(np.zeros((active_len, 1)))\\n lag_rhs_bar = np.asmatrix(np.zeros((self.M - active_len, 1)))\\n for r, val in enumerate(active):\\n counter = 0\\n bar_counter = 0\\n for c in range(self.M):\\n if c in active:\\n lag_lhs[r, counter] = lhs_m[val, c]\\n counter += 1\\n else:\\n lag_lhs_bar[r, bar_counter] = lhs_m[val, c]\\n bar_counter += 1\\n counter = 0\\n bar_counter = 0\\n for c in range(self.M):\\n if c in active:\\n lag_rhs[counter, 0] = rhs_m[c, p]\\n counter += 1\\n else:\\n lag_rhs_bar[bar_counter, 0] = rhs_m[c, p]\\n bar_counter += 1\\n lag_lambda = (-2 * np.linalg.pinv(lag_lhs) * lag_lhs_bar * lag_rhs_bar - 2 * lag_rhs).T\\n # print('lag_lambda\\\\n', lag_lambda)\\n # print('lag_lhs\\\\n', lag_lhs)\\n # print('lag_lhs_bar\\\\n', lag_lhs_bar)\\n # print('lag_rhs\\\\n', lag_rhs)\\n # print('lag_rhs_bar\\\\n', lag_rhs_bar)\\n expected_theta = lag_lhs * (lag_rhs + 1/2*lag_lambda.T) + lag_lhs_bar * lag_rhs_bar\\n # print('expected theta\\\\n', expected_theta) \\n \\n active_remove = set()\\n # print(\\\"[Neg lag]\\\", lag_lambda)\\n for c, val in enumerate(active):\\n if lag_lambda[:, c] < 0:\\n print(\\\"[Neg lag]\\\", lag_lambda[:, c])\\n active_remove.add(val)\\n active = active.difference(active_remove)\\n if len(active_remove) == 0:\\n done = True\\n for c, val in enumerate(active):\\n lag_lambda_m[:, val] = lag_lambda[:, c]\\n else:\\n break\\n if not self.check_positive(old_z):\\n print(\\\"[Warning]: get negative z value\\\")\\n print(old_z)\\n # raise Exception('[z]:Non positive state')\\n new_z_m[p, :] = old_z[:, 0].T\\n self.theta_m = new_z_m.T.copy()\",\n \"def cmdVelCallback(self, req):\\n x = req.linear.x # m/s\\n th = req.angular.z # rad/s\\n\\n if x == 0:\\n # Turn in place\\n right = th * self.wheel_track * self.gear_reduction / 2.0\\n left = -right\\n elif th == 0: \\n # Pure forward/backward motion\\n left = right = x\\n else:\\n # Rotation about a point in space\\n left = x - th * self.wheel_track * self.gear_reduction / 2.0\\n right = x + th * self.wheel_track * self.gear_reduction / 2.0\\n\\n # Set motor speeds in meters per second.\\n self.mySerializer.mogo_m_per_s([1, 2], [left, right])\",\n \"def test3(self):\\n sig1 = np.array([0, 1, 0])\\n sig2 = np.array([0, 1, 0, 0])\\n d, p = EventSync.estimate_delay(sig1, sig2)\\n self.assertTrue(d == 0)\",\n \"def test_force(self):\\n group = hoomd.group.all()\\n\\n # compute forces\\n f = azplugins.restrain.plane(group=group, point=(0,0,0), normal=(1,0,0), k=2.0)\\n hoomd.run(1)\\n np.testing.assert_array_almost_equal(f.forces[0].force, (-2.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 2.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 6.,0,0))\\n self.assertAlmostEqual(f.forces[0].energy, 1.)\\n self.assertAlmostEqual(f.forces[1].energy, 1.)\\n self.assertAlmostEqual(f.forces[2].energy, 9.)\\n np.testing.assert_array_almost_equal(f.forces[0].virial, (-2.,0,0,0,0,0))\\n np.testing.assert_array_almost_equal(f.forces[1].virial, (-2.,0,4.,0,0,0))\\n np.testing.assert_array_almost_equal(f.forces[2].virial, (12.,0,0,0,0,0))\\n\\n # change the spring constant\\n f.set_params(k=1.0)\\n hoomd.run(1)\\n np.testing.assert_array_almost_equal(f.forces[0].force, (-1.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 1.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 3.,0,0))\\n self.assertAlmostEqual(f.forces[0].energy, 0.5)\\n self.assertAlmostEqual(f.forces[1].energy, 0.5)\\n self.assertAlmostEqual(f.forces[2].energy, 4.5)\\n\\n # shift the plane down\\n f.set_params(point=(-1,0,0))\\n hoomd.run(1)\\n np.testing.assert_array_almost_equal(f.forces[0].force, (-2.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[1].force, ( 0.,0,0))\\n np.testing.assert_array_almost_equal(f.forces[2].force, ( 2.,0,0))\\n self.assertAlmostEqual(f.forces[0].energy, 2.0)\\n self.assertAlmostEqual(f.forces[1].energy, 0.0)\\n self.assertAlmostEqual(f.forces[2].energy, 2.0)\\n\\n # rotate the plane so that only particle 1 is off the line\\n f.set_params(point=(0,0,0), normal=(0,0,1))\\n hoomd.run(1)\\n np.testing.assert_array_almost_equal(f.forces[0].force, (0,0,0))\\n np.testing.assert_array_almost_equal(f.forces[1].force, (0,0,-2))\\n np.testing.assert_array_almost_equal(f.forces[2].force, (0,0,0))\\n self.assertAlmostEqual(f.forces[0].energy, 0.0)\\n self.assertAlmostEqual(f.forces[1].energy, 2.0)\\n self.assertAlmostEqual(f.forces[2].energy, 0.0)\",\n \"def _compute_correction(self, initial_state, final_state, a, b, c, s):\\r\\n pertub = self.pertub\\r\\n pertub_s = pertub *10\\r\\n \\r\\n pred_no_pertub = self._motion_update_one_shot(initial_state, a, b, c, s)\\r\\n pred_pertub_a = self._motion_update_one_shot(initial_state, a +pertub, b, c, s)\\r\\n pred_pertub_b = self._motion_update_one_shot(initial_state, a, b +pertub, c, s)\\r\\n # no need to correct C, C is constrained by kappa_final\\r\\n # # pred_pertub_c = self._motion_update_one_shot(initial_state, a, b, c +pertub, s)\\r\\n pred_pertub_s = self._motion_update_one_shot(initial_state, a, b, c, s +pertub_s)\\r\\n\\r\\n d_state = np.zeros((3,1))\\r\\n d_pertub_state = np.zeros((3,3))\\r\\n Jacobian = np.zeros((3,3))\\r\\n for i in range(0, 3):\\r\\n d_pertub_state[i][0] = (final_state[i] - pred_pertub_a[i]) # a\\r\\n d_pertub_state[i][1] = (final_state[i] - pred_pertub_b[i]) # b\\r\\n # d_pertub_state[i][2] = (final_state[i] - pred_pertub_c[i]) # c (no update)\\r\\n d_pertub_state[i][2] = (final_state[i] - pred_pertub_s[i]) # s\\r\\n \\r\\n d_state[i] = final_state[i] - pred_no_pertub[i]\\r\\n \\r\\n Jacobian[i][0] = (d_pertub_state[i][0] - d_state[i])/pertub # a\\r\\n Jacobian[i][1] = (d_pertub_state[i][1] - d_state[i])/pertub # b\\r\\n # Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub # c (no update)\\r\\n Jacobian[i][2] = (d_pertub_state[i][2] - d_state[i])/pertub_s # s\\r\\n\\r\\n # inv_Jacobian = np.linalg.inv(Jacobian)\\r\\n inv_Jacobian = np.linalg.pinv(Jacobian)\\r\\n correction = np.dot(inv_Jacobian, d_state)\\r\\n # pdb.set_trace()\\r\\n return correction\",\n \"def test_vertical_velocity(self):\\n L_x = self.x_edge[-1]\\n np.testing.assert_array_equal(self.dVbox_dz(self.t, 0), 0)\\n np.testing.assert_array_less(self.dVbox_dz(self.t, 0.5 * L_x), 0)\\n np.testing.assert_array_equal(self.dVbox_dz(self.t, L_x), 0)\",\n \"def rk4(accel,m,r,h,v): \\n k1v = accel(m,r) \\n k1r = v \\n k2v = accel(m,r + h*0.5*k1r) \\n k2r = v+k1v*h*0.5 \\n k3v = accel(m,r + h*0.5*k2r) \\n k3r = v+k2v*h*0.5\\n k4v = accel(m,r + h*k3r) \\n k4r = v+k3v*h\\n new_v = v + h*(k1v + 2*k2v + 2*k3v + k4v)/float(6)\\n new_r = r + h*(k1r + 2*k2r + 2*k3r + k4r)/float(6)\\n return new_v,new_r\",\n \"def check_change_power_spectrum(test_knotpos, test_knotval, matpow):\\n #Get the modified power spectrum\\n kval = matpow[:,0]\\n newpk = lyasimulation.change_power_spectrum_knots(test_knotpos, test_knotval, matpow)\\n #Check the kvalues are still the same for comparison to the transfer function\\n assert np.all([k in newpk[:,0] for k in kval])\\n #Build interpolators for the new power spectrum\\n #Only interpolate a subset of Pk for speed\\n newpkint = build_restrict_interp(newpk, test_knotpos[0]/3., test_knotpos[-1]*3)\\n #Build interpolator for old power spectrum\\n pkint = build_restrict_interp(matpow, test_knotpos[0]/3., test_knotpos[-1]*3)\\n #Build interpolator for knots\\n ext_knotpos = np.concatenate([[kval[0],],test_knotpos, [kval[-1],]])\\n ext_knotval = np.concatenate([[test_knotval[0],],test_knotval, [test_knotval[-1],]])\\n knotint = interp.interp1d(ext_knotpos, ext_knotval, kind='linear')\\n #Check that the interpolator works\\n assert np.all(np.abs(knotint(test_knotpos) / test_knotval-1) < 1e-5)\\n lg_knotpos = np.log(test_knotpos)\\n #Check modification worked at the knot values\\n assert np.all(np.abs(np.exp(newpkint(lg_knotpos)) / (np.exp(pkint(lg_knotpos)) * test_knotval) - 1) < 1e-3)\\n #Pick some random k values distributed uniformly in log space\\n krand = (lg_knotpos[-1]-lg_knotpos[0]+0.2)*np.random.random(250)+lg_knotpos[0]-0.1\\n #Check that the modification was accurate at random positions\\n #print(np.max(np.abs(np.exp(newpkint(krand)) / (np.exp(pkint(krand)) * knotint(np.exp(krand))) - 1)))\\n assert np.all(np.abs(np.exp(newpkint(krand)) / (np.exp(pkint(krand)) * knotint(np.exp(krand))) - 1) < 0.01)\",\n \"def get_correction(d, a, hfov, img_x):\\n\\n width = 2 * d*math.tan((hfov/2)*math.pi/180) # in meters\\n one_meter = img_x / width\\n return int(a*one_meter)\",\n \"def loss_comparison(warp_fname, offpar_fname, moving_rmli_fname,\\n fixed_rmli_fname, moved_fname, crop_center, crop_size,\\n reg_weight, ncc_win, debug=False):\\n rg_crop = crop_size[0]\\n az_crop = crop_size[1]\\n rg_cen = crop_center[0]\\n az_cen = crop_center[1]\\n\\n # Import voxelmorph with pytorch backend\\n os.environ['VXM_BACKEND'] = 'pytorch'\\n import voxelmorph as vxm\\n\\n # Read the voxelmorph warp file\\n warp_file = np.load(warp_fname)\\n warp = warp_file['offs']\\n warp = warp[np.newaxis, :, :, :]\\n\\n # Read moved scene\\n moved_file = np.load(moved_fname)\\n moved = moved_file['scene']\\n moved = moved[np.newaxis, np.newaxis, :, :]\\n\\n # Read, crop, and scale the fixed RMLI\\n fixed_rmli = gx.MLI(fixed_rmli_fname,\\n par=gx.MLI_Par(fixed_rmli_fname + '.par'))\\n rmli_dim = fixed_rmli.dim\\n fixed_full = fixed_rmli.array\\n fixed = fixed_full[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\\n az_cen - az_crop // 2:az_cen + az_crop // 2]\\n fixed = scale_rmli(fixed)\\n fixed = fixed[np.newaxis, np.newaxis, :, :]\\n\\n # Read, crop, and scale the moving RMLI\\n moving_rmli = gx.MLI(moving_rmli_fname,\\n par=gx.MLI_Par(moving_rmli_fname + '.par'))\\n moving_full = moving_rmli.array\\n moving = moving_full[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\\n az_cen - az_crop // 2:az_cen + az_crop // 2]\\n moving = scale_rmli(moving)\\n moving = moving[np.newaxis, np.newaxis, :, :]\\n\\n # Read in the Gamma offsets\\n # Scale the Gamma offsets to be the same size as the original data that\\n # was cropped to feed into voxelmorph.\\n offs_basename, _ = os.path.splitext(os.path.basename(offpar_fname))\\n offs_fname = os.path.join(os.path.dirname(offpar_fname), offs_basename + '.offs')\\n offpar = gx.OFF_Par(offpar_fname)\\n offs_dim = (offpar['offset_estimation_range_samples'],\\n offpar['offset_estimation_azimuth_samples'])\\n gx_offs = gx.readBin(offs_fname, offs_dim, _dtype='complex64')\\n zoom_factor = (rmli_dim[0] / offs_dim[0], rmli_dim[1] / offs_dim[1])\\n multilook = (fixed_rmli.par['range_looks'],\\n fixed_rmli.par['azimuth_looks'])\\n gamma_rg_offs = scipy.ndimage.zoom(np.real(gx_offs), zoom_factor)\\n gamma_az_offs = scipy.ndimage.zoom(np.imag(gx_offs), zoom_factor)\\n gamma_rg_offs = gamma_rg_offs[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\\n az_cen - az_crop // 2:az_cen + az_crop // 2]\\n gamma_rg_offs /= multilook[0]\\n gamma_az_offs = gamma_az_offs[rg_cen - rg_crop // 2:rg_cen + rg_crop // 2,\\n az_cen - az_crop // 2:az_cen + az_crop // 2]\\n gamma_az_offs /= multilook[1]\\n gamma_warp = np.stack((gamma_rg_offs, gamma_az_offs), axis=0)\\n gamma_warp = gamma_warp[np.newaxis, :, :, :]\\n\\n # Create a moved image with the gamma offsets\\n transformer = vxm.layers.SpatialTransformer(crop_size)\\n gamma_moved = transformer(torch.from_numpy(moving).float(),\\n torch.from_numpy(gamma_warp).float())\\n\\n # Prepare ncc loss with square window\\n ndims = len(list(fixed.shape)) - 2\\n assert ndims in [1, 2, 3], \\\"volumes should be 1 to 3 dimensions. found: %d\\\" % ndims\\n ncc_win_sq = [ncc_win] * ndims # Build a square window\\n ncc = vxm.losses.NCC(ncc_win_sq, cuda=False)\\n\\n # Now we have all the data, compute the losses\\n loss_sim_vxm = ncc.loss(torch.from_numpy(fixed).float(),\\n torch.from_numpy(moved).float())\\n loss_sim_gamma = ncc.loss(torch.from_numpy(fixed).float(), gamma_moved)\\n\\n grad = vxm.losses.Grad(penalty='l2')\\n loss_smooth_vxm = grad.loss(None, torch.from_numpy(warp).float())\\n loss_smooth_gamma = grad.loss(None, torch.from_numpy(gamma_warp).float())\\n\\n loss_total_vxm = loss_sim_vxm + (reg_weight * loss_smooth_vxm)\\n loss_total_gamma = loss_sim_gamma + (reg_weight * loss_smooth_gamma)\\n\\n # Print everything\\n print('Lambda: {}\\\\n'.format(reg_weight))\\n print('Voxelmorph:\\\\nSimilarity loss: {}\\\\nSmoothness loss: {}\\\\n'\\n 'Total: {}\\\\n'.format(loss_sim_vxm, loss_smooth_vxm, loss_total_vxm))\\n print('Gamma:\\\\nSimilarity loss: {}\\\\nSmoothness loss: {}\\\\n'\\n 'Total: {}\\\\n'.format(loss_sim_gamma, loss_smooth_gamma, loss_total_gamma))\\n\\n if debug:\\n plt.figure()\\n plt.imshow(moved[0, 0, :, :])\\n plt.title('moved')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(gamma_moved[0, 0, :, :])\\n plt.title('gamma_moved')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(fixed[0, 0, :, :])\\n plt.title('fixed')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(warp[0, 0, :, :])\\n plt.title('warp_rg')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(warp[0, 1, :, :])\\n plt.title('warp_az')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(gamma_warp[0, 0, :, :])\\n plt.title('gamma_warp_rg')\\n plt.colorbar()\\n plt.figure()\\n plt.imshow(gamma_warp[0, 1, :, :])\\n plt.title('gamma_warp_az')\\n plt.colorbar()\\n plt.show()\",\n \"def update_apc12(self, delta_t=None):\\n\\n delta_t = delta_t or self.delta_t\\n\\n kap = (self.vel, self.force(self.pos,\\n self.vel,\\n self.time, drag=False), self.time)\\n\\n pos = self.pos+delta_t*self.vel\\n vel = self.vel+delta_t*kap[1]\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(self.pos, self.vel, self.time, delta_t)\\n\\n\\n force = self.force(pos,\\n vel,\\n self.time+delta_t, drag=False)\\n\\n pos = self.pos+delta_t/2.0*(vel+self.vel)\\n vel = self.vel+delta_t/2.0*(force+kap[1])\\n\\n for cback in self.pos_callbacks:\\n pos += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n for cback in self.vel_callbacks:\\n vel += delta_t*cback(pos, vel, self.time+delta_t, delta_t)\\n\\n try:\\n self.pos, self.vel = self.check_collision_full(pos, self.pos,\\n vel, self.vel,\\n delta_t, drag=True)\\n except Collision.CollisionException as col:\\n vel = self.vel+col.delta_t*kap[1]\\n C, fvel = self.drag_coefficient(col.pos, vel, self.time+col.delta_t, nearest = True)\\n col.vel = (self.vel+col.delta_t*(kap[1]+C*fvel))/(1.0+col.delta_t*C)\\n raise col\\n \\n self.time += delta_t\\n\\n return kap\",\n \"def test_compute_after_smooth_goddard_2013(\\r\\n PM_ds_initialized_3d_full, PM_ds_control_3d_full\\r\\n):\\r\\n PM_ds_control_3d_full = smooth_goddard_2013(\\r\\n PM_ds_control_3d_full,\\r\\n )\\r\\n PM_ds_initialized_3d_full = smooth_goddard_2013(\\r\\n PM_ds_initialized_3d_full,\\r\\n )\\r\\n actual = compute_perfect_model(PM_ds_initialized_3d_full, PM_ds_control_3d_full).tos\\r\\n\\r\\n north_atlantic = actual.sel(lat=slice(40, 50), lon=slice(-30, -20))\\r\\n assert not north_atlantic.isnull().any()\",\n \"def calc(self):\\n\\n # the following if query ensures that volume- and interaction-terms\\n # are only calculated if tau > 0.\\n # (to avoid nan-values from invalid function-evaluations)\\n\\n if self.V.tau.shape == (1,):\\n Isurf = self.surface()\\n # differentiation for non-existing canopy, as otherwise NAN values\\n if self.V.tau > 0.:\\n Ivol = self.volume()\\n if self.int_Q is True:\\n Iint = self.interaction()\\n else:\\n Iint = np.array([0.])\\n else:\\n Ivol = np.array([0.])\\n Iint = np.array([0.])\\n else:\\n # calculate surface-term (valid for any tau-value)\\n Isurf = self.surface()\\n\\n # store initial parameter-values\\n old_t_0 = self.t_0\\n old_p_0 = self.p_0\\n old_t_ex = self.t_ex\\n old_p_ex = self.p_ex\\n\\n old_tau = self.V._get_tau()\\n old_omega = self.V._get_omega()\\n old_NN = self.SRF._get_NormBRDF()\\n\\n # set mask for tau > 0.\\n mask = old_tau > 0.\\n valid_index = np.where(mask)\\n inval_index = np.where(~mask)\\n\\n # set parameter-values to valid values for calculation\\n self.t_0 = old_t_0[valid_index[0]]\\n self.p_0 = old_p_0[valid_index[0]]\\n self.t_ex = old_t_ex[valid_index[0]]\\n self.p_ex = old_p_ex[valid_index[0]]\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\\n if np.array(self.V.omega).size != 1:\\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\\n if np.array(self.SRF.NormBRDF).size != 1:\\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\\n\\n # calculate volume and interaction term where tau-values are valid\\n _Ivol = self.volume()\\n if self.int_Q is True:\\n _Iint = self.interaction()\\n else:\\n _Iint = np.full_like(self.t_0, 0.)\\n\\n # reset parameter values to old values\\n self.t_0 = old_t_0\\n self.p_0 = old_p_0\\n self.t_ex = old_t_ex\\n self.p_ex = old_p_ex\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically add an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau)\\n self.V.omega = np.squeeze(old_omega)\\n self.SRF.NormBRDF = np.squeeze(old_NN)\\n\\n # combine calculated volume-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n Ivol = np.ones_like(self.t_0)\\n Ivol[valid_index[0]] = _Ivol\\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\\n\\n # combine calculated interaction-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n if self.int_Q is True:\\n Iint = np.ones_like(self.t_0)\\n Iint[valid_index[0]] = _Iint\\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\\n else:\\n Iint = np.full_like(self.t_0, 0.)\\n\\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint\",\n \"def single_volume_inference_unpadded(self, volume):\\n \\n # normalize the data volume \\n image = (volume.astype(np.single) - np.min(volume))/(np.max(volume) - np.min(volume))\\n # reshape the image volume to the same patch size used for training\\n img_reshaped = med_reshape(image, new_shape=(self.patch_size, self.patch_size, image.shape[2]))\\n # create a new 3d mask to store predicted results\\n mask3d = np.zeros(img_reshaped.shape)\\n # iterate over the image array and predict the all the slices\\n for slc_idx in range(img_reshaped.shape[2]):\\n # compute for each slice\\n slc = torch.from_numpy(img_reshaped[:,:,slc_idx].astype(np.single)).unsqueeze(0).unsqueeze(0)\\n # make prediction\\n pred = self.model(slc.to(self.device))\\n pred = np.squeeze(pred.cpu().detach())\\n # store predicted data\\n mask3d[:,:,slc_idx] = torch.argmax(pred, dim=0)\\n # return the predicted volume\\n return mask3d\",\n \"def update_speed_input_step(self,curr_v):\\n \\n # update speed inputs \\n self.speed_inputs_east*=0\\n self.speed_inputs_west*=0\\n self.speed_inputs_north*=0\\n self.speed_inputs_south*=0\\n\\n if self.use_eight_directions is True: \\n self.speed_inputs_north_east*=0\\n self.speed_inputs_north_west*=0\\n self.speed_inputs_south_east*=0\\n self.speed_inputs_south_west*=0\\n \\n #speed_values=self.rr[:self.N_e,0] \\n speed_values=np.ones((self.N_e,1))\\n\\n if curr_v[0]>0:\\n \\n # north-east\\n if self.use_eight_directions is True and curr_v[1]>0:\\n self.speed_inputs_north_east=speed_values \\n \\n # south-east \\n elif self.use_eight_directions is True and curr_v[1]<0:\\n self.speed_inputs_south_east=speed_values\\n \\n #east \\n else:\\n self.speed_inputs_east=speed_values\\n\\n\\n elif curr_v[0]<0:\\n\\n # north-west \\n if self.use_eight_directions is True and curr_v[1]>0:\\n self.speed_inputs_north_west=speed_values\\n\\n # south-west \\n elif self.use_eight_directions is True and curr_v[1]<0:\\n self.speed_inputs_south_west=speed_values\\n \\n # west \\n else:\\n self.speed_inputs_west=speed_values\\n\\n else: \\n # north\\n if curr_v[1]>0:\\n self.speed_inputs_north=speed_values\\n\\n # south\\n elif curr_v[1]<0:\\n self.speed_inputs_south=speed_values\",\n \"def update_focal_axes(self):\\n self.update_sigma()\\n self.updateGL()\",\n \"def compute_desired_velocity(self):\\n mask_red = (self.image_red == 255) \\\\\\n *(self.image_green == 0) \\\\\\n *(self.image_blue == 0)\\n ind_red = sp.where( mask_red )\\n phi = sp.ones(self.image_red.shape)\\n phi[ind_red] = 0\\n phi = sp.ma.MaskedArray(phi, mask=self.mask)\\n numpy.set_printoptions(threshold=sys.maxsize)\\n self.door_distance = skfmm.distance(phi, dx=self.pixel_size)\\n tmp_dist = self.door_distance.filled(9999)\\n grad = sp.gradient(tmp_dist,edge_order=2)\\n grad_X = -grad[1]/self.pixel_size\\n grad_Y = -grad[0]/self.pixel_size\\n norm = sp.sqrt(grad_X**2+grad_Y**2)\\n norm = (norm>0)*norm+(norm==0)*0.001\\n self.desired_velocity_X = self.vmax * (grad_X/norm)\\n self.desired_velocity_Y = self.vmax * (grad_Y/norm)\\n '''plt.subplot(1,2,1)\\n plt.imshow(self.desired_velocity_X, cmap='hot', interpolation='nearest')\\n plt.gca().invert_yaxis()\\n plt.colorbar()\\n plt.subplot(1,2,2)\\n plt.imshow(self.desired_velocity_X, cmap='hot', interpolation='nearest')\\n plt.gca().invert_yaxis()\\n plt.colorbar()\\n plt.show()'''\\n return self.door_distance, self.desired_velocity_X, self.desired_velocity_Y\",\n \"def _update_motion_data(self, msg):\\n if self._auv_motion != msg.motion:\\n self._target_euler[\\\"alpha\\\"] = self._actual_euler[\\\"alpha\\\"]\\n self._target_euler[\\\"beta\\\"] = self._actual_euler[\\\"beta\\\"]\\n self._target_euler[\\\"gamma\\\"] = self._actual_euler[\\\"gamma\\\"]\\n self._auv_motion = msg.motion\\n self._thrusters_actual_speed[\\\"1\\\"] = msg.thrusters_speed.thruster_id1_speed\\n self._thrusters_actual_speed[\\\"2\\\"] = msg.thrusters_speed.thruster_id2_speed\\n self._thrusters_actual_speed[\\\"3\\\"] = msg.thrusters_speed.thruster_id3_speed\\n self._thrusters_actual_speed[\\\"4\\\"] = msg.thrusters_speed.thruster_id4_speed\\n self._thrusters_actual_speed[\\\"5\\\"] = msg.thrusters_speed.thruster_id5_speed\\n self._thrusters_actual_speed[\\\"6\\\"] = msg.thrusters_speed.thruster_id6_speed\\n self._thrusters_actual_speed[\\\"7\\\"] = msg.thrusters_speed.thruster_id7_speed\\n self._thrusters_actual_speed[\\\"8\\\"] = msg.thrusters_speed.thruster_id8_speed\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65022635","0.5807314","0.5593249","0.55692303","0.5540268","0.5456648","0.5415684","0.539106","0.5349843","0.5269265","0.52284133","0.52205676","0.5212929","0.5210599","0.52055985","0.52035886","0.5195111","0.51901066","0.5163019","0.5134201","0.5127333","0.51255727","0.5121156","0.50821805","0.5077219","0.50729513","0.5065775","0.50641704","0.50640464","0.50625086","0.506009","0.50583297","0.50481755","0.5041057","0.50132453","0.4998375","0.49947917","0.49931207","0.49851322","0.4972273","0.49561992","0.49521556","0.4950889","0.49484318","0.4939295","0.49340686","0.49319807","0.49311328","0.4929449","0.49292374","0.49283203","0.4927944","0.4917543","0.49148524","0.49142018","0.49086624","0.49062523","0.4903244","0.4900945","0.48831967","0.4881095","0.48802766","0.48793578","0.48705056","0.48566672","0.48508668","0.48499233","0.4842123","0.48409745","0.48354304","0.48342195","0.48321646","0.481446","0.48138908","0.4811763","0.48102644","0.4806386","0.48030627","0.479695","0.47931904","0.47839934","0.47836858","0.47831953","0.47822326","0.47803816","0.47802228","0.47764271","0.4776352","0.47750345","0.47746554","0.4774472","0.4771635","0.47636148","0.4761327","0.47604194","0.47583973","0.47582766","0.475587","0.47513977","0.47502747"],"string":"[\n \"0.65022635\",\n \"0.5807314\",\n \"0.5593249\",\n \"0.55692303\",\n \"0.5540268\",\n \"0.5456648\",\n \"0.5415684\",\n \"0.539106\",\n \"0.5349843\",\n \"0.5269265\",\n \"0.52284133\",\n \"0.52205676\",\n \"0.5212929\",\n \"0.5210599\",\n \"0.52055985\",\n \"0.52035886\",\n \"0.5195111\",\n \"0.51901066\",\n \"0.5163019\",\n \"0.5134201\",\n \"0.5127333\",\n \"0.51255727\",\n \"0.5121156\",\n \"0.50821805\",\n \"0.5077219\",\n \"0.50729513\",\n \"0.5065775\",\n \"0.50641704\",\n \"0.50640464\",\n \"0.50625086\",\n \"0.506009\",\n \"0.50583297\",\n \"0.50481755\",\n \"0.5041057\",\n \"0.50132453\",\n \"0.4998375\",\n \"0.49947917\",\n \"0.49931207\",\n \"0.49851322\",\n \"0.4972273\",\n \"0.49561992\",\n \"0.49521556\",\n \"0.4950889\",\n \"0.49484318\",\n \"0.4939295\",\n \"0.49340686\",\n \"0.49319807\",\n \"0.49311328\",\n \"0.4929449\",\n \"0.49292374\",\n \"0.49283203\",\n \"0.4927944\",\n \"0.4917543\",\n \"0.49148524\",\n \"0.49142018\",\n \"0.49086624\",\n \"0.49062523\",\n \"0.4903244\",\n \"0.4900945\",\n \"0.48831967\",\n \"0.4881095\",\n \"0.48802766\",\n \"0.48793578\",\n \"0.48705056\",\n \"0.48566672\",\n \"0.48508668\",\n \"0.48499233\",\n \"0.4842123\",\n \"0.48409745\",\n \"0.48354304\",\n \"0.48342195\",\n \"0.48321646\",\n \"0.481446\",\n \"0.48138908\",\n \"0.4811763\",\n \"0.48102644\",\n \"0.4806386\",\n \"0.48030627\",\n \"0.479695\",\n \"0.47931904\",\n \"0.47839934\",\n \"0.47836858\",\n \"0.47831953\",\n \"0.47822326\",\n \"0.47803816\",\n \"0.47802228\",\n \"0.47764271\",\n \"0.4776352\",\n \"0.47750345\",\n \"0.47746554\",\n \"0.4774472\",\n \"0.4771635\",\n \"0.47636148\",\n \"0.4761327\",\n \"0.47604194\",\n \"0.47583973\",\n \"0.47582766\",\n \"0.475587\",\n \"0.47513977\",\n \"0.47502747\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.47712728"},"document_rank":{"kind":"string","value":"92"}}},{"rowIdx":8453,"cells":{"query":{"kind":"string","value":"Call the jump_censor program to characterize the degree of motion."},"document":{"kind":"string","value":"def JumpCensor(self):\n if self.verbose:\n print 'Computing censor files.'\n for entry in self.entry_map['epi']:\n if self.censor_interleave:\n input_file = '%s+orig' % self.info[entry]['imgfile']\n interleave = '--interleave'\n else:\n interleave = ''\n if os.path.exists(self.info[entry]['mot_file']):\n input_file = self.info[entry]['mot_file']\n else:\n input_file = '%s+orig' % self.info[entry]['imgfile']\n cmd = \\\n \"jump_censor -v --prefix=%s %s --store-plot --threshold=%f %s\" % \\\n (self.info[entry]['censor_prefix'],\n interleave,\n self.censor_thresh,\n input_file)\n try:\n self.CheckExec(cmd, ['%s_censor.1D' %\n self.info[entry]['censor_prefix']],\n force=False)\n except:\n print 'Error computing censor files.'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def censoring_fcn(self, q):\n return 1.0","def jump(self):\n\t\tself.vel = -10\n\t\tself.tick_count = 0\n\t\tself.height = self.y","def on_jump_press(self) -> None:\r\n if not self.node:\r\n return\r\n if don.jumpFly:\r\n self.node.handlemessage(\"impulse\",self.node.position[0],self.node.position[1]-2,self.node.position[2],self.node.velocity[0]*0,20 +0.01,self.node.velocity[2]*0,10,5,0,0,self.node.velocity[0]*-0.1,20 + 0.01,self.node.velocity[2]*0)\r\n self.node.handlemessage(\"impulse\",self.node.position[0],self.node.position[1],self.node.position[2],self.node.velocity[0]*0,20 +0.01,self.node.velocity[2]*0,10,5,0,0,self.node.velocity[0]*-0.1,20 + 0.01,self.node.velocity[2]*0)\r\n self.node.color = ((0+random.random()*6.5),(0+random.random()*6.5),(0+random.random()*6.5))\r\n self.node.highlight = ((0+random.random()*6.5),(0+random.random()*6.5),(0+random.random()*6.5)) \r\n self.node.jump_pressed = True\r\n self._turbo_filter_add_press('jump')","def jump(self, x):\n self.change_y += x * self.speed","def jump(self):\n self.vy = -9","def censor(text: str) -> str:\n\n # Split up individual words in the text\n tokens: List[str] = text.split(\" \")\n\n # Create a mapping of 0 if the word is okay, 1 if it should be censored\n censor_mask: List[int] = predict([word for word in tokens])\n\n # A list of tuples with the first element being the word and the second being 0 or 1\n censor_map: List[Tuple[str, int]] = list(zip(tokens, censor_mask))\n\n # A list of the words that make up the censored text\n censored_text: List[str] = [\n censor_word(word) if should_censor else word\n for word, should_censor in censor_map\n ]\n\n return \" \".join(censored_text)","def content_jump(self, jump: np.ndarray, data: np.ndarray) -> None:\n if len(jump) != 1:\n raise ValueError(\"`jump` must be a one dimensional vector\")\n if jump > 0.5:\n euc_dist = np.abs(self._buffer - data).sum(axis=1)\n self._head = np.argmin(euc_dist)","def censor(text: Optional[str]) -> str:\n char = \"*\"\n text = text if text else \"\"\n return text[0] + (len(text) - 1) * char if text else text","def jog(self, axis:str=\"x\", distance:float=1):\n self.sendCommand(\"G91\")\n axis.capitalize()\n self.sendCommand(f'$J={axis}{distance} F1000')","def evaluate(self,joystick,keys):\n \n self.AG_twinklers.do() \n \n \n if joystick.isUp(keys)==True and self.solomon.current_state[\"jumping\"]==0: \n self.solomon.current_state[\"jumping\"]=1 \n\n\n walkcheck=False\n \n if self.solomon.A_wandswish.overide==False:\n \n self.solomon.current_state[\"wandswish\"]=0 \n\n if joystick.isDown(keys)==True: \n self.solomon.current_state[\"crouching\"]=1 \n self.solomon.current_state[\"standing\"]=0\n else: \n self.solomon.current_state[\"crouching\"]=0 \n \n if joystick.isRight(keys)==True:\n self.solomon.facing=1 \n self.solomon.current_state[\"walking\"]=1\n self.solomon.current_state[\"standing\"]=1\n walkcheck=True\n elif joystick.isLeft(keys)==True: \n self.solomon.facing=-1 \n walkcheck=True\n self.solomon.current_state[\"walking\"]=1\n self.solomon.current_state[\"standing\"]=0\n else:\n self.solomon.current_state[\"walking\"]=0 \n self.solomon.current_state[\"standing\"]=1\n\n canwalk=False\n if walkcheck:\n result=self.detect(self.solomon.x+self.solomon.facing*self.solomon.step*5.0,self.solomon.y) \n if (len(result)==0 or result[0][0]==\".\") and self.solomon.current_state[\"walking\"]==1:\n #self.solomon.x+=self.solomon.step*self.solomon.facing \n self.solomon.current_state[\"standing\"]=0 \n self.solomon.current_state[\"walking\"]=1\n canwalk=True\n #elif result[0][0] in [\"]\n\n result1=self.grid[int(self.solomon.y-0)][int(self.solomon.x+0.5+self.solomon.step*2*self.solomon.facing)]\n result2=self.grid[int(self.solomon.y-0)][int(self.solomon.x+0.5-self.solomon.step*2*self.solomon.facing)]\n #print \"fall check\" + str((result1,result2,self.solomon.x,self.solomon.y))\n if result1==\".\" and result2==\".\":\n self.solomon.y-=self.solomon.step\n self.solomon.current_state[\"walking\"]=0\n canwalk=False\n\n if canwalk==True: self.solomon.x+=self.solomon.step*self.solomon.facing\n\n if joystick.isFire(keys)==True and self.solomon.current_state[\"wandswish\"]==0: \n self.solomon.A_wandswish.kick()\n self.solomon.A_wandswish.overide=True\n self.solomon.current_state[\"wandswish\"]=1 \n\n \n if self.solomon.current_state[\"jumping\"]==1:\n self.solomon.AG_jump.do()\n print \"he's jumping\"\n print str(self.solomon.AG_jump.action(\"jump_displacement\").tick)\n self.solomon.y+=0.2\n #print \"co-ordinates \"+str((self.solomon.x,self.solomon.y))","def on_r_joy_y(self):\r\n self.log()","def jump(self):\n \n # move down a bit and see if there is a platform below us.\n # Move down 2 pixels because it doesn't work well if we only move down 1\n # when working with a platform moving down.\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n self.rect.y -= 2\n \n # If it is ok to jump, set our speed upwards\n if len(platform_hit_list) > 0 or self.rect.bottom >= SCR_HEIGHT:\n self.change_y = -8","def jump(self):\n\n # move down a bit and see if there is a platform below us.\n # Move down 2 pixels because it doesn't work well if we only move down\n # 1 when working with a platform moving down.\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(\n self, self.platforms, False)\n self.rect.y -= 2\n\n # If it is ok to jump, set our speed upwards\n if len(platform_hit_list) > 0 or self.rect.bottom >= WIN_HEIGHT:\n self.change_y = -10","def teleopPeriodic(self):\n self.drive.arcadeDrive(1, 0)\n self.brushless.set(1)\n self.spark.set(self.joystick.getY())","def jump(self):\n if self.y_pos > self.max_pos_y + self.height:\n self.isJump = True\n self.y_velocity = -13.5\n sounds[\"jump\"].play()","def on_key(window, key, scancode, action, mods):\n if action != glfw.PRESS:\n return\n \n global controller\n\n if key == glfw.KEY_SPACE:\n controller.fillPolygon = not controller.fillPolygon\n\n elif key == glfw.KEY_ESCAPE:\n glfw.set_window_should_close(window, True)\n\n # Si detecta la tecla [Q] cambia el estado del efecto 1 : zoom\n elif key == glfw.KEY_Z:\n controller.effect1 = not controller.effect1\n\n # Si detecta la tecla [W] cambia el estado del efecto 2 : corte\n elif key == glfw.KEY_C:\n controller.effect2 = not controller.effect2\n\n else:\n print('Unknown key')","def jump(self):\n \n # move down a bit and see if there is a platform below us.\n # Move down 2 pixels because it doesn't work well if we only move down 1\n # when working with a platform moving down.\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n self.rect.y -= 2\n \n # If it is ok to jump, set our speed upwards\n if len(platform_hit_list) > 0: #or self.rect.bottom >= SCREEN_HEIGHT:\n self.change_y = -10","def apply_action(self, action):\n robot_state = self.get_state('turtlebot3_waffle_pi','world')\n robot_x = robot_state.pose.position.x\n robot_y = robot_state.pose.position.y\n # Set the distance moved in an action such that it is at least as large as the\n # minimum distance that would let a robot in the middle of the goal go to either side\n #self.move_dist = max(((C.GOAL_TOP + C.GOAL_BOTTOM) / 2) / C.NUM_POS_SENDS, 0.5)\n if action == Learn.MOVE_LEFT:\n print(\"Move left\")\n self.set_robot(robot_x, robot_y+self.move_dist)\n elif action == Learn.MOVE_RIGHT:\n print(\"Move right\")\n self.set_robot(robot_x, robot_y-self.move_dist)\n else:\n print(\"Stay put\")","def ev_controlleraxismotion(self, event: tcod.event.ControllerAxis) -> T | None:","def jump(self):\n \n # move down and see if there's a platform below us.\n # Move down 2 pixels because it doesn't work well if you only move down\n # 1 when working with a platform moving down.\n self.rect.y += 2\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\n self.rect.y -= 2\n \n # If it is ok to jump, set the speed upwards\n if len(platform_hit_list) > 0 or self.rect.bottom >= SCREEN_HEIGHT:\n self.change_y = -10","def jump(self):\n\t\tself._is_falling = True\n\t\tself._dy = -5","def ev_joyhatmotion(self, event: tcod.event.JoystickHat) -> T | None:","def on_r_joy_x(self):\r\n self.log()","def on_l_joy_y(self):\r\n self.log()","def ev_joyaxismotion(self, event: tcod.event.JoystickAxis) -> T | None:","def jump(self, xvel = 0, yvel = 0): #TODO: figure out how a monster's jumping ability is determined.\n self.xvel += xvel\n self.yvel -= yvel\n self.animation.iter()\n self.ai_count = 25 #TEMP\n self.onGround = False","def _take_action(self, action):\n\n if isinstance(action, list) or isinstance(action, np.ndarray):\n action = action[0]\n\n if self.continuous:\n increment = np.array([1.5*np.cos(action),1.5*np.sin(action)])\n else:\n increment = np.array([0.0,0.0])\n if action == 0:\n increment[0] = 1.5\n elif action == 1:\n increment[0] = 1.225\n increment[1] = 1.225\n elif action == 2:\n increment[1] = 1.5\n elif action == 3:\n increment[0] = -1.225\n increment[1] = 1.225\n elif action == 4:\n increment[0] = -1.5\n elif action == 5:\n increment[0] = -1.225\n increment[1] = -1.225\n elif action == 6:\n increment[1] = -1.5\n elif action == 7:\n increment[0] = 1.225\n increment[1] = -1.225\n else:\n print('NOP!')\n\n self.dog_pose += increment\n self._update_environment()","def jump(self):\n print(\"Inside ElfRider.jump\")","def censor_a_word(text, censor):\n censored_item = \"\"\n for x in range(len(censor)):\n if censor[x] == \" \":\n censored_item = censored_item + \" \"\n else:\n censored_item = censored_item + \"X\"\n return text.replace(censor, censored_item)","def on_cmyk_slide(self,c,m,y,k):\n if not self.active:\n return\n cyan = c / 100.0\n magenta = m / 100.0\n yellow = y / 100.0\n black = k / 100.0\n self.cmyk = colormodel.CMYK(cyan, magenta, yellow, black)\n temp = a3.cmyk_to_rgb(self.cmyk)\n assert (temp == None or type(temp) == colormodel.RGB), 'cmyk_to_rgb does not return a RGB object'\n self.rgb = self.rgb if temp is None else temp\n self.hsv = a3.rgb_to_hsv(self.rgb)\n assert (self.hsv == None or type(self.hsv) == colormodel.HSV), 'rgb_to_hsv does not return a HSV object'\n self.update()","def jump(self):\n if (self.falling or self.rising) and self.doubleJump:\n self.speed_y = -20 # //////Aquí se cambia la velocidad incial cuando se salta//////\n self.fallin = False\n self.rising = True\n self.doubleJump = False\n\n if not self.falling and not self.rising:\n self.speed_y = -20 # //////Aquí se cambia la velocidad incial cuando se salta//////\n self.rising = True","def _jump(self):\n # can't jump while jump\n if self._player.is_jumping():\n # no hard-code, set jump with 2*max_velocity\n self._player.set_velocity((0, -2*self._max_velocity))\n self._player.set_jumping(False) # means can't jump","def jump(self):\r\n if self.grounded == True:\r\n self.vel.y = -13","def _compute_gravity_torque(self):\n pass","def cryptensor(*args, backend=None, **kwargs):\n if backend is None:\n backend = get_default_backend()\n if backend == crypten.mpc:\n return backend.MPCTensor(*args, **kwargs)\n else:\n raise TypeError(\"Backend %s is not supported\" % backend)","def perform_action(self, car, action):\n action[0]=action[0]*10+20\n action[1]=action[1]*0.5\n p.setJointMotorControl2(car, 3, p.POSITION_CONTROL, targetPosition =action[1],force = self.maxForce)\n for i in [0,1]:\n p.setJointMotorControl2(car, i, p.VELOCITY_CONTROL, targetVelocity =action[0],force = self.maxForce)\n p.setJointMotorControl2(car, 7, p.VELOCITY_CONTROL, targetVelocity =action[0]*7,force = self.maxForce)\n pos1, ori1 = p.getBasePositionAndOrientation(car)\n lin, ang = p.getBaseVelocity(car)\n '''\n if(pos1[0]<-self.max_dist_x):\n p.resetBasePositionAndOrientation(car, [pos1[0]+2*self.max_dist_x,pos1[1],pos1[2]], ori1)\n vel = p.resetBaseVelocity(car, lin)\n if(pos1[0]>self.max_dist_x):\n p.resetBasePositionAndOrientation(car, [pos1[0]-2*self.max_dist_x,pos1[1],pos1[2]], ori1)\n vel = p.resetBaseVelocity(car, lin)\n if(pos1[1]<-self.max_dist_y):\n p.resetBasePositionAndOrientation(car, [pos1[0],pos1[1]+2*self.max_dist_y,pos1[2]], ori1)\n vel = p.resetBaseVelocity(car, lin)\n if(pos1[1]>self.max_dist_y):\n p.resetBasePositionAndOrientation(car, [pos1[0],pos1[1]-2*self.max_dist_y,pos1[2]], ori1)\n vel = p.resetBaseVelocity(car, lin)\n '''","def jump(distance):\r\n t.penup()\r\n t.forward(200)\r\n t.pendown()\r\n return None","def cooldown():\n print camera.CoolerON()\n camera.status.update()","def ev_joyballmotion(self, event: tcod.event.JoystickBall) -> T | None:","def main():\n raw_input()\n clouds = map(int, raw_input().split(' '))\n print jumping_on_the_clouds(clouds)","def on_l_joy_x(self):\r\n self.log()","def compute_controller(self):\n \n # here we implement an example for a consensus algorithm\n neig = self.get_neighbors()\n messages = self.get_messages()\n pos, rot = self.get_pos_and_orientation()\n \n #send message of positions to all neighbors indicating our position\n for n in neig:\n self.send_message(n, pos)\n \n # check if we received the position of our neighbors and compute desired change in position\n # as a function of the neighbors (message is composed of [neighbors id, position])\n dx = 0.\n dy = 0.\n if messages:\n for m in messages:\n dx += m[1][0] - pos[0]\n dy += m[1][1] - pos[1]\n # integrate\n des_pos_x = pos[0] + self.dt * dx\n des_pos_y = pos[1] + self.dt * dy\n \n #compute velocity change for the wheels\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\n if vel_norm < 0.01:\n vel_norm = 0.01\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\n self.set_wheel_velocity([left_wheel, right_wheel])","def do_impact(self, car):\r\n\r\n if car is not None:\r\n if self.head_of_jump is None:\r\n self.head_of_jump = Jump()\r\n else:\r\n jj = Jump()\r\n jj.next = self.head_of_jump\r\n self.head_of_jump = jj\r\n\r\n # self.current_jump = self.head_of_jump\r","def cox_cc_loss(g_case: Tensor, g_control: Tensor, shrink : float = 0.,\n clamp: Tuple[float, float] = (-3e+38, 80.)) -> Tensor:\n control_sum = 0.\n shrink_control = 0.\n if g_case.shape != g_control[0].shape:\n raise ValueError(f\"Need `g_case` and `g_control[0]` to have same shape. Got {g_case.shape}\"+\n f\" and {g_control[0].shape}\")\n for ctr in g_control:\n shrink_control += ctr.abs().mean()\n ctr = ctr - g_case\n ctr = torch.clamp(ctr, *clamp) # Kills grads for very bad cases (should instead cap grads!!!).\n control_sum += torch.exp(ctr)\n loss = torch.log(1. + control_sum)\n shrink_zero = shrink * (g_case.abs().mean() + shrink_control) / len(g_control)\n return torch.mean(loss) + shrink_zero.abs()","def _twist_callback(self, cmd):\n self.set_velocity(cmd.linear.x, cmd.angular.z)","def short_circ():\n \n set_mode(mode_cv)\n time.sleep(.250)\n set_CV_volts(0.1)\n time.sleep(.250)\n \n sc_vals = get_input_values()\n sc_data_point = data_point(sc_vals)\n jsc = sc_data_point[4]\n print('Short circuit current: ', jsc)\n write_data_tofile(sc_data_point)\n\n return jsc","def print_usage():\n print \"Censor a document\\n\"\n print \"Use: python censor.py x NUM_DOCS s t p heuristic flag\\n\"\n print \"Arguments:\"\n print \"\\tif Greedy, x = index of the target document to censor (0 <= x < NUM_DOCS)\\n\\tif KillNodes, x = percentage of nodes to be shut down (0 < x < 1)\\n\"\n print \"\\tNUM_DOCS = total number of docs in the archive\\n\"\n print \"\\ts = number of source blocks\\n\"\n print \"\\tt = number of pointer blocks\\n\"\n print \"\\tp = number of parity blocks\\n\"\n print \"\\theuristic = MinimumAttack or LeapingAttack or CreepingAttack or TailoredAttack or KillNodes or KillDocs or KillRand\\n\"\n print \"\\tflag = write 'draw' if you want graphical visualization\\n\"","def _do_mc_action(self):\n goal = self._current_mc_goal\n self._position_control_client.send_goal(\n goal,\n done_cb = self._motion_control_callback\n )","def kick_from_accretion(self):\n\n accth_mag = -self.v * self.pair.dmdt_accr / self.pair.mass\n kick0 = -self.vth_vec * accth_mag[0] * self.dt\n kick1 = self.vth_vec * accth_mag[1] * self.dt\n\n self.pair[0].velocity += kick0\n self.pair[1].velocity += kick1","def put_on_the_floor(device, q_init):\n global key_pressed\n key_pressed = False\n Kp_pos = 3.\n Kd_pos = 0.01\n imax = 3.0\n pos = np.zeros(device.nb_motors)\n for motor in range(device.nb_motors):\n pos[motor] = q_init[device.motorToUrdf[motor]] * device.gearRatioSigned[motor]\n listener = keyboard.Listener(on_press=on_press)\n listener.start()\n print(\"Put the robot on the floor and press Enter\")\n while not key_pressed:\n device.UpdateMeasurment()\n for motor in range(device.nb_motors):\n ref = Kp_pos*(pos[motor] - device.hardware.GetMotor(motor).GetPosition() - Kd_pos*device.hardware.GetMotor(motor).GetVelocity())\n ref = min(imax, max(-imax, ref))\n device.hardware.GetMotor(motor).SetCurrentReference(ref)\n device.SendCommand(WaitEndOfCycle=True)\n\n print(\"Start the motion.\")","def _perform_landing(self):\n self.y += self.settings.mario_jump_speed\n if self.y >= self.settings.mario_y_pos:\n self.y = self.settings.mario_y_pos\n self.jumping = 0\n self.is_currently_jumping = False","def step1c(self):\n\t\tif (self.ends(\"y\") and self.vowelinstem()):\n\t\t\tself.b = self.b[:self.k] + 'i' + self.b[self.k+1:]","def ocr_correction(token):","def requestAccel(self) -> None:\n self._protocol.write_line(CMD_ACCELEROMETER)","def jump_factor(self):\n return self._jump_factor","def jumped_on(self):\r\n pass","def warmup():\n print camera.CoolerOFF()\n camera.status.update()","async def c(self, f : float):\n c = (f-32) * 5/9\n await self.bot.say(\"{0} Celsius\".format(c))","def do_updates(self):\n if self.words.check_for_dashes():\n self.console.write(\"Great job! We are so proud of you!\")\n self.keep_playing = False\n \n if not self.good_guess:\n del self.jumper.jumper_list[0]\n if self.jumper.jumper_list[0] == \" 0 \":\n webbrowser.open(\"https://www.youtube.com/watch?v=oHg5SJYRHA0&ab_channel=cotter548\")\n self.keep_playing = False","def set_actuator(self, action):\n deltav = action[0]\n vt = np.clip(self.vt + deltav, -self.maxV, self.maxV)\n self.vt = vt\n p.setJointMotorControl2(bodyUniqueId=self.botId,\n jointIndex=0,\n controlMode=p.VELOCITY_CONTROL,\n targetVelocity=vt)\n p.setJointMotorControl2(bodyUniqueId=self.botId,\n jointIndex=1,\n controlMode=p.VELOCITY_CONTROL,\n targetVelocity=-vt)","def runDisplacement(self,distance,axis):\n if axis == \"x\":\n self.M1.runDisplacement(distance)\n elif axis == \"y\":\n self.M2.runDisplacement(distance)","def dynamicMoon(moonPosX, moonPosY, velocityXMoon, velocityYMoon, h):\r\n kPosMoon = [[0 for x in range(4)] for y in range(2)] # initialising the 2x2 k matricies\r\n kVMoon = [[0 for x in range(4)] for y in range(2)]\r\n \r\n kPosMoon[0][0] = velocityXMoon # this value is k1 for the x position. It is just the velocity of the rocket at its current position.\r\n kPosMoon[1][0] = velocityYMoon #this value is k1 for the y position\r\n kVMoon[0][0] = accelerationCalcX(moonPosX, moonPosY) #this value is k1 for the x velocity. At its current position what is the acceleration of the projectile\r\n kVMoon[1][0] = accelerationCalcY(moonPosX, moonPosY) # this value is k1 for the y velocity\r\n \r\n #k2s\r\n kPosMoon[0][1] = velocityXMoon + h*kVMoon[0][0]/2 #what would its velocity be if it carried on at its initial acceleration (calculated in k1 for x velocity) for half a time step\r\n kPosMoon[1][1] = velocityYMoon + h*kVMoon[1][0]/2\r\n kVMoon[0][1] = accelerationCalcX(moonPosX + h*kPosMoon[0][0]/2, moonPosY + h*kPosMoon[1][0]/2) # if it continued at the velocity in k2 for x position for half a time step what would the acceleration on the projectile be.\r\n kVMoon[1][1] = accelerationCalcY(moonPosX + h*kPosMoon[0][0]/2, moonPosY + h*kPosMoon[1][0]/2)\r\n \r\n #k3s\r\n kPosMoon[0][2] = velocityXMoon + h*kVMoon[0][1]/2 # if it carried on at the acceleration calculated for k2 in x velocity for half a time step, what would its velocity be\r\n kPosMoon[1][2] = velocityYMoon + h*kVMoon[1][1]/2\r\n kVMoon[0][2] = accelerationCalcX(moonPosX + h*kPosMoon[0][1]/2, moonPosY + h*kPosMoon[1][1]/2) # if carried on at the velocity calculated in k2 for half a time step then what would its accelaration be\r\n kVMoon[1][2] = accelerationCalcY(moonPosX + h*kPosMoon[0][1]/2, moonPosY + h*kPosMoon[1][1]/2)\r\n \r\n #k4s\r\n kPosMoon[0][3] = velocityXMoon + h*kVMoon[0][2] # if it carried on at the acceleration calcualted in k3 fro a whole timestep, then what would its velocity be \r\n kPosMoon[1][3] = velocityYMoon + h*kVMoon[1][2]\r\n kVMoon[0][3] = accelerationCalcX(moonPosX + h*kPosMoon[0][2], moonPosY + h*kPosMoon[1][2]) #if it continued at the velocity calculated in k3 for a whole time step, then what would its accelaration be\r\n kVMoon[1][3] = accelerationCalcY(moonPosX + h*kPosMoon[0][2], moonPosY + h*kPosMoon[1][2])\r\n \r\n velocityXMoon = velocityXMoon+(h/6)*(kVMoon[0][0]+2*kVMoon[0][1]+2*kVMoon[0][2]+kVMoon[0][3]) # the velocity in x is appended, after combining the ks for velocity in x\r\n velocityYMoon = velocityYMoon+(h/6)*(kVMoon[1][0]+2*kVMoon[1][1]+2*kVMoon[1][2]+kVMoon[1][3]) # the velocity in y is appended, after combining the ks for velocity in y\r\n moonPosX = moonPosX+(h/6)*(kPosMoon[0][0]+2*kPosMoon[0][1]+2*kPosMoon[0][2]+kPosMoon[0][3]) # the x position is appended, after combinging the ks for x position\r\n moonPosY = moonPosY+(h/6)*(kPosMoon[1][0]+2*kPosMoon[1][1]+2*kPosMoon[1][2]+kPosMoon[1][3]) # the y position is appended, after combinging the ks for y position\r\n \r\n return moonPosX, moonPosY, velocityXMoon, velocityYMoon # return the position and velocity back to moonPass\r","def moove_character(self, case_list):\n\t\tself.actual_hero.start_moove(case_list)","def _do_updates(self):\n is_right = self._puzzle.is_guess_right()\n if is_right:\n self._puzzle.reveal_puzzle()\n else:\n self._jumper.cut_line()","def rotate_cw(self, deg):\r\n self.send_command_without_response(f'cw {deg}')","def Keyboard(self, key):\r\n\t\tif key == Keys.K_u:\r\n\t\t\tself.kp+=self.dp\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_j:\r\n\t\t\tself.kp-=self.dp\r\n\t\t\tif self.kp<0:\r\n\t\t\t\tself.kp=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_i:\r\n\t\t\tself.ki+=self.di\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_k:\r\n\t\t\tself.ki-=self.di\r\n\t\t\tif self.ki<0:\r\n\t\t\t\tself.ki=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_o:\r\n\t\t\tself.kd+=self.dd\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\tif key == Keys.K_l:\r\n\t\t\tself.kd-=self.dd\r\n\t\t\tif self.kd<0:\r\n\t\t\t\tself.kd=0.0\r\n\t\t\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\r\n\t\t\r\n\t\tif key == Keys.K_f:\r\n\t\t\tself.center+=1\r\n\t\t\tif self.center>2:\r\n\t\t\t\tself.center = 0\r\n\t\t\r\n\t\tif key == Keys.K_t:\r\n\t\t\tself.cut=1-self.cut\r\n\t\t\tself.q[0].SetCut(self.cut)\t\t\t\r\n\t\t\t\r\n\t\tif key == Keys.K_r:\r\n\t\t\tpass\t\r\n\t\t\r\n\t\tif key == Keys.K_s:\r\n\t\t\tself.q[0].saveConf()","def set_jump(self, jump):\n self.jump = jump","def apply_action(self, action):\n real_action = self.policy_action_to_robot_action(action)\n p.setGravity(0, 0, 0)\n p.resetBaseVelocity(\n self.robot_ids[0], real_action[:3], real_action[3:])","def before_step(self, action, physics):\n # # Support legacy internal code.\n\n physics.named.data.xfrc_applied[:,:3]=np.zeros((3,))\n\n if self._random_location and not self._maxq:\n index = self._current_loc\n else:\n one_hot = action[:4]\n index = np.argmax(one_hot)\n action = action[4:]\n\n goal_position = action * 0.05\n corner_action = CORNER_INDEX_ACTION[index]\n corner_geom = CORNER_INDEX_POSITION[index]\n\n\n # apply consecutive force to move the point to the target position\n position = goal_position + physics.named.data.geom_xpos[corner_geom]\n dist = position - physics.named.data.geom_xpos[corner_geom]\n\n loop = 0\n while np.linalg.norm(dist) > 0.025:\n loop += 1\n if loop > 40:\n break\n physics.named.data.xfrc_applied[corner_action, :3] = dist * 20\n physics.step()\n self.after_step(physics)\n dist = position - physics.named.data.geom_xpos[corner_geom]\n\n if self._random_location and not self._maxq:\n self._current_loc = self._generate_loc()","def CreateMotionKernel(kernel):\r\n TrajSize = 64\r\n anxiety = 0.2* np.random.rand()\r\n numT = 10\r\n MaxTotalLength =10\r\n TotLength = 0\r\n #term determining, at each sample, the strengh of the component leating towards the previous position\r\n centripetal = 0.7 * np.random.rand()\r\n #term determining, at each sample, the random component of the new direction\r\n gaussianTerm =10 * np.random.rand()\r\n #probability of having a big shake, e.g. due to pressing camera button or abrupt hand movements\r\n freqBigShakes = 3 *np.random.rand()\r\n #v is the initial velocity vector, initialized at random direction\r\n init_angle = 360 * np.random.rand()\r\n #initial velocity vector having norm 1\r\n v0 = math.cos(init_angle / 180.0 * math.pi) + 1.0j * math.sin(init_angle/ 180.0 * math.pi)\r\n #the speed of the initial velocity vector\r\n v = v0* MaxTotalLength/(numT-1);\r\n\r\n if anxiety > 0:\r\n v = v0 * anxiety\r\n # initialize the trajectory vector\r\n x = np.zeros(numT,dtype = np.complex);\r\n\r\n abruptShakesCounter = 0\r\n for t in range(numT-1):\r\n # determine if there is an abrupt (impulsive) shake\r\n if np.random.rand() < freqBigShakes * anxiety:\r\n #if yes, determine the next direction which is likely to be opposite to the previous one\r\n nextDirection = 2 * v * (np.exp( 1.0j * (math.pi + (np.random.rand() - 0.5))))\r\n abruptShakesCounter = abruptShakesCounter + 1\r\n else:\r\n nextDirection=0\r\n\r\n #determine the random component motion vector at the next step\r\n dv = nextDirection + anxiety * (gaussianTerm * (np.random.randn()- + 1.0j * np.random.randn()) - centripetal * x[t]) * (MaxTotalLength / (numT - 1))\r\n v = v + dv\r\n # velocity vector normalization\r\n v = (v / np.abs(v)) * MaxTotalLength / (numT - 1)\r\n #print v\r\n x[t + 1] = x[t] + v\r\n # compute total length\r\n #TotLength=TotLength+np.abs(x([t+1]-x[t]))\r\n x_real = []\r\n x_imag = []\r\n for elem in x:\r\n x_real.append(elem.real)\r\n x_imag.append(elem.imag)\r\n x_real = np.round((x_real - np.min(x_real))/(np.max(x_real) - np.min(x_real)) * kernel-0.5)\r\n x_imag = np.round((x_imag - np.min(x_imag))/(np.max(x_imag) - np.min(x_imag)) * kernel-0.5)\r\n for idx in range(len(x_real)):\r\n if x_real[idx] < 0:\r\n x_real[idx] = 0\r\n if x_imag[idx] < 0:\r\n x_imag[idx] = 0\r\n if x_real[idx] > kernel -1:\r\n x_real[idx] = kernel -1\r\n if x_imag[idx] > kernel -1:\r\n x_imag[idx] = kernel -1\r\n\r\n ker = np.zeros((kernel, kernel))\r\n for idx in range(len(x_real)):\r\n ker[np.int(x_real[idx])][np.int(x_imag[idx])] = 1\r\n ker = ker/np.sum(np.sum(ker))\r\n return ker","def cmu_mocap_35_walk_jog(data_set='cmu_mocap'):\r\n train_motions = ['01', '02', '03', '04', '05', '06',\r\n '07', '08', '09', '10', '11', '12',\r\n '13', '14', '15', '16', '17', '19',\r\n '20', '21', '22', '23', '24', '25',\r\n '26', '28', '30', '31', '32', '33', '34']\r\n test_motions = ['18', '29']\r\n data = cmu_mocap('35', train_motions, test_motions, sample_every=4, data_set=data_set)\r\n data['info'] = \"Walk and jog data from CMU data base subject 35. As used in Tayor, Roweis and Hinton at NIPS 2007, but without their pre-processing (i.e. as used by Lawrence at AISTATS 2007). It consists of \" + data['info']\r\n return data","def moco_loss_func(\n query: torch.Tensor, key: torch.Tensor, queue: torch.Tensor, temperature=0.1\n) -> torch.Tensor:\n\n pos = torch.einsum(\"nc,nc->n\", [query, key]).unsqueeze(-1)\n neg = torch.einsum(\"nc,ck->nk\", [query, queue])\n logits = torch.cat([pos, neg], dim=1)\n logits /= temperature\n targets = torch.zeros(query.size(0), device=query.device, dtype=torch.long)\n return F.cross_entropy(logits, targets)","def put_cmyk(self, path, res, mode, color_correction):\n\n ### Stratasys_J750\n if mode == \"stratasys\":\n _c_ = (0, 90, 158, 255)\n _m_ = (166, 33, 98, 255)\n _y_ = (200, 189, 3, 255)\n _k_ = (26, 26, 29, 255)\n\n ### cmyk\n else:\n _c_ = (0, 255, 255, 255)\n _m_ = (255, 0, 255, 255)\n _y_ = (255, 255, 0, 255)\n \n\n\n ut = util.UTIL()\n \n image = Image.open(path)\n image_size = image.size\n\n clr_list = self.get_color_to_memory(image)\n\n # print(\"Memory : \", ut.ll_size(clr_list))\n\n new_image = self.up_scale(image, res)\n new_image_size = new_image.size\n \n # print(\"PIL : \", image_size)\n # print(new_image_size)\n\n vectors = ut.set_vector(res)\n\n for i in range(image_size[0]):\n for j in range(image_size[1]):\n\n pt = (i * res, j * res)\n rgb = clr_list[i][j]\n new_vectors = random.sample(vectors, len(vectors))\n\n cmyk = self.calc_rgb_cmyk(rgb)\n\n\n ### ========== CMYK ==========\n # cc, mm, yy, kk = self.calc_cmyk_count(cmyk, res)\n # new_length = cc + mm + yy + kk\n ### ========== CMYK ==========\n\n\n ### ========== CMY ==========\n cc, mm, yy = self.calc_cmy_count(cmyk, res, color_correction)\n _length = cc + mm + yy\n ### ========== CMY ==========\n\n\n if _length > (res * res):\n new_length = (res * res)\n else:\n new_length = _length\n\n new_pt = []\n\n for k in range(new_length):\n\n new_pt = ut.pt2d_add(pt, new_vectors[k])\n # print(new_pt)\n\n if k < cc:\n new_image.putpixel(new_pt, (_c_))\n elif k < (cc + mm):\n new_image.putpixel(new_pt, (_m_))\n elif k < (cc + mm + yy):\n new_image.putpixel(new_pt, (_y_))\n # else:\n # new_image.putpixel(new_pt, (_k_))\n \n return new_image","def cox_cc_loss_single_ctrl(g_case: Tensor, g_control: Tensor, shrink: float = 0.) -> Tensor:\n loss = F.softplus(g_control - g_case).mean()\n if shrink != 0:\n loss += shrink * (g_case.abs().mean() + g_control.abs().mean())\n return loss","def handle_continuous_keys(self):\n shift = pygame.K_LSHIFT in self.held\n ctrl = pygame.K_LCTRL in self.held\n factor = 3 if shift else 1/3 if ctrl else 1\n for key in self.held:\n if not self.followmode:\n # if self.held_delay[key] == 0:\n if key in (pygame.K_w, pygame.K_UP): # up\n # self.canvas.move_offset(0, 5 * factor)\n self.canvas.move_focus(0, 5 * factor)\n elif key in (pygame.K_s, pygame.K_DOWN): # down\n # self.canvas.move_offset(0, -5 * factor)\n self.canvas.move_focus(0, -5 * factor)\n elif key in (pygame.K_d, pygame.K_RIGHT): # right\n # self.canvas.move_offset(-5 * factor, 0)\n self.canvas.move_focus(5 * factor, 0)\n elif key in (pygame.K_a, pygame.K_LEFT): # left\n # self.canvas.move_offset(5 * factor, 0)\n self.canvas.move_focus(-5 * factor, 0)\n if key in (pygame.K_e, pygame.K_KP_PLUS):\n self.canvas.zoom(2 * factor)\n elif key in (pygame.K_q, pygame.K_KP_MINUS):\n self.canvas.zoom(-2 * factor)\n for key in self.held:\n self.held_delay[key] = (self.held_delay[key] + 1) % 5","def _motion_control_callback(self, state, result):\n if len(self._mc_goals) > 0:\n self._current_mc_goal = self._mc_goals.pop(0)\n self._do_mc_action()\n else :\n self._current_mc_goal = None","def acceleration(degrees, finalSpeed, steering = 0, robot = MoveSteering(OUTPUT_A, OUTPUT_B), motorA = LargeMotor(OUTPUT_A)): # Function to accelerate while moving so the robot can get traction before moving fast\n\n motorA.reset() #reseting how many degrees the robot has moved\n motorA.position = 0 # setting how many degrees the robot has moved\n\n accelerateDegree = degrees * 0.8\n # declerationDegree = degrees * 0.2'\n speed = 0 #starting speed\n while motorA.position < degrees and False == Constants.STOP: #while the robot hasen't moved the target amount of degree's(distance)\n if motorA.position < accelerateDegree and False == Constants.STOP: \n if speed < finalSpeed: # while the robot hasen't accelerated to the target speed\n speed += 5 #speed up\n robot.on(steering = steering, speed = speed) # Start Moving\n sleep(0.1) #wait so that it doesn't accelerate immidiatly\n else:\n robot.on(steering = steering, speed = finalSpeed)# otherwise just keep moving\n sleep(0.01)\n elif False == Constants.STOP:\n if speed > 10:\n speed -= 5\n robot.on(steering = steering, speed = speed)\n sleep(0.05)\n else:\n robot.on(steering = steering, speed = speed)\n sleep(0.01)\n \n robot.off() # Stop Moving","def sit(self):\n\n\t\tself.pose.goToPosture(\"Crouch\")","def cohen_kappa(*args, **kwargs):\n return ConfusionMatrix2.from_ccw(*args, **kwargs).kappa()","def set_hybrid_control(self, model, max_force_torque, timeout=5.0, stop_on_target_force=False):\n\n reduced_speed = np.deg2rad([100, 100, 100, 150, 150, 150])\n q_last = self.joint_angles()\n\n # Timeout for motion\n initime = rospy.get_time()\n xb = self.end_effector()\n failure_counter = 0\n\n while not rospy.is_shutdown() \\\n and (rospy.get_time() - initime) < timeout:\n\n # Transform wrench to the base_link frame\n Wb = self.get_ee_wrench()\n\n # Current Force in task-space\n Fb = -1 * Wb\n # Safety limits: max force\n if np.any(np.abs(Fb) > max_force_torque):\n rospy.logerr('Maximum force/torque exceeded {}'.format(np.round(Wb, 3)))\n self.set_target_pose_flex(pose=xb, t=model.dt)\n return FORCE_TORQUE_EXCEEDED\n\n if stop_on_target_force and np.any(np.abs(Fb)[model.target_force != 0] > model.target_force[model.target_force != 0]):\n rospy.loginfo('Target F/T reached {}'.format(np.round(Wb, 3)) + ' Stopping!')\n self.set_target_pose_flex(pose=xb, t=model.dt)\n return STOP_ON_TARGET_FORCE\n\n # Current position in task-space\n xb = self.end_effector()\n\n dxf = model.control_position_orientation(Fb, xb) # angular velocity\n\n # Limit linear/angular velocity\n dxf[:3] = np.clip(dxf[:3], -0.5, 0.5)\n dxf[3:] = np.clip(dxf[3:], -5., 5.)\n\n xc = transformations.pose_from_angular_velocity(xb, dxf, dt=model.dt)\n\n # Avoid extra acceleration when a point failed due to IK or other violation\n # So, this corrects the allowed time for the next point\n dt = model.dt * (failure_counter+1)\n\n q = self._solve_ik(xc)\n if q is None:\n rospy.logwarn(\"IK not found\")\n result = IK_NOT_FOUND\n else:\n q_speed = (q_last - q)/dt\n if np.any(np.abs(q_speed) > reduced_speed):\n rospy.logwarn(\"Exceeded reduced max speed %s deg/s, Ignoring command\" % np.round(np.rad2deg(q_speed), 0))\n result = SPEED_LIMIT_EXCEEDED\n else:\n result = self.set_joint_positions_flex(position=q, t=dt)\n\n if result != DONE:\n failure_counter += 1\n continue # Don't wait since there is not motion\n else:\n failure_counter = 0\n\n # Compensate the time allocated to the next command when there are failures\n for _ in range(failure_counter+1):\n self.rate.sleep()\n\n q_last = self.joint_angles()\n return DONE","def _content_jump(self, target):\n head = self._relative_head_pos()\n similarities = 1 - np.sqrt(np.sum((self.memory - target) ** 2, 1)) / self.memory_unit_size\n pos = int(np.argmax(similarities).item())\n if similarities[pos] > self.min_similarity_to_jump:\n self.head_pos = pos\n else:\n self.head_pos = 0\n if self.history is not None:\n self.history[\"loc\"][-1].append((head, 0.1))","def rotateCW(device, runTime = 0.1):\n return execute(device, ROTATE_CCW_CMD, STATUS_LIMIT_ROTATE, runTime)","def omega(self):\n self.cosineSequences()","def cauchy(self, loc, gamma):\n c = loc + gamma * np.tan(np.pi * (self.random() - 0.5))\n return c if c > 0 else self.cauchy(loc, gamma)","def attack(self, robot):\n pass","def move_to_coc(self):\n coc = scale(self.center_of_charge(), -1.0)\n self.translate(coc)","def run(self):\n self.coffee_machine.water_tank.decrease_weight(self.coffee_machine.chosen_coffee_data.get('water_weight'))","def k_2_jy(freq: float, theta_major: float,\n theta_minor: float, brightness: float) -> float:\n conv = (1.222E3 * (freq ** -2) / theta_minor / theta_major) ** -1\n return brightness * conv","def joy_callback(self, msg):\n mappings = gamepad_mappings.set_gamepad_mappings(msg)\n self.move_vertical = mappings[\"button_vertical\"] # up: +1.0, down: -1.0\n self.move_horizontal = mappings[\"button_horizontal\"] # left: +1.0, right: -1.0","def recommend_cosim():\n pass","def execute(self):\n # Check buttons\n self.check_enable_pids(self.inputs[JoyInput.START])\n self.check_disable_pids(self.inputs[JoyInput.BACK])\n self.check_toggle(self.inputs[JoyInput.LS], self.inputs[JoyInput.LS_Y], self.saved[JoyInput.LS_Y])\n\n # Check axes\n #self.input_forward(JoyInput.LS_Y)\n self.input_depth(JoyInput.CROSS_Y)\n self.set_raw_pwm(JoyInput.LT, JoyInput.RT) # uses LT/RT for depth\n #self.input_rotate(JoyInput.LT, JoyInput.RT) # uses RT for rotation\n\n # yaw setpoint\n magnitude, angle = self.input_yaw_setpoint(JoyInput.RS_X, JoyInput.RS_Y, Joystick.RS_ANGLE)\n self.check_yaw_setpoint_toggle(JoyInput.RS, Joystick.RS_ANGLE, magnitude, angle)","def take_action(self, action):\r\n\r\n self._update_velocity(action)\r\n self._update_position()\r\n if self.is_terminal_state():\r\n return 100.0\r\n\r\n return -1.0","def teleopPeriodic(self):\n\n turningValue = (self.angleSetpoint - self.gyro.getAngle()) * self.pGain\n if self.joystick.getY() <= 0:\n # forwards\n self.myRobot.arcadeDrive(self.joystick.getY(), turningValue)\n elif self.joystick.getY() > 0:\n # backwards\n self.myRobot.arcadeDrive(self.joystick.getY(), -turningValue)","def JogMode(port):\n\tport.write(\"Q\")","def runTask1(self):\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.initialize()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.sweepDuckie()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieAlignX()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieAlignY()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieLowerEE()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieSuctionOn()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.duckieLiftEE()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.ppSweep()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.ppAlignX()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.ppAlignY()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.ppLowerEE()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.ppSuctionOff()\r\n\t\tif not self._motion.breakout():\r\n\t\t\tself._motion.reset()\r\n\t\tself._motion.terminate()","def control_change(self, channel, cc, value):\n knob, bank = self.decode_mpd218_cc(cc)\n log.debug(\"Winch control change %d on knob %d bank %d\", cc, knob, bank)\n\n if knob == 1: # Knob #1 on MPD218, use to control resonant frequency\n #self.frequency = 0.05 + 0.1 * value\n self.frequency = 5.00\n self.set_freq_damping()\n\n elif knob == 2: # Knob #2 on on MPD218, use to control damping ratio\n #self.damping_ratio = 0.05 + 0.01 * value\n self.damping_ratio = 1.32\n self.set_freq_damping()","async def get_words():\n global CENSORED_WORDS\n CENSORED_WORDS = get_censor()","def first_time_run(self, w):\n \n if w in ['half', 'quarter']:\n self._first_time_channel = getattr(self,'_'+w+'_channel')\n else:\n raise ValueError('Input type for \"w\" should be \"half\" or \"quarter\"')\n\n #measure position before optimizing\n getattr(self.rotator, 'set_zero_position')(getattr(self,'_'+w+'_channel')) \n pos_before = getattr(self.rotator, 'get_noof_steps_ch'+\\\n str(getattr(self,'_'+w+'_channel')))()\n\n #turn waveplates\n data, qtdata, dataplot, premature_quit = self.run('first_time', self._first_opt_red_power)\n qtdata.close_file()\n\n if not premature_quit:\n\n print '\\tGuessing optimal waveplate position...'\n optim_pos = data['wp_step'][self.find_nearest(data['counts'],\n min(data['counts']))]\n\n if self.get_plot_degrees():\n print '\\tOptimal waveplate position determined at %.0f degrees.'%(optim_pos*self.get_conversion_factor(w))\n else:\n print '\\tOptimal waveplate position determined at %d steps.'%optim_pos\n \n #BEWARE: never ask the current position in noof_steps\n curr_pos = data['wp_step'][len(data['wp_step'])-1]\n\n #set the position to the optimal position\n self.rotator.quick_scan(optim_pos-curr_pos, getattr(self,'_'+w+'_channel'))\n\n else:\n #ways to get a premature quit action: q key stroke or > threshold\n #BEWARE: never ask the current position in noof_steps\n curr_pos = data['wp_step'][len(data['wp_step'])-1] \n\n print '\\tReturning to initial position...'\n\n #set the position to the optimal position\n self.rotator.quick_scan(pos_before-curr_pos, getattr(self,'_'+w+'_channel'))\n\n\n #measure position after optimizing\n pos_after = getattr(self.rotator, 'get_noof_steps_ch'+\\\n str(getattr(self,'_'+w+'_channel') ) )()\n\n #print \"\\tPosition of %s waveplate changed %d steps\"\\\n # %(w, pos_after-pos_before)","def keyboardController(self):\n # get pressed keys\n keypress = pygame.key.get_pressed()\n\n # randomize world if R is pressed and enough frames have passed since last time\n if keypress[pygame.K_r]:\n if not self.seedWorldTimer:\n self.seedWorldTimer = int(self.FPS / 4)\n self.calculateWorldValues()\n self.findPolygons()\n # decrement delay between allowed randomizations\n if self.seedWorldTimer:\n self.seedWorldTimer -= 1\n\n # change displayMode if necessary\n if keypress[pygame.K_p]:\n self.displayMode = self.DISPLAYMODE_POINTS\n glEnable(GL_CULL_FACE) # enable culling\n glCullFace(GL_BACK)\n elif keypress[pygame.K_o]:\n self.displayMode = self.DISPLAYMODE_MESH\n glDisable(GL_CULL_FACE) # disable culling for mesh viewing\n elif keypress[pygame.K_i]:\n self.displayMode = self.DISPLAYMODE_WIREFRAME\n\n # apply polar camera movement\n if keypress[pygame.K_w]:\n self.cameraPolar[2] -= 0.08 * self.dt\n if self.cameraPolar[2] < 1:\n self.cameraPolar[2] = 1.0\n if keypress[pygame.K_s]:\n self.cameraPolar[2] += 0.08 * self.dt\n if self.cameraPolar[2] > 179:\n self.cameraPolar[2] = 179\n if keypress[pygame.K_d]:\n self.cameraPolar[1] += 0.08 * self.dt\n if self.cameraPolar[1] > 180:\n self.cameraPolar[1] -= 360\n if keypress[pygame.K_a]:\n self.cameraPolar[1] -= 0.08 * self.dt\n if self.cameraPolar[1] <= -180:\n self.cameraPolar[1] += 360\n if keypress[pygame.K_b]:\n self.t = 'b'\n self.calculateWorldValues()\n self.findPolygons()\n if keypress[pygame.K_n]:\n self.t = 'n'\n self.calculateWorldValues()\n self.findPolygons()\n if keypress[pygame.K_m]:\n self.t = 'm'\n self.calculateWorldValues()\n self.findPolygons()\n # update camera cartesian position\n self.polarCameraToCartesian()","def switch_on(self,name):\n self.circles[name].switch_on()\n self.cursor.execute(\"\"\"UPDATE sensors_powersensor SET state=1 WHERE target=%s\"\"\", (name,))"],"string":"[\n \"def censoring_fcn(self, q):\\n return 1.0\",\n \"def jump(self):\\n\\t\\tself.vel = -10\\n\\t\\tself.tick_count = 0\\n\\t\\tself.height = self.y\",\n \"def on_jump_press(self) -> None:\\r\\n if not self.node:\\r\\n return\\r\\n if don.jumpFly:\\r\\n self.node.handlemessage(\\\"impulse\\\",self.node.position[0],self.node.position[1]-2,self.node.position[2],self.node.velocity[0]*0,20 +0.01,self.node.velocity[2]*0,10,5,0,0,self.node.velocity[0]*-0.1,20 + 0.01,self.node.velocity[2]*0)\\r\\n self.node.handlemessage(\\\"impulse\\\",self.node.position[0],self.node.position[1],self.node.position[2],self.node.velocity[0]*0,20 +0.01,self.node.velocity[2]*0,10,5,0,0,self.node.velocity[0]*-0.1,20 + 0.01,self.node.velocity[2]*0)\\r\\n self.node.color = ((0+random.random()*6.5),(0+random.random()*6.5),(0+random.random()*6.5))\\r\\n self.node.highlight = ((0+random.random()*6.5),(0+random.random()*6.5),(0+random.random()*6.5)) \\r\\n self.node.jump_pressed = True\\r\\n self._turbo_filter_add_press('jump')\",\n \"def jump(self, x):\\n self.change_y += x * self.speed\",\n \"def jump(self):\\n self.vy = -9\",\n \"def censor(text: str) -> str:\\n\\n # Split up individual words in the text\\n tokens: List[str] = text.split(\\\" \\\")\\n\\n # Create a mapping of 0 if the word is okay, 1 if it should be censored\\n censor_mask: List[int] = predict([word for word in tokens])\\n\\n # A list of tuples with the first element being the word and the second being 0 or 1\\n censor_map: List[Tuple[str, int]] = list(zip(tokens, censor_mask))\\n\\n # A list of the words that make up the censored text\\n censored_text: List[str] = [\\n censor_word(word) if should_censor else word\\n for word, should_censor in censor_map\\n ]\\n\\n return \\\" \\\".join(censored_text)\",\n \"def content_jump(self, jump: np.ndarray, data: np.ndarray) -> None:\\n if len(jump) != 1:\\n raise ValueError(\\\"`jump` must be a one dimensional vector\\\")\\n if jump > 0.5:\\n euc_dist = np.abs(self._buffer - data).sum(axis=1)\\n self._head = np.argmin(euc_dist)\",\n \"def censor(text: Optional[str]) -> str:\\n char = \\\"*\\\"\\n text = text if text else \\\"\\\"\\n return text[0] + (len(text) - 1) * char if text else text\",\n \"def jog(self, axis:str=\\\"x\\\", distance:float=1):\\n self.sendCommand(\\\"G91\\\")\\n axis.capitalize()\\n self.sendCommand(f'$J={axis}{distance} F1000')\",\n \"def evaluate(self,joystick,keys):\\n \\n self.AG_twinklers.do() \\n \\n \\n if joystick.isUp(keys)==True and self.solomon.current_state[\\\"jumping\\\"]==0: \\n self.solomon.current_state[\\\"jumping\\\"]=1 \\n\\n\\n walkcheck=False\\n \\n if self.solomon.A_wandswish.overide==False:\\n \\n self.solomon.current_state[\\\"wandswish\\\"]=0 \\n\\n if joystick.isDown(keys)==True: \\n self.solomon.current_state[\\\"crouching\\\"]=1 \\n self.solomon.current_state[\\\"standing\\\"]=0\\n else: \\n self.solomon.current_state[\\\"crouching\\\"]=0 \\n \\n if joystick.isRight(keys)==True:\\n self.solomon.facing=1 \\n self.solomon.current_state[\\\"walking\\\"]=1\\n self.solomon.current_state[\\\"standing\\\"]=1\\n walkcheck=True\\n elif joystick.isLeft(keys)==True: \\n self.solomon.facing=-1 \\n walkcheck=True\\n self.solomon.current_state[\\\"walking\\\"]=1\\n self.solomon.current_state[\\\"standing\\\"]=0\\n else:\\n self.solomon.current_state[\\\"walking\\\"]=0 \\n self.solomon.current_state[\\\"standing\\\"]=1\\n\\n canwalk=False\\n if walkcheck:\\n result=self.detect(self.solomon.x+self.solomon.facing*self.solomon.step*5.0,self.solomon.y) \\n if (len(result)==0 or result[0][0]==\\\".\\\") and self.solomon.current_state[\\\"walking\\\"]==1:\\n #self.solomon.x+=self.solomon.step*self.solomon.facing \\n self.solomon.current_state[\\\"standing\\\"]=0 \\n self.solomon.current_state[\\\"walking\\\"]=1\\n canwalk=True\\n #elif result[0][0] in [\\\"]\\n\\n result1=self.grid[int(self.solomon.y-0)][int(self.solomon.x+0.5+self.solomon.step*2*self.solomon.facing)]\\n result2=self.grid[int(self.solomon.y-0)][int(self.solomon.x+0.5-self.solomon.step*2*self.solomon.facing)]\\n #print \\\"fall check\\\" + str((result1,result2,self.solomon.x,self.solomon.y))\\n if result1==\\\".\\\" and result2==\\\".\\\":\\n self.solomon.y-=self.solomon.step\\n self.solomon.current_state[\\\"walking\\\"]=0\\n canwalk=False\\n\\n if canwalk==True: self.solomon.x+=self.solomon.step*self.solomon.facing\\n\\n if joystick.isFire(keys)==True and self.solomon.current_state[\\\"wandswish\\\"]==0: \\n self.solomon.A_wandswish.kick()\\n self.solomon.A_wandswish.overide=True\\n self.solomon.current_state[\\\"wandswish\\\"]=1 \\n\\n \\n if self.solomon.current_state[\\\"jumping\\\"]==1:\\n self.solomon.AG_jump.do()\\n print \\\"he's jumping\\\"\\n print str(self.solomon.AG_jump.action(\\\"jump_displacement\\\").tick)\\n self.solomon.y+=0.2\\n #print \\\"co-ordinates \\\"+str((self.solomon.x,self.solomon.y))\",\n \"def on_r_joy_y(self):\\r\\n self.log()\",\n \"def jump(self):\\n \\n # move down a bit and see if there is a platform below us.\\n # Move down 2 pixels because it doesn't work well if we only move down 1\\n # when working with a platform moving down.\\n self.rect.y += 2\\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\\n self.rect.y -= 2\\n \\n # If it is ok to jump, set our speed upwards\\n if len(platform_hit_list) > 0 or self.rect.bottom >= SCR_HEIGHT:\\n self.change_y = -8\",\n \"def jump(self):\\n\\n # move down a bit and see if there is a platform below us.\\n # Move down 2 pixels because it doesn't work well if we only move down\\n # 1 when working with a platform moving down.\\n self.rect.y += 2\\n platform_hit_list = pygame.sprite.spritecollide(\\n self, self.platforms, False)\\n self.rect.y -= 2\\n\\n # If it is ok to jump, set our speed upwards\\n if len(platform_hit_list) > 0 or self.rect.bottom >= WIN_HEIGHT:\\n self.change_y = -10\",\n \"def teleopPeriodic(self):\\n self.drive.arcadeDrive(1, 0)\\n self.brushless.set(1)\\n self.spark.set(self.joystick.getY())\",\n \"def jump(self):\\n if self.y_pos > self.max_pos_y + self.height:\\n self.isJump = True\\n self.y_velocity = -13.5\\n sounds[\\\"jump\\\"].play()\",\n \"def on_key(window, key, scancode, action, mods):\\n if action != glfw.PRESS:\\n return\\n \\n global controller\\n\\n if key == glfw.KEY_SPACE:\\n controller.fillPolygon = not controller.fillPolygon\\n\\n elif key == glfw.KEY_ESCAPE:\\n glfw.set_window_should_close(window, True)\\n\\n # Si detecta la tecla [Q] cambia el estado del efecto 1 : zoom\\n elif key == glfw.KEY_Z:\\n controller.effect1 = not controller.effect1\\n\\n # Si detecta la tecla [W] cambia el estado del efecto 2 : corte\\n elif key == glfw.KEY_C:\\n controller.effect2 = not controller.effect2\\n\\n else:\\n print('Unknown key')\",\n \"def jump(self):\\n \\n # move down a bit and see if there is a platform below us.\\n # Move down 2 pixels because it doesn't work well if we only move down 1\\n # when working with a platform moving down.\\n self.rect.y += 2\\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\\n self.rect.y -= 2\\n \\n # If it is ok to jump, set our speed upwards\\n if len(platform_hit_list) > 0: #or self.rect.bottom >= SCREEN_HEIGHT:\\n self.change_y = -10\",\n \"def apply_action(self, action):\\n robot_state = self.get_state('turtlebot3_waffle_pi','world')\\n robot_x = robot_state.pose.position.x\\n robot_y = robot_state.pose.position.y\\n # Set the distance moved in an action such that it is at least as large as the\\n # minimum distance that would let a robot in the middle of the goal go to either side\\n #self.move_dist = max(((C.GOAL_TOP + C.GOAL_BOTTOM) / 2) / C.NUM_POS_SENDS, 0.5)\\n if action == Learn.MOVE_LEFT:\\n print(\\\"Move left\\\")\\n self.set_robot(robot_x, robot_y+self.move_dist)\\n elif action == Learn.MOVE_RIGHT:\\n print(\\\"Move right\\\")\\n self.set_robot(robot_x, robot_y-self.move_dist)\\n else:\\n print(\\\"Stay put\\\")\",\n \"def ev_controlleraxismotion(self, event: tcod.event.ControllerAxis) -> T | None:\",\n \"def jump(self):\\n \\n # move down and see if there's a platform below us.\\n # Move down 2 pixels because it doesn't work well if you only move down\\n # 1 when working with a platform moving down.\\n self.rect.y += 2\\n platform_hit_list = pygame.sprite.spritecollide(self, self.level.platform_list, False)\\n self.rect.y -= 2\\n \\n # If it is ok to jump, set the speed upwards\\n if len(platform_hit_list) > 0 or self.rect.bottom >= SCREEN_HEIGHT:\\n self.change_y = -10\",\n \"def jump(self):\\n\\t\\tself._is_falling = True\\n\\t\\tself._dy = -5\",\n \"def ev_joyhatmotion(self, event: tcod.event.JoystickHat) -> T | None:\",\n \"def on_r_joy_x(self):\\r\\n self.log()\",\n \"def on_l_joy_y(self):\\r\\n self.log()\",\n \"def ev_joyaxismotion(self, event: tcod.event.JoystickAxis) -> T | None:\",\n \"def jump(self, xvel = 0, yvel = 0): #TODO: figure out how a monster's jumping ability is determined.\\n self.xvel += xvel\\n self.yvel -= yvel\\n self.animation.iter()\\n self.ai_count = 25 #TEMP\\n self.onGround = False\",\n \"def _take_action(self, action):\\n\\n if isinstance(action, list) or isinstance(action, np.ndarray):\\n action = action[0]\\n\\n if self.continuous:\\n increment = np.array([1.5*np.cos(action),1.5*np.sin(action)])\\n else:\\n increment = np.array([0.0,0.0])\\n if action == 0:\\n increment[0] = 1.5\\n elif action == 1:\\n increment[0] = 1.225\\n increment[1] = 1.225\\n elif action == 2:\\n increment[1] = 1.5\\n elif action == 3:\\n increment[0] = -1.225\\n increment[1] = 1.225\\n elif action == 4:\\n increment[0] = -1.5\\n elif action == 5:\\n increment[0] = -1.225\\n increment[1] = -1.225\\n elif action == 6:\\n increment[1] = -1.5\\n elif action == 7:\\n increment[0] = 1.225\\n increment[1] = -1.225\\n else:\\n print('NOP!')\\n\\n self.dog_pose += increment\\n self._update_environment()\",\n \"def jump(self):\\n print(\\\"Inside ElfRider.jump\\\")\",\n \"def censor_a_word(text, censor):\\n censored_item = \\\"\\\"\\n for x in range(len(censor)):\\n if censor[x] == \\\" \\\":\\n censored_item = censored_item + \\\" \\\"\\n else:\\n censored_item = censored_item + \\\"X\\\"\\n return text.replace(censor, censored_item)\",\n \"def on_cmyk_slide(self,c,m,y,k):\\n if not self.active:\\n return\\n cyan = c / 100.0\\n magenta = m / 100.0\\n yellow = y / 100.0\\n black = k / 100.0\\n self.cmyk = colormodel.CMYK(cyan, magenta, yellow, black)\\n temp = a3.cmyk_to_rgb(self.cmyk)\\n assert (temp == None or type(temp) == colormodel.RGB), 'cmyk_to_rgb does not return a RGB object'\\n self.rgb = self.rgb if temp is None else temp\\n self.hsv = a3.rgb_to_hsv(self.rgb)\\n assert (self.hsv == None or type(self.hsv) == colormodel.HSV), 'rgb_to_hsv does not return a HSV object'\\n self.update()\",\n \"def jump(self):\\n if (self.falling or self.rising) and self.doubleJump:\\n self.speed_y = -20 # //////Aquí se cambia la velocidad incial cuando se salta//////\\n self.fallin = False\\n self.rising = True\\n self.doubleJump = False\\n\\n if not self.falling and not self.rising:\\n self.speed_y = -20 # //////Aquí se cambia la velocidad incial cuando se salta//////\\n self.rising = True\",\n \"def _jump(self):\\n # can't jump while jump\\n if self._player.is_jumping():\\n # no hard-code, set jump with 2*max_velocity\\n self._player.set_velocity((0, -2*self._max_velocity))\\n self._player.set_jumping(False) # means can't jump\",\n \"def jump(self):\\r\\n if self.grounded == True:\\r\\n self.vel.y = -13\",\n \"def _compute_gravity_torque(self):\\n pass\",\n \"def cryptensor(*args, backend=None, **kwargs):\\n if backend is None:\\n backend = get_default_backend()\\n if backend == crypten.mpc:\\n return backend.MPCTensor(*args, **kwargs)\\n else:\\n raise TypeError(\\\"Backend %s is not supported\\\" % backend)\",\n \"def perform_action(self, car, action):\\n action[0]=action[0]*10+20\\n action[1]=action[1]*0.5\\n p.setJointMotorControl2(car, 3, p.POSITION_CONTROL, targetPosition =action[1],force = self.maxForce)\\n for i in [0,1]:\\n p.setJointMotorControl2(car, i, p.VELOCITY_CONTROL, targetVelocity =action[0],force = self.maxForce)\\n p.setJointMotorControl2(car, 7, p.VELOCITY_CONTROL, targetVelocity =action[0]*7,force = self.maxForce)\\n pos1, ori1 = p.getBasePositionAndOrientation(car)\\n lin, ang = p.getBaseVelocity(car)\\n '''\\n if(pos1[0]<-self.max_dist_x):\\n p.resetBasePositionAndOrientation(car, [pos1[0]+2*self.max_dist_x,pos1[1],pos1[2]], ori1)\\n vel = p.resetBaseVelocity(car, lin)\\n if(pos1[0]>self.max_dist_x):\\n p.resetBasePositionAndOrientation(car, [pos1[0]-2*self.max_dist_x,pos1[1],pos1[2]], ori1)\\n vel = p.resetBaseVelocity(car, lin)\\n if(pos1[1]<-self.max_dist_y):\\n p.resetBasePositionAndOrientation(car, [pos1[0],pos1[1]+2*self.max_dist_y,pos1[2]], ori1)\\n vel = p.resetBaseVelocity(car, lin)\\n if(pos1[1]>self.max_dist_y):\\n p.resetBasePositionAndOrientation(car, [pos1[0],pos1[1]-2*self.max_dist_y,pos1[2]], ori1)\\n vel = p.resetBaseVelocity(car, lin)\\n '''\",\n \"def jump(distance):\\r\\n t.penup()\\r\\n t.forward(200)\\r\\n t.pendown()\\r\\n return None\",\n \"def cooldown():\\n print camera.CoolerON()\\n camera.status.update()\",\n \"def ev_joyballmotion(self, event: tcod.event.JoystickBall) -> T | None:\",\n \"def main():\\n raw_input()\\n clouds = map(int, raw_input().split(' '))\\n print jumping_on_the_clouds(clouds)\",\n \"def on_l_joy_x(self):\\r\\n self.log()\",\n \"def compute_controller(self):\\n \\n # here we implement an example for a consensus algorithm\\n neig = self.get_neighbors()\\n messages = self.get_messages()\\n pos, rot = self.get_pos_and_orientation()\\n \\n #send message of positions to all neighbors indicating our position\\n for n in neig:\\n self.send_message(n, pos)\\n \\n # check if we received the position of our neighbors and compute desired change in position\\n # as a function of the neighbors (message is composed of [neighbors id, position])\\n dx = 0.\\n dy = 0.\\n if messages:\\n for m in messages:\\n dx += m[1][0] - pos[0]\\n dy += m[1][1] - pos[1]\\n # integrate\\n des_pos_x = pos[0] + self.dt * dx\\n des_pos_y = pos[1] + self.dt * dy\\n \\n #compute velocity change for the wheels\\n vel_norm = np.linalg.norm([dx, dy]) #norm of desired velocity\\n if vel_norm < 0.01:\\n vel_norm = 0.01\\n des_theta = np.arctan2(dy/vel_norm, dx/vel_norm)\\n right_wheel = np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n left_wheel = -np.sin(des_theta-rot)*vel_norm + np.cos(des_theta-rot)*vel_norm\\n self.set_wheel_velocity([left_wheel, right_wheel])\",\n \"def do_impact(self, car):\\r\\n\\r\\n if car is not None:\\r\\n if self.head_of_jump is None:\\r\\n self.head_of_jump = Jump()\\r\\n else:\\r\\n jj = Jump()\\r\\n jj.next = self.head_of_jump\\r\\n self.head_of_jump = jj\\r\\n\\r\\n # self.current_jump = self.head_of_jump\\r\",\n \"def cox_cc_loss(g_case: Tensor, g_control: Tensor, shrink : float = 0.,\\n clamp: Tuple[float, float] = (-3e+38, 80.)) -> Tensor:\\n control_sum = 0.\\n shrink_control = 0.\\n if g_case.shape != g_control[0].shape:\\n raise ValueError(f\\\"Need `g_case` and `g_control[0]` to have same shape. Got {g_case.shape}\\\"+\\n f\\\" and {g_control[0].shape}\\\")\\n for ctr in g_control:\\n shrink_control += ctr.abs().mean()\\n ctr = ctr - g_case\\n ctr = torch.clamp(ctr, *clamp) # Kills grads for very bad cases (should instead cap grads!!!).\\n control_sum += torch.exp(ctr)\\n loss = torch.log(1. + control_sum)\\n shrink_zero = shrink * (g_case.abs().mean() + shrink_control) / len(g_control)\\n return torch.mean(loss) + shrink_zero.abs()\",\n \"def _twist_callback(self, cmd):\\n self.set_velocity(cmd.linear.x, cmd.angular.z)\",\n \"def short_circ():\\n \\n set_mode(mode_cv)\\n time.sleep(.250)\\n set_CV_volts(0.1)\\n time.sleep(.250)\\n \\n sc_vals = get_input_values()\\n sc_data_point = data_point(sc_vals)\\n jsc = sc_data_point[4]\\n print('Short circuit current: ', jsc)\\n write_data_tofile(sc_data_point)\\n\\n return jsc\",\n \"def print_usage():\\n print \\\"Censor a document\\\\n\\\"\\n print \\\"Use: python censor.py x NUM_DOCS s t p heuristic flag\\\\n\\\"\\n print \\\"Arguments:\\\"\\n print \\\"\\\\tif Greedy, x = index of the target document to censor (0 <= x < NUM_DOCS)\\\\n\\\\tif KillNodes, x = percentage of nodes to be shut down (0 < x < 1)\\\\n\\\"\\n print \\\"\\\\tNUM_DOCS = total number of docs in the archive\\\\n\\\"\\n print \\\"\\\\ts = number of source blocks\\\\n\\\"\\n print \\\"\\\\tt = number of pointer blocks\\\\n\\\"\\n print \\\"\\\\tp = number of parity blocks\\\\n\\\"\\n print \\\"\\\\theuristic = MinimumAttack or LeapingAttack or CreepingAttack or TailoredAttack or KillNodes or KillDocs or KillRand\\\\n\\\"\\n print \\\"\\\\tflag = write 'draw' if you want graphical visualization\\\\n\\\"\",\n \"def _do_mc_action(self):\\n goal = self._current_mc_goal\\n self._position_control_client.send_goal(\\n goal,\\n done_cb = self._motion_control_callback\\n )\",\n \"def kick_from_accretion(self):\\n\\n accth_mag = -self.v * self.pair.dmdt_accr / self.pair.mass\\n kick0 = -self.vth_vec * accth_mag[0] * self.dt\\n kick1 = self.vth_vec * accth_mag[1] * self.dt\\n\\n self.pair[0].velocity += kick0\\n self.pair[1].velocity += kick1\",\n \"def put_on_the_floor(device, q_init):\\n global key_pressed\\n key_pressed = False\\n Kp_pos = 3.\\n Kd_pos = 0.01\\n imax = 3.0\\n pos = np.zeros(device.nb_motors)\\n for motor in range(device.nb_motors):\\n pos[motor] = q_init[device.motorToUrdf[motor]] * device.gearRatioSigned[motor]\\n listener = keyboard.Listener(on_press=on_press)\\n listener.start()\\n print(\\\"Put the robot on the floor and press Enter\\\")\\n while not key_pressed:\\n device.UpdateMeasurment()\\n for motor in range(device.nb_motors):\\n ref = Kp_pos*(pos[motor] - device.hardware.GetMotor(motor).GetPosition() - Kd_pos*device.hardware.GetMotor(motor).GetVelocity())\\n ref = min(imax, max(-imax, ref))\\n device.hardware.GetMotor(motor).SetCurrentReference(ref)\\n device.SendCommand(WaitEndOfCycle=True)\\n\\n print(\\\"Start the motion.\\\")\",\n \"def _perform_landing(self):\\n self.y += self.settings.mario_jump_speed\\n if self.y >= self.settings.mario_y_pos:\\n self.y = self.settings.mario_y_pos\\n self.jumping = 0\\n self.is_currently_jumping = False\",\n \"def step1c(self):\\n\\t\\tif (self.ends(\\\"y\\\") and self.vowelinstem()):\\n\\t\\t\\tself.b = self.b[:self.k] + 'i' + self.b[self.k+1:]\",\n \"def ocr_correction(token):\",\n \"def requestAccel(self) -> None:\\n self._protocol.write_line(CMD_ACCELEROMETER)\",\n \"def jump_factor(self):\\n return self._jump_factor\",\n \"def jumped_on(self):\\r\\n pass\",\n \"def warmup():\\n print camera.CoolerOFF()\\n camera.status.update()\",\n \"async def c(self, f : float):\\n c = (f-32) * 5/9\\n await self.bot.say(\\\"{0} Celsius\\\".format(c))\",\n \"def do_updates(self):\\n if self.words.check_for_dashes():\\n self.console.write(\\\"Great job! We are so proud of you!\\\")\\n self.keep_playing = False\\n \\n if not self.good_guess:\\n del self.jumper.jumper_list[0]\\n if self.jumper.jumper_list[0] == \\\" 0 \\\":\\n webbrowser.open(\\\"https://www.youtube.com/watch?v=oHg5SJYRHA0&ab_channel=cotter548\\\")\\n self.keep_playing = False\",\n \"def set_actuator(self, action):\\n deltav = action[0]\\n vt = np.clip(self.vt + deltav, -self.maxV, self.maxV)\\n self.vt = vt\\n p.setJointMotorControl2(bodyUniqueId=self.botId,\\n jointIndex=0,\\n controlMode=p.VELOCITY_CONTROL,\\n targetVelocity=vt)\\n p.setJointMotorControl2(bodyUniqueId=self.botId,\\n jointIndex=1,\\n controlMode=p.VELOCITY_CONTROL,\\n targetVelocity=-vt)\",\n \"def runDisplacement(self,distance,axis):\\n if axis == \\\"x\\\":\\n self.M1.runDisplacement(distance)\\n elif axis == \\\"y\\\":\\n self.M2.runDisplacement(distance)\",\n \"def dynamicMoon(moonPosX, moonPosY, velocityXMoon, velocityYMoon, h):\\r\\n kPosMoon = [[0 for x in range(4)] for y in range(2)] # initialising the 2x2 k matricies\\r\\n kVMoon = [[0 for x in range(4)] for y in range(2)]\\r\\n \\r\\n kPosMoon[0][0] = velocityXMoon # this value is k1 for the x position. It is just the velocity of the rocket at its current position.\\r\\n kPosMoon[1][0] = velocityYMoon #this value is k1 for the y position\\r\\n kVMoon[0][0] = accelerationCalcX(moonPosX, moonPosY) #this value is k1 for the x velocity. At its current position what is the acceleration of the projectile\\r\\n kVMoon[1][0] = accelerationCalcY(moonPosX, moonPosY) # this value is k1 for the y velocity\\r\\n \\r\\n #k2s\\r\\n kPosMoon[0][1] = velocityXMoon + h*kVMoon[0][0]/2 #what would its velocity be if it carried on at its initial acceleration (calculated in k1 for x velocity) for half a time step\\r\\n kPosMoon[1][1] = velocityYMoon + h*kVMoon[1][0]/2\\r\\n kVMoon[0][1] = accelerationCalcX(moonPosX + h*kPosMoon[0][0]/2, moonPosY + h*kPosMoon[1][0]/2) # if it continued at the velocity in k2 for x position for half a time step what would the acceleration on the projectile be.\\r\\n kVMoon[1][1] = accelerationCalcY(moonPosX + h*kPosMoon[0][0]/2, moonPosY + h*kPosMoon[1][0]/2)\\r\\n \\r\\n #k3s\\r\\n kPosMoon[0][2] = velocityXMoon + h*kVMoon[0][1]/2 # if it carried on at the acceleration calculated for k2 in x velocity for half a time step, what would its velocity be\\r\\n kPosMoon[1][2] = velocityYMoon + h*kVMoon[1][1]/2\\r\\n kVMoon[0][2] = accelerationCalcX(moonPosX + h*kPosMoon[0][1]/2, moonPosY + h*kPosMoon[1][1]/2) # if carried on at the velocity calculated in k2 for half a time step then what would its accelaration be\\r\\n kVMoon[1][2] = accelerationCalcY(moonPosX + h*kPosMoon[0][1]/2, moonPosY + h*kPosMoon[1][1]/2)\\r\\n \\r\\n #k4s\\r\\n kPosMoon[0][3] = velocityXMoon + h*kVMoon[0][2] # if it carried on at the acceleration calcualted in k3 fro a whole timestep, then what would its velocity be \\r\\n kPosMoon[1][3] = velocityYMoon + h*kVMoon[1][2]\\r\\n kVMoon[0][3] = accelerationCalcX(moonPosX + h*kPosMoon[0][2], moonPosY + h*kPosMoon[1][2]) #if it continued at the velocity calculated in k3 for a whole time step, then what would its accelaration be\\r\\n kVMoon[1][3] = accelerationCalcY(moonPosX + h*kPosMoon[0][2], moonPosY + h*kPosMoon[1][2])\\r\\n \\r\\n velocityXMoon = velocityXMoon+(h/6)*(kVMoon[0][0]+2*kVMoon[0][1]+2*kVMoon[0][2]+kVMoon[0][3]) # the velocity in x is appended, after combining the ks for velocity in x\\r\\n velocityYMoon = velocityYMoon+(h/6)*(kVMoon[1][0]+2*kVMoon[1][1]+2*kVMoon[1][2]+kVMoon[1][3]) # the velocity in y is appended, after combining the ks for velocity in y\\r\\n moonPosX = moonPosX+(h/6)*(kPosMoon[0][0]+2*kPosMoon[0][1]+2*kPosMoon[0][2]+kPosMoon[0][3]) # the x position is appended, after combinging the ks for x position\\r\\n moonPosY = moonPosY+(h/6)*(kPosMoon[1][0]+2*kPosMoon[1][1]+2*kPosMoon[1][2]+kPosMoon[1][3]) # the y position is appended, after combinging the ks for y position\\r\\n \\r\\n return moonPosX, moonPosY, velocityXMoon, velocityYMoon # return the position and velocity back to moonPass\\r\",\n \"def moove_character(self, case_list):\\n\\t\\tself.actual_hero.start_moove(case_list)\",\n \"def _do_updates(self):\\n is_right = self._puzzle.is_guess_right()\\n if is_right:\\n self._puzzle.reveal_puzzle()\\n else:\\n self._jumper.cut_line()\",\n \"def rotate_cw(self, deg):\\r\\n self.send_command_without_response(f'cw {deg}')\",\n \"def Keyboard(self, key):\\r\\n\\t\\tif key == Keys.K_u:\\r\\n\\t\\t\\tself.kp+=self.dp\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_j:\\r\\n\\t\\t\\tself.kp-=self.dp\\r\\n\\t\\t\\tif self.kp<0:\\r\\n\\t\\t\\t\\tself.kp=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_i:\\r\\n\\t\\t\\tself.ki+=self.di\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_k:\\r\\n\\t\\t\\tself.ki-=self.di\\r\\n\\t\\t\\tif self.ki<0:\\r\\n\\t\\t\\t\\tself.ki=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_o:\\r\\n\\t\\t\\tself.kd+=self.dd\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\tif key == Keys.K_l:\\r\\n\\t\\t\\tself.kd-=self.dd\\r\\n\\t\\t\\tif self.kd<0:\\r\\n\\t\\t\\t\\tself.kd=0.0\\r\\n\\t\\t\\tself.q[0].SetCoefs(self.q[0].configer,self.kp,self.ki,self.kd)\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_f:\\r\\n\\t\\t\\tself.center+=1\\r\\n\\t\\t\\tif self.center>2:\\r\\n\\t\\t\\t\\tself.center = 0\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_t:\\r\\n\\t\\t\\tself.cut=1-self.cut\\r\\n\\t\\t\\tself.q[0].SetCut(self.cut)\\t\\t\\t\\r\\n\\t\\t\\t\\r\\n\\t\\tif key == Keys.K_r:\\r\\n\\t\\t\\tpass\\t\\r\\n\\t\\t\\r\\n\\t\\tif key == Keys.K_s:\\r\\n\\t\\t\\tself.q[0].saveConf()\",\n \"def set_jump(self, jump):\\n self.jump = jump\",\n \"def apply_action(self, action):\\n real_action = self.policy_action_to_robot_action(action)\\n p.setGravity(0, 0, 0)\\n p.resetBaseVelocity(\\n self.robot_ids[0], real_action[:3], real_action[3:])\",\n \"def before_step(self, action, physics):\\n # # Support legacy internal code.\\n\\n physics.named.data.xfrc_applied[:,:3]=np.zeros((3,))\\n\\n if self._random_location and not self._maxq:\\n index = self._current_loc\\n else:\\n one_hot = action[:4]\\n index = np.argmax(one_hot)\\n action = action[4:]\\n\\n goal_position = action * 0.05\\n corner_action = CORNER_INDEX_ACTION[index]\\n corner_geom = CORNER_INDEX_POSITION[index]\\n\\n\\n # apply consecutive force to move the point to the target position\\n position = goal_position + physics.named.data.geom_xpos[corner_geom]\\n dist = position - physics.named.data.geom_xpos[corner_geom]\\n\\n loop = 0\\n while np.linalg.norm(dist) > 0.025:\\n loop += 1\\n if loop > 40:\\n break\\n physics.named.data.xfrc_applied[corner_action, :3] = dist * 20\\n physics.step()\\n self.after_step(physics)\\n dist = position - physics.named.data.geom_xpos[corner_geom]\\n\\n if self._random_location and not self._maxq:\\n self._current_loc = self._generate_loc()\",\n \"def CreateMotionKernel(kernel):\\r\\n TrajSize = 64\\r\\n anxiety = 0.2* np.random.rand()\\r\\n numT = 10\\r\\n MaxTotalLength =10\\r\\n TotLength = 0\\r\\n #term determining, at each sample, the strengh of the component leating towards the previous position\\r\\n centripetal = 0.7 * np.random.rand()\\r\\n #term determining, at each sample, the random component of the new direction\\r\\n gaussianTerm =10 * np.random.rand()\\r\\n #probability of having a big shake, e.g. due to pressing camera button or abrupt hand movements\\r\\n freqBigShakes = 3 *np.random.rand()\\r\\n #v is the initial velocity vector, initialized at random direction\\r\\n init_angle = 360 * np.random.rand()\\r\\n #initial velocity vector having norm 1\\r\\n v0 = math.cos(init_angle / 180.0 * math.pi) + 1.0j * math.sin(init_angle/ 180.0 * math.pi)\\r\\n #the speed of the initial velocity vector\\r\\n v = v0* MaxTotalLength/(numT-1);\\r\\n\\r\\n if anxiety > 0:\\r\\n v = v0 * anxiety\\r\\n # initialize the trajectory vector\\r\\n x = np.zeros(numT,dtype = np.complex);\\r\\n\\r\\n abruptShakesCounter = 0\\r\\n for t in range(numT-1):\\r\\n # determine if there is an abrupt (impulsive) shake\\r\\n if np.random.rand() < freqBigShakes * anxiety:\\r\\n #if yes, determine the next direction which is likely to be opposite to the previous one\\r\\n nextDirection = 2 * v * (np.exp( 1.0j * (math.pi + (np.random.rand() - 0.5))))\\r\\n abruptShakesCounter = abruptShakesCounter + 1\\r\\n else:\\r\\n nextDirection=0\\r\\n\\r\\n #determine the random component motion vector at the next step\\r\\n dv = nextDirection + anxiety * (gaussianTerm * (np.random.randn()- + 1.0j * np.random.randn()) - centripetal * x[t]) * (MaxTotalLength / (numT - 1))\\r\\n v = v + dv\\r\\n # velocity vector normalization\\r\\n v = (v / np.abs(v)) * MaxTotalLength / (numT - 1)\\r\\n #print v\\r\\n x[t + 1] = x[t] + v\\r\\n # compute total length\\r\\n #TotLength=TotLength+np.abs(x([t+1]-x[t]))\\r\\n x_real = []\\r\\n x_imag = []\\r\\n for elem in x:\\r\\n x_real.append(elem.real)\\r\\n x_imag.append(elem.imag)\\r\\n x_real = np.round((x_real - np.min(x_real))/(np.max(x_real) - np.min(x_real)) * kernel-0.5)\\r\\n x_imag = np.round((x_imag - np.min(x_imag))/(np.max(x_imag) - np.min(x_imag)) * kernel-0.5)\\r\\n for idx in range(len(x_real)):\\r\\n if x_real[idx] < 0:\\r\\n x_real[idx] = 0\\r\\n if x_imag[idx] < 0:\\r\\n x_imag[idx] = 0\\r\\n if x_real[idx] > kernel -1:\\r\\n x_real[idx] = kernel -1\\r\\n if x_imag[idx] > kernel -1:\\r\\n x_imag[idx] = kernel -1\\r\\n\\r\\n ker = np.zeros((kernel, kernel))\\r\\n for idx in range(len(x_real)):\\r\\n ker[np.int(x_real[idx])][np.int(x_imag[idx])] = 1\\r\\n ker = ker/np.sum(np.sum(ker))\\r\\n return ker\",\n \"def cmu_mocap_35_walk_jog(data_set='cmu_mocap'):\\r\\n train_motions = ['01', '02', '03', '04', '05', '06',\\r\\n '07', '08', '09', '10', '11', '12',\\r\\n '13', '14', '15', '16', '17', '19',\\r\\n '20', '21', '22', '23', '24', '25',\\r\\n '26', '28', '30', '31', '32', '33', '34']\\r\\n test_motions = ['18', '29']\\r\\n data = cmu_mocap('35', train_motions, test_motions, sample_every=4, data_set=data_set)\\r\\n data['info'] = \\\"Walk and jog data from CMU data base subject 35. As used in Tayor, Roweis and Hinton at NIPS 2007, but without their pre-processing (i.e. as used by Lawrence at AISTATS 2007). It consists of \\\" + data['info']\\r\\n return data\",\n \"def moco_loss_func(\\n query: torch.Tensor, key: torch.Tensor, queue: torch.Tensor, temperature=0.1\\n) -> torch.Tensor:\\n\\n pos = torch.einsum(\\\"nc,nc->n\\\", [query, key]).unsqueeze(-1)\\n neg = torch.einsum(\\\"nc,ck->nk\\\", [query, queue])\\n logits = torch.cat([pos, neg], dim=1)\\n logits /= temperature\\n targets = torch.zeros(query.size(0), device=query.device, dtype=torch.long)\\n return F.cross_entropy(logits, targets)\",\n \"def put_cmyk(self, path, res, mode, color_correction):\\n\\n ### Stratasys_J750\\n if mode == \\\"stratasys\\\":\\n _c_ = (0, 90, 158, 255)\\n _m_ = (166, 33, 98, 255)\\n _y_ = (200, 189, 3, 255)\\n _k_ = (26, 26, 29, 255)\\n\\n ### cmyk\\n else:\\n _c_ = (0, 255, 255, 255)\\n _m_ = (255, 0, 255, 255)\\n _y_ = (255, 255, 0, 255)\\n \\n\\n\\n ut = util.UTIL()\\n \\n image = Image.open(path)\\n image_size = image.size\\n\\n clr_list = self.get_color_to_memory(image)\\n\\n # print(\\\"Memory : \\\", ut.ll_size(clr_list))\\n\\n new_image = self.up_scale(image, res)\\n new_image_size = new_image.size\\n \\n # print(\\\"PIL : \\\", image_size)\\n # print(new_image_size)\\n\\n vectors = ut.set_vector(res)\\n\\n for i in range(image_size[0]):\\n for j in range(image_size[1]):\\n\\n pt = (i * res, j * res)\\n rgb = clr_list[i][j]\\n new_vectors = random.sample(vectors, len(vectors))\\n\\n cmyk = self.calc_rgb_cmyk(rgb)\\n\\n\\n ### ========== CMYK ==========\\n # cc, mm, yy, kk = self.calc_cmyk_count(cmyk, res)\\n # new_length = cc + mm + yy + kk\\n ### ========== CMYK ==========\\n\\n\\n ### ========== CMY ==========\\n cc, mm, yy = self.calc_cmy_count(cmyk, res, color_correction)\\n _length = cc + mm + yy\\n ### ========== CMY ==========\\n\\n\\n if _length > (res * res):\\n new_length = (res * res)\\n else:\\n new_length = _length\\n\\n new_pt = []\\n\\n for k in range(new_length):\\n\\n new_pt = ut.pt2d_add(pt, new_vectors[k])\\n # print(new_pt)\\n\\n if k < cc:\\n new_image.putpixel(new_pt, (_c_))\\n elif k < (cc + mm):\\n new_image.putpixel(new_pt, (_m_))\\n elif k < (cc + mm + yy):\\n new_image.putpixel(new_pt, (_y_))\\n # else:\\n # new_image.putpixel(new_pt, (_k_))\\n \\n return new_image\",\n \"def cox_cc_loss_single_ctrl(g_case: Tensor, g_control: Tensor, shrink: float = 0.) -> Tensor:\\n loss = F.softplus(g_control - g_case).mean()\\n if shrink != 0:\\n loss += shrink * (g_case.abs().mean() + g_control.abs().mean())\\n return loss\",\n \"def handle_continuous_keys(self):\\n shift = pygame.K_LSHIFT in self.held\\n ctrl = pygame.K_LCTRL in self.held\\n factor = 3 if shift else 1/3 if ctrl else 1\\n for key in self.held:\\n if not self.followmode:\\n # if self.held_delay[key] == 0:\\n if key in (pygame.K_w, pygame.K_UP): # up\\n # self.canvas.move_offset(0, 5 * factor)\\n self.canvas.move_focus(0, 5 * factor)\\n elif key in (pygame.K_s, pygame.K_DOWN): # down\\n # self.canvas.move_offset(0, -5 * factor)\\n self.canvas.move_focus(0, -5 * factor)\\n elif key in (pygame.K_d, pygame.K_RIGHT): # right\\n # self.canvas.move_offset(-5 * factor, 0)\\n self.canvas.move_focus(5 * factor, 0)\\n elif key in (pygame.K_a, pygame.K_LEFT): # left\\n # self.canvas.move_offset(5 * factor, 0)\\n self.canvas.move_focus(-5 * factor, 0)\\n if key in (pygame.K_e, pygame.K_KP_PLUS):\\n self.canvas.zoom(2 * factor)\\n elif key in (pygame.K_q, pygame.K_KP_MINUS):\\n self.canvas.zoom(-2 * factor)\\n for key in self.held:\\n self.held_delay[key] = (self.held_delay[key] + 1) % 5\",\n \"def _motion_control_callback(self, state, result):\\n if len(self._mc_goals) > 0:\\n self._current_mc_goal = self._mc_goals.pop(0)\\n self._do_mc_action()\\n else :\\n self._current_mc_goal = None\",\n \"def acceleration(degrees, finalSpeed, steering = 0, robot = MoveSteering(OUTPUT_A, OUTPUT_B), motorA = LargeMotor(OUTPUT_A)): # Function to accelerate while moving so the robot can get traction before moving fast\\n\\n motorA.reset() #reseting how many degrees the robot has moved\\n motorA.position = 0 # setting how many degrees the robot has moved\\n\\n accelerateDegree = degrees * 0.8\\n # declerationDegree = degrees * 0.2'\\n speed = 0 #starting speed\\n while motorA.position < degrees and False == Constants.STOP: #while the robot hasen't moved the target amount of degree's(distance)\\n if motorA.position < accelerateDegree and False == Constants.STOP: \\n if speed < finalSpeed: # while the robot hasen't accelerated to the target speed\\n speed += 5 #speed up\\n robot.on(steering = steering, speed = speed) # Start Moving\\n sleep(0.1) #wait so that it doesn't accelerate immidiatly\\n else:\\n robot.on(steering = steering, speed = finalSpeed)# otherwise just keep moving\\n sleep(0.01)\\n elif False == Constants.STOP:\\n if speed > 10:\\n speed -= 5\\n robot.on(steering = steering, speed = speed)\\n sleep(0.05)\\n else:\\n robot.on(steering = steering, speed = speed)\\n sleep(0.01)\\n \\n robot.off() # Stop Moving\",\n \"def sit(self):\\n\\n\\t\\tself.pose.goToPosture(\\\"Crouch\\\")\",\n \"def cohen_kappa(*args, **kwargs):\\n return ConfusionMatrix2.from_ccw(*args, **kwargs).kappa()\",\n \"def set_hybrid_control(self, model, max_force_torque, timeout=5.0, stop_on_target_force=False):\\n\\n reduced_speed = np.deg2rad([100, 100, 100, 150, 150, 150])\\n q_last = self.joint_angles()\\n\\n # Timeout for motion\\n initime = rospy.get_time()\\n xb = self.end_effector()\\n failure_counter = 0\\n\\n while not rospy.is_shutdown() \\\\\\n and (rospy.get_time() - initime) < timeout:\\n\\n # Transform wrench to the base_link frame\\n Wb = self.get_ee_wrench()\\n\\n # Current Force in task-space\\n Fb = -1 * Wb\\n # Safety limits: max force\\n if np.any(np.abs(Fb) > max_force_torque):\\n rospy.logerr('Maximum force/torque exceeded {}'.format(np.round(Wb, 3)))\\n self.set_target_pose_flex(pose=xb, t=model.dt)\\n return FORCE_TORQUE_EXCEEDED\\n\\n if stop_on_target_force and np.any(np.abs(Fb)[model.target_force != 0] > model.target_force[model.target_force != 0]):\\n rospy.loginfo('Target F/T reached {}'.format(np.round(Wb, 3)) + ' Stopping!')\\n self.set_target_pose_flex(pose=xb, t=model.dt)\\n return STOP_ON_TARGET_FORCE\\n\\n # Current position in task-space\\n xb = self.end_effector()\\n\\n dxf = model.control_position_orientation(Fb, xb) # angular velocity\\n\\n # Limit linear/angular velocity\\n dxf[:3] = np.clip(dxf[:3], -0.5, 0.5)\\n dxf[3:] = np.clip(dxf[3:], -5., 5.)\\n\\n xc = transformations.pose_from_angular_velocity(xb, dxf, dt=model.dt)\\n\\n # Avoid extra acceleration when a point failed due to IK or other violation\\n # So, this corrects the allowed time for the next point\\n dt = model.dt * (failure_counter+1)\\n\\n q = self._solve_ik(xc)\\n if q is None:\\n rospy.logwarn(\\\"IK not found\\\")\\n result = IK_NOT_FOUND\\n else:\\n q_speed = (q_last - q)/dt\\n if np.any(np.abs(q_speed) > reduced_speed):\\n rospy.logwarn(\\\"Exceeded reduced max speed %s deg/s, Ignoring command\\\" % np.round(np.rad2deg(q_speed), 0))\\n result = SPEED_LIMIT_EXCEEDED\\n else:\\n result = self.set_joint_positions_flex(position=q, t=dt)\\n\\n if result != DONE:\\n failure_counter += 1\\n continue # Don't wait since there is not motion\\n else:\\n failure_counter = 0\\n\\n # Compensate the time allocated to the next command when there are failures\\n for _ in range(failure_counter+1):\\n self.rate.sleep()\\n\\n q_last = self.joint_angles()\\n return DONE\",\n \"def _content_jump(self, target):\\n head = self._relative_head_pos()\\n similarities = 1 - np.sqrt(np.sum((self.memory - target) ** 2, 1)) / self.memory_unit_size\\n pos = int(np.argmax(similarities).item())\\n if similarities[pos] > self.min_similarity_to_jump:\\n self.head_pos = pos\\n else:\\n self.head_pos = 0\\n if self.history is not None:\\n self.history[\\\"loc\\\"][-1].append((head, 0.1))\",\n \"def rotateCW(device, runTime = 0.1):\\n return execute(device, ROTATE_CCW_CMD, STATUS_LIMIT_ROTATE, runTime)\",\n \"def omega(self):\\n self.cosineSequences()\",\n \"def cauchy(self, loc, gamma):\\n c = loc + gamma * np.tan(np.pi * (self.random() - 0.5))\\n return c if c > 0 else self.cauchy(loc, gamma)\",\n \"def attack(self, robot):\\n pass\",\n \"def move_to_coc(self):\\n coc = scale(self.center_of_charge(), -1.0)\\n self.translate(coc)\",\n \"def run(self):\\n self.coffee_machine.water_tank.decrease_weight(self.coffee_machine.chosen_coffee_data.get('water_weight'))\",\n \"def k_2_jy(freq: float, theta_major: float,\\n theta_minor: float, brightness: float) -> float:\\n conv = (1.222E3 * (freq ** -2) / theta_minor / theta_major) ** -1\\n return brightness * conv\",\n \"def joy_callback(self, msg):\\n mappings = gamepad_mappings.set_gamepad_mappings(msg)\\n self.move_vertical = mappings[\\\"button_vertical\\\"] # up: +1.0, down: -1.0\\n self.move_horizontal = mappings[\\\"button_horizontal\\\"] # left: +1.0, right: -1.0\",\n \"def recommend_cosim():\\n pass\",\n \"def execute(self):\\n # Check buttons\\n self.check_enable_pids(self.inputs[JoyInput.START])\\n self.check_disable_pids(self.inputs[JoyInput.BACK])\\n self.check_toggle(self.inputs[JoyInput.LS], self.inputs[JoyInput.LS_Y], self.saved[JoyInput.LS_Y])\\n\\n # Check axes\\n #self.input_forward(JoyInput.LS_Y)\\n self.input_depth(JoyInput.CROSS_Y)\\n self.set_raw_pwm(JoyInput.LT, JoyInput.RT) # uses LT/RT for depth\\n #self.input_rotate(JoyInput.LT, JoyInput.RT) # uses RT for rotation\\n\\n # yaw setpoint\\n magnitude, angle = self.input_yaw_setpoint(JoyInput.RS_X, JoyInput.RS_Y, Joystick.RS_ANGLE)\\n self.check_yaw_setpoint_toggle(JoyInput.RS, Joystick.RS_ANGLE, magnitude, angle)\",\n \"def take_action(self, action):\\r\\n\\r\\n self._update_velocity(action)\\r\\n self._update_position()\\r\\n if self.is_terminal_state():\\r\\n return 100.0\\r\\n\\r\\n return -1.0\",\n \"def teleopPeriodic(self):\\n\\n turningValue = (self.angleSetpoint - self.gyro.getAngle()) * self.pGain\\n if self.joystick.getY() <= 0:\\n # forwards\\n self.myRobot.arcadeDrive(self.joystick.getY(), turningValue)\\n elif self.joystick.getY() > 0:\\n # backwards\\n self.myRobot.arcadeDrive(self.joystick.getY(), -turningValue)\",\n \"def JogMode(port):\\n\\tport.write(\\\"Q\\\")\",\n \"def runTask1(self):\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.initialize()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.sweepDuckie()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieSuctionOn()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.duckieLiftEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppSweep()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppAlignX()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppAlignY()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppLowerEE()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.ppSuctionOff()\\r\\n\\t\\tif not self._motion.breakout():\\r\\n\\t\\t\\tself._motion.reset()\\r\\n\\t\\tself._motion.terminate()\",\n \"def control_change(self, channel, cc, value):\\n knob, bank = self.decode_mpd218_cc(cc)\\n log.debug(\\\"Winch control change %d on knob %d bank %d\\\", cc, knob, bank)\\n\\n if knob == 1: # Knob #1 on MPD218, use to control resonant frequency\\n #self.frequency = 0.05 + 0.1 * value\\n self.frequency = 5.00\\n self.set_freq_damping()\\n\\n elif knob == 2: # Knob #2 on on MPD218, use to control damping ratio\\n #self.damping_ratio = 0.05 + 0.01 * value\\n self.damping_ratio = 1.32\\n self.set_freq_damping()\",\n \"async def get_words():\\n global CENSORED_WORDS\\n CENSORED_WORDS = get_censor()\",\n \"def first_time_run(self, w):\\n \\n if w in ['half', 'quarter']:\\n self._first_time_channel = getattr(self,'_'+w+'_channel')\\n else:\\n raise ValueError('Input type for \\\"w\\\" should be \\\"half\\\" or \\\"quarter\\\"')\\n\\n #measure position before optimizing\\n getattr(self.rotator, 'set_zero_position')(getattr(self,'_'+w+'_channel')) \\n pos_before = getattr(self.rotator, 'get_noof_steps_ch'+\\\\\\n str(getattr(self,'_'+w+'_channel')))()\\n\\n #turn waveplates\\n data, qtdata, dataplot, premature_quit = self.run('first_time', self._first_opt_red_power)\\n qtdata.close_file()\\n\\n if not premature_quit:\\n\\n print '\\\\tGuessing optimal waveplate position...'\\n optim_pos = data['wp_step'][self.find_nearest(data['counts'],\\n min(data['counts']))]\\n\\n if self.get_plot_degrees():\\n print '\\\\tOptimal waveplate position determined at %.0f degrees.'%(optim_pos*self.get_conversion_factor(w))\\n else:\\n print '\\\\tOptimal waveplate position determined at %d steps.'%optim_pos\\n \\n #BEWARE: never ask the current position in noof_steps\\n curr_pos = data['wp_step'][len(data['wp_step'])-1]\\n\\n #set the position to the optimal position\\n self.rotator.quick_scan(optim_pos-curr_pos, getattr(self,'_'+w+'_channel'))\\n\\n else:\\n #ways to get a premature quit action: q key stroke or > threshold\\n #BEWARE: never ask the current position in noof_steps\\n curr_pos = data['wp_step'][len(data['wp_step'])-1] \\n\\n print '\\\\tReturning to initial position...'\\n\\n #set the position to the optimal position\\n self.rotator.quick_scan(pos_before-curr_pos, getattr(self,'_'+w+'_channel'))\\n\\n\\n #measure position after optimizing\\n pos_after = getattr(self.rotator, 'get_noof_steps_ch'+\\\\\\n str(getattr(self,'_'+w+'_channel') ) )()\\n\\n #print \\\"\\\\tPosition of %s waveplate changed %d steps\\\"\\\\\\n # %(w, pos_after-pos_before)\",\n \"def keyboardController(self):\\n # get pressed keys\\n keypress = pygame.key.get_pressed()\\n\\n # randomize world if R is pressed and enough frames have passed since last time\\n if keypress[pygame.K_r]:\\n if not self.seedWorldTimer:\\n self.seedWorldTimer = int(self.FPS / 4)\\n self.calculateWorldValues()\\n self.findPolygons()\\n # decrement delay between allowed randomizations\\n if self.seedWorldTimer:\\n self.seedWorldTimer -= 1\\n\\n # change displayMode if necessary\\n if keypress[pygame.K_p]:\\n self.displayMode = self.DISPLAYMODE_POINTS\\n glEnable(GL_CULL_FACE) # enable culling\\n glCullFace(GL_BACK)\\n elif keypress[pygame.K_o]:\\n self.displayMode = self.DISPLAYMODE_MESH\\n glDisable(GL_CULL_FACE) # disable culling for mesh viewing\\n elif keypress[pygame.K_i]:\\n self.displayMode = self.DISPLAYMODE_WIREFRAME\\n\\n # apply polar camera movement\\n if keypress[pygame.K_w]:\\n self.cameraPolar[2] -= 0.08 * self.dt\\n if self.cameraPolar[2] < 1:\\n self.cameraPolar[2] = 1.0\\n if keypress[pygame.K_s]:\\n self.cameraPolar[2] += 0.08 * self.dt\\n if self.cameraPolar[2] > 179:\\n self.cameraPolar[2] = 179\\n if keypress[pygame.K_d]:\\n self.cameraPolar[1] += 0.08 * self.dt\\n if self.cameraPolar[1] > 180:\\n self.cameraPolar[1] -= 360\\n if keypress[pygame.K_a]:\\n self.cameraPolar[1] -= 0.08 * self.dt\\n if self.cameraPolar[1] <= -180:\\n self.cameraPolar[1] += 360\\n if keypress[pygame.K_b]:\\n self.t = 'b'\\n self.calculateWorldValues()\\n self.findPolygons()\\n if keypress[pygame.K_n]:\\n self.t = 'n'\\n self.calculateWorldValues()\\n self.findPolygons()\\n if keypress[pygame.K_m]:\\n self.t = 'm'\\n self.calculateWorldValues()\\n self.findPolygons()\\n # update camera cartesian position\\n self.polarCameraToCartesian()\",\n \"def switch_on(self,name):\\n self.circles[name].switch_on()\\n self.cursor.execute(\\\"\\\"\\\"UPDATE sensors_powersensor SET state=1 WHERE target=%s\\\"\\\"\\\", (name,))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59280413","0.541607","0.5290688","0.52872306","0.5283519","0.51918846","0.5162083","0.5111304","0.50946474","0.50678855","0.5063725","0.50507736","0.50380087","0.5027665","0.50178564","0.49831063","0.4976855","0.4957412","0.49509645","0.49407175","0.49123913","0.49025196","0.48670948","0.48562348","0.48345843","0.48306543","0.48277506","0.48266277","0.4821847","0.48047793","0.47947925","0.4777308","0.47706807","0.47633016","0.47522616","0.47461694","0.47306776","0.46897304","0.46725744","0.46627864","0.46619913","0.460711","0.45884004","0.457084","0.45661137","0.45659515","0.45629004","0.45610657","0.45566863","0.45564222","0.45481992","0.45399728","0.4534141","0.45305654","0.4521202","0.45067325","0.45059216","0.44972107","0.44857696","0.4459462","0.44584933","0.44425666","0.44363317","0.44331554","0.4408211","0.44071746","0.4403953","0.44030344","0.4402658","0.43947783","0.4391479","0.43854916","0.4384707","0.4349547","0.43463236","0.43445387","0.43428218","0.43296224","0.4327063","0.43196416","0.43099603","0.43088573","0.43073884","0.43072504","0.430633","0.4306214","0.43053004","0.43022972","0.4301223","0.43007088","0.43005797","0.42994553","0.42946732","0.42940536","0.4293233","0.4283806","0.4278969","0.42773655","0.4277172","0.42746904"],"string":"[\n \"0.59280413\",\n \"0.541607\",\n \"0.5290688\",\n \"0.52872306\",\n \"0.5283519\",\n \"0.51918846\",\n \"0.5162083\",\n \"0.5111304\",\n \"0.50946474\",\n \"0.50678855\",\n \"0.5063725\",\n \"0.50507736\",\n \"0.50380087\",\n \"0.5027665\",\n \"0.50178564\",\n \"0.49831063\",\n \"0.4976855\",\n \"0.4957412\",\n \"0.49509645\",\n \"0.49407175\",\n \"0.49123913\",\n \"0.49025196\",\n \"0.48670948\",\n \"0.48562348\",\n \"0.48345843\",\n \"0.48306543\",\n \"0.48277506\",\n \"0.48266277\",\n \"0.4821847\",\n \"0.48047793\",\n \"0.47947925\",\n \"0.4777308\",\n \"0.47706807\",\n \"0.47633016\",\n \"0.47522616\",\n \"0.47461694\",\n \"0.47306776\",\n \"0.46897304\",\n \"0.46725744\",\n \"0.46627864\",\n \"0.46619913\",\n \"0.460711\",\n \"0.45884004\",\n \"0.457084\",\n \"0.45661137\",\n \"0.45659515\",\n \"0.45629004\",\n \"0.45610657\",\n \"0.45566863\",\n \"0.45564222\",\n \"0.45481992\",\n \"0.45399728\",\n \"0.4534141\",\n \"0.45305654\",\n \"0.4521202\",\n \"0.45067325\",\n \"0.45059216\",\n \"0.44972107\",\n \"0.44857696\",\n \"0.4459462\",\n \"0.44584933\",\n \"0.44425666\",\n \"0.44363317\",\n \"0.44331554\",\n \"0.4408211\",\n \"0.44071746\",\n \"0.4403953\",\n \"0.44030344\",\n \"0.4402658\",\n \"0.43947783\",\n \"0.4391479\",\n \"0.43854916\",\n \"0.4384707\",\n \"0.4349547\",\n \"0.43463236\",\n \"0.43445387\",\n \"0.43428218\",\n \"0.43296224\",\n \"0.4327063\",\n \"0.43196416\",\n \"0.43099603\",\n \"0.43088573\",\n \"0.43073884\",\n \"0.43072504\",\n \"0.430633\",\n \"0.4306214\",\n \"0.43053004\",\n \"0.43022972\",\n \"0.4301223\",\n \"0.43007088\",\n \"0.43005797\",\n \"0.42994553\",\n \"0.42946732\",\n \"0.42940536\",\n \"0.4293233\",\n \"0.4283806\",\n \"0.4278969\",\n \"0.42773655\",\n \"0.4277172\",\n \"0.42746904\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7094361"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8454,"cells":{"query":{"kind":"string","value":"Check if output file exists, then execute commmand. If there is more than one output file, the command will be executed if at least one is missing."},"document":{"kind":"string","value":"def CheckExec(self, cmd, checknames, force=False, halt_on_error=True):\n gone = False\n names = []\n for name in checknames:\n if '+orig' in name:\n if name.endswith('+orig'):\n names.append('%s.HEAD' % name)\n names.append('%s.BRIK' % name)\n elif name.endswith('HEAD'):\n names.append(name)\n newname = name[:-4] + 'BRIK'\n if newname not in checknames:\n names.append(newname)\n elif name.endswith('BRIK'):\n newname = name[:-4] + 'HEAD'\n if newname not in checknames:\n names.append(newname)\n names.append(name)\n else:\n names.append(name)\n for name in names:\n if not os.path.exists(name) and not os.path.exists('%s.gz'%name):\n gone = True\n elif self.redo or force or gone:\n os.remove(name)\n gone = True\n if self.redo or gone:\n self.ExecCmd(cmd, halt_on_error=halt_on_error)\n if '+orig.' in names[0]:\n name = names[0].replace('.BRIK','')\n name = name.replace('.HEAD','')\n append_history_note(name, cmd)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check_for_preexisting_output_file(output_file_path):\n if path.exists(f\"{output_file_path}\"):\n print(\"Output file at specified save location file path already exists!\")\n print(\"Aborting operation!\")\n sys.exit()","def exec_command_string_one_file(command_str,output):\n print(command_str)\n # (status, result) = subprocess.check_output(command_str, universal_newlines=True, stderr=sys.stdout) #available in both Python 2.x and 3.x\n\n (status, result) = getstatusoutput(command_str)\n\n if os.path.isfile(output):\n return output\n else:\n outputlogMessage(result)\n # syslog.outputlogMessage('The version of GDAL must be great than 2.0 in order to use the r option ')\n return False","def exec_command_args_list_one_file(args_list,output):\n outputlogMessage(output_commandString_from_args_list(args_list))\n ps = subprocess.Popen(args_list)\n returncode = ps.wait()\n if os.path.isfile(output):\n return output\n else:\n outputlogMessage('return codes: '+ str(returncode))\n return False","def exec_to_file ( cmd, output_file, cwd = '/tmp/' ):\n\n try:\n dn = open(os.devnull, 'r')\n with open(output_file, 'w') as fo:\n vlog(4, 'Running command: %s > %s from %s '% (cmd, output_file, cwd))\n p = subprocess.Popen(\n cmd, \n stdin=dn,\n stdout=fo, \n stderr=fo, \n cwd=cwd, \n close_fds=True\n )\n\n if p:\n p.wait()\n return p.returncode\n\n except Exception as e:\n vlog(1, 'Command Error: %s'% (str(e)))\n\n vlog(1, 'Failed to run command: %s > %s '% (cmd, output_file))\n return None","def do_command(cmd, output_file):\n global txt_output_dir\n output_path = os.path.join(txt_output_dir, output_file)\n print \"doing: %s > %s\" % (cmd, output_path)\n output = check_output(cmd.split(\" \"))\n with open(output_path, \"w\") as f:\n f.write(output)","def output_file_exists(self):\n file = self.config['OUT_FOLDER'] + '/' + self.config['OUTPUT_FOLDER'] + '/' + self.output_filename + '.' + \\\n self.options['image_format'].lower()\n self.__log(f'Checking if output file: \"{file}\" already exists.')\n if os.path.exists(file) and not os.stat(file).st_size == 0:\n self.__log(f'Output file: \"{file}\" does exist.')\n return True\n self.__log(f'Output file: \"{file}\" does not exist.')\n return False","def check_output(self, args):\n if isinstance(args, str):\n args = [args]\n try:\n return check_output(args)\n\n except IOError as e:\n raise ScriptError(e)\n\n except OSError as e:\n raise ScriptError(e)\n\n except CalledProcessError as e:\n raise ScriptError(e)","def test_check_if_output_file_exists():\n input_file = os.path.join(os.getcwd(), 'tests', 'input_test_file.docx')\n output_file = os.path.join(os.getcwd(), 'tests', 'output_test_file.txt')\n\n questions_parser = QuestionsParser()\n questions_parser.main(argv=['-i', input_file, '-o', output_file])\n assert os.path.exists(output_file)\n os.unlink(output_file)","def get_output(self, output_dir=\"tools_output\"):\n\n output_dir = self.project_dir / output_dir / self.name\n # create output directory if didn't exist\n if not output_dir.exists():\n os.makedirs(output_dir)\n logger.info(f\"Created {output_dir}\")\n\n for outfile in self.output:\n outfile = self.project_dir / outfile\n if outfile.exists():\n src = os.fspath(outfile)\n dst = os.fspath(output_dir / outfile.name)\n shutil.move(src, dst)\n logger.info(f\"Moved {outfile.name} to {output_dir}\")\n else:\n msg = f\"File not found: {outfile} - did you execute run() before?\"\n logger.error(msg)\n raise FileNotFoundError(msg)","def _execute_internal(cmd, log_std_out, log_std_err, log_cmd, also_output_to_file=None,\r\n return_output=False):\r\n tmp_filename = None\r\n if log_std_out or log_std_err:\r\n tmp_filename = tempfile.mktemp()\r\n tmp_file = open(tmp_filename, \"w\")\r\n\r\n if (not log_std_err) or (not log_std_out):\r\n dev_null_file = open(os.devnull, \"w\")\r\n\r\n if log_std_out:\r\n if also_output_to_file:\r\n std_out_file = open(also_output_to_file, \"w\")\r\n else:\r\n std_out_file = tmp_file\r\n else:\r\n std_out_file = dev_null_file\r\n\r\n if log_std_err:\r\n std_err_file = tmp_file\r\n else:\r\n std_err_file = dev_null_file\r\n\r\n if log_cmd:\r\n log.info(\"Executing: %s\" % \" \".join(cmd))\r\n #Call subprocess.call to run the command.\r\n try:\r\n ret = subprocess.call(cmd, stdout=std_out_file, stderr=std_err_file)\r\n text = None\r\n #Check if there is some output or error captured to log\r\n if tmp_filename:\r\n with open(tmp_filename, 'r') as file_read:\r\n text = file_read.read()\r\n os.remove(tmp_filename)\r\n if text:\r\n log.info(\"External output:\\n%s\" % text)\r\n return (ret, text) if return_output else ret\r\n except subprocess.CalledProcessError as exception:\r\n log.error(\"Subprocess Exception occurred %s\" % exception)\r\n return (ret, None) if return_output else ret\r\n except OSError as exception:\r\n log.error(\"OS Exception occurred %s\" % exception)\r\n #IF this exception occurs the ret value is not initialized hence return non-zero\r\n return (1, None) if return_output else 1","def get_output_file():\n if len(sys.argv) < 4:\n return -1\n return sys.argv[3]","def execute(cmd, output_file, env={}):\n return subprocess.Popen(shlex.split(cmd),\n stderr=subprocess.STDOUT,\n stdout=open(output_file, \"w\"),\n env = dict(os.environ, **env))","def check_output(self):\n directory, file = split(self.target)\n if not exists(directory):\n mkdir(directory)\n if exists(self.target):\n unlink(self.target)","def ensure_file(command, filename, overwrite=False, shell=False):\n print(command)\n if overwrite or not os.path.exists(filename):\n ret = subprocess.call(command, shell=shell)\n if ret:\n raise Exception(ret)\n return ret","def _execute_cmd(cmd, outdir: str = DEFAULT_OUTDIR, stdout_file=None, stderr_file=None):\n if cmd is None:\n raise Exception(\"cmd was not specified\")\n\n logging.info(f\"Will attempt to execute '{cmd}'\")\n\n if outdir is None:\n outdir = '/tmp'\n logging.info(f\"outdir was not defined and therefore was set to default '{outdir}'\")\n\n if stdout_file is None:\n stdout_file = os.path.join(outdir, os.path.basename(__file__) + '.stdout')\n logging.info(f\"stdout_file was not specified and therefore was set to '{stdout_file}'\")\n\n if stderr_file is None:\n stderr_file = os.path.join(outdir, os.path.basename(__file__) + '.stderr')\n logging.info(f\"stderr_file was not specified and therefore was set to '{stderr_file}'\")\n\n if os.path.exists(stdout_file):\n logging.info(f\"STDOUT file '{stdout_file}' already exists so will delete it now\")\n os.remove(stdout_file)\n\n if os.path.exists(stderr_file):\n logging.info(f\"STDERR file '{stderr_file}' already exists so will delete it now\")\n os.remove(stderr_file)\n\n p = subprocess.Popen(cmd, shell=True)\n\n (stdout, stderr) = p.communicate()\n\n pid = p.pid\n\n logging.info(f\"The child process ID is '{pid}'\")\n\n p_status = p.wait()\n\n p_returncode = p.returncode\n\n if p_returncode is not None:\n logging.info(f\"The return code was '{p_returncode}'\")\n else:\n logging.info(\"There was no return code\")\n\n if p_status == 0:\n logging.info(f\"Execution of cmd '{cmd}' has completed\")\n else:\n raise Exception(f\"Received status '{p_status}'\")\n\n if stdout is not None:\n logging.info(\"stdout is: \" + stdout)\n\n if stderr is not None:\n logging.info(\"stderr is: \" + stderr)\n\n return stdout_file","def fileoutput(cd, bin, args=[]):\n cmd = \"cd %s; ./%s %s\" % (cd, bin, ' '.join(args))\n logger.info(cmd)\n\n return exec_command(cmd)","def check_file_output(self, actual: str, expected: str):\n assert self._program_executed, f\"You first need to `execute` the program before checking its outputs!\"\n assert actual in self._write_files, f\"Unknown output file {actual}. Did you forget to provide it to the program by calling input_write_filename?\"\n full_expected = _root_dir / expected\n assert full_expected.is_file(), f\"Reference file {full_expected} does not exist!\"\n # check to make sure the output file exists\n full_actual = _root_dir / actual\n self._test.assertTrue(full_actual.is_file(), f\"It seems like the program never created the output file {full_actual}\")\n # open and compare the files\n with open(full_actual, 'rb') as a:\n actual_bin = a.read()\n with open(full_expected, 'rb') as e:\n expected_bin = e.read()\n self._test.assertEqual(actual_bin, expected_bin, f\"Bytes of {actual} and {expected} did not match!\")","def check_output_wrapper(*args, **kwargs):\n\n logger.debug('Executing %s, %s', args, kwargs)\n try:\n return check_output(*args, **kwargs)\n except CalledProcessError as msg:\n logger.warning('Error %s,%s,%s from command.', msg.returncode, msg.output, msg.stderr)\n logger.debug('Output: %s', msg.output)\n sys.exit(ERROR_EXECUTING_COMMAND);","def runTool(self, filename, expected_out, args):\n\n input_path = os.path.join(self.inputs_dir, filename)\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\n actual_output = actual_output.decode('utf-8')\n\n self.assertEqual(return_value, 0)\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')","def process_cleanup(self, output_file=None, output_list=None):\n if output_file:\n self.check_output_file( output_file )\n elif output_list:\n for output_file in output_list:\n self.check_output_file( output_file )\n log.info('All expected output files found - process successful!\\n')","def call_files():\n try:\n predicted_proteins = sys.argv[1]\n except IndexError:\n predicted_proteins = input('Please input AUGUSTUS file for analysis: ')\n try:\n protein_db = sys.argv[2]\n except IndexError:\n protein_db = input('Please input a protein database file: ')\n\n try:\n output_file_aug_to_fasta = sys.argv[3]\n output_to_file = True\n except IndexError:\n output_to_file = input('Write output to file?'\n + ' [Yes/No]: ')\n if output_to_file.upper() in 'YES':\n output_to_file = True\n output_file_aug_to_fasta = input('Please supply output file name '\n + 'for AUGUSTUS conversion to '\n + 'FASTA: ')\n else:\n output_to_file = False\n output_file_aug_to_fasta = None\n\n try:\n output_file_proteins_to_db = sys.argv[4]\n except IndexError:\n if output_to_file:\n output_file_proteins_to_db = input('Please supply output file name'\n + 'for blast database: ')\n else:\n output_file_proteins_to_db = None\n\n try:\n blastp_output = sys.argv[5]\n except IndexError:\n if output_to_file:\n blastp_output = input('Please supply output file name for blastp: ')\n else:\n blastp_output = None\n\n finally:\n if len(sys.argv) >= 7:\n overwrite = sys.argv[6]\n elif output_file and os.path.exists(output_file):\n overwrite = input('Output file already exists. Overwrite? '\n + '[Yes/No]: ')\n if overwrite.upper() in 'YES':\n overwrite = True\n else:\n overwrite = False\n else: overwrite = False\n\n return (predicted_proteins, protein_db, output_file_aug_to_fasta, \n output_file_proteins_to_db, blastp_output, \n output_to_file, overwrite)","def load_job_output(output_title, output_summary, output):\n def read_if_file(val):\n if os.path.exists(val):\n logger.info(\"Reading file: %s\", val)\n with open(val, \"r\") as inf:\n return inf.read()\n else:\n return val\n\n if output_title:\n assert output_summary\n return checks.Output(\n title = output_title,\n summary = read_if_file(output_summary),\n text = read_if_file(output) if output else None\n )\n else:\n return None","def check_output(self, cmd, nonzero_e = tc.error_e):\n _exitcode, stdoutf, _stderrf = self.run(cmd, nonzero_e = nonzero_e)\n return stdoutf.read()","def check_output(*args, **kwargs):\n if \"stdout\" in kwargs:\n raise ValueError(\"stdout argument not allowed, it will be overriden.\")\n p = Popen(stdout=PIPE, *args, **kwargs)\n out, _ = p.communicate()\n rc = p.poll()\n if rc != 0:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = args[0]\n raise CalledProcessError(rc, cmd, output=out)\n return out","def check_output(command):\n process = Popen(command, shell=True, stdout=PIPE)\n output, err = process.communicate()\n if process.returncode == 0: # success\n return output\n else:\n raise RuntimeError(\"Command {0} running unsuccessfully\".format(command))","def try_process_output_file(ins_file, output_file=None):\n if output_file is None:\n output_file = ins_file.replace(\".ins\", \"\")\n df = None\n i = InstructionFile(ins_file)\n try:\n df = i.read_output_file(output_file)\n except Exception as e:\n print(\"error processing instruction/output file pair: {0}\".format(str(e)))\n return df","def process_tool_output(self, proc, log_fn, name):\n output, error = proc.communicate()\n if proc.returncode != 0:\n fd = open(log_fn, \"a+\")\n fd.write(\"Error: %s returned the following output:\"\n \"\\n%s\" % (name, error))\n fd.close()\n raise Exception(\"%s returned the following output:\"\n \"\\n%s\" % (name, error))\n else:\n fd = open(log_fn, \"a+\")\n fd.write(\"%s output: %s %s\" % (name, output, error))\n # printing stdout and stderr though bowtie2 currently will always\n # output to stderr instead of stdout even if no error occurred\n fd.close()","def run(self):\n try:\n self.parse_args(None)\n self.execute_command()\n except FileExistsException, e:\n print \"Can't copy file as destination already exists.\"\n print \"Exiting...\"\n except Exception, e:\n print \"Exception occured: %s\\nExiting...\" % e","def check_output(*popenargs, **kwargs):\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n error = subprocess.CalledProcessError(retcode, cmd)\n error.output = output\n raise error\n return output","def test_execution(self):\n\n # This process will echo the input and output file name to stdout.\n the_process_unit = ProcessUnit([self.a_pattern_ds], '/another/%file%/%pattern%.txt',\n 'echo')\n\n ds_result = the_process_unit.execute(simulate=True)\n\n outfiles = [file_thing for file_thing in ds_result.files]\n self.assertEqual(len(outfiles), 1)\n\n expected_string = self.script_header + \"mkdir -p /another/file_1\\necho test_file1 /another/file_1/pattern_1.txt\\n\"\n self.assertEqual(expected_string, the_process_unit.scheduler.job.to_str())","def collect(self,outfilename):\n # TODO actually gather results and check if run is successful\n if os.path.isfile(outfilename):\n self.completed=True\n else:\n self.completed=False","def setup_molecule_output_check(exp_builder_db, mol_id, output_path):\n exp_builder_db._setup_molecules(mol_id)\n assert os.path.exists(output_path)\n assert os.path.getsize(output_path) > 0","def test_output_exists():\n global out_dir, cor_dir\n assert(path.exists(path.join(out_dir, 'oshea_similarity.json')))","def ExecuteIf(self, args, src_files, dst_files):\n if self.ShouldBuild(src_files, dst_files):\n self.MakeDestinationDirectories(dst_files)\n self.Execute(args)\n if self.execute and not self.VerifyExists(dst_files):\n raise RuntimeError(\"FAILED: build did not create all required files\")","def output_files_exist(self):\n return all([split.exists() for split in self.split_files])","def eval_output(ifile,cfile,ofile):\n print('Evaluating {} on combo {}'.format(ifile, cfile))\n\tglobal EVAL_TABLE_PATH\n\tEVAL_TABLE_ARGS = \" -i %s -C %s -o %s -u %s \"%(ifile,cfile,ofile,OUT)\n\tresult=os.system(EVAL_TABLE_PATH + \" \" + EVAL_TABLE_ARGS) \n\tif result!=0:\n\t\tprint \"error while executing\"\n\t\texit(0)","def blankOutputFiles():\n print(\"Checking for blank output files\")\n find_output = re.compile(r\"/\\* Output:(.*)\\*/\", re.DOTALL)\n for java in config.example_dir.rglob(\"*.java\"):\n with java.open() as codeFile:\n output = find_output.search(codeFile.read())\n if output:\n # print(output.group(1))\n if not output.group(1).strip():\n print(java)","def check_output(*popenargs, **kwargs):\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n error = subprocess.CalledProcessError(retcode, cmd)\n error.output = output\n raise error\n return output","def check_output(*popenargs, **kwargs):\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n error = subprocess.CalledProcessError(retcode, cmd)\n error.output = output\n raise error\n return output","def execute(cmd, log_cmd=True, also_output_to_file=None):\r\n return CommandUtil._execute_internal(cmd, True, True, log_cmd, also_output_to_file)","def execute(cmd, path):\n oldPath = os.getcwd()\n os.chdir(path)\n\n exitcode, output = subprocess.getstatusoutput(cmd)\n\n os.chdir(oldPath)\n\n ok = not exitcode\n\n return ok, output","def executeCommand(commandtoexecute):\n try:\n _output = commands.getstatusoutput(commandtoexecute)\n except Exception as er:\n print \"not able to execute command\"\n return False\n return _output","def optional_output_file(func):\n\n @click.argument(\n \"output\", required=False, type=click.Path(exists=False, )\n )\n @wraps(func)\n def wrapped(*args, **kwargs):\n return func(*args, **kwargs)\n return wrapped","def check_output(args, **kwargs):\n kwargs.setdefault('stdin', DEVNULL)\n if 'stdout' in kwargs:\n raise ValueError('stdout argument not allowed, it would be overridden.')\n return check_call_out(args, stdout=PIPE, **kwargs)[0]","def present_output(self, output: str):\n\n if self._config.use_stdout():\n print(output, flush=True, end='')\n else:\n try:\n # print(\"OUTPUT FILE IS THERE!!!!\", self._config.values['output'])\n with open(self._config.values['output'], mode='w', newline='') as f:\n # print(\"PRINITNG OUTPUT>>>>\"+output+\"<<<<<\")\n f.write(output)\n # print(\"OUTPUT FILE IS THERE!!!!\", \"After write\")\n except:\n raise OutputFileException('Error writing the output')","def shell_execute(command, output, test=None, options=None,\n data=None, test_split=None):\n command = check_debug(command)\n world.directory = os.path.dirname(output)\n world.folders.append(world.directory)\n try:\n retcode = check_call(command, shell=True)\n if retcode < 0:\n assert False\n else:\n if test is not None:\n world.test_lines = file_number_of_lines(test)\n if options is None or options.find('--prediction-header') == -1:\n # test file has headers in it, so first line must be ignored\n world.test_lines -= 1\n if test_split is not None:\n data_lines = file_number_of_lines(data) - 1\n world.test_lines = int(data_lines * float(test_split))\n \n world.output = output\n assert True\n except (OSError, CalledProcessError, IOError) as exc:\n assert False, str(exc)","def exists(parser, output_name):\n return parser.setParseAction(isNotEmpty).setResultsName(output_name)","def checkForCommand(quickLogger, commandList):\n\n for command in commandList:\n\n cmd = \"which -s \" + command + \" > \" + os.devnull + \" 2>&1\"\n retcode = os.system(cmd)\n \n if(retcode):\n quickLogger.critical(\"unix command \"+command+\" not found.\")\n raise CommandNotFound","def executeSAGATool(self, tool_cmd, f_out, desc=''):\n\n if self.runSep(self.p_uid):\n\n self.logger.debug('[SAGA cmd]: %s', tool_cmd)\n subprocess.call(self.saga_cmd + tool_cmd)\n\n # if output file exist log workflow step\n if os.path.isfile(f_out):\n if not desc:\n desc = '{0}: Processing step {1}'.format(self.wf_name, self.p_uid)\n\n self.logWorkflowStep(self.p_uid, desc)\n else:\n raise workflowException('SAGA process \\'{0}\\' output file is missing: {1}'.format(desc, f_out))\n else:\n self.logger.info(desc + ': done.')\n\n # increment p_uid\n self.p_uid += 10\n\n return","def get_output(command):\n if is_debug_environment():\n dir_name = get_test_dir_argument()\n filename = '{}.txt'.format(command)\n full_path = os.path.join(dir_name, filename)\n if os.path.isfile(full_path):\n with open(full_path) as f:\n return f.read()\n raise IOError(\"File {} is not found\".format(full_path))\n else:\n import cli\n result = cli.cli(command)\n i = 0\n # Occasionally, cli library returns an empty output, in this case we try multiple time\n while result.count('\\n') <= 2 and i < 3:\n result = cli.cli(command)\n i += 1\n return result","def execute_task(self, filename):\n stdin, stdout, stderr = self.ssh.exec_command(open(filename).read())\n if stdout.channel.recv_exit_status() == 0:\n return True, stdout.read().strip(), stderr.read().strip()\n else:\n return False, stdout.read().strip(), stderr.read().strip()","def test_missing_file():\n\n rv, out = getstatusoutput(f'{prg} -o {outfile}')\n assert rv != 0\n assert re.search('the following arguments are required: -f/--file', out)","def command_exe(cmd, file_name, folder=None):\r\n if folder:\r\n file_path = os.path.join(folder, file_name)\r\n subprocess.call('%s > %s' % (cmd, file_path), shell=True)\r\n logging.info(file_path)\r\n else:\r\n file_path = os.path.join(os.getcwd(), file_name)\r\n subprocess.call('%s > %s' % (cmd, file_path), shell=True)\r\n logging.info(file_path)","def get_prog_file():\n get_file()\n ## Executa\n file = ARGS.output\n os.system(\"chmod +x \" + file)\n subprocess.call([file])","def execute(self, *args, **options):\n self.output_file = options.get('output_file')\n try:\n super(CsvMixin, self).execute(*args, **options)\n finally:\n self.close_file_handle()","def test_call_output_to_file(self):\r\n\r\n fd, tmp_result_filepath = mkstemp(\r\n prefix='CdHitOtuPickerTest.test_call_output_to_file_',\r\n suffix='.txt')\r\n close(fd)\r\n\r\n app = CdHitOtuPicker(params={'Similarity': 0.90})\r\n obs = app(self.tmp_seq_filepath1, result_path=tmp_result_filepath)\r\n\r\n result_file = open(tmp_result_filepath)\r\n result_file_str = result_file.read()\r\n result_file.close()\r\n # remove the result file before running the test, so in\r\n # case it fails the temp file is still cleaned up\r\n remove(tmp_result_filepath)\r\n\r\n # compare data in result file to fake expected file\r\n self.assertEqual(result_file_str, dna_seqs_result_file_90_exp)\r\n # confirm that nothing is returned when result_path is specified\r\n self.assertEqual(obs, None)","def check_output(*popenargs, **kwargs):\n if 'stdout' in kwargs:\n raise ValueError('stdout argument not allowed, it will be overridden.')\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n raise subprocess.CalledProcessError(retcode, cmd)#, output=output)\n return output","def check_for(command):\n if shutil.which(command) is None:\n print(colored(\"{} not available on system\".format(command),\"red\"))\n sys.exit(1)","def check_output(*popenargs, **kwargs):\n if 'stdout' in kwargs:\n raise ValueError('stdout argument not allowed, it will be overridden.')\n logging.debug(' '.join(popenargs[0]))\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\n output, unused_err = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n raise subprocess.CalledProcessError(retcode, cmd)\n return output","def execute(cmd) :\n return os.system( cmd )","def command(self, command, out=False, err=False):\n \n if out:\n std_out = subprocess.PIPE\n else:\n std_out = None\n \n if not err:\n std_err = subprocess.PIPE\n else:\n std_err = None\n \n \n proc = subprocess.Popen(command, stdout = std_out, stderr=std_err)#std_out)\n out, err = proc.communicate()\n \n return out, err","def testGetOutput(self):\n #f = open(\"src_output.root\", 'w')\n #f.close()\n\n #1) missing required -d option (the other required option, -r, is ignored)\n go = getoutput(self.logger, self.maplistopt)\n res = go()\n expRes = CommandResult(2001, 'ERROR: Task option is required')\n self.assertEquals(expRes, res)\n\n #2) -d option is present but -r is missing\n analysisDir = self.reqarea\n go = getoutput(self.logger, self.maplistopt + [\"-d\", analysisDir])\n res = go()\n expRes = CommandResult(2002, 'ERROR: Range option is required')\n self.assertEquals(expRes, res)\n\n #3) request passed with the -d option does not exist\n #res = go([\"-d\", analysisDir + \"asdf\"])\n #TODO we expect an appropriate answer from the server.\n #By now, the server just answer an empty list\n\n #4) check correct behaviour without specifying output directory\n #N.B.: -p options is required for tests to skip proxy creation and delegation\n destDir = os.path.join(analysisDir, 'results')\n go = getoutput(self.logger, self.maplistopt + [\"-d\", analysisDir, \"-r\", \"1\", \"-p\"])\n res = go()\n expRes = CommandResult(0, '\\n')\n #check if the result directory has been created\n self.assertTrue(os.path.isdir(destDir))\n self.assertTrue(os.path.isfile(os.path.join(destDir, '1.root')))\n #Remove the directory\n shutil.rmtree(destDir)\n self.assertFalse(os.path.isdir(destDir))\n self.assertEquals(expRes, res)\n\n #5) correct behavior and output directory specified which exists\n go = getoutput(self.logger, self.maplistopt + [\"-d\", analysisDir, \"-r\", \"1\", \"-o\", \"/tmp\", \"-p\"])\n res = go()\n expRes = CommandResult(0, '\\n')\n #check if the result directory has been created\n self.assertTrue(os.path.isdir('/tmp'))\n destFile = os.path.join('/tmp', '1.root')\n self.assertTrue(os.path.isfile(destFile))\n os.remove(destFile)\n self.assertFalse(os.path.isfile(destFile))\n self.assertEquals(expRes, res)\n\n #6) correct behavior and output directory specified which does not exists\n go = getoutput(self.logger, self.maplistopt + [\"-d\", analysisDir, \"-r\", \"1\", \"-o\", \"/tmp/asdf/qwerty\", \"-p\"])\n res = go()\n expRes = CommandResult(0, '\\n')\n #check if the result directory has been created\n self.assertTrue(os.path.isdir('/tmp/asdf/qwerty'))\n #Remove the directory\n shutil.rmtree('/tmp/asdf/qwerty')\n self.assertEquals(expRes, res)\n\n #7) correct behavior and output directory specified which does not exists (relative path)\n go = getoutput(self.logger, self.maplistopt + [\"-d\", analysisDir, \"-r\", \"1\", \"-o\", \"qwerty\", \"-p\"])\n res = go()\n expRes = CommandResult(0, '\\n')\n #check if the result directory has been created\n self.assertTrue(os.path.isdir('qwerty'))\n #Remove the directory\n shutil.rmtree('qwerty')\n self.assertEquals(expRes, res)","def check_output_file(output_dir,\n identifier,\n shuffle_labels,\n model_options,\n predictor='classify',\n fold_no=None,\n titration_ratio=None):\n\n signal = 'shuffled' if shuffle_labels else 'signal'\n\n if not isinstance(model_options.training_data, str):\n training_data = '.'.join(model_options.training_data)\n else:\n training_data = model_options.training_data\n\n if isinstance(model_options.n_dim, list):\n n_dim = '.'.join(map(str, model_options.n_dim))\n else:\n n_dim = model_options.n_dim\n\n check_file = construct_filename(output_dir,\n 'coefficients',\n '.tsv.gz',\n identifier,\n training_data,\n model_options.model,\n signal,\n predictor,\n s=model_options.seed,\n n=n_dim,\n f=fold_no,\n t=titration_ratio)\n if check_file.is_file():\n raise ResultsFileExistsError(\n 'Results file already exists for identifier: {}\\n'.format(\n identifier)\n )\n return check_file","def fetch_output(self, path, name, working_directory, action_type, output_type):\n if output_type in ['output_workdir', 'output_metadata']:\n self._populate_output_path(name, path, action_type, output_type)\n elif output_type == 'output':\n self._fetch_output(path=path, name=name, action_type=action_type)\n else:\n raise Exception(\"Unknown output_type %s\" % output_type)","def assertExecutedOnce(self, component: Text) -> None:\n component_path = os.path.join(self._pipeline_root, component)\n self.assertTrue(fileio.exists(component_path))\n outputs = fileio.listdir(component_path)\n\n self.assertIn(\".system\", outputs)\n outputs.remove(\".system\")\n system_paths = [\n os.path.join(\".system\", path)\n for path in fileio.listdir(os.path.join(component_path, \".system\"))\n ]\n self.assertNotEmpty(system_paths)\n self.assertIn(\".system/executor_execution\", system_paths)\n outputs.extend(system_paths)\n self.assertNotEmpty(outputs)\n for output in outputs:\n execution = fileio.listdir(os.path.join(component_path, output))\n self.assertLen(execution, 1)","def get_output(self, output, download_dir, overwrite=False, callback=None, block=4096):\n download = self._get_file(output, download_dir, overwrite, callback=callback, block=block)\n if download.success:\n return os.path.join(download_dir, output.get('name', ''))\n else:\n raise download.result","def __fetch_output_task(\n self, task, download_dir, overwrite, changed_only, **extra_args):\n return task.fetch_output(\n download_dir, overwrite, changed_only, **extra_args)","def check_output(*popenargs, **kwargs):\n process = subprocess.Popen(stdout=subprocess.PIPE,\n universal_newlines=True,\n *popenargs,\n **kwargs)\n output, _ = process.communicate()\n retcode = process.poll()\n if retcode:\n cmd = kwargs.get(\"args\")\n if cmd is None:\n cmd = popenargs[0]\n error = subprocess.CalledProcessError(retcode, cmd)\n error.output = output\n raise error\n return output.strip()","def _execute_run(prefix, osiris_path, run_dir):\n # Cf. run_config\n p = subprocess.Popen(prefix + osiris_path + \" > out.txt 2> err.txt\", shell=True, cwd=path.abspath(run_dir))\n p.wait()","def run_command(self,command):\n from subprocess import Popen, PIPE, STDOUT\n if command == '':\n raise RuntimeError('no command for run_command :(')\n # print 'Running: ', command #debug\n proc = Popen([command], shell=True, stderr=PIPE)\n proc.wait()\n exitcode = proc.returncode\n if exitcode != 0:\n # print exitcode,'label:', self.calc_dir\n error='%s exited with error code %i in %s' % (\n command,exitcode,self.calc_dir)\n stdout,stderr = proc.communicate()\n print 'shell output: ',stdout,stderr\n raise RuntimeError(error)\n return 0","def run_file(self, value=None):\n self.save_file()\n self.p = Popen(\"./Project/myfile.py\", stdout=PIPE, stderr=PIPE)\n output, errors = self.p.communicate()\n self.my_output.delete(\"1.0\", END)\n self.my_output.insert(\"1.0\", output)\n if errors != \"\":\n print_to_log(errors)\n self.my_output.configure(fg=\"red\")\n else:\n self.my_output.configure(fg=\"white\")\n self.my_output.insert(\"1.0\", errors)","def _check_output(*args, **kwargs):\n kwargs['stdout'] = subprocess.PIPE\n p = subprocess.Popen(*args, **kwargs)\n stdout, stderr = p.communicate()\n if p.returncode != 0:\n raise ValueError(\n 'subprocess exited with return code %s' % p.returncode\n )\n return stdout","def subprocess_check_output(*popenargs, **kwargs):\r\n if 'stdout' in kwargs:\r\n raise ValueError('stdout argument not allowed, it will be overridden.')\r\n if 'stderr' in kwargs:\r\n raise ValueError('stderr argument not allowed, it will be overridden.')\r\n\r\n #executable_exists(popenargs[0][0])\r\n\r\n # NOTE: it is very, very important that we use temporary files for\r\n # collecting stdout and stderr here. There is a nasty bug in python\r\n # subprocess; if your process produces more than 64k of data on an fd that\r\n # is using subprocess.PIPE, the whole thing will hang. To avoid this, we\r\n # use temporary fds to capture the data\r\n stdouttmp = TemporaryFile()\r\n stderrtmp = TemporaryFile()\r\n\r\n process = subprocess.Popen(stdout=stdouttmp, stderr=stderrtmp, *popenargs,\r\n **kwargs)\r\n process.communicate()\r\n retcode = process.poll()\r\n\r\n stdouttmp.seek(0, 0)\r\n stdout = stdouttmp.read()\r\n stdouttmp.close()\r\n\r\n stderrtmp.seek(0, 0)\r\n stderr = stderrtmp.read()\r\n stderrtmp.close()\r\n\r\n if retcode:\r\n cmd = ' '.join(*popenargs)\r\n raise Exception(\"'%s' failed(%d): %s\" % (cmd, retcode, stderr), retcode)\r\n return (stdout, stderr, retcode)","def assertOutput(self, toExec, argList, expectedStdout=None, \n\t\t\texpectedStderr=\"\", expectedRetcode=0, input=None,\n\t\t\tstdoutStrings=None):\n\t\tfor name in [\"output.stderr\", \"output.stdout\"]:\n\t\t\ttry:\n\t\t\t\tos.unlink(name)\n\t\t\texcept os.error:\n\t\t\t\tpass\n\n\t\tif isinstance(toExec, basestring):\n\t\t\tp = subprocess.Popen([toExec]+argList, executable=toExec, \n\t\t\t\tstdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\telse:\n\t\t\tp = ForkingSubprocess([\"test harness\"]+argList, executable=toExec, \n\t\t\t\tstdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tout, err = p.communicate(input=input)\n\t\tretcode = p.wait()\n\n\t\ttry:\n\t\t\tself.assertEqual(expectedRetcode, retcode)\n\n\t\t\tif isinstance(expectedStderr, basestring):\n\t\t\t\tself.assertEqual(err, expectedStderr)\n\t\t\telse:\n\t\t\t\tself.failUnless(expectedStderr(err))\n\t\texcept AssertionError:\n\t\t\twith open(\"output.stdout\", \"w\") as f:\n\t\t\t\tf.write(out)\n\t\t\twith open(\"output.stderr\", \"w\") as f:\n\t\t\t\tf.write(err)\n\t\t\traise\n\n\t\ttry:\n\t\t\tif isinstance(expectedStdout, basestring):\n\t\t\t\tself.assertEqual(out, expectedStdout)\n\t\t\telif expectedStdout is not None:\n\t\t\t\tself.failUnless(expectedStdout(out))\n\t\t\tif stdoutStrings:\n\t\t\t\tfor s in stdoutStrings:\n\t\t\t\t\tself.failIf(s not in out, \"%s missing\"%s)\n\t\texcept AssertionError:\n\t\t\twith open(\"output.stdout\", \"w\") as f:\n\t\t\t\tf.write(out)\n\t\t\traise","def get_output(self, download_dir, output=None, overwrite=False, callback=None, block=4096):\n if output:\n name = output.get('name', \"\")\n download = self._get_intermediate_output(output,\n download_dir,\n overwrite,\n callback=callback,\n block=block)\n\n elif self.output_url and self.output_filename:\n name = self.output_filename\n download = self._get_final_output(download_dir, overwrite,\n callback=callback, block=block)\n\n else:\n raise FileDownloadException(\n \"Job has no reference to an output file, \"\n \"please update to check if the output is ready\")\n\n if download.success:\n return os.path.join(download_dir, name)\n\n else:\n raise download.result","def _output_result(result: str, arguments: Dict[str, str]) -> None:\n if (\"ciphered_file\" in arguments) or (\"deciphered_file\" in arguments):\n output_filename = arguments[\"ciphered_file\"] if \"ciphered_file\" in arguments else arguments[\"deciphered_file\"]\n with open(output_filename, mode=\"w\") as output_file:\n output_file.write(result)\n output_file.flush()\n else:\n print(result)","def command_output(cmd):\n import subprocess\n return subprocess.Popen(\n cmd.split(\";\"), stdout=subprocess.PIPE).communicate()[0]","def get_output_path():\n return os.getcwd() + \"/output/\"","def eichertest():\n try:\n eicher = ('echo ZQZXJVBVT >> mcafee1.txt', 'echo ZQZXJVBVT >> mcafee2.txt','echo ZQZXJVBVT >> mcafee2.txt')\n for test in eicher:\n executeCommand(test)\n except Exception as er:\n print \"Not able to do install check\"\n return False","def __subprocess_out(self):\n file_out = open(os.path.join(self.report_path, \"cyclomatic-complexity.csv\"), \"w\")\n status = subprocess.call(r'%s' % self.cmd, stdout=file_out)\n if status:\n print(\"There was error while processing the sub process command\") # pragma: no mutate\n file_out.close()\n return status","def write_actual_output(self, output):\n actual_output_file = path.splitext(self.source_name)[0] + \".actual\"\n with open(actual_output_file, \"w\") as f:\n f.write(output)","def ExecuteScript(script):\n os.system(\"%s > /dev/null 2>&1\" % script)","def test_DDSim_runIt_success_OutputFile_1(self):\n self.ddsim.platform = \"Windows\"\n self.ddsim.applicationLog = self.logFileName\n self.ddsim.SteeringFile = \"mySteering.py\"\n self.ddsim.OutputFile = \"grailDiary.root\"\n ## side effect for Steering1a, Steering1b, Steering2, Script, userlibs, log, logAfter\n with patch(\"os.path.exists\", new=Mock(side_effect=[False, True, True, False, False, False, True] ) ):\n res = self.ddsim.runIt()\n assertDiracSucceeds( res, self )\n self.assertIn( \" --outputFile grailDiary.root \", self.ddsim.extraCLIarguments )","def execute(container, output_strategy, *args, **kwargs):\n env = os.environ.copy()\n\n if kwargs.get(\"env\"):\n env.update(kwargs[\"env\"])\n\n try:\n cmd = list(process_shebang(args))\n\n # On Windows, we need to explicitly specify the\n # executable, since PATH is only read in its\n # state at the time this process started and\n # not at the time Popen is called.\n if not os.path.exists(cmd[0]):\n cmd[0] = which(cmd[0])\n assert cmd[0] is not None\n\n # Sanity-check for non-unicode environment\n # variables and print the name of the\n # variable and its value/type on error.\n for key, value in env.items():\n if not isinstance(value, str):\n raise RuntimeError(\"\"\"{}'s value {} is not a \"\"\"\n \"\"\"str but a {}\"\"\".format(key,\n repr(value),\n type(value)))\n\n process = subprocess.Popen(cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n env=env)\n except OSError as error:\n raise Exception(u\"\"\"Failed to execute \"\"\"\n u\"\"\"{0} - {1}\"\"\".format(\" \".join(cmd), str(error)))\n\n with close_file_pair((process.stdout, process.stderr)) as outputs:\n status = output_strategy(process, outputs)\n\n instant_fail = kwargs.get(\"instant_fail\") or False\n\n if status != 0:\n IndentedLogger.message(u\"\"\"!!! Process {0}\\n\"\"\".format(cmd[0]))\n for arg in cmd[1:]:\n IndentedLogger.message(u\"\"\"!!! {0}\\n\"\"\".format(arg))\n IndentedLogger.message(u\"\"\"!!! failed with {0}\\n\"\"\".format(status))\n if not kwargs.get(\"allow_failure\", None):\n container.note_failure(instant_fail)\n\n return status","def get_output(self, cmd, *args, **kwargs):\n return self.exec(cmd, *args, **kwargs, capture_output=True)","def _call(self, filename, *args, **kwargs):\n #hard code tmp_output as the output name since we don't save it anyway\n #self.cli('{} -log {log_output} {seqfile} > {tmp_path}'.format(self.command(), tmp_path=os.path.join(tmpd,'tmp_output'), log_output=logfile, seqfile=filename), wait=True)\n self.cli('{} {seq_file}'.format(self.command(), seq_file=filename), wait=True)\n\n return (self.cli.get_stdout(), self.cli.get_stderr())","def postProcessOutput(self):\n\n logging.info(\" ========> Analysis %20s called postProcessOutput:\"%(self.name))\n\n if self.checkExpectedOutputFiles() == False:\n raise Exception(\"Missing expected output files. Number missing are [%d]\"%(len(self.missing_output_files)))\n\n FileUtils.checkDirExists(self.output_dir)\n\n tmpfiles = []\n\n logging.info(\" ========> Analysis %20s called postProcessOutput: Moving files from %s to %s \"%(self.name,self.working_dir,self.output_dir))\n try:\n for srcfile in self.expected_output_files:\n\n fullsrcfile = os.path.join(self.working_dir,srcfile)\n destfile = os.path.join(self.output_dir,srcfile)\n\n FileUtils.checkDirExistsForFile(destfile)\n\n res = shutil.move(fullsrcfile,destfile)\n\n if res == None:\n res = \"OK\"\n else:\n res = \"FAILED\"\n\n print \"Checking %s\"%destfile\n tmpfiles.append(destfile)\n \n logging.info(\" ========> Analysis %20s called postProcessOutput: Result of file move for %s = %s\" % (self.name,srcfile,res))\n\n except Exception as e:\n logging.info(\" ========> Analysis %20s file move failed %s\"%(self.name,e))\n raise\n\n self.output_files = tmpfiles\n\n for f in self.temp_output_files:\n logging.info(\" ========> Analysis %20s removing temp file %s \"%(self.name,f))\n\t res = os.remove(f)","def test_outfile():\n\n out_file = random_filename()\n if os.path.isfile(out_file):\n os.remove(out_file)\n\n try:\n cmd = f'{prg} --cdhit {cdhit} --proteins {proteins} -o {out_file}'\n rv, out = getstatusoutput(cmd)\n assert rv == 0\n\n assert out == ('Wrote 309 of 220,520 unclustered '\n f'proteins to \"{out_file}\"')\n\n assert os.path.isfile(out_file)\n\n seqs = list(SeqIO.parse(out_file, 'fasta'))\n assert len(seqs) == 309\n\n finally:\n if os.path.isfile(out_file):\n os.remove(out_file)","def get_output_file_name(argn=2, std_name='output.txt'):\n try:\n name = sys.argv[argn]\n except IndexError:\n name = std_name\n print(\"Warning: no output file name received. Output will be\"\n \" written to '%s'.\" % name)\n return name","def execute_tool(description, *args):\n command_line = list(args) + files_and_directories\n click.echo(f\"{description}: {' '.join(command_line)}\")\n rv = call(command_line)\n if rv != 0:\n exit(rv)","def test_empty_file(self):\n input_file = \"does_not_exist.fasta\"\n self.assertFalse(os.path.isfile(input_file))\n\n cline = XXmotifCommandline(outdir=self.out_dir, seqfile=input_file)\n\n try:\n stdout, stderr = cline()\n except ApplicationError as err:\n self.assertEqual(err.returncode, 255)\n else:\n self.fail(f\"Should have failed, returned:\\n{stdout}\\n{stderr}\")","def execute(self, log_out, log_err):\n EventGenerator.execute(self, log_out, log_err)\n if 'moller' not in self.name:\n src = os.path.join(self.rundir, 'brems.stdhep')\n dest = os.path.join(self.rundir, self.output_files()[0])\n logger.debug(\"Copying '%s' to '%s'\" % (src, dest))\n shutil.copy(src, dest)","def execute_command(self, command):\n\n if os.system(command) != 0:\n self.log('ERROR: Command \"' + command + '\" returned with a non-zero exit code')\n raise NonZeroExitCodeException('Non-zero exit code')","def has_fileout(self):\n return self.fileout is not None","async def collect_final_outputs(self) -> None: # pylint: disable=too-many-branches\n self._become_current()\n\n missing_outputs = False\n assert self.step is not None\n\n did_sleep = False\n\n for pattern in sorted(self.step.output): # pylint: disable=too-many-nested-blocks\n formatted_pattern = fmt_capture(self.kwargs, pattern)\n if is_phony(pattern):\n Invocation.up_to_date[formatted_pattern] = UpToDate(self.name, self.newest_input_mtime_ns + 1)\n continue\n\n try:\n paths = glob_paths(formatted_pattern)\n if not paths:\n Logger.debug(f\"Did not make the optional output(s): {pattern}\")\n else:\n for path in paths:\n self.built_outputs.append(path)\n\n global touch_success_outputs # pylint: disable=invalid-name\n if touch_success_outputs.value:\n if not did_sleep:\n await self.done(asyncio.sleep(1.0))\n did_sleep = True\n Logger.file(f\"Touch the output: {path}\")\n Stat.touch(path)\n\n mtime_ns = Stat.stat(path).st_mtime_ns\n Invocation.up_to_date[path] = UpToDate(self.name, mtime_ns)\n\n if Logger.isEnabledFor(logging.DEBUG):\n if path == formatted_pattern:\n Logger.debug(f\"Has the output: {path} \" f\"time: {_datetime_from_nanoseconds(mtime_ns)}\")\n else:\n Logger.debug(\n f\"Has the output: {pattern} -> {path} \"\n f\"time: {_datetime_from_nanoseconds(mtime_ns)}\"\n )\n\n except NonOptionalException:\n self._become_current()\n Logger.error(f\"Missing the output(s): {pattern}\")\n missing_outputs = True\n break\n\n if missing_outputs:\n self.abort(\"Missing some output(s)\")","def _write_output(output: List[str], output_file: Optional[str]) -> None:\n if output_file:\n with open(output_file, 'w+') as file:\n file.write('\\n'.join(output))\n else:\n for line in output:\n print(line)","def command_exists(name, path=None):\n if path is None:\n path = sys.path\n\n for prefix in path:\n filename = os.path.join(prefix, name)\n is_executable = os.access(filename, os.X_OK)\n is_file = os.path.isfile(filename)\n if is_executable and is_file:\n return True\n\n return False","def _exec_command(command):\n\n log(\"Run command for '%s'\" % command)\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\n (output, err) = p.communicate()\n p_status = p.wait()\n return p_status, output","def has_output(self, output_ref):\n outputs = self.get_recipe_outputs()\n for (output_role_name, output_role) in outputs.items():\n for item in output_role.get(\"items\", []):\n if item.get(\"ref\", None) == output_ref:\n return True\n return False","def _get_sys_cmd(self, file_path_input, fold_coverage, file_path_output_prefix):\n assert self.validate_file(file_path_input)\n assert isinstance(fold_coverage, (int, float))\n assert self.validate_dir(file_path_output_prefix, only_parent=True)\n\n # TODO: mask 'N' default: '-nf 1'\n read_length = self._art_read_length[self._profile]\n error_profile = os.path.join(self._directory_error_profiles, self._art_error_profiles[self._profile])\n arguments = [\n \"-sam\", \"-na\",\n \"-i '{}'\".format(file_path_input),\n \"-l\", str(read_length),\n \"-m\", str(self._fragment_size_mean),\n \"-s\", str(self._fragment_size_standard_deviation),\n \"-f\", str(fold_coverage),\n \"-o '{}'\".format(file_path_output_prefix),\n \"-1 '{}'\".format(error_profile+'1.txt'),\n \"-2 '{}'\".format(error_profile+'2.txt'),\n ]\n\n if self._logfile:\n arguments.append(\">> '{}'\".format(self._logfile))\n\n # art illumina only accepts integer as seed!\n arguments.append(\"-rs '{}'\".format(self._get_seed()))\n\n cmd = \"{exe} {args}\".format(exe=self._file_path_executable, args=\" \".join(arguments))\n return cmd","def execute(self, parameters, messages):\n #3.4\n try:\n importlib.reload (arcsdm.combine_outputnnfiles)\n except :\n reload(arcsdm.combine_outputnnfiles);\n arcsdm.combine_outputnnfiles.execute(self, parameters, messages)\n return"],"string":"[\n \"def check_for_preexisting_output_file(output_file_path):\\n if path.exists(f\\\"{output_file_path}\\\"):\\n print(\\\"Output file at specified save location file path already exists!\\\")\\n print(\\\"Aborting operation!\\\")\\n sys.exit()\",\n \"def exec_command_string_one_file(command_str,output):\\n print(command_str)\\n # (status, result) = subprocess.check_output(command_str, universal_newlines=True, stderr=sys.stdout) #available in both Python 2.x and 3.x\\n\\n (status, result) = getstatusoutput(command_str)\\n\\n if os.path.isfile(output):\\n return output\\n else:\\n outputlogMessage(result)\\n # syslog.outputlogMessage('The version of GDAL must be great than 2.0 in order to use the r option ')\\n return False\",\n \"def exec_command_args_list_one_file(args_list,output):\\n outputlogMessage(output_commandString_from_args_list(args_list))\\n ps = subprocess.Popen(args_list)\\n returncode = ps.wait()\\n if os.path.isfile(output):\\n return output\\n else:\\n outputlogMessage('return codes: '+ str(returncode))\\n return False\",\n \"def exec_to_file ( cmd, output_file, cwd = '/tmp/' ):\\n\\n try:\\n dn = open(os.devnull, 'r')\\n with open(output_file, 'w') as fo:\\n vlog(4, 'Running command: %s > %s from %s '% (cmd, output_file, cwd))\\n p = subprocess.Popen(\\n cmd, \\n stdin=dn,\\n stdout=fo, \\n stderr=fo, \\n cwd=cwd, \\n close_fds=True\\n )\\n\\n if p:\\n p.wait()\\n return p.returncode\\n\\n except Exception as e:\\n vlog(1, 'Command Error: %s'% (str(e)))\\n\\n vlog(1, 'Failed to run command: %s > %s '% (cmd, output_file))\\n return None\",\n \"def do_command(cmd, output_file):\\n global txt_output_dir\\n output_path = os.path.join(txt_output_dir, output_file)\\n print \\\"doing: %s > %s\\\" % (cmd, output_path)\\n output = check_output(cmd.split(\\\" \\\"))\\n with open(output_path, \\\"w\\\") as f:\\n f.write(output)\",\n \"def output_file_exists(self):\\n file = self.config['OUT_FOLDER'] + '/' + self.config['OUTPUT_FOLDER'] + '/' + self.output_filename + '.' + \\\\\\n self.options['image_format'].lower()\\n self.__log(f'Checking if output file: \\\"{file}\\\" already exists.')\\n if os.path.exists(file) and not os.stat(file).st_size == 0:\\n self.__log(f'Output file: \\\"{file}\\\" does exist.')\\n return True\\n self.__log(f'Output file: \\\"{file}\\\" does not exist.')\\n return False\",\n \"def check_output(self, args):\\n if isinstance(args, str):\\n args = [args]\\n try:\\n return check_output(args)\\n\\n except IOError as e:\\n raise ScriptError(e)\\n\\n except OSError as e:\\n raise ScriptError(e)\\n\\n except CalledProcessError as e:\\n raise ScriptError(e)\",\n \"def test_check_if_output_file_exists():\\n input_file = os.path.join(os.getcwd(), 'tests', 'input_test_file.docx')\\n output_file = os.path.join(os.getcwd(), 'tests', 'output_test_file.txt')\\n\\n questions_parser = QuestionsParser()\\n questions_parser.main(argv=['-i', input_file, '-o', output_file])\\n assert os.path.exists(output_file)\\n os.unlink(output_file)\",\n \"def get_output(self, output_dir=\\\"tools_output\\\"):\\n\\n output_dir = self.project_dir / output_dir / self.name\\n # create output directory if didn't exist\\n if not output_dir.exists():\\n os.makedirs(output_dir)\\n logger.info(f\\\"Created {output_dir}\\\")\\n\\n for outfile in self.output:\\n outfile = self.project_dir / outfile\\n if outfile.exists():\\n src = os.fspath(outfile)\\n dst = os.fspath(output_dir / outfile.name)\\n shutil.move(src, dst)\\n logger.info(f\\\"Moved {outfile.name} to {output_dir}\\\")\\n else:\\n msg = f\\\"File not found: {outfile} - did you execute run() before?\\\"\\n logger.error(msg)\\n raise FileNotFoundError(msg)\",\n \"def _execute_internal(cmd, log_std_out, log_std_err, log_cmd, also_output_to_file=None,\\r\\n return_output=False):\\r\\n tmp_filename = None\\r\\n if log_std_out or log_std_err:\\r\\n tmp_filename = tempfile.mktemp()\\r\\n tmp_file = open(tmp_filename, \\\"w\\\")\\r\\n\\r\\n if (not log_std_err) or (not log_std_out):\\r\\n dev_null_file = open(os.devnull, \\\"w\\\")\\r\\n\\r\\n if log_std_out:\\r\\n if also_output_to_file:\\r\\n std_out_file = open(also_output_to_file, \\\"w\\\")\\r\\n else:\\r\\n std_out_file = tmp_file\\r\\n else:\\r\\n std_out_file = dev_null_file\\r\\n\\r\\n if log_std_err:\\r\\n std_err_file = tmp_file\\r\\n else:\\r\\n std_err_file = dev_null_file\\r\\n\\r\\n if log_cmd:\\r\\n log.info(\\\"Executing: %s\\\" % \\\" \\\".join(cmd))\\r\\n #Call subprocess.call to run the command.\\r\\n try:\\r\\n ret = subprocess.call(cmd, stdout=std_out_file, stderr=std_err_file)\\r\\n text = None\\r\\n #Check if there is some output or error captured to log\\r\\n if tmp_filename:\\r\\n with open(tmp_filename, 'r') as file_read:\\r\\n text = file_read.read()\\r\\n os.remove(tmp_filename)\\r\\n if text:\\r\\n log.info(\\\"External output:\\\\n%s\\\" % text)\\r\\n return (ret, text) if return_output else ret\\r\\n except subprocess.CalledProcessError as exception:\\r\\n log.error(\\\"Subprocess Exception occurred %s\\\" % exception)\\r\\n return (ret, None) if return_output else ret\\r\\n except OSError as exception:\\r\\n log.error(\\\"OS Exception occurred %s\\\" % exception)\\r\\n #IF this exception occurs the ret value is not initialized hence return non-zero\\r\\n return (1, None) if return_output else 1\",\n \"def get_output_file():\\n if len(sys.argv) < 4:\\n return -1\\n return sys.argv[3]\",\n \"def execute(cmd, output_file, env={}):\\n return subprocess.Popen(shlex.split(cmd),\\n stderr=subprocess.STDOUT,\\n stdout=open(output_file, \\\"w\\\"),\\n env = dict(os.environ, **env))\",\n \"def check_output(self):\\n directory, file = split(self.target)\\n if not exists(directory):\\n mkdir(directory)\\n if exists(self.target):\\n unlink(self.target)\",\n \"def ensure_file(command, filename, overwrite=False, shell=False):\\n print(command)\\n if overwrite or not os.path.exists(filename):\\n ret = subprocess.call(command, shell=shell)\\n if ret:\\n raise Exception(ret)\\n return ret\",\n \"def _execute_cmd(cmd, outdir: str = DEFAULT_OUTDIR, stdout_file=None, stderr_file=None):\\n if cmd is None:\\n raise Exception(\\\"cmd was not specified\\\")\\n\\n logging.info(f\\\"Will attempt to execute '{cmd}'\\\")\\n\\n if outdir is None:\\n outdir = '/tmp'\\n logging.info(f\\\"outdir was not defined and therefore was set to default '{outdir}'\\\")\\n\\n if stdout_file is None:\\n stdout_file = os.path.join(outdir, os.path.basename(__file__) + '.stdout')\\n logging.info(f\\\"stdout_file was not specified and therefore was set to '{stdout_file}'\\\")\\n\\n if stderr_file is None:\\n stderr_file = os.path.join(outdir, os.path.basename(__file__) + '.stderr')\\n logging.info(f\\\"stderr_file was not specified and therefore was set to '{stderr_file}'\\\")\\n\\n if os.path.exists(stdout_file):\\n logging.info(f\\\"STDOUT file '{stdout_file}' already exists so will delete it now\\\")\\n os.remove(stdout_file)\\n\\n if os.path.exists(stderr_file):\\n logging.info(f\\\"STDERR file '{stderr_file}' already exists so will delete it now\\\")\\n os.remove(stderr_file)\\n\\n p = subprocess.Popen(cmd, shell=True)\\n\\n (stdout, stderr) = p.communicate()\\n\\n pid = p.pid\\n\\n logging.info(f\\\"The child process ID is '{pid}'\\\")\\n\\n p_status = p.wait()\\n\\n p_returncode = p.returncode\\n\\n if p_returncode is not None:\\n logging.info(f\\\"The return code was '{p_returncode}'\\\")\\n else:\\n logging.info(\\\"There was no return code\\\")\\n\\n if p_status == 0:\\n logging.info(f\\\"Execution of cmd '{cmd}' has completed\\\")\\n else:\\n raise Exception(f\\\"Received status '{p_status}'\\\")\\n\\n if stdout is not None:\\n logging.info(\\\"stdout is: \\\" + stdout)\\n\\n if stderr is not None:\\n logging.info(\\\"stderr is: \\\" + stderr)\\n\\n return stdout_file\",\n \"def fileoutput(cd, bin, args=[]):\\n cmd = \\\"cd %s; ./%s %s\\\" % (cd, bin, ' '.join(args))\\n logger.info(cmd)\\n\\n return exec_command(cmd)\",\n \"def check_file_output(self, actual: str, expected: str):\\n assert self._program_executed, f\\\"You first need to `execute` the program before checking its outputs!\\\"\\n assert actual in self._write_files, f\\\"Unknown output file {actual}. Did you forget to provide it to the program by calling input_write_filename?\\\"\\n full_expected = _root_dir / expected\\n assert full_expected.is_file(), f\\\"Reference file {full_expected} does not exist!\\\"\\n # check to make sure the output file exists\\n full_actual = _root_dir / actual\\n self._test.assertTrue(full_actual.is_file(), f\\\"It seems like the program never created the output file {full_actual}\\\")\\n # open and compare the files\\n with open(full_actual, 'rb') as a:\\n actual_bin = a.read()\\n with open(full_expected, 'rb') as e:\\n expected_bin = e.read()\\n self._test.assertEqual(actual_bin, expected_bin, f\\\"Bytes of {actual} and {expected} did not match!\\\")\",\n \"def check_output_wrapper(*args, **kwargs):\\n\\n logger.debug('Executing %s, %s', args, kwargs)\\n try:\\n return check_output(*args, **kwargs)\\n except CalledProcessError as msg:\\n logger.warning('Error %s,%s,%s from command.', msg.returncode, msg.output, msg.stderr)\\n logger.debug('Output: %s', msg.output)\\n sys.exit(ERROR_EXECUTING_COMMAND);\",\n \"def runTool(self, filename, expected_out, args):\\n\\n input_path = os.path.join(self.inputs_dir, filename)\\n return_value, actual_output = create_subprocess(self.executable_binary, args + [input_path] + ['--'])\\n actual_output = actual_output.decode('utf-8')\\n\\n self.assertEqual(return_value, 0)\\n self.evaluate(expected_out, actual_output, command=f'{[self.executable_binary] + args} {filename}')\",\n \"def process_cleanup(self, output_file=None, output_list=None):\\n if output_file:\\n self.check_output_file( output_file )\\n elif output_list:\\n for output_file in output_list:\\n self.check_output_file( output_file )\\n log.info('All expected output files found - process successful!\\\\n')\",\n \"def call_files():\\n try:\\n predicted_proteins = sys.argv[1]\\n except IndexError:\\n predicted_proteins = input('Please input AUGUSTUS file for analysis: ')\\n try:\\n protein_db = sys.argv[2]\\n except IndexError:\\n protein_db = input('Please input a protein database file: ')\\n\\n try:\\n output_file_aug_to_fasta = sys.argv[3]\\n output_to_file = True\\n except IndexError:\\n output_to_file = input('Write output to file?'\\n + ' [Yes/No]: ')\\n if output_to_file.upper() in 'YES':\\n output_to_file = True\\n output_file_aug_to_fasta = input('Please supply output file name '\\n + 'for AUGUSTUS conversion to '\\n + 'FASTA: ')\\n else:\\n output_to_file = False\\n output_file_aug_to_fasta = None\\n\\n try:\\n output_file_proteins_to_db = sys.argv[4]\\n except IndexError:\\n if output_to_file:\\n output_file_proteins_to_db = input('Please supply output file name'\\n + 'for blast database: ')\\n else:\\n output_file_proteins_to_db = None\\n\\n try:\\n blastp_output = sys.argv[5]\\n except IndexError:\\n if output_to_file:\\n blastp_output = input('Please supply output file name for blastp: ')\\n else:\\n blastp_output = None\\n\\n finally:\\n if len(sys.argv) >= 7:\\n overwrite = sys.argv[6]\\n elif output_file and os.path.exists(output_file):\\n overwrite = input('Output file already exists. Overwrite? '\\n + '[Yes/No]: ')\\n if overwrite.upper() in 'YES':\\n overwrite = True\\n else:\\n overwrite = False\\n else: overwrite = False\\n\\n return (predicted_proteins, protein_db, output_file_aug_to_fasta, \\n output_file_proteins_to_db, blastp_output, \\n output_to_file, overwrite)\",\n \"def load_job_output(output_title, output_summary, output):\\n def read_if_file(val):\\n if os.path.exists(val):\\n logger.info(\\\"Reading file: %s\\\", val)\\n with open(val, \\\"r\\\") as inf:\\n return inf.read()\\n else:\\n return val\\n\\n if output_title:\\n assert output_summary\\n return checks.Output(\\n title = output_title,\\n summary = read_if_file(output_summary),\\n text = read_if_file(output) if output else None\\n )\\n else:\\n return None\",\n \"def check_output(self, cmd, nonzero_e = tc.error_e):\\n _exitcode, stdoutf, _stderrf = self.run(cmd, nonzero_e = nonzero_e)\\n return stdoutf.read()\",\n \"def check_output(*args, **kwargs):\\n if \\\"stdout\\\" in kwargs:\\n raise ValueError(\\\"stdout argument not allowed, it will be overriden.\\\")\\n p = Popen(stdout=PIPE, *args, **kwargs)\\n out, _ = p.communicate()\\n rc = p.poll()\\n if rc != 0:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = args[0]\\n raise CalledProcessError(rc, cmd, output=out)\\n return out\",\n \"def check_output(command):\\n process = Popen(command, shell=True, stdout=PIPE)\\n output, err = process.communicate()\\n if process.returncode == 0: # success\\n return output\\n else:\\n raise RuntimeError(\\\"Command {0} running unsuccessfully\\\".format(command))\",\n \"def try_process_output_file(ins_file, output_file=None):\\n if output_file is None:\\n output_file = ins_file.replace(\\\".ins\\\", \\\"\\\")\\n df = None\\n i = InstructionFile(ins_file)\\n try:\\n df = i.read_output_file(output_file)\\n except Exception as e:\\n print(\\\"error processing instruction/output file pair: {0}\\\".format(str(e)))\\n return df\",\n \"def process_tool_output(self, proc, log_fn, name):\\n output, error = proc.communicate()\\n if proc.returncode != 0:\\n fd = open(log_fn, \\\"a+\\\")\\n fd.write(\\\"Error: %s returned the following output:\\\"\\n \\\"\\\\n%s\\\" % (name, error))\\n fd.close()\\n raise Exception(\\\"%s returned the following output:\\\"\\n \\\"\\\\n%s\\\" % (name, error))\\n else:\\n fd = open(log_fn, \\\"a+\\\")\\n fd.write(\\\"%s output: %s %s\\\" % (name, output, error))\\n # printing stdout and stderr though bowtie2 currently will always\\n # output to stderr instead of stdout even if no error occurred\\n fd.close()\",\n \"def run(self):\\n try:\\n self.parse_args(None)\\n self.execute_command()\\n except FileExistsException, e:\\n print \\\"Can't copy file as destination already exists.\\\"\\n print \\\"Exiting...\\\"\\n except Exception, e:\\n print \\\"Exception occured: %s\\\\nExiting...\\\" % e\",\n \"def check_output(*popenargs, **kwargs):\\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\\n output, unused_err = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n error = subprocess.CalledProcessError(retcode, cmd)\\n error.output = output\\n raise error\\n return output\",\n \"def test_execution(self):\\n\\n # This process will echo the input and output file name to stdout.\\n the_process_unit = ProcessUnit([self.a_pattern_ds], '/another/%file%/%pattern%.txt',\\n 'echo')\\n\\n ds_result = the_process_unit.execute(simulate=True)\\n\\n outfiles = [file_thing for file_thing in ds_result.files]\\n self.assertEqual(len(outfiles), 1)\\n\\n expected_string = self.script_header + \\\"mkdir -p /another/file_1\\\\necho test_file1 /another/file_1/pattern_1.txt\\\\n\\\"\\n self.assertEqual(expected_string, the_process_unit.scheduler.job.to_str())\",\n \"def collect(self,outfilename):\\n # TODO actually gather results and check if run is successful\\n if os.path.isfile(outfilename):\\n self.completed=True\\n else:\\n self.completed=False\",\n \"def setup_molecule_output_check(exp_builder_db, mol_id, output_path):\\n exp_builder_db._setup_molecules(mol_id)\\n assert os.path.exists(output_path)\\n assert os.path.getsize(output_path) > 0\",\n \"def test_output_exists():\\n global out_dir, cor_dir\\n assert(path.exists(path.join(out_dir, 'oshea_similarity.json')))\",\n \"def ExecuteIf(self, args, src_files, dst_files):\\n if self.ShouldBuild(src_files, dst_files):\\n self.MakeDestinationDirectories(dst_files)\\n self.Execute(args)\\n if self.execute and not self.VerifyExists(dst_files):\\n raise RuntimeError(\\\"FAILED: build did not create all required files\\\")\",\n \"def output_files_exist(self):\\n return all([split.exists() for split in self.split_files])\",\n \"def eval_output(ifile,cfile,ofile):\\n print('Evaluating {} on combo {}'.format(ifile, cfile))\\n\\tglobal EVAL_TABLE_PATH\\n\\tEVAL_TABLE_ARGS = \\\" -i %s -C %s -o %s -u %s \\\"%(ifile,cfile,ofile,OUT)\\n\\tresult=os.system(EVAL_TABLE_PATH + \\\" \\\" + EVAL_TABLE_ARGS) \\n\\tif result!=0:\\n\\t\\tprint \\\"error while executing\\\"\\n\\t\\texit(0)\",\n \"def blankOutputFiles():\\n print(\\\"Checking for blank output files\\\")\\n find_output = re.compile(r\\\"/\\\\* Output:(.*)\\\\*/\\\", re.DOTALL)\\n for java in config.example_dir.rglob(\\\"*.java\\\"):\\n with java.open() as codeFile:\\n output = find_output.search(codeFile.read())\\n if output:\\n # print(output.group(1))\\n if not output.group(1).strip():\\n print(java)\",\n \"def check_output(*popenargs, **kwargs):\\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\\n output, unused_err = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n error = subprocess.CalledProcessError(retcode, cmd)\\n error.output = output\\n raise error\\n return output\",\n \"def check_output(*popenargs, **kwargs):\\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\\n output, unused_err = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n error = subprocess.CalledProcessError(retcode, cmd)\\n error.output = output\\n raise error\\n return output\",\n \"def execute(cmd, log_cmd=True, also_output_to_file=None):\\r\\n return CommandUtil._execute_internal(cmd, True, True, log_cmd, also_output_to_file)\",\n \"def execute(cmd, path):\\n oldPath = os.getcwd()\\n os.chdir(path)\\n\\n exitcode, output = subprocess.getstatusoutput(cmd)\\n\\n os.chdir(oldPath)\\n\\n ok = not exitcode\\n\\n return ok, output\",\n \"def executeCommand(commandtoexecute):\\n try:\\n _output = commands.getstatusoutput(commandtoexecute)\\n except Exception as er:\\n print \\\"not able to execute command\\\"\\n return False\\n return _output\",\n \"def optional_output_file(func):\\n\\n @click.argument(\\n \\\"output\\\", required=False, type=click.Path(exists=False, )\\n )\\n @wraps(func)\\n def wrapped(*args, **kwargs):\\n return func(*args, **kwargs)\\n return wrapped\",\n \"def check_output(args, **kwargs):\\n kwargs.setdefault('stdin', DEVNULL)\\n if 'stdout' in kwargs:\\n raise ValueError('stdout argument not allowed, it would be overridden.')\\n return check_call_out(args, stdout=PIPE, **kwargs)[0]\",\n \"def present_output(self, output: str):\\n\\n if self._config.use_stdout():\\n print(output, flush=True, end='')\\n else:\\n try:\\n # print(\\\"OUTPUT FILE IS THERE!!!!\\\", self._config.values['output'])\\n with open(self._config.values['output'], mode='w', newline='') as f:\\n # print(\\\"PRINITNG OUTPUT>>>>\\\"+output+\\\"<<<<<\\\")\\n f.write(output)\\n # print(\\\"OUTPUT FILE IS THERE!!!!\\\", \\\"After write\\\")\\n except:\\n raise OutputFileException('Error writing the output')\",\n \"def shell_execute(command, output, test=None, options=None,\\n data=None, test_split=None):\\n command = check_debug(command)\\n world.directory = os.path.dirname(output)\\n world.folders.append(world.directory)\\n try:\\n retcode = check_call(command, shell=True)\\n if retcode < 0:\\n assert False\\n else:\\n if test is not None:\\n world.test_lines = file_number_of_lines(test)\\n if options is None or options.find('--prediction-header') == -1:\\n # test file has headers in it, so first line must be ignored\\n world.test_lines -= 1\\n if test_split is not None:\\n data_lines = file_number_of_lines(data) - 1\\n world.test_lines = int(data_lines * float(test_split))\\n \\n world.output = output\\n assert True\\n except (OSError, CalledProcessError, IOError) as exc:\\n assert False, str(exc)\",\n \"def exists(parser, output_name):\\n return parser.setParseAction(isNotEmpty).setResultsName(output_name)\",\n \"def checkForCommand(quickLogger, commandList):\\n\\n for command in commandList:\\n\\n cmd = \\\"which -s \\\" + command + \\\" > \\\" + os.devnull + \\\" 2>&1\\\"\\n retcode = os.system(cmd)\\n \\n if(retcode):\\n quickLogger.critical(\\\"unix command \\\"+command+\\\" not found.\\\")\\n raise CommandNotFound\",\n \"def executeSAGATool(self, tool_cmd, f_out, desc=''):\\n\\n if self.runSep(self.p_uid):\\n\\n self.logger.debug('[SAGA cmd]: %s', tool_cmd)\\n subprocess.call(self.saga_cmd + tool_cmd)\\n\\n # if output file exist log workflow step\\n if os.path.isfile(f_out):\\n if not desc:\\n desc = '{0}: Processing step {1}'.format(self.wf_name, self.p_uid)\\n\\n self.logWorkflowStep(self.p_uid, desc)\\n else:\\n raise workflowException('SAGA process \\\\'{0}\\\\' output file is missing: {1}'.format(desc, f_out))\\n else:\\n self.logger.info(desc + ': done.')\\n\\n # increment p_uid\\n self.p_uid += 10\\n\\n return\",\n \"def get_output(command):\\n if is_debug_environment():\\n dir_name = get_test_dir_argument()\\n filename = '{}.txt'.format(command)\\n full_path = os.path.join(dir_name, filename)\\n if os.path.isfile(full_path):\\n with open(full_path) as f:\\n return f.read()\\n raise IOError(\\\"File {} is not found\\\".format(full_path))\\n else:\\n import cli\\n result = cli.cli(command)\\n i = 0\\n # Occasionally, cli library returns an empty output, in this case we try multiple time\\n while result.count('\\\\n') <= 2 and i < 3:\\n result = cli.cli(command)\\n i += 1\\n return result\",\n \"def execute_task(self, filename):\\n stdin, stdout, stderr = self.ssh.exec_command(open(filename).read())\\n if stdout.channel.recv_exit_status() == 0:\\n return True, stdout.read().strip(), stderr.read().strip()\\n else:\\n return False, stdout.read().strip(), stderr.read().strip()\",\n \"def test_missing_file():\\n\\n rv, out = getstatusoutput(f'{prg} -o {outfile}')\\n assert rv != 0\\n assert re.search('the following arguments are required: -f/--file', out)\",\n \"def command_exe(cmd, file_name, folder=None):\\r\\n if folder:\\r\\n file_path = os.path.join(folder, file_name)\\r\\n subprocess.call('%s > %s' % (cmd, file_path), shell=True)\\r\\n logging.info(file_path)\\r\\n else:\\r\\n file_path = os.path.join(os.getcwd(), file_name)\\r\\n subprocess.call('%s > %s' % (cmd, file_path), shell=True)\\r\\n logging.info(file_path)\",\n \"def get_prog_file():\\n get_file()\\n ## Executa\\n file = ARGS.output\\n os.system(\\\"chmod +x \\\" + file)\\n subprocess.call([file])\",\n \"def execute(self, *args, **options):\\n self.output_file = options.get('output_file')\\n try:\\n super(CsvMixin, self).execute(*args, **options)\\n finally:\\n self.close_file_handle()\",\n \"def test_call_output_to_file(self):\\r\\n\\r\\n fd, tmp_result_filepath = mkstemp(\\r\\n prefix='CdHitOtuPickerTest.test_call_output_to_file_',\\r\\n suffix='.txt')\\r\\n close(fd)\\r\\n\\r\\n app = CdHitOtuPicker(params={'Similarity': 0.90})\\r\\n obs = app(self.tmp_seq_filepath1, result_path=tmp_result_filepath)\\r\\n\\r\\n result_file = open(tmp_result_filepath)\\r\\n result_file_str = result_file.read()\\r\\n result_file.close()\\r\\n # remove the result file before running the test, so in\\r\\n # case it fails the temp file is still cleaned up\\r\\n remove(tmp_result_filepath)\\r\\n\\r\\n # compare data in result file to fake expected file\\r\\n self.assertEqual(result_file_str, dna_seqs_result_file_90_exp)\\r\\n # confirm that nothing is returned when result_path is specified\\r\\n self.assertEqual(obs, None)\",\n \"def check_output(*popenargs, **kwargs):\\n if 'stdout' in kwargs:\\n raise ValueError('stdout argument not allowed, it will be overridden.')\\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\\n output, unused_err = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n raise subprocess.CalledProcessError(retcode, cmd)#, output=output)\\n return output\",\n \"def check_for(command):\\n if shutil.which(command) is None:\\n print(colored(\\\"{} not available on system\\\".format(command),\\\"red\\\"))\\n sys.exit(1)\",\n \"def check_output(*popenargs, **kwargs):\\n if 'stdout' in kwargs:\\n raise ValueError('stdout argument not allowed, it will be overridden.')\\n logging.debug(' '.join(popenargs[0]))\\n process = subprocess.Popen(stdout=subprocess.PIPE, *popenargs, **kwargs)\\n output, unused_err = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n raise subprocess.CalledProcessError(retcode, cmd)\\n return output\",\n \"def execute(cmd) :\\n return os.system( cmd )\",\n \"def command(self, command, out=False, err=False):\\n \\n if out:\\n std_out = subprocess.PIPE\\n else:\\n std_out = None\\n \\n if not err:\\n std_err = subprocess.PIPE\\n else:\\n std_err = None\\n \\n \\n proc = subprocess.Popen(command, stdout = std_out, stderr=std_err)#std_out)\\n out, err = proc.communicate()\\n \\n return out, err\",\n \"def testGetOutput(self):\\n #f = open(\\\"src_output.root\\\", 'w')\\n #f.close()\\n\\n #1) missing required -d option (the other required option, -r, is ignored)\\n go = getoutput(self.logger, self.maplistopt)\\n res = go()\\n expRes = CommandResult(2001, 'ERROR: Task option is required')\\n self.assertEquals(expRes, res)\\n\\n #2) -d option is present but -r is missing\\n analysisDir = self.reqarea\\n go = getoutput(self.logger, self.maplistopt + [\\\"-d\\\", analysisDir])\\n res = go()\\n expRes = CommandResult(2002, 'ERROR: Range option is required')\\n self.assertEquals(expRes, res)\\n\\n #3) request passed with the -d option does not exist\\n #res = go([\\\"-d\\\", analysisDir + \\\"asdf\\\"])\\n #TODO we expect an appropriate answer from the server.\\n #By now, the server just answer an empty list\\n\\n #4) check correct behaviour without specifying output directory\\n #N.B.: -p options is required for tests to skip proxy creation and delegation\\n destDir = os.path.join(analysisDir, 'results')\\n go = getoutput(self.logger, self.maplistopt + [\\\"-d\\\", analysisDir, \\\"-r\\\", \\\"1\\\", \\\"-p\\\"])\\n res = go()\\n expRes = CommandResult(0, '\\\\n')\\n #check if the result directory has been created\\n self.assertTrue(os.path.isdir(destDir))\\n self.assertTrue(os.path.isfile(os.path.join(destDir, '1.root')))\\n #Remove the directory\\n shutil.rmtree(destDir)\\n self.assertFalse(os.path.isdir(destDir))\\n self.assertEquals(expRes, res)\\n\\n #5) correct behavior and output directory specified which exists\\n go = getoutput(self.logger, self.maplistopt + [\\\"-d\\\", analysisDir, \\\"-r\\\", \\\"1\\\", \\\"-o\\\", \\\"/tmp\\\", \\\"-p\\\"])\\n res = go()\\n expRes = CommandResult(0, '\\\\n')\\n #check if the result directory has been created\\n self.assertTrue(os.path.isdir('/tmp'))\\n destFile = os.path.join('/tmp', '1.root')\\n self.assertTrue(os.path.isfile(destFile))\\n os.remove(destFile)\\n self.assertFalse(os.path.isfile(destFile))\\n self.assertEquals(expRes, res)\\n\\n #6) correct behavior and output directory specified which does not exists\\n go = getoutput(self.logger, self.maplistopt + [\\\"-d\\\", analysisDir, \\\"-r\\\", \\\"1\\\", \\\"-o\\\", \\\"/tmp/asdf/qwerty\\\", \\\"-p\\\"])\\n res = go()\\n expRes = CommandResult(0, '\\\\n')\\n #check if the result directory has been created\\n self.assertTrue(os.path.isdir('/tmp/asdf/qwerty'))\\n #Remove the directory\\n shutil.rmtree('/tmp/asdf/qwerty')\\n self.assertEquals(expRes, res)\\n\\n #7) correct behavior and output directory specified which does not exists (relative path)\\n go = getoutput(self.logger, self.maplistopt + [\\\"-d\\\", analysisDir, \\\"-r\\\", \\\"1\\\", \\\"-o\\\", \\\"qwerty\\\", \\\"-p\\\"])\\n res = go()\\n expRes = CommandResult(0, '\\\\n')\\n #check if the result directory has been created\\n self.assertTrue(os.path.isdir('qwerty'))\\n #Remove the directory\\n shutil.rmtree('qwerty')\\n self.assertEquals(expRes, res)\",\n \"def check_output_file(output_dir,\\n identifier,\\n shuffle_labels,\\n model_options,\\n predictor='classify',\\n fold_no=None,\\n titration_ratio=None):\\n\\n signal = 'shuffled' if shuffle_labels else 'signal'\\n\\n if not isinstance(model_options.training_data, str):\\n training_data = '.'.join(model_options.training_data)\\n else:\\n training_data = model_options.training_data\\n\\n if isinstance(model_options.n_dim, list):\\n n_dim = '.'.join(map(str, model_options.n_dim))\\n else:\\n n_dim = model_options.n_dim\\n\\n check_file = construct_filename(output_dir,\\n 'coefficients',\\n '.tsv.gz',\\n identifier,\\n training_data,\\n model_options.model,\\n signal,\\n predictor,\\n s=model_options.seed,\\n n=n_dim,\\n f=fold_no,\\n t=titration_ratio)\\n if check_file.is_file():\\n raise ResultsFileExistsError(\\n 'Results file already exists for identifier: {}\\\\n'.format(\\n identifier)\\n )\\n return check_file\",\n \"def fetch_output(self, path, name, working_directory, action_type, output_type):\\n if output_type in ['output_workdir', 'output_metadata']:\\n self._populate_output_path(name, path, action_type, output_type)\\n elif output_type == 'output':\\n self._fetch_output(path=path, name=name, action_type=action_type)\\n else:\\n raise Exception(\\\"Unknown output_type %s\\\" % output_type)\",\n \"def assertExecutedOnce(self, component: Text) -> None:\\n component_path = os.path.join(self._pipeline_root, component)\\n self.assertTrue(fileio.exists(component_path))\\n outputs = fileio.listdir(component_path)\\n\\n self.assertIn(\\\".system\\\", outputs)\\n outputs.remove(\\\".system\\\")\\n system_paths = [\\n os.path.join(\\\".system\\\", path)\\n for path in fileio.listdir(os.path.join(component_path, \\\".system\\\"))\\n ]\\n self.assertNotEmpty(system_paths)\\n self.assertIn(\\\".system/executor_execution\\\", system_paths)\\n outputs.extend(system_paths)\\n self.assertNotEmpty(outputs)\\n for output in outputs:\\n execution = fileio.listdir(os.path.join(component_path, output))\\n self.assertLen(execution, 1)\",\n \"def get_output(self, output, download_dir, overwrite=False, callback=None, block=4096):\\n download = self._get_file(output, download_dir, overwrite, callback=callback, block=block)\\n if download.success:\\n return os.path.join(download_dir, output.get('name', ''))\\n else:\\n raise download.result\",\n \"def __fetch_output_task(\\n self, task, download_dir, overwrite, changed_only, **extra_args):\\n return task.fetch_output(\\n download_dir, overwrite, changed_only, **extra_args)\",\n \"def check_output(*popenargs, **kwargs):\\n process = subprocess.Popen(stdout=subprocess.PIPE,\\n universal_newlines=True,\\n *popenargs,\\n **kwargs)\\n output, _ = process.communicate()\\n retcode = process.poll()\\n if retcode:\\n cmd = kwargs.get(\\\"args\\\")\\n if cmd is None:\\n cmd = popenargs[0]\\n error = subprocess.CalledProcessError(retcode, cmd)\\n error.output = output\\n raise error\\n return output.strip()\",\n \"def _execute_run(prefix, osiris_path, run_dir):\\n # Cf. run_config\\n p = subprocess.Popen(prefix + osiris_path + \\\" > out.txt 2> err.txt\\\", shell=True, cwd=path.abspath(run_dir))\\n p.wait()\",\n \"def run_command(self,command):\\n from subprocess import Popen, PIPE, STDOUT\\n if command == '':\\n raise RuntimeError('no command for run_command :(')\\n # print 'Running: ', command #debug\\n proc = Popen([command], shell=True, stderr=PIPE)\\n proc.wait()\\n exitcode = proc.returncode\\n if exitcode != 0:\\n # print exitcode,'label:', self.calc_dir\\n error='%s exited with error code %i in %s' % (\\n command,exitcode,self.calc_dir)\\n stdout,stderr = proc.communicate()\\n print 'shell output: ',stdout,stderr\\n raise RuntimeError(error)\\n return 0\",\n \"def run_file(self, value=None):\\n self.save_file()\\n self.p = Popen(\\\"./Project/myfile.py\\\", stdout=PIPE, stderr=PIPE)\\n output, errors = self.p.communicate()\\n self.my_output.delete(\\\"1.0\\\", END)\\n self.my_output.insert(\\\"1.0\\\", output)\\n if errors != \\\"\\\":\\n print_to_log(errors)\\n self.my_output.configure(fg=\\\"red\\\")\\n else:\\n self.my_output.configure(fg=\\\"white\\\")\\n self.my_output.insert(\\\"1.0\\\", errors)\",\n \"def _check_output(*args, **kwargs):\\n kwargs['stdout'] = subprocess.PIPE\\n p = subprocess.Popen(*args, **kwargs)\\n stdout, stderr = p.communicate()\\n if p.returncode != 0:\\n raise ValueError(\\n 'subprocess exited with return code %s' % p.returncode\\n )\\n return stdout\",\n \"def subprocess_check_output(*popenargs, **kwargs):\\r\\n if 'stdout' in kwargs:\\r\\n raise ValueError('stdout argument not allowed, it will be overridden.')\\r\\n if 'stderr' in kwargs:\\r\\n raise ValueError('stderr argument not allowed, it will be overridden.')\\r\\n\\r\\n #executable_exists(popenargs[0][0])\\r\\n\\r\\n # NOTE: it is very, very important that we use temporary files for\\r\\n # collecting stdout and stderr here. There is a nasty bug in python\\r\\n # subprocess; if your process produces more than 64k of data on an fd that\\r\\n # is using subprocess.PIPE, the whole thing will hang. To avoid this, we\\r\\n # use temporary fds to capture the data\\r\\n stdouttmp = TemporaryFile()\\r\\n stderrtmp = TemporaryFile()\\r\\n\\r\\n process = subprocess.Popen(stdout=stdouttmp, stderr=stderrtmp, *popenargs,\\r\\n **kwargs)\\r\\n process.communicate()\\r\\n retcode = process.poll()\\r\\n\\r\\n stdouttmp.seek(0, 0)\\r\\n stdout = stdouttmp.read()\\r\\n stdouttmp.close()\\r\\n\\r\\n stderrtmp.seek(0, 0)\\r\\n stderr = stderrtmp.read()\\r\\n stderrtmp.close()\\r\\n\\r\\n if retcode:\\r\\n cmd = ' '.join(*popenargs)\\r\\n raise Exception(\\\"'%s' failed(%d): %s\\\" % (cmd, retcode, stderr), retcode)\\r\\n return (stdout, stderr, retcode)\",\n \"def assertOutput(self, toExec, argList, expectedStdout=None, \\n\\t\\t\\texpectedStderr=\\\"\\\", expectedRetcode=0, input=None,\\n\\t\\t\\tstdoutStrings=None):\\n\\t\\tfor name in [\\\"output.stderr\\\", \\\"output.stdout\\\"]:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tos.unlink(name)\\n\\t\\t\\texcept os.error:\\n\\t\\t\\t\\tpass\\n\\n\\t\\tif isinstance(toExec, basestring):\\n\\t\\t\\tp = subprocess.Popen([toExec]+argList, executable=toExec, \\n\\t\\t\\t\\tstdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t\\telse:\\n\\t\\t\\tp = ForkingSubprocess([\\\"test harness\\\"]+argList, executable=toExec, \\n\\t\\t\\t\\tstdout=subprocess.PIPE, stdin=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t\\tout, err = p.communicate(input=input)\\n\\t\\tretcode = p.wait()\\n\\n\\t\\ttry:\\n\\t\\t\\tself.assertEqual(expectedRetcode, retcode)\\n\\n\\t\\t\\tif isinstance(expectedStderr, basestring):\\n\\t\\t\\t\\tself.assertEqual(err, expectedStderr)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.failUnless(expectedStderr(err))\\n\\t\\texcept AssertionError:\\n\\t\\t\\twith open(\\\"output.stdout\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(out)\\n\\t\\t\\twith open(\\\"output.stderr\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(err)\\n\\t\\t\\traise\\n\\n\\t\\ttry:\\n\\t\\t\\tif isinstance(expectedStdout, basestring):\\n\\t\\t\\t\\tself.assertEqual(out, expectedStdout)\\n\\t\\t\\telif expectedStdout is not None:\\n\\t\\t\\t\\tself.failUnless(expectedStdout(out))\\n\\t\\t\\tif stdoutStrings:\\n\\t\\t\\t\\tfor s in stdoutStrings:\\n\\t\\t\\t\\t\\tself.failIf(s not in out, \\\"%s missing\\\"%s)\\n\\t\\texcept AssertionError:\\n\\t\\t\\twith open(\\\"output.stdout\\\", \\\"w\\\") as f:\\n\\t\\t\\t\\tf.write(out)\\n\\t\\t\\traise\",\n \"def get_output(self, download_dir, output=None, overwrite=False, callback=None, block=4096):\\n if output:\\n name = output.get('name', \\\"\\\")\\n download = self._get_intermediate_output(output,\\n download_dir,\\n overwrite,\\n callback=callback,\\n block=block)\\n\\n elif self.output_url and self.output_filename:\\n name = self.output_filename\\n download = self._get_final_output(download_dir, overwrite,\\n callback=callback, block=block)\\n\\n else:\\n raise FileDownloadException(\\n \\\"Job has no reference to an output file, \\\"\\n \\\"please update to check if the output is ready\\\")\\n\\n if download.success:\\n return os.path.join(download_dir, name)\\n\\n else:\\n raise download.result\",\n \"def _output_result(result: str, arguments: Dict[str, str]) -> None:\\n if (\\\"ciphered_file\\\" in arguments) or (\\\"deciphered_file\\\" in arguments):\\n output_filename = arguments[\\\"ciphered_file\\\"] if \\\"ciphered_file\\\" in arguments else arguments[\\\"deciphered_file\\\"]\\n with open(output_filename, mode=\\\"w\\\") as output_file:\\n output_file.write(result)\\n output_file.flush()\\n else:\\n print(result)\",\n \"def command_output(cmd):\\n import subprocess\\n return subprocess.Popen(\\n cmd.split(\\\";\\\"), stdout=subprocess.PIPE).communicate()[0]\",\n \"def get_output_path():\\n return os.getcwd() + \\\"/output/\\\"\",\n \"def eichertest():\\n try:\\n eicher = ('echo ZQZXJVBVT >> mcafee1.txt', 'echo ZQZXJVBVT >> mcafee2.txt','echo ZQZXJVBVT >> mcafee2.txt')\\n for test in eicher:\\n executeCommand(test)\\n except Exception as er:\\n print \\\"Not able to do install check\\\"\\n return False\",\n \"def __subprocess_out(self):\\n file_out = open(os.path.join(self.report_path, \\\"cyclomatic-complexity.csv\\\"), \\\"w\\\")\\n status = subprocess.call(r'%s' % self.cmd, stdout=file_out)\\n if status:\\n print(\\\"There was error while processing the sub process command\\\") # pragma: no mutate\\n file_out.close()\\n return status\",\n \"def write_actual_output(self, output):\\n actual_output_file = path.splitext(self.source_name)[0] + \\\".actual\\\"\\n with open(actual_output_file, \\\"w\\\") as f:\\n f.write(output)\",\n \"def ExecuteScript(script):\\n os.system(\\\"%s > /dev/null 2>&1\\\" % script)\",\n \"def test_DDSim_runIt_success_OutputFile_1(self):\\n self.ddsim.platform = \\\"Windows\\\"\\n self.ddsim.applicationLog = self.logFileName\\n self.ddsim.SteeringFile = \\\"mySteering.py\\\"\\n self.ddsim.OutputFile = \\\"grailDiary.root\\\"\\n ## side effect for Steering1a, Steering1b, Steering2, Script, userlibs, log, logAfter\\n with patch(\\\"os.path.exists\\\", new=Mock(side_effect=[False, True, True, False, False, False, True] ) ):\\n res = self.ddsim.runIt()\\n assertDiracSucceeds( res, self )\\n self.assertIn( \\\" --outputFile grailDiary.root \\\", self.ddsim.extraCLIarguments )\",\n \"def execute(container, output_strategy, *args, **kwargs):\\n env = os.environ.copy()\\n\\n if kwargs.get(\\\"env\\\"):\\n env.update(kwargs[\\\"env\\\"])\\n\\n try:\\n cmd = list(process_shebang(args))\\n\\n # On Windows, we need to explicitly specify the\\n # executable, since PATH is only read in its\\n # state at the time this process started and\\n # not at the time Popen is called.\\n if not os.path.exists(cmd[0]):\\n cmd[0] = which(cmd[0])\\n assert cmd[0] is not None\\n\\n # Sanity-check for non-unicode environment\\n # variables and print the name of the\\n # variable and its value/type on error.\\n for key, value in env.items():\\n if not isinstance(value, str):\\n raise RuntimeError(\\\"\\\"\\\"{}'s value {} is not a \\\"\\\"\\\"\\n \\\"\\\"\\\"str but a {}\\\"\\\"\\\".format(key,\\n repr(value),\\n type(value)))\\n\\n process = subprocess.Popen(cmd,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n env=env)\\n except OSError as error:\\n raise Exception(u\\\"\\\"\\\"Failed to execute \\\"\\\"\\\"\\n u\\\"\\\"\\\"{0} - {1}\\\"\\\"\\\".format(\\\" \\\".join(cmd), str(error)))\\n\\n with close_file_pair((process.stdout, process.stderr)) as outputs:\\n status = output_strategy(process, outputs)\\n\\n instant_fail = kwargs.get(\\\"instant_fail\\\") or False\\n\\n if status != 0:\\n IndentedLogger.message(u\\\"\\\"\\\"!!! Process {0}\\\\n\\\"\\\"\\\".format(cmd[0]))\\n for arg in cmd[1:]:\\n IndentedLogger.message(u\\\"\\\"\\\"!!! {0}\\\\n\\\"\\\"\\\".format(arg))\\n IndentedLogger.message(u\\\"\\\"\\\"!!! failed with {0}\\\\n\\\"\\\"\\\".format(status))\\n if not kwargs.get(\\\"allow_failure\\\", None):\\n container.note_failure(instant_fail)\\n\\n return status\",\n \"def get_output(self, cmd, *args, **kwargs):\\n return self.exec(cmd, *args, **kwargs, capture_output=True)\",\n \"def _call(self, filename, *args, **kwargs):\\n #hard code tmp_output as the output name since we don't save it anyway\\n #self.cli('{} -log {log_output} {seqfile} > {tmp_path}'.format(self.command(), tmp_path=os.path.join(tmpd,'tmp_output'), log_output=logfile, seqfile=filename), wait=True)\\n self.cli('{} {seq_file}'.format(self.command(), seq_file=filename), wait=True)\\n\\n return (self.cli.get_stdout(), self.cli.get_stderr())\",\n \"def postProcessOutput(self):\\n\\n logging.info(\\\" ========> Analysis %20s called postProcessOutput:\\\"%(self.name))\\n\\n if self.checkExpectedOutputFiles() == False:\\n raise Exception(\\\"Missing expected output files. Number missing are [%d]\\\"%(len(self.missing_output_files)))\\n\\n FileUtils.checkDirExists(self.output_dir)\\n\\n tmpfiles = []\\n\\n logging.info(\\\" ========> Analysis %20s called postProcessOutput: Moving files from %s to %s \\\"%(self.name,self.working_dir,self.output_dir))\\n try:\\n for srcfile in self.expected_output_files:\\n\\n fullsrcfile = os.path.join(self.working_dir,srcfile)\\n destfile = os.path.join(self.output_dir,srcfile)\\n\\n FileUtils.checkDirExistsForFile(destfile)\\n\\n res = shutil.move(fullsrcfile,destfile)\\n\\n if res == None:\\n res = \\\"OK\\\"\\n else:\\n res = \\\"FAILED\\\"\\n\\n print \\\"Checking %s\\\"%destfile\\n tmpfiles.append(destfile)\\n \\n logging.info(\\\" ========> Analysis %20s called postProcessOutput: Result of file move for %s = %s\\\" % (self.name,srcfile,res))\\n\\n except Exception as e:\\n logging.info(\\\" ========> Analysis %20s file move failed %s\\\"%(self.name,e))\\n raise\\n\\n self.output_files = tmpfiles\\n\\n for f in self.temp_output_files:\\n logging.info(\\\" ========> Analysis %20s removing temp file %s \\\"%(self.name,f))\\n\\t res = os.remove(f)\",\n \"def test_outfile():\\n\\n out_file = random_filename()\\n if os.path.isfile(out_file):\\n os.remove(out_file)\\n\\n try:\\n cmd = f'{prg} --cdhit {cdhit} --proteins {proteins} -o {out_file}'\\n rv, out = getstatusoutput(cmd)\\n assert rv == 0\\n\\n assert out == ('Wrote 309 of 220,520 unclustered '\\n f'proteins to \\\"{out_file}\\\"')\\n\\n assert os.path.isfile(out_file)\\n\\n seqs = list(SeqIO.parse(out_file, 'fasta'))\\n assert len(seqs) == 309\\n\\n finally:\\n if os.path.isfile(out_file):\\n os.remove(out_file)\",\n \"def get_output_file_name(argn=2, std_name='output.txt'):\\n try:\\n name = sys.argv[argn]\\n except IndexError:\\n name = std_name\\n print(\\\"Warning: no output file name received. Output will be\\\"\\n \\\" written to '%s'.\\\" % name)\\n return name\",\n \"def execute_tool(description, *args):\\n command_line = list(args) + files_and_directories\\n click.echo(f\\\"{description}: {' '.join(command_line)}\\\")\\n rv = call(command_line)\\n if rv != 0:\\n exit(rv)\",\n \"def test_empty_file(self):\\n input_file = \\\"does_not_exist.fasta\\\"\\n self.assertFalse(os.path.isfile(input_file))\\n\\n cline = XXmotifCommandline(outdir=self.out_dir, seqfile=input_file)\\n\\n try:\\n stdout, stderr = cline()\\n except ApplicationError as err:\\n self.assertEqual(err.returncode, 255)\\n else:\\n self.fail(f\\\"Should have failed, returned:\\\\n{stdout}\\\\n{stderr}\\\")\",\n \"def execute(self, log_out, log_err):\\n EventGenerator.execute(self, log_out, log_err)\\n if 'moller' not in self.name:\\n src = os.path.join(self.rundir, 'brems.stdhep')\\n dest = os.path.join(self.rundir, self.output_files()[0])\\n logger.debug(\\\"Copying '%s' to '%s'\\\" % (src, dest))\\n shutil.copy(src, dest)\",\n \"def execute_command(self, command):\\n\\n if os.system(command) != 0:\\n self.log('ERROR: Command \\\"' + command + '\\\" returned with a non-zero exit code')\\n raise NonZeroExitCodeException('Non-zero exit code')\",\n \"def has_fileout(self):\\n return self.fileout is not None\",\n \"async def collect_final_outputs(self) -> None: # pylint: disable=too-many-branches\\n self._become_current()\\n\\n missing_outputs = False\\n assert self.step is not None\\n\\n did_sleep = False\\n\\n for pattern in sorted(self.step.output): # pylint: disable=too-many-nested-blocks\\n formatted_pattern = fmt_capture(self.kwargs, pattern)\\n if is_phony(pattern):\\n Invocation.up_to_date[formatted_pattern] = UpToDate(self.name, self.newest_input_mtime_ns + 1)\\n continue\\n\\n try:\\n paths = glob_paths(formatted_pattern)\\n if not paths:\\n Logger.debug(f\\\"Did not make the optional output(s): {pattern}\\\")\\n else:\\n for path in paths:\\n self.built_outputs.append(path)\\n\\n global touch_success_outputs # pylint: disable=invalid-name\\n if touch_success_outputs.value:\\n if not did_sleep:\\n await self.done(asyncio.sleep(1.0))\\n did_sleep = True\\n Logger.file(f\\\"Touch the output: {path}\\\")\\n Stat.touch(path)\\n\\n mtime_ns = Stat.stat(path).st_mtime_ns\\n Invocation.up_to_date[path] = UpToDate(self.name, mtime_ns)\\n\\n if Logger.isEnabledFor(logging.DEBUG):\\n if path == formatted_pattern:\\n Logger.debug(f\\\"Has the output: {path} \\\" f\\\"time: {_datetime_from_nanoseconds(mtime_ns)}\\\")\\n else:\\n Logger.debug(\\n f\\\"Has the output: {pattern} -> {path} \\\"\\n f\\\"time: {_datetime_from_nanoseconds(mtime_ns)}\\\"\\n )\\n\\n except NonOptionalException:\\n self._become_current()\\n Logger.error(f\\\"Missing the output(s): {pattern}\\\")\\n missing_outputs = True\\n break\\n\\n if missing_outputs:\\n self.abort(\\\"Missing some output(s)\\\")\",\n \"def _write_output(output: List[str], output_file: Optional[str]) -> None:\\n if output_file:\\n with open(output_file, 'w+') as file:\\n file.write('\\\\n'.join(output))\\n else:\\n for line in output:\\n print(line)\",\n \"def command_exists(name, path=None):\\n if path is None:\\n path = sys.path\\n\\n for prefix in path:\\n filename = os.path.join(prefix, name)\\n is_executable = os.access(filename, os.X_OK)\\n is_file = os.path.isfile(filename)\\n if is_executable and is_file:\\n return True\\n\\n return False\",\n \"def _exec_command(command):\\n\\n log(\\\"Run command for '%s'\\\" % command)\\n p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE)\\n (output, err) = p.communicate()\\n p_status = p.wait()\\n return p_status, output\",\n \"def has_output(self, output_ref):\\n outputs = self.get_recipe_outputs()\\n for (output_role_name, output_role) in outputs.items():\\n for item in output_role.get(\\\"items\\\", []):\\n if item.get(\\\"ref\\\", None) == output_ref:\\n return True\\n return False\",\n \"def _get_sys_cmd(self, file_path_input, fold_coverage, file_path_output_prefix):\\n assert self.validate_file(file_path_input)\\n assert isinstance(fold_coverage, (int, float))\\n assert self.validate_dir(file_path_output_prefix, only_parent=True)\\n\\n # TODO: mask 'N' default: '-nf 1'\\n read_length = self._art_read_length[self._profile]\\n error_profile = os.path.join(self._directory_error_profiles, self._art_error_profiles[self._profile])\\n arguments = [\\n \\\"-sam\\\", \\\"-na\\\",\\n \\\"-i '{}'\\\".format(file_path_input),\\n \\\"-l\\\", str(read_length),\\n \\\"-m\\\", str(self._fragment_size_mean),\\n \\\"-s\\\", str(self._fragment_size_standard_deviation),\\n \\\"-f\\\", str(fold_coverage),\\n \\\"-o '{}'\\\".format(file_path_output_prefix),\\n \\\"-1 '{}'\\\".format(error_profile+'1.txt'),\\n \\\"-2 '{}'\\\".format(error_profile+'2.txt'),\\n ]\\n\\n if self._logfile:\\n arguments.append(\\\">> '{}'\\\".format(self._logfile))\\n\\n # art illumina only accepts integer as seed!\\n arguments.append(\\\"-rs '{}'\\\".format(self._get_seed()))\\n\\n cmd = \\\"{exe} {args}\\\".format(exe=self._file_path_executable, args=\\\" \\\".join(arguments))\\n return cmd\",\n \"def execute(self, parameters, messages):\\n #3.4\\n try:\\n importlib.reload (arcsdm.combine_outputnnfiles)\\n except :\\n reload(arcsdm.combine_outputnnfiles);\\n arcsdm.combine_outputnnfiles.execute(self, parameters, messages)\\n return\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6835208","0.6785431","0.667483","0.6603357","0.6349663","0.606884","0.60539275","0.5998481","0.59256774","0.58965987","0.5828717","0.57747966","0.5732897","0.56906545","0.56636125","0.5657635","0.56451756","0.5630182","0.56291837","0.5592528","0.55353385","0.54916877","0.54818755","0.5480598","0.54655564","0.54590255","0.5442302","0.5441071","0.5426283","0.5395376","0.53868616","0.53861314","0.5381742","0.53598833","0.5359872","0.5340553","0.533075","0.5317391","0.5317391","0.53045946","0.53038675","0.5284631","0.5283594","0.5282211","0.5238213","0.5235603","0.52337116","0.52323663","0.52290547","0.52142435","0.52139276","0.5202516","0.52010083","0.51969004","0.5188644","0.5181365","0.5178959","0.51771265","0.51748395","0.5161045","0.5153142","0.51437736","0.5129286","0.51269037","0.5122244","0.51185095","0.5118305","0.51145816","0.510891","0.51055145","0.50926816","0.5077752","0.5075787","0.5075702","0.5073708","0.5063155","0.5058177","0.5046708","0.50432163","0.50414026","0.50273204","0.5024844","0.5019808","0.50060666","0.5005501","0.5004443","0.5002541","0.5000222","0.49910882","0.4990591","0.49896017","0.49840474","0.4977657","0.49773848","0.49745914","0.49669817","0.4966776","0.49649832","0.49636388","0.49552718","0.4950346"],"string":"[\n \"0.6835208\",\n \"0.6785431\",\n \"0.667483\",\n \"0.6603357\",\n \"0.6349663\",\n \"0.606884\",\n \"0.60539275\",\n \"0.5998481\",\n \"0.59256774\",\n \"0.58965987\",\n \"0.5828717\",\n \"0.57747966\",\n \"0.5732897\",\n \"0.56906545\",\n \"0.56636125\",\n \"0.5657635\",\n \"0.56451756\",\n \"0.5630182\",\n \"0.56291837\",\n \"0.5592528\",\n \"0.55353385\",\n \"0.54916877\",\n \"0.54818755\",\n \"0.5480598\",\n \"0.54655564\",\n \"0.54590255\",\n \"0.5442302\",\n \"0.5441071\",\n \"0.5426283\",\n \"0.5395376\",\n \"0.53868616\",\n \"0.53861314\",\n \"0.5381742\",\n \"0.53598833\",\n \"0.5359872\",\n \"0.5340553\",\n \"0.533075\",\n \"0.5317391\",\n \"0.5317391\",\n \"0.53045946\",\n \"0.53038675\",\n \"0.5284631\",\n \"0.5283594\",\n \"0.5282211\",\n \"0.5238213\",\n \"0.5235603\",\n \"0.52337116\",\n \"0.52323663\",\n \"0.52290547\",\n \"0.52142435\",\n \"0.52139276\",\n \"0.5202516\",\n \"0.52010083\",\n \"0.51969004\",\n \"0.5188644\",\n \"0.5181365\",\n \"0.5178959\",\n \"0.51771265\",\n \"0.51748395\",\n \"0.5161045\",\n \"0.5153142\",\n \"0.51437736\",\n \"0.5129286\",\n \"0.51269037\",\n \"0.5122244\",\n \"0.51185095\",\n \"0.5118305\",\n \"0.51145816\",\n \"0.510891\",\n \"0.51055145\",\n \"0.50926816\",\n \"0.5077752\",\n \"0.5075787\",\n \"0.5075702\",\n \"0.5073708\",\n \"0.5063155\",\n \"0.5058177\",\n \"0.5046708\",\n \"0.50432163\",\n \"0.50414026\",\n \"0.50273204\",\n \"0.5024844\",\n \"0.5019808\",\n \"0.50060666\",\n \"0.5005501\",\n \"0.5004443\",\n \"0.5002541\",\n \"0.5000222\",\n \"0.49910882\",\n \"0.4990591\",\n \"0.49896017\",\n \"0.49840474\",\n \"0.4977657\",\n \"0.49773848\",\n \"0.49745914\",\n \"0.49669817\",\n \"0.4966776\",\n \"0.49649832\",\n \"0.49636388\",\n \"0.49552718\",\n \"0.4950346\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8455,"cells":{"query":{"kind":"string","value":"Compute the temporal SNR for each epi, save in a nifti file, and store a summmary in a png file."},"document":{"kind":"string","value":"def ComputeSNR(self):\n for epi in self.entry_map['epi']:\n epifile = self.info[epi]['imgfile_final'] + self.info[epi]['suffix']\n prefix = self.info[epi]['imgfile_final'] + '_snr'\n if not os.path.exists('%s_snr.png' % prefix):\n if self.verbose:\n print 'TemporalSnr(epifile=%s, prefix=%s)' % \\\n (epifile, prefix)\n try:\n TemporalSnr(epifile=epifile, prefix=prefix)()\n except:\n print(\"Error computing temporal SNR\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculate_SNR(snid: int, photo_data: pd.DataFrame, \n head_data: pd.DataFrame, code_zenodo: int, \n snana_file_index: int, code_snana: int):\n \n types_names = {90: 'Ia', 62: 'Ibc', 42: 'II', 67: '91bg', 52: 'Iax',\n 64:'KN', 95: 'SLSN', 994: 'PISN', 992: 'ILOT', \n 993: 'CaRT', 15: 'TDE', 88: 'AGN', 92: 'RRL', \n 65: 'M-dw', 16: 'EB', 53: 'Mira', 991: 'BMicroL',\n 6: 'MicroL'}\n \n # LSST filters\n filters = [b'u ', b'g ', b'r ', b'i ', b'z ', b'Y ']\n\n flag_id_photo = photo_data['SNID'] == snid\n\n flux = photo_data[flag_id_photo]['FLUXCAL'].values\n fluxerr = photo_data[flag_id_photo]['FLUXCALERR'].values\n\n SNR_all = flux/fluxerr\n \n indx = np.random.choice(range(flux.shape[0]))\n\n flag_id_head = head_data['SNID'].values == snid\n redshift = head_data['SIM_REDSHIFT_CMB'].values[flag_id_head][0]\n \n # store values\n line = [snid, snana_file_index, code_zenodo, code_snana, \n types_names[code_zenodo], redshift]\n \n for fil in filters: \n line.append(head_data['SIM_PEAKMAG_' + str(fil)[2]].values[flag_id_head][0])\n \n # calculate SNR statistics \n for f in [np.mean, max, np.std]: \n for fil in filters: \n \n flag_fil = photo_data[flag_id_photo]['FLT'] == fil\n neg_flag = flux > -100\n flag2 = np.logical_and(flag_fil, neg_flag)\n \n if sum(flag2) > 0:\n SNR_fil = SNR_all[flag2] \n line.append(f(SNR_fil))\n \n if len(line) == 30:\n return line\n else:\n return []","def save(self, compute_snrs=True):\n if not self.save_mode:\n raise RuntimeError('Need to enable save mode to save')\n\n fn = os.path.join(self.output_dir,\n 'data_' + time_string() + '.h5')\n save_dict(fn=fn, d=self.data)\n if compute_snrs:\n from src.analyzer import DataAnalyzer\n da = DataAnalyzer.fromfilename(fn)\n da.snr_list()\n return fn","def do_SEIR(self, t_max=200, dt=1.):\n dt = float(dt)\n g = Graph()\n\n for node in ['S', 'E', 'I', 'R']:\n g.add_node(node, 0)\n\n g.set_node('S', self.population)\n g.set_node('E', 0)\n g.set_node('I', self.N_init)\n g.set_node('R', 0)\n\n # cumulative time series\n S = [g.get_node_value('S')] # Susceptible\n E = [g.get_node_value('E')] # Exposed\n I = [g.get_node_value('I')] # noqa Infected\n R = [g.get_node_value('R')] # Recovered\n\n ts = [0.] # time series\n nms = ['prob', 'lag']\n\n g.add_edge('S', 'S', nms, [0.1, 2])\n g.add_edge('E', 'E', nms, [0.4, 21])\n g.add_edge('I', 'I', nms, [0.1, 2])\n\n g.add_edge('S', 'E', nms, [1.2, 1])\n g.add_edge('E', 'I', nms, [0.1, 14]) # [, tiempo de incubacion]\n g.add_edge('I', 'R', nms, [0.7, 2]) # [, tiempo de recuperacion]\n\n t, time_steps = 0., 0\n while t < t_max:\n\n time_steps = time_steps + 1\n\n t = t + dt\n ts.append(t)\n\n # (( S ))\n prob_SS = g.get_edge('S', 'S', 'prob') # beta\n\n dS = - S[-1] * (I[-1] / self.population) * prob_SS\n\n # n_S = min(S[-1] + min(dS * dt, 0), self.population)\n n_S = S[-1] + dS * dt\n\n # (( E ))\n prob_EE = g.get_edge('E', 'E', 'prob')\n dE = - dS - prob_EE * E[-1]\n\n # n_E = min(E[-1] + max(dE * dt, 0), self.population)\n n_E = E[-1] + dE * dt\n\n # (( I ))\n prob_EI = g.get_edge('E', 'I', 'prob')\n lag_EI = g.get_edge('E', 'I', 'lag')\n update_EI = E[-lag_EI] if lag_EI < len(E) else 0.\n\n prob_IR = g.get_edge('I', 'R', 'prob')\n lag_IR = g.get_edge('I', 'R', 'lag')\n update_IR = I[-lag_IR] if lag_IR < len(I) else 0.\n\n prob_II = g.get_edge('I', 'I', 'prob')\n\n dI = prob_EI * update_EI - prob_IR * update_IR\n dI = -dI # porque ????\n n_I = min(I[-1] + dI * dt, self.population)\n\n # (( R ))\n prob_II = g.get_edge('I', 'I', 'prob')\n dR = prob_II * I[-1]\n n_R = min(R[-1] + max(dR * dt, 0), self.population)\n\n S.append(n_S)\n E.append(n_E)\n I.append(n_I)\n R.append(n_R)\n\n df = pd.DataFrame(\n {'ts': ts, 'S': S, 'E': E, 'I': I, 'R': R}).set_index(\"ts\")\n\n extra = attr.asdict(self)\n extra[\"model_name\"] = \"SEIR\"\n return ModelResultFrame(df=df, extra=extra)","def run(self):\r\n #print 'WriteFITS_IDI.run'\r\n\r\n # construct the name of the file\r\n readfits = self.previous_results['readfits']\r\n obs_date = readfits['obs date']\r\n idifitsfile = '%s.idi.fits' % obs_date\r\n\r\n configxml = 'firi.xml'\r\n\r\n # midnight on date to Julian day\r\n obs_date_midnight = astro_time.Time('%s-%s-%sT00:00:00' %\r\n (obs_date[:4], obs_date[4:6], obs_date[6:8]), format='isot')\r\n obs_date_midnight = obs_date_midnight.jd\r\n\r\n rdate = astro_time.Time(obs_date_midnight, format='jd',\r\n out_subfmt='date')\r\n rdate = rdate.iso\r\n\r\n # number of days after midnight at obs start\r\n obs_date_time = astro_time.Time('%s-%s-%s:%s:%s' %\r\n (obs_date[:4], obs_date[4:6], obs_date[6:11], obs_date[11:13],\r\n obs_date[13:]), format='isot')\r\n obs_date_time = obs_date_time.jd - obs_date_midnight\r\n\r\n # get specific items from the results that will be need in\r\n # the reduction\r\n reduce_interferogram = self.previous_results['reduceinterferogram']\r\n data_quality = reduce_interferogram['data_quality']\r\n scan_uvspectra = reduce_interferogram['scan_uvspectra']\r\n\r\n wavenumber = scan_uvspectra[0].wavenumber\r\n\r\n # construct lists of the values to be stored in each Table column\r\n n_uvspectra = max(scan_uvspectra.keys()) + 1\r\n mcomplex = 3\r\n mstokes = 1\r\n mfreq = len(wavenumber)\r\n mra = 1\r\n mdec = 1\r\n\r\n uv_data = np.zeros([n_uvspectra, mdec, mra, mfreq, mstokes, mcomplex])\r\n u = np.zeros([n_uvspectra])\r\n v = np.zeros([n_uvspectra])\r\n w = np.zeros([n_uvspectra])\r\n dates = np.zeros([n_uvspectra])\r\n times = np.zeros([n_uvspectra])\r\n baselines = np.zeros([n_uvspectra], dtype=np.int)\r\n freqid = np.ones([n_uvspectra], dtype=np.int)\r\n\r\n for k,val in scan_uvspectra.items():\r\n uv_data[k,0,0,:,0,0] = val.spectrum.real\r\n uv_data[k,0,0,:,0,1] = val.spectrum.imag\r\n uv_data[k,0,0,:,0,2] = np.ones(val.spectrum.real.shape)\r\n u[k] = np.mean(val.baseline_x)\r\n v[k] = np.mean(val.baseline_y)\r\n w[k] = np.mean(val.baseline_z)\r\n dates[k] = obs_date_midnight\r\n times[k] = obs_date_time + (np.mean(val.time) / (3600 * 24))\r\n baselines[k] = 258\r\n\r\n # external_params is referred to inside config.xml and can be\r\n # used to set parameters there\r\n light_speed = constants.c.to('m/s').value\r\n external_params = {'NCHAN':len(wavenumber),\r\n 'RDATE':rdate,\r\n 'REF_FREQ':0.0 * 100 * light_speed,\r\n 'CHAN_BW':np.abs(wavenumber[1] - wavenumber[0]) * \\\r\n 100 * light_speed}\r\n\r\n print \"Out: %s\\nConfig: %s\"%(idifitsfile, configxml)\r\n\r\n print('\\nConfiguring Array geography')\r\n print('--------------------------')\r\n # Meaningless numbers, hopefully not needed by any CASA method \r\n # that we want to use\r\n (latitude, longitude, elevation) = ('00:00:00.00', '00:00:00.00', 0)\r\n now = datetime.datetime.now()\r\n\r\n # Make ourselves an Array (pyEphem observer)\r\n array_geometry_m = np.array([\r\n [0.0, 0.0, 0.0],\r\n [0.0, 80.0, 0.0]], dtype = 'float32')\r\n beach = Array(lat=latitude, long=longitude, elev=elevation, date=now,\r\n antennas=array_geometry_m)\r\n\r\n print('\\nConfiguring phase source')\r\n print('--------------------------')\r\n # The source is our phase centre for UVW coordinates\r\n line = \"%s,f,%s,%s,%s,%d\" % ('Deep Space', '00:00:00',\r\n '00:00:00', '1', 2000)\r\n source = ephem.readdb(line)\r\n source.compute(beach)\r\n print \"Name: %s \\nRA: %s \\nDEC: %s\"%(source.name, source.ra, source.dec)\r\n\r\n # Make a new blank FITS HDU\r\n print('\\nCreating PRIMARY HDU')\r\n print('------------------------------------')\r\n hdu = make_primary(config=configxml, external_params=external_params)\r\n print repr(hdu.header)\r\n\r\n # Go through and generate required tables\r\n print('\\nCreating ARRAY_GEOMETRY')\r\n print('------------------------------------')\r\n tbl_array_geometry = make_array_geometry(config=configxml, num_rows=2,\r\n external_params=external_params)\r\n tbl_array_geometry = config_array_geometry(tbl_array_geometry,\r\n array_geometry_m)\r\n print repr(tbl_array_geometry.header)\r\n\r\n print('\\nCreating FREQUENCY')\r\n print('------------------------------------')\r\n tbl_frequency = make_frequency(config=configxml, num_rows=1,\r\n external_params=external_params)\r\n tbl_frequency = config_frequency(tbl_frequency,\r\n external_params=external_params)\r\n print repr(tbl_frequency.header)\r\n\r\n print('\\nCreating SOURCE')\r\n print('------------------------------------')\r\n tbl_source = make_source(config=configxml, num_rows=1,\r\n external_params=external_params)\r\n tbl_source = config_source(tbl_source, source)\r\n print repr(tbl_source.header)\r\n\r\n print('\\nCreating ANTENNA')\r\n print('------------------------------------')\r\n tbl_antenna = make_antenna(config=configxml, num_rows=2,\r\n external_params=external_params)\r\n tbl_antenna = config_antenna(tbl_antenna)\r\n print repr(tbl_antenna.header)\r\n\r\n print('\\nCreating UV_DATA')\r\n print('------------------------------------')\r\n\r\n print 'Data dimensions: %i dumps, %i chans, %i pols, %i data' % (\r\n n_uvspectra, mfreq, mstokes, mcomplex)\r\n\r\n print('Generating blank UV_DATA rows...')\r\n tbl_uv_data = make_uv_data(config=configxml, num_rows=n_uvspectra,\r\n external_params=external_params)\r\n\r\n timesorted = np.argsort(times)\r\n\r\n for k in timesorted:\r\n tbl_uv_data.data[k]['FLUX'] = uv_data[k,0,0,:,0,:].ravel()\r\n tbl_uv_data.data[k]['UU'] = u[k] / light_speed\r\n tbl_uv_data.data[k]['VV'] = v[k] / light_speed\r\n tbl_uv_data.data[k]['WW'] = w[k] / light_speed\r\n tbl_uv_data.data[k]['BASELINE'] = baselines[k]\r\n tbl_uv_data.data[k]['DATE'] = dates[k]\r\n tbl_uv_data.data[k]['TIME'] = times[k]\r\n tbl_uv_data.data[k]['SOURCE'] = 1\r\n tbl_uv_data.data[k]['FREQID'] = 1\r\n tbl_uv_data.data[k]['INTTIM'] = 3\r\n\r\n print repr(tbl_uv_data.header)\r\n \r\n hdulist = pyfits.HDUList(hdus=\r\n [hdu,\r\n tbl_array_geometry,\r\n tbl_source, \r\n tbl_frequency,\r\n tbl_antenna,\r\n tbl_uv_data])\r\n\r\n print('Verifying integrity...') \r\n hdulist.verify()\r\n \r\n if(os.path.isfile(idifitsfile)):\r\n print('Removing existing file...')\r\n os.remove(idifitsfile)\r\n print('Writing to file...')\r\n hdulist.writeto(idifitsfile)\r\n\r\n print('Done.')\r\n\r\n self.result['idifitsfile'] = idifitsfile\r\n\r\n return self.result","def save_nifti(self, path):\n meta = {'te': self.te, 'tr': self.tr, 'sw': self.sw}\n if self.sequence_type == 'STEAM':\n meta['tm'] = self.tm\n\n # store real and imaginary components in last 2 dims\n component_fid = np.stack((np.real(self.fid),np.imag(self.fid)), -2)\n nifti = nib.Nifti2Image(component_fid, self.transform.get_matrix(), extra=meta)\n nib.save(nifti, path)","def write_seisan(filename, args):\n bf = BaikalFile(filename)\n if not bf.valid:\n print(\"Invalid file {}\".format(filename))\n return\n header = bf.MainHeader\n # datetime\n date = datetime.datetime(header[\"year\"], header[\"month\"], header[\"day\"])\n delta = datetime.timedelta(seconds=header[\"to\"])\n dt = date + delta\n _time = dt.time() # time\n # make utc datetime\n utcdatetime = UTCDateTime(date.year, date.month, date.day,\n _time.hour, _time.minute, _time.second, _time.microsecond, precision=3)\n bf.traces = bf.traces.astype(np.int32)\n bf.traces = bf.traces[:3]\n traces = []\n for channel, data in zip(CHANNELS, bf.traces):\n stats = DEFAULT_STATS.copy()\n stats.update({\n \"station\": header['station'].upper()[:3],\n 'channel': channel,\n 'sampling_rate': int( 1./header[\"dt\"] ),\n \"delta\": header[\"dt\"],\n \"npts\": data.size,#shape[0]\n 'starttime': utcdatetime,\n })\n # save coordinates\n stats['gse2'][\"lat\"] = header['latitude']\n stats['gse2'][\"lon\"] = header[\"longitude\"]\n trace = Trace(data=data, header=stats)\n traces.append(trace)\n # create Stream\n stream = Stream(traces)\n #== write seisan\n # date\n name = \"{year:04}-{month:02}-{day:02}\".format(**header)\n # time\n name += \"-{t.hour:02}-{t.minute:02}\".format(t=stats['starttime'])\n # + station name + Day_of_Year\n name += \"{0}__{1:03}\".format(stats[\"station\"], stats['starttime'].timetuple().tm_yday)\n print('Writing GSE2 file %s.' % name)\n writeGSE2(stream, os.path.join(args.outdir, name))","def in_situ_tair_snd(sno0, year0=2016, npr_date=-1, ascat_date=-1):\n if npr_date < 0:\n npr_date = 100*24*3600 + bxy.get_total_sec('%d0101' % year0)\n if ascat_date < 0:\n ascat_date = 100*24*3600 + bxy.get_total_sec('%d0101' % year0)\n snd_name = \"snow\"\n print 'the %d was processing' % sno0\n sno = str(sno0)\n tair_name = \"Air Temperature Observed (degC)\"\n if sno0 in [2065, 2081]:\n if year0 == 2016:\n tair_name = \"Air Temperature Average (degC)\"\n # read measurements\n hr_list = [5, 7, 9, 14, 18, 21]\n t_air_one_year = read_site.in_situ_series(sno, y=year0, hr=hr_list) # [:, :, 0] temperature at 7:00 (local)\n # time_above_zero_0 = data_process.zero_find(t_air_one_year[:, :, 0], w=10, th=-0.1) #\n # time_above_zero_1 = data_process.zero_find(t_air_one_year[:, :, 1], w=10, th=-0.1)\n # time_above_zero_2 = data_process.zero_find(t_air_one_year[:, :, 3], w=10, th=-0.1)\n time_above_zero_list = [data_process.zero_find(t_air_one_year[:, :, i], w=10, th=-0.1)\n for i in range(0, len(hr_list))]\n date_tuple = bxy.time_getlocaltime(time_above_zero_list, ref_time=[2000, 1, 1, 0], t_source='US/Alaska')\n t_value, t_date = read_site.read_measurements\\\n (sno, tair_name, np.arange(1, 365), year0=year0, hr=18, t_unit='sec')\n\n\n tair_zero_day2 = data_process.zero_find(np.array([t_date, -t_value]), w=7, th=0) # in unit of sec\n tair_zero_day1 = data_process.zero_find_gt(np.array([t_date, t_value]), w=7, th=1)\n air_win = 7 # check days during window shown air temperature gt 0 degC\n w, w_valid = data_process.n_convolve3(t_value, air_win)\n air0_index0 = np.where(w>5)\n for ind0 in air0_index0[0]:\n if t_date[ind0] > bxy.get_total_sec('%d0307' % year0):\n tair_zero_day = t_date[ind0] - air_win*24*3600\n break\n # check\n zero_date = bxy.time_getlocaltime([tair_zero_day,tair_zero_day2, npr_date[0], ascat_date[0]],\n ref_time=[2000, 1, 1, 0], t_source=\"US/Alaska\")[-2]\n i_zero = np.where(bxy.time_getlocaltime(t_date, ref_time=[2000, 1, 1, 0],\n t_source=\"US/Alaska\")[-2] == zero_date[0])[0][0]\n t_check = t_value[i_zero - 3: i_zero + 4]\n air_0, air00 = read_site.read_measurements(sno, tair_name, 366+np.arange(50, 70), hr=18)\n a_extend = np.array([-3600*24, 3600*24])\n period0, period1 = np.array(sorted([tair_zero_day, npr_date])) + a_extend, \\\n np.array(sorted([tair_zero_day, ascat_date])) + a_extend\n snow_value, snow_date = read_site.read_measurements\\\n (sno, snd_name, np.arange(1, 365), year0=year0, hr=0, t_unit='sec')\n # get the in situ measurements during a period\n snow2date0 = data_process.measurements_slice(np.array([snow_date, snow_value]),\n peroid=period0)\n snow2date1 = data_process.measurements_slice(np.array([snow_date, snow_value]),\n peroid=period1)\n air2date0, air2date1 = data_process.measurements_slice(np.array([t_date, t_value]),\n peroid=period0),\\\n data_process.measurements_slice(np.array([t_date, t_value]),\n peroid=period1)\n return tair_zero_day, snow2date0, snow2date1, air2date0, air2date1","def plot_seaice_trend(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/trends/SIC/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' SIE trends'\n\n\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n\n if seaice_source == 'nsidc':\n seaice = seaice * area /250\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = xr.apply_ufunc(scipy.stats.linregress, seaice[seaicename].time.values.astype(float), seaice[seaicename], input_core_dims=[['time'],['time']], vectorize=True, dask='parallelized', output_dtypes=[float]*5, output_core_dims=[[]]*5)\n if seaice_source =='ecmwf':\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename])\n \n seaice_m = seaice_m * 1e9 * 60 * 60 * 24 * 365\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n # seaice_m = seaice_m*area\n seaice_m = seaice_m.where(seaice_m != 0)\n # seaice_m = seaice_m.where(seaice_p_value <= 0.05)\n max_ = seaice_m.max()\n min_ = seaice_m.min() \n # max_ = 1\n divnorm = TwoSlopeNorm(vmin=min_, vcenter=0, vmax=max_)\n fig = plt.figure(figsize = (5,5))\n ax = fig.add_subplot(111, projection = ccrs.SouthPolarStereo())\n # Plotting\n contor = ax.contourf(seaice_m.x, seaice_m.y, seaice_m, cmap = 'RdBu', levels = 11, norm = divnorm, transform=ccrs.SouthPolarStereo())\n ax.coastlines()\n ax.set_axis_off()\n cbar = plt.colorbar(contor)\n cbar.set_label('Trend in SIE (km$^2$ yr$^{-1}$)')\n plt.title(title)\n plt.savefig(imagefolder + seaicename + '.pdf')\n plt.show()","def plot_seaice_predict(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' SIE prediction'\n\n\n# Loading Seaice Trends\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n seaice = seaice\n\n\n# Index contributions\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\n dataset = xr.open_dataset(filename + '.nc')\n indicies = np.array([i for i in dataset])\n values = np.array([dataset[i].values for i in dataset])\n index_data = {}\n for indexname in indicies[:-1]:\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\n\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\n\n prediction = seaice.copy() * 0\n for indexname in indicies:\n if indexname in index_data.keys():\n prediction += index_data[indexname] * dataset[indexname]\n else:\n prediction += dataset[indexname]\n\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n # ax2 = ax.twinx()\n # ax2.plot([],[])\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'SIE')\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'Prediction')\n # ax.set_xlim([min(times),max(times)])\n\n lines = ln1 + ln2\n labels = [line.get_label() for line in lines]\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\n\n\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89')\n plt.title(title)\n\n plt.savefig(imagefolder + seaicename + '.pdf')\n plt.show()","def plot_index_sic_timeseries(anomlous = False, temporal_resolution = 'monthly', detrend = False, imagefolder = 'images/timeseries/SIC_INDICIES', indexname = 'SAM', n = 5, seaice_source = 'nsidc'):\n output_folder = 'processed_data/'\n\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n if detrend:\n dt = 'detrended'\n else:\n dt = 'raw'\n\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\n indicies = xr.open_dataset(output_folder + 'INDICIES/' + filename +'.nc')[indexname]\n data = indicies.copy()\n data = data.loc[data.time.dt.year >= 1979]\n seaicename = f'{temp_decomp}_{temporal_resolution}_{n}_{dt}'\n\n seaice = xr.open_dataset(output_folder + 'SIC/' + seaicename +'.nc')\n\n\n times = list(set.intersection(set(seaice.time.values), set(data.time.values)))\n\n seaice = seaice_area_mean(seaice.sel(time=times).sortby('time'), 1)\n data = data.sel(time=times).sortby('time')\n\n\n if seaice_source == 'ecmwf':\n seaice = xr.open_dataset(output_folder + 'ERA5/SIC/' + seaicename +'.nc')\n if seaice_source == 'ecmwf':\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename].mean(dim = ('longitude', 'latitude')))\n if seaice_source == 'nsidc':\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(mean_seaice.time.values.astype(float), mean_seaice)\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n title += dt + ' '\n\n title += temporal_resolution\n title += f' mean {indexname} and SIC'\n fig, ax = plt.subplots()\n ax2 = plt.twinx(ax)\n ax2.plot([],[])\n\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\n\n ln1 = ax.plot(data.time, data, label = f'{indexname}', color = '#EA1B10')\n ax.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\n if seaice_source == 'ecmwf':\n ln2 = ax2.plot(seaice.time, seaice[seaicename].mean(dim = ('longitude', 'latitude')), label = 'SIC', color = '#177E89')\n if seaice_source == 'nsidc':\n ln2 = ax2.plot(mean_seaice.time, mean_seaice, label = 'SIC', color = '#177E89')\n ax2.plot(seaice.time, seaice_m * seaice.time.values.astype(float) + seaice_b, color = '#177E89')\n\n yabs_max = abs(max(ax.get_ylim(), key=abs))\n ax.set_ylim(ymin=-yabs_max, ymax=yabs_max)\n if anomlous or detrend:\n yabs_max = abs(max(ax2.get_ylim(), key=abs))\n ax2.set_ylim(ymin=-yabs_max, ymax=yabs_max)\n\n # ylabels\n ax.set_ylabel(f'{indexname}')\n ax2.set_ylabel(f'Mean SIC')\n\n # legend\n lines = ln1 + ln2\n labels = [line.get_label() for line in lines]\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\n\n plt.title(title)\n plt.savefig(imagefolder + f'/SIC_{indexname}_{filename}_{seaice_source}' + '.pdf')\n plt.show()","def snr(mag=20, itime=1., read=24.5, sky=8.43, npix=24., zero=26.44, dark=0.0):\n # 2009-02-20 14:40 IJC: Initiated\n \n star = itime * 10**(0.4*(zero-mag))\n noise = npix * (itime*(sky+dark)+read**2)\n\n return star * (star+noise)**-0.5","def writeNoise(self):\n\n if (self.noise_file == None or self.noise_file == \"\"):\n return\n ofname = self.noise_file\n ofh = open(ofname,'w')\n\n # these have to be there as long as we've read the FAST file already\n ## not true: we don't store these in the dict.\n have_data = False\n if (\"TipRad\" in self.fstDict and 'TowerHt' in self.fstDict and 'Twr2Shft' in self.fstDict):\n tiprad = self.fstDict['TipRad']\n towerht = self.fstDict['TowerHt']\n twr2shft = self.fstDict['Twr2Shft']\n have_data = True\n\n for line in self.lines_noise:\n if (have_data and line.find('Observer location') >= 0):\n xdist = -1.0 * (tiprad + (towerht + twr2shft))\n ofh.write('{:.1f} 0.0 0.0'.format(xdist))\n ofh.write(' (x,y,z) Observer location in tower-base coordinate system. Use -(RotRad+HubHt)\\n')\n else:\n ofh.write(line)\n ofh.close()","def generate_nrrd(file):\n for root, dirs, files in os.walk(file):\n path = root.split(file)\n if path[1] != \"\":\n patient_id = int(path[1].split('/')[1][3:])\n path = file + \"_nrrd\" + path[1]\n # if path.find(\"frisk\")==-1 & path.find(\"M+\")==-1 & path.find('T2M')==-1:\n if (path.find('T2M') != -1 & path.find('frisk') ==\n -1 & path.find('+') == -1\n ) or path.find('masks') != -1: # Only T2M or mask can be found\n os.makedirs(path, exist_ok=True)\n print(path)\n Nrrd = ImageCollection(os.path.join(root, \"*.tiff\"),\n plugin='tifffile',\n load_func=convert_to_gray)\n Nrrd = np.asarray(Nrrd)\n\n if get_image_info(patient_id):\n print(patient_id)\n (spacings, thickness) = get_image_info(patient_id)\n thicknesses = [float('nan'), float('nan'), thickness]\n spacing_direction = np.eye(3)\n # Note: All header fields are specified in Fortran order,\n # per the NRRD specification, regardless of the index order. For example,\n # a C-ordered array with shape (60, 800, 600) would have a sizes field of (600, 800, 60).\n if len(Nrrd) > 0:\n header = {\n 'spacings': spacings,\n 'thicknesses': thicknesses\n \n }\n nrrd.write(os.path.join(path,\n str(patient_id) + '.nrrd'),\n Nrrd,\n header,\n index_order='C')","def plot_snr(tseries, lb=0, ub=None, fig=None):\r\n\r\n if fig is None:\r\n fig = plt.figure()\r\n\r\n ax_spectra = fig.add_subplot(1, 2, 1)\r\n ax_snr_info = fig.add_subplot(1, 2, 2)\r\n\r\n A = []\r\n info = []\r\n s_n_r = []\r\n coh = []\r\n noise_spectra = []\r\n signal_spectra = []\r\n #If you only have one channel, make sure that everything still works by\r\n #adding an axis\r\n if len(tseries.data.shape) < 3:\r\n this = tseries.data[np.newaxis, :, :]\r\n else:\r\n this = tseries.data\r\n\r\n for i in range(this.shape[0]):\r\n A.append(nta.SNRAnalyzer(ts.TimeSeries(this[i],\r\n sampling_rate=tseries.sampling_rate)))\r\n info.append(A[-1].mt_information)\r\n s_n_r.append(A[-1].mt_snr)\r\n coh.append(A[-1].mt_coherence)\r\n noise_spectra.append(A[-1].mt_noise_psd)\r\n signal_spectra.append(A[-1].mt_signal_psd)\r\n\r\n freqs = A[-1].mt_frequencies\r\n\r\n lb_idx, ub_idx = tsu.get_bounds(freqs, lb, ub)\r\n freqs = freqs[lb_idx:ub_idx]\r\n\r\n coh_mean = np.mean(coh, 0)\r\n snr_mean = np.mean(s_n_r, 0)\r\n info_mean = np.mean(info, 0)\r\n n_spec_mean = np.mean(noise_spectra, 0)\r\n s_spec_mean = np.mean(signal_spectra, 0)\r\n\r\n ax_spectra.plot(freqs, np.log(s_spec_mean[lb_idx:ub_idx]), label='Signal')\r\n ax_spectra.plot(freqs, np.log(n_spec_mean[lb_idx:ub_idx]), label='Noise')\r\n ax_spectra.set_xlabel('Frequency (Hz)')\r\n ax_spectra.set_ylabel('Spectral power (dB)')\r\n\r\n ax_snr_info.plot(freqs, snr_mean[lb_idx:ub_idx], label='SNR')\r\n ax_snr_info.plot(np.nan, np.nan, 'r', label='Info')\r\n ax_snr_info.set_ylabel('SNR')\r\n ax_snr_info.set_xlabel('Frequency (Hz)')\r\n ax_info = ax_snr_info.twinx()\r\n ax_info.plot(freqs, np.cumsum(info_mean[lb_idx:ub_idx]), 'r')\r\n ax_info.set_ylabel('Cumulative information rate (bits/sec)')\r\n return fig","def noe_analysis():\n\n files = ['f34k_7p2_noe_heights.txt', 'dphs_7p4_noe_heights.txt']\n proteins = ['f34k','dphs']\n\n heights = [nmrfn.parse_noe(f) for f in files]\n heights = pd.concat(heights, axis=1, keys=proteins)\n\n results = [noe_calcs(heights[i]) for i in proteins]\n results = pd.concat(results, axis=1, keys=proteins)\n\n fig = plt.figure(figsize=(10,6))\n val = plt.subplot(211)\n dif = plt.subplot(212)\n \n seqmin = 6\n seqmax = 144\n noemin = 0.5\n noemax = 0.95\n\n dy = results.dphs.noe - results.f34k.noe\n dif_plot(val, dy.index, dy.ix[8:141], seqmin, seqmax)\n noe_plot(proteins, results, seqmin, seqmax, noemin, noemax, xax=False)\n\n s1 = plt.Rectangle((0, 0), 1, 1, fc=proteins['f34k'])\n s2 = plt.Rectangle((0, 0), 1, 1, fc=proteins['dphs'])\n val.legend([s1, s2], ['F34K', '∆+PHS'], loc=8)\n\n title = 'HN NOE: F34K pH 7.2 compared with ∆+PHS pH 7.4'\n savefile = 'f34k_7p2_noe_analysis.pdf'\n save_plot(title, savefile)","def plot_seaice_timeseries(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/SIC/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' mean SIC in Antarctica'\n\n\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n\n if seaice_source == 'nsidc':\n seaice = seaice\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(mean_seaice.time.values.astype(float), mean_seaice)\n if seaice_source =='ecmwf':\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename].sum(dim = ('longitude', 'latitude')))\n ax = plt.gca()\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\n if seaice_source == 'nsidc':\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\n plt.plot(seaice.time, mean_seaice)\n\n if seaice_source == 'ecmwf':\n plt.plot(seaice.time, seaice[seaicename].mean(dim = ('longitude', 'latitude')))\n plt.plot(seaice.time, (seaice_m * seaice.time.values.astype(float) + seaice_b), color = '#177E89')\n plt.title(title)\n plt.savefig(imagefolder + seaicename+f'_{seaice_source}.pdf')\n plt.show()","def SaveNIFTI(data, file_path):\n if(np.iscomplex(data).any()):\n data = abs(data)\n nii = nib.Nifti1Image(data, np.eye(4)) \n nib.save(nii, file_path)","def plotSate(s,i,seed):\r\n fig, ax = plt.subplots()\r\n\r\n im = ax.imshow(s)\r\n\r\n plt.xticks([i for i in range(dim)], \"\")\r\n plt.yticks([i for i in range(dim)], \"\")\r\n\r\n fig.tight_layout()\r\n plt.savefig(\"Systems/\" + str(dim) + \"_\" + str(seed) + \"/Images/\" + str(i) +\r\n \".jpeg\",quality=80,optimize=True,\r\n dpi=80,progressive=True,transparent=True)\r\n fig.clear()\r\n plt.close(fig)","def fun_cnoise_Stim(self, t_stim = 10*s, sexp = 0, cutf = 0, do_csd = 1, t_qual = 0, freq_used = np.array([]), K_mat_old = np.array([]), inh_factor = [1], onf = None, equi = 0):\n self.barrier() # wait for other nodes\n \n filename = str(self.pickle_prefix) + \"_results_pop_cnoise.p\"\n filepath = self.data_dir + \"/\" + filename\n \n if self.id == 0: print \"- filepath:\", filepath \n \n if self.do_run or (os.path.isfile(filepath) is False):\n\n tstart = 0; \n fs = 1 / self.dt # sampling rate \n fmax = fs / 2 # maximum frequency (nyquist)\n \n t_noise = arange(tstart, t_stim, self.dt) # create stimulus time vector, make sure stimulus is even!!!\n\n #print self.syn_ex_dist\n #print self.syn_inh_dist\n #exit()\n \n if (self.syn_ex_dist == []):\n for nt in range(self.n_celltypes): # loop over all cells\n #print \"nt\", nt\n if hasattr(self.cells[nt][0], 'input_vec'):\n self.syn_ex_dist.append([1] * len(self.cells[nt][0].input_vec)) # default ex for all by default!!!\n else: \n self.syn_ex_dist.append([1] * self.n_syn_ex[nt]) # default ex for all by default!!!\n \n #print self.syn_ex_dist\n \n if (self.syn_ex_dist[0] == []):\n nemax = 1\n else:\n nemax = max([item for sublist in self.syn_ex_dist for item in sublist])\n \n if (self.syn_inh_dist == []): # and (any(self.n_syn_inh) > 0)\n for nt in range(self.n_celltypes): # loop over all cells\n self.syn_inh_dist.append([0] * self.n_syn_inh[nt]) # default no inh for all by default!!!\n \n #print self.syn_inh_dist\n #exit()\n \n if (self.syn_inh_dist[0] == []):\n nimax = 0\n else:\n nimax = max([item for sublist in self.syn_inh_dist for item in sublist]) \n \n #print \"self.syn_inh_dist, self.syn_ex_dist\", self.syn_inh_dist, self.syn_ex_dist\n \n n_noise = max([nemax,nimax]) # number of noise sources\n #print n_noise,nemax,nimax\n # create reproduceable input\n noise_data = []\n\n for nj in range(n_noise):\n \n if self.id == 0: # make sure all have the same signal !!!\n if len(freq_used) == 0: \n noise_data0 = create_colnoise(t_noise, sexp, cutf, self.seed+nj, onf = onf)\n else:\n noise_data0, _, _, _ = create_multisines(t_noise, freq_used) # create multi sine signal\n else:\n noise_data0 = np.empty(len(t_noise), dtype=np.float64)\n\n noise_data0 = self.broadcast(noise_data0, fast = True) \n \n noise_data.append(noise_data0)\n noise_data0 = [] \n \n noise_data_points = len(noise_data[0]) \n\n # Create signal weight vector inh_factor if it is not fully given\n if len(noise_data) > len(inh_factor):\n inh_factor = [inh_factor[0]] * len(noise_data) \n print \"inh_factor:\", inh_factor\n\n #if equi:\n #pass\n # tstop = t_stim\n \n if max(self.n_syn_ex) == 0: # this means current input\n \n self.set_IStim() # sets amp\n \n if self.fluct_s != []:\n if self.fluct_s[self.a_celltype[0]] > 0:\n if self.id == 0: print \"- adding i fluct\"\n self.connect_fluct()\n \n for i, m in enumerate(self.method_interpol):\n if \"syn\" in m: self.method_interpol[i] = \"syn \" + str(self.syn_tau1/ms) + \"/\" + str(self.syn_tau2/ms) + \"ms\"\n if \"bin\" in m: self.method_interpol[i] = \"bin \" + str(self.bin_width/ms) + \"ms\"\n \n stimulus = []\n for nj in range(len(noise_data)):\n stimulus0, t, t_startstop = construct_Stimulus(noise_data[nj], fs, self.amp[self.a_celltype[0]], ihold = 0, delay_baseline = self.delay_baseline) # , tail_points = 0\n stimulus.append(stimulus0)\n tstop = t[-1]\n \n self.set_IPlay2(stimulus, t)\n if self.id == 0: print \"- starting colored noise transfer function estimation! with amp = \" + str(np.round(self.amp[self.a_celltype[0]],4)) + \", ihold = \" + str(np.round(self.ihold[self.a_celltype[0]],4)) + \", ihold_sigma = \" + str(np.round(self.ihold_sigma,4)) + \", dt = \" + str(self.dt) + \" => maximum frequency = \" + str(fmax) + \"\\r\" \n \n else:\n\n self.give_freq = False\n ihold = self.set_i(self.ihold) # just sets amp, ihold should not change! \n\n if 'gsyn_in' not in self.method_interpol: \n pass\n else:\n self.g_syn_ex = [1]*len(self.N)\n \n \n if ((self.fluct_g_e0 != []) or (self.fluct_g_i0 != [])):\n if ((self.fluct_g_e0[self.a_celltype[0]] > 0) or (self.fluct_g_i0[self.a_celltype[0]] > 0)):\n if self.id == 0: print \"- adding g fluct\"\n self.connect_gfluct(E_i=-65)\n \n stimulus = []\n for nj in range(len(noise_data)):\n stimulus0, t, t_startstop = construct_Stimulus(noise_data[nj], fs, amp=1, ihold = 0, tail_points = 0, delay_baseline = self.delay_baseline) # self.amp\n stimulus.append(stimulus0)\n \n noise_data = [] \n tstop = t[-1]\n \n if self.N[self.a_celltype[0]] > 1:\n self.set_IStim(ihold = [0]*self.n_celltypes, ihold_sigma = [0]*self.n_celltypes, random_start = True, tstart_offset = 1)\n if self.id == 0: print \"- add random start\"\n \n #print \"Enter Synplay()\"\n self.set_SynPlay(stimulus, t, t_startstop = t_startstop) \n #print \"Exit Synplay()\"\n\n if self.id == 0: print \"- starting colored noise transfer function estimation with synaptic input! with amp = \" + str(np.round(self.amp,4)) + \", ihold = \" + str(np.round(self.ihold,4)) + \", ihold_sigma = \" + str(np.round(self.ihold_sigma,4)) + \", dt = \" + str(self.dt) + \" => maximum frequency = \" + str(fmax) + \"\\r\" \n \n amp_vec = []\n mag_vec = [] \n pha_vec = []\n freq_used = []\n ca = []\n SNR_mat = []\n VAFf_mat = []\n Qual_mat = []\n CF_mat = [] \n VAF_mat = []\n stim = []\n stim_re_mat = []\n resp_mat = []\n current_re = []\n ihold1 = []\n tk = []\n K_mat = []\n gsyn_in = []\n fmean = []\n fmax = [] \n fmstd = [] \n fcvm = [] \n fmeanA = []\n fmaxA = [] \n fmstdA = [] \n fcvmA = [] \n t_all_vec_input_sorted = []\n id_all_vec_input_sorted = []\n \n if (self.id == 0) and (max(self.n_syn_ex) > 0):\n print range(self.n_celltypes), np.shape(self.t_all_vec_input)\n for l in range(self.n_celltypes): \n ie = argsort(self.t_all_vec_input[l]) \n t_all_vec_input_sorted.append( self.t_all_vec_input[l][ie] )\n id_all_vec_input_sorted.append( self.id_all_vec_input[l][ie].astype(int) )\n \n #if (self.id == 0): \n # print self.g_syn_ex\n # print np.array(self.g_syn_ex)>= 0\n \n #print \"g_syn_ex:\",self.g_syn_ex\n if np.array(np.array(self.g_syn_ex)>= 0).any():\n \n if hasattr(self.cells[self.a_celltype[0]][0], 'get_states') and equi:\n print \"- Equilibrate!\"\n self.run(tstop, do_loadstate = False)\n m = md5.new()\n cell_exe_new = self.cell_exe[0]\n m.update(cell_exe_new)\n filename = './states_' + self.celltype[0] + '_' + m.hexdigest() + '_Population.b'\n self.cells[self.a_celltype[0]][0].get_states(filename)\n else:\n self.run(tstop, do_loadstate = False)\n \n i_startstop = []\n \n results = self.get(t_startstop, i_startstop) \n time = results.get('time')\n current = results.get('current') \n voltage = results.get('voltage') \n fmean = results.get('fmean') \n gsyn = results.get('gsyn') \n freq_times = results.get('freq_times')\n spike_freq = results.get('spike_freq')\n t_all_vec_vec = results.get('t_all_vec_vec')\n id_all_vec_vec = results.get('id_all_vec_vec')\n gsyns = results.get('gsyns')\n gsyn_in = results.get('gsyn_in')\n \n fmax = results.get('fmax')\n fmstd = results.get('fmstd')\n fcvm = results.get('fcvm')\n \n fmeanA = results.get('fmeanA') \n fmaxA = results.get('fmaxA')\n fmstdA = results.get('fmstdA')\n fcvmA = results.get('fcvmA')\n \n fbaseA = results.get('fbaseA') \n fbase = results.get('fbase')\n fbstdA = results.get('fbstdA')\n \n \n else: # do not run, analyse input!!!\n \n time = t\n voltage = []\n for l in range(self.n_celltypes): \n voltage.append(np.zeros(len(t)))\n current = []\n \n freq_times = []\n spike_freq = []\n gsyn = []\n gsyn_in = []\n \n t_all_vec_vec = []\n id_all_vec_vec = []\n \n fmean = []\n fmax = []\n fmstd = []\n fcvm = []\n fstdm = []\n \n fmeanA = []\n fmaxA = []\n fmstdA = []\n fcvmA = []\n fbaseA = []\n fbase = []\n fbstdA = []\n \n if self.id == 0:\n \n current = self.n_train_ex\n \n #t_all_vec = self.t_all_vec_input\n #id_all_vec = self.id_all_vec_input\n\n #ie = argsort(t_all_vec) \n #t_all_vec_vec.append( t_all_vec[ie] )\n #id_all_vec_vec.append( id_all_vec[ie].astype(int) )\n \n t_all_vec_vec = t_all_vec_input_sorted\n id_all_vec_vec = id_all_vec_input_sorted\n \n freq_times = arange(0, tstop, self.bin_width)\n spike_freq = np.zeros(len(freq_times))\n \n for j in self.a_celltype:\n \n [num_spikes, _] = neuronpy.util.spiketrain.get_histogram(t_all_vec_vec[j], bins = freq_times)\n\n if self.tau2_ex[0] > 0:\n spike_freq = np.concatenate((zeros(1),num_spikes)) \n print \"NOSYN TEST: start convolution with Ksyn\"\n Ksyn = syn_kernel(arange(0,10*self.tau2_ex[0],self.bin_width), self.tau1_ex[0], self.tau2_ex[0]) \n Ksyn = np.concatenate((zeros(len(Ksyn)-1),Ksyn))\n spike_freq = np.convolve(Ksyn, spike_freq, mode='same')\n print \"NOSYN TEST: convolution finished\"\n else:\n\n if isinstance(self.factor_celltype[j], ( int, long ) ):\n f = self.factor_celltype[j] \n else:\n f = self.factor_celltype[j][0] \n \n spike_freq = spike_freq + f * np.concatenate((zeros(1),num_spikes)) / self.bin_width\n\n fmean.append(self.fmean_input)\n fmax.append(self.fmax_input) \n fmstd.append(self.fmstd_input) \n fcvm.append(self.fcvm_input) \n fstdm.append(self.fstdm_input)\n\n if self.no_fmean == True:\n fmean.append(ihold)\n \n #plt.figure('spike_freq') \n #plt.plot(freq_times, spike_freq)\n #plt.savefig(\"./figs/Pub/Spike_freq_\" + str(self.pickle_prefix) + \".pdf\", dpi = 300, transparent=True) # save it \n #plt.clf()\n \n fmeanA = fmean[0]\n fmaxA = fmax[0]\n fmstdA = fmstd [0] \n fcvmA = fcvm[0]\n fstdmA = fstdm[0]\n \n \n if self.id == 0: \n \n if any([i<0 for i in inh_factor]):\n \n p0 = []\n inhf_idx = []\n for i, inhf in enumerate(inh_factor):\n if inhf < 0: \n p0.append(0) \n inhf_idx.append(i)\n \n plsq = fmin(self.residuals_compute_Transfer, p0, args=(stimulus, spike_freq, freq_times, t, noise_data_points, gsyn, gsyn_in, do_csd, t_qual, K_mat_old, t_startstop, inh_factor))\n p = plsq\n \n ip = 0\n for i in inhf_idx:\n inh_factor[i] = p[ip]\n ip += 1\n \n\n print \"Final inh_factor: \", inh_factor\n \n \n results = self.compute_Transfer(stimulus, spike_freq = spike_freq, freq_times = freq_times, \n t = t, noise_data_points = noise_data_points, gsyn = gsyn, gsyn_in = gsyn_in, \n do_csd = do_csd, t_qual = t_qual, K_mat_old = K_mat_old, t_startstop = t_startstop, inh_factor=inh_factor)\n \n mag_vec, pha_vec, ca, freq, freq_used, fmean_all = results.get('mag_mat'), results.get('pha_mat'), results.get('ca_mat'), results.get('freq'), results.get('freq_used'), results.get('fmean') \n SNR_mat, VAFf_mat, Qual_mat, CF_mat, VAF_mat = results.get('SNR_mat'), results.get('VAFf_mat'), results.get('Qual_mat'), results.get('CF_mat'), results.get('VAF_mat') \n stim, resp_mat, stim_re_mat, tk, K_mat = results.get('stim'), results.get('resp_mat'), results.get('stim_re_mat'), results.get('tk'), results.get('K_mat') \n \n \n self.barrier() # wait for other nodes\n \n \n if self.id == 0:\n \n if t_qual > 0:\n #print t_startstop[0], t_startstop[0]/self.dt, (t_startstop[0]+t_qual)/self.dt\n current_re = current[int(t_startstop[0]/self.dt):int((t_startstop[0]+t_qual)/self.dt)]\n current_re = current_re[int(len(K_mat[self.a_celltype[0]])):int(len(current_re))-int(len(K_mat[self.a_celltype[0]]))]\n \n if len(self.i_holdrs) > 0:\n ihold1 = self.i_holdrs[self.a_celltype[0]][0]\n else:\n ihold1 = []\n \n for l in range(len(self.method_interpol)): # unwrap \n pha_vec[l,:] = unwrap(pha_vec[l,:] * (pi / 180)) * (180 / pi) # unwrap for smooth phase\n \n # only return fraction of actual signal, it is too long!!! \n if time[-1] > self.tmax: \n imax = -1*int(self.tmax/self.dt)\n time = time[imax:]; current = current[imax:]; gsyn = gsyn[imax:]; gsyn_in = gsyn_in[imax:]\n for n in range(self.n_celltypes): \n voltage[n] = voltage[n][imax:]\n \n if freq_times != []: \n if freq_times[-1] > self.tmax:\n imax2 = where(freq_times > self.tmax)[0][0] # for spike frequency \n freq_times = freq_times[0:imax2]; spike_freq = spike_freq[0:imax2] \n \n bvec = [\"_syn\" in st for st in self.method_interpol]\n if np.any(bvec):\n # normalize synaptic integration with others \n mag_vec[1,:]= mag_vec[0,0]*mag_vec[1,:]/mag_vec[1,0] \n \n if self.id == 0: print \"start pickle\"\n \n results = {'freq_used':freq_used, 'amp':amp_vec,'mag':mag_vec,'pha':pha_vec,'ca':ca,'voltage':voltage,'tk':tk,'K_mat':K_mat, 'ihold1': ihold1, 't_startstop':t_startstop, #'stimulus':stimulus,\n 'current':current,'t1':time,'freq_times':freq_times,'spike_freq':spike_freq, 'stim':stim, 'stim_re_mat':stim_re_mat, 'resp_mat':resp_mat, 'current_re':current_re, 'gsyn_in':gsyn_in, 'fmeanA':fmeanA, 'fmaxA':fmaxA, 'fmstdA':fmstdA, 'fcvmA':fcvmA, 'fbaseA':fbaseA, 'fbase':fbase, 'fbstdA':fbstdA,\n 'fmean':fmean,'method_interpol':self.method_interpol, 'SNR':SNR_mat, 'VAF':VAFf_mat, 'Qual':Qual_mat, 'CF':CF_mat, 'VAFs':VAF_mat, 'fmax':fmax, 'fmstd':fmstd, 'fcvm':fcvm, 'inh_factor':inh_factor, 't_all_vec_vec':t_all_vec_vec, 'id_all_vec_vec':id_all_vec_vec} \n \n if self.id == 0:\n if self.dumpsave == 1:\n pickle.dump( results, gzip.GzipFile( filepath, \"wb\" ) )\n print \"pickle done\" \n \n \n if self.plot_train:\n \n for a in self.a_celltype:\n\n #i_start = mlab.find(t_all_vec_vec[a] >= 0)[0]\n #i_stop = mlab.find(t_all_vec_vec[a] >= 5)[0]\n \n #t_all_cut = t_all_vec_vec[a][i_start:i_stop]\n #id_all_cut = id_all_vec_vec[a][i_start:i_stop]\n \n t_all_cut = t_all_vec_vec[a]\n id_all_cut = id_all_vec_vec[a]\n \n f_start_in = mlab.find(t_all_cut >= 0) \n f_stop_in = mlab.find(t_all_cut <= 10) \n \n f_start = f_start_in[0] \n f_stop = f_stop_in[-1]+1 \n use_spikes = t_all_cut[f_start:f_stop]\n use_id = id_all_cut[f_start:f_stop]\n \n plt.figure('results_train') \n ax99 = plt.subplot(1,1,1)\n ax99.plot(use_spikes,use_id,'|', ms=2)\n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\n plt.savefig(\"./figs/Pub/Train_\" + str(self.pickle_prefix) + \"_cell\" + str(a) + \"_N\" + str(self.N[a]) + \".pdf\", dpi = 300, transparent=True) # save it \n \n plt.clf()\n \n if len(t_all_cut) > 0:\n \n tbin = 100*ms\n tb = np.arange(0,t[-1],tbin)\n [all_rate, _] = neuronpy.util.spiketrain.get_histogram(t_all_cut, bins = tb)\n all_rate = np.concatenate((np.zeros(1),all_rate)) / self.N[a] / tbin\n \n plt.figure('results_train2') \n plt.plot(tb,all_rate)\n plt.savefig(\"./figs/Pub/PSTH_\" + str(self.pickle_prefix) + \"_cell\" + str(a) + \"_N\" + str(self.N[a]) + \".pdf\", dpi = 300, transparent=True) # save it \n plt.clf()\n \n plt.figure('results_noise') \n plt.plot(time,current)\n plt.savefig(\"./figs/Pub/Noise_\" + str(self.pickle_prefix) + \"_cell\" + str(a) + \"_N\" + str(self.N[a]) + \".pdf\", dpi = 300, transparent=True) # save it \n plt.clf()\n \n \n if self.plot_input:\n \n if len(t_all_vec_input_sorted[0]) > 0:\n \n i_start = mlab.find(t_all_vec_input_sorted[0] >= 0)[0]\n i_stop = mlab.find(t_all_vec_input_sorted[0] >= 5)[0]\n \n t_all_cut = t_all_vec_input_sorted[0][i_start:i_stop]\n id_all_cut = id_all_vec_input_sorted[0][i_start:i_stop]\n \n plt.figure('results_input') \n ax99 = plt.subplot(1,1,1)\n ax99.plot(t_all_cut,id_all_cut,'|', ms=2)\n plt.text(0.5, 1.1, r'fmean=' + str(round(self.fmean_input,1)) + ',fmax=' + str(round(self.fmax_input,1)) + ',fmstd=' + str(round(self.fmstd_input,1)) + ',fcvm=' + str(round(self.fcvm_input,1)) + ',fstdm=' + str(round(self.fstdm_input,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\n plt.savefig(\"./figs/Pub/Input_\" + str(self.pickle_prefix) + \"_N\" + str(self.N[self.a_celltype[0]]) + \".pdf\", dpi = 300, transparent=True) # save it \n plt.clf()\n \n\n else:\n \n if self.id == 0:\n results = pickle.load( gzip.GzipFile( filepath, \"rb\" ) )\n \n #print results\n #print {key:np.shape(value) for key,value in results.iteritems()}\n \n if self.minimal_dir: # save only info needed for plot\n \n print {key:np.shape(value) for key,value in results.iteritems()}\n \n if \"Fig6_pop_transfer_grc_syngr_nsyn4_cn_a1_noisesynlow_inhlow_adjfinh_varih_N100_CFo6.0_results_pop_cnoise.p\" in filename:\n results['ca'] = [] \n results['resp_mat'] = []\n results['stim'] = []\n results['current'] = []\n results['tk'] = []\n results['K_mat'] = []\n results['freq_times'] = []\n results['spike_freq'] = []\n results['stim_re_mat'] = []\n results['current_re'] = []\n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = [] \n results['gsyn_in'] = []\n \n elif (\"Fig8_pop_transfer_none_synno_cn_cutf30_a1_noisesynlow_ih20_varih_N100_CFo-1_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_none_synno_cn_cutf30_a10_noisesynlow_ih20_varih_N100_CFo-1_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_CFo9.0_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a10_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_is0.14_CFo9.0_results_pop_cnoise.p\" in filename) \\\n :\n\n results['ca'] = [] \n results['resp_mat'] = []\n results['current'] = []\n results['tk'] = []\n results['K_mat'] = []\n results['voltage'] = [] \n results['current_re'] = []\n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = []\n results['t1'] = []\n results['gsyn_in'] = []\n \n elif (\"Fig8_pop_transfer_none_synno_cn_cutf30_a1_noisesynlow_ih20_varih_N50_twopop_CFo-1_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_none_synno_cn_cutf30_a10_noisesynlow_ih20_varih_N50_twopop_CFo-1_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_twopop_CFo9.0_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a10_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_is0.14_twopop_CFo9.0_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf5_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_CFo14.0_results_pop_cnoise.p\" in filename) \\\n or (\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf5_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_twopop_CFo14.0_results_pop_cnoise.p\" in filename) \\\n :\n \n results['ca'] = [] \n results['resp_mat'] = []\n results['current'] = []\n results['tk'] = []\n results['K_mat'] = []\n results['voltage'] = [] \n results['current_re'] = []\n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = []\n results['t1'] = []\n results['gsyn_in'] = []\n results['freq_times'] = []\n results['spike_freq'] = []\n \n elif (\"Fig4_pop_transfer_grc_cn_addn100_N[100]_CF[40]_amod[1]_results_pop_cnoise.p\" in filename) \\\n or (\"Fig4_pop_transfer_grc_cn_addn1_N[100]_CF[40]_amod[1]_results_pop_cnoise.p\" in filename) \\\n or (\"Fig4b_pop_transfer_grc_lowcf_cn_twopop_N[50, 50]_CF[0.0055, 0.0055]_amod[None, None]_results_pop_cnoise.p\" in filename) \\\n or (\"Fig4b_pop_transfer_grc_lowcf_cn_N[100]_CF[0.0055]_amod[None]_results_pop_cnoise.p\" in filename) \\\n or (\"Fig4b_pop_transfer_grc_lowcf_slownoise_cn_twopop_N[50, 50]_CF[0.0051, 0.0051]_amod[None, None]_results_pop_cnoise.p\" in filename) \\\n or (\"Fig4b_pop_transfer_grc_lowcf_slownoise_cn_N[100]_CF[0.0051]_amod[None]_results_pop_cnoise.p\" in filename) \\\n :\n \n results['ca'] = [] \n results['resp_mat'] = []\n results['current'] = []\n results['tk'] = []\n results['K_mat'] = []\n results['voltage'] = [] \n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = []\n results['t1'] = []\n results['gsyn_in'] = []\n results['freq_times'] = []\n results['spike_freq'] = []\n \n elif (\"Fig2_pop_transfer_\" in filename) \\\n :\n \n results['ca'] = [] \n results['resp_mat'] = []\n results['current'] = []\n results['t1'] = []\n results['voltage'] = [] \n results['freq_times'] = []\n results['spike_freq'] = []\n results['current_re'] = []\n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = []\n results['gsyn_in'] = []\n \n else:\n results['ca'] = [] \n results['resp_mat'] = []\n results['stim'] = []\n results['current'] = []\n results['tk'] = []\n results['K_mat'] = []\n results['t1'] = []\n results['voltage'] = [] \n results['freq_times'] = []\n results['spike_freq'] = []\n results['stim_re_mat'] = []\n results['current_re'] = []\n results['t_all_vec_vec'] = []\n results['id_all_vec_vec'] = []\n results['gsyn_in'] = []\n\n print {key:np.shape(value) for key,value in results.iteritems()}\n\n pickle.dump( results, gzip.GzipFile( self.minimal_dir + \"/\" + filename, \"wb\" ) ) \n \n else:\n results = {'freq_used':[], 'amp':[],'mag':[],'pha':[],'ca':[],'voltage':[], 'tk':[],'K_mat':[], 'ihold1':[], 't_startstop':[], #'stimulus':[],\n 'current':[],'t1':[],'freq_times':[],'spike_freq':[], 'stim':[], 'stim_re_mat':[], 'current_re':[], 'gsyn_in':[], 'fmeanA':[], 'fmaxA':[], 'fmstdA':[], 'fcvmA':[], 'fbaseA':[], 'fbase':[], 'fbstdA':[],\n 'fmean':[],'method_interpol':self.method_interpol, 'SNR':[], 'VAF':[], 'Qual':[], 'CF':[], 'VAFs':[], 'fmax':[], 'fmstd':[], 'fcvm':[], 'inh_factor':[], 't_all_vec_vec':[], 'id_all_vec_vec':[]} \n \n if self.id == 0: \n\n if self.plot_train: \n\n for a in self.a_celltype:\n \n t1 = results.get('t1') \n voltage = results.get('voltage') \n fmean = results.get('fmean') \n fmax = results.get('fmax') \n fmstd = results.get('fmstd') \n \n \n if results.has_key('t_all_vec_vec'):\n \n if len(results['t_all_vec_vec']) > 0: \n t_all_vec_vec = results.get('t_all_vec_vec') \n id_all_vec_vec = results.get('id_all_vec_vec') \n \n t_all_cut = t_all_vec_vec[a]\n id_all_cut = id_all_vec_vec[a]\n \n f_start_in = mlab.find(t_all_cut >= 0) \n f_stop_in = mlab.find(t_all_cut <= 10) \n \n f_start = f_start_in[0] \n f_stop = f_stop_in[-1]+1 \n use_spikes = t_all_cut[f_start:f_stop]\n use_id = id_all_cut[f_start:f_stop]\n \n plt.figure('results_train') \n ax97 = plt.subplot(1,1,1)\n ax97.plot(use_spikes,use_id,'|', ms=6)\n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax97.transAxes, fontsize=10, va='center', ha='center')\n plt.savefig(\"./figs/Pub/Train_\" + str(self.pickle_prefix) + \"_cell\" + str(a) + \"_N\" + str(self.N[a]) + \".pdf\", dpi = 300, transparent=True) # save it \n\n \n plt.figure('results_voltage') \n ax99 = plt.subplot(2,1,1)\n ax99.plot(t1,voltage[a])\n \n t_noise = arange(0, t_stim, self.dt)\n noise_data = create_colnoise(t_noise, sexp, cutf, 50, onf = onf)\n stimulus, t, t_startstop = construct_Stimulus(noise_data, 1/self.dt, amp=1, ihold = 0, tail_points = 0, delay_baseline = self.delay_baseline) \n ax98 = plt.subplot(2,1,2)\n ax98.plot(t[0:10/self.dt],stimulus[0:10/self.dt],color='k')\n \n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\n plt.savefig(\"./figs/Pub/Voltage_\" + str(self.pickle_prefix) + \"_cell\" + str(a) + \"_N\" + str(self.N[a]) + \".pdf\", dpi = 300, transparent=True) # save it \n plt.show()\n plt.clf()\n \n if (self.id == 0) and (do_csd == 1):\n Qual = results.get('Qual') \n for i, ii in enumerate(self.method_interpol):\n print \"\\n[QUAL:] Interpol:\", ii, \"SNR0:\", Qual[i,0,0], \"SNR_cutff:\", Qual[i,0,1], \"SNR_mean:\", Qual[i,0,2], \"\\n VAF0:\", Qual[i,1,0], \"VAF_cutff:\", Qual[i,1,1], \"VAF_mean:\", Qual[i,1,2], \"\\n CF(subtracted):\", Qual[i,2,0], \"VAF(subtracted):\", Qual[i,2,1] \n \n VAF = results.get('VAF')\n freq_used = results.get('freq_used') \n iend = mlab.find(freq_used >= self.xmax)[0] \n print 'm(VAF)=' + str(np.mean(VAF[1][0,0:iend])) \n \n self.barrier() # wait for other nodes\n \n return results","def plot_sic_sic_timeseries(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/SIC/'):\n output_folder = 'processed_data/'\n\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n if detrend:\n dt = 'detrended'\n else:\n dt = 'raw'\n\n filename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n indicies = xr.open_dataset(output_folder + 'ERA5/SIC/' + filename +'.nc')[filename].mean(dim = ('longitude', 'latitude'))\n data = indicies.copy()\n data = data.loc[data.time.dt.year >= 1979]\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + 'SIC/' + seaicename +'.nc')[seaicename]\n\n times = list(set.intersection(set(seaice.time.values), set(data.time.values)))\n\n seaice = seaice_area_mean(seaice.sel(time=times).sortby('time'), 1)\n data = data.sel(time=times).sortby('time')\n\n seaice = seaice_area_mean(seaice,1)\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice.time.values.astype(float), seaice)\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n title += dt + ' '\n\n title += temporal_resolution\n title += f' mean ERA5 and SIC'\n fig, ax = plt.subplots()\n ax2 = plt.twinx(ax)\n ax2.plot([],[])\n\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\n\n ln1 = ax.plot(data.time, data, label = f'ERA5', color = '#EA1B10')\n ax.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\n ln2 = ax2.plot(seaice.time, seaice, label = 'SIC', color = '#177E89')\n ax2.plot(seaice.time, seaice_m * seaice.time.values.astype(float) + seaice_b, color = '#177E89')\n\n if anomlous or detrend:\n yabs_max = abs(max(ax2.get_ylim(), key=abs))\n ax2.set_ylim(ymin=-yabs_max, ymax=yabs_max)\n\n yabs_max = abs(max(ax.get_ylim(), key=abs))\n ax.set_ylim(ymin=-yabs_max, ymax=yabs_max)\n\n # ylabels\n ax.set_ylabel(f'ECMWF')\n ax2.set_ylabel(f'Mean SIC')\n\n # legend\n lines = ln1 + ln2\n labels = [line.get_label() for line in lines]\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\n\n plt.title(title)\n plt.savefig(imagefolder + f'/SIC_ERA5_{filename}' + '.pdf')\n plt.show()","def TICwriter(TIC, dataFile, saveDirectory):\n #Create savename from data file name:\n savefile = dataFile.split('/')[-1].split('.')[0] + '_TIC.png'\n #Create ouput directory:\n saveDirectory = os.path.join(saveDirectory, 'output/')\n os.makedirs(os.path.dirname(saveDirectory), exist_ok=True)\n #Plot figure:\n Plot = pl.figure()\n TICplot = Plot.add_subplot(111)\n TICplot.plot([d[0] for d in TIC], [d[1] for d in TIC])\n \n #Save and close plot:\n pl.savefig(saveDirectory + savefile)\n pl.close(Plot)","def fn_intensitytrace(file_name,folder,data,time,i,x,y):\n import numpy as np\n import matplotlib.pyplot as plt\n\n\n figure_name=file_name+'_intensity_trace'\n for a in range(0,len(data),int(len(data)/2)):\n if i==a:\n x_coord=x[i+1]\n y_coord=y[i+1]\n max_int=np.max(data[i])\n min_int=np.min(data[i])\n #norm_int = [b / max_int for b in data[i]]\n plt.figure()\n #plt.plot(time[0:len(time)-1],norm_int,'g')\n plt.plot(time[0:len(time)-1],data[i],'g')\n plt.xlim(0, 100)\n plt.ylim(min_int, (max_int+100))\n plt.xlabel('Time (s)', fontname='Arial', fontsize=12)\n plt.ylabel('Photon counts (photons)', fontname='Arial', fontsize=12)\n plt.xticks(fontname='Arial',fontsize=12)\n plt.yticks(fontname='Arial', fontsize=12)\n plt.savefig(folder+'/Figures/PDFs'+ '/' + figure_name + '_'+str(x_coord)+','+str(y_coord)+'.pdf', dpi=500)\n plt.savefig(folder+'/Figures/PNGs'+ '/' + figure_name + '_'+str(x_coord)+','+str(y_coord)+'.png', dpi=500)\n\n return (plt.show())","def plot_TSNE(fig_name):\n dir = \"log/peps mini\"\n pattern = r'(internal|access|lock)\\\\\\d{1,2}.csv$'\n pattern_valid = r'(3|6|9|12).csv$'\n utils.construct_set(dir, pattern, pattern_valid)\n X, y = utils.load_all()\n utils.plot_TSNE(X, y)\n plt.title(fig_name)\n if not os.path.exists(dir_fig):\n os.makedirs(dir_fig)\n plt.savefig(dir_fig + '/' + fig_name + '.png')","def process_nitrate(store, site):\n constituent = 'NitrateSurr'\n db_path = '/said/{}/'.format(site['id'])\n iv_path = db_path + 'iv'\n df = store.get(iv_path)\n\n n_error = np.maximum(0.5, df[constituent]*.1)\n df[constituent+'_U90.0'] = df[constituent] + n_error\n df['NitrateSurr_L90.0'] = df.NitrateSurr - n_error\n #clip values below 0\n df['NitrateSurr_L90.0'] = np.maximum(0, df['NitrateSurr_L90.0'])\n\n update_table(store, iv_path, df)","def snr_stats(\r\n t,\r\n y,\r\n period,\r\n duration,\r\n T0,\r\n transit_times,\r\n transit_duration_in_days,\r\n per_transit_count,\r\n):\r\n\r\n snr_per_transit = numpy.zeros([len(transit_times)])\r\n snr_pink_per_transit = numpy.zeros([len(transit_times)])\r\n intransit = transit_mask(t, period, 2 * duration, T0)\r\n flux_ootr = y[~intransit]\r\n\r\n try:\r\n pinknoise = pink_noise(flux_ootr, int(numpy.mean(per_transit_count)))\r\n except:\r\n pinknoise = numpy.nan\r\n\r\n # Estimate SNR and pink SNR\r\n # Second run because now the out of transit points are known\r\n if len(flux_ootr) > 0:\r\n std = numpy.std(flux_ootr)\r\n else:\r\n std = numpy.nan\r\n for i in range(len(transit_times)):\r\n mid_transit = transit_times[i]\r\n tmin = mid_transit - 0.5 * transit_duration_in_days\r\n tmax = mid_transit + 0.5 * transit_duration_in_days\r\n if numpy.isnan(tmin) or numpy.isnan(tmax):\r\n idx_intransit = []\r\n mean_flux = numpy.nan\r\n else:\r\n idx_intransit = numpy.where(numpy.logical_and(t > tmin, t < tmax))\r\n if len(y[idx_intransit]) > 0:\r\n mean_flux = numpy.mean(y[idx_intransit])\r\n else:\r\n mean_flux = numpy.nan\r\n\r\n intransit_points = numpy.size(y[idx_intransit])\r\n try:\r\n snr_pink_per_transit[i] = (1 - mean_flux) / pinknoise\r\n if intransit_points > 0 and not numpy.isnan(std):\r\n std_binned = std / intransit_points ** 0.5\r\n snr_per_transit[i] = (1 - mean_flux) / std_binned\r\n else:\r\n snr_per_transit[i] = 0\r\n snr_pink_per_transit[i] = 0\r\n except:\r\n snr_per_transit[i] = 0\r\n snr_pink_per_transit[i] = 0\r\n\r\n return snr_per_transit, snr_pink_per_transit","def writetipsy(self, outfile=None, hubble=None):\n from . import analysis\n from . import tipsy\n from .analysis import cosmology\n from snapshot import _new as new\n import math\n s = self._base()\n if outfile is None: outfile = s.filename+'.gtp'\n print \"write tipsy file to \", outfile\n sout = new(star=self._nhalos) # create new tipsy snapshot written as halos.\n sout.properties['a'] = s.properties['a']\n sout.properties['z'] = s.properties['z']\n sout.properties['boxsize'] = s.properties['boxsize']\n if hubble is None: hubble = s.properties['h']\n sout.properties['h'] = hubble\n ### ! dangerous -- rho_crit function and unit conversions needs simplifying\n rhocrithhco = cosmology.rho_crit(s, z=0, unit=\"Msol Mpc^-3 h^2\")\n lboxkpc = sout.properties['boxsize'].ratio(\"kpc a\")\n lboxkpch = lboxkpc*sout.properties['h']\n lboxmpch = lboxkpc*sout.properties['h']/1000.\n tipsyvunitkms = lboxmpch * 100. / (math.pi * 8./3.)**.5\n tipsymunitmsun = rhocrithhco * lboxmpch**3 / sout.properties['h']\n\n print \"transforming \", self._nhalos, \" halos into tipsy star particles\"\n for ii in xrange(self._nhalos):\n h = self[ii+1].properties\n sout.star[ii]['mass'] = h['m']/hubble / tipsymunitmsun\n ## tipsy units: box centered at 0. (assume 0<=x<=1)\n sout.star[ii]['x'] = h['pos'][0][0]/lboxmpch - 0.5\n sout.star[ii]['y'] = h['pos'][0][1]/lboxmpch - 0.5\n sout.star[ii]['z'] = h['pos'][0][2]/lboxmpch - 0.5\n sout.star[ii]['vx'] = h['vel'][0][0]/tipsyvunitkms\n sout.star[ii]['vy'] = h['vel'][0][1]/tipsyvunitkms\n sout.star[ii]['vz'] = h['vel'][0][2]/tipsyvunitkms\n sout.star[ii]['eps'] = h['r']/lboxkpch\n sout.star[ii]['metals'] = 0.\n sout.star[ii]['phi'] = 0.\n sout.star[ii]['tform'] = 0.\n print \"writing tipsy outfile %s\"%outfile\n sout.write(fmt=tipsy.TipsySnap, filename=outfile)\n return sout","def summarize_series(fglob, outfile):\n with open(outfile, mode='w') as of:\n #Iterate over files\n flist = glob(fglob)\n flist = sorted(flist)\n lgrho = [] #list of log(rho) values, parallel to the list of rxnmap maps\n rxns = [] #list of maps of form 'rxn_name' --> energy release [erg/g/s]\n for f in flist:\n rxnmap = {}\n currxn = ''\n eps_nuc = ''\n for line in open(f,mode='r'):\n if not currxn and line.count('reaction name') == 1:\n i1 = line.index('<') + 1\n i2 = line.index('>')\n currxn = line[i1:i2]\n elif currxn and line.count('eps_nuc') == 1:\n eps_nuc = float(line.partition(':')[2].strip())\n rxnmap[currxn] = eps_nuc\n currxn = ''\n elif line.count('log rho') == 1:\n lgrho.append(line.partition('rho')[2].strip())\n srtmap = sorted(rxnmap.items(), key=operator.itemgetter(1), reverse=True) #sort on values\n rxns.append(srtmap)\n\n #Write header\n of.write('log(rho): ' + (' {:3.3s} |'*len(lgrho)).format(*lgrho) + '\\n')\n\n #Write rows of data for each logrho, include top ten rxns\n start = ' '\n for i in range(10):\n of.write(start)\n for tup in rxns:\n of.write('{:23s}'.format(tup[i][0]))\n of.write('\\n')\n of.write(start)\n for tup in rxns:\n of.write('{:<23.8e}'.format(tup[i][1]))\n of.write('\\n\\n')","def runErdosRenyi(n,p):\n s = z.Optimize()\n g = ig.Graph.Erdos_Renyi(n, p, directed=True, loops=True)\n while g.is_dag():\n g = ig.Graph.Erdos_Renyi(n, p, directed=True, loops=True)\n\n return MFAS_set_cover(s,g), u.get_feedback_arc_set(g)","def simul_and_export(file, config, i):\n\n simulate_UVSPEC(file, config)\n\n load_skymap(config)\n\n sim = files_sim(config)[i]\n export_sim_rad(sim, config)","def science_reduction(input_file):\n #name of the planet\n planet = input_file['exoplanet']\n #set original directory\n original_path = os.getcwd()\n save_path = input_file['save_path']\n data_path = input_file['data_path']\n #Change your directory to data diretory\n os.chdir(data_path)\n #list all flat images\n exoplanet = glob.glob(planet+'*.fits')\n print '\\nLoading exoplanet images \\nTotal of '+planet+'*.fits files = ',len(exoplanet),'\\nFiles = \\n'\n print exoplanet\n #if save_path exist, continue; if not, create.\n if not os.path.exists(save_path):\n os.makedirs(save_path)\n #create a list of bias images and copy images to save_path\n print '\\nCopy science images to save_path directory to main reduction: ....'\n os.system('cp '+planet+'*.fits '+save_path)\n print '\\n .... done. \\n'\n #change to save_path\n os.chdir(save_path)\n #create the names for exoplanet science mages with bias subtracted\n bexoplanet = []\n for i in exoplanet:\n bexoplanet.append('B'+i)\n #verify if previous superbias exist\n if os.path.isfile('B'+i) == True:\n os.system('rm B'+i)\n print '\\n Will be create this images: \\n'\n print bexoplanet\n #exoplanet = string.join(exoplanet,',') #create the list string of exoplanet science images\n #bexoplanet = string.join(bexoplanet,',')#create the list string of bexoplanet science images\n print '\\nSubtracting superbias.fits from all '+planet+'*.fits images ....\\n'\n for i in range(len(exoplanet)):\n iraf.imarith(exoplanet[i],'-','superbias.fits',bexoplanet[i])\n use.update_progress((i+1.)/len(bexoplanet))\n print '\\n.... cleaning '+planet+'*.fits images\\n'\n os.system('rm '+planet+'*.fits')\n print '\\n Statistics of B'+planet+'*.fits images: \\n'\n for i in range(len(bexoplanet)):\n iraf.imstat(bexoplanet[i])\n print '\\nFlatfielding the B'+planet+'*.fits ....\\n'\n #create the names for exoplanet science images with bias subtracted and flatfielding\n abexoplanet = []\n for i in bexoplanet:\n abexoplanet.append('A'+i)\n #verify if previous superbias exist\n if os.path.isfile('A'+i) == True:\n os.system('rm A'+i)\n print '\\n Will be create this images: \\n'\n print abexoplanet\n #flatifielding images\n for i in range(len(abexoplanet)):\n iraf.imarith(bexoplanet[i],'/','superflat.fits',abexoplanet[i])\n use.update_progress((i+1.)/len(abexoplanet))\n # print '\\n.... cleaning B'+planet+'*.fits images\\n'\n # os.system('rm B'+planet+'*.fits')\n print '\\n Statistics of AB'+planet+'*.fits images: \\n'\n for i in range(len(abexoplanet)):\n iraf.imstat(abexoplanet[i])\n os.chdir(original_path) #change to save_path\n return","def AssignEpiNames(self):\n# Sort each run in the series by its acquisition time.\n epi_sort = self.epi_times.keys()\n epi_sort.sort()\n# Rewrite pfiles as an ordered list of p-files to be reconstructed.\n for idx in xrange(len(epi_sort)):\n entry = self.epi_times[epi_sort[idx]]\n info = self.info[entry]\n if info['data_filetype'] == 'ge_data':\n self.pfiles_recon.append(entry)\n info['run'] = '%0d' % (self.n_epi)\n self.n_epi = self.n_epi + 1\n plane = info['plane']\n if not self.epinames.has_key(plane):\n plane = 'any'\n n_epi = self.epinames[plane]['n_epi']\n if n_epi > len(self.epinames[plane]['names'])-1:\n if self.epinames.has_key('any') and \\\n n_epi < len(self.epinames['any']):\n plane = 'any'\n n_epi = self.epinames[plane]['n_epi']\n else:\n self.DumpInfo()\n errstr = 'Not enough EPI names in template file'\n raise RuntimeError(errstr)\n# epiname = self.epinames[plane]['names'][n_epi]\n\n filebase = os.path.basename(self.epinames[plane]['names'][n_epi])\n epi_mf_outdir = os.path.dirname(\\\n self.epinames[plane]['names'][n_epi])\n\n epi_base = self.epinames[plane]['subdir'][n_epi]\n tmp_outdir = '%s/%s' % (self.tmpdir, epi_base)\n# Get output directory for raw epis.\n if self.no_motcorr:\n epi_r_outdir = epi_mf_outdir\n elif self.keep_epi_raw:\n epi_r_outdir = self.epi_scratch_space\n else:\n epi_r_outdir = tmp_outdir\n\n# Get output directory for motion-corrected epis.\n if self.keep_epi_mot:\n epi_m_outdir = self.epi_scratch_space\n else:\n epi_m_outdir = tmp_outdir\n info['outdir'] = epi_mf_outdir\n if n_epi < len(self.epinames[plane]['names']):\n epiname = self.epinames[plane]['names'][n_epi]\n info['imgfile'] = '%s/%s' % (epi_r_outdir, filebase)\n else:\n info['imgfile'] = '%s/s%0d_epi_run%0d' % \\\n (epi_r_outdir, n_epi, idx+1)\n self.epinames[plane]['n_epi'] += 1\n\n info['mot_file'] = '%s/%s_mtn.txt' % (epi_mf_outdir, filebase)\n info['censor_prefix'] = '%s/%s' % (epi_mf_outdir, filebase)\n info['imgfile_t'] = '%s/%s_t' % (epi_m_outdir, filebase)\n if self.no_motcorr:\n info['imgfile_m'] = None\n info['imgfile_mf'] = None\n info['imgfile_final'] = info['imgfile']\n else:\n info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)\n if self.no_fmapcorr or info['fmap_entry'] is None:\n info['imgfile_m'] = '%s/%s_m' % (epi_mf_outdir, filebase)\n info['imgfile_mf'] = None\n info['imgfile_final'] = info['imgfile_m']\n else:\n info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)\n info['imgfile_mf'] = '%s/%s_mf' % (epi_mf_outdir, filebase)\n info['imgfile_final'] = info['imgfile_mf']\n info['skip'] = self.skip\n info['motion_ref_frame'] = self.tmplt['motion_ref_frame']\n\n info['motion_interp'] = self.tmplt['epi_motion_interp']\n if not info['motion_interp'].startswith('-'):\n info['motion_interp'] = '-%s' % info['motion_interp']\n\n info['filetype'] = self.tmplt['epi_file_format']\n info['valid'] = True\n self.info[entry] = info\n\n if not self.no_motcorr:\n epi_base = os.path.basename(info['imgfile_m'])\n info['matfile_m'] = '%s/%s.aff12.1D' % (info['outdir'], epi_base)\n info['matfile_mcat'] = '%s/%scat.aff12.1D' % (info['outdir'], epi_base)","def writeRotNSeosfile(self, filename, tempPrescription, ye=None, addNeutrinoTerms=False):\n assert not(ye == 'NuFull' and 'quantity' not in tempPrescription), \\\n \"NuFull Beta-Eq not supported for fixedQuantityPrescription\"\n\n tempOfLog10Rhob = self.tempOfLog10RhobFuncFromPrescription(tempPrescription, ye)\n\n log10numberdensityMin = 2.67801536139756E+01\n log10numberdensityMax = 3.97601536139756E+01 # = 1e16 g/cm^3\n\n npoints = 600\n\n #dlogn = (log10numberdensityMax - log10numberdensityMin) / (npoints - 1.0)\n\n logns = numpy.linspace(log10numberdensityMin, log10numberdensityMax, npoints)\n\n outfile = open(filename, 'w')\n\n for logn in logns:\n numberdensityCGS = numpy.power(10.0, logn)\n rho_b_CGS = numberdensityCGS * consts.CGS_AMU\n logrho_b_CGS = numpy.log10(rho_b_CGS)\n\n temp = tempOfLog10Rhob(logrho_b_CGS)\n\n if ye is None and temp is not None:\n self.setBetaEqState({'rho':rho_b_CGS, 'temp': temp})\n elif ye is None and temp is None:\n quantity = tempPrescription['quantity']\n target = tempPrescription['target']\n self.setConstQuantityAndBetaEqState({'rho': rho_b_CGS},\n quantity,\n target)\n elif ye == 'NuFull':\n self.setNuFullBetaEqState({'rho': rho_b_CGS, 'temp': temp})\n else:\n self.setState({'rho': rho_b_CGS, 'temp': temp, 'ye': ye})\n\n logpress, logeps, munu = self.query(['logpress', 'logenergy', 'munu'])\n #print logpress, rho_b_CGS\n P_nu = 0.0\n if addNeutrinoTerms:\n P_nu = P_nu_of(rho_b_CGS, temp, munu)\n logpress = numpy.log10(numpy.power(10.0, logpress) + P_nu)\n eps_nu = 3.0 * P_nu / rho_b_CGS\n\n #Total energy density (1.0 + eps) is in AGEO units\n eps = (numpy.power(10.0, logeps) - self.energy_shift + eps_nu)\\\n * consts.AGEO_ERG / consts.AGEO_GRAM\n #print logpress\n #print\n totalEnergyDensity = rho_b_CGS * (1.0 + eps)\n logTotalEnergyDensity = numpy.log10(totalEnergyDensity)\n #print logrho_b_CGS ,logeps, numpy.power(10.0, logeps), eps, self.energy_shift\n #print logrho_b_CGS, eps, logpress\n outfile.write(\"{:24.14e}{:24.14e}{:24.14e}\\n\".format(logn,\n logTotalEnergyDensity,\n logpress))","def TNG(self):\n \n import h5py as h5\n filename = localpath+'input/yields/TNG/SNIa.hdf5'\n # Read H5 file\n f = h5.File(filename, \"r\")\n \n indexing = {}\n indexing['H'] = 'Hydrogen'\n indexing['He'] = 'Helium'\n indexing['Li'] = 'Lithium'\n indexing['Be'] = 'Beryllium'\n indexing['B'] = 'Boron'\n indexing['C'] = 'Carbon'\n indexing['N'] = 'Nitrogen'\n indexing['O'] = 'Oxygen'\n indexing['F'] = 'Fluorine'\n indexing['Ne'] = 'Neon'\n indexing['Na'] = 'Sodium'\n indexing['Mg'] = 'Magnesium'\n indexing['Al'] = 'Aluminum'\n indexing['Si'] = 'Silicon'\n indexing['P'] = 'Phosphorus'\n indexing['S'] = 'Sulphur'\n indexing['Cl'] = 'Chlorine'\n indexing['Ar'] = 'Argon'\n indexing['K'] = 'Potassium'\n indexing['Ca'] = 'Calcium'\n indexing['Sc'] = 'Scandium'\n indexing['Ti'] = 'Titanium'\n indexing['V'] = 'Vanadium'\n indexing['Cr'] = 'Chromium'\n indexing['Mn'] = 'Manganese'\n indexing['Fe'] = 'Iron'\n indexing['Co'] = 'Cobalt'\n indexing['Ni'] = 'Nickel'\n indexing['Cu'] = 'Copper'\n indexing['Zn'] = 'Zinc'\n indexing['Ga'] = 'Gallium'\n indexing['Ge'] = 'Germanium'\n indexing['As'] = 'Arsenic'\n indexing['Se'] = 'Selenium'\n indexing['Br'] = 'Bromine'\n indexing['Kr'] = 'Krypton'\n indexing['Rb'] = 'Rubidium'\n indexing['Sr'] = 'Strontium'\n indexing['Y'] = 'Yttrium'\n indexing['Zr'] = 'Zirconium'\n indexing['Nb'] = 'Niobium'\n indexing['Mo'] = 'Molybdenum'\n \n \n self.elements = list(indexing.keys())\n \n self.table = {}\n \n self.metallicities = list([0.02]) # arbitrary since only one value\n self.masses = list([np.sum(f['Yield'].value)]) # sum of all yields\n \n names = ['Mass','mass_in_remnants']+self.elements\n \n yield_subtable = {}\n \n base = np.zeros(len(self.masses))\n list_of_arrays = []\n for i in range(len(names)):\n list_of_arrays.append(base)\n \n yield_subtable = np.core.records.fromarrays(list_of_arrays,names=names)\n \n yield_subtable['Mass'] = self.masses\n yield_subtable['mass_in_remnants'] = np.asarray([-1*m for m in self.masses])\n \n for el_index,el in enumerate(self.elements):\n yield_subtable[el] = np.divide(f['Yield'][el_index],self.masses)\n \n self.table[self.metallicities[0]] = yield_subtable","def plot_seaice_predict_components(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' SIE prediction'\n\n\n# Loading Seaice Trends\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n seaice = seaice\n\n\n# Index contributions\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\n dataset = xr.open_dataset(filename + '.nc')\n indicies = np.array([i for i in dataset])\n values = np.array([dataset[i].values for i in dataset])\n index_data = {}\n for indexname in indicies[:-1]:\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\n\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\n\n prediction = seaice.copy() * 0\n predictions = []\n for indexname in indicies:\n if indexname in index_data.keys():\n prediction += index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\n predictions += [index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]]\n else:\n prediction += dataset[indexname]\n predictions += [dataset[indexname]]\n\n\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.axhline(y=0, alpha = 0.5)\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10', alpha = 0.5)\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89', alpha = 0.5)\n\n\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'SIE', alpha = 0.5)\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'Prediction', alpha = 0.5)\n\n lines = ln1 + ln2\n i = 0\n for indexname in indicies[:-1]:\n predict = index_data[indexname] * dataset[indexname]\n lines += ax.plot(predict.time, (predict*area/250).sum(dim = ('x', 'y')), '--', label = f'{indexname}', linewidth=1)\n i += 1\n\n labels = [line.get_label() for line in lines]\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 3, loc = 'upper right')\n plt.title(title)\n\n plt.savefig(imagefolder + seaicename + '_components.pdf')\n plt.show()\n\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.axhline(y=0, alpha = 0.5)\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n # plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10', alpha = 0.5)\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\n # plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89', alpha = 0.5)\n\n\n norm = (seaice[seaicename]*area/250).sum(dim = ('x', 'y')) / 100\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')).sortby('time')/norm, label = 'SIE', alpha = 0.5)\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')).sortby('time')/norm, label = 'Prediction', alpha = 0.5)\n\n lines = ln1 + ln2\n i = 0\n for indexname in indicies[:-1]:\n predict = index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\n lines += ax.plot(predict.time, (predict*area/250).sum(dim = ('x', 'y'))/norm, '--', label = f'{indexname}', linewidth=1)\n i += 1\n\n labels = [line.get_label() for line in lines]\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 3, loc = 'upper right')\n plt.title(title)\n\n plt.savefig(imagefolder + seaicename + '_components_normalised.pdf')\n plt.show()","def s(t,n, T=2*np.pi):\n k = np.arange(1, n)\n s = ((1/(2*k-1))*np.sin((2*(2*k-1)*np.pi*t)/ T))\n return ((4/np.pi)*np.sum(s))","def process_nitrate(self):\n constituent = 'NitrateSurr'\n db_path = '/said/{}/'.format(self.site['id'])\n iv_path = db_path + 'iv'\n df = self.store.get(iv_path)\n\n n_error = np.maximum(0.5, df[constituent]*.1)\n df[constituent+'_U90.0'] = df[constituent] + n_error\n df['NitrateSurr_L90.0'] = df.NitrateSurr - n_error\n #clip values below 0\n df['NitrateSurr_L90.0'] = np.maximum(0, df['NitrateSurr_L90.0'])\n\n update_table(self.store, iv_path, df)","def test(nifti_region_to_save, path_where_store_out=\"pet_regions_segmented\"):\n regions_used = \"three\"\n list_regions = session.select_regions_to_evaluate(regions_used)\n dic_regions_segmented = load_pet_regions_segmented(list_regions)\n\n region_container_3d = dic_regions_segmented[\n nifti_region_to_save] # [patients x heigh, width, depth]\n\n for patient in range(0, region_container_3d.shape[0], 1):\n img = nib.Nifti1Image(region_container_3d[patient, :, :, :], np.eye(4))\n img.to_filename(os.path.join(path_where_store_out,\n \"region_{0},patient_{1}.nii\".format(\n regions_used, patient)))","def rainfall_series(self):\n\n # assign local temporal variables\n datatype = 'strds'\n increment = str(self.rain_interval)+\" minutes\"\n raster = 'raster'\n rain_excess = 'rain_excess'\n net_difference = 'net_difference'\n #iterations = sum(1 for row in precip)\n\n # create a raster space time dataset\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.elevation_timeseries,\n title=self.elevation_title,\n description=self.elevation_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.depth_timeseries,\n title=self.depth_title,\n description=self.depth_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.erdep_timeseries,\n title=self.erdep_title,\n description=self.erdep_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.flux_timeseries,\n title=self.flux_title,\n description=self.flux_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.difference_timeseries,\n title=self.difference_title,\n description=self.difference_description,\n overwrite=True)\n\n # register the initial digital elevation model\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=self.elevation,\n start=self.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # create evolution object\n evol = Evolution(\n elevation=self.elevation,\n precipitation=self.precipitation,\n start=self.start,\n rain_intensity=self.rain_intensity,\n rain_interval=self.rain_interval,\n walkers=self.walkers,\n runoff=self.runoff,\n mannings=self.mannings,\n detachment=self.detachment,\n transport=self.transport,\n shearstress=self.shearstress,\n density=self.density,\n mass=self.mass,\n grav_diffusion=self.grav_diffusion,\n erdepmin=self.erdepmin,\n erdepmax=self.erdepmax,\n k_factor=self.k_factor,\n c_factor=self.c_factor,\n m=self.m,\n n=self.n,\n threads=self.threads,\n fill_depressions=self.fill_depressions)\n\n # open txt file with precipitation data\n with open(evol.precipitation) as csvfile:\n\n # check for header\n has_header = csv.Sniffer().has_header(csvfile.read(1024))\n\n # rewind\n csvfile.seek(0)\n\n # skip header\n if has_header:\n next(csvfile)\n\n # parse time and precipitation\n precip = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\n\n # initial run\n initial = next(precip)\n evol.start = initial[0]\n evol.rain_intensity = 'rain_intensity'\n # compute rainfall intensity (mm/hr)\n # from rainfall observation (mm)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity}\"\n \"={rain_observation}\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_intensity=evol.rain_intensity,\n rain_observation=float(initial[1]),\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # determine mode and run model\n if self.mode == \"simwe_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps\n # from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # run the landscape evolution model for each rainfall record\n for row in precip:\n\n # update the elevation\n evol.elevation=evolved_elevation\n\n # update time\n evol.start=row[0]\n\n # compute rainfall intensity (mm/hr)\n # from rainfall observation (mm)\n rain_intensity = 'rain_intensity'\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity}\"\n \"={rain_observation}\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_intensity=rain_intensity,\n rain_observation=float(row[1]),\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\n # plus the depth (m) per rainfall interval (min)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_excess}\"\n \"={rain_intensity}\"\n \"+{depth}\"\n \"/1000.\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_excess=rain_excess,\n rain_intensity=rain_intensity,\n depth=depth,\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # update excess rainfall\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity} = {rain_excess}\".format(\n rain_intensity='rain_intensity',\n rain_excess=rain_excess),\n overwrite=True)\n evol.rain_intensity = rain_intensity\n\n # determine mode and run model\n if self.mode == \"simwe_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps\n # from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # remove temporary maps\n gscript.run_command(\n 'g.remove',\n type='raster',\n name=['rain_excess'],\n flags='f')\n\n # compute net elevation change\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{net_difference}\"\n \"= {evolved_elevation}-{elevation}\".format(\n net_difference=net_difference,\n elevation=self.elevation,\n evolved_elevation=evol.elevation),\n overwrite=True)\n gscript.write_command(\n 'r.colors',\n map=net_difference,\n rules='-',\n stdin=difference_colors)","def niriss_header(ra=53.1592277508136, dec=-27.782056346146, pa_aper=128.589, \n filter='F150W', grism='GR150R'): \n naxis = 2048, 2048\n crpix = 1024, 1024\n \n cd = np.array([[ -0.0658, 0], [0, 0.0654]])/3600.\n cd_rot = rotate_CD_matrix(cd, pa_aper)\n \n h = pyfits.Header()\n \n h['CRVAL1'] = ra\n h['CRVAL2'] = dec\n \n h['WCSAXES'] = 2\n h['CTYPE1'] = 'RA---TAN'\n h['CTYPE2'] = 'DEC--TAN'\n \n for i in range(2):\n h['NAXIS%d' %(i+1)] = naxis[i]\n h['CRPIX%d' %(i+1)] = crpix[i]\n h['CDELT%d' %(i+1)] = 1.0\n for j in range(2):\n h['CD%d_%d' %(i+1, j+1)] = cd_rot[i,j]\n \n ### Backgrounds\n # http://www.stsci.edu/jwst/instruments/niriss/software-tools/wfss-simulations/niriss-wfss-cookbook.pdf\n bg = {'F090W':0.50, 'F115W':0.47, 'F140M':0.23, 'F150W':0.48, 'F158M':0.25, 'F200W':0.44}\n \n h['BACKGR'] = bg[filter], 'Total, e/s'\n h['FILTER'] = filter\n h['INSTRUME'] = 'NIRISS'\n h['READN'] = 6 , 'Rough, per pixel per 1 ks exposure' # e/pix/per\n h['PHOTFLAM'] = 1.\n h['PHOTPLAM'] = 1.\n \n if grism == 'GR150R':\n h['GRISM'] = 'GR150R', 'Spectral trace along X'\n else:\n h['GRISM'] = 'GR150C', 'Spectral trace along Y'\n \n wcs = pywcs.WCS(h)\n h['EXTVER'] = 1\n \n return h, wcs","def time_calibration(input_file):\n original_path = os.getcwd()\n save_path = input_file['save_path']\n #change to save data reduction directory\n os.chdir(save_path)\n print '\\n Reading the list of images ....\\n'\n planet = input_file['exoplanet'] #set exoplanet name\n images = sorted(glob.glob('AB'+planet+'*.fits'))\n print images\n #include de RA,DEC and epoch of the exoplanet\n RA,DEC,epoch = input_file['RA'],input_file['DEC'],input_file['epoch']\n #obtain ST JD using iraf task and introduce in the header\n for i in range(len(images)):\n hdr = fits.getheader(images[i])\n if int(split(hdr['UT'],':')[0]) < int(hdr['timezone']):\n new_date = use.yesterday(hdr['date-obs'])\n #print images[i], new_date\n else:\n new_date = hdr['date-obs']\n year,month,day = split(new_date,'-')\n iraf.asttimes(year=year,month=month,day=day,time=hdr['loctime'],obs=input_file['observatory'])\n JD = iraf.asttimes.jd #obtain julian date\n LMST = iraf.asttimes.lmst #obtain the sideral time\n LMST = use.sexagesimal_format(LMST) #convert sideral time in sexagesimal format\n iraf.hedit(images[i],'ST',LMST,add='yes',verify='no',show='no',update='yes') #create the ST keyword in the header\n iraf.ccdhedit(images[i],'LMST',LMST,type='string') #include the mean sideral time in the header\n iraf.ccdhedit(images[i],'JD',JD,type='string') #include de julian date in the header\n #include RA, and DEC of the object in your header\n iraf.ccdhedit(images[i],\"RA\",RA,type=\"string\") #include right ascention in the header\n iraf.ccdhedit(images[i],\"DEC\",DEC,type=\"string\") #include declination in the header\n iraf.ccdhedit(images[i],\"epoch\",epoch,type=\"string\") #include epoch in the header\n # use.update_progress((i+1.)/len(images))\n print '\\n Setting airmass ....\\n'\n for i in range(len(images)):\n print '# ',images[i]\n #iraf.hedit(images[i],'airmass',airmass,add='yes')\n #iraf.hedit(images[i],'HJD',HJD,add='yes')\n iraf.setairmass.observatory = input_file['observatory']\n iraf.setairmass(images[i])\n iraf.setjd.time = 'ut'\n iraf.setjd(images[i])\n print '\\n.... done.\\n'\n #export information\n hjd, jd, airmass, st = [],[],[],[]\n for i in range(len(images)):\n hdr = fits.getheader(images[i])\n hjd.append(hdr['HJD'])\n jd.append(hdr['JD'])\n airmass.append(hdr['airmass'])\n st.append(hdr['st'])\n #saving the data\n data = DataFrame([list(hjd),list(jd),list(st),list(airmass)]).T\n data.columns = ['HJD','JD','ST','Airmass']\n data.to_csv('results_iraf_calibrations.csv')\n #change to workings directory\n os.chdir(original_path)\n return","def calculateSNR(self):\n pass","def save2nifti(self, file_path):\n #Define nifti1 datatype codes\n NIFTI_TYPE_UINT8 = 2 # unsigned char\n NIFTI_TYPE_INT16 = 4 # signed short\n NIFTI_TYPE_INT32 = 8 # signed int.\n NIFTI_TYPE_FLOAT32 = 16 # 32 bit float.\n NIFTI_TYPE_COMPLEX64 = 32 # 64 bit complex = 2 32 bit floats\n NIFTI_TYPE_FLOAT64 = 64 # 64 bit float = double.\n NIFTI_TYPE_RGB24 = 128 # 3 8 bit bytes.\n NIFTI_TYPE_INT8 = 256 # signed char.\n NIFTI_TYPE_UINT16 = 512 # unsigned short.\n NIFTI_TYPE_UINT32 = 768 # unsigned int.\n NIFTI_TYPE_INT64 = 1024 #signed long long.\n NIFTI_TYPE_UINT64 = 1280 # unsigned long long.\n NIFTI_TYPE_FLOAT128 = 1536 # 128 bit float = long double.\n NIFTI_TYPE_COMPLEX128 = 1792 #128 bit complex = 2 64 bit floats.\n NIFTI_TYPE_COMPLEX256 = 2048 # 256 bit complex = 2 128 bit floats\n NIFTI_TYPE_RGBA32 = 2304 # 4 8 bit bytes.\n\n #Detect the data type of the input data.\n data_type = {\n np.uint8: NIFTI_TYPE_UINT8,\n np.uint16: NIFTI_TYPE_UINT16,\n np.uint32: NIFTI_TYPE_UINT32,\n np.float32: NIFTI_TYPE_FLOAT32,\n np.int16: NIFTI_TYPE_INT16,\n np.int32: NIFTI_TYPE_INT32,\n np.int8: NIFTI_TYPE_INT8\n }\n if sys.maxint > 2 ** 32: # The platform is 64 bit\n data_type[np.float128] = NIFTI_TYPE_FLOAT128\n data_type[np.float64] = NIFTI_TYPE_FLOAT64\n data_type[np.int64] = NIFTI_TYPE_INT64\n data_type[np.uint64] = NIFTI_TYPE_UINT64\n data_type[np.complex64] = NIFTI_TYPE_COMPLEX64\n data_type[np.complex128] = NIFTI_TYPE_COMPLEX128\n data_type[np.complex256] = NIFTI_TYPE_COMPLEX256\n\n data = np.rot90(self._data, 3)\n if data_type.has_key(data.dtype.type):\n self._header['datatype'] = data_type[data.dtype.type]\n self._header['cal_max'] = data.max()\n self._header['cal_min'] = 0\n image = nib.nifti1.Nifti1Image(data, None, self._header)\n nib.nifti1.save(image, file_path)","def add_noise(self, snr, unit=None):\n return self.from_time(self.fs, noisify(self.in_time, snr, unit=unit))","def _make_s2n(self, ffile, hdul):\n bname = os.path.basename(ffile)\n phu = hdul[0].copy()\n phu.data = None\n if 'S/N' in hdul:\n hdu = hdul['S/N']\n else:\n if 'FLUX' in hdul and 'ERROR' in hdul:\n log.debug(f'Making S/N image from FLUX and ERROR '\n f'extensions for {bname}.')\n hdu = fits.ImageHDU(hdul['FLUX'].data,\n hdul['FLUX'].header)\n s = hdul['FLUX'].data\n n = hdul['ERROR'].data\n elif 'FLUX' in hdul and 'STDDEV' in hdul:\n log.debug(f'Making S/N image from FLUX and STDDEV '\n f'extensions for {bname}.')\n hdu = fits.ImageHDU(hdul['FLUX'].data,\n hdul['FLUX'].header)\n s = hdul['FLUX'].data\n n = hdul['STDDEV'].data\n elif 'STOKES I' in hdul and 'ERROR I' in hdul:\n log.debug(f'Making S/N image from STOKES I and '\n f'ERROR I extensions for {bname}.')\n hdu = fits.ImageHDU(hdul['STOKES I'].data,\n hdul['STOKES I'].header)\n s = hdul['STOKES I'].data\n n = hdul['ERROR I'].data\n else:\n raise ValueError(f'Cannot determine S/N from extensions '\n f'in file {bname}')\n\n hdu.data = s / n\n hdu.header['EXTNAME'] = 'S/N'\n hdu.header['BUNIT'] = ''\n\n # blank out data outside of range\n try:\n low, high = self.disp_parameters['s2n_range']\n hdu.data[hdu.data < float(low)] = np.nan\n hdu.data[hdu.data > float(high)] = np.nan\n except (ValueError, AttributeError, IndexError, TypeError):\n pass\n\n s2n = fits.HDUList([phu, hdu])\n return s2n","def save_to_nii(im, filename, outdir=\"\", mode=\"image\", system=\"sitk\"):\n if system == \"sitk\":\n if mode == 'label':\n img = sitk.GetImageFromArray(im.astype(np.uint8))\n else:\n img = sitk.GetImageFromArray(im.astype(np.float32))\n if not os.path.exists(\"./{}\".format(outdir)):\n os.mkdir(\"./{}\".format(outdir))\n sitk.WriteImage(img, \"./{}/{}.nii.gz\".format(outdir, filename))\n else:\n img = np.rot90(im, k=2, axes= (1,2))\n OUTPUT_AFFINE = np.array(\n [[0, 0, 1, 0],\n [0, 1, 0, 0],\n [1, 0, 0, 0],\n [0, 0, 0, 1]])\n if mode == 'label':\n img = nibabel.Nifti1Image(img.astype(np.uint8), OUTPUT_AFFINE)\n else:\n img = nibabel.Nifti1Image(img.astype(np.float32), OUTPUT_AFFINE)\n if not os.path.exists(\"./{}\".format(outdir)):\n os.mkdir(\"./{}\".format(outdir))\n nibabel.save(img, \"./{}/{}.nii.gz\".format(outdir, filename))","def Writefile(self, outfile, verbose=True):\n \n self.outfile = outfile\n \n # Write SUNTANS grid to file\n nc = Dataset(outfile, 'w', format='NETCDF3_CLASSIC')\n nc.Description = 'SUNTANS subsetted history file'\n nc.Author = ''\n nc.Created = datetime.now().isoformat()\n nc.type = 'SUNTANS HIS file'\n #pdb.set_trace()\n nc.createDimension('Nc', self.Nc)\n nc.createDimension('Np', self.Np)\n nc.createDimension('Ne', self.Ne)\n nc.createDimension('Nk', self.Nk)\n nc.createDimension('numsides', self.numsides)\n \n nc.createDimension('time', None)\n \n def write_nc_var(var, name, dimensions, units=None):\n nc.createVariable(name, 'f8', dimensions)\n if units is not None:\n nc.variables[name].units = units\n nc.variables[name][:] = var\n if verbose:\n print ' ... wrote ', name\n \n def create_nc_var(name, dimensions, units=None):\n nc.createVariable(name, 'f8', dimensions)\n if units is not None:\n nc.variables[name].units = units\n if verbose:\n print ' ... wrote ', name\n \n # Grid variables\n write_nc_var(self.xv, 'xv', ('Nc'))\n write_nc_var(self.yv, 'yv', ('Nc'))\n write_nc_var(self.xp, 'xp', ('Np'))\n write_nc_var(self.yp, 'yp', ('Np'))\n write_nc_var(self.xe, 'xe', ('Ne'))\n write_nc_var(self.ye, 'ye', ('Ne'))\n write_nc_var(self.dz, 'dz', ('Nk'))\n write_nc_var(self.dv, 'dv', ('Nc'))\n write_nc_var(self.Ac, 'Ac', ('Nc'))\n write_nc_var(self.Nk, 'Nk', ('Nc'))\n write_nc_var(self.face, 'face', ('Nc','numsides'))\n write_nc_var(self.mark, 'mark', ('Ne'))\n write_nc_var(self.cells, 'cells', ('Nc','numsides'))\n \n \n # Create the data variables\n create_nc_var('time',('time'),'seconds since 1990-01-01 00:00:00')\n create_nc_var('salt',('time','Nk','Nc'),'psu')\n create_nc_var('temp',('time','Nk','Nc'),'degrees C')\n create_nc_var('uc',('time','Nk','Nc'),'meter second-1')\n create_nc_var('vc',('time','Nk','Nc'),'meter second-1')\n create_nc_var('nu_v',('time','Nk','Nc'),'m2 s-1')\n create_nc_var('rho',('time','Nk','Nc'),'kg m-3')\n create_nc_var('tau_x',('time','Nc'),'N m-2')\n create_nc_var('tau_y',('time','Nc'),'N m-2')\n create_nc_var('eta',('time','Nc'),'m')\n \n nc.close()","def fractalise(fractals,n,fpath):\n # Use different fractals for MP\n n = nnn-n\n # Load numpy array\n for f in fractals:\n if f.endswith('{:02d}.npy'.format(n)):\n frac_arr = N.load(f)\n f_out = f\n \n # Normalise to +/- 1%\n frac_arr_pc = normalise(frac_arr)\n\n # Load virgin data\n nc = Dataset(fpath,'r+')\n virgindata = nc.variables['SMOIS'][:,:,:,:]\n newdata = N.zeros_like(virgindata)\n shp = virgindata[0,0,:,:].shape\n\n # Interpolate fractal to wrfinput grid\n frac_arr_interp = interpolate_to_wrfgrid(frac_arr_pc,shp)\n \n # Perturb each soil level (broadcasting is scary)\n for lv in N.arange(virgindata.shape[1]):\n newdata[0,lv,:,:] = virgindata[0,lv,:,:] + (virgindata[0,lv,:,:]*frac_arr_interp)\n\n # Write back\n assert N.all(newdata > 0.0)\n # pdb.set_trace()\n nc.variables['SMOIS'][:,:,:,:] = newdata\n nc.close()\n return f_out","def SNR(self, flux_sky, n_pix_star, flux_star, gain, ron):\n SNR = (gain*flux_star/sqrt(gain*flux_star + n_pix_star*gain*flux_sky + n_pix_star*ron**2)) \n return SNR","def plot_subplot_trend(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/subplots/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' SIE trends'\n\n\n# Loading Seaice Trends\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n if seaice_source == 'nsidc':\n seaice = seaice/250\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = xr.apply_ufunc(scipy.stats.linregress, seaice[seaicename].time.values.astype(float), seaice[seaicename], input_core_dims=[['time'],['time']], vectorize=True, dask='parallelized', output_dtypes=[float]*5, output_core_dims=[[]]*5)\n if seaice_source =='ecmwf':\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename])\n seaice_m = seaice_m * 1e9 * 60 * 60 * 24 * 365\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n seaice_m = seaice_m*area\n seaice_m = seaice_m.where(seaice_m != 0)\n seaice_m = seaice_m.where(seaice_p_value <= 0.05)\n\n\n# Index xontributions\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\n dataset = xr.open_dataset(filename + '.nc')\n indicies = np.array([i for i in dataset])\n values = np.array([dataset[i].values for i in dataset])\n index_data = {}\n for indexname in indicies[:-1]:\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\n newdata = {} \n for indexname in indicies[:-1]:\n a = scipy.stats.linregress(index_data[indexname].time.values.astype(float), index_data[indexname])\n newdata[indexname] = a[0] * dataset[indexname] * 24*60*60*365e9\n title = temp_decomp.capitalize() + ' '\n if detrend == 'detrended':\n title += detrend + ' '\n title += temporal_resolution\n title += f' SIC trend contributions'\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n # Plotting\n for i in range(len(indicies)-1):\n indexname = indicies[i]\n newdata[indexname] = newdata[indexname] * area / 250\n newdata[indexname] = newdata[indexname].where(newdata[indexname] !=0)\n\n\n\n fig = plt.figure(figsize = (15,5))\n\n # seaice_m = log_data(seaice_m)\n max_ = min(seaice_m.max(),-seaice_m.min())\n # max_ = 1\n divnorm = TwoSlopeNorm(vmin=-max_, vcenter=0, vmax=max_)\n ax = fig.add_subplot(131, projection = ccrs.SouthPolarStereo())\n # Plotting\n contor = ax.contourf(seaice_m.x, seaice_m.y, seaice_m, cmap = 'RdBu', levels = 100, norm = divnorm, transform=ccrs.SouthPolarStereo())\n ax.coastlines()\n ax.set_axis_off()\n # cbar = plt.colorbar(contor)\n # cbar.set_label('Trend in SIE (km$^2$ yr$^{-1}$)')\n # plt.title(title)\n ax = [fig.add_subplot(2,6,3, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,4, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,9, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,10, projection = ccrs.SouthPolarStereo())]\n for i in range(len(indicies)-1):\n indexname = indicies[i]\n # newdata[indexname] = log_data(newdata[indexname])\n newdata[indexname] = newdata[indexname].where(newdata[indexname] !=0)\n contor = ax[i].contourf(dataset.x, dataset.y, newdata[indexname], cmap = 'RdBu', norm = divnorm, transform=ccrs.SouthPolarStereo(), levels = 100)\n ax[i].coastlines()\n ax[i].set_axis_off()\n ax[i].set_title(indicies[i])\n\n ax = fig.add_subplot(1,3,3, projection = ccrs.SouthPolarStereo())\n data = seaice_m\n for i in range(len(indicies)-1):\n indexname = indicies[i]\n data = data - newdata[indexname]\n ax.contourf(dataset.x, dataset.y, data, cmap = 'RdBu', norm = divnorm, levels = 100, transform=ccrs.SouthPolarStereo())\n ax.coastlines()\n fig.subplots_adjust(right=0.9)\n cbar_ax = fig.add_axes([0.95, 0.15, 0.05, 0.7])\n cbar = fig.colorbar(cm.ScalarMappable(norm=divnorm, cmap='RdBu'), cax=cbar_ax, shrink=0.88)\n\n plt.savefig(imagefolder + seaicename + '.pdf')\n plt.show()","def calculate_piN_piS(codonseqs, method, codon_table, het=False):\n analysis = {\"seqname\": \"\", \"piN\": -1, \"piS\": -1, \"piNpiS\": -1, \"pi\": -1, \"method\":method}\n x = seqfreqs(codonseqs)\n #if 'piNpiS' in options.debug:\n # print(\"freqs are: {}\".format(x))\n # print(\"len codonseqs is: \", len(codonseqs))\n piN = 0\n piS = 0\n for i in range(len(codonseqs)):\n for j in range(i+1, len(codonseqs)):\n #print(codonseqs[i], codonseqs[j])\n if not het:\n dN, dS = cal_dn_ds(codonseqs[i], codonseqs[j], codon_table=codon_table, method=method)\n piN = piN + (x[i] * x[j] * dN)\n piS = piS + (x[i] * x[j] * dS)\n #if 'piNpiS' in options.debug:\n # print(\"{0} dN{1}{2}={3} dS{1}{2}={4}\".format(method, i, j, dN, dS))\n else:\n try:\n dN, dS = cal_dn_ds(codonseqs[i], codonseqs[j], codon_table=codon_table, method=method)\n piN = piN + (x[i] * x[j] * dN)\n piS = piS + (x[i] * x[j] * dS)\n except:\n pass\n\n analysis['piN'] = piN\n analysis['piS'] = piS\n try:\n analysis['piNpiS'] = piN/piS\n except:\n analysis['piNpiS'] = 0\n #if 'piNpiS' in options.debug:\n # print (\"{0} dN={1:.3f} dS={2:.3f} piN/piS = {3:.3f}\".format(\n # method, analysis['piN'], analysis['piS'], analysis['piNpiS']))\n\n return analysis","def save():\n pl.savefig('/home/filippini/Documents/plot/RUN55/compa'+INFO_RUN+'.png')","def visualize(self, save: bool = False) -> None:\n import matplotlib.pyplot as plt\n import inspect\n\n plt.style.use('seaborn-whitegrid')\n plt.rcParams['figure.figsize'] = [10, 5]\n if not self.inverse_transformation:\n grid = np.linspace(0, 1, 10000)\n func = self.pdf(np.linspace(0, 1, 10000))\n try:\n plt.plot(grid, func)\n except:\n plt.plot(grid, np.repeat(func, 10000))\n plt.title('Intensity function')\n plt.xlabel('time')\n plt.ylabel('value')\n if save:\n try:\n plt.savefig('intensity_function_' + inspect.getsource(self.pdf).split('return')[\n 1].strip() + '.png')\n print('Saved as ' + 'intensity_function_' + inspect.getsource(self.pdf).split('return')[\n 1].strip() + '.png')\n except:\n warnings.warn(\"Saving intensity function failed!\")\n plt.show()\n plt.clf()\n\n t = self.generate()\n plt.step(t, list(range(0, len(t))))\n plt.title('Simulated trajectory')\n plt.xlabel('time')\n plt.ylabel('value')\n if save:\n try:\n plt.savefig(\n 'trajectory_' + inspect.getsource(self.pdf).split('return')[1].strip() + '.png')\n print('Saved as ' + 'trajectory_' + inspect.getsource(self.pdf).split('return')[\n 1].strip() + '.png')\n except:\n warnings.warn(\"Saving trajectory failed!\")\n plt.show()\n plt.clf()\n\n plt.plot(t, list(np.repeat(0, len(t))), '.')\n plt.title('Simulated points')\n plt.xlabel('time')\n if save:\n try:\n plt.savefig('points_' + inspect.getsource(self.pdf).split('return')[1].strip() + '.png')\n print('Saved as ' + 'points_' + inspect.getsource(self.pdf).split('return')[\n 1].strip() + '.png')\n except:\n warnings.warn(\"Saving points failed!\")\n plt.show()\n plt.clf()","def time_info(input_file):\n original_path = os.getcwd() #set original directory\n save_path = input_file['save_path']\n planet = input_file['exoplanet'] #set exoplanet name\n print '\\nObtain the images .... \\n'\n print 'Change to ', save_path\n os.chdir(save_path) #change to save directory where is our scvience images\n images = sorted(glob.glob('AB'+input_file['exoplanet']+'*.fits'))\n print '\\nImages = \\n',images\n tempo_loc = [] #time object\n SUN = [] #Sun coordinate object\n ra_sun, dec_sun, dsun = np.zeros(len(images)),np.zeros(len(images)),np.zeros(len(images)) #sun coordinates\n JD = np.zeros(len(images)) #julian date from time object\n ST = np.zeros(len(images))\n HJD = np.zeros(len(images))\n #create the exoplanet object coordianate\n exoplanet = SkyCoord(dec=input_file['DEC'],ra=input_file['RA'],unit=('deg','deg'),frame=input_file['frame'])\n print '\\nObtain data info from header ....\\n'\n for i in range(len(images)):\n hdr = fits.getheader(images[i])\n UTC = hdr['date-obs']+'T'+hdr['UT'] #string that contain the time in UTC in isot format\n tempo_loc.append(Time(UTC,scale=input_file['scale-time'],format='isot',location=(input_file['lon-obs'],input_file['lat-obs'])))#,input_data['altitude'])))\n JD[i] = tempo_loc[i].jd\n ST[i] = tempo_loc[i].sidereal_time('apparent').hour\n SUN.append(get_sun(tempo_loc[i]))\n ra_sun[i],dec_sun[i] = SUN[i].ra.deg, SUN[i].dec.deg\n dsun[i] = SUN[i].distance.value\n HJD[i] = use.hjd_date(JD[i],dsun[i],dec_sun[i],ra_sun[i],exoplanet.dec.deg,exoplanet.ra.deg,circular_orbit=input_file['circular_orbit'])\n use.update_progress((i+1.)/len(images))\n print '\\n.... done.\\n'\n print '\\n Time from header = \\n'\n #print '\\nImages ** UTC (YYYY-MM-DDTHH:MM:SS) ** JD (7d.5d) ** ST (hours) ** ST (HH:MM:SS) ** Sun Coordinate (epoch,RA,DEC,Distance) (deg,deg,AU) \\n'\n ST_string = []\n for i in range(len(images)):\n ST1 = int(ST[i])\n ST2 = int((ST[i]-ST1)*60.)\n ST3 = (((ST[i]-ST1)*60.)-ST2)*60\n ST_string.append(str(ST1)+':'+str(ST2)+':'+str(ST3))\n tempo_loc[i] = tempo_loc[i].value\n use.update_progress((i+1.)/len(images))\n #print images[i], ' ** ',tempo_loc[i], ' ** ', JD[i], ' ** ', ST[i],' ** ',ST_string[i],' ** ',sun_loc[i],' ** ',HJD[i]\n print '\\nSave data file ... \\n'\n data = DataFrame([images,tempo_loc,list(JD),list(ST),list(ST_string),list(ra_sun),list(dec_sun),list(dsun),list(HJD)]).T\n data.columns=['images','UTC','JD','ST','ST_isot','RA_SUN','DEC_SUN','D_SUN','HJD']\n print data\n data.to_csv('results.csv')\n os.chdir(original_path)\n return","def get_sinogram( self, N = 128, theta_vec = None ):\n\n if theta_vec is None:\n theta_vec = np.deg2rad(np.linspace(0, 359, 360))\n if self.phantom_type in ['ellipses', 'shepp_logan', 'modified_shepp_logan']:\n analytical_sinogram = radon_ellipses(N, theta_vec, np.copy(self.matrix), circle=self.circle);\n elif self.phantom_type == 'squares':\n analytical_sinogram = radon_squares(N, theta_vec, np.copy(self.matrix), circle = self.circle);\n elif self.phantom_type == 'rectangles':\n analytical_sinogram = radon_rectangles(N, theta_vec, np.copy(self.matrix), circle=self.circle);\n else:\n print('error on the choice of phantom type')\n return analytical_sinogram","def intensityRatioSave(self,outFile=0):\n if not outFile:\n outFile = self.IntensityRatio['filename']\n print(' saving ratio to filename = %s'%(outFile))\n if hasattr(self, 'IntensityRatio'):\n temperature=self.IntensityRatio['temperature']\n eDensity=self.IntensityRatio['eDensity']\n ratio=self.IntensityRatio['ratio']\n out = open(outFile,'w')\n nvalues=len(ratio)\n # need to add 7 lines to maintain IDL like files\n out.write(outFile+'\\n') #1\n out.write(self.IntensityRatio['desc']+'\\n') #2\n out.write(' created with ChiantiPy using CHIANTI version '+ chdata.ChiantiVersion +'\\n') #3\n out.write(' columns are temperature, eDensity, ratio'+'\\n') #5\n tunit = 'K'\n out.write(' temperature in '+tunit+', electron eDensity in cm^(-3)'+'\\n') #6\n out.write(' ratio given in '+self.Defaults['flux']+'\\n') #4\n out.write(' '+'\\n') #7\n for ivalue in range(nvalues):\n s='%12.3e %12.3e %12.3e \\n' % (temperature[ivalue],eDensity[ivalue],ratio[ivalue])\n out.write(s)\n out.close()\n else:\n print(' in .intensityRatioSave(), no IntensityRatio is found')","def analyseSNRfluctuations(self, fsu='FSUA', snr='PD', plot=True,\n xlims=None, title='', normalized=False):\n t = self.raw['IMAGING_DATA_'+fsu].data.field('TIME')\n\n if (fsu=='FSUB' and self.insmode=='NORMAL') or \\\n (fsu=='FSUA' and self.insmode=='SWAPPED'):\n wno = np.where((np.interp(t, self.raw['OPDC'].data.field('TIME'),\n self.raw['OPDC'].data.field('STATE'))<3))\n else:\n wno = np.where(np.interp(t, self.raw['DOPDC'].data.field('TIME'),\n self.raw['DOPDC'].data.field('STATE'))<3)\n if (fsu=='FSUB' and self.insmode=='NORMAL') or \\\n (fsu=='FSUA' and self.insmode=='SWAPPED'):\n wftk = np.where((np.interp(t, self.raw['OPDC'].data.field('TIME'),\n self.raw['OPDC'].data.field('STATE'))>=7))\n else:\n wftk = np.where(np.interp(t, self.raw['DOPDC'].data.field('TIME'),\n self.raw['DOPDC'].data.field('STATE'))>=7)\n\n snrNo = self.raw['IMAGING_DATA_'+fsu].data.field(snr+'SNR')[wno[0]]\n snrFtk = self.raw['IMAGING_DATA_'+fsu].data.field(snr+'SNR')[wftk[0]]\n\n if plot:\n fig = plt.figure(1, figsize=(8,4))\n plt.subplots_adjust(left=0.08, right=.98)\n fig.clf()\n if normalized:\n norma = np.median(snrNo)\n plt.xlabel('normalized SNR to out-of-fringes')\n else:\n norma = 1\n plt.xlabel('SNR')\n\n plt.hist(snrNo/norma, bins=50, normed=True, alpha=0.5,\n color='r', label='NOT FTK')\n hno = np.histogram(snrNo, bins=50, normed=True)\n plt.hist(snrFtk/norma, bins=50, normed=True, alpha=0.5,\n color='g', label='FTK')\n hftk = np.histogram(snrFtk, bins=50, normed=True)\n\n if not xlims is None:\n plt.xlim(xlims[0], xlims[1])\n plt.title(title)\n poissonDist = lambda x,p:\\\n poisson(p['m']*p['p']).pmf(np.int_(np.floor(x*p['p'])))*p['p'] +\\\n (x*p['p']-np.floor(x*p['p']))/\\\n (np.ceil(x*p['p'])-np.floor(x*p['p']))*\\\n (poisson(p['m']*p['p']).pmf(np.int_(np.ceil(x*p['p'])))*p['p'] -\n poisson(p['m']*p['p']).pmf(np.int_(np.floor(x*p['p'])))*p['p'])\n\n guess = {'m':np.median(snrNo), 'p':1}\n X = 0.5*(hno[1][:-1]+hno[1][1:])\n fit = dpfit.leastsqFit(poissonDist, X, guess, hno[0])\n guessNo = fit['best']\n uncer = fit['uncer']\n chi2 = fit['chi2']\n model = fit['model']\n print guessNo\n print 'NOFTK; POISSON: LAMBDA', guessNo['p']*guessNo['m']\n print 'NOFTK; POISSON: STD/MEAN', 1/np.sqrt(guessNo['p']*guessNo['m'])\n plt.plot(X/norma, poissonDist(X, guessNo)*norma, '-r',\n linewidth=3, alpha=0.8, linestyle='dashed')\n\n guess = {'m':np.median(snrNo), 'p':1/10.}\n X = 0.5*(hftk[1][:-1]+hftk[1][1:]) \n fit = dpfit.leastsqFit(poissonDist, X, guess, hftk[0])\n guess = fit['best']\n uncer = fit['uncer']\n chi2 = fit['chi2']\n model = fit['model']\n print guess\n print ' FTK; POISSON: LAMBDA', guess['p']*guess['m']\n print ' FTK; POISSON: STD/MEAN', 1/np.sqrt(guess['p']*guess['m'])\n plt.plot(X/norma, poissonDist(X, guess)*norma, '-g',\n linewidth=3, alpha=0.8, linestyle='dashed')\n plt.legend( loc='upper left')\n #plt.xscale('log')\n\n print 'DIFFERENCIATION',\\\n np.abs(guess['m']-guessNo['m'])/\\\n (np.sqrt(guessNo['m']/guessNo['p']) +\n np.sqrt(guess['m']/guess['p']))\n return","def generate_siaf_pre_flight_reference_files_nircam():\n\n instrument = 'NIRCam'\n overwrite_wedge_file = False\n overwrite_grism_file = False\n\n\n # wedge definitions\n wedge_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_wedge_offsets.txt'.format(instrument.lower()))\n\n if (not os.path.isfile(wedge_file) or (overwrite_wedge_file)):\n\n wedge_offsets = Table.read(os.path.join(JWST_SOURCE_DATA_ROOT, instrument, 'wedge_offsets.txt'), format='ascii.basic', delimiter=' ', guess=False)\n\n comments = []\n comments.append('{} detector parameter definition file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the wedge offsets.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n wedge_offsets.meta['comments'] = comments\n wedge_offsets.write(wedge_file, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False)\n\n # grism definitions\n grism_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_grism_parameters.txt'.format(instrument.lower()))\n\n if (not os.path.isfile(wedge_file) or (overwrite_grism_file)):\n # grism parameters, see WFSS worksheet in EXCEL SIAF\n grism_parameters = Table.read(grism_file, format='ascii.basic', delimiter=',', guess=False)\n\n # Save a backup copy of the grism file\n cmd = 'cp {} {}'.format(grism_file,os.path.join(JWST_TEMPORARY_DATA_ROOT, instrument, 'nircam_siaf_grism_parameters_backup.txt'))\n os.system(cmd)\n\n # different sign in Y for NRCB apertures\n factor = np.array(\n [1. if 'NRCA' in grism_parameters['aperture_name'][i] else -1. for i in range(len(grism_parameters))])\n\n for col in grism_parameters.colnames[1:]:\n # these are Sci coordinates\n if col[0] != 'D':\n if 'X' in col:\n grism_parameters['D{}'.format(col)] = grism_parameters[col].data - 1024.5\n elif 'Y' in col:\n grism_parameters['D{}'.format(col)] = factor * (grism_parameters[col].data - 1024.5)\n\n\n\n comments = []\n comments.append('{} grism parameter definition file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the grism parameters.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n grism_parameters.meta['comments'] = comments\n grism_parameters.write(grism_file, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False)\n\n # Transformation parameters, mapping used to select rows in cold_fit_[] file\n coldfit_name_mapping = {\n 'NRCA1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\n },\n 'NRCA5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RT_20170307121022',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALWToOTESKY_RT_20170307121022',\n },\n 'NRCB1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_1_20161025081604',\n 'pixels_to_mm':'NIRCAMBSW_1ToNIRCAMBSW_20161025081604',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_2_20161025081912',\n 'pixels_to_mm':'NIRCAMBSW_2ToNIRCAMBSW_20161025081912',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_3_20161025082300',\n 'pixels_to_mm':'NIRCAMBSW_3ToNIRCAMBSW_20161025082300',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_4_20161025082647',\n 'pixels_to_mm':'NIRCAMBSW_4ToNIRCAMBSW_20161025082647',\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\n },\n 'NRCB5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMBLW_RT_20170307121023',\n 'mm_to_pixels':'NIRCAMBLWToNIRCAMBLW_1_20161227162336',\n 'pixels_to_mm':'NIRCAMBLW_1ToNIRCAMBLW_20161227162336',\n 'mm_to_degrees':'NIRCAMBLWToOTESKY_RT_20170307121023',\n },\n 'NRCA1_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\n },\n 'NRCA2_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\n },\n 'NRCA3_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\n },\n 'NRCA4_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\n },\n 'NRCA1_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\n },\n 'NRCA2_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\n },\n 'NRCA3_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\n },\n 'NRCA4_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\n },\n 'NRCA5_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RND_202005150434',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALW_RNDToOTESKY_202005150434',\n },\n 'NRCA5_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMALW_BAR_202005150434',\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\n 'mm_to_degrees':'NIRCAMALW_BARToOTESKY_202005150434',\n }\n }\n\n # coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('cold_fit_201703071210.csv'))\n coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('nircam_cold_fit.txt'))\n print('NIRCam coldfit data from', coldfit_source_data_file)\n t = open(coldfit_source_data_file)\n coldfit_source_data = t.readlines()\n t.close()\n # remove comments from read content\n coldfit_source_data = [line for line in coldfit_source_data if line[0] != '#']\n\n siaf_detector_layout = iando.read.read_siaf_detector_layout()\n # siaf_alignment_parameters = iando.read.read_siaf_alignment_parameters(instrument)\n siaf_aperture_definitions = iando.read.read_siaf_aperture_definitions(instrument)\n # aperture_dict = {}\n aperture_name_list = siaf_aperture_definitions['AperName'].tolist()\n\n # generate alignment reference file, one file for all master apertures\n outfile = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_alignment.txt'.format(instrument.lower()))\n siaf_alignment = Table()\n\n for AperName in aperture_name_list:\n\n # process the master apertures of NIRCam\n if AperName in siaf_detector_layout['AperName']:\n (A, B, C, D, betaX, betaY, V2Ref, V3Ref) = nircam_get_polynomial_both(AperName, siaf_aperture_definitions, coldfit_name_mapping, coldfit_source_data)\n\n #generate distortion reference file\n number_of_coefficients = len(A)\n polynomial_degree = int((np.sqrt(8 * number_of_coefficients + 1) - 3) / 2)\n siaf_index = []\n exponent_x = []\n exponent_y = []\n for i in range(polynomial_degree + 1):\n for j in np.arange(i + 1):\n siaf_index.append('{:d}{:d}'.format(i, j))\n exponent_x.append(i - j)\n exponent_y.append(j)\n\n distortion_reference_table = Table((siaf_index, exponent_x, exponent_y, A, B, C, D), names=(\n 'siaf_index', 'exponent_x', 'exponent_y', 'Sci2IdlX', 'Sci2IdlY', 'Idl2SciX', 'Idl2SciY'))\n distortion_reference_table.add_column(\n Column([AperName] * len(distortion_reference_table), name='AperName'), index=0)\n distortion_reference_file_name = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\n '{}_siaf_distortion_{}.txt'.format(instrument.lower(),\n AperName.lower()))\n # distortion_reference_table.pprint()\n comments = []\n comments.append('{} distortion reference file for SIAF\\n'.format(instrument))\n comments.append('Aperture: {}\\n'.format(AperName))\n comments.append('Based on coefficients given in {},'.format(os.path.basename(coldfit_source_data_file)))\n # comments.append('that were rescaled, shifted for a different reference pixel location, and rotated:')\n # comments.append('Rotation of {:2.3f} deg was removed and is carried separately in V3IdlYangle.'.format(\n # np.rad2deg(V3angle))) # *units.deg.to(units.arcsecond)\n # if 'may_2015' in distortion_file_name:\n # comments.append(\n # 'These parameters are stored in the currently (January 2018) active SIAF (PRDOPSSOC-G-012). ')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n distortion_reference_table.meta['comments'] = comments\n distortion_reference_table.write(distortion_reference_file_name, format='ascii.fixed_width',\n delimiter=',', delimiter_pad=' ', bookend=False, overwrite=True)\n\n V3SciYAngle = betaY\n V3SciXAngle = betaX\n if np.abs(V3SciYAngle) < 90.:\n V3IdlYAngle = V3SciYAngle\n else:\n V3IdlYAngle = V3SciYAngle - np.sign(V3SciYAngle) * 180.\n\n if len(siaf_alignment) == 0: # first entry\n siaf_alignment['AperName'] = ['{:>30}'.format(AperName)]\n siaf_alignment['V3IdlYAngle'] = [V3IdlYAngle]\n siaf_alignment['V3SciXAngle'] = V3SciXAngle #[np.rad2deg(betaX)]\n siaf_alignment['V3SciYAngle'] = V3SciYAngle #[np.rad2deg(betaY)]\n siaf_alignment['V2Ref'] = [V2Ref]\n siaf_alignment['V3Ref'] = [V3Ref]\n else:\n siaf_alignment.add_row(['{:>30}'.format(AperName), V3IdlYAngle, V3SciXAngle, V3SciYAngle, V2Ref,V3Ref])\n comments = []\n comments.append('{} alignment parameter reference file for SIAF'.format(instrument))\n comments.append('')\n comments.append('This file contains the focal plane alignment parameters of master apertures calibrated')\n comments.append('during FGS-SI alignment.')\n comments.append('')\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\n comments.append('by {}'.format(username))\n comments.append('')\n siaf_alignment.meta['comments'] = comments\n siaf_alignment.write(outfile, format='ascii.fixed_width', delimiter=',',\n delimiter_pad=' ', bookend=False, overwrite=True)","def calc_rsi(image):\n\n # roll axes to conventional row,col,depth\n img = np.rollaxis(image, 0, 3)\n\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\n COAST = img[:, :, 0]\n B = img[:, :, 1]\n G = img[:, :, 2]\n Y = img[:, :, 3]\n R = img[:, :, 4]\n RE = img[:, :, 5]\n NIR1 = img[:, :, 6]\n NIR2 = img[:, :, 7]\n\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\n dd = (2 * NIR1 - R) - (G - B)\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\n gndvi = old_div((NIR1 - G), (NIR1 + G))\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\n ndvi = old_div((NIR1 - R), (NIR1 + R))\n ndvi35 = old_div((G - R), (G + R))\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\n nirry = old_div((NIR1), (R + Y))\n normnir = old_div(NIR1, (NIR1 + R + G))\n psri = old_div((R - B), RE)\n rey = old_div((RE - Y), (RE + Y))\n rvi = old_div(NIR1, R)\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\n vire = old_div(NIR1, RE)\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\n gr = old_div(G, R)\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\n\n ###Built-Up indices\n wvbi = old_div((COAST - RE), (COAST + RE))\n wvnhfd = old_div((RE - COAST), (RE + COAST))\n\n ###SIs\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\n rgi = old_div(R, G)\n bri = old_div(B, R)\n\n rsi = np.stack(\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\n axis=2)\n\n return rsi","def save_time_spent(self):\n\n ratings_dir = Path(self.out_dir).resolve() / cfg.suffix_ratings_dir\n if not ratings_dir.exists():\n makedirs(ratings_dir, exist_ok=True)\n\n timer_file = ratings_dir / '{}_{}_{}'.format(\n self.vis_type, self.suffix, cfg.file_name_timer)\n\n lines = '\\n'.join(['{},{}'.format(sid, elapsed_time)\n for sid, elapsed_time in self.timer.items()])\n\n # saving to disk\n try:\n with open(timer_file, 'w') as tf:\n tf.write(lines)\n except:\n print('Unable to save timer info to disk -- printing them to log:')\n print(lines)\n raise IOError('Error in saving timer info to file!')\n\n # printing summary\n times = np.array(list(self.timer.values()))\n if len(times) < 10:\n print('\\n\\ntimes spent per subject in seconds:\\n{}'.format(lines))\n\n print('\\nMedian time per subject : {} seconds'.format(np.median(times)))\n print('\\t5th and 95th percentile of distribution of times spent '\n ': {} seconds'.format(np.nanpercentile(times, [5, 95])))","def make_sn(sn_thres=0, format=\"png\", snsig=False):\r\n ###############################################\r\n # Read values of S/N\r\n sn = np.loadtxt(outtable, usecols=(14,))\r\n if snsig:\r\n sigma = np.loadtxt(outtable, usecols=(3,))\r\n sn /= sigma / 100.\r\n ###############################################\r\n # Find good (and bad) regions according to S/N\r\n good = np.where(((~np.isnan(sn)) & (sn >= sn_thres)))[0]\r\n bad = np.where((sn < sn_thres))[0]\r\n ###############################################\r\n # Filter S/N\r\n sn = sn[good]\r\n ###############################################\r\n # Colorbar limits\r\n vmin, vmax = 10, 50\r\n # Set limits for the plot\r\n norm = Normalize(vmin, vmax)\r\n ###############################################\r\n # Set colormap\r\n cmap = \"cubelaw_r\"\r\n cmap = \"Spectral\"\r\n # Produces a collection of polygons with colors according to S/N values\r\n coll = PolyCollection(polygons_bins[good], array=sn, cmap=cmap,\r\n edgecolors='w', norm=norm, linewidths=1.)\r\n ###############################################\r\n # Initiate figure and axis for matplotlib\r\n fig, ax = plt.subplots(1, 1, figsize=(6.4, 6), )\r\n fig.subplots_adjust(left=0.09, right=0.985, bottom=0.092, top=0.98,\r\n hspace=0.05, wspace=0.06)\r\n ###############################################\r\n # ax.add_patch(Rectangle((-100, -100), 200, 200, facecolor=\"0.8\", zorder=0,\r\n # alpha=0.5))\r\n ###############################################\r\n # Draw the polygons\r\n draw_map(fig, ax, coll, lims=40)\r\n ###############################################\r\n # Add contours according to V-band image\r\n # draw_contours(\"residual\", fig, ax, c=\"k\")\r\n draw_contours(\"vband\", fig, ax, c=\"k\")\r\n # Draw actual slit positions\r\n # canvas.draw_slits(ax, slit_type=1, fc=\"r\", ec=\"r\", ignore=ignore_slits )\r\n # canvas.draw_slits(ax, slit_type=3, fc=\"r\", ec=\"r\", ignore=ignore_slits )\r\n canvas.draw_slits_ids(ax, slits, fc=\"r\", ec=\"r\")\r\n ###############################################\r\n # Draw white rectangle in the position of the colorbar so background\r\n # stars do not overplot the labels and ticks\r\n plt.gca().add_patch(Rectangle((18, -36), 20, 10, alpha=1, zorder=10000,\r\n color=\"w\"))\r\n ###############################################\r\n # Draw the colorbar\r\n label = r\"100 S/N [pix] / $\\sigma$\" if snsig else r\"S/N\"\r\n draw_colorbar(fig, ax, coll, ticks=np.linspace(vmin, vmax, 5),\r\n cblabel=label, cbar_pos=[0.16, 0.15, 0.17, 0.04])\r\n ##############################################\r\n # Write labels\r\n xylabels(ax)\r\n ##############################################\r\n # Draw positions of galaxies\r\n # draw_galaxies(fig, ax)\r\n ##############################################\r\n # Save the figure\r\n plt.savefig(\"figs/sn.{0}\".format(format), dpi=300)\r\n # plt.savefig(\"figs/sn.pdf\", dpi=100)\r\n # plt.savefig(\"figs/sn.eps\", dpi=2500, format=\"eps\")\r\n # plt.savefig(\"figs/sn.png\", dpi=300)\r\n return","def tiled_writing(red, nir, output):\n \n #open datasets\n src_red = rio.open(red)\n src_nir = rio.open(nir)\n \n #define raster properies and update datatype\n meta = src_red.meta.copy()\n meta.update({'dtype':'float32'}) # meta is a dictionary\n outfile = output\n #open outfile in writing mode with the properties of defined raster band\n with rio.open(outfile, 'w', **meta) as dst:\n #iterate over blocks of the bands, calculate ndvi for each block \n # and put the blocks back together\n for window in calc_tiles(src_red, tile_size_x, tile_size_y):\n red_block = src_red.read(window=window, masked=True)\n nir_block = src_nir.read(window=window, masked=True)\n #cast ndarrays to Float32 type\n red = red_block.astype('f4')\n nir = nir_block.astype('f4')\n #allow division by zero\n np.seterr(divide='ignore', invalid='ignore')\n #calculate ndvi and write raster\n ndvi = (nir - red) / (nir + red)\n dst.write(ndvi, window=window)\n\n #close dataset\n src_red.close()\n src_nir.close()\n return outfile","def save(self, inst):\n n = inst.dimensions[\"n\"]\n with open(self.location, \"wt\") as f:\n f.write(f\"measurements: {n}\\n\")\n f.write(f\"time temperature\\n\")\n for time, temp in zip(inst.time, inst.temperature):\n f.write(f\"{time:4} {temp:12}\\n\")","def SNR_vs_NSA():\n NSA = np.array([1, 2, 3, 4])\n noise = np.array([0.7, 0.5, 0.4, 0.3]) # **2\n comp1 = np.array([39.3, 40.8, 41.3, 41.2])\n comp2 = np.array([55.3, 56.5, 56.8, 56.4])\n comp3 = np.array([69.4, 69.2, 69.1, 68.7])\n comp4 = np.array([53.5, 53.3, 53.7, 53.0])\n comp5 = np.array([65.1, 65.4, 65.7, 65.7])\n\n comp = [comp1, comp2, comp3, comp4, comp5]\n colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\"]\n for i, j, f in zip(comp, range(1, 6), colors):\n popt, _ = curve_fit(SNR, (NSA, i / noise), i / noise, p0=np.array([2, 0.5]))\n k, n = popt\n y_new = SNR((NSA, i / noise), k, n)\n plt.plot(NSA, y_new, c=f)\n plt.plot(NSA, i / noise, \"o\", label=\"Compartment %i, n=%.2f\" % (j, n))\n\n plt.grid()\n plt.legend()\n plt.ylabel(\"SNR\")\n plt.xlabel(\"NSA\")\n plt.show()\n\n # curve fit to see if SNR goes as root of NSA","def run_psavg_sims(bursttimefile):\n\n nfolder = [5,6,8,12]\n datadirs = [\"P20165/20165-01-01-000\", \"P20165/20165-01-01-001\", \"P20165/20165-01-01-002\",\n \"P10223/10223-01-03-01\", \"P10223/10223-01-03-010\" ]\n\n data_all, unbary_all, tstart_all, tend_all, t0_all, pcus_all, std1dir_all = [], [], [], [], [], [], []\n\n for d in datadirs:\n print(\"I am on directory %s\" %d)\n files = rxteburst.search_filenames_recursively(\"./%s/\"%d, \"*1div8192*.asc\")\n if len(files) == 0:\n files = rxteburst.search_filenames_recursively(\"./%s/\"%d, \"*1div-32768s*.asc\")\n if len(files) == 0:\n files = rxteburst.search_filenames_recursively(\"./%s/\"%d, \"*1div8*.asc\")\n #print(\"File to use %s\" %files[0])\n data = rxte.RXTEData(times=None, channels=None, datafile=files[0], npcus=None, ra=None, dec=None, emid=None, emiddir=None, bary=True)\n\n len_datafile = len(files[0].split(\"/\")[-1])\n len_processed = len(files[0].split(\"/\")[-2])\n std1dir_all.append(files[0][:-(len_datafile+len_processed+1)])\n\n data_all.append(np.array([p.time for p in data.photons])+data.t0)\n unbary_all.append(np.array([p.unbary for p in data.photons])+data.t0)\n tstart_all.append(data.photons[0].unbary+data.t0)\n tend_all.append(data.photons[-1].unbary+data.t0)\n t0_all.append(data.t0)\n pcus_all.append(data.pcus)\n\n t0_sorted, tstart_sorted, tend_sorted, data_sorted, pcus_sorted, std1dir_sorted, unbary_sorted = \\\n zip(*sorted(zip(t0_all, tstart_all, tend_all, data_all, pcus_all, std1dir_all, unbary_all)))\n t0_sorted = np.array(t0_sorted)\n\n psno = [5,6,8,12]\n m_all = [30, 23, 23, 50]\n\n for n,m in zip(psno, m_all):\n psavg_all = sgr1900_results.make_randomly_sampled_periodograms(datadirs, bursttimefile, m, n=1000,\n save_step=100, fileroot=\"sgr1806_psavg%i\"%n,\n data_sorted=data_sorted, t0_sorted=t0_sorted,\n pcus_sorted=pcus_sorted, tend_sorted=tend_sorted,\n tstart_sorted=tstart_sorted,\n unbary_sorted=unbary_sorted)\n\n return","def plot_seaice_predict_components_scatter(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\n output_folder = 'processed_data/SIC/'\n if seaice_source == 'ecmwf':\n output_folder = 'processed_data/ERA5/SIC/'\n\n if anomlous:\n temp_decomp = 'anomalous'\n else:\n temp_decomp = 'raw'\n\n\n title = temp_decomp.capitalize() + ' '\n\n if detrend:\n dt = 'detrended'\n title += dt + ' '\n else:\n dt = 'raw'\n\n title += temporal_resolution\n title += ' SIE prediction'\n\n\n# Loading Seaice Trends\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\n seaice = seaice\n\n\n# Index contributions\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\n dataset = xr.open_dataset(filename + '.nc')\n indicies = np.array([i for i in dataset])\n values = np.array([dataset[i].values for i in dataset])\n index_data = {}\n for indexname in indicies[:-1]:\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\n\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\n\n prediction = seaice.copy() * 0\n predictions = []\n for indexname in indicies:\n if indexname in index_data.keys():\n prediction += index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\n predictions += [index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]]\n else:\n prediction += dataset[indexname]\n predictions += [dataset[indexname]]\n\n\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\n\n\n fig = plt.figure(figsize = [7,14])\n\n ax1 = fig.add_subplot(4,2,1)\n ax2 = fig.add_subplot(4,2,2)\n ax3 = fig.add_subplot(4,2,3)\n ax4 = fig.add_subplot(4,2,4)\n smallaxes = [ax1,ax2,ax3,ax4]\n AX = fig.add_subplot(2,1,2)\n x_data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\n for i in range(4):\n ax = smallaxes[i]\n indexname = indicies[i]\n predict = index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\n y_data = (predict*area/250).sum(dim = ('x', 'y'))\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(x_data, y_data)\n r, p = scipy.stats.pearsonr(x_data, y_data)\n ax.scatter(x_data, y_data)\n ax.plot(x_data,data_m*x_data+data_b)\n ax.set_title(indexname)\n lim = max([-min(x_data),-min(y_data),max(x_data),max(y_data)])*1.05\n ax.text(-lim*0.9, lim*0.55, f'gradient = {data_m:.2f}\\ncorrelation = {r:.2f}\\np-value = {p:.2f}')\n ax.set_ylim([-lim, lim])\n ax.set_xlim([-lim, lim])\n ax.axhline(0, alpha = 0.2)\n ax.axvline(0, alpha = 0.2)\n y_data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\n AX.scatter(x_data, y_data)\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(x_data, y_data)\n r, p = scipy.stats.pearsonr(x_data, y_data)\n AX.plot(x_data,data_m*x_data+data_b)\n AX.set_title('Total Prediction')\n lim = max([-min(x_data),-min(y_data),max(x_data),max(y_data)])*1.05\n AX.text(-lim*0.9, lim*0.7, f'gradient = {data_m:.2f}\\ncorrelation = {r:.2f}\\np-value = {p:.2f}')\n AX.set_ylim([-lim, lim])\n AX.set_xlim([-lim, lim])\n AX.axhline(0, alpha = 0.2)\n AX.axvline(0, alpha = 0.2)\n fig.suptitle(title)\n\n plt.savefig(imagefolder + seaicename + 'scatter.pdf')\n plt.show()","def _write_nemo_hr_file(rpn_hr_ds_path, nemo_hr_ds_path):\n with xarray.open_dataset(rpn_hr_ds_path) as rpn_hr:\n logging.debug(\n f\"calculating specific humidity & incoming longwave radiation from {rpn_hr_ds_path}\"\n )\n qair, ilwr, rh = _calc_qair_ilwr(rpn_hr)\n u_out, v_out = _rotate_winds(rpn_hr)\n data_vars = {\n \"nav_lon\": rpn_hr.nav_lon,\n \"nav_lat\": rpn_hr.nav_lat,\n # [:, 0] drops z dimension that NEMO will not tolerate\n \"qair\": qair[:, 0],\n \"RH_2maboveground\": rh[:, 0],\n \"therm_rad\": ilwr[:, 0],\n \"u_wind\": u_out[:, 0],\n \"v_wind\": v_out[:, 0],\n # \"LHTFL_surface\": ** needs to be calculated**,\n }\n nemo_rpn_vars = (\n (\"atmpres\", \"PN\"),\n (\"percentcloud\", \"NT\"),\n (\"PRATE_surface\", \"RT\"),\n (\"precip\", \"PR\"),\n (\"solar\", \"FB\"),\n (\"tair\", \"TT\"),\n )\n missing_vars = \"\"\n for nemo_var, rpn_var in nemo_rpn_vars:\n try:\n # [:, 0] drops z dimension that NEMO will not tolerate\n data_vars.update({nemo_var: getattr(rpn_hr, rpn_var)[:, 0]})\n except AttributeError:\n # Variable is missing from RPN dataset, so provide a placeholder DataArray\n # full of NaNs that we will deal with later via interpolation\n data_vars.update(\n {nemo_var: xarray.DataArray(numpy.full_like(qair[:, 0], numpy.nan))}\n )\n missing_vars = (\n \", \".join((missing_vars, nemo_var)) if missing_vars else nemo_var\n )\n logging.warning(f\"missing RPN variable {rpn_var} from {rpn_hr_ds_path}\")\n nemo_hr = xarray.Dataset(\n data_vars=data_vars, coords=rpn_hr.coords, attrs=rpn_hr.attrs\n )\n nemo_hr.attrs[\"history\"] += (\n f\"\\n{arrow.now().format('ddd MMM DD HH:mm:ss YYYY')}: \"\n f\"Add specific and relative humidity and incoming longwave radiation variables from \"\n f\"correlations\"\n )\n if missing_vars:\n nemo_hr.attrs[\"missing_variables\"] = missing_vars\n _add_vars_metadata(nemo_hr)\n _write_netcdf_file(nemo_hr, nemo_hr_ds_path)","def _check_PSNR(self, dataset, is_test=False):\n\n # process one image per iter for test phase\n if is_test:\n batch_size = 1\n else:\n batch_size = 1 # self.batch_size\n\n dataloader = DataLoader(dataset, batch_size=batch_size,\n shuffle=False, num_workers=1)\n\n avr_psnr = 0\n avr_ssim = 0\n\n # book keeping variables for test phase\n psnrs = [] # psnr for each image\n ssims = [] # ssim for each image\n proc_time = [] # processing time\n outputs = [] # output for each image\n names = []\n\n for batch, sample in enumerate(dataloader):\n input_batch, label_batch, name = sample['lr'], sample['hr'], sample['im_name']\n\n # Wrap with torch Variable\n input_batch, label_batch = self._wrap_variable(input_batch,\n label_batch,\n self.use_gpu)\n\n if is_test:\n start = time.time()\n if self.model_name in ['TDAN']:\n output_batch = chop_forward(input_batch, self.model, 4)\n #output_batch = chop_forward(input_batch, self.model, 4)\n #output_batch = forward_x8(input_batch, self.model).unsqueeze(0)\n #print(output_batch.size())\n # _, lrs = self.model(input_batch)\n # output_batch = lrs[:, -1, :, :, :]\n else:\n output_batch = self.model(input_batch)\n elapsed_time = time.time() - start\n else:\n if self.model_name in ['TDAN']:\n #output_batch, _ = self.model(input_batch)\n output_batch = chop_forward(input_batch, self.model, 4)\n else:\n output_batch = self.model(input_batch)\n # ssim is calculated with the normalize (range [0, 1]) image\n ssim = pytorch_ssim.ssim(output_batch + 0.5, label_batch + 0.5, size_average=False)\n ssim = torch.sum(ssim.data)\n avr_ssim += ssim\n\n # calculate PSRN\n output = output_batch.data\n label = label_batch.data\n\n output = (output + 0.5) * 255\n label = (label + 0.5) * 255\n\n output = quantize(output, 255)\n label = quantize(label, 255)\n # diff = input - target\n\n output = output.squeeze(dim=0)\n label = label.squeeze(dim=0)\n\n psnr = self._comput_PSNR(output / 255.0, label / 255.0)\n # print(psnr)\n avr_psnr += psnr\n\n # save psnrs and outputs for statistics and generate image at test time\n if is_test:\n psnrs.append(psnr)\n ssims.append(ssim)\n proc_time.append(elapsed_time)\n np_output = output.cpu().numpy()\n outputs.append(np_output)\n names.append(name)\n\n epoch_size = len(dataset)\n avr_psnr /= epoch_size\n avr_ssim /= epoch_size\n stats = (psnrs, ssims, proc_time)\n\n return avr_psnr, avr_ssim, stats, outputs, names","def write_file(self):\n if self.it_num % 5 == 0:\n #plt.imshow(self.grid)\n #plt.savefig(\"output%.4d.png\" % self.it_num, bbox_inches='tight')\n io.savemat(\"MLOutput%.4d\" % self.it_num, { \"Grid\":self.grid})","def writetipsy(self, snapshot, halos, tipsyoutfile, hubble=None):\n from . import analysis\n from . import tipsy\n from .analysis import cosmology\n from snapshot import _new as new\n import math\n s = snapshot\n outfile = tipsyoutfile\n nhalos = halos._nhalos\n nstar = nhalos\n sout = new(star=nstar) # create new tipsy snapshot written as halos.\n sout.properties['a'] = s.properties['a']\n sout.properties['z'] = s.properties['z']\n sout.properties['boxsize'] = s.properties['boxsize']\n if hubble is None:\n hubble = s.properties['h']\n sout.properties['h'] = hubble\n # ! dangerous -- rho_crit function and unit conversions needs simplifying\n rhocrithhco = cosmology.rho_crit(s, z=0, unit=\"Msol Mpc^-3 h^2\")\n lboxkpc = sout.properties['boxsize'].ratio(\"kpc a\")\n lboxkpch = lboxkpc * sout.properties['h']\n lboxmpch = lboxkpc * sout.properties['h'] / 1000.\n tipsyvunitkms = lboxmpch * 100. / (math.pi * 8. / 3.) ** .5\n tipsymunitmsun = rhocrithhco * lboxmpch ** 3 / sout.properties['h']\n\n for ii in xrange(nhalos):\n h = halos[ii + 1].properties\n sout.star[ii]['mass'] = h['mass'] / hubble / tipsymunitmsun\n # tipsy units: box centered at 0. (assume 0<=x<=1)\n sout.star[ii]['x'] = h['Xc'] / lboxkpch - 0.5\n sout.star[ii]['y'] = h['Yc'] / lboxkpch - 0.5\n sout.star[ii]['z'] = h['Zc'] / lboxkpch - 0.5\n sout.star[ii]['vx'] = h['VXc'] / tipsyvunitkms\n sout.star[ii]['vy'] = h['VYc'] / tipsyvunitkms\n sout.star[ii]['vz'] = h['VZc'] / tipsyvunitkms\n sout.star[ii]['eps'] = h['Rvir'] / lboxkpch\n sout.star[ii]['metals'] = 0.\n sout.star[ii]['phi'] = 0.\n sout.star[ii]['tform'] = 0.\n\n sout.write(fmt=tipsy.TipsySnap, filename=outfile)\n return sout","def save2nifti(self, file_path):\n # Define nifti1 datatype codes\n NIFTI_TYPE_UINT8 = 2 # unsigned char\n NIFTI_TYPE_INT16 = 4 # signed short\n NIFTI_TYPE_INT32 = 8 # signed int.\n NIFTI_TYPE_FLOAT32 = 16 # 32 bit float.\n NIFTI_TYPE_COMPLEX64 = 32 # 64 bit complex = 2 32 bit floats\n NIFTI_TYPE_FLOAT64 = 64 # 64 bit float = double.\n NIFTI_TYPE_RGB24 = 128 # 3 8 bit bytes.\n NIFTI_TYPE_INT8 = 256 # signed char.\n NIFTI_TYPE_UINT16 = 512 # unsigned short.\n NIFTI_TYPE_UINT32 = 768 # unsigned int.\n NIFTI_TYPE_INT64 = 1024 # signed long long.\n NIFTI_TYPE_UINT64 = 1280 # unsigned long long.\n NIFTI_TYPE_FLOAT128 = 1536 # 128 bit float = long double.\n NIFTI_TYPE_COMPLEX128 = 1792 # 128 bit complex = 2 64 bit floats.\n NIFTI_TYPE_COMPLEX256 = 2048 # 256 bit complex = 2 128 bit floats\n NIFTI_TYPE_RGBA32 = 2304 # 4 8 bit bytes.\n\n # Detect the data type of the input data.\n data_type = {\n np.uint8: NIFTI_TYPE_UINT8,\n np.uint16: NIFTI_TYPE_UINT16,\n np.uint32: NIFTI_TYPE_UINT32,\n np.float32: NIFTI_TYPE_FLOAT32,\n np.int16: NIFTI_TYPE_INT16,\n np.int32: NIFTI_TYPE_INT32,\n np.int8: NIFTI_TYPE_INT8\n }\n if sys.maxint > 2 ** 32: # The platform is 64 bit\n data_type[np.float128] = NIFTI_TYPE_FLOAT128\n data_type[np.float64] = NIFTI_TYPE_FLOAT64\n data_type[np.int64] = NIFTI_TYPE_INT64\n data_type[np.uint64] = NIFTI_TYPE_UINT64\n data_type[np.complex64] = NIFTI_TYPE_COMPLEX64\n data_type[np.complex128] = NIFTI_TYPE_COMPLEX128\n data_type[np.complex256] = NIFTI_TYPE_COMPLEX256\n\n header = nib.Nifti1Header()\n if self.data.shape[1] == 1:\n new_shape = (self.data.shape[0], 1, 1)\n else:\n new_shape = (self.data.shape[0], 1, 1, self.data.shape[1])\n data = self.data.reshape(new_shape)\n\n if data.dtype.type in data_type:\n header['datatype'] = data_type[data.dtype.type]\n header['cal_max'] = data.max()\n header['cal_min'] = data.min()\n image = nib.Nifti1Image(data, None, header)\n nib.nifti1.save(image, file_path)","def IR():\n s = np.array(\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\n )\n\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\n\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\n\n comp = [comp1, comp2, comp3, comp4, comp5]\n MSE = []\n colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#9467bd\"]\n x_new = np.linspace(0, 3000, 10000)\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\n plt.scatter(TI, i, c=j)\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\n # M_z0, T1, M0 = popt\n M0, T1 = popt\n y_new = MZ(x_new, *popt)\n plt.plot(x_new, y_new, \"--\", c=j, label=f\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\")\n MSE.append(mean_squared_error(i,y_new[TI]))\n print(MSE)\n print(np.mean(MSE))\n plt.grid()\n plt.legend(loc=\"best\")\n plt.xlabel(\"TI\")\n plt.ylabel(r\"Singal Intensity $M_z$\")\n plt.show()","def save_ttest_metrics(self, ttest_metrics, fname, no_genes=20):\n\n top_genes = self.fetch_gene_descriptions(ttest_metrics, nih_fetch_num=no_genes, printme=False)\n eids = [int(i[0]) for i in top_genes]\n myfig = self.effect_size_distr(ttest_metrics, genes_of_interest=eids[0:no_genes], return_fig=True)\n plt.savefig(fname+'.png')\n\n with open(fname+'.csv', 'wb') as csvfile:\n writer = csv.writer(csvfile)\n for i in top_genes:\n writer.writerow([i[0], i[3], i[1], i[2], i[4]])","def IRIS_network(input):\n\n t_iris_1 = datetime.now()\n \n global events\n \n len_events = len(events)\n Period = input['min_date'].split('T')[0] + '_' + \\\n input['max_date'].split('T')[0] + '_' + \\\n str(input['min_mag']) + '_' + str(input['max_mag'])\n eventpath = os.path.join(input['datapath'], Period)\n \n create_foders_files(events, eventpath)\n\n print 'IRIS-Folders are Created!'\n print \"--------------------\"\n \n Stas_iris = []\n \n for i in range(0, len_events):\n \n target_path = os.path.join(eventpath, events[i]['event_id'])\n Sta_iris = IRIS_available(input, events[i], target_path, event_number = i)\n Stas_iris.append(Sta_iris)\n \n if input['iris_bulk'] != 'Y':\n print 'IRIS-Availability for event: ' + str(i+1) + str('/') + \\\n str(len_events) + ' ---> ' + 'DONE'\n else:\n print 'IRIS-bulkfile for event : ' + str(i+1) + str('/') + \\\n str(len_events) + ' ---> ' + 'DONE'\n \n if input['get_continuous'] == 'Y' and input['iris_bulk'] == 'Y':\n for j in range(1, len_events):\n Stas_iris.append(Sta_iris)\n target_path = os.path.join(eventpath, events[j]['event_id'])\n shutil.copy2(os.path.join(eventpath, events[0]['event_id'], \\\n 'info', 'bulkdata-0.txt'), \\\n os.path.join(target_path, \\\n 'info', 'bulkdata-' + str(j) + '.txt'))\n print 'IRIS-Availability for event: ' + str(j+1) + str('/') + \\\n str(len_events) + ' ---> ' + 'DONE'\n break\n \n t_iris_2 = datetime.now()\n t_iris = t_iris_2 - t_iris_1\n print \"--------------------\"\n print 'IRIS-Time: (Availability)'\n print t_iris \n \n return Stas_iris","def log(pro, logname, savepng=True):\n\n # Load processed data variables\n rawfiles = pro['rawfiles']\n transect = pro['transect']\n t120 = pro['t120' ]\n r120 = pro['r120' ]\n Sv120 = pro['Sv120' ]\n Sv120sw = pro['Sv120sw' ]\n t120r = pro['t120r' ]\n t120intrvls = pro['t120intervals']\n nm120r = pro['nm120r' ]\n lon120r = pro['lon120r' ]\n lat120r = pro['lat120r' ]\n sbline120r = pro['sbliner' ][0,:] \n NASC120swr = pro['NASC120swr'][0,:]\n pc120swr = pro['pc120swr'][0,:]\n \n # Build summary results\n results = {'Time' : np.array(t120r , dtype=str) ,\n 'Longitude': np.round(lon120r , 5) ,\n 'Latitude' : np.round(lat120r , 5) ,\n 'Transect' : np.ones(len(t120r ), dtype=int)*transect,\n 'Miles' : nm120r ,\n 'Seabed' : np.round(sbline120r , 1) ,\n 'NASC' : np.round(NASC120swr , 2) ,\n '% samples': np.round(pc120swr , 1) }\n results = pd.DataFrame(results, columns= ['Time' , 'Longitude',\n 'Latitude' , 'Transect' ,\n 'Miles' , 'Seabed' ,\n 'NASC' , '% samples'])\n \n # Create new log subdirectory\n path = os.path.join(os.path.dirname(__file__), '..', 'log', logname, '')\n if not os.path.exists(path):\n os.makedirs(path)\n \n # Write results in CSV log file\n with open(path+logname+'.csv', 'a') as f:\n results.to_csv(path+logname+'.csv', index=False, mode='a',\n header=f.tell()==0) \n \n # save png image\n if savepng:\n \n # set figure\n plt.close()\n plt.figure(figsize=(8, 8))\n plt.subplots_adjust(left=0.066, right=1.055, bottom=0.065, top=0.985,\n wspace=0, hspace=0.05)\n plt.rcParams.update({'font.size': 9, 'lines.linewidth': 1})\n \n # plot raw echogram\n plt.subplot(211).invert_yaxis()\n im=plt.pcolormesh(t120, r120, Sv120,\n vmin=-80, vmax=-50, cmap=cmaps().ek500)\n plt.colorbar(im).set_label('Sv raw (dB re 1m$^{-1}$)')\n plt.gca().set_ylim(270,0)\n plt.gca().set_ylabel('Depth (m)')\n plt.gca().set_xlim(t120intrvls[0], t120intrvls[-1])\n plt.gca().set_xticks(t120intrvls[[0,-1]])\n plt.tick_params(labelright=False, labelbottom=False)\n \n # plot processed echogram\n ax= plt.subplot(212)\n ax = [ax, ax.twinx()]\n im=ax[0].pcolormesh(t120, r120, Sv120sw,\n vmin=-80,vmax=-50, cmap=cmaps().ek500)\n plt.colorbar(im).set_label('Sv pro (dB re 1m$^{-1}$)')\n ax[0].invert_yaxis()\n ax[0].set_ylim(270,0)\n ax[0].set_ylabel('Depth (m)')\n \n # overlay distance/NASC info\n for t, nm, NASC in zip(t120r, nm120r, NASC120swr):\n ax[1].plot([t, t], [0, 1], color=[0,.8,0], linewidth=2)\n ax[1].text(t, .95, ' ' + str(transect) + ': ' + str(round(nm,2)),\n fontweight='bold', color=[0,.8,0])\n ax[1].text(t, .02, ' ' + str(round(NASC,2)),\n fontweight='bold', color=[1,0,0]) \n ax[1].set_ylim(0, 1)\n ax[1].set_xlim(t120intrvls[0], t120intrvls[-1])\n ax[1].set_xticks(t120intrvls[[0,-1]])\n ax[1].tick_params(labelright=False)\n ax[1].xaxis.set_major_formatter(mdates.DateFormatter('%d%b-%H:%M:%S'))\n \n # save figure\n pf = rawfiles[0].split('-')[0]\n fn = pd.to_datetime(str(t120[0])).strftime(pf + '-D%Y%m%d-T%H%M%S')\n plt.savefig(path+fn+'.png' ,figsize=(8, 8), dpi=100)\n plt.close()","def make_sim(args):\n flux, nread, iexp = args\n satlevel=65535\n\n #outdir = \"sim_data_flux\" + str(flux)\n #outdir = \"sim_data_flux\" + str(flux) + \"_nonl\"\n outdir = \"sim_\" + \"exposure\" + str(iexp) + \"_flux\" + str(flux)\n pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) \n \n for i in range(1, nread+1):\n logger.info(\"Looping through UTR {}\".format(i))\n #outfileprefix = outdir + \"/exposure\" + str(iexp) + \"_utr\" + str(i)\n outfileprefix = outdir + \"/utr\" + str(i)\n refoutfileprefix = outdir + \"/utr\" + str(i) + \"_ref\"\n\n primary_hdu = fits.PrimaryHDU()\n outhdus = fits.HDUList([primary_hdu])\n outhdus_ref = fits.HDUList([primary_hdu])\n\n ahdu = fits.HDUList([primary_hdu]) #for a coefficients\n bhdu = fits.HDUList([primary_hdu]) #for b coefficients\n\n #loop all 16 detectors\n ndet = 16\n logger.info(\"Looping through all 16 detectors\")\n for idet in range(ndet):\n detid = detids[idet]\n logger.info(\"Looping through the detector {}, {}\".format(idet+1, detid)) \n\n flux_actual = np.zeros((2048, 2048))\n #start with the measured flux, constant spatially but varying temporally\n flux_actual[4:2044,4:2044] = flux * TREAD * i #fill data region with measured flux, (ADU/f) * s = ADU ???\n flux_actual *= Gain #e-??\n logger.info(\"Filled with measured flux\")\n\n #calculate nonlinearity from calibration data, varing spatially but not temporally, unit e-??\n flux_nonlinear = make_nonlinear(flux_actual, outfileprefix, ahdu, bhdu, detid) \n #flux_nonlinear = flux_actual #no non-linearity\n logger.info(\"Made non-linearized flux\")\n\n #add pedestal, constant spatially very varying temporally\n pedestal = random.randint(0, 1000) #set the random pedistal (between 0 - 1000) for this exposure, unit e-??\n flux_nonlinear[:, :] += pedestal #add the pedestal to all of the data including the reference pixels.\n logger.info(\"Added pedestal\")\n\n #add noise (e-)\n readnoise_array = np.random.normal(size=[2048,2048]) * ReadNoise #generate the read noise, the reference pixels should also have noise. \n flux_nonlinear += readnoise_array #add the read noise to the data array\n logger.info(\"Added read noise\")\n\n # set any pixel above the saturation limit to the saturation limit.\n # This can set to 65535 for all pixels for now, but should be able to \n # take a map since this will vary pixel to pixel. \n ind = np.where(flux_nonlinear > satlevel)\n flux_nonlinear[ind] = satlevel\n logger.info(\"Checked saturation level\")\n\n #add this dector to the final simulated data\n hdu = fits.ImageHDU(data=flux_nonlinear, name=detid)\n outhdus.append(hdu)\n\n #do reference pixel correction\n updownCorr(flux_nonlinear)\n leftrightCorr(flux_nonlinear)\n\n #add this dector to another output\n hdu1 = fits.ImageHDU(data=flux_nonlinear, name=detid)\n outhdus_ref.append(hdu1)\n \n\n #write the sim data for this UTR\n outfile = outfileprefix + \".fits\"\n #logger.info(\"Writing sim data to: {}\".format(outfile))\n #outhdus.writeto(outfile)\n\n refoutfile = refoutfileprefix + \".fits\"\n logger.info(\"Writing reference pixel corrected sim data to: {}\".format(refoutfile))\n outhdus_ref.writeto(refoutfile)\n\n #ahdu.writeto(outfileprefix + \"_a_coef.fits\")\n #bhdu.writeto(outfileprefix + \"_b_coef.fits\")","def read_taiwan_ntu_dsi():\n dipy_home = pjoin(os.path.expanduser('~'), '.dipy')\n folder = pjoin(dipy_home, 'taiwan_ntu_dsi')\n fraw = pjoin(folder, 'DSI203.nii.gz')\n fbval = pjoin(folder, 'DSI203.bval')\n fbvec = pjoin(folder, 'DSI203.bvec')\n md5_dict = {'data': '950408c0980a7154cb188666a885a91f',\n 'bval': '602e5cb5fad2e7163e8025011d8a6755',\n 'bvec': 'a95eb1be44748c20214dc7aa654f9e6b',\n 'license': '7fa1d5e272533e832cc7453eeba23f44'}\n\n check_md5(fraw, md5_dict['data'])\n check_md5(fbval, md5_dict['bval'])\n check_md5(fbvec, md5_dict['bvec'])\n check_md5(pjoin(folder, 'DSI203_license.txt'), md5_dict['license'])\n\n bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\n bvecs[1:] = bvecs[1:] / np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None]\n\n gtab = gradient_table(bvals, bvecs)\n img = nib.load(fraw)\n return img, gtab","def to_nifti(self,folder_path: str):\n data_path = settings.STORAGE_DIR\n path = folder_path \n nifti=series.get_series_object(path) \n nifti_str=str(nifti)\n nifti_str=nifti_str[1:44]\n if nifti_str=='dicom_to_cnn.model.reader.SeriesCT.SeriesCT': \n nifti.get_instances_ordered() \n nifti.get_numpy_array()\n image_md5 = hashlib.md5(str(nifti).encode())\n image_id = image_md5.hexdigest()\n img=nifti.export_nifti(data_path+'/image/image_'+image_id+'.nii')\n if nifti_str=='dicom_to_cnn.model.reader.SeriesPT.SeriesPT':\n nifti.get_instances_ordered() \n nifti.get_numpy_array()\n nifti.set_ExportType('suv')\n image_md5 = hashlib.md5(str(nifti).encode())\n image_id = image_md5.hexdigest()\n img=nifti.export_nifti(data_path+'/image/image_'+image_id+'.nii')","def store_std_dev_of_noises(src_file: H5File) -> None:\n mean_perp_noise = src_file.attrs['perp_noise_mean']\n mean_par_noise = src_file.attrs['par_noise_mean']\n mean_ref_noise = src_file.attrs['ref_noise_mean']\n perp_sum = 0\n par_sum = 0\n ref_sum = 0\n counts = 0\n for path in rawnav.pump_group_paths(src_file):\n perp_path = path + '/perp'\n par_path = path + '/par'\n ref_path = path + '/ref'\n perp_noise = src_file[perp_path].attrs['noise']\n par_noise = src_file[par_path].attrs['noise']\n ref_noise = src_file[ref_path].attrs['noise']\n perp_sum += (perp_noise - mean_perp_noise) ** 2\n par_sum += (par_noise - mean_par_noise) ** 2\n ref_sum += (ref_noise - mean_ref_noise) ** 2\n counts += 1\n src_file.attrs['perp_noise_std_dev'] = np.sqrt(perp_sum / (counts - 1))\n src_file.attrs['par_noise_std_dev'] = np.sqrt(par_sum / (counts - 1))\n src_file.attrs['ref_noise_std_dev'] = np.sqrt(ref_sum / (counts - 1))\n return","def calculate_psf_tilts():\n for order in [1, 2]:\n\n # Get the file\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\n psf_file = resource_filename('awesimsoss', path)\n\n # Dimensions\n subarray = 'SUBSTRIP256'\n X = range(2048)\n Y = range(256)\n\n # Get the wave map\n wave_map = utils.wave_solutions(subarray, order).astype(float)\n\n # Get the y-coordinate of the trace polynomial in this column\n # (center of the trace)\n coeffs = trace_polynomials(subarray=subarray, order=order)\n trace = np.polyval(coeffs, X)\n\n # Interpolate to get the wavelength value at the center\n wave = interp2d(X, Y, wave_map)\n\n # Get the wavelength of the trace center in each column\n trace_wave = []\n for x, y in zip(X, trace):\n trace_wave.append(wave(x, y)[0])\n\n # For each column wavelength (defined by the wavelength at\n # the trace center) define an isowavelength contour\n angles = []\n for n, x in enumerate(X):\n\n w = trace_wave[x]\n\n # Edge cases\n try:\n w0 = trace_wave[x-1]\n except IndexError:\n w0 = 0\n\n try:\n w1 = trace_wave[x+1]\n except IndexError:\n w1 = 10\n\n # Define the width of the wavelength bin as half-way\n # between neighboring points\n dw0 = np.mean([w0, w])\n dw1 = np.mean([w1, w])\n\n # Get the coordinates of all the pixels in that range\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\n\n # Find the angle between the vertical and the tilted wavelength bin\n if len(xx) >= 1:\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\n else:\n angle = 0\n\n # Don't flip them upside down\n angle = angle % 180\n\n # Add to the array\n angles.append(angle)\n\n # Save the file\n np.save(psf_file, np.array(angles))\n print('Angles saved to', psf_file)","def PCO1S12Noise():\n #Get data\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/' \\\n 'NoiseStudy/TestOptics_PCO1S12/'\n d1,dx1 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas1.fits')\n d2,dx2 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas2.fits')\n d3,dx3 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas3.fits')\n\n #Construct power spectra\n f12,pow12 = fourier.meanPSD((d1-d2)[:,100:-100],\\\n win=np.hanning,dx=dx1,irregular=True)\n f23,pow23 = fourier.meanPSD((d2-d3)[:,100:-100],\\\n win=np.hanning,dx=dx1,irregular=True)\n f13,pow13 = fourier.meanPSD((d1-d3)[:,100:-100],\\\n win=np.hanning,dx=dx1,irregular=True)\n\n #Mid frequency\n midfreq = [1000*np.sqrt(np.sum(p[np.logical_and(f>.1,f<1.)])) \\\n for f,p in zip([f12,f23,f13],[pow12,pow23,pow13])]\n\n #Plot\n plt.loglog(f12,pow12/f12[0],label='1-2: %.2f' % midfreq[0])\n plt.loglog(f23,pow23/f23[0],label='2-3: %.2f' % midfreq[1])\n plt.loglog(f13,pow13/f13[0],label='1-3: %.2f' % midfreq[2])\n plt.legend(loc='lower left')\n plt.grid()\n plt.title('4D Repeatability: PCO1S12')\n plt.xlabel('Frequency (1/mm)')\n plt.ylabel('Power ($\\mu$m$^2$ mm)')\n\n print midfreq\n\n return f12,pow12","def final_states_figure_file(self, it, avg):\n return self.output_file(self.figures_dir(), 'final_states', it, avg, 'png')","def n27_and_stark():\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4))\n # n=27 without static field.\n folder = os.path.join(\"..\", \"..\", \"2018-09-09\")\n fname = \"11_freq_diode.txt\"\n fname = os.path.join(folder, fname)\n data = pmu.fscan_import(fname)\n data['afpoly'] = data['fpoly'] + 4511\n ax.axhline(0, color='grey')\n data.plot(x='afpoly', y='sig', c='k', ax=ax)\n # n=27 with static field.\n fname = \"10_freq_diode.txt\"\n fname = os.path.join(folder, fname)\n data = pmu.fscan_import(fname)\n data['afpoly'] = data['fpoly'] + 4511\n data['asig'] = data['sig'] + 0.15\n ax.axhline(0.15, color='grey')\n data.plot(x='afpoly', y='asig', c='k', ax=ax)\n # text\n ax.text(-5.5, 0.2, \"3 V/cm\", horizontalalignment='right')\n ax.text(-5.5, 0.01, \"0 V/cm\", horizontalalignment='right')\n # pretty figure\n ax.legend().remove()\n ax.set_ylabel(r\"$e^-$ Signal\")\n ax.set_yticks([])\n ax.set_xlabel(r\"Frequency (GHz from $3d_{5/2} \\rightarrow 27f$)\")\n ax.set_xlim(-8, 7)\n # save\n fig.tight_layout()\n fig.savefig('n27_and_stark.pdf')\n return","def plot_seir(sir, T, tmin = 0, tmax = 200, absolute=True, figsize=(10,10), facecolor='LightGrey'):\n fig = plt.figure(figsize=figsize)\n ax = fig.add_subplot(111, axisbelow=True)\n ax.set_facecolor(facecolor)\n\n if absolute:\n S = sir.S * sir.N\n E = sir.E * sir.N\n I = sir.I * sir.N\n R = sir.R * sir.N\n Y = 1.1 * sir.N\n else:\n S = sir.S\n E = sir.E\n I = sir.I\n R = sir.R\n Y = 1.1\n\n ax.plot(sir.t, S, 'k', alpha=0.5, lw=3, label='Susceptible')\n ax.plot(sir.t, I, 'b', alpha=0.5, lw=3, label='Infected')\n ax.plot(sir.t, E, 'r', alpha=0.5, lw=3, label='Exposed')\n ax.plot(sir.t, R, 'g', alpha=0.5, lw=3, label='Recovered')\n xlabel = 'Time /days'\n if absolute:\n ylabel = 'Total population'\n else:\n ylabel = 'Fraction of population'\n\n set_pretty_ax(ax, facecolor, xlabel, ylabel, tmin, tmax, Y)\n plt.title(T)\n plt.show()","def tsne_projection(train_file, gen_file, i):\n train_data = pd.read_csv(train_file)\n def get_ori_prom(row):\n if row['gaps'] <= 2 and row['dips'] <=2:\n return 'Model'+str(i)+'_train_promising'\n else:\n return 'Model'+str(i)+'_train'\n train_data['label'] = train_data.apply(get_ori_prom, axis=1)\n train_data = train_data.drop(['id', 'gaps', 'dips'], axis=1)\n gen_prom_smiles = pd.read_csv(gen_file)\n gen_prom_smiles = gen_prom_smiles.drop(['Group', 'Gaps', 'Dips'], axis=1)\n gen_prom_smiles['label'] = 'Generated_promising'\n all_smi = pd.concat([train_data, gen_prom_smiles])\n mols = get_mols(all_smi.SMILES)\n fps, _ = get_fingerprints(mols)\n fp_embeded = TSNE(n_components=2, perplexity=100).fit_transform(fps)\n all_smi['tsne1'] = fp_embeded[:, 0]\n all_smi['tsne2'] = fp_embeded[:, 1]\n return all_smi, len(train_data)","def plot_stairway_Ne_estimate(infile, outfile):\n nt = pandas.read_csv(infile, sep=\"\\t\", skiprows=5)\n nt = nt[nt['year'] > 10]\n f, ax = plt.subplots(figsize=(7, 7))\n ax.set(xscale=\"log\", yscale=\"log\")\n ax.plot(nt['year'], nt['Ne_median'], c=\"red\")\n ax.plot(nt['year'], nt['Ne_2.5%'], c='grey')\n ax.plot(nt['year'], nt['Ne_97.5%'], c='grey')\n f.savefig(outfile, bbox_inches='tight')","def make_fakeSN_spectrum(snfile, galfile, params, outfile, err=False, signal_noise=20.0, z=0.0, wave_range=[3842, 8195]):\n snt=loadtext(snfile)\n galt=loadsdss(fits.open(galfile))\n minsalt=list(snt.wavelength).index(3840)\n maxsalt=list(snt.wavelength).index(8200)\n snt.wavelength=snt.wavelength[minsalt:maxsalt+1]\n snt.flux=snt.flux[minsalt:maxsalt+1]\n igal=np.interp(snt.wavelength, galt.wavelength, galt.flux)\n \n #foflux=fake observed flux\n foflux=(snt.flux*params[0] + igal*params[1]) \n \n if err:\n foflux = signal_noise**2 * foflux\n foflux = np.random.poisson(foflux)\n fofluxerr=foflux**0.5\n \n# if err:\n# foflux = foflux + np.random.normal(0.0, 0.1, foflux.shape)\n# fofluxerr=0.1*foflux\n \n if not err:\n fofluxerr=0.1*foflux\n \n fout=open(outfile, 'w')\n for i in range(len(snt.wavelength)):\n fout.write('%f %f %f\\n' % (snt.wavelength[i], foflux[i], fofluxerr[i]))\n fout.close()\n \n return fout","def to_nii(self, outbase, spirec='spirec', saveInOut=False):\n if self.image_data is None:\n self.recon(spirec)\n\n image_tlhc = np.array([self.header.image.tlhc_R, self.header.image.tlhc_A, self.header.image.tlhc_S])\n image_trhc = np.array([self.header.image.trhc_R, self.header.image.trhc_A, self.header.image.trhc_S])\n image_brhc = np.array([self.header.image.brhc_R, self.header.image.brhc_A, self.header.image.brhc_S])\n #image_cent = np.array([self.header.image.ctr_R, self.header.image.ctr_A, self.header.image.ctr_S])\n\n row_vec = (image_trhc-image_tlhc)/np.sqrt(np.dot(image_trhc-image_tlhc, image_trhc-image_tlhc))\n col_vec = -(image_trhc-image_brhc)/np.sqrt(np.dot(image_trhc-image_brhc, image_trhc-image_brhc))\n # The DICOM standard defines these two unit vectors in an LPS coordinate frame, but we'll\n # need RAS (+x is right, +y is anterior, +z is superior) for NIFTI. So, we compute them\n # such that row_vec points to the right and col_vec points up.\n # Not sure if we need to negate the slice_norm. From the NIFTI-1 header:\n # The third column of R will be either the cross-product of the first 2 columns or\n # its negative. It is possible to infer the sign of the 3rd column by examining\n # the coordinates in DICOM attribute (0020,0032) \"Image Position (Patient)\" for\n # successive slices. However, this method occasionally fails for reasons that I\n # (RW Cox) do not understand.\n\n # can also get slice_norm from: slice_norm = np.cross(row_vec, col_vec)\n slice_norm = np.array([self.header.image.norm_R, self.header.image.norm_A, self.header.image.norm_S])\n slice_fov = np.abs(self.header.series.start_loc - self.header.series.end_loc)\n\n # This is either the first slice tlhc (image_tlhc) or the last slice tlhc. How to decide?\n # And is it related to wheather I have to negate the slice_norm?\n # Tuned this empirically by comparing spiral and EPI data with the sam Rx.\n # Everything seems reasonable, except the test for axial orientation (start_ras==S|I).\n # I have no idea why I need that! But the flipping only seems necessary for axials, not\n # coronals or the few obliques I've tested.\n # FIXME: haven't tested sagittals! (to test for spiral: 'sprt' in self.psd_name.lower())\n if (self.header.series.start_ras=='S' or self.header.series.start_ras=='I') and self.header.series.start_loc > self.header.series.end_loc:\n pos = image_tlhc - slice_norm*slice_fov\n # FIXME: since we are reversing the slice order here, should we change the slice_order field below?\n self.image_data = self.image_data[:,:,::-1,]\n if self.fm_data is not None:\n self.fm_data = self.fm_data[:,:,::-1,]\n else:\n pos = image_tlhc\n\n if self.num_bands > 1:\n pos = pos - slice_norm * self.band_spacing_mm * (self.num_bands - 1.0) / 2.0\n\n qto_xyz = np.zeros((4,4))\n qto_xyz[0,0] = row_vec[0]\n qto_xyz[0,1] = col_vec[0]\n qto_xyz[0,2] = slice_norm[0]\n\n qto_xyz[1,0] = row_vec[1]\n qto_xyz[1,1] = col_vec[1]\n qto_xyz[1,2] = slice_norm[1]\n\n qto_xyz[2,0] = row_vec[2]\n qto_xyz[2,1] = col_vec[2]\n qto_xyz[2,2] = slice_norm[2]\n\n qto_xyz[:,3] = np.append(pos, 1).T\n qto_xyz[0:3,0:3] = np.dot(qto_xyz[0:3,0:3], np.diag(self.mm_per_vox))\n\n nii_header = nibabel.Nifti1Header()\n nii_header.set_xyzt_units('mm', 'sec')\n nii_header.set_qform(qto_xyz, 'scanner')\n nii_header.set_sform(qto_xyz, 'scanner')\n\n nii_header['slice_start'] = 0\n nii_header['slice_end'] = self.num_slices - 1\n # nifti slice order codes: 0 = unknown, 1 = sequential incrementing, 2 = seq. dec., 3 = alternating inc., 4 = alt. dec.\n slice_order = 0\n nii_header['slice_duration'] = self.tr * 1000 / self.num_slices\n # FIXME: check that this is correct.\n if self.header.series.se_sortorder == 0:\n slice_order = 1 # or 2?\n elif self.header.series.se_sortorder == 1:\n slice_order = 3 # or 4?\n nii_header['slice_code'] = slice_order\n\n # Note: the freq/phase dir isn't meaningful for spiral trajectories.\n if self.header.image.freq_dir==1:\n nii_header.set_dim_info(freq=1, phase=0, slice=2)\n else:\n nii_header.set_dim_info(freq=0, phase=1, slice=2)\n\n # FIXME: There must be a cleaner way to set the TR! Maybe bug Matthew about it.\n nii_header.structarr['pixdim'][4] = self.tr\n nii_header.set_slice_duration(nii_header.structarr['pixdim'][4] / self.num_slices)\n nii_header.structarr['cal_max'] = self.image_data.max()\n nii_header.structarr['cal_min'] = self.image_data.min()\n\n if self.num_echoes == 1:\n nifti = nibabel.Nifti1Image(self.image_data, None, nii_header)\n nibabel.save(nifti, outbase + '.nii.gz')\n elif self.num_echoes == 2:\n if saveInOut:\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,0], None, nii_header)\n nibabel.save(nifti, outbase + '_in.nii.gz')\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,1], None, nii_header)\n nibabel.save(nifti, outbase + '_out.nii.gz')\n # FIXME: Do a more robust test for spiralio!\n # Assume spiralio, so do a weighted average of the two echos.\n # FIXME: should do a quick motion correction here\n w_in = np.mean(self.image_data[:,:,:,:,0], 3)\n w_out = np.mean(self.image_data[:,:,:,:,1], 3)\n inout_sum = w_in + w_out\n w_in = w_in / inout_sum\n w_out = w_out / inout_sum\n avg = np.zeros(self.image_data.shape[0:4])\n for tp in range(self.image_data.shape[3]):\n avg[:,:,:,tp] = w_in*self.image_data[:,:,:,tp,0] + w_out*self.image_data[:,:,:,tp,1]\n nifti = nibabel.Nifti1Image(avg, None, nii_header)\n nibabel.save(nifti, outbase + '.nii.gz')\n else:\n for echo in range(self.num_echoes):\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,echo], None, nii_header)\n nibabel.save(nifti, outbase + '_echo%02d.nii.gz' % echo)\n\n if self.fm_data is not None:\n nii_header.structarr['cal_max'] = self.fm_data.max()\n nii_header.structarr['cal_min'] = self.fm_data.min()\n nifti = nibabel.Nifti1Image(self.fm_data, None, nii_header)\n nibabel.save(nifti, outbase + '_B0.nii.gz')","def plotTI():\n min_dl = dlam[dlam != 0].min()\n S = int(0.4/min_dl)\n fig = pl.figure(figsize = (8,6))\n ax = fig.add_subplot(1,1,1)\n ax.spines['bottom'].set_position('zero')\n ax.spines['top'].set_color('none')\n ax.spines['right'].set_color('none')\n ax.xaxis.set_ticks_position('bottom')\n ax.yaxis.set_ticks_position('left')\n\n for k, spine in ax.spines.items():\n spine.set_zorder(12.2)\n\n xs, ndx, dx = [0], 0, 0.001\n colors = ['r', 'g', '#7F38EC', '#9F000F', 'b', 'y']\n min_y, max_y = 0, 0\n\n lines = tuple()\n ## lv_names2 = [r'$Coulomb$', r'$vdWaals$'] ## for the paper\n lv_names2 = []\n for j in range(n_components):\n y = ave_dhdl[:,j]\n if not (y == 0).all():\n lv_names2.append(r'$%s$' % P.lv_names[j].capitalize())\n\n for j in range(n_components):\n\n y = ave_dhdl[:,j]\n if not (y == 0).all():\n\n # Get the coordinates.\n lj = lchange[:,j]\n x = lv[:,j][lj]\n y = y[lj]/P.beta_report\n\n if 'TI' in P.methods:\n # Plot the TI integration area.\n ss = 'TI'\n for i in range(len(x)-1):\n min_y = min(y.min(), min_y)\n max_y = max(y.max(), max_y)\n #pl.plot(x,y)\n if i%2==0:\n pl.fill_between(x[i:i+2]+ndx, 0, y[i:i+2], color=colors[ndx], alpha=1.0)\n else:\n pl.fill_between(x[i:i+2]+ndx, 0, y[i:i+2], color=colors[ndx], alpha=0.5)\n xlegend = [-100*wnum for wnum in range(len(lv_names2))]\n pl.plot(xlegend, [0*wnum for wnum in xlegend], ls='-', color=colors[ndx], label=lv_names2[ndx]) ## for the paper\n\n if 'TI-CUBIC' in P.methods and not cubspl[j]==0:\n # Plot the TI-CUBIC interpolation curve.\n ss += ' and TI-CUBIC'\n xnew = numpy.arange(0, 1+dx, dx)\n ynew = cubspl[j].interpolate(y, xnew)\n min_y = min(ynew.min(), min_y)\n max_y = max(ynew.max(), max_y)\n pl.plot(xnew+ndx, ynew, color='#B6B6B4', ls ='-', solid_capstyle='round', lw=3.0)\n\n else:\n # Plot the TI-CUBIC integration area.\n ss = 'TI-CUBIC'\n for i in range(len(x)-1):\n xnew = numpy.arange(x[i], x[i+1]+dx, dx)\n ynew = cubspl[j].interpolate(y, xnew)\n ynew[0], ynew[-1] = y[i], y[i+1]\n min_y = min(ynew.min(), min_y)\n max_y = max(ynew.max(), max_y)\n if i%2==0:\n pl.fill_between(xnew+ndx, 0, ynew, color=colors[ndx], alpha=1.0)\n else:\n pl.fill_between(xnew+ndx, 0, ynew, color=colors[ndx], alpha=0.5)\n\n # Store the abscissa values and update the subplot index.\n xs += (x+ndx).tolist()[1:]\n ndx += 1\n\n # Make sure the tick labels are not overcrowded.\n xs = numpy.array(xs)\n dl_mat = numpy.array([xs-i for i in xs])\n ri = range(len(xs))\n\n def getInd(r=ri, z=[0]):\n primo = r[0]\n min_dl=ndx*0.02*2**(primo>10)\n if dl_mat[primo].max()<min_dl:\n return z\n for i in r:\n for j in range(len(xs)):\n if dl_mat[i,j]>min_dl:\n z.append(j)\n return getInd(ri[j:], z)\n\n xt = [i if (i in getInd()) else '' for i in range(K)]\n pl.xticks(xs[1:], xt[1:], fontsize=10)\n pl.yticks(fontsize=10)\n #ax = pl.gca()\n #for label in ax.get_xticklabels():\n # label.set_bbox(dict(fc='w', ec='None', alpha=0.5))\n\n # Remove the abscissa ticks and set up the axes limits.\n for tick in ax.get_xticklines():\n tick.set_visible(False)\n pl.xlim(0, ndx)\n min_y *= 1.01\n max_y *= 1.01\n pl.ylim(min_y, max_y)\n\n for i,j in zip(xs[1:], xt[1:]):\n pl.annotate(('%.2f' % (i-1.0 if i>1.0 else i) if not j=='' else ''), xy=(i, 0), xytext=(i, 0.01), size=10, rotation=90, textcoords=('data', 'axes fraction'), va='bottom', ha='center', color='#151B54')\n if ndx>1:\n lenticks = len(ax.get_ymajorticklabels()) - 1\n if min_y<0: lenticks -= 1\n if lenticks < 5:\n from matplotlib.ticker import AutoMinorLocator as AML\n ax.yaxis.set_minor_locator(AML())\n pl.grid(which='both', color='w', lw=0.25, axis='y', zorder=12)\n pl.ylabel(r'$\\mathrm{\\langle{\\frac{ \\partial U } { \\partial \\lambda }}\\rangle_{\\lambda}\\/%s}$' % P.units, fontsize=20, color='#151B54')\n pl.annotate('$\\mathit{\\lambda}$', xy=(0, 0), xytext=(0.5, -0.05), size=18, textcoords='axes fraction', va='top', ha='center', color='#151B54')\n if not P.software.title()=='Sire':\n lege = ax.legend(prop=FP(size=14), frameon=False, loc=1)\n for l in lege.legendHandles:\n l.set_linewidth(10)\n pl.savefig(os.path.join(P.output_directory, 'dhdl_TI.pdf'))\n pl.close(fig)\n return","def do(sts):\n sts = sts.replace(tzinfo=pytz.timezone(\"UTC\"))\n\n ets = sts + datetime.timedelta(days=1)\n\n interval = datetime.timedelta(hours=3)\n\n now = sts\n while now < ets:\n archivefn = now.strftime((\"/mesonet/ARCHIVE/data/%Y/%m/%d/model/\"\n \"NARR/rad_%Y%m%d%H%M.nc\"))\n if os.path.isfile(archivefn):\n now += interval\n continue\n uri = now.strftime((\"http://nomads.ncdc.noaa.gov/thredds/ncss/\"\n \"grid/narr-a/%Y%m/%Y%m%d/\"\n \"narr-a_221_%Y%m%d_%H00_000.grb?\"\n \"var=Downward_shortwave_radiation_flux&spatial=all\"\n \"&temporal=all\"))\n\n try:\n req = urllib2.Request(uri)\n data = urllib2.urlopen(req).read()\n except:\n print 'NARR Download failed for: %s' % (uri,)\n sys.exit()\n\n tmpfn = tempfile.mktemp()\n o = open(tmpfn, 'w')\n o.write(data)\n o.close()\n\n cmd = (\"/home/ldm/bin/pqinsert -p 'data a %s bogus \"\n \"model/NARR/rad_%s.nc nc' %s\"\n ) % (now.strftime(\"%Y%m%d%H%M\"),\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(cmd, shell=True)\n\n os.remove(tmpfn)\n now += interval","def save_grtrans_image(grt_obj):\n I_im = grt_obj.ivals[:,0,0].reshape(npix,npix).flatten()\n Q_im = grt_obj.ivals[:,1,0].reshape(npix,npix).flatten()\n U_im = grt_obj.ivals[:,2,0].reshape(npix,npix).flatten()\n V_im = grt_obj.ivals[:,3,0].reshape(npix,npix).flatten()\n\n # convert to Tb\n factor = 3.254e13/(RF**2 * psize_rad**2)\n I_im *= factor\n Q_im *= factor\n U_im *= factor\n V_im *= factor\n\n x = np.array([[i for i in range(npix)] for j in range(npix)]).flatten()\n y = np.array([[j for i in range(npix)] for j in range(npix)]).flatten()\n\n x -= npix/2\n y -= npix/2\n x = x*psize_uas\n y = y*psize_uas\n\n outdat = np.vstack((x.T,y.T,I_im.T,Q_im.T,U_im.T,V_im.T)).T\n np.savetxt('../rrjet_and_riaf/'+FNAME,outdat)\n #np.savetxt('../rrjet_and_riaf/grtrans_jet_compare_positron_noconv.txt',outdat)\n return","def store_individual_noises(src_file: H5File) -> None:\n paths = rawnav.pump_group_paths(src_file)\n for path in paths:\n par_path = path + '/par'\n perp_path = path + '/perp'\n ref_path = path + '/ref'\n points = len(src_file[perp_path])\n for channel in [perp_path, par_path, ref_path]:\n signal_data = np.empty(points, dtype=np.float64)\n src_file[channel].read_direct(signal_data)\n src_file[channel].attrs['noise'] = noise(signal_data)\n return","def ns_analysis():\r\n\r\n global params\r\n n = params['n']\r\n level = params['level']\r\n nsmin = params['nsmin']\r\n nsmax = params['nsmax']\r\n nsstep = params['nsstep']\r\n bf = params['bf']\r\n po = params['po']\r\n pe = params['pe']\r\n pf = params['pf']\r\n ns_dir = create_dir(TIMES_PATH + 'ns')\r\n filename = datetime.datetime.now().strftime(DATE_FORMAT) + '.csv'\r\n with open(ns_dir + filename, 'wb') as csvfile:\r\n fw = csv.writer(csvfile)\r\n fw.writerow(('lv', 'ns', 'nt', 'no', 'ne', 'nf',\r\n 'method1', 'method2', 'method3_1', 'method3_2'))\r\n ns = nsmin\r\n while ns <= nsmax:\r\n nt = int(round(bf * ns))\r\n no = max(int(round(po * nt)), 1)\r\n ne = max(int(round(pe * no)), 1)\r\n nf = max(int(round(pf * nt)), 1)\r\n times = [0] * 4\r\n for i in range(n):\r\n times = map(sum, zip(times, get_times(level, ns, nt, no, ne, nf)))\r\n show_progress('ns', ns, nsmin, nsmax, nsstep, i, n)\r\n times = map(lambda x: float(x) / n, times)\r\n fw.writerow((level, ns, nt, no, ne, nf) + tuple(times))\r\n ns += nsstep\r\n csvfile.close()","def do_SIR(self, t_max=200, dt=1.):\n dt = float(dt)\n\n g = Graph()\n\n for node in ['I', 'C', 'R', 'H', 'B', 'U', 'D']:\n g.add_node(node, 0)\n\n g.set_node('I', self.N_init)\n\n # cumulative time series\n I = [g.get_node_value('I')] # noqa Infected\n C = [g.get_node_value('C')] # Confirmed\n R = [g.get_node_value('R')] # Recovered\n\n ts = [0.] # time series\n nms = ['prob', 'lag']\n\n # En este modelo todos los infectados se confirman a los 10\n # dias y se curan a los 20 dias de confirmados\n T_IC = int(self.t_incubation / dt)\n T_CR = 20\n f_IC = 1.\n f_CR = 1.\n\n g.add_edge('I', 'I', nms, [self.R, 0])\n g.add_edge('I', 'C', nms, [f_IC, T_IC])\n g.add_edge('C', 'R', nms, [f_CR, T_CR])\n\n t, time_steps = 0., 0\n while t < t_max:\n\n time_steps = time_steps + 1\n\n t = t + dt\n ts.append(t)\n\n # (( I ))\n prob_II = g.get_edge('I', 'I', 'prob')\n\n prob_IC = g.get_edge('I', 'C', 'prob')\n lag_IC = g.get_edge('I', 'C', 'lag')\n update_IC = I[-lag_IC] if lag_IC < len(I) else 0.\n\n n_I = (\n min(I[-1] + I[-1] * prob_II * dt, self.population) - # noqa\n update_IC * prob_IC * dt)\n n_I = max(n_I, 0)\n\n I.append(n_I)\n\n # (( C ))\n prob_CR = g.get_edge('C', 'R', 'prob')\n lag_CR = g.get_edge('C', 'R', 'lag')\n update_CR = C[-lag_CR] if lag_CR < len(C) else 0.\n\n n_C = (\n min(C[-1] + update_IC * prob_IC * dt, self.population) - # noqa\n update_CR * prob_CR * dt)\n n_C = max(n_C, 0)\n C.append(n_C)\n\n # (( R ))\n # recuperados nuevos\n n_R = min(R[-1] + update_CR * prob_CR * dt, self.population)\n n_R = max(n_R, 0)\n R.append(n_R)\n\n df = pd.DataFrame(\n {'ts': ts, 'I': I, 'C': C, 'R': R}).set_index(\"ts\")\n\n extra = attr.asdict(self)\n extra[\"model_name\"] = \"SIR\"\n return ModelResultFrame(df=df, extra=extra)","def save_nii(img_path, data, affine, header):\n nimg = nib.Nifti1Image(data, affine=affine, header=header)\n nimg.to_filename(img_path)","def save(self, rs, ths, xs, ys, vals, file=None):\n\n\t\tif file is None:\n\t\t\tfile = 'PR_c%d_r%d_th%d.h5' % (self.cbins, self.rbins, self.thbins)\n\n\t\twith File(file, 'w') as file:\n\t\t\tfile.create_dataset('cbins' , data=self.cbins)\n\t\t\tfile.create_dataset('rbins' , data=self.rbins)\n\t\t\tfile.create_dataset('thbins', data=self.thbins)\n\t\t\tfile.create_dataset('rs' , data=rs)\n\t\t\tfile.create_dataset('ths' , data=ths)\n\t\t\tfile.create_dataset('xs' , data=xs)\n\t\t\tfile.create_dataset('ys' , data=ys)\n\t\t\tfile.create_dataset('vals' , data=vals)","def astrometryScan(self, plot=False, writeOut=False, overwrite=False,\n store_intermediate=False, max_err_um = .5,\n max_GD_um=None, sigma_clipping=10.0, Nmax=None,\n min_snr=4):\n et = []\n t = [] # time at maximum\n snr = [] # SNR at max\n if Nmax==None:\n Nmax = self.scan_nscans\n for k in range(np.minimum(self.scan_nscans, int(Nmax))):\n tmp = self.scanGetTimeMaxEnv(k)\n # time of maximum:\n t.append( tmp[0])\n # err bar in t:\n et.append(10e-6)\n snr.append(tmp[1])\n\n t = np.array(t)\n et = np.array(et)\n snr = np.array(snr)\n\n # get FTK status around the time of maximum\n ftk_status = []\n for tau in t:\n w = np.where(np.abs(self.raw['OPDC'].data.field('TIME')-tau)<1e5)\n tmp = self.raw['OPDC'].data.field('STATE')[w].mean()\n ftk_status.append(tmp)\n ftk_status = np.array(ftk_status)\n\n w = np.where((ftk_status>=5.0))\n print ' useful data: %d/%d' % ( len(w[0]), len(t))\n t = t[w]\n et = et[w]\n snr = snr[w]\n ftk_status=ftk_status[w]\n\n # FSU which is tracking\n fsu_opdc = self.getKeyword('DEL FT SENSOR')\n\n if self.insmode=='SWAPPED' and \\\n self.getKeyword('DEL FT SENSOR')=='FSUB':\n print \" -> I suspect a wrong FSU config in \"+\\\n \"the header based on the INS.MODE\"\n if fsu_opdc == 'FSUB':\n fsu_dopdc = 'FSUA'\n else:\n fsu_dopdc = 'FSUB'\n\n ### group delay dOPDC\n GDdopdc = lambda x: np.interp(x,\n self.raw['IMAGING_DATA_'+fsu_dopdc].data.field('TIME'),\n self.raw['IMAGING_DATA_'+fsu_dopdc].data.field('GD'))\n\n ### group delay OPDC\n GDopdc = lambda x: np.interp(x,\n self.raw['IMAGING_DATA_'+fsu_opdc].data.field('TIME'),\n self.raw['IMAGING_DATA_'+fsu_opdc].data.field('GD'))\n\n ### primet A-B\n deltal = lambda x: np.interp(x,\n self.raw['METROLOGY_DATA'].data.field('TIME'),\n self.raw['METROLOGY_DATA'].data.field('DELTAL'))\n\n ### astrometric observable\n deltal_2 = deltal(t) + GDopdc(t) - GDdopdc(t)\n\n ### error bar = agreement between AC and BD\n err = 0.5*np.abs(deltal(t+et) + GDopdc(t+et) -\n deltal(t-et) - GDopdc(t-et) )\n\n ### select points with good agreement\n if not min_snr is None:\n w = np.where(snr > min_snr)\n print 'error clipping:', len(err), '->', len(w[0])\n else:\n w = range(len(err))\n\n deltal_2 = deltal_2[w]\n t = t[w]\n err = err[w]\n snr = snr[w]\n mjd = t*1e-6/(24*3600.0)+self.mjd_start\n\n if not sigma_clipping is None:\n n_before = len(mjd)\n for k in range(10):\n c = np.polyfit(mjd-mjd.mean(), deltal_2,1)\n # residuals\n res = deltal_2 - np.polyval(c,mjd-mjd.mean())\n s = res.argsort()\n pseudoSTD = 0.5*(res[s[int(.84*len(s))]] -\n res[s[int(.16*len(s))]])\n print k, 'STD=', pseudoSTD\n werr = np.where(np.abs(res)<sigma_clipping*pseudoSTD)\n if len(werr[0]) == len(err):\n break\n deltal_2 = deltal_2[werr]\n t = t[werr]\n err = err[werr]\n mjd = mjd[werr]\n snr = snr[werr]\n err = np.ones(len(err))*pseudoSTD/np.sqrt(snr-min_snr+1)\n print 'sigma clipping:', n_before, '->', len(mjd)\n\n if writeOut:\n hdu = pyfits.PrimaryHDU(None)\n # copy original header\n for i in self.raw[0].header.items():\n if len(i[0])>8:\n hie = 'HIERARCH '\n else:\n hie = ''\n if len(i)==2:\n hdu.header.update(hie+i[0], i[1])\n elif len(i)==3:\n hdu.header.update(hie+i[0], i[1], i[2])\n # prepare data for a binary table\n cols=[]\n cols.append(pyfits.Column(name='MJD', format='F12.5',\n array=mjd))\n cols.append(pyfits.Column(name='D_AL', format='E', unit='m',\n array=deltal_2))\n cols.append(pyfits.Column(name='D_AL_ERR', format='E', unit='m',\n array=err))\n hducols = pyfits.ColDefs(cols)\n hdub = pyfits.new_table(hducols)\n hdub.header.update('EXTNAME', 'ASTROMETRY_BINNED', '')\n\n # combine all HDUs\n thdulist = pyfits.HDUList([hdu, hdub])\n # write file\n outfile = os.path.join(\n self.dirname, self.filename.split('.')[0]+'_RED.fits')\n if overwrite and os.path.exists(outfile):\n os.remove(outfile)\n print 'writting ->', outfile\n thdulist.writeto(outfile)\n thdulist=[]\n return outfile\n\n if plot:\n plt.plot(13)\n plt.clf()\n ax1 = plt.subplot(211)\n plt.plot(mjd, deltal_2, '.b')\n if not sigma_clipping is None:\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean()), 'b-')\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean())+pseudoSTD, 'y-')\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean())-pseudoSTD, 'y-')\n\n plt.ylabel('position of max env')\n plt.subplot(212, sharex=ax1)\n plt.plot(mjd, snr, '.k')\n #plt.yscale('log')\n plt.ylabel('SNR')\n return","def findROICentres(smt_file): #{\n rois = []\n ary = None\n state = 0\n roi_centres = { \n 'head_ct': None,\n 'neck_cb': None,\n 'shoulder_l': None,\n 'shoulder_r': None,\n 'elbow_l': None,\n 'elbow_r': None,\n 'pelvis_c': None,\n 'wrist_l': None,\n 'wrist_r': None,\n 'hip_l': None,\n 'hip_r': None,\n 'knee_l': None,\n 'knee_r': None,\n 'ankle_l': None,\n 'ankle_r': None,\n 'toes_e': None}\n\n vrbMsg(1, 'findROICentres() smt_file = ' + smt_file)\n # Read the smoothed Woolz object and create a NumPy array from it\n err_num, smt_obj = readWoolzObj(smt_file)\n if(not bool(err_num)): #{\n\t err_num, org, ary = wlzObjToNP(smt_obj)\n #}\n sz = np.shape(ary)\n vrbMsg(5, 'findROICentres() object size = ' + str(sz))\n # Work down the scan finding coordinates, ordering has dependency\n # but this is checked in the individual functions\n if((sz[0] >= img_size_min) and (sz[1] >= img_size_min)): #{\n findProfileHeadCT(roi_centres, ary)\n findProfileToes(roi_centres, ary)\n #}\n if(bool(roi_centres['toes_e']) and bool(roi_centres['head_ct'])): #{\n findProfileShoulders(roi_centres, ary)\n findProfilePelvis(roi_centres, ary)\n findProfileHip(roi_centres, ary)\n findProfileElbow(roi_centres, ary)\n findProfileWrist(roi_centres, ary)\n findProfileKnee(roi_centres, ary)\n findProfileAnkle(roi_centres, ary)\n rois = roi_centres\n for cen in rois: #{\n # numpy gives int64 which is not always handled (eg by json) so convert\n pos = rois[cen]\n if(not (pos is None)): #{\n rois[cen] = [int(pos[0]), int(pos[1])]\n #}\n #}\n #}\n vrbMsg(1, 'findROICentres() rois = ' + str(rois))\n return rois","def _ion_densities_datafiles(self):\n ne = self.ne_in\n nD = self.ni_in[0,:]\n nC = (ne-nD)/6.\n print(\"nC/nD: \"+str(np.mean(nC/nD)*100.)+\" %\")\n self.ni_in[0,:] = nD\n self.ni_in[1,:] = nC","def G_SNR(T2s, TEs):\r\n N = len(TEs)\r\n TEs = TEs-TEs[0]\r\n \r\n return N / np.sqrt(np.sum(np.exp(TEs / T2s)**2))","def write_nifti(self, output_path):\n nib.save(self.niftiImage, output_path)\n print('Image saved at: {}'.format(output_path))"],"string":"[\n \"def calculate_SNR(snid: int, photo_data: pd.DataFrame, \\n head_data: pd.DataFrame, code_zenodo: int, \\n snana_file_index: int, code_snana: int):\\n \\n types_names = {90: 'Ia', 62: 'Ibc', 42: 'II', 67: '91bg', 52: 'Iax',\\n 64:'KN', 95: 'SLSN', 994: 'PISN', 992: 'ILOT', \\n 993: 'CaRT', 15: 'TDE', 88: 'AGN', 92: 'RRL', \\n 65: 'M-dw', 16: 'EB', 53: 'Mira', 991: 'BMicroL',\\n 6: 'MicroL'}\\n \\n # LSST filters\\n filters = [b'u ', b'g ', b'r ', b'i ', b'z ', b'Y ']\\n\\n flag_id_photo = photo_data['SNID'] == snid\\n\\n flux = photo_data[flag_id_photo]['FLUXCAL'].values\\n fluxerr = photo_data[flag_id_photo]['FLUXCALERR'].values\\n\\n SNR_all = flux/fluxerr\\n \\n indx = np.random.choice(range(flux.shape[0]))\\n\\n flag_id_head = head_data['SNID'].values == snid\\n redshift = head_data['SIM_REDSHIFT_CMB'].values[flag_id_head][0]\\n \\n # store values\\n line = [snid, snana_file_index, code_zenodo, code_snana, \\n types_names[code_zenodo], redshift]\\n \\n for fil in filters: \\n line.append(head_data['SIM_PEAKMAG_' + str(fil)[2]].values[flag_id_head][0])\\n \\n # calculate SNR statistics \\n for f in [np.mean, max, np.std]: \\n for fil in filters: \\n \\n flag_fil = photo_data[flag_id_photo]['FLT'] == fil\\n neg_flag = flux > -100\\n flag2 = np.logical_and(flag_fil, neg_flag)\\n \\n if sum(flag2) > 0:\\n SNR_fil = SNR_all[flag2] \\n line.append(f(SNR_fil))\\n \\n if len(line) == 30:\\n return line\\n else:\\n return []\",\n \"def save(self, compute_snrs=True):\\n if not self.save_mode:\\n raise RuntimeError('Need to enable save mode to save')\\n\\n fn = os.path.join(self.output_dir,\\n 'data_' + time_string() + '.h5')\\n save_dict(fn=fn, d=self.data)\\n if compute_snrs:\\n from src.analyzer import DataAnalyzer\\n da = DataAnalyzer.fromfilename(fn)\\n da.snr_list()\\n return fn\",\n \"def do_SEIR(self, t_max=200, dt=1.):\\n dt = float(dt)\\n g = Graph()\\n\\n for node in ['S', 'E', 'I', 'R']:\\n g.add_node(node, 0)\\n\\n g.set_node('S', self.population)\\n g.set_node('E', 0)\\n g.set_node('I', self.N_init)\\n g.set_node('R', 0)\\n\\n # cumulative time series\\n S = [g.get_node_value('S')] # Susceptible\\n E = [g.get_node_value('E')] # Exposed\\n I = [g.get_node_value('I')] # noqa Infected\\n R = [g.get_node_value('R')] # Recovered\\n\\n ts = [0.] # time series\\n nms = ['prob', 'lag']\\n\\n g.add_edge('S', 'S', nms, [0.1, 2])\\n g.add_edge('E', 'E', nms, [0.4, 21])\\n g.add_edge('I', 'I', nms, [0.1, 2])\\n\\n g.add_edge('S', 'E', nms, [1.2, 1])\\n g.add_edge('E', 'I', nms, [0.1, 14]) # [, tiempo de incubacion]\\n g.add_edge('I', 'R', nms, [0.7, 2]) # [, tiempo de recuperacion]\\n\\n t, time_steps = 0., 0\\n while t < t_max:\\n\\n time_steps = time_steps + 1\\n\\n t = t + dt\\n ts.append(t)\\n\\n # (( S ))\\n prob_SS = g.get_edge('S', 'S', 'prob') # beta\\n\\n dS = - S[-1] * (I[-1] / self.population) * prob_SS\\n\\n # n_S = min(S[-1] + min(dS * dt, 0), self.population)\\n n_S = S[-1] + dS * dt\\n\\n # (( E ))\\n prob_EE = g.get_edge('E', 'E', 'prob')\\n dE = - dS - prob_EE * E[-1]\\n\\n # n_E = min(E[-1] + max(dE * dt, 0), self.population)\\n n_E = E[-1] + dE * dt\\n\\n # (( I ))\\n prob_EI = g.get_edge('E', 'I', 'prob')\\n lag_EI = g.get_edge('E', 'I', 'lag')\\n update_EI = E[-lag_EI] if lag_EI < len(E) else 0.\\n\\n prob_IR = g.get_edge('I', 'R', 'prob')\\n lag_IR = g.get_edge('I', 'R', 'lag')\\n update_IR = I[-lag_IR] if lag_IR < len(I) else 0.\\n\\n prob_II = g.get_edge('I', 'I', 'prob')\\n\\n dI = prob_EI * update_EI - prob_IR * update_IR\\n dI = -dI # porque ????\\n n_I = min(I[-1] + dI * dt, self.population)\\n\\n # (( R ))\\n prob_II = g.get_edge('I', 'I', 'prob')\\n dR = prob_II * I[-1]\\n n_R = min(R[-1] + max(dR * dt, 0), self.population)\\n\\n S.append(n_S)\\n E.append(n_E)\\n I.append(n_I)\\n R.append(n_R)\\n\\n df = pd.DataFrame(\\n {'ts': ts, 'S': S, 'E': E, 'I': I, 'R': R}).set_index(\\\"ts\\\")\\n\\n extra = attr.asdict(self)\\n extra[\\\"model_name\\\"] = \\\"SEIR\\\"\\n return ModelResultFrame(df=df, extra=extra)\",\n \"def run(self):\\r\\n #print 'WriteFITS_IDI.run'\\r\\n\\r\\n # construct the name of the file\\r\\n readfits = self.previous_results['readfits']\\r\\n obs_date = readfits['obs date']\\r\\n idifitsfile = '%s.idi.fits' % obs_date\\r\\n\\r\\n configxml = 'firi.xml'\\r\\n\\r\\n # midnight on date to Julian day\\r\\n obs_date_midnight = astro_time.Time('%s-%s-%sT00:00:00' %\\r\\n (obs_date[:4], obs_date[4:6], obs_date[6:8]), format='isot')\\r\\n obs_date_midnight = obs_date_midnight.jd\\r\\n\\r\\n rdate = astro_time.Time(obs_date_midnight, format='jd',\\r\\n out_subfmt='date')\\r\\n rdate = rdate.iso\\r\\n\\r\\n # number of days after midnight at obs start\\r\\n obs_date_time = astro_time.Time('%s-%s-%s:%s:%s' %\\r\\n (obs_date[:4], obs_date[4:6], obs_date[6:11], obs_date[11:13],\\r\\n obs_date[13:]), format='isot')\\r\\n obs_date_time = obs_date_time.jd - obs_date_midnight\\r\\n\\r\\n # get specific items from the results that will be need in\\r\\n # the reduction\\r\\n reduce_interferogram = self.previous_results['reduceinterferogram']\\r\\n data_quality = reduce_interferogram['data_quality']\\r\\n scan_uvspectra = reduce_interferogram['scan_uvspectra']\\r\\n\\r\\n wavenumber = scan_uvspectra[0].wavenumber\\r\\n\\r\\n # construct lists of the values to be stored in each Table column\\r\\n n_uvspectra = max(scan_uvspectra.keys()) + 1\\r\\n mcomplex = 3\\r\\n mstokes = 1\\r\\n mfreq = len(wavenumber)\\r\\n mra = 1\\r\\n mdec = 1\\r\\n\\r\\n uv_data = np.zeros([n_uvspectra, mdec, mra, mfreq, mstokes, mcomplex])\\r\\n u = np.zeros([n_uvspectra])\\r\\n v = np.zeros([n_uvspectra])\\r\\n w = np.zeros([n_uvspectra])\\r\\n dates = np.zeros([n_uvspectra])\\r\\n times = np.zeros([n_uvspectra])\\r\\n baselines = np.zeros([n_uvspectra], dtype=np.int)\\r\\n freqid = np.ones([n_uvspectra], dtype=np.int)\\r\\n\\r\\n for k,val in scan_uvspectra.items():\\r\\n uv_data[k,0,0,:,0,0] = val.spectrum.real\\r\\n uv_data[k,0,0,:,0,1] = val.spectrum.imag\\r\\n uv_data[k,0,0,:,0,2] = np.ones(val.spectrum.real.shape)\\r\\n u[k] = np.mean(val.baseline_x)\\r\\n v[k] = np.mean(val.baseline_y)\\r\\n w[k] = np.mean(val.baseline_z)\\r\\n dates[k] = obs_date_midnight\\r\\n times[k] = obs_date_time + (np.mean(val.time) / (3600 * 24))\\r\\n baselines[k] = 258\\r\\n\\r\\n # external_params is referred to inside config.xml and can be\\r\\n # used to set parameters there\\r\\n light_speed = constants.c.to('m/s').value\\r\\n external_params = {'NCHAN':len(wavenumber),\\r\\n 'RDATE':rdate,\\r\\n 'REF_FREQ':0.0 * 100 * light_speed,\\r\\n 'CHAN_BW':np.abs(wavenumber[1] - wavenumber[0]) * \\\\\\r\\n 100 * light_speed}\\r\\n\\r\\n print \\\"Out: %s\\\\nConfig: %s\\\"%(idifitsfile, configxml)\\r\\n\\r\\n print('\\\\nConfiguring Array geography')\\r\\n print('--------------------------')\\r\\n # Meaningless numbers, hopefully not needed by any CASA method \\r\\n # that we want to use\\r\\n (latitude, longitude, elevation) = ('00:00:00.00', '00:00:00.00', 0)\\r\\n now = datetime.datetime.now()\\r\\n\\r\\n # Make ourselves an Array (pyEphem observer)\\r\\n array_geometry_m = np.array([\\r\\n [0.0, 0.0, 0.0],\\r\\n [0.0, 80.0, 0.0]], dtype = 'float32')\\r\\n beach = Array(lat=latitude, long=longitude, elev=elevation, date=now,\\r\\n antennas=array_geometry_m)\\r\\n\\r\\n print('\\\\nConfiguring phase source')\\r\\n print('--------------------------')\\r\\n # The source is our phase centre for UVW coordinates\\r\\n line = \\\"%s,f,%s,%s,%s,%d\\\" % ('Deep Space', '00:00:00',\\r\\n '00:00:00', '1', 2000)\\r\\n source = ephem.readdb(line)\\r\\n source.compute(beach)\\r\\n print \\\"Name: %s \\\\nRA: %s \\\\nDEC: %s\\\"%(source.name, source.ra, source.dec)\\r\\n\\r\\n # Make a new blank FITS HDU\\r\\n print('\\\\nCreating PRIMARY HDU')\\r\\n print('------------------------------------')\\r\\n hdu = make_primary(config=configxml, external_params=external_params)\\r\\n print repr(hdu.header)\\r\\n\\r\\n # Go through and generate required tables\\r\\n print('\\\\nCreating ARRAY_GEOMETRY')\\r\\n print('------------------------------------')\\r\\n tbl_array_geometry = make_array_geometry(config=configxml, num_rows=2,\\r\\n external_params=external_params)\\r\\n tbl_array_geometry = config_array_geometry(tbl_array_geometry,\\r\\n array_geometry_m)\\r\\n print repr(tbl_array_geometry.header)\\r\\n\\r\\n print('\\\\nCreating FREQUENCY')\\r\\n print('------------------------------------')\\r\\n tbl_frequency = make_frequency(config=configxml, num_rows=1,\\r\\n external_params=external_params)\\r\\n tbl_frequency = config_frequency(tbl_frequency,\\r\\n external_params=external_params)\\r\\n print repr(tbl_frequency.header)\\r\\n\\r\\n print('\\\\nCreating SOURCE')\\r\\n print('------------------------------------')\\r\\n tbl_source = make_source(config=configxml, num_rows=1,\\r\\n external_params=external_params)\\r\\n tbl_source = config_source(tbl_source, source)\\r\\n print repr(tbl_source.header)\\r\\n\\r\\n print('\\\\nCreating ANTENNA')\\r\\n print('------------------------------------')\\r\\n tbl_antenna = make_antenna(config=configxml, num_rows=2,\\r\\n external_params=external_params)\\r\\n tbl_antenna = config_antenna(tbl_antenna)\\r\\n print repr(tbl_antenna.header)\\r\\n\\r\\n print('\\\\nCreating UV_DATA')\\r\\n print('------------------------------------')\\r\\n\\r\\n print 'Data dimensions: %i dumps, %i chans, %i pols, %i data' % (\\r\\n n_uvspectra, mfreq, mstokes, mcomplex)\\r\\n\\r\\n print('Generating blank UV_DATA rows...')\\r\\n tbl_uv_data = make_uv_data(config=configxml, num_rows=n_uvspectra,\\r\\n external_params=external_params)\\r\\n\\r\\n timesorted = np.argsort(times)\\r\\n\\r\\n for k in timesorted:\\r\\n tbl_uv_data.data[k]['FLUX'] = uv_data[k,0,0,:,0,:].ravel()\\r\\n tbl_uv_data.data[k]['UU'] = u[k] / light_speed\\r\\n tbl_uv_data.data[k]['VV'] = v[k] / light_speed\\r\\n tbl_uv_data.data[k]['WW'] = w[k] / light_speed\\r\\n tbl_uv_data.data[k]['BASELINE'] = baselines[k]\\r\\n tbl_uv_data.data[k]['DATE'] = dates[k]\\r\\n tbl_uv_data.data[k]['TIME'] = times[k]\\r\\n tbl_uv_data.data[k]['SOURCE'] = 1\\r\\n tbl_uv_data.data[k]['FREQID'] = 1\\r\\n tbl_uv_data.data[k]['INTTIM'] = 3\\r\\n\\r\\n print repr(tbl_uv_data.header)\\r\\n \\r\\n hdulist = pyfits.HDUList(hdus=\\r\\n [hdu,\\r\\n tbl_array_geometry,\\r\\n tbl_source, \\r\\n tbl_frequency,\\r\\n tbl_antenna,\\r\\n tbl_uv_data])\\r\\n\\r\\n print('Verifying integrity...') \\r\\n hdulist.verify()\\r\\n \\r\\n if(os.path.isfile(idifitsfile)):\\r\\n print('Removing existing file...')\\r\\n os.remove(idifitsfile)\\r\\n print('Writing to file...')\\r\\n hdulist.writeto(idifitsfile)\\r\\n\\r\\n print('Done.')\\r\\n\\r\\n self.result['idifitsfile'] = idifitsfile\\r\\n\\r\\n return self.result\",\n \"def save_nifti(self, path):\\n meta = {'te': self.te, 'tr': self.tr, 'sw': self.sw}\\n if self.sequence_type == 'STEAM':\\n meta['tm'] = self.tm\\n\\n # store real and imaginary components in last 2 dims\\n component_fid = np.stack((np.real(self.fid),np.imag(self.fid)), -2)\\n nifti = nib.Nifti2Image(component_fid, self.transform.get_matrix(), extra=meta)\\n nib.save(nifti, path)\",\n \"def write_seisan(filename, args):\\n bf = BaikalFile(filename)\\n if not bf.valid:\\n print(\\\"Invalid file {}\\\".format(filename))\\n return\\n header = bf.MainHeader\\n # datetime\\n date = datetime.datetime(header[\\\"year\\\"], header[\\\"month\\\"], header[\\\"day\\\"])\\n delta = datetime.timedelta(seconds=header[\\\"to\\\"])\\n dt = date + delta\\n _time = dt.time() # time\\n # make utc datetime\\n utcdatetime = UTCDateTime(date.year, date.month, date.day,\\n _time.hour, _time.minute, _time.second, _time.microsecond, precision=3)\\n bf.traces = bf.traces.astype(np.int32)\\n bf.traces = bf.traces[:3]\\n traces = []\\n for channel, data in zip(CHANNELS, bf.traces):\\n stats = DEFAULT_STATS.copy()\\n stats.update({\\n \\\"station\\\": header['station'].upper()[:3],\\n 'channel': channel,\\n 'sampling_rate': int( 1./header[\\\"dt\\\"] ),\\n \\\"delta\\\": header[\\\"dt\\\"],\\n \\\"npts\\\": data.size,#shape[0]\\n 'starttime': utcdatetime,\\n })\\n # save coordinates\\n stats['gse2'][\\\"lat\\\"] = header['latitude']\\n stats['gse2'][\\\"lon\\\"] = header[\\\"longitude\\\"]\\n trace = Trace(data=data, header=stats)\\n traces.append(trace)\\n # create Stream\\n stream = Stream(traces)\\n #== write seisan\\n # date\\n name = \\\"{year:04}-{month:02}-{day:02}\\\".format(**header)\\n # time\\n name += \\\"-{t.hour:02}-{t.minute:02}\\\".format(t=stats['starttime'])\\n # + station name + Day_of_Year\\n name += \\\"{0}__{1:03}\\\".format(stats[\\\"station\\\"], stats['starttime'].timetuple().tm_yday)\\n print('Writing GSE2 file %s.' % name)\\n writeGSE2(stream, os.path.join(args.outdir, name))\",\n \"def in_situ_tair_snd(sno0, year0=2016, npr_date=-1, ascat_date=-1):\\n if npr_date < 0:\\n npr_date = 100*24*3600 + bxy.get_total_sec('%d0101' % year0)\\n if ascat_date < 0:\\n ascat_date = 100*24*3600 + bxy.get_total_sec('%d0101' % year0)\\n snd_name = \\\"snow\\\"\\n print 'the %d was processing' % sno0\\n sno = str(sno0)\\n tair_name = \\\"Air Temperature Observed (degC)\\\"\\n if sno0 in [2065, 2081]:\\n if year0 == 2016:\\n tair_name = \\\"Air Temperature Average (degC)\\\"\\n # read measurements\\n hr_list = [5, 7, 9, 14, 18, 21]\\n t_air_one_year = read_site.in_situ_series(sno, y=year0, hr=hr_list) # [:, :, 0] temperature at 7:00 (local)\\n # time_above_zero_0 = data_process.zero_find(t_air_one_year[:, :, 0], w=10, th=-0.1) #\\n # time_above_zero_1 = data_process.zero_find(t_air_one_year[:, :, 1], w=10, th=-0.1)\\n # time_above_zero_2 = data_process.zero_find(t_air_one_year[:, :, 3], w=10, th=-0.1)\\n time_above_zero_list = [data_process.zero_find(t_air_one_year[:, :, i], w=10, th=-0.1)\\n for i in range(0, len(hr_list))]\\n date_tuple = bxy.time_getlocaltime(time_above_zero_list, ref_time=[2000, 1, 1, 0], t_source='US/Alaska')\\n t_value, t_date = read_site.read_measurements\\\\\\n (sno, tair_name, np.arange(1, 365), year0=year0, hr=18, t_unit='sec')\\n\\n\\n tair_zero_day2 = data_process.zero_find(np.array([t_date, -t_value]), w=7, th=0) # in unit of sec\\n tair_zero_day1 = data_process.zero_find_gt(np.array([t_date, t_value]), w=7, th=1)\\n air_win = 7 # check days during window shown air temperature gt 0 degC\\n w, w_valid = data_process.n_convolve3(t_value, air_win)\\n air0_index0 = np.where(w>5)\\n for ind0 in air0_index0[0]:\\n if t_date[ind0] > bxy.get_total_sec('%d0307' % year0):\\n tair_zero_day = t_date[ind0] - air_win*24*3600\\n break\\n # check\\n zero_date = bxy.time_getlocaltime([tair_zero_day,tair_zero_day2, npr_date[0], ascat_date[0]],\\n ref_time=[2000, 1, 1, 0], t_source=\\\"US/Alaska\\\")[-2]\\n i_zero = np.where(bxy.time_getlocaltime(t_date, ref_time=[2000, 1, 1, 0],\\n t_source=\\\"US/Alaska\\\")[-2] == zero_date[0])[0][0]\\n t_check = t_value[i_zero - 3: i_zero + 4]\\n air_0, air00 = read_site.read_measurements(sno, tair_name, 366+np.arange(50, 70), hr=18)\\n a_extend = np.array([-3600*24, 3600*24])\\n period0, period1 = np.array(sorted([tair_zero_day, npr_date])) + a_extend, \\\\\\n np.array(sorted([tair_zero_day, ascat_date])) + a_extend\\n snow_value, snow_date = read_site.read_measurements\\\\\\n (sno, snd_name, np.arange(1, 365), year0=year0, hr=0, t_unit='sec')\\n # get the in situ measurements during a period\\n snow2date0 = data_process.measurements_slice(np.array([snow_date, snow_value]),\\n peroid=period0)\\n snow2date1 = data_process.measurements_slice(np.array([snow_date, snow_value]),\\n peroid=period1)\\n air2date0, air2date1 = data_process.measurements_slice(np.array([t_date, t_value]),\\n peroid=period0),\\\\\\n data_process.measurements_slice(np.array([t_date, t_value]),\\n peroid=period1)\\n return tair_zero_day, snow2date0, snow2date1, air2date0, air2date1\",\n \"def plot_seaice_trend(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/trends/SIC/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' SIE trends'\\n\\n\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n\\n if seaice_source == 'nsidc':\\n seaice = seaice * area /250\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = xr.apply_ufunc(scipy.stats.linregress, seaice[seaicename].time.values.astype(float), seaice[seaicename], input_core_dims=[['time'],['time']], vectorize=True, dask='parallelized', output_dtypes=[float]*5, output_core_dims=[[]]*5)\\n if seaice_source =='ecmwf':\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename])\\n \\n seaice_m = seaice_m * 1e9 * 60 * 60 * 24 * 365\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n # seaice_m = seaice_m*area\\n seaice_m = seaice_m.where(seaice_m != 0)\\n # seaice_m = seaice_m.where(seaice_p_value <= 0.05)\\n max_ = seaice_m.max()\\n min_ = seaice_m.min() \\n # max_ = 1\\n divnorm = TwoSlopeNorm(vmin=min_, vcenter=0, vmax=max_)\\n fig = plt.figure(figsize = (5,5))\\n ax = fig.add_subplot(111, projection = ccrs.SouthPolarStereo())\\n # Plotting\\n contor = ax.contourf(seaice_m.x, seaice_m.y, seaice_m, cmap = 'RdBu', levels = 11, norm = divnorm, transform=ccrs.SouthPolarStereo())\\n ax.coastlines()\\n ax.set_axis_off()\\n cbar = plt.colorbar(contor)\\n cbar.set_label('Trend in SIE (km$^2$ yr$^{-1}$)')\\n plt.title(title)\\n plt.savefig(imagefolder + seaicename + '.pdf')\\n plt.show()\",\n \"def plot_seaice_predict(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' SIE prediction'\\n\\n\\n# Loading Seaice Trends\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n seaice = seaice\\n\\n\\n# Index contributions\\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\\n dataset = xr.open_dataset(filename + '.nc')\\n indicies = np.array([i for i in dataset])\\n values = np.array([dataset[i].values for i in dataset])\\n index_data = {}\\n for indexname in indicies[:-1]:\\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \\n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\\n\\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\\n\\n prediction = seaice.copy() * 0\\n for indexname in indicies:\\n if indexname in index_data.keys():\\n prediction += index_data[indexname] * dataset[indexname]\\n else:\\n prediction += dataset[indexname]\\n\\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n # ax2 = ax.twinx()\\n # ax2.plot([],[])\\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'SIE')\\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'Prediction')\\n # ax.set_xlim([min(times),max(times)])\\n\\n lines = ln1 + ln2\\n labels = [line.get_label() for line in lines]\\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\\n\\n\\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89')\\n plt.title(title)\\n\\n plt.savefig(imagefolder + seaicename + '.pdf')\\n plt.show()\",\n \"def plot_index_sic_timeseries(anomlous = False, temporal_resolution = 'monthly', detrend = False, imagefolder = 'images/timeseries/SIC_INDICIES', indexname = 'SAM', n = 5, seaice_source = 'nsidc'):\\n output_folder = 'processed_data/'\\n\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n if detrend:\\n dt = 'detrended'\\n else:\\n dt = 'raw'\\n\\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\\n indicies = xr.open_dataset(output_folder + 'INDICIES/' + filename +'.nc')[indexname]\\n data = indicies.copy()\\n data = data.loc[data.time.dt.year >= 1979]\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{n}_{dt}'\\n\\n seaice = xr.open_dataset(output_folder + 'SIC/' + seaicename +'.nc')\\n\\n\\n times = list(set.intersection(set(seaice.time.values), set(data.time.values)))\\n\\n seaice = seaice_area_mean(seaice.sel(time=times).sortby('time'), 1)\\n data = data.sel(time=times).sortby('time')\\n\\n\\n if seaice_source == 'ecmwf':\\n seaice = xr.open_dataset(output_folder + 'ERA5/SIC/' + seaicename +'.nc')\\n if seaice_source == 'ecmwf':\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename].mean(dim = ('longitude', 'latitude')))\\n if seaice_source == 'nsidc':\\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(mean_seaice.time.values.astype(float), mean_seaice)\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n title += dt + ' '\\n\\n title += temporal_resolution\\n title += f' mean {indexname} and SIC'\\n fig, ax = plt.subplots()\\n ax2 = plt.twinx(ax)\\n ax2.plot([],[])\\n\\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\\n\\n ln1 = ax.plot(data.time, data, label = f'{indexname}', color = '#EA1B10')\\n ax.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\\n if seaice_source == 'ecmwf':\\n ln2 = ax2.plot(seaice.time, seaice[seaicename].mean(dim = ('longitude', 'latitude')), label = 'SIC', color = '#177E89')\\n if seaice_source == 'nsidc':\\n ln2 = ax2.plot(mean_seaice.time, mean_seaice, label = 'SIC', color = '#177E89')\\n ax2.plot(seaice.time, seaice_m * seaice.time.values.astype(float) + seaice_b, color = '#177E89')\\n\\n yabs_max = abs(max(ax.get_ylim(), key=abs))\\n ax.set_ylim(ymin=-yabs_max, ymax=yabs_max)\\n if anomlous or detrend:\\n yabs_max = abs(max(ax2.get_ylim(), key=abs))\\n ax2.set_ylim(ymin=-yabs_max, ymax=yabs_max)\\n\\n # ylabels\\n ax.set_ylabel(f'{indexname}')\\n ax2.set_ylabel(f'Mean SIC')\\n\\n # legend\\n lines = ln1 + ln2\\n labels = [line.get_label() for line in lines]\\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\\n\\n plt.title(title)\\n plt.savefig(imagefolder + f'/SIC_{indexname}_{filename}_{seaice_source}' + '.pdf')\\n plt.show()\",\n \"def snr(mag=20, itime=1., read=24.5, sky=8.43, npix=24., zero=26.44, dark=0.0):\\n # 2009-02-20 14:40 IJC: Initiated\\n \\n star = itime * 10**(0.4*(zero-mag))\\n noise = npix * (itime*(sky+dark)+read**2)\\n\\n return star * (star+noise)**-0.5\",\n \"def writeNoise(self):\\n\\n if (self.noise_file == None or self.noise_file == \\\"\\\"):\\n return\\n ofname = self.noise_file\\n ofh = open(ofname,'w')\\n\\n # these have to be there as long as we've read the FAST file already\\n ## not true: we don't store these in the dict.\\n have_data = False\\n if (\\\"TipRad\\\" in self.fstDict and 'TowerHt' in self.fstDict and 'Twr2Shft' in self.fstDict):\\n tiprad = self.fstDict['TipRad']\\n towerht = self.fstDict['TowerHt']\\n twr2shft = self.fstDict['Twr2Shft']\\n have_data = True\\n\\n for line in self.lines_noise:\\n if (have_data and line.find('Observer location') >= 0):\\n xdist = -1.0 * (tiprad + (towerht + twr2shft))\\n ofh.write('{:.1f} 0.0 0.0'.format(xdist))\\n ofh.write(' (x,y,z) Observer location in tower-base coordinate system. Use -(RotRad+HubHt)\\\\n')\\n else:\\n ofh.write(line)\\n ofh.close()\",\n \"def generate_nrrd(file):\\n for root, dirs, files in os.walk(file):\\n path = root.split(file)\\n if path[1] != \\\"\\\":\\n patient_id = int(path[1].split('/')[1][3:])\\n path = file + \\\"_nrrd\\\" + path[1]\\n # if path.find(\\\"frisk\\\")==-1 & path.find(\\\"M+\\\")==-1 & path.find('T2M')==-1:\\n if (path.find('T2M') != -1 & path.find('frisk') ==\\n -1 & path.find('+') == -1\\n ) or path.find('masks') != -1: # Only T2M or mask can be found\\n os.makedirs(path, exist_ok=True)\\n print(path)\\n Nrrd = ImageCollection(os.path.join(root, \\\"*.tiff\\\"),\\n plugin='tifffile',\\n load_func=convert_to_gray)\\n Nrrd = np.asarray(Nrrd)\\n\\n if get_image_info(patient_id):\\n print(patient_id)\\n (spacings, thickness) = get_image_info(patient_id)\\n thicknesses = [float('nan'), float('nan'), thickness]\\n spacing_direction = np.eye(3)\\n # Note: All header fields are specified in Fortran order,\\n # per the NRRD specification, regardless of the index order. For example,\\n # a C-ordered array with shape (60, 800, 600) would have a sizes field of (600, 800, 60).\\n if len(Nrrd) > 0:\\n header = {\\n 'spacings': spacings,\\n 'thicknesses': thicknesses\\n \\n }\\n nrrd.write(os.path.join(path,\\n str(patient_id) + '.nrrd'),\\n Nrrd,\\n header,\\n index_order='C')\",\n \"def plot_snr(tseries, lb=0, ub=None, fig=None):\\r\\n\\r\\n if fig is None:\\r\\n fig = plt.figure()\\r\\n\\r\\n ax_spectra = fig.add_subplot(1, 2, 1)\\r\\n ax_snr_info = fig.add_subplot(1, 2, 2)\\r\\n\\r\\n A = []\\r\\n info = []\\r\\n s_n_r = []\\r\\n coh = []\\r\\n noise_spectra = []\\r\\n signal_spectra = []\\r\\n #If you only have one channel, make sure that everything still works by\\r\\n #adding an axis\\r\\n if len(tseries.data.shape) < 3:\\r\\n this = tseries.data[np.newaxis, :, :]\\r\\n else:\\r\\n this = tseries.data\\r\\n\\r\\n for i in range(this.shape[0]):\\r\\n A.append(nta.SNRAnalyzer(ts.TimeSeries(this[i],\\r\\n sampling_rate=tseries.sampling_rate)))\\r\\n info.append(A[-1].mt_information)\\r\\n s_n_r.append(A[-1].mt_snr)\\r\\n coh.append(A[-1].mt_coherence)\\r\\n noise_spectra.append(A[-1].mt_noise_psd)\\r\\n signal_spectra.append(A[-1].mt_signal_psd)\\r\\n\\r\\n freqs = A[-1].mt_frequencies\\r\\n\\r\\n lb_idx, ub_idx = tsu.get_bounds(freqs, lb, ub)\\r\\n freqs = freqs[lb_idx:ub_idx]\\r\\n\\r\\n coh_mean = np.mean(coh, 0)\\r\\n snr_mean = np.mean(s_n_r, 0)\\r\\n info_mean = np.mean(info, 0)\\r\\n n_spec_mean = np.mean(noise_spectra, 0)\\r\\n s_spec_mean = np.mean(signal_spectra, 0)\\r\\n\\r\\n ax_spectra.plot(freqs, np.log(s_spec_mean[lb_idx:ub_idx]), label='Signal')\\r\\n ax_spectra.plot(freqs, np.log(n_spec_mean[lb_idx:ub_idx]), label='Noise')\\r\\n ax_spectra.set_xlabel('Frequency (Hz)')\\r\\n ax_spectra.set_ylabel('Spectral power (dB)')\\r\\n\\r\\n ax_snr_info.plot(freqs, snr_mean[lb_idx:ub_idx], label='SNR')\\r\\n ax_snr_info.plot(np.nan, np.nan, 'r', label='Info')\\r\\n ax_snr_info.set_ylabel('SNR')\\r\\n ax_snr_info.set_xlabel('Frequency (Hz)')\\r\\n ax_info = ax_snr_info.twinx()\\r\\n ax_info.plot(freqs, np.cumsum(info_mean[lb_idx:ub_idx]), 'r')\\r\\n ax_info.set_ylabel('Cumulative information rate (bits/sec)')\\r\\n return fig\",\n \"def noe_analysis():\\n\\n files = ['f34k_7p2_noe_heights.txt', 'dphs_7p4_noe_heights.txt']\\n proteins = ['f34k','dphs']\\n\\n heights = [nmrfn.parse_noe(f) for f in files]\\n heights = pd.concat(heights, axis=1, keys=proteins)\\n\\n results = [noe_calcs(heights[i]) for i in proteins]\\n results = pd.concat(results, axis=1, keys=proteins)\\n\\n fig = plt.figure(figsize=(10,6))\\n val = plt.subplot(211)\\n dif = plt.subplot(212)\\n \\n seqmin = 6\\n seqmax = 144\\n noemin = 0.5\\n noemax = 0.95\\n\\n dy = results.dphs.noe - results.f34k.noe\\n dif_plot(val, dy.index, dy.ix[8:141], seqmin, seqmax)\\n noe_plot(proteins, results, seqmin, seqmax, noemin, noemax, xax=False)\\n\\n s1 = plt.Rectangle((0, 0), 1, 1, fc=proteins['f34k'])\\n s2 = plt.Rectangle((0, 0), 1, 1, fc=proteins['dphs'])\\n val.legend([s1, s2], ['F34K', '∆+PHS'], loc=8)\\n\\n title = 'HN NOE: F34K pH 7.2 compared with ∆+PHS pH 7.4'\\n savefile = 'f34k_7p2_noe_analysis.pdf'\\n save_plot(title, savefile)\",\n \"def plot_seaice_timeseries(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/SIC/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' mean SIC in Antarctica'\\n\\n\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n\\n if seaice_source == 'nsidc':\\n seaice = seaice\\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(mean_seaice.time.values.astype(float), mean_seaice)\\n if seaice_source =='ecmwf':\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename].sum(dim = ('longitude', 'latitude')))\\n ax = plt.gca()\\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\\n if seaice_source == 'nsidc':\\n mean_seaice = seaice_area_mean(seaice[seaicename],1)\\n plt.plot(seaice.time, mean_seaice)\\n\\n if seaice_source == 'ecmwf':\\n plt.plot(seaice.time, seaice[seaicename].mean(dim = ('longitude', 'latitude')))\\n plt.plot(seaice.time, (seaice_m * seaice.time.values.astype(float) + seaice_b), color = '#177E89')\\n plt.title(title)\\n plt.savefig(imagefolder + seaicename+f'_{seaice_source}.pdf')\\n plt.show()\",\n \"def SaveNIFTI(data, file_path):\\n if(np.iscomplex(data).any()):\\n data = abs(data)\\n nii = nib.Nifti1Image(data, np.eye(4)) \\n nib.save(nii, file_path)\",\n \"def plotSate(s,i,seed):\\r\\n fig, ax = plt.subplots()\\r\\n\\r\\n im = ax.imshow(s)\\r\\n\\r\\n plt.xticks([i for i in range(dim)], \\\"\\\")\\r\\n plt.yticks([i for i in range(dim)], \\\"\\\")\\r\\n\\r\\n fig.tight_layout()\\r\\n plt.savefig(\\\"Systems/\\\" + str(dim) + \\\"_\\\" + str(seed) + \\\"/Images/\\\" + str(i) +\\r\\n \\\".jpeg\\\",quality=80,optimize=True,\\r\\n dpi=80,progressive=True,transparent=True)\\r\\n fig.clear()\\r\\n plt.close(fig)\",\n \"def fun_cnoise_Stim(self, t_stim = 10*s, sexp = 0, cutf = 0, do_csd = 1, t_qual = 0, freq_used = np.array([]), K_mat_old = np.array([]), inh_factor = [1], onf = None, equi = 0):\\n self.barrier() # wait for other nodes\\n \\n filename = str(self.pickle_prefix) + \\\"_results_pop_cnoise.p\\\"\\n filepath = self.data_dir + \\\"/\\\" + filename\\n \\n if self.id == 0: print \\\"- filepath:\\\", filepath \\n \\n if self.do_run or (os.path.isfile(filepath) is False):\\n\\n tstart = 0; \\n fs = 1 / self.dt # sampling rate \\n fmax = fs / 2 # maximum frequency (nyquist)\\n \\n t_noise = arange(tstart, t_stim, self.dt) # create stimulus time vector, make sure stimulus is even!!!\\n\\n #print self.syn_ex_dist\\n #print self.syn_inh_dist\\n #exit()\\n \\n if (self.syn_ex_dist == []):\\n for nt in range(self.n_celltypes): # loop over all cells\\n #print \\\"nt\\\", nt\\n if hasattr(self.cells[nt][0], 'input_vec'):\\n self.syn_ex_dist.append([1] * len(self.cells[nt][0].input_vec)) # default ex for all by default!!!\\n else: \\n self.syn_ex_dist.append([1] * self.n_syn_ex[nt]) # default ex for all by default!!!\\n \\n #print self.syn_ex_dist\\n \\n if (self.syn_ex_dist[0] == []):\\n nemax = 1\\n else:\\n nemax = max([item for sublist in self.syn_ex_dist for item in sublist])\\n \\n if (self.syn_inh_dist == []): # and (any(self.n_syn_inh) > 0)\\n for nt in range(self.n_celltypes): # loop over all cells\\n self.syn_inh_dist.append([0] * self.n_syn_inh[nt]) # default no inh for all by default!!!\\n \\n #print self.syn_inh_dist\\n #exit()\\n \\n if (self.syn_inh_dist[0] == []):\\n nimax = 0\\n else:\\n nimax = max([item for sublist in self.syn_inh_dist for item in sublist]) \\n \\n #print \\\"self.syn_inh_dist, self.syn_ex_dist\\\", self.syn_inh_dist, self.syn_ex_dist\\n \\n n_noise = max([nemax,nimax]) # number of noise sources\\n #print n_noise,nemax,nimax\\n # create reproduceable input\\n noise_data = []\\n\\n for nj in range(n_noise):\\n \\n if self.id == 0: # make sure all have the same signal !!!\\n if len(freq_used) == 0: \\n noise_data0 = create_colnoise(t_noise, sexp, cutf, self.seed+nj, onf = onf)\\n else:\\n noise_data0, _, _, _ = create_multisines(t_noise, freq_used) # create multi sine signal\\n else:\\n noise_data0 = np.empty(len(t_noise), dtype=np.float64)\\n\\n noise_data0 = self.broadcast(noise_data0, fast = True) \\n \\n noise_data.append(noise_data0)\\n noise_data0 = [] \\n \\n noise_data_points = len(noise_data[0]) \\n\\n # Create signal weight vector inh_factor if it is not fully given\\n if len(noise_data) > len(inh_factor):\\n inh_factor = [inh_factor[0]] * len(noise_data) \\n print \\\"inh_factor:\\\", inh_factor\\n\\n #if equi:\\n #pass\\n # tstop = t_stim\\n \\n if max(self.n_syn_ex) == 0: # this means current input\\n \\n self.set_IStim() # sets amp\\n \\n if self.fluct_s != []:\\n if self.fluct_s[self.a_celltype[0]] > 0:\\n if self.id == 0: print \\\"- adding i fluct\\\"\\n self.connect_fluct()\\n \\n for i, m in enumerate(self.method_interpol):\\n if \\\"syn\\\" in m: self.method_interpol[i] = \\\"syn \\\" + str(self.syn_tau1/ms) + \\\"/\\\" + str(self.syn_tau2/ms) + \\\"ms\\\"\\n if \\\"bin\\\" in m: self.method_interpol[i] = \\\"bin \\\" + str(self.bin_width/ms) + \\\"ms\\\"\\n \\n stimulus = []\\n for nj in range(len(noise_data)):\\n stimulus0, t, t_startstop = construct_Stimulus(noise_data[nj], fs, self.amp[self.a_celltype[0]], ihold = 0, delay_baseline = self.delay_baseline) # , tail_points = 0\\n stimulus.append(stimulus0)\\n tstop = t[-1]\\n \\n self.set_IPlay2(stimulus, t)\\n if self.id == 0: print \\\"- starting colored noise transfer function estimation! with amp = \\\" + str(np.round(self.amp[self.a_celltype[0]],4)) + \\\", ihold = \\\" + str(np.round(self.ihold[self.a_celltype[0]],4)) + \\\", ihold_sigma = \\\" + str(np.round(self.ihold_sigma,4)) + \\\", dt = \\\" + str(self.dt) + \\\" => maximum frequency = \\\" + str(fmax) + \\\"\\\\r\\\" \\n \\n else:\\n\\n self.give_freq = False\\n ihold = self.set_i(self.ihold) # just sets amp, ihold should not change! \\n\\n if 'gsyn_in' not in self.method_interpol: \\n pass\\n else:\\n self.g_syn_ex = [1]*len(self.N)\\n \\n \\n if ((self.fluct_g_e0 != []) or (self.fluct_g_i0 != [])):\\n if ((self.fluct_g_e0[self.a_celltype[0]] > 0) or (self.fluct_g_i0[self.a_celltype[0]] > 0)):\\n if self.id == 0: print \\\"- adding g fluct\\\"\\n self.connect_gfluct(E_i=-65)\\n \\n stimulus = []\\n for nj in range(len(noise_data)):\\n stimulus0, t, t_startstop = construct_Stimulus(noise_data[nj], fs, amp=1, ihold = 0, tail_points = 0, delay_baseline = self.delay_baseline) # self.amp\\n stimulus.append(stimulus0)\\n \\n noise_data = [] \\n tstop = t[-1]\\n \\n if self.N[self.a_celltype[0]] > 1:\\n self.set_IStim(ihold = [0]*self.n_celltypes, ihold_sigma = [0]*self.n_celltypes, random_start = True, tstart_offset = 1)\\n if self.id == 0: print \\\"- add random start\\\"\\n \\n #print \\\"Enter Synplay()\\\"\\n self.set_SynPlay(stimulus, t, t_startstop = t_startstop) \\n #print \\\"Exit Synplay()\\\"\\n\\n if self.id == 0: print \\\"- starting colored noise transfer function estimation with synaptic input! with amp = \\\" + str(np.round(self.amp,4)) + \\\", ihold = \\\" + str(np.round(self.ihold,4)) + \\\", ihold_sigma = \\\" + str(np.round(self.ihold_sigma,4)) + \\\", dt = \\\" + str(self.dt) + \\\" => maximum frequency = \\\" + str(fmax) + \\\"\\\\r\\\" \\n \\n amp_vec = []\\n mag_vec = [] \\n pha_vec = []\\n freq_used = []\\n ca = []\\n SNR_mat = []\\n VAFf_mat = []\\n Qual_mat = []\\n CF_mat = [] \\n VAF_mat = []\\n stim = []\\n stim_re_mat = []\\n resp_mat = []\\n current_re = []\\n ihold1 = []\\n tk = []\\n K_mat = []\\n gsyn_in = []\\n fmean = []\\n fmax = [] \\n fmstd = [] \\n fcvm = [] \\n fmeanA = []\\n fmaxA = [] \\n fmstdA = [] \\n fcvmA = [] \\n t_all_vec_input_sorted = []\\n id_all_vec_input_sorted = []\\n \\n if (self.id == 0) and (max(self.n_syn_ex) > 0):\\n print range(self.n_celltypes), np.shape(self.t_all_vec_input)\\n for l in range(self.n_celltypes): \\n ie = argsort(self.t_all_vec_input[l]) \\n t_all_vec_input_sorted.append( self.t_all_vec_input[l][ie] )\\n id_all_vec_input_sorted.append( self.id_all_vec_input[l][ie].astype(int) )\\n \\n #if (self.id == 0): \\n # print self.g_syn_ex\\n # print np.array(self.g_syn_ex)>= 0\\n \\n #print \\\"g_syn_ex:\\\",self.g_syn_ex\\n if np.array(np.array(self.g_syn_ex)>= 0).any():\\n \\n if hasattr(self.cells[self.a_celltype[0]][0], 'get_states') and equi:\\n print \\\"- Equilibrate!\\\"\\n self.run(tstop, do_loadstate = False)\\n m = md5.new()\\n cell_exe_new = self.cell_exe[0]\\n m.update(cell_exe_new)\\n filename = './states_' + self.celltype[0] + '_' + m.hexdigest() + '_Population.b'\\n self.cells[self.a_celltype[0]][0].get_states(filename)\\n else:\\n self.run(tstop, do_loadstate = False)\\n \\n i_startstop = []\\n \\n results = self.get(t_startstop, i_startstop) \\n time = results.get('time')\\n current = results.get('current') \\n voltage = results.get('voltage') \\n fmean = results.get('fmean') \\n gsyn = results.get('gsyn') \\n freq_times = results.get('freq_times')\\n spike_freq = results.get('spike_freq')\\n t_all_vec_vec = results.get('t_all_vec_vec')\\n id_all_vec_vec = results.get('id_all_vec_vec')\\n gsyns = results.get('gsyns')\\n gsyn_in = results.get('gsyn_in')\\n \\n fmax = results.get('fmax')\\n fmstd = results.get('fmstd')\\n fcvm = results.get('fcvm')\\n \\n fmeanA = results.get('fmeanA') \\n fmaxA = results.get('fmaxA')\\n fmstdA = results.get('fmstdA')\\n fcvmA = results.get('fcvmA')\\n \\n fbaseA = results.get('fbaseA') \\n fbase = results.get('fbase')\\n fbstdA = results.get('fbstdA')\\n \\n \\n else: # do not run, analyse input!!!\\n \\n time = t\\n voltage = []\\n for l in range(self.n_celltypes): \\n voltage.append(np.zeros(len(t)))\\n current = []\\n \\n freq_times = []\\n spike_freq = []\\n gsyn = []\\n gsyn_in = []\\n \\n t_all_vec_vec = []\\n id_all_vec_vec = []\\n \\n fmean = []\\n fmax = []\\n fmstd = []\\n fcvm = []\\n fstdm = []\\n \\n fmeanA = []\\n fmaxA = []\\n fmstdA = []\\n fcvmA = []\\n fbaseA = []\\n fbase = []\\n fbstdA = []\\n \\n if self.id == 0:\\n \\n current = self.n_train_ex\\n \\n #t_all_vec = self.t_all_vec_input\\n #id_all_vec = self.id_all_vec_input\\n\\n #ie = argsort(t_all_vec) \\n #t_all_vec_vec.append( t_all_vec[ie] )\\n #id_all_vec_vec.append( id_all_vec[ie].astype(int) )\\n \\n t_all_vec_vec = t_all_vec_input_sorted\\n id_all_vec_vec = id_all_vec_input_sorted\\n \\n freq_times = arange(0, tstop, self.bin_width)\\n spike_freq = np.zeros(len(freq_times))\\n \\n for j in self.a_celltype:\\n \\n [num_spikes, _] = neuronpy.util.spiketrain.get_histogram(t_all_vec_vec[j], bins = freq_times)\\n\\n if self.tau2_ex[0] > 0:\\n spike_freq = np.concatenate((zeros(1),num_spikes)) \\n print \\\"NOSYN TEST: start convolution with Ksyn\\\"\\n Ksyn = syn_kernel(arange(0,10*self.tau2_ex[0],self.bin_width), self.tau1_ex[0], self.tau2_ex[0]) \\n Ksyn = np.concatenate((zeros(len(Ksyn)-1),Ksyn))\\n spike_freq = np.convolve(Ksyn, spike_freq, mode='same')\\n print \\\"NOSYN TEST: convolution finished\\\"\\n else:\\n\\n if isinstance(self.factor_celltype[j], ( int, long ) ):\\n f = self.factor_celltype[j] \\n else:\\n f = self.factor_celltype[j][0] \\n \\n spike_freq = spike_freq + f * np.concatenate((zeros(1),num_spikes)) / self.bin_width\\n\\n fmean.append(self.fmean_input)\\n fmax.append(self.fmax_input) \\n fmstd.append(self.fmstd_input) \\n fcvm.append(self.fcvm_input) \\n fstdm.append(self.fstdm_input)\\n\\n if self.no_fmean == True:\\n fmean.append(ihold)\\n \\n #plt.figure('spike_freq') \\n #plt.plot(freq_times, spike_freq)\\n #plt.savefig(\\\"./figs/Pub/Spike_freq_\\\" + str(self.pickle_prefix) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n #plt.clf()\\n \\n fmeanA = fmean[0]\\n fmaxA = fmax[0]\\n fmstdA = fmstd [0] \\n fcvmA = fcvm[0]\\n fstdmA = fstdm[0]\\n \\n \\n if self.id == 0: \\n \\n if any([i<0 for i in inh_factor]):\\n \\n p0 = []\\n inhf_idx = []\\n for i, inhf in enumerate(inh_factor):\\n if inhf < 0: \\n p0.append(0) \\n inhf_idx.append(i)\\n \\n plsq = fmin(self.residuals_compute_Transfer, p0, args=(stimulus, spike_freq, freq_times, t, noise_data_points, gsyn, gsyn_in, do_csd, t_qual, K_mat_old, t_startstop, inh_factor))\\n p = plsq\\n \\n ip = 0\\n for i in inhf_idx:\\n inh_factor[i] = p[ip]\\n ip += 1\\n \\n\\n print \\\"Final inh_factor: \\\", inh_factor\\n \\n \\n results = self.compute_Transfer(stimulus, spike_freq = spike_freq, freq_times = freq_times, \\n t = t, noise_data_points = noise_data_points, gsyn = gsyn, gsyn_in = gsyn_in, \\n do_csd = do_csd, t_qual = t_qual, K_mat_old = K_mat_old, t_startstop = t_startstop, inh_factor=inh_factor)\\n \\n mag_vec, pha_vec, ca, freq, freq_used, fmean_all = results.get('mag_mat'), results.get('pha_mat'), results.get('ca_mat'), results.get('freq'), results.get('freq_used'), results.get('fmean') \\n SNR_mat, VAFf_mat, Qual_mat, CF_mat, VAF_mat = results.get('SNR_mat'), results.get('VAFf_mat'), results.get('Qual_mat'), results.get('CF_mat'), results.get('VAF_mat') \\n stim, resp_mat, stim_re_mat, tk, K_mat = results.get('stim'), results.get('resp_mat'), results.get('stim_re_mat'), results.get('tk'), results.get('K_mat') \\n \\n \\n self.barrier() # wait for other nodes\\n \\n \\n if self.id == 0:\\n \\n if t_qual > 0:\\n #print t_startstop[0], t_startstop[0]/self.dt, (t_startstop[0]+t_qual)/self.dt\\n current_re = current[int(t_startstop[0]/self.dt):int((t_startstop[0]+t_qual)/self.dt)]\\n current_re = current_re[int(len(K_mat[self.a_celltype[0]])):int(len(current_re))-int(len(K_mat[self.a_celltype[0]]))]\\n \\n if len(self.i_holdrs) > 0:\\n ihold1 = self.i_holdrs[self.a_celltype[0]][0]\\n else:\\n ihold1 = []\\n \\n for l in range(len(self.method_interpol)): # unwrap \\n pha_vec[l,:] = unwrap(pha_vec[l,:] * (pi / 180)) * (180 / pi) # unwrap for smooth phase\\n \\n # only return fraction of actual signal, it is too long!!! \\n if time[-1] > self.tmax: \\n imax = -1*int(self.tmax/self.dt)\\n time = time[imax:]; current = current[imax:]; gsyn = gsyn[imax:]; gsyn_in = gsyn_in[imax:]\\n for n in range(self.n_celltypes): \\n voltage[n] = voltage[n][imax:]\\n \\n if freq_times != []: \\n if freq_times[-1] > self.tmax:\\n imax2 = where(freq_times > self.tmax)[0][0] # for spike frequency \\n freq_times = freq_times[0:imax2]; spike_freq = spike_freq[0:imax2] \\n \\n bvec = [\\\"_syn\\\" in st for st in self.method_interpol]\\n if np.any(bvec):\\n # normalize synaptic integration with others \\n mag_vec[1,:]= mag_vec[0,0]*mag_vec[1,:]/mag_vec[1,0] \\n \\n if self.id == 0: print \\\"start pickle\\\"\\n \\n results = {'freq_used':freq_used, 'amp':amp_vec,'mag':mag_vec,'pha':pha_vec,'ca':ca,'voltage':voltage,'tk':tk,'K_mat':K_mat, 'ihold1': ihold1, 't_startstop':t_startstop, #'stimulus':stimulus,\\n 'current':current,'t1':time,'freq_times':freq_times,'spike_freq':spike_freq, 'stim':stim, 'stim_re_mat':stim_re_mat, 'resp_mat':resp_mat, 'current_re':current_re, 'gsyn_in':gsyn_in, 'fmeanA':fmeanA, 'fmaxA':fmaxA, 'fmstdA':fmstdA, 'fcvmA':fcvmA, 'fbaseA':fbaseA, 'fbase':fbase, 'fbstdA':fbstdA,\\n 'fmean':fmean,'method_interpol':self.method_interpol, 'SNR':SNR_mat, 'VAF':VAFf_mat, 'Qual':Qual_mat, 'CF':CF_mat, 'VAFs':VAF_mat, 'fmax':fmax, 'fmstd':fmstd, 'fcvm':fcvm, 'inh_factor':inh_factor, 't_all_vec_vec':t_all_vec_vec, 'id_all_vec_vec':id_all_vec_vec} \\n \\n if self.id == 0:\\n if self.dumpsave == 1:\\n pickle.dump( results, gzip.GzipFile( filepath, \\\"wb\\\" ) )\\n print \\\"pickle done\\\" \\n \\n \\n if self.plot_train:\\n \\n for a in self.a_celltype:\\n\\n #i_start = mlab.find(t_all_vec_vec[a] >= 0)[0]\\n #i_stop = mlab.find(t_all_vec_vec[a] >= 5)[0]\\n \\n #t_all_cut = t_all_vec_vec[a][i_start:i_stop]\\n #id_all_cut = id_all_vec_vec[a][i_start:i_stop]\\n \\n t_all_cut = t_all_vec_vec[a]\\n id_all_cut = id_all_vec_vec[a]\\n \\n f_start_in = mlab.find(t_all_cut >= 0) \\n f_stop_in = mlab.find(t_all_cut <= 10) \\n \\n f_start = f_start_in[0] \\n f_stop = f_stop_in[-1]+1 \\n use_spikes = t_all_cut[f_start:f_stop]\\n use_id = id_all_cut[f_start:f_stop]\\n \\n plt.figure('results_train') \\n ax99 = plt.subplot(1,1,1)\\n ax99.plot(use_spikes,use_id,'|', ms=2)\\n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\\n plt.savefig(\\\"./figs/Pub/Train_\\\" + str(self.pickle_prefix) + \\\"_cell\\\" + str(a) + \\\"_N\\\" + str(self.N[a]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n \\n plt.clf()\\n \\n if len(t_all_cut) > 0:\\n \\n tbin = 100*ms\\n tb = np.arange(0,t[-1],tbin)\\n [all_rate, _] = neuronpy.util.spiketrain.get_histogram(t_all_cut, bins = tb)\\n all_rate = np.concatenate((np.zeros(1),all_rate)) / self.N[a] / tbin\\n \\n plt.figure('results_train2') \\n plt.plot(tb,all_rate)\\n plt.savefig(\\\"./figs/Pub/PSTH_\\\" + str(self.pickle_prefix) + \\\"_cell\\\" + str(a) + \\\"_N\\\" + str(self.N[a]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n plt.clf()\\n \\n plt.figure('results_noise') \\n plt.plot(time,current)\\n plt.savefig(\\\"./figs/Pub/Noise_\\\" + str(self.pickle_prefix) + \\\"_cell\\\" + str(a) + \\\"_N\\\" + str(self.N[a]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n plt.clf()\\n \\n \\n if self.plot_input:\\n \\n if len(t_all_vec_input_sorted[0]) > 0:\\n \\n i_start = mlab.find(t_all_vec_input_sorted[0] >= 0)[0]\\n i_stop = mlab.find(t_all_vec_input_sorted[0] >= 5)[0]\\n \\n t_all_cut = t_all_vec_input_sorted[0][i_start:i_stop]\\n id_all_cut = id_all_vec_input_sorted[0][i_start:i_stop]\\n \\n plt.figure('results_input') \\n ax99 = plt.subplot(1,1,1)\\n ax99.plot(t_all_cut,id_all_cut,'|', ms=2)\\n plt.text(0.5, 1.1, r'fmean=' + str(round(self.fmean_input,1)) + ',fmax=' + str(round(self.fmax_input,1)) + ',fmstd=' + str(round(self.fmstd_input,1)) + ',fcvm=' + str(round(self.fcvm_input,1)) + ',fstdm=' + str(round(self.fstdm_input,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\\n plt.savefig(\\\"./figs/Pub/Input_\\\" + str(self.pickle_prefix) + \\\"_N\\\" + str(self.N[self.a_celltype[0]]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n plt.clf()\\n \\n\\n else:\\n \\n if self.id == 0:\\n results = pickle.load( gzip.GzipFile( filepath, \\\"rb\\\" ) )\\n \\n #print results\\n #print {key:np.shape(value) for key,value in results.iteritems()}\\n \\n if self.minimal_dir: # save only info needed for plot\\n \\n print {key:np.shape(value) for key,value in results.iteritems()}\\n \\n if \\\"Fig6_pop_transfer_grc_syngr_nsyn4_cn_a1_noisesynlow_inhlow_adjfinh_varih_N100_CFo6.0_results_pop_cnoise.p\\\" in filename:\\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['stim'] = []\\n results['current'] = []\\n results['tk'] = []\\n results['K_mat'] = []\\n results['freq_times'] = []\\n results['spike_freq'] = []\\n results['stim_re_mat'] = []\\n results['current_re'] = []\\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = [] \\n results['gsyn_in'] = []\\n \\n elif (\\\"Fig8_pop_transfer_none_synno_cn_cutf30_a1_noisesynlow_ih20_varih_N100_CFo-1_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_none_synno_cn_cutf30_a10_noisesynlow_ih20_varih_N100_CFo-1_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_CFo9.0_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a10_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_is0.14_CFo9.0_results_pop_cnoise.p\\\" in filename) \\\\\\n :\\n\\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['current'] = []\\n results['tk'] = []\\n results['K_mat'] = []\\n results['voltage'] = [] \\n results['current_re'] = []\\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = []\\n results['t1'] = []\\n results['gsyn_in'] = []\\n \\n elif (\\\"Fig8_pop_transfer_none_synno_cn_cutf30_a1_noisesynlow_ih20_varih_N50_twopop_CFo-1_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_none_synno_cn_cutf30_a10_noisesynlow_ih20_varih_N50_twopop_CFo-1_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_twopop_CFo9.0_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf30_a10_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_is0.14_twopop_CFo9.0_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf5_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N100_CFo14.0_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig8_pop_transfer_grc_syngr_nsyn4_cn_cutf5_a1_noisesynlow_inhlow_adjfinh_varih_varinhn_N50_twopop_CFo14.0_results_pop_cnoise.p\\\" in filename) \\\\\\n :\\n \\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['current'] = []\\n results['tk'] = []\\n results['K_mat'] = []\\n results['voltage'] = [] \\n results['current_re'] = []\\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = []\\n results['t1'] = []\\n results['gsyn_in'] = []\\n results['freq_times'] = []\\n results['spike_freq'] = []\\n \\n elif (\\\"Fig4_pop_transfer_grc_cn_addn100_N[100]_CF[40]_amod[1]_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig4_pop_transfer_grc_cn_addn1_N[100]_CF[40]_amod[1]_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig4b_pop_transfer_grc_lowcf_cn_twopop_N[50, 50]_CF[0.0055, 0.0055]_amod[None, None]_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig4b_pop_transfer_grc_lowcf_cn_N[100]_CF[0.0055]_amod[None]_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig4b_pop_transfer_grc_lowcf_slownoise_cn_twopop_N[50, 50]_CF[0.0051, 0.0051]_amod[None, None]_results_pop_cnoise.p\\\" in filename) \\\\\\n or (\\\"Fig4b_pop_transfer_grc_lowcf_slownoise_cn_N[100]_CF[0.0051]_amod[None]_results_pop_cnoise.p\\\" in filename) \\\\\\n :\\n \\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['current'] = []\\n results['tk'] = []\\n results['K_mat'] = []\\n results['voltage'] = [] \\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = []\\n results['t1'] = []\\n results['gsyn_in'] = []\\n results['freq_times'] = []\\n results['spike_freq'] = []\\n \\n elif (\\\"Fig2_pop_transfer_\\\" in filename) \\\\\\n :\\n \\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['current'] = []\\n results['t1'] = []\\n results['voltage'] = [] \\n results['freq_times'] = []\\n results['spike_freq'] = []\\n results['current_re'] = []\\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = []\\n results['gsyn_in'] = []\\n \\n else:\\n results['ca'] = [] \\n results['resp_mat'] = []\\n results['stim'] = []\\n results['current'] = []\\n results['tk'] = []\\n results['K_mat'] = []\\n results['t1'] = []\\n results['voltage'] = [] \\n results['freq_times'] = []\\n results['spike_freq'] = []\\n results['stim_re_mat'] = []\\n results['current_re'] = []\\n results['t_all_vec_vec'] = []\\n results['id_all_vec_vec'] = []\\n results['gsyn_in'] = []\\n\\n print {key:np.shape(value) for key,value in results.iteritems()}\\n\\n pickle.dump( results, gzip.GzipFile( self.minimal_dir + \\\"/\\\" + filename, \\\"wb\\\" ) ) \\n \\n else:\\n results = {'freq_used':[], 'amp':[],'mag':[],'pha':[],'ca':[],'voltage':[], 'tk':[],'K_mat':[], 'ihold1':[], 't_startstop':[], #'stimulus':[],\\n 'current':[],'t1':[],'freq_times':[],'spike_freq':[], 'stim':[], 'stim_re_mat':[], 'current_re':[], 'gsyn_in':[], 'fmeanA':[], 'fmaxA':[], 'fmstdA':[], 'fcvmA':[], 'fbaseA':[], 'fbase':[], 'fbstdA':[],\\n 'fmean':[],'method_interpol':self.method_interpol, 'SNR':[], 'VAF':[], 'Qual':[], 'CF':[], 'VAFs':[], 'fmax':[], 'fmstd':[], 'fcvm':[], 'inh_factor':[], 't_all_vec_vec':[], 'id_all_vec_vec':[]} \\n \\n if self.id == 0: \\n\\n if self.plot_train: \\n\\n for a in self.a_celltype:\\n \\n t1 = results.get('t1') \\n voltage = results.get('voltage') \\n fmean = results.get('fmean') \\n fmax = results.get('fmax') \\n fmstd = results.get('fmstd') \\n \\n \\n if results.has_key('t_all_vec_vec'):\\n \\n if len(results['t_all_vec_vec']) > 0: \\n t_all_vec_vec = results.get('t_all_vec_vec') \\n id_all_vec_vec = results.get('id_all_vec_vec') \\n \\n t_all_cut = t_all_vec_vec[a]\\n id_all_cut = id_all_vec_vec[a]\\n \\n f_start_in = mlab.find(t_all_cut >= 0) \\n f_stop_in = mlab.find(t_all_cut <= 10) \\n \\n f_start = f_start_in[0] \\n f_stop = f_stop_in[-1]+1 \\n use_spikes = t_all_cut[f_start:f_stop]\\n use_id = id_all_cut[f_start:f_stop]\\n \\n plt.figure('results_train') \\n ax97 = plt.subplot(1,1,1)\\n ax97.plot(use_spikes,use_id,'|', ms=6)\\n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax97.transAxes, fontsize=10, va='center', ha='center')\\n plt.savefig(\\\"./figs/Pub/Train_\\\" + str(self.pickle_prefix) + \\\"_cell\\\" + str(a) + \\\"_N\\\" + str(self.N[a]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n\\n \\n plt.figure('results_voltage') \\n ax99 = plt.subplot(2,1,1)\\n ax99.plot(t1,voltage[a])\\n \\n t_noise = arange(0, t_stim, self.dt)\\n noise_data = create_colnoise(t_noise, sexp, cutf, 50, onf = onf)\\n stimulus, t, t_startstop = construct_Stimulus(noise_data, 1/self.dt, amp=1, ihold = 0, tail_points = 0, delay_baseline = self.delay_baseline) \\n ax98 = plt.subplot(2,1,2)\\n ax98.plot(t[0:10/self.dt],stimulus[0:10/self.dt],color='k')\\n \\n plt.text(0.5, 1.1, r'CF=' + str(round(fmean,1)) + ',fmax=' + str(round(fmax,1)) + ',fmstd=' + str(round(fmstd,1)), transform=ax99.transAxes, fontsize=10, va='center', ha='center')\\n plt.savefig(\\\"./figs/Pub/Voltage_\\\" + str(self.pickle_prefix) + \\\"_cell\\\" + str(a) + \\\"_N\\\" + str(self.N[a]) + \\\".pdf\\\", dpi = 300, transparent=True) # save it \\n plt.show()\\n plt.clf()\\n \\n if (self.id == 0) and (do_csd == 1):\\n Qual = results.get('Qual') \\n for i, ii in enumerate(self.method_interpol):\\n print \\\"\\\\n[QUAL:] Interpol:\\\", ii, \\\"SNR0:\\\", Qual[i,0,0], \\\"SNR_cutff:\\\", Qual[i,0,1], \\\"SNR_mean:\\\", Qual[i,0,2], \\\"\\\\n VAF0:\\\", Qual[i,1,0], \\\"VAF_cutff:\\\", Qual[i,1,1], \\\"VAF_mean:\\\", Qual[i,1,2], \\\"\\\\n CF(subtracted):\\\", Qual[i,2,0], \\\"VAF(subtracted):\\\", Qual[i,2,1] \\n \\n VAF = results.get('VAF')\\n freq_used = results.get('freq_used') \\n iend = mlab.find(freq_used >= self.xmax)[0] \\n print 'm(VAF)=' + str(np.mean(VAF[1][0,0:iend])) \\n \\n self.barrier() # wait for other nodes\\n \\n return results\",\n \"def plot_sic_sic_timeseries(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/SIC/'):\\n output_folder = 'processed_data/'\\n\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n if detrend:\\n dt = 'detrended'\\n else:\\n dt = 'raw'\\n\\n filename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n indicies = xr.open_dataset(output_folder + 'ERA5/SIC/' + filename +'.nc')[filename].mean(dim = ('longitude', 'latitude'))\\n data = indicies.copy()\\n data = data.loc[data.time.dt.year >= 1979]\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + 'SIC/' + seaicename +'.nc')[seaicename]\\n\\n times = list(set.intersection(set(seaice.time.values), set(data.time.values)))\\n\\n seaice = seaice_area_mean(seaice.sel(time=times).sortby('time'), 1)\\n data = data.sel(time=times).sortby('time')\\n\\n seaice = seaice_area_mean(seaice,1)\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice.time.values.astype(float), seaice)\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n title += dt + ' '\\n\\n title += temporal_resolution\\n title += f' mean ERA5 and SIC'\\n fig, ax = plt.subplots()\\n ax2 = plt.twinx(ax)\\n ax2.plot([],[])\\n\\n if anomlous or detrend: ax.axhline(0, alpha = 0.5)\\n\\n ln1 = ax.plot(data.time, data, label = f'ERA5', color = '#EA1B10')\\n ax.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10')\\n ln2 = ax2.plot(seaice.time, seaice, label = 'SIC', color = '#177E89')\\n ax2.plot(seaice.time, seaice_m * seaice.time.values.astype(float) + seaice_b, color = '#177E89')\\n\\n if anomlous or detrend:\\n yabs_max = abs(max(ax2.get_ylim(), key=abs))\\n ax2.set_ylim(ymin=-yabs_max, ymax=yabs_max)\\n\\n yabs_max = abs(max(ax.get_ylim(), key=abs))\\n ax.set_ylim(ymin=-yabs_max, ymax=yabs_max)\\n\\n # ylabels\\n ax.set_ylabel(f'ECMWF')\\n ax2.set_ylabel(f'Mean SIC')\\n\\n # legend\\n lines = ln1 + ln2\\n labels = [line.get_label() for line in lines]\\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 2, loc = 'upper right')\\n\\n plt.title(title)\\n plt.savefig(imagefolder + f'/SIC_ERA5_{filename}' + '.pdf')\\n plt.show()\",\n \"def TICwriter(TIC, dataFile, saveDirectory):\\n #Create savename from data file name:\\n savefile = dataFile.split('/')[-1].split('.')[0] + '_TIC.png'\\n #Create ouput directory:\\n saveDirectory = os.path.join(saveDirectory, 'output/')\\n os.makedirs(os.path.dirname(saveDirectory), exist_ok=True)\\n #Plot figure:\\n Plot = pl.figure()\\n TICplot = Plot.add_subplot(111)\\n TICplot.plot([d[0] for d in TIC], [d[1] for d in TIC])\\n \\n #Save and close plot:\\n pl.savefig(saveDirectory + savefile)\\n pl.close(Plot)\",\n \"def fn_intensitytrace(file_name,folder,data,time,i,x,y):\\n import numpy as np\\n import matplotlib.pyplot as plt\\n\\n\\n figure_name=file_name+'_intensity_trace'\\n for a in range(0,len(data),int(len(data)/2)):\\n if i==a:\\n x_coord=x[i+1]\\n y_coord=y[i+1]\\n max_int=np.max(data[i])\\n min_int=np.min(data[i])\\n #norm_int = [b / max_int for b in data[i]]\\n plt.figure()\\n #plt.plot(time[0:len(time)-1],norm_int,'g')\\n plt.plot(time[0:len(time)-1],data[i],'g')\\n plt.xlim(0, 100)\\n plt.ylim(min_int, (max_int+100))\\n plt.xlabel('Time (s)', fontname='Arial', fontsize=12)\\n plt.ylabel('Photon counts (photons)', fontname='Arial', fontsize=12)\\n plt.xticks(fontname='Arial',fontsize=12)\\n plt.yticks(fontname='Arial', fontsize=12)\\n plt.savefig(folder+'/Figures/PDFs'+ '/' + figure_name + '_'+str(x_coord)+','+str(y_coord)+'.pdf', dpi=500)\\n plt.savefig(folder+'/Figures/PNGs'+ '/' + figure_name + '_'+str(x_coord)+','+str(y_coord)+'.png', dpi=500)\\n\\n return (plt.show())\",\n \"def plot_TSNE(fig_name):\\n dir = \\\"log/peps mini\\\"\\n pattern = r'(internal|access|lock)\\\\\\\\\\\\d{1,2}.csv$'\\n pattern_valid = r'(3|6|9|12).csv$'\\n utils.construct_set(dir, pattern, pattern_valid)\\n X, y = utils.load_all()\\n utils.plot_TSNE(X, y)\\n plt.title(fig_name)\\n if not os.path.exists(dir_fig):\\n os.makedirs(dir_fig)\\n plt.savefig(dir_fig + '/' + fig_name + '.png')\",\n \"def process_nitrate(store, site):\\n constituent = 'NitrateSurr'\\n db_path = '/said/{}/'.format(site['id'])\\n iv_path = db_path + 'iv'\\n df = store.get(iv_path)\\n\\n n_error = np.maximum(0.5, df[constituent]*.1)\\n df[constituent+'_U90.0'] = df[constituent] + n_error\\n df['NitrateSurr_L90.0'] = df.NitrateSurr - n_error\\n #clip values below 0\\n df['NitrateSurr_L90.0'] = np.maximum(0, df['NitrateSurr_L90.0'])\\n\\n update_table(store, iv_path, df)\",\n \"def snr_stats(\\r\\n t,\\r\\n y,\\r\\n period,\\r\\n duration,\\r\\n T0,\\r\\n transit_times,\\r\\n transit_duration_in_days,\\r\\n per_transit_count,\\r\\n):\\r\\n\\r\\n snr_per_transit = numpy.zeros([len(transit_times)])\\r\\n snr_pink_per_transit = numpy.zeros([len(transit_times)])\\r\\n intransit = transit_mask(t, period, 2 * duration, T0)\\r\\n flux_ootr = y[~intransit]\\r\\n\\r\\n try:\\r\\n pinknoise = pink_noise(flux_ootr, int(numpy.mean(per_transit_count)))\\r\\n except:\\r\\n pinknoise = numpy.nan\\r\\n\\r\\n # Estimate SNR and pink SNR\\r\\n # Second run because now the out of transit points are known\\r\\n if len(flux_ootr) > 0:\\r\\n std = numpy.std(flux_ootr)\\r\\n else:\\r\\n std = numpy.nan\\r\\n for i in range(len(transit_times)):\\r\\n mid_transit = transit_times[i]\\r\\n tmin = mid_transit - 0.5 * transit_duration_in_days\\r\\n tmax = mid_transit + 0.5 * transit_duration_in_days\\r\\n if numpy.isnan(tmin) or numpy.isnan(tmax):\\r\\n idx_intransit = []\\r\\n mean_flux = numpy.nan\\r\\n else:\\r\\n idx_intransit = numpy.where(numpy.logical_and(t > tmin, t < tmax))\\r\\n if len(y[idx_intransit]) > 0:\\r\\n mean_flux = numpy.mean(y[idx_intransit])\\r\\n else:\\r\\n mean_flux = numpy.nan\\r\\n\\r\\n intransit_points = numpy.size(y[idx_intransit])\\r\\n try:\\r\\n snr_pink_per_transit[i] = (1 - mean_flux) / pinknoise\\r\\n if intransit_points > 0 and not numpy.isnan(std):\\r\\n std_binned = std / intransit_points ** 0.5\\r\\n snr_per_transit[i] = (1 - mean_flux) / std_binned\\r\\n else:\\r\\n snr_per_transit[i] = 0\\r\\n snr_pink_per_transit[i] = 0\\r\\n except:\\r\\n snr_per_transit[i] = 0\\r\\n snr_pink_per_transit[i] = 0\\r\\n\\r\\n return snr_per_transit, snr_pink_per_transit\",\n \"def writetipsy(self, outfile=None, hubble=None):\\n from . import analysis\\n from . import tipsy\\n from .analysis import cosmology\\n from snapshot import _new as new\\n import math\\n s = self._base()\\n if outfile is None: outfile = s.filename+'.gtp'\\n print \\\"write tipsy file to \\\", outfile\\n sout = new(star=self._nhalos) # create new tipsy snapshot written as halos.\\n sout.properties['a'] = s.properties['a']\\n sout.properties['z'] = s.properties['z']\\n sout.properties['boxsize'] = s.properties['boxsize']\\n if hubble is None: hubble = s.properties['h']\\n sout.properties['h'] = hubble\\n ### ! dangerous -- rho_crit function and unit conversions needs simplifying\\n rhocrithhco = cosmology.rho_crit(s, z=0, unit=\\\"Msol Mpc^-3 h^2\\\")\\n lboxkpc = sout.properties['boxsize'].ratio(\\\"kpc a\\\")\\n lboxkpch = lboxkpc*sout.properties['h']\\n lboxmpch = lboxkpc*sout.properties['h']/1000.\\n tipsyvunitkms = lboxmpch * 100. / (math.pi * 8./3.)**.5\\n tipsymunitmsun = rhocrithhco * lboxmpch**3 / sout.properties['h']\\n\\n print \\\"transforming \\\", self._nhalos, \\\" halos into tipsy star particles\\\"\\n for ii in xrange(self._nhalos):\\n h = self[ii+1].properties\\n sout.star[ii]['mass'] = h['m']/hubble / tipsymunitmsun\\n ## tipsy units: box centered at 0. (assume 0<=x<=1)\\n sout.star[ii]['x'] = h['pos'][0][0]/lboxmpch - 0.5\\n sout.star[ii]['y'] = h['pos'][0][1]/lboxmpch - 0.5\\n sout.star[ii]['z'] = h['pos'][0][2]/lboxmpch - 0.5\\n sout.star[ii]['vx'] = h['vel'][0][0]/tipsyvunitkms\\n sout.star[ii]['vy'] = h['vel'][0][1]/tipsyvunitkms\\n sout.star[ii]['vz'] = h['vel'][0][2]/tipsyvunitkms\\n sout.star[ii]['eps'] = h['r']/lboxkpch\\n sout.star[ii]['metals'] = 0.\\n sout.star[ii]['phi'] = 0.\\n sout.star[ii]['tform'] = 0.\\n print \\\"writing tipsy outfile %s\\\"%outfile\\n sout.write(fmt=tipsy.TipsySnap, filename=outfile)\\n return sout\",\n \"def summarize_series(fglob, outfile):\\n with open(outfile, mode='w') as of:\\n #Iterate over files\\n flist = glob(fglob)\\n flist = sorted(flist)\\n lgrho = [] #list of log(rho) values, parallel to the list of rxnmap maps\\n rxns = [] #list of maps of form 'rxn_name' --> energy release [erg/g/s]\\n for f in flist:\\n rxnmap = {}\\n currxn = ''\\n eps_nuc = ''\\n for line in open(f,mode='r'):\\n if not currxn and line.count('reaction name') == 1:\\n i1 = line.index('<') + 1\\n i2 = line.index('>')\\n currxn = line[i1:i2]\\n elif currxn and line.count('eps_nuc') == 1:\\n eps_nuc = float(line.partition(':')[2].strip())\\n rxnmap[currxn] = eps_nuc\\n currxn = ''\\n elif line.count('log rho') == 1:\\n lgrho.append(line.partition('rho')[2].strip())\\n srtmap = sorted(rxnmap.items(), key=operator.itemgetter(1), reverse=True) #sort on values\\n rxns.append(srtmap)\\n\\n #Write header\\n of.write('log(rho): ' + (' {:3.3s} |'*len(lgrho)).format(*lgrho) + '\\\\n')\\n\\n #Write rows of data for each logrho, include top ten rxns\\n start = ' '\\n for i in range(10):\\n of.write(start)\\n for tup in rxns:\\n of.write('{:23s}'.format(tup[i][0]))\\n of.write('\\\\n')\\n of.write(start)\\n for tup in rxns:\\n of.write('{:<23.8e}'.format(tup[i][1]))\\n of.write('\\\\n\\\\n')\",\n \"def runErdosRenyi(n,p):\\n s = z.Optimize()\\n g = ig.Graph.Erdos_Renyi(n, p, directed=True, loops=True)\\n while g.is_dag():\\n g = ig.Graph.Erdos_Renyi(n, p, directed=True, loops=True)\\n\\n return MFAS_set_cover(s,g), u.get_feedback_arc_set(g)\",\n \"def simul_and_export(file, config, i):\\n\\n simulate_UVSPEC(file, config)\\n\\n load_skymap(config)\\n\\n sim = files_sim(config)[i]\\n export_sim_rad(sim, config)\",\n \"def science_reduction(input_file):\\n #name of the planet\\n planet = input_file['exoplanet']\\n #set original directory\\n original_path = os.getcwd()\\n save_path = input_file['save_path']\\n data_path = input_file['data_path']\\n #Change your directory to data diretory\\n os.chdir(data_path)\\n #list all flat images\\n exoplanet = glob.glob(planet+'*.fits')\\n print '\\\\nLoading exoplanet images \\\\nTotal of '+planet+'*.fits files = ',len(exoplanet),'\\\\nFiles = \\\\n'\\n print exoplanet\\n #if save_path exist, continue; if not, create.\\n if not os.path.exists(save_path):\\n os.makedirs(save_path)\\n #create a list of bias images and copy images to save_path\\n print '\\\\nCopy science images to save_path directory to main reduction: ....'\\n os.system('cp '+planet+'*.fits '+save_path)\\n print '\\\\n .... done. \\\\n'\\n #change to save_path\\n os.chdir(save_path)\\n #create the names for exoplanet science mages with bias subtracted\\n bexoplanet = []\\n for i in exoplanet:\\n bexoplanet.append('B'+i)\\n #verify if previous superbias exist\\n if os.path.isfile('B'+i) == True:\\n os.system('rm B'+i)\\n print '\\\\n Will be create this images: \\\\n'\\n print bexoplanet\\n #exoplanet = string.join(exoplanet,',') #create the list string of exoplanet science images\\n #bexoplanet = string.join(bexoplanet,',')#create the list string of bexoplanet science images\\n print '\\\\nSubtracting superbias.fits from all '+planet+'*.fits images ....\\\\n'\\n for i in range(len(exoplanet)):\\n iraf.imarith(exoplanet[i],'-','superbias.fits',bexoplanet[i])\\n use.update_progress((i+1.)/len(bexoplanet))\\n print '\\\\n.... cleaning '+planet+'*.fits images\\\\n'\\n os.system('rm '+planet+'*.fits')\\n print '\\\\n Statistics of B'+planet+'*.fits images: \\\\n'\\n for i in range(len(bexoplanet)):\\n iraf.imstat(bexoplanet[i])\\n print '\\\\nFlatfielding the B'+planet+'*.fits ....\\\\n'\\n #create the names for exoplanet science images with bias subtracted and flatfielding\\n abexoplanet = []\\n for i in bexoplanet:\\n abexoplanet.append('A'+i)\\n #verify if previous superbias exist\\n if os.path.isfile('A'+i) == True:\\n os.system('rm A'+i)\\n print '\\\\n Will be create this images: \\\\n'\\n print abexoplanet\\n #flatifielding images\\n for i in range(len(abexoplanet)):\\n iraf.imarith(bexoplanet[i],'/','superflat.fits',abexoplanet[i])\\n use.update_progress((i+1.)/len(abexoplanet))\\n # print '\\\\n.... cleaning B'+planet+'*.fits images\\\\n'\\n # os.system('rm B'+planet+'*.fits')\\n print '\\\\n Statistics of AB'+planet+'*.fits images: \\\\n'\\n for i in range(len(abexoplanet)):\\n iraf.imstat(abexoplanet[i])\\n os.chdir(original_path) #change to save_path\\n return\",\n \"def AssignEpiNames(self):\\n# Sort each run in the series by its acquisition time.\\n epi_sort = self.epi_times.keys()\\n epi_sort.sort()\\n# Rewrite pfiles as an ordered list of p-files to be reconstructed.\\n for idx in xrange(len(epi_sort)):\\n entry = self.epi_times[epi_sort[idx]]\\n info = self.info[entry]\\n if info['data_filetype'] == 'ge_data':\\n self.pfiles_recon.append(entry)\\n info['run'] = '%0d' % (self.n_epi)\\n self.n_epi = self.n_epi + 1\\n plane = info['plane']\\n if not self.epinames.has_key(plane):\\n plane = 'any'\\n n_epi = self.epinames[plane]['n_epi']\\n if n_epi > len(self.epinames[plane]['names'])-1:\\n if self.epinames.has_key('any') and \\\\\\n n_epi < len(self.epinames['any']):\\n plane = 'any'\\n n_epi = self.epinames[plane]['n_epi']\\n else:\\n self.DumpInfo()\\n errstr = 'Not enough EPI names in template file'\\n raise RuntimeError(errstr)\\n# epiname = self.epinames[plane]['names'][n_epi]\\n\\n filebase = os.path.basename(self.epinames[plane]['names'][n_epi])\\n epi_mf_outdir = os.path.dirname(\\\\\\n self.epinames[plane]['names'][n_epi])\\n\\n epi_base = self.epinames[plane]['subdir'][n_epi]\\n tmp_outdir = '%s/%s' % (self.tmpdir, epi_base)\\n# Get output directory for raw epis.\\n if self.no_motcorr:\\n epi_r_outdir = epi_mf_outdir\\n elif self.keep_epi_raw:\\n epi_r_outdir = self.epi_scratch_space\\n else:\\n epi_r_outdir = tmp_outdir\\n\\n# Get output directory for motion-corrected epis.\\n if self.keep_epi_mot:\\n epi_m_outdir = self.epi_scratch_space\\n else:\\n epi_m_outdir = tmp_outdir\\n info['outdir'] = epi_mf_outdir\\n if n_epi < len(self.epinames[plane]['names']):\\n epiname = self.epinames[plane]['names'][n_epi]\\n info['imgfile'] = '%s/%s' % (epi_r_outdir, filebase)\\n else:\\n info['imgfile'] = '%s/s%0d_epi_run%0d' % \\\\\\n (epi_r_outdir, n_epi, idx+1)\\n self.epinames[plane]['n_epi'] += 1\\n\\n info['mot_file'] = '%s/%s_mtn.txt' % (epi_mf_outdir, filebase)\\n info['censor_prefix'] = '%s/%s' % (epi_mf_outdir, filebase)\\n info['imgfile_t'] = '%s/%s_t' % (epi_m_outdir, filebase)\\n if self.no_motcorr:\\n info['imgfile_m'] = None\\n info['imgfile_mf'] = None\\n info['imgfile_final'] = info['imgfile']\\n else:\\n info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)\\n if self.no_fmapcorr or info['fmap_entry'] is None:\\n info['imgfile_m'] = '%s/%s_m' % (epi_mf_outdir, filebase)\\n info['imgfile_mf'] = None\\n info['imgfile_final'] = info['imgfile_m']\\n else:\\n info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)\\n info['imgfile_mf'] = '%s/%s_mf' % (epi_mf_outdir, filebase)\\n info['imgfile_final'] = info['imgfile_mf']\\n info['skip'] = self.skip\\n info['motion_ref_frame'] = self.tmplt['motion_ref_frame']\\n\\n info['motion_interp'] = self.tmplt['epi_motion_interp']\\n if not info['motion_interp'].startswith('-'):\\n info['motion_interp'] = '-%s' % info['motion_interp']\\n\\n info['filetype'] = self.tmplt['epi_file_format']\\n info['valid'] = True\\n self.info[entry] = info\\n\\n if not self.no_motcorr:\\n epi_base = os.path.basename(info['imgfile_m'])\\n info['matfile_m'] = '%s/%s.aff12.1D' % (info['outdir'], epi_base)\\n info['matfile_mcat'] = '%s/%scat.aff12.1D' % (info['outdir'], epi_base)\",\n \"def writeRotNSeosfile(self, filename, tempPrescription, ye=None, addNeutrinoTerms=False):\\n assert not(ye == 'NuFull' and 'quantity' not in tempPrescription), \\\\\\n \\\"NuFull Beta-Eq not supported for fixedQuantityPrescription\\\"\\n\\n tempOfLog10Rhob = self.tempOfLog10RhobFuncFromPrescription(tempPrescription, ye)\\n\\n log10numberdensityMin = 2.67801536139756E+01\\n log10numberdensityMax = 3.97601536139756E+01 # = 1e16 g/cm^3\\n\\n npoints = 600\\n\\n #dlogn = (log10numberdensityMax - log10numberdensityMin) / (npoints - 1.0)\\n\\n logns = numpy.linspace(log10numberdensityMin, log10numberdensityMax, npoints)\\n\\n outfile = open(filename, 'w')\\n\\n for logn in logns:\\n numberdensityCGS = numpy.power(10.0, logn)\\n rho_b_CGS = numberdensityCGS * consts.CGS_AMU\\n logrho_b_CGS = numpy.log10(rho_b_CGS)\\n\\n temp = tempOfLog10Rhob(logrho_b_CGS)\\n\\n if ye is None and temp is not None:\\n self.setBetaEqState({'rho':rho_b_CGS, 'temp': temp})\\n elif ye is None and temp is None:\\n quantity = tempPrescription['quantity']\\n target = tempPrescription['target']\\n self.setConstQuantityAndBetaEqState({'rho': rho_b_CGS},\\n quantity,\\n target)\\n elif ye == 'NuFull':\\n self.setNuFullBetaEqState({'rho': rho_b_CGS, 'temp': temp})\\n else:\\n self.setState({'rho': rho_b_CGS, 'temp': temp, 'ye': ye})\\n\\n logpress, logeps, munu = self.query(['logpress', 'logenergy', 'munu'])\\n #print logpress, rho_b_CGS\\n P_nu = 0.0\\n if addNeutrinoTerms:\\n P_nu = P_nu_of(rho_b_CGS, temp, munu)\\n logpress = numpy.log10(numpy.power(10.0, logpress) + P_nu)\\n eps_nu = 3.0 * P_nu / rho_b_CGS\\n\\n #Total energy density (1.0 + eps) is in AGEO units\\n eps = (numpy.power(10.0, logeps) - self.energy_shift + eps_nu)\\\\\\n * consts.AGEO_ERG / consts.AGEO_GRAM\\n #print logpress\\n #print\\n totalEnergyDensity = rho_b_CGS * (1.0 + eps)\\n logTotalEnergyDensity = numpy.log10(totalEnergyDensity)\\n #print logrho_b_CGS ,logeps, numpy.power(10.0, logeps), eps, self.energy_shift\\n #print logrho_b_CGS, eps, logpress\\n outfile.write(\\\"{:24.14e}{:24.14e}{:24.14e}\\\\n\\\".format(logn,\\n logTotalEnergyDensity,\\n logpress))\",\n \"def TNG(self):\\n \\n import h5py as h5\\n filename = localpath+'input/yields/TNG/SNIa.hdf5'\\n # Read H5 file\\n f = h5.File(filename, \\\"r\\\")\\n \\n indexing = {}\\n indexing['H'] = 'Hydrogen'\\n indexing['He'] = 'Helium'\\n indexing['Li'] = 'Lithium'\\n indexing['Be'] = 'Beryllium'\\n indexing['B'] = 'Boron'\\n indexing['C'] = 'Carbon'\\n indexing['N'] = 'Nitrogen'\\n indexing['O'] = 'Oxygen'\\n indexing['F'] = 'Fluorine'\\n indexing['Ne'] = 'Neon'\\n indexing['Na'] = 'Sodium'\\n indexing['Mg'] = 'Magnesium'\\n indexing['Al'] = 'Aluminum'\\n indexing['Si'] = 'Silicon'\\n indexing['P'] = 'Phosphorus'\\n indexing['S'] = 'Sulphur'\\n indexing['Cl'] = 'Chlorine'\\n indexing['Ar'] = 'Argon'\\n indexing['K'] = 'Potassium'\\n indexing['Ca'] = 'Calcium'\\n indexing['Sc'] = 'Scandium'\\n indexing['Ti'] = 'Titanium'\\n indexing['V'] = 'Vanadium'\\n indexing['Cr'] = 'Chromium'\\n indexing['Mn'] = 'Manganese'\\n indexing['Fe'] = 'Iron'\\n indexing['Co'] = 'Cobalt'\\n indexing['Ni'] = 'Nickel'\\n indexing['Cu'] = 'Copper'\\n indexing['Zn'] = 'Zinc'\\n indexing['Ga'] = 'Gallium'\\n indexing['Ge'] = 'Germanium'\\n indexing['As'] = 'Arsenic'\\n indexing['Se'] = 'Selenium'\\n indexing['Br'] = 'Bromine'\\n indexing['Kr'] = 'Krypton'\\n indexing['Rb'] = 'Rubidium'\\n indexing['Sr'] = 'Strontium'\\n indexing['Y'] = 'Yttrium'\\n indexing['Zr'] = 'Zirconium'\\n indexing['Nb'] = 'Niobium'\\n indexing['Mo'] = 'Molybdenum'\\n \\n \\n self.elements = list(indexing.keys())\\n \\n self.table = {}\\n \\n self.metallicities = list([0.02]) # arbitrary since only one value\\n self.masses = list([np.sum(f['Yield'].value)]) # sum of all yields\\n \\n names = ['Mass','mass_in_remnants']+self.elements\\n \\n yield_subtable = {}\\n \\n base = np.zeros(len(self.masses))\\n list_of_arrays = []\\n for i in range(len(names)):\\n list_of_arrays.append(base)\\n \\n yield_subtable = np.core.records.fromarrays(list_of_arrays,names=names)\\n \\n yield_subtable['Mass'] = self.masses\\n yield_subtable['mass_in_remnants'] = np.asarray([-1*m for m in self.masses])\\n \\n for el_index,el in enumerate(self.elements):\\n yield_subtable[el] = np.divide(f['Yield'][el_index],self.masses)\\n \\n self.table[self.metallicities[0]] = yield_subtable\",\n \"def plot_seaice_predict_components(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' SIE prediction'\\n\\n\\n# Loading Seaice Trends\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n seaice = seaice\\n\\n\\n# Index contributions\\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\\n dataset = xr.open_dataset(filename + '.nc')\\n indicies = np.array([i for i in dataset])\\n values = np.array([dataset[i].values for i in dataset])\\n index_data = {}\\n for indexname in indicies[:-1]:\\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \\n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\\n\\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\\n\\n prediction = seaice.copy() * 0\\n predictions = []\\n for indexname in indicies:\\n if indexname in index_data.keys():\\n prediction += index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\\n predictions += [index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]]\\n else:\\n prediction += dataset[indexname]\\n predictions += [dataset[indexname]]\\n\\n\\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.axhline(y=0, alpha = 0.5)\\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10', alpha = 0.5)\\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89', alpha = 0.5)\\n\\n\\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'SIE', alpha = 0.5)\\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')), label = 'Prediction', alpha = 0.5)\\n\\n lines = ln1 + ln2\\n i = 0\\n for indexname in indicies[:-1]:\\n predict = index_data[indexname] * dataset[indexname]\\n lines += ax.plot(predict.time, (predict*area/250).sum(dim = ('x', 'y')), '--', label = f'{indexname}', linewidth=1)\\n i += 1\\n\\n labels = [line.get_label() for line in lines]\\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 3, loc = 'upper right')\\n plt.title(title)\\n\\n plt.savefig(imagefolder + seaicename + '_components.pdf')\\n plt.show()\\n\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.axhline(y=0, alpha = 0.5)\\n data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n # plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#EA1B10', alpha = 0.5)\\n data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(data.time.values.astype(float), data)\\n # plt.plot(data.time, data_m * data.time.values.astype(float) + data_b, color = '#177E89', alpha = 0.5)\\n\\n\\n norm = (seaice[seaicename]*area/250).sum(dim = ('x', 'y')) / 100\\n ln1 = ax.plot(seaice.time, (seaice[seaicename]*area/250).sum(dim = ('x', 'y')).sortby('time')/norm, label = 'SIE', alpha = 0.5)\\n ln2 = ax.plot(seaice.time, (prediction[seaicename]*area/250).sum(dim = ('x', 'y')).sortby('time')/norm, label = 'Prediction', alpha = 0.5)\\n\\n lines = ln1 + ln2\\n i = 0\\n for indexname in indicies[:-1]:\\n predict = index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\\n lines += ax.plot(predict.time, (predict*area/250).sum(dim = ('x', 'y'))/norm, '--', label = f'{indexname}', linewidth=1)\\n i += 1\\n\\n labels = [line.get_label() for line in lines]\\n plt.legend(lines,labels,bbox_to_anchor=(0.99, -0.15), ncol = 3, loc = 'upper right')\\n plt.title(title)\\n\\n plt.savefig(imagefolder + seaicename + '_components_normalised.pdf')\\n plt.show()\",\n \"def s(t,n, T=2*np.pi):\\n k = np.arange(1, n)\\n s = ((1/(2*k-1))*np.sin((2*(2*k-1)*np.pi*t)/ T))\\n return ((4/np.pi)*np.sum(s))\",\n \"def process_nitrate(self):\\n constituent = 'NitrateSurr'\\n db_path = '/said/{}/'.format(self.site['id'])\\n iv_path = db_path + 'iv'\\n df = self.store.get(iv_path)\\n\\n n_error = np.maximum(0.5, df[constituent]*.1)\\n df[constituent+'_U90.0'] = df[constituent] + n_error\\n df['NitrateSurr_L90.0'] = df.NitrateSurr - n_error\\n #clip values below 0\\n df['NitrateSurr_L90.0'] = np.maximum(0, df['NitrateSurr_L90.0'])\\n\\n update_table(self.store, iv_path, df)\",\n \"def test(nifti_region_to_save, path_where_store_out=\\\"pet_regions_segmented\\\"):\\n regions_used = \\\"three\\\"\\n list_regions = session.select_regions_to_evaluate(regions_used)\\n dic_regions_segmented = load_pet_regions_segmented(list_regions)\\n\\n region_container_3d = dic_regions_segmented[\\n nifti_region_to_save] # [patients x heigh, width, depth]\\n\\n for patient in range(0, region_container_3d.shape[0], 1):\\n img = nib.Nifti1Image(region_container_3d[patient, :, :, :], np.eye(4))\\n img.to_filename(os.path.join(path_where_store_out,\\n \\\"region_{0},patient_{1}.nii\\\".format(\\n regions_used, patient)))\",\n \"def rainfall_series(self):\\n\\n # assign local temporal variables\\n datatype = 'strds'\\n increment = str(self.rain_interval)+\\\" minutes\\\"\\n raster = 'raster'\\n rain_excess = 'rain_excess'\\n net_difference = 'net_difference'\\n #iterations = sum(1 for row in precip)\\n\\n # create a raster space time dataset\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.elevation_timeseries,\\n title=self.elevation_title,\\n description=self.elevation_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.depth_timeseries,\\n title=self.depth_title,\\n description=self.depth_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.erdep_timeseries,\\n title=self.erdep_title,\\n description=self.erdep_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.flux_timeseries,\\n title=self.flux_title,\\n description=self.flux_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.difference_timeseries,\\n title=self.difference_title,\\n description=self.difference_description,\\n overwrite=True)\\n\\n # register the initial digital elevation model\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=self.elevation,\\n start=self.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # create evolution object\\n evol = Evolution(\\n elevation=self.elevation,\\n precipitation=self.precipitation,\\n start=self.start,\\n rain_intensity=self.rain_intensity,\\n rain_interval=self.rain_interval,\\n walkers=self.walkers,\\n runoff=self.runoff,\\n mannings=self.mannings,\\n detachment=self.detachment,\\n transport=self.transport,\\n shearstress=self.shearstress,\\n density=self.density,\\n mass=self.mass,\\n grav_diffusion=self.grav_diffusion,\\n erdepmin=self.erdepmin,\\n erdepmax=self.erdepmax,\\n k_factor=self.k_factor,\\n c_factor=self.c_factor,\\n m=self.m,\\n n=self.n,\\n threads=self.threads,\\n fill_depressions=self.fill_depressions)\\n\\n # open txt file with precipitation data\\n with open(evol.precipitation) as csvfile:\\n\\n # check for header\\n has_header = csv.Sniffer().has_header(csvfile.read(1024))\\n\\n # rewind\\n csvfile.seek(0)\\n\\n # skip header\\n if has_header:\\n next(csvfile)\\n\\n # parse time and precipitation\\n precip = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\\n\\n # initial run\\n initial = next(precip)\\n evol.start = initial[0]\\n evol.rain_intensity = 'rain_intensity'\\n # compute rainfall intensity (mm/hr)\\n # from rainfall observation (mm)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity}\\\"\\n \\\"={rain_observation}\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_intensity=evol.rain_intensity,\\n rain_observation=float(initial[1]),\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # determine mode and run model\\n if self.mode == \\\"simwe_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps\\n # from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # run the landscape evolution model for each rainfall record\\n for row in precip:\\n\\n # update the elevation\\n evol.elevation=evolved_elevation\\n\\n # update time\\n evol.start=row[0]\\n\\n # compute rainfall intensity (mm/hr)\\n # from rainfall observation (mm)\\n rain_intensity = 'rain_intensity'\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity}\\\"\\n \\\"={rain_observation}\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_intensity=rain_intensity,\\n rain_observation=float(row[1]),\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\\n # plus the depth (m) per rainfall interval (min)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_excess}\\\"\\n \\\"={rain_intensity}\\\"\\n \\\"+{depth}\\\"\\n \\\"/1000.\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_excess=rain_excess,\\n rain_intensity=rain_intensity,\\n depth=depth,\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # update excess rainfall\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity} = {rain_excess}\\\".format(\\n rain_intensity='rain_intensity',\\n rain_excess=rain_excess),\\n overwrite=True)\\n evol.rain_intensity = rain_intensity\\n\\n # determine mode and run model\\n if self.mode == \\\"simwe_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps\\n # from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # remove temporary maps\\n gscript.run_command(\\n 'g.remove',\\n type='raster',\\n name=['rain_excess'],\\n flags='f')\\n\\n # compute net elevation change\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{net_difference}\\\"\\n \\\"= {evolved_elevation}-{elevation}\\\".format(\\n net_difference=net_difference,\\n elevation=self.elevation,\\n evolved_elevation=evol.elevation),\\n overwrite=True)\\n gscript.write_command(\\n 'r.colors',\\n map=net_difference,\\n rules='-',\\n stdin=difference_colors)\",\n \"def niriss_header(ra=53.1592277508136, dec=-27.782056346146, pa_aper=128.589, \\n filter='F150W', grism='GR150R'): \\n naxis = 2048, 2048\\n crpix = 1024, 1024\\n \\n cd = np.array([[ -0.0658, 0], [0, 0.0654]])/3600.\\n cd_rot = rotate_CD_matrix(cd, pa_aper)\\n \\n h = pyfits.Header()\\n \\n h['CRVAL1'] = ra\\n h['CRVAL2'] = dec\\n \\n h['WCSAXES'] = 2\\n h['CTYPE1'] = 'RA---TAN'\\n h['CTYPE2'] = 'DEC--TAN'\\n \\n for i in range(2):\\n h['NAXIS%d' %(i+1)] = naxis[i]\\n h['CRPIX%d' %(i+1)] = crpix[i]\\n h['CDELT%d' %(i+1)] = 1.0\\n for j in range(2):\\n h['CD%d_%d' %(i+1, j+1)] = cd_rot[i,j]\\n \\n ### Backgrounds\\n # http://www.stsci.edu/jwst/instruments/niriss/software-tools/wfss-simulations/niriss-wfss-cookbook.pdf\\n bg = {'F090W':0.50, 'F115W':0.47, 'F140M':0.23, 'F150W':0.48, 'F158M':0.25, 'F200W':0.44}\\n \\n h['BACKGR'] = bg[filter], 'Total, e/s'\\n h['FILTER'] = filter\\n h['INSTRUME'] = 'NIRISS'\\n h['READN'] = 6 , 'Rough, per pixel per 1 ks exposure' # e/pix/per\\n h['PHOTFLAM'] = 1.\\n h['PHOTPLAM'] = 1.\\n \\n if grism == 'GR150R':\\n h['GRISM'] = 'GR150R', 'Spectral trace along X'\\n else:\\n h['GRISM'] = 'GR150C', 'Spectral trace along Y'\\n \\n wcs = pywcs.WCS(h)\\n h['EXTVER'] = 1\\n \\n return h, wcs\",\n \"def time_calibration(input_file):\\n original_path = os.getcwd()\\n save_path = input_file['save_path']\\n #change to save data reduction directory\\n os.chdir(save_path)\\n print '\\\\n Reading the list of images ....\\\\n'\\n planet = input_file['exoplanet'] #set exoplanet name\\n images = sorted(glob.glob('AB'+planet+'*.fits'))\\n print images\\n #include de RA,DEC and epoch of the exoplanet\\n RA,DEC,epoch = input_file['RA'],input_file['DEC'],input_file['epoch']\\n #obtain ST JD using iraf task and introduce in the header\\n for i in range(len(images)):\\n hdr = fits.getheader(images[i])\\n if int(split(hdr['UT'],':')[0]) < int(hdr['timezone']):\\n new_date = use.yesterday(hdr['date-obs'])\\n #print images[i], new_date\\n else:\\n new_date = hdr['date-obs']\\n year,month,day = split(new_date,'-')\\n iraf.asttimes(year=year,month=month,day=day,time=hdr['loctime'],obs=input_file['observatory'])\\n JD = iraf.asttimes.jd #obtain julian date\\n LMST = iraf.asttimes.lmst #obtain the sideral time\\n LMST = use.sexagesimal_format(LMST) #convert sideral time in sexagesimal format\\n iraf.hedit(images[i],'ST',LMST,add='yes',verify='no',show='no',update='yes') #create the ST keyword in the header\\n iraf.ccdhedit(images[i],'LMST',LMST,type='string') #include the mean sideral time in the header\\n iraf.ccdhedit(images[i],'JD',JD,type='string') #include de julian date in the header\\n #include RA, and DEC of the object in your header\\n iraf.ccdhedit(images[i],\\\"RA\\\",RA,type=\\\"string\\\") #include right ascention in the header\\n iraf.ccdhedit(images[i],\\\"DEC\\\",DEC,type=\\\"string\\\") #include declination in the header\\n iraf.ccdhedit(images[i],\\\"epoch\\\",epoch,type=\\\"string\\\") #include epoch in the header\\n # use.update_progress((i+1.)/len(images))\\n print '\\\\n Setting airmass ....\\\\n'\\n for i in range(len(images)):\\n print '# ',images[i]\\n #iraf.hedit(images[i],'airmass',airmass,add='yes')\\n #iraf.hedit(images[i],'HJD',HJD,add='yes')\\n iraf.setairmass.observatory = input_file['observatory']\\n iraf.setairmass(images[i])\\n iraf.setjd.time = 'ut'\\n iraf.setjd(images[i])\\n print '\\\\n.... done.\\\\n'\\n #export information\\n hjd, jd, airmass, st = [],[],[],[]\\n for i in range(len(images)):\\n hdr = fits.getheader(images[i])\\n hjd.append(hdr['HJD'])\\n jd.append(hdr['JD'])\\n airmass.append(hdr['airmass'])\\n st.append(hdr['st'])\\n #saving the data\\n data = DataFrame([list(hjd),list(jd),list(st),list(airmass)]).T\\n data.columns = ['HJD','JD','ST','Airmass']\\n data.to_csv('results_iraf_calibrations.csv')\\n #change to workings directory\\n os.chdir(original_path)\\n return\",\n \"def calculateSNR(self):\\n pass\",\n \"def save2nifti(self, file_path):\\n #Define nifti1 datatype codes\\n NIFTI_TYPE_UINT8 = 2 # unsigned char\\n NIFTI_TYPE_INT16 = 4 # signed short\\n NIFTI_TYPE_INT32 = 8 # signed int.\\n NIFTI_TYPE_FLOAT32 = 16 # 32 bit float.\\n NIFTI_TYPE_COMPLEX64 = 32 # 64 bit complex = 2 32 bit floats\\n NIFTI_TYPE_FLOAT64 = 64 # 64 bit float = double.\\n NIFTI_TYPE_RGB24 = 128 # 3 8 bit bytes.\\n NIFTI_TYPE_INT8 = 256 # signed char.\\n NIFTI_TYPE_UINT16 = 512 # unsigned short.\\n NIFTI_TYPE_UINT32 = 768 # unsigned int.\\n NIFTI_TYPE_INT64 = 1024 #signed long long.\\n NIFTI_TYPE_UINT64 = 1280 # unsigned long long.\\n NIFTI_TYPE_FLOAT128 = 1536 # 128 bit float = long double.\\n NIFTI_TYPE_COMPLEX128 = 1792 #128 bit complex = 2 64 bit floats.\\n NIFTI_TYPE_COMPLEX256 = 2048 # 256 bit complex = 2 128 bit floats\\n NIFTI_TYPE_RGBA32 = 2304 # 4 8 bit bytes.\\n\\n #Detect the data type of the input data.\\n data_type = {\\n np.uint8: NIFTI_TYPE_UINT8,\\n np.uint16: NIFTI_TYPE_UINT16,\\n np.uint32: NIFTI_TYPE_UINT32,\\n np.float32: NIFTI_TYPE_FLOAT32,\\n np.int16: NIFTI_TYPE_INT16,\\n np.int32: NIFTI_TYPE_INT32,\\n np.int8: NIFTI_TYPE_INT8\\n }\\n if sys.maxint > 2 ** 32: # The platform is 64 bit\\n data_type[np.float128] = NIFTI_TYPE_FLOAT128\\n data_type[np.float64] = NIFTI_TYPE_FLOAT64\\n data_type[np.int64] = NIFTI_TYPE_INT64\\n data_type[np.uint64] = NIFTI_TYPE_UINT64\\n data_type[np.complex64] = NIFTI_TYPE_COMPLEX64\\n data_type[np.complex128] = NIFTI_TYPE_COMPLEX128\\n data_type[np.complex256] = NIFTI_TYPE_COMPLEX256\\n\\n data = np.rot90(self._data, 3)\\n if data_type.has_key(data.dtype.type):\\n self._header['datatype'] = data_type[data.dtype.type]\\n self._header['cal_max'] = data.max()\\n self._header['cal_min'] = 0\\n image = nib.nifti1.Nifti1Image(data, None, self._header)\\n nib.nifti1.save(image, file_path)\",\n \"def add_noise(self, snr, unit=None):\\n return self.from_time(self.fs, noisify(self.in_time, snr, unit=unit))\",\n \"def _make_s2n(self, ffile, hdul):\\n bname = os.path.basename(ffile)\\n phu = hdul[0].copy()\\n phu.data = None\\n if 'S/N' in hdul:\\n hdu = hdul['S/N']\\n else:\\n if 'FLUX' in hdul and 'ERROR' in hdul:\\n log.debug(f'Making S/N image from FLUX and ERROR '\\n f'extensions for {bname}.')\\n hdu = fits.ImageHDU(hdul['FLUX'].data,\\n hdul['FLUX'].header)\\n s = hdul['FLUX'].data\\n n = hdul['ERROR'].data\\n elif 'FLUX' in hdul and 'STDDEV' in hdul:\\n log.debug(f'Making S/N image from FLUX and STDDEV '\\n f'extensions for {bname}.')\\n hdu = fits.ImageHDU(hdul['FLUX'].data,\\n hdul['FLUX'].header)\\n s = hdul['FLUX'].data\\n n = hdul['STDDEV'].data\\n elif 'STOKES I' in hdul and 'ERROR I' in hdul:\\n log.debug(f'Making S/N image from STOKES I and '\\n f'ERROR I extensions for {bname}.')\\n hdu = fits.ImageHDU(hdul['STOKES I'].data,\\n hdul['STOKES I'].header)\\n s = hdul['STOKES I'].data\\n n = hdul['ERROR I'].data\\n else:\\n raise ValueError(f'Cannot determine S/N from extensions '\\n f'in file {bname}')\\n\\n hdu.data = s / n\\n hdu.header['EXTNAME'] = 'S/N'\\n hdu.header['BUNIT'] = ''\\n\\n # blank out data outside of range\\n try:\\n low, high = self.disp_parameters['s2n_range']\\n hdu.data[hdu.data < float(low)] = np.nan\\n hdu.data[hdu.data > float(high)] = np.nan\\n except (ValueError, AttributeError, IndexError, TypeError):\\n pass\\n\\n s2n = fits.HDUList([phu, hdu])\\n return s2n\",\n \"def save_to_nii(im, filename, outdir=\\\"\\\", mode=\\\"image\\\", system=\\\"sitk\\\"):\\n if system == \\\"sitk\\\":\\n if mode == 'label':\\n img = sitk.GetImageFromArray(im.astype(np.uint8))\\n else:\\n img = sitk.GetImageFromArray(im.astype(np.float32))\\n if not os.path.exists(\\\"./{}\\\".format(outdir)):\\n os.mkdir(\\\"./{}\\\".format(outdir))\\n sitk.WriteImage(img, \\\"./{}/{}.nii.gz\\\".format(outdir, filename))\\n else:\\n img = np.rot90(im, k=2, axes= (1,2))\\n OUTPUT_AFFINE = np.array(\\n [[0, 0, 1, 0],\\n [0, 1, 0, 0],\\n [1, 0, 0, 0],\\n [0, 0, 0, 1]])\\n if mode == 'label':\\n img = nibabel.Nifti1Image(img.astype(np.uint8), OUTPUT_AFFINE)\\n else:\\n img = nibabel.Nifti1Image(img.astype(np.float32), OUTPUT_AFFINE)\\n if not os.path.exists(\\\"./{}\\\".format(outdir)):\\n os.mkdir(\\\"./{}\\\".format(outdir))\\n nibabel.save(img, \\\"./{}/{}.nii.gz\\\".format(outdir, filename))\",\n \"def Writefile(self, outfile, verbose=True):\\n \\n self.outfile = outfile\\n \\n # Write SUNTANS grid to file\\n nc = Dataset(outfile, 'w', format='NETCDF3_CLASSIC')\\n nc.Description = 'SUNTANS subsetted history file'\\n nc.Author = ''\\n nc.Created = datetime.now().isoformat()\\n nc.type = 'SUNTANS HIS file'\\n #pdb.set_trace()\\n nc.createDimension('Nc', self.Nc)\\n nc.createDimension('Np', self.Np)\\n nc.createDimension('Ne', self.Ne)\\n nc.createDimension('Nk', self.Nk)\\n nc.createDimension('numsides', self.numsides)\\n \\n nc.createDimension('time', None)\\n \\n def write_nc_var(var, name, dimensions, units=None):\\n nc.createVariable(name, 'f8', dimensions)\\n if units is not None:\\n nc.variables[name].units = units\\n nc.variables[name][:] = var\\n if verbose:\\n print ' ... wrote ', name\\n \\n def create_nc_var(name, dimensions, units=None):\\n nc.createVariable(name, 'f8', dimensions)\\n if units is not None:\\n nc.variables[name].units = units\\n if verbose:\\n print ' ... wrote ', name\\n \\n # Grid variables\\n write_nc_var(self.xv, 'xv', ('Nc'))\\n write_nc_var(self.yv, 'yv', ('Nc'))\\n write_nc_var(self.xp, 'xp', ('Np'))\\n write_nc_var(self.yp, 'yp', ('Np'))\\n write_nc_var(self.xe, 'xe', ('Ne'))\\n write_nc_var(self.ye, 'ye', ('Ne'))\\n write_nc_var(self.dz, 'dz', ('Nk'))\\n write_nc_var(self.dv, 'dv', ('Nc'))\\n write_nc_var(self.Ac, 'Ac', ('Nc'))\\n write_nc_var(self.Nk, 'Nk', ('Nc'))\\n write_nc_var(self.face, 'face', ('Nc','numsides'))\\n write_nc_var(self.mark, 'mark', ('Ne'))\\n write_nc_var(self.cells, 'cells', ('Nc','numsides'))\\n \\n \\n # Create the data variables\\n create_nc_var('time',('time'),'seconds since 1990-01-01 00:00:00')\\n create_nc_var('salt',('time','Nk','Nc'),'psu')\\n create_nc_var('temp',('time','Nk','Nc'),'degrees C')\\n create_nc_var('uc',('time','Nk','Nc'),'meter second-1')\\n create_nc_var('vc',('time','Nk','Nc'),'meter second-1')\\n create_nc_var('nu_v',('time','Nk','Nc'),'m2 s-1')\\n create_nc_var('rho',('time','Nk','Nc'),'kg m-3')\\n create_nc_var('tau_x',('time','Nc'),'N m-2')\\n create_nc_var('tau_y',('time','Nc'),'N m-2')\\n create_nc_var('eta',('time','Nc'),'m')\\n \\n nc.close()\",\n \"def fractalise(fractals,n,fpath):\\n # Use different fractals for MP\\n n = nnn-n\\n # Load numpy array\\n for f in fractals:\\n if f.endswith('{:02d}.npy'.format(n)):\\n frac_arr = N.load(f)\\n f_out = f\\n \\n # Normalise to +/- 1%\\n frac_arr_pc = normalise(frac_arr)\\n\\n # Load virgin data\\n nc = Dataset(fpath,'r+')\\n virgindata = nc.variables['SMOIS'][:,:,:,:]\\n newdata = N.zeros_like(virgindata)\\n shp = virgindata[0,0,:,:].shape\\n\\n # Interpolate fractal to wrfinput grid\\n frac_arr_interp = interpolate_to_wrfgrid(frac_arr_pc,shp)\\n \\n # Perturb each soil level (broadcasting is scary)\\n for lv in N.arange(virgindata.shape[1]):\\n newdata[0,lv,:,:] = virgindata[0,lv,:,:] + (virgindata[0,lv,:,:]*frac_arr_interp)\\n\\n # Write back\\n assert N.all(newdata > 0.0)\\n # pdb.set_trace()\\n nc.variables['SMOIS'][:,:,:,:] = newdata\\n nc.close()\\n return f_out\",\n \"def SNR(self, flux_sky, n_pix_star, flux_star, gain, ron):\\n SNR = (gain*flux_star/sqrt(gain*flux_star + n_pix_star*gain*flux_sky + n_pix_star*ron**2)) \\n return SNR\",\n \"def plot_subplot_trend(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/subplots/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' SIE trends'\\n\\n\\n# Loading Seaice Trends\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n if seaice_source == 'nsidc':\\n seaice = seaice/250\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = xr.apply_ufunc(scipy.stats.linregress, seaice[seaicename].time.values.astype(float), seaice[seaicename], input_core_dims=[['time'],['time']], vectorize=True, dask='parallelized', output_dtypes=[float]*5, output_core_dims=[[]]*5)\\n if seaice_source =='ecmwf':\\n seaice_m, seaice_b, seaice_r_value, seaice_p_value, seaice_std_err = scipy.stats.linregress(seaice[seaicename].time.values.astype(float), seaice[seaicename])\\n seaice_m = seaice_m * 1e9 * 60 * 60 * 24 * 365\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n seaice_m = seaice_m*area\\n seaice_m = seaice_m.where(seaice_m != 0)\\n seaice_m = seaice_m.where(seaice_p_value <= 0.05)\\n\\n\\n# Index xontributions\\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\\n dataset = xr.open_dataset(filename + '.nc')\\n indicies = np.array([i for i in dataset])\\n values = np.array([dataset[i].values for i in dataset])\\n index_data = {}\\n for indexname in indicies[:-1]:\\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \\n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\\n newdata = {} \\n for indexname in indicies[:-1]:\\n a = scipy.stats.linregress(index_data[indexname].time.values.astype(float), index_data[indexname])\\n newdata[indexname] = a[0] * dataset[indexname] * 24*60*60*365e9\\n title = temp_decomp.capitalize() + ' '\\n if detrend == 'detrended':\\n title += detrend + ' '\\n title += temporal_resolution\\n title += f' SIC trend contributions'\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n # Plotting\\n for i in range(len(indicies)-1):\\n indexname = indicies[i]\\n newdata[indexname] = newdata[indexname] * area / 250\\n newdata[indexname] = newdata[indexname].where(newdata[indexname] !=0)\\n\\n\\n\\n fig = plt.figure(figsize = (15,5))\\n\\n # seaice_m = log_data(seaice_m)\\n max_ = min(seaice_m.max(),-seaice_m.min())\\n # max_ = 1\\n divnorm = TwoSlopeNorm(vmin=-max_, vcenter=0, vmax=max_)\\n ax = fig.add_subplot(131, projection = ccrs.SouthPolarStereo())\\n # Plotting\\n contor = ax.contourf(seaice_m.x, seaice_m.y, seaice_m, cmap = 'RdBu', levels = 100, norm = divnorm, transform=ccrs.SouthPolarStereo())\\n ax.coastlines()\\n ax.set_axis_off()\\n # cbar = plt.colorbar(contor)\\n # cbar.set_label('Trend in SIE (km$^2$ yr$^{-1}$)')\\n # plt.title(title)\\n ax = [fig.add_subplot(2,6,3, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,4, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,9, projection = ccrs.SouthPolarStereo()),fig.add_subplot(2,6,10, projection = ccrs.SouthPolarStereo())]\\n for i in range(len(indicies)-1):\\n indexname = indicies[i]\\n # newdata[indexname] = log_data(newdata[indexname])\\n newdata[indexname] = newdata[indexname].where(newdata[indexname] !=0)\\n contor = ax[i].contourf(dataset.x, dataset.y, newdata[indexname], cmap = 'RdBu', norm = divnorm, transform=ccrs.SouthPolarStereo(), levels = 100)\\n ax[i].coastlines()\\n ax[i].set_axis_off()\\n ax[i].set_title(indicies[i])\\n\\n ax = fig.add_subplot(1,3,3, projection = ccrs.SouthPolarStereo())\\n data = seaice_m\\n for i in range(len(indicies)-1):\\n indexname = indicies[i]\\n data = data - newdata[indexname]\\n ax.contourf(dataset.x, dataset.y, data, cmap = 'RdBu', norm = divnorm, levels = 100, transform=ccrs.SouthPolarStereo())\\n ax.coastlines()\\n fig.subplots_adjust(right=0.9)\\n cbar_ax = fig.add_axes([0.95, 0.15, 0.05, 0.7])\\n cbar = fig.colorbar(cm.ScalarMappable(norm=divnorm, cmap='RdBu'), cax=cbar_ax, shrink=0.88)\\n\\n plt.savefig(imagefolder + seaicename + '.pdf')\\n plt.show()\",\n \"def calculate_piN_piS(codonseqs, method, codon_table, het=False):\\n analysis = {\\\"seqname\\\": \\\"\\\", \\\"piN\\\": -1, \\\"piS\\\": -1, \\\"piNpiS\\\": -1, \\\"pi\\\": -1, \\\"method\\\":method}\\n x = seqfreqs(codonseqs)\\n #if 'piNpiS' in options.debug:\\n # print(\\\"freqs are: {}\\\".format(x))\\n # print(\\\"len codonseqs is: \\\", len(codonseqs))\\n piN = 0\\n piS = 0\\n for i in range(len(codonseqs)):\\n for j in range(i+1, len(codonseqs)):\\n #print(codonseqs[i], codonseqs[j])\\n if not het:\\n dN, dS = cal_dn_ds(codonseqs[i], codonseqs[j], codon_table=codon_table, method=method)\\n piN = piN + (x[i] * x[j] * dN)\\n piS = piS + (x[i] * x[j] * dS)\\n #if 'piNpiS' in options.debug:\\n # print(\\\"{0} dN{1}{2}={3} dS{1}{2}={4}\\\".format(method, i, j, dN, dS))\\n else:\\n try:\\n dN, dS = cal_dn_ds(codonseqs[i], codonseqs[j], codon_table=codon_table, method=method)\\n piN = piN + (x[i] * x[j] * dN)\\n piS = piS + (x[i] * x[j] * dS)\\n except:\\n pass\\n\\n analysis['piN'] = piN\\n analysis['piS'] = piS\\n try:\\n analysis['piNpiS'] = piN/piS\\n except:\\n analysis['piNpiS'] = 0\\n #if 'piNpiS' in options.debug:\\n # print (\\\"{0} dN={1:.3f} dS={2:.3f} piN/piS = {3:.3f}\\\".format(\\n # method, analysis['piN'], analysis['piS'], analysis['piNpiS']))\\n\\n return analysis\",\n \"def save():\\n pl.savefig('/home/filippini/Documents/plot/RUN55/compa'+INFO_RUN+'.png')\",\n \"def visualize(self, save: bool = False) -> None:\\n import matplotlib.pyplot as plt\\n import inspect\\n\\n plt.style.use('seaborn-whitegrid')\\n plt.rcParams['figure.figsize'] = [10, 5]\\n if not self.inverse_transformation:\\n grid = np.linspace(0, 1, 10000)\\n func = self.pdf(np.linspace(0, 1, 10000))\\n try:\\n plt.plot(grid, func)\\n except:\\n plt.plot(grid, np.repeat(func, 10000))\\n plt.title('Intensity function')\\n plt.xlabel('time')\\n plt.ylabel('value')\\n if save:\\n try:\\n plt.savefig('intensity_function_' + inspect.getsource(self.pdf).split('return')[\\n 1].strip() + '.png')\\n print('Saved as ' + 'intensity_function_' + inspect.getsource(self.pdf).split('return')[\\n 1].strip() + '.png')\\n except:\\n warnings.warn(\\\"Saving intensity function failed!\\\")\\n plt.show()\\n plt.clf()\\n\\n t = self.generate()\\n plt.step(t, list(range(0, len(t))))\\n plt.title('Simulated trajectory')\\n plt.xlabel('time')\\n plt.ylabel('value')\\n if save:\\n try:\\n plt.savefig(\\n 'trajectory_' + inspect.getsource(self.pdf).split('return')[1].strip() + '.png')\\n print('Saved as ' + 'trajectory_' + inspect.getsource(self.pdf).split('return')[\\n 1].strip() + '.png')\\n except:\\n warnings.warn(\\\"Saving trajectory failed!\\\")\\n plt.show()\\n plt.clf()\\n\\n plt.plot(t, list(np.repeat(0, len(t))), '.')\\n plt.title('Simulated points')\\n plt.xlabel('time')\\n if save:\\n try:\\n plt.savefig('points_' + inspect.getsource(self.pdf).split('return')[1].strip() + '.png')\\n print('Saved as ' + 'points_' + inspect.getsource(self.pdf).split('return')[\\n 1].strip() + '.png')\\n except:\\n warnings.warn(\\\"Saving points failed!\\\")\\n plt.show()\\n plt.clf()\",\n \"def time_info(input_file):\\n original_path = os.getcwd() #set original directory\\n save_path = input_file['save_path']\\n planet = input_file['exoplanet'] #set exoplanet name\\n print '\\\\nObtain the images .... \\\\n'\\n print 'Change to ', save_path\\n os.chdir(save_path) #change to save directory where is our scvience images\\n images = sorted(glob.glob('AB'+input_file['exoplanet']+'*.fits'))\\n print '\\\\nImages = \\\\n',images\\n tempo_loc = [] #time object\\n SUN = [] #Sun coordinate object\\n ra_sun, dec_sun, dsun = np.zeros(len(images)),np.zeros(len(images)),np.zeros(len(images)) #sun coordinates\\n JD = np.zeros(len(images)) #julian date from time object\\n ST = np.zeros(len(images))\\n HJD = np.zeros(len(images))\\n #create the exoplanet object coordianate\\n exoplanet = SkyCoord(dec=input_file['DEC'],ra=input_file['RA'],unit=('deg','deg'),frame=input_file['frame'])\\n print '\\\\nObtain data info from header ....\\\\n'\\n for i in range(len(images)):\\n hdr = fits.getheader(images[i])\\n UTC = hdr['date-obs']+'T'+hdr['UT'] #string that contain the time in UTC in isot format\\n tempo_loc.append(Time(UTC,scale=input_file['scale-time'],format='isot',location=(input_file['lon-obs'],input_file['lat-obs'])))#,input_data['altitude'])))\\n JD[i] = tempo_loc[i].jd\\n ST[i] = tempo_loc[i].sidereal_time('apparent').hour\\n SUN.append(get_sun(tempo_loc[i]))\\n ra_sun[i],dec_sun[i] = SUN[i].ra.deg, SUN[i].dec.deg\\n dsun[i] = SUN[i].distance.value\\n HJD[i] = use.hjd_date(JD[i],dsun[i],dec_sun[i],ra_sun[i],exoplanet.dec.deg,exoplanet.ra.deg,circular_orbit=input_file['circular_orbit'])\\n use.update_progress((i+1.)/len(images))\\n print '\\\\n.... done.\\\\n'\\n print '\\\\n Time from header = \\\\n'\\n #print '\\\\nImages ** UTC (YYYY-MM-DDTHH:MM:SS) ** JD (7d.5d) ** ST (hours) ** ST (HH:MM:SS) ** Sun Coordinate (epoch,RA,DEC,Distance) (deg,deg,AU) \\\\n'\\n ST_string = []\\n for i in range(len(images)):\\n ST1 = int(ST[i])\\n ST2 = int((ST[i]-ST1)*60.)\\n ST3 = (((ST[i]-ST1)*60.)-ST2)*60\\n ST_string.append(str(ST1)+':'+str(ST2)+':'+str(ST3))\\n tempo_loc[i] = tempo_loc[i].value\\n use.update_progress((i+1.)/len(images))\\n #print images[i], ' ** ',tempo_loc[i], ' ** ', JD[i], ' ** ', ST[i],' ** ',ST_string[i],' ** ',sun_loc[i],' ** ',HJD[i]\\n print '\\\\nSave data file ... \\\\n'\\n data = DataFrame([images,tempo_loc,list(JD),list(ST),list(ST_string),list(ra_sun),list(dec_sun),list(dsun),list(HJD)]).T\\n data.columns=['images','UTC','JD','ST','ST_isot','RA_SUN','DEC_SUN','D_SUN','HJD']\\n print data\\n data.to_csv('results.csv')\\n os.chdir(original_path)\\n return\",\n \"def get_sinogram( self, N = 128, theta_vec = None ):\\n\\n if theta_vec is None:\\n theta_vec = np.deg2rad(np.linspace(0, 359, 360))\\n if self.phantom_type in ['ellipses', 'shepp_logan', 'modified_shepp_logan']:\\n analytical_sinogram = radon_ellipses(N, theta_vec, np.copy(self.matrix), circle=self.circle);\\n elif self.phantom_type == 'squares':\\n analytical_sinogram = radon_squares(N, theta_vec, np.copy(self.matrix), circle = self.circle);\\n elif self.phantom_type == 'rectangles':\\n analytical_sinogram = radon_rectangles(N, theta_vec, np.copy(self.matrix), circle=self.circle);\\n else:\\n print('error on the choice of phantom type')\\n return analytical_sinogram\",\n \"def intensityRatioSave(self,outFile=0):\\n if not outFile:\\n outFile = self.IntensityRatio['filename']\\n print(' saving ratio to filename = %s'%(outFile))\\n if hasattr(self, 'IntensityRatio'):\\n temperature=self.IntensityRatio['temperature']\\n eDensity=self.IntensityRatio['eDensity']\\n ratio=self.IntensityRatio['ratio']\\n out = open(outFile,'w')\\n nvalues=len(ratio)\\n # need to add 7 lines to maintain IDL like files\\n out.write(outFile+'\\\\n') #1\\n out.write(self.IntensityRatio['desc']+'\\\\n') #2\\n out.write(' created with ChiantiPy using CHIANTI version '+ chdata.ChiantiVersion +'\\\\n') #3\\n out.write(' columns are temperature, eDensity, ratio'+'\\\\n') #5\\n tunit = 'K'\\n out.write(' temperature in '+tunit+', electron eDensity in cm^(-3)'+'\\\\n') #6\\n out.write(' ratio given in '+self.Defaults['flux']+'\\\\n') #4\\n out.write(' '+'\\\\n') #7\\n for ivalue in range(nvalues):\\n s='%12.3e %12.3e %12.3e \\\\n' % (temperature[ivalue],eDensity[ivalue],ratio[ivalue])\\n out.write(s)\\n out.close()\\n else:\\n print(' in .intensityRatioSave(), no IntensityRatio is found')\",\n \"def analyseSNRfluctuations(self, fsu='FSUA', snr='PD', plot=True,\\n xlims=None, title='', normalized=False):\\n t = self.raw['IMAGING_DATA_'+fsu].data.field('TIME')\\n\\n if (fsu=='FSUB' and self.insmode=='NORMAL') or \\\\\\n (fsu=='FSUA' and self.insmode=='SWAPPED'):\\n wno = np.where((np.interp(t, self.raw['OPDC'].data.field('TIME'),\\n self.raw['OPDC'].data.field('STATE'))<3))\\n else:\\n wno = np.where(np.interp(t, self.raw['DOPDC'].data.field('TIME'),\\n self.raw['DOPDC'].data.field('STATE'))<3)\\n if (fsu=='FSUB' and self.insmode=='NORMAL') or \\\\\\n (fsu=='FSUA' and self.insmode=='SWAPPED'):\\n wftk = np.where((np.interp(t, self.raw['OPDC'].data.field('TIME'),\\n self.raw['OPDC'].data.field('STATE'))>=7))\\n else:\\n wftk = np.where(np.interp(t, self.raw['DOPDC'].data.field('TIME'),\\n self.raw['DOPDC'].data.field('STATE'))>=7)\\n\\n snrNo = self.raw['IMAGING_DATA_'+fsu].data.field(snr+'SNR')[wno[0]]\\n snrFtk = self.raw['IMAGING_DATA_'+fsu].data.field(snr+'SNR')[wftk[0]]\\n\\n if plot:\\n fig = plt.figure(1, figsize=(8,4))\\n plt.subplots_adjust(left=0.08, right=.98)\\n fig.clf()\\n if normalized:\\n norma = np.median(snrNo)\\n plt.xlabel('normalized SNR to out-of-fringes')\\n else:\\n norma = 1\\n plt.xlabel('SNR')\\n\\n plt.hist(snrNo/norma, bins=50, normed=True, alpha=0.5,\\n color='r', label='NOT FTK')\\n hno = np.histogram(snrNo, bins=50, normed=True)\\n plt.hist(snrFtk/norma, bins=50, normed=True, alpha=0.5,\\n color='g', label='FTK')\\n hftk = np.histogram(snrFtk, bins=50, normed=True)\\n\\n if not xlims is None:\\n plt.xlim(xlims[0], xlims[1])\\n plt.title(title)\\n poissonDist = lambda x,p:\\\\\\n poisson(p['m']*p['p']).pmf(np.int_(np.floor(x*p['p'])))*p['p'] +\\\\\\n (x*p['p']-np.floor(x*p['p']))/\\\\\\n (np.ceil(x*p['p'])-np.floor(x*p['p']))*\\\\\\n (poisson(p['m']*p['p']).pmf(np.int_(np.ceil(x*p['p'])))*p['p'] -\\n poisson(p['m']*p['p']).pmf(np.int_(np.floor(x*p['p'])))*p['p'])\\n\\n guess = {'m':np.median(snrNo), 'p':1}\\n X = 0.5*(hno[1][:-1]+hno[1][1:])\\n fit = dpfit.leastsqFit(poissonDist, X, guess, hno[0])\\n guessNo = fit['best']\\n uncer = fit['uncer']\\n chi2 = fit['chi2']\\n model = fit['model']\\n print guessNo\\n print 'NOFTK; POISSON: LAMBDA', guessNo['p']*guessNo['m']\\n print 'NOFTK; POISSON: STD/MEAN', 1/np.sqrt(guessNo['p']*guessNo['m'])\\n plt.plot(X/norma, poissonDist(X, guessNo)*norma, '-r',\\n linewidth=3, alpha=0.8, linestyle='dashed')\\n\\n guess = {'m':np.median(snrNo), 'p':1/10.}\\n X = 0.5*(hftk[1][:-1]+hftk[1][1:]) \\n fit = dpfit.leastsqFit(poissonDist, X, guess, hftk[0])\\n guess = fit['best']\\n uncer = fit['uncer']\\n chi2 = fit['chi2']\\n model = fit['model']\\n print guess\\n print ' FTK; POISSON: LAMBDA', guess['p']*guess['m']\\n print ' FTK; POISSON: STD/MEAN', 1/np.sqrt(guess['p']*guess['m'])\\n plt.plot(X/norma, poissonDist(X, guess)*norma, '-g',\\n linewidth=3, alpha=0.8, linestyle='dashed')\\n plt.legend( loc='upper left')\\n #plt.xscale('log')\\n\\n print 'DIFFERENCIATION',\\\\\\n np.abs(guess['m']-guessNo['m'])/\\\\\\n (np.sqrt(guessNo['m']/guessNo['p']) +\\n np.sqrt(guess['m']/guess['p']))\\n return\",\n \"def generate_siaf_pre_flight_reference_files_nircam():\\n\\n instrument = 'NIRCam'\\n overwrite_wedge_file = False\\n overwrite_grism_file = False\\n\\n\\n # wedge definitions\\n wedge_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_wedge_offsets.txt'.format(instrument.lower()))\\n\\n if (not os.path.isfile(wedge_file) or (overwrite_wedge_file)):\\n\\n wedge_offsets = Table.read(os.path.join(JWST_SOURCE_DATA_ROOT, instrument, 'wedge_offsets.txt'), format='ascii.basic', delimiter=' ', guess=False)\\n\\n comments = []\\n comments.append('{} detector parameter definition file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the wedge offsets.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n wedge_offsets.meta['comments'] = comments\\n wedge_offsets.write(wedge_file, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False)\\n\\n # grism definitions\\n grism_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_grism_parameters.txt'.format(instrument.lower()))\\n\\n if (not os.path.isfile(wedge_file) or (overwrite_grism_file)):\\n # grism parameters, see WFSS worksheet in EXCEL SIAF\\n grism_parameters = Table.read(grism_file, format='ascii.basic', delimiter=',', guess=False)\\n\\n # Save a backup copy of the grism file\\n cmd = 'cp {} {}'.format(grism_file,os.path.join(JWST_TEMPORARY_DATA_ROOT, instrument, 'nircam_siaf_grism_parameters_backup.txt'))\\n os.system(cmd)\\n\\n # different sign in Y for NRCB apertures\\n factor = np.array(\\n [1. if 'NRCA' in grism_parameters['aperture_name'][i] else -1. for i in range(len(grism_parameters))])\\n\\n for col in grism_parameters.colnames[1:]:\\n # these are Sci coordinates\\n if col[0] != 'D':\\n if 'X' in col:\\n grism_parameters['D{}'.format(col)] = grism_parameters[col].data - 1024.5\\n elif 'Y' in col:\\n grism_parameters['D{}'.format(col)] = factor * (grism_parameters[col].data - 1024.5)\\n\\n\\n\\n comments = []\\n comments.append('{} grism parameter definition file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the grism parameters.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n grism_parameters.meta['comments'] = comments\\n grism_parameters.write(grism_file, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False)\\n\\n # Transformation parameters, mapping used to select rows in cold_fit_[] file\\n coldfit_name_mapping = {\\n 'NRCA1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMASW_201609161431',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASWoOTESKY_201609161431',\\n },\\n 'NRCA5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RT_20170307121022',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALWToOTESKY_RT_20170307121022',\\n },\\n 'NRCB1_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_1_20161025081604',\\n 'pixels_to_mm':'NIRCAMBSW_1ToNIRCAMBSW_20161025081604',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB2_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_2_20161025081912',\\n 'pixels_to_mm':'NIRCAMBSW_2ToNIRCAMBSW_20161025081912',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB3_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_3_20161025082300',\\n 'pixels_to_mm':'NIRCAMBSW_3ToNIRCAMBSW_20161025082300',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB4_FULL': {'degrees_to_mm':'OTESKYToNIRCAMBSW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBSWToNIRCAMBSW_4_20161025082647',\\n 'pixels_to_mm':'NIRCAMBSW_4ToNIRCAMBSW_20161025082647',\\n 'mm_to_degrees':'NIRCAMBSWToOTESKY_RT_20170307121024',\\n },\\n 'NRCB5_FULL' :{'degrees_to_mm':'OTESKYToNIRCAMBLW_RT_20170307121023',\\n 'mm_to_pixels':'NIRCAMBLWToNIRCAMBLW_1_20161227162336',\\n 'pixels_to_mm':'NIRCAMBLW_1ToNIRCAMBLW_20161227162336',\\n 'mm_to_degrees':'NIRCAMBLWToOTESKY_RT_20170307121023',\\n },\\n 'NRCA1_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\\n },\\n 'NRCA2_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA3_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RNDNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASW_RNDNCToOTESKY_202110261138',\\n },\\n 'NRCA4_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMASW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA1_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_1_20161025081540',\\n 'pixels_to_mm':'NIRCAMASW_1ToNIRCAMASW_20161025081540',\\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\\n },\\n 'NRCA2_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_2_20161025081547',\\n 'pixels_to_mm':'NIRCAMASW_2ToNIRCAMASW_20161025081547',\\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\\n },\\n 'NRCA3_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BARNC_202110261138',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_3_20161025081552',\\n 'pixels_to_mm':'NIRCAMASW_3ToNIRCAMASW_20161025081552',\\n 'mm_to_degrees':'NIRCAMASW_BARNCToOTESKY_202110261138',\\n },\\n 'NRCA4_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMASW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMASWToNIRCAMASW_4_20161025081557',\\n 'pixels_to_mm':'NIRCAMASW_4ToNIRCAMASW_20161025081557',\\n 'mm_to_degrees':'NIRCAMASW_BARToOTESKY_202005150434',\\n },\\n 'NRCA5_FULL_WEDGE_RND' :{'degrees_to_mm':'OTESKYToNIRCAMALW_RND_202005150434',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALW_RNDToOTESKY_202005150434',\\n },\\n 'NRCA5_FULL_WEDGE_BAR' :{'degrees_to_mm':'OTESKYToNIRCAMALW_BAR_202005150434',\\n 'mm_to_pixels':'NIRCAMALWToNIRCAMALW_1_20161227162042',\\n 'pixels_to_mm':'NIRCAMALW_1ToNIRCAMALW_20161227162042',\\n 'mm_to_degrees':'NIRCAMALW_BARToOTESKY_202005150434',\\n }\\n }\\n\\n # coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('cold_fit_201703071210.csv'))\\n coldfit_source_data_file = os.path.join(JWST_SOURCE_DATA_ROOT, instrument, '{}'.format('nircam_cold_fit.txt'))\\n print('NIRCam coldfit data from', coldfit_source_data_file)\\n t = open(coldfit_source_data_file)\\n coldfit_source_data = t.readlines()\\n t.close()\\n # remove comments from read content\\n coldfit_source_data = [line for line in coldfit_source_data if line[0] != '#']\\n\\n siaf_detector_layout = iando.read.read_siaf_detector_layout()\\n # siaf_alignment_parameters = iando.read.read_siaf_alignment_parameters(instrument)\\n siaf_aperture_definitions = iando.read.read_siaf_aperture_definitions(instrument)\\n # aperture_dict = {}\\n aperture_name_list = siaf_aperture_definitions['AperName'].tolist()\\n\\n # generate alignment reference file, one file for all master apertures\\n outfile = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_alignment.txt'.format(instrument.lower()))\\n siaf_alignment = Table()\\n\\n for AperName in aperture_name_list:\\n\\n # process the master apertures of NIRCam\\n if AperName in siaf_detector_layout['AperName']:\\n (A, B, C, D, betaX, betaY, V2Ref, V3Ref) = nircam_get_polynomial_both(AperName, siaf_aperture_definitions, coldfit_name_mapping, coldfit_source_data)\\n\\n #generate distortion reference file\\n number_of_coefficients = len(A)\\n polynomial_degree = int((np.sqrt(8 * number_of_coefficients + 1) - 3) / 2)\\n siaf_index = []\\n exponent_x = []\\n exponent_y = []\\n for i in range(polynomial_degree + 1):\\n for j in np.arange(i + 1):\\n siaf_index.append('{:d}{:d}'.format(i, j))\\n exponent_x.append(i - j)\\n exponent_y.append(j)\\n\\n distortion_reference_table = Table((siaf_index, exponent_x, exponent_y, A, B, C, D), names=(\\n 'siaf_index', 'exponent_x', 'exponent_y', 'Sci2IdlX', 'Sci2IdlY', 'Idl2SciX', 'Idl2SciY'))\\n distortion_reference_table.add_column(\\n Column([AperName] * len(distortion_reference_table), name='AperName'), index=0)\\n distortion_reference_file_name = os.path.join(JWST_SOURCE_DATA_ROOT, instrument,\\n '{}_siaf_distortion_{}.txt'.format(instrument.lower(),\\n AperName.lower()))\\n # distortion_reference_table.pprint()\\n comments = []\\n comments.append('{} distortion reference file for SIAF\\\\n'.format(instrument))\\n comments.append('Aperture: {}\\\\n'.format(AperName))\\n comments.append('Based on coefficients given in {},'.format(os.path.basename(coldfit_source_data_file)))\\n # comments.append('that were rescaled, shifted for a different reference pixel location, and rotated:')\\n # comments.append('Rotation of {:2.3f} deg was removed and is carried separately in V3IdlYangle.'.format(\\n # np.rad2deg(V3angle))) # *units.deg.to(units.arcsecond)\\n # if 'may_2015' in distortion_file_name:\\n # comments.append(\\n # 'These parameters are stored in the currently (January 2018) active SIAF (PRDOPSSOC-G-012). ')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n distortion_reference_table.meta['comments'] = comments\\n distortion_reference_table.write(distortion_reference_file_name, format='ascii.fixed_width',\\n delimiter=',', delimiter_pad=' ', bookend=False, overwrite=True)\\n\\n V3SciYAngle = betaY\\n V3SciXAngle = betaX\\n if np.abs(V3SciYAngle) < 90.:\\n V3IdlYAngle = V3SciYAngle\\n else:\\n V3IdlYAngle = V3SciYAngle - np.sign(V3SciYAngle) * 180.\\n\\n if len(siaf_alignment) == 0: # first entry\\n siaf_alignment['AperName'] = ['{:>30}'.format(AperName)]\\n siaf_alignment['V3IdlYAngle'] = [V3IdlYAngle]\\n siaf_alignment['V3SciXAngle'] = V3SciXAngle #[np.rad2deg(betaX)]\\n siaf_alignment['V3SciYAngle'] = V3SciYAngle #[np.rad2deg(betaY)]\\n siaf_alignment['V2Ref'] = [V2Ref]\\n siaf_alignment['V3Ref'] = [V3Ref]\\n else:\\n siaf_alignment.add_row(['{:>30}'.format(AperName), V3IdlYAngle, V3SciXAngle, V3SciYAngle, V2Ref,V3Ref])\\n comments = []\\n comments.append('{} alignment parameter reference file for SIAF'.format(instrument))\\n comments.append('')\\n comments.append('This file contains the focal plane alignment parameters of master apertures calibrated')\\n comments.append('during FGS-SI alignment.')\\n comments.append('')\\n comments.append('Generated {} {}'.format(timestamp.isot, timestamp.scale))\\n comments.append('by {}'.format(username))\\n comments.append('')\\n siaf_alignment.meta['comments'] = comments\\n siaf_alignment.write(outfile, format='ascii.fixed_width', delimiter=',',\\n delimiter_pad=' ', bookend=False, overwrite=True)\",\n \"def calc_rsi(image):\\n\\n # roll axes to conventional row,col,depth\\n img = np.rollaxis(image, 0, 3)\\n\\n # bands: Coastal(0), Blue(1), Green(2), Yellow(3), Red(4), Red-edge(5), NIR1(6), NIR2(7)) Multispectral\\n COAST = img[:, :, 0]\\n B = img[:, :, 1]\\n G = img[:, :, 2]\\n Y = img[:, :, 3]\\n R = img[:, :, 4]\\n RE = img[:, :, 5]\\n NIR1 = img[:, :, 6]\\n NIR2 = img[:, :, 7]\\n\\n arvi = old_div((NIR1 - (R - (B - R))), (NIR1 + (R - (B - R))))\\n dd = (2 * NIR1 - R) - (G - B)\\n gi2 = (B * -0.2848 + G * -0.2434 + R * -0.5436 + NIR1 * 0.7243 + NIR2 * 0.0840) * 5\\n gndvi = old_div((NIR1 - G), (NIR1 + G))\\n ndre = old_div((NIR1 - RE), (NIR1 + RE))\\n ndvi = old_div((NIR1 - R), (NIR1 + R))\\n ndvi35 = old_div((G - R), (G + R))\\n ndvi84 = old_div((NIR2 - Y), (NIR2 + Y))\\n nirry = old_div((NIR1), (R + Y))\\n normnir = old_div(NIR1, (NIR1 + R + G))\\n psri = old_div((R - B), RE)\\n rey = old_div((RE - Y), (RE + Y))\\n rvi = old_div(NIR1, R)\\n sa = old_div(((Y + R) * 0.35), 2) + old_div((0.7 * (NIR1 + NIR2)), 2) - 0.69\\n vi1 = old_div((10000 * NIR1), (RE) ** 2)\\n vire = old_div(NIR1, RE)\\n br = (old_div(R, B)) * (old_div(G, B)) * (old_div(RE, B)) * (old_div(NIR1, B))\\n gr = old_div(G, R)\\n rr = (old_div(NIR1, R)) * (old_div(G, R)) * (old_div(NIR1, RE))\\n\\n ###Built-Up indices\\n wvbi = old_div((COAST - RE), (COAST + RE))\\n wvnhfd = old_div((RE - COAST), (RE + COAST))\\n\\n ###SIs\\n evi = old_div((2.5 * (NIR2 - R)), (NIR2 + 6 * R - 7.5 * B + 1))\\n L = 0.5 # some coefficient for Soil Adjusted Vegetation Index (SAVI) DO NOT INCLUDE IN FEATURES\\n savi = old_div(((1 + L) * (NIR2 - R)), (NIR2 + R + L))\\n msavi = old_div((2 * NIR2 + 1 - ((2 * NIR2 + 1) ** 2 - 8 * (NIR2 - R)) ** 0.5), 2)\\n bai = old_div(1.0, ((0.1 + R) ** 2 + 0.06 + NIR2))\\n rgi = old_div(R, G)\\n bri = old_div(B, R)\\n\\n rsi = np.stack(\\n [arvi, dd, gi2, gndvi, ndre, ndvi, ndvi35, ndvi84, nirry, normnir, psri, rey, rvi, sa, vi1, vire, br, gr, rr,\\n wvbi, wvnhfd, evi, savi, msavi, bai, rgi, bri],\\n axis=2)\\n\\n return rsi\",\n \"def save_time_spent(self):\\n\\n ratings_dir = Path(self.out_dir).resolve() / cfg.suffix_ratings_dir\\n if not ratings_dir.exists():\\n makedirs(ratings_dir, exist_ok=True)\\n\\n timer_file = ratings_dir / '{}_{}_{}'.format(\\n self.vis_type, self.suffix, cfg.file_name_timer)\\n\\n lines = '\\\\n'.join(['{},{}'.format(sid, elapsed_time)\\n for sid, elapsed_time in self.timer.items()])\\n\\n # saving to disk\\n try:\\n with open(timer_file, 'w') as tf:\\n tf.write(lines)\\n except:\\n print('Unable to save timer info to disk -- printing them to log:')\\n print(lines)\\n raise IOError('Error in saving timer info to file!')\\n\\n # printing summary\\n times = np.array(list(self.timer.values()))\\n if len(times) < 10:\\n print('\\\\n\\\\ntimes spent per subject in seconds:\\\\n{}'.format(lines))\\n\\n print('\\\\nMedian time per subject : {} seconds'.format(np.median(times)))\\n print('\\\\t5th and 95th percentile of distribution of times spent '\\n ': {} seconds'.format(np.nanpercentile(times, [5, 95])))\",\n \"def make_sn(sn_thres=0, format=\\\"png\\\", snsig=False):\\r\\n ###############################################\\r\\n # Read values of S/N\\r\\n sn = np.loadtxt(outtable, usecols=(14,))\\r\\n if snsig:\\r\\n sigma = np.loadtxt(outtable, usecols=(3,))\\r\\n sn /= sigma / 100.\\r\\n ###############################################\\r\\n # Find good (and bad) regions according to S/N\\r\\n good = np.where(((~np.isnan(sn)) & (sn >= sn_thres)))[0]\\r\\n bad = np.where((sn < sn_thres))[0]\\r\\n ###############################################\\r\\n # Filter S/N\\r\\n sn = sn[good]\\r\\n ###############################################\\r\\n # Colorbar limits\\r\\n vmin, vmax = 10, 50\\r\\n # Set limits for the plot\\r\\n norm = Normalize(vmin, vmax)\\r\\n ###############################################\\r\\n # Set colormap\\r\\n cmap = \\\"cubelaw_r\\\"\\r\\n cmap = \\\"Spectral\\\"\\r\\n # Produces a collection of polygons with colors according to S/N values\\r\\n coll = PolyCollection(polygons_bins[good], array=sn, cmap=cmap,\\r\\n edgecolors='w', norm=norm, linewidths=1.)\\r\\n ###############################################\\r\\n # Initiate figure and axis for matplotlib\\r\\n fig, ax = plt.subplots(1, 1, figsize=(6.4, 6), )\\r\\n fig.subplots_adjust(left=0.09, right=0.985, bottom=0.092, top=0.98,\\r\\n hspace=0.05, wspace=0.06)\\r\\n ###############################################\\r\\n # ax.add_patch(Rectangle((-100, -100), 200, 200, facecolor=\\\"0.8\\\", zorder=0,\\r\\n # alpha=0.5))\\r\\n ###############################################\\r\\n # Draw the polygons\\r\\n draw_map(fig, ax, coll, lims=40)\\r\\n ###############################################\\r\\n # Add contours according to V-band image\\r\\n # draw_contours(\\\"residual\\\", fig, ax, c=\\\"k\\\")\\r\\n draw_contours(\\\"vband\\\", fig, ax, c=\\\"k\\\")\\r\\n # Draw actual slit positions\\r\\n # canvas.draw_slits(ax, slit_type=1, fc=\\\"r\\\", ec=\\\"r\\\", ignore=ignore_slits )\\r\\n # canvas.draw_slits(ax, slit_type=3, fc=\\\"r\\\", ec=\\\"r\\\", ignore=ignore_slits )\\r\\n canvas.draw_slits_ids(ax, slits, fc=\\\"r\\\", ec=\\\"r\\\")\\r\\n ###############################################\\r\\n # Draw white rectangle in the position of the colorbar so background\\r\\n # stars do not overplot the labels and ticks\\r\\n plt.gca().add_patch(Rectangle((18, -36), 20, 10, alpha=1, zorder=10000,\\r\\n color=\\\"w\\\"))\\r\\n ###############################################\\r\\n # Draw the colorbar\\r\\n label = r\\\"100 S/N [pix] / $\\\\sigma$\\\" if snsig else r\\\"S/N\\\"\\r\\n draw_colorbar(fig, ax, coll, ticks=np.linspace(vmin, vmax, 5),\\r\\n cblabel=label, cbar_pos=[0.16, 0.15, 0.17, 0.04])\\r\\n ##############################################\\r\\n # Write labels\\r\\n xylabels(ax)\\r\\n ##############################################\\r\\n # Draw positions of galaxies\\r\\n # draw_galaxies(fig, ax)\\r\\n ##############################################\\r\\n # Save the figure\\r\\n plt.savefig(\\\"figs/sn.{0}\\\".format(format), dpi=300)\\r\\n # plt.savefig(\\\"figs/sn.pdf\\\", dpi=100)\\r\\n # plt.savefig(\\\"figs/sn.eps\\\", dpi=2500, format=\\\"eps\\\")\\r\\n # plt.savefig(\\\"figs/sn.png\\\", dpi=300)\\r\\n return\",\n \"def tiled_writing(red, nir, output):\\n \\n #open datasets\\n src_red = rio.open(red)\\n src_nir = rio.open(nir)\\n \\n #define raster properies and update datatype\\n meta = src_red.meta.copy()\\n meta.update({'dtype':'float32'}) # meta is a dictionary\\n outfile = output\\n #open outfile in writing mode with the properties of defined raster band\\n with rio.open(outfile, 'w', **meta) as dst:\\n #iterate over blocks of the bands, calculate ndvi for each block \\n # and put the blocks back together\\n for window in calc_tiles(src_red, tile_size_x, tile_size_y):\\n red_block = src_red.read(window=window, masked=True)\\n nir_block = src_nir.read(window=window, masked=True)\\n #cast ndarrays to Float32 type\\n red = red_block.astype('f4')\\n nir = nir_block.astype('f4')\\n #allow division by zero\\n np.seterr(divide='ignore', invalid='ignore')\\n #calculate ndvi and write raster\\n ndvi = (nir - red) / (nir + red)\\n dst.write(ndvi, window=window)\\n\\n #close dataset\\n src_red.close()\\n src_nir.close()\\n return outfile\",\n \"def save(self, inst):\\n n = inst.dimensions[\\\"n\\\"]\\n with open(self.location, \\\"wt\\\") as f:\\n f.write(f\\\"measurements: {n}\\\\n\\\")\\n f.write(f\\\"time temperature\\\\n\\\")\\n for time, temp in zip(inst.time, inst.temperature):\\n f.write(f\\\"{time:4} {temp:12}\\\\n\\\")\",\n \"def SNR_vs_NSA():\\n NSA = np.array([1, 2, 3, 4])\\n noise = np.array([0.7, 0.5, 0.4, 0.3]) # **2\\n comp1 = np.array([39.3, 40.8, 41.3, 41.2])\\n comp2 = np.array([55.3, 56.5, 56.8, 56.4])\\n comp3 = np.array([69.4, 69.2, 69.1, 68.7])\\n comp4 = np.array([53.5, 53.3, 53.7, 53.0])\\n comp5 = np.array([65.1, 65.4, 65.7, 65.7])\\n\\n comp = [comp1, comp2, comp3, comp4, comp5]\\n colors = [\\\"#1f77b4\\\", \\\"#ff7f0e\\\", \\\"#2ca02c\\\", \\\"#d62728\\\", \\\"#9467bd\\\"]\\n for i, j, f in zip(comp, range(1, 6), colors):\\n popt, _ = curve_fit(SNR, (NSA, i / noise), i / noise, p0=np.array([2, 0.5]))\\n k, n = popt\\n y_new = SNR((NSA, i / noise), k, n)\\n plt.plot(NSA, y_new, c=f)\\n plt.plot(NSA, i / noise, \\\"o\\\", label=\\\"Compartment %i, n=%.2f\\\" % (j, n))\\n\\n plt.grid()\\n plt.legend()\\n plt.ylabel(\\\"SNR\\\")\\n plt.xlabel(\\\"NSA\\\")\\n plt.show()\\n\\n # curve fit to see if SNR goes as root of NSA\",\n \"def run_psavg_sims(bursttimefile):\\n\\n nfolder = [5,6,8,12]\\n datadirs = [\\\"P20165/20165-01-01-000\\\", \\\"P20165/20165-01-01-001\\\", \\\"P20165/20165-01-01-002\\\",\\n \\\"P10223/10223-01-03-01\\\", \\\"P10223/10223-01-03-010\\\" ]\\n\\n data_all, unbary_all, tstart_all, tend_all, t0_all, pcus_all, std1dir_all = [], [], [], [], [], [], []\\n\\n for d in datadirs:\\n print(\\\"I am on directory %s\\\" %d)\\n files = rxteburst.search_filenames_recursively(\\\"./%s/\\\"%d, \\\"*1div8192*.asc\\\")\\n if len(files) == 0:\\n files = rxteburst.search_filenames_recursively(\\\"./%s/\\\"%d, \\\"*1div-32768s*.asc\\\")\\n if len(files) == 0:\\n files = rxteburst.search_filenames_recursively(\\\"./%s/\\\"%d, \\\"*1div8*.asc\\\")\\n #print(\\\"File to use %s\\\" %files[0])\\n data = rxte.RXTEData(times=None, channels=None, datafile=files[0], npcus=None, ra=None, dec=None, emid=None, emiddir=None, bary=True)\\n\\n len_datafile = len(files[0].split(\\\"/\\\")[-1])\\n len_processed = len(files[0].split(\\\"/\\\")[-2])\\n std1dir_all.append(files[0][:-(len_datafile+len_processed+1)])\\n\\n data_all.append(np.array([p.time for p in data.photons])+data.t0)\\n unbary_all.append(np.array([p.unbary for p in data.photons])+data.t0)\\n tstart_all.append(data.photons[0].unbary+data.t0)\\n tend_all.append(data.photons[-1].unbary+data.t0)\\n t0_all.append(data.t0)\\n pcus_all.append(data.pcus)\\n\\n t0_sorted, tstart_sorted, tend_sorted, data_sorted, pcus_sorted, std1dir_sorted, unbary_sorted = \\\\\\n zip(*sorted(zip(t0_all, tstart_all, tend_all, data_all, pcus_all, std1dir_all, unbary_all)))\\n t0_sorted = np.array(t0_sorted)\\n\\n psno = [5,6,8,12]\\n m_all = [30, 23, 23, 50]\\n\\n for n,m in zip(psno, m_all):\\n psavg_all = sgr1900_results.make_randomly_sampled_periodograms(datadirs, bursttimefile, m, n=1000,\\n save_step=100, fileroot=\\\"sgr1806_psavg%i\\\"%n,\\n data_sorted=data_sorted, t0_sorted=t0_sorted,\\n pcus_sorted=pcus_sorted, tend_sorted=tend_sorted,\\n tstart_sorted=tstart_sorted,\\n unbary_sorted=unbary_sorted)\\n\\n return\",\n \"def plot_seaice_predict_components_scatter(anomlous = False, temporal_resolution = 'monthly', spatial_resolution = 1, detrend = False, imagefolder = 'images/timeseries/prediction/',seaice_source='nsidc'):\\n output_folder = 'processed_data/SIC/'\\n if seaice_source == 'ecmwf':\\n output_folder = 'processed_data/ERA5/SIC/'\\n\\n if anomlous:\\n temp_decomp = 'anomalous'\\n else:\\n temp_decomp = 'raw'\\n\\n\\n title = temp_decomp.capitalize() + ' '\\n\\n if detrend:\\n dt = 'detrended'\\n title += dt + ' '\\n else:\\n dt = 'raw'\\n\\n title += temporal_resolution\\n title += ' SIE prediction'\\n\\n\\n# Loading Seaice Trends\\n seaicename = f'{temp_decomp}_{temporal_resolution}_{spatial_resolution}_{dt}'\\n seaice = xr.open_dataset(output_folder + seaicename +'.nc')\\n area = xr.open_dataset('data/area_files/processed_nsidc.nc').area\\n seaice = seaice\\n\\n\\n# Index contributions\\n filename = f'processed_data/regressions/spatial_multiple/regr_{temp_decomp}_{temporal_resolution}_{dt}_{spatial_resolution}'\\n dataset = xr.open_dataset(filename + '.nc')\\n indicies = np.array([i for i in dataset])\\n values = np.array([dataset[i].values for i in dataset])\\n index_data = {}\\n for indexname in indicies[:-1]:\\n filename = f'{indexname}_{temp_decomp}_{temporal_resolution}_{dt}'\\n index_data[indexname] = xr.open_dataset('processed_data/INDICIES/' + filename +'.nc')[indexname]\\n index_data[indexname] = (index_data[indexname] - index_data[indexname].mean()) \\n index_data[indexname] = index_data[indexname] / index_data[indexname].std()\\n\\n times = list(set.intersection(set(seaice.time.values), *(set(index_data[i].time.values)for i in indicies[:-1])))\\n\\n prediction = seaice.copy() * 0\\n predictions = []\\n for indexname in indicies:\\n if indexname in index_data.keys():\\n prediction += index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\\n predictions += [index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]]\\n else:\\n prediction += dataset[indexname]\\n predictions += [dataset[indexname]]\\n\\n\\n seaice = seaice.sortby('time').sel(time=times).sortby('time')\\n prediction = prediction.sortby('time').sel(time=times).sortby('time')\\n\\n\\n fig = plt.figure(figsize = [7,14])\\n\\n ax1 = fig.add_subplot(4,2,1)\\n ax2 = fig.add_subplot(4,2,2)\\n ax3 = fig.add_subplot(4,2,3)\\n ax4 = fig.add_subplot(4,2,4)\\n smallaxes = [ax1,ax2,ax3,ax4]\\n AX = fig.add_subplot(2,1,2)\\n x_data = (seaice[seaicename]*area/250).sum(dim = ('x', 'y'))\\n for i in range(4):\\n ax = smallaxes[i]\\n indexname = indicies[i]\\n predict = index_data[indexname].sel(time=times).sortby('time') * dataset[indexname]\\n y_data = (predict*area/250).sum(dim = ('x', 'y'))\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(x_data, y_data)\\n r, p = scipy.stats.pearsonr(x_data, y_data)\\n ax.scatter(x_data, y_data)\\n ax.plot(x_data,data_m*x_data+data_b)\\n ax.set_title(indexname)\\n lim = max([-min(x_data),-min(y_data),max(x_data),max(y_data)])*1.05\\n ax.text(-lim*0.9, lim*0.55, f'gradient = {data_m:.2f}\\\\ncorrelation = {r:.2f}\\\\np-value = {p:.2f}')\\n ax.set_ylim([-lim, lim])\\n ax.set_xlim([-lim, lim])\\n ax.axhline(0, alpha = 0.2)\\n ax.axvline(0, alpha = 0.2)\\n y_data = (prediction[seaicename]*area/250).sum(dim = ('x', 'y'))\\n AX.scatter(x_data, y_data)\\n data_m, data_b, data_r_value, data_p_value, data_std_err = scipy.stats.linregress(x_data, y_data)\\n r, p = scipy.stats.pearsonr(x_data, y_data)\\n AX.plot(x_data,data_m*x_data+data_b)\\n AX.set_title('Total Prediction')\\n lim = max([-min(x_data),-min(y_data),max(x_data),max(y_data)])*1.05\\n AX.text(-lim*0.9, lim*0.7, f'gradient = {data_m:.2f}\\\\ncorrelation = {r:.2f}\\\\np-value = {p:.2f}')\\n AX.set_ylim([-lim, lim])\\n AX.set_xlim([-lim, lim])\\n AX.axhline(0, alpha = 0.2)\\n AX.axvline(0, alpha = 0.2)\\n fig.suptitle(title)\\n\\n plt.savefig(imagefolder + seaicename + 'scatter.pdf')\\n plt.show()\",\n \"def _write_nemo_hr_file(rpn_hr_ds_path, nemo_hr_ds_path):\\n with xarray.open_dataset(rpn_hr_ds_path) as rpn_hr:\\n logging.debug(\\n f\\\"calculating specific humidity & incoming longwave radiation from {rpn_hr_ds_path}\\\"\\n )\\n qair, ilwr, rh = _calc_qair_ilwr(rpn_hr)\\n u_out, v_out = _rotate_winds(rpn_hr)\\n data_vars = {\\n \\\"nav_lon\\\": rpn_hr.nav_lon,\\n \\\"nav_lat\\\": rpn_hr.nav_lat,\\n # [:, 0] drops z dimension that NEMO will not tolerate\\n \\\"qair\\\": qair[:, 0],\\n \\\"RH_2maboveground\\\": rh[:, 0],\\n \\\"therm_rad\\\": ilwr[:, 0],\\n \\\"u_wind\\\": u_out[:, 0],\\n \\\"v_wind\\\": v_out[:, 0],\\n # \\\"LHTFL_surface\\\": ** needs to be calculated**,\\n }\\n nemo_rpn_vars = (\\n (\\\"atmpres\\\", \\\"PN\\\"),\\n (\\\"percentcloud\\\", \\\"NT\\\"),\\n (\\\"PRATE_surface\\\", \\\"RT\\\"),\\n (\\\"precip\\\", \\\"PR\\\"),\\n (\\\"solar\\\", \\\"FB\\\"),\\n (\\\"tair\\\", \\\"TT\\\"),\\n )\\n missing_vars = \\\"\\\"\\n for nemo_var, rpn_var in nemo_rpn_vars:\\n try:\\n # [:, 0] drops z dimension that NEMO will not tolerate\\n data_vars.update({nemo_var: getattr(rpn_hr, rpn_var)[:, 0]})\\n except AttributeError:\\n # Variable is missing from RPN dataset, so provide a placeholder DataArray\\n # full of NaNs that we will deal with later via interpolation\\n data_vars.update(\\n {nemo_var: xarray.DataArray(numpy.full_like(qair[:, 0], numpy.nan))}\\n )\\n missing_vars = (\\n \\\", \\\".join((missing_vars, nemo_var)) if missing_vars else nemo_var\\n )\\n logging.warning(f\\\"missing RPN variable {rpn_var} from {rpn_hr_ds_path}\\\")\\n nemo_hr = xarray.Dataset(\\n data_vars=data_vars, coords=rpn_hr.coords, attrs=rpn_hr.attrs\\n )\\n nemo_hr.attrs[\\\"history\\\"] += (\\n f\\\"\\\\n{arrow.now().format('ddd MMM DD HH:mm:ss YYYY')}: \\\"\\n f\\\"Add specific and relative humidity and incoming longwave radiation variables from \\\"\\n f\\\"correlations\\\"\\n )\\n if missing_vars:\\n nemo_hr.attrs[\\\"missing_variables\\\"] = missing_vars\\n _add_vars_metadata(nemo_hr)\\n _write_netcdf_file(nemo_hr, nemo_hr_ds_path)\",\n \"def _check_PSNR(self, dataset, is_test=False):\\n\\n # process one image per iter for test phase\\n if is_test:\\n batch_size = 1\\n else:\\n batch_size = 1 # self.batch_size\\n\\n dataloader = DataLoader(dataset, batch_size=batch_size,\\n shuffle=False, num_workers=1)\\n\\n avr_psnr = 0\\n avr_ssim = 0\\n\\n # book keeping variables for test phase\\n psnrs = [] # psnr for each image\\n ssims = [] # ssim for each image\\n proc_time = [] # processing time\\n outputs = [] # output for each image\\n names = []\\n\\n for batch, sample in enumerate(dataloader):\\n input_batch, label_batch, name = sample['lr'], sample['hr'], sample['im_name']\\n\\n # Wrap with torch Variable\\n input_batch, label_batch = self._wrap_variable(input_batch,\\n label_batch,\\n self.use_gpu)\\n\\n if is_test:\\n start = time.time()\\n if self.model_name in ['TDAN']:\\n output_batch = chop_forward(input_batch, self.model, 4)\\n #output_batch = chop_forward(input_batch, self.model, 4)\\n #output_batch = forward_x8(input_batch, self.model).unsqueeze(0)\\n #print(output_batch.size())\\n # _, lrs = self.model(input_batch)\\n # output_batch = lrs[:, -1, :, :, :]\\n else:\\n output_batch = self.model(input_batch)\\n elapsed_time = time.time() - start\\n else:\\n if self.model_name in ['TDAN']:\\n #output_batch, _ = self.model(input_batch)\\n output_batch = chop_forward(input_batch, self.model, 4)\\n else:\\n output_batch = self.model(input_batch)\\n # ssim is calculated with the normalize (range [0, 1]) image\\n ssim = pytorch_ssim.ssim(output_batch + 0.5, label_batch + 0.5, size_average=False)\\n ssim = torch.sum(ssim.data)\\n avr_ssim += ssim\\n\\n # calculate PSRN\\n output = output_batch.data\\n label = label_batch.data\\n\\n output = (output + 0.5) * 255\\n label = (label + 0.5) * 255\\n\\n output = quantize(output, 255)\\n label = quantize(label, 255)\\n # diff = input - target\\n\\n output = output.squeeze(dim=0)\\n label = label.squeeze(dim=0)\\n\\n psnr = self._comput_PSNR(output / 255.0, label / 255.0)\\n # print(psnr)\\n avr_psnr += psnr\\n\\n # save psnrs and outputs for statistics and generate image at test time\\n if is_test:\\n psnrs.append(psnr)\\n ssims.append(ssim)\\n proc_time.append(elapsed_time)\\n np_output = output.cpu().numpy()\\n outputs.append(np_output)\\n names.append(name)\\n\\n epoch_size = len(dataset)\\n avr_psnr /= epoch_size\\n avr_ssim /= epoch_size\\n stats = (psnrs, ssims, proc_time)\\n\\n return avr_psnr, avr_ssim, stats, outputs, names\",\n \"def write_file(self):\\n if self.it_num % 5 == 0:\\n #plt.imshow(self.grid)\\n #plt.savefig(\\\"output%.4d.png\\\" % self.it_num, bbox_inches='tight')\\n io.savemat(\\\"MLOutput%.4d\\\" % self.it_num, { \\\"Grid\\\":self.grid})\",\n \"def writetipsy(self, snapshot, halos, tipsyoutfile, hubble=None):\\n from . import analysis\\n from . import tipsy\\n from .analysis import cosmology\\n from snapshot import _new as new\\n import math\\n s = snapshot\\n outfile = tipsyoutfile\\n nhalos = halos._nhalos\\n nstar = nhalos\\n sout = new(star=nstar) # create new tipsy snapshot written as halos.\\n sout.properties['a'] = s.properties['a']\\n sout.properties['z'] = s.properties['z']\\n sout.properties['boxsize'] = s.properties['boxsize']\\n if hubble is None:\\n hubble = s.properties['h']\\n sout.properties['h'] = hubble\\n # ! dangerous -- rho_crit function and unit conversions needs simplifying\\n rhocrithhco = cosmology.rho_crit(s, z=0, unit=\\\"Msol Mpc^-3 h^2\\\")\\n lboxkpc = sout.properties['boxsize'].ratio(\\\"kpc a\\\")\\n lboxkpch = lboxkpc * sout.properties['h']\\n lboxmpch = lboxkpc * sout.properties['h'] / 1000.\\n tipsyvunitkms = lboxmpch * 100. / (math.pi * 8. / 3.) ** .5\\n tipsymunitmsun = rhocrithhco * lboxmpch ** 3 / sout.properties['h']\\n\\n for ii in xrange(nhalos):\\n h = halos[ii + 1].properties\\n sout.star[ii]['mass'] = h['mass'] / hubble / tipsymunitmsun\\n # tipsy units: box centered at 0. (assume 0<=x<=1)\\n sout.star[ii]['x'] = h['Xc'] / lboxkpch - 0.5\\n sout.star[ii]['y'] = h['Yc'] / lboxkpch - 0.5\\n sout.star[ii]['z'] = h['Zc'] / lboxkpch - 0.5\\n sout.star[ii]['vx'] = h['VXc'] / tipsyvunitkms\\n sout.star[ii]['vy'] = h['VYc'] / tipsyvunitkms\\n sout.star[ii]['vz'] = h['VZc'] / tipsyvunitkms\\n sout.star[ii]['eps'] = h['Rvir'] / lboxkpch\\n sout.star[ii]['metals'] = 0.\\n sout.star[ii]['phi'] = 0.\\n sout.star[ii]['tform'] = 0.\\n\\n sout.write(fmt=tipsy.TipsySnap, filename=outfile)\\n return sout\",\n \"def save2nifti(self, file_path):\\n # Define nifti1 datatype codes\\n NIFTI_TYPE_UINT8 = 2 # unsigned char\\n NIFTI_TYPE_INT16 = 4 # signed short\\n NIFTI_TYPE_INT32 = 8 # signed int.\\n NIFTI_TYPE_FLOAT32 = 16 # 32 bit float.\\n NIFTI_TYPE_COMPLEX64 = 32 # 64 bit complex = 2 32 bit floats\\n NIFTI_TYPE_FLOAT64 = 64 # 64 bit float = double.\\n NIFTI_TYPE_RGB24 = 128 # 3 8 bit bytes.\\n NIFTI_TYPE_INT8 = 256 # signed char.\\n NIFTI_TYPE_UINT16 = 512 # unsigned short.\\n NIFTI_TYPE_UINT32 = 768 # unsigned int.\\n NIFTI_TYPE_INT64 = 1024 # signed long long.\\n NIFTI_TYPE_UINT64 = 1280 # unsigned long long.\\n NIFTI_TYPE_FLOAT128 = 1536 # 128 bit float = long double.\\n NIFTI_TYPE_COMPLEX128 = 1792 # 128 bit complex = 2 64 bit floats.\\n NIFTI_TYPE_COMPLEX256 = 2048 # 256 bit complex = 2 128 bit floats\\n NIFTI_TYPE_RGBA32 = 2304 # 4 8 bit bytes.\\n\\n # Detect the data type of the input data.\\n data_type = {\\n np.uint8: NIFTI_TYPE_UINT8,\\n np.uint16: NIFTI_TYPE_UINT16,\\n np.uint32: NIFTI_TYPE_UINT32,\\n np.float32: NIFTI_TYPE_FLOAT32,\\n np.int16: NIFTI_TYPE_INT16,\\n np.int32: NIFTI_TYPE_INT32,\\n np.int8: NIFTI_TYPE_INT8\\n }\\n if sys.maxint > 2 ** 32: # The platform is 64 bit\\n data_type[np.float128] = NIFTI_TYPE_FLOAT128\\n data_type[np.float64] = NIFTI_TYPE_FLOAT64\\n data_type[np.int64] = NIFTI_TYPE_INT64\\n data_type[np.uint64] = NIFTI_TYPE_UINT64\\n data_type[np.complex64] = NIFTI_TYPE_COMPLEX64\\n data_type[np.complex128] = NIFTI_TYPE_COMPLEX128\\n data_type[np.complex256] = NIFTI_TYPE_COMPLEX256\\n\\n header = nib.Nifti1Header()\\n if self.data.shape[1] == 1:\\n new_shape = (self.data.shape[0], 1, 1)\\n else:\\n new_shape = (self.data.shape[0], 1, 1, self.data.shape[1])\\n data = self.data.reshape(new_shape)\\n\\n if data.dtype.type in data_type:\\n header['datatype'] = data_type[data.dtype.type]\\n header['cal_max'] = data.max()\\n header['cal_min'] = data.min()\\n image = nib.Nifti1Image(data, None, header)\\n nib.nifti1.save(image, file_path)\",\n \"def IR():\\n s = np.array(\\n [2.40774137,2.287696084,2.203613927,2.048710132,1.899829585,1.591776247,\\n 2.021218754,2.572949552,3.298381484,3.635993426,3.788266224,3.8307278,3.834208811]\\n )\\n\\n TI = np.array([50, 75, 100, 150, 200, 300, 400, 500, 750, 1000, 1500, 2000, 3000])\\n\\n comp1 = s * np.array([-159.1,-134.2,-109.1,-64.7,25.0,40.1,88.6,126.8,187.6,219.4,245.4,253.6,256.1])\\n comp2 = s * np.array([-368.3,-356.9,-343.8,-318.1,-292.0,-242.5,-199.3,-158.4,-68.8,14.2,131.9,219.5,333.5])\\n comp3 = s * np.array([-77.5,-51.9,-29.8,9.9,40.2,85.7,115.4,135.1,160.1,167.6,172.3,171.7,171.8])\\n comp4 = s * np.array([-265.0,-240.6,-216.7,-170.5,-128.2,-53.5,9.6,62.3,159.7,223.8,296.5,328.3,346.7])\\n comp5 = s * np.array([-346.5,-328.9,-312.1,-278.5,-244.4,-182.3,-128.0,-80.0,30.8,109.3,225.1,299.5,372.2])\\n\\n comp = [comp1, comp2, comp3, comp4, comp5]\\n MSE = []\\n colors = [\\\"#1f77b4\\\", \\\"#ff7f0e\\\", \\\"#2ca02c\\\", \\\"#d62728\\\", \\\"#9467bd\\\"]\\n x_new = np.linspace(0, 3000, 10000)\\n for i, j, k in zip(comp, colors, np.arange(1, 6)):\\n plt.scatter(TI, i, c=j)\\n # popt, _ = curve_fit(MZ, TI, i, p0=np.array([200, 220, 300]))\\n popt, _ = curve_fit(MZ, TI, i, p0=np.array([300, 220]))\\n # M_z0, T1, M0 = popt\\n M0, T1 = popt\\n y_new = MZ(x_new, *popt)\\n plt.plot(x_new, y_new, \\\"--\\\", c=j, label=f\\\"Fit Comp. {k:d} : $T_1$={T1:3.2f}\\\")\\n MSE.append(mean_squared_error(i,y_new[TI]))\\n print(MSE)\\n print(np.mean(MSE))\\n plt.grid()\\n plt.legend(loc=\\\"best\\\")\\n plt.xlabel(\\\"TI\\\")\\n plt.ylabel(r\\\"Singal Intensity $M_z$\\\")\\n plt.show()\",\n \"def save_ttest_metrics(self, ttest_metrics, fname, no_genes=20):\\n\\n top_genes = self.fetch_gene_descriptions(ttest_metrics, nih_fetch_num=no_genes, printme=False)\\n eids = [int(i[0]) for i in top_genes]\\n myfig = self.effect_size_distr(ttest_metrics, genes_of_interest=eids[0:no_genes], return_fig=True)\\n plt.savefig(fname+'.png')\\n\\n with open(fname+'.csv', 'wb') as csvfile:\\n writer = csv.writer(csvfile)\\n for i in top_genes:\\n writer.writerow([i[0], i[3], i[1], i[2], i[4]])\",\n \"def IRIS_network(input):\\n\\n t_iris_1 = datetime.now()\\n \\n global events\\n \\n len_events = len(events)\\n Period = input['min_date'].split('T')[0] + '_' + \\\\\\n input['max_date'].split('T')[0] + '_' + \\\\\\n str(input['min_mag']) + '_' + str(input['max_mag'])\\n eventpath = os.path.join(input['datapath'], Period)\\n \\n create_foders_files(events, eventpath)\\n\\n print 'IRIS-Folders are Created!'\\n print \\\"--------------------\\\"\\n \\n Stas_iris = []\\n \\n for i in range(0, len_events):\\n \\n target_path = os.path.join(eventpath, events[i]['event_id'])\\n Sta_iris = IRIS_available(input, events[i], target_path, event_number = i)\\n Stas_iris.append(Sta_iris)\\n \\n if input['iris_bulk'] != 'Y':\\n print 'IRIS-Availability for event: ' + str(i+1) + str('/') + \\\\\\n str(len_events) + ' ---> ' + 'DONE'\\n else:\\n print 'IRIS-bulkfile for event : ' + str(i+1) + str('/') + \\\\\\n str(len_events) + ' ---> ' + 'DONE'\\n \\n if input['get_continuous'] == 'Y' and input['iris_bulk'] == 'Y':\\n for j in range(1, len_events):\\n Stas_iris.append(Sta_iris)\\n target_path = os.path.join(eventpath, events[j]['event_id'])\\n shutil.copy2(os.path.join(eventpath, events[0]['event_id'], \\\\\\n 'info', 'bulkdata-0.txt'), \\\\\\n os.path.join(target_path, \\\\\\n 'info', 'bulkdata-' + str(j) + '.txt'))\\n print 'IRIS-Availability for event: ' + str(j+1) + str('/') + \\\\\\n str(len_events) + ' ---> ' + 'DONE'\\n break\\n \\n t_iris_2 = datetime.now()\\n t_iris = t_iris_2 - t_iris_1\\n print \\\"--------------------\\\"\\n print 'IRIS-Time: (Availability)'\\n print t_iris \\n \\n return Stas_iris\",\n \"def log(pro, logname, savepng=True):\\n\\n # Load processed data variables\\n rawfiles = pro['rawfiles']\\n transect = pro['transect']\\n t120 = pro['t120' ]\\n r120 = pro['r120' ]\\n Sv120 = pro['Sv120' ]\\n Sv120sw = pro['Sv120sw' ]\\n t120r = pro['t120r' ]\\n t120intrvls = pro['t120intervals']\\n nm120r = pro['nm120r' ]\\n lon120r = pro['lon120r' ]\\n lat120r = pro['lat120r' ]\\n sbline120r = pro['sbliner' ][0,:] \\n NASC120swr = pro['NASC120swr'][0,:]\\n pc120swr = pro['pc120swr'][0,:]\\n \\n # Build summary results\\n results = {'Time' : np.array(t120r , dtype=str) ,\\n 'Longitude': np.round(lon120r , 5) ,\\n 'Latitude' : np.round(lat120r , 5) ,\\n 'Transect' : np.ones(len(t120r ), dtype=int)*transect,\\n 'Miles' : nm120r ,\\n 'Seabed' : np.round(sbline120r , 1) ,\\n 'NASC' : np.round(NASC120swr , 2) ,\\n '% samples': np.round(pc120swr , 1) }\\n results = pd.DataFrame(results, columns= ['Time' , 'Longitude',\\n 'Latitude' , 'Transect' ,\\n 'Miles' , 'Seabed' ,\\n 'NASC' , '% samples'])\\n \\n # Create new log subdirectory\\n path = os.path.join(os.path.dirname(__file__), '..', 'log', logname, '')\\n if not os.path.exists(path):\\n os.makedirs(path)\\n \\n # Write results in CSV log file\\n with open(path+logname+'.csv', 'a') as f:\\n results.to_csv(path+logname+'.csv', index=False, mode='a',\\n header=f.tell()==0) \\n \\n # save png image\\n if savepng:\\n \\n # set figure\\n plt.close()\\n plt.figure(figsize=(8, 8))\\n plt.subplots_adjust(left=0.066, right=1.055, bottom=0.065, top=0.985,\\n wspace=0, hspace=0.05)\\n plt.rcParams.update({'font.size': 9, 'lines.linewidth': 1})\\n \\n # plot raw echogram\\n plt.subplot(211).invert_yaxis()\\n im=plt.pcolormesh(t120, r120, Sv120,\\n vmin=-80, vmax=-50, cmap=cmaps().ek500)\\n plt.colorbar(im).set_label('Sv raw (dB re 1m$^{-1}$)')\\n plt.gca().set_ylim(270,0)\\n plt.gca().set_ylabel('Depth (m)')\\n plt.gca().set_xlim(t120intrvls[0], t120intrvls[-1])\\n plt.gca().set_xticks(t120intrvls[[0,-1]])\\n plt.tick_params(labelright=False, labelbottom=False)\\n \\n # plot processed echogram\\n ax= plt.subplot(212)\\n ax = [ax, ax.twinx()]\\n im=ax[0].pcolormesh(t120, r120, Sv120sw,\\n vmin=-80,vmax=-50, cmap=cmaps().ek500)\\n plt.colorbar(im).set_label('Sv pro (dB re 1m$^{-1}$)')\\n ax[0].invert_yaxis()\\n ax[0].set_ylim(270,0)\\n ax[0].set_ylabel('Depth (m)')\\n \\n # overlay distance/NASC info\\n for t, nm, NASC in zip(t120r, nm120r, NASC120swr):\\n ax[1].plot([t, t], [0, 1], color=[0,.8,0], linewidth=2)\\n ax[1].text(t, .95, ' ' + str(transect) + ': ' + str(round(nm,2)),\\n fontweight='bold', color=[0,.8,0])\\n ax[1].text(t, .02, ' ' + str(round(NASC,2)),\\n fontweight='bold', color=[1,0,0]) \\n ax[1].set_ylim(0, 1)\\n ax[1].set_xlim(t120intrvls[0], t120intrvls[-1])\\n ax[1].set_xticks(t120intrvls[[0,-1]])\\n ax[1].tick_params(labelright=False)\\n ax[1].xaxis.set_major_formatter(mdates.DateFormatter('%d%b-%H:%M:%S'))\\n \\n # save figure\\n pf = rawfiles[0].split('-')[0]\\n fn = pd.to_datetime(str(t120[0])).strftime(pf + '-D%Y%m%d-T%H%M%S')\\n plt.savefig(path+fn+'.png' ,figsize=(8, 8), dpi=100)\\n plt.close()\",\n \"def make_sim(args):\\n flux, nread, iexp = args\\n satlevel=65535\\n\\n #outdir = \\\"sim_data_flux\\\" + str(flux)\\n #outdir = \\\"sim_data_flux\\\" + str(flux) + \\\"_nonl\\\"\\n outdir = \\\"sim_\\\" + \\\"exposure\\\" + str(iexp) + \\\"_flux\\\" + str(flux)\\n pathlib.Path(outdir).mkdir(parents=True, exist_ok=True) \\n \\n for i in range(1, nread+1):\\n logger.info(\\\"Looping through UTR {}\\\".format(i))\\n #outfileprefix = outdir + \\\"/exposure\\\" + str(iexp) + \\\"_utr\\\" + str(i)\\n outfileprefix = outdir + \\\"/utr\\\" + str(i)\\n refoutfileprefix = outdir + \\\"/utr\\\" + str(i) + \\\"_ref\\\"\\n\\n primary_hdu = fits.PrimaryHDU()\\n outhdus = fits.HDUList([primary_hdu])\\n outhdus_ref = fits.HDUList([primary_hdu])\\n\\n ahdu = fits.HDUList([primary_hdu]) #for a coefficients\\n bhdu = fits.HDUList([primary_hdu]) #for b coefficients\\n\\n #loop all 16 detectors\\n ndet = 16\\n logger.info(\\\"Looping through all 16 detectors\\\")\\n for idet in range(ndet):\\n detid = detids[idet]\\n logger.info(\\\"Looping through the detector {}, {}\\\".format(idet+1, detid)) \\n\\n flux_actual = np.zeros((2048, 2048))\\n #start with the measured flux, constant spatially but varying temporally\\n flux_actual[4:2044,4:2044] = flux * TREAD * i #fill data region with measured flux, (ADU/f) * s = ADU ???\\n flux_actual *= Gain #e-??\\n logger.info(\\\"Filled with measured flux\\\")\\n\\n #calculate nonlinearity from calibration data, varing spatially but not temporally, unit e-??\\n flux_nonlinear = make_nonlinear(flux_actual, outfileprefix, ahdu, bhdu, detid) \\n #flux_nonlinear = flux_actual #no non-linearity\\n logger.info(\\\"Made non-linearized flux\\\")\\n\\n #add pedestal, constant spatially very varying temporally\\n pedestal = random.randint(0, 1000) #set the random pedistal (between 0 - 1000) for this exposure, unit e-??\\n flux_nonlinear[:, :] += pedestal #add the pedestal to all of the data including the reference pixels.\\n logger.info(\\\"Added pedestal\\\")\\n\\n #add noise (e-)\\n readnoise_array = np.random.normal(size=[2048,2048]) * ReadNoise #generate the read noise, the reference pixels should also have noise. \\n flux_nonlinear += readnoise_array #add the read noise to the data array\\n logger.info(\\\"Added read noise\\\")\\n\\n # set any pixel above the saturation limit to the saturation limit.\\n # This can set to 65535 for all pixels for now, but should be able to \\n # take a map since this will vary pixel to pixel. \\n ind = np.where(flux_nonlinear > satlevel)\\n flux_nonlinear[ind] = satlevel\\n logger.info(\\\"Checked saturation level\\\")\\n\\n #add this dector to the final simulated data\\n hdu = fits.ImageHDU(data=flux_nonlinear, name=detid)\\n outhdus.append(hdu)\\n\\n #do reference pixel correction\\n updownCorr(flux_nonlinear)\\n leftrightCorr(flux_nonlinear)\\n\\n #add this dector to another output\\n hdu1 = fits.ImageHDU(data=flux_nonlinear, name=detid)\\n outhdus_ref.append(hdu1)\\n \\n\\n #write the sim data for this UTR\\n outfile = outfileprefix + \\\".fits\\\"\\n #logger.info(\\\"Writing sim data to: {}\\\".format(outfile))\\n #outhdus.writeto(outfile)\\n\\n refoutfile = refoutfileprefix + \\\".fits\\\"\\n logger.info(\\\"Writing reference pixel corrected sim data to: {}\\\".format(refoutfile))\\n outhdus_ref.writeto(refoutfile)\\n\\n #ahdu.writeto(outfileprefix + \\\"_a_coef.fits\\\")\\n #bhdu.writeto(outfileprefix + \\\"_b_coef.fits\\\")\",\n \"def read_taiwan_ntu_dsi():\\n dipy_home = pjoin(os.path.expanduser('~'), '.dipy')\\n folder = pjoin(dipy_home, 'taiwan_ntu_dsi')\\n fraw = pjoin(folder, 'DSI203.nii.gz')\\n fbval = pjoin(folder, 'DSI203.bval')\\n fbvec = pjoin(folder, 'DSI203.bvec')\\n md5_dict = {'data': '950408c0980a7154cb188666a885a91f',\\n 'bval': '602e5cb5fad2e7163e8025011d8a6755',\\n 'bvec': 'a95eb1be44748c20214dc7aa654f9e6b',\\n 'license': '7fa1d5e272533e832cc7453eeba23f44'}\\n\\n check_md5(fraw, md5_dict['data'])\\n check_md5(fbval, md5_dict['bval'])\\n check_md5(fbvec, md5_dict['bvec'])\\n check_md5(pjoin(folder, 'DSI203_license.txt'), md5_dict['license'])\\n\\n bvals, bvecs = read_bvals_bvecs(fbval, fbvec)\\n bvecs[1:] = bvecs[1:] / np.sqrt(np.sum(bvecs[1:] * bvecs[1:], axis=1))[:, None]\\n\\n gtab = gradient_table(bvals, bvecs)\\n img = nib.load(fraw)\\n return img, gtab\",\n \"def to_nifti(self,folder_path: str):\\n data_path = settings.STORAGE_DIR\\n path = folder_path \\n nifti=series.get_series_object(path) \\n nifti_str=str(nifti)\\n nifti_str=nifti_str[1:44]\\n if nifti_str=='dicom_to_cnn.model.reader.SeriesCT.SeriesCT': \\n nifti.get_instances_ordered() \\n nifti.get_numpy_array()\\n image_md5 = hashlib.md5(str(nifti).encode())\\n image_id = image_md5.hexdigest()\\n img=nifti.export_nifti(data_path+'/image/image_'+image_id+'.nii')\\n if nifti_str=='dicom_to_cnn.model.reader.SeriesPT.SeriesPT':\\n nifti.get_instances_ordered() \\n nifti.get_numpy_array()\\n nifti.set_ExportType('suv')\\n image_md5 = hashlib.md5(str(nifti).encode())\\n image_id = image_md5.hexdigest()\\n img=nifti.export_nifti(data_path+'/image/image_'+image_id+'.nii')\",\n \"def store_std_dev_of_noises(src_file: H5File) -> None:\\n mean_perp_noise = src_file.attrs['perp_noise_mean']\\n mean_par_noise = src_file.attrs['par_noise_mean']\\n mean_ref_noise = src_file.attrs['ref_noise_mean']\\n perp_sum = 0\\n par_sum = 0\\n ref_sum = 0\\n counts = 0\\n for path in rawnav.pump_group_paths(src_file):\\n perp_path = path + '/perp'\\n par_path = path + '/par'\\n ref_path = path + '/ref'\\n perp_noise = src_file[perp_path].attrs['noise']\\n par_noise = src_file[par_path].attrs['noise']\\n ref_noise = src_file[ref_path].attrs['noise']\\n perp_sum += (perp_noise - mean_perp_noise) ** 2\\n par_sum += (par_noise - mean_par_noise) ** 2\\n ref_sum += (ref_noise - mean_ref_noise) ** 2\\n counts += 1\\n src_file.attrs['perp_noise_std_dev'] = np.sqrt(perp_sum / (counts - 1))\\n src_file.attrs['par_noise_std_dev'] = np.sqrt(par_sum / (counts - 1))\\n src_file.attrs['ref_noise_std_dev'] = np.sqrt(ref_sum / (counts - 1))\\n return\",\n \"def calculate_psf_tilts():\\n for order in [1, 2]:\\n\\n # Get the file\\n path = 'files/SOSS_PSF_tilt_order{}.npy'.format(order)\\n psf_file = resource_filename('awesimsoss', path)\\n\\n # Dimensions\\n subarray = 'SUBSTRIP256'\\n X = range(2048)\\n Y = range(256)\\n\\n # Get the wave map\\n wave_map = utils.wave_solutions(subarray, order).astype(float)\\n\\n # Get the y-coordinate of the trace polynomial in this column\\n # (center of the trace)\\n coeffs = trace_polynomials(subarray=subarray, order=order)\\n trace = np.polyval(coeffs, X)\\n\\n # Interpolate to get the wavelength value at the center\\n wave = interp2d(X, Y, wave_map)\\n\\n # Get the wavelength of the trace center in each column\\n trace_wave = []\\n for x, y in zip(X, trace):\\n trace_wave.append(wave(x, y)[0])\\n\\n # For each column wavelength (defined by the wavelength at\\n # the trace center) define an isowavelength contour\\n angles = []\\n for n, x in enumerate(X):\\n\\n w = trace_wave[x]\\n\\n # Edge cases\\n try:\\n w0 = trace_wave[x-1]\\n except IndexError:\\n w0 = 0\\n\\n try:\\n w1 = trace_wave[x+1]\\n except IndexError:\\n w1 = 10\\n\\n # Define the width of the wavelength bin as half-way\\n # between neighboring points\\n dw0 = np.mean([w0, w])\\n dw1 = np.mean([w1, w])\\n\\n # Get the coordinates of all the pixels in that range\\n yy, xx = np.where(np.logical_and(wave_map >= dw0, wave_map < dw1))\\n\\n # Find the angle between the vertical and the tilted wavelength bin\\n if len(xx) >= 1:\\n angle = get_angle([xx[-1], yy[-1]], [x, trace[x]])\\n else:\\n angle = 0\\n\\n # Don't flip them upside down\\n angle = angle % 180\\n\\n # Add to the array\\n angles.append(angle)\\n\\n # Save the file\\n np.save(psf_file, np.array(angles))\\n print('Angles saved to', psf_file)\",\n \"def PCO1S12Noise():\\n #Get data\\n wdir = '/home/rallured/Dropbox/AXRO/Metrology/' \\\\\\n 'NoiseStudy/TestOptics_PCO1S12/'\\n d1,dx1 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas1.fits')\\n d2,dx2 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas2.fits')\\n d3,dx3 = met.read4DFits(wdir+'161202_PCO1S12_4InchCut_Avg8_Meas3.fits')\\n\\n #Construct power spectra\\n f12,pow12 = fourier.meanPSD((d1-d2)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f23,pow23 = fourier.meanPSD((d2-d3)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n f13,pow13 = fourier.meanPSD((d1-d3)[:,100:-100],\\\\\\n win=np.hanning,dx=dx1,irregular=True)\\n\\n #Mid frequency\\n midfreq = [1000*np.sqrt(np.sum(p[np.logical_and(f>.1,f<1.)])) \\\\\\n for f,p in zip([f12,f23,f13],[pow12,pow23,pow13])]\\n\\n #Plot\\n plt.loglog(f12,pow12/f12[0],label='1-2: %.2f' % midfreq[0])\\n plt.loglog(f23,pow23/f23[0],label='2-3: %.2f' % midfreq[1])\\n plt.loglog(f13,pow13/f13[0],label='1-3: %.2f' % midfreq[2])\\n plt.legend(loc='lower left')\\n plt.grid()\\n plt.title('4D Repeatability: PCO1S12')\\n plt.xlabel('Frequency (1/mm)')\\n plt.ylabel('Power ($\\\\mu$m$^2$ mm)')\\n\\n print midfreq\\n\\n return f12,pow12\",\n \"def final_states_figure_file(self, it, avg):\\n return self.output_file(self.figures_dir(), 'final_states', it, avg, 'png')\",\n \"def n27_and_stark():\\n fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(4.5, 4))\\n # n=27 without static field.\\n folder = os.path.join(\\\"..\\\", \\\"..\\\", \\\"2018-09-09\\\")\\n fname = \\\"11_freq_diode.txt\\\"\\n fname = os.path.join(folder, fname)\\n data = pmu.fscan_import(fname)\\n data['afpoly'] = data['fpoly'] + 4511\\n ax.axhline(0, color='grey')\\n data.plot(x='afpoly', y='sig', c='k', ax=ax)\\n # n=27 with static field.\\n fname = \\\"10_freq_diode.txt\\\"\\n fname = os.path.join(folder, fname)\\n data = pmu.fscan_import(fname)\\n data['afpoly'] = data['fpoly'] + 4511\\n data['asig'] = data['sig'] + 0.15\\n ax.axhline(0.15, color='grey')\\n data.plot(x='afpoly', y='asig', c='k', ax=ax)\\n # text\\n ax.text(-5.5, 0.2, \\\"3 V/cm\\\", horizontalalignment='right')\\n ax.text(-5.5, 0.01, \\\"0 V/cm\\\", horizontalalignment='right')\\n # pretty figure\\n ax.legend().remove()\\n ax.set_ylabel(r\\\"$e^-$ Signal\\\")\\n ax.set_yticks([])\\n ax.set_xlabel(r\\\"Frequency (GHz from $3d_{5/2} \\\\rightarrow 27f$)\\\")\\n ax.set_xlim(-8, 7)\\n # save\\n fig.tight_layout()\\n fig.savefig('n27_and_stark.pdf')\\n return\",\n \"def plot_seir(sir, T, tmin = 0, tmax = 200, absolute=True, figsize=(10,10), facecolor='LightGrey'):\\n fig = plt.figure(figsize=figsize)\\n ax = fig.add_subplot(111, axisbelow=True)\\n ax.set_facecolor(facecolor)\\n\\n if absolute:\\n S = sir.S * sir.N\\n E = sir.E * sir.N\\n I = sir.I * sir.N\\n R = sir.R * sir.N\\n Y = 1.1 * sir.N\\n else:\\n S = sir.S\\n E = sir.E\\n I = sir.I\\n R = sir.R\\n Y = 1.1\\n\\n ax.plot(sir.t, S, 'k', alpha=0.5, lw=3, label='Susceptible')\\n ax.plot(sir.t, I, 'b', alpha=0.5, lw=3, label='Infected')\\n ax.plot(sir.t, E, 'r', alpha=0.5, lw=3, label='Exposed')\\n ax.plot(sir.t, R, 'g', alpha=0.5, lw=3, label='Recovered')\\n xlabel = 'Time /days'\\n if absolute:\\n ylabel = 'Total population'\\n else:\\n ylabel = 'Fraction of population'\\n\\n set_pretty_ax(ax, facecolor, xlabel, ylabel, tmin, tmax, Y)\\n plt.title(T)\\n plt.show()\",\n \"def tsne_projection(train_file, gen_file, i):\\n train_data = pd.read_csv(train_file)\\n def get_ori_prom(row):\\n if row['gaps'] <= 2 and row['dips'] <=2:\\n return 'Model'+str(i)+'_train_promising'\\n else:\\n return 'Model'+str(i)+'_train'\\n train_data['label'] = train_data.apply(get_ori_prom, axis=1)\\n train_data = train_data.drop(['id', 'gaps', 'dips'], axis=1)\\n gen_prom_smiles = pd.read_csv(gen_file)\\n gen_prom_smiles = gen_prom_smiles.drop(['Group', 'Gaps', 'Dips'], axis=1)\\n gen_prom_smiles['label'] = 'Generated_promising'\\n all_smi = pd.concat([train_data, gen_prom_smiles])\\n mols = get_mols(all_smi.SMILES)\\n fps, _ = get_fingerprints(mols)\\n fp_embeded = TSNE(n_components=2, perplexity=100).fit_transform(fps)\\n all_smi['tsne1'] = fp_embeded[:, 0]\\n all_smi['tsne2'] = fp_embeded[:, 1]\\n return all_smi, len(train_data)\",\n \"def plot_stairway_Ne_estimate(infile, outfile):\\n nt = pandas.read_csv(infile, sep=\\\"\\\\t\\\", skiprows=5)\\n nt = nt[nt['year'] > 10]\\n f, ax = plt.subplots(figsize=(7, 7))\\n ax.set(xscale=\\\"log\\\", yscale=\\\"log\\\")\\n ax.plot(nt['year'], nt['Ne_median'], c=\\\"red\\\")\\n ax.plot(nt['year'], nt['Ne_2.5%'], c='grey')\\n ax.plot(nt['year'], nt['Ne_97.5%'], c='grey')\\n f.savefig(outfile, bbox_inches='tight')\",\n \"def make_fakeSN_spectrum(snfile, galfile, params, outfile, err=False, signal_noise=20.0, z=0.0, wave_range=[3842, 8195]):\\n snt=loadtext(snfile)\\n galt=loadsdss(fits.open(galfile))\\n minsalt=list(snt.wavelength).index(3840)\\n maxsalt=list(snt.wavelength).index(8200)\\n snt.wavelength=snt.wavelength[minsalt:maxsalt+1]\\n snt.flux=snt.flux[minsalt:maxsalt+1]\\n igal=np.interp(snt.wavelength, galt.wavelength, galt.flux)\\n \\n #foflux=fake observed flux\\n foflux=(snt.flux*params[0] + igal*params[1]) \\n \\n if err:\\n foflux = signal_noise**2 * foflux\\n foflux = np.random.poisson(foflux)\\n fofluxerr=foflux**0.5\\n \\n# if err:\\n# foflux = foflux + np.random.normal(0.0, 0.1, foflux.shape)\\n# fofluxerr=0.1*foflux\\n \\n if not err:\\n fofluxerr=0.1*foflux\\n \\n fout=open(outfile, 'w')\\n for i in range(len(snt.wavelength)):\\n fout.write('%f %f %f\\\\n' % (snt.wavelength[i], foflux[i], fofluxerr[i]))\\n fout.close()\\n \\n return fout\",\n \"def to_nii(self, outbase, spirec='spirec', saveInOut=False):\\n if self.image_data is None:\\n self.recon(spirec)\\n\\n image_tlhc = np.array([self.header.image.tlhc_R, self.header.image.tlhc_A, self.header.image.tlhc_S])\\n image_trhc = np.array([self.header.image.trhc_R, self.header.image.trhc_A, self.header.image.trhc_S])\\n image_brhc = np.array([self.header.image.brhc_R, self.header.image.brhc_A, self.header.image.brhc_S])\\n #image_cent = np.array([self.header.image.ctr_R, self.header.image.ctr_A, self.header.image.ctr_S])\\n\\n row_vec = (image_trhc-image_tlhc)/np.sqrt(np.dot(image_trhc-image_tlhc, image_trhc-image_tlhc))\\n col_vec = -(image_trhc-image_brhc)/np.sqrt(np.dot(image_trhc-image_brhc, image_trhc-image_brhc))\\n # The DICOM standard defines these two unit vectors in an LPS coordinate frame, but we'll\\n # need RAS (+x is right, +y is anterior, +z is superior) for NIFTI. So, we compute them\\n # such that row_vec points to the right and col_vec points up.\\n # Not sure if we need to negate the slice_norm. From the NIFTI-1 header:\\n # The third column of R will be either the cross-product of the first 2 columns or\\n # its negative. It is possible to infer the sign of the 3rd column by examining\\n # the coordinates in DICOM attribute (0020,0032) \\\"Image Position (Patient)\\\" for\\n # successive slices. However, this method occasionally fails for reasons that I\\n # (RW Cox) do not understand.\\n\\n # can also get slice_norm from: slice_norm = np.cross(row_vec, col_vec)\\n slice_norm = np.array([self.header.image.norm_R, self.header.image.norm_A, self.header.image.norm_S])\\n slice_fov = np.abs(self.header.series.start_loc - self.header.series.end_loc)\\n\\n # This is either the first slice tlhc (image_tlhc) or the last slice tlhc. How to decide?\\n # And is it related to wheather I have to negate the slice_norm?\\n # Tuned this empirically by comparing spiral and EPI data with the sam Rx.\\n # Everything seems reasonable, except the test for axial orientation (start_ras==S|I).\\n # I have no idea why I need that! But the flipping only seems necessary for axials, not\\n # coronals or the few obliques I've tested.\\n # FIXME: haven't tested sagittals! (to test for spiral: 'sprt' in self.psd_name.lower())\\n if (self.header.series.start_ras=='S' or self.header.series.start_ras=='I') and self.header.series.start_loc > self.header.series.end_loc:\\n pos = image_tlhc - slice_norm*slice_fov\\n # FIXME: since we are reversing the slice order here, should we change the slice_order field below?\\n self.image_data = self.image_data[:,:,::-1,]\\n if self.fm_data is not None:\\n self.fm_data = self.fm_data[:,:,::-1,]\\n else:\\n pos = image_tlhc\\n\\n if self.num_bands > 1:\\n pos = pos - slice_norm * self.band_spacing_mm * (self.num_bands - 1.0) / 2.0\\n\\n qto_xyz = np.zeros((4,4))\\n qto_xyz[0,0] = row_vec[0]\\n qto_xyz[0,1] = col_vec[0]\\n qto_xyz[0,2] = slice_norm[0]\\n\\n qto_xyz[1,0] = row_vec[1]\\n qto_xyz[1,1] = col_vec[1]\\n qto_xyz[1,2] = slice_norm[1]\\n\\n qto_xyz[2,0] = row_vec[2]\\n qto_xyz[2,1] = col_vec[2]\\n qto_xyz[2,2] = slice_norm[2]\\n\\n qto_xyz[:,3] = np.append(pos, 1).T\\n qto_xyz[0:3,0:3] = np.dot(qto_xyz[0:3,0:3], np.diag(self.mm_per_vox))\\n\\n nii_header = nibabel.Nifti1Header()\\n nii_header.set_xyzt_units('mm', 'sec')\\n nii_header.set_qform(qto_xyz, 'scanner')\\n nii_header.set_sform(qto_xyz, 'scanner')\\n\\n nii_header['slice_start'] = 0\\n nii_header['slice_end'] = self.num_slices - 1\\n # nifti slice order codes: 0 = unknown, 1 = sequential incrementing, 2 = seq. dec., 3 = alternating inc., 4 = alt. dec.\\n slice_order = 0\\n nii_header['slice_duration'] = self.tr * 1000 / self.num_slices\\n # FIXME: check that this is correct.\\n if self.header.series.se_sortorder == 0:\\n slice_order = 1 # or 2?\\n elif self.header.series.se_sortorder == 1:\\n slice_order = 3 # or 4?\\n nii_header['slice_code'] = slice_order\\n\\n # Note: the freq/phase dir isn't meaningful for spiral trajectories.\\n if self.header.image.freq_dir==1:\\n nii_header.set_dim_info(freq=1, phase=0, slice=2)\\n else:\\n nii_header.set_dim_info(freq=0, phase=1, slice=2)\\n\\n # FIXME: There must be a cleaner way to set the TR! Maybe bug Matthew about it.\\n nii_header.structarr['pixdim'][4] = self.tr\\n nii_header.set_slice_duration(nii_header.structarr['pixdim'][4] / self.num_slices)\\n nii_header.structarr['cal_max'] = self.image_data.max()\\n nii_header.structarr['cal_min'] = self.image_data.min()\\n\\n if self.num_echoes == 1:\\n nifti = nibabel.Nifti1Image(self.image_data, None, nii_header)\\n nibabel.save(nifti, outbase + '.nii.gz')\\n elif self.num_echoes == 2:\\n if saveInOut:\\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,0], None, nii_header)\\n nibabel.save(nifti, outbase + '_in.nii.gz')\\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,1], None, nii_header)\\n nibabel.save(nifti, outbase + '_out.nii.gz')\\n # FIXME: Do a more robust test for spiralio!\\n # Assume spiralio, so do a weighted average of the two echos.\\n # FIXME: should do a quick motion correction here\\n w_in = np.mean(self.image_data[:,:,:,:,0], 3)\\n w_out = np.mean(self.image_data[:,:,:,:,1], 3)\\n inout_sum = w_in + w_out\\n w_in = w_in / inout_sum\\n w_out = w_out / inout_sum\\n avg = np.zeros(self.image_data.shape[0:4])\\n for tp in range(self.image_data.shape[3]):\\n avg[:,:,:,tp] = w_in*self.image_data[:,:,:,tp,0] + w_out*self.image_data[:,:,:,tp,1]\\n nifti = nibabel.Nifti1Image(avg, None, nii_header)\\n nibabel.save(nifti, outbase + '.nii.gz')\\n else:\\n for echo in range(self.num_echoes):\\n nifti = nibabel.Nifti1Image(self.image_data[:,:,:,:,echo], None, nii_header)\\n nibabel.save(nifti, outbase + '_echo%02d.nii.gz' % echo)\\n\\n if self.fm_data is not None:\\n nii_header.structarr['cal_max'] = self.fm_data.max()\\n nii_header.structarr['cal_min'] = self.fm_data.min()\\n nifti = nibabel.Nifti1Image(self.fm_data, None, nii_header)\\n nibabel.save(nifti, outbase + '_B0.nii.gz')\",\n \"def plotTI():\\n min_dl = dlam[dlam != 0].min()\\n S = int(0.4/min_dl)\\n fig = pl.figure(figsize = (8,6))\\n ax = fig.add_subplot(1,1,1)\\n ax.spines['bottom'].set_position('zero')\\n ax.spines['top'].set_color('none')\\n ax.spines['right'].set_color('none')\\n ax.xaxis.set_ticks_position('bottom')\\n ax.yaxis.set_ticks_position('left')\\n\\n for k, spine in ax.spines.items():\\n spine.set_zorder(12.2)\\n\\n xs, ndx, dx = [0], 0, 0.001\\n colors = ['r', 'g', '#7F38EC', '#9F000F', 'b', 'y']\\n min_y, max_y = 0, 0\\n\\n lines = tuple()\\n ## lv_names2 = [r'$Coulomb$', r'$vdWaals$'] ## for the paper\\n lv_names2 = []\\n for j in range(n_components):\\n y = ave_dhdl[:,j]\\n if not (y == 0).all():\\n lv_names2.append(r'$%s$' % P.lv_names[j].capitalize())\\n\\n for j in range(n_components):\\n\\n y = ave_dhdl[:,j]\\n if not (y == 0).all():\\n\\n # Get the coordinates.\\n lj = lchange[:,j]\\n x = lv[:,j][lj]\\n y = y[lj]/P.beta_report\\n\\n if 'TI' in P.methods:\\n # Plot the TI integration area.\\n ss = 'TI'\\n for i in range(len(x)-1):\\n min_y = min(y.min(), min_y)\\n max_y = max(y.max(), max_y)\\n #pl.plot(x,y)\\n if i%2==0:\\n pl.fill_between(x[i:i+2]+ndx, 0, y[i:i+2], color=colors[ndx], alpha=1.0)\\n else:\\n pl.fill_between(x[i:i+2]+ndx, 0, y[i:i+2], color=colors[ndx], alpha=0.5)\\n xlegend = [-100*wnum for wnum in range(len(lv_names2))]\\n pl.plot(xlegend, [0*wnum for wnum in xlegend], ls='-', color=colors[ndx], label=lv_names2[ndx]) ## for the paper\\n\\n if 'TI-CUBIC' in P.methods and not cubspl[j]==0:\\n # Plot the TI-CUBIC interpolation curve.\\n ss += ' and TI-CUBIC'\\n xnew = numpy.arange(0, 1+dx, dx)\\n ynew = cubspl[j].interpolate(y, xnew)\\n min_y = min(ynew.min(), min_y)\\n max_y = max(ynew.max(), max_y)\\n pl.plot(xnew+ndx, ynew, color='#B6B6B4', ls ='-', solid_capstyle='round', lw=3.0)\\n\\n else:\\n # Plot the TI-CUBIC integration area.\\n ss = 'TI-CUBIC'\\n for i in range(len(x)-1):\\n xnew = numpy.arange(x[i], x[i+1]+dx, dx)\\n ynew = cubspl[j].interpolate(y, xnew)\\n ynew[0], ynew[-1] = y[i], y[i+1]\\n min_y = min(ynew.min(), min_y)\\n max_y = max(ynew.max(), max_y)\\n if i%2==0:\\n pl.fill_between(xnew+ndx, 0, ynew, color=colors[ndx], alpha=1.0)\\n else:\\n pl.fill_between(xnew+ndx, 0, ynew, color=colors[ndx], alpha=0.5)\\n\\n # Store the abscissa values and update the subplot index.\\n xs += (x+ndx).tolist()[1:]\\n ndx += 1\\n\\n # Make sure the tick labels are not overcrowded.\\n xs = numpy.array(xs)\\n dl_mat = numpy.array([xs-i for i in xs])\\n ri = range(len(xs))\\n\\n def getInd(r=ri, z=[0]):\\n primo = r[0]\\n min_dl=ndx*0.02*2**(primo>10)\\n if dl_mat[primo].max()<min_dl:\\n return z\\n for i in r:\\n for j in range(len(xs)):\\n if dl_mat[i,j]>min_dl:\\n z.append(j)\\n return getInd(ri[j:], z)\\n\\n xt = [i if (i in getInd()) else '' for i in range(K)]\\n pl.xticks(xs[1:], xt[1:], fontsize=10)\\n pl.yticks(fontsize=10)\\n #ax = pl.gca()\\n #for label in ax.get_xticklabels():\\n # label.set_bbox(dict(fc='w', ec='None', alpha=0.5))\\n\\n # Remove the abscissa ticks and set up the axes limits.\\n for tick in ax.get_xticklines():\\n tick.set_visible(False)\\n pl.xlim(0, ndx)\\n min_y *= 1.01\\n max_y *= 1.01\\n pl.ylim(min_y, max_y)\\n\\n for i,j in zip(xs[1:], xt[1:]):\\n pl.annotate(('%.2f' % (i-1.0 if i>1.0 else i) if not j=='' else ''), xy=(i, 0), xytext=(i, 0.01), size=10, rotation=90, textcoords=('data', 'axes fraction'), va='bottom', ha='center', color='#151B54')\\n if ndx>1:\\n lenticks = len(ax.get_ymajorticklabels()) - 1\\n if min_y<0: lenticks -= 1\\n if lenticks < 5:\\n from matplotlib.ticker import AutoMinorLocator as AML\\n ax.yaxis.set_minor_locator(AML())\\n pl.grid(which='both', color='w', lw=0.25, axis='y', zorder=12)\\n pl.ylabel(r'$\\\\mathrm{\\\\langle{\\\\frac{ \\\\partial U } { \\\\partial \\\\lambda }}\\\\rangle_{\\\\lambda}\\\\/%s}$' % P.units, fontsize=20, color='#151B54')\\n pl.annotate('$\\\\mathit{\\\\lambda}$', xy=(0, 0), xytext=(0.5, -0.05), size=18, textcoords='axes fraction', va='top', ha='center', color='#151B54')\\n if not P.software.title()=='Sire':\\n lege = ax.legend(prop=FP(size=14), frameon=False, loc=1)\\n for l in lege.legendHandles:\\n l.set_linewidth(10)\\n pl.savefig(os.path.join(P.output_directory, 'dhdl_TI.pdf'))\\n pl.close(fig)\\n return\",\n \"def do(sts):\\n sts = sts.replace(tzinfo=pytz.timezone(\\\"UTC\\\"))\\n\\n ets = sts + datetime.timedelta(days=1)\\n\\n interval = datetime.timedelta(hours=3)\\n\\n now = sts\\n while now < ets:\\n archivefn = now.strftime((\\\"/mesonet/ARCHIVE/data/%Y/%m/%d/model/\\\"\\n \\\"NARR/rad_%Y%m%d%H%M.nc\\\"))\\n if os.path.isfile(archivefn):\\n now += interval\\n continue\\n uri = now.strftime((\\\"http://nomads.ncdc.noaa.gov/thredds/ncss/\\\"\\n \\\"grid/narr-a/%Y%m/%Y%m%d/\\\"\\n \\\"narr-a_221_%Y%m%d_%H00_000.grb?\\\"\\n \\\"var=Downward_shortwave_radiation_flux&spatial=all\\\"\\n \\\"&temporal=all\\\"))\\n\\n try:\\n req = urllib2.Request(uri)\\n data = urllib2.urlopen(req).read()\\n except:\\n print 'NARR Download failed for: %s' % (uri,)\\n sys.exit()\\n\\n tmpfn = tempfile.mktemp()\\n o = open(tmpfn, 'w')\\n o.write(data)\\n o.close()\\n\\n cmd = (\\\"/home/ldm/bin/pqinsert -p 'data a %s bogus \\\"\\n \\\"model/NARR/rad_%s.nc nc' %s\\\"\\n ) % (now.strftime(\\\"%Y%m%d%H%M\\\"),\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(cmd, shell=True)\\n\\n os.remove(tmpfn)\\n now += interval\",\n \"def save_grtrans_image(grt_obj):\\n I_im = grt_obj.ivals[:,0,0].reshape(npix,npix).flatten()\\n Q_im = grt_obj.ivals[:,1,0].reshape(npix,npix).flatten()\\n U_im = grt_obj.ivals[:,2,0].reshape(npix,npix).flatten()\\n V_im = grt_obj.ivals[:,3,0].reshape(npix,npix).flatten()\\n\\n # convert to Tb\\n factor = 3.254e13/(RF**2 * psize_rad**2)\\n I_im *= factor\\n Q_im *= factor\\n U_im *= factor\\n V_im *= factor\\n\\n x = np.array([[i for i in range(npix)] for j in range(npix)]).flatten()\\n y = np.array([[j for i in range(npix)] for j in range(npix)]).flatten()\\n\\n x -= npix/2\\n y -= npix/2\\n x = x*psize_uas\\n y = y*psize_uas\\n\\n outdat = np.vstack((x.T,y.T,I_im.T,Q_im.T,U_im.T,V_im.T)).T\\n np.savetxt('../rrjet_and_riaf/'+FNAME,outdat)\\n #np.savetxt('../rrjet_and_riaf/grtrans_jet_compare_positron_noconv.txt',outdat)\\n return\",\n \"def store_individual_noises(src_file: H5File) -> None:\\n paths = rawnav.pump_group_paths(src_file)\\n for path in paths:\\n par_path = path + '/par'\\n perp_path = path + '/perp'\\n ref_path = path + '/ref'\\n points = len(src_file[perp_path])\\n for channel in [perp_path, par_path, ref_path]:\\n signal_data = np.empty(points, dtype=np.float64)\\n src_file[channel].read_direct(signal_data)\\n src_file[channel].attrs['noise'] = noise(signal_data)\\n return\",\n \"def ns_analysis():\\r\\n\\r\\n global params\\r\\n n = params['n']\\r\\n level = params['level']\\r\\n nsmin = params['nsmin']\\r\\n nsmax = params['nsmax']\\r\\n nsstep = params['nsstep']\\r\\n bf = params['bf']\\r\\n po = params['po']\\r\\n pe = params['pe']\\r\\n pf = params['pf']\\r\\n ns_dir = create_dir(TIMES_PATH + 'ns')\\r\\n filename = datetime.datetime.now().strftime(DATE_FORMAT) + '.csv'\\r\\n with open(ns_dir + filename, 'wb') as csvfile:\\r\\n fw = csv.writer(csvfile)\\r\\n fw.writerow(('lv', 'ns', 'nt', 'no', 'ne', 'nf',\\r\\n 'method1', 'method2', 'method3_1', 'method3_2'))\\r\\n ns = nsmin\\r\\n while ns <= nsmax:\\r\\n nt = int(round(bf * ns))\\r\\n no = max(int(round(po * nt)), 1)\\r\\n ne = max(int(round(pe * no)), 1)\\r\\n nf = max(int(round(pf * nt)), 1)\\r\\n times = [0] * 4\\r\\n for i in range(n):\\r\\n times = map(sum, zip(times, get_times(level, ns, nt, no, ne, nf)))\\r\\n show_progress('ns', ns, nsmin, nsmax, nsstep, i, n)\\r\\n times = map(lambda x: float(x) / n, times)\\r\\n fw.writerow((level, ns, nt, no, ne, nf) + tuple(times))\\r\\n ns += nsstep\\r\\n csvfile.close()\",\n \"def do_SIR(self, t_max=200, dt=1.):\\n dt = float(dt)\\n\\n g = Graph()\\n\\n for node in ['I', 'C', 'R', 'H', 'B', 'U', 'D']:\\n g.add_node(node, 0)\\n\\n g.set_node('I', self.N_init)\\n\\n # cumulative time series\\n I = [g.get_node_value('I')] # noqa Infected\\n C = [g.get_node_value('C')] # Confirmed\\n R = [g.get_node_value('R')] # Recovered\\n\\n ts = [0.] # time series\\n nms = ['prob', 'lag']\\n\\n # En este modelo todos los infectados se confirman a los 10\\n # dias y se curan a los 20 dias de confirmados\\n T_IC = int(self.t_incubation / dt)\\n T_CR = 20\\n f_IC = 1.\\n f_CR = 1.\\n\\n g.add_edge('I', 'I', nms, [self.R, 0])\\n g.add_edge('I', 'C', nms, [f_IC, T_IC])\\n g.add_edge('C', 'R', nms, [f_CR, T_CR])\\n\\n t, time_steps = 0., 0\\n while t < t_max:\\n\\n time_steps = time_steps + 1\\n\\n t = t + dt\\n ts.append(t)\\n\\n # (( I ))\\n prob_II = g.get_edge('I', 'I', 'prob')\\n\\n prob_IC = g.get_edge('I', 'C', 'prob')\\n lag_IC = g.get_edge('I', 'C', 'lag')\\n update_IC = I[-lag_IC] if lag_IC < len(I) else 0.\\n\\n n_I = (\\n min(I[-1] + I[-1] * prob_II * dt, self.population) - # noqa\\n update_IC * prob_IC * dt)\\n n_I = max(n_I, 0)\\n\\n I.append(n_I)\\n\\n # (( C ))\\n prob_CR = g.get_edge('C', 'R', 'prob')\\n lag_CR = g.get_edge('C', 'R', 'lag')\\n update_CR = C[-lag_CR] if lag_CR < len(C) else 0.\\n\\n n_C = (\\n min(C[-1] + update_IC * prob_IC * dt, self.population) - # noqa\\n update_CR * prob_CR * dt)\\n n_C = max(n_C, 0)\\n C.append(n_C)\\n\\n # (( R ))\\n # recuperados nuevos\\n n_R = min(R[-1] + update_CR * prob_CR * dt, self.population)\\n n_R = max(n_R, 0)\\n R.append(n_R)\\n\\n df = pd.DataFrame(\\n {'ts': ts, 'I': I, 'C': C, 'R': R}).set_index(\\\"ts\\\")\\n\\n extra = attr.asdict(self)\\n extra[\\\"model_name\\\"] = \\\"SIR\\\"\\n return ModelResultFrame(df=df, extra=extra)\",\n \"def save_nii(img_path, data, affine, header):\\n nimg = nib.Nifti1Image(data, affine=affine, header=header)\\n nimg.to_filename(img_path)\",\n \"def save(self, rs, ths, xs, ys, vals, file=None):\\n\\n\\t\\tif file is None:\\n\\t\\t\\tfile = 'PR_c%d_r%d_th%d.h5' % (self.cbins, self.rbins, self.thbins)\\n\\n\\t\\twith File(file, 'w') as file:\\n\\t\\t\\tfile.create_dataset('cbins' , data=self.cbins)\\n\\t\\t\\tfile.create_dataset('rbins' , data=self.rbins)\\n\\t\\t\\tfile.create_dataset('thbins', data=self.thbins)\\n\\t\\t\\tfile.create_dataset('rs' , data=rs)\\n\\t\\t\\tfile.create_dataset('ths' , data=ths)\\n\\t\\t\\tfile.create_dataset('xs' , data=xs)\\n\\t\\t\\tfile.create_dataset('ys' , data=ys)\\n\\t\\t\\tfile.create_dataset('vals' , data=vals)\",\n \"def astrometryScan(self, plot=False, writeOut=False, overwrite=False,\\n store_intermediate=False, max_err_um = .5,\\n max_GD_um=None, sigma_clipping=10.0, Nmax=None,\\n min_snr=4):\\n et = []\\n t = [] # time at maximum\\n snr = [] # SNR at max\\n if Nmax==None:\\n Nmax = self.scan_nscans\\n for k in range(np.minimum(self.scan_nscans, int(Nmax))):\\n tmp = self.scanGetTimeMaxEnv(k)\\n # time of maximum:\\n t.append( tmp[0])\\n # err bar in t:\\n et.append(10e-6)\\n snr.append(tmp[1])\\n\\n t = np.array(t)\\n et = np.array(et)\\n snr = np.array(snr)\\n\\n # get FTK status around the time of maximum\\n ftk_status = []\\n for tau in t:\\n w = np.where(np.abs(self.raw['OPDC'].data.field('TIME')-tau)<1e5)\\n tmp = self.raw['OPDC'].data.field('STATE')[w].mean()\\n ftk_status.append(tmp)\\n ftk_status = np.array(ftk_status)\\n\\n w = np.where((ftk_status>=5.0))\\n print ' useful data: %d/%d' % ( len(w[0]), len(t))\\n t = t[w]\\n et = et[w]\\n snr = snr[w]\\n ftk_status=ftk_status[w]\\n\\n # FSU which is tracking\\n fsu_opdc = self.getKeyword('DEL FT SENSOR')\\n\\n if self.insmode=='SWAPPED' and \\\\\\n self.getKeyword('DEL FT SENSOR')=='FSUB':\\n print \\\" -> I suspect a wrong FSU config in \\\"+\\\\\\n \\\"the header based on the INS.MODE\\\"\\n if fsu_opdc == 'FSUB':\\n fsu_dopdc = 'FSUA'\\n else:\\n fsu_dopdc = 'FSUB'\\n\\n ### group delay dOPDC\\n GDdopdc = lambda x: np.interp(x,\\n self.raw['IMAGING_DATA_'+fsu_dopdc].data.field('TIME'),\\n self.raw['IMAGING_DATA_'+fsu_dopdc].data.field('GD'))\\n\\n ### group delay OPDC\\n GDopdc = lambda x: np.interp(x,\\n self.raw['IMAGING_DATA_'+fsu_opdc].data.field('TIME'),\\n self.raw['IMAGING_DATA_'+fsu_opdc].data.field('GD'))\\n\\n ### primet A-B\\n deltal = lambda x: np.interp(x,\\n self.raw['METROLOGY_DATA'].data.field('TIME'),\\n self.raw['METROLOGY_DATA'].data.field('DELTAL'))\\n\\n ### astrometric observable\\n deltal_2 = deltal(t) + GDopdc(t) - GDdopdc(t)\\n\\n ### error bar = agreement between AC and BD\\n err = 0.5*np.abs(deltal(t+et) + GDopdc(t+et) -\\n deltal(t-et) - GDopdc(t-et) )\\n\\n ### select points with good agreement\\n if not min_snr is None:\\n w = np.where(snr > min_snr)\\n print 'error clipping:', len(err), '->', len(w[0])\\n else:\\n w = range(len(err))\\n\\n deltal_2 = deltal_2[w]\\n t = t[w]\\n err = err[w]\\n snr = snr[w]\\n mjd = t*1e-6/(24*3600.0)+self.mjd_start\\n\\n if not sigma_clipping is None:\\n n_before = len(mjd)\\n for k in range(10):\\n c = np.polyfit(mjd-mjd.mean(), deltal_2,1)\\n # residuals\\n res = deltal_2 - np.polyval(c,mjd-mjd.mean())\\n s = res.argsort()\\n pseudoSTD = 0.5*(res[s[int(.84*len(s))]] -\\n res[s[int(.16*len(s))]])\\n print k, 'STD=', pseudoSTD\\n werr = np.where(np.abs(res)<sigma_clipping*pseudoSTD)\\n if len(werr[0]) == len(err):\\n break\\n deltal_2 = deltal_2[werr]\\n t = t[werr]\\n err = err[werr]\\n mjd = mjd[werr]\\n snr = snr[werr]\\n err = np.ones(len(err))*pseudoSTD/np.sqrt(snr-min_snr+1)\\n print 'sigma clipping:', n_before, '->', len(mjd)\\n\\n if writeOut:\\n hdu = pyfits.PrimaryHDU(None)\\n # copy original header\\n for i in self.raw[0].header.items():\\n if len(i[0])>8:\\n hie = 'HIERARCH '\\n else:\\n hie = ''\\n if len(i)==2:\\n hdu.header.update(hie+i[0], i[1])\\n elif len(i)==3:\\n hdu.header.update(hie+i[0], i[1], i[2])\\n # prepare data for a binary table\\n cols=[]\\n cols.append(pyfits.Column(name='MJD', format='F12.5',\\n array=mjd))\\n cols.append(pyfits.Column(name='D_AL', format='E', unit='m',\\n array=deltal_2))\\n cols.append(pyfits.Column(name='D_AL_ERR', format='E', unit='m',\\n array=err))\\n hducols = pyfits.ColDefs(cols)\\n hdub = pyfits.new_table(hducols)\\n hdub.header.update('EXTNAME', 'ASTROMETRY_BINNED', '')\\n\\n # combine all HDUs\\n thdulist = pyfits.HDUList([hdu, hdub])\\n # write file\\n outfile = os.path.join(\\n self.dirname, self.filename.split('.')[0]+'_RED.fits')\\n if overwrite and os.path.exists(outfile):\\n os.remove(outfile)\\n print 'writting ->', outfile\\n thdulist.writeto(outfile)\\n thdulist=[]\\n return outfile\\n\\n if plot:\\n plt.plot(13)\\n plt.clf()\\n ax1 = plt.subplot(211)\\n plt.plot(mjd, deltal_2, '.b')\\n if not sigma_clipping is None:\\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean()), 'b-')\\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean())+pseudoSTD, 'y-')\\n plt.plot(mjd, np.polyval(c,mjd-mjd.mean())-pseudoSTD, 'y-')\\n\\n plt.ylabel('position of max env')\\n plt.subplot(212, sharex=ax1)\\n plt.plot(mjd, snr, '.k')\\n #plt.yscale('log')\\n plt.ylabel('SNR')\\n return\",\n \"def findROICentres(smt_file): #{\\n rois = []\\n ary = None\\n state = 0\\n roi_centres = { \\n 'head_ct': None,\\n 'neck_cb': None,\\n 'shoulder_l': None,\\n 'shoulder_r': None,\\n 'elbow_l': None,\\n 'elbow_r': None,\\n 'pelvis_c': None,\\n 'wrist_l': None,\\n 'wrist_r': None,\\n 'hip_l': None,\\n 'hip_r': None,\\n 'knee_l': None,\\n 'knee_r': None,\\n 'ankle_l': None,\\n 'ankle_r': None,\\n 'toes_e': None}\\n\\n vrbMsg(1, 'findROICentres() smt_file = ' + smt_file)\\n # Read the smoothed Woolz object and create a NumPy array from it\\n err_num, smt_obj = readWoolzObj(smt_file)\\n if(not bool(err_num)): #{\\n\\t err_num, org, ary = wlzObjToNP(smt_obj)\\n #}\\n sz = np.shape(ary)\\n vrbMsg(5, 'findROICentres() object size = ' + str(sz))\\n # Work down the scan finding coordinates, ordering has dependency\\n # but this is checked in the individual functions\\n if((sz[0] >= img_size_min) and (sz[1] >= img_size_min)): #{\\n findProfileHeadCT(roi_centres, ary)\\n findProfileToes(roi_centres, ary)\\n #}\\n if(bool(roi_centres['toes_e']) and bool(roi_centres['head_ct'])): #{\\n findProfileShoulders(roi_centres, ary)\\n findProfilePelvis(roi_centres, ary)\\n findProfileHip(roi_centres, ary)\\n findProfileElbow(roi_centres, ary)\\n findProfileWrist(roi_centres, ary)\\n findProfileKnee(roi_centres, ary)\\n findProfileAnkle(roi_centres, ary)\\n rois = roi_centres\\n for cen in rois: #{\\n # numpy gives int64 which is not always handled (eg by json) so convert\\n pos = rois[cen]\\n if(not (pos is None)): #{\\n rois[cen] = [int(pos[0]), int(pos[1])]\\n #}\\n #}\\n #}\\n vrbMsg(1, 'findROICentres() rois = ' + str(rois))\\n return rois\",\n \"def _ion_densities_datafiles(self):\\n ne = self.ne_in\\n nD = self.ni_in[0,:]\\n nC = (ne-nD)/6.\\n print(\\\"nC/nD: \\\"+str(np.mean(nC/nD)*100.)+\\\" %\\\")\\n self.ni_in[0,:] = nD\\n self.ni_in[1,:] = nC\",\n \"def G_SNR(T2s, TEs):\\r\\n N = len(TEs)\\r\\n TEs = TEs-TEs[0]\\r\\n \\r\\n return N / np.sqrt(np.sum(np.exp(TEs / T2s)**2))\",\n \"def write_nifti(self, output_path):\\n nib.save(self.niftiImage, output_path)\\n print('Image saved at: {}'.format(output_path))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5856596","0.58353704","0.58029324","0.5760767","0.57194346","0.5685501","0.565635","0.5610267","0.5596072","0.5580801","0.5550373","0.55146116","0.55122143","0.54595554","0.53850317","0.53831476","0.53568536","0.5345135","0.53432506","0.5322765","0.53195083","0.53150904","0.5302821","0.52760804","0.52736145","0.5260181","0.5254817","0.5254216","0.52344","0.5229716","0.5220142","0.52192575","0.5204042","0.52023816","0.51953423","0.5193932","0.51785934","0.51547456","0.5154285","0.51498765","0.514857","0.5143521","0.51421195","0.51262033","0.5123879","0.5122884","0.51143014","0.5112798","0.5095889","0.50789094","0.50750756","0.506163","0.5052254","0.50511503","0.5049222","0.5045908","0.5045428","0.504462","0.50369334","0.5034644","0.5033192","0.50259715","0.50240034","0.5023302","0.50165206","0.50113857","0.50038594","0.500213","0.49938756","0.49925864","0.49857765","0.49842328","0.4969718","0.49659893","0.4964805","0.49613428","0.4959475","0.49505994","0.49436814","0.49411607","0.49392116","0.49369198","0.49339637","0.49323758","0.49283168","0.49233967","0.49222884","0.4918073","0.4905633","0.48999876","0.4894735","0.48910412","0.4890585","0.48867083","0.4877011","0.48760593","0.4871597","0.4870998","0.48682794","0.48654935"],"string":"[\n \"0.5856596\",\n \"0.58353704\",\n \"0.58029324\",\n \"0.5760767\",\n \"0.57194346\",\n \"0.5685501\",\n \"0.565635\",\n \"0.5610267\",\n \"0.5596072\",\n \"0.5580801\",\n \"0.5550373\",\n \"0.55146116\",\n \"0.55122143\",\n \"0.54595554\",\n \"0.53850317\",\n \"0.53831476\",\n \"0.53568536\",\n \"0.5345135\",\n \"0.53432506\",\n \"0.5322765\",\n \"0.53195083\",\n \"0.53150904\",\n \"0.5302821\",\n \"0.52760804\",\n \"0.52736145\",\n \"0.5260181\",\n \"0.5254817\",\n \"0.5254216\",\n \"0.52344\",\n \"0.5229716\",\n \"0.5220142\",\n \"0.52192575\",\n \"0.5204042\",\n \"0.52023816\",\n \"0.51953423\",\n \"0.5193932\",\n \"0.51785934\",\n \"0.51547456\",\n \"0.5154285\",\n \"0.51498765\",\n \"0.514857\",\n \"0.5143521\",\n \"0.51421195\",\n \"0.51262033\",\n \"0.5123879\",\n \"0.5122884\",\n \"0.51143014\",\n \"0.5112798\",\n \"0.5095889\",\n \"0.50789094\",\n \"0.50750756\",\n \"0.506163\",\n \"0.5052254\",\n \"0.50511503\",\n \"0.5049222\",\n \"0.5045908\",\n \"0.5045428\",\n \"0.504462\",\n \"0.50369334\",\n \"0.5034644\",\n \"0.5033192\",\n \"0.50259715\",\n \"0.50240034\",\n \"0.5023302\",\n \"0.50165206\",\n \"0.50113857\",\n \"0.50038594\",\n \"0.500213\",\n \"0.49938756\",\n \"0.49925864\",\n \"0.49857765\",\n \"0.49842328\",\n \"0.4969718\",\n \"0.49659893\",\n \"0.4964805\",\n \"0.49613428\",\n \"0.4959475\",\n \"0.49505994\",\n \"0.49436814\",\n \"0.49411607\",\n \"0.49392116\",\n \"0.49369198\",\n \"0.49339637\",\n \"0.49323758\",\n \"0.49283168\",\n \"0.49233967\",\n \"0.49222884\",\n \"0.4918073\",\n \"0.4905633\",\n \"0.48999876\",\n \"0.4894735\",\n \"0.48910412\",\n \"0.4890585\",\n \"0.48867083\",\n \"0.4877011\",\n \"0.48760593\",\n \"0.4871597\",\n \"0.4870998\",\n \"0.48682794\",\n \"0.48654935\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.78557074"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8456,"cells":{"query":{"kind":"string","value":"Flip axes to orientation fslview expects."},"document":{"kind":"string","value":"def FSLFlip(self, infile, prefix):\n cmd = '3dresample -orient LPI -prefix %s.nii -inset %s+orig' % \\\n (prefix, infile)\n self.CheckExec(cmd, ['%s.nii' % prefix])\n fname = '%s+orig.BRIK' % infile\n if os.path.exists(fname):\n os.remove(fname)\n fname = '%s+orig.HEAD' % infile\n if os.path.exists(fname):\n os.remove(fname)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def flip(self, axes=None):\n axes = self._make_axes_as_num(axes)\n vals = self.values\n if 0 in axes:\n vals = vals[::-1, :]\n if 1 in axes:\n vals = vals[:, ::-1]\n return Signal2D(vals, index=self.index, columns=self.columns)","def flip_axes(input_file, flipx=True, flipy=True, flipz=False,\n use_matrix=False, use_header=True):\n import os\n import numpy as np\n import nibabel as nb\n\n # Load image volume\n img = nb.load(input_file)\n dat = img.get_data()\n if use_matrix:\n mat = img.get_affine()\n if use_header:\n hdr = img.get_header()\n lenx, leny, lenz = np.shape(dat)\n dat_new = np.zeros((lenx, leny, lenz))\n\n # Flip x\n if flipx:\n for x in range(lenx):\n dat_new[lenx-1-x,:,:] = dat[x,:,:]\n\n # Flip y\n if flipy:\n for y in range(leny):\n dat_new[:,leny-1-y,:] = dat[:,y,:]\n\n # Flip z\n if flipz:\n for z in range(lenz):\n dat_new[:,:,lenz-1-z] = dat[:,:,z]\n\n # Save output\n out_file = 'reorient_' + os.path.basename(input_file)\n if use_matrix:\n if use_header:\n img = nb.Nifti1Image(dat_new, mat, hdr)\n else:\n img = nb.Nifti1Image(dat_new, mat)\n elif use_header:\n img = nb.Nifti1Image(dat_new, np.eye(4,4), hdr)\n else:\n img = nb.Nifti1Image(dat_new, np.eye(4,4))\n\n img.to_filename(out_file)\n\n return out_file","def flip_plot_axes(self):\n self.plot_inverted = not self.plot_inverted\n\n if not self.plot_inverted:\n self.draw_plot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)\n else:\n self.draw_plot(self.data_y_axis, self.data_x_axis, self.label_y_axis, self.label_x_axis)","def transform(x: np.array, params: TransformParams) -> np.array:\n if params.do_hor_flip:\n x = flip_axis(x, 1)\n\n if params.do_vert_flip:\n x = flip_axis(x, 0)\n\n return x","def td_flip(self):\n self.cw_rotate()\n self.cw_rotate()\n self.lr_flip()\n self.find_edges()","def flip(self, axes=None, inplace=False, i=False):\n d = _inplace_enabled_define_and_cleanup(self)\n super(DimensionCoordinate, d).flip(axes=axes, inplace=True)\n\n direction = d._custom.get(\"direction\")\n if direction is not None:\n d._custom[\"direction\"] = not direction\n\n return d","def flip(self, mode='h'):\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\n # is_vertical_flip.\n if mode == 'h':\n self.is_horizontal_flip = True\n self.x = np.flipud(self.x)\n elif mode == 'v':\n self.is_vertical_flip = True\n self.x = np.fliplr(self.x)\n else:\n self.is_vertical_flip = True\n self.is_horizontal_flip = True\n self.x = np.fliplr(self.x)\n self.x = np.flipud(self.x)\n # raise NotImplementedError\n #######################################################################\n # #\n # #\n # TODO: YOUR CODE HERE #\n # #\n # #\n #######################################################################","def mirror_axis(self, axis_index=0, reverse=False):\n self.smd3.mirror(axis_index=axis_index, reverse=reverse)\n self.logic.mirror(axis_index=axis_index, reverse=reverse)\n min_vector, max_vector = self.smd3.get_min_max_vector()\n self.header.set_box(min_vector, max_vector)\n self.header.update(self.smd3)\n self.logic.update(self.smd3)\n # self.meta.mirror(axis_index=axis_index, reverse=reverse)","def toggle_axis(self):\n # cycle through three axis states\n states = [False, 'world', 'all']\n # the state after toggling\n index = (states.index(self.view['axis']) + 1) % len(states)\n # update state to next index\n self.view['axis'] = states[index]\n # perform gl actions\n self.update_flags()","def set_orientation(self, axes):\n if debug:\n logger.debug('set_orientation ...')\n logger.debug('%s -> %s', str(self.axes_names), str(axes))\n\n if set(axes) != set(self.axes_names):\n raise Exception('Required orientation %s does not contain '\n 'all axes %s' % (str(axes), str(self.axes_names)))\n\n if axes == self.axes_names: # already in the asked orientation\n return\n\n for i, axis in enumerate(axes):\n logger.debug('Rolling axis %s, cur pos=%d -> dest pos=%d',\n axis, self.axes_names.index(axis), i)\n logger.debug('Shape: %s', str(self.data.shape))\n cur_i = self.axes_names.index(axis)\n self.data = np.rollaxis(self.data, cur_i, i)\n self.axes_names.pop(cur_i)\n self.axes_names.insert(i, axis)\n logger.debug('After rolling. Shape: %s, new axes: %s',\n str(self.data.shape), str(self.axes_names))\n logger.debug('')\n\n self.axes_ids = dict([(a, i) for i, a in enumerate(self.axes_names)])","def flip(tensor, axis=None):\n raise NotImplementedError","def flip(a, axis):\n a_ndim = a.ndim\n if a_ndim < 1:\n raise core.core._AxisError('Input must be >= 1-d')\n\n axis = int(axis)\n if not -a_ndim <= axis < a_ndim:\n raise core.core._AxisError(\n 'axis must be >= %d and < %d' % (-a_ndim, a_ndim))\n\n return _flip(a, axis)","def _update_data_transforms(self, axisOrder='col-major'):\n self._dataTransform = QtGui.QTransform()\n self._inverseDataTransform = QtGui.QTransform()\n if self.axisOrder == 'row-major': # transpose both\n self._dataTransform.scale(1, -1)\n self._dataTransform.rotate(-90)\n self._inverseDataTransform.scale(1, -1)\n self._inverseDataTransform.rotate(-90)","def convert_flip(g, op, block):\n\n x = g.get_node(op.input(\"X\")[0])\n axis = op.attr(\"axis\")\n\n for i, ax in enumerate(axis):\n if i == 0:\n out = _op.reverse(x, ax)\n else:\n out = _op.reverse(out, ax)\n\n g.add_node(op.output(\"Out\")[0], out)","def _apply_swap(self, state, axes, **kwargs):\n all_axes = list(range(len(state.shape)))\n all_axes[axes[0]] = axes[1]\n all_axes[axes[1]] = axes[0]\n return self._transpose(state, all_axes)","def flip_dir(self, axis):\n res = self.empty_like()\n # Negate directions and quantum numbers in form data.\n res.dirs[axis] *= -1\n res.qhape[axis] = [self._qod_func(-q) for q in res.qhape[axis]]\n # Flip the quantum numbers of the keys of self.sects.\n keys = set(self.sects.keys())\n while keys:\n k = keys.pop()\n kf = list(k)\n kf[axis] = self._qod_func(-kf[axis])\n kf = tuple(kf)\n # Swap self[kf] and self[k], at least if both exist.\n vf = self[k]\n if kf in keys:\n v = self[kf]\n keys.discard(kf)\n res[k] = v\n res[kf] = vf\n return res","def change_orientation(self):\n self.shape = self.shape.T","def reorderAxesEvent(self):\n axisB = self.sender().text()\n self.myParent.swapAxes(self.axisName, axisB)\n self.myParent.setVistrailsVariableAxes()","def flip(self):","def inverse_transform(data, flip, rotate):\n if flip == 'FLIP_LEFT_RIGHT':\n data = np.fliplr(data)\n\n if rotate == 'ROTATE_90':\n data = np.rot90(data, 3)\n\n if rotate == 'ROTATE_180':\n data = np.rot90(data, 2)\n\n if rotate == 'ROTATE_270':\n data = np.rot90(data, 1)\n\n return data","def swapaxes(self, a1, a2):\n an = self.axes_names[:]\n ia1, ia2 = self.get_axis_id(a1), self.get_axis_id(a2)\n an[ia2], an[ia1] = an[ia1], an[ia2]\n return xndarray(np.swapaxes(self.data, ia1, ia2), an, self.axes_domains,\n self.value_label, self.meta_data)","def change_axis_names(self, axis_map):\n axes = self.axes\n\n # Partition axes\n self.axes = [axis_map[axis] for axis in axes]\n\n # Flipped axes\n flip = self.flip\n if flip:\n self.flip = [axis_map[axis] for axis in flip]","def flip(self, xflip=True, yflip=False):\n self.drawer.flush()\n img = self.img\n if xflip: img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n if yflip: img = img.transpose(PIL.Image.FLIP_TOP_BOTTOM)\n self.img = img\n self.update_drawer_img()\n return self","def flip_vertical(img):\r\n #reading image\r\n im = Image.open(\"filename\")\r\n\r\n #flipping image vertically\r\n newimg = im.transpose(PIL.Image.FLIP_TOP_BOTTOM)\r\n return img","def flip(self):\n self.width, self.height = self.height, self.width","def flip_x(self):\n self.x_lim_helper.flip_limits()","def flip_x(self):\n self.x_lim_helper.flip_limits()","def flip(self, horizontal):\n try:\n self._is_transformable()\n horizontal = get_int(horizontal)\n except NotTransformable as e:\n self._app[\"statusbar\"].message(str(e) + \" flip\", \"error\")\n return\n except StringConversionError as e:\n self._app[\"statusbar\"].message(str(e), \"error\")\n return\n images = self.get_images(\"Flipped\")\n # Apply changes\n for fil in images:\n if fil not in self._changes:\n self._changes[fil] = [0, 0, 0]\n if horizontal:\n self._changes[fil][1] = \\\n (self._changes[fil][1] + 1) % 2\n else:\n self._changes[fil][2] = \\\n (self._changes[fil][2] + 1) % 2\n # Flip the image shown\n if self._app.get_path() in images:\n self.emit(\"changed\", \"flip\", horizontal)\n # Reload thumbnails of flipped images immediately\n if self._app[\"thumbnail\"].toggled:\n self.apply()","def saveflip(image, fname, outpath, axis='x', preserve_name=False):\n if not preserve_name:\n fpath = genSavePath(outpath, fname, modstring=f\"mirror_{axis}\")\n else:\n fpath = genSavePath(outpath, fname)\n im = copy(image)\n if axis == 'x':\n im = im.transpose(Image.FLIP_LEFT_RIGHT)\n elif axis == 'y':\n im = im.transpose(Image.FLIP_TOP_BOTTOM)\n else:\n raise Exception('No valid axis procvided for flipping: {} - received value: {}'.format(fname, axis))\n try:\n im.save(fpath, subsample=\"keep\", qtables=image.quantization, optimize=True)\n\n except IOError as m:\n print( \"Flipped({}) image creation failed for: {}. \\nReason:{}\".format(axis,fname,m))","def flip(self, bev_direction: str = 'horizontal') -> None:\n pass","def reverse_axis(self, x: bool, y: bool, z: bool) -> None:\n current_directions = self.i2c.read(self.Register.AXIS_MAP_SIGN, 1)[0]\n if x:\n current_directions |= (1 << 2)\n else:\n current_directions &= ~(1 << 2)\n if y:\n current_directions |= (1 << 1)\n else:\n current_directions &= ~(1 << 1)\n if z:\n current_directions |= (1 << 0)\n else:\n current_directions &= ~(1 << 0)\n self.i2c.write(self.Register.AXIS_MAP_SIGN, current_directions)","def swapaxes(a, axis1, axis2):\n # TODO(okuta): check type\n return a.swapaxes(axis1, axis2)","def vflip(img):\n #if not _is_pil_image(img):\n # raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\n\n return img.transpose(Image.FLIP_TOP_BOTTOM)","def flip(self, horizontally):\n\t\tself.currentPixbuf = self.currentPixbuf.flip(horizontally)\n\t\tself.scaleCache[1] = 0\n\t\tgc.collect()","def set_flipped(self, x, y):\n self.pieces[x + (y * self.width)].set_flipped()","def flip_image(img, vert=True):\n if vert:\n return img.transpose(Image.FLIP_TOP_BOTTOM)\n else:\n return img.transpose(Image.FLIP_LEFT_RIGHT)","def reorder_axes(cls, array: np.ndarray, axes: str) -> np.ndarray:\n return cls._reorder_axes(array, axes, cls.array_axis_order)","def flip_horizontal(img):\r\n #reading image\r\n im = Image.open(\"filename\")\r\n\r\n #flipping image horizontally\r\n newimg = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\r\n \r\n return img","def fl_flip_yorigin():\n _fl_flip_yorigin = library.cfuncproto(\n library.load_so_libforms(), \"fl_flip_yorigin\",\\\n None, [],\\\n \"\"\"void fl_flip_yorigin()\"\"\")\n _fl_flip_yorigin()","def orient_img_hwd(data, slice_axis):\n if slice_axis == 0:\n return data.transpose(2, 1, 0)\n elif slice_axis == 1:\n return data.transpose(2, 0, 1)\n elif slice_axis == 2:\n return data","def flip(self):\n self.align = self._left if self.align == self._right else self._right\n self.group.layout_all()","def adjust_axes(axes):\n # TODO: Uncomment & decide for each subplot!\n for ax in axes.itervalues():\n core.hide_axis(ax)\n\n for k in [\n \"placeholder\",\n \"placeholder1\",\n \"placeholder2\",\n \"spikes_stim\",\n \"spikes_stim1\",\n \"spikes_stim2\",\n \"spikes_post\",\n \"stimulation_schema\"\n ]:\n axes[k].set_frame_on(False)","def _update_axes(self):\n data_shape = self.data.shape\n if len(self.axes) < self.data.ndim + 1:\n self._axes.append(Axis())\n for index in range(self.data.ndim):\n if len(self.axes[index].values) != data_shape[index]:\n self.axes[index].values = np.arange(data_shape[index],\n dtype=np.float64)","def flip(self, bev_direction='horizontal', points=None):\n assert bev_direction in ('horizontal', 'vertical')\n if bev_direction == 'horizontal':\n self.tensor[:, 0::7] = -self.tensor[:, 0::7]\n if self.with_yaw:\n self.tensor[:, 6] = -self.tensor[:, 6] + np.pi\n elif bev_direction == 'vertical':\n self.tensor[:, 2::7] = -self.tensor[:, 2::7]\n if self.with_yaw:\n self.tensor[:, 6] = -self.tensor[:, 6]\n\n if points is not None:\n assert isinstance(points, (torch.Tensor, np.ndarray, BasePoints))\n if isinstance(points, (torch.Tensor, np.ndarray)):\n if bev_direction == 'horizontal':\n points[:, 0] = -points[:, 0]\n elif bev_direction == 'vertical':\n points[:, 2] = -points[:, 2]\n elif isinstance(points, BasePoints):\n points.flip(bev_direction)\n return points","def flipud(self) -> xr.DataArray | xr.Dataset:\n y_dim = self.y_dim\n # NOTE don't use ycoords to work for rotated grids\n yrev = self._obj[y_dim].values[::-1]\n obj_filpud = self._obj.reindex({y_dim: yrev})\n # y_dim is typically a dimension without coords in rotated grids\n if y_dim not in self._obj.coords:\n obj_filpud = obj_filpud.drop_vars(y_dim)\n return obj_filpud","def reorient(self, orientation):\n if orientation == self.axes_names:\n return self\n else:\n new_c = self.copy()\n new_c.set_orientation(orientation)\n return new_c","def lr_flip(self):\n for g in self.grid:\n g.reverse()","def SwapAxis(self, axis0, axis1):\n\n axis0 = int(axis0)\n axis1 = int(axis1)\n\n self.points[:,[axis0,axis1]] = self.points[:,[axis1,axis0]]","def flip_augmentation():\n return lambda image: ImageOps.flip(image)","def test_tranform_1(self):\n fig_test = plt.figure()\n ax = fig_test.add_subplot(111, projection='ternary')\n ax.plot([0, 1], [0, 1], transform=ax.transAxes)","def vflip(img, data_format='CHW'):\n _assert_image_tensor(img, data_format)\n\n h_axis = _get_image_h_axis(data_format)\n\n return img.flip(axis=[h_axis])","def reverseXZrotation(self):\n rot = np.zeros((3, 3), dtype = 'float64')\n rot[0, 0] = np.cos(self.currtheta)\n rot[0, 1] = -np.sin(self.currtheta)\n rot[0, 2] = 0.0\n rot[1, 0] = np.sin(self.currtheta) * np.cos(self.currphi)\n rot[1, 1] = np.cos(self.currtheta) * np.cos(self.currphi)\n rot[1, 2] = - np.sin(self.currphi)\n rot[2, 0] = np.sin(self.currtheta) * np.sin(self.currphi)\n rot[2, 1] = np.cos(self.currtheta) * np.sin(self.currphi)\n rot[2, 2] = np.cos(self.currphi)\n\n self.ds = np.dot(rot, self.ds1)","def mirrorImage(self):\n\n im = Image.open(self.ActivePhoto)\n out = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\n out.save(self.ActivePhoto)\n self.photo.setPixmap(QtGui.QPixmap(self.ActivePhoto))\n print (\"Flipped image\")","def flip(self):\n \n if self.faceup:\n self.faceup = False\n else:\n self.faceup = True","def set_MRI_orientation(self):\n\n if self.has_axes(MRI3Daxes):\n orientation = MRI3Daxes[:]\n if self.has_axis('time'):\n orientation += ['time']\n if self.has_axis('iteration'):\n orientation += ['iteration']\n if self.has_axis('condition'):\n orientation += ['condition']\n\n orientation += sorted(set(self.axes_names).difference(orientation))\n\n self.set_orientation(orientation)","def forward_transform(data, flip, rotate):\n if flip == 'FLIP_LEFT_RIGHT':\n data = np.fliplr(data)\n\n if rotate == 'ROTATE_90':\n data = np.rot90(data, 1)\n\n if rotate == 'ROTATE_180':\n data = np.rot90(data, 2)\n\n if rotate == 'ROTATE_270':\n data = np.rot90(data, 3)\n\n return data","def __flip(img, flip, flip_type=Image.FLIP_LEFT_RIGHT):\n if flip:\n return img.transpose(flip_type)\n return img","def flip_image_vertical(image):\n return cv.flip(image, 1)","def flip(self, x, y):\n self.pieces[x + (y * self.width)].flip()","def setHorizontalFlip(self, flag):\n\t\tself.flipHorizontally = flag","def clean_axes(axl):\n cleanAxes(axl)","def flip_image(image_path, saved_location):\n image_obj = Image.open(image_path)\n rotated_image = image_obj.transpose(Image.FLIP_LEFT_RIGHT)\n rotated_image.save(saved_location)","def ipix_swap_axes(self, ipix, colwise=False):\n xy = self.ipix_to_xypix(ipix, colwise)\n return self.xypix_to_ipix(xy, not colwise)","def clean_axes(axes, left=True, right=False):\n if not type(axes) in [np.ndarray, list]:\n axes = [axes]\n elif type(axes) == np.ndarray:\n axes = axes.flatten()\n for axis in axes:\n axis.tick_params(direction='out')\n axis.spines['top'].set_visible(False)\n if not right:\n axis.spines['right'].set_visible(False)\n if not left:\n axis.spines['left'].set_visible(False)\n axis.get_xaxis().tick_bottom()\n axis.get_yaxis().tick_left()","def edges_flip_orientation(edges):\n edges_flipped = []\n for e in edges:\n edges_flipped.insert(0, (e[1], e[0]))\n return edges_flipped","def flip(self, xbool, ybool):\n self._surf = pygame.transform.flip(self._surf, xbool, ybool).convert_alpha()","def flipped_dimensions(transformation, size):\n dim = len(size)\n # transform start point\n start = [0.0] * dim\n transformed_start = transformation.TransformPoint(start)\n flipped = [False] * dim\n for i in range(dim):\n # set current end point and transform it\n end = [0.0] * dim\n end[i] = size[i]\n transformed_end = transformation.TransformPoint(end)\n # check, if transformed_start and transformed_end changed position\n flipped[i] = transformed_start[i] > transformed_end[i]\n return flipped","def flip_image(image, rects, axis = 1):\n\n if axis not in [-1,0,1]:\n return image, rects\n\n height = image.shape[0]\n widht = image.shape[1]\n \n image = cv2.flip(image,axis)\n\n if axis == 1:\n rects[:,1] = widht - rects[:,1]\n rects[:,3] = widht - rects[:,3]\n rects[:,3], rects[:,1] = rects[:,1].copy(),rects[:,3].copy()\n elif axis == 0:\n rects[:,2] = height - rects[:,2]\n rects[:,4] = height - rects[:,4]\n rects[:,4], rects[:,2] = rects[:,2].copy(),rects[:,4].copy()\n elif axis == -1:\n rects[:,1] = widht - rects[:,1]\n rects[:,3] = widht - rects[:,3]\n rects[:,3], rects[:,1] = rects[:,1].copy(),rects[:,3].copy()\n rects[:,2] = height - rects[:,2]\n rects[:,4] = height - rects[:,4]\n rects[:,4], rects[:,2] = rects[:,2].copy(),rects[:,4].copy()\n\n return image, rects","def reflect(self, axis):\n if axis == \"x\":\n self.y = - self.y\n elif axis == \"y\":\n self.x = - self.x\n else:\n print(\"The argument axis only accepts values 'x' and 'y'!\")","def _flip_isolated_fluxes(l: Lattice, bonds: np.ndarray, fluxes: np.ndarray):\n indices_to_flip = np.where(fluxes == -1)[0]\n\n def pos(p):\n return l.plaquettes[p].center\n\n def distance_func(a, b):\n return straight_line_length(pos(a), pos(b))\n\n close_pairs = _greedy_plaquette_pairing(indices_to_flip, distance_func)\n\n for a, b in close_pairs:\n plaquettes, edges_to_flip = path_between_plaquettes(l,\n a,\n b,\n maxits=l.n_edges)\n bonds[edges_to_flip] *= -1\n fluxes[a] *= -1\n fluxes[b] *= -1\n\n return bonds, fluxes","def rescale_axes(self, x=True, y=True, xlim=None, ylim=None, \n tighten_up=0): \n \n # First, figure out what limits should be\n col_xlim = [[1e10, -1e10] for i in range(self.dims[0])]\n row_ylim = [[1e10, -1e10] for i in range(self.dims[1])]\n \n # Loop over axes\n for i in range(self.N):\n if self.grid[i] is None:\n continue\n \n # column, row\n j, k = self.axis_position(i)\n \n if self.above_diagonal(i):\n continue\n \n if x and xlim is None:\n col_xlim[j][0] = min(col_xlim[j][0], self.grid[i].dataLim.min[0])\n col_xlim[j][1] = max(col_xlim[j][1], self.grid[i].dataLim.max[0]) \n elif x:\n col_xlim[j][0] = xlim[0]\n col_xlim[j][1] = xlim[1]\n \n if self.diagonal is not None and i in self.diag:\n continue\n \n if y and (ylim is None): \n row_ylim[k][0] = min(row_ylim[k][0], self.grid[i].dataLim.min[1])\n row_ylim[k][1] = max(row_ylim[k][1], self.grid[i].dataLim.max[1]) \n elif y:\n row_ylim[k][0] = ylim[0]\n row_ylim[k][1] = ylim[1] \n \n # Apply limits \n for i in range(self.N):\n if self.grid[i] is None:\n continue\n \n # column, row \n j, k = self.axis_position(i)\n \n col_tmp = [col_xlim[j][0] * (1. + tighten_up * np.sign(col_xlim[j][0])),\n col_xlim[j][1] * (1. - tighten_up * np.sign(col_xlim[j][1]))]\n \n row_tmp = [row_ylim[k][0] * (1. + tighten_up * np.sign(row_ylim[k][0])),\n row_ylim[k][1] * (1. - tighten_up * np.sign(row_ylim[k][1]))]\n\n # Kludge\n if np.all(np.isfinite(col_tmp)):\n self.grid[i].set_xlim(col_tmp)\n \n if self.diagonal and i in self.diag:\n continue\n\n if np.all(np.isfinite(row_tmp)):\n self.grid[i].set_ylim(row_tmp)\n\n pl.draw()","def flip_vertical(original_image: Image) -> Image :\r\n \r\n new_image = copy(original_image)\r\n \r\n pixel_width = get_width(original_image)\r\n pixel_height = get_height(original_image) \r\n\r\n \r\n for x in range(pixel_width) :\r\n for y in range(pixel_height) :\r\n original_vertical_pixel = get_color(original_image, x, y)\r\n opposite_vertical_pixel = pixel_height - 1 - y\r\n set_color(new_image, x, opposite_vertical_pixel, original_vertical_pixel)\r\n \r\n return new_image","def apply_transforms(self):\n self.axes.set_xlim(self._curr_xlim)\n self.axes.set_ylim(self._curr_ylim)","def reverse_transformation(self, rot90, flip_lr=False):\n\n transformed_ROI = copy.deepcopy(self) #Make a copy of itself to transform and return\n\n # Undo the left-right flip\n if flip_lr:\n transformed_ROI.center = (transformed_ROI.center[0], self.img_shape[1] - transformed_ROI.center[1])\n\n #Undo the rotation of the image\n cent = transformed_ROI.center\n if rot90 == 1:\n transformed_ROI.center = (cent[1], self.img_shape[0] - cent[0])\n transformed_ROI.size = (transformed_ROI.size[1], transformed_ROI.size[0])\n elif rot90 == 2:\n transformed_ROI.center = (self.img_shape[0] - cent[0], self.img_shape[1] - cent[1])\n elif rot90 == 3:\n transformed_ROI.center = (self.img_shape[1] - cent[1], cent[0])\n transformed_ROI.size = (transformed_ROI.size[1], transformed_ROI.size[0])\n\n return transformed_ROI","def flipNormals(self):\n self.flip = not self.flip","def side_wheel_from_axis():","def _swap_axis(input_tensor, dim_index, last_index, name=None):\n return array_ops.transpose(\n input_tensor,\n array_ops.concat([\n math_ops.range(dim_index), [last_index],\n math_ops.range(dim_index + 1, last_index), [dim_index]\n ], 0),\n name=name)","def test_surfer6binary_invert_axis_methods(text_grd_file):\n\n grd = Surfer6TextGrid.load(text_grd_file)\n\n dm = np.arange(150).reshape(15, 10)\n\n assert np.alltrue(grd.dm == dm)\n assert grd.ylo == 10\n assert grd.yhi == 38\n assert grd.xhi == 9\n assert grd.xlo == 0\n grd.invert_yaxis()\n assert np.alltrue(grd.dm == dm[::-1, :])\n assert grd.ylo == 38\n assert grd.yhi == 10\n grd.invert_xaxis()\n assert np.alltrue(grd.dm == dm[::-1, ::-1])\n assert grd.xlo == 9\n assert grd.xhi == 0\n grd.invert_yaxis()\n assert np.alltrue(grd.dm == dm[:, ::-1])\n grd.invert_xaxis()\n assert np.alltrue(grd.dm == dm)\n assert grd.ylo == 10\n assert grd.yhi == 38\n assert grd.xhi == 9\n assert grd.xlo == 0","def swap_axis(ogr_geom):\n\n osr_sref = ogr_geom.GetSpatialReference()\n sref = SpatialRef.from_osr(osr_sref)\n if (sref.epsg == 4326) and GDAL_3_ENABLED and (osr_sref.GetAxisMappingStrategy() == 1):\n ogr_geom.SwapXY()\n osr_sref.SetAxisMappingStrategy(0)\n ogr_geom.AssignSpatialReference(osr_sref)\n\n return ogr_geom","def flip(self, x: bool, y: bool) -> 'BaseImage':\n assert isinstance(x, bool)\n assert isinstance(y, bool)\n assert (x or y), 'at least one axis should be True'\n self._surface = pygame.transform.flip(self._surface, x, y)\n return self","def right_angle_axes(self, right_angle_axes):\n\n self.container['right_angle_axes'] = right_angle_axes","def flip(x, dim):\n dim = x.dim() + dim if dim < 0 else dim\n inds = tuple(slice(None, None) if i != dim\n else x.new(torch.arange(x.size(i) - 1, -1, -1).tolist()).long()\n for i in range(x.dim()))\n return x[inds]","def setVerticalFlip(self, flag):\n\t\tif self.ext.lower() in [\"png\", \"jpg\", \"jpeg\"]:\n\t\t\tself.flipVertically = not flag\n\t\telse:\n\t\t\tself.flipVertically = flag","def inverse_transform(self, Xt):\n # Inverse transform\n columns = []\n start = 0\n\n for j in range(self.n_dims):\n dim = self.dimensions[j]\n offset = dim.transformed_size\n\n if offset == 1:\n columns.append(dim.inverse_transform(Xt[:, start]))\n else:\n columns.append(dim.inverse_transform(Xt[:, start : start + offset]))\n\n start += offset\n\n # Transpose\n rows = []\n\n for i in range(len(Xt)):\n r = []\n for j in range(self.n_dims):\n r.append(columns[j][i])\n\n rows.append(r)\n\n return rows","def shift_axes(self, shift, axes=None):\n axes = self._get_axes_numbers(axes)\n shift = self._cook_args(shift, axes, [0.0, 0.0])\n return Signal2D(self.values, index=self.index-shift[0], columns=self.columns-shift[1])","def setaxesnames(self):\n if not self._axesnames or self.prop['skipsai']:\n return\n debug('ControllerStartup.setaxesnames()')\n oldaxes = self.pidevice.qSAI_ALL()\n for i, newaxis in enumerate(self.axesnames):\n if newaxis != oldaxes[i] or self.prop['forcesai']:\n setstage = False\n if self.pidevice.HasqCST():\n if self.pidevice.qCST()[oldaxes[i]] == 'NOSTAGE':\n try:\n debug('try rename NOSTAGE to TEMP (0x3C)')\n self.pidevice.SPA(oldaxes[i], 0x3c, 'TEMP')\n setstage = True\n except GCSError:\n pass\n self.pidevice.SAI(oldaxes[i], newaxis)\n if setstage:\n self.pidevice.SPA(newaxis, 0x3c, 'NOSTAGE')\n debug('restore NOSTAGE (0x3C)')","def set_axes(self, a):\r\n self.axes = a","def reverse_x():\n plt.xlim(plt.xlim()[::-1])","def setLabelOrientation(orientation='horizontal',axes='XYZ'):\n orientdict = {'horizontal':'HORI', 'vertical':'VERT'}\n dislin.labtyp(orientdict[orientation],axes)","def flip_quat(orientation):\n r,p,y = quat_to_euler(orientation)\n r += np.pi\n return euler_to_quat(r,p,y)","def rotate_ship(self):\n\t\tif self.cur_orient == GameBoard.O_HORIZONTAL:\n\t\t\tself.cur_orient = GameBoard.O_VERTICAL\n\t\telse:\n\t\t\tself.cur_orient = GameBoard.O_HORIZONTAL","def SetAxisOrientation(*args, **kwargs):\n return _gdi_.DC_SetAxisOrientation(*args, **kwargs)","def _flip_adjacent_fluxes(l: Lattice, bonds: np.ndarray, fluxes: np.ndarray):\n for edge_index, (p_a, p_b) in enumerate(l.edges.adjacent_plaquettes):\n if (p_a == INVALID) or (p_b == INVALID):\n break\n if (fluxes[p_a] == -1) and (fluxes[p_b] == -1):\n bonds[edge_index] *= -1\n fluxes[p_a] *= -1\n fluxes[p_b] *= -1\n\n #attempt at vectorising, check this at somepoint\n #adj_fluxes = fluxes[l.edges.adjacent_plaquettes]\n #to_flip = np.where((adj_fluxes[:, 0] == -1) & (adj_fluxes[:, 1] == -1))\n #bonds[to_flip] *= -1\n #fluxes_to_flip = l.edges.adjacent_plaquettes[to_flip].flatten()\n #fluxes[fluxes_to_flip] *= -1\n\n return bonds, fluxes","def flip_boxes_horizontally(boxes):\n # Flip boxes horizontally.\n ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)\n flipped_xmin = tf.subtract(1.0, xmax)\n flipped_xmax = tf.subtract(1.0, xmin)\n flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)\n return flipped_boxes","def restore_orientation_matrix(self):\n self.Umat = self.init_p","def flip(h):\n return np.flip(h)","def flip(h):\n return np.flip(h)","def convert_axis( mv, axisold, axisindnew ):\n (axisnew, indexina3) = axisindnew\n axes = allAxes(mv)\n kold = None\n for k in range(len(axes)):\n if axes[k]==axisold: kold=k\n if kold==None:\n print \"ERROR. convert_axis cannot find axis\",axisold,\" in variable\",mv\n if len(axisold)==len(axisnew):\n mv.setAxis( kold, axisnew )\n return\n # Here's what we would do in 1-D:\n # newdata = ma.ones(len(axisnew))*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\n # for i in range(len(axisold)):\n # newdata[ indexina3[i] ] = ma[i]\n # newmv = cdms2.createVariable( newdata, id=mv.id )\n # >1-D is the same idea, but more dimensions are coming along for the ride,\n # making it more complicated...\n shape0 = mv.shape\n shape0[kold] = len(axisnew)\n newdata = ma.ones(shape0)*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\n # We want to copy ma to newdata - except that we need indirect indexing for the kold-th axis.\n # There seems to be nothing in numpy for treating one axis differently from the rest\n # (except for ellipsis, but it makes sense to use only one ellipsis and we would need two here).\n # The following will do the job. It would be very slow for an array with many big dimensions,\n # but the arrays here have already been reduced for graphics; the index sets will be small or\n # empty...\n ranges = map( range, shape0[0:kold] )\n for i in range(len(axisold)):\n for idx in apply(itertools.product,ranges):\n idx = idx + [indexina3(i)] + [Ellipsis]\n idxo = idx + [i] + [Ellipsis]\n newdata[ tuple(idx) ] = mv[idxo]\n newmv = cdms2.createVariable( newdata, id=mv.id )","def extend_dataset_flip_axis(data,\n labels,\n height=90,\n width=160,\n channels=3):\n all_images = []\n all_labels = []\n flat_shape = data.shape[1]\n for i in range(data.shape[0]):\n orig_label = labels[i]\n if orig_label == 0:\n continue\n frame = data[i].reshape((height, width, channels))\n\n if orig_label == 1:\n flip_cmd = 2\n else:\n flip_cmd = 1\n flip = np.flip(frame, axis=1)\n flip = np.array(flip.reshape(flat_shape))\n all_images.append(flip)\n all_labels.append(flip_cmd)\n all_labels = np.array(all_labels).astype('uint8')\n all_labels = all_labels.reshape((all_labels.shape[0], 1))\n extended_images = np.concatenate((data, all_images), axis=0)\n extended_labels = np.concatenate((labels, all_labels), axis=0)\n return extended_images, extended_labels","def flip(self, index):\n head, tail = self.get_extreme_points(index)\n centroid = self.get_position(index)\n\n if tail is not None and centroid is not None:\n self.set_angle(index, points_angle(centroid, tail) )","def flip_ras_lps(vol, affine):\n vol_flipped = np.flip(vol, (0,1))\n affine_flipped = affine.copy()\n affine_flipped[0,-1] = (-1 * affine @ np.array([vol.shape[0]-1,0,0,1]))[0]\n affine_flipped[1,-1] = (-1 * affine @ np.array([0,vol.shape[1]-1,0,1]))[1]\n\n return vol_flipped, affine_flipped"],"string":"[\n \"def flip(self, axes=None):\\n axes = self._make_axes_as_num(axes)\\n vals = self.values\\n if 0 in axes:\\n vals = vals[::-1, :]\\n if 1 in axes:\\n vals = vals[:, ::-1]\\n return Signal2D(vals, index=self.index, columns=self.columns)\",\n \"def flip_axes(input_file, flipx=True, flipy=True, flipz=False,\\n use_matrix=False, use_header=True):\\n import os\\n import numpy as np\\n import nibabel as nb\\n\\n # Load image volume\\n img = nb.load(input_file)\\n dat = img.get_data()\\n if use_matrix:\\n mat = img.get_affine()\\n if use_header:\\n hdr = img.get_header()\\n lenx, leny, lenz = np.shape(dat)\\n dat_new = np.zeros((lenx, leny, lenz))\\n\\n # Flip x\\n if flipx:\\n for x in range(lenx):\\n dat_new[lenx-1-x,:,:] = dat[x,:,:]\\n\\n # Flip y\\n if flipy:\\n for y in range(leny):\\n dat_new[:,leny-1-y,:] = dat[:,y,:]\\n\\n # Flip z\\n if flipz:\\n for z in range(lenz):\\n dat_new[:,:,lenz-1-z] = dat[:,:,z]\\n\\n # Save output\\n out_file = 'reorient_' + os.path.basename(input_file)\\n if use_matrix:\\n if use_header:\\n img = nb.Nifti1Image(dat_new, mat, hdr)\\n else:\\n img = nb.Nifti1Image(dat_new, mat)\\n elif use_header:\\n img = nb.Nifti1Image(dat_new, np.eye(4,4), hdr)\\n else:\\n img = nb.Nifti1Image(dat_new, np.eye(4,4))\\n\\n img.to_filename(out_file)\\n\\n return out_file\",\n \"def flip_plot_axes(self):\\n self.plot_inverted = not self.plot_inverted\\n\\n if not self.plot_inverted:\\n self.draw_plot(self.data_x_axis, self.data_y_axis, self.label_x_axis, self.label_y_axis)\\n else:\\n self.draw_plot(self.data_y_axis, self.data_x_axis, self.label_y_axis, self.label_x_axis)\",\n \"def transform(x: np.array, params: TransformParams) -> np.array:\\n if params.do_hor_flip:\\n x = flip_axis(x, 1)\\n\\n if params.do_vert_flip:\\n x = flip_axis(x, 0)\\n\\n return x\",\n \"def td_flip(self):\\n self.cw_rotate()\\n self.cw_rotate()\\n self.lr_flip()\\n self.find_edges()\",\n \"def flip(self, axes=None, inplace=False, i=False):\\n d = _inplace_enabled_define_and_cleanup(self)\\n super(DimensionCoordinate, d).flip(axes=axes, inplace=True)\\n\\n direction = d._custom.get(\\\"direction\\\")\\n if direction is not None:\\n d._custom[\\\"direction\\\"] = not direction\\n\\n return d\",\n \"def flip(self, mode='h'):\\n # TODO: Implement the flip function. Remember to record the boolean values is_horizontal_flip and\\n # is_vertical_flip.\\n if mode == 'h':\\n self.is_horizontal_flip = True\\n self.x = np.flipud(self.x)\\n elif mode == 'v':\\n self.is_vertical_flip = True\\n self.x = np.fliplr(self.x)\\n else:\\n self.is_vertical_flip = True\\n self.is_horizontal_flip = True\\n self.x = np.fliplr(self.x)\\n self.x = np.flipud(self.x)\\n # raise NotImplementedError\\n #######################################################################\\n # #\\n # #\\n # TODO: YOUR CODE HERE #\\n # #\\n # #\\n #######################################################################\",\n \"def mirror_axis(self, axis_index=0, reverse=False):\\n self.smd3.mirror(axis_index=axis_index, reverse=reverse)\\n self.logic.mirror(axis_index=axis_index, reverse=reverse)\\n min_vector, max_vector = self.smd3.get_min_max_vector()\\n self.header.set_box(min_vector, max_vector)\\n self.header.update(self.smd3)\\n self.logic.update(self.smd3)\\n # self.meta.mirror(axis_index=axis_index, reverse=reverse)\",\n \"def toggle_axis(self):\\n # cycle through three axis states\\n states = [False, 'world', 'all']\\n # the state after toggling\\n index = (states.index(self.view['axis']) + 1) % len(states)\\n # update state to next index\\n self.view['axis'] = states[index]\\n # perform gl actions\\n self.update_flags()\",\n \"def set_orientation(self, axes):\\n if debug:\\n logger.debug('set_orientation ...')\\n logger.debug('%s -> %s', str(self.axes_names), str(axes))\\n\\n if set(axes) != set(self.axes_names):\\n raise Exception('Required orientation %s does not contain '\\n 'all axes %s' % (str(axes), str(self.axes_names)))\\n\\n if axes == self.axes_names: # already in the asked orientation\\n return\\n\\n for i, axis in enumerate(axes):\\n logger.debug('Rolling axis %s, cur pos=%d -> dest pos=%d',\\n axis, self.axes_names.index(axis), i)\\n logger.debug('Shape: %s', str(self.data.shape))\\n cur_i = self.axes_names.index(axis)\\n self.data = np.rollaxis(self.data, cur_i, i)\\n self.axes_names.pop(cur_i)\\n self.axes_names.insert(i, axis)\\n logger.debug('After rolling. Shape: %s, new axes: %s',\\n str(self.data.shape), str(self.axes_names))\\n logger.debug('')\\n\\n self.axes_ids = dict([(a, i) for i, a in enumerate(self.axes_names)])\",\n \"def flip(tensor, axis=None):\\n raise NotImplementedError\",\n \"def flip(a, axis):\\n a_ndim = a.ndim\\n if a_ndim < 1:\\n raise core.core._AxisError('Input must be >= 1-d')\\n\\n axis = int(axis)\\n if not -a_ndim <= axis < a_ndim:\\n raise core.core._AxisError(\\n 'axis must be >= %d and < %d' % (-a_ndim, a_ndim))\\n\\n return _flip(a, axis)\",\n \"def _update_data_transforms(self, axisOrder='col-major'):\\n self._dataTransform = QtGui.QTransform()\\n self._inverseDataTransform = QtGui.QTransform()\\n if self.axisOrder == 'row-major': # transpose both\\n self._dataTransform.scale(1, -1)\\n self._dataTransform.rotate(-90)\\n self._inverseDataTransform.scale(1, -1)\\n self._inverseDataTransform.rotate(-90)\",\n \"def convert_flip(g, op, block):\\n\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n axis = op.attr(\\\"axis\\\")\\n\\n for i, ax in enumerate(axis):\\n if i == 0:\\n out = _op.reverse(x, ax)\\n else:\\n out = _op.reverse(out, ax)\\n\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def _apply_swap(self, state, axes, **kwargs):\\n all_axes = list(range(len(state.shape)))\\n all_axes[axes[0]] = axes[1]\\n all_axes[axes[1]] = axes[0]\\n return self._transpose(state, all_axes)\",\n \"def flip_dir(self, axis):\\n res = self.empty_like()\\n # Negate directions and quantum numbers in form data.\\n res.dirs[axis] *= -1\\n res.qhape[axis] = [self._qod_func(-q) for q in res.qhape[axis]]\\n # Flip the quantum numbers of the keys of self.sects.\\n keys = set(self.sects.keys())\\n while keys:\\n k = keys.pop()\\n kf = list(k)\\n kf[axis] = self._qod_func(-kf[axis])\\n kf = tuple(kf)\\n # Swap self[kf] and self[k], at least if both exist.\\n vf = self[k]\\n if kf in keys:\\n v = self[kf]\\n keys.discard(kf)\\n res[k] = v\\n res[kf] = vf\\n return res\",\n \"def change_orientation(self):\\n self.shape = self.shape.T\",\n \"def reorderAxesEvent(self):\\n axisB = self.sender().text()\\n self.myParent.swapAxes(self.axisName, axisB)\\n self.myParent.setVistrailsVariableAxes()\",\n \"def flip(self):\",\n \"def inverse_transform(data, flip, rotate):\\n if flip == 'FLIP_LEFT_RIGHT':\\n data = np.fliplr(data)\\n\\n if rotate == 'ROTATE_90':\\n data = np.rot90(data, 3)\\n\\n if rotate == 'ROTATE_180':\\n data = np.rot90(data, 2)\\n\\n if rotate == 'ROTATE_270':\\n data = np.rot90(data, 1)\\n\\n return data\",\n \"def swapaxes(self, a1, a2):\\n an = self.axes_names[:]\\n ia1, ia2 = self.get_axis_id(a1), self.get_axis_id(a2)\\n an[ia2], an[ia1] = an[ia1], an[ia2]\\n return xndarray(np.swapaxes(self.data, ia1, ia2), an, self.axes_domains,\\n self.value_label, self.meta_data)\",\n \"def change_axis_names(self, axis_map):\\n axes = self.axes\\n\\n # Partition axes\\n self.axes = [axis_map[axis] for axis in axes]\\n\\n # Flipped axes\\n flip = self.flip\\n if flip:\\n self.flip = [axis_map[axis] for axis in flip]\",\n \"def flip(self, xflip=True, yflip=False):\\n self.drawer.flush()\\n img = self.img\\n if xflip: img = img.transpose(PIL.Image.FLIP_LEFT_RIGHT)\\n if yflip: img = img.transpose(PIL.Image.FLIP_TOP_BOTTOM)\\n self.img = img\\n self.update_drawer_img()\\n return self\",\n \"def flip_vertical(img):\\r\\n #reading image\\r\\n im = Image.open(\\\"filename\\\")\\r\\n\\r\\n #flipping image vertically\\r\\n newimg = im.transpose(PIL.Image.FLIP_TOP_BOTTOM)\\r\\n return img\",\n \"def flip(self):\\n self.width, self.height = self.height, self.width\",\n \"def flip_x(self):\\n self.x_lim_helper.flip_limits()\",\n \"def flip_x(self):\\n self.x_lim_helper.flip_limits()\",\n \"def flip(self, horizontal):\\n try:\\n self._is_transformable()\\n horizontal = get_int(horizontal)\\n except NotTransformable as e:\\n self._app[\\\"statusbar\\\"].message(str(e) + \\\" flip\\\", \\\"error\\\")\\n return\\n except StringConversionError as e:\\n self._app[\\\"statusbar\\\"].message(str(e), \\\"error\\\")\\n return\\n images = self.get_images(\\\"Flipped\\\")\\n # Apply changes\\n for fil in images:\\n if fil not in self._changes:\\n self._changes[fil] = [0, 0, 0]\\n if horizontal:\\n self._changes[fil][1] = \\\\\\n (self._changes[fil][1] + 1) % 2\\n else:\\n self._changes[fil][2] = \\\\\\n (self._changes[fil][2] + 1) % 2\\n # Flip the image shown\\n if self._app.get_path() in images:\\n self.emit(\\\"changed\\\", \\\"flip\\\", horizontal)\\n # Reload thumbnails of flipped images immediately\\n if self._app[\\\"thumbnail\\\"].toggled:\\n self.apply()\",\n \"def saveflip(image, fname, outpath, axis='x', preserve_name=False):\\n if not preserve_name:\\n fpath = genSavePath(outpath, fname, modstring=f\\\"mirror_{axis}\\\")\\n else:\\n fpath = genSavePath(outpath, fname)\\n im = copy(image)\\n if axis == 'x':\\n im = im.transpose(Image.FLIP_LEFT_RIGHT)\\n elif axis == 'y':\\n im = im.transpose(Image.FLIP_TOP_BOTTOM)\\n else:\\n raise Exception('No valid axis procvided for flipping: {} - received value: {}'.format(fname, axis))\\n try:\\n im.save(fpath, subsample=\\\"keep\\\", qtables=image.quantization, optimize=True)\\n\\n except IOError as m:\\n print( \\\"Flipped({}) image creation failed for: {}. \\\\nReason:{}\\\".format(axis,fname,m))\",\n \"def flip(self, bev_direction: str = 'horizontal') -> None:\\n pass\",\n \"def reverse_axis(self, x: bool, y: bool, z: bool) -> None:\\n current_directions = self.i2c.read(self.Register.AXIS_MAP_SIGN, 1)[0]\\n if x:\\n current_directions |= (1 << 2)\\n else:\\n current_directions &= ~(1 << 2)\\n if y:\\n current_directions |= (1 << 1)\\n else:\\n current_directions &= ~(1 << 1)\\n if z:\\n current_directions |= (1 << 0)\\n else:\\n current_directions &= ~(1 << 0)\\n self.i2c.write(self.Register.AXIS_MAP_SIGN, current_directions)\",\n \"def swapaxes(a, axis1, axis2):\\n # TODO(okuta): check type\\n return a.swapaxes(axis1, axis2)\",\n \"def vflip(img):\\n #if not _is_pil_image(img):\\n # raise TypeError('img should be PIL Image. Got {}'.format(type(img)))\\n\\n return img.transpose(Image.FLIP_TOP_BOTTOM)\",\n \"def flip(self, horizontally):\\n\\t\\tself.currentPixbuf = self.currentPixbuf.flip(horizontally)\\n\\t\\tself.scaleCache[1] = 0\\n\\t\\tgc.collect()\",\n \"def set_flipped(self, x, y):\\n self.pieces[x + (y * self.width)].set_flipped()\",\n \"def flip_image(img, vert=True):\\n if vert:\\n return img.transpose(Image.FLIP_TOP_BOTTOM)\\n else:\\n return img.transpose(Image.FLIP_LEFT_RIGHT)\",\n \"def reorder_axes(cls, array: np.ndarray, axes: str) -> np.ndarray:\\n return cls._reorder_axes(array, axes, cls.array_axis_order)\",\n \"def flip_horizontal(img):\\r\\n #reading image\\r\\n im = Image.open(\\\"filename\\\")\\r\\n\\r\\n #flipping image horizontally\\r\\n newimg = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\\r\\n \\r\\n return img\",\n \"def fl_flip_yorigin():\\n _fl_flip_yorigin = library.cfuncproto(\\n library.load_so_libforms(), \\\"fl_flip_yorigin\\\",\\\\\\n None, [],\\\\\\n \\\"\\\"\\\"void fl_flip_yorigin()\\\"\\\"\\\")\\n _fl_flip_yorigin()\",\n \"def orient_img_hwd(data, slice_axis):\\n if slice_axis == 0:\\n return data.transpose(2, 1, 0)\\n elif slice_axis == 1:\\n return data.transpose(2, 0, 1)\\n elif slice_axis == 2:\\n return data\",\n \"def flip(self):\\n self.align = self._left if self.align == self._right else self._right\\n self.group.layout_all()\",\n \"def adjust_axes(axes):\\n # TODO: Uncomment & decide for each subplot!\\n for ax in axes.itervalues():\\n core.hide_axis(ax)\\n\\n for k in [\\n \\\"placeholder\\\",\\n \\\"placeholder1\\\",\\n \\\"placeholder2\\\",\\n \\\"spikes_stim\\\",\\n \\\"spikes_stim1\\\",\\n \\\"spikes_stim2\\\",\\n \\\"spikes_post\\\",\\n \\\"stimulation_schema\\\"\\n ]:\\n axes[k].set_frame_on(False)\",\n \"def _update_axes(self):\\n data_shape = self.data.shape\\n if len(self.axes) < self.data.ndim + 1:\\n self._axes.append(Axis())\\n for index in range(self.data.ndim):\\n if len(self.axes[index].values) != data_shape[index]:\\n self.axes[index].values = np.arange(data_shape[index],\\n dtype=np.float64)\",\n \"def flip(self, bev_direction='horizontal', points=None):\\n assert bev_direction in ('horizontal', 'vertical')\\n if bev_direction == 'horizontal':\\n self.tensor[:, 0::7] = -self.tensor[:, 0::7]\\n if self.with_yaw:\\n self.tensor[:, 6] = -self.tensor[:, 6] + np.pi\\n elif bev_direction == 'vertical':\\n self.tensor[:, 2::7] = -self.tensor[:, 2::7]\\n if self.with_yaw:\\n self.tensor[:, 6] = -self.tensor[:, 6]\\n\\n if points is not None:\\n assert isinstance(points, (torch.Tensor, np.ndarray, BasePoints))\\n if isinstance(points, (torch.Tensor, np.ndarray)):\\n if bev_direction == 'horizontal':\\n points[:, 0] = -points[:, 0]\\n elif bev_direction == 'vertical':\\n points[:, 2] = -points[:, 2]\\n elif isinstance(points, BasePoints):\\n points.flip(bev_direction)\\n return points\",\n \"def flipud(self) -> xr.DataArray | xr.Dataset:\\n y_dim = self.y_dim\\n # NOTE don't use ycoords to work for rotated grids\\n yrev = self._obj[y_dim].values[::-1]\\n obj_filpud = self._obj.reindex({y_dim: yrev})\\n # y_dim is typically a dimension without coords in rotated grids\\n if y_dim not in self._obj.coords:\\n obj_filpud = obj_filpud.drop_vars(y_dim)\\n return obj_filpud\",\n \"def reorient(self, orientation):\\n if orientation == self.axes_names:\\n return self\\n else:\\n new_c = self.copy()\\n new_c.set_orientation(orientation)\\n return new_c\",\n \"def lr_flip(self):\\n for g in self.grid:\\n g.reverse()\",\n \"def SwapAxis(self, axis0, axis1):\\n\\n axis0 = int(axis0)\\n axis1 = int(axis1)\\n\\n self.points[:,[axis0,axis1]] = self.points[:,[axis1,axis0]]\",\n \"def flip_augmentation():\\n return lambda image: ImageOps.flip(image)\",\n \"def test_tranform_1(self):\\n fig_test = plt.figure()\\n ax = fig_test.add_subplot(111, projection='ternary')\\n ax.plot([0, 1], [0, 1], transform=ax.transAxes)\",\n \"def vflip(img, data_format='CHW'):\\n _assert_image_tensor(img, data_format)\\n\\n h_axis = _get_image_h_axis(data_format)\\n\\n return img.flip(axis=[h_axis])\",\n \"def reverseXZrotation(self):\\n rot = np.zeros((3, 3), dtype = 'float64')\\n rot[0, 0] = np.cos(self.currtheta)\\n rot[0, 1] = -np.sin(self.currtheta)\\n rot[0, 2] = 0.0\\n rot[1, 0] = np.sin(self.currtheta) * np.cos(self.currphi)\\n rot[1, 1] = np.cos(self.currtheta) * np.cos(self.currphi)\\n rot[1, 2] = - np.sin(self.currphi)\\n rot[2, 0] = np.sin(self.currtheta) * np.sin(self.currphi)\\n rot[2, 1] = np.cos(self.currtheta) * np.sin(self.currphi)\\n rot[2, 2] = np.cos(self.currphi)\\n\\n self.ds = np.dot(rot, self.ds1)\",\n \"def mirrorImage(self):\\n\\n im = Image.open(self.ActivePhoto)\\n out = im.transpose(PIL.Image.FLIP_LEFT_RIGHT)\\n out.save(self.ActivePhoto)\\n self.photo.setPixmap(QtGui.QPixmap(self.ActivePhoto))\\n print (\\\"Flipped image\\\")\",\n \"def flip(self):\\n \\n if self.faceup:\\n self.faceup = False\\n else:\\n self.faceup = True\",\n \"def set_MRI_orientation(self):\\n\\n if self.has_axes(MRI3Daxes):\\n orientation = MRI3Daxes[:]\\n if self.has_axis('time'):\\n orientation += ['time']\\n if self.has_axis('iteration'):\\n orientation += ['iteration']\\n if self.has_axis('condition'):\\n orientation += ['condition']\\n\\n orientation += sorted(set(self.axes_names).difference(orientation))\\n\\n self.set_orientation(orientation)\",\n \"def forward_transform(data, flip, rotate):\\n if flip == 'FLIP_LEFT_RIGHT':\\n data = np.fliplr(data)\\n\\n if rotate == 'ROTATE_90':\\n data = np.rot90(data, 1)\\n\\n if rotate == 'ROTATE_180':\\n data = np.rot90(data, 2)\\n\\n if rotate == 'ROTATE_270':\\n data = np.rot90(data, 3)\\n\\n return data\",\n \"def __flip(img, flip, flip_type=Image.FLIP_LEFT_RIGHT):\\n if flip:\\n return img.transpose(flip_type)\\n return img\",\n \"def flip_image_vertical(image):\\n return cv.flip(image, 1)\",\n \"def flip(self, x, y):\\n self.pieces[x + (y * self.width)].flip()\",\n \"def setHorizontalFlip(self, flag):\\n\\t\\tself.flipHorizontally = flag\",\n \"def clean_axes(axl):\\n cleanAxes(axl)\",\n \"def flip_image(image_path, saved_location):\\n image_obj = Image.open(image_path)\\n rotated_image = image_obj.transpose(Image.FLIP_LEFT_RIGHT)\\n rotated_image.save(saved_location)\",\n \"def ipix_swap_axes(self, ipix, colwise=False):\\n xy = self.ipix_to_xypix(ipix, colwise)\\n return self.xypix_to_ipix(xy, not colwise)\",\n \"def clean_axes(axes, left=True, right=False):\\n if not type(axes) in [np.ndarray, list]:\\n axes = [axes]\\n elif type(axes) == np.ndarray:\\n axes = axes.flatten()\\n for axis in axes:\\n axis.tick_params(direction='out')\\n axis.spines['top'].set_visible(False)\\n if not right:\\n axis.spines['right'].set_visible(False)\\n if not left:\\n axis.spines['left'].set_visible(False)\\n axis.get_xaxis().tick_bottom()\\n axis.get_yaxis().tick_left()\",\n \"def edges_flip_orientation(edges):\\n edges_flipped = []\\n for e in edges:\\n edges_flipped.insert(0, (e[1], e[0]))\\n return edges_flipped\",\n \"def flip(self, xbool, ybool):\\n self._surf = pygame.transform.flip(self._surf, xbool, ybool).convert_alpha()\",\n \"def flipped_dimensions(transformation, size):\\n dim = len(size)\\n # transform start point\\n start = [0.0] * dim\\n transformed_start = transformation.TransformPoint(start)\\n flipped = [False] * dim\\n for i in range(dim):\\n # set current end point and transform it\\n end = [0.0] * dim\\n end[i] = size[i]\\n transformed_end = transformation.TransformPoint(end)\\n # check, if transformed_start and transformed_end changed position\\n flipped[i] = transformed_start[i] > transformed_end[i]\\n return flipped\",\n \"def flip_image(image, rects, axis = 1):\\n\\n if axis not in [-1,0,1]:\\n return image, rects\\n\\n height = image.shape[0]\\n widht = image.shape[1]\\n \\n image = cv2.flip(image,axis)\\n\\n if axis == 1:\\n rects[:,1] = widht - rects[:,1]\\n rects[:,3] = widht - rects[:,3]\\n rects[:,3], rects[:,1] = rects[:,1].copy(),rects[:,3].copy()\\n elif axis == 0:\\n rects[:,2] = height - rects[:,2]\\n rects[:,4] = height - rects[:,4]\\n rects[:,4], rects[:,2] = rects[:,2].copy(),rects[:,4].copy()\\n elif axis == -1:\\n rects[:,1] = widht - rects[:,1]\\n rects[:,3] = widht - rects[:,3]\\n rects[:,3], rects[:,1] = rects[:,1].copy(),rects[:,3].copy()\\n rects[:,2] = height - rects[:,2]\\n rects[:,4] = height - rects[:,4]\\n rects[:,4], rects[:,2] = rects[:,2].copy(),rects[:,4].copy()\\n\\n return image, rects\",\n \"def reflect(self, axis):\\n if axis == \\\"x\\\":\\n self.y = - self.y\\n elif axis == \\\"y\\\":\\n self.x = - self.x\\n else:\\n print(\\\"The argument axis only accepts values 'x' and 'y'!\\\")\",\n \"def _flip_isolated_fluxes(l: Lattice, bonds: np.ndarray, fluxes: np.ndarray):\\n indices_to_flip = np.where(fluxes == -1)[0]\\n\\n def pos(p):\\n return l.plaquettes[p].center\\n\\n def distance_func(a, b):\\n return straight_line_length(pos(a), pos(b))\\n\\n close_pairs = _greedy_plaquette_pairing(indices_to_flip, distance_func)\\n\\n for a, b in close_pairs:\\n plaquettes, edges_to_flip = path_between_plaquettes(l,\\n a,\\n b,\\n maxits=l.n_edges)\\n bonds[edges_to_flip] *= -1\\n fluxes[a] *= -1\\n fluxes[b] *= -1\\n\\n return bonds, fluxes\",\n \"def rescale_axes(self, x=True, y=True, xlim=None, ylim=None, \\n tighten_up=0): \\n \\n # First, figure out what limits should be\\n col_xlim = [[1e10, -1e10] for i in range(self.dims[0])]\\n row_ylim = [[1e10, -1e10] for i in range(self.dims[1])]\\n \\n # Loop over axes\\n for i in range(self.N):\\n if self.grid[i] is None:\\n continue\\n \\n # column, row\\n j, k = self.axis_position(i)\\n \\n if self.above_diagonal(i):\\n continue\\n \\n if x and xlim is None:\\n col_xlim[j][0] = min(col_xlim[j][0], self.grid[i].dataLim.min[0])\\n col_xlim[j][1] = max(col_xlim[j][1], self.grid[i].dataLim.max[0]) \\n elif x:\\n col_xlim[j][0] = xlim[0]\\n col_xlim[j][1] = xlim[1]\\n \\n if self.diagonal is not None and i in self.diag:\\n continue\\n \\n if y and (ylim is None): \\n row_ylim[k][0] = min(row_ylim[k][0], self.grid[i].dataLim.min[1])\\n row_ylim[k][1] = max(row_ylim[k][1], self.grid[i].dataLim.max[1]) \\n elif y:\\n row_ylim[k][0] = ylim[0]\\n row_ylim[k][1] = ylim[1] \\n \\n # Apply limits \\n for i in range(self.N):\\n if self.grid[i] is None:\\n continue\\n \\n # column, row \\n j, k = self.axis_position(i)\\n \\n col_tmp = [col_xlim[j][0] * (1. + tighten_up * np.sign(col_xlim[j][0])),\\n col_xlim[j][1] * (1. - tighten_up * np.sign(col_xlim[j][1]))]\\n \\n row_tmp = [row_ylim[k][0] * (1. + tighten_up * np.sign(row_ylim[k][0])),\\n row_ylim[k][1] * (1. - tighten_up * np.sign(row_ylim[k][1]))]\\n\\n # Kludge\\n if np.all(np.isfinite(col_tmp)):\\n self.grid[i].set_xlim(col_tmp)\\n \\n if self.diagonal and i in self.diag:\\n continue\\n\\n if np.all(np.isfinite(row_tmp)):\\n self.grid[i].set_ylim(row_tmp)\\n\\n pl.draw()\",\n \"def flip_vertical(original_image: Image) -> Image :\\r\\n \\r\\n new_image = copy(original_image)\\r\\n \\r\\n pixel_width = get_width(original_image)\\r\\n pixel_height = get_height(original_image) \\r\\n\\r\\n \\r\\n for x in range(pixel_width) :\\r\\n for y in range(pixel_height) :\\r\\n original_vertical_pixel = get_color(original_image, x, y)\\r\\n opposite_vertical_pixel = pixel_height - 1 - y\\r\\n set_color(new_image, x, opposite_vertical_pixel, original_vertical_pixel)\\r\\n \\r\\n return new_image\",\n \"def apply_transforms(self):\\n self.axes.set_xlim(self._curr_xlim)\\n self.axes.set_ylim(self._curr_ylim)\",\n \"def reverse_transformation(self, rot90, flip_lr=False):\\n\\n transformed_ROI = copy.deepcopy(self) #Make a copy of itself to transform and return\\n\\n # Undo the left-right flip\\n if flip_lr:\\n transformed_ROI.center = (transformed_ROI.center[0], self.img_shape[1] - transformed_ROI.center[1])\\n\\n #Undo the rotation of the image\\n cent = transformed_ROI.center\\n if rot90 == 1:\\n transformed_ROI.center = (cent[1], self.img_shape[0] - cent[0])\\n transformed_ROI.size = (transformed_ROI.size[1], transformed_ROI.size[0])\\n elif rot90 == 2:\\n transformed_ROI.center = (self.img_shape[0] - cent[0], self.img_shape[1] - cent[1])\\n elif rot90 == 3:\\n transformed_ROI.center = (self.img_shape[1] - cent[1], cent[0])\\n transformed_ROI.size = (transformed_ROI.size[1], transformed_ROI.size[0])\\n\\n return transformed_ROI\",\n \"def flipNormals(self):\\n self.flip = not self.flip\",\n \"def side_wheel_from_axis():\",\n \"def _swap_axis(input_tensor, dim_index, last_index, name=None):\\n return array_ops.transpose(\\n input_tensor,\\n array_ops.concat([\\n math_ops.range(dim_index), [last_index],\\n math_ops.range(dim_index + 1, last_index), [dim_index]\\n ], 0),\\n name=name)\",\n \"def test_surfer6binary_invert_axis_methods(text_grd_file):\\n\\n grd = Surfer6TextGrid.load(text_grd_file)\\n\\n dm = np.arange(150).reshape(15, 10)\\n\\n assert np.alltrue(grd.dm == dm)\\n assert grd.ylo == 10\\n assert grd.yhi == 38\\n assert grd.xhi == 9\\n assert grd.xlo == 0\\n grd.invert_yaxis()\\n assert np.alltrue(grd.dm == dm[::-1, :])\\n assert grd.ylo == 38\\n assert grd.yhi == 10\\n grd.invert_xaxis()\\n assert np.alltrue(grd.dm == dm[::-1, ::-1])\\n assert grd.xlo == 9\\n assert grd.xhi == 0\\n grd.invert_yaxis()\\n assert np.alltrue(grd.dm == dm[:, ::-1])\\n grd.invert_xaxis()\\n assert np.alltrue(grd.dm == dm)\\n assert grd.ylo == 10\\n assert grd.yhi == 38\\n assert grd.xhi == 9\\n assert grd.xlo == 0\",\n \"def swap_axis(ogr_geom):\\n\\n osr_sref = ogr_geom.GetSpatialReference()\\n sref = SpatialRef.from_osr(osr_sref)\\n if (sref.epsg == 4326) and GDAL_3_ENABLED and (osr_sref.GetAxisMappingStrategy() == 1):\\n ogr_geom.SwapXY()\\n osr_sref.SetAxisMappingStrategy(0)\\n ogr_geom.AssignSpatialReference(osr_sref)\\n\\n return ogr_geom\",\n \"def flip(self, x: bool, y: bool) -> 'BaseImage':\\n assert isinstance(x, bool)\\n assert isinstance(y, bool)\\n assert (x or y), 'at least one axis should be True'\\n self._surface = pygame.transform.flip(self._surface, x, y)\\n return self\",\n \"def right_angle_axes(self, right_angle_axes):\\n\\n self.container['right_angle_axes'] = right_angle_axes\",\n \"def flip(x, dim):\\n dim = x.dim() + dim if dim < 0 else dim\\n inds = tuple(slice(None, None) if i != dim\\n else x.new(torch.arange(x.size(i) - 1, -1, -1).tolist()).long()\\n for i in range(x.dim()))\\n return x[inds]\",\n \"def setVerticalFlip(self, flag):\\n\\t\\tif self.ext.lower() in [\\\"png\\\", \\\"jpg\\\", \\\"jpeg\\\"]:\\n\\t\\t\\tself.flipVertically = not flag\\n\\t\\telse:\\n\\t\\t\\tself.flipVertically = flag\",\n \"def inverse_transform(self, Xt):\\n # Inverse transform\\n columns = []\\n start = 0\\n\\n for j in range(self.n_dims):\\n dim = self.dimensions[j]\\n offset = dim.transformed_size\\n\\n if offset == 1:\\n columns.append(dim.inverse_transform(Xt[:, start]))\\n else:\\n columns.append(dim.inverse_transform(Xt[:, start : start + offset]))\\n\\n start += offset\\n\\n # Transpose\\n rows = []\\n\\n for i in range(len(Xt)):\\n r = []\\n for j in range(self.n_dims):\\n r.append(columns[j][i])\\n\\n rows.append(r)\\n\\n return rows\",\n \"def shift_axes(self, shift, axes=None):\\n axes = self._get_axes_numbers(axes)\\n shift = self._cook_args(shift, axes, [0.0, 0.0])\\n return Signal2D(self.values, index=self.index-shift[0], columns=self.columns-shift[1])\",\n \"def setaxesnames(self):\\n if not self._axesnames or self.prop['skipsai']:\\n return\\n debug('ControllerStartup.setaxesnames()')\\n oldaxes = self.pidevice.qSAI_ALL()\\n for i, newaxis in enumerate(self.axesnames):\\n if newaxis != oldaxes[i] or self.prop['forcesai']:\\n setstage = False\\n if self.pidevice.HasqCST():\\n if self.pidevice.qCST()[oldaxes[i]] == 'NOSTAGE':\\n try:\\n debug('try rename NOSTAGE to TEMP (0x3C)')\\n self.pidevice.SPA(oldaxes[i], 0x3c, 'TEMP')\\n setstage = True\\n except GCSError:\\n pass\\n self.pidevice.SAI(oldaxes[i], newaxis)\\n if setstage:\\n self.pidevice.SPA(newaxis, 0x3c, 'NOSTAGE')\\n debug('restore NOSTAGE (0x3C)')\",\n \"def set_axes(self, a):\\r\\n self.axes = a\",\n \"def reverse_x():\\n plt.xlim(plt.xlim()[::-1])\",\n \"def setLabelOrientation(orientation='horizontal',axes='XYZ'):\\n orientdict = {'horizontal':'HORI', 'vertical':'VERT'}\\n dislin.labtyp(orientdict[orientation],axes)\",\n \"def flip_quat(orientation):\\n r,p,y = quat_to_euler(orientation)\\n r += np.pi\\n return euler_to_quat(r,p,y)\",\n \"def rotate_ship(self):\\n\\t\\tif self.cur_orient == GameBoard.O_HORIZONTAL:\\n\\t\\t\\tself.cur_orient = GameBoard.O_VERTICAL\\n\\t\\telse:\\n\\t\\t\\tself.cur_orient = GameBoard.O_HORIZONTAL\",\n \"def SetAxisOrientation(*args, **kwargs):\\n return _gdi_.DC_SetAxisOrientation(*args, **kwargs)\",\n \"def _flip_adjacent_fluxes(l: Lattice, bonds: np.ndarray, fluxes: np.ndarray):\\n for edge_index, (p_a, p_b) in enumerate(l.edges.adjacent_plaquettes):\\n if (p_a == INVALID) or (p_b == INVALID):\\n break\\n if (fluxes[p_a] == -1) and (fluxes[p_b] == -1):\\n bonds[edge_index] *= -1\\n fluxes[p_a] *= -1\\n fluxes[p_b] *= -1\\n\\n #attempt at vectorising, check this at somepoint\\n #adj_fluxes = fluxes[l.edges.adjacent_plaquettes]\\n #to_flip = np.where((adj_fluxes[:, 0] == -1) & (adj_fluxes[:, 1] == -1))\\n #bonds[to_flip] *= -1\\n #fluxes_to_flip = l.edges.adjacent_plaquettes[to_flip].flatten()\\n #fluxes[fluxes_to_flip] *= -1\\n\\n return bonds, fluxes\",\n \"def flip_boxes_horizontally(boxes):\\n # Flip boxes horizontally.\\n ymin, xmin, ymax, xmax = tf.split(value=boxes, num_or_size_splits=4, axis=1)\\n flipped_xmin = tf.subtract(1.0, xmax)\\n flipped_xmax = tf.subtract(1.0, xmin)\\n flipped_boxes = tf.concat([ymin, flipped_xmin, ymax, flipped_xmax], 1)\\n return flipped_boxes\",\n \"def restore_orientation_matrix(self):\\n self.Umat = self.init_p\",\n \"def flip(h):\\n return np.flip(h)\",\n \"def flip(h):\\n return np.flip(h)\",\n \"def convert_axis( mv, axisold, axisindnew ):\\n (axisnew, indexina3) = axisindnew\\n axes = allAxes(mv)\\n kold = None\\n for k in range(len(axes)):\\n if axes[k]==axisold: kold=k\\n if kold==None:\\n print \\\"ERROR. convert_axis cannot find axis\\\",axisold,\\\" in variable\\\",mv\\n if len(axisold)==len(axisnew):\\n mv.setAxis( kold, axisnew )\\n return\\n # Here's what we would do in 1-D:\\n # newdata = ma.ones(len(axisnew))*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\\n # for i in range(len(axisold)):\\n # newdata[ indexina3[i] ] = ma[i]\\n # newmv = cdms2.createVariable( newdata, id=mv.id )\\n # >1-D is the same idea, but more dimensions are coming along for the ride,\\n # making it more complicated...\\n shape0 = mv.shape\\n shape0[kold] = len(axisnew)\\n newdata = ma.ones(shape0)*mv.missing_value # Note that a FileVariable's missing_value is a tuple.\\n # We want to copy ma to newdata - except that we need indirect indexing for the kold-th axis.\\n # There seems to be nothing in numpy for treating one axis differently from the rest\\n # (except for ellipsis, but it makes sense to use only one ellipsis and we would need two here).\\n # The following will do the job. It would be very slow for an array with many big dimensions,\\n # but the arrays here have already been reduced for graphics; the index sets will be small or\\n # empty...\\n ranges = map( range, shape0[0:kold] )\\n for i in range(len(axisold)):\\n for idx in apply(itertools.product,ranges):\\n idx = idx + [indexina3(i)] + [Ellipsis]\\n idxo = idx + [i] + [Ellipsis]\\n newdata[ tuple(idx) ] = mv[idxo]\\n newmv = cdms2.createVariable( newdata, id=mv.id )\",\n \"def extend_dataset_flip_axis(data,\\n labels,\\n height=90,\\n width=160,\\n channels=3):\\n all_images = []\\n all_labels = []\\n flat_shape = data.shape[1]\\n for i in range(data.shape[0]):\\n orig_label = labels[i]\\n if orig_label == 0:\\n continue\\n frame = data[i].reshape((height, width, channels))\\n\\n if orig_label == 1:\\n flip_cmd = 2\\n else:\\n flip_cmd = 1\\n flip = np.flip(frame, axis=1)\\n flip = np.array(flip.reshape(flat_shape))\\n all_images.append(flip)\\n all_labels.append(flip_cmd)\\n all_labels = np.array(all_labels).astype('uint8')\\n all_labels = all_labels.reshape((all_labels.shape[0], 1))\\n extended_images = np.concatenate((data, all_images), axis=0)\\n extended_labels = np.concatenate((labels, all_labels), axis=0)\\n return extended_images, extended_labels\",\n \"def flip(self, index):\\n head, tail = self.get_extreme_points(index)\\n centroid = self.get_position(index)\\n\\n if tail is not None and centroid is not None:\\n self.set_angle(index, points_angle(centroid, tail) )\",\n \"def flip_ras_lps(vol, affine):\\n vol_flipped = np.flip(vol, (0,1))\\n affine_flipped = affine.copy()\\n affine_flipped[0,-1] = (-1 * affine @ np.array([vol.shape[0]-1,0,0,1]))[0]\\n affine_flipped[1,-1] = (-1 * affine @ np.array([0,vol.shape[1]-1,0,1]))[1]\\n\\n return vol_flipped, affine_flipped\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7156261","0.7035609","0.7033058","0.6533171","0.64487714","0.63454676","0.6332849","0.6305558","0.63048905","0.62795544","0.61534804","0.6124441","0.6114931","0.60523576","0.6050565","0.5979607","0.5953482","0.5949251","0.5904345","0.5891284","0.58893","0.5879466","0.5845572","0.57949686","0.5777062","0.5748649","0.5748649","0.574592","0.57452726","0.57370806","0.5698076","0.5681145","0.5655644","0.5645772","0.5605551","0.5590453","0.5557051","0.554656","0.5545495","0.55450004","0.5515334","0.5510841","0.54881805","0.54848915","0.5481436","0.54800075","0.5474346","0.54656833","0.54523575","0.5433069","0.54280853","0.5422308","0.54112506","0.53531396","0.5352776","0.5344959","0.5332802","0.53171","0.53090584","0.5306327","0.53024125","0.5301364","0.53003776","0.52988166","0.5297362","0.52850664","0.5283662","0.52676886","0.5260868","0.5252846","0.52467304","0.52409464","0.52387637","0.52311796","0.5223342","0.5217415","0.52127457","0.52086085","0.5206685","0.52021664","0.52006316","0.51946306","0.51904565","0.51874137","0.5172381","0.51654476","0.5162056","0.51590645","0.514877","0.51478404","0.51408875","0.51385564","0.5133834","0.5128809","0.5125853","0.5119835","0.5119835","0.5116832","0.51139814","0.5099972","0.50993276"],"string":"[\n \"0.7156261\",\n \"0.7035609\",\n \"0.7033058\",\n \"0.6533171\",\n \"0.64487714\",\n \"0.63454676\",\n \"0.6332849\",\n \"0.6305558\",\n \"0.63048905\",\n \"0.62795544\",\n \"0.61534804\",\n \"0.6124441\",\n \"0.6114931\",\n \"0.60523576\",\n \"0.6050565\",\n \"0.5979607\",\n \"0.5953482\",\n \"0.5949251\",\n \"0.5904345\",\n \"0.5891284\",\n \"0.58893\",\n \"0.5879466\",\n \"0.5845572\",\n \"0.57949686\",\n \"0.5777062\",\n \"0.5748649\",\n \"0.5748649\",\n \"0.574592\",\n \"0.57452726\",\n \"0.57370806\",\n \"0.5698076\",\n \"0.5681145\",\n \"0.5655644\",\n \"0.5645772\",\n \"0.5605551\",\n \"0.5590453\",\n \"0.5557051\",\n \"0.554656\",\n \"0.5545495\",\n \"0.55450004\",\n \"0.5515334\",\n \"0.5510841\",\n \"0.54881805\",\n \"0.54848915\",\n \"0.5481436\",\n \"0.54800075\",\n \"0.5474346\",\n \"0.54656833\",\n \"0.54523575\",\n \"0.5433069\",\n \"0.54280853\",\n \"0.5422308\",\n \"0.54112506\",\n \"0.53531396\",\n \"0.5352776\",\n \"0.5344959\",\n \"0.5332802\",\n \"0.53171\",\n \"0.53090584\",\n \"0.5306327\",\n \"0.53024125\",\n \"0.5301364\",\n \"0.53003776\",\n \"0.52988166\",\n \"0.5297362\",\n \"0.52850664\",\n \"0.5283662\",\n \"0.52676886\",\n \"0.5260868\",\n \"0.5252846\",\n \"0.52467304\",\n \"0.52409464\",\n \"0.52387637\",\n \"0.52311796\",\n \"0.5223342\",\n \"0.5217415\",\n \"0.52127457\",\n \"0.52086085\",\n \"0.5206685\",\n \"0.52021664\",\n \"0.52006316\",\n \"0.51946306\",\n \"0.51904565\",\n \"0.51874137\",\n \"0.5172381\",\n \"0.51654476\",\n \"0.5162056\",\n \"0.51590645\",\n \"0.514877\",\n \"0.51478404\",\n \"0.51408875\",\n \"0.51385564\",\n \"0.5133834\",\n \"0.5128809\",\n \"0.5125853\",\n \"0.5119835\",\n \"0.5119835\",\n \"0.5116832\",\n \"0.51139814\",\n \"0.5099972\",\n \"0.50993276\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8457,"cells":{"query":{"kind":"string","value":"Change ownership to group read read/write."},"document":{"kind":"string","value":"def Chown(self):\n cmd = 'chmod -R 0775 %s' % self.procdir\n self.ExecCmd(cmd)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def changeOwnership(self, document):\n document.changeOwnership(getSecurityManager().getUser(), False)","def chown(self, user, group, rec=0):\n uid = getuserid(user)\n gid = getgroupid(group)\n if rec:\n for x in self.visit(rec=lambda x: x.check(link=0)):\n if x.check(link=0):\n error.checked_call(os.chown, str(x), uid, gid)\n error.checked_call(os.chown, str(self), uid, gid)","def changeOwn():\n os.system('sudo chown -R test:users /etc/resolv.conf')\n os.system('sudo chown -R test:named /etc/named.conf')","def chown(self, path, owner=None, group=None):\n kwargs = {}\n if owner is not None:\n kwargs[\"owner\"] = owner\n if group is not None:\n kwargs[\"group\"] = group\n self._call(\"SETOWNER\", method=\"put\", path=path, **kwargs)","def _set_rw_permissions_for_all(self, nms, path):\n nms.appliance.execute('chmod ugo+rw %s' % path)","def set_ownership(self):\n\n os.chmod(os.path.join(\"%s\" % NetworkManager_conf_dir, self.connection._id), 0600)","def chgrp_file(filename, group, sudo=True):\n LOG.info(\"Changing file permissions for {}\".format(filename))\n cmd = \"chgrp {} {}\".format(group, filename)\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)","def chgrp_perms( path, group='climatew', permissions=None ):\n global std_file_perms, std_dir_perms\n if group is None:\n _group = -1 # means don't change the group\n elif not isinstance(group, int):\n _group = grp.getgrnam(group)[2]\n if permissions is None:\n if os.path.isdir(path):\n permissions = std_file_perms\n else:\n permissions = std_dir_perms\n os.chown( path, -1, _group )\n os.chmod( path, permissions )","def change_ownership(obj, userid):\n assert isinstance(userid, string_types)\n old_owner = obj.creators[0]\n if userid == old_owner:\n return\n #Remove Owner group from old owner\n obj.local_roles.remove(old_owner, ROLE_OWNER)\n #Add new owner\n obj.local_roles.add(userid, ROLE_OWNER)\n #Set new owner in creators attr - this will also trigger reindex catalog event so keep it last!\n obj.set_field_appstruct({'creators': (userid,)})\n return userid","def _switch_group_right(group_id, action, value, workspace, request_user):\n group = group_api.get_group_by_id(group_id)\n\n if action == workspace_constants.ACTION_READ:\n if value:\n workspace_api.add_group_read_access_to_workspace(workspace, group, request_user)\n else:\n workspace_api.remove_group_read_access_to_workspace(workspace, group, request_user)\n elif action == workspace_constants.ACTION_WRITE:\n if value:\n workspace_api.add_group_write_access_to_workspace(workspace, group, request_user)\n else:\n workspace_api.remove_group_write_access_to_workspace(workspace, group, request_user)","def set_read_only(self, bReadOnly):\n\t\tcall_sdk_function('PrlShare_SetReadOnly', self.handle, bReadOnly)","def _ensure_read_write_access(tarfileobj):\n dir_perm = tarfile.TUREAD | tarfile.TUWRITE | tarfile.TUEXEC\n file_perm = tarfile.TUREAD | tarfile.TUWRITE\n\n for tarinfo in tarfileobj.getmembers():\n tarinfo.mode |= (dir_perm if tarinfo.isdir() else file_perm)","def _set_read_only(self, read_only):\n self._read_only = read_only","def request_access_to_groups(self, ceph):\n for ceph_group in (\"volumes\", \"images\", \"vms\"):\n ceph.request_access_to_group(\n name=ceph_group,\n object_prefix_permissions={\"class-read\": [\"rbd_children\"]},\n permission=\"rwx\",\n )","def renounceOwnership():\n\n assert msg.sender == self.owner, \"Access is denied.\"\n\n log.OwnershipRenounced(msg.sender)\n self.owner = ZERO_ADDRESS","def grant_access_rw(message, db, reason):\n hf.grant(message, db.lower(), reason, \"readwrite\")","def test_00_mail_group_access_rights(self):\n cr, uid, user_bert_id, user_raoul_id = self.cr, self.uid, self.user_bert_id, self.user_raoul_id\n\n # Do: Bert reads Jobs -> ok, public\n self.mail_group.read(cr, user_bert_id, [self.group_jobs_id])\n # Do: Bert read Pigs -> ko, restricted to employees\n self.assertRaises(except_orm, self.mail_group.read,\n cr, user_bert_id, [self.group_pigs_id])\n # Do: Raoul read Pigs -> ok, belong to employees\n self.mail_group.read(cr, user_raoul_id, [self.group_pigs_id])\n\n # Do: Bert creates a group -> ko, no access rights\n self.assertRaises(except_orm, self.mail_group.create,\n cr, user_bert_id, {'name': 'Test'})\n # Do: Raoul creates a restricted group -> ok\n new_group_id = self.mail_group.create(cr, user_raoul_id, {'name': 'Test'})\n # Do: Bert added in followers, read -> ok, in followers\n self.mail_group.message_subscribe_users(cr, uid, [new_group_id], [user_bert_id])\n self.mail_group.read(cr, user_bert_id, [new_group_id])\n\n # Do: Raoul reads Priv -> ko, private\n self.assertRaises(except_orm, self.mail_group.read,\n cr, user_raoul_id, [self.group_priv_id])\n # Do: Raoul added in follower, read -> ok, in followers\n self.mail_group.message_subscribe_users(cr, uid, [self.group_priv_id], [user_raoul_id])\n self.mail_group.read(cr, user_raoul_id, [self.group_priv_id])\n\n # Do: Raoul write on Jobs -> ok\n self.mail_group.write(cr, user_raoul_id, [self.group_priv_id], {'name': 'modified'})\n # Do: Bert cannot write on Private -> ko (read but no write)\n self.assertRaises(except_orm, self.mail_group.write,\n cr, user_bert_id, [self.group_priv_id], {'name': 're-modified'})\n # Test: Bert cannot unlink the group\n self.assertRaises(except_orm,\n self.mail_group.unlink,\n cr, user_bert_id, [self.group_priv_id])\n # Do: Raoul unlinks the group, there are no followers and messages left\n self.mail_group.unlink(cr, user_raoul_id, [self.group_priv_id])\n fol_ids = self.mail_followers.search(cr, uid, [('res_model', '=', 'mail.group'), ('res_id', '=', self.group_priv_id)])\n self.assertFalse(fol_ids, 'unlinked document should not have any followers left')\n msg_ids = self.mail_message.search(cr, uid, [('model', '=', 'mail.group'), ('res_id', '=', self.group_priv_id)])\n self.assertFalse(msg_ids, 'unlinked document should not have any followers left')","async def claim_group_owner(self, group_id: int):\n r = await self.request.request(url=f'https://groups.roblox.com/v1/groups/{group_id}/claim-ownership',\n method='post')\n return r","def test_protect_owner(self):\n self.collection.set_permission(Permission.SHARE, self.user1)\n\n # User with share permission cannot grant ``owner`` permission\n data = {\"users\": {self.user2.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n self.assertNotIn(\"owner\", self.collection.get_permissions(self.user2))\n self.assertFalse(PermissionModel.objects.filter(user=self.user2).exists())\n\n # User with share permission cannot revoke ``owner`` permission\n self.collection.set_permission(Permission.OWNER, self.user2)\n data = {\"users\": {self.user2.pk: \"editor\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\n self.assertEqual(\n self.collection.get_permissions(self.user2),\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\n )\n self.collection.set_permission(Permission.NONE, self.user2)\n\n # Now let user1 be owner on collection.\n set_permission(Permission.OWNER, self.user1, self.collection)\n\n # ``owner`` permission cannot be assigned to a group\n data = {\"groups\": {self.group.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertFalse(PermissionModel.objects.filter(group=self.group).exists())\n\n # User with owner permission can grant ``owner`` permission\n data = {\"users\": {self.user2.pk: \"owner\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertEqual(\n self.collection.get_permissions(self.user2),\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\n )\n\n # User with owner permission can revoke ``owner`` permission\n data = {\"users\": {self.user2.pk: \"edit\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertFalse(\n PermissionModel.objects.filter(\n user=self.user2, value=Permission.OWNER.value\n ).exists()\n )\n\n # User with owner permission cannot remove all owners\n data = {\"users\": {self.user1.pk: \"edit\", self.owner.pk: \"edit\"}}\n\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\n self.assertEqual(resp.data[\"detail\"], \"Object must have at least one owner.\")\n\n owner_permissions = self.collection.permission_group.permissions.filter(\n value=Permission.OWNER.value\n )\n owner_count = owner_permissions.count()\n self.assertEqual(owner_count, 2)\n\n # User can delete his owner permission if there is at least one other owner\n self.assertTrue(owner_permissions.filter(user=self.user1).exists())\n data = {\"users\": {self.user1.pk: \"view\"}}\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\n self.assertFalse(owner_permissions.filter(user=self.user1.pk).exists())","def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\n ...","def chown_chmod ( fspath, uid=None, gid=None, mode=None, pretend=False ):\n return ChownChmod ( uid, gid, mode, pretend ).chown_chmod ( fspath )","def set_file_owner_perm(path, permission, user, group):\n uid = pwd.getpwnam(user).pw_uid\n gid = grp.getgrnam(group).gr_gid\n\n current_perm = get_permissions(path)\n try:\n logger.debug('Current permission: {0}, changing to {1}'.format(current_perm, oct(permission)))\n os.chmod(path, permission)\n os.chown(path, uid, gid)\n except Exception as e:\n logger.warning('Unable to change permissions on {0}: {1}'.format(path, e))","def pid_permissions():\n config = Config()\n try:\n user = pwd.getpwnam(config.user)\n group = grp.getgrnam(config.group)\n os.chown(config.pidfile, user.pw_uid, group.gr_gid)\n except (KeyError, PermissionError):\n logger.error(\"Unable to change pidfile ownership permissions.\")\n raise SystemExit(os.EX_USAGE)","def set_owner(self, role, check_mode=True):\n query = 'ALTER SUBSCRIPTION %s OWNER TO \"%s\"' % (self.name, role)\n return self.__exec_sql(query, check_mode=check_mode)","def make_readonly(path):\n mode = Path.stat(path).st_mode\n Path.chmod(path, mode & ~stat.S_IWRITE)","def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\n return jsii.invoke(self, \"grantReadWrite\", [identity, objects_key_pattern])","def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\n return jsii.invoke(self, \"grantReadWrite\", [identity, objects_key_pattern])","def chown(config):\n\n path = config.device_path()\n\n args = [\"sudo\", \"chown\", \"%d:%d\" % (os.getuid(), os.getgid()), path]\n iotests.log(\" \".join(args), filters=[iotests.filter_chown])\n proc = subprocess.Popen(args,\n stdin=subprocess.PIPE,\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n\n msg = proc.communicate()[0]\n\n if proc.returncode != 0:\n raise Exception(msg)","def chown_file(filename, file_owner, sudo=True):\n LOG.info(\"Changing the user that owns {}\".format(filename))\n cmd = \"chown {} {}\".format(file_owner, filename)\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)","def sync_volumeaccessright_record( vac ):\n \n syndicate_caps = \"UNKNOWN\" # for exception handling\n \n # get arguments\n config = observer_core.get_config()\n principal_id = vac.owner_id.email\n volume_name = vac.volume.name\n syndicate_caps = observer_core.opencloud_caps_to_syndicate_caps( vac.cap_read_data, vac.cap_write_data, vac.cap_host_data ) \n \n logger.info( \"Sync VolumeAccessRight for (%s, %s)\" % (principal_id, volume_name) )\n \n # validate config\n try:\n observer_secret = observer_core.get_syndicate_observer_secret( config.SYNDICATE_OBSERVER_SECRET )\n except Exception, e:\n traceback.print_exc()\n logger.error(\"syndicatelib config is missing SYNDICATE_RG_DEFAULT_PORT, SYNDICATE_OBSERVER_SECRET\")\n raise e\n \n # ensure the user exists and has credentials\n try:\n rc, user = observer_core.ensure_principal_exists( principal_id, observer_secret, is_admin=False, max_UGs=1100, max_RGs=1 )\n assert rc is True, \"Failed to ensure principal %s exists (rc = %s,%s)\" % (principal_id, rc, user)\n except Exception, e:\n traceback.print_exc()\n logger.error(\"Failed to ensure user '%s' exists\" % principal_id )\n raise e\n\n # grant the slice-owning user the ability to provision UGs in this Volume\n try:\n rc = observer_core.ensure_volume_access_right_exists( principal_id, volume_name, syndicate_caps )\n assert rc is True, \"Failed to set up Volume access right for slice %s in %s\" % (principal_id, volume_name)\n \n except Exception, e:\n traceback.print_exc()\n logger.error(\"Failed to set up Volume access right for slice %s in %s\" % (principal_id, volume_name))\n raise e\n \n except Exception, e:\n traceback.print_exc()\n logger.error(\"Faoed to ensure user %s can access Volume %s with rights %s\" % (principal_id, volume_name, syndicate_caps))\n raise e\n\n return True","def test_component_chown_ok(self):\n test_name = sys._getframe().f_code.co_name\n self._execute('component chown component2 changed_owner')\n rv, output = self._execute('component list')\n self.assertEqual(0, rv)\n self.assertEqual(self.expected_results[test_name], output)","def chown_file ( self, fspath ):\n return","def testChAttrs(self):\n def _check(results):\n self.flushLoggedErrors()\n self.assertTrue(results[0].startswith(b'-rw-r--r--'))\n self.assertEqual(results[1], b'')\n self.assertTrue(results[2].startswith(b'----------'), results[2])\n self.assertEqual(results[3], b'')\n\n d = self.runScript('ls -l testfile1', 'chmod 0 testfile1',\n 'ls -l testfile1', 'chmod 644 testfile1')\n return d.addCallback(_check)\n # XXX test chgrp/own","def _remove_group_rights(object_id, workspace, request_user):\n group = group_api.get_group_by_id(object_id)\n workspace_api.remove_group_read_access_to_workspace(workspace, group, request_user)\n workspace_api.remove_group_write_access_to_workspace(workspace, group, request_user)","def manage_changeOwnershipType(\n self,\n explicit=1,\n RESPONSE=None,\n REQUEST=None\n ):\n old = getattr(self, '_owner', None)\n if explicit:\n if old is not None:\n return\n owner = self.getOwnerTuple()\n if owner is not None and owner is not UnownableOwner:\n self._owner = owner\n else:\n if old is None:\n return\n new = aq_get(aq_parent(self), '_owner', None, 1)\n _m = object()\n if old is new and (self.__dict__.get('_owner', _m) is not _m):\n del self._owner\n\n if RESPONSE is not None:\n RESPONSE.redirect(REQUEST['HTTP_REFERER'])","def setup_volume_access( user_email, volume_name, caps, RG_port, slice_secret, RG_closure=None ):\n client = connect_syndicate()\n \n try:\n rc = ensure_volume_access_right_exists( user_email, volume_name, caps )\n assert rc is True, \"Failed to create access right for %s in %s\" % (user_email, volume_name)\n \n except Exception, e:\n logger.exception(e)\n return False\n \n RG_name = syndicate_provisioning.make_gateway_name( \"OpenCloud\", \"RG\", volume_name, \"localhost\" )\n RG_key_password = syndicate_provisioning.make_gateway_private_key_password( RG_name, slice_secret )\n \n try:\n rc = syndicate_provisioning.ensure_RG_exists( client, user_email, volume_name, RG_name, \"localhost\", RG_port, RG_key_password, closure=RG_closure )\n except Exception, e:\n logger.exception(e)\n return False\n \n return True","def test_transfer_new_inherited_owner(self):\n self.assertEqual(self.project.get_owner().user, self.user_owner)\n self.assertEqual(\n self.project.get_owners(inherited_only=True)[0].user,\n self.user_owner_cat,\n )\n url = reverse(\n 'projectroles:api_role_owner_transfer',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'new_owner': self.user_owner_cat.username,\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 200, msg=response.content)\n self.assertEqual(self.project.get_owner().user, self.user_owner_cat)\n self.owner_as.refresh_from_db()\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\n self.assertEqual(self.owner_as.role, self.role_contributor)\n self.assertEqual(\n self.project.get_role(self.user_owner_cat),\n RoleAssignment.objects.get(\n project=self.project,\n user=self.user_owner_cat,\n role=self.role_owner,\n ),\n )","def grant_access_rw(message, db, reason):\n grant_sql_access(message, db, reason, False)","def test_get_role_set_inherited_only(self):\n self.make_assignment(self.project, self.user_alice, self.role_owner)\n self.assertEqual(\n self.project.get_role(self.user_alice, inherited_only=True),\n self.owner_as_cat,\n )","def testOwnershipAfterEdit(self):\n self.simulateATGUIInteraction(task='edit')\n self.failUnlessEqual(self.person.getOwnerTuple()[1], 'abc123')","def read_only(self, reddit):\n # Require tests to explicitly disable read_only mode.\n reddit.read_only = True","def _change_access(course, user, level, action):\r\n\r\n try:\r\n role = ROLES[level](course.id)\r\n except KeyError:\r\n raise ValueError(\"unrecognized level '{}'\".format(level))\r\n\r\n if action == 'allow':\r\n role.add_users(user)\r\n elif action == 'revoke':\r\n role.remove_users(user)\r\n else:\r\n raise ValueError(\"unrecognized action '{}'\".format(action))","def privilegedStartService(self):\n super(GroupOwnedUNIXServer, self).privilegedStartService()\n\n # Unfortunately, there's no public way to access this. -glyph\n fileName = self._port.port\n chown(fileName, getuid(), self.gid)","def set_read_only(self, read_only):\n self.__aceQLHttpApi.set_read_only(read_only)","def grant_write(self, grantee: aws_cdk.aws_iam.IGrantable) -> aws_cdk.aws_iam.Grant:\n ...","def read_only(self, read_only):\n\n self._read_only = read_only","def read_only(self, read_only):\n\n self._read_only = read_only","def test_read_group(self):\n pass","def chgrp_perms_updown( path, group='climatew' ):\n global std_file_perms, std_dir_perms\n # First go down\n for (root,dirs,files) in os.walk(path):\n chgrp_perms( root, group, std_dir_perms )\n for file in files:\n chgrp_perms( os.path.join(root,file), group, std_file_perms )\n dir = path\n try:\n while len(dir)>21: # len(\"/p/css03/esgf_publish\")\n chgrp_perms( dir, group, std_dir_perms )\n dir = os.path.split(dir)[0]\n except: # If we don't have write permission, we're finished.\n pass","def _setup_permissions(self, chown, chmod):\n if chown is not None:\n if isinstance(chown, str):\n user, group = chown, None\n\n else:\n try:\n # Try to extract tuple.\n user, group = chown\n\n except ValueError:\n # If length of iterable is not 2, then allow 1.\n assert len(chown) == 1, 'chown must be user or tuple'\n user, group = chown[0], None\n\n except TypeError:\n # If not iterable, use given value as user.\n user, group = chown, None\n\n # Lookup user id.\n if isinstance(user, str):\n user_info = pwd.getpwnam(user)\n user = user_info.pw_uid\n\n # Lookup group id, or use -1 (do not change group)\n if isinstance(group, str):\n group = grp.getgrnam(group).pw_gid\n\n elif group is None:\n group = -1\n\n # Return tuple usable by os.chown().\n chown = (user, group)\n\n # Ensure chmod is numeric if given.\n if chmod is not None:\n assert isinstance(chmod, numbers.Number), 'chmod must be a number'\n\n return chown, chmod","def test_transfer_old_inherited_owner(self):\n self.owner_as_cat.user = self.user_owner\n self.owner_as_cat.save()\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\n url = reverse(\n 'projectroles:api_role_owner_transfer',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'new_owner': self.user_guest.username,\n 'old_owner_role': PROJECT_ROLE_OWNER,\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 200, msg=response.content)\n self.assertEqual(self.project.get_owner().user, self.user_guest)\n self.assertIsNone(\n RoleAssignment.objects.filter(\n project=self.project, user=self.user_owner\n ).first()\n )\n self.assertEqual(\n self.project.get_role(self.user_owner), self.owner_as_cat\n )\n self.assertEqual(self.owner_as.role, self.role_owner)","def set_owner_allowed(self, data):\n self._owner_allowed = self._uni(data)","def test_filter_owner_permission(self):\n User = get_user_model()\n user1 = User.objects.create(username=\"test_user1\", email=\"user1@test.com\")\n obj = DescriptorSchema.objects.create(contributor=user1)\n obj.set_permission(Permission.VIEW, user1)\n\n data_template = {\n \"users\": {user1.id: \"view\"},\n \"groups\": {1: \"edit\", 2: \"NONE\"},\n }\n\n check_owner_permission(data_template, False, obj)\n\n # Check that only owner can set owner permission.\n data = deepcopy(data_template)\n data[\"users\"][1] = \"owner\"\n with self.assertRaises(exceptions.PermissionDenied):\n check_owner_permission(data, False, obj)\n check_owner_permission(data, True, obj)\n\n # Check that only owner can rewoke owner permission.\n obj.set_permission(Permission.OWNER, user1)\n data = deepcopy(data_template)\n data[\"users\"][1] = \"edit\"\n with self.assertRaises(exceptions.PermissionDenied):\n check_owner_permission(data, False, obj)\n check_owner_permission(data, True, obj)\n\n # Check that group can not be owner.\n obj.set_permission(Permission.VIEW, user1)\n data = deepcopy(data_template)\n data[\"groups\"][1] = \"owner\"\n with self.assertRaises(exceptions.ParseError):\n check_owner_permission(data, False, obj)\n with self.assertRaises(exceptions.ParseError):\n check_owner_permission(data, True, obj)","def test_mark_entry_as_read_groups(self):\r\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\r\n pass","def writable(name):","def room_to_read_only(self, room_id):\n # set room to read-only\n response = requests.patch(\n f\"{self.uri}/rooms/{room_id}/attribute/id/text\",\n json={\"attribute\": \"readonly\", \"value\": \"True\"},\n headers={\"Authorization\": f\"Bearer {self.token}\"},\n )\n if not response.ok:\n logging.error(f\"Could not set room to read_only: {response.status_code}\")\n response.raise_for_status()\n response = requests.patch(\n f\"{self.uri}/rooms/{room_id}/attribute/id/text\",\n json={\"attribute\": \"placeholder\", \"value\": \"This room is read-only\"},\n headers={\"Authorization\": f\"Bearer {self.token}\"},\n )\n if not response.ok:\n logging.error(f\"Could not set room to read_only: {response.status_code}\")\n response.raise_for_status()\n\n for user in self.players_per_room[room_id]:\n response = requests.get(\n f\"{self.uri}/users/{user['id']}\",\n headers={\"Authorization\": f\"Bearer {self.token}\"},\n )\n if not response.ok:\n logging.error(f\"Could not get user: {response.status_code}\")\n response.raise_for_status()\n etag = response.headers[\"ETag\"]\n\n response = requests.delete(\n f\"{self.uri}/users/{user['id']}/rooms/{room_id}\",\n headers={\"If-Match\": etag, \"Authorization\": f\"Bearer {self.token}\"},\n )\n if not response.ok:\n logging.error(\n f\"Could not remove user from task room: {response.status_code}\"\n )\n response.raise_for_status()\n logging.debug(\"Removing user from task room was successful.\")","def test_leave_accrual_access_rights(self):\n accrual = self.employee.get_leave_accrual(self.leave_type.id)\n accrual.write({\n 'line_ids': [(0, 0, {\n 'name': 'Test',\n 'amount_cash': 100,\n 'date': datetime.now(),\n })],\n })\n\n self.assertRaises(\n Exception,\n accrual.sudo(self.user_3.id).check_access_rule, 'read')\n\n self.assertRaises(\n Exception,\n accrual.sudo(self.user_2.id).check_access_rights, 'write')\n\n accrual.sudo(self.user_1.id).check_access_rule('read')\n self.assertTrue(\n accrual.sudo(self.user_1.id).check_access_rights('read'))\n\n # The manager can not access the leave accruals of the employee 2\n # because he is not the employee's manager\n accrual_2 = self.employee_2.get_leave_accrual(self.leave_type.id)\n\n self.assertRaises(\n Exception,\n accrual_2.sudo(self.user_1.id).check_access_rule, 'read')\n\n self.user_1.write({\n 'groups_id': [(4, self.ref('base.group_hr_manager'))]})\n\n for operation in ['read', 'write', 'create', 'unlink']:\n accrual_2.sudo(self.user_1.id).check_access_rule(operation)\n self.assertTrue(\n accrual_2.sudo(self.user_1.id).check_access_rights(operation))","def test_transfer_old_inherited_member(self):\n self.make_assignment(\n self.category, self.user_owner, self.role_contributor\n )\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\n url = reverse(\n 'projectroles:api_role_owner_transfer',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'new_owner': self.user_guest.username,\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 200, msg=response.content)\n self.assertEqual(self.project.get_owner().user, self.user_guest)\n self.owner_as.refresh_from_db()\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\n self.assertEqual(self.owner_as.role, self.role_contributor)","def raise_for_ownership(self, resource: Model) -> None:\n\n # pylint: disable=import-outside-toplevel\n from superset import db\n\n if self.is_admin():\n return\n\n # Set of wners that works across ORM models.\n owners: List[User] = []\n\n orig_resource = db.session.query(resource.__class__).get(resource.id)\n\n if orig_resource:\n if hasattr(resource, \"owners\"):\n owners += orig_resource.owners\n\n if hasattr(resource, \"owner\"):\n owners.append(orig_resource.owner)\n\n if hasattr(resource, \"created_by\"):\n owners.append(orig_resource.created_by)\n\n if g.user.is_anonymous or g.user not in owners:\n raise SupersetSecurityException(\n SupersetError(\n error_type=SupersetErrorType.MISSING_OWNERSHIP_ERROR,\n message=f\"You don't have the rights to alter [{resource}]\",\n level=ErrorLevel.ERROR,\n )\n )","def read_release(self):\n self.is_locked = False\n self.rwlock = RWLock().read_acquire()","def setup_permissions():\n sudo('chown %s:%s -R %s' % (env.apache_user, env.apache_user, env.whole_path_symlinked))","def _RemountRootAsReadWrite(self):\n self.RunCmdOnDevice(['mount', '-o', 'remount,rw', '/'])","def chown_chmod ( self, fspath ):\n # should be renamed to chmod_chown()\n return (\n self.chmod ( fspath ),\n self.chown ( fspath )\n )","def del_ro(action, name, exc):\n os.chmod(name, stat.S_IWRITE)\n os.remove(name)","def transferOwnership(_newOwner: address):\n assert msg.sender == self.owner, \"Access is denied.\"\n assert _newOwner != ZERO_ADDRESS, \"Invalid owner supplied.\"\n\n log.OwnershipTransferred(msg.sender, _newOwner)\n self.owner = _newOwner","def is_read_only(self):\n\t\treturn bool(call_sdk_function('PrlShare_IsReadOnly', self.handle))","def test_06_self_cannot_upgrade_group(self):\n meowers = self.meowers\n cat = self.cat\n dog = self.dog\n cat.uaccess.share_group_with_user(meowers, dog, PrivilegeCodes.VIEW)\n self.assertFalse(dog in meowers.gaccess.edit_users)\n self.assertTrue(dog in meowers.gaccess.members)\n self.assertTrue(\n is_equal_to_as_set(\n [dog],\n dog.uaccess.get_group_unshare_users(meowers)))\n with self.assertRaises(PermissionDenied):\n dog.uaccess.share_group_with_user(\n meowers, dog, PrivilegeCodes.VIEW)\n with self.assertRaises(PermissionDenied):\n dog.uaccess.share_group_with_user(\n meowers, dog, PrivilegeCodes.CHANGE)\n self.assertTrue(dog in meowers.gaccess.members)\n self.assertTrue(\n is_equal_to_as_set(\n [dog],\n dog.uaccess.get_group_unshare_users(meowers)))","def match_owner_group(dest_path, source_path):\n source_stat = os.stat(source_path)\n return os.chown(dest_path, source_stat[stat.ST_UID], source_stat[stat.ST_GID])","def assign_permissions(sender, instance, created, **kwargs):\n if created:\n assign_perm('view_strand', instance.owner.group, instance)\n assign_perm('change_strand', instance.saver, instance)\n assign_perm('delete_strand', instance.saver, instance)\n assign_perm('view_strand', instance.saver, instance)","def chown ( self, fspath ):\n if os.path.isdir ( fspath ):\n return self.chown_dir ( fspath )\n else:\n return self.chown_file ( fspath )","def set_permissions(self, object, replace=False):\r\n if isinstance(self.config.origin, S3Origin):\r\n if self.config.origin.origin_access_identity:\r\n id = self.config.origin.origin_access_identity.split('/')[-1]\r\n oai = self.connection.get_origin_access_identity_info(id)\r\n policy = object.get_acl()\r\n if replace:\r\n policy.acl = ACL()\r\n policy.acl.add_user_grant('READ', oai.s3_user_id)\r\n object.set_acl(policy)\r\n else:\r\n object.set_canned_acl('public-read')","def test_set_permission(self):\n collection = Collection.objects.create(\n contributor=self.owner, name=\"Test collection 2\"\n )\n self.assertEqual(collection.get_permission(self.user1), Permission.NONE)\n self.assertEqual(self.collection.get_permission(self.user1), Permission.NONE)\n Collection.objects.filter(name__startswith=\"Test collection\").set_permission(\n Permission.VIEW, self.user1\n )\n self.assertEqual(collection.get_permission(self.user1), Permission.VIEW)\n self.assertEqual(self.collection.get_permission(self.user1), Permission.VIEW)","def changePermissions(self, event):\n pass","def set_read_group( in_bam_path, out_bam_path, id:str, pl:str, lb:str, sm:str, pu:str, threads=4 ):\n\n \"\"\"\n read_groups(set/dict) : set or dictionary which contains read groups. The dictionary should have the format { read_group_id (str)\n { 'ID': ID, 'LB':library,\n 'PL':platform,\n 'SM':sampleLib,\n 'PU':readGroup }\n \"\"\"\n\n read_groups = {id:{ 'ID': id, 'LB':lb,\n 'PL':pl,\n 'SM':sm,\n 'PU':pu }}\n\n with pysam.AlignmentFile(in_bam_path, threads = threads) as input_bam:\n\n input_header = input_bam.header.as_dict()\n\n # Write provenance information to BAM header\n write_program_tag(\n input_header,\n program_name='bamReadGroupFormat',\n command_line=\" \".join(\n sys.argv),\n version=singlecellmultiomics.__version__,\n description=f'SingleCellMultiOmics read group formatting, executed at {datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")}')\n\n with sorted_bam_file(out_bam_path, header=input_header, read_groups=read_groups, input_is_sorted=True) as out:\n print('Started writing')\n for read in input_bam:\n rg_id = id\n read.set_tag('RG',rg_id)\n out.write(read)","def chown_dir ( self, fspath ):\n return","def read_acquire(self):\n self.is_locked = True\n self.rwlock = RWLock().read_acquire()","def update_readonly_flag(self, volume, read_only):\n return self._impl.update_readonly_flag(volume, read_only=read_only)","def change_permissions(path, permission='777'):\r\n if os.path.exists(path):\r\n subprocess.call('chmod -R %s %s'%(permission,path),shell=True)\r\n else:\r\n raise NameError('invalid path %s'% path)","def setReadOnly(self, state: bool) -> None:\n ...","async def reacrole(self, ctx: commands.Context):\n pass","def pre_security_group_read(self, resource_id):\n pass","def MakeWorldReadable(path):\n\n # No need to do anything special on Windows.\n if IsWindows():\n return\n\n perms = stat.S_IMODE(os.stat(path)[stat.ST_MODE])\n if os.path.isdir(path):\n # Directories need read and exec.\n os.chmod(path, perms | 0555)\n else:\n os.chmod(path, perms | 0444)","def set_object_copy_only_permissions(self, agent):\n\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 1, PermissionsMask.Copy)\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Modify)\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Transfer)","def MakeWritable():\n return shell.ShellCommand(\n name = \"make writable\",\n haltOnFailure = 1,\n description = [\"making writable\"],\n descriptionDone = [\"made writable\"],\n command = [\"chmod\", \"-R\", \"+w\", \".\"],\n )","def __reader_get_new_ownership(self):\n if self._transfer_cmp_event.is_set() and not self.put_queue_flag:\n self.logger.info(\"Received transfer/accept request event in reader\") \n for key in self._reader_map.keys():\n if int(key) not in self.msg.get_ownershipList():\n del self._reader_map[key]\n self.msg.put_into_Queue()\n self.put_queue_flag = True\n elif not self._transfer_cmp_event.is_set():\n self.put_queue_flag = False","def post_security_group_read(self, resource_id, resource_dict):\n pass","def grant_read(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\n ...","def set_group_access(self, group, allowed_signals, allowed_controls):\n self._access_lists.set_group_access(group, allowed_signals, allowed_controls)","def _fix_r_res(res, o, g, p):\n print green(\"Setting remote resource %s parameters %s, %s, %s\" %\n (res, o, g, p))\n run('chown %s:%s %s' % (o, g, res))\n run('chmod %s %s' % (p, res))","def enable_acm_readonly(self):\n self._request({\"enable-acm-readonly\": True})","def set_object_transfer_only_permissions(self, agent):\n\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Copy)\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Modify)\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 1, PermissionsMask.Transfer)","def transfer_ownership(self, user):\n new_owner = get_user_model().objects.filter(is_active=True) \\\n .get(pk=user.pk)\n self.owner = new_owner","def grant(self, group, repo, privilege):\r\n url = '{0}/group-privileges/{1}/{2}/{1}/{3}/'.format(\r\n self.parent.parent.get_url(), self.user, repo, group)\r\n\r\n return http.Request('PUT', url, privilege), parsers.parse_empty","def _make_writeable(filename):\n import stat\n if sys.platform.startswith('java'):\n # On Jython there is no os.access()\n return\n if not os.access(filename, os.W_OK):\n st = os.stat(filename)\n new_permissions = stat.S_IMODE(st.st_mode) | stat.S_IWUSR\n os.chmod(filename, new_permissions)","def set_access_for_others(self, nAccessForOthers):\n\t\tcall_sdk_function('PrlAcl_SetAccessForOthers', self.handle, nAccessForOthers)","def update_readonly_flag(self, volume, read_only):\n aname = \"cinder_v%s.update_readonly_flag\" % self.version\n with atomic.ActionTimer(self, aname):\n return self._get_client().volumes.update_readonly_flag(\n volume, read_only)","def allowed_group_access_change(user, group):\n try:\n up = user.get_profile()\n except AttributeError:\n return False\n\n return (user.has_perm(\"vnswww.group_change_any\")\n or (user.has_perm(\"vnswww.group_change_org\")\n and group.org == up.org))","def _read_only(object, name, value):\n raise TraitError(\n \"The '%s' trait of %s instance is 'read only'.\"\n % (name, class_of(object))\n )","def adjust_permission_base_dir(base_dir, destination):\n\n if destination==\"tegner-login-1\":\n #Change group and set permissions for PDC Stockholm\n user_group = DATA_USER_PDC + \":\" + DATA_GROUP_PDC\n \n subprocess.Popen( [\"chown\", \"-R\", user_group, base_dir],\n stdout=subprocess.PIPE )\n \n\n subprocess.Popen( [\"setfacl\", \"-R\", \"-M\", \"/cfs/klemming/projects/xenon/misc/basic\", base_dir],\n stdout=subprocess.PIPE )","def test_transfer_new_inherited_member(self):\n self.make_assignment(\n self.category, self.user_new, self.role_contributor\n )\n self.assertEqual(\n self.project.get_owners(inherited_only=True)[0].user,\n self.user_owner_cat,\n )\n url = reverse(\n 'projectroles:api_role_owner_transfer',\n kwargs={'project': self.project.sodar_uuid},\n )\n post_data = {\n 'new_owner': self.user_new.username,\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\n }\n response = self.request_knox(url, method='POST', data=post_data)\n self.assertEqual(response.status_code, 200, msg=response.content)\n self.assertEqual(self.project.get_owner().user, self.user_new)\n self.owner_as.refresh_from_db()\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\n self.assertEqual(self.owner_as.role, self.role_contributor)"],"string":"[\n \"def changeOwnership(self, document):\\n document.changeOwnership(getSecurityManager().getUser(), False)\",\n \"def chown(self, user, group, rec=0):\\n uid = getuserid(user)\\n gid = getgroupid(group)\\n if rec:\\n for x in self.visit(rec=lambda x: x.check(link=0)):\\n if x.check(link=0):\\n error.checked_call(os.chown, str(x), uid, gid)\\n error.checked_call(os.chown, str(self), uid, gid)\",\n \"def changeOwn():\\n os.system('sudo chown -R test:users /etc/resolv.conf')\\n os.system('sudo chown -R test:named /etc/named.conf')\",\n \"def chown(self, path, owner=None, group=None):\\n kwargs = {}\\n if owner is not None:\\n kwargs[\\\"owner\\\"] = owner\\n if group is not None:\\n kwargs[\\\"group\\\"] = group\\n self._call(\\\"SETOWNER\\\", method=\\\"put\\\", path=path, **kwargs)\",\n \"def _set_rw_permissions_for_all(self, nms, path):\\n nms.appliance.execute('chmod ugo+rw %s' % path)\",\n \"def set_ownership(self):\\n\\n os.chmod(os.path.join(\\\"%s\\\" % NetworkManager_conf_dir, self.connection._id), 0600)\",\n \"def chgrp_file(filename, group, sudo=True):\\n LOG.info(\\\"Changing file permissions for {}\\\".format(filename))\\n cmd = \\\"chgrp {} {}\\\".format(group, filename)\\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)\",\n \"def chgrp_perms( path, group='climatew', permissions=None ):\\n global std_file_perms, std_dir_perms\\n if group is None:\\n _group = -1 # means don't change the group\\n elif not isinstance(group, int):\\n _group = grp.getgrnam(group)[2]\\n if permissions is None:\\n if os.path.isdir(path):\\n permissions = std_file_perms\\n else:\\n permissions = std_dir_perms\\n os.chown( path, -1, _group )\\n os.chmod( path, permissions )\",\n \"def change_ownership(obj, userid):\\n assert isinstance(userid, string_types)\\n old_owner = obj.creators[0]\\n if userid == old_owner:\\n return\\n #Remove Owner group from old owner\\n obj.local_roles.remove(old_owner, ROLE_OWNER)\\n #Add new owner\\n obj.local_roles.add(userid, ROLE_OWNER)\\n #Set new owner in creators attr - this will also trigger reindex catalog event so keep it last!\\n obj.set_field_appstruct({'creators': (userid,)})\\n return userid\",\n \"def _switch_group_right(group_id, action, value, workspace, request_user):\\n group = group_api.get_group_by_id(group_id)\\n\\n if action == workspace_constants.ACTION_READ:\\n if value:\\n workspace_api.add_group_read_access_to_workspace(workspace, group, request_user)\\n else:\\n workspace_api.remove_group_read_access_to_workspace(workspace, group, request_user)\\n elif action == workspace_constants.ACTION_WRITE:\\n if value:\\n workspace_api.add_group_write_access_to_workspace(workspace, group, request_user)\\n else:\\n workspace_api.remove_group_write_access_to_workspace(workspace, group, request_user)\",\n \"def set_read_only(self, bReadOnly):\\n\\t\\tcall_sdk_function('PrlShare_SetReadOnly', self.handle, bReadOnly)\",\n \"def _ensure_read_write_access(tarfileobj):\\n dir_perm = tarfile.TUREAD | tarfile.TUWRITE | tarfile.TUEXEC\\n file_perm = tarfile.TUREAD | tarfile.TUWRITE\\n\\n for tarinfo in tarfileobj.getmembers():\\n tarinfo.mode |= (dir_perm if tarinfo.isdir() else file_perm)\",\n \"def _set_read_only(self, read_only):\\n self._read_only = read_only\",\n \"def request_access_to_groups(self, ceph):\\n for ceph_group in (\\\"volumes\\\", \\\"images\\\", \\\"vms\\\"):\\n ceph.request_access_to_group(\\n name=ceph_group,\\n object_prefix_permissions={\\\"class-read\\\": [\\\"rbd_children\\\"]},\\n permission=\\\"rwx\\\",\\n )\",\n \"def renounceOwnership():\\n\\n assert msg.sender == self.owner, \\\"Access is denied.\\\"\\n\\n log.OwnershipRenounced(msg.sender)\\n self.owner = ZERO_ADDRESS\",\n \"def grant_access_rw(message, db, reason):\\n hf.grant(message, db.lower(), reason, \\\"readwrite\\\")\",\n \"def test_00_mail_group_access_rights(self):\\n cr, uid, user_bert_id, user_raoul_id = self.cr, self.uid, self.user_bert_id, self.user_raoul_id\\n\\n # Do: Bert reads Jobs -> ok, public\\n self.mail_group.read(cr, user_bert_id, [self.group_jobs_id])\\n # Do: Bert read Pigs -> ko, restricted to employees\\n self.assertRaises(except_orm, self.mail_group.read,\\n cr, user_bert_id, [self.group_pigs_id])\\n # Do: Raoul read Pigs -> ok, belong to employees\\n self.mail_group.read(cr, user_raoul_id, [self.group_pigs_id])\\n\\n # Do: Bert creates a group -> ko, no access rights\\n self.assertRaises(except_orm, self.mail_group.create,\\n cr, user_bert_id, {'name': 'Test'})\\n # Do: Raoul creates a restricted group -> ok\\n new_group_id = self.mail_group.create(cr, user_raoul_id, {'name': 'Test'})\\n # Do: Bert added in followers, read -> ok, in followers\\n self.mail_group.message_subscribe_users(cr, uid, [new_group_id], [user_bert_id])\\n self.mail_group.read(cr, user_bert_id, [new_group_id])\\n\\n # Do: Raoul reads Priv -> ko, private\\n self.assertRaises(except_orm, self.mail_group.read,\\n cr, user_raoul_id, [self.group_priv_id])\\n # Do: Raoul added in follower, read -> ok, in followers\\n self.mail_group.message_subscribe_users(cr, uid, [self.group_priv_id], [user_raoul_id])\\n self.mail_group.read(cr, user_raoul_id, [self.group_priv_id])\\n\\n # Do: Raoul write on Jobs -> ok\\n self.mail_group.write(cr, user_raoul_id, [self.group_priv_id], {'name': 'modified'})\\n # Do: Bert cannot write on Private -> ko (read but no write)\\n self.assertRaises(except_orm, self.mail_group.write,\\n cr, user_bert_id, [self.group_priv_id], {'name': 're-modified'})\\n # Test: Bert cannot unlink the group\\n self.assertRaises(except_orm,\\n self.mail_group.unlink,\\n cr, user_bert_id, [self.group_priv_id])\\n # Do: Raoul unlinks the group, there are no followers and messages left\\n self.mail_group.unlink(cr, user_raoul_id, [self.group_priv_id])\\n fol_ids = self.mail_followers.search(cr, uid, [('res_model', '=', 'mail.group'), ('res_id', '=', self.group_priv_id)])\\n self.assertFalse(fol_ids, 'unlinked document should not have any followers left')\\n msg_ids = self.mail_message.search(cr, uid, [('model', '=', 'mail.group'), ('res_id', '=', self.group_priv_id)])\\n self.assertFalse(msg_ids, 'unlinked document should not have any followers left')\",\n \"async def claim_group_owner(self, group_id: int):\\n r = await self.request.request(url=f'https://groups.roblox.com/v1/groups/{group_id}/claim-ownership',\\n method='post')\\n return r\",\n \"def test_protect_owner(self):\\n self.collection.set_permission(Permission.SHARE, self.user1)\\n\\n # User with share permission cannot grant ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertNotIn(\\\"owner\\\", self.collection.get_permissions(self.user2))\\n self.assertFalse(PermissionModel.objects.filter(user=self.user2).exists())\\n\\n # User with share permission cannot revoke ``owner`` permission\\n self.collection.set_permission(Permission.OWNER, self.user2)\\n data = {\\\"users\\\": {self.user2.pk: \\\"editor\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_403_FORBIDDEN)\\n self.assertEqual(\\n self.collection.get_permissions(self.user2),\\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\\n )\\n self.collection.set_permission(Permission.NONE, self.user2)\\n\\n # Now let user1 be owner on collection.\\n set_permission(Permission.OWNER, self.user1, self.collection)\\n\\n # ``owner`` permission cannot be assigned to a group\\n data = {\\\"groups\\\": {self.group.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertFalse(PermissionModel.objects.filter(group=self.group).exists())\\n\\n # User with owner permission can grant ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"owner\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertEqual(\\n self.collection.get_permissions(self.user2),\\n [Permission.VIEW, Permission.EDIT, Permission.SHARE, Permission.OWNER],\\n )\\n\\n # User with owner permission can revoke ``owner`` permission\\n data = {\\\"users\\\": {self.user2.pk: \\\"edit\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertFalse(\\n PermissionModel.objects.filter(\\n user=self.user2, value=Permission.OWNER.value\\n ).exists()\\n )\\n\\n # User with owner permission cannot remove all owners\\n data = {\\\"users\\\": {self.user1.pk: \\\"edit\\\", self.owner.pk: \\\"edit\\\"}}\\n\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_400_BAD_REQUEST)\\n self.assertEqual(resp.data[\\\"detail\\\"], \\\"Object must have at least one owner.\\\")\\n\\n owner_permissions = self.collection.permission_group.permissions.filter(\\n value=Permission.OWNER.value\\n )\\n owner_count = owner_permissions.count()\\n self.assertEqual(owner_count, 2)\\n\\n # User can delete his owner permission if there is at least one other owner\\n self.assertTrue(owner_permissions.filter(user=self.user1).exists())\\n data = {\\\"users\\\": {self.user1.pk: \\\"view\\\"}}\\n resp = self._detail_permissions(self.collection.pk, data, self.user1)\\n self.assertEqual(resp.status_code, status.HTTP_200_OK)\\n self.assertFalse(owner_permissions.filter(user=self.user1.pk).exists())\",\n \"def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\\n ...\",\n \"def chown_chmod ( fspath, uid=None, gid=None, mode=None, pretend=False ):\\n return ChownChmod ( uid, gid, mode, pretend ).chown_chmod ( fspath )\",\n \"def set_file_owner_perm(path, permission, user, group):\\n uid = pwd.getpwnam(user).pw_uid\\n gid = grp.getgrnam(group).gr_gid\\n\\n current_perm = get_permissions(path)\\n try:\\n logger.debug('Current permission: {0}, changing to {1}'.format(current_perm, oct(permission)))\\n os.chmod(path, permission)\\n os.chown(path, uid, gid)\\n except Exception as e:\\n logger.warning('Unable to change permissions on {0}: {1}'.format(path, e))\",\n \"def pid_permissions():\\n config = Config()\\n try:\\n user = pwd.getpwnam(config.user)\\n group = grp.getgrnam(config.group)\\n os.chown(config.pidfile, user.pw_uid, group.gr_gid)\\n except (KeyError, PermissionError):\\n logger.error(\\\"Unable to change pidfile ownership permissions.\\\")\\n raise SystemExit(os.EX_USAGE)\",\n \"def set_owner(self, role, check_mode=True):\\n query = 'ALTER SUBSCRIPTION %s OWNER TO \\\"%s\\\"' % (self.name, role)\\n return self.__exec_sql(query, check_mode=check_mode)\",\n \"def make_readonly(path):\\n mode = Path.stat(path).st_mode\\n Path.chmod(path, mode & ~stat.S_IWRITE)\",\n \"def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\\n return jsii.invoke(self, \\\"grantReadWrite\\\", [identity, objects_key_pattern])\",\n \"def grant_read_write(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\\n return jsii.invoke(self, \\\"grantReadWrite\\\", [identity, objects_key_pattern])\",\n \"def chown(config):\\n\\n path = config.device_path()\\n\\n args = [\\\"sudo\\\", \\\"chown\\\", \\\"%d:%d\\\" % (os.getuid(), os.getgid()), path]\\n iotests.log(\\\" \\\".join(args), filters=[iotests.filter_chown])\\n proc = subprocess.Popen(args,\\n stdin=subprocess.PIPE,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT)\\n\\n msg = proc.communicate()[0]\\n\\n if proc.returncode != 0:\\n raise Exception(msg)\",\n \"def chown_file(filename, file_owner, sudo=True):\\n LOG.info(\\\"Changing the user that owns {}\\\".format(filename))\\n cmd = \\\"chown {} {}\\\".format(file_owner, filename)\\n _exec_cmd(cmd=cmd, sudo=sudo, fail_ok=False)\",\n \"def sync_volumeaccessright_record( vac ):\\n \\n syndicate_caps = \\\"UNKNOWN\\\" # for exception handling\\n \\n # get arguments\\n config = observer_core.get_config()\\n principal_id = vac.owner_id.email\\n volume_name = vac.volume.name\\n syndicate_caps = observer_core.opencloud_caps_to_syndicate_caps( vac.cap_read_data, vac.cap_write_data, vac.cap_host_data ) \\n \\n logger.info( \\\"Sync VolumeAccessRight for (%s, %s)\\\" % (principal_id, volume_name) )\\n \\n # validate config\\n try:\\n observer_secret = observer_core.get_syndicate_observer_secret( config.SYNDICATE_OBSERVER_SECRET )\\n except Exception, e:\\n traceback.print_exc()\\n logger.error(\\\"syndicatelib config is missing SYNDICATE_RG_DEFAULT_PORT, SYNDICATE_OBSERVER_SECRET\\\")\\n raise e\\n \\n # ensure the user exists and has credentials\\n try:\\n rc, user = observer_core.ensure_principal_exists( principal_id, observer_secret, is_admin=False, max_UGs=1100, max_RGs=1 )\\n assert rc is True, \\\"Failed to ensure principal %s exists (rc = %s,%s)\\\" % (principal_id, rc, user)\\n except Exception, e:\\n traceback.print_exc()\\n logger.error(\\\"Failed to ensure user '%s' exists\\\" % principal_id )\\n raise e\\n\\n # grant the slice-owning user the ability to provision UGs in this Volume\\n try:\\n rc = observer_core.ensure_volume_access_right_exists( principal_id, volume_name, syndicate_caps )\\n assert rc is True, \\\"Failed to set up Volume access right for slice %s in %s\\\" % (principal_id, volume_name)\\n \\n except Exception, e:\\n traceback.print_exc()\\n logger.error(\\\"Failed to set up Volume access right for slice %s in %s\\\" % (principal_id, volume_name))\\n raise e\\n \\n except Exception, e:\\n traceback.print_exc()\\n logger.error(\\\"Faoed to ensure user %s can access Volume %s with rights %s\\\" % (principal_id, volume_name, syndicate_caps))\\n raise e\\n\\n return True\",\n \"def test_component_chown_ok(self):\\n test_name = sys._getframe().f_code.co_name\\n self._execute('component chown component2 changed_owner')\\n rv, output = self._execute('component list')\\n self.assertEqual(0, rv)\\n self.assertEqual(self.expected_results[test_name], output)\",\n \"def chown_file ( self, fspath ):\\n return\",\n \"def testChAttrs(self):\\n def _check(results):\\n self.flushLoggedErrors()\\n self.assertTrue(results[0].startswith(b'-rw-r--r--'))\\n self.assertEqual(results[1], b'')\\n self.assertTrue(results[2].startswith(b'----------'), results[2])\\n self.assertEqual(results[3], b'')\\n\\n d = self.runScript('ls -l testfile1', 'chmod 0 testfile1',\\n 'ls -l testfile1', 'chmod 644 testfile1')\\n return d.addCallback(_check)\\n # XXX test chgrp/own\",\n \"def _remove_group_rights(object_id, workspace, request_user):\\n group = group_api.get_group_by_id(object_id)\\n workspace_api.remove_group_read_access_to_workspace(workspace, group, request_user)\\n workspace_api.remove_group_write_access_to_workspace(workspace, group, request_user)\",\n \"def manage_changeOwnershipType(\\n self,\\n explicit=1,\\n RESPONSE=None,\\n REQUEST=None\\n ):\\n old = getattr(self, '_owner', None)\\n if explicit:\\n if old is not None:\\n return\\n owner = self.getOwnerTuple()\\n if owner is not None and owner is not UnownableOwner:\\n self._owner = owner\\n else:\\n if old is None:\\n return\\n new = aq_get(aq_parent(self), '_owner', None, 1)\\n _m = object()\\n if old is new and (self.__dict__.get('_owner', _m) is not _m):\\n del self._owner\\n\\n if RESPONSE is not None:\\n RESPONSE.redirect(REQUEST['HTTP_REFERER'])\",\n \"def setup_volume_access( user_email, volume_name, caps, RG_port, slice_secret, RG_closure=None ):\\n client = connect_syndicate()\\n \\n try:\\n rc = ensure_volume_access_right_exists( user_email, volume_name, caps )\\n assert rc is True, \\\"Failed to create access right for %s in %s\\\" % (user_email, volume_name)\\n \\n except Exception, e:\\n logger.exception(e)\\n return False\\n \\n RG_name = syndicate_provisioning.make_gateway_name( \\\"OpenCloud\\\", \\\"RG\\\", volume_name, \\\"localhost\\\" )\\n RG_key_password = syndicate_provisioning.make_gateway_private_key_password( RG_name, slice_secret )\\n \\n try:\\n rc = syndicate_provisioning.ensure_RG_exists( client, user_email, volume_name, RG_name, \\\"localhost\\\", RG_port, RG_key_password, closure=RG_closure )\\n except Exception, e:\\n logger.exception(e)\\n return False\\n \\n return True\",\n \"def test_transfer_new_inherited_owner(self):\\n self.assertEqual(self.project.get_owner().user, self.user_owner)\\n self.assertEqual(\\n self.project.get_owners(inherited_only=True)[0].user,\\n self.user_owner_cat,\\n )\\n url = reverse(\\n 'projectroles:api_role_owner_transfer',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'new_owner': self.user_owner_cat.username,\\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 200, msg=response.content)\\n self.assertEqual(self.project.get_owner().user, self.user_owner_cat)\\n self.owner_as.refresh_from_db()\\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\\n self.assertEqual(self.owner_as.role, self.role_contributor)\\n self.assertEqual(\\n self.project.get_role(self.user_owner_cat),\\n RoleAssignment.objects.get(\\n project=self.project,\\n user=self.user_owner_cat,\\n role=self.role_owner,\\n ),\\n )\",\n \"def grant_access_rw(message, db, reason):\\n grant_sql_access(message, db, reason, False)\",\n \"def test_get_role_set_inherited_only(self):\\n self.make_assignment(self.project, self.user_alice, self.role_owner)\\n self.assertEqual(\\n self.project.get_role(self.user_alice, inherited_only=True),\\n self.owner_as_cat,\\n )\",\n \"def testOwnershipAfterEdit(self):\\n self.simulateATGUIInteraction(task='edit')\\n self.failUnlessEqual(self.person.getOwnerTuple()[1], 'abc123')\",\n \"def read_only(self, reddit):\\n # Require tests to explicitly disable read_only mode.\\n reddit.read_only = True\",\n \"def _change_access(course, user, level, action):\\r\\n\\r\\n try:\\r\\n role = ROLES[level](course.id)\\r\\n except KeyError:\\r\\n raise ValueError(\\\"unrecognized level '{}'\\\".format(level))\\r\\n\\r\\n if action == 'allow':\\r\\n role.add_users(user)\\r\\n elif action == 'revoke':\\r\\n role.remove_users(user)\\r\\n else:\\r\\n raise ValueError(\\\"unrecognized action '{}'\\\".format(action))\",\n \"def privilegedStartService(self):\\n super(GroupOwnedUNIXServer, self).privilegedStartService()\\n\\n # Unfortunately, there's no public way to access this. -glyph\\n fileName = self._port.port\\n chown(fileName, getuid(), self.gid)\",\n \"def set_read_only(self, read_only):\\n self.__aceQLHttpApi.set_read_only(read_only)\",\n \"def grant_write(self, grantee: aws_cdk.aws_iam.IGrantable) -> aws_cdk.aws_iam.Grant:\\n ...\",\n \"def read_only(self, read_only):\\n\\n self._read_only = read_only\",\n \"def read_only(self, read_only):\\n\\n self._read_only = read_only\",\n \"def test_read_group(self):\\n pass\",\n \"def chgrp_perms_updown( path, group='climatew' ):\\n global std_file_perms, std_dir_perms\\n # First go down\\n for (root,dirs,files) in os.walk(path):\\n chgrp_perms( root, group, std_dir_perms )\\n for file in files:\\n chgrp_perms( os.path.join(root,file), group, std_file_perms )\\n dir = path\\n try:\\n while len(dir)>21: # len(\\\"/p/css03/esgf_publish\\\")\\n chgrp_perms( dir, group, std_dir_perms )\\n dir = os.path.split(dir)[0]\\n except: # If we don't have write permission, we're finished.\\n pass\",\n \"def _setup_permissions(self, chown, chmod):\\n if chown is not None:\\n if isinstance(chown, str):\\n user, group = chown, None\\n\\n else:\\n try:\\n # Try to extract tuple.\\n user, group = chown\\n\\n except ValueError:\\n # If length of iterable is not 2, then allow 1.\\n assert len(chown) == 1, 'chown must be user or tuple'\\n user, group = chown[0], None\\n\\n except TypeError:\\n # If not iterable, use given value as user.\\n user, group = chown, None\\n\\n # Lookup user id.\\n if isinstance(user, str):\\n user_info = pwd.getpwnam(user)\\n user = user_info.pw_uid\\n\\n # Lookup group id, or use -1 (do not change group)\\n if isinstance(group, str):\\n group = grp.getgrnam(group).pw_gid\\n\\n elif group is None:\\n group = -1\\n\\n # Return tuple usable by os.chown().\\n chown = (user, group)\\n\\n # Ensure chmod is numeric if given.\\n if chmod is not None:\\n assert isinstance(chmod, numbers.Number), 'chmod must be a number'\\n\\n return chown, chmod\",\n \"def test_transfer_old_inherited_owner(self):\\n self.owner_as_cat.user = self.user_owner\\n self.owner_as_cat.save()\\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\\n url = reverse(\\n 'projectroles:api_role_owner_transfer',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'new_owner': self.user_guest.username,\\n 'old_owner_role': PROJECT_ROLE_OWNER,\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 200, msg=response.content)\\n self.assertEqual(self.project.get_owner().user, self.user_guest)\\n self.assertIsNone(\\n RoleAssignment.objects.filter(\\n project=self.project, user=self.user_owner\\n ).first()\\n )\\n self.assertEqual(\\n self.project.get_role(self.user_owner), self.owner_as_cat\\n )\\n self.assertEqual(self.owner_as.role, self.role_owner)\",\n \"def set_owner_allowed(self, data):\\n self._owner_allowed = self._uni(data)\",\n \"def test_filter_owner_permission(self):\\n User = get_user_model()\\n user1 = User.objects.create(username=\\\"test_user1\\\", email=\\\"user1@test.com\\\")\\n obj = DescriptorSchema.objects.create(contributor=user1)\\n obj.set_permission(Permission.VIEW, user1)\\n\\n data_template = {\\n \\\"users\\\": {user1.id: \\\"view\\\"},\\n \\\"groups\\\": {1: \\\"edit\\\", 2: \\\"NONE\\\"},\\n }\\n\\n check_owner_permission(data_template, False, obj)\\n\\n # Check that only owner can set owner permission.\\n data = deepcopy(data_template)\\n data[\\\"users\\\"][1] = \\\"owner\\\"\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_owner_permission(data, False, obj)\\n check_owner_permission(data, True, obj)\\n\\n # Check that only owner can rewoke owner permission.\\n obj.set_permission(Permission.OWNER, user1)\\n data = deepcopy(data_template)\\n data[\\\"users\\\"][1] = \\\"edit\\\"\\n with self.assertRaises(exceptions.PermissionDenied):\\n check_owner_permission(data, False, obj)\\n check_owner_permission(data, True, obj)\\n\\n # Check that group can not be owner.\\n obj.set_permission(Permission.VIEW, user1)\\n data = deepcopy(data_template)\\n data[\\\"groups\\\"][1] = \\\"owner\\\"\\n with self.assertRaises(exceptions.ParseError):\\n check_owner_permission(data, False, obj)\\n with self.assertRaises(exceptions.ParseError):\\n check_owner_permission(data, True, obj)\",\n \"def test_mark_entry_as_read_groups(self):\\r\\n # This method utilises the PUT request method and will make changes to the Canvas instance. This needs consideration.\\r\\n pass\",\n \"def writable(name):\",\n \"def room_to_read_only(self, room_id):\\n # set room to read-only\\n response = requests.patch(\\n f\\\"{self.uri}/rooms/{room_id}/attribute/id/text\\\",\\n json={\\\"attribute\\\": \\\"readonly\\\", \\\"value\\\": \\\"True\\\"},\\n headers={\\\"Authorization\\\": f\\\"Bearer {self.token}\\\"},\\n )\\n if not response.ok:\\n logging.error(f\\\"Could not set room to read_only: {response.status_code}\\\")\\n response.raise_for_status()\\n response = requests.patch(\\n f\\\"{self.uri}/rooms/{room_id}/attribute/id/text\\\",\\n json={\\\"attribute\\\": \\\"placeholder\\\", \\\"value\\\": \\\"This room is read-only\\\"},\\n headers={\\\"Authorization\\\": f\\\"Bearer {self.token}\\\"},\\n )\\n if not response.ok:\\n logging.error(f\\\"Could not set room to read_only: {response.status_code}\\\")\\n response.raise_for_status()\\n\\n for user in self.players_per_room[room_id]:\\n response = requests.get(\\n f\\\"{self.uri}/users/{user['id']}\\\",\\n headers={\\\"Authorization\\\": f\\\"Bearer {self.token}\\\"},\\n )\\n if not response.ok:\\n logging.error(f\\\"Could not get user: {response.status_code}\\\")\\n response.raise_for_status()\\n etag = response.headers[\\\"ETag\\\"]\\n\\n response = requests.delete(\\n f\\\"{self.uri}/users/{user['id']}/rooms/{room_id}\\\",\\n headers={\\\"If-Match\\\": etag, \\\"Authorization\\\": f\\\"Bearer {self.token}\\\"},\\n )\\n if not response.ok:\\n logging.error(\\n f\\\"Could not remove user from task room: {response.status_code}\\\"\\n )\\n response.raise_for_status()\\n logging.debug(\\\"Removing user from task room was successful.\\\")\",\n \"def test_leave_accrual_access_rights(self):\\n accrual = self.employee.get_leave_accrual(self.leave_type.id)\\n accrual.write({\\n 'line_ids': [(0, 0, {\\n 'name': 'Test',\\n 'amount_cash': 100,\\n 'date': datetime.now(),\\n })],\\n })\\n\\n self.assertRaises(\\n Exception,\\n accrual.sudo(self.user_3.id).check_access_rule, 'read')\\n\\n self.assertRaises(\\n Exception,\\n accrual.sudo(self.user_2.id).check_access_rights, 'write')\\n\\n accrual.sudo(self.user_1.id).check_access_rule('read')\\n self.assertTrue(\\n accrual.sudo(self.user_1.id).check_access_rights('read'))\\n\\n # The manager can not access the leave accruals of the employee 2\\n # because he is not the employee's manager\\n accrual_2 = self.employee_2.get_leave_accrual(self.leave_type.id)\\n\\n self.assertRaises(\\n Exception,\\n accrual_2.sudo(self.user_1.id).check_access_rule, 'read')\\n\\n self.user_1.write({\\n 'groups_id': [(4, self.ref('base.group_hr_manager'))]})\\n\\n for operation in ['read', 'write', 'create', 'unlink']:\\n accrual_2.sudo(self.user_1.id).check_access_rule(operation)\\n self.assertTrue(\\n accrual_2.sudo(self.user_1.id).check_access_rights(operation))\",\n \"def test_transfer_old_inherited_member(self):\\n self.make_assignment(\\n self.category, self.user_owner, self.role_contributor\\n )\\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\\n url = reverse(\\n 'projectroles:api_role_owner_transfer',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'new_owner': self.user_guest.username,\\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 200, msg=response.content)\\n self.assertEqual(self.project.get_owner().user, self.user_guest)\\n self.owner_as.refresh_from_db()\\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\\n self.assertEqual(self.owner_as.role, self.role_contributor)\",\n \"def raise_for_ownership(self, resource: Model) -> None:\\n\\n # pylint: disable=import-outside-toplevel\\n from superset import db\\n\\n if self.is_admin():\\n return\\n\\n # Set of wners that works across ORM models.\\n owners: List[User] = []\\n\\n orig_resource = db.session.query(resource.__class__).get(resource.id)\\n\\n if orig_resource:\\n if hasattr(resource, \\\"owners\\\"):\\n owners += orig_resource.owners\\n\\n if hasattr(resource, \\\"owner\\\"):\\n owners.append(orig_resource.owner)\\n\\n if hasattr(resource, \\\"created_by\\\"):\\n owners.append(orig_resource.created_by)\\n\\n if g.user.is_anonymous or g.user not in owners:\\n raise SupersetSecurityException(\\n SupersetError(\\n error_type=SupersetErrorType.MISSING_OWNERSHIP_ERROR,\\n message=f\\\"You don't have the rights to alter [{resource}]\\\",\\n level=ErrorLevel.ERROR,\\n )\\n )\",\n \"def read_release(self):\\n self.is_locked = False\\n self.rwlock = RWLock().read_acquire()\",\n \"def setup_permissions():\\n sudo('chown %s:%s -R %s' % (env.apache_user, env.apache_user, env.whole_path_symlinked))\",\n \"def _RemountRootAsReadWrite(self):\\n self.RunCmdOnDevice(['mount', '-o', 'remount,rw', '/'])\",\n \"def chown_chmod ( self, fspath ):\\n # should be renamed to chmod_chown()\\n return (\\n self.chmod ( fspath ),\\n self.chown ( fspath )\\n )\",\n \"def del_ro(action, name, exc):\\n os.chmod(name, stat.S_IWRITE)\\n os.remove(name)\",\n \"def transferOwnership(_newOwner: address):\\n assert msg.sender == self.owner, \\\"Access is denied.\\\"\\n assert _newOwner != ZERO_ADDRESS, \\\"Invalid owner supplied.\\\"\\n\\n log.OwnershipTransferred(msg.sender, _newOwner)\\n self.owner = _newOwner\",\n \"def is_read_only(self):\\n\\t\\treturn bool(call_sdk_function('PrlShare_IsReadOnly', self.handle))\",\n \"def test_06_self_cannot_upgrade_group(self):\\n meowers = self.meowers\\n cat = self.cat\\n dog = self.dog\\n cat.uaccess.share_group_with_user(meowers, dog, PrivilegeCodes.VIEW)\\n self.assertFalse(dog in meowers.gaccess.edit_users)\\n self.assertTrue(dog in meowers.gaccess.members)\\n self.assertTrue(\\n is_equal_to_as_set(\\n [dog],\\n dog.uaccess.get_group_unshare_users(meowers)))\\n with self.assertRaises(PermissionDenied):\\n dog.uaccess.share_group_with_user(\\n meowers, dog, PrivilegeCodes.VIEW)\\n with self.assertRaises(PermissionDenied):\\n dog.uaccess.share_group_with_user(\\n meowers, dog, PrivilegeCodes.CHANGE)\\n self.assertTrue(dog in meowers.gaccess.members)\\n self.assertTrue(\\n is_equal_to_as_set(\\n [dog],\\n dog.uaccess.get_group_unshare_users(meowers)))\",\n \"def match_owner_group(dest_path, source_path):\\n source_stat = os.stat(source_path)\\n return os.chown(dest_path, source_stat[stat.ST_UID], source_stat[stat.ST_GID])\",\n \"def assign_permissions(sender, instance, created, **kwargs):\\n if created:\\n assign_perm('view_strand', instance.owner.group, instance)\\n assign_perm('change_strand', instance.saver, instance)\\n assign_perm('delete_strand', instance.saver, instance)\\n assign_perm('view_strand', instance.saver, instance)\",\n \"def chown ( self, fspath ):\\n if os.path.isdir ( fspath ):\\n return self.chown_dir ( fspath )\\n else:\\n return self.chown_file ( fspath )\",\n \"def set_permissions(self, object, replace=False):\\r\\n if isinstance(self.config.origin, S3Origin):\\r\\n if self.config.origin.origin_access_identity:\\r\\n id = self.config.origin.origin_access_identity.split('/')[-1]\\r\\n oai = self.connection.get_origin_access_identity_info(id)\\r\\n policy = object.get_acl()\\r\\n if replace:\\r\\n policy.acl = ACL()\\r\\n policy.acl.add_user_grant('READ', oai.s3_user_id)\\r\\n object.set_acl(policy)\\r\\n else:\\r\\n object.set_canned_acl('public-read')\",\n \"def test_set_permission(self):\\n collection = Collection.objects.create(\\n contributor=self.owner, name=\\\"Test collection 2\\\"\\n )\\n self.assertEqual(collection.get_permission(self.user1), Permission.NONE)\\n self.assertEqual(self.collection.get_permission(self.user1), Permission.NONE)\\n Collection.objects.filter(name__startswith=\\\"Test collection\\\").set_permission(\\n Permission.VIEW, self.user1\\n )\\n self.assertEqual(collection.get_permission(self.user1), Permission.VIEW)\\n self.assertEqual(self.collection.get_permission(self.user1), Permission.VIEW)\",\n \"def changePermissions(self, event):\\n pass\",\n \"def set_read_group( in_bam_path, out_bam_path, id:str, pl:str, lb:str, sm:str, pu:str, threads=4 ):\\n\\n \\\"\\\"\\\"\\n read_groups(set/dict) : set or dictionary which contains read groups. The dictionary should have the format { read_group_id (str)\\n { 'ID': ID, 'LB':library,\\n 'PL':platform,\\n 'SM':sampleLib,\\n 'PU':readGroup }\\n \\\"\\\"\\\"\\n\\n read_groups = {id:{ 'ID': id, 'LB':lb,\\n 'PL':pl,\\n 'SM':sm,\\n 'PU':pu }}\\n\\n with pysam.AlignmentFile(in_bam_path, threads = threads) as input_bam:\\n\\n input_header = input_bam.header.as_dict()\\n\\n # Write provenance information to BAM header\\n write_program_tag(\\n input_header,\\n program_name='bamReadGroupFormat',\\n command_line=\\\" \\\".join(\\n sys.argv),\\n version=singlecellmultiomics.__version__,\\n description=f'SingleCellMultiOmics read group formatting, executed at {datetime.now().strftime(\\\"%d/%m/%Y %H:%M:%S\\\")}')\\n\\n with sorted_bam_file(out_bam_path, header=input_header, read_groups=read_groups, input_is_sorted=True) as out:\\n print('Started writing')\\n for read in input_bam:\\n rg_id = id\\n read.set_tag('RG',rg_id)\\n out.write(read)\",\n \"def chown_dir ( self, fspath ):\\n return\",\n \"def read_acquire(self):\\n self.is_locked = True\\n self.rwlock = RWLock().read_acquire()\",\n \"def update_readonly_flag(self, volume, read_only):\\n return self._impl.update_readonly_flag(volume, read_only=read_only)\",\n \"def change_permissions(path, permission='777'):\\r\\n if os.path.exists(path):\\r\\n subprocess.call('chmod -R %s %s'%(permission,path),shell=True)\\r\\n else:\\r\\n raise NameError('invalid path %s'% path)\",\n \"def setReadOnly(self, state: bool) -> None:\\n ...\",\n \"async def reacrole(self, ctx: commands.Context):\\n pass\",\n \"def pre_security_group_read(self, resource_id):\\n pass\",\n \"def MakeWorldReadable(path):\\n\\n # No need to do anything special on Windows.\\n if IsWindows():\\n return\\n\\n perms = stat.S_IMODE(os.stat(path)[stat.ST_MODE])\\n if os.path.isdir(path):\\n # Directories need read and exec.\\n os.chmod(path, perms | 0555)\\n else:\\n os.chmod(path, perms | 0444)\",\n \"def set_object_copy_only_permissions(self, agent):\\n\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 1, PermissionsMask.Copy)\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Modify)\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Transfer)\",\n \"def MakeWritable():\\n return shell.ShellCommand(\\n name = \\\"make writable\\\",\\n haltOnFailure = 1,\\n description = [\\\"making writable\\\"],\\n descriptionDone = [\\\"made writable\\\"],\\n command = [\\\"chmod\\\", \\\"-R\\\", \\\"+w\\\", \\\".\\\"],\\n )\",\n \"def __reader_get_new_ownership(self):\\n if self._transfer_cmp_event.is_set() and not self.put_queue_flag:\\n self.logger.info(\\\"Received transfer/accept request event in reader\\\") \\n for key in self._reader_map.keys():\\n if int(key) not in self.msg.get_ownershipList():\\n del self._reader_map[key]\\n self.msg.put_into_Queue()\\n self.put_queue_flag = True\\n elif not self._transfer_cmp_event.is_set():\\n self.put_queue_flag = False\",\n \"def post_security_group_read(self, resource_id, resource_dict):\\n pass\",\n \"def grant_read(self, identity: aws_cdk.aws_iam.IGrantable, objects_key_pattern: typing.Any=None) -> aws_cdk.aws_iam.Grant:\\n ...\",\n \"def set_group_access(self, group, allowed_signals, allowed_controls):\\n self._access_lists.set_group_access(group, allowed_signals, allowed_controls)\",\n \"def _fix_r_res(res, o, g, p):\\n print green(\\\"Setting remote resource %s parameters %s, %s, %s\\\" %\\n (res, o, g, p))\\n run('chown %s:%s %s' % (o, g, res))\\n run('chmod %s %s' % (p, res))\",\n \"def enable_acm_readonly(self):\\n self._request({\\\"enable-acm-readonly\\\": True})\",\n \"def set_object_transfer_only_permissions(self, agent):\\n\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Copy)\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 0, PermissionsMask.Modify)\\n self.update_object_permissions(agent, PermissionsTarget.NextOwner, 1, PermissionsMask.Transfer)\",\n \"def transfer_ownership(self, user):\\n new_owner = get_user_model().objects.filter(is_active=True) \\\\\\n .get(pk=user.pk)\\n self.owner = new_owner\",\n \"def grant(self, group, repo, privilege):\\r\\n url = '{0}/group-privileges/{1}/{2}/{1}/{3}/'.format(\\r\\n self.parent.parent.get_url(), self.user, repo, group)\\r\\n\\r\\n return http.Request('PUT', url, privilege), parsers.parse_empty\",\n \"def _make_writeable(filename):\\n import stat\\n if sys.platform.startswith('java'):\\n # On Jython there is no os.access()\\n return\\n if not os.access(filename, os.W_OK):\\n st = os.stat(filename)\\n new_permissions = stat.S_IMODE(st.st_mode) | stat.S_IWUSR\\n os.chmod(filename, new_permissions)\",\n \"def set_access_for_others(self, nAccessForOthers):\\n\\t\\tcall_sdk_function('PrlAcl_SetAccessForOthers', self.handle, nAccessForOthers)\",\n \"def update_readonly_flag(self, volume, read_only):\\n aname = \\\"cinder_v%s.update_readonly_flag\\\" % self.version\\n with atomic.ActionTimer(self, aname):\\n return self._get_client().volumes.update_readonly_flag(\\n volume, read_only)\",\n \"def allowed_group_access_change(user, group):\\n try:\\n up = user.get_profile()\\n except AttributeError:\\n return False\\n\\n return (user.has_perm(\\\"vnswww.group_change_any\\\")\\n or (user.has_perm(\\\"vnswww.group_change_org\\\")\\n and group.org == up.org))\",\n \"def _read_only(object, name, value):\\n raise TraitError(\\n \\\"The '%s' trait of %s instance is 'read only'.\\\"\\n % (name, class_of(object))\\n )\",\n \"def adjust_permission_base_dir(base_dir, destination):\\n\\n if destination==\\\"tegner-login-1\\\":\\n #Change group and set permissions for PDC Stockholm\\n user_group = DATA_USER_PDC + \\\":\\\" + DATA_GROUP_PDC\\n \\n subprocess.Popen( [\\\"chown\\\", \\\"-R\\\", user_group, base_dir],\\n stdout=subprocess.PIPE )\\n \\n\\n subprocess.Popen( [\\\"setfacl\\\", \\\"-R\\\", \\\"-M\\\", \\\"/cfs/klemming/projects/xenon/misc/basic\\\", base_dir],\\n stdout=subprocess.PIPE )\",\n \"def test_transfer_new_inherited_member(self):\\n self.make_assignment(\\n self.category, self.user_new, self.role_contributor\\n )\\n self.assertEqual(\\n self.project.get_owners(inherited_only=True)[0].user,\\n self.user_owner_cat,\\n )\\n url = reverse(\\n 'projectroles:api_role_owner_transfer',\\n kwargs={'project': self.project.sodar_uuid},\\n )\\n post_data = {\\n 'new_owner': self.user_new.username,\\n 'old_owner_role': PROJECT_ROLE_CONTRIBUTOR,\\n }\\n response = self.request_knox(url, method='POST', data=post_data)\\n self.assertEqual(response.status_code, 200, msg=response.content)\\n self.assertEqual(self.project.get_owner().user, self.user_new)\\n self.owner_as.refresh_from_db()\\n self.assertEqual(self.project.get_role(self.user_owner), self.owner_as)\\n self.assertEqual(self.owner_as.role, self.role_contributor)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66892415","0.62186366","0.6200737","0.6195593","0.6075884","0.6041262","0.599601","0.59780955","0.5939623","0.5920451","0.58722764","0.5808822","0.5740759","0.565018","0.56067586","0.55544484","0.5519573","0.5503808","0.5484997","0.5481513","0.54399353","0.5434692","0.54086477","0.53880817","0.53870636","0.53663975","0.53663975","0.5362936","0.53531086","0.52928156","0.52316505","0.5224458","0.5214807","0.5198643","0.5172099","0.514449","0.51427233","0.51405805","0.5130294","0.5129552","0.5111754","0.5108394","0.5105414","0.5105336","0.5105204","0.50948834","0.50948834","0.50774235","0.50690544","0.50651205","0.5063244","0.5060551","0.5058698","0.5042138","0.5035244","0.50251853","0.501794","0.50176466","0.50074345","0.5007332","0.4999267","0.49988243","0.4995351","0.49906075","0.49769816","0.4970408","0.4962522","0.49497515","0.49486372","0.49462727","0.4944149","0.4938897","0.4934152","0.49274203","0.49268585","0.49222368","0.49142677","0.49121448","0.49084613","0.49000874","0.48985815","0.4897585","0.4895833","0.48876172","0.48873466","0.48850584","0.48792762","0.48760295","0.48701686","0.48562813","0.48550895","0.48497063","0.48446828","0.48436925","0.48400295","0.48332655","0.48287657","0.48270518","0.48266616","0.4819047"],"string":"[\n \"0.66892415\",\n \"0.62186366\",\n \"0.6200737\",\n \"0.6195593\",\n \"0.6075884\",\n \"0.6041262\",\n \"0.599601\",\n \"0.59780955\",\n \"0.5939623\",\n \"0.5920451\",\n \"0.58722764\",\n \"0.5808822\",\n \"0.5740759\",\n \"0.565018\",\n \"0.56067586\",\n \"0.55544484\",\n \"0.5519573\",\n \"0.5503808\",\n \"0.5484997\",\n \"0.5481513\",\n \"0.54399353\",\n \"0.5434692\",\n \"0.54086477\",\n \"0.53880817\",\n \"0.53870636\",\n \"0.53663975\",\n \"0.53663975\",\n \"0.5362936\",\n \"0.53531086\",\n \"0.52928156\",\n \"0.52316505\",\n \"0.5224458\",\n \"0.5214807\",\n \"0.5198643\",\n \"0.5172099\",\n \"0.514449\",\n \"0.51427233\",\n \"0.51405805\",\n \"0.5130294\",\n \"0.5129552\",\n \"0.5111754\",\n \"0.5108394\",\n \"0.5105414\",\n \"0.5105336\",\n \"0.5105204\",\n \"0.50948834\",\n \"0.50948834\",\n \"0.50774235\",\n \"0.50690544\",\n \"0.50651205\",\n \"0.5063244\",\n \"0.5060551\",\n \"0.5058698\",\n \"0.5042138\",\n \"0.5035244\",\n \"0.50251853\",\n \"0.501794\",\n \"0.50176466\",\n \"0.50074345\",\n \"0.5007332\",\n \"0.4999267\",\n \"0.49988243\",\n \"0.4995351\",\n \"0.49906075\",\n \"0.49769816\",\n \"0.4970408\",\n \"0.4962522\",\n \"0.49497515\",\n \"0.49486372\",\n \"0.49462727\",\n \"0.4944149\",\n \"0.4938897\",\n \"0.4934152\",\n \"0.49274203\",\n \"0.49268585\",\n \"0.49222368\",\n \"0.49142677\",\n \"0.49121448\",\n \"0.49084613\",\n \"0.49000874\",\n \"0.48985815\",\n \"0.4897585\",\n \"0.4895833\",\n \"0.48876172\",\n \"0.48873466\",\n \"0.48850584\",\n \"0.48792762\",\n \"0.48760295\",\n \"0.48701686\",\n \"0.48562813\",\n \"0.48550895\",\n \"0.48497063\",\n \"0.48446828\",\n \"0.48436925\",\n \"0.48400295\",\n \"0.48332655\",\n \"0.48287657\",\n \"0.48270518\",\n \"0.48266616\",\n \"0.4819047\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56320053"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":8458,"cells":{"query":{"kind":"string","value":"Store some useful information in the log file."},"document":{"kind":"string","value":"def LogProcess(self):\n time = datetime.today().strftime('%a %Y%b%d %X')\n# Get user name.\n f = os.popen(\"whoami\",\"r\")\n user = f.read().strip()\n f.close()\n\n entry = '%s\\t%s\\t%s\\t%s\\n' % (time, self.topdir, user, self.version)\n\n if ismounted(c.exams_file):\n# Append info to the exams file.\n try:\n f = open(c.exams_file,'a+')\n f.seek(0, 2)\n f.write(entry)\n f.close()\n except:\n# Not a huge problem if this doesn't work.\n pass"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def log_data(self):\n\n self.check_dir()\n with open(self.log_file, \"a\") as logger_file:\n logger_file.write(\"{}, {}\\n\".format(self.time, self.msg))","def log(self):\n f = open(self.log_dir + 'parsed.log', 'a')\n try:\n # Write: local time | CurrentCost \"time\" | id | temp/C | power/W \n f.write(\"%s\\t%s\\t%s\\t%s\\t%s\\n\" \n % (self.ts('now'), self.ts('cc'), self.id, self.temp, self.watts))\n finally:\n f.close()","def logsave(self):\n log_file = open(self.conf[\"output_prefix\"] + \"_log.txt\", \"w\")\n try:\n log_file.write(self.log)\n finally:\n log_file.close()","def log_info(info):\n log = open(log_path, 'a+')\n log.write(info + '\\n')\n log.close()","def log(self):\n\t\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\n\t\tfileobj = open(filename, 'w');\n\t\tfileobj.write(self.title)\n\t\tfor (key, value) in self.argv.items():\n\t\t\tfileobj.write('%s: %s\\n' % (key, value))\n\t\tfileobj.close()\n\t\tprint \"[%s] Log saved '%s'\" % (ctime(), filename)","def _log_to_file(self, message):\n if self.log is not None:\n message = \"[%s] %s\" % (datetime.datetime.utcnow().strftime('%H:%M:%S'), message)\n self.log.write(\"%s\\n\" % (message,))\n self.log.flush()\n print message","def writeLog(self):\n if self.logBuffer != None and self.logging :\n f = open(self.logfileName, 'w')\n self.logBuffer += \"Final Fitness: %f\\n\" % self.getTotalReward()\n self.logBuffer += \"\\n\"\n f.write(self.logBuffer)\n f.close()","def log(self, loginfo):\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(filename)s:%(message)s',\n datefmt='%d %b %Y %H:%M:%S',\n filename=self.logfilepath,\n filemode='w')\n filelog = logging.FileHandler(self.logfilepath)\n logging.getLogger('Functest').addHandler(filelog)\n logging.info(loginfo)","def log(self, msg):\n current_datetime = self.get_date_time()\n self.file.write(\"%s %s\\n\" % (current_datetime, msg))","def write_log(self, log_filename, data):\n open(log_filename, 'a').write(str(data))","def write_data(self, data):\n print \"Writing data...\"\n # Write data into log\n self.log.write_file(data)\n\n # Close log so information can be sent\n self.log.close_log()","def __log__(self, val):\n if lm_settings[\"debug\"]:\n try:\n log_file = open(\"language_manager/info/language_manager.log\", \"a\")\n except FileNotFoundError:\n log_file = open(lm_settings[\"logfile\"], \"w\")\n log_file.write(val)\n log_file.close()","def write_log(self, msg: str):\n self.cta_engine.write_log(msg, self)","def _logger(self):\r\n\r\n # Create filename for log\r\n filenameF = self._vna.getDateFormatted() + \".txt\"\r\n filenameF = \"Logs/\" + filenameF \r\n f = open(filenameF, \"a+\") # Log saved in directory named logs located in same directory as this file\r\n \r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages)):\r\n # f.write('%s\\t\\t\\t' % self._voltages[i][0])\r\n # else:\r\n for i in range(len(self._voltages)):\r\n f.write('%s\\t\\t' % self._voltages[i][0])\r\n f.write('\\n')\r\n\r\n # if self._vna.isTwoComponents():\r\n # for i in range(len(self._voltages[0])):\r\n # line = \"\"\r\n # for j in range(len(self._voltages)):\r\n # line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][2*i]) + \\\r\n # str(self._intensity[j][2*i + 1]) + '\\t'\r\n # f.write(line)\r\n # f.write('\\n')\r\n # else: \r\n for i in range(len(self._voltages[0])):\r\n line = \"\"\r\n for j in range(len(self._voltages)):\r\n line = line + str(self._frequency[j][i]) + '\\t' + str(self._intensity[j][i]) + '\\t' \r\n f.write(line)\r\n f.write('\\n')","def log(self, message):\n timestamp = time.strftime(\"[%H:%M:%S]\", time.localtime(time.time()))\n self.file.write('%s %s\\n' % (timestamp, message))\n self.file.flush()","def log(self):\n self.logger = logging.getLogger(self.log_name)\n self.logger.info(f\"Name: {self.name}\")\n self.logger.info(f\"Grid points: {self.gp}\")\n self.logger.info(f\"Nadir points: {self.nadir_p}\")\n self.logger.info(f\"Penalty weight: {self.eps}\")\n self.logger.info(f\"Early exit: {self.early_exit}\")\n self.logger.info(f\"Bypass coefficient: {self.bypass}\")\n self.logger.info(f\"Flag array: {self.flag}\")\n self.logger.info(f\"CPU Count: {self.cpu_count}\")\n self.logger.info(f\"Redivide work: {self.redivide_work}\")\n self.logger.info(f\"Shared flag array: {self.shared_flag}\")\n self.logger.info(Helper.separator())","def writeToLogFile(self, event):\n outPutStr = '{:013}'.format(0)\n logOutPutStr = outPutStr + '\\t' + '{:.2f}'.format (time ()) + '\\t' + event + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\n printOutPutStr = outPutStr + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\t' + event\n print (printOutPutStr)\n if self.logFP is not None:\n self.logFP.write(logOutPutStr + '\\n')\n self.logFP.flush()","def saveToLogFile(self, msg):\n path = os.path.join(self.parent.progpath, \"logfile.txt\")\n fo = open(path, 'a')\n # prefix with current date and time from now variable\n msg = \"\\n#{0}\\n\".format(datetime.datetime.now()) + msg\n fo.write(msg)\n fo.close()","def saveToLogFile(self, msg):\n path = os.path.join(self.parent.progpath, \"logfile.txt\")\n fo = open(path, 'a')\n # prefix with current date and time from now variable\n msg = \"\\n#{0}\\n\".format(datetime.datetime.now()) + msg\n fo.write(msg)\n fo.close()","def log(self, line):\n now = datetime.datetime.now()\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\n with open(self.logfile, 'a') as log:\n log.write(time + ' ' + line + '\\n')","def log(self, txt):\n if self.logfile:\n self.logfile.write(txt)","def set_info(self, message):\n if self.log_file_exist(self.file_path_name):\n logging.info(message)\n else:\n print \"The log \"+ self.name_log + \"does not exist in the directory\"","def log_to_file(text, status='INFO'):\n outfile = open(LogName, 'a')\n outfile.write(timestamp()+' - '+status+' - '+str(text)+'\\n')\n outfile.close()","def __logtofile(self, log_name):\n logger = logging.getLogger(log_name)\n\n file_path = os.path.join(self.log_file_path, log_name + '.txt')\n\n formatter = logging.Formatter('<%(asctime)s> %(levelname)-8s %(message)s',\n datefmt='%y-%m-%d %H:%M:%S')\n self.file_handlers[logger] = logging.FileHandler(file_path, mode='w')\n self.file_handlers[logger].setFormatter(formatter)\n self.file_handlers[logger].setLevel(logging.DEBUG)\n logger.addHandler(self.file_handlers[logger])\n\n logger.info('SAVING LOGS IN: %s' % file_path)","def log(self, msg=None):\n f = open(self.logbook, 'a')\n # if send or receive, write message\n if msg: \n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + \n \" \" + str(msg) + '\\n')\n # if it is an internal event just write the system time and current\n # logical clock time\n else:\n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + '\\n')\n f.close()","def save(*messages):\n data = Parser.parse_texts(*messages[1:])\n hour = time.strftime(\"_%H_%M_%S\")\n today = time.strftime(\"_%d_%m_%Y\")\n title = Parser.parse_text(messages[0])\n\n file = open(\"./logs/\"+threading.currentThread().getName()+today+\".log\",'a+')\n file.write(\"\\n==\"+title+hour+\"==\\n\")\n if type(data) is dict: #Dictionary with each value being a triplet. From get_all_items\n for key in data.keys():\n file.write(Parser.parse_text(key) + \" -> \"+ Parser.parse_text(str(data[key].x)) +\"\\n\")\n elif type(data) is list: #From get_item, market item, attribute listings\n for listing in data:\n file.write(str(listing.id)+\" - \"+str(listing.price/100)+\" euros\\n\")\n else: #plain text\n file.write(Parser.parse_text(data))\n file.write(\"=====================================\\n\")\n file.close()","def logFile(self):\n\n event = 'stim'\n mStr = '{:013}'.format(self.mouse.tag) + '\\t'\n outPutStr = mStr + \\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\t' + event\n print (outPutStr)\n if self.textfp != None:\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\t' + event\n self.textfp.write(outPutStr + '\\n')\n self.textfp.flush()","def write_debug_log(self, msg):\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n with open(self.debug_log, 'a+') as logfile:\n logfile.write(\"%s: %s\\n\" % (now, msg))","def saveLogFile(self, fname = \"data/status.txt\"):\n with open(fname, 'w') as f:\n f.write(\"<br>\\n\".join(self.logLines))\n self.log(\"wrote \"+fname)","def _write_log(self, log_data):\n # for data in log_data:\n # self.log_file.write(\"{}\\n\".format(data).encode('utf-8'))\n self.log_file.write(\"{}\\n\".format(log_data).encode('utf-8'))\n self.log_file.flush()","def _log2mylog(self, msg):\n time_str = mod_time.strftime(\n \"%Y-%m-%d %H:%M:%S\", mod_time.localtime(mod_time.time())\n )\n msg = str(msg)\n content = \"%s [%s]\\n\" % (time_str, msg)\n fa = open(self.mylogfile, \"a\")\n fa.write(content)\n fa.close()","def dump_to_log(self):\n # self._send_request(\"/dumpToLog\")\n pass","def store_action_log(self, filename):\n t = self.get_current_timeindex()\n camera_obs = self.get_camera_observation(t)\n self._action_log[\"final_object_pose\"] = {\n \"t\": t,\n \"pose\": camera_obs.object_pose,\n }\n\n with open(filename, \"wb\") as fh:\n pickle.dump(self._action_log, fh)","def write(self, msg, flag_print=True):\n file = open(self.log_path, \"a\")\n insert_time=datetime.now().strftime('%H:%M:%S.%f')[:-3]\n current_time = \"[\"+insert_time+\"]\"\n log_msg = current_time + \" \" + msg + \"$\" +\"\\n\" \n file.write(log_msg)\n # if flag_print is True:\n print(log_msg)","def print_info(self):\n\n self.logging.info(str(self.filename) + ':' + str(self.__info_dict))","def InsertLog():","def _write_log(self, log_data):\n self.log_file.write(ensure_bytes(log_data + \"\\n\"))\n self.log_file.flush()","def printToLogfile (self, text):\n if self.logFile is not None:\n self.logFile.write(text)\n self.logFile.flush()","def log_it(logdata=None):\n with open(\"bloomhack.log\", \"a\") as fp:\n fp.write(logdata)\n return","def logentry(self, string=None):\n if (self._OIFlogging):\n oiflogfile = open(self._commslogfilename, \"a\")\n oiflogfile.write(\"# \" + \"%04.6fs: \" % (self._gettime() - self._logstarttime) + string + \"\\n\")\n oiflogfile.flush()\n else:\n# if self._print_once:\n# self._print_once = 0\n# print self.hilite(\"Warning: Not logging OIF transactions. Use\\n it.logfile(<filename>) to set log filename and\\n it.logging(True) to enable logging\", False, True)\n print 'Unable to write log entry', string\n return","def _log(self, log, message):\n log_entry = '[%s] %s\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\n log.write(log_entry)\n if self.verbose:\n print log_entry.rstrip()","def dump_to_log(self, log_dir, log_filename):\n\t\twith open(os.path.join(log_dir, log_filename), \"w\") as f:\n\t\t\tf.write(\"================ Arguments ==================\\n\")\n\t\t\tfor k, v in vars(self).items():\n\t\t\t\tf.write(\"{} : {}\\n\".format(k, v))\n\t\t\tf.write(\"=============================================\\n\")","def store(status, message, **kwargs):\r\n LogRecord(status, message, **kwargs)","def _init_log(self):\n if not os_path_exists(self.log_file):\n self._write('', 'w')","def _write(self, data, mode):\n check_path(self.config_path)\n\n with open(self.log_file, mode) as log:\n if mode == 'a' and self.add_time:\n msg = self.TIME_TEMPLATE.format(time=strftime('%c'), error_msg=data)\n else:\n msg = data\n\n log.write(msg.encode(self._encoding, 'ignore'))","def to_log(self, namefile=None):\n if namefile is None:\n namefile = self.name.replace(' ', '_')+'.log'\n f = open(namefile, 'w')\n f.write(self.__str__())\n f.close()","def log_all(self):\n self.save_raw()\n self.log()","def write_log(self):\n if self.hash_log_curr:\n temp_dict = {}\n count = 0\n for key, value in self.hash_log_curr.iteritems():\n temp_dict[value[4] + str(count)] = key\n count += 1\n temp_sort = temp_dict.keys()\n temp_sort.sort()\n temp_sort.reverse()\n\n try:\n log = open(self.log_path + r'\\hash_log.txt', 'w')\n # log header\n log.write(self.log_header)\n # write hash_log_content to log\n for key in temp_sort:\n value = self.hash_log_curr[temp_dict[key]]\n log.write(value[0]+'|'+value[1]+'|'+value[2]+'|'+value[3]+'|'+value[4]+'|'+value[5] + '\\n')\n log.close()\n self.print_to_log('New log writen to file: ' + self.log_path + r'\\hash_log.txt' )\n except IOError:\n self.print_to_log('Cannot open log file to write')\n raise\n except:\n self.print_to_log('Unknown Error')\n raise","def add_log(self, log):\n log = str(datetime.datetime.now()) + \": \"+log+\"\\n\"\n print(log)\n self.logs.append(log)\n if len(self.logs) > 10:\n self.append_to_logfile()","def logInfo(self, timestamp, info):\n self.logs['messages'].write(','.join([str(i) for i in [\n self.formatTimestamp(timestamp), info]]) + '\\n')\n self.logs['messages'].flush()","def write_to_log(self, log_file, log_data):\n with open(self.gamelogs_path + log_file, 'a') as f:\n writer = csv.writer(f)\n writer.writerow(log_data)\n f.close()","def writeLog(self, log_path):\r\n f = open(log_path, 'w')\r\n f.write(str(self))\r\n f.close()","def save_exception(exc):\n LOG.error(\"Error - %s\", str(exc))\n hour = time.strftime(\"_%H_%M_%S\")\n today = time.strftime(\"_%d_%m_%Y\")\n data = (str(exc)+traceback.format_exc())\n\n file = open(\"./logs/ERROR_\"+threading.currentThread().getName()+today+\".log\",'a+') #Replace to fix OSError\n file.write(\"\\n==\"+hour+\"==\\n\")\n file.write(Parser.parse_text(data))\n file.write(\"=====================================\\n\")\n file.close()","def log(self, msg):\n logging.info(\"Logging Message\")\n ml = self.monk_logs\n today = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n ml.insert({today: {\"log\": msg,\n \"sentiment\": self.get_sentiment(msg),\n \"weather\": Weather.get_weather()}})","def save_log(file_path=None, stat=\"\"):\n fp = open(file_path, mode=\"a+\")\n fp.write(stat + \"\\n\")\n fp.close()","def local(self, text):\n\t\tlogf = open(\"update_log.txt\", \"a\")\n\t\tdate = datetime.datetime.now()\n\t\tlogf.writelines(\"[\" + str(date) + \"] \" + text + \"\\n\")\n\t\tlogf.close()","def save(self, log_information):\n with self.connection.cursor() as cursor:\n # Create a new record\n sql = \"\"\"INSERT INTO `logs` (\n `latitude`, `longitude`, `address`, `number`,\n `neighborhood`, `city`, `postal_code`, `state`,\n `country`\n ) VALUES (\n %(latitude)s, %(longitude)s, %(address)s, %(number)s,\n %(neighborhood)s, %(city)s, %(postal_code)s, %(state)s,\n %(country)s\n )\"\"\"\n cursor.execute(sql, log_information)\n\n # connection is not autocommit by default. So we must commit to save\n # changes.\n self.connection.commit()","def data_log(self, file, spectra):\n if self.datalogflag:\n with open(file, 'a') as f:\n f.write('{0}, '.format(spectra))\n self.vprint(\n 2, 'Writing spectra to data log at {}'.format(file))","def log(self, message):\n try:\n stat = os.stat(self.logpath)\n if stat.st_size >= 1000000:\n os.rename(self.logpath, self.logpath + '.1')\n except:\n pass\n logfile = open(self.logpath, 'a+')\n logfile.write(message + \"\\n\")\n logfile.close()","def _addLogEntry(request, action, pagename, filename):\n from MoinMoin.logfile import editlog\n t = wikiutil.timestamp2version(time.time())\n fname = wikiutil.url_quote(filename)\n\n # Write to global log\n log = editlog.EditLog(request)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\n\n # Write to local log\n log = editlog.EditLog(request, rootpagename=pagename)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)","def log(self, my_string):\n ## Open/Close each call is ridiculously inefficient.\n ## This was just a quick solution to build from\n ## TODO: Improve the logging mechanism\n logto = open(self.logfile, 'a')\n logto.write(my_string)\n logto.close()","def log(self, message):","def logging_data(self):\n with open('sensor_data.log','w') as f:\n json.dump(self.read_continuous_data, f)","def write(self, identifier: str, message: str):\n self.log[identifier] = message","def _log(self, data):\n if self.log_data is not None:\n self.log_data(data)","def write_log(text):\n write_file(read_file(log_file), log + '\\n' + text)","def write_info_to_file(self):\n\n self.info.write_mission_info()\n\n self.logger.info(\"Mission instance write succeeded.\")","def log_to_file(self, filename=None):\n if not filename:\n filename = '%s/../../output/sentimentpy.log' % os.path.dirname(os.path.realpath(__file__))\n file_handler = RotatingFileHandler(filename, 'a', 1000000, 1)\n file_handler.setLevel(logging.DEBUG)\n file_handler.setFormatter(self.formatter)\n self.log.addHandler(file_handler)\n return self","def loginfo(self, msg):\n self.logger.info(msg)","def putLog(self, log):\n \n moduleCoordinator.ModuleCoordinator().addEvent(moduleCoordinator.LOG_EVENT, log, self.hash, self.config)","def update_log(er=\"File created\",log_path=log_path, s3_path_log = s3_path_log, upload=False):\n print(er)\n with open(log_path, 'a') as file:\n file.write(str(datetime.now()) + ',' + str(er) + '\\n')\n # if upload is True:\n # s3.meta.client.upload_file(log_path, bucket_name, s3_path_log)","def _log(self, message):\n pass","def write(self):\n with open(\"log.txt\", 'w') as f:\n for message in self.message_list:\n f.write(message + \"\\n\")","def WriteErrorsToFile():\n if(not __errorsTracked__ is None):\n if(len(__errorsTracked__)>0):\n formattedLogName = '_'.join[\"ErrorLog\",\"GarageChecker\",datetime.date,datetime.time]\n WriteToFile(formattedLogName,__errorsTracked__)\n __errorsTracked__ = []","def log_tofile(self, inst):\n self._tick += 1\n if self._tick >= self._second:\n self.logger.log(inst)\n self._tick = 0","def log_message(self, message):\n with open(LOGFILE, \"a\") as f:\n currentDt = datetime.now().strftime(\"%d-%b-%Y (%H:%M:%S.%f)\")\n message = \"\\n\" + currentDt + '---' + message\n f.write(message)","def log(self, message: str):","def log(self, msg, alwaysPrint = False):\n if self.fileObject is None or alwaysPrint:\n print msg\n if self.fileObject:\n self.fileObject.write( msg + '\\n' )","def write_log(self):\n with open(self.trav_stat_file, 'a') as stat_file:\n travel_writer = csv.writer(stat_file)\n # Every row starts with the start and destnation\n row = [self.start, self.dest]\n # This uses a static list so that the order is fixed\n for state in [\"waiting\", \"riding\", \"transferring\"]:\n state_total = sum(self.time_record[state])\n row.append(state_total)\n travel_writer.writerow(row)","def data_log(self, file, **kwargs):\n time_string = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n average_data = kwargs.get('average_data')\n if self.datalogflag:\n with open(file, 'a') as f:\n f.write('{0}, {1}'.format(time_string, average_data))\n f.write('\\n')\n self.vprint(2, 'Writing average air quality data to data log at {}'.format(file))","def append_to_logfile(self):\n with open(self.path, \"a+\") as f:\n for item in self.logs:\n f.write(item)\n self.logs.clear()","def write_to_file(self, *args, **kwargs) -> None:\n with open(self._log_file, 'a') as file:\n print(file=file, *args, **kwargs)","def log_and_print(self, message):\n self.f.write(message + \"\\n\")\n print message","def set_log_file(filename):\n pass","def _createLogFile(LogFile,date,LocalPath,ShowTagsResult):\n try:\n LOG = open(LogFile,\"w\")\n if _verbose:\n print(\"Writing Production Host, Location, Release and Tags information in %s\" % LogFile) \n LOG.write(\"These performance tests were executed on host %s and published on %s\" % (HOST,date))\n LOG.write(\"They were run in %s\" % LocalPath)\n LOG.write(\"Results of showtags -r in the local release:\\n%s\" % ShowTagsResult)\n LOG.close()\n except IOError as detail:\n print(\"WARNING: Can't create log file\") \n print(detail)","def create_log(self, num_machines):\n\n # generates a folder for logs if one does not exist\n os.makedirs('logs', exist_ok=True)\n\n # record extra info at the top of the log file\n extra_info = [f'num machines: {num_machines}', f'ticks per second: {self.ticks_per_second}', f'lifetime: {self.lifetime}']\n dummy_info_dict = {k:info for k, info in zip(LogEntry.ENTRY_ORDER, extra_info)}\n\n with open(self.log_filename, mode='a') as log_file:\n writer = csv.DictWriter(log_file, fieldnames=LogEntry.ENTRY_ORDER)\n writer.writerow(dummy_info_dict)\n writer.writeheader()","def _stdlog(self, msg):\n print msg\n logger.info(msg)","def log(self):\n\n\t\t# Only every 1/10 second (or so) to avoid flooding networktables\n\t\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\n\t\t\treturn\n\n\t\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\n\t\twpilib.SmartDashboard.putBoolean(\"Garbo?\", self.is_pbot)\n\n\t\tself.drive.log()\n\t\tself.elevator.log()\n\t\tself.intake.log()","def saveStatsFile(self):\n if not os.path.exists(\"stats\"):\n os.mkdir(\"stats\")\n now = datetime.datetime.now()\n parts = [now.year, now.month, now.day]\n parts = [\"%02d\"%x for x in parts]\n todaysFileName = \"-\".join(parts)+\".txt\" \n timeStamp = time.strftime(\"%y%m%d%H%M\", time.localtime())\n log = \",\".join(self.logLinesStats)\n fname = \"stats/\"+todaysFileName\n with open(fname, 'a') as f:\n f.write(timeStamp+\",\"+log+\"\\n\")\n self.log(\"wrote \"+fname)","def putlog(self,s):\n if not self.logqueue == None:\n# print s\n self.logqueue.put(\"Spectrum: (\"+time.ctime()+\"):\\n\"+s)","def log_info(self, msg):\n self.logger.info(msg)","def recordLog(project, status, memo):\n path = getPath(project)\n log = open(path, 'a')\n writer = csv.writer(log, lineterminator='\\n')\n writer.writerow((time.time(), status, memo))\n log.close()\n if status == 'a':\n print(\"Tracking your time on \" + project)\n if status == 's':\n print(\"Tracking suspended on \" + project)\n if status == 't':\n print(\"Time shifted on \" + project)\n if not path == '.sourglass':\n store = open(os.path.join(basepath, 'last'), 'w')\n store.write(project)\n store.close","def init_log_file(self):\r\n try:\r\n os.makedirs(config[\"server_log_path\"])\r\n except OSError:\r\n if not os.path.isdir(config[\"server_log_path\"]):\r\n raise\r\n server_log_file = logging.FileHandler(\r\n config[\"server_log_path\"] + 'server_log_' + time.strftime('%Y-%m-%d_%H.%M.%S') + '.txt')\r\n server_log_file.setLevel(logging.DEBUG)\r\n server_log_file.setFormatter(file_formatter)\r\n server_log.addHandler(server_log_file)","def _print_log(self, step, data=None):\n \n # Set mode to append to log file\n mode = 'a'\n\n if self.logfile is None:\n # Increment log counter for the class. Each instance of the class generates a new log.\n self.__class__.log_no += 1\n\n # Create a log file for the instance\n # Logs will be stored in ..\\logs\\SKLearn Log <n>.txt\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\n \n if step == 1:\n # Output log header\n output = \"\\nSKLearnForQlik Log: {0} \\n\\n\".format(time.ctime(time.time()))\n # Set mode to write new log file\n mode = 'w'\n \n elif step == 2:\n # Output the parameters\n output = \"Model Name: {0}\\n\\n\".format(self.model.name)\n output += \"Execution arguments: {0}\\n\\n\".format(self.exec_params)\n \n try:\n output += \"Scaler: {0}, missing: {1}, scale_hashed: {2}, scale_vectors: {3}\\n\".format(\\\n self.model.scaler, self.model.missing,self.model.scale_hashed, self.model.scale_vectors)\n output += \"Scaler kwargs: {0}\\n\\n\".format(self.model.scaler_kwargs)\n except AttributeError:\n output += \"scale_hashed: {0}, scale_vectors: {1}\\n\".format(self.model.scale_hashed, self.model.scale_vectors)\n\n try:\n if self.model.dim_reduction:\n output += \"Reduction: {0}\\nReduction kwargs: {1}\\n\\n\".format(self.model.reduction, self.model.dim_reduction_args)\n except AttributeError:\n pass\n \n output += \"Estimator: {0}\\nEstimator kwargs: {1}\\n\\n\".format(self.model.estimator, self.model.estimator_kwargs)\n \n elif step == 3: \n # Output the request dataframe\n output = \"REQUEST: {0} rows x cols\\nSample Data:\\n\\n\".format(self.request_df.shape)\n output += \"{0}\\n...\\n{1}\\n\\n\".format(self.request_df.head().to_string(), self.request_df.tail().to_string())\n \n elif step == 4:\n # Output the response dataframe/series\n output = \"RESPONSE: {0} rows x cols\\nSample Data:\\n\\n\".format(self.response.shape)\n output += \"{0}\\n...\\n{1}\\n\\n\".format(self.response.head().to_string(), self.response.tail().to_string())\n \n elif step == 5:\n # Print the table description if the call was made from the load script\n output = \"\\nTABLE DESCRIPTION SENT TO QLIK:\\n\\n{0} \\n\\n\".format(self.table)\n \n elif step == 6:\n # Message when model is loaded from cache\n output = \"\\nModel {0} loaded from cache.\\n\\n\".format(self.model.name)\n \n elif step == 7:\n # Message when model is loaded from disk\n output = \"\\nModel {0} loaded from disk.\\n\\n\".format(self.model.name)\n \n elif step == 8:\n # Message when cache is updated\n output = \"\\nCache updated. Models in cache:\\n{0}\\n\\n\".format([k for k,v in self.__class__.model_cache.items()])\n \n elif step == 9:\n # Output when a parameter grid is set up\n output = \"Model Name: {0}, Estimator: {1}\\n\\nGrid Search Arguments: {2}\\n\\nParameter Grid: {3}\\n\\n\".\\\n format(self.model.name, self.model.estimator, self.model.grid_search_args, self.model.param_grid)\n \n elif step == 10:\n # self.model.estimator_kwargs['architecture']\n output = \"\\nKeras architecture added to Model {0}:\\n\\n{1}\\n\\n\".format(self.model.name,\\\n self.model.architecture.to_string())\n\n elif step == 11:\n # Output after adding lag observations to input data\n output = \"Lag observations added ({0} per sample). New input shape of X is {1}.\\n\\n\".format(self.model.lags, data.shape)\n output += \"Feature Definitions:\\n{0}\\n\\n\".format(self.model.features_df.to_string())\n output += \"Sample Data:\\n{0}\\n...\\n{1}\\n\\n\".format(data.head(5).to_string(), data.tail(5).to_string())\n \n sys.stdout.write(output)\n with open(self.logfile, mode, encoding='utf-8') as f:\n f.write(output)","def log_success(self):\n with open(self.logfile, 'a+') as f:\n f.write(self.BEGIN + self.message + '\\n' + self.END)\n self.message = ''","def writeLog(pid):\n\tglobal processes,logfile,strikes,sleep\n\tproc = processes[pid]\n\tlogfile.write('[%s] %d %s %f%%cpu %f%%mem (over %d s): %s\\n'%(time.strftime('%b %d %H:%M:%S'),pid,proc.user,proc.cpu,proc.mem,proc.count*sleep,proc.command))","def store(self, **stats):\n if self.first_row:\n self.log_headers = list(stats.keys())\n for key in stats:\n assert key in self.log_headers, f\"Can't introduce a new key that you didn't include before: {key}\"\n\n # Write to output file\n if self.first_row:\n self.file_writer.writerow(self.log_headers)\n self.file_writer.writerow(stats.values())\n self.output_file.flush()\n\n # Display in stdout\n if self.log_freq > 0 and self.counter % self.log_freq == 0:\n _print_table(stats)\n\n self.first_row = False\n self.counter += 1","def log(self, message):\n self._log += \"%s\\n\" % message\n print message","def send_log():\n log.info(f\"UUID={UUID}\")\n log.info(f\"SPLIT={SPLIT}\")\n log.info(f\"BATCH_SIZE={BATCH_SIZE}\")\n log.info(f\"EPOCHS={EPOCHS}\")\n log.info(f\"PATIENCE={PATIENCE}\")\n log.info(f\"X_FREQ={X_FREQ}\")\n log.info(f\"LOOK_BACK={LOOK_BACK}\")\n log.info(f\"LOOK_AHEAD={LOOK_AHEAD}\")\n log.info(f\"KERNEL_SIZE={KERNEL_SIZE}\")\n log.info(f\"FILTERS={FILTERS}\")\n log.info(f\"L1L2={L1L2}\")\n log.info(f\"D1={D1}\")\n log.info(f\"D2={D2}\")\n log.info(f\"DOUT={DOUT}\")\n log.info(f\"PLOT={PLOT}\")\n log.info(f\"SHUFFLE={SHUFFLE}\")","def appendLog(self):\n if self.logBuffer == None :\n self.logBuffer = \"Some header\\nhere\\n\\n\"\n self.logBuffer += \"\\tx\\ty\\ttheta : ul\\tur\\tt-neur\\n\";\n \n self.logBuffer += '%2.1f: %2.6f\\t %2.6f\\t %2.6f : ' % \\\n\t ( self.t, self.env.state[0], self.env.state[2], self.env.state[4] )\n self.logBuffer += '%1.3f\\t %1.3f \\t%1.2f \\t' % \\\n ( self.env.action[0], self.env.action[1], self.env.action[2] )\n self.logBuffer += 'Dst/Theta/Speed: \\t%f\\t%f\\t%f \\tF: %.2f \\n' % \\\n ( self.env.getDistance(), self.env.getOrientation(), self.env.getDistance(), self.getReward() )"],"string":"[\n \"def log_data(self):\\n\\n self.check_dir()\\n with open(self.log_file, \\\"a\\\") as logger_file:\\n logger_file.write(\\\"{}, {}\\\\n\\\".format(self.time, self.msg))\",\n \"def log(self):\\n f = open(self.log_dir + 'parsed.log', 'a')\\n try:\\n # Write: local time | CurrentCost \\\"time\\\" | id | temp/C | power/W \\n f.write(\\\"%s\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\n\\\" \\n % (self.ts('now'), self.ts('cc'), self.id, self.temp, self.watts))\\n finally:\\n f.close()\",\n \"def logsave(self):\\n log_file = open(self.conf[\\\"output_prefix\\\"] + \\\"_log.txt\\\", \\\"w\\\")\\n try:\\n log_file.write(self.log)\\n finally:\\n log_file.close()\",\n \"def log_info(info):\\n log = open(log_path, 'a+')\\n log.write(info + '\\\\n')\\n log.close()\",\n \"def log(self):\\n\\t\\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\\n\\t\\tfileobj = open(filename, 'w');\\n\\t\\tfileobj.write(self.title)\\n\\t\\tfor (key, value) in self.argv.items():\\n\\t\\t\\tfileobj.write('%s: %s\\\\n' % (key, value))\\n\\t\\tfileobj.close()\\n\\t\\tprint \\\"[%s] Log saved '%s'\\\" % (ctime(), filename)\",\n \"def _log_to_file(self, message):\\n if self.log is not None:\\n message = \\\"[%s] %s\\\" % (datetime.datetime.utcnow().strftime('%H:%M:%S'), message)\\n self.log.write(\\\"%s\\\\n\\\" % (message,))\\n self.log.flush()\\n print message\",\n \"def writeLog(self):\\n if self.logBuffer != None and self.logging :\\n f = open(self.logfileName, 'w')\\n self.logBuffer += \\\"Final Fitness: %f\\\\n\\\" % self.getTotalReward()\\n self.logBuffer += \\\"\\\\n\\\"\\n f.write(self.logBuffer)\\n f.close()\",\n \"def log(self, loginfo):\\n logging.basicConfig(level=logging.INFO,\\n format='%(asctime)s %(filename)s:%(message)s',\\n datefmt='%d %b %Y %H:%M:%S',\\n filename=self.logfilepath,\\n filemode='w')\\n filelog = logging.FileHandler(self.logfilepath)\\n logging.getLogger('Functest').addHandler(filelog)\\n logging.info(loginfo)\",\n \"def log(self, msg):\\n current_datetime = self.get_date_time()\\n self.file.write(\\\"%s %s\\\\n\\\" % (current_datetime, msg))\",\n \"def write_log(self, log_filename, data):\\n open(log_filename, 'a').write(str(data))\",\n \"def write_data(self, data):\\n print \\\"Writing data...\\\"\\n # Write data into log\\n self.log.write_file(data)\\n\\n # Close log so information can be sent\\n self.log.close_log()\",\n \"def __log__(self, val):\\n if lm_settings[\\\"debug\\\"]:\\n try:\\n log_file = open(\\\"language_manager/info/language_manager.log\\\", \\\"a\\\")\\n except FileNotFoundError:\\n log_file = open(lm_settings[\\\"logfile\\\"], \\\"w\\\")\\n log_file.write(val)\\n log_file.close()\",\n \"def write_log(self, msg: str):\\n self.cta_engine.write_log(msg, self)\",\n \"def _logger(self):\\r\\n\\r\\n # Create filename for log\\r\\n filenameF = self._vna.getDateFormatted() + \\\".txt\\\"\\r\\n filenameF = \\\"Logs/\\\" + filenameF \\r\\n f = open(filenameF, \\\"a+\\\") # Log saved in directory named logs located in same directory as this file\\r\\n \\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages)):\\r\\n # f.write('%s\\\\t\\\\t\\\\t' % self._voltages[i][0])\\r\\n # else:\\r\\n for i in range(len(self._voltages)):\\r\\n f.write('%s\\\\t\\\\t' % self._voltages[i][0])\\r\\n f.write('\\\\n')\\r\\n\\r\\n # if self._vna.isTwoComponents():\\r\\n # for i in range(len(self._voltages[0])):\\r\\n # line = \\\"\\\"\\r\\n # for j in range(len(self._voltages)):\\r\\n # line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][2*i]) + \\\\\\r\\n # str(self._intensity[j][2*i + 1]) + '\\\\t'\\r\\n # f.write(line)\\r\\n # f.write('\\\\n')\\r\\n # else: \\r\\n for i in range(len(self._voltages[0])):\\r\\n line = \\\"\\\"\\r\\n for j in range(len(self._voltages)):\\r\\n line = line + str(self._frequency[j][i]) + '\\\\t' + str(self._intensity[j][i]) + '\\\\t' \\r\\n f.write(line)\\r\\n f.write('\\\\n')\",\n \"def log(self, message):\\n timestamp = time.strftime(\\\"[%H:%M:%S]\\\", time.localtime(time.time()))\\n self.file.write('%s %s\\\\n' % (timestamp, message))\\n self.file.flush()\",\n \"def log(self):\\n self.logger = logging.getLogger(self.log_name)\\n self.logger.info(f\\\"Name: {self.name}\\\")\\n self.logger.info(f\\\"Grid points: {self.gp}\\\")\\n self.logger.info(f\\\"Nadir points: {self.nadir_p}\\\")\\n self.logger.info(f\\\"Penalty weight: {self.eps}\\\")\\n self.logger.info(f\\\"Early exit: {self.early_exit}\\\")\\n self.logger.info(f\\\"Bypass coefficient: {self.bypass}\\\")\\n self.logger.info(f\\\"Flag array: {self.flag}\\\")\\n self.logger.info(f\\\"CPU Count: {self.cpu_count}\\\")\\n self.logger.info(f\\\"Redivide work: {self.redivide_work}\\\")\\n self.logger.info(f\\\"Shared flag array: {self.shared_flag}\\\")\\n self.logger.info(Helper.separator())\",\n \"def writeToLogFile(self, event):\\n outPutStr = '{:013}'.format(0)\\n logOutPutStr = outPutStr + '\\\\t' + '{:.2f}'.format (time ()) + '\\\\t' + event + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\\n printOutPutStr = outPutStr + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\\\t' + event\\n print (printOutPutStr)\\n if self.logFP is not None:\\n self.logFP.write(logOutPutStr + '\\\\n')\\n self.logFP.flush()\",\n \"def saveToLogFile(self, msg):\\n path = os.path.join(self.parent.progpath, \\\"logfile.txt\\\")\\n fo = open(path, 'a')\\n # prefix with current date and time from now variable\\n msg = \\\"\\\\n#{0}\\\\n\\\".format(datetime.datetime.now()) + msg\\n fo.write(msg)\\n fo.close()\",\n \"def saveToLogFile(self, msg):\\n path = os.path.join(self.parent.progpath, \\\"logfile.txt\\\")\\n fo = open(path, 'a')\\n # prefix with current date and time from now variable\\n msg = \\\"\\\\n#{0}\\\\n\\\".format(datetime.datetime.now()) + msg\\n fo.write(msg)\\n fo.close()\",\n \"def log(self, line):\\n now = datetime.datetime.now()\\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\\n with open(self.logfile, 'a') as log:\\n log.write(time + ' ' + line + '\\\\n')\",\n \"def log(self, txt):\\n if self.logfile:\\n self.logfile.write(txt)\",\n \"def set_info(self, message):\\n if self.log_file_exist(self.file_path_name):\\n logging.info(message)\\n else:\\n print \\\"The log \\\"+ self.name_log + \\\"does not exist in the directory\\\"\",\n \"def log_to_file(text, status='INFO'):\\n outfile = open(LogName, 'a')\\n outfile.write(timestamp()+' - '+status+' - '+str(text)+'\\\\n')\\n outfile.close()\",\n \"def __logtofile(self, log_name):\\n logger = logging.getLogger(log_name)\\n\\n file_path = os.path.join(self.log_file_path, log_name + '.txt')\\n\\n formatter = logging.Formatter('<%(asctime)s> %(levelname)-8s %(message)s',\\n datefmt='%y-%m-%d %H:%M:%S')\\n self.file_handlers[logger] = logging.FileHandler(file_path, mode='w')\\n self.file_handlers[logger].setFormatter(formatter)\\n self.file_handlers[logger].setLevel(logging.DEBUG)\\n logger.addHandler(self.file_handlers[logger])\\n\\n logger.info('SAVING LOGS IN: %s' % file_path)\",\n \"def log(self, msg=None):\\n f = open(self.logbook, 'a')\\n # if send or receive, write message\\n if msg: \\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + \\n \\\" \\\" + str(msg) + '\\\\n')\\n # if it is an internal event just write the system time and current\\n # logical clock time\\n else:\\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + '\\\\n')\\n f.close()\",\n \"def save(*messages):\\n data = Parser.parse_texts(*messages[1:])\\n hour = time.strftime(\\\"_%H_%M_%S\\\")\\n today = time.strftime(\\\"_%d_%m_%Y\\\")\\n title = Parser.parse_text(messages[0])\\n\\n file = open(\\\"./logs/\\\"+threading.currentThread().getName()+today+\\\".log\\\",'a+')\\n file.write(\\\"\\\\n==\\\"+title+hour+\\\"==\\\\n\\\")\\n if type(data) is dict: #Dictionary with each value being a triplet. From get_all_items\\n for key in data.keys():\\n file.write(Parser.parse_text(key) + \\\" -> \\\"+ Parser.parse_text(str(data[key].x)) +\\\"\\\\n\\\")\\n elif type(data) is list: #From get_item, market item, attribute listings\\n for listing in data:\\n file.write(str(listing.id)+\\\" - \\\"+str(listing.price/100)+\\\" euros\\\\n\\\")\\n else: #plain text\\n file.write(Parser.parse_text(data))\\n file.write(\\\"=====================================\\\\n\\\")\\n file.close()\",\n \"def logFile(self):\\n\\n event = 'stim'\\n mStr = '{:013}'.format(self.mouse.tag) + '\\\\t'\\n outPutStr = mStr + \\\\\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\\\t' + event\\n print (outPutStr)\\n if self.textfp != None:\\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\\\t' + event\\n self.textfp.write(outPutStr + '\\\\n')\\n self.textfp.flush()\",\n \"def write_debug_log(self, msg):\\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n with open(self.debug_log, 'a+') as logfile:\\n logfile.write(\\\"%s: %s\\\\n\\\" % (now, msg))\",\n \"def saveLogFile(self, fname = \\\"data/status.txt\\\"):\\n with open(fname, 'w') as f:\\n f.write(\\\"<br>\\\\n\\\".join(self.logLines))\\n self.log(\\\"wrote \\\"+fname)\",\n \"def _write_log(self, log_data):\\n # for data in log_data:\\n # self.log_file.write(\\\"{}\\\\n\\\".format(data).encode('utf-8'))\\n self.log_file.write(\\\"{}\\\\n\\\".format(log_data).encode('utf-8'))\\n self.log_file.flush()\",\n \"def _log2mylog(self, msg):\\n time_str = mod_time.strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\", mod_time.localtime(mod_time.time())\\n )\\n msg = str(msg)\\n content = \\\"%s [%s]\\\\n\\\" % (time_str, msg)\\n fa = open(self.mylogfile, \\\"a\\\")\\n fa.write(content)\\n fa.close()\",\n \"def dump_to_log(self):\\n # self._send_request(\\\"/dumpToLog\\\")\\n pass\",\n \"def store_action_log(self, filename):\\n t = self.get_current_timeindex()\\n camera_obs = self.get_camera_observation(t)\\n self._action_log[\\\"final_object_pose\\\"] = {\\n \\\"t\\\": t,\\n \\\"pose\\\": camera_obs.object_pose,\\n }\\n\\n with open(filename, \\\"wb\\\") as fh:\\n pickle.dump(self._action_log, fh)\",\n \"def write(self, msg, flag_print=True):\\n file = open(self.log_path, \\\"a\\\")\\n insert_time=datetime.now().strftime('%H:%M:%S.%f')[:-3]\\n current_time = \\\"[\\\"+insert_time+\\\"]\\\"\\n log_msg = current_time + \\\" \\\" + msg + \\\"$\\\" +\\\"\\\\n\\\" \\n file.write(log_msg)\\n # if flag_print is True:\\n print(log_msg)\",\n \"def print_info(self):\\n\\n self.logging.info(str(self.filename) + ':' + str(self.__info_dict))\",\n \"def InsertLog():\",\n \"def _write_log(self, log_data):\\n self.log_file.write(ensure_bytes(log_data + \\\"\\\\n\\\"))\\n self.log_file.flush()\",\n \"def printToLogfile (self, text):\\n if self.logFile is not None:\\n self.logFile.write(text)\\n self.logFile.flush()\",\n \"def log_it(logdata=None):\\n with open(\\\"bloomhack.log\\\", \\\"a\\\") as fp:\\n fp.write(logdata)\\n return\",\n \"def logentry(self, string=None):\\n if (self._OIFlogging):\\n oiflogfile = open(self._commslogfilename, \\\"a\\\")\\n oiflogfile.write(\\\"# \\\" + \\\"%04.6fs: \\\" % (self._gettime() - self._logstarttime) + string + \\\"\\\\n\\\")\\n oiflogfile.flush()\\n else:\\n# if self._print_once:\\n# self._print_once = 0\\n# print self.hilite(\\\"Warning: Not logging OIF transactions. Use\\\\n it.logfile(<filename>) to set log filename and\\\\n it.logging(True) to enable logging\\\", False, True)\\n print 'Unable to write log entry', string\\n return\",\n \"def _log(self, log, message):\\n log_entry = '[%s] %s\\\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\\n log.write(log_entry)\\n if self.verbose:\\n print log_entry.rstrip()\",\n \"def dump_to_log(self, log_dir, log_filename):\\n\\t\\twith open(os.path.join(log_dir, log_filename), \\\"w\\\") as f:\\n\\t\\t\\tf.write(\\\"================ Arguments ==================\\\\n\\\")\\n\\t\\t\\tfor k, v in vars(self).items():\\n\\t\\t\\t\\tf.write(\\\"{} : {}\\\\n\\\".format(k, v))\\n\\t\\t\\tf.write(\\\"=============================================\\\\n\\\")\",\n \"def store(status, message, **kwargs):\\r\\n LogRecord(status, message, **kwargs)\",\n \"def _init_log(self):\\n if not os_path_exists(self.log_file):\\n self._write('', 'w')\",\n \"def _write(self, data, mode):\\n check_path(self.config_path)\\n\\n with open(self.log_file, mode) as log:\\n if mode == 'a' and self.add_time:\\n msg = self.TIME_TEMPLATE.format(time=strftime('%c'), error_msg=data)\\n else:\\n msg = data\\n\\n log.write(msg.encode(self._encoding, 'ignore'))\",\n \"def to_log(self, namefile=None):\\n if namefile is None:\\n namefile = self.name.replace(' ', '_')+'.log'\\n f = open(namefile, 'w')\\n f.write(self.__str__())\\n f.close()\",\n \"def log_all(self):\\n self.save_raw()\\n self.log()\",\n \"def write_log(self):\\n if self.hash_log_curr:\\n temp_dict = {}\\n count = 0\\n for key, value in self.hash_log_curr.iteritems():\\n temp_dict[value[4] + str(count)] = key\\n count += 1\\n temp_sort = temp_dict.keys()\\n temp_sort.sort()\\n temp_sort.reverse()\\n\\n try:\\n log = open(self.log_path + r'\\\\hash_log.txt', 'w')\\n # log header\\n log.write(self.log_header)\\n # write hash_log_content to log\\n for key in temp_sort:\\n value = self.hash_log_curr[temp_dict[key]]\\n log.write(value[0]+'|'+value[1]+'|'+value[2]+'|'+value[3]+'|'+value[4]+'|'+value[5] + '\\\\n')\\n log.close()\\n self.print_to_log('New log writen to file: ' + self.log_path + r'\\\\hash_log.txt' )\\n except IOError:\\n self.print_to_log('Cannot open log file to write')\\n raise\\n except:\\n self.print_to_log('Unknown Error')\\n raise\",\n \"def add_log(self, log):\\n log = str(datetime.datetime.now()) + \\\": \\\"+log+\\\"\\\\n\\\"\\n print(log)\\n self.logs.append(log)\\n if len(self.logs) > 10:\\n self.append_to_logfile()\",\n \"def logInfo(self, timestamp, info):\\n self.logs['messages'].write(','.join([str(i) for i in [\\n self.formatTimestamp(timestamp), info]]) + '\\\\n')\\n self.logs['messages'].flush()\",\n \"def write_to_log(self, log_file, log_data):\\n with open(self.gamelogs_path + log_file, 'a') as f:\\n writer = csv.writer(f)\\n writer.writerow(log_data)\\n f.close()\",\n \"def writeLog(self, log_path):\\r\\n f = open(log_path, 'w')\\r\\n f.write(str(self))\\r\\n f.close()\",\n \"def save_exception(exc):\\n LOG.error(\\\"Error - %s\\\", str(exc))\\n hour = time.strftime(\\\"_%H_%M_%S\\\")\\n today = time.strftime(\\\"_%d_%m_%Y\\\")\\n data = (str(exc)+traceback.format_exc())\\n\\n file = open(\\\"./logs/ERROR_\\\"+threading.currentThread().getName()+today+\\\".log\\\",'a+') #Replace to fix OSError\\n file.write(\\\"\\\\n==\\\"+hour+\\\"==\\\\n\\\")\\n file.write(Parser.parse_text(data))\\n file.write(\\\"=====================================\\\\n\\\")\\n file.close()\",\n \"def log(self, msg):\\n logging.info(\\\"Logging Message\\\")\\n ml = self.monk_logs\\n today = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n ml.insert({today: {\\\"log\\\": msg,\\n \\\"sentiment\\\": self.get_sentiment(msg),\\n \\\"weather\\\": Weather.get_weather()}})\",\n \"def save_log(file_path=None, stat=\\\"\\\"):\\n fp = open(file_path, mode=\\\"a+\\\")\\n fp.write(stat + \\\"\\\\n\\\")\\n fp.close()\",\n \"def local(self, text):\\n\\t\\tlogf = open(\\\"update_log.txt\\\", \\\"a\\\")\\n\\t\\tdate = datetime.datetime.now()\\n\\t\\tlogf.writelines(\\\"[\\\" + str(date) + \\\"] \\\" + text + \\\"\\\\n\\\")\\n\\t\\tlogf.close()\",\n \"def save(self, log_information):\\n with self.connection.cursor() as cursor:\\n # Create a new record\\n sql = \\\"\\\"\\\"INSERT INTO `logs` (\\n `latitude`, `longitude`, `address`, `number`,\\n `neighborhood`, `city`, `postal_code`, `state`,\\n `country`\\n ) VALUES (\\n %(latitude)s, %(longitude)s, %(address)s, %(number)s,\\n %(neighborhood)s, %(city)s, %(postal_code)s, %(state)s,\\n %(country)s\\n )\\\"\\\"\\\"\\n cursor.execute(sql, log_information)\\n\\n # connection is not autocommit by default. So we must commit to save\\n # changes.\\n self.connection.commit()\",\n \"def data_log(self, file, spectra):\\n if self.datalogflag:\\n with open(file, 'a') as f:\\n f.write('{0}, '.format(spectra))\\n self.vprint(\\n 2, 'Writing spectra to data log at {}'.format(file))\",\n \"def log(self, message):\\n try:\\n stat = os.stat(self.logpath)\\n if stat.st_size >= 1000000:\\n os.rename(self.logpath, self.logpath + '.1')\\n except:\\n pass\\n logfile = open(self.logpath, 'a+')\\n logfile.write(message + \\\"\\\\n\\\")\\n logfile.close()\",\n \"def _addLogEntry(request, action, pagename, filename):\\n from MoinMoin.logfile import editlog\\n t = wikiutil.timestamp2version(time.time())\\n fname = wikiutil.url_quote(filename)\\n\\n # Write to global log\\n log = editlog.EditLog(request)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\\n\\n # Write to local log\\n log = editlog.EditLog(request, rootpagename=pagename)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\",\n \"def log(self, my_string):\\n ## Open/Close each call is ridiculously inefficient.\\n ## This was just a quick solution to build from\\n ## TODO: Improve the logging mechanism\\n logto = open(self.logfile, 'a')\\n logto.write(my_string)\\n logto.close()\",\n \"def log(self, message):\",\n \"def logging_data(self):\\n with open('sensor_data.log','w') as f:\\n json.dump(self.read_continuous_data, f)\",\n \"def write(self, identifier: str, message: str):\\n self.log[identifier] = message\",\n \"def _log(self, data):\\n if self.log_data is not None:\\n self.log_data(data)\",\n \"def write_log(text):\\n write_file(read_file(log_file), log + '\\\\n' + text)\",\n \"def write_info_to_file(self):\\n\\n self.info.write_mission_info()\\n\\n self.logger.info(\\\"Mission instance write succeeded.\\\")\",\n \"def log_to_file(self, filename=None):\\n if not filename:\\n filename = '%s/../../output/sentimentpy.log' % os.path.dirname(os.path.realpath(__file__))\\n file_handler = RotatingFileHandler(filename, 'a', 1000000, 1)\\n file_handler.setLevel(logging.DEBUG)\\n file_handler.setFormatter(self.formatter)\\n self.log.addHandler(file_handler)\\n return self\",\n \"def loginfo(self, msg):\\n self.logger.info(msg)\",\n \"def putLog(self, log):\\n \\n moduleCoordinator.ModuleCoordinator().addEvent(moduleCoordinator.LOG_EVENT, log, self.hash, self.config)\",\n \"def update_log(er=\\\"File created\\\",log_path=log_path, s3_path_log = s3_path_log, upload=False):\\n print(er)\\n with open(log_path, 'a') as file:\\n file.write(str(datetime.now()) + ',' + str(er) + '\\\\n')\\n # if upload is True:\\n # s3.meta.client.upload_file(log_path, bucket_name, s3_path_log)\",\n \"def _log(self, message):\\n pass\",\n \"def write(self):\\n with open(\\\"log.txt\\\", 'w') as f:\\n for message in self.message_list:\\n f.write(message + \\\"\\\\n\\\")\",\n \"def WriteErrorsToFile():\\n if(not __errorsTracked__ is None):\\n if(len(__errorsTracked__)>0):\\n formattedLogName = '_'.join[\\\"ErrorLog\\\",\\\"GarageChecker\\\",datetime.date,datetime.time]\\n WriteToFile(formattedLogName,__errorsTracked__)\\n __errorsTracked__ = []\",\n \"def log_tofile(self, inst):\\n self._tick += 1\\n if self._tick >= self._second:\\n self.logger.log(inst)\\n self._tick = 0\",\n \"def log_message(self, message):\\n with open(LOGFILE, \\\"a\\\") as f:\\n currentDt = datetime.now().strftime(\\\"%d-%b-%Y (%H:%M:%S.%f)\\\")\\n message = \\\"\\\\n\\\" + currentDt + '---' + message\\n f.write(message)\",\n \"def log(self, message: str):\",\n \"def log(self, msg, alwaysPrint = False):\\n if self.fileObject is None or alwaysPrint:\\n print msg\\n if self.fileObject:\\n self.fileObject.write( msg + '\\\\n' )\",\n \"def write_log(self):\\n with open(self.trav_stat_file, 'a') as stat_file:\\n travel_writer = csv.writer(stat_file)\\n # Every row starts with the start and destnation\\n row = [self.start, self.dest]\\n # This uses a static list so that the order is fixed\\n for state in [\\\"waiting\\\", \\\"riding\\\", \\\"transferring\\\"]:\\n state_total = sum(self.time_record[state])\\n row.append(state_total)\\n travel_writer.writerow(row)\",\n \"def data_log(self, file, **kwargs):\\n time_string = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\")\\n average_data = kwargs.get('average_data')\\n if self.datalogflag:\\n with open(file, 'a') as f:\\n f.write('{0}, {1}'.format(time_string, average_data))\\n f.write('\\\\n')\\n self.vprint(2, 'Writing average air quality data to data log at {}'.format(file))\",\n \"def append_to_logfile(self):\\n with open(self.path, \\\"a+\\\") as f:\\n for item in self.logs:\\n f.write(item)\\n self.logs.clear()\",\n \"def write_to_file(self, *args, **kwargs) -> None:\\n with open(self._log_file, 'a') as file:\\n print(file=file, *args, **kwargs)\",\n \"def log_and_print(self, message):\\n self.f.write(message + \\\"\\\\n\\\")\\n print message\",\n \"def set_log_file(filename):\\n pass\",\n \"def _createLogFile(LogFile,date,LocalPath,ShowTagsResult):\\n try:\\n LOG = open(LogFile,\\\"w\\\")\\n if _verbose:\\n print(\\\"Writing Production Host, Location, Release and Tags information in %s\\\" % LogFile) \\n LOG.write(\\\"These performance tests were executed on host %s and published on %s\\\" % (HOST,date))\\n LOG.write(\\\"They were run in %s\\\" % LocalPath)\\n LOG.write(\\\"Results of showtags -r in the local release:\\\\n%s\\\" % ShowTagsResult)\\n LOG.close()\\n except IOError as detail:\\n print(\\\"WARNING: Can't create log file\\\") \\n print(detail)\",\n \"def create_log(self, num_machines):\\n\\n # generates a folder for logs if one does not exist\\n os.makedirs('logs', exist_ok=True)\\n\\n # record extra info at the top of the log file\\n extra_info = [f'num machines: {num_machines}', f'ticks per second: {self.ticks_per_second}', f'lifetime: {self.lifetime}']\\n dummy_info_dict = {k:info for k, info in zip(LogEntry.ENTRY_ORDER, extra_info)}\\n\\n with open(self.log_filename, mode='a') as log_file:\\n writer = csv.DictWriter(log_file, fieldnames=LogEntry.ENTRY_ORDER)\\n writer.writerow(dummy_info_dict)\\n writer.writeheader()\",\n \"def _stdlog(self, msg):\\n print msg\\n logger.info(msg)\",\n \"def log(self):\\n\\n\\t\\t# Only every 1/10 second (or so) to avoid flooding networktables\\n\\t\\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\\n\\t\\t\\treturn\\n\\n\\t\\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\\n\\t\\twpilib.SmartDashboard.putBoolean(\\\"Garbo?\\\", self.is_pbot)\\n\\n\\t\\tself.drive.log()\\n\\t\\tself.elevator.log()\\n\\t\\tself.intake.log()\",\n \"def saveStatsFile(self):\\n if not os.path.exists(\\\"stats\\\"):\\n os.mkdir(\\\"stats\\\")\\n now = datetime.datetime.now()\\n parts = [now.year, now.month, now.day]\\n parts = [\\\"%02d\\\"%x for x in parts]\\n todaysFileName = \\\"-\\\".join(parts)+\\\".txt\\\" \\n timeStamp = time.strftime(\\\"%y%m%d%H%M\\\", time.localtime())\\n log = \\\",\\\".join(self.logLinesStats)\\n fname = \\\"stats/\\\"+todaysFileName\\n with open(fname, 'a') as f:\\n f.write(timeStamp+\\\",\\\"+log+\\\"\\\\n\\\")\\n self.log(\\\"wrote \\\"+fname)\",\n \"def putlog(self,s):\\n if not self.logqueue == None:\\n# print s\\n self.logqueue.put(\\\"Spectrum: (\\\"+time.ctime()+\\\"):\\\\n\\\"+s)\",\n \"def log_info(self, msg):\\n self.logger.info(msg)\",\n \"def recordLog(project, status, memo):\\n path = getPath(project)\\n log = open(path, 'a')\\n writer = csv.writer(log, lineterminator='\\\\n')\\n writer.writerow((time.time(), status, memo))\\n log.close()\\n if status == 'a':\\n print(\\\"Tracking your time on \\\" + project)\\n if status == 's':\\n print(\\\"Tracking suspended on \\\" + project)\\n if status == 't':\\n print(\\\"Time shifted on \\\" + project)\\n if not path == '.sourglass':\\n store = open(os.path.join(basepath, 'last'), 'w')\\n store.write(project)\\n store.close\",\n \"def init_log_file(self):\\r\\n try:\\r\\n os.makedirs(config[\\\"server_log_path\\\"])\\r\\n except OSError:\\r\\n if not os.path.isdir(config[\\\"server_log_path\\\"]):\\r\\n raise\\r\\n server_log_file = logging.FileHandler(\\r\\n config[\\\"server_log_path\\\"] + 'server_log_' + time.strftime('%Y-%m-%d_%H.%M.%S') + '.txt')\\r\\n server_log_file.setLevel(logging.DEBUG)\\r\\n server_log_file.setFormatter(file_formatter)\\r\\n server_log.addHandler(server_log_file)\",\n \"def _print_log(self, step, data=None):\\n \\n # Set mode to append to log file\\n mode = 'a'\\n\\n if self.logfile is None:\\n # Increment log counter for the class. Each instance of the class generates a new log.\\n self.__class__.log_no += 1\\n\\n # Create a log file for the instance\\n # Logs will be stored in ..\\\\logs\\\\SKLearn Log <n>.txt\\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\\n \\n if step == 1:\\n # Output log header\\n output = \\\"\\\\nSKLearnForQlik Log: {0} \\\\n\\\\n\\\".format(time.ctime(time.time()))\\n # Set mode to write new log file\\n mode = 'w'\\n \\n elif step == 2:\\n # Output the parameters\\n output = \\\"Model Name: {0}\\\\n\\\\n\\\".format(self.model.name)\\n output += \\\"Execution arguments: {0}\\\\n\\\\n\\\".format(self.exec_params)\\n \\n try:\\n output += \\\"Scaler: {0}, missing: {1}, scale_hashed: {2}, scale_vectors: {3}\\\\n\\\".format(\\\\\\n self.model.scaler, self.model.missing,self.model.scale_hashed, self.model.scale_vectors)\\n output += \\\"Scaler kwargs: {0}\\\\n\\\\n\\\".format(self.model.scaler_kwargs)\\n except AttributeError:\\n output += \\\"scale_hashed: {0}, scale_vectors: {1}\\\\n\\\".format(self.model.scale_hashed, self.model.scale_vectors)\\n\\n try:\\n if self.model.dim_reduction:\\n output += \\\"Reduction: {0}\\\\nReduction kwargs: {1}\\\\n\\\\n\\\".format(self.model.reduction, self.model.dim_reduction_args)\\n except AttributeError:\\n pass\\n \\n output += \\\"Estimator: {0}\\\\nEstimator kwargs: {1}\\\\n\\\\n\\\".format(self.model.estimator, self.model.estimator_kwargs)\\n \\n elif step == 3: \\n # Output the request dataframe\\n output = \\\"REQUEST: {0} rows x cols\\\\nSample Data:\\\\n\\\\n\\\".format(self.request_df.shape)\\n output += \\\"{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(self.request_df.head().to_string(), self.request_df.tail().to_string())\\n \\n elif step == 4:\\n # Output the response dataframe/series\\n output = \\\"RESPONSE: {0} rows x cols\\\\nSample Data:\\\\n\\\\n\\\".format(self.response.shape)\\n output += \\\"{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(self.response.head().to_string(), self.response.tail().to_string())\\n \\n elif step == 5:\\n # Print the table description if the call was made from the load script\\n output = \\\"\\\\nTABLE DESCRIPTION SENT TO QLIK:\\\\n\\\\n{0} \\\\n\\\\n\\\".format(self.table)\\n \\n elif step == 6:\\n # Message when model is loaded from cache\\n output = \\\"\\\\nModel {0} loaded from cache.\\\\n\\\\n\\\".format(self.model.name)\\n \\n elif step == 7:\\n # Message when model is loaded from disk\\n output = \\\"\\\\nModel {0} loaded from disk.\\\\n\\\\n\\\".format(self.model.name)\\n \\n elif step == 8:\\n # Message when cache is updated\\n output = \\\"\\\\nCache updated. Models in cache:\\\\n{0}\\\\n\\\\n\\\".format([k for k,v in self.__class__.model_cache.items()])\\n \\n elif step == 9:\\n # Output when a parameter grid is set up\\n output = \\\"Model Name: {0}, Estimator: {1}\\\\n\\\\nGrid Search Arguments: {2}\\\\n\\\\nParameter Grid: {3}\\\\n\\\\n\\\".\\\\\\n format(self.model.name, self.model.estimator, self.model.grid_search_args, self.model.param_grid)\\n \\n elif step == 10:\\n # self.model.estimator_kwargs['architecture']\\n output = \\\"\\\\nKeras architecture added to Model {0}:\\\\n\\\\n{1}\\\\n\\\\n\\\".format(self.model.name,\\\\\\n self.model.architecture.to_string())\\n\\n elif step == 11:\\n # Output after adding lag observations to input data\\n output = \\\"Lag observations added ({0} per sample). New input shape of X is {1}.\\\\n\\\\n\\\".format(self.model.lags, data.shape)\\n output += \\\"Feature Definitions:\\\\n{0}\\\\n\\\\n\\\".format(self.model.features_df.to_string())\\n output += \\\"Sample Data:\\\\n{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(data.head(5).to_string(), data.tail(5).to_string())\\n \\n sys.stdout.write(output)\\n with open(self.logfile, mode, encoding='utf-8') as f:\\n f.write(output)\",\n \"def log_success(self):\\n with open(self.logfile, 'a+') as f:\\n f.write(self.BEGIN + self.message + '\\\\n' + self.END)\\n self.message = ''\",\n \"def writeLog(pid):\\n\\tglobal processes,logfile,strikes,sleep\\n\\tproc = processes[pid]\\n\\tlogfile.write('[%s] %d %s %f%%cpu %f%%mem (over %d s): %s\\\\n'%(time.strftime('%b %d %H:%M:%S'),pid,proc.user,proc.cpu,proc.mem,proc.count*sleep,proc.command))\",\n \"def store(self, **stats):\\n if self.first_row:\\n self.log_headers = list(stats.keys())\\n for key in stats:\\n assert key in self.log_headers, f\\\"Can't introduce a new key that you didn't include before: {key}\\\"\\n\\n # Write to output file\\n if self.first_row:\\n self.file_writer.writerow(self.log_headers)\\n self.file_writer.writerow(stats.values())\\n self.output_file.flush()\\n\\n # Display in stdout\\n if self.log_freq > 0 and self.counter % self.log_freq == 0:\\n _print_table(stats)\\n\\n self.first_row = False\\n self.counter += 1\",\n \"def log(self, message):\\n self._log += \\\"%s\\\\n\\\" % message\\n print message\",\n \"def send_log():\\n log.info(f\\\"UUID={UUID}\\\")\\n log.info(f\\\"SPLIT={SPLIT}\\\")\\n log.info(f\\\"BATCH_SIZE={BATCH_SIZE}\\\")\\n log.info(f\\\"EPOCHS={EPOCHS}\\\")\\n log.info(f\\\"PATIENCE={PATIENCE}\\\")\\n log.info(f\\\"X_FREQ={X_FREQ}\\\")\\n log.info(f\\\"LOOK_BACK={LOOK_BACK}\\\")\\n log.info(f\\\"LOOK_AHEAD={LOOK_AHEAD}\\\")\\n log.info(f\\\"KERNEL_SIZE={KERNEL_SIZE}\\\")\\n log.info(f\\\"FILTERS={FILTERS}\\\")\\n log.info(f\\\"L1L2={L1L2}\\\")\\n log.info(f\\\"D1={D1}\\\")\\n log.info(f\\\"D2={D2}\\\")\\n log.info(f\\\"DOUT={DOUT}\\\")\\n log.info(f\\\"PLOT={PLOT}\\\")\\n log.info(f\\\"SHUFFLE={SHUFFLE}\\\")\",\n \"def appendLog(self):\\n if self.logBuffer == None :\\n self.logBuffer = \\\"Some header\\\\nhere\\\\n\\\\n\\\"\\n self.logBuffer += \\\"\\\\tx\\\\ty\\\\ttheta : ul\\\\tur\\\\tt-neur\\\\n\\\";\\n \\n self.logBuffer += '%2.1f: %2.6f\\\\t %2.6f\\\\t %2.6f : ' % \\\\\\n\\t ( self.t, self.env.state[0], self.env.state[2], self.env.state[4] )\\n self.logBuffer += '%1.3f\\\\t %1.3f \\\\t%1.2f \\\\t' % \\\\\\n ( self.env.action[0], self.env.action[1], self.env.action[2] )\\n self.logBuffer += 'Dst/Theta/Speed: \\\\t%f\\\\t%f\\\\t%f \\\\tF: %.2f \\\\n' % \\\\\\n ( self.env.getDistance(), self.env.getOrientation(), self.env.getDistance(), self.getReward() )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7579466","0.7374402","0.7347465","0.70612556","0.69241893","0.69229144","0.69018483","0.6860829","0.68271667","0.67728823","0.6762465","0.6749048","0.6685187","0.66748893","0.6671111","0.66622746","0.6618556","0.6599946","0.6599946","0.6597023","0.65964586","0.65901273","0.6573837","0.65586334","0.65402126","0.6531082","0.65134573","0.6505944","0.6489775","0.648779","0.64620155","0.6453281","0.6441042","0.64346576","0.64226913","0.6420089","0.6416695","0.64069146","0.63937783","0.6378739","0.6324976","0.6317836","0.63045454","0.63021135","0.63015926","0.62966657","0.6273819","0.6263009","0.62533414","0.62488246","0.6246205","0.62461054","0.6233486","0.6233434","0.6217805","0.6186134","0.61666036","0.61629546","0.61588585","0.6157825","0.6151425","0.6140395","0.61398184","0.6139343","0.61342895","0.6128415","0.6128036","0.61238927","0.6105306","0.60935354","0.6080301","0.60608804","0.6059511","0.60579944","0.60486996","0.60485244","0.60454506","0.60436535","0.6036961","0.60369337","0.603646","0.6028476","0.60211504","0.6019191","0.59965575","0.5990896","0.59853864","0.5973779","0.5970662","0.59681076","0.59668326","0.59646153","0.5962683","0.59614235","0.59512365","0.59496","0.59411025","0.59394187","0.59342223","0.59315306"],"string":"[\n \"0.7579466\",\n \"0.7374402\",\n \"0.7347465\",\n \"0.70612556\",\n \"0.69241893\",\n \"0.69229144\",\n \"0.69018483\",\n \"0.6860829\",\n \"0.68271667\",\n \"0.67728823\",\n \"0.6762465\",\n \"0.6749048\",\n \"0.6685187\",\n \"0.66748893\",\n \"0.6671111\",\n \"0.66622746\",\n \"0.6618556\",\n \"0.6599946\",\n \"0.6599946\",\n \"0.6597023\",\n \"0.65964586\",\n \"0.65901273\",\n \"0.6573837\",\n \"0.65586334\",\n \"0.65402126\",\n \"0.6531082\",\n \"0.65134573\",\n \"0.6505944\",\n \"0.6489775\",\n \"0.648779\",\n \"0.64620155\",\n \"0.6453281\",\n \"0.6441042\",\n \"0.64346576\",\n \"0.64226913\",\n \"0.6420089\",\n \"0.6416695\",\n \"0.64069146\",\n \"0.63937783\",\n \"0.6378739\",\n \"0.6324976\",\n \"0.6317836\",\n \"0.63045454\",\n \"0.63021135\",\n \"0.63015926\",\n \"0.62966657\",\n \"0.6273819\",\n \"0.6263009\",\n \"0.62533414\",\n \"0.62488246\",\n \"0.6246205\",\n \"0.62461054\",\n \"0.6233486\",\n \"0.6233434\",\n \"0.6217805\",\n \"0.6186134\",\n \"0.61666036\",\n \"0.61629546\",\n \"0.61588585\",\n \"0.6157825\",\n \"0.6151425\",\n \"0.6140395\",\n \"0.61398184\",\n \"0.6139343\",\n \"0.61342895\",\n \"0.6128415\",\n \"0.6128036\",\n \"0.61238927\",\n \"0.6105306\",\n \"0.60935354\",\n \"0.6080301\",\n \"0.60608804\",\n \"0.6059511\",\n \"0.60579944\",\n \"0.60486996\",\n \"0.60485244\",\n \"0.60454506\",\n \"0.60436535\",\n \"0.6036961\",\n \"0.60369337\",\n \"0.603646\",\n \"0.6028476\",\n \"0.60211504\",\n \"0.6019191\",\n \"0.59965575\",\n \"0.5990896\",\n \"0.59853864\",\n \"0.5973779\",\n \"0.5970662\",\n \"0.59681076\",\n \"0.59668326\",\n \"0.59646153\",\n \"0.5962683\",\n \"0.59614235\",\n \"0.59512365\",\n \"0.59496\",\n \"0.59411025\",\n \"0.59394187\",\n \"0.59342223\",\n \"0.59315306\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64186484"},"document_rank":{"kind":"string","value":"36"}}},{"rowIdx":8459,"cells":{"query":{"kind":"string","value":"Delete temporary files, close log files and email results."},"document":{"kind":"string","value":"def CleanUp(self):\n if (not self.keep_epi_raw or not self.keep_epi_mot) \\\n and not self.opts.debug_tmp:\n self.tmp.Clean()\n overall_msg = self.SummaryErrorMessage()\n if self.tmplt and not self.no_email:\n EmailResults(self.tmplt['email'], overall_msg, \\\n self.topdir, self.dumpfile, self.logfile, self.motcor_summary)\n\n# Write the error message to the log file.\n if self.f_log is None:\n# Log file not opened yet, do it now.\n if self.logdir is not None:\n logfile = '%s/preprocess.log' % self.logdir\n f_log = open(logfile,'w')\n f_log.write('\\n%s\\n' % overall_msg)\n f_log.close()\n else:\n self.f_log.write('\\n%s\\n' % overall_msg)\n sys.exit()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def teardown():\n for filename in files_to_delete:\n delete_file(filename)","def cleanup(self):\n if self.log_fo:\n self.log_fo.close()","def classCleanup(cls):\n cls.RemoveTempFile(\"child_send1.txt\")\n cls.RemoveTempFile(\"child_read1.txt\")\n cls.RemoveTempFile(\"child_send2.txt\")\n cls.RemoveTempFile(\"child_read2.txt\")","def tearDown(self):\r\n\r\n # turn off the alarm\r\n signal.alarm(0)\r\n\r\n remove_files(self.files_to_remove, False)\r\n if self.server_socket:\r\n self.server_socket.close()\r\n # give clients time to clean up\r\n sleep(1)\r\n if exists(self.tmp_dir):\r\n try:\r\n rmdir(self.tmp_dir)\r\n except OSError:\r\n # give clients some more time, fail if still error\r\n sleep(5)\r\n rmdir(self.tmp_dir)","def clear_log_files(log_files):\n for log_file in log_files:\n try:\n open(log_file, 'w', 0).close()\n except IOError:\n pass","def __call__(self):\n for tmp_file in filter(lambda x: x.exists(), self.temp_files):\n tmp_file.unlink()\n\n for proc in self.processes:\n try:\n os.kill(proc, signal.SIGTERM)\n except ProcessLookupError:\n pass","def clean_up_temp_dir():\n files = glob.glob(f'{CONFIG_DIR}/tmp/*')\n for f in files:\n try:\n os.remove(f)\n except Exception:\n pass","def tearDown(self):\r\n remove_files(self.files_to_remove, False)\r\n if self.tmpdir:\r\n rmtree(self.tmpdir)\r\n\r\n # clean up the file from init_flowgram_file\r\n if (hasattr(self, \"tmp_filename\") and exists(self.tmp_filename)):\r\n remove(self.tmp_filename)","def clean(self):\n # Wait for all threads to finish\n [x.join() for x in self.__threads]\n\n if self.recording:\n self.record_file.close()\n\n if self.simulating:\n self.simulate_file.close()\n\n self.s.shutdown(socket.SHUT_RDWR)\n self.s.close()\n print(\"Cleaned\")","def cleanup_files(self):\n\n self.backup_files()\n self.delete_files()","def tearDown(self):\n try:\n shutil.rmtree(self._msgdir)\n shutil.rmtree(self._tmp_dir)\n except OSError as e:\n print('Error removing temporary directory %s' % self._tmp_dir)\n print(e)","def cleanup_logs(self):\n\n _now = time.time()\n\n for _id in self.open_logs.keys():\n try:\n if _now > (self.open_logs[_id]['last_time'] + self.FILE_ACTIVITY_TIMEOUT):\n # Flush and close the log file, and pop this element from the dictionary.\n self.open_logs[_id]['log'].flush()\n self.open_logs[_id]['log'].close()\n self.open_logs.pop(_id, None)\n self.log_info(\"Closed log file for %s\" % _id)\n except Exception as e:\n self.log_error(\"Error closing log for %s - %s\" % (_id, str(e)))","def exitLogCleanup(*args):\n for logFile in args:\n os.unlink(logFile)\n return None","def __del__(self):\n self.close_files()","def delete_temporary_files(request, tmp_path_factory):\r\n _tmp_path_factory = tmp_path_factory\r\n\r\n def cleanup():\r\n tmp_path = _tmp_path_factory.getbasetemp()\r\n if pathlib.Path(tmp_path).exists() and pathlib.Path(tmp_path).is_dir():\r\n shutil.rmtree(tmp_path)\r\n\r\n request.addfinalizer(cleanup)","def _delete_temp():\n global _TEMP_NAME\n\n try:\n database.delete_temp(_TEMP_NAME)\n outputtools.delete_temp(_TEMP_NAME)\n except:\n raise","def __del__(self):\n\t\tif self.temp_dir:\n\t\t\tself.temp_dir.cleanup()","def exit_and_clean_up(temp_folder):\n # Capture the traceback\n logging.info(\"There was an unexpected failure\")\n exc_type, exc_value, exc_traceback = sys.exc_info()\n for line in traceback.format_tb(exc_traceback):\n logging.info(line)\n\n # Delete any files that were created for this sample\n logging.info(\"Removing temporary folder: \" + temp_folder)\n shutil.rmtree(temp_folder)\n\n # Exit\n logging.info(\"Exit type: {}\".format(exc_type))\n logging.info(\"Exit code: {}\".format(exc_value))\n sys.exit(exc_value)","def cleanup(bot, client, servers, print_all):\n try:\n if bot:\n bot.stop()\n finally:\n if servers:\n servers.stop()\n if print_all:\n if bot:\n bot.dump_log()\n if servers:\n servers.dump_log()\n if client:\n client.dump_log()\n if not Test.leak:\n shutil.rmtree(Test.tmpdir)","def pytest_sessionfinish(session, exitstatus):\n\n # dat files are created when using attachements\n print(\"\\n-------------------------\\nClean dpytest_*.dat files\")\n fileList = glob.glob('./dpytest_*.dat')\n for filePath in fileList:\n try:\n os.remove(filePath)\n except Exception:\n print(\"Error while deleting file : \", filePath)","def dispose(self):\n rmtree(self._temp_path)","def remove_temporary_files():\n try:\n xml_file_path, bin_file_path = get_ida_exported_files()\n if os.path.isfile(xml_file_path):\n os.remove(xml_file_path)\n\n if os.path.isfile(bin_file_path):\n os.remove(bin_file_path)\n\n except Exception:\n print(\"GhIDA:: [!] Unexpected error while removing temporary files.\")","def delete_log(self):\n os.system('rm -rf *.log')\n os.system('rm -rf *.log~')\n os.system('rm -rf *.last')\n os.system('rm -rf *.last~')","def tearDown(self):\n if os.path.isfile(LOGFILENAME):\n os.remove(LOGFILENAME)","def _remove_tmpfiles():\n for f in tmpfiles:\n try:\n os.remove(f)\n except OSError:\n pass","def tearDown(self):\n self.tempdir.cleanup()","def _clean_up_temporary_files(dataset_dir):\n return","def cleanup(self):\n async def close_session():\n await self.logi.close()\n\n self.loop.run_until_complete(close_session())\n\n self.loop.close()\n self.logi = None\n if os.path.isfile(CACHE_FILE):\n os.remove(CACHE_FILE)","def __del__(self):\n shutil.rmtree('tmp')\n self.quit_browser()","def close(self):\n try:\n process_id = get_pid(\"broker_mqtts\")\n except subprocess.CalledProcessError:\n pass\n else:\n os.system(\"kill -9 {}\".format(process_id))\n if os.path.isfile(self.log):\n os.system(\"rm -rf \" + self.log)","def release_logger_files():\n for hl in logging.getLogger().handlers:\n if isinstance(hl, logging.FileHandler):\n hl.close()\n logging.getLogger().removeHandler(hl)","def release_logger_files():\n for hl in logging.getLogger().handlers:\n if isinstance(hl, logging.FileHandler):\n hl.close()\n logging.getLogger().removeHandler(hl)","def unload(self):\n for f in self.logs.values():\n f.close()","def __del__(self):\n\n self.logfd.close()","def delete_logs(self):\n if self.etw_log is not None:\n files = sorted(glob.glob(self.etw_log + '*'))\n for path in files:\n try:\n os.remove(path)\n except Exception:\n pass","def __del__(self):\n for f in self._files:\n f.close()","def clean_up_temp_files():\n global __tmp_model_dir\n\n if __tmp_model_dir is not None:\n FileUtils.deleteDirectory(__tmp_model_dir)\n __tmp_model_dir = None","def cleanup():\n default_log_dir = Path(DEFAULT_LOGDIR)\n if default_log_dir.exists():\n shutil.rmtree(default_log_dir)\n yield \"Cleanup\"\n if default_log_dir.exists():\n shutil.rmtree(default_log_dir)","def delFiles(self):\r\n \r\n logStr = \"{0:s}.{1:s}: \".format(self.__class__.__name__, sys._getframe().f_code.co_name)\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'Start.')) \r\n \r\n try: \r\n if os.path.exists(self.h5File): \r\n os.remove(self.h5File) \r\n logger.debug(\"{0:s} File {1:s} deleted.\".format(logStr,self.h5File)) \r\n except XmError:\r\n raise \r\n except Exception as e:\r\n logStrFinal=\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\r\n logger.error(logStrFinal) \r\n raise XmError(logStrFinal) \r\n finally:\r\n logger.debug(\"{0:s}{1:s}\".format(logStr,'_Done.'))","def _CloseOutputFiles(self):\n self.gfile.close()\n self.efile.close()","def _remove_mp_logs(self):\n for i, fn in enumerate(self.logfiles):\n os.remove(fn)","def tearDown(self):\n print(\n \"\\nDeleting temporary files...\\n\")\n try:\n shutil.rmtree(TEST_DIR)\n except OSError:\n pass","def cleanup(self):\r\n if self.tempDirectory != None:\r\n shutil.rmtree(self.tempDirectory, True)\r\n self.tempDirectory = None","def tearDown(self):\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\n self.logging_handler.assertNoLogsOver(logging.INFO)\n\n logging.getLogger('COT').removeHandler(self.logging_handler)\n\n self.validate_with_ovftool(self.temp_file)\n\n # Delete the temporary directory\n if os.path.exists(self.temp_dir):\n logger.debug(\"Deleting temp dir %s\", self.temp_dir)\n shutil.rmtree(self.temp_dir)\n self.temp_dir = None\n self.temp_file = None\n\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\n delta = tmps2 - self.tmps\n if delta:\n self.fail(\"Temp directory(s) {0} left over after test!\"\n .format(delta))\n\n # Clear output caches for helper commands:\n for helper in helpers.values():\n helper.cached_output.clear()\n\n # Let's try to keep things lean...\n delta_t = time.time() - self.start_time\n if delta_t > 5.0:\n print(\"\\nWARNING: Test {0} took {1:.3f} seconds to execute. \"\n \"Consider refactoring it to be more efficient.\"\n .format(self.id(), delta_t))","def cleanup(self, keep_logs : bool = False):\n if self.path.exists():\n try:\n shutil.rmtree(self.path)\n except Exception as e:\n warn(e)\n if self.log_path.exists() and not keep_logs:\n try:\n os.remove(self.log_path)\n except Exception as e:\n warn(e)","def cleanup(self):\r\n for f in [i for d in self.data.values() for i in d[\"filenames\"]]:\r\n try:\r\n os.unlink(f)\r\n except Exception: pass\r\n self.Destroy()","def __exit__(self, exc_type, exc_value, traceback):\n logger.debug(\"Cleaning up temporary directory: %s\", self.temporary_directory)\n shutil.rmtree(self.temporary_directory)\n del self.temporary_directory","def cleanup(self):\n files = self.nlst()\n latest = self.latest_filename\n for filename in files:\n if filename != latest:\n result = self.delete(filename)\n logger.info(f\"Deleted old export from FTP: {result}\")","def tearDown(self):\n utils.rm_rf(TMP_DIR_PATH)","def cleanup_temp_dir(context):\n\n try:\n os.chdir(context.cwd)\n except:\n print(\"Current working file record does not exist\")\n\n try:\n context.tempdir.cleanup()\n except:\n print(\"Temporary directory cannot be cleaned up - does it exist?\")","def tearDown(self):\n self.tmp.cleanup()","def teardown_class(cls):\n os.remove(logfilename)","def _cleanup(self):\n # delete stdout/stderr\n if os.path.isfile(self.stdout):\n os.unlink(self.stdout)","def tearDown(self):\n for f in os.listdir('/tmp'):\n if not f.startswith(self.FILE_PREFIX):\n continue\n\n os.remove(os.path.join('/tmp', f))","def clear_tmp_folder(self):\r\n for file in os.listdir(self.temp_dir):\r\n if file.endswith('.png') or file.endswith('.jpg'):\r\n path = os.path.join(self.temp_dir, file)\r\n print ('Cleaned up {}'.format(path))\r\n os.remove(path)","def cleanUp(self):\n self.dirMonitor.stop()\n self.filesList.cleanUp()","def cleanup():\n if config.get('global').get('no_cleanup'):\n return\n logging.info('Cleaning up temp directories')\n try:\n tmp_path = config.get('global').get('tmp_path')\n if os.path.exists(tmp_path):\n rmtree(tmp_path)\n except Exception as e:\n logging.error(format_debug(e))\n print_message('Error removing temp directories')\n\n try:\n archive_path = os.path.join(\n config.get('global').get('output_path'),\n 'script_archive',\n time.strftime(\"%Y-%m-%d-%I-%M\"))\n if not os.path.exists(archive_path):\n os.makedirs(archive_path)\n run_script_path = config.get('global').get('run_scripts_path')\n if os.path.exists(run_script_path):\n move(run_script_path, archive_path)\n except Exception as e:\n logging.error(format_debug(e))\n logging.error('Error archiving run_scripts directory')","def close(self):\n if not self.__closed:\n self.counters = { \"error\": 0, \"warning\": 0, \"success\": 0, \"failure\": 0 }\n\n try:\n self.__flush_count = 0\n for handler in self.__filehandlers:\n handler.flush()\n self.__logger.removeHandler(handler)\n handler.close()\n except:\n # do nothing\n pass\n self.__closed = True","def clean_temp_storage_dir(self, filenames):\n for fn in filenames:\n try:\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\n except FileNotFoundError:\n pass","def close(self):\r\n self._report_file.close()\r\n # Make sure everything's closed.\r\n for files in self._output_files.values():\r\n for f in files.values():\r\n f.close()","def close_files(self):\n self.wb_alm.close()\n self.wb_defect.close()\n self.wb_enhancement.close()\n self.wb_incident.close()\n self.wb_destination.close()","def release(self):\n self.errorlog.close()\n del self.errorlog\n del self.printer","def classCleanup(cls):\n cls.RemoveTempFile(SettingsCommandTestCase.output_file_name)","def cleanup(self):\n\n # NOTE(jbresnah) call stop on each of the servers instead of\n # checking the pid file. stop() will wait until the child\n # server is dead. This eliminates the possibility of a race\n # between a child process listening on a port actually dying\n # and a new process being started\n servers = [self.api_server, self.conductor_server, ]\n for s in servers:\n try:\n s.stop()\n except Exception:\n pass\n\n for f in self.files_to_destroy:\n if os.path.exists(f):\n os.unlink(f)","def main():\n dir_path = '/home/ubuntu/test_files' # path for the log files that needs to be pruned\n stat_file_name = 'file_status_info' # temp file will be created to store the stat of each files to calculate when to delete\n \n # Get the list of all the files where we want to perfrom the delete operations\n file_list = get_list_of_files_in_dir(dir_path)\n\n # Get the current system date\n current_date = get_current_date()\n\n # Iterate through all the log, error, info files in the specified directory path and check for the criteria of file older than 5 days and delete.\n for fil in file_list:\n get_file_stat(dir_path, stat_file_name, fil)\n filename, file_date = get_file_last_modification_date(stat_file_name)\n\n print(\"*********** %s file stat is written **************\" % fil)\n days = abs(current_date - file_date).days\n \n # Check if the file modification date if older than 5 days.\n if days > 5:\n remove_files(os.path.join(dir_path, fil))\n else:\n print(\"No eligible file(s) found to be deleted\")","def clean_file_before_test():\n\n if os.path.exists(LOG_FOLDER):\n for file in os.listdir(LOG_FOLDER):\n os.remove(LOG_FOLDER + \"/\" + file)","def clean_cwd():\n\n # Generator of the files generated for each runs\n del_files = (file for file in os.listdir() if file.endswith('.vtk')\n or file.endswith('.dat')\n or file.startswith('eeldata')\n or file.endswith('.log'))\n\n for file in del_files:\n try:\n os.remove(file)\n print(\"\\rRemoved {:s} succesfully!\".format(file), end=' '*15)\n except:\n print(\"\\rFailed to remove {:s}\".format(file))\n raise\n\n print('')","def clean_outputs(self) -> None:\n\n def _delete_if_not_none(fn: Optional[str]) -> None:\n if fn is not None:\n Path(fn).unlink()\n\n _delete_if_not_none(self.config[\"LOG_FILE\"])\n\n for file_ in self.exporter.get_all_files():\n file_.unlink()","def _cleanup(self):\n try:\n tmpdir = self.tmpdir\n except AttributeError:\n # Don't need to do anything if the temp dir isn't set\n return\n shutil.rmtree(tmpdir)","def __del__(self) -> None:\n try:\n shutil.rmtree(self.temp_path)\n except FileNotFoundError:\n pass","def close_log():\n\n global log_file\n if log_file is not None:\n try:\n log_file.flush()\n finally:\n log_file.close()","def on_cleanup(self):\n self.close()","def on_cleanup(self):\n self.close()","def teardown(self):\n super(TestCisPickleOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def tearDown(self):\n logging.info(\"Delete %s\", self._temp_dir)\n shutil.rmtree(self._temp_dir)","def close(self):\n\n sp.call([\"convert\", \"{}_*\".format(self.tmp_prefix),\n self.filename])\n\n sp.call(\"rm {}_*\".format(self.tmp_prefix), shell=True)\n sp.call([\"rmdir\", self.tmp_dir])","def reopen_files(self):\r\n for log in (self.error_log, self.access_log):\r\n for h in log.handlers:\r\n if isinstance(h, logging.FileHandler):\r\n h.acquire()\r\n h.stream.close()\r\n h.stream = open(h.baseFilename, h.mode)\r\n h.release()","def __del__(self):\n\n # cleanup logging handlers\n for handler in self.logger.handlers[::-1]:\n try:\n handler.acquire()\n handler.flush()\n handler.close()\n except (OSError, ValueError):\n pass\n finally:\n handler.release()","def tearDown(self):\n \tshutil.rmtree(self.tempdir)","def _cleanup(self):\n if self.current_session is not None:\n self.current_session.close()\n self.current_session = None\n\n for handler in list(self.logger.root_logger.handlers):\n self.logger.root_logger.removeHandler(handler)\n handler.flush()\n handler.close()","def _cleanup(self):\n if self.pidfile:\n os.unlink(self.pidfile)","def cleanup(self):\n self.all_wav_to_mp3()\n self.past_songs_db.close()\n self.move_tracks_to_music_folder( )\n self.delete_leftovers()\n print \"Cleanup finished\"","def tearDown(self):\n\n for fname in self.fnames:\n FileSystem.unlink(fname)","def cleanup(self):\n try:\n self.disconnect()\n self.log_file.close()\n SerialAsyncLoop.stop_loop(self.logger)\n except Exception as e:\n self.applog.exception(f\"Exception --> {SerialConsole.cleanup.__qualname__}\", exc_info=e)\n raise","def cleanup(self):\n\n print \"Cleaning up...\",\n sys.stdout.flush()\n\n builddir = os.path.join(self.build)\n\n comm = 'rm -rf '+builddir\n #+' '+libdir+' '+logdir\n (output, error, retz) = runShellCommand(comm)\n\n print \"done.\"","def LogAndRemoveFiles(temp_dir, regex_pattern):\n regex = re.compile(regex_pattern)\n if not os.path.isdir(temp_dir):\n return\n for dir_item in os.listdir(temp_dir):\n if regex.search(dir_item):\n full_path = os.path.join(temp_dir, dir_item)\n print 'Removing leaked temp item: %s' % full_path\n try:\n if os.path.islink(full_path) or os.path.isfile(full_path):\n os.remove(full_path)\n elif os.path.isdir(full_path):\n chromium_utils.RemoveDirectory(full_path)\n else:\n print 'Temp item wasn\\'t a file or directory?'\n except OSError, e:\n print >> sys.stderr, e\n # Don't fail.","def tearDown(self):\n self.clearTempDir()","def tearDown(self):\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\n self.logging_handler.assertNoLogsOver(logging.INFO)\n\n logging.getLogger('COT').removeHandler(self.logging_handler)\n\n if hasattr(self, 'instance'):\n self.instance.destroy()\n self.instance = None\n\n self.validate_with_ovftool(self.temp_file)\n\n # Delete the temporary directory\n if os.path.exists(self.temp_dir):\n logger.debug(\"Deleting temp dir {0}\".format(self.temp_dir))\n shutil.rmtree(self.temp_dir)\n self.temp_dir = None\n self.temp_file = None\n\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\n delta = tmps2 - self.tmps\n if delta:\n self.fail(\"Temp directory(s) {0} left over after test!\"\n .format(delta))\n\n # Let's try to keep things lean...\n delta_t = time.time() - self.start_time\n if delta_t > 5.0:\n print(\"\\nWARNING: Test {0} took {1:.3f} seconds to execute. \"\n \"Consider refactoring it to be more efficient.\"\n .format(self.id(), delta_t))","def delete_log():\n log_path = Path.cwd() / \"premise.log\"\n if log_path.exists():\n log_path.unlink()","def del_tmp() -> None:\n for elem in os.listdir('./tmp'):\n path = f\"./tmp/{elem}\"\n if os.path.isfile(path):\n os.remove(path)\n else:\n shutil.rmtree(path)","def cleanup(tempdir):\n try:\n shutil.rmtree(tempdir)\n except OSError:\n pass","def tearDown(self):\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\n for f in glob.glob(os.path.join(testing_dir, \"*\")):\n if f.split(\".\")[-1] in [\"o\", \"out\", \"pyc\", \"log\"]:\n subprocess.call(['rm', f])","def __exit__(self, exc_type, exc_val, exc_tb):\n os.rmdir(self._tempdir)","def tearDown(self):\n with contextlib.suppress(FileNotFoundError):\n Path(\"test.xlsx\").absolute().unlink()","def test_monitor_correctly_deletes_temporary_directory_in_the_case_of_any_error(\n self,\n ):\n # Arrange\n feed_pages = [fixtures.PROQUEST_FEED_PAGE_1, fixtures.PROQUEST_FEED_PAGE_2]\n\n client = create_autospec(spec=ProQuestAPIClient)\n client.download_all_feed_pages = MagicMock(\n return_value=list(map(fixtures.serialize, feed_pages))\n )\n\n client_factory = create_autospec(spec=ProQuestAPIClientFactory)\n client_factory.create = MagicMock(return_value=client)\n\n monitor = ProQuestOPDS2ImportMonitor(\n client_factory, self._db, self._proquest_collection, ProQuestOPDS2Importer\n )\n monitor.import_one_feed = MagicMock(return_value=([], []))\n\n results = {\"temp_directory\": None, \"temp_files\": []}\n original_mkdtemp = tempfile.mkdtemp\n original_temp_file_constructor = tempfile.NamedTemporaryFile\n original_rmtree = shutil.rmtree\n\n def create_temp_directory():\n results[\"temp_directory\"] = original_mkdtemp()\n\n return results[\"temp_directory\"]\n\n def create_temp_file(**kwargs):\n temp_file = original_temp_file_constructor(**kwargs)\n results[\"temp_files\"].append(temp_file.name)\n\n return temp_file\n\n # Act\n with patch(\"tempfile.mkdtemp\") as mkdtemp_mock, patch(\n \"tempfile.NamedTemporaryFile\"\n ) as named_temporary_file_constructor_mock, patch(\n \"shutil.rmtree\"\n ) as rmtree_mock, patch(\n \"api.proquest.importer.parse_feed\"\n ) as parse_feed_mock:\n mkdtemp_mock.side_effect = create_temp_directory\n named_temporary_file_constructor_mock.side_effect = create_temp_file\n rmtree_mock.side_effect = original_rmtree\n parse_feed_mock.side_effect = core.opds2_import.parse_feed\n\n # An exception will be raised while trying to parse the feed page.\n parse_feed_mock.side_effect = Exception(\"\")\n\n monitor.run_once(False)\n\n # Assert\n # Ensure that the temp directory was successfully created.\n tempfile.mkdtemp.assert_called_once()\n\n # Ensure that only one temp file was created, after this an exception was raised and the process stopped.\n tempfile.NamedTemporaryFile.assert_has_calls(\n [call(mode=\"r+\", dir=results[\"temp_directory\"], delete=False)]\n )\n\n # Ensure that parse_feed method was called only once.\n parse_feed_mock.assert_has_calls([call(ANY, silent=False)])\n\n # Ensure that the temp directory was successfully removed.\n shutil.rmtree.assert_called_once_with(results[\"temp_directory\"])\n assert False == os.path.exists(results[\"temp_directory\"])","def teardown(self):\n super(TestCisPlyOutput, self).teardown()\n if os.path.isfile(self.tempfile):\n os.remove(self.tempfile)","def teardown(self):\n try:\n self._close(True)\n except:\n pass\n try:\n os.unlink(os.path.join(self.home_dir, DB_FILE))\n except FileNotFoundError as _:\n pass","def _call_cleanup(self,\r\n input_fp,\r\n output_dir,\r\n params,\r\n job_prefix,\r\n poll_directly,\r\n suppress_submit_jobs):\r\n pass","def __del__(self):\n for handle in self._filehandles:\n handle.close()","def __del__(self):\n\n # close socket\n try:\n self.socket.shutdown(socket.SHUT_RDWR)\n self.socket.close()\n except OSError:\n pass\n\n # quit selenium driver\n if self.driver:\n self.driver.quit()\n\n # make sure to delete linkfinder temporary files\n files = glob.glob(\"linkfinder_tmp*\")\n for file in files:\n os.remove(file)\n\n # make sure helper process is dead\n try:\n os.kill(self.helper_pid, signal.SIGKILL)\n except ProcessLookupError:\n pass\n\n if os.path.exists(UNIX_SOCK_ADDR):\n os.remove(UNIX_SOCK_ADDR)"],"string":"[\n \"def teardown():\\n for filename in files_to_delete:\\n delete_file(filename)\",\n \"def cleanup(self):\\n if self.log_fo:\\n self.log_fo.close()\",\n \"def classCleanup(cls):\\n cls.RemoveTempFile(\\\"child_send1.txt\\\")\\n cls.RemoveTempFile(\\\"child_read1.txt\\\")\\n cls.RemoveTempFile(\\\"child_send2.txt\\\")\\n cls.RemoveTempFile(\\\"child_read2.txt\\\")\",\n \"def tearDown(self):\\r\\n\\r\\n # turn off the alarm\\r\\n signal.alarm(0)\\r\\n\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.server_socket:\\r\\n self.server_socket.close()\\r\\n # give clients time to clean up\\r\\n sleep(1)\\r\\n if exists(self.tmp_dir):\\r\\n try:\\r\\n rmdir(self.tmp_dir)\\r\\n except OSError:\\r\\n # give clients some more time, fail if still error\\r\\n sleep(5)\\r\\n rmdir(self.tmp_dir)\",\n \"def clear_log_files(log_files):\\n for log_file in log_files:\\n try:\\n open(log_file, 'w', 0).close()\\n except IOError:\\n pass\",\n \"def __call__(self):\\n for tmp_file in filter(lambda x: x.exists(), self.temp_files):\\n tmp_file.unlink()\\n\\n for proc in self.processes:\\n try:\\n os.kill(proc, signal.SIGTERM)\\n except ProcessLookupError:\\n pass\",\n \"def clean_up_temp_dir():\\n files = glob.glob(f'{CONFIG_DIR}/tmp/*')\\n for f in files:\\n try:\\n os.remove(f)\\n except Exception:\\n pass\",\n \"def tearDown(self):\\r\\n remove_files(self.files_to_remove, False)\\r\\n if self.tmpdir:\\r\\n rmtree(self.tmpdir)\\r\\n\\r\\n # clean up the file from init_flowgram_file\\r\\n if (hasattr(self, \\\"tmp_filename\\\") and exists(self.tmp_filename)):\\r\\n remove(self.tmp_filename)\",\n \"def clean(self):\\n # Wait for all threads to finish\\n [x.join() for x in self.__threads]\\n\\n if self.recording:\\n self.record_file.close()\\n\\n if self.simulating:\\n self.simulate_file.close()\\n\\n self.s.shutdown(socket.SHUT_RDWR)\\n self.s.close()\\n print(\\\"Cleaned\\\")\",\n \"def cleanup_files(self):\\n\\n self.backup_files()\\n self.delete_files()\",\n \"def tearDown(self):\\n try:\\n shutil.rmtree(self._msgdir)\\n shutil.rmtree(self._tmp_dir)\\n except OSError as e:\\n print('Error removing temporary directory %s' % self._tmp_dir)\\n print(e)\",\n \"def cleanup_logs(self):\\n\\n _now = time.time()\\n\\n for _id in self.open_logs.keys():\\n try:\\n if _now > (self.open_logs[_id]['last_time'] + self.FILE_ACTIVITY_TIMEOUT):\\n # Flush and close the log file, and pop this element from the dictionary.\\n self.open_logs[_id]['log'].flush()\\n self.open_logs[_id]['log'].close()\\n self.open_logs.pop(_id, None)\\n self.log_info(\\\"Closed log file for %s\\\" % _id)\\n except Exception as e:\\n self.log_error(\\\"Error closing log for %s - %s\\\" % (_id, str(e)))\",\n \"def exitLogCleanup(*args):\\n for logFile in args:\\n os.unlink(logFile)\\n return None\",\n \"def __del__(self):\\n self.close_files()\",\n \"def delete_temporary_files(request, tmp_path_factory):\\r\\n _tmp_path_factory = tmp_path_factory\\r\\n\\r\\n def cleanup():\\r\\n tmp_path = _tmp_path_factory.getbasetemp()\\r\\n if pathlib.Path(tmp_path).exists() and pathlib.Path(tmp_path).is_dir():\\r\\n shutil.rmtree(tmp_path)\\r\\n\\r\\n request.addfinalizer(cleanup)\",\n \"def _delete_temp():\\n global _TEMP_NAME\\n\\n try:\\n database.delete_temp(_TEMP_NAME)\\n outputtools.delete_temp(_TEMP_NAME)\\n except:\\n raise\",\n \"def __del__(self):\\n\\t\\tif self.temp_dir:\\n\\t\\t\\tself.temp_dir.cleanup()\",\n \"def exit_and_clean_up(temp_folder):\\n # Capture the traceback\\n logging.info(\\\"There was an unexpected failure\\\")\\n exc_type, exc_value, exc_traceback = sys.exc_info()\\n for line in traceback.format_tb(exc_traceback):\\n logging.info(line)\\n\\n # Delete any files that were created for this sample\\n logging.info(\\\"Removing temporary folder: \\\" + temp_folder)\\n shutil.rmtree(temp_folder)\\n\\n # Exit\\n logging.info(\\\"Exit type: {}\\\".format(exc_type))\\n logging.info(\\\"Exit code: {}\\\".format(exc_value))\\n sys.exit(exc_value)\",\n \"def cleanup(bot, client, servers, print_all):\\n try:\\n if bot:\\n bot.stop()\\n finally:\\n if servers:\\n servers.stop()\\n if print_all:\\n if bot:\\n bot.dump_log()\\n if servers:\\n servers.dump_log()\\n if client:\\n client.dump_log()\\n if not Test.leak:\\n shutil.rmtree(Test.tmpdir)\",\n \"def pytest_sessionfinish(session, exitstatus):\\n\\n # dat files are created when using attachements\\n print(\\\"\\\\n-------------------------\\\\nClean dpytest_*.dat files\\\")\\n fileList = glob.glob('./dpytest_*.dat')\\n for filePath in fileList:\\n try:\\n os.remove(filePath)\\n except Exception:\\n print(\\\"Error while deleting file : \\\", filePath)\",\n \"def dispose(self):\\n rmtree(self._temp_path)\",\n \"def remove_temporary_files():\\n try:\\n xml_file_path, bin_file_path = get_ida_exported_files()\\n if os.path.isfile(xml_file_path):\\n os.remove(xml_file_path)\\n\\n if os.path.isfile(bin_file_path):\\n os.remove(bin_file_path)\\n\\n except Exception:\\n print(\\\"GhIDA:: [!] Unexpected error while removing temporary files.\\\")\",\n \"def delete_log(self):\\n os.system('rm -rf *.log')\\n os.system('rm -rf *.log~')\\n os.system('rm -rf *.last')\\n os.system('rm -rf *.last~')\",\n \"def tearDown(self):\\n if os.path.isfile(LOGFILENAME):\\n os.remove(LOGFILENAME)\",\n \"def _remove_tmpfiles():\\n for f in tmpfiles:\\n try:\\n os.remove(f)\\n except OSError:\\n pass\",\n \"def tearDown(self):\\n self.tempdir.cleanup()\",\n \"def _clean_up_temporary_files(dataset_dir):\\n return\",\n \"def cleanup(self):\\n async def close_session():\\n await self.logi.close()\\n\\n self.loop.run_until_complete(close_session())\\n\\n self.loop.close()\\n self.logi = None\\n if os.path.isfile(CACHE_FILE):\\n os.remove(CACHE_FILE)\",\n \"def __del__(self):\\n shutil.rmtree('tmp')\\n self.quit_browser()\",\n \"def close(self):\\n try:\\n process_id = get_pid(\\\"broker_mqtts\\\")\\n except subprocess.CalledProcessError:\\n pass\\n else:\\n os.system(\\\"kill -9 {}\\\".format(process_id))\\n if os.path.isfile(self.log):\\n os.system(\\\"rm -rf \\\" + self.log)\",\n \"def release_logger_files():\\n for hl in logging.getLogger().handlers:\\n if isinstance(hl, logging.FileHandler):\\n hl.close()\\n logging.getLogger().removeHandler(hl)\",\n \"def release_logger_files():\\n for hl in logging.getLogger().handlers:\\n if isinstance(hl, logging.FileHandler):\\n hl.close()\\n logging.getLogger().removeHandler(hl)\",\n \"def unload(self):\\n for f in self.logs.values():\\n f.close()\",\n \"def __del__(self):\\n\\n self.logfd.close()\",\n \"def delete_logs(self):\\n if self.etw_log is not None:\\n files = sorted(glob.glob(self.etw_log + '*'))\\n for path in files:\\n try:\\n os.remove(path)\\n except Exception:\\n pass\",\n \"def __del__(self):\\n for f in self._files:\\n f.close()\",\n \"def clean_up_temp_files():\\n global __tmp_model_dir\\n\\n if __tmp_model_dir is not None:\\n FileUtils.deleteDirectory(__tmp_model_dir)\\n __tmp_model_dir = None\",\n \"def cleanup():\\n default_log_dir = Path(DEFAULT_LOGDIR)\\n if default_log_dir.exists():\\n shutil.rmtree(default_log_dir)\\n yield \\\"Cleanup\\\"\\n if default_log_dir.exists():\\n shutil.rmtree(default_log_dir)\",\n \"def delFiles(self):\\r\\n \\r\\n logStr = \\\"{0:s}.{1:s}: \\\".format(self.__class__.__name__, sys._getframe().f_code.co_name)\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'Start.')) \\r\\n \\r\\n try: \\r\\n if os.path.exists(self.h5File): \\r\\n os.remove(self.h5File) \\r\\n logger.debug(\\\"{0:s} File {1:s} deleted.\\\".format(logStr,self.h5File)) \\r\\n except XmError:\\r\\n raise \\r\\n except Exception as e:\\r\\n logStrFinal=\\\"{:s}Exception: Line: {:d}: {!s:s}: {:s}\\\".format(logStr,sys.exc_info()[-1].tb_lineno,type(e),str(e))\\r\\n logger.error(logStrFinal) \\r\\n raise XmError(logStrFinal) \\r\\n finally:\\r\\n logger.debug(\\\"{0:s}{1:s}\\\".format(logStr,'_Done.'))\",\n \"def _CloseOutputFiles(self):\\n self.gfile.close()\\n self.efile.close()\",\n \"def _remove_mp_logs(self):\\n for i, fn in enumerate(self.logfiles):\\n os.remove(fn)\",\n \"def tearDown(self):\\n print(\\n \\\"\\\\nDeleting temporary files...\\\\n\\\")\\n try:\\n shutil.rmtree(TEST_DIR)\\n except OSError:\\n pass\",\n \"def cleanup(self):\\r\\n if self.tempDirectory != None:\\r\\n shutil.rmtree(self.tempDirectory, True)\\r\\n self.tempDirectory = None\",\n \"def tearDown(self):\\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\\n self.logging_handler.assertNoLogsOver(logging.INFO)\\n\\n logging.getLogger('COT').removeHandler(self.logging_handler)\\n\\n self.validate_with_ovftool(self.temp_file)\\n\\n # Delete the temporary directory\\n if os.path.exists(self.temp_dir):\\n logger.debug(\\\"Deleting temp dir %s\\\", self.temp_dir)\\n shutil.rmtree(self.temp_dir)\\n self.temp_dir = None\\n self.temp_file = None\\n\\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\\n delta = tmps2 - self.tmps\\n if delta:\\n self.fail(\\\"Temp directory(s) {0} left over after test!\\\"\\n .format(delta))\\n\\n # Clear output caches for helper commands:\\n for helper in helpers.values():\\n helper.cached_output.clear()\\n\\n # Let's try to keep things lean...\\n delta_t = time.time() - self.start_time\\n if delta_t > 5.0:\\n print(\\\"\\\\nWARNING: Test {0} took {1:.3f} seconds to execute. \\\"\\n \\\"Consider refactoring it to be more efficient.\\\"\\n .format(self.id(), delta_t))\",\n \"def cleanup(self, keep_logs : bool = False):\\n if self.path.exists():\\n try:\\n shutil.rmtree(self.path)\\n except Exception as e:\\n warn(e)\\n if self.log_path.exists() and not keep_logs:\\n try:\\n os.remove(self.log_path)\\n except Exception as e:\\n warn(e)\",\n \"def cleanup(self):\\r\\n for f in [i for d in self.data.values() for i in d[\\\"filenames\\\"]]:\\r\\n try:\\r\\n os.unlink(f)\\r\\n except Exception: pass\\r\\n self.Destroy()\",\n \"def __exit__(self, exc_type, exc_value, traceback):\\n logger.debug(\\\"Cleaning up temporary directory: %s\\\", self.temporary_directory)\\n shutil.rmtree(self.temporary_directory)\\n del self.temporary_directory\",\n \"def cleanup(self):\\n files = self.nlst()\\n latest = self.latest_filename\\n for filename in files:\\n if filename != latest:\\n result = self.delete(filename)\\n logger.info(f\\\"Deleted old export from FTP: {result}\\\")\",\n \"def tearDown(self):\\n utils.rm_rf(TMP_DIR_PATH)\",\n \"def cleanup_temp_dir(context):\\n\\n try:\\n os.chdir(context.cwd)\\n except:\\n print(\\\"Current working file record does not exist\\\")\\n\\n try:\\n context.tempdir.cleanup()\\n except:\\n print(\\\"Temporary directory cannot be cleaned up - does it exist?\\\")\",\n \"def tearDown(self):\\n self.tmp.cleanup()\",\n \"def teardown_class(cls):\\n os.remove(logfilename)\",\n \"def _cleanup(self):\\n # delete stdout/stderr\\n if os.path.isfile(self.stdout):\\n os.unlink(self.stdout)\",\n \"def tearDown(self):\\n for f in os.listdir('/tmp'):\\n if not f.startswith(self.FILE_PREFIX):\\n continue\\n\\n os.remove(os.path.join('/tmp', f))\",\n \"def clear_tmp_folder(self):\\r\\n for file in os.listdir(self.temp_dir):\\r\\n if file.endswith('.png') or file.endswith('.jpg'):\\r\\n path = os.path.join(self.temp_dir, file)\\r\\n print ('Cleaned up {}'.format(path))\\r\\n os.remove(path)\",\n \"def cleanUp(self):\\n self.dirMonitor.stop()\\n self.filesList.cleanUp()\",\n \"def cleanup():\\n if config.get('global').get('no_cleanup'):\\n return\\n logging.info('Cleaning up temp directories')\\n try:\\n tmp_path = config.get('global').get('tmp_path')\\n if os.path.exists(tmp_path):\\n rmtree(tmp_path)\\n except Exception as e:\\n logging.error(format_debug(e))\\n print_message('Error removing temp directories')\\n\\n try:\\n archive_path = os.path.join(\\n config.get('global').get('output_path'),\\n 'script_archive',\\n time.strftime(\\\"%Y-%m-%d-%I-%M\\\"))\\n if not os.path.exists(archive_path):\\n os.makedirs(archive_path)\\n run_script_path = config.get('global').get('run_scripts_path')\\n if os.path.exists(run_script_path):\\n move(run_script_path, archive_path)\\n except Exception as e:\\n logging.error(format_debug(e))\\n logging.error('Error archiving run_scripts directory')\",\n \"def close(self):\\n if not self.__closed:\\n self.counters = { \\\"error\\\": 0, \\\"warning\\\": 0, \\\"success\\\": 0, \\\"failure\\\": 0 }\\n\\n try:\\n self.__flush_count = 0\\n for handler in self.__filehandlers:\\n handler.flush()\\n self.__logger.removeHandler(handler)\\n handler.close()\\n except:\\n # do nothing\\n pass\\n self.__closed = True\",\n \"def clean_temp_storage_dir(self, filenames):\\n for fn in filenames:\\n try:\\n pathlib.Path(pathlib.PurePath(self.temp_storage_dir, fn)).unlink()\\n except FileNotFoundError:\\n pass\",\n \"def close(self):\\r\\n self._report_file.close()\\r\\n # Make sure everything's closed.\\r\\n for files in self._output_files.values():\\r\\n for f in files.values():\\r\\n f.close()\",\n \"def close_files(self):\\n self.wb_alm.close()\\n self.wb_defect.close()\\n self.wb_enhancement.close()\\n self.wb_incident.close()\\n self.wb_destination.close()\",\n \"def release(self):\\n self.errorlog.close()\\n del self.errorlog\\n del self.printer\",\n \"def classCleanup(cls):\\n cls.RemoveTempFile(SettingsCommandTestCase.output_file_name)\",\n \"def cleanup(self):\\n\\n # NOTE(jbresnah) call stop on each of the servers instead of\\n # checking the pid file. stop() will wait until the child\\n # server is dead. This eliminates the possibility of a race\\n # between a child process listening on a port actually dying\\n # and a new process being started\\n servers = [self.api_server, self.conductor_server, ]\\n for s in servers:\\n try:\\n s.stop()\\n except Exception:\\n pass\\n\\n for f in self.files_to_destroy:\\n if os.path.exists(f):\\n os.unlink(f)\",\n \"def main():\\n dir_path = '/home/ubuntu/test_files' # path for the log files that needs to be pruned\\n stat_file_name = 'file_status_info' # temp file will be created to store the stat of each files to calculate when to delete\\n \\n # Get the list of all the files where we want to perfrom the delete operations\\n file_list = get_list_of_files_in_dir(dir_path)\\n\\n # Get the current system date\\n current_date = get_current_date()\\n\\n # Iterate through all the log, error, info files in the specified directory path and check for the criteria of file older than 5 days and delete.\\n for fil in file_list:\\n get_file_stat(dir_path, stat_file_name, fil)\\n filename, file_date = get_file_last_modification_date(stat_file_name)\\n\\n print(\\\"*********** %s file stat is written **************\\\" % fil)\\n days = abs(current_date - file_date).days\\n \\n # Check if the file modification date if older than 5 days.\\n if days > 5:\\n remove_files(os.path.join(dir_path, fil))\\n else:\\n print(\\\"No eligible file(s) found to be deleted\\\")\",\n \"def clean_file_before_test():\\n\\n if os.path.exists(LOG_FOLDER):\\n for file in os.listdir(LOG_FOLDER):\\n os.remove(LOG_FOLDER + \\\"/\\\" + file)\",\n \"def clean_cwd():\\n\\n # Generator of the files generated for each runs\\n del_files = (file for file in os.listdir() if file.endswith('.vtk')\\n or file.endswith('.dat')\\n or file.startswith('eeldata')\\n or file.endswith('.log'))\\n\\n for file in del_files:\\n try:\\n os.remove(file)\\n print(\\\"\\\\rRemoved {:s} succesfully!\\\".format(file), end=' '*15)\\n except:\\n print(\\\"\\\\rFailed to remove {:s}\\\".format(file))\\n raise\\n\\n print('')\",\n \"def clean_outputs(self) -> None:\\n\\n def _delete_if_not_none(fn: Optional[str]) -> None:\\n if fn is not None:\\n Path(fn).unlink()\\n\\n _delete_if_not_none(self.config[\\\"LOG_FILE\\\"])\\n\\n for file_ in self.exporter.get_all_files():\\n file_.unlink()\",\n \"def _cleanup(self):\\n try:\\n tmpdir = self.tmpdir\\n except AttributeError:\\n # Don't need to do anything if the temp dir isn't set\\n return\\n shutil.rmtree(tmpdir)\",\n \"def __del__(self) -> None:\\n try:\\n shutil.rmtree(self.temp_path)\\n except FileNotFoundError:\\n pass\",\n \"def close_log():\\n\\n global log_file\\n if log_file is not None:\\n try:\\n log_file.flush()\\n finally:\\n log_file.close()\",\n \"def on_cleanup(self):\\n self.close()\",\n \"def on_cleanup(self):\\n self.close()\",\n \"def teardown(self):\\n super(TestCisPickleOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def tearDown(self):\\n logging.info(\\\"Delete %s\\\", self._temp_dir)\\n shutil.rmtree(self._temp_dir)\",\n \"def close(self):\\n\\n sp.call([\\\"convert\\\", \\\"{}_*\\\".format(self.tmp_prefix),\\n self.filename])\\n\\n sp.call(\\\"rm {}_*\\\".format(self.tmp_prefix), shell=True)\\n sp.call([\\\"rmdir\\\", self.tmp_dir])\",\n \"def reopen_files(self):\\r\\n for log in (self.error_log, self.access_log):\\r\\n for h in log.handlers:\\r\\n if isinstance(h, logging.FileHandler):\\r\\n h.acquire()\\r\\n h.stream.close()\\r\\n h.stream = open(h.baseFilename, h.mode)\\r\\n h.release()\",\n \"def __del__(self):\\n\\n # cleanup logging handlers\\n for handler in self.logger.handlers[::-1]:\\n try:\\n handler.acquire()\\n handler.flush()\\n handler.close()\\n except (OSError, ValueError):\\n pass\\n finally:\\n handler.release()\",\n \"def tearDown(self):\\n \\tshutil.rmtree(self.tempdir)\",\n \"def _cleanup(self):\\n if self.current_session is not None:\\n self.current_session.close()\\n self.current_session = None\\n\\n for handler in list(self.logger.root_logger.handlers):\\n self.logger.root_logger.removeHandler(handler)\\n handler.flush()\\n handler.close()\",\n \"def _cleanup(self):\\n if self.pidfile:\\n os.unlink(self.pidfile)\",\n \"def cleanup(self):\\n self.all_wav_to_mp3()\\n self.past_songs_db.close()\\n self.move_tracks_to_music_folder( )\\n self.delete_leftovers()\\n print \\\"Cleanup finished\\\"\",\n \"def tearDown(self):\\n\\n for fname in self.fnames:\\n FileSystem.unlink(fname)\",\n \"def cleanup(self):\\n try:\\n self.disconnect()\\n self.log_file.close()\\n SerialAsyncLoop.stop_loop(self.logger)\\n except Exception as e:\\n self.applog.exception(f\\\"Exception --> {SerialConsole.cleanup.__qualname__}\\\", exc_info=e)\\n raise\",\n \"def cleanup(self):\\n\\n print \\\"Cleaning up...\\\",\\n sys.stdout.flush()\\n\\n builddir = os.path.join(self.build)\\n\\n comm = 'rm -rf '+builddir\\n #+' '+libdir+' '+logdir\\n (output, error, retz) = runShellCommand(comm)\\n\\n print \\\"done.\\\"\",\n \"def LogAndRemoveFiles(temp_dir, regex_pattern):\\n regex = re.compile(regex_pattern)\\n if not os.path.isdir(temp_dir):\\n return\\n for dir_item in os.listdir(temp_dir):\\n if regex.search(dir_item):\\n full_path = os.path.join(temp_dir, dir_item)\\n print 'Removing leaked temp item: %s' % full_path\\n try:\\n if os.path.islink(full_path) or os.path.isfile(full_path):\\n os.remove(full_path)\\n elif os.path.isdir(full_path):\\n chromium_utils.RemoveDirectory(full_path)\\n else:\\n print 'Temp item wasn\\\\'t a file or directory?'\\n except OSError, e:\\n print >> sys.stderr, e\\n # Don't fail.\",\n \"def tearDown(self):\\n self.clearTempDir()\",\n \"def tearDown(self):\\n # Fail if any WARNING/ERROR/CRITICAL logs were generated\\n self.logging_handler.assertNoLogsOver(logging.INFO)\\n\\n logging.getLogger('COT').removeHandler(self.logging_handler)\\n\\n if hasattr(self, 'instance'):\\n self.instance.destroy()\\n self.instance = None\\n\\n self.validate_with_ovftool(self.temp_file)\\n\\n # Delete the temporary directory\\n if os.path.exists(self.temp_dir):\\n logger.debug(\\\"Deleting temp dir {0}\\\".format(self.temp_dir))\\n shutil.rmtree(self.temp_dir)\\n self.temp_dir = None\\n self.temp_file = None\\n\\n tmps2 = set(glob.glob(os.path.join(tempfile.gettempdir(), 'cot*')))\\n delta = tmps2 - self.tmps\\n if delta:\\n self.fail(\\\"Temp directory(s) {0} left over after test!\\\"\\n .format(delta))\\n\\n # Let's try to keep things lean...\\n delta_t = time.time() - self.start_time\\n if delta_t > 5.0:\\n print(\\\"\\\\nWARNING: Test {0} took {1:.3f} seconds to execute. \\\"\\n \\\"Consider refactoring it to be more efficient.\\\"\\n .format(self.id(), delta_t))\",\n \"def delete_log():\\n log_path = Path.cwd() / \\\"premise.log\\\"\\n if log_path.exists():\\n log_path.unlink()\",\n \"def del_tmp() -> None:\\n for elem in os.listdir('./tmp'):\\n path = f\\\"./tmp/{elem}\\\"\\n if os.path.isfile(path):\\n os.remove(path)\\n else:\\n shutil.rmtree(path)\",\n \"def cleanup(tempdir):\\n try:\\n shutil.rmtree(tempdir)\\n except OSError:\\n pass\",\n \"def tearDown(self):\\n testing_dir = os.path.split(os.path.realpath(__file__))[0]\\n for f in glob.glob(os.path.join(testing_dir, \\\"*\\\")):\\n if f.split(\\\".\\\")[-1] in [\\\"o\\\", \\\"out\\\", \\\"pyc\\\", \\\"log\\\"]:\\n subprocess.call(['rm', f])\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n os.rmdir(self._tempdir)\",\n \"def tearDown(self):\\n with contextlib.suppress(FileNotFoundError):\\n Path(\\\"test.xlsx\\\").absolute().unlink()\",\n \"def test_monitor_correctly_deletes_temporary_directory_in_the_case_of_any_error(\\n self,\\n ):\\n # Arrange\\n feed_pages = [fixtures.PROQUEST_FEED_PAGE_1, fixtures.PROQUEST_FEED_PAGE_2]\\n\\n client = create_autospec(spec=ProQuestAPIClient)\\n client.download_all_feed_pages = MagicMock(\\n return_value=list(map(fixtures.serialize, feed_pages))\\n )\\n\\n client_factory = create_autospec(spec=ProQuestAPIClientFactory)\\n client_factory.create = MagicMock(return_value=client)\\n\\n monitor = ProQuestOPDS2ImportMonitor(\\n client_factory, self._db, self._proquest_collection, ProQuestOPDS2Importer\\n )\\n monitor.import_one_feed = MagicMock(return_value=([], []))\\n\\n results = {\\\"temp_directory\\\": None, \\\"temp_files\\\": []}\\n original_mkdtemp = tempfile.mkdtemp\\n original_temp_file_constructor = tempfile.NamedTemporaryFile\\n original_rmtree = shutil.rmtree\\n\\n def create_temp_directory():\\n results[\\\"temp_directory\\\"] = original_mkdtemp()\\n\\n return results[\\\"temp_directory\\\"]\\n\\n def create_temp_file(**kwargs):\\n temp_file = original_temp_file_constructor(**kwargs)\\n results[\\\"temp_files\\\"].append(temp_file.name)\\n\\n return temp_file\\n\\n # Act\\n with patch(\\\"tempfile.mkdtemp\\\") as mkdtemp_mock, patch(\\n \\\"tempfile.NamedTemporaryFile\\\"\\n ) as named_temporary_file_constructor_mock, patch(\\n \\\"shutil.rmtree\\\"\\n ) as rmtree_mock, patch(\\n \\\"api.proquest.importer.parse_feed\\\"\\n ) as parse_feed_mock:\\n mkdtemp_mock.side_effect = create_temp_directory\\n named_temporary_file_constructor_mock.side_effect = create_temp_file\\n rmtree_mock.side_effect = original_rmtree\\n parse_feed_mock.side_effect = core.opds2_import.parse_feed\\n\\n # An exception will be raised while trying to parse the feed page.\\n parse_feed_mock.side_effect = Exception(\\\"\\\")\\n\\n monitor.run_once(False)\\n\\n # Assert\\n # Ensure that the temp directory was successfully created.\\n tempfile.mkdtemp.assert_called_once()\\n\\n # Ensure that only one temp file was created, after this an exception was raised and the process stopped.\\n tempfile.NamedTemporaryFile.assert_has_calls(\\n [call(mode=\\\"r+\\\", dir=results[\\\"temp_directory\\\"], delete=False)]\\n )\\n\\n # Ensure that parse_feed method was called only once.\\n parse_feed_mock.assert_has_calls([call(ANY, silent=False)])\\n\\n # Ensure that the temp directory was successfully removed.\\n shutil.rmtree.assert_called_once_with(results[\\\"temp_directory\\\"])\\n assert False == os.path.exists(results[\\\"temp_directory\\\"])\",\n \"def teardown(self):\\n super(TestCisPlyOutput, self).teardown()\\n if os.path.isfile(self.tempfile):\\n os.remove(self.tempfile)\",\n \"def teardown(self):\\n try:\\n self._close(True)\\n except:\\n pass\\n try:\\n os.unlink(os.path.join(self.home_dir, DB_FILE))\\n except FileNotFoundError as _:\\n pass\",\n \"def _call_cleanup(self,\\r\\n input_fp,\\r\\n output_dir,\\r\\n params,\\r\\n job_prefix,\\r\\n poll_directly,\\r\\n suppress_submit_jobs):\\r\\n pass\",\n \"def __del__(self):\\n for handle in self._filehandles:\\n handle.close()\",\n \"def __del__(self):\\n\\n # close socket\\n try:\\n self.socket.shutdown(socket.SHUT_RDWR)\\n self.socket.close()\\n except OSError:\\n pass\\n\\n # quit selenium driver\\n if self.driver:\\n self.driver.quit()\\n\\n # make sure to delete linkfinder temporary files\\n files = glob.glob(\\\"linkfinder_tmp*\\\")\\n for file in files:\\n os.remove(file)\\n\\n # make sure helper process is dead\\n try:\\n os.kill(self.helper_pid, signal.SIGKILL)\\n except ProcessLookupError:\\n pass\\n\\n if os.path.exists(UNIX_SOCK_ADDR):\\n os.remove(UNIX_SOCK_ADDR)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68277276","0.67437613","0.660802","0.6526464","0.6455161","0.6444915","0.64396834","0.64194924","0.6357929","0.635103","0.6348235","0.6337223","0.631956","0.62995","0.6263754","0.6261455","0.6259881","0.6235108","0.6226701","0.62038773","0.6193479","0.619078","0.6185075","0.61716455","0.6162405","0.6148543","0.61366403","0.61211395","0.6110119","0.61008394","0.6094485","0.6094485","0.60817474","0.60717994","0.6071512","0.60656995","0.60620594","0.6057521","0.60528797","0.603094","0.60286385","0.60231495","0.60209477","0.6004534","0.5996872","0.5996733","0.5996421","0.5984027","0.5979224","0.59701145","0.59607434","0.5959775","0.5945069","0.5935149","0.5897033","0.58951557","0.5885507","0.5880851","0.587962","0.58740205","0.5870042","0.5868677","0.58653396","0.5851836","0.5847082","0.58420503","0.5836608","0.583468","0.5834509","0.58197486","0.5819063","0.5814661","0.5814661","0.58087206","0.58036685","0.5800616","0.57937884","0.57905906","0.5789693","0.57806677","0.57727456","0.57724655","0.5770342","0.5752853","0.5746332","0.5739621","0.573046","0.5728316","0.57260895","0.5721934","0.5716965","0.57097477","0.5701906","0.5698013","0.56977355","0.56958807","0.5694483","0.56871206","0.5684093","0.568106"],"string":"[\n \"0.68277276\",\n \"0.67437613\",\n \"0.660802\",\n \"0.6526464\",\n \"0.6455161\",\n \"0.6444915\",\n \"0.64396834\",\n \"0.64194924\",\n \"0.6357929\",\n \"0.635103\",\n \"0.6348235\",\n \"0.6337223\",\n \"0.631956\",\n \"0.62995\",\n \"0.6263754\",\n \"0.6261455\",\n \"0.6259881\",\n \"0.6235108\",\n \"0.6226701\",\n \"0.62038773\",\n \"0.6193479\",\n \"0.619078\",\n \"0.6185075\",\n \"0.61716455\",\n \"0.6162405\",\n \"0.6148543\",\n \"0.61366403\",\n \"0.61211395\",\n \"0.6110119\",\n \"0.61008394\",\n \"0.6094485\",\n \"0.6094485\",\n \"0.60817474\",\n \"0.60717994\",\n \"0.6071512\",\n \"0.60656995\",\n \"0.60620594\",\n \"0.6057521\",\n \"0.60528797\",\n \"0.603094\",\n \"0.60286385\",\n \"0.60231495\",\n \"0.60209477\",\n \"0.6004534\",\n \"0.5996872\",\n \"0.5996733\",\n \"0.5996421\",\n \"0.5984027\",\n \"0.5979224\",\n \"0.59701145\",\n \"0.59607434\",\n \"0.5959775\",\n \"0.5945069\",\n \"0.5935149\",\n \"0.5897033\",\n \"0.58951557\",\n \"0.5885507\",\n \"0.5880851\",\n \"0.587962\",\n \"0.58740205\",\n \"0.5870042\",\n \"0.5868677\",\n \"0.58653396\",\n \"0.5851836\",\n \"0.5847082\",\n \"0.58420503\",\n \"0.5836608\",\n \"0.583468\",\n \"0.5834509\",\n \"0.58197486\",\n \"0.5819063\",\n \"0.5814661\",\n \"0.5814661\",\n \"0.58087206\",\n \"0.58036685\",\n \"0.5800616\",\n \"0.57937884\",\n \"0.57905906\",\n \"0.5789693\",\n \"0.57806677\",\n \"0.57727456\",\n \"0.57724655\",\n \"0.5770342\",\n \"0.5752853\",\n \"0.5746332\",\n \"0.5739621\",\n \"0.573046\",\n \"0.5728316\",\n \"0.57260895\",\n \"0.5721934\",\n \"0.5716965\",\n \"0.57097477\",\n \"0.5701906\",\n \"0.5698013\",\n \"0.56977355\",\n \"0.56958807\",\n \"0.5694483\",\n \"0.56871206\",\n \"0.5684093\",\n \"0.568106\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58914703"},"document_rank":{"kind":"string","value":"56"}}},{"rowIdx":8460,"cells":{"query":{"kind":"string","value":"Create summary message for email."},"document":{"kind":"string","value":"def SummaryErrorMessage(self, error_log=None):\n if error_log is None:\n error_log = self.error_log\n# server = socket.gethostname().split('.')[0]\n mssg = '\\nPreprocessing script complete for data in %s\\n\\nServer: %s\\n'\\\n % (self.topdir, self.server)\n# Log time.\n ms = time.time()\n ms = int(1000*(ms - int(ms)))\n mssg += '\\nTime: %s:%03d\\n' % \\\n (datetime.today().strftime('%a %b %d, %Y; %X'), ms)\n if len(error_log) > 0:\n mssg += 'Command: %s\\n\\nSummary:\\n' % (' '.join(sys.argv))\n lines = error_log.split('\\n')\n for line in lines:\n if line.startswith('Description:'):\n mssg += line[12:]\n mssg += '\\n\\nDetails:' + error_log\n else:\n mssg += '\\nNo problems detected (this does NOT imply that everything was computed.).\\n\\n'\n return mssg"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def composeSummaryEmail(self):\r\n message = \"\"\"From: Douglas Gregor <dgregor@osl.iu.edu>\r\nTo: boost@lists.boost.org\r\nReply-To: boost@lists.boost.org\r\nSubject: [Report] \"\"\"\r\n message += str(self.numFailures()) + \" failures on \" + branch\r\n if branch != 'trunk':\r\n message += ' branch'\r\n message += \" (\" + str(datetime.date.today()) + \")\"\r\n message += \"\"\"\r\n\r\nBoost regression test failures\r\n\"\"\"\r\n message += \"Report time: \" + self.date + \"\"\"\r\n\r\nThis report lists all regression test failures on high-priority platforms.\r\n\r\nDetailed report:\r\n\"\"\"\r\n\r\n message += ' ' + self.url + '\\n\\n'\r\n\r\n if self.numFailures() == 0:\r\n message += \"No failures! Yay!\\n\"\r\n return message\r\n \r\n # List the platforms that are broken\r\n any_broken_platforms = self.numReportableFailures() < self.numFailures()\r\n if any_broken_platforms:\r\n message += \"\"\"The following platforms have a large number of failures:\r\n\"\"\"\r\n for platform in sorted_keys( self.platforms ):\r\n if self.platforms[platform].isBroken():\r\n message += (' ' + platform + ' ('\r\n + str(len(self.platforms[platform].failures))\r\n + ' failures)\\n')\r\n\r\n message += \"\"\"\r\nFailures on these \"broken\" platforms will be omitted from the results below.\r\nPlease see the full report for information about these failures.\r\n\r\n\"\"\"\r\n \r\n # Display the number of failures\r\n message += (str(self.numReportableFailures()) + ' failures in ' + \r\n str(len(self.libraries)) + ' libraries')\r\n if any_broken_platforms:\r\n message += (' (plus ' + str(self.numFailures() - self.numReportableFailures())\r\n + ' from broken platforms)')\r\n \r\n message += '\\n'\r\n\r\n # Display the number of failures per library\r\n for k in sorted_keys( self.libraries ):\r\n library = self.libraries[k]\r\n num_failures = library.numFailures()\r\n message += ' ' + library.name + ' ('\r\n \r\n if library.numReportableFailures() > 0:\r\n message += (str(library.numReportableFailures())\r\n + \" failures\")\r\n \r\n if library.numReportableFailures() < num_failures:\r\n if library.numReportableFailures() > 0:\r\n message += ', plus '\r\n \r\n message += (str(num_failures-library.numReportableFailures()) \r\n + ' failures on broken platforms')\r\n message += ')\\n'\r\n pass\r\n\r\n message += '\\n'\r\n\r\n # Provide the details for the failures in each library.\r\n for k in sorted_keys( self.libraries ):\r\n library = self.libraries[k]\r\n if library.numReportableFailures() > 0:\r\n message += '\\n|' + library.name + '|\\n'\r\n for test in library.tests:\r\n if test.numReportableFailures() > 0:\r\n message += ' ' + test.name + ':'\r\n for failure in test.failures:\r\n platform = failure.platform\r\n if not platform.isBroken():\r\n message += ' ' + platform.name\r\n message += '\\n'\r\n\r\n return message","def generateNotifyMessage(self):\n now = datetime.now()\n current_time = now.strftime(\"%H:%M:%S\")\n today = date.today()\n current_date = today.strftime(\"%B %d, %Y\")\n\n subject = \"Progam operating warning - Not Running\"\n body = \"Since \" + current_date + \" at \" + current_time \n msg = f'Subject: {subject} \\n\\n{body}'\n return msg","def summary_string(self) -> str:","def construct_email_content(self):\n # Construct header of the message\n content = MAIL_HEAD_CONTENT.replace(\"TITLE_HOLDER\", self.title).replace('FAIL_JOB_HOLDER',\n self.fail_job_content).replace(\n \"TIME_HOLDER\", os.getenv(\"START_TIME\")).replace(\"GRAPH_HOLDER\", os.getenv(\"BENCHMARK_GRAPH\")).replace(\n \"JOB_HOLDER\", os.getenv(\"BENCHMARK_TYPE\")).replace(\"DEVICE_HOLDER\", os.getenv(\"DEVICE_TYPE\")).replace(\"CUDA_HOLDER\", os.getenv(\"VERSION_CUDA\")).replace('DISPLAY', self.job_display)\n\n if not self.alarm_info:\n return\n # Construct alarm content\n content += self.alarm_info\n # Construct the tail of the message\n content += MAIL_TAIL_CONTENT.replace(\"BENCHMARK_WEBSITE1\", os.getenv(\"BENCHMARK_WEBSITE1\", \"\")).strip().replace(\n 'RUN_ENV_HOLDER', self.env_content).replace(\"BENCHMARK_WEBSITE2\", os.getenv(\"BENCHMARK_WEBSITE2\"))\n\n with open(os.path.join(self.log_path, \"mail.html\"), \"w\") as f_object:\n f_object.write(content)","def formatted_message(self):\n message = MIMEMultipart()\n message['From'] = self.sender\n message['To'] = self.receiver\n message['Subject'] = self.subject.format(**self.params)\n message.attach(MIMEText(self.body.format(**self.params), 'plain'))\n return message.as_string()","def _construct_message(self):\n self.message[\"text\"] = \"\"\n if self.from_:\n self.message[\"text\"] += \"From: \" + self.from_ + \"\\n\"\n if self.subject:\n self.message[\"text\"] += \"Subject: \" + self.subject + \"\\n\"\n\n self.message[\"text\"] += self.body\n self._add_attachments()","def compose_email(self, donation_id=-1, write_totals=False):\n if not write_totals:\n amount = self.donations[donation_id]\n else:\n amount = self.total_donations\n email_string = f\"\\nDear {self.name},\\n Thank you for your generous\\\n gift of ${amount:.2f}! It will help Local Charity achieve our mission.\\n\\\n Best regards,\\n\\\n Local Charity\\n\\n\"\n return email_string","def build_message():\n outgoing_mail = Mail()\n outgoing_mail.from_email = Email(email_from_address, email_from_name)\n outgoing_mail.subject = subject\n personalization = Personalization()\n for recipient in email_to_addresses:\n personalization.add_to(Email(recipient))\n outgoing_mail.add_personalization(personalization)\n outgoing_mail.add_content(Content(\"text/plain\", str.join('\\n', _log)))\n outgoing_mail.add_content(Content(\"text/html\", \"<html><body> {} </body></html>\".format(str.join(' <br /> ', _log))))\n return outgoing_mail.get()","def make_email_message(itrf_begin, epoch_begin, itrf_final, epoch_final, velocity, date):\n\n message = \"Estimado Usuario,\\n\\nEn adjunto encontrará los resultados de la transformacion ITRF de acuerdo a la siguiente configuración:\\n\\nITRF inicial: \"+str(itrf_begin)+\"\\nEpoca inicial: \"+str(epoch_begin)+\"\\nITRF final: \"+str(itrf_final)+\"\\nEpoca final: \"+str(epoch_final)+\"\\nModelo de velocidad: \"+velocity+\"\\nFecha de la solicitud de la transformación: \"+date+\"\\n\\n\\nSaludos Cordiales,\\n\\nEquipo de Geodesia del IGVSB.\"\n return message","def _create_message(self, msg):\n head = msg[\"head\"]\n body = msg[\"body\"]\n body = body.format(**self.data)\n length = len(body)\n head = head.format(length=length, **self.data)\n return head + body","def generate_plain_mesg(info, open_quests, owner, tags):\n\n msg = (\n \"This email is being sent to {} because that is the owner listed\\n\"\n \"for the systems with open Hermes labors listed below.\\n\\n\"\n \"Due dates, if any, are noted with each quest.\\n\".format(owner)\n )\n msg += (\n \"\\nTo throw an event manually, you can run the following command \"\n \"on a shell server:\"\n \"\\n\\n\"\n \"$ hermes event create [event] --host [hostname].\\n\\n\"\n \"Or you can visit the quests linked below.\\n\\n\".format(\n settings.frontend)\n )\n for quest_id in info[owner]:\n quest = find_quest(open_quests, quest_id)\n if quest:\n msg += (\n \"==[ QUEST {} ]================================\\n\"\n \"CREATOR: {}\\n\"\n ).format(\n quest_id, quest.creator\n )\n if quest.target_time:\n msg += \"DUE: {}\\n\".format(quest.target_time)\n msg += \"DESC: \\\"{}\\\"\\n\".format(textwrap.fill(\n quest.description,\n width=60, subsequent_indent=\"\"\n ))\n msg += \"LINK: {}/v1/quests/{}\\n\\n\".format(\n settings.frontend, quest_id\n )\n else:\n msg += \" Labors not associated with a quest:\\n\\n\"\n\n msg += \"Machines with labors:\\n\"\n\n for hostname in sorted(info[owner][quest_id]):\n if tags[hostname]:\n tags_str = \"{}\".format((\", \".join(tags[hostname])))\n else:\n tags_str = \"no services\"\n msg += \" {} ({})\\n\".format(hostname, tags_str)\n\n msg += \"\\n\\n\"\n\n return msg","def email_article_summary(to_address, summary_filename, start_year, start_month, end_year, end_month, num_articles):\n \n host = HOST\n from_address = FROM_ADDRESS\n body = \"\"\"\n Good morning,\n \n There were %i peer-reviewed papers produced by researchers at this institute between %i/%i and %i/%i. A summary file containing the front page from each article is attached with this email. Please print out these summary pages, highlight the author(s) on each article and pin them to the monthly papers noticeboard.\n \n Thanks a bunch,\n \n Skynet.\n \n \"\"\" % (num_articles, start_month, start_year, end_month, end_year, )\n \n recipients = [to_address, ADMIN_ADDRESS]\n \n logging.info(\"Preparing summary email report for %s\" % (', '.join(recipients), ))\n \n successful = True\n for recipient in recipients:\n \n message = MIMEMultipart()\n message[\"From\"] = from_address\n message[\"To\"] = recipient\n message[\"Subject\"] = \"Refereed papers summary between %i/%i and %i/%i\" % (start_month, start_year, end_month, end_year, )\n message[\"Date\"] = formatdate(localtime=True)\n \n message.attach(MIMEText(textwrap.dedent(body).lstrip()))\n \n part = MIMEBase('application', 'octet-stream')\n part.set_payload(open(summary_filename, 'rb').read())\n Encoders.encode_base64(part)\n part.add_header('Content-Disposition', 'attachment; filename=\"%s\"' % os.path.basename(summary_filename))\n message.attach(part)\n \n server = smtplib.SMTP(host)\n \n try:\n failed = server.sendmail(from_address, to_address, message.as_string())\n server.close()\n \n except Exception as e:\n logging.critical(\"Unable to send email to %s. Error: %s\" % (recipient, str(e), ))\n successful = False\n \n else:\n logging.info(\"Email successfully sent to %s\" % recipient)\n \n \n return successful","def composeTestingSummaryEmail(self):\r\n brokenPlatforms = 0\r\n for platform in sorted_keys( self.platforms ):\r\n if self.platforms[platform].isBroken():\r\n brokenPlatforms = brokenPlatforms + 1\r\n\r\n if brokenPlatforms == 0:\r\n return None;\r\n \r\n message = \"\"\"From: Douglas Gregor <dgregor@osl.iu.edu>\r\nTo: boost-testing@lists.boost.org\r\nReply-To: boost-testing@lists.boost.org\r\nSubject: [Report] \"\"\"\r\n message += str(brokenPlatforms) + \" potentially broken platforms on \" + branch\r\n if branch != 'trunk':\r\n message += ' branch'\r\n message += \" (\" + str(datetime.date.today()) + \")\"\r\n message += \"\"\"\r\n\r\nPotentially broken platforms for Boost regression testing\r\n\"\"\"\r\n message += \"Report time: \" + self.date + \"\"\"\r\n\r\nThis report lists the high-priority platforms that are exhibiting a\r\nlarge number of regression test failures, which might indicate a problem\r\nwith the test machines or testing harness.\r\n\r\nDetailed report:\r\n\"\"\"\r\n\r\n message += ' ' + self.url + '\\n'\r\n\r\n message += \"\"\"\r\nPlatforms with a large number of failures:\r\n\"\"\"\r\n for platform in sorted_keys( self.platforms ):\r\n if self.platforms[platform].isBroken():\r\n message += (' ' + platform + ' ('\r\n + str(len(self.platforms[platform].failures))\r\n + ' failures)\\n')\r\n\r\n return message","def construct_message(self):\n msg_type = self.msg_type\n if msg_type == \"PUBMSG\":\n msg_type = \"PRIVMSG\"\n ret = \"{} {}\".format(msg_type, self.target)\n if self.content:\n ret += \" :{}\".format(self.content)\n return ret + \"\\r\\n\"","def massage_addinfo(self) -> str:\n self.message_str = f'{self.time}\\n{self.sent_by}\\n'\n return self.message_str","def summary(self):\n return ''","def test_email_content():\n\n time_of_day = alerts.current_time()\n hostname = alerts.host_name()\n\n subject = \"Subject: Raspi-Sump Email Test\"\n message = \"Raspi-Sump Test Email\"\n\n return \"\\r\\n\".join(\n (\n f\"From: {configs['email_from']}\",\n f\"To: {configs['email_to']}\",\n f\"{subject}\",\n \"\",\n f\"{hostname} - {time_of_day} - {message}.\",\n )\n )","def summary(self):\r\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\r\n GREEN, self.description,\r\n NORMAL, self.link)","def massage_addinfo(self) -> str:\n self.message_str= \"{}, {}\\n\".format(self.sent_by, self.time)","def create_test_summary(args, TEST_RESULTS):\n logging.error(\"Creating test summary report...\")\n\n try:\n test_summary = \"Performance Metrics of {APP} Application Tested from this PR\\n\".format(APP=args.bundle_id)\n test_summary += \"---------------------------------------------------------------\\n\"\n\n for element in TEST_RESULTS:\n if element != LAUNCHES:\n test_summary += \"> {KEY}: {VALUE}\".format(KEY=element, VALUE=TEST_RESULTS[element])\n if element == INSTALL_LAUNCH_DURATION:\n if int(TEST_RESULTS[INSTALL_LAUNCH_DURATION]) > args.duration_limit:\n test_summary += \"ms :x:\\n\"\n else:\n test_summary += \"ms :white_check_mark:\\n\"\n\n if element == INSTALL_MEMORY_USAGE:\n if int(TEST_RESULTS[INSTALL_MEMORY_USAGE]) > args.memory_limit:\n test_summary += \"MB :x:\\n\"\n else:\n test_summary += \"MB :white_check_mark:\\n\"\n\n if element == APP_SIZE:\n if int(TEST_RESULTS[APP_SIZE]) > args.size_limit:\n test_summary += \"MB :x:\\n\"\n else:\n test_summary += \"MB :white_check_mark:\\n\"\n test_summary += \"---------------------------------------------------------------\\n\"\n\n for element in TEST_RESULTS[LAUNCHES]:\n test_summary += \"> DEVICE: {DEVICE} | LAUNCH TYPE: {LAUNCH_TYPE} | \".format(DEVICE=element[DEVICE], LAUNCH_TYPE=element[LAUNCH_TYPE])\n test_summary += \"DURATION: {DURATION}ms \".format(DURATION=element[LAUNCH_DURATION])\n if int(element[LAUNCH_DURATION]) > args.duration_limit:\n test_summary += \" :x: | \"\n else:\n test_summary += \" :white_check_mark: | \"\n\n test_summary += \"MEMORY USAGE: {MEMORY_USAGE}MB \".format(MEMORY_USAGE=element[MEMORY_USAGE])\n if int(element[MEMORY_USAGE]) > args.memory_limit:\n test_summary += \" :x:\\n\"\n else:\n test_summary += \" :white_check_mark:\\n\"\n test_summary += \"----------------------------------------------------\\n\"\n\n except Exception as e:\n logging.error(\"Creating test summary failed with error '{ERROR}'\".format(ERROR=e))\n return None\n\n logging.info(test_summary)\n return test_summary","def add_summary_header(self):\n self.fontSize(22, bold=True)\n self.PDF.setFillColor(\"black\")\n self.PDF.drawString(75, 260, \"Summary\")\n self.fontSize(FONT_XXS)\n self.PDF.setFillColor(HexColor(\"#9CA3AF\"))\n # self.PDF.drawString(\n # 185,\n # 260,\n # f\"{self.invoice.subscription.start_date} - {self.invoice.subscription.end_date}\",\n # )\n self.PDF.setFillColor(\"black\")\n self.fontSize(FONT_XS)\n self.PDF.setFillColor(HexColor(\"#9CA3AF\"))\n self.PDF.drawString(75, 290, \"Services\")\n self.PDF.drawString(475, 290, \"Amount\")\n self.PDF.setFillColor(\"black\")\n self.draw_line(305)","async def summarise(self, ctx, start=None, end=None):\n if ctx.message.author.bot:\n return\n\n if not start or not end:\n await ctx.send(\n \"Insufficient arguments!\\n Arguements: <start ID> <end ID>\"\n )\n return\n\n summary, keywords, clean_messages = await convert_to_summary(\n ctx, start, end\n )\n\n if summary:\n summary = \"```\\n\" + summary + \"```\"\n await ctx.send(summary)\n else:\n await ctx.send(\"```Not enough messages to generate summary```\")\n\n if keywords:\n keyword_str = \"Keywords: \"\n for word in keywords:\n keyword_str += f\"{word}, \"\n\n keyword_str = \"```\\n\" + keyword_str + \"```\"\n await ctx.send(keyword_str)\n else:\n await ctx.send(\"```Not enough messages to generate keywords```\")","def __str__(self):\n email_template = '\\n'.join((f'\\n\\nDear {self._full_name},\\n',\n f'Thank you for your very kind donation of ${self.last_donation:.2f}.\\n',\n 'It will be put to very good use.\\n',\n ' Sincerely,',\n ' -The Team\\n'))\n return email_template","def send_created_email(self):\n if settings.NOTIFY_NEW_REG:\n to = settings.NOTIFY_NEW_REG\n message = \"\"\"\\\nGreetings,<br><br>\n\nA new vehicle registration has been submitted by %s.<br><br>\n\nGo here to view or edit the request: <br>\n<a href=\"%s\">%s</a>\n<br><br>\nSincerely,<br><br>\nThe Janelia Parking Permit Program\n \"\"\" % (self.user_display_name(), self.get_edit_url(True), self.get_edit_url(True))\n subject = 'A new parking permit request has been entered'\n from_email = 'parkingpermit-donotreply@janelia.hhmi.org'\n text_content = re.sub(r'<[^>]+>','',message)\n html_content = message\n msg = EmailMultiAlternatives(subject, text_content, from_email, to)\n msg.attach_alternative(html_content, \"text/html\")\n msg.send()","def summary(self) -> str:\n pass","def _render_mail(self, rebuild, success, canceled):\n subject_template = 'Image %(image)s; Status %(endstate)s; Submitted by %(user)s'\n body_template = '\\n'.join([\n 'Image: %(image)s',\n 'Status: %(endstate)s',\n 'Submitted by: %(user)s',\n 'Logs: %(logs)s',\n ])\n\n endstate = None\n if canceled:\n endstate = 'canceled'\n else:\n endstate = 'successful' if success else 'failed'\n url = None\n if self.url and self.workflow.openshift_build_selflink:\n url = urljoin(self.url, self.workflow.openshift_build_selflink + '/log')\n\n formatting_dict = {\n 'image': self.workflow.image,\n 'endstate': endstate,\n 'user': '<autorebuild>' if rebuild else self.submitter,\n 'logs': url\n }\n return (subject_template % formatting_dict, body_template % formatting_dict)","def write_plaintext_body(self, recipient: str) -> str:\n body = f'Dear {recipient},\\n\\n' \\\n + 'You are receiving this message because you participated ' \\\n + f'in {self.trip_title}.\\n\\n' \\\n + 'I hope you had a pleasant time.\\n\\n' \\\n + 'If you have debts to repay, please be courteous and ' \\\n + 'reimburse your fellow participant(s) in a timely fashion.\\n' \\\n\n subbody: str = ''\n subbody_debt: List = list()\n subbody_credit: List = list()\n for currency, reimbs in self.reimbursement_matrices.items():\n debts = reimbs[recipient].dropna()\n credits = reimbs.loc[recipient].dropna()\n for creditor, credit in debts.iteritems():\n #creditor = creditor.rjust(self._max_name_len)\n amount = f'{credit} {currency}'.ljust(6+1+3)\n subbody_debt.append('\\t' + creditor + ' | ' + amount)\n for debtor, debt in credits.iteritems():\n #debtor = debtor.rjust(self._max_name_len)\n amount = f'{debt} {currency}'.ljust(6+1+3)\n subbody_credit.append('\\t' + debtor + ' | ' + amount)\n\n if len(subbody_debt) == 1:\n subbody += 'Please reimburse the following participant:\\n' \\\n + subbody_debt[0]\n elif len(subbody_debt) > 1:\n subbody += 'Please reimburse the following participants:\\n' \\\n + '\\n'.join(subbody_debt)\n else:\n subbody += 'You don\\'t have any payable reimbursements.'\n\n subbody += '\\n\\n'\n\n if len(subbody_credit) == 1:\n subbody += 'The following participant is obligated to ' \\\n + 'reimburse you:\\n' + subbody_credit[0]\n elif len(subbody_credit) > 1:\n subbody += 'The following participants are obligated to ' \\\n + 'reimburse you:\\n' + '\\n'.join(subbody_credit)\n else:\n subbody += 'You don\\'t have any receivable reimbursements.' \\\n\n body += '\\n' + subbody\n\n return body","def summary(self):\n name = 'name : ' + self.get_name()\n damage = 'damage : ' + str(self.get_damage())\n ammos = 'ammo : ' + str(self.get_ammos())\n owner = 'owner : ' + str(self.get_owner())\n return '\\n'.join([name, damage, ammos, owner])","def summary_str(self):\n if not self.results:\n return self.summary.empty() or ''\n elif self.state == Ok:\n return self.summary.ok(self.results) or ''\n return self.summary.problem(self.results) or ''","def initialize_error_summary() -> str:\n error_summary = '\\nSummary of <span class=\"tex-fatal\">Critical Errors:</span>\\n\\n<ul>\\n'\n return error_summary","def summary_print(self):\r\n self.ensure_one()\r\n self.sent = True\r\n #return self.env['ir.actions.report'].report_action(self, 'proandsys_purchase_14.summary_landed_report')\r\n return self.env.ref('proandsys_purchase_14.summary_landedcost').report_action(self)","def create_group_message(group_name,subject,message,status,createdby):\n query=\"INSERT INTO groupmails(group_name,subject,message,status,createdby)VALUES('{}','{}','{}','{}','{}')\".format(\n group_name,subject,message,status,createdby\n )\n cur.execute(query)","def _message(self, recipient, connection, context=None):\n base_subject = '{{ event.calendar.course.name }} {{ event.title }}'\n if not self.event.get_documents(True):\n template_name = self.REQUEST_TEMPLATE\n subject = 'Got a {} study guide?'.format(base_subject)\n else:\n template_name = self.PUBLISH_TEMPLATE\n subject = '{} study guide'.format(base_subject)\n\n subject = Template(subject).render(context)\n body = get_template(template_name).render(context)\n\n return make_email_message(subject, body,\n make_display_email(\n self.sender_address,\n self.sender_name),\n recipient, connection)","def email_body_review_reminder():\n\tmsg = '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr><td align=\"center\" valign=\"top\"></td></tr></tbody></table>'\n\tmsg = msg + '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr>'\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\" cellspacing=\"0\" cellpadding=\"10\" width=\"600\">'\n\tmsg = msg + '<tbody>'\n\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<a href=\"https://insprite.co\"><img src=\"http://ryanfbaker.com/insprite/inspriteLogoA.png\" border=\"0\" alt=\"Insprite\" align=\"center\" width=\"200px\" height=\"55px\" /></a>'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</tbody>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"85\" width=\"600\" height=\"350\">'\n\tmsg = msg + '\\t<tr>td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px; padding-right:75px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '\\t\\t <font style=\"font-family:Helvetica Neue;color:#555555;font-size:14px;\">We hope you had a great appointment!<br>'\n\tmsg = msg + '\\t\\t\\t Your opinion goes a long way—write up your review of the appointment so others can learn from your experience with <a href=\"#\" style=\"color:#1488CC\">{user\\'s name}</a></font><br><br>'\n\tmsg = msg + '\\t</td></tr>'\n\n\tmsg = msg + '<td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:200px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '<a href=\"#\" style=\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:100px;text-align:center;\" target=\"_blank\">Rate & Review</a>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/facebookIcon.png\">'\n\tmsg = msg + '\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/twitterIcon.png\">'\n\tmsg = msg + '\\t\\t<img src=\"http://ryanfbaker.com/insprite/instagramIcon.png\">'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<img src=\"http://ryanfbaker.com/insprite/spacer-2.png\">'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</table>'\n\treturn msg","def email_body_to_user_sending_msg(profile, message):\n\tmsg = '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr><td align=\"center\" valign=\"top\"></td></tr></tbody></table>'\n\tmsg = msg + '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr>'\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\" cellspacing=\"0\" cellpadding=\"10\" width=\"600\">'\n\tmsg = msg + '<tbody>'\n\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<a href=\"https://insprite.co\"><img src=\"http://ryanfbaker.com/insprite/inspriteLogoA.png\" border=\"0\" alt=\"Insprite\" align=\"center\" width=\"200px\" height=\"55px\" /></a>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</tbody>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"85\" width=\"600\" height=\"350\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '<font style=\"font-family:Helvetica Neue;color:#555555;font-size:14px;\">Way to get the conversation started! You messaged <a href=\\\"https://127.0.0.1:5000/profile?hero=' + profile.prof_id + '\\\" style=\"color:#1488CC\">' + profile.prof_name.encode('utf8', 'ignore') + '</a> and should get a response soon.<br><br>'\n\tmsg = msg + 'Until then, stand tight. <br><br>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/facebookIcon.png\">'\n\tmsg = msg + '<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/twitterIcon.png\">'\n\tmsg = msg + '<img src=\"http://ryanfbaker.com/insprite/instagramIcon.png\">'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<img src=\"http://ryanfbaker.com/insprite/spacer-2.png\">'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\treturn msg","def generate_email(self, length=10):\n self.update()\n # Generate the emails\n lines = []\n line_count = 0\n while line_count < length:\n sent = self.markov.make_sentence()\n if sent != None:\n lines.append(sent)\n line_count +=1\n else:\n print(sent)\n email_body = '\\n'.join(lines)\n email_header = random.choice(get_best_trigrams(filter(email_body),5))\n return email_header.decode('utf-8').strip() + \"\\n\" + email_body.decode('utf-8').strip()","def summary(self, summary: str):\n return self.swag({\n 'summary': summary\n })","def generate_email(self):\n email_dict = {'donor_name':self.name,\n 'donation_amount':self.last_donation(),\n 'total_amount':self.total_donations()}\n\n # Create formatted email that can be copied & pasted\n email = ('\\n'.join(['Dear {donor_name},','',\n 'Thank you for your generous donation of ${donation_amount:.2f}.',\n 'To date, you have donated a total of ${total_amount:.2f} to our charity.',\n 'Your contributions help new arrivals receive the highest quality care possible.',\n 'Please know that your donations make a world of difference!',\n '','Sincerely,','The Good Place Team'])).format(**email_dict)\n\n return(email)","def mysummary(self):\n return self.sprintf(\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\")","def text_summary_message(self):\n failed = [e for e in self.evaluations if not e.passes]\n if failed == []:\n return \"SUCCESS - all constraints evaluations pass\"\n else:\n return \"FAILURE: %d constraints evaluations failed\" % len(failed)","def create_messages(application, action, remedy):\n\n messages = [] \n messages.append(\"\"\"Your Resources: </br><pre style=\"margin-left: 40px\">\"\"\" + application + \"</br></pre>\" + action + \"\"\" in AWS. <strong style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\">\"\"\" + remedy +\"\"\"</strong>\n </td>\n </tr><tr style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\"><td class=\"content-block\" style=\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; vertical-align: top; margin: 0; padding: 0 0 20px;\" valign=\"top\">\n This message was sent to inform you of changes happening to your resources.\n <ul>\n <li>New instances are auto-tagged with an expiration date, an NT ID, and a patch group if invalid.</li>\n <li>Instances without the necessary tags are notified through email and Slack.</li>\n </ul>\n If you have any further questions, please reply to this email.\"\"\")\n \n messages.append(\"Your Resources:\\n\\n\" + application + \"\\n\\n\" + action + \" in AWS. \" + remedy + \"\\n\" + \n (\"\\nThis message was sent to inform you of changes happening to your resources.\\n\"\n \"\\nNew instances are auto-tagged with an expiration date, an NT ID, and a patch group if invalid.\"\n \"Instances without Owner Mail and Owner Team tags are notified through email and slack.\\n\"\n \"\\nIf you have any further questions, please reply to this email.\")) \n\n return messages","def createThankYouEmail(self):\n result = (\"\\nDear {:s},\\n\\n\"\n \"\\tThank you so much for your generous donation of ${:,.2f}!\\n\\n\"\n \"\\tIt will be put to very good use.\\n\\n\"\n \"\\t\\tSincerely,\\n\\t\\t\\t- The Team\".format(self.name, self.getTotDonation())\n )\n return result","def summary(self, fromdt, todt):\r\n totalSaved = self.miser.totalSaved(fromdt, todt) \r\n sumStr = \"%s: %s to %s\\n\" % (self.miser.name, fromdt, todt)\r\n sumStr += \"Total saved: %.2f\" % totalSaved\r\n\r\n sumStr += \"\\n\\nGoals:\\n\"\r\n sumStr += self._goalsMetStr(fromdt, todt, totalSaved)\r\n\r\n return sumStr","def _create_msg(self, tr_id, i_triples, i_type, r_triples, r_type, confirm):\n params = SSAP_UPDATE_PARAM_TEMPLATE % (str(i_type).upper(),\n str(i_triples),\n str(r_type).upper(),\n str(r_triples),\n str(confirm).upper())\n tmp = SSAP_MESSAGE_TEMPLATE % (str(self.node_id), str(self.targetSS),\n self.tr_type, str(tr_id), params)\n return tmp","def produce_message_for_sending() -> str:\n return f\"You can donate your money here:\\n`{card_donations}`\"","async def report(cls, description, **kwargs):\n return await cls.message(description, color = discord.Color(0xbfbfbf))","def verification_email_body(case_name, url, display_name, category, subcategory, breakpoint_1, breakpoint_2, hgnc_symbol, panels, gtcalls, tx_changes, name, comment):\n html = \"\"\"\n <ul>\n <li>\n <strong>Case {case_name}</strong>: <a href=\"{url}\">{display_name}</a>\n </li>\n <li><strong>Variant type</strong>: {category} ({subcategory})\n <li><strong>Breakpoint 1</strong>: {breakpoint_1}</li>\n <li><strong>Breakpoint 2</strong>: {breakpoint_2}</li>\n <li><strong>HGNC symbols</strong>: {hgnc_symbol}</li>\n <li><strong>Gene panels</strong>: {panels}</li>\n <li><strong>GT call</strong></li>\n {gtcalls}\n <li><strong>Amino acid changes</strong></li>\n {tx_changes}\n <li><strong>Comment</strong>: {comment}</li>\n <li><strong>Ordered by</strong>: {name}</li>\n </ul>\n \"\"\".format(\n case_name=case_name,\n url=url,\n display_name=display_name,\n category=category,\n subcategory=subcategory,\n breakpoint_1=breakpoint_1,\n breakpoint_2=breakpoint_2,\n hgnc_symbol=hgnc_symbol,\n panels=panels,\n gtcalls=gtcalls,\n tx_changes=tx_changes,\n name=name,\n comment=comment)\n\n return html","def generate_html_mesg(info, open_quests, owner, tags):\n\n msg = '<html>' \\\n '<body style=\"font-family: Verdana; font-size: 1em; color: #000\">'\n msg += (\n \"<div style='padding: 10px; border-radius: 5px; background: #232f3e; \"\n \"color: #fff; font-weight: bold; font-size: 1.25em;'>\"\n \"Hermes Notifications\"\n \"</div>\"\n \"<div style='padding: 10px;'><p>This email is being sent to {} because that is the owner listed\\n\"\n \"for the systems with open Hermes labors listed below.</p>\"\n \"<p>Due dates, if any, are noted with each quest.</p>\"\n \"\".format(owner)\n )\n msg += (\n \"<p>To throw an event manually, you can run the following command \"\n \"on a shell server:</p>\"\n \"<pre style='font-size: 1.2em'>$ hermes event create [event] --host \"\n \"[hostname]</pre>\"\n \"<p>Or you can visit the quests linked below.</p></div>\".format(\n settings.frontend)\n )\n for quest_id in info[owner]:\n quest = find_quest(open_quests, quest_id)\n if quest:\n msg += (\n \"<div style='border-radius: 5px; background: #dce1e6; \"\n \"padding: 10px; margin-bottom: 10px;'>\"\n \"<span style='font-size: 1.1em; font-weight: bold'>QUEST {}</span><br/>\"\n \"<strong>CREATOR:</strong> {}<br />\"\n ).format(\n quest_id, quest.creator\n )\n if quest.target_time:\n msg += \"<strong>DUE:</strong> {}<br/>\".format(quest.target_time)\n msg += \"<strong>DESC:</strong><p> \\\"{}\\\"</p>\".format(quest.description)\n msg += \"<strong>LINK:</strong> <code>{}/v1/quests/{}</code><br/>\".format(\n settings.frontend, quest_id\n )\n else:\n msg += (\n \"<div style='border-radius: 5px; background: #dce1e6; \"\n \"padding: 10px; margin-bottom: 10px;'>\"\n \"<span style='font-size: 1.1em; font-weight: bold'>Labors not \"\n \"associated with a quest:</span><br />\"\n )\n\n msg += \"<p>Machines with labors:</p>\"\n\n msg += \"<pre style='margin-left: 10px; font-size: 1.2em'>\"\n for hostname in sorted(info[owner][quest_id]):\n if tags[hostname]:\n tags_str = \"{}\".format((\", \".join(tags[hostname])))\n else:\n tags_str = \"no services\"\n msg += \"{} ({})\\n\".format(hostname, tags_str)\n\n msg += \"</pre></div>\"\n\n msg += \"</body>\"\n\n return msg","def email_body_meeting_reminder():\n\tmsg = '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr><td align=\"center\" valign=\"top\"></td></tr></tbody></table>'\n\tmsg = msg + '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr>'\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\" cellspacing=\"0\" cellpadding=\"10\" width=\"600\">'\n\tmsg = msg + '<tbody>'\n\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<a href=\"https://insprite.co\"><img src=\"http://ryanfbaker.com/insprite/inspriteLogoA.png\" border=\"0\" alt=\"Insprite\" align=\"center\" width=\"200px\" height=\"55px\" /></a>'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</tbody>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"85\" width=\"600\" height=\"350\">'\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '\\t\\t<font style=\"font-family:Helvetica Neue;color:#555555;font-size:14px;\">Drats. <a href=\"#\" style=\"color:#1488CC\">{insert seller name} cancelled your appointment</a>.<br><br>'\n\tmsg = msg + '\\t\\t\\t <a href=\"#\" style=\"color:#1488CC\">Reschedule</a> or you can send a message to inquire about the cancellation. <br><br>'\n\tmsg = msg + '\\t\\t\\t And, don\\'t worry! You won\\'t be charged, promise. </font><br><br>'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/facebookIcon.png\">'\n\tmsg = msg + '\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/twitterIcon.png\">'\n\tmsg = msg + '\\t\\t<img src=\"http://ryanfbaker.com/insprite/instagramIcon.png\">'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\t\\t<img src=\"http://ryanfbaker.com/insprite/spacer-2.png\">'\n\tmsg = msg + '\\t</td></tr>'\n\tmsg = msg + '</table>'\n\treturn msg","def summary(self) -> str:\n return pulumi.get(self, \"summary\")","def create_base_message(subject, msg=None):\n message = EmailMessage()\n message[\"From\"] = email_config.from_address\n message[\"To\"] = email_config.to_addresses\n message[\"Subject\"] = subject\n message.set_content(\"\\n\".join([\n 50 * \"_\",\n f\"Hostname: {gethostname()}\",\n f\"Time: {datetime.now().strftime(config.datetime_format)}\",\n f\"Log file: {LOGGER.log_file_path}\",\n 50 * \"_\",\n ]))\n # Add `msg` to the contents if it is not None\n if msg is not None:\n _append_content(message, msg)\n # Attach the log file if it is available\n if LOGGER.log_file_path is not None and config.email_attach_log_file:\n _attach_log_file(message)\n return message.as_string()","def _create_msg(self, tr_id, payload, confirm, expire_time, encoding):\n tmp = [\"<SSAP_message><transaction_type>INSERT</transaction_type>\",\n \"<message_type>REQUEST</message_type>\"]\n tmp.extend([\"<transaction_id>\", str(tr_id), \"</transaction_id>\"])\n tmp.extend([\"<node_id>\", str(self.node_id), \"</node_id>\"])\n tmp.extend([\"<space_id>\", str(self.targetSS), \"</space_id>\"])\n tmp.extend(['<parameter name=\"insert_graph\" encoding=\"%s\">' % encoding.upper(),\n str(payload), \"</parameter>\"])\n tmp.extend(['<parameter name = \"confirm\">',\n str(confirm).upper(),\n \"</parameter>\",\n \"</SSAP_message>\"])\n return \"\".join(tmp)","def mergeSummary(self, *args):\n if len(args) == 1:\n lvl = args[0].level\n msg = args[0].message\n elif len(args) == 2:\n lvl = args[0]\n msg = args[1]\n\n if (lvl > DiagnosticStatus.OK) == (self.level > DiagnosticStatus.OK):\n if len(self.message) > 0:\n self.message += '; '\n self.message += msg\n elif lvl > self.level:\n self.message = msg\n\n if lvl > self.level:\n self.level = lvl","def create_mail_content(daily: bool = False):\n if not daily:\n order = STATE['order'] if STATE['order'] else get_closed_order()\n trade_part = create_report_part_trade(order)\n performance_part = create_report_part_performance(daily)\n advice_part = create_report_part_advice()\n settings_part = create_report_part_settings()\n general_part = create_mail_part_general()\n\n if not daily:\n trade = [\"Last trade\", \"----------\", '\\n'.join(trade_part['mail']), '\\n\\n']\n performance = [\"Performance\", \"-----------\",\n '\\n'.join(performance_part['mail']) + '\\n* (change within 24 hours)', '\\n\\n']\n advice = [\"Assessment / advice\", \"-------------------\", '\\n'.join(advice_part['mail']), '\\n\\n']\n settings = [\"Your settings\", \"-------------\", '\\n'.join(settings_part['mail']), '\\n\\n']\n general = [\"General\", \"-------\", '\\n'.join(general_part), '\\n\\n']\n\n bcs_url = 'https://bitcoin-schweiz.ch/bot/'\n text = '' if daily else '\\n'.join(trade)\n\n if not CONF.info:\n text += '\\n'.join(performance) + '\\n'.join(advice) + '\\n'.join(settings) + '\\n'.join(general) + bcs_url + '\\n'\n else:\n text += '\\n'.join(performance) + '\\n'.join(advice) + '\\n'.join(settings) + '\\n'.join(general) + CONF.info \\\n + '\\n\\n' + bcs_url + '\\n'\n\n csv = None if not daily else INSTANCE + ';' + str(datetime.datetime.utcnow().replace(microsecond=0)) + ' UTC;' + \\\n (';'.join(performance_part['csv']) + ';' + ';'.join(advice_part['csv']) + ';' +\n ';'.join(settings_part['csv']) + ';' + CONF.info + '\\n')\n\n return {'text': text, 'csv': csv}","def format(self, message):","def __str__(self):\n return f'{self.message} {self.description}'","def summary(self, *args):\n if len(args) == 1:\n self.level = args[0].level\n self.message = args[0].message\n elif len(args) == 2:\n self.level = args[0]\n self.message = str(args[1])","def msg(self):\n ended = time.time()\n started_wait = datetime.datetime.fromtimestamp(self.started).strftime(\n \"%Y-%m-%d %H:%M:%S\"\n )\n raised_date = datetime.datetime.fromtimestamp(ended).strftime(\n \"%Y-%m-%d %H:%M:%S\"\n )\n duration = ended - self.started\n return \"Info[started at {}, raised at {} after {}s]\".format(\n started_wait, raised_date, round(duration, 2)\n )","def summaryText(self):\n\n print('\\nReport Summary:\\n')\n for author in self.lowQuality.keys():\n if len(self.lowQuality[author]) > 0:\n print('Author: ' + author)\n print('---------------------')\n # do some sorting for readability\n files = []\n file2rating = {}\n for fileRating in self.lowQuality[author]:\n files.append(fileRating[1])\n file2rating[fileRating[1]] = fileRating[0]\n files.sort()\n for fileRating in files:\n print(file2rating[fileRating] + ' :: ' + fileRating)\n print('\\n\\n')","def create_workflow_info(html=True):\n if html:\n hdr = \"<h3>Workflow Theses</h3>\"\n state_info = \"<p><b>stav:</b>\" + self.workflow.workflow.state + \"</p>\"\n started_info = \"<p><b>spuštěno:</b>\" + str(self.workflow.workflow.started) + \"</p>\"\n stopped_info = \"<p><b>zastaveno:</b>\" + str(self.workflow.workflow.stopped) + \"</p>\"\n double_stop = \"<br><br>\"\n batches_count_info = \"<p><b>počet zpracovaných dávek:</b>\" + \\\n str(len(self.workflow.batches_processing)) + \"</p><br><br>\"\n else:\n hdr = \"Workflow Theses\\n\\n\"\n state_info = \"stav:\" + self.workflow.workflow.state + \"\\n\"\n started_info = \"spuštěno:\" + str(self.workflow.workflow.started) + \"\\n\"\n stopped_info = \"zastaveno:\" + str(self.workflow.workflow.stopped) + \"\\n\"\n double_stop = \"\\n\\n\"\n batches_count_info = \"počet zpracovaných dávek:\" + str(len(self.workflow.batches_processing)) + \"\\n\\n\"\n\n msg_part = hdr + state_info + started_info + stopped_info + double_stop + batches_count_info\n\n return msg_part","def generate_test_report(self, message):\n pass","def printSummary(self):\n pass","def publish_summary(self, jobs):\n pass","def summary(self):\n name = 'name : ' + self.get_name()\n description = 'description : ' + self.get_description()\n agility = 'agility : ' + str(self.get_agility())\n strength = 'strength : ' + str(self.get_strength())\n health_points = 'health_points : ' + str(self.get_health_points())\n summary = '\\n'.join([name, description, agility, strength, health_points])\n if self.take_weapon():\n summary += self.take_weapon().summary()\n return summary","def main_email(name, total, answered, not_answered, declines, remaining):\n\n start = smtplib.SMTP(host=HOST, port=PORT)\n start.starttls()\n start.login(ADDRESS, PASSWORD)\n\n date = datetime.datetime.now()\n date_now = date.strftime(\"%m-%d-%Y\")\n\n print_list, email_dict = simple_contacts('contacts.txt')\n\n emails = get_emails(print_list, email_dict)\n\n message_template = read_template()\n\n for mail in emails:\n pretty_print(f\"Sending email to {mail}\", \"!\")\n msg = MIMEMultipart()\n\n message = message_template.substitute(PERSON_NAME=name, DATE=date_now, TOTAL_CALLED=total, ANSWERED=answered, NOT_ANSWERED=not_answered, DECLINES=declines, REMAINING=remaining)\n\n msg['From'] = ADDRESS\n msg['To'] = mail\n msg['Subject'] = f\"{name} - Calling Campaign Summary - {date_now}\"\n\n msg.attach(MIMEText(message, 'plain'))\n start.send_message(msg)\n pretty_print(f\"Mail sent to {mail}\", \"!\")\n\n del msg\n\n start.quit()","def make_incident_description(self, incident):\n i_summary = incident.get('summary', \"Proofpoint TRAP Incident - Lacks a summary\")\n i_event_count = len(incident['events'])\n i_sources = incident.get('event_sources', 'No Sources Found')\n\n description = '{}\\n\\nTotal Events in incident: {}\\n\\nFrom: {}\\n'.format(i_summary, i_event_count, i_sources)\n return {'format': 'text', 'content': description}","def format_body(self, notice, fullname=None):\n if not fullname:\n fullname = self.fullname or self.name\n\n out = StringIO()\n out.write(\"Attention: {0}\\n\".format(fullname))\n out.write(\"Notification Type: {0}\\n\".format(notice.type))\n if notice.origin:\n out.write(\"Origin: {0}\\n\".format(notice.origin))\n out.write(\"\\n\")\n if notice.title:\n out.write(textwrap.fill(notice.title, 80) + \"\\n\\n\")\n\n desc = notice.description\n if desc:\n if not isinstance(desc, list):\n desc = [desc]\n for d in desc:\n if notice.doformat:\n d = textwrap.fill(d, 80)\n out.write(d)\n out.write('\\n\\n')\n\n out.write(\"Issued: {0}\\n\".format(notice.issued))\n for key in notice.metadata:\n val = notice.metadata[key]\n if isinstance(val, list):\n val = json.dumps(val, indent=2, separators=(', ', ': '))\n else:\n val = str(val)\n out.write(\"{0}: {1}\\n\".format(key, val))\n\n return out.getvalue()","def composeEmail(self, report):\r\n\r\n # Determine if we need to send a message to this developer.\r\n requires_message = False\r\n for platform in self.platforms:\r\n if platform.isBroken():\r\n requires_message = True\r\n break\r\n\r\n if not requires_message:\r\n return None\r\n\r\n # Build the message header\r\n message = \"\"\"From: Douglas Gregor <dgregor@osl.iu.edu>\r\nTo: \"\"\"\r\n message += self.name + ' <' + self.email + '>'\r\n message += \"\"\"\r\nReply-To: boost@lists.boost.org\r\nSubject: Large number of Boost failures on a platform you maintain as of \"\"\"\r\n message += str(datetime.date.today()) + \" [\" + report.branch + \"]\"\r\n message += \"\"\"\r\n\r\nYou are receiving this report because one or more of the testing\r\nplatforms that you maintain has a large number of Boost failures that\r\nare not accounted for. A full version of the report is sent to the\r\nBoost developer's mailing list.\r\n\r\nDetailed report:\r\n\"\"\"\r\n message += ' ' + report.url + \"\"\"\r\n\r\nThe following platforms have a large number of failures:\r\n\"\"\"\r\n\r\n for platform in self.platforms:\r\n if platform.isBroken():\r\n message += (' ' + platform.name + ' ('\r\n + str(len(platform.failures)) + ' failures)\\n')\r\n\r\n return message","def GetKMsgSummary(kmsgp, prefix_str=\"\"):\n kmsghp = kmsgp.ikm_header\n kmsgh = dereference(kmsghp)\n out_string = \"\"\n out_string += \"{0: <20s} {1: <#019x} {2: <8s} {3: <#011x} \".format(\n ' ', unsigned(kmsgp), ' '*8, kmsgh.msgh_id)\n prefix_str = \"{0: <20s} \".format(' ') + prefix_str\n disposition = \"\"\n bits = kmsgh.msgh_bits & 0xff\n \n # remote port\n if bits == 17:\n disposition = \"rS\"\n elif bits == 18:\n disposition = \"rO\"\n else :\n disposition = \"rX\" # invalid\n \n out_string += \"{0: <2s}\".format(disposition)\n \n # local port\n disposition = \"\"\n bits = (kmsgh.msgh_bits & 0xff00) >> 8\n \n if bits == 17:\n disposition = \"lS\"\n elif bits == 18:\n disposition = \"lO\"\n elif bits == 0:\n disposition = \"l-\"\n else:\n disposition = \"lX\" # invalid\n \n out_string += \"{0: <2s}\".format(disposition)\n \n # voucher\n disposition = \"\"\n bits = (kmsgh.msgh_bits & 0xff0000) >> 16\n \n if bits == 17:\n disposition = \"vS\"\n elif bits == 0:\n disposition = \"v-\"\n else:\n disposition = \"vX\"\n\n out_string += \"{0: <2s}\".format(disposition) \n \n # complex message\n if kmsgh.msgh_bits & 0x80000000:\n out_string += \"{0: <1s}\".format(\"c\")\n else:\n out_string += \"{0: <1s}\".format(\"s\")\n \n # importance boost\n if kmsgh.msgh_bits & 0x20000000:\n out_string += \"{0: <1s}\".format(\"I\")\n else:\n out_string += \"{0: <1s}\".format(\"-\")\n \n dest_proc_name = \"\"\n if kmsgp.ikm_header.msgh_remote_port:\n dest_proc_name = GetDestinationProcessFromPort(kmsgp.ikm_header.msgh_remote_port)\n\n out_string += \"{0: ^6d} {1: <#019x} {2: <26s} {3: <26s}\\n\".format(\n unsigned(kmsgh.msgh_size), unsigned(kmsgh.msgh_local_port),\n GetKMsgSrc(kmsgp), dest_proc_name)\n \n if kmsgh.msgh_bits & 0x80000000:\n out_string += prefix_str + \"\\t\" + GetKMsgComplexBodyDesc.header + \"\\n\"\n out_string += prefix_str + \"\\t\" + GetKMsgComplexBodyDesc(kmsgp, prefix_str + \"\\t\") + \"\\n\"\n \n return out_string","def summarize(self):\n txtStr = \"%s to %s, %d flight legs.\" %\\\n (self.origin, self.destination, self.nlegs)\n txtStr += \"\\nTakeoff at %s\\nLanding at %s\\n\" %\\\n (self.takeoff, self.landing)\n txtStr += \"Flight duration of %s including %s observing time\" %\\\n (str(self.flighttime), self.obstime)\n\n return txtStr","def add_daily_summary(self):\n auth_date = self.report_date.strftime(\"%b %-d, %Y\")\n now = datetime.now().strftime(\"%x %X\")\n report_title = ' '.join([\n f'Report for {self.origin_value} participant consents authored on: {auth_date} 12:00AM-11:59PM UTC',\n f'(generated on {now} Central)'\n ])\n\n report_notes = [\n ['Notes:'],\n [f'Validation details on this sheet for {self.origin_value} participants only'],\n ['Checkbox validation currently only performed on GROR consents'],\n ['Total Errors can exceed Consents with Errors if any consents had multiple validation errors']\n ]\n\n self._add_text_rows(text_rows=[[report_title]], format_spec=self.format_specs.get('bold_text'))\n # Add any explanatory text / details about the report that have been included in the layout\n self._add_text_rows(text_rows=report_notes, format_spec=self.format_specs.get('legend_text'),\n row_pos=self.row_pos + 1)\n\n if not self._has_needs_correcting(self.consent_df):\n self._add_text_rows(text_rows=[['No consent validation errors detected']],\n format_spec=self.format_specs.get('italic_text'), row_pos=self.row_pos+1)\n\n # Daily summary counts for all the recently authored consents that were processed (regardless of errors)\n self._add_text_rows([['Total Consent Validation Counts']],\n format_spec=self.format_specs.get('bold_text'), row_pos=self.row_pos+1)\n self._add_consent_issue_count_header_section(hpo='All Entities')\n self._add_consent_issue_counts(self.consent_df, show_all_counts=True)","def generate_audit_email_body(audit_id):\n msg = None\n try:\n ad_list, err = get_entries(audit_id)\n if err:\n raise Exception(err)\n if ad_list:\n ad = ad_list[0]\n msg = ' Audit time: %s\\n Performed from: %s\\n Performed by: %s\\n Action: %s.' % (\n ad['time'], ad['ip'], ad['username'], ad['action_str'])\n except Exception, e:\n return None, 'Error generating audit email message body : %s' % str(e)\n else:\n return msg, None","def createMessage( self, *args, **kw ):\n if not kw.has_key('charset'):\n kw['charset'] = self.getOutputCharset()\n kw['to_mail'] = 1\n return MailServerBase.createMessage( self, *args, **kw )","def generate_withno_attachement(sender, recipient, subject, body):\n # Basic Email formatting\n message = email.message.EmailMessage()\n message[\"From\"] = sender\n message[\"To\"] = recipient\n message[\"Subject\"] = subject\n message.set_content(body)\n return message","def email_body_to_user_receiving_msg(profile, message):\n\tmsg = '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr><td align=\"center\" valign=\"top\"></td></tr></tbody></table>'\n\tmsg = msg + '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\"><tbody><tr>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\" cellspacing=\"0\" cellpadding=\"10\" width=\"600\">'\n\tmsg = msg + '<tbody>'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<a href=\"https://insprite.co\"><img src=\"http://ryanfbaker.com/insprite/inspriteLogoA.png\" border=\"0\" alt=\"Insprite\" align=\"center\" width=\"200px\" height=\"55px\" /></a>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</tbody>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '<font style=\"font-family:Helvetica Neue;color:#555555;font-size:14px;\">'\n\tmsg = msg + 'You\\'ve got mail. It\\'s from <a href=\\\"https://127.0.0.1:5000/profile?hero=' + str(profile.prof_id) + '\\\" style=\"color:#1488CC\">' + profile.prof_name.encode('utf8', 'ignore') + '</a>.'\n\tmsg = msg + '<br><i>' + message.msg_content + '</i>'\n\tmsg = msg + '</font><br>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '<td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:200px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '<a href=\"#\" style=\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center;\" target=\"_blank\">Reply</a>'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/facebookIcon.png\">'\n\tmsg = msg + '<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/twitterIcon.png\">'\n\tmsg = msg + '<img src=\"http://ryanfbaker.com/insprite/instagramIcon.png\">'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\n\tmsg = msg + '<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '<img src=\"http://ryanfbaker.com/insprite/spacer-2.png\">'\n\tmsg = msg + '</td></tr>'\n\tmsg = msg + '</table>'\n\treturn msg","def consolidate_messages(self, msg):","def generate_message(self, mtu):\r\n raise SystemExit(self.sm.__end_msg__)","def summary(self):\n if self.intact and self.valid:\n return 'INTACT:' + ','.join(self.summary_fields())\n else:\n return 'INVALID'","def print_summary(self):\n #outcomes = self.get_outcomes()\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\n print('')\n print ('Passes: %i' % self.get_pass_count())\n print ('Fails: %i' % self.get_fail_count())\n print ('Errors: %i' % self.get_error_count())\n print ('Untested: %i' % self.get_untested_count())\n print ('Skipped: %i' % self.get_skipped_count())","def send_status_mail(self):\n from django.core.mail import send_mail\n subject = \"App Load Status | %s - %s | S%02d/-R%02d\" % (\n self.publication.account, self.title, self.load_count, self.reject_count)\n body = \"Account: %s\\nPublication: %s\\nStatus: %s\\nTime:%s - %s\\nLoaded: %02d\\nRejected: %02d\\n\\nComments\\n:%s\\n\" % (\n self.publication.account, self.publication, self.load_status, self.start_time, self.end_time, self.load_count, self.reject_count, self.comments)\n body = body + settings.EMAIL_DEFAULT_SIGNATURE\n if self.publication.id in [2,3,60,61,62,63,370,39]:\n return send_mail(subject, body, settings.DEFAULT_FROM_EMAIL,\n [settings.ADMIN_EMAIL, 'anand.kumar@contify.com', 'rajesh.swain@contify.com', 'tapan.puhan@contify.com'], fail_silently=True)\n else:\n return send_mail(subject, body, settings.DEFAULT_FROM_EMAIL,\n [settings.ADMIN_EMAIL], fail_silently=True)","def summary_line_and_description():","def generate_web_service_email(details):\n subject = details[\"subject\"]\n body = details[\"message\"]\n from_email = settings.DEFAULT_FROM_ADDR\n reply_to_email = [settings.EMAIL_TARGET_W]\n to_email = details[\"email_to\"]\n\n email = GenericEmailGenerator(subject=subject, to_emails=to_email, bcc=reply_to_email, from_email=from_email,\n reply_to=reply_to_email, body=body, context={'mrkdwn': True})\n\n return email","def makeAMQPmsg(self):\n msg = {'msgType' : 'AgentUpdate',\n 'AgentType': 'Bus',\n 'Extnum':self.Extnum,\n 'Vm': self.cv['Vm'],\n 'Va': self.cv['Va'],\n }\n return msg","def make_info(self, v_queue_mean, v_tr_cv):\n info = '\\n - average queue size: %.2f packets'\n info += '\\n - CV for transmission rate: %.2f'\n return info % (v_queue_mean, v_tr_cv)","def generate_selfservice_notice_email(context):\n subject = \"Self Service Form Submission\"\n from_email = settings.DEFAULT_FROM_ADDR\n to_email = [settings.EMAIL_TARGET_W, settings.EMAIL_TARGET_VP]\n\n cont_html = render_to_string('emails/email_selfservice.html', context)\n cont_text = render_to_string('emails/email_selfservice.txt', context)\n\n email = EmailMultiAlternatives(subject, cont_text, from_email, to_email)\n email.attach_alternative(cont_html, \"text/html\")\n\n return email","def summarize(self):\n\n if self.summarizer == None:\n return \"The summarizer has not been initialised\"\n elif not isinstance(self.summarizer, Summarizer):\n raise TypeError(\"summarizer is not a Summarizer object\")\n\n self.summary = self.summarizer.summarize(self.article)\n return self.summary","def create_message(self, subject, message, destination, origin = None):\n msg = MIMEText(message)\n if origin is not None:\n msg['From'] = origin\n else:\n msg['From'] = self.origin\n msg['Subject'] = subject\n msg['To'] = destination\n self.messages.append(msg)","def generate_message(ctx, question, answer):\n return preamble.format(channel=rules_channel(ctx).id) + question + answer","def gen_report(self):\n self.report = '#Report for {0}\\n'.format(self.ip)\n self.report += 'This report was generated by the chameleon pentest bot. We cannot grant 100% accurate results.\\n'\n self.report += '###Services:\\n'\n for service in self.services:\n self.report += '#####{0}:\\n- Port: {1}\\n- Info:{2}'.format(service.name, service.port, service.info)\n self.report += '###Vulnerabilities:\\n'\n for vuln in self.vulns:\n self.report += '- {0}\\n'.format(vuln.name)\n self.report += 'Open an issue for wrong results at github.com/coretool/chameleon.'","def create_report(self):\n text_string = \"\\n\"\n text_string += \"Donor Name | Total Given | Num Gifts |\\\n Average Gift\\n\\\n ------------------------------------------------------------------\\n\"\n for donor in sorted(self.donors, key=Donor.sort_key, reverse=True):\n text_string += f\"{donor.name:<26} $ {donor.total_donations:>11.2f}\\\n {donor.number_of_donations:>10} $ {donor.average_gift:>11.2f}\\n\"\n\n return text_string","def create_message(sender, to, subject, message_text_html, message_text_plain):\r\n message = MIMEMultipart('alternative')\r\n message['to'] = to\r\n message['from'] = sender\r\n message['subject'] = subject\r\n message_html = MIMEText(message_text_html, 'html') # HTML version\r\n message_plain = MIMEText(message_text_plain) # plain text version\r\n message.attach(message_plain)\r\n message.attach(message_html)\r\n return {'raw': base64.urlsafe_b64encode(message.as_string().encode()).decode()}","def summary_string(self) -> str:\n return f\"dixonoid: {self.plain_rules}\"","def summaryView(request):\n\n alert_errors = []\n alert_infos = []\n alert_filters = []\n\n runs = get_runs_from_request_filters(\n request, alert_errors, alert_infos, alert_filters\n )\n\n summary = SummaryReport(runs)\n\n context = {\n \"refs\": summary.reference_runs(),\n \"runs\": summary.runs_checked_per_type(),\n \"tk_maps\": summary.tracker_maps_per_type(),\n \"certified_runs\": summary.certified_runs_per_type(),\n \"sums\": summary.sum_of_quantities_per_type(),\n \"alert_errors\": alert_errors,\n \"alert_infos\": alert_infos,\n \"alert_filters\": alert_filters,\n }\n\n return render(request, \"certhelper/summary.html\", context)","def summarize(self):\n txtSumm = ''\n\n if self.legtype == 'Takeoff':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Landing':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Other':\n txtSumm = \"%02d -- %s\" %\\\n (self.legno, self.legtype)\n elif self.legtype == 'Observing':\n txtSumm = \"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\" %\\\n (self.legno, self.target, self.ra, self.dec,\n str(self.duration),\n str(self.obsdur))\n txtSumm += \"\\n\"\n if self.nonsid is True:\n txtSumm += \"NONSIDERIAL TARGET -- NAIFID: %d\" % (self.naifid)\n txtSumm += \"\\n\"\n txtSumm += \"(The SOFIA project sincerely hopes you enjoy \"\n txtSumm += \"your observing breaks due to XFORMS crashes)\"\n txtSumm += \"\\n\"\n txtSumm += \"ObsPlan: %s, ObsBlk: %s\" % (self.obsplan, self.obsblk)\n txtSumm += \"\\n\\n\"\n txtSumm += \"Elevation Range: %.1f, %.1f\" % (self.range_elev[0],\n self.range_elev[1])\n txtSumm += \"\\n\\n\"\n txtSumm += \"ROF Range: %.1f, %.1f\" % (self.range_rof[0],\n self.range_rof[1])\n txtSumm += \"\\n\"\n txtSumm += \"ROF Rate Range: %.1f, %.1f %s\" % (self.range_rofrt[0],\n self.range_rofrt[1],\n self.range_rofrtu)\n txtSumm += \"\\n\\n\"\n txtSumm += \"True Heading Range: %.1f, %.1f\" % (self.range_thdg[0],\n self.range_thdg[1])\n txtSumm += \"\\n\"\n txtSumm += \"True Heading Rate Range: %.1f, %.1f %s\" %\\\n (self.range_thdgrt[0],\n self.range_thdgrt[1],\n self.range_thdgrtu)\n txtSumm += \"\\n\"\n txtSumm += \"Moon Angle: %.1f, Moon Illumination: %s\" %\\\n (self.moonangle, self.moonillum)\n\n return txtSumm","def present_summary(services, methods, count, backup):\n print_heading(\"Summary\")\n if backup is not None:\n writer(f\"Backup: {backup}\")\n writer(f\"Showing {count[0]}/{len(services)} Services\")\n writer(f\"Showing {count[1]}/{len(methods)} Methods\\n\")","def show_summary(self, lang):\n return self.summary % self.vars","def _show_summary(self):\n print 'Summary:'\n print ' Reports downloaded successfully: %d' % self.counts\n print ' Reports not downloaded: %d\\n' % self.failed","def email_body_new_proposal_notification_to_seller(meeting, buyer_name, buyer_profile_id):\n\tmsg = '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\">\\n<tbody>\\n\\t<tr><td align=\"center\" valign=\"top\">\\n\\t</td></tr>\\n</tbody>\\n</table>'\n\n\tmsg = msg + '<table cellspacing=\"0\" cellpadding=\"0\" width=\"100%\" bgcolor=\"#ffffff\">'\n\tmsg = msg + '\\n<tbody><tr>'\n\n\tmsg = msg + '\\n\\t<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\" cellspacing=\"0\" cellpadding=\"10\" width=\"600\">'\n\tmsg = msg + '\\n\\t\\t<tbody>'\n\tmsg = msg + '\\n\\t\\t\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\n\\t\\t\\t\\t<a href=\"https://insprite.co\"><img src=\"http://ryanfbaker.com/insprite/inspriteLogoA.png\" border=\"0\" alt=\"Insprite\" align=\"center\" width=\"200px\" height=\"55px\" />'\n\tmsg = msg + '\\n\\t\\t\\t\\t</a>'\n\tmsg = msg + '\\n\\t\\t\\t</td></tr>'\n\tmsg = msg + '\\n\\t\\t</tbody>'\n\tmsg = msg + '\\n\\t</table>'\n\n\n\tmsg = msg + '\\n\\t<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\n\\t\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '\\n\\t\\t\\t<font style=\"font-family:Helvetica Neue;color:#555555;font-size:14px;\">'\n\tmsg = msg + '\\n\\t\\t\\t\\tGreat! You received a new proposal from <a href=\\\"https://127.0.0.1:5000/profile?hero=' + buyer_profile_id + '\\\" style=\"color:#1488CC\">'+ buyer_name + '</a>.'\n\tmsg = msg + '\\n\\t\\t\\t\\t<br><br><br>'\n\tmsg = msg + '\\n\\t\\t\\t\\tTime: ' + meeting.meet_ts.strftime('%A, %b %d, %Y %H:%M %p') + '<br>'\n\tmsg = msg + '\\n\\t\\t\\t\\tDuration: ' + meeting.get_duration_in_hours() + ' hours<br>'\n\tmsg = msg + '\\n\\t\\t\\t\\tLocation: ' + str(meeting.meet_location) + '<br>'\n\tmsg = msg + '\\n\\t\\t\\t\\tFee: $' + str(meeting.meet_cost) + '<br><br>'\n\tmsg = msg + '\\n\\t\\t\\t\\tDescription: ' + meeting.get_description_html() + '<br><br>'\n\tmsg = msg + '\\n\\t\\t\\t</font><br><br>'\n\tmsg = msg + '\\n\\t\\t</td></tr>'\n\n\tmsg = msg + '\\n\\t\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:150px\" align=\"left\" valign=\"top\">'\n\tmsg = msg + '\\n\\t\\t\\t<a href=\\\"'+ meeting.accept_url() +'\\\" style=\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center;\" target=\"_blank\">Accept</a> '\n\tmsg = msg + '\\n\\t\\t\\t<a href=\\\"'+ meeting.reject_url() +'\\\" style=\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #e55e62;font-size: 14px;border-radius: 3px;border: 1px solid #e55e62;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center\" target=\"_blank\">Reject</a> '\n\tmsg = msg + '\\n\\t\\t</td></tr>'\n\tmsg = msg + '\\n\\t</table>'\n\n\tmsg = msg + '\\n\\t<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\n\\t\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\n\\t\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/facebookIcon.png\">'\n\tmsg = msg + '\\n\\t\\t\\t<img style=\"padding-right: 6px\" src=\"http://ryanfbaker.com/insprite/twitterIcon.png\">'\n\tmsg = msg + '\\n\\t\\t\\t<img src=\"http://ryanfbaker.com/insprite/instagramIcon.png\">'\n\tmsg = msg + '\\n\\t\\t</td></tr>'\n\tmsg = msg + '\\n\\t</table>'\n\n\tmsg = msg + '\\n\\t<table style=\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\">'\n\tmsg = msg + '\\n\\t\\t<tr><td style=\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\" align=\"center\" valign=\"middle\">'\n\tmsg = msg + '\\n\\t\\t\\t<img src=\"http://ryanfbaker.com/insprite/spacer-2.png\">'\n\tmsg = msg + '\\n\\t\\t</td></tr>'\n\tmsg = msg + '\\n\\t</table>'\n\n\tmsg = msg + '\\n</tr></tbody>'\n\tmsg = msg + '</table>'\n\treturn msg","def __str__(self):\n return '- ' + str.join('\\n- ', [self.format_message(record)\n for record in self._messages]) + '\\n'","def build_message(self, subject, body, attachment=None):\n msg = MIMEMultipart()\n msg['From'] = self.src_addr\n msg['To'] = self.dest_addr\n msg['Subject'] = subject\n msg.attach(MIMEText(body, 'plain'))\n\n if attachment is not None:\n if os.path.exists(attachment):\n with open(attachment, 'rb') as a:\n part = MIMEBase('application', 'octet-stream')\n part.set_payload(a.read())\n encoders.encode_base64(part)\n part.add_header('Content-Disposition', 'attachment; filename= {}'.format(attachment))\n msg.attach(part)\n\n self.outbox.append(msg)"],"string":"[\n \"def composeSummaryEmail(self):\\r\\n message = \\\"\\\"\\\"From: Douglas Gregor <dgregor@osl.iu.edu>\\r\\nTo: boost@lists.boost.org\\r\\nReply-To: boost@lists.boost.org\\r\\nSubject: [Report] \\\"\\\"\\\"\\r\\n message += str(self.numFailures()) + \\\" failures on \\\" + branch\\r\\n if branch != 'trunk':\\r\\n message += ' branch'\\r\\n message += \\\" (\\\" + str(datetime.date.today()) + \\\")\\\"\\r\\n message += \\\"\\\"\\\"\\r\\n\\r\\nBoost regression test failures\\r\\n\\\"\\\"\\\"\\r\\n message += \\\"Report time: \\\" + self.date + \\\"\\\"\\\"\\r\\n\\r\\nThis report lists all regression test failures on high-priority platforms.\\r\\n\\r\\nDetailed report:\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n message += ' ' + self.url + '\\\\n\\\\n'\\r\\n\\r\\n if self.numFailures() == 0:\\r\\n message += \\\"No failures! Yay!\\\\n\\\"\\r\\n return message\\r\\n \\r\\n # List the platforms that are broken\\r\\n any_broken_platforms = self.numReportableFailures() < self.numFailures()\\r\\n if any_broken_platforms:\\r\\n message += \\\"\\\"\\\"The following platforms have a large number of failures:\\r\\n\\\"\\\"\\\"\\r\\n for platform in sorted_keys( self.platforms ):\\r\\n if self.platforms[platform].isBroken():\\r\\n message += (' ' + platform + ' ('\\r\\n + str(len(self.platforms[platform].failures))\\r\\n + ' failures)\\\\n')\\r\\n\\r\\n message += \\\"\\\"\\\"\\r\\nFailures on these \\\"broken\\\" platforms will be omitted from the results below.\\r\\nPlease see the full report for information about these failures.\\r\\n\\r\\n\\\"\\\"\\\"\\r\\n \\r\\n # Display the number of failures\\r\\n message += (str(self.numReportableFailures()) + ' failures in ' + \\r\\n str(len(self.libraries)) + ' libraries')\\r\\n if any_broken_platforms:\\r\\n message += (' (plus ' + str(self.numFailures() - self.numReportableFailures())\\r\\n + ' from broken platforms)')\\r\\n \\r\\n message += '\\\\n'\\r\\n\\r\\n # Display the number of failures per library\\r\\n for k in sorted_keys( self.libraries ):\\r\\n library = self.libraries[k]\\r\\n num_failures = library.numFailures()\\r\\n message += ' ' + library.name + ' ('\\r\\n \\r\\n if library.numReportableFailures() > 0:\\r\\n message += (str(library.numReportableFailures())\\r\\n + \\\" failures\\\")\\r\\n \\r\\n if library.numReportableFailures() < num_failures:\\r\\n if library.numReportableFailures() > 0:\\r\\n message += ', plus '\\r\\n \\r\\n message += (str(num_failures-library.numReportableFailures()) \\r\\n + ' failures on broken platforms')\\r\\n message += ')\\\\n'\\r\\n pass\\r\\n\\r\\n message += '\\\\n'\\r\\n\\r\\n # Provide the details for the failures in each library.\\r\\n for k in sorted_keys( self.libraries ):\\r\\n library = self.libraries[k]\\r\\n if library.numReportableFailures() > 0:\\r\\n message += '\\\\n|' + library.name + '|\\\\n'\\r\\n for test in library.tests:\\r\\n if test.numReportableFailures() > 0:\\r\\n message += ' ' + test.name + ':'\\r\\n for failure in test.failures:\\r\\n platform = failure.platform\\r\\n if not platform.isBroken():\\r\\n message += ' ' + platform.name\\r\\n message += '\\\\n'\\r\\n\\r\\n return message\",\n \"def generateNotifyMessage(self):\\n now = datetime.now()\\n current_time = now.strftime(\\\"%H:%M:%S\\\")\\n today = date.today()\\n current_date = today.strftime(\\\"%B %d, %Y\\\")\\n\\n subject = \\\"Progam operating warning - Not Running\\\"\\n body = \\\"Since \\\" + current_date + \\\" at \\\" + current_time \\n msg = f'Subject: {subject} \\\\n\\\\n{body}'\\n return msg\",\n \"def summary_string(self) -> str:\",\n \"def construct_email_content(self):\\n # Construct header of the message\\n content = MAIL_HEAD_CONTENT.replace(\\\"TITLE_HOLDER\\\", self.title).replace('FAIL_JOB_HOLDER',\\n self.fail_job_content).replace(\\n \\\"TIME_HOLDER\\\", os.getenv(\\\"START_TIME\\\")).replace(\\\"GRAPH_HOLDER\\\", os.getenv(\\\"BENCHMARK_GRAPH\\\")).replace(\\n \\\"JOB_HOLDER\\\", os.getenv(\\\"BENCHMARK_TYPE\\\")).replace(\\\"DEVICE_HOLDER\\\", os.getenv(\\\"DEVICE_TYPE\\\")).replace(\\\"CUDA_HOLDER\\\", os.getenv(\\\"VERSION_CUDA\\\")).replace('DISPLAY', self.job_display)\\n\\n if not self.alarm_info:\\n return\\n # Construct alarm content\\n content += self.alarm_info\\n # Construct the tail of the message\\n content += MAIL_TAIL_CONTENT.replace(\\\"BENCHMARK_WEBSITE1\\\", os.getenv(\\\"BENCHMARK_WEBSITE1\\\", \\\"\\\")).strip().replace(\\n 'RUN_ENV_HOLDER', self.env_content).replace(\\\"BENCHMARK_WEBSITE2\\\", os.getenv(\\\"BENCHMARK_WEBSITE2\\\"))\\n\\n with open(os.path.join(self.log_path, \\\"mail.html\\\"), \\\"w\\\") as f_object:\\n f_object.write(content)\",\n \"def formatted_message(self):\\n message = MIMEMultipart()\\n message['From'] = self.sender\\n message['To'] = self.receiver\\n message['Subject'] = self.subject.format(**self.params)\\n message.attach(MIMEText(self.body.format(**self.params), 'plain'))\\n return message.as_string()\",\n \"def _construct_message(self):\\n self.message[\\\"text\\\"] = \\\"\\\"\\n if self.from_:\\n self.message[\\\"text\\\"] += \\\"From: \\\" + self.from_ + \\\"\\\\n\\\"\\n if self.subject:\\n self.message[\\\"text\\\"] += \\\"Subject: \\\" + self.subject + \\\"\\\\n\\\"\\n\\n self.message[\\\"text\\\"] += self.body\\n self._add_attachments()\",\n \"def compose_email(self, donation_id=-1, write_totals=False):\\n if not write_totals:\\n amount = self.donations[donation_id]\\n else:\\n amount = self.total_donations\\n email_string = f\\\"\\\\nDear {self.name},\\\\n Thank you for your generous\\\\\\n gift of ${amount:.2f}! It will help Local Charity achieve our mission.\\\\n\\\\\\n Best regards,\\\\n\\\\\\n Local Charity\\\\n\\\\n\\\"\\n return email_string\",\n \"def build_message():\\n outgoing_mail = Mail()\\n outgoing_mail.from_email = Email(email_from_address, email_from_name)\\n outgoing_mail.subject = subject\\n personalization = Personalization()\\n for recipient in email_to_addresses:\\n personalization.add_to(Email(recipient))\\n outgoing_mail.add_personalization(personalization)\\n outgoing_mail.add_content(Content(\\\"text/plain\\\", str.join('\\\\n', _log)))\\n outgoing_mail.add_content(Content(\\\"text/html\\\", \\\"<html><body> {} </body></html>\\\".format(str.join(' <br /> ', _log))))\\n return outgoing_mail.get()\",\n \"def make_email_message(itrf_begin, epoch_begin, itrf_final, epoch_final, velocity, date):\\n\\n message = \\\"Estimado Usuario,\\\\n\\\\nEn adjunto encontrará los resultados de la transformacion ITRF de acuerdo a la siguiente configuración:\\\\n\\\\nITRF inicial: \\\"+str(itrf_begin)+\\\"\\\\nEpoca inicial: \\\"+str(epoch_begin)+\\\"\\\\nITRF final: \\\"+str(itrf_final)+\\\"\\\\nEpoca final: \\\"+str(epoch_final)+\\\"\\\\nModelo de velocidad: \\\"+velocity+\\\"\\\\nFecha de la solicitud de la transformación: \\\"+date+\\\"\\\\n\\\\n\\\\nSaludos Cordiales,\\\\n\\\\nEquipo de Geodesia del IGVSB.\\\"\\n return message\",\n \"def _create_message(self, msg):\\n head = msg[\\\"head\\\"]\\n body = msg[\\\"body\\\"]\\n body = body.format(**self.data)\\n length = len(body)\\n head = head.format(length=length, **self.data)\\n return head + body\",\n \"def generate_plain_mesg(info, open_quests, owner, tags):\\n\\n msg = (\\n \\\"This email is being sent to {} because that is the owner listed\\\\n\\\"\\n \\\"for the systems with open Hermes labors listed below.\\\\n\\\\n\\\"\\n \\\"Due dates, if any, are noted with each quest.\\\\n\\\".format(owner)\\n )\\n msg += (\\n \\\"\\\\nTo throw an event manually, you can run the following command \\\"\\n \\\"on a shell server:\\\"\\n \\\"\\\\n\\\\n\\\"\\n \\\"$ hermes event create [event] --host [hostname].\\\\n\\\\n\\\"\\n \\\"Or you can visit the quests linked below.\\\\n\\\\n\\\".format(\\n settings.frontend)\\n )\\n for quest_id in info[owner]:\\n quest = find_quest(open_quests, quest_id)\\n if quest:\\n msg += (\\n \\\"==[ QUEST {} ]================================\\\\n\\\"\\n \\\"CREATOR: {}\\\\n\\\"\\n ).format(\\n quest_id, quest.creator\\n )\\n if quest.target_time:\\n msg += \\\"DUE: {}\\\\n\\\".format(quest.target_time)\\n msg += \\\"DESC: \\\\\\\"{}\\\\\\\"\\\\n\\\".format(textwrap.fill(\\n quest.description,\\n width=60, subsequent_indent=\\\"\\\"\\n ))\\n msg += \\\"LINK: {}/v1/quests/{}\\\\n\\\\n\\\".format(\\n settings.frontend, quest_id\\n )\\n else:\\n msg += \\\" Labors not associated with a quest:\\\\n\\\\n\\\"\\n\\n msg += \\\"Machines with labors:\\\\n\\\"\\n\\n for hostname in sorted(info[owner][quest_id]):\\n if tags[hostname]:\\n tags_str = \\\"{}\\\".format((\\\", \\\".join(tags[hostname])))\\n else:\\n tags_str = \\\"no services\\\"\\n msg += \\\" {} ({})\\\\n\\\".format(hostname, tags_str)\\n\\n msg += \\\"\\\\n\\\\n\\\"\\n\\n return msg\",\n \"def email_article_summary(to_address, summary_filename, start_year, start_month, end_year, end_month, num_articles):\\n \\n host = HOST\\n from_address = FROM_ADDRESS\\n body = \\\"\\\"\\\"\\n Good morning,\\n \\n There were %i peer-reviewed papers produced by researchers at this institute between %i/%i and %i/%i. A summary file containing the front page from each article is attached with this email. Please print out these summary pages, highlight the author(s) on each article and pin them to the monthly papers noticeboard.\\n \\n Thanks a bunch,\\n \\n Skynet.\\n \\n \\\"\\\"\\\" % (num_articles, start_month, start_year, end_month, end_year, )\\n \\n recipients = [to_address, ADMIN_ADDRESS]\\n \\n logging.info(\\\"Preparing summary email report for %s\\\" % (', '.join(recipients), ))\\n \\n successful = True\\n for recipient in recipients:\\n \\n message = MIMEMultipart()\\n message[\\\"From\\\"] = from_address\\n message[\\\"To\\\"] = recipient\\n message[\\\"Subject\\\"] = \\\"Refereed papers summary between %i/%i and %i/%i\\\" % (start_month, start_year, end_month, end_year, )\\n message[\\\"Date\\\"] = formatdate(localtime=True)\\n \\n message.attach(MIMEText(textwrap.dedent(body).lstrip()))\\n \\n part = MIMEBase('application', 'octet-stream')\\n part.set_payload(open(summary_filename, 'rb').read())\\n Encoders.encode_base64(part)\\n part.add_header('Content-Disposition', 'attachment; filename=\\\"%s\\\"' % os.path.basename(summary_filename))\\n message.attach(part)\\n \\n server = smtplib.SMTP(host)\\n \\n try:\\n failed = server.sendmail(from_address, to_address, message.as_string())\\n server.close()\\n \\n except Exception as e:\\n logging.critical(\\\"Unable to send email to %s. Error: %s\\\" % (recipient, str(e), ))\\n successful = False\\n \\n else:\\n logging.info(\\\"Email successfully sent to %s\\\" % recipient)\\n \\n \\n return successful\",\n \"def composeTestingSummaryEmail(self):\\r\\n brokenPlatforms = 0\\r\\n for platform in sorted_keys( self.platforms ):\\r\\n if self.platforms[platform].isBroken():\\r\\n brokenPlatforms = brokenPlatforms + 1\\r\\n\\r\\n if brokenPlatforms == 0:\\r\\n return None;\\r\\n \\r\\n message = \\\"\\\"\\\"From: Douglas Gregor <dgregor@osl.iu.edu>\\r\\nTo: boost-testing@lists.boost.org\\r\\nReply-To: boost-testing@lists.boost.org\\r\\nSubject: [Report] \\\"\\\"\\\"\\r\\n message += str(brokenPlatforms) + \\\" potentially broken platforms on \\\" + branch\\r\\n if branch != 'trunk':\\r\\n message += ' branch'\\r\\n message += \\\" (\\\" + str(datetime.date.today()) + \\\")\\\"\\r\\n message += \\\"\\\"\\\"\\r\\n\\r\\nPotentially broken platforms for Boost regression testing\\r\\n\\\"\\\"\\\"\\r\\n message += \\\"Report time: \\\" + self.date + \\\"\\\"\\\"\\r\\n\\r\\nThis report lists the high-priority platforms that are exhibiting a\\r\\nlarge number of regression test failures, which might indicate a problem\\r\\nwith the test machines or testing harness.\\r\\n\\r\\nDetailed report:\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n message += ' ' + self.url + '\\\\n'\\r\\n\\r\\n message += \\\"\\\"\\\"\\r\\nPlatforms with a large number of failures:\\r\\n\\\"\\\"\\\"\\r\\n for platform in sorted_keys( self.platforms ):\\r\\n if self.platforms[platform].isBroken():\\r\\n message += (' ' + platform + ' ('\\r\\n + str(len(self.platforms[platform].failures))\\r\\n + ' failures)\\\\n')\\r\\n\\r\\n return message\",\n \"def construct_message(self):\\n msg_type = self.msg_type\\n if msg_type == \\\"PUBMSG\\\":\\n msg_type = \\\"PRIVMSG\\\"\\n ret = \\\"{} {}\\\".format(msg_type, self.target)\\n if self.content:\\n ret += \\\" :{}\\\".format(self.content)\\n return ret + \\\"\\\\r\\\\n\\\"\",\n \"def massage_addinfo(self) -> str:\\n self.message_str = f'{self.time}\\\\n{self.sent_by}\\\\n'\\n return self.message_str\",\n \"def summary(self):\\n return ''\",\n \"def test_email_content():\\n\\n time_of_day = alerts.current_time()\\n hostname = alerts.host_name()\\n\\n subject = \\\"Subject: Raspi-Sump Email Test\\\"\\n message = \\\"Raspi-Sump Test Email\\\"\\n\\n return \\\"\\\\r\\\\n\\\".join(\\n (\\n f\\\"From: {configs['email_from']}\\\",\\n f\\\"To: {configs['email_to']}\\\",\\n f\\\"{subject}\\\",\\n \\\"\\\",\\n f\\\"{hostname} - {time_of_day} - {message}.\\\",\\n )\\n )\",\n \"def summary(self):\\r\\n return '%s%s: %s%s %s%s' % (BLUE, self.title,\\r\\n GREEN, self.description,\\r\\n NORMAL, self.link)\",\n \"def massage_addinfo(self) -> str:\\n self.message_str= \\\"{}, {}\\\\n\\\".format(self.sent_by, self.time)\",\n \"def create_test_summary(args, TEST_RESULTS):\\n logging.error(\\\"Creating test summary report...\\\")\\n\\n try:\\n test_summary = \\\"Performance Metrics of {APP} Application Tested from this PR\\\\n\\\".format(APP=args.bundle_id)\\n test_summary += \\\"---------------------------------------------------------------\\\\n\\\"\\n\\n for element in TEST_RESULTS:\\n if element != LAUNCHES:\\n test_summary += \\\"> {KEY}: {VALUE}\\\".format(KEY=element, VALUE=TEST_RESULTS[element])\\n if element == INSTALL_LAUNCH_DURATION:\\n if int(TEST_RESULTS[INSTALL_LAUNCH_DURATION]) > args.duration_limit:\\n test_summary += \\\"ms :x:\\\\n\\\"\\n else:\\n test_summary += \\\"ms :white_check_mark:\\\\n\\\"\\n\\n if element == INSTALL_MEMORY_USAGE:\\n if int(TEST_RESULTS[INSTALL_MEMORY_USAGE]) > args.memory_limit:\\n test_summary += \\\"MB :x:\\\\n\\\"\\n else:\\n test_summary += \\\"MB :white_check_mark:\\\\n\\\"\\n\\n if element == APP_SIZE:\\n if int(TEST_RESULTS[APP_SIZE]) > args.size_limit:\\n test_summary += \\\"MB :x:\\\\n\\\"\\n else:\\n test_summary += \\\"MB :white_check_mark:\\\\n\\\"\\n test_summary += \\\"---------------------------------------------------------------\\\\n\\\"\\n\\n for element in TEST_RESULTS[LAUNCHES]:\\n test_summary += \\\"> DEVICE: {DEVICE} | LAUNCH TYPE: {LAUNCH_TYPE} | \\\".format(DEVICE=element[DEVICE], LAUNCH_TYPE=element[LAUNCH_TYPE])\\n test_summary += \\\"DURATION: {DURATION}ms \\\".format(DURATION=element[LAUNCH_DURATION])\\n if int(element[LAUNCH_DURATION]) > args.duration_limit:\\n test_summary += \\\" :x: | \\\"\\n else:\\n test_summary += \\\" :white_check_mark: | \\\"\\n\\n test_summary += \\\"MEMORY USAGE: {MEMORY_USAGE}MB \\\".format(MEMORY_USAGE=element[MEMORY_USAGE])\\n if int(element[MEMORY_USAGE]) > args.memory_limit:\\n test_summary += \\\" :x:\\\\n\\\"\\n else:\\n test_summary += \\\" :white_check_mark:\\\\n\\\"\\n test_summary += \\\"----------------------------------------------------\\\\n\\\"\\n\\n except Exception as e:\\n logging.error(\\\"Creating test summary failed with error '{ERROR}'\\\".format(ERROR=e))\\n return None\\n\\n logging.info(test_summary)\\n return test_summary\",\n \"def add_summary_header(self):\\n self.fontSize(22, bold=True)\\n self.PDF.setFillColor(\\\"black\\\")\\n self.PDF.drawString(75, 260, \\\"Summary\\\")\\n self.fontSize(FONT_XXS)\\n self.PDF.setFillColor(HexColor(\\\"#9CA3AF\\\"))\\n # self.PDF.drawString(\\n # 185,\\n # 260,\\n # f\\\"{self.invoice.subscription.start_date} - {self.invoice.subscription.end_date}\\\",\\n # )\\n self.PDF.setFillColor(\\\"black\\\")\\n self.fontSize(FONT_XS)\\n self.PDF.setFillColor(HexColor(\\\"#9CA3AF\\\"))\\n self.PDF.drawString(75, 290, \\\"Services\\\")\\n self.PDF.drawString(475, 290, \\\"Amount\\\")\\n self.PDF.setFillColor(\\\"black\\\")\\n self.draw_line(305)\",\n \"async def summarise(self, ctx, start=None, end=None):\\n if ctx.message.author.bot:\\n return\\n\\n if not start or not end:\\n await ctx.send(\\n \\\"Insufficient arguments!\\\\n Arguements: <start ID> <end ID>\\\"\\n )\\n return\\n\\n summary, keywords, clean_messages = await convert_to_summary(\\n ctx, start, end\\n )\\n\\n if summary:\\n summary = \\\"```\\\\n\\\" + summary + \\\"```\\\"\\n await ctx.send(summary)\\n else:\\n await ctx.send(\\\"```Not enough messages to generate summary```\\\")\\n\\n if keywords:\\n keyword_str = \\\"Keywords: \\\"\\n for word in keywords:\\n keyword_str += f\\\"{word}, \\\"\\n\\n keyword_str = \\\"```\\\\n\\\" + keyword_str + \\\"```\\\"\\n await ctx.send(keyword_str)\\n else:\\n await ctx.send(\\\"```Not enough messages to generate keywords```\\\")\",\n \"def __str__(self):\\n email_template = '\\\\n'.join((f'\\\\n\\\\nDear {self._full_name},\\\\n',\\n f'Thank you for your very kind donation of ${self.last_donation:.2f}.\\\\n',\\n 'It will be put to very good use.\\\\n',\\n ' Sincerely,',\\n ' -The Team\\\\n'))\\n return email_template\",\n \"def send_created_email(self):\\n if settings.NOTIFY_NEW_REG:\\n to = settings.NOTIFY_NEW_REG\\n message = \\\"\\\"\\\"\\\\\\nGreetings,<br><br>\\n\\nA new vehicle registration has been submitted by %s.<br><br>\\n\\nGo here to view or edit the request: <br>\\n<a href=\\\"%s\\\">%s</a>\\n<br><br>\\nSincerely,<br><br>\\nThe Janelia Parking Permit Program\\n \\\"\\\"\\\" % (self.user_display_name(), self.get_edit_url(True), self.get_edit_url(True))\\n subject = 'A new parking permit request has been entered'\\n from_email = 'parkingpermit-donotreply@janelia.hhmi.org'\\n text_content = re.sub(r'<[^>]+>','',message)\\n html_content = message\\n msg = EmailMultiAlternatives(subject, text_content, from_email, to)\\n msg.attach_alternative(html_content, \\\"text/html\\\")\\n msg.send()\",\n \"def summary(self) -> str:\\n pass\",\n \"def _render_mail(self, rebuild, success, canceled):\\n subject_template = 'Image %(image)s; Status %(endstate)s; Submitted by %(user)s'\\n body_template = '\\\\n'.join([\\n 'Image: %(image)s',\\n 'Status: %(endstate)s',\\n 'Submitted by: %(user)s',\\n 'Logs: %(logs)s',\\n ])\\n\\n endstate = None\\n if canceled:\\n endstate = 'canceled'\\n else:\\n endstate = 'successful' if success else 'failed'\\n url = None\\n if self.url and self.workflow.openshift_build_selflink:\\n url = urljoin(self.url, self.workflow.openshift_build_selflink + '/log')\\n\\n formatting_dict = {\\n 'image': self.workflow.image,\\n 'endstate': endstate,\\n 'user': '<autorebuild>' if rebuild else self.submitter,\\n 'logs': url\\n }\\n return (subject_template % formatting_dict, body_template % formatting_dict)\",\n \"def write_plaintext_body(self, recipient: str) -> str:\\n body = f'Dear {recipient},\\\\n\\\\n' \\\\\\n + 'You are receiving this message because you participated ' \\\\\\n + f'in {self.trip_title}.\\\\n\\\\n' \\\\\\n + 'I hope you had a pleasant time.\\\\n\\\\n' \\\\\\n + 'If you have debts to repay, please be courteous and ' \\\\\\n + 'reimburse your fellow participant(s) in a timely fashion.\\\\n' \\\\\\n\\n subbody: str = ''\\n subbody_debt: List = list()\\n subbody_credit: List = list()\\n for currency, reimbs in self.reimbursement_matrices.items():\\n debts = reimbs[recipient].dropna()\\n credits = reimbs.loc[recipient].dropna()\\n for creditor, credit in debts.iteritems():\\n #creditor = creditor.rjust(self._max_name_len)\\n amount = f'{credit} {currency}'.ljust(6+1+3)\\n subbody_debt.append('\\\\t' + creditor + ' | ' + amount)\\n for debtor, debt in credits.iteritems():\\n #debtor = debtor.rjust(self._max_name_len)\\n amount = f'{debt} {currency}'.ljust(6+1+3)\\n subbody_credit.append('\\\\t' + debtor + ' | ' + amount)\\n\\n if len(subbody_debt) == 1:\\n subbody += 'Please reimburse the following participant:\\\\n' \\\\\\n + subbody_debt[0]\\n elif len(subbody_debt) > 1:\\n subbody += 'Please reimburse the following participants:\\\\n' \\\\\\n + '\\\\n'.join(subbody_debt)\\n else:\\n subbody += 'You don\\\\'t have any payable reimbursements.'\\n\\n subbody += '\\\\n\\\\n'\\n\\n if len(subbody_credit) == 1:\\n subbody += 'The following participant is obligated to ' \\\\\\n + 'reimburse you:\\\\n' + subbody_credit[0]\\n elif len(subbody_credit) > 1:\\n subbody += 'The following participants are obligated to ' \\\\\\n + 'reimburse you:\\\\n' + '\\\\n'.join(subbody_credit)\\n else:\\n subbody += 'You don\\\\'t have any receivable reimbursements.' \\\\\\n\\n body += '\\\\n' + subbody\\n\\n return body\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n damage = 'damage : ' + str(self.get_damage())\\n ammos = 'ammo : ' + str(self.get_ammos())\\n owner = 'owner : ' + str(self.get_owner())\\n return '\\\\n'.join([name, damage, ammos, owner])\",\n \"def summary_str(self):\\n if not self.results:\\n return self.summary.empty() or ''\\n elif self.state == Ok:\\n return self.summary.ok(self.results) or ''\\n return self.summary.problem(self.results) or ''\",\n \"def initialize_error_summary() -> str:\\n error_summary = '\\\\nSummary of <span class=\\\"tex-fatal\\\">Critical Errors:</span>\\\\n\\\\n<ul>\\\\n'\\n return error_summary\",\n \"def summary_print(self):\\r\\n self.ensure_one()\\r\\n self.sent = True\\r\\n #return self.env['ir.actions.report'].report_action(self, 'proandsys_purchase_14.summary_landed_report')\\r\\n return self.env.ref('proandsys_purchase_14.summary_landedcost').report_action(self)\",\n \"def create_group_message(group_name,subject,message,status,createdby):\\n query=\\\"INSERT INTO groupmails(group_name,subject,message,status,createdby)VALUES('{}','{}','{}','{}','{}')\\\".format(\\n group_name,subject,message,status,createdby\\n )\\n cur.execute(query)\",\n \"def _message(self, recipient, connection, context=None):\\n base_subject = '{{ event.calendar.course.name }} {{ event.title }}'\\n if not self.event.get_documents(True):\\n template_name = self.REQUEST_TEMPLATE\\n subject = 'Got a {} study guide?'.format(base_subject)\\n else:\\n template_name = self.PUBLISH_TEMPLATE\\n subject = '{} study guide'.format(base_subject)\\n\\n subject = Template(subject).render(context)\\n body = get_template(template_name).render(context)\\n\\n return make_email_message(subject, body,\\n make_display_email(\\n self.sender_address,\\n self.sender_name),\\n recipient, connection)\",\n \"def email_body_review_reminder():\\n\\tmsg = '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr><td align=\\\"center\\\" valign=\\\"top\\\"></td></tr></tbody></table>'\\n\\tmsg = msg + '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr>'\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\\\" cellspacing=\\\"0\\\" cellpadding=\\\"10\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tbody>'\\n\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<a href=\\\"https://insprite.co\\\"><img src=\\\"http://ryanfbaker.com/insprite/inspriteLogoA.png\\\" border=\\\"0\\\" alt=\\\"Insprite\\\" align=\\\"center\\\" width=\\\"200px\\\" height=\\\"55px\\\" /></a>'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</tbody>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"85\\\" width=\\\"600\\\" height=\\\"350\\\">'\\n\\tmsg = msg + '\\\\t<tr>td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px; padding-right:75px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '\\\\t\\\\t <font style=\\\"font-family:Helvetica Neue;color:#555555;font-size:14px;\\\">We hope you had a great appointment!<br>'\\n\\tmsg = msg + '\\\\t\\\\t\\\\t Your opinion goes a long way—write up your review of the appointment so others can learn from your experience with <a href=\\\"#\\\" style=\\\"color:#1488CC\\\">{user\\\\'s name}</a></font><br><br>'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\n\\tmsg = msg + '<td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:200px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '<a href=\\\"#\\\" style=\\\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:100px;text-align:center;\\\" target=\\\"_blank\\\">Rate & Review</a>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/facebookIcon.png\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/twitterIcon.png\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/instagramIcon.png\\\">'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/spacer-2.png\\\">'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\treturn msg\",\n \"def email_body_to_user_sending_msg(profile, message):\\n\\tmsg = '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr><td align=\\\"center\\\" valign=\\\"top\\\"></td></tr></tbody></table>'\\n\\tmsg = msg + '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr>'\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\\\" cellspacing=\\\"0\\\" cellpadding=\\\"10\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tbody>'\\n\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<a href=\\\"https://insprite.co\\\"><img src=\\\"http://ryanfbaker.com/insprite/inspriteLogoA.png\\\" border=\\\"0\\\" alt=\\\"Insprite\\\" align=\\\"center\\\" width=\\\"200px\\\" height=\\\"55px\\\" /></a>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</tbody>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"85\\\" width=\\\"600\\\" height=\\\"350\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '<font style=\\\"font-family:Helvetica Neue;color:#555555;font-size:14px;\\\">Way to get the conversation started! You messaged <a href=\\\\\\\"https://127.0.0.1:5000/profile?hero=' + profile.prof_id + '\\\\\\\" style=\\\"color:#1488CC\\\">' + profile.prof_name.encode('utf8', 'ignore') + '</a> and should get a response soon.<br><br>'\\n\\tmsg = msg + 'Until then, stand tight. <br><br>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/facebookIcon.png\\\">'\\n\\tmsg = msg + '<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/twitterIcon.png\\\">'\\n\\tmsg = msg + '<img src=\\\"http://ryanfbaker.com/insprite/instagramIcon.png\\\">'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<img src=\\\"http://ryanfbaker.com/insprite/spacer-2.png\\\">'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\treturn msg\",\n \"def generate_email(self, length=10):\\n self.update()\\n # Generate the emails\\n lines = []\\n line_count = 0\\n while line_count < length:\\n sent = self.markov.make_sentence()\\n if sent != None:\\n lines.append(sent)\\n line_count +=1\\n else:\\n print(sent)\\n email_body = '\\\\n'.join(lines)\\n email_header = random.choice(get_best_trigrams(filter(email_body),5))\\n return email_header.decode('utf-8').strip() + \\\"\\\\n\\\" + email_body.decode('utf-8').strip()\",\n \"def summary(self, summary: str):\\n return self.swag({\\n 'summary': summary\\n })\",\n \"def generate_email(self):\\n email_dict = {'donor_name':self.name,\\n 'donation_amount':self.last_donation(),\\n 'total_amount':self.total_donations()}\\n\\n # Create formatted email that can be copied & pasted\\n email = ('\\\\n'.join(['Dear {donor_name},','',\\n 'Thank you for your generous donation of ${donation_amount:.2f}.',\\n 'To date, you have donated a total of ${total_amount:.2f} to our charity.',\\n 'Your contributions help new arrivals receive the highest quality care possible.',\\n 'Please know that your donations make a world of difference!',\\n '','Sincerely,','The Good Place Team'])).format(**email_dict)\\n\\n return(email)\",\n \"def mysummary(self):\\n return self.sprintf(\\\"IGMPv3 Group Record %IGMPv3gr.type% %IGMPv3gr.maddr%\\\")\",\n \"def text_summary_message(self):\\n failed = [e for e in self.evaluations if not e.passes]\\n if failed == []:\\n return \\\"SUCCESS - all constraints evaluations pass\\\"\\n else:\\n return \\\"FAILURE: %d constraints evaluations failed\\\" % len(failed)\",\n \"def create_messages(application, action, remedy):\\n\\n messages = [] \\n messages.append(\\\"\\\"\\\"Your Resources: </br><pre style=\\\"margin-left: 40px\\\">\\\"\\\"\\\" + application + \\\"</br></pre>\\\" + action + \\\"\\\"\\\" in AWS. <strong style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\\\">\\\"\\\"\\\" + remedy +\\\"\\\"\\\"</strong>\\n </td>\\n </tr><tr style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; margin: 0;\\\"><td class=\\\"content-block\\\" style=\\\"font-family: 'Helvetica Neue',Helvetica,Arial,sans-serif; box-sizing: border-box; font-size: 14px; vertical-align: top; margin: 0; padding: 0 0 20px;\\\" valign=\\\"top\\\">\\n This message was sent to inform you of changes happening to your resources.\\n <ul>\\n <li>New instances are auto-tagged with an expiration date, an NT ID, and a patch group if invalid.</li>\\n <li>Instances without the necessary tags are notified through email and Slack.</li>\\n </ul>\\n If you have any further questions, please reply to this email.\\\"\\\"\\\")\\n \\n messages.append(\\\"Your Resources:\\\\n\\\\n\\\" + application + \\\"\\\\n\\\\n\\\" + action + \\\" in AWS. \\\" + remedy + \\\"\\\\n\\\" + \\n (\\\"\\\\nThis message was sent to inform you of changes happening to your resources.\\\\n\\\"\\n \\\"\\\\nNew instances are auto-tagged with an expiration date, an NT ID, and a patch group if invalid.\\\"\\n \\\"Instances without Owner Mail and Owner Team tags are notified through email and slack.\\\\n\\\"\\n \\\"\\\\nIf you have any further questions, please reply to this email.\\\")) \\n\\n return messages\",\n \"def createThankYouEmail(self):\\n result = (\\\"\\\\nDear {:s},\\\\n\\\\n\\\"\\n \\\"\\\\tThank you so much for your generous donation of ${:,.2f}!\\\\n\\\\n\\\"\\n \\\"\\\\tIt will be put to very good use.\\\\n\\\\n\\\"\\n \\\"\\\\t\\\\tSincerely,\\\\n\\\\t\\\\t\\\\t- The Team\\\".format(self.name, self.getTotDonation())\\n )\\n return result\",\n \"def summary(self, fromdt, todt):\\r\\n totalSaved = self.miser.totalSaved(fromdt, todt) \\r\\n sumStr = \\\"%s: %s to %s\\\\n\\\" % (self.miser.name, fromdt, todt)\\r\\n sumStr += \\\"Total saved: %.2f\\\" % totalSaved\\r\\n\\r\\n sumStr += \\\"\\\\n\\\\nGoals:\\\\n\\\"\\r\\n sumStr += self._goalsMetStr(fromdt, todt, totalSaved)\\r\\n\\r\\n return sumStr\",\n \"def _create_msg(self, tr_id, i_triples, i_type, r_triples, r_type, confirm):\\n params = SSAP_UPDATE_PARAM_TEMPLATE % (str(i_type).upper(),\\n str(i_triples),\\n str(r_type).upper(),\\n str(r_triples),\\n str(confirm).upper())\\n tmp = SSAP_MESSAGE_TEMPLATE % (str(self.node_id), str(self.targetSS),\\n self.tr_type, str(tr_id), params)\\n return tmp\",\n \"def produce_message_for_sending() -> str:\\n return f\\\"You can donate your money here:\\\\n`{card_donations}`\\\"\",\n \"async def report(cls, description, **kwargs):\\n return await cls.message(description, color = discord.Color(0xbfbfbf))\",\n \"def verification_email_body(case_name, url, display_name, category, subcategory, breakpoint_1, breakpoint_2, hgnc_symbol, panels, gtcalls, tx_changes, name, comment):\\n html = \\\"\\\"\\\"\\n <ul>\\n <li>\\n <strong>Case {case_name}</strong>: <a href=\\\"{url}\\\">{display_name}</a>\\n </li>\\n <li><strong>Variant type</strong>: {category} ({subcategory})\\n <li><strong>Breakpoint 1</strong>: {breakpoint_1}</li>\\n <li><strong>Breakpoint 2</strong>: {breakpoint_2}</li>\\n <li><strong>HGNC symbols</strong>: {hgnc_symbol}</li>\\n <li><strong>Gene panels</strong>: {panels}</li>\\n <li><strong>GT call</strong></li>\\n {gtcalls}\\n <li><strong>Amino acid changes</strong></li>\\n {tx_changes}\\n <li><strong>Comment</strong>: {comment}</li>\\n <li><strong>Ordered by</strong>: {name}</li>\\n </ul>\\n \\\"\\\"\\\".format(\\n case_name=case_name,\\n url=url,\\n display_name=display_name,\\n category=category,\\n subcategory=subcategory,\\n breakpoint_1=breakpoint_1,\\n breakpoint_2=breakpoint_2,\\n hgnc_symbol=hgnc_symbol,\\n panels=panels,\\n gtcalls=gtcalls,\\n tx_changes=tx_changes,\\n name=name,\\n comment=comment)\\n\\n return html\",\n \"def generate_html_mesg(info, open_quests, owner, tags):\\n\\n msg = '<html>' \\\\\\n '<body style=\\\"font-family: Verdana; font-size: 1em; color: #000\\\">'\\n msg += (\\n \\\"<div style='padding: 10px; border-radius: 5px; background: #232f3e; \\\"\\n \\\"color: #fff; font-weight: bold; font-size: 1.25em;'>\\\"\\n \\\"Hermes Notifications\\\"\\n \\\"</div>\\\"\\n \\\"<div style='padding: 10px;'><p>This email is being sent to {} because that is the owner listed\\\\n\\\"\\n \\\"for the systems with open Hermes labors listed below.</p>\\\"\\n \\\"<p>Due dates, if any, are noted with each quest.</p>\\\"\\n \\\"\\\".format(owner)\\n )\\n msg += (\\n \\\"<p>To throw an event manually, you can run the following command \\\"\\n \\\"on a shell server:</p>\\\"\\n \\\"<pre style='font-size: 1.2em'>$ hermes event create [event] --host \\\"\\n \\\"[hostname]</pre>\\\"\\n \\\"<p>Or you can visit the quests linked below.</p></div>\\\".format(\\n settings.frontend)\\n )\\n for quest_id in info[owner]:\\n quest = find_quest(open_quests, quest_id)\\n if quest:\\n msg += (\\n \\\"<div style='border-radius: 5px; background: #dce1e6; \\\"\\n \\\"padding: 10px; margin-bottom: 10px;'>\\\"\\n \\\"<span style='font-size: 1.1em; font-weight: bold'>QUEST {}</span><br/>\\\"\\n \\\"<strong>CREATOR:</strong> {}<br />\\\"\\n ).format(\\n quest_id, quest.creator\\n )\\n if quest.target_time:\\n msg += \\\"<strong>DUE:</strong> {}<br/>\\\".format(quest.target_time)\\n msg += \\\"<strong>DESC:</strong><p> \\\\\\\"{}\\\\\\\"</p>\\\".format(quest.description)\\n msg += \\\"<strong>LINK:</strong> <code>{}/v1/quests/{}</code><br/>\\\".format(\\n settings.frontend, quest_id\\n )\\n else:\\n msg += (\\n \\\"<div style='border-radius: 5px; background: #dce1e6; \\\"\\n \\\"padding: 10px; margin-bottom: 10px;'>\\\"\\n \\\"<span style='font-size: 1.1em; font-weight: bold'>Labors not \\\"\\n \\\"associated with a quest:</span><br />\\\"\\n )\\n\\n msg += \\\"<p>Machines with labors:</p>\\\"\\n\\n msg += \\\"<pre style='margin-left: 10px; font-size: 1.2em'>\\\"\\n for hostname in sorted(info[owner][quest_id]):\\n if tags[hostname]:\\n tags_str = \\\"{}\\\".format((\\\", \\\".join(tags[hostname])))\\n else:\\n tags_str = \\\"no services\\\"\\n msg += \\\"{} ({})\\\\n\\\".format(hostname, tags_str)\\n\\n msg += \\\"</pre></div>\\\"\\n\\n msg += \\\"</body>\\\"\\n\\n return msg\",\n \"def email_body_meeting_reminder():\\n\\tmsg = '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr><td align=\\\"center\\\" valign=\\\"top\\\"></td></tr></tbody></table>'\\n\\tmsg = msg + '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr>'\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\\\" cellspacing=\\\"0\\\" cellpadding=\\\"10\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tbody>'\\n\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<a href=\\\"https://insprite.co\\\"><img src=\\\"http://ryanfbaker.com/insprite/inspriteLogoA.png\\\" border=\\\"0\\\" alt=\\\"Insprite\\\" align=\\\"center\\\" width=\\\"200px\\\" height=\\\"55px\\\" /></a>'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</tbody>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"85\\\" width=\\\"600\\\" height=\\\"350\\\">'\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<font style=\\\"font-family:Helvetica Neue;color:#555555;font-size:14px;\\\">Drats. <a href=\\\"#\\\" style=\\\"color:#1488CC\\\">{insert seller name} cancelled your appointment</a>.<br><br>'\\n\\tmsg = msg + '\\\\t\\\\t\\\\t <a href=\\\"#\\\" style=\\\"color:#1488CC\\\">Reschedule</a> or you can send a message to inquire about the cancellation. <br><br>'\\n\\tmsg = msg + '\\\\t\\\\t\\\\t And, don\\\\'t worry! You won\\\\'t be charged, promise. </font><br><br>'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/facebookIcon.png\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/twitterIcon.png\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/instagramIcon.png\\\">'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/spacer-2.png\\\">'\\n\\tmsg = msg + '\\\\t</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\treturn msg\",\n \"def summary(self) -> str:\\n return pulumi.get(self, \\\"summary\\\")\",\n \"def create_base_message(subject, msg=None):\\n message = EmailMessage()\\n message[\\\"From\\\"] = email_config.from_address\\n message[\\\"To\\\"] = email_config.to_addresses\\n message[\\\"Subject\\\"] = subject\\n message.set_content(\\\"\\\\n\\\".join([\\n 50 * \\\"_\\\",\\n f\\\"Hostname: {gethostname()}\\\",\\n f\\\"Time: {datetime.now().strftime(config.datetime_format)}\\\",\\n f\\\"Log file: {LOGGER.log_file_path}\\\",\\n 50 * \\\"_\\\",\\n ]))\\n # Add `msg` to the contents if it is not None\\n if msg is not None:\\n _append_content(message, msg)\\n # Attach the log file if it is available\\n if LOGGER.log_file_path is not None and config.email_attach_log_file:\\n _attach_log_file(message)\\n return message.as_string()\",\n \"def _create_msg(self, tr_id, payload, confirm, expire_time, encoding):\\n tmp = [\\\"<SSAP_message><transaction_type>INSERT</transaction_type>\\\",\\n \\\"<message_type>REQUEST</message_type>\\\"]\\n tmp.extend([\\\"<transaction_id>\\\", str(tr_id), \\\"</transaction_id>\\\"])\\n tmp.extend([\\\"<node_id>\\\", str(self.node_id), \\\"</node_id>\\\"])\\n tmp.extend([\\\"<space_id>\\\", str(self.targetSS), \\\"</space_id>\\\"])\\n tmp.extend(['<parameter name=\\\"insert_graph\\\" encoding=\\\"%s\\\">' % encoding.upper(),\\n str(payload), \\\"</parameter>\\\"])\\n tmp.extend(['<parameter name = \\\"confirm\\\">',\\n str(confirm).upper(),\\n \\\"</parameter>\\\",\\n \\\"</SSAP_message>\\\"])\\n return \\\"\\\".join(tmp)\",\n \"def mergeSummary(self, *args):\\n if len(args) == 1:\\n lvl = args[0].level\\n msg = args[0].message\\n elif len(args) == 2:\\n lvl = args[0]\\n msg = args[1]\\n\\n if (lvl > DiagnosticStatus.OK) == (self.level > DiagnosticStatus.OK):\\n if len(self.message) > 0:\\n self.message += '; '\\n self.message += msg\\n elif lvl > self.level:\\n self.message = msg\\n\\n if lvl > self.level:\\n self.level = lvl\",\n \"def create_mail_content(daily: bool = False):\\n if not daily:\\n order = STATE['order'] if STATE['order'] else get_closed_order()\\n trade_part = create_report_part_trade(order)\\n performance_part = create_report_part_performance(daily)\\n advice_part = create_report_part_advice()\\n settings_part = create_report_part_settings()\\n general_part = create_mail_part_general()\\n\\n if not daily:\\n trade = [\\\"Last trade\\\", \\\"----------\\\", '\\\\n'.join(trade_part['mail']), '\\\\n\\\\n']\\n performance = [\\\"Performance\\\", \\\"-----------\\\",\\n '\\\\n'.join(performance_part['mail']) + '\\\\n* (change within 24 hours)', '\\\\n\\\\n']\\n advice = [\\\"Assessment / advice\\\", \\\"-------------------\\\", '\\\\n'.join(advice_part['mail']), '\\\\n\\\\n']\\n settings = [\\\"Your settings\\\", \\\"-------------\\\", '\\\\n'.join(settings_part['mail']), '\\\\n\\\\n']\\n general = [\\\"General\\\", \\\"-------\\\", '\\\\n'.join(general_part), '\\\\n\\\\n']\\n\\n bcs_url = 'https://bitcoin-schweiz.ch/bot/'\\n text = '' if daily else '\\\\n'.join(trade)\\n\\n if not CONF.info:\\n text += '\\\\n'.join(performance) + '\\\\n'.join(advice) + '\\\\n'.join(settings) + '\\\\n'.join(general) + bcs_url + '\\\\n'\\n else:\\n text += '\\\\n'.join(performance) + '\\\\n'.join(advice) + '\\\\n'.join(settings) + '\\\\n'.join(general) + CONF.info \\\\\\n + '\\\\n\\\\n' + bcs_url + '\\\\n'\\n\\n csv = None if not daily else INSTANCE + ';' + str(datetime.datetime.utcnow().replace(microsecond=0)) + ' UTC;' + \\\\\\n (';'.join(performance_part['csv']) + ';' + ';'.join(advice_part['csv']) + ';' +\\n ';'.join(settings_part['csv']) + ';' + CONF.info + '\\\\n')\\n\\n return {'text': text, 'csv': csv}\",\n \"def format(self, message):\",\n \"def __str__(self):\\n return f'{self.message} {self.description}'\",\n \"def summary(self, *args):\\n if len(args) == 1:\\n self.level = args[0].level\\n self.message = args[0].message\\n elif len(args) == 2:\\n self.level = args[0]\\n self.message = str(args[1])\",\n \"def msg(self):\\n ended = time.time()\\n started_wait = datetime.datetime.fromtimestamp(self.started).strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\"\\n )\\n raised_date = datetime.datetime.fromtimestamp(ended).strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\"\\n )\\n duration = ended - self.started\\n return \\\"Info[started at {}, raised at {} after {}s]\\\".format(\\n started_wait, raised_date, round(duration, 2)\\n )\",\n \"def summaryText(self):\\n\\n print('\\\\nReport Summary:\\\\n')\\n for author in self.lowQuality.keys():\\n if len(self.lowQuality[author]) > 0:\\n print('Author: ' + author)\\n print('---------------------')\\n # do some sorting for readability\\n files = []\\n file2rating = {}\\n for fileRating in self.lowQuality[author]:\\n files.append(fileRating[1])\\n file2rating[fileRating[1]] = fileRating[0]\\n files.sort()\\n for fileRating in files:\\n print(file2rating[fileRating] + ' :: ' + fileRating)\\n print('\\\\n\\\\n')\",\n \"def create_workflow_info(html=True):\\n if html:\\n hdr = \\\"<h3>Workflow Theses</h3>\\\"\\n state_info = \\\"<p><b>stav:</b>\\\" + self.workflow.workflow.state + \\\"</p>\\\"\\n started_info = \\\"<p><b>spuštěno:</b>\\\" + str(self.workflow.workflow.started) + \\\"</p>\\\"\\n stopped_info = \\\"<p><b>zastaveno:</b>\\\" + str(self.workflow.workflow.stopped) + \\\"</p>\\\"\\n double_stop = \\\"<br><br>\\\"\\n batches_count_info = \\\"<p><b>počet zpracovaných dávek:</b>\\\" + \\\\\\n str(len(self.workflow.batches_processing)) + \\\"</p><br><br>\\\"\\n else:\\n hdr = \\\"Workflow Theses\\\\n\\\\n\\\"\\n state_info = \\\"stav:\\\" + self.workflow.workflow.state + \\\"\\\\n\\\"\\n started_info = \\\"spuštěno:\\\" + str(self.workflow.workflow.started) + \\\"\\\\n\\\"\\n stopped_info = \\\"zastaveno:\\\" + str(self.workflow.workflow.stopped) + \\\"\\\\n\\\"\\n double_stop = \\\"\\\\n\\\\n\\\"\\n batches_count_info = \\\"počet zpracovaných dávek:\\\" + str(len(self.workflow.batches_processing)) + \\\"\\\\n\\\\n\\\"\\n\\n msg_part = hdr + state_info + started_info + stopped_info + double_stop + batches_count_info\\n\\n return msg_part\",\n \"def generate_test_report(self, message):\\n pass\",\n \"def printSummary(self):\\n pass\",\n \"def publish_summary(self, jobs):\\n pass\",\n \"def summary(self):\\n name = 'name : ' + self.get_name()\\n description = 'description : ' + self.get_description()\\n agility = 'agility : ' + str(self.get_agility())\\n strength = 'strength : ' + str(self.get_strength())\\n health_points = 'health_points : ' + str(self.get_health_points())\\n summary = '\\\\n'.join([name, description, agility, strength, health_points])\\n if self.take_weapon():\\n summary += self.take_weapon().summary()\\n return summary\",\n \"def main_email(name, total, answered, not_answered, declines, remaining):\\n\\n start = smtplib.SMTP(host=HOST, port=PORT)\\n start.starttls()\\n start.login(ADDRESS, PASSWORD)\\n\\n date = datetime.datetime.now()\\n date_now = date.strftime(\\\"%m-%d-%Y\\\")\\n\\n print_list, email_dict = simple_contacts('contacts.txt')\\n\\n emails = get_emails(print_list, email_dict)\\n\\n message_template = read_template()\\n\\n for mail in emails:\\n pretty_print(f\\\"Sending email to {mail}\\\", \\\"!\\\")\\n msg = MIMEMultipart()\\n\\n message = message_template.substitute(PERSON_NAME=name, DATE=date_now, TOTAL_CALLED=total, ANSWERED=answered, NOT_ANSWERED=not_answered, DECLINES=declines, REMAINING=remaining)\\n\\n msg['From'] = ADDRESS\\n msg['To'] = mail\\n msg['Subject'] = f\\\"{name} - Calling Campaign Summary - {date_now}\\\"\\n\\n msg.attach(MIMEText(message, 'plain'))\\n start.send_message(msg)\\n pretty_print(f\\\"Mail sent to {mail}\\\", \\\"!\\\")\\n\\n del msg\\n\\n start.quit()\",\n \"def make_incident_description(self, incident):\\n i_summary = incident.get('summary', \\\"Proofpoint TRAP Incident - Lacks a summary\\\")\\n i_event_count = len(incident['events'])\\n i_sources = incident.get('event_sources', 'No Sources Found')\\n\\n description = '{}\\\\n\\\\nTotal Events in incident: {}\\\\n\\\\nFrom: {}\\\\n'.format(i_summary, i_event_count, i_sources)\\n return {'format': 'text', 'content': description}\",\n \"def format_body(self, notice, fullname=None):\\n if not fullname:\\n fullname = self.fullname or self.name\\n\\n out = StringIO()\\n out.write(\\\"Attention: {0}\\\\n\\\".format(fullname))\\n out.write(\\\"Notification Type: {0}\\\\n\\\".format(notice.type))\\n if notice.origin:\\n out.write(\\\"Origin: {0}\\\\n\\\".format(notice.origin))\\n out.write(\\\"\\\\n\\\")\\n if notice.title:\\n out.write(textwrap.fill(notice.title, 80) + \\\"\\\\n\\\\n\\\")\\n\\n desc = notice.description\\n if desc:\\n if not isinstance(desc, list):\\n desc = [desc]\\n for d in desc:\\n if notice.doformat:\\n d = textwrap.fill(d, 80)\\n out.write(d)\\n out.write('\\\\n\\\\n')\\n\\n out.write(\\\"Issued: {0}\\\\n\\\".format(notice.issued))\\n for key in notice.metadata:\\n val = notice.metadata[key]\\n if isinstance(val, list):\\n val = json.dumps(val, indent=2, separators=(', ', ': '))\\n else:\\n val = str(val)\\n out.write(\\\"{0}: {1}\\\\n\\\".format(key, val))\\n\\n return out.getvalue()\",\n \"def composeEmail(self, report):\\r\\n\\r\\n # Determine if we need to send a message to this developer.\\r\\n requires_message = False\\r\\n for platform in self.platforms:\\r\\n if platform.isBroken():\\r\\n requires_message = True\\r\\n break\\r\\n\\r\\n if not requires_message:\\r\\n return None\\r\\n\\r\\n # Build the message header\\r\\n message = \\\"\\\"\\\"From: Douglas Gregor <dgregor@osl.iu.edu>\\r\\nTo: \\\"\\\"\\\"\\r\\n message += self.name + ' <' + self.email + '>'\\r\\n message += \\\"\\\"\\\"\\r\\nReply-To: boost@lists.boost.org\\r\\nSubject: Large number of Boost failures on a platform you maintain as of \\\"\\\"\\\"\\r\\n message += str(datetime.date.today()) + \\\" [\\\" + report.branch + \\\"]\\\"\\r\\n message += \\\"\\\"\\\"\\r\\n\\r\\nYou are receiving this report because one or more of the testing\\r\\nplatforms that you maintain has a large number of Boost failures that\\r\\nare not accounted for. A full version of the report is sent to the\\r\\nBoost developer's mailing list.\\r\\n\\r\\nDetailed report:\\r\\n\\\"\\\"\\\"\\r\\n message += ' ' + report.url + \\\"\\\"\\\"\\r\\n\\r\\nThe following platforms have a large number of failures:\\r\\n\\\"\\\"\\\"\\r\\n\\r\\n for platform in self.platforms:\\r\\n if platform.isBroken():\\r\\n message += (' ' + platform.name + ' ('\\r\\n + str(len(platform.failures)) + ' failures)\\\\n')\\r\\n\\r\\n return message\",\n \"def GetKMsgSummary(kmsgp, prefix_str=\\\"\\\"):\\n kmsghp = kmsgp.ikm_header\\n kmsgh = dereference(kmsghp)\\n out_string = \\\"\\\"\\n out_string += \\\"{0: <20s} {1: <#019x} {2: <8s} {3: <#011x} \\\".format(\\n ' ', unsigned(kmsgp), ' '*8, kmsgh.msgh_id)\\n prefix_str = \\\"{0: <20s} \\\".format(' ') + prefix_str\\n disposition = \\\"\\\"\\n bits = kmsgh.msgh_bits & 0xff\\n \\n # remote port\\n if bits == 17:\\n disposition = \\\"rS\\\"\\n elif bits == 18:\\n disposition = \\\"rO\\\"\\n else :\\n disposition = \\\"rX\\\" # invalid\\n \\n out_string += \\\"{0: <2s}\\\".format(disposition)\\n \\n # local port\\n disposition = \\\"\\\"\\n bits = (kmsgh.msgh_bits & 0xff00) >> 8\\n \\n if bits == 17:\\n disposition = \\\"lS\\\"\\n elif bits == 18:\\n disposition = \\\"lO\\\"\\n elif bits == 0:\\n disposition = \\\"l-\\\"\\n else:\\n disposition = \\\"lX\\\" # invalid\\n \\n out_string += \\\"{0: <2s}\\\".format(disposition)\\n \\n # voucher\\n disposition = \\\"\\\"\\n bits = (kmsgh.msgh_bits & 0xff0000) >> 16\\n \\n if bits == 17:\\n disposition = \\\"vS\\\"\\n elif bits == 0:\\n disposition = \\\"v-\\\"\\n else:\\n disposition = \\\"vX\\\"\\n\\n out_string += \\\"{0: <2s}\\\".format(disposition) \\n \\n # complex message\\n if kmsgh.msgh_bits & 0x80000000:\\n out_string += \\\"{0: <1s}\\\".format(\\\"c\\\")\\n else:\\n out_string += \\\"{0: <1s}\\\".format(\\\"s\\\")\\n \\n # importance boost\\n if kmsgh.msgh_bits & 0x20000000:\\n out_string += \\\"{0: <1s}\\\".format(\\\"I\\\")\\n else:\\n out_string += \\\"{0: <1s}\\\".format(\\\"-\\\")\\n \\n dest_proc_name = \\\"\\\"\\n if kmsgp.ikm_header.msgh_remote_port:\\n dest_proc_name = GetDestinationProcessFromPort(kmsgp.ikm_header.msgh_remote_port)\\n\\n out_string += \\\"{0: ^6d} {1: <#019x} {2: <26s} {3: <26s}\\\\n\\\".format(\\n unsigned(kmsgh.msgh_size), unsigned(kmsgh.msgh_local_port),\\n GetKMsgSrc(kmsgp), dest_proc_name)\\n \\n if kmsgh.msgh_bits & 0x80000000:\\n out_string += prefix_str + \\\"\\\\t\\\" + GetKMsgComplexBodyDesc.header + \\\"\\\\n\\\"\\n out_string += prefix_str + \\\"\\\\t\\\" + GetKMsgComplexBodyDesc(kmsgp, prefix_str + \\\"\\\\t\\\") + \\\"\\\\n\\\"\\n \\n return out_string\",\n \"def summarize(self):\\n txtStr = \\\"%s to %s, %d flight legs.\\\" %\\\\\\n (self.origin, self.destination, self.nlegs)\\n txtStr += \\\"\\\\nTakeoff at %s\\\\nLanding at %s\\\\n\\\" %\\\\\\n (self.takeoff, self.landing)\\n txtStr += \\\"Flight duration of %s including %s observing time\\\" %\\\\\\n (str(self.flighttime), self.obstime)\\n\\n return txtStr\",\n \"def add_daily_summary(self):\\n auth_date = self.report_date.strftime(\\\"%b %-d, %Y\\\")\\n now = datetime.now().strftime(\\\"%x %X\\\")\\n report_title = ' '.join([\\n f'Report for {self.origin_value} participant consents authored on: {auth_date} 12:00AM-11:59PM UTC',\\n f'(generated on {now} Central)'\\n ])\\n\\n report_notes = [\\n ['Notes:'],\\n [f'Validation details on this sheet for {self.origin_value} participants only'],\\n ['Checkbox validation currently only performed on GROR consents'],\\n ['Total Errors can exceed Consents with Errors if any consents had multiple validation errors']\\n ]\\n\\n self._add_text_rows(text_rows=[[report_title]], format_spec=self.format_specs.get('bold_text'))\\n # Add any explanatory text / details about the report that have been included in the layout\\n self._add_text_rows(text_rows=report_notes, format_spec=self.format_specs.get('legend_text'),\\n row_pos=self.row_pos + 1)\\n\\n if not self._has_needs_correcting(self.consent_df):\\n self._add_text_rows(text_rows=[['No consent validation errors detected']],\\n format_spec=self.format_specs.get('italic_text'), row_pos=self.row_pos+1)\\n\\n # Daily summary counts for all the recently authored consents that were processed (regardless of errors)\\n self._add_text_rows([['Total Consent Validation Counts']],\\n format_spec=self.format_specs.get('bold_text'), row_pos=self.row_pos+1)\\n self._add_consent_issue_count_header_section(hpo='All Entities')\\n self._add_consent_issue_counts(self.consent_df, show_all_counts=True)\",\n \"def generate_audit_email_body(audit_id):\\n msg = None\\n try:\\n ad_list, err = get_entries(audit_id)\\n if err:\\n raise Exception(err)\\n if ad_list:\\n ad = ad_list[0]\\n msg = ' Audit time: %s\\\\n Performed from: %s\\\\n Performed by: %s\\\\n Action: %s.' % (\\n ad['time'], ad['ip'], ad['username'], ad['action_str'])\\n except Exception, e:\\n return None, 'Error generating audit email message body : %s' % str(e)\\n else:\\n return msg, None\",\n \"def createMessage( self, *args, **kw ):\\n if not kw.has_key('charset'):\\n kw['charset'] = self.getOutputCharset()\\n kw['to_mail'] = 1\\n return MailServerBase.createMessage( self, *args, **kw )\",\n \"def generate_withno_attachement(sender, recipient, subject, body):\\n # Basic Email formatting\\n message = email.message.EmailMessage()\\n message[\\\"From\\\"] = sender\\n message[\\\"To\\\"] = recipient\\n message[\\\"Subject\\\"] = subject\\n message.set_content(body)\\n return message\",\n \"def email_body_to_user_receiving_msg(profile, message):\\n\\tmsg = '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr><td align=\\\"center\\\" valign=\\\"top\\\"></td></tr></tbody></table>'\\n\\tmsg = msg + '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\"><tbody><tr>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\\\" cellspacing=\\\"0\\\" cellpadding=\\\"10\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tbody>'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<a href=\\\"https://insprite.co\\\"><img src=\\\"http://ryanfbaker.com/insprite/inspriteLogoA.png\\\" border=\\\"0\\\" alt=\\\"Insprite\\\" align=\\\"center\\\" width=\\\"200px\\\" height=\\\"55px\\\" /></a>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</tbody>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '<font style=\\\"font-family:Helvetica Neue;color:#555555;font-size:14px;\\\">'\\n\\tmsg = msg + 'You\\\\'ve got mail. It\\\\'s from <a href=\\\\\\\"https://127.0.0.1:5000/profile?hero=' + str(profile.prof_id) + '\\\\\\\" style=\\\"color:#1488CC\\\">' + profile.prof_name.encode('utf8', 'ignore') + '</a>.'\\n\\tmsg = msg + '<br><i>' + message.msg_content + '</i>'\\n\\tmsg = msg + '</font><br>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '<td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:200px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '<a href=\\\"#\\\" style=\\\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center;\\\" target=\\\"_blank\\\">Reply</a>'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/facebookIcon.png\\\">'\\n\\tmsg = msg + '<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/twitterIcon.png\\\">'\\n\\tmsg = msg + '<img src=\\\"http://ryanfbaker.com/insprite/instagramIcon.png\\\">'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\n\\tmsg = msg + '<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '<img src=\\\"http://ryanfbaker.com/insprite/spacer-2.png\\\">'\\n\\tmsg = msg + '</td></tr>'\\n\\tmsg = msg + '</table>'\\n\\treturn msg\",\n \"def consolidate_messages(self, msg):\",\n \"def generate_message(self, mtu):\\r\\n raise SystemExit(self.sm.__end_msg__)\",\n \"def summary(self):\\n if self.intact and self.valid:\\n return 'INTACT:' + ','.join(self.summary_fields())\\n else:\\n return 'INVALID'\",\n \"def print_summary(self):\\n #outcomes = self.get_outcomes()\\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\\n print('')\\n print ('Passes: %i' % self.get_pass_count())\\n print ('Fails: %i' % self.get_fail_count())\\n print ('Errors: %i' % self.get_error_count())\\n print ('Untested: %i' % self.get_untested_count())\\n print ('Skipped: %i' % self.get_skipped_count())\",\n \"def send_status_mail(self):\\n from django.core.mail import send_mail\\n subject = \\\"App Load Status | %s - %s | S%02d/-R%02d\\\" % (\\n self.publication.account, self.title, self.load_count, self.reject_count)\\n body = \\\"Account: %s\\\\nPublication: %s\\\\nStatus: %s\\\\nTime:%s - %s\\\\nLoaded: %02d\\\\nRejected: %02d\\\\n\\\\nComments\\\\n:%s\\\\n\\\" % (\\n self.publication.account, self.publication, self.load_status, self.start_time, self.end_time, self.load_count, self.reject_count, self.comments)\\n body = body + settings.EMAIL_DEFAULT_SIGNATURE\\n if self.publication.id in [2,3,60,61,62,63,370,39]:\\n return send_mail(subject, body, settings.DEFAULT_FROM_EMAIL,\\n [settings.ADMIN_EMAIL, 'anand.kumar@contify.com', 'rajesh.swain@contify.com', 'tapan.puhan@contify.com'], fail_silently=True)\\n else:\\n return send_mail(subject, body, settings.DEFAULT_FROM_EMAIL,\\n [settings.ADMIN_EMAIL], fail_silently=True)\",\n \"def summary_line_and_description():\",\n \"def generate_web_service_email(details):\\n subject = details[\\\"subject\\\"]\\n body = details[\\\"message\\\"]\\n from_email = settings.DEFAULT_FROM_ADDR\\n reply_to_email = [settings.EMAIL_TARGET_W]\\n to_email = details[\\\"email_to\\\"]\\n\\n email = GenericEmailGenerator(subject=subject, to_emails=to_email, bcc=reply_to_email, from_email=from_email,\\n reply_to=reply_to_email, body=body, context={'mrkdwn': True})\\n\\n return email\",\n \"def makeAMQPmsg(self):\\n msg = {'msgType' : 'AgentUpdate',\\n 'AgentType': 'Bus',\\n 'Extnum':self.Extnum,\\n 'Vm': self.cv['Vm'],\\n 'Va': self.cv['Va'],\\n }\\n return msg\",\n \"def make_info(self, v_queue_mean, v_tr_cv):\\n info = '\\\\n - average queue size: %.2f packets'\\n info += '\\\\n - CV for transmission rate: %.2f'\\n return info % (v_queue_mean, v_tr_cv)\",\n \"def generate_selfservice_notice_email(context):\\n subject = \\\"Self Service Form Submission\\\"\\n from_email = settings.DEFAULT_FROM_ADDR\\n to_email = [settings.EMAIL_TARGET_W, settings.EMAIL_TARGET_VP]\\n\\n cont_html = render_to_string('emails/email_selfservice.html', context)\\n cont_text = render_to_string('emails/email_selfservice.txt', context)\\n\\n email = EmailMultiAlternatives(subject, cont_text, from_email, to_email)\\n email.attach_alternative(cont_html, \\\"text/html\\\")\\n\\n return email\",\n \"def summarize(self):\\n\\n if self.summarizer == None:\\n return \\\"The summarizer has not been initialised\\\"\\n elif not isinstance(self.summarizer, Summarizer):\\n raise TypeError(\\\"summarizer is not a Summarizer object\\\")\\n\\n self.summary = self.summarizer.summarize(self.article)\\n return self.summary\",\n \"def create_message(self, subject, message, destination, origin = None):\\n msg = MIMEText(message)\\n if origin is not None:\\n msg['From'] = origin\\n else:\\n msg['From'] = self.origin\\n msg['Subject'] = subject\\n msg['To'] = destination\\n self.messages.append(msg)\",\n \"def generate_message(ctx, question, answer):\\n return preamble.format(channel=rules_channel(ctx).id) + question + answer\",\n \"def gen_report(self):\\n self.report = '#Report for {0}\\\\n'.format(self.ip)\\n self.report += 'This report was generated by the chameleon pentest bot. We cannot grant 100% accurate results.\\\\n'\\n self.report += '###Services:\\\\n'\\n for service in self.services:\\n self.report += '#####{0}:\\\\n- Port: {1}\\\\n- Info:{2}'.format(service.name, service.port, service.info)\\n self.report += '###Vulnerabilities:\\\\n'\\n for vuln in self.vulns:\\n self.report += '- {0}\\\\n'.format(vuln.name)\\n self.report += 'Open an issue for wrong results at github.com/coretool/chameleon.'\",\n \"def create_report(self):\\n text_string = \\\"\\\\n\\\"\\n text_string += \\\"Donor Name | Total Given | Num Gifts |\\\\\\n Average Gift\\\\n\\\\\\n ------------------------------------------------------------------\\\\n\\\"\\n for donor in sorted(self.donors, key=Donor.sort_key, reverse=True):\\n text_string += f\\\"{donor.name:<26} $ {donor.total_donations:>11.2f}\\\\\\n {donor.number_of_donations:>10} $ {donor.average_gift:>11.2f}\\\\n\\\"\\n\\n return text_string\",\n \"def create_message(sender, to, subject, message_text_html, message_text_plain):\\r\\n message = MIMEMultipart('alternative')\\r\\n message['to'] = to\\r\\n message['from'] = sender\\r\\n message['subject'] = subject\\r\\n message_html = MIMEText(message_text_html, 'html') # HTML version\\r\\n message_plain = MIMEText(message_text_plain) # plain text version\\r\\n message.attach(message_plain)\\r\\n message.attach(message_html)\\r\\n return {'raw': base64.urlsafe_b64encode(message.as_string().encode()).decode()}\",\n \"def summary_string(self) -> str:\\n return f\\\"dixonoid: {self.plain_rules}\\\"\",\n \"def summaryView(request):\\n\\n alert_errors = []\\n alert_infos = []\\n alert_filters = []\\n\\n runs = get_runs_from_request_filters(\\n request, alert_errors, alert_infos, alert_filters\\n )\\n\\n summary = SummaryReport(runs)\\n\\n context = {\\n \\\"refs\\\": summary.reference_runs(),\\n \\\"runs\\\": summary.runs_checked_per_type(),\\n \\\"tk_maps\\\": summary.tracker_maps_per_type(),\\n \\\"certified_runs\\\": summary.certified_runs_per_type(),\\n \\\"sums\\\": summary.sum_of_quantities_per_type(),\\n \\\"alert_errors\\\": alert_errors,\\n \\\"alert_infos\\\": alert_infos,\\n \\\"alert_filters\\\": alert_filters,\\n }\\n\\n return render(request, \\\"certhelper/summary.html\\\", context)\",\n \"def summarize(self):\\n txtSumm = ''\\n\\n if self.legtype == 'Takeoff':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Landing':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Other':\\n txtSumm = \\\"%02d -- %s\\\" %\\\\\\n (self.legno, self.legtype)\\n elif self.legtype == 'Observing':\\n txtSumm = \\\"%02d -- %s, RA: %s, Dec: %s, LegDur: %s, ObsDur: %s\\\" %\\\\\\n (self.legno, self.target, self.ra, self.dec,\\n str(self.duration),\\n str(self.obsdur))\\n txtSumm += \\\"\\\\n\\\"\\n if self.nonsid is True:\\n txtSumm += \\\"NONSIDERIAL TARGET -- NAIFID: %d\\\" % (self.naifid)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"(The SOFIA project sincerely hopes you enjoy \\\"\\n txtSumm += \\\"your observing breaks due to XFORMS crashes)\\\"\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ObsPlan: %s, ObsBlk: %s\\\" % (self.obsplan, self.obsblk)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"Elevation Range: %.1f, %.1f\\\" % (self.range_elev[0],\\n self.range_elev[1])\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"ROF Range: %.1f, %.1f\\\" % (self.range_rof[0],\\n self.range_rof[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"ROF Rate Range: %.1f, %.1f %s\\\" % (self.range_rofrt[0],\\n self.range_rofrt[1],\\n self.range_rofrtu)\\n txtSumm += \\\"\\\\n\\\\n\\\"\\n txtSumm += \\\"True Heading Range: %.1f, %.1f\\\" % (self.range_thdg[0],\\n self.range_thdg[1])\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"True Heading Rate Range: %.1f, %.1f %s\\\" %\\\\\\n (self.range_thdgrt[0],\\n self.range_thdgrt[1],\\n self.range_thdgrtu)\\n txtSumm += \\\"\\\\n\\\"\\n txtSumm += \\\"Moon Angle: %.1f, Moon Illumination: %s\\\" %\\\\\\n (self.moonangle, self.moonillum)\\n\\n return txtSumm\",\n \"def present_summary(services, methods, count, backup):\\n print_heading(\\\"Summary\\\")\\n if backup is not None:\\n writer(f\\\"Backup: {backup}\\\")\\n writer(f\\\"Showing {count[0]}/{len(services)} Services\\\")\\n writer(f\\\"Showing {count[1]}/{len(methods)} Methods\\\\n\\\")\",\n \"def show_summary(self, lang):\\n return self.summary % self.vars\",\n \"def _show_summary(self):\\n print 'Summary:'\\n print ' Reports downloaded successfully: %d' % self.counts\\n print ' Reports not downloaded: %d\\\\n' % self.failed\",\n \"def email_body_new_proposal_notification_to_seller(meeting, buyer_name, buyer_profile_id):\\n\\tmsg = '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\">\\\\n<tbody>\\\\n\\\\t<tr><td align=\\\"center\\\" valign=\\\"top\\\">\\\\n\\\\t</td></tr>\\\\n</tbody>\\\\n</table>'\\n\\n\\tmsg = msg + '<table cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\" bgcolor=\\\"#ffffff\\\">'\\n\\tmsg = msg + '\\\\n<tbody><tr>'\\n\\n\\tmsg = msg + '\\\\n\\\\t<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6; border-top: 2px solid #e6e6e6\\\" cellspacing=\\\"0\\\" cellpadding=\\\"10\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t<tbody>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #e6e6e6; border-bottom: 10px solid #FFFFFF; padding-top:75px; padding-left:58px\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\t<a href=\\\"https://insprite.co\\\"><img src=\\\"http://ryanfbaker.com/insprite/inspriteLogoA.png\\\" border=\\\"0\\\" alt=\\\"Insprite\\\" align=\\\"center\\\" width=\\\"200px\\\" height=\\\"55px\\\" />'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\t</a>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t</td></tr>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t</tbody>'\\n\\tmsg = msg + '\\\\n\\\\t</table>'\\n\\n\\n\\tmsg = msg + '\\\\n\\\\t<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:0px;padding-left:75px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<font style=\\\"font-family:Helvetica Neue;color:#555555;font-size:14px;\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tGreat! You received a new proposal from <a href=\\\\\\\"https://127.0.0.1:5000/profile?hero=' + buyer_profile_id + '\\\\\\\" style=\\\"color:#1488CC\\\">'+ buyer_name + '</a>.'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\t<br><br><br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tTime: ' + meeting.meet_ts.strftime('%A, %b %d, %Y %H:%M %p') + '<br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tDuration: ' + meeting.get_duration_in_hours() + ' hours<br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tLocation: ' + str(meeting.meet_location) + '<br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tFee: $' + str(meeting.meet_cost) + '<br><br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t\\\\tDescription: ' + meeting.get_description_html() + '<br><br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t</font><br><br>'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t</td></tr>'\\n\\n\\tmsg = msg + '\\\\n\\\\t\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 10px solid #FFFFFF;padding-top:10px;padding-left:75px;padding-bottom:150px\\\" align=\\\"left\\\" valign=\\\"top\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<a href=\\\\\\\"'+ meeting.accept_url() +'\\\\\\\" style=\\\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #1488CC;font-size: 14px;border-radius: 3px;border: 1px solid #1488CC;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center;\\\" target=\\\"_blank\\\">Accept</a> '\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<a href=\\\\\\\"'+ meeting.reject_url() +'\\\\\\\" style=\\\"color:#ffffff;text-decoration: none;display: inline-block;min-height: 38px;line-height: 39px;padding-right: 16px;padding-left: 16px;background: #e55e62;font-size: 14px;border-radius: 3px;border: 1px solid #e55e62;font-family:Garamond, EB Garamond, Georgia, serif; width:50px;text-align:center\\\" target=\\\"_blank\\\">Reject</a> '\\n\\tmsg = msg + '\\\\n\\\\t\\\\t</td></tr>'\\n\\tmsg = msg + '\\\\n\\\\t</table>'\\n\\n\\tmsg = msg + '\\\\n\\\\t<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/facebookIcon.png\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<img style=\\\"padding-right: 6px\\\" src=\\\"http://ryanfbaker.com/insprite/twitterIcon.png\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/instagramIcon.png\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t</td></tr>'\\n\\tmsg = msg + '\\\\n\\\\t</table>'\\n\\n\\tmsg = msg + '\\\\n\\\\t<table style=\\\"border-left: 2px solid #e6e6e6; border-right: 2px solid #e6e6e6;\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"600\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t<tr><td style=\\\"background-color: #ffffff; border-top: 0px solid #333333; border-bottom: 5px solid #FFFFFF;\\\" align=\\\"center\\\" valign=\\\"middle\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t\\\\t<img src=\\\"http://ryanfbaker.com/insprite/spacer-2.png\\\">'\\n\\tmsg = msg + '\\\\n\\\\t\\\\t</td></tr>'\\n\\tmsg = msg + '\\\\n\\\\t</table>'\\n\\n\\tmsg = msg + '\\\\n</tr></tbody>'\\n\\tmsg = msg + '</table>'\\n\\treturn msg\",\n \"def __str__(self):\\n return '- ' + str.join('\\\\n- ', [self.format_message(record)\\n for record in self._messages]) + '\\\\n'\",\n \"def build_message(self, subject, body, attachment=None):\\n msg = MIMEMultipart()\\n msg['From'] = self.src_addr\\n msg['To'] = self.dest_addr\\n msg['Subject'] = subject\\n msg.attach(MIMEText(body, 'plain'))\\n\\n if attachment is not None:\\n if os.path.exists(attachment):\\n with open(attachment, 'rb') as a:\\n part = MIMEBase('application', 'octet-stream')\\n part.set_payload(a.read())\\n encoders.encode_base64(part)\\n part.add_header('Content-Disposition', 'attachment; filename= {}'.format(attachment))\\n msg.attach(part)\\n\\n self.outbox.append(msg)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.63322425","0.61821836","0.61753136","0.6175085","0.61733943","0.6131707","0.61256206","0.60573125","0.6047461","0.6034194","0.60252714","0.6003194","0.5889488","0.5864201","0.584809","0.58364767","0.58144146","0.5763619","0.57625306","0.5760647","0.5760176","0.57419","0.57328933","0.57066536","0.570077","0.5686128","0.5668326","0.566309","0.5615482","0.5602021","0.55937946","0.55879915","0.5584277","0.55776787","0.55430233","0.55384487","0.55302644","0.5523392","0.5522491","0.54989254","0.5488263","0.54727036","0.54683685","0.5459027","0.54586744","0.54437554","0.54390126","0.5437434","0.5433293","0.5432784","0.54015404","0.5399378","0.53976834","0.5397042","0.5394274","0.5392775","0.53848547","0.53840697","0.53795433","0.53764665","0.53734994","0.5372144","0.53712076","0.5368467","0.5365127","0.5360165","0.5359771","0.5358905","0.5358369","0.5348092","0.53448355","0.53347886","0.5332572","0.5330909","0.53070706","0.530156","0.53015006","0.52921396","0.5280913","0.52801985","0.52743185","0.5270821","0.5268508","0.5256264","0.52554184","0.52455723","0.52455634","0.52440715","0.524214","0.52418166","0.52381957","0.5234165","0.5231968","0.523013","0.52233344","0.521263","0.5202129","0.5198537","0.5195391","0.51933044"],"string":"[\n \"0.63322425\",\n \"0.61821836\",\n \"0.61753136\",\n \"0.6175085\",\n \"0.61733943\",\n \"0.6131707\",\n \"0.61256206\",\n \"0.60573125\",\n \"0.6047461\",\n \"0.6034194\",\n \"0.60252714\",\n \"0.6003194\",\n \"0.5889488\",\n \"0.5864201\",\n \"0.584809\",\n \"0.58364767\",\n \"0.58144146\",\n \"0.5763619\",\n \"0.57625306\",\n \"0.5760647\",\n \"0.5760176\",\n \"0.57419\",\n \"0.57328933\",\n \"0.57066536\",\n \"0.570077\",\n \"0.5686128\",\n \"0.5668326\",\n \"0.566309\",\n \"0.5615482\",\n \"0.5602021\",\n \"0.55937946\",\n \"0.55879915\",\n \"0.5584277\",\n \"0.55776787\",\n \"0.55430233\",\n \"0.55384487\",\n \"0.55302644\",\n \"0.5523392\",\n \"0.5522491\",\n \"0.54989254\",\n \"0.5488263\",\n \"0.54727036\",\n \"0.54683685\",\n \"0.5459027\",\n \"0.54586744\",\n \"0.54437554\",\n \"0.54390126\",\n \"0.5437434\",\n \"0.5433293\",\n \"0.5432784\",\n \"0.54015404\",\n \"0.5399378\",\n \"0.53976834\",\n \"0.5397042\",\n \"0.5394274\",\n \"0.5392775\",\n \"0.53848547\",\n \"0.53840697\",\n \"0.53795433\",\n \"0.53764665\",\n \"0.53734994\",\n \"0.5372144\",\n \"0.53712076\",\n \"0.5368467\",\n \"0.5365127\",\n \"0.5360165\",\n \"0.5359771\",\n \"0.5358905\",\n \"0.5358369\",\n \"0.5348092\",\n \"0.53448355\",\n \"0.53347886\",\n \"0.5332572\",\n \"0.5330909\",\n \"0.53070706\",\n \"0.530156\",\n \"0.53015006\",\n \"0.52921396\",\n \"0.5280913\",\n \"0.52801985\",\n \"0.52743185\",\n \"0.5270821\",\n \"0.5268508\",\n \"0.5256264\",\n \"0.52554184\",\n \"0.52455723\",\n \"0.52455634\",\n \"0.52440715\",\n \"0.524214\",\n \"0.52418166\",\n \"0.52381957\",\n \"0.5234165\",\n \"0.5231968\",\n \"0.523013\",\n \"0.52233344\",\n \"0.521263\",\n \"0.5202129\",\n \"0.5198537\",\n \"0.5195391\",\n \"0.51933044\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5509327"},"document_rank":{"kind":"string","value":"39"}}},{"rowIdx":8461,"cells":{"query":{"kind":"string","value":"Bear in mind some simulations cannot contain neither peds nor objs"},"document":{"kind":"string","value":"def __init__(self, no_peds=0, peds_topics=[], num_s_samples=1, objs=None):\n self.global_ts = -1\n self.PEDESTRIAN_TOPICS = peds_topics\n\n self.drone = DroneModel(num_s_samples)\n self.subject = SubjectModel(num_s_samples)\n \n if no_peds > 0:\n self.peds = {\n str(i): PedestrianModel(num_s_samples) for i in range(no_peds)\n }\n else:\n self.peds = None\n \n self.objs = objs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def make_simulations(self):\n pass","def simulation():\n\n return {\n \"type\": \"class\",\n \"base\": \"iso.process_step\",\n \"is_abstract\": False,\n \"is_document\": True,\n \"pstr\": (\"({}/{}/{})\", (\"used\", \"ran_for_experiments\", \"ensemble_id\")),\n \"properties\": [\n (\n \"part_of_project\",\n \"linked_to(designing.project)\",\n \"1.N\",\n \"Project or projects for which simulation was run\",\n ),\n (\n \"ran_for_experiments\",\n \"linked_to(designing.numerical_experiment)\",\n \"1.N\",\n \"One or more experiments with which the simulation is \"\n \"associated\",\n ),\n (\n \"sub_experiment\",\n \"linked_to(designing.numerical_experiment)\",\n \"0.1\",\n \"For start-date ensembles, this will indicate the beginning \"\n \"year; for offline models driven by output from another \"\n \"model, this will provide the source_id and variant_label \"\n \"for the 'driving' model.\",\n ),\n (\n \"used\",\n \"linked_to(science.model)\",\n \"1.1\",\n \"The model used to run the simulation\",\n ),\n (\n \"primary_ensemble\",\n \"linked_to(activity.ensemble)\",\n \"0.1\",\n \"Primary Ensemble (ensemble for which this simulation was \"\n \"first run).\",\n ),\n (\n \"institution\",\n \"linked_to(shared.party)\",\n \"0.1\",\n \"institution which carried out the simulation\",\n ),\n (\n \"parent_of\",\n \"linked_to(activity.child_simulation)\",\n \"0.N\",\n \"If appropriate, links to simulations which branched from \"\n \"this one\",\n ),\n (\n \"produced\",\n \"linked_to(data.dataset)\",\n \"0.N\",\n \"Products of the simulation\",\n ),\n (\n \"had_performance\",\n \"linked_to(platform.performance)\",\n \"0.1\",\n \"Performance of the simulation.\",\n ),\n (\n \"ran_on\",\n \"linked_to(platform.machine)\",\n \"0.1\",\n \"The machine on which the simulation was run.\",\n ),\n (\n \"errata\",\n \"shared.online_resource\",\n \"0.1\",\n \"Link to errata associated with this simulation.\",\n ),\n (\n \"ensemble_id\",\n \"activity.axis_member\",\n \"0.N\",\n \"Identification within ensemble axes via axis member. \"\n \"(Multiple axis members within a simulation cannot share the \"\n \"same ensemble_axis.) (There must be an axis_member instance \"\n \"for each ensemble axis in a parent ensemble.)\",\n ),\n # Time\n (\n \"start_time\",\n \"time.date_time\",\n \"0.1\",\n \"The start date-time of the simulation. e.g. \"\n \"2012-04-01 00:00:00\",\n ),\n (\n \"end_time\",\n \"time.date_time\",\n \"0.1\",\n \"The end date-time of the simulation. e.g. \"\n \"2087-11-30 12:00:00\",\n ),\n (\n \"calendar\",\n \"time.calendar\",\n \"0.1\",\n \"The calendar used in the simulation\",\n ),\n # Further Info URL\n (\n \"documentation\",\n \"shared.online_resource\",\n \"0.1\",\n \"On-line location of additional documentation\",\n ),\n # Extra attributes\n (\n \"extra_attributes\",\n \"shared.extra_attribute\",\n \"0.N\",\n \"Additional attributes provided with simulation.\",\n ),\n ],\n \"constraints\": [\n (\"cardinality\", \"rationale\", \"0.0\"),\n ],\n }","def make_simulation(self):\n pass","def test_empty() -> None:\n for class_name in [SimulationData, SystemData, ObservableData, TrajectoryData]:\n print(f\"Testing empty {class_name.__name__} objects\")\n object1 = class_name()\n object2 = class_name()\n assert object1 == object2\n\n object1 = EnsembleData(\"NVE\", natoms=10, volume=1)\n object2 = EnsembleData(\"NVE\", natoms=10, volume=1)\n assert object1 == object2","def __init__(self,ensemble_obj,truths):\n\n# if ensemble_obj==None or truths==None:\n# print 'Warning: inputs not complete'\n self.ensemble_obj = ensemble_obj\n self.truths = truths","def test_poms_unwrapped(self):\n import electrochemistry\n import numpy as np\n # Create model\n model = electrochemistry.POMModel(DEFAULT_POMS)\n times = model.suggest_times()\n values = model.simulate(times)\n\n self.assertEqual(len(values), len(times))","def run_Simulation2(k,N=100,T=10,start = 1,p=0.5,q=0.08,startcenter = False,startcorner=False):\n recover = [0]\n infect = [start]\n suspect = [N-start]\n pop = [Person() for i in range(N)]\n ##we need to change the code for the case start people infected\n for i in range(start):\n pop[i].get_infected();\n if(startcenter):\n resetcenter(start,pop)\n if(startcorner):\n resetcorner(start,pop)\n np.random.seed(10)\n for i in range(T):\n for j in range(N):\n pop[j].movepos(p)\n X = calculatedistance(pop)\n tree = cKDTree(X)\n for j in range(N):\n if pop[j].is_infected():\n addvalue = np.array([X[j]])\n inds = tree.query_ball_point(addvalue, q)\n inds = inds[0]\n #may have problem here\n for l in inds:\n if pop[l].is_willinfected():\n pop[l].get_infected()\n\n for j in range(N):\n if pop[j].is_infected():\n if np.random.rand()< k:\n pop[j].get_recovered()\n\n recover.append(count_recover(pop))\n infect.append(count_infect(pop))\n suspect.append(count_suspectial(pop))\n newrecover = [i/N for i in recover]\n newsuspect = [s/N for s in suspect]\n newinfect = [i/N for i in infect]\n plt.plot(range(T+1),newrecover,label = \"r: percentage of removed \")\n plt.plot(range(T+1),newsuspect,label = \"s: percentage of susceptible\")\n plt.plot(range(T+1),newinfect,label = \"i: percentage of infected\")\n plt.xlabel(\"T\")\n plt.ylabel(\"percentage\")\n plt.title(\"Percentage of Population, Discrete\")\n plt.legend()\n plt.show()","def test_particle_obj():\n # Set up the base parameters describing a particle object\n T = 273.15 + 15.\n P = 150e5\n Sa = 35.\n Ta = 273.15 + 4.\n composition = ['methane', 'ethane', 'propane', 'oxygen']\n yk = np.array([0.85, 0.07, 0.08, 0.0])\n de = 0.005\n K = 1.\n Kt = 1.\n fdis = 1e-6\n\n # Compute a few derived quantities\n bub = dbm.FluidParticle(composition)\n m0 = bub.masses_by_diameter(de, T, P, yk)\n\n # Create a `SingleParticle` object\n bub_obj = dispersed_phases.SingleParticle(bub, m0, T, K, fdis=fdis,\n K_T=Kt)\n\n # Check if the initial attributes are correct\n for i in range(len(composition)):\n assert bub_obj.composition[i] == composition[i]\n assert_array_almost_equal(bub_obj.m0, m0, decimal=6)\n assert bub_obj.T0 == T\n assert bub_obj.cp == seawater.cp() * 0.5\n assert bub_obj.K == K\n assert bub_obj.K_T == Kt\n assert bub_obj.fdis == fdis\n for i in range(len(composition)-1):\n assert bub_obj.diss_indices[i] == True\n assert bub_obj.diss_indices[-1] == False\n\n # Check if the values returned by the `properties` method match the input\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m0, T, P,\n Sa, Ta, 0.)\n us_ans = bub.slip_velocity(m0, T, P, Sa, Ta)\n rho_p_ans = bub.density(m0, T, P)\n A_ans = bub.surface_area(m0, T, P, Sa, Ta)\n Cs_ans = bub.solubility(m0, T, P, Sa)\n beta_ans = bub.mass_transfer(m0, T, P, Sa, Ta)\n beta_T_ans = bub.heat_transfer(m0, T, P, Sa, Ta)\n assert us == us_ans\n assert rho_p == rho_p_ans\n assert A == A_ans\n assert_array_almost_equal(Cs, Cs_ans, decimal=6)\n assert_array_almost_equal(beta, beta_ans, decimal=6)\n assert beta_T == beta_T_ans\n assert T == T_ans\n\n # Check that dissolution shuts down correctly\n m_dis = np.array([m0[0]*1e-10, m0[1]*1e-8, m0[2]*1e-3, 1.5e-5])\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\n Sa, Ta, 0)\n assert beta[0] == 0.\n assert beta[1] == 0.\n assert beta[2] > 0.\n assert beta[3] > 0.\n m_dis = np.array([m0[0]*1e-10, m0[1]*1e-8, m0[2]*1e-7, 1.5e-16])\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\n Sa, Ta, 0.)\n assert np.sum(beta[0:-1]) == 0.\n assert us == 0.\n assert rho_p == seawater.density(Ta, Sa, P)\n\n # Check that heat transfer shuts down correctly\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, Ta, P,\n Sa, Ta, 0)\n assert beta_T == 0.\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\n Sa, Ta, 0)\n assert beta_T == 0.\n\n # Check the value returned by the `diameter` method\n de_p = bub_obj.diameter(m0, T, P, Sa, Ta)\n assert_approx_equal(de_p, de, significant=6)\n\n # Check functionality of insoluble particle\n drop = dbm.InsolubleParticle(isfluid=True, iscompressible=True)\n m0 = drop.mass_by_diameter(de, T, P, Sa, Ta)\n\n # Create a `Particle` object\n drop_obj = dispersed_phases.SingleParticle(drop, m0, T, K, fdis=fdis,\n K_T=Kt)\n\n # Check if the values returned by the `properties` method match the input\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(\n np.array([m0]), T, P, Sa, Ta, 0)\n us_ans = drop.slip_velocity(m0, T, P, Sa, Ta)\n rho_p_ans = drop.density(T, P, Sa, Ta)\n A_ans = drop.surface_area(m0, T, P, Sa, Ta)\n beta_T_ans = drop.heat_transfer(m0, T, P, Sa, Ta)\n assert us == us_ans\n assert rho_p == rho_p_ans\n assert A == A_ans\n assert beta_T == beta_T_ans\n\n # Check that heat transfer shuts down correctly\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(m_dis, Ta, P,\n Sa, Ta, 0)\n assert beta_T == 0.\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(m_dis, T, P,\n Sa, Ta, 0)\n assert beta_T == 0.\n\n # Check the value returned by the `diameter` method\n de_p = drop_obj.diameter(m0, T, P, Sa, Ta)\n assert_approx_equal(de_p, de, significant=6)","def check_learning_mol(\n n_representations,\n p_representations,\n n_atoms_list,\n p_atoms_list,\n properties,\n n_training=DEFAULT_N_TRAINING):\n\n properties = np.array(properties)\n\n n_points = len(properties)\n indexes = np.arange(n_points, dtype=int)\n np.random.shuffle(indexes)\n\n n_valid = 50\n v_idxs = indexes[-n_valid:]\n vn_repr = n_representations[v_idxs]\n vp_repr = p_representations[v_idxs]\n vn_atoms = [n_atoms_list[i] for i in v_idxs]\n vp_atoms = [p_atoms_list[i] for i in v_idxs]\n v_props = properties[v_idxs]\n\n\n errors = []\n\n hp = {\n \"sigma\": 4\n }\n\n for n in n_training:\n\n start_time = time.time()\n\n t_idxs = indexes[:n]\n t_props = properties[t_idxs]\n\n tn_repr = n_representations[t_idxs]\n tn_atoms = [n_atoms_list[i] for i in t_idxs]\n\n tp_repr = p_representations[t_idxs]\n tp_atoms = [p_atoms_list[i] for i in t_idxs]\n\n # Train\n tn_K = create_local_kernel(tn_repr, tn_repr, tn_atoms, tn_atoms, **hp)\n tp_K = create_local_kernel(tp_repr, tp_repr, tp_atoms, tp_atoms, **hp)\n # tnp_K = create_local_kernel(tn_repr, tp_repr, tn_atoms, tp_atoms)\n # tpn_K = create_local_kernel(tp_repr, tn_repr, tp_atoms, tn_atoms)\n\n # print(tn_K)\n # print(tp_K)\n # print(tnp_K)\n # print(tpn_K)\n\n # K_bind = tn_K + tp_K - tnp_K - tpn_K\n K_bind = tp_K - tn_K\n\n if False:\n\n pca = kpca(K_bind, n=2)\n\n # plot\n fig, axs = plt.subplots(2, 1, figsize=(5,10))\n\n sc = axs[0].scatter(*pca, c=t_props)\n fig.colorbar(sc, ax=axs[0])\n\n im = axs[1].imshow(K_bind)\n fig.colorbar(im, ax=axs[1])\n\n fig.savefig(\"_tmp_pca_{:}.png\".format(n))\n\n\n # Train model\n t_alpha = krr(K_bind, t_props, solver=\"svd\", rcond=10**-4)\n\n # Test and predict\n vn_K = create_local_kernel(tn_repr, vn_repr, tn_atoms, vn_atoms, **hp)\n vp_K = create_local_kernel(tp_repr, vp_repr, tp_atoms, vp_atoms, **hp)\n\n v_K = vp_K - vn_K\n\n p_props = np.dot(v_K, t_alpha)\n\n # rmse error\n p_rmse, le, ue = rmse(p_props, v_props)\n\n end_time = time.time()\n\n print(\"{:5d}\".format(n), \"{:10.2f} ± {:4.2f} t={:4.1f}\".format(p_rmse, ue, end_time-start_time))\n\n errors.append([p_rmse, le, ue])\n\n errors = np.array(errors)\n\n return errors","def potential_new_obs(self) -> Iterable[GriddedPerm]:","def test_outV_traversals(self):\r\n results = self.eric.outV()\r\n assert len(results) == 1\r\n assert self.physics in results","def test_population_movements_without_compilation(self):\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01, False)',\n block=True)\n print self._pystepx._rc[0]['elems']","def test_order(self):\n class Mock(object):\n def __init__(self):\n self.spike = [False]\n\n def evolve(self, t, dt):\n self.spike = [True]\n\n G0 = Mock()\n M0 = simulation.EventMonitor(G0)\n sim = simulation.Simulation(G0, M0)\n sim.run(sim.dt)\n\n self.assertEqual(len(M0.t), 1)\n self.assertEqual(len(M0.i), 1)","def simulate_memories(simulation_length):\n \n \n pass","def simulationTwoDrugsVirusPopulations():\n #TODO","def test_when_opponent_all_Ds(self):\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\n random_seed=5)","def initialise_sim(self):\n pass","def test_simulation(self):\r\n positions = [1, 10, 100,1000]\r\n num_trials = 10000\r\n result = Investment(positions, num_trials)\r\n result = result.simulation(positions, num_trials) \r\n for pos in positions:\r\n self.assertEqual(len(result[pos]), num_trials)\r\n self.assertTrue(result[pos].all() == 1 or result[pos].all() == -1)","def run_test0():\r\n \r\n ndia, nadi, nnucl, ntraj = 1, 1, 2, 500\r\n\r\n # ======= Hierarchy of Hamiltonians =======\r\n ham = nHamiltonian(ndia, nadi, nnucl)\r\n ham.init_all(2)\r\n print \"id=\", ham.id, \" level=\", ham.level\r\n\r\n ham1 = [] \r\n for tr in xrange(ntraj):\r\n ham1.append( nHamiltonian(ndia, nadi, nnucl) ) \r\n print ham1[tr].id, ham1[tr].level\r\n ham1[tr].init_all(2)\r\n ham.add_child(ham1[tr])\r\n print Cpp2Py(ham1[tr].get_full_id())\r\n\r\n # Set up the models and compute internal variables\r\n # Initialization\r\n # Model parameters \r\n params = { \"model\":1 }\r\n\r\n # Simulation parameters\r\n dt = 1.0\r\n\r\n # Dynamical variables and system-specific properties\r\n mean_q = MATRIX(nnucl,1); \r\n sigma_q = MATRIX(nnucl,1); \r\n mean_p = MATRIX(nnucl,1); \r\n sigma_p = MATRIX(nnucl,1); \r\n iM = MATRIX(nnucl,1);\r\n\r\n for i in xrange(nnucl):\r\n mean_q.set(i,0, -1.0) \r\n sigma_q.set(i,0, 0.05) \r\n mean_p.set(i,0, 0.0) \r\n sigma_p.set(i,0, 0.0)\r\n iM.set(i,0, 1.0/2000.0)\r\n\r\n rnd = Random()\r\n q = MATRIX(nnucl,ntraj); aux_functs.sample(q, mean_q, sigma_q, rnd)\r\n p = MATRIX(nnucl,ntraj); aux_functs.sample(p, mean_p, sigma_p, rnd) \r\n\r\n # Initial calculations\r\n q.show_matrix()\r\n\r\n # Compute Initial trajectory probability distributions for all dof\r\n #bin(q, -2.0, 2.0, 0.01)\r\n\r\n ham.compute_diabatic(compute_model, q, params, 1)\r\n ham.compute_adiabatic(1, 1);\r\n ham.add_ethd_adi(q, iM, 1)\r\n\r\n os.system(\"mkdir _2D_dist\")\r\n out1 = open(\"_output.txt\", \"w\"); out1.close() \r\n\r\n # Do the propagation\r\n for i in xrange(100):\r\n\r\n aux_functs.bin2(q, -2.0, 2.0, 0.1, -2.0, 2.0, 0.1, \"_2D_dist/_2D_distrib_\"+str(i)+\"_.txt\")\r\n\r\n Verlet1(dt, q, p, iM, ham, compute_model, params, 1)\r\n\r\n #=========== Properties ==========\r\n\r\n Ekin, Epot, Etot = aux_functs.compute_etot(ham, p, iM)\r\n\r\n # Print the ensemble average - kinetic, potential, and total energies\r\n # Print the tunneling information. Here, we count each trajectory across the barrier.\r\n out1 = open(\"_output.txt\", \"a\")\r\n out1.write( \" %8.5f %8.5f %8.5f %8.5f\\n\" % ( i*dt, Ekin, Epot, Etot ) )\r\n out1.close()","def test_sample(self):\n seed = 5\n space = Space()\n probs = (0.1, 0.2, 0.3, 0.4)\n categories = (\"asdfa\", 2, 3, 4)\n dim1 = Categorical(\"yolo\", OrderedDict(zip(categories, probs)), shape=(2, 2))\n space.register(dim1)\n dim2 = Integer(\"yolo2\", \"uniform\", -3, 6)\n space.register(dim2)\n dim3 = Real(\"yolo3\", \"norm\", 0.9)\n space.register(dim3)\n\n point = space.sample(seed=seed)\n rng = check_random_state(seed)\n test_point = [\n dict(\n yolo=dim1.sample(seed=rng)[0],\n yolo2=dim2.sample(seed=rng)[0],\n yolo3=dim3.sample(seed=rng)[0],\n )\n ]\n assert len(point) == len(test_point) == 1\n assert len(point[0].params) == len(test_point[0]) == 3\n assert np.all(point[0].params[\"yolo\"] == test_point[0][\"yolo\"])\n assert point[0].params[\"yolo2\"] == test_point[0][\"yolo2\"]\n assert point[0].params[\"yolo3\"] == test_point[0][\"yolo3\"]\n\n points = space.sample(2, seed=seed)\n rng = check_random_state(seed)\n points1 = dim1.sample(2, seed=rng)\n points2 = dim2.sample(2, seed=rng)\n points3 = dim3.sample(2, seed=rng)\n test_points = [\n dict(yolo=points1[0], yolo2=points2[0], yolo3=points3[0]),\n dict(yolo=points1[1], yolo2=points2[1], yolo3=points3[1]),\n ]\n assert len(points) == len(test_points) == 2\n for i in range(2):\n assert len(points[i].params) == len(test_points[i]) == 3\n assert np.all(points[i].params[\"yolo\"] == test_points[i][\"yolo\"])\n assert points[i].params[\"yolo2\"] == test_points[i][\"yolo2\"]\n assert points[i].params[\"yolo3\"] == test_points[i][\"yolo3\"]","def testInvisiblePositive(self):\n slhafile=\"./testFiles/slha/higgsinoStop.slha\"\n model = Model(BSMList,SMList)\n model.updateParticles(slhafile)\n topos = decomposer.decompose ( model, .1*fb, False, True, 5.*GeV )\n tested = False\n for topo in topos:\n if str(topo)!=\"[][]\":\n continue\n for element in topo.elementList:\n if str(element)!=\"[[],[]]\":\n continue\n tested = True\n trueMothers = [mother for mother in element.motherElements if not mother is element]\n if not trueMothers: continue\n self.assertEqual(str(trueMothers[0]),\"[[],[[nu,nu]]]\")\n self.assertEqual(len(trueMothers), 1)\n self.assertTrue(tested)","def simulationTwoDrugsDelayedTreatment():\n\n # TODO","def test_contains():\n world, bodies, _ = example_world()\n\n geometry = metis.geometry.ManyShapeGeometry(world, bodies)\n dynamics = metis.dynamics.MagneticDynamics(bodies)\n factored_graph = FactoredRandomGeometricGraph(\n geometry, dynamics, default_count=5, blacklist=NoObjectContactBlacklist())\n\n assert {'robot': (None, 0), 'box1': (None, 0), 'box2': (None, 0)} in factored_graph\n assert {'robot': ('box1', 0), 'box1': (None, 0), 'box2': (None, 0)} in factored_graph\n assert {'robot': ('box1', 5), 'box1': (None, 0), 'box2': (None, 0)} not in factored_graph\n assert {'robot': ('robot', 0), 'box1': (None, 0), 'box2': (None, 0)} not in factored_graph\n assert {'robot': ('box1', 0), 'box1': ('robot', 0), 'box2': (None, 0)} not in factored_graph","def test_object_with_observables() -> None:\n system_name = \"Water900\"\n simulation_id_1 = \"NPT-lowT-lowP\"\n simulation_id_2 = \"NPT-highT-lowP\"\n system = database.system(system_name)\n parser = FlatfileParser()\n simulation_data_1 = parser.get_simulation_data(\n units=system.units,\n ensemble=system.ensemble(simulation_id_1),\n system=system.system_data,\n kinetic_ene_file=system.observable_flat_file(\n simulation_id_1, \"kinetic_energy\"\n ),\n potential_ene_file=system.observable_flat_file(\n simulation_id_1, \"potential_energy\"\n ),\n total_ene_file=system.observable_flat_file(simulation_id_1, \"total_energy\"),\n volume_file=system.observable_flat_file(simulation_id_1, \"volume\"),\n )\n simulation_data_1_copy = parser.get_simulation_data(\n units=system.units,\n ensemble=system.ensemble(simulation_id_1),\n system=system.system_data,\n kinetic_ene_file=system.observable_flat_file(\n simulation_id_1, \"kinetic_energy\"\n ),\n potential_ene_file=system.observable_flat_file(\n simulation_id_1, \"potential_energy\"\n ),\n total_ene_file=system.observable_flat_file(simulation_id_1, \"total_energy\"),\n volume_file=system.observable_flat_file(simulation_id_1, \"volume\"),\n )\n simulation_data_2 = parser.get_simulation_data(\n units=system.units,\n ensemble=system.ensemble(simulation_id_2),\n system=system.system_data,\n kinetic_ene_file=system.observable_flat_file(\n simulation_id_2, \"kinetic_energy\"\n ),\n potential_ene_file=system.observable_flat_file(\n simulation_id_2, \"potential_energy\"\n ),\n total_ene_file=system.observable_flat_file(simulation_id_2, \"total_energy\"),\n volume_file=system.observable_flat_file(simulation_id_2, \"volume\"),\n )\n\n assert simulation_data_1 == simulation_data_1_copy\n assert simulation_data_1 != simulation_data_2","def test_run_sim_1():\n rnd = rand.Arrivals(36, 41)\n sim.run_sim(3, 2, 5, 6, 22, rnd)","def test_w_and_without():\n A = Node(\"A\", [\"B\"], {\"B\": np.array([[1,0],[1,.1]])})\n B = Node(\"B\", [], {})\n net = CyberNet([A,B])\n T=10\n data = gen_data(T, net, {\"A\": \"normal\", \"B\":\"normal\"})\n logn_fact = gen_logn_fact(data)\n pdata_no_a = prob_model_no_attacker(net, data, T, logn_fact)\n pdata_a = prob_model_given_data_times(net, data, {}, T, logn_fact,\n {\"A\": \"normal\",\n \"B\":\"normal\"})\n np.testing.assert_almost_equal(pdata_no_a, pdata_a)\n\n np.testing.assert_almost_equal(np.log(poisson.pmf(len(data[0]), 10)), pdata_a)","def xo(mom:list,eval_mom,dad:list,eval_dad,slices):\n legal = False\n legal_son = False\n legal_daughter = False\n son = []\n daughter = []\n point_track = set()\n while not legal:\n\n slice_point = randint(0, len(dad)-1)\n if slice_point in point_track:\n continue\n point_track.add(slice_point)\n if len(point_track) == len(dad): # exhausted all possible points with no success\n if legal_son is True: # and legal_daughter is False\n return son,eval_son,mom,eval_mom\n if legal_daughter is True: # and legal_son is False\n return dad, eval_dad, daughter, eval_daughter\n if legal_son is False:\n son = dad[:slice_point]+mom[slice_point:]\n eval_son = evaluateOne(son)\n if eval_son > -1:\n legal_son = True\n if legal_daughter is False:\n daughter = mom[:slice_point]+dad[slice_point:]\n eval_daughter = evaluateOne(daughter)\n if eval_daughter > -1:\n legal_daughter = True\n\n legal = legal_son and legal_daughter\n\n return son,eval_son,daughter,eval_daughter","def test_inE_traversals(self):\r\n results = self.jon.inE()\r\n assert len(results) == 1\r\n assert self.jon_physics in results","def simulate_trajectories(kav):\n print \"Simulating \"+str(kav)\n wt_trajectories = []\n avp_trajectories = []\n vip_trajectories = []\n for tn in range(100):\n # get random initial condition\n # initial phases\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \n for i in range(AVPcells+VIPcells)]\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\n for i in range(NAVcells)]\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\n\n # do the simulation\n model = GonzeModelManyCells(param, kav=kav, \n initial_values=y0_random)\n wt_trajectories.append(model.run(show_labels=False, seed=0))\n\n # avp bmalko\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \n initial_values=y0_random)\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\n\n # vip bmalko\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \n initial_values=y0_random)\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\n\n # save results\n with open(\"Data/params/wt_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(wt_trajectories, output_file)\n with open(\"Data/params/avp_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(avp_trajectories, output_file)\n with open(\"Data/params/vip_\"+str(kav)+\".pickle\", \"wb\") as output_file:\n pickle.dump(vip_trajectories, output_file)\n\n return {'wt': wt_trajectories,\n 'avp': avp_trajectories,\n 'vip': vip_trajectories}","def test_active_inference_SPM_1b(self):","def build_and_run_(spec):\n opt = spec['opt']\n print \"pool starting \", opt\n\n # lenght of simulation \n tf = float(opt.get('tf', 100))\n\n # model # coupling function # connectivity \n simargs = {}\n for mod, key in [(models, 'model'), \n (connectivity, 'connectivity'),\n (coupling, 'coupling')]:\n simargs[key] = build_sim_part(mod, opt[key])\n\n # noise # integrator \n optint = opt['integrator']\n if 'noise' in optint:\n optint['noise'] = build_sim_part(noise, optint['noise'])\n simargs['integrator'] = build_sim_part(integrators, optint)\n\n # monitors \n if not type(opt['monitors']) in (list,):\n opt['monitors'] = [opt['monitors']]\n simargs['monitors'] = []\n for mon in opt['monitors']:\n simargs['monitors'].append(build_sim_part(monitors, mon))\n\n # stimulus \n # NotImplemented\n\n # simulator \n sim = simulator.Simulator(**simargs)\n sim.configure()\n\n # TODO open HDF5 first, figure out correct sizes, etc\n\n # loop, writing data to h5\n ts = [[] for _ in opt['monitors']]\n ys = [[] for _ in opt['monitors']]\n for i, all_monitor_data in enumerate(sim(tf)):\n for j, mondata in enumerate(all_monitor_data):\n if not mondata is None:\n t, y = mondata\n ts[j].append(t)\n ys[j].append(y)\n\n # write data to hdf5 file\n path = os.path.abspath(opt.get('wd', './'))\n h5fname = os.path.join(path, \"tvb_%s.h5\" % (spec['md5sum'], ))\n h5 = h5py.File(h5fname, 'w')\n\n for i, (mon, (t, y)) in enumerate(zip(simargs['monitors'], zip(ts, ys))):\n mname = \"mon_%d_%s\" % (i, mon.__class__.__name__)\n g = h5.create_group(mname)\n g.create_dataset('ts', data=t)\n g.create_dataset('ys', data=y)\n\n h5.close()\n\n # return filename\n print \"pool finished\", opt\n return h5fname","def test_sim(self):\n nxfe = 4\n ipopt = get_solver(\"ipopt\")\n\n m_steady = self.make_steady_model(nfe=nxfe)\n self.fix_model_inlets(m_steady, inlet_pressure=50.0 * pyo.units.bar)\n m_steady.fs.compressor.boost_pressure[:].fix(7.0 * pyo.units.bar)\n ipopt.solve(m_steady, tee=True)\n time_steady = m_steady.fs.time\n scalar_data = self.get_scalar_data_from_model(m_steady, time_steady)\n initial_data = self.get_data_from_model_at_time(m_steady, time_steady)\n\n m = pyo.ConcreteModel()\n default = {\n \"dynamic\": True,\n \"time_set\": [0.0, 20.0],\n \"time_units\": pyo.units.hr,\n }\n m.fs = idaes.FlowsheetBlock(**default)\n m.fs.properties = NaturalGasParameterBlock()\n pipeline_config = {\n \"property_package\": m.fs.properties,\n \"finite_elements\": nxfe,\n }\n m.fs.pipeline = GasPipeline(**pipeline_config)\n pipeline = m.fs.pipeline\n compressor_config = {\"property_package\": m.fs.properties}\n m.fs.compressor = Compressor(**compressor_config)\n compressor = m.fs.compressor\n m._compressor_to_pipeline = Arc(\n ports=(compressor.outlet_port, pipeline.inlet_port),\n )\n expand_arcs = pyo.TransformationFactory(\"network.expand_arcs\")\n expand_arcs.apply_to(m)\n\n cv = m.fs.pipeline.control_volume\n assert_units_consistent(m)\n\n disc = pyo.TransformationFactory(\"dae.finite_difference\")\n ntfe = 20\n disc.apply_to(m, nfe=ntfe, wrt=m.fs.time, scheme=\"BACKWARD\")\n\n time = m.fs.time\n t0 = m.fs.time.first()\n x0 = cv.length_domain.first()\n xf = cv.length_domain.last()\n j = next(iter(m.fs.properties.component_list))\n\n # Fix geometry variables\n m.fs.pipeline.diameter.fix(0.92 * pyo.units.m)\n cv.length.fix(300.0 * pyo.units.km)\n\n # Fix boost pressure\n compressor.boost_pressure[:].fix()\n\n # Inlets to the compressor are fixed, except for flow, where\n # the outlet is fixed.\n state = compressor.inlet_state\n state[:].pressure.fix()\n state[:].mole_frac_comp[j].fix()\n state[:].temperature.fix()\n cv.flow_mass[:, xf].fix()\n\n # Fix initial conditions. Here, pressure and volume for all\n # non-specified points.\n for x in cv.length_domain:\n if x != x0:\n cv.pressure[t0, x].fix()\n if x != xf:\n cv.flow_mass[t0, x].fix()\n\n # I want to deactivate differential equations at (t0, xf)\n # Material balance already doesn't exist here.\n cv.momentum_balance[t0, xf].deactivate()\n\n self.assertEqual(degrees_of_freedom(m), 0)\n\n # Load initial steady state into model at all time points.\n for name, val in initial_data.items():\n var = m.find_component(name)\n for t in time:\n var[t].set_value(val)\n # Load scalar data from initial steady state\n # (initialize area, basically)\n for name, val in scalar_data.items():\n var = m.find_component(name)\n var.set_value(val)\n\n cv.material_accumulation[...].set_value(0.0)\n cv.flow_mass_dt[...].set_value(0.0)\n\n for con in large_residuals_set(m):\n resid = pyo.value(con.body - con.upper)\n print(resid, con.name)\n ipopt.solve(m, tee=True)\n\n # Load input sequence into model\n sample_points = [4.0, 20.0]\n input_name = \"fs.pipeline.control_volume.flow_mass[*,1.0]\"\n nominal_density = 0.72\n val = 12.0 * 1e6 / 24 * nominal_density # 12 (1e6 SCM)/day\n input_series_data = (\n sample_points,\n {input_name: [val, val]},\n )\n input_interval_data = interval_data_from_time_series(input_series_data)\n load_inputs_into_model(m, time, input_interval_data)\n # Solve with loaded inputs\n res = ipopt.solve(m, tee=True)\n self.assertIs(\n res.solver.termination_condition,\n pyo.TerminationCondition.optimal,\n )\n\n # These predicted values come from a simulation of a single pipeline\n # model from the Pyomo DAE example. flow_mass has been converted\n # to kg/hr from (1e4 SCM/hr) by a factor of 0.72*1e4, where\n # 0.72 kg/m**3 is the gas density at standard conditions.\n pred_values = (\n list(time),\n {\n \"fs.pipeline.control_volume.flow_mass[*,%s]\"\n % x0: [\n 3.000e5,\n 2.999e5,\n 2.999e5,\n 2.999e5,\n 3.000e5,\n 3.174e5,\n 3.301e5,\n 3.389e5,\n 3.449e5,\n 3.492e5,\n 3.523e5,\n 3.544e5,\n 3.560e5,\n 3.571e5,\n 3.579e5,\n 3.585e5,\n 3.589e5,\n 3.592e5,\n 3.594e5,\n 3.595e5,\n 3.597e5,\n ],\n \"fs.pipeline.control_volume.pressure[*,%s]\"\n % xf: [\n 50.90,\n 50.90,\n 50.90,\n 50.90,\n 50.90,\n 49.83,\n 49.31,\n 48.95,\n 48.69,\n 48.51,\n 48.38,\n 48.29,\n 48.22,\n 48.17,\n 48.14,\n 48.11,\n 48.10,\n 48.08,\n 48.07,\n 48.07,\n 48.06,\n ],\n \"fs.compressor.power[*]\": [\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.590e3,\n 1.682e3,\n 1.750e3,\n 1.796e3,\n 1.828e3,\n 1.851e3,\n 1.867e3,\n 1.878e3,\n 1.887e3,\n 1.892e3,\n 1.897e3,\n 1.900e3,\n 1.902e3,\n 1.904e3,\n 1.905e3,\n 1.906e3,\n 1.906e3,\n ],\n },\n )\n output_names = [\n \"fs.pipeline.control_volume.flow_mass[*,%s]\" % x0,\n \"fs.pipeline.control_volume.pressure[*,%s]\" % xf,\n \"fs.compressor.power[*]\",\n ]\n actual_values = (\n list(time),\n {\n name: [var.value for var in m.find_component(name).values()]\n for name in output_names\n },\n )\n # Note: We fail with a reltol of 0.01, due to flow rate discrepancies\n # in positions 6, 7, 8, and 9. A reltol of 0.02 seems reasonable to me.\n self.assertStructuredAlmostEqual(pred_values, actual_values, reltol=0.02)","def test_variability(self):\n # some reproducible arbitrariness\n np.random.seed(343143)\n\n n = 10\n t_max = 20.0\n dt = 0.1\n G = RandomLayer(n)\n\n M1 = simulation.EventMonitor(G)\n\n sim1 = simulation.Simulation(G, M1, dt=dt)\n sim1.run(t_max)\n \n M2 = simulation.EventMonitor(G)\n sim2 = simulation.Simulation(G, M2, dt=dt)\n sim2.run(t_max)\n\n self.assertNotEqual(len(M1.t), 0)\n self.assertNotEqual(len(M2.t), 0)\n self.assertNotEqual(M1.t, M2.t)","def test_no_jitter(self):\n n = 10\n t_max = 25\n dt = 0.1\n G = RateHVCLayer(n)\n G.burst_noise = 0.0\n\n M1 = simulation.StateMonitor(G, 'out')\n\n sim1 = simulation.Simulation(G, M1, dt=dt)\n sim1.run(t_max)\n \n M2 = simulation.StateMonitor(G, 'out')\n sim2 = simulation.Simulation(G, M2, dt=dt)\n sim2.run(t_max)\n\n self.assertTrue(np.allclose(M1.out, M2.out))","def test_shape(self):\n M = simulation.EventMonitor(self.G)\n sim = simulation.Simulation(self.G, M, dt=self.dt)\n sim.run(self.t_max)\n\n self.assertEqual(len(M.t), len(M.i))","def test_shared_members_N(self):\r\n def populate_module(m,x):\r\n m.x=x\r\n m.lx=[x]\r\n m.llx=[[x],[x]]\r\n m.ltx=[(x,)]\r\n m.ldx=[{'x':x}]\r\n m.tx=(x,)\r\n m.tlx=([x],)\r\n m.ttx=((x,),)\r\n m.tdx=({'x':x},)\r\n m.dx={'x':x}\r\n m.dlx={'x':[x]}\r\n m.dtx={'x':(x,)}\r\n m.ddx={'x':{'x':x}}\r\n\r\n def get_element(i):\r\n return [i.x,i.lx[0],i.tx[0],i.dx['x'],i.llx[0][0], i.llx[1][0], i.ltx[0][0], i.ldx[0]['x'], i.tlx[0][0], i.tlx[0][0], i.tdx[0]['x'], i.dlx['x'][0], i.dtx['x'][0], i.ddx['x']['x']]\r\n m1=Module()\r\n m2=Module()\r\n m3=Module()\r\n m4=Module()\r\n x=T.dscalar()\r\n populate_module(m1,x)\r\n populate_module(m2,(x))\r\n populate_module(m4,(x))\r\n #m1.x and m2.x should not be shared as their is no hierarchi link between them.\r\n inst1=m1.make()\r\n inst2=m2.make()\r\n m1.m2=m2\r\n m2.m3=m3\r\n m3.m4=m4\r\n #m1.x and m2.x should be shared as their is a hierarchi link between them.\r\n inst3=m1.make()\r\n inst1.x=1\r\n inst2.x=2\r\n inst3.x=3\r\n for f in get_element(inst1):\r\n assert f==1\r\n for f in get_element(inst2):\r\n assert f==2\r\n for f in get_element(inst3)+get_element(inst3.m2)+get_element(inst3.m2.m3.m4):\r\n assert f==3\r\n\r\n inst3.m2.x=4\r\n for f in get_element(inst3)+get_element(inst3.m2)+get_element(inst3.m2.m3.m4):\r\n assert f==4","def test_OBJT_pass(self):\n for O in self.mod.objts.itervalues():\n self.assertTrue(O.isset)","def test_basic_setup(self):\n random_vars = ['D', 'I', 'G', 'S', 'L']\n\n for rv in random_vars:\n self.assertTrue(rv in self.Gs.nodes)\n self.assertTrue(isinstance(self.Gs.nodes[rv], DiscreteNetworkNode))","def test_single_game_seed_works(self):\n sim = ss.Simulation(seed=23)\n game1 = sim.single_game()\n sim = ss.Simulation(seed=23)\n game2 = sim.single_game()\n assert game1 == game2, 'Your seed in Simulation class is not working.'","def test_no_jitter(self):\n # some reproducible arbitrariness\n np.random.seed(3249823)\n\n n = 10\n t_max = 25\n dt = 0.1\n G = HVCLikeLayer(n)\n G.burst_noise = 0.0\n G.spike_noise = 0.0\n\n M1 = simulation.EventMonitor(G)\n\n sim1 = simulation.Simulation(G, M1, dt=dt)\n sim1.run(t_max)\n \n M2 = simulation.EventMonitor(G)\n sim2 = simulation.Simulation(G, M2, dt=dt)\n sim2.run(t_max)\n\n self.assertEqual(M1.t, M2.t)","def __post_init__(self):\n assert self.data.shape == self.theory.shape","def v1_kcat_validate():\n\n name = MPI.Get_processor_name()\n rank = MPI.COMM_WORLD.Get_rank()\n size = MPI.COMM_WORLD.Get_size()\n\n if rank == 0:\n # create ident object first\n v1_ident = ModelIdent(ident_fun=kotte_model.flux_1_kcat_ident,\n arranged_data_file_name=os.path.join(os.getcwd(), 'exp/exp_v1_2_experiments'),\n ident_data_file_name=os.path.join(os.getcwd(), 'ident/ident_v1_kcat'),\n **{'original_exp_file': os.path.join(os.getcwd(), 'exp/experiments'),\n 'flux_id': 1, 'flux_choice': 2,\n 'values_figure': os.path.join(os.getcwd(), 'results/v1_kcat_parameter_values.eps'),\n 'ident_figure': os.path.join(os.getcwd(), 'results/v1_kcat_ident.eps'),\n 'exp_figure': os.path.join(os.getcwd(), 'results/v1_kcat_exp.eps'),\n 'figure_format': 'eps',\n 'ident_index_label': ['sample_name', 'data_set_id']})\n\n # retrieve identifiability data and process it for validation\n v1_ident.validation_info()\n\n user_ode_opts = {'iter': 'Newton', 'discr': 'Adams', 'atol': 1e-10, 'rtol': 1e-10,\n 'time_points': 200, 'display_progress': True, 'verbosity': 30}\n # initial ss to begin all simulations from\n y0 = np.array([5, 1, 1])\n # get and set true parameter values, if available separately\n default_parameters = true_parameter_values()\n\n v1_valid_obj = ValidateSim(kotte_model.kotte_ck_ode, kotte_model.kotte_ck_flux,\n **{'kinetics': 2, 'ode_opts': user_ode_opts, 't_final': 200, 'wt_y0': y0,\n 'i_parameter': default_parameters, 'sample_size': 1, 'noise_std': 0.05,\n 'validate_index_label': ['estimate_id', 'sample_name', 'data_set_id',\n 'experiment_id'],\n 'validate_file_name': os.path.join(os.getcwd(), 'validate/v1_kcat_validate'),\n 'original_exp_file': v1_ident.original_exp_file,\n 'c_validate_file': os.path.join(os.getcwd(), 'results/v1_kcat_c_validate.eps'),\n 'f_validate_file': os.path.join(os.getcwd(), 'results/v1_kcat_f_validate.eps'),\n 'c_exp_file': os.path.join(os.getcwd(), 'results/v1_kcat_c_exp.eps'),\n 'v_exp_file': os.path.join(os.getcwd(), 'results/v1_kcat_f_exp.eps'),\n 'format': v1_ident.figure_format})\n\n parameter_estimates, estimate_info = v1_valid_obj.create_parameter_list(v1_ident.select_values)\n\n job = ParallelValidate(slaves=range(1, size))\n\n validate_results = job.run_all(task='initial_sim', **{'parameters': parameter_estimates,\n 'estimate_info': estimate_info, 'sim_obj': v1_valid_obj})\n job.terminate_slaves()\n\n # process validation data for plots\n validate_processing(v1_valid_obj, validate_results)\n\n else:\n\n print('I am %s Slave with rank %s of %s' % (name, str(rank), str(size)))\n ValidateSlave().run()\n\n return None","def mewe_misspecified(M,N,m,n,target):\r\n\toutput = []\r\n\tfor k in tqdm(range(0,M)):\r\n\t\t# Allocate space for output\r\n\t\tmewe_store = np.zeros((len(n),target['thetadim']))\r\n\t\tmewe_runtimes = np.zeros(len(n))\r\n\t\tmewe_evals = np.zeros(len(n))\r\n\t\t\r\n\t\t# generate all observations and sets of randomness to be used\r\n\t\t\r\n\t\tif target[\"observed_law\"] == \"Gamma\":\r\n\t\t\tobs_all = np.random.gamma(true_theta[0], true_theta[1],np.max(n))\r\n\t\telif target[\"observed_law\"] == \"Cauchy\":\r\n\t\t\tobs_all = np.random.standard_cauchy(np.max(n))\r\n\t\telse : \r\n\t\t\treturn(\"Not implemented law\")\r\n\t\t\tbreak\r\n\t\t# la ligne du dessus est modifiée pour générer un échantillon contaminé\r\n\t\t\r\n\t\t# generate the synthetic randomness, sort.\r\n\t\t\r\n\t\trandomness = [target['generate_randomness'](m) for i in range(N)]\r\n\t\t\r\n\t\tfor i in range(0,len(n)):\r\n\t\t\t# subset observations and sort\r\n\t\t\tobs = obs_all[:n[i]]\r\n\t\t\tsort_obs = np.sort(obs)\r\n\t\t\tsort_obs_mult = np.repeat(sort_obs, m / n[i], axis = 0)\r\n\t\t\t\r\n\t\t\t# Define the objective to be minimized to find the MEWE\r\n\t\t\t\r\n\t\t\tdef obj1(theta):\r\n\t\t\t\tif(theta[1] < 0 ):\r\n\t\t\t\t\tout = 10e6\r\n\t\t\t\telse :\r\n\t\t\t\t\twass_dists = [target['dist'](sort_obs_mult, np.sort(target['simulation'](theta, x))) for x in randomness]\r\n\t\t\t\t\tout = np.mean(wass_dists)\r\n\t\t\t\t\r\n\t\t\t\treturn out\r\n\t\t\t\t\r\n\t\t\t# Optimization\r\n\t\t\t\r\n\t\t\tt_mewe = time.process_time()\r\n\t\t\tmewe = minimize(fun = obj1, x0 = true_theta)\r\n\t\t\tt_mewe = time.process_time() - t_mewe\r\n\t\t\t\r\n\t\t\t# Save the results\r\n\t\t\tmewe_store[i] = mewe.x\r\n\t\t\tmewe_runtimes[i] = t_mewe\r\n\t\t\tmewe_evals[i] = mewe.nit\r\n\t\t\r\n\t\toutput_cbind = np.c_[mewe_store, mewe_runtimes, mewe_evals, n, np.arange(len(n))]\r\n\t\toutput.append(output_cbind)\r\n\t\t\r\n\treturn output","def simulation(G, # graph object\n pos = None, # positions of nodes\n n = 5, # number of simulation steps\n \n # wrapped args for simulation_step function\n kernel = 'weights', # simulation kernel\n custom_kernel = None, # custom simulation kernel\n WERE_multiplier = 10, # multiplier for WERE kernel\n oblivion = False, # enable information oblivion\n engagement_enforcement = 1.01,\n draw = False, # draw graph\n show_attr = False): # show attributes \n \n #=======================================#\n # append nodes data from 0 step to list #\n #=======================================#\n \n graph_list = []\n graph_list.append(copy.deepcopy(list(G.nodes.data() ) ) )\n \n\n #===================#\n # Run n simulations #\n #===================#\n \n for i in range(n):\n dp.simulation_step(G = G, \n pos = pos, \n \n kernel = kernel,\n custom_kernel = custom_kernel,\n WERE_multiplier = WERE_multiplier, \n oblivion = oblivion, \n engagement_enforcement = engagement_enforcement,\n draw = draw, \n show_attr = show_attr)\n\n # save nodes data to to list\n graph_list.append(copy.deepcopy(list(G.nodes.data() ) ) )\n \n \n #======================================================#\n # Count aware agents before and after simulation steps #\n #======================================================#\n \n # Check number of aware agents in 0 step\n #global aware_first\n aware_first = []\n for i in range(len(graph_list[0])):\n aware_first.append(graph_list[0][i][1]['state'])\n aware_first_c = aware_first.count('aware')\n \n # graph_list[0][1][1]['state']\n \n # Check number of aware agents in the last step\n #global aware_last\n aware_last = []\n graph_list_len = len(graph_list) - 1\n for i in range(len(graph_list[0])):\n aware_last.append(graph_list[graph_list_len][i][1]['state']) # n is the last sim\n aware_last_c = aware_last.count('aware')\n \n #graph_list[5][0][1]['state']\n \n #=================================#\n # diffusion performance measuring #\n #=================================#\n \n # equation for diffusion performance measuring\n avg_aware_inc_per_step = (aware_last_c - aware_first_c) / n\n \n # show graph statistics\n return graph_list, avg_aware_inc_per_step","def test_multiple_rng(self):\r\n rng1 = RandomStreams(1234)\r\n rng2 = RandomStreams(2392)\r\n assert rng1.random_state_variables is not rng2.random_state_variables","def test_find_singletons(self):\n self._build_sample_graph()\n # Adding singletons\n sg = self.skill_graph.add(Skill.build('g', ''))\n sh = self.skill_graph.add(Skill.build('h', ''))\n skill_map = SkillMap.load(self.course)\n result = SkillMapMetrics(skill_map).singletons()\n expected = [sg.id, sh.id]\n self.assertEqual(sorted(expected), sorted(result))","def random_structure (params) :\n\n import random\n random.seed(0)\n from scitbx.array_family import flex\n flex.set_random_seed(0)\n from cctbx.development import random_structure\n\n uc_volume = params.target_unit_cell.volume()\n asu_volume = uc_volume / params.target_space_group.group().order_z()\n target_number_scatterers = int(asu_volume)//128 # Very approximate rule of thumb for proteins with ~50% solvent content\n element_unit = ['O']*19 + ['N']*18 + ['C']*62 + ['S']*1\n element_pallet = element_unit * (1 + ( target_number_scatterers//len(element_unit) ))\n assert len(element_pallet) >= target_number_scatterers\n # XXX Ersatz hard limit to prevent excessive execution time of\n # xray_structure() below.\n elements = element_pallet[:min(1000, target_number_scatterers)]\n\n xs = random_structure.xray_structure(\n space_group_info = params.target_space_group,\n unit_cell = params.target_unit_cell,\n elements=elements,\n min_distance=1.2)\n xs.show_summary()\n phil2 = mmtbx.command_line.fmodel.fmodel_from_xray_structure_master_params\n params2 = phil2.extract()\n # adjust the cutoff of the generated intensities to assure that\n # statistics will be reported to the desired high-resolution limit\n # even if the observed unit cell differs slightly from the reference.\n params2.high_resolution = params.d_min / math.pow(\n 1 + params.unit_cell_length_tolerance, 1 / 3)\n params2.output.type = \"real\"\n if (params.include_bulk_solvent) :\n print(\"Sorry, can't include bulk solvent for randomly-generated sites.\")\n f_model = mmtbx.utils.fmodel_from_xray_structure(\n xray_structure = xs,\n f_obs = None,\n add_sigmas = True,\n params = params2).f_model\n if not params.merge_anomalous:\n f_model_possibly_anomalous = f_model.generate_bijvoet_mates()\n else:\n f_model_possibly_anomalous = f_model\n i_model = f_model_possibly_anomalous.as_intensity_array()\n\n if params.scaling.mtz_file is not None:\n f_fake = f_model.as_amplitude_array()\n # as the code that consumes the mtz f-obs expects non-anomalous data\n mtzdata = f_fake.as_mtz_dataset(column_root_label=\"f-obs\")\n mtzdata.mtz_object().write(params.scaling.mtz_file)\n\n return i_model","def test_shared_members(self):\r\n\r\n def populate_module(m,x):\r\n m.x=x\r\n m.lx=[x]\r\n m.llx=[[x],[x]]\r\n m.ltx=[(x,)]\r\n m.ldx=[{'x':x}]\r\n m.tx=(x,)\r\n m.tlx=([x],)\r\n m.ttx=((x,),)\r\n m.tdx=({'x':x},)\r\n m.dx={'x':x}\r\n m.dlx={'x':[x]}\r\n m.dtx={'x':(x,)}\r\n m.ddx={'x':{'x':x}}\r\n\r\n def get_element(i):\r\n return [i.x,i.lx[0],i.tx[0],i.dx['x'],i.llx[0][0], i.llx[1][0], i.ltx[0][0], i.ldx[0]['x'], i.tlx[0][0], i.tlx[0][0], i.tdx[0]['x'], i.dlx['x'][0], i.dtx['x'][0], i.ddx['x']['x']]\r\n\r\n m1=Module()\r\n m2=Module()\r\n x=T.dscalar()\r\n populate_module(m1,x)\r\n populate_module(m2,x)\r\n #m1.x and m2.x should not be shared as their is no hierarchi link between them.\r\n inst1=m1.make()\r\n inst2=m2.make()\r\n m1.m2=m2\r\n #m1.x and m2.x should be shared as their is a hierarchi link between them.\r\n inst3=m1.make()\r\n inst1.x=1\r\n inst2.x=2\r\n inst3.x=3\r\n for f in get_element(inst1):\r\n assert f==1\r\n for f in get_element(inst2):\r\n assert f==2\r\n for f in get_element(inst3)+get_element(inst3.m2):\r\n assert f==3\r\n\r\n inst3.m2.x=4\r\n for f in get_element(inst3)+get_element(inst3.m2):\r\n assert f==4","def test_speciesDestruction():\n sys = LVsystem.Ecosystem()\n sys.addSpecies('rabbit')\n sys.addSpecies('fox')\n sys.addSpecies('wolf')\n \n sys.removeSpecies('fox')\n \n assert len(sys.species_list) == 2\n assert not ('fox' in sys.species_list)\n for key in sys.intMatrix:\n assert not ('fox' in key)\n \n sys.removeSpecies('wolf')\n \n assert sys.species_list == ['rabbit']\n for key in sys.intMatrix:\n assert not ('wolf' in key)\n \n sys.removeSpecies('rabbit') \n \n assert sys.intMatrix == {}\n assert sys.species_list == []\n for key in sys.intMatrix:\n assert not ('rabbit' in key)","def __init__(self,\n num_masks=1,\n num_fake=0,\n epsilon_greedy=0.0,\n num_classes = 2,\n num_samples = 1000,\n max_length = 30,\n ambiguity=False,\n num_bondtypes=1):\n\n self.num_masks = num_masks\n self.num_fake = num_fake\n self.epsilon_greedy = epsilon_greedy\n self.num_classes = num_classes\n self.num_samples = num_samples\n\n self.molecule_generator = MoleculeGenerator(num_classes=num_classes, \n max_length=max_length, \n ambiguity=ambiguity,\n num_bondtypes=num_bondtypes)\n\n self.corruption = CorruptionTransform(num_masks=num_masks, num_fake=num_fake, epsilon=epsilon_greedy)\n\n\n self.data = []\n\n\n for i in range(self.num_samples):\n molecule = self.molecule_generator.generate_molecule()\n molecule = chem.AddHs(molecule)\n\n\n Adj = chem.rdmolops.GetAdjacencyMatrix(molecule)\n atoms = np.asarray([periodic_table[atom.GetAtomicNum()] for atom in molecule.GetAtoms()])\n smiles = chem.MolToSmiles(molecule)\n self.data += [MoleculeSample(atoms, Adj, {}, smiles)]","def test_determinism_2():\n\n def run_sgd(mode):\n # Must be seeded the same both times run_sgd is called\n disturb_mem.disturb_mem()\n rng = np.random.RandomState([2012, 11, 27])\n\n batch_size = 5\n train_batches = 3\n valid_batches = 4\n num_features = 2\n\n # Synthesize dataset with a linear decision boundary\n w = rng.randn(num_features)\n\n def make_dataset(num_batches):\n disturb_mem.disturb_mem()\n m = num_batches*batch_size\n X = rng.randn(m, num_features)\n y = np.zeros((m, 1))\n y[:, 0] = np.dot(X, w) > 0.\n\n rval = DenseDesignMatrix(X=X, y=y)\n\n rval.yaml_src = \"\" # suppress no yaml_src warning\n\n X = rval.get_batch_design(batch_size)\n assert X.shape == (batch_size, num_features)\n\n return rval\n\n train = make_dataset(train_batches)\n valid = make_dataset(valid_batches)\n\n num_chunks = 10\n chunk_width = 2\n\n class ManyParamsModel(Model):\n \"\"\"\n Make a model with lots of parameters, so that there are many\n opportunities for their updates to get accidentally re-ordered\n non-deterministically. This makes non-determinism bugs manifest\n more frequently.\n \"\"\"\n\n def __init__(self):\n super(ManyParamsModel, self).__init__()\n self.W1 = [sharedX(rng.randn(num_features, chunk_width)) for i\n in xrange(num_chunks)]\n disturb_mem.disturb_mem()\n self.W2 = [sharedX(rng.randn(chunk_width))\n for i in xrange(num_chunks)]\n self._params = safe_union(self.W1, self.W2)\n self.input_space = VectorSpace(num_features)\n self.output_space = VectorSpace(1)\n\n disturb_mem.disturb_mem()\n model = ManyParamsModel()\n disturb_mem.disturb_mem()\n\n class LotsOfSummingCost(Cost):\n \"\"\"\n Make a cost whose gradient on the parameters involves summing many\n terms together, so that T.grad is more likely to sum things in a\n random order.\n \"\"\"\n\n supervised = True\n\n def expr(self, model, data, **kwargs):\n self.get_data_specs(model)[0].validate(data)\n X, Y = data\n disturb_mem.disturb_mem()\n\n def mlp_pred(non_linearity):\n Z = [T.dot(X, W) for W in model.W1]\n H = [non_linearity(z) for z in Z]\n Z = [T.dot(h, W) for h, W in safe_izip(H, model.W2)]\n pred = sum(Z)\n return pred\n\n nonlinearity_predictions = map(mlp_pred,\n [T.nnet.sigmoid,\n T.nnet.softplus,\n T.sqr,\n T.sin])\n pred = sum(nonlinearity_predictions)\n disturb_mem.disturb_mem()\n\n return abs(pred-Y[:, 0]).sum()\n\n def get_data_specs(self, model):\n data = CompositeSpace((model.get_input_space(),\n model.get_output_space()))\n source = (model.get_input_source(), model.get_target_source())\n return (data, source)\n\n cost = LotsOfSummingCost()\n\n disturb_mem.disturb_mem()\n\n algorithm = SGD(cost=cost,\n batch_size=batch_size,\n learning_rule=Momentum(.5),\n learning_rate=1e-3,\n monitoring_dataset={'train': train, 'valid': valid},\n update_callbacks=[ExponentialDecay(decay_factor=2.,\n min_lr=.0001)],\n termination_criterion=EpochCounter(max_epochs=5))\n\n disturb_mem.disturb_mem()\n\n train_object = Train(dataset=train,\n model=model,\n algorithm=algorithm,\n extensions=[PolyakAveraging(start=0),\n MomentumAdjustor(final_momentum=.9,\n start=1,\n saturate=5), ],\n save_freq=0)\n\n disturb_mem.disturb_mem()\n\n train_object.main_loop()\n\n output = cStringIO()\n record = Record(file_object=output, replay=False)\n record_mode = RecordMode(record)\n\n run_sgd(record_mode)\n\n output = cStringIO(output.getvalue())\n playback = Record(file_object=output, replay=True)\n playback_mode = RecordMode(playback)\n\n run_sgd(playback_mode)","def test_checks_population_size(self):\n with pm.Model() as model:\n n = pm.Normal(\"n\", mu=0, sigma=1)\n for stepper in TestPopulationSamplers.steppers:\n step = stepper()\n with pytest.raises(ValueError, match=\"requires at least 3 chains\"):\n pm.sample(draws=10, tune=10, chains=1, cores=1, step=step)\n # don't parallelize to make test faster\n pm.sample(\n draws=10,\n tune=10,\n chains=4,\n cores=1,\n step=step,\n compute_convergence_checks=False,\n )","def test_when_opponent_all_Ds(self):\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)","def exists(sim):\n return (sim['N'] > 0)","def testMass(self):\n tested = False\n slhafile=\"./testFiles/slha/higgsinoStop.slha\"\n model = Model(BSMList,SMList)\n model.updateParticles(slhafile,promptWidth = 1e-12*GeV)\n topos = decomposer.decompose(model, .1*fb, True, False, 5.*GeV)\n for topo in topos:\n if str(topo)!=\"[1][1]\":\n continue\n for element in topo.elementList:\n if str(element)!=\"[[[b]],[[b]]]\":\n continue\n masses=element.motherElements[0].mass\n tested = True\n dm=abs(masses[0][1]-masses[0][2])/GeV\n #If intermediate BSM states are compared there are two elements ([[[b],[c,q]],[[b],[q,q]]])\n # which do not get combined because their branches differ by the charges of the intermediate states\n self.assertEqual(len(element.motherElements),24)\n self.assertTrue(dm < 5.0)\n self.assertTrue(tested)","def test_creature(self):\n self.assertEqual(len(self.processor), 3)","def test_am_basic(Simulator, plt, seed, rng):\n\n D = 64\n vocab = Vocabulary(D, rng=rng)\n vocab.parse('A+B+C+D')\n\n with nengo.Network('model', seed=seed) as m:\n am = AssociativeMemory(vocab)\n in_node = nengo.Node(output=vocab.parse(\"A\").v, label='input')\n nengo.Connection(in_node, am.input)\n\n in_p = nengo.Probe(in_node)\n out_p = nengo.Probe(am.output, synapse=0.03)\n\n sim = Simulator(m)\n sim.run(0.2)\n t = sim.trange()\n\n plt.subplot(2, 1, 1)\n plt.plot(t, nengo.spa.similarity(sim.data[in_p], vocab))\n plt.ylabel(\"Input\")\n plt.ylim(top=1.1)\n plt.legend(vocab.keys, loc='best')\n plt.subplot(2, 1, 2)\n plt.plot(t, nengo.spa.similarity(sim.data[out_p], vocab))\n plt.plot(t[t > 0.15], np.ones(t.shape)[t > 0.15] * 0.8, c='g', lw=2)\n plt.ylabel(\"Output\")\n plt.legend(vocab.keys, loc='best')\n\n assert similarity(sim.data[in_p][t > 0.15], vocab.parse(\"A\").v) > 0.99\n assert similarity(sim.data[out_p][t > 0.15], vocab.parse(\"A\").v) > 0.8","def sngl_obj_evo(self, lacking):\n prob, algo = self.probinit('jde', 0)\n l = list()\n u = 6+(self.N-3)*4\n for i in range(lacking):\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\n for j in range(u):\n archi.evolve(5)\n stdout.write(\"\\r{0} / {1}\".format(i*u+j+1, lacking*u))\n stdout.flush()\n tmp = [isl for isl in archi]\n tmp.sort(key = lambda x: x.population.champion.f[0]);\n l.append(tmp[0].population.champion)\n stdout.write(\" Done. \")\n return l, prob","def test_observed_species(self):\n c = array([4,3,4,0,1,0,2])\n obs = observed_species(c)\n exp = 5\n self.assertEqual(obs, exp)\n c = array([0,0,0])\n obs = observed_species(c)\n exp = 0\n self.assertEqual(obs, exp)\n self.assertEqual(observed_species(self.TestData), 9)","def test_population_movements_with_compilation(self):\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\n print self._pystepx._rc[0]['gp_engine']\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\n block=True)\n print self._pystepx._rc[0]['elems']","def test_6(self):\n params_spec, options_spec = generate_random_model()\n respy_obj = RespyCls(params_spec, options_spec)\n respy_obj = simulate_observed(respy_obj)\n\n # Extract class attributes\n (\n periods_rewards_systematic,\n mapping_state_idx,\n seed_prob,\n periods_emax,\n num_periods,\n states_all,\n num_points_interp,\n edu_spec,\n num_draws_emax,\n is_myopic,\n is_debug,\n is_interpolated,\n optim_paras,\n optimizer_options,\n file_sim,\n num_types,\n ) = dist_class_attributes(\n respy_obj,\n \"periods_rewards_systematic\",\n \"mapping_state_idx\",\n \"seed_prob\",\n \"periods_emax\",\n \"num_periods\",\n \"states_all\",\n \"num_points_interp\",\n \"edu_spec\",\n \"num_draws_emax\",\n \"is_myopic\",\n \"is_debug\",\n \"is_interpolated\",\n \"optim_paras\",\n \"optimizer_options\",\n \"file_sim\",\n \"num_types\",\n )\n\n shocks_cholesky = optim_paras[\"shocks_cholesky\"]\n shocks_cov = shocks_cholesky.dot(shocks_cholesky.T)\n coeffs_common = optim_paras[\"coeffs_common\"]\n coeffs_a = optim_paras[\"coeffs_a\"]\n coeffs_b = optim_paras[\"coeffs_b\"]\n delta = optim_paras[\"delta\"]\n\n # Add some additional objects required for the interfaces to the functions.\n period = np.random.choice(num_periods)\n\n periods_draws_emax = create_draws(\n num_periods, num_draws_emax, seed_prob, is_debug\n )\n\n draws_emax_standard = periods_draws_emax[period, :, :]\n\n draws_emax_risk = transform_disturbances(\n draws_emax_standard, np.zeros(4), shocks_cholesky\n )\n\n # Initialize Python version and solve.\n state_space = StateSpace(\n num_periods, num_types, edu_spec[\"start\"], edu_spec[\"max\"], optim_paras\n )\n\n # Integrate periods_emax in state_space\n state_space.emaxs = np.column_stack(\n (\n np.zeros((state_space.num_states, 4)),\n periods_emax[~np.isnan(periods_emax) & (periods_emax != MISSING_FLOAT)],\n )\n )\n\n # Fill emaxs_a - emaxs_home in the requested period\n states_period = state_space.get_attribute_from_period(\"states\", period)\n\n # Do not get the emaxs from the previous period if we are in the last one.\n if period != state_space.num_periods - 1:\n state_space.emaxs = get_emaxs_of_subsequent_period(\n states_period, state_space.indexer, state_space.emaxs, edu_spec[\"max\"]\n )\n\n num_states = state_space.states_per_period[period]\n\n shifts = np.random.randn(4)\n\n # Slight modification of request which assures that the interpolation code is\n # working.\n num_points_interp = min(num_points_interp, num_states)\n\n # Get the IS_SIMULATED indicator for the subset of points which are used for the\n # predication model.\n is_simulated = get_simulated_indicator(\n num_points_interp, num_states, period, is_debug\n )\n\n # Unpack necessary attributes\n rewards_period = state_space.get_attribute_from_period(\"rewards\", period)\n emaxs_period = state_space.get_attribute_from_period(\"emaxs\", period)[:, :4]\n max_education = (\n state_space.get_attribute_from_period(\"states\", period)[:, 3]\n >= edu_spec[\"max\"]\n )\n\n # Construct the exogenous variables for all points of the state space.\n exogenous, max_emax = get_exogenous_variables(\n rewards_period, emaxs_period, shifts, optim_paras[\"delta\"], max_education\n )\n\n # Align output between Python and Fortran version.\n py = (exogenous, max_emax)\n\n f90 = fort_debug.wrapper_get_exogenous_variables(\n period,\n num_periods,\n num_states,\n periods_rewards_systematic,\n shifts,\n mapping_state_idx,\n periods_emax,\n states_all,\n edu_spec[\"start\"],\n edu_spec[\"max\"],\n delta,\n coeffs_common,\n coeffs_a,\n coeffs_b,\n num_types,\n )\n\n assert_almost_equal(py[0], f90[0])\n assert_almost_equal(py[1], f90[1])\n\n # Construct endogenous variable so that the prediction model can be fitted.\n endogenous = get_endogenous_variable(\n rewards_period,\n emaxs_period,\n max_emax,\n is_simulated,\n draws_emax_risk,\n optim_paras[\"delta\"],\n max_education,\n )\n\n f90 = fort_debug.wrapper_get_endogenous_variable(\n period,\n num_periods,\n num_states,\n periods_rewards_systematic,\n mapping_state_idx,\n periods_emax,\n states_all,\n is_simulated,\n num_draws_emax,\n max_emax,\n draws_emax_risk,\n edu_spec[\"start\"],\n edu_spec[\"max\"],\n shocks_cov,\n delta,\n coeffs_common,\n coeffs_a,\n coeffs_b,\n )\n assert_almost_equal(endogenous, replace_missing_values(f90))\n\n py = get_predictions(endogenous, exogenous, max_emax, is_simulated)\n\n f90 = fort_debug.wrapper_get_predictions(\n endogenous,\n exogenous,\n max_emax,\n is_simulated,\n num_points_interp,\n num_states,\n file_sim,\n False,\n )\n\n # This assertion fails if a column is all zeros.\n if not exogenous.any(axis=0).any():\n assert_array_almost_equal(py, f90)","def prove_NN() -> Proof:\n # Optional Task 6.7c","def test_pickling(simulation_factory, two_particle_snapshot_factory,\n external_params):\n # unpack parameters\n cls_obj, param_attr, list_params, evaluator = external_params\n\n # create class instance, get/set params when not attached\n obj_instance = cls_obj()\n getattr(obj_instance, param_attr)['A'] = list_params[0]\n\n pickling_check(obj_instance)\n # set up simulation\n snap = two_particle_snapshot_factory(d=3.7)\n sim = simulation_factory(snap)\n sim.operations.integrator = hoomd.md.Integrator(dt=0.001)\n sim.operations.integrator.forces.append(obj_instance)\n sim.run(0)\n pickling_check(obj_instance)","def test_ne():\n # Define some universal gsps\n gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)\n\n # Pixel. Params include scale, flux, gsparams.\n # gsparams.\n # The following should all test unequal:\n gals = [galsim.Pixel(scale=1.0),\n galsim.Pixel(scale=1.1),\n galsim.Pixel(scale=1.0, flux=1.1),\n galsim.Pixel(scale=1.0, gsparams=gsp)]\n all_obj_diff(gals)\n\n # Box. Params include width, height, flux, gsparams.\n # gsparams.\n # The following should all test unequal:\n gals = [galsim.Box(width=1.0, height=1.0),\n galsim.Box(width=1.1, height=1.0),\n galsim.Box(width=1.0, height=1.1),\n galsim.Box(width=1.0, height=1.0, flux=1.1),\n galsim.Box(width=1.0, height=1.0, gsparams=gsp)]\n all_obj_diff(gals)\n\n # TopHat. Params include radius, flux, gsparams.\n # gsparams.\n # The following should all test unequal:\n gals = [galsim.TopHat(radius=1.0),\n galsim.TopHat(radius=1.1),\n galsim.TopHat(radius=1.0, flux=1.1),\n galsim.TopHat(radius=1.0, gsparams=gsp)]\n all_obj_diff(gals)","def test_identity_oddballs(self):\n from keras.constraints import identity\n identity_instance = identity()\n\n oddball_examples = [\"Hello\", [1], -1, None]\n assert(oddball_examples == identity_instance(oddball_examples))","def test_run_simulation_returns_nothing(self):\n sim = ss.Simulation()\n assert sim.run_simulation(10) is None","def __init__(self):\n self.persons = []\n self.partnerships = []\n self.straight_males = set()\n self.females = set()\n self.high_sexual_activity = set()\n self.singles = set()\n self.infected = set()\n self.resistant = set()\n self.age_group = [set() for x in range(5)]\n self.age_group_mixing_prob = age_group_mixing()\n self.age_groups = [(i,j) for i in range(5) for j in range(5)]\n self.time = 0\n self.number_of_symptomatic = 0\n self.number_of_asymptomatic = 0\n self.number_of_res_symp = 0\n self.number_of_res_asymp = 0\n self.number_of_steady = 0\n self.r0_infected = []","def test_run_sim():\n rnd = rand.Arrivals(31, 40)\n sim.run_sim(2, 1, 3, 4, 24, rnd)","def make_sim(sim_name: str, object_names: List[str], epoch: datetime, \n integrator: str, steps_per_day: int, save_file: bool) -> rebound.Simulation:\n # Filename for archive\n file_date: str = epoch.strftime('%Y-%m-%d_%H-%M')\n fname_archive: str = f'../data/planets/{sim_name}_{file_date}.bin'\n\n # If this file already exists, load it and check for both extra and missing bodies\n sim: rebound.Simulation\n try:\n # Attempt to load the named file\n sim = rebound.Simulation(fname_archive)\n # print(f'Loaded {fname_archive}')\n\n # Generate list of missing object names\n objects_missing: List[str] = [nm for nm in object_names if nm not in sim.particles]\n\n # Extend the simulation and save it with the augmented bodies\n if objects_missing:\n print(f'Found missing objects in {fname_archive}:')\n print(objects_missing)\n extend_sim_horizons(sim, object_names = objects_missing, epoch=epoch)\n\n # Sets of named and input object hashes\n hashes_sim: Set[int] = set(p.hash.value for p in sim.particles)\n hashes_input: Set[str] = set(rebound.hash(nm).value for nm in object_names)\n\n # Filter the simulation so only the named objects are included\n hashes_remove: List[str] = [h for h in hashes_sim if h not in hashes_input] \n for h in hashes_remove:\n sim.remove(hash=h)\n\n except: \n # Initialize simulation\n sim = make_sim_horizons(object_names=object_names, epoch=epoch)\n\n # Move to center of momentum\n sim.move_to_com()\n\n # Set integrator and time step\n sim.integrator = integrator\n dt: float = 1.0 / steps_per_day if steps_per_day > 0 else 0\n sim.dt = dt\n if integrator == 'ias15':\n ias15 = sim.ri_ias15\n ias15.min_dt = dt\n\n # Save a snapshot to the archive file if requested\n if save_file:\n sim.simulationarchive_snapshot(filename=fname_archive, deletefile=True)\n\n # Return the simulation\n return sim","def recurse(hp):\n global G\n nodes = G.nodes(data=True)\n p_insert = hp.p_insert if count_boxes() > hp.initial_boxes else 1.\n for node in nodes:\n try:\n if node[1][\"shape\"] is \"square\":\n if random.random() < p_insert:\n insert_motif(hp, id=node[0])\n except Exception as e:\n log('exception in recurse', e)","def test_randomize_mp_fits(self):\n\t\tdetails = self.watcher.analyze(mp_fit=True, randomize=True, pool=True)\n\t\tself.assertTrue((details.rand_sigma_mp < 1.10).all())\n\t\tself.assertTrue((details.rand_sigma_mp > 0.96).all())\n\t\tself.assertTrue((details.rand_num_spikes.to_numpy() < 80).all())","def test_setup(sim):\n n = 3\n data = []\n dt = 1\n\n for i in range(n):\n sim.setup(timestep=dt, min_delay=dt)\n p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))\n p.record('v')\n print('starting run ', i)\n sim.run(10.0)\n print('finished run ', i)\n\n print('start get_data run ', i)\n data.append(p.get_data())\n print('finished get_data run ', i)\n\n print('start sim end ', i)\n sim.end()\n print('finished sim end ', i)\n\n assert len(data) == n\n for block in data:\n assert len(block.segments) == 1\n signals = block.segments[0].analogsignals\n assert len(signals) == 1\n assert_array_equal(signals[0], data[0].segments[0].analogsignals[0])","def __init__(self,input_dict):\n self.no_monomers = int(input_dict['Monomer types'])\n self.occupancy = float(input_dict['Site occupancy fraction'])\n \n # Mole fraction of monomers\n self.monomer_fraction = []\n \n # Functionality of monomers\n self.functionality = []\n \n # Molecular weight of monomers\n self.molecular_wt = []\n \n # Molecular volume of monomers\n self.molecular_vol = []\n \n # Molecular hopping rates\n self.hopping_rate = []\n \n # Interaction energy of monomers\n self.eps_self = []\n self.mean_eps_self = 0.0\n \n # Diffusivity factors\n self.d_factor_monomers = []\n \n # Propagation rate constant of monomers\n self.k_p = np.zeros((self.no_monomers,self.no_monomers))\n \n # Termination rate constant of monomers\n self.k_t = np.zeros((self.no_monomers,self.no_monomers))\n \n # Termination rate constant of monomers\n self.eps = np.zeros((self.no_monomers,self.no_monomers))\n \n # Probability distribution of monomers\n self.prob_set = np.zeros(shape=(self.no_monomers))\n \n # Population of monomers added to the network\n self.added_set = np.zeros(shape=(self.no_monomers))\n \n # Network size\n self.network_size = 0\n \n for mo_type in range(0,self.no_monomers):\n prefix = 'Monomer '+str(mo_type + 1)+': '\n \n self.monomer_fraction.append(float(input_dict[prefix+'Mole fraction']))\n self.functionality.append(float(input_dict[prefix+'Functionality']))\n self.molecular_wt.append(float(input_dict[prefix+'Molecular weight (in Da)']))\n self.molecular_vol.append(float(input_dict[prefix+'Molecular volume (in Angstroms^3)']))\n self.eps_self.append(float(input_dict[prefix+'Self interaction energy (in eV)']))\n self.hopping_rate.append(float(input_dict[prefix+'Hopping rate']))\n self.d_factor_monomers.append(1.0)\n \n self.mean_eps_self = sum(self.eps_self)/float(self.no_monomers)\n \n rate_constants_file = input_dict['Rate constants file']\n self.read_rate_constants(rate_constants_file)\n \n term_constants_file = input_dict['Termination constants file']\n self.read_term_constants(rate_constants_file)\n \n #interactions_file = input_dict['Interaction energy file']\n #self.read_interactions(interactions_file)\n \n self.interaction_range = int(input_dict['Interaction range'])\n \n self.create_interaction_matrix()","def test_clear(self):\n from supvisors.statistics import StatisticsCompiler\n compiler = StatisticsCompiler(self.supvisors)\n # set data to a given address\n for address, period_instance in compiler.data.items():\n for period, instance in period_instance.items():\n instance.counter = 28\n instance.ref_stats = ('dummy', 0)\n instance.cpu = [13.2, 14.8]\n instance.mem = [56.4, 71.3, 68.9]\n instance.io = {'eth0': (123465, 654321), 'lo': (321, 321)}\n instance.proc = {('myself', 5888): (25.0, 12.5)}\n # check clearance of instance\n compiler.clear('10.0.0.2')\n for address, period_instance in compiler.data.items():\n if address == '10.0.0.2':\n for period, instance in period_instance.items():\n self.assertEqual(period / 5, instance.period)\n self.assertEqual(10, instance.depth)\n self.assertEqual(-1, instance.counter)\n self.assertIsNone(instance.ref_stats)\n self.assertIs(list, type(instance.cpu))\n self.assertFalse(instance.cpu)\n self.assertIs(list, type(instance.mem))\n self.assertFalse(instance.mem)\n self.assertIs(dict, type(instance.io))\n self.assertFalse(instance.io)\n self.assertIs(dict, type(instance.proc))\n self.assertFalse(instance.proc)\n else:\n for period, instance in period_instance.items():\n self.assertEqual(period / 5, instance.period)\n self.assertEqual(10, instance.depth)\n self.assertEqual(28, instance.counter)\n self.assertTupleEqual(('dummy', 0), instance.ref_stats)\n self.assertListEqual([13.2, 14.8], instance.cpu)\n self.assertListEqual([56.4, 71.3, 68.9], instance.mem)\n self.assertDictEqual({'eth0': (123465, 654321), 'lo': (321, 321)}, instance.io)\n self.assertDictEqual({('myself', 5888): (25.0, 12.5)}, instance.proc)","def simulate_universe():\n\n # untreated_survival is the probability to survive if not treated\n # this is an exact law of the universe, the player will not have this information\n untreated_survival = random.uniform(MIN_DISEASE_SURVIVAL, MAX_DISEASE_SURVIVAL)\n\n trials: list[Trial] = []\n\n treated_survivals: dict[Trial, float] = {}\n\n for _ in range(random.randint(MIN_TREATMENTS_COUNT, MAX_TREATMENTS_COUNT)):\n group_size = random.randint(MIN_GROUP_SIZE, MAX_GROUP_SIZE)\n\n # treated_survival is the probability to survive if treated\n # this is an exact law of the universe, the player will not have this information\n # therefore it is stored in a separate dict and not in the given-to-player Trial object\n treated_survival = random.uniform(MIN_TREATED_SURVIVAL, MAX_TREATED_SURVIVAL)\n\n trial = Trial(group_size, untreated_survival, treated_survival)\n\n trials.append(trial)\n treated_survivals[trial] = treated_survival\n\n chosen_trial = playground.choose_trial(trials)\n\n if chosen_trial is None: # None means no treatment\n chosen_survival = untreated_survival\n else:\n chosen_survival = treated_survivals[chosen_trial]\n\n return random.uniform(0, 1) <= chosen_survival","def test_fit_many_comps():\n\n run_name = 'stationary'\n savedir = 'temp_data/{}_expectmax_{}/'.format(PY_VERS, run_name)\n mkpath(savedir)\n data_filename = savedir + '{}_expectmax_{}_data.fits'.format(PY_VERS,\n run_name)\n # log_filename = 'temp_data/{}_expectmax_{}/log.log'.format(PY_VERS,\n # run_name)\n\n logging.basicConfig(level=logging.INFO, filemode='w',\n filename=log_filename)\n uniform_age = 1e-10\n sphere_comp_pars = np.array([\n # X, Y, Z, U, V, W, dX, dV, age,\n [-50,-50,-50, 0, 0, 0, 10., 5, uniform_age],\n [ 50, 50, 50, 0, 0, 0, 10., 5, uniform_age],\n ])\n starcounts = [200,200]\n ncomps = sphere_comp_pars.shape[0]\n\n # initialise z appropriately\n # start = 0\n # for i in range(ngroups):\n # nstars_in_group = int(group_pars[i,-1])\n # z[start:start+nstars_in_group,i] = 1.0\n # start += nstars_in_group\n\n true_memb_probs = np.zeros((np.sum(starcounts), ncomps))\n true_memb_probs[:200,0] = 1.\n true_memb_probs[200:,1] = 1.\n\n synth_data = SynthData(pars=sphere_comp_pars, starcounts=starcounts,\n Components=SphereComponent,\n )\n synth_data.synthesise_everything()\n tabletool.convert_table_astro2cart(synth_data.table,\n write_table=True,\n filename=data_filename)\n\n origins = [SphereComponent(pars) for pars in sphere_comp_pars]\n\n init_memb_probs = np.ones(true_memb_probs.shape) / ncomps\n\n best_comps, med_and_spans, memb_probs = \\\n expectmax.fit_many_comps(data=synth_data.table,\n ncomps=ncomps,\n rdir=savedir,\n init_memb_probs=init_memb_probs,\n trace_orbit_func=dummy_trace_orbit_func, )\n\n # compare fit with input\n try:\n assert np.allclose(true_memb_probs, memb_probs)\n except AssertionError:\n # If not close, check if flipping component order fixes things\n memb_probs = memb_probs[:,::-1]\n best_comps = best_comps[::-1]\n assert np.allclose(true_memb_probs, memb_probs)\n for origin, best_comp in zip(origins, best_comps):\n assert (isinstance(origin, SphereComponent) and\n isinstance(best_comp, SphereComponent))\n o_pars = origin.get_pars()\n b_pars = best_comp.get_pars()\n\n logging.info(\"origin pars: {}\".format(o_pars))\n logging.info(\"best fit pars: {}\".format(b_pars))\n assert np.allclose(origin.get_mean(),\n best_comp.get_mean(),\n atol=5.)\n assert np.allclose(origin.get_sphere_dx(),\n best_comp.get_sphere_dx(),\n atol=2.)\n assert np.allclose(origin.get_sphere_dv(),\n best_comp.get_sphere_dv(),\n atol=2.)\n assert np.allclose(origin.get_age(),\n best_comp.get_age(),\n atol=1.)","def test_scrublet_simulate_doublets():\n pytest.importorskip(\"scrublet\")\n\n adata_obs = sc.datasets.pbmc3k()\n sc.pp.filter_genes(adata_obs, min_cells=3)\n sc.pp.filter_cells(adata_obs, min_genes=3)\n adata_obs.layers['raw'] = adata_obs.X\n sc.pp.normalize_total(adata_obs)\n logged = sc.pp.log1p(adata_obs, copy=True)\n\n _ = sc.pp.highly_variable_genes(logged)\n adata_obs = adata_obs[:, logged.var['highly_variable']]\n\n adata_sim = sce.pp.scrublet_simulate_doublets(adata_obs, layer='raw')\n\n assert 'doublet_parents' in adata_sim.obsm.keys()","def test_subset_reconstruction_iterable(self, wires):\n circuit = hadamard_circuit(wires)\n bits, recipes = circuit()\n shadow = ClassicalShadow(bits, recipes)\n\n # choose 1000 random indices\n snapshots = np.random.choice(np.arange(10000, dtype=np.int64), size=1000, replace=False)\n state = shadow.global_snapshots(snapshots=snapshots)\n assert state.shape == (len(snapshots), 2**wires, 2**wires)\n\n # check the results against obtaining the full global snapshots\n expected = shadow.global_snapshots()\n for i, t in enumerate(snapshots):\n assert np.allclose(expected[t], state[i])","def test_getters():\n\n A = np.random.randint(2, size=(10, 10))\n S = np.random.randint(2, size=10)\n stocks = np.random.rand(10)\n ones = np.ones(10)\n # Dummy values\n m = ExploitCore(A, S, stocks, ones, ones, ones, 0.5, 2.0)\n assert (m.get_adjacency() == A).all()\n assert (m.get_strategies() == S).all()\n assert (m.get_stocks() == stocks).all()\n assert m.get_time() == 0","def experiment4():\n np.random.seed()\n state['result'] = np.random.rand(1)","def sim_grasp_set_row(scene):\n gripper_model = burg.gripper.Robotiq2F85()\n\n target_object = None\n\n found = False\n while found == False:\n print([obj.object_type.identifier for obj in scene.objects])\n buffer_target_object = input(\"Wich object in the previous list do you want to grab ? \")\n\n i = 0\n while i < len(scene.objects):\n if (scene.objects[i].object_type.identifier == buffer_target_object):\n target_object = scene.objects[i]\n found = True\n break\n i += 1\n if found == False :\n print(\"the selected object is not in the list\")\n\n #test a grasp set and create a list of successful grasp\n grasp_set, contact_points, normals, approach_vectors = create_antipodal_grasp_set(target_object)\n sim = burg.sim.SceneGraspSimulator(target_object = target_object, gripper= gripper_model, scene=scene, verbose=False)\n scores = sim.simulate_grasp_set(grasp_set)\n successful_grasps = burg.grasp.GraspSet()\n index_successfull = []\n params_successfull = []\n for index in range(len(scores)):\n if scores[index] == 5:\n successful_grasps.add(grasp_set[index].as_grasp_set())\n index_successfull += [index]\n params_successfull+=[[contact_points[index], normals[index], approach_vectors[index]]]\n sim.dismiss()\n\n print(len(successful_grasps))\n print(\"hellllo\")\n\n time.sleep(10)\n\n #Evaluate each successfull grasp\n sim2 = burg.sim.SceneGraspSimulator(target_object = target_object, gripper= gripper_model, scene=scene, verbose=False)\n results = []\n for i, grasp in enumerate(successful_grasps):\n n_graspset, n_contact_points, n_normals, n_approach_vectors = create_antipodal_noisy_grasp_set(target_object, grasp, params_successfull[i][0][0])\n #n_graspset = perturbations.generate_perturb_grasp_set(grasp = grasp, nb_grasps = 50)\n #burg.visualization.show_grasp_set(objects = [target_object.object_type.mesh], gs = n_graspset, gripper = gripper_model, with_plane = True)\n params_noised = []\n for k in range(len(n_contact_points)):\n params_noised += [[n_contact_points[i], n_normals[i], n_approach_vectors[i]]]\n scores = sim2.simulate_grasp_set(n_graspset)\n robust = sum(scores)/len(scores)\n #metric = quality.probability_force_closure(n_graspset, params_noised)\n metric = quality.epsilon_quality(grasp, params_successfull[i] )\n results += [[grasp, robust, metric]]\n print([grasp, robust, metric])\n #results+= [[grasp, metric]]\n #results += [metric]\n\n print(results)\n sim2.dismiss()\n return results","def independent(self):\n return True","def test_single_game_works(self):\n sim = ss.Simulation(seed=154)\n game1 = sim.single_game()\n sim = ss.Simulation(seed=79)\n game2 = sim.single_game()\n assert game1 != game2, 'Your method single_game is not working.'","def test_parameterisation_model(self):\n self.topo.assign_force_field(self.ff)\n for atom in self.topo.get_atoms(inc_alt_states=True):\n if atom.element != 'H':\n self.assertTrue(atom._ff_id is not None)","def test_empty_structure():\n empty = SME_Struct()\n\n assert isinstance(empty.version, str)\n assert empty.teff is not None\n assert empty.logg is not None\n assert empty.vmic == 0\n assert empty.vmac == 0\n assert empty.vsini == 0\n\n assert empty.nseg == 0\n assert empty.wave is None\n assert empty.spec is None\n assert empty.uncs is None\n assert empty.synth is None\n assert empty.cont is None\n assert empty.mask is None\n assert empty.mask_good is None\n assert empty.mask_bad is None\n # assert empty.mask_line is None\n # assert empty.mask_continuum is None\n\n assert empty.cscale.shape == (0, 1)\n assert empty.vrad.shape == (0,)\n assert empty.cscale_flag == \"none\"\n assert empty.vrad_flag == \"none\"\n assert empty.cscale_degree == 0\n\n assert empty.mu is not None\n assert empty.nmu == 7\n\n # assert empty.md5 is not None\n\n assert empty.linelist is not None\n assert empty.species is not None\n assert len(empty.species) == 0\n assert empty.atomic is not None\n\n assert empty.monh == 0\n assert not np.isnan(empty[\"abund Fe\"])\n assert empty.abund[\"H\"] == 12\n assert not np.isnan(empty.abund()[\"Mg\"])\n\n assert empty.system_info is not None\n assert empty.system_info.arch == \"\"\n\n assert len(empty.fitparameters) == 0\n assert empty.fitresults is not None\n assert empty.fitresults.covariance is None\n\n assert empty.atmo is not None\n assert empty.atmo.depth is None\n\n assert empty.nlte is not None\n assert empty.nlte.elements == []","def test_serializing_solvers(self):\n ratio = 0.5\n l_enet = 1e-2\n sd = ratio * l_enet\n\n proxes = [\n ProxZero(),\n ProxTV(2),\n ProxL1(2),\n ProxGroupL1(strength=1, blocks_start=[0, 3, 8],\n blocks_length=[3, 5, 2])\n ]\n solvers = [\n AdaGrad(step=1e-3, max_iter=100, verbose=False, tol=0),\n SGD(step=1e-3, max_iter=100, verbose=False, tol=0),\n SDCA(l_l2sq=sd, max_iter=100, verbose=False, tol=0),\n SAGA(step=1e-3, max_iter=100, verbose=False, tol=0),\n SVRG(step=1e-3, max_iter=100, verbose=False, tol=0)\n ]\n model_map = {\n ModelLinReg: SimuLinReg,\n ModelLogReg: SimuLogReg,\n ModelPoisReg: SimuPoisReg,\n ModelHinge: SimuLogReg,\n ModelQuadraticHinge: SimuLogReg,\n ModelSmoothedHinge: SimuLogReg,\n ModelAbsoluteRegression: SimuLinReg,\n ModelEpsilonInsensitive: SimuLinReg,\n ModelHuber: SimuLinReg,\n ModelLinRegWithIntercepts: SimuLinReg,\n ModelModifiedHuber: SimuLogReg\n }\n\n for solver in solvers:\n for mod in model_map:\n for prox in proxes:\n\n np.random.seed(12)\n n_samples, n_features = 100, 5\n w0 = np.random.randn(n_features)\n intercept0 = 50 * weights_sparse_gauss(n_weights=n_samples,\n nnz=30)\n c0 = None\n X, y = SimuLinReg(w0, c0, n_samples=n_samples, verbose=False,\n seed=2038).simulate()\n if mod == ModelLinRegWithIntercepts:\n y += intercept0\n\n model = mod(fit_intercept=False).fit(X, y)\n\n # prox = ProxZero() #(2.)\n solver.set_model(model)\n solver.set_prox(prox)\n\n pickled = pickle.loads(pickle.dumps(solver))\n\n self.assertTrue(solver._solver.compare(pickled._solver))\n\n self.assertTrue(\n solver.model._model.compare(pickled.model._model))\n\n self.assertTrue(solver.prox._prox.compare(pickled.prox._prox))\n\n if mod == ModelLinRegWithIntercepts:\n test_vector = np.hstack((X[0], np.ones(n_samples)))\n self.assertEqual(\n model.loss(test_vector),\n solver.model.loss(test_vector))\n else:\n self.assertEqual(model.loss(X[0]), solver.model.loss(X[0]))","def simulation(nepisodes):\n # Initialize robots\n # print('I am inside the simulation')\n agents = [] # List containing all robots\n a1 = Agent(start = [0, 0], end = [grid_size-1, grid_size-1], nr = 1) # Create agent 1\n a2 = Agent(start = [0, grid_size-1], end = [grid_size-1, 0], nr = 2) # Create agent 2\n a3 = Agent(start = [grid_size-1, 0], end = [0, grid_size-1], nr = 3) # Create agent 3\n a4 = Agent(start = [grid_size-1, grid_size-1], end = [0, 0], nr = 4) # Create agent 4\n agents.append(a1)\n agents.append(a2)\n agents.append(a3)\n agents.append(a4)\n\n # for agent in agents:\n # agent.load_target('target_weights_{}.h5'.format(agent.nr))\n # agent.load_policy('policy_weights_{}.h5'.format(agent.nr))\n # print('loaded')\n\n steps_list = [[] for i in range(len(agents))]\n reward_list = [[] for i in range(len(agents))]\n cumulative_rewards = [[] for i in range(len(agents))]\n collisions_list = [[] for i in range(len(agents))]\n\n t = 0 # Set time to zero\n for i in range(nepisodes):\n t = episode(agents, t, i+1) # Run one episode\n\n print('End of episode ', i+1)\n agent_index = 0\n for agent in agents:\n steps_list[agent_index].append(agent.steps)\n reward_list[agent_index].append(agent.reward)\n collisions_list[agent_index].append(agent.collisions)\n if i == 0:\n cumulative_rewards[agent_index].append(agent.reward)\n else:\n cumulative_rewards[agent_index].append(agent.reward + cumulative_rewards[agent_index][i-1])\n agent_index += 1\n\n if i % 1000 == 0:\n with open('reward_4_agents_{}'.format(i),'wb') as f:\n pickle.dump(reward_list,f)\n\n with open('steps_4_agents_{}'.format(i), 'wb') as f:\n pickle.dump(steps_list, f)\n\n with open('cols_4_agents_{}'.format(i), 'wb') as f:\n pickle.dump(collisions_list, f)\n\n\n return steps_list, reward_list, collisions_list, cumulative_rewards","def test_bothE_traversals(self):\r\n results = self.jon.bothE()\r\n assert len(results) == 2\r\n assert self.jon_physics in results\r\n assert self.jon_in_beekeeping in results","def experiment(self) -> Any:","def test_createData():\n\n sys = LVsystem.Ecosystem()\n\n sys.addSpecies('rabbit')\n sys.setInteraction('rabbit', 'hen', 0)\n sys.setInteraction('rabbit', 'fox', -1)\n sys.setInitialCond('rabbit', 30)\n sys.setGrowthRate('rabbit', 0.09)\n sys.setCarrCap('rabbit', 10000)\n sys.setChangeRate('rabbit', 400)\n\n sys.addSpecies('hen')\n sys.setInteraction('hen', 'rabbit', 0)\n sys.setInteraction('hen', 'fox', -1)\n sys.setInitialCond('hen', 10)\n sys.setGrowthRate('hen', 0.07)\n sys.setCarrCap('hen', 10000)\n sys.setChangeRate('hen', 500)\n\n sys.addSpecies('fox')\n sys.setInteraction('fox', 'rabbit', 1)\n sys.setInteraction('fox', 'hen', 1)\n sys.setInitialCond('fox', 20)\n sys.setGrowthRate('fox', -0.06)\n sys.setCarrCap('fox', 1)\n sys.setChangeRate('fox', 250)\n\n \n data = sys.create_data()\n \n assert data[0] == 3\n assert data[1] == ['rabbit', 'hen', 'fox']\n assert data[2] == [30,10,20]\n assert data[3] == [0.09,0.07,-0.06] \n assert data[4] == [10000,10000,1]\n assert data[5] == [400,500,250]\n assert data[6][1][2] == -data[6][2][1]\n assert data[6][2][2] == 0\n \n sys.removeSpecies('rabbit')\n sys.removeSpecies('fox')\n sys.removeSpecies('hen')","def none_model_vars():\n obj_fun = None\n time_sol = None\n gap = None\n s_pos = None\n b_target = None\n threads = None\n\n return obj_fun, time_sol, gap, s_pos, b_target, threads","def test_order(self):\n n = 3\n was_called = n*[False]\n class Mock(object):\n def __init__(self, i):\n self.i = i\n\n def evolve(self1, t, dt):\n was_called[self1.i] = True\n self.assertTrue(all(was_called[:self1.i]))\n\n sim = simulation.Simulation(*[Mock(_) for _ in xrange(n)])\n sim.run(sim.dt)","def nits(self):","def test_duplicate(self):\n test_file = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-225-1-0.mdd')\n\n mdd.procall([test_file])\n \n self.compare_node58()","def prepare_simulation(master_seed, n_populations):\n nest.ResetKernel()\n # set global kernel parameters\n nest.SetKernelStatus(\n {\"communicate_allgather\": sim.allgather,\n \"overwrite_files\": sim.overwrite_existing_files,\n \"resolution\": sim.dt,\n \"total_num_virtual_procs\": sim.n_vp})\n if sim.to_text_file:\n nest.SetKernelStatus({\"data_path\": data_path_test})\n \n # Set random seeds\n \n # PYNEST\n #nest.sli_run('0 << /rngs [%i %i] Range { rngdict/gsl_mt19937 :: exch CreateRNG } Map >> SetStatus'%(\n # master_seed, master_seed + sim.n_vp - 1))\n #nest.SetKernelStatus({\"rng_seeds\" : range(master_seed, master_seed + sim.n_vp)})\n #nest.sli_run('0 << /grng rngdict/gsl_mt19937 :: %i CreateRNG >> SetStatus'%(master_seed + sim.n_vp))\n #nest.SetKernelStatus({\"grng_seed\" : master_seed + sim.n_vp})\n #pyrngs = [np.random.RandomState(s) for s in \n # range(master_seed + sim.n_vp + 1, master_seed + 2 * sim.n_vp + 1)]\n\n # SLI VERSION\n sli_str = \"0 << \\n\"\n #sli_str += \"/rngs %i [0 %i 1 sub] add Range { rngdict/gsl_mt19937 :: exch CreateRNG } Map\\n\"%(master_seed, sim.n_vp) # local RNG, seeded\n #sli_str += \"/grng rngdict/gsl_mt19937 :: %i %i add CreateRNG\\n\"%(master_seed, sim.n_vp) # global RNG\n sli_str += \"/rng_seeds %i [0 %i 1 sub] add Range\\n\"%(master_seed, sim.n_vp) # local RNG seeds\n sli_str += \"/grng_seed %i %i add\\n\"%(master_seed, sim.n_vp) # global RNG seed\n sli_str += \">> SetStatus\"\n nest.sli_run(sli_str)\n sli_str2 = \"/script_rngs [%i]\\n\"%sim.n_vp\n sli_str2 += \"{%i add rngdict /gsl_mt19937 get exch CreateRNG } Table def\\n\"%(master_seed + sim.n_vp)\n sli_str2 += \"/normal_rdvs script_rngs { rdevdict /normal get CreateRDV } Map def\"\n nest.sli_run(sli_str2)\n pyrngs = None\n return pyrngs","def test_perfectModelEnsemble_init_da(PM_ds_initialized_1d):\n pm = PerfectModelEnsemble(PM_ds_initialized_1d.tos)\n assert pm","def ncore(self):","def test_molecule_loop_no_data(self):\n\n # Reset relax.\n reset()\n\n # Add a data pipe to the data store.\n ds.add(pipe_name='orig', pipe_type='mf')\n\n # Loop over the molecules.\n i = 0\n for molecule in mol_res_spin.molecule_loop():\n i = i + 1\n\n # Test loop length.\n self.assertEqual(i, 0)","def generate(\n seeds=10,\n param_num_nodes=7,\n mode='train',\n param_dim=10,\n param_sel=100,\n param_mu=10,\n param_br=0.05,\n param_activity_wt=None,\n A=None,\n sp_to_id=None,\n min_coord=None,\n max_coord=None,\n org_pts=None,\n ):\n global dim, sel, mu, br, activity_wt, tree_lc, tree_rc, num_nodes\n\n dim=param_dim\n sel=param_sel\n mu=param_mu\n br=param_br\n activity_wt=param_activity_wt\n num_nodes = param_num_nodes\n\n sp_root = 0\n tree = None\n\n if mode == 'train':\n tree, tree_lc, tree_rc = generate_tree(sp_root, num_nodes)\n if param_activity_wt is None:\n # weights for the linear activity function\n num_wts = int(((dim * (dim + 1))/2) + 1)\n activity_wt = np.random.normal(0, 1, num_wts)\n\n if org_pts is None:\n org_pts = []\n # simulate data points\n # format: exampleID, species, values\n # region, species, coord1, coord2, ...., activity_value\n\n for i in tqdm(range(int(seeds))):\n pt_id = i\n\n # pick a random point of d-dimension\n rand_pt = np.random.uniform(min_coord, max_coord, dim)\n curr_pt = np.append([pt_id, sp_root], rand_pt)\n curr_activity = get_activity(modify_pt(rand_pt), activity_wt)\n # print('curr_pt:', curr_pt, 'curr_activity:', curr_activity); exit(0)\n org_pts.append(np.append(curr_pt, curr_activity))\n\n generated_points = []\n full_org_pts = []\n\n if mode == 'train':\n pool = Pool(16)\n sample_bag = pool.map(generate_bag, org_pts)\n for item in sample_bag:\n for val in item:\n val = list(val)\n full_org_pts.append(val)\n generated_points.append(val[:2]+modify_pt(val[2:-1])+[val[-1]])\n else:\n for val in org_pts:\n val = list(val)\n generated_points.append(val[:2]+modify_pt(val[2:-1])+[val[-1]])\n\n return generated_points, activity_wt, org_pts, full_org_pts, tree","def solution(self) -> State:"],"string":"[\n \"def make_simulations(self):\\n pass\",\n \"def simulation():\\n\\n return {\\n \\\"type\\\": \\\"class\\\",\\n \\\"base\\\": \\\"iso.process_step\\\",\\n \\\"is_abstract\\\": False,\\n \\\"is_document\\\": True,\\n \\\"pstr\\\": (\\\"({}/{}/{})\\\", (\\\"used\\\", \\\"ran_for_experiments\\\", \\\"ensemble_id\\\")),\\n \\\"properties\\\": [\\n (\\n \\\"part_of_project\\\",\\n \\\"linked_to(designing.project)\\\",\\n \\\"1.N\\\",\\n \\\"Project or projects for which simulation was run\\\",\\n ),\\n (\\n \\\"ran_for_experiments\\\",\\n \\\"linked_to(designing.numerical_experiment)\\\",\\n \\\"1.N\\\",\\n \\\"One or more experiments with which the simulation is \\\"\\n \\\"associated\\\",\\n ),\\n (\\n \\\"sub_experiment\\\",\\n \\\"linked_to(designing.numerical_experiment)\\\",\\n \\\"0.1\\\",\\n \\\"For start-date ensembles, this will indicate the beginning \\\"\\n \\\"year; for offline models driven by output from another \\\"\\n \\\"model, this will provide the source_id and variant_label \\\"\\n \\\"for the 'driving' model.\\\",\\n ),\\n (\\n \\\"used\\\",\\n \\\"linked_to(science.model)\\\",\\n \\\"1.1\\\",\\n \\\"The model used to run the simulation\\\",\\n ),\\n (\\n \\\"primary_ensemble\\\",\\n \\\"linked_to(activity.ensemble)\\\",\\n \\\"0.1\\\",\\n \\\"Primary Ensemble (ensemble for which this simulation was \\\"\\n \\\"first run).\\\",\\n ),\\n (\\n \\\"institution\\\",\\n \\\"linked_to(shared.party)\\\",\\n \\\"0.1\\\",\\n \\\"institution which carried out the simulation\\\",\\n ),\\n (\\n \\\"parent_of\\\",\\n \\\"linked_to(activity.child_simulation)\\\",\\n \\\"0.N\\\",\\n \\\"If appropriate, links to simulations which branched from \\\"\\n \\\"this one\\\",\\n ),\\n (\\n \\\"produced\\\",\\n \\\"linked_to(data.dataset)\\\",\\n \\\"0.N\\\",\\n \\\"Products of the simulation\\\",\\n ),\\n (\\n \\\"had_performance\\\",\\n \\\"linked_to(platform.performance)\\\",\\n \\\"0.1\\\",\\n \\\"Performance of the simulation.\\\",\\n ),\\n (\\n \\\"ran_on\\\",\\n \\\"linked_to(platform.machine)\\\",\\n \\\"0.1\\\",\\n \\\"The machine on which the simulation was run.\\\",\\n ),\\n (\\n \\\"errata\\\",\\n \\\"shared.online_resource\\\",\\n \\\"0.1\\\",\\n \\\"Link to errata associated with this simulation.\\\",\\n ),\\n (\\n \\\"ensemble_id\\\",\\n \\\"activity.axis_member\\\",\\n \\\"0.N\\\",\\n \\\"Identification within ensemble axes via axis member. \\\"\\n \\\"(Multiple axis members within a simulation cannot share the \\\"\\n \\\"same ensemble_axis.) (There must be an axis_member instance \\\"\\n \\\"for each ensemble axis in a parent ensemble.)\\\",\\n ),\\n # Time\\n (\\n \\\"start_time\\\",\\n \\\"time.date_time\\\",\\n \\\"0.1\\\",\\n \\\"The start date-time of the simulation. e.g. \\\"\\n \\\"2012-04-01 00:00:00\\\",\\n ),\\n (\\n \\\"end_time\\\",\\n \\\"time.date_time\\\",\\n \\\"0.1\\\",\\n \\\"The end date-time of the simulation. e.g. \\\"\\n \\\"2087-11-30 12:00:00\\\",\\n ),\\n (\\n \\\"calendar\\\",\\n \\\"time.calendar\\\",\\n \\\"0.1\\\",\\n \\\"The calendar used in the simulation\\\",\\n ),\\n # Further Info URL\\n (\\n \\\"documentation\\\",\\n \\\"shared.online_resource\\\",\\n \\\"0.1\\\",\\n \\\"On-line location of additional documentation\\\",\\n ),\\n # Extra attributes\\n (\\n \\\"extra_attributes\\\",\\n \\\"shared.extra_attribute\\\",\\n \\\"0.N\\\",\\n \\\"Additional attributes provided with simulation.\\\",\\n ),\\n ],\\n \\\"constraints\\\": [\\n (\\\"cardinality\\\", \\\"rationale\\\", \\\"0.0\\\"),\\n ],\\n }\",\n \"def make_simulation(self):\\n pass\",\n \"def test_empty() -> None:\\n for class_name in [SimulationData, SystemData, ObservableData, TrajectoryData]:\\n print(f\\\"Testing empty {class_name.__name__} objects\\\")\\n object1 = class_name()\\n object2 = class_name()\\n assert object1 == object2\\n\\n object1 = EnsembleData(\\\"NVE\\\", natoms=10, volume=1)\\n object2 = EnsembleData(\\\"NVE\\\", natoms=10, volume=1)\\n assert object1 == object2\",\n \"def __init__(self,ensemble_obj,truths):\\n\\n# if ensemble_obj==None or truths==None:\\n# print 'Warning: inputs not complete'\\n self.ensemble_obj = ensemble_obj\\n self.truths = truths\",\n \"def test_poms_unwrapped(self):\\n import electrochemistry\\n import numpy as np\\n # Create model\\n model = electrochemistry.POMModel(DEFAULT_POMS)\\n times = model.suggest_times()\\n values = model.simulate(times)\\n\\n self.assertEqual(len(values), len(times))\",\n \"def run_Simulation2(k,N=100,T=10,start = 1,p=0.5,q=0.08,startcenter = False,startcorner=False):\\n recover = [0]\\n infect = [start]\\n suspect = [N-start]\\n pop = [Person() for i in range(N)]\\n ##we need to change the code for the case start people infected\\n for i in range(start):\\n pop[i].get_infected();\\n if(startcenter):\\n resetcenter(start,pop)\\n if(startcorner):\\n resetcorner(start,pop)\\n np.random.seed(10)\\n for i in range(T):\\n for j in range(N):\\n pop[j].movepos(p)\\n X = calculatedistance(pop)\\n tree = cKDTree(X)\\n for j in range(N):\\n if pop[j].is_infected():\\n addvalue = np.array([X[j]])\\n inds = tree.query_ball_point(addvalue, q)\\n inds = inds[0]\\n #may have problem here\\n for l in inds:\\n if pop[l].is_willinfected():\\n pop[l].get_infected()\\n\\n for j in range(N):\\n if pop[j].is_infected():\\n if np.random.rand()< k:\\n pop[j].get_recovered()\\n\\n recover.append(count_recover(pop))\\n infect.append(count_infect(pop))\\n suspect.append(count_suspectial(pop))\\n newrecover = [i/N for i in recover]\\n newsuspect = [s/N for s in suspect]\\n newinfect = [i/N for i in infect]\\n plt.plot(range(T+1),newrecover,label = \\\"r: percentage of removed \\\")\\n plt.plot(range(T+1),newsuspect,label = \\\"s: percentage of susceptible\\\")\\n plt.plot(range(T+1),newinfect,label = \\\"i: percentage of infected\\\")\\n plt.xlabel(\\\"T\\\")\\n plt.ylabel(\\\"percentage\\\")\\n plt.title(\\\"Percentage of Population, Discrete\\\")\\n plt.legend()\\n plt.show()\",\n \"def test_particle_obj():\\n # Set up the base parameters describing a particle object\\n T = 273.15 + 15.\\n P = 150e5\\n Sa = 35.\\n Ta = 273.15 + 4.\\n composition = ['methane', 'ethane', 'propane', 'oxygen']\\n yk = np.array([0.85, 0.07, 0.08, 0.0])\\n de = 0.005\\n K = 1.\\n Kt = 1.\\n fdis = 1e-6\\n\\n # Compute a few derived quantities\\n bub = dbm.FluidParticle(composition)\\n m0 = bub.masses_by_diameter(de, T, P, yk)\\n\\n # Create a `SingleParticle` object\\n bub_obj = dispersed_phases.SingleParticle(bub, m0, T, K, fdis=fdis,\\n K_T=Kt)\\n\\n # Check if the initial attributes are correct\\n for i in range(len(composition)):\\n assert bub_obj.composition[i] == composition[i]\\n assert_array_almost_equal(bub_obj.m0, m0, decimal=6)\\n assert bub_obj.T0 == T\\n assert bub_obj.cp == seawater.cp() * 0.5\\n assert bub_obj.K == K\\n assert bub_obj.K_T == Kt\\n assert bub_obj.fdis == fdis\\n for i in range(len(composition)-1):\\n assert bub_obj.diss_indices[i] == True\\n assert bub_obj.diss_indices[-1] == False\\n\\n # Check if the values returned by the `properties` method match the input\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m0, T, P,\\n Sa, Ta, 0.)\\n us_ans = bub.slip_velocity(m0, T, P, Sa, Ta)\\n rho_p_ans = bub.density(m0, T, P)\\n A_ans = bub.surface_area(m0, T, P, Sa, Ta)\\n Cs_ans = bub.solubility(m0, T, P, Sa)\\n beta_ans = bub.mass_transfer(m0, T, P, Sa, Ta)\\n beta_T_ans = bub.heat_transfer(m0, T, P, Sa, Ta)\\n assert us == us_ans\\n assert rho_p == rho_p_ans\\n assert A == A_ans\\n assert_array_almost_equal(Cs, Cs_ans, decimal=6)\\n assert_array_almost_equal(beta, beta_ans, decimal=6)\\n assert beta_T == beta_T_ans\\n assert T == T_ans\\n\\n # Check that dissolution shuts down correctly\\n m_dis = np.array([m0[0]*1e-10, m0[1]*1e-8, m0[2]*1e-3, 1.5e-5])\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\\n Sa, Ta, 0)\\n assert beta[0] == 0.\\n assert beta[1] == 0.\\n assert beta[2] > 0.\\n assert beta[3] > 0.\\n m_dis = np.array([m0[0]*1e-10, m0[1]*1e-8, m0[2]*1e-7, 1.5e-16])\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\\n Sa, Ta, 0.)\\n assert np.sum(beta[0:-1]) == 0.\\n assert us == 0.\\n assert rho_p == seawater.density(Ta, Sa, P)\\n\\n # Check that heat transfer shuts down correctly\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, Ta, P,\\n Sa, Ta, 0)\\n assert beta_T == 0.\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = bub_obj.properties(m_dis, T, P,\\n Sa, Ta, 0)\\n assert beta_T == 0.\\n\\n # Check the value returned by the `diameter` method\\n de_p = bub_obj.diameter(m0, T, P, Sa, Ta)\\n assert_approx_equal(de_p, de, significant=6)\\n\\n # Check functionality of insoluble particle\\n drop = dbm.InsolubleParticle(isfluid=True, iscompressible=True)\\n m0 = drop.mass_by_diameter(de, T, P, Sa, Ta)\\n\\n # Create a `Particle` object\\n drop_obj = dispersed_phases.SingleParticle(drop, m0, T, K, fdis=fdis,\\n K_T=Kt)\\n\\n # Check if the values returned by the `properties` method match the input\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(\\n np.array([m0]), T, P, Sa, Ta, 0)\\n us_ans = drop.slip_velocity(m0, T, P, Sa, Ta)\\n rho_p_ans = drop.density(T, P, Sa, Ta)\\n A_ans = drop.surface_area(m0, T, P, Sa, Ta)\\n beta_T_ans = drop.heat_transfer(m0, T, P, Sa, Ta)\\n assert us == us_ans\\n assert rho_p == rho_p_ans\\n assert A == A_ans\\n assert beta_T == beta_T_ans\\n\\n # Check that heat transfer shuts down correctly\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(m_dis, Ta, P,\\n Sa, Ta, 0)\\n assert beta_T == 0.\\n (us, rho_p, A, Cs, beta, beta_T, T_ans) = drop_obj.properties(m_dis, T, P,\\n Sa, Ta, 0)\\n assert beta_T == 0.\\n\\n # Check the value returned by the `diameter` method\\n de_p = drop_obj.diameter(m0, T, P, Sa, Ta)\\n assert_approx_equal(de_p, de, significant=6)\",\n \"def check_learning_mol(\\n n_representations,\\n p_representations,\\n n_atoms_list,\\n p_atoms_list,\\n properties,\\n n_training=DEFAULT_N_TRAINING):\\n\\n properties = np.array(properties)\\n\\n n_points = len(properties)\\n indexes = np.arange(n_points, dtype=int)\\n np.random.shuffle(indexes)\\n\\n n_valid = 50\\n v_idxs = indexes[-n_valid:]\\n vn_repr = n_representations[v_idxs]\\n vp_repr = p_representations[v_idxs]\\n vn_atoms = [n_atoms_list[i] for i in v_idxs]\\n vp_atoms = [p_atoms_list[i] for i in v_idxs]\\n v_props = properties[v_idxs]\\n\\n\\n errors = []\\n\\n hp = {\\n \\\"sigma\\\": 4\\n }\\n\\n for n in n_training:\\n\\n start_time = time.time()\\n\\n t_idxs = indexes[:n]\\n t_props = properties[t_idxs]\\n\\n tn_repr = n_representations[t_idxs]\\n tn_atoms = [n_atoms_list[i] for i in t_idxs]\\n\\n tp_repr = p_representations[t_idxs]\\n tp_atoms = [p_atoms_list[i] for i in t_idxs]\\n\\n # Train\\n tn_K = create_local_kernel(tn_repr, tn_repr, tn_atoms, tn_atoms, **hp)\\n tp_K = create_local_kernel(tp_repr, tp_repr, tp_atoms, tp_atoms, **hp)\\n # tnp_K = create_local_kernel(tn_repr, tp_repr, tn_atoms, tp_atoms)\\n # tpn_K = create_local_kernel(tp_repr, tn_repr, tp_atoms, tn_atoms)\\n\\n # print(tn_K)\\n # print(tp_K)\\n # print(tnp_K)\\n # print(tpn_K)\\n\\n # K_bind = tn_K + tp_K - tnp_K - tpn_K\\n K_bind = tp_K - tn_K\\n\\n if False:\\n\\n pca = kpca(K_bind, n=2)\\n\\n # plot\\n fig, axs = plt.subplots(2, 1, figsize=(5,10))\\n\\n sc = axs[0].scatter(*pca, c=t_props)\\n fig.colorbar(sc, ax=axs[0])\\n\\n im = axs[1].imshow(K_bind)\\n fig.colorbar(im, ax=axs[1])\\n\\n fig.savefig(\\\"_tmp_pca_{:}.png\\\".format(n))\\n\\n\\n # Train model\\n t_alpha = krr(K_bind, t_props, solver=\\\"svd\\\", rcond=10**-4)\\n\\n # Test and predict\\n vn_K = create_local_kernel(tn_repr, vn_repr, tn_atoms, vn_atoms, **hp)\\n vp_K = create_local_kernel(tp_repr, vp_repr, tp_atoms, vp_atoms, **hp)\\n\\n v_K = vp_K - vn_K\\n\\n p_props = np.dot(v_K, t_alpha)\\n\\n # rmse error\\n p_rmse, le, ue = rmse(p_props, v_props)\\n\\n end_time = time.time()\\n\\n print(\\\"{:5d}\\\".format(n), \\\"{:10.2f} ± {:4.2f} t={:4.1f}\\\".format(p_rmse, ue, end_time-start_time))\\n\\n errors.append([p_rmse, le, ue])\\n\\n errors = np.array(errors)\\n\\n return errors\",\n \"def potential_new_obs(self) -> Iterable[GriddedPerm]:\",\n \"def test_outV_traversals(self):\\r\\n results = self.eric.outV()\\r\\n assert len(results) == 1\\r\\n assert self.physics in results\",\n \"def test_population_movements_without_compilation(self):\\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01, False)',\\n block=True)\\n print self._pystepx._rc[0]['elems']\",\n \"def test_order(self):\\n class Mock(object):\\n def __init__(self):\\n self.spike = [False]\\n\\n def evolve(self, t, dt):\\n self.spike = [True]\\n\\n G0 = Mock()\\n M0 = simulation.EventMonitor(G0)\\n sim = simulation.Simulation(G0, M0)\\n sim.run(sim.dt)\\n\\n self.assertEqual(len(M0.t), 1)\\n self.assertEqual(len(M0.i), 1)\",\n \"def simulate_memories(simulation_length):\\n \\n \\n pass\",\n \"def simulationTwoDrugsVirusPopulations():\\n #TODO\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([D, D, D, D], [D, D, D, D], [D, D, D],\\n random_seed=5)\",\n \"def initialise_sim(self):\\n pass\",\n \"def test_simulation(self):\\r\\n positions = [1, 10, 100,1000]\\r\\n num_trials = 10000\\r\\n result = Investment(positions, num_trials)\\r\\n result = result.simulation(positions, num_trials) \\r\\n for pos in positions:\\r\\n self.assertEqual(len(result[pos]), num_trials)\\r\\n self.assertTrue(result[pos].all() == 1 or result[pos].all() == -1)\",\n \"def run_test0():\\r\\n \\r\\n ndia, nadi, nnucl, ntraj = 1, 1, 2, 500\\r\\n\\r\\n # ======= Hierarchy of Hamiltonians =======\\r\\n ham = nHamiltonian(ndia, nadi, nnucl)\\r\\n ham.init_all(2)\\r\\n print \\\"id=\\\", ham.id, \\\" level=\\\", ham.level\\r\\n\\r\\n ham1 = [] \\r\\n for tr in xrange(ntraj):\\r\\n ham1.append( nHamiltonian(ndia, nadi, nnucl) ) \\r\\n print ham1[tr].id, ham1[tr].level\\r\\n ham1[tr].init_all(2)\\r\\n ham.add_child(ham1[tr])\\r\\n print Cpp2Py(ham1[tr].get_full_id())\\r\\n\\r\\n # Set up the models and compute internal variables\\r\\n # Initialization\\r\\n # Model parameters \\r\\n params = { \\\"model\\\":1 }\\r\\n\\r\\n # Simulation parameters\\r\\n dt = 1.0\\r\\n\\r\\n # Dynamical variables and system-specific properties\\r\\n mean_q = MATRIX(nnucl,1); \\r\\n sigma_q = MATRIX(nnucl,1); \\r\\n mean_p = MATRIX(nnucl,1); \\r\\n sigma_p = MATRIX(nnucl,1); \\r\\n iM = MATRIX(nnucl,1);\\r\\n\\r\\n for i in xrange(nnucl):\\r\\n mean_q.set(i,0, -1.0) \\r\\n sigma_q.set(i,0, 0.05) \\r\\n mean_p.set(i,0, 0.0) \\r\\n sigma_p.set(i,0, 0.0)\\r\\n iM.set(i,0, 1.0/2000.0)\\r\\n\\r\\n rnd = Random()\\r\\n q = MATRIX(nnucl,ntraj); aux_functs.sample(q, mean_q, sigma_q, rnd)\\r\\n p = MATRIX(nnucl,ntraj); aux_functs.sample(p, mean_p, sigma_p, rnd) \\r\\n\\r\\n # Initial calculations\\r\\n q.show_matrix()\\r\\n\\r\\n # Compute Initial trajectory probability distributions for all dof\\r\\n #bin(q, -2.0, 2.0, 0.01)\\r\\n\\r\\n ham.compute_diabatic(compute_model, q, params, 1)\\r\\n ham.compute_adiabatic(1, 1);\\r\\n ham.add_ethd_adi(q, iM, 1)\\r\\n\\r\\n os.system(\\\"mkdir _2D_dist\\\")\\r\\n out1 = open(\\\"_output.txt\\\", \\\"w\\\"); out1.close() \\r\\n\\r\\n # Do the propagation\\r\\n for i in xrange(100):\\r\\n\\r\\n aux_functs.bin2(q, -2.0, 2.0, 0.1, -2.0, 2.0, 0.1, \\\"_2D_dist/_2D_distrib_\\\"+str(i)+\\\"_.txt\\\")\\r\\n\\r\\n Verlet1(dt, q, p, iM, ham, compute_model, params, 1)\\r\\n\\r\\n #=========== Properties ==========\\r\\n\\r\\n Ekin, Epot, Etot = aux_functs.compute_etot(ham, p, iM)\\r\\n\\r\\n # Print the ensemble average - kinetic, potential, and total energies\\r\\n # Print the tunneling information. Here, we count each trajectory across the barrier.\\r\\n out1 = open(\\\"_output.txt\\\", \\\"a\\\")\\r\\n out1.write( \\\" %8.5f %8.5f %8.5f %8.5f\\\\n\\\" % ( i*dt, Ekin, Epot, Etot ) )\\r\\n out1.close()\",\n \"def test_sample(self):\\n seed = 5\\n space = Space()\\n probs = (0.1, 0.2, 0.3, 0.4)\\n categories = (\\\"asdfa\\\", 2, 3, 4)\\n dim1 = Categorical(\\\"yolo\\\", OrderedDict(zip(categories, probs)), shape=(2, 2))\\n space.register(dim1)\\n dim2 = Integer(\\\"yolo2\\\", \\\"uniform\\\", -3, 6)\\n space.register(dim2)\\n dim3 = Real(\\\"yolo3\\\", \\\"norm\\\", 0.9)\\n space.register(dim3)\\n\\n point = space.sample(seed=seed)\\n rng = check_random_state(seed)\\n test_point = [\\n dict(\\n yolo=dim1.sample(seed=rng)[0],\\n yolo2=dim2.sample(seed=rng)[0],\\n yolo3=dim3.sample(seed=rng)[0],\\n )\\n ]\\n assert len(point) == len(test_point) == 1\\n assert len(point[0].params) == len(test_point[0]) == 3\\n assert np.all(point[0].params[\\\"yolo\\\"] == test_point[0][\\\"yolo\\\"])\\n assert point[0].params[\\\"yolo2\\\"] == test_point[0][\\\"yolo2\\\"]\\n assert point[0].params[\\\"yolo3\\\"] == test_point[0][\\\"yolo3\\\"]\\n\\n points = space.sample(2, seed=seed)\\n rng = check_random_state(seed)\\n points1 = dim1.sample(2, seed=rng)\\n points2 = dim2.sample(2, seed=rng)\\n points3 = dim3.sample(2, seed=rng)\\n test_points = [\\n dict(yolo=points1[0], yolo2=points2[0], yolo3=points3[0]),\\n dict(yolo=points1[1], yolo2=points2[1], yolo3=points3[1]),\\n ]\\n assert len(points) == len(test_points) == 2\\n for i in range(2):\\n assert len(points[i].params) == len(test_points[i]) == 3\\n assert np.all(points[i].params[\\\"yolo\\\"] == test_points[i][\\\"yolo\\\"])\\n assert points[i].params[\\\"yolo2\\\"] == test_points[i][\\\"yolo2\\\"]\\n assert points[i].params[\\\"yolo3\\\"] == test_points[i][\\\"yolo3\\\"]\",\n \"def testInvisiblePositive(self):\\n slhafile=\\\"./testFiles/slha/higgsinoStop.slha\\\"\\n model = Model(BSMList,SMList)\\n model.updateParticles(slhafile)\\n topos = decomposer.decompose ( model, .1*fb, False, True, 5.*GeV )\\n tested = False\\n for topo in topos:\\n if str(topo)!=\\\"[][]\\\":\\n continue\\n for element in topo.elementList:\\n if str(element)!=\\\"[[],[]]\\\":\\n continue\\n tested = True\\n trueMothers = [mother for mother in element.motherElements if not mother is element]\\n if not trueMothers: continue\\n self.assertEqual(str(trueMothers[0]),\\\"[[],[[nu,nu]]]\\\")\\n self.assertEqual(len(trueMothers), 1)\\n self.assertTrue(tested)\",\n \"def simulationTwoDrugsDelayedTreatment():\\n\\n # TODO\",\n \"def test_contains():\\n world, bodies, _ = example_world()\\n\\n geometry = metis.geometry.ManyShapeGeometry(world, bodies)\\n dynamics = metis.dynamics.MagneticDynamics(bodies)\\n factored_graph = FactoredRandomGeometricGraph(\\n geometry, dynamics, default_count=5, blacklist=NoObjectContactBlacklist())\\n\\n assert {'robot': (None, 0), 'box1': (None, 0), 'box2': (None, 0)} in factored_graph\\n assert {'robot': ('box1', 0), 'box1': (None, 0), 'box2': (None, 0)} in factored_graph\\n assert {'robot': ('box1', 5), 'box1': (None, 0), 'box2': (None, 0)} not in factored_graph\\n assert {'robot': ('robot', 0), 'box1': (None, 0), 'box2': (None, 0)} not in factored_graph\\n assert {'robot': ('box1', 0), 'box1': ('robot', 0), 'box2': (None, 0)} not in factored_graph\",\n \"def test_object_with_observables() -> None:\\n system_name = \\\"Water900\\\"\\n simulation_id_1 = \\\"NPT-lowT-lowP\\\"\\n simulation_id_2 = \\\"NPT-highT-lowP\\\"\\n system = database.system(system_name)\\n parser = FlatfileParser()\\n simulation_data_1 = parser.get_simulation_data(\\n units=system.units,\\n ensemble=system.ensemble(simulation_id_1),\\n system=system.system_data,\\n kinetic_ene_file=system.observable_flat_file(\\n simulation_id_1, \\\"kinetic_energy\\\"\\n ),\\n potential_ene_file=system.observable_flat_file(\\n simulation_id_1, \\\"potential_energy\\\"\\n ),\\n total_ene_file=system.observable_flat_file(simulation_id_1, \\\"total_energy\\\"),\\n volume_file=system.observable_flat_file(simulation_id_1, \\\"volume\\\"),\\n )\\n simulation_data_1_copy = parser.get_simulation_data(\\n units=system.units,\\n ensemble=system.ensemble(simulation_id_1),\\n system=system.system_data,\\n kinetic_ene_file=system.observable_flat_file(\\n simulation_id_1, \\\"kinetic_energy\\\"\\n ),\\n potential_ene_file=system.observable_flat_file(\\n simulation_id_1, \\\"potential_energy\\\"\\n ),\\n total_ene_file=system.observable_flat_file(simulation_id_1, \\\"total_energy\\\"),\\n volume_file=system.observable_flat_file(simulation_id_1, \\\"volume\\\"),\\n )\\n simulation_data_2 = parser.get_simulation_data(\\n units=system.units,\\n ensemble=system.ensemble(simulation_id_2),\\n system=system.system_data,\\n kinetic_ene_file=system.observable_flat_file(\\n simulation_id_2, \\\"kinetic_energy\\\"\\n ),\\n potential_ene_file=system.observable_flat_file(\\n simulation_id_2, \\\"potential_energy\\\"\\n ),\\n total_ene_file=system.observable_flat_file(simulation_id_2, \\\"total_energy\\\"),\\n volume_file=system.observable_flat_file(simulation_id_2, \\\"volume\\\"),\\n )\\n\\n assert simulation_data_1 == simulation_data_1_copy\\n assert simulation_data_1 != simulation_data_2\",\n \"def test_run_sim_1():\\n rnd = rand.Arrivals(36, 41)\\n sim.run_sim(3, 2, 5, 6, 22, rnd)\",\n \"def test_w_and_without():\\n A = Node(\\\"A\\\", [\\\"B\\\"], {\\\"B\\\": np.array([[1,0],[1,.1]])})\\n B = Node(\\\"B\\\", [], {})\\n net = CyberNet([A,B])\\n T=10\\n data = gen_data(T, net, {\\\"A\\\": \\\"normal\\\", \\\"B\\\":\\\"normal\\\"})\\n logn_fact = gen_logn_fact(data)\\n pdata_no_a = prob_model_no_attacker(net, data, T, logn_fact)\\n pdata_a = prob_model_given_data_times(net, data, {}, T, logn_fact,\\n {\\\"A\\\": \\\"normal\\\",\\n \\\"B\\\":\\\"normal\\\"})\\n np.testing.assert_almost_equal(pdata_no_a, pdata_a)\\n\\n np.testing.assert_almost_equal(np.log(poisson.pmf(len(data[0]), 10)), pdata_a)\",\n \"def xo(mom:list,eval_mom,dad:list,eval_dad,slices):\\n legal = False\\n legal_son = False\\n legal_daughter = False\\n son = []\\n daughter = []\\n point_track = set()\\n while not legal:\\n\\n slice_point = randint(0, len(dad)-1)\\n if slice_point in point_track:\\n continue\\n point_track.add(slice_point)\\n if len(point_track) == len(dad): # exhausted all possible points with no success\\n if legal_son is True: # and legal_daughter is False\\n return son,eval_son,mom,eval_mom\\n if legal_daughter is True: # and legal_son is False\\n return dad, eval_dad, daughter, eval_daughter\\n if legal_son is False:\\n son = dad[:slice_point]+mom[slice_point:]\\n eval_son = evaluateOne(son)\\n if eval_son > -1:\\n legal_son = True\\n if legal_daughter is False:\\n daughter = mom[:slice_point]+dad[slice_point:]\\n eval_daughter = evaluateOne(daughter)\\n if eval_daughter > -1:\\n legal_daughter = True\\n\\n legal = legal_son and legal_daughter\\n\\n return son,eval_son,daughter,eval_daughter\",\n \"def test_inE_traversals(self):\\r\\n results = self.jon.inE()\\r\\n assert len(results) == 1\\r\\n assert self.jon_physics in results\",\n \"def simulate_trajectories(kav):\\n print \\\"Simulating \\\"+str(kav)\\n wt_trajectories = []\\n avp_trajectories = []\\n vip_trajectories = []\\n for tn in range(100):\\n # get random initial condition\\n # initial phases\\n init_conditions_AV = [single_osc.lc(wt_T*np.random.rand()) \\n for i in range(AVPcells+VIPcells)]\\n init_conditions_NAV = [single_osc.lc(wt_T*np.random.rand())[:-1]\\n for i in range(NAVcells)]\\n y0_random = np.hstack(init_conditions_AV+init_conditions_NAV)\\n\\n # do the simulation\\n model = GonzeModelManyCells(param, kav=kav, \\n initial_values=y0_random)\\n wt_trajectories.append(model.run(show_labels=False, seed=0))\\n\\n # avp bmalko\\n avp_model = GonzeModelManyCells(param, bmalko='AVP', kav=kav, \\n initial_values=y0_random)\\n avp_trajectories.append(avp_model.run(show_labels=False, seed=0))\\n\\n # vip bmalko\\n vip_model = GonzeModelManyCells(param, bmalko='VIP', kav=kav, \\n initial_values=y0_random)\\n vip_trajectories.append(vip_model.run(show_labels=False, seed=0))\\n\\n # save results\\n with open(\\\"Data/params/wt_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(wt_trajectories, output_file)\\n with open(\\\"Data/params/avp_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(avp_trajectories, output_file)\\n with open(\\\"Data/params/vip_\\\"+str(kav)+\\\".pickle\\\", \\\"wb\\\") as output_file:\\n pickle.dump(vip_trajectories, output_file)\\n\\n return {'wt': wt_trajectories,\\n 'avp': avp_trajectories,\\n 'vip': vip_trajectories}\",\n \"def test_active_inference_SPM_1b(self):\",\n \"def build_and_run_(spec):\\n opt = spec['opt']\\n print \\\"pool starting \\\", opt\\n\\n # lenght of simulation \\n tf = float(opt.get('tf', 100))\\n\\n # model # coupling function # connectivity \\n simargs = {}\\n for mod, key in [(models, 'model'), \\n (connectivity, 'connectivity'),\\n (coupling, 'coupling')]:\\n simargs[key] = build_sim_part(mod, opt[key])\\n\\n # noise # integrator \\n optint = opt['integrator']\\n if 'noise' in optint:\\n optint['noise'] = build_sim_part(noise, optint['noise'])\\n simargs['integrator'] = build_sim_part(integrators, optint)\\n\\n # monitors \\n if not type(opt['monitors']) in (list,):\\n opt['monitors'] = [opt['monitors']]\\n simargs['monitors'] = []\\n for mon in opt['monitors']:\\n simargs['monitors'].append(build_sim_part(monitors, mon))\\n\\n # stimulus \\n # NotImplemented\\n\\n # simulator \\n sim = simulator.Simulator(**simargs)\\n sim.configure()\\n\\n # TODO open HDF5 first, figure out correct sizes, etc\\n\\n # loop, writing data to h5\\n ts = [[] for _ in opt['monitors']]\\n ys = [[] for _ in opt['monitors']]\\n for i, all_monitor_data in enumerate(sim(tf)):\\n for j, mondata in enumerate(all_monitor_data):\\n if not mondata is None:\\n t, y = mondata\\n ts[j].append(t)\\n ys[j].append(y)\\n\\n # write data to hdf5 file\\n path = os.path.abspath(opt.get('wd', './'))\\n h5fname = os.path.join(path, \\\"tvb_%s.h5\\\" % (spec['md5sum'], ))\\n h5 = h5py.File(h5fname, 'w')\\n\\n for i, (mon, (t, y)) in enumerate(zip(simargs['monitors'], zip(ts, ys))):\\n mname = \\\"mon_%d_%s\\\" % (i, mon.__class__.__name__)\\n g = h5.create_group(mname)\\n g.create_dataset('ts', data=t)\\n g.create_dataset('ys', data=y)\\n\\n h5.close()\\n\\n # return filename\\n print \\\"pool finished\\\", opt\\n return h5fname\",\n \"def test_sim(self):\\n nxfe = 4\\n ipopt = get_solver(\\\"ipopt\\\")\\n\\n m_steady = self.make_steady_model(nfe=nxfe)\\n self.fix_model_inlets(m_steady, inlet_pressure=50.0 * pyo.units.bar)\\n m_steady.fs.compressor.boost_pressure[:].fix(7.0 * pyo.units.bar)\\n ipopt.solve(m_steady, tee=True)\\n time_steady = m_steady.fs.time\\n scalar_data = self.get_scalar_data_from_model(m_steady, time_steady)\\n initial_data = self.get_data_from_model_at_time(m_steady, time_steady)\\n\\n m = pyo.ConcreteModel()\\n default = {\\n \\\"dynamic\\\": True,\\n \\\"time_set\\\": [0.0, 20.0],\\n \\\"time_units\\\": pyo.units.hr,\\n }\\n m.fs = idaes.FlowsheetBlock(**default)\\n m.fs.properties = NaturalGasParameterBlock()\\n pipeline_config = {\\n \\\"property_package\\\": m.fs.properties,\\n \\\"finite_elements\\\": nxfe,\\n }\\n m.fs.pipeline = GasPipeline(**pipeline_config)\\n pipeline = m.fs.pipeline\\n compressor_config = {\\\"property_package\\\": m.fs.properties}\\n m.fs.compressor = Compressor(**compressor_config)\\n compressor = m.fs.compressor\\n m._compressor_to_pipeline = Arc(\\n ports=(compressor.outlet_port, pipeline.inlet_port),\\n )\\n expand_arcs = pyo.TransformationFactory(\\\"network.expand_arcs\\\")\\n expand_arcs.apply_to(m)\\n\\n cv = m.fs.pipeline.control_volume\\n assert_units_consistent(m)\\n\\n disc = pyo.TransformationFactory(\\\"dae.finite_difference\\\")\\n ntfe = 20\\n disc.apply_to(m, nfe=ntfe, wrt=m.fs.time, scheme=\\\"BACKWARD\\\")\\n\\n time = m.fs.time\\n t0 = m.fs.time.first()\\n x0 = cv.length_domain.first()\\n xf = cv.length_domain.last()\\n j = next(iter(m.fs.properties.component_list))\\n\\n # Fix geometry variables\\n m.fs.pipeline.diameter.fix(0.92 * pyo.units.m)\\n cv.length.fix(300.0 * pyo.units.km)\\n\\n # Fix boost pressure\\n compressor.boost_pressure[:].fix()\\n\\n # Inlets to the compressor are fixed, except for flow, where\\n # the outlet is fixed.\\n state = compressor.inlet_state\\n state[:].pressure.fix()\\n state[:].mole_frac_comp[j].fix()\\n state[:].temperature.fix()\\n cv.flow_mass[:, xf].fix()\\n\\n # Fix initial conditions. Here, pressure and volume for all\\n # non-specified points.\\n for x in cv.length_domain:\\n if x != x0:\\n cv.pressure[t0, x].fix()\\n if x != xf:\\n cv.flow_mass[t0, x].fix()\\n\\n # I want to deactivate differential equations at (t0, xf)\\n # Material balance already doesn't exist here.\\n cv.momentum_balance[t0, xf].deactivate()\\n\\n self.assertEqual(degrees_of_freedom(m), 0)\\n\\n # Load initial steady state into model at all time points.\\n for name, val in initial_data.items():\\n var = m.find_component(name)\\n for t in time:\\n var[t].set_value(val)\\n # Load scalar data from initial steady state\\n # (initialize area, basically)\\n for name, val in scalar_data.items():\\n var = m.find_component(name)\\n var.set_value(val)\\n\\n cv.material_accumulation[...].set_value(0.0)\\n cv.flow_mass_dt[...].set_value(0.0)\\n\\n for con in large_residuals_set(m):\\n resid = pyo.value(con.body - con.upper)\\n print(resid, con.name)\\n ipopt.solve(m, tee=True)\\n\\n # Load input sequence into model\\n sample_points = [4.0, 20.0]\\n input_name = \\\"fs.pipeline.control_volume.flow_mass[*,1.0]\\\"\\n nominal_density = 0.72\\n val = 12.0 * 1e6 / 24 * nominal_density # 12 (1e6 SCM)/day\\n input_series_data = (\\n sample_points,\\n {input_name: [val, val]},\\n )\\n input_interval_data = interval_data_from_time_series(input_series_data)\\n load_inputs_into_model(m, time, input_interval_data)\\n # Solve with loaded inputs\\n res = ipopt.solve(m, tee=True)\\n self.assertIs(\\n res.solver.termination_condition,\\n pyo.TerminationCondition.optimal,\\n )\\n\\n # These predicted values come from a simulation of a single pipeline\\n # model from the Pyomo DAE example. flow_mass has been converted\\n # to kg/hr from (1e4 SCM/hr) by a factor of 0.72*1e4, where\\n # 0.72 kg/m**3 is the gas density at standard conditions.\\n pred_values = (\\n list(time),\\n {\\n \\\"fs.pipeline.control_volume.flow_mass[*,%s]\\\"\\n % x0: [\\n 3.000e5,\\n 2.999e5,\\n 2.999e5,\\n 2.999e5,\\n 3.000e5,\\n 3.174e5,\\n 3.301e5,\\n 3.389e5,\\n 3.449e5,\\n 3.492e5,\\n 3.523e5,\\n 3.544e5,\\n 3.560e5,\\n 3.571e5,\\n 3.579e5,\\n 3.585e5,\\n 3.589e5,\\n 3.592e5,\\n 3.594e5,\\n 3.595e5,\\n 3.597e5,\\n ],\\n \\\"fs.pipeline.control_volume.pressure[*,%s]\\\"\\n % xf: [\\n 50.90,\\n 50.90,\\n 50.90,\\n 50.90,\\n 50.90,\\n 49.83,\\n 49.31,\\n 48.95,\\n 48.69,\\n 48.51,\\n 48.38,\\n 48.29,\\n 48.22,\\n 48.17,\\n 48.14,\\n 48.11,\\n 48.10,\\n 48.08,\\n 48.07,\\n 48.07,\\n 48.06,\\n ],\\n \\\"fs.compressor.power[*]\\\": [\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.590e3,\\n 1.682e3,\\n 1.750e3,\\n 1.796e3,\\n 1.828e3,\\n 1.851e3,\\n 1.867e3,\\n 1.878e3,\\n 1.887e3,\\n 1.892e3,\\n 1.897e3,\\n 1.900e3,\\n 1.902e3,\\n 1.904e3,\\n 1.905e3,\\n 1.906e3,\\n 1.906e3,\\n ],\\n },\\n )\\n output_names = [\\n \\\"fs.pipeline.control_volume.flow_mass[*,%s]\\\" % x0,\\n \\\"fs.pipeline.control_volume.pressure[*,%s]\\\" % xf,\\n \\\"fs.compressor.power[*]\\\",\\n ]\\n actual_values = (\\n list(time),\\n {\\n name: [var.value for var in m.find_component(name).values()]\\n for name in output_names\\n },\\n )\\n # Note: We fail with a reltol of 0.01, due to flow rate discrepancies\\n # in positions 6, 7, 8, and 9. A reltol of 0.02 seems reasonable to me.\\n self.assertStructuredAlmostEqual(pred_values, actual_values, reltol=0.02)\",\n \"def test_variability(self):\\n # some reproducible arbitrariness\\n np.random.seed(343143)\\n\\n n = 10\\n t_max = 20.0\\n dt = 0.1\\n G = RandomLayer(n)\\n\\n M1 = simulation.EventMonitor(G)\\n\\n sim1 = simulation.Simulation(G, M1, dt=dt)\\n sim1.run(t_max)\\n \\n M2 = simulation.EventMonitor(G)\\n sim2 = simulation.Simulation(G, M2, dt=dt)\\n sim2.run(t_max)\\n\\n self.assertNotEqual(len(M1.t), 0)\\n self.assertNotEqual(len(M2.t), 0)\\n self.assertNotEqual(M1.t, M2.t)\",\n \"def test_no_jitter(self):\\n n = 10\\n t_max = 25\\n dt = 0.1\\n G = RateHVCLayer(n)\\n G.burst_noise = 0.0\\n\\n M1 = simulation.StateMonitor(G, 'out')\\n\\n sim1 = simulation.Simulation(G, M1, dt=dt)\\n sim1.run(t_max)\\n \\n M2 = simulation.StateMonitor(G, 'out')\\n sim2 = simulation.Simulation(G, M2, dt=dt)\\n sim2.run(t_max)\\n\\n self.assertTrue(np.allclose(M1.out, M2.out))\",\n \"def test_shape(self):\\n M = simulation.EventMonitor(self.G)\\n sim = simulation.Simulation(self.G, M, dt=self.dt)\\n sim.run(self.t_max)\\n\\n self.assertEqual(len(M.t), len(M.i))\",\n \"def test_shared_members_N(self):\\r\\n def populate_module(m,x):\\r\\n m.x=x\\r\\n m.lx=[x]\\r\\n m.llx=[[x],[x]]\\r\\n m.ltx=[(x,)]\\r\\n m.ldx=[{'x':x}]\\r\\n m.tx=(x,)\\r\\n m.tlx=([x],)\\r\\n m.ttx=((x,),)\\r\\n m.tdx=({'x':x},)\\r\\n m.dx={'x':x}\\r\\n m.dlx={'x':[x]}\\r\\n m.dtx={'x':(x,)}\\r\\n m.ddx={'x':{'x':x}}\\r\\n\\r\\n def get_element(i):\\r\\n return [i.x,i.lx[0],i.tx[0],i.dx['x'],i.llx[0][0], i.llx[1][0], i.ltx[0][0], i.ldx[0]['x'], i.tlx[0][0], i.tlx[0][0], i.tdx[0]['x'], i.dlx['x'][0], i.dtx['x'][0], i.ddx['x']['x']]\\r\\n m1=Module()\\r\\n m2=Module()\\r\\n m3=Module()\\r\\n m4=Module()\\r\\n x=T.dscalar()\\r\\n populate_module(m1,x)\\r\\n populate_module(m2,(x))\\r\\n populate_module(m4,(x))\\r\\n #m1.x and m2.x should not be shared as their is no hierarchi link between them.\\r\\n inst1=m1.make()\\r\\n inst2=m2.make()\\r\\n m1.m2=m2\\r\\n m2.m3=m3\\r\\n m3.m4=m4\\r\\n #m1.x and m2.x should be shared as their is a hierarchi link between them.\\r\\n inst3=m1.make()\\r\\n inst1.x=1\\r\\n inst2.x=2\\r\\n inst3.x=3\\r\\n for f in get_element(inst1):\\r\\n assert f==1\\r\\n for f in get_element(inst2):\\r\\n assert f==2\\r\\n for f in get_element(inst3)+get_element(inst3.m2)+get_element(inst3.m2.m3.m4):\\r\\n assert f==3\\r\\n\\r\\n inst3.m2.x=4\\r\\n for f in get_element(inst3)+get_element(inst3.m2)+get_element(inst3.m2.m3.m4):\\r\\n assert f==4\",\n \"def test_OBJT_pass(self):\\n for O in self.mod.objts.itervalues():\\n self.assertTrue(O.isset)\",\n \"def test_basic_setup(self):\\n random_vars = ['D', 'I', 'G', 'S', 'L']\\n\\n for rv in random_vars:\\n self.assertTrue(rv in self.Gs.nodes)\\n self.assertTrue(isinstance(self.Gs.nodes[rv], DiscreteNetworkNode))\",\n \"def test_single_game_seed_works(self):\\n sim = ss.Simulation(seed=23)\\n game1 = sim.single_game()\\n sim = ss.Simulation(seed=23)\\n game2 = sim.single_game()\\n assert game1 == game2, 'Your seed in Simulation class is not working.'\",\n \"def test_no_jitter(self):\\n # some reproducible arbitrariness\\n np.random.seed(3249823)\\n\\n n = 10\\n t_max = 25\\n dt = 0.1\\n G = HVCLikeLayer(n)\\n G.burst_noise = 0.0\\n G.spike_noise = 0.0\\n\\n M1 = simulation.EventMonitor(G)\\n\\n sim1 = simulation.Simulation(G, M1, dt=dt)\\n sim1.run(t_max)\\n \\n M2 = simulation.EventMonitor(G)\\n sim2 = simulation.Simulation(G, M2, dt=dt)\\n sim2.run(t_max)\\n\\n self.assertEqual(M1.t, M2.t)\",\n \"def __post_init__(self):\\n assert self.data.shape == self.theory.shape\",\n \"def v1_kcat_validate():\\n\\n name = MPI.Get_processor_name()\\n rank = MPI.COMM_WORLD.Get_rank()\\n size = MPI.COMM_WORLD.Get_size()\\n\\n if rank == 0:\\n # create ident object first\\n v1_ident = ModelIdent(ident_fun=kotte_model.flux_1_kcat_ident,\\n arranged_data_file_name=os.path.join(os.getcwd(), 'exp/exp_v1_2_experiments'),\\n ident_data_file_name=os.path.join(os.getcwd(), 'ident/ident_v1_kcat'),\\n **{'original_exp_file': os.path.join(os.getcwd(), 'exp/experiments'),\\n 'flux_id': 1, 'flux_choice': 2,\\n 'values_figure': os.path.join(os.getcwd(), 'results/v1_kcat_parameter_values.eps'),\\n 'ident_figure': os.path.join(os.getcwd(), 'results/v1_kcat_ident.eps'),\\n 'exp_figure': os.path.join(os.getcwd(), 'results/v1_kcat_exp.eps'),\\n 'figure_format': 'eps',\\n 'ident_index_label': ['sample_name', 'data_set_id']})\\n\\n # retrieve identifiability data and process it for validation\\n v1_ident.validation_info()\\n\\n user_ode_opts = {'iter': 'Newton', 'discr': 'Adams', 'atol': 1e-10, 'rtol': 1e-10,\\n 'time_points': 200, 'display_progress': True, 'verbosity': 30}\\n # initial ss to begin all simulations from\\n y0 = np.array([5, 1, 1])\\n # get and set true parameter values, if available separately\\n default_parameters = true_parameter_values()\\n\\n v1_valid_obj = ValidateSim(kotte_model.kotte_ck_ode, kotte_model.kotte_ck_flux,\\n **{'kinetics': 2, 'ode_opts': user_ode_opts, 't_final': 200, 'wt_y0': y0,\\n 'i_parameter': default_parameters, 'sample_size': 1, 'noise_std': 0.05,\\n 'validate_index_label': ['estimate_id', 'sample_name', 'data_set_id',\\n 'experiment_id'],\\n 'validate_file_name': os.path.join(os.getcwd(), 'validate/v1_kcat_validate'),\\n 'original_exp_file': v1_ident.original_exp_file,\\n 'c_validate_file': os.path.join(os.getcwd(), 'results/v1_kcat_c_validate.eps'),\\n 'f_validate_file': os.path.join(os.getcwd(), 'results/v1_kcat_f_validate.eps'),\\n 'c_exp_file': os.path.join(os.getcwd(), 'results/v1_kcat_c_exp.eps'),\\n 'v_exp_file': os.path.join(os.getcwd(), 'results/v1_kcat_f_exp.eps'),\\n 'format': v1_ident.figure_format})\\n\\n parameter_estimates, estimate_info = v1_valid_obj.create_parameter_list(v1_ident.select_values)\\n\\n job = ParallelValidate(slaves=range(1, size))\\n\\n validate_results = job.run_all(task='initial_sim', **{'parameters': parameter_estimates,\\n 'estimate_info': estimate_info, 'sim_obj': v1_valid_obj})\\n job.terminate_slaves()\\n\\n # process validation data for plots\\n validate_processing(v1_valid_obj, validate_results)\\n\\n else:\\n\\n print('I am %s Slave with rank %s of %s' % (name, str(rank), str(size)))\\n ValidateSlave().run()\\n\\n return None\",\n \"def mewe_misspecified(M,N,m,n,target):\\r\\n\\toutput = []\\r\\n\\tfor k in tqdm(range(0,M)):\\r\\n\\t\\t# Allocate space for output\\r\\n\\t\\tmewe_store = np.zeros((len(n),target['thetadim']))\\r\\n\\t\\tmewe_runtimes = np.zeros(len(n))\\r\\n\\t\\tmewe_evals = np.zeros(len(n))\\r\\n\\t\\t\\r\\n\\t\\t# generate all observations and sets of randomness to be used\\r\\n\\t\\t\\r\\n\\t\\tif target[\\\"observed_law\\\"] == \\\"Gamma\\\":\\r\\n\\t\\t\\tobs_all = np.random.gamma(true_theta[0], true_theta[1],np.max(n))\\r\\n\\t\\telif target[\\\"observed_law\\\"] == \\\"Cauchy\\\":\\r\\n\\t\\t\\tobs_all = np.random.standard_cauchy(np.max(n))\\r\\n\\t\\telse : \\r\\n\\t\\t\\treturn(\\\"Not implemented law\\\")\\r\\n\\t\\t\\tbreak\\r\\n\\t\\t# la ligne du dessus est modifiée pour générer un échantillon contaminé\\r\\n\\t\\t\\r\\n\\t\\t# generate the synthetic randomness, sort.\\r\\n\\t\\t\\r\\n\\t\\trandomness = [target['generate_randomness'](m) for i in range(N)]\\r\\n\\t\\t\\r\\n\\t\\tfor i in range(0,len(n)):\\r\\n\\t\\t\\t# subset observations and sort\\r\\n\\t\\t\\tobs = obs_all[:n[i]]\\r\\n\\t\\t\\tsort_obs = np.sort(obs)\\r\\n\\t\\t\\tsort_obs_mult = np.repeat(sort_obs, m / n[i], axis = 0)\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Define the objective to be minimized to find the MEWE\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tdef obj1(theta):\\r\\n\\t\\t\\t\\tif(theta[1] < 0 ):\\r\\n\\t\\t\\t\\t\\tout = 10e6\\r\\n\\t\\t\\t\\telse :\\r\\n\\t\\t\\t\\t\\twass_dists = [target['dist'](sort_obs_mult, np.sort(target['simulation'](theta, x))) for x in randomness]\\r\\n\\t\\t\\t\\t\\tout = np.mean(wass_dists)\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t\\treturn out\\r\\n\\t\\t\\t\\t\\r\\n\\t\\t\\t# Optimization\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tt_mewe = time.process_time()\\r\\n\\t\\t\\tmewe = minimize(fun = obj1, x0 = true_theta)\\r\\n\\t\\t\\tt_mewe = time.process_time() - t_mewe\\r\\n\\t\\t\\t\\r\\n\\t\\t\\t# Save the results\\r\\n\\t\\t\\tmewe_store[i] = mewe.x\\r\\n\\t\\t\\tmewe_runtimes[i] = t_mewe\\r\\n\\t\\t\\tmewe_evals[i] = mewe.nit\\r\\n\\t\\t\\r\\n\\t\\toutput_cbind = np.c_[mewe_store, mewe_runtimes, mewe_evals, n, np.arange(len(n))]\\r\\n\\t\\toutput.append(output_cbind)\\r\\n\\t\\t\\r\\n\\treturn output\",\n \"def simulation(G, # graph object\\n pos = None, # positions of nodes\\n n = 5, # number of simulation steps\\n \\n # wrapped args for simulation_step function\\n kernel = 'weights', # simulation kernel\\n custom_kernel = None, # custom simulation kernel\\n WERE_multiplier = 10, # multiplier for WERE kernel\\n oblivion = False, # enable information oblivion\\n engagement_enforcement = 1.01,\\n draw = False, # draw graph\\n show_attr = False): # show attributes \\n \\n #=======================================#\\n # append nodes data from 0 step to list #\\n #=======================================#\\n \\n graph_list = []\\n graph_list.append(copy.deepcopy(list(G.nodes.data() ) ) )\\n \\n\\n #===================#\\n # Run n simulations #\\n #===================#\\n \\n for i in range(n):\\n dp.simulation_step(G = G, \\n pos = pos, \\n \\n kernel = kernel,\\n custom_kernel = custom_kernel,\\n WERE_multiplier = WERE_multiplier, \\n oblivion = oblivion, \\n engagement_enforcement = engagement_enforcement,\\n draw = draw, \\n show_attr = show_attr)\\n\\n # save nodes data to to list\\n graph_list.append(copy.deepcopy(list(G.nodes.data() ) ) )\\n \\n \\n #======================================================#\\n # Count aware agents before and after simulation steps #\\n #======================================================#\\n \\n # Check number of aware agents in 0 step\\n #global aware_first\\n aware_first = []\\n for i in range(len(graph_list[0])):\\n aware_first.append(graph_list[0][i][1]['state'])\\n aware_first_c = aware_first.count('aware')\\n \\n # graph_list[0][1][1]['state']\\n \\n # Check number of aware agents in the last step\\n #global aware_last\\n aware_last = []\\n graph_list_len = len(graph_list) - 1\\n for i in range(len(graph_list[0])):\\n aware_last.append(graph_list[graph_list_len][i][1]['state']) # n is the last sim\\n aware_last_c = aware_last.count('aware')\\n \\n #graph_list[5][0][1]['state']\\n \\n #=================================#\\n # diffusion performance measuring #\\n #=================================#\\n \\n # equation for diffusion performance measuring\\n avg_aware_inc_per_step = (aware_last_c - aware_first_c) / n\\n \\n # show graph statistics\\n return graph_list, avg_aware_inc_per_step\",\n \"def test_multiple_rng(self):\\r\\n rng1 = RandomStreams(1234)\\r\\n rng2 = RandomStreams(2392)\\r\\n assert rng1.random_state_variables is not rng2.random_state_variables\",\n \"def test_find_singletons(self):\\n self._build_sample_graph()\\n # Adding singletons\\n sg = self.skill_graph.add(Skill.build('g', ''))\\n sh = self.skill_graph.add(Skill.build('h', ''))\\n skill_map = SkillMap.load(self.course)\\n result = SkillMapMetrics(skill_map).singletons()\\n expected = [sg.id, sh.id]\\n self.assertEqual(sorted(expected), sorted(result))\",\n \"def random_structure (params) :\\n\\n import random\\n random.seed(0)\\n from scitbx.array_family import flex\\n flex.set_random_seed(0)\\n from cctbx.development import random_structure\\n\\n uc_volume = params.target_unit_cell.volume()\\n asu_volume = uc_volume / params.target_space_group.group().order_z()\\n target_number_scatterers = int(asu_volume)//128 # Very approximate rule of thumb for proteins with ~50% solvent content\\n element_unit = ['O']*19 + ['N']*18 + ['C']*62 + ['S']*1\\n element_pallet = element_unit * (1 + ( target_number_scatterers//len(element_unit) ))\\n assert len(element_pallet) >= target_number_scatterers\\n # XXX Ersatz hard limit to prevent excessive execution time of\\n # xray_structure() below.\\n elements = element_pallet[:min(1000, target_number_scatterers)]\\n\\n xs = random_structure.xray_structure(\\n space_group_info = params.target_space_group,\\n unit_cell = params.target_unit_cell,\\n elements=elements,\\n min_distance=1.2)\\n xs.show_summary()\\n phil2 = mmtbx.command_line.fmodel.fmodel_from_xray_structure_master_params\\n params2 = phil2.extract()\\n # adjust the cutoff of the generated intensities to assure that\\n # statistics will be reported to the desired high-resolution limit\\n # even if the observed unit cell differs slightly from the reference.\\n params2.high_resolution = params.d_min / math.pow(\\n 1 + params.unit_cell_length_tolerance, 1 / 3)\\n params2.output.type = \\\"real\\\"\\n if (params.include_bulk_solvent) :\\n print(\\\"Sorry, can't include bulk solvent for randomly-generated sites.\\\")\\n f_model = mmtbx.utils.fmodel_from_xray_structure(\\n xray_structure = xs,\\n f_obs = None,\\n add_sigmas = True,\\n params = params2).f_model\\n if not params.merge_anomalous:\\n f_model_possibly_anomalous = f_model.generate_bijvoet_mates()\\n else:\\n f_model_possibly_anomalous = f_model\\n i_model = f_model_possibly_anomalous.as_intensity_array()\\n\\n if params.scaling.mtz_file is not None:\\n f_fake = f_model.as_amplitude_array()\\n # as the code that consumes the mtz f-obs expects non-anomalous data\\n mtzdata = f_fake.as_mtz_dataset(column_root_label=\\\"f-obs\\\")\\n mtzdata.mtz_object().write(params.scaling.mtz_file)\\n\\n return i_model\",\n \"def test_shared_members(self):\\r\\n\\r\\n def populate_module(m,x):\\r\\n m.x=x\\r\\n m.lx=[x]\\r\\n m.llx=[[x],[x]]\\r\\n m.ltx=[(x,)]\\r\\n m.ldx=[{'x':x}]\\r\\n m.tx=(x,)\\r\\n m.tlx=([x],)\\r\\n m.ttx=((x,),)\\r\\n m.tdx=({'x':x},)\\r\\n m.dx={'x':x}\\r\\n m.dlx={'x':[x]}\\r\\n m.dtx={'x':(x,)}\\r\\n m.ddx={'x':{'x':x}}\\r\\n\\r\\n def get_element(i):\\r\\n return [i.x,i.lx[0],i.tx[0],i.dx['x'],i.llx[0][0], i.llx[1][0], i.ltx[0][0], i.ldx[0]['x'], i.tlx[0][0], i.tlx[0][0], i.tdx[0]['x'], i.dlx['x'][0], i.dtx['x'][0], i.ddx['x']['x']]\\r\\n\\r\\n m1=Module()\\r\\n m2=Module()\\r\\n x=T.dscalar()\\r\\n populate_module(m1,x)\\r\\n populate_module(m2,x)\\r\\n #m1.x and m2.x should not be shared as their is no hierarchi link between them.\\r\\n inst1=m1.make()\\r\\n inst2=m2.make()\\r\\n m1.m2=m2\\r\\n #m1.x and m2.x should be shared as their is a hierarchi link between them.\\r\\n inst3=m1.make()\\r\\n inst1.x=1\\r\\n inst2.x=2\\r\\n inst3.x=3\\r\\n for f in get_element(inst1):\\r\\n assert f==1\\r\\n for f in get_element(inst2):\\r\\n assert f==2\\r\\n for f in get_element(inst3)+get_element(inst3.m2):\\r\\n assert f==3\\r\\n\\r\\n inst3.m2.x=4\\r\\n for f in get_element(inst3)+get_element(inst3.m2):\\r\\n assert f==4\",\n \"def test_speciesDestruction():\\n sys = LVsystem.Ecosystem()\\n sys.addSpecies('rabbit')\\n sys.addSpecies('fox')\\n sys.addSpecies('wolf')\\n \\n sys.removeSpecies('fox')\\n \\n assert len(sys.species_list) == 2\\n assert not ('fox' in sys.species_list)\\n for key in sys.intMatrix:\\n assert not ('fox' in key)\\n \\n sys.removeSpecies('wolf')\\n \\n assert sys.species_list == ['rabbit']\\n for key in sys.intMatrix:\\n assert not ('wolf' in key)\\n \\n sys.removeSpecies('rabbit') \\n \\n assert sys.intMatrix == {}\\n assert sys.species_list == []\\n for key in sys.intMatrix:\\n assert not ('rabbit' in key)\",\n \"def __init__(self,\\n num_masks=1,\\n num_fake=0,\\n epsilon_greedy=0.0,\\n num_classes = 2,\\n num_samples = 1000,\\n max_length = 30,\\n ambiguity=False,\\n num_bondtypes=1):\\n\\n self.num_masks = num_masks\\n self.num_fake = num_fake\\n self.epsilon_greedy = epsilon_greedy\\n self.num_classes = num_classes\\n self.num_samples = num_samples\\n\\n self.molecule_generator = MoleculeGenerator(num_classes=num_classes, \\n max_length=max_length, \\n ambiguity=ambiguity,\\n num_bondtypes=num_bondtypes)\\n\\n self.corruption = CorruptionTransform(num_masks=num_masks, num_fake=num_fake, epsilon=epsilon_greedy)\\n\\n\\n self.data = []\\n\\n\\n for i in range(self.num_samples):\\n molecule = self.molecule_generator.generate_molecule()\\n molecule = chem.AddHs(molecule)\\n\\n\\n Adj = chem.rdmolops.GetAdjacencyMatrix(molecule)\\n atoms = np.asarray([periodic_table[atom.GetAtomicNum()] for atom in molecule.GetAtoms()])\\n smiles = chem.MolToSmiles(molecule)\\n self.data += [MoleculeSample(atoms, Adj, {}, smiles)]\",\n \"def test_determinism_2():\\n\\n def run_sgd(mode):\\n # Must be seeded the same both times run_sgd is called\\n disturb_mem.disturb_mem()\\n rng = np.random.RandomState([2012, 11, 27])\\n\\n batch_size = 5\\n train_batches = 3\\n valid_batches = 4\\n num_features = 2\\n\\n # Synthesize dataset with a linear decision boundary\\n w = rng.randn(num_features)\\n\\n def make_dataset(num_batches):\\n disturb_mem.disturb_mem()\\n m = num_batches*batch_size\\n X = rng.randn(m, num_features)\\n y = np.zeros((m, 1))\\n y[:, 0] = np.dot(X, w) > 0.\\n\\n rval = DenseDesignMatrix(X=X, y=y)\\n\\n rval.yaml_src = \\\"\\\" # suppress no yaml_src warning\\n\\n X = rval.get_batch_design(batch_size)\\n assert X.shape == (batch_size, num_features)\\n\\n return rval\\n\\n train = make_dataset(train_batches)\\n valid = make_dataset(valid_batches)\\n\\n num_chunks = 10\\n chunk_width = 2\\n\\n class ManyParamsModel(Model):\\n \\\"\\\"\\\"\\n Make a model with lots of parameters, so that there are many\\n opportunities for their updates to get accidentally re-ordered\\n non-deterministically. This makes non-determinism bugs manifest\\n more frequently.\\n \\\"\\\"\\\"\\n\\n def __init__(self):\\n super(ManyParamsModel, self).__init__()\\n self.W1 = [sharedX(rng.randn(num_features, chunk_width)) for i\\n in xrange(num_chunks)]\\n disturb_mem.disturb_mem()\\n self.W2 = [sharedX(rng.randn(chunk_width))\\n for i in xrange(num_chunks)]\\n self._params = safe_union(self.W1, self.W2)\\n self.input_space = VectorSpace(num_features)\\n self.output_space = VectorSpace(1)\\n\\n disturb_mem.disturb_mem()\\n model = ManyParamsModel()\\n disturb_mem.disturb_mem()\\n\\n class LotsOfSummingCost(Cost):\\n \\\"\\\"\\\"\\n Make a cost whose gradient on the parameters involves summing many\\n terms together, so that T.grad is more likely to sum things in a\\n random order.\\n \\\"\\\"\\\"\\n\\n supervised = True\\n\\n def expr(self, model, data, **kwargs):\\n self.get_data_specs(model)[0].validate(data)\\n X, Y = data\\n disturb_mem.disturb_mem()\\n\\n def mlp_pred(non_linearity):\\n Z = [T.dot(X, W) for W in model.W1]\\n H = [non_linearity(z) for z in Z]\\n Z = [T.dot(h, W) for h, W in safe_izip(H, model.W2)]\\n pred = sum(Z)\\n return pred\\n\\n nonlinearity_predictions = map(mlp_pred,\\n [T.nnet.sigmoid,\\n T.nnet.softplus,\\n T.sqr,\\n T.sin])\\n pred = sum(nonlinearity_predictions)\\n disturb_mem.disturb_mem()\\n\\n return abs(pred-Y[:, 0]).sum()\\n\\n def get_data_specs(self, model):\\n data = CompositeSpace((model.get_input_space(),\\n model.get_output_space()))\\n source = (model.get_input_source(), model.get_target_source())\\n return (data, source)\\n\\n cost = LotsOfSummingCost()\\n\\n disturb_mem.disturb_mem()\\n\\n algorithm = SGD(cost=cost,\\n batch_size=batch_size,\\n learning_rule=Momentum(.5),\\n learning_rate=1e-3,\\n monitoring_dataset={'train': train, 'valid': valid},\\n update_callbacks=[ExponentialDecay(decay_factor=2.,\\n min_lr=.0001)],\\n termination_criterion=EpochCounter(max_epochs=5))\\n\\n disturb_mem.disturb_mem()\\n\\n train_object = Train(dataset=train,\\n model=model,\\n algorithm=algorithm,\\n extensions=[PolyakAveraging(start=0),\\n MomentumAdjustor(final_momentum=.9,\\n start=1,\\n saturate=5), ],\\n save_freq=0)\\n\\n disturb_mem.disturb_mem()\\n\\n train_object.main_loop()\\n\\n output = cStringIO()\\n record = Record(file_object=output, replay=False)\\n record_mode = RecordMode(record)\\n\\n run_sgd(record_mode)\\n\\n output = cStringIO(output.getvalue())\\n playback = Record(file_object=output, replay=True)\\n playback_mode = RecordMode(playback)\\n\\n run_sgd(playback_mode)\",\n \"def test_checks_population_size(self):\\n with pm.Model() as model:\\n n = pm.Normal(\\\"n\\\", mu=0, sigma=1)\\n for stepper in TestPopulationSamplers.steppers:\\n step = stepper()\\n with pytest.raises(ValueError, match=\\\"requires at least 3 chains\\\"):\\n pm.sample(draws=10, tune=10, chains=1, cores=1, step=step)\\n # don't parallelize to make test faster\\n pm.sample(\\n draws=10,\\n tune=10,\\n chains=4,\\n cores=1,\\n step=step,\\n compute_convergence_checks=False,\\n )\",\n \"def test_when_opponent_all_Ds(self):\\n self.responses_test([C, C, C, C], [D, D, D, D], [D, D, D], random_seed=5)\",\n \"def exists(sim):\\n return (sim['N'] > 0)\",\n \"def testMass(self):\\n tested = False\\n slhafile=\\\"./testFiles/slha/higgsinoStop.slha\\\"\\n model = Model(BSMList,SMList)\\n model.updateParticles(slhafile,promptWidth = 1e-12*GeV)\\n topos = decomposer.decompose(model, .1*fb, True, False, 5.*GeV)\\n for topo in topos:\\n if str(topo)!=\\\"[1][1]\\\":\\n continue\\n for element in topo.elementList:\\n if str(element)!=\\\"[[[b]],[[b]]]\\\":\\n continue\\n masses=element.motherElements[0].mass\\n tested = True\\n dm=abs(masses[0][1]-masses[0][2])/GeV\\n #If intermediate BSM states are compared there are two elements ([[[b],[c,q]],[[b],[q,q]]])\\n # which do not get combined because their branches differ by the charges of the intermediate states\\n self.assertEqual(len(element.motherElements),24)\\n self.assertTrue(dm < 5.0)\\n self.assertTrue(tested)\",\n \"def test_creature(self):\\n self.assertEqual(len(self.processor), 3)\",\n \"def test_am_basic(Simulator, plt, seed, rng):\\n\\n D = 64\\n vocab = Vocabulary(D, rng=rng)\\n vocab.parse('A+B+C+D')\\n\\n with nengo.Network('model', seed=seed) as m:\\n am = AssociativeMemory(vocab)\\n in_node = nengo.Node(output=vocab.parse(\\\"A\\\").v, label='input')\\n nengo.Connection(in_node, am.input)\\n\\n in_p = nengo.Probe(in_node)\\n out_p = nengo.Probe(am.output, synapse=0.03)\\n\\n sim = Simulator(m)\\n sim.run(0.2)\\n t = sim.trange()\\n\\n plt.subplot(2, 1, 1)\\n plt.plot(t, nengo.spa.similarity(sim.data[in_p], vocab))\\n plt.ylabel(\\\"Input\\\")\\n plt.ylim(top=1.1)\\n plt.legend(vocab.keys, loc='best')\\n plt.subplot(2, 1, 2)\\n plt.plot(t, nengo.spa.similarity(sim.data[out_p], vocab))\\n plt.plot(t[t > 0.15], np.ones(t.shape)[t > 0.15] * 0.8, c='g', lw=2)\\n plt.ylabel(\\\"Output\\\")\\n plt.legend(vocab.keys, loc='best')\\n\\n assert similarity(sim.data[in_p][t > 0.15], vocab.parse(\\\"A\\\").v) > 0.99\\n assert similarity(sim.data[out_p][t > 0.15], vocab.parse(\\\"A\\\").v) > 0.8\",\n \"def sngl_obj_evo(self, lacking):\\n prob, algo = self.probinit('jde', 0)\\n l = list()\\n u = 6+(self.N-3)*4\\n for i in range(lacking):\\n archi = archipelago(algo,prob,8,16, topology=fully_connected())\\n for j in range(u):\\n archi.evolve(5)\\n stdout.write(\\\"\\\\r{0} / {1}\\\".format(i*u+j+1, lacking*u))\\n stdout.flush()\\n tmp = [isl for isl in archi]\\n tmp.sort(key = lambda x: x.population.champion.f[0]);\\n l.append(tmp[0].population.champion)\\n stdout.write(\\\" Done. \\\")\\n return l, prob\",\n \"def test_observed_species(self):\\n c = array([4,3,4,0,1,0,2])\\n obs = observed_species(c)\\n exp = 5\\n self.assertEqual(obs, exp)\\n c = array([0,0,0])\\n obs = observed_species(c)\\n exp = 0\\n self.assertEqual(obs, exp)\\n self.assertEqual(observed_species(self.TestData), 9)\",\n \"def test_population_movements_with_compilation(self):\\n self._pystepx = PySTEPXIsland(nb_islands=4, init_script=init_script)\\n print self._pystepx._rc[0]['gp_engine']\\n self._pystepx._rc[0].execute('elems = gp_engine.get_evolver().select_and_remove_individuals(0.01)',\\n block=True)\\n print self._pystepx._rc[0]['elems']\",\n \"def test_6(self):\\n params_spec, options_spec = generate_random_model()\\n respy_obj = RespyCls(params_spec, options_spec)\\n respy_obj = simulate_observed(respy_obj)\\n\\n # Extract class attributes\\n (\\n periods_rewards_systematic,\\n mapping_state_idx,\\n seed_prob,\\n periods_emax,\\n num_periods,\\n states_all,\\n num_points_interp,\\n edu_spec,\\n num_draws_emax,\\n is_myopic,\\n is_debug,\\n is_interpolated,\\n optim_paras,\\n optimizer_options,\\n file_sim,\\n num_types,\\n ) = dist_class_attributes(\\n respy_obj,\\n \\\"periods_rewards_systematic\\\",\\n \\\"mapping_state_idx\\\",\\n \\\"seed_prob\\\",\\n \\\"periods_emax\\\",\\n \\\"num_periods\\\",\\n \\\"states_all\\\",\\n \\\"num_points_interp\\\",\\n \\\"edu_spec\\\",\\n \\\"num_draws_emax\\\",\\n \\\"is_myopic\\\",\\n \\\"is_debug\\\",\\n \\\"is_interpolated\\\",\\n \\\"optim_paras\\\",\\n \\\"optimizer_options\\\",\\n \\\"file_sim\\\",\\n \\\"num_types\\\",\\n )\\n\\n shocks_cholesky = optim_paras[\\\"shocks_cholesky\\\"]\\n shocks_cov = shocks_cholesky.dot(shocks_cholesky.T)\\n coeffs_common = optim_paras[\\\"coeffs_common\\\"]\\n coeffs_a = optim_paras[\\\"coeffs_a\\\"]\\n coeffs_b = optim_paras[\\\"coeffs_b\\\"]\\n delta = optim_paras[\\\"delta\\\"]\\n\\n # Add some additional objects required for the interfaces to the functions.\\n period = np.random.choice(num_periods)\\n\\n periods_draws_emax = create_draws(\\n num_periods, num_draws_emax, seed_prob, is_debug\\n )\\n\\n draws_emax_standard = periods_draws_emax[period, :, :]\\n\\n draws_emax_risk = transform_disturbances(\\n draws_emax_standard, np.zeros(4), shocks_cholesky\\n )\\n\\n # Initialize Python version and solve.\\n state_space = StateSpace(\\n num_periods, num_types, edu_spec[\\\"start\\\"], edu_spec[\\\"max\\\"], optim_paras\\n )\\n\\n # Integrate periods_emax in state_space\\n state_space.emaxs = np.column_stack(\\n (\\n np.zeros((state_space.num_states, 4)),\\n periods_emax[~np.isnan(periods_emax) & (periods_emax != MISSING_FLOAT)],\\n )\\n )\\n\\n # Fill emaxs_a - emaxs_home in the requested period\\n states_period = state_space.get_attribute_from_period(\\\"states\\\", period)\\n\\n # Do not get the emaxs from the previous period if we are in the last one.\\n if period != state_space.num_periods - 1:\\n state_space.emaxs = get_emaxs_of_subsequent_period(\\n states_period, state_space.indexer, state_space.emaxs, edu_spec[\\\"max\\\"]\\n )\\n\\n num_states = state_space.states_per_period[period]\\n\\n shifts = np.random.randn(4)\\n\\n # Slight modification of request which assures that the interpolation code is\\n # working.\\n num_points_interp = min(num_points_interp, num_states)\\n\\n # Get the IS_SIMULATED indicator for the subset of points which are used for the\\n # predication model.\\n is_simulated = get_simulated_indicator(\\n num_points_interp, num_states, period, is_debug\\n )\\n\\n # Unpack necessary attributes\\n rewards_period = state_space.get_attribute_from_period(\\\"rewards\\\", period)\\n emaxs_period = state_space.get_attribute_from_period(\\\"emaxs\\\", period)[:, :4]\\n max_education = (\\n state_space.get_attribute_from_period(\\\"states\\\", period)[:, 3]\\n >= edu_spec[\\\"max\\\"]\\n )\\n\\n # Construct the exogenous variables for all points of the state space.\\n exogenous, max_emax = get_exogenous_variables(\\n rewards_period, emaxs_period, shifts, optim_paras[\\\"delta\\\"], max_education\\n )\\n\\n # Align output between Python and Fortran version.\\n py = (exogenous, max_emax)\\n\\n f90 = fort_debug.wrapper_get_exogenous_variables(\\n period,\\n num_periods,\\n num_states,\\n periods_rewards_systematic,\\n shifts,\\n mapping_state_idx,\\n periods_emax,\\n states_all,\\n edu_spec[\\\"start\\\"],\\n edu_spec[\\\"max\\\"],\\n delta,\\n coeffs_common,\\n coeffs_a,\\n coeffs_b,\\n num_types,\\n )\\n\\n assert_almost_equal(py[0], f90[0])\\n assert_almost_equal(py[1], f90[1])\\n\\n # Construct endogenous variable so that the prediction model can be fitted.\\n endogenous = get_endogenous_variable(\\n rewards_period,\\n emaxs_period,\\n max_emax,\\n is_simulated,\\n draws_emax_risk,\\n optim_paras[\\\"delta\\\"],\\n max_education,\\n )\\n\\n f90 = fort_debug.wrapper_get_endogenous_variable(\\n period,\\n num_periods,\\n num_states,\\n periods_rewards_systematic,\\n mapping_state_idx,\\n periods_emax,\\n states_all,\\n is_simulated,\\n num_draws_emax,\\n max_emax,\\n draws_emax_risk,\\n edu_spec[\\\"start\\\"],\\n edu_spec[\\\"max\\\"],\\n shocks_cov,\\n delta,\\n coeffs_common,\\n coeffs_a,\\n coeffs_b,\\n )\\n assert_almost_equal(endogenous, replace_missing_values(f90))\\n\\n py = get_predictions(endogenous, exogenous, max_emax, is_simulated)\\n\\n f90 = fort_debug.wrapper_get_predictions(\\n endogenous,\\n exogenous,\\n max_emax,\\n is_simulated,\\n num_points_interp,\\n num_states,\\n file_sim,\\n False,\\n )\\n\\n # This assertion fails if a column is all zeros.\\n if not exogenous.any(axis=0).any():\\n assert_array_almost_equal(py, f90)\",\n \"def prove_NN() -> Proof:\\n # Optional Task 6.7c\",\n \"def test_pickling(simulation_factory, two_particle_snapshot_factory,\\n external_params):\\n # unpack parameters\\n cls_obj, param_attr, list_params, evaluator = external_params\\n\\n # create class instance, get/set params when not attached\\n obj_instance = cls_obj()\\n getattr(obj_instance, param_attr)['A'] = list_params[0]\\n\\n pickling_check(obj_instance)\\n # set up simulation\\n snap = two_particle_snapshot_factory(d=3.7)\\n sim = simulation_factory(snap)\\n sim.operations.integrator = hoomd.md.Integrator(dt=0.001)\\n sim.operations.integrator.forces.append(obj_instance)\\n sim.run(0)\\n pickling_check(obj_instance)\",\n \"def test_ne():\\n # Define some universal gsps\\n gsp = galsim.GSParams(maxk_threshold=1.1e-3, folding_threshold=5.1e-3)\\n\\n # Pixel. Params include scale, flux, gsparams.\\n # gsparams.\\n # The following should all test unequal:\\n gals = [galsim.Pixel(scale=1.0),\\n galsim.Pixel(scale=1.1),\\n galsim.Pixel(scale=1.0, flux=1.1),\\n galsim.Pixel(scale=1.0, gsparams=gsp)]\\n all_obj_diff(gals)\\n\\n # Box. Params include width, height, flux, gsparams.\\n # gsparams.\\n # The following should all test unequal:\\n gals = [galsim.Box(width=1.0, height=1.0),\\n galsim.Box(width=1.1, height=1.0),\\n galsim.Box(width=1.0, height=1.1),\\n galsim.Box(width=1.0, height=1.0, flux=1.1),\\n galsim.Box(width=1.0, height=1.0, gsparams=gsp)]\\n all_obj_diff(gals)\\n\\n # TopHat. Params include radius, flux, gsparams.\\n # gsparams.\\n # The following should all test unequal:\\n gals = [galsim.TopHat(radius=1.0),\\n galsim.TopHat(radius=1.1),\\n galsim.TopHat(radius=1.0, flux=1.1),\\n galsim.TopHat(radius=1.0, gsparams=gsp)]\\n all_obj_diff(gals)\",\n \"def test_identity_oddballs(self):\\n from keras.constraints import identity\\n identity_instance = identity()\\n\\n oddball_examples = [\\\"Hello\\\", [1], -1, None]\\n assert(oddball_examples == identity_instance(oddball_examples))\",\n \"def test_run_simulation_returns_nothing(self):\\n sim = ss.Simulation()\\n assert sim.run_simulation(10) is None\",\n \"def __init__(self):\\n self.persons = []\\n self.partnerships = []\\n self.straight_males = set()\\n self.females = set()\\n self.high_sexual_activity = set()\\n self.singles = set()\\n self.infected = set()\\n self.resistant = set()\\n self.age_group = [set() for x in range(5)]\\n self.age_group_mixing_prob = age_group_mixing()\\n self.age_groups = [(i,j) for i in range(5) for j in range(5)]\\n self.time = 0\\n self.number_of_symptomatic = 0\\n self.number_of_asymptomatic = 0\\n self.number_of_res_symp = 0\\n self.number_of_res_asymp = 0\\n self.number_of_steady = 0\\n self.r0_infected = []\",\n \"def test_run_sim():\\n rnd = rand.Arrivals(31, 40)\\n sim.run_sim(2, 1, 3, 4, 24, rnd)\",\n \"def make_sim(sim_name: str, object_names: List[str], epoch: datetime, \\n integrator: str, steps_per_day: int, save_file: bool) -> rebound.Simulation:\\n # Filename for archive\\n file_date: str = epoch.strftime('%Y-%m-%d_%H-%M')\\n fname_archive: str = f'../data/planets/{sim_name}_{file_date}.bin'\\n\\n # If this file already exists, load it and check for both extra and missing bodies\\n sim: rebound.Simulation\\n try:\\n # Attempt to load the named file\\n sim = rebound.Simulation(fname_archive)\\n # print(f'Loaded {fname_archive}')\\n\\n # Generate list of missing object names\\n objects_missing: List[str] = [nm for nm in object_names if nm not in sim.particles]\\n\\n # Extend the simulation and save it with the augmented bodies\\n if objects_missing:\\n print(f'Found missing objects in {fname_archive}:')\\n print(objects_missing)\\n extend_sim_horizons(sim, object_names = objects_missing, epoch=epoch)\\n\\n # Sets of named and input object hashes\\n hashes_sim: Set[int] = set(p.hash.value for p in sim.particles)\\n hashes_input: Set[str] = set(rebound.hash(nm).value for nm in object_names)\\n\\n # Filter the simulation so only the named objects are included\\n hashes_remove: List[str] = [h for h in hashes_sim if h not in hashes_input] \\n for h in hashes_remove:\\n sim.remove(hash=h)\\n\\n except: \\n # Initialize simulation\\n sim = make_sim_horizons(object_names=object_names, epoch=epoch)\\n\\n # Move to center of momentum\\n sim.move_to_com()\\n\\n # Set integrator and time step\\n sim.integrator = integrator\\n dt: float = 1.0 / steps_per_day if steps_per_day > 0 else 0\\n sim.dt = dt\\n if integrator == 'ias15':\\n ias15 = sim.ri_ias15\\n ias15.min_dt = dt\\n\\n # Save a snapshot to the archive file if requested\\n if save_file:\\n sim.simulationarchive_snapshot(filename=fname_archive, deletefile=True)\\n\\n # Return the simulation\\n return sim\",\n \"def recurse(hp):\\n global G\\n nodes = G.nodes(data=True)\\n p_insert = hp.p_insert if count_boxes() > hp.initial_boxes else 1.\\n for node in nodes:\\n try:\\n if node[1][\\\"shape\\\"] is \\\"square\\\":\\n if random.random() < p_insert:\\n insert_motif(hp, id=node[0])\\n except Exception as e:\\n log('exception in recurse', e)\",\n \"def test_randomize_mp_fits(self):\\n\\t\\tdetails = self.watcher.analyze(mp_fit=True, randomize=True, pool=True)\\n\\t\\tself.assertTrue((details.rand_sigma_mp < 1.10).all())\\n\\t\\tself.assertTrue((details.rand_sigma_mp > 0.96).all())\\n\\t\\tself.assertTrue((details.rand_num_spikes.to_numpy() < 80).all())\",\n \"def test_setup(sim):\\n n = 3\\n data = []\\n dt = 1\\n\\n for i in range(n):\\n sim.setup(timestep=dt, min_delay=dt)\\n p = sim.Population(1, sim.IF_curr_exp(i_offset=0.1))\\n p.record('v')\\n print('starting run ', i)\\n sim.run(10.0)\\n print('finished run ', i)\\n\\n print('start get_data run ', i)\\n data.append(p.get_data())\\n print('finished get_data run ', i)\\n\\n print('start sim end ', i)\\n sim.end()\\n print('finished sim end ', i)\\n\\n assert len(data) == n\\n for block in data:\\n assert len(block.segments) == 1\\n signals = block.segments[0].analogsignals\\n assert len(signals) == 1\\n assert_array_equal(signals[0], data[0].segments[0].analogsignals[0])\",\n \"def __init__(self,input_dict):\\n self.no_monomers = int(input_dict['Monomer types'])\\n self.occupancy = float(input_dict['Site occupancy fraction'])\\n \\n # Mole fraction of monomers\\n self.monomer_fraction = []\\n \\n # Functionality of monomers\\n self.functionality = []\\n \\n # Molecular weight of monomers\\n self.molecular_wt = []\\n \\n # Molecular volume of monomers\\n self.molecular_vol = []\\n \\n # Molecular hopping rates\\n self.hopping_rate = []\\n \\n # Interaction energy of monomers\\n self.eps_self = []\\n self.mean_eps_self = 0.0\\n \\n # Diffusivity factors\\n self.d_factor_monomers = []\\n \\n # Propagation rate constant of monomers\\n self.k_p = np.zeros((self.no_monomers,self.no_monomers))\\n \\n # Termination rate constant of monomers\\n self.k_t = np.zeros((self.no_monomers,self.no_monomers))\\n \\n # Termination rate constant of monomers\\n self.eps = np.zeros((self.no_monomers,self.no_monomers))\\n \\n # Probability distribution of monomers\\n self.prob_set = np.zeros(shape=(self.no_monomers))\\n \\n # Population of monomers added to the network\\n self.added_set = np.zeros(shape=(self.no_monomers))\\n \\n # Network size\\n self.network_size = 0\\n \\n for mo_type in range(0,self.no_monomers):\\n prefix = 'Monomer '+str(mo_type + 1)+': '\\n \\n self.monomer_fraction.append(float(input_dict[prefix+'Mole fraction']))\\n self.functionality.append(float(input_dict[prefix+'Functionality']))\\n self.molecular_wt.append(float(input_dict[prefix+'Molecular weight (in Da)']))\\n self.molecular_vol.append(float(input_dict[prefix+'Molecular volume (in Angstroms^3)']))\\n self.eps_self.append(float(input_dict[prefix+'Self interaction energy (in eV)']))\\n self.hopping_rate.append(float(input_dict[prefix+'Hopping rate']))\\n self.d_factor_monomers.append(1.0)\\n \\n self.mean_eps_self = sum(self.eps_self)/float(self.no_monomers)\\n \\n rate_constants_file = input_dict['Rate constants file']\\n self.read_rate_constants(rate_constants_file)\\n \\n term_constants_file = input_dict['Termination constants file']\\n self.read_term_constants(rate_constants_file)\\n \\n #interactions_file = input_dict['Interaction energy file']\\n #self.read_interactions(interactions_file)\\n \\n self.interaction_range = int(input_dict['Interaction range'])\\n \\n self.create_interaction_matrix()\",\n \"def test_clear(self):\\n from supvisors.statistics import StatisticsCompiler\\n compiler = StatisticsCompiler(self.supvisors)\\n # set data to a given address\\n for address, period_instance in compiler.data.items():\\n for period, instance in period_instance.items():\\n instance.counter = 28\\n instance.ref_stats = ('dummy', 0)\\n instance.cpu = [13.2, 14.8]\\n instance.mem = [56.4, 71.3, 68.9]\\n instance.io = {'eth0': (123465, 654321), 'lo': (321, 321)}\\n instance.proc = {('myself', 5888): (25.0, 12.5)}\\n # check clearance of instance\\n compiler.clear('10.0.0.2')\\n for address, period_instance in compiler.data.items():\\n if address == '10.0.0.2':\\n for period, instance in period_instance.items():\\n self.assertEqual(period / 5, instance.period)\\n self.assertEqual(10, instance.depth)\\n self.assertEqual(-1, instance.counter)\\n self.assertIsNone(instance.ref_stats)\\n self.assertIs(list, type(instance.cpu))\\n self.assertFalse(instance.cpu)\\n self.assertIs(list, type(instance.mem))\\n self.assertFalse(instance.mem)\\n self.assertIs(dict, type(instance.io))\\n self.assertFalse(instance.io)\\n self.assertIs(dict, type(instance.proc))\\n self.assertFalse(instance.proc)\\n else:\\n for period, instance in period_instance.items():\\n self.assertEqual(period / 5, instance.period)\\n self.assertEqual(10, instance.depth)\\n self.assertEqual(28, instance.counter)\\n self.assertTupleEqual(('dummy', 0), instance.ref_stats)\\n self.assertListEqual([13.2, 14.8], instance.cpu)\\n self.assertListEqual([56.4, 71.3, 68.9], instance.mem)\\n self.assertDictEqual({'eth0': (123465, 654321), 'lo': (321, 321)}, instance.io)\\n self.assertDictEqual({('myself', 5888): (25.0, 12.5)}, instance.proc)\",\n \"def simulate_universe():\\n\\n # untreated_survival is the probability to survive if not treated\\n # this is an exact law of the universe, the player will not have this information\\n untreated_survival = random.uniform(MIN_DISEASE_SURVIVAL, MAX_DISEASE_SURVIVAL)\\n\\n trials: list[Trial] = []\\n\\n treated_survivals: dict[Trial, float] = {}\\n\\n for _ in range(random.randint(MIN_TREATMENTS_COUNT, MAX_TREATMENTS_COUNT)):\\n group_size = random.randint(MIN_GROUP_SIZE, MAX_GROUP_SIZE)\\n\\n # treated_survival is the probability to survive if treated\\n # this is an exact law of the universe, the player will not have this information\\n # therefore it is stored in a separate dict and not in the given-to-player Trial object\\n treated_survival = random.uniform(MIN_TREATED_SURVIVAL, MAX_TREATED_SURVIVAL)\\n\\n trial = Trial(group_size, untreated_survival, treated_survival)\\n\\n trials.append(trial)\\n treated_survivals[trial] = treated_survival\\n\\n chosen_trial = playground.choose_trial(trials)\\n\\n if chosen_trial is None: # None means no treatment\\n chosen_survival = untreated_survival\\n else:\\n chosen_survival = treated_survivals[chosen_trial]\\n\\n return random.uniform(0, 1) <= chosen_survival\",\n \"def test_fit_many_comps():\\n\\n run_name = 'stationary'\\n savedir = 'temp_data/{}_expectmax_{}/'.format(PY_VERS, run_name)\\n mkpath(savedir)\\n data_filename = savedir + '{}_expectmax_{}_data.fits'.format(PY_VERS,\\n run_name)\\n # log_filename = 'temp_data/{}_expectmax_{}/log.log'.format(PY_VERS,\\n # run_name)\\n\\n logging.basicConfig(level=logging.INFO, filemode='w',\\n filename=log_filename)\\n uniform_age = 1e-10\\n sphere_comp_pars = np.array([\\n # X, Y, Z, U, V, W, dX, dV, age,\\n [-50,-50,-50, 0, 0, 0, 10., 5, uniform_age],\\n [ 50, 50, 50, 0, 0, 0, 10., 5, uniform_age],\\n ])\\n starcounts = [200,200]\\n ncomps = sphere_comp_pars.shape[0]\\n\\n # initialise z appropriately\\n # start = 0\\n # for i in range(ngroups):\\n # nstars_in_group = int(group_pars[i,-1])\\n # z[start:start+nstars_in_group,i] = 1.0\\n # start += nstars_in_group\\n\\n true_memb_probs = np.zeros((np.sum(starcounts), ncomps))\\n true_memb_probs[:200,0] = 1.\\n true_memb_probs[200:,1] = 1.\\n\\n synth_data = SynthData(pars=sphere_comp_pars, starcounts=starcounts,\\n Components=SphereComponent,\\n )\\n synth_data.synthesise_everything()\\n tabletool.convert_table_astro2cart(synth_data.table,\\n write_table=True,\\n filename=data_filename)\\n\\n origins = [SphereComponent(pars) for pars in sphere_comp_pars]\\n\\n init_memb_probs = np.ones(true_memb_probs.shape) / ncomps\\n\\n best_comps, med_and_spans, memb_probs = \\\\\\n expectmax.fit_many_comps(data=synth_data.table,\\n ncomps=ncomps,\\n rdir=savedir,\\n init_memb_probs=init_memb_probs,\\n trace_orbit_func=dummy_trace_orbit_func, )\\n\\n # compare fit with input\\n try:\\n assert np.allclose(true_memb_probs, memb_probs)\\n except AssertionError:\\n # If not close, check if flipping component order fixes things\\n memb_probs = memb_probs[:,::-1]\\n best_comps = best_comps[::-1]\\n assert np.allclose(true_memb_probs, memb_probs)\\n for origin, best_comp in zip(origins, best_comps):\\n assert (isinstance(origin, SphereComponent) and\\n isinstance(best_comp, SphereComponent))\\n o_pars = origin.get_pars()\\n b_pars = best_comp.get_pars()\\n\\n logging.info(\\\"origin pars: {}\\\".format(o_pars))\\n logging.info(\\\"best fit pars: {}\\\".format(b_pars))\\n assert np.allclose(origin.get_mean(),\\n best_comp.get_mean(),\\n atol=5.)\\n assert np.allclose(origin.get_sphere_dx(),\\n best_comp.get_sphere_dx(),\\n atol=2.)\\n assert np.allclose(origin.get_sphere_dv(),\\n best_comp.get_sphere_dv(),\\n atol=2.)\\n assert np.allclose(origin.get_age(),\\n best_comp.get_age(),\\n atol=1.)\",\n \"def test_scrublet_simulate_doublets():\\n pytest.importorskip(\\\"scrublet\\\")\\n\\n adata_obs = sc.datasets.pbmc3k()\\n sc.pp.filter_genes(adata_obs, min_cells=3)\\n sc.pp.filter_cells(adata_obs, min_genes=3)\\n adata_obs.layers['raw'] = adata_obs.X\\n sc.pp.normalize_total(adata_obs)\\n logged = sc.pp.log1p(adata_obs, copy=True)\\n\\n _ = sc.pp.highly_variable_genes(logged)\\n adata_obs = adata_obs[:, logged.var['highly_variable']]\\n\\n adata_sim = sce.pp.scrublet_simulate_doublets(adata_obs, layer='raw')\\n\\n assert 'doublet_parents' in adata_sim.obsm.keys()\",\n \"def test_subset_reconstruction_iterable(self, wires):\\n circuit = hadamard_circuit(wires)\\n bits, recipes = circuit()\\n shadow = ClassicalShadow(bits, recipes)\\n\\n # choose 1000 random indices\\n snapshots = np.random.choice(np.arange(10000, dtype=np.int64), size=1000, replace=False)\\n state = shadow.global_snapshots(snapshots=snapshots)\\n assert state.shape == (len(snapshots), 2**wires, 2**wires)\\n\\n # check the results against obtaining the full global snapshots\\n expected = shadow.global_snapshots()\\n for i, t in enumerate(snapshots):\\n assert np.allclose(expected[t], state[i])\",\n \"def test_getters():\\n\\n A = np.random.randint(2, size=(10, 10))\\n S = np.random.randint(2, size=10)\\n stocks = np.random.rand(10)\\n ones = np.ones(10)\\n # Dummy values\\n m = ExploitCore(A, S, stocks, ones, ones, ones, 0.5, 2.0)\\n assert (m.get_adjacency() == A).all()\\n assert (m.get_strategies() == S).all()\\n assert (m.get_stocks() == stocks).all()\\n assert m.get_time() == 0\",\n \"def experiment4():\\n np.random.seed()\\n state['result'] = np.random.rand(1)\",\n \"def sim_grasp_set_row(scene):\\n gripper_model = burg.gripper.Robotiq2F85()\\n\\n target_object = None\\n\\n found = False\\n while found == False:\\n print([obj.object_type.identifier for obj in scene.objects])\\n buffer_target_object = input(\\\"Wich object in the previous list do you want to grab ? \\\")\\n\\n i = 0\\n while i < len(scene.objects):\\n if (scene.objects[i].object_type.identifier == buffer_target_object):\\n target_object = scene.objects[i]\\n found = True\\n break\\n i += 1\\n if found == False :\\n print(\\\"the selected object is not in the list\\\")\\n\\n #test a grasp set and create a list of successful grasp\\n grasp_set, contact_points, normals, approach_vectors = create_antipodal_grasp_set(target_object)\\n sim = burg.sim.SceneGraspSimulator(target_object = target_object, gripper= gripper_model, scene=scene, verbose=False)\\n scores = sim.simulate_grasp_set(grasp_set)\\n successful_grasps = burg.grasp.GraspSet()\\n index_successfull = []\\n params_successfull = []\\n for index in range(len(scores)):\\n if scores[index] == 5:\\n successful_grasps.add(grasp_set[index].as_grasp_set())\\n index_successfull += [index]\\n params_successfull+=[[contact_points[index], normals[index], approach_vectors[index]]]\\n sim.dismiss()\\n\\n print(len(successful_grasps))\\n print(\\\"hellllo\\\")\\n\\n time.sleep(10)\\n\\n #Evaluate each successfull grasp\\n sim2 = burg.sim.SceneGraspSimulator(target_object = target_object, gripper= gripper_model, scene=scene, verbose=False)\\n results = []\\n for i, grasp in enumerate(successful_grasps):\\n n_graspset, n_contact_points, n_normals, n_approach_vectors = create_antipodal_noisy_grasp_set(target_object, grasp, params_successfull[i][0][0])\\n #n_graspset = perturbations.generate_perturb_grasp_set(grasp = grasp, nb_grasps = 50)\\n #burg.visualization.show_grasp_set(objects = [target_object.object_type.mesh], gs = n_graspset, gripper = gripper_model, with_plane = True)\\n params_noised = []\\n for k in range(len(n_contact_points)):\\n params_noised += [[n_contact_points[i], n_normals[i], n_approach_vectors[i]]]\\n scores = sim2.simulate_grasp_set(n_graspset)\\n robust = sum(scores)/len(scores)\\n #metric = quality.probability_force_closure(n_graspset, params_noised)\\n metric = quality.epsilon_quality(grasp, params_successfull[i] )\\n results += [[grasp, robust, metric]]\\n print([grasp, robust, metric])\\n #results+= [[grasp, metric]]\\n #results += [metric]\\n\\n print(results)\\n sim2.dismiss()\\n return results\",\n \"def independent(self):\\n return True\",\n \"def test_single_game_works(self):\\n sim = ss.Simulation(seed=154)\\n game1 = sim.single_game()\\n sim = ss.Simulation(seed=79)\\n game2 = sim.single_game()\\n assert game1 != game2, 'Your method single_game is not working.'\",\n \"def test_parameterisation_model(self):\\n self.topo.assign_force_field(self.ff)\\n for atom in self.topo.get_atoms(inc_alt_states=True):\\n if atom.element != 'H':\\n self.assertTrue(atom._ff_id is not None)\",\n \"def test_empty_structure():\\n empty = SME_Struct()\\n\\n assert isinstance(empty.version, str)\\n assert empty.teff is not None\\n assert empty.logg is not None\\n assert empty.vmic == 0\\n assert empty.vmac == 0\\n assert empty.vsini == 0\\n\\n assert empty.nseg == 0\\n assert empty.wave is None\\n assert empty.spec is None\\n assert empty.uncs is None\\n assert empty.synth is None\\n assert empty.cont is None\\n assert empty.mask is None\\n assert empty.mask_good is None\\n assert empty.mask_bad is None\\n # assert empty.mask_line is None\\n # assert empty.mask_continuum is None\\n\\n assert empty.cscale.shape == (0, 1)\\n assert empty.vrad.shape == (0,)\\n assert empty.cscale_flag == \\\"none\\\"\\n assert empty.vrad_flag == \\\"none\\\"\\n assert empty.cscale_degree == 0\\n\\n assert empty.mu is not None\\n assert empty.nmu == 7\\n\\n # assert empty.md5 is not None\\n\\n assert empty.linelist is not None\\n assert empty.species is not None\\n assert len(empty.species) == 0\\n assert empty.atomic is not None\\n\\n assert empty.monh == 0\\n assert not np.isnan(empty[\\\"abund Fe\\\"])\\n assert empty.abund[\\\"H\\\"] == 12\\n assert not np.isnan(empty.abund()[\\\"Mg\\\"])\\n\\n assert empty.system_info is not None\\n assert empty.system_info.arch == \\\"\\\"\\n\\n assert len(empty.fitparameters) == 0\\n assert empty.fitresults is not None\\n assert empty.fitresults.covariance is None\\n\\n assert empty.atmo is not None\\n assert empty.atmo.depth is None\\n\\n assert empty.nlte is not None\\n assert empty.nlte.elements == []\",\n \"def test_serializing_solvers(self):\\n ratio = 0.5\\n l_enet = 1e-2\\n sd = ratio * l_enet\\n\\n proxes = [\\n ProxZero(),\\n ProxTV(2),\\n ProxL1(2),\\n ProxGroupL1(strength=1, blocks_start=[0, 3, 8],\\n blocks_length=[3, 5, 2])\\n ]\\n solvers = [\\n AdaGrad(step=1e-3, max_iter=100, verbose=False, tol=0),\\n SGD(step=1e-3, max_iter=100, verbose=False, tol=0),\\n SDCA(l_l2sq=sd, max_iter=100, verbose=False, tol=0),\\n SAGA(step=1e-3, max_iter=100, verbose=False, tol=0),\\n SVRG(step=1e-3, max_iter=100, verbose=False, tol=0)\\n ]\\n model_map = {\\n ModelLinReg: SimuLinReg,\\n ModelLogReg: SimuLogReg,\\n ModelPoisReg: SimuPoisReg,\\n ModelHinge: SimuLogReg,\\n ModelQuadraticHinge: SimuLogReg,\\n ModelSmoothedHinge: SimuLogReg,\\n ModelAbsoluteRegression: SimuLinReg,\\n ModelEpsilonInsensitive: SimuLinReg,\\n ModelHuber: SimuLinReg,\\n ModelLinRegWithIntercepts: SimuLinReg,\\n ModelModifiedHuber: SimuLogReg\\n }\\n\\n for solver in solvers:\\n for mod in model_map:\\n for prox in proxes:\\n\\n np.random.seed(12)\\n n_samples, n_features = 100, 5\\n w0 = np.random.randn(n_features)\\n intercept0 = 50 * weights_sparse_gauss(n_weights=n_samples,\\n nnz=30)\\n c0 = None\\n X, y = SimuLinReg(w0, c0, n_samples=n_samples, verbose=False,\\n seed=2038).simulate()\\n if mod == ModelLinRegWithIntercepts:\\n y += intercept0\\n\\n model = mod(fit_intercept=False).fit(X, y)\\n\\n # prox = ProxZero() #(2.)\\n solver.set_model(model)\\n solver.set_prox(prox)\\n\\n pickled = pickle.loads(pickle.dumps(solver))\\n\\n self.assertTrue(solver._solver.compare(pickled._solver))\\n\\n self.assertTrue(\\n solver.model._model.compare(pickled.model._model))\\n\\n self.assertTrue(solver.prox._prox.compare(pickled.prox._prox))\\n\\n if mod == ModelLinRegWithIntercepts:\\n test_vector = np.hstack((X[0], np.ones(n_samples)))\\n self.assertEqual(\\n model.loss(test_vector),\\n solver.model.loss(test_vector))\\n else:\\n self.assertEqual(model.loss(X[0]), solver.model.loss(X[0]))\",\n \"def simulation(nepisodes):\\n # Initialize robots\\n # print('I am inside the simulation')\\n agents = [] # List containing all robots\\n a1 = Agent(start = [0, 0], end = [grid_size-1, grid_size-1], nr = 1) # Create agent 1\\n a2 = Agent(start = [0, grid_size-1], end = [grid_size-1, 0], nr = 2) # Create agent 2\\n a3 = Agent(start = [grid_size-1, 0], end = [0, grid_size-1], nr = 3) # Create agent 3\\n a4 = Agent(start = [grid_size-1, grid_size-1], end = [0, 0], nr = 4) # Create agent 4\\n agents.append(a1)\\n agents.append(a2)\\n agents.append(a3)\\n agents.append(a4)\\n\\n # for agent in agents:\\n # agent.load_target('target_weights_{}.h5'.format(agent.nr))\\n # agent.load_policy('policy_weights_{}.h5'.format(agent.nr))\\n # print('loaded')\\n\\n steps_list = [[] for i in range(len(agents))]\\n reward_list = [[] for i in range(len(agents))]\\n cumulative_rewards = [[] for i in range(len(agents))]\\n collisions_list = [[] for i in range(len(agents))]\\n\\n t = 0 # Set time to zero\\n for i in range(nepisodes):\\n t = episode(agents, t, i+1) # Run one episode\\n\\n print('End of episode ', i+1)\\n agent_index = 0\\n for agent in agents:\\n steps_list[agent_index].append(agent.steps)\\n reward_list[agent_index].append(agent.reward)\\n collisions_list[agent_index].append(agent.collisions)\\n if i == 0:\\n cumulative_rewards[agent_index].append(agent.reward)\\n else:\\n cumulative_rewards[agent_index].append(agent.reward + cumulative_rewards[agent_index][i-1])\\n agent_index += 1\\n\\n if i % 1000 == 0:\\n with open('reward_4_agents_{}'.format(i),'wb') as f:\\n pickle.dump(reward_list,f)\\n\\n with open('steps_4_agents_{}'.format(i), 'wb') as f:\\n pickle.dump(steps_list, f)\\n\\n with open('cols_4_agents_{}'.format(i), 'wb') as f:\\n pickle.dump(collisions_list, f)\\n\\n\\n return steps_list, reward_list, collisions_list, cumulative_rewards\",\n \"def test_bothE_traversals(self):\\r\\n results = self.jon.bothE()\\r\\n assert len(results) == 2\\r\\n assert self.jon_physics in results\\r\\n assert self.jon_in_beekeeping in results\",\n \"def experiment(self) -> Any:\",\n \"def test_createData():\\n\\n sys = LVsystem.Ecosystem()\\n\\n sys.addSpecies('rabbit')\\n sys.setInteraction('rabbit', 'hen', 0)\\n sys.setInteraction('rabbit', 'fox', -1)\\n sys.setInitialCond('rabbit', 30)\\n sys.setGrowthRate('rabbit', 0.09)\\n sys.setCarrCap('rabbit', 10000)\\n sys.setChangeRate('rabbit', 400)\\n\\n sys.addSpecies('hen')\\n sys.setInteraction('hen', 'rabbit', 0)\\n sys.setInteraction('hen', 'fox', -1)\\n sys.setInitialCond('hen', 10)\\n sys.setGrowthRate('hen', 0.07)\\n sys.setCarrCap('hen', 10000)\\n sys.setChangeRate('hen', 500)\\n\\n sys.addSpecies('fox')\\n sys.setInteraction('fox', 'rabbit', 1)\\n sys.setInteraction('fox', 'hen', 1)\\n sys.setInitialCond('fox', 20)\\n sys.setGrowthRate('fox', -0.06)\\n sys.setCarrCap('fox', 1)\\n sys.setChangeRate('fox', 250)\\n\\n \\n data = sys.create_data()\\n \\n assert data[0] == 3\\n assert data[1] == ['rabbit', 'hen', 'fox']\\n assert data[2] == [30,10,20]\\n assert data[3] == [0.09,0.07,-0.06] \\n assert data[4] == [10000,10000,1]\\n assert data[5] == [400,500,250]\\n assert data[6][1][2] == -data[6][2][1]\\n assert data[6][2][2] == 0\\n \\n sys.removeSpecies('rabbit')\\n sys.removeSpecies('fox')\\n sys.removeSpecies('hen')\",\n \"def none_model_vars():\\n obj_fun = None\\n time_sol = None\\n gap = None\\n s_pos = None\\n b_target = None\\n threads = None\\n\\n return obj_fun, time_sol, gap, s_pos, b_target, threads\",\n \"def test_order(self):\\n n = 3\\n was_called = n*[False]\\n class Mock(object):\\n def __init__(self, i):\\n self.i = i\\n\\n def evolve(self1, t, dt):\\n was_called[self1.i] = True\\n self.assertTrue(all(was_called[:self1.i]))\\n\\n sim = simulation.Simulation(*[Mock(_) for _ in xrange(n)])\\n sim.run(sim.dt)\",\n \"def nits(self):\",\n \"def test_duplicate(self):\\n test_file = os.path.join(INPUT_HYPM_PATH, 'unit_364-2013-225-1-0.mdd')\\n\\n mdd.procall([test_file])\\n \\n self.compare_node58()\",\n \"def prepare_simulation(master_seed, n_populations):\\n nest.ResetKernel()\\n # set global kernel parameters\\n nest.SetKernelStatus(\\n {\\\"communicate_allgather\\\": sim.allgather,\\n \\\"overwrite_files\\\": sim.overwrite_existing_files,\\n \\\"resolution\\\": sim.dt,\\n \\\"total_num_virtual_procs\\\": sim.n_vp})\\n if sim.to_text_file:\\n nest.SetKernelStatus({\\\"data_path\\\": data_path_test})\\n \\n # Set random seeds\\n \\n # PYNEST\\n #nest.sli_run('0 << /rngs [%i %i] Range { rngdict/gsl_mt19937 :: exch CreateRNG } Map >> SetStatus'%(\\n # master_seed, master_seed + sim.n_vp - 1))\\n #nest.SetKernelStatus({\\\"rng_seeds\\\" : range(master_seed, master_seed + sim.n_vp)})\\n #nest.sli_run('0 << /grng rngdict/gsl_mt19937 :: %i CreateRNG >> SetStatus'%(master_seed + sim.n_vp))\\n #nest.SetKernelStatus({\\\"grng_seed\\\" : master_seed + sim.n_vp})\\n #pyrngs = [np.random.RandomState(s) for s in \\n # range(master_seed + sim.n_vp + 1, master_seed + 2 * sim.n_vp + 1)]\\n\\n # SLI VERSION\\n sli_str = \\\"0 << \\\\n\\\"\\n #sli_str += \\\"/rngs %i [0 %i 1 sub] add Range { rngdict/gsl_mt19937 :: exch CreateRNG } Map\\\\n\\\"%(master_seed, sim.n_vp) # local RNG, seeded\\n #sli_str += \\\"/grng rngdict/gsl_mt19937 :: %i %i add CreateRNG\\\\n\\\"%(master_seed, sim.n_vp) # global RNG\\n sli_str += \\\"/rng_seeds %i [0 %i 1 sub] add Range\\\\n\\\"%(master_seed, sim.n_vp) # local RNG seeds\\n sli_str += \\\"/grng_seed %i %i add\\\\n\\\"%(master_seed, sim.n_vp) # global RNG seed\\n sli_str += \\\">> SetStatus\\\"\\n nest.sli_run(sli_str)\\n sli_str2 = \\\"/script_rngs [%i]\\\\n\\\"%sim.n_vp\\n sli_str2 += \\\"{%i add rngdict /gsl_mt19937 get exch CreateRNG } Table def\\\\n\\\"%(master_seed + sim.n_vp)\\n sli_str2 += \\\"/normal_rdvs script_rngs { rdevdict /normal get CreateRDV } Map def\\\"\\n nest.sli_run(sli_str2)\\n pyrngs = None\\n return pyrngs\",\n \"def test_perfectModelEnsemble_init_da(PM_ds_initialized_1d):\\n pm = PerfectModelEnsemble(PM_ds_initialized_1d.tos)\\n assert pm\",\n \"def ncore(self):\",\n \"def test_molecule_loop_no_data(self):\\n\\n # Reset relax.\\n reset()\\n\\n # Add a data pipe to the data store.\\n ds.add(pipe_name='orig', pipe_type='mf')\\n\\n # Loop over the molecules.\\n i = 0\\n for molecule in mol_res_spin.molecule_loop():\\n i = i + 1\\n\\n # Test loop length.\\n self.assertEqual(i, 0)\",\n \"def generate(\\n seeds=10,\\n param_num_nodes=7,\\n mode='train',\\n param_dim=10,\\n param_sel=100,\\n param_mu=10,\\n param_br=0.05,\\n param_activity_wt=None,\\n A=None,\\n sp_to_id=None,\\n min_coord=None,\\n max_coord=None,\\n org_pts=None,\\n ):\\n global dim, sel, mu, br, activity_wt, tree_lc, tree_rc, num_nodes\\n\\n dim=param_dim\\n sel=param_sel\\n mu=param_mu\\n br=param_br\\n activity_wt=param_activity_wt\\n num_nodes = param_num_nodes\\n\\n sp_root = 0\\n tree = None\\n\\n if mode == 'train':\\n tree, tree_lc, tree_rc = generate_tree(sp_root, num_nodes)\\n if param_activity_wt is None:\\n # weights for the linear activity function\\n num_wts = int(((dim * (dim + 1))/2) + 1)\\n activity_wt = np.random.normal(0, 1, num_wts)\\n\\n if org_pts is None:\\n org_pts = []\\n # simulate data points\\n # format: exampleID, species, values\\n # region, species, coord1, coord2, ...., activity_value\\n\\n for i in tqdm(range(int(seeds))):\\n pt_id = i\\n\\n # pick a random point of d-dimension\\n rand_pt = np.random.uniform(min_coord, max_coord, dim)\\n curr_pt = np.append([pt_id, sp_root], rand_pt)\\n curr_activity = get_activity(modify_pt(rand_pt), activity_wt)\\n # print('curr_pt:', curr_pt, 'curr_activity:', curr_activity); exit(0)\\n org_pts.append(np.append(curr_pt, curr_activity))\\n\\n generated_points = []\\n full_org_pts = []\\n\\n if mode == 'train':\\n pool = Pool(16)\\n sample_bag = pool.map(generate_bag, org_pts)\\n for item in sample_bag:\\n for val in item:\\n val = list(val)\\n full_org_pts.append(val)\\n generated_points.append(val[:2]+modify_pt(val[2:-1])+[val[-1]])\\n else:\\n for val in org_pts:\\n val = list(val)\\n generated_points.append(val[:2]+modify_pt(val[2:-1])+[val[-1]])\\n\\n return generated_points, activity_wt, org_pts, full_org_pts, tree\",\n \"def solution(self) -> State:\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62294304","0.5881555","0.5799523","0.5603874","0.5549049","0.55471927","0.5523414","0.55085677","0.5462701","0.54552025","0.54473364","0.5418987","0.5409605","0.54048645","0.5397913","0.5395056","0.53824604","0.53729665","0.53604305","0.535905","0.535402","0.53459674","0.5342061","0.5338783","0.5338299","0.5330204","0.5315376","0.5310694","0.53080636","0.53077054","0.5307604","0.53001386","0.53000486","0.52976155","0.5296482","0.5286949","0.52734643","0.526898","0.52682656","0.526406","0.5239242","0.5238306","0.5236805","0.523653","0.52342206","0.52296776","0.5227946","0.5221033","0.52183276","0.521822","0.52090883","0.5208045","0.5206338","0.5205761","0.52033246","0.519695","0.518926","0.5180175","0.51753825","0.5174641","0.51690036","0.5164724","0.51556885","0.51537895","0.5150561","0.5148141","0.51432526","0.51358813","0.5116993","0.51056975","0.510137","0.5100057","0.5097287","0.5091948","0.5088143","0.50834423","0.50675","0.5067474","0.5064519","0.50584126","0.50580305","0.50535214","0.50534064","0.5052544","0.50453496","0.50411016","0.50386566","0.5038581","0.50372666","0.5035926","0.5035543","0.50354517","0.5029772","0.50289667","0.5026618","0.5025631","0.50195867","0.5017724","0.501725","0.5009026"],"string":"[\n \"0.62294304\",\n \"0.5881555\",\n \"0.5799523\",\n \"0.5603874\",\n \"0.5549049\",\n \"0.55471927\",\n \"0.5523414\",\n \"0.55085677\",\n \"0.5462701\",\n \"0.54552025\",\n \"0.54473364\",\n \"0.5418987\",\n \"0.5409605\",\n \"0.54048645\",\n \"0.5397913\",\n \"0.5395056\",\n \"0.53824604\",\n \"0.53729665\",\n \"0.53604305\",\n \"0.535905\",\n \"0.535402\",\n \"0.53459674\",\n \"0.5342061\",\n \"0.5338783\",\n \"0.5338299\",\n \"0.5330204\",\n \"0.5315376\",\n \"0.5310694\",\n \"0.53080636\",\n \"0.53077054\",\n \"0.5307604\",\n \"0.53001386\",\n \"0.53000486\",\n \"0.52976155\",\n \"0.5296482\",\n \"0.5286949\",\n \"0.52734643\",\n \"0.526898\",\n \"0.52682656\",\n \"0.526406\",\n \"0.5239242\",\n \"0.5238306\",\n \"0.5236805\",\n \"0.523653\",\n \"0.52342206\",\n \"0.52296776\",\n \"0.5227946\",\n \"0.5221033\",\n \"0.52183276\",\n \"0.521822\",\n \"0.52090883\",\n \"0.5208045\",\n \"0.5206338\",\n \"0.5205761\",\n \"0.52033246\",\n \"0.519695\",\n \"0.518926\",\n \"0.5180175\",\n \"0.51753825\",\n \"0.5174641\",\n \"0.51690036\",\n \"0.5164724\",\n \"0.51556885\",\n \"0.51537895\",\n \"0.5150561\",\n \"0.5148141\",\n \"0.51432526\",\n \"0.51358813\",\n \"0.5116993\",\n \"0.51056975\",\n \"0.510137\",\n \"0.5100057\",\n \"0.5097287\",\n \"0.5091948\",\n \"0.5088143\",\n \"0.50834423\",\n \"0.50675\",\n \"0.5067474\",\n \"0.5064519\",\n \"0.50584126\",\n \"0.50580305\",\n \"0.50535214\",\n \"0.50534064\",\n \"0.5052544\",\n \"0.50453496\",\n \"0.50411016\",\n \"0.50386566\",\n \"0.5038581\",\n \"0.50372666\",\n \"0.5035926\",\n \"0.5035543\",\n \"0.50354517\",\n \"0.5029772\",\n \"0.50289667\",\n \"0.5026618\",\n \"0.5025631\",\n \"0.50195867\",\n \"0.5017724\",\n \"0.501725\",\n \"0.5009026\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53972495"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":8462,"cells":{"query":{"kind":"string","value":"Check if the given data is not filled already in the bag"},"document":{"kind":"string","value":"def is_coord_empty(self, data):\n check = False\n if data[\"topic\"] in DRONE_POS_TOPICS:\n check = self.drone.check_if_pos(data[\"coord\"])\n elif data[\"topic\"] in DRONE_VEL_TOPICS:\n check = self.drone.check_if_vel(data[\"coord\"])\n elif data[\"topic\"] in DRONE_ACC_TOPICS:\n check = self.drone.check_if_acc(data[\"coord\"])\n elif data[\"topic\"] in SUBJECT_TOPICS:\n check = self.subject.check_if_pos(data[\"coord\"])\n elif data[\"topic\"] in self.PEDESTRIAN_TOPICS:\n check = self.peds[data[\"pid\"]].check_if_pos(data[\"coord\"])\n return check"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def has_data(self):\n return ([0] != self.__contexts) and ([0] != self.__weights)","def empty(self):\n return 0 >= len(self.__data)","def verify_if_basket_is_empty(self):\n self._basket.verify_if_basket_is_empty()","def not_empty(entry):\n gt_boxes = entry['boxes']\n return gt_boxes.shape[0] > 0","def has_data(self):\n return len(self.data) > 0","def tied(self):\n for (x, y) in self.fields:\n if self.fields[x, y] == self.empty:\n return False\n return True","def is_empty(self):","def is_empty(self):","def is_data(i):\n keys = ['_id', '_time']\n return all(i != k for k in keys)","def is_empty(self):\n return len(self.data) == 0","def is_empty(self):\r\n return self.buff==[]","def is_empty(self):\n for key, dataset in self.datasets.items():\n try:\n has_data = dataset.has_data()\n except MFDataException as mfde:\n raise MFDataException(\n mfdata_except=mfde,\n model=self._container_package.model_name,\n package=self._container_package._get_pname(),\n message=\"Error occurred while verifying\"\n ' data of dataset \"{}\" in block '\n '\"{}\"'.format(dataset.structure.name, self.structure.name),\n )\n\n if has_data is not None and has_data:\n return False\n return True","def f_is_empty(self):\n return self._data is None","def empty(self):\n return not self.mystack1 and not self.mystack2","def filled(self):\n return self.list.count(0) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def is_empty(self):\n return len(self._data) == 0","def check_unique(self, data: Union['LedGroup', 'Sequencer', 'Step'], datatype: str, seq: Optional['Sequencer']) \\\n -> bool:\n if datatype == 'LedGroup':\n return data.Name.lower() not in self.get_group_list()\n elif datatype == 'Sequencer':\n return data.Name.lower() not in self.get_seq_names()\n else:\n if seq is not None:\n return data.Name.lower() not in seq.get_steps_names()\n return False","def its_empty(self) -> bool:\n return self.items == []","def f_is_empty(self):\n return len(self._data) == 0","def empty(self) -> bool:\n return not bool(self.data)","def checkIfThereIsData(self, i):\n _, amountOfThings = self.weaviate.Get(\"/\" + i)\n if len(amountOfThings[i]) == 0:\n return False\n else:\n return True","def is_empty(self):\n return not self.size()","def add_data(self, data):\n for i, row in enumerate(self._grid):\n for j, column in enumerate(row):\n if self._grid[i][j] is None:\n self._grid[i][j] = data\n return True\n return False","def store_in_bag(self, data):\n # timestamp is (s, nanos): data[\"ts\"], data[\"tnanos\"]\n\n self.bag.add(data)\n\n # Ensure that all data have the same timestamp and are not None\n # Also there can't be more than a sample per second.\n if self.bag.is_full():\n if random() > 0.99999:\n print(\"Telemetry data: \", data[\"topic\"])\n print(\"Bag data: \", self.bag.print_data())\n\n # Then flush the data to process it and empty the bag\n data = self.bag.get_data()\n self.on_full(data)","def validate(self, data: Dict):\n for key in self.__dict__.keys():\n if not key.startswith('__') and key != 'id':\n if data[key] == '' or data[key] is None:\n raise ValidationError(\n message=f'{key} should not be \"{data[key]}\"'\n )","def has_data(self, *args, **kwargs):\n return False","def empty(self) -> bool:\n return self.data.empty()","def is_empty(self):\n if len(self._data) == 0:\n return True\n return False","def _check_data_valid(self):\n\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\n if not is_valid:\n raise FITSException(f\"No data in {self.survey}\")","def _check(data, path):\n if 'loading' in data:\n empties = (k for k, v in data.items() if not v)\n for empty in empties:\n logging.info('No data collected for %s in file %s.', empty, path)\n if 'errors' in data:\n logging.warning('\\n'.join(data['errors']))","def is_empty(self) -> bool:","def test_overFill(self):\r\n high = 15\r\n for _ in range(high):\r\n self.nb.add(_)\r\n\r\n self.assertFalse(self.nb.isEmpty())\r\n self.assertTrue(self.nb.isFull())\r\n self.assertEqual(5, len(self.nb))\r\n\r\n # check all are still present\r\n for _ in range(high-1, high - SIZE-1, -1):\r\n self.assertTrue(_ in self.nb)","def empty(self) -> bool:\n return self.data.get_size() == 0","def _check_onehot_data(self, data):\n if data.ndim > 1 and np.equal(data ** 2, data).all():\n shp = (data.shape[0],) + data.shape[2:]\n if np.equal(np.ones(shp), data.sum(axis=1)).all():\n return True\n return False","def empty(self) -> bool:","def has_data(self):\n return self._data is not None","def check_Data(self):\r\n \r\n if self._target_data is None:\r\n self.processData()","def isExist(data):\n return True/False","def derives_empty(self):\n if self.known_to_derive_empty:\n return True\n for item in self.first():\n if item.is_empty():\n self.known_to_derive_empty = True\n return True\n return False","def _is_empty(self):\n return self.size == 0","def _check_already_present(self, new_da):\n for da in self:\n self._id_of_DataArrays_equal(da, new_da)","def _is_missing(self, item):\n pass","def is_empty(self):\n return self.items == []","def is_empty(self) -> bool:\n return len(self._data) == 0","def _isvalid(self, data):\n if data is None:\n return False\n elif isinstance(data, (list,tuple)):\n if len(data) <= 0:\n return False\n else:\n return True\n elif isinstance(data, (np.ndarray)):\n if data.size <= 0:\n return False\n else:\n return True\n elif not data:\n return False\n else:\n return True","def __validate_node_data(self, data):\n\n # skipping check of 'grapheap_node_id' optimisation key\n if all(key in data for key in self.optimisation_keys[1:]):\n return True\n\n else:\n missing_keys = [\n x for x in self.optimisation_keys[1:] if x not in data]\n raise ValueError(\"Grapheap Error: \" + str(missing_keys) +\n \" optimisation keys missing in data\")","def is_empty(self):\n return self.count == 0","def is_empty(self):\n return self.count == 0","def is_populated(self) -> bool:\n return 0 < self.count_compounds()","def exists(self, data):\n return data in self.__stack","def is_empty(self):\n return self.size == []","def is_empty(self):\n\n # If the queue is an empty list, self._data would return False\n # So if the queue is empty we want to return true\n # modify with not self._data\n return not self._data","def is_empty(self):\n return len(self) == 0","def check(self, data):# ->bool:\r\n return check(self.gd, data)","def is_empty( self ):\n \n return len(self.__deck) == 0","def empty(self):\n return len(self.deck) == 0","def is_empty(self):\n return not (\n self.has_label\n or self.has_name\n or self.has_points\n or self.has_attributes\n )","def is_consistent_dataset(data: dict, parameter_box: np.ndarray = None) -> bool:\n train_set = copy.deepcopy(data)\n y, phi = train_set[\"outputs\"].pop(-1), train_set[\"features\"].pop(-1)\n y, phi = np.array(y)[..., np.newaxis], np.array(phi)[..., np.newaxis]\n if train_set[\"outputs\"] and train_set[\"features\"]:\n theta, _, gramian, beta = confidence_polytope(train_set, parameter_box=parameter_box)\n return is_valid_observation(y, phi, theta, gramian, beta)\n else:\n return True","def is_empty(self): # -> bool:\n ...","def is_empty(self):\n # type: () -> bool\n # If the Histogram contains at least one value, at least one element of\n # self.counts will be not null\n return not any(self.counts)","def is_valid(self, dataset):\n pass","def has_data(self) -> bool:\n raise NotImplementedError","def is_empty(self) -> bool:\n return self._symmetries == [] or self._sq_paulis == [] or self._sq_list == []","def empty(self):\n return len(self.a) + len(self.b) == 0","def empty(self) -> bool:\n return self.q1==[]","def isTileBlank(tile):\n for b in tile:\n if b: return False\n return True","def is_empty(self, trace) -> bool:\n return len(trace) == 0","def check_empty(self):\n if self.size():\n raise AttributeError","def empty(self):\n return self.value == []","def verify_queue_empty(self):\n self.assert_sample_queue_size(DataParticleType.VELOCITY_PARTICLE, 0)\n self.assert_sample_queue_size(DataParticleType.TIME_PARTICLE, 0)","def has_data(self):\n\n return self._data is not None","def has_data(self):\n\n return self._data is not None","def is_empty(self):\n return len(self.items) == 0","def is_empty(self):\n return len(self.values) == 0","def is_empty(self):\n return self._sum() == 0","def is_empty(self):\r\n return len(self) == 0","def is_empty(self):\n\n return self.items == []","def is_empty(self):\n\n return self.items == []","def is_empty(self):\n return self._size == 0","def empty(self) -> bool:\n return self.sk1_len==0","def is_empty(self):\n return (self.counter == 0)","def is_empty(self):\n #return not self.vulnerable_in and not self.fixed_in\n return not self.fixed_in","def _isEmpty(self, x, y):\n\t\treturn self.getValue(x, y) == None","def is_empty(self):\n return False","def is_empty(self):\n return False","def is_empty(self):\n return False","def is_empty(self):\n return False","def is_empty(self):\n return False","def is_empty(self):\n return False","def is_empty(self):\n return len(self) == 0","def is_empty(self):\n return self.__size == 0","def checkOpsimData(self, otherdata=None):\n data2check = self.opsim_data\n if otherdata:\n data2check = otherdata\n if type(data2check) != list:\n if type(data2check) == dict:\n if set(self._opsim_keys).issubset(set(data2check.keys())):\n return True, 'good'\n else:\n return False, 'Data is not complete'\n else:\n return False, 'Data is not a list nor a dictionary'\n else:\n nodict = 0\n baddict = 0\n for data in data2check:\n if type(data) != dict:\n nodict += 1\n else:\n if not set(self._opsim_keys).issubset(set(data.keys())):\n baddict += 1\n if (nodict == 0 and baddict == 0):\n return True, 'good'\n else:\n statement = '{0} non dictionary element(s) and {1} uncomplete\\\n dictionary(ies) in data list'.format(nodict, baddict)\n return (False, statement)","def is_empty(self):\n return self.id is None or self.nb_cart_items == 0","def missing_data(self, data):\n missing_fields = []\n for key in data:\n if not key in request.json:\n missing_fields.append(key)\n if missing_fields:\n message = 'Missing ' + ', '.join(missing_fields)\n return self.bad_request(message)\n return None","def validate(self, data):\n if HashTag.objects.filter(name=data[\"hash_tag\"][\"name\"]).first() is None:\n raise serializers.ValidationError(\n {\"hash_tag\": data[\"hash_tag\"][\"name\"] + \" \" + _(\"HashTag does not exist.\")}\n )\n\n return data"],"string":"[\n \"def has_data(self):\\n return ([0] != self.__contexts) and ([0] != self.__weights)\",\n \"def empty(self):\\n return 0 >= len(self.__data)\",\n \"def verify_if_basket_is_empty(self):\\n self._basket.verify_if_basket_is_empty()\",\n \"def not_empty(entry):\\n gt_boxes = entry['boxes']\\n return gt_boxes.shape[0] > 0\",\n \"def has_data(self):\\n return len(self.data) > 0\",\n \"def tied(self):\\n for (x, y) in self.fields:\\n if self.fields[x, y] == self.empty:\\n return False\\n return True\",\n \"def is_empty(self):\",\n \"def is_empty(self):\",\n \"def is_data(i):\\n keys = ['_id', '_time']\\n return all(i != k for k in keys)\",\n \"def is_empty(self):\\n return len(self.data) == 0\",\n \"def is_empty(self):\\r\\n return self.buff==[]\",\n \"def is_empty(self):\\n for key, dataset in self.datasets.items():\\n try:\\n has_data = dataset.has_data()\\n except MFDataException as mfde:\\n raise MFDataException(\\n mfdata_except=mfde,\\n model=self._container_package.model_name,\\n package=self._container_package._get_pname(),\\n message=\\\"Error occurred while verifying\\\"\\n ' data of dataset \\\"{}\\\" in block '\\n '\\\"{}\\\"'.format(dataset.structure.name, self.structure.name),\\n )\\n\\n if has_data is not None and has_data:\\n return False\\n return True\",\n \"def f_is_empty(self):\\n return self._data is None\",\n \"def empty(self):\\n return not self.mystack1 and not self.mystack2\",\n \"def filled(self):\\n return self.list.count(0) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def is_empty(self):\\n return len(self._data) == 0\",\n \"def check_unique(self, data: Union['LedGroup', 'Sequencer', 'Step'], datatype: str, seq: Optional['Sequencer']) \\\\\\n -> bool:\\n if datatype == 'LedGroup':\\n return data.Name.lower() not in self.get_group_list()\\n elif datatype == 'Sequencer':\\n return data.Name.lower() not in self.get_seq_names()\\n else:\\n if seq is not None:\\n return data.Name.lower() not in seq.get_steps_names()\\n return False\",\n \"def its_empty(self) -> bool:\\n return self.items == []\",\n \"def f_is_empty(self):\\n return len(self._data) == 0\",\n \"def empty(self) -> bool:\\n return not bool(self.data)\",\n \"def checkIfThereIsData(self, i):\\n _, amountOfThings = self.weaviate.Get(\\\"/\\\" + i)\\n if len(amountOfThings[i]) == 0:\\n return False\\n else:\\n return True\",\n \"def is_empty(self):\\n return not self.size()\",\n \"def add_data(self, data):\\n for i, row in enumerate(self._grid):\\n for j, column in enumerate(row):\\n if self._grid[i][j] is None:\\n self._grid[i][j] = data\\n return True\\n return False\",\n \"def store_in_bag(self, data):\\n # timestamp is (s, nanos): data[\\\"ts\\\"], data[\\\"tnanos\\\"]\\n\\n self.bag.add(data)\\n\\n # Ensure that all data have the same timestamp and are not None\\n # Also there can't be more than a sample per second.\\n if self.bag.is_full():\\n if random() > 0.99999:\\n print(\\\"Telemetry data: \\\", data[\\\"topic\\\"])\\n print(\\\"Bag data: \\\", self.bag.print_data())\\n\\n # Then flush the data to process it and empty the bag\\n data = self.bag.get_data()\\n self.on_full(data)\",\n \"def validate(self, data: Dict):\\n for key in self.__dict__.keys():\\n if not key.startswith('__') and key != 'id':\\n if data[key] == '' or data[key] is None:\\n raise ValidationError(\\n message=f'{key} should not be \\\"{data[key]}\\\"'\\n )\",\n \"def has_data(self, *args, **kwargs):\\n return False\",\n \"def empty(self) -> bool:\\n return self.data.empty()\",\n \"def is_empty(self):\\n if len(self._data) == 0:\\n return True\\n return False\",\n \"def _check_data_valid(self):\\n\\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\\n if not is_valid:\\n raise FITSException(f\\\"No data in {self.survey}\\\")\",\n \"def _check(data, path):\\n if 'loading' in data:\\n empties = (k for k, v in data.items() if not v)\\n for empty in empties:\\n logging.info('No data collected for %s in file %s.', empty, path)\\n if 'errors' in data:\\n logging.warning('\\\\n'.join(data['errors']))\",\n \"def is_empty(self) -> bool:\",\n \"def test_overFill(self):\\r\\n high = 15\\r\\n for _ in range(high):\\r\\n self.nb.add(_)\\r\\n\\r\\n self.assertFalse(self.nb.isEmpty())\\r\\n self.assertTrue(self.nb.isFull())\\r\\n self.assertEqual(5, len(self.nb))\\r\\n\\r\\n # check all are still present\\r\\n for _ in range(high-1, high - SIZE-1, -1):\\r\\n self.assertTrue(_ in self.nb)\",\n \"def empty(self) -> bool:\\n return self.data.get_size() == 0\",\n \"def _check_onehot_data(self, data):\\n if data.ndim > 1 and np.equal(data ** 2, data).all():\\n shp = (data.shape[0],) + data.shape[2:]\\n if np.equal(np.ones(shp), data.sum(axis=1)).all():\\n return True\\n return False\",\n \"def empty(self) -> bool:\",\n \"def has_data(self):\\n return self._data is not None\",\n \"def check_Data(self):\\r\\n \\r\\n if self._target_data is None:\\r\\n self.processData()\",\n \"def isExist(data):\\n return True/False\",\n \"def derives_empty(self):\\n if self.known_to_derive_empty:\\n return True\\n for item in self.first():\\n if item.is_empty():\\n self.known_to_derive_empty = True\\n return True\\n return False\",\n \"def _is_empty(self):\\n return self.size == 0\",\n \"def _check_already_present(self, new_da):\\n for da in self:\\n self._id_of_DataArrays_equal(da, new_da)\",\n \"def _is_missing(self, item):\\n pass\",\n \"def is_empty(self):\\n return self.items == []\",\n \"def is_empty(self) -> bool:\\n return len(self._data) == 0\",\n \"def _isvalid(self, data):\\n if data is None:\\n return False\\n elif isinstance(data, (list,tuple)):\\n if len(data) <= 0:\\n return False\\n else:\\n return True\\n elif isinstance(data, (np.ndarray)):\\n if data.size <= 0:\\n return False\\n else:\\n return True\\n elif not data:\\n return False\\n else:\\n return True\",\n \"def __validate_node_data(self, data):\\n\\n # skipping check of 'grapheap_node_id' optimisation key\\n if all(key in data for key in self.optimisation_keys[1:]):\\n return True\\n\\n else:\\n missing_keys = [\\n x for x in self.optimisation_keys[1:] if x not in data]\\n raise ValueError(\\\"Grapheap Error: \\\" + str(missing_keys) +\\n \\\" optimisation keys missing in data\\\")\",\n \"def is_empty(self):\\n return self.count == 0\",\n \"def is_empty(self):\\n return self.count == 0\",\n \"def is_populated(self) -> bool:\\n return 0 < self.count_compounds()\",\n \"def exists(self, data):\\n return data in self.__stack\",\n \"def is_empty(self):\\n return self.size == []\",\n \"def is_empty(self):\\n\\n # If the queue is an empty list, self._data would return False\\n # So if the queue is empty we want to return true\\n # modify with not self._data\\n return not self._data\",\n \"def is_empty(self):\\n return len(self) == 0\",\n \"def check(self, data):# ->bool:\\r\\n return check(self.gd, data)\",\n \"def is_empty( self ):\\n \\n return len(self.__deck) == 0\",\n \"def empty(self):\\n return len(self.deck) == 0\",\n \"def is_empty(self):\\n return not (\\n self.has_label\\n or self.has_name\\n or self.has_points\\n or self.has_attributes\\n )\",\n \"def is_consistent_dataset(data: dict, parameter_box: np.ndarray = None) -> bool:\\n train_set = copy.deepcopy(data)\\n y, phi = train_set[\\\"outputs\\\"].pop(-1), train_set[\\\"features\\\"].pop(-1)\\n y, phi = np.array(y)[..., np.newaxis], np.array(phi)[..., np.newaxis]\\n if train_set[\\\"outputs\\\"] and train_set[\\\"features\\\"]:\\n theta, _, gramian, beta = confidence_polytope(train_set, parameter_box=parameter_box)\\n return is_valid_observation(y, phi, theta, gramian, beta)\\n else:\\n return True\",\n \"def is_empty(self): # -> bool:\\n ...\",\n \"def is_empty(self):\\n # type: () -> bool\\n # If the Histogram contains at least one value, at least one element of\\n # self.counts will be not null\\n return not any(self.counts)\",\n \"def is_valid(self, dataset):\\n pass\",\n \"def has_data(self) -> bool:\\n raise NotImplementedError\",\n \"def is_empty(self) -> bool:\\n return self._symmetries == [] or self._sq_paulis == [] or self._sq_list == []\",\n \"def empty(self):\\n return len(self.a) + len(self.b) == 0\",\n \"def empty(self) -> bool:\\n return self.q1==[]\",\n \"def isTileBlank(tile):\\n for b in tile:\\n if b: return False\\n return True\",\n \"def is_empty(self, trace) -> bool:\\n return len(trace) == 0\",\n \"def check_empty(self):\\n if self.size():\\n raise AttributeError\",\n \"def empty(self):\\n return self.value == []\",\n \"def verify_queue_empty(self):\\n self.assert_sample_queue_size(DataParticleType.VELOCITY_PARTICLE, 0)\\n self.assert_sample_queue_size(DataParticleType.TIME_PARTICLE, 0)\",\n \"def has_data(self):\\n\\n return self._data is not None\",\n \"def has_data(self):\\n\\n return self._data is not None\",\n \"def is_empty(self):\\n return len(self.items) == 0\",\n \"def is_empty(self):\\n return len(self.values) == 0\",\n \"def is_empty(self):\\n return self._sum() == 0\",\n \"def is_empty(self):\\r\\n return len(self) == 0\",\n \"def is_empty(self):\\n\\n return self.items == []\",\n \"def is_empty(self):\\n\\n return self.items == []\",\n \"def is_empty(self):\\n return self._size == 0\",\n \"def empty(self) -> bool:\\n return self.sk1_len==0\",\n \"def is_empty(self):\\n return (self.counter == 0)\",\n \"def is_empty(self):\\n #return not self.vulnerable_in and not self.fixed_in\\n return not self.fixed_in\",\n \"def _isEmpty(self, x, y):\\n\\t\\treturn self.getValue(x, y) == None\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return False\",\n \"def is_empty(self):\\n return len(self) == 0\",\n \"def is_empty(self):\\n return self.__size == 0\",\n \"def checkOpsimData(self, otherdata=None):\\n data2check = self.opsim_data\\n if otherdata:\\n data2check = otherdata\\n if type(data2check) != list:\\n if type(data2check) == dict:\\n if set(self._opsim_keys).issubset(set(data2check.keys())):\\n return True, 'good'\\n else:\\n return False, 'Data is not complete'\\n else:\\n return False, 'Data is not a list nor a dictionary'\\n else:\\n nodict = 0\\n baddict = 0\\n for data in data2check:\\n if type(data) != dict:\\n nodict += 1\\n else:\\n if not set(self._opsim_keys).issubset(set(data.keys())):\\n baddict += 1\\n if (nodict == 0 and baddict == 0):\\n return True, 'good'\\n else:\\n statement = '{0} non dictionary element(s) and {1} uncomplete\\\\\\n dictionary(ies) in data list'.format(nodict, baddict)\\n return (False, statement)\",\n \"def is_empty(self):\\n return self.id is None or self.nb_cart_items == 0\",\n \"def missing_data(self, data):\\n missing_fields = []\\n for key in data:\\n if not key in request.json:\\n missing_fields.append(key)\\n if missing_fields:\\n message = 'Missing ' + ', '.join(missing_fields)\\n return self.bad_request(message)\\n return None\",\n \"def validate(self, data):\\n if HashTag.objects.filter(name=data[\\\"hash_tag\\\"][\\\"name\\\"]).first() is None:\\n raise serializers.ValidationError(\\n {\\\"hash_tag\\\": data[\\\"hash_tag\\\"][\\\"name\\\"] + \\\" \\\" + _(\\\"HashTag does not exist.\\\")}\\n )\\n\\n return data\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65717185","0.63385725","0.6331197","0.62527806","0.61652166","0.61505044","0.608145","0.608145","0.60609126","0.60432065","0.60016364","0.5989013","0.59870905","0.5972411","0.59252816","0.59170127","0.59170127","0.59170127","0.59170127","0.59170127","0.59170127","0.59170127","0.5893321","0.5889449","0.58882296","0.5886396","0.5865447","0.58651257","0.58586276","0.58512163","0.58308935","0.5826826","0.58224964","0.5809509","0.57886654","0.5776007","0.57752746","0.5774196","0.5769365","0.57595897","0.5756273","0.573443","0.5734022","0.5728281","0.5728167","0.5726175","0.5723506","0.5716772","0.5713306","0.5712191","0.56938726","0.5693762","0.56878406","0.56878406","0.56843567","0.56766623","0.5672347","0.56722265","0.5644812","0.5640878","0.5636179","0.56295294","0.5628514","0.5625296","0.561773","0.5609455","0.5608869","0.5601043","0.5595306","0.5592583","0.558852","0.5583733","0.5580688","0.5577714","0.5577599","0.55761325","0.5575769","0.5575769","0.5575537","0.5575068","0.55735487","0.5572166","0.55629694","0.55629694","0.55602","0.554836","0.55469877","0.55456215","0.5544786","0.55434275","0.55434275","0.55434275","0.55434275","0.55434275","0.55434275","0.5542141","0.5541512","0.5536896","0.5533101","0.55323005","0.5531319"],"string":"[\n \"0.65717185\",\n \"0.63385725\",\n \"0.6331197\",\n \"0.62527806\",\n \"0.61652166\",\n \"0.61505044\",\n \"0.608145\",\n \"0.608145\",\n \"0.60609126\",\n \"0.60432065\",\n \"0.60016364\",\n \"0.5989013\",\n \"0.59870905\",\n \"0.5972411\",\n \"0.59252816\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.59170127\",\n \"0.5893321\",\n \"0.5889449\",\n \"0.58882296\",\n \"0.5886396\",\n \"0.5865447\",\n \"0.58651257\",\n \"0.58586276\",\n \"0.58512163\",\n \"0.58308935\",\n \"0.5826826\",\n \"0.58224964\",\n \"0.5809509\",\n \"0.57886654\",\n \"0.5776007\",\n \"0.57752746\",\n \"0.5774196\",\n \"0.5769365\",\n \"0.57595897\",\n \"0.5756273\",\n \"0.573443\",\n \"0.5734022\",\n \"0.5728281\",\n \"0.5728167\",\n \"0.5726175\",\n \"0.5723506\",\n \"0.5716772\",\n \"0.5713306\",\n \"0.5712191\",\n \"0.56938726\",\n \"0.5693762\",\n \"0.56878406\",\n \"0.56878406\",\n \"0.56843567\",\n \"0.56766623\",\n \"0.5672347\",\n \"0.56722265\",\n \"0.5644812\",\n \"0.5640878\",\n \"0.5636179\",\n \"0.56295294\",\n \"0.5628514\",\n \"0.5625296\",\n \"0.561773\",\n \"0.5609455\",\n \"0.5608869\",\n \"0.5601043\",\n \"0.5595306\",\n \"0.5592583\",\n \"0.558852\",\n \"0.5583733\",\n \"0.5580688\",\n \"0.5577714\",\n \"0.5577599\",\n \"0.55761325\",\n \"0.5575769\",\n \"0.5575769\",\n \"0.5575537\",\n \"0.5575068\",\n \"0.55735487\",\n \"0.5572166\",\n \"0.55629694\",\n \"0.55629694\",\n \"0.55602\",\n \"0.554836\",\n \"0.55469877\",\n \"0.55456215\",\n \"0.5544786\",\n \"0.55434275\",\n \"0.55434275\",\n \"0.55434275\",\n \"0.55434275\",\n \"0.55434275\",\n \"0.55434275\",\n \"0.5542141\",\n \"0.5541512\",\n \"0.5536896\",\n \"0.5533101\",\n \"0.55323005\",\n \"0.5531319\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8463,"cells":{"query":{"kind":"string","value":"Stores the given data"},"document":{"kind":"string","value":"def add(self, data):\n if data[\"topic\"] in DRONE_POS_TOPICS:\n self.drone.set_pos_val(data[\"ts\"], data[\"coord\"], data[\"value\"])\n elif data[\"topic\"] in DRONE_VEL_TOPICS:\n self.drone.set_vel_val(data[\"ts\"], data[\"coord\"], data[\"value\"])\n elif data[\"topic\"] in DRONE_ACC_TOPICS:\n self.drone.set_acc_val(data[\"ts\"], data[\"coord\"], data[\"value\"])\n elif data[\"topic\"] in SUBJECT_TOPICS:\n self.subject.set_val(data[\"ts\"], data[\"coord\"], data[\"value\"])\n elif data[\"topic\"] in self.PEDESTRIAN_TOPICS:\n self.peds[data[\"pid\"]].set_val(data[\"ts\"], data[\"coord\"], data[\"value\"])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def store_data(self, data):\n self.data.append(data)","def store_data(self, store_data):\n self._store_data = store_data","def saveData(self):\n pass","def save_data(self):\n pass","def save(self, data):\n\t\tif self.value:\n\t\t\tdata['value'] = self.value","def store_data(self, data):\n if not self.light.hasAttr(self.custom_data_storage_attr_name):\n pm.addAttr(\n self.light,\n ln=self.custom_data_storage_attr_name,\n dt='string'\n )\n\n self.light.setAttr(self.custom_data_storage_attr_name, data)","def save(self, data):\n self.write(data)","def save_data(self):\n data = self.data\n if data is not None:\n data = base64.encodestring(pickle.dumps(data))\n connection = self._open_db()\n cursor = connection.cursor()\n cursor.execute('UPDATE sessions SET data = ? WHERE id = ?;',\n (data, self.sid))\n cursor.close()\n connection.commit()\n connection.close()","def save(self, data):\n data['id'] = self.id\n\n self.db.append(data)","def _storeData(self, data, table, query=None):\n print ('Storing data')\n conn = dbo.getConnection()\n\n if query == None:\n num_cols = len(data[0])\n cols = ','.join(['%s ' for i in range(0, num_cols)])\n query = \"INSERT INTO \" + table + \" VALUES (\" + cols + \")\"\n\n dbo.execute_query(conn, query, data, multiple=True)\n dbo.closeConnection(conn)\n return","def put(data):","def on_data(self, data):\n # store the data\n self._storage.append(data)","def store_data(self, data):\n self.data = data\n # HERE\n the_main_dict = {**self.user_data(), **self.entities_data(), **self.extract_relevant(), **self.locate(),\n **self.calculate_days(), **self.clean_user_desc()}\n # The below is the reason that the table creation must be written in alphabetical order. This is simpler than\n # writing the complex joins that would otherwise be needed.\n my_keys_list = sorted(the_main_dict.keys())\n my_items = list(map(lambda x: str(the_main_dict[x]).replace(\"'\", ''), my_keys_list))\n try:\n # Unpacks the items into an insert statement for the SQLite table\n self.conn.execute(\"INSERT INTO {0} VALUES('{1}','{2}','{3}','{4}','{5}','{6}','{7}','{8}','{9}',\"\n \"'{10}','{11}','{12}','{13}','{14}','{15}','{16}','{17}','{18}','{19}','{20}',\"\n \"'{21}','{22}','{23}','{24}','{25}','{26}','{27}','{28}')\".format(self.table, *my_items))\n self.limiting += 1\n return 0\n except sqlite3.IntegrityError:\n return 1","def store(self):\n\n pass","def save_data(self, record):\n self.dbm.addRecord(record)","def add_data(self, data):\n self.data = data","def put_data(data):\n at_write = airtable.Airtable(app.config['AIRTABLE_BASE'],\n app.config['AIRTABLE_WRITE_KEY'])\n return at_write.create(app.config['AIRTABLE_TABLE'] , data)","def _put(self, key, data):\n path = self._get_key_path(key)\n with open(path, \"wb\") as pickle_file:\n pickle.dump(data, pickle_file)","def set_data(self, data):\n\n pass","def save(self, data, file_id=None, metadata={}):\n pass","def save_data(self):\n db.session.add(self)\n db.session.commit( )","def setData(self, data):\n self._data = pickle.dumps(data)","def data(self, data):\n self.__data = data","def data(self, data):\n self._data = data","def data(self, data):\n self._data = data","def _make_information_storable( self, data ):\n\t\tpass","def save_session_data(self, session_id, data):\n raise NotImplementedError()","def write_data(self, data: Dict):\n raise NotImplementedError","def store(self, key, a):\n if key in self.SMGData.keys():\n self.SMGData[key] = a\n else:\n raise Exception('Key does not exist in the data structure')","def set_data(self, data):\n self.data = data","def write_data():","def store(self,key,start,end,data):\n\n pass","def data(self, data):\n\n self._data = data","def data(self, data):\n\n self._data = data","def data(self, data):\n\n self._data = data","def data(self, data):\n\n self._data = data","def data(self, data):\n\n self._data = data","def data(self, data):\n\n self._data = data","def save_data(db, dict_key, url, data_to_store):\n if dict_key not in db:\n db[dict_key] = []\n data = db[dict_key]\n data.append({\n 'url': url,\n 'data': data_to_store,\n })\n db[dict_key] = data","def save(self, **data):\n self.applyChanges(data)","def save(self, *args, **kwargs):\n raise NotImplementedError('missing data mixin')","def save(self, data, place):\n if isinstance(data, TrustList):\n self.__save_trust_list(place, data)\n\n if isinstance(data, (bytes, bytearray)):\n self.__save_blob(place, data)","def save(self, output, data):\n pass","def setData(self, data):\n self.data = data","def setData(self, data):\n self.data = data","def store_data(self, data, info_dict):\n\n size_in_MB = np.prod(data.shape) * data.dtype.itemsize / 1024 / 1024\n\n with mongo_connection(self.cfg_mongo) as mongo:\n client, coll = mongo\n if self.datastore == \"gridfs\":\n with mongo_storage_gridfs(client.get_database()) as fs:\n tmp = Binary(pickle.dumps(data))\n tic_io = time.perf_counter()\n fid = fs.put(tmp)\n toc_io = time.perf_counter()\n info_dict.update({\"result_gridfs\": fid})\n\n elif self.datastore == \"numpy\":\n with mongo_storage_numpy(self.cfg_mongo) as fname:\n tic_io = time.perf_counter()\n np.savez(fname, data=data)\n toc_io = time.perf_counter()\n info_dict.update({\"unique_filename\": fname})\n\n elif self.datastore == \"adios2\":\n # Use adios2's context manager\n datadir = join(self.cfg_mongo[\"datadir\"], self.cfg_mongo[\"run_id\"])\n if (isdir(datadir) == False):\n try:\n mkdir(datadir)\n except:\n self.logger.error(f\"Could not access path {datadir}\")\n raise ValueError(f\"Could not access path {datadir}\")\n\n fname = join(datadir, uuid.uuid1().__str__() + \".bp\")\n info_dict.update({\"unique_filename\": fname})\n\n # with open(fname, \"w\") as df:\n # tic_io = time.perf_counter()\n # df.write(info_dict[\"analysis_name\"])\n with adios2.open(fname, \"w\") as fh:\n tic_io = time.perf_counter()\n fh.write(info_dict[\"analysis_name\"], data, data.shape, [0] * data.ndim, data.shape)\n toc_io = time.perf_counter()\n \n\n # Calculate performance metric\n MB_per_sec = size_in_MB / (toc_io - tic_io)\n info_dict.update({\"Performance\": MB_per_sec})\n\n info_dict.update({\"timestamp\": datetime.datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")})\n info_dict.update({\"description\": \"analysis results\"})\n\n try:\n inserted_id = coll.insert_one(info_dict)\n except:\n self.logger.error(\"Unexpected error:\", sys.exc_info()[0])\n\n return None","def store(self, key: object, value: object):\n self._user_data.update({key: value})","def put(self,data):\n\n \n try:\n\n db = getDatabase()\n connection = db.connect()\n \n connection.put(self,data)\n except Exception as e:\n raise e\n finally:\n db.dispose()","def save(self, data, **kwargs):\n if self.persist==['data']: # 1 data shortcut\n self.output().save(data, **kwargs)\n else:\n targets = self.output()\n if not set(data.keys())==set(targets.keys()):\n raise ValueError('Save dictionary needs to consistent with Task.persist')\n for k, v in data.items():\n targets[k].save(v, **kwargs)","def save_to_file(self, data):\n\t\tif self.data_file.write(data):\n\t\t\tprint(\"Data successfully added to file\")\n\t\telse:\n\t\t\tPrint(\"Problem occured during adding to file\")","def save_data(self, payload):\n obj = Data(\n key=payload['UUID'],\n device_id=payload['ID'],\n rate=payload['RATE'],\n state=payload['VALUE'],\n date=payload['TIME']\n )\n obj.save()","def save_data(self):\n # Validate\n try:\n self._data = self._schema(self._data)\n except vol.Invalid as ex:\n _LOGGER.error(\"Can't parse data: %s\",\n humanize_error(self._data, ex))\n\n # Load last valid data\n _LOGGER.warning(\"Reset %s to last version\", self._file)\n self.read_data()\n return\n\n # write\n try:\n write_json_file(self._file, self._data)\n except (OSError, json.JSONDecodeError) as err:\n _LOGGER.error(\"Can't store config in %s: %s\", self._file, err)","def save_data(self, gauge_name, date_key, data):\n pass","def add(self, data):\n if self._filter(data):\n id = self.db._generate_id(data)\n \n if not id == None:\n if self.db._store:\n self.db.append(id, str(data))\n print id, \"stored to\", self.db._generate_path(id)\n else:\n print id\n print data.show2()","def _save(self, data: np.ndarray) -> None:\n ...","def put(self, id, data):\n assert isinstance(data, dict)\n self._shelf[str(id)] = data","def save(self, output, data):\n return","def store_data(self):\n return self._store_data","def save_data(self):\n with open(self.storage_path, 'w') as cache_file:\n json.dump(self.data, cache_file)","def put(self, filename, data, **kw):\n\n file_path = os.path.join(self.storage_path, filename)\n file_obj = open(file_path, \"w\")\n file_obj.write(data)","def setData(self, data):\n return None","def store(self, args):\n pass","def persist(data):\n conn = psycopg2.connect(host=\"localhost\", database=\"integration\", user=\"postgres\", password=\"postgres\")\n cursor = conn.cursor()\n cursor.execute(INSERT_SQL, (data[\"name\"], data[\"gender\"], data[\"age\"]))\n conn.commit()\n cursor.close()","def save(self, output, data):","def store(self, key, value):\n pass","def store(self, key, headers, value):","def _write(self, data):\n self.db.append(data)\n\n with open(self.DB_FILE, 'w') as outfile:\n json.dump(self.db, outfile)","def store_in_bag(self, data):\n # timestamp is (s, nanos): data[\"ts\"], data[\"tnanos\"]\n\n self.bag.add(data)\n\n # Ensure that all data have the same timestamp and are not None\n # Also there can't be more than a sample per second.\n if self.bag.is_full():\n if random() > 0.99999:\n print(\"Telemetry data: \", data[\"topic\"])\n print(\"Bag data: \", self.bag.print_data())\n\n # Then flush the data to process it and empty the bag\n data = self.bag.get_data()\n self.on_full(data)","def handle_data(self, d):\n\n # write the data to our file handler\n self.save_fh.write(d)","def setData(self, data):\n self._data = data","def writeData(self, dataDict):\n pass","def save_event(self, data):\n rdb.table(self.rdb_table).insert(data)","def save_to_users(self):\n Data.add_data(self.user_data())","def storeData(data, filename='laue.dat'):\r\n import cPickle\r\n with open(filename, 'wb') as fp:\r\n cPickle.dump(data, fp)","def set_data(self, data):\n\n self._data = data","def set_data(self, data):\n\n self._data = data","def set_data(self, data):\n\n self._data = data","def _save_data(self, data):\n path = os.path.join(self._cache_path, '%s.data' % self._name)\n\n f = bz2.BZ2File(path, 'w')\n f.write(pickle.dumps(data))\n f.close()","def setData(self,data):\n self.data = data\n self.size = len(data)","def setData(self,data):\n self.data = data\n self.size = len(data)","def handle_data(self, data):\r\n self.fed.append(data)","def store_introspection_data(node_uuid, data, processed=True):\n introspection_data_manager = plugins_base.introspection_data_manager()\n store = CONF.processing.store_data\n ext = introspection_data_manager[store].obj\n ext.save(node_uuid, data, processed)","def callback_object(self, data):\n\n try:\n # TODO support multiple of the same object\n # Save an array of object locations\n self.redis.set(self.prefix+\"_\"+data.name, json.dumps([{\n \"name\": data.name,\n \"time\": data.time,\n \"x\": data.x,\n \"y\": data.y,\n \"z\": data.z\n }]))\n except:\n rospy.logerr(\"Cannot insert row\")","def save(self):\n self.wallet.storage.put(\n \"slp_data_version\", None\n ) # clear key of other older formats.\n data = {\n \"validity\": self.validity,\n \"token_quantities\": {\n k: [[v0, v1] for v0, v1 in v.items()]\n for k, v in self.token_quantities.items()\n },\n \"txo_byaddr\": {\n k.to_storage_string(): list(v) for k, v in self.txo_byaddr.items()\n },\n \"version\": self.DATA_VERSION,\n }\n self.wallet.storage.put(\"slp\", data)","async def save(cls, data, save_file, *args, **kwargs):\n raise NotImplementedError()","def store(self, data: Union[str, bytes, int, float]) -> str:\n key = str(uuid.uuid4())\n self._redis.set(key, data)\n return key","def save(self, key, value):\n # deepcopy so that later modifications to value aren't reflected in the db\n self.data[key] = copy.deepcopy(value)","def setData(self,newData):\r\n pass","def add_data(self, data):\n self.data = self.data + data","def save_shelf(self, shelf_name, data):\r\n shelf_path = os.path.join(self.full_dir, shelf_name)\r\n with shelve.open(shelf_path, 'c') as shelf:\r\n shelf['data'] = data","def set_data(self, data):\n self._set_data(data)","def store(self, data: Union[str, bytes, int, float]) -> str:\n k = str(uuid.uuid4())\n self._redis[k] = data\n return k","def save(self):\n data = self.serialize()\n\n self.validate(data)\n\n saved_data = DATABASE_CONNECTION.insert(self.__class__.__name__, data)\n\n self.__dict__.update(saved_data)","def store(self, data: dict) -> tuple:\n item = self.model(**data)\n try:\n item.save()\n items = self.get_all()\n\n return items\n except DataBaseException as e:\n return {'error': {'message': e.message}}, 500","def store(self, quantitiy):\n self._stored = self._stored + quantitiy","def SetData(self, data):\r\n\r\n self._data = data","def insert(self, data):\r\n pass","def set_data(self, new_data):\n self.data = new_data","def persist(self, values):\n pass","def save_monitor_data(self, data_type, data, metadata=None):\n if isinstance(data, np.ndarray):\n data = {'data': data.tolist()}\n data['data_type'] = data_type\n if metadata is not None:\n data.update(metadata)\n self.monitor_cache.append((data_type, data))","def save(self, values):"],"string":"[\n \"def store_data(self, data):\\n self.data.append(data)\",\n \"def store_data(self, store_data):\\n self._store_data = store_data\",\n \"def saveData(self):\\n pass\",\n \"def save_data(self):\\n pass\",\n \"def save(self, data):\\n\\t\\tif self.value:\\n\\t\\t\\tdata['value'] = self.value\",\n \"def store_data(self, data):\\n if not self.light.hasAttr(self.custom_data_storage_attr_name):\\n pm.addAttr(\\n self.light,\\n ln=self.custom_data_storage_attr_name,\\n dt='string'\\n )\\n\\n self.light.setAttr(self.custom_data_storage_attr_name, data)\",\n \"def save(self, data):\\n self.write(data)\",\n \"def save_data(self):\\n data = self.data\\n if data is not None:\\n data = base64.encodestring(pickle.dumps(data))\\n connection = self._open_db()\\n cursor = connection.cursor()\\n cursor.execute('UPDATE sessions SET data = ? WHERE id = ?;',\\n (data, self.sid))\\n cursor.close()\\n connection.commit()\\n connection.close()\",\n \"def save(self, data):\\n data['id'] = self.id\\n\\n self.db.append(data)\",\n \"def _storeData(self, data, table, query=None):\\n print ('Storing data')\\n conn = dbo.getConnection()\\n\\n if query == None:\\n num_cols = len(data[0])\\n cols = ','.join(['%s ' for i in range(0, num_cols)])\\n query = \\\"INSERT INTO \\\" + table + \\\" VALUES (\\\" + cols + \\\")\\\"\\n\\n dbo.execute_query(conn, query, data, multiple=True)\\n dbo.closeConnection(conn)\\n return\",\n \"def put(data):\",\n \"def on_data(self, data):\\n # store the data\\n self._storage.append(data)\",\n \"def store_data(self, data):\\n self.data = data\\n # HERE\\n the_main_dict = {**self.user_data(), **self.entities_data(), **self.extract_relevant(), **self.locate(),\\n **self.calculate_days(), **self.clean_user_desc()}\\n # The below is the reason that the table creation must be written in alphabetical order. This is simpler than\\n # writing the complex joins that would otherwise be needed.\\n my_keys_list = sorted(the_main_dict.keys())\\n my_items = list(map(lambda x: str(the_main_dict[x]).replace(\\\"'\\\", ''), my_keys_list))\\n try:\\n # Unpacks the items into an insert statement for the SQLite table\\n self.conn.execute(\\\"INSERT INTO {0} VALUES('{1}','{2}','{3}','{4}','{5}','{6}','{7}','{8}','{9}',\\\"\\n \\\"'{10}','{11}','{12}','{13}','{14}','{15}','{16}','{17}','{18}','{19}','{20}',\\\"\\n \\\"'{21}','{22}','{23}','{24}','{25}','{26}','{27}','{28}')\\\".format(self.table, *my_items))\\n self.limiting += 1\\n return 0\\n except sqlite3.IntegrityError:\\n return 1\",\n \"def store(self):\\n\\n pass\",\n \"def save_data(self, record):\\n self.dbm.addRecord(record)\",\n \"def add_data(self, data):\\n self.data = data\",\n \"def put_data(data):\\n at_write = airtable.Airtable(app.config['AIRTABLE_BASE'],\\n app.config['AIRTABLE_WRITE_KEY'])\\n return at_write.create(app.config['AIRTABLE_TABLE'] , data)\",\n \"def _put(self, key, data):\\n path = self._get_key_path(key)\\n with open(path, \\\"wb\\\") as pickle_file:\\n pickle.dump(data, pickle_file)\",\n \"def set_data(self, data):\\n\\n pass\",\n \"def save(self, data, file_id=None, metadata={}):\\n pass\",\n \"def save_data(self):\\n db.session.add(self)\\n db.session.commit( )\",\n \"def setData(self, data):\\n self._data = pickle.dumps(data)\",\n \"def data(self, data):\\n self.__data = data\",\n \"def data(self, data):\\n self._data = data\",\n \"def data(self, data):\\n self._data = data\",\n \"def _make_information_storable( self, data ):\\n\\t\\tpass\",\n \"def save_session_data(self, session_id, data):\\n raise NotImplementedError()\",\n \"def write_data(self, data: Dict):\\n raise NotImplementedError\",\n \"def store(self, key, a):\\n if key in self.SMGData.keys():\\n self.SMGData[key] = a\\n else:\\n raise Exception('Key does not exist in the data structure')\",\n \"def set_data(self, data):\\n self.data = data\",\n \"def write_data():\",\n \"def store(self,key,start,end,data):\\n\\n pass\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def data(self, data):\\n\\n self._data = data\",\n \"def save_data(db, dict_key, url, data_to_store):\\n if dict_key not in db:\\n db[dict_key] = []\\n data = db[dict_key]\\n data.append({\\n 'url': url,\\n 'data': data_to_store,\\n })\\n db[dict_key] = data\",\n \"def save(self, **data):\\n self.applyChanges(data)\",\n \"def save(self, *args, **kwargs):\\n raise NotImplementedError('missing data mixin')\",\n \"def save(self, data, place):\\n if isinstance(data, TrustList):\\n self.__save_trust_list(place, data)\\n\\n if isinstance(data, (bytes, bytearray)):\\n self.__save_blob(place, data)\",\n \"def save(self, output, data):\\n pass\",\n \"def setData(self, data):\\n self.data = data\",\n \"def setData(self, data):\\n self.data = data\",\n \"def store_data(self, data, info_dict):\\n\\n size_in_MB = np.prod(data.shape) * data.dtype.itemsize / 1024 / 1024\\n\\n with mongo_connection(self.cfg_mongo) as mongo:\\n client, coll = mongo\\n if self.datastore == \\\"gridfs\\\":\\n with mongo_storage_gridfs(client.get_database()) as fs:\\n tmp = Binary(pickle.dumps(data))\\n tic_io = time.perf_counter()\\n fid = fs.put(tmp)\\n toc_io = time.perf_counter()\\n info_dict.update({\\\"result_gridfs\\\": fid})\\n\\n elif self.datastore == \\\"numpy\\\":\\n with mongo_storage_numpy(self.cfg_mongo) as fname:\\n tic_io = time.perf_counter()\\n np.savez(fname, data=data)\\n toc_io = time.perf_counter()\\n info_dict.update({\\\"unique_filename\\\": fname})\\n\\n elif self.datastore == \\\"adios2\\\":\\n # Use adios2's context manager\\n datadir = join(self.cfg_mongo[\\\"datadir\\\"], self.cfg_mongo[\\\"run_id\\\"])\\n if (isdir(datadir) == False):\\n try:\\n mkdir(datadir)\\n except:\\n self.logger.error(f\\\"Could not access path {datadir}\\\")\\n raise ValueError(f\\\"Could not access path {datadir}\\\")\\n\\n fname = join(datadir, uuid.uuid1().__str__() + \\\".bp\\\")\\n info_dict.update({\\\"unique_filename\\\": fname})\\n\\n # with open(fname, \\\"w\\\") as df:\\n # tic_io = time.perf_counter()\\n # df.write(info_dict[\\\"analysis_name\\\"])\\n with adios2.open(fname, \\\"w\\\") as fh:\\n tic_io = time.perf_counter()\\n fh.write(info_dict[\\\"analysis_name\\\"], data, data.shape, [0] * data.ndim, data.shape)\\n toc_io = time.perf_counter()\\n \\n\\n # Calculate performance metric\\n MB_per_sec = size_in_MB / (toc_io - tic_io)\\n info_dict.update({\\\"Performance\\\": MB_per_sec})\\n\\n info_dict.update({\\\"timestamp\\\": datetime.datetime.utcnow().strftime(\\\"%Y-%m-%d %H:%M:%S\\\")})\\n info_dict.update({\\\"description\\\": \\\"analysis results\\\"})\\n\\n try:\\n inserted_id = coll.insert_one(info_dict)\\n except:\\n self.logger.error(\\\"Unexpected error:\\\", sys.exc_info()[0])\\n\\n return None\",\n \"def store(self, key: object, value: object):\\n self._user_data.update({key: value})\",\n \"def put(self,data):\\n\\n \\n try:\\n\\n db = getDatabase()\\n connection = db.connect()\\n \\n connection.put(self,data)\\n except Exception as e:\\n raise e\\n finally:\\n db.dispose()\",\n \"def save(self, data, **kwargs):\\n if self.persist==['data']: # 1 data shortcut\\n self.output().save(data, **kwargs)\\n else:\\n targets = self.output()\\n if not set(data.keys())==set(targets.keys()):\\n raise ValueError('Save dictionary needs to consistent with Task.persist')\\n for k, v in data.items():\\n targets[k].save(v, **kwargs)\",\n \"def save_to_file(self, data):\\n\\t\\tif self.data_file.write(data):\\n\\t\\t\\tprint(\\\"Data successfully added to file\\\")\\n\\t\\telse:\\n\\t\\t\\tPrint(\\\"Problem occured during adding to file\\\")\",\n \"def save_data(self, payload):\\n obj = Data(\\n key=payload['UUID'],\\n device_id=payload['ID'],\\n rate=payload['RATE'],\\n state=payload['VALUE'],\\n date=payload['TIME']\\n )\\n obj.save()\",\n \"def save_data(self):\\n # Validate\\n try:\\n self._data = self._schema(self._data)\\n except vol.Invalid as ex:\\n _LOGGER.error(\\\"Can't parse data: %s\\\",\\n humanize_error(self._data, ex))\\n\\n # Load last valid data\\n _LOGGER.warning(\\\"Reset %s to last version\\\", self._file)\\n self.read_data()\\n return\\n\\n # write\\n try:\\n write_json_file(self._file, self._data)\\n except (OSError, json.JSONDecodeError) as err:\\n _LOGGER.error(\\\"Can't store config in %s: %s\\\", self._file, err)\",\n \"def save_data(self, gauge_name, date_key, data):\\n pass\",\n \"def add(self, data):\\n if self._filter(data):\\n id = self.db._generate_id(data)\\n \\n if not id == None:\\n if self.db._store:\\n self.db.append(id, str(data))\\n print id, \\\"stored to\\\", self.db._generate_path(id)\\n else:\\n print id\\n print data.show2()\",\n \"def _save(self, data: np.ndarray) -> None:\\n ...\",\n \"def put(self, id, data):\\n assert isinstance(data, dict)\\n self._shelf[str(id)] = data\",\n \"def save(self, output, data):\\n return\",\n \"def store_data(self):\\n return self._store_data\",\n \"def save_data(self):\\n with open(self.storage_path, 'w') as cache_file:\\n json.dump(self.data, cache_file)\",\n \"def put(self, filename, data, **kw):\\n\\n file_path = os.path.join(self.storage_path, filename)\\n file_obj = open(file_path, \\\"w\\\")\\n file_obj.write(data)\",\n \"def setData(self, data):\\n return None\",\n \"def store(self, args):\\n pass\",\n \"def persist(data):\\n conn = psycopg2.connect(host=\\\"localhost\\\", database=\\\"integration\\\", user=\\\"postgres\\\", password=\\\"postgres\\\")\\n cursor = conn.cursor()\\n cursor.execute(INSERT_SQL, (data[\\\"name\\\"], data[\\\"gender\\\"], data[\\\"age\\\"]))\\n conn.commit()\\n cursor.close()\",\n \"def save(self, output, data):\",\n \"def store(self, key, value):\\n pass\",\n \"def store(self, key, headers, value):\",\n \"def _write(self, data):\\n self.db.append(data)\\n\\n with open(self.DB_FILE, 'w') as outfile:\\n json.dump(self.db, outfile)\",\n \"def store_in_bag(self, data):\\n # timestamp is (s, nanos): data[\\\"ts\\\"], data[\\\"tnanos\\\"]\\n\\n self.bag.add(data)\\n\\n # Ensure that all data have the same timestamp and are not None\\n # Also there can't be more than a sample per second.\\n if self.bag.is_full():\\n if random() > 0.99999:\\n print(\\\"Telemetry data: \\\", data[\\\"topic\\\"])\\n print(\\\"Bag data: \\\", self.bag.print_data())\\n\\n # Then flush the data to process it and empty the bag\\n data = self.bag.get_data()\\n self.on_full(data)\",\n \"def handle_data(self, d):\\n\\n # write the data to our file handler\\n self.save_fh.write(d)\",\n \"def setData(self, data):\\n self._data = data\",\n \"def writeData(self, dataDict):\\n pass\",\n \"def save_event(self, data):\\n rdb.table(self.rdb_table).insert(data)\",\n \"def save_to_users(self):\\n Data.add_data(self.user_data())\",\n \"def storeData(data, filename='laue.dat'):\\r\\n import cPickle\\r\\n with open(filename, 'wb') as fp:\\r\\n cPickle.dump(data, fp)\",\n \"def set_data(self, data):\\n\\n self._data = data\",\n \"def set_data(self, data):\\n\\n self._data = data\",\n \"def set_data(self, data):\\n\\n self._data = data\",\n \"def _save_data(self, data):\\n path = os.path.join(self._cache_path, '%s.data' % self._name)\\n\\n f = bz2.BZ2File(path, 'w')\\n f.write(pickle.dumps(data))\\n f.close()\",\n \"def setData(self,data):\\n self.data = data\\n self.size = len(data)\",\n \"def setData(self,data):\\n self.data = data\\n self.size = len(data)\",\n \"def handle_data(self, data):\\r\\n self.fed.append(data)\",\n \"def store_introspection_data(node_uuid, data, processed=True):\\n introspection_data_manager = plugins_base.introspection_data_manager()\\n store = CONF.processing.store_data\\n ext = introspection_data_manager[store].obj\\n ext.save(node_uuid, data, processed)\",\n \"def callback_object(self, data):\\n\\n try:\\n # TODO support multiple of the same object\\n # Save an array of object locations\\n self.redis.set(self.prefix+\\\"_\\\"+data.name, json.dumps([{\\n \\\"name\\\": data.name,\\n \\\"time\\\": data.time,\\n \\\"x\\\": data.x,\\n \\\"y\\\": data.y,\\n \\\"z\\\": data.z\\n }]))\\n except:\\n rospy.logerr(\\\"Cannot insert row\\\")\",\n \"def save(self):\\n self.wallet.storage.put(\\n \\\"slp_data_version\\\", None\\n ) # clear key of other older formats.\\n data = {\\n \\\"validity\\\": self.validity,\\n \\\"token_quantities\\\": {\\n k: [[v0, v1] for v0, v1 in v.items()]\\n for k, v in self.token_quantities.items()\\n },\\n \\\"txo_byaddr\\\": {\\n k.to_storage_string(): list(v) for k, v in self.txo_byaddr.items()\\n },\\n \\\"version\\\": self.DATA_VERSION,\\n }\\n self.wallet.storage.put(\\\"slp\\\", data)\",\n \"async def save(cls, data, save_file, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def store(self, data: Union[str, bytes, int, float]) -> str:\\n key = str(uuid.uuid4())\\n self._redis.set(key, data)\\n return key\",\n \"def save(self, key, value):\\n # deepcopy so that later modifications to value aren't reflected in the db\\n self.data[key] = copy.deepcopy(value)\",\n \"def setData(self,newData):\\r\\n pass\",\n \"def add_data(self, data):\\n self.data = self.data + data\",\n \"def save_shelf(self, shelf_name, data):\\r\\n shelf_path = os.path.join(self.full_dir, shelf_name)\\r\\n with shelve.open(shelf_path, 'c') as shelf:\\r\\n shelf['data'] = data\",\n \"def set_data(self, data):\\n self._set_data(data)\",\n \"def store(self, data: Union[str, bytes, int, float]) -> str:\\n k = str(uuid.uuid4())\\n self._redis[k] = data\\n return k\",\n \"def save(self):\\n data = self.serialize()\\n\\n self.validate(data)\\n\\n saved_data = DATABASE_CONNECTION.insert(self.__class__.__name__, data)\\n\\n self.__dict__.update(saved_data)\",\n \"def store(self, data: dict) -> tuple:\\n item = self.model(**data)\\n try:\\n item.save()\\n items = self.get_all()\\n\\n return items\\n except DataBaseException as e:\\n return {'error': {'message': e.message}}, 500\",\n \"def store(self, quantitiy):\\n self._stored = self._stored + quantitiy\",\n \"def SetData(self, data):\\r\\n\\r\\n self._data = data\",\n \"def insert(self, data):\\r\\n pass\",\n \"def set_data(self, new_data):\\n self.data = new_data\",\n \"def persist(self, values):\\n pass\",\n \"def save_monitor_data(self, data_type, data, metadata=None):\\n if isinstance(data, np.ndarray):\\n data = {'data': data.tolist()}\\n data['data_type'] = data_type\\n if metadata is not None:\\n data.update(metadata)\\n self.monitor_cache.append((data_type, data))\",\n \"def save(self, values):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82004386","0.788964","0.7607003","0.75672203","0.753746","0.7510255","0.74823844","0.74805313","0.7291415","0.7250676","0.71713775","0.7075155","0.7072606","0.7048692","0.69521874","0.69172645","0.6839893","0.68263394","0.68209374","0.6810317","0.6782455","0.67814493","0.67521983","0.67308974","0.67308974","0.6730723","0.6726373","0.6720174","0.6705719","0.6697373","0.66660845","0.6664942","0.66636825","0.66636825","0.66636825","0.66636825","0.66636825","0.66636825","0.66521025","0.6646962","0.66350234","0.6634842","0.65851295","0.65849197","0.65849197","0.656612","0.6563253","0.6559191","0.6547565","0.65417063","0.65318894","0.6531413","0.65297925","0.65249795","0.6513164","0.6502445","0.64880747","0.6478076","0.6474736","0.64546853","0.6453926","0.6452969","0.64454305","0.64404595","0.64350015","0.64319366","0.64231986","0.64219236","0.64117515","0.6401982","0.63949","0.63905567","0.6389426","0.6378031","0.6354721","0.6354721","0.6354721","0.63404995","0.6338161","0.6338161","0.6331593","0.63310194","0.632789","0.6319098","0.6316953","0.6312859","0.63088477","0.6302199","0.6290043","0.62809753","0.6274785","0.62699175","0.62610215","0.62586147","0.62504756","0.6246361","0.62458694","0.624478","0.6233857","0.62307936","0.62240505"],"string":"[\n \"0.82004386\",\n \"0.788964\",\n \"0.7607003\",\n \"0.75672203\",\n \"0.753746\",\n \"0.7510255\",\n \"0.74823844\",\n \"0.74805313\",\n \"0.7291415\",\n \"0.7250676\",\n \"0.71713775\",\n \"0.7075155\",\n \"0.7072606\",\n \"0.7048692\",\n \"0.69521874\",\n \"0.69172645\",\n \"0.6839893\",\n \"0.68263394\",\n \"0.68209374\",\n \"0.6810317\",\n \"0.6782455\",\n \"0.67814493\",\n \"0.67521983\",\n \"0.67308974\",\n \"0.67308974\",\n \"0.6730723\",\n \"0.6726373\",\n \"0.6720174\",\n \"0.6705719\",\n \"0.6697373\",\n \"0.66660845\",\n \"0.6664942\",\n \"0.66636825\",\n \"0.66636825\",\n \"0.66636825\",\n \"0.66636825\",\n \"0.66636825\",\n \"0.66636825\",\n \"0.66521025\",\n \"0.6646962\",\n \"0.66350234\",\n \"0.6634842\",\n \"0.65851295\",\n \"0.65849197\",\n \"0.65849197\",\n \"0.656612\",\n \"0.6563253\",\n \"0.6559191\",\n \"0.6547565\",\n \"0.65417063\",\n \"0.65318894\",\n \"0.6531413\",\n \"0.65297925\",\n \"0.65249795\",\n \"0.6513164\",\n \"0.6502445\",\n \"0.64880747\",\n \"0.6478076\",\n \"0.6474736\",\n \"0.64546853\",\n \"0.6453926\",\n \"0.6452969\",\n \"0.64454305\",\n \"0.64404595\",\n \"0.64350015\",\n \"0.64319366\",\n \"0.64231986\",\n \"0.64219236\",\n \"0.64117515\",\n \"0.6401982\",\n \"0.63949\",\n \"0.63905567\",\n \"0.6389426\",\n \"0.6378031\",\n \"0.6354721\",\n \"0.6354721\",\n \"0.6354721\",\n \"0.63404995\",\n \"0.6338161\",\n \"0.6338161\",\n \"0.6331593\",\n \"0.63310194\",\n \"0.632789\",\n \"0.6319098\",\n \"0.6316953\",\n \"0.6312859\",\n \"0.63088477\",\n \"0.6302199\",\n \"0.6290043\",\n \"0.62809753\",\n \"0.6274785\",\n \"0.62699175\",\n \"0.62610215\",\n \"0.62586147\",\n \"0.62504756\",\n \"0.6246361\",\n \"0.62458694\",\n \"0.624478\",\n \"0.6233857\",\n \"0.62307936\",\n \"0.62240505\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8464,"cells":{"query":{"kind":"string","value":"Check if all models stored are complete for the given timestamp."},"document":{"kind":"string","value":"def is_full(self):\n core_full = self.drone.complete() and self.subject.complete()\n if self.peds is None:\n return core_full\n else:\n return core_full and all([p.complete() for p in self.peds.values()])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def complete(self, verbose=False):\r\n if (self.__num_tasks == 0 or\r\n self.__arrival_time == 0 or\r\n self.__num_tasks != len(self.__tasks)):\r\n #\r\n if verbose:\r\n print (\"Request %s incomplete. %d expected tasks, %d recorded tasks, \"\r\n \"arrival time %s\") % (self.__id, self.__num_tasks,\r\n len(self.__tasks), self.__arrival_time)\r\n return False\r\n for task in self.__tasks.values():\r\n if not task.complete(verbose):\r\n return False\r\n return True","def complete(self, verbose=False):\r\n #if self.scheduler_launch_time == INVALID_TIME: print \"scheduler launch\"\r\n #if self.node_monitor_launch_time == INVALID_TIME: print \"nm launch\"\r\n #if self.completion_time == INVALID_TIME: print \"completion\"\r\n if verbose:\r\n if self.node_monitor_get_task_time == INVALID_TIME:\r\n print \"Task %s incomplete: node monitor get_task time missing\" % self.id\r\n elif self.scheduler_launch_time == INVALID_TIME:\r\n print \"Task %s incomplete: Scheduler launch time missing\" % self.id\r\n elif self.node_monitor_launch_time == INVALID_TIME:\r\n print \"Task %s incomplete: Node monitor launch time missing\" % self.id\r\n elif self.completion_time == INVALID_TIME:\r\n print \"Task %s incomplete: Completion time missing\" % self.id\r\n return (self.node_monitor_get_task_time != INVALID_TIME and\r\n self.scheduler_launch_time != INVALID_TIME and\r\n self.node_monitor_launch_time != INVALID_TIME and\r\n self.completion_time != INVALID_TIME)","def complete(self):\r\n if self.scheduler_launch_time == INVALID_TIME:\r\n print \"Missing task scheduler launch time\"\r\n return False\r\n if self.node_monitor_launch_time == INVALID_TIME:\r\n\t print \"Missing task node monitor launch time\"\r\n\t return False\r\n\tif self.completion_time == INVALID_TIME:\r\n\t print \"Missing task completion time\"\r\n\t return False\r\n\tif self.clock_skew == INVALID_TIME_DELTA:\r\n print \"Missing task clock skew\"\r\n\t return False\r\n\treturn True","def Complete(self):\n return self.date.Complete() and self.time.Complete()","def complete(self):\n\n return any(\n [\n self.is_complete(),\n self.is_failed(),\n self.is_out_of_memory(),\n self.is_timeout(),\n ]\n )","def has_full_batch(self) -> bool:","def complete(self):\r\n if (self.__num_tasks == 0 or\r\n self.__arrival_time == 0 or\r\n self.__num_tasks != len(self.__tasks)):\r\n print (\"Expected to find %s tasks; found %s\" %\r\n (self.__num_tasks, len(self.__tasks)))\r\n return False\r\n for task in self.__tasks.values():\r\n if not task.complete():\r\n return False\r\n if len(self.__probes) == 0:\r\n return False # Don't consider non-probing requests\r\n return True","def __updater_check_complete_all_event(self):\n if self._complete_all_event.is_set():\n self.logger.info(\"Received complete all request event in updater\")\n self._updater_map = {}\n self.msg.put_into_Queue()\n return True","def is_complete(self) -> bool:\r\n return path.exists(self._has_completed_path)","def complete(self):\r\n\tif self.launch_time == INVALID_TIME:\r\n\t print \"Missing probe launch time\"\r\n return False\r\n if self.received_time == INVALID_TIME:\r\n print \"Missing probe received time\"\r\n return False\r\n if self.completion_time == INVALID_TIME:\r\n print \"Missing probe completion time\"\r\n return False\r\n return True","def is_all_done():\n for job_set in job_sets:\n if job_set.status != SetStatus.COMPLETED:\n return False\n return True","def is_blast_db_up_to_date(self):\n if not self.have_blast_db():\n return False\n\n # get time creation blast database files\n modification_times = []\n files = glob.glob(self.path)\n for i in files:\n mod_time_in_secs = os.stat(i).st_ctime\n modification_times.append(datetime.datetime.fromtimestamp(mod_time_in_secs))\n modification_times.sort(reverse=True)\n time_creation_blast = modification_times[0].replace(tzinfo=pytz.utc)\n\n # get time creation time edited sequences in our database\n time_created_queryset = Sequences.objects.all().order_by('-time_created')[:1]\n time_created = time_created_queryset[0].time_created\n\n time_edited_queryset = Sequences.objects.all().order_by('-time_edited')[:1]\n time_edited = time_edited_queryset[0].time_edited\n\n if time_created > time_creation_blast or time_edited > time_creation_blast:\n return False\n else:\n return True","def is_complete(self):\n return not (self.year is None or\n self.month is None or\n self.day is None)","def is_valid(self):\n return self.startTime <= ApiKey.get_now() < self.endTime","def event_processing_finished(self):\n if self.status in ACTIVE_STATES:\n return False # tally of events is only available at end of run\n try:\n event_qs = self.get_event_queryset()\n except NotImplementedError:\n return True # Model without events, such as WFJT\n return self.emitted_events == event_qs.count()","def should_update(self) -> bool:\n if CoronaCaseRaw.objects.all().count() == 0:\n return True\n last_updated = CoronaCaseRaw.objects.latest('date_received').date_received\n return timezone.now() >= last_updated + timezone.timedelta(seconds=self.interval)","def is_available_at(self, datetime):\n for booking in self.booking_set.all():\n if booking.schedule_start <= datetime < booking.schedule_end and not booking.is_cancelled():\n return False\n return True","def is_ready_to_run(self, at_time):\n return (self.next_time - at_time) <= 0","def is_done(self):\n return time.time() - self._start > self._time","def is_complete(self):\n status = self.get_status()\n return status[\"status\"] == 4","def is_complete(self) -> bool:\n return (\n (\n self.materialized_subset | self.failed_and_downstream_subset\n ).num_partitions_and_non_partitioned_assets\n == self.target_subset.num_partitions_and_non_partitioned_assets\n )","def check(self):\n\n self.check_auto_update()\n assert not self.empty()","async def is_model_ready(\n self,\n model_name: str,\n model_version: str = ...,\n headers: dict[str, t.Any] = ...,\n ) -> bool:","def is_complete(self):\n return all([\n len(strip_tags(score.notes)) > 0 for score in self.scores.all()\n ])","def is_all_obsolete(self, now):\n if self._timestamps:\n return max(timestamps.last_active for timestamps in self._timestamps.itervalues()) + CANDIDATE_OBSOLETE < now\n return True","def checkGameComplete(self):\n for rowKey in self.table:\n for ele in self.table[rowKey]:\n if type(ele) == int:\n return False # means not complete\n return True","def _check_all_systems_ready(self):\n self._check_all_sensors_ready()\n return True","def _check_all_systems_ready(self):\n self._check_all_sensors_ready()\n return True","def is_complete(self):\n pass","def is_complete(self, assignment):\n for a in self.agents:\n if self.calc_agent_budget(a, assignment):\n return False\n for t in self.tasks:\n if self.calc_task_budget(t, assignment):\n return False\n return True","def _check_all_systems_ready(self):\n for r in self.robots:\n r.joints = None\n while r.joints is None and not rospy.is_shutdown():\n try:\n r.joints = rospy.wait_for_message(\n r.ns + '/joint_states', JointState, timeout=3.0)\n except:\n rospy.logerr(\"Current /joint_states not ready yet.\\n\\\n Do you spawn the robot and launch ros_control?\")\n try:\n r.model_index = rospy.wait_for_message('/gazebo/model_states', ModelStates, 3).name.index(r.ns[1:])\n except rospy.exceptions.ROSException:\n rospy.logerr(\"Robot model does not exist.\")\n\n # rospy.logdebug(\"ALL SYSTEMS READY\")\n return True","def isFresh(self, timestamp):\n pass;","def isFresh(self, timestamp):\n if not os.path.exists(self.__resource):\n return False;\n return os.path.getmtime(self.__resource) < timestamp;","def is_ready_to_reap(self):\n self.calc_progress()\n return self._num_results > 0 and (\n self._num_results == self.num_sown_batches\n )","def _check_all_systems_ready(self):\n \n self._check_all_sensors_ready()\n #self._check_joint_states_ready()\n self._check_cmd_vel_pub()\n \n return True","def isComplete(self):\n for n in range(9):\n for m in range(9):\n if self.puzzle[n][m] == 0:\n return False\n return True","def must_run(self):\r\n self.current_time = datetime.now()\r\n return all([self._minute(), self._hour(), self._day_of_month(), self._month(), self._day_of_week()])","def ready(self, flush_time):\n return len(self.t) > 0 and self.t[-1] >= flush_time","def check_if_ok_to_update(self):\n current_time = int(time.time())\n last_refresh = self.last_refresh\n if last_refresh is None:\n last_refresh = 0\n if current_time >= (last_refresh + self.refresh_rate):\n return True\n return False","def is_loaded(self):\n return self.known_stations != {}","def is_complete(self):\n qstat = self._grep_qstat('complete')\n comp = self._grep_status('complete')\n if qstat and comp:\n return True\n return False","def phone_timezones_have_been_processed():\n if settings.UNIT_TESTING:\n override = getattr(\n settings, 'PHONE_TIMEZONES_HAVE_BEEN_PROCESSED', None)\n if override is not None:\n return override\n return (_get_migration_status_from_threadlocals()\n == MigrationStatus.COMPLETE)","def is_updated(self):\n return self.timestamp > 0","def needs_update(self):\n now = time.time()/60\n return (self.last_update_time_in_minutes+self.timeout) < now","def check_consistency(object) -> bool:\n time = np.array(list(object.keys()))\n time_diff = time[1:] - time[0:-1]\n return np.all(time_diff == 1)","def has_t(self):\n return any(map(lambda s: s.is_temporal, self))","def db_status_ok():\n for Model in apps.get_models():\n table_name = Model._meta.db_table\n if not db_table_exists(table_name):\n return False\n return True","def needs_update(self, *path):\n dt_fmt = \"%Y-%m-%d %H:%M:%S\"\n try:\n linfo = self.info(*path)\n dt_local = datetime.datetime.strptime(\n linfo[\"datetime\"][:19], dt_fmt)\n dt_server = datetime.datetime.strptime(\n self.serverfiles.info(*path)[\"datetime\"][:19], dt_fmt)\n return dt_server > dt_local\n except FileNotFoundError:\n return True\n except KeyError:\n return True","def check_if_full(self):\n pass","def _check_if_statistics_calculation_is_needed():\n expiration_date = datetime.datetime.now(tz=datetime.timezone.utc) - datetime.timedelta(\n seconds=UploadHandler.EXPIRATION_TIME_IN_SECONDS)\n not_expired_data = UnprocessedData.objects.filter(uploaded_at__gte=expiration_date)\n sites_of_not_expired_data = not_expired_data.values_list('site_id', flat=True).distinct()\n all_sites = UnprocessedData.objects.filter(uploaded_at__lte=expiration_date).values_list('site_id',\n flat=True).distinct()\n for s in all_sites:\n if s not in sites_of_not_expired_data:\n from_date = UnprocessedData.objects.filter(site_id_id=s).order_by('from_date')[0].from_date\n to_date = UnprocessedData.objects.filter(site_id_id=s).order_by('-to_date')[0].to_date\n logger.info(\"should create stats for {} from {} to {}\".format(s, from_date, to_date))\n site_obj = get_object_or_404(Site, pk=s)\n UploadHandler.create_statistics(site=site_obj, from_date=from_date, to_date=to_date)\n UnprocessedData.objects.filter(site_id_id=s).delete()\n\n if len(sites_of_not_expired_data):\n Timer(UploadHandler.INTERVAL, UploadHandler._check_if_statistics_calculation_is_needed).start()\n else:\n UploadHandler.is_interval_running = False","def found_schedules(self) -> bool:\n return self._schedule_list != []","def is_done_in_the_past(self):\n return any(self.hash == rec['hash'] for rec in self.records)","def testAllTimesExists(self):\n times = []\n for ref in self.coal.get_community_references():\n times.append(self.coal.get_community_parameters(ref)[\"time\"])\n for time in times:\n self.assertTrue(time in self.times, msg=\"Time {} not in times.\".format(time))\n for time in self.times:\n self.assertTrue(time in times, msg=\"Time {} not in times.\".format(time))","def is_complete(self) -> bool:\n raise NotImplementedError(\"Base method not implemented\")","def toc(self,timestamp):\n return self._timestamp > timestamp","def objectsReady(self, n):\n return len(self.files) >= n","def is_any_active(self, now):\n if self._timestamps:\n return (now < max(timestamps.last_walk for timestamps in self._timestamps.itervalues()) + CANDIDATE_WALK_LIFETIME or\n now < max(timestamps.last_stumble for timestamps in self._timestamps.itervalues()) + CANDIDATE_STUMBLE_LIFETIME)\n return False","def is_complete(self):\n acquired_points = self.dset.shape[0]\n total_nr_pts = np.shape(self.get_sweep_points())[0]\n if acquired_points < total_nr_pts:\n return False\n elif acquired_points >= total_nr_pts:\n if self.soft_avg() != 1 and self.soft_iteration == 0:\n return False\n else:\n return True","def complete(self):\n return (self.memberDevices <= len(self.members)) or not self.exists","def complete(self):\n for runner in self._runners:\n if not runner.complete:\n return False\n return True","def check_models_ready(self):\n if not self.models_ready:\n raise RuntimeError(\"Models aren't loaded yet.\")","def allready(antReady) :\n return numNotready(antReady) == 0","def isComplete(self):\n assert len(self._x) > 0\n assert len(self._y) > 0\n assert 2 == len(self._data_array.shape)\n assert self.wkt is not None\n assert self.wkt != ''\n\n return True","def is_finished(self):\n self.refresh()\n return self.progress.remaining_budget is not None and self.progress.remaining_budget <= 0","def valid(self):\n delta = datetime.datetime.now() - self._last_access\n return ((delta.seconds < 7200) and not self.is_empty()) or \\\n (delta.seconds < 60)","def detect_completion(self):\n results_dir = glob.glob(f\"{self.production.rundir}\")\n if len(results_dir)>0: # dynesty_merge_result.json\n if len(glob.glob(os.path.join(results_dir[0], f\"extrinsic_posterior_samples.dat\"))) > 0:\n return True\n else:\n return False\n else:\n return False","def _check_completed(self):\n current_rung_df = self.sieve_board.loc[\n self.sieve_board['status'].isin(\n [StatusType.WAITTING, StatusType.RUNNING])\n ]\n if current_rung_df.empty:\n return True\n else:\n return False","def complete(self):\n return all((constraint.satisfied() for constraint in self.constraints))","def has_happened(self):\n\n return self.end < timezone.now()","def valid_update_flags(self) -> bool:\n if CoronaCaseRaw.objects.all().count() < 2:\n return True\n return not CoronaCaseRaw.objects.filter(update_flag=(not self.latest_flag())).exists()","def is_active(self):\n return (datetime.now() - self.updated).days < 100","def is_full(self):\n return len(self.cache_data) >= self.MAX_ITEMS","def completed(self):\n content = self.getContent()\n for field in self.fields.values():\n if not field.field.required:\n continue\n dm = zope.component.getMultiAdapter(\n (content, field.field), IDataManager)\n if dm.query(\n field.field.missing_value) is field.field.missing_value:\n return False\n return True","def is_training_completed(self) -> bool:\n return (self.model_dir/self.TRAINING_COMPLETED_FILE_NAME).is_file()","def validate_ts(self):\n try:\n self.get_log_file()\n\n self.parse_vibrations()\n\n self.obtain_geometries()\n\n self.percent_changes = self.obtain_percent_changes()\n\n\n center_values = np.log(\n self.percent_changes[self.percent_changes.center].percent_change.mean())\n shell_values = np.log(\n self.percent_changes[self.percent_changes.center != True].percent_change.mean())\n\n if center_values > shell_values + 1:\n logging.info(\"Vibrational analysis was successful\")\n return True\n else:\n logging.info(\n \"Cannot reasonably say that we have arrived at a TS through vibrational analysis.\")\n return False\n except AssertionError:\n logging.info(\"Something went wrong when attempting vibrational analysis...\")\n logging.info(\"Cannot verify via vibrational analysis\")\n return False","def _is_finished(self, as_of):\n if self.is_one_off():\n last_billing_cycle = self.get_billing_cycles()[self.total_billing_cycles - 1]\n return last_billing_cycle.date_range.upper <= as_of\n else:\n return False","def available(self) -> bool:\n return self._coordinator.last_update_success","def has_finished():","def valid(t):\n return float(t) > time.time()","def check_all_done(self, label: str):\n all_converged = True\n if not self.output[label]['convergence']:\n for job_type, spawn_job_type in self.job_types.items():\n if spawn_job_type and not self.output[label]['job_types'][job_type] \\\n and not ((self.species_dict[label].is_ts and job_type in ['scan', 'conformers'])\n or (self.species_dict[label].number_of_atoms == 1\n and job_type in ['conformers', 'opt', 'fine', 'freq', 'rotors', 'bde'])\n or job_type == 'bde' and self.species_dict[label].bdes is None\n or job_type == 'conformers'\n or job_type == 'irc'\n or job_type == 'tsg'):\n logger.debug(f'Species {label} did not converge.')\n all_converged = False\n break\n if all_converged:\n self.output[label]['convergence'] = True\n if self.species_dict[label].is_ts:\n self.species_dict[label].make_ts_report()\n logger.info(self.species_dict[label].ts_report + '\\n')\n zero_delta = datetime.timedelta(0)\n conf_time = extremum_list([job.run_time for job in self.job_dict[label]['conformers'].values()],\n return_min=False) \\\n if 'conformers' in self.job_dict[label].keys() else zero_delta\n tsg_time = extremum_list([job.run_time for job in self.job_dict[label]['tsg'].values()], return_min=False) \\\n if 'tsg' in self.job_dict[label].keys() else zero_delta\n opt_time = sum_time_delta([job.run_time for job in self.job_dict[label]['opt'].values()]) \\\n if 'opt' in self.job_dict[label].keys() else zero_delta\n comp_time = sum_time_delta([job.run_time for job in self.job_dict[label]['composite'].values()]) \\\n if 'composite' in self.job_dict[label].keys() else zero_delta\n other_time = extremum_list([sum_time_delta([job.run_time for job in job_dictionary.values()])\n for job_type, job_dictionary in self.job_dict[label].items()\n if job_type not in ['conformers', 'opt', 'composite']], return_min=False) \\\n if any([job_type not in ['conformers', 'opt', 'composite']\n for job_type in self.job_dict[label].keys()]) else zero_delta\n self.species_dict[label].run_time = self.species_dict[label].run_time \\\n or (conf_time or zero_delta) + \\\n (tsg_time or zero_delta) + \\\n (opt_time or zero_delta) + \\\n (comp_time or zero_delta) + \\\n (other_time or zero_delta)\n logger.info(f'\\nAll jobs for species {label} successfully converged. '\n f'Run time: {self.species_dict[label].run_time}')\n # Todo: any TS which did not converged (any rxn not calculated) should be reported here with full status: Was the family identified? Were TS guesses found? IF so, what's wrong?\n elif not self.species_dict[label].is_ts or self.species_dict[label].ts_guesses_exhausted:\n job_type_status = {key: val for key, val in self.output[label]['job_types'].items()\n if key in self.job_types and self.job_types[key]\n and (key != 'irc' or self.species_dict[label].is_ts)}\n logger.error(f'Species {label} did not converge. Job type status is: {job_type_status}')\n # Update restart dictionary and save the yaml restart file:\n self.save_restart_dict()","def status_check(self):\n from coordinator.tasks import cancel_release\n # Check if we hit the time limit\n last_update = self.events.order_by('-created_at')\\\n .first().created_at\n diff = datetime.datetime.utcnow() - last_update.replace(tzinfo=None)\n\n if diff.total_seconds() > settings.RELEASE_TIMEOUT:\n if self.state == 'canceling':\n return\n logger.error(f'canceling release {self.kf_id} for time out.')\n self.cancel()\n self.save()\n django_rq.enqueue(cancel_release, self.kf_id)\n return\n\n # Check if any contained tasks have failed/canceled\n for task in self.tasks.all():\n if task.state in ['failed', 'canceled', 'rejected']:\n if self.state == 'canceling':\n return\n logger.error(f'canceling release: {self.kf_id} task is ' +\n f'{task.state}')\n self.cancel()\n self.save()\n django_rq.enqueue(cancel_release, self.kf_id)\n return","def mark_completed_trips_from_obs(session, trip_ids_from_observations):\n print('checking {} observations for completed trips'.format(len(trip_ids_from_observations)))\n uncompleted = Trip.uncompleted(session)\n trips_with_no_observations = [trip for trip in uncompleted if trip.trip_id not in trip_ids_from_observations]\n for trip in trips_with_no_observations:\n trip.trip_end = TrainActivity.most_recent_for_trip_id(trip.trip_id).timestamp\n\n return len(trips_with_no_observations)","def full(self):\n return len(self.future_buffer) == self.CAPACITY","def is_ready_update(self):\n size_of_buffer = len(self.training_buffer.update_buffer['actions'])\n return size_of_buffer > max(int(self.trainer_parameters['buffer_size'] / self.policy.sequence_length), 1)","def check_pickle(src: \"list[str]\", targets: \"list[str]\"):\n src_time = np.array([os.path.getmtime(item) for item in src])\n targets_time = np.array([os.path.getmtime(item) for item in targets])\n for time in targets_time:\n if np.any(src_time > time):\n csv_pickle()\n print('new pickle data were successfully made.')\n break","def latest_flag(self) -> bool:\n if CoronaCaseRaw.objects.all().count() == 0:\n return True\n return CoronaCaseRaw.objects.latest('date_received').update_flag","def is_complete(self):\r\n # We normally don't want django dependencies in xmodule. foldit is\r\n # special. Import this late to avoid errors with things not yet being\r\n # initialized.\r\n from foldit.models import PuzzleComplete\r\n\r\n complete = PuzzleComplete.is_level_complete(\r\n self.system.anonymous_student_id,\r\n self.required_level,\r\n self.required_sublevel,\r\n self.due_time)\r\n return complete","def is_ready_update(self):\n size_of_buffer = len(self.training_buffer.update_buffer[\"actions\"])\n return size_of_buffer > max(\n int(self.trainer_parameters[\"buffer_size\"] / self.policy.sequence_length), 1\n )","def probe_completed(self, job, queue_lengths, current_time):\r\n events = []\r\n task_arrival_time = current_time + get_param(\"network_delay\")\r\n used = {}\r\n all_empty_queues = True\r\n for counter, task_constraints in enumerate(job.constraints):\r\n servers = [s for s in queue_lengths if s[0] in task_constraints]\r\n best = min(servers, key=lambda x: x[1])\r\n if best[1] > 0:\r\n all_empty_queues = False \r\n # Increment the load on the chosen server. Assumes value returned\r\n # is in units of one task.\r\n queue_lengths.remove(best)\r\n queue_lengths.append((best[0], best[1] + 1))\r\n if get_param(\"record_task_info\"):\r\n job.record_probe_result(counter, best[1])\r\n events.append((task_arrival_time,\r\n TaskArrival(best[0], job, counter)))\r\n if all_empty_queues:\r\n self.stats_manager.record_job_with_all_empty_queues()\r\n return events","def is_full(self):\n return len(self.keys) == self.order","def isValidationInProgress(self):\n validation = self.getLatestValidValidation()\n today = date.today()\n if validation and validation.getDownTo():\n validfrom = validation.getDownFrom().asdatetime().date()\n validto = validation.getDownTo().asdatetime().date()\n if validfrom <= today <= validto:\n return True\n return False","def has_timestamp(self):\n return (self.data_type() & 0x100 == 0x100) and (self.raw_data_length() >= 8)","def _check_all_systems_ready(self):\n raise NotImplementedError()","def Complete(self):\n return self.hour is not None and self.minute is not None and self.second is not None","def __is_complete__(self,config,mockdb):\n if GenericProcess.__is_complete__(self):\n return True\n if self.pipelines is None:\n return False\n for pipeline in self.__current_pipeline_list__(mockdb):\n if not pipeline.__is_complete__():\n return False\n return True","def is_ready(self):\n return self.prep_job.is_done()","def is_all_dates(self) -> bool:\n return False","def tasks_are_available(tasks):\n task_not_finished_not_scheduled_count = len(tasks)\n for task in tasks:\n if task.getisTaskFinished():\n continue\n if task.getisTaskScheduled():\n continue\n else:\n task_not_finished_not_scheduled_count -= 1\n if task_not_finished_not_scheduled_count < len(tasks):\n return True\n else:\n return False","def _should_run_now(self):\n # Assumes the unit/all values will have values.\n if not len(self._device_values.keys()) > 0:\n return False\n return not len(self._needed_devices) > 0","def __reader_check_complete_all_event(self):\n if self._complete_all_event.is_set():\n self.logger.info(\"Received complete all request event in reader\")\n self._reader_map = {}\n self.msg.put_into_Queue()\n return True"],"string":"[\n \"def complete(self, verbose=False):\\r\\n if (self.__num_tasks == 0 or\\r\\n self.__arrival_time == 0 or\\r\\n self.__num_tasks != len(self.__tasks)):\\r\\n #\\r\\n if verbose:\\r\\n print (\\\"Request %s incomplete. %d expected tasks, %d recorded tasks, \\\"\\r\\n \\\"arrival time %s\\\") % (self.__id, self.__num_tasks,\\r\\n len(self.__tasks), self.__arrival_time)\\r\\n return False\\r\\n for task in self.__tasks.values():\\r\\n if not task.complete(verbose):\\r\\n return False\\r\\n return True\",\n \"def complete(self, verbose=False):\\r\\n #if self.scheduler_launch_time == INVALID_TIME: print \\\"scheduler launch\\\"\\r\\n #if self.node_monitor_launch_time == INVALID_TIME: print \\\"nm launch\\\"\\r\\n #if self.completion_time == INVALID_TIME: print \\\"completion\\\"\\r\\n if verbose:\\r\\n if self.node_monitor_get_task_time == INVALID_TIME:\\r\\n print \\\"Task %s incomplete: node monitor get_task time missing\\\" % self.id\\r\\n elif self.scheduler_launch_time == INVALID_TIME:\\r\\n print \\\"Task %s incomplete: Scheduler launch time missing\\\" % self.id\\r\\n elif self.node_monitor_launch_time == INVALID_TIME:\\r\\n print \\\"Task %s incomplete: Node monitor launch time missing\\\" % self.id\\r\\n elif self.completion_time == INVALID_TIME:\\r\\n print \\\"Task %s incomplete: Completion time missing\\\" % self.id\\r\\n return (self.node_monitor_get_task_time != INVALID_TIME and\\r\\n self.scheduler_launch_time != INVALID_TIME and\\r\\n self.node_monitor_launch_time != INVALID_TIME and\\r\\n self.completion_time != INVALID_TIME)\",\n \"def complete(self):\\r\\n if self.scheduler_launch_time == INVALID_TIME:\\r\\n print \\\"Missing task scheduler launch time\\\"\\r\\n return False\\r\\n if self.node_monitor_launch_time == INVALID_TIME:\\r\\n\\t print \\\"Missing task node monitor launch time\\\"\\r\\n\\t return False\\r\\n\\tif self.completion_time == INVALID_TIME:\\r\\n\\t print \\\"Missing task completion time\\\"\\r\\n\\t return False\\r\\n\\tif self.clock_skew == INVALID_TIME_DELTA:\\r\\n print \\\"Missing task clock skew\\\"\\r\\n\\t return False\\r\\n\\treturn True\",\n \"def Complete(self):\\n return self.date.Complete() and self.time.Complete()\",\n \"def complete(self):\\n\\n return any(\\n [\\n self.is_complete(),\\n self.is_failed(),\\n self.is_out_of_memory(),\\n self.is_timeout(),\\n ]\\n )\",\n \"def has_full_batch(self) -> bool:\",\n \"def complete(self):\\r\\n if (self.__num_tasks == 0 or\\r\\n self.__arrival_time == 0 or\\r\\n self.__num_tasks != len(self.__tasks)):\\r\\n print (\\\"Expected to find %s tasks; found %s\\\" %\\r\\n (self.__num_tasks, len(self.__tasks)))\\r\\n return False\\r\\n for task in self.__tasks.values():\\r\\n if not task.complete():\\r\\n return False\\r\\n if len(self.__probes) == 0:\\r\\n return False # Don't consider non-probing requests\\r\\n return True\",\n \"def __updater_check_complete_all_event(self):\\n if self._complete_all_event.is_set():\\n self.logger.info(\\\"Received complete all request event in updater\\\")\\n self._updater_map = {}\\n self.msg.put_into_Queue()\\n return True\",\n \"def is_complete(self) -> bool:\\r\\n return path.exists(self._has_completed_path)\",\n \"def complete(self):\\r\\n\\tif self.launch_time == INVALID_TIME:\\r\\n\\t print \\\"Missing probe launch time\\\"\\r\\n return False\\r\\n if self.received_time == INVALID_TIME:\\r\\n print \\\"Missing probe received time\\\"\\r\\n return False\\r\\n if self.completion_time == INVALID_TIME:\\r\\n print \\\"Missing probe completion time\\\"\\r\\n return False\\r\\n return True\",\n \"def is_all_done():\\n for job_set in job_sets:\\n if job_set.status != SetStatus.COMPLETED:\\n return False\\n return True\",\n \"def is_blast_db_up_to_date(self):\\n if not self.have_blast_db():\\n return False\\n\\n # get time creation blast database files\\n modification_times = []\\n files = glob.glob(self.path)\\n for i in files:\\n mod_time_in_secs = os.stat(i).st_ctime\\n modification_times.append(datetime.datetime.fromtimestamp(mod_time_in_secs))\\n modification_times.sort(reverse=True)\\n time_creation_blast = modification_times[0].replace(tzinfo=pytz.utc)\\n\\n # get time creation time edited sequences in our database\\n time_created_queryset = Sequences.objects.all().order_by('-time_created')[:1]\\n time_created = time_created_queryset[0].time_created\\n\\n time_edited_queryset = Sequences.objects.all().order_by('-time_edited')[:1]\\n time_edited = time_edited_queryset[0].time_edited\\n\\n if time_created > time_creation_blast or time_edited > time_creation_blast:\\n return False\\n else:\\n return True\",\n \"def is_complete(self):\\n return not (self.year is None or\\n self.month is None or\\n self.day is None)\",\n \"def is_valid(self):\\n return self.startTime <= ApiKey.get_now() < self.endTime\",\n \"def event_processing_finished(self):\\n if self.status in ACTIVE_STATES:\\n return False # tally of events is only available at end of run\\n try:\\n event_qs = self.get_event_queryset()\\n except NotImplementedError:\\n return True # Model without events, such as WFJT\\n return self.emitted_events == event_qs.count()\",\n \"def should_update(self) -> bool:\\n if CoronaCaseRaw.objects.all().count() == 0:\\n return True\\n last_updated = CoronaCaseRaw.objects.latest('date_received').date_received\\n return timezone.now() >= last_updated + timezone.timedelta(seconds=self.interval)\",\n \"def is_available_at(self, datetime):\\n for booking in self.booking_set.all():\\n if booking.schedule_start <= datetime < booking.schedule_end and not booking.is_cancelled():\\n return False\\n return True\",\n \"def is_ready_to_run(self, at_time):\\n return (self.next_time - at_time) <= 0\",\n \"def is_done(self):\\n return time.time() - self._start > self._time\",\n \"def is_complete(self):\\n status = self.get_status()\\n return status[\\\"status\\\"] == 4\",\n \"def is_complete(self) -> bool:\\n return (\\n (\\n self.materialized_subset | self.failed_and_downstream_subset\\n ).num_partitions_and_non_partitioned_assets\\n == self.target_subset.num_partitions_and_non_partitioned_assets\\n )\",\n \"def check(self):\\n\\n self.check_auto_update()\\n assert not self.empty()\",\n \"async def is_model_ready(\\n self,\\n model_name: str,\\n model_version: str = ...,\\n headers: dict[str, t.Any] = ...,\\n ) -> bool:\",\n \"def is_complete(self):\\n return all([\\n len(strip_tags(score.notes)) > 0 for score in self.scores.all()\\n ])\",\n \"def is_all_obsolete(self, now):\\n if self._timestamps:\\n return max(timestamps.last_active for timestamps in self._timestamps.itervalues()) + CANDIDATE_OBSOLETE < now\\n return True\",\n \"def checkGameComplete(self):\\n for rowKey in self.table:\\n for ele in self.table[rowKey]:\\n if type(ele) == int:\\n return False # means not complete\\n return True\",\n \"def _check_all_systems_ready(self):\\n self._check_all_sensors_ready()\\n return True\",\n \"def _check_all_systems_ready(self):\\n self._check_all_sensors_ready()\\n return True\",\n \"def is_complete(self):\\n pass\",\n \"def is_complete(self, assignment):\\n for a in self.agents:\\n if self.calc_agent_budget(a, assignment):\\n return False\\n for t in self.tasks:\\n if self.calc_task_budget(t, assignment):\\n return False\\n return True\",\n \"def _check_all_systems_ready(self):\\n for r in self.robots:\\n r.joints = None\\n while r.joints is None and not rospy.is_shutdown():\\n try:\\n r.joints = rospy.wait_for_message(\\n r.ns + '/joint_states', JointState, timeout=3.0)\\n except:\\n rospy.logerr(\\\"Current /joint_states not ready yet.\\\\n\\\\\\n Do you spawn the robot and launch ros_control?\\\")\\n try:\\n r.model_index = rospy.wait_for_message('/gazebo/model_states', ModelStates, 3).name.index(r.ns[1:])\\n except rospy.exceptions.ROSException:\\n rospy.logerr(\\\"Robot model does not exist.\\\")\\n\\n # rospy.logdebug(\\\"ALL SYSTEMS READY\\\")\\n return True\",\n \"def isFresh(self, timestamp):\\n pass;\",\n \"def isFresh(self, timestamp):\\n if not os.path.exists(self.__resource):\\n return False;\\n return os.path.getmtime(self.__resource) < timestamp;\",\n \"def is_ready_to_reap(self):\\n self.calc_progress()\\n return self._num_results > 0 and (\\n self._num_results == self.num_sown_batches\\n )\",\n \"def _check_all_systems_ready(self):\\n \\n self._check_all_sensors_ready()\\n #self._check_joint_states_ready()\\n self._check_cmd_vel_pub()\\n \\n return True\",\n \"def isComplete(self):\\n for n in range(9):\\n for m in range(9):\\n if self.puzzle[n][m] == 0:\\n return False\\n return True\",\n \"def must_run(self):\\r\\n self.current_time = datetime.now()\\r\\n return all([self._minute(), self._hour(), self._day_of_month(), self._month(), self._day_of_week()])\",\n \"def ready(self, flush_time):\\n return len(self.t) > 0 and self.t[-1] >= flush_time\",\n \"def check_if_ok_to_update(self):\\n current_time = int(time.time())\\n last_refresh = self.last_refresh\\n if last_refresh is None:\\n last_refresh = 0\\n if current_time >= (last_refresh + self.refresh_rate):\\n return True\\n return False\",\n \"def is_loaded(self):\\n return self.known_stations != {}\",\n \"def is_complete(self):\\n qstat = self._grep_qstat('complete')\\n comp = self._grep_status('complete')\\n if qstat and comp:\\n return True\\n return False\",\n \"def phone_timezones_have_been_processed():\\n if settings.UNIT_TESTING:\\n override = getattr(\\n settings, 'PHONE_TIMEZONES_HAVE_BEEN_PROCESSED', None)\\n if override is not None:\\n return override\\n return (_get_migration_status_from_threadlocals()\\n == MigrationStatus.COMPLETE)\",\n \"def is_updated(self):\\n return self.timestamp > 0\",\n \"def needs_update(self):\\n now = time.time()/60\\n return (self.last_update_time_in_minutes+self.timeout) < now\",\n \"def check_consistency(object) -> bool:\\n time = np.array(list(object.keys()))\\n time_diff = time[1:] - time[0:-1]\\n return np.all(time_diff == 1)\",\n \"def has_t(self):\\n return any(map(lambda s: s.is_temporal, self))\",\n \"def db_status_ok():\\n for Model in apps.get_models():\\n table_name = Model._meta.db_table\\n if not db_table_exists(table_name):\\n return False\\n return True\",\n \"def needs_update(self, *path):\\n dt_fmt = \\\"%Y-%m-%d %H:%M:%S\\\"\\n try:\\n linfo = self.info(*path)\\n dt_local = datetime.datetime.strptime(\\n linfo[\\\"datetime\\\"][:19], dt_fmt)\\n dt_server = datetime.datetime.strptime(\\n self.serverfiles.info(*path)[\\\"datetime\\\"][:19], dt_fmt)\\n return dt_server > dt_local\\n except FileNotFoundError:\\n return True\\n except KeyError:\\n return True\",\n \"def check_if_full(self):\\n pass\",\n \"def _check_if_statistics_calculation_is_needed():\\n expiration_date = datetime.datetime.now(tz=datetime.timezone.utc) - datetime.timedelta(\\n seconds=UploadHandler.EXPIRATION_TIME_IN_SECONDS)\\n not_expired_data = UnprocessedData.objects.filter(uploaded_at__gte=expiration_date)\\n sites_of_not_expired_data = not_expired_data.values_list('site_id', flat=True).distinct()\\n all_sites = UnprocessedData.objects.filter(uploaded_at__lte=expiration_date).values_list('site_id',\\n flat=True).distinct()\\n for s in all_sites:\\n if s not in sites_of_not_expired_data:\\n from_date = UnprocessedData.objects.filter(site_id_id=s).order_by('from_date')[0].from_date\\n to_date = UnprocessedData.objects.filter(site_id_id=s).order_by('-to_date')[0].to_date\\n logger.info(\\\"should create stats for {} from {} to {}\\\".format(s, from_date, to_date))\\n site_obj = get_object_or_404(Site, pk=s)\\n UploadHandler.create_statistics(site=site_obj, from_date=from_date, to_date=to_date)\\n UnprocessedData.objects.filter(site_id_id=s).delete()\\n\\n if len(sites_of_not_expired_data):\\n Timer(UploadHandler.INTERVAL, UploadHandler._check_if_statistics_calculation_is_needed).start()\\n else:\\n UploadHandler.is_interval_running = False\",\n \"def found_schedules(self) -> bool:\\n return self._schedule_list != []\",\n \"def is_done_in_the_past(self):\\n return any(self.hash == rec['hash'] for rec in self.records)\",\n \"def testAllTimesExists(self):\\n times = []\\n for ref in self.coal.get_community_references():\\n times.append(self.coal.get_community_parameters(ref)[\\\"time\\\"])\\n for time in times:\\n self.assertTrue(time in self.times, msg=\\\"Time {} not in times.\\\".format(time))\\n for time in self.times:\\n self.assertTrue(time in times, msg=\\\"Time {} not in times.\\\".format(time))\",\n \"def is_complete(self) -> bool:\\n raise NotImplementedError(\\\"Base method not implemented\\\")\",\n \"def toc(self,timestamp):\\n return self._timestamp > timestamp\",\n \"def objectsReady(self, n):\\n return len(self.files) >= n\",\n \"def is_any_active(self, now):\\n if self._timestamps:\\n return (now < max(timestamps.last_walk for timestamps in self._timestamps.itervalues()) + CANDIDATE_WALK_LIFETIME or\\n now < max(timestamps.last_stumble for timestamps in self._timestamps.itervalues()) + CANDIDATE_STUMBLE_LIFETIME)\\n return False\",\n \"def is_complete(self):\\n acquired_points = self.dset.shape[0]\\n total_nr_pts = np.shape(self.get_sweep_points())[0]\\n if acquired_points < total_nr_pts:\\n return False\\n elif acquired_points >= total_nr_pts:\\n if self.soft_avg() != 1 and self.soft_iteration == 0:\\n return False\\n else:\\n return True\",\n \"def complete(self):\\n return (self.memberDevices <= len(self.members)) or not self.exists\",\n \"def complete(self):\\n for runner in self._runners:\\n if not runner.complete:\\n return False\\n return True\",\n \"def check_models_ready(self):\\n if not self.models_ready:\\n raise RuntimeError(\\\"Models aren't loaded yet.\\\")\",\n \"def allready(antReady) :\\n return numNotready(antReady) == 0\",\n \"def isComplete(self):\\n assert len(self._x) > 0\\n assert len(self._y) > 0\\n assert 2 == len(self._data_array.shape)\\n assert self.wkt is not None\\n assert self.wkt != ''\\n\\n return True\",\n \"def is_finished(self):\\n self.refresh()\\n return self.progress.remaining_budget is not None and self.progress.remaining_budget <= 0\",\n \"def valid(self):\\n delta = datetime.datetime.now() - self._last_access\\n return ((delta.seconds < 7200) and not self.is_empty()) or \\\\\\n (delta.seconds < 60)\",\n \"def detect_completion(self):\\n results_dir = glob.glob(f\\\"{self.production.rundir}\\\")\\n if len(results_dir)>0: # dynesty_merge_result.json\\n if len(glob.glob(os.path.join(results_dir[0], f\\\"extrinsic_posterior_samples.dat\\\"))) > 0:\\n return True\\n else:\\n return False\\n else:\\n return False\",\n \"def _check_completed(self):\\n current_rung_df = self.sieve_board.loc[\\n self.sieve_board['status'].isin(\\n [StatusType.WAITTING, StatusType.RUNNING])\\n ]\\n if current_rung_df.empty:\\n return True\\n else:\\n return False\",\n \"def complete(self):\\n return all((constraint.satisfied() for constraint in self.constraints))\",\n \"def has_happened(self):\\n\\n return self.end < timezone.now()\",\n \"def valid_update_flags(self) -> bool:\\n if CoronaCaseRaw.objects.all().count() < 2:\\n return True\\n return not CoronaCaseRaw.objects.filter(update_flag=(not self.latest_flag())).exists()\",\n \"def is_active(self):\\n return (datetime.now() - self.updated).days < 100\",\n \"def is_full(self):\\n return len(self.cache_data) >= self.MAX_ITEMS\",\n \"def completed(self):\\n content = self.getContent()\\n for field in self.fields.values():\\n if not field.field.required:\\n continue\\n dm = zope.component.getMultiAdapter(\\n (content, field.field), IDataManager)\\n if dm.query(\\n field.field.missing_value) is field.field.missing_value:\\n return False\\n return True\",\n \"def is_training_completed(self) -> bool:\\n return (self.model_dir/self.TRAINING_COMPLETED_FILE_NAME).is_file()\",\n \"def validate_ts(self):\\n try:\\n self.get_log_file()\\n\\n self.parse_vibrations()\\n\\n self.obtain_geometries()\\n\\n self.percent_changes = self.obtain_percent_changes()\\n\\n\\n center_values = np.log(\\n self.percent_changes[self.percent_changes.center].percent_change.mean())\\n shell_values = np.log(\\n self.percent_changes[self.percent_changes.center != True].percent_change.mean())\\n\\n if center_values > shell_values + 1:\\n logging.info(\\\"Vibrational analysis was successful\\\")\\n return True\\n else:\\n logging.info(\\n \\\"Cannot reasonably say that we have arrived at a TS through vibrational analysis.\\\")\\n return False\\n except AssertionError:\\n logging.info(\\\"Something went wrong when attempting vibrational analysis...\\\")\\n logging.info(\\\"Cannot verify via vibrational analysis\\\")\\n return False\",\n \"def _is_finished(self, as_of):\\n if self.is_one_off():\\n last_billing_cycle = self.get_billing_cycles()[self.total_billing_cycles - 1]\\n return last_billing_cycle.date_range.upper <= as_of\\n else:\\n return False\",\n \"def available(self) -> bool:\\n return self._coordinator.last_update_success\",\n \"def has_finished():\",\n \"def valid(t):\\n return float(t) > time.time()\",\n \"def check_all_done(self, label: str):\\n all_converged = True\\n if not self.output[label]['convergence']:\\n for job_type, spawn_job_type in self.job_types.items():\\n if spawn_job_type and not self.output[label]['job_types'][job_type] \\\\\\n and not ((self.species_dict[label].is_ts and job_type in ['scan', 'conformers'])\\n or (self.species_dict[label].number_of_atoms == 1\\n and job_type in ['conformers', 'opt', 'fine', 'freq', 'rotors', 'bde'])\\n or job_type == 'bde' and self.species_dict[label].bdes is None\\n or job_type == 'conformers'\\n or job_type == 'irc'\\n or job_type == 'tsg'):\\n logger.debug(f'Species {label} did not converge.')\\n all_converged = False\\n break\\n if all_converged:\\n self.output[label]['convergence'] = True\\n if self.species_dict[label].is_ts:\\n self.species_dict[label].make_ts_report()\\n logger.info(self.species_dict[label].ts_report + '\\\\n')\\n zero_delta = datetime.timedelta(0)\\n conf_time = extremum_list([job.run_time for job in self.job_dict[label]['conformers'].values()],\\n return_min=False) \\\\\\n if 'conformers' in self.job_dict[label].keys() else zero_delta\\n tsg_time = extremum_list([job.run_time for job in self.job_dict[label]['tsg'].values()], return_min=False) \\\\\\n if 'tsg' in self.job_dict[label].keys() else zero_delta\\n opt_time = sum_time_delta([job.run_time for job in self.job_dict[label]['opt'].values()]) \\\\\\n if 'opt' in self.job_dict[label].keys() else zero_delta\\n comp_time = sum_time_delta([job.run_time for job in self.job_dict[label]['composite'].values()]) \\\\\\n if 'composite' in self.job_dict[label].keys() else zero_delta\\n other_time = extremum_list([sum_time_delta([job.run_time for job in job_dictionary.values()])\\n for job_type, job_dictionary in self.job_dict[label].items()\\n if job_type not in ['conformers', 'opt', 'composite']], return_min=False) \\\\\\n if any([job_type not in ['conformers', 'opt', 'composite']\\n for job_type in self.job_dict[label].keys()]) else zero_delta\\n self.species_dict[label].run_time = self.species_dict[label].run_time \\\\\\n or (conf_time or zero_delta) + \\\\\\n (tsg_time or zero_delta) + \\\\\\n (opt_time or zero_delta) + \\\\\\n (comp_time or zero_delta) + \\\\\\n (other_time or zero_delta)\\n logger.info(f'\\\\nAll jobs for species {label} successfully converged. '\\n f'Run time: {self.species_dict[label].run_time}')\\n # Todo: any TS which did not converged (any rxn not calculated) should be reported here with full status: Was the family identified? Were TS guesses found? IF so, what's wrong?\\n elif not self.species_dict[label].is_ts or self.species_dict[label].ts_guesses_exhausted:\\n job_type_status = {key: val for key, val in self.output[label]['job_types'].items()\\n if key in self.job_types and self.job_types[key]\\n and (key != 'irc' or self.species_dict[label].is_ts)}\\n logger.error(f'Species {label} did not converge. Job type status is: {job_type_status}')\\n # Update restart dictionary and save the yaml restart file:\\n self.save_restart_dict()\",\n \"def status_check(self):\\n from coordinator.tasks import cancel_release\\n # Check if we hit the time limit\\n last_update = self.events.order_by('-created_at')\\\\\\n .first().created_at\\n diff = datetime.datetime.utcnow() - last_update.replace(tzinfo=None)\\n\\n if diff.total_seconds() > settings.RELEASE_TIMEOUT:\\n if self.state == 'canceling':\\n return\\n logger.error(f'canceling release {self.kf_id} for time out.')\\n self.cancel()\\n self.save()\\n django_rq.enqueue(cancel_release, self.kf_id)\\n return\\n\\n # Check if any contained tasks have failed/canceled\\n for task in self.tasks.all():\\n if task.state in ['failed', 'canceled', 'rejected']:\\n if self.state == 'canceling':\\n return\\n logger.error(f'canceling release: {self.kf_id} task is ' +\\n f'{task.state}')\\n self.cancel()\\n self.save()\\n django_rq.enqueue(cancel_release, self.kf_id)\\n return\",\n \"def mark_completed_trips_from_obs(session, trip_ids_from_observations):\\n print('checking {} observations for completed trips'.format(len(trip_ids_from_observations)))\\n uncompleted = Trip.uncompleted(session)\\n trips_with_no_observations = [trip for trip in uncompleted if trip.trip_id not in trip_ids_from_observations]\\n for trip in trips_with_no_observations:\\n trip.trip_end = TrainActivity.most_recent_for_trip_id(trip.trip_id).timestamp\\n\\n return len(trips_with_no_observations)\",\n \"def full(self):\\n return len(self.future_buffer) == self.CAPACITY\",\n \"def is_ready_update(self):\\n size_of_buffer = len(self.training_buffer.update_buffer['actions'])\\n return size_of_buffer > max(int(self.trainer_parameters['buffer_size'] / self.policy.sequence_length), 1)\",\n \"def check_pickle(src: \\\"list[str]\\\", targets: \\\"list[str]\\\"):\\n src_time = np.array([os.path.getmtime(item) for item in src])\\n targets_time = np.array([os.path.getmtime(item) for item in targets])\\n for time in targets_time:\\n if np.any(src_time > time):\\n csv_pickle()\\n print('new pickle data were successfully made.')\\n break\",\n \"def latest_flag(self) -> bool:\\n if CoronaCaseRaw.objects.all().count() == 0:\\n return True\\n return CoronaCaseRaw.objects.latest('date_received').update_flag\",\n \"def is_complete(self):\\r\\n # We normally don't want django dependencies in xmodule. foldit is\\r\\n # special. Import this late to avoid errors with things not yet being\\r\\n # initialized.\\r\\n from foldit.models import PuzzleComplete\\r\\n\\r\\n complete = PuzzleComplete.is_level_complete(\\r\\n self.system.anonymous_student_id,\\r\\n self.required_level,\\r\\n self.required_sublevel,\\r\\n self.due_time)\\r\\n return complete\",\n \"def is_ready_update(self):\\n size_of_buffer = len(self.training_buffer.update_buffer[\\\"actions\\\"])\\n return size_of_buffer > max(\\n int(self.trainer_parameters[\\\"buffer_size\\\"] / self.policy.sequence_length), 1\\n )\",\n \"def probe_completed(self, job, queue_lengths, current_time):\\r\\n events = []\\r\\n task_arrival_time = current_time + get_param(\\\"network_delay\\\")\\r\\n used = {}\\r\\n all_empty_queues = True\\r\\n for counter, task_constraints in enumerate(job.constraints):\\r\\n servers = [s for s in queue_lengths if s[0] in task_constraints]\\r\\n best = min(servers, key=lambda x: x[1])\\r\\n if best[1] > 0:\\r\\n all_empty_queues = False \\r\\n # Increment the load on the chosen server. Assumes value returned\\r\\n # is in units of one task.\\r\\n queue_lengths.remove(best)\\r\\n queue_lengths.append((best[0], best[1] + 1))\\r\\n if get_param(\\\"record_task_info\\\"):\\r\\n job.record_probe_result(counter, best[1])\\r\\n events.append((task_arrival_time,\\r\\n TaskArrival(best[0], job, counter)))\\r\\n if all_empty_queues:\\r\\n self.stats_manager.record_job_with_all_empty_queues()\\r\\n return events\",\n \"def is_full(self):\\n return len(self.keys) == self.order\",\n \"def isValidationInProgress(self):\\n validation = self.getLatestValidValidation()\\n today = date.today()\\n if validation and validation.getDownTo():\\n validfrom = validation.getDownFrom().asdatetime().date()\\n validto = validation.getDownTo().asdatetime().date()\\n if validfrom <= today <= validto:\\n return True\\n return False\",\n \"def has_timestamp(self):\\n return (self.data_type() & 0x100 == 0x100) and (self.raw_data_length() >= 8)\",\n \"def _check_all_systems_ready(self):\\n raise NotImplementedError()\",\n \"def Complete(self):\\n return self.hour is not None and self.minute is not None and self.second is not None\",\n \"def __is_complete__(self,config,mockdb):\\n if GenericProcess.__is_complete__(self):\\n return True\\n if self.pipelines is None:\\n return False\\n for pipeline in self.__current_pipeline_list__(mockdb):\\n if not pipeline.__is_complete__():\\n return False\\n return True\",\n \"def is_ready(self):\\n return self.prep_job.is_done()\",\n \"def is_all_dates(self) -> bool:\\n return False\",\n \"def tasks_are_available(tasks):\\n task_not_finished_not_scheduled_count = len(tasks)\\n for task in tasks:\\n if task.getisTaskFinished():\\n continue\\n if task.getisTaskScheduled():\\n continue\\n else:\\n task_not_finished_not_scheduled_count -= 1\\n if task_not_finished_not_scheduled_count < len(tasks):\\n return True\\n else:\\n return False\",\n \"def _should_run_now(self):\\n # Assumes the unit/all values will have values.\\n if not len(self._device_values.keys()) > 0:\\n return False\\n return not len(self._needed_devices) > 0\",\n \"def __reader_check_complete_all_event(self):\\n if self._complete_all_event.is_set():\\n self.logger.info(\\\"Received complete all request event in reader\\\")\\n self._reader_map = {}\\n self.msg.put_into_Queue()\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.617297","0.6029285","0.59201694","0.5905041","0.5833025","0.5806174","0.58009607","0.5789535","0.5736804","0.56675947","0.566142","0.56097996","0.55602163","0.55594164","0.555323","0.55530936","0.55497646","0.55493504","0.5537272","0.5535819","0.55284566","0.55262035","0.55253464","0.549965","0.5499578","0.54865664","0.5479413","0.5479413","0.54698735","0.54668367","0.5461132","0.5460218","0.5455605","0.5453121","0.5451642","0.5440424","0.5439322","0.54299","0.54235196","0.5388577","0.53882843","0.538827","0.5375584","0.53749406","0.53450537","0.5338437","0.53345966","0.5329436","0.53242314","0.53082365","0.5307732","0.5302545","0.5291354","0.52841103","0.5276517","0.52654487","0.52581376","0.52532995","0.52503395","0.5235934","0.52306503","0.52237797","0.5223372","0.5217331","0.52121717","0.520216","0.52004707","0.5189944","0.5186668","0.5179705","0.51797014","0.5176425","0.51747656","0.5173572","0.5160912","0.51510674","0.5119131","0.5116034","0.51115155","0.510899","0.5108539","0.5108255","0.51034886","0.5102479","0.50999635","0.50980234","0.50967324","0.5096016","0.50959855","0.50876546","0.50867087","0.5085624","0.5080901","0.5072522","0.5069816","0.5061298","0.5057441","0.5044814","0.5043063","0.5041278"],"string":"[\n \"0.617297\",\n \"0.6029285\",\n \"0.59201694\",\n \"0.5905041\",\n \"0.5833025\",\n \"0.5806174\",\n \"0.58009607\",\n \"0.5789535\",\n \"0.5736804\",\n \"0.56675947\",\n \"0.566142\",\n \"0.56097996\",\n \"0.55602163\",\n \"0.55594164\",\n \"0.555323\",\n \"0.55530936\",\n \"0.55497646\",\n \"0.55493504\",\n \"0.5537272\",\n \"0.5535819\",\n \"0.55284566\",\n \"0.55262035\",\n \"0.55253464\",\n \"0.549965\",\n \"0.5499578\",\n \"0.54865664\",\n \"0.5479413\",\n \"0.5479413\",\n \"0.54698735\",\n \"0.54668367\",\n \"0.5461132\",\n \"0.5460218\",\n \"0.5455605\",\n \"0.5453121\",\n \"0.5451642\",\n \"0.5440424\",\n \"0.5439322\",\n \"0.54299\",\n \"0.54235196\",\n \"0.5388577\",\n \"0.53882843\",\n \"0.538827\",\n \"0.5375584\",\n \"0.53749406\",\n \"0.53450537\",\n \"0.5338437\",\n \"0.53345966\",\n \"0.5329436\",\n \"0.53242314\",\n \"0.53082365\",\n \"0.5307732\",\n \"0.5302545\",\n \"0.5291354\",\n \"0.52841103\",\n \"0.5276517\",\n \"0.52654487\",\n \"0.52581376\",\n \"0.52532995\",\n \"0.52503395\",\n \"0.5235934\",\n \"0.52306503\",\n \"0.52237797\",\n \"0.5223372\",\n \"0.5217331\",\n \"0.52121717\",\n \"0.520216\",\n \"0.52004707\",\n \"0.5189944\",\n \"0.5186668\",\n \"0.5179705\",\n \"0.51797014\",\n \"0.5176425\",\n \"0.51747656\",\n \"0.5173572\",\n \"0.5160912\",\n \"0.51510674\",\n \"0.5119131\",\n \"0.5116034\",\n \"0.51115155\",\n \"0.510899\",\n \"0.5108539\",\n \"0.5108255\",\n \"0.51034886\",\n \"0.5102479\",\n \"0.50999635\",\n \"0.50980234\",\n \"0.50967324\",\n \"0.5096016\",\n \"0.50959855\",\n \"0.50876546\",\n \"0.50867087\",\n \"0.5085624\",\n \"0.5080901\",\n \"0.5072522\",\n \"0.5069816\",\n \"0.5061298\",\n \"0.5057441\",\n \"0.5044814\",\n \"0.5043063\",\n \"0.5041278\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.57210994"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":8465,"cells":{"query":{"kind":"string","value":"Empties the models within the bag"},"document":{"kind":"string","value":"def empty_bag(self):\n if self.peds is not None:\n for _, model in self.peds.items():\n model.reset()\n self.drone.reset()\n self.subject.reset()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clear():\n\t\tModel.counter = 0","def ClearModels(self):\n self._modelFileNames = []\n self._models = []\n self.Modified(readAgain=True)","def clearmodels(self):\n \n dbpath, config = self._start() \n ModelDescriptionTable(dbpath).empty()\n ModelPhenotypeTable(dbpath).empty()\n ModelScoreTable(dbpath).empty() \n self._end()","def reset_bag(self):","def clear(self) -> None:\n self.objects = []","def clear(self):\n self.beginResetModel()\n self.root_item = RootItem()\n self.requests_items = {}\n self.endResetModel()","def resetmodel(self):\n for key, value in self._dentsvertsdata.items():\n value.free()\n self._dentsvertsdata.clear()","def clear_batch(self):\n self._batch_idx = 0\n self.variant_states = None\n self.object_specs = None\n self.object_attribute_values = None","def clear(self):\n for ob in self.obs:\n ob.clear()\n return","def clear(self):\r\n\t\tself.free_objects[:] = []","def clear(self) -> None:\n # Creates a new, empty bag and assigns self.da to the new, empty bag.\n new_bag = Bag()\n self.da = new_bag.da","def clear():\n MIGRATIONS.clear()","def clear(self):\n self.vars = []","def clear_all():\n bpy.ops.object.select_all(action='SELECT')\n bpy.ops.object.delete()","def _finalize(self):\n for model in self.models:\n model._finalize()","def clear(self):\n self._items = []","def clear(self) -> None:\n self.saved.clear()","def clear(self):\n\n\t\tfor chain in self.chain:\n\t\t\tchain.clear()\n\n\t\tself.chain = []\n\t\tself.remark = []","def clear(self):\r\n self.orderitem_set.all().delete()","def reset(self):\n self.vna.write(edit_list(self.model))\n self.vna.write(clear_list(self.model))","def clear(self):\n self._pkcache = {}\n self._typecache = defaultdict(dict)\n self.init()","def clear(self):\n self._store = {}","def reset(self):\n self.entities = set()\n self.frozen = False","def reset(self):\n logging.info(\"Resetting DINTModel.\")\n if self.classifier:\n self.server.remove_model(self.classifier)\n # for ds in self.server.datasets:\n # self.server.remove_dataset(ds)\n # TODO: remove datasets?\n self.classifier = None","def reset(self):\n self._setupObjects()","def reset(self):\n # Clear mutable data, but leave the immutables intact\n self.train_data = {}\n self.val_data = {}\n self.test_data = {}\n self.model_files = []\n self.custom_data = {}\n # Remove all the physical assets\n for item in os.scandir(self.root_path):\n os.remove(item.path)\n # Reserialize\n self.serialize()","def clear(self):\n self.versions = {}\n self.clearItems()","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def tearDown(self):\n for model in MODELS:\n for obj in model.objects.all():\n obj.delete()","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def reset(self):\n for provider in self.providers.values():\n provider.reset()\n\n for observation in self.observations.values():\n observation.reset()","def clear_dummy_obj(self):\n for d in self.dummies:\n self.map.remove_node(d)\n\n self.dummies = []","def clear(self):\n # Orphan all objects\n for obj in self.uow:\n state(obj).session = None\n self.uow.clear()\n self.imap.clear()","def clear_all(self):\n raise NotImplementedError","def _data_reset(self):\n conn = self.get_connection()\n\n elements = {\n **self.domain.registry.aggregates,\n **self.domain.registry.entities,\n **self.domain.registry.views,\n }\n for _, element_record in elements.items():\n provider = current_domain.providers[element_record.cls.meta_.provider]\n repo = self.domain.repository_for(element_record.cls)\n\n model_cls = repo._model\n if provider.conn_info[\n \"DATABASE\"\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\n model_cls._index._name\n ):\n conn.delete_by_query(\n refresh=True,\n index=model_cls._index._name,\n body={\"query\": {\"match_all\": {}}},\n )","def reset(self):\n self.dict_lock.acquire()\n self.list_lock.acquire()\n\n self.beginResetModel()\n self.levels_dict = {}\n self.view_list = []\n self.endResetModel()\n \n self.list_lock.release()\n self.dict_lock.release()","def clear(self) -> None:\n self.items.all().delete()\n self._cached_items = None","def clear_model_cache():\n global __model_cache\n __model_cache = {}","def remove_objects(self):\n logger.debug('Removing all objects from model.')\n del self._objects[:]","def clear(self) -> None:","def full_clear(self):\n self.clear()\n self.class_hooks.clear()","def reset(self):\n for layer in self.network:\n layer.clean()","def clear(self):\n ...","def clear(self) -> None:\n self._store.clear()","def clear(self):\n self.__dict__.clear()","def clear(self):\n if self.debug:\n print(\"DIMS cleared\")\n self.sp_dicts.clear()","def remove_all_recs(self):\n return self.storage.clear()","def clear_data():\n logger.info(\"Delete Structure instances\")\n Structure.objects.all().delete()\n logger.info(\"Delete StructureType instances\")\n StructureType.objects.all().delete()\n logger.info(\"Delete Industry instances\")\n Industry.objects.all().delete()\n logger.info(\"Delete Price instances\")\n PriceList.objects.all().delete()\n logger.info(\"Delete Stock instances\")\n Stock.objects.all().delete()\n logger.info(\"Delete News instances\")\n News.objects.all().delete()\n logger.info(\"Delete NewsImages instances\")\n NewsImage.objects.all().delete()\n logger.info(\"Delete News Sections instances\")\n NewsCategorySection.objects.all().delete()\n logger.info(\"Delete Analysis instances\")\n AnalysisOpinion.objects.all().delete()\n logger.info(\"Delete Analysis Images instances\")\n AnalysisImage.objects.all().delete()\n logger.info(\"Delete Analysis Sections instances\")\n AnalysisCategorySection.objects.all().delete()","def clear_data(self):\n self.game_list.clear()\n self.game_scores.clear()","def clear(self, using=None):\n backend = self.get_backend(using)\n\n if backend is not None:\n backend.clear(models=[self.get_model()])","def clear(self):\n pass","def clear(self):\n pass","def clear(self):\n pass","def _reset(self) -> None:\n self.images = []\n self.activations = []\n self.labels = []\n self.preds = []\n self.n_found = 0","def clear_all(self):\n self._data = {}\n self.uncache()\n self.dirty = True\n self.shipping_method = None\n self.payment_method = None\n self.customer_comment = \"\"","def _reset(self):\n self._model._reset()\n super(RDPAnalyzer, self)._reset()","def clear(self):\n self.recorders = set([])\n self.reset()\n\n # Stop any currently running SpiNNaker application\n self.stop()","def empty(self):\n self.items = []\n self.totalWeight = 0","def clear_model_sigs(self):\n self._model_sigs.clear()","def clear(self):\n for key in self.__data.keys():\n del self.__data[key]","def clear():","def reset():\n teardown_db()\n build()","def clearStore(self):\n os.remove(self.uid+\".pcl\")\n self.items = []","def clear_collection(self):\n self._cards = []","def clean(self):\r\n self.roadrunnerModel = None\r\n return self","def clearList(self):\r\n self.skills.clear()\r\n del self.orderedSkills[:]","def clear(self):\r\n try:\r\n while True:\r\n self.pop()\r\n except KeyError:\r\n pass","def reset(self):\r\n self.pop()","def _purge(self):\n for _ in self.all():\n self.delete(_)","def reset(self):\n self.__sets = []\n self._computed = False","def clear(self):\n try:\n self._load(False)\n except KeyError:\n return\n\n for i in xrange(self.size):\n try:\n del self.db[i]\n except KeyError:\n pass\n del self.db['count']\n del self.db['head']\n del self.db['size']","def _unload(apps, schema_editor):\n for modelname in models:\n model = apps.get_model(appname, modelname)\n model.objects.all().delete()","def clear(self):\n for key in self.keys():\n del self[key]","def __reset__(self):\n\n for i in self.__dict__.keys():\n self.__dict__[i] = None","def clear(self) -> None:\n ...","def clear(self):\n self.__list = []","def reset(self):\n self._pkgs.clear()\n self._catalogs.clear()\n self._categories.clear()\n self._command_to_category.clear()\n self._version = None","def clear(self):\n self._in_memory_objects.clear()\n self._disk_objects.clear()\n self._object_to_list_node.clear()\n for bucket in self._buckets:\n bucket.clear()","def reset(self):\n for item in TextChannelFilterItem.objects(channel_filter=self):\n item.delete()\n self.reset_counters()\n self.retrain()","def clear(self):\n self._zombie_list = []\n self._human_list = []\n poc_grid.Grid.clear(self)","def clear(self) -> None:\n self.selected = {}\n self.orderings = []\n self.predicate = None\n self.limit_index = None\n self.offset_index = None\n self.callbacks.clear()","def reset(self, model):\n for obs_func in self.observation_functions:\n obs_func.reset(model)","def clear(self) -> None:\n self._items = []\n self._size = 0","def clear(self):\n self._clear()","def clear(self):\n self._clear()","def clear(self):\n self.tensor_store.clear()\n self.i = 0","def clear(self):\r\n poc_grid.Grid.clear(self)\r\n self._zombie_list = []\r\n self._human_list = []","def reset(self):\n self.satisfiability = Satisfiability.UNTESTED\n self.model = None\n self.unsatCore = []","def clear(self):\n self.keyvaluepair_set.all().delete()","def clear(self):\n self._list.clear()","def clear(self):\n self.weight = 0","def clear(self):\n # we want to use self.inputs.clear() but it's not in python2\n del self.inputs[:]\n del self.outputs[:]","def clear(self):\n poc_grid.Grid.clear(self)\n self._zombie_list = []\n self._human_list = []\n # need proof it works"],"string":"[\n \"def clear():\\n\\t\\tModel.counter = 0\",\n \"def ClearModels(self):\\n self._modelFileNames = []\\n self._models = []\\n self.Modified(readAgain=True)\",\n \"def clearmodels(self):\\n \\n dbpath, config = self._start() \\n ModelDescriptionTable(dbpath).empty()\\n ModelPhenotypeTable(dbpath).empty()\\n ModelScoreTable(dbpath).empty() \\n self._end()\",\n \"def reset_bag(self):\",\n \"def clear(self) -> None:\\n self.objects = []\",\n \"def clear(self):\\n self.beginResetModel()\\n self.root_item = RootItem()\\n self.requests_items = {}\\n self.endResetModel()\",\n \"def resetmodel(self):\\n for key, value in self._dentsvertsdata.items():\\n value.free()\\n self._dentsvertsdata.clear()\",\n \"def clear_batch(self):\\n self._batch_idx = 0\\n self.variant_states = None\\n self.object_specs = None\\n self.object_attribute_values = None\",\n \"def clear(self):\\n for ob in self.obs:\\n ob.clear()\\n return\",\n \"def clear(self):\\r\\n\\t\\tself.free_objects[:] = []\",\n \"def clear(self) -> None:\\n # Creates a new, empty bag and assigns self.da to the new, empty bag.\\n new_bag = Bag()\\n self.da = new_bag.da\",\n \"def clear():\\n MIGRATIONS.clear()\",\n \"def clear(self):\\n self.vars = []\",\n \"def clear_all():\\n bpy.ops.object.select_all(action='SELECT')\\n bpy.ops.object.delete()\",\n \"def _finalize(self):\\n for model in self.models:\\n model._finalize()\",\n \"def clear(self):\\n self._items = []\",\n \"def clear(self) -> None:\\n self.saved.clear()\",\n \"def clear(self):\\n\\n\\t\\tfor chain in self.chain:\\n\\t\\t\\tchain.clear()\\n\\n\\t\\tself.chain = []\\n\\t\\tself.remark = []\",\n \"def clear(self):\\r\\n self.orderitem_set.all().delete()\",\n \"def reset(self):\\n self.vna.write(edit_list(self.model))\\n self.vna.write(clear_list(self.model))\",\n \"def clear(self):\\n self._pkcache = {}\\n self._typecache = defaultdict(dict)\\n self.init()\",\n \"def clear(self):\\n self._store = {}\",\n \"def reset(self):\\n self.entities = set()\\n self.frozen = False\",\n \"def reset(self):\\n logging.info(\\\"Resetting DINTModel.\\\")\\n if self.classifier:\\n self.server.remove_model(self.classifier)\\n # for ds in self.server.datasets:\\n # self.server.remove_dataset(ds)\\n # TODO: remove datasets?\\n self.classifier = None\",\n \"def reset(self):\\n self._setupObjects()\",\n \"def reset(self):\\n # Clear mutable data, but leave the immutables intact\\n self.train_data = {}\\n self.val_data = {}\\n self.test_data = {}\\n self.model_files = []\\n self.custom_data = {}\\n # Remove all the physical assets\\n for item in os.scandir(self.root_path):\\n os.remove(item.path)\\n # Reserialize\\n self.serialize()\",\n \"def clear(self):\\n self.versions = {}\\n self.clearItems()\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def tearDown(self):\\n for model in MODELS:\\n for obj in model.objects.all():\\n obj.delete()\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def reset(self):\\n for provider in self.providers.values():\\n provider.reset()\\n\\n for observation in self.observations.values():\\n observation.reset()\",\n \"def clear_dummy_obj(self):\\n for d in self.dummies:\\n self.map.remove_node(d)\\n\\n self.dummies = []\",\n \"def clear(self):\\n # Orphan all objects\\n for obj in self.uow:\\n state(obj).session = None\\n self.uow.clear()\\n self.imap.clear()\",\n \"def clear_all(self):\\n raise NotImplementedError\",\n \"def _data_reset(self):\\n conn = self.get_connection()\\n\\n elements = {\\n **self.domain.registry.aggregates,\\n **self.domain.registry.entities,\\n **self.domain.registry.views,\\n }\\n for _, element_record in elements.items():\\n provider = current_domain.providers[element_record.cls.meta_.provider]\\n repo = self.domain.repository_for(element_record.cls)\\n\\n model_cls = repo._model\\n if provider.conn_info[\\n \\\"DATABASE\\\"\\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\\n model_cls._index._name\\n ):\\n conn.delete_by_query(\\n refresh=True,\\n index=model_cls._index._name,\\n body={\\\"query\\\": {\\\"match_all\\\": {}}},\\n )\",\n \"def reset(self):\\n self.dict_lock.acquire()\\n self.list_lock.acquire()\\n\\n self.beginResetModel()\\n self.levels_dict = {}\\n self.view_list = []\\n self.endResetModel()\\n \\n self.list_lock.release()\\n self.dict_lock.release()\",\n \"def clear(self) -> None:\\n self.items.all().delete()\\n self._cached_items = None\",\n \"def clear_model_cache():\\n global __model_cache\\n __model_cache = {}\",\n \"def remove_objects(self):\\n logger.debug('Removing all objects from model.')\\n del self._objects[:]\",\n \"def clear(self) -> None:\",\n \"def full_clear(self):\\n self.clear()\\n self.class_hooks.clear()\",\n \"def reset(self):\\n for layer in self.network:\\n layer.clean()\",\n \"def clear(self):\\n ...\",\n \"def clear(self) -> None:\\n self._store.clear()\",\n \"def clear(self):\\n self.__dict__.clear()\",\n \"def clear(self):\\n if self.debug:\\n print(\\\"DIMS cleared\\\")\\n self.sp_dicts.clear()\",\n \"def remove_all_recs(self):\\n return self.storage.clear()\",\n \"def clear_data():\\n logger.info(\\\"Delete Structure instances\\\")\\n Structure.objects.all().delete()\\n logger.info(\\\"Delete StructureType instances\\\")\\n StructureType.objects.all().delete()\\n logger.info(\\\"Delete Industry instances\\\")\\n Industry.objects.all().delete()\\n logger.info(\\\"Delete Price instances\\\")\\n PriceList.objects.all().delete()\\n logger.info(\\\"Delete Stock instances\\\")\\n Stock.objects.all().delete()\\n logger.info(\\\"Delete News instances\\\")\\n News.objects.all().delete()\\n logger.info(\\\"Delete NewsImages instances\\\")\\n NewsImage.objects.all().delete()\\n logger.info(\\\"Delete News Sections instances\\\")\\n NewsCategorySection.objects.all().delete()\\n logger.info(\\\"Delete Analysis instances\\\")\\n AnalysisOpinion.objects.all().delete()\\n logger.info(\\\"Delete Analysis Images instances\\\")\\n AnalysisImage.objects.all().delete()\\n logger.info(\\\"Delete Analysis Sections instances\\\")\\n AnalysisCategorySection.objects.all().delete()\",\n \"def clear_data(self):\\n self.game_list.clear()\\n self.game_scores.clear()\",\n \"def clear(self, using=None):\\n backend = self.get_backend(using)\\n\\n if backend is not None:\\n backend.clear(models=[self.get_model()])\",\n \"def clear(self):\\n pass\",\n \"def clear(self):\\n pass\",\n \"def clear(self):\\n pass\",\n \"def _reset(self) -> None:\\n self.images = []\\n self.activations = []\\n self.labels = []\\n self.preds = []\\n self.n_found = 0\",\n \"def clear_all(self):\\n self._data = {}\\n self.uncache()\\n self.dirty = True\\n self.shipping_method = None\\n self.payment_method = None\\n self.customer_comment = \\\"\\\"\",\n \"def _reset(self):\\n self._model._reset()\\n super(RDPAnalyzer, self)._reset()\",\n \"def clear(self):\\n self.recorders = set([])\\n self.reset()\\n\\n # Stop any currently running SpiNNaker application\\n self.stop()\",\n \"def empty(self):\\n self.items = []\\n self.totalWeight = 0\",\n \"def clear_model_sigs(self):\\n self._model_sigs.clear()\",\n \"def clear(self):\\n for key in self.__data.keys():\\n del self.__data[key]\",\n \"def clear():\",\n \"def reset():\\n teardown_db()\\n build()\",\n \"def clearStore(self):\\n os.remove(self.uid+\\\".pcl\\\")\\n self.items = []\",\n \"def clear_collection(self):\\n self._cards = []\",\n \"def clean(self):\\r\\n self.roadrunnerModel = None\\r\\n return self\",\n \"def clearList(self):\\r\\n self.skills.clear()\\r\\n del self.orderedSkills[:]\",\n \"def clear(self):\\r\\n try:\\r\\n while True:\\r\\n self.pop()\\r\\n except KeyError:\\r\\n pass\",\n \"def reset(self):\\r\\n self.pop()\",\n \"def _purge(self):\\n for _ in self.all():\\n self.delete(_)\",\n \"def reset(self):\\n self.__sets = []\\n self._computed = False\",\n \"def clear(self):\\n try:\\n self._load(False)\\n except KeyError:\\n return\\n\\n for i in xrange(self.size):\\n try:\\n del self.db[i]\\n except KeyError:\\n pass\\n del self.db['count']\\n del self.db['head']\\n del self.db['size']\",\n \"def _unload(apps, schema_editor):\\n for modelname in models:\\n model = apps.get_model(appname, modelname)\\n model.objects.all().delete()\",\n \"def clear(self):\\n for key in self.keys():\\n del self[key]\",\n \"def __reset__(self):\\n\\n for i in self.__dict__.keys():\\n self.__dict__[i] = None\",\n \"def clear(self) -> None:\\n ...\",\n \"def clear(self):\\n self.__list = []\",\n \"def reset(self):\\n self._pkgs.clear()\\n self._catalogs.clear()\\n self._categories.clear()\\n self._command_to_category.clear()\\n self._version = None\",\n \"def clear(self):\\n self._in_memory_objects.clear()\\n self._disk_objects.clear()\\n self._object_to_list_node.clear()\\n for bucket in self._buckets:\\n bucket.clear()\",\n \"def reset(self):\\n for item in TextChannelFilterItem.objects(channel_filter=self):\\n item.delete()\\n self.reset_counters()\\n self.retrain()\",\n \"def clear(self):\\n self._zombie_list = []\\n self._human_list = []\\n poc_grid.Grid.clear(self)\",\n \"def clear(self) -> None:\\n self.selected = {}\\n self.orderings = []\\n self.predicate = None\\n self.limit_index = None\\n self.offset_index = None\\n self.callbacks.clear()\",\n \"def reset(self, model):\\n for obs_func in self.observation_functions:\\n obs_func.reset(model)\",\n \"def clear(self) -> None:\\n self._items = []\\n self._size = 0\",\n \"def clear(self):\\n self._clear()\",\n \"def clear(self):\\n self._clear()\",\n \"def clear(self):\\n self.tensor_store.clear()\\n self.i = 0\",\n \"def clear(self):\\r\\n poc_grid.Grid.clear(self)\\r\\n self._zombie_list = []\\r\\n self._human_list = []\",\n \"def reset(self):\\n self.satisfiability = Satisfiability.UNTESTED\\n self.model = None\\n self.unsatCore = []\",\n \"def clear(self):\\n self.keyvaluepair_set.all().delete()\",\n \"def clear(self):\\n self._list.clear()\",\n \"def clear(self):\\n self.weight = 0\",\n \"def clear(self):\\n # we want to use self.inputs.clear() but it's not in python2\\n del self.inputs[:]\\n del self.outputs[:]\",\n \"def clear(self):\\n poc_grid.Grid.clear(self)\\n self._zombie_list = []\\n self._human_list = []\\n # need proof it works\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77507436","0.76478666","0.7637113","0.74050826","0.7388874","0.73118776","0.7252325","0.709604","0.7079502","0.6967123","0.69053054","0.6871042","0.68186563","0.6815605","0.68100023","0.68016225","0.6794553","0.6774137","0.67709094","0.67597353","0.6744278","0.6739675","0.6722856","0.67019886","0.6701017","0.6696451","0.66916823","0.6679739","0.6679739","0.6679739","0.667821","0.667821","0.667821","0.667821","0.667821","0.667821","0.667821","0.66702384","0.6670163","0.66570276","0.6644759","0.6627977","0.6627338","0.66188836","0.6617718","0.66142803","0.66092324","0.65913755","0.65902776","0.6579889","0.65718615","0.65622425","0.6538044","0.65273887","0.6523186","0.6520358","0.65177184","0.65150136","0.65150136","0.65150136","0.65143377","0.65091443","0.6507829","0.6505601","0.64999413","0.6488509","0.64877206","0.648671","0.6478849","0.6478075","0.6476727","0.6474495","0.64703935","0.64685756","0.64683497","0.6465181","0.64621246","0.6456299","0.64552265","0.6455104","0.6451751","0.6448085","0.64293444","0.6427194","0.64233106","0.64203227","0.64196324","0.6414099","0.6407899","0.6406558","0.6404729","0.6404729","0.6390545","0.63808584","0.6380032","0.6377471","0.63693184","0.6368381","0.6367519","0.6367357"],"string":"[\n \"0.77507436\",\n \"0.76478666\",\n \"0.7637113\",\n \"0.74050826\",\n \"0.7388874\",\n \"0.73118776\",\n \"0.7252325\",\n \"0.709604\",\n \"0.7079502\",\n \"0.6967123\",\n \"0.69053054\",\n \"0.6871042\",\n \"0.68186563\",\n \"0.6815605\",\n \"0.68100023\",\n \"0.68016225\",\n \"0.6794553\",\n \"0.6774137\",\n \"0.67709094\",\n \"0.67597353\",\n \"0.6744278\",\n \"0.6739675\",\n \"0.6722856\",\n \"0.67019886\",\n \"0.6701017\",\n \"0.6696451\",\n \"0.66916823\",\n \"0.6679739\",\n \"0.6679739\",\n \"0.6679739\",\n \"0.667821\",\n \"0.667821\",\n \"0.667821\",\n \"0.667821\",\n \"0.667821\",\n \"0.667821\",\n \"0.667821\",\n \"0.66702384\",\n \"0.6670163\",\n \"0.66570276\",\n \"0.6644759\",\n \"0.6627977\",\n \"0.6627338\",\n \"0.66188836\",\n \"0.6617718\",\n \"0.66142803\",\n \"0.66092324\",\n \"0.65913755\",\n \"0.65902776\",\n \"0.6579889\",\n \"0.65718615\",\n \"0.65622425\",\n \"0.6538044\",\n \"0.65273887\",\n \"0.6523186\",\n \"0.6520358\",\n \"0.65177184\",\n \"0.65150136\",\n \"0.65150136\",\n \"0.65150136\",\n \"0.65143377\",\n \"0.65091443\",\n \"0.6507829\",\n \"0.6505601\",\n \"0.64999413\",\n \"0.6488509\",\n \"0.64877206\",\n \"0.648671\",\n \"0.6478849\",\n \"0.6478075\",\n \"0.6476727\",\n \"0.6474495\",\n \"0.64703935\",\n \"0.64685756\",\n \"0.64683497\",\n \"0.6465181\",\n \"0.64621246\",\n \"0.6456299\",\n \"0.64552265\",\n \"0.6455104\",\n \"0.6451751\",\n \"0.6448085\",\n \"0.64293444\",\n \"0.6427194\",\n \"0.64233106\",\n \"0.64203227\",\n \"0.64196324\",\n \"0.6414099\",\n \"0.6407899\",\n \"0.6406558\",\n \"0.6404729\",\n \"0.6404729\",\n \"0.6390545\",\n \"0.63808584\",\n \"0.6380032\",\n \"0.6377471\",\n \"0.63693184\",\n \"0.6368381\",\n \"0.6367519\",\n \"0.6367357\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8674077"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8466,"cells":{"query":{"kind":"string","value":"Flushes the stored data and empties the bag. Use the drone ts"},"document":{"kind":"string","value":"def get_data(self):\n data = {\n \"ts\": self.drone.pos[0][0],\n \"drone\": self.drone,\n \"subject\": self.subject,\n \"peds\": self.peds, # can be None\n \"objs\": self.objs # can be None\n }\n self.empty_bag()\n return data"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clean_data():\n redis_db.flushdb()","def _flush(self):\n self._d = {}","def flush(self):\n self.cur_stocks = self.cur_stocks.drop(self.cur_stocks.index)\n # add history that flushed whole stocks","def reset(self):\n self.temp_data.clear()","def empty_bag(self):\n if self.peds is not None:\n for _, model in self.peds.items():\n model.reset()\n self.drone.reset()\n self.subject.reset()","def delete_all(self):\n with self.__lock:\n self.__data = dict()\n self.flush()","def purge(self):\n pass","def finalize(self):\n self.storage.finalize(basket=self)\n self.uncache()\n self._data = None\n self.dirty = False","def clear(self):\r\n self._state[\"data\"].clear()\r\n self._state[\"session\"].request_rerun()","def clear(self):\r\n self._state[\"data\"].clear()\r\n self._state[\"session\"].request_rerun()","def _flush(self):\n tempbuf = self.databuffer\n self.databuffer = []\n self.database.runInteraction(self._executemany, tempbuf)","def gc(self):\n if self.verbose:\n t0=time.time()\n self.data = None\n self.data_orig = None\n self.weights = None\n self.weights_orig = None\n if self.verbose:\n t1=time.time()\n print(\"Unload time: %0.2f s\" % (t1-t0))\n g.collect()","def clear(self):\n self._state[\"data\"].clear()\n self._state[\"session\"].request_rerun()","def clear(self):\n self._state[\"data\"].clear()\n self._state[\"session\"].request_rerun()","def clear(self):\n self._state[\"data\"].clear()\n self._state[\"session\"].request_rerun()","def clear(self):\n self._grasp_data = None\n self._status = None\n self._cache = dict()\n self._trajectory_result = None","def flush(self):\n self.table = []","def flush(self):\n self.genomes = []","def reset(self):\n self._data = []","def cleanup(self) -> None:\n self.min_insert_size = -1 # ensure everything gets flushed\n self.insert_data()\n self.db_event_time = sum(self.event_times)","def removeall(self):\n\n # If there used to be a key, there must exist an old value blob somewhere in the database. It should be deallocated after a successful commit to disk.\n for key in self.keys:\n if self.keys[key] is not None:\n punchat,punchlen = self.keys[key]\n self.awaitingpunch.append((punchat, punchlen))\n \n self.keys = {}\n self.buffered = {}\n self.cache = {}\n \n if self.autocommit:\n commit()","def clear_data():\n conn = get_connect()\n #conn.execute(\"DELETE from match\")\n #conn.execute(\"DELETE from account\")\n #conn.execute(\"DELETE from championMatchData\")\n conn.execute(\"DELETE from championData\")\n conn.commit()\n conn.close()\n print(\"all data in info.db has been cleared\")\n return","def reset_bag(self):","def reset_data(self):\n self.data = []","def clear(self) -> None:\n # Creates a new, empty bag and assigns self.da to the new, empty bag.\n new_bag = Bag()\n self.da = new_bag.da","def flush(self):\r\n if stub_manager.active_stubs == 'remote':\r\n import random\r\n import string\r\n code = ''.join([random.choice(string.ascii_letters)\r\n for x in range(4)])\r\n print \"\\n\\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\"\r\n print \"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\"\r\n print \"Warning! You're about to delete the *production* datastore!\"\r\n print \"Only models defined in your INSTALLED_APPS can be removed!\"\r\n print \"If you want to clear the whole datastore you have to use \" \\\r\n \"the datastore viewer in the dashboard. Also, in order to \" \\\r\n \"delete all unneeded indexes you have to run appcfg.py \" \\\r\n \"vacuum_indexes.\"\r\n print \"In order to proceed you have to enter the following code:\"\r\n print code\r\n response = raw_input(\"Repeat: \")\r\n if code == response:\r\n print \"Deleting...\"\r\n delete_all_entities()\r\n print \"Datastore flushed! Please check your dashboard's \" \\\r\n \"datastore viewer for any remaining entities and \" \\\r\n \"remove all unneeded indexes with appcfg.py \" \\\r\n \"vacuum_indexes.\"\r\n else:\r\n print \"Aborting.\"\r\n exit()\r\n elif stub_manager.active_stubs == 'test':\r\n stub_manager.deactivate_test_stubs()\r\n stub_manager.activate_test_stubs(self)\r\n else:\r\n destroy_datastore(get_datastore_paths(self.settings_dict))\r\n stub_manager.setup_local_stubs(self)","def tearDown(self):\n self.all_ob = storage.all()\n self.all_ob.clear()\n storage.save()","def clear_mem(self):\n dbe.upload_trials(self.trials, self.name)\n self.trials = []","def clear(self):\n self._data = []","def clear(self):\n self._data = []","def flush(self):\n super().flush()\n self.dists = {}","def finish(self):\r\n\r\n\t\t# Write any unempty buffers to file\r\n logger.info('Writing un-empty buffers...')\r\n self._writeBuffers()\r\n\r\n\t\t# Compact datasets\r\n logger.info('Compacting datasets...')\r\n for ds in self.datasets.keys():\r\n\r\n self._resizeDataset(self.datasets[ds], ds, finished=self.idxs[ds])\r\n\r\n\t\t# close the database\r\n self.db.close()","def _purge():\r\n _cache.clear()","def last_buy(self):\n multi_data = []\n while not self.infoQueue.empty():\n multi_data.append(self.infoQueue.get_nowait())\n self.redisHandle.set_multiple_data(multi_data)\n print(\"flush all data\")","def flush(self):","def flush(self):","def flush(self):","def flush(self):","def flush(self):","def flush(self):","def reset_data(self):\n self.data = None","def clearStore(self):\n os.remove(self.uid+\".pcl\")\n self.items = []","def clear():","def flush(self):\n return","def flush(self):\n # flush entire inventory\n self.__flushGrids(self.keys())","def __del__(self):\n self.evaler.db.flush()","def flush(self):\n pass","def flush(self):\n pass","def flush(self):\n pass","def flush(self):\n pass","def clear(self):\n try:\n self._load(False)\n except KeyError:\n return\n\n for i in xrange(self.size):\n try:\n del self.db[i]\n except KeyError:\n pass\n del self.db['count']\n del self.db['head']\n del self.db['size']","def clearData():\n Co8PersistentData.__dataDict.clear()","def clear(self):\n self._store = {}","def clear_data(self):\n self.game_list.clear()\n self.game_scores.clear()","def clear(self):\n self._data.clear()","def clear_data(cls):\n cls.__data.clear()\n cls.__counters.clear()","def reset_the_db(_step):\r\n reset_data(None)","def flush(self) -> None:\n pass","def reset(self):\n if self.monotonic_energy is not None:\n self.monotonic_energy.reset()\n if self.chunk_energy is not None:\n self.chunk_energy.reset()\n self.bd_L_prev = 0\n self.key_tail = None","def clear(self) -> None:\n self.data = {} # defaultdict fails (T282865)\n self.size = 0","def releaseTraingingData(self):\n del(self.documents)\n #del(self.sumsOfVectors)\n self.documents = {}\n #self.sumsOfVectors = {}","def clear(self):\n self._storage.clear()","def clear_storage(self):\r\n raise NotImplementedError('override me')","def flush(self):\n cursor = self.db.cursor()\n cursor.execute(\"DELETE FROM triples\")\n self.db.commit()","def clear(self):\n for key in self.__data.keys():\n del self.__data[key]","def shush(self):\n cancel_all()","def reset_storage(self):\n self.set_storage()","def resetData(self):\n self.alive = 1\n self.fromSystem = self.toSystem\n self.systemGrid = 5\n mySystem = self.myGalaxy.systems[self.toSystem]\n mySystem.availSYC += self.repair","def resetmodel(self):\n for key, value in self._dentsvertsdata.items():\n value.free()\n self._dentsvertsdata.clear()","def takedown(self):\n self.data_handler.send_all_to_backlog()\n\n del(self)","def cleanup():\n redis_client.flushall()","def cleanup_and_reset(self):\n self.mem.set(self.mem.META_PLAN, None)\n self.mem.set(self.mem.META_GOALS, None)\n self.mem.set(self.mem.META_CURR_GOAL, None)","def clear(self) -> None:\n self._store.clear()","def clear(self):\n if self.debug:\n print(\"DIMS cleared\")\n self.sp_dicts.clear()","def clear_data(self):\n if isinstance(self.data, DataManager):\n self.data._update_keys(clear=True)\n else:\n self.data = {}","def purge(self):\n self.remaining = 0","def flush(self):\r\n # this flush method is needed for python 3 compatibility.\r\n # this handles the flush command by doing nothing.\r\n pass","def flush(self):\n super().flush()\n self._targetEvaluation = None\n self._solutionExport = None","def flush():\n for k in cache._thecache.keys():\n del cache._thecache[k]","def clear():\n\t\tModel.counter = 0","def _data_reset(self):\n conn = self.get_connection()\n\n elements = {\n **self.domain.registry.aggregates,\n **self.domain.registry.entities,\n **self.domain.registry.views,\n }\n for _, element_record in elements.items():\n provider = current_domain.providers[element_record.cls.meta_.provider]\n repo = self.domain.repository_for(element_record.cls)\n\n model_cls = repo._model\n if provider.conn_info[\n \"DATABASE\"\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\n model_cls._index._name\n ):\n conn.delete_by_query(\n refresh=True,\n index=model_cls._index._name,\n body={\"query\": {\"match_all\": {}}},\n )","def __del__(self): \n \n self.flush()","def flush(self):\n self._getMemcacheClient().flush_all()","def clear_data():\n logger.info(\"Delete Structure instances\")\n Structure.objects.all().delete()\n logger.info(\"Delete StructureType instances\")\n StructureType.objects.all().delete()\n logger.info(\"Delete Industry instances\")\n Industry.objects.all().delete()\n logger.info(\"Delete Price instances\")\n PriceList.objects.all().delete()\n logger.info(\"Delete Stock instances\")\n Stock.objects.all().delete()\n logger.info(\"Delete News instances\")\n News.objects.all().delete()\n logger.info(\"Delete NewsImages instances\")\n NewsImage.objects.all().delete()\n logger.info(\"Delete News Sections instances\")\n NewsCategorySection.objects.all().delete()\n logger.info(\"Delete Analysis instances\")\n AnalysisOpinion.objects.all().delete()\n logger.info(\"Delete Analysis Images instances\")\n AnalysisImage.objects.all().delete()\n logger.info(\"Delete Analysis Sections instances\")\n AnalysisCategorySection.objects.all().delete()","def reset(self):\n self.reset_count += 1\n self._init_data()","def _flush(self):\n pass","def flush_cache(cls, ):\n cls.Lock.acquire()\n cls.UsbDevices.clear()\n cls.Lock.release()","def clear(self) -> None:","def reset(self):\n if not self._data_writer.is_ran_last():\n if not self._data_writer.is_ran_ever():\n logger.error(\"Ignoring the reset before the run\")\n else:\n logger.error(\"Ignoring the repeated reset call\")\n return\n for population in self._data_writer.iterate_populations():\n population._cache_data() # pylint: disable=protected-access\n\n # Call superclass implementation\n AbstractSpinnakerBase.reset(self)","def discart(self):\n self.queue.clear()\n self.fetchable = 0","def clear(self):\r\n\t\tself.free_objects[:] = []","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def clear(self):","def flush(self):\n # spectra/*.csv | Age distribution of various subpopulations (e.g. population that died of genetic causes)\n for key, val in self.collection.items():\n with open(self.paths[\"visor_spectra\"] / f\"{key}.csv\", \"ab\") as f:\n array = np.array(val)\n np.savetxt(f, [array], delimiter=\",\", fmt=\"%i\")\n\n # Reinitialize the collection\n self.collection = copy.deepcopy(self._collection)","def flush_structure(self):\n ...","async def clear(self):"],"string":"[\n \"def clean_data():\\n redis_db.flushdb()\",\n \"def _flush(self):\\n self._d = {}\",\n \"def flush(self):\\n self.cur_stocks = self.cur_stocks.drop(self.cur_stocks.index)\\n # add history that flushed whole stocks\",\n \"def reset(self):\\n self.temp_data.clear()\",\n \"def empty_bag(self):\\n if self.peds is not None:\\n for _, model in self.peds.items():\\n model.reset()\\n self.drone.reset()\\n self.subject.reset()\",\n \"def delete_all(self):\\n with self.__lock:\\n self.__data = dict()\\n self.flush()\",\n \"def purge(self):\\n pass\",\n \"def finalize(self):\\n self.storage.finalize(basket=self)\\n self.uncache()\\n self._data = None\\n self.dirty = False\",\n \"def clear(self):\\r\\n self._state[\\\"data\\\"].clear()\\r\\n self._state[\\\"session\\\"].request_rerun()\",\n \"def clear(self):\\r\\n self._state[\\\"data\\\"].clear()\\r\\n self._state[\\\"session\\\"].request_rerun()\",\n \"def _flush(self):\\n tempbuf = self.databuffer\\n self.databuffer = []\\n self.database.runInteraction(self._executemany, tempbuf)\",\n \"def gc(self):\\n if self.verbose:\\n t0=time.time()\\n self.data = None\\n self.data_orig = None\\n self.weights = None\\n self.weights_orig = None\\n if self.verbose:\\n t1=time.time()\\n print(\\\"Unload time: %0.2f s\\\" % (t1-t0))\\n g.collect()\",\n \"def clear(self):\\n self._state[\\\"data\\\"].clear()\\n self._state[\\\"session\\\"].request_rerun()\",\n \"def clear(self):\\n self._state[\\\"data\\\"].clear()\\n self._state[\\\"session\\\"].request_rerun()\",\n \"def clear(self):\\n self._state[\\\"data\\\"].clear()\\n self._state[\\\"session\\\"].request_rerun()\",\n \"def clear(self):\\n self._grasp_data = None\\n self._status = None\\n self._cache = dict()\\n self._trajectory_result = None\",\n \"def flush(self):\\n self.table = []\",\n \"def flush(self):\\n self.genomes = []\",\n \"def reset(self):\\n self._data = []\",\n \"def cleanup(self) -> None:\\n self.min_insert_size = -1 # ensure everything gets flushed\\n self.insert_data()\\n self.db_event_time = sum(self.event_times)\",\n \"def removeall(self):\\n\\n # If there used to be a key, there must exist an old value blob somewhere in the database. It should be deallocated after a successful commit to disk.\\n for key in self.keys:\\n if self.keys[key] is not None:\\n punchat,punchlen = self.keys[key]\\n self.awaitingpunch.append((punchat, punchlen))\\n \\n self.keys = {}\\n self.buffered = {}\\n self.cache = {}\\n \\n if self.autocommit:\\n commit()\",\n \"def clear_data():\\n conn = get_connect()\\n #conn.execute(\\\"DELETE from match\\\")\\n #conn.execute(\\\"DELETE from account\\\")\\n #conn.execute(\\\"DELETE from championMatchData\\\")\\n conn.execute(\\\"DELETE from championData\\\")\\n conn.commit()\\n conn.close()\\n print(\\\"all data in info.db has been cleared\\\")\\n return\",\n \"def reset_bag(self):\",\n \"def reset_data(self):\\n self.data = []\",\n \"def clear(self) -> None:\\n # Creates a new, empty bag and assigns self.da to the new, empty bag.\\n new_bag = Bag()\\n self.da = new_bag.da\",\n \"def flush(self):\\r\\n if stub_manager.active_stubs == 'remote':\\r\\n import random\\r\\n import string\\r\\n code = ''.join([random.choice(string.ascii_letters)\\r\\n for x in range(4)])\\r\\n print \\\"\\\\n\\\\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\\"\\r\\n print \\\"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\\\"\\r\\n print \\\"Warning! You're about to delete the *production* datastore!\\\"\\r\\n print \\\"Only models defined in your INSTALLED_APPS can be removed!\\\"\\r\\n print \\\"If you want to clear the whole datastore you have to use \\\" \\\\\\r\\n \\\"the datastore viewer in the dashboard. Also, in order to \\\" \\\\\\r\\n \\\"delete all unneeded indexes you have to run appcfg.py \\\" \\\\\\r\\n \\\"vacuum_indexes.\\\"\\r\\n print \\\"In order to proceed you have to enter the following code:\\\"\\r\\n print code\\r\\n response = raw_input(\\\"Repeat: \\\")\\r\\n if code == response:\\r\\n print \\\"Deleting...\\\"\\r\\n delete_all_entities()\\r\\n print \\\"Datastore flushed! Please check your dashboard's \\\" \\\\\\r\\n \\\"datastore viewer for any remaining entities and \\\" \\\\\\r\\n \\\"remove all unneeded indexes with appcfg.py \\\" \\\\\\r\\n \\\"vacuum_indexes.\\\"\\r\\n else:\\r\\n print \\\"Aborting.\\\"\\r\\n exit()\\r\\n elif stub_manager.active_stubs == 'test':\\r\\n stub_manager.deactivate_test_stubs()\\r\\n stub_manager.activate_test_stubs(self)\\r\\n else:\\r\\n destroy_datastore(get_datastore_paths(self.settings_dict))\\r\\n stub_manager.setup_local_stubs(self)\",\n \"def tearDown(self):\\n self.all_ob = storage.all()\\n self.all_ob.clear()\\n storage.save()\",\n \"def clear_mem(self):\\n dbe.upload_trials(self.trials, self.name)\\n self.trials = []\",\n \"def clear(self):\\n self._data = []\",\n \"def clear(self):\\n self._data = []\",\n \"def flush(self):\\n super().flush()\\n self.dists = {}\",\n \"def finish(self):\\r\\n\\r\\n\\t\\t# Write any unempty buffers to file\\r\\n logger.info('Writing un-empty buffers...')\\r\\n self._writeBuffers()\\r\\n\\r\\n\\t\\t# Compact datasets\\r\\n logger.info('Compacting datasets...')\\r\\n for ds in self.datasets.keys():\\r\\n\\r\\n self._resizeDataset(self.datasets[ds], ds, finished=self.idxs[ds])\\r\\n\\r\\n\\t\\t# close the database\\r\\n self.db.close()\",\n \"def _purge():\\r\\n _cache.clear()\",\n \"def last_buy(self):\\n multi_data = []\\n while not self.infoQueue.empty():\\n multi_data.append(self.infoQueue.get_nowait())\\n self.redisHandle.set_multiple_data(multi_data)\\n print(\\\"flush all data\\\")\",\n \"def flush(self):\",\n \"def flush(self):\",\n \"def flush(self):\",\n \"def flush(self):\",\n \"def flush(self):\",\n \"def flush(self):\",\n \"def reset_data(self):\\n self.data = None\",\n \"def clearStore(self):\\n os.remove(self.uid+\\\".pcl\\\")\\n self.items = []\",\n \"def clear():\",\n \"def flush(self):\\n return\",\n \"def flush(self):\\n # flush entire inventory\\n self.__flushGrids(self.keys())\",\n \"def __del__(self):\\n self.evaler.db.flush()\",\n \"def flush(self):\\n pass\",\n \"def flush(self):\\n pass\",\n \"def flush(self):\\n pass\",\n \"def flush(self):\\n pass\",\n \"def clear(self):\\n try:\\n self._load(False)\\n except KeyError:\\n return\\n\\n for i in xrange(self.size):\\n try:\\n del self.db[i]\\n except KeyError:\\n pass\\n del self.db['count']\\n del self.db['head']\\n del self.db['size']\",\n \"def clearData():\\n Co8PersistentData.__dataDict.clear()\",\n \"def clear(self):\\n self._store = {}\",\n \"def clear_data(self):\\n self.game_list.clear()\\n self.game_scores.clear()\",\n \"def clear(self):\\n self._data.clear()\",\n \"def clear_data(cls):\\n cls.__data.clear()\\n cls.__counters.clear()\",\n \"def reset_the_db(_step):\\r\\n reset_data(None)\",\n \"def flush(self) -> None:\\n pass\",\n \"def reset(self):\\n if self.monotonic_energy is not None:\\n self.monotonic_energy.reset()\\n if self.chunk_energy is not None:\\n self.chunk_energy.reset()\\n self.bd_L_prev = 0\\n self.key_tail = None\",\n \"def clear(self) -> None:\\n self.data = {} # defaultdict fails (T282865)\\n self.size = 0\",\n \"def releaseTraingingData(self):\\n del(self.documents)\\n #del(self.sumsOfVectors)\\n self.documents = {}\\n #self.sumsOfVectors = {}\",\n \"def clear(self):\\n self._storage.clear()\",\n \"def clear_storage(self):\\r\\n raise NotImplementedError('override me')\",\n \"def flush(self):\\n cursor = self.db.cursor()\\n cursor.execute(\\\"DELETE FROM triples\\\")\\n self.db.commit()\",\n \"def clear(self):\\n for key in self.__data.keys():\\n del self.__data[key]\",\n \"def shush(self):\\n cancel_all()\",\n \"def reset_storage(self):\\n self.set_storage()\",\n \"def resetData(self):\\n self.alive = 1\\n self.fromSystem = self.toSystem\\n self.systemGrid = 5\\n mySystem = self.myGalaxy.systems[self.toSystem]\\n mySystem.availSYC += self.repair\",\n \"def resetmodel(self):\\n for key, value in self._dentsvertsdata.items():\\n value.free()\\n self._dentsvertsdata.clear()\",\n \"def takedown(self):\\n self.data_handler.send_all_to_backlog()\\n\\n del(self)\",\n \"def cleanup():\\n redis_client.flushall()\",\n \"def cleanup_and_reset(self):\\n self.mem.set(self.mem.META_PLAN, None)\\n self.mem.set(self.mem.META_GOALS, None)\\n self.mem.set(self.mem.META_CURR_GOAL, None)\",\n \"def clear(self) -> None:\\n self._store.clear()\",\n \"def clear(self):\\n if self.debug:\\n print(\\\"DIMS cleared\\\")\\n self.sp_dicts.clear()\",\n \"def clear_data(self):\\n if isinstance(self.data, DataManager):\\n self.data._update_keys(clear=True)\\n else:\\n self.data = {}\",\n \"def purge(self):\\n self.remaining = 0\",\n \"def flush(self):\\r\\n # this flush method is needed for python 3 compatibility.\\r\\n # this handles the flush command by doing nothing.\\r\\n pass\",\n \"def flush(self):\\n super().flush()\\n self._targetEvaluation = None\\n self._solutionExport = None\",\n \"def flush():\\n for k in cache._thecache.keys():\\n del cache._thecache[k]\",\n \"def clear():\\n\\t\\tModel.counter = 0\",\n \"def _data_reset(self):\\n conn = self.get_connection()\\n\\n elements = {\\n **self.domain.registry.aggregates,\\n **self.domain.registry.entities,\\n **self.domain.registry.views,\\n }\\n for _, element_record in elements.items():\\n provider = current_domain.providers[element_record.cls.meta_.provider]\\n repo = self.domain.repository_for(element_record.cls)\\n\\n model_cls = repo._model\\n if provider.conn_info[\\n \\\"DATABASE\\\"\\n ] == Database.ELASTICSEARCH.value and conn.indices.exists(\\n model_cls._index._name\\n ):\\n conn.delete_by_query(\\n refresh=True,\\n index=model_cls._index._name,\\n body={\\\"query\\\": {\\\"match_all\\\": {}}},\\n )\",\n \"def __del__(self): \\n \\n self.flush()\",\n \"def flush(self):\\n self._getMemcacheClient().flush_all()\",\n \"def clear_data():\\n logger.info(\\\"Delete Structure instances\\\")\\n Structure.objects.all().delete()\\n logger.info(\\\"Delete StructureType instances\\\")\\n StructureType.objects.all().delete()\\n logger.info(\\\"Delete Industry instances\\\")\\n Industry.objects.all().delete()\\n logger.info(\\\"Delete Price instances\\\")\\n PriceList.objects.all().delete()\\n logger.info(\\\"Delete Stock instances\\\")\\n Stock.objects.all().delete()\\n logger.info(\\\"Delete News instances\\\")\\n News.objects.all().delete()\\n logger.info(\\\"Delete NewsImages instances\\\")\\n NewsImage.objects.all().delete()\\n logger.info(\\\"Delete News Sections instances\\\")\\n NewsCategorySection.objects.all().delete()\\n logger.info(\\\"Delete Analysis instances\\\")\\n AnalysisOpinion.objects.all().delete()\\n logger.info(\\\"Delete Analysis Images instances\\\")\\n AnalysisImage.objects.all().delete()\\n logger.info(\\\"Delete Analysis Sections instances\\\")\\n AnalysisCategorySection.objects.all().delete()\",\n \"def reset(self):\\n self.reset_count += 1\\n self._init_data()\",\n \"def _flush(self):\\n pass\",\n \"def flush_cache(cls, ):\\n cls.Lock.acquire()\\n cls.UsbDevices.clear()\\n cls.Lock.release()\",\n \"def clear(self) -> None:\",\n \"def reset(self):\\n if not self._data_writer.is_ran_last():\\n if not self._data_writer.is_ran_ever():\\n logger.error(\\\"Ignoring the reset before the run\\\")\\n else:\\n logger.error(\\\"Ignoring the repeated reset call\\\")\\n return\\n for population in self._data_writer.iterate_populations():\\n population._cache_data() # pylint: disable=protected-access\\n\\n # Call superclass implementation\\n AbstractSpinnakerBase.reset(self)\",\n \"def discart(self):\\n self.queue.clear()\\n self.fetchable = 0\",\n \"def clear(self):\\r\\n\\t\\tself.free_objects[:] = []\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def clear(self):\",\n \"def flush(self):\\n # spectra/*.csv | Age distribution of various subpopulations (e.g. population that died of genetic causes)\\n for key, val in self.collection.items():\\n with open(self.paths[\\\"visor_spectra\\\"] / f\\\"{key}.csv\\\", \\\"ab\\\") as f:\\n array = np.array(val)\\n np.savetxt(f, [array], delimiter=\\\",\\\", fmt=\\\"%i\\\")\\n\\n # Reinitialize the collection\\n self.collection = copy.deepcopy(self._collection)\",\n \"def flush_structure(self):\\n ...\",\n \"async def clear(self):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7044794","0.6967814","0.68903756","0.6873136","0.6852722","0.68504673","0.68156374","0.67802024","0.6756149","0.6756149","0.67459303","0.67278725","0.6683704","0.6683704","0.6683704","0.66800135","0.6664512","0.66624105","0.66586465","0.66405725","0.6590448","0.6590231","0.65853405","0.6579776","0.6576952","0.65745366","0.6567904","0.6564967","0.6553601","0.6553601","0.654088","0.65355086","0.6525888","0.6516929","0.65021837","0.65021837","0.65021837","0.65021837","0.65021837","0.65021837","0.6480298","0.647572","0.6473111","0.64661205","0.64570177","0.64500004","0.64488","0.64488","0.64488","0.64488","0.6437207","0.64286035","0.6421688","0.64180064","0.6400853","0.6378791","0.6371526","0.6370279","0.6366992","0.63663596","0.63610494","0.6359789","0.63412917","0.63289505","0.6315172","0.6314009","0.63111216","0.6300127","0.6295129","0.62950665","0.62898225","0.6288781","0.6281203","0.6275641","0.62742645","0.62727374","0.62663305","0.62586296","0.6258362","0.6244612","0.62397385","0.6239727","0.6230636","0.62139523","0.62130773","0.6206182","0.61966693","0.6190101","0.6187571","0.6180998","0.6180977","0.6179025","0.6179025","0.6179025","0.6179025","0.6179025","0.6179025","0.6179025","0.61771494","0.617269","0.61721224"],"string":"[\n \"0.7044794\",\n \"0.6967814\",\n \"0.68903756\",\n \"0.6873136\",\n \"0.6852722\",\n \"0.68504673\",\n \"0.68156374\",\n \"0.67802024\",\n \"0.6756149\",\n \"0.6756149\",\n \"0.67459303\",\n \"0.67278725\",\n \"0.6683704\",\n \"0.6683704\",\n \"0.6683704\",\n \"0.66800135\",\n \"0.6664512\",\n \"0.66624105\",\n \"0.66586465\",\n \"0.66405725\",\n \"0.6590448\",\n \"0.6590231\",\n \"0.65853405\",\n \"0.6579776\",\n \"0.6576952\",\n \"0.65745366\",\n \"0.6567904\",\n \"0.6564967\",\n \"0.6553601\",\n \"0.6553601\",\n \"0.654088\",\n \"0.65355086\",\n \"0.6525888\",\n \"0.6516929\",\n \"0.65021837\",\n \"0.65021837\",\n \"0.65021837\",\n \"0.65021837\",\n \"0.65021837\",\n \"0.65021837\",\n \"0.6480298\",\n \"0.647572\",\n \"0.6473111\",\n \"0.64661205\",\n \"0.64570177\",\n \"0.64500004\",\n \"0.64488\",\n \"0.64488\",\n \"0.64488\",\n \"0.64488\",\n \"0.6437207\",\n \"0.64286035\",\n \"0.6421688\",\n \"0.64180064\",\n \"0.6400853\",\n \"0.6378791\",\n \"0.6371526\",\n \"0.6370279\",\n \"0.6366992\",\n \"0.63663596\",\n \"0.63610494\",\n \"0.6359789\",\n \"0.63412917\",\n \"0.63289505\",\n \"0.6315172\",\n \"0.6314009\",\n \"0.63111216\",\n \"0.6300127\",\n \"0.6295129\",\n \"0.62950665\",\n \"0.62898225\",\n \"0.6288781\",\n \"0.6281203\",\n \"0.6275641\",\n \"0.62742645\",\n \"0.62727374\",\n \"0.62663305\",\n \"0.62586296\",\n \"0.6258362\",\n \"0.6244612\",\n \"0.62397385\",\n \"0.6239727\",\n \"0.6230636\",\n \"0.62139523\",\n \"0.62130773\",\n \"0.6206182\",\n \"0.61966693\",\n \"0.6190101\",\n \"0.6187571\",\n \"0.6180998\",\n \"0.6180977\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.6179025\",\n \"0.61771494\",\n \"0.617269\",\n \"0.61721224\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8467,"cells":{"query":{"kind":"string","value":"Store data samples sent in multiple batches"},"document":{"kind":"string","value":"def store_in_bag(self, data):\n # timestamp is (s, nanos): data[\"ts\"], data[\"tnanos\"]\n\n self.bag.add(data)\n\n # Ensure that all data have the same timestamp and are not None\n # Also there can't be more than a sample per second.\n if self.bag.is_full():\n if random() > 0.99999:\n print(\"Telemetry data: \", data[\"topic\"])\n print(\"Bag data: \", self.bag.print_data())\n\n # Then flush the data to process it and empty the bag\n data = self.bag.get_data()\n self.on_full(data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def train(self, num_batches: int):","def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:","def set_batch_data():\r\n if not os.path.exists(filepath):\r\n download_data()\r\n for n in range(0,6):\r\n d = read(filepath + flist[n])\r\n metadata = read(filepath + flist[-1])\r\n ndata = metadata['num_cases_per_batch']\r\n ndim = metadata['num_vis']\r\n\r\n data, trts = {}, {}\r\n data['labels'] = metadata['label_names']\r\n data['ntraindata'] = metadata['num_cases_per_batch'] * (len(flist) - 2)\r\n data['ntestdata'] = metadata['num_cases_per_batch']\r\n data['ndim'] = metadata['num_vis']\r\n trts['x'], trts['y'] = d['data'], d['labels']\r\n trtsflag = ['train', 'train', 'train', 'train', 'train', 'test']\r\n\r\n data['flag'] = trtsflag[n]\r\n data[trtsflag[n]] = trts\r\n save_pkl(data, savename=flist[n]+'.pkl')","def batch_data(data, state_queue):\n while True:\n time.sleep(BATCH_SLEEP_TIME)\n state_queue.put((\"device_values\", [data]))","def prepare_data(self, train_data, **kwargs):\n data_len = len(train_data[\"done\"])\n for index in range(data_len):\n if self.multi_step == 1:\n self.buff.add(train_data[\"cur_state\"][index],\n train_data[\"action\"][index],\n train_data[\"reward\"][index],\n train_data[\"next_state\"][index],\n float(train_data[\"done\"][index])) # Add replay buffer","def _defineBatches(self):\n # extract all ids\n all_keys = list(self.data_dict.unique_ids)\n\n # randomly shuffle keys\n if self.random_shuffle_batches:\n random.shuffle(all_keys)\n\n # create batches based on number of batches\n if self.n_big_batches is not None:\n self.n_big_batches += 1\n # define cuts for batches\n cuts = np.linspace(0, self.n_observations,\n self.n_big_batches).round()\n # create batches based on batch size\n elif self.batch_size is not None:\n cuts = [x for x in range(0, self.n_observations,\n int(self.batch_size))]\n if cuts[-1] < self.n_observations:\n cuts.append(self.n_observations)\n\n # convert batch sizes to integers\n cuts = [int(x) for x in cuts]\n\n # save batches into dictionary\n batches = dict()\n for i in range(0, (len(cuts) - 1)):\n # create DataBatch object\n current_batch = DataBatch(ids=all_keys[cuts[i]:cuts[i+1]],\n batch_id=i)\n current_batch.setDiskStoragePath(self.disk_scratch)\n batches[i] = current_batch\n\n # save batches\n self.n_batches = len(batches.keys())\n self.batches = batches","def _train(self):\n epoch_training_time = 0\n epoch_metrics_time = 0\n self.epoch_ += 1\n for i_batch, sample_batched in enumerate(self.dataloader):\n self.global_step_ += 1\n batch_start_time = time.time()\n data_sample = sample_batched[0].to(self.device)\n\n # Get model samples, either from replay buffer or noise.\n if self.model_samples_ is None:\n self.model_samples_ = deque(\n [\n self.net_.sample_from_prior(\n data_sample.shape[0], device=self.device\n ).detach()\n ]\n )\n elif len(self.model_samples_) > self.max_replay:\n self.model_samples_.popleft()\n replay_sample = random.choices(\n self.model_samples_,\n # favor more recent samples:\n weights=list(range(1, len(self.model_samples_) + 1)),\n )[0]\n noise_sample = self.net_.sample_from_prior(\n replay_sample.shape[0], device=self.device\n )\n mask = torch.rand(replay_sample.shape[0]) < self.replay_prob\n while len(mask.shape) < len(replay_sample.shape):\n # Add extra feature-dims\n mask.unsqueeze_(dim=-1)\n\n model_sample = torch.where(\n mask.to(self.device), replay_sample, noise_sample\n )\n\n self.net_.eval()\n # Run at least one iteration\n model_sample = self.net_.sample_fantasy(\n model_sample,\n num_mc_steps=self.num_mc_steps,\n mc_dynamics=self.sampler,\n ).detach()\n\n self.model_samples_.append(model_sample)\n\n # Sanity checks:\n assert (\n data_sample.shape[1:] == self.net_.input_shape\n ), \"Data is incompatible with network.\"\n assert (\n model_sample.shape[1:] == data_sample.shape[1:]\n ), \"Model and data samples are incompatible.\"\n\n # Forward gradient:\n self.net_.train()\n self.net_.zero_grad()\n data_energy_mean = self.net_(data_sample).mean()\n model_energy = self.net_(model_sample)\n model_energy_mean = model_energy.mean()\n\n # Estimate the odds of the data's energy based on a normal fitted to\n # model samples:\n data_erf = torch.erf(\n (data_energy_mean - model_energy_mean) / model_energy.std()\n )\n\n objective = data_energy_mean - model_energy_mean\n objective.backward()\n torch.nn.utils.clip_grad.clip_grad_value_(self.net_.parameters(), 1e2)\n self.optimizer_.step()\n\n batch_training_time = time.time() - batch_start_time\n epoch_training_time += batch_training_time\n self.logger_(energy_diff=float(objective))\n self.logger_(data_erf=float(data_erf))\n\n tr_metrics_start_time = time.time()\n for callback in self.step_callbacks:\n callback(\n net=self.net_,\n data_sample=data_sample,\n model_sample=model_sample,\n epoch=self.epoch_,\n global_step=self.global_step_,\n validation=False,\n )\n tr_metrics_time = time.time() - tr_metrics_start_time\n epoch_metrics_time += tr_metrics_time\n if self.verbose:\n print(\n f\"on epoch {self.epoch_}, batch {i_batch}, data erf: {data_erf}, objective: {objective}\"\n )\n print(f\"model energy: {model_energy_mean} +- {model_energy.std()}\")\n print(f\"data energy: {data_energy_mean}\")\n print(\n f\"training time: {batch_training_time:0.3f}s, metrics time: {tr_metrics_time:0.3f}s\"\n )\n means = self.logger_.means()\n if self.verbose:\n print(f\"on epoch {self.epoch_}\")\n for k, v in means.items():\n print(f\"{k}: {v}\")\n self.logger_.flush()\n means[\"loss\"] = energy_model.utils.constraints.add_soft_constraint(\n means[\"loss_ais\"], means[\"data_erf\"], lower_bound=-1\n )\n return means","def gen_batch(self):\n batch_size = self.batch_size\n shuffle = self.shuffle\n data = np.array(self.sentences)\n\n data_size = len(data)\n num_batches_per_epoch = int((len(data) - 1) / batch_size) + 1\n while True:\n # shuffle the data at starting of each epoch\n shuffled_data = data\n if shuffle:\n shuffle_indices = np.random.permutation(np.arange(data_size))\n shuffled_data = data[shuffle_indices]\n \n for batch_num in range(num_batches_per_epoch):\n start_index = batch_num * batch_size\n end_index = min((batch_num + 1) * batch_size, data_size)\n yield self._format_samples(shuffled_data[start_index:end_index], self.max_length)\n\n if self.mode in ['train', \"pred\"]:\n break","def train(self, batch):\n pass","def batch_size(self) -> int:\n ...","def _local_train(self, dataloader_with_memory, num_updates):\n # Local train\n _size = len(dataloader_with_memory)\n self.model = self.model.train()\n for _batch in range(num_updates):\n X, y = dataloader_with_memory.get_samples()\n X, y = X.to(self._device), y.to(self._device)\n if _batch == 0:\n # Initialize the batch-size using the first batch to avoid\n # edge cases with drop_last=False\n _batch_size = X.shape[0]\n _num_batches_per_epoch = (_size // _batch_size) + int(\n (_size % _batch_size) != 0\n )\n # Compute prediction and loss\n _pred = self.model(X)\n _loss = self._loss(_pred, y)\n\n # Backpropagation\n _loss.backward()\n self._optimizer.step()\n self._optimizer.zero_grad()\n self.num_batches_seen += 1\n _loss, _current_epoch = (\n _loss.item(),\n self.num_batches_seen // _num_batches_per_epoch,\n )\n\n if self.log:\n if _batch % self.log_period == 0:\n print(\n f\"loss: {_loss:>7f} after {self.num_batches_seen:>5d}\"\n f\" batches of data amounting to {_current_epoch:>5d}\"\n \" epochs.\"\n )\n self.writer.add_scalar(\n f\"client{self.client_id}/train/Loss\",\n _loss,\n self.num_batches_seen,\n )\n\n if _current_epoch > self.current_epoch:\n # At each epoch we look at the histograms of all the\n # network's parameters\n for name, p in self.model.named_parameters():\n self.writer.add_histogram(\n f\"client{self.client_id}/{name}\", p, _current_epoch\n )\n\n self.current_epoch = _current_epoch","def _train_batch(self):\n\n # start epoch\n for i, (source, target) in enumerate(self.train_dataset):\n result = self._batch_iter(source, target, i)\n\n # yield\n yield result","def train_batch(self, data, num_iteration, verbose=False):\n self.train(data, num_iteration, random_order=False, verbose=verbose)","def batch_data(cls, train_data, train_labels, batch_size):\n for batch in range(int(np.ceil(train_data.shape[0] / batch_size))):\n start = batch_size * batch\n end = start + batch_size\n if end > train_data.shape[0]:\n yield batch, (train_data[start:train_data.shape[0]], \\\n train_labels[start:train_data.shape[0]])\n else:\n yield batch, (train_data[start:end], \\\n train_labels[start:end])","def prepareDataBatches(self, traindata, trainlabel):\n index = np.random.permutation(len(traindata))\n traindata = traindata[index]\n trainlabel = trainlabel[index]\n split_no = int(len(traindata) / self.batchSize)\n return zip(np.split(traindata[:split_no*self.batchSize], split_no), np.split(trainlabel[:split_no*self.batchSize], split_no))","def _upload_samples(self, samples):\n # Iterate over the full set of provided samples, uploading them in chunks.\n for offset in range(0, len(samples), self.upload_chunk_size):\n chunk = samples[offset:offset + self.upload_chunk_size]\n self.api.upload_samples(offset, chunk)","def prepare_batches(self, data):\n batches = []\n start, end = 0, 100\n if len(data) > 100:\n while True:\n data_batch = data[start:end]\n if not data_batch:\n break\n temp = end + 100\n start, end = end, temp\n if data_batch:\n batches.append(data_batch)\n else:\n batches.append(data)\n return batches","def _batch(self, X, y, batch_size=16):\n X, y = self._shuffle(X, y) # shuffle the data\n self.batches = []\n idx = 0\n while idx < len(X):\n batch = (X[idx:idx+batch_size], y[idx:idx+batch_size])\n self.batches.append(batch)\n idx += batch_size","def static_batch(data, batch_size=16):\n buf = []\n for sample in data:\n buf.append(sample)\n if len(buf) >= batch_size:\n yield buf\n buf = []\n if len(buf) > 0:\n yield buf","def train_next_batch(self, batch_size=None):","def write_batch(self, batch):\n for item in batch:\n self.write_buffer.buffer(item)\n key = self.write_buffer.get_key_from_item(item)\n if self.write_buffer.should_write_buffer(key):\n self._write_current_buffer_for_group_key(key)\n self.increment_written_items()\n self._check_items_limit()","def storeBatch(self, storage, X_data, y_data):\n # data to store\n data_dict = {'X_data': X_data,\n 'y_data': y_data}\n # store in memory\n if storage == \"memory\":\n # dict with data\n self.raw_data = data_dict\n # store on disk\n elif storage == \"disk\":\n # check batch directory\n batch_file = 'batch' + str(self.batch_id) + '.pkl'\n if batch_file in os.listdir(self.disk_storage_path):\n print(\"Overwriting: %s\" % batch_file)\n else:\n pkl.dump(data_dict,\n open(self.disk_storage_path + batch_file, \"wb\"))\n # save in native numpy format\n elif storage == \"numpy\":\n batch_file = 'batch' + str(self.batch_id) + '.npy'\n if batch_file in os.listdir(self.disk_storage_path):\n print(\"Overwriting: %s\" % batch_file)\n else:\n np.save(self.disk_storage_path + \"X_\" + batch_file, X_data)\n np.save(self.disk_storage_path + \"y_\" + batch_file, y_data)\n # save to disk as images\n elif storage == \"disk_raw\":\n # create new directory for batch\n batch_dir = self.disk_storage_path + \"batch\" + str(self.batch_id)\n os.mkdir(batch_dir)\n # loop through all images and store\n for i in range(0, X_data.shape[0]):\n img = array_to_img(X_data[i, :, :, :])\n img.save(batch_dir + \"/\" + i + \".jpeg\")\n else:\n # do nothing\n return None\n\n # set flags and parameters\n self.is_stored = True\n self.storage = storage","def on_train_batch_begin(self, step, logs=None):","def hook_store_batch_size(module):\n if self._batch_size == {}:\n batch_axis = 0\n batch_size = module.input0.shape[batch_axis]\n\n for group in param_groups:\n group_id = id(group)\n\n if self._verbose:\n print(f\"Group {group_id}: Store 'batch_size'\")\n\n self._batch_size[group_id] = batch_size","def batches(set_name):\n global num_batches, args, ds_sizes \n # num_batches = how many batches in each dataset(train, valid, test)\n # ds_sizes = dataset_sizes \n for b in range(num_batches[set_name]):\n bi = b * args.batch_size # one batch mul batch_size \n bj = (b + 1) * args.batch_size \n if b == num_batches[set_name] - 1:\n bj = ds_sizes[set_name] # maybe only remainer set\n yield bi, bj","def data_gen(voc_size, batch, nbatches, seq_len = 15):\r\n for i in range(nbatches):\r\n # (batch_size, seq_len)\r\n data = torch.from_numpy(\r\n np.random.randint(1, voc_size, size=(batch, seq_len)))\r\n data[:, 0] = 1 # add start token\r\n src = Variable(data, requires_grad=False)\r\n tgt = Variable(data, requires_grad=False)\r\n yield Batch(src, tgt, 0) # Accessed by next function one by one\r","def next_batch(self, batch_size):\n batch_data = np.zeros([batch_size,] + list(self.example_shape))\n for i in range(batch_size):\n index = self.q.pop()\n batch_data[i,...] = self.data[index]\n if len(self.q)==0:\n self.__new_epoch()\n\n return batch_data","def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\n for real_data in data_stream:\n tmp = []\n tmp.append(real_data.state)\n tmp.append(real_data.action)\n tmp.append(real_data.reward)\n tmp.append(real_data.next_state)\n tmp.append(real_data.done)\n tmp.append(actor_set[k]['w'][real_data.idx])\n tmp.append(actor_set[k]['i'][real_data.idx])\n tmp.append(actor_set[k]['t'][real_data.idx])\n tmp.append(real_data.timestamp)\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['states'].append(decom_state) #.to(device))\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\n # is the data overwrited?\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\n local_dict = {}\n while True:\n batch_timestamp_real = []\n batch_timestamp_store = []\n batch_weights = []\n batch_idxes = []\n\n states, actions, rewards, next_states, dones = [], [], [], [], []\n\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\n actor_set = {}\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\n for i in range(len(actor_ids)):\n set_a = actor_set.get(actor_ids[i], False)\n if set_a == False:\n actor_set[actor_ids[i]] = {}\n set_a = actor_set[actor_ids[i]]\n set_a['d'] = []\n set_a['w'] = []\n set_a['i'] = []\n set_a['t'] = []\n cached_value['states'][actor_ids[i]] = []\n cached_value['actions'][actor_ids[i]] = []\n cached_value['rewards'][actor_ids[i]] = []\n cached_value['next_states'][actor_ids[i]] = []\n cached_value['dones'][actor_ids[i]] = []\n cached_value['batch_weights'][actor_ids[i]] = []\n cached_value['batch_idxes'][actor_ids[i]] = []\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\n cache_id = actor_ids[i]*local_size+data_ids[i]\n cache_trans = cache_array[cache_id]\n if cache_trans & 2**pid == 0:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\n del local_dict[cache_id]\n else:\n try:\n state_tmp = local_dict[cache_id][0]\n action_tmp = local_dict[cache_id][1]\n reward_tmp = local_dict[cache_id][2] \n next_state_tmp = local_dict[cache_id][3] \n done_tmp = local_dict[cache_id][4] \n batch_weight_tmp = local_dict[cache_id][5] \n batch_idx_tmp = local_dict[cache_id][6] \n batch_store_tmp = local_dict[cache_id][7] \n batch_real_tmp = local_dict[cache_id][8] \n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['states'][actor_ids[i]].append(decom_state)\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\n except:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n real_data_links = {}\n real_data_tasks = {}\n for k, v in actor_set.items():\n actor_ip, data_port = actor_id_to_ip_dataport[k]\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\n real_data_tasks[k] = {}\n real_data_tasks[k]['states'] = cached_value['states'][k]\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\n threads = []\n for k, v in real_data_links.items():\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\n threads.append(t)\n t.start()\n\n for t in threads:\n t.join()\n\n for k, v in real_data_tasks.items():\n states += v['states']\n actions += v['actions']\n rewards += v['rewards']\n next_states += v['next_states']\n dones += v['dones']\n batch_weights += v['batch_weights']\n batch_idxes += v['batch_idxes']\n batch_timestamp_real += v['batch_timestamp_real']\n batch_timestamp_store += v['batch_timestamp_store']\n\n states = torch.cat(states,0).to(device)\n actions = torch.cat(actions,0).to(device)\n rewards = torch.cat(rewards,0).to(device)\n next_states = torch.cat(next_states,0).to(device)\n dones = torch.cat(dones,0).to(device)\n batch_weights = torch.cat(batch_weights,0).to(device)\n\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\n batch_queue.put(batch)\n data, batch = None, None","def batch_split(self) -> np.array:\n pass","def _one_mini_batch(self, data, indices, pad_id):\n batch_data = {'raw_data': [data[i] for i in indices],\n 'question_token_ids': [],\n 'question_length': [],\n 'passage_token_ids': [],\n 'passage_length': [],\n 'start_id': [],\n 'end_id': []}\n max_passage_num = max([len(sample['passages']) for sample in batch_data['raw_data']])\n max_passage_num = min(self.max_p_num, max_passage_num)\n for sidx, sample in enumerate(batch_data['raw_data']):\n for pidx in range(max_passage_num):\n if pidx < len(sample['passages']):\n batch_data['question_token_ids'].append(sample['question_token_ids'])\n batch_data['question_length'].append(len(sample['question_token_ids']))\n passage_token_ids = sample['passages'][pidx]['passage_token_ids']\n batch_data['passage_token_ids'].append(passage_token_ids)\n batch_data['passage_length'].append(min(len(passage_token_ids), self.max_p_len))\n else:\n batch_data['question_token_ids'].append([])\n batch_data['question_length'].append(0)\n batch_data['passage_token_ids'].append([])\n batch_data['passage_length'].append(0)\n batch_data, padded_p_len, padded_q_len = self._dynamic_padding(batch_data, pad_id)\n for sample in batch_data['raw_data']:\n if 'answer_passages' in sample and len(sample['answer_passages']):\n gold_passage_offset = padded_p_len * sample['answer_passages'][0]\n batch_data['start_id'].append(gold_passage_offset + sample['answer_spans'][0][0])\n batch_data['end_id'].append(gold_passage_offset + sample['answer_spans'][0][1])\n else:\n # fake span for some samples, only valid for testing\n batch_data['start_id'].append(0)\n batch_data['end_id'].append(0)\n return batch_data","def __call__(self):\n if self.numbatches is None:\n pool = self.pooler()\n if self.batchsize is None:\n self.batchsize = self.pooler.nInPool()\n self.numbatches = self.pooler.nInPool()//self.batchsize\n for i in xrange(self.numbatches):\n pool = self.pooler()\n self._reset_batch()\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\n batchinds,keyids = self._samplebalanced(pool)\n elif self.samplemethod == 'uniform':\n batchinds,keyids = self._sampleuniform(pool)\n else:\n batchinds,keyids = self._samplesequential(i)\n batch = self._extractInds(pool,batchinds,keyids)\n for k in batch:\n batch[k][np.isnan(batch[k])] = self.nanreplacement\n yield batch","def make_batch(self, batch_size):\n filenames = self.get_filenames()\n if self.mode == tf.estimator.ModeKeys.PREDICT and self.imagenet_train_predict_partial:\n # Sort and shuffle with seed to randomize deterministically.\n random.seed(self.imagenet_train_predict_shuffle_seed)\n random.shuffle(filenames)\n dataset = tf.contrib.data.TFRecordDataset(filenames)\n\n # Parse records.\n dataset = dataset.map(self.parser,\n num_threads=batch_size,\n output_buffer_size=2 * batch_size)\n\n # If training, shuffle and repeat indefinitely.\n if self.mode == tf.estimator.ModeKeys.TRAIN:\n dataset = dataset.shuffle(buffer_size=50000 + 3 * batch_size)\n dataset = dataset.repeat(-1)\n elif self.mode == tf.estimator.ModeKeys.PREDICT:\n if self.predict_split == 'train':\n if self.imagenet_train_predict_partial:\n MAX_EXAMPLES = 50000\n # Skip to start at a random spot in the first TFRecord.\n random.seed(self.imagenet_train_predict_shuffle_seed)\n skip_examples = random.randint(0, 1251)\n dataset = dataset.skip(skip_examples)\n # Continue shuffling amongst at least as many examples\n # as it could see in 3 cross validations.\n dataset.shuffle(buffer_size=3 * MAX_EXAMPLES,\n seed=self.imagenet_train_predict_shuffle_seed)\n num_examples = MAX_EXAMPLES\n else:\n # Take whole training set.\n num_examples = self.num_examples_per_epoch(tf.estimator.ModeKeys.TRAIN)\n else:\n # Take whole validation set.\n num_examples = self.num_examples_per_epoch(tf.estimator.ModeKeys.EVAL)\n # Take as much of the dataset as possible that can be evenly\n # divided by batch_size.\n while True:\n if num_examples % batch_size == 0:\n break\n else:\n num_examples -= 1\n dataset = dataset.take(num_examples)\n dataset = dataset.repeat(1)\n\n # dataset = dataset.take(1000) # For fast debugging!\n else:\n dataset = dataset.repeat(1)\n\n # Batch it up.\n dataset = dataset.batch(batch_size)\n iterator = dataset.make_one_shot_iterator()\n image_batch, label_batch = iterator.get_next()\n\n return image_batch, label_batch","def sample_batch(self, batch_size):\n batch = []\n\n # Sample using prorities\n if(self.with_per):\n T = self.buffer.total() // batch_size\n #print(\"T is \",T)\n for i in range(batch_size):\n a, b = T * i, T * (i + 1)\n s = random.uniform(a, b)\n idx, error, data = self.buffer.get(s)\n #print(\"sampled data \", s, \" \",data, end=\" \")\n batch.append((*data, idx))\n\n idx = np.array([i[2] for i in batch])\n #idx in the offline buffer\n \n # Sample randomly from Buffer\n elif self.count < batch_size:\n idx = None\n batch = random.sample(self.buffer, self.count)\n else:\n idx = None\n batch = random.sample(self.buffer, batch_size)\n\n # Return a batch of experience\n names_batch = np.array([i[1] for i in batch])\n\n return names_batch, idx","def split_and_load(batch_data, num_gpus):\n return [batch_data[i].data[0] for i in range(num_gpus)], \\\n [batch_data[i].label[0].as_in_context(mx.gpu(i)) for i in range(num_gpus)]","def on_train_batch_end(self, step, logs=None):","def dynamic_batch(data, max_frames_in_batch=12000):\n buf = []\n longest_frames = 0\n for sample in data:\n assert \"feat\" in sample\n assert isinstance(sample[\"feat\"], torch.Tensor)\n new_sample_frames = sample[\"feat\"].size(0)\n longest_frames = max(longest_frames, new_sample_frames)\n frames_after_padding = longest_frames * (len(buf) + 1)\n if frames_after_padding > max_frames_in_batch:\n yield buf\n buf = [sample]\n longest_frames = new_sample_frames\n else:\n buf.append(sample)\n if len(buf) > 0:\n yield buf","def on_batch_begin(self, batch, logs=None):","def _send_data(self):\n \n # Do not send more than 100 datasets each time (totally arbitrary)\n MAX_DATA_SETS_PER_POST = 100\n data_to_send = self._data_buffer[0:MAX_DATA_SETS_PER_POST]\n data_to_keep = self._data_buffer[MAX_DATA_SETS_PER_POST:]\n\n # Prepare data string with the values in data buffer\n now = time.time()\n data_string = '[' \n for (timestamp, data) in data_to_send:\n data_string += '['\n data_string += str(round(timestamp-now,2))\n for sample in data:\n data_string += ','\n data_string += str(sample)\n data_string += '],'\n # Remove trailing comma and close bracket\n data_string = data_string[0:-1]+']'\n\n self._log.debug(\"Data string: \" + data_string)\n \n # Prepare URL string of the form\n # 'http://domain.tld/emoncms/input/bulk.json?apikey=\n # 12345&data=[[-10,10,1806],[-5,10,1806],[0,10,1806]]'\n url_string = self._settings['protocol'] + self._settings['domain'] + \\\n self._settings['path'] + \"/input/bulk_json?apikey=\" + \\\n self._settings['apikey'] + \"&data=\" + data_string\n self._log.debug(\"URL string: \" + url_string)\n\n # Send data to server\n self._log.info(\"Sending to \" + \n self._settings['domain'] + self._settings['path'])\n try:\n result = urllib2.urlopen(url_string, timeout=60)\n except urllib2.HTTPError as e:\n self._log.warning(\"Couldn't send to server, HTTPError: \" + \n str(e.code))\n except urllib2.URLError as e:\n self._log.warning(\"Couldn't send to server, URLError: \" + \n str(e.reason))\n except httplib.HTTPException:\n self._log.warning(\"Couldn't send to server, HTTPException\")\n except Exception:\n import traceback\n self._log.warning(\"Couldn't send to server, Exception: \" + \n traceback.format_exc())\n else:\n if (result.readline() == 'ok'):\n self._log.debug(\"Send ok\")\n # Send ok -> empty buffer\n self._data_buffer = data_to_keep\n return True\n else:\n self._log.warning(\"Send failure\")","def load_batch(self):\r\n\r\n #if we've seen all the data, start again with them in a new random order\r\n if self.batchcounter+self.batchsize > self.num_data:\r\n self.batchcounter = 0\r\n self.epochs += 1\r\n self._permutation = np.random.permutation(self.num_data)\r\n\r\n this_perm = self._permutation[self.batchcounter:self.batchcounter+self.batchsize]\r\n\r\n self.X_batch = self.X[this_perm]\r\n self.likelihood.set_data(self.Y[this_perm])\r\n if self.has_uncertain_inputs:\r\n self.X_variance_batch = self.X_variance[this_perm]\r\n\r\n self.batchcounter += self.batchsize\r\n\r\n self.data_prop = float(self.batchsize)/self.num_data\r\n\r\n self._compute_kernel_matrices()\r\n self._computations()","def __iter__(self):\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0\n\n while True:\n\n # Randomizing wav lists\n random.shuffle(self._lst_spk_files)\n random.shuffle(self._lst_noise_files)\n\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\n\n # Read wav files\n sig_spk = self.__read_wav_file(spk_file)\n sig_noise = self.__read_wav_file(noise_file)\n\n # Align signal\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\n\n if min_length < self._fftsize:\n raise Exception(\"ERROR: Too short signals in dataset\")\n\n sig_spk = sig_spk[:min_length]\n sig_noise = sig_noise[:min_length]\n\n # Generate need SNR\n need_snr = random.uniform(self._min_snr, self._max_snr)\n\n # Calc scaled signals\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\n\n # Calc STFT\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\n stft_mix = stft_spk + stft_noise\n\n # Skip small segments\n frames, bin = stft_mix.shape\n if frames <= self._context_size:\n continue\n\n # Collect batch\n i = 0\n while i + self._context_size < frames:\n\n batch_sp.append(stft_spk[i:i + self._context_size, :])\n batch_noise.append(stft_noise[i:i + self._context_size, :])\n batch_mix.append(stft_mix[i:i + self._context_size, :])\n\n i += self._context_size // 2\n batch_count += 1\n\n if batch_count == self._batch_size:\n sp = np.array(batch_sp).reshape((self._batch_size,\n self._context_size, -1))\n noise = np.array(batch_noise).reshape((self._batch_size,\n self._context_size, -1))\n mix = np.array(batch_mix).reshape((self._batch_size,\n self._context_size, -1))\n yield sp, noise, mix\n\n batch_sp = []\n batch_noise = []\n batch_mix = []\n batch_count = 0","def generate_batch():\n\n # Initialize variables\n example = np.zeros(self.batch_size)\n labels = np.zeros((self.batch_size, 1))\n alphas = np.zeros(self.batch_size)\n n_items = 0\n index = 0\n\n while index < len(data):\n reduced_window = random.randint(0, self.window_size)\n if data[index] is not None:\n\n left = max(0, index - self.window_size + reduced_window)\n right = min((index + self.window_size + 1 -\n reduced_window), len(data) - 1)\n for pos2 in range(left, right, 1):\n\n if n_items == self.batch_size:\n queue.put((example, labels, index))\n example = np.zeros(self.batch_size)\n labels = np.zeros((self.batch_size, 1))\n n_items = 0\n\n if pos2 != index and data[pos2] is not None:\n example[n_items] = data[pos2]\n labels[n_items] = data[index]\n alpha = self.learning_rate - \\\n (self.learning_rate - 0.001) * (index / self.n_words)\n alphas[n_items] = max(0.001, alpha)\n n_items += 1\n index += 1\n\n # Poison pills\n for _ in range(n_workers):\n queue.put(None)","def batch(self, batch):\n\n self._batch = batch","def _batch_train(self, batch, training_step, step):\n lstm_size = (self.batch_size, self.Qmain.h_size)\n batch_mem = np.zeros(lstm_size)\n batch_carry = np.zeros(lstm_size)\n input_shape = (self.batch_size,\n self.trace_length,\n self.observation_size)\n m_data = np.vstack(batch[:, 0])\n m_data = m_data.reshape(input_shape)\n t_data = np.vstack(batch[:, 4])\n t_data = t_data.reshape(input_shape)\n q_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(m_data)]\n q1_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(t_data)]\n\n # Batch predict\n self.Qmain.trace_length.assign(self.trace_length)\n self.Qmain.dropout_rate.assign(0.0)\n self.Qtarget.trace_length.assign(self.trace_length)\n self.Qtarget.dropout_rate.assign(0.0)\n\n # Save the graph just the first time\n if training_step == 0:\n tf.summary.trace_on()\n\n # T batch predict\n pred = self.Qmain.model.predict(q_input,\n batch_size=self.batch_size)\n Q = pred[0]\n batch_bus = pred[1]\n batch_line = pred[2]\n batch_disp = pred[3]\n\n ## Log graph once and disable graph logging\n if training_step == 0:\n with self.tf_writer.as_default():\n tf.summary.trace_export(self.name + \"-graph\", step)\n\n # T+1 batch predict\n Qn, *_ = self.Qtarget.model.predict(q1_input,\n batch_size=self.batch_size)\n \n # Compute batch Q update to Qtarget\n for i in range(self.batch_size):\n idx = i * (self.trace_length - 1)\n a = batch[idx][1]\n grid = a[0]\n batch_bus[i][:] = a[1][:]\n batch_line[i][:] = a[2][:]\n batch_disp[i][:] = a[3][:]\n r = batch[idx][2]\n d = batch[idx][3]\n Q[i][grid] = r\n if d == False:\n Q[i][grid] += DISCOUNT_FACTOR * Qn[i][grid]\n\n # Batch train\n batch_x = [batch_mem, batch_carry, m_data]\n batch_y = [\n Q,\n batch_bus, batch_line, batch_disp,\n batch_mem, batch_carry\n ]\n loss = self.Qmain.model.train_on_batch(batch_x, batch_y)\n loss = loss[0]\n\n # Log to tensorboard\n self._tf_log_summary(loss, step)","def sample_batch(self) -> List:\n return self.buffer.sample(self.batch_size)","def generate_batches(data, labels, batch_size):\n for start in range(0, len(data), batch_size):\n yield Tensor(data[start:start+batch_size, ...]), Tensor(labels[start:start+batch_size, ...])","def batches(self):\n return [self.get_batch(i) for i in range(self.num_batches)]","def save_batch(self):\n self._batch_counter += 1\n write_to_disk(\n self._batch_cases,\n os.path.join(\n self.crop.location,\n \"batches\",\n BTCH_NM.format(self._batch_counter),\n ),\n )\n self._batch_cases = []\n self._counter = 0","def process_batch(self, data):\n [embedding_batch] = self._sess.run([self._embedding_tensor],\n feed_dict={self._features_tensor: data})\n return embedding_batch","def append(self, batch: Batch):","def instantiate_batch(self, inputs):\n return inputs","def data(self, train=True, batch_size=2):\n if train:\n elements = self.prepare_batch(self.training_albums)\n else:\n elements = self.prepare_batch(self.validation_albums)\n\n while len(elements) > 0:\n # Collect the batch\n batch = []\n for _ in range(min(batch_size, len(elements))):\n batch.append(elements.pop())\n\n # Get same sequence size for all elements of the batch\n albums, labels = self.batchify(batch)\n yield albums, labels","def init_batch(self):\n pass","def train_minibatches(self):\n batch_size = self.params['batch_size']\n start_index = 0\n while start_index + batch_size < 500:\n end_index = start_index + batch_size\n yield self.input[start_index:end_index], self.y[start_index:end_index]\n start_index = end_index","def _update_num_batches(self):\n # maximum possible number of batches is equal to number of whole times\n # batch_size divides in to the number of data points which can be\n # found using integer division\n possible_num_batches = self.inputs.shape[0] // self.batch_size\n if self.max_num_batches == -1:\n self.num_batches = possible_num_batches\n else:\n self.num_batches = min(self.max_num_batches, possible_num_batches)","def create_batch(client, generator: DataGenerator):\n try:\n event_data_batch = client.create_batch()\n for device in generator.devices:\n # event_data_batch.add(EventData(gen.generate_payload(device)))\n event_data_batch.add(EventData(generator.generate_payload(device)))\n return event_data_batch\n except Exception as e:\n print(str(e))","def multiple_batch(self, handler, dataset):\n iterator = tf.data.Iterator.from_string_handle(handler, dataset.output_types, dataset.output_shapes)\n (src_ids, tgt_input_ids, tgt_output_ids, src_seq_len, tgt_seq_len) = (iterator.get_next())\n return BatchedInput(iterator=None,\n batched_dataset=None,\n handle=None,\n initializer=None,\n source=src_ids,\n target_input=tgt_input_ids,\n target_output=tgt_output_ids,\n source_sequence_length=src_seq_len,\n target_sequence_length=tgt_seq_len)","def data_gen(\n v: int, batch: int, nbatches: int, device: torch.device = torch.device(\"cpu\")\n) -> Iterator[Batch]: # TODO bad name\n for i in range(nbatches):\n data = np.random.randint(1, v, size=(batch, 10))\n data[:, 0] = 1\n src: LongTensorType = torch.from_numpy(data)\n tgt: LongTensorType = torch.from_numpy(data)\n src, tgt = src.to(device), tgt.to(device)\n yield Batch(src, tgt, 0)","def save_feature(self):\n import scipy.io as sio\n testdp = self.test_data_provider\n num_batches = len(testdp.batch_range)\n print 'There are ' + str(testdp.get_num_batches(self.data_path)) + ' in directory'\n if self.test_data_provider.batch_size > 0:\n num_batches = (num_batches - 1)/ self.test_data_provider.batch_size + 1\n if self.test_one:\n num_batches = min(num_batches, 1)\n print 'There are ' + str( num_batches ) + ' in range'\n iu.ensure_dir(self.save_feature_path)\n feature_name = self.op.get_value('save_feature_name')\n feature_dim = self.model_state['layers'][self.feature_idx]['outputs']\n print 'Feature dim is %d' % feature_dim\n for b in range(num_batches):\n epoch, b_num, data = self.get_next_batch(train=False)\n print ' Start writing batch......\\t' + str(b_num)\n num_data = data[0].shape[-1]\n data += [n.zeros((num_data, feature_dim), dtype=n.single)]\n save_name = 'batch_feature_' + str(b_num) + '_' + feature_name \n save_path = iu.fullfile(self.save_feature_path, save_name)\n self.libmodel.startFeatureWriter(data, self.feature_idx)\n self.finish_batch()\n d = dict()\n d['X'] = data[-1].transpose()\n d['batch_num'] = b_num\n d['Y'] = data[1]\n cur_batch_indexes = self.test_data_provider.data_dic['cur_batch_indexes']\n # d['Y_other'] = data[2:-1] if len(data) > 3 else []\n ####### WARN BEGIN ################\n # for human eva fake experiments\n # d['images_path'] = [self.test_data_provider.images_path[x] for x in cur_batch_indexes]\n # d['Y'] = np.concatenate(map(lambda x:self.test_data_provider.batch_meta['RelativeSkel_Y3d_mono_body_backup'][...,x].reshape((-1,1),order='F'), cur_batch_indexes),axis=1)\n print d['Y'].shape\n d['cur_batch_indexes'] = cur_batch_indexes\n ####### WARN END ################\n print 'The len of data is ' + str(len(data))\n print 'The shape of X is' + str(d['X'].shape)\n print 'The shape of Y is' + str(d['Y'].shape)\n ##sio.savemat(save_path, d)\n pickle(save_path, d)","def collect_samples(self):\n self.replay_buffer = self.collect_initial_batch(\n self.replay_buffer, self.acm_pre_train_samples\n )","def _get_batch(self):\n # index = self._index[self._current]\n # im_path = self._imdb.image_path_from_index(0)\n # im_path = 'data/demo/dog.jpg'\n # with open(im_path, 'rb') as fp:\n # img_content = fp.read()\n\n batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))\n batch_label = [] \n global imgi\n # img = mx.nd.array(imgi)\n # imgr = mx.img.imdecode(img_content)\n data = self._data_augmentation(imgi)\n batch_data[0] = data\n \n self._data = {'data': batch_data}\n self._label = {'label': None}","def save_and_upload_batch_sample_sets(batch_samples, batch_tumors, batch_normals, tsca_id, namespace, workspace):\n # Save to file\n os.system('mkdir -p %s'%tsca_id)\n batch_samples_filename = './%s/fc_upload_sample_set_tsca_%s.txt' % (tsca_id, tsca_id)\n batch_tumors_filename = './%s/fc_upload_sample_set_tsca_%s_tumors.txt' % (tsca_id, tsca_id)\n batch_normals_filename = './%s/fc_upload_sample_set_tsca_%s_normals.txt' % (tsca_id, tsca_id)\n \n batch_samples.to_csv(batch_samples_filename , sep=\"\\t\", index=False )\n batch_tumors.to_csv(batch_tumors_filename , sep=\"\\t\", index=False )\n batch_normals.to_csv(batch_normals_filename , sep=\"\\t\", index=False )\n\n r1 = upload_entities_from_tsv(namespace, workspace, batch_samples_filename)\n r2 = upload_entities_from_tsv(namespace, workspace, batch_tumors_filename)\n r3 = upload_entities_from_tsv(namespace, workspace, batch_normals_filename)\n return (r1, r2, r3)","def get_batches(self, batch_size):\n if self.data.shape[0] % batch_size != 0:\n raise RuntimeError('num of data tuples is not a multiple of batch size')\n num_batch = self.data.shape[0] // batch_size\n for b in range(num_batch):\n yield self.data[b*batch_size:(b+1)*batch_size, :], \\\n self.target[b*batch_size:(b+1)*batch_size, :]","def next_batch(self, batch_size, shuffle=True):","def batches(self, batch_size): \n if self.shuffle:\n idx = np.arange(len(dataset.train_x))\n np.random.shuffle(idx)\n self.train_x = self.train_x[idx]\n \n n_batches = len(self.train_x) // batch_size\n for ii in range(0, len(self.train_x), batch_size):\n x = self.train_x[ii:ii+batch_size]\n \n yield self.scaler(x)","def data_generator(delta=1, batch_size=32):\n while True:\n yield generate_samples(delta=delta, n=batch_size)","def data_iter(data, vocab, batch_size, shuffle=True, cuda=False):\n\n buckets = defaultdict(list)\n for pair in data:\n src_sent = pair[0]\n buckets[len(src_sent)].append(pair)\n\n batched_data = []\n for src_len in buckets:\n tuples = buckets[src_len]\n if shuffle: np.random.shuffle(tuples)\n batched_data.extend(list(HNCMDataLoader.batch_slice(tuples, batch_size)))\n\n if shuffle:\n np.random.shuffle(batched_data)\n for src_sents, trg_sents, fact_sents in batched_data:\n num_trg_word = sum(len(s[:-1]) for s in trg_sents)\n src_lengths = [len(s) for s in src_sents]\n src_seqs_var = to_input_var(src_sents, vocab.src, cuda)\n trg_seqs_var = to_input_var(trg_sents, vocab.trg, cuda)\n fact_lengths = [[len (s) for s in fact_sent] for fact_sent in fact_sents]\n fact_seqs_var = to_input_var_2d(fact_sents, vocab.src, cuda)\n\n yield {\n 'src_seq': src_seqs_var, 'src_lengths': src_lengths,\n 'fact_seq': fact_seqs_var, 'fact_lengths': fact_lengths,\n 'trg_seq': trg_seqs_var[:, :-1],\n 'target': trg_seqs_var[:, 1:],\n 'num_trg_word': num_trg_word, 'num_trg_seq': len(trg_sents)\n }","def train(batch_size, num_sample=128):\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)","def on_test_batch_begin(self, batch, logs=None):","def produce_query_batches(self):\n pass","def process(self, data_batch: Sequence[Dict],\n data_samples: Sequence[Dict]) -> None:\n for data_sample in data_samples:\n pred_labels = data_sample.get('pred_instances').get(self.key).cpu()\n gt_labels = data_sample.get('gt_instances').get(self.key).cpu()\n\n result = dict(\n pred_labels=pred_labels.flatten(),\n gt_labels=gt_labels.flatten())\n self.results.append(result)","def sample_train_batch(self):\r\n batch = []\r\n labels =[]\r\n num_groups = self.batch_size // self.batch_k\r\n sampleed_classes = np.random.choice(self.train_class_ids,num_groups,replace=False)\r\n for class_id in sampleed_classes:\r\n img_fname = np.random.choice(self.train_image_files[class_id],self.batch_k,replace=False)\r\n batch += img_fname.tolist()\r\n labels += [class_id]*self.batch_k\r\n return batch,labels","def test_batch(self):\n pass","def next_batch(self, batch_size):\n raise NotImplementedError","def gen_batches(data, batch_size=2048):\n indices = torch.randperm(len(data))\n indices = indices.cuda()\n\n for idx in range(0, len(data) - batch_size + 1, batch_size):\n sample = indices[idx:idx + batch_size]\n l_words, r_words = data.L_words[sample], data.R_words[sample]\n l_vecs = data.l_vecs[l_words]\n r_vecs = data.r_vecs[r_words]\n l_bias = data.l_biases[l_words]\n r_bias = data.r_biases[r_words]\n weight = data.weights[sample]\n y = data.y[sample]\n yield weight, l_vecs, r_vecs, y, l_bias, r_bias","def generate_next_batch(self, data): \n \n batch_words = np.array(data[self.batch_lookup[self.batch_index]][0])\n batch_labels = np.array(data[self.batch_lookup[self.batch_index]][1])\n self.batch_index += 1\n if self.batch_index == len(data) - 1:\n self.epoch += 1\n return batch_words, batch_labels","def __iter__(self):\n batch = []\n for idx in self._sampler:\n batch.append(idx)\n if len(batch) == self._batch_size:\n batch = sum(batch, [])\n yield batch\n batch = []\n if len(batch) > 0:\n batch = sum(batch, [])\n yield batch","def _generate_and_save_examples(self, epoch):\n\n if self.fn_save_examples:\n z_in = tf.random.uniform((self.batch_size, self.z_dim), -1, 1)\n x_save = self.raw_x_dataset[0:self.batch_size]\n c_save = self.raw_dataset[0:self.batch_size]\n\n generations = tf.squeeze(self.generator(z_in, c_save, training=False))\n self.fn_save_examples(epoch, x_save, generations)","def upload_data_new_batch(tsca_id, latest_tsca_id, paths_to_batches_info, namespace, workspace, google_bucket_id):\n paths_to_batches_info_df = pd.read_excel(paths_to_batches_info, index_col=0)\n path_to_samples_info = paths_to_batches_info_df.loc[tsca_id, 'path_to_samples_info']\n\n # DF of remote [sample < > sample set ]\n remote_sample_sets = pd.read_table('remote_files/sample_set_membership_%s.tsv'%latest_tsca_id)\n # DF of remote [pair < > pair set]\n remote_pair_sets = pd.read_table('remote_files/pair_set_membership_%s.tsv'%latest_tsca_id)\n\n all_samples = get_samples(paths_to_batches_info, google_bucket_id)\n # Add cohorts for older batches\n all_samples = add_cohort_to_old_batches(all_samples)\n\n ##### Remove blacklisted samples ##\n # Blacklisted samples\n blacklisted = pd.read_table(\"samples_blacklist.txt\", header=None, names=[\"entity:sample_id\"])\n blacklisted_sample_ids = blacklisted[\"entity:sample_id\"].values.tolist()\n all_samples = all_samples[~all_samples[\"entity:sample_id\"].isin(blacklisted_sample_ids)]\n\n ########## Participants ##########\n print(\"Uploading participants...\") \n participants = prepare_participants_for_metadata_export(path_to_samples_info, tsca_id)\n r1 = save_and_upload_participants(participants, namespace, workspace, tsca_id)\n ##################################\n\n ########## Samples ############\n print(\"Uploading samples...\")\n batch_samples = prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id)\n r2 = save_and_upload_samples(batch_samples, namespace, workspace, tsca_id)\n #################################\n\n ########## Pairs #############\n print(\"Uploading pairs...\")\n pairs = create_pairs_list(all_samples)\n r3 = save_and_upload_pairs(namespace, workspace, pairs)\n #################################\n\n ########## Sample Sets #########\n print(\"Uploading sample sets...\")\n batch_sample_set, batch_tumor_set, batch_normal_set = prepare_batch_sample_set_for_metadata_export(path_to_samples_info, tsca_id)\n # Remove the samples that have already been uploaded \n uploaded_sample_ids = remote_sample_sets['sample'].tolist()\n batch_sample_set_clean = batch_sample_set[~batch_sample_set['sample_id'].isin(uploaded_sample_ids)]\n batch_tumor_set_clean = batch_tumor_set[~batch_tumor_set['sample_id'].isin(uploaded_sample_ids)]\n batch_normal_set_clean = batch_normal_set[~batch_normal_set['sample_id'].isin(uploaded_sample_ids)]\n r4a, r4b, r4c = save_and_upload_batch_sample_sets(batch_sample_set_clean, batch_tumor_set_clean, batch_normal_set_clean, tsca_id, namespace, workspace)\n #################################\n\n ########## PoNs ###############\n print(\"Uploading PoNs...\")\n \n # Number of latest tsca id\n latest_tsca_id_int = int(re.findall('\\d+', latest_tsca_id )[0])\n # Array with list of all previous TSCA ids\n previous_tsca_ids = [\"TSCA%s\"%i for i in np.arange(14, latest_tsca_id_int+1)]\n previous_tsca_ids.insert(0, \"TSCA1213\")\n\n pon, name = create_panel_of_normals_advanced(tsca_id, all_samples,\\\n num_normals_per_cohort_involved = 3, \\\n batches_to_pick_from = previous_tsca_ids)\n\n # Only upload PoN if it hasn't been uploaded already\n if not name in remote_sample_sets['membership:sample_set_id'].unique().tolist():\n r5 = upload_pon(pon, name, namespace, workspace) \n else: \n print(\"PoN already exists...\")\n r5 = {}\n #################################\n \n ########## Pair Set ###########\n print(\"Uploading pair sets...\")\n # Upload cumulative pair sets\n tn_cum_pairsets, tp_cum_pairsets = prepare_cumulative_pairsets_for_metadata_export(pairs, tsca_id)\n r6 = upload_pairsets(namespace, workspace, tn_cum_pairsets, \"TN\")\n r7 = upload_pairsets(namespace, workspace, tp_cum_pairsets, \"TP\")\n\n # Batch pair sets\n tn_pairsets, tp_pairsets = prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id)\n uploaded_pair_ids = remote_pair_sets['pair'].tolist()\n tn_pairsets_clean = tn_pairsets[~tn_pairsets['pair_id'].isin(uploaded_pair_ids)]\n tp_pairsets_clean = tp_pairsets[~tp_pairsets['pair_id'].isin(uploaded_pair_ids)]\n\n r8 = upload_pairsets(namespace, workspace, tn_pairsets_clean, \"TN\")\n r9 = upload_pairsets(namespace, workspace, tp_pairsets_clean, \"TP\")\n #################################\n\n return (r1, r2, r3, r4a, r4b, r4c, r5, r6, r7, r8, r9)","def on_train_begin(self, logs={}):\n self._data = []","def sample_batch(self, batch_size):\n batch = []\n\n # Sample using prorities\n if(self.with_per):\n T = self.buffer.total() // batch_size\n for i in range(batch_size):\n a, b = T * i, T * (i + 1)\n s = random.uniform(a, b)\n idx, error, data = self.buffer.get(s)\n batch.append((*data, idx))\n idx = np.array([i[5] for i in batch])\n #TD errors are only updated for transitions that are replayed\n \n # Sample randomly from Buffer\n elif self.count < batch_size:\n idx = None\n batch = random.sample(self.buffer, self.count)\n else:\n idx = None\n batch = random.sample(self.buffer, batch_size)\n\n # Return a batch of experience\n s_batch = np.array([i[0] for i in batch])\n a_batch = np.array([i[1] for i in batch])\n r_batch = np.array([i[2] for i in batch])\n d_batch = np.array([i[3] for i in batch])\n new_s_batch = np.array([i[4] for i in batch])\n\n return s_batch, a_batch, r_batch, d_batch, new_s_batch, idx","def _train(self, samples):\n for i, sample in enumerate(samples):\n # add extra info to samples\n sample = {\n 'src_tokens': sample[0],\n 'input_tokens': self._right_shifted_ys(sample[1]),\n 'target': sample[1],\n 'id': None\n }\n sample['ntokens'] = sum(len(t) for t in sample['target'])\n sample['src_positions'] = self._positions_for_tokens(\n sample['src_tokens'])\n sample['input_positions'] = self._positions_for_tokens(\n sample['input_tokens'])\n samples[i] = sample\n return self.trainer.train_step(samples)","def batch(self, data, size):\n\n return [data[x : x + size] for x in range(0, len(data), size)]","def setUp(self):\n self.batch_size = 8\n num_keypoints = 16\n self.data_batch = []\n self.data_samples = []\n\n for i in range(self.batch_size):\n gt_instances = InstanceData()\n keypoints = np.zeros((1, num_keypoints, 2))\n keypoints[0, i] = [0.5 * i, 0.5 * i]\n gt_instances.keypoints = keypoints + 1.0\n gt_instances.keypoints_visible = np.ones(\n (1, num_keypoints, 1)).astype(bool)\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\n\n pred_instances = InstanceData()\n pred_instances.keypoints = keypoints\n\n data = {'inputs': None}\n data_sample = {\n 'gt_instances': gt_instances.to_dict(),\n 'pred_instances': pred_instances.to_dict(),\n }\n\n self.data_batch.append(data)\n self.data_samples.append(data_sample)","def split_to_batches(self, train_data, batch_size):\n num_of_training_examples = len(train_data)\n for i in range(0, num_of_training_examples, batch_size):\n x, y = zip(*train_data[i: i+batch_size])\n yield np.vstack(x), np.vstack(y)","def train_dynamic(batch_size=10):\n \n return","def next_batch(self):\n # Whether an epoch is done.\n done = False\n samples = []\n for _ in range(self.batch_size):\n # Indeed, `>` will not occur.\n if self.ptr >= self.dataset_size:\n done = True\n break\n else:\n self.ptr += 1\n sample = self.enqueuer.queue.get()\n samples.append(sample)\n # print 'queue size: {}'.format(self.enqueuer.queue.qsize())\n # Indeed, `>` will not occur.\n if self.ptr >= self.dataset_size:\n done = True\n return samples, done","def batch_fit(self, data_loader: torch.utils.data.DataLoader, epochs: int):\n pass","def batch_start(self, batch_idx, batch_data):\n self.batch = batch_idx","def next_batch(self, batch_size):\r\n raise NotImplementedError","def gen_batches(data, batch_size):\n data = np.array(data)\n\n for i in range(0, data.shape[0], batch_size):\n yield data[i:i+batch_size]","def generate_batch(self):\n\n # sbatch = list()\n # tbatch = list()\n # for i in range(self.dict_paras['batch_size']):\n # sbatch.append(self.lst_triplet_train_map[self.data_index])\n # self.data_index = (self.data_index + 1) % self.triplet_train_size\n\n sbatch = random.sample(self.lst_triplet_train_map, self.dict_paras['batch_size'])\n tbatch = list()\n\n for ele in sbatch:\n corrupted1, corrupted2 = self.get_corrupted_triplet(ele)\n tbatch.append((ele, corrupted1))\n tbatch.append((ele, corrupted2))\n return tbatch","def prepare_batch(data,BATCH_SIZE, filename):\n ### select the last BATCH_SIZE rows from batch dataset\n batch = data.iloc[-BATCH_SIZE:].values.tolist()\n batch_data = []\n \n ### remove white spaces in the list because predtion server expects no white spaces between elements\n for i in batch:\n str_row = str(i)\n str_row = str_row.replace(' ','')\n batch_data.append(str_row)\n \n ### write values in a file called filename\n with open(filename, 'w') as f:\n f.write(','.join(str(i) for i in batch_data))","def batch_data(source, target, batch_size):\n for batch_i in range(0, len(source)//batch_size):\n start_i = batch_i * batch_size\n source_batch = source[start_i:start_i + batch_size]\n target_batch = target[start_i:start_i + batch_size]\n yield np.array(pad_sentence_batch(source_batch)), np.array(pad_sentence_batch(target_batch))","def samples(self, samples):\n\n self._samples = samples","def next_batch(self, batch_size=8):\n raise NotImplementedError()","def _fillBatches(self):\n\n batchRE = r\"\"\"\n B\n (?P<observebatch>\\d+?)\n (?P<startend>[SE])\n (?P<sequence>\\d+?)\n _SR\n (?:_(?P<extraInjections>\\d+?|\\w+?))?\n $\n \"\"\"\n batchRE = re.compile(batchRE, re.VERBOSE)\n # We canot infer batches unless we have runorder\n if 'Run Order' in self.sampleMetadata.keys():\n currentBatch = 0\n # Loop over samples in run order\n for index, row in self.sampleMetadata.sort_values(by='Run Order').iterrows():\n nameComponents = batchRE.search(row['Sample File Name'])\n if nameComponents:\n # Batch start\n if nameComponents.group('startend') == 'S':\n # New batch - increment batch no\n if nameComponents.group('sequence') == '1':\n currentBatch = currentBatch + 1\n\n # Don't include the dilution series or blanks\n if not ((row['AssayRole'] == AssayRole.LinearityReference) or (row['SampleType'] == SampleType.ProceduralBlank)):\n self.sampleMetadata.loc[index, 'Batch'] = currentBatch\n self.sampleMetadata.loc[index, 'Correction Batch'] = currentBatch\n\n else:\n warnings.warn('Unable to infer batches without run order, skipping.')\n return","def _send_batch(self):\n batch = RPLogBatch(self._batch)\n http_request = HttpRequest(\n self.session.post, self._log_endpoint, files=batch.payload,\n verify_ssl=self.verify_ssl)\n batch.http_request = http_request\n self._worker.send(batch)\n self._batch = []\n self._payload_size = helpers.TYPICAL_MULTIPART_FOOTER_LENGTH","def setUp(self):\n self.batch_size = 8\n num_keypoints = 15\n self.data_batch = []\n self.data_samples = []\n\n for i in range(self.batch_size):\n gt_instances = InstanceData()\n keypoints = np.zeros((1, num_keypoints, 2))\n keypoints[0, i] = [0.5 * i, 0.5 * i]\n gt_instances.keypoints = keypoints\n gt_instances.keypoints_visible = np.ones(\n (1, num_keypoints, 1)).astype(bool)\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\n\n pred_instances = InstanceData()\n pred_instances.keypoints = keypoints\n\n data = {'inputs': None}\n data_sample = {\n 'gt_instances': gt_instances.to_dict(),\n 'pred_instances': pred_instances.to_dict(),\n }\n\n self.data_batch.append(data)\n self.data_samples.append(data_sample)","def setUp(self):\n self.batch_size = 8\n num_keypoints = 15\n self.data_batch = []\n self.data_samples = []\n\n for i in range(self.batch_size):\n gt_instances = InstanceData()\n keypoints = np.zeros((1, num_keypoints, 2))\n keypoints[0, i] = [0.5 * i, 0.5 * i]\n gt_instances.keypoints = keypoints\n gt_instances.keypoints_visible = np.ones(\n (1, num_keypoints, 1)).astype(bool)\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\n\n pred_instances = InstanceData()\n pred_instances.keypoints = keypoints\n\n data = {'inputs': None}\n data_sample = {\n 'gt_instances': gt_instances.to_dict(),\n 'pred_instances': pred_instances.to_dict(),\n }\n\n self.data_batch.append(data)\n self.data_samples.append(data_sample)","def next(self):\n\n if self.i_sample < self.n_sample:\n df_batch = self.grouped[self.i_sample:min(self.n_sample, self.i_sample + self.batch_size)]\n # at end of epoch, number of sample remains may be smaller than batch size\n if len(df_batch) < self.batch_size:\n df_sample = random.sample(self.grouped, self.batch_size-len(df_batch))\n df_batch = df_batch + df_sample\n try:\n assert len(df_batch) == self.batch_size\n except AssertionError:\n print(self.i_sample, df_sample, df_batch)\n\n # get random frame_idxs\n if self.train:\n flips = np.random.choice(a=[False, True], size=(self.batch_size,), p=[0.5, 0.5])\n else:\n flips = np.zeros(self.batch_size, dtype=bool)\n\n\n video = sample_clips(df_batch, flips, self.batch_size, self.n_frame,\n self.scale_w, self.scale_h, self.sample_half_time, self.train)\n\n bboxes = np.zeros((self.batch_size, self.n_frame // self.temporal_scale, self.n_bbox, 5))\n labels = np.zeros((self.batch_size, self.n_bbox, self.num_class))\n for i in range(len(df_batch)):\n tmp_bbox, tmp_label = self.get_bbox_and_label(df_batch[i], flips[i], i, self.scale_w, self.scale_h)\n bboxes[i] = tmp_bbox\n labels[i] = tmp_label\n\n if self.debug_dataloader:\n with open('dataset/AVA_v2.1/ava_action_list_v2.1.pbtxt') as fd:\n lines = fd.readlines()\n\n labels_info = []\n for i in range(80):\n name_line = lines[i * 5 + 1]\n label_id_line = lines[i * 5 + 2]\n label_type_line = lines[i * 5 + 3]\n\n name = name_line[name_line.find('\"') + 1:name_line.rfind('\"')]\n label_id = int(label_id_line.strip().split(':')[1].strip())\n label_type = label_type_line.strip().split(':')[1].strip()\n\n assert label_id == i + 1\n labels_info.append({\n 'name': name,\n 'label_type': label_type\n })\n\n for bidx in range(self.batch_size):\n s_video = video[bidx, ...]\n s_bboxes = bboxes[bidx, ...]\n s_labels = labels[bidx, ...]\n\n window_name = 'batch_idx_'+str(bidx)\n if self.train:\n window_name += '_train'\n else:\n window_name += '_val'\n\n\n bbox = s_bboxes[0, 0, 1:].astype(np.int32)\n label_indices = np.where(s_labels[0, :])[0]\n\n for fidx in range(self.n_frame):\n # print('fidx', fidx)\n save_name = window_name + '_' + str(fidx)\n tmp_img = (s_video[:, fidx, :, :].transpose((1,2,0))).astype(np.uint8).copy()\n\n cv2.rectangle(tmp_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=(0,0,255), thickness=2)\n for en_idx, label_index in enumerate(label_indices):\n # print('label_index', label_index, 'len', len(labels_info))\n cv2.putText(tmp_img, labels_info[label_index]['name'], (bbox[0], bbox[1] + en_idx * 10), cv2.FONT_HERSHEY_SIMPLEX, 0.3, color=(0, 255, 0), thickness=1)\n\n cv2.imwrite(save_name+'.jpg', tmp_img)\n\n\n #print(video.shape, bboxes.shape, labels.shape)\n ret = mx.io.DataBatch(data=[mx.nd.array(video), mx.nd.array(bboxes)],\n label=[mx.nd.array(labels),],\n provide_data=self.provide_data,\n provide_label=self.provide_label)\n\n self.i_sample += self.batch_size\n return ret\n else:\n raise StopIteration"],"string":"[\n \"def train(self, num_batches: int):\",\n \"def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:\",\n \"def set_batch_data():\\r\\n if not os.path.exists(filepath):\\r\\n download_data()\\r\\n for n in range(0,6):\\r\\n d = read(filepath + flist[n])\\r\\n metadata = read(filepath + flist[-1])\\r\\n ndata = metadata['num_cases_per_batch']\\r\\n ndim = metadata['num_vis']\\r\\n\\r\\n data, trts = {}, {}\\r\\n data['labels'] = metadata['label_names']\\r\\n data['ntraindata'] = metadata['num_cases_per_batch'] * (len(flist) - 2)\\r\\n data['ntestdata'] = metadata['num_cases_per_batch']\\r\\n data['ndim'] = metadata['num_vis']\\r\\n trts['x'], trts['y'] = d['data'], d['labels']\\r\\n trtsflag = ['train', 'train', 'train', 'train', 'train', 'test']\\r\\n\\r\\n data['flag'] = trtsflag[n]\\r\\n data[trtsflag[n]] = trts\\r\\n save_pkl(data, savename=flist[n]+'.pkl')\",\n \"def batch_data(data, state_queue):\\n while True:\\n time.sleep(BATCH_SLEEP_TIME)\\n state_queue.put((\\\"device_values\\\", [data]))\",\n \"def prepare_data(self, train_data, **kwargs):\\n data_len = len(train_data[\\\"done\\\"])\\n for index in range(data_len):\\n if self.multi_step == 1:\\n self.buff.add(train_data[\\\"cur_state\\\"][index],\\n train_data[\\\"action\\\"][index],\\n train_data[\\\"reward\\\"][index],\\n train_data[\\\"next_state\\\"][index],\\n float(train_data[\\\"done\\\"][index])) # Add replay buffer\",\n \"def _defineBatches(self):\\n # extract all ids\\n all_keys = list(self.data_dict.unique_ids)\\n\\n # randomly shuffle keys\\n if self.random_shuffle_batches:\\n random.shuffle(all_keys)\\n\\n # create batches based on number of batches\\n if self.n_big_batches is not None:\\n self.n_big_batches += 1\\n # define cuts for batches\\n cuts = np.linspace(0, self.n_observations,\\n self.n_big_batches).round()\\n # create batches based on batch size\\n elif self.batch_size is not None:\\n cuts = [x for x in range(0, self.n_observations,\\n int(self.batch_size))]\\n if cuts[-1] < self.n_observations:\\n cuts.append(self.n_observations)\\n\\n # convert batch sizes to integers\\n cuts = [int(x) for x in cuts]\\n\\n # save batches into dictionary\\n batches = dict()\\n for i in range(0, (len(cuts) - 1)):\\n # create DataBatch object\\n current_batch = DataBatch(ids=all_keys[cuts[i]:cuts[i+1]],\\n batch_id=i)\\n current_batch.setDiskStoragePath(self.disk_scratch)\\n batches[i] = current_batch\\n\\n # save batches\\n self.n_batches = len(batches.keys())\\n self.batches = batches\",\n \"def _train(self):\\n epoch_training_time = 0\\n epoch_metrics_time = 0\\n self.epoch_ += 1\\n for i_batch, sample_batched in enumerate(self.dataloader):\\n self.global_step_ += 1\\n batch_start_time = time.time()\\n data_sample = sample_batched[0].to(self.device)\\n\\n # Get model samples, either from replay buffer or noise.\\n if self.model_samples_ is None:\\n self.model_samples_ = deque(\\n [\\n self.net_.sample_from_prior(\\n data_sample.shape[0], device=self.device\\n ).detach()\\n ]\\n )\\n elif len(self.model_samples_) > self.max_replay:\\n self.model_samples_.popleft()\\n replay_sample = random.choices(\\n self.model_samples_,\\n # favor more recent samples:\\n weights=list(range(1, len(self.model_samples_) + 1)),\\n )[0]\\n noise_sample = self.net_.sample_from_prior(\\n replay_sample.shape[0], device=self.device\\n )\\n mask = torch.rand(replay_sample.shape[0]) < self.replay_prob\\n while len(mask.shape) < len(replay_sample.shape):\\n # Add extra feature-dims\\n mask.unsqueeze_(dim=-1)\\n\\n model_sample = torch.where(\\n mask.to(self.device), replay_sample, noise_sample\\n )\\n\\n self.net_.eval()\\n # Run at least one iteration\\n model_sample = self.net_.sample_fantasy(\\n model_sample,\\n num_mc_steps=self.num_mc_steps,\\n mc_dynamics=self.sampler,\\n ).detach()\\n\\n self.model_samples_.append(model_sample)\\n\\n # Sanity checks:\\n assert (\\n data_sample.shape[1:] == self.net_.input_shape\\n ), \\\"Data is incompatible with network.\\\"\\n assert (\\n model_sample.shape[1:] == data_sample.shape[1:]\\n ), \\\"Model and data samples are incompatible.\\\"\\n\\n # Forward gradient:\\n self.net_.train()\\n self.net_.zero_grad()\\n data_energy_mean = self.net_(data_sample).mean()\\n model_energy = self.net_(model_sample)\\n model_energy_mean = model_energy.mean()\\n\\n # Estimate the odds of the data's energy based on a normal fitted to\\n # model samples:\\n data_erf = torch.erf(\\n (data_energy_mean - model_energy_mean) / model_energy.std()\\n )\\n\\n objective = data_energy_mean - model_energy_mean\\n objective.backward()\\n torch.nn.utils.clip_grad.clip_grad_value_(self.net_.parameters(), 1e2)\\n self.optimizer_.step()\\n\\n batch_training_time = time.time() - batch_start_time\\n epoch_training_time += batch_training_time\\n self.logger_(energy_diff=float(objective))\\n self.logger_(data_erf=float(data_erf))\\n\\n tr_metrics_start_time = time.time()\\n for callback in self.step_callbacks:\\n callback(\\n net=self.net_,\\n data_sample=data_sample,\\n model_sample=model_sample,\\n epoch=self.epoch_,\\n global_step=self.global_step_,\\n validation=False,\\n )\\n tr_metrics_time = time.time() - tr_metrics_start_time\\n epoch_metrics_time += tr_metrics_time\\n if self.verbose:\\n print(\\n f\\\"on epoch {self.epoch_}, batch {i_batch}, data erf: {data_erf}, objective: {objective}\\\"\\n )\\n print(f\\\"model energy: {model_energy_mean} +- {model_energy.std()}\\\")\\n print(f\\\"data energy: {data_energy_mean}\\\")\\n print(\\n f\\\"training time: {batch_training_time:0.3f}s, metrics time: {tr_metrics_time:0.3f}s\\\"\\n )\\n means = self.logger_.means()\\n if self.verbose:\\n print(f\\\"on epoch {self.epoch_}\\\")\\n for k, v in means.items():\\n print(f\\\"{k}: {v}\\\")\\n self.logger_.flush()\\n means[\\\"loss\\\"] = energy_model.utils.constraints.add_soft_constraint(\\n means[\\\"loss_ais\\\"], means[\\\"data_erf\\\"], lower_bound=-1\\n )\\n return means\",\n \"def gen_batch(self):\\n batch_size = self.batch_size\\n shuffle = self.shuffle\\n data = np.array(self.sentences)\\n\\n data_size = len(data)\\n num_batches_per_epoch = int((len(data) - 1) / batch_size) + 1\\n while True:\\n # shuffle the data at starting of each epoch\\n shuffled_data = data\\n if shuffle:\\n shuffle_indices = np.random.permutation(np.arange(data_size))\\n shuffled_data = data[shuffle_indices]\\n \\n for batch_num in range(num_batches_per_epoch):\\n start_index = batch_num * batch_size\\n end_index = min((batch_num + 1) * batch_size, data_size)\\n yield self._format_samples(shuffled_data[start_index:end_index], self.max_length)\\n\\n if self.mode in ['train', \\\"pred\\\"]:\\n break\",\n \"def train(self, batch):\\n pass\",\n \"def batch_size(self) -> int:\\n ...\",\n \"def _local_train(self, dataloader_with_memory, num_updates):\\n # Local train\\n _size = len(dataloader_with_memory)\\n self.model = self.model.train()\\n for _batch in range(num_updates):\\n X, y = dataloader_with_memory.get_samples()\\n X, y = X.to(self._device), y.to(self._device)\\n if _batch == 0:\\n # Initialize the batch-size using the first batch to avoid\\n # edge cases with drop_last=False\\n _batch_size = X.shape[0]\\n _num_batches_per_epoch = (_size // _batch_size) + int(\\n (_size % _batch_size) != 0\\n )\\n # Compute prediction and loss\\n _pred = self.model(X)\\n _loss = self._loss(_pred, y)\\n\\n # Backpropagation\\n _loss.backward()\\n self._optimizer.step()\\n self._optimizer.zero_grad()\\n self.num_batches_seen += 1\\n _loss, _current_epoch = (\\n _loss.item(),\\n self.num_batches_seen // _num_batches_per_epoch,\\n )\\n\\n if self.log:\\n if _batch % self.log_period == 0:\\n print(\\n f\\\"loss: {_loss:>7f} after {self.num_batches_seen:>5d}\\\"\\n f\\\" batches of data amounting to {_current_epoch:>5d}\\\"\\n \\\" epochs.\\\"\\n )\\n self.writer.add_scalar(\\n f\\\"client{self.client_id}/train/Loss\\\",\\n _loss,\\n self.num_batches_seen,\\n )\\n\\n if _current_epoch > self.current_epoch:\\n # At each epoch we look at the histograms of all the\\n # network's parameters\\n for name, p in self.model.named_parameters():\\n self.writer.add_histogram(\\n f\\\"client{self.client_id}/{name}\\\", p, _current_epoch\\n )\\n\\n self.current_epoch = _current_epoch\",\n \"def _train_batch(self):\\n\\n # start epoch\\n for i, (source, target) in enumerate(self.train_dataset):\\n result = self._batch_iter(source, target, i)\\n\\n # yield\\n yield result\",\n \"def train_batch(self, data, num_iteration, verbose=False):\\n self.train(data, num_iteration, random_order=False, verbose=verbose)\",\n \"def batch_data(cls, train_data, train_labels, batch_size):\\n for batch in range(int(np.ceil(train_data.shape[0] / batch_size))):\\n start = batch_size * batch\\n end = start + batch_size\\n if end > train_data.shape[0]:\\n yield batch, (train_data[start:train_data.shape[0]], \\\\\\n train_labels[start:train_data.shape[0]])\\n else:\\n yield batch, (train_data[start:end], \\\\\\n train_labels[start:end])\",\n \"def prepareDataBatches(self, traindata, trainlabel):\\n index = np.random.permutation(len(traindata))\\n traindata = traindata[index]\\n trainlabel = trainlabel[index]\\n split_no = int(len(traindata) / self.batchSize)\\n return zip(np.split(traindata[:split_no*self.batchSize], split_no), np.split(trainlabel[:split_no*self.batchSize], split_no))\",\n \"def _upload_samples(self, samples):\\n # Iterate over the full set of provided samples, uploading them in chunks.\\n for offset in range(0, len(samples), self.upload_chunk_size):\\n chunk = samples[offset:offset + self.upload_chunk_size]\\n self.api.upload_samples(offset, chunk)\",\n \"def prepare_batches(self, data):\\n batches = []\\n start, end = 0, 100\\n if len(data) > 100:\\n while True:\\n data_batch = data[start:end]\\n if not data_batch:\\n break\\n temp = end + 100\\n start, end = end, temp\\n if data_batch:\\n batches.append(data_batch)\\n else:\\n batches.append(data)\\n return batches\",\n \"def _batch(self, X, y, batch_size=16):\\n X, y = self._shuffle(X, y) # shuffle the data\\n self.batches = []\\n idx = 0\\n while idx < len(X):\\n batch = (X[idx:idx+batch_size], y[idx:idx+batch_size])\\n self.batches.append(batch)\\n idx += batch_size\",\n \"def static_batch(data, batch_size=16):\\n buf = []\\n for sample in data:\\n buf.append(sample)\\n if len(buf) >= batch_size:\\n yield buf\\n buf = []\\n if len(buf) > 0:\\n yield buf\",\n \"def train_next_batch(self, batch_size=None):\",\n \"def write_batch(self, batch):\\n for item in batch:\\n self.write_buffer.buffer(item)\\n key = self.write_buffer.get_key_from_item(item)\\n if self.write_buffer.should_write_buffer(key):\\n self._write_current_buffer_for_group_key(key)\\n self.increment_written_items()\\n self._check_items_limit()\",\n \"def storeBatch(self, storage, X_data, y_data):\\n # data to store\\n data_dict = {'X_data': X_data,\\n 'y_data': y_data}\\n # store in memory\\n if storage == \\\"memory\\\":\\n # dict with data\\n self.raw_data = data_dict\\n # store on disk\\n elif storage == \\\"disk\\\":\\n # check batch directory\\n batch_file = 'batch' + str(self.batch_id) + '.pkl'\\n if batch_file in os.listdir(self.disk_storage_path):\\n print(\\\"Overwriting: %s\\\" % batch_file)\\n else:\\n pkl.dump(data_dict,\\n open(self.disk_storage_path + batch_file, \\\"wb\\\"))\\n # save in native numpy format\\n elif storage == \\\"numpy\\\":\\n batch_file = 'batch' + str(self.batch_id) + '.npy'\\n if batch_file in os.listdir(self.disk_storage_path):\\n print(\\\"Overwriting: %s\\\" % batch_file)\\n else:\\n np.save(self.disk_storage_path + \\\"X_\\\" + batch_file, X_data)\\n np.save(self.disk_storage_path + \\\"y_\\\" + batch_file, y_data)\\n # save to disk as images\\n elif storage == \\\"disk_raw\\\":\\n # create new directory for batch\\n batch_dir = self.disk_storage_path + \\\"batch\\\" + str(self.batch_id)\\n os.mkdir(batch_dir)\\n # loop through all images and store\\n for i in range(0, X_data.shape[0]):\\n img = array_to_img(X_data[i, :, :, :])\\n img.save(batch_dir + \\\"/\\\" + i + \\\".jpeg\\\")\\n else:\\n # do nothing\\n return None\\n\\n # set flags and parameters\\n self.is_stored = True\\n self.storage = storage\",\n \"def on_train_batch_begin(self, step, logs=None):\",\n \"def hook_store_batch_size(module):\\n if self._batch_size == {}:\\n batch_axis = 0\\n batch_size = module.input0.shape[batch_axis]\\n\\n for group in param_groups:\\n group_id = id(group)\\n\\n if self._verbose:\\n print(f\\\"Group {group_id}: Store 'batch_size'\\\")\\n\\n self._batch_size[group_id] = batch_size\",\n \"def batches(set_name):\\n global num_batches, args, ds_sizes \\n # num_batches = how many batches in each dataset(train, valid, test)\\n # ds_sizes = dataset_sizes \\n for b in range(num_batches[set_name]):\\n bi = b * args.batch_size # one batch mul batch_size \\n bj = (b + 1) * args.batch_size \\n if b == num_batches[set_name] - 1:\\n bj = ds_sizes[set_name] # maybe only remainer set\\n yield bi, bj\",\n \"def data_gen(voc_size, batch, nbatches, seq_len = 15):\\r\\n for i in range(nbatches):\\r\\n # (batch_size, seq_len)\\r\\n data = torch.from_numpy(\\r\\n np.random.randint(1, voc_size, size=(batch, seq_len)))\\r\\n data[:, 0] = 1 # add start token\\r\\n src = Variable(data, requires_grad=False)\\r\\n tgt = Variable(data, requires_grad=False)\\r\\n yield Batch(src, tgt, 0) # Accessed by next function one by one\\r\",\n \"def next_batch(self, batch_size):\\n batch_data = np.zeros([batch_size,] + list(self.example_shape))\\n for i in range(batch_size):\\n index = self.q.pop()\\n batch_data[i,...] = self.data[index]\\n if len(self.q)==0:\\n self.__new_epoch()\\n\\n return batch_data\",\n \"def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\\n for real_data in data_stream:\\n tmp = []\\n tmp.append(real_data.state)\\n tmp.append(real_data.action)\\n tmp.append(real_data.reward)\\n tmp.append(real_data.next_state)\\n tmp.append(real_data.done)\\n tmp.append(actor_set[k]['w'][real_data.idx])\\n tmp.append(actor_set[k]['i'][real_data.idx])\\n tmp.append(actor_set[k]['t'][real_data.idx])\\n tmp.append(real_data.timestamp)\\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['states'].append(decom_state) #.to(device))\\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\\n # is the data overwrited?\\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\\n local_dict = {}\\n while True:\\n batch_timestamp_real = []\\n batch_timestamp_store = []\\n batch_weights = []\\n batch_idxes = []\\n\\n states, actions, rewards, next_states, dones = [], [], [], [], []\\n\\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\\n actor_set = {}\\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\\n for i in range(len(actor_ids)):\\n set_a = actor_set.get(actor_ids[i], False)\\n if set_a == False:\\n actor_set[actor_ids[i]] = {}\\n set_a = actor_set[actor_ids[i]]\\n set_a['d'] = []\\n set_a['w'] = []\\n set_a['i'] = []\\n set_a['t'] = []\\n cached_value['states'][actor_ids[i]] = []\\n cached_value['actions'][actor_ids[i]] = []\\n cached_value['rewards'][actor_ids[i]] = []\\n cached_value['next_states'][actor_ids[i]] = []\\n cached_value['dones'][actor_ids[i]] = []\\n cached_value['batch_weights'][actor_ids[i]] = []\\n cached_value['batch_idxes'][actor_ids[i]] = []\\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\\n cache_id = actor_ids[i]*local_size+data_ids[i]\\n cache_trans = cache_array[cache_id]\\n if cache_trans & 2**pid == 0:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\\n del local_dict[cache_id]\\n else:\\n try:\\n state_tmp = local_dict[cache_id][0]\\n action_tmp = local_dict[cache_id][1]\\n reward_tmp = local_dict[cache_id][2] \\n next_state_tmp = local_dict[cache_id][3] \\n done_tmp = local_dict[cache_id][4] \\n batch_weight_tmp = local_dict[cache_id][5] \\n batch_idx_tmp = local_dict[cache_id][6] \\n batch_store_tmp = local_dict[cache_id][7] \\n batch_real_tmp = local_dict[cache_id][8] \\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['states'][actor_ids[i]].append(decom_state)\\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\\n except:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n real_data_links = {}\\n real_data_tasks = {}\\n for k, v in actor_set.items():\\n actor_ip, data_port = actor_id_to_ip_dataport[k]\\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\\n real_data_tasks[k] = {}\\n real_data_tasks[k]['states'] = cached_value['states'][k]\\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\\n threads = []\\n for k, v in real_data_links.items():\\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\\n threads.append(t)\\n t.start()\\n\\n for t in threads:\\n t.join()\\n\\n for k, v in real_data_tasks.items():\\n states += v['states']\\n actions += v['actions']\\n rewards += v['rewards']\\n next_states += v['next_states']\\n dones += v['dones']\\n batch_weights += v['batch_weights']\\n batch_idxes += v['batch_idxes']\\n batch_timestamp_real += v['batch_timestamp_real']\\n batch_timestamp_store += v['batch_timestamp_store']\\n\\n states = torch.cat(states,0).to(device)\\n actions = torch.cat(actions,0).to(device)\\n rewards = torch.cat(rewards,0).to(device)\\n next_states = torch.cat(next_states,0).to(device)\\n dones = torch.cat(dones,0).to(device)\\n batch_weights = torch.cat(batch_weights,0).to(device)\\n\\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\\n batch_queue.put(batch)\\n data, batch = None, None\",\n \"def batch_split(self) -> np.array:\\n pass\",\n \"def _one_mini_batch(self, data, indices, pad_id):\\n batch_data = {'raw_data': [data[i] for i in indices],\\n 'question_token_ids': [],\\n 'question_length': [],\\n 'passage_token_ids': [],\\n 'passage_length': [],\\n 'start_id': [],\\n 'end_id': []}\\n max_passage_num = max([len(sample['passages']) for sample in batch_data['raw_data']])\\n max_passage_num = min(self.max_p_num, max_passage_num)\\n for sidx, sample in enumerate(batch_data['raw_data']):\\n for pidx in range(max_passage_num):\\n if pidx < len(sample['passages']):\\n batch_data['question_token_ids'].append(sample['question_token_ids'])\\n batch_data['question_length'].append(len(sample['question_token_ids']))\\n passage_token_ids = sample['passages'][pidx]['passage_token_ids']\\n batch_data['passage_token_ids'].append(passage_token_ids)\\n batch_data['passage_length'].append(min(len(passage_token_ids), self.max_p_len))\\n else:\\n batch_data['question_token_ids'].append([])\\n batch_data['question_length'].append(0)\\n batch_data['passage_token_ids'].append([])\\n batch_data['passage_length'].append(0)\\n batch_data, padded_p_len, padded_q_len = self._dynamic_padding(batch_data, pad_id)\\n for sample in batch_data['raw_data']:\\n if 'answer_passages' in sample and len(sample['answer_passages']):\\n gold_passage_offset = padded_p_len * sample['answer_passages'][0]\\n batch_data['start_id'].append(gold_passage_offset + sample['answer_spans'][0][0])\\n batch_data['end_id'].append(gold_passage_offset + sample['answer_spans'][0][1])\\n else:\\n # fake span for some samples, only valid for testing\\n batch_data['start_id'].append(0)\\n batch_data['end_id'].append(0)\\n return batch_data\",\n \"def __call__(self):\\n if self.numbatches is None:\\n pool = self.pooler()\\n if self.batchsize is None:\\n self.batchsize = self.pooler.nInPool()\\n self.numbatches = self.pooler.nInPool()//self.batchsize\\n for i in xrange(self.numbatches):\\n pool = self.pooler()\\n self._reset_batch()\\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\\n batchinds,keyids = self._samplebalanced(pool)\\n elif self.samplemethod == 'uniform':\\n batchinds,keyids = self._sampleuniform(pool)\\n else:\\n batchinds,keyids = self._samplesequential(i)\\n batch = self._extractInds(pool,batchinds,keyids)\\n for k in batch:\\n batch[k][np.isnan(batch[k])] = self.nanreplacement\\n yield batch\",\n \"def make_batch(self, batch_size):\\n filenames = self.get_filenames()\\n if self.mode == tf.estimator.ModeKeys.PREDICT and self.imagenet_train_predict_partial:\\n # Sort and shuffle with seed to randomize deterministically.\\n random.seed(self.imagenet_train_predict_shuffle_seed)\\n random.shuffle(filenames)\\n dataset = tf.contrib.data.TFRecordDataset(filenames)\\n\\n # Parse records.\\n dataset = dataset.map(self.parser,\\n num_threads=batch_size,\\n output_buffer_size=2 * batch_size)\\n\\n # If training, shuffle and repeat indefinitely.\\n if self.mode == tf.estimator.ModeKeys.TRAIN:\\n dataset = dataset.shuffle(buffer_size=50000 + 3 * batch_size)\\n dataset = dataset.repeat(-1)\\n elif self.mode == tf.estimator.ModeKeys.PREDICT:\\n if self.predict_split == 'train':\\n if self.imagenet_train_predict_partial:\\n MAX_EXAMPLES = 50000\\n # Skip to start at a random spot in the first TFRecord.\\n random.seed(self.imagenet_train_predict_shuffle_seed)\\n skip_examples = random.randint(0, 1251)\\n dataset = dataset.skip(skip_examples)\\n # Continue shuffling amongst at least as many examples\\n # as it could see in 3 cross validations.\\n dataset.shuffle(buffer_size=3 * MAX_EXAMPLES,\\n seed=self.imagenet_train_predict_shuffle_seed)\\n num_examples = MAX_EXAMPLES\\n else:\\n # Take whole training set.\\n num_examples = self.num_examples_per_epoch(tf.estimator.ModeKeys.TRAIN)\\n else:\\n # Take whole validation set.\\n num_examples = self.num_examples_per_epoch(tf.estimator.ModeKeys.EVAL)\\n # Take as much of the dataset as possible that can be evenly\\n # divided by batch_size.\\n while True:\\n if num_examples % batch_size == 0:\\n break\\n else:\\n num_examples -= 1\\n dataset = dataset.take(num_examples)\\n dataset = dataset.repeat(1)\\n\\n # dataset = dataset.take(1000) # For fast debugging!\\n else:\\n dataset = dataset.repeat(1)\\n\\n # Batch it up.\\n dataset = dataset.batch(batch_size)\\n iterator = dataset.make_one_shot_iterator()\\n image_batch, label_batch = iterator.get_next()\\n\\n return image_batch, label_batch\",\n \"def sample_batch(self, batch_size):\\n batch = []\\n\\n # Sample using prorities\\n if(self.with_per):\\n T = self.buffer.total() // batch_size\\n #print(\\\"T is \\\",T)\\n for i in range(batch_size):\\n a, b = T * i, T * (i + 1)\\n s = random.uniform(a, b)\\n idx, error, data = self.buffer.get(s)\\n #print(\\\"sampled data \\\", s, \\\" \\\",data, end=\\\" \\\")\\n batch.append((*data, idx))\\n\\n idx = np.array([i[2] for i in batch])\\n #idx in the offline buffer\\n \\n # Sample randomly from Buffer\\n elif self.count < batch_size:\\n idx = None\\n batch = random.sample(self.buffer, self.count)\\n else:\\n idx = None\\n batch = random.sample(self.buffer, batch_size)\\n\\n # Return a batch of experience\\n names_batch = np.array([i[1] for i in batch])\\n\\n return names_batch, idx\",\n \"def split_and_load(batch_data, num_gpus):\\n return [batch_data[i].data[0] for i in range(num_gpus)], \\\\\\n [batch_data[i].label[0].as_in_context(mx.gpu(i)) for i in range(num_gpus)]\",\n \"def on_train_batch_end(self, step, logs=None):\",\n \"def dynamic_batch(data, max_frames_in_batch=12000):\\n buf = []\\n longest_frames = 0\\n for sample in data:\\n assert \\\"feat\\\" in sample\\n assert isinstance(sample[\\\"feat\\\"], torch.Tensor)\\n new_sample_frames = sample[\\\"feat\\\"].size(0)\\n longest_frames = max(longest_frames, new_sample_frames)\\n frames_after_padding = longest_frames * (len(buf) + 1)\\n if frames_after_padding > max_frames_in_batch:\\n yield buf\\n buf = [sample]\\n longest_frames = new_sample_frames\\n else:\\n buf.append(sample)\\n if len(buf) > 0:\\n yield buf\",\n \"def on_batch_begin(self, batch, logs=None):\",\n \"def _send_data(self):\\n \\n # Do not send more than 100 datasets each time (totally arbitrary)\\n MAX_DATA_SETS_PER_POST = 100\\n data_to_send = self._data_buffer[0:MAX_DATA_SETS_PER_POST]\\n data_to_keep = self._data_buffer[MAX_DATA_SETS_PER_POST:]\\n\\n # Prepare data string with the values in data buffer\\n now = time.time()\\n data_string = '[' \\n for (timestamp, data) in data_to_send:\\n data_string += '['\\n data_string += str(round(timestamp-now,2))\\n for sample in data:\\n data_string += ','\\n data_string += str(sample)\\n data_string += '],'\\n # Remove trailing comma and close bracket\\n data_string = data_string[0:-1]+']'\\n\\n self._log.debug(\\\"Data string: \\\" + data_string)\\n \\n # Prepare URL string of the form\\n # 'http://domain.tld/emoncms/input/bulk.json?apikey=\\n # 12345&data=[[-10,10,1806],[-5,10,1806],[0,10,1806]]'\\n url_string = self._settings['protocol'] + self._settings['domain'] + \\\\\\n self._settings['path'] + \\\"/input/bulk_json?apikey=\\\" + \\\\\\n self._settings['apikey'] + \\\"&data=\\\" + data_string\\n self._log.debug(\\\"URL string: \\\" + url_string)\\n\\n # Send data to server\\n self._log.info(\\\"Sending to \\\" + \\n self._settings['domain'] + self._settings['path'])\\n try:\\n result = urllib2.urlopen(url_string, timeout=60)\\n except urllib2.HTTPError as e:\\n self._log.warning(\\\"Couldn't send to server, HTTPError: \\\" + \\n str(e.code))\\n except urllib2.URLError as e:\\n self._log.warning(\\\"Couldn't send to server, URLError: \\\" + \\n str(e.reason))\\n except httplib.HTTPException:\\n self._log.warning(\\\"Couldn't send to server, HTTPException\\\")\\n except Exception:\\n import traceback\\n self._log.warning(\\\"Couldn't send to server, Exception: \\\" + \\n traceback.format_exc())\\n else:\\n if (result.readline() == 'ok'):\\n self._log.debug(\\\"Send ok\\\")\\n # Send ok -> empty buffer\\n self._data_buffer = data_to_keep\\n return True\\n else:\\n self._log.warning(\\\"Send failure\\\")\",\n \"def load_batch(self):\\r\\n\\r\\n #if we've seen all the data, start again with them in a new random order\\r\\n if self.batchcounter+self.batchsize > self.num_data:\\r\\n self.batchcounter = 0\\r\\n self.epochs += 1\\r\\n self._permutation = np.random.permutation(self.num_data)\\r\\n\\r\\n this_perm = self._permutation[self.batchcounter:self.batchcounter+self.batchsize]\\r\\n\\r\\n self.X_batch = self.X[this_perm]\\r\\n self.likelihood.set_data(self.Y[this_perm])\\r\\n if self.has_uncertain_inputs:\\r\\n self.X_variance_batch = self.X_variance[this_perm]\\r\\n\\r\\n self.batchcounter += self.batchsize\\r\\n\\r\\n self.data_prop = float(self.batchsize)/self.num_data\\r\\n\\r\\n self._compute_kernel_matrices()\\r\\n self._computations()\",\n \"def __iter__(self):\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\\n\\n while True:\\n\\n # Randomizing wav lists\\n random.shuffle(self._lst_spk_files)\\n random.shuffle(self._lst_noise_files)\\n\\n for spk_file, noise_file in zip(self._lst_spk_files, self._lst_noise_files):\\n\\n # Read wav files\\n sig_spk = self.__read_wav_file(spk_file)\\n sig_noise = self.__read_wav_file(noise_file)\\n\\n # Align signal\\n min_length = min(sig_spk.shape[0], sig_noise.shape[0])\\n\\n if min_length < self._fftsize:\\n raise Exception(\\\"ERROR: Too short signals in dataset\\\")\\n\\n sig_spk = sig_spk[:min_length]\\n sig_noise = sig_noise[:min_length]\\n\\n # Generate need SNR\\n need_snr = random.uniform(self._min_snr, self._max_snr)\\n\\n # Calc scaled signals\\n sig_spk, sig_noise = self.__mix_with_snr(sig_spk, sig_noise, need_snr)\\n\\n # Calc STFT\\n stft_spk = stft(sig_spk, fftsize=self._fftsize, overlap=self._overlap)\\n stft_noise = stft(sig_noise, fftsize=self._fftsize, overlap=self._overlap)\\n stft_mix = stft_spk + stft_noise\\n\\n # Skip small segments\\n frames, bin = stft_mix.shape\\n if frames <= self._context_size:\\n continue\\n\\n # Collect batch\\n i = 0\\n while i + self._context_size < frames:\\n\\n batch_sp.append(stft_spk[i:i + self._context_size, :])\\n batch_noise.append(stft_noise[i:i + self._context_size, :])\\n batch_mix.append(stft_mix[i:i + self._context_size, :])\\n\\n i += self._context_size // 2\\n batch_count += 1\\n\\n if batch_count == self._batch_size:\\n sp = np.array(batch_sp).reshape((self._batch_size,\\n self._context_size, -1))\\n noise = np.array(batch_noise).reshape((self._batch_size,\\n self._context_size, -1))\\n mix = np.array(batch_mix).reshape((self._batch_size,\\n self._context_size, -1))\\n yield sp, noise, mix\\n\\n batch_sp = []\\n batch_noise = []\\n batch_mix = []\\n batch_count = 0\",\n \"def generate_batch():\\n\\n # Initialize variables\\n example = np.zeros(self.batch_size)\\n labels = np.zeros((self.batch_size, 1))\\n alphas = np.zeros(self.batch_size)\\n n_items = 0\\n index = 0\\n\\n while index < len(data):\\n reduced_window = random.randint(0, self.window_size)\\n if data[index] is not None:\\n\\n left = max(0, index - self.window_size + reduced_window)\\n right = min((index + self.window_size + 1 -\\n reduced_window), len(data) - 1)\\n for pos2 in range(left, right, 1):\\n\\n if n_items == self.batch_size:\\n queue.put((example, labels, index))\\n example = np.zeros(self.batch_size)\\n labels = np.zeros((self.batch_size, 1))\\n n_items = 0\\n\\n if pos2 != index and data[pos2] is not None:\\n example[n_items] = data[pos2]\\n labels[n_items] = data[index]\\n alpha = self.learning_rate - \\\\\\n (self.learning_rate - 0.001) * (index / self.n_words)\\n alphas[n_items] = max(0.001, alpha)\\n n_items += 1\\n index += 1\\n\\n # Poison pills\\n for _ in range(n_workers):\\n queue.put(None)\",\n \"def batch(self, batch):\\n\\n self._batch = batch\",\n \"def _batch_train(self, batch, training_step, step):\\n lstm_size = (self.batch_size, self.Qmain.h_size)\\n batch_mem = np.zeros(lstm_size)\\n batch_carry = np.zeros(lstm_size)\\n input_shape = (self.batch_size,\\n self.trace_length,\\n self.observation_size)\\n m_data = np.vstack(batch[:, 0])\\n m_data = m_data.reshape(input_shape)\\n t_data = np.vstack(batch[:, 4])\\n t_data = t_data.reshape(input_shape)\\n q_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(m_data)]\\n q1_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(t_data)]\\n\\n # Batch predict\\n self.Qmain.trace_length.assign(self.trace_length)\\n self.Qmain.dropout_rate.assign(0.0)\\n self.Qtarget.trace_length.assign(self.trace_length)\\n self.Qtarget.dropout_rate.assign(0.0)\\n\\n # Save the graph just the first time\\n if training_step == 0:\\n tf.summary.trace_on()\\n\\n # T batch predict\\n pred = self.Qmain.model.predict(q_input,\\n batch_size=self.batch_size)\\n Q = pred[0]\\n batch_bus = pred[1]\\n batch_line = pred[2]\\n batch_disp = pred[3]\\n\\n ## Log graph once and disable graph logging\\n if training_step == 0:\\n with self.tf_writer.as_default():\\n tf.summary.trace_export(self.name + \\\"-graph\\\", step)\\n\\n # T+1 batch predict\\n Qn, *_ = self.Qtarget.model.predict(q1_input,\\n batch_size=self.batch_size)\\n \\n # Compute batch Q update to Qtarget\\n for i in range(self.batch_size):\\n idx = i * (self.trace_length - 1)\\n a = batch[idx][1]\\n grid = a[0]\\n batch_bus[i][:] = a[1][:]\\n batch_line[i][:] = a[2][:]\\n batch_disp[i][:] = a[3][:]\\n r = batch[idx][2]\\n d = batch[idx][3]\\n Q[i][grid] = r\\n if d == False:\\n Q[i][grid] += DISCOUNT_FACTOR * Qn[i][grid]\\n\\n # Batch train\\n batch_x = [batch_mem, batch_carry, m_data]\\n batch_y = [\\n Q,\\n batch_bus, batch_line, batch_disp,\\n batch_mem, batch_carry\\n ]\\n loss = self.Qmain.model.train_on_batch(batch_x, batch_y)\\n loss = loss[0]\\n\\n # Log to tensorboard\\n self._tf_log_summary(loss, step)\",\n \"def sample_batch(self) -> List:\\n return self.buffer.sample(self.batch_size)\",\n \"def generate_batches(data, labels, batch_size):\\n for start in range(0, len(data), batch_size):\\n yield Tensor(data[start:start+batch_size, ...]), Tensor(labels[start:start+batch_size, ...])\",\n \"def batches(self):\\n return [self.get_batch(i) for i in range(self.num_batches)]\",\n \"def save_batch(self):\\n self._batch_counter += 1\\n write_to_disk(\\n self._batch_cases,\\n os.path.join(\\n self.crop.location,\\n \\\"batches\\\",\\n BTCH_NM.format(self._batch_counter),\\n ),\\n )\\n self._batch_cases = []\\n self._counter = 0\",\n \"def process_batch(self, data):\\n [embedding_batch] = self._sess.run([self._embedding_tensor],\\n feed_dict={self._features_tensor: data})\\n return embedding_batch\",\n \"def append(self, batch: Batch):\",\n \"def instantiate_batch(self, inputs):\\n return inputs\",\n \"def data(self, train=True, batch_size=2):\\n if train:\\n elements = self.prepare_batch(self.training_albums)\\n else:\\n elements = self.prepare_batch(self.validation_albums)\\n\\n while len(elements) > 0:\\n # Collect the batch\\n batch = []\\n for _ in range(min(batch_size, len(elements))):\\n batch.append(elements.pop())\\n\\n # Get same sequence size for all elements of the batch\\n albums, labels = self.batchify(batch)\\n yield albums, labels\",\n \"def init_batch(self):\\n pass\",\n \"def train_minibatches(self):\\n batch_size = self.params['batch_size']\\n start_index = 0\\n while start_index + batch_size < 500:\\n end_index = start_index + batch_size\\n yield self.input[start_index:end_index], self.y[start_index:end_index]\\n start_index = end_index\",\n \"def _update_num_batches(self):\\n # maximum possible number of batches is equal to number of whole times\\n # batch_size divides in to the number of data points which can be\\n # found using integer division\\n possible_num_batches = self.inputs.shape[0] // self.batch_size\\n if self.max_num_batches == -1:\\n self.num_batches = possible_num_batches\\n else:\\n self.num_batches = min(self.max_num_batches, possible_num_batches)\",\n \"def create_batch(client, generator: DataGenerator):\\n try:\\n event_data_batch = client.create_batch()\\n for device in generator.devices:\\n # event_data_batch.add(EventData(gen.generate_payload(device)))\\n event_data_batch.add(EventData(generator.generate_payload(device)))\\n return event_data_batch\\n except Exception as e:\\n print(str(e))\",\n \"def multiple_batch(self, handler, dataset):\\n iterator = tf.data.Iterator.from_string_handle(handler, dataset.output_types, dataset.output_shapes)\\n (src_ids, tgt_input_ids, tgt_output_ids, src_seq_len, tgt_seq_len) = (iterator.get_next())\\n return BatchedInput(iterator=None,\\n batched_dataset=None,\\n handle=None,\\n initializer=None,\\n source=src_ids,\\n target_input=tgt_input_ids,\\n target_output=tgt_output_ids,\\n source_sequence_length=src_seq_len,\\n target_sequence_length=tgt_seq_len)\",\n \"def data_gen(\\n v: int, batch: int, nbatches: int, device: torch.device = torch.device(\\\"cpu\\\")\\n) -> Iterator[Batch]: # TODO bad name\\n for i in range(nbatches):\\n data = np.random.randint(1, v, size=(batch, 10))\\n data[:, 0] = 1\\n src: LongTensorType = torch.from_numpy(data)\\n tgt: LongTensorType = torch.from_numpy(data)\\n src, tgt = src.to(device), tgt.to(device)\\n yield Batch(src, tgt, 0)\",\n \"def save_feature(self):\\n import scipy.io as sio\\n testdp = self.test_data_provider\\n num_batches = len(testdp.batch_range)\\n print 'There are ' + str(testdp.get_num_batches(self.data_path)) + ' in directory'\\n if self.test_data_provider.batch_size > 0:\\n num_batches = (num_batches - 1)/ self.test_data_provider.batch_size + 1\\n if self.test_one:\\n num_batches = min(num_batches, 1)\\n print 'There are ' + str( num_batches ) + ' in range'\\n iu.ensure_dir(self.save_feature_path)\\n feature_name = self.op.get_value('save_feature_name')\\n feature_dim = self.model_state['layers'][self.feature_idx]['outputs']\\n print 'Feature dim is %d' % feature_dim\\n for b in range(num_batches):\\n epoch, b_num, data = self.get_next_batch(train=False)\\n print ' Start writing batch......\\\\t' + str(b_num)\\n num_data = data[0].shape[-1]\\n data += [n.zeros((num_data, feature_dim), dtype=n.single)]\\n save_name = 'batch_feature_' + str(b_num) + '_' + feature_name \\n save_path = iu.fullfile(self.save_feature_path, save_name)\\n self.libmodel.startFeatureWriter(data, self.feature_idx)\\n self.finish_batch()\\n d = dict()\\n d['X'] = data[-1].transpose()\\n d['batch_num'] = b_num\\n d['Y'] = data[1]\\n cur_batch_indexes = self.test_data_provider.data_dic['cur_batch_indexes']\\n # d['Y_other'] = data[2:-1] if len(data) > 3 else []\\n ####### WARN BEGIN ################\\n # for human eva fake experiments\\n # d['images_path'] = [self.test_data_provider.images_path[x] for x in cur_batch_indexes]\\n # d['Y'] = np.concatenate(map(lambda x:self.test_data_provider.batch_meta['RelativeSkel_Y3d_mono_body_backup'][...,x].reshape((-1,1),order='F'), cur_batch_indexes),axis=1)\\n print d['Y'].shape\\n d['cur_batch_indexes'] = cur_batch_indexes\\n ####### WARN END ################\\n print 'The len of data is ' + str(len(data))\\n print 'The shape of X is' + str(d['X'].shape)\\n print 'The shape of Y is' + str(d['Y'].shape)\\n ##sio.savemat(save_path, d)\\n pickle(save_path, d)\",\n \"def collect_samples(self):\\n self.replay_buffer = self.collect_initial_batch(\\n self.replay_buffer, self.acm_pre_train_samples\\n )\",\n \"def _get_batch(self):\\n # index = self._index[self._current]\\n # im_path = self._imdb.image_path_from_index(0)\\n # im_path = 'data/demo/dog.jpg'\\n # with open(im_path, 'rb') as fp:\\n # img_content = fp.read()\\n\\n batch_data = mx.nd.zeros((self.batch_size, 3, self._data_shape[0], self._data_shape[1]))\\n batch_label = [] \\n global imgi\\n # img = mx.nd.array(imgi)\\n # imgr = mx.img.imdecode(img_content)\\n data = self._data_augmentation(imgi)\\n batch_data[0] = data\\n \\n self._data = {'data': batch_data}\\n self._label = {'label': None}\",\n \"def save_and_upload_batch_sample_sets(batch_samples, batch_tumors, batch_normals, tsca_id, namespace, workspace):\\n # Save to file\\n os.system('mkdir -p %s'%tsca_id)\\n batch_samples_filename = './%s/fc_upload_sample_set_tsca_%s.txt' % (tsca_id, tsca_id)\\n batch_tumors_filename = './%s/fc_upload_sample_set_tsca_%s_tumors.txt' % (tsca_id, tsca_id)\\n batch_normals_filename = './%s/fc_upload_sample_set_tsca_%s_normals.txt' % (tsca_id, tsca_id)\\n \\n batch_samples.to_csv(batch_samples_filename , sep=\\\"\\\\t\\\", index=False )\\n batch_tumors.to_csv(batch_tumors_filename , sep=\\\"\\\\t\\\", index=False )\\n batch_normals.to_csv(batch_normals_filename , sep=\\\"\\\\t\\\", index=False )\\n\\n r1 = upload_entities_from_tsv(namespace, workspace, batch_samples_filename)\\n r2 = upload_entities_from_tsv(namespace, workspace, batch_tumors_filename)\\n r3 = upload_entities_from_tsv(namespace, workspace, batch_normals_filename)\\n return (r1, r2, r3)\",\n \"def get_batches(self, batch_size):\\n if self.data.shape[0] % batch_size != 0:\\n raise RuntimeError('num of data tuples is not a multiple of batch size')\\n num_batch = self.data.shape[0] // batch_size\\n for b in range(num_batch):\\n yield self.data[b*batch_size:(b+1)*batch_size, :], \\\\\\n self.target[b*batch_size:(b+1)*batch_size, :]\",\n \"def next_batch(self, batch_size, shuffle=True):\",\n \"def batches(self, batch_size): \\n if self.shuffle:\\n idx = np.arange(len(dataset.train_x))\\n np.random.shuffle(idx)\\n self.train_x = self.train_x[idx]\\n \\n n_batches = len(self.train_x) // batch_size\\n for ii in range(0, len(self.train_x), batch_size):\\n x = self.train_x[ii:ii+batch_size]\\n \\n yield self.scaler(x)\",\n \"def data_generator(delta=1, batch_size=32):\\n while True:\\n yield generate_samples(delta=delta, n=batch_size)\",\n \"def data_iter(data, vocab, batch_size, shuffle=True, cuda=False):\\n\\n buckets = defaultdict(list)\\n for pair in data:\\n src_sent = pair[0]\\n buckets[len(src_sent)].append(pair)\\n\\n batched_data = []\\n for src_len in buckets:\\n tuples = buckets[src_len]\\n if shuffle: np.random.shuffle(tuples)\\n batched_data.extend(list(HNCMDataLoader.batch_slice(tuples, batch_size)))\\n\\n if shuffle:\\n np.random.shuffle(batched_data)\\n for src_sents, trg_sents, fact_sents in batched_data:\\n num_trg_word = sum(len(s[:-1]) for s in trg_sents)\\n src_lengths = [len(s) for s in src_sents]\\n src_seqs_var = to_input_var(src_sents, vocab.src, cuda)\\n trg_seqs_var = to_input_var(trg_sents, vocab.trg, cuda)\\n fact_lengths = [[len (s) for s in fact_sent] for fact_sent in fact_sents]\\n fact_seqs_var = to_input_var_2d(fact_sents, vocab.src, cuda)\\n\\n yield {\\n 'src_seq': src_seqs_var, 'src_lengths': src_lengths,\\n 'fact_seq': fact_seqs_var, 'fact_lengths': fact_lengths,\\n 'trg_seq': trg_seqs_var[:, :-1],\\n 'target': trg_seqs_var[:, 1:],\\n 'num_trg_word': num_trg_word, 'num_trg_seq': len(trg_sents)\\n }\",\n \"def train(batch_size, num_sample=128):\\n return paddle.batch(_read_creater(num_sample=num_sample), batch_size)\",\n \"def on_test_batch_begin(self, batch, logs=None):\",\n \"def produce_query_batches(self):\\n pass\",\n \"def process(self, data_batch: Sequence[Dict],\\n data_samples: Sequence[Dict]) -> None:\\n for data_sample in data_samples:\\n pred_labels = data_sample.get('pred_instances').get(self.key).cpu()\\n gt_labels = data_sample.get('gt_instances').get(self.key).cpu()\\n\\n result = dict(\\n pred_labels=pred_labels.flatten(),\\n gt_labels=gt_labels.flatten())\\n self.results.append(result)\",\n \"def sample_train_batch(self):\\r\\n batch = []\\r\\n labels =[]\\r\\n num_groups = self.batch_size // self.batch_k\\r\\n sampleed_classes = np.random.choice(self.train_class_ids,num_groups,replace=False)\\r\\n for class_id in sampleed_classes:\\r\\n img_fname = np.random.choice(self.train_image_files[class_id],self.batch_k,replace=False)\\r\\n batch += img_fname.tolist()\\r\\n labels += [class_id]*self.batch_k\\r\\n return batch,labels\",\n \"def test_batch(self):\\n pass\",\n \"def next_batch(self, batch_size):\\n raise NotImplementedError\",\n \"def gen_batches(data, batch_size=2048):\\n indices = torch.randperm(len(data))\\n indices = indices.cuda()\\n\\n for idx in range(0, len(data) - batch_size + 1, batch_size):\\n sample = indices[idx:idx + batch_size]\\n l_words, r_words = data.L_words[sample], data.R_words[sample]\\n l_vecs = data.l_vecs[l_words]\\n r_vecs = data.r_vecs[r_words]\\n l_bias = data.l_biases[l_words]\\n r_bias = data.r_biases[r_words]\\n weight = data.weights[sample]\\n y = data.y[sample]\\n yield weight, l_vecs, r_vecs, y, l_bias, r_bias\",\n \"def generate_next_batch(self, data): \\n \\n batch_words = np.array(data[self.batch_lookup[self.batch_index]][0])\\n batch_labels = np.array(data[self.batch_lookup[self.batch_index]][1])\\n self.batch_index += 1\\n if self.batch_index == len(data) - 1:\\n self.epoch += 1\\n return batch_words, batch_labels\",\n \"def __iter__(self):\\n batch = []\\n for idx in self._sampler:\\n batch.append(idx)\\n if len(batch) == self._batch_size:\\n batch = sum(batch, [])\\n yield batch\\n batch = []\\n if len(batch) > 0:\\n batch = sum(batch, [])\\n yield batch\",\n \"def _generate_and_save_examples(self, epoch):\\n\\n if self.fn_save_examples:\\n z_in = tf.random.uniform((self.batch_size, self.z_dim), -1, 1)\\n x_save = self.raw_x_dataset[0:self.batch_size]\\n c_save = self.raw_dataset[0:self.batch_size]\\n\\n generations = tf.squeeze(self.generator(z_in, c_save, training=False))\\n self.fn_save_examples(epoch, x_save, generations)\",\n \"def upload_data_new_batch(tsca_id, latest_tsca_id, paths_to_batches_info, namespace, workspace, google_bucket_id):\\n paths_to_batches_info_df = pd.read_excel(paths_to_batches_info, index_col=0)\\n path_to_samples_info = paths_to_batches_info_df.loc[tsca_id, 'path_to_samples_info']\\n\\n # DF of remote [sample < > sample set ]\\n remote_sample_sets = pd.read_table('remote_files/sample_set_membership_%s.tsv'%latest_tsca_id)\\n # DF of remote [pair < > pair set]\\n remote_pair_sets = pd.read_table('remote_files/pair_set_membership_%s.tsv'%latest_tsca_id)\\n\\n all_samples = get_samples(paths_to_batches_info, google_bucket_id)\\n # Add cohorts for older batches\\n all_samples = add_cohort_to_old_batches(all_samples)\\n\\n ##### Remove blacklisted samples ##\\n # Blacklisted samples\\n blacklisted = pd.read_table(\\\"samples_blacklist.txt\\\", header=None, names=[\\\"entity:sample_id\\\"])\\n blacklisted_sample_ids = blacklisted[\\\"entity:sample_id\\\"].values.tolist()\\n all_samples = all_samples[~all_samples[\\\"entity:sample_id\\\"].isin(blacklisted_sample_ids)]\\n\\n ########## Participants ##########\\n print(\\\"Uploading participants...\\\") \\n participants = prepare_participants_for_metadata_export(path_to_samples_info, tsca_id)\\n r1 = save_and_upload_participants(participants, namespace, workspace, tsca_id)\\n ##################################\\n\\n ########## Samples ############\\n print(\\\"Uploading samples...\\\")\\n batch_samples = prepare_batch_samples_for_metadata_export(path_to_samples_info, tsca_id, google_bucket_id)\\n r2 = save_and_upload_samples(batch_samples, namespace, workspace, tsca_id)\\n #################################\\n\\n ########## Pairs #############\\n print(\\\"Uploading pairs...\\\")\\n pairs = create_pairs_list(all_samples)\\n r3 = save_and_upload_pairs(namespace, workspace, pairs)\\n #################################\\n\\n ########## Sample Sets #########\\n print(\\\"Uploading sample sets...\\\")\\n batch_sample_set, batch_tumor_set, batch_normal_set = prepare_batch_sample_set_for_metadata_export(path_to_samples_info, tsca_id)\\n # Remove the samples that have already been uploaded \\n uploaded_sample_ids = remote_sample_sets['sample'].tolist()\\n batch_sample_set_clean = batch_sample_set[~batch_sample_set['sample_id'].isin(uploaded_sample_ids)]\\n batch_tumor_set_clean = batch_tumor_set[~batch_tumor_set['sample_id'].isin(uploaded_sample_ids)]\\n batch_normal_set_clean = batch_normal_set[~batch_normal_set['sample_id'].isin(uploaded_sample_ids)]\\n r4a, r4b, r4c = save_and_upload_batch_sample_sets(batch_sample_set_clean, batch_tumor_set_clean, batch_normal_set_clean, tsca_id, namespace, workspace)\\n #################################\\n\\n ########## PoNs ###############\\n print(\\\"Uploading PoNs...\\\")\\n \\n # Number of latest tsca id\\n latest_tsca_id_int = int(re.findall('\\\\d+', latest_tsca_id )[0])\\n # Array with list of all previous TSCA ids\\n previous_tsca_ids = [\\\"TSCA%s\\\"%i for i in np.arange(14, latest_tsca_id_int+1)]\\n previous_tsca_ids.insert(0, \\\"TSCA1213\\\")\\n\\n pon, name = create_panel_of_normals_advanced(tsca_id, all_samples,\\\\\\n num_normals_per_cohort_involved = 3, \\\\\\n batches_to_pick_from = previous_tsca_ids)\\n\\n # Only upload PoN if it hasn't been uploaded already\\n if not name in remote_sample_sets['membership:sample_set_id'].unique().tolist():\\n r5 = upload_pon(pon, name, namespace, workspace) \\n else: \\n print(\\\"PoN already exists...\\\")\\n r5 = {}\\n #################################\\n \\n ########## Pair Set ###########\\n print(\\\"Uploading pair sets...\\\")\\n # Upload cumulative pair sets\\n tn_cum_pairsets, tp_cum_pairsets = prepare_cumulative_pairsets_for_metadata_export(pairs, tsca_id)\\n r6 = upload_pairsets(namespace, workspace, tn_cum_pairsets, \\\"TN\\\")\\n r7 = upload_pairsets(namespace, workspace, tp_cum_pairsets, \\\"TP\\\")\\n\\n # Batch pair sets\\n tn_pairsets, tp_pairsets = prepare_batch_pairsets_for_metadata_export(all_samples, pairs, tsca_id)\\n uploaded_pair_ids = remote_pair_sets['pair'].tolist()\\n tn_pairsets_clean = tn_pairsets[~tn_pairsets['pair_id'].isin(uploaded_pair_ids)]\\n tp_pairsets_clean = tp_pairsets[~tp_pairsets['pair_id'].isin(uploaded_pair_ids)]\\n\\n r8 = upload_pairsets(namespace, workspace, tn_pairsets_clean, \\\"TN\\\")\\n r9 = upload_pairsets(namespace, workspace, tp_pairsets_clean, \\\"TP\\\")\\n #################################\\n\\n return (r1, r2, r3, r4a, r4b, r4c, r5, r6, r7, r8, r9)\",\n \"def on_train_begin(self, logs={}):\\n self._data = []\",\n \"def sample_batch(self, batch_size):\\n batch = []\\n\\n # Sample using prorities\\n if(self.with_per):\\n T = self.buffer.total() // batch_size\\n for i in range(batch_size):\\n a, b = T * i, T * (i + 1)\\n s = random.uniform(a, b)\\n idx, error, data = self.buffer.get(s)\\n batch.append((*data, idx))\\n idx = np.array([i[5] for i in batch])\\n #TD errors are only updated for transitions that are replayed\\n \\n # Sample randomly from Buffer\\n elif self.count < batch_size:\\n idx = None\\n batch = random.sample(self.buffer, self.count)\\n else:\\n idx = None\\n batch = random.sample(self.buffer, batch_size)\\n\\n # Return a batch of experience\\n s_batch = np.array([i[0] for i in batch])\\n a_batch = np.array([i[1] for i in batch])\\n r_batch = np.array([i[2] for i in batch])\\n d_batch = np.array([i[3] for i in batch])\\n new_s_batch = np.array([i[4] for i in batch])\\n\\n return s_batch, a_batch, r_batch, d_batch, new_s_batch, idx\",\n \"def _train(self, samples):\\n for i, sample in enumerate(samples):\\n # add extra info to samples\\n sample = {\\n 'src_tokens': sample[0],\\n 'input_tokens': self._right_shifted_ys(sample[1]),\\n 'target': sample[1],\\n 'id': None\\n }\\n sample['ntokens'] = sum(len(t) for t in sample['target'])\\n sample['src_positions'] = self._positions_for_tokens(\\n sample['src_tokens'])\\n sample['input_positions'] = self._positions_for_tokens(\\n sample['input_tokens'])\\n samples[i] = sample\\n return self.trainer.train_step(samples)\",\n \"def batch(self, data, size):\\n\\n return [data[x : x + size] for x in range(0, len(data), size)]\",\n \"def setUp(self):\\n self.batch_size = 8\\n num_keypoints = 16\\n self.data_batch = []\\n self.data_samples = []\\n\\n for i in range(self.batch_size):\\n gt_instances = InstanceData()\\n keypoints = np.zeros((1, num_keypoints, 2))\\n keypoints[0, i] = [0.5 * i, 0.5 * i]\\n gt_instances.keypoints = keypoints + 1.0\\n gt_instances.keypoints_visible = np.ones(\\n (1, num_keypoints, 1)).astype(bool)\\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\\n\\n pred_instances = InstanceData()\\n pred_instances.keypoints = keypoints\\n\\n data = {'inputs': None}\\n data_sample = {\\n 'gt_instances': gt_instances.to_dict(),\\n 'pred_instances': pred_instances.to_dict(),\\n }\\n\\n self.data_batch.append(data)\\n self.data_samples.append(data_sample)\",\n \"def split_to_batches(self, train_data, batch_size):\\n num_of_training_examples = len(train_data)\\n for i in range(0, num_of_training_examples, batch_size):\\n x, y = zip(*train_data[i: i+batch_size])\\n yield np.vstack(x), np.vstack(y)\",\n \"def train_dynamic(batch_size=10):\\n \\n return\",\n \"def next_batch(self):\\n # Whether an epoch is done.\\n done = False\\n samples = []\\n for _ in range(self.batch_size):\\n # Indeed, `>` will not occur.\\n if self.ptr >= self.dataset_size:\\n done = True\\n break\\n else:\\n self.ptr += 1\\n sample = self.enqueuer.queue.get()\\n samples.append(sample)\\n # print 'queue size: {}'.format(self.enqueuer.queue.qsize())\\n # Indeed, `>` will not occur.\\n if self.ptr >= self.dataset_size:\\n done = True\\n return samples, done\",\n \"def batch_fit(self, data_loader: torch.utils.data.DataLoader, epochs: int):\\n pass\",\n \"def batch_start(self, batch_idx, batch_data):\\n self.batch = batch_idx\",\n \"def next_batch(self, batch_size):\\r\\n raise NotImplementedError\",\n \"def gen_batches(data, batch_size):\\n data = np.array(data)\\n\\n for i in range(0, data.shape[0], batch_size):\\n yield data[i:i+batch_size]\",\n \"def generate_batch(self):\\n\\n # sbatch = list()\\n # tbatch = list()\\n # for i in range(self.dict_paras['batch_size']):\\n # sbatch.append(self.lst_triplet_train_map[self.data_index])\\n # self.data_index = (self.data_index + 1) % self.triplet_train_size\\n\\n sbatch = random.sample(self.lst_triplet_train_map, self.dict_paras['batch_size'])\\n tbatch = list()\\n\\n for ele in sbatch:\\n corrupted1, corrupted2 = self.get_corrupted_triplet(ele)\\n tbatch.append((ele, corrupted1))\\n tbatch.append((ele, corrupted2))\\n return tbatch\",\n \"def prepare_batch(data,BATCH_SIZE, filename):\\n ### select the last BATCH_SIZE rows from batch dataset\\n batch = data.iloc[-BATCH_SIZE:].values.tolist()\\n batch_data = []\\n \\n ### remove white spaces in the list because predtion server expects no white spaces between elements\\n for i in batch:\\n str_row = str(i)\\n str_row = str_row.replace(' ','')\\n batch_data.append(str_row)\\n \\n ### write values in a file called filename\\n with open(filename, 'w') as f:\\n f.write(','.join(str(i) for i in batch_data))\",\n \"def batch_data(source, target, batch_size):\\n for batch_i in range(0, len(source)//batch_size):\\n start_i = batch_i * batch_size\\n source_batch = source[start_i:start_i + batch_size]\\n target_batch = target[start_i:start_i + batch_size]\\n yield np.array(pad_sentence_batch(source_batch)), np.array(pad_sentence_batch(target_batch))\",\n \"def samples(self, samples):\\n\\n self._samples = samples\",\n \"def next_batch(self, batch_size=8):\\n raise NotImplementedError()\",\n \"def _fillBatches(self):\\n\\n batchRE = r\\\"\\\"\\\"\\n B\\n (?P<observebatch>\\\\d+?)\\n (?P<startend>[SE])\\n (?P<sequence>\\\\d+?)\\n _SR\\n (?:_(?P<extraInjections>\\\\d+?|\\\\w+?))?\\n $\\n \\\"\\\"\\\"\\n batchRE = re.compile(batchRE, re.VERBOSE)\\n # We canot infer batches unless we have runorder\\n if 'Run Order' in self.sampleMetadata.keys():\\n currentBatch = 0\\n # Loop over samples in run order\\n for index, row in self.sampleMetadata.sort_values(by='Run Order').iterrows():\\n nameComponents = batchRE.search(row['Sample File Name'])\\n if nameComponents:\\n # Batch start\\n if nameComponents.group('startend') == 'S':\\n # New batch - increment batch no\\n if nameComponents.group('sequence') == '1':\\n currentBatch = currentBatch + 1\\n\\n # Don't include the dilution series or blanks\\n if not ((row['AssayRole'] == AssayRole.LinearityReference) or (row['SampleType'] == SampleType.ProceduralBlank)):\\n self.sampleMetadata.loc[index, 'Batch'] = currentBatch\\n self.sampleMetadata.loc[index, 'Correction Batch'] = currentBatch\\n\\n else:\\n warnings.warn('Unable to infer batches without run order, skipping.')\\n return\",\n \"def _send_batch(self):\\n batch = RPLogBatch(self._batch)\\n http_request = HttpRequest(\\n self.session.post, self._log_endpoint, files=batch.payload,\\n verify_ssl=self.verify_ssl)\\n batch.http_request = http_request\\n self._worker.send(batch)\\n self._batch = []\\n self._payload_size = helpers.TYPICAL_MULTIPART_FOOTER_LENGTH\",\n \"def setUp(self):\\n self.batch_size = 8\\n num_keypoints = 15\\n self.data_batch = []\\n self.data_samples = []\\n\\n for i in range(self.batch_size):\\n gt_instances = InstanceData()\\n keypoints = np.zeros((1, num_keypoints, 2))\\n keypoints[0, i] = [0.5 * i, 0.5 * i]\\n gt_instances.keypoints = keypoints\\n gt_instances.keypoints_visible = np.ones(\\n (1, num_keypoints, 1)).astype(bool)\\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\\n\\n pred_instances = InstanceData()\\n pred_instances.keypoints = keypoints\\n\\n data = {'inputs': None}\\n data_sample = {\\n 'gt_instances': gt_instances.to_dict(),\\n 'pred_instances': pred_instances.to_dict(),\\n }\\n\\n self.data_batch.append(data)\\n self.data_samples.append(data_sample)\",\n \"def setUp(self):\\n self.batch_size = 8\\n num_keypoints = 15\\n self.data_batch = []\\n self.data_samples = []\\n\\n for i in range(self.batch_size):\\n gt_instances = InstanceData()\\n keypoints = np.zeros((1, num_keypoints, 2))\\n keypoints[0, i] = [0.5 * i, 0.5 * i]\\n gt_instances.keypoints = keypoints\\n gt_instances.keypoints_visible = np.ones(\\n (1, num_keypoints, 1)).astype(bool)\\n gt_instances.keypoints_visible[0, (2 * i) % 8, 0] = False\\n gt_instances.bboxes = np.random.random((1, 4)) * 20 * i\\n gt_instances.head_size = np.random.random((1, 1)) * 10 * i\\n\\n pred_instances = InstanceData()\\n pred_instances.keypoints = keypoints\\n\\n data = {'inputs': None}\\n data_sample = {\\n 'gt_instances': gt_instances.to_dict(),\\n 'pred_instances': pred_instances.to_dict(),\\n }\\n\\n self.data_batch.append(data)\\n self.data_samples.append(data_sample)\",\n \"def next(self):\\n\\n if self.i_sample < self.n_sample:\\n df_batch = self.grouped[self.i_sample:min(self.n_sample, self.i_sample + self.batch_size)]\\n # at end of epoch, number of sample remains may be smaller than batch size\\n if len(df_batch) < self.batch_size:\\n df_sample = random.sample(self.grouped, self.batch_size-len(df_batch))\\n df_batch = df_batch + df_sample\\n try:\\n assert len(df_batch) == self.batch_size\\n except AssertionError:\\n print(self.i_sample, df_sample, df_batch)\\n\\n # get random frame_idxs\\n if self.train:\\n flips = np.random.choice(a=[False, True], size=(self.batch_size,), p=[0.5, 0.5])\\n else:\\n flips = np.zeros(self.batch_size, dtype=bool)\\n\\n\\n video = sample_clips(df_batch, flips, self.batch_size, self.n_frame,\\n self.scale_w, self.scale_h, self.sample_half_time, self.train)\\n\\n bboxes = np.zeros((self.batch_size, self.n_frame // self.temporal_scale, self.n_bbox, 5))\\n labels = np.zeros((self.batch_size, self.n_bbox, self.num_class))\\n for i in range(len(df_batch)):\\n tmp_bbox, tmp_label = self.get_bbox_and_label(df_batch[i], flips[i], i, self.scale_w, self.scale_h)\\n bboxes[i] = tmp_bbox\\n labels[i] = tmp_label\\n\\n if self.debug_dataloader:\\n with open('dataset/AVA_v2.1/ava_action_list_v2.1.pbtxt') as fd:\\n lines = fd.readlines()\\n\\n labels_info = []\\n for i in range(80):\\n name_line = lines[i * 5 + 1]\\n label_id_line = lines[i * 5 + 2]\\n label_type_line = lines[i * 5 + 3]\\n\\n name = name_line[name_line.find('\\\"') + 1:name_line.rfind('\\\"')]\\n label_id = int(label_id_line.strip().split(':')[1].strip())\\n label_type = label_type_line.strip().split(':')[1].strip()\\n\\n assert label_id == i + 1\\n labels_info.append({\\n 'name': name,\\n 'label_type': label_type\\n })\\n\\n for bidx in range(self.batch_size):\\n s_video = video[bidx, ...]\\n s_bboxes = bboxes[bidx, ...]\\n s_labels = labels[bidx, ...]\\n\\n window_name = 'batch_idx_'+str(bidx)\\n if self.train:\\n window_name += '_train'\\n else:\\n window_name += '_val'\\n\\n\\n bbox = s_bboxes[0, 0, 1:].astype(np.int32)\\n label_indices = np.where(s_labels[0, :])[0]\\n\\n for fidx in range(self.n_frame):\\n # print('fidx', fidx)\\n save_name = window_name + '_' + str(fidx)\\n tmp_img = (s_video[:, fidx, :, :].transpose((1,2,0))).astype(np.uint8).copy()\\n\\n cv2.rectangle(tmp_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=(0,0,255), thickness=2)\\n for en_idx, label_index in enumerate(label_indices):\\n # print('label_index', label_index, 'len', len(labels_info))\\n cv2.putText(tmp_img, labels_info[label_index]['name'], (bbox[0], bbox[1] + en_idx * 10), cv2.FONT_HERSHEY_SIMPLEX, 0.3, color=(0, 255, 0), thickness=1)\\n\\n cv2.imwrite(save_name+'.jpg', tmp_img)\\n\\n\\n #print(video.shape, bboxes.shape, labels.shape)\\n ret = mx.io.DataBatch(data=[mx.nd.array(video), mx.nd.array(bboxes)],\\n label=[mx.nd.array(labels),],\\n provide_data=self.provide_data,\\n provide_label=self.provide_label)\\n\\n self.i_sample += self.batch_size\\n return ret\\n else:\\n raise StopIteration\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67769766","0.6642692","0.66230625","0.6600419","0.6592506","0.64439774","0.64208335","0.63826704","0.62283033","0.619053","0.61626637","0.6160936","0.61458206","0.6139684","0.61335284","0.61245483","0.61134374","0.6109564","0.60928875","0.60751015","0.6073456","0.6071311","0.606895","0.6056902","0.6046535","0.6035673","0.6028624","0.602325","0.6013657","0.6011615","0.60091263","0.60024095","0.5989787","0.59784913","0.59779084","0.597138","0.5968291","0.59593225","0.5957691","0.5952634","0.59508944","0.59502244","0.59386414","0.59365547","0.5929014","0.59207505","0.59126765","0.5907854","0.59069496","0.5904922","0.5903771","0.5895348","0.58947015","0.58899456","0.5882897","0.5878427","0.5876755","0.58759","0.5873941","0.5869417","0.5854498","0.58533704","0.5852776","0.58495307","0.5846189","0.58433634","0.58340675","0.5822984","0.5822408","0.581928","0.58147705","0.5813848","0.5808603","0.5803609","0.58030504","0.5797823","0.57958287","0.5794992","0.5794419","0.57933116","0.5790247","0.57872224","0.57849574","0.578335","0.57809675","0.57798266","0.5779718","0.5773251","0.57640976","0.57616305","0.5751019","0.57492554","0.5749143","0.5742852","0.5736812","0.5734309","0.5734299","0.5725935","0.5725935","0.5724539"],"string":"[\n \"0.67769766\",\n \"0.6642692\",\n \"0.66230625\",\n \"0.6600419\",\n \"0.6592506\",\n \"0.64439774\",\n \"0.64208335\",\n \"0.63826704\",\n \"0.62283033\",\n \"0.619053\",\n \"0.61626637\",\n \"0.6160936\",\n \"0.61458206\",\n \"0.6139684\",\n \"0.61335284\",\n \"0.61245483\",\n \"0.61134374\",\n \"0.6109564\",\n \"0.60928875\",\n \"0.60751015\",\n \"0.6073456\",\n \"0.6071311\",\n \"0.606895\",\n \"0.6056902\",\n \"0.6046535\",\n \"0.6035673\",\n \"0.6028624\",\n \"0.602325\",\n \"0.6013657\",\n \"0.6011615\",\n \"0.60091263\",\n \"0.60024095\",\n \"0.5989787\",\n \"0.59784913\",\n \"0.59779084\",\n \"0.597138\",\n \"0.5968291\",\n \"0.59593225\",\n \"0.5957691\",\n \"0.5952634\",\n \"0.59508944\",\n \"0.59502244\",\n \"0.59386414\",\n \"0.59365547\",\n \"0.5929014\",\n \"0.59207505\",\n \"0.59126765\",\n \"0.5907854\",\n \"0.59069496\",\n \"0.5904922\",\n \"0.5903771\",\n \"0.5895348\",\n \"0.58947015\",\n \"0.58899456\",\n \"0.5882897\",\n \"0.5878427\",\n \"0.5876755\",\n \"0.58759\",\n \"0.5873941\",\n \"0.5869417\",\n \"0.5854498\",\n \"0.58533704\",\n \"0.5852776\",\n \"0.58495307\",\n \"0.5846189\",\n \"0.58433634\",\n \"0.58340675\",\n \"0.5822984\",\n \"0.5822408\",\n \"0.581928\",\n \"0.58147705\",\n \"0.5813848\",\n \"0.5808603\",\n \"0.5803609\",\n \"0.58030504\",\n \"0.5797823\",\n \"0.57958287\",\n \"0.5794992\",\n \"0.5794419\",\n \"0.57933116\",\n \"0.5790247\",\n \"0.57872224\",\n \"0.57849574\",\n \"0.578335\",\n \"0.57809675\",\n \"0.57798266\",\n \"0.5779718\",\n \"0.5773251\",\n \"0.57640976\",\n \"0.57616305\",\n \"0.5751019\",\n \"0.57492554\",\n \"0.5749143\",\n \"0.5742852\",\n \"0.5736812\",\n \"0.5734309\",\n \"0.5734299\",\n \"0.5725935\",\n \"0.5725935\",\n \"0.5724539\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.58928144"},"document_rank":{"kind":"string","value":"53"}}},{"rowIdx":8468,"cells":{"query":{"kind":"string","value":"Do computations when multiple samples with equal timestamp are received"},"document":{"kind":"string","value":"def on_full(self, bag_data):\n # bag_data is a dict {ts, drone, subject, peds, objs}\n # Bear in mind some simulations cannot contain neither peds nor objs\n dr_pos, dr_vel, dr_acc = bag_data[\"drone\"].get_data()\n subj_pos = bag_data[\"subject\"].get_pos()\n\n peds_poses = []\n if bag_data[\"peds\"] is not None:\n peds_poses = [p.get_pos() for p in bag_data[\"peds\"].values()]\n objs_poses = []\n if bag_data[\"objs\"] is not None:\n objs_poses = [m.pose for m in bag_data[\"objs\"]]\n\n # Bear in mind some simulations cannot contain neither peds nor objs\n # (ped and obj force would be 0.0)\n forces = [0,0,0]\n\n if not self.file.closed:\n rowdata = [ \n bag_data[\"ts\"], dr_pos, dr_vel, dr_acc, subj_pos, forces[0], forces[1], forces[2]\n ] + peds_poses + objs_poses\n\n # print(\"\\n\\nROWDATA: \", rowdata, \"\\n\\n\")\n self.csv_writer.writerow(rowdata)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def audioEpochFeats(cur,uid,timestamp):\n\tuidA = uid +'audio'\n\n\tvar_stats = []\n\tstd_stats = []\n\tnoise = []\n\tvoiceToSilenceRatio = []\n\n\tfor i in range(1,24):\n\t\ths_timestamp = timestamp-86400+(i-1)*hour\n\t\the_timestamp = timestamp-86400+i*hour\n\t\t# Determining if start/end time of given hour is in the night\n\t\t# If yes, proceed with feature calculation, if not skip\n\t\ts_epoch = epochCalc(hs_timestamp)\n\t\te_epoch = epochCalc(he_timestamp)\n\n\t\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\n\t\t\tcur.execute('SELECT audio FROM {0} WHERE time_stamp >= {1} AND time_stamp<= {2}'\n\t\t\t\t.format(uidA,timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\n\t\t\trecords = cur.fetchall()\n\n\t\t\tvar_stats.append(np.var(records))\n\t\t\tstd_stats.append(np.std(records))\n\n\t\t\t# Calculating number of silence and voice/noise occurences\n\t\t\tsilence = len([item for item in records if item==0])\n\t\t\tvoice = len([item for item in records if item==1 or item==2])\n\t\t\tnoise.append(len([item for item in records if item==3]))\n\t\t\tif silence>0:\n\t\t\t\tvoiceToSilenceRatio.append(float(voice) / silence)\n\t\t\telse:\n\t\t\t\tvoiceToSilenceRatio.append(0)\n\treturn(np.nan_to_num(np.hstack((voiceToSilenceRatio,var_stats,std_stats,noise))))\n\t\"\"\"\ndef main():\n\tcon = psycopg2.connect(database='dataset', user='tabrianos')\n\tcur = con.cursor()\n\t#warnings.simplefilter(\"error\")\n\t#centers = np.load('visualizations/clustercenters.npy')\n\n# ------------TEST CASE-----------------------------\n\tfor loso in uids1:\n\t\tytest=[]\n\t\taccuracies =[]\n\t\tacc=0\n\t\tmaxminAcc =[]\n\t\tXbig = np.zeros([1,132])\t\n\t\tYbig = np.zeros([1])\n\t\tlabels=[]\n\t\tlabels.append(19)\n\t\t# loso means leave one student out: forest is trained on other users data\n\t\t# then tests are run on 'loso' student \n\t\tuids2.remove(loso)\n\t\tuids2.append(loso)\n\t\tprint('LOSO: {0}'.format(loso))\n\t\tfor testUser in uids2:\n\t\t\tprint(testUser)\n\t\t\t# lists that temporary store features before concatenation\n\t\t\t\n\t\t\tcolocationList =[]\n\t\t\tconversationList =[]\n\t\t\tactivityList=[]\n\t\t\taudioList = []\n\n\t\t\t# loading stress labels from database (currently on 0-5 scale)\n\t\t\trecords = loadSleepLabels(cur,testUser) \n\t\t\n\n\t\t\t\n\t\t\t#X,Y store initially the dataset and the labels accordingly\n\t\t\tY = np.zeros(len(records))\n\t\t\tX = np.array(records)\n\n\t\n\n\n\t\t\tfor i in range(0,len(records)):\n\t\t\t\tcolocationList.append( colocationEpochFeats(cur,testUser,X[i][0]))\n\t\t\t\tconversationList.append( convEpochFeats(cur,testUser,X[i][0]))\n\t\t\t\tactivityList.append(activityEpochFeats(cur,testUser,X[i][0]))\n\t\t\t#\tScreenList.append( screenStatFeatures(cur,testUser,X[i][0],day) )\n\t\t\t\taudioList.append(audioEpochFeats(cur,testUser,X[i][0]))\n\t\t\n\t\t\t\tif testUser==loso:\n\t\t\t\t\tytest.append(X[i][1])\n\t\t\t\t#labels list holds user ids to be used in LeaveOneOut pipeline\n\t\t\t\tlabels.append(testUser[-2:])\n\t\t\t\tY[i] = X[i][2]\n\n\t\t\t\n\t\t\t#concatenating features in one array \n\n\t\t\tXtt = np.concatenate((np.array(activityList),np.array(conversationList),np.array(colocationList),np.array(audioList)),axis=1)\n\t\t\tprint(Xtt.shape)\n\n\t\t\t#initiating and training forest, n_jobs indicates threads, -1 means all available\n\t\t\t# while the test student is not reached, training data are merged into one big matrix\n\t\t\tXbig = np.concatenate((Xbig,Xtt),axis=0)\n\t\t\tYbig = np.concatenate((Ybig,Y),axis=0)\n\n\t\t\tdel colocationList[:]\n\t\t\tdel conversationList[:]\n\t\t\tdel activityList[:]\n\t\t\tdel audioList[:]\n\n\n\n\t\t\tif testUser!=loso:\n\t\t\t\tXbig = Xbig.astype(np.float64)\n\t\t\t\tprint(Xbig.dtype)\n\t\t\t\t\n\n\t\t\t# when loso, tests are run\n\t\t\telif testUser==loso:\n\t\t\t\t#Xbig = preprocessing.scale(Xbig)\n\t\t\t\tnp.save('numdata/withgps/sleephourlyX.npy',Xbig)\n\t\t\t\tnp.save('numdata/withgps/sleephourlyY.npy',Ybig)\n\t\t\t\tnp.save('numdata/withgps/sleephourlyLOO.npy',np.array(labels))\n\t\t\t\tprint(Xbig.shape[0],Ybig.shape[0],len(labels))\n\t\t\t\tprint('train matrix saved')\n\t\t\t\ta = raw_input()\n\t\t\t\tforest = RandomForestClassifier(n_estimators=100, n_jobs = -1)\n\t\t\t\tforest.fit(Xbig,Ybig)\n\t\t\t\tef = forest.score(Xtt,ytest)\n\t\t\t\tprint(ef*100)\n\n\t\t\t\toutput = np.array(forest.predict(Xtt))\n\t\t\t\tscored = output - np.array(ytest)\n\n\t\t\t\t# Counting as correct predictions the ones which fall in +/-1, not only exact\n\t\t\t\t# I call it the 'Tolerance technique'\n\t\t\t\tcorrect=0\n\t\t\t\tc = Counter(scored)\n\t\t\t\tfor k in c.keys():\n\t\t\t\t\tif k<2 and k>-2:\n\t\t\t\t\t\tcorrect += c[k]\n\t\t\t\t\n\t\t\t\tscore = float(correct)/len(scored)\n\t\t\t\tprint(score*100)\n\n\n\n\t\tprint(Xbig.shape)\n\t\n\t\t\n\n\n\nif __name__ == '__main__':\n\tmain()\n\n\n\n\t\"\"\"","def record_data(self, no_of_samples, interval):\r\n\r\n #tempory storage while function is completing\r\n temp_return_list = []\r\n\r\n #colec\r\n for i in range(0,no_of_samples):\r\n\r\n print(i)\r\n sensor_value = self.sen.get_sensor_value()\r\n\r\n temp_return_list.append([sensor_value,(i*interval)])\r\n\r\n time.sleep(interval)\r\n\r\n \r\n \r\n self.return_data = temp_return_list","def processData(pipe, event, pulses):\n logging.info(\"Started processData\")\n start_time = time.time()\n for pulse in range(pulses):\n samples = pipe.get()\n# time.sleep(0.001)\n end_time = time.time()\n elapsed = end_time - start_time\n samplesProcessed = (pulses * len(samples[0]) * len(samples))\n logging.info(\"processData processed %d Msamples in %.3f s\",\n samplesProcessed / 1e6,\n elapsed)\n logging.info(\"processData rate: %.3f Msa/s in lumps of %d samples\",\n samplesProcessed / elapsed / 1e6,\n dig.pointsPerCycle)","def _got_chunk(self, chunk, timestamp):\n log.debug(\"_got_chunk: chunk=%s\", chunk)\n self._extract_sample(D1000TemperatureDataParticle, D1000TemperatureDataParticle.regex_compiled(), chunk,\n timestamp)","def UTC_times(times, \n trace, \n diff_thres = 30.0):\n # set times values to seconds\n \n #AUTOMATE THIS SECTION!\n #CHECK THAT THIS IS CORRECT\n times = times / trace.stats.sampling_rate\n #remove unwanted parts of times numpy array \n times = times[:,0]\n \n #remove the first instance of time because it is \n #somehow always of the wrong format!\n #times = np.delete(times, 0) \n \n event_times = []\n event = [times[0]]\n \n start_time = trace.stats.starttime\n \n #for item in times:\n # print start_time + item\n\n for i in range(1, len(times)):\n \n # check if two events in times array have a difference < diff_thres, \n #if not, run average of those times, if so append that events to a \n #new events_times list\n \n #time_diff = times[i + 1] - times[i]\n \n time_diff = times[i] - times[i-1]\n\n #save info until events are far enough apart! \n if time_diff < diff_thres:\n\n event.append(times[i])\n \n \n #raise conditional for if events are far enough apart! \n else:\n\n event_start = event[0] - 2 #minus 5 seconds\n event_end = max(event) + 2 #add 5 seconds\n\n event_times.append([event_start, event_end])\n \n event = [] \n \n event.append(times[i])\n\n #if event still contains something for any reason, add it to event times\n if len(event) > 0: \n event_start = event[0] - 2 #minus 5 seconds\n event_end = max(event) + 2 #add 5 seconds\n event_times.append([event_start, event_end])\n event = [] \n \n\n\n #if len(event_times) == 0 and len(event) > 0 or time_diff > diff_thres and len(event) > 0:\n \n #event_times.append(sum(event) / len(event))\n \n # event_start = event[0] - 2 #minus 5 seconds\n # event_end = event[-1] + 2 #add 5 seconds\n \n # event_times.append([event_start, event_end])\n \n # event = []\n \n #event_times.append(times[i])\n \n # else:\n # event.append(times[i])\n \n\n UTC_events = []\n\n #earthquake length threshold is 10 seconds and above!\n eq_len = 0#5.0\n\n for i in event_times:\n estart = start_time + i[0]\n eend = start_time + i[1]\n \n if eend - estart > eq_len:\n UTC_events.append([estart, eend])\n \n #UTC_events = np.unique(np.asarray(UTC_events))\n\n \n return UTC_events","def _got_chunk(self, chunk, timestamp):\n if self._extract_sample(SBE19DataParticle, SBE19DataParticle.regex_compiled(), chunk, timestamp):\n self._sampling = True\n return\n\n for particle_class in SBE19HardwareParticle, \\\n SBE19CalibrationParticle, \\\n SBE19ConfigurationParticle, \\\n SBE19StatusParticle, \\\n OptodeSettingsParticle:\n if self._extract_sample(particle_class, particle_class.regex_compiled(), chunk, timestamp):\n return","def sample_run(df, n_epochs = 10, window_size = 500, com = 12, p_anoms = .5):\n import numpy as np\n\n # create arrays that will hold the results of batch AD (y_true) and online AD (y_pred)\n y_true = []\n y_pred = []\n run_times = []\n \n # check which unique machines, sensors, and timestamps we have in the dataset\n machineIDs = df['machineID'].unique()\n sensors = df.columns[2:]\n timestamps = df['datetime'].unique()[window_size:]\n \n # sample n_machines_test random machines and sensors \n random_machines = np.random.choice(machineIDs, n_epochs)\n random_sensors = np.random.choice(sensors, n_epochs)\n\n # we intialize an array with that will later hold a sample of timetamps\n random_timestamps = np.random.choice(timestamps, n_epochs)\n \n for i in range(0, n_epochs):\n # take a slice of the dataframe that only contains the measures of one random machine\n df_s = df[df['machineID'] == random_machines[i]]\n \n # smooth the values of one random sensor, using our run_avg function\n smooth_values = run_avg(df_s[random_sensors[i]].values, com)\n \n # create a data frame with two columns: timestamp, and smoothed values\n df_smooth = pd.DataFrame(data={'timestamp': df_s['datetime'].values, 'value': smooth_values})\n\n # load the results of batch AD for this machine and sensor\n anoms_s = anoms_batch[((anoms_batch['machineID'] == random_machines[i]) & (anoms_batch['errorID'] == random_sensors[i]))]\n \n # find the location of the t'th random timestamp in the data frame\n if np.random.random() < p_anoms:\n anoms_timestamps = anoms_s['datetime'].values\n np.random.shuffle(anoms_timestamps)\n counter = 0\n while anoms_timestamps[0] < timestamps[0]:\n if counter > 100:\n return 0.0, 9999.0\n np.random.shuffle(anoms_timestamps)\n counter += 1\n random_timestamps[i] = anoms_timestamps[0]\n \n # select the test case\n test_case = df_smooth[df_smooth['timestamp'] == random_timestamps[i]]\n test_case_index = test_case.index.values[0]\n\n\n # check whether the batch AD found an anomaly at that time stamps and copy into y_true at idx\n y_true_i = random_timestamps[i] in anoms_s['datetime'].values\n\n # perform online AD, and write result to y_pred\n y_pred_i, run_times_i = detect_ts_online(df_smooth, window_size, test_case_index)\n \n y_true.append(y_true_i)\n y_pred.append(y_pred_i)\n run_times.append(run_times_i)\n \n return fbeta_score(y_true, y_pred, beta=2), np.mean(run_times)","def collect_samples(serialPort,NO_SENSORS,NO_SAMPLES,log):\n run = '1'\n badSamples = 0\n count = 1\n log_temp = []\n temp = [0] * 20\n NO_FIELDS = (NO_SENSORS * 3) + 1\n \n while (run == '1'):\n # If the input buffer is not empty read the data out into rawData using \\n as a delimiter.\n if (serialPort.inWaiting()>0):\n rawData = serialPort.readline()\n print(rawData)\n \n # If invalid data is recieved this prevents program crash\n try:\n # Decode the bytes into a string\n data = rawData.decode()\n \n # Split x, y, z and newline values into a list\n if (count >= (NO_SAMPLES + 1)):\n endTime_temp = data.split(\" \", 2)\n if (len(endTime_temp) == 2 and '' not in endTime_temp):\n endTime = int(endTime_temp[0])\n else:\n endTime = 780\n print('Time not recieved')\n print('Lost Samples: ' + str(badSamples))\n run = '0'\n else:\n data_readings = data.split(\" \", NO_FIELDS)\n print(data_readings)\n \n # A correct sample should contain 16 values and not include null and so this is used\n # to validate the data and record any samples that are discarded in this way\n if (len(data_readings) == NO_FIELDS and '' not in data_readings):\n # Discard newline characters before saving data\n int_data_readings = list(map(int,data_readings[:(NO_FIELDS - 1)]))\n log_temp.append(int_data_readings)\n else:\n badSamples += 1\n except:\n print('Invalid data recieved')\n \n count += 1\n\n samplingPeriod = (endTime/NO_SAMPLES)/NO_SENSORS\n timeStamp = 0.0\n\n for i in range(0,len(log_temp)):\n for j in range(0,NO_SENSORS):\n temp[0+(j*4)] = log_temp[i][0+(j*3)]\n temp[1+(j*4)] = log_temp[i][1+(j*3)]\n temp[2+(j*4)] = log_temp[i][2+(j*3)]\n temp[3+(j*4)] = timeStamp\n timeStamp += samplingPeriod\n log.append(temp.copy())","def test_data_handlers(timeseries, handler):\n handler = handler(timeseries, HISTORICAL_CONTEXT_NUMBER)\n updates = []\n i = 0\n while True:\n try:\n context, update = handler.next()\n except NoMoreData:\n break\n i += 1\n\n updates.append(update)\n # import pdb; pdb.set_trace()\n assert context[-1].datetime < update.datetime\n\n # total lenght of series(25) - context(5) which aren't provided as updates\n assert i == 20\n updates = [x.datetime.date().isoformat() for x in updates]\n assert updates[10] == \"2018-01-22\"","def _got_chunk(self, chunk, timestamp):\n new_timestamp_match = _NEW_DATFILE_PARTICLE_TIMESTAMP_MATCHER.match(chunk)\n if new_timestamp_match:\n date = datetime.strptime(new_timestamp_match.group(1), '%Y-%m-%dT%H:%M:%S.%f')\n self.log_timestamp = (date - datetime(1900, 1, 1)).total_seconds()\n return\n\n new_body_match = _NEW_DATFILE_PARTICLE_BODY_MATCHER.match(chunk)\n if new_body_match:\n # Only publish D1000 playback if we have a timestamp.\n if self.log_timestamp is not None:\n self._extract_sample(\n D1000TemperatureDataParticle,\n _NEW_DATFILE_PARTICLE_BODY_MATCHER,\n chunk,\n self.log_timestamp\n )\n\n return\n\n old_timestamp_match = _OLD_DATFILE_PARTICLE_TIMESTAMP_MATCHER.match(chunk)\n if old_timestamp_match:\n date = datetime.strptime(old_timestamp_match.group(1), '%Y-%m-%d %H:%M:%S')\n self.log_timestamp = (date - datetime(1900, 1, 1)).total_seconds()\n return\n\n old_body_match = _OLD_DATFILE_PARTICLE_BODY_MATCHER.match(chunk)\n if old_body_match:\n # Only publish D1000 playback if we have a timestamp.\n if self.log_timestamp is not None:\n self._extract_sample(\n D1000TemperatureDataParticle,\n _OLD_DATFILE_PARTICLE_BODY_MATCHER,\n chunk,\n self.log_timestamp\n )","def process_original_data():\n audioSamples = AudioDataOriginal.query.filter().all()\n\n currentSampleDatetime = None\n maxValueRecord = None\n for sample in audioSamples:\n if currentSampleDatetime is None:\n currentSampleDatetime = sample.datetime\n if maxValueRecord is None:\n maxValueRecord = sample\n # Check if we've reached a new sample batch.\n if currentSampleDatetime != sample.datetime:\n # If we reach a new sample batch, update the `processedValue` of the\n # record held in `maxValueRecord` to be the `audio` value.\n maxValueRecord.processedValue = maxValueRecord.audio\n # Update to the first value of the new sample batch.\n currentSampleDatetime = sample.datetime\n maxValueRecord = sample\n elif sample.audio > maxValueRecord.audio:\n # If not, then check if the value is higher than the current one\n # and overwrite if so.\n maxValueRecord = sample\n # Update the last record, since this won't be caught in the if statement\n # inside the loop (it compares the current.datetime vs a new.datetime after\n # all).\n maxValueRecord.processedValue = maxValueRecord.audio\n db.session.commit()","def update_live_data(n, last_time, id1, id2, power):\n if power:\n raise PreventUpdate\n\n timer_start = perf_counter()\n # 1 sec delay so server has time to add live data\n end_time = datetime.now(timezone.utc) - timedelta(seconds=1)\n\n # Initialization and lag prevention\n if last_time is None or end_time - strptime_fix(last_time) > timedelta(seconds=3):\n logging.warning('Falling behind! Start %s End %s', last_time, end_time)\n return dash.no_update, dash.no_update, end_time.isoformat(), dash.no_update\n\n # Query data from SMIP\n logging.info(f'start_time {last_time} end_time {end_time}')\n timer_query_start = perf_counter()\n r = conn.get_data(last_time, end_time.isoformat(),\n [id1, id2], timeout=1)\n timer_query_end = perf_counter()\n response_json: dict = r.json()\n logging.debug(response_json.keys())\n if 'errors' in response_json:\n logging.error(response_json)\n raise Exception()\n data = response_json['data']['getRawHistoryDataWithSampling']\n logging.info('Got %s responses in %s seconds', len(\n data), timer_query_end - timer_query_start)\n\n # Used for measuring performance\n start_processing = perf_counter()\n\n # Unpack data\n def unpack(id: int):\n \"\"\"Unpacks return data into time and value lists\"\"\"\n id = int(id)\n time_list = [i['ts'] for i in data if int(i['id']) == id]\n val_list = [i['floatvalue'] for i in data if int(i['id']) == id]\n # SMIP always returns one entry before the start time for each ID, we don't need this\n if len(time_list) < 2 or len(val_list) < 2:\n return dash.no_update\n time_list.pop(0)\n val_list.pop(0)\n # Measure sampling rate\n rate = nan\n if len(time_list) > 1:\n rate = (strptime_fix(time_list[1])\n - strptime_fix(time_list[0])).total_seconds()\n return {'time_list': time_list, 'val_list': val_list, 'rate': rate}\n\n # Used for measuring performance\n data_processed = perf_counter()\n logging.info('Total %s Query %s Processing %s', data_processed - timer_start, timer_query_end - timer_query_start,\n data_processed - start_processing)\n\n return unpack(id1), unpack(id2), end_time.isoformat(), \\\n [f'Last updated {end_time.astimezone()},',\n html.Br(),\n f'received {len(data)} samples in {round(data_processed - timer_start, 3)} seconds']","def process(self, timeframe, timestamp):\n pass","def predict(self):\n\n global pos\n pos = (pos + 1) % len(ue_data) # iterate through entire list one by one in cycle manner and will be updated when live feed will be coming through KPIMON to influxDB\n sample = ue_data[pos]\n ue_df = pd.DataFrame([sample], columns=db.data.columns)\n val = predict_anomaly(self, ue_df)\n if (val is not None) and (len(val) > 2):\n msg_to_ts(self, val)","def _compute_samples(self, samples):\n return samples","def timings(samples):\n groups = samples.groupby(axis=1, level=0)\n return groups.apply(lambda group: group.iloc[:, 1] - group.iloc[:, 0])","def _query_data(self, index, tag):\n version, datapoints = yield self.quasar.stream_get(self.name, tag, tag+(15*qdf.MINUTE))\n values = np.empty((BLOCK_SIZE,), dtype=(type(datapoints[0])))\n values[:] = None\n \n for point in datapoints:\n time = float(point.time - tag)\n time_index = int(round(time*SAMPLE_RATE/qdf.SECOND))\n values[time_index] = point\n\n self.cache[index][CACHE_INDEX_TAG] = tag\n self.cache[index][CACHE_INDEX_DATA] = values","def run():\n\n import matplotlib.pyplot as plt\n\n anomalies_t = []\n anomalies_v = []\n anomalies_c = []\n\n all_t = []\n all_v = []\n\n rows = []\n for i, row in dataSet.iterrows():\n\n inputData = row.to_dict()\n\n detectorValues = handleRecord(inputData)\n\n if (detectorValues[0] > 0.65):\n anomalies_t.append(inputData[\"timestamp\"])\n anomalies_v.append(inputData[\"value\"])\n anomalies_c.append(detectorValues[0])\n\n all_t.append(inputData[\"timestamp\"])\n all_v.append(inputData[\"value\"])\n\n outputRow = list(row) + list(detectorValues)\n\n rows.append(outputRow)\n\n # Progress report\n if (i % 1000) == 0:\n print \".\",\n sys.stdout.flush()\n\n fig, ax = plt.subplots()\n\n ax.plot(all_t, all_v)\n ax.plot(anomalies_t, anomalies_v, 'ro')\n\n plt.show()\n\n ans = pandas.DataFrame(rows)\n return ans","def process_point(meta, data):\n point = dfparser.Point()\n point.ParseFromString(data)\n\n sample_freq = meta['params']['sample_freq']\n threshold = meta['process_params']['threshold']\n\n events_all = []\n for channel in point.channels:\n for i, block in enumerate(channel.blocks):\n SOCKETIO.emit('progress',\n {'val': int((i/len(channel.blocks))*100)})\n eventlet.sleep(0)\n events = []\n for event in block.events:\n data = np.frombuffer(event.data, np.int16)\n events.append(extract_amps_approx2(data, event.time,\n threshold,\n sample_freq)[0])\n events = np.hstack(events)[0::2]\n events_all.append(events)\n\n events_all = np.hstack(events_all)\n return events_all","def hit(self, timestamp):\n idx = timestamp % 300\n if self.counter[idx][1] == timestamp:\n self.counter[idx][0] += 1\n else:\n self.counter[idx] = [1, timestamp]","def test_time_series_from_file():\r\n\r\n TR = 1.35\r\n ts_ff = io.time_series_from_file\r\n\r\n #File names:\r\n fmri_file1 = os.path.join(data_path,'fmri1.nii.gz')\r\n fmri_file2 = os.path.join(data_path,'fmri2.nii.gz')\r\n\r\n #Spatial coordinates into the volumes:\r\n coords1 = np.array([[5,5,5,5],[5,5,5,5],[1,2,3,4]])\r\n coords2 = np.array([[6,6,6,6],[6,6,6,6],[3,4,5,6]])\r\n\r\n #No averaging, no normalization:\r\n t1 = ts_ff([fmri_file1,fmri_file2],[coords1,coords2],TR)\r\n\r\n npt.assert_equal(t1[0].shape,(4,80)) # 4 coordinates, 80 time-points\r\n\r\n t2 = ts_ff([fmri_file1,fmri_file2],[coords1,coords2],TR,average=True)\r\n\r\n npt.assert_equal(t2[0].shape,(80,)) # collapse coordinates,80 time-points\r\n\r\n t3 = ts_ff(fmri_file1,coords1,TR,normalize='zscore')\r\n\r\n #The mean of each channel should be almost equal to 0:\r\n npt.assert_almost_equal(t3.data[0].mean(),0)\r\n #And the standard deviation should be almost equal to 1:\r\n npt.assert_almost_equal(t3.data[0].std(),1)\r\n\r\n t4 = ts_ff(fmri_file1,coords1,TR,normalize='percent')\r\n\r\n #In this case, the average is almost equal to 0, but no constraint on the\r\n #std:\r\n npt.assert_almost_equal(t4.data[0].mean(),0)\r\n\r\n #Make sure that we didn't mess up the sampling interval:\r\n npt.assert_equal(t4.sampling_interval,nitime.TimeArray(1.35))\r\n\r\n # Test the default behavior:\r\n data = io.load(fmri_file1).get_data()\r\n t5 = ts_ff(fmri_file1)\r\n npt.assert_equal(t5.shape, data.shape)\r\n npt.assert_equal(t5.sampling_interval, ts.TimeArray(1, time_unit='s'))\r\n\r\n # Test initializing TR with a TimeArray:\r\n t6= ts_ff(fmri_file1, TR=ts.TimeArray(1350, time_unit='ms'))\r\n npt.assert_equal(t4.sampling_interval, t6.sampling_interval)\r\n\r\n # Check the concatenation dimensions:\r\n t7 = ts_ff([fmri_file1, fmri_file2])\r\n npt.assert_equal([t7.shape[:3], t7.shape[-1]], [data.shape[:3], data.shape[-1]*2])\r\n\r\n t8 = ts_ff([fmri_file1, fmri_file2], average=True)\r\n npt.assert_equal(t8.shape[0], data.shape[-1]*2)\r\n\r\n t9 = ts_ff([fmri_file1, fmri_file2], average=True, normalize='zscore')\r\n npt.assert_almost_equal(t9.data.mean(), 0)","def newChunk(self, data, sample_period):\n added = False\n found = False\n for r in self._receiving:\n if r[0] == data.timestamp:\n r[1][data.sensor - 1] = data\n found = True\n break\n if found is False:\n self._receiving.append((data.timestamp, [None] * self._sensors))\n self._receiving[-1][1][data.sensor - 1] = data\n self._receiving.sort(key=lambda x: x[0])\n\n # all data for given timestamp received\n while len(self._receiving) > 0:\n r = self._receiving[0]\n if r[1].count(None) > 0:\n break\n dl = len(data.accelerationX)\n timestamps = np.arange(r[0], r[0] + sample_period * dl, sample_period)\n\n def copy_data(start, l):\n self.data[\"timestamp\"][\n self.current_index : self.current_index + l\n ] = timestamps[start : start + l]\n for s in range(1, self._sensors + 1):\n self.data[f\"{s} X\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationX[start : start + l]\n self.data[f\"{s} Y\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationY[start : start + l]\n self.data[f\"{s} Z\"][\n self.current_index : self.current_index + l\n ] = r[1][s - 1].accelerationZ[start : start + l]\n\n l = min(dl, self._size - self.current_index)\n if l > 0:\n copy_data(0, l)\n self.current_index += l\n dl -= l\n\n if dl > 0:\n self.current_index = 0\n self.filled = True\n copy_data(l, dl)\n self.current_index = dl\n\n self._receiving.remove(r)\n added = True\n\n chunk_removed = False\n if len(self._receiving) > self._window:\n self._receiving = self._receiving[1:]\n chunk_removed = True\n return (added, chunk_removed)","def _compute_epochs(events, rel_start_ms, rel_stop_ms, timestamps, sr):\n\n # THIS IS SO MUCH FASTER THAN NP.WHERE, CRAZY\n offsets = events.stTime.apply(lambda x: np.searchsorted(timestamps, x))\n # offsets = events.stTime.apply(lambda x: np.where(timestamps >= x)[0][0])\n rel_start_micro = int(rel_start_ms * sr / 1e3)\n rel_stop_micro = int(rel_stop_ms * sr / 1e3)\n epochs = np.array([(offset + rel_start_micro, offset + rel_stop_micro) for offset in offsets])\n return epochs","def aggregated_second(self, data):\n if self.results:\n if self.results[-1][\"ts\"] >= data[\"ts\"]:\n raise AssertionError(\"TS sequence wrong: %s>=%s\" % (self.results[-1][\"ts\"], data[\"ts\"]))\n self.results.append(data)","def _init_sample(self):\n self.timestamps = np.zeros(5)\n self.data = np.zeros((5, 12))","def _sample_from_null_frm_dist(mean_spike_count, total_baseline_time, total_effect_time, sample_size=10 ** 6):\n total_time = total_baseline_time + total_effect_time\n\n samples = (\n st.poisson(mean_spike_count * total_effect_time / total_time).rvs(sample_size) / total_effect_time\n -\n st.poisson(mean_spike_count * total_baseline_time / total_time).rvs(sample_size) / total_baseline_time\n )\n\n # convert 1/ms to 1/s (Hz)\n samples = samples / MS_TO_S\n\n return samples","def testQueryWithTimestamp(self):\n for i in range(5):\n row_name = \"aff4:/row:query_with_ts\"\n data_store.DB.Set(row_name, \"metadata:5\", \"test\", timestamp=i + 10,\n replace=False, token=self.token)\n data_store.DB.Set(row_name, \"aff4:type\", \"test\", timestamp=i + 10,\n replace=False, token=self.token)\n\n # Read all timestamps.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=data_store.DB.ALL_TIMESTAMPS, token=self.token)]\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n self.assertEqual(len(attributes[\"aff4:type\"]), 5)\n\n # Read latest timestamp.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=data_store.DB.NEWEST_TIMESTAMP, token=self.token)]\n\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n self.assertEqual(len(attributes[\"aff4:type\"]), 1)\n self.assertEqual(attributes[\"aff4:type\"][0][0], \"test\")\n\n # Newest timestamp is 4.\n self.assertEqual(attributes[\"aff4:type\"][0][1], 14)\n\n # Now query for a timestamp range.\n rows = [row for row in data_store.DB.Query(\n [], data_store.DB.filter.HasPredicateFilter(\"metadata:5\"),\n subject_prefix=\"aff4:/row:query_with_ts\",\n timestamp=(11, 13), token=self.token)]\n\n attributes = rows[0]\n self.assertEqual(attributes[\"subject\"][0][0], \"aff4:/row:query_with_ts\")\n # Now we should have three timestamps.\n self.assertEqual(len(attributes[\"aff4:type\"]), 3)\n\n timestamps = [attribute[1] for attribute in attributes[\"aff4:type\"]]\n self.assertListEqual(sorted(timestamps), [11, 12, 13])","def run(self):\n while(not self.stop_event.is_set()):\n # read values until stop is sent\n response1 = _read_once(1,self.serial)\n response2 = _read_once(2,self.serial)\n #print(response)\n self.data1[\"d\"].append(response1) # Push response to the data list for later\n self.data2[\"d\"].append(response2) # Push response to the data list for later\n curTime = time.time()\n self.data1[\"t\"].append(curTime)\n self.data2[\"t\"].append(curTime)\n #sleep(0.0001) # I need to be small enough to capture peaks.\n return","def statistical_feature_extraction(window_size, signal, axis, device, subject_ID):\n\n start_running = timeit.default_timer()\n try:\n directory = f'data/row_data/{device}_{signal}/S{subject_ID}_{device}_{signal}.csv'\n sampling_rate = 20\n window_size = int(sampling_rate * window_size)\n # print(window_size)\n except:\n print('Error! Can not find such directory.')\n\n raw_signal = pd.read_csv(directory)\n win_count = 0\n total_win_count = 0\n features_for_all_windows_one_activity = []\n features_for_all_windows_all_activities = []\n column_title = f'{axis}_{device}_{signal}'\n for class_label in np.append(range(1, 14), range(15, 20)):\n activity_ID = chr(class_label + 64)\n raw_data_one_activity = np.array(raw_signal.loc[raw_signal['activity_ID'] == activity_ID, [column_title]])\n raw_data_one_activity = pd.DataFrame(raw_data_one_activity)\n\n for data_point in range(0, len(raw_data_one_activity), window_size):\n win_count += 1\n start = data_point\n end = start + window_size\n time_domain_window = raw_data_one_activity[start:end]\n\n time_mean = pd.Series(time_domain_window.mean()).rename(f'{axis}_{signal}_mean')\n time_min = pd.Series(time_domain_window.min()).rename(f'{axis}_{signal}_min')\n time_max = pd.Series(time_domain_window.max()).rename(f'{axis}_{signal}_max')\n time_std = pd.Series(time_domain_window.std()).rename(f'{axis}_{signal}_std')\n time_median = pd.Series(time_domain_window.median()).rename(f'{axis}_{signal}_median')\n time_variance = pd.Series(time_domain_window.var()).rename(f'{axis}_{signal}_variance')\n zero_crossing_rate = pd.Series(zero_crossing(time_domain_window)).rename(\n f'{axis}_{signal}_zero_crossing')\n mean_crossing = pd.Series(mean_crossing_rate(time_domain_window)).rename(\n f'{axis}_{signal}_mean_crossing')\n activity_id_ = pd.Series(activity_ID).rename('Activity_ID')\n\n features_for_one_window_one_activity = pd.concat(\n [time_mean, time_min, time_max, time_std, time_median, time_variance, zero_crossing_rate, mean_crossing,\n activity_id_], axis=1)\n features_for_all_windows_one_activity.append(features_for_one_window_one_activity)\n # print(features_for_all_windows)\n\n print('Window count', win_count)\n total_win_count += win_count\n win_count = 0\n features_for_all_windows_all_activities.append(features_for_all_windows_one_activity)\n features = pd.concat(features_for_all_windows_all_activities[0], ignore_index=False)\n print(features)\n save_as_directory = f'feature_label_tables/feature_{device}_{signal}/feature_S{subject_ID}_{axis}_{device}_{signal}.csv'\n features.to_csv(save_as_directory, encoding='utf-8', index=False)\n finish_running = timeit.default_timer()\n print('Total number of windows: ', total_win_count)\n print('Running time: ', finish_running - start_running)","def _handler_sample(self, *args, **kwargs):\n next_state = None\n timeout = time.time() + SAMPLE_TIMEOUT\n\n for i in self._units:\n self._do_command(Command.READ, i)\n\n particles = self.wait_for_particles([DataParticleType.D1000_PARSED], timeout)\n\n return next_state, (next_state, particles)","def timestamp_processor(timestamps, with_tz=False, check_delta=False):\n if timestamps.count(timestamps[0]) == len(timestamps):\n unified_timestamp = timestamps[0]\n else:\n average_timestamp = sum([dt.timestamp for dt in timestamps])/len(timestamps)\n unified_timestamp = arrow.get(average_timestamp)\n\n if check_delta:\n for ts in timestamps:\n delta = unified_timestamp - arrow.get(ts)\n second_difference = abs(delta.total_seconds())\n\n if second_difference > 3600:\n # more than 1 hour difference\n raise ValueError(\"\"\"South Korea generation data is more than 1 hour apart,\n saw {} hours difference\"\"\".format(second_difference/3600))\n\n if with_tz:\n unified_timestamp = unified_timestamp.replace(tzinfo='Asia/Seoul')\n\n return unified_timestamp","def collect_samples(self):\n self.__running = True\n with open(self.__filename, 'a') as output:\n next_sample_time = time.time()\n while self.__running:\n sensor_name = self.__sensor.get_sensor_type_name()\n sensor_id = self.__sensor.get_sensor_id()\n data = self.__sensor.retrieve_data_string() \n if DEBUG:\n print('data: \"{}\"'.format(data),\n file = sys.stderr, flush=True)\n when = datetime.datetime.now(datetime.timezone.utc).isoformat()\n result = OUTPUT_FORMAT.format(when,\n sensor_name, \n sensor_id, \n data)\n output.write(result)\n output.flush()\n \n next_sample_time = next_sample_time + self.__interval\n delay_time = next_sample_time - time.time()\n if DEBUG:\n print('delay_time = {}'.format(delay_time),\n file=sys.stderr, flush=True)\n \n if 0 < delay_time: # don't sleep if already next sample time\n time.sleep(delay_time)","def get_observation(self, ts: int) -> np.ndarray:\n \n self.connect_db()\n c = self.conn.cursor()\n c.arraysize = self.num_clients * self.ticks_per_observation + 10\n \n # Create np array of size [clients x ticks_per_observation x parameters]\n result = np.zeros((len(self.client_list),\n self.ticks_per_observation,\n len(self.conf['ceph-param'])), dtype=float)\n \n # Get performance indicators within time step\n # sql = '''SELECT clientid, ts, pis from perfs WHERE ts <= ? AND ts > ? ORDER BY clientid ASC, ts ASC'''\n c.execute('''SELECT clientid, ts, pis from perfs WHERE ts <= ? AND \n ts > ? ORDER BY clientid ASC, ts ASC''',\n (ts,ts - self.ticks_per_observation))\n \n data = c.fetchall()\n if len(data) < self.num_clients * self.ticks_per_observation - self.missing_entry_tolerance:\n raise NotEnoughDataError\n elif len(data) != self.num_clients * self.ticks_per_observation:\n logger.debug('Observation at ts {0} has {1} missing entries'.format(\n ts, self.num_clients * self.ticks_per_observation - len(data)))\n for row in data:\n client_id = row[0]\n pi = row[2]\n if client_id not in self.client_list:\n # non client MA should send in zero length data for now\n assert len(pickle.loads(pi)) == 0\n continue\n \n client_id_idx = self.client_list.index(client_id)\n ts_idx = row[1] - (ts - self.ticks_per_observation) - 1\n # numpy assignments also check that pi is in right shape\n result[client_id_idx, ts_idx] = pickle.loads(pi)\n \n self.conn.close()\n return result.reshape((self.observation_size,))","def _set_sample(self, sample, PB_X, t):\n for sensor in PB_X.keys():\n sample.set(sensor, np.array(PB_X[sensor]), t=t+1)","def testGenerateSamplesRuntimeAndTimestamps(self):\n timer = timing_util.IntervalTimer()\n with timer.Measure('First'):\n pass\n with timer.Measure('Second'):\n pass\n start0 = timer.intervals[0][1]\n stop0 = timer.intervals[0][2]\n start1 = timer.intervals[1][1]\n stop1 = timer.intervals[1][2]\n with mock.patch(\n 'perfkitbenchmarker.timing_util.TimestampMeasurementsEnabled',\n return_value=True):\n samples = timer.GenerateSamples()\n exp_samples = [\n sample.Sample('First Runtime', stop0 - start0, 'seconds'),\n sample.Sample('First Start Timestamp', start0, 'seconds'),\n sample.Sample('First Stop Timestamp', stop0, 'seconds'),\n sample.Sample('Second Runtime', stop1 - start1, 'seconds'),\n sample.Sample('Second Start Timestamp', start1, 'seconds'),\n sample.Sample('Second Stop Timestamp', stop1, 'seconds')]\n self.assertSampleListsEqualUpToTimestamp(samples, exp_samples)","def create_msgs():\n getcontext().prec = 3 # will round to 3 decimal places\n orig_times = sorted(dat)\n for n in range(len(dat) - 1):\n linfun = interp1d([orig_times[n], orig_times[n+1]], \\\n [dat[orig_times[n]], dat[orig_times[n+1]]])\n dt = orig_times[n+1] - orig_times[n] # current\n freq = 1/dt # current\n if dt < (1/desHz):\n print('found instance where Freq already at/above desired Freq')\n else:\n new_dt = dt*freq/desHz\n new_times = linspace(orig_times[n],orig_times[n+1],floor(dt/new_dt))\n # print(new_times)\n new_values = linfun(new_times)\n # rounded_values = [float(Decimal(\"%.3f\" % e)) for e in new_values]\n rounded_times = [float(Decimal(\"%.3f\" % e)) for e in new_times]\n for m in range(len(rounded_times)):\n # this_time = int(new_times[m]*100000)/100000 # 5 decimal places in timstamp\n self.outData[sens][meas][rounded_times[m]] = new_values[m]","def test_single_ended_matching_sections_synthetic():\n from dtscalibration import DataStore\n\n cable_len = 100.0\n nt = 50\n nx = 200\n time = np.arange(nt)\n x = np.linspace(0.0, cable_len, nx)\n ts_cold = np.ones(nt) * 4.0\n ts_warm = np.ones(nt) * 20.0\n ts_ambient = np.ones(nt) * 12\n ts_valid = np.ones(nt) * 16\n\n C_p = 15246\n C_m = 2400.0\n dalpha_r = 0.0005284\n dalpha_m = 0.0004961\n dalpha_p = 0.0005607\n gamma = 482.6\n cold_mask1 = np.logical_and(x > 0.125 * cable_len, x < 0.25 * cable_len)\n cold_mask2 = np.logical_and(x > 0.625 * cable_len, x < 0.75 * cable_len)\n warm_mask1 = np.logical_and(x > 0.75 * cable_len, x < 0.875 * cable_len)\n warm_mask2 = np.logical_and(x > 0.25 * cable_len, x < 0.375 * cable_len)\n valid_mask = np.logical_and(x > 0.40 * cable_len, x < 0.50 * cable_len)\n temp_real = np.ones((len(x), nt)) * 12 + 273.15\n temp_real[cold_mask1 + cold_mask2] = ts_cold + 273.15\n temp_real[warm_mask1 + warm_mask2] = ts_warm + 273.15\n temp_real[valid_mask] = ts_valid + 273.15\n\n st = (\n C_p\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_p * x[:, None])\n * np.exp(gamma / temp_real)\n / (np.exp(gamma / temp_real) - 1)\n )\n ast = (\n C_m\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_m * x[:, None])\n / (np.exp(gamma / temp_real) - 1)\n )\n\n # Add attenuation\n tr_att = np.random.rand(nt) * 0.2 + 0.8\n st[int(x.size * 0.4) :] *= tr_att\n tr_att2 = np.random.rand(nt) * 0.2 + 0.8\n st[int(x.size * 0.6) :] *= tr_att2\n\n ds = DataStore(\n {\n \"st\": ([\"x\", \"time\"], st),\n \"ast\": ([\"x\", \"time\"], ast),\n \"userAcquisitionTimeFW\": ([\"time\"], np.ones(nt)),\n \"cold\": ([\"time\"], ts_cold),\n \"warm\": ([\"time\"], ts_warm),\n \"ambient\": ([\"time\"], ts_ambient),\n },\n coords={\"x\": x, \"time\": time},\n attrs={\"isDoubleEnded\": \"0\"},\n )\n\n sections = {\n \"cold\": [slice(0.13 * cable_len, 0.24 * cable_len)],\n \"warm\": [slice(0.26 * cable_len, 0.365 * cable_len)],\n }\n\n matching_sections = [\n (\n slice(0.01 * cable_len, 0.09 * cable_len),\n slice(0.51 * cable_len, 0.59 * cable_len),\n True,\n ),\n (\n slice(0.01 * cable_len, 0.09 * cable_len),\n slice(0.91 * cable_len, 0.99 * cable_len),\n True,\n ),\n ]\n\n ds_test = ds.copy(deep=True)\n\n # WLS\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n matching_sections=matching_sections,\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n ds_test = ds.copy(deep=True)\n\n # Test fixing gamma + transient att.\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n fix_gamma=(482.6, 0),\n matching_sections=matching_sections,\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n ds_test = ds.copy(deep=True)\n\n # Test fixing dalpha + transient att.\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n fix_dalpha=(6.46e-05, 0),\n matching_sections=matching_sections,\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n ds_test = ds.copy(deep=True)\n\n # Test fixing gamma & dalpha + transient att.\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n fix_gamma=(482.6, 0),\n fix_dalpha=(6.46e-05, 0),\n matching_sections=matching_sections,\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n # Test conf. ints. for the combination of everything\n ds_test.conf_int_single_ended(\n p_val=\"p_val\",\n p_cov=\"p_cov\",\n st_var=1.0,\n ast_var=1.0,\n conf_ints=[2.5, 50.0, 97.5],\n mc_sample_size=50,\n )\n\n ds_test_1 = ds_test.isel(time=-1)\n # ds_test_1.tmpf\n # ds_test_1.tmpf_mc.isel(CI=0).values\n # ds_test_1.tmpf_mc.isel(CI=2).values\n\n assert np.all(\n np.less(ds_test_1.tmpf_mc.isel(CI=0).values, ds_test_1.tmpf)\n ), \"Single-ended, trans. att.; 2.5% confidence interval is incorrect\"\n\n assert np.all(\n np.greater(ds_test_1.tmpf_mc.isel(CI=2).values, ds_test_1.tmpf)\n ), \"Single-ended, trans. att.; 97.5% confidence interval is incorrect\"","def time_record(record, sampling_rate, detector, n_runs):\n start = timer()\n for _ in range(n_runs):\n detector(record, sampling_rate=sampling_rate)\n end = timer()\n avg_time = (end - start) / n_runs * 1000\n\n return avg_time","def testData(nTimes = 1000, taus = [1.0e-6, 1.0e-4, 5.0e-3], amps = [0.3, 0.3, 0.4], sigma = 0.05, linear=False):\n\n if linear:\n Times = rangeLin(1.0e-7, 1.0e-3, nTimes) # in seconds\n else:\n Times = rangeLog(1.0e-9, 1.0, nTimes) # in seconds\n\n Data = np.zeros( Times.shape )\n for i in range(len(taus)):\n Data += amps[i]*np.exp(-1.*Times/taus[i])\n Data += sigma*np.random.randn( len(Data) )\n\n return Times, Data","def job_gen(self, time_frame):","def get_datapoints(self, rid, t0, t1, nmax = 300):\n self.read_curs.execute(\"SELECT COUNT(*) FROM readings WHERE readout_id = ? AND time >= ? AND time <= ?\", (int(rid), t0, t1))\n if self.read_curs.fetchone()[0] > nmax:\n self.read_curs.execute(\"SELECT avg(time),avg(value) FROM readings WHERE readout_id = ? AND time >= ? AND time <= ? GROUP BY round(time/?) ORDER BY time DESC\", (int(rid), t0, t1, (t1-t0)/nmax));\n else:\n self.read_curs.execute(\"SELECT time,value FROM readings WHERE readout_id = ? AND time >= ? AND time <= ? ORDER BY time DESC\", (int(rid), t0, t1))\n return self.read_curs.fetchall()","def get_accumulated_data(self, topic, start_time, end_time, units):\n ignore_start_time = self._get_value('ignore_start_time', topic)\n ignore_end_time = self._get_value('ignore_end_time', topic)\n adjust_start_time = self._get_value('adjust_start_time', topic)\n adjust_end_time = self._get_value('adjust_end_time', topic)\n\n if ignore_start_time:\n self.logger.debug(\"Service ignoring start time.\")\n start_ts = self.peek_datetime(topic) - adjust_start_time\n else:\n start_ts = start_time - adjust_start_time\n\n if ignore_end_time:\n self.logger.debug(\"Service ignoring end time.\")\n end_ts = self.peek_last_datetime(topic) + adjust_end_time\n else:\n end_ts = end_time + adjust_end_time\n\n self.logger.debug(\"Service processing interval: %f %f\" %(start_ts, end_ts))\n accumulator = weewx.accum.Accum(weeutil.weeutil.TimeSpan(start_ts, end_ts))\n\n for data in self.get_data(topic, end_ts):\n if data:\n try:\n self.logger.debug(\"Service data to accumulate: %s %s\"\n % (weeutil.weeutil.timestamp_to_string(data['dateTime']), to_sorted_string(data)))\n accumulator.addRecord(data)\n except weewx.accum.OutOfSpan:\n self.logger.info(\"Service ignoring record outside of interval %f %f %f %s\"\n %(start_ts, end_ts, data['dateTime'], (to_sorted_string(data))))\n else:\n break\n\n target_data = {}\n if not accumulator.isEmpty:\n aggregate_data = accumulator.getRecord()\n self.logger.debug(\"Service data prior to conversion is: %s %s\"\n % (weeutil.weeutil.timestamp_to_string(aggregate_data['dateTime']), to_sorted_string(aggregate_data)))\n target_data = weewx.units.to_std_system(aggregate_data, units)\n self.logger.debug(\"Service data after to conversion is: %s %s\"\n % (weeutil.weeutil.timestamp_to_string(target_data['dateTime']), to_sorted_string(target_data)))\n else:\n self.logger.debug(\"Dervice queue was empty\")\n\n # Force dateTime to packet's datetime so that the packet datetime is not updated to the MQTT datetime\n if ignore_end_time:\n target_data['dateTime'] = end_time\n\n return target_data","def test_aggregate(self):\n\n #10 Minute sampleing\n result = export.processExport(houseId=1,\n aggregate=\"10Min\")\n\n self.assertEqual(result.shape, (1440, 2))\n #And the second sample should be 10 minutes in\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 10, 00))\n\n #1/2 hourly\n result = export.processExport(houseId=1,\n aggregate=\"30Min\")\n\n #2 * 24 * 10 = 480\n self.assertEqual(result.shape, (480, 2))\n #And the second sample should be 10 minutes in\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 30, 00))\n\n #Hourly\n result = export.processExport(houseId=1,\n aggregate=\"1H\")\n\n self.assertEqual(result.shape, (240, 2))\n #And the second sample should be 10 minutes in\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 1, 00, 00))\n\n\n #daily\n result = export.processExport(houseId=1,\n aggregate=\"1D\")\n\n self.assertEqual(result.shape, (10, 2))\n #And the second sample should be 10 minutes in\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 02, 0, 00, 00))","def polling_experiment_multiple(dataset_id, times, **kwargs):\n total_yeahs = 0\n total_noes = 0\n for i in range(0, times):\n yeah, noes = polling_experiment(dataset_id, **kwargs)\n total_yeahs += yeah\n total_noes += noes\n logging.info(\"After \"+str(times)+\" \"+ str(dataset_id) + \" experiments:\")\n logging.info(\"\"+ str(total_yeahs) + \" correct predictions\")\n logging.info(\"\"+ str(total_noes) + \" wrong predictions\")\n logging.info(\"\"+ str(total_yeahs/(total_yeahs + total_noes)) + \" accuracy\")","def test_dejitter_timestamps():\n n_steps = 100\n n_tests = 50\n sfreqs = np.linspace(1, 5000, n_tests).astype(int)\n last_times = np.random.randint(-100, 100, size=n_tests)\n test_timestamps = np.random.random((n_tests, n_steps)) + np.arange(n_steps)\n expected_timestamps = [np.arange(n_steps)/sfreq + last_times[i] + 1/sfreq\n for i, sfreq in enumerate(sfreqs)]\n for i, args in enumerate(zip(test_timestamps, sfreqs, last_times)):\n dejittered = acquire.dejitter_timestamps(*args)\n # there may be some floating-point errors, so just make sure the\n # difference is tiny\n assert np.all((dejittered - expected_timestamps[i]) < 1e-14)","def data_point(inputs: list):\n \n opv = '1'\n \n sample_id = 0\n \n timenow = strftime(\"%#m/%#d/%Y %#H:%M\")\n volts = inputs[0]\n current = inputs[1]\n power = inputs[2]\n \n data_point = [opv, sample_id, timenow, volts, current, power]\n\n if data_point == True:\n sample_id += 1\n \n return data_point","def run_resample(self):\n\n self.in_data.open()\n self.out_data.open()\n\n try:\n # Get the fields from the input file and set them/write headers in output:\n self.all_fields = self.in_data.fields\n\n self.out_data.set_fields(self.all_fields)\n self.out_data.write_headers()\n\n # Set up the sensor fields by removing non-sensor fields:\n self.set_sensor_only_fields()\n\n # Read the first event from the input file:\n self.get_next_input_event()\n\n # Warn and exit if we have no input data to read:\n if self.next_input_event is None:\n msg = f\"The input file {self.in_file} did not have any data rows\"\n warn(msg)\n\n return\n\n self.first_event_stamp = self.next_input_event[self.stamp_field]\n\n # Set up the sample tracking (here mostly to set the start of the first interval):\n self.reset_sample_tracking()\n\n # Now iterate through the output intervals:\n while True:\n self.process_next_interval()\n except EOFError: # catch when we are at the end of the file\n pass\n finally:\n self.in_data.close()\n self.out_data.close()\n\n print() # make sure we go to a new output line","def filter(self, newer_than=None):\r\n if newer_than is None:\r\n newer_than = self._clock.time() - self._window.as_(Time.SECONDS)\r\n self._samples = [sample for sample in self._samples if sample[0] >= newer_than]","def update_data():\n values = temp_serial_placeholder()\n time = current_time_milli() - __start\n points = [ [time, values[0]], [time, values[1]] ]\n __data.append(points)\n return points","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def test_stream_loop(self):\n chans, gains, scans, rate = (10,10,10,10), (1,2,4,5), 1024, 500\n v = [v[0] for v in self.l.stream_sync(\n channels=chans, gains=gains,\n num_scans=scans, rate=rate)]\n for vi in v:\n for r in vi:\n self.assertTrue(abs(r-2.5) < .1,\n \"%s should be cal, 2.5v\" % vi[0])","def get_peaks(\n self,\n new_sample: np.ndarray,\n threshold: float,\n min_peaks_interval=None,\n ) -> tuple:\n tic = time.time()\n nb_peaks = []\n if len(new_sample.shape) == 1:\n new_sample = np.expand_dims(new_sample, 0)\n sample_proc = np.copy(new_sample)\n if not self._is_one:\n self._is_one = [False] * new_sample.shape[0]\n\n for i in range(new_sample.shape[0]):\n for j in range(new_sample.shape[1]):\n if new_sample[i, j] < threshold:\n sample_proc[i, j] = 0\n self._is_one[i] = False\n elif new_sample[i, j] >= threshold:\n if not self._is_one[i]:\n sample_proc[i, j] = 1\n self._is_one[i] = True\n else:\n sample_proc[i, j] = 0\n\n if len(self.raw_data_buffer) == 0:\n self.raw_data_buffer = new_sample\n self.processed_data_buffer = sample_proc\n nb_peaks = None\n\n elif self.raw_data_buffer.shape[1] < self.processing_window:\n self.raw_data_buffer = np.append(self.raw_data_buffer, new_sample, axis=1)\n self.processed_data_buffer = np.append(self.processed_data_buffer, sample_proc, axis=1)\n nb_peaks = None\n\n else:\n self.raw_data_buffer = np.append(self.raw_data_buffer[:, new_sample.shape[1] :], new_sample, axis=1)\n self.processed_data_buffer = np.append(\n self.processed_data_buffer[:, new_sample.shape[1] :], sample_proc, axis=1\n )\n\n if min_peaks_interval:\n self.processed_data_buffer = RealTimeProcessing._check_and_adjust_interval(\n self.processed_data_buffer, min_peaks_interval\n )\n if isinstance(nb_peaks, list):\n for i in range(self.processed_data_buffer.shape[0]):\n nb_peaks.append(np.count_nonzero(self.processed_data_buffer[i, :]))\n self.process_time.append(time.time() - tic)\n return nb_peaks, self.processed_data_buffer","def test_TimeSeries():\r\n\r\n #Test initialization with duration:\r\n tseries1 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], duration=10)\r\n tseries2 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], sampling_interval=1)\r\n npt.assert_equal(tseries1.time, tseries2.time)\r\n\r\n #downsampling:\r\n t1 = ts.UniformTime(length=8, sampling_rate=2)\r\n #duration is the same, but we're downsampling to 1Hz\r\n tseries1 = ts.TimeSeries(data=[1, 2, 3, 4], time=t1, sampling_rate=1)\r\n #If you didn't explicitely provide the rate you want to downsample to, that\r\n #is an error:\r\n npt.assert_raises(ValueError, ts.TimeSeries, dict(data=[1, 2, 3, 4],\r\n time=t1))\r\n\r\n tseries2 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_rate=1)\r\n tseries3 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_rate=1000,\r\n time_unit='ms')\r\n #you can specify the sampling_rate or the sampling_interval, to the same\r\n #effect, where specificying the sampling_interval is in the units of that\r\n #time-series:\r\n tseries4 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_interval=1,\r\n time_unit='ms')\r\n npt.assert_equal(tseries4.time, tseries3.time)\r\n\r\n #The units you use shouldn't matter - time is time:\r\n tseries6 = ts.TimeSeries(data=[1, 2, 3, 4],\r\n sampling_interval=0.001,\r\n time_unit='s')\r\n npt.assert_equal(tseries6.time, tseries3.time)\r\n\r\n #And this too - perverse, but should be possible:\r\n tseries5 = ts.TimeSeries(data=[1, 2, 3, 4],\r\n sampling_interval=ts.TimeArray(0.001,\r\n time_unit='s'),\r\n time_unit='ms')\r\n\r\n npt.assert_equal(tseries5.time, tseries3.time)\r\n\r\n #initializing with a UniformTime object:\r\n t = ts.UniformTime(length=3, sampling_rate=3)\r\n\r\n data = [1, 2, 3]\r\n\r\n tseries7 = ts.TimeSeries(data=data, time=t)\r\n\r\n npt.assert_equal(tseries7.data, data)\r\n\r\n data = [1, 2, 3, 4]\r\n #If the data is not the right length, that should throw an error:\r\n npt.assert_raises(ValueError,\r\n ts.TimeSeries, dict(data=data, time=t))\r\n\r\n # test basic arithmetics wiht TimeSeries\r\n tseries1 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], sampling_rate=1)\r\n tseries2 = tseries1 + 1\r\n npt.assert_equal(tseries1.data + 1, tseries2.data)\r\n npt.assert_equal(tseries1.time, tseries2.time)\r\n tseries2 -= 1\r\n npt.assert_equal(tseries1.data, tseries2.data)\r\n npt.assert_equal(tseries1.time, tseries2.time)\r\n tseries2 = tseries1 * 2\r\n npt.assert_equal(tseries1.data * 2, tseries2.data)\r\n npt.assert_equal(tseries1.time, tseries2.time)\r\n tseries2 /= 2\r\n npt.assert_equal(tseries1.data, tseries2.data)\r\n npt.assert_equal(tseries1.time, tseries2.time)","def process(self,timeStamp,values,queueNo):\n\t\tdatain = values[0]\n\t\tcurValue = float(datain)\n\t\tif self.isBaselineRunning:\n\t\t\tself.testMinMax(curValue)\n\t\t\n\t\tif self.hasBaselineEnded:\n\t\t\tscaledValue = self.scale(curValue)\n\t\t\t#print scaledValue\n\t\t\tself.addProcessedValues(scaledValue)","def AddSample(self, machine, timestamp, value):\n self.machine_data.setdefault(machine, list()).append([timestamp, value])\n if len(self.cluster_total) == 0 or timestamp > self.cluster_total[-1][0]:\n self.cluster_total.append([timestamp, 0])\n self.cluster_avg.append([timestamp, 0])\n self.cluster_total[-1][1] += value\n self.cluster_avg[-1][1] = self.cluster_total[-1][1] / float(len(self.machine_data))","def get_preprocessed_from_raw(sess_no, raw_path, align_on, from_time, to_time) :\n \n #params\n sess = '01'\n \n trial_length = abs(from_time - to_time)\n\n # Paths\n #raw_path = base_path + 'data/raw/' + sess_no + '/session' + sess + '/'\n rinfo_path = raw_path + 'recording_info.mat'\n tinfo_path = raw_path + 'trial_info.mat'\n\n # Define and loop over intervals\n \n srate = io.get_sfreq(rinfo_path) # = 1 000\n n_trials = io.get_number_of_trials(tinfo_path) \n last_trial = int(max(io.get_trial_ids(raw_path)))\n n_chans = io.get_number_of_channels(rinfo_path)\n channels = [ch for ch in range(n_chans)]\n\n # Pre-process data\n filtered = np.empty([n_trials,\n len(channels),\n int(trial_length * srate/1000)])\n\n trial_counter = 0; counter = 0\n while trial_counter < last_trial:\n n_zeros = 4-len(str(trial_counter+1))\n trial_str = '0' * n_zeros + str(trial_counter+1) # fills leading 0s\n if sess == '01' :\n file_in = sess_no + '01.' + trial_str + '.mat'\n else :\n file_in = sess_no + '02.' + trial_str + '.mat'\n \n if align_on == 'sample' : \n onset = io.get_sample_on(tinfo_path)[trial_counter].item()\n elif align_on == 'match' :\n onset = io.get_match_on(tinfo_path)[trial_counter].item()\n else :\n print(\"Petit problème avec align_on : 'sample' ou 'match' \")\n \n\n \n if np.isnan(onset): # drop trials for which there is no onset info\n print('No onset for ' + file_in)\n trial_counter += 1\n if trial_counter == last_trial:\n break\n else:\n counter += 1\n continue\n print(file_in)\n try:\n raw = io.get_data(raw_path + file_in)\n temp = pp.strip_data(raw,\n rinfo_path,\n onset,\n start=from_time,\n length=trial_length)\n \n if temp.shape[1] == trial_length: # drop trials shorter than length\n filtered[counter] = temp\n counter += 1\n except IOError:\n print('No file ' + file_in)\n trial_counter += 1\n\n # Return data\n\n filtered = np.array(filtered)\n return(filtered)","def calc_observables(samples):\n n = samples.shape[1]\n obs = np.zeros((samples.shape[0], n+n*(n-1)//2))\n \n k = 0\n for i in range(n):\n obs[:,i] = samples[:,i]\n for j in range(i+1,n):\n obs[:,n+k] = samples[:,i] * samples[:,j]\n k += 1\n return obs","def _process_data_sorted(self):\n n, t, trials = self.raw_data.shape[0], self.raw_data.shape[1], self.raw_data.shape[2]\n if self.PD:\n if self.use_silent_channels:\n clean_channels, red_n = self.raw_data, n\n _, _, invalid_ch = self._discard_channels(self.raw_data)\n else:\n clean_channels, red_n, invalid_ch = self._discard_channels(self.raw_data)\n\n session_onmeds = clean_channels[:, :, :, :, self.pd_ses_order[self.i_sub][0]]\n session_offmeds = clean_channels[:, :, :, :, self.pd_ses_order[self.i_sub][1]]\n subsets = [session_onmeds, session_offmeds]\n else:\n if self.use_silent_channels:\n clean_channels, red_n = self.raw_data, n\n _, _, invalid_ch = self._discard_channels(self.raw_data)\n else:\n clean_channels, red_n, invalid_ch = self._discard_channels(self.raw_data)\n session1 = clean_channels[:, :, :, 0::2]\n session2 = clean_channels[:, :, :, 1::2]\n subsets = [session1, session2]\n\n ts = np.empty(shape=(0, 2, self.N_MOTIV, trials, t, red_n))\n for ds in subsets:\n # session(1/2 or on/off) - rg1/rg2 - motiv - trial - sec - signal\n ts_tmp_new = np.zeros((2, self.N_MOTIV, trials, t, red_n))\n\n # rg1:\n ts_tmp_new[0, :, :, :] = np.array(\n (ds[:, :, :, 0].T, ds[:, :, :, 1].T, ds[:, :, :, 2].T)\n )\n # rg2:\n ts_tmp_new[1, :, :, :] = np.array(\n (ds[:, :, :, 3].T, ds[:, :, :, 4].T, ds[:, :, :, 5].T)\n )\n\n ts = np.concatenate((ts, np.array([ts_tmp_new])))\n\n return ts, invalid_ch","def generate_sample_data(point_numbers, interval):\n src_names = ['test.example.com', 'salmon.example.com', 'www.example.com']\n sources = []\n for name in src_names:\n sources.append(models.Source.objects.get_or_create(name=name)[0])\n sources.append(None)\n metric_names = ['ps.virtual_memory_usage',\n 'ps.disk_io_counters.write_bytes',\n 'ps.disk_io_counters.read_bytes',\n 'ps.disk_usage.percent',\n 'ps.physical_memory_usage.percent']\n\n for source in sources:\n for name in metric_names:\n metric = models.Metric.objects.get_or_create(source=source,\n name=name)[0]\n start = datetime.datetime.now() - datetime.timedelta(\n minutes=interval * point_numbers)\n for i in range(point_numbers):\n metric.latest_value = random.randint(1, 100)\n metric.last_updated = (start +\n datetime.timedelta(minutes=interval * i))\n metric.save()","def get_beat_data(data,columns,seconds='last',starteqend=False,preloadduration=0.3):\r\n if ispanda(data) and isastr(columns[0]):\r\n time=data.as_matrix(['X'])\r\n data=data.as_matrix(columns)\r\n elif isnparray(data) and isanum(columns[0]):\r\n data=np.array(data)\r\n time=data[:,[0]].copy()\r\n data=data[:,columns].copy()\r\n else:\r\n return None\r\n time=time.transpose()[0]\r\n if str(seconds)=='last':\r\n seconds=[time[-1]-1,time[-1]]\r\n elif isanum(seconds):\r\n if len(np.array([seconds]))>1:\r\n pass\r\n #now check if we wanted a beat#\r\n elif isinteger(seconds):\r\n pld=preloadduration\r\n seconds=[seconds-1+pld,seconds+pld]\r\n else:\r\n return None\r\n #start row(sr) and end row (er)\r\n sr=np.argmin(abs(time-seconds[0]))\r\n er=np.argmin(abs(time-seconds[-1]))\r\n data=data[sr:er+1]\r\n timeabs=time[sr:er+1].copy()\r\n timerel=timeabs-timeabs[0]\r\n if starteqend:\r\n if timerel[-1]<0.05:\r\n return None\r\n #fifty ms row\r\n fiftymsr=np.argmin(abs(timerel-0.05))\r\n #time as a column again, multiplied horizontally to reach the desired dim\r\n t=np.array([timerel[:fiftymsr+1]]).T \r\n t=np.tile(t,(1,data.shape[1]))\r\n #vf/vi (relationship between final value (vf) and initial value (vi))\r\n vf_vi=np.tile(data[-1]/data[0],(fiftymsr+1,1))\r\n adj_mat=(1-vf_vi)/0.05*t+vf_vi\r\n data[:fiftymsr+1,:]=data[:fiftymsr+1,:]*adj_mat\r\n return data,np.array([timeabs]).T,np.array([timerel]).T","def compare(self):\n samples = self.data[-2:]\n if len(samples) != 2:\n return\n\n timestamp_a, data_a = samples[0]\n timestamp_b, data_b = samples[1]\n LOG.debug(\"%s comparing sample from %s to %s\", self, timestamp_a, timestamp_b)\n changes = dict_compare(data_a, data_b)\n for key in changes:\n OUTPUT.info(\"%s:%s: %s -> %s\", self, key, get_value(data_a, key), get_value(data_b, key))","def get_sampling_updates(self, k=1):\n # perform actual negative phase\n [nh_samples, nv_samples, nv_mean, beta, mixstat, E, labels, \n swapstat, rtime, avg_rtime, nup, ndown], updates = \\\n theano.scan(self.pt_step, \n outputs_info = \\\n [{'initial': None, 'return_steps': 1}, # h1_sample\n {'initial': self.buffer, 'return_steps': 1}, # v1_sample\n {'initial': None, 'return_steps': 1}, # v1_mean \n {'initial': self.beta, 'return_steps': 1}, # beta\n {'initial': self.mixstat, 'return_steps': 1}, # mixstat\n {'initial': None, 'return_steps': 1}, # E\n {'initial': self.labels, 'return_steps': 1}, # labels\n {'initial': self.swapstat, 'return_steps': 1}, # swapstat\n {'initial': self.rtime, 'return_steps': 1}, # rtime\n {'initial': self.avg_rtime,'return_steps': 1}, # avg_rtime\n {'initial': self.nup, 'return_steps': 1}, # nup\n {'initial': self.ndown, 'return_steps': 1}],# ndown\n n_steps = k)\n\n updates = {}\n\n # update particle states\n updates[self._buffer] = T.set_subtensor(self._buffer[:self.n_chain_total], nv_samples)\n updates[self.mf_buffer] = T.set_subtensor(self.mf_buffer[:self.n_chain_total], nv_mean)\n \n # update energy of each particle\n updates[self._E] = T.set_subtensor(self._E[:self.n_chain_total], E)\n\n # update particle<->temperature mappings\n updates[self._mixstat] = T.set_subtensor(self._mixstat[:,:self.n_beta], mixstat)\n updates[self._beta] = T.set_subtensor(self._beta[:self.n_chain], beta)\n\n # updates for beta adaptation\n updates[self.labels] = labels\n updates[self.rtime] = rtime\n updates[self.avg_rtime] = avg_rtime\n updates[self._nup] = T.set_subtensor(self._nup[:self.n_beta], nup)\n updates[self._ndown] = T.set_subtensor(self._ndown[:self.n_beta], ndown)\n\n # updates for chain spawning\n updates[self._swapstat] = T.set_subtensor(self._swapstat[:self.n_beta], swapstat)\n\n return [nh_samples, nv_samples, beta, mixstat, E], updates","def test_single_ended_trans_att_synthetic():\n from dtscalibration import DataStore\n\n cable_len = 100.0\n nt = 50\n nx = 200\n time = np.arange(nt)\n x = np.linspace(0.0, cable_len, nx)\n ts_cold = np.ones(nt) * 4.0\n ts_warm = np.ones(nt) * 20.0\n ts_ambient = np.ones(nt) * 12\n ts_valid = np.ones(nt) * 16\n\n C_p = 15246\n C_m = 2400.0\n dalpha_r = 0.0005284\n dalpha_m = 0.0004961\n dalpha_p = 0.0005607\n gamma = 482.6\n cold_mask1 = np.logical_and(x > 0.125 * cable_len, x < 0.25 * cable_len)\n cold_mask2 = np.logical_and(x > 0.625 * cable_len, x < 0.75 * cable_len)\n warm_mask1 = np.logical_and(x > 0.75 * cable_len, x < 0.875 * cable_len)\n warm_mask2 = np.logical_and(x > 0.25 * cable_len, x < 0.375 * cable_len)\n valid_mask = np.logical_and(x > 0.40 * cable_len, x < 0.50 * cable_len)\n temp_real = np.ones((len(x), nt)) * 12 + 273.15\n temp_real[cold_mask1 + cold_mask2] = ts_cold + 273.15\n temp_real[warm_mask1 + warm_mask2] = ts_warm + 273.15\n temp_real[valid_mask] = ts_valid + 273.15\n\n st = (\n C_p\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_p * x[:, None])\n * np.exp(gamma / temp_real)\n / (np.exp(gamma / temp_real) - 1)\n )\n ast = (\n C_m\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_m * x[:, None])\n / (np.exp(gamma / temp_real) - 1)\n )\n\n # Add attenuation\n tr_att = np.random.rand(nt) * 0.2 + 0.8\n st[int(x.size * 0.4) :] *= tr_att\n tr_att2 = np.random.rand(nt) * 0.2 + 0.8\n st[int(x.size * 0.6) :] *= tr_att2\n\n ds = DataStore(\n {\n \"st\": ([\"x\", \"time\"], st),\n \"ast\": ([\"x\", \"time\"], ast),\n \"userAcquisitionTimeFW\": ([\"time\"], np.ones(nt)),\n \"cold\": ([\"time\"], ts_cold),\n \"warm\": ([\"time\"], ts_warm),\n \"ambient\": ([\"time\"], ts_ambient),\n },\n coords={\"x\": x, \"time\": time},\n attrs={\"isDoubleEnded\": \"0\"},\n )\n\n sections = {\n \"ambient\": [slice(0.52 * cable_len, 0.58 * cable_len)],\n \"cold\": [\n slice(0.125 * cable_len, 0.25 * cable_len),\n slice(0.65 * cable_len, 0.70 * cable_len),\n ],\n \"warm\": [slice(0.25 * cable_len, 0.375 * cable_len)],\n }\n\n ds_test = ds.copy(deep=True)\n\n # WLS\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n # test `trans_att` related functions\n # Clear out old results\n ds_test.set_trans_att([])\n\n assert ds_test.trans_att.size == 0, \"clear out trans_att config\"\n\n del_keys = []\n for k, v in ds_test.data_vars.items():\n if \"trans_att\" in v.dims:\n del_keys.append(k)\n\n assert len(del_keys) == 0, \"clear out trans_att config\"\n\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n ds_test = ds.copy(deep=True)\n\n # Test fixing gamma + transient att.\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n fix_gamma=(482.6, 0),\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )\n\n ds_test = ds.copy(deep=True)\n\n # Test fixing alpha + transient att.\n ds_test.calibration_single_ended(\n sections=sections,\n st_var=1.0,\n ast_var=1.0,\n method=\"wls\",\n fix_dalpha=(6.46e-05, 0),\n trans_att=[40, 60],\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\n )\n assert_almost_equal_verbose(\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\n )","def getUnscaledSamples(self, **kwargs) -> TimeData:\n # initialise chans, startSample and endSample with the whole dataset\n options = self.parseGetDataKeywords(kwargs)\n\n # get the files to read and the samples to take from them, in the correct order\n dataFilesToRead, samplesToRead, scalings = self.getDataFilesForSamples(\n options[\"startSample\"], options[\"endSample\"]\n )\n numSamples = options[\"endSample\"] - options[\"startSample\"] + 1\n # set up the dictionary to hold the data\n data = {}\n for chan in options[\"chans\"]:\n data[chan] = np.zeros(shape=(numSamples), dtype=self.dtype)\n\n # loop through chans and get data\n sampleCounter = 0\n for dFile, sToRead, scalar in zip(dataFilesToRead, samplesToRead, scalings):\n # get samples - this is inclusive\n dSamples = sToRead[1] - sToRead[0] + 1\n # spam files always record 5 channels\n dSamplesRead = dSamples * self.recChannels[dFile]\n # read the data\n byteOff = (\n self.dataByteOffset[dFile]\n + sToRead[0] * self.recChannels[dFile] * self.dataByteSize\n )\n dFilePath = os.path.join(self.dataPath, dFile)\n dataRead = np.memmap(\n dFilePath,\n dtype=self.dtype,\n mode=\"r\",\n offset=byteOff,\n shape=(dSamplesRead),\n )\n # now need to unpack this\n for chan in options[\"chans\"]:\n # check to make sure channel exists\n self.checkChan(chan)\n # get the channel index - the chanIndex should give the right order in the data file\n # as it is the same order as in the header file\n chanIndex = self.chanMap[chan]\n # use the range sampleCounter -> sampleCounter + dSamples, because this actually means sampleCounter + dSamples - 1 as python ranges are not inclusive of the end value\n # scale by the lsb scalar here - note that these can be different for each file in the run\n data[chan][sampleCounter : sampleCounter + dSamples] = (\n dataRead[chanIndex : dSamplesRead : self.recChannels[dFile]]\n * scalar[chan]\n )\n # increment sample counter\n sampleCounter = sampleCounter + dSamples # get ready for the next data read\n\n # return data\n startTime, stopTime = self.sample2time(\n options[\"startSample\"], options[\"endSample\"]\n )\n comments = []\n comments.append(\n \"Unscaled data {} to {} read in from measurement {}, samples {} to {}\".format(\n startTime,\n stopTime,\n self.dataPath,\n options[\"startSample\"],\n options[\"endSample\"],\n )\n )\n comments.append(\"Data read from {} files in total\".format(len(dataFilesToRead)))\n comments.append(\n \"Data scaled to mV for all channels using scalings in header files\"\n )\n comments.append(\"Sampling frequency {}\".format(self.getSampleFreq()))\n return TimeData(\n sampleFreq=self.getSampleFreq(),\n startTime=startTime,\n stopTime=stopTime,\n data=data,\n comments=comments,\n )","def test_callback_multiple_entries(self):\n TestStorage.set_timeout(20)\n TestStorage.set_time(100)\n store = RatedStatisticStorage()\n entity_c = TestStorage._gen_entity(\n \"ram_usage_mean\", [\"20\", \"40\"],\n [chr(Outcome.HIGH), chr(Outcome.LOW)])\n msg = TestStorage._gen_msg(\"n!node\", 100, [entity_c])\n store.callback_rated_statistic(msg)\n\n self.assertEqual(\n store.get_outcome(\"n!node\", \"ram_usage_mean_0\"), Outcome.HIGH)\n self.assertEqual(\n store.get_outcome(\"n!node\", \"ram_usage_mean_1\"), Outcome.LOW)","def minutes_of_new_data(symbol, kline_size, data, source, client):\n if len(data) > 0:\n old = parser.parse(data[\"timestamp\"].iloc[-1])\n elif source == \"binance\":\n old = datetime.strptime('1 Jan 2017', '%d %b %Y')\n elif source == \"bitmex\":\n old = client.Trade.Trade_getBucketed(symbol=symbol, binSize=kline_size, count=1, reverse=False).result()[0][0][\n 'timestamp'] \n if source == \"binance\": new = pd.to_datetime(client.get_klines(symbol=symbol, interval=kline_size)[-1][0],\n unit='ms')\n if source == \"bitmex\": new = \\\n client.Trade.Trade_getBucketed(symbol=symbol, binSize=kline_size, count=1, reverse=True).result()[0][0]['timestamp']\n return old, new","def _compute_observations(self):\n observations = {}\n for ts in self.ts_ids:\n if self.traffic_signals[ts].time_to_act() or self.traffic_signals[ts].regular_obs() :\n observations[ts] = self.traffic_signals[ts]._compute_observation()\n return observations","def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:","def micsample(listentime):\n frames, sampling_rate = record_audio(listentime)\n samples = np.hstack([np.frombuffer(i, np.int16) for i in frames])\n times = np.arange(samples.size) / sampling_rate\n return samples, times","def callback(in_data, frame_count, time_info, status):\n assert frame_count == window\n try:\n samples = outq.get(block=False)\n except queue.Empty:\n print(\"underrun\", file=sys.stderr)\n samples = zeros\n\n # Return samples and continue signal.\n return (samples, pa.paContinue)","def add_running_values(audioSamples, step):\n if (step + 1) * groupSize > 2400:\n print(\"We've reached the end of the run!\")\n return None\n samples = audioSamples[step * groupSize: (step + 1) * groupSize]\n noiseValues = [-1, 0, 1]\n for sample in samples:\n # Add noise to the sample.\n noiseValue = random.choice(noiseValues)\n value = sample.audio + noiseValue\n if value < 0:\n value = 0\n newSample = AudioData(\n value,\n \"W6\",\n 1,\n \"Washing Room\"\n )\n newSample.processedValue = value\n db.session.add(newSample)\n db.session.commit()","def test_double_ended_wls_estimate_synthetic():\n from dtscalibration import DataStore\n\n cable_len = 100.0\n nt = 50\n time = np.arange(nt)\n x = np.linspace(0.0, cable_len, 100)\n ts_cold = np.ones(nt) * 4.0\n ts_warm = np.ones(nt) * 20.0\n\n C_p = 15246\n C_m = 2400.0\n dalpha_r = 0.0005284\n dalpha_m = 0.0004961\n dalpha_p = 0.0005607\n gamma = 482.6\n cold_mask = x < 0.5 * cable_len\n warm_mask = np.invert(cold_mask) # == False\n temp_real = np.ones((len(x), nt))\n temp_real[cold_mask] *= ts_cold + 273.15\n temp_real[warm_mask] *= ts_warm + 273.15\n\n st = (\n C_p\n * np.exp(-(dalpha_r + dalpha_p) * x[:, None])\n * np.exp(gamma / temp_real)\n / (np.exp(gamma / temp_real) - 1)\n )\n ast = (\n C_m\n * np.exp(-(dalpha_r + dalpha_m) * x[:, None])\n / (np.exp(gamma / temp_real) - 1)\n )\n rst = (\n C_p\n * np.exp(-(dalpha_r + dalpha_p) * (-x[:, None] + cable_len))\n * np.exp(gamma / temp_real)\n / (np.exp(gamma / temp_real) - 1)\n )\n rast = (\n C_m\n * np.exp(-(dalpha_r + dalpha_m) * (-x[:, None] + cable_len))\n / (np.exp(gamma / temp_real) - 1)\n )\n\n alpha = np.mean(np.log(rst / rast) - np.log(st / ast), axis=1) / 2\n alpha -= alpha[0] # the first x-index is where to start counting\n\n ds = DataStore(\n {\n \"st\": ([\"x\", \"time\"], st),\n \"ast\": ([\"x\", \"time\"], ast),\n \"rst\": ([\"x\", \"time\"], rst),\n \"rast\": ([\"x\", \"time\"], rast),\n \"userAcquisitionTimeFW\": ([\"time\"], np.ones(nt)),\n \"userAcquisitionTimeBW\": ([\"time\"], np.ones(nt)),\n \"cold\": ([\"time\"], ts_cold),\n \"warm\": ([\"time\"], ts_warm),\n },\n coords={\"x\": x, \"time\": time},\n attrs={\"isDoubleEnded\": \"1\"},\n )\n\n sections = {\n \"cold\": [slice(0.0, 0.4 * cable_len)],\n \"warm\": [slice(0.65 * cable_len, cable_len)],\n }\n\n # WLS\n ds.calibration_double_ended(\n sections=sections,\n st_var=1e-7,\n ast_var=1e-7,\n rst_var=1e-7,\n rast_var=1e-7,\n method=\"wls\",\n solver=\"sparse\",\n )\n\n assert_almost_equal_verbose(ds.gamma.values, gamma, decimal=10)\n assert_almost_equal_verbose(ds.alpha.values, alpha, decimal=8)\n assert_almost_equal_verbose(ds.tmpf.values, temp_real - 273.15, decimal=6)\n assert_almost_equal_verbose(ds.tmpb.values, temp_real - 273.15, decimal=6)\n assert_almost_equal_verbose(ds.tmpw.values, temp_real - 273.15, decimal=6)","def _update(self, datapoints):\r\n if len(datapoints) == 1:\r\n timestamp, value = datapoints[0]\r\n whisper.update(self.path, value, timestamp)\r\n else:\r\n whisper.update_many(self.path, datapoints)","def addSample(self, time, x, y, z):\n\t\tself.numSamples += 1\n\t\tif self.prevTime != None:\n\t\t\tdt = abs(time - self.prevTime)\n\t\t\tself.timeDifferences[dt] += 1\n\t\t\tif dt > self.highDT:\n\t\t\t\tif getTimeDifference(self.rawData[self.currIdx]) >= self.minSampleTime:\n\t\t\t\t\tself.currIdx += 1\n\t\t\t\t\tself.rawData.append(list())\n\t\t\t\telse:\n\t\t\t\t\tself.rawData[self.currIdx] = list()\n\t\t\t\t\n\t\t\telse: \n\t\t\t\tself.rawData[self.currIdx].append(preProcess.resultantAcceleration(time, x, y, z))\n\n\t\tself.prevTime = time","def monitor(data_feeder):\n _total_time = 0.\n _costs = []\n _data_feeder = data_feeder(BATCH_SIZE,\n SEQ_LEN,\n OVERLAP,\n Q_LEVELS,\n Q_ZERO,\n Q_TYPE)\n\n for _seqs, _reset, _mask in _data_feeder:\n _start_time = time.time()\n _cost = test_fn(_seqs, _mask)\n _total_time += time.time() - _start_time\n\n _costs.append(_cost)\n\n return numpy.mean(_costs), _total_time","def two_in_one(obs_file,et,subevent):\r\n \r\n #in this function, the \"original time window\" talked about in the comments\r\n #refers to the start and end times that were input to create the file obs_file,\r\n #which will likely have been created using the database_extraction function\r\n \r\n #opening first output file created by operational_sep_quantities\r\n with open(obs_file, 'r') as o:\r\n out = js.load(o)\r\n \r\n #all events recorded in that output file\r\n ongoing_events = (out['sep_forecast_submission']['triggers'][0]['particle_intensity']\r\n ['ongoing_events'])\r\n \r\n #creating lists for values from each event\r\n end_times = [] \r\n start_times = []\r\n energy_thresholds = []\r\n flux_thresholds = []\r\n out_names = []\r\n \r\n #appending values to lists for each event\r\n for i in range(len(ongoing_events)):\r\n start_times.append(parse(ongoing_events[i]['start_time']))\r\n end_times.append(parse(ongoing_events[i]['end_time']))\r\n energy_thresholds.append(ongoing_events[i]['energy_min'])\r\n flux_thresholds.append(float(ongoing_events[i]['threshold']))\r\n \r\n #checking if there was a second event for each threshold\r\n for i in range(len(end_times)):\r\n end = end_times[i]\r\n #if the end time of an event for any threshold was a day before the last day\r\n #in the original time window given, will check if ONLY THAT THRESHOLD\r\n #had another event after the first one, using the end time of the first\r\n #event of that threshold as the new start time of the event window\r\n if end.date() < et.date():\r\n print('end time to use as new start time: %s' %end)\r\n #figuring out which threshold this end time was for\r\n flux_thresh = int(flux_thresholds[i])\r\n energy_thresh = int(energy_thresholds[i])\r\n print('extracting second event for threshold ' + str(flux_thresh) + ' MeV '\r\n + str(energy_thresh) + ' pfu')\r\n #new start time (2 days in advance bc the database_extraction function\r\n #makes the start time 2 days prior, so will cancel that out)\r\n st = end + timedelta(days=2)\r\n #thresholds in correct format\r\n thresholds = str(energy_thresh) + ',' + str(flux_thresh)\r\n print('thresholds: %s' %thresholds)\r\n #creating observation data for second event for thresholds given\r\n out_names.append(Path(cfg.obs_path) /\r\n database_extraction(st,et,instrument_chosen,subevent,\r\n thresholds = thresholds,\r\n one_thresh = True))\r\n \r\n #returns list of all new files created by this function\r\n return(out_names)","def simulate_queued_data(event_times: typing.Union[list, np.ndarray],\n processing_time: typing.Union[list, np.ndarray],\n num_queues: int = 1) -> np.ndarray:\n num_points = len(event_times)\n thread_available_times = np.ones(num_queues) * event_times[0]\n thread_available_times[0] += processing_time[0]\n\n event_total_times = np.ndarray(num_points)\n\n event_total_times[0] = processing_time[0]\n\n for i in range(1, num_points):\n current_time = event_times[i]\n current_process_time = processing_time[i]\n oldest_thread = thread_available_times.argmin()\n\n # If there is a thread that is empty at the current time, latency and processing time are the same\n if thread_available_times[oldest_thread] < current_time:\n event_total_times[i] = current_process_time\n # Thread will be busy from current time until process finishes\n thread_available_times[oldest_thread] = current_time + current_process_time\n # If there is not a thread empty, the requests latency will be the shortest wait time plus the processing time\n else:\n event_total_times[i] = thread_available_times[oldest_thread] - current_time + current_process_time\n # Thread will continue to be busy this amount of additional time\n thread_available_times[oldest_thread] += current_process_time\n\n return event_total_times","def _copy(source, track, filter_f=lambda x: True, coef=1000):\n for msg in source:\n if filter_f(msg):\n track.append(msg.copy(time=int(msg.time*coef)))","def read_timed_multi(\n adcs: tuple[ADC, ...], bufs: tuple[AnyWritableBuf, ...], timer: Timer, /\n ) -> bool:","def _filterTimes(self):\n print(self.tRange)\n idT = np.where((self.tRange[0] > np.array(self.rawD['Epoch'][:])) & \n (self.tRange[1] < np.array(self.rawD['Epoch'][:])))[0]\n #print(self.rawD['Epoch'][:100])\n print(idT)\n # Filter data\n for key in filter(lambda x: ('Epoch' in x or \n ('Counts' in x and x[-1] == 's')), self.rawD.keys()):\n self.d[key] = self.rawD[key].copy()[idT]\n return","def process_sample_val(self):\n raise NotImplementedError","def pre_process(self):\n t1_start = perf_counter()\n wav_arr_raw = np.array(self.raw_data['spectrum_0'].attrs['wavelengths'])\n self.wavelengths = wav_arr_raw\n self.back_spectra_arr = np.array(self.raw_data['spectrum_0'].attrs['background'])\n\n corr_data = []\n times_proc = []\n\n # extract reference point for 0 seconds\n time_ref = str(self.raw_data['spectrum_0'].attrs['creation_timestamp'])\n\n # spectrometer adds 'b' and quotation marks to timestamps that must be removed\n # some spectra are taken on X.000000s which does not have a .%f component - use try and except\n try:\n time_ref = datetime.strptime((time_ref.replace('b','')).replace('\\'',''),\"%Y-%m-%dT%H:%M:%S.%f\")\n except ValueError:\n time_ref = datetime.strptime((time_ref.replace('b','')).replace('\\'',''),\"%Y-%m-%dT%H:%M:%S\")\n\n print('Measurement was started at {}, \\n normalising times and applying a background correction \\n'.format(time_ref))\n\n # applies background correction\n for counter, spectra in enumerate(self.raw_data.keys()):\n corr_data.append(self.raw_data[spectra]-self.back_spectra_arr)\n time = str(self.raw_data[spectra].attrs['creation_timestamp'])\n try:\n time = datetime.strptime((time.replace('b','')).replace('\\'',''),\"%Y-%m-%dT%H:%M:%S.%f\")\n except ValueError:\n time = datetime.strptime((time.replace('b','')).replace('\\'',''),\"%Y-%m-%dT%H:%M:%S\")\n deltatime = time - time_ref\n times_proc.append(deltatime.total_seconds())\n\n self.times = np.array(times_proc)\n print('Measurement contains {} spectra with {} wavelengths \\n'.format(len(self.times),len(self.wavelengths)))\n\n # data is stored as a pd Dataframe with elapsed times as indices and wavelengths as columns\n pre_proc_data = pd.DataFrame(corr_data, index = self.times, columns = self.wavelengths)\n\n # data may be disordered in time when iterated through\n # sort the data by elapsed time\n self.pre_proc_data = pre_proc_data.sort_index(axis=0)\n self.times = np.sort(self.times)\n\n t1_stop = perf_counter()\n print(\"Elapsed time for pre-processing:\", t1_stop-t1_start)\n\n return self.pre_proc_data","def test_make_sampled_format(self):\n for num_inputs in [1, 3]:\n for num_outputs in [1, 2, 4]:\n for num_time_steps in [4, 10, 12]:\n # Generate data\n # P=2 format [0, 1, 2, 3, ...]\n sample_interval = 2\n dt_system = np.random.random()\n dt_sample = sample_interval * dt_system\n outputs = np.random.random(\n (num_time_steps, num_outputs, num_inputs))\n time_steps = make_time_steps(\n num_time_steps, sample_interval)\n time_values = time_steps * dt_system\n\n # Compute using modred\n my_ERA = era.ERA()\n time_steps_computed, outputs_computed =\\\n era.make_sampled_format(time_values, outputs)\n #self.assertEqual(dt_system_computed, dt_system)\n\n # Reference values\n num_time_steps_true = (num_time_steps - 1) * 2\n time_steps_true = make_time_steps(num_time_steps_true, 1)\n outputs_true = np.zeros(\n (num_time_steps_true, num_outputs, num_inputs))\n outputs_true[::2] = outputs[:-1]\n outputs_true[1::2] = outputs[1:]\n\n # Compare values\n np.testing.assert_equal(\n time_steps_computed, time_steps_true)\n np.testing.assert_equal(outputs_computed, outputs_true)\n\n # Test that if there is a wrong time value, get an error\n time_values[num_time_steps // 2] = -1\n self.assertRaises(\n ValueError, era.make_sampled_format, time_values,\n outputs)","def convEpochFeats(cur,uid,timestamp):\n\n\thour = 3600\n\ttotalConvTime = []\n\ttotalConvs = []\n\tfor i in range(1,8):\n\t\ths_timestamp = timestamp-86400+(i-1)*hour3\n\t\the_timestamp = timestamp-86400+i*hour3\n\t\t# Determining if start/end time of given hour is in the night\n\t\t# If yes, proceed with feature calculation, if not skip\n\t\ts_epoch = epochCalc(hs_timestamp)\n\t\te_epoch = epochCalc(he_timestamp)\n\t\n\t\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\n\t\t\tcur.execute('SELECT * FROM {0} WHERE start_timestamp >= {1} AND end_timestamp<= {2}'\n\t\t\t\t.format(uid+'con',timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\n\n\t\t\trecords = cur.fetchall()\n\t\t\t# Sum over the duration of all conversation in that hour\n\t\t\ttotalConvTime.append( sum([(records[i][1]-records[i][0]) for i in range(0,len(records))]))\n\t\t\t# Count their total number\n\t\t\ttotalConvs.append(len(records))\n\t# Concatenate to one row before returning\n\treturn(np.hstack((totalConvTime,totalConvs)))","def interval_multivariate(inputspikes, outputspikes, samples=1):\n times = []\n krdists = []\n for prv, nxt in zip(outputspikes[:-1], outputspikes[1:]):\n krd = multivariate(inputspikes, prv, nxt, samples)\n times.append(krd[0])\n krdists.append(krd[1])\n return times, krdists","def get_rate(timestamps):\n return (timestamps[1, 1] - timestamps[0, 1]) / (timestamps[1, 0])","def variable_time_collate_fn(batch, args, device = torch.device(\"cpu\"), data_type = \"train\", \n\tdata_min = None, data_max = None):\n\tD = batch[0][2].shape[1]\n\tcombined_tt, inverse_indices = torch.unique(torch.cat([ex[1] for ex in batch]), sorted=True, return_inverse=True)\n\tcombined_tt = combined_tt.to(device)\n\n\toffset = 0\n\tcombined_vals = torch.zeros([len(batch), len(combined_tt), D]).to(device)\n\tcombined_mask = torch.zeros([len(batch), len(combined_tt), D]).to(device)\n\t\n\tcombined_labels = None\n\tN_labels = 1\n\n\tcombined_labels = torch.zeros(len(batch), N_labels) + torch.tensor(float('nan'))\n\tcombined_labels = combined_labels.to(device = device)\n\t\n\tfor b, (record_id, tt, vals, mask, labels) in enumerate(batch):\n\t\ttt = tt.to(device)\n\t\tvals = vals.to(device)\n\t\tmask = mask.to(device)\n\t\tif labels is not None:\n\t\t\tlabels = labels.to(device)\n\n\t\tindices = inverse_indices[offset:offset + len(tt)]\n\t\toffset += len(tt)\n\n\t\tcombined_vals[b, indices] = vals\n\t\tcombined_mask[b, indices] = mask\n\n\t\tif labels is not None:\n\t\t\tcombined_labels[b] = labels\n\n\tcombined_vals, _, _ = utils.normalize_masked_data(combined_vals, combined_mask, \n\t\tatt_min = data_min, att_max = data_max)\n\n\tif torch.max(combined_tt) != 0.:\n\t\tcombined_tt = combined_tt / torch.max(combined_tt)\n\t\t\n\tdata_dict = {\n\t\t\"data\": combined_vals, \n\t\t\"time_steps\": combined_tt,\n\t\t\"mask\": combined_mask,\n\t\t\"labels\": combined_labels}\n\n\tdata_dict = utils.split_and_subsample_batch(data_dict, args, data_type = data_type)\n\treturn data_dict","def get_next_sample(self):","def test_1d_time():\n \n dic,data = ng.pipe.read(\"common_data/1d_pipe/test.fid\")\n assert data.shape == (1500,)\n assert data.dtype == 'complex64'\n assert round(data[0].real,2) == 91899.24\n assert round(data[0].imag,2) == 1964.70\n assert round(data[1].real,2) == 168844.25\n assert round(data[1].imag,2) == 49503.41\n write_readback(dic,data)","def calc_observables(samples):\n n = samples.shape[1]\n obs = np.zeros((samples.shape[0],n*(n-1)//2))\n \n k = 0\n for i in range(n):\n for j in range(i+1,n):\n obs[:,k] = samples[:,i]*samples[:,j]\n k += 1\n return obs","def analyze2(ys, freqs, ts):","def activityEpochFeats(cur,uid,timestamp):\n\tstatToMovingRatio = []\n\tvar_stats = []\n\tstd_stats = []\n\tuidS = uid +'act'\n\tfor i in range(1,8):\n\t\ths_timestamp = timestamp-86400+(i-1)*hour3\n\t\the_timestamp = timestamp-86400+i*hour3\n\t\t# Determining if start/end time of given hour is in the night\n\t\t# If yes, proceed with feature calculation, if not skip\n\t\ts_epoch = epochCalc(hs_timestamp)\n\t\te_epoch = epochCalc(he_timestamp)\n\n\t\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\n\t\t\t# Retrieving data in hour intervals spanning over one day\n\t\t\tcur.execute('SELECT activity FROM {0} WHERE time_stamp >= {1} AND time_stamp<= {2}'\n\t\t\t\t.format(uidS,timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\n\t\t\trecords = cur.fetchall()\n\n\t\t\tvar_stats.append(np.var(records))\n\t\t\tstd_stats.append(np.std(records))\n\n\t\t\t# Calculating number of stationary and walking/running occurences\n\t\t\tstationary = len([item for item in records if item==0])\n\t\t\tmoving = len([item for item in records if item==1 or item==2])\n\n\t\t\tif moving>0:\n\t\t\t\tstatToMovingRatio.append(float(stationary) / moving)\n\t\t\telse:\n\t\t\t\tstatToMovingRatio.append(0)\n\treturn(np.nan_to_num(np.hstack((statToMovingRatio,var_stats,std_stats))))","def raw_data(\n n: int = 8, limit: int = 1000, arrival_function: ArrivalF = arrival1\n) -> Counter[int]:\n data = samples(limit, arrival_function(n))\n wait_times = collections.Counter(coupon_collector(n, data))\n return wait_times","def samples_keep(self,index):\n\n\t\tif isinstance(index, (int, long)): index = range(self.samples)[-index:]\n\n\t\tself.sampled_topics = np.take(self.sampled_topics,index,axis=0)\n\t\tself.tt = np.take(self.tt,index,axis=2)\n\t\tself.dt = np.take(self.dt,index,axis=2)\n\n\t\tself.samples = len(index)","def test_single_ended_wls_estimate_synthetic():\n\n from dtscalibration import DataStore\n\n cable_len = 100.0\n nt = 50\n time = np.arange(nt)\n x = np.linspace(0.0, cable_len, 500)\n ts_cold = np.ones(nt) * 4.0\n ts_warm = np.ones(nt) * 20.0\n\n C_p = 15246\n C_m = 2400.0\n dalpha_r = 0.0005284\n dalpha_m = 0.0004961\n dalpha_p = 0.0005607\n gamma = 482.6\n cold_mask = x < 0.5 * cable_len\n warm_mask = np.invert(cold_mask) # == False\n temp_real = np.ones((len(x), nt))\n temp_real[cold_mask] *= ts_cold + 273.15\n temp_real[warm_mask] *= ts_warm + 273.15\n\n st = (\n C_p\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_p * x[:, None])\n * np.exp(gamma / temp_real)\n / (np.exp(gamma / temp_real) - 1)\n )\n ast = (\n C_m\n * np.exp(-dalpha_r * x[:, None])\n * np.exp(-dalpha_m * x[:, None])\n / (np.exp(gamma / temp_real) - 1)\n )\n\n print(\"alphaint\", cable_len * (dalpha_p - dalpha_m))\n print(\"alpha\", dalpha_p - dalpha_m)\n print(\"C\", np.log(C_p / C_m))\n print(\"x0\", x.max())\n\n ds = DataStore(\n {\n \"st\": ([\"x\", \"time\"], st),\n \"ast\": ([\"x\", \"time\"], ast),\n \"userAcquisitionTimeFW\": ([\"time\"], np.ones(nt)),\n \"cold\": ([\"time\"], ts_cold),\n \"warm\": ([\"time\"], ts_warm),\n },\n coords={\"x\": x, \"time\": time},\n attrs={\"isDoubleEnded\": \"0\"},\n )\n\n sections = {\n \"cold\": [slice(0.0, 0.5 * cable_len)],\n \"warm\": [slice(0.5 * cable_len, cable_len)],\n }\n\n # WLS\n ds.calibration_single_ended(\n sections=sections, st_var=1.0, ast_var=1.0, method=\"wls\", solver=\"sparse\"\n )\n\n assert_almost_equal_verbose(ds.gamma.values, gamma, decimal=6)\n assert_almost_equal_verbose(ds.dalpha.values, dalpha_p - dalpha_m, decimal=8)\n assert_almost_equal_verbose(ds.tmpf.values, temp_real - 273.15, decimal=4)\n\n pass","def sample_next(self, time, samples, errors):\n if self.previous_value is None:\n output = self.start_value\n else:\n time_diff = time - self.previous_time\n noise = np.random.normal(loc=0.0, scale=1.0, size=1)\n output = (np.power(self.ar_param, time_diff))*self.previous_value+\\\n self.sigma*np.sqrt(1-np.power(self.ar_param, time_diff))*noise\n self.previous_time = time\n self.previous_value = output\n return output","def getData(dig, pipe, event, pulses):\n logging.info(\"Started getData\")\n start_time = time.time()\n for pulse in range(pulses):\n samples = dig.get_data_raw()\n# logging.info(\"GetData retrieved: %d\", len(samples))\n pipe.put(samples)\n end_time = time.time()\n elapsed = end_time - start_time\n samplesProcessed = (pulses * len(samples[0]) * len(samples))\n logging.info(\"getData processed %d Msamples in %.3f s\",\n samplesProcessed / 1e6,\n elapsed)\n logging.info(\"getData rate: %.3f Msa/s in lumps of %d samples\",\n samplesProcessed / elapsed / 1e6,\n dig.pointsPerCycle)","def spike_count(spikeTime, start, stop, dt):\n\n\n #Spike time turned into a numpy array\n spikeTime = np.array(spikeTime)\n # print('Spike Times: ', spikeTime)\n\n #Creat interval array - intervals in which to break up the time array - sub time interval array\n duration = stop-start #Total run time\n n = duration/dt #How many subintervals from time horizon results from user defined interval\n splitInterval = np.linspace(0, duration, n+1) #create numpy array of subinterval over which to count spikes\n # print ('split interval: ', splitInterval)\n\n ##Find length over which to iterate in for loop\n length_splitInt = len(splitInterval)\n # print('length splitInterval: ', length_splitInt)\n length_time = len(spikeTime)\n # print('length time: ', length_time)\n length = length_splitInt + ((length_time) - 2)\n # print('length :', length)\n\n i=0 #inex for time array\n j=0 #index for splitInterval array.\n k=0 #index for new matrix that will store the grouped values from the split time array\n counter = 0 #counter variable to keep track of spike count for each subinterval through loop\n SpikeCount = [] #Initialize array to collect the number of spikes occuring wihtin each subinterval\n\n for i in range(length):\n if (i == 0) and (spikeTime[0] == splitInterval[0]):\n counter += 1\n i += 1\n\n # Spot check\n # print('if counter: ', counter)\n # print('time element: ', spikeTime[k])\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\n # print('i: ', i)\n # print('if k: ', k)\n\n if k < (len(spikeTime) - 1):\n k += 1\n\n # Spot check\n # print('iff k: ', k)\n # print('iff counter: ', counter)\n\n else:\n j += 1\n\n # Spot check\n # print('iff counter: ', counter)\n # print(SpikeCount)\n # print('iff j: ', j)\n\n elif (spikeTime[k] > splitInterval[j]) and (spikeTime[k] <= splitInterval[j + 1]):\n counter += 1\n i += 1\n\n # Spot check\n # print('if counter: ', counter)\n # print('time element: ', spikeTime[k])\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\n # print('i: ', i)\n # print('if k: ', k)\n\n if k < (len(spikeTime) - 1):\n k += 1\n\n # Spot check\n # print('iff k: ', k)\n # print('iff counter: ', counter)\n\n else:\n j += 1\n # Spot check\n SpikeCount.append(counter)\n # print('iff counter: ', counter)\n # print(SpikeCount)\n # print('iff j: ', j)\n\n\n\n else:\n SpikeCount.append(counter)\n counter = 0\n j += 1\n i += 1\n\n # Spot Check\n # print('else counter: ', counter)\n # print(SpikeCount)\n # print('time element: ', spikeTime[k])\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\n # print('else j: ', j)\n # print('else i: ', i)\n # print('else k: ', k)\n\n return (SpikeCount, splitInterval)","def test_high_delta_sample_stays_the_same(self, sampling_class,\n sampling_method):\n s = private_sampling.ThresholdSample(0.5, sampling_method)\n for i in range(2000):\n s.process(i, 1)\n private_priority_sample = sampling_class.from_non_private(\n s, eps=0.1, delta=1.0)\n self.assertCountEqual(s.elements.keys(), private_priority_sample.elements)","def test_4d_stream_time():\n fmask = \"common_data/4d_pipe/full4D.fid\"\n dic,data = ng.pipe.read_lowmem(fmask)\n\n fname = \"common_data/4d_pipe/time_2index/test02006.fid\"\n sdic,sdata = ng.pipe.read(fname)\n\n assert data.shape == (8, 12, 16, 1400)\n assert data.dtype == 'complex64'\n assert round(data[0,1,2,3].real,2) == -395.11\n assert round(data[0,1,2,3].imag,2) == 52.72\n assert round(data[5,9,11,987].real,2) == -35.09\n assert round(data[5,9,11,987].imag,2) == 33.07\n\n # check the slice\n assert sdata.shape == (16, 1400)\n assert sdata.dtype == 'complex64'\n assert round(sdata[1,2].real,2) == 75.93\n assert round(sdata[1,2].imag,2) == 5.55\n assert round(sdata[7,800].real,2) == -8.93\n assert round(sdata[7,800].imag,2) == -10.24\n\n # slice/data matching\n assert_array_equal(data[1,5],sdata)\n\n lowmem_write_readback(dic,data)","def split_by_interval(array, interval: int):\n\n prev_timestamp = array[0][\"timestamp\"]\n values = 0\n count = 0\n averages = []\n\n for el in array:\n if el[\"timestamp\"] - prev_timestamp >= timedelta(interval):\n prev_timestamp = el[\"timestamp\"]\n average = values / count\n averages.append(average)\n values = count = 0\n else:\n values += el[\"value\"]\n count += 1"],"string":"[\n \"def audioEpochFeats(cur,uid,timestamp):\\n\\tuidA = uid +'audio'\\n\\n\\tvar_stats = []\\n\\tstd_stats = []\\n\\tnoise = []\\n\\tvoiceToSilenceRatio = []\\n\\n\\tfor i in range(1,24):\\n\\t\\ths_timestamp = timestamp-86400+(i-1)*hour\\n\\t\\the_timestamp = timestamp-86400+i*hour\\n\\t\\t# Determining if start/end time of given hour is in the night\\n\\t\\t# If yes, proceed with feature calculation, if not skip\\n\\t\\ts_epoch = epochCalc(hs_timestamp)\\n\\t\\te_epoch = epochCalc(he_timestamp)\\n\\n\\t\\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\\n\\t\\t\\tcur.execute('SELECT audio FROM {0} WHERE time_stamp >= {1} AND time_stamp<= {2}'\\n\\t\\t\\t\\t.format(uidA,timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\\n\\t\\t\\trecords = cur.fetchall()\\n\\n\\t\\t\\tvar_stats.append(np.var(records))\\n\\t\\t\\tstd_stats.append(np.std(records))\\n\\n\\t\\t\\t# Calculating number of silence and voice/noise occurences\\n\\t\\t\\tsilence = len([item for item in records if item==0])\\n\\t\\t\\tvoice = len([item for item in records if item==1 or item==2])\\n\\t\\t\\tnoise.append(len([item for item in records if item==3]))\\n\\t\\t\\tif silence>0:\\n\\t\\t\\t\\tvoiceToSilenceRatio.append(float(voice) / silence)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvoiceToSilenceRatio.append(0)\\n\\treturn(np.nan_to_num(np.hstack((voiceToSilenceRatio,var_stats,std_stats,noise))))\\n\\t\\\"\\\"\\\"\\ndef main():\\n\\tcon = psycopg2.connect(database='dataset', user='tabrianos')\\n\\tcur = con.cursor()\\n\\t#warnings.simplefilter(\\\"error\\\")\\n\\t#centers = np.load('visualizations/clustercenters.npy')\\n\\n# ------------TEST CASE-----------------------------\\n\\tfor loso in uids1:\\n\\t\\tytest=[]\\n\\t\\taccuracies =[]\\n\\t\\tacc=0\\n\\t\\tmaxminAcc =[]\\n\\t\\tXbig = np.zeros([1,132])\\t\\n\\t\\tYbig = np.zeros([1])\\n\\t\\tlabels=[]\\n\\t\\tlabels.append(19)\\n\\t\\t# loso means leave one student out: forest is trained on other users data\\n\\t\\t# then tests are run on 'loso' student \\n\\t\\tuids2.remove(loso)\\n\\t\\tuids2.append(loso)\\n\\t\\tprint('LOSO: {0}'.format(loso))\\n\\t\\tfor testUser in uids2:\\n\\t\\t\\tprint(testUser)\\n\\t\\t\\t# lists that temporary store features before concatenation\\n\\t\\t\\t\\n\\t\\t\\tcolocationList =[]\\n\\t\\t\\tconversationList =[]\\n\\t\\t\\tactivityList=[]\\n\\t\\t\\taudioList = []\\n\\n\\t\\t\\t# loading stress labels from database (currently on 0-5 scale)\\n\\t\\t\\trecords = loadSleepLabels(cur,testUser) \\n\\t\\t\\n\\n\\t\\t\\t\\n\\t\\t\\t#X,Y store initially the dataset and the labels accordingly\\n\\t\\t\\tY = np.zeros(len(records))\\n\\t\\t\\tX = np.array(records)\\n\\n\\t\\n\\n\\n\\t\\t\\tfor i in range(0,len(records)):\\n\\t\\t\\t\\tcolocationList.append( colocationEpochFeats(cur,testUser,X[i][0]))\\n\\t\\t\\t\\tconversationList.append( convEpochFeats(cur,testUser,X[i][0]))\\n\\t\\t\\t\\tactivityList.append(activityEpochFeats(cur,testUser,X[i][0]))\\n\\t\\t\\t#\\tScreenList.append( screenStatFeatures(cur,testUser,X[i][0],day) )\\n\\t\\t\\t\\taudioList.append(audioEpochFeats(cur,testUser,X[i][0]))\\n\\t\\t\\n\\t\\t\\t\\tif testUser==loso:\\n\\t\\t\\t\\t\\tytest.append(X[i][1])\\n\\t\\t\\t\\t#labels list holds user ids to be used in LeaveOneOut pipeline\\n\\t\\t\\t\\tlabels.append(testUser[-2:])\\n\\t\\t\\t\\tY[i] = X[i][2]\\n\\n\\t\\t\\t\\n\\t\\t\\t#concatenating features in one array \\n\\n\\t\\t\\tXtt = np.concatenate((np.array(activityList),np.array(conversationList),np.array(colocationList),np.array(audioList)),axis=1)\\n\\t\\t\\tprint(Xtt.shape)\\n\\n\\t\\t\\t#initiating and training forest, n_jobs indicates threads, -1 means all available\\n\\t\\t\\t# while the test student is not reached, training data are merged into one big matrix\\n\\t\\t\\tXbig = np.concatenate((Xbig,Xtt),axis=0)\\n\\t\\t\\tYbig = np.concatenate((Ybig,Y),axis=0)\\n\\n\\t\\t\\tdel colocationList[:]\\n\\t\\t\\tdel conversationList[:]\\n\\t\\t\\tdel activityList[:]\\n\\t\\t\\tdel audioList[:]\\n\\n\\n\\n\\t\\t\\tif testUser!=loso:\\n\\t\\t\\t\\tXbig = Xbig.astype(np.float64)\\n\\t\\t\\t\\tprint(Xbig.dtype)\\n\\t\\t\\t\\t\\n\\n\\t\\t\\t# when loso, tests are run\\n\\t\\t\\telif testUser==loso:\\n\\t\\t\\t\\t#Xbig = preprocessing.scale(Xbig)\\n\\t\\t\\t\\tnp.save('numdata/withgps/sleephourlyX.npy',Xbig)\\n\\t\\t\\t\\tnp.save('numdata/withgps/sleephourlyY.npy',Ybig)\\n\\t\\t\\t\\tnp.save('numdata/withgps/sleephourlyLOO.npy',np.array(labels))\\n\\t\\t\\t\\tprint(Xbig.shape[0],Ybig.shape[0],len(labels))\\n\\t\\t\\t\\tprint('train matrix saved')\\n\\t\\t\\t\\ta = raw_input()\\n\\t\\t\\t\\tforest = RandomForestClassifier(n_estimators=100, n_jobs = -1)\\n\\t\\t\\t\\tforest.fit(Xbig,Ybig)\\n\\t\\t\\t\\tef = forest.score(Xtt,ytest)\\n\\t\\t\\t\\tprint(ef*100)\\n\\n\\t\\t\\t\\toutput = np.array(forest.predict(Xtt))\\n\\t\\t\\t\\tscored = output - np.array(ytest)\\n\\n\\t\\t\\t\\t# Counting as correct predictions the ones which fall in +/-1, not only exact\\n\\t\\t\\t\\t# I call it the 'Tolerance technique'\\n\\t\\t\\t\\tcorrect=0\\n\\t\\t\\t\\tc = Counter(scored)\\n\\t\\t\\t\\tfor k in c.keys():\\n\\t\\t\\t\\t\\tif k<2 and k>-2:\\n\\t\\t\\t\\t\\t\\tcorrect += c[k]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tscore = float(correct)/len(scored)\\n\\t\\t\\t\\tprint(score*100)\\n\\n\\n\\n\\t\\tprint(Xbig.shape)\\n\\t\\n\\t\\t\\n\\n\\n\\nif __name__ == '__main__':\\n\\tmain()\\n\\n\\n\\n\\t\\\"\\\"\\\"\",\n \"def record_data(self, no_of_samples, interval):\\r\\n\\r\\n #tempory storage while function is completing\\r\\n temp_return_list = []\\r\\n\\r\\n #colec\\r\\n for i in range(0,no_of_samples):\\r\\n\\r\\n print(i)\\r\\n sensor_value = self.sen.get_sensor_value()\\r\\n\\r\\n temp_return_list.append([sensor_value,(i*interval)])\\r\\n\\r\\n time.sleep(interval)\\r\\n\\r\\n \\r\\n \\r\\n self.return_data = temp_return_list\",\n \"def processData(pipe, event, pulses):\\n logging.info(\\\"Started processData\\\")\\n start_time = time.time()\\n for pulse in range(pulses):\\n samples = pipe.get()\\n# time.sleep(0.001)\\n end_time = time.time()\\n elapsed = end_time - start_time\\n samplesProcessed = (pulses * len(samples[0]) * len(samples))\\n logging.info(\\\"processData processed %d Msamples in %.3f s\\\",\\n samplesProcessed / 1e6,\\n elapsed)\\n logging.info(\\\"processData rate: %.3f Msa/s in lumps of %d samples\\\",\\n samplesProcessed / elapsed / 1e6,\\n dig.pointsPerCycle)\",\n \"def _got_chunk(self, chunk, timestamp):\\n log.debug(\\\"_got_chunk: chunk=%s\\\", chunk)\\n self._extract_sample(D1000TemperatureDataParticle, D1000TemperatureDataParticle.regex_compiled(), chunk,\\n timestamp)\",\n \"def UTC_times(times, \\n trace, \\n diff_thres = 30.0):\\n # set times values to seconds\\n \\n #AUTOMATE THIS SECTION!\\n #CHECK THAT THIS IS CORRECT\\n times = times / trace.stats.sampling_rate\\n #remove unwanted parts of times numpy array \\n times = times[:,0]\\n \\n #remove the first instance of time because it is \\n #somehow always of the wrong format!\\n #times = np.delete(times, 0) \\n \\n event_times = []\\n event = [times[0]]\\n \\n start_time = trace.stats.starttime\\n \\n #for item in times:\\n # print start_time + item\\n\\n for i in range(1, len(times)):\\n \\n # check if two events in times array have a difference < diff_thres, \\n #if not, run average of those times, if so append that events to a \\n #new events_times list\\n \\n #time_diff = times[i + 1] - times[i]\\n \\n time_diff = times[i] - times[i-1]\\n\\n #save info until events are far enough apart! \\n if time_diff < diff_thres:\\n\\n event.append(times[i])\\n \\n \\n #raise conditional for if events are far enough apart! \\n else:\\n\\n event_start = event[0] - 2 #minus 5 seconds\\n event_end = max(event) + 2 #add 5 seconds\\n\\n event_times.append([event_start, event_end])\\n \\n event = [] \\n \\n event.append(times[i])\\n\\n #if event still contains something for any reason, add it to event times\\n if len(event) > 0: \\n event_start = event[0] - 2 #minus 5 seconds\\n event_end = max(event) + 2 #add 5 seconds\\n event_times.append([event_start, event_end])\\n event = [] \\n \\n\\n\\n #if len(event_times) == 0 and len(event) > 0 or time_diff > diff_thres and len(event) > 0:\\n \\n #event_times.append(sum(event) / len(event))\\n \\n # event_start = event[0] - 2 #minus 5 seconds\\n # event_end = event[-1] + 2 #add 5 seconds\\n \\n # event_times.append([event_start, event_end])\\n \\n # event = []\\n \\n #event_times.append(times[i])\\n \\n # else:\\n # event.append(times[i])\\n \\n\\n UTC_events = []\\n\\n #earthquake length threshold is 10 seconds and above!\\n eq_len = 0#5.0\\n\\n for i in event_times:\\n estart = start_time + i[0]\\n eend = start_time + i[1]\\n \\n if eend - estart > eq_len:\\n UTC_events.append([estart, eend])\\n \\n #UTC_events = np.unique(np.asarray(UTC_events))\\n\\n \\n return UTC_events\",\n \"def _got_chunk(self, chunk, timestamp):\\n if self._extract_sample(SBE19DataParticle, SBE19DataParticle.regex_compiled(), chunk, timestamp):\\n self._sampling = True\\n return\\n\\n for particle_class in SBE19HardwareParticle, \\\\\\n SBE19CalibrationParticle, \\\\\\n SBE19ConfigurationParticle, \\\\\\n SBE19StatusParticle, \\\\\\n OptodeSettingsParticle:\\n if self._extract_sample(particle_class, particle_class.regex_compiled(), chunk, timestamp):\\n return\",\n \"def sample_run(df, n_epochs = 10, window_size = 500, com = 12, p_anoms = .5):\\n import numpy as np\\n\\n # create arrays that will hold the results of batch AD (y_true) and online AD (y_pred)\\n y_true = []\\n y_pred = []\\n run_times = []\\n \\n # check which unique machines, sensors, and timestamps we have in the dataset\\n machineIDs = df['machineID'].unique()\\n sensors = df.columns[2:]\\n timestamps = df['datetime'].unique()[window_size:]\\n \\n # sample n_machines_test random machines and sensors \\n random_machines = np.random.choice(machineIDs, n_epochs)\\n random_sensors = np.random.choice(sensors, n_epochs)\\n\\n # we intialize an array with that will later hold a sample of timetamps\\n random_timestamps = np.random.choice(timestamps, n_epochs)\\n \\n for i in range(0, n_epochs):\\n # take a slice of the dataframe that only contains the measures of one random machine\\n df_s = df[df['machineID'] == random_machines[i]]\\n \\n # smooth the values of one random sensor, using our run_avg function\\n smooth_values = run_avg(df_s[random_sensors[i]].values, com)\\n \\n # create a data frame with two columns: timestamp, and smoothed values\\n df_smooth = pd.DataFrame(data={'timestamp': df_s['datetime'].values, 'value': smooth_values})\\n\\n # load the results of batch AD for this machine and sensor\\n anoms_s = anoms_batch[((anoms_batch['machineID'] == random_machines[i]) & (anoms_batch['errorID'] == random_sensors[i]))]\\n \\n # find the location of the t'th random timestamp in the data frame\\n if np.random.random() < p_anoms:\\n anoms_timestamps = anoms_s['datetime'].values\\n np.random.shuffle(anoms_timestamps)\\n counter = 0\\n while anoms_timestamps[0] < timestamps[0]:\\n if counter > 100:\\n return 0.0, 9999.0\\n np.random.shuffle(anoms_timestamps)\\n counter += 1\\n random_timestamps[i] = anoms_timestamps[0]\\n \\n # select the test case\\n test_case = df_smooth[df_smooth['timestamp'] == random_timestamps[i]]\\n test_case_index = test_case.index.values[0]\\n\\n\\n # check whether the batch AD found an anomaly at that time stamps and copy into y_true at idx\\n y_true_i = random_timestamps[i] in anoms_s['datetime'].values\\n\\n # perform online AD, and write result to y_pred\\n y_pred_i, run_times_i = detect_ts_online(df_smooth, window_size, test_case_index)\\n \\n y_true.append(y_true_i)\\n y_pred.append(y_pred_i)\\n run_times.append(run_times_i)\\n \\n return fbeta_score(y_true, y_pred, beta=2), np.mean(run_times)\",\n \"def collect_samples(serialPort,NO_SENSORS,NO_SAMPLES,log):\\n run = '1'\\n badSamples = 0\\n count = 1\\n log_temp = []\\n temp = [0] * 20\\n NO_FIELDS = (NO_SENSORS * 3) + 1\\n \\n while (run == '1'):\\n # If the input buffer is not empty read the data out into rawData using \\\\n as a delimiter.\\n if (serialPort.inWaiting()>0):\\n rawData = serialPort.readline()\\n print(rawData)\\n \\n # If invalid data is recieved this prevents program crash\\n try:\\n # Decode the bytes into a string\\n data = rawData.decode()\\n \\n # Split x, y, z and newline values into a list\\n if (count >= (NO_SAMPLES + 1)):\\n endTime_temp = data.split(\\\" \\\", 2)\\n if (len(endTime_temp) == 2 and '' not in endTime_temp):\\n endTime = int(endTime_temp[0])\\n else:\\n endTime = 780\\n print('Time not recieved')\\n print('Lost Samples: ' + str(badSamples))\\n run = '0'\\n else:\\n data_readings = data.split(\\\" \\\", NO_FIELDS)\\n print(data_readings)\\n \\n # A correct sample should contain 16 values and not include null and so this is used\\n # to validate the data and record any samples that are discarded in this way\\n if (len(data_readings) == NO_FIELDS and '' not in data_readings):\\n # Discard newline characters before saving data\\n int_data_readings = list(map(int,data_readings[:(NO_FIELDS - 1)]))\\n log_temp.append(int_data_readings)\\n else:\\n badSamples += 1\\n except:\\n print('Invalid data recieved')\\n \\n count += 1\\n\\n samplingPeriod = (endTime/NO_SAMPLES)/NO_SENSORS\\n timeStamp = 0.0\\n\\n for i in range(0,len(log_temp)):\\n for j in range(0,NO_SENSORS):\\n temp[0+(j*4)] = log_temp[i][0+(j*3)]\\n temp[1+(j*4)] = log_temp[i][1+(j*3)]\\n temp[2+(j*4)] = log_temp[i][2+(j*3)]\\n temp[3+(j*4)] = timeStamp\\n timeStamp += samplingPeriod\\n log.append(temp.copy())\",\n \"def test_data_handlers(timeseries, handler):\\n handler = handler(timeseries, HISTORICAL_CONTEXT_NUMBER)\\n updates = []\\n i = 0\\n while True:\\n try:\\n context, update = handler.next()\\n except NoMoreData:\\n break\\n i += 1\\n\\n updates.append(update)\\n # import pdb; pdb.set_trace()\\n assert context[-1].datetime < update.datetime\\n\\n # total lenght of series(25) - context(5) which aren't provided as updates\\n assert i == 20\\n updates = [x.datetime.date().isoformat() for x in updates]\\n assert updates[10] == \\\"2018-01-22\\\"\",\n \"def _got_chunk(self, chunk, timestamp):\\n new_timestamp_match = _NEW_DATFILE_PARTICLE_TIMESTAMP_MATCHER.match(chunk)\\n if new_timestamp_match:\\n date = datetime.strptime(new_timestamp_match.group(1), '%Y-%m-%dT%H:%M:%S.%f')\\n self.log_timestamp = (date - datetime(1900, 1, 1)).total_seconds()\\n return\\n\\n new_body_match = _NEW_DATFILE_PARTICLE_BODY_MATCHER.match(chunk)\\n if new_body_match:\\n # Only publish D1000 playback if we have a timestamp.\\n if self.log_timestamp is not None:\\n self._extract_sample(\\n D1000TemperatureDataParticle,\\n _NEW_DATFILE_PARTICLE_BODY_MATCHER,\\n chunk,\\n self.log_timestamp\\n )\\n\\n return\\n\\n old_timestamp_match = _OLD_DATFILE_PARTICLE_TIMESTAMP_MATCHER.match(chunk)\\n if old_timestamp_match:\\n date = datetime.strptime(old_timestamp_match.group(1), '%Y-%m-%d %H:%M:%S')\\n self.log_timestamp = (date - datetime(1900, 1, 1)).total_seconds()\\n return\\n\\n old_body_match = _OLD_DATFILE_PARTICLE_BODY_MATCHER.match(chunk)\\n if old_body_match:\\n # Only publish D1000 playback if we have a timestamp.\\n if self.log_timestamp is not None:\\n self._extract_sample(\\n D1000TemperatureDataParticle,\\n _OLD_DATFILE_PARTICLE_BODY_MATCHER,\\n chunk,\\n self.log_timestamp\\n )\",\n \"def process_original_data():\\n audioSamples = AudioDataOriginal.query.filter().all()\\n\\n currentSampleDatetime = None\\n maxValueRecord = None\\n for sample in audioSamples:\\n if currentSampleDatetime is None:\\n currentSampleDatetime = sample.datetime\\n if maxValueRecord is None:\\n maxValueRecord = sample\\n # Check if we've reached a new sample batch.\\n if currentSampleDatetime != sample.datetime:\\n # If we reach a new sample batch, update the `processedValue` of the\\n # record held in `maxValueRecord` to be the `audio` value.\\n maxValueRecord.processedValue = maxValueRecord.audio\\n # Update to the first value of the new sample batch.\\n currentSampleDatetime = sample.datetime\\n maxValueRecord = sample\\n elif sample.audio > maxValueRecord.audio:\\n # If not, then check if the value is higher than the current one\\n # and overwrite if so.\\n maxValueRecord = sample\\n # Update the last record, since this won't be caught in the if statement\\n # inside the loop (it compares the current.datetime vs a new.datetime after\\n # all).\\n maxValueRecord.processedValue = maxValueRecord.audio\\n db.session.commit()\",\n \"def update_live_data(n, last_time, id1, id2, power):\\n if power:\\n raise PreventUpdate\\n\\n timer_start = perf_counter()\\n # 1 sec delay so server has time to add live data\\n end_time = datetime.now(timezone.utc) - timedelta(seconds=1)\\n\\n # Initialization and lag prevention\\n if last_time is None or end_time - strptime_fix(last_time) > timedelta(seconds=3):\\n logging.warning('Falling behind! Start %s End %s', last_time, end_time)\\n return dash.no_update, dash.no_update, end_time.isoformat(), dash.no_update\\n\\n # Query data from SMIP\\n logging.info(f'start_time {last_time} end_time {end_time}')\\n timer_query_start = perf_counter()\\n r = conn.get_data(last_time, end_time.isoformat(),\\n [id1, id2], timeout=1)\\n timer_query_end = perf_counter()\\n response_json: dict = r.json()\\n logging.debug(response_json.keys())\\n if 'errors' in response_json:\\n logging.error(response_json)\\n raise Exception()\\n data = response_json['data']['getRawHistoryDataWithSampling']\\n logging.info('Got %s responses in %s seconds', len(\\n data), timer_query_end - timer_query_start)\\n\\n # Used for measuring performance\\n start_processing = perf_counter()\\n\\n # Unpack data\\n def unpack(id: int):\\n \\\"\\\"\\\"Unpacks return data into time and value lists\\\"\\\"\\\"\\n id = int(id)\\n time_list = [i['ts'] for i in data if int(i['id']) == id]\\n val_list = [i['floatvalue'] for i in data if int(i['id']) == id]\\n # SMIP always returns one entry before the start time for each ID, we don't need this\\n if len(time_list) < 2 or len(val_list) < 2:\\n return dash.no_update\\n time_list.pop(0)\\n val_list.pop(0)\\n # Measure sampling rate\\n rate = nan\\n if len(time_list) > 1:\\n rate = (strptime_fix(time_list[1])\\n - strptime_fix(time_list[0])).total_seconds()\\n return {'time_list': time_list, 'val_list': val_list, 'rate': rate}\\n\\n # Used for measuring performance\\n data_processed = perf_counter()\\n logging.info('Total %s Query %s Processing %s', data_processed - timer_start, timer_query_end - timer_query_start,\\n data_processed - start_processing)\\n\\n return unpack(id1), unpack(id2), end_time.isoformat(), \\\\\\n [f'Last updated {end_time.astimezone()},',\\n html.Br(),\\n f'received {len(data)} samples in {round(data_processed - timer_start, 3)} seconds']\",\n \"def process(self, timeframe, timestamp):\\n pass\",\n \"def predict(self):\\n\\n global pos\\n pos = (pos + 1) % len(ue_data) # iterate through entire list one by one in cycle manner and will be updated when live feed will be coming through KPIMON to influxDB\\n sample = ue_data[pos]\\n ue_df = pd.DataFrame([sample], columns=db.data.columns)\\n val = predict_anomaly(self, ue_df)\\n if (val is not None) and (len(val) > 2):\\n msg_to_ts(self, val)\",\n \"def _compute_samples(self, samples):\\n return samples\",\n \"def timings(samples):\\n groups = samples.groupby(axis=1, level=0)\\n return groups.apply(lambda group: group.iloc[:, 1] - group.iloc[:, 0])\",\n \"def _query_data(self, index, tag):\\n version, datapoints = yield self.quasar.stream_get(self.name, tag, tag+(15*qdf.MINUTE))\\n values = np.empty((BLOCK_SIZE,), dtype=(type(datapoints[0])))\\n values[:] = None\\n \\n for point in datapoints:\\n time = float(point.time - tag)\\n time_index = int(round(time*SAMPLE_RATE/qdf.SECOND))\\n values[time_index] = point\\n\\n self.cache[index][CACHE_INDEX_TAG] = tag\\n self.cache[index][CACHE_INDEX_DATA] = values\",\n \"def run():\\n\\n import matplotlib.pyplot as plt\\n\\n anomalies_t = []\\n anomalies_v = []\\n anomalies_c = []\\n\\n all_t = []\\n all_v = []\\n\\n rows = []\\n for i, row in dataSet.iterrows():\\n\\n inputData = row.to_dict()\\n\\n detectorValues = handleRecord(inputData)\\n\\n if (detectorValues[0] > 0.65):\\n anomalies_t.append(inputData[\\\"timestamp\\\"])\\n anomalies_v.append(inputData[\\\"value\\\"])\\n anomalies_c.append(detectorValues[0])\\n\\n all_t.append(inputData[\\\"timestamp\\\"])\\n all_v.append(inputData[\\\"value\\\"])\\n\\n outputRow = list(row) + list(detectorValues)\\n\\n rows.append(outputRow)\\n\\n # Progress report\\n if (i % 1000) == 0:\\n print \\\".\\\",\\n sys.stdout.flush()\\n\\n fig, ax = plt.subplots()\\n\\n ax.plot(all_t, all_v)\\n ax.plot(anomalies_t, anomalies_v, 'ro')\\n\\n plt.show()\\n\\n ans = pandas.DataFrame(rows)\\n return ans\",\n \"def process_point(meta, data):\\n point = dfparser.Point()\\n point.ParseFromString(data)\\n\\n sample_freq = meta['params']['sample_freq']\\n threshold = meta['process_params']['threshold']\\n\\n events_all = []\\n for channel in point.channels:\\n for i, block in enumerate(channel.blocks):\\n SOCKETIO.emit('progress',\\n {'val': int((i/len(channel.blocks))*100)})\\n eventlet.sleep(0)\\n events = []\\n for event in block.events:\\n data = np.frombuffer(event.data, np.int16)\\n events.append(extract_amps_approx2(data, event.time,\\n threshold,\\n sample_freq)[0])\\n events = np.hstack(events)[0::2]\\n events_all.append(events)\\n\\n events_all = np.hstack(events_all)\\n return events_all\",\n \"def hit(self, timestamp):\\n idx = timestamp % 300\\n if self.counter[idx][1] == timestamp:\\n self.counter[idx][0] += 1\\n else:\\n self.counter[idx] = [1, timestamp]\",\n \"def test_time_series_from_file():\\r\\n\\r\\n TR = 1.35\\r\\n ts_ff = io.time_series_from_file\\r\\n\\r\\n #File names:\\r\\n fmri_file1 = os.path.join(data_path,'fmri1.nii.gz')\\r\\n fmri_file2 = os.path.join(data_path,'fmri2.nii.gz')\\r\\n\\r\\n #Spatial coordinates into the volumes:\\r\\n coords1 = np.array([[5,5,5,5],[5,5,5,5],[1,2,3,4]])\\r\\n coords2 = np.array([[6,6,6,6],[6,6,6,6],[3,4,5,6]])\\r\\n\\r\\n #No averaging, no normalization:\\r\\n t1 = ts_ff([fmri_file1,fmri_file2],[coords1,coords2],TR)\\r\\n\\r\\n npt.assert_equal(t1[0].shape,(4,80)) # 4 coordinates, 80 time-points\\r\\n\\r\\n t2 = ts_ff([fmri_file1,fmri_file2],[coords1,coords2],TR,average=True)\\r\\n\\r\\n npt.assert_equal(t2[0].shape,(80,)) # collapse coordinates,80 time-points\\r\\n\\r\\n t3 = ts_ff(fmri_file1,coords1,TR,normalize='zscore')\\r\\n\\r\\n #The mean of each channel should be almost equal to 0:\\r\\n npt.assert_almost_equal(t3.data[0].mean(),0)\\r\\n #And the standard deviation should be almost equal to 1:\\r\\n npt.assert_almost_equal(t3.data[0].std(),1)\\r\\n\\r\\n t4 = ts_ff(fmri_file1,coords1,TR,normalize='percent')\\r\\n\\r\\n #In this case, the average is almost equal to 0, but no constraint on the\\r\\n #std:\\r\\n npt.assert_almost_equal(t4.data[0].mean(),0)\\r\\n\\r\\n #Make sure that we didn't mess up the sampling interval:\\r\\n npt.assert_equal(t4.sampling_interval,nitime.TimeArray(1.35))\\r\\n\\r\\n # Test the default behavior:\\r\\n data = io.load(fmri_file1).get_data()\\r\\n t5 = ts_ff(fmri_file1)\\r\\n npt.assert_equal(t5.shape, data.shape)\\r\\n npt.assert_equal(t5.sampling_interval, ts.TimeArray(1, time_unit='s'))\\r\\n\\r\\n # Test initializing TR with a TimeArray:\\r\\n t6= ts_ff(fmri_file1, TR=ts.TimeArray(1350, time_unit='ms'))\\r\\n npt.assert_equal(t4.sampling_interval, t6.sampling_interval)\\r\\n\\r\\n # Check the concatenation dimensions:\\r\\n t7 = ts_ff([fmri_file1, fmri_file2])\\r\\n npt.assert_equal([t7.shape[:3], t7.shape[-1]], [data.shape[:3], data.shape[-1]*2])\\r\\n\\r\\n t8 = ts_ff([fmri_file1, fmri_file2], average=True)\\r\\n npt.assert_equal(t8.shape[0], data.shape[-1]*2)\\r\\n\\r\\n t9 = ts_ff([fmri_file1, fmri_file2], average=True, normalize='zscore')\\r\\n npt.assert_almost_equal(t9.data.mean(), 0)\",\n \"def newChunk(self, data, sample_period):\\n added = False\\n found = False\\n for r in self._receiving:\\n if r[0] == data.timestamp:\\n r[1][data.sensor - 1] = data\\n found = True\\n break\\n if found is False:\\n self._receiving.append((data.timestamp, [None] * self._sensors))\\n self._receiving[-1][1][data.sensor - 1] = data\\n self._receiving.sort(key=lambda x: x[0])\\n\\n # all data for given timestamp received\\n while len(self._receiving) > 0:\\n r = self._receiving[0]\\n if r[1].count(None) > 0:\\n break\\n dl = len(data.accelerationX)\\n timestamps = np.arange(r[0], r[0] + sample_period * dl, sample_period)\\n\\n def copy_data(start, l):\\n self.data[\\\"timestamp\\\"][\\n self.current_index : self.current_index + l\\n ] = timestamps[start : start + l]\\n for s in range(1, self._sensors + 1):\\n self.data[f\\\"{s} X\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationX[start : start + l]\\n self.data[f\\\"{s} Y\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationY[start : start + l]\\n self.data[f\\\"{s} Z\\\"][\\n self.current_index : self.current_index + l\\n ] = r[1][s - 1].accelerationZ[start : start + l]\\n\\n l = min(dl, self._size - self.current_index)\\n if l > 0:\\n copy_data(0, l)\\n self.current_index += l\\n dl -= l\\n\\n if dl > 0:\\n self.current_index = 0\\n self.filled = True\\n copy_data(l, dl)\\n self.current_index = dl\\n\\n self._receiving.remove(r)\\n added = True\\n\\n chunk_removed = False\\n if len(self._receiving) > self._window:\\n self._receiving = self._receiving[1:]\\n chunk_removed = True\\n return (added, chunk_removed)\",\n \"def _compute_epochs(events, rel_start_ms, rel_stop_ms, timestamps, sr):\\n\\n # THIS IS SO MUCH FASTER THAN NP.WHERE, CRAZY\\n offsets = events.stTime.apply(lambda x: np.searchsorted(timestamps, x))\\n # offsets = events.stTime.apply(lambda x: np.where(timestamps >= x)[0][0])\\n rel_start_micro = int(rel_start_ms * sr / 1e3)\\n rel_stop_micro = int(rel_stop_ms * sr / 1e3)\\n epochs = np.array([(offset + rel_start_micro, offset + rel_stop_micro) for offset in offsets])\\n return epochs\",\n \"def aggregated_second(self, data):\\n if self.results:\\n if self.results[-1][\\\"ts\\\"] >= data[\\\"ts\\\"]:\\n raise AssertionError(\\\"TS sequence wrong: %s>=%s\\\" % (self.results[-1][\\\"ts\\\"], data[\\\"ts\\\"]))\\n self.results.append(data)\",\n \"def _init_sample(self):\\n self.timestamps = np.zeros(5)\\n self.data = np.zeros((5, 12))\",\n \"def _sample_from_null_frm_dist(mean_spike_count, total_baseline_time, total_effect_time, sample_size=10 ** 6):\\n total_time = total_baseline_time + total_effect_time\\n\\n samples = (\\n st.poisson(mean_spike_count * total_effect_time / total_time).rvs(sample_size) / total_effect_time\\n -\\n st.poisson(mean_spike_count * total_baseline_time / total_time).rvs(sample_size) / total_baseline_time\\n )\\n\\n # convert 1/ms to 1/s (Hz)\\n samples = samples / MS_TO_S\\n\\n return samples\",\n \"def testQueryWithTimestamp(self):\\n for i in range(5):\\n row_name = \\\"aff4:/row:query_with_ts\\\"\\n data_store.DB.Set(row_name, \\\"metadata:5\\\", \\\"test\\\", timestamp=i + 10,\\n replace=False, token=self.token)\\n data_store.DB.Set(row_name, \\\"aff4:type\\\", \\\"test\\\", timestamp=i + 10,\\n replace=False, token=self.token)\\n\\n # Read all timestamps.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=data_store.DB.ALL_TIMESTAMPS, token=self.token)]\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 5)\\n\\n # Read latest timestamp.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=data_store.DB.NEWEST_TIMESTAMP, token=self.token)]\\n\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 1)\\n self.assertEqual(attributes[\\\"aff4:type\\\"][0][0], \\\"test\\\")\\n\\n # Newest timestamp is 4.\\n self.assertEqual(attributes[\\\"aff4:type\\\"][0][1], 14)\\n\\n # Now query for a timestamp range.\\n rows = [row for row in data_store.DB.Query(\\n [], data_store.DB.filter.HasPredicateFilter(\\\"metadata:5\\\"),\\n subject_prefix=\\\"aff4:/row:query_with_ts\\\",\\n timestamp=(11, 13), token=self.token)]\\n\\n attributes = rows[0]\\n self.assertEqual(attributes[\\\"subject\\\"][0][0], \\\"aff4:/row:query_with_ts\\\")\\n # Now we should have three timestamps.\\n self.assertEqual(len(attributes[\\\"aff4:type\\\"]), 3)\\n\\n timestamps = [attribute[1] for attribute in attributes[\\\"aff4:type\\\"]]\\n self.assertListEqual(sorted(timestamps), [11, 12, 13])\",\n \"def run(self):\\n while(not self.stop_event.is_set()):\\n # read values until stop is sent\\n response1 = _read_once(1,self.serial)\\n response2 = _read_once(2,self.serial)\\n #print(response)\\n self.data1[\\\"d\\\"].append(response1) # Push response to the data list for later\\n self.data2[\\\"d\\\"].append(response2) # Push response to the data list for later\\n curTime = time.time()\\n self.data1[\\\"t\\\"].append(curTime)\\n self.data2[\\\"t\\\"].append(curTime)\\n #sleep(0.0001) # I need to be small enough to capture peaks.\\n return\",\n \"def statistical_feature_extraction(window_size, signal, axis, device, subject_ID):\\n\\n start_running = timeit.default_timer()\\n try:\\n directory = f'data/row_data/{device}_{signal}/S{subject_ID}_{device}_{signal}.csv'\\n sampling_rate = 20\\n window_size = int(sampling_rate * window_size)\\n # print(window_size)\\n except:\\n print('Error! Can not find such directory.')\\n\\n raw_signal = pd.read_csv(directory)\\n win_count = 0\\n total_win_count = 0\\n features_for_all_windows_one_activity = []\\n features_for_all_windows_all_activities = []\\n column_title = f'{axis}_{device}_{signal}'\\n for class_label in np.append(range(1, 14), range(15, 20)):\\n activity_ID = chr(class_label + 64)\\n raw_data_one_activity = np.array(raw_signal.loc[raw_signal['activity_ID'] == activity_ID, [column_title]])\\n raw_data_one_activity = pd.DataFrame(raw_data_one_activity)\\n\\n for data_point in range(0, len(raw_data_one_activity), window_size):\\n win_count += 1\\n start = data_point\\n end = start + window_size\\n time_domain_window = raw_data_one_activity[start:end]\\n\\n time_mean = pd.Series(time_domain_window.mean()).rename(f'{axis}_{signal}_mean')\\n time_min = pd.Series(time_domain_window.min()).rename(f'{axis}_{signal}_min')\\n time_max = pd.Series(time_domain_window.max()).rename(f'{axis}_{signal}_max')\\n time_std = pd.Series(time_domain_window.std()).rename(f'{axis}_{signal}_std')\\n time_median = pd.Series(time_domain_window.median()).rename(f'{axis}_{signal}_median')\\n time_variance = pd.Series(time_domain_window.var()).rename(f'{axis}_{signal}_variance')\\n zero_crossing_rate = pd.Series(zero_crossing(time_domain_window)).rename(\\n f'{axis}_{signal}_zero_crossing')\\n mean_crossing = pd.Series(mean_crossing_rate(time_domain_window)).rename(\\n f'{axis}_{signal}_mean_crossing')\\n activity_id_ = pd.Series(activity_ID).rename('Activity_ID')\\n\\n features_for_one_window_one_activity = pd.concat(\\n [time_mean, time_min, time_max, time_std, time_median, time_variance, zero_crossing_rate, mean_crossing,\\n activity_id_], axis=1)\\n features_for_all_windows_one_activity.append(features_for_one_window_one_activity)\\n # print(features_for_all_windows)\\n\\n print('Window count', win_count)\\n total_win_count += win_count\\n win_count = 0\\n features_for_all_windows_all_activities.append(features_for_all_windows_one_activity)\\n features = pd.concat(features_for_all_windows_all_activities[0], ignore_index=False)\\n print(features)\\n save_as_directory = f'feature_label_tables/feature_{device}_{signal}/feature_S{subject_ID}_{axis}_{device}_{signal}.csv'\\n features.to_csv(save_as_directory, encoding='utf-8', index=False)\\n finish_running = timeit.default_timer()\\n print('Total number of windows: ', total_win_count)\\n print('Running time: ', finish_running - start_running)\",\n \"def _handler_sample(self, *args, **kwargs):\\n next_state = None\\n timeout = time.time() + SAMPLE_TIMEOUT\\n\\n for i in self._units:\\n self._do_command(Command.READ, i)\\n\\n particles = self.wait_for_particles([DataParticleType.D1000_PARSED], timeout)\\n\\n return next_state, (next_state, particles)\",\n \"def timestamp_processor(timestamps, with_tz=False, check_delta=False):\\n if timestamps.count(timestamps[0]) == len(timestamps):\\n unified_timestamp = timestamps[0]\\n else:\\n average_timestamp = sum([dt.timestamp for dt in timestamps])/len(timestamps)\\n unified_timestamp = arrow.get(average_timestamp)\\n\\n if check_delta:\\n for ts in timestamps:\\n delta = unified_timestamp - arrow.get(ts)\\n second_difference = abs(delta.total_seconds())\\n\\n if second_difference > 3600:\\n # more than 1 hour difference\\n raise ValueError(\\\"\\\"\\\"South Korea generation data is more than 1 hour apart,\\n saw {} hours difference\\\"\\\"\\\".format(second_difference/3600))\\n\\n if with_tz:\\n unified_timestamp = unified_timestamp.replace(tzinfo='Asia/Seoul')\\n\\n return unified_timestamp\",\n \"def collect_samples(self):\\n self.__running = True\\n with open(self.__filename, 'a') as output:\\n next_sample_time = time.time()\\n while self.__running:\\n sensor_name = self.__sensor.get_sensor_type_name()\\n sensor_id = self.__sensor.get_sensor_id()\\n data = self.__sensor.retrieve_data_string() \\n if DEBUG:\\n print('data: \\\"{}\\\"'.format(data),\\n file = sys.stderr, flush=True)\\n when = datetime.datetime.now(datetime.timezone.utc).isoformat()\\n result = OUTPUT_FORMAT.format(when,\\n sensor_name, \\n sensor_id, \\n data)\\n output.write(result)\\n output.flush()\\n \\n next_sample_time = next_sample_time + self.__interval\\n delay_time = next_sample_time - time.time()\\n if DEBUG:\\n print('delay_time = {}'.format(delay_time),\\n file=sys.stderr, flush=True)\\n \\n if 0 < delay_time: # don't sleep if already next sample time\\n time.sleep(delay_time)\",\n \"def get_observation(self, ts: int) -> np.ndarray:\\n \\n self.connect_db()\\n c = self.conn.cursor()\\n c.arraysize = self.num_clients * self.ticks_per_observation + 10\\n \\n # Create np array of size [clients x ticks_per_observation x parameters]\\n result = np.zeros((len(self.client_list),\\n self.ticks_per_observation,\\n len(self.conf['ceph-param'])), dtype=float)\\n \\n # Get performance indicators within time step\\n # sql = '''SELECT clientid, ts, pis from perfs WHERE ts <= ? AND ts > ? ORDER BY clientid ASC, ts ASC'''\\n c.execute('''SELECT clientid, ts, pis from perfs WHERE ts <= ? AND \\n ts > ? ORDER BY clientid ASC, ts ASC''',\\n (ts,ts - self.ticks_per_observation))\\n \\n data = c.fetchall()\\n if len(data) < self.num_clients * self.ticks_per_observation - self.missing_entry_tolerance:\\n raise NotEnoughDataError\\n elif len(data) != self.num_clients * self.ticks_per_observation:\\n logger.debug('Observation at ts {0} has {1} missing entries'.format(\\n ts, self.num_clients * self.ticks_per_observation - len(data)))\\n for row in data:\\n client_id = row[0]\\n pi = row[2]\\n if client_id not in self.client_list:\\n # non client MA should send in zero length data for now\\n assert len(pickle.loads(pi)) == 0\\n continue\\n \\n client_id_idx = self.client_list.index(client_id)\\n ts_idx = row[1] - (ts - self.ticks_per_observation) - 1\\n # numpy assignments also check that pi is in right shape\\n result[client_id_idx, ts_idx] = pickle.loads(pi)\\n \\n self.conn.close()\\n return result.reshape((self.observation_size,))\",\n \"def _set_sample(self, sample, PB_X, t):\\n for sensor in PB_X.keys():\\n sample.set(sensor, np.array(PB_X[sensor]), t=t+1)\",\n \"def testGenerateSamplesRuntimeAndTimestamps(self):\\n timer = timing_util.IntervalTimer()\\n with timer.Measure('First'):\\n pass\\n with timer.Measure('Second'):\\n pass\\n start0 = timer.intervals[0][1]\\n stop0 = timer.intervals[0][2]\\n start1 = timer.intervals[1][1]\\n stop1 = timer.intervals[1][2]\\n with mock.patch(\\n 'perfkitbenchmarker.timing_util.TimestampMeasurementsEnabled',\\n return_value=True):\\n samples = timer.GenerateSamples()\\n exp_samples = [\\n sample.Sample('First Runtime', stop0 - start0, 'seconds'),\\n sample.Sample('First Start Timestamp', start0, 'seconds'),\\n sample.Sample('First Stop Timestamp', stop0, 'seconds'),\\n sample.Sample('Second Runtime', stop1 - start1, 'seconds'),\\n sample.Sample('Second Start Timestamp', start1, 'seconds'),\\n sample.Sample('Second Stop Timestamp', stop1, 'seconds')]\\n self.assertSampleListsEqualUpToTimestamp(samples, exp_samples)\",\n \"def create_msgs():\\n getcontext().prec = 3 # will round to 3 decimal places\\n orig_times = sorted(dat)\\n for n in range(len(dat) - 1):\\n linfun = interp1d([orig_times[n], orig_times[n+1]], \\\\\\n [dat[orig_times[n]], dat[orig_times[n+1]]])\\n dt = orig_times[n+1] - orig_times[n] # current\\n freq = 1/dt # current\\n if dt < (1/desHz):\\n print('found instance where Freq already at/above desired Freq')\\n else:\\n new_dt = dt*freq/desHz\\n new_times = linspace(orig_times[n],orig_times[n+1],floor(dt/new_dt))\\n # print(new_times)\\n new_values = linfun(new_times)\\n # rounded_values = [float(Decimal(\\\"%.3f\\\" % e)) for e in new_values]\\n rounded_times = [float(Decimal(\\\"%.3f\\\" % e)) for e in new_times]\\n for m in range(len(rounded_times)):\\n # this_time = int(new_times[m]*100000)/100000 # 5 decimal places in timstamp\\n self.outData[sens][meas][rounded_times[m]] = new_values[m]\",\n \"def test_single_ended_matching_sections_synthetic():\\n from dtscalibration import DataStore\\n\\n cable_len = 100.0\\n nt = 50\\n nx = 200\\n time = np.arange(nt)\\n x = np.linspace(0.0, cable_len, nx)\\n ts_cold = np.ones(nt) * 4.0\\n ts_warm = np.ones(nt) * 20.0\\n ts_ambient = np.ones(nt) * 12\\n ts_valid = np.ones(nt) * 16\\n\\n C_p = 15246\\n C_m = 2400.0\\n dalpha_r = 0.0005284\\n dalpha_m = 0.0004961\\n dalpha_p = 0.0005607\\n gamma = 482.6\\n cold_mask1 = np.logical_and(x > 0.125 * cable_len, x < 0.25 * cable_len)\\n cold_mask2 = np.logical_and(x > 0.625 * cable_len, x < 0.75 * cable_len)\\n warm_mask1 = np.logical_and(x > 0.75 * cable_len, x < 0.875 * cable_len)\\n warm_mask2 = np.logical_and(x > 0.25 * cable_len, x < 0.375 * cable_len)\\n valid_mask = np.logical_and(x > 0.40 * cable_len, x < 0.50 * cable_len)\\n temp_real = np.ones((len(x), nt)) * 12 + 273.15\\n temp_real[cold_mask1 + cold_mask2] = ts_cold + 273.15\\n temp_real[warm_mask1 + warm_mask2] = ts_warm + 273.15\\n temp_real[valid_mask] = ts_valid + 273.15\\n\\n st = (\\n C_p\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_p * x[:, None])\\n * np.exp(gamma / temp_real)\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n ast = (\\n C_m\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_m * x[:, None])\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n\\n # Add attenuation\\n tr_att = np.random.rand(nt) * 0.2 + 0.8\\n st[int(x.size * 0.4) :] *= tr_att\\n tr_att2 = np.random.rand(nt) * 0.2 + 0.8\\n st[int(x.size * 0.6) :] *= tr_att2\\n\\n ds = DataStore(\\n {\\n \\\"st\\\": ([\\\"x\\\", \\\"time\\\"], st),\\n \\\"ast\\\": ([\\\"x\\\", \\\"time\\\"], ast),\\n \\\"userAcquisitionTimeFW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"cold\\\": ([\\\"time\\\"], ts_cold),\\n \\\"warm\\\": ([\\\"time\\\"], ts_warm),\\n \\\"ambient\\\": ([\\\"time\\\"], ts_ambient),\\n },\\n coords={\\\"x\\\": x, \\\"time\\\": time},\\n attrs={\\\"isDoubleEnded\\\": \\\"0\\\"},\\n )\\n\\n sections = {\\n \\\"cold\\\": [slice(0.13 * cable_len, 0.24 * cable_len)],\\n \\\"warm\\\": [slice(0.26 * cable_len, 0.365 * cable_len)],\\n }\\n\\n matching_sections = [\\n (\\n slice(0.01 * cable_len, 0.09 * cable_len),\\n slice(0.51 * cable_len, 0.59 * cable_len),\\n True,\\n ),\\n (\\n slice(0.01 * cable_len, 0.09 * cable_len),\\n slice(0.91 * cable_len, 0.99 * cable_len),\\n True,\\n ),\\n ]\\n\\n ds_test = ds.copy(deep=True)\\n\\n # WLS\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n matching_sections=matching_sections,\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n ds_test = ds.copy(deep=True)\\n\\n # Test fixing gamma + transient att.\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n fix_gamma=(482.6, 0),\\n matching_sections=matching_sections,\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n ds_test = ds.copy(deep=True)\\n\\n # Test fixing dalpha + transient att.\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n fix_dalpha=(6.46e-05, 0),\\n matching_sections=matching_sections,\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n ds_test = ds.copy(deep=True)\\n\\n # Test fixing gamma & dalpha + transient att.\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n fix_gamma=(482.6, 0),\\n fix_dalpha=(6.46e-05, 0),\\n matching_sections=matching_sections,\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n # Test conf. ints. for the combination of everything\\n ds_test.conf_int_single_ended(\\n p_val=\\\"p_val\\\",\\n p_cov=\\\"p_cov\\\",\\n st_var=1.0,\\n ast_var=1.0,\\n conf_ints=[2.5, 50.0, 97.5],\\n mc_sample_size=50,\\n )\\n\\n ds_test_1 = ds_test.isel(time=-1)\\n # ds_test_1.tmpf\\n # ds_test_1.tmpf_mc.isel(CI=0).values\\n # ds_test_1.tmpf_mc.isel(CI=2).values\\n\\n assert np.all(\\n np.less(ds_test_1.tmpf_mc.isel(CI=0).values, ds_test_1.tmpf)\\n ), \\\"Single-ended, trans. att.; 2.5% confidence interval is incorrect\\\"\\n\\n assert np.all(\\n np.greater(ds_test_1.tmpf_mc.isel(CI=2).values, ds_test_1.tmpf)\\n ), \\\"Single-ended, trans. att.; 97.5% confidence interval is incorrect\\\"\",\n \"def time_record(record, sampling_rate, detector, n_runs):\\n start = timer()\\n for _ in range(n_runs):\\n detector(record, sampling_rate=sampling_rate)\\n end = timer()\\n avg_time = (end - start) / n_runs * 1000\\n\\n return avg_time\",\n \"def testData(nTimes = 1000, taus = [1.0e-6, 1.0e-4, 5.0e-3], amps = [0.3, 0.3, 0.4], sigma = 0.05, linear=False):\\n\\n if linear:\\n Times = rangeLin(1.0e-7, 1.0e-3, nTimes) # in seconds\\n else:\\n Times = rangeLog(1.0e-9, 1.0, nTimes) # in seconds\\n\\n Data = np.zeros( Times.shape )\\n for i in range(len(taus)):\\n Data += amps[i]*np.exp(-1.*Times/taus[i])\\n Data += sigma*np.random.randn( len(Data) )\\n\\n return Times, Data\",\n \"def job_gen(self, time_frame):\",\n \"def get_datapoints(self, rid, t0, t1, nmax = 300):\\n self.read_curs.execute(\\\"SELECT COUNT(*) FROM readings WHERE readout_id = ? AND time >= ? AND time <= ?\\\", (int(rid), t0, t1))\\n if self.read_curs.fetchone()[0] > nmax:\\n self.read_curs.execute(\\\"SELECT avg(time),avg(value) FROM readings WHERE readout_id = ? AND time >= ? AND time <= ? GROUP BY round(time/?) ORDER BY time DESC\\\", (int(rid), t0, t1, (t1-t0)/nmax));\\n else:\\n self.read_curs.execute(\\\"SELECT time,value FROM readings WHERE readout_id = ? AND time >= ? AND time <= ? ORDER BY time DESC\\\", (int(rid), t0, t1))\\n return self.read_curs.fetchall()\",\n \"def get_accumulated_data(self, topic, start_time, end_time, units):\\n ignore_start_time = self._get_value('ignore_start_time', topic)\\n ignore_end_time = self._get_value('ignore_end_time', topic)\\n adjust_start_time = self._get_value('adjust_start_time', topic)\\n adjust_end_time = self._get_value('adjust_end_time', topic)\\n\\n if ignore_start_time:\\n self.logger.debug(\\\"Service ignoring start time.\\\")\\n start_ts = self.peek_datetime(topic) - adjust_start_time\\n else:\\n start_ts = start_time - adjust_start_time\\n\\n if ignore_end_time:\\n self.logger.debug(\\\"Service ignoring end time.\\\")\\n end_ts = self.peek_last_datetime(topic) + adjust_end_time\\n else:\\n end_ts = end_time + adjust_end_time\\n\\n self.logger.debug(\\\"Service processing interval: %f %f\\\" %(start_ts, end_ts))\\n accumulator = weewx.accum.Accum(weeutil.weeutil.TimeSpan(start_ts, end_ts))\\n\\n for data in self.get_data(topic, end_ts):\\n if data:\\n try:\\n self.logger.debug(\\\"Service data to accumulate: %s %s\\\"\\n % (weeutil.weeutil.timestamp_to_string(data['dateTime']), to_sorted_string(data)))\\n accumulator.addRecord(data)\\n except weewx.accum.OutOfSpan:\\n self.logger.info(\\\"Service ignoring record outside of interval %f %f %f %s\\\"\\n %(start_ts, end_ts, data['dateTime'], (to_sorted_string(data))))\\n else:\\n break\\n\\n target_data = {}\\n if not accumulator.isEmpty:\\n aggregate_data = accumulator.getRecord()\\n self.logger.debug(\\\"Service data prior to conversion is: %s %s\\\"\\n % (weeutil.weeutil.timestamp_to_string(aggregate_data['dateTime']), to_sorted_string(aggregate_data)))\\n target_data = weewx.units.to_std_system(aggregate_data, units)\\n self.logger.debug(\\\"Service data after to conversion is: %s %s\\\"\\n % (weeutil.weeutil.timestamp_to_string(target_data['dateTime']), to_sorted_string(target_data)))\\n else:\\n self.logger.debug(\\\"Dervice queue was empty\\\")\\n\\n # Force dateTime to packet's datetime so that the packet datetime is not updated to the MQTT datetime\\n if ignore_end_time:\\n target_data['dateTime'] = end_time\\n\\n return target_data\",\n \"def test_aggregate(self):\\n\\n #10 Minute sampleing\\n result = export.processExport(houseId=1,\\n aggregate=\\\"10Min\\\")\\n\\n self.assertEqual(result.shape, (1440, 2))\\n #And the second sample should be 10 minutes in\\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 10, 00))\\n\\n #1/2 hourly\\n result = export.processExport(houseId=1,\\n aggregate=\\\"30Min\\\")\\n\\n #2 * 24 * 10 = 480\\n self.assertEqual(result.shape, (480, 2))\\n #And the second sample should be 10 minutes in\\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 0, 30, 00))\\n\\n #Hourly\\n result = export.processExport(houseId=1,\\n aggregate=\\\"1H\\\")\\n\\n self.assertEqual(result.shape, (240, 2))\\n #And the second sample should be 10 minutes in\\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 01, 1, 00, 00))\\n\\n\\n #daily\\n result = export.processExport(houseId=1,\\n aggregate=\\\"1D\\\")\\n\\n self.assertEqual(result.shape, (10, 2))\\n #And the second sample should be 10 minutes in\\n self.assertEqual(result.index[1], datetime.datetime(2013, 01, 02, 0, 00, 00))\",\n \"def polling_experiment_multiple(dataset_id, times, **kwargs):\\n total_yeahs = 0\\n total_noes = 0\\n for i in range(0, times):\\n yeah, noes = polling_experiment(dataset_id, **kwargs)\\n total_yeahs += yeah\\n total_noes += noes\\n logging.info(\\\"After \\\"+str(times)+\\\" \\\"+ str(dataset_id) + \\\" experiments:\\\")\\n logging.info(\\\"\\\"+ str(total_yeahs) + \\\" correct predictions\\\")\\n logging.info(\\\"\\\"+ str(total_noes) + \\\" wrong predictions\\\")\\n logging.info(\\\"\\\"+ str(total_yeahs/(total_yeahs + total_noes)) + \\\" accuracy\\\")\",\n \"def test_dejitter_timestamps():\\n n_steps = 100\\n n_tests = 50\\n sfreqs = np.linspace(1, 5000, n_tests).astype(int)\\n last_times = np.random.randint(-100, 100, size=n_tests)\\n test_timestamps = np.random.random((n_tests, n_steps)) + np.arange(n_steps)\\n expected_timestamps = [np.arange(n_steps)/sfreq + last_times[i] + 1/sfreq\\n for i, sfreq in enumerate(sfreqs)]\\n for i, args in enumerate(zip(test_timestamps, sfreqs, last_times)):\\n dejittered = acquire.dejitter_timestamps(*args)\\n # there may be some floating-point errors, so just make sure the\\n # difference is tiny\\n assert np.all((dejittered - expected_timestamps[i]) < 1e-14)\",\n \"def data_point(inputs: list):\\n \\n opv = '1'\\n \\n sample_id = 0\\n \\n timenow = strftime(\\\"%#m/%#d/%Y %#H:%M\\\")\\n volts = inputs[0]\\n current = inputs[1]\\n power = inputs[2]\\n \\n data_point = [opv, sample_id, timenow, volts, current, power]\\n\\n if data_point == True:\\n sample_id += 1\\n \\n return data_point\",\n \"def run_resample(self):\\n\\n self.in_data.open()\\n self.out_data.open()\\n\\n try:\\n # Get the fields from the input file and set them/write headers in output:\\n self.all_fields = self.in_data.fields\\n\\n self.out_data.set_fields(self.all_fields)\\n self.out_data.write_headers()\\n\\n # Set up the sensor fields by removing non-sensor fields:\\n self.set_sensor_only_fields()\\n\\n # Read the first event from the input file:\\n self.get_next_input_event()\\n\\n # Warn and exit if we have no input data to read:\\n if self.next_input_event is None:\\n msg = f\\\"The input file {self.in_file} did not have any data rows\\\"\\n warn(msg)\\n\\n return\\n\\n self.first_event_stamp = self.next_input_event[self.stamp_field]\\n\\n # Set up the sample tracking (here mostly to set the start of the first interval):\\n self.reset_sample_tracking()\\n\\n # Now iterate through the output intervals:\\n while True:\\n self.process_next_interval()\\n except EOFError: # catch when we are at the end of the file\\n pass\\n finally:\\n self.in_data.close()\\n self.out_data.close()\\n\\n print() # make sure we go to a new output line\",\n \"def filter(self, newer_than=None):\\r\\n if newer_than is None:\\r\\n newer_than = self._clock.time() - self._window.as_(Time.SECONDS)\\r\\n self._samples = [sample for sample in self._samples if sample[0] >= newer_than]\",\n \"def update_data():\\n values = temp_serial_placeholder()\\n time = current_time_milli() - __start\\n points = [ [time, values[0]], [time, values[1]] ]\\n __data.append(points)\\n return points\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def test_stream_loop(self):\\n chans, gains, scans, rate = (10,10,10,10), (1,2,4,5), 1024, 500\\n v = [v[0] for v in self.l.stream_sync(\\n channels=chans, gains=gains,\\n num_scans=scans, rate=rate)]\\n for vi in v:\\n for r in vi:\\n self.assertTrue(abs(r-2.5) < .1,\\n \\\"%s should be cal, 2.5v\\\" % vi[0])\",\n \"def get_peaks(\\n self,\\n new_sample: np.ndarray,\\n threshold: float,\\n min_peaks_interval=None,\\n ) -> tuple:\\n tic = time.time()\\n nb_peaks = []\\n if len(new_sample.shape) == 1:\\n new_sample = np.expand_dims(new_sample, 0)\\n sample_proc = np.copy(new_sample)\\n if not self._is_one:\\n self._is_one = [False] * new_sample.shape[0]\\n\\n for i in range(new_sample.shape[0]):\\n for j in range(new_sample.shape[1]):\\n if new_sample[i, j] < threshold:\\n sample_proc[i, j] = 0\\n self._is_one[i] = False\\n elif new_sample[i, j] >= threshold:\\n if not self._is_one[i]:\\n sample_proc[i, j] = 1\\n self._is_one[i] = True\\n else:\\n sample_proc[i, j] = 0\\n\\n if len(self.raw_data_buffer) == 0:\\n self.raw_data_buffer = new_sample\\n self.processed_data_buffer = sample_proc\\n nb_peaks = None\\n\\n elif self.raw_data_buffer.shape[1] < self.processing_window:\\n self.raw_data_buffer = np.append(self.raw_data_buffer, new_sample, axis=1)\\n self.processed_data_buffer = np.append(self.processed_data_buffer, sample_proc, axis=1)\\n nb_peaks = None\\n\\n else:\\n self.raw_data_buffer = np.append(self.raw_data_buffer[:, new_sample.shape[1] :], new_sample, axis=1)\\n self.processed_data_buffer = np.append(\\n self.processed_data_buffer[:, new_sample.shape[1] :], sample_proc, axis=1\\n )\\n\\n if min_peaks_interval:\\n self.processed_data_buffer = RealTimeProcessing._check_and_adjust_interval(\\n self.processed_data_buffer, min_peaks_interval\\n )\\n if isinstance(nb_peaks, list):\\n for i in range(self.processed_data_buffer.shape[0]):\\n nb_peaks.append(np.count_nonzero(self.processed_data_buffer[i, :]))\\n self.process_time.append(time.time() - tic)\\n return nb_peaks, self.processed_data_buffer\",\n \"def test_TimeSeries():\\r\\n\\r\\n #Test initialization with duration:\\r\\n tseries1 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], duration=10)\\r\\n tseries2 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], sampling_interval=1)\\r\\n npt.assert_equal(tseries1.time, tseries2.time)\\r\\n\\r\\n #downsampling:\\r\\n t1 = ts.UniformTime(length=8, sampling_rate=2)\\r\\n #duration is the same, but we're downsampling to 1Hz\\r\\n tseries1 = ts.TimeSeries(data=[1, 2, 3, 4], time=t1, sampling_rate=1)\\r\\n #If you didn't explicitely provide the rate you want to downsample to, that\\r\\n #is an error:\\r\\n npt.assert_raises(ValueError, ts.TimeSeries, dict(data=[1, 2, 3, 4],\\r\\n time=t1))\\r\\n\\r\\n tseries2 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_rate=1)\\r\\n tseries3 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_rate=1000,\\r\\n time_unit='ms')\\r\\n #you can specify the sampling_rate or the sampling_interval, to the same\\r\\n #effect, where specificying the sampling_interval is in the units of that\\r\\n #time-series:\\r\\n tseries4 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_interval=1,\\r\\n time_unit='ms')\\r\\n npt.assert_equal(tseries4.time, tseries3.time)\\r\\n\\r\\n #The units you use shouldn't matter - time is time:\\r\\n tseries6 = ts.TimeSeries(data=[1, 2, 3, 4],\\r\\n sampling_interval=0.001,\\r\\n time_unit='s')\\r\\n npt.assert_equal(tseries6.time, tseries3.time)\\r\\n\\r\\n #And this too - perverse, but should be possible:\\r\\n tseries5 = ts.TimeSeries(data=[1, 2, 3, 4],\\r\\n sampling_interval=ts.TimeArray(0.001,\\r\\n time_unit='s'),\\r\\n time_unit='ms')\\r\\n\\r\\n npt.assert_equal(tseries5.time, tseries3.time)\\r\\n\\r\\n #initializing with a UniformTime object:\\r\\n t = ts.UniformTime(length=3, sampling_rate=3)\\r\\n\\r\\n data = [1, 2, 3]\\r\\n\\r\\n tseries7 = ts.TimeSeries(data=data, time=t)\\r\\n\\r\\n npt.assert_equal(tseries7.data, data)\\r\\n\\r\\n data = [1, 2, 3, 4]\\r\\n #If the data is not the right length, that should throw an error:\\r\\n npt.assert_raises(ValueError,\\r\\n ts.TimeSeries, dict(data=data, time=t))\\r\\n\\r\\n # test basic arithmetics wiht TimeSeries\\r\\n tseries1 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], sampling_rate=1)\\r\\n tseries2 = tseries1 + 1\\r\\n npt.assert_equal(tseries1.data + 1, tseries2.data)\\r\\n npt.assert_equal(tseries1.time, tseries2.time)\\r\\n tseries2 -= 1\\r\\n npt.assert_equal(tseries1.data, tseries2.data)\\r\\n npt.assert_equal(tseries1.time, tseries2.time)\\r\\n tseries2 = tseries1 * 2\\r\\n npt.assert_equal(tseries1.data * 2, tseries2.data)\\r\\n npt.assert_equal(tseries1.time, tseries2.time)\\r\\n tseries2 /= 2\\r\\n npt.assert_equal(tseries1.data, tseries2.data)\\r\\n npt.assert_equal(tseries1.time, tseries2.time)\",\n \"def process(self,timeStamp,values,queueNo):\\n\\t\\tdatain = values[0]\\n\\t\\tcurValue = float(datain)\\n\\t\\tif self.isBaselineRunning:\\n\\t\\t\\tself.testMinMax(curValue)\\n\\t\\t\\n\\t\\tif self.hasBaselineEnded:\\n\\t\\t\\tscaledValue = self.scale(curValue)\\n\\t\\t\\t#print scaledValue\\n\\t\\t\\tself.addProcessedValues(scaledValue)\",\n \"def AddSample(self, machine, timestamp, value):\\n self.machine_data.setdefault(machine, list()).append([timestamp, value])\\n if len(self.cluster_total) == 0 or timestamp > self.cluster_total[-1][0]:\\n self.cluster_total.append([timestamp, 0])\\n self.cluster_avg.append([timestamp, 0])\\n self.cluster_total[-1][1] += value\\n self.cluster_avg[-1][1] = self.cluster_total[-1][1] / float(len(self.machine_data))\",\n \"def get_preprocessed_from_raw(sess_no, raw_path, align_on, from_time, to_time) :\\n \\n #params\\n sess = '01'\\n \\n trial_length = abs(from_time - to_time)\\n\\n # Paths\\n #raw_path = base_path + 'data/raw/' + sess_no + '/session' + sess + '/'\\n rinfo_path = raw_path + 'recording_info.mat'\\n tinfo_path = raw_path + 'trial_info.mat'\\n\\n # Define and loop over intervals\\n \\n srate = io.get_sfreq(rinfo_path) # = 1 000\\n n_trials = io.get_number_of_trials(tinfo_path) \\n last_trial = int(max(io.get_trial_ids(raw_path)))\\n n_chans = io.get_number_of_channels(rinfo_path)\\n channels = [ch for ch in range(n_chans)]\\n\\n # Pre-process data\\n filtered = np.empty([n_trials,\\n len(channels),\\n int(trial_length * srate/1000)])\\n\\n trial_counter = 0; counter = 0\\n while trial_counter < last_trial:\\n n_zeros = 4-len(str(trial_counter+1))\\n trial_str = '0' * n_zeros + str(trial_counter+1) # fills leading 0s\\n if sess == '01' :\\n file_in = sess_no + '01.' + trial_str + '.mat'\\n else :\\n file_in = sess_no + '02.' + trial_str + '.mat'\\n \\n if align_on == 'sample' : \\n onset = io.get_sample_on(tinfo_path)[trial_counter].item()\\n elif align_on == 'match' :\\n onset = io.get_match_on(tinfo_path)[trial_counter].item()\\n else :\\n print(\\\"Petit problème avec align_on : 'sample' ou 'match' \\\")\\n \\n\\n \\n if np.isnan(onset): # drop trials for which there is no onset info\\n print('No onset for ' + file_in)\\n trial_counter += 1\\n if trial_counter == last_trial:\\n break\\n else:\\n counter += 1\\n continue\\n print(file_in)\\n try:\\n raw = io.get_data(raw_path + file_in)\\n temp = pp.strip_data(raw,\\n rinfo_path,\\n onset,\\n start=from_time,\\n length=trial_length)\\n \\n if temp.shape[1] == trial_length: # drop trials shorter than length\\n filtered[counter] = temp\\n counter += 1\\n except IOError:\\n print('No file ' + file_in)\\n trial_counter += 1\\n\\n # Return data\\n\\n filtered = np.array(filtered)\\n return(filtered)\",\n \"def calc_observables(samples):\\n n = samples.shape[1]\\n obs = np.zeros((samples.shape[0], n+n*(n-1)//2))\\n \\n k = 0\\n for i in range(n):\\n obs[:,i] = samples[:,i]\\n for j in range(i+1,n):\\n obs[:,n+k] = samples[:,i] * samples[:,j]\\n k += 1\\n return obs\",\n \"def _process_data_sorted(self):\\n n, t, trials = self.raw_data.shape[0], self.raw_data.shape[1], self.raw_data.shape[2]\\n if self.PD:\\n if self.use_silent_channels:\\n clean_channels, red_n = self.raw_data, n\\n _, _, invalid_ch = self._discard_channels(self.raw_data)\\n else:\\n clean_channels, red_n, invalid_ch = self._discard_channels(self.raw_data)\\n\\n session_onmeds = clean_channels[:, :, :, :, self.pd_ses_order[self.i_sub][0]]\\n session_offmeds = clean_channels[:, :, :, :, self.pd_ses_order[self.i_sub][1]]\\n subsets = [session_onmeds, session_offmeds]\\n else:\\n if self.use_silent_channels:\\n clean_channels, red_n = self.raw_data, n\\n _, _, invalid_ch = self._discard_channels(self.raw_data)\\n else:\\n clean_channels, red_n, invalid_ch = self._discard_channels(self.raw_data)\\n session1 = clean_channels[:, :, :, 0::2]\\n session2 = clean_channels[:, :, :, 1::2]\\n subsets = [session1, session2]\\n\\n ts = np.empty(shape=(0, 2, self.N_MOTIV, trials, t, red_n))\\n for ds in subsets:\\n # session(1/2 or on/off) - rg1/rg2 - motiv - trial - sec - signal\\n ts_tmp_new = np.zeros((2, self.N_MOTIV, trials, t, red_n))\\n\\n # rg1:\\n ts_tmp_new[0, :, :, :] = np.array(\\n (ds[:, :, :, 0].T, ds[:, :, :, 1].T, ds[:, :, :, 2].T)\\n )\\n # rg2:\\n ts_tmp_new[1, :, :, :] = np.array(\\n (ds[:, :, :, 3].T, ds[:, :, :, 4].T, ds[:, :, :, 5].T)\\n )\\n\\n ts = np.concatenate((ts, np.array([ts_tmp_new])))\\n\\n return ts, invalid_ch\",\n \"def generate_sample_data(point_numbers, interval):\\n src_names = ['test.example.com', 'salmon.example.com', 'www.example.com']\\n sources = []\\n for name in src_names:\\n sources.append(models.Source.objects.get_or_create(name=name)[0])\\n sources.append(None)\\n metric_names = ['ps.virtual_memory_usage',\\n 'ps.disk_io_counters.write_bytes',\\n 'ps.disk_io_counters.read_bytes',\\n 'ps.disk_usage.percent',\\n 'ps.physical_memory_usage.percent']\\n\\n for source in sources:\\n for name in metric_names:\\n metric = models.Metric.objects.get_or_create(source=source,\\n name=name)[0]\\n start = datetime.datetime.now() - datetime.timedelta(\\n minutes=interval * point_numbers)\\n for i in range(point_numbers):\\n metric.latest_value = random.randint(1, 100)\\n metric.last_updated = (start +\\n datetime.timedelta(minutes=interval * i))\\n metric.save()\",\n \"def get_beat_data(data,columns,seconds='last',starteqend=False,preloadduration=0.3):\\r\\n if ispanda(data) and isastr(columns[0]):\\r\\n time=data.as_matrix(['X'])\\r\\n data=data.as_matrix(columns)\\r\\n elif isnparray(data) and isanum(columns[0]):\\r\\n data=np.array(data)\\r\\n time=data[:,[0]].copy()\\r\\n data=data[:,columns].copy()\\r\\n else:\\r\\n return None\\r\\n time=time.transpose()[0]\\r\\n if str(seconds)=='last':\\r\\n seconds=[time[-1]-1,time[-1]]\\r\\n elif isanum(seconds):\\r\\n if len(np.array([seconds]))>1:\\r\\n pass\\r\\n #now check if we wanted a beat#\\r\\n elif isinteger(seconds):\\r\\n pld=preloadduration\\r\\n seconds=[seconds-1+pld,seconds+pld]\\r\\n else:\\r\\n return None\\r\\n #start row(sr) and end row (er)\\r\\n sr=np.argmin(abs(time-seconds[0]))\\r\\n er=np.argmin(abs(time-seconds[-1]))\\r\\n data=data[sr:er+1]\\r\\n timeabs=time[sr:er+1].copy()\\r\\n timerel=timeabs-timeabs[0]\\r\\n if starteqend:\\r\\n if timerel[-1]<0.05:\\r\\n return None\\r\\n #fifty ms row\\r\\n fiftymsr=np.argmin(abs(timerel-0.05))\\r\\n #time as a column again, multiplied horizontally to reach the desired dim\\r\\n t=np.array([timerel[:fiftymsr+1]]).T \\r\\n t=np.tile(t,(1,data.shape[1]))\\r\\n #vf/vi (relationship between final value (vf) and initial value (vi))\\r\\n vf_vi=np.tile(data[-1]/data[0],(fiftymsr+1,1))\\r\\n adj_mat=(1-vf_vi)/0.05*t+vf_vi\\r\\n data[:fiftymsr+1,:]=data[:fiftymsr+1,:]*adj_mat\\r\\n return data,np.array([timeabs]).T,np.array([timerel]).T\",\n \"def compare(self):\\n samples = self.data[-2:]\\n if len(samples) != 2:\\n return\\n\\n timestamp_a, data_a = samples[0]\\n timestamp_b, data_b = samples[1]\\n LOG.debug(\\\"%s comparing sample from %s to %s\\\", self, timestamp_a, timestamp_b)\\n changes = dict_compare(data_a, data_b)\\n for key in changes:\\n OUTPUT.info(\\\"%s:%s: %s -> %s\\\", self, key, get_value(data_a, key), get_value(data_b, key))\",\n \"def get_sampling_updates(self, k=1):\\n # perform actual negative phase\\n [nh_samples, nv_samples, nv_mean, beta, mixstat, E, labels, \\n swapstat, rtime, avg_rtime, nup, ndown], updates = \\\\\\n theano.scan(self.pt_step, \\n outputs_info = \\\\\\n [{'initial': None, 'return_steps': 1}, # h1_sample\\n {'initial': self.buffer, 'return_steps': 1}, # v1_sample\\n {'initial': None, 'return_steps': 1}, # v1_mean \\n {'initial': self.beta, 'return_steps': 1}, # beta\\n {'initial': self.mixstat, 'return_steps': 1}, # mixstat\\n {'initial': None, 'return_steps': 1}, # E\\n {'initial': self.labels, 'return_steps': 1}, # labels\\n {'initial': self.swapstat, 'return_steps': 1}, # swapstat\\n {'initial': self.rtime, 'return_steps': 1}, # rtime\\n {'initial': self.avg_rtime,'return_steps': 1}, # avg_rtime\\n {'initial': self.nup, 'return_steps': 1}, # nup\\n {'initial': self.ndown, 'return_steps': 1}],# ndown\\n n_steps = k)\\n\\n updates = {}\\n\\n # update particle states\\n updates[self._buffer] = T.set_subtensor(self._buffer[:self.n_chain_total], nv_samples)\\n updates[self.mf_buffer] = T.set_subtensor(self.mf_buffer[:self.n_chain_total], nv_mean)\\n \\n # update energy of each particle\\n updates[self._E] = T.set_subtensor(self._E[:self.n_chain_total], E)\\n\\n # update particle<->temperature mappings\\n updates[self._mixstat] = T.set_subtensor(self._mixstat[:,:self.n_beta], mixstat)\\n updates[self._beta] = T.set_subtensor(self._beta[:self.n_chain], beta)\\n\\n # updates for beta adaptation\\n updates[self.labels] = labels\\n updates[self.rtime] = rtime\\n updates[self.avg_rtime] = avg_rtime\\n updates[self._nup] = T.set_subtensor(self._nup[:self.n_beta], nup)\\n updates[self._ndown] = T.set_subtensor(self._ndown[:self.n_beta], ndown)\\n\\n # updates for chain spawning\\n updates[self._swapstat] = T.set_subtensor(self._swapstat[:self.n_beta], swapstat)\\n\\n return [nh_samples, nv_samples, beta, mixstat, E], updates\",\n \"def test_single_ended_trans_att_synthetic():\\n from dtscalibration import DataStore\\n\\n cable_len = 100.0\\n nt = 50\\n nx = 200\\n time = np.arange(nt)\\n x = np.linspace(0.0, cable_len, nx)\\n ts_cold = np.ones(nt) * 4.0\\n ts_warm = np.ones(nt) * 20.0\\n ts_ambient = np.ones(nt) * 12\\n ts_valid = np.ones(nt) * 16\\n\\n C_p = 15246\\n C_m = 2400.0\\n dalpha_r = 0.0005284\\n dalpha_m = 0.0004961\\n dalpha_p = 0.0005607\\n gamma = 482.6\\n cold_mask1 = np.logical_and(x > 0.125 * cable_len, x < 0.25 * cable_len)\\n cold_mask2 = np.logical_and(x > 0.625 * cable_len, x < 0.75 * cable_len)\\n warm_mask1 = np.logical_and(x > 0.75 * cable_len, x < 0.875 * cable_len)\\n warm_mask2 = np.logical_and(x > 0.25 * cable_len, x < 0.375 * cable_len)\\n valid_mask = np.logical_and(x > 0.40 * cable_len, x < 0.50 * cable_len)\\n temp_real = np.ones((len(x), nt)) * 12 + 273.15\\n temp_real[cold_mask1 + cold_mask2] = ts_cold + 273.15\\n temp_real[warm_mask1 + warm_mask2] = ts_warm + 273.15\\n temp_real[valid_mask] = ts_valid + 273.15\\n\\n st = (\\n C_p\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_p * x[:, None])\\n * np.exp(gamma / temp_real)\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n ast = (\\n C_m\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_m * x[:, None])\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n\\n # Add attenuation\\n tr_att = np.random.rand(nt) * 0.2 + 0.8\\n st[int(x.size * 0.4) :] *= tr_att\\n tr_att2 = np.random.rand(nt) * 0.2 + 0.8\\n st[int(x.size * 0.6) :] *= tr_att2\\n\\n ds = DataStore(\\n {\\n \\\"st\\\": ([\\\"x\\\", \\\"time\\\"], st),\\n \\\"ast\\\": ([\\\"x\\\", \\\"time\\\"], ast),\\n \\\"userAcquisitionTimeFW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"cold\\\": ([\\\"time\\\"], ts_cold),\\n \\\"warm\\\": ([\\\"time\\\"], ts_warm),\\n \\\"ambient\\\": ([\\\"time\\\"], ts_ambient),\\n },\\n coords={\\\"x\\\": x, \\\"time\\\": time},\\n attrs={\\\"isDoubleEnded\\\": \\\"0\\\"},\\n )\\n\\n sections = {\\n \\\"ambient\\\": [slice(0.52 * cable_len, 0.58 * cable_len)],\\n \\\"cold\\\": [\\n slice(0.125 * cable_len, 0.25 * cable_len),\\n slice(0.65 * cable_len, 0.70 * cable_len),\\n ],\\n \\\"warm\\\": [slice(0.25 * cable_len, 0.375 * cable_len)],\\n }\\n\\n ds_test = ds.copy(deep=True)\\n\\n # WLS\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n # test `trans_att` related functions\\n # Clear out old results\\n ds_test.set_trans_att([])\\n\\n assert ds_test.trans_att.size == 0, \\\"clear out trans_att config\\\"\\n\\n del_keys = []\\n for k, v in ds_test.data_vars.items():\\n if \\\"trans_att\\\" in v.dims:\\n del_keys.append(k)\\n\\n assert len(del_keys) == 0, \\\"clear out trans_att config\\\"\\n\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n ds_test = ds.copy(deep=True)\\n\\n # Test fixing gamma + transient att.\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n fix_gamma=(482.6, 0),\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=10)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\\n\\n ds_test = ds.copy(deep=True)\\n\\n # Test fixing alpha + transient att.\\n ds_test.calibration_single_ended(\\n sections=sections,\\n st_var=1.0,\\n ast_var=1.0,\\n method=\\\"wls\\\",\\n fix_dalpha=(6.46e-05, 0),\\n trans_att=[40, 60],\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds_test.gamma.values, gamma, decimal=8)\\n assert_almost_equal_verbose(ds_test.tmpf.values, temp_real - 273.15, decimal=8)\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=0).talpha_fw, -np.log(tr_att), decimal=8\\n )\\n assert_almost_equal_verbose(\\n ds_test.isel(trans_att=1).talpha_fw, -np.log(tr_att2), decimal=8\\n )\",\n \"def getUnscaledSamples(self, **kwargs) -> TimeData:\\n # initialise chans, startSample and endSample with the whole dataset\\n options = self.parseGetDataKeywords(kwargs)\\n\\n # get the files to read and the samples to take from them, in the correct order\\n dataFilesToRead, samplesToRead, scalings = self.getDataFilesForSamples(\\n options[\\\"startSample\\\"], options[\\\"endSample\\\"]\\n )\\n numSamples = options[\\\"endSample\\\"] - options[\\\"startSample\\\"] + 1\\n # set up the dictionary to hold the data\\n data = {}\\n for chan in options[\\\"chans\\\"]:\\n data[chan] = np.zeros(shape=(numSamples), dtype=self.dtype)\\n\\n # loop through chans and get data\\n sampleCounter = 0\\n for dFile, sToRead, scalar in zip(dataFilesToRead, samplesToRead, scalings):\\n # get samples - this is inclusive\\n dSamples = sToRead[1] - sToRead[0] + 1\\n # spam files always record 5 channels\\n dSamplesRead = dSamples * self.recChannels[dFile]\\n # read the data\\n byteOff = (\\n self.dataByteOffset[dFile]\\n + sToRead[0] * self.recChannels[dFile] * self.dataByteSize\\n )\\n dFilePath = os.path.join(self.dataPath, dFile)\\n dataRead = np.memmap(\\n dFilePath,\\n dtype=self.dtype,\\n mode=\\\"r\\\",\\n offset=byteOff,\\n shape=(dSamplesRead),\\n )\\n # now need to unpack this\\n for chan in options[\\\"chans\\\"]:\\n # check to make sure channel exists\\n self.checkChan(chan)\\n # get the channel index - the chanIndex should give the right order in the data file\\n # as it is the same order as in the header file\\n chanIndex = self.chanMap[chan]\\n # use the range sampleCounter -> sampleCounter + dSamples, because this actually means sampleCounter + dSamples - 1 as python ranges are not inclusive of the end value\\n # scale by the lsb scalar here - note that these can be different for each file in the run\\n data[chan][sampleCounter : sampleCounter + dSamples] = (\\n dataRead[chanIndex : dSamplesRead : self.recChannels[dFile]]\\n * scalar[chan]\\n )\\n # increment sample counter\\n sampleCounter = sampleCounter + dSamples # get ready for the next data read\\n\\n # return data\\n startTime, stopTime = self.sample2time(\\n options[\\\"startSample\\\"], options[\\\"endSample\\\"]\\n )\\n comments = []\\n comments.append(\\n \\\"Unscaled data {} to {} read in from measurement {}, samples {} to {}\\\".format(\\n startTime,\\n stopTime,\\n self.dataPath,\\n options[\\\"startSample\\\"],\\n options[\\\"endSample\\\"],\\n )\\n )\\n comments.append(\\\"Data read from {} files in total\\\".format(len(dataFilesToRead)))\\n comments.append(\\n \\\"Data scaled to mV for all channels using scalings in header files\\\"\\n )\\n comments.append(\\\"Sampling frequency {}\\\".format(self.getSampleFreq()))\\n return TimeData(\\n sampleFreq=self.getSampleFreq(),\\n startTime=startTime,\\n stopTime=stopTime,\\n data=data,\\n comments=comments,\\n )\",\n \"def test_callback_multiple_entries(self):\\n TestStorage.set_timeout(20)\\n TestStorage.set_time(100)\\n store = RatedStatisticStorage()\\n entity_c = TestStorage._gen_entity(\\n \\\"ram_usage_mean\\\", [\\\"20\\\", \\\"40\\\"],\\n [chr(Outcome.HIGH), chr(Outcome.LOW)])\\n msg = TestStorage._gen_msg(\\\"n!node\\\", 100, [entity_c])\\n store.callback_rated_statistic(msg)\\n\\n self.assertEqual(\\n store.get_outcome(\\\"n!node\\\", \\\"ram_usage_mean_0\\\"), Outcome.HIGH)\\n self.assertEqual(\\n store.get_outcome(\\\"n!node\\\", \\\"ram_usage_mean_1\\\"), Outcome.LOW)\",\n \"def minutes_of_new_data(symbol, kline_size, data, source, client):\\n if len(data) > 0:\\n old = parser.parse(data[\\\"timestamp\\\"].iloc[-1])\\n elif source == \\\"binance\\\":\\n old = datetime.strptime('1 Jan 2017', '%d %b %Y')\\n elif source == \\\"bitmex\\\":\\n old = client.Trade.Trade_getBucketed(symbol=symbol, binSize=kline_size, count=1, reverse=False).result()[0][0][\\n 'timestamp'] \\n if source == \\\"binance\\\": new = pd.to_datetime(client.get_klines(symbol=symbol, interval=kline_size)[-1][0],\\n unit='ms')\\n if source == \\\"bitmex\\\": new = \\\\\\n client.Trade.Trade_getBucketed(symbol=symbol, binSize=kline_size, count=1, reverse=True).result()[0][0]['timestamp']\\n return old, new\",\n \"def _compute_observations(self):\\n observations = {}\\n for ts in self.ts_ids:\\n if self.traffic_signals[ts].time_to_act() or self.traffic_signals[ts].regular_obs() :\\n observations[ts] = self.traffic_signals[ts]._compute_observation()\\n return observations\",\n \"def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:\",\n \"def micsample(listentime):\\n frames, sampling_rate = record_audio(listentime)\\n samples = np.hstack([np.frombuffer(i, np.int16) for i in frames])\\n times = np.arange(samples.size) / sampling_rate\\n return samples, times\",\n \"def callback(in_data, frame_count, time_info, status):\\n assert frame_count == window\\n try:\\n samples = outq.get(block=False)\\n except queue.Empty:\\n print(\\\"underrun\\\", file=sys.stderr)\\n samples = zeros\\n\\n # Return samples and continue signal.\\n return (samples, pa.paContinue)\",\n \"def add_running_values(audioSamples, step):\\n if (step + 1) * groupSize > 2400:\\n print(\\\"We've reached the end of the run!\\\")\\n return None\\n samples = audioSamples[step * groupSize: (step + 1) * groupSize]\\n noiseValues = [-1, 0, 1]\\n for sample in samples:\\n # Add noise to the sample.\\n noiseValue = random.choice(noiseValues)\\n value = sample.audio + noiseValue\\n if value < 0:\\n value = 0\\n newSample = AudioData(\\n value,\\n \\\"W6\\\",\\n 1,\\n \\\"Washing Room\\\"\\n )\\n newSample.processedValue = value\\n db.session.add(newSample)\\n db.session.commit()\",\n \"def test_double_ended_wls_estimate_synthetic():\\n from dtscalibration import DataStore\\n\\n cable_len = 100.0\\n nt = 50\\n time = np.arange(nt)\\n x = np.linspace(0.0, cable_len, 100)\\n ts_cold = np.ones(nt) * 4.0\\n ts_warm = np.ones(nt) * 20.0\\n\\n C_p = 15246\\n C_m = 2400.0\\n dalpha_r = 0.0005284\\n dalpha_m = 0.0004961\\n dalpha_p = 0.0005607\\n gamma = 482.6\\n cold_mask = x < 0.5 * cable_len\\n warm_mask = np.invert(cold_mask) # == False\\n temp_real = np.ones((len(x), nt))\\n temp_real[cold_mask] *= ts_cold + 273.15\\n temp_real[warm_mask] *= ts_warm + 273.15\\n\\n st = (\\n C_p\\n * np.exp(-(dalpha_r + dalpha_p) * x[:, None])\\n * np.exp(gamma / temp_real)\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n ast = (\\n C_m\\n * np.exp(-(dalpha_r + dalpha_m) * x[:, None])\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n rst = (\\n C_p\\n * np.exp(-(dalpha_r + dalpha_p) * (-x[:, None] + cable_len))\\n * np.exp(gamma / temp_real)\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n rast = (\\n C_m\\n * np.exp(-(dalpha_r + dalpha_m) * (-x[:, None] + cable_len))\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n\\n alpha = np.mean(np.log(rst / rast) - np.log(st / ast), axis=1) / 2\\n alpha -= alpha[0] # the first x-index is where to start counting\\n\\n ds = DataStore(\\n {\\n \\\"st\\\": ([\\\"x\\\", \\\"time\\\"], st),\\n \\\"ast\\\": ([\\\"x\\\", \\\"time\\\"], ast),\\n \\\"rst\\\": ([\\\"x\\\", \\\"time\\\"], rst),\\n \\\"rast\\\": ([\\\"x\\\", \\\"time\\\"], rast),\\n \\\"userAcquisitionTimeFW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"userAcquisitionTimeBW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"cold\\\": ([\\\"time\\\"], ts_cold),\\n \\\"warm\\\": ([\\\"time\\\"], ts_warm),\\n },\\n coords={\\\"x\\\": x, \\\"time\\\": time},\\n attrs={\\\"isDoubleEnded\\\": \\\"1\\\"},\\n )\\n\\n sections = {\\n \\\"cold\\\": [slice(0.0, 0.4 * cable_len)],\\n \\\"warm\\\": [slice(0.65 * cable_len, cable_len)],\\n }\\n\\n # WLS\\n ds.calibration_double_ended(\\n sections=sections,\\n st_var=1e-7,\\n ast_var=1e-7,\\n rst_var=1e-7,\\n rast_var=1e-7,\\n method=\\\"wls\\\",\\n solver=\\\"sparse\\\",\\n )\\n\\n assert_almost_equal_verbose(ds.gamma.values, gamma, decimal=10)\\n assert_almost_equal_verbose(ds.alpha.values, alpha, decimal=8)\\n assert_almost_equal_verbose(ds.tmpf.values, temp_real - 273.15, decimal=6)\\n assert_almost_equal_verbose(ds.tmpb.values, temp_real - 273.15, decimal=6)\\n assert_almost_equal_verbose(ds.tmpw.values, temp_real - 273.15, decimal=6)\",\n \"def _update(self, datapoints):\\r\\n if len(datapoints) == 1:\\r\\n timestamp, value = datapoints[0]\\r\\n whisper.update(self.path, value, timestamp)\\r\\n else:\\r\\n whisper.update_many(self.path, datapoints)\",\n \"def addSample(self, time, x, y, z):\\n\\t\\tself.numSamples += 1\\n\\t\\tif self.prevTime != None:\\n\\t\\t\\tdt = abs(time - self.prevTime)\\n\\t\\t\\tself.timeDifferences[dt] += 1\\n\\t\\t\\tif dt > self.highDT:\\n\\t\\t\\t\\tif getTimeDifference(self.rawData[self.currIdx]) >= self.minSampleTime:\\n\\t\\t\\t\\t\\tself.currIdx += 1\\n\\t\\t\\t\\t\\tself.rawData.append(list())\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself.rawData[self.currIdx] = list()\\n\\t\\t\\t\\t\\n\\t\\t\\telse: \\n\\t\\t\\t\\tself.rawData[self.currIdx].append(preProcess.resultantAcceleration(time, x, y, z))\\n\\n\\t\\tself.prevTime = time\",\n \"def monitor(data_feeder):\\n _total_time = 0.\\n _costs = []\\n _data_feeder = data_feeder(BATCH_SIZE,\\n SEQ_LEN,\\n OVERLAP,\\n Q_LEVELS,\\n Q_ZERO,\\n Q_TYPE)\\n\\n for _seqs, _reset, _mask in _data_feeder:\\n _start_time = time.time()\\n _cost = test_fn(_seqs, _mask)\\n _total_time += time.time() - _start_time\\n\\n _costs.append(_cost)\\n\\n return numpy.mean(_costs), _total_time\",\n \"def two_in_one(obs_file,et,subevent):\\r\\n \\r\\n #in this function, the \\\"original time window\\\" talked about in the comments\\r\\n #refers to the start and end times that were input to create the file obs_file,\\r\\n #which will likely have been created using the database_extraction function\\r\\n \\r\\n #opening first output file created by operational_sep_quantities\\r\\n with open(obs_file, 'r') as o:\\r\\n out = js.load(o)\\r\\n \\r\\n #all events recorded in that output file\\r\\n ongoing_events = (out['sep_forecast_submission']['triggers'][0]['particle_intensity']\\r\\n ['ongoing_events'])\\r\\n \\r\\n #creating lists for values from each event\\r\\n end_times = [] \\r\\n start_times = []\\r\\n energy_thresholds = []\\r\\n flux_thresholds = []\\r\\n out_names = []\\r\\n \\r\\n #appending values to lists for each event\\r\\n for i in range(len(ongoing_events)):\\r\\n start_times.append(parse(ongoing_events[i]['start_time']))\\r\\n end_times.append(parse(ongoing_events[i]['end_time']))\\r\\n energy_thresholds.append(ongoing_events[i]['energy_min'])\\r\\n flux_thresholds.append(float(ongoing_events[i]['threshold']))\\r\\n \\r\\n #checking if there was a second event for each threshold\\r\\n for i in range(len(end_times)):\\r\\n end = end_times[i]\\r\\n #if the end time of an event for any threshold was a day before the last day\\r\\n #in the original time window given, will check if ONLY THAT THRESHOLD\\r\\n #had another event after the first one, using the end time of the first\\r\\n #event of that threshold as the new start time of the event window\\r\\n if end.date() < et.date():\\r\\n print('end time to use as new start time: %s' %end)\\r\\n #figuring out which threshold this end time was for\\r\\n flux_thresh = int(flux_thresholds[i])\\r\\n energy_thresh = int(energy_thresholds[i])\\r\\n print('extracting second event for threshold ' + str(flux_thresh) + ' MeV '\\r\\n + str(energy_thresh) + ' pfu')\\r\\n #new start time (2 days in advance bc the database_extraction function\\r\\n #makes the start time 2 days prior, so will cancel that out)\\r\\n st = end + timedelta(days=2)\\r\\n #thresholds in correct format\\r\\n thresholds = str(energy_thresh) + ',' + str(flux_thresh)\\r\\n print('thresholds: %s' %thresholds)\\r\\n #creating observation data for second event for thresholds given\\r\\n out_names.append(Path(cfg.obs_path) /\\r\\n database_extraction(st,et,instrument_chosen,subevent,\\r\\n thresholds = thresholds,\\r\\n one_thresh = True))\\r\\n \\r\\n #returns list of all new files created by this function\\r\\n return(out_names)\",\n \"def simulate_queued_data(event_times: typing.Union[list, np.ndarray],\\n processing_time: typing.Union[list, np.ndarray],\\n num_queues: int = 1) -> np.ndarray:\\n num_points = len(event_times)\\n thread_available_times = np.ones(num_queues) * event_times[0]\\n thread_available_times[0] += processing_time[0]\\n\\n event_total_times = np.ndarray(num_points)\\n\\n event_total_times[0] = processing_time[0]\\n\\n for i in range(1, num_points):\\n current_time = event_times[i]\\n current_process_time = processing_time[i]\\n oldest_thread = thread_available_times.argmin()\\n\\n # If there is a thread that is empty at the current time, latency and processing time are the same\\n if thread_available_times[oldest_thread] < current_time:\\n event_total_times[i] = current_process_time\\n # Thread will be busy from current time until process finishes\\n thread_available_times[oldest_thread] = current_time + current_process_time\\n # If there is not a thread empty, the requests latency will be the shortest wait time plus the processing time\\n else:\\n event_total_times[i] = thread_available_times[oldest_thread] - current_time + current_process_time\\n # Thread will continue to be busy this amount of additional time\\n thread_available_times[oldest_thread] += current_process_time\\n\\n return event_total_times\",\n \"def _copy(source, track, filter_f=lambda x: True, coef=1000):\\n for msg in source:\\n if filter_f(msg):\\n track.append(msg.copy(time=int(msg.time*coef)))\",\n \"def read_timed_multi(\\n adcs: tuple[ADC, ...], bufs: tuple[AnyWritableBuf, ...], timer: Timer, /\\n ) -> bool:\",\n \"def _filterTimes(self):\\n print(self.tRange)\\n idT = np.where((self.tRange[0] > np.array(self.rawD['Epoch'][:])) & \\n (self.tRange[1] < np.array(self.rawD['Epoch'][:])))[0]\\n #print(self.rawD['Epoch'][:100])\\n print(idT)\\n # Filter data\\n for key in filter(lambda x: ('Epoch' in x or \\n ('Counts' in x and x[-1] == 's')), self.rawD.keys()):\\n self.d[key] = self.rawD[key].copy()[idT]\\n return\",\n \"def process_sample_val(self):\\n raise NotImplementedError\",\n \"def pre_process(self):\\n t1_start = perf_counter()\\n wav_arr_raw = np.array(self.raw_data['spectrum_0'].attrs['wavelengths'])\\n self.wavelengths = wav_arr_raw\\n self.back_spectra_arr = np.array(self.raw_data['spectrum_0'].attrs['background'])\\n\\n corr_data = []\\n times_proc = []\\n\\n # extract reference point for 0 seconds\\n time_ref = str(self.raw_data['spectrum_0'].attrs['creation_timestamp'])\\n\\n # spectrometer adds 'b' and quotation marks to timestamps that must be removed\\n # some spectra are taken on X.000000s which does not have a .%f component - use try and except\\n try:\\n time_ref = datetime.strptime((time_ref.replace('b','')).replace('\\\\'',''),\\\"%Y-%m-%dT%H:%M:%S.%f\\\")\\n except ValueError:\\n time_ref = datetime.strptime((time_ref.replace('b','')).replace('\\\\'',''),\\\"%Y-%m-%dT%H:%M:%S\\\")\\n\\n print('Measurement was started at {}, \\\\n normalising times and applying a background correction \\\\n'.format(time_ref))\\n\\n # applies background correction\\n for counter, spectra in enumerate(self.raw_data.keys()):\\n corr_data.append(self.raw_data[spectra]-self.back_spectra_arr)\\n time = str(self.raw_data[spectra].attrs['creation_timestamp'])\\n try:\\n time = datetime.strptime((time.replace('b','')).replace('\\\\'',''),\\\"%Y-%m-%dT%H:%M:%S.%f\\\")\\n except ValueError:\\n time = datetime.strptime((time.replace('b','')).replace('\\\\'',''),\\\"%Y-%m-%dT%H:%M:%S\\\")\\n deltatime = time - time_ref\\n times_proc.append(deltatime.total_seconds())\\n\\n self.times = np.array(times_proc)\\n print('Measurement contains {} spectra with {} wavelengths \\\\n'.format(len(self.times),len(self.wavelengths)))\\n\\n # data is stored as a pd Dataframe with elapsed times as indices and wavelengths as columns\\n pre_proc_data = pd.DataFrame(corr_data, index = self.times, columns = self.wavelengths)\\n\\n # data may be disordered in time when iterated through\\n # sort the data by elapsed time\\n self.pre_proc_data = pre_proc_data.sort_index(axis=0)\\n self.times = np.sort(self.times)\\n\\n t1_stop = perf_counter()\\n print(\\\"Elapsed time for pre-processing:\\\", t1_stop-t1_start)\\n\\n return self.pre_proc_data\",\n \"def test_make_sampled_format(self):\\n for num_inputs in [1, 3]:\\n for num_outputs in [1, 2, 4]:\\n for num_time_steps in [4, 10, 12]:\\n # Generate data\\n # P=2 format [0, 1, 2, 3, ...]\\n sample_interval = 2\\n dt_system = np.random.random()\\n dt_sample = sample_interval * dt_system\\n outputs = np.random.random(\\n (num_time_steps, num_outputs, num_inputs))\\n time_steps = make_time_steps(\\n num_time_steps, sample_interval)\\n time_values = time_steps * dt_system\\n\\n # Compute using modred\\n my_ERA = era.ERA()\\n time_steps_computed, outputs_computed =\\\\\\n era.make_sampled_format(time_values, outputs)\\n #self.assertEqual(dt_system_computed, dt_system)\\n\\n # Reference values\\n num_time_steps_true = (num_time_steps - 1) * 2\\n time_steps_true = make_time_steps(num_time_steps_true, 1)\\n outputs_true = np.zeros(\\n (num_time_steps_true, num_outputs, num_inputs))\\n outputs_true[::2] = outputs[:-1]\\n outputs_true[1::2] = outputs[1:]\\n\\n # Compare values\\n np.testing.assert_equal(\\n time_steps_computed, time_steps_true)\\n np.testing.assert_equal(outputs_computed, outputs_true)\\n\\n # Test that if there is a wrong time value, get an error\\n time_values[num_time_steps // 2] = -1\\n self.assertRaises(\\n ValueError, era.make_sampled_format, time_values,\\n outputs)\",\n \"def convEpochFeats(cur,uid,timestamp):\\n\\n\\thour = 3600\\n\\ttotalConvTime = []\\n\\ttotalConvs = []\\n\\tfor i in range(1,8):\\n\\t\\ths_timestamp = timestamp-86400+(i-1)*hour3\\n\\t\\the_timestamp = timestamp-86400+i*hour3\\n\\t\\t# Determining if start/end time of given hour is in the night\\n\\t\\t# If yes, proceed with feature calculation, if not skip\\n\\t\\ts_epoch = epochCalc(hs_timestamp)\\n\\t\\te_epoch = epochCalc(he_timestamp)\\n\\t\\n\\t\\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\\n\\t\\t\\tcur.execute('SELECT * FROM {0} WHERE start_timestamp >= {1} AND end_timestamp<= {2}'\\n\\t\\t\\t\\t.format(uid+'con',timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\\n\\n\\t\\t\\trecords = cur.fetchall()\\n\\t\\t\\t# Sum over the duration of all conversation in that hour\\n\\t\\t\\ttotalConvTime.append( sum([(records[i][1]-records[i][0]) for i in range(0,len(records))]))\\n\\t\\t\\t# Count their total number\\n\\t\\t\\ttotalConvs.append(len(records))\\n\\t# Concatenate to one row before returning\\n\\treturn(np.hstack((totalConvTime,totalConvs)))\",\n \"def interval_multivariate(inputspikes, outputspikes, samples=1):\\n times = []\\n krdists = []\\n for prv, nxt in zip(outputspikes[:-1], outputspikes[1:]):\\n krd = multivariate(inputspikes, prv, nxt, samples)\\n times.append(krd[0])\\n krdists.append(krd[1])\\n return times, krdists\",\n \"def get_rate(timestamps):\\n return (timestamps[1, 1] - timestamps[0, 1]) / (timestamps[1, 0])\",\n \"def variable_time_collate_fn(batch, args, device = torch.device(\\\"cpu\\\"), data_type = \\\"train\\\", \\n\\tdata_min = None, data_max = None):\\n\\tD = batch[0][2].shape[1]\\n\\tcombined_tt, inverse_indices = torch.unique(torch.cat([ex[1] for ex in batch]), sorted=True, return_inverse=True)\\n\\tcombined_tt = combined_tt.to(device)\\n\\n\\toffset = 0\\n\\tcombined_vals = torch.zeros([len(batch), len(combined_tt), D]).to(device)\\n\\tcombined_mask = torch.zeros([len(batch), len(combined_tt), D]).to(device)\\n\\t\\n\\tcombined_labels = None\\n\\tN_labels = 1\\n\\n\\tcombined_labels = torch.zeros(len(batch), N_labels) + torch.tensor(float('nan'))\\n\\tcombined_labels = combined_labels.to(device = device)\\n\\t\\n\\tfor b, (record_id, tt, vals, mask, labels) in enumerate(batch):\\n\\t\\ttt = tt.to(device)\\n\\t\\tvals = vals.to(device)\\n\\t\\tmask = mask.to(device)\\n\\t\\tif labels is not None:\\n\\t\\t\\tlabels = labels.to(device)\\n\\n\\t\\tindices = inverse_indices[offset:offset + len(tt)]\\n\\t\\toffset += len(tt)\\n\\n\\t\\tcombined_vals[b, indices] = vals\\n\\t\\tcombined_mask[b, indices] = mask\\n\\n\\t\\tif labels is not None:\\n\\t\\t\\tcombined_labels[b] = labels\\n\\n\\tcombined_vals, _, _ = utils.normalize_masked_data(combined_vals, combined_mask, \\n\\t\\tatt_min = data_min, att_max = data_max)\\n\\n\\tif torch.max(combined_tt) != 0.:\\n\\t\\tcombined_tt = combined_tt / torch.max(combined_tt)\\n\\t\\t\\n\\tdata_dict = {\\n\\t\\t\\\"data\\\": combined_vals, \\n\\t\\t\\\"time_steps\\\": combined_tt,\\n\\t\\t\\\"mask\\\": combined_mask,\\n\\t\\t\\\"labels\\\": combined_labels}\\n\\n\\tdata_dict = utils.split_and_subsample_batch(data_dict, args, data_type = data_type)\\n\\treturn data_dict\",\n \"def get_next_sample(self):\",\n \"def test_1d_time():\\n \\n dic,data = ng.pipe.read(\\\"common_data/1d_pipe/test.fid\\\")\\n assert data.shape == (1500,)\\n assert data.dtype == 'complex64'\\n assert round(data[0].real,2) == 91899.24\\n assert round(data[0].imag,2) == 1964.70\\n assert round(data[1].real,2) == 168844.25\\n assert round(data[1].imag,2) == 49503.41\\n write_readback(dic,data)\",\n \"def calc_observables(samples):\\n n = samples.shape[1]\\n obs = np.zeros((samples.shape[0],n*(n-1)//2))\\n \\n k = 0\\n for i in range(n):\\n for j in range(i+1,n):\\n obs[:,k] = samples[:,i]*samples[:,j]\\n k += 1\\n return obs\",\n \"def analyze2(ys, freqs, ts):\",\n \"def activityEpochFeats(cur,uid,timestamp):\\n\\tstatToMovingRatio = []\\n\\tvar_stats = []\\n\\tstd_stats = []\\n\\tuidS = uid +'act'\\n\\tfor i in range(1,8):\\n\\t\\ths_timestamp = timestamp-86400+(i-1)*hour3\\n\\t\\the_timestamp = timestamp-86400+i*hour3\\n\\t\\t# Determining if start/end time of given hour is in the night\\n\\t\\t# If yes, proceed with feature calculation, if not skip\\n\\t\\ts_epoch = epochCalc(hs_timestamp)\\n\\t\\te_epoch = epochCalc(he_timestamp)\\n\\n\\t\\tif s_epoch[0][0]=='night' or e_epoch[0][0]=='night':\\n\\t\\t\\t# Retrieving data in hour intervals spanning over one day\\n\\t\\t\\tcur.execute('SELECT activity FROM {0} WHERE time_stamp >= {1} AND time_stamp<= {2}'\\n\\t\\t\\t\\t.format(uidS,timestamp-86400+(i-1)*hour,timestamp-86400+i*hour))\\n\\t\\t\\trecords = cur.fetchall()\\n\\n\\t\\t\\tvar_stats.append(np.var(records))\\n\\t\\t\\tstd_stats.append(np.std(records))\\n\\n\\t\\t\\t# Calculating number of stationary and walking/running occurences\\n\\t\\t\\tstationary = len([item for item in records if item==0])\\n\\t\\t\\tmoving = len([item for item in records if item==1 or item==2])\\n\\n\\t\\t\\tif moving>0:\\n\\t\\t\\t\\tstatToMovingRatio.append(float(stationary) / moving)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tstatToMovingRatio.append(0)\\n\\treturn(np.nan_to_num(np.hstack((statToMovingRatio,var_stats,std_stats))))\",\n \"def raw_data(\\n n: int = 8, limit: int = 1000, arrival_function: ArrivalF = arrival1\\n) -> Counter[int]:\\n data = samples(limit, arrival_function(n))\\n wait_times = collections.Counter(coupon_collector(n, data))\\n return wait_times\",\n \"def samples_keep(self,index):\\n\\n\\t\\tif isinstance(index, (int, long)): index = range(self.samples)[-index:]\\n\\n\\t\\tself.sampled_topics = np.take(self.sampled_topics,index,axis=0)\\n\\t\\tself.tt = np.take(self.tt,index,axis=2)\\n\\t\\tself.dt = np.take(self.dt,index,axis=2)\\n\\n\\t\\tself.samples = len(index)\",\n \"def test_single_ended_wls_estimate_synthetic():\\n\\n from dtscalibration import DataStore\\n\\n cable_len = 100.0\\n nt = 50\\n time = np.arange(nt)\\n x = np.linspace(0.0, cable_len, 500)\\n ts_cold = np.ones(nt) * 4.0\\n ts_warm = np.ones(nt) * 20.0\\n\\n C_p = 15246\\n C_m = 2400.0\\n dalpha_r = 0.0005284\\n dalpha_m = 0.0004961\\n dalpha_p = 0.0005607\\n gamma = 482.6\\n cold_mask = x < 0.5 * cable_len\\n warm_mask = np.invert(cold_mask) # == False\\n temp_real = np.ones((len(x), nt))\\n temp_real[cold_mask] *= ts_cold + 273.15\\n temp_real[warm_mask] *= ts_warm + 273.15\\n\\n st = (\\n C_p\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_p * x[:, None])\\n * np.exp(gamma / temp_real)\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n ast = (\\n C_m\\n * np.exp(-dalpha_r * x[:, None])\\n * np.exp(-dalpha_m * x[:, None])\\n / (np.exp(gamma / temp_real) - 1)\\n )\\n\\n print(\\\"alphaint\\\", cable_len * (dalpha_p - dalpha_m))\\n print(\\\"alpha\\\", dalpha_p - dalpha_m)\\n print(\\\"C\\\", np.log(C_p / C_m))\\n print(\\\"x0\\\", x.max())\\n\\n ds = DataStore(\\n {\\n \\\"st\\\": ([\\\"x\\\", \\\"time\\\"], st),\\n \\\"ast\\\": ([\\\"x\\\", \\\"time\\\"], ast),\\n \\\"userAcquisitionTimeFW\\\": ([\\\"time\\\"], np.ones(nt)),\\n \\\"cold\\\": ([\\\"time\\\"], ts_cold),\\n \\\"warm\\\": ([\\\"time\\\"], ts_warm),\\n },\\n coords={\\\"x\\\": x, \\\"time\\\": time},\\n attrs={\\\"isDoubleEnded\\\": \\\"0\\\"},\\n )\\n\\n sections = {\\n \\\"cold\\\": [slice(0.0, 0.5 * cable_len)],\\n \\\"warm\\\": [slice(0.5 * cable_len, cable_len)],\\n }\\n\\n # WLS\\n ds.calibration_single_ended(\\n sections=sections, st_var=1.0, ast_var=1.0, method=\\\"wls\\\", solver=\\\"sparse\\\"\\n )\\n\\n assert_almost_equal_verbose(ds.gamma.values, gamma, decimal=6)\\n assert_almost_equal_verbose(ds.dalpha.values, dalpha_p - dalpha_m, decimal=8)\\n assert_almost_equal_verbose(ds.tmpf.values, temp_real - 273.15, decimal=4)\\n\\n pass\",\n \"def sample_next(self, time, samples, errors):\\n if self.previous_value is None:\\n output = self.start_value\\n else:\\n time_diff = time - self.previous_time\\n noise = np.random.normal(loc=0.0, scale=1.0, size=1)\\n output = (np.power(self.ar_param, time_diff))*self.previous_value+\\\\\\n self.sigma*np.sqrt(1-np.power(self.ar_param, time_diff))*noise\\n self.previous_time = time\\n self.previous_value = output\\n return output\",\n \"def getData(dig, pipe, event, pulses):\\n logging.info(\\\"Started getData\\\")\\n start_time = time.time()\\n for pulse in range(pulses):\\n samples = dig.get_data_raw()\\n# logging.info(\\\"GetData retrieved: %d\\\", len(samples))\\n pipe.put(samples)\\n end_time = time.time()\\n elapsed = end_time - start_time\\n samplesProcessed = (pulses * len(samples[0]) * len(samples))\\n logging.info(\\\"getData processed %d Msamples in %.3f s\\\",\\n samplesProcessed / 1e6,\\n elapsed)\\n logging.info(\\\"getData rate: %.3f Msa/s in lumps of %d samples\\\",\\n samplesProcessed / elapsed / 1e6,\\n dig.pointsPerCycle)\",\n \"def spike_count(spikeTime, start, stop, dt):\\n\\n\\n #Spike time turned into a numpy array\\n spikeTime = np.array(spikeTime)\\n # print('Spike Times: ', spikeTime)\\n\\n #Creat interval array - intervals in which to break up the time array - sub time interval array\\n duration = stop-start #Total run time\\n n = duration/dt #How many subintervals from time horizon results from user defined interval\\n splitInterval = np.linspace(0, duration, n+1) #create numpy array of subinterval over which to count spikes\\n # print ('split interval: ', splitInterval)\\n\\n ##Find length over which to iterate in for loop\\n length_splitInt = len(splitInterval)\\n # print('length splitInterval: ', length_splitInt)\\n length_time = len(spikeTime)\\n # print('length time: ', length_time)\\n length = length_splitInt + ((length_time) - 2)\\n # print('length :', length)\\n\\n i=0 #inex for time array\\n j=0 #index for splitInterval array.\\n k=0 #index for new matrix that will store the grouped values from the split time array\\n counter = 0 #counter variable to keep track of spike count for each subinterval through loop\\n SpikeCount = [] #Initialize array to collect the number of spikes occuring wihtin each subinterval\\n\\n for i in range(length):\\n if (i == 0) and (spikeTime[0] == splitInterval[0]):\\n counter += 1\\n i += 1\\n\\n # Spot check\\n # print('if counter: ', counter)\\n # print('time element: ', spikeTime[k])\\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\\n # print('i: ', i)\\n # print('if k: ', k)\\n\\n if k < (len(spikeTime) - 1):\\n k += 1\\n\\n # Spot check\\n # print('iff k: ', k)\\n # print('iff counter: ', counter)\\n\\n else:\\n j += 1\\n\\n # Spot check\\n # print('iff counter: ', counter)\\n # print(SpikeCount)\\n # print('iff j: ', j)\\n\\n elif (spikeTime[k] > splitInterval[j]) and (spikeTime[k] <= splitInterval[j + 1]):\\n counter += 1\\n i += 1\\n\\n # Spot check\\n # print('if counter: ', counter)\\n # print('time element: ', spikeTime[k])\\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\\n # print('i: ', i)\\n # print('if k: ', k)\\n\\n if k < (len(spikeTime) - 1):\\n k += 1\\n\\n # Spot check\\n # print('iff k: ', k)\\n # print('iff counter: ', counter)\\n\\n else:\\n j += 1\\n # Spot check\\n SpikeCount.append(counter)\\n # print('iff counter: ', counter)\\n # print(SpikeCount)\\n # print('iff j: ', j)\\n\\n\\n\\n else:\\n SpikeCount.append(counter)\\n counter = 0\\n j += 1\\n i += 1\\n\\n # Spot Check\\n # print('else counter: ', counter)\\n # print(SpikeCount)\\n # print('time element: ', spikeTime[k])\\n # print('splitInt: ', splitInterval[j], splitInterval[j + 1])\\n # print('else j: ', j)\\n # print('else i: ', i)\\n # print('else k: ', k)\\n\\n return (SpikeCount, splitInterval)\",\n \"def test_high_delta_sample_stays_the_same(self, sampling_class,\\n sampling_method):\\n s = private_sampling.ThresholdSample(0.5, sampling_method)\\n for i in range(2000):\\n s.process(i, 1)\\n private_priority_sample = sampling_class.from_non_private(\\n s, eps=0.1, delta=1.0)\\n self.assertCountEqual(s.elements.keys(), private_priority_sample.elements)\",\n \"def test_4d_stream_time():\\n fmask = \\\"common_data/4d_pipe/full4D.fid\\\"\\n dic,data = ng.pipe.read_lowmem(fmask)\\n\\n fname = \\\"common_data/4d_pipe/time_2index/test02006.fid\\\"\\n sdic,sdata = ng.pipe.read(fname)\\n\\n assert data.shape == (8, 12, 16, 1400)\\n assert data.dtype == 'complex64'\\n assert round(data[0,1,2,3].real,2) == -395.11\\n assert round(data[0,1,2,3].imag,2) == 52.72\\n assert round(data[5,9,11,987].real,2) == -35.09\\n assert round(data[5,9,11,987].imag,2) == 33.07\\n\\n # check the slice\\n assert sdata.shape == (16, 1400)\\n assert sdata.dtype == 'complex64'\\n assert round(sdata[1,2].real,2) == 75.93\\n assert round(sdata[1,2].imag,2) == 5.55\\n assert round(sdata[7,800].real,2) == -8.93\\n assert round(sdata[7,800].imag,2) == -10.24\\n\\n # slice/data matching\\n assert_array_equal(data[1,5],sdata)\\n\\n lowmem_write_readback(dic,data)\",\n \"def split_by_interval(array, interval: int):\\n\\n prev_timestamp = array[0][\\\"timestamp\\\"]\\n values = 0\\n count = 0\\n averages = []\\n\\n for el in array:\\n if el[\\\"timestamp\\\"] - prev_timestamp >= timedelta(interval):\\n prev_timestamp = el[\\\"timestamp\\\"]\\n average = values / count\\n averages.append(average)\\n values = count = 0\\n else:\\n values += el[\\\"value\\\"]\\n count += 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.60448503","0.5995645","0.58302253","0.57981944","0.5796712","0.57483286","0.57325566","0.57073534","0.56920207","0.5675369","0.56446433","0.5632045","0.5621465","0.5616494","0.559965","0.5543054","0.55349135","0.55306476","0.5527352","0.55071265","0.54715633","0.54674643","0.54631126","0.5436526","0.5403363","0.54024506","0.53941214","0.53931826","0.538854","0.5364179","0.5361968","0.53616434","0.5337768","0.5334232","0.5327028","0.53251815","0.5313115","0.5309612","0.53082424","0.5304992","0.5301247","0.5299155","0.5289329","0.5288964","0.527963","0.5279242","0.52725583","0.525972","0.5247218","0.5247172","0.5245574","0.52394915","0.52296805","0.52274007","0.5223142","0.5220494","0.52177536","0.52086973","0.5203484","0.51996785","0.5192449","0.5187594","0.5182446","0.51818377","0.51754755","0.51748526","0.5171783","0.5170953","0.51687473","0.5165193","0.51621586","0.51564026","0.51542616","0.51521146","0.514832","0.51478344","0.5141622","0.51376826","0.51356465","0.5134733","0.5129225","0.5127647","0.51250935","0.5124748","0.5118706","0.51161355","0.511333","0.5109825","0.5109059","0.51058","0.5095116","0.5094746","0.5086436","0.5086009","0.50813997","0.5078517","0.50745046","0.50730157","0.50704646","0.5068577","0.50618887"],"string":"[\n \"0.60448503\",\n \"0.5995645\",\n \"0.58302253\",\n \"0.57981944\",\n \"0.5796712\",\n \"0.57483286\",\n \"0.57325566\",\n \"0.57073534\",\n \"0.56920207\",\n \"0.5675369\",\n \"0.56446433\",\n \"0.5632045\",\n \"0.5621465\",\n \"0.5616494\",\n \"0.559965\",\n \"0.5543054\",\n \"0.55349135\",\n \"0.55306476\",\n \"0.5527352\",\n \"0.55071265\",\n \"0.54715633\",\n \"0.54674643\",\n \"0.54631126\",\n \"0.5436526\",\n \"0.5403363\",\n \"0.54024506\",\n \"0.53941214\",\n \"0.53931826\",\n \"0.538854\",\n \"0.5364179\",\n \"0.5361968\",\n \"0.53616434\",\n \"0.5337768\",\n \"0.5334232\",\n \"0.5327028\",\n \"0.53251815\",\n \"0.5313115\",\n \"0.5309612\",\n \"0.53082424\",\n \"0.5304992\",\n \"0.5301247\",\n \"0.5299155\",\n \"0.5289329\",\n \"0.5288964\",\n \"0.527963\",\n \"0.5279242\",\n \"0.52725583\",\n \"0.525972\",\n \"0.5247218\",\n \"0.5247172\",\n \"0.5245574\",\n \"0.52394915\",\n \"0.52296805\",\n \"0.52274007\",\n \"0.5223142\",\n \"0.5220494\",\n \"0.52177536\",\n \"0.52086973\",\n \"0.5203484\",\n \"0.51996785\",\n \"0.5192449\",\n \"0.5187594\",\n \"0.5182446\",\n \"0.51818377\",\n \"0.51754755\",\n \"0.51748526\",\n \"0.5171783\",\n \"0.5170953\",\n \"0.51687473\",\n \"0.5165193\",\n \"0.51621586\",\n \"0.51564026\",\n \"0.51542616\",\n \"0.51521146\",\n \"0.514832\",\n \"0.51478344\",\n \"0.5141622\",\n \"0.51376826\",\n \"0.51356465\",\n \"0.5134733\",\n \"0.5129225\",\n \"0.5127647\",\n \"0.51250935\",\n \"0.5124748\",\n \"0.5118706\",\n \"0.51161355\",\n \"0.511333\",\n \"0.5109825\",\n \"0.5109059\",\n \"0.51058\",\n \"0.5095116\",\n \"0.5094746\",\n \"0.5086436\",\n \"0.5086009\",\n \"0.50813997\",\n \"0.5078517\",\n \"0.50745046\",\n \"0.50730157\",\n \"0.50704646\",\n \"0.5068577\",\n \"0.50618887\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8469,"cells":{"query":{"kind":"string","value":"Get the list of pedestrian topics"},"document":{"kind":"string","value":"def get_ped_topics(n_peds):\n ls = []\n for n in range(n_peds):\n ls += [coord.format(n) for coord in PED_TEMPL_TOPICS]\n return ls"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_topics(self):\r\n return [x[0] for x in get_published_topics()]","def topics(ctx):\n pass","def topics(self):\r\n return topics.Topics(self)","def topics(self, project: str) -> list:\n assert self.exists(project), f'Project {project} inesistente'\n\n cursor = self.projects(\n {\n 'url': project\n }\n )\n try:\n return cursor.next()['topics']\n except StopIteration:\n return []","def topics(self) -> pulumi.Output[Optional[Sequence[str]]]:\n return pulumi.get(self, \"topics\")","def topics(self):\n return topics.Topics(self)","def get_topics(self):\n try:\n with self.__db_lock:\n sql = \"SELECT * FROM 'topics' ORDER BY 'name' ASC\"\n self.__cursor.execute(sql)\n topics = self.__cursor.fetchall()\n if topics is None or len(topics) == 0:\n return []\n return [topic[1] for topic in topics]\n except Exception as e:\n logging.error(\n \"Exception when trying to get topics list: {}\".format(e))\n return []","def list_topics(project_id):\n project_path = f\"projects/{project_id}\"\n for topic in PUBLISHER_CLIENT.list_topics(request={\"project\": project_path}):\n print(topic)","def topics(self):\r\n return contents.Topics(self)","def get_topics(self):\n return self.client.cluster.topics()","def get_topics():\n topics, _ = base_query(db_session)\n return jsonify([p.serialize for p in topics])","def topics(self):\r\n return Topics(self)","def topics(self):\n # use get_model to avoid circular dependency\n topic_model = apps.get_model('tags', 'Topic')\n return topic_model.objects.filter(tag__in=self.tags.all()).distinct()","def get_teacher_topic_all():\n topic_data = query_db(\n \"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \"\n \"ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n topics = []\n for topic in topic_data:\n topic_dict_teacher = {}\n topic_dict_teacher[\"id\"] = topic[0]\n topic_dict_teacher[\"name\"] = flask.escape(str(topic[1]))\n topic_dict_teacher[\"class\"] = flask.escape(str(topic[2]))\n topics.append(topic_dict_teacher)\n return topics","def topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"topics\")","def get_custom_phrases():\n return [x[0] for x in all_topics if x[2] == \"1\"]","def getGroupTopics(group_id): \r\n return Group.getGroupTopics(group_id)","def get_topics_for_alt(alt_list, pgm_dict):\n for epv in alt_list:\n name = epv.get('package', {}).get('name', [''])[0]\n if name:\n for pgm_pkg_key, pgm_list in pgm_dict.items():\n for pgm_epv in pgm_list:\n if name == pgm_epv.get('package_name', ''):\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\n\n return alt_list","def topics(self):\r\n return ProjectTopics(self)","def list(self, topic, **options):\n pass","def my_posttopic_list(request, show_user=False):\n\tif not show_user:\n\t\tshow_user = str(request.user)\n\ttry:\n\t\ttopics = Post.objects.order_by('-post_date').filter(post_author=show_user).values('post_topic').distinct()[:50]\n\t\tposts = []\n\t\tfor i in topics:\n\t\t\tposts.append(int(i['post_topic']))\n\t\ttopics = Topic.objects.order_by('-topic_modification_date').filter(id__in=posts)\n\t\tfor i in topics:\n\t\t\tpmax = i.post_set.all().count()/10\n\t\t\tpmaxten = i.post_set.all().count()%10\n\t\t\tif pmaxten != 0:\n\t\t\t\ti.pagination_max = pmax+1\n\t\t\telse:\n\t\t\t\ti.pagination_max = pmax\n\t\tforum_name = _('User Posts in Latest Topics')\n\texcept:\n\t\treturn render_to_response('myghtyboard/mytopics_list.html', {'perms': list_perms(request)}, context_instance=RequestContext(request))\n\treturn render_to_response(\n\t\t'myghtyboard/mytopics_list.html',\n\t\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\n\t\tcontext_instance=RequestContext(request))","def get_topics(self, tags=None):\r\n params = {}\r\n if tags:\r\n params['tags'] = ','.join(tags)\r\n resp = self._make_request('get', 'topics', params=params)\r\n return resp.json()","def getSubscriptionTopics(self) -> List[str]:\n return self.cpp.getSubscriptionTopics()","def topics(self):\n return set([n.text for n in self.node.xpath('.//domClass')\n if n.text is not None])","def get_topic_words(self, topics):\n topic_words = []\n for topic, top_n_words in topics.items():\n words = [word for word, c_tf_idf in top_n_words]\n topic_words.append(words)\n return topic_words","def topic_list(request, forum_id, pagination_id=1):\n\ttry:\n\t\ttopics = Topic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1'))\n\t\tforum_name = Forum.objects.get(id=forum_id)\n\t\tforum_name = forum_name.forum_name\n\texcept:\n\t\treturn HttpResponseRedirect('/forum/')\n\treturn object_list(\n\t\trequest,\n\t\tTopic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1')),\n\t\tpaginate_by = 10,\n\t\tallow_empty = True,\n\t\tpage = pagination_id,\n\t\textra_context = {'forum': forum_id, 'perms': list_perms(request), 'forum_name': forum_name},\n\t\ttemplate_name = 'myghtyboard/topics_list.html')","def resolve_topics(self, info, **kwargs):\n return Topic.objects.all()","def get_topics(mods):\n output = []\n topics = mods.findall(\"{{{0}}}subject/{{{0}}}topic\".format(common.MODS_NS))\n for topic in topics:\n output.append(topic.text)\n return output","def get_topics(self):\n topics = self.word_topics\n return topics / topics.sum(axis=1)[:, None]","def trendingTopics():\n api = twitter.Api()\n\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\n for topic in trending_topics:\n util.safe_print(topic.name)","def get_valid_phrases():\n return [x[0] for x in all_topics if x[1] == \"1\"]","def cmd_TOPICS(self):\r\n return self._ros.get_topics()","def get_class_topic(class_id):\n topic_data = query_db(\"SELECT id, name FROM topics WHERE class_id=?\", [class_id])\n topics = []\n for topic in topic_data:\n topic_dict_class = {}\n topic_dict_class[\"id\"] = topic[0]\n topic_dict_class[\"name\"] = topic[1]\n topics.append(topic_dict_class)\n return topics","def parse_topics(self, n=10):\n assert(self.is_trained)\n raw_topics = self._lda_model.print_topics(self._lda_model.num_topics)\n topics = map(lambda x: x.split(' + '), raw_topics)\n top_words = [\n map(\n lambda x: x.split('*')[1], \n topic[:n]\n ) \n for topic in topics]\n self.topics = top_words\n self.has_topics = True\n return top_words","def get_top_topics(self, model_name, data):\n\n if model_name == 'lda':\n return list(self.lda_model.top_topics(data))\n elif model_name == 'lsa':\n return list(self.lsa_model.print_topics(num_topics= 10))","def display_topic(self):\n return ', '.join(topic.name for topic in self.topic.all()[:3])","def my_topic_list(request, show_user=False):\n\tif not show_user:\n\t\tshow_user = str(request.user)\n\tif request.user.is_authenticated():\n\t\ttopics = Topic.objects.order_by('-topic_modification_date').filter(topic_author=show_user)[:50]\n\t\tforum_name = _('User Topics')\n\t\treturn render_to_response(\n\t\t\t'myghtyboard/mytopics_list.html',\n\t\t\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\n\t\t\tcontext_instance=RequestContext(request))\n\telse:\n\t\treturn render_to_response('pages/bug.html', {'bug': _('You aren\\'t logged in')}, context_instance=RequestContext(request))","def _topics_words(self, num_of_words):\n x = self.model.show_topics(-1, num_of_words, formatted=False)\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\n # where `topic` is a list of `(word, probability)` tuples.\n return [[i[0] for i in topic[1]] for topic in x]","def help_topics():\n pass","def top_level_discussion_topic_ids(self):\r\n topics = self.discussion_topics\r\n return [d[\"id\"] for d in topics.values()]","def print_topics(self, time=0, top_terms=20):\n return [self.print_topic(topic, time, top_terms) for topic in range(self.num_topics)]","def test_get_all_topics(mock_send_message_json):\n assert OranDmaap.get_all_topics_url == f\"{BASE_URL}/topics/listAll\"","def get_exchange_topics(conf):\n return [\n plugin.ExchangeTopics(\n exchange=conf.akanda_notification_exchange,\n topics=set(topic + '.info'\n for topic in conf.akanda_notification_topics)),\n ]","def get_queryset(self, request):\n qs = super(TopicAdmin, self).get_queryset(request)\n if request.user.is_superuser:\n return qs\n return qs.filter(id__in=request.user.profile.topics.all())","def check_exists(cls, topics):\n\t\tresult = []\n\t\tfor known_feed in cls.get([cls.create_key(url) for url in set(topics)]):\n\t\t\tif known_feed is not None:\n\t\t\t\tresult.append(known_feed.topic)\n\t\treturn result","def get_queryset(self):\n return models.ProfileTopic.objects.filter(\n profile__pk=self.kwargs.get(\"pk\")\n )","def last_topic_list(request):\n\ttopics = Topic.objects.order_by('-topic_modification_date')[:50]\n\tfor i in topics:\n\t\tpmax = i.post_set.all().count()/10\n\t\tpmaxten = i.post_set.all().count()%10\n\t\tif pmaxten != 0:\n\t\t\ti.pagination_max = pmax+1\n\t\telse:\n\t\t\ti.pagination_max = pmax\n\tforum_name = _('Last Active Topics')\n\treturn render_to_response(\n\t\t'myghtyboard/mytopics_list.html',\n\t\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\n\t\tcontext_instance=RequestContext(request))","def test_topics_for_products(self):\n desktop_topics = topics_for(product=self.desktop)\n eq_(len(desktop_topics), 3)\n\n mobile_topics = topics_for(product=self.mobile)\n eq_(len(mobile_topics), 2)","def get_topics(category):\n page = requests.get(BASE_URL, verify=False)\n soup = BeautifulSoup(page.content)\n output = []\n get_lesson_id = lambda url: url.split('=')[-1]\n\n if category == 'Top 10 Courses':\n playlist = soup.find(id='featured_playlists')\n for item in playlist.findAll('div', 'item'):\n link = item.find('a', 'featured-playlist-title')\n output.append({\n 'thumbnail': item.find('img').get('src'),\n 'title': link.text.replace(' ', '').strip(),\n 'lesson_id': get_lesson_id(link['href'])})\n else:\n sidebar = soup.find(id='main_aside')\n for dl in sidebar.findAll('dl'):\n if dl.find('h4').text == category:\n for item in dl.findAll('dd'):\n link = item.find('a', 'category-name')\n output.append({\n 'title': link.getText(' '),\n 'lesson_id': get_lesson_id(link['href'])})\n\n return output","def get_topics_for_comp(comp_list, pgm_list):\n for epv in comp_list:\n name = epv.get('package', {}).get('name', [''])[0]\n if name:\n for pgm_epv in pgm_list:\n if name == pgm_epv.get('package_name', ''):\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\n epv['package']['cooccurrence_probability'] = pgm_epv.get(\n 'cooccurrence_probability', 0)\n epv['package']['cooccurrence_count'] = pgm_epv.get(\n 'cooccurrence_count', 0)\n\n return comp_list","def getHelpTopics(self):\n return self.helpTopics.values()","def print_topics(lda):\n topics = lda.show_topics(num_topics=100, num_words=10, formatted=False)\n for ti, topic in enumerate(topics):\n print('topic %d: %s' % (ti, ' '.join('%s/%.2f' % (t[1], t[0]) for t in topic)))","def muckrack_trending_topics():\n\tresponse = requests.get('http://muckrack.com')\n\thtml = response.text\n\tdom = pq(html)\n\ttrending_list = dom('.trending')\n\treturn [topic.text for topic in trending_list]","def __init__(self, topics=None):\n self.topics = topics or []","def describe_topics(self, topics=None):\n return self._client.describe_topics(topics)","def get_topics(all_jsons):\n topics = []\n for j in all_jsons:\n if 'GeneralObjectsDetected' in j:\n for obj in j['GeneralObjectsDetected']:\n topics.append(obj.split(':')[0])\n return list(set(topics))","def get_topics(model, nlp_model, n_top_words):\n\n words = nlp_model.get_feature_names()\n\n return [convert_to_string([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]) for topic_idx, topic in enumerate(model.components_)]","def subscribed_topics(self):\n return self.manager.subscribed_topics","def print_topic_times(self, topic, top_terms=20):\n topics = []\n for time in range(self.num_time_slices):\n topics.append(self.print_topic(topic, time, top_terms))\n\n return topics","def get_probable_topic(self, script):\n script_topics = script.topic_probabilities\\\n .filter(topic_model=self)\\\n .only('topic', 'probability')\n\n max_prob = -100000\n probable_topic = None\n for mt in script_topics:\n if mt.probability > max_prob:\n probable_topic = mt.topic\n max_prob = mt.probability\n\n return probable_topic","def show_topics(self, num_topics=10, num_words=10, log=False, formatted=True):\n if num_topics < 0 or num_topics >= self.num_topics:\n num_topics = self.num_topics\n chosen_topics = range(num_topics)\n else:\n num_topics = min(num_topics, self.num_topics)\n # add a little random jitter, to randomize results around the same alpha\n sort_alpha = self.alpha + 0.0001 * numpy.random.rand(len(self.alpha))\n sorted_topics = list(matutils.argsort(sort_alpha))\n chosen_topics = sorted_topics[: num_topics // 2] + sorted_topics[-num_topics // 2:]\n shown = []\n for i in chosen_topics:\n if formatted:\n topic = self.print_topic(i, topn=num_words)\n else:\n topic = self.show_topic(i, topn=num_words)\n shown.append((i, topic))\n if log:\n logger.info(\"topic #%i (%.3f): %s\", i, self.alpha[i], topic)\n return shown","def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \n pass","def describe_event_topics(DirectoryId=None, TopicNames=None):\n pass","def generate_initial_topics(self):\n initial_topics = random.sample(self.remaining_topics, self.num_topics)\n self.remaining_topics = [topic for topic in self.remaining_topics if topic not in initial_topics]\n return initial_topics","def format_topics(topics):\n return '|'.join([topic.get('title', '') for topic in topics])","def extract_queries(self, path_topics=\"../topics-rnd5.xml\"):\n \n topic_queries = []\n with open(path_topics, \"r\") as f:\n for line in f:\n match = re.match(\".*<query>([^<]*)<\\/query>.*\", line)\n if match:\n topic_queries.append(match.group(1))\n if len(topic_queries) != 50:\n sys.exit(\"There should be 50 topics, found {}\".format(\n len(topic_queries)))\n \n return topic_queries","def _get_topic_for_response():\n return _get_topic_base() + \"res/\"","def get_most_relevant_topics(topics_list):\n topics_list.sort(cmp=lambda x, y: 1 if x[1] < y[1] else -1)\n topics_list = topics_list[:3] # ARBITRARY (at most 3 topic names)\n topics_id, _ = zip(*topics_list)\n return [topics_tools.lda_topic_names[tid] for tid in topics_id]","def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = response.get(\"Items\")\n return questions","def topicnews(topic):\n urlnews=urltop\n url=urlnews+topic\n urlapi=url+'&'+'apiKey='\n urlcoun=urlapi+apikey\n response=requests.get(urlcoun)\n data=response.json()\n return data","def suppress_topics ( *topics ) :\n if topics and 1 == len( topics ) :\n t = str ( topics [ 0 ] ).lower()\n if 'config' == t : return suppress_topics() \n\n if not topics :\n newtopics = [] \n import ostap.core.config as CONFIG\n if 'RooFit' in CONFIG.config :\n import string\n ws = string.whitespace \n node = CONFIG.config [ 'RooFit' ]\n data = node.get('RemoveTopics','(,)' )\n topics = tuple ( i.strip ( ws ) for i in data.split ( ',' ) if i.strip ( ws ) ) \n \n if topics : \n svc = ROOT.RooMsgService.instance()\n svc.saveState () \n topic = msg_topic ( *topics ) \n num = svc.numStreams()\n for i in range ( num ) : ok = Ostap.Utils.remove_topic ( i , topic )","def describe_topic(self, index):\n assert(self.has_topics)\n assert(0 <= index < self.K)\n return self.topics[index]","def topic_index():\n topic = db.topic(request.args(0)) or redirect(URL('default', 'index'))\n return dict(topic=topic)","def print_all_topics(model, num_topics=10, num_words=20, try_to_disambiguate=False,\n min_word_probabity_for_disambiguation=0.010):\n print('Print {0} topics'.format(num_topics))\n print('------------')\n for t in model.show_topics(num_topics=num_topics, num_words=num_words, formatted=False):\n if try_to_disambiguate:\n possible_labels = disambiguate_topic(model.show_topic(t[0]), min_word_probability=min_word_probabity_for_disambiguation)[:2]\n print('{0}:\\t{1}\\n'.format(t[0], possible_labels))\n print('{0}\\n'.format(t[1]))\n else:\n print('{0}:\\t{1}\\n'.format(t[0], t[1]))","def print_topics(model, feature_names, n_top_words, topic_prev):\n\ti = 0\n\tmessage_list =[]\n\tfor topic_idx, topic in enumerate(model.components_):\n\t\tmessage = \"%f Topic #%d: \" % (topic_prev[i],topic_idx)\n\t\ti +=1\n\t\tlist_feat = [feature_names[i]\n\t\t\t\t\t\t\tfor i in topic.argsort()[:-n_top_words - 1:-1]]\n\t\tfeat_freq = sorted(topic, reverse=True)\n\t\tfor j in range(0, len(list_feat)):\n\t\t\tlist_feat[j] += \" \" + str(round(feat_freq[j], 3)) + \",\"\n\n\t\tmessage += \" \".join(list_feat)\n\t\tmessage_list.append(message)\n\t\tprint(message)\n\tprint()\n\treturn message_list","def get_publishers(self):","def get_topic(self):\n return self.topic","def print_top_topics_custom(topic_model, start_year, end_year, n_topics=10, out=sys.stdout, debug=False):\n papers_count = get_paper_count_per_topic(topic_model, start_year, end_year, debug)\n topic_ids = []\n out.write('#\\ttopic id\\t#docs\\ttopic\\n')\n for i in range(min(n_topics, len(papers_count))):\n topic_id = papers_count[i][0]\n topic_ids.append(topic_id)\n out.write(\n '{0}\\t{3}\\t\\t{1}\\t{2}\\n\\n'.format(i, papers_count[i][1], topic_model.print_topic(topic_id, 30), topic_id))\n\n return topic_ids","def test_extract_topics():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model = BERTopic()\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def __init__(self):\n self.topics = {}","def test_wiki_topics(self):\n t1 = TopicFactory(slug='doesnotexist')\n t2 = TopicFactory(slug='extant')\n t3 = TopicFactory(slug='tagged')\n\n doc = DocumentFactory(locale=u'en-US', category=10)\n doc.topics.add(t2)\n RevisionFactory(document=doc, is_approved=True)\n\n doc = DocumentFactory(locale=u'en-US', category=10)\n doc.topics.add(t2)\n doc.topics.add(t3)\n RevisionFactory(document=doc, is_approved=True)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n ([t2.slug, t3.slug], 1),\n )\n\n qs = {'a': 1, 'w': 1, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def get_topics_paged_get(self, categoryFilter, group, locales, page, pageSize, quickDate, sort, tagstring):\n # TODO: Assuming first server is good - need to make fallback logic\n return self.session.get_any(\"{base}{request_url}\".format(base=self.servers[0],\n request_url=F\"/Forum/GetTopicsPaged/{page}/{pageSize}/{group}/{sort}/{quickDate}/{categoryFilter}/\"))","def test_topic_notification_list_show_private_topic(self):\n TopicNotification.objects.all().delete()\n\n topic_a = utils.create_private_topic(user=self.user)\n topic_notif = TopicNotification.objects.create(\n user=self.user, topic=topic_a.topic,\n comment=self.comment, is_active=True, action=COMMENT)\n\n utils.login(self)\n response = self.client.get(reverse('spirit:topic:notification:index'))\n self.assertEqual(\n list(response.context['notifications']),\n [topic_notif, ])\n\n # list unread should behave the same\n response = self.client.get(\n reverse('spirit:topic:notification:index-unread'))\n self.assertEqual(list(response.context['page']), [topic_notif, ])\n\n # ajax list should behave the same\n response = self.client.get(\n reverse('spirit:topic:notification:index-ajax'),\n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\n res = json.loads(response.content.decode('utf-8'))\n self.assertEqual(len(res['n']), 1)","def test_topic_list_view_unauthenticated(self):\n response = self.client.get(self.url)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(len(response.context[\"topics\"]), 3)","def get_topics_strings(\n topics_words, mu, sigma, vocabulary, topics_to_print=10, words_per_topic=30\n):\n mu = np.squeeze(mu, axis=0)\n sigma = np.squeeze(sigma, axis=0)\n # Use a stable sorting algorithm so that when alpha is fixed\n # we always get the same topics.\n highest_weight_topics = np.argsort(-mu, kind=\"mergesort\")\n top_words = np.argsort(-topics_words, axis=1)\n\n res = []\n # try:\n for topic_idx in highest_weight_topics[:topics_to_print]:\n lst = [\n \"index={} mu={:.2f} sigma={:.2f}\".format(\n topic_idx, mu[topic_idx], sigma[topic_idx]\n )\n ]\n lst += [vocabulary[word] for word in top_words[topic_idx, :words_per_topic]]\n res.append(\" \".join(lst))\n # except:\n # res.append('')\n\n return np.array(res)","def test_get_topics(self):\n\n for m in self.models:\n\n topics = m.topics\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertEqual(topics.num_rows(), 25)\n self.assertEqual(topics.num_columns(), 2)\n z = m.topics[\"topic_probabilities\"]\n for k in range(m.num_topics):\n self.assertTrue(\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\n \"Returned probabilities do not sum to 1.\",\n )\n\n # Make sure returned object is an SFrame of the right size\n topics = m.get_topics()\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertTrue(\n topics.num_columns() == 3,\n \"Returned SFrame should have a topic, word, and probs.\",\n )\n\n # Make sure that requesting a single topic returns only that topic\n num_words = 8\n topics = m.get_topics([5], num_words=num_words)\n self.assertTrue(\n all(topics[\"topic\"] == 5), \"Returned topics do not have the right id.\"\n )\n self.assertEqual(topics.num_rows(), num_words)\n topics = m.get_topics([2, 4], num_words=num_words)\n self.assertEqual(set(list(topics[\"topic\"])), set([2, 4]))\n self.assertEqual(topics.num_rows(), num_words + num_words)\n\n # Make sure the cumulative probability of the returned words is\n # is less than the cutoff we provided.\n # A cutoff of 1.0 should return num_words for every topic.\n cutoff = 1.0\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\n totals = topics.groupby(\n \"topic\", {\"total_score\": turicreate.aggregate.SUM(\"score\")}\n )\n self.assertTrue(\n all(totals[\"total_score\"] <= (cutoff + DELTA)),\n \"More words were returned than expected for this cutoff.\",\n )\n\n # Make sure we raise errors for bad input\n with self.assertRaises(ValueError):\n m.get_topics([-1])\n with self.assertRaises(ValueError):\n m.get_topics([10000])\n with self.assertRaises(ToolkitError):\n topics = m.get_topics(output_type=\"other\")\n\n # Test getting topic_words\n topic_words = m.get_topics(output_type=\"topic_words\", num_words=5)\n self.assertEqual(type(topic_words), turicreate.SFrame)\n\n # Test words are sorted correctly for the first topic\n # TODO: Make this more deterministic.\n\n # topic_probs = m.get_topics(num_words=5)\n # expected = [w for w in topic_probs['word'][:5]]\n # observed = topic_words['words'][0]\n # self.assertEqual(observed[0], expected[0])","def get_related_topics(self,keyword,cut=0.5):\n \n ret = []\n\n if type(keyword) == str:\n if keyword in self.topic_map.keys():\n ret = [(keyword,1.0)]\n keyword = \"\"\n else:\n _keyword = []\n for k in keyword:\n if k in self.topic_map.keys():\n ret.append((k,1.0))\n else:\n _keyword.append(k) \n keyword = _keyword\n\n keyword_rels = set(self.get_related_keywords(keyword,self.keyword_map_rel,_score=False))\n\n if len(keyword_rels) > 0:\n for topic,topic_rels in self.topic_map.items():\n alike = keyword_rels.intersection(topic_rels)\n score = (len(alike) * (100/len(keyword_rels)))/100\n ret.append((topic,round(score,3)))\n ret.sort(key=lambda x : x[1], reverse=True)\n ret = [t for t in ret if t[1] >= cut]\n \n return ret","def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n tuple_result.append(each_tuple)\n\n # sort by the the second element, which is value, of each tuple in the list\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\n\n # separate the topic and count into two lists for graphing purpose\n topics = []\n counts = []\n\n for each_tuple in tuple_result:\n topics.append(each_tuple[0])\n counts.append(each_tuple[1])\n\n return counts, topics","def getMessages(self, topic=False):\n ret = []\n catalog = getToolByName(self.context, 'portal_catalog')\n theme = ''\n if topic:\n theme = getTheme(self.context)\n query = {\n 'portal_type': 'KeyMessage',\n 'review_state': 'published'\n }\n if theme:\n query['getThemes'] = theme\n brains = catalog.searchResults(query)\n for brain in brains:\n text = self._prepareText(brain)\n obj = brain.getObject()\n parent = obj.aq_parent\n ret.append({\n 'text': text,\n 'url': brain.getURL,\n 'parent_url': parent.absolute_url(),\n 'parent_title': parent.Title(),\n })\n return ret","def target_lang_titles(self):\n return self.target_lang_topics.keys()","def main_topic_doc(ldamodel, corpus=corpus): \n \n doc_topics = pd.DataFrame()\n\n for i, row in enumerate(ldamodel[corpus]):\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\n\n for j, (topic_num, prop_topic) in enumerate(row):\n if j == 0:\n wp = ldamodel.show_topic(topic_num)\n topic_keywords = \"' \".join([word for word, prop in wp])\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n else:\n break\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\n return doc_topics","def get_topics_articles(topic_id):\n articles = db_session.query(Article).filter_by(topic_id=topic_id)\n return jsonify([p.serialize for p in articles])","def get_partitions_for_topic(self, topic):\n return self.client.cluster._partitions[topic]","def explore_topic_nouns(topic_number, topn=25, model=10):\n #\n if model==10:\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\n topicname=topic_names_10[topic_number]\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\n gensimSTR=str(gensimdic[topic_number])\n \n # \n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\n')\n \n dic={}\n j=0\n \n print('top 5 terms')\n for term, frequency in lda.show_topic(topic_number, topn):\n if dfff[dfff['nouns']==term].empty: ## dfff is loaded from pilot_path/bow_nouns.csv\n pass\n else:\n j=j+1\n if j<6:\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\n dic[term]=frequency\n dff=pd.DataFrame.from_dict(dic,orient='index')\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \n return(dff)","def include_topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"include_topics\")","def delete_topic():\n return dict()","def topic(df, num_topics=5):\r\n\r\n lda = LatentDirichletAllocation(n_topics=num_topics,\r\n max_iter=5,\r\n learning_method='online',\r\n learning_offset=50.,\r\n random_state=0)\r\n return lda.fit_transform(df)","async def create_unconsumed_topics():\n # ################################################ #\n # TODO: remove these once there is someone consuming the topics\n unconsumed_topics = ['dummy']\n\n logger.warning(\n f'Creating topics on the publisher: {unconsumed_topics} due to lack of consumers. '\n 'Remove them once there are consumers'\n )\n for topic in unconsumed_topics:\n await kafka.topic(topic).maybe_declare()\n\n # ################################################ #","def unread_forum_topics(context):\n request = context.get('request', None)\n\n # Get all topics\n all_forum_topics = Topic.objects.all()\n\n # Retrieve the unread topics\n return TrackingHandler(request=request).get_unread_topics(all_forum_topics, request.user)","def get_queryset(self, request):\n qs = super(TaskAdmin, self).get_queryset(request)\n if request.user.is_superuser:\n return qs.filter()\n return qs.filter(topic_id__in=request.user.profile.topics.all())"],"string":"[\n \"def get_topics(self):\\r\\n return [x[0] for x in get_published_topics()]\",\n \"def topics(ctx):\\n pass\",\n \"def topics(self):\\r\\n return topics.Topics(self)\",\n \"def topics(self, project: str) -> list:\\n assert self.exists(project), f'Project {project} inesistente'\\n\\n cursor = self.projects(\\n {\\n 'url': project\\n }\\n )\\n try:\\n return cursor.next()['topics']\\n except StopIteration:\\n return []\",\n \"def topics(self) -> pulumi.Output[Optional[Sequence[str]]]:\\n return pulumi.get(self, \\\"topics\\\")\",\n \"def topics(self):\\n return topics.Topics(self)\",\n \"def get_topics(self):\\n try:\\n with self.__db_lock:\\n sql = \\\"SELECT * FROM 'topics' ORDER BY 'name' ASC\\\"\\n self.__cursor.execute(sql)\\n topics = self.__cursor.fetchall()\\n if topics is None or len(topics) == 0:\\n return []\\n return [topic[1] for topic in topics]\\n except Exception as e:\\n logging.error(\\n \\\"Exception when trying to get topics list: {}\\\".format(e))\\n return []\",\n \"def list_topics(project_id):\\n project_path = f\\\"projects/{project_id}\\\"\\n for topic in PUBLISHER_CLIENT.list_topics(request={\\\"project\\\": project_path}):\\n print(topic)\",\n \"def topics(self):\\r\\n return contents.Topics(self)\",\n \"def get_topics(self):\\n return self.client.cluster.topics()\",\n \"def get_topics():\\n topics, _ = base_query(db_session)\\n return jsonify([p.serialize for p in topics])\",\n \"def topics(self):\\r\\n return Topics(self)\",\n \"def topics(self):\\n # use get_model to avoid circular dependency\\n topic_model = apps.get_model('tags', 'Topic')\\n return topic_model.objects.filter(tag__in=self.tags.all()).distinct()\",\n \"def get_teacher_topic_all():\\n topic_data = query_db(\\n \\\"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \\\"\\n \\\"ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n topics = []\\n for topic in topic_data:\\n topic_dict_teacher = {}\\n topic_dict_teacher[\\\"id\\\"] = topic[0]\\n topic_dict_teacher[\\\"name\\\"] = flask.escape(str(topic[1]))\\n topic_dict_teacher[\\\"class\\\"] = flask.escape(str(topic[2]))\\n topics.append(topic_dict_teacher)\\n return topics\",\n \"def topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"topics\\\")\",\n \"def get_custom_phrases():\\n return [x[0] for x in all_topics if x[2] == \\\"1\\\"]\",\n \"def getGroupTopics(group_id): \\r\\n return Group.getGroupTopics(group_id)\",\n \"def get_topics_for_alt(alt_list, pgm_dict):\\n for epv in alt_list:\\n name = epv.get('package', {}).get('name', [''])[0]\\n if name:\\n for pgm_pkg_key, pgm_list in pgm_dict.items():\\n for pgm_epv in pgm_list:\\n if name == pgm_epv.get('package_name', ''):\\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\\n\\n return alt_list\",\n \"def topics(self):\\r\\n return ProjectTopics(self)\",\n \"def list(self, topic, **options):\\n pass\",\n \"def my_posttopic_list(request, show_user=False):\\n\\tif not show_user:\\n\\t\\tshow_user = str(request.user)\\n\\ttry:\\n\\t\\ttopics = Post.objects.order_by('-post_date').filter(post_author=show_user).values('post_topic').distinct()[:50]\\n\\t\\tposts = []\\n\\t\\tfor i in topics:\\n\\t\\t\\tposts.append(int(i['post_topic']))\\n\\t\\ttopics = Topic.objects.order_by('-topic_modification_date').filter(id__in=posts)\\n\\t\\tfor i in topics:\\n\\t\\t\\tpmax = i.post_set.all().count()/10\\n\\t\\t\\tpmaxten = i.post_set.all().count()%10\\n\\t\\t\\tif pmaxten != 0:\\n\\t\\t\\t\\ti.pagination_max = pmax+1\\n\\t\\t\\telse:\\n\\t\\t\\t\\ti.pagination_max = pmax\\n\\t\\tforum_name = _('User Posts in Latest Topics')\\n\\texcept:\\n\\t\\treturn render_to_response('myghtyboard/mytopics_list.html', {'perms': list_perms(request)}, context_instance=RequestContext(request))\\n\\treturn render_to_response(\\n\\t\\t'myghtyboard/mytopics_list.html',\\n\\t\\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\\n\\t\\tcontext_instance=RequestContext(request))\",\n \"def get_topics(self, tags=None):\\r\\n params = {}\\r\\n if tags:\\r\\n params['tags'] = ','.join(tags)\\r\\n resp = self._make_request('get', 'topics', params=params)\\r\\n return resp.json()\",\n \"def getSubscriptionTopics(self) -> List[str]:\\n return self.cpp.getSubscriptionTopics()\",\n \"def topics(self):\\n return set([n.text for n in self.node.xpath('.//domClass')\\n if n.text is not None])\",\n \"def get_topic_words(self, topics):\\n topic_words = []\\n for topic, top_n_words in topics.items():\\n words = [word for word, c_tf_idf in top_n_words]\\n topic_words.append(words)\\n return topic_words\",\n \"def topic_list(request, forum_id, pagination_id=1):\\n\\ttry:\\n\\t\\ttopics = Topic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1'))\\n\\t\\tforum_name = Forum.objects.get(id=forum_id)\\n\\t\\tforum_name = forum_name.forum_name\\n\\texcept:\\n\\t\\treturn HttpResponseRedirect('/forum/')\\n\\treturn object_list(\\n\\t\\trequest,\\n\\t\\tTopic.objects.order_by('-is_global', '-is_sticky', '-topic_modification_date').filter(Q(topic_forum=forum_id) | Q(is_global='1')),\\n\\t\\tpaginate_by = 10,\\n\\t\\tallow_empty = True,\\n\\t\\tpage = pagination_id,\\n\\t\\textra_context = {'forum': forum_id, 'perms': list_perms(request), 'forum_name': forum_name},\\n\\t\\ttemplate_name = 'myghtyboard/topics_list.html')\",\n \"def resolve_topics(self, info, **kwargs):\\n return Topic.objects.all()\",\n \"def get_topics(mods):\\n output = []\\n topics = mods.findall(\\\"{{{0}}}subject/{{{0}}}topic\\\".format(common.MODS_NS))\\n for topic in topics:\\n output.append(topic.text)\\n return output\",\n \"def get_topics(self):\\n topics = self.word_topics\\n return topics / topics.sum(axis=1)[:, None]\",\n \"def trendingTopics():\\n api = twitter.Api()\\n\\n trending_topics = api.GetTrendsWoeid(PHILA_WOEID)\\n for topic in trending_topics:\\n util.safe_print(topic.name)\",\n \"def get_valid_phrases():\\n return [x[0] for x in all_topics if x[1] == \\\"1\\\"]\",\n \"def cmd_TOPICS(self):\\r\\n return self._ros.get_topics()\",\n \"def get_class_topic(class_id):\\n topic_data = query_db(\\\"SELECT id, name FROM topics WHERE class_id=?\\\", [class_id])\\n topics = []\\n for topic in topic_data:\\n topic_dict_class = {}\\n topic_dict_class[\\\"id\\\"] = topic[0]\\n topic_dict_class[\\\"name\\\"] = topic[1]\\n topics.append(topic_dict_class)\\n return topics\",\n \"def parse_topics(self, n=10):\\n assert(self.is_trained)\\n raw_topics = self._lda_model.print_topics(self._lda_model.num_topics)\\n topics = map(lambda x: x.split(' + '), raw_topics)\\n top_words = [\\n map(\\n lambda x: x.split('*')[1], \\n topic[:n]\\n ) \\n for topic in topics]\\n self.topics = top_words\\n self.has_topics = True\\n return top_words\",\n \"def get_top_topics(self, model_name, data):\\n\\n if model_name == 'lda':\\n return list(self.lda_model.top_topics(data))\\n elif model_name == 'lsa':\\n return list(self.lsa_model.print_topics(num_topics= 10))\",\n \"def display_topic(self):\\n return ', '.join(topic.name for topic in self.topic.all()[:3])\",\n \"def my_topic_list(request, show_user=False):\\n\\tif not show_user:\\n\\t\\tshow_user = str(request.user)\\n\\tif request.user.is_authenticated():\\n\\t\\ttopics = Topic.objects.order_by('-topic_modification_date').filter(topic_author=show_user)[:50]\\n\\t\\tforum_name = _('User Topics')\\n\\t\\treturn render_to_response(\\n\\t\\t\\t'myghtyboard/mytopics_list.html',\\n\\t\\t\\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\\n\\t\\t\\tcontext_instance=RequestContext(request))\\n\\telse:\\n\\t\\treturn render_to_response('pages/bug.html', {'bug': _('You aren\\\\'t logged in')}, context_instance=RequestContext(request))\",\n \"def _topics_words(self, num_of_words):\\n x = self.model.show_topics(-1, num_of_words, formatted=False)\\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\\n # where `topic` is a list of `(word, probability)` tuples.\\n return [[i[0] for i in topic[1]] for topic in x]\",\n \"def help_topics():\\n pass\",\n \"def top_level_discussion_topic_ids(self):\\r\\n topics = self.discussion_topics\\r\\n return [d[\\\"id\\\"] for d in topics.values()]\",\n \"def print_topics(self, time=0, top_terms=20):\\n return [self.print_topic(topic, time, top_terms) for topic in range(self.num_topics)]\",\n \"def test_get_all_topics(mock_send_message_json):\\n assert OranDmaap.get_all_topics_url == f\\\"{BASE_URL}/topics/listAll\\\"\",\n \"def get_exchange_topics(conf):\\n return [\\n plugin.ExchangeTopics(\\n exchange=conf.akanda_notification_exchange,\\n topics=set(topic + '.info'\\n for topic in conf.akanda_notification_topics)),\\n ]\",\n \"def get_queryset(self, request):\\n qs = super(TopicAdmin, self).get_queryset(request)\\n if request.user.is_superuser:\\n return qs\\n return qs.filter(id__in=request.user.profile.topics.all())\",\n \"def check_exists(cls, topics):\\n\\t\\tresult = []\\n\\t\\tfor known_feed in cls.get([cls.create_key(url) for url in set(topics)]):\\n\\t\\t\\tif known_feed is not None:\\n\\t\\t\\t\\tresult.append(known_feed.topic)\\n\\t\\treturn result\",\n \"def get_queryset(self):\\n return models.ProfileTopic.objects.filter(\\n profile__pk=self.kwargs.get(\\\"pk\\\")\\n )\",\n \"def last_topic_list(request):\\n\\ttopics = Topic.objects.order_by('-topic_modification_date')[:50]\\n\\tfor i in topics:\\n\\t\\tpmax = i.post_set.all().count()/10\\n\\t\\tpmaxten = i.post_set.all().count()%10\\n\\t\\tif pmaxten != 0:\\n\\t\\t\\ti.pagination_max = pmax+1\\n\\t\\telse:\\n\\t\\t\\ti.pagination_max = pmax\\n\\tforum_name = _('Last Active Topics')\\n\\treturn render_to_response(\\n\\t\\t'myghtyboard/mytopics_list.html',\\n\\t\\t{'topics': topics, 'forum_name': forum_name, 'perms': list_perms(request)},\\n\\t\\tcontext_instance=RequestContext(request))\",\n \"def test_topics_for_products(self):\\n desktop_topics = topics_for(product=self.desktop)\\n eq_(len(desktop_topics), 3)\\n\\n mobile_topics = topics_for(product=self.mobile)\\n eq_(len(mobile_topics), 2)\",\n \"def get_topics(category):\\n page = requests.get(BASE_URL, verify=False)\\n soup = BeautifulSoup(page.content)\\n output = []\\n get_lesson_id = lambda url: url.split('=')[-1]\\n\\n if category == 'Top 10 Courses':\\n playlist = soup.find(id='featured_playlists')\\n for item in playlist.findAll('div', 'item'):\\n link = item.find('a', 'featured-playlist-title')\\n output.append({\\n 'thumbnail': item.find('img').get('src'),\\n 'title': link.text.replace(' ', '').strip(),\\n 'lesson_id': get_lesson_id(link['href'])})\\n else:\\n sidebar = soup.find(id='main_aside')\\n for dl in sidebar.findAll('dl'):\\n if dl.find('h4').text == category:\\n for item in dl.findAll('dd'):\\n link = item.find('a', 'category-name')\\n output.append({\\n 'title': link.getText(' '),\\n 'lesson_id': get_lesson_id(link['href'])})\\n\\n return output\",\n \"def get_topics_for_comp(comp_list, pgm_list):\\n for epv in comp_list:\\n name = epv.get('package', {}).get('name', [''])[0]\\n if name:\\n for pgm_epv in pgm_list:\\n if name == pgm_epv.get('package_name', ''):\\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\\n epv['package']['cooccurrence_probability'] = pgm_epv.get(\\n 'cooccurrence_probability', 0)\\n epv['package']['cooccurrence_count'] = pgm_epv.get(\\n 'cooccurrence_count', 0)\\n\\n return comp_list\",\n \"def getHelpTopics(self):\\n return self.helpTopics.values()\",\n \"def print_topics(lda):\\n topics = lda.show_topics(num_topics=100, num_words=10, formatted=False)\\n for ti, topic in enumerate(topics):\\n print('topic %d: %s' % (ti, ' '.join('%s/%.2f' % (t[1], t[0]) for t in topic)))\",\n \"def muckrack_trending_topics():\\n\\tresponse = requests.get('http://muckrack.com')\\n\\thtml = response.text\\n\\tdom = pq(html)\\n\\ttrending_list = dom('.trending')\\n\\treturn [topic.text for topic in trending_list]\",\n \"def __init__(self, topics=None):\\n self.topics = topics or []\",\n \"def describe_topics(self, topics=None):\\n return self._client.describe_topics(topics)\",\n \"def get_topics(all_jsons):\\n topics = []\\n for j in all_jsons:\\n if 'GeneralObjectsDetected' in j:\\n for obj in j['GeneralObjectsDetected']:\\n topics.append(obj.split(':')[0])\\n return list(set(topics))\",\n \"def get_topics(model, nlp_model, n_top_words):\\n\\n words = nlp_model.get_feature_names()\\n\\n return [convert_to_string([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]) for topic_idx, topic in enumerate(model.components_)]\",\n \"def subscribed_topics(self):\\n return self.manager.subscribed_topics\",\n \"def print_topic_times(self, topic, top_terms=20):\\n topics = []\\n for time in range(self.num_time_slices):\\n topics.append(self.print_topic(topic, time, top_terms))\\n\\n return topics\",\n \"def get_probable_topic(self, script):\\n script_topics = script.topic_probabilities\\\\\\n .filter(topic_model=self)\\\\\\n .only('topic', 'probability')\\n\\n max_prob = -100000\\n probable_topic = None\\n for mt in script_topics:\\n if mt.probability > max_prob:\\n probable_topic = mt.topic\\n max_prob = mt.probability\\n\\n return probable_topic\",\n \"def show_topics(self, num_topics=10, num_words=10, log=False, formatted=True):\\n if num_topics < 0 or num_topics >= self.num_topics:\\n num_topics = self.num_topics\\n chosen_topics = range(num_topics)\\n else:\\n num_topics = min(num_topics, self.num_topics)\\n # add a little random jitter, to randomize results around the same alpha\\n sort_alpha = self.alpha + 0.0001 * numpy.random.rand(len(self.alpha))\\n sorted_topics = list(matutils.argsort(sort_alpha))\\n chosen_topics = sorted_topics[: num_topics // 2] + sorted_topics[-num_topics // 2:]\\n shown = []\\n for i in chosen_topics:\\n if formatted:\\n topic = self.print_topic(i, topn=num_words)\\n else:\\n topic = self.show_topic(i, topn=num_words)\\n shown.append((i, topic))\\n if log:\\n logger.info(\\\"topic #%i (%.3f): %s\\\", i, self.alpha[i], topic)\\n return shown\",\n \"def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \\n pass\",\n \"def describe_event_topics(DirectoryId=None, TopicNames=None):\\n pass\",\n \"def generate_initial_topics(self):\\n initial_topics = random.sample(self.remaining_topics, self.num_topics)\\n self.remaining_topics = [topic for topic in self.remaining_topics if topic not in initial_topics]\\n return initial_topics\",\n \"def format_topics(topics):\\n return '|'.join([topic.get('title', '') for topic in topics])\",\n \"def extract_queries(self, path_topics=\\\"../topics-rnd5.xml\\\"):\\n \\n topic_queries = []\\n with open(path_topics, \\\"r\\\") as f:\\n for line in f:\\n match = re.match(\\\".*<query>([^<]*)<\\\\/query>.*\\\", line)\\n if match:\\n topic_queries.append(match.group(1))\\n if len(topic_queries) != 50:\\n sys.exit(\\\"There should be 50 topics, found {}\\\".format(\\n len(topic_queries)))\\n \\n return topic_queries\",\n \"def _get_topic_for_response():\\n return _get_topic_base() + \\\"res/\\\"\",\n \"def get_most_relevant_topics(topics_list):\\n topics_list.sort(cmp=lambda x, y: 1 if x[1] < y[1] else -1)\\n topics_list = topics_list[:3] # ARBITRARY (at most 3 topic names)\\n topics_id, _ = zip(*topics_list)\\n return [topics_tools.lda_topic_names[tid] for tid in topics_id]\",\n \"def get_questions_of_topic(topic):\\n\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n fe = Attr(\\\"TopicId\\\").eq(topic.get(\\\"TopicId\\\"))\\n response = question_table.scan(FilterExpression=fe)\\n questions = response.get(\\\"Items\\\")\\n return questions\",\n \"def topicnews(topic):\\n urlnews=urltop\\n url=urlnews+topic\\n urlapi=url+'&'+'apiKey='\\n urlcoun=urlapi+apikey\\n response=requests.get(urlcoun)\\n data=response.json()\\n return data\",\n \"def suppress_topics ( *topics ) :\\n if topics and 1 == len( topics ) :\\n t = str ( topics [ 0 ] ).lower()\\n if 'config' == t : return suppress_topics() \\n\\n if not topics :\\n newtopics = [] \\n import ostap.core.config as CONFIG\\n if 'RooFit' in CONFIG.config :\\n import string\\n ws = string.whitespace \\n node = CONFIG.config [ 'RooFit' ]\\n data = node.get('RemoveTopics','(,)' )\\n topics = tuple ( i.strip ( ws ) for i in data.split ( ',' ) if i.strip ( ws ) ) \\n \\n if topics : \\n svc = ROOT.RooMsgService.instance()\\n svc.saveState () \\n topic = msg_topic ( *topics ) \\n num = svc.numStreams()\\n for i in range ( num ) : ok = Ostap.Utils.remove_topic ( i , topic )\",\n \"def describe_topic(self, index):\\n assert(self.has_topics)\\n assert(0 <= index < self.K)\\n return self.topics[index]\",\n \"def topic_index():\\n topic = db.topic(request.args(0)) or redirect(URL('default', 'index'))\\n return dict(topic=topic)\",\n \"def print_all_topics(model, num_topics=10, num_words=20, try_to_disambiguate=False,\\n min_word_probabity_for_disambiguation=0.010):\\n print('Print {0} topics'.format(num_topics))\\n print('------------')\\n for t in model.show_topics(num_topics=num_topics, num_words=num_words, formatted=False):\\n if try_to_disambiguate:\\n possible_labels = disambiguate_topic(model.show_topic(t[0]), min_word_probability=min_word_probabity_for_disambiguation)[:2]\\n print('{0}:\\\\t{1}\\\\n'.format(t[0], possible_labels))\\n print('{0}\\\\n'.format(t[1]))\\n else:\\n print('{0}:\\\\t{1}\\\\n'.format(t[0], t[1]))\",\n \"def print_topics(model, feature_names, n_top_words, topic_prev):\\n\\ti = 0\\n\\tmessage_list =[]\\n\\tfor topic_idx, topic in enumerate(model.components_):\\n\\t\\tmessage = \\\"%f Topic #%d: \\\" % (topic_prev[i],topic_idx)\\n\\t\\ti +=1\\n\\t\\tlist_feat = [feature_names[i]\\n\\t\\t\\t\\t\\t\\t\\tfor i in topic.argsort()[:-n_top_words - 1:-1]]\\n\\t\\tfeat_freq = sorted(topic, reverse=True)\\n\\t\\tfor j in range(0, len(list_feat)):\\n\\t\\t\\tlist_feat[j] += \\\" \\\" + str(round(feat_freq[j], 3)) + \\\",\\\"\\n\\n\\t\\tmessage += \\\" \\\".join(list_feat)\\n\\t\\tmessage_list.append(message)\\n\\t\\tprint(message)\\n\\tprint()\\n\\treturn message_list\",\n \"def get_publishers(self):\",\n \"def get_topic(self):\\n return self.topic\",\n \"def print_top_topics_custom(topic_model, start_year, end_year, n_topics=10, out=sys.stdout, debug=False):\\n papers_count = get_paper_count_per_topic(topic_model, start_year, end_year, debug)\\n topic_ids = []\\n out.write('#\\\\ttopic id\\\\t#docs\\\\ttopic\\\\n')\\n for i in range(min(n_topics, len(papers_count))):\\n topic_id = papers_count[i][0]\\n topic_ids.append(topic_id)\\n out.write(\\n '{0}\\\\t{3}\\\\t\\\\t{1}\\\\t{2}\\\\n\\\\n'.format(i, papers_count[i][1], topic_model.print_topic(topic_id, 30), topic_id))\\n\\n return topic_ids\",\n \"def test_extract_topics():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model = BERTopic()\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def __init__(self):\\n self.topics = {}\",\n \"def test_wiki_topics(self):\\n t1 = TopicFactory(slug='doesnotexist')\\n t2 = TopicFactory(slug='extant')\\n t3 = TopicFactory(slug='tagged')\\n\\n doc = DocumentFactory(locale=u'en-US', category=10)\\n doc.topics.add(t2)\\n RevisionFactory(document=doc, is_approved=True)\\n\\n doc = DocumentFactory(locale=u'en-US', category=10)\\n doc.topics.add(t2)\\n doc.topics.add(t3)\\n RevisionFactory(document=doc, is_approved=True)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n ([t2.slug, t3.slug], 1),\\n )\\n\\n qs = {'a': 1, 'w': 1, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def get_topics_paged_get(self, categoryFilter, group, locales, page, pageSize, quickDate, sort, tagstring):\\n # TODO: Assuming first server is good - need to make fallback logic\\n return self.session.get_any(\\\"{base}{request_url}\\\".format(base=self.servers[0],\\n request_url=F\\\"/Forum/GetTopicsPaged/{page}/{pageSize}/{group}/{sort}/{quickDate}/{categoryFilter}/\\\"))\",\n \"def test_topic_notification_list_show_private_topic(self):\\n TopicNotification.objects.all().delete()\\n\\n topic_a = utils.create_private_topic(user=self.user)\\n topic_notif = TopicNotification.objects.create(\\n user=self.user, topic=topic_a.topic,\\n comment=self.comment, is_active=True, action=COMMENT)\\n\\n utils.login(self)\\n response = self.client.get(reverse('spirit:topic:notification:index'))\\n self.assertEqual(\\n list(response.context['notifications']),\\n [topic_notif, ])\\n\\n # list unread should behave the same\\n response = self.client.get(\\n reverse('spirit:topic:notification:index-unread'))\\n self.assertEqual(list(response.context['page']), [topic_notif, ])\\n\\n # ajax list should behave the same\\n response = self.client.get(\\n reverse('spirit:topic:notification:index-ajax'),\\n HTTP_X_REQUESTED_WITH='XMLHttpRequest')\\n res = json.loads(response.content.decode('utf-8'))\\n self.assertEqual(len(res['n']), 1)\",\n \"def test_topic_list_view_unauthenticated(self):\\n response = self.client.get(self.url)\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(len(response.context[\\\"topics\\\"]), 3)\",\n \"def get_topics_strings(\\n topics_words, mu, sigma, vocabulary, topics_to_print=10, words_per_topic=30\\n):\\n mu = np.squeeze(mu, axis=0)\\n sigma = np.squeeze(sigma, axis=0)\\n # Use a stable sorting algorithm so that when alpha is fixed\\n # we always get the same topics.\\n highest_weight_topics = np.argsort(-mu, kind=\\\"mergesort\\\")\\n top_words = np.argsort(-topics_words, axis=1)\\n\\n res = []\\n # try:\\n for topic_idx in highest_weight_topics[:topics_to_print]:\\n lst = [\\n \\\"index={} mu={:.2f} sigma={:.2f}\\\".format(\\n topic_idx, mu[topic_idx], sigma[topic_idx]\\n )\\n ]\\n lst += [vocabulary[word] for word in top_words[topic_idx, :words_per_topic]]\\n res.append(\\\" \\\".join(lst))\\n # except:\\n # res.append('')\\n\\n return np.array(res)\",\n \"def test_get_topics(self):\\n\\n for m in self.models:\\n\\n topics = m.topics\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertEqual(topics.num_rows(), 25)\\n self.assertEqual(topics.num_columns(), 2)\\n z = m.topics[\\\"topic_probabilities\\\"]\\n for k in range(m.num_topics):\\n self.assertTrue(\\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\\n \\\"Returned probabilities do not sum to 1.\\\",\\n )\\n\\n # Make sure returned object is an SFrame of the right size\\n topics = m.get_topics()\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertTrue(\\n topics.num_columns() == 3,\\n \\\"Returned SFrame should have a topic, word, and probs.\\\",\\n )\\n\\n # Make sure that requesting a single topic returns only that topic\\n num_words = 8\\n topics = m.get_topics([5], num_words=num_words)\\n self.assertTrue(\\n all(topics[\\\"topic\\\"] == 5), \\\"Returned topics do not have the right id.\\\"\\n )\\n self.assertEqual(topics.num_rows(), num_words)\\n topics = m.get_topics([2, 4], num_words=num_words)\\n self.assertEqual(set(list(topics[\\\"topic\\\"])), set([2, 4]))\\n self.assertEqual(topics.num_rows(), num_words + num_words)\\n\\n # Make sure the cumulative probability of the returned words is\\n # is less than the cutoff we provided.\\n # A cutoff of 1.0 should return num_words for every topic.\\n cutoff = 1.0\\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\\n totals = topics.groupby(\\n \\\"topic\\\", {\\\"total_score\\\": turicreate.aggregate.SUM(\\\"score\\\")}\\n )\\n self.assertTrue(\\n all(totals[\\\"total_score\\\"] <= (cutoff + DELTA)),\\n \\\"More words were returned than expected for this cutoff.\\\",\\n )\\n\\n # Make sure we raise errors for bad input\\n with self.assertRaises(ValueError):\\n m.get_topics([-1])\\n with self.assertRaises(ValueError):\\n m.get_topics([10000])\\n with self.assertRaises(ToolkitError):\\n topics = m.get_topics(output_type=\\\"other\\\")\\n\\n # Test getting topic_words\\n topic_words = m.get_topics(output_type=\\\"topic_words\\\", num_words=5)\\n self.assertEqual(type(topic_words), turicreate.SFrame)\\n\\n # Test words are sorted correctly for the first topic\\n # TODO: Make this more deterministic.\\n\\n # topic_probs = m.get_topics(num_words=5)\\n # expected = [w for w in topic_probs['word'][:5]]\\n # observed = topic_words['words'][0]\\n # self.assertEqual(observed[0], expected[0])\",\n \"def get_related_topics(self,keyword,cut=0.5):\\n \\n ret = []\\n\\n if type(keyword) == str:\\n if keyword in self.topic_map.keys():\\n ret = [(keyword,1.0)]\\n keyword = \\\"\\\"\\n else:\\n _keyword = []\\n for k in keyword:\\n if k in self.topic_map.keys():\\n ret.append((k,1.0))\\n else:\\n _keyword.append(k) \\n keyword = _keyword\\n\\n keyword_rels = set(self.get_related_keywords(keyword,self.keyword_map_rel,_score=False))\\n\\n if len(keyword_rels) > 0:\\n for topic,topic_rels in self.topic_map.items():\\n alike = keyword_rels.intersection(topic_rels)\\n score = (len(alike) * (100/len(keyword_rels)))/100\\n ret.append((topic,round(score,3)))\\n ret.sort(key=lambda x : x[1], reverse=True)\\n ret = [t for t in ret if t[1] >= cut]\\n \\n return ret\",\n \"def topic_count():\\n # get the number topics and their counts as tuples: ('Topic', 123)\\n query = peewee.RawQuery(Post, \\\"select topic, count(topic) from post group by topic\\\").tuples()\\n\\n # turn the result of the query object into a list of tuples\\n tuple_result = []\\n for each_tuple in query:\\n tuple_result.append(each_tuple)\\n\\n # sort by the the second element, which is value, of each tuple in the list\\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\\n\\n # separate the topic and count into two lists for graphing purpose\\n topics = []\\n counts = []\\n\\n for each_tuple in tuple_result:\\n topics.append(each_tuple[0])\\n counts.append(each_tuple[1])\\n\\n return counts, topics\",\n \"def getMessages(self, topic=False):\\n ret = []\\n catalog = getToolByName(self.context, 'portal_catalog')\\n theme = ''\\n if topic:\\n theme = getTheme(self.context)\\n query = {\\n 'portal_type': 'KeyMessage',\\n 'review_state': 'published'\\n }\\n if theme:\\n query['getThemes'] = theme\\n brains = catalog.searchResults(query)\\n for brain in brains:\\n text = self._prepareText(brain)\\n obj = brain.getObject()\\n parent = obj.aq_parent\\n ret.append({\\n 'text': text,\\n 'url': brain.getURL,\\n 'parent_url': parent.absolute_url(),\\n 'parent_title': parent.Title(),\\n })\\n return ret\",\n \"def target_lang_titles(self):\\n return self.target_lang_topics.keys()\",\n \"def main_topic_doc(ldamodel, corpus=corpus): \\n \\n doc_topics = pd.DataFrame()\\n\\n for i, row in enumerate(ldamodel[corpus]):\\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\\n\\n for j, (topic_num, prop_topic) in enumerate(row):\\n if j == 0:\\n wp = ldamodel.show_topic(topic_num)\\n topic_keywords = \\\"' \\\".join([word for word, prop in wp])\\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\\n else:\\n break\\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\\n return doc_topics\",\n \"def get_topics_articles(topic_id):\\n articles = db_session.query(Article).filter_by(topic_id=topic_id)\\n return jsonify([p.serialize for p in articles])\",\n \"def get_partitions_for_topic(self, topic):\\n return self.client.cluster._partitions[topic]\",\n \"def explore_topic_nouns(topic_number, topn=25, model=10):\\n #\\n if model==10:\\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\\n topicname=topic_names_10[topic_number]\\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\\n gensimSTR=str(gensimdic[topic_number])\\n \\n # \\n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\\\n')\\n \\n dic={}\\n j=0\\n \\n print('top 5 terms')\\n for term, frequency in lda.show_topic(topic_number, topn):\\n if dfff[dfff['nouns']==term].empty: ## dfff is loaded from pilot_path/bow_nouns.csv\\n pass\\n else:\\n j=j+1\\n if j<6:\\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\\n dic[term]=frequency\\n dff=pd.DataFrame.from_dict(dic,orient='index')\\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \\n return(dff)\",\n \"def include_topics(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"include_topics\\\")\",\n \"def delete_topic():\\n return dict()\",\n \"def topic(df, num_topics=5):\\r\\n\\r\\n lda = LatentDirichletAllocation(n_topics=num_topics,\\r\\n max_iter=5,\\r\\n learning_method='online',\\r\\n learning_offset=50.,\\r\\n random_state=0)\\r\\n return lda.fit_transform(df)\",\n \"async def create_unconsumed_topics():\\n # ################################################ #\\n # TODO: remove these once there is someone consuming the topics\\n unconsumed_topics = ['dummy']\\n\\n logger.warning(\\n f'Creating topics on the publisher: {unconsumed_topics} due to lack of consumers. '\\n 'Remove them once there are consumers'\\n )\\n for topic in unconsumed_topics:\\n await kafka.topic(topic).maybe_declare()\\n\\n # ################################################ #\",\n \"def unread_forum_topics(context):\\n request = context.get('request', None)\\n\\n # Get all topics\\n all_forum_topics = Topic.objects.all()\\n\\n # Retrieve the unread topics\\n return TrackingHandler(request=request).get_unread_topics(all_forum_topics, request.user)\",\n \"def get_queryset(self, request):\\n qs = super(TaskAdmin, self).get_queryset(request)\\n if request.user.is_superuser:\\n return qs.filter()\\n return qs.filter(topic_id__in=request.user.profile.topics.all())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7867339","0.73566186","0.733255","0.73222786","0.72298735","0.71772873","0.7127868","0.7058027","0.7025698","0.6943055","0.68688285","0.6814466","0.67832524","0.6769526","0.6753593","0.6670809","0.660663","0.6604779","0.6540701","0.65293837","0.650491","0.647983","0.6411239","0.63960874","0.63816625","0.6375652","0.6361237","0.6351099","0.6350313","0.6305629","0.6255691","0.6232363","0.6173439","0.6145597","0.614464","0.61225873","0.6079282","0.60713136","0.60563374","0.6038992","0.6029752","0.6016739","0.5996351","0.5983899","0.5979008","0.5975755","0.5968089","0.5965385","0.5960553","0.5954899","0.5954757","0.5946697","0.59444726","0.5940261","0.5933061","0.5926648","0.59000725","0.58572507","0.5856428","0.5851768","0.5839545","0.5829418","0.5808505","0.5803146","0.5782622","0.57816726","0.5768138","0.57652295","0.5763986","0.5758385","0.57409227","0.57249683","0.5721607","0.5715178","0.5711881","0.5708401","0.57020795","0.5701426","0.5699075","0.5693329","0.56839204","0.56791806","0.5662876","0.56581336","0.56429994","0.5624922","0.56230205","0.5622304","0.56129634","0.5592812","0.5587868","0.5577466","0.5571116","0.55681175","0.55617374","0.5537945","0.55322146","0.5528402","0.5516151","0.55110574"],"string":"[\n \"0.7867339\",\n \"0.73566186\",\n \"0.733255\",\n \"0.73222786\",\n \"0.72298735\",\n \"0.71772873\",\n \"0.7127868\",\n \"0.7058027\",\n \"0.7025698\",\n \"0.6943055\",\n \"0.68688285\",\n \"0.6814466\",\n \"0.67832524\",\n \"0.6769526\",\n \"0.6753593\",\n \"0.6670809\",\n \"0.660663\",\n \"0.6604779\",\n \"0.6540701\",\n \"0.65293837\",\n \"0.650491\",\n \"0.647983\",\n \"0.6411239\",\n \"0.63960874\",\n \"0.63816625\",\n \"0.6375652\",\n \"0.6361237\",\n \"0.6351099\",\n \"0.6350313\",\n \"0.6305629\",\n \"0.6255691\",\n \"0.6232363\",\n \"0.6173439\",\n \"0.6145597\",\n \"0.614464\",\n \"0.61225873\",\n \"0.6079282\",\n \"0.60713136\",\n \"0.60563374\",\n \"0.6038992\",\n \"0.6029752\",\n \"0.6016739\",\n \"0.5996351\",\n \"0.5983899\",\n \"0.5979008\",\n \"0.5975755\",\n \"0.5968089\",\n \"0.5965385\",\n \"0.5960553\",\n \"0.5954899\",\n \"0.5954757\",\n \"0.5946697\",\n \"0.59444726\",\n \"0.5940261\",\n \"0.5933061\",\n \"0.5926648\",\n \"0.59000725\",\n \"0.58572507\",\n \"0.5856428\",\n \"0.5851768\",\n \"0.5839545\",\n \"0.5829418\",\n \"0.5808505\",\n \"0.5803146\",\n \"0.5782622\",\n \"0.57816726\",\n \"0.5768138\",\n \"0.57652295\",\n \"0.5763986\",\n \"0.5758385\",\n \"0.57409227\",\n \"0.57249683\",\n \"0.5721607\",\n \"0.5715178\",\n \"0.5711881\",\n \"0.5708401\",\n \"0.57020795\",\n \"0.5701426\",\n \"0.5699075\",\n \"0.5693329\",\n \"0.56839204\",\n \"0.56791806\",\n \"0.5662876\",\n \"0.56581336\",\n \"0.56429994\",\n \"0.5624922\",\n \"0.56230205\",\n \"0.5622304\",\n \"0.56129634\",\n \"0.5592812\",\n \"0.5587868\",\n \"0.5577466\",\n \"0.5571116\",\n \"0.55681175\",\n \"0.55617374\",\n \"0.5537945\",\n \"0.55322146\",\n \"0.5528402\",\n \"0.5516151\",\n \"0.55110574\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.68354625"},"document_rank":{"kind":"string","value":"11"}}},{"rowIdx":8470,"cells":{"query":{"kind":"string","value":"Stop timer when user enter a nonescape command."},"document":{"kind":"string","value":"def stop(self):\n command = input(\"Enter anything to finish (or 'exit' to cancel)>>>\")\n return command != 'exit'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handleKeyboardInterupt():\n System.stopExecution(TERMINATED_BY_USER)","def on_KeyboardInterrupt(player):\n print(\"paused by KeyboardInterrupt\")\n player.edit()","def term():\n curses.endwin()\n unicornhathd.off()","def stop_timer(self):\r\n self.countdownTimer.stop()","def cb_check_esc(data, remaining_calls):\n global esc_pressed, vi_buffer, cmd_text, catching_keys_data\n if last_signal_time == float(data):\n esc_pressed += 1\n set_mode(\"NORMAL\")\n # Cancel any current partial commands.\n vi_buffer = \"\"\n cmd_text = \"\"\n weechat.command(\"\", \"/bar hide vi_cmd\")\n catching_keys_data = {'amount': 0}\n weechat.bar_item_update(\"vi_buffer\")\n return weechat.WEECHAT_RC_OK","def prompt_stop(cls):\n\n cls._set_mode_prompt_stop()\n TimeDisplay.stop_time()\n for callback in cls.pause_callback:\n callback()","def stop_timer(self):\n self.end_time = datetime.now()","def stop(self):\r\n self.inst.write(':STOP')","def emergencyStop(self):\n return self.set_command(\"!\")","def stopLong(self, reject=False):\n if self.longCommand:\n p = self.spawnProc\n #print 'stopLong: sending ctrl-c'\n p.send(CTRL_C)\n match = p.expect([self.prompt,\n pexpect.TIMEOUT], 2)\n if match == 0:\n\t\tif reject: return\n trailingJunk = '\\^C' + '\\r\\n' + self.promptChunk\n output = re.sub(self.longCommand+'\\r\\n', '', p.before)\n output = re.sub(trailingJunk, '', output)\n return output\n else:\n return \"timed out\"","def end_stimulus(win,end_stim):\n #start core clock\n clock = core.Clock()\n\n #while space bar is not pressed continue to show end stimulus\n #if 50 seconds pass, then stop showing end stimulus\n end_stim.setAutoDraw(True)\n while not event.getKeys(['space']):\n win.flip()\n if int(clock.getTime()) > 50:\n break\n end_stim.setAutoDraw(False)","def kill_switch(disable_after, keys):\n watchdog(disable_after, keys)","def loop_stop(self):\n super(TimerLoop, self).loop_stop()\n self.timer.cancel()\n self.loop_confirm_stopped()","def stop() -> None:","def esc_cancel_completion(event):\n event.cli.current_buffer.cancel_completion()","def __exit__(self, exc_type, exc_val, exc_tb):\n self.stdscr.keypad(False)\n self.stdscr.nodelay(False)\n curses.echo()\n curses.nocbreak()\n curses.endwin()","def stop():","def stop():","def stop():","def stop():","def anti_idle_timer_handler(self):\n if not self.connected(): return\n self.send_nop()\n self.start_anti_idle_timer()","def _user_hit_escape(b=None):\n if b is None:\n if GLOBAL['GUI']['escape']:\n GLOBAL['GUI']['escape'] = False # toggle it\n return True\n else:\n return False\n else:\n # Set in vipy.gui.using_matplotlib.escape_to_exit()\n assert isinstance(b, bool)\n GLOBAL['GUI']['escape'] = b","def kill(self):\n self._serial.write('\\x03\\r\\n'.encode('utf-8'))\n self._serial.write('\\x03\\r\\n'.encode('utf-8'))\n sleep(2)\n self._serial.reset_input_buffer()","def stop(self):\n self.idle = True\n # pass","def TerminalClientStop(self, exitCode=200):\n pass","def ctrl_c(signum, frame):\n global shutdown_event\n raise SystemExit('\\nCancelling...')","def stop_console(self):\n return","def on_press_escape(self, event):\n del event\n self.destroy()","def stop(self):\r\n self.terminating = True","def _stop(self):\n self.display_end_message()","def set_stop(self):\n self.timer.stop=True\n final_message = \"Time %s: Interrupted %s\"%(self.sim.timestr(),\n self.timer.func.__name__)\n self._close(final_message)","def pause_handler(term):\n inp = None\n while inp not in (\"p\", \"P\", \"q\", \"Q\"):\n print(term.home + term.clear + term.move_y(term.height // 2))\n print(term.black_on_white(term.center(\"press P to continue.\")))\n\n inp = term.inkey(timeout=10)","def _control_stop(self):\n self.player.stop()","def stop_recording():\n do_command('PlayStop')\n print('Stopped')","def __time_key_release_event(self, event):\n\t\tif event.key() == QtCore.Qt.Key_Space:\n\t\t\tself._player.stop() if self._player.is_playing else _player._video.play()","def stop_timing_no_callback(self) -> None:\n self._is_timing = False","def stop_timer(self):\n self.log.info(\"{} timer stopped ({} seconds)\".format(self.name, self.interval))\n self.start_event.clear()\n # self.count = self.interval / self.sleep_chunk","def disable_keypress(self):\r\n self.cursor_visible = True","def event2600():\n header(2600)\n rune_effect, rune_flag = define_args('ii')\n\n if_player_has_special_effect(0, rune_effect)\n flag.disable_chunk(EVENT.SableRuneActive, EVENT.RhythmRuneActive)\n flag.enable(rune_flag)\n if_player_does_not_have_special_effect(0, rune_effect)\n restart()","def _cancel_timeout(_, dc):\n if dc.active():\n dc.cancel()\n return _","def stop(self):\n self.started = False\n self.i.clear_keymap()\n self.i.stop_listen()","def stop(self, signum, dummy):\n if signum == signal.SIGINT:\n self.writer.lock.acquire()\n self.stopped = True\n logging.info('Ctrl-C issued')\n self.writer.lock.release()","def do_exit(self, args) :\r\n\r\n self.__Logger.warn(\"stopping the timer loop\")\r\n\r\n self.cmds[\"SimulatorStartup\"] = True\r\n self.cmds[\"SimulatorShutdown\"] = True\r\n\r\n\r\n return True","def kill(self):\n Character.kill(self)\n cblocals.global_controlsEnabled = False","def handle_stop(_):\n loop.force_unmute()","def sigterm(signum, frame):\n loop.stop()","def stop_shiftr(event):\n mqttc.disconnect()","def stop(self):\n\n command = [0x00, 0x00, 0x00, 0x00]\n self.send_command(command)","def stop(self):\r\n self.running = False","def stop(self):\r\n self.running = False","def power_off(timeout: int = 0) -> None:","def eStop(self):\n Step(speed=0, coils=1, steps=0, dir=Step.BRAKE)\n # #######################################################\n # Need to blink Stop and wait until Stop is pressed again\n # #######################################################","def cb_key_pressed(data, signal, signal_data):\n global last_signal_time\n last_signal_time = time.time()\n if signal_data == \"\\x01[\":\n # In 50ms, check if any other keys were pressed. If not, it's Esc!\n weechat.hook_timer(50, 0, 1, \"cb_check_esc\",\n \"{:f}\".format(last_signal_time))\n return weechat.WEECHAT_RC_OK","def stop(self):\n self.setWindowTitle(self.name + ': stopped')\n self._timer.stop()","def __call__(self,duration=np.inf):\n\n clock = core.Clock()\n t=0\n while t<duration: #Keep going for the duration\n t=clock.getTime()\n\n self.text.draw()\n self.win.flip()\n\n for key in event.getKeys():\n if key:\n return","def timer():\r\n\r\n T = 0\r\n while True:\r\n print (term.white + term.move_xy(82,1) + 'TIMER : ', end='')\r\n print(T, end='\\r')\r\n time.sleep(1)\r\n T = T + 1","def __call__(self,duration=np.inf,key_press=True):\n\n clock = core.Clock()\n t=0\n while t<duration: #Keep going for the duration\n t=clock.getTime()\n\n self.text.draw()\n\n #Keep checking for time:\n if clock.getTime()>=duration:\n break\n\n self.win.flip()\n \n #Keep checking for time:\n if clock.getTime()>=duration:\n break\n\n if key_press:\n for key in event.getKeys():\n if key:\n return","def _cancel_automation(self) -> None:\n if HANDLE_VACATION_MODE in self.handles:\n handle = self.handles.pop(HANDLE_VACATION_MODE)\n self.cancel_timer(handle)","def skip_control_z(event):\n pass","def cancel_main():\n entry1.delete(0, END)\n output_on_display.delete(1.0, END)\n output()","def stop_scroll():\n send_command(0x2E)","def signal_handler(self, signal, frame):\n logger.info('CTRL+C pressed')\n self.trigger_stop()","def clean_exit(self):\n if self.client:\n if self.client.key:\n self.client.send(\"!exit\")\n self.client.cli = None\n self.stdscr.keypad(False)\n curses.echo()\n curses.nocbreak()\n curses.endwin()","def loop_exit_on_q(self, stats_period):\n start_time = time.time() # This is the only way I managed to make a curse application with\n while time.time() - start_time <= stats_period: # screen refreshing exit on key pressed: make window.getch()\n key = self.myscreen.getch() # non blocking with curses.nodelay(1) (otherwise main loop is interrupted)\n if key == ord('q'): # and check it every [10-50]ms to be responsive.\n curses.endwin()\n hacked_print(\"Monitoring ended by user\") # cf hacked_print method\n return 1\n curses.napms(self.GETCH_REFRESH_MS)","def stopMeasurement_pmt(self):\r\n self.pmtTest.aboutToQuitHandler()","def timeout_cmd(cmd, timeout):\n return \"timeout -sKILL %us stdbuf -o0 -e0 %s\" % (timeout, cmd)","def setinterrupt(self, chr: int, /) -> None:","def stop_auto_quotes(self):\n for AQ in self.auto_quotes_timers:\n self.auto_quotes_timers[AQ].cancel()\n time.sleep(1)\n self.auto_quotes_timers[AQ].cancel()","def cancel_stop(cls):\n cls._set_mode_running()","def _stop_bot(_event):\n pass","def stop( self ):\n self.data_source.si.daqStop()\n self.timer.stop()\n \n #re-enable the play button\n self.play_button.setEnabled(True)\n self.stop_button.setEnabled(False)\n self.spinbox_timestep.setEnabled(True)","def ShutDown(self):\n self.stop = True","def signal_handler(signal, frame): \n import signal\n import sys\n from time import localtime, strftime\n time = strftime(\"%H:%M:%S\", localtime())\n sel = raw_input('\\n\\n%s: Paused. Press return to resume, or type exit to quit: \\n' % time)\n if sel.startswith('e') or sel.startswith('E'):\n sys.exit(0)\n else:\n time = strftime(\"%H:%M:%S\", localtime())\n print '%s: Interrogation resumed.\\n' % time","def clean_up_terminal(self) -> None:\n if self.stdscr:\n # Disable the Keypad mode\n self.stdscr.keypad(False)\n # Renable caracters echoing\n curses.echo()\n # Disable the interrupts\n curses.nocbreak()\n # Restore the terimnal to it's orginial operating mode\n curses.endwin()","def stop():\n set_power(0)","async def stop_signal():\n global user_exit\n user_exit = True\n await cancel_async_tasks()","def timer_ffmpeg_process_timeout():\n try:\n if not self.ffmpeg_process_ps.is_alive():\n timer_ffmpeg_process.stop()\n self.w.hide()\n del (self.w)\n self.ffmpeg_process_ps = None\n except:\n pass","def stop(self):\n self._stop_event.set()","def pytest_timeout_cancel_timer(item):\n tle.lib.cancel()\n return True","def timeout(self):\n self.timeout_scan_flag=True\n self.timer.stop()\n self.status_sig.emit([\"Update_Status\",\"Timeout during acquisition\",'log'])\n self.status_sig.emit([\"Timeout\"])","def command_clearterm():\n subprocess.call(\"reset\")","def StopTimer(self):\n return time.time() - self._start_time","def shutdown():\n # command executed after Ctrl+C is pressed\n rospy.loginfo(\"Stop ASRControl\")\n rospy.sleep(1)","def end():\n curses.endwin()","def pause(question='PRESS ENTER TO CONTINUE ...'):\n try: input(question)\n except KeyboardInterrupt:\n global shutDown\n shutDown = True\n except: pass","def stop(self):\n self.active = False","def stopGame(event):\n if event.action == sense_hat.ACTION_RELEASED:\n global playAgain, alive\n playAgain = False\n alive = False","def stop_media(self):\n self.stdin_queue.put(\"stop\")","def on_stop(self):\n self.write_log(\"策略停止\")\n self.cta_engine.event_engine.unregister(EVENT_TIMER, self.process_timer_event)","def OnStopPress(self, event):\n\t\tself.onOffText.SetLabel('Off')\n\t\tself.isBaselineRunning = False\n\t\tself.hasBaselineEnded = True","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def stop(self):\n self.running = False","def handler(signum, frame):\n\tglobal stop_flag\n\tstop_flag=True\n\tprint(\"Query timeout!!!!!!!!!!!\")\n\traise Exception(\"Timeout\")","def stopDetection(self):\n self.statusWrite(\"stop\")\n self.p.sleep()\n self.birdHere = 0","def _D(stdscr):\n curses.nocbreak()\n stdscr.keypad(0)\n curses.echo()\n curses.endwin()\n import pdb; pdb.set_trace()","def ctrl_d_condition() -> bool:\n app = get_app()\n return (\n app.current_buffer.name == DEFAULT_BUFFER\n and not app.current_buffer.text\n )","def Stop():\n timer.stop()\n global total_stop\n global success_stop\n total_stop += 1\n if n % 10 == 0:\n success_stop = success_stop + 1"],"string":"[\n \"def handleKeyboardInterupt():\\n System.stopExecution(TERMINATED_BY_USER)\",\n \"def on_KeyboardInterrupt(player):\\n print(\\\"paused by KeyboardInterrupt\\\")\\n player.edit()\",\n \"def term():\\n curses.endwin()\\n unicornhathd.off()\",\n \"def stop_timer(self):\\r\\n self.countdownTimer.stop()\",\n \"def cb_check_esc(data, remaining_calls):\\n global esc_pressed, vi_buffer, cmd_text, catching_keys_data\\n if last_signal_time == float(data):\\n esc_pressed += 1\\n set_mode(\\\"NORMAL\\\")\\n # Cancel any current partial commands.\\n vi_buffer = \\\"\\\"\\n cmd_text = \\\"\\\"\\n weechat.command(\\\"\\\", \\\"/bar hide vi_cmd\\\")\\n catching_keys_data = {'amount': 0}\\n weechat.bar_item_update(\\\"vi_buffer\\\")\\n return weechat.WEECHAT_RC_OK\",\n \"def prompt_stop(cls):\\n\\n cls._set_mode_prompt_stop()\\n TimeDisplay.stop_time()\\n for callback in cls.pause_callback:\\n callback()\",\n \"def stop_timer(self):\\n self.end_time = datetime.now()\",\n \"def stop(self):\\r\\n self.inst.write(':STOP')\",\n \"def emergencyStop(self):\\n return self.set_command(\\\"!\\\")\",\n \"def stopLong(self, reject=False):\\n if self.longCommand:\\n p = self.spawnProc\\n #print 'stopLong: sending ctrl-c'\\n p.send(CTRL_C)\\n match = p.expect([self.prompt,\\n pexpect.TIMEOUT], 2)\\n if match == 0:\\n\\t\\tif reject: return\\n trailingJunk = '\\\\^C' + '\\\\r\\\\n' + self.promptChunk\\n output = re.sub(self.longCommand+'\\\\r\\\\n', '', p.before)\\n output = re.sub(trailingJunk, '', output)\\n return output\\n else:\\n return \\\"timed out\\\"\",\n \"def end_stimulus(win,end_stim):\\n #start core clock\\n clock = core.Clock()\\n\\n #while space bar is not pressed continue to show end stimulus\\n #if 50 seconds pass, then stop showing end stimulus\\n end_stim.setAutoDraw(True)\\n while not event.getKeys(['space']):\\n win.flip()\\n if int(clock.getTime()) > 50:\\n break\\n end_stim.setAutoDraw(False)\",\n \"def kill_switch(disable_after, keys):\\n watchdog(disable_after, keys)\",\n \"def loop_stop(self):\\n super(TimerLoop, self).loop_stop()\\n self.timer.cancel()\\n self.loop_confirm_stopped()\",\n \"def stop() -> None:\",\n \"def esc_cancel_completion(event):\\n event.cli.current_buffer.cancel_completion()\",\n \"def __exit__(self, exc_type, exc_val, exc_tb):\\n self.stdscr.keypad(False)\\n self.stdscr.nodelay(False)\\n curses.echo()\\n curses.nocbreak()\\n curses.endwin()\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def stop():\",\n \"def anti_idle_timer_handler(self):\\n if not self.connected(): return\\n self.send_nop()\\n self.start_anti_idle_timer()\",\n \"def _user_hit_escape(b=None):\\n if b is None:\\n if GLOBAL['GUI']['escape']:\\n GLOBAL['GUI']['escape'] = False # toggle it\\n return True\\n else:\\n return False\\n else:\\n # Set in vipy.gui.using_matplotlib.escape_to_exit()\\n assert isinstance(b, bool)\\n GLOBAL['GUI']['escape'] = b\",\n \"def kill(self):\\n self._serial.write('\\\\x03\\\\r\\\\n'.encode('utf-8'))\\n self._serial.write('\\\\x03\\\\r\\\\n'.encode('utf-8'))\\n sleep(2)\\n self._serial.reset_input_buffer()\",\n \"def stop(self):\\n self.idle = True\\n # pass\",\n \"def TerminalClientStop(self, exitCode=200):\\n pass\",\n \"def ctrl_c(signum, frame):\\n global shutdown_event\\n raise SystemExit('\\\\nCancelling...')\",\n \"def stop_console(self):\\n return\",\n \"def on_press_escape(self, event):\\n del event\\n self.destroy()\",\n \"def stop(self):\\r\\n self.terminating = True\",\n \"def _stop(self):\\n self.display_end_message()\",\n \"def set_stop(self):\\n self.timer.stop=True\\n final_message = \\\"Time %s: Interrupted %s\\\"%(self.sim.timestr(),\\n self.timer.func.__name__)\\n self._close(final_message)\",\n \"def pause_handler(term):\\n inp = None\\n while inp not in (\\\"p\\\", \\\"P\\\", \\\"q\\\", \\\"Q\\\"):\\n print(term.home + term.clear + term.move_y(term.height // 2))\\n print(term.black_on_white(term.center(\\\"press P to continue.\\\")))\\n\\n inp = term.inkey(timeout=10)\",\n \"def _control_stop(self):\\n self.player.stop()\",\n \"def stop_recording():\\n do_command('PlayStop')\\n print('Stopped')\",\n \"def __time_key_release_event(self, event):\\n\\t\\tif event.key() == QtCore.Qt.Key_Space:\\n\\t\\t\\tself._player.stop() if self._player.is_playing else _player._video.play()\",\n \"def stop_timing_no_callback(self) -> None:\\n self._is_timing = False\",\n \"def stop_timer(self):\\n self.log.info(\\\"{} timer stopped ({} seconds)\\\".format(self.name, self.interval))\\n self.start_event.clear()\\n # self.count = self.interval / self.sleep_chunk\",\n \"def disable_keypress(self):\\r\\n self.cursor_visible = True\",\n \"def event2600():\\n header(2600)\\n rune_effect, rune_flag = define_args('ii')\\n\\n if_player_has_special_effect(0, rune_effect)\\n flag.disable_chunk(EVENT.SableRuneActive, EVENT.RhythmRuneActive)\\n flag.enable(rune_flag)\\n if_player_does_not_have_special_effect(0, rune_effect)\\n restart()\",\n \"def _cancel_timeout(_, dc):\\n if dc.active():\\n dc.cancel()\\n return _\",\n \"def stop(self):\\n self.started = False\\n self.i.clear_keymap()\\n self.i.stop_listen()\",\n \"def stop(self, signum, dummy):\\n if signum == signal.SIGINT:\\n self.writer.lock.acquire()\\n self.stopped = True\\n logging.info('Ctrl-C issued')\\n self.writer.lock.release()\",\n \"def do_exit(self, args) :\\r\\n\\r\\n self.__Logger.warn(\\\"stopping the timer loop\\\")\\r\\n\\r\\n self.cmds[\\\"SimulatorStartup\\\"] = True\\r\\n self.cmds[\\\"SimulatorShutdown\\\"] = True\\r\\n\\r\\n\\r\\n return True\",\n \"def kill(self):\\n Character.kill(self)\\n cblocals.global_controlsEnabled = False\",\n \"def handle_stop(_):\\n loop.force_unmute()\",\n \"def sigterm(signum, frame):\\n loop.stop()\",\n \"def stop_shiftr(event):\\n mqttc.disconnect()\",\n \"def stop(self):\\n\\n command = [0x00, 0x00, 0x00, 0x00]\\n self.send_command(command)\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def stop(self):\\r\\n self.running = False\",\n \"def power_off(timeout: int = 0) -> None:\",\n \"def eStop(self):\\n Step(speed=0, coils=1, steps=0, dir=Step.BRAKE)\\n # #######################################################\\n # Need to blink Stop and wait until Stop is pressed again\\n # #######################################################\",\n \"def cb_key_pressed(data, signal, signal_data):\\n global last_signal_time\\n last_signal_time = time.time()\\n if signal_data == \\\"\\\\x01[\\\":\\n # In 50ms, check if any other keys were pressed. If not, it's Esc!\\n weechat.hook_timer(50, 0, 1, \\\"cb_check_esc\\\",\\n \\\"{:f}\\\".format(last_signal_time))\\n return weechat.WEECHAT_RC_OK\",\n \"def stop(self):\\n self.setWindowTitle(self.name + ': stopped')\\n self._timer.stop()\",\n \"def __call__(self,duration=np.inf):\\n\\n clock = core.Clock()\\n t=0\\n while t<duration: #Keep going for the duration\\n t=clock.getTime()\\n\\n self.text.draw()\\n self.win.flip()\\n\\n for key in event.getKeys():\\n if key:\\n return\",\n \"def timer():\\r\\n\\r\\n T = 0\\r\\n while True:\\r\\n print (term.white + term.move_xy(82,1) + 'TIMER : ', end='')\\r\\n print(T, end='\\\\r')\\r\\n time.sleep(1)\\r\\n T = T + 1\",\n \"def __call__(self,duration=np.inf,key_press=True):\\n\\n clock = core.Clock()\\n t=0\\n while t<duration: #Keep going for the duration\\n t=clock.getTime()\\n\\n self.text.draw()\\n\\n #Keep checking for time:\\n if clock.getTime()>=duration:\\n break\\n\\n self.win.flip()\\n \\n #Keep checking for time:\\n if clock.getTime()>=duration:\\n break\\n\\n if key_press:\\n for key in event.getKeys():\\n if key:\\n return\",\n \"def _cancel_automation(self) -> None:\\n if HANDLE_VACATION_MODE in self.handles:\\n handle = self.handles.pop(HANDLE_VACATION_MODE)\\n self.cancel_timer(handle)\",\n \"def skip_control_z(event):\\n pass\",\n \"def cancel_main():\\n entry1.delete(0, END)\\n output_on_display.delete(1.0, END)\\n output()\",\n \"def stop_scroll():\\n send_command(0x2E)\",\n \"def signal_handler(self, signal, frame):\\n logger.info('CTRL+C pressed')\\n self.trigger_stop()\",\n \"def clean_exit(self):\\n if self.client:\\n if self.client.key:\\n self.client.send(\\\"!exit\\\")\\n self.client.cli = None\\n self.stdscr.keypad(False)\\n curses.echo()\\n curses.nocbreak()\\n curses.endwin()\",\n \"def loop_exit_on_q(self, stats_period):\\n start_time = time.time() # This is the only way I managed to make a curse application with\\n while time.time() - start_time <= stats_period: # screen refreshing exit on key pressed: make window.getch()\\n key = self.myscreen.getch() # non blocking with curses.nodelay(1) (otherwise main loop is interrupted)\\n if key == ord('q'): # and check it every [10-50]ms to be responsive.\\n curses.endwin()\\n hacked_print(\\\"Monitoring ended by user\\\") # cf hacked_print method\\n return 1\\n curses.napms(self.GETCH_REFRESH_MS)\",\n \"def stopMeasurement_pmt(self):\\r\\n self.pmtTest.aboutToQuitHandler()\",\n \"def timeout_cmd(cmd, timeout):\\n return \\\"timeout -sKILL %us stdbuf -o0 -e0 %s\\\" % (timeout, cmd)\",\n \"def setinterrupt(self, chr: int, /) -> None:\",\n \"def stop_auto_quotes(self):\\n for AQ in self.auto_quotes_timers:\\n self.auto_quotes_timers[AQ].cancel()\\n time.sleep(1)\\n self.auto_quotes_timers[AQ].cancel()\",\n \"def cancel_stop(cls):\\n cls._set_mode_running()\",\n \"def _stop_bot(_event):\\n pass\",\n \"def stop( self ):\\n self.data_source.si.daqStop()\\n self.timer.stop()\\n \\n #re-enable the play button\\n self.play_button.setEnabled(True)\\n self.stop_button.setEnabled(False)\\n self.spinbox_timestep.setEnabled(True)\",\n \"def ShutDown(self):\\n self.stop = True\",\n \"def signal_handler(signal, frame): \\n import signal\\n import sys\\n from time import localtime, strftime\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n sel = raw_input('\\\\n\\\\n%s: Paused. Press return to resume, or type exit to quit: \\\\n' % time)\\n if sel.startswith('e') or sel.startswith('E'):\\n sys.exit(0)\\n else:\\n time = strftime(\\\"%H:%M:%S\\\", localtime())\\n print '%s: Interrogation resumed.\\\\n' % time\",\n \"def clean_up_terminal(self) -> None:\\n if self.stdscr:\\n # Disable the Keypad mode\\n self.stdscr.keypad(False)\\n # Renable caracters echoing\\n curses.echo()\\n # Disable the interrupts\\n curses.nocbreak()\\n # Restore the terimnal to it's orginial operating mode\\n curses.endwin()\",\n \"def stop():\\n set_power(0)\",\n \"async def stop_signal():\\n global user_exit\\n user_exit = True\\n await cancel_async_tasks()\",\n \"def timer_ffmpeg_process_timeout():\\n try:\\n if not self.ffmpeg_process_ps.is_alive():\\n timer_ffmpeg_process.stop()\\n self.w.hide()\\n del (self.w)\\n self.ffmpeg_process_ps = None\\n except:\\n pass\",\n \"def stop(self):\\n self._stop_event.set()\",\n \"def pytest_timeout_cancel_timer(item):\\n tle.lib.cancel()\\n return True\",\n \"def timeout(self):\\n self.timeout_scan_flag=True\\n self.timer.stop()\\n self.status_sig.emit([\\\"Update_Status\\\",\\\"Timeout during acquisition\\\",'log'])\\n self.status_sig.emit([\\\"Timeout\\\"])\",\n \"def command_clearterm():\\n subprocess.call(\\\"reset\\\")\",\n \"def StopTimer(self):\\n return time.time() - self._start_time\",\n \"def shutdown():\\n # command executed after Ctrl+C is pressed\\n rospy.loginfo(\\\"Stop ASRControl\\\")\\n rospy.sleep(1)\",\n \"def end():\\n curses.endwin()\",\n \"def pause(question='PRESS ENTER TO CONTINUE ...'):\\n try: input(question)\\n except KeyboardInterrupt:\\n global shutDown\\n shutDown = True\\n except: pass\",\n \"def stop(self):\\n self.active = False\",\n \"def stopGame(event):\\n if event.action == sense_hat.ACTION_RELEASED:\\n global playAgain, alive\\n playAgain = False\\n alive = False\",\n \"def stop_media(self):\\n self.stdin_queue.put(\\\"stop\\\")\",\n \"def on_stop(self):\\n self.write_log(\\\"策略停止\\\")\\n self.cta_engine.event_engine.unregister(EVENT_TIMER, self.process_timer_event)\",\n \"def OnStopPress(self, event):\\n\\t\\tself.onOffText.SetLabel('Off')\\n\\t\\tself.isBaselineRunning = False\\n\\t\\tself.hasBaselineEnded = True\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def stop(self):\\n self.running = False\",\n \"def handler(signum, frame):\\n\\tglobal stop_flag\\n\\tstop_flag=True\\n\\tprint(\\\"Query timeout!!!!!!!!!!!\\\")\\n\\traise Exception(\\\"Timeout\\\")\",\n \"def stopDetection(self):\\n self.statusWrite(\\\"stop\\\")\\n self.p.sleep()\\n self.birdHere = 0\",\n \"def _D(stdscr):\\n curses.nocbreak()\\n stdscr.keypad(0)\\n curses.echo()\\n curses.endwin()\\n import pdb; pdb.set_trace()\",\n \"def ctrl_d_condition() -> bool:\\n app = get_app()\\n return (\\n app.current_buffer.name == DEFAULT_BUFFER\\n and not app.current_buffer.text\\n )\",\n \"def Stop():\\n timer.stop()\\n global total_stop\\n global success_stop\\n total_stop += 1\\n if n % 10 == 0:\\n success_stop = success_stop + 1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61809236","0.59877855","0.590018","0.5879762","0.58237314","0.5802075","0.57889146","0.57686067","0.5766843","0.57374257","0.5713238","0.5673947","0.5642621","0.564084","0.5605514","0.5603989","0.55528235","0.55528235","0.55528235","0.55528235","0.5546439","0.5542056","0.5526183","0.551357","0.54676926","0.5462781","0.5438458","0.54364985","0.5430201","0.54087824","0.5406931","0.537861","0.535127","0.53455067","0.53432494","0.53406966","0.5318454","0.5317229","0.5287121","0.52847767","0.52767205","0.5272566","0.527218","0.5270632","0.5265245","0.5258437","0.524834","0.52459884","0.5239925","0.5239925","0.523847","0.5213012","0.5211838","0.5209146","0.52053845","0.5199889","0.51993465","0.5193347","0.5191202","0.51910985","0.5183301","0.5183241","0.51827437","0.51755595","0.5165957","0.5164364","0.51598334","0.5158349","0.5157558","0.5157219","0.5157075","0.51523584","0.515105","0.5149021","0.51465344","0.51446164","0.513314","0.51254195","0.5125212","0.51227665","0.51190096","0.51181054","0.5117727","0.51126724","0.5107727","0.5105046","0.5102107","0.5100808","0.5098018","0.5093533","0.50921154","0.50921154","0.50921154","0.50921154","0.50921154","0.5091357","0.5090711","0.50863403","0.50855815","0.50801355"],"string":"[\n \"0.61809236\",\n \"0.59877855\",\n \"0.590018\",\n \"0.5879762\",\n \"0.58237314\",\n \"0.5802075\",\n \"0.57889146\",\n \"0.57686067\",\n \"0.5766843\",\n \"0.57374257\",\n \"0.5713238\",\n \"0.5673947\",\n \"0.5642621\",\n \"0.564084\",\n \"0.5605514\",\n \"0.5603989\",\n \"0.55528235\",\n \"0.55528235\",\n \"0.55528235\",\n \"0.55528235\",\n \"0.5546439\",\n \"0.5542056\",\n \"0.5526183\",\n \"0.551357\",\n \"0.54676926\",\n \"0.5462781\",\n \"0.5438458\",\n \"0.54364985\",\n \"0.5430201\",\n \"0.54087824\",\n \"0.5406931\",\n \"0.537861\",\n \"0.535127\",\n \"0.53455067\",\n \"0.53432494\",\n \"0.53406966\",\n \"0.5318454\",\n \"0.5317229\",\n \"0.5287121\",\n \"0.52847767\",\n \"0.52767205\",\n \"0.5272566\",\n \"0.527218\",\n \"0.5270632\",\n \"0.5265245\",\n \"0.5258437\",\n \"0.524834\",\n \"0.52459884\",\n \"0.5239925\",\n \"0.5239925\",\n \"0.523847\",\n \"0.5213012\",\n \"0.5211838\",\n \"0.5209146\",\n \"0.52053845\",\n \"0.5199889\",\n \"0.51993465\",\n \"0.5193347\",\n \"0.5191202\",\n \"0.51910985\",\n \"0.5183301\",\n \"0.5183241\",\n \"0.51827437\",\n \"0.51755595\",\n \"0.5165957\",\n \"0.5164364\",\n \"0.51598334\",\n \"0.5158349\",\n \"0.5157558\",\n \"0.5157219\",\n \"0.5157075\",\n \"0.51523584\",\n \"0.515105\",\n \"0.5149021\",\n \"0.51465344\",\n \"0.51446164\",\n \"0.513314\",\n \"0.51254195\",\n \"0.5125212\",\n \"0.51227665\",\n \"0.51190096\",\n \"0.51181054\",\n \"0.5117727\",\n \"0.51126724\",\n \"0.5107727\",\n \"0.5105046\",\n \"0.5102107\",\n \"0.5100808\",\n \"0.5098018\",\n \"0.5093533\",\n \"0.50921154\",\n \"0.50921154\",\n \"0.50921154\",\n \"0.50921154\",\n \"0.50921154\",\n \"0.5091357\",\n \"0.5090711\",\n \"0.50863403\",\n \"0.50855815\",\n \"0.50801355\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5726188"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":8471,"cells":{"query":{"kind":"string","value":"Logs an entry for the homework log using a timer"},"document":{"kind":"string","value":"def do_start(self, input):\n course_name = course.course_name(input)\n\n if course_name in config.current_courses:\n timer = Timer(course_name)\n timer.start()\n\n if self.stop():\n timer.stop()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def timer(work_log):\n start = time.time()\n print '\\nyou started working at %s\\n' % time.ctime(int(start))\n\n input = raw_input(\"\\ntype 'stop' to stop timer...\\n\")\n while (input != 'stop'):\n input = raw_input(\"\\ntype 'stop' to stop timer...\\n\")\n work = raw_input(\"\\nwhat'd you work on?\\n\")\n stop = time.time()\n print_to_file(start, stop, (stop-start), work, work_log)","def internal_event(self):\n # log activity\n self.log_activity(LogEntry(\n sys_time=time(),\n logical_time=self.logical_clock,\n action=\"work\"\n ))","def entry(entry,fhandle): \n\n import time\n timestamp = time.strftime('%H:%M:%S')\n print entry\n try: fhandle.write('{0}: {1}\\n'.format(timestamp, entry))\n except: print 'Log File Error: entry could not be logged'\n return","def log(self, label, times, overlapping=False):\r\n self._timings.append(Timing(label, times, overlapping))","def internal_event (self):\n self.clock_time += 1\n self.log()","def log_tofile(self, inst):\n self._tick += 1\n if self._tick >= self._second:\n self.logger.log(inst)\n self._tick = 0","def add_line_in_log():\n logging.info(' ' + '-' * 60 + '\\n')","def log_entry(debug, out, text):\n # Format log entry\n monthday = make_time_stamp('%m%d')\n time_stamp = make_time_stamp('%H:%M:%S')\n now = time.time()\n ms = \".\"+str('%06d' % int((now - int(now)) * 1000000))\n line_form = \"I{monthday} {time_stamp} 0000 main.py:00] {text}\\n\"\n entry = line_form.format(monthday=monthday, time_stamp=time_stamp+ms, text=text)\n\n # Log entry to stderr\n sys.stderr.write(entry)\n pass","def logFile(self):\n\n event = 'stim'\n mStr = '{:013}'.format(self.mouse.tag) + '\\t'\n outPutStr = mStr + \\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\t' + event\n print (outPutStr)\n if self.textfp != None:\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\t' + event\n self.textfp.write(outPutStr + '\\n')\n self.textfp.flush()","def log(self, game: str, outcome: str):\n current_time = datetime.now()\n self.user.record.append([current_time.strftime(\"%c\"), game, outcome, self.user.balance])","def log(exercise):\n global logfile\n msg = raw_input(\"Enter your message. \")\n logfile.write(exercise + \" >>> \" + msg + \"\\n\")","def recordLog(project, status, memo):\n path = getPath(project)\n log = open(path, 'a')\n writer = csv.writer(log, lineterminator='\\n')\n writer.writerow((time.time(), status, memo))\n log.close()\n if status == 'a':\n print(\"Tracking your time on \" + project)\n if status == 's':\n print(\"Tracking suspended on \" + project)\n if status == 't':\n print(\"Time shifted on \" + project)\n if not path == '.sourglass':\n store = open(os.path.join(basepath, 'last'), 'w')\n store.write(project)\n store.close","def log(self, line):\n now = datetime.datetime.now()\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\n with open(self.logfile, 'a') as log:\n log.write(time + ' ' + line + '\\n')","def putlog(self,s):\n if not self.logqueue == None:\n# print s\n self.logqueue.put(\"Spectrum: (\"+time.ctime()+\"):\\n\"+s)","def task_display_funny_time():\n print(\"funny time is %s\" % datetime.datetime.now())\n logger.info(\"Hurray its working\")","def Log(self, times):\n\n print '--'\n print times.PrettyPrintLog()\n\n return","def log_schedule(self):\n self.logger.log_schedule(self.params.schedule)","def logWork(self, id, logData):\n\t\tnow = datetime.datetime.now()\n\t\ttimeText = now.strftime(LOG_DATE_FORMAT)\n\n\t\tlogToEdit = self.LOGS.get(id).get(\"log\")\n\t\t#If inside this case and there is already a log entry for this time\n\t\talreadyEntryForThisTime = False\n\t\tfor entry in logToEdit:\n\t\t\tif timeText == entry[0]:\n\t\t\t\tentry[1] += logData\n\t\t\t\talreadyEntryForThisTime = True\n\n\t\tif not alreadyEntryForThisTime:\n\t\t\tlogToEdit.append([timeText, logData])\n\t\t\tself.logByDate(now.strftime(CASE_DATE_FORMAT), id)\n\n\t\tself.save_logs(\"c\")\n\t\tself.autoCommit()\n\t\treturn 0","def on_log(self):\n monitors = self.monitors\n if self.monitors is None:\n monitors = self.trainer.metrics.keys()\n\n\n hparams = self.hparams\n if self.hparams is None:\n hparams = self.trainer.hparams.keys()\n\n metrics = {name: format_metric(self.trainer.metrics[name])\n for name in monitors\n if name in self.trainer.metrics}\n hparams = {name: format_metric(self.trainer.hparams[name])\n for name in hparams\n if name in self.trainer.hparams}\n\n\n step_bar = self.step_bars[-1]\n step_bar.set_description(\"Epoch {}\".format(self.trainer.epoch+1))\n step_bar.set_postfix(**metrics, **hparams)\n step_bar.update(self.trainer.steps_trained - self.last_step)\n self.last_step = self.trainer.steps_trained","def logentry(jobid, label, typ, content=None, path=None):\n ud = str(uuid.uuid4())\n db.logs.save({\"uuid\":ud, \"jobid\":jobid, \"label\":label, \"type\":typ, \"content\":content, \"date\":tstamp(), \"containerurl\":path})","def log(text):\n print \"%s: %s\" % (str(datetime.datetime.now()), text)","def log_time(name):\n if DEBUG:\n now = time.time()\n logging.debug('emcc step \"%s\" took %.2f seconds', name, now - TimeLogger.last)\n TimeLogger.update()","def log(self):\n\n\t\t# Only every 1/10 second (or so) to avoid flooding networktables\n\t\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\n\t\t\treturn\n\n\t\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\n\t\twpilib.SmartDashboard.putBoolean(\"Garbo?\", self.is_pbot)\n\n\t\tself.drive.log()\n\t\tself.elevator.log()\n\t\tself.intake.log()","def log(self, event_cls, *args, **kw):\n args = list(args)\n args.append(self.time)\n args.append(self)\n args.append(self.worldview.locality_copy())\n self.diary.log(self.time, event_cls(*args, **kw))","def _log(self, runtime, extra):\n\t\tif extra is None:\n\t\t\tdebug(\"Timer - %s took %d ms\" % (self._item, 1000 * runtime))\n\t\telse:\n\t\t\tdebug(\"Timer - %s [%s] took %d ms\" % (self._item, str(extra), 1000 * runtime))\n\t\treturn self","def add_log(self, log):\n log = str(datetime.datetime.now()) + \": \"+log+\"\\n\"\n print(log)\n self.logs.append(log)\n if len(self.logs) > 10:\n self.append_to_logfile()","def logentry(self, string=None):\n if (self._OIFlogging):\n oiflogfile = open(self._commslogfilename, \"a\")\n oiflogfile.write(\"# \" + \"%04.6fs: \" % (self._gettime() - self._logstarttime) + string + \"\\n\")\n oiflogfile.flush()\n else:\n# if self._print_once:\n# self._print_once = 0\n# print self.hilite(\"Warning: Not logging OIF transactions. Use\\n it.logfile(<filename>) to set log filename and\\n it.logging(True) to enable logging\", False, True)\n print 'Unable to write log entry', string\n return","def log_time(label: str) -> None:\n print(label, datetime.now())","def log(self, msg=\"\"):\n if len(msg):\n msg = \"[%.03fs] %s\" % (time.time()-self.timeStart, msg)\n print(msg)\n self.logLines.append(msg)","def log(self, extra=None):\n\t\tself.stop()\n\t\treturn self._log(self.time(), extra)","def add_entry(name, title, duration, notes):\n clear()\n print('Entry added to work log!')\n return Entry.create(\n employee_name=name,\n task_title=title,\n time_spent=duration,\n task_notes=notes\n )","def timer_callback(self):\n # There're 5 logger-level in ROS 2 get_logger() System.\n # Try out and watch whats difference.\n self.get_logger().debug(f'==== Hello ROS 2 : {self.count}====')\n self.get_logger().info(f'==== Hello ROS 2 : {self.count}====')\n self.get_logger().warn(f'==== Hello ROS 2 : {self.count}====')\n self.get_logger().error(f'==== Hello ROS 2 : {self.count}====')\n self.get_logger().fatal(f'==== Hello ROS 2 : {self.count}====')\n\n self.count += 1","def log_entry(self, timestamp, entry):\n if timestamp in self.log:\n self.log[timestamp].update(entry)\n else:\n self.log[timestamp] = entry","def logger(start_time, file):\n with open(file, \"a\") as text:\n text.write(\"\"\"\n\n Current date and time: {}\n Program ran in {} seconds.\n \"\"\".format(datetime.datetime.now(), time.process_time() - start_time))\n\n return 'hello'","def log(self, *args, **kwargs):\n self.game_view.log(*args, **kwargs)","def log(self, msg):\n current_datetime = self.get_date_time()\n self.file.write(\"%s %s\\n\" % (current_datetime, msg))","def log(message):\n print(\"{0}: {1}\".format(acm.Time.TimeNow(), message))","def add_log(conn, task, start_time):\n cursor = conn.cursor()\n cursor.execute('INSERT INTO timelogs (task, start_time) VALUES (?, ?);', (task, start_time))","async def log_time(self, event):\n sender = await event.get_sender()\n user = utils.get_display_name(sender)\n\n message = event.message\n\n time = message.date.astimezone(self.__to_zone).time().hour\n\n logging.debug(\"Got the following message: \\\"\" + event.raw_text + \"\\\" at time \" + str(time))\n\n self.__contact_times.labels(user).observe(time)","def on_start(self):\r\n self.log()","def new_entry():\n clear_screen()\n entry = {}\n entry['id'] = get_next_id()\n entry['name'] = input_name()\n print(\"How many minutes did you spend on {}?\".format(entry['name']))\n print(\"Or you may specify a format after the time, seperated by a comma\")\n entry['time_spent'] = input_time_spent()\n add_notes = input(\"Add notes? Y/n \").lower()\n if add_notes != 'n':\n entry['notes'] = input_notes()\n entry['date'] = datetime.now().strftime(FMT_MONTH_DAY_YEAR)\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\n work_log_writer.writerow(entry)","def on_a(self):\r\n self.log()","def logManager(self):\n time.sleep(0.1)\n while True:\n try:\n time.sleep(0.2)\n data = self.logQ.get(block=False)\n except Queue.Empty:\n pass\n else:\n try:\n self.log_lock.acquire() \n self.log_file.logEntry(data)\n time.sleep(0.1)\n self.log_lock.release()\n except:\n print '*Unable to write to log file*'","def print_to_file(start, stop, time_worked, work_text, work_log):\n today = datetime.date.today()\n\n record = ' || %.2f || %.2f || %.4f hours || %s\\n' % (start, stop, time_worked/3600, work_text)\n\n #if it is a new file you have the option to set a start time for the project\n # and how many hours a week you want to work\n if not os.path.isfile(work_log):\n while True:\n option = raw_input('\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\n if option == 'y':\n date = raw_input('\\nplease enter the start date of the project (dd-mm-yyyy): ')\n hours_per_week = raw_input('\\nplease enter the number of hours you intend to work on the project per week: ')\n try:\n datetime.datetime.strptime(date, '%d-%m-%Y')\n if hours_per_week.isdigit():\n f = open(work_log, 'a')\n f.write('#! || ' + date + ':' + hours_per_week + '\\n')\n f.close()\n break\n else:\n print \"\\nPlease enter a valid number for hours to work!\\n\"\n except ValueError:\n print \"\\nPlease enter a valid date!\\n\"\n\n else:\n break\n\n\n f = open(work_log, 'a')\n print '\\n\\n' + today.strftime('%b-%d-%Y') + record\n f.write(today.strftime('%b-%d-%Y') + record)\n f.close()","def main():\n\n # check database for tracking options\n # if empty prompt to add subject\n\n # present tracking options\n\n # calculate timedelta\n\n # printing/updating the time","def main_log(logfile, entry, print_tag=False):\n if logfile != None:\n with open(logfile, 'a') as lf:\n lf.write('{}\\n'.format(entry))\n\n if print_tag:\n print entry","def InsertLog():","def log_time(self, batch_idx, duration, loss):\n samples_per_sec = self.opt.batch_size / duration\n time_so_far = time.time() - self.start_time\n print_string = \"exp_name {} \\n| dataset: {:>5} | epoch {:>3} | batch {:>6}/{:>6} | \" \\\n \"examples/s: {:5.1f} | loss: {:.5f}\"\n print(print_string.format(self.log_path.split('/')[-1], self.syn_or_real, self.epoch, batch_idx,\n self.num_total_batch, samples_per_sec, loss))","def record(self, time, increment):\n raise NotImplementedError(\"Abstract method not implemented.\")","def after_epoch(self):\n line = ' '.join([str(k) + ': ' + str(v) for k, v in self.trainer.status.items()])\n with open(os.path.join(self.root_path, 'log.txt'), 'a+') as fout:\n fout.write(line + '\\n')","def log(self, message):\n timestamp = time.strftime(\"[%H:%M:%S]\", time.localtime(time.time()))\n self.file.write('%s %s\\n' % (timestamp, message))\n self.file.flush()","def _addLogEntry(request, action, pagename, filename):\n from MoinMoin.logfile import editlog\n t = wikiutil.timestamp2version(time.time())\n fname = wikiutil.url_quote(filename)\n\n # Write to global log\n log = editlog.EditLog(request)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\n\n # Write to local log\n log = editlog.EditLog(request, rootpagename=pagename)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)","def logStarted(build, step, log):","def time_automation_listener(now):\n action()","def time_in(self):\n if self.is_logged():\n self.time_out()\n else:\n TaskLog.objects.create(task=self)","def log_message(self, srcid, msg, t = None):\n t = time.time() if t is None else t\n self.insert(\"textlog\", {\"readout_id\":srcid, \"time\":t, \"msg\":msg})","def _log_results(self, results):\n log.new_entry(results)\n self.new_entry = 2","def QueueLog(self):\n # Check every 100ms if there is a new message in the queue to display\n while True:\n try:\n record = self.log_queue.get(block=False)\n\n except queue.Empty:\n\n break\n else:\n self.display(record)\n self.fram.after(100, self.QueueLog)","def _log(self, action: types.NestedArray) -> None:\n if self._logger is None:\n return\n self._logger.info('{}, {}, {}, {}, {}, {}, {}'.format(\n self._last_timestep.observation['STAGE'],\n self._last_timestep.observation['CHIPS'],\n self._last_timestep.observation['PLAYER_TOTAL'],\n self._last_timestep.observation['PLAYER_ACES'],\n self._last_timestep.observation['DEALER_TOTAL'],\n action,\n self._deck_distribution))","def __log_trial__(self, trial_data):\n from klibs.KLDatabase import EntryTemplate\n\n trial_template = EntryTemplate('trials')\n trial_template.log(P.id_field_name, P.participant_id)\n for attr in trial_data:\n trial_template.log(attr, trial_data[attr])\n\n return self.database.insert(trial_template)","def log(self, message):","def _log_progress(self, t):\n\n # Run the update only 2 step before the actual logging happens in order to\n # make sure that the most recent possible values will be stored in\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\n # which runs 2 threads without coordination\n if (t+2) % self.log_freq == 0 and self.learn_started:\n episode_rewards = self.env_monitor.get_episode_rewards()\n self.episode_rewards = np.asarray(episode_rewards)\n if self.episode_rewards.size > 0:\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\n\n if t % self.log_freq == 0 and self.learn_started:\n stats_logger.info(\"\")\n for s, lambda_v in self.log_info:\n stats_logger.info(s.format(lambda_v(t)))\n stats_logger.info(\"\")\n\n if self.summary:\n # Log with TensorBoard\n self.tb_writer.add_summary(self.summary, global_step=t)","def writeToLog(self, type, line):\r\n self._log.append((type, time(), line))\r\n if len(self._log[self._logIndex:]) >= self.config.get('logging', 'loginterval'):\r\n self.logFlush()\r\n return True\r\n return False","def timer_callback(*args):\n logging.debug(\"timer callback at %s\" % datetime.now())","def log(self, txt, dt=None):\n try:\n dt = dt or self.datas[0].datetime.datetime(0)\n print('%s, %s' % (dt.isoformat(), txt))\n except IndexError:\n print('%s' % txt)","def logIt(self, msg):\n\n\t\tif( self.logger ): self.logger.logIt( msg )","def record_task_attempt(task_name: str):\n\n from common.models import InvenTreeSetting\n\n logger.info(f\"Logging task attempt for '{task_name}'\")\n\n InvenTreeSetting.set_setting(f'_{task_name}_ATTEMPT', datetime.now().isoformat(), None)","def run_game_log(player, game_log):\n\n\tgame_log.update_room_type()\n\tcurrent_msg = game_log.get_current_message()\n\n\tprint('HP: {} GOLD: {}'.format(player.stats['HP'], player.stats['GOLD']))\n\tprint('CURRENT ROOM: {}\\n'.format(game_log.current_room))\n\tprint(\"{}'s LOG: {}\".format(player.info['Name'].upper(), current_msg))","def add_log(self,txt):\n try:\n now=datetime.datetime.now()\n new_item=QtWidgets.QListWidgetItem(now.strftime('%Y/%m/%d %H:%M:%S')+\": \"+txt)\n self.ui.logger_list.addItem(new_item)\n if self.h5saver.h5_file.isopen:\n self.h5saver.append(self.h5saver.logger_array, now.strftime('%Y/%m/%d %H:%M:%S')+\": \"+txt)\n\n except:\n pass","def log(self, obj, action):\n action_dict = {'time': time.time(),\n 'action': action}\n self.log_data[obj.get_obj_id()]['actions'].append(action_dict)","def listener(q, output_file):\n\n with open(output_file, 'w') as time_code_log:\n while True:\n m = q.get()\n if m == 'kill':\n time_code_log.write('killed')\n break\n\n time_code_log.write(m[0] + '\\n')\n time_code_log.write(m[1] + '\\n')\n time_code_log.write(str(m[2]) + '\\n')\n time_code_log.flush()","def on_L1(self):\r\n self.log()","def log(msg=\"\"):\n print(msg)\n sys.stdout.flush()\n f = open(\"/target/testdriver.log\", \"a\")\n f.write('{:%Y-%m-%d %H:%M:%S.%s} :: '.format(datetime.datetime.now()))\n f.write(f\"{msg}\\n\")\n f.close()","def record_event(self, description, time=None, additional=None):\n if time is None:\n time = datetime.datetime.now()\n if additional is not None:\n self.history.append((time, (description, additional)))\n else:\n self.history.append((time, description))","def writeToLogFile(self, event):\n outPutStr = '{:013}'.format(0)\n logOutPutStr = outPutStr + '\\t' + '{:.2f}'.format (time ()) + '\\t' + event + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\n printOutPutStr = outPutStr + '\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\t' + event\n print (printOutPutStr)\n if self.logFP is not None:\n self.logFP.write(logOutPutStr + '\\n')\n self.logFP.flush()","def log(self, client_addr, request):\n with codecs.open(self.log_path, \"a\", 'UTF-8') as fh_out:\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \"\\t\" +\n ':'.join([str(i) for i in client_addr]) + \"\\t\" +\n request)","def simulate(self, path):\n prev_time = 0\n for entry in self.read_log(path):\n event = Event(entry)\n if event.time != prev_time: # Show frame at time i\n self.show()\n prev_time = event.time\n self.process_event(event)","def log_time(self, batch_idx, duration, loss):\n samples_per_sec = self.batch_size / duration\n time_sofar = time.time() - self.start_time\n training_time_left = (\n self.num_total_steps / self.step - 1.0) * time_sofar if self.step > 0 else 0\n print_string = \"epoch {:>3} | batch {:>6} | examples/s: {:5.1f}\" + \\\n \" | loss: {:.5f} | time elapsed: {} | time left: {}\"\n print(print_string.format(self.epoch, batch_idx, samples_per_sec, loss,\n sec_to_hm_str(time_sofar), sec_to_hm_str(training_time_left)))","def log(self, msg):\n logging.info(\"Logging Message\")\n ml = self.monk_logs\n today = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n ml.insert({today: {\"log\": msg,\n \"sentiment\": self.get_sentiment(msg),\n \"weather\": Weather.get_weather()}})","def log_time(self, batch_idx, duration, loss):\n samples_per_sec = self.opt.batch_size / duration\n time_sofar = time.time() - self.start_time\n training_time_left = (\n self.num_total_steps / self.step - 1.0) * time_sofar if self.step > 0 else 0\n print_string = \"epoch {:>3} | batch {:>6} | examples/s: {:5.1f}\" + \\\n \" | loss: {:.5f} | time elapsed: {} | time left: {}\"\n print(print_string.format(self.epoch, batch_idx, samples_per_sec, loss,\n sec_to_hm_str(time_sofar), sec_to_hm_str(training_time_left)))","def log_every(self, identifier, message, seconds=1):\r\n if self.has_been_n_seconds_since_last(identifier, seconds):\r\n logger.info(message)\r\n else:\r\n logger.debug(message)","def run_timing():\n time_log = []\n while True:\n one_run = input(\"Enter your time for this 10 km: \")\n if not one_run:\n break\n try:\n time_log.append(float(one_run))\n except ValueError:\n print(\n \"Hey, you enter something strange, \"\n \"please enter a valid number\")\n avg_time = sum(time_log) / len(time_log)\n return f\"Your average time is about: {avg_time:.1f} \" \\\n f\"over {len(time_log)} runs\"","def log_completed_run(self, log_file_path):\n with open(log_file_path, \"r\") as f:\n stats = f.read().splitlines()\n\n self._parse_log_entry(stats)\n self.experiment.log_other(\"log_file_path\", log_file_path)","def time_tracking(self):\n fb = FreshBooks()\n tg = Toggl()\n self.print_splash()\n self.print(\"Tip: You can always enter 'skip' when you want to skip a time entry.\", format='warn')\n days = self.get_interactive_days() # number of days to go back\n self.print(\"OK, I'll run you through the Toggl time entries of the past %i day(s).\" % (days))\n timestamp = self.get_timestamp(days) # unix timestamp including tz\n time_entries = tg.get_time_entries(timestamp)\n if len(time_entries) == 0:\n self.print(\"No Toggl entries in this time span!\", 'warn')\n return False\n time_entries = self.merge_toggl_time_entries(time_entries) # merge Toggl entries\n fb_projects = fb.get_projects()\n # Loop through merged Toggl time entries:\n for entry in time_entries:\n # Get and convert all necessary info:\n client_id = tg.get_client_id(project_id=entry.get('pid'))\n client_name = tg.get_client_name(client_id)\n project = tg.get_project(entry.get('pid'))\n duration = int(entry['duration']) / 60 / 60 # convert duration to hours\n duration = round(duration * 4 ) / 4 # round hours to nearest .25\n description = self.format_description(project['name'], entry['description'])\n date = str(parser.parse(entry['start']).date())\n # Print info in a nice way:\n self.print_divider(30)\n self.print(\"Description: \" + description)\n self.print(\"Date: \" + date)\n self.print(\"Hours spent: \" + str(duration))\n # Skip if Toggl entry is already booked:\n if entry.get('tags') and tg.BOOKED_TAG in entry['tags']:\n self.print(\"Skipping this entry because it is already in Freshbooks.\", 'cross')\n # Skip if duration is below 0.25:\n elif duration < 0.25:\n self.print(\"Skipping this entry because there are less than 0.25 hours spent.\", 'cross')\n # If billable, add to Freshbooks:\n elif entry['billable']:\n # Get FreshBooks project name through interactive search:\n try:\n self.print(\"Project: \\U0001F50D \")\n fb_project_name = self.interactive_search(fb_projects.keys(), client_name)\n # Handle KeyboardInterrupt\n except KeyboardInterrupt:\n answer = input(\"\\nKeyboardInterrupt! Skip current entry or quit time tracking? (S/q) \")\n if answer.lower() == 's' or answer == '':\n self.clear_lines(1)\n self.print(\"Skipping this entry.\", 'cross')\n continue\n else:\n self.clear_lines(1)\n self.print(\"Ok, stopping time tracking.\", 'cross')\n sys.exit()\n # If user requests so, skip this entry:\n self.clear_lines(1)\n if not fb_project_name:\n self.print(\"Skipping this entry.\", 'cross')\n continue\n # Otherwise, add entry to FreshBooks and tag Toggl entry/entries:\n self.print(\"Project: \" + fb_project_name)\n project_id = fb.get_project_id(fb_project_name)\n fb.add_entry(project_id, duration, description, date)\n tg.tag_projects(entry['merged_ids'], tg.BOOKED_TAG)\n # If not billable, skip entry:\n else:\n self.print(\"Skipping this entry because it is not billable.\", 'cross')\n self.print_divider(30)\n answer = input(\"All done! Open FreshBooks in browser to verify? (Y/n) \")\n if answer.lower() == 'y' or answer == '':\n webbrowser.open('https://%s.freshbooks.com/timesheet' % fb.fb_creds['subdomain'])","def __init__(self, logfile):\n\n self.logfile = logfile\n if self. logfile:\n self.file = open(logfile, \"w\")\n self.starttime = time.time()\n self.file.write(\"%.2f %s Starting log\\n\" % (time.time() - self.starttime, time.asctime()))","def log(self, level, *msg_elements):\r\n self.report.log(self._threadlocal.current_workunit, level, *msg_elements)","def print_log(*content):\n now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\")\n print(\"MODEL INFO: \" + str(now)+ \" \", end='')\n print(*content)","def log(self, txt):\n if self.logfile:\n self.logfile.write(txt)","def _log(self, log, message):\n log_entry = '[%s] %s\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\n log.write(log_entry)\n if self.verbose:\n print log_entry.rstrip()","def logByDate(self, timeText, id):\n\t\t# Array of IDs for a day\n\t\tdayLog = self.LOGS.get(\"by_date\").get(timeText)\n\n\t\t# If day already made\n\t\tif dayLog:\n\t\t\tif not id in dayLog:\n\t\t\t\tdayLog.append(id)\n\t\telse:\n\t\t\tself.LOGS.get(\"by_date\")[timeText] = [id]","def record_time(t):\n\n f = open('time.out', 'w')\n f.write(str(t))\n f.close()","def debug_started(self, command):\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n lines = [\n \"%s\\n\" % (\"*\" * self.line_lenght),\n \"Command: %s\\n\" % command,\n \"DateTime: %s\\n\" % now,\n \"%s\\n\" % (\"*\" * self.line_lenght)\n ]\n\n with open(self.debug_log, 'a+') as logfile:\n logfile.writelines(lines)","def log(message):\n path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n with open(os.path.join(path, logfile_name), 'a+') as f:\n t = strftime(\"%d %b %Y %H:%M:%S\", gmtime())\n f.write(\"\\n\" + t + \" \" + message)","def check_timer():\n end = time.time()\n time_elapsed = end - target_time[0]\n durationMSG = fg.cyan + f\"Scans Completed for {args.target} in: \" + fg.rs\n print(durationMSG, display_time(time_elapsed))","def _log2mylog(self, msg):\n time_str = mod_time.strftime(\n \"%Y-%m-%d %H:%M:%S\", mod_time.localtime(mod_time.time())\n )\n msg = str(msg)\n content = \"%s [%s]\\n\" % (time_str, msg)\n fa = open(self.mylogfile, \"a\")\n fa.write(content)\n fa.close()","def write(self, txt): \n self.log.appendtext(txt)\n self.log.update_idletasks()\n return","def log(self, msg):\n\n\t\tself.eyetribe.log_message(msg)","def log(self, msg=None):\n f = open(self.logbook, 'a')\n # if send or receive, write message\n if msg: \n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + \n \" \" + str(msg) + '\\n')\n # if it is an internal event just write the system time and current\n # logical clock time\n else:\n f.write(\" System time: \" + str(datetime.now()) + \n \" Logical clock time: \" + str(self.clock_time) + '\\n')\n f.close()","def log(self, loginfo):\n logging.basicConfig(level=logging.INFO,\n format='%(asctime)s %(filename)s:%(message)s',\n datefmt='%d %b %Y %H:%M:%S',\n filename=self.logfilepath,\n filemode='w')\n filelog = logging.FileHandler(self.logfilepath)\n logging.getLogger('Functest').addHandler(filelog)\n logging.info(loginfo)","def _print_logs_info(job_id, project_id):\n print(\"\\nJob submitted successfully.\")\n print(\"Your job ID is: \", job_id)\n print(\"\\nPlease access your training job information here:\")\n print(\n \"https://console.cloud.google.com/mlengine/jobs/{}?project={}\".format(\n job_id, project_id))\n print(\"\\nPlease access your training job logs here: \"\n \"https://console.cloud.google.com/logs/viewer?resource=ml_job%2F\"\n \"job_id%2F{}&interval=NO_LIMIT&project={}\\n\".format(\n job_id, project_id))","def log(message):\n path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n with open(os.path.join(path, logfile_name), 'a+') as f:\n t = strftime(\"%d %b %Y %H:%M:%S\", gmtime())\n f.write(\"\\n\" + t + \" %s\" % message)"],"string":"[\n \"def timer(work_log):\\n start = time.time()\\n print '\\\\nyou started working at %s\\\\n' % time.ctime(int(start))\\n\\n input = raw_input(\\\"\\\\ntype 'stop' to stop timer...\\\\n\\\")\\n while (input != 'stop'):\\n input = raw_input(\\\"\\\\ntype 'stop' to stop timer...\\\\n\\\")\\n work = raw_input(\\\"\\\\nwhat'd you work on?\\\\n\\\")\\n stop = time.time()\\n print_to_file(start, stop, (stop-start), work, work_log)\",\n \"def internal_event(self):\\n # log activity\\n self.log_activity(LogEntry(\\n sys_time=time(),\\n logical_time=self.logical_clock,\\n action=\\\"work\\\"\\n ))\",\n \"def entry(entry,fhandle): \\n\\n import time\\n timestamp = time.strftime('%H:%M:%S')\\n print entry\\n try: fhandle.write('{0}: {1}\\\\n'.format(timestamp, entry))\\n except: print 'Log File Error: entry could not be logged'\\n return\",\n \"def log(self, label, times, overlapping=False):\\r\\n self._timings.append(Timing(label, times, overlapping))\",\n \"def internal_event (self):\\n self.clock_time += 1\\n self.log()\",\n \"def log_tofile(self, inst):\\n self._tick += 1\\n if self._tick >= self._second:\\n self.logger.log(inst)\\n self._tick = 0\",\n \"def add_line_in_log():\\n logging.info(' ' + '-' * 60 + '\\\\n')\",\n \"def log_entry(debug, out, text):\\n # Format log entry\\n monthday = make_time_stamp('%m%d')\\n time_stamp = make_time_stamp('%H:%M:%S')\\n now = time.time()\\n ms = \\\".\\\"+str('%06d' % int((now - int(now)) * 1000000))\\n line_form = \\\"I{monthday} {time_stamp} 0000 main.py:00] {text}\\\\n\\\"\\n entry = line_form.format(monthday=monthday, time_stamp=time_stamp+ms, text=text)\\n\\n # Log entry to stderr\\n sys.stderr.write(entry)\\n pass\",\n \"def logFile(self):\\n\\n event = 'stim'\\n mStr = '{:013}'.format(self.mouse.tag) + '\\\\t'\\n outPutStr = mStr + \\\\\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\\\t' + event\\n print (outPutStr)\\n if self.textfp != None:\\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\\\t' + event\\n self.textfp.write(outPutStr + '\\\\n')\\n self.textfp.flush()\",\n \"def log(self, game: str, outcome: str):\\n current_time = datetime.now()\\n self.user.record.append([current_time.strftime(\\\"%c\\\"), game, outcome, self.user.balance])\",\n \"def log(exercise):\\n global logfile\\n msg = raw_input(\\\"Enter your message. \\\")\\n logfile.write(exercise + \\\" >>> \\\" + msg + \\\"\\\\n\\\")\",\n \"def recordLog(project, status, memo):\\n path = getPath(project)\\n log = open(path, 'a')\\n writer = csv.writer(log, lineterminator='\\\\n')\\n writer.writerow((time.time(), status, memo))\\n log.close()\\n if status == 'a':\\n print(\\\"Tracking your time on \\\" + project)\\n if status == 's':\\n print(\\\"Tracking suspended on \\\" + project)\\n if status == 't':\\n print(\\\"Time shifted on \\\" + project)\\n if not path == '.sourglass':\\n store = open(os.path.join(basepath, 'last'), 'w')\\n store.write(project)\\n store.close\",\n \"def log(self, line):\\n now = datetime.datetime.now()\\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\\n with open(self.logfile, 'a') as log:\\n log.write(time + ' ' + line + '\\\\n')\",\n \"def putlog(self,s):\\n if not self.logqueue == None:\\n# print s\\n self.logqueue.put(\\\"Spectrum: (\\\"+time.ctime()+\\\"):\\\\n\\\"+s)\",\n \"def task_display_funny_time():\\n print(\\\"funny time is %s\\\" % datetime.datetime.now())\\n logger.info(\\\"Hurray its working\\\")\",\n \"def Log(self, times):\\n\\n print '--'\\n print times.PrettyPrintLog()\\n\\n return\",\n \"def log_schedule(self):\\n self.logger.log_schedule(self.params.schedule)\",\n \"def logWork(self, id, logData):\\n\\t\\tnow = datetime.datetime.now()\\n\\t\\ttimeText = now.strftime(LOG_DATE_FORMAT)\\n\\n\\t\\tlogToEdit = self.LOGS.get(id).get(\\\"log\\\")\\n\\t\\t#If inside this case and there is already a log entry for this time\\n\\t\\talreadyEntryForThisTime = False\\n\\t\\tfor entry in logToEdit:\\n\\t\\t\\tif timeText == entry[0]:\\n\\t\\t\\t\\tentry[1] += logData\\n\\t\\t\\t\\talreadyEntryForThisTime = True\\n\\n\\t\\tif not alreadyEntryForThisTime:\\n\\t\\t\\tlogToEdit.append([timeText, logData])\\n\\t\\t\\tself.logByDate(now.strftime(CASE_DATE_FORMAT), id)\\n\\n\\t\\tself.save_logs(\\\"c\\\")\\n\\t\\tself.autoCommit()\\n\\t\\treturn 0\",\n \"def on_log(self):\\n monitors = self.monitors\\n if self.monitors is None:\\n monitors = self.trainer.metrics.keys()\\n\\n\\n hparams = self.hparams\\n if self.hparams is None:\\n hparams = self.trainer.hparams.keys()\\n\\n metrics = {name: format_metric(self.trainer.metrics[name])\\n for name in monitors\\n if name in self.trainer.metrics}\\n hparams = {name: format_metric(self.trainer.hparams[name])\\n for name in hparams\\n if name in self.trainer.hparams}\\n\\n\\n step_bar = self.step_bars[-1]\\n step_bar.set_description(\\\"Epoch {}\\\".format(self.trainer.epoch+1))\\n step_bar.set_postfix(**metrics, **hparams)\\n step_bar.update(self.trainer.steps_trained - self.last_step)\\n self.last_step = self.trainer.steps_trained\",\n \"def logentry(jobid, label, typ, content=None, path=None):\\n ud = str(uuid.uuid4())\\n db.logs.save({\\\"uuid\\\":ud, \\\"jobid\\\":jobid, \\\"label\\\":label, \\\"type\\\":typ, \\\"content\\\":content, \\\"date\\\":tstamp(), \\\"containerurl\\\":path})\",\n \"def log(text):\\n print \\\"%s: %s\\\" % (str(datetime.datetime.now()), text)\",\n \"def log_time(name):\\n if DEBUG:\\n now = time.time()\\n logging.debug('emcc step \\\"%s\\\" took %.2f seconds', name, now - TimeLogger.last)\\n TimeLogger.update()\",\n \"def log(self):\\n\\n\\t\\t# Only every 1/10 second (or so) to avoid flooding networktables\\n\\t\\tif not self.log_timer.running or not self.log_timer.hasPeriodPassed(self.log_timer_delay):\\n\\t\\t\\treturn\\n\\n\\t\\twpilib.SmartDashboard.putString('Pressure', '{0:.2f}'.format(self.get_pressure()))\\n\\t\\twpilib.SmartDashboard.putBoolean(\\\"Garbo?\\\", self.is_pbot)\\n\\n\\t\\tself.drive.log()\\n\\t\\tself.elevator.log()\\n\\t\\tself.intake.log()\",\n \"def log(self, event_cls, *args, **kw):\\n args = list(args)\\n args.append(self.time)\\n args.append(self)\\n args.append(self.worldview.locality_copy())\\n self.diary.log(self.time, event_cls(*args, **kw))\",\n \"def _log(self, runtime, extra):\\n\\t\\tif extra is None:\\n\\t\\t\\tdebug(\\\"Timer - %s took %d ms\\\" % (self._item, 1000 * runtime))\\n\\t\\telse:\\n\\t\\t\\tdebug(\\\"Timer - %s [%s] took %d ms\\\" % (self._item, str(extra), 1000 * runtime))\\n\\t\\treturn self\",\n \"def add_log(self, log):\\n log = str(datetime.datetime.now()) + \\\": \\\"+log+\\\"\\\\n\\\"\\n print(log)\\n self.logs.append(log)\\n if len(self.logs) > 10:\\n self.append_to_logfile()\",\n \"def logentry(self, string=None):\\n if (self._OIFlogging):\\n oiflogfile = open(self._commslogfilename, \\\"a\\\")\\n oiflogfile.write(\\\"# \\\" + \\\"%04.6fs: \\\" % (self._gettime() - self._logstarttime) + string + \\\"\\\\n\\\")\\n oiflogfile.flush()\\n else:\\n# if self._print_once:\\n# self._print_once = 0\\n# print self.hilite(\\\"Warning: Not logging OIF transactions. Use\\\\n it.logfile(<filename>) to set log filename and\\\\n it.logging(True) to enable logging\\\", False, True)\\n print 'Unable to write log entry', string\\n return\",\n \"def log_time(label: str) -> None:\\n print(label, datetime.now())\",\n \"def log(self, msg=\\\"\\\"):\\n if len(msg):\\n msg = \\\"[%.03fs] %s\\\" % (time.time()-self.timeStart, msg)\\n print(msg)\\n self.logLines.append(msg)\",\n \"def log(self, extra=None):\\n\\t\\tself.stop()\\n\\t\\treturn self._log(self.time(), extra)\",\n \"def add_entry(name, title, duration, notes):\\n clear()\\n print('Entry added to work log!')\\n return Entry.create(\\n employee_name=name,\\n task_title=title,\\n time_spent=duration,\\n task_notes=notes\\n )\",\n \"def timer_callback(self):\\n # There're 5 logger-level in ROS 2 get_logger() System.\\n # Try out and watch whats difference.\\n self.get_logger().debug(f'==== Hello ROS 2 : {self.count}====')\\n self.get_logger().info(f'==== Hello ROS 2 : {self.count}====')\\n self.get_logger().warn(f'==== Hello ROS 2 : {self.count}====')\\n self.get_logger().error(f'==== Hello ROS 2 : {self.count}====')\\n self.get_logger().fatal(f'==== Hello ROS 2 : {self.count}====')\\n\\n self.count += 1\",\n \"def log_entry(self, timestamp, entry):\\n if timestamp in self.log:\\n self.log[timestamp].update(entry)\\n else:\\n self.log[timestamp] = entry\",\n \"def logger(start_time, file):\\n with open(file, \\\"a\\\") as text:\\n text.write(\\\"\\\"\\\"\\n\\n Current date and time: {}\\n Program ran in {} seconds.\\n \\\"\\\"\\\".format(datetime.datetime.now(), time.process_time() - start_time))\\n\\n return 'hello'\",\n \"def log(self, *args, **kwargs):\\n self.game_view.log(*args, **kwargs)\",\n \"def log(self, msg):\\n current_datetime = self.get_date_time()\\n self.file.write(\\\"%s %s\\\\n\\\" % (current_datetime, msg))\",\n \"def log(message):\\n print(\\\"{0}: {1}\\\".format(acm.Time.TimeNow(), message))\",\n \"def add_log(conn, task, start_time):\\n cursor = conn.cursor()\\n cursor.execute('INSERT INTO timelogs (task, start_time) VALUES (?, ?);', (task, start_time))\",\n \"async def log_time(self, event):\\n sender = await event.get_sender()\\n user = utils.get_display_name(sender)\\n\\n message = event.message\\n\\n time = message.date.astimezone(self.__to_zone).time().hour\\n\\n logging.debug(\\\"Got the following message: \\\\\\\"\\\" + event.raw_text + \\\"\\\\\\\" at time \\\" + str(time))\\n\\n self.__contact_times.labels(user).observe(time)\",\n \"def on_start(self):\\r\\n self.log()\",\n \"def new_entry():\\n clear_screen()\\n entry = {}\\n entry['id'] = get_next_id()\\n entry['name'] = input_name()\\n print(\\\"How many minutes did you spend on {}?\\\".format(entry['name']))\\n print(\\\"Or you may specify a format after the time, seperated by a comma\\\")\\n entry['time_spent'] = input_time_spent()\\n add_notes = input(\\\"Add notes? Y/n \\\").lower()\\n if add_notes != 'n':\\n entry['notes'] = input_notes()\\n entry['date'] = datetime.now().strftime(FMT_MONTH_DAY_YEAR)\\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\\n work_log_writer.writerow(entry)\",\n \"def on_a(self):\\r\\n self.log()\",\n \"def logManager(self):\\n time.sleep(0.1)\\n while True:\\n try:\\n time.sleep(0.2)\\n data = self.logQ.get(block=False)\\n except Queue.Empty:\\n pass\\n else:\\n try:\\n self.log_lock.acquire() \\n self.log_file.logEntry(data)\\n time.sleep(0.1)\\n self.log_lock.release()\\n except:\\n print '*Unable to write to log file*'\",\n \"def print_to_file(start, stop, time_worked, work_text, work_log):\\n today = datetime.date.today()\\n\\n record = ' || %.2f || %.2f || %.4f hours || %s\\\\n' % (start, stop, time_worked/3600, work_text)\\n\\n #if it is a new file you have the option to set a start time for the project\\n # and how many hours a week you want to work\\n if not os.path.isfile(work_log):\\n while True:\\n option = raw_input('\\\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\\n if option == 'y':\\n date = raw_input('\\\\nplease enter the start date of the project (dd-mm-yyyy): ')\\n hours_per_week = raw_input('\\\\nplease enter the number of hours you intend to work on the project per week: ')\\n try:\\n datetime.datetime.strptime(date, '%d-%m-%Y')\\n if hours_per_week.isdigit():\\n f = open(work_log, 'a')\\n f.write('#! || ' + date + ':' + hours_per_week + '\\\\n')\\n f.close()\\n break\\n else:\\n print \\\"\\\\nPlease enter a valid number for hours to work!\\\\n\\\"\\n except ValueError:\\n print \\\"\\\\nPlease enter a valid date!\\\\n\\\"\\n\\n else:\\n break\\n\\n\\n f = open(work_log, 'a')\\n print '\\\\n\\\\n' + today.strftime('%b-%d-%Y') + record\\n f.write(today.strftime('%b-%d-%Y') + record)\\n f.close()\",\n \"def main():\\n\\n # check database for tracking options\\n # if empty prompt to add subject\\n\\n # present tracking options\\n\\n # calculate timedelta\\n\\n # printing/updating the time\",\n \"def main_log(logfile, entry, print_tag=False):\\n if logfile != None:\\n with open(logfile, 'a') as lf:\\n lf.write('{}\\\\n'.format(entry))\\n\\n if print_tag:\\n print entry\",\n \"def InsertLog():\",\n \"def log_time(self, batch_idx, duration, loss):\\n samples_per_sec = self.opt.batch_size / duration\\n time_so_far = time.time() - self.start_time\\n print_string = \\\"exp_name {} \\\\n| dataset: {:>5} | epoch {:>3} | batch {:>6}/{:>6} | \\\" \\\\\\n \\\"examples/s: {:5.1f} | loss: {:.5f}\\\"\\n print(print_string.format(self.log_path.split('/')[-1], self.syn_or_real, self.epoch, batch_idx,\\n self.num_total_batch, samples_per_sec, loss))\",\n \"def record(self, time, increment):\\n raise NotImplementedError(\\\"Abstract method not implemented.\\\")\",\n \"def after_epoch(self):\\n line = ' '.join([str(k) + ': ' + str(v) for k, v in self.trainer.status.items()])\\n with open(os.path.join(self.root_path, 'log.txt'), 'a+') as fout:\\n fout.write(line + '\\\\n')\",\n \"def log(self, message):\\n timestamp = time.strftime(\\\"[%H:%M:%S]\\\", time.localtime(time.time()))\\n self.file.write('%s %s\\\\n' % (timestamp, message))\\n self.file.flush()\",\n \"def _addLogEntry(request, action, pagename, filename):\\n from MoinMoin.logfile import editlog\\n t = wikiutil.timestamp2version(time.time())\\n fname = wikiutil.url_quote(filename)\\n\\n # Write to global log\\n log = editlog.EditLog(request)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\\n\\n # Write to local log\\n log = editlog.EditLog(request, rootpagename=pagename)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\",\n \"def logStarted(build, step, log):\",\n \"def time_automation_listener(now):\\n action()\",\n \"def time_in(self):\\n if self.is_logged():\\n self.time_out()\\n else:\\n TaskLog.objects.create(task=self)\",\n \"def log_message(self, srcid, msg, t = None):\\n t = time.time() if t is None else t\\n self.insert(\\\"textlog\\\", {\\\"readout_id\\\":srcid, \\\"time\\\":t, \\\"msg\\\":msg})\",\n \"def _log_results(self, results):\\n log.new_entry(results)\\n self.new_entry = 2\",\n \"def QueueLog(self):\\n # Check every 100ms if there is a new message in the queue to display\\n while True:\\n try:\\n record = self.log_queue.get(block=False)\\n\\n except queue.Empty:\\n\\n break\\n else:\\n self.display(record)\\n self.fram.after(100, self.QueueLog)\",\n \"def _log(self, action: types.NestedArray) -> None:\\n if self._logger is None:\\n return\\n self._logger.info('{}, {}, {}, {}, {}, {}, {}'.format(\\n self._last_timestep.observation['STAGE'],\\n self._last_timestep.observation['CHIPS'],\\n self._last_timestep.observation['PLAYER_TOTAL'],\\n self._last_timestep.observation['PLAYER_ACES'],\\n self._last_timestep.observation['DEALER_TOTAL'],\\n action,\\n self._deck_distribution))\",\n \"def __log_trial__(self, trial_data):\\n from klibs.KLDatabase import EntryTemplate\\n\\n trial_template = EntryTemplate('trials')\\n trial_template.log(P.id_field_name, P.participant_id)\\n for attr in trial_data:\\n trial_template.log(attr, trial_data[attr])\\n\\n return self.database.insert(trial_template)\",\n \"def log(self, message):\",\n \"def _log_progress(self, t):\\n\\n # Run the update only 2 step before the actual logging happens in order to\\n # make sure that the most recent possible values will be stored in\\n # self.summary. This is a hacky workaround in order to support OffPolicyAgent\\n # which runs 2 threads without coordination\\n if (t+2) % self.log_freq == 0 and self.learn_started:\\n episode_rewards = self.env_monitor.get_episode_rewards()\\n self.episode_rewards = np.asarray(episode_rewards)\\n if self.episode_rewards.size > 0:\\n self.mean_ep_rew = np.mean(episode_rewards[-self.stats_n:])\\n self.best_mean_ep_rew = max(self.best_mean_ep_rew, self.mean_ep_rew)\\n\\n if t % self.log_freq == 0 and self.learn_started:\\n stats_logger.info(\\\"\\\")\\n for s, lambda_v in self.log_info:\\n stats_logger.info(s.format(lambda_v(t)))\\n stats_logger.info(\\\"\\\")\\n\\n if self.summary:\\n # Log with TensorBoard\\n self.tb_writer.add_summary(self.summary, global_step=t)\",\n \"def writeToLog(self, type, line):\\r\\n self._log.append((type, time(), line))\\r\\n if len(self._log[self._logIndex:]) >= self.config.get('logging', 'loginterval'):\\r\\n self.logFlush()\\r\\n return True\\r\\n return False\",\n \"def timer_callback(*args):\\n logging.debug(\\\"timer callback at %s\\\" % datetime.now())\",\n \"def log(self, txt, dt=None):\\n try:\\n dt = dt or self.datas[0].datetime.datetime(0)\\n print('%s, %s' % (dt.isoformat(), txt))\\n except IndexError:\\n print('%s' % txt)\",\n \"def logIt(self, msg):\\n\\n\\t\\tif( self.logger ): self.logger.logIt( msg )\",\n \"def record_task_attempt(task_name: str):\\n\\n from common.models import InvenTreeSetting\\n\\n logger.info(f\\\"Logging task attempt for '{task_name}'\\\")\\n\\n InvenTreeSetting.set_setting(f'_{task_name}_ATTEMPT', datetime.now().isoformat(), None)\",\n \"def run_game_log(player, game_log):\\n\\n\\tgame_log.update_room_type()\\n\\tcurrent_msg = game_log.get_current_message()\\n\\n\\tprint('HP: {} GOLD: {}'.format(player.stats['HP'], player.stats['GOLD']))\\n\\tprint('CURRENT ROOM: {}\\\\n'.format(game_log.current_room))\\n\\tprint(\\\"{}'s LOG: {}\\\".format(player.info['Name'].upper(), current_msg))\",\n \"def add_log(self,txt):\\n try:\\n now=datetime.datetime.now()\\n new_item=QtWidgets.QListWidgetItem(now.strftime('%Y/%m/%d %H:%M:%S')+\\\": \\\"+txt)\\n self.ui.logger_list.addItem(new_item)\\n if self.h5saver.h5_file.isopen:\\n self.h5saver.append(self.h5saver.logger_array, now.strftime('%Y/%m/%d %H:%M:%S')+\\\": \\\"+txt)\\n\\n except:\\n pass\",\n \"def log(self, obj, action):\\n action_dict = {'time': time.time(),\\n 'action': action}\\n self.log_data[obj.get_obj_id()]['actions'].append(action_dict)\",\n \"def listener(q, output_file):\\n\\n with open(output_file, 'w') as time_code_log:\\n while True:\\n m = q.get()\\n if m == 'kill':\\n time_code_log.write('killed')\\n break\\n\\n time_code_log.write(m[0] + '\\\\n')\\n time_code_log.write(m[1] + '\\\\n')\\n time_code_log.write(str(m[2]) + '\\\\n')\\n time_code_log.flush()\",\n \"def on_L1(self):\\r\\n self.log()\",\n \"def log(msg=\\\"\\\"):\\n print(msg)\\n sys.stdout.flush()\\n f = open(\\\"/target/testdriver.log\\\", \\\"a\\\")\\n f.write('{:%Y-%m-%d %H:%M:%S.%s} :: '.format(datetime.datetime.now()))\\n f.write(f\\\"{msg}\\\\n\\\")\\n f.close()\",\n \"def record_event(self, description, time=None, additional=None):\\n if time is None:\\n time = datetime.datetime.now()\\n if additional is not None:\\n self.history.append((time, (description, additional)))\\n else:\\n self.history.append((time, description))\",\n \"def writeToLogFile(self, event):\\n outPutStr = '{:013}'.format(0)\\n logOutPutStr = outPutStr + '\\\\t' + '{:.2f}'.format (time ()) + '\\\\t' + event + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ')\\n printOutPutStr = outPutStr + '\\\\t' + datetime.fromtimestamp (int (time())).isoformat (' ') + '\\\\t' + event\\n print (printOutPutStr)\\n if self.logFP is not None:\\n self.logFP.write(logOutPutStr + '\\\\n')\\n self.logFP.flush()\",\n \"def log(self, client_addr, request):\\n with codecs.open(self.log_path, \\\"a\\\", 'UTF-8') as fh_out:\\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \\\"\\\\t\\\" +\\n ':'.join([str(i) for i in client_addr]) + \\\"\\\\t\\\" +\\n request)\",\n \"def simulate(self, path):\\n prev_time = 0\\n for entry in self.read_log(path):\\n event = Event(entry)\\n if event.time != prev_time: # Show frame at time i\\n self.show()\\n prev_time = event.time\\n self.process_event(event)\",\n \"def log_time(self, batch_idx, duration, loss):\\n samples_per_sec = self.batch_size / duration\\n time_sofar = time.time() - self.start_time\\n training_time_left = (\\n self.num_total_steps / self.step - 1.0) * time_sofar if self.step > 0 else 0\\n print_string = \\\"epoch {:>3} | batch {:>6} | examples/s: {:5.1f}\\\" + \\\\\\n \\\" | loss: {:.5f} | time elapsed: {} | time left: {}\\\"\\n print(print_string.format(self.epoch, batch_idx, samples_per_sec, loss,\\n sec_to_hm_str(time_sofar), sec_to_hm_str(training_time_left)))\",\n \"def log(self, msg):\\n logging.info(\\\"Logging Message\\\")\\n ml = self.monk_logs\\n today = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n ml.insert({today: {\\\"log\\\": msg,\\n \\\"sentiment\\\": self.get_sentiment(msg),\\n \\\"weather\\\": Weather.get_weather()}})\",\n \"def log_time(self, batch_idx, duration, loss):\\n samples_per_sec = self.opt.batch_size / duration\\n time_sofar = time.time() - self.start_time\\n training_time_left = (\\n self.num_total_steps / self.step - 1.0) * time_sofar if self.step > 0 else 0\\n print_string = \\\"epoch {:>3} | batch {:>6} | examples/s: {:5.1f}\\\" + \\\\\\n \\\" | loss: {:.5f} | time elapsed: {} | time left: {}\\\"\\n print(print_string.format(self.epoch, batch_idx, samples_per_sec, loss,\\n sec_to_hm_str(time_sofar), sec_to_hm_str(training_time_left)))\",\n \"def log_every(self, identifier, message, seconds=1):\\r\\n if self.has_been_n_seconds_since_last(identifier, seconds):\\r\\n logger.info(message)\\r\\n else:\\r\\n logger.debug(message)\",\n \"def run_timing():\\n time_log = []\\n while True:\\n one_run = input(\\\"Enter your time for this 10 km: \\\")\\n if not one_run:\\n break\\n try:\\n time_log.append(float(one_run))\\n except ValueError:\\n print(\\n \\\"Hey, you enter something strange, \\\"\\n \\\"please enter a valid number\\\")\\n avg_time = sum(time_log) / len(time_log)\\n return f\\\"Your average time is about: {avg_time:.1f} \\\" \\\\\\n f\\\"over {len(time_log)} runs\\\"\",\n \"def log_completed_run(self, log_file_path):\\n with open(log_file_path, \\\"r\\\") as f:\\n stats = f.read().splitlines()\\n\\n self._parse_log_entry(stats)\\n self.experiment.log_other(\\\"log_file_path\\\", log_file_path)\",\n \"def time_tracking(self):\\n fb = FreshBooks()\\n tg = Toggl()\\n self.print_splash()\\n self.print(\\\"Tip: You can always enter 'skip' when you want to skip a time entry.\\\", format='warn')\\n days = self.get_interactive_days() # number of days to go back\\n self.print(\\\"OK, I'll run you through the Toggl time entries of the past %i day(s).\\\" % (days))\\n timestamp = self.get_timestamp(days) # unix timestamp including tz\\n time_entries = tg.get_time_entries(timestamp)\\n if len(time_entries) == 0:\\n self.print(\\\"No Toggl entries in this time span!\\\", 'warn')\\n return False\\n time_entries = self.merge_toggl_time_entries(time_entries) # merge Toggl entries\\n fb_projects = fb.get_projects()\\n # Loop through merged Toggl time entries:\\n for entry in time_entries:\\n # Get and convert all necessary info:\\n client_id = tg.get_client_id(project_id=entry.get('pid'))\\n client_name = tg.get_client_name(client_id)\\n project = tg.get_project(entry.get('pid'))\\n duration = int(entry['duration']) / 60 / 60 # convert duration to hours\\n duration = round(duration * 4 ) / 4 # round hours to nearest .25\\n description = self.format_description(project['name'], entry['description'])\\n date = str(parser.parse(entry['start']).date())\\n # Print info in a nice way:\\n self.print_divider(30)\\n self.print(\\\"Description: \\\" + description)\\n self.print(\\\"Date: \\\" + date)\\n self.print(\\\"Hours spent: \\\" + str(duration))\\n # Skip if Toggl entry is already booked:\\n if entry.get('tags') and tg.BOOKED_TAG in entry['tags']:\\n self.print(\\\"Skipping this entry because it is already in Freshbooks.\\\", 'cross')\\n # Skip if duration is below 0.25:\\n elif duration < 0.25:\\n self.print(\\\"Skipping this entry because there are less than 0.25 hours spent.\\\", 'cross')\\n # If billable, add to Freshbooks:\\n elif entry['billable']:\\n # Get FreshBooks project name through interactive search:\\n try:\\n self.print(\\\"Project: \\\\U0001F50D \\\")\\n fb_project_name = self.interactive_search(fb_projects.keys(), client_name)\\n # Handle KeyboardInterrupt\\n except KeyboardInterrupt:\\n answer = input(\\\"\\\\nKeyboardInterrupt! Skip current entry or quit time tracking? (S/q) \\\")\\n if answer.lower() == 's' or answer == '':\\n self.clear_lines(1)\\n self.print(\\\"Skipping this entry.\\\", 'cross')\\n continue\\n else:\\n self.clear_lines(1)\\n self.print(\\\"Ok, stopping time tracking.\\\", 'cross')\\n sys.exit()\\n # If user requests so, skip this entry:\\n self.clear_lines(1)\\n if not fb_project_name:\\n self.print(\\\"Skipping this entry.\\\", 'cross')\\n continue\\n # Otherwise, add entry to FreshBooks and tag Toggl entry/entries:\\n self.print(\\\"Project: \\\" + fb_project_name)\\n project_id = fb.get_project_id(fb_project_name)\\n fb.add_entry(project_id, duration, description, date)\\n tg.tag_projects(entry['merged_ids'], tg.BOOKED_TAG)\\n # If not billable, skip entry:\\n else:\\n self.print(\\\"Skipping this entry because it is not billable.\\\", 'cross')\\n self.print_divider(30)\\n answer = input(\\\"All done! Open FreshBooks in browser to verify? (Y/n) \\\")\\n if answer.lower() == 'y' or answer == '':\\n webbrowser.open('https://%s.freshbooks.com/timesheet' % fb.fb_creds['subdomain'])\",\n \"def __init__(self, logfile):\\n\\n self.logfile = logfile\\n if self. logfile:\\n self.file = open(logfile, \\\"w\\\")\\n self.starttime = time.time()\\n self.file.write(\\\"%.2f %s Starting log\\\\n\\\" % (time.time() - self.starttime, time.asctime()))\",\n \"def log(self, level, *msg_elements):\\r\\n self.report.log(self._threadlocal.current_workunit, level, *msg_elements)\",\n \"def print_log(*content):\\n now = datetime.datetime.now().strftime(\\\"%y-%m-%d %H:%M:%S\\\")\\n print(\\\"MODEL INFO: \\\" + str(now)+ \\\" \\\", end='')\\n print(*content)\",\n \"def log(self, txt):\\n if self.logfile:\\n self.logfile.write(txt)\",\n \"def _log(self, log, message):\\n log_entry = '[%s] %s\\\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\\n log.write(log_entry)\\n if self.verbose:\\n print log_entry.rstrip()\",\n \"def logByDate(self, timeText, id):\\n\\t\\t# Array of IDs for a day\\n\\t\\tdayLog = self.LOGS.get(\\\"by_date\\\").get(timeText)\\n\\n\\t\\t# If day already made\\n\\t\\tif dayLog:\\n\\t\\t\\tif not id in dayLog:\\n\\t\\t\\t\\tdayLog.append(id)\\n\\t\\telse:\\n\\t\\t\\tself.LOGS.get(\\\"by_date\\\")[timeText] = [id]\",\n \"def record_time(t):\\n\\n f = open('time.out', 'w')\\n f.write(str(t))\\n f.close()\",\n \"def debug_started(self, command):\\n now = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\\n lines = [\\n \\\"%s\\\\n\\\" % (\\\"*\\\" * self.line_lenght),\\n \\\"Command: %s\\\\n\\\" % command,\\n \\\"DateTime: %s\\\\n\\\" % now,\\n \\\"%s\\\\n\\\" % (\\\"*\\\" * self.line_lenght)\\n ]\\n\\n with open(self.debug_log, 'a+') as logfile:\\n logfile.writelines(lines)\",\n \"def log(message):\\n path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\\n with open(os.path.join(path, logfile_name), 'a+') as f:\\n t = strftime(\\\"%d %b %Y %H:%M:%S\\\", gmtime())\\n f.write(\\\"\\\\n\\\" + t + \\\" \\\" + message)\",\n \"def check_timer():\\n end = time.time()\\n time_elapsed = end - target_time[0]\\n durationMSG = fg.cyan + f\\\"Scans Completed for {args.target} in: \\\" + fg.rs\\n print(durationMSG, display_time(time_elapsed))\",\n \"def _log2mylog(self, msg):\\n time_str = mod_time.strftime(\\n \\\"%Y-%m-%d %H:%M:%S\\\", mod_time.localtime(mod_time.time())\\n )\\n msg = str(msg)\\n content = \\\"%s [%s]\\\\n\\\" % (time_str, msg)\\n fa = open(self.mylogfile, \\\"a\\\")\\n fa.write(content)\\n fa.close()\",\n \"def write(self, txt): \\n self.log.appendtext(txt)\\n self.log.update_idletasks()\\n return\",\n \"def log(self, msg):\\n\\n\\t\\tself.eyetribe.log_message(msg)\",\n \"def log(self, msg=None):\\n f = open(self.logbook, 'a')\\n # if send or receive, write message\\n if msg: \\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + \\n \\\" \\\" + str(msg) + '\\\\n')\\n # if it is an internal event just write the system time and current\\n # logical clock time\\n else:\\n f.write(\\\" System time: \\\" + str(datetime.now()) + \\n \\\" Logical clock time: \\\" + str(self.clock_time) + '\\\\n')\\n f.close()\",\n \"def log(self, loginfo):\\n logging.basicConfig(level=logging.INFO,\\n format='%(asctime)s %(filename)s:%(message)s',\\n datefmt='%d %b %Y %H:%M:%S',\\n filename=self.logfilepath,\\n filemode='w')\\n filelog = logging.FileHandler(self.logfilepath)\\n logging.getLogger('Functest').addHandler(filelog)\\n logging.info(loginfo)\",\n \"def _print_logs_info(job_id, project_id):\\n print(\\\"\\\\nJob submitted successfully.\\\")\\n print(\\\"Your job ID is: \\\", job_id)\\n print(\\\"\\\\nPlease access your training job information here:\\\")\\n print(\\n \\\"https://console.cloud.google.com/mlengine/jobs/{}?project={}\\\".format(\\n job_id, project_id))\\n print(\\\"\\\\nPlease access your training job logs here: \\\"\\n \\\"https://console.cloud.google.com/logs/viewer?resource=ml_job%2F\\\"\\n \\\"job_id%2F{}&interval=NO_LIMIT&project={}\\\\n\\\".format(\\n job_id, project_id))\",\n \"def log(message):\\n path = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\\n with open(os.path.join(path, logfile_name), 'a+') as f:\\n t = strftime(\\\"%d %b %Y %H:%M:%S\\\", gmtime())\\n f.write(\\\"\\\\n\\\" + t + \\\" %s\\\" % message)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70600265","0.64849997","0.64838594","0.6340308","0.626186","0.62565213","0.6244451","0.61845344","0.6172261","0.61576736","0.61439645","0.6137266","0.61355144","0.61291295","0.60836816","0.60411006","0.60167503","0.6007121","0.59954494","0.5931569","0.59120244","0.59064573","0.58965003","0.58921224","0.58653826","0.5857319","0.5856026","0.5855597","0.58551645","0.58194077","0.5807463","0.580267","0.5792795","0.5787527","0.5776082","0.57709527","0.5751121","0.5742101","0.5717202","0.5714032","0.57138205","0.56932104","0.56879073","0.5687035","0.56595445","0.5649528","0.5640544","0.5619968","0.5614741","0.5593052","0.55851716","0.558189","0.55804276","0.5566729","0.5558903","0.5541059","0.5522113","0.55192804","0.5498454","0.54628766","0.54506767","0.5441389","0.5440478","0.54370886","0.5436044","0.54190946","0.5410207","0.5405395","0.5404787","0.5389913","0.5389582","0.5382065","0.5377675","0.5365362","0.5357453","0.5353378","0.5348371","0.53434265","0.53412867","0.5336479","0.5334991","0.5334269","0.53336316","0.53226274","0.5322209","0.5318494","0.53139496","0.5312586","0.53125644","0.53110665","0.5302052","0.530113","0.5299679","0.5298444","0.5286686","0.5283248","0.52771425","0.527601","0.5273054","0.5267496","0.52626884"],"string":"[\n \"0.70600265\",\n \"0.64849997\",\n \"0.64838594\",\n \"0.6340308\",\n \"0.626186\",\n \"0.62565213\",\n \"0.6244451\",\n \"0.61845344\",\n \"0.6172261\",\n \"0.61576736\",\n \"0.61439645\",\n \"0.6137266\",\n \"0.61355144\",\n \"0.61291295\",\n \"0.60836816\",\n \"0.60411006\",\n \"0.60167503\",\n \"0.6007121\",\n \"0.59954494\",\n \"0.5931569\",\n \"0.59120244\",\n \"0.59064573\",\n \"0.58965003\",\n \"0.58921224\",\n \"0.58653826\",\n \"0.5857319\",\n \"0.5856026\",\n \"0.5855597\",\n \"0.58551645\",\n \"0.58194077\",\n \"0.5807463\",\n \"0.580267\",\n \"0.5792795\",\n \"0.5787527\",\n \"0.5776082\",\n \"0.57709527\",\n \"0.5751121\",\n \"0.5742101\",\n \"0.5717202\",\n \"0.5714032\",\n \"0.57138205\",\n \"0.56932104\",\n \"0.56879073\",\n \"0.5687035\",\n \"0.56595445\",\n \"0.5649528\",\n \"0.5640544\",\n \"0.5619968\",\n \"0.5614741\",\n \"0.5593052\",\n \"0.55851716\",\n \"0.558189\",\n \"0.55804276\",\n \"0.5566729\",\n \"0.5558903\",\n \"0.5541059\",\n \"0.5522113\",\n \"0.55192804\",\n \"0.5498454\",\n \"0.54628766\",\n \"0.54506767\",\n \"0.5441389\",\n \"0.5440478\",\n \"0.54370886\",\n \"0.5436044\",\n \"0.54190946\",\n \"0.5410207\",\n \"0.5405395\",\n \"0.5404787\",\n \"0.5389913\",\n \"0.5389582\",\n \"0.5382065\",\n \"0.5377675\",\n \"0.5365362\",\n \"0.5357453\",\n \"0.5353378\",\n \"0.5348371\",\n \"0.53434265\",\n \"0.53412867\",\n \"0.5336479\",\n \"0.5334991\",\n \"0.5334269\",\n \"0.53336316\",\n \"0.53226274\",\n \"0.5322209\",\n \"0.5318494\",\n \"0.53139496\",\n \"0.5312586\",\n \"0.53125644\",\n \"0.53110665\",\n \"0.5302052\",\n \"0.530113\",\n \"0.5299679\",\n \"0.5298444\",\n \"0.5286686\",\n \"0.5283248\",\n \"0.52771425\",\n \"0.527601\",\n \"0.5273054\",\n \"0.5267496\",\n \"0.52626884\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8472,"cells":{"query":{"kind":"string","value":"Determines if an input is a float."},"document":{"kind":"string","value":"def is_float(self, input):\n try:\n float(input)\n return True\n except ValueError:\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_float(x):\r\n try:\r\n float(x)\r\n except ValueError:\r\n return False\r\n return True","def isfloat(value):\r\n try:\r\n float(value)\r\n return True\r\n except ValueError:\r\n return False","def isFloat(value): \n try:\n float(value)\n return True\n except ValueError:\n return False","def isfloat(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def is_float(self, value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def isfloat(s):\n try:\n x = float(s)\n return True\n except:\n return False","def is_float(input_string):\r\n try:\r\n float(input_string)\r\n return True\r\n except ValueError:\r\n return False","def is_float(input_string):\r\n try:\r\n float(input_string)\r\n return True\r\n except ValueError:\r\n return False","def is_float(value):\n try:\n float(value)\n except ValueError:\n return False\n else:\n return True","def is_float(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def is_float(val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def is_float(self, val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def isit_float(s):\r\n try:\r\n int(s)\r\n return False\r\n except ValueError:\r\n try:\r\n float(s)\r\n return True\r\n except ValueError:\r\n return False","def could_be_float(val):\n if val == None:\n return False\n\n if isinstance(val, float):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n f = float(val)\n if not isinstance(f, float):\n raise ValueError\n else:\n return True\n except:\n return False\n\n #otherwise\n return False","def is_floatable(value):\n\n try:\n float(value)\n return True\n except:\n return False","def is_float(string):\n try:\n return float(string)\n except ValueError:\n return False","def isfloat(string):\n try:\n float(string)\n return True\n except ValueError:\n return False","def isFloat(string):\n return (True)","def is_float(string):\n try:\n float(string)\n return True\n except ValueError:\n return False","def isFloat(string):\n try: float(string)\n except ValueError: return 0\n else: return 1","def is_valid_float(input_string):\n assert input_string is not None\n try:\n float(input_string)\n return True\n except ValueError:\n return False","def is_float(string):\n try:\n float(string)\n return True\n except ValueError:\n return False","def is_float(string: str) -> bool:\n try:\n float(string)\n return True\n except ValueError:\n return False","def isfloat(string:str) -> bool:\n try:\n float(string)\n return True\n except ValueError:\n return False","def is_float(self, string):\n try:\n return decimal.Decimal(string)\n except decimal.DecimalException:\n return False","def is_float(*args): \n try:\n for i in args:\n float(i)\n return True\n except Exception:\n return False","def is_float_or_int(value):\n if type(value) is float:\n return True\n elif type(value) is int:\n return True\n else:\n return False","def check_for_float(check):","def isfloat(str):\n\n try:\n float(str)\n return True\t\t\t#Returns true if the string is a floating point number\n except (ValueError, TypeError):\n return False\t\t\t#Returns false otherwise","def is_float(value):\n if isinstance(value, float):\n return True\n\n if isinstance(value, np.ndarray):\n return value.dtype == np.float64\n\n return False","def is_float(word):\n try:\n float(word)\n return True\n except ValueError:\n return False","def is_float(space, w_obj):\n return space.wrap(w_obj.tp == space.tp_float)","def is_float(self, size=None):\n return False","def check_type_force_float(x, name):\n if type(x) is int:\n return float(x)\n elif type(x) is not float and type(x) is not numpy.float64:\n raise TypeError(\"%r should be a float\" % (name,))\n else:\n return x","def is_float(possible_number):\r\n try:\r\n float(possible_number)\r\n return True\r\n except ValueError:\r\n return False","def is_number_parse_float(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def isFloat(val):\n\tvalFloat = True\n\ttry:\n\t\ttVal = float(val)\n\texcept ValueError:\n\t\tvalFloat = False\n\ttVal = None\n\tr = (valFloat, tVal)\n\treturn r","def isFloat(string):\n return all(c in \"0123456789.\" for c in string)","def _is_positive_float(item):\n if not isinstance(item, (int, float)):\n return False\n return item > 0","def is_float_like(val):\n try:\n return str(float(val)) == str(val)\n except Exception:\n return False","def isnumber(x):\n try:\n float(x)\n return True\n except ValueError:\n return False","def _is_number(s) -> bool:\n try:\n float(s)\n except ValueError:\n return False\n else:\n return True","def value_is_float_not_int(value):\n # this is klugy and only needed to display deprecation warnings\n try:\n int(value)\n return False\n except ValueError:\n try:\n float(value)\n return True\n except ValueError:\n return False\n except TypeError:\n return False","def is_floating_point(type):\n float_def = (\n create_cv_types(cpptypes.float_t()) +\n create_cv_types(cpptypes.double_t()) +\n create_cv_types(cpptypes.long_double_t()))\n\n return remove_alias(type) in float_def","def isFloat(data):\n\tif type(data) == list or type(data) == np.ndarray:\n\t\tcol = pd.Series(data)\n\telse:\n\t\tcol = data\n\treturn col.dtype == np.float32 or col.dtype == np.float64","def is_floatscalar(x: Any) -> bool:\r\n return isinstance(x, (\r\n float,\r\n np.float16,\r\n np.float32,\r\n np.float64,\r\n ))","def isNumber(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_floating_point(self, size=None):\n return False","def _is_number(value):\n try:\n float(value)\n return True\n except (TypeError, ValueError):\n return False","def convertFloat(s):\n try:\n float(s)\n return \"FLOAT\"\n except:\n return s","def str_is_float(value):\n return all(\n [[any([i.isnumeric(), i in ['.', 'e']]) for i in value],\n len(value.split('.')) == 2])","def _is_non_negative_float(item):\n if not isinstance(item, (int, float)):\n return False\n return item >= 0","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def _float(data):\n try:\n return float(data)\n except ValueError as err:\n if data in ('None', 'NA', 'nan'):\n return nan\n else:\n raise ValueError(err)","def is_number(s):\r\n try:\r\n float(s)\r\n return True\r\n except ValueError:\r\n return False","def tryFloat(value):\n try:\n return float(value)\n except:\n return value","def possible_float(arg):\n try:\n return float(arg)\n except ValueError:\n logging.info(f'failed to parse {arg} as a float, treating it as a string')\n return arg","def isNumber(x):\n return isinstance(x, (int, float))","def is_number(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def isNumber(self, s):\n try:\n tmp = float(s)\n return True\n except:\n return False","def isNumber(s):\n\ttry:\n\t\tfloat(s)\n\t\treturn True\n\texcept ValueError:\n\t\treturn False","def float_or_none(s):\n if s:\n return float(s)","def test_wiki_toc_isfloat_true(self):\n from .wiki_toc import isfloat\n value = isfloat(value='40.22222')\n self.assertTrue(value is True)","def operand_present(input_str): # HELPER\n try:\n float(input_str)\n return True\n except ValueError:\n return False","def is_number(self,val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def is_sequence_of_float(items):\n return all(isinstance(item, float) for item in items)","def isnum(value):\n\n try:\n return bool(isinstance(value, (float, int)))\n except RuntimeError:\n return False","def test_float():\n floatify = fields.FloatField().adapt\n\n for input, expect in [\n (1.1, 1.1),\n (11, 11.0),\n (int(5.7), 5)\n ]:\n assert floatify(input) == expect","def IsNumber(s):\n try:\n v = float(s)\n return True\n except ValueError:\n return False","def checkNumberFloat(value):\n try:\n value = float(value)\n return value\n except ValueError:\n print(\"You did not enter a number\")\n newNum = input(\"Please enter a number: \")\n return checkNumberFloat(newNum)","def check_for_float_and_int(check):","def char_float(inp_char):\n try:\n nFloat = float(inp_char)\n except:\n nFloat = 0.0\n return nFloat","def test_float(self):\n self.assertFalse(validate_measure_input('0.0', self.measures))\n self.assertFalse(validate_measure_input('1.0', self.measures))\n self.assertFalse(validate_measure_input('1.1', self.measures))","def test_float_type(self):\n\n input_ = 1.2\n expected = ValueError\n with self.assertRaises(expected):\n math.factorial(input_)","def test_float(self, env: yaenv.Env):\n _val = env.float('FLOAT_VAR')\n assert _val == 10.0 and type(_val) == float\n _val = env.float('MISSING', -3.1)\n assert _val == -3.1 and type(_val) == float\n with pytest.raises(yaenv.EnvError) as err:\n _ = env.float('LIST_VAR')\n assert 'Invalid numerical' in str(err.value)\n assert env.float('MISSING') is None","def isNumber(x):\n\treturn type(x) in [int, float]","def check_pos_float(v):\n status = True\n try:\n val = float(v)\n if val <= 0:\n status = False\n except ValueError:\n status = False\n return status","def is_number_tryexcept(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(num):\n try:\n float(num)\n return True\n except ValueError:\n return False","def float(x) -> float:\n pass","def _assert_float_dtype(dtype):\n if not dtype.is_floating:\n raise ValueError(\"Expected floating point type, got %s.\" % dtype)\n return dtype","def ffloat(string):\n try:\n return float(string.strip())\n except:\n return 0","def convert_to_float(s):\n try:\n return float(s)\n except TypeError:\n return None","def is_number_tryexcept(s):\n try:\n float(s)\n return True\n except ValueError:\n return False","def _is_real(symbol):\n return isa(symbol, float) or is_int(symbol)","def test_float(self):\n htype = h5t.py_create('f')\n self.assertIsInstance(htype, h5t.TypeFloatID)","def test_wiki_toc_isfloat_false(self):\n from .wiki_toc import isfloat\n value = isfloat(value='test_float')\n self.assertTrue(value is False)","def is_float3(items):\n return len(items) == 3 and all(isinstance(item, float) for item in items)","def is_number(s: Union[str, int, float]):\n if isinstance(s, str) and s.lower() == \"nan\":\n return True\n try:\n float(s)\n return True\n except ValueError:\n return False","def is_number(x):\n if isinstance(x, (int, float)):\n return True\n else:\n return False","def CheckNumber(userInput):\n try:\n float(userInput)\n return True\n except(ValueError):\n return False","def validate_float(self, p_str):\n # may validate only '[+-].' which needs to be handled later\n float_pattern = r\"^[\\+\\-]?([0-9]*[.])?[0-9]*$\"\n if re.search(float_pattern, p_str) or p_str == \"\":\n return True\n self.frame.bell()\n return False","def is_numeric(value):\n return isinstance(value, int) or isinstance(value, float)","def IsFloatable(self):\r\n\r\n return self.HasFlag(self.optionFloatable)","def safe_float(float_string: str = \"0.0\") -> float:\n float_things = [None, \"\", \"-\", \"0\"]\n\n if float_string in float_things:\n return 0.0\n else:\n return float(float_string)","def could_be_number(val):\n if val == None:\n return False\n\n if isinstance(val, (float, int, long)):\n return True\n\n # allow coercion from str\n if isinstance(val, (str, unicode)):\n try:\n n = float(val)\n if not isinstance(n, float):\n raise ValueError\n else:\n return True\n except:\n return False\n\n #otherwise\n return False","def isrealnum(variable):\n return bool(math.isfinite(variable))"],"string":"[\n \"def is_float(x):\\r\\n try:\\r\\n float(x)\\r\\n except ValueError:\\r\\n return False\\r\\n return True\",\n \"def isfloat(value):\\r\\n try:\\r\\n float(value)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def isFloat(value): \\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def isfloat(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(self, value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def isfloat(s):\\n try:\\n x = float(s)\\n return True\\n except:\\n return False\",\n \"def is_float(input_string):\\r\\n try:\\r\\n float(input_string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_float(input_string):\\r\\n try:\\r\\n float(input_string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_float(value):\\n try:\\n float(value)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def is_float(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(self, val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def isit_float(s):\\r\\n try:\\r\\n int(s)\\r\\n return False\\r\\n except ValueError:\\r\\n try:\\r\\n float(s)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def could_be_float(val):\\n if val == None:\\n return False\\n\\n if isinstance(val, float):\\n return True\\n\\n # allow coercion from str\\n if isinstance(val, (str, unicode)):\\n try:\\n f = float(val)\\n if not isinstance(f, float):\\n raise ValueError\\n else:\\n return True\\n except:\\n return False\\n\\n #otherwise\\n return False\",\n \"def is_floatable(value):\\n\\n try:\\n float(value)\\n return True\\n except:\\n return False\",\n \"def is_float(string):\\n try:\\n return float(string)\\n except ValueError:\\n return False\",\n \"def isfloat(string):\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def isFloat(string):\\n return (True)\",\n \"def is_float(string):\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def isFloat(string):\\n try: float(string)\\n except ValueError: return 0\\n else: return 1\",\n \"def is_valid_float(input_string):\\n assert input_string is not None\\n try:\\n float(input_string)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(string):\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(string: str) -> bool:\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def isfloat(string:str) -> bool:\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(self, string):\\n try:\\n return decimal.Decimal(string)\\n except decimal.DecimalException:\\n return False\",\n \"def is_float(*args): \\n try:\\n for i in args:\\n float(i)\\n return True\\n except Exception:\\n return False\",\n \"def is_float_or_int(value):\\n if type(value) is float:\\n return True\\n elif type(value) is int:\\n return True\\n else:\\n return False\",\n \"def check_for_float(check):\",\n \"def isfloat(str):\\n\\n try:\\n float(str)\\n return True\\t\\t\\t#Returns true if the string is a floating point number\\n except (ValueError, TypeError):\\n return False\\t\\t\\t#Returns false otherwise\",\n \"def is_float(value):\\n if isinstance(value, float):\\n return True\\n\\n if isinstance(value, np.ndarray):\\n return value.dtype == np.float64\\n\\n return False\",\n \"def is_float(word):\\n try:\\n float(word)\\n return True\\n except ValueError:\\n return False\",\n \"def is_float(space, w_obj):\\n return space.wrap(w_obj.tp == space.tp_float)\",\n \"def is_float(self, size=None):\\n return False\",\n \"def check_type_force_float(x, name):\\n if type(x) is int:\\n return float(x)\\n elif type(x) is not float and type(x) is not numpy.float64:\\n raise TypeError(\\\"%r should be a float\\\" % (name,))\\n else:\\n return x\",\n \"def is_float(possible_number):\\r\\n try:\\r\\n float(possible_number)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_number_parse_float(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def isFloat(val):\\n\\tvalFloat = True\\n\\ttry:\\n\\t\\ttVal = float(val)\\n\\texcept ValueError:\\n\\t\\tvalFloat = False\\n\\ttVal = None\\n\\tr = (valFloat, tVal)\\n\\treturn r\",\n \"def isFloat(string):\\n return all(c in \\\"0123456789.\\\" for c in string)\",\n \"def _is_positive_float(item):\\n if not isinstance(item, (int, float)):\\n return False\\n return item > 0\",\n \"def is_float_like(val):\\n try:\\n return str(float(val)) == str(val)\\n except Exception:\\n return False\",\n \"def isnumber(x):\\n try:\\n float(x)\\n return True\\n except ValueError:\\n return False\",\n \"def _is_number(s) -> bool:\\n try:\\n float(s)\\n except ValueError:\\n return False\\n else:\\n return True\",\n \"def value_is_float_not_int(value):\\n # this is klugy and only needed to display deprecation warnings\\n try:\\n int(value)\\n return False\\n except ValueError:\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\\n except TypeError:\\n return False\",\n \"def is_floating_point(type):\\n float_def = (\\n create_cv_types(cpptypes.float_t()) +\\n create_cv_types(cpptypes.double_t()) +\\n create_cv_types(cpptypes.long_double_t()))\\n\\n return remove_alias(type) in float_def\",\n \"def isFloat(data):\\n\\tif type(data) == list or type(data) == np.ndarray:\\n\\t\\tcol = pd.Series(data)\\n\\telse:\\n\\t\\tcol = data\\n\\treturn col.dtype == np.float32 or col.dtype == np.float64\",\n \"def is_floatscalar(x: Any) -> bool:\\r\\n return isinstance(x, (\\r\\n float,\\r\\n np.float16,\\r\\n np.float32,\\r\\n np.float64,\\r\\n ))\",\n \"def isNumber(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_floating_point(self, size=None):\\n return False\",\n \"def _is_number(value):\\n try:\\n float(value)\\n return True\\n except (TypeError, ValueError):\\n return False\",\n \"def convertFloat(s):\\n try:\\n float(s)\\n return \\\"FLOAT\\\"\\n except:\\n return s\",\n \"def str_is_float(value):\\n return all(\\n [[any([i.isnumeric(), i in ['.', 'e']]) for i in value],\\n len(value.split('.')) == 2])\",\n \"def _is_non_negative_float(item):\\n if not isinstance(item, (int, float)):\\n return False\\n return item >= 0\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def _float(data):\\n try:\\n return float(data)\\n except ValueError as err:\\n if data in ('None', 'NA', 'nan'):\\n return nan\\n else:\\n raise ValueError(err)\",\n \"def is_number(s):\\r\\n try:\\r\\n float(s)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def tryFloat(value):\\n try:\\n return float(value)\\n except:\\n return value\",\n \"def possible_float(arg):\\n try:\\n return float(arg)\\n except ValueError:\\n logging.info(f'failed to parse {arg} as a float, treating it as a string')\\n return arg\",\n \"def isNumber(x):\\n return isinstance(x, (int, float))\",\n \"def is_number(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def isNumber(self, s):\\n try:\\n tmp = float(s)\\n return True\\n except:\\n return False\",\n \"def isNumber(s):\\n\\ttry:\\n\\t\\tfloat(s)\\n\\t\\treturn True\\n\\texcept ValueError:\\n\\t\\treturn False\",\n \"def float_or_none(s):\\n if s:\\n return float(s)\",\n \"def test_wiki_toc_isfloat_true(self):\\n from .wiki_toc import isfloat\\n value = isfloat(value='40.22222')\\n self.assertTrue(value is True)\",\n \"def operand_present(input_str): # HELPER\\n try:\\n float(input_str)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(self,val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def is_sequence_of_float(items):\\n return all(isinstance(item, float) for item in items)\",\n \"def isnum(value):\\n\\n try:\\n return bool(isinstance(value, (float, int)))\\n except RuntimeError:\\n return False\",\n \"def test_float():\\n floatify = fields.FloatField().adapt\\n\\n for input, expect in [\\n (1.1, 1.1),\\n (11, 11.0),\\n (int(5.7), 5)\\n ]:\\n assert floatify(input) == expect\",\n \"def IsNumber(s):\\n try:\\n v = float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def checkNumberFloat(value):\\n try:\\n value = float(value)\\n return value\\n except ValueError:\\n print(\\\"You did not enter a number\\\")\\n newNum = input(\\\"Please enter a number: \\\")\\n return checkNumberFloat(newNum)\",\n \"def check_for_float_and_int(check):\",\n \"def char_float(inp_char):\\n try:\\n nFloat = float(inp_char)\\n except:\\n nFloat = 0.0\\n return nFloat\",\n \"def test_float(self):\\n self.assertFalse(validate_measure_input('0.0', self.measures))\\n self.assertFalse(validate_measure_input('1.0', self.measures))\\n self.assertFalse(validate_measure_input('1.1', self.measures))\",\n \"def test_float_type(self):\\n\\n input_ = 1.2\\n expected = ValueError\\n with self.assertRaises(expected):\\n math.factorial(input_)\",\n \"def test_float(self, env: yaenv.Env):\\n _val = env.float('FLOAT_VAR')\\n assert _val == 10.0 and type(_val) == float\\n _val = env.float('MISSING', -3.1)\\n assert _val == -3.1 and type(_val) == float\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = env.float('LIST_VAR')\\n assert 'Invalid numerical' in str(err.value)\\n assert env.float('MISSING') is None\",\n \"def isNumber(x):\\n\\treturn type(x) in [int, float]\",\n \"def check_pos_float(v):\\n status = True\\n try:\\n val = float(v)\\n if val <= 0:\\n status = False\\n except ValueError:\\n status = False\\n return status\",\n \"def is_number_tryexcept(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(num):\\n try:\\n float(num)\\n return True\\n except ValueError:\\n return False\",\n \"def float(x) -> float:\\n pass\",\n \"def _assert_float_dtype(dtype):\\n if not dtype.is_floating:\\n raise ValueError(\\\"Expected floating point type, got %s.\\\" % dtype)\\n return dtype\",\n \"def ffloat(string):\\n try:\\n return float(string.strip())\\n except:\\n return 0\",\n \"def convert_to_float(s):\\n try:\\n return float(s)\\n except TypeError:\\n return None\",\n \"def is_number_tryexcept(s):\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def _is_real(symbol):\\n return isa(symbol, float) or is_int(symbol)\",\n \"def test_float(self):\\n htype = h5t.py_create('f')\\n self.assertIsInstance(htype, h5t.TypeFloatID)\",\n \"def test_wiki_toc_isfloat_false(self):\\n from .wiki_toc import isfloat\\n value = isfloat(value='test_float')\\n self.assertTrue(value is False)\",\n \"def is_float3(items):\\n return len(items) == 3 and all(isinstance(item, float) for item in items)\",\n \"def is_number(s: Union[str, int, float]):\\n if isinstance(s, str) and s.lower() == \\\"nan\\\":\\n return True\\n try:\\n float(s)\\n return True\\n except ValueError:\\n return False\",\n \"def is_number(x):\\n if isinstance(x, (int, float)):\\n return True\\n else:\\n return False\",\n \"def CheckNumber(userInput):\\n try:\\n float(userInput)\\n return True\\n except(ValueError):\\n return False\",\n \"def validate_float(self, p_str):\\n # may validate only '[+-].' which needs to be handled later\\n float_pattern = r\\\"^[\\\\+\\\\-]?([0-9]*[.])?[0-9]*$\\\"\\n if re.search(float_pattern, p_str) or p_str == \\\"\\\":\\n return True\\n self.frame.bell()\\n return False\",\n \"def is_numeric(value):\\n return isinstance(value, int) or isinstance(value, float)\",\n \"def IsFloatable(self):\\r\\n\\r\\n return self.HasFlag(self.optionFloatable)\",\n \"def safe_float(float_string: str = \\\"0.0\\\") -> float:\\n float_things = [None, \\\"\\\", \\\"-\\\", \\\"0\\\"]\\n\\n if float_string in float_things:\\n return 0.0\\n else:\\n return float(float_string)\",\n \"def could_be_number(val):\\n if val == None:\\n return False\\n\\n if isinstance(val, (float, int, long)):\\n return True\\n\\n # allow coercion from str\\n if isinstance(val, (str, unicode)):\\n try:\\n n = float(val)\\n if not isinstance(n, float):\\n raise ValueError\\n else:\\n return True\\n except:\\n return False\\n\\n #otherwise\\n return False\",\n \"def isrealnum(variable):\\n return bool(math.isfinite(variable))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82458997","0.8190943","0.81595534","0.81558645","0.81085587","0.8093256","0.807079","0.807079","0.80703425","0.8061959","0.8060793","0.8011374","0.7838578","0.7823464","0.77867234","0.77559304","0.7752695","0.7747797","0.7746936","0.77243704","0.7696662","0.7658992","0.7640902","0.76312405","0.76285326","0.76165867","0.7559674","0.7531927","0.75238013","0.7520814","0.74827254","0.7369767","0.7336208","0.7332111","0.7326844","0.71557266","0.7097585","0.7084261","0.7077825","0.70132476","0.69466156","0.6942867","0.693276","0.69027615","0.6882085","0.6837131","0.68317866","0.6823439","0.68204474","0.68148977","0.681379","0.67892045","0.678092","0.678092","0.678092","0.67802936","0.6779412","0.67675513","0.67405176","0.6728251","0.6716626","0.6710665","0.6708011","0.67048496","0.6698156","0.6694513","0.66868407","0.6673555","0.66717637","0.6667786","0.66517895","0.6631107","0.66027755","0.65864027","0.6576154","0.6573517","0.65604794","0.6555999","0.655375","0.6524634","0.65217745","0.65208524","0.6518815","0.6515827","0.6509773","0.6499663","0.6494604","0.6492649","0.64817905","0.6477013","0.6460381","0.645624","0.6451185","0.64445364","0.6443786","0.6433912","0.64223486","0.6420339","0.6412904","0.64071983"],"string":"[\n \"0.82458997\",\n \"0.8190943\",\n \"0.81595534\",\n \"0.81558645\",\n \"0.81085587\",\n \"0.8093256\",\n \"0.807079\",\n \"0.807079\",\n \"0.80703425\",\n \"0.8061959\",\n \"0.8060793\",\n \"0.8011374\",\n \"0.7838578\",\n \"0.7823464\",\n \"0.77867234\",\n \"0.77559304\",\n \"0.7752695\",\n \"0.7747797\",\n \"0.7746936\",\n \"0.77243704\",\n \"0.7696662\",\n \"0.7658992\",\n \"0.7640902\",\n \"0.76312405\",\n \"0.76285326\",\n \"0.76165867\",\n \"0.7559674\",\n \"0.7531927\",\n \"0.75238013\",\n \"0.7520814\",\n \"0.74827254\",\n \"0.7369767\",\n \"0.7336208\",\n \"0.7332111\",\n \"0.7326844\",\n \"0.71557266\",\n \"0.7097585\",\n \"0.7084261\",\n \"0.7077825\",\n \"0.70132476\",\n \"0.69466156\",\n \"0.6942867\",\n \"0.693276\",\n \"0.69027615\",\n \"0.6882085\",\n \"0.6837131\",\n \"0.68317866\",\n \"0.6823439\",\n \"0.68204474\",\n \"0.68148977\",\n \"0.681379\",\n \"0.67892045\",\n \"0.678092\",\n \"0.678092\",\n \"0.678092\",\n \"0.67802936\",\n \"0.6779412\",\n \"0.67675513\",\n \"0.67405176\",\n \"0.6728251\",\n \"0.6716626\",\n \"0.6710665\",\n \"0.6708011\",\n \"0.67048496\",\n \"0.6698156\",\n \"0.6694513\",\n \"0.66868407\",\n \"0.6673555\",\n \"0.66717637\",\n \"0.6667786\",\n \"0.66517895\",\n \"0.6631107\",\n \"0.66027755\",\n \"0.65864027\",\n \"0.6576154\",\n \"0.6573517\",\n \"0.65604794\",\n \"0.6555999\",\n \"0.655375\",\n \"0.6524634\",\n \"0.65217745\",\n \"0.65208524\",\n \"0.6518815\",\n \"0.6515827\",\n \"0.6509773\",\n \"0.6499663\",\n \"0.6494604\",\n \"0.6492649\",\n \"0.64817905\",\n \"0.6477013\",\n \"0.6460381\",\n \"0.645624\",\n \"0.6451185\",\n \"0.64445364\",\n \"0.6443786\",\n \"0.6433912\",\n \"0.64223486\",\n \"0.6420339\",\n \"0.6412904\",\n \"0.64071983\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.884747"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8473,"cells":{"query":{"kind":"string","value":"Takes a valid float from the user or the escape command"},"document":{"kind":"string","value":"def hours_studied(self):\n value = input(\"Enter value (or 'exit')>>>\")\n while not self.is_float(value):\n value = input(\"Enter value (or 'exit')>>>\")\n\n # Escape command\n if value == 'exit':\n return value\n\n return float(value)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def prompt_float_input(prompt_name: str, get_user_input: GetInputFunc) -> float:\n try:\n return float(get_user_input(f\"{prompt_name}:\"))\n except (ValueError, IndexError) as e:\n raise InvalidInput(str(e))","def get_float(self, prompt=\"> \"):\n\t\twhile True:\n\t\t\tans = raw_input(prompt)\n\t\t\ttry: \t\n\t\t\t\tans = float(ans)\n\t\t\t\treturn ans\n\t\t\texcept ValueError:\n\t\t\t\tif ans == \"quit\": quit()\n\t\t\t\telse: print \"Please enter a number using decimal notation.\"","def check_for_float(check):","def test_normal_decimal_input(self):\r\n ws_leader = \"S. O'Neal (14.9)\"\r\n res = treat_input(ws_leader, type=\"float\")\r\n assert res == 14.9","def restricted_float(x):\n try:\n x = float(x)\n except ValueError:\n raise argparse.ArgumentTypeError(\"%r not a floating-point literal\" % (x,))\n return x","def main():\n\ttry:\n\t\tx = input(\"Type in a number to be converted to a float: \")\n\t\tfloatnum = float(x)\n\t\tprint(floatnum)\n\texcept(ValueError):\n\t\tprint(\"Could not convert the string to a float\")","def checkNumberFloat(value):\n try:\n value = float(value)\n return value\n except ValueError:\n print(\"You did not enter a number\")\n newNum = input(\"Please enter a number: \")\n return checkNumberFloat(newNum)","def read_float(message, default=None, help=None):\n while True:\n option = raw_input(message)\n if not option:\n if default is not None:\n return default\n else:\n print(\"Please provide a value.\")\n elif option == \"?\":\n if help:\n print(help)\n else:\n print(\"Please provide a number\")\n else:\n try:\n return float(option)\n except ValueError:\n print(\"Expected a number.\")\n pass","def sanitizeFloatFromKeyboard(s,range_start=0,range_end=0):\n try:\n\tx = float(s)\n except ValueError:\n\terr = 1\n\treturn err,0\n\n if (x >= range_start) and (x <= range_end):\n\terr = 0\n return err,x\n else:\n\terr = 1\n\treturn err,x","def _restricted_float(val: float):\n try:\n val = float(val)\n except ValueError:\n raise argparse.ArgumentTypeError(f\"{val} not a floating-point literal\")\n\n if 0.0 < val > 1.0:\n raise argparse.ArgumentTypeError(f\"{val} not in range [0.0, 1.0]\")\n return val","def validate_float(self, p_str):\n # may validate only '[+-].' which needs to be handled later\n float_pattern = r\"^[\\+\\-]?([0-9]*[.])?[0-9]*$\"\n if re.search(float_pattern, p_str) or p_str == \"\":\n return True\n self.frame.bell()\n return False","def char_float(inp_char):\n try:\n nFloat = float(inp_char)\n except:\n nFloat = 0.0\n return nFloat","def getFloat(fl):\n while True:\n try:\n return float(input(fl))\n except ValueError:\n print(\"Give a proper value please\")","def possible_float(arg):\n try:\n return float(arg)\n except ValueError:\n logging.info(f'failed to parse {arg} as a float, treating it as a string')\n return arg","def gravedad(frm):\r\n x=int(frm.txtGrav.GetValue())\r\n while type(x)!=float:\r\n try:\r\n x=float(x)\r\n\r\n if type(x)==float:\r\n return -x\r\n\r\n else:\r\n pass\r\n\r\n except:\r\n print \"Ingrese un valor valido para la gravedad\"\r\n print\r\n x=raw_input(\"?Cu?l es la aceleracion de la gravedad?: m/s\")","def get_user_input():\n return float(input('Your transaction amount please: '))","def number_format(num):\n while True:\n try:\n user_input = float(input(num))\n return user_input\n except ValueError:\n print(\"Error. Please enter the desired number. You may use \"\n \"decimals.\")\n except:\n print(\"Error: unknown.\")","def test_try():\n numb = input(\"Enter a number\")\n print(type(numb))\n try:\n num = float(numb)\n print(num)\n except Exception as e: # if there is exception,we wont crash,we will catch it\n print(e)\n print(\"Exiting the program\")","def give_me_a_float():\n return 5.8\n pass","def handle_commas(x):\n if type(x) != str:\n print (\"Please enter a string\")\n return False\n else:\n x = float(x.replace(\",\",\"\"))\n return x","def CheckNumber(userInput):\n try:\n float(userInput)\n return True\n except(ValueError):\n return False","def setfloat(self, strcommand, value):\n command = ct.c_wchar_p(strcommand)\n value = ct.c_double(value)\n self.lib.AT_SetFloat(self.AT_H, command, value)","def _ensure_positive_float(f):\n if float(f) < 0:\n raise argparse.ArgumentTypeError(\n 'Argument must be greater than zero')\n return float(f)","def get_check_fn():\r\n fn = input(\"Enter your favorite number.\\n\\t\")\r\n while True:\r\n try:\r\n fn = float(fn)\r\n except ValueError:\r\n fn = input(\"Must enter a number.\\n\\t\")\r\n else:\r\n return fn","def isFloat(string):\n return (True)","def velocidad(frm):\r\n x=int(frm.txtvel.GetValue())\r\n while type(x)!=float:\r\n try:\r\n x=float(x)\r\n\r\n if type(x)==float:\r\n return x\r\n\r\n else:\r\n pass\r\n\r\n except:\r\n print \"Ingrese un valor v?lido para la fuerza\"\r\n print\r\n x=raw_input(\"?Cu?l es la velocidad inicial del objeto? (m/s): \")","def default(self, line):\n if line == \"EOF\":\n return True\n elif line in self.operations:\n try:\n self.calc.operation(self.operations[line])\n\t\tself._debug_stack()\n except ReversePolishCalcError, errmsg:\n self._debug_calc_error(errmsg)\n elif re.match(\"[0-9.eE]+\", line):\n try:\n self.calc.insert_value(float(line))\n except ValueError:\n debug(\"Invalid float value: %s\" % line)\n else:\n debug(\"Unknown command: %s\" % line)","def Float(val):\n try:\n return float(val)\n except ValueError:\n return ''","def clean_input(prompt='Error'): # A special input function that will reject a\r\n # user's input of text when a number is requested -- if no prompt is\r\n # specified in the program, it will display \"Error\"\r\n text = True\r\n phrase = '0'\r\n while text:\r\n phrase = input(prompt + '\\n')\r\n try: # Adapted from an example in the ThinkPython textbook (15.7) -\r\n # Checks whether the input is a number, positive or negative. If\r\n # not, rejects the input and user gets to try again\r\n float(phrase)\r\n text = False\r\n except ValueError:\r\n print(\"Error: Non-Numeric Entry Detected\")\r\n # if phrase.isnumeric(): # Checks for a positive number (negative\r\n # rejected as well as text) - replaced with superior form from textbook\r\n # example\r\n # return float(phrase) # Return the number the user entered\r\n # else:\r\n # print(\"Error: Non-Numeric Entry Detected\")\r\n return float(phrase) # Return the number the user entered\r","def validation_method(input_value):\r\n while True:\r\n try:\r\n valor = float(input(input_value))\r\n return valor\r\n except ValueError:\r\n print(\" ingresa un número\")","def validate_calibration(prompt,name):\n \n while True:\n temp_value = input(prompt)\n try:\n float(temp_value)\n\n if temp_value > 0:\n return float(temp_value)\n else:\n print(\"invalid {}\".format(name))\n except:\n print(\"invalid {}\".format(name))","def test_float(self):\n self.assertFalse(validate_measure_input('0.0', self.measures))\n self.assertFalse(validate_measure_input('1.0', self.measures))\n self.assertFalse(validate_measure_input('1.1', self.measures))","def test_float(self, env: yaenv.Env):\n _val = env.float('FLOAT_VAR')\n assert _val == 10.0 and type(_val) == float\n _val = env.float('MISSING', -3.1)\n assert _val == -3.1 and type(_val) == float\n with pytest.raises(yaenv.EnvError) as err:\n _ = env.float('LIST_VAR')\n assert 'Invalid numerical' in str(err.value)\n assert env.float('MISSING') is None","def get_float(message, high, low=0):\r\n\r\n while True:\r\n try:\r\n floatValue = float(input(message))\r\n except ValueError:\r\n print (\"ERROR, Entry must be a number. Please try again.\")\r\n continue\r\n if floatValue <= low or floatValue > high:\r\n print (\"ERROR, Entry must be greater than \" + str(low) + \" and, less than or equal to \"\\\r\n + str(high) + \". Please try again.\")\r\n continue\r\n break\r\n return floatValue","def usetf(self, prompt=None, default=None):\n\n i = 0\n abak = copy(default) # Backup our default value\n\n while(i<self.maxTries):\n tmp = self.uset(prompt,default)\n try:\n a = float(tmp)\n i = self.maxTries # preload failure\n except:\n # Print warning\n print\n print \" WARNING: Invalid Entry. Please enter a floating point number \"\n print \n # reload the default\n a = abak\n i = i+1\n\n return(a)","def validate_insert(self, s, internal=True):\n super(FieldNumeric, self).validate_insert(s, internal) # mandatory check\n if s:\n try:\n float(s)\n except:\n raise FilemanError(\"\"\"[%s] is not a valid number\"\"\" % s)","def operand_present(input_str): # HELPER\n try:\n float(input_str)\n return True\n except ValueError:\n return False","def is_valid_float(input_string):\n assert input_string is not None\n try:\n float(input_string)\n return True\n except ValueError:\n return False","def check_arguments(arguments):\n quit = False\n for argument, value in vars(arguments).items():\n try:\n float(value)\n except:\n print(\"{} must be numeric\".format(argument))\n quit = True\n if quit:\n exit(1)","def is_float(self, input):\n try:\n float(input)\n return True\n except ValueError:\n return False","def checkfloat(name, val, mn=None, mx=None):\n try:\n\tx = string.atof(val)\n\tif ((mn is not None and x < mn) or\n\t (mx is not None and x > mx)):\n\t\traise ValidationError, \\\n\t\t 'parameter \"%s\", value \"%s\" is out of range' % \\\n\t\t (name, val)\n\treturn\n except ValueError:\n\traise ValidationError, '%s is not a valid floating point number' % val","def posfloat_p(value):\n # check if the value has the expected type\n if type(value) is not float:\n raise Invalid(\"invalid value type {value}\".format(value=value))\n if value <= 0.0:\n raise Invalid(\"invalid value {value}, positive float expected\".format(value=value))","def float_or_none(s):\n if s:\n return float(s)","def value_input(unit):\n print(Fore.CYAN + \"\\n Enter the temperature in \\u00b0\" + unit + \":\\n\" +\n Fore.RESET)\n while True:\n try:\n value = float(input()) # <=== Make sure input is a float\n return value\n break\n except ValueError:\n print(Fore.RED + \"\\n Input must be an integer!\\n\" + Fore.RESET)","def check_for_float_and_int(check):","def convert_str_float(x):\n\ttry:\n\t\treturn float(x)\n\texcept ValueError:\n\t\tprint(\"must be a number\")","def price_product():\n while True:\n price = raw_input(\"Add the price: \")\n try:\n price = float(price)\n return price\n except ValueError: #if the price is a number\n print \"Insert a number\"\n return price","def handle_value(value):\n\n if value[-1] == 'x':\n return float(value[0:-1])\n\n if value[-1] == '%':\n return float(value[0:-1])\n\n if value[0].isdigit():\n return bytify(value)\n\n raise ValueError","def read_float(v):\n if v.strip() == '':\n return 0.\n try:\n return float(v)\n except ValueError:\n # ENDF6 may omit the e for exponent\n return float(v[0] + v[1:].replace('+', 'e+').replace('-', 'e-')) # don't replace leading negative sign","def ffloat(string):\n try:\n return float(string.strip())\n except:\n return 0","def test_get_value1(monkeypatch):\n mr.initialize_donors()\n monkeypatch.setattr('builtins.input', lambda x: 4)\n value = mr.get_value('Enter a float', float)\n assert value == float('4')","def tryFloat(value):\n try:\n return float(value)\n except:\n return value","def get_price():\n\n while (True):\n price = input(\"Enter the purchase price (xx.xx) or 'q' to quit: \")\n if(price.capitalize() == 'Q'):\n return -1\n elif price.replace('.', '').isdigit() and not is_valid(price):\n print(\"Illegal price: Must be a non-negative multiple of 5 cents.\\n\")\n elif not price.replace('.', '').isdigit():\n print(\"Illegal entry: Must be a price like (1.75) or 'q' for quit.\\n\")\n else:\n return float(price)","def convertFloat(num):\n try:\n num = float(num)\n return num\n except (TypeError, ValueError, ArithmeticError):\n print(\"num cannot convert float\")","def float_or_auto(val):\n try:\n return float(val)\n except:\n if isinstance(val, basestring) and val == \"auto\":\n return val\n raise TraitError, \"Tick interval must be a number or 'auto'.\"","def is_float(*args): \n try:\n for i in args:\n float(i)\n return True\n except Exception:\n return False","def parse_float_value(self, value: str):\n value, power = self.parse_value_ending(value)\n try:\n value = float(value)\n return value * 10 ** power\n except:\n raise Exception(\"Failed to parse the __value.\")","def maybe_float(v):\n try:\n return float(v)\n except ValueError:\n return v","def validate_number(value_if_allowed):\n if value_if_allowed == '':\n return True\n try:\n float(value_if_allowed)\n return True\n except ValueError:\n return False","def evaluate(self):\n self.getInput()\n try:\n self.result = eval(self.userInput)\n except ZeroDivisionError:\n self.entry.delete(0, END)\n self.entry.insert(0, \"Not a number\")\n except SyntaxError:\n self.entry.delete(0, END)\n self.entry.insert(0, \"Input error\")\n else:\n self.entry.delete(0, END)\n self.entry.insert(0, self.result)","def parse_float(val, fn):\n return float(val)","def test_number_input(self):\r\n easy_eval = lambda x: calc.evaluator({}, {}, x)\r\n\r\n self.assertEqual(easy_eval(\"13\"), 13)\r\n self.assertEqual(easy_eval(\"3.14\"), 3.14)\r\n self.assertEqual(easy_eval(\".618033989\"), 0.618033989)\r\n\r\n self.assertEqual(easy_eval(\"-13\"), -13)\r\n self.assertEqual(easy_eval(\"-3.14\"), -3.14)\r\n self.assertEqual(easy_eval(\"-.618033989\"), -0.618033989)","def test_float_type(self):\n\n input_ = 1.2\n expected = ValueError\n with self.assertRaises(expected):\n math.factorial(input_)","def validate_answer(answer):\r\n try:\r\n float(answer)\r\n return True\r\n except ValueError:\r\n return False","def val_parser(parser, inputstring):\n\n inputstring = inputstring.strip()\n\n if float(inputstring) == 9.9e37:\n output = float('inf')\n else:\n output = float(inputstring)\n if parser == int:\n output = parser(output)\n\n return output","def is_float(input_string):\r\n try:\r\n float(input_string)\r\n return True\r\n except ValueError:\r\n return False","def is_float(input_string):\r\n try:\r\n float(input_string)\r\n return True\r\n except ValueError:\r\n return False","def isfloat(s):\n try:\n x = float(s)\n return True\n except:\n return False","def set_float(val,default=None):\n if val in (None,''): return default\n try:\n return float(val)\n except:\n return default","def is_float(word):\n try:\n float(word)\n return True\n except ValueError:\n return False","def float(x) -> float:\n pass","def proper(inputnum):\n if isinstance(inputnum, (bool, complex)):\n return inputnum\n else:\n return float(inputnum)","def expt(e):\n # print(e)\n print(\"An error has occurred\")\n if 'float division by zero' in e:\n print(\"Unable to divide by zero\")\n cls()\n menu()\n elif 'factorial() argument should not exceed 2147483647' in e:\n print('That number is too large, sorry')\n cls()\n menu()\n elif 'generic' in e:\n print(\"Please try again\")\n cls()\n menu()\n elif 'could not convert string' or \"'NoneType' object is not subscriptable\" in e:\n print('Incorrect input, please try again')\n cls()\n menu()","def negfloat_p(value):\n # check if the value has the expected type\n if type(value) is not float:\n raise Invalid(\"invalid value type {value}\".format(value=value))\n if value >= 0.0:\n raise Invalid(\"invalid value {value}, negative float expected\".format(value=value))","def test_float():\n floatify = fields.FloatField().adapt\n\n for input, expect in [\n (1.1, 1.1),\n (11, 11.0),\n (int(5.7), 5)\n ]:\n assert floatify(input) == expect","def force_float(element, surpress_error = False):\n \n if isinstance(element, float):\n # element is a float, return it\n return element\n else:\n try:\n # try if the element is a number\n return float(element)\n except (ValueError, TypeError):\n # replace all non-digit characters\n element = str(element)\n matches = convert_pattern.match(element)\n \n if matches != None:\n element = matches.group(0)\n \n try:\n return float(element)\n except (ValueError, TypeError):\n if surpress_error:\n return 0\n else:\n raise","def isfloat(value):\r\n try:\r\n float(value)\r\n return True\r\n except ValueError:\r\n return False","def _process_numerical_parameter(param, value, allow_neg=False, is_float=False):\n try:\n if is_float:\n value = float(value)\n else:\n value = int(value)\n except:\n raise ValueError(\"The value of parameter {} was set to {}, but must be numerical!\".format(param, value))\n if not allow_neg and value < 0:\n raise ValueError(\"Parameter {} has value {}, but must be >= 0!\".format(param, value))\n return \"{}={}\".format(param, value)","def isfloat(value):\n try:\n float(value)\n return True\n except ValueError:\n return False","def convertFloat(s):\n try:\n float(s)\n return \"FLOAT\"\n except:\n return s","def isFloat(value): \n try:\n float(value)\n return True\n except ValueError:\n return False","def get_employee_hours_Float(message):\n while True:\n user_input = input('{}: '.format(message))\n\n # Type validation\n try:\n number = float(user_input)\n print(\"You entered this hour \", number)\n break\n except ValueError:\n print('You must enter a Float number.')\n continue\n\n #Range Validation\n # if valid_range and number not in valid_range:\n # _min = min(valid_range)\n # _max = max(valid_range)\n # print('You must enter a number from {} to {}.'.format(_min, _max))\n # continue\n return number","def _float(data):\n try:\n return float(data)\n except ValueError as err:\n if data in ('None', 'NA', 'nan'):\n return nan\n else:\n raise ValueError(err)","def check_if_number(list):\n for item in list:\n try:\n float(item)\n except ValueError as e:\n print WrongTypePointError(item)\n sys.exit()","def get_amount():\n while True:\n try:\n amount = input(\"How much did they donate: \")\n if str(amount).lower() == 'exit':\n return amount\n else:\n return float(amount)\n except ValueError:\n print(\"you have made an invalid choice, try again.\")","def test_get_value3(monkeypatch):\n mr.initialize_donors()\n monkeypatch.setattr('builtins.input', lambda x: 10.2)\n value = mr.get_value('Enter a string', float)\n assert value == 10.2","def read_endf_float(string):\n if string.strip() == \"\":\n return 0.0\n if \".\" in string:\n strsplit = string.split('.')\n return float(strsplit[0]+\".\"+strsplit[1].replace(\"-\",\"e-\").replace(\"+\",\"e+\"))\n else:\n return float(string)","def is_float(val):\n try:\n float(val)\n return True\n except ValueError:\n return False","def get_input():\n\n end_loop = True # Used to stop the loop for user input\n while end_loop:\n try:\n user_input = str(float(input(\"Please enter a number: \")))\n end_loop = False # The loop breaks once the user has entered valid input\n except():\n print(\"Invalid input, please try again.\")\n\n return user_input","def opr1():\n try:\n a = input(\"Enter the number 'a': \")\n b = input(\"Enter the number 'b': \")\n z = (float(a) ** 2) / float(b)\n return z\n except (ValueError, ZeroDivisionError):\n return 'Something is entered incorrectly!'","def opr():\n try:\n a = input(\"Enter the number 'a': \")\n b = input(\"Enter the number 'b': \")\n z = (float(a) ** 2) / float(b)\n return z\n except ValueError:\n return 'You entered not a number!'\n except ZeroDivisionError:\n return 'Only stupid divide by zero'","def check_valid_frac(num) -> float:\n if (num < 0) or (num > 1):\n raise argparse.ArgumentTypeError(f\"{num} is an invalid percentage\")\n return num","def is_float(x):\r\n try:\r\n float(x)\r\n except ValueError:\r\n return False\r\n return True","def try_float(data):\n try:\n return float(data)\n except (ValueError, TypeError ):\n return data","def isfloat(string:str) -> bool:\n try:\n float(string)\n return True\n except ValueError:\n return False","def acceleration():\n \"\"\"\n This block allowed the user to choose whether to use m/s^2 or g as the unit of acceleration.\n valid_units = [\"m\", \"M\", \"g\", \"G\"]\n unit = raw_input(\"Would you like to enter burn acceleration in meters per second squared (press M) or in g-forces (press G)? \")\n while unit not in valid_units:\n unit = raw_input(\"Invalid input. Please press M for m/s^2 or G for g-forces. \")\n \"\"\"\n acceleration = raw_input(\"What is the ship's acceleration in g? \")\n while True:\n try:\n float(acceleration)\n except ValueError:\n acceleration = raw_input(\"Invalid input. Please enter a positive number with no other non-decimal \"\n \"characters. \")\n continue\n else:\n break\n while float(acceleration) <= 0:\n acceleration = raw_input(\"Invalid input. Please enter a positive number. \")\n try:\n float(acceleration)\n except ValueError:\n acceleration = raw_input(\"Invalid input. Please enter a positive number with no other non-decimal \"\n \"characters. \")\n continue\n else:\n break\n # These two lines assisted the block above in converting to g if m/s^2 was chosen as the unit of acceleration.\n '''if unit in [\"m\", \"M\"]:\n acceleration = round(float(acceleration) / 9.8, 2)'''\n return float(acceleration)","def _float_or_nan(ent):\n try:\n return float(ent)\n except ValueError:\n return float('nan')","def getFloat(string):\n return (0.0)","def write_endf_float(value):\n if(abs(value) < 1e-9 or abs(value) > 9.999e9):\n raise ValueError(\"value is too small or too big\")\n valstring = \"{:>13.6e}\".format(value).replace('e','').replace('+0','+').replace('-0','-')\n # with AMPX written files we use \"-0\" instead of \"+0\" for some reason\n if( '+0' in valstring ):\n valstring = valstring.replace('+0','-0')\n return valstring","def parse_float(value):\n try:\n return float(value)\n except (ValueError, TypeError):\n return None"],"string":"[\n \"def prompt_float_input(prompt_name: str, get_user_input: GetInputFunc) -> float:\\n try:\\n return float(get_user_input(f\\\"{prompt_name}:\\\"))\\n except (ValueError, IndexError) as e:\\n raise InvalidInput(str(e))\",\n \"def get_float(self, prompt=\\\"> \\\"):\\n\\t\\twhile True:\\n\\t\\t\\tans = raw_input(prompt)\\n\\t\\t\\ttry: \\t\\n\\t\\t\\t\\tans = float(ans)\\n\\t\\t\\t\\treturn ans\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\tif ans == \\\"quit\\\": quit()\\n\\t\\t\\t\\telse: print \\\"Please enter a number using decimal notation.\\\"\",\n \"def check_for_float(check):\",\n \"def test_normal_decimal_input(self):\\r\\n ws_leader = \\\"S. O'Neal (14.9)\\\"\\r\\n res = treat_input(ws_leader, type=\\\"float\\\")\\r\\n assert res == 14.9\",\n \"def restricted_float(x):\\n try:\\n x = float(x)\\n except ValueError:\\n raise argparse.ArgumentTypeError(\\\"%r not a floating-point literal\\\" % (x,))\\n return x\",\n \"def main():\\n\\ttry:\\n\\t\\tx = input(\\\"Type in a number to be converted to a float: \\\")\\n\\t\\tfloatnum = float(x)\\n\\t\\tprint(floatnum)\\n\\texcept(ValueError):\\n\\t\\tprint(\\\"Could not convert the string to a float\\\")\",\n \"def checkNumberFloat(value):\\n try:\\n value = float(value)\\n return value\\n except ValueError:\\n print(\\\"You did not enter a number\\\")\\n newNum = input(\\\"Please enter a number: \\\")\\n return checkNumberFloat(newNum)\",\n \"def read_float(message, default=None, help=None):\\n while True:\\n option = raw_input(message)\\n if not option:\\n if default is not None:\\n return default\\n else:\\n print(\\\"Please provide a value.\\\")\\n elif option == \\\"?\\\":\\n if help:\\n print(help)\\n else:\\n print(\\\"Please provide a number\\\")\\n else:\\n try:\\n return float(option)\\n except ValueError:\\n print(\\\"Expected a number.\\\")\\n pass\",\n \"def sanitizeFloatFromKeyboard(s,range_start=0,range_end=0):\\n try:\\n\\tx = float(s)\\n except ValueError:\\n\\terr = 1\\n\\treturn err,0\\n\\n if (x >= range_start) and (x <= range_end):\\n\\terr = 0\\n return err,x\\n else:\\n\\terr = 1\\n\\treturn err,x\",\n \"def _restricted_float(val: float):\\n try:\\n val = float(val)\\n except ValueError:\\n raise argparse.ArgumentTypeError(f\\\"{val} not a floating-point literal\\\")\\n\\n if 0.0 < val > 1.0:\\n raise argparse.ArgumentTypeError(f\\\"{val} not in range [0.0, 1.0]\\\")\\n return val\",\n \"def validate_float(self, p_str):\\n # may validate only '[+-].' which needs to be handled later\\n float_pattern = r\\\"^[\\\\+\\\\-]?([0-9]*[.])?[0-9]*$\\\"\\n if re.search(float_pattern, p_str) or p_str == \\\"\\\":\\n return True\\n self.frame.bell()\\n return False\",\n \"def char_float(inp_char):\\n try:\\n nFloat = float(inp_char)\\n except:\\n nFloat = 0.0\\n return nFloat\",\n \"def getFloat(fl):\\n while True:\\n try:\\n return float(input(fl))\\n except ValueError:\\n print(\\\"Give a proper value please\\\")\",\n \"def possible_float(arg):\\n try:\\n return float(arg)\\n except ValueError:\\n logging.info(f'failed to parse {arg} as a float, treating it as a string')\\n return arg\",\n \"def gravedad(frm):\\r\\n x=int(frm.txtGrav.GetValue())\\r\\n while type(x)!=float:\\r\\n try:\\r\\n x=float(x)\\r\\n\\r\\n if type(x)==float:\\r\\n return -x\\r\\n\\r\\n else:\\r\\n pass\\r\\n\\r\\n except:\\r\\n print \\\"Ingrese un valor valido para la gravedad\\\"\\r\\n print\\r\\n x=raw_input(\\\"?Cu?l es la aceleracion de la gravedad?: m/s\\\")\",\n \"def get_user_input():\\n return float(input('Your transaction amount please: '))\",\n \"def number_format(num):\\n while True:\\n try:\\n user_input = float(input(num))\\n return user_input\\n except ValueError:\\n print(\\\"Error. Please enter the desired number. You may use \\\"\\n \\\"decimals.\\\")\\n except:\\n print(\\\"Error: unknown.\\\")\",\n \"def test_try():\\n numb = input(\\\"Enter a number\\\")\\n print(type(numb))\\n try:\\n num = float(numb)\\n print(num)\\n except Exception as e: # if there is exception,we wont crash,we will catch it\\n print(e)\\n print(\\\"Exiting the program\\\")\",\n \"def give_me_a_float():\\n return 5.8\\n pass\",\n \"def handle_commas(x):\\n if type(x) != str:\\n print (\\\"Please enter a string\\\")\\n return False\\n else:\\n x = float(x.replace(\\\",\\\",\\\"\\\"))\\n return x\",\n \"def CheckNumber(userInput):\\n try:\\n float(userInput)\\n return True\\n except(ValueError):\\n return False\",\n \"def setfloat(self, strcommand, value):\\n command = ct.c_wchar_p(strcommand)\\n value = ct.c_double(value)\\n self.lib.AT_SetFloat(self.AT_H, command, value)\",\n \"def _ensure_positive_float(f):\\n if float(f) < 0:\\n raise argparse.ArgumentTypeError(\\n 'Argument must be greater than zero')\\n return float(f)\",\n \"def get_check_fn():\\r\\n fn = input(\\\"Enter your favorite number.\\\\n\\\\t\\\")\\r\\n while True:\\r\\n try:\\r\\n fn = float(fn)\\r\\n except ValueError:\\r\\n fn = input(\\\"Must enter a number.\\\\n\\\\t\\\")\\r\\n else:\\r\\n return fn\",\n \"def isFloat(string):\\n return (True)\",\n \"def velocidad(frm):\\r\\n x=int(frm.txtvel.GetValue())\\r\\n while type(x)!=float:\\r\\n try:\\r\\n x=float(x)\\r\\n\\r\\n if type(x)==float:\\r\\n return x\\r\\n\\r\\n else:\\r\\n pass\\r\\n\\r\\n except:\\r\\n print \\\"Ingrese un valor v?lido para la fuerza\\\"\\r\\n print\\r\\n x=raw_input(\\\"?Cu?l es la velocidad inicial del objeto? (m/s): \\\")\",\n \"def default(self, line):\\n if line == \\\"EOF\\\":\\n return True\\n elif line in self.operations:\\n try:\\n self.calc.operation(self.operations[line])\\n\\t\\tself._debug_stack()\\n except ReversePolishCalcError, errmsg:\\n self._debug_calc_error(errmsg)\\n elif re.match(\\\"[0-9.eE]+\\\", line):\\n try:\\n self.calc.insert_value(float(line))\\n except ValueError:\\n debug(\\\"Invalid float value: %s\\\" % line)\\n else:\\n debug(\\\"Unknown command: %s\\\" % line)\",\n \"def Float(val):\\n try:\\n return float(val)\\n except ValueError:\\n return ''\",\n \"def clean_input(prompt='Error'): # A special input function that will reject a\\r\\n # user's input of text when a number is requested -- if no prompt is\\r\\n # specified in the program, it will display \\\"Error\\\"\\r\\n text = True\\r\\n phrase = '0'\\r\\n while text:\\r\\n phrase = input(prompt + '\\\\n')\\r\\n try: # Adapted from an example in the ThinkPython textbook (15.7) -\\r\\n # Checks whether the input is a number, positive or negative. If\\r\\n # not, rejects the input and user gets to try again\\r\\n float(phrase)\\r\\n text = False\\r\\n except ValueError:\\r\\n print(\\\"Error: Non-Numeric Entry Detected\\\")\\r\\n # if phrase.isnumeric(): # Checks for a positive number (negative\\r\\n # rejected as well as text) - replaced with superior form from textbook\\r\\n # example\\r\\n # return float(phrase) # Return the number the user entered\\r\\n # else:\\r\\n # print(\\\"Error: Non-Numeric Entry Detected\\\")\\r\\n return float(phrase) # Return the number the user entered\\r\",\n \"def validation_method(input_value):\\r\\n while True:\\r\\n try:\\r\\n valor = float(input(input_value))\\r\\n return valor\\r\\n except ValueError:\\r\\n print(\\\" ingresa un número\\\")\",\n \"def validate_calibration(prompt,name):\\n \\n while True:\\n temp_value = input(prompt)\\n try:\\n float(temp_value)\\n\\n if temp_value > 0:\\n return float(temp_value)\\n else:\\n print(\\\"invalid {}\\\".format(name))\\n except:\\n print(\\\"invalid {}\\\".format(name))\",\n \"def test_float(self):\\n self.assertFalse(validate_measure_input('0.0', self.measures))\\n self.assertFalse(validate_measure_input('1.0', self.measures))\\n self.assertFalse(validate_measure_input('1.1', self.measures))\",\n \"def test_float(self, env: yaenv.Env):\\n _val = env.float('FLOAT_VAR')\\n assert _val == 10.0 and type(_val) == float\\n _val = env.float('MISSING', -3.1)\\n assert _val == -3.1 and type(_val) == float\\n with pytest.raises(yaenv.EnvError) as err:\\n _ = env.float('LIST_VAR')\\n assert 'Invalid numerical' in str(err.value)\\n assert env.float('MISSING') is None\",\n \"def get_float(message, high, low=0):\\r\\n\\r\\n while True:\\r\\n try:\\r\\n floatValue = float(input(message))\\r\\n except ValueError:\\r\\n print (\\\"ERROR, Entry must be a number. Please try again.\\\")\\r\\n continue\\r\\n if floatValue <= low or floatValue > high:\\r\\n print (\\\"ERROR, Entry must be greater than \\\" + str(low) + \\\" and, less than or equal to \\\"\\\\\\r\\n + str(high) + \\\". Please try again.\\\")\\r\\n continue\\r\\n break\\r\\n return floatValue\",\n \"def usetf(self, prompt=None, default=None):\\n\\n i = 0\\n abak = copy(default) # Backup our default value\\n\\n while(i<self.maxTries):\\n tmp = self.uset(prompt,default)\\n try:\\n a = float(tmp)\\n i = self.maxTries # preload failure\\n except:\\n # Print warning\\n print\\n print \\\" WARNING: Invalid Entry. Please enter a floating point number \\\"\\n print \\n # reload the default\\n a = abak\\n i = i+1\\n\\n return(a)\",\n \"def validate_insert(self, s, internal=True):\\n super(FieldNumeric, self).validate_insert(s, internal) # mandatory check\\n if s:\\n try:\\n float(s)\\n except:\\n raise FilemanError(\\\"\\\"\\\"[%s] is not a valid number\\\"\\\"\\\" % s)\",\n \"def operand_present(input_str): # HELPER\\n try:\\n float(input_str)\\n return True\\n except ValueError:\\n return False\",\n \"def is_valid_float(input_string):\\n assert input_string is not None\\n try:\\n float(input_string)\\n return True\\n except ValueError:\\n return False\",\n \"def check_arguments(arguments):\\n quit = False\\n for argument, value in vars(arguments).items():\\n try:\\n float(value)\\n except:\\n print(\\\"{} must be numeric\\\".format(argument))\\n quit = True\\n if quit:\\n exit(1)\",\n \"def is_float(self, input):\\n try:\\n float(input)\\n return True\\n except ValueError:\\n return False\",\n \"def checkfloat(name, val, mn=None, mx=None):\\n try:\\n\\tx = string.atof(val)\\n\\tif ((mn is not None and x < mn) or\\n\\t (mx is not None and x > mx)):\\n\\t\\traise ValidationError, \\\\\\n\\t\\t 'parameter \\\"%s\\\", value \\\"%s\\\" is out of range' % \\\\\\n\\t\\t (name, val)\\n\\treturn\\n except ValueError:\\n\\traise ValidationError, '%s is not a valid floating point number' % val\",\n \"def posfloat_p(value):\\n # check if the value has the expected type\\n if type(value) is not float:\\n raise Invalid(\\\"invalid value type {value}\\\".format(value=value))\\n if value <= 0.0:\\n raise Invalid(\\\"invalid value {value}, positive float expected\\\".format(value=value))\",\n \"def float_or_none(s):\\n if s:\\n return float(s)\",\n \"def value_input(unit):\\n print(Fore.CYAN + \\\"\\\\n Enter the temperature in \\\\u00b0\\\" + unit + \\\":\\\\n\\\" +\\n Fore.RESET)\\n while True:\\n try:\\n value = float(input()) # <=== Make sure input is a float\\n return value\\n break\\n except ValueError:\\n print(Fore.RED + \\\"\\\\n Input must be an integer!\\\\n\\\" + Fore.RESET)\",\n \"def check_for_float_and_int(check):\",\n \"def convert_str_float(x):\\n\\ttry:\\n\\t\\treturn float(x)\\n\\texcept ValueError:\\n\\t\\tprint(\\\"must be a number\\\")\",\n \"def price_product():\\n while True:\\n price = raw_input(\\\"Add the price: \\\")\\n try:\\n price = float(price)\\n return price\\n except ValueError: #if the price is a number\\n print \\\"Insert a number\\\"\\n return price\",\n \"def handle_value(value):\\n\\n if value[-1] == 'x':\\n return float(value[0:-1])\\n\\n if value[-1] == '%':\\n return float(value[0:-1])\\n\\n if value[0].isdigit():\\n return bytify(value)\\n\\n raise ValueError\",\n \"def read_float(v):\\n if v.strip() == '':\\n return 0.\\n try:\\n return float(v)\\n except ValueError:\\n # ENDF6 may omit the e for exponent\\n return float(v[0] + v[1:].replace('+', 'e+').replace('-', 'e-')) # don't replace leading negative sign\",\n \"def ffloat(string):\\n try:\\n return float(string.strip())\\n except:\\n return 0\",\n \"def test_get_value1(monkeypatch):\\n mr.initialize_donors()\\n monkeypatch.setattr('builtins.input', lambda x: 4)\\n value = mr.get_value('Enter a float', float)\\n assert value == float('4')\",\n \"def tryFloat(value):\\n try:\\n return float(value)\\n except:\\n return value\",\n \"def get_price():\\n\\n while (True):\\n price = input(\\\"Enter the purchase price (xx.xx) or 'q' to quit: \\\")\\n if(price.capitalize() == 'Q'):\\n return -1\\n elif price.replace('.', '').isdigit() and not is_valid(price):\\n print(\\\"Illegal price: Must be a non-negative multiple of 5 cents.\\\\n\\\")\\n elif not price.replace('.', '').isdigit():\\n print(\\\"Illegal entry: Must be a price like (1.75) or 'q' for quit.\\\\n\\\")\\n else:\\n return float(price)\",\n \"def convertFloat(num):\\n try:\\n num = float(num)\\n return num\\n except (TypeError, ValueError, ArithmeticError):\\n print(\\\"num cannot convert float\\\")\",\n \"def float_or_auto(val):\\n try:\\n return float(val)\\n except:\\n if isinstance(val, basestring) and val == \\\"auto\\\":\\n return val\\n raise TraitError, \\\"Tick interval must be a number or 'auto'.\\\"\",\n \"def is_float(*args): \\n try:\\n for i in args:\\n float(i)\\n return True\\n except Exception:\\n return False\",\n \"def parse_float_value(self, value: str):\\n value, power = self.parse_value_ending(value)\\n try:\\n value = float(value)\\n return value * 10 ** power\\n except:\\n raise Exception(\\\"Failed to parse the __value.\\\")\",\n \"def maybe_float(v):\\n try:\\n return float(v)\\n except ValueError:\\n return v\",\n \"def validate_number(value_if_allowed):\\n if value_if_allowed == '':\\n return True\\n try:\\n float(value_if_allowed)\\n return True\\n except ValueError:\\n return False\",\n \"def evaluate(self):\\n self.getInput()\\n try:\\n self.result = eval(self.userInput)\\n except ZeroDivisionError:\\n self.entry.delete(0, END)\\n self.entry.insert(0, \\\"Not a number\\\")\\n except SyntaxError:\\n self.entry.delete(0, END)\\n self.entry.insert(0, \\\"Input error\\\")\\n else:\\n self.entry.delete(0, END)\\n self.entry.insert(0, self.result)\",\n \"def parse_float(val, fn):\\n return float(val)\",\n \"def test_number_input(self):\\r\\n easy_eval = lambda x: calc.evaluator({}, {}, x)\\r\\n\\r\\n self.assertEqual(easy_eval(\\\"13\\\"), 13)\\r\\n self.assertEqual(easy_eval(\\\"3.14\\\"), 3.14)\\r\\n self.assertEqual(easy_eval(\\\".618033989\\\"), 0.618033989)\\r\\n\\r\\n self.assertEqual(easy_eval(\\\"-13\\\"), -13)\\r\\n self.assertEqual(easy_eval(\\\"-3.14\\\"), -3.14)\\r\\n self.assertEqual(easy_eval(\\\"-.618033989\\\"), -0.618033989)\",\n \"def test_float_type(self):\\n\\n input_ = 1.2\\n expected = ValueError\\n with self.assertRaises(expected):\\n math.factorial(input_)\",\n \"def validate_answer(answer):\\r\\n try:\\r\\n float(answer)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def val_parser(parser, inputstring):\\n\\n inputstring = inputstring.strip()\\n\\n if float(inputstring) == 9.9e37:\\n output = float('inf')\\n else:\\n output = float(inputstring)\\n if parser == int:\\n output = parser(output)\\n\\n return output\",\n \"def is_float(input_string):\\r\\n try:\\r\\n float(input_string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def is_float(input_string):\\r\\n try:\\r\\n float(input_string)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def isfloat(s):\\n try:\\n x = float(s)\\n return True\\n except:\\n return False\",\n \"def set_float(val,default=None):\\n if val in (None,''): return default\\n try:\\n return float(val)\\n except:\\n return default\",\n \"def is_float(word):\\n try:\\n float(word)\\n return True\\n except ValueError:\\n return False\",\n \"def float(x) -> float:\\n pass\",\n \"def proper(inputnum):\\n if isinstance(inputnum, (bool, complex)):\\n return inputnum\\n else:\\n return float(inputnum)\",\n \"def expt(e):\\n # print(e)\\n print(\\\"An error has occurred\\\")\\n if 'float division by zero' in e:\\n print(\\\"Unable to divide by zero\\\")\\n cls()\\n menu()\\n elif 'factorial() argument should not exceed 2147483647' in e:\\n print('That number is too large, sorry')\\n cls()\\n menu()\\n elif 'generic' in e:\\n print(\\\"Please try again\\\")\\n cls()\\n menu()\\n elif 'could not convert string' or \\\"'NoneType' object is not subscriptable\\\" in e:\\n print('Incorrect input, please try again')\\n cls()\\n menu()\",\n \"def negfloat_p(value):\\n # check if the value has the expected type\\n if type(value) is not float:\\n raise Invalid(\\\"invalid value type {value}\\\".format(value=value))\\n if value >= 0.0:\\n raise Invalid(\\\"invalid value {value}, negative float expected\\\".format(value=value))\",\n \"def test_float():\\n floatify = fields.FloatField().adapt\\n\\n for input, expect in [\\n (1.1, 1.1),\\n (11, 11.0),\\n (int(5.7), 5)\\n ]:\\n assert floatify(input) == expect\",\n \"def force_float(element, surpress_error = False):\\n \\n if isinstance(element, float):\\n # element is a float, return it\\n return element\\n else:\\n try:\\n # try if the element is a number\\n return float(element)\\n except (ValueError, TypeError):\\n # replace all non-digit characters\\n element = str(element)\\n matches = convert_pattern.match(element)\\n \\n if matches != None:\\n element = matches.group(0)\\n \\n try:\\n return float(element)\\n except (ValueError, TypeError):\\n if surpress_error:\\n return 0\\n else:\\n raise\",\n \"def isfloat(value):\\r\\n try:\\r\\n float(value)\\r\\n return True\\r\\n except ValueError:\\r\\n return False\",\n \"def _process_numerical_parameter(param, value, allow_neg=False, is_float=False):\\n try:\\n if is_float:\\n value = float(value)\\n else:\\n value = int(value)\\n except:\\n raise ValueError(\\\"The value of parameter {} was set to {}, but must be numerical!\\\".format(param, value))\\n if not allow_neg and value < 0:\\n raise ValueError(\\\"Parameter {} has value {}, but must be >= 0!\\\".format(param, value))\\n return \\\"{}={}\\\".format(param, value)\",\n \"def isfloat(value):\\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def convertFloat(s):\\n try:\\n float(s)\\n return \\\"FLOAT\\\"\\n except:\\n return s\",\n \"def isFloat(value): \\n try:\\n float(value)\\n return True\\n except ValueError:\\n return False\",\n \"def get_employee_hours_Float(message):\\n while True:\\n user_input = input('{}: '.format(message))\\n\\n # Type validation\\n try:\\n number = float(user_input)\\n print(\\\"You entered this hour \\\", number)\\n break\\n except ValueError:\\n print('You must enter a Float number.')\\n continue\\n\\n #Range Validation\\n # if valid_range and number not in valid_range:\\n # _min = min(valid_range)\\n # _max = max(valid_range)\\n # print('You must enter a number from {} to {}.'.format(_min, _max))\\n # continue\\n return number\",\n \"def _float(data):\\n try:\\n return float(data)\\n except ValueError as err:\\n if data in ('None', 'NA', 'nan'):\\n return nan\\n else:\\n raise ValueError(err)\",\n \"def check_if_number(list):\\n for item in list:\\n try:\\n float(item)\\n except ValueError as e:\\n print WrongTypePointError(item)\\n sys.exit()\",\n \"def get_amount():\\n while True:\\n try:\\n amount = input(\\\"How much did they donate: \\\")\\n if str(amount).lower() == 'exit':\\n return amount\\n else:\\n return float(amount)\\n except ValueError:\\n print(\\\"you have made an invalid choice, try again.\\\")\",\n \"def test_get_value3(monkeypatch):\\n mr.initialize_donors()\\n monkeypatch.setattr('builtins.input', lambda x: 10.2)\\n value = mr.get_value('Enter a string', float)\\n assert value == 10.2\",\n \"def read_endf_float(string):\\n if string.strip() == \\\"\\\":\\n return 0.0\\n if \\\".\\\" in string:\\n strsplit = string.split('.')\\n return float(strsplit[0]+\\\".\\\"+strsplit[1].replace(\\\"-\\\",\\\"e-\\\").replace(\\\"+\\\",\\\"e+\\\"))\\n else:\\n return float(string)\",\n \"def is_float(val):\\n try:\\n float(val)\\n return True\\n except ValueError:\\n return False\",\n \"def get_input():\\n\\n end_loop = True # Used to stop the loop for user input\\n while end_loop:\\n try:\\n user_input = str(float(input(\\\"Please enter a number: \\\")))\\n end_loop = False # The loop breaks once the user has entered valid input\\n except():\\n print(\\\"Invalid input, please try again.\\\")\\n\\n return user_input\",\n \"def opr1():\\n try:\\n a = input(\\\"Enter the number 'a': \\\")\\n b = input(\\\"Enter the number 'b': \\\")\\n z = (float(a) ** 2) / float(b)\\n return z\\n except (ValueError, ZeroDivisionError):\\n return 'Something is entered incorrectly!'\",\n \"def opr():\\n try:\\n a = input(\\\"Enter the number 'a': \\\")\\n b = input(\\\"Enter the number 'b': \\\")\\n z = (float(a) ** 2) / float(b)\\n return z\\n except ValueError:\\n return 'You entered not a number!'\\n except ZeroDivisionError:\\n return 'Only stupid divide by zero'\",\n \"def check_valid_frac(num) -> float:\\n if (num < 0) or (num > 1):\\n raise argparse.ArgumentTypeError(f\\\"{num} is an invalid percentage\\\")\\n return num\",\n \"def is_float(x):\\r\\n try:\\r\\n float(x)\\r\\n except ValueError:\\r\\n return False\\r\\n return True\",\n \"def try_float(data):\\n try:\\n return float(data)\\n except (ValueError, TypeError ):\\n return data\",\n \"def isfloat(string:str) -> bool:\\n try:\\n float(string)\\n return True\\n except ValueError:\\n return False\",\n \"def acceleration():\\n \\\"\\\"\\\"\\n This block allowed the user to choose whether to use m/s^2 or g as the unit of acceleration.\\n valid_units = [\\\"m\\\", \\\"M\\\", \\\"g\\\", \\\"G\\\"]\\n unit = raw_input(\\\"Would you like to enter burn acceleration in meters per second squared (press M) or in g-forces (press G)? \\\")\\n while unit not in valid_units:\\n unit = raw_input(\\\"Invalid input. Please press M for m/s^2 or G for g-forces. \\\")\\n \\\"\\\"\\\"\\n acceleration = raw_input(\\\"What is the ship's acceleration in g? \\\")\\n while True:\\n try:\\n float(acceleration)\\n except ValueError:\\n acceleration = raw_input(\\\"Invalid input. Please enter a positive number with no other non-decimal \\\"\\n \\\"characters. \\\")\\n continue\\n else:\\n break\\n while float(acceleration) <= 0:\\n acceleration = raw_input(\\\"Invalid input. Please enter a positive number. \\\")\\n try:\\n float(acceleration)\\n except ValueError:\\n acceleration = raw_input(\\\"Invalid input. Please enter a positive number with no other non-decimal \\\"\\n \\\"characters. \\\")\\n continue\\n else:\\n break\\n # These two lines assisted the block above in converting to g if m/s^2 was chosen as the unit of acceleration.\\n '''if unit in [\\\"m\\\", \\\"M\\\"]:\\n acceleration = round(float(acceleration) / 9.8, 2)'''\\n return float(acceleration)\",\n \"def _float_or_nan(ent):\\n try:\\n return float(ent)\\n except ValueError:\\n return float('nan')\",\n \"def getFloat(string):\\n return (0.0)\",\n \"def write_endf_float(value):\\n if(abs(value) < 1e-9 or abs(value) > 9.999e9):\\n raise ValueError(\\\"value is too small or too big\\\")\\n valstring = \\\"{:>13.6e}\\\".format(value).replace('e','').replace('+0','+').replace('-0','-')\\n # with AMPX written files we use \\\"-0\\\" instead of \\\"+0\\\" for some reason\\n if( '+0' in valstring ):\\n valstring = valstring.replace('+0','-0')\\n return valstring\",\n \"def parse_float(value):\\n try:\\n return float(value)\\n except (ValueError, TypeError):\\n return None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71381927","0.7047657","0.7025061","0.69696033","0.67903113","0.6774239","0.67697823","0.66692364","0.6616977","0.6591584","0.656603","0.65500516","0.6470003","0.64432317","0.64170504","0.6386314","0.6362336","0.6322216","0.6319651","0.63095284","0.628306","0.62816256","0.6269164","0.6263565","0.6245321","0.62437123","0.61984056","0.61793274","0.61704344","0.6168855","0.6155719","0.6142138","0.61419845","0.61346006","0.60798055","0.60771513","0.6073596","0.60633755","0.6049336","0.6036392","0.60126865","0.6007995","0.59937626","0.5987921","0.5979778","0.59756565","0.59702","0.59663445","0.5961952","0.595345","0.5950798","0.5935071","0.5934135","0.59280664","0.5885621","0.5883926","0.5878518","0.5866909","0.5864821","0.58452237","0.58417326","0.583745","0.5833384","0.58262163","0.5820567","0.5808444","0.5808444","0.58062863","0.580619","0.57638144","0.5753542","0.5748542","0.5739517","0.57318246","0.5706658","0.56967","0.569051","0.5685406","0.5680785","0.56797516","0.56676847","0.5667651","0.56305856","0.5623565","0.5623404","0.5620492","0.56173575","0.56139874","0.5613908","0.56126136","0.56078464","0.5603325","0.5597563","0.5595402","0.5593233","0.5589791","0.55767083","0.5570844","0.5568808","0.556633"],"string":"[\n \"0.71381927\",\n \"0.7047657\",\n \"0.7025061\",\n \"0.69696033\",\n \"0.67903113\",\n \"0.6774239\",\n \"0.67697823\",\n \"0.66692364\",\n \"0.6616977\",\n \"0.6591584\",\n \"0.656603\",\n \"0.65500516\",\n \"0.6470003\",\n \"0.64432317\",\n \"0.64170504\",\n \"0.6386314\",\n \"0.6362336\",\n \"0.6322216\",\n \"0.6319651\",\n \"0.63095284\",\n \"0.628306\",\n \"0.62816256\",\n \"0.6269164\",\n \"0.6263565\",\n \"0.6245321\",\n \"0.62437123\",\n \"0.61984056\",\n \"0.61793274\",\n \"0.61704344\",\n \"0.6168855\",\n \"0.6155719\",\n \"0.6142138\",\n \"0.61419845\",\n \"0.61346006\",\n \"0.60798055\",\n \"0.60771513\",\n \"0.6073596\",\n \"0.60633755\",\n \"0.6049336\",\n \"0.6036392\",\n \"0.60126865\",\n \"0.6007995\",\n \"0.59937626\",\n \"0.5987921\",\n \"0.5979778\",\n \"0.59756565\",\n \"0.59702\",\n \"0.59663445\",\n \"0.5961952\",\n \"0.595345\",\n \"0.5950798\",\n \"0.5935071\",\n \"0.5934135\",\n \"0.59280664\",\n \"0.5885621\",\n \"0.5883926\",\n \"0.5878518\",\n \"0.5866909\",\n \"0.5864821\",\n \"0.58452237\",\n \"0.58417326\",\n \"0.583745\",\n \"0.5833384\",\n \"0.58262163\",\n \"0.5820567\",\n \"0.5808444\",\n \"0.5808444\",\n \"0.58062863\",\n \"0.580619\",\n \"0.57638144\",\n \"0.5753542\",\n \"0.5748542\",\n \"0.5739517\",\n \"0.57318246\",\n \"0.5706658\",\n \"0.56967\",\n \"0.569051\",\n \"0.5685406\",\n \"0.5680785\",\n \"0.56797516\",\n \"0.56676847\",\n \"0.5667651\",\n \"0.56305856\",\n \"0.5623565\",\n \"0.5623404\",\n \"0.5620492\",\n \"0.56173575\",\n \"0.56139874\",\n \"0.5613908\",\n \"0.56126136\",\n \"0.56078464\",\n \"0.5603325\",\n \"0.5597563\",\n \"0.5595402\",\n \"0.5593233\",\n \"0.5589791\",\n \"0.55767083\",\n \"0.5570844\",\n \"0.5568808\",\n \"0.556633\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6430425"},"document_rank":{"kind":"string","value":"14"}}},{"rowIdx":8474,"cells":{"query":{"kind":"string","value":"Logs an entry for the homework log using a userentered value"},"document":{"kind":"string","value":"def do_record(self, input):\n course_name = course.course_name(input)\n\n if course_name in config.current_courses:\n hours_studied = self.hours_studied()\n\n # Checks for escape command\n if hours_studied != 'exit':\n write.to_csv(course_name, hours_studied)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def log(exercise):\n global logfile\n msg = raw_input(\"Enter your message. \")\n logfile.write(exercise + \" >>> \" + msg + \"\\n\")","def logentry(jobid, label, typ, content=None, path=None):\n ud = str(uuid.uuid4())\n db.logs.save({\"uuid\":ud, \"jobid\":jobid, \"label\":label, \"type\":typ, \"content\":content, \"date\":tstamp(), \"containerurl\":path})","def log(self, game: str, outcome: str):\n current_time = datetime.now()\n self.user.record.append([current_time.strftime(\"%c\"), game, outcome, self.user.balance])","def new_entry():\n clear_screen()\n entry = {}\n entry['id'] = get_next_id()\n entry['name'] = input_name()\n print(\"How many minutes did you spend on {}?\".format(entry['name']))\n print(\"Or you may specify a format after the time, seperated by a comma\")\n entry['time_spent'] = input_time_spent()\n add_notes = input(\"Add notes? Y/n \").lower()\n if add_notes != 'n':\n entry['notes'] = input_notes()\n entry['date'] = datetime.now().strftime(FMT_MONTH_DAY_YEAR)\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\n work_log_writer.writerow(entry)","def entry(entry,fhandle): \n\n import time\n timestamp = time.strftime('%H:%M:%S')\n print entry\n try: fhandle.write('{0}: {1}\\n'.format(timestamp, entry))\n except: print 'Log File Error: entry could not be logged'\n return","def log_entry(debug, out, text):\n # Format log entry\n monthday = make_time_stamp('%m%d')\n time_stamp = make_time_stamp('%H:%M:%S')\n now = time.time()\n ms = \".\"+str('%06d' % int((now - int(now)) * 1000000))\n line_form = \"I{monthday} {time_stamp} 0000 main.py:00] {text}\\n\"\n entry = line_form.format(monthday=monthday, time_stamp=time_stamp+ms, text=text)\n\n # Log entry to stderr\n sys.stderr.write(entry)\n pass","def logentry(self, string=None):\n if (self._OIFlogging):\n oiflogfile = open(self._commslogfilename, \"a\")\n oiflogfile.write(\"# \" + \"%04.6fs: \" % (self._gettime() - self._logstarttime) + string + \"\\n\")\n oiflogfile.flush()\n else:\n# if self._print_once:\n# self._print_once = 0\n# print self.hilite(\"Warning: Not logging OIF transactions. Use\\n it.logfile(<filename>) to set log filename and\\n it.logging(True) to enable logging\", False, True)\n print 'Unable to write log entry', string\n return","def log(self, inp, print_result: bool = None):\n if print_result is None: print_result = self.log_print\n # Parse input\n if type(inp) == str:\n logged_inp = inp\n else:\n raise NotImplementedError(f\"Given type '{type(inp)}' is not supported for logging.\")\n \n # Print if requested\n if print_result: print(logged_inp)\n \n # Append the string to the file\n append_log(logged_inp, f\"population{'_backup' if self.use_backup else ''}/\"\n f\"storage/\"\n f\"{self.folder_name}/\"\n f\"{self}/\"\n f\"logbook.log\")","def main_log(logfile, entry, print_tag=False):\n if logfile != None:\n with open(logfile, 'a') as lf:\n lf.write('{}\\n'.format(entry))\n\n if print_tag:\n print entry","def log_entry(self, timestamp, entry):\n if timestamp in self.log:\n self.log[timestamp].update(entry)\n else:\n self.log[timestamp] = entry","def _addLogEntry(request, action, pagename, filename):\n from MoinMoin.logfile import editlog\n t = wikiutil.timestamp2version(time.time())\n fname = wikiutil.url_quote(filename)\n\n # Write to global log\n log = editlog.EditLog(request)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\n\n # Write to local log\n log = editlog.EditLog(request, rootpagename=pagename)\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)","def do_log(self, arg):\n arg = \" %s :custom log\" % (arg)\n log(arg)","def __log__(self, val):\n if lm_settings[\"debug\"]:\n try:\n log_file = open(\"language_manager/info/language_manager.log\", \"a\")\n except FileNotFoundError:\n log_file = open(lm_settings[\"logfile\"], \"w\")\n log_file.write(val)\n log_file.close()","def InsertLog():","def log(self, message):","def log(self, *args, **kwargs):\n self.game_view.log(*args, **kwargs)","def add_entry(name, title, duration, notes):\n clear()\n print('Entry added to work log!')\n return Entry.create(\n employee_name=name,\n task_title=title,\n time_spent=duration,\n task_notes=notes\n )","def __log_trial__(self, trial_data):\n from klibs.KLDatabase import EntryTemplate\n\n trial_template = EntryTemplate('trials')\n trial_template.log(P.id_field_name, P.participant_id)\n for attr in trial_data:\n trial_template.log(attr, trial_data[attr])\n\n return self.database.insert(trial_template)","def log(self, message: str):","async def add_log(self, value):\n log_string = value\n print(log_string)\n self.embed.title = log_string\n self.embed.timestamp = datetime.datetime.now()\n self.embed.description = \"\"","def addLog(log_info,seed_keyword=\"\",meta_keyword=\"\"):\n payload = {\n \"user\" : os.getlogin(),\n \"seed_keyword\":seed_keyword,\n \"meta_keyword\":meta_keyword,\n \"log_info\":log_info\n }\n res = requests.post('{}add/issue/'.format(base_url),data=payload)\n return res.status_code","def record(*args, **kwargs):\n LOG.info(\"args={}, kwargs={}\".format(args, kwargs))","def logWork(self, id, logData):\n\t\tnow = datetime.datetime.now()\n\t\ttimeText = now.strftime(LOG_DATE_FORMAT)\n\n\t\tlogToEdit = self.LOGS.get(id).get(\"log\")\n\t\t#If inside this case and there is already a log entry for this time\n\t\talreadyEntryForThisTime = False\n\t\tfor entry in logToEdit:\n\t\t\tif timeText == entry[0]:\n\t\t\t\tentry[1] += logData\n\t\t\t\talreadyEntryForThisTime = True\n\n\t\tif not alreadyEntryForThisTime:\n\t\t\tlogToEdit.append([timeText, logData])\n\t\t\tself.logByDate(now.strftime(CASE_DATE_FORMAT), id)\n\n\t\tself.save_logs(\"c\")\n\t\tself.autoCommit()\n\t\treturn 0","def log(info):\n print(f\"[{info}]\")","def logger(self, value):\n pass","def log_lesson(request):\n user = User.objects.get(email=request.user.email)\n user_info = UserInformation.objects.get(user=user)\n lesson_set = user_info.current_lesson_set\n lesson = Lesson.objects.get(lesson_index=user_info.current_lesson_index)\n main_set = user_info.current_main_set\n\n print(\"lesson_logged\")\n\n lesson_to_log = LessonLog.objects.create(user=user,\n time_stamp=timezone.now(),\n lesson_set_key=lesson_set,\n lesson_key=lesson,\n lesson_index=lesson.lesson_index,\n main_set_key=main_set)\n lesson_to_log.save()","def log(self, inputText, saveLog=True):\n\n self.logView.insertPlainText(\">> %s \\n\" % inputText)\n\n self.logView.moveCursor(QtGui.QTextCursor.End)\n\n if saveLog == True:\n f = open(self.logFileName, \"a\")\n\n f.write(inputText + '\\n')\n\n f.close()","def recordLog(project, status, memo):\n path = getPath(project)\n log = open(path, 'a')\n writer = csv.writer(log, lineterminator='\\n')\n writer.writerow((time.time(), status, memo))\n log.close()\n if status == 'a':\n print(\"Tracking your time on \" + project)\n if status == 's':\n print(\"Tracking suspended on \" + project)\n if status == 't':\n print(\"Time shifted on \" + project)\n if not path == '.sourglass':\n store = open(os.path.join(basepath, 'last'), 'w')\n store.write(project)\n store.close","def log(text):\n print \"%s: %s\" % (str(datetime.datetime.now()), text)","def logtool(self, action, **options):\n pass","def ENTRY(entry_code):\n\tif check_user(entry_code) ==True:\n\t\t###workday = Workday.objects.filter(date=get_time()).get()\n\t\tenter_workday(entry_code)","def logFile(self):\n\n event = 'stim'\n mStr = '{:013}'.format(self.mouse.tag) + '\\t'\n outPutStr = mStr + \\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\t' + event\n print (outPutStr)\n if self.textfp != None:\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\t' + event\n self.textfp.write(outPutStr + '\\n')\n self.textfp.flush()","def logline(msg):\n print msg","def Log(self, msg):\n self.DBExecute(\"INSERT INTO Log (class, instance, event) VALUES (%s, %s, %s)\",\n self.__class__.__name__, self._instance, msg)\n print '%s/%s: %s' % (self.__class__.__name__, self._instance, msg)","def create_log_entry(self, i): \r\n fitness_vector = self.get_fitness_vector()\r\n \r\n best = min(fitness_vector)\r\n worst = max(fitness_vector)\r\n mean = rython.mean(fitness_vector)\r\n std = rython.std(fitness_vector)\r\n \r\n return \"{0},{1},{2},{3},{4}\\n\".format(i, 1.0/best, 1.0/worst, 1.0/mean, std)","def _log(self, format, args, level=None):\n if level is None:\n level = self.log_level\n xbmc.log(\n \"metadata.movie.stupid: %s - - [%s] %s\\n\" % (\n self.client_address[0], self.log_date_time_string(),\n format % args),\n level)","def scriptlog(entry, subarray=DEFAULT) :\n multiSubarray('log', subarray, \"SCRIPT: \" + entry)","def on_a(self):\r\n self.log()","def log(string):\n\n print string\n\n# data and time\n dt = datetime.now().strftime(\"%b %d %H:%M:%S\")\n\n# check if log file exist / if not create it\n check_logf = os.path.isfile(logfile)\n if check_logf == False:\n os.system(\"touch %s\" % (logfile))\n firstlog = \"%s %s jadm: jadm log file was created!\" % (dt, os.uname()[1])\n os.system(\"echo '%s' > %s\" % (firstlog, logfile))\n\n# applay string to log file\n string = \"%s %s jadm: %s\" % (dt, os.uname()[1], string)\n os.system(\"echo '%s' >> %s\" % (string, logfile))","def print_to_file(start, stop, time_worked, work_text, work_log):\n today = datetime.date.today()\n\n record = ' || %.2f || %.2f || %.4f hours || %s\\n' % (start, stop, time_worked/3600, work_text)\n\n #if it is a new file you have the option to set a start time for the project\n # and how many hours a week you want to work\n if not os.path.isfile(work_log):\n while True:\n option = raw_input('\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\n if option == 'y':\n date = raw_input('\\nplease enter the start date of the project (dd-mm-yyyy): ')\n hours_per_week = raw_input('\\nplease enter the number of hours you intend to work on the project per week: ')\n try:\n datetime.datetime.strptime(date, '%d-%m-%Y')\n if hours_per_week.isdigit():\n f = open(work_log, 'a')\n f.write('#! || ' + date + ':' + hours_per_week + '\\n')\n f.close()\n break\n else:\n print \"\\nPlease enter a valid number for hours to work!\\n\"\n except ValueError:\n print \"\\nPlease enter a valid date!\\n\"\n\n else:\n break\n\n\n f = open(work_log, 'a')\n print '\\n\\n' + today.strftime('%b-%d-%Y') + record\n f.write(today.strftime('%b-%d-%Y') + record)\n f.close()","def _log_results(self, results):\n log.new_entry(results)\n self.new_entry = 2","def logP(self, history, word):","def make_record(self):\n answer = self.input('enter your name: ')\n if answer:\n print('hi ' + answer + '! Your answer has been recorded.')\n self.record_score(answer, len(self.board.tiles.blacks))\n self.game_on = False\n else:\n self.game_on = False # Canceled dialog will turn off","def log(self):\n\t\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\n\t\tfileobj = open(filename, 'w');\n\t\tfileobj.write(self.title)\n\t\tfor (key, value) in self.argv.items():\n\t\t\tfileobj.write('%s: %s\\n' % (key, value))\n\t\tfileobj.close()\n\t\tprint \"[%s] Log saved '%s'\" % (ctime(), filename)","def log(self, key: str, val: Any, iteration: int = None) -> None:\n assert key is not None and val is not None, \"Please set key and val\"\n\n if self._tb_writer is not None:\n assert (\n iteration is not None\n ), \"Must specify iteration when logging to tensorboard\"\n self._tb_writer.add_scalar(key, val, iteration)\n if self._tqdm_bar is not None:\n # update tqdm bar\n self._tqdm_data[key] = val\n self._tqdm_bar.set_postfix(self._tqdm_data, refresh=True)","def logOutput(self, line):\r\n self.writeToLog('output', line)","def log_metric(self, name, val, step):\n raise NotImplementedError","def log_message(self, format, *args):","def get_log(request, **kwargs):\n\n #Creating the command for the logs \n try:\n\tprint(kwargs)\n\tprint(request.GET['project_id'])\n\toutputStr = sidecar.events.test_logs(project_id=request.GET['project_id'])\n\tlog_data = outputStr.log_data\n\toutputStr = \" <br>\".join(log_data.split(\"\\n\"))\n except Exception, e:\n outputStr = \"Updating the logs...\"\t\n #Making the output\n context = {\n \"page_title\": _(\"Test Details\"),\n \"test_lists\": 'report_list', #tests_list\n \"log_data\": outputStr\n }\n return render(request, 'rally_dashboard/events/test_logs.html', context)","def log(self, line):\n now = datetime.datetime.now()\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\n with open(self.logfile, 'a') as log:\n log.write(time + ' ' + line + '\\n')","def _log(self, data):\n if self.log_data is not None:\n self.log_data(data)","def run_game_log(player, game_log):\n\n\tgame_log.update_room_type()\n\tcurrent_msg = game_log.get_current_message()\n\n\tprint('HP: {} GOLD: {}'.format(player.stats['HP'], player.stats['GOLD']))\n\tprint('CURRENT ROOM: {}\\n'.format(game_log.current_room))\n\tprint(\"{}'s LOG: {}\".format(player.info['Name'].upper(), current_msg))","def log(entry, subarray=DEFAULT) :\n multiSubarray('log', subarray, \"LOG: \" + entry)","def plain(self, *args):\n self.mylog.log(logging.INFO + 1, *args)","def on_log(self):\n monitors = self.monitors\n if self.monitors is None:\n monitors = self.trainer.metrics.keys()\n\n\n hparams = self.hparams\n if self.hparams is None:\n hparams = self.trainer.hparams.keys()\n\n metrics = {name: format_metric(self.trainer.metrics[name])\n for name in monitors\n if name in self.trainer.metrics}\n hparams = {name: format_metric(self.trainer.hparams[name])\n for name in hparams\n if name in self.trainer.hparams}\n\n\n step_bar = self.step_bars[-1]\n step_bar.set_description(\"Epoch {}\".format(self.trainer.epoch+1))\n step_bar.set_postfix(**metrics, **hparams)\n step_bar.update(self.trainer.steps_trained - self.last_step)\n self.last_step = self.trainer.steps_trained","def logIt(self, msg):\n\n\t\tif( self.logger ): self.logger.logIt( msg )","def log_it(logdata=None):\n with open(\"bloomhack.log\", \"a\") as fp:\n fp.write(logdata)\n return","def putlog(self,s):\n if not self.logqueue == None:\n# print s\n self.logqueue.put(\"Spectrum: (\"+time.ctime()+\"):\\n\"+s)","def print_entry(text):\n print \"Text entered: \\n '%s'\" % text","def log_dialogue_input(log_dict, dynamodb_table):\n if not isinstance(log_dict, dict):\n raise NameError(f\"Logging information must be dictionary, not type {type(log_dict)}\")\n\n # Log in PST\n log_dict[\"time\"] = pst()\n if dynamodb_table is not None:\n try:\n dynamodb_table.put_item(Item=log_dict)\n app.logger.info(\"DB write successful\")\n except Exception as e:\n app.logger.info(f\"Could not write to database: {e}\")\n # If no db is specified, write logs to info\n app.logger.info(log_dict)","def view(\n id: int = typer.Argument(\n ...,\n help=\"ID of the log entry\"\n )\n):\n manager = LogBookManager()\n log_entry = manager.get(id)\n\n if log_entry:\n log_entry_id = (\n typer.style(\"Log Entry ID: \", fg=typer.colors.BRIGHT_BLUE, bold=True) +\n str(log_entry.id)\n )\n typer.echo(log_entry_id)\n\n log_datetime = (\n typer.style(\"Log Date & Time: \", fg=typer.colors.BRIGHT_BLUE, bold=True) +\n log_entry.log_datetime.strftime(\"%Y-%m-%d %I:%M %p\")\n )\n typer.echo(log_datetime)\n\n typer.echo(\n typer.style(\"\\nDescription:\\n\", fg=typer.colors.BRIGHT_BLUE, bold=True)\n )\n typer.echo(log_entry.description + '\\n')\n\n created_at = (\n typer.style(\"Created at: \", fg=typer.colors.BRIGHT_BLUE, bold=True) +\n log_entry.created_at.strftime(\"%Y-%m-%d %I:%M %p\")\n )\n typer.echo(created_at)\n\n updated_at = (\n typer.style(\"Updated at: \", fg=typer.colors.BRIGHT_BLUE, bold=True) +\n log_entry.updated_at.strftime(\"%Y-%m-%d %I:%M %p\")\n )\n typer.echo(updated_at)\n else:\n typer.echo(\n typer.style(\n f'No Log Entry Found with id={id}',\n fg=typer.colors.RED,\n bold=True\n )\n )","def render_entry_log(self):\n self.render_log(self.selenium_testcase_entry_template)","def log(self, user: TelegramController.User, input: str = \"None\", output: str = \"None\") -> NONE:\n\n header = connect(self.__path)\n curs = header.cursor()\n encrypted_id = md5((str(user.id) + \"typicaluser\").encode()).hexdigest()\n if not self.has_history(user):\n curs.execute(\"INSERT INTO users VALUES(?, ?)\", (encrypted_id, \"\"))\n\n curs.execute(\"SELECT * FROM users WHERE id = (?)\", (encrypted_id,))\n data = curs.fetchall()[0][1]\n data += f\"input: {input}\\noutput: {output}\\n\" + \"-\" * 30 + \"\\n\"\n curs.execute(\"UPDATE users SET history = (?) WHERE id = (?)\", (data, encrypted_id))\n header.commit()","def log_info(request, piece_id):\n\n asset = AssetMap.objects.get(piece=piece_id)\n url = '/'.join([FTP_URL, asset.folder, asset.name+'.log',])\n context = {\n 'keyform': KeySearchForm(auto_id=False),\n 'piece' : Piece.objects.get(pk=piece_id),\n 'logfile': requests.get(url),\n }\n return render(request, 'mutopia/piece_log.html', context)","def _print_log(self, step, data=None):\n \n # Set mode to append to log file\n mode = 'a'\n\n if self.logfile is None:\n # Increment log counter for the class. Each instance of the class generates a new log.\n self.__class__.log_no += 1\n\n # Create a log file for the instance\n # Logs will be stored in ..\\logs\\SKLearn Log <n>.txt\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\n \n if step == 1:\n # Output log header\n output = \"\\nSKLearnForQlik Log: {0} \\n\\n\".format(time.ctime(time.time()))\n # Set mode to write new log file\n mode = 'w'\n \n elif step == 2:\n # Output the parameters\n output = \"Model Name: {0}\\n\\n\".format(self.model.name)\n output += \"Execution arguments: {0}\\n\\n\".format(self.exec_params)\n \n try:\n output += \"Scaler: {0}, missing: {1}, scale_hashed: {2}, scale_vectors: {3}\\n\".format(\\\n self.model.scaler, self.model.missing,self.model.scale_hashed, self.model.scale_vectors)\n output += \"Scaler kwargs: {0}\\n\\n\".format(self.model.scaler_kwargs)\n except AttributeError:\n output += \"scale_hashed: {0}, scale_vectors: {1}\\n\".format(self.model.scale_hashed, self.model.scale_vectors)\n\n try:\n if self.model.dim_reduction:\n output += \"Reduction: {0}\\nReduction kwargs: {1}\\n\\n\".format(self.model.reduction, self.model.dim_reduction_args)\n except AttributeError:\n pass\n \n output += \"Estimator: {0}\\nEstimator kwargs: {1}\\n\\n\".format(self.model.estimator, self.model.estimator_kwargs)\n \n elif step == 3: \n # Output the request dataframe\n output = \"REQUEST: {0} rows x cols\\nSample Data:\\n\\n\".format(self.request_df.shape)\n output += \"{0}\\n...\\n{1}\\n\\n\".format(self.request_df.head().to_string(), self.request_df.tail().to_string())\n \n elif step == 4:\n # Output the response dataframe/series\n output = \"RESPONSE: {0} rows x cols\\nSample Data:\\n\\n\".format(self.response.shape)\n output += \"{0}\\n...\\n{1}\\n\\n\".format(self.response.head().to_string(), self.response.tail().to_string())\n \n elif step == 5:\n # Print the table description if the call was made from the load script\n output = \"\\nTABLE DESCRIPTION SENT TO QLIK:\\n\\n{0} \\n\\n\".format(self.table)\n \n elif step == 6:\n # Message when model is loaded from cache\n output = \"\\nModel {0} loaded from cache.\\n\\n\".format(self.model.name)\n \n elif step == 7:\n # Message when model is loaded from disk\n output = \"\\nModel {0} loaded from disk.\\n\\n\".format(self.model.name)\n \n elif step == 8:\n # Message when cache is updated\n output = \"\\nCache updated. Models in cache:\\n{0}\\n\\n\".format([k for k,v in self.__class__.model_cache.items()])\n \n elif step == 9:\n # Output when a parameter grid is set up\n output = \"Model Name: {0}, Estimator: {1}\\n\\nGrid Search Arguments: {2}\\n\\nParameter Grid: {3}\\n\\n\".\\\n format(self.model.name, self.model.estimator, self.model.grid_search_args, self.model.param_grid)\n \n elif step == 10:\n # self.model.estimator_kwargs['architecture']\n output = \"\\nKeras architecture added to Model {0}:\\n\\n{1}\\n\\n\".format(self.model.name,\\\n self.model.architecture.to_string())\n\n elif step == 11:\n # Output after adding lag observations to input data\n output = \"Lag observations added ({0} per sample). New input shape of X is {1}.\\n\\n\".format(self.model.lags, data.shape)\n output += \"Feature Definitions:\\n{0}\\n\\n\".format(self.model.features_df.to_string())\n output += \"Sample Data:\\n{0}\\n...\\n{1}\\n\\n\".format(data.head(5).to_string(), data.tail(5).to_string())\n \n sys.stdout.write(output)\n with open(self.logfile, mode, encoding='utf-8') as f:\n f.write(output)","def _Log(self, logf, s):\r\n if logf:\r\n logf(s + '\\n')","def log_message(self, formate, *args):\n return","def print_entry(entry):\n print(\"\"\"\nEmployee Name: {name}\nTask Name: {task_name}\nDate: {date}\nTime Spent: {time_spent}\nNotes: {notes}\n\"\"\".format(**entry))","def log_example(var):\n\n log.info('example code started')\n log.debug('calling settings')\n test_settings()\n log2.error('there is no error this is example ')\n log2.info('finished')","def log(self, reward, action):\n self.logs.append([reward, action])","def logStarted(build, step, log):","def commandlog(entry, subarray=DEFAULT) :\n multiSubarray('log', subarray, \"COMMAND: \" + entry)","def log_metric(key, value, step=None):\n mlflow.log_metric(key, value, step=step)","def submit_entry(self, entry_type=\"Log\"):\n # Gratitude List\n gratitude_li = []\n for grat_entry in self.grat_entry_li:\n grat_txt = grat_entry.get()\n if grat_txt != \"\":\n gratitude_li.append(grat_txt.replace(\",\", \"\\comma\"))\n\n # Goals List\n goals_li = []\n\n for goal_entry in self.goals_entry_li:\n goal_txt = goal_entry.get()\n if goal_txt != \"\":\n goals_li.append(goal_txt.replace(\",\", \"\\comma\"))\n\n \"\"\"\n Plans List: {\"Description\": description entry box,\n \"Priority\": priority combo box, \"Steps\": list of step entry boxes }\n \n Step list: {\"Description\": new_step_box, \"Status\": check_var}\n \"\"\"\n plans_li = []\n\n for plan_entry in self.plans_entry_li:\n if plan_entry[\"Description\"].get() == \"\" and len(plan_entry[\"Steps\"]) == 0:\n pass\n else:\n plan = {}\n plan[\"Plan_Type\"] = plan_entry[\"Plan_Type\"].get()\n plan[\"Description\"] = plan_entry[\"Description\"].get().replace(\n \",\", \"\\comma\")\n plan[\"Status\"] = plan_entry[\"Status\"].get()\n plan[\"Priority\"] = plan_entry[\"Priority\"].get()\n plan[\"Steps\"] = []\n for step_entry in plan_entry[\"Steps\"]:\n step_txt = step_entry[\"Description\"].get()\n if step_txt == \"\":\n pass\n else:\n full_step = {\"Status\": step_entry[\"Status\"].get(),\n \"Description\": step_txt}\n plan[\"Steps\"].append(full_step)\n plans_li.append(plan)\n\n # Affirmation Entry\n affirmation = self.affirm_entry.get(\"1.0\", 'end-1c')\n\n # Additional Comment Entry\n additional_notes = self.notes_entry.get(\"1.0\", 'end-1c')\n\n add_new_entry(entry_type=entry_type,\n gratitude=gratitude_li,\n goals=goals_li,\n plans=plans_li,\n affirmation=affirmation,\n additional_notes=additional_notes,\n test=True)\n\n self.root.switch_page(self.root._HomePage)","def log3(request):\n if request.method == \"GET\":\n bValid = True\n (usernumber_str, bValid) = getValue(request, u\"uid\", bValid)\n (timelapse_str, bValid) = getValue(request, u\"timelapse\", bValid)\n (propkind_str, bValid) = getValue(request, u\"propkind\", bValid)\n (figure_str, bValid) = getValue(request, u\"figure\", bValid)\n (correctness_str, bValid) = getValue(request, u\"corr\", bValid)\n (term_S_str, bValid) = getValue(request, u\"S\", bValid)\n (term_M_str, bValid) = getValue(request, u\"M\", bValid)\n (term_P_str, bValid) = getValue(request, u\"P\", bValid)\n (term_Q1_str, bValid) = getValue(request, u\"q1\", bValid)\n (term_Q2_str, bValid) = getValue(request, u\"q2\", bValid)\n (term_Q3_str, bValid) = getValue(request, u\"q3\", bValid)\n (conclusion_truth_value_str, bValid) = getValue(request, u\"conclusion\", bValid)\n (validity_answer, bValid) = getValue(request, u\"validity\", bValid)\n\n if not bValid:\n return render(request, 'log.html')\n else:\n if correctness_str == \"true\":\n bCorrect = True\n else:\n bCorrect = False\n\n if conclusion_truth_value_str == \"1\":\n bConclusionTruthValue = True\n else:\n bConclusionTruthValue = False\n\n newobj3 = Answer3(usernumber=int(usernumber_str),\n timelapse=int(timelapse_str),\n figure=int(figure_str),\n quantor_1=term_Q1_str,\n quantor_2=term_Q2_str,\n quantor_3=term_Q3_str,\n term_S=int(term_S_str),\n term_M=int(term_M_str),\n term_P=int(term_P_str),\n propkind=propkind_str,\n student_answer=validity_answer,\n correctness=bCorrect,\n conclusion_truth_value=bConclusionTruthValue)\n\n newobj3.save()\n\n return render(request, 'log.html')\n else:\n return render(request, 'log.html')","def enter_linking_log_patient(project_path: str, project_name: str):\n # Get Linking Log\n linking_log_path = os.path.join(project_path, 'logs', 'logs_with_phi', 'Master_Linking_Log.xlsx')\n\n while True:\n choice = input(\"Would you like to enter a new patient onto the Linking log log [Y/N]? \")\n\n if choice and choice.strip() and choice.lower() == 'y':\n patient_data = rsm.get_data_from_user.get_master_linking_log_data()\n rsm.add_patient_to_excel_file.add_patient(linking_log_path,\n patient_data,\n 'Master_Linking_Log')\n print(\"Successfully added patient to {} Master Linking log\".format(project_name))\n\n elif choice and choice.strip() and choice.lower() == 'n':\n break\n else:\n print(\"Please enter Y or N\")","def create_log_entry_when_user_logs_in(sender, request, user, **kwargs):\n create_user_log(\n request=request,\n user=user,\n type=_account_const.AUTHENTICATION,\n action=_account_const.LOGIN\n )","def display_entry(self, entry):\n border = '-' * 50\n print(border)\n print('Employee: {}'.format(entry.employee_name))\n print('Task Name: {}'.format(entry.task_name))\n print(\"Date: {}\".format(entry.date))\n print(\"Time Spent: {}\".format(entry.time_spent))\n if entry.notes != '':\n print(\"Notes:\\n{}\\n{}\".format('----------', entry.notes))\n print(border)","def log_step(step: int, message: str, stdout: bool = True) -> None:\n log(f\"Step {step:6d}: {message}\", stdout=stdout)","def add_line_in_log():\n logging.info(' ' + '-' * 60 + '\\n')","def save_result(self, value: Any) -> None:\n self.run_logger.set_tags({self.name: value})","def on_L1(self):\r\n self.log()","def log(self):\n self.logger = logging.getLogger(self.log_name)\n self.logger.info(f\"Name: {self.name}\")\n self.logger.info(f\"Grid points: {self.gp}\")\n self.logger.info(f\"Nadir points: {self.nadir_p}\")\n self.logger.info(f\"Penalty weight: {self.eps}\")\n self.logger.info(f\"Early exit: {self.early_exit}\")\n self.logger.info(f\"Bypass coefficient: {self.bypass}\")\n self.logger.info(f\"Flag array: {self.flag}\")\n self.logger.info(f\"CPU Count: {self.cpu_count}\")\n self.logger.info(f\"Redivide work: {self.redivide_work}\")\n self.logger.info(f\"Shared flag array: {self.shared_flag}\")\n self.logger.info(Helper.separator())","def log(msg):\n print msg","def log(self, msg):\n logging.info(\"Logging Message\")\n ml = self.monk_logs\n today = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M\")\n ml.insert({today: {\"log\": msg,\n \"sentiment\": self.get_sentiment(msg),\n \"weather\": Weather.get_weather()}})","def log_successful_login(sender, request, user, **kwargs):\r\n if settings.FEATURES['SQUELCH_PII_IN_LOGS']:\r\n AUDIT_LOG.info(u\"Login success - user.id: {0}\".format(user.id))\r\n else:\r\n AUDIT_LOG.info(u\"Login success - {0} ({1})\".format(user.username, user.email))","def log_message(self, fmt, *args):\n pass","def log(self, *args):\n self._check_private_key(\"log data\")\n params = {'private_key': self.privateKey}\n params.update(dict((k, self._encoder.serialize(v))\n for k, v in zip(self.fields, args)))\n response = self._post(self.inputUrl(), params=params)\n\n self._last_headers = response.headers\n self._stats = None","def add_log(self, log):\n log = str(datetime.datetime.now()) + \": \"+log+\"\\n\"\n print(log)\n self.logs.append(log)\n if len(self.logs) > 10:\n self.append_to_logfile()","def log_request(req: 'Flask_Request', results: str) -> None:\n\n with UseDatabase(app.config['dbconfig']) as cursor:\n _SQL_INSERT = \"\"\"insert into log_table\n (phrase, letters, ip, browser_string, results)\n values\n (?, ?, ?, ?, ?)\"\"\"\n\n cursor.execute(_SQL_INSERT,\n (req.form['phrase'], req.form['letter'], req.remote_addr, req.user_agent.browser,\n results,))","def log(self, message, *args, newline):\n\n self.current_character.log(message, *args, newline=newline)","def log(self, client_addr, request):\n with codecs.open(self.log_path, \"a\", 'UTF-8') as fh_out:\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \"\\t\" +\n ':'.join([str(i) for i in client_addr]) + \"\\t\" +\n request)","def _print_logs_info(job_id, project_id):\n print(\"\\nJob submitted successfully.\")\n print(\"Your job ID is: \", job_id)\n print(\"\\nPlease access your training job information here:\")\n print(\n \"https://console.cloud.google.com/mlengine/jobs/{}?project={}\".format(\n job_id, project_id))\n print(\"\\nPlease access your training job logs here: \"\n \"https://console.cloud.google.com/logs/viewer?resource=ml_job%2F\"\n \"job_id%2F{}&interval=NO_LIMIT&project={}\\n\".format(\n job_id, project_id))","def history_log(self, user, action=CHANGE, message=''):\n LogEntry.objects.log_action(\n user_id=user.pk,\n content_type_id=ContentType.objects.get_for_model(self).pk,\n object_id=self.pk,\n object_repr=force_text(self),\n action_flag=action,\n change_message=message\n )","def _log(self, log, message):\n log_entry = '[%s] %s\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\n log.write(log_entry)\n if self.verbose:\n print log_entry.rstrip()","def log(self, obj, action):\n action_dict = {'time': time.time(),\n 'action': action}\n self.log_data[obj.get_obj_id()]['actions'].append(action_dict)","def log(self, *args, **kwargs):\n return self.event.log(*args, meta={'contribution_id': self.id}, **kwargs)","def get_action_logfile():\n return \"action\" + get_day() + \".log\"","def record_task_attempt(task_name: str):\n\n from common.models import InvenTreeSetting\n\n logger.info(f\"Logging task attempt for '{task_name}'\")\n\n InvenTreeSetting.set_setting(f'_{task_name}_ATTEMPT', datetime.now().isoformat(), None)","def log(data):\n return _make.log(data)"],"string":"[\n \"def log(exercise):\\n global logfile\\n msg = raw_input(\\\"Enter your message. \\\")\\n logfile.write(exercise + \\\" >>> \\\" + msg + \\\"\\\\n\\\")\",\n \"def logentry(jobid, label, typ, content=None, path=None):\\n ud = str(uuid.uuid4())\\n db.logs.save({\\\"uuid\\\":ud, \\\"jobid\\\":jobid, \\\"label\\\":label, \\\"type\\\":typ, \\\"content\\\":content, \\\"date\\\":tstamp(), \\\"containerurl\\\":path})\",\n \"def log(self, game: str, outcome: str):\\n current_time = datetime.now()\\n self.user.record.append([current_time.strftime(\\\"%c\\\"), game, outcome, self.user.balance])\",\n \"def new_entry():\\n clear_screen()\\n entry = {}\\n entry['id'] = get_next_id()\\n entry['name'] = input_name()\\n print(\\\"How many minutes did you spend on {}?\\\".format(entry['name']))\\n print(\\\"Or you may specify a format after the time, seperated by a comma\\\")\\n entry['time_spent'] = input_time_spent()\\n add_notes = input(\\\"Add notes? Y/n \\\").lower()\\n if add_notes != 'n':\\n entry['notes'] = input_notes()\\n entry['date'] = datetime.now().strftime(FMT_MONTH_DAY_YEAR)\\n with open(WORK_LOG_FILENAME, 'a', newline='') as work_log:\\n work_log_writer = csv.DictWriter(work_log, fieldnames=FIELDNAMES)\\n work_log_writer.writerow(entry)\",\n \"def entry(entry,fhandle): \\n\\n import time\\n timestamp = time.strftime('%H:%M:%S')\\n print entry\\n try: fhandle.write('{0}: {1}\\\\n'.format(timestamp, entry))\\n except: print 'Log File Error: entry could not be logged'\\n return\",\n \"def log_entry(debug, out, text):\\n # Format log entry\\n monthday = make_time_stamp('%m%d')\\n time_stamp = make_time_stamp('%H:%M:%S')\\n now = time.time()\\n ms = \\\".\\\"+str('%06d' % int((now - int(now)) * 1000000))\\n line_form = \\\"I{monthday} {time_stamp} 0000 main.py:00] {text}\\\\n\\\"\\n entry = line_form.format(monthday=monthday, time_stamp=time_stamp+ms, text=text)\\n\\n # Log entry to stderr\\n sys.stderr.write(entry)\\n pass\",\n \"def logentry(self, string=None):\\n if (self._OIFlogging):\\n oiflogfile = open(self._commslogfilename, \\\"a\\\")\\n oiflogfile.write(\\\"# \\\" + \\\"%04.6fs: \\\" % (self._gettime() - self._logstarttime) + string + \\\"\\\\n\\\")\\n oiflogfile.flush()\\n else:\\n# if self._print_once:\\n# self._print_once = 0\\n# print self.hilite(\\\"Warning: Not logging OIF transactions. Use\\\\n it.logfile(<filename>) to set log filename and\\\\n it.logging(True) to enable logging\\\", False, True)\\n print 'Unable to write log entry', string\\n return\",\n \"def log(self, inp, print_result: bool = None):\\n if print_result is None: print_result = self.log_print\\n # Parse input\\n if type(inp) == str:\\n logged_inp = inp\\n else:\\n raise NotImplementedError(f\\\"Given type '{type(inp)}' is not supported for logging.\\\")\\n \\n # Print if requested\\n if print_result: print(logged_inp)\\n \\n # Append the string to the file\\n append_log(logged_inp, f\\\"population{'_backup' if self.use_backup else ''}/\\\"\\n f\\\"storage/\\\"\\n f\\\"{self.folder_name}/\\\"\\n f\\\"{self}/\\\"\\n f\\\"logbook.log\\\")\",\n \"def main_log(logfile, entry, print_tag=False):\\n if logfile != None:\\n with open(logfile, 'a') as lf:\\n lf.write('{}\\\\n'.format(entry))\\n\\n if print_tag:\\n print entry\",\n \"def log_entry(self, timestamp, entry):\\n if timestamp in self.log:\\n self.log[timestamp].update(entry)\\n else:\\n self.log[timestamp] = entry\",\n \"def _addLogEntry(request, action, pagename, filename):\\n from MoinMoin.logfile import editlog\\n t = wikiutil.timestamp2version(time.time())\\n fname = wikiutil.url_quote(filename)\\n\\n # Write to global log\\n log = editlog.EditLog(request)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\\n\\n # Write to local log\\n log = editlog.EditLog(request, rootpagename=pagename)\\n log.add(request, t, 99999999, action, pagename, request.remote_addr, fname)\",\n \"def do_log(self, arg):\\n arg = \\\" %s :custom log\\\" % (arg)\\n log(arg)\",\n \"def __log__(self, val):\\n if lm_settings[\\\"debug\\\"]:\\n try:\\n log_file = open(\\\"language_manager/info/language_manager.log\\\", \\\"a\\\")\\n except FileNotFoundError:\\n log_file = open(lm_settings[\\\"logfile\\\"], \\\"w\\\")\\n log_file.write(val)\\n log_file.close()\",\n \"def InsertLog():\",\n \"def log(self, message):\",\n \"def log(self, *args, **kwargs):\\n self.game_view.log(*args, **kwargs)\",\n \"def add_entry(name, title, duration, notes):\\n clear()\\n print('Entry added to work log!')\\n return Entry.create(\\n employee_name=name,\\n task_title=title,\\n time_spent=duration,\\n task_notes=notes\\n )\",\n \"def __log_trial__(self, trial_data):\\n from klibs.KLDatabase import EntryTemplate\\n\\n trial_template = EntryTemplate('trials')\\n trial_template.log(P.id_field_name, P.participant_id)\\n for attr in trial_data:\\n trial_template.log(attr, trial_data[attr])\\n\\n return self.database.insert(trial_template)\",\n \"def log(self, message: str):\",\n \"async def add_log(self, value):\\n log_string = value\\n print(log_string)\\n self.embed.title = log_string\\n self.embed.timestamp = datetime.datetime.now()\\n self.embed.description = \\\"\\\"\",\n \"def addLog(log_info,seed_keyword=\\\"\\\",meta_keyword=\\\"\\\"):\\n payload = {\\n \\\"user\\\" : os.getlogin(),\\n \\\"seed_keyword\\\":seed_keyword,\\n \\\"meta_keyword\\\":meta_keyword,\\n \\\"log_info\\\":log_info\\n }\\n res = requests.post('{}add/issue/'.format(base_url),data=payload)\\n return res.status_code\",\n \"def record(*args, **kwargs):\\n LOG.info(\\\"args={}, kwargs={}\\\".format(args, kwargs))\",\n \"def logWork(self, id, logData):\\n\\t\\tnow = datetime.datetime.now()\\n\\t\\ttimeText = now.strftime(LOG_DATE_FORMAT)\\n\\n\\t\\tlogToEdit = self.LOGS.get(id).get(\\\"log\\\")\\n\\t\\t#If inside this case and there is already a log entry for this time\\n\\t\\talreadyEntryForThisTime = False\\n\\t\\tfor entry in logToEdit:\\n\\t\\t\\tif timeText == entry[0]:\\n\\t\\t\\t\\tentry[1] += logData\\n\\t\\t\\t\\talreadyEntryForThisTime = True\\n\\n\\t\\tif not alreadyEntryForThisTime:\\n\\t\\t\\tlogToEdit.append([timeText, logData])\\n\\t\\t\\tself.logByDate(now.strftime(CASE_DATE_FORMAT), id)\\n\\n\\t\\tself.save_logs(\\\"c\\\")\\n\\t\\tself.autoCommit()\\n\\t\\treturn 0\",\n \"def log(info):\\n print(f\\\"[{info}]\\\")\",\n \"def logger(self, value):\\n pass\",\n \"def log_lesson(request):\\n user = User.objects.get(email=request.user.email)\\n user_info = UserInformation.objects.get(user=user)\\n lesson_set = user_info.current_lesson_set\\n lesson = Lesson.objects.get(lesson_index=user_info.current_lesson_index)\\n main_set = user_info.current_main_set\\n\\n print(\\\"lesson_logged\\\")\\n\\n lesson_to_log = LessonLog.objects.create(user=user,\\n time_stamp=timezone.now(),\\n lesson_set_key=lesson_set,\\n lesson_key=lesson,\\n lesson_index=lesson.lesson_index,\\n main_set_key=main_set)\\n lesson_to_log.save()\",\n \"def log(self, inputText, saveLog=True):\\n\\n self.logView.insertPlainText(\\\">> %s \\\\n\\\" % inputText)\\n\\n self.logView.moveCursor(QtGui.QTextCursor.End)\\n\\n if saveLog == True:\\n f = open(self.logFileName, \\\"a\\\")\\n\\n f.write(inputText + '\\\\n')\\n\\n f.close()\",\n \"def recordLog(project, status, memo):\\n path = getPath(project)\\n log = open(path, 'a')\\n writer = csv.writer(log, lineterminator='\\\\n')\\n writer.writerow((time.time(), status, memo))\\n log.close()\\n if status == 'a':\\n print(\\\"Tracking your time on \\\" + project)\\n if status == 's':\\n print(\\\"Tracking suspended on \\\" + project)\\n if status == 't':\\n print(\\\"Time shifted on \\\" + project)\\n if not path == '.sourglass':\\n store = open(os.path.join(basepath, 'last'), 'w')\\n store.write(project)\\n store.close\",\n \"def log(text):\\n print \\\"%s: %s\\\" % (str(datetime.datetime.now()), text)\",\n \"def logtool(self, action, **options):\\n pass\",\n \"def ENTRY(entry_code):\\n\\tif check_user(entry_code) ==True:\\n\\t\\t###workday = Workday.objects.filter(date=get_time()).get()\\n\\t\\tenter_workday(entry_code)\",\n \"def logFile(self):\\n\\n event = 'stim'\\n mStr = '{:013}'.format(self.mouse.tag) + '\\\\t'\\n outPutStr = mStr + \\\\\\n datetime.fromtimestamp(int(time())).isoformat(' ') + '\\\\t' + event\\n print (outPutStr)\\n if self.textfp != None:\\n outPutStr = mStr + '{:.2f}'.format(time()) + '\\\\t' + event\\n self.textfp.write(outPutStr + '\\\\n')\\n self.textfp.flush()\",\n \"def logline(msg):\\n print msg\",\n \"def Log(self, msg):\\n self.DBExecute(\\\"INSERT INTO Log (class, instance, event) VALUES (%s, %s, %s)\\\",\\n self.__class__.__name__, self._instance, msg)\\n print '%s/%s: %s' % (self.__class__.__name__, self._instance, msg)\",\n \"def create_log_entry(self, i): \\r\\n fitness_vector = self.get_fitness_vector()\\r\\n \\r\\n best = min(fitness_vector)\\r\\n worst = max(fitness_vector)\\r\\n mean = rython.mean(fitness_vector)\\r\\n std = rython.std(fitness_vector)\\r\\n \\r\\n return \\\"{0},{1},{2},{3},{4}\\\\n\\\".format(i, 1.0/best, 1.0/worst, 1.0/mean, std)\",\n \"def _log(self, format, args, level=None):\\n if level is None:\\n level = self.log_level\\n xbmc.log(\\n \\\"metadata.movie.stupid: %s - - [%s] %s\\\\n\\\" % (\\n self.client_address[0], self.log_date_time_string(),\\n format % args),\\n level)\",\n \"def scriptlog(entry, subarray=DEFAULT) :\\n multiSubarray('log', subarray, \\\"SCRIPT: \\\" + entry)\",\n \"def on_a(self):\\r\\n self.log()\",\n \"def log(string):\\n\\n print string\\n\\n# data and time\\n dt = datetime.now().strftime(\\\"%b %d %H:%M:%S\\\")\\n\\n# check if log file exist / if not create it\\n check_logf = os.path.isfile(logfile)\\n if check_logf == False:\\n os.system(\\\"touch %s\\\" % (logfile))\\n firstlog = \\\"%s %s jadm: jadm log file was created!\\\" % (dt, os.uname()[1])\\n os.system(\\\"echo '%s' > %s\\\" % (firstlog, logfile))\\n\\n# applay string to log file\\n string = \\\"%s %s jadm: %s\\\" % (dt, os.uname()[1], string)\\n os.system(\\\"echo '%s' >> %s\\\" % (string, logfile))\",\n \"def print_to_file(start, stop, time_worked, work_text, work_log):\\n today = datetime.date.today()\\n\\n record = ' || %.2f || %.2f || %.4f hours || %s\\\\n' % (start, stop, time_worked/3600, work_text)\\n\\n #if it is a new file you have the option to set a start time for the project\\n # and how many hours a week you want to work\\n if not os.path.isfile(work_log):\\n while True:\\n option = raw_input('\\\\nThis is a new log, would you like to specify a start date and a hours per week goal for the project? (y/n): ').lower()\\n if option == 'y':\\n date = raw_input('\\\\nplease enter the start date of the project (dd-mm-yyyy): ')\\n hours_per_week = raw_input('\\\\nplease enter the number of hours you intend to work on the project per week: ')\\n try:\\n datetime.datetime.strptime(date, '%d-%m-%Y')\\n if hours_per_week.isdigit():\\n f = open(work_log, 'a')\\n f.write('#! || ' + date + ':' + hours_per_week + '\\\\n')\\n f.close()\\n break\\n else:\\n print \\\"\\\\nPlease enter a valid number for hours to work!\\\\n\\\"\\n except ValueError:\\n print \\\"\\\\nPlease enter a valid date!\\\\n\\\"\\n\\n else:\\n break\\n\\n\\n f = open(work_log, 'a')\\n print '\\\\n\\\\n' + today.strftime('%b-%d-%Y') + record\\n f.write(today.strftime('%b-%d-%Y') + record)\\n f.close()\",\n \"def _log_results(self, results):\\n log.new_entry(results)\\n self.new_entry = 2\",\n \"def logP(self, history, word):\",\n \"def make_record(self):\\n answer = self.input('enter your name: ')\\n if answer:\\n print('hi ' + answer + '! Your answer has been recorded.')\\n self.record_score(answer, len(self.board.tiles.blacks))\\n self.game_on = False\\n else:\\n self.game_on = False # Canceled dialog will turn off\",\n \"def log(self):\\n\\t\\tfilename = '/var/log/postunsuspendacct-%s.log' % self.argv.get('user')\\n\\t\\tfileobj = open(filename, 'w');\\n\\t\\tfileobj.write(self.title)\\n\\t\\tfor (key, value) in self.argv.items():\\n\\t\\t\\tfileobj.write('%s: %s\\\\n' % (key, value))\\n\\t\\tfileobj.close()\\n\\t\\tprint \\\"[%s] Log saved '%s'\\\" % (ctime(), filename)\",\n \"def log(self, key: str, val: Any, iteration: int = None) -> None:\\n assert key is not None and val is not None, \\\"Please set key and val\\\"\\n\\n if self._tb_writer is not None:\\n assert (\\n iteration is not None\\n ), \\\"Must specify iteration when logging to tensorboard\\\"\\n self._tb_writer.add_scalar(key, val, iteration)\\n if self._tqdm_bar is not None:\\n # update tqdm bar\\n self._tqdm_data[key] = val\\n self._tqdm_bar.set_postfix(self._tqdm_data, refresh=True)\",\n \"def logOutput(self, line):\\r\\n self.writeToLog('output', line)\",\n \"def log_metric(self, name, val, step):\\n raise NotImplementedError\",\n \"def log_message(self, format, *args):\",\n \"def get_log(request, **kwargs):\\n\\n #Creating the command for the logs \\n try:\\n\\tprint(kwargs)\\n\\tprint(request.GET['project_id'])\\n\\toutputStr = sidecar.events.test_logs(project_id=request.GET['project_id'])\\n\\tlog_data = outputStr.log_data\\n\\toutputStr = \\\" <br>\\\".join(log_data.split(\\\"\\\\n\\\"))\\n except Exception, e:\\n outputStr = \\\"Updating the logs...\\\"\\t\\n #Making the output\\n context = {\\n \\\"page_title\\\": _(\\\"Test Details\\\"),\\n \\\"test_lists\\\": 'report_list', #tests_list\\n \\\"log_data\\\": outputStr\\n }\\n return render(request, 'rally_dashboard/events/test_logs.html', context)\",\n \"def log(self, line):\\n now = datetime.datetime.now()\\n time = datetime.datetime.strftime(now, '(%d %b %Y %H:%M:%S)')\\n with open(self.logfile, 'a') as log:\\n log.write(time + ' ' + line + '\\\\n')\",\n \"def _log(self, data):\\n if self.log_data is not None:\\n self.log_data(data)\",\n \"def run_game_log(player, game_log):\\n\\n\\tgame_log.update_room_type()\\n\\tcurrent_msg = game_log.get_current_message()\\n\\n\\tprint('HP: {} GOLD: {}'.format(player.stats['HP'], player.stats['GOLD']))\\n\\tprint('CURRENT ROOM: {}\\\\n'.format(game_log.current_room))\\n\\tprint(\\\"{}'s LOG: {}\\\".format(player.info['Name'].upper(), current_msg))\",\n \"def log(entry, subarray=DEFAULT) :\\n multiSubarray('log', subarray, \\\"LOG: \\\" + entry)\",\n \"def plain(self, *args):\\n self.mylog.log(logging.INFO + 1, *args)\",\n \"def on_log(self):\\n monitors = self.monitors\\n if self.monitors is None:\\n monitors = self.trainer.metrics.keys()\\n\\n\\n hparams = self.hparams\\n if self.hparams is None:\\n hparams = self.trainer.hparams.keys()\\n\\n metrics = {name: format_metric(self.trainer.metrics[name])\\n for name in monitors\\n if name in self.trainer.metrics}\\n hparams = {name: format_metric(self.trainer.hparams[name])\\n for name in hparams\\n if name in self.trainer.hparams}\\n\\n\\n step_bar = self.step_bars[-1]\\n step_bar.set_description(\\\"Epoch {}\\\".format(self.trainer.epoch+1))\\n step_bar.set_postfix(**metrics, **hparams)\\n step_bar.update(self.trainer.steps_trained - self.last_step)\\n self.last_step = self.trainer.steps_trained\",\n \"def logIt(self, msg):\\n\\n\\t\\tif( self.logger ): self.logger.logIt( msg )\",\n \"def log_it(logdata=None):\\n with open(\\\"bloomhack.log\\\", \\\"a\\\") as fp:\\n fp.write(logdata)\\n return\",\n \"def putlog(self,s):\\n if not self.logqueue == None:\\n# print s\\n self.logqueue.put(\\\"Spectrum: (\\\"+time.ctime()+\\\"):\\\\n\\\"+s)\",\n \"def print_entry(text):\\n print \\\"Text entered: \\\\n '%s'\\\" % text\",\n \"def log_dialogue_input(log_dict, dynamodb_table):\\n if not isinstance(log_dict, dict):\\n raise NameError(f\\\"Logging information must be dictionary, not type {type(log_dict)}\\\")\\n\\n # Log in PST\\n log_dict[\\\"time\\\"] = pst()\\n if dynamodb_table is not None:\\n try:\\n dynamodb_table.put_item(Item=log_dict)\\n app.logger.info(\\\"DB write successful\\\")\\n except Exception as e:\\n app.logger.info(f\\\"Could not write to database: {e}\\\")\\n # If no db is specified, write logs to info\\n app.logger.info(log_dict)\",\n \"def view(\\n id: int = typer.Argument(\\n ...,\\n help=\\\"ID of the log entry\\\"\\n )\\n):\\n manager = LogBookManager()\\n log_entry = manager.get(id)\\n\\n if log_entry:\\n log_entry_id = (\\n typer.style(\\\"Log Entry ID: \\\", fg=typer.colors.BRIGHT_BLUE, bold=True) +\\n str(log_entry.id)\\n )\\n typer.echo(log_entry_id)\\n\\n log_datetime = (\\n typer.style(\\\"Log Date & Time: \\\", fg=typer.colors.BRIGHT_BLUE, bold=True) +\\n log_entry.log_datetime.strftime(\\\"%Y-%m-%d %I:%M %p\\\")\\n )\\n typer.echo(log_datetime)\\n\\n typer.echo(\\n typer.style(\\\"\\\\nDescription:\\\\n\\\", fg=typer.colors.BRIGHT_BLUE, bold=True)\\n )\\n typer.echo(log_entry.description + '\\\\n')\\n\\n created_at = (\\n typer.style(\\\"Created at: \\\", fg=typer.colors.BRIGHT_BLUE, bold=True) +\\n log_entry.created_at.strftime(\\\"%Y-%m-%d %I:%M %p\\\")\\n )\\n typer.echo(created_at)\\n\\n updated_at = (\\n typer.style(\\\"Updated at: \\\", fg=typer.colors.BRIGHT_BLUE, bold=True) +\\n log_entry.updated_at.strftime(\\\"%Y-%m-%d %I:%M %p\\\")\\n )\\n typer.echo(updated_at)\\n else:\\n typer.echo(\\n typer.style(\\n f'No Log Entry Found with id={id}',\\n fg=typer.colors.RED,\\n bold=True\\n )\\n )\",\n \"def render_entry_log(self):\\n self.render_log(self.selenium_testcase_entry_template)\",\n \"def log(self, user: TelegramController.User, input: str = \\\"None\\\", output: str = \\\"None\\\") -> NONE:\\n\\n header = connect(self.__path)\\n curs = header.cursor()\\n encrypted_id = md5((str(user.id) + \\\"typicaluser\\\").encode()).hexdigest()\\n if not self.has_history(user):\\n curs.execute(\\\"INSERT INTO users VALUES(?, ?)\\\", (encrypted_id, \\\"\\\"))\\n\\n curs.execute(\\\"SELECT * FROM users WHERE id = (?)\\\", (encrypted_id,))\\n data = curs.fetchall()[0][1]\\n data += f\\\"input: {input}\\\\noutput: {output}\\\\n\\\" + \\\"-\\\" * 30 + \\\"\\\\n\\\"\\n curs.execute(\\\"UPDATE users SET history = (?) WHERE id = (?)\\\", (data, encrypted_id))\\n header.commit()\",\n \"def log_info(request, piece_id):\\n\\n asset = AssetMap.objects.get(piece=piece_id)\\n url = '/'.join([FTP_URL, asset.folder, asset.name+'.log',])\\n context = {\\n 'keyform': KeySearchForm(auto_id=False),\\n 'piece' : Piece.objects.get(pk=piece_id),\\n 'logfile': requests.get(url),\\n }\\n return render(request, 'mutopia/piece_log.html', context)\",\n \"def _print_log(self, step, data=None):\\n \\n # Set mode to append to log file\\n mode = 'a'\\n\\n if self.logfile is None:\\n # Increment log counter for the class. Each instance of the class generates a new log.\\n self.__class__.log_no += 1\\n\\n # Create a log file for the instance\\n # Logs will be stored in ..\\\\logs\\\\SKLearn Log <n>.txt\\n self.logfile = os.path.join(os.getcwd(), 'logs', 'SKLearn Log {}.txt'.format(self.log_no))\\n \\n if step == 1:\\n # Output log header\\n output = \\\"\\\\nSKLearnForQlik Log: {0} \\\\n\\\\n\\\".format(time.ctime(time.time()))\\n # Set mode to write new log file\\n mode = 'w'\\n \\n elif step == 2:\\n # Output the parameters\\n output = \\\"Model Name: {0}\\\\n\\\\n\\\".format(self.model.name)\\n output += \\\"Execution arguments: {0}\\\\n\\\\n\\\".format(self.exec_params)\\n \\n try:\\n output += \\\"Scaler: {0}, missing: {1}, scale_hashed: {2}, scale_vectors: {3}\\\\n\\\".format(\\\\\\n self.model.scaler, self.model.missing,self.model.scale_hashed, self.model.scale_vectors)\\n output += \\\"Scaler kwargs: {0}\\\\n\\\\n\\\".format(self.model.scaler_kwargs)\\n except AttributeError:\\n output += \\\"scale_hashed: {0}, scale_vectors: {1}\\\\n\\\".format(self.model.scale_hashed, self.model.scale_vectors)\\n\\n try:\\n if self.model.dim_reduction:\\n output += \\\"Reduction: {0}\\\\nReduction kwargs: {1}\\\\n\\\\n\\\".format(self.model.reduction, self.model.dim_reduction_args)\\n except AttributeError:\\n pass\\n \\n output += \\\"Estimator: {0}\\\\nEstimator kwargs: {1}\\\\n\\\\n\\\".format(self.model.estimator, self.model.estimator_kwargs)\\n \\n elif step == 3: \\n # Output the request dataframe\\n output = \\\"REQUEST: {0} rows x cols\\\\nSample Data:\\\\n\\\\n\\\".format(self.request_df.shape)\\n output += \\\"{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(self.request_df.head().to_string(), self.request_df.tail().to_string())\\n \\n elif step == 4:\\n # Output the response dataframe/series\\n output = \\\"RESPONSE: {0} rows x cols\\\\nSample Data:\\\\n\\\\n\\\".format(self.response.shape)\\n output += \\\"{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(self.response.head().to_string(), self.response.tail().to_string())\\n \\n elif step == 5:\\n # Print the table description if the call was made from the load script\\n output = \\\"\\\\nTABLE DESCRIPTION SENT TO QLIK:\\\\n\\\\n{0} \\\\n\\\\n\\\".format(self.table)\\n \\n elif step == 6:\\n # Message when model is loaded from cache\\n output = \\\"\\\\nModel {0} loaded from cache.\\\\n\\\\n\\\".format(self.model.name)\\n \\n elif step == 7:\\n # Message when model is loaded from disk\\n output = \\\"\\\\nModel {0} loaded from disk.\\\\n\\\\n\\\".format(self.model.name)\\n \\n elif step == 8:\\n # Message when cache is updated\\n output = \\\"\\\\nCache updated. Models in cache:\\\\n{0}\\\\n\\\\n\\\".format([k for k,v in self.__class__.model_cache.items()])\\n \\n elif step == 9:\\n # Output when a parameter grid is set up\\n output = \\\"Model Name: {0}, Estimator: {1}\\\\n\\\\nGrid Search Arguments: {2}\\\\n\\\\nParameter Grid: {3}\\\\n\\\\n\\\".\\\\\\n format(self.model.name, self.model.estimator, self.model.grid_search_args, self.model.param_grid)\\n \\n elif step == 10:\\n # self.model.estimator_kwargs['architecture']\\n output = \\\"\\\\nKeras architecture added to Model {0}:\\\\n\\\\n{1}\\\\n\\\\n\\\".format(self.model.name,\\\\\\n self.model.architecture.to_string())\\n\\n elif step == 11:\\n # Output after adding lag observations to input data\\n output = \\\"Lag observations added ({0} per sample). New input shape of X is {1}.\\\\n\\\\n\\\".format(self.model.lags, data.shape)\\n output += \\\"Feature Definitions:\\\\n{0}\\\\n\\\\n\\\".format(self.model.features_df.to_string())\\n output += \\\"Sample Data:\\\\n{0}\\\\n...\\\\n{1}\\\\n\\\\n\\\".format(data.head(5).to_string(), data.tail(5).to_string())\\n \\n sys.stdout.write(output)\\n with open(self.logfile, mode, encoding='utf-8') as f:\\n f.write(output)\",\n \"def _Log(self, logf, s):\\r\\n if logf:\\r\\n logf(s + '\\\\n')\",\n \"def log_message(self, formate, *args):\\n return\",\n \"def print_entry(entry):\\n print(\\\"\\\"\\\"\\nEmployee Name: {name}\\nTask Name: {task_name}\\nDate: {date}\\nTime Spent: {time_spent}\\nNotes: {notes}\\n\\\"\\\"\\\".format(**entry))\",\n \"def log_example(var):\\n\\n log.info('example code started')\\n log.debug('calling settings')\\n test_settings()\\n log2.error('there is no error this is example ')\\n log2.info('finished')\",\n \"def log(self, reward, action):\\n self.logs.append([reward, action])\",\n \"def logStarted(build, step, log):\",\n \"def commandlog(entry, subarray=DEFAULT) :\\n multiSubarray('log', subarray, \\\"COMMAND: \\\" + entry)\",\n \"def log_metric(key, value, step=None):\\n mlflow.log_metric(key, value, step=step)\",\n \"def submit_entry(self, entry_type=\\\"Log\\\"):\\n # Gratitude List\\n gratitude_li = []\\n for grat_entry in self.grat_entry_li:\\n grat_txt = grat_entry.get()\\n if grat_txt != \\\"\\\":\\n gratitude_li.append(grat_txt.replace(\\\",\\\", \\\"\\\\comma\\\"))\\n\\n # Goals List\\n goals_li = []\\n\\n for goal_entry in self.goals_entry_li:\\n goal_txt = goal_entry.get()\\n if goal_txt != \\\"\\\":\\n goals_li.append(goal_txt.replace(\\\",\\\", \\\"\\\\comma\\\"))\\n\\n \\\"\\\"\\\"\\n Plans List: {\\\"Description\\\": description entry box,\\n \\\"Priority\\\": priority combo box, \\\"Steps\\\": list of step entry boxes }\\n \\n Step list: {\\\"Description\\\": new_step_box, \\\"Status\\\": check_var}\\n \\\"\\\"\\\"\\n plans_li = []\\n\\n for plan_entry in self.plans_entry_li:\\n if plan_entry[\\\"Description\\\"].get() == \\\"\\\" and len(plan_entry[\\\"Steps\\\"]) == 0:\\n pass\\n else:\\n plan = {}\\n plan[\\\"Plan_Type\\\"] = plan_entry[\\\"Plan_Type\\\"].get()\\n plan[\\\"Description\\\"] = plan_entry[\\\"Description\\\"].get().replace(\\n \\\",\\\", \\\"\\\\comma\\\")\\n plan[\\\"Status\\\"] = plan_entry[\\\"Status\\\"].get()\\n plan[\\\"Priority\\\"] = plan_entry[\\\"Priority\\\"].get()\\n plan[\\\"Steps\\\"] = []\\n for step_entry in plan_entry[\\\"Steps\\\"]:\\n step_txt = step_entry[\\\"Description\\\"].get()\\n if step_txt == \\\"\\\":\\n pass\\n else:\\n full_step = {\\\"Status\\\": step_entry[\\\"Status\\\"].get(),\\n \\\"Description\\\": step_txt}\\n plan[\\\"Steps\\\"].append(full_step)\\n plans_li.append(plan)\\n\\n # Affirmation Entry\\n affirmation = self.affirm_entry.get(\\\"1.0\\\", 'end-1c')\\n\\n # Additional Comment Entry\\n additional_notes = self.notes_entry.get(\\\"1.0\\\", 'end-1c')\\n\\n add_new_entry(entry_type=entry_type,\\n gratitude=gratitude_li,\\n goals=goals_li,\\n plans=plans_li,\\n affirmation=affirmation,\\n additional_notes=additional_notes,\\n test=True)\\n\\n self.root.switch_page(self.root._HomePage)\",\n \"def log3(request):\\n if request.method == \\\"GET\\\":\\n bValid = True\\n (usernumber_str, bValid) = getValue(request, u\\\"uid\\\", bValid)\\n (timelapse_str, bValid) = getValue(request, u\\\"timelapse\\\", bValid)\\n (propkind_str, bValid) = getValue(request, u\\\"propkind\\\", bValid)\\n (figure_str, bValid) = getValue(request, u\\\"figure\\\", bValid)\\n (correctness_str, bValid) = getValue(request, u\\\"corr\\\", bValid)\\n (term_S_str, bValid) = getValue(request, u\\\"S\\\", bValid)\\n (term_M_str, bValid) = getValue(request, u\\\"M\\\", bValid)\\n (term_P_str, bValid) = getValue(request, u\\\"P\\\", bValid)\\n (term_Q1_str, bValid) = getValue(request, u\\\"q1\\\", bValid)\\n (term_Q2_str, bValid) = getValue(request, u\\\"q2\\\", bValid)\\n (term_Q3_str, bValid) = getValue(request, u\\\"q3\\\", bValid)\\n (conclusion_truth_value_str, bValid) = getValue(request, u\\\"conclusion\\\", bValid)\\n (validity_answer, bValid) = getValue(request, u\\\"validity\\\", bValid)\\n\\n if not bValid:\\n return render(request, 'log.html')\\n else:\\n if correctness_str == \\\"true\\\":\\n bCorrect = True\\n else:\\n bCorrect = False\\n\\n if conclusion_truth_value_str == \\\"1\\\":\\n bConclusionTruthValue = True\\n else:\\n bConclusionTruthValue = False\\n\\n newobj3 = Answer3(usernumber=int(usernumber_str),\\n timelapse=int(timelapse_str),\\n figure=int(figure_str),\\n quantor_1=term_Q1_str,\\n quantor_2=term_Q2_str,\\n quantor_3=term_Q3_str,\\n term_S=int(term_S_str),\\n term_M=int(term_M_str),\\n term_P=int(term_P_str),\\n propkind=propkind_str,\\n student_answer=validity_answer,\\n correctness=bCorrect,\\n conclusion_truth_value=bConclusionTruthValue)\\n\\n newobj3.save()\\n\\n return render(request, 'log.html')\\n else:\\n return render(request, 'log.html')\",\n \"def enter_linking_log_patient(project_path: str, project_name: str):\\n # Get Linking Log\\n linking_log_path = os.path.join(project_path, 'logs', 'logs_with_phi', 'Master_Linking_Log.xlsx')\\n\\n while True:\\n choice = input(\\\"Would you like to enter a new patient onto the Linking log log [Y/N]? \\\")\\n\\n if choice and choice.strip() and choice.lower() == 'y':\\n patient_data = rsm.get_data_from_user.get_master_linking_log_data()\\n rsm.add_patient_to_excel_file.add_patient(linking_log_path,\\n patient_data,\\n 'Master_Linking_Log')\\n print(\\\"Successfully added patient to {} Master Linking log\\\".format(project_name))\\n\\n elif choice and choice.strip() and choice.lower() == 'n':\\n break\\n else:\\n print(\\\"Please enter Y or N\\\")\",\n \"def create_log_entry_when_user_logs_in(sender, request, user, **kwargs):\\n create_user_log(\\n request=request,\\n user=user,\\n type=_account_const.AUTHENTICATION,\\n action=_account_const.LOGIN\\n )\",\n \"def display_entry(self, entry):\\n border = '-' * 50\\n print(border)\\n print('Employee: {}'.format(entry.employee_name))\\n print('Task Name: {}'.format(entry.task_name))\\n print(\\\"Date: {}\\\".format(entry.date))\\n print(\\\"Time Spent: {}\\\".format(entry.time_spent))\\n if entry.notes != '':\\n print(\\\"Notes:\\\\n{}\\\\n{}\\\".format('----------', entry.notes))\\n print(border)\",\n \"def log_step(step: int, message: str, stdout: bool = True) -> None:\\n log(f\\\"Step {step:6d}: {message}\\\", stdout=stdout)\",\n \"def add_line_in_log():\\n logging.info(' ' + '-' * 60 + '\\\\n')\",\n \"def save_result(self, value: Any) -> None:\\n self.run_logger.set_tags({self.name: value})\",\n \"def on_L1(self):\\r\\n self.log()\",\n \"def log(self):\\n self.logger = logging.getLogger(self.log_name)\\n self.logger.info(f\\\"Name: {self.name}\\\")\\n self.logger.info(f\\\"Grid points: {self.gp}\\\")\\n self.logger.info(f\\\"Nadir points: {self.nadir_p}\\\")\\n self.logger.info(f\\\"Penalty weight: {self.eps}\\\")\\n self.logger.info(f\\\"Early exit: {self.early_exit}\\\")\\n self.logger.info(f\\\"Bypass coefficient: {self.bypass}\\\")\\n self.logger.info(f\\\"Flag array: {self.flag}\\\")\\n self.logger.info(f\\\"CPU Count: {self.cpu_count}\\\")\\n self.logger.info(f\\\"Redivide work: {self.redivide_work}\\\")\\n self.logger.info(f\\\"Shared flag array: {self.shared_flag}\\\")\\n self.logger.info(Helper.separator())\",\n \"def log(msg):\\n print msg\",\n \"def log(self, msg):\\n logging.info(\\\"Logging Message\\\")\\n ml = self.monk_logs\\n today = datetime.datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\")\\n ml.insert({today: {\\\"log\\\": msg,\\n \\\"sentiment\\\": self.get_sentiment(msg),\\n \\\"weather\\\": Weather.get_weather()}})\",\n \"def log_successful_login(sender, request, user, **kwargs):\\r\\n if settings.FEATURES['SQUELCH_PII_IN_LOGS']:\\r\\n AUDIT_LOG.info(u\\\"Login success - user.id: {0}\\\".format(user.id))\\r\\n else:\\r\\n AUDIT_LOG.info(u\\\"Login success - {0} ({1})\\\".format(user.username, user.email))\",\n \"def log_message(self, fmt, *args):\\n pass\",\n \"def log(self, *args):\\n self._check_private_key(\\\"log data\\\")\\n params = {'private_key': self.privateKey}\\n params.update(dict((k, self._encoder.serialize(v))\\n for k, v in zip(self.fields, args)))\\n response = self._post(self.inputUrl(), params=params)\\n\\n self._last_headers = response.headers\\n self._stats = None\",\n \"def add_log(self, log):\\n log = str(datetime.datetime.now()) + \\\": \\\"+log+\\\"\\\\n\\\"\\n print(log)\\n self.logs.append(log)\\n if len(self.logs) > 10:\\n self.append_to_logfile()\",\n \"def log_request(req: 'Flask_Request', results: str) -> None:\\n\\n with UseDatabase(app.config['dbconfig']) as cursor:\\n _SQL_INSERT = \\\"\\\"\\\"insert into log_table\\n (phrase, letters, ip, browser_string, results)\\n values\\n (?, ?, ?, ?, ?)\\\"\\\"\\\"\\n\\n cursor.execute(_SQL_INSERT,\\n (req.form['phrase'], req.form['letter'], req.remote_addr, req.user_agent.browser,\\n results,))\",\n \"def log(self, message, *args, newline):\\n\\n self.current_character.log(message, *args, newline=newline)\",\n \"def log(self, client_addr, request):\\n with codecs.open(self.log_path, \\\"a\\\", 'UTF-8') as fh_out:\\n print >> fh_out, (time.strftime('%Y-%m-%d %H:%M:%S') + \\\"\\\\t\\\" +\\n ':'.join([str(i) for i in client_addr]) + \\\"\\\\t\\\" +\\n request)\",\n \"def _print_logs_info(job_id, project_id):\\n print(\\\"\\\\nJob submitted successfully.\\\")\\n print(\\\"Your job ID is: \\\", job_id)\\n print(\\\"\\\\nPlease access your training job information here:\\\")\\n print(\\n \\\"https://console.cloud.google.com/mlengine/jobs/{}?project={}\\\".format(\\n job_id, project_id))\\n print(\\\"\\\\nPlease access your training job logs here: \\\"\\n \\\"https://console.cloud.google.com/logs/viewer?resource=ml_job%2F\\\"\\n \\\"job_id%2F{}&interval=NO_LIMIT&project={}\\\\n\\\".format(\\n job_id, project_id))\",\n \"def history_log(self, user, action=CHANGE, message=''):\\n LogEntry.objects.log_action(\\n user_id=user.pk,\\n content_type_id=ContentType.objects.get_for_model(self).pk,\\n object_id=self.pk,\\n object_repr=force_text(self),\\n action_flag=action,\\n change_message=message\\n )\",\n \"def _log(self, log, message):\\n log_entry = '[%s] %s\\\\n' % (time.strftime('%Y/%m/%d %H:%M:%S'), message)\\n log.write(log_entry)\\n if self.verbose:\\n print log_entry.rstrip()\",\n \"def log(self, obj, action):\\n action_dict = {'time': time.time(),\\n 'action': action}\\n self.log_data[obj.get_obj_id()]['actions'].append(action_dict)\",\n \"def log(self, *args, **kwargs):\\n return self.event.log(*args, meta={'contribution_id': self.id}, **kwargs)\",\n \"def get_action_logfile():\\n return \\\"action\\\" + get_day() + \\\".log\\\"\",\n \"def record_task_attempt(task_name: str):\\n\\n from common.models import InvenTreeSetting\\n\\n logger.info(f\\\"Logging task attempt for '{task_name}'\\\")\\n\\n InvenTreeSetting.set_setting(f'_{task_name}_ATTEMPT', datetime.now().isoformat(), None)\",\n \"def log(data):\\n return _make.log(data)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.68945855","0.6559437","0.6485974","0.62908894","0.62812376","0.6237484","0.6155005","0.60995555","0.60548156","0.6035026","0.60128146","0.59510344","0.5913017","0.59052706","0.5875756","0.58669865","0.5864817","0.5801178","0.5774821","0.57367194","0.56955934","0.56485015","0.5620402","0.56164557","0.55982274","0.5586754","0.5570456","0.5569306","0.55621547","0.555175","0.5551224","0.554468","0.5528927","0.55110824","0.5506672","0.5501592","0.54945815","0.5494404","0.54781675","0.5477084","0.5475177","0.5468415","0.54650044","0.54586476","0.5457204","0.54539627","0.5453257","0.54469234","0.5435364","0.5420035","0.54141724","0.5409191","0.5406821","0.5406493","0.53991663","0.53926677","0.53923625","0.5390717","0.5389024","0.5388456","0.53867424","0.5385702","0.5383189","0.537404","0.5363159","0.53628963","0.53588384","0.5356544","0.5352027","0.53381604","0.53378874","0.533786","0.5332198","0.5331161","0.5327184","0.53262144","0.53227776","0.5320269","0.5319203","0.53183365","0.53111476","0.5308007","0.53015375","0.5296755","0.529649","0.52934474","0.5291275","0.52901256","0.5289721","0.5286743","0.52845395","0.5283241","0.5277811","0.52707493","0.5251437","0.52470165","0.5244757","0.52435553","0.5238904","0.5237906"],"string":"[\n \"0.68945855\",\n \"0.6559437\",\n \"0.6485974\",\n \"0.62908894\",\n \"0.62812376\",\n \"0.6237484\",\n \"0.6155005\",\n \"0.60995555\",\n \"0.60548156\",\n \"0.6035026\",\n \"0.60128146\",\n \"0.59510344\",\n \"0.5913017\",\n \"0.59052706\",\n \"0.5875756\",\n \"0.58669865\",\n \"0.5864817\",\n \"0.5801178\",\n \"0.5774821\",\n \"0.57367194\",\n \"0.56955934\",\n \"0.56485015\",\n \"0.5620402\",\n \"0.56164557\",\n \"0.55982274\",\n \"0.5586754\",\n \"0.5570456\",\n \"0.5569306\",\n \"0.55621547\",\n \"0.555175\",\n \"0.5551224\",\n \"0.554468\",\n \"0.5528927\",\n \"0.55110824\",\n \"0.5506672\",\n \"0.5501592\",\n \"0.54945815\",\n \"0.5494404\",\n \"0.54781675\",\n \"0.5477084\",\n \"0.5475177\",\n \"0.5468415\",\n \"0.54650044\",\n \"0.54586476\",\n \"0.5457204\",\n \"0.54539627\",\n \"0.5453257\",\n \"0.54469234\",\n \"0.5435364\",\n \"0.5420035\",\n \"0.54141724\",\n \"0.5409191\",\n \"0.5406821\",\n \"0.5406493\",\n \"0.53991663\",\n \"0.53926677\",\n \"0.53923625\",\n \"0.5390717\",\n \"0.5389024\",\n \"0.5388456\",\n \"0.53867424\",\n \"0.5385702\",\n \"0.5383189\",\n \"0.537404\",\n \"0.5363159\",\n \"0.53628963\",\n \"0.53588384\",\n \"0.5356544\",\n \"0.5352027\",\n \"0.53381604\",\n \"0.53378874\",\n \"0.533786\",\n \"0.5332198\",\n \"0.5331161\",\n \"0.5327184\",\n \"0.53262144\",\n \"0.53227776\",\n \"0.5320269\",\n \"0.5319203\",\n \"0.53183365\",\n \"0.53111476\",\n \"0.5308007\",\n \"0.53015375\",\n \"0.5296755\",\n \"0.529649\",\n \"0.52934474\",\n \"0.5291275\",\n \"0.52901256\",\n \"0.5289721\",\n \"0.5286743\",\n \"0.52845395\",\n \"0.5283241\",\n \"0.5277811\",\n \"0.52707493\",\n \"0.5251437\",\n \"0.52470165\",\n \"0.5244757\",\n \"0.52435553\",\n \"0.5238904\",\n \"0.5237906\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54031944"},"document_rank":{"kind":"string","value":"54"}}},{"rowIdx":8475,"cells":{"query":{"kind":"string","value":"Given a latitude and longitude calculate distance to airplane including altitude, return kilometers"},"document":{"kind":"string","value":"def distance(self, lat: float, long: float) -> float:\n # Initial euclidian formula below\n # diff_lat = self.lat - lat\n # diff_long = self.long - long\n # euclidian = math.sqrt((diff_lat ** 2 + diff_long ** 2 + self.altitude ** 2))\n\n return self._haversine(lat, long) + self.altitude / 1000"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_distance_meters(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n dlong *= math.cos( aLocation2.lat * math.pi / 180.0 )\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat * dlat) + (dlong * dlong)) * 1.113195e5","def get_distance_metres(aLocation1, aLocation2):\n \n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def get_distance_meters(location1, location2):\n dlat = location2.lat - location1.lat\n dlong = location2.lon - location1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5","def calculate_distance(long_x, lat_x, long_y, lat_y):\n if not (long_x and lat_x and long_y and lat_y):\n return 0\n\n dlon = long_y - long_x if long_y != long_x else long_y\n dlat = lat_y - lat_x if lat_y != lat_x else lat_y\n\n a = (sin(dlat / 2) ** 2) + cos(lat_x) * cos(lat_y) * (sin(dlon / 2) ** 2)\n c = 2 * asin(sqrt(a))\n r = 6371 # radius of earth in kms.\n return (c * r)","def calcMetresDistance(lat1, long1, lat2, long2):\n return (abs(lat1 - lat2) + abs(long1 - long2)) * 100\n # no, like, really badly","def lnglat_to_meters(longitude, latitude):\n if isinstance(longitude, (list, tuple)):\n longitude = numpy.array(longitude)\n if isinstance(latitude, (list, tuple)):\n latitude = numpy.array(latitude)\n\n origin_shift = numpy.pi * 6378137\n easting = longitude * origin_shift / 180.0\n northing = numpy.log(numpy.tan((90 + latitude) * numpy.pi / 360.0)) * origin_shift / numpy.pi\n return (easting, northing)","def get_distance(latitude, longitude, del_latitude, del_longitude):\n coord = (latitude, longitude)\n del_coord = (del_latitude, del_longitude)\n return distance.geodesic(coord, del_coord).km","def get_distance_between_point(test_long, test_lat, lab_long, lab_lat):\r\n test = (test_lat, test_long)\r\n lab = (lab_lat, lab_long)\r\n return geodesic(test, lab).miles","def coord_dist_meters(lat1, lon1, lat2, lon2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat/2)**2 + math.cos(lat1) * \\\n math.cos(lat2) * math.sin(dlon/2)**2\n c = 2 * math.asin(math.sqrt(a))\n r = 6371000 # Radius of earth in meters. Use 3956 for miles\n return c * r","def distance_in_meters(coord1, coord2):\n return vincenty(coord1, coord2).meters","def closest_cruising_altitude(altitude):\n return 1000 * ((altitude + 500) // 1000)","def find_distance_to_store(latitude1, longitude1, latitude2, longitude2):\n lat1 = float(latitude1)\n lon1 = float(longitude1)\n lat2 = float(latitude2)\n lon2 = float(longitude2)\n\n dlat = radians(lat2-lat1)\n dlon = radians(lon2-lon1)\n a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) \\\n * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\n c = 2 * atan2(sqrt(a), sqrt(1-a))\n distance_in_kilometers = RADIUS_OF_THE_EARTH_KILOMETERS * c\n\n return distance_in_kilometers","def coord_distance(lat1, lon1, lat2, lon2):\n\tlon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n\tdlon = lon2 - lon1\n\tdlat = lat2 - lat1\n\ta = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n\tc = 2 * math.asin(math.sqrt(a))\n\tkm = 6367 * c \n\treturn km","def distance(lat0, lng0, lat1, lng1):\n # convert decimal degrees to radians \n lat0, lng0, lat1, lng1 = map(radians, [lat0, lng0, lat1, lng1])\n # haversine formula \n dlng = lng1 - lng0 \n dlat = lat1 - lat0 \n a = sin(dlat/2)**2 + cos(lat0) * cos(lat1) * sin(dlng/2)**2\n c = 2 * asin(sqrt(a)) \n m = 6367000 * c\n return m","def get_distance(lat1, lon1, lat2, lon2):\n phi1 = math.radians(lat1)\n phi2 = math.radians(lat2)\n d_phi = math.radians(lat2 - lat1)\n d_lam = math.radians(lon2 - lon1)\n a = math.sin(d_phi/2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(d_lam/2)**2\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n return 6371000 * c","def calcDistance(lat1, lon1, lat2, lon2):\n # convert decimal degrees to radians \n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula \n dlon = lon2 - lon1 \n dlat = lat2 - lat1 \n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a)) \n km = 6367 * c\n return km * 1000","def coord_distance(lat1, lon1, lat2, lon2):\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\n km = 2 * 6367 * math.asin(math.sqrt(a))\n mi = 0.621371 * km\n return mi","def distance_between(lat_1, lon_1, lat_2, lon_2):\n lat_1, lon_1 = math.radians(lat_1), math.radians(lon_1)\n lat_2, lon_2 = math.radians(lat_2), math.radians(lon_2)\n theta = lon_1 - lon_2\n dist = math.sin(lat_1)*math.sin(lat_2) + math.cos(lat_1)*math.cos(lat_2)*math.cos(theta)\n dist = math.acos(dist)\n dist = math.degrees(dist)\n dist = dist * 69.06 # 69.09 = circumference of earth in miles / 360 degrees\n return dist","def distance(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians \n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula \n dlon = lon2 - lon1 \n dlat = lat2 - lat1 \n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a)) \n km = 6367 * c\n return km","def get_distance_metres(aLocation1, aLocation2):\n [dNorth, dEast, dDown] = get_position_error(aLocation1, aLocation2)\n \n return math.sqrt((dNorth*dNorth) + (dEast*dEast))","def test_get_kilometers() -> None:\n kilometers = location_util.vincenty(COORDINATES_PARIS, COORDINATES_NEW_YORK)\n assert round(kilometers, 2) == DISTANCE_KM","def get_euclidian_distance(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n c = 2 * math.asin(math.sqrt(a))\n # Radius of earth in kilometers is 6371\n km = 6371 * c\n return km","def test_get_distance() -> None:\n meters = location_util.distance(\n COORDINATES_PARIS[0],\n COORDINATES_PARIS[1],\n COORDINATES_NEW_YORK[0],\n COORDINATES_NEW_YORK[1],\n )\n\n assert meters / 1000 - DISTANCE_KM < 0.01","def get_distance(lat1, long1, lat2, long2):\n x = 69.1*(lat2 - lat1)\n y = 69.1*(long2 - long1) * math.cos(lat1/57.3)\n dist = math.sqrt(x*x + y*y)\n return dist","def haversine_distance_ignore_altitude(loc1_lat, loc1_lon, loc2_lat, loc2_lon):\n R = 6372797.560856 # radius of the earth in meters\n\n lat_arc = math.radians(loc1_lat - loc2_lat)\n lon_arc = math.radians(loc1_lon - loc2_lon)\n\n lat_h = math.sin(lat_arc * 0.5)\n lat_h = lat_h * lat_h\n\n lon_h = math.sin(lon_arc * 0.5)\n lon_h = lon_h * lon_h\n\n tmp = math.cos(math.radians(loc1_lat)) * math.cos(math.radians(loc2_lat))\n rad = 2.0 * math.asin(math.sqrt(lat_h + tmp * lon_h))\n\n return rad * R","def getAltitudeFromLatLon(self, lat, lon):\r\n # print \"-----\\nFromLatLon\", lon, lat\r\n lat -= self.lat\r\n lon -= self.lon\r\n # print \"lon, lat\", lon, lat\r\n if lat < 0.0 or lat >= 1.0 or lon < 0.0 or lon >= 1.0:\r\n raise WrongTileError(self.lat, self.lon, self.lat+lat, self.lon+lon)\r\n x = lon * (self.size - 1)\r\n y = lat * (self.size - 1)\r\n # print \"x,y\", x, y\r\n x_int = int(x)\r\n x_frac = x - int(x)\r\n y_int = int(y)\r\n y_frac = y - int(y)\r\n # print \"frac\", x_int, x_frac, y_int, y_frac\r\n value00 = self.getPixelValue(x_int, y_int)\r\n value10 = self.getPixelValue(x_int+1, y_int)\r\n value01 = self.getPixelValue(x_int, y_int+1)\r\n value11 = self.getPixelValue(x_int+1, y_int+1)\r\n value1 = self._avg(value00, value10, x_frac)\r\n value2 = self._avg(value01, value11, x_frac)\r\n value = self._avg(value1, value2, y_frac)\r\n # print \"%4d %4d | %4d\\n%4d %4d | %4d\\n-------------\\n%4d\" % (\r\n # value00, value10, value1, value01, value11, value2, value)\r\n return value","def earth_distance(lat1: float, lon1: float, lat2: float, lon2: float)\\\n -> float:\n # R = 6373.0 # earth radius in km\n R = 3963.0 # earth radius in miles\n lat1 = radians(lat1)\n lon1 = radians(lon1)\n lat2 = radians(lat2)\n lon2 = radians(lon2)\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\n distance = R * c\n return distance","def calculate_distance(x: float, y: float) -> float:\n # return geopy.distance.vincenty(x, y).km\n R = 6370\n lat1 = radians(x[0]) #insert value\n lon1 = radians(x[1])\n lat2 = radians(y[0])\n lon2 = radians(y[1])\n\n dlon = lon2 - lon1\n dlat = lat2- lat1\n\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1-a))\n distance = R * c\n return distance","def get_location_meters(original_location, north, east, alt=0):\n # Radius of \"spherical\" earth\n earth_radius = 6378137.0\n # Coordinate offsets in radians\n lat = north / earth_radius\n lon = east / (earth_radius * math.cos(original_location.lat * math.pi/180))\n\n # New position in decimal degrees\n newlat = original_location.lat + (lat * 180/math.pi)\n newlon = original_location.lon + (lon * 180/math.pi)\n return Location(newlat, newlon, original_location.alt + alt, original_location.is_relative)","def distance(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * asin(sqrt(a))\n m = 6367 * c * 1000\n return m","def road_distance(lat1, lon1, lat2, lon2):\n point1 = lat1, lon1\n point2 = lat2, lon2\n url = \"https://maps.googleapis.com/maps/api/distancematrix/json?origins={0},{1}&destinations={2},{3}&mode=driving&language=en-EN&sensor=false&key={4}\".format(str(lat1),str(lon1),str(lat2),str(lon2), google_api_key)\n response = api_call(url)\n km = response['rows'][0]['elements'][0]['distance']['value']\n return round(km/1000,1)","def _earth_distance(time='now'):\n return get_earth(time).radius","def calc_dist(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\n c = 2 * asin(sqrt(a))\n # Radius of earth in kilometers is 6371\n mtr = 6371000 * c\n return mtr","def distance(lat1, lon1, lat2, lon2):\r\n earth_radius=3959.0 #miles\r\n if lat1==lat2 and lon1==lon2:\r\n dst=0\r\n else:\r\n dst = acos(\r\n (sin(radians(lat1)) * sin(radians(lat2))) +\r\n (cos(radians(lat1)) * cos(radians(lat2)) * cos(radians(lon1) - radians(lon2)))\r\n ) * earth_radius\r\n return dst","def calculate_distance_based_on_lon_lat(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians \n lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\n # haversine formula \n dlon = lon2 - lon1 \n dlat = lat2 - lat1 \n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * np.arcsin(sqrt(a)) \n # Radius of earth in kilometers. Use 3956 for miles\n r = 6378137.0\n return c * r","def distance(latitude_1: float, longitude_1: float, latitude_2: float, longitude_2: float) -> float:\n lat1, lon1, lat2, lon2 = map(radians, (latitude_1, longitude_1, latitude_2, longitude_2))\n return (\n 2\n * EARTH_RADIUS\n * asin(\n sqrt(\n sin((lat2 - lat1) / 2) ** 2 + cos(lat1) * cos(lat2) * (sin((lon2 - lon1) / 2) ** 2)\n )\n )\n )","def distance_in_meters(self, latlng):\n return self.distance_to(latlng)","def getDist(lat1,long1,lat2,long2):\n\tlat1 = math.radians(lat1)\n\tlong1 = math.radians(long1)\n\tlat2 = math.radians(lat2)\n\tlong2 = math.radians(long2)\n\tR = 6371 # km\n\td = cmath.acos(cmath.sin(lat1) * cmath.sin(lat2) + \\\n\tcmath.cos(lat1) * cmath.cos(lat2) *\n\tcmath.cos(long2 - long1)) * R\n\treturn abs(d) # cast to float","def test_get_distance(self):\n meters = location_util.distance(COORDINATES_PARIS[0],\n COORDINATES_PARIS[1],\n COORDINATES_NEW_YORK[0],\n COORDINATES_NEW_YORK[1])\n self.assertAlmostEqual(meters / 1000, DISTANCE_KM, places=2)","def get_distance(lat1, lon1, lat2, lon2) -> float:\n # Earth radius in meters\n radius = 6371000\n\n # Degress to radian\n lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\n\n # Deltas\n dlat = lat2 - lat1\n dlon = lon2 - lon1\n\n # Calculate distance\n arch = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n arch_sin = 2 * np.arcsin(np.sqrt(arch))\n\n return radius * arch_sin","def latlon2distance(lat1, long1, lat2, long2, miles=False):\n global verbose\n\n if lat1 == lat2 and long1 == long2:\n return 0\n\n\n # Convert latitude and longitude to\n # spherical coordinates in radians.\n degrees_to_radians = math.pi / 180.0\n\n # phi = 90 - latitude\n phi1 = (90.0 - lat1) * degrees_to_radians\n phi2 = (90.0 - lat2) * degrees_to_radians\n\n # theta = longitude\n theta1 = long1 * degrees_to_radians\n theta2 = long2 * degrees_to_radians\n\n # Compute spherical distance from spherical coordinates.\n\n # For two locations in spherical coordinates\n # (1, theta, phi) and (1, theta, phi)\n # cosine( arc length ) =\n # sin phi sin phi' cos(theta-theta') + cos phi cos phi'\n # distance = rho * arc length\n\n cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) + math.cos(phi1) * math.cos(phi2))\n try:\n arc = math.acos(cos)\n except Exception as err:\n sys.stderr.write(\"There was an err: {} trying to take the acos of ({})\\n\".format(err, cos))\n arc=0\n # Remember to multiply arc by the radius of the earth\n # in your favorite set of units to get length.\n #\n # To convert to miles multiple arc by 3960\n # To convert to kilometers multiply arc by 6373\n\n if miles:\n arc *= 3960\n else:\n arc *= 6373\n\n return arc","def distance(lat1,lon1, lat2, lon2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\n c = 2 * math.asin(math.sqrt(a))\n miles = earth_radius * c\n return miles","def get_spherical_distance(lat1,lat2,long1,long2):\n q=radians(lat2-lat1)\n r=radians(long2-long1)\n lat2r=radians(lat2)\n lat1r=radians(lat1)\n a=sin(q/2)*sin(q/2)+cos(lat1r)*cos(lat2r)*sin(r/2)*sin(r/2)\n c=2*atan2(sqrt(a),sqrt(1-a))\n R=6371*1000\n d=R*c\n return d","def calculateDistanceBetweenPoints(lat1,lon1,lat2,lon2):\n\treturn Geodesic.WGS84.Inverse(lat1,lon1, lat2, lon2)['s12']","def _calculate_distance(self, passenger, driver):\n londriver, latdriver = driver['lon'], driver['lat']\n lonpassenger, latpassenger = passenger['lon'], passenger['lat']\n lon_p, lat_p, lon_d, lat_d = map(radians,\n [float(lonpassenger), float(latpassenger), float(londriver), float(latdriver)])\n lon_distance = lon_d - lon_p\n lat_distance = lat_d - lat_p\n a = sin(lat_distance / 2) ** 2 + cos(lat_p) * cos(lat_d) * sin(lon_distance / 2) ** 2\n c = 2 * asin(sqrt(a))\n km = 6367 * c\n return km","def _calculate_distance(self, passenger, driver):\n londriver, latdriver = driver['lon'], driver['lat']\n lonpassenger, latpassenger = passenger['lon'], passenger['lat']\n lon_p, lat_p, lon_d, lat_d = map(radians,\n [float(lonpassenger), float(latpassenger), float(londriver), float(latdriver)])\n lon_distance = lon_d - lon_p\n lat_distance = lat_d - lat_p\n a = sin(lat_distance / 2) ** 2 + cos(lat_p) * cos(lat_d) * sin(lon_distance / 2) ** 2\n c = 2 * asin(sqrt(a))\n km = 6367 * c\n return km","def harversine(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n # harversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat/2.)**2. + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2.)**2.\n c = 2. * math.asin(math.sqrt(a))\n km = 6371. * c # radius of earth\n return km","def distance(lat1, lon1, lat2, lon2):\n lon1, lat1 = math.radians(lon1), math.radians(lat1)\n lon2, lat2 = math.radians(lon2), math.radians(lat2)\n a = (math.sin((lat2 - lat1) / 2) ** 2 +\n math.cos(lat1) * math.cos(lat2) * math.sin((lon2 - lon1) / 2) ** 2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n d = 6371000 * c\n\n return d","def calculate_distance(srcLong, srcLat, dstLong, dstLat):\n return math.sqrt( (srcLong-dstLong) ** 2 + (srcLat - dstLat) ** 2)","def calcDistance(lat1, lon1, lat2, lon2):\n yDistance = (lat2 - lat1) * nauticalMilePerLat\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2) \n distance = math.sqrt( yDistance**2 + xDistance**2 )\n return distance * milesPerNauticalMile","def topographic_altitude(lat, lon):\n global __model\n type_output = type(lat)\n lat = prepare_input_array(lat)\n lon = prepare_input_array(lon)\n lon = np.mod(lon, 360)\n val = __model.topographic_altitude(lat, lon)\n val = np.maximum(val, 1e-7)\n return prepare_output_array(val, type_output) * u.km","def calculateDistance(lat1, lon1, lat2, lon2):\n # convert decimal degrees to radians\n lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\n\n # haversine formula\n dlat = lat2 - lat1\n dlon = lon2 - lon1\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a))\n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\n return c * r","def get_spherical_distance(lat1,lat2,long1,long2):\n lat1,lat2,long1,long2= float(lat1),float(lat2),float(long1),float(long2)\n q=radians(lat2-lat1)\n r=radians(long2-long1)\n lat2r=radians(lat2)\n lat1r=radians(lat1)\n a=sin(q/2)*sin(q/2)+cos(lat1r)*cos(lat2r)*sin(r/2)*sin(r/2)\n c=2*atan2(sqrt(a),sqrt(1-a))\n R=6371*1000\n d=R*c\n return d","def calcDistanceOptimized(lat1, lon1, lat2, lon2):\n rad = 0.017453292519943\n yDistance = (lat2 - lat1) * 60.00721\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * 30.053965\n distance = math.sqrt( yDistance**2 + xDistance**2 )\n return distance * 1852.00088832","def get_distance_from_point(long1, lati1, long2, lati2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [long1, lati1, long2, lati2])\n\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\n c = 2 * asin(sqrt(a))\n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\n return c * r","def dist_between(lon1, lat1, lon2, lat2):\n # convert decimal degrees to radians \n lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n # haversine formula \n dlon = lon2 - lon1 \n dlat = lat2 - lat1 \n a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\n c = 2 * np.arcsin(np.sqrt(a)) \n km = 6367 * c\n return km","def test_get_kilometers(self):\n kilometers = location_util.vincenty(COORDINATES_PARIS,\n COORDINATES_NEW_YORK)\n self.assertEqual(round(kilometers, 2), DISTANCE_KM)","def distance(self, lat: float, lon: float) -> float:\n return distance((self.lat, self.lon), (lat, lon))","def calcDistance(lat1, lon1, lat2, lon2):\n yDistance = (lat2 - lat1) * nauticalMilePerLat\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2)\n distance = math.sqrt( yDistance**2 + xDistance**2 )\n return distance * milesPerNauticalMile","def compute_distance_to_city_in_km(self, city):\n lat = math.radians(self.latitude - city.latitude)\n long = math.radians(self.longitude - city.longitude)\n a = math.pow(math.sin(lat/2), 2) \\\n + math.cos(math.radians(self.latitude)) * math.cos(math.radians(city.latitude)) * pow(math.sin(long/2), 2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n\n radius_earth = 6378.7 # in kilometers\n return radius_earth * c","def distance_between_m(lat1, lon1, lat2, lon2):\n phi1 = (90. - lat1) * pi / 180.\n phi2 = (90. - lat2) * pi / 180.\n theta1 = lon1 * pi / 180.\n theta2 = lon2 * pi / 180.\n arc_length = acos(sin(phi1) * sin(phi2) * cos(theta1 - theta2) +\n cos(phi1) * cos(phi2))\n return arc_length * EARTH_RADIUS_M","def get_distance_from_hq(self, lat, lon):\n coords_1 = (40.646860, -8.642999) # Coords to the office\n coords_2 = (lat, lon)\n return distance.distance(coords_1, coords_2).km","def haversine(lon1, lat1, lon2, lat2): \r\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \r\n #print 34\r\n dlon = lon2 - lon1 \r\n dlat = lat2 - lat1 \r\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \r\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \r\n r = 6371 \r\n d=c * r\r\n #print type(d)\r\n return d","def get_earth_radius(latitude):\n a = R_EARTH_MAX\n b = R_EARTH_MIN\n num = ((a ** 2 * np.cos(latitude)) ** 2 +\n (b ** 2 * np.sin(latitude)) ** 2)\n den = ((a * np.cos(latitude)) ** 2 +\n (b * np.sin(latitude)) ** 2)\n\n earth_radius = np.sqrt(num / den)\n\n return earth_radius","def distance(s_lat, s_lng, e_lat, e_lng):\n\n # approximate radius of earth in km\n R = 6373.0\n\n# s_lat = s_lat*np.pi/180.0\n s_lat = np.deg2rad(s_lat)\n s_lng = np.deg2rad(s_lng)\n e_lat = np.deg2rad(e_lat)\n e_lng = np.deg2rad(e_lng)\n\n d = (np.sin((e_lat - s_lat)/2)**2 + np.cos(s_lat)*np.cos(e_lat) *\n np.sin((e_lng - s_lng)/2)**2)\n distance = 2 * R * np.arcsin(np.sqrt(d))\n\n return distance","def haversine_distance(loc1_lat, loc1_lon, loc1_alt, loc2_lat, loc2_lon, loc2_alt):\n R = 6372797.560856 # radius of the earth in meters\n R = R + loc2_alt - loc1_alt\n\n lat_arc = math.radians(loc1_lat - loc2_lat)\n lon_arc = math.radians(loc1_lon - loc2_lon)\n\n lat_h = math.sin(lat_arc * 0.5)\n lat_h = lat_h * lat_h\n\n lon_h = math.sin(lon_arc * 0.5)\n lon_h = lon_h * lon_h\n\n tmp = math.cos(math.radians(loc1_lat)) * math.cos(math.radians(loc2_lat))\n rad = 2.0 * math.asin(math.sqrt(lat_h + tmp * lon_h))\n\n return rad * R","def gpx_distance(lat1, lon1, lat2, lon2):\n theta = lon1 - lon2\n rads = sin(radians(lat1)) * sin(radians(lat2)) + cos(radians(lat1)) * cos(radians(lat2)) * cos(radians(theta))\n\n # make sure rads is [-1, 1]\n rads = 1 if rads > 1 else rads\n rads = -1 if rads < -1 else rads\n\n rads = acos(rads)\n\n # multiply by radius of the earth to get distance\n return rads * 6367","def distance(a, b):\n return vincenty((float(a.longitude), float(a.latitude)),\n (float(b.longitude), float(b.latitude))).km","def distance(lat1, lon1, lat2, lon2):\r\n radius = 6373 * 1000\r\n dlon = lon2 - lon1\r\n dlat = lat2 - lat1\r\n a = (math.sin(dlat/2))**2 + math.cos(lat1) * math.cos(lat2) * (math.sin(dlon/2))**2\r\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\r\n return radius * c","def aversine(lon1, lat1, lon2, lat2):\n\n lon1 = float(lon1)\n lon2 = float(lon2)\n lat1 = float(lat1)\n lat2 = float(lat2)\n\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\n c = 2 * math.asin(math.sqrt(a))\n meters = 6356988 * c\n\n return meters","def get_location_offset_meters(self, dNorth, dEast, alt):\n earth_radius=6378137.0 #Radius of \"spherical\" earth\n #Coordinate offsets in radians\n dLat = dNorth/earth_radius\n dLon = dEast/(earth_radius*math.cos(math.pi*self.home.lat/180))\n\n #New position in decimal degrees\n newlat = self.home.lat + (dLat * 180/math.pi)\n newlon = self.home.lon + (dLon * 180/math.pi)\n return LocationGlobal(newlat, newlon,self.home.alt+alt)","def get_nearest_station(latitude, longitude):\n urlbase = \"http://realtime.mbta.com/developer/api/v2/stopsbylocation?api_key=wX9NwuHnZU2ToO7GmGR9uw&lat=\"\n urlbase += str(latitude)\n urlbase += '&lon='\n urlbase += str(longitude)\n urlbase += '&format=json'\n response_data = get_json(urlbase)\n station_name = response_data[\"stop\"][0][\"stop_name\"]\n distance = response_data[\"stop\"][0][\"distance\"]\n return station_name, distance","def dist_in_meters(coords, pt, is_geo=False):\n xe = coords[:, 0]\n ye = coords[:, 1]\n xp = pt[0]\n yp = pt[1]\n if is_geo:\n d = _get_dist_geo(xe, ye, xp, yp)\n else:\n d = np.sqrt(np.square(xe - xp) + np.square(ye - yp))\n return d","def calc_distance(start_lat, start_lng, end_lat, end_lng):\n # approximate radius of earth in km\n r = 6373.0\n\n # Convert to radians\n lat1 = math.radians(start_lat)\n lon1 = math.radians(start_lng)\n lat2 = math.radians(end_lat)\n lon2 = math.radians(end_lng)\n\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n\n return r * c","def get_fast_distance(lat1, lon1, lat2, lon2):\n KM = 6371.393\n lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\n dlat = lat2 - lat1\n dlon = lon2 - lon1\n a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\n c = 2 * np.arcsin(np.sqrt(a))\n distance = KM * c\n return distance","def calculate_H(s_lat,s_lon,e_lat,e_lon):\n R = 6371.0\n snlat = radians(s_lat)\n snlon = radians(s_lon)\n elat = radians(e_lat)\n elon = radians(e_lon)\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\n actual_dist = actual_dist * 1000\n return actual_dist","def latlong_distance(p1, p2):\n radius = 6371 # km\n\n lat1 = p1[1] * math.pi / 180.0\n lat2 = p2[1] * math.pi / 180.0\n lon1 = p1[0] * math.pi / 180.0\n lon2 = p2[0] * math.pi / 180.0\n\n deltaLat = lat2 - lat1\n deltaLon = lon2 - lon1\n a = (math.sin(deltaLat / 2)**2\n + math.cos(lat1) * math.cos(lat2) * math.sin(deltaLon / 2)**2)\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n d = radius * c\n d = d * 1e3 # Return in m\n return d","def test_get_distance_to_same_place() -> None:\n meters = location_util.distance(\n COORDINATES_PARIS[0],\n COORDINATES_PARIS[1],\n COORDINATES_PARIS[0],\n COORDINATES_PARIS[1],\n )\n\n assert meters == 0","def get_hike_distance(df1lat, df1long, df2lat, df2long):\n # approximate radius of earth in km\n R = 6373.0\n\n lat1 = radians(df1lat)\n lon1 = radians(df1long)\n lat2 = radians(df2lat)\n lon2 = radians(df2long)\n\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\n\n distance = R * c\n return distance","def spherical_distance(lat1, lon1, lat2, lon2):\n # convert decimal degrees to radians\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n # haversine formula\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a))\n km = 6373 * c\n km = '%d' % km\n return float(km)","def dist(lat1, lon1, lat2, lon2):\n # convert decimal degrees to radians \n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n\n # haversine formula \n dlon = lon2 - lon1 \n dlat = lat2 - lat1 \n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n c = 2 * asin(sqrt(a)) \n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\n return c * r","def distance(lat1, lon1, lat2, lon2):\n coord = map(lambda x: float(x) * pi / 180.0, [lat1, lon1, lat2, lon2])\n inverse_arc = sin(coord[0]) * sin(coord[2]) + \\\n cos(coord[0]) * cos(coord[2]) * cos(coord[1] - (coord[3]))\n arc_dist = acos(min(1, max(inverse_arc, -1))) * 6371\n return arc_dist","def delta_lat_miles(self, delta_lat):\n\n return delta_lat.dist_from_radius(EARTH_RADIUS)","def measure_gps(lat1, lon1, lat2, lon2):\n R = 6378.137; # Radius of earth in KM\n dLat = radians(lat2) - radians(lat1)\n dLon = radians(lon2) - radians(lon1)\n a = sin(dLat/2) * sin(dLat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dLon/2) * sin(dLon/2)\n c = 2 * atan2(sqrt(a), sqrt(1-a))\n d = R * c\n return d * 1000 # meters","def calc_distance_two_points(lat_from, long_from, lat_to, long_to):\n distance_in_km = haversine(\n (lat_from, long_from),\n (lat_to, long_to),\n unit='km')\n\n return distance_in_km","def distance_between_coordinates(current_coordinate, target_coordinate):\n distance_in_km = haversine(current_coordinate, target_coordinate)\n distance_in_metres = math.ceil(distance_in_km * 1000)\n return distance_in_metres","def get_nearest_station(latitude, longitude):\n url1 = \"http://realtime.mbta.com/developer/api/v2/stopsbylocation\"\n params1 = {'api_key':'lm1M_mXgq0O6dsH9xduPAQ','lat':latitude,'lon':longitude,'format':'json'}\n req1 = requests.get(url1,params=params1)\n stat1 = req1.status_code\n stop_name = req1.json()['stop'][0]['stop_name']\n distance = req1.json()['stop'][0]['distance']\n return stop_name, distance","def distance_coordinates(lat1: Decimal, lon1: Decimal, lat2: Decimal, lon2: Decimal) -> Decimal:\n lat1 = math.radians(lat1)\n lon1 = math.radians(lon1)\n lat2 = math.radians(lat2)\n lon2 = math.radians(lon2)\n\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\n distance = Decimal(R * c)\n\n return distance","def calcDistance(lat1, lon1, lat2, lon2):\n nauticalMilePerLat = 60.00721\n nauticalMilePerLongitude = 60.10793\n rad = math.pi / 180.0\n milesPerNauticalMile = 1.15078\n \n yDistance = (lat2 - lat1) * nauticalMilePerLat\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2)\n\n distance = math.sqrt( yDistance**2 + xDistance**2 )\n\n return distance * milesPerNauticalMile * 1609.344","def calc_distance(user_loc, space):\n geocode_result = gmaps.geocode(space['_location'])\n dest_loc = geocode_result[0]['geometry']['location']\n direction = gmaps.distance_matrix(user_loc, dest_loc, mode=\"walking\")\n distance = direction['rows'][0]['elements'][0]['distance']['value']\n # convert to mile\n distance = distance * 0.000621371\n return distance","def calculate_distance(source,dest):\n\n ### Earth radius in miles\n R = 3960.0\n\n lat1, lon1 = source\n lat2, lon2 = dest\n lat1 = radians(lat1)\n lon1 = radians(lon1)\n lat2 = radians(lat2)\n lon2 = radians(lon2)\n\n dlon = lon2 - lon1\n dlat = lat2 - lat1\n\n ### compute distance in spherical coordinates\n\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\n\n distance = R * c\n\n return distance","def get_distance_to(self, latitude, longitude):\n\n if not self.latitude or not self.longitude:\n return None\n return distance_between_coordinates(self.latitude, latitude, self.longitude, longitude)","def distance_intermediate_formula(lat1, long1, lat2, long2):\n return pow(sin(radians(col(lat1) - col(lat2)) / 2), 2) + (\n pow(sin(radians(col(long1) - col(long2)) / 2), 2)\n * cos(radians(col(lat1)))\n * cos(radians(col(lat2)))\n )"],"string":"[\n \"def get_distance_meters(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n dlong *= math.cos( aLocation2.lat * math.pi / 180.0 )\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat * dlat) + (dlong * dlong)) * 1.113195e5\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n \\n dlat = aLocation2.lat - aLocation1.lat\\n dlong = aLocation2.lon - aLocation1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def get_distance_meters(location1, location2):\\n dlat = location2.lat - location1.lat\\n dlong = location2.lon - location1.lon\\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5\",\n \"def calculate_distance(long_x, lat_x, long_y, lat_y):\\n if not (long_x and lat_x and long_y and lat_y):\\n return 0\\n\\n dlon = long_y - long_x if long_y != long_x else long_y\\n dlat = lat_y - lat_x if lat_y != lat_x else lat_y\\n\\n a = (sin(dlat / 2) ** 2) + cos(lat_x) * cos(lat_y) * (sin(dlon / 2) ** 2)\\n c = 2 * asin(sqrt(a))\\n r = 6371 # radius of earth in kms.\\n return (c * r)\",\n \"def calcMetresDistance(lat1, long1, lat2, long2):\\n return (abs(lat1 - lat2) + abs(long1 - long2)) * 100\\n # no, like, really badly\",\n \"def lnglat_to_meters(longitude, latitude):\\n if isinstance(longitude, (list, tuple)):\\n longitude = numpy.array(longitude)\\n if isinstance(latitude, (list, tuple)):\\n latitude = numpy.array(latitude)\\n\\n origin_shift = numpy.pi * 6378137\\n easting = longitude * origin_shift / 180.0\\n northing = numpy.log(numpy.tan((90 + latitude) * numpy.pi / 360.0)) * origin_shift / numpy.pi\\n return (easting, northing)\",\n \"def get_distance(latitude, longitude, del_latitude, del_longitude):\\n coord = (latitude, longitude)\\n del_coord = (del_latitude, del_longitude)\\n return distance.geodesic(coord, del_coord).km\",\n \"def get_distance_between_point(test_long, test_lat, lab_long, lab_lat):\\r\\n test = (test_lat, test_long)\\r\\n lab = (lab_lat, lab_long)\\r\\n return geodesic(test, lab).miles\",\n \"def coord_dist_meters(lat1, lon1, lat2, lon2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat/2)**2 + math.cos(lat1) * \\\\\\n math.cos(lat2) * math.sin(dlon/2)**2\\n c = 2 * math.asin(math.sqrt(a))\\n r = 6371000 # Radius of earth in meters. Use 3956 for miles\\n return c * r\",\n \"def distance_in_meters(coord1, coord2):\\n return vincenty(coord1, coord2).meters\",\n \"def closest_cruising_altitude(altitude):\\n return 1000 * ((altitude + 500) // 1000)\",\n \"def find_distance_to_store(latitude1, longitude1, latitude2, longitude2):\\n lat1 = float(latitude1)\\n lon1 = float(longitude1)\\n lat2 = float(latitude2)\\n lon2 = float(longitude2)\\n\\n dlat = radians(lat2-lat1)\\n dlon = radians(lon2-lon1)\\n a = sin(dlat/2) * sin(dlat/2) + cos(radians(lat1)) \\\\\\n * cos(radians(lat2)) * sin(dlon/2) * sin(dlon/2)\\n c = 2 * atan2(sqrt(a), sqrt(1-a))\\n distance_in_kilometers = RADIUS_OF_THE_EARTH_KILOMETERS * c\\n\\n return distance_in_kilometers\",\n \"def coord_distance(lat1, lon1, lat2, lon2):\\n\\tlon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n\\tdlon = lon2 - lon1\\n\\tdlat = lat2 - lat1\\n\\ta = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\\n\\tc = 2 * math.asin(math.sqrt(a))\\n\\tkm = 6367 * c \\n\\treturn km\",\n \"def distance(lat0, lng0, lat1, lng1):\\n # convert decimal degrees to radians \\n lat0, lng0, lat1, lng1 = map(radians, [lat0, lng0, lat1, lng1])\\n # haversine formula \\n dlng = lng1 - lng0 \\n dlat = lat1 - lat0 \\n a = sin(dlat/2)**2 + cos(lat0) * cos(lat1) * sin(dlng/2)**2\\n c = 2 * asin(sqrt(a)) \\n m = 6367000 * c\\n return m\",\n \"def get_distance(lat1, lon1, lat2, lon2):\\n phi1 = math.radians(lat1)\\n phi2 = math.radians(lat2)\\n d_phi = math.radians(lat2 - lat1)\\n d_lam = math.radians(lon2 - lon1)\\n a = math.sin(d_phi/2) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(d_lam/2)**2\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\\n return 6371000 * c\",\n \"def calcDistance(lat1, lon1, lat2, lon2):\\n # convert decimal degrees to radians \\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n # haversine formula \\n dlon = lon2 - lon1 \\n dlat = lat2 - lat1 \\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * asin(sqrt(a)) \\n km = 6367 * c\\n return km * 1000\",\n \"def coord_distance(lat1, lon1, lat2, lon2):\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\\n km = 2 * 6367 * math.asin(math.sqrt(a))\\n mi = 0.621371 * km\\n return mi\",\n \"def distance_between(lat_1, lon_1, lat_2, lon_2):\\n lat_1, lon_1 = math.radians(lat_1), math.radians(lon_1)\\n lat_2, lon_2 = math.radians(lat_2), math.radians(lon_2)\\n theta = lon_1 - lon_2\\n dist = math.sin(lat_1)*math.sin(lat_2) + math.cos(lat_1)*math.cos(lat_2)*math.cos(theta)\\n dist = math.acos(dist)\\n dist = math.degrees(dist)\\n dist = dist * 69.06 # 69.09 = circumference of earth in miles / 360 degrees\\n return dist\",\n \"def distance(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians \\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n # haversine formula \\n dlon = lon2 - lon1 \\n dlat = lat2 - lat1 \\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * asin(sqrt(a)) \\n km = 6367 * c\\n return km\",\n \"def get_distance_metres(aLocation1, aLocation2):\\n [dNorth, dEast, dDown] = get_position_error(aLocation1, aLocation2)\\n \\n return math.sqrt((dNorth*dNorth) + (dEast*dEast))\",\n \"def test_get_kilometers() -> None:\\n kilometers = location_util.vincenty(COORDINATES_PARIS, COORDINATES_NEW_YORK)\\n assert round(kilometers, 2) == DISTANCE_KM\",\n \"def get_euclidian_distance(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\\n c = 2 * math.asin(math.sqrt(a))\\n # Radius of earth in kilometers is 6371\\n km = 6371 * c\\n return km\",\n \"def test_get_distance() -> None:\\n meters = location_util.distance(\\n COORDINATES_PARIS[0],\\n COORDINATES_PARIS[1],\\n COORDINATES_NEW_YORK[0],\\n COORDINATES_NEW_YORK[1],\\n )\\n\\n assert meters / 1000 - DISTANCE_KM < 0.01\",\n \"def get_distance(lat1, long1, lat2, long2):\\n x = 69.1*(lat2 - lat1)\\n y = 69.1*(long2 - long1) * math.cos(lat1/57.3)\\n dist = math.sqrt(x*x + y*y)\\n return dist\",\n \"def haversine_distance_ignore_altitude(loc1_lat, loc1_lon, loc2_lat, loc2_lon):\\n R = 6372797.560856 # radius of the earth in meters\\n\\n lat_arc = math.radians(loc1_lat - loc2_lat)\\n lon_arc = math.radians(loc1_lon - loc2_lon)\\n\\n lat_h = math.sin(lat_arc * 0.5)\\n lat_h = lat_h * lat_h\\n\\n lon_h = math.sin(lon_arc * 0.5)\\n lon_h = lon_h * lon_h\\n\\n tmp = math.cos(math.radians(loc1_lat)) * math.cos(math.radians(loc2_lat))\\n rad = 2.0 * math.asin(math.sqrt(lat_h + tmp * lon_h))\\n\\n return rad * R\",\n \"def getAltitudeFromLatLon(self, lat, lon):\\r\\n # print \\\"-----\\\\nFromLatLon\\\", lon, lat\\r\\n lat -= self.lat\\r\\n lon -= self.lon\\r\\n # print \\\"lon, lat\\\", lon, lat\\r\\n if lat < 0.0 or lat >= 1.0 or lon < 0.0 or lon >= 1.0:\\r\\n raise WrongTileError(self.lat, self.lon, self.lat+lat, self.lon+lon)\\r\\n x = lon * (self.size - 1)\\r\\n y = lat * (self.size - 1)\\r\\n # print \\\"x,y\\\", x, y\\r\\n x_int = int(x)\\r\\n x_frac = x - int(x)\\r\\n y_int = int(y)\\r\\n y_frac = y - int(y)\\r\\n # print \\\"frac\\\", x_int, x_frac, y_int, y_frac\\r\\n value00 = self.getPixelValue(x_int, y_int)\\r\\n value10 = self.getPixelValue(x_int+1, y_int)\\r\\n value01 = self.getPixelValue(x_int, y_int+1)\\r\\n value11 = self.getPixelValue(x_int+1, y_int+1)\\r\\n value1 = self._avg(value00, value10, x_frac)\\r\\n value2 = self._avg(value01, value11, x_frac)\\r\\n value = self._avg(value1, value2, y_frac)\\r\\n # print \\\"%4d %4d | %4d\\\\n%4d %4d | %4d\\\\n-------------\\\\n%4d\\\" % (\\r\\n # value00, value10, value1, value01, value11, value2, value)\\r\\n return value\",\n \"def earth_distance(lat1: float, lon1: float, lat2: float, lon2: float)\\\\\\n -> float:\\n # R = 6373.0 # earth radius in km\\n R = 3963.0 # earth radius in miles\\n lat1 = radians(lat1)\\n lon1 = radians(lon1)\\n lat2 = radians(lat2)\\n lon2 = radians(lon2)\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\\n distance = R * c\\n return distance\",\n \"def calculate_distance(x: float, y: float) -> float:\\n # return geopy.distance.vincenty(x, y).km\\n R = 6370\\n lat1 = radians(x[0]) #insert value\\n lon1 = radians(x[1])\\n lat2 = radians(y[0])\\n lon2 = radians(y[1])\\n\\n dlon = lon2 - lon1\\n dlat = lat2- lat1\\n\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * atan2(sqrt(a), sqrt(1-a))\\n distance = R * c\\n return distance\",\n \"def get_location_meters(original_location, north, east, alt=0):\\n # Radius of \\\"spherical\\\" earth\\n earth_radius = 6378137.0\\n # Coordinate offsets in radians\\n lat = north / earth_radius\\n lon = east / (earth_radius * math.cos(original_location.lat * math.pi/180))\\n\\n # New position in decimal degrees\\n newlat = original_location.lat + (lat * 180/math.pi)\\n newlon = original_location.lon + (lon * 180/math.pi)\\n return Location(newlat, newlon, original_location.alt + alt, original_location.is_relative)\",\n \"def distance(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * asin(sqrt(a))\\n m = 6367 * c * 1000\\n return m\",\n \"def road_distance(lat1, lon1, lat2, lon2):\\n point1 = lat1, lon1\\n point2 = lat2, lon2\\n url = \\\"https://maps.googleapis.com/maps/api/distancematrix/json?origins={0},{1}&destinations={2},{3}&mode=driving&language=en-EN&sensor=false&key={4}\\\".format(str(lat1),str(lon1),str(lat2),str(lon2), google_api_key)\\n response = api_call(url)\\n km = response['rows'][0]['elements'][0]['distance']['value']\\n return round(km/1000,1)\",\n \"def _earth_distance(time='now'):\\n return get_earth(time).radius\",\n \"def calc_dist(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\\n c = 2 * asin(sqrt(a))\\n # Radius of earth in kilometers is 6371\\n mtr = 6371000 * c\\n return mtr\",\n \"def distance(lat1, lon1, lat2, lon2):\\r\\n earth_radius=3959.0 #miles\\r\\n if lat1==lat2 and lon1==lon2:\\r\\n dst=0\\r\\n else:\\r\\n dst = acos(\\r\\n (sin(radians(lat1)) * sin(radians(lat2))) +\\r\\n (cos(radians(lat1)) * cos(radians(lat2)) * cos(radians(lon1) - radians(lon2)))\\r\\n ) * earth_radius\\r\\n return dst\",\n \"def calculate_distance_based_on_lon_lat(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians \\n lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n\\n # haversine formula \\n dlon = lon2 - lon1 \\n dlat = lat2 - lat1 \\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * np.arcsin(sqrt(a)) \\n # Radius of earth in kilometers. Use 3956 for miles\\n r = 6378137.0\\n return c * r\",\n \"def distance(latitude_1: float, longitude_1: float, latitude_2: float, longitude_2: float) -> float:\\n lat1, lon1, lat2, lon2 = map(radians, (latitude_1, longitude_1, latitude_2, longitude_2))\\n return (\\n 2\\n * EARTH_RADIUS\\n * asin(\\n sqrt(\\n sin((lat2 - lat1) / 2) ** 2 + cos(lat1) * cos(lat2) * (sin((lon2 - lon1) / 2) ** 2)\\n )\\n )\\n )\",\n \"def distance_in_meters(self, latlng):\\n return self.distance_to(latlng)\",\n \"def getDist(lat1,long1,lat2,long2):\\n\\tlat1 = math.radians(lat1)\\n\\tlong1 = math.radians(long1)\\n\\tlat2 = math.radians(lat2)\\n\\tlong2 = math.radians(long2)\\n\\tR = 6371 # km\\n\\td = cmath.acos(cmath.sin(lat1) * cmath.sin(lat2) + \\\\\\n\\tcmath.cos(lat1) * cmath.cos(lat2) *\\n\\tcmath.cos(long2 - long1)) * R\\n\\treturn abs(d) # cast to float\",\n \"def test_get_distance(self):\\n meters = location_util.distance(COORDINATES_PARIS[0],\\n COORDINATES_PARIS[1],\\n COORDINATES_NEW_YORK[0],\\n COORDINATES_NEW_YORK[1])\\n self.assertAlmostEqual(meters / 1000, DISTANCE_KM, places=2)\",\n \"def get_distance(lat1, lon1, lat2, lon2) -> float:\\n # Earth radius in meters\\n radius = 6371000\\n\\n # Degress to radian\\n lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\\n\\n # Deltas\\n dlat = lat2 - lat1\\n dlon = lon2 - lon1\\n\\n # Calculate distance\\n arch = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\\n arch_sin = 2 * np.arcsin(np.sqrt(arch))\\n\\n return radius * arch_sin\",\n \"def latlon2distance(lat1, long1, lat2, long2, miles=False):\\n global verbose\\n\\n if lat1 == lat2 and long1 == long2:\\n return 0\\n\\n\\n # Convert latitude and longitude to\\n # spherical coordinates in radians.\\n degrees_to_radians = math.pi / 180.0\\n\\n # phi = 90 - latitude\\n phi1 = (90.0 - lat1) * degrees_to_radians\\n phi2 = (90.0 - lat2) * degrees_to_radians\\n\\n # theta = longitude\\n theta1 = long1 * degrees_to_radians\\n theta2 = long2 * degrees_to_radians\\n\\n # Compute spherical distance from spherical coordinates.\\n\\n # For two locations in spherical coordinates\\n # (1, theta, phi) and (1, theta, phi)\\n # cosine( arc length ) =\\n # sin phi sin phi' cos(theta-theta') + cos phi cos phi'\\n # distance = rho * arc length\\n\\n cos = (math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2) + math.cos(phi1) * math.cos(phi2))\\n try:\\n arc = math.acos(cos)\\n except Exception as err:\\n sys.stderr.write(\\\"There was an err: {} trying to take the acos of ({})\\\\n\\\".format(err, cos))\\n arc=0\\n # Remember to multiply arc by the radius of the earth\\n # in your favorite set of units to get length.\\n #\\n # To convert to miles multiple arc by 3960\\n # To convert to kilometers multiply arc by 6373\\n\\n if miles:\\n arc *= 3960\\n else:\\n arc *= 6373\\n\\n return arc\",\n \"def distance(lat1,lon1, lat2, lon2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat/2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2)**2\\n c = 2 * math.asin(math.sqrt(a))\\n miles = earth_radius * c\\n return miles\",\n \"def get_spherical_distance(lat1,lat2,long1,long2):\\n q=radians(lat2-lat1)\\n r=radians(long2-long1)\\n lat2r=radians(lat2)\\n lat1r=radians(lat1)\\n a=sin(q/2)*sin(q/2)+cos(lat1r)*cos(lat2r)*sin(r/2)*sin(r/2)\\n c=2*atan2(sqrt(a),sqrt(1-a))\\n R=6371*1000\\n d=R*c\\n return d\",\n \"def calculateDistanceBetweenPoints(lat1,lon1,lat2,lon2):\\n\\treturn Geodesic.WGS84.Inverse(lat1,lon1, lat2, lon2)['s12']\",\n \"def _calculate_distance(self, passenger, driver):\\n londriver, latdriver = driver['lon'], driver['lat']\\n lonpassenger, latpassenger = passenger['lon'], passenger['lat']\\n lon_p, lat_p, lon_d, lat_d = map(radians,\\n [float(lonpassenger), float(latpassenger), float(londriver), float(latdriver)])\\n lon_distance = lon_d - lon_p\\n lat_distance = lat_d - lat_p\\n a = sin(lat_distance / 2) ** 2 + cos(lat_p) * cos(lat_d) * sin(lon_distance / 2) ** 2\\n c = 2 * asin(sqrt(a))\\n km = 6367 * c\\n return km\",\n \"def _calculate_distance(self, passenger, driver):\\n londriver, latdriver = driver['lon'], driver['lat']\\n lonpassenger, latpassenger = passenger['lon'], passenger['lat']\\n lon_p, lat_p, lon_d, lat_d = map(radians,\\n [float(lonpassenger), float(latpassenger), float(londriver), float(latdriver)])\\n lon_distance = lon_d - lon_p\\n lat_distance = lat_d - lat_p\\n a = sin(lat_distance / 2) ** 2 + cos(lat_p) * cos(lat_d) * sin(lon_distance / 2) ** 2\\n c = 2 * asin(sqrt(a))\\n km = 6367 * c\\n return km\",\n \"def harversine(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n # harversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat/2.)**2. + math.cos(lat1) * math.cos(lat2) * math.sin(dlon/2.)**2.\\n c = 2. * math.asin(math.sqrt(a))\\n km = 6371. * c # radius of earth\\n return km\",\n \"def distance(lat1, lon1, lat2, lon2):\\n lon1, lat1 = math.radians(lon1), math.radians(lat1)\\n lon2, lat2 = math.radians(lon2), math.radians(lat2)\\n a = (math.sin((lat2 - lat1) / 2) ** 2 +\\n math.cos(lat1) * math.cos(lat2) * math.sin((lon2 - lon1) / 2) ** 2)\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\\n d = 6371000 * c\\n\\n return d\",\n \"def calculate_distance(srcLong, srcLat, dstLong, dstLat):\\n return math.sqrt( (srcLong-dstLong) ** 2 + (srcLat - dstLat) ** 2)\",\n \"def calcDistance(lat1, lon1, lat2, lon2):\\n yDistance = (lat2 - lat1) * nauticalMilePerLat\\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2) \\n distance = math.sqrt( yDistance**2 + xDistance**2 )\\n return distance * milesPerNauticalMile\",\n \"def topographic_altitude(lat, lon):\\n global __model\\n type_output = type(lat)\\n lat = prepare_input_array(lat)\\n lon = prepare_input_array(lon)\\n lon = np.mod(lon, 360)\\n val = __model.topographic_altitude(lat, lon)\\n val = np.maximum(val, 1e-7)\\n return prepare_output_array(val, type_output) * u.km\",\n \"def calculateDistance(lat1, lon1, lat2, lon2):\\n # convert decimal degrees to radians\\n lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])\\n\\n # haversine formula\\n dlat = lat2 - lat1\\n dlon = lon2 - lon1\\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * asin(sqrt(a))\\n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\\n return c * r\",\n \"def get_spherical_distance(lat1,lat2,long1,long2):\\n lat1,lat2,long1,long2= float(lat1),float(lat2),float(long1),float(long2)\\n q=radians(lat2-lat1)\\n r=radians(long2-long1)\\n lat2r=radians(lat2)\\n lat1r=radians(lat1)\\n a=sin(q/2)*sin(q/2)+cos(lat1r)*cos(lat2r)*sin(r/2)*sin(r/2)\\n c=2*atan2(sqrt(a),sqrt(1-a))\\n R=6371*1000\\n d=R*c\\n return d\",\n \"def calcDistanceOptimized(lat1, lon1, lat2, lon2):\\n rad = 0.017453292519943\\n yDistance = (lat2 - lat1) * 60.00721\\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * 30.053965\\n distance = math.sqrt( yDistance**2 + xDistance**2 )\\n return distance * 1852.00088832\",\n \"def get_distance_from_point(long1, lati1, long2, lati2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(radians, [long1, lati1, long2, lati2])\\n\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = sin(dlat / 2) ** 2 + cos(lat1) * cos(lat2) * sin(dlon / 2) ** 2\\n c = 2 * asin(sqrt(a))\\n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\\n return c * r\",\n \"def dist_between(lon1, lat1, lon2, lat2):\\n # convert decimal degrees to radians \\n lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\\n # haversine formula \\n dlon = lon2 - lon1 \\n dlat = lat2 - lat1 \\n a = np.sin(dlat/2)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2)**2\\n c = 2 * np.arcsin(np.sqrt(a)) \\n km = 6367 * c\\n return km\",\n \"def test_get_kilometers(self):\\n kilometers = location_util.vincenty(COORDINATES_PARIS,\\n COORDINATES_NEW_YORK)\\n self.assertEqual(round(kilometers, 2), DISTANCE_KM)\",\n \"def distance(self, lat: float, lon: float) -> float:\\n return distance((self.lat, self.lon), (lat, lon))\",\n \"def calcDistance(lat1, lon1, lat2, lon2):\\n yDistance = (lat2 - lat1) * nauticalMilePerLat\\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2)\\n distance = math.sqrt( yDistance**2 + xDistance**2 )\\n return distance * milesPerNauticalMile\",\n \"def compute_distance_to_city_in_km(self, city):\\n lat = math.radians(self.latitude - city.latitude)\\n long = math.radians(self.longitude - city.longitude)\\n a = math.pow(math.sin(lat/2), 2) \\\\\\n + math.cos(math.radians(self.latitude)) * math.cos(math.radians(city.latitude)) * pow(math.sin(long/2), 2)\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\\n\\n radius_earth = 6378.7 # in kilometers\\n return radius_earth * c\",\n \"def distance_between_m(lat1, lon1, lat2, lon2):\\n phi1 = (90. - lat1) * pi / 180.\\n phi2 = (90. - lat2) * pi / 180.\\n theta1 = lon1 * pi / 180.\\n theta2 = lon2 * pi / 180.\\n arc_length = acos(sin(phi1) * sin(phi2) * cos(theta1 - theta2) +\\n cos(phi1) * cos(phi2))\\n return arc_length * EARTH_RADIUS_M\",\n \"def get_distance_from_hq(self, lat, lon):\\n coords_1 = (40.646860, -8.642999) # Coords to the office\\n coords_2 = (lat, lon)\\n return distance.distance(coords_1, coords_2).km\",\n \"def haversine(lon1, lat1, lon2, lat2): \\r\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2]) \\r\\n #print 34\\r\\n dlon = lon2 - lon1 \\r\\n dlat = lat2 - lat1 \\r\\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2 \\r\\n c = 2 * atan(sqrt(a)/sqrt(1-a)) \\r\\n r = 6371 \\r\\n d=c * r\\r\\n #print type(d)\\r\\n return d\",\n \"def get_earth_radius(latitude):\\n a = R_EARTH_MAX\\n b = R_EARTH_MIN\\n num = ((a ** 2 * np.cos(latitude)) ** 2 +\\n (b ** 2 * np.sin(latitude)) ** 2)\\n den = ((a * np.cos(latitude)) ** 2 +\\n (b * np.sin(latitude)) ** 2)\\n\\n earth_radius = np.sqrt(num / den)\\n\\n return earth_radius\",\n \"def distance(s_lat, s_lng, e_lat, e_lng):\\n\\n # approximate radius of earth in km\\n R = 6373.0\\n\\n# s_lat = s_lat*np.pi/180.0\\n s_lat = np.deg2rad(s_lat)\\n s_lng = np.deg2rad(s_lng)\\n e_lat = np.deg2rad(e_lat)\\n e_lng = np.deg2rad(e_lng)\\n\\n d = (np.sin((e_lat - s_lat)/2)**2 + np.cos(s_lat)*np.cos(e_lat) *\\n np.sin((e_lng - s_lng)/2)**2)\\n distance = 2 * R * np.arcsin(np.sqrt(d))\\n\\n return distance\",\n \"def haversine_distance(loc1_lat, loc1_lon, loc1_alt, loc2_lat, loc2_lon, loc2_alt):\\n R = 6372797.560856 # radius of the earth in meters\\n R = R + loc2_alt - loc1_alt\\n\\n lat_arc = math.radians(loc1_lat - loc2_lat)\\n lon_arc = math.radians(loc1_lon - loc2_lon)\\n\\n lat_h = math.sin(lat_arc * 0.5)\\n lat_h = lat_h * lat_h\\n\\n lon_h = math.sin(lon_arc * 0.5)\\n lon_h = lon_h * lon_h\\n\\n tmp = math.cos(math.radians(loc1_lat)) * math.cos(math.radians(loc2_lat))\\n rad = 2.0 * math.asin(math.sqrt(lat_h + tmp * lon_h))\\n\\n return rad * R\",\n \"def gpx_distance(lat1, lon1, lat2, lon2):\\n theta = lon1 - lon2\\n rads = sin(radians(lat1)) * sin(radians(lat2)) + cos(radians(lat1)) * cos(radians(lat2)) * cos(radians(theta))\\n\\n # make sure rads is [-1, 1]\\n rads = 1 if rads > 1 else rads\\n rads = -1 if rads < -1 else rads\\n\\n rads = acos(rads)\\n\\n # multiply by radius of the earth to get distance\\n return rads * 6367\",\n \"def distance(a, b):\\n return vincenty((float(a.longitude), float(a.latitude)),\\n (float(b.longitude), float(b.latitude))).km\",\n \"def distance(lat1, lon1, lat2, lon2):\\r\\n radius = 6373 * 1000\\r\\n dlon = lon2 - lon1\\r\\n dlat = lat2 - lat1\\r\\n a = (math.sin(dlat/2))**2 + math.cos(lat1) * math.cos(lat2) * (math.sin(dlon/2))**2\\r\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\\r\\n return radius * c\",\n \"def aversine(lon1, lat1, lon2, lat2):\\n\\n lon1 = float(lon1)\\n lon2 = float(lon2)\\n lat1 = float(lat1)\\n lat2 = float(lat2)\\n\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(math.radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\\n c = 2 * math.asin(math.sqrt(a))\\n meters = 6356988 * c\\n\\n return meters\",\n \"def get_location_offset_meters(self, dNorth, dEast, alt):\\n earth_radius=6378137.0 #Radius of \\\"spherical\\\" earth\\n #Coordinate offsets in radians\\n dLat = dNorth/earth_radius\\n dLon = dEast/(earth_radius*math.cos(math.pi*self.home.lat/180))\\n\\n #New position in decimal degrees\\n newlat = self.home.lat + (dLat * 180/math.pi)\\n newlon = self.home.lon + (dLon * 180/math.pi)\\n return LocationGlobal(newlat, newlon,self.home.alt+alt)\",\n \"def get_nearest_station(latitude, longitude):\\n urlbase = \\\"http://realtime.mbta.com/developer/api/v2/stopsbylocation?api_key=wX9NwuHnZU2ToO7GmGR9uw&lat=\\\"\\n urlbase += str(latitude)\\n urlbase += '&lon='\\n urlbase += str(longitude)\\n urlbase += '&format=json'\\n response_data = get_json(urlbase)\\n station_name = response_data[\\\"stop\\\"][0][\\\"stop_name\\\"]\\n distance = response_data[\\\"stop\\\"][0][\\\"distance\\\"]\\n return station_name, distance\",\n \"def dist_in_meters(coords, pt, is_geo=False):\\n xe = coords[:, 0]\\n ye = coords[:, 1]\\n xp = pt[0]\\n yp = pt[1]\\n if is_geo:\\n d = _get_dist_geo(xe, ye, xp, yp)\\n else:\\n d = np.sqrt(np.square(xe - xp) + np.square(ye - yp))\\n return d\",\n \"def calc_distance(start_lat, start_lng, end_lat, end_lng):\\n # approximate radius of earth in km\\n r = 6373.0\\n\\n # Convert to radians\\n lat1 = math.radians(start_lat)\\n lon1 = math.radians(start_lng)\\n lat2 = math.radians(end_lat)\\n lon2 = math.radians(end_lng)\\n\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n\\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\\n\\n return r * c\",\n \"def get_fast_distance(lat1, lon1, lat2, lon2):\\n KM = 6371.393\\n lat1, lon1, lat2, lon2 = map(np.deg2rad, [lat1, lon1, lat2, lon2])\\n dlat = lat2 - lat1\\n dlon = lon2 - lon1\\n a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2\\n c = 2 * np.arcsin(np.sqrt(a))\\n distance = KM * c\\n return distance\",\n \"def calculate_H(s_lat,s_lon,e_lat,e_lon):\\n R = 6371.0\\n snlat = radians(s_lat)\\n snlon = radians(s_lon)\\n elat = radians(e_lat)\\n elon = radians(e_lon)\\n actual_dist = 6371.01 * acos(sin(snlat) * sin(elat) + cos(snlat) * cos(elat) * cos(snlon - elon))\\n actual_dist = actual_dist * 1000\\n return actual_dist\",\n \"def latlong_distance(p1, p2):\\n radius = 6371 # km\\n\\n lat1 = p1[1] * math.pi / 180.0\\n lat2 = p2[1] * math.pi / 180.0\\n lon1 = p1[0] * math.pi / 180.0\\n lon2 = p2[0] * math.pi / 180.0\\n\\n deltaLat = lat2 - lat1\\n deltaLon = lon2 - lon1\\n a = (math.sin(deltaLat / 2)**2\\n + math.cos(lat1) * math.cos(lat2) * math.sin(deltaLon / 2)**2)\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\\n d = radius * c\\n d = d * 1e3 # Return in m\\n return d\",\n \"def test_get_distance_to_same_place() -> None:\\n meters = location_util.distance(\\n COORDINATES_PARIS[0],\\n COORDINATES_PARIS[1],\\n COORDINATES_PARIS[0],\\n COORDINATES_PARIS[1],\\n )\\n\\n assert meters == 0\",\n \"def get_hike_distance(df1lat, df1long, df2lat, df2long):\\n # approximate radius of earth in km\\n R = 6373.0\\n\\n lat1 = radians(df1lat)\\n lon1 = radians(df1long)\\n lat2 = radians(df2lat)\\n lon2 = radians(df2long)\\n\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\\n\\n distance = R * c\\n return distance\",\n \"def spherical_distance(lat1, lon1, lat2, lon2):\\n # convert decimal degrees to radians\\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n # haversine formula\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * asin(sqrt(a))\\n km = 6373 * c\\n km = '%d' % km\\n return float(km)\",\n \"def dist(lat1, lon1, lat2, lon2):\\n # convert decimal degrees to radians \\n lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\\n\\n # haversine formula \\n dlon = lon2 - lon1 \\n dlat = lat2 - lat1 \\n a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\\n c = 2 * asin(sqrt(a)) \\n r = 6371 # Radius of earth in kilometers. Use 3956 for miles\\n return c * r\",\n \"def distance(lat1, lon1, lat2, lon2):\\n coord = map(lambda x: float(x) * pi / 180.0, [lat1, lon1, lat2, lon2])\\n inverse_arc = sin(coord[0]) * sin(coord[2]) + \\\\\\n cos(coord[0]) * cos(coord[2]) * cos(coord[1] - (coord[3]))\\n arc_dist = acos(min(1, max(inverse_arc, -1))) * 6371\\n return arc_dist\",\n \"def delta_lat_miles(self, delta_lat):\\n\\n return delta_lat.dist_from_radius(EARTH_RADIUS)\",\n \"def measure_gps(lat1, lon1, lat2, lon2):\\n R = 6378.137; # Radius of earth in KM\\n dLat = radians(lat2) - radians(lat1)\\n dLon = radians(lon2) - radians(lon1)\\n a = sin(dLat/2) * sin(dLat/2) + cos(radians(lat1)) * cos(radians(lat2)) * sin(dLon/2) * sin(dLon/2)\\n c = 2 * atan2(sqrt(a), sqrt(1-a))\\n d = R * c\\n return d * 1000 # meters\",\n \"def calc_distance_two_points(lat_from, long_from, lat_to, long_to):\\n distance_in_km = haversine(\\n (lat_from, long_from),\\n (lat_to, long_to),\\n unit='km')\\n\\n return distance_in_km\",\n \"def distance_between_coordinates(current_coordinate, target_coordinate):\\n distance_in_km = haversine(current_coordinate, target_coordinate)\\n distance_in_metres = math.ceil(distance_in_km * 1000)\\n return distance_in_metres\",\n \"def get_nearest_station(latitude, longitude):\\n url1 = \\\"http://realtime.mbta.com/developer/api/v2/stopsbylocation\\\"\\n params1 = {'api_key':'lm1M_mXgq0O6dsH9xduPAQ','lat':latitude,'lon':longitude,'format':'json'}\\n req1 = requests.get(url1,params=params1)\\n stat1 = req1.status_code\\n stop_name = req1.json()['stop'][0]['stop_name']\\n distance = req1.json()['stop'][0]['distance']\\n return stop_name, distance\",\n \"def distance_coordinates(lat1: Decimal, lon1: Decimal, lat2: Decimal, lon2: Decimal) -> Decimal:\\n lat1 = math.radians(lat1)\\n lon1 = math.radians(lon1)\\n lat2 = math.radians(lat2)\\n lon2 = math.radians(lon2)\\n\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n\\n a = math.sin(dlat / 2) ** 2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2) ** 2\\n c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))\\n distance = Decimal(R * c)\\n\\n return distance\",\n \"def calcDistance(lat1, lon1, lat2, lon2):\\n nauticalMilePerLat = 60.00721\\n nauticalMilePerLongitude = 60.10793\\n rad = math.pi / 180.0\\n milesPerNauticalMile = 1.15078\\n \\n yDistance = (lat2 - lat1) * nauticalMilePerLat\\n xDistance = (math.cos(lat1 * rad) + math.cos(lat2 * rad)) * (lon2 - lon1) * (nauticalMilePerLongitude / 2)\\n\\n distance = math.sqrt( yDistance**2 + xDistance**2 )\\n\\n return distance * milesPerNauticalMile * 1609.344\",\n \"def calc_distance(user_loc, space):\\n geocode_result = gmaps.geocode(space['_location'])\\n dest_loc = geocode_result[0]['geometry']['location']\\n direction = gmaps.distance_matrix(user_loc, dest_loc, mode=\\\"walking\\\")\\n distance = direction['rows'][0]['elements'][0]['distance']['value']\\n # convert to mile\\n distance = distance * 0.000621371\\n return distance\",\n \"def calculate_distance(source,dest):\\n\\n ### Earth radius in miles\\n R = 3960.0\\n\\n lat1, lon1 = source\\n lat2, lon2 = dest\\n lat1 = radians(lat1)\\n lon1 = radians(lon1)\\n lat2 = radians(lat2)\\n lon2 = radians(lon2)\\n\\n dlon = lon2 - lon1\\n dlat = lat2 - lat1\\n\\n ### compute distance in spherical coordinates\\n\\n a = sin(dlat / 2)**2 + cos(lat1) * cos(lat2) * sin(dlon / 2)**2\\n c = 2 * atan2(sqrt(a), sqrt(1 - a))\\n\\n distance = R * c\\n\\n return distance\",\n \"def get_distance_to(self, latitude, longitude):\\n\\n if not self.latitude or not self.longitude:\\n return None\\n return distance_between_coordinates(self.latitude, latitude, self.longitude, longitude)\",\n \"def distance_intermediate_formula(lat1, long1, lat2, long2):\\n return pow(sin(radians(col(lat1) - col(lat2)) / 2), 2) + (\\n pow(sin(radians(col(long1) - col(long2)) / 2), 2)\\n * cos(radians(col(lat1)))\\n * cos(radians(col(lat2)))\\n )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70389205","0.6926336","0.6884272","0.6884272","0.6884272","0.6884272","0.6884272","0.68767065","0.68557805","0.67505145","0.67494184","0.67259705","0.6697738","0.6696931","0.66856134","0.66700953","0.65975","0.6567814","0.6560275","0.6525618","0.6513142","0.6469947","0.64646846","0.6464619","0.6458212","0.6453903","0.64422905","0.64214396","0.6416447","0.6408516","0.6407694","0.63815165","0.63763237","0.6375057","0.63644433","0.6357537","0.6357159","0.63551754","0.63334054","0.6330632","0.6322481","0.63093776","0.6309073","0.63064146","0.6305236","0.628643","0.628607","0.62803155","0.6262236","0.6252615","0.6244191","0.6241631","0.6241631","0.62402874","0.6235937","0.62281615","0.6221061","0.6219821","0.6207887","0.6205054","0.6190647","0.618942","0.6187081","0.6171453","0.6144029","0.61433804","0.61379933","0.6106372","0.61038923","0.60972476","0.6093409","0.608716","0.6085234","0.6081149","0.60803646","0.60400105","0.6037376","0.60317636","0.6027537","0.60274786","0.6026757","0.60231334","0.60209394","0.6013335","0.60113496","0.6002163","0.59987","0.59906363","0.5984894","0.5983949","0.59832454","0.59773344","0.5976753","0.59587574","0.5951377","0.5949854","0.5946117","0.59437156","0.5936631","0.5932907"],"string":"[\n \"0.70389205\",\n \"0.6926336\",\n \"0.6884272\",\n \"0.6884272\",\n \"0.6884272\",\n \"0.6884272\",\n \"0.6884272\",\n \"0.68767065\",\n \"0.68557805\",\n \"0.67505145\",\n \"0.67494184\",\n \"0.67259705\",\n \"0.6697738\",\n \"0.6696931\",\n \"0.66856134\",\n \"0.66700953\",\n \"0.65975\",\n \"0.6567814\",\n \"0.6560275\",\n \"0.6525618\",\n \"0.6513142\",\n \"0.6469947\",\n \"0.64646846\",\n \"0.6464619\",\n \"0.6458212\",\n \"0.6453903\",\n \"0.64422905\",\n \"0.64214396\",\n \"0.6416447\",\n \"0.6408516\",\n \"0.6407694\",\n \"0.63815165\",\n \"0.63763237\",\n \"0.6375057\",\n \"0.63644433\",\n \"0.6357537\",\n \"0.6357159\",\n \"0.63551754\",\n \"0.63334054\",\n \"0.6330632\",\n \"0.6322481\",\n \"0.63093776\",\n \"0.6309073\",\n \"0.63064146\",\n \"0.6305236\",\n \"0.628643\",\n \"0.628607\",\n \"0.62803155\",\n \"0.6262236\",\n \"0.6252615\",\n \"0.6244191\",\n \"0.6241631\",\n \"0.6241631\",\n \"0.62402874\",\n \"0.6235937\",\n \"0.62281615\",\n \"0.6221061\",\n \"0.6219821\",\n \"0.6207887\",\n \"0.6205054\",\n \"0.6190647\",\n \"0.618942\",\n \"0.6187081\",\n \"0.6171453\",\n \"0.6144029\",\n \"0.61433804\",\n \"0.61379933\",\n \"0.6106372\",\n \"0.61038923\",\n \"0.60972476\",\n \"0.6093409\",\n \"0.608716\",\n \"0.6085234\",\n \"0.6081149\",\n \"0.60803646\",\n \"0.60400105\",\n \"0.6037376\",\n \"0.60317636\",\n \"0.6027537\",\n \"0.60274786\",\n \"0.6026757\",\n \"0.60231334\",\n \"0.60209394\",\n \"0.6013335\",\n \"0.60113496\",\n \"0.6002163\",\n \"0.59987\",\n \"0.59906363\",\n \"0.5984894\",\n \"0.5983949\",\n \"0.59832454\",\n \"0.59773344\",\n \"0.5976753\",\n \"0.59587574\",\n \"0.5951377\",\n \"0.5949854\",\n \"0.5946117\",\n \"0.59437156\",\n \"0.5936631\",\n \"0.5932907\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6852247"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":8476,"cells":{"query":{"kind":"string","value":"Both paths should be full."},"document":{"kind":"string","value":"def _put(self, src_fname, dst_fname):\n logging.info('Transferring file %s to %s', src_fname, self._ip_addr)\n sftp_cli = self._get_sftp_client()\n if sftp_cli is None:\n raise Exception('Not supported without ssh.')\n return sftp_cli.put(src_fname, dst_fname)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __combine_path(self, other):\n self.path = other.path + self.path","def join(self, path, *paths):","def aix_path_join(path_one, path_two):\n if path_one.endswith('/'):\n path_one = path_one.rstrip('/')\n\n if path_two.startswith('/'):\n path_two = path_two.lstrip('/')\n\n final_path = path_one + '/' + path_two\n return final_path","def samepath(p1, p2):\n # type: (str, str) -> bool\n return pathnormalize(p1) == pathnormalize(p2)","def join_paths(path_1, path_2):\r\n a = lib_path.join(path_1, path_2)\r\n return a","def test_verify_path_2(self):\n result = basic.verify_path(str(self.test_filepath1) + \"abcxyz\", \"file\")\n self.assertFalse(result)","def _issubpath(self, a, b):\n p1 = a.rstrip(os.sep).split(os.sep)\n p2 = b.rstrip(os.sep).split(os.sep)\n return p1[:len(p2)] == p2","def test_AppendPath(self) -> None:\n p1 = r'C:\\dir\\num\\one;C:\\dir\\num\\two'\n p2 = r'C:\\mydir\\num\\one;C:\\mydir\\num\\two'\n # have to include the pathsep here so that the test will work on UNIX too.\n p1 = AppendPath(p1, r'C:\\dir\\num\\two', sep=';')\n p1 = AppendPath(p1, r'C:\\dir\\num\\three', sep=';')\n assert p1 == r'C:\\dir\\num\\one;C:\\dir\\num\\two;C:\\dir\\num\\three', p1\n\n p2 = AppendPath(p2, r'C:\\mydir\\num\\three', sep=';')\n p2 = AppendPath(p2, r'C:\\mydir\\num\\one', sep=';')\n assert p2 == r'C:\\mydir\\num\\two;C:\\mydir\\num\\three;C:\\mydir\\num\\one', p2\n\n # check (only) last one is kept if there are dupes in new\n p3 = r'C:\\dir\\num\\one'\n p3 = AppendPath(p3, r'C:\\dir\\num\\two;C:\\dir\\num\\three;C:\\dir\\num\\two', sep=';')\n assert p3 == r'C:\\dir\\num\\one;C:\\dir\\num\\three;C:\\dir\\num\\two', p3","def append_path(path1, path2):\n\n # Get the first absolute path\n abs_path1 = abspath(path1)\n\n # Return the joined paths\n return os.path.join(abs_path1, path2).replace(\"\\\\\", \"/\")","def joinPath(path, *args):","def test_set_path_5(self, verify_path2_mock):\n home = Path(\"~\")\n home = home.expanduser()\n test_file = Path(\"~/dir1/dir2/../file.txt\")\n verify_path2_mock.return_value = (True, None)\n output = basic.set_path(test_file, kind=\"file\", expect=True)\n exp = Path(home, \"dir1/file.txt\")\n self.assertEqual(output, exp)","def areSamePaths(path1, path2):\n\n path1 = os.path.abspath(os.path.normpath(path1))\n path2 = os.path.abspath(os.path.normpath(path2))\n\n if os.path.exists(path1) and os.path.exists(path2):\n path1 = getExternalUsePath(path1)\n path2 = getExternalUsePath(path2)\n\n path1 = os.path.normcase(path1)\n path2 = os.path.normcase(path2)\n\n return path1 == path2","def commonpath(a, b):\r\n a = normpath(normcase(a))\r\n b = normpath(normcase(b))\r\n\r\n if a == b:\r\n return a\r\n\r\n while len(a) > 0:\r\n if a == b:\r\n return a\r\n\r\n if len(a) > len(b):\r\n a = dirname(a)\r\n else:\r\n b = dirname(b)\r\n\r\n return None","def on_same_mount(cls, path1: os.PathLike, path2: os.PathLike) -> bool:\n return cls.get_mount(path1)[0] == cls.get_mount(path2)[0]","def test_set_path_4(self, verify_path2_mock):\n test_file = Path(\"/dir1/dir2/../file.txt\")\n verify_path2_mock.return_value = (True, None)\n output = basic.set_path(test_file, kind=\"file\", expect=True)\n exp = Path(\"/dir1/file.txt\")\n self.assertEqual(output, exp)","def test_verify_path2_2(self):\n self.file.touch()\n result, msg = basic.verify_path2(self.file, kind=\"file\", expect=False)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_is_compatible_both_dirs(self):\n\n i = Interface('/foo[0:4]')\n i['/foo[0:2]', 'interface', 'io'] = [0, 'out']\n i['/foo[2:4]', 'interface', 'io'] = [0, 'in']\n j = Interface('/foo[0:4]')\n j['/foo[0:2]', 'interface', 'io'] = [1, 'in']\n j['/foo[2:4]', 'interface', 'io'] = [1, 'out']\n assert i.is_compatible(0, j, 1)","def test_bad_paths(self):\n self.do_test_bad_path('frog', '/frog') # no permission to write","def test_relativise_sameparent():\n src = pathlib.Path(\"/tmp/foo/bar/src.txt\")\n dst = pathlib.Path(\"/tmp/foo/bar/dst.txt\")\n rel = relativise(src, dst)\n assert rel == pathlib.Path(\"dst.txt\")","def test_AppendPathPreserveOld(self) -> None:\n p1 = r'C:\\dir\\num\\one;C:\\dir\\num\\two'\n # have to include the pathsep here so that the test will work on UNIX too.\n p1 = AppendPath(p1, r'C:\\dir\\num\\one', sep=';', delete_existing=0)\n p1 = AppendPath(p1, r'C:\\dir\\num\\three', sep=';')\n assert p1 == r'C:\\dir\\num\\one;C:\\dir\\num\\two;C:\\dir\\num\\three', p1","def test_verify_path2_1(self):\n self.file.touch()\n result, msg = basic.verify_path2(self.file, kind=\"file\", expect=True)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def test_set_path_1(self):\n self.file.touch()\n # Since using tempfile, there is an added quirk.\n # the tempfile path may be a symlink, so passing it through set path\n # will resolve the symlink, changing the path, and breaking the test.\n self.file = self.file.resolve()\n output = basic.set_path(self.file, kind=\"file\", expect=True)\n with self.subTest():\n self.assertIsInstance(output, Path)\n with self.subTest():\n self.assertEqual(str(self.file), str(output))","def test_verify_path2_16(self):\n self.dir.mkdir()\n result, msg = basic.verify_path2(self.dir, kind=None, expect=True)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def test_verify_path2_13(self):\n result, msg = basic.verify_path2(self.dir, kind=\"dir\", expect=False)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def areInSamePaths(path1, path2):\n return areSamePaths(os.path.dirname(path1), os.path.dirname(path2))","def path_join(first: str, second: str) -> str:\n first = first.rstrip('/\\\\')\n second = second.lstrip('/\\\\')\n if not first: return second\n if not second: return first\n return first + '/' + second","def test_verify_path2_7(self):\n self.file.touch()\n result, msg = basic.verify_path2(self.file, kind=None, expect=False)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_verify_path2_12(self):\n self.dir.mkdir()\n result, msg = basic.verify_path2(self.dir, kind=\"dir\", expect=False)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_verify_path2_5(self):\n self.file.touch()\n result, msg = basic.verify_path2(self.dir, kind=\"dir\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_fromFullPath(self):\n log1 = logfile.LogFile(self.name, self.dir, 10, defaultMode=0o777)\n self.addCleanup(log1.close)\n log2 = logfile.LogFile.fromFullPath(self.path, 10, defaultMode=0o777)\n self.addCleanup(log2.close)\n self.assertEqual(log1.name, log2.name)\n self.assertEqual(os.path.abspath(log1.path), log2.path)\n self.assertEqual(log1.rotateLength, log2.rotateLength)\n self.assertEqual(log1.defaultMode, log2.defaultMode)","def __check_exist_path(self):\n if 'path_out' not in self.params:\n raise ValueError('missing \"path_out\" among parameters')\n self.params['path_out'] = update_path(self.params.get('path_out'))\n list_names = [n for n in self.params if any(m in n.lower() for m in ['path', 'dir', 'file'])]\n for n in list_names:\n p = os.path.abspath(os.path.expanduser(self.params[n]))\n if not os.path.exists(p):\n raise FileNotFoundError('given path/file/dir \"%s\" does not exist!' % p)\n self.params[n] = p\n for n in [n for n in self.params if 'exec' in n]:\n # in case you define executable in your home\n if os.path.expanduser(self.params[n]) != self.params[n]:\n self.params[n] = os.path.expanduser(self.params[n])","def test_fromFullPath(self):\n log1 = logfile.LogFile(self.name, self.dir, 10, defaultMode=0777)\n log2 = logfile.LogFile.fromFullPath(self.path, 10, defaultMode=0777)\n self.assertEquals(log1.name, log2.name)\n self.assertEquals(os.path.abspath(log1.path), log2.path)\n self.assertEquals(log1.rotateLength, log2.rotateLength)\n self.assertEquals(log1.defaultMode, log2.defaultMode)","def test_relativise_src_under():\n src = pathlib.Path(\"/tmp/foo/bar/baz/src.txt\")\n dst = pathlib.Path(\"/tmp/foo/dst.txt\")\n rel = relativise(src, dst)\n assert rel == pathlib.Path(\"../../dst.txt\")","def check_relpath(path1, path2, exception=True):\r\n p1 = op.normpath(path1)\r\n p2 = op.normpath(op.join(path1, path2))\r\n if op.relpath(p1, p2).endswith(op.basename(p1)):\r\n if exception:\r\n raise ValueError(\"Invalid path '%s'\" % path2)\r\n return False\r\n return p2","def file_path_short(self):\r\n if not hasattr(self, '_file_path_short'):\r\n if self.file_path:\r\n result = None\r\n\r\n for path in sys.path:\r\n candidate = os.path.relpath(self.file_path, path)\r\n if not result or (len(candidate.split('/')) < len(result.split('/'))):\r\n result = candidate\r\n\r\n self._file_path_short = result\r\n else: \r\n self._file_path_short = None\r\n\r\n return self._file_path_short","def test_relativise_dst_under():\n src = pathlib.Path(\"/tmp/foo/src.txt\")\n dst = pathlib.Path(\"/tmp/foo/bar/baz/dst.txt\")\n rel = relativise(src, dst)\n assert rel == pathlib.Path(\"bar/baz/dst.txt\")","def test_merge_not_fail_draftpath_intersection(self):\n path_a = PathFactory.create(name=\"A\", geom=LineString((0, 0), (10, 0)))\n path_b = PathFactory.create(name=\"B\", geom=LineString((10, 0), (20, 0)))\n PathFactory.create(name=\"C\", geom=LineString((10, 0), (10, 10)), draft=True)\n response = self.client.post(reverse('core:path-drf-merge-path'), {'path[]': [path_a.pk, path_b.pk]})\n self.assertIn('success', response.json())","def test_is_compatible_both_dirs_types(self):\n\n i = Interface('/foo[0:4]')\n i['/foo[0:2]'] = [0, 'out', 'gpot']\n i['/foo[2:4]'] = [0, 'in', 'spike']\n j = Interface('/foo[0:4]')\n j['/foo[0:2]'] = [1, 'in', 'gpot']\n j['/foo[2:4]'] = [1, 'out', 'spike']\n assert i.is_compatible(0, j, 1)","def lpath(file0, file1):\n return os.path.abspath(os.path.join(os.path.dirname(file0), file1))","def test_verify_path2_15(self):\n self.dir.mkdir()\n result, msg = basic.verify_path2(self.dir, kind=\"file\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_outpath_multi_unequal(tmpdir):\n base = glob.glob(\"%s/dummy/mm0\" % DATA_DIR)[0]\n paths = sorted(glob.glob(base + \"/*.ufo\"))\n # the reference font is modified in-place, make a temp copy first\n referenceSrc = py.path.local(paths[0])\n referenceDst = tmpdir / referenceSrc.basename\n referenceSrc.copy(referenceDst)\n reference = str(referenceDst)\n inpaths = paths[1:]\n outpaths = [str(tmpdir / basename(p)) for p in inpaths][1:]\n\n with pytest.raises(SystemExit):\n psautohint(inpaths + ['-o'] + outpaths + ['-r', reference])","def test_verify_path2_14(self):\n result, msg = basic.verify_path2(self.dir, kind=\"dir\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_expand_path_2(self):\n input_path = \"/fake/path\"\n expanded_path = basic.expand_path(input_path)\n expected_path = input_path\n self.assertEqual(expanded_path, expected_path)","def test_file_empty_dir_conflict(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo\")\n self.write_dir(dir1, \"foo\")\n self.sync_all()\n # Directory wins. File is deleted in dir0\n self.assertDirPresent(dir0, \"foo\")\n self.assertDirPresent(dir1, \"foo\")","def testJoinPath(self):\n test_file_path = self._GetTestFilePath(['utmp-linux_libc6'])\n self._SkipIfPathNotExists(test_file_path)\n\n test_helper = dfvfs_helpers.DFVFSFileSystemHelper(None)\n\n path_spec = path_spec_factory.Factory.NewPathSpec(\n dfvfs_definitions.TYPE_INDICATOR_OS, location=test_file_path)\n test_helper.OpenFileSystem(path_spec)\n\n path_segments = os.path.split(test_file_path)\n\n path = test_helper.JoinPath(path_segments)\n self.assertEqual(path, test_file_path)","def samefile(self, other):\n other = os.fspath(other)\n if not isabs(other):\n other = abspath(other)\n if self == other:\n return True\n if not hasattr(os.path, \"samefile\"):\n return False\n return error.checked_call(os.path.samefile, self.strpath, other)","def test_verify_path2_17(self):\n self.dir.mkdir()\n result, msg = basic.verify_path2(self.dir, kind=\"invalid\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_relativise_different_parents_shallow():\n src = pathlib.Path(\"/tmp/foo/bar/src.txt\")\n dst = pathlib.Path(\"/tmp/foo/baz/dst.txt\")\n rel = relativise(src, dst)\n assert rel == pathlib.Path(\"../baz/dst.txt\")","def validate_short_path(short_path):","def test_verify_path2_6(self):\n self.file.touch()\n result, msg = basic.verify_path2(self.file, kind=None, expect=True)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def test_verify_path2_4(self):\n result, msg = basic.verify_path2(self.file, kind=\"file\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def existShortestPath(self):\r\n # s, t = self.findSourceDest(source, dest)\r\n return self.run()","def __eq__(self, other):\n return type(self) == type(other) and self._full_path == other.full_path","def test_verify_path2_11(self):\n self.dir.mkdir()\n result, msg = basic.verify_path2(self.dir, kind=\"dir\", expect=True)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def test_filePathNoCommonElements(self):\n self.assertEqual(\n filePathDelta(FilePath(\"/foo/bar\"), FilePath(\"/baz/quux\")),\n [\"..\", \"..\", \"baz\", \"quux\"],\n )","def test_verify_path_4(self):\n result = basic.verify_path(str(self.test_directory1) + \"abcxyz\", \"dir\")\n self.assertFalse(result)","def test_verify_path2_9(self):\n result, msg = basic.verify_path2(self.file, kind=None, expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def testJoin(self):\r\n P=lambda p:ufsi.NativeUnixPath(p)\r\n data={\r\n # 1\r\n 'relativePath':\r\n ['/dir1/',P('dir2/fileBase.ext'),'/dir1/dir2/fileBase.ext'],\r\n\r\n # 2\r\n 'absolutePath':\r\n ['/dir1/',P('/dir2/fileBase.ext'),'/dir2/fileBase.ext'],\r\n\r\n # 3\r\n 'notSeparatorTerminatedPath':\r\n ['dir1',P('dir2/fileBase.ext'),'dir1/dir2/fileBase.ext'],\r\n\r\n # 4\r\n 'emptyPath':\r\n ['dir1',P(''),'dir1/'],\r\n\r\n # 5\r\n 'nonNativePath':\r\n ['dir1',ufsi.HttpPath('http://www.google.com.au/'),\r\n 'http://www.google.com.au/']\r\n }\r\n\r\n for k in data.iterkeys():\r\n p1=P(data[k][0])\r\n p2=data[k][1]\r\n r1=str(p1.join(p2))\r\n r2=data[k][2]\r\n self.assertEquals(r1,r2,\r\n '%s: join result was %r but should have been %r'\r\n %(k,r1,r2))","def test_too_short_path_but_root_correct(self):\n result = self.runner.invoke(\n cli, [*CLI_LOG_OPTION, \"config\", \"get\", \"agent\"], standalone_mode=False\n )\n assert result.exit_code == 1\n assert (\n result.exception.message\n == \"The path is too short. Please specify a path up to an attribute name.\"\n )\n\n result = self.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"config\", \"get\", \"skills.dummy\"],\n standalone_mode=False,\n )\n assert result.exit_code == 1\n assert (\n result.exception.message\n == \"The path is too short. Please specify a path up to an attribute name.\"\n )","def samefile(path1, path2):\n try:\n return os.path.samefile(path1, path2)\n except OSError as err:\n if err.errno == 2: # ENOENT\n return False\n else:\n raise","def findShortestPath(self):\r\n pass","def test_expand_path_3(self):\n partial_path = \"/fake/path\"\n input_path = \".\" + partial_path\n expanded_path = basic.expand_path(input_path)\n local_path = Path(\".\").resolve()\n expected_path = str(local_path) + partial_path\n self.assertEqual(expanded_path, expected_path)","def test_set_path_3(self, verify_path2_mock):\n home = Path(\"~\")\n home = home.expanduser()\n test_file = Path(\"~/path/to/file.txt\")\n verify_path2_mock.return_value = (True, None)\n output = basic.set_path(test_file, kind=\"file\", expect=True)\n exp = Path(home, \"path/to/file.txt\")\n self.assertEqual(output, exp)","def test_verify_path_6(self):\n result = basic.verify_path(str(self.test_directory1) + \"abcxyz\")\n self.assertFalse(result)","def test_PrependPath(self) -> None:\n p1 = r'C:\\dir\\num\\one;C:\\dir\\num\\two'\n p2 = r'C:\\mydir\\num\\one;C:\\mydir\\num\\two'\n # have to include the pathsep here so that the test will work on UNIX too.\n p1 = PrependPath(p1, r'C:\\dir\\num\\two', sep=';')\n p1 = PrependPath(p1, r'C:\\dir\\num\\three', sep=';')\n assert p1 == r'C:\\dir\\num\\three;C:\\dir\\num\\two;C:\\dir\\num\\one', p1\n\n p2 = PrependPath(p2, r'C:\\mydir\\num\\three', sep=';')\n p2 = PrependPath(p2, r'C:\\mydir\\num\\one', sep=';')\n assert p2 == r'C:\\mydir\\num\\one;C:\\mydir\\num\\three;C:\\mydir\\num\\two', p2\n\n # check (only) first one is kept if there are dupes in new\n p3 = r'C:\\dir\\num\\one'\n p3 = PrependPath(p3, r'C:\\dir\\num\\two;C:\\dir\\num\\three;C:\\dir\\num\\two', sep=';')\n assert p3 == r'C:\\dir\\num\\two;C:\\dir\\num\\three;C:\\dir\\num\\one', p3","def assertEqualPathsList(first: Iterable[str], second: Iterable[str]) -> None: # pragma: no cover\n if any(isPass(path) for path in first):\n return\n if any(isPass(path) for path in second):\n return\n for fpath in first:\n assert any(fnmatch.fnmatch(fpath, spath) for spath in second)\n for spath in second:\n assert any(fnmatch.fnmatch(fpath, spath) for fpath in first)","def test_verify_path_1(self):\n result = basic.verify_path(self.test_filepath1, \"file\")\n self.assertTrue(result)","def test_expand_path_1(self):\n partial_path = \"/fake/path\"\n input_path = \"~\" + partial_path\n expanded_path = basic.expand_path(input_path)\n home_dir = Path(\"~\").expanduser()\n expected_path = str(home_dir) + partial_path\n self.assertEqual(expanded_path, expected_path)","def make_full_path(self, path, name):\n full_path = (path + \"/\" + name) if path != '' else name\n # remove any duplicate slashes\n full_path = re.sub(r'//+',r'/', full_path)\n self.validate_path(full_path)\n return full_path","def test_filePathNoCommonElements(self):\n self.assertEquals(filePathDelta(FilePath(\"/foo/bar\"),\n FilePath(\"/baz/quux\")),\n [\"..\", \"..\", \"baz\", \"quux\"])","def test_too_short_path_but_root_correct(self):\n result = self.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"config\", \"set\", \"agent\", \"data\"],\n standalone_mode=False,\n )\n assert result.exit_code == 1\n assert (\n result.exception.message\n == \"The path is too short. Please specify a path up to an attribute name.\"\n )\n\n result = self.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"config\", \"set\", \"skills.dummy\", \"value\"],\n standalone_mode=False,\n )\n assert result.exit_code == 1\n assert (\n result.exception.message\n == \"The path is too short. Please specify a path up to an attribute name.\"\n )","def test_relativise_different_parents_deep():\n src = pathlib.Path(\"/tmp/foo/bar1/bar2/src.txt\")\n dst = pathlib.Path(\"/tmp/foo/baz1/baz2/baz3/dst.txt\")\n rel = relativise(src, dst)\n assert rel == pathlib.Path(\"../../baz1/baz2/baz3/dst.txt\")","def test_verify_path2_8(self):\n result, msg = basic.verify_path2(self.file, kind=None, expect=False)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def test_verify_path2_10(self):\n result, msg = basic.verify_path2(self.file, kind=\"invalid\", expect=True)\n with self.subTest():\n self.assertFalse(result)\n with self.subTest():\n self.assertIsNotNone(msg)","def test_file_dir_conflict(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo\")\n self.write_file(dir1, \"foo/bar\", \"baz\")\n self.sync_all()\n # Directory wins. File is deleted in dir0\n self.assertFile(dir0, \"foo/bar\", \"baz\")\n self.assertFile(dir1, \"foo/bar\", \"baz\")","def empty_paths(self):\n self.paths[:]","def samefile(path1, path2):\n # Handles path-like objects and checks if storage\n path1, path1_is_storage = format_and_is_storage(path1)\n path2, path2_is_storage = format_and_is_storage(path2)\n\n # Local files: Redirects to \"os.path.samefile\"\n if not path1_is_storage and not path2_is_storage:\n return os_path_samefile(path1, path2)\n\n # One path is local, the other storage\n if not path1_is_storage or not path2_is_storage:\n return False\n\n with handle_os_exceptions():\n # Paths don't use same storage\n system = get_instance(path1)\n if system is not get_instance(path2):\n return False\n\n # Relative path are different\n elif system.relpath(path1) != system.relpath(path2):\n return False\n\n # Same files\n return True","def has_path(self, source, target):\n try:\n sp = nx.shortest_path(self.G, source, target)\n except nx.NetworkXNoPath:\n return False\n return True","def constructShortestPath(self):","def check(self):\n super().check()\n\n # scratch directory\n if 'ORTHO' not in PATH:\n setattr(PATH, 'ORTHO', join(PATH.SCRATCH, 'ortho'))","def test_filePathDeltaSimilarEndElements(self):\n self.assertEqual(\n filePathDelta(FilePath(\"/foo/bar/bar/spam\"), FilePath(\"/foo/bar/baz/spam\")),\n [\"..\", \"..\", \"baz\", \"spam\"],\n )","def check_paths(self):\n self.data[\"app_path\"] = list(map(\n self.replace_vars_path, self.data[\"app_path\"]))\n self.data[\"icons_path\"] = list(map(\n self.replace_vars_path, self.data[\"icons_path\"]))\n new_app_path = []\n for app_path in self.data[\"app_path\"]:\n if path.isdir(app_path) or path.isfile(app_path):\n new_app_path.append(app_path)\n self.data[\"app_path\"] = new_app_path\n if not len(self.data[\"app_path\"]) == 0:\n new_icons_path = []\n for icon_path in self.data[\"icons_path\"]:\n if (self.data[\"force_create_folder\"] and\n not path.exists(icon_path)):\n log(\"Creating application folder for {0}\".format(self.data[\"name\"]))\n create_dir(icon_path)\n if path.isdir(icon_path):\n if (\"binary\" in self.data.keys()\n and path.isfile(icon_path + self.data[\"binary\"])):\n new_icons_path.append(icon_path)\n elif \"binary\" not in self.data.keys():\n new_icons_path.append(icon_path)\n self.data[\"icons_path\"] = new_icons_path","def test_verify_path_7(self):\n result = basic.verify_path(str(self.test_directory1), \"invalid\")\n self.assertFalse(result)","def join(cls, *args):\n return AbsolutePath(os.path.join(*(str(piece) for piece in args)))","def full_path(self):\n fullpath = os.path.join(self.path, self.name)\n if self.path == \"\":\n fullpath = self.name\n return fullpath","def test_predicates_on_unsanitized_paths(self):\n self.mfs.add_entries({'/just/another/pythonista': ''})\n\n self.assertTrue(os.path.isdir('///just'))\n self.assertTrue(os.path.isdir('///just/////another'))\n self.assertTrue(os.path.exists('///just////another////////pythonista'))\n self.assertTrue(os.path.isfile('///just////another////////pythonista'))","def build_paths():\n print(\"Creating Paths!\")\n try:\n os.stat(FILES_PATH)\n except:\n os.mkdir(FILES_PATH)\n \n try:\n os.stat(BARRELS_PATH)\n except:\n os.mkdir(BARRELS_PATH)\n\n try:\n os.stat(FORWARD_BARRELS_PATH)\n except:\n os.mkdir(FORWARD_BARRELS_PATH)\n\n try:\n os.stat(INVERTED_BARRELS_PATH)\n except:\n os.mkdir(INVERTED_BARRELS_PATH)\n\n try:\n os.stat(SHORT_BARRELS)\n except:\n os.mkdir(SHORT_BARRELS)\n\n try:\n os.stat(SHORT_FORWARD_BARRELS_PATH)\n except:\n os.mkdir(SHORT_FORWARD_BARRELS_PATH)\n\n try:\n os.stat(SHORT_INVERTED_BARRELS_PATH)\n except:\n os.mkdir(SHORT_INVERTED_BARRELS_PATH)\n\n try:\n os.stat(EXTRA_PATH)\n except:\n os.mkdir(EXTRA_PATH)","def test_getLinkrelToSameDirectory(self):\n linkrel = self.builder.getLinkrel(FilePath(\"/foo/bar\"),\n FilePath(\"/foo/bar\"))\n self.assertEquals(linkrel, \"\")","def ensure_path(full_path):\n full_path = Path(full_path)\n if not full_path.exists():\n full_path.mkdir(parents=True, exist_ok=True)","def test_change_non_empty_dir_to_file(self):\n dir0, dir1 = self.make_temp_dirs(2)\n self.write_file(dir0, \"foo/bar\", \"baz\")\n self.sync_all()\n self.assertFile(dir0, \"foo/bar\", \"baz\")\n self.assertFile(dir1, \"foo/bar\", \"baz\")\n\n self.delete_file(dir0, \"foo/bar\")\n self.delete_dir(dir0, \"foo\")\n self.write_file(dir0, \"foo\", \"bar\")\n self.sync_all()\n self.assertFile(dir0, \"foo\", \"bar\")\n self.assertFile(dir1, \"foo\", \"bar\")","def check_paths(self):\r\n\t\tself.check_line_edits_and_refresh_filestate()\r\n\t\t# paths\r\n\t\tsource_img_filename = self.source_img_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_dir_name = self.sink_dir_entry.text().replace(\"\\\\\", \"/\")\r\n\t\tsink_db_name_entry_text = self.sink_db_name_entry.text()\r\n\t\tdb_ext = \".db\" if not sink_db_name_entry_text.lower().endswith(\".db\") else \"\"\r\n\t\tsink_db_filename = os.path.join(sink_dir_name, sink_db_name_entry_text + db_ext).replace(\"\\\\\", \"/\")\r\n\t\tsource_db_filename = \"\"\r\n\r\n\t\t# check validity\r\n\t\tsource_img_filename_valid = self.filestate.is_valid(source_img_filename, SOURCE_IMG)\r\n\t\tsink_dir_name_valid = self.filestate.is_valid(sink_dir_name, SINK_DIR)\r\n\t\tsink_db_filename_valid = self.filestate.is_valid(sink_db_filename, SINK_DB)\r\n\t\tsource_db_filename_valid = True\r\n\r\n\t\tall_paths_valid = source_img_filename_valid and sink_dir_name_valid and sink_db_filename_valid\r\n\r\n\t\tif self.existing_case:\r\n\t\t\tsource_db_filename = self.source_db_entry.text()\r\n\t\t\tsource_db_filename_valid = self.filestate.is_valid(source_db_filename, SOURCE_DB)\r\n\t\t\tall_paths_valid = all_paths_valid and source_db_filename_valid\r\n\r\n\t\tif all_paths_valid:\r\n\t\t\tself.filestate.set_source_img_filename(source_img_filename)\r\n\t\t\tself.filestate.set_sink_dir_name(sink_dir_name)\r\n\t\t\tself.filestate.set_sink_db_filename(sink_db_filename)\r\n\t\t\tif self.existing_case:\r\n\t\t\t\tself.filestate.set_source_db_filename(source_db_filename)\r\n\t\t\tself.refresh_UI()\r\n\t\t\treturn True\r\n\r\n\t\t# in the case of invalidity\r\n\t\tif not source_img_filename_valid:\r\n\t\t\tif not self.filestate.source_img_file_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source image file at does not exist.\")\r\n\t\t\telif not self.filestate.source_img_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source image file type is invalid (must be .npy).\")\r\n\t\t\tself.filestate.set_source_img_filename(\"\")\r\n\t\tif not source_db_filename_valid: # only if existing case\r\n\t\t\tif not self.source_db_file_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source database file does not exist.\")\r\n\t\t\telif not self.filestate.source_db_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided source database file type is invalid (must be .db)\")\r\n\t\t\tself.filestate.set_source_db_filename(\"\")\r\n\t\tif not sink_dir_name_valid:\r\n\t\t\tif not self.filestate.sink_dir_exists:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided sink directory does not exist.\")\r\n\t\t\telif not self.sink_dir_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Provided sink directory format is invalid.\")\r\n\t\t\tself.filestate.set_sink_dir_name(\"\")\r\n\t\tif not sink_db_filename_valid:\r\n\t\t\tif sink_dir_name_valid and not self.filestate.sink_db_file_preexists and \\\r\n\t\t\t\t\tself.filestate.sink_db_file_format_valid and \\\r\n\t\t\t\t\tdisplay_yes_no_message(self, \"Create file at \" + sink_db_filename + \"?\"):\r\n\t\t\t\t# create file with read write permissions\r\n\t\t\t\t###########################################\r\n\t\t\t\ttry:\r\n\t\t\t\t\tsink_db_file = open(sink_db_filename, \"w+\")\r\n\t\t\t\t\tsink_db_file.close()\r\n\t\t\t\texcept IOError as error:\r\n\t\t\t\t\tdisplay_warning_message(self, \"Failed to create provided sink database file: \" + error)\r\n\t\t\t\t###########################################\r\n\t\t\t\t# set sink db filename\r\n\t\t\t\telse:\r\n\t\t\t\t\tself.filestate.set_sink_db_filename(sink_db_filename)\r\n\t\t\t\t\tself.refresh_UI()\r\n\t\t\t\t\treturn True\r\n\t\t\telif not self.filestate.sink_db_file_format_valid:\r\n\t\t\t\tdisplay_warning_message(self, \"Be sure to specify a name for the sink database.\")\r\n\t\t\tself.filestate.set_sink_db_filename(\"\")\r\n\r\n\t\t# print(\"paths invalid\")\r\n\t\tself.refresh_UI()\r\n\t\treturn False","def update_path():\n #TODO update path information\n pass","def test_realpath(self):\n print real_upath(\"ref with space\")\n self.assertTrue(real_upath(\"ref with space\").endswith(\"ref\\ with\\ space\"))\n self.assertTrue(real_upath(\"ref\\ with\\ space\").endswith(\"ref\\ with\\ space\"))\n self.assertTrue(real_ppath(\"ref with space\").endswith(\"ref with space\"))\n self.assertTrue(real_ppath(\"ref\\ with\\ space\").endswith(\"ref with space\"))","def test_verify_path_5(self):\n result = basic.verify_path(str(self.test_directory1))\n self.assertTrue(result)","def _set_rel_paths(self):\n if self.working_dir is not None:\n self._rel_working_dir = os.path.relpath(self.working_dir)\n if self.alignment is not None:\n self._rel_alignment = os.path.relpath(self.alignment, \n self.working_dir)\n if self.out_file is not None:\n self._rel_out_file = os.path.relpath(self.out_file, \n self.working_dir)","def check_abs_path_file(self):\n with in_temp_dir() as dir1:\n filename = self._make_random_file(self.tempdir)\n abspath_filename = os.path.abspath(filename)\n\n # since we're using absolute path to file, we should be able to run the compress command from anywhere\n with in_temp_dir() as dir2:\n assert dir1 != dir2\n\n os.system(_compress_cmd(abspath_filename))\n self._validate_compressed(abspath_filename)","def test_verify_path2_3(self):\n result, msg = basic.verify_path2(self.file, kind=\"file\", expect=False)\n with self.subTest():\n self.assertTrue(result)\n with self.subTest():\n self.assertIsNone(msg)","def compactuser(path):\n userPath = expanduser('~')\n otherPath = expanduser(path)\n \n prefix = commonprefix([userPath, otherPath])\n if prefix == userPath:\n return '~' + otherPath[len(prefix):]\n else:\n return otherPath","def prepare(self):\n if not self._base:\n self.error = \"path= must be specified\"\n return False\n if self._volume:\n if \"://\" not in self._volume:\n self.error = \"mount= can only be an URL\"\n return False\n if self._base.startswith(\"/\"):\n self._base = os.path.join(self._volume, self._base[1:])\n # do the prefix check anyway, for sanity\n if not is_parent_of(self._volume, self._base):\n self.error = \"mount= must be a prefix of path=\"\n return False\n return True","def force_absolute(base, path):\n if os.path.abspath(path) and os.path.exists(path):\n return path\n else:\n return path_format(base + path)","def chkPath(fullPath: str) -> None:\n\n # Check if path already exist.\n p = os.path.split(fullPath)\n exists = os.path.exists(p[0])\n # If not then create it.\n if exists == False:\n try:\n os.makedirs(p[0])\n except:\n print(\"Failed to create requested path.\")"],"string":"[\n \"def __combine_path(self, other):\\n self.path = other.path + self.path\",\n \"def join(self, path, *paths):\",\n \"def aix_path_join(path_one, path_two):\\n if path_one.endswith('/'):\\n path_one = path_one.rstrip('/')\\n\\n if path_two.startswith('/'):\\n path_two = path_two.lstrip('/')\\n\\n final_path = path_one + '/' + path_two\\n return final_path\",\n \"def samepath(p1, p2):\\n # type: (str, str) -> bool\\n return pathnormalize(p1) == pathnormalize(p2)\",\n \"def join_paths(path_1, path_2):\\r\\n a = lib_path.join(path_1, path_2)\\r\\n return a\",\n \"def test_verify_path_2(self):\\n result = basic.verify_path(str(self.test_filepath1) + \\\"abcxyz\\\", \\\"file\\\")\\n self.assertFalse(result)\",\n \"def _issubpath(self, a, b):\\n p1 = a.rstrip(os.sep).split(os.sep)\\n p2 = b.rstrip(os.sep).split(os.sep)\\n return p1[:len(p2)] == p2\",\n \"def test_AppendPath(self) -> None:\\n p1 = r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\two'\\n p2 = r'C:\\\\mydir\\\\num\\\\one;C:\\\\mydir\\\\num\\\\two'\\n # have to include the pathsep here so that the test will work on UNIX too.\\n p1 = AppendPath(p1, r'C:\\\\dir\\\\num\\\\two', sep=';')\\n p1 = AppendPath(p1, r'C:\\\\dir\\\\num\\\\three', sep=';')\\n assert p1 == r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\three', p1\\n\\n p2 = AppendPath(p2, r'C:\\\\mydir\\\\num\\\\three', sep=';')\\n p2 = AppendPath(p2, r'C:\\\\mydir\\\\num\\\\one', sep=';')\\n assert p2 == r'C:\\\\mydir\\\\num\\\\two;C:\\\\mydir\\\\num\\\\three;C:\\\\mydir\\\\num\\\\one', p2\\n\\n # check (only) last one is kept if there are dupes in new\\n p3 = r'C:\\\\dir\\\\num\\\\one'\\n p3 = AppendPath(p3, r'C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\three;C:\\\\dir\\\\num\\\\two', sep=';')\\n assert p3 == r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\three;C:\\\\dir\\\\num\\\\two', p3\",\n \"def append_path(path1, path2):\\n\\n # Get the first absolute path\\n abs_path1 = abspath(path1)\\n\\n # Return the joined paths\\n return os.path.join(abs_path1, path2).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\",\n \"def joinPath(path, *args):\",\n \"def test_set_path_5(self, verify_path2_mock):\\n home = Path(\\\"~\\\")\\n home = home.expanduser()\\n test_file = Path(\\\"~/dir1/dir2/../file.txt\\\")\\n verify_path2_mock.return_value = (True, None)\\n output = basic.set_path(test_file, kind=\\\"file\\\", expect=True)\\n exp = Path(home, \\\"dir1/file.txt\\\")\\n self.assertEqual(output, exp)\",\n \"def areSamePaths(path1, path2):\\n\\n path1 = os.path.abspath(os.path.normpath(path1))\\n path2 = os.path.abspath(os.path.normpath(path2))\\n\\n if os.path.exists(path1) and os.path.exists(path2):\\n path1 = getExternalUsePath(path1)\\n path2 = getExternalUsePath(path2)\\n\\n path1 = os.path.normcase(path1)\\n path2 = os.path.normcase(path2)\\n\\n return path1 == path2\",\n \"def commonpath(a, b):\\r\\n a = normpath(normcase(a))\\r\\n b = normpath(normcase(b))\\r\\n\\r\\n if a == b:\\r\\n return a\\r\\n\\r\\n while len(a) > 0:\\r\\n if a == b:\\r\\n return a\\r\\n\\r\\n if len(a) > len(b):\\r\\n a = dirname(a)\\r\\n else:\\r\\n b = dirname(b)\\r\\n\\r\\n return None\",\n \"def on_same_mount(cls, path1: os.PathLike, path2: os.PathLike) -> bool:\\n return cls.get_mount(path1)[0] == cls.get_mount(path2)[0]\",\n \"def test_set_path_4(self, verify_path2_mock):\\n test_file = Path(\\\"/dir1/dir2/../file.txt\\\")\\n verify_path2_mock.return_value = (True, None)\\n output = basic.set_path(test_file, kind=\\\"file\\\", expect=True)\\n exp = Path(\\\"/dir1/file.txt\\\")\\n self.assertEqual(output, exp)\",\n \"def test_verify_path2_2(self):\\n self.file.touch()\\n result, msg = basic.verify_path2(self.file, kind=\\\"file\\\", expect=False)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_is_compatible_both_dirs(self):\\n\\n i = Interface('/foo[0:4]')\\n i['/foo[0:2]', 'interface', 'io'] = [0, 'out']\\n i['/foo[2:4]', 'interface', 'io'] = [0, 'in']\\n j = Interface('/foo[0:4]')\\n j['/foo[0:2]', 'interface', 'io'] = [1, 'in']\\n j['/foo[2:4]', 'interface', 'io'] = [1, 'out']\\n assert i.is_compatible(0, j, 1)\",\n \"def test_bad_paths(self):\\n self.do_test_bad_path('frog', '/frog') # no permission to write\",\n \"def test_relativise_sameparent():\\n src = pathlib.Path(\\\"/tmp/foo/bar/src.txt\\\")\\n dst = pathlib.Path(\\\"/tmp/foo/bar/dst.txt\\\")\\n rel = relativise(src, dst)\\n assert rel == pathlib.Path(\\\"dst.txt\\\")\",\n \"def test_AppendPathPreserveOld(self) -> None:\\n p1 = r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\two'\\n # have to include the pathsep here so that the test will work on UNIX too.\\n p1 = AppendPath(p1, r'C:\\\\dir\\\\num\\\\one', sep=';', delete_existing=0)\\n p1 = AppendPath(p1, r'C:\\\\dir\\\\num\\\\three', sep=';')\\n assert p1 == r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\three', p1\",\n \"def test_verify_path2_1(self):\\n self.file.touch()\\n result, msg = basic.verify_path2(self.file, kind=\\\"file\\\", expect=True)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def test_set_path_1(self):\\n self.file.touch()\\n # Since using tempfile, there is an added quirk.\\n # the tempfile path may be a symlink, so passing it through set path\\n # will resolve the symlink, changing the path, and breaking the test.\\n self.file = self.file.resolve()\\n output = basic.set_path(self.file, kind=\\\"file\\\", expect=True)\\n with self.subTest():\\n self.assertIsInstance(output, Path)\\n with self.subTest():\\n self.assertEqual(str(self.file), str(output))\",\n \"def test_verify_path2_16(self):\\n self.dir.mkdir()\\n result, msg = basic.verify_path2(self.dir, kind=None, expect=True)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def test_verify_path2_13(self):\\n result, msg = basic.verify_path2(self.dir, kind=\\\"dir\\\", expect=False)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def areInSamePaths(path1, path2):\\n return areSamePaths(os.path.dirname(path1), os.path.dirname(path2))\",\n \"def path_join(first: str, second: str) -> str:\\n first = first.rstrip('/\\\\\\\\')\\n second = second.lstrip('/\\\\\\\\')\\n if not first: return second\\n if not second: return first\\n return first + '/' + second\",\n \"def test_verify_path2_7(self):\\n self.file.touch()\\n result, msg = basic.verify_path2(self.file, kind=None, expect=False)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_verify_path2_12(self):\\n self.dir.mkdir()\\n result, msg = basic.verify_path2(self.dir, kind=\\\"dir\\\", expect=False)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_verify_path2_5(self):\\n self.file.touch()\\n result, msg = basic.verify_path2(self.dir, kind=\\\"dir\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_fromFullPath(self):\\n log1 = logfile.LogFile(self.name, self.dir, 10, defaultMode=0o777)\\n self.addCleanup(log1.close)\\n log2 = logfile.LogFile.fromFullPath(self.path, 10, defaultMode=0o777)\\n self.addCleanup(log2.close)\\n self.assertEqual(log1.name, log2.name)\\n self.assertEqual(os.path.abspath(log1.path), log2.path)\\n self.assertEqual(log1.rotateLength, log2.rotateLength)\\n self.assertEqual(log1.defaultMode, log2.defaultMode)\",\n \"def __check_exist_path(self):\\n if 'path_out' not in self.params:\\n raise ValueError('missing \\\"path_out\\\" among parameters')\\n self.params['path_out'] = update_path(self.params.get('path_out'))\\n list_names = [n for n in self.params if any(m in n.lower() for m in ['path', 'dir', 'file'])]\\n for n in list_names:\\n p = os.path.abspath(os.path.expanduser(self.params[n]))\\n if not os.path.exists(p):\\n raise FileNotFoundError('given path/file/dir \\\"%s\\\" does not exist!' % p)\\n self.params[n] = p\\n for n in [n for n in self.params if 'exec' in n]:\\n # in case you define executable in your home\\n if os.path.expanduser(self.params[n]) != self.params[n]:\\n self.params[n] = os.path.expanduser(self.params[n])\",\n \"def test_fromFullPath(self):\\n log1 = logfile.LogFile(self.name, self.dir, 10, defaultMode=0777)\\n log2 = logfile.LogFile.fromFullPath(self.path, 10, defaultMode=0777)\\n self.assertEquals(log1.name, log2.name)\\n self.assertEquals(os.path.abspath(log1.path), log2.path)\\n self.assertEquals(log1.rotateLength, log2.rotateLength)\\n self.assertEquals(log1.defaultMode, log2.defaultMode)\",\n \"def test_relativise_src_under():\\n src = pathlib.Path(\\\"/tmp/foo/bar/baz/src.txt\\\")\\n dst = pathlib.Path(\\\"/tmp/foo/dst.txt\\\")\\n rel = relativise(src, dst)\\n assert rel == pathlib.Path(\\\"../../dst.txt\\\")\",\n \"def check_relpath(path1, path2, exception=True):\\r\\n p1 = op.normpath(path1)\\r\\n p2 = op.normpath(op.join(path1, path2))\\r\\n if op.relpath(p1, p2).endswith(op.basename(p1)):\\r\\n if exception:\\r\\n raise ValueError(\\\"Invalid path '%s'\\\" % path2)\\r\\n return False\\r\\n return p2\",\n \"def file_path_short(self):\\r\\n if not hasattr(self, '_file_path_short'):\\r\\n if self.file_path:\\r\\n result = None\\r\\n\\r\\n for path in sys.path:\\r\\n candidate = os.path.relpath(self.file_path, path)\\r\\n if not result or (len(candidate.split('/')) < len(result.split('/'))):\\r\\n result = candidate\\r\\n\\r\\n self._file_path_short = result\\r\\n else: \\r\\n self._file_path_short = None\\r\\n\\r\\n return self._file_path_short\",\n \"def test_relativise_dst_under():\\n src = pathlib.Path(\\\"/tmp/foo/src.txt\\\")\\n dst = pathlib.Path(\\\"/tmp/foo/bar/baz/dst.txt\\\")\\n rel = relativise(src, dst)\\n assert rel == pathlib.Path(\\\"bar/baz/dst.txt\\\")\",\n \"def test_merge_not_fail_draftpath_intersection(self):\\n path_a = PathFactory.create(name=\\\"A\\\", geom=LineString((0, 0), (10, 0)))\\n path_b = PathFactory.create(name=\\\"B\\\", geom=LineString((10, 0), (20, 0)))\\n PathFactory.create(name=\\\"C\\\", geom=LineString((10, 0), (10, 10)), draft=True)\\n response = self.client.post(reverse('core:path-drf-merge-path'), {'path[]': [path_a.pk, path_b.pk]})\\n self.assertIn('success', response.json())\",\n \"def test_is_compatible_both_dirs_types(self):\\n\\n i = Interface('/foo[0:4]')\\n i['/foo[0:2]'] = [0, 'out', 'gpot']\\n i['/foo[2:4]'] = [0, 'in', 'spike']\\n j = Interface('/foo[0:4]')\\n j['/foo[0:2]'] = [1, 'in', 'gpot']\\n j['/foo[2:4]'] = [1, 'out', 'spike']\\n assert i.is_compatible(0, j, 1)\",\n \"def lpath(file0, file1):\\n return os.path.abspath(os.path.join(os.path.dirname(file0), file1))\",\n \"def test_verify_path2_15(self):\\n self.dir.mkdir()\\n result, msg = basic.verify_path2(self.dir, kind=\\\"file\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_outpath_multi_unequal(tmpdir):\\n base = glob.glob(\\\"%s/dummy/mm0\\\" % DATA_DIR)[0]\\n paths = sorted(glob.glob(base + \\\"/*.ufo\\\"))\\n # the reference font is modified in-place, make a temp copy first\\n referenceSrc = py.path.local(paths[0])\\n referenceDst = tmpdir / referenceSrc.basename\\n referenceSrc.copy(referenceDst)\\n reference = str(referenceDst)\\n inpaths = paths[1:]\\n outpaths = [str(tmpdir / basename(p)) for p in inpaths][1:]\\n\\n with pytest.raises(SystemExit):\\n psautohint(inpaths + ['-o'] + outpaths + ['-r', reference])\",\n \"def test_verify_path2_14(self):\\n result, msg = basic.verify_path2(self.dir, kind=\\\"dir\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_expand_path_2(self):\\n input_path = \\\"/fake/path\\\"\\n expanded_path = basic.expand_path(input_path)\\n expected_path = input_path\\n self.assertEqual(expanded_path, expected_path)\",\n \"def test_file_empty_dir_conflict(self):\\n dir0, dir1 = self.make_temp_dirs(2)\\n self.write_file(dir0, \\\"foo\\\")\\n self.write_dir(dir1, \\\"foo\\\")\\n self.sync_all()\\n # Directory wins. File is deleted in dir0\\n self.assertDirPresent(dir0, \\\"foo\\\")\\n self.assertDirPresent(dir1, \\\"foo\\\")\",\n \"def testJoinPath(self):\\n test_file_path = self._GetTestFilePath(['utmp-linux_libc6'])\\n self._SkipIfPathNotExists(test_file_path)\\n\\n test_helper = dfvfs_helpers.DFVFSFileSystemHelper(None)\\n\\n path_spec = path_spec_factory.Factory.NewPathSpec(\\n dfvfs_definitions.TYPE_INDICATOR_OS, location=test_file_path)\\n test_helper.OpenFileSystem(path_spec)\\n\\n path_segments = os.path.split(test_file_path)\\n\\n path = test_helper.JoinPath(path_segments)\\n self.assertEqual(path, test_file_path)\",\n \"def samefile(self, other):\\n other = os.fspath(other)\\n if not isabs(other):\\n other = abspath(other)\\n if self == other:\\n return True\\n if not hasattr(os.path, \\\"samefile\\\"):\\n return False\\n return error.checked_call(os.path.samefile, self.strpath, other)\",\n \"def test_verify_path2_17(self):\\n self.dir.mkdir()\\n result, msg = basic.verify_path2(self.dir, kind=\\\"invalid\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_relativise_different_parents_shallow():\\n src = pathlib.Path(\\\"/tmp/foo/bar/src.txt\\\")\\n dst = pathlib.Path(\\\"/tmp/foo/baz/dst.txt\\\")\\n rel = relativise(src, dst)\\n assert rel == pathlib.Path(\\\"../baz/dst.txt\\\")\",\n \"def validate_short_path(short_path):\",\n \"def test_verify_path2_6(self):\\n self.file.touch()\\n result, msg = basic.verify_path2(self.file, kind=None, expect=True)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def test_verify_path2_4(self):\\n result, msg = basic.verify_path2(self.file, kind=\\\"file\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def existShortestPath(self):\\r\\n # s, t = self.findSourceDest(source, dest)\\r\\n return self.run()\",\n \"def __eq__(self, other):\\n return type(self) == type(other) and self._full_path == other.full_path\",\n \"def test_verify_path2_11(self):\\n self.dir.mkdir()\\n result, msg = basic.verify_path2(self.dir, kind=\\\"dir\\\", expect=True)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def test_filePathNoCommonElements(self):\\n self.assertEqual(\\n filePathDelta(FilePath(\\\"/foo/bar\\\"), FilePath(\\\"/baz/quux\\\")),\\n [\\\"..\\\", \\\"..\\\", \\\"baz\\\", \\\"quux\\\"],\\n )\",\n \"def test_verify_path_4(self):\\n result = basic.verify_path(str(self.test_directory1) + \\\"abcxyz\\\", \\\"dir\\\")\\n self.assertFalse(result)\",\n \"def test_verify_path2_9(self):\\n result, msg = basic.verify_path2(self.file, kind=None, expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def testJoin(self):\\r\\n P=lambda p:ufsi.NativeUnixPath(p)\\r\\n data={\\r\\n # 1\\r\\n 'relativePath':\\r\\n ['/dir1/',P('dir2/fileBase.ext'),'/dir1/dir2/fileBase.ext'],\\r\\n\\r\\n # 2\\r\\n 'absolutePath':\\r\\n ['/dir1/',P('/dir2/fileBase.ext'),'/dir2/fileBase.ext'],\\r\\n\\r\\n # 3\\r\\n 'notSeparatorTerminatedPath':\\r\\n ['dir1',P('dir2/fileBase.ext'),'dir1/dir2/fileBase.ext'],\\r\\n\\r\\n # 4\\r\\n 'emptyPath':\\r\\n ['dir1',P(''),'dir1/'],\\r\\n\\r\\n # 5\\r\\n 'nonNativePath':\\r\\n ['dir1',ufsi.HttpPath('http://www.google.com.au/'),\\r\\n 'http://www.google.com.au/']\\r\\n }\\r\\n\\r\\n for k in data.iterkeys():\\r\\n p1=P(data[k][0])\\r\\n p2=data[k][1]\\r\\n r1=str(p1.join(p2))\\r\\n r2=data[k][2]\\r\\n self.assertEquals(r1,r2,\\r\\n '%s: join result was %r but should have been %r'\\r\\n %(k,r1,r2))\",\n \"def test_too_short_path_but_root_correct(self):\\n result = self.runner.invoke(\\n cli, [*CLI_LOG_OPTION, \\\"config\\\", \\\"get\\\", \\\"agent\\\"], standalone_mode=False\\n )\\n assert result.exit_code == 1\\n assert (\\n result.exception.message\\n == \\\"The path is too short. Please specify a path up to an attribute name.\\\"\\n )\\n\\n result = self.runner.invoke(\\n cli,\\n [*CLI_LOG_OPTION, \\\"config\\\", \\\"get\\\", \\\"skills.dummy\\\"],\\n standalone_mode=False,\\n )\\n assert result.exit_code == 1\\n assert (\\n result.exception.message\\n == \\\"The path is too short. Please specify a path up to an attribute name.\\\"\\n )\",\n \"def samefile(path1, path2):\\n try:\\n return os.path.samefile(path1, path2)\\n except OSError as err:\\n if err.errno == 2: # ENOENT\\n return False\\n else:\\n raise\",\n \"def findShortestPath(self):\\r\\n pass\",\n \"def test_expand_path_3(self):\\n partial_path = \\\"/fake/path\\\"\\n input_path = \\\".\\\" + partial_path\\n expanded_path = basic.expand_path(input_path)\\n local_path = Path(\\\".\\\").resolve()\\n expected_path = str(local_path) + partial_path\\n self.assertEqual(expanded_path, expected_path)\",\n \"def test_set_path_3(self, verify_path2_mock):\\n home = Path(\\\"~\\\")\\n home = home.expanduser()\\n test_file = Path(\\\"~/path/to/file.txt\\\")\\n verify_path2_mock.return_value = (True, None)\\n output = basic.set_path(test_file, kind=\\\"file\\\", expect=True)\\n exp = Path(home, \\\"path/to/file.txt\\\")\\n self.assertEqual(output, exp)\",\n \"def test_verify_path_6(self):\\n result = basic.verify_path(str(self.test_directory1) + \\\"abcxyz\\\")\\n self.assertFalse(result)\",\n \"def test_PrependPath(self) -> None:\\n p1 = r'C:\\\\dir\\\\num\\\\one;C:\\\\dir\\\\num\\\\two'\\n p2 = r'C:\\\\mydir\\\\num\\\\one;C:\\\\mydir\\\\num\\\\two'\\n # have to include the pathsep here so that the test will work on UNIX too.\\n p1 = PrependPath(p1, r'C:\\\\dir\\\\num\\\\two', sep=';')\\n p1 = PrependPath(p1, r'C:\\\\dir\\\\num\\\\three', sep=';')\\n assert p1 == r'C:\\\\dir\\\\num\\\\three;C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\one', p1\\n\\n p2 = PrependPath(p2, r'C:\\\\mydir\\\\num\\\\three', sep=';')\\n p2 = PrependPath(p2, r'C:\\\\mydir\\\\num\\\\one', sep=';')\\n assert p2 == r'C:\\\\mydir\\\\num\\\\one;C:\\\\mydir\\\\num\\\\three;C:\\\\mydir\\\\num\\\\two', p2\\n\\n # check (only) first one is kept if there are dupes in new\\n p3 = r'C:\\\\dir\\\\num\\\\one'\\n p3 = PrependPath(p3, r'C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\three;C:\\\\dir\\\\num\\\\two', sep=';')\\n assert p3 == r'C:\\\\dir\\\\num\\\\two;C:\\\\dir\\\\num\\\\three;C:\\\\dir\\\\num\\\\one', p3\",\n \"def assertEqualPathsList(first: Iterable[str], second: Iterable[str]) -> None: # pragma: no cover\\n if any(isPass(path) for path in first):\\n return\\n if any(isPass(path) for path in second):\\n return\\n for fpath in first:\\n assert any(fnmatch.fnmatch(fpath, spath) for spath in second)\\n for spath in second:\\n assert any(fnmatch.fnmatch(fpath, spath) for fpath in first)\",\n \"def test_verify_path_1(self):\\n result = basic.verify_path(self.test_filepath1, \\\"file\\\")\\n self.assertTrue(result)\",\n \"def test_expand_path_1(self):\\n partial_path = \\\"/fake/path\\\"\\n input_path = \\\"~\\\" + partial_path\\n expanded_path = basic.expand_path(input_path)\\n home_dir = Path(\\\"~\\\").expanduser()\\n expected_path = str(home_dir) + partial_path\\n self.assertEqual(expanded_path, expected_path)\",\n \"def make_full_path(self, path, name):\\n full_path = (path + \\\"/\\\" + name) if path != '' else name\\n # remove any duplicate slashes\\n full_path = re.sub(r'//+',r'/', full_path)\\n self.validate_path(full_path)\\n return full_path\",\n \"def test_filePathNoCommonElements(self):\\n self.assertEquals(filePathDelta(FilePath(\\\"/foo/bar\\\"),\\n FilePath(\\\"/baz/quux\\\")),\\n [\\\"..\\\", \\\"..\\\", \\\"baz\\\", \\\"quux\\\"])\",\n \"def test_too_short_path_but_root_correct(self):\\n result = self.runner.invoke(\\n cli,\\n [*CLI_LOG_OPTION, \\\"config\\\", \\\"set\\\", \\\"agent\\\", \\\"data\\\"],\\n standalone_mode=False,\\n )\\n assert result.exit_code == 1\\n assert (\\n result.exception.message\\n == \\\"The path is too short. Please specify a path up to an attribute name.\\\"\\n )\\n\\n result = self.runner.invoke(\\n cli,\\n [*CLI_LOG_OPTION, \\\"config\\\", \\\"set\\\", \\\"skills.dummy\\\", \\\"value\\\"],\\n standalone_mode=False,\\n )\\n assert result.exit_code == 1\\n assert (\\n result.exception.message\\n == \\\"The path is too short. Please specify a path up to an attribute name.\\\"\\n )\",\n \"def test_relativise_different_parents_deep():\\n src = pathlib.Path(\\\"/tmp/foo/bar1/bar2/src.txt\\\")\\n dst = pathlib.Path(\\\"/tmp/foo/baz1/baz2/baz3/dst.txt\\\")\\n rel = relativise(src, dst)\\n assert rel == pathlib.Path(\\\"../../baz1/baz2/baz3/dst.txt\\\")\",\n \"def test_verify_path2_8(self):\\n result, msg = basic.verify_path2(self.file, kind=None, expect=False)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def test_verify_path2_10(self):\\n result, msg = basic.verify_path2(self.file, kind=\\\"invalid\\\", expect=True)\\n with self.subTest():\\n self.assertFalse(result)\\n with self.subTest():\\n self.assertIsNotNone(msg)\",\n \"def test_file_dir_conflict(self):\\n dir0, dir1 = self.make_temp_dirs(2)\\n self.write_file(dir0, \\\"foo\\\")\\n self.write_file(dir1, \\\"foo/bar\\\", \\\"baz\\\")\\n self.sync_all()\\n # Directory wins. File is deleted in dir0\\n self.assertFile(dir0, \\\"foo/bar\\\", \\\"baz\\\")\\n self.assertFile(dir1, \\\"foo/bar\\\", \\\"baz\\\")\",\n \"def empty_paths(self):\\n self.paths[:]\",\n \"def samefile(path1, path2):\\n # Handles path-like objects and checks if storage\\n path1, path1_is_storage = format_and_is_storage(path1)\\n path2, path2_is_storage = format_and_is_storage(path2)\\n\\n # Local files: Redirects to \\\"os.path.samefile\\\"\\n if not path1_is_storage and not path2_is_storage:\\n return os_path_samefile(path1, path2)\\n\\n # One path is local, the other storage\\n if not path1_is_storage or not path2_is_storage:\\n return False\\n\\n with handle_os_exceptions():\\n # Paths don't use same storage\\n system = get_instance(path1)\\n if system is not get_instance(path2):\\n return False\\n\\n # Relative path are different\\n elif system.relpath(path1) != system.relpath(path2):\\n return False\\n\\n # Same files\\n return True\",\n \"def has_path(self, source, target):\\n try:\\n sp = nx.shortest_path(self.G, source, target)\\n except nx.NetworkXNoPath:\\n return False\\n return True\",\n \"def constructShortestPath(self):\",\n \"def check(self):\\n super().check()\\n\\n # scratch directory\\n if 'ORTHO' not in PATH:\\n setattr(PATH, 'ORTHO', join(PATH.SCRATCH, 'ortho'))\",\n \"def test_filePathDeltaSimilarEndElements(self):\\n self.assertEqual(\\n filePathDelta(FilePath(\\\"/foo/bar/bar/spam\\\"), FilePath(\\\"/foo/bar/baz/spam\\\")),\\n [\\\"..\\\", \\\"..\\\", \\\"baz\\\", \\\"spam\\\"],\\n )\",\n \"def check_paths(self):\\n self.data[\\\"app_path\\\"] = list(map(\\n self.replace_vars_path, self.data[\\\"app_path\\\"]))\\n self.data[\\\"icons_path\\\"] = list(map(\\n self.replace_vars_path, self.data[\\\"icons_path\\\"]))\\n new_app_path = []\\n for app_path in self.data[\\\"app_path\\\"]:\\n if path.isdir(app_path) or path.isfile(app_path):\\n new_app_path.append(app_path)\\n self.data[\\\"app_path\\\"] = new_app_path\\n if not len(self.data[\\\"app_path\\\"]) == 0:\\n new_icons_path = []\\n for icon_path in self.data[\\\"icons_path\\\"]:\\n if (self.data[\\\"force_create_folder\\\"] and\\n not path.exists(icon_path)):\\n log(\\\"Creating application folder for {0}\\\".format(self.data[\\\"name\\\"]))\\n create_dir(icon_path)\\n if path.isdir(icon_path):\\n if (\\\"binary\\\" in self.data.keys()\\n and path.isfile(icon_path + self.data[\\\"binary\\\"])):\\n new_icons_path.append(icon_path)\\n elif \\\"binary\\\" not in self.data.keys():\\n new_icons_path.append(icon_path)\\n self.data[\\\"icons_path\\\"] = new_icons_path\",\n \"def test_verify_path_7(self):\\n result = basic.verify_path(str(self.test_directory1), \\\"invalid\\\")\\n self.assertFalse(result)\",\n \"def join(cls, *args):\\n return AbsolutePath(os.path.join(*(str(piece) for piece in args)))\",\n \"def full_path(self):\\n fullpath = os.path.join(self.path, self.name)\\n if self.path == \\\"\\\":\\n fullpath = self.name\\n return fullpath\",\n \"def test_predicates_on_unsanitized_paths(self):\\n self.mfs.add_entries({'/just/another/pythonista': ''})\\n\\n self.assertTrue(os.path.isdir('///just'))\\n self.assertTrue(os.path.isdir('///just/////another'))\\n self.assertTrue(os.path.exists('///just////another////////pythonista'))\\n self.assertTrue(os.path.isfile('///just////another////////pythonista'))\",\n \"def build_paths():\\n print(\\\"Creating Paths!\\\")\\n try:\\n os.stat(FILES_PATH)\\n except:\\n os.mkdir(FILES_PATH)\\n \\n try:\\n os.stat(BARRELS_PATH)\\n except:\\n os.mkdir(BARRELS_PATH)\\n\\n try:\\n os.stat(FORWARD_BARRELS_PATH)\\n except:\\n os.mkdir(FORWARD_BARRELS_PATH)\\n\\n try:\\n os.stat(INVERTED_BARRELS_PATH)\\n except:\\n os.mkdir(INVERTED_BARRELS_PATH)\\n\\n try:\\n os.stat(SHORT_BARRELS)\\n except:\\n os.mkdir(SHORT_BARRELS)\\n\\n try:\\n os.stat(SHORT_FORWARD_BARRELS_PATH)\\n except:\\n os.mkdir(SHORT_FORWARD_BARRELS_PATH)\\n\\n try:\\n os.stat(SHORT_INVERTED_BARRELS_PATH)\\n except:\\n os.mkdir(SHORT_INVERTED_BARRELS_PATH)\\n\\n try:\\n os.stat(EXTRA_PATH)\\n except:\\n os.mkdir(EXTRA_PATH)\",\n \"def test_getLinkrelToSameDirectory(self):\\n linkrel = self.builder.getLinkrel(FilePath(\\\"/foo/bar\\\"),\\n FilePath(\\\"/foo/bar\\\"))\\n self.assertEquals(linkrel, \\\"\\\")\",\n \"def ensure_path(full_path):\\n full_path = Path(full_path)\\n if not full_path.exists():\\n full_path.mkdir(parents=True, exist_ok=True)\",\n \"def test_change_non_empty_dir_to_file(self):\\n dir0, dir1 = self.make_temp_dirs(2)\\n self.write_file(dir0, \\\"foo/bar\\\", \\\"baz\\\")\\n self.sync_all()\\n self.assertFile(dir0, \\\"foo/bar\\\", \\\"baz\\\")\\n self.assertFile(dir1, \\\"foo/bar\\\", \\\"baz\\\")\\n\\n self.delete_file(dir0, \\\"foo/bar\\\")\\n self.delete_dir(dir0, \\\"foo\\\")\\n self.write_file(dir0, \\\"foo\\\", \\\"bar\\\")\\n self.sync_all()\\n self.assertFile(dir0, \\\"foo\\\", \\\"bar\\\")\\n self.assertFile(dir1, \\\"foo\\\", \\\"bar\\\")\",\n \"def check_paths(self):\\r\\n\\t\\tself.check_line_edits_and_refresh_filestate()\\r\\n\\t\\t# paths\\r\\n\\t\\tsource_img_filename = self.source_img_entry.text().replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsink_dir_name = self.sink_dir_entry.text().replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsink_db_name_entry_text = self.sink_db_name_entry.text()\\r\\n\\t\\tdb_ext = \\\".db\\\" if not sink_db_name_entry_text.lower().endswith(\\\".db\\\") else \\\"\\\"\\r\\n\\t\\tsink_db_filename = os.path.join(sink_dir_name, sink_db_name_entry_text + db_ext).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\r\\n\\t\\tsource_db_filename = \\\"\\\"\\r\\n\\r\\n\\t\\t# check validity\\r\\n\\t\\tsource_img_filename_valid = self.filestate.is_valid(source_img_filename, SOURCE_IMG)\\r\\n\\t\\tsink_dir_name_valid = self.filestate.is_valid(sink_dir_name, SINK_DIR)\\r\\n\\t\\tsink_db_filename_valid = self.filestate.is_valid(sink_db_filename, SINK_DB)\\r\\n\\t\\tsource_db_filename_valid = True\\r\\n\\r\\n\\t\\tall_paths_valid = source_img_filename_valid and sink_dir_name_valid and sink_db_filename_valid\\r\\n\\r\\n\\t\\tif self.existing_case:\\r\\n\\t\\t\\tsource_db_filename = self.source_db_entry.text()\\r\\n\\t\\t\\tsource_db_filename_valid = self.filestate.is_valid(source_db_filename, SOURCE_DB)\\r\\n\\t\\t\\tall_paths_valid = all_paths_valid and source_db_filename_valid\\r\\n\\r\\n\\t\\tif all_paths_valid:\\r\\n\\t\\t\\tself.filestate.set_source_img_filename(source_img_filename)\\r\\n\\t\\t\\tself.filestate.set_sink_dir_name(sink_dir_name)\\r\\n\\t\\t\\tself.filestate.set_sink_db_filename(sink_db_filename)\\r\\n\\t\\t\\tif self.existing_case:\\r\\n\\t\\t\\t\\tself.filestate.set_source_db_filename(source_db_filename)\\r\\n\\t\\t\\tself.refresh_UI()\\r\\n\\t\\t\\treturn True\\r\\n\\r\\n\\t\\t# in the case of invalidity\\r\\n\\t\\tif not source_img_filename_valid:\\r\\n\\t\\t\\tif not self.filestate.source_img_file_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source image file at does not exist.\\\")\\r\\n\\t\\t\\telif not self.filestate.source_img_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source image file type is invalid (must be .npy).\\\")\\r\\n\\t\\t\\tself.filestate.set_source_img_filename(\\\"\\\")\\r\\n\\t\\tif not source_db_filename_valid: # only if existing case\\r\\n\\t\\t\\tif not self.source_db_file_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source database file does not exist.\\\")\\r\\n\\t\\t\\telif not self.filestate.source_db_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided source database file type is invalid (must be .db)\\\")\\r\\n\\t\\t\\tself.filestate.set_source_db_filename(\\\"\\\")\\r\\n\\t\\tif not sink_dir_name_valid:\\r\\n\\t\\t\\tif not self.filestate.sink_dir_exists:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided sink directory does not exist.\\\")\\r\\n\\t\\t\\telif not self.sink_dir_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Provided sink directory format is invalid.\\\")\\r\\n\\t\\t\\tself.filestate.set_sink_dir_name(\\\"\\\")\\r\\n\\t\\tif not sink_db_filename_valid:\\r\\n\\t\\t\\tif sink_dir_name_valid and not self.filestate.sink_db_file_preexists and \\\\\\r\\n\\t\\t\\t\\t\\tself.filestate.sink_db_file_format_valid and \\\\\\r\\n\\t\\t\\t\\t\\tdisplay_yes_no_message(self, \\\"Create file at \\\" + sink_db_filename + \\\"?\\\"):\\r\\n\\t\\t\\t\\t# create file with read write permissions\\r\\n\\t\\t\\t\\t###########################################\\r\\n\\t\\t\\t\\ttry:\\r\\n\\t\\t\\t\\t\\tsink_db_file = open(sink_db_filename, \\\"w+\\\")\\r\\n\\t\\t\\t\\t\\tsink_db_file.close()\\r\\n\\t\\t\\t\\texcept IOError as error:\\r\\n\\t\\t\\t\\t\\tdisplay_warning_message(self, \\\"Failed to create provided sink database file: \\\" + error)\\r\\n\\t\\t\\t\\t###########################################\\r\\n\\t\\t\\t\\t# set sink db filename\\r\\n\\t\\t\\t\\telse:\\r\\n\\t\\t\\t\\t\\tself.filestate.set_sink_db_filename(sink_db_filename)\\r\\n\\t\\t\\t\\t\\tself.refresh_UI()\\r\\n\\t\\t\\t\\t\\treturn True\\r\\n\\t\\t\\telif not self.filestate.sink_db_file_format_valid:\\r\\n\\t\\t\\t\\tdisplay_warning_message(self, \\\"Be sure to specify a name for the sink database.\\\")\\r\\n\\t\\t\\tself.filestate.set_sink_db_filename(\\\"\\\")\\r\\n\\r\\n\\t\\t# print(\\\"paths invalid\\\")\\r\\n\\t\\tself.refresh_UI()\\r\\n\\t\\treturn False\",\n \"def update_path():\\n #TODO update path information\\n pass\",\n \"def test_realpath(self):\\n print real_upath(\\\"ref with space\\\")\\n self.assertTrue(real_upath(\\\"ref with space\\\").endswith(\\\"ref\\\\ with\\\\ space\\\"))\\n self.assertTrue(real_upath(\\\"ref\\\\ with\\\\ space\\\").endswith(\\\"ref\\\\ with\\\\ space\\\"))\\n self.assertTrue(real_ppath(\\\"ref with space\\\").endswith(\\\"ref with space\\\"))\\n self.assertTrue(real_ppath(\\\"ref\\\\ with\\\\ space\\\").endswith(\\\"ref with space\\\"))\",\n \"def test_verify_path_5(self):\\n result = basic.verify_path(str(self.test_directory1))\\n self.assertTrue(result)\",\n \"def _set_rel_paths(self):\\n if self.working_dir is not None:\\n self._rel_working_dir = os.path.relpath(self.working_dir)\\n if self.alignment is not None:\\n self._rel_alignment = os.path.relpath(self.alignment, \\n self.working_dir)\\n if self.out_file is not None:\\n self._rel_out_file = os.path.relpath(self.out_file, \\n self.working_dir)\",\n \"def check_abs_path_file(self):\\n with in_temp_dir() as dir1:\\n filename = self._make_random_file(self.tempdir)\\n abspath_filename = os.path.abspath(filename)\\n\\n # since we're using absolute path to file, we should be able to run the compress command from anywhere\\n with in_temp_dir() as dir2:\\n assert dir1 != dir2\\n\\n os.system(_compress_cmd(abspath_filename))\\n self._validate_compressed(abspath_filename)\",\n \"def test_verify_path2_3(self):\\n result, msg = basic.verify_path2(self.file, kind=\\\"file\\\", expect=False)\\n with self.subTest():\\n self.assertTrue(result)\\n with self.subTest():\\n self.assertIsNone(msg)\",\n \"def compactuser(path):\\n userPath = expanduser('~')\\n otherPath = expanduser(path)\\n \\n prefix = commonprefix([userPath, otherPath])\\n if prefix == userPath:\\n return '~' + otherPath[len(prefix):]\\n else:\\n return otherPath\",\n \"def prepare(self):\\n if not self._base:\\n self.error = \\\"path= must be specified\\\"\\n return False\\n if self._volume:\\n if \\\"://\\\" not in self._volume:\\n self.error = \\\"mount= can only be an URL\\\"\\n return False\\n if self._base.startswith(\\\"/\\\"):\\n self._base = os.path.join(self._volume, self._base[1:])\\n # do the prefix check anyway, for sanity\\n if not is_parent_of(self._volume, self._base):\\n self.error = \\\"mount= must be a prefix of path=\\\"\\n return False\\n return True\",\n \"def force_absolute(base, path):\\n if os.path.abspath(path) and os.path.exists(path):\\n return path\\n else:\\n return path_format(base + path)\",\n \"def chkPath(fullPath: str) -> None:\\n\\n # Check if path already exist.\\n p = os.path.split(fullPath)\\n exists = os.path.exists(p[0])\\n # If not then create it.\\n if exists == False:\\n try:\\n os.makedirs(p[0])\\n except:\\n print(\\\"Failed to create requested path.\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.645393","0.6182501","0.61075705","0.60068315","0.60003424","0.5978063","0.5929083","0.592156","0.59077317","0.59016716","0.58891624","0.58888334","0.584172","0.5833566","0.5822319","0.579614","0.57439196","0.5732762","0.57041806","0.5696847","0.5693155","0.5675337","0.56707925","0.5650782","0.56467646","0.5644818","0.56414324","0.5639067","0.5620646","0.5617122","0.5600285","0.55982727","0.55940837","0.55937153","0.5590871","0.5586046","0.55795944","0.5575766","0.5569317","0.55622745","0.5553674","0.55435175","0.5537959","0.55328465","0.55303335","0.5528622","0.5524984","0.5515486","0.55146545","0.5506945","0.5497049","0.54952633","0.54928726","0.54869545","0.54765564","0.5470671","0.5464428","0.54553473","0.5452744","0.54519695","0.5450707","0.5445118","0.544475","0.54400104","0.5432142","0.54269093","0.54200923","0.53963995","0.5389728","0.53860444","0.537189","0.5371793","0.5371756","0.5369218","0.5360853","0.5355881","0.53544825","0.5352988","0.53405154","0.5333561","0.5327588","0.53259367","0.5307375","0.53032476","0.5301489","0.5285494","0.52829564","0.52813005","0.5279314","0.5271664","0.52713645","0.52678597","0.52621025","0.52572864","0.5250519","0.5250345","0.52489316","0.5244003","0.5241094","0.5236389","0.5223881"],"string":"[\n \"0.645393\",\n \"0.6182501\",\n \"0.61075705\",\n \"0.60068315\",\n \"0.60003424\",\n \"0.5978063\",\n \"0.5929083\",\n \"0.592156\",\n \"0.59077317\",\n \"0.59016716\",\n \"0.58891624\",\n \"0.58888334\",\n \"0.584172\",\n \"0.5833566\",\n \"0.5822319\",\n \"0.579614\",\n \"0.57439196\",\n \"0.5732762\",\n \"0.57041806\",\n \"0.5696847\",\n \"0.5693155\",\n \"0.5675337\",\n \"0.56707925\",\n \"0.5650782\",\n \"0.56467646\",\n \"0.5644818\",\n \"0.56414324\",\n \"0.5639067\",\n \"0.5620646\",\n \"0.5617122\",\n \"0.5600285\",\n \"0.55982727\",\n \"0.55940837\",\n \"0.55937153\",\n \"0.5590871\",\n \"0.5586046\",\n \"0.55795944\",\n \"0.5575766\",\n \"0.5569317\",\n \"0.55622745\",\n \"0.5553674\",\n \"0.55435175\",\n \"0.5537959\",\n \"0.55328465\",\n \"0.55303335\",\n \"0.5528622\",\n \"0.5524984\",\n \"0.5515486\",\n \"0.55146545\",\n \"0.5506945\",\n \"0.5497049\",\n \"0.54952633\",\n \"0.54928726\",\n \"0.54869545\",\n \"0.54765564\",\n \"0.5470671\",\n \"0.5464428\",\n \"0.54553473\",\n \"0.5452744\",\n \"0.54519695\",\n \"0.5450707\",\n \"0.5445118\",\n \"0.544475\",\n \"0.54400104\",\n \"0.5432142\",\n \"0.54269093\",\n \"0.54200923\",\n \"0.53963995\",\n \"0.5389728\",\n \"0.53860444\",\n \"0.537189\",\n \"0.5371793\",\n \"0.5371756\",\n \"0.5369218\",\n \"0.5360853\",\n \"0.5355881\",\n \"0.53544825\",\n \"0.5352988\",\n \"0.53405154\",\n \"0.5333561\",\n \"0.5327588\",\n \"0.53259367\",\n \"0.5307375\",\n \"0.53032476\",\n \"0.5301489\",\n \"0.5285494\",\n \"0.52829564\",\n \"0.52813005\",\n \"0.5279314\",\n \"0.5271664\",\n \"0.52713645\",\n \"0.52678597\",\n \"0.52621025\",\n \"0.52572864\",\n \"0.5250519\",\n \"0.5250345\",\n \"0.52489316\",\n \"0.5244003\",\n \"0.5241094\",\n \"0.5236389\",\n \"0.5223881\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8477,"cells":{"query":{"kind":"string","value":"dst_fname should be full path. Creates directories if required."},"document":{"kind":"string","value":"def put_file(self, src_fname, dst_fname):\n dst_fname = os.path.normpath(dst_fname)\n self.mkdirs(os.path.dirname(dst_fname))\n self._put(src_fname, dst_fname)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _prepare_dst_dir(self, dst, src=None, perm=None, **kwargs):\n if self.isdir(dst):\n full_dst = os.path.join(dst, os.path.basename(src)) if src else dst\n\n elif self.isfile(dst):\n full_dst = dst\n\n else:\n # interpret dst as a file name, create missing dirs\n dst_dir = self.dirname(dst)\n if dst_dir and self.create_file_dir and not self.isdir(dst_dir):\n self.mkdir(dst_dir, perm=perm, recursive=True)\n full_dst = dst\n\n return full_dst","def _prepare_dst_dir(self, dst, src=None, perm=None, **kwargs):\n rstat = self.exists(dst, stat=True)\n\n if rstat:\n if self.file_interface.isdir(dst, stat=rstat) and src:\n full_dst = os.path.join(dst, os.path.basename(src))\n else:\n full_dst = dst\n\n else:\n # interpret dst as a file name, create missing dirs\n dst_dir = self.dirname(dst)\n if dst_dir and self.create_file_dir and not self.isdir(dst_dir):\n self.mkdir(dst_dir, perm=perm, recursive=True, **kwargs)\n full_dst = dst\n\n return full_dst","def prepare(self, dst, options):\n self.checkExisting(dst)\n self.makedirs(dst.parent())","def MakeDestinationDirectories(self, dst_files):\n for dst in dst_files:\n path = os.path.dirname(dst);\n if (len(path) > 0) and (not os.path.exists(path)):\n self.VerboseMsg(\"Make Directory: \" + path)\n if self.execute:\n os.makedirs(path)","def copy_file(filename, dst):\n # Create dir if needed\n dir_path = os.path.dirname(os.path.expanduser(dst))\n if not os.path.isdir(dir_path):\n os.makedirs(dir_path)\n\n src = os.path.join(get_data(''), filename)\n dst = os.path.expanduser(dir_path)\n shutil.copy2(src, dst)","def _copy_file_with_parents(src, dst, ignore_no_src=False):\n if not os.path.isfile(src) and ignore_no_src:\n return\n\n dst_dir = os.path.dirname(dst)\n create_directories(dst_dir)\n\n copyfile(src, dst)","def make_local_copy(outdir, subdir, fname):\n\n destdir = path.join(outdir, subdir)\n mkdir(destdir)\n shutil.copyfile(package_data(fname), path.join(destdir, fname))\n return path.join(subdir, fname)","def copy_deep(src: str, dst: str, create_dst_dir: bool = False) -> None:\n system_is_darwin = platform.system().lower() == \"darwin\"\n if create_dst_dir:\n mkdir_p(os.path.dirname(dst))\n src_is_link = os.path.islink(src)\n dst_exists = os.path.lexists(dst)\n if os.path.isdir(src) and not src_is_link:\n logging.debug(\"Copying directory {} to {}\".format(src, dst))\n mkdir_p(dst)\n for name in os.listdir(src):\n copy_deep(os.path.join(src, name), os.path.join(dst, name))\n elif src_is_link:\n if dst_exists:\n return\n target = os.readlink(src)\n logging.debug(\"Creating symlink {} -> {}\".format(dst, target))\n os.symlink(target, dst)\n else:\n if dst_exists:\n if not system_is_darwin:\n return\n # Only overwrite the file if the source is newer than the destination.\n if os.path.getmtime(src) <= os.path.getmtime(dst):\n return\n logging.debug(\"Copying file {} to {}\".format(src, dst))\n # Preserve the file attributes.\n shutil.copy2(src, dst)","def safecopy(src, dst):\r\n abs_src = os.path.abspath(src)\r\n abs_dst = os.path.abspath(dst)\r\n if (abs_src != abs_dst) \\\r\n and os.path.isfile(abs_src): \r\n dirname = os.path.dirname(abs_dst)\r\n recurse_mkdir(dirname)\r\n shutil.copy(abs_src, abs_dst)","def copy_to_se(self, src, dst, create_parent_directory=True):\n mgm, dst = self._safe_split_mgm(dst)\n dst = self._join_mgm_lfn(mgm, dst)\n if create_parent_directory:\n parent_directory = osp.dirname(dst)\n self.create_directory(parent_directory)\n logger.warning('Copying {0} to {1}'.format(src, dst))\n cmd = [ 'xrdcp', '-s', src, dst ]\n svj.core.utils.run_command(cmd)","def cp(src, dst):\n os.makedirs(os.path.dirname(dst), exist_ok=True)\n src_stat = os.stat(src)\n try:\n dst_stat = os.stat(dst)\n except FileNotFoundError:\n dst_stat = (0,)*10\n src_modif_time = src_stat[stat.ST_MTIME]\n dst_modif_time = dst_stat[stat.ST_MTIME]\n if src_modif_time > dst_modif_time:\n shutil.copyfile(src, dst)\n print(\" ++\", dst[len(THESIS_DIR):])\n else:\n print(\" --\", dst[len(THESIS_DIR):])","def cp_to_dir(fn0, d):\n\n # keep rewriting attributes\n shutil.copy(fn0, d)","def _copy_dir(src, dst):\n if os.path.isdir(src):\n os.makedirs(dst, exist_ok=True)\n for item in os.listdir(src):\n s = os.path.join(src, item)\n d = os.path.join(dst, item)\n\n if os.path.isdir(s):\n _copy_dir(s, d)\n else:\n shutil.copy2(s, d)\n\n else:\n os.makedirs(os.path.dirname(dst), exist_ok=True)\n _delete_file(dst)\n shutil.copy2(src, dst)","def copy_dir(src: Text, dst: Text) -> None:\n\n if tf.io.gfile.exists(dst):\n tf.io.gfile.rmtree(dst)\n tf.io.gfile.makedirs(dst)\n\n for dir_name, sub_dirs, leaf_files in tf.io.gfile.walk(src):\n for leaf_file in leaf_files:\n leaf_file_path = os.path.join(dir_name, leaf_file)\n new_file_path = os.path.join(dir_name.replace(src, dst, 1), leaf_file)\n tf.io.gfile.copy(leaf_file_path, new_file_path)\n\n for sub_dir in sub_dirs:\n tf.io.gfile.makedirs(os.path.join(dir_name.replace(src, dst, 1), sub_dir))","def convert_tmpfile(src_file_name:str, dest_path:str):\n src_path = os.path.join(\n current_app.config['UPLOAD_FOLDER'],\n src_file_name\n )\n if not os.path.exists(src_path):\n abort(http.HTTPStatus.BAD_REQUEST, message='raw file not exist')\n pathlib.Path(os.path.dirname(dest_path)).mkdir(parents=True, exist_ok=True)\n shutil.move(src_path, dest_path)","def makeOutputPath(outputPath, filePath, filename):\n if filePath == \"\" or filePath == None:\n return None\n if outputPath[-1] == FOLDER_DELIM: # remove an eventual / at the end\n outputPath = outputPath[:-1]\n\n fullPath = outputPath + FOLDER_DELIM + filePath\n fullPath += FOLDER_DELIM # add a '/' at the end of the path\n\n if not os.path.isfile(fullPath): # if the path doesn't exist, create it!\n try:\n os.makedirs(fullPath)\n except FileExistsError:\n pass # nothing really went wrong if the folder already exists, continue quietly\n return fullPath + filename + TARGET # return the path and target filename","def copy_file_to_multiple_subfolders(src, dst, *args, **kwargs):\n print '\\nSource: {}\\nDestinations parent folder: {}'.format(src, dst)\n filename = os.path.basename(src)\n for folder in (d for d in os.listdir(dst) if os.path.isdir(d)):\n print '\\nCopying {} to {}...'.format(filename, folder)\n try:\n shutil.copy(src, os.path.abspath(dst) + '\\\\' + folder)\n except Exception as e:\n print e","def move_files(src_dir, dst_dir):\n for f in os.listdir(src_dir):\n try:\n name, season, episode = FILENAME_PATTERN.search(f).groups()\n except AttributeError:\n try:\n name, season, episode = FILENAME_PATTERN2.search(f).groups()\n except AttributeError:\n print \"Cannot parse\", f\n pass\n\n name = name.replace('.', ' ').replace('_', ' ').strip().title()\n\n dir_path = os.path.join(dst_dir, name, 'Season %02d' % int(season))\n full_path = os.path.join(dir_path, f)\n source_path = os.path.join(src_dir, f)\n\n if not os.path.exists(dir_path):\n os.makedirs(dir_path, 0777)\n\n if not os.path.exists(full_path):\n shutil.move(source_path, full_path)\n os.symlink(full_path, source_path)","def copy(src, dst):\n os.makedirs(os.path.dirname(dst), exist_ok=True)\n shutil.copy2(src, dst)","def cpsym(src,dest):\n \n src = os.path.normpath(src)\n dest = os.path.normpath(dest)\n \n if not os.path.exists(src):\n return\n \n for dirpath,dirnames,filenames in os.walk(src):\n rel_dirpath = os.path.relpath(dirpath,src)\n dest_dirpath = os.path.join(dest,rel_dirpath)\n mkdir(dest_dirpath,isfull=True)\n \n for filename in filenames:\n src_filename = os.path.join(dirpath,filename)\n rel_filename = os.path.relpath(src_filename,src)\n \n dest_filename = os.path.join(dest,rel_filename)\n try:\n os.symlink(src_filename,dest_filename)\n except OSError:\n pass","def create_path_by_date(dest, dt):\n if not os.path.isdir(dest):\n raise FileNotFoundError(f\"dest {dest} must be valid path\")\n yyyy, mm, dd = dt[0:3]\n yyyy = str(yyyy).zfill(4)\n mm = str(mm).zfill(2)\n dd = str(dd).zfill(2)\n new_dest = os.path.join(dest, yyyy, mm, dd)\n if not os.path.isdir(new_dest):\n os.makedirs(new_dest)\n return new_dest","def create_dir_for_file(f_path):\n d = os.path.dirname(f_path)\n if d and not os.path.exists(d):\n os.makedirs(d)","def _make_output_directory(self):\n fs = self._filesystem\n output_filename = fs.join(self._root_output_dir, self._test_name)\n fs.maybe_make_directory(fs.dirname(output_filename))","def create(self, basedir, outdir, name, prefix=None):","def create_output_dir(self):\n if self.output_dir is None:\n new_path = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n self.output_dir = os.path.expanduser(os.path.join(self.input_dir, new_path))\n try:\n os.makedirs(self.output_dir)\n except OSError:\n pass","def buildDestination(self, mapping, options, src):\n prefixTemplate = options['prefix']\n if prefixTemplate is None:\n prefixTemplate = self.defaultPrefixTemplate\n\n if prefixTemplate is not None:\n prefix = os.path.expanduser(\n prefixTemplate.safe_substitute(mapping))\n else:\n prefixTemplate = string.Template(src.dirname())\n prefix = prefixTemplate.template\n\n ext = src.splitext()[-1]\n\n nameTemplate = options['name']\n if nameTemplate is None:\n nameTemplate = self.defaultNameTemplate\n\n filename = nameTemplate.safe_substitute(mapping)\n logging.msg(\n 'Building filename: prefix=%r name=%r mapping=%r' % (\n prefixTemplate.template, nameTemplate.template, mapping),\n verbosity=3)\n return FilePath(prefix).child(filename).siblingExtension(ext)","def clone(src: str, dst: str):\n if dst is None:\n dst = getcwd()\n destination = path.abspath(dst)\n # TODO: replace with false this is just for testing:\n makedirs(destination, exist_ok=True)\n\n sync_chunk(src, destination)\n copy(src, destination)","def create_path(inputfile, outputdir):\n pathdata = '/'.join(inputfile.split('/')[-3:])\n newpath = join(outputdir, pathdata)\n dirout = dirname(newpath)\n if not isdir(dirout):\n os.makedirs(dirout)\n return newpath","def task():\n if os.path.isdir(orig):\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*')) if \\\n os.path.isdir(fP) ]:\n if not os.path.exists(dest + fP[len(orig):]):\n os.makedirs(dest + fP[len(orig):])\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*/%s.log' %fmt.get_date())) if \\\n os.path.isfile(fP) ]:\n convert(fP, dest + fP[len(orig):])","def copyFile(src_dir, dst_dir, f_name):\n\n try:\n src_file = open(osp.join(src_dir, f_name),\"rb\")\n dst_file = open(osp.join(dst_dir, f_name),\"wb\")\n dst_file.write(src_file.read())\n dst_file.close()\n src_file.close()\n except Exception, e:\n msg = \"!!! In copying files from < %s > dir to < %s > dir exception occur. Details: %s.\" % (src_dir,dst_dir, str(e))\n print >> import_out, msg\n LOG('performImportToPortal',INFO,'copyFile', msg)","def move(self,src,dst):\n src = os.path.join(self.testpath,src)\n dst = os.path.join(self.testpath,dst)\n directory = os.path.split(dst)[0]\n try:\n os.makedirs(directory)\n except OSError:\n pass\n\n shutil.move(src,dst)","def copydir(src, dst):\n for item in os.listdir(src):\n s, d = os.path.join(src, item), os.path.join(dst, item)\n if os.path.isdir(s):\n if not os.path.isdir(d):\n os.mkdir(d)\n copydir(s, d)\n else:\n shutil.copy(s, d)","def _createDirsAbove(fname):\n\n dirToCreate = os.path.dirname(fname)\n # https://stackoverflow.com/a/600612\n try:\n os.makedirs(dirToCreate)\n except OSError as e:\n if e.errno == errno.EEXIST and os.path.isdir(dirToCreate):\n pass\n else:\n raise","def create_dir(name_new_path):\n\n try:\n os.mkdir(config_tools.full_dest + name_new_path)\n except OSError:\n print(\"Создать директорию %s не удалось\" % name_new_path)\n else:\n print(\"Успешно создана директория %s \" % name_new_path)","def makefilename(self):\n fp= (pathlib.Path(self.vr_folder).expanduser()/(time.strftime(self.vr_filename))).with_suffix('')\n fp.parent.mkdir(parents=True, exist_ok=True)\n print('files setup', str(fp))\n return fp","def install(src, dst):\n try:\n dst = os.path.join(install_dir, dst, os.path.basename(src))\n src = os.path.join(source_dir, src)\n assert os.path.isfile(src)\n assert not os.path.isdir(dst)\n if not os.path.isdir(os.path.dirname(dst)):\n os.makedirs(os.path.dirname(dst))\n shutil.copy(src, dst)\n print 'Installed', dst\n except Exception:\n print 'Could not install', dst","def create_folder(output_directory: str, fldrname: str):\n\n os.makedirs(output_directory, exist_ok=True)\n tstmp = datetime.now().strftime('%Y%m%d_%H%M%S')\n try:\n fldr_path = os.path.join(output_directory, fldrname)\n os.mkdir(fldr_path)\n except FileExistsError:\n fldr_path = os.path.join(output_directory, fldrname + '_{}'.format(tstmp))\n os.mkdir(fldr_path)\n return fldr_path","def create_directory_for_file_if_needed(file_name):\n directory_name = os.path.dirname(file_name)\n create_directory_if_needed(directory_name)\n return directory_name","def ensure_directory(self, name, dest, mode=0777):\n self.m.path.assert_absolute(dest)\n self._run(\n name, ['ensure-directory', '--mode', oct(mode), dest])\n self.m.path.mock_add_paths(dest)","def _create_dir(filename):\n head = os.path.dirname(filename)\n if head != '' and not os.path.isdir(head):\n os.makedirs(head)","def create_symlink_dir(src_dir, src_list, dst):\n if not src_list:\n return\n message = \"creating symlink directory at {dst} with files {src_list}\".format(\n dst=dst,\n src_list=pformat(src_list))\n logging.info(message)\n if not os.path.exists(dst):\n os.makedirs(dst)\n for src_file in src_list:\n if not src_file:\n continue\n source = os.path.join(src_dir, src_file)\n destination = os.path.join(dst, src_file)\n if os.path.lexists(destination):\n continue\n try:\n os.symlink(source, destination)\n except Exception as e:\n msg = format_debug(e)\n logging.error(e)","def copy_file(src_file,dst_folder):\n from shutil import copyfile\n from os.path import split\n copyfile(src_file, dst_folder+split(src_file)[1])\n return","def add_to_split(rec_dir, target, label):\n for file_name in os.listdir(rec_dir):\n path = os.path.join(rec_dir, file_name)\n if (os.path.isfile(path)):\n if not os.path.isdir(os.path.join(target, str(label))):\n os.makedirs(os.path.join(target, str(label)))\n shutil.copy(path, os.path.join(target, str(label), file_name))","def _copy_files(src_paths,dst_dir,class_numbers):\n\n class_dirs=[os.path.join(dst_dir,class_name+\"/\")for class_name in self.class_names]\n\n for dir in class_dirs:\n if not os.path.exists(dir):\n os.makedirs(dir)\n\n for src,cls in zip(src_paths,class_numbers):\n shutil.copy(src=src,dst=class_dirs[cls])","def create_dir(output_path):\n if not os.path.exists(output_path) and is_directory(output_path):\n os.makedirs(output_path)","def prepare_output_dir(out_dir, test_dir):\r\n\r\n if not out_dir.exists():\r\n out_dir.mkdir()\r\n\r\n # get the necessary file names\r\n file_names = get_file_names(test_dir, args.distance, print_file_names=False)\r\n\r\n # copy the images in the firstIms into the output folder\r\n for name in file_names[1][0]:\r\n file_path = Path(test_dir / name)\r\n copy_to = Path(out_dir / name)\r\n shutil.copy(file_path, copy_to)\r\n\r\n # the firstIms list does not contain the last image,\r\n # so we need to also copy the last image of the secIms into the output folder\r\n last_im = file_names[1][1][-1]\r\n shutil.copy(Path(test_dir/last_im), Path(out_dir/last_im))\r\n\r\n return file_names","def CopyFolder(src, dst, overwrite=True):\n\n if not src.endswith((\"/\", \"\\\\\")):\n src = src + \"\\\\\"\n if not dst.endswith((\"/\", \"\\\\\")):\n dst = dst + \"\\\\\"\n os.makedirs(dst, exist_ok=True)\n\n for file in os.listdir(src):\n if not overwrite and os.path.isfile(dst + file):\n continue\n if os.path.isfile(src + file):\n shutil.copy(src + file, dst + file)\n elif os.path.isdir(src + file):\n CopyFolder(src + file, dst + file, overwrite)\n return 0","def move_files(fname_fout, root_dir, dest_dir):\n fname, f_ext = os.path.splitext(fname_fout)\n # Find files which filename of fname_fout\n matches = []\n pattern = fname + '*'\n root_fnames = os.listdir(root_dir)\n for filename in fnmatch.filter(root_fnames, pattern):\n matches.append([filename, os.path.join(root_dir, filename)])\n # Extract new folder name based on fname_fout\n new_folder_name = reshape_fname(fname_fout, ['nairfoil', 'nsetup'])\n dest_dir = os.path.join(dest_dir, new_folder_name)\n # Move files\n for cur_file in matches:\n os.renames(cur_file[1], os.path.join(dest_dir, cur_file[0]))","def convertPath(srcpath, dstdir):\n bits = srcpath.split(\"/\")\n bits.pop(0)\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\n if bits[0] == 'unsigned':\n bits.pop(0)\n return os.path.join(dstdir, *bits)","def folder_to_s3(src, dst, region, max_parallelism=1, force_copy=False, **kwargs):\n bucket, root = utils.path.reverse_split(dst)\n\n s3 = boto3.resource('s3')\n\n # check if the bucket exists\n if not __bucket_exists(bucket):\n\n if force_copy:\n print('creating bucket: ' + bucket)\n\n try:\n s3.create_bucket(Bucket=bucket,\n CreateBucketConfiguration={'LocationConstraint': region})\n except botocore.exceptions.ClientError as e:\n raise e\n else:\n exit(-1)\n\n # instanciate transfer configuration\n conf = boto3.s3.transfer.TransferConfig(use_threads=True, **kwargs)\n\n # start uploading\n with ProcessPoolExecutor(max_workers=max_parallelism) as executor:\n try:\n for file in utils.path.dir_tree(src):\n # removes the root so that it can be\n # later added to the input string\n suffix = file.replace(src, '')\n executor.submit(file_to_s3,\n bucket,\n file,\n os.path.join(root, suffix),\n conf,\n utils.path.progress\n )\n\n except (BrokenProcessPool):\n try:\n # deleting the bucket if created\n # to do so, the bucket must be empty\n print(\"removing %s from %s\" % (root, bucket))\n delete_folder(bucket, root, region)\n if force_copy:\n print(\"attempting to delete %s\" % bucket)\n s3.Bucket(bucket).delete()\n\n except botocore.exceptions.ClientError as e:\n print(\"operation failed: %s\" % e)\n exit(-1)\n\n else:\n print(\"operation aborted. exiting...\")\n exit(0)","def createFolder(self):\n self.destination = self.getPath() #Find the destination to create the folder\n try:\n os.makedirs(self.destination) #Try and make a folder\n except FileExistsError:\n pass #Otherwise continue if an error is encountered because the file exists already","def path_src_to_dest(src_pathname, dest_filename_suffix=None):\n src_relpath = Path(src_pathname).relative_to(config[\"topdir\"])\n dest_pathname = Path(config[\"outdir\"]).joinpath(src_relpath)\n if dest_filename_suffix:\n dest_pathname = dest_pathname.with_suffix(dest_filename_suffix)\n return dest_pathname","def compress(src,dstfile):\n\tafile = zipfile.ZipFile(dstfile,\"w\",zipfile.ZIP_DEFLATED)\n\tfor root,dirs,files in os.walk(src):\n\t\tfor filename in files:\n\t\t\tabspath = osp.join(root,filename)\n\t\t\trelpath = osp.relpath(abspath,src)\n\t\t\tafile.write(abspath, relpath)\n\tafile.close();","def safe_copy(file_path, out_dir, dst=None):\n name = dst or os.path.basename(file_path)\n if not os.path.exists(os.path.join(out_dir, name)):\n shutil.copy(file_path, os.path.join(out_dir, name))","def create_output_folder(campaign_name: str, path: str) -> str:\n folder_name = \"\".join([campaign_name, \"_druckfiles\"])\n path = os.path.split(path)[0]\n out_path = os.path.join(path, folder_name)\n if not os.path.exists(out_path):\n os.makedirs(out_path)\n return out_path","def copy(self, src, dst, label=None):\n self._tag(dst, label)\n self._mkdir_for(dst)\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))","def applyDir(self,srcDir,destDir,exts): \n for srcFile in os.listdir(srcDir):\n srcExt = os.path.splitext(srcFile)[-1].lower()\n srcPath = os.path.join(srcDir,srcFile)\n destPath = os.path.join(destDir,srcFile)\n if srcExt in exts:\n if not os.path.exists(destDir):\n os.makedirs(destDir)\n shutil.copyfile(srcPath,destPath)\n if self.progress: \n self.cumSize += os.path.getsize(srcPath)\n self.progress(self.cumSize,_('Copying Files...'))\n elif os.path.isdir(srcPath):\n self.applyDir(srcPath,destPath,exts)","def _prepare_directory(destination, connector_id):\n\n # Use the destination directory when provided\n if destination:\n if not os.path.exists(destination):\n # Create all sub-directories\n os.makedirs(destination)\n # Create a sub-directory in the current directory\n # when a destination isn't provided\n else:\n if not os.path.isdir(connector_id):\n os.mkdir(connector_id)\n destination = connector_id\n\n if os.path.isdir(destination):\n os.chdir(destination)\n else:\n error = 'Couldn\\'t download to the desination directory {}.'\n raise CLIError(error.format(destination))\n\n return os.getcwd()","def m_cp(*args):\n if (not args or len(args) < 2) :\n print(\"parameter is invalid\")\n return\n\n src = args[0:len(args)-1]\n dst = args[len(args)-1]\n\n if not os.path.exists(dst) :\n os.mkdir(dst)\n\n for s in src:\n print(\"cp %s ==> %s\" % (s, dst))\n cpfile(s, dst)","def save(url, dst, force=False):\n if not os.path.isfile(dst) or force:\n # Test if the directory exist or create\n d = os.path.dirname(dst)\n if not os.path.exists(d):\n os.makedirs(d)\n print(u\"\\nDownloading: {0} to {1}\".format(url, dst))\n urllib.urlretrieve(url, dst, report)","def make_dir(file_name): # output_file_loc = des\n for i in os.walk(f'{tmp_path}/{file_name}'):\n fld = i[0].split(file_name)[-1]\n if fld:\n loc = f\"{output_path}{fld}\"\n if not os.path.exists(f'{output_path}/{fld}'):\n os.makedirs(f'{output_path}/{fld}')\n # print(\"MAKE_DIR completed...\") \n return","def dst_to_src(self,dst_file):\n rel_path=os.path.relpath(dst_file,start=self.dst_root)\n if (rel_path == '.'):\n rel_path=''\n else:\n rel_path= '/'+rel_path\n if (os.sep != '/'):\n # if directoty path sep isn't / then translate for URI \n rel_path=rel_path.replace(os.sep,'/')\n return(self.src_root+rel_path)","def copy_dir(src, dst):\n try:\n debug.log(\"copy dir from \"+ src, \"to \"+ dst)\n shutil.copytree(src, dst)\n except Exception as e:\n debug.log(\"Error: happened while copying!\\n%s\\n\"%e)","def set_dest(self, mode, dest, prefix):\n try: \n os.mkdir(dest)\n os.mkdir(dest+'/images')\n os.mkdir(dest+'/images'+'/train')\n os.mkdir(dest+'/images'+'/test')\n os.mkdir(dest+'/labels')\n os.mkdir(dest+'/labels'+'/train')\n os.mkdir(dest+'/labels'+'/test')\n except FileExistsError:\n pass\n\n if mode == 'train':\n self.detImage = dest+'images/'+'train/'\n self.detLabel = dest+'labels/'+'train/'\n self.detList = dest+'train.txt'\n elif mode == 'test':\n self.detImage = dest+'images/'+'test/'\n self.detLabel = dest+'labels/'+'test/'\n self.detList = dest+'test.txt'\n self.prefix = prefix","def copy(self, src, dst, label=None):\r\n self._tag(dst, label)\r\n self._mkdir_for(dst)\r\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))","def copy(self, src, dst, label=None):\r\n self._tag(dst, label)\r\n self._mkdir_for(dst)\r\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))","def make_dir(name='results'):\n if os.path.isabs(name):\n output_path = name\n else:\n output_path = os.path.join(os.getcwd(), name)\n\n if ('.' not in output_path):\n directory = os.path.dirname(os.path.join(output_path, 'toto')) # doesn't work w/o 'toto'\n else :\n directory = os.path.dirname(output_path);\n\n try:\n os.makedirs(directory)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n return output_path","def make_path(self, basename):\n return os.path.join(self.output_folder, basename.format(self.sample_name))","def copy(self, fname):\n _, ext = osp.splitext(fname)\n spath = osp.join(self.src, fname)\n oname = fname\n path = osp.join(self.dst, oname)\n os.makedirs(osp.dirname(path), exist_ok=True)\n if ext in [\".css\"]:\n content = self.include(fname)\n with open(path, \"wt\") as fp:\n fp.write(content)\n else:\n shutil.copyfile(spath, path)\n return osp.relpath(oname, self.root)","def copyDir(src, dst, includes, excludes = []):\n\tmultiFilesReplacements([], dst, src, includes, excludes)","def _install_file(srcdir, filename, dstdir):\n srcfilename = os.path.join(srcdir, filename)\n dstfilename = os.path.join(dstdir, filename)\n if not os.path.exists(srcfilename):\n if os.path.exists(dstfilename):\n subprocess.run(['rm', dstfilename], check=True)\n return (False, True)\n return (False, False)\n\n equal = subprocess.run(['diff', '-q', srcfilename, dstfilename],\n check=False,\n stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL).returncode == 0\n if not equal:\n subprocess.run(['mv', srcfilename, dstfilename], check=True)\n return (True, not equal)","def check_path(fp):\n if not Path(fp).exists():\n\n if len(Path(fp).suffix) > 0: # check if file\n Path(fp).parent.mkdir(exist_ok=True, parents=True)\n\n else: # or directory\n Path(fp).mkdir(exist_ok=True, parents=True)","def copy_dir(src=\"\", dst=\"\", header=\"\", footer=\"\", clip=0, ext=\"\", test=False):\n failed = []\n nfiles = 0\n if not os.path.exists(dst):\n os.makedirs(dst)\n if not os.path.exists(src):\n raise argparse.ArgumentError(\"source does not exist! It must be a directory.\")\n else:\n for root, dirs, files in os.walk(src, topdown=False):\n for name in files:\n name_wo_ext, file_ext = os.path.splitext(name)\n\n src_path = os.path.join(root, name)\n dstfilename = header + os.path.join(root[len(src)+1:], name_wo_ext[clip:]) + footer + file_ext\n dst_path = os.path.join(dst, dstfilename)\n\n dst_pdir = os.path.dirname(dst_path)\n if not os.path.exists(dst_pdir):\n os.makedirs(dst_pdir)\n\n if not os.path.exists(dst_path):\n if ext == \"\" or ext == file_ext[1:]:\n try:\n shutil.copy(src_path, dst_path)\n except:\n failed.append(src_path)\n print(f\"... {src_path} failed\")\n else:\n print(f\"... {dst_path} already exists'. Skipping\")\n nfiles += 1\n\n if test:\n break\n if test:\n break\n print(f\"{nfiles - len(failed)} / {nfiles} files were copied.\")\n return failed","def validate_file_destination(namespace):\n try:\n path = namespace.destination\n except AttributeError:\n return\n else:\n # TODO: Need to confirm this logic...\n file_path = path\n file_dir = os.path.dirname(path)\n if os.path.isdir(path):\n file_name = os.path.basename(namespace.file_name)\n file_path = os.path.join(path, file_name)\n elif not os.path.isdir(file_dir):\n try:\n os.mkdir(file_dir)\n except EnvironmentError as exp:\n message = \"Directory {} does not exist, and cannot be created: {}\"\n raise ValueError(message.format(file_dir, exp))\n if os.path.isfile(file_path):\n raise ValueError(\"File {} already exists.\".format(file_path))\n namespace.destination = file_path","def put(self, src, dst):\r\n abs_src = os.path.expanduser(src)\r\n assert os.path.exists(abs_src), 'File does not exist, cannot copy: %s' % abs_src\r\n return self._do_put(abs_src, dst)","def ConstructDstUri(self, src_uri, exp_src_uri, base_dst_uri):\n if base_dst_uri.names_container():\n # To match naming semantics of UNIX 'cp' command, copying files\n # to buckets/dirs should result in objects/files named by just the\n # final filename component; while copying directories should result\n # in objects/files mirroring the directory hierarchy. Example of the\n # first case:\n # gsutil cp dir1/file1 gs://bucket\n # should create object gs://bucket/file1\n # Example of the second case:\n # gsutil cp dir1/dir2 gs://bucket\n # should create object gs://bucket/dir2/file2 (assuming dir1/dir2\n # contains file2).\n if src_uri.names_container():\n dst_path_start = (src_uri.object_name.rstrip(os.sep)\n .rpartition(os.sep)[-1])\n start_pos = exp_src_uri.object_name.find(dst_path_start)\n dst_key_name = exp_src_uri.object_name[start_pos:]\n else:\n # src is a file or object, so use final component of src name.\n dst_key_name = os.path.basename(exp_src_uri.object_name)\n if base_dst_uri.is_file_uri():\n # dst names a directory, so append src obj name to dst obj name.\n dst_key_name = '%s%s%s' % (base_dst_uri.object_name, os.sep,\n dst_key_name)\n self.CheckForDirFileConflict(exp_src_uri, dst_key_name)\n else:\n # dest is an object or file: use dst obj name\n dst_key_name = base_dst_uri.object_name\n return base_dst_uri.clone_replace_name(dst_key_name)","def create_out_dir(out): \n out_path = os.path.join(out,out_dir_name)\n try:\n os.stat(out_path)\n except:\n os.mkdir(out_path)","def _make_dirs(filepath, mode):\n parent = filepath.parent\n if \"w\" in mode and parent:\n os.makedirs(parent, exist_ok=True)","def prepare_destination(self):\n self.movie_root_path = self.config.share_movie_root_path % (\n self.share_path, self.title)\n\n if os.path.isdir(self.movie_root_path):\n if self.capacity_reached():\n Logger.log(\n '[!] Capacity reached. Skipping adding movie %s.' % self.title)\n else:\n if not os.path.isdir(self.movie_root_path):\n Logger.log('[+] Adding Movie: %s' % self.title)\n os.mkdir(self.movie_root_path)","def create_file(backup_file, input_root, output_root):\n input_path = get_input_path(backup_file, input_root)\n if input_path is None:\n logging.warning(f\"Missing file: {backup_file.file_id} ({backup_file.relative_path})\")\n return 0\n output_path = get_output_path(backup_file, output_root)\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\n copyfile(input_path, output_path)","def save(annotation, new_filename, original_path):\n \n destination = \"../../standardized-data/\"\n if os.path.isdir(destination + \"/\" + annotation) == False:\n os.mkdir(destination + \"/\" + annotation)\n print(annotation, \"FOLDER CREATED\")\n if os.path.exists(destination + \"/\" + annotation + \"/\" + new_filename):\n print('FILE EXISTS: DOUBLE CHECK FOR DUPLICATION :', new_filename)\n else:\n shutil.copyfile(original_path, destination + \"/\" + annotation + \"/\" + new_filename)\n return","def create_file_path(fname, direc=\"data/result/\"):\n path = os.path.join(TOP_LEVEL, direc, fname)\n return path","def _make_fname(song, ext=None, av=None, subdir=None):\n # pylint: disable=E1103\n # Instance of 'bool' has no 'extension' member (some types not inferable)\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\n if not os.path.exists(ddir):\n os.makedirs(ddir)\n\n if ext:\n extension = ext\n\n else:\n stream = streams.select(streams.get(song),\n audio=av == \"audio\", m4a_ok=True)\n extension = stream['ext']\n\n # filename = song.title[:59] + \".\" + extension\n filename = song.title + \".\" + extension\n filename = os.path.join(ddir, mswinfn(filename.replace(\"/\", \"-\")))\n filename = filename.replace('\"', '')\n return filename","def ensure_path_exists(filename):\n targetdir = dirname(expanduser(filename))\n if exists(targetdir):\n return\n os.makedirs(abspath(targetdir))","def createWorkingFolder(newpath):\n newpath = pdbName[:-4]+'_ARM_input'\n if not os.path.exists(newpath):\n os.makedirs(newpath)\n globals().update({ \"workingFolder\" : newpath+\"/\"})\n\n shutil.move( pdbName, newpath+\"/\"+pdbName) \n if glob.glob(\"*.seqmut\"):\n MutFile = glob.glob(\"*.seqmut\")[0]\n shutil.copyfile(str(MutFile), newpath+\"/\"+pdbName[:-4]+\".seqmut\")\n else:\n pass\n os.chdir(newpath)","def make_source_dir():\n\n os.makedirs(files['source_dir'].rel)","def create_files(save_dir, vid_name):\n file_name = vid_name.split('/')[-1].split('.')[0]\n if not os.path.isdir(os.path.join(save_dir, file_name)):\n os.makedirs(os.path.join(save_dir, file_name))\n return file_name","def setup(self, newdir=None):\n if not os.path.exists(self.output_path):\n os.makedirs(self.output_path)\n if newdir:\n _new = os.path.join(self.output_path, newdir)\n if not os.path.exists(_new):\n os.makedirs(_new)","def moveAsset(self, src, dst):\n if not self.exists( self.dirname(dst) ):\n self.makedirs( self.dirname(dst) )\n self.move(src, dst)\n\n cache_src = self.cache_path(src)\n if not os.path.exists(cache_src):\n return \n\n cache_dst = self.cache_path(dst)\n if not os.path.exists( os.path.dirname(cache_dst) ):\n os.makedirs( os.path.dirname(cache_dst) )\n shutil.move(cache_src, cache_dst)","def copy_one(self, src, dest):\n if self.manager.no_sourcemaps and self.is_ignored_sourcemap(src.name):\n return\n\n if dest.is_dir():\n shutil.rmtree(dest)\n elif dest.exists():\n dest.unlink()\n\n if not dest.parent.exists():\n self.log.debug(f\"creating folder {dest.parent}\")\n dest.parent.mkdir(parents=True)\n\n self.maybe_timestamp(dest.parent)\n\n copytree_kwargs = {}\n\n if self.manager.no_sourcemaps:\n copytree_kwargs[\"ignore\"] = SOURCEMAP_IGNORE_PATTERNS\n\n if src.is_dir():\n shutil.copytree(src, dest, **copytree_kwargs)\n else:\n shutil.copy2(src, dest)\n\n self.maybe_timestamp(dest)","def copy_files_and_create_dirs(files) -> None:\r\n for file in files:\r\n target_dir_name = os.path.dirname(file[1])\r\n\r\n # will create all intermediate-level directories\r\n if not os.path.exists(target_dir_name):\r\n os.makedirs(target_dir_name)\r\n\r\n shutil.copyfile(file[0], file[1])","def generate_filename_template_path(output_dir, filename_template):\n if output_dir:\n os.makedirs(output_dir, exist_ok=True)\n return os.path.join(output_dir, filename_template)\n return None","def prep_folder(args):\n if(args.save_folder[-1]!='/'):\n args.save_folder += '/'\n if(not os.path.isdir(args.save_folder)):\n os.mkdir(args.save_folder)","def CheckForDirFileConflict(self, src_uri, dst_path):\n final_dir = os.path.dirname(dst_path)\n if os.path.isfile(final_dir):\n raise CommandException('Cannot retrieve %s because it a file exists '\n 'where a directory needs to be created (%s).' %\n (src_uri, final_dir))\n if os.path.isdir(dst_path):\n raise CommandException('Cannot retrieve %s because a directory exists '\n '(%s) where the file needs to be created.' %\n (src_uri, dst_path))","def make_output_folders():\n call([\"mkdir\", \"-p\", args.out_folder.strip()])\n call([\"mkdir\", args.out_folder.strip() + \"/files\"])\n call([\"mkdir\", args.out_folder.strip() + \"/fasta\"])","def make_zipfile(output_filename, source_dir):\n relroot = os.path.abspath(os.path.join(source_dir, os.pardir))\n with zipfile.ZipFile(output_filename, \"w\", zipfile.ZIP_DEFLATED) as zip:\n for root, dirs, files in os.walk(source_dir):\n # add directory (needed for empty dirs)\n zip.write(root, os.path.relpath(root, relroot))\n for file in files:\n filename = os.path.join(root, file)\n if os.path.isfile(filename): # regular files only\n arcname = os.path.join(os.path.relpath(root, relroot), file)\n zip.write(filename, arcname)","def copyDir(self, src, subpath):\n dst = self.output_path + \"/\" + subpath\n shutil.copytree(src, dst)","def get_path(dir_name, file_format, args):\n fname = \"{exp}-excl-{excl}\".format(exp=args.exp,\n excl=args.feature_set)\n path = os.path.join(SAVE_ROOT, dir_name, args.rbp,\n fname + file_format)\n\n # make the directory if it doesn't exist_ok\n os.makedirs(os.path.dirname(path), exist_ok=True)\n\n return path","def copy_file(self, dst, tmpdir=None):\n if tmpdir is None:\n tmpfn = sameDir\n else:\n tmpfn = lambda _: tmpdir._path\n assert isinstance(dst, Path)\n with open(self._path, 'rb') as src_fd:\n with safeopen(dst._path, 'wb', useDir=tmpfn) as dst_fd:\n copyfileobj(src_fd, dst_fd)","def cp_dir_or_files(self):\n if self.recursive:\n if self.cmdtype == 'upload' and not self.srcpath.endswith(os.path.sep):\n basename = os.path.basename(self.srcpath)\n self.destpath = join_obs_path(self.destpath, basename)\n elif self.cmdtype == 'download' and not self.srcpath.endswith('/'):\n bucket, key = split_bucket_key(self.srcpath)\n basename = key.split('/')[-1]\n if basename:\n self.destpath = os.path.join(self.destpath, basename)\n elif not self.srcpath.endswith('/'):\n bucket, key = split_bucket_key(self.srcpath)\n basename = key.split('/')[-1]\n if basename:\n self.destpath = join_obs_path(self.destpath, basename)"],"string":"[\n \"def _prepare_dst_dir(self, dst, src=None, perm=None, **kwargs):\\n if self.isdir(dst):\\n full_dst = os.path.join(dst, os.path.basename(src)) if src else dst\\n\\n elif self.isfile(dst):\\n full_dst = dst\\n\\n else:\\n # interpret dst as a file name, create missing dirs\\n dst_dir = self.dirname(dst)\\n if dst_dir and self.create_file_dir and not self.isdir(dst_dir):\\n self.mkdir(dst_dir, perm=perm, recursive=True)\\n full_dst = dst\\n\\n return full_dst\",\n \"def _prepare_dst_dir(self, dst, src=None, perm=None, **kwargs):\\n rstat = self.exists(dst, stat=True)\\n\\n if rstat:\\n if self.file_interface.isdir(dst, stat=rstat) and src:\\n full_dst = os.path.join(dst, os.path.basename(src))\\n else:\\n full_dst = dst\\n\\n else:\\n # interpret dst as a file name, create missing dirs\\n dst_dir = self.dirname(dst)\\n if dst_dir and self.create_file_dir and not self.isdir(dst_dir):\\n self.mkdir(dst_dir, perm=perm, recursive=True, **kwargs)\\n full_dst = dst\\n\\n return full_dst\",\n \"def prepare(self, dst, options):\\n self.checkExisting(dst)\\n self.makedirs(dst.parent())\",\n \"def MakeDestinationDirectories(self, dst_files):\\n for dst in dst_files:\\n path = os.path.dirname(dst);\\n if (len(path) > 0) and (not os.path.exists(path)):\\n self.VerboseMsg(\\\"Make Directory: \\\" + path)\\n if self.execute:\\n os.makedirs(path)\",\n \"def copy_file(filename, dst):\\n # Create dir if needed\\n dir_path = os.path.dirname(os.path.expanduser(dst))\\n if not os.path.isdir(dir_path):\\n os.makedirs(dir_path)\\n\\n src = os.path.join(get_data(''), filename)\\n dst = os.path.expanduser(dir_path)\\n shutil.copy2(src, dst)\",\n \"def _copy_file_with_parents(src, dst, ignore_no_src=False):\\n if not os.path.isfile(src) and ignore_no_src:\\n return\\n\\n dst_dir = os.path.dirname(dst)\\n create_directories(dst_dir)\\n\\n copyfile(src, dst)\",\n \"def make_local_copy(outdir, subdir, fname):\\n\\n destdir = path.join(outdir, subdir)\\n mkdir(destdir)\\n shutil.copyfile(package_data(fname), path.join(destdir, fname))\\n return path.join(subdir, fname)\",\n \"def copy_deep(src: str, dst: str, create_dst_dir: bool = False) -> None:\\n system_is_darwin = platform.system().lower() == \\\"darwin\\\"\\n if create_dst_dir:\\n mkdir_p(os.path.dirname(dst))\\n src_is_link = os.path.islink(src)\\n dst_exists = os.path.lexists(dst)\\n if os.path.isdir(src) and not src_is_link:\\n logging.debug(\\\"Copying directory {} to {}\\\".format(src, dst))\\n mkdir_p(dst)\\n for name in os.listdir(src):\\n copy_deep(os.path.join(src, name), os.path.join(dst, name))\\n elif src_is_link:\\n if dst_exists:\\n return\\n target = os.readlink(src)\\n logging.debug(\\\"Creating symlink {} -> {}\\\".format(dst, target))\\n os.symlink(target, dst)\\n else:\\n if dst_exists:\\n if not system_is_darwin:\\n return\\n # Only overwrite the file if the source is newer than the destination.\\n if os.path.getmtime(src) <= os.path.getmtime(dst):\\n return\\n logging.debug(\\\"Copying file {} to {}\\\".format(src, dst))\\n # Preserve the file attributes.\\n shutil.copy2(src, dst)\",\n \"def safecopy(src, dst):\\r\\n abs_src = os.path.abspath(src)\\r\\n abs_dst = os.path.abspath(dst)\\r\\n if (abs_src != abs_dst) \\\\\\r\\n and os.path.isfile(abs_src): \\r\\n dirname = os.path.dirname(abs_dst)\\r\\n recurse_mkdir(dirname)\\r\\n shutil.copy(abs_src, abs_dst)\",\n \"def copy_to_se(self, src, dst, create_parent_directory=True):\\n mgm, dst = self._safe_split_mgm(dst)\\n dst = self._join_mgm_lfn(mgm, dst)\\n if create_parent_directory:\\n parent_directory = osp.dirname(dst)\\n self.create_directory(parent_directory)\\n logger.warning('Copying {0} to {1}'.format(src, dst))\\n cmd = [ 'xrdcp', '-s', src, dst ]\\n svj.core.utils.run_command(cmd)\",\n \"def cp(src, dst):\\n os.makedirs(os.path.dirname(dst), exist_ok=True)\\n src_stat = os.stat(src)\\n try:\\n dst_stat = os.stat(dst)\\n except FileNotFoundError:\\n dst_stat = (0,)*10\\n src_modif_time = src_stat[stat.ST_MTIME]\\n dst_modif_time = dst_stat[stat.ST_MTIME]\\n if src_modif_time > dst_modif_time:\\n shutil.copyfile(src, dst)\\n print(\\\" ++\\\", dst[len(THESIS_DIR):])\\n else:\\n print(\\\" --\\\", dst[len(THESIS_DIR):])\",\n \"def cp_to_dir(fn0, d):\\n\\n # keep rewriting attributes\\n shutil.copy(fn0, d)\",\n \"def _copy_dir(src, dst):\\n if os.path.isdir(src):\\n os.makedirs(dst, exist_ok=True)\\n for item in os.listdir(src):\\n s = os.path.join(src, item)\\n d = os.path.join(dst, item)\\n\\n if os.path.isdir(s):\\n _copy_dir(s, d)\\n else:\\n shutil.copy2(s, d)\\n\\n else:\\n os.makedirs(os.path.dirname(dst), exist_ok=True)\\n _delete_file(dst)\\n shutil.copy2(src, dst)\",\n \"def copy_dir(src: Text, dst: Text) -> None:\\n\\n if tf.io.gfile.exists(dst):\\n tf.io.gfile.rmtree(dst)\\n tf.io.gfile.makedirs(dst)\\n\\n for dir_name, sub_dirs, leaf_files in tf.io.gfile.walk(src):\\n for leaf_file in leaf_files:\\n leaf_file_path = os.path.join(dir_name, leaf_file)\\n new_file_path = os.path.join(dir_name.replace(src, dst, 1), leaf_file)\\n tf.io.gfile.copy(leaf_file_path, new_file_path)\\n\\n for sub_dir in sub_dirs:\\n tf.io.gfile.makedirs(os.path.join(dir_name.replace(src, dst, 1), sub_dir))\",\n \"def convert_tmpfile(src_file_name:str, dest_path:str):\\n src_path = os.path.join(\\n current_app.config['UPLOAD_FOLDER'],\\n src_file_name\\n )\\n if not os.path.exists(src_path):\\n abort(http.HTTPStatus.BAD_REQUEST, message='raw file not exist')\\n pathlib.Path(os.path.dirname(dest_path)).mkdir(parents=True, exist_ok=True)\\n shutil.move(src_path, dest_path)\",\n \"def makeOutputPath(outputPath, filePath, filename):\\n if filePath == \\\"\\\" or filePath == None:\\n return None\\n if outputPath[-1] == FOLDER_DELIM: # remove an eventual / at the end\\n outputPath = outputPath[:-1]\\n\\n fullPath = outputPath + FOLDER_DELIM + filePath\\n fullPath += FOLDER_DELIM # add a '/' at the end of the path\\n\\n if not os.path.isfile(fullPath): # if the path doesn't exist, create it!\\n try:\\n os.makedirs(fullPath)\\n except FileExistsError:\\n pass # nothing really went wrong if the folder already exists, continue quietly\\n return fullPath + filename + TARGET # return the path and target filename\",\n \"def copy_file_to_multiple_subfolders(src, dst, *args, **kwargs):\\n print '\\\\nSource: {}\\\\nDestinations parent folder: {}'.format(src, dst)\\n filename = os.path.basename(src)\\n for folder in (d for d in os.listdir(dst) if os.path.isdir(d)):\\n print '\\\\nCopying {} to {}...'.format(filename, folder)\\n try:\\n shutil.copy(src, os.path.abspath(dst) + '\\\\\\\\' + folder)\\n except Exception as e:\\n print e\",\n \"def move_files(src_dir, dst_dir):\\n for f in os.listdir(src_dir):\\n try:\\n name, season, episode = FILENAME_PATTERN.search(f).groups()\\n except AttributeError:\\n try:\\n name, season, episode = FILENAME_PATTERN2.search(f).groups()\\n except AttributeError:\\n print \\\"Cannot parse\\\", f\\n pass\\n\\n name = name.replace('.', ' ').replace('_', ' ').strip().title()\\n\\n dir_path = os.path.join(dst_dir, name, 'Season %02d' % int(season))\\n full_path = os.path.join(dir_path, f)\\n source_path = os.path.join(src_dir, f)\\n\\n if not os.path.exists(dir_path):\\n os.makedirs(dir_path, 0777)\\n\\n if not os.path.exists(full_path):\\n shutil.move(source_path, full_path)\\n os.symlink(full_path, source_path)\",\n \"def copy(src, dst):\\n os.makedirs(os.path.dirname(dst), exist_ok=True)\\n shutil.copy2(src, dst)\",\n \"def cpsym(src,dest):\\n \\n src = os.path.normpath(src)\\n dest = os.path.normpath(dest)\\n \\n if not os.path.exists(src):\\n return\\n \\n for dirpath,dirnames,filenames in os.walk(src):\\n rel_dirpath = os.path.relpath(dirpath,src)\\n dest_dirpath = os.path.join(dest,rel_dirpath)\\n mkdir(dest_dirpath,isfull=True)\\n \\n for filename in filenames:\\n src_filename = os.path.join(dirpath,filename)\\n rel_filename = os.path.relpath(src_filename,src)\\n \\n dest_filename = os.path.join(dest,rel_filename)\\n try:\\n os.symlink(src_filename,dest_filename)\\n except OSError:\\n pass\",\n \"def create_path_by_date(dest, dt):\\n if not os.path.isdir(dest):\\n raise FileNotFoundError(f\\\"dest {dest} must be valid path\\\")\\n yyyy, mm, dd = dt[0:3]\\n yyyy = str(yyyy).zfill(4)\\n mm = str(mm).zfill(2)\\n dd = str(dd).zfill(2)\\n new_dest = os.path.join(dest, yyyy, mm, dd)\\n if not os.path.isdir(new_dest):\\n os.makedirs(new_dest)\\n return new_dest\",\n \"def create_dir_for_file(f_path):\\n d = os.path.dirname(f_path)\\n if d and not os.path.exists(d):\\n os.makedirs(d)\",\n \"def _make_output_directory(self):\\n fs = self._filesystem\\n output_filename = fs.join(self._root_output_dir, self._test_name)\\n fs.maybe_make_directory(fs.dirname(output_filename))\",\n \"def create(self, basedir, outdir, name, prefix=None):\",\n \"def create_output_dir(self):\\n if self.output_dir is None:\\n new_path = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\\n self.output_dir = os.path.expanduser(os.path.join(self.input_dir, new_path))\\n try:\\n os.makedirs(self.output_dir)\\n except OSError:\\n pass\",\n \"def buildDestination(self, mapping, options, src):\\n prefixTemplate = options['prefix']\\n if prefixTemplate is None:\\n prefixTemplate = self.defaultPrefixTemplate\\n\\n if prefixTemplate is not None:\\n prefix = os.path.expanduser(\\n prefixTemplate.safe_substitute(mapping))\\n else:\\n prefixTemplate = string.Template(src.dirname())\\n prefix = prefixTemplate.template\\n\\n ext = src.splitext()[-1]\\n\\n nameTemplate = options['name']\\n if nameTemplate is None:\\n nameTemplate = self.defaultNameTemplate\\n\\n filename = nameTemplate.safe_substitute(mapping)\\n logging.msg(\\n 'Building filename: prefix=%r name=%r mapping=%r' % (\\n prefixTemplate.template, nameTemplate.template, mapping),\\n verbosity=3)\\n return FilePath(prefix).child(filename).siblingExtension(ext)\",\n \"def clone(src: str, dst: str):\\n if dst is None:\\n dst = getcwd()\\n destination = path.abspath(dst)\\n # TODO: replace with false this is just for testing:\\n makedirs(destination, exist_ok=True)\\n\\n sync_chunk(src, destination)\\n copy(src, destination)\",\n \"def create_path(inputfile, outputdir):\\n pathdata = '/'.join(inputfile.split('/')[-3:])\\n newpath = join(outputdir, pathdata)\\n dirout = dirname(newpath)\\n if not isdir(dirout):\\n os.makedirs(dirout)\\n return newpath\",\n \"def task():\\n if os.path.isdir(orig):\\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*')) if \\\\\\n os.path.isdir(fP) ]:\\n if not os.path.exists(dest + fP[len(orig):]):\\n os.makedirs(dest + fP[len(orig):])\\n for fP in [ fP for fP in glob.glob(os.path.join(orig, '*-*/*/%s.log' %fmt.get_date())) if \\\\\\n os.path.isfile(fP) ]:\\n convert(fP, dest + fP[len(orig):])\",\n \"def copyFile(src_dir, dst_dir, f_name):\\n\\n try:\\n src_file = open(osp.join(src_dir, f_name),\\\"rb\\\")\\n dst_file = open(osp.join(dst_dir, f_name),\\\"wb\\\")\\n dst_file.write(src_file.read())\\n dst_file.close()\\n src_file.close()\\n except Exception, e:\\n msg = \\\"!!! In copying files from < %s > dir to < %s > dir exception occur. Details: %s.\\\" % (src_dir,dst_dir, str(e))\\n print >> import_out, msg\\n LOG('performImportToPortal',INFO,'copyFile', msg)\",\n \"def move(self,src,dst):\\n src = os.path.join(self.testpath,src)\\n dst = os.path.join(self.testpath,dst)\\n directory = os.path.split(dst)[0]\\n try:\\n os.makedirs(directory)\\n except OSError:\\n pass\\n\\n shutil.move(src,dst)\",\n \"def copydir(src, dst):\\n for item in os.listdir(src):\\n s, d = os.path.join(src, item), os.path.join(dst, item)\\n if os.path.isdir(s):\\n if not os.path.isdir(d):\\n os.mkdir(d)\\n copydir(s, d)\\n else:\\n shutil.copy(s, d)\",\n \"def _createDirsAbove(fname):\\n\\n dirToCreate = os.path.dirname(fname)\\n # https://stackoverflow.com/a/600612\\n try:\\n os.makedirs(dirToCreate)\\n except OSError as e:\\n if e.errno == errno.EEXIST and os.path.isdir(dirToCreate):\\n pass\\n else:\\n raise\",\n \"def create_dir(name_new_path):\\n\\n try:\\n os.mkdir(config_tools.full_dest + name_new_path)\\n except OSError:\\n print(\\\"Создать директорию %s не удалось\\\" % name_new_path)\\n else:\\n print(\\\"Успешно создана директория %s \\\" % name_new_path)\",\n \"def makefilename(self):\\n fp= (pathlib.Path(self.vr_folder).expanduser()/(time.strftime(self.vr_filename))).with_suffix('')\\n fp.parent.mkdir(parents=True, exist_ok=True)\\n print('files setup', str(fp))\\n return fp\",\n \"def install(src, dst):\\n try:\\n dst = os.path.join(install_dir, dst, os.path.basename(src))\\n src = os.path.join(source_dir, src)\\n assert os.path.isfile(src)\\n assert not os.path.isdir(dst)\\n if not os.path.isdir(os.path.dirname(dst)):\\n os.makedirs(os.path.dirname(dst))\\n shutil.copy(src, dst)\\n print 'Installed', dst\\n except Exception:\\n print 'Could not install', dst\",\n \"def create_folder(output_directory: str, fldrname: str):\\n\\n os.makedirs(output_directory, exist_ok=True)\\n tstmp = datetime.now().strftime('%Y%m%d_%H%M%S')\\n try:\\n fldr_path = os.path.join(output_directory, fldrname)\\n os.mkdir(fldr_path)\\n except FileExistsError:\\n fldr_path = os.path.join(output_directory, fldrname + '_{}'.format(tstmp))\\n os.mkdir(fldr_path)\\n return fldr_path\",\n \"def create_directory_for_file_if_needed(file_name):\\n directory_name = os.path.dirname(file_name)\\n create_directory_if_needed(directory_name)\\n return directory_name\",\n \"def ensure_directory(self, name, dest, mode=0777):\\n self.m.path.assert_absolute(dest)\\n self._run(\\n name, ['ensure-directory', '--mode', oct(mode), dest])\\n self.m.path.mock_add_paths(dest)\",\n \"def _create_dir(filename):\\n head = os.path.dirname(filename)\\n if head != '' and not os.path.isdir(head):\\n os.makedirs(head)\",\n \"def create_symlink_dir(src_dir, src_list, dst):\\n if not src_list:\\n return\\n message = \\\"creating symlink directory at {dst} with files {src_list}\\\".format(\\n dst=dst,\\n src_list=pformat(src_list))\\n logging.info(message)\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n for src_file in src_list:\\n if not src_file:\\n continue\\n source = os.path.join(src_dir, src_file)\\n destination = os.path.join(dst, src_file)\\n if os.path.lexists(destination):\\n continue\\n try:\\n os.symlink(source, destination)\\n except Exception as e:\\n msg = format_debug(e)\\n logging.error(e)\",\n \"def copy_file(src_file,dst_folder):\\n from shutil import copyfile\\n from os.path import split\\n copyfile(src_file, dst_folder+split(src_file)[1])\\n return\",\n \"def add_to_split(rec_dir, target, label):\\n for file_name in os.listdir(rec_dir):\\n path = os.path.join(rec_dir, file_name)\\n if (os.path.isfile(path)):\\n if not os.path.isdir(os.path.join(target, str(label))):\\n os.makedirs(os.path.join(target, str(label)))\\n shutil.copy(path, os.path.join(target, str(label), file_name))\",\n \"def _copy_files(src_paths,dst_dir,class_numbers):\\n\\n class_dirs=[os.path.join(dst_dir,class_name+\\\"/\\\")for class_name in self.class_names]\\n\\n for dir in class_dirs:\\n if not os.path.exists(dir):\\n os.makedirs(dir)\\n\\n for src,cls in zip(src_paths,class_numbers):\\n shutil.copy(src=src,dst=class_dirs[cls])\",\n \"def create_dir(output_path):\\n if not os.path.exists(output_path) and is_directory(output_path):\\n os.makedirs(output_path)\",\n \"def prepare_output_dir(out_dir, test_dir):\\r\\n\\r\\n if not out_dir.exists():\\r\\n out_dir.mkdir()\\r\\n\\r\\n # get the necessary file names\\r\\n file_names = get_file_names(test_dir, args.distance, print_file_names=False)\\r\\n\\r\\n # copy the images in the firstIms into the output folder\\r\\n for name in file_names[1][0]:\\r\\n file_path = Path(test_dir / name)\\r\\n copy_to = Path(out_dir / name)\\r\\n shutil.copy(file_path, copy_to)\\r\\n\\r\\n # the firstIms list does not contain the last image,\\r\\n # so we need to also copy the last image of the secIms into the output folder\\r\\n last_im = file_names[1][1][-1]\\r\\n shutil.copy(Path(test_dir/last_im), Path(out_dir/last_im))\\r\\n\\r\\n return file_names\",\n \"def CopyFolder(src, dst, overwrite=True):\\n\\n if not src.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n src = src + \\\"\\\\\\\\\\\"\\n if not dst.endswith((\\\"/\\\", \\\"\\\\\\\\\\\")):\\n dst = dst + \\\"\\\\\\\\\\\"\\n os.makedirs(dst, exist_ok=True)\\n\\n for file in os.listdir(src):\\n if not overwrite and os.path.isfile(dst + file):\\n continue\\n if os.path.isfile(src + file):\\n shutil.copy(src + file, dst + file)\\n elif os.path.isdir(src + file):\\n CopyFolder(src + file, dst + file, overwrite)\\n return 0\",\n \"def move_files(fname_fout, root_dir, dest_dir):\\n fname, f_ext = os.path.splitext(fname_fout)\\n # Find files which filename of fname_fout\\n matches = []\\n pattern = fname + '*'\\n root_fnames = os.listdir(root_dir)\\n for filename in fnmatch.filter(root_fnames, pattern):\\n matches.append([filename, os.path.join(root_dir, filename)])\\n # Extract new folder name based on fname_fout\\n new_folder_name = reshape_fname(fname_fout, ['nairfoil', 'nsetup'])\\n dest_dir = os.path.join(dest_dir, new_folder_name)\\n # Move files\\n for cur_file in matches:\\n os.renames(cur_file[1], os.path.join(dest_dir, cur_file[0]))\",\n \"def convertPath(srcpath, dstdir):\\n bits = srcpath.split(\\\"/\\\")\\n bits.pop(0)\\n # Strip out leading 'unsigned' from paths like unsigned/update/win32/...\\n if bits[0] == 'unsigned':\\n bits.pop(0)\\n return os.path.join(dstdir, *bits)\",\n \"def folder_to_s3(src, dst, region, max_parallelism=1, force_copy=False, **kwargs):\\n bucket, root = utils.path.reverse_split(dst)\\n\\n s3 = boto3.resource('s3')\\n\\n # check if the bucket exists\\n if not __bucket_exists(bucket):\\n\\n if force_copy:\\n print('creating bucket: ' + bucket)\\n\\n try:\\n s3.create_bucket(Bucket=bucket,\\n CreateBucketConfiguration={'LocationConstraint': region})\\n except botocore.exceptions.ClientError as e:\\n raise e\\n else:\\n exit(-1)\\n\\n # instanciate transfer configuration\\n conf = boto3.s3.transfer.TransferConfig(use_threads=True, **kwargs)\\n\\n # start uploading\\n with ProcessPoolExecutor(max_workers=max_parallelism) as executor:\\n try:\\n for file in utils.path.dir_tree(src):\\n # removes the root so that it can be\\n # later added to the input string\\n suffix = file.replace(src, '')\\n executor.submit(file_to_s3,\\n bucket,\\n file,\\n os.path.join(root, suffix),\\n conf,\\n utils.path.progress\\n )\\n\\n except (BrokenProcessPool):\\n try:\\n # deleting the bucket if created\\n # to do so, the bucket must be empty\\n print(\\\"removing %s from %s\\\" % (root, bucket))\\n delete_folder(bucket, root, region)\\n if force_copy:\\n print(\\\"attempting to delete %s\\\" % bucket)\\n s3.Bucket(bucket).delete()\\n\\n except botocore.exceptions.ClientError as e:\\n print(\\\"operation failed: %s\\\" % e)\\n exit(-1)\\n\\n else:\\n print(\\\"operation aborted. exiting...\\\")\\n exit(0)\",\n \"def createFolder(self):\\n self.destination = self.getPath() #Find the destination to create the folder\\n try:\\n os.makedirs(self.destination) #Try and make a folder\\n except FileExistsError:\\n pass #Otherwise continue if an error is encountered because the file exists already\",\n \"def path_src_to_dest(src_pathname, dest_filename_suffix=None):\\n src_relpath = Path(src_pathname).relative_to(config[\\\"topdir\\\"])\\n dest_pathname = Path(config[\\\"outdir\\\"]).joinpath(src_relpath)\\n if dest_filename_suffix:\\n dest_pathname = dest_pathname.with_suffix(dest_filename_suffix)\\n return dest_pathname\",\n \"def compress(src,dstfile):\\n\\tafile = zipfile.ZipFile(dstfile,\\\"w\\\",zipfile.ZIP_DEFLATED)\\n\\tfor root,dirs,files in os.walk(src):\\n\\t\\tfor filename in files:\\n\\t\\t\\tabspath = osp.join(root,filename)\\n\\t\\t\\trelpath = osp.relpath(abspath,src)\\n\\t\\t\\tafile.write(abspath, relpath)\\n\\tafile.close();\",\n \"def safe_copy(file_path, out_dir, dst=None):\\n name = dst or os.path.basename(file_path)\\n if not os.path.exists(os.path.join(out_dir, name)):\\n shutil.copy(file_path, os.path.join(out_dir, name))\",\n \"def create_output_folder(campaign_name: str, path: str) -> str:\\n folder_name = \\\"\\\".join([campaign_name, \\\"_druckfiles\\\"])\\n path = os.path.split(path)[0]\\n out_path = os.path.join(path, folder_name)\\n if not os.path.exists(out_path):\\n os.makedirs(out_path)\\n return out_path\",\n \"def copy(self, src, dst, label=None):\\n self._tag(dst, label)\\n self._mkdir_for(dst)\\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))\",\n \"def applyDir(self,srcDir,destDir,exts): \\n for srcFile in os.listdir(srcDir):\\n srcExt = os.path.splitext(srcFile)[-1].lower()\\n srcPath = os.path.join(srcDir,srcFile)\\n destPath = os.path.join(destDir,srcFile)\\n if srcExt in exts:\\n if not os.path.exists(destDir):\\n os.makedirs(destDir)\\n shutil.copyfile(srcPath,destPath)\\n if self.progress: \\n self.cumSize += os.path.getsize(srcPath)\\n self.progress(self.cumSize,_('Copying Files...'))\\n elif os.path.isdir(srcPath):\\n self.applyDir(srcPath,destPath,exts)\",\n \"def _prepare_directory(destination, connector_id):\\n\\n # Use the destination directory when provided\\n if destination:\\n if not os.path.exists(destination):\\n # Create all sub-directories\\n os.makedirs(destination)\\n # Create a sub-directory in the current directory\\n # when a destination isn't provided\\n else:\\n if not os.path.isdir(connector_id):\\n os.mkdir(connector_id)\\n destination = connector_id\\n\\n if os.path.isdir(destination):\\n os.chdir(destination)\\n else:\\n error = 'Couldn\\\\'t download to the desination directory {}.'\\n raise CLIError(error.format(destination))\\n\\n return os.getcwd()\",\n \"def m_cp(*args):\\n if (not args or len(args) < 2) :\\n print(\\\"parameter is invalid\\\")\\n return\\n\\n src = args[0:len(args)-1]\\n dst = args[len(args)-1]\\n\\n if not os.path.exists(dst) :\\n os.mkdir(dst)\\n\\n for s in src:\\n print(\\\"cp %s ==> %s\\\" % (s, dst))\\n cpfile(s, dst)\",\n \"def save(url, dst, force=False):\\n if not os.path.isfile(dst) or force:\\n # Test if the directory exist or create\\n d = os.path.dirname(dst)\\n if not os.path.exists(d):\\n os.makedirs(d)\\n print(u\\\"\\\\nDownloading: {0} to {1}\\\".format(url, dst))\\n urllib.urlretrieve(url, dst, report)\",\n \"def make_dir(file_name): # output_file_loc = des\\n for i in os.walk(f'{tmp_path}/{file_name}'):\\n fld = i[0].split(file_name)[-1]\\n if fld:\\n loc = f\\\"{output_path}{fld}\\\"\\n if not os.path.exists(f'{output_path}/{fld}'):\\n os.makedirs(f'{output_path}/{fld}')\\n # print(\\\"MAKE_DIR completed...\\\") \\n return\",\n \"def dst_to_src(self,dst_file):\\n rel_path=os.path.relpath(dst_file,start=self.dst_root)\\n if (rel_path == '.'):\\n rel_path=''\\n else:\\n rel_path= '/'+rel_path\\n if (os.sep != '/'):\\n # if directoty path sep isn't / then translate for URI \\n rel_path=rel_path.replace(os.sep,'/')\\n return(self.src_root+rel_path)\",\n \"def copy_dir(src, dst):\\n try:\\n debug.log(\\\"copy dir from \\\"+ src, \\\"to \\\"+ dst)\\n shutil.copytree(src, dst)\\n except Exception as e:\\n debug.log(\\\"Error: happened while copying!\\\\n%s\\\\n\\\"%e)\",\n \"def set_dest(self, mode, dest, prefix):\\n try: \\n os.mkdir(dest)\\n os.mkdir(dest+'/images')\\n os.mkdir(dest+'/images'+'/train')\\n os.mkdir(dest+'/images'+'/test')\\n os.mkdir(dest+'/labels')\\n os.mkdir(dest+'/labels'+'/train')\\n os.mkdir(dest+'/labels'+'/test')\\n except FileExistsError:\\n pass\\n\\n if mode == 'train':\\n self.detImage = dest+'images/'+'train/'\\n self.detLabel = dest+'labels/'+'train/'\\n self.detList = dest+'train.txt'\\n elif mode == 'test':\\n self.detImage = dest+'images/'+'test/'\\n self.detLabel = dest+'labels/'+'test/'\\n self.detList = dest+'test.txt'\\n self.prefix = prefix\",\n \"def copy(self, src, dst, label=None):\\r\\n self._tag(dst, label)\\r\\n self._mkdir_for(dst)\\r\\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))\",\n \"def copy(self, src, dst, label=None):\\r\\n self._tag(dst, label)\\r\\n self._mkdir_for(dst)\\r\\n shutil.copyfile(self._rootjoin(src), os.path.join(self.chroot, dst))\",\n \"def make_dir(name='results'):\\n if os.path.isabs(name):\\n output_path = name\\n else:\\n output_path = os.path.join(os.getcwd(), name)\\n\\n if ('.' not in output_path):\\n directory = os.path.dirname(os.path.join(output_path, 'toto')) # doesn't work w/o 'toto'\\n else :\\n directory = os.path.dirname(output_path);\\n\\n try:\\n os.makedirs(directory)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n return output_path\",\n \"def make_path(self, basename):\\n return os.path.join(self.output_folder, basename.format(self.sample_name))\",\n \"def copy(self, fname):\\n _, ext = osp.splitext(fname)\\n spath = osp.join(self.src, fname)\\n oname = fname\\n path = osp.join(self.dst, oname)\\n os.makedirs(osp.dirname(path), exist_ok=True)\\n if ext in [\\\".css\\\"]:\\n content = self.include(fname)\\n with open(path, \\\"wt\\\") as fp:\\n fp.write(content)\\n else:\\n shutil.copyfile(spath, path)\\n return osp.relpath(oname, self.root)\",\n \"def copyDir(src, dst, includes, excludes = []):\\n\\tmultiFilesReplacements([], dst, src, includes, excludes)\",\n \"def _install_file(srcdir, filename, dstdir):\\n srcfilename = os.path.join(srcdir, filename)\\n dstfilename = os.path.join(dstdir, filename)\\n if not os.path.exists(srcfilename):\\n if os.path.exists(dstfilename):\\n subprocess.run(['rm', dstfilename], check=True)\\n return (False, True)\\n return (False, False)\\n\\n equal = subprocess.run(['diff', '-q', srcfilename, dstfilename],\\n check=False,\\n stdout=subprocess.DEVNULL,\\n stderr=subprocess.DEVNULL).returncode == 0\\n if not equal:\\n subprocess.run(['mv', srcfilename, dstfilename], check=True)\\n return (True, not equal)\",\n \"def check_path(fp):\\n if not Path(fp).exists():\\n\\n if len(Path(fp).suffix) > 0: # check if file\\n Path(fp).parent.mkdir(exist_ok=True, parents=True)\\n\\n else: # or directory\\n Path(fp).mkdir(exist_ok=True, parents=True)\",\n \"def copy_dir(src=\\\"\\\", dst=\\\"\\\", header=\\\"\\\", footer=\\\"\\\", clip=0, ext=\\\"\\\", test=False):\\n failed = []\\n nfiles = 0\\n if not os.path.exists(dst):\\n os.makedirs(dst)\\n if not os.path.exists(src):\\n raise argparse.ArgumentError(\\\"source does not exist! It must be a directory.\\\")\\n else:\\n for root, dirs, files in os.walk(src, topdown=False):\\n for name in files:\\n name_wo_ext, file_ext = os.path.splitext(name)\\n\\n src_path = os.path.join(root, name)\\n dstfilename = header + os.path.join(root[len(src)+1:], name_wo_ext[clip:]) + footer + file_ext\\n dst_path = os.path.join(dst, dstfilename)\\n\\n dst_pdir = os.path.dirname(dst_path)\\n if not os.path.exists(dst_pdir):\\n os.makedirs(dst_pdir)\\n\\n if not os.path.exists(dst_path):\\n if ext == \\\"\\\" or ext == file_ext[1:]:\\n try:\\n shutil.copy(src_path, dst_path)\\n except:\\n failed.append(src_path)\\n print(f\\\"... {src_path} failed\\\")\\n else:\\n print(f\\\"... {dst_path} already exists'. Skipping\\\")\\n nfiles += 1\\n\\n if test:\\n break\\n if test:\\n break\\n print(f\\\"{nfiles - len(failed)} / {nfiles} files were copied.\\\")\\n return failed\",\n \"def validate_file_destination(namespace):\\n try:\\n path = namespace.destination\\n except AttributeError:\\n return\\n else:\\n # TODO: Need to confirm this logic...\\n file_path = path\\n file_dir = os.path.dirname(path)\\n if os.path.isdir(path):\\n file_name = os.path.basename(namespace.file_name)\\n file_path = os.path.join(path, file_name)\\n elif not os.path.isdir(file_dir):\\n try:\\n os.mkdir(file_dir)\\n except EnvironmentError as exp:\\n message = \\\"Directory {} does not exist, and cannot be created: {}\\\"\\n raise ValueError(message.format(file_dir, exp))\\n if os.path.isfile(file_path):\\n raise ValueError(\\\"File {} already exists.\\\".format(file_path))\\n namespace.destination = file_path\",\n \"def put(self, src, dst):\\r\\n abs_src = os.path.expanduser(src)\\r\\n assert os.path.exists(abs_src), 'File does not exist, cannot copy: %s' % abs_src\\r\\n return self._do_put(abs_src, dst)\",\n \"def ConstructDstUri(self, src_uri, exp_src_uri, base_dst_uri):\\n if base_dst_uri.names_container():\\n # To match naming semantics of UNIX 'cp' command, copying files\\n # to buckets/dirs should result in objects/files named by just the\\n # final filename component; while copying directories should result\\n # in objects/files mirroring the directory hierarchy. Example of the\\n # first case:\\n # gsutil cp dir1/file1 gs://bucket\\n # should create object gs://bucket/file1\\n # Example of the second case:\\n # gsutil cp dir1/dir2 gs://bucket\\n # should create object gs://bucket/dir2/file2 (assuming dir1/dir2\\n # contains file2).\\n if src_uri.names_container():\\n dst_path_start = (src_uri.object_name.rstrip(os.sep)\\n .rpartition(os.sep)[-1])\\n start_pos = exp_src_uri.object_name.find(dst_path_start)\\n dst_key_name = exp_src_uri.object_name[start_pos:]\\n else:\\n # src is a file or object, so use final component of src name.\\n dst_key_name = os.path.basename(exp_src_uri.object_name)\\n if base_dst_uri.is_file_uri():\\n # dst names a directory, so append src obj name to dst obj name.\\n dst_key_name = '%s%s%s' % (base_dst_uri.object_name, os.sep,\\n dst_key_name)\\n self.CheckForDirFileConflict(exp_src_uri, dst_key_name)\\n else:\\n # dest is an object or file: use dst obj name\\n dst_key_name = base_dst_uri.object_name\\n return base_dst_uri.clone_replace_name(dst_key_name)\",\n \"def create_out_dir(out): \\n out_path = os.path.join(out,out_dir_name)\\n try:\\n os.stat(out_path)\\n except:\\n os.mkdir(out_path)\",\n \"def _make_dirs(filepath, mode):\\n parent = filepath.parent\\n if \\\"w\\\" in mode and parent:\\n os.makedirs(parent, exist_ok=True)\",\n \"def prepare_destination(self):\\n self.movie_root_path = self.config.share_movie_root_path % (\\n self.share_path, self.title)\\n\\n if os.path.isdir(self.movie_root_path):\\n if self.capacity_reached():\\n Logger.log(\\n '[!] Capacity reached. Skipping adding movie %s.' % self.title)\\n else:\\n if not os.path.isdir(self.movie_root_path):\\n Logger.log('[+] Adding Movie: %s' % self.title)\\n os.mkdir(self.movie_root_path)\",\n \"def create_file(backup_file, input_root, output_root):\\n input_path = get_input_path(backup_file, input_root)\\n if input_path is None:\\n logging.warning(f\\\"Missing file: {backup_file.file_id} ({backup_file.relative_path})\\\")\\n return 0\\n output_path = get_output_path(backup_file, output_root)\\n os.makedirs(os.path.dirname(output_path), exist_ok=True)\\n copyfile(input_path, output_path)\",\n \"def save(annotation, new_filename, original_path):\\n \\n destination = \\\"../../standardized-data/\\\"\\n if os.path.isdir(destination + \\\"/\\\" + annotation) == False:\\n os.mkdir(destination + \\\"/\\\" + annotation)\\n print(annotation, \\\"FOLDER CREATED\\\")\\n if os.path.exists(destination + \\\"/\\\" + annotation + \\\"/\\\" + new_filename):\\n print('FILE EXISTS: DOUBLE CHECK FOR DUPLICATION :', new_filename)\\n else:\\n shutil.copyfile(original_path, destination + \\\"/\\\" + annotation + \\\"/\\\" + new_filename)\\n return\",\n \"def create_file_path(fname, direc=\\\"data/result/\\\"):\\n path = os.path.join(TOP_LEVEL, direc, fname)\\n return path\",\n \"def _make_fname(song, ext=None, av=None, subdir=None):\\n # pylint: disable=E1103\\n # Instance of 'bool' has no 'extension' member (some types not inferable)\\n ddir = os.path.join(Config.DDIR.get, subdir) if subdir else Config.DDIR.get\\n if not os.path.exists(ddir):\\n os.makedirs(ddir)\\n\\n if ext:\\n extension = ext\\n\\n else:\\n stream = streams.select(streams.get(song),\\n audio=av == \\\"audio\\\", m4a_ok=True)\\n extension = stream['ext']\\n\\n # filename = song.title[:59] + \\\".\\\" + extension\\n filename = song.title + \\\".\\\" + extension\\n filename = os.path.join(ddir, mswinfn(filename.replace(\\\"/\\\", \\\"-\\\")))\\n filename = filename.replace('\\\"', '')\\n return filename\",\n \"def ensure_path_exists(filename):\\n targetdir = dirname(expanduser(filename))\\n if exists(targetdir):\\n return\\n os.makedirs(abspath(targetdir))\",\n \"def createWorkingFolder(newpath):\\n newpath = pdbName[:-4]+'_ARM_input'\\n if not os.path.exists(newpath):\\n os.makedirs(newpath)\\n globals().update({ \\\"workingFolder\\\" : newpath+\\\"/\\\"})\\n\\n shutil.move( pdbName, newpath+\\\"/\\\"+pdbName) \\n if glob.glob(\\\"*.seqmut\\\"):\\n MutFile = glob.glob(\\\"*.seqmut\\\")[0]\\n shutil.copyfile(str(MutFile), newpath+\\\"/\\\"+pdbName[:-4]+\\\".seqmut\\\")\\n else:\\n pass\\n os.chdir(newpath)\",\n \"def make_source_dir():\\n\\n os.makedirs(files['source_dir'].rel)\",\n \"def create_files(save_dir, vid_name):\\n file_name = vid_name.split('/')[-1].split('.')[0]\\n if not os.path.isdir(os.path.join(save_dir, file_name)):\\n os.makedirs(os.path.join(save_dir, file_name))\\n return file_name\",\n \"def setup(self, newdir=None):\\n if not os.path.exists(self.output_path):\\n os.makedirs(self.output_path)\\n if newdir:\\n _new = os.path.join(self.output_path, newdir)\\n if not os.path.exists(_new):\\n os.makedirs(_new)\",\n \"def moveAsset(self, src, dst):\\n if not self.exists( self.dirname(dst) ):\\n self.makedirs( self.dirname(dst) )\\n self.move(src, dst)\\n\\n cache_src = self.cache_path(src)\\n if not os.path.exists(cache_src):\\n return \\n\\n cache_dst = self.cache_path(dst)\\n if not os.path.exists( os.path.dirname(cache_dst) ):\\n os.makedirs( os.path.dirname(cache_dst) )\\n shutil.move(cache_src, cache_dst)\",\n \"def copy_one(self, src, dest):\\n if self.manager.no_sourcemaps and self.is_ignored_sourcemap(src.name):\\n return\\n\\n if dest.is_dir():\\n shutil.rmtree(dest)\\n elif dest.exists():\\n dest.unlink()\\n\\n if not dest.parent.exists():\\n self.log.debug(f\\\"creating folder {dest.parent}\\\")\\n dest.parent.mkdir(parents=True)\\n\\n self.maybe_timestamp(dest.parent)\\n\\n copytree_kwargs = {}\\n\\n if self.manager.no_sourcemaps:\\n copytree_kwargs[\\\"ignore\\\"] = SOURCEMAP_IGNORE_PATTERNS\\n\\n if src.is_dir():\\n shutil.copytree(src, dest, **copytree_kwargs)\\n else:\\n shutil.copy2(src, dest)\\n\\n self.maybe_timestamp(dest)\",\n \"def copy_files_and_create_dirs(files) -> None:\\r\\n for file in files:\\r\\n target_dir_name = os.path.dirname(file[1])\\r\\n\\r\\n # will create all intermediate-level directories\\r\\n if not os.path.exists(target_dir_name):\\r\\n os.makedirs(target_dir_name)\\r\\n\\r\\n shutil.copyfile(file[0], file[1])\",\n \"def generate_filename_template_path(output_dir, filename_template):\\n if output_dir:\\n os.makedirs(output_dir, exist_ok=True)\\n return os.path.join(output_dir, filename_template)\\n return None\",\n \"def prep_folder(args):\\n if(args.save_folder[-1]!='/'):\\n args.save_folder += '/'\\n if(not os.path.isdir(args.save_folder)):\\n os.mkdir(args.save_folder)\",\n \"def CheckForDirFileConflict(self, src_uri, dst_path):\\n final_dir = os.path.dirname(dst_path)\\n if os.path.isfile(final_dir):\\n raise CommandException('Cannot retrieve %s because it a file exists '\\n 'where a directory needs to be created (%s).' %\\n (src_uri, final_dir))\\n if os.path.isdir(dst_path):\\n raise CommandException('Cannot retrieve %s because a directory exists '\\n '(%s) where the file needs to be created.' %\\n (src_uri, dst_path))\",\n \"def make_output_folders():\\n call([\\\"mkdir\\\", \\\"-p\\\", args.out_folder.strip()])\\n call([\\\"mkdir\\\", args.out_folder.strip() + \\\"/files\\\"])\\n call([\\\"mkdir\\\", args.out_folder.strip() + \\\"/fasta\\\"])\",\n \"def make_zipfile(output_filename, source_dir):\\n relroot = os.path.abspath(os.path.join(source_dir, os.pardir))\\n with zipfile.ZipFile(output_filename, \\\"w\\\", zipfile.ZIP_DEFLATED) as zip:\\n for root, dirs, files in os.walk(source_dir):\\n # add directory (needed for empty dirs)\\n zip.write(root, os.path.relpath(root, relroot))\\n for file in files:\\n filename = os.path.join(root, file)\\n if os.path.isfile(filename): # regular files only\\n arcname = os.path.join(os.path.relpath(root, relroot), file)\\n zip.write(filename, arcname)\",\n \"def copyDir(self, src, subpath):\\n dst = self.output_path + \\\"/\\\" + subpath\\n shutil.copytree(src, dst)\",\n \"def get_path(dir_name, file_format, args):\\n fname = \\\"{exp}-excl-{excl}\\\".format(exp=args.exp,\\n excl=args.feature_set)\\n path = os.path.join(SAVE_ROOT, dir_name, args.rbp,\\n fname + file_format)\\n\\n # make the directory if it doesn't exist_ok\\n os.makedirs(os.path.dirname(path), exist_ok=True)\\n\\n return path\",\n \"def copy_file(self, dst, tmpdir=None):\\n if tmpdir is None:\\n tmpfn = sameDir\\n else:\\n tmpfn = lambda _: tmpdir._path\\n assert isinstance(dst, Path)\\n with open(self._path, 'rb') as src_fd:\\n with safeopen(dst._path, 'wb', useDir=tmpfn) as dst_fd:\\n copyfileobj(src_fd, dst_fd)\",\n \"def cp_dir_or_files(self):\\n if self.recursive:\\n if self.cmdtype == 'upload' and not self.srcpath.endswith(os.path.sep):\\n basename = os.path.basename(self.srcpath)\\n self.destpath = join_obs_path(self.destpath, basename)\\n elif self.cmdtype == 'download' and not self.srcpath.endswith('/'):\\n bucket, key = split_bucket_key(self.srcpath)\\n basename = key.split('/')[-1]\\n if basename:\\n self.destpath = os.path.join(self.destpath, basename)\\n elif not self.srcpath.endswith('/'):\\n bucket, key = split_bucket_key(self.srcpath)\\n basename = key.split('/')[-1]\\n if basename:\\n self.destpath = join_obs_path(self.destpath, basename)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7712215","0.7621111","0.6710671","0.6514752","0.63302606","0.62029976","0.6054697","0.6051175","0.6038802","0.5964814","0.5951201","0.59485644","0.5948254","0.5946226","0.59095293","0.58884376","0.58736056","0.58708906","0.5866591","0.5849726","0.58469474","0.5839048","0.5807639","0.5806094","0.5761289","0.57328033","0.57309127","0.57285875","0.57232267","0.5713726","0.5706059","0.56945825","0.5693603","0.5680116","0.5671909","0.56689256","0.56661475","0.5640255","0.56282455","0.562352","0.55943","0.5582576","0.557928","0.55291486","0.5526603","0.5513261","0.5511419","0.5509769","0.5505771","0.55011344","0.54961807","0.5490744","0.5487028","0.5486514","0.548605","0.5478819","0.54769206","0.5468459","0.5464938","0.54597336","0.5459439","0.54578114","0.5454339","0.5443674","0.54409295","0.54409295","0.54396343","0.543266","0.54291797","0.54105395","0.54103184","0.5404263","0.5378119","0.537359","0.5372133","0.5371678","0.53660464","0.53643304","0.5357919","0.5355391","0.53483963","0.5341635","0.5338536","0.5332116","0.532981","0.5329335","0.5327814","0.5316662","0.5312688","0.53121144","0.5311315","0.5311232","0.5306307","0.53056496","0.53034556","0.5292448","0.5291944","0.5285185","0.527991","0.52796346"],"string":"[\n \"0.7712215\",\n \"0.7621111\",\n \"0.6710671\",\n \"0.6514752\",\n \"0.63302606\",\n \"0.62029976\",\n \"0.6054697\",\n \"0.6051175\",\n \"0.6038802\",\n \"0.5964814\",\n \"0.5951201\",\n \"0.59485644\",\n \"0.5948254\",\n \"0.5946226\",\n \"0.59095293\",\n \"0.58884376\",\n \"0.58736056\",\n \"0.58708906\",\n \"0.5866591\",\n \"0.5849726\",\n \"0.58469474\",\n \"0.5839048\",\n \"0.5807639\",\n \"0.5806094\",\n \"0.5761289\",\n \"0.57328033\",\n \"0.57309127\",\n \"0.57285875\",\n \"0.57232267\",\n \"0.5713726\",\n \"0.5706059\",\n \"0.56945825\",\n \"0.5693603\",\n \"0.5680116\",\n \"0.5671909\",\n \"0.56689256\",\n \"0.56661475\",\n \"0.5640255\",\n \"0.56282455\",\n \"0.562352\",\n \"0.55943\",\n \"0.5582576\",\n \"0.557928\",\n \"0.55291486\",\n \"0.5526603\",\n \"0.5513261\",\n \"0.5511419\",\n \"0.5509769\",\n \"0.5505771\",\n \"0.55011344\",\n \"0.54961807\",\n \"0.5490744\",\n \"0.5487028\",\n \"0.5486514\",\n \"0.548605\",\n \"0.5478819\",\n \"0.54769206\",\n \"0.5468459\",\n \"0.5464938\",\n \"0.54597336\",\n \"0.5459439\",\n \"0.54578114\",\n \"0.5454339\",\n \"0.5443674\",\n \"0.54409295\",\n \"0.54409295\",\n \"0.54396343\",\n \"0.543266\",\n \"0.54291797\",\n \"0.54105395\",\n \"0.54103184\",\n \"0.5404263\",\n \"0.5378119\",\n \"0.537359\",\n \"0.5372133\",\n \"0.5371678\",\n \"0.53660464\",\n \"0.53643304\",\n \"0.5357919\",\n \"0.5355391\",\n \"0.53483963\",\n \"0.5341635\",\n \"0.5338536\",\n \"0.5332116\",\n \"0.532981\",\n \"0.5329335\",\n \"0.5327814\",\n \"0.5316662\",\n \"0.5312688\",\n \"0.53121144\",\n \"0.5311315\",\n \"0.5311232\",\n \"0.5306307\",\n \"0.53056496\",\n \"0.53034556\",\n \"0.5292448\",\n \"0.5291944\",\n \"0.5285185\",\n \"0.527991\",\n \"0.52796346\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71607935"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":8478,"cells":{"query":{"kind":"string","value":"gpu_model_to_scale is a dict from model string to scale."},"document":{"kind":"string","value":"def avail_gpu_compute(self, gpu_model_to_scale):\n self._check_spy_stats_available()\n l = []\n for u, model in zip(self._util.gpu_compute, self._capacity.gpu_model):\n found = False\n for k, scale in gpu_model_to_scale.items():\n if k in model:\n found = True\n break\n\n if found:\n l.append(scale * (1 - u))\n else:\n raise Exception('Unknown GPU model %s found on host %s' %\n (model, self.name))\n\n return l"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def scale_model(model, scale):\n params = model.named_parameters()\n dict_params = dict(params)\n with torch.no_grad():\n for name, param in dict_params.items():\n dict_params[name].set_(dict_params[name].data * scale)","def scale_model(model,scaleparname='A',scaleval=1):\n model = get_model_instance(model)\n if scaleparname in model.params:\n scaleparname += '1'\n if isinstance(model,FunctionModel1D):\n compclass = CompositeModel1D\n else:\n compclass = CompositeModel\n res = compclass((model,'constant'),operation='*',\n parnames={'C1':scaleparname})\n setattr(res,scaleparname,scaleval)\n return res","def petab_scale_to_amici_scale(scale_str):\n\n if scale_str == 'lin':\n return amici.ParameterScaling_none\n if scale_str == 'log':\n return amici.ParameterScaling_ln\n if scale_str == 'log10':\n return amici.ParameterScaling_log10\n raise ValueError(\"Invalid pscale \" + scale_str)","def _scaling_model_from_dict(obj):\n for entry_point in pkg_resources.iter_entry_points(\"dxtbx.scaling_model_ext\"):\n if entry_point.name == obj[\"__id__\"]:\n return entry_point.load().from_dict(obj)","def get_scales(min_scale=0.2, max_scale=0.9,num_layers=6):\n\n # this code follows the original implementation of wei liu\n # for more, look at ssd/score_ssd_pascal.py:310 in the original caffe implementation\n min_ratio = int(min_scale * 100)\n max_ratio = int(max_scale * 100)\n step = int(np.floor((max_ratio - min_ratio) / (num_layers - 2)))\n min_sizes = []\n max_sizes = []\n for ratio in xrange(min_ratio, max_ratio + 1, step):\n min_sizes.append(ratio / 100.)\n max_sizes.append((ratio + step) / 100.)\n min_sizes = [int(100*min_scale / 2.0) / 100.0] + min_sizes\n max_sizes = [min_scale] + max_sizes\n\n # convert it back to this implementation's notation:\n scales = []\n for layer_idx in range(num_layers):\n scales.append([min_sizes[layer_idx], np.single(np.sqrt(min_sizes[layer_idx] * max_sizes[layer_idx]))])\n return scales","def convert_scale(g, op, block):\n\n scale = op.attr(\"scale\")\n bias = op.attr(\"bias\")\n bias_after_scale = op.attr(\"bias_after_scale\")\n x = g.get_node(op.input(\"X\")[0])\n if np.isclose(scale, 1.0) and np.isclose(bias, 0.0):\n out = x\n else:\n if np.isclose(bias, 0.0):\n out = x * _expr.const(np.array(scale).astype(\"float32\"))\n elif np.isclose(scale, 1.0):\n out = x + _expr.const(np.array(bias).astype(\"float32\"))\n else:\n if bias_after_scale:\n out = x * _expr.const(np.array(scale).astype(\"float32\")) + _expr.const(\n np.array(bias).astype(\"float32\")\n )\n else:\n out = (x + _expr.const(np.array(bias).astype(\"float32\"))) * _expr.const(\n np.array(scale).astype(\"float32\")\n )\n g.add_node(op.output(\"Out\")[0], out)","def scale_module(module, scale):\n for p in module.parameters():\n p.detach().mul_(scale)\n return module","def _hyperparam_to_scale(self, hyperparam):\n\n # If logscale is used, input hyperparam is log of the scale.\n if self.use_log_scale:\n scale = 10.0**hyperparam\n else:\n scale = numpy.abs(hyperparam)\n\n return scale","def set_scale(self, motor_model):\n for driver_re, motor_dict in self.__SCALE_FACTORS_BY_MODEL.iteritems():\n if driver_re.match(self._apt.model_number) is not None:\n if motor_model in motor_dict:\n self.scale_factors = motor_dict[motor_model]\n return\n else:\n break\n # If we've made it down here, emit a warning that we didn't find the\n # model.\n logger.warning(\n \"Scale factors for controller {} and motor {} are unknown\".format(\n self._apt.model_number, motor_model\n )\n )","def make_feature_scale_factors():\n X, y = make_X_and_y()\n sqm = make_sqm_X()\n scale_factors = {\n \"indoor_temp\": np.max(X[:,:,0]),\n \"outdoor_temp\": np.max(X[:,:,1]),\n \"gas_kwh\": np.max(X[:,:,2]),\n \"elec_kwh\": np.max(X[:,:,3]),\n \"floor_area\": np.max(sqm),\n \"htc\": np.max(y),\n }\n\n with open(os.path.join(_TRAINING_DATA_PATH, \"scalefactors.json\"), \"w+\") as f:\n json.dump(scale_factors, f)","def _update_model_params(self, params, model_ID, model, param_grid):\n \n params = params.copy()\n param_grid = param_grid.copy()\n \n params_transform = {}\n \n for key in params.keys():\n \n if 'log10.' in key:\n log10_transform = True\n else:\n log10_transform = False\n \n key = key.replace('log10.','')\n \n type_str = str(type(param_grid[key][0]))\n \n if 'int' in type_str: \n if log10_transform:\n params_transform[key] = int(10**params['log10.'+key])\n else:\n params_transform[key] = int(params[key])\n \n elif 'float' in type_str:\n if log10_transform:\n params_transform[key] = float(10**params['log10.'+key])\n \n else:\n params_transform[key] = float(params[key])\n \n elif 'str' in type_str: #index the param grid for hyperparams using 'choice'\n params_transform[key] = param_grid[key][params[key]]\n \n if 'densenet' not in model_ID.lower(): \n model.__dict__[key] = params_transform[key]\n \n assert(type_str == str(type(params_transform[key]))), 'type(param_grid[key][0]) changed from '+type_str+' to '+str(type(param_grid[key][0]))+' after updating params for key:'+str(key)\n \n if 'str' in type_str:\n assert(params_transform[key] in param_grid[key]), 'params_transform['+key+']='+str(params_transform[key])+' is not in the list of valid parameter choices:'+str(param_grid[key])\n \n else:\n assert(params_transform[key]<=max(param_grid[key]) and params_transform[key]>=min(param_grid[key])), 'params_transform['+key+']='+str(params_transform[key])+' does not lie in the range of valid values:'+str([min(param_grid[key]),max(param_grid[key])] )\n \n if 'densenet' in model_ID.lower(): \n model = model(**params_transform)\n \n return params_transform, model","def scale_data(x, y, x_scale_f = '../saved_models/scalers/9_params_21_2_x_scaler.pkl', y_scale_f = '../saved_models/scalers/9_params_21_2_y_scaler.pkl', par_slice = range(7) + range(8,9)):\n\tx_scaler = sklearn.externals.joblib.load(x_scale_f)\n\ty_scaler = sklearn.externals.joblib.load(y_scale_f)\n\tx_scaler.transform(x)\n\ty_scaler.transform(y)\n\tx = x[:,par_slice] \n\treturn x, y, x_scaler, y_scaler","def get_scale():\r\n\r\n \r\n return 0.5","def scale(self):\n return self._gev_bijector.scale","def scale(self, data: np.ndarray):\n if self.scale_type == \"min_max\":\n scaled_data = (data - self.predictor_min) / (\n self.predictor_max - self.predictor_mean\n )\n elif self.scale_type == \"normalize\":\n scaled_data = (data - self.predictor_mean) / (\n self.predictor_max - self.predictor_min\n )\n elif self.scale_type == \"standardize\":\n scaled_data = (data - self.predictor_mean) / self.predictor_std\n elif self.scale_type == \"scale\":\n scaled_data = data - self.predictor_mean\n else:\n scaled_data = data\n return scaled_data","def _call_scale(vecObj, sc):\n res = vecObj.scale(sc)\n return res","def task_scaling(input_array, scaling_factor):\n return(np.multiply(scaling_factor, input_array))","def colorscale(self):\n return self[\"colorscale\"]","def any_scale(scale):\n return scale","def scale_to_factor(scale):\n return (B.pi / 2) / (2 * scale**2)","def scale(self) -> Optional[pulumi.Input['ScaleArgs']]:\n return pulumi.get(self, \"scale\")","def yscale(value):\n impl.yscale(**locals())","def scale_data(cube, user_input, plot_type):\n\n scaled = False\n for input_pair in user_input:\n scale_factor, user_plot_type = input_pair\n if user_plot_type == plot_type:\n assert scaled == False, \"Multiple scale factors entered for single plot type\"\n cube.data = cube.data / 10**int(scale_factor)\n cube.units = '10^%s %s' %(scale_factor, str(cube.units))\n scaled = True\n\n return cube","def scale_uv(mesh_name, scale):\n mesh = bpy.data.meshes[mesh_name]\n if len(mesh.uv_layers) == 0:\n return\n uv_layer = mesh.uv_layers[0].data\n for uv_index in range(0, len(uv_layer)):\n uv = uv_layer[uv_index].uv\n uv *= scale","def scale(self, factor):\n for a in self.symbol_attributes:\n a.scale(factor)","def _scale_independent_metrics() -> list:\n return ['mape', 'r2', 'nse']","def scaleProcess(process,scale):\n #print '>>> scaleProcess(\"%s\",%.3f):'%(process.process(),scale)\n #print \">>> rate before = %s\"%(process.rate())\n process.set_rate(process.rate()*scale)\n #print \">>> rate after = %s\"%(process.rate())","def _scale_to_hyperparam(self, scale):\n\n # If logscale is used, output hyperparam is log of scale.\n if self.use_log_scale:\n hyperparam = numpy.log10(numpy.abs(scale))\n else:\n hyperparam = numpy.abs(scale)\n\n return hyperparam","def get_scale(units, compartmentId, volume, extracellularVolume):\r\n if compartmentId == 'c':\r\n V = volume\r\n else:\r\n V = extracellularVolume\r\n\r\n if units == 'uM':\r\n return 1. / N_AVOGADRO / V * 1e6\r\n elif units == 'mM':\r\n return 1. / N_AVOGADRO / V * 1e3\r\n elif units == 'molecules':\r\n return 1.\r\n else:\r\n raise Exception('Invalid units \"%s\"' % units)","def get_scale_op(self):\n\t\treturn self.variables.get('scale')","def write_scale_map(f: h5py.File, parameter_scale_mapping: List[List[str]],\n parameter_df: pd.DataFrame, amici_model: amici.Model):\n\n # for simulation\n # set parameter scaling for all parameters\n pscale = np.zeros(shape=(len(parameter_scale_mapping),\n len(parameter_scale_mapping[0])))\n for i, cond_scale_list in enumerate(parameter_scale_mapping):\n for j, s in enumerate(cond_scale_list):\n pscale[i, j] = petab_scale_to_amici_scale(s)\n\n f.require_dataset('/parameters/pscaleSimulation',\n shape=pscale.shape,\n dtype=\"<i4\",\n data=pscale)\n\n # for cost function parameters\n pscale = np.array([petab_scale_to_amici_scale(s)\n for s in parameter_df.parameterScale.values[\n (parameter_df.estimate == 1)\n & ~parameter_df.index.isin(\n amici_model.getFixedParameterIds())]])\n f.require_dataset('/parameters/pscaleOptimization',\n shape=pscale.shape,\n dtype=\"<i4\",\n data=pscale)","def colorscale(self):\n return self['colorscale']","def b_scale_object():\n \n bpy.ops.transform.resize(value=(7.5,1,1), constraint_axis=(True,False,False))\n bpy.ops.transform.resize(value=(1,7.5,1), constraint_axis=(False,True,False))\n bpy.ops.transform.resize(value=(1,1,3.5), constraint_axis=(False,False,True))\n bpy.ops.object.transform_apply(scale=True)","def to_multi_gpu(model, n_gpus=4):\n\n with tf.device('/cpu:0'):\n x = Input(model.input_shape[1:], name=model.input_names[0])\n towers = []\n device=[0,1,2,3]\n for g in range(n_gpus):\n with tf.device('/gpu:' + str(device[g])):\n slice_g = Lambda(slice_batch, lambda shape: shape,\n arguments={'n_gpus':n_gpus, 'part':g})(x)\n towers.append(model(slice_g))\n\n with tf.device('/cpu:0'):\n merged = merge(towers, mode='concat', concat_axis=0)\n\n return Model(inputs=[x], outputs=merged)","def scale(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"scale\")","def _scale_bbox(bbox_list: List[List[int]], dst_dims: Tuple[int, int]) -> List[List[int]]:\n ret_val = []\n [x_scale, y_scale] = [dst / src for dst, src in zip(dst_dims, CityScapesDataset.base_size)]\n for bbox in bbox_list:\n ret_val.append([bbox[0] * x_scale, bbox[1] * y_scale,\n bbox[2] * x_scale, bbox[3] * y_scale])\n return ret_val","def scale_mvs_camera(cams, scale=1):\n for view in range(FLAGS.view_num):\n cams[view] = scale_camera(cams[view], scale=scale)\n return cams","def test_replace_namespaced_scale_scale(self):\n pass","def with_scale_op(self, scale):\n\t\tself.variables['scale'] = scale\n\t\treturn self","def scale_to(scale_to, group,\n allow_zero_scale=False, allow_unknown_scale=False):\n if isinstance(scale_to, numbers.Number):\n scale = scale_to\n else:\n # get scale to be used\n scale_to = lena.flow.Selector(scale_to)\n cands = [val for val in group if scale_to(val)]\n if len(cands) > 1:\n raise lena.core.LenaValueError(\n \"only one candidate to provide scale must be selected, \"\n \"{} found\".format(cands)\n )\n elif not cands:\n raise lena.core.LenaValueError(\n \"at least one item to get scale from must be selected\"\n )\n else:\n cand = cands[0]\n scale = lena.flow.get_data(cand).scale()\n\n # rescale\n for val in group:\n data, context = lena.flow.get_data_context(val)\n try:\n data.scale(scale)\n except AttributeError as err:\n # scale was not set and can't be determined\n if not allow_unknown_scale:\n raise lena.core.LenaValueError(\n \"could not determine the scale of {}\"\n .format(val)\n )\n except lena.core.LenaValueError as err:\n # scale is zero and can't be changed\n if not allow_zero_scale:\n raise err\n return None","def scale(self, fname, **kw):\n return self.scales.scale(fname, **kw)","def scale(train, test):\n # fit scaler\n scaler = MinMaxScaler(feature_range=(-1, 1))\n scaler = scaler.fit(train)\n # transform train\n train = train.reshape(train.shape[0], train.shape[1])\n train_scaled = scaler.transform(train)\n # transform test\n test = test.reshape(test.shape[0], test.shape[1])\n test_scaled = scaler.transform(test)\n return scaler, train_scaled, test_scaled","def test_read_namespaced_scale_scale(self):\n pass","def action_scaling(env, action_scaler):\n try:\n state_dim = len(env.observation_space.low)\n except AttributeError:\n print(\"Using dm_control so need to get state_dim differently\")\n state_dim = len(env.observation_space['observations'].low)\n\n action_dim = len(env.action_space.low)\n\n # state_scaling = float(state_scaling)\n action_scaler = float(action_scaler)\n\n state_scaler_array = np.ones((state_dim,), dtype=np.float32)\n action_scaler_array = np.ones((action_dim,), dtype=np.float32) * action_scaler\n\n return np.concatenate([state_scaler_array, action_scaler_array], axis=0)","def get_image_scale (wcs):\n return list(proj_plane_pixel_scales(wcs))","def get_scale_net():\n return nn.Sequential(nn.Linear(2, 64), nn.LeakyReLU(), nn.Linear(64, 64), nn.LeakyReLU(), nn.Linear(64, 2), nn.Tanh())","def get_scaler(scaler):\n if scaler == 'standard':\n from sklearn.preprocessing import StandardScaler\n return StandardScaler()\n if scaler == 'minmax':\n from sklearn.preprocessing import MinMaxScaler\n return MinMaxScaler()","def scale(self):","def set_constraint_scaling_factor(self, con):\n condata = self.get_representative_data_object(con)\n vardata = self.con2var[condata]\n scaling_factor = self.scaling_factor\n\n var_factor = scaling_factor[vardata]\n if self.dim == 0:\n scaling_factor[con] = var_factor\n else:\n for c in con.values():\n scaling_factor[c] = var_factor","def __scale(data, max_value_list, min_value_list, scale_value_list, process_cols_list):\n features = np.array(data.features, dtype=float)\n for i in process_cols_list:\n value = features[i]\n if value > max_value_list[i]:\n value = max_value_list[i]\n elif value < min_value_list[i]:\n value = min_value_list[i]\n\n features[i] = (value - min_value_list[i]) / scale_value_list[i]\n _data = copy.deepcopy(data)\n _data.features = features\n return _data","def scale_and_offset_model(model,scaleparname='A',offsetparname='C',scaleval=1,offsetval=0):\n model = get_model_instance(model)\n if scaleparname in model.params:\n scaleparname += '1'\n if offsetparname in model.params:\n offsetparname += '2'\n if isinstance(model,FunctionModel1D):\n compclass = CompositeModel1D\n else:\n compclass = CompositeModel\n res = compclass((model,'constant','constant'),operation=['*','+'],\n parnames={'C1':scaleparname,'C2':offsetparname})\n setattr(res,scaleparname,scaleval)\n setattr(res,offsetparname,offsetval)\n return res","def scale(self):\n return self.distribution.scale","def set_ui_scale():\n # TODO test on other OS and resolutions\n moniter_h = QtWidgets.QDesktopWidget().screenGeometry(-1).height()\n if sys.platform == 'win32':\n if moniter_h == 1080:\n scale = 1.0\n elif moniter_h == 1440:\n scale = 1.0\n else:\n scale = 1.0\n elif sys.platform == 'linux':\n if moniter_h == 1080:\n scale = 1.0\n elif moniter_h == 1440:\n scale = 1.23\n else:\n scale = 1.4\n elif sys.platform == 'darwin':\n if moniter_h == 1080:\n scale = 1.0\n elif moniter_h == 1440:\n scale = 1.25\n else:\n scale = 1.55\n return scale","def factor_to_scale(factor):\n return 1 / B.sqrt(4 * factor / B.pi)","def parallel_scale(self, value):\n self.camera.parallel_scale = value\n self.Modified()","def scale_it(val):\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])","def get_scale_factor(value_dict, max_length=os.get_terminal_size().columns):\n max_value = max(value_dict.values(), key=abs)\n try:\n scale = max_length / abs(max_value)\n except ZeroDivisionError:\n scale = 1\n return scale","def parallel_scale(self):\n return self.camera.parallel_scale","def keypoints_scale(keypoints, scale_x, scale_y):\n keypoints[:, :2] = keypoints[:, :2] * (scale_x, scale_y)\n return keypoints","def GetScale(self):\n ...","def _adjust_for_gpus(cls, model: DeviceAwareModule, config: ModelConfigBase,\n model_execution_mode: ModelExecutionMode) -> DeviceAwareModule:\n if config.use_gpu:\n model = model.cuda()\n logging.info(\"Adjusting the model to use mixed precision training.\")\n # If model parallel is set to True, then partition the network across all available gpus.\n if config.use_model_parallel:\n devices = config.get_cuda_devices()\n assert devices is not None # for mypy\n model.partition_model(devices=devices) # type: ignore\n else:\n logging.info(\"Making no adjustments to the model because no GPU was found.\")\n\n # Update model related config attributes (After Model Parallel Activated)\n config.adjust_after_mixed_precision_and_parallel(model)\n\n # DataParallel enables running the model with multiple gpus by splitting samples across GPUs\n # If the model is used in training mode, data parallel is activated by default.\n # Similarly, if model parallel is not activated, data parallel is used as a backup option\n use_data_parallel = (model_execution_mode == ModelExecutionMode.TRAIN) or (not config.use_model_parallel)\n if config.use_gpu and use_data_parallel:\n logging.info(\"Adjusting the model to use DataParallel\")\n # Move all layers to the default GPU before activating data parallel.\n # This needs to happen even though we put the model to the GPU at the beginning of the method,\n # but we may have spread it across multiple GPUs later.\n model = model.cuda()\n model = DataParallelModel(model, device_ids=config.get_cuda_devices())\n\n return model","def test_scale_value(make_rampmodel):\n\n datmod = make_rampmodel(2, 2, 4, 2048, 2048)\n\n # Calculate the scale based off of the input.\n scale = datmod.meta.exposure.frame_divisor / datmod.meta.exposure.nframes\n\n output = GroupScaleStep.call(datmod)\n\n scale_from_data = np.unique(output.data / datmod.data)\n\n # Since the scale value is applied uniformly to the array, if we divide the output\n # by the input then we should get a single unique value (ie the scale) calculated\n # by the pipeline.\n assert len(scale_from_data) == 1\n\n # Make sure the scale calculated manually from the data model above matched what the\n # pipeline calculated.\n assert scale == scale_from_data[0]","def _rescale_module(module):\n for sub in module.modules():\n if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):\n std = sub.weight.std().detach()\n scale = (std / 0.1) ** 0.5\n sub.weight.data /= scale\n if sub.bias is not None:\n sub.bias.data /= scale","def set_scale_factors_to_output_size(self):\n # Compute the scale_factor using rounded scaled image size.\n height = tf.shape(self._image)[0]\n width = tf.shape(self._image)[1]\n max_image_size = tf.to_float(tf.maximum(height, width))\n image_scale = tf.to_float(self._output_size) / max_image_size\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\n self._image_scale = image_scale\n self._scaled_height = scaled_height\n self._scaled_width = scaled_width","def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:\n return sample","def __scale_bboxes(self, bboxes, scale_x, scale_y):\n with tf.variable_scope('scale_bboxes'):\n return tf.multiply(bboxes, tf.tile([[scale_y, scale_x, scale_y,\n scale_x]],\n [tf.shape(bboxes)[0], 1]))","def scaling():\n \n for i in range(cfg.nfea):\n dm = 0\n var = 0\n for j in range(cfg.ntrain):\n dm += cfg.a[j,i]\n dm = dm/cfg.ntrain\n \n for j in range(cfg.ntrain):\n var += (cfg.a[j,i]-dm)**2\n\n var = var/cfg.ntrain\n var = np.sqrt(var)\n \n if var >= 10**(-5):\n cfg.clin[i] = 1.0/var \n cfg.dlin[i] = -dm/var \n \n else: \n if np.abs(dm)<=1.0:\n cfg.clin[i] = 1.0\n cfg.dlin[i] = 0.0 \n else: \n cfg.clin[i] = 1.0/dm\n cfg.dlin[i] = 0.0 \n \n for j in range(cfg.ntrain):\n cfg.a_scaled[j,i] = cfg.clin[i]*cfg.a[j,i] + cfg.dlin[i]\n \n return","def create_scaling_model(params, experiments, reflections):\n autos = [None, Auto, \"auto\", \"Auto\"]\n use_auto_model = params.model in autos\n # Determine non-auto model to use outside the loop over datasets.\n if not use_auto_model:\n model_class = None\n for entry_point in pkg_resources.iter_entry_points(\"dxtbx.scaling_model_ext\"):\n if entry_point.name == params.model:\n model_class = entry_point.load()\n break\n if not model_class:\n raise ValueError(f\"Unable to create scaling model of type {params.model}\")\n\n for expt, refl in zip(experiments, reflections):\n if not expt.scaling_model or params.overwrite_existing_models:\n # need to make a new model\n if use_auto_model:\n if not expt.scan:\n model = KBScalingModel\n else: # set model as physical unless scan < 1.0 degree\n osc_range = expt.scan.get_oscillation_range()\n abs_osc_range = abs(osc_range[1] - osc_range[0])\n if abs_osc_range < 1.0:\n model = KBScalingModel\n else:\n model = PhysicalScalingModel\n else:\n model = model_class\n expt.scaling_model = model.from_data(params, expt, refl)\n else:\n # allow for updating of an existing model.\n expt.scaling_model.update(params)\n return experiments","def plate_scale(platescale):\n if platescale.unit.is_equivalent(si.arcsec / si.m):\n platescale_val = platescale.to_value(si.radian / si.m)\n elif platescale.unit.is_equivalent(si.m / si.arcsec):\n platescale_val = (1 / platescale).to_value(si.radian / si.m)\n else:\n raise UnitsError(\"The pixel scale must be in angle/distance or distance/angle\")\n\n return Equivalency(\n [(si.m, si.radian, lambda d: d * platescale_val, lambda a: a / platescale_val)],\n \"plate_scale\",\n {\"platescale\": platescale},\n )","def supervised_cost_scale(\n scale, loss_supervised, output_noise_labelled, labelled_target\n):\n cost_supervised = loss_supervised.forward(output_noise_labelled, labelled_target)\n\n cost_supervised *= scale\n return cost_supervised","def _linear_to_mel_scale(linear_scale_in, n_mel_bins, mel_lower_hertz_edge,\n mel_upper_hertz_edge):\n\n linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(\n n_mel_bins, linear_scale_in.shape[-1], _SAMPLE_RATE, mel_lower_hertz_edge,\n mel_upper_hertz_edge\n )\n\n mel_scale_out = tf.tensordot(linear_scale_in, linear_to_mel_weight_matrix, 1)\n return mel_scale_out","def _score_converter_fn_with_logit_scale(tf_score_converter_fn, logit_scale):\n\n def score_converter_fn(logits):\n scaled_logits = np.divide(logits, logit_scale)\n\n return tf_score_converter_fn(scaled_logits)\n\n return score_converter_fn","def scale(original_train, new_train):\n # find magnitude original training data\n o_mag = np.linalg.norm(np.stack(original_train[:,1]))\n # find magnitude new data\n n_mag = np.linalg.norm(np.stack(new_train[:,1]))\n # scale new data\n scale = o_mag / n_mag\n return scale","def scale(self):\n return self._scale","def scaling(self) -> Optional['outputs.AiFeatureStoreOnlineServingConfigScaling']:\n return pulumi.get(self, \"scaling\")","def compute_scaler(args):\n workspace = args.workspace\n data_type = args.data_type\n dir_name = args.dir_name \n # Load data. \n t1 = time.time()\n hdf5_path = os.path.join(workspace, \"packed_features\", \"spectrogram\", data_type, dir_name, \"data.h5\")\n with h5py.File(hdf5_path, 'r') as hf:\n x = hf.get('x') \n x = np.array(x) # (n_segs, n_concat, n_freq)\n \n # Compute scaler. \n (n_segs, n_concat, n_freq) = x.shape\n x2d = x.reshape((n_segs * n_concat, n_freq))\n scaler = preprocessing.StandardScaler(with_mean=True, with_std=True).fit(x2d)\n print(scaler.mean_)\n print(scaler.scale_)\n \n # Write out scaler. \n out_path = os.path.join(workspace, \"packed_features\", \"spectrogram\", data_type, dir_name, \"scaler.p\")\n create_folder(os.path.dirname(out_path))\n pickle.dump(scaler, open(out_path, 'wb'))\n \n print(\"Save scaler to %s\" % out_path)\n print(\"Compute scaler finished! %s s\" % (time.time() - t1,))","def get_scaling_type(df_mfactors: pd.DataFrame, cmdl_args):\n eps = 0.9\n normalized_inst_ratio = 0\n\n # Check if there is only one trace\n if len(df_mfactors.index) == 1:\n return 'strong'\n\n for index, row in df_mfactors.iterrows():\n inst_ratio = float(row[MOD_FACTORS_DOC['useful_ins']]) / float(df_mfactors[MOD_FACTORS_DOC['useful_ins']][0])\n proc_ratio = float(row[MOD_FACTORS_DOC['num_processes']]) / float(\n df_mfactors[MOD_FACTORS_DOC['num_processes']][0])\n normalized_inst_ratio += inst_ratio / proc_ratio\n\n # Get the average inst increase. Ignore ratio of first trace (1.0)\n normalized_inst_ratio = (normalized_inst_ratio - 1) / (len(df_mfactors.index) - 1)\n\n scaling_computed = ''\n\n if normalized_inst_ratio > eps:\n scaling_computed = 'weak'\n else:\n scaling_computed = 'strong'\n\n if cmdl_args.scaling == 'auto':\n if cmdl_args.debug:\n print(f'==DEBUG== Detected {scaling_computed} scaling.')\n print('')\n return scaling_computed\n\n if cmdl_args.scaling == 'weak':\n if scaling_computed == 'strong':\n print('==WARNING== Scaling set to weak scaling but detected strong scaling.')\n print('')\n return 'strong'\n\n if cmdl_args.scaling == 'strong':\n if scaling_computed == 'weak':\n print('==WARNING== Scaling set to strong scaling but detected weak scaling.')\n print('')\n return 'strong'\n\n print('==ERROR== Reached undefined control flow state.')\n sys.exit(1)","def scale(requestContext, seriesList, factor):\n for series in seriesList:\n series.name = \"scale(%s,%g)\" % (series.name,float(factor))\n series.pathExpression = series.name\n for i,value in enumerate(series):\n series[i] = safeMul(value,factor)\n return seriesList","def scale(inp, ab):\n\n return inp * ab[0] + ab[1]","def find_scale(dataset, log_scales, path=_default_path):\n fname = \"\".join([\"evaluate_\", dataset, \"_64x64.h5\"])\n store = h5py.File(join(path, fname))\n\n rocs = dict()\n dists = [\"L1\", \"L2\", \"COSINE\"]\n norms = [\"l1\", \"l2\", \"id\"]\n for scale in log_scales:\n sift = \"\".join([\"evaluate_\", dataset, \"_sift_\", str(scale), \".h5\"])\n sift = h5py.File(join(path, sift), \"w\")\n store_as_sift(store, sift, scale=2**scale)\n rocs[scale] = evaluate(sift, distances=dists, normalizations=norms)\n sift.close()\n return rocs","def find_suggested_tonemap_scale(session):\n avg_film_luminance = session.GetFilm().GetFilmY()\n return (1.25 / avg_film_luminance * (118 / 255))\n\n # TODO\n # measure this all the time, show a message to the user if\n # abs(old - new) > threshold\n # so the user can set the new value with one click\n\n # imagepipeline = scene.camera.data.luxcore.imagepipeline\n # imagepipeline.tonemapper.linear_scale = suggested_linear_scale\n # imagepipeline.tonemapper.use_autolinear = False","def _scale_param(self, resid_us):\n return((resid_us**2).sum().sum() / self.dof)","def test_auto_scale_batch_size_trainer_arg(tmpdir, scale_arg):\n tutils.reset_seed()\n hparams = EvalModelTemplate.get_default_hparams()\n model = EvalModelTemplate(**hparams)\n before_batch_size = hparams.get('batch_size')\n trainer = Trainer(default_root_dir=tmpdir,\n max_epochs=1,\n auto_scale_batch_size=scale_arg,\n gpus=1)\n trainer.tune(model)\n after_batch_size = model.batch_size\n assert before_batch_size != after_batch_size, \\\n 'Batch size was not altered after running auto scaling of batch size'","def price_user_scale_factor(self, new_price_scale_factor):\n self._price_user_scale_factor = new_price_scale_factor","def scale(self,bvp):\n\n sol = bvp.solution\n # Additional aux entries for initial and terminal BCs\n extras = [{'type':'initial','vars':self.problem_data['state_list']},\n {'type':'terminal','vars':self.problem_data['state_list']}]\n\n # Scale the states and costates\n for idx,state in enumerate(self.problem_data['state_list']):\n sol.y[idx,:] /= self.scale_vals['states'][state]\n\n # Scale auxiliary variables\n for aux in (self.problem_data['aux_list']+extras):\n if aux['type'] not in Scaling.excluded_aux:\n for var in aux['vars']:\n sol.aux[aux['type']][var] /= self.scale_vals[aux['type']][var]\n\n # Scale parameters\n for idx, param in enumerate(self.problem_data['parameter_list']):\n sol.parameters[idx] /= self.scale_vals['parameters'][param]","def __get_scale_units(self, device_type_name, channel_number):\n if device_type_name == \"BPMS\":\n if channel_number == 0:\n fault_name = \"X\"\n elif channel_number == 1:\n fault_name = \"Y\"\n elif channel_number == 2:\n fault_name = \"TMIT\"\n else:\n raise ValueError(\"Function \\\"get_app_name(device_type_name={}, channel_number={})\\\". Invalid channel number for BPMS device type\"\n .format(device_type_name, channel_number))\n return self.__get_app_units(device_type_name, fault_name)\n else:\n return self.__get_app_units(device_type_name, \"\")","def ConvertGpuToVendorName(gpu):\n if not gpu:\n return 'No GPU'\n elif '8086' in gpu:\n return 'Intel'\n elif '10de' in gpu:\n return 'NVIDIA'\n elif '1002' in gpu:\n return 'AMD'\n return gpu","def plane_scale(self, scale):\n cmd = '{}testPlaneScale {}'.format(self.console, scale)\n self.write_command(cmd)","def scale(self, X_train, X_test):\n\n #X_train, X_test, y_train, y_test = self.split_X_y_sets()\n self.scaler.fit(X_train)\n X_train_sc = self.scaler.transform(X_train)\n X_test_sc = self.scaler.transform(X_test)\n\n return X_train_sc, X_test_sc #, y_train, y_test","def freqscale(self):\n index = self._ordered_input_names.index('freqscale')\n return self._inputs[index]","def scale_matrix(self, matrix_df):\n\n print('scaling features...')\n\n originalkeys = matrix_df[['segment_id','date','time']].reset_index()\n originalkeys['index'] = originalkeys.index\n\n columns = list(matrix_df)\n columns.remove('segment_id')\n columns.remove('date')\n columns.remove('time')\n data_to_scale = matrix_df[columns]\n matrix_df_scaled_tmp = pd.DataFrame(preprocessing.scale(data_to_scale), columns = columns).reset_index()\n matrix_df_scaled_tmp['index']=matrix_df_scaled_tmp.index\n\n matrix_df_scaled_labels_tmp = pd.merge(matrix_df_scaled_tmp, originalkeys, on=['index'])\n matrix_df_scaled = matrix_df_scaled_tmp.drop(columns=['index'])\n matrix_df_scaled_withlabels = matrix_df_scaled_labels_tmp.drop(columns=['index'])\n \n return matrix_df_scaled_withlabels","def scale(image):\n image = tf.cast(image, tf.float32)\n image /= 255\n return image","def scaled_component(self, key):\n\n if key in self.components:\n dat = self.components[key] \n # Aliases\n elif key in component_from_alias:\n comp = component_from_alias[key]\n if comp in self.components:\n dat = self.components[comp] \n else:\n # Component not present, make zeros\n return np.zeros(self.shape)\n else:\n raise ValueError(f'Component not available: {key}')\n \n # Multiply by scale factor\n factor = self.factor \n \n if factor != 1:\n return factor*dat\n else:\n return dat","def test_patch_namespaced_scale_scale(self):\n pass","def config_from_pytorch_model(\n model,\n granularity='model',\n backend=None,\n default_precision='ap_fixed<16,6>',\n default_reuse_factor=1,\n inputs_channel_last=False,\n transpose_outputs=True,\n):\n\n config = {}\n\n model_config = {}\n model_config['Precision'] = default_precision\n model_config['ReuseFactor'] = default_reuse_factor\n model_config['InputsChannelLast'] = inputs_channel_last\n model_config['TransposeOutputs'] = transpose_outputs\n model_config['Strategy'] = 'Latency'\n\n config['Model'] = model_config\n\n return config","def scale(self):\n return self.scale_factor / CONSTANTS.AU","def getscales(self):\n return self.scales","def convert_scaling_to_form_factors(qz, scale):\n apply_absorption_correction(qz, scale)\n apply_Lorentz_correction(qz, scale)\n for i in xrange(len(scale)):\n scale[i] = np.sign(scale[i]) * math.sqrt(abs(scale[i]))","def matScale(mat, scale):\n shape=matShape(mat)\n return [[matGet(mat,x,y)*scale for y in range(shape[1])] \\\n for x in range(shape[0])]","def scale_ticks_params(tick_scale='linear'):\n if tick_scale == 'linear':\n base = None\n label_scale = 'Linear Scale'\n else:\n if tick_scale == 'log2':\n base = 2\n label_scale = 'Log2 Scale'\n elif tick_scale == 'log10':\n base = 10\n label_scale = 'Log10 Scale'\n else:\n raise ValueError('The specified tick scale is not supported.')\n return base, label_scale"],"string":"[\n \"def scale_model(model, scale):\\n params = model.named_parameters()\\n dict_params = dict(params)\\n with torch.no_grad():\\n for name, param in dict_params.items():\\n dict_params[name].set_(dict_params[name].data * scale)\",\n \"def scale_model(model,scaleparname='A',scaleval=1):\\n model = get_model_instance(model)\\n if scaleparname in model.params:\\n scaleparname += '1'\\n if isinstance(model,FunctionModel1D):\\n compclass = CompositeModel1D\\n else:\\n compclass = CompositeModel\\n res = compclass((model,'constant'),operation='*',\\n parnames={'C1':scaleparname})\\n setattr(res,scaleparname,scaleval)\\n return res\",\n \"def petab_scale_to_amici_scale(scale_str):\\n\\n if scale_str == 'lin':\\n return amici.ParameterScaling_none\\n if scale_str == 'log':\\n return amici.ParameterScaling_ln\\n if scale_str == 'log10':\\n return amici.ParameterScaling_log10\\n raise ValueError(\\\"Invalid pscale \\\" + scale_str)\",\n \"def _scaling_model_from_dict(obj):\\n for entry_point in pkg_resources.iter_entry_points(\\\"dxtbx.scaling_model_ext\\\"):\\n if entry_point.name == obj[\\\"__id__\\\"]:\\n return entry_point.load().from_dict(obj)\",\n \"def get_scales(min_scale=0.2, max_scale=0.9,num_layers=6):\\n\\n # this code follows the original implementation of wei liu\\n # for more, look at ssd/score_ssd_pascal.py:310 in the original caffe implementation\\n min_ratio = int(min_scale * 100)\\n max_ratio = int(max_scale * 100)\\n step = int(np.floor((max_ratio - min_ratio) / (num_layers - 2)))\\n min_sizes = []\\n max_sizes = []\\n for ratio in xrange(min_ratio, max_ratio + 1, step):\\n min_sizes.append(ratio / 100.)\\n max_sizes.append((ratio + step) / 100.)\\n min_sizes = [int(100*min_scale / 2.0) / 100.0] + min_sizes\\n max_sizes = [min_scale] + max_sizes\\n\\n # convert it back to this implementation's notation:\\n scales = []\\n for layer_idx in range(num_layers):\\n scales.append([min_sizes[layer_idx], np.single(np.sqrt(min_sizes[layer_idx] * max_sizes[layer_idx]))])\\n return scales\",\n \"def convert_scale(g, op, block):\\n\\n scale = op.attr(\\\"scale\\\")\\n bias = op.attr(\\\"bias\\\")\\n bias_after_scale = op.attr(\\\"bias_after_scale\\\")\\n x = g.get_node(op.input(\\\"X\\\")[0])\\n if np.isclose(scale, 1.0) and np.isclose(bias, 0.0):\\n out = x\\n else:\\n if np.isclose(bias, 0.0):\\n out = x * _expr.const(np.array(scale).astype(\\\"float32\\\"))\\n elif np.isclose(scale, 1.0):\\n out = x + _expr.const(np.array(bias).astype(\\\"float32\\\"))\\n else:\\n if bias_after_scale:\\n out = x * _expr.const(np.array(scale).astype(\\\"float32\\\")) + _expr.const(\\n np.array(bias).astype(\\\"float32\\\")\\n )\\n else:\\n out = (x + _expr.const(np.array(bias).astype(\\\"float32\\\"))) * _expr.const(\\n np.array(scale).astype(\\\"float32\\\")\\n )\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def scale_module(module, scale):\\n for p in module.parameters():\\n p.detach().mul_(scale)\\n return module\",\n \"def _hyperparam_to_scale(self, hyperparam):\\n\\n # If logscale is used, input hyperparam is log of the scale.\\n if self.use_log_scale:\\n scale = 10.0**hyperparam\\n else:\\n scale = numpy.abs(hyperparam)\\n\\n return scale\",\n \"def set_scale(self, motor_model):\\n for driver_re, motor_dict in self.__SCALE_FACTORS_BY_MODEL.iteritems():\\n if driver_re.match(self._apt.model_number) is not None:\\n if motor_model in motor_dict:\\n self.scale_factors = motor_dict[motor_model]\\n return\\n else:\\n break\\n # If we've made it down here, emit a warning that we didn't find the\\n # model.\\n logger.warning(\\n \\\"Scale factors for controller {} and motor {} are unknown\\\".format(\\n self._apt.model_number, motor_model\\n )\\n )\",\n \"def make_feature_scale_factors():\\n X, y = make_X_and_y()\\n sqm = make_sqm_X()\\n scale_factors = {\\n \\\"indoor_temp\\\": np.max(X[:,:,0]),\\n \\\"outdoor_temp\\\": np.max(X[:,:,1]),\\n \\\"gas_kwh\\\": np.max(X[:,:,2]),\\n \\\"elec_kwh\\\": np.max(X[:,:,3]),\\n \\\"floor_area\\\": np.max(sqm),\\n \\\"htc\\\": np.max(y),\\n }\\n\\n with open(os.path.join(_TRAINING_DATA_PATH, \\\"scalefactors.json\\\"), \\\"w+\\\") as f:\\n json.dump(scale_factors, f)\",\n \"def _update_model_params(self, params, model_ID, model, param_grid):\\n \\n params = params.copy()\\n param_grid = param_grid.copy()\\n \\n params_transform = {}\\n \\n for key in params.keys():\\n \\n if 'log10.' in key:\\n log10_transform = True\\n else:\\n log10_transform = False\\n \\n key = key.replace('log10.','')\\n \\n type_str = str(type(param_grid[key][0]))\\n \\n if 'int' in type_str: \\n if log10_transform:\\n params_transform[key] = int(10**params['log10.'+key])\\n else:\\n params_transform[key] = int(params[key])\\n \\n elif 'float' in type_str:\\n if log10_transform:\\n params_transform[key] = float(10**params['log10.'+key])\\n \\n else:\\n params_transform[key] = float(params[key])\\n \\n elif 'str' in type_str: #index the param grid for hyperparams using 'choice'\\n params_transform[key] = param_grid[key][params[key]]\\n \\n if 'densenet' not in model_ID.lower(): \\n model.__dict__[key] = params_transform[key]\\n \\n assert(type_str == str(type(params_transform[key]))), 'type(param_grid[key][0]) changed from '+type_str+' to '+str(type(param_grid[key][0]))+' after updating params for key:'+str(key)\\n \\n if 'str' in type_str:\\n assert(params_transform[key] in param_grid[key]), 'params_transform['+key+']='+str(params_transform[key])+' is not in the list of valid parameter choices:'+str(param_grid[key])\\n \\n else:\\n assert(params_transform[key]<=max(param_grid[key]) and params_transform[key]>=min(param_grid[key])), 'params_transform['+key+']='+str(params_transform[key])+' does not lie in the range of valid values:'+str([min(param_grid[key]),max(param_grid[key])] )\\n \\n if 'densenet' in model_ID.lower(): \\n model = model(**params_transform)\\n \\n return params_transform, model\",\n \"def scale_data(x, y, x_scale_f = '../saved_models/scalers/9_params_21_2_x_scaler.pkl', y_scale_f = '../saved_models/scalers/9_params_21_2_y_scaler.pkl', par_slice = range(7) + range(8,9)):\\n\\tx_scaler = sklearn.externals.joblib.load(x_scale_f)\\n\\ty_scaler = sklearn.externals.joblib.load(y_scale_f)\\n\\tx_scaler.transform(x)\\n\\ty_scaler.transform(y)\\n\\tx = x[:,par_slice] \\n\\treturn x, y, x_scaler, y_scaler\",\n \"def get_scale():\\r\\n\\r\\n \\r\\n return 0.5\",\n \"def scale(self):\\n return self._gev_bijector.scale\",\n \"def scale(self, data: np.ndarray):\\n if self.scale_type == \\\"min_max\\\":\\n scaled_data = (data - self.predictor_min) / (\\n self.predictor_max - self.predictor_mean\\n )\\n elif self.scale_type == \\\"normalize\\\":\\n scaled_data = (data - self.predictor_mean) / (\\n self.predictor_max - self.predictor_min\\n )\\n elif self.scale_type == \\\"standardize\\\":\\n scaled_data = (data - self.predictor_mean) / self.predictor_std\\n elif self.scale_type == \\\"scale\\\":\\n scaled_data = data - self.predictor_mean\\n else:\\n scaled_data = data\\n return scaled_data\",\n \"def _call_scale(vecObj, sc):\\n res = vecObj.scale(sc)\\n return res\",\n \"def task_scaling(input_array, scaling_factor):\\n return(np.multiply(scaling_factor, input_array))\",\n \"def colorscale(self):\\n return self[\\\"colorscale\\\"]\",\n \"def any_scale(scale):\\n return scale\",\n \"def scale_to_factor(scale):\\n return (B.pi / 2) / (2 * scale**2)\",\n \"def scale(self) -> Optional[pulumi.Input['ScaleArgs']]:\\n return pulumi.get(self, \\\"scale\\\")\",\n \"def yscale(value):\\n impl.yscale(**locals())\",\n \"def scale_data(cube, user_input, plot_type):\\n\\n scaled = False\\n for input_pair in user_input:\\n scale_factor, user_plot_type = input_pair\\n if user_plot_type == plot_type:\\n assert scaled == False, \\\"Multiple scale factors entered for single plot type\\\"\\n cube.data = cube.data / 10**int(scale_factor)\\n cube.units = '10^%s %s' %(scale_factor, str(cube.units))\\n scaled = True\\n\\n return cube\",\n \"def scale_uv(mesh_name, scale):\\n mesh = bpy.data.meshes[mesh_name]\\n if len(mesh.uv_layers) == 0:\\n return\\n uv_layer = mesh.uv_layers[0].data\\n for uv_index in range(0, len(uv_layer)):\\n uv = uv_layer[uv_index].uv\\n uv *= scale\",\n \"def scale(self, factor):\\n for a in self.symbol_attributes:\\n a.scale(factor)\",\n \"def _scale_independent_metrics() -> list:\\n return ['mape', 'r2', 'nse']\",\n \"def scaleProcess(process,scale):\\n #print '>>> scaleProcess(\\\"%s\\\",%.3f):'%(process.process(),scale)\\n #print \\\">>> rate before = %s\\\"%(process.rate())\\n process.set_rate(process.rate()*scale)\\n #print \\\">>> rate after = %s\\\"%(process.rate())\",\n \"def _scale_to_hyperparam(self, scale):\\n\\n # If logscale is used, output hyperparam is log of scale.\\n if self.use_log_scale:\\n hyperparam = numpy.log10(numpy.abs(scale))\\n else:\\n hyperparam = numpy.abs(scale)\\n\\n return hyperparam\",\n \"def get_scale(units, compartmentId, volume, extracellularVolume):\\r\\n if compartmentId == 'c':\\r\\n V = volume\\r\\n else:\\r\\n V = extracellularVolume\\r\\n\\r\\n if units == 'uM':\\r\\n return 1. / N_AVOGADRO / V * 1e6\\r\\n elif units == 'mM':\\r\\n return 1. / N_AVOGADRO / V * 1e3\\r\\n elif units == 'molecules':\\r\\n return 1.\\r\\n else:\\r\\n raise Exception('Invalid units \\\"%s\\\"' % units)\",\n \"def get_scale_op(self):\\n\\t\\treturn self.variables.get('scale')\",\n \"def write_scale_map(f: h5py.File, parameter_scale_mapping: List[List[str]],\\n parameter_df: pd.DataFrame, amici_model: amici.Model):\\n\\n # for simulation\\n # set parameter scaling for all parameters\\n pscale = np.zeros(shape=(len(parameter_scale_mapping),\\n len(parameter_scale_mapping[0])))\\n for i, cond_scale_list in enumerate(parameter_scale_mapping):\\n for j, s in enumerate(cond_scale_list):\\n pscale[i, j] = petab_scale_to_amici_scale(s)\\n\\n f.require_dataset('/parameters/pscaleSimulation',\\n shape=pscale.shape,\\n dtype=\\\"<i4\\\",\\n data=pscale)\\n\\n # for cost function parameters\\n pscale = np.array([petab_scale_to_amici_scale(s)\\n for s in parameter_df.parameterScale.values[\\n (parameter_df.estimate == 1)\\n & ~parameter_df.index.isin(\\n amici_model.getFixedParameterIds())]])\\n f.require_dataset('/parameters/pscaleOptimization',\\n shape=pscale.shape,\\n dtype=\\\"<i4\\\",\\n data=pscale)\",\n \"def colorscale(self):\\n return self['colorscale']\",\n \"def b_scale_object():\\n \\n bpy.ops.transform.resize(value=(7.5,1,1), constraint_axis=(True,False,False))\\n bpy.ops.transform.resize(value=(1,7.5,1), constraint_axis=(False,True,False))\\n bpy.ops.transform.resize(value=(1,1,3.5), constraint_axis=(False,False,True))\\n bpy.ops.object.transform_apply(scale=True)\",\n \"def to_multi_gpu(model, n_gpus=4):\\n\\n with tf.device('/cpu:0'):\\n x = Input(model.input_shape[1:], name=model.input_names[0])\\n towers = []\\n device=[0,1,2,3]\\n for g in range(n_gpus):\\n with tf.device('/gpu:' + str(device[g])):\\n slice_g = Lambda(slice_batch, lambda shape: shape,\\n arguments={'n_gpus':n_gpus, 'part':g})(x)\\n towers.append(model(slice_g))\\n\\n with tf.device('/cpu:0'):\\n merged = merge(towers, mode='concat', concat_axis=0)\\n\\n return Model(inputs=[x], outputs=merged)\",\n \"def scale(self) -> pulumi.Input[int]:\\n return pulumi.get(self, \\\"scale\\\")\",\n \"def _scale_bbox(bbox_list: List[List[int]], dst_dims: Tuple[int, int]) -> List[List[int]]:\\n ret_val = []\\n [x_scale, y_scale] = [dst / src for dst, src in zip(dst_dims, CityScapesDataset.base_size)]\\n for bbox in bbox_list:\\n ret_val.append([bbox[0] * x_scale, bbox[1] * y_scale,\\n bbox[2] * x_scale, bbox[3] * y_scale])\\n return ret_val\",\n \"def scale_mvs_camera(cams, scale=1):\\n for view in range(FLAGS.view_num):\\n cams[view] = scale_camera(cams[view], scale=scale)\\n return cams\",\n \"def test_replace_namespaced_scale_scale(self):\\n pass\",\n \"def with_scale_op(self, scale):\\n\\t\\tself.variables['scale'] = scale\\n\\t\\treturn self\",\n \"def scale_to(scale_to, group,\\n allow_zero_scale=False, allow_unknown_scale=False):\\n if isinstance(scale_to, numbers.Number):\\n scale = scale_to\\n else:\\n # get scale to be used\\n scale_to = lena.flow.Selector(scale_to)\\n cands = [val for val in group if scale_to(val)]\\n if len(cands) > 1:\\n raise lena.core.LenaValueError(\\n \\\"only one candidate to provide scale must be selected, \\\"\\n \\\"{} found\\\".format(cands)\\n )\\n elif not cands:\\n raise lena.core.LenaValueError(\\n \\\"at least one item to get scale from must be selected\\\"\\n )\\n else:\\n cand = cands[0]\\n scale = lena.flow.get_data(cand).scale()\\n\\n # rescale\\n for val in group:\\n data, context = lena.flow.get_data_context(val)\\n try:\\n data.scale(scale)\\n except AttributeError as err:\\n # scale was not set and can't be determined\\n if not allow_unknown_scale:\\n raise lena.core.LenaValueError(\\n \\\"could not determine the scale of {}\\\"\\n .format(val)\\n )\\n except lena.core.LenaValueError as err:\\n # scale is zero and can't be changed\\n if not allow_zero_scale:\\n raise err\\n return None\",\n \"def scale(self, fname, **kw):\\n return self.scales.scale(fname, **kw)\",\n \"def scale(train, test):\\n # fit scaler\\n scaler = MinMaxScaler(feature_range=(-1, 1))\\n scaler = scaler.fit(train)\\n # transform train\\n train = train.reshape(train.shape[0], train.shape[1])\\n train_scaled = scaler.transform(train)\\n # transform test\\n test = test.reshape(test.shape[0], test.shape[1])\\n test_scaled = scaler.transform(test)\\n return scaler, train_scaled, test_scaled\",\n \"def test_read_namespaced_scale_scale(self):\\n pass\",\n \"def action_scaling(env, action_scaler):\\n try:\\n state_dim = len(env.observation_space.low)\\n except AttributeError:\\n print(\\\"Using dm_control so need to get state_dim differently\\\")\\n state_dim = len(env.observation_space['observations'].low)\\n\\n action_dim = len(env.action_space.low)\\n\\n # state_scaling = float(state_scaling)\\n action_scaler = float(action_scaler)\\n\\n state_scaler_array = np.ones((state_dim,), dtype=np.float32)\\n action_scaler_array = np.ones((action_dim,), dtype=np.float32) * action_scaler\\n\\n return np.concatenate([state_scaler_array, action_scaler_array], axis=0)\",\n \"def get_image_scale (wcs):\\n return list(proj_plane_pixel_scales(wcs))\",\n \"def get_scale_net():\\n return nn.Sequential(nn.Linear(2, 64), nn.LeakyReLU(), nn.Linear(64, 64), nn.LeakyReLU(), nn.Linear(64, 2), nn.Tanh())\",\n \"def get_scaler(scaler):\\n if scaler == 'standard':\\n from sklearn.preprocessing import StandardScaler\\n return StandardScaler()\\n if scaler == 'minmax':\\n from sklearn.preprocessing import MinMaxScaler\\n return MinMaxScaler()\",\n \"def scale(self):\",\n \"def set_constraint_scaling_factor(self, con):\\n condata = self.get_representative_data_object(con)\\n vardata = self.con2var[condata]\\n scaling_factor = self.scaling_factor\\n\\n var_factor = scaling_factor[vardata]\\n if self.dim == 0:\\n scaling_factor[con] = var_factor\\n else:\\n for c in con.values():\\n scaling_factor[c] = var_factor\",\n \"def __scale(data, max_value_list, min_value_list, scale_value_list, process_cols_list):\\n features = np.array(data.features, dtype=float)\\n for i in process_cols_list:\\n value = features[i]\\n if value > max_value_list[i]:\\n value = max_value_list[i]\\n elif value < min_value_list[i]:\\n value = min_value_list[i]\\n\\n features[i] = (value - min_value_list[i]) / scale_value_list[i]\\n _data = copy.deepcopy(data)\\n _data.features = features\\n return _data\",\n \"def scale_and_offset_model(model,scaleparname='A',offsetparname='C',scaleval=1,offsetval=0):\\n model = get_model_instance(model)\\n if scaleparname in model.params:\\n scaleparname += '1'\\n if offsetparname in model.params:\\n offsetparname += '2'\\n if isinstance(model,FunctionModel1D):\\n compclass = CompositeModel1D\\n else:\\n compclass = CompositeModel\\n res = compclass((model,'constant','constant'),operation=['*','+'],\\n parnames={'C1':scaleparname,'C2':offsetparname})\\n setattr(res,scaleparname,scaleval)\\n setattr(res,offsetparname,offsetval)\\n return res\",\n \"def scale(self):\\n return self.distribution.scale\",\n \"def set_ui_scale():\\n # TODO test on other OS and resolutions\\n moniter_h = QtWidgets.QDesktopWidget().screenGeometry(-1).height()\\n if sys.platform == 'win32':\\n if moniter_h == 1080:\\n scale = 1.0\\n elif moniter_h == 1440:\\n scale = 1.0\\n else:\\n scale = 1.0\\n elif sys.platform == 'linux':\\n if moniter_h == 1080:\\n scale = 1.0\\n elif moniter_h == 1440:\\n scale = 1.23\\n else:\\n scale = 1.4\\n elif sys.platform == 'darwin':\\n if moniter_h == 1080:\\n scale = 1.0\\n elif moniter_h == 1440:\\n scale = 1.25\\n else:\\n scale = 1.55\\n return scale\",\n \"def factor_to_scale(factor):\\n return 1 / B.sqrt(4 * factor / B.pi)\",\n \"def parallel_scale(self, value):\\n self.camera.parallel_scale = value\\n self.Modified()\",\n \"def scale_it(val):\\n return scale(val, 0, 1, bpm_range[0], bpm_range[1])\",\n \"def get_scale_factor(value_dict, max_length=os.get_terminal_size().columns):\\n max_value = max(value_dict.values(), key=abs)\\n try:\\n scale = max_length / abs(max_value)\\n except ZeroDivisionError:\\n scale = 1\\n return scale\",\n \"def parallel_scale(self):\\n return self.camera.parallel_scale\",\n \"def keypoints_scale(keypoints, scale_x, scale_y):\\n keypoints[:, :2] = keypoints[:, :2] * (scale_x, scale_y)\\n return keypoints\",\n \"def GetScale(self):\\n ...\",\n \"def _adjust_for_gpus(cls, model: DeviceAwareModule, config: ModelConfigBase,\\n model_execution_mode: ModelExecutionMode) -> DeviceAwareModule:\\n if config.use_gpu:\\n model = model.cuda()\\n logging.info(\\\"Adjusting the model to use mixed precision training.\\\")\\n # If model parallel is set to True, then partition the network across all available gpus.\\n if config.use_model_parallel:\\n devices = config.get_cuda_devices()\\n assert devices is not None # for mypy\\n model.partition_model(devices=devices) # type: ignore\\n else:\\n logging.info(\\\"Making no adjustments to the model because no GPU was found.\\\")\\n\\n # Update model related config attributes (After Model Parallel Activated)\\n config.adjust_after_mixed_precision_and_parallel(model)\\n\\n # DataParallel enables running the model with multiple gpus by splitting samples across GPUs\\n # If the model is used in training mode, data parallel is activated by default.\\n # Similarly, if model parallel is not activated, data parallel is used as a backup option\\n use_data_parallel = (model_execution_mode == ModelExecutionMode.TRAIN) or (not config.use_model_parallel)\\n if config.use_gpu and use_data_parallel:\\n logging.info(\\\"Adjusting the model to use DataParallel\\\")\\n # Move all layers to the default GPU before activating data parallel.\\n # This needs to happen even though we put the model to the GPU at the beginning of the method,\\n # but we may have spread it across multiple GPUs later.\\n model = model.cuda()\\n model = DataParallelModel(model, device_ids=config.get_cuda_devices())\\n\\n return model\",\n \"def test_scale_value(make_rampmodel):\\n\\n datmod = make_rampmodel(2, 2, 4, 2048, 2048)\\n\\n # Calculate the scale based off of the input.\\n scale = datmod.meta.exposure.frame_divisor / datmod.meta.exposure.nframes\\n\\n output = GroupScaleStep.call(datmod)\\n\\n scale_from_data = np.unique(output.data / datmod.data)\\n\\n # Since the scale value is applied uniformly to the array, if we divide the output\\n # by the input then we should get a single unique value (ie the scale) calculated\\n # by the pipeline.\\n assert len(scale_from_data) == 1\\n\\n # Make sure the scale calculated manually from the data model above matched what the\\n # pipeline calculated.\\n assert scale == scale_from_data[0]\",\n \"def _rescale_module(module):\\n for sub in module.modules():\\n if isinstance(sub, (nn.Conv1d, nn.ConvTranspose1d, nn.Conv2d, nn.ConvTranspose2d)):\\n std = sub.weight.std().detach()\\n scale = (std / 0.1) ** 0.5\\n sub.weight.data /= scale\\n if sub.bias is not None:\\n sub.bias.data /= scale\",\n \"def set_scale_factors_to_output_size(self):\\n # Compute the scale_factor using rounded scaled image size.\\n height = tf.shape(self._image)[0]\\n width = tf.shape(self._image)[1]\\n max_image_size = tf.to_float(tf.maximum(height, width))\\n image_scale = tf.to_float(self._output_size) / max_image_size\\n scaled_height = tf.to_int32(tf.to_float(height) * image_scale)\\n scaled_width = tf.to_int32(tf.to_float(width) * image_scale)\\n self._image_scale = image_scale\\n self._scaled_height = scaled_height\\n self._scaled_width = scaled_width\",\n \"def scale_model_input(self, sample: torch.FloatTensor, *args, **kwargs) -> torch.FloatTensor:\\n return sample\",\n \"def __scale_bboxes(self, bboxes, scale_x, scale_y):\\n with tf.variable_scope('scale_bboxes'):\\n return tf.multiply(bboxes, tf.tile([[scale_y, scale_x, scale_y,\\n scale_x]],\\n [tf.shape(bboxes)[0], 1]))\",\n \"def scaling():\\n \\n for i in range(cfg.nfea):\\n dm = 0\\n var = 0\\n for j in range(cfg.ntrain):\\n dm += cfg.a[j,i]\\n dm = dm/cfg.ntrain\\n \\n for j in range(cfg.ntrain):\\n var += (cfg.a[j,i]-dm)**2\\n\\n var = var/cfg.ntrain\\n var = np.sqrt(var)\\n \\n if var >= 10**(-5):\\n cfg.clin[i] = 1.0/var \\n cfg.dlin[i] = -dm/var \\n \\n else: \\n if np.abs(dm)<=1.0:\\n cfg.clin[i] = 1.0\\n cfg.dlin[i] = 0.0 \\n else: \\n cfg.clin[i] = 1.0/dm\\n cfg.dlin[i] = 0.0 \\n \\n for j in range(cfg.ntrain):\\n cfg.a_scaled[j,i] = cfg.clin[i]*cfg.a[j,i] + cfg.dlin[i]\\n \\n return\",\n \"def create_scaling_model(params, experiments, reflections):\\n autos = [None, Auto, \\\"auto\\\", \\\"Auto\\\"]\\n use_auto_model = params.model in autos\\n # Determine non-auto model to use outside the loop over datasets.\\n if not use_auto_model:\\n model_class = None\\n for entry_point in pkg_resources.iter_entry_points(\\\"dxtbx.scaling_model_ext\\\"):\\n if entry_point.name == params.model:\\n model_class = entry_point.load()\\n break\\n if not model_class:\\n raise ValueError(f\\\"Unable to create scaling model of type {params.model}\\\")\\n\\n for expt, refl in zip(experiments, reflections):\\n if not expt.scaling_model or params.overwrite_existing_models:\\n # need to make a new model\\n if use_auto_model:\\n if not expt.scan:\\n model = KBScalingModel\\n else: # set model as physical unless scan < 1.0 degree\\n osc_range = expt.scan.get_oscillation_range()\\n abs_osc_range = abs(osc_range[1] - osc_range[0])\\n if abs_osc_range < 1.0:\\n model = KBScalingModel\\n else:\\n model = PhysicalScalingModel\\n else:\\n model = model_class\\n expt.scaling_model = model.from_data(params, expt, refl)\\n else:\\n # allow for updating of an existing model.\\n expt.scaling_model.update(params)\\n return experiments\",\n \"def plate_scale(platescale):\\n if platescale.unit.is_equivalent(si.arcsec / si.m):\\n platescale_val = platescale.to_value(si.radian / si.m)\\n elif platescale.unit.is_equivalent(si.m / si.arcsec):\\n platescale_val = (1 / platescale).to_value(si.radian / si.m)\\n else:\\n raise UnitsError(\\\"The pixel scale must be in angle/distance or distance/angle\\\")\\n\\n return Equivalency(\\n [(si.m, si.radian, lambda d: d * platescale_val, lambda a: a / platescale_val)],\\n \\\"plate_scale\\\",\\n {\\\"platescale\\\": platescale},\\n )\",\n \"def supervised_cost_scale(\\n scale, loss_supervised, output_noise_labelled, labelled_target\\n):\\n cost_supervised = loss_supervised.forward(output_noise_labelled, labelled_target)\\n\\n cost_supervised *= scale\\n return cost_supervised\",\n \"def _linear_to_mel_scale(linear_scale_in, n_mel_bins, mel_lower_hertz_edge,\\n mel_upper_hertz_edge):\\n\\n linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(\\n n_mel_bins, linear_scale_in.shape[-1], _SAMPLE_RATE, mel_lower_hertz_edge,\\n mel_upper_hertz_edge\\n )\\n\\n mel_scale_out = tf.tensordot(linear_scale_in, linear_to_mel_weight_matrix, 1)\\n return mel_scale_out\",\n \"def _score_converter_fn_with_logit_scale(tf_score_converter_fn, logit_scale):\\n\\n def score_converter_fn(logits):\\n scaled_logits = np.divide(logits, logit_scale)\\n\\n return tf_score_converter_fn(scaled_logits)\\n\\n return score_converter_fn\",\n \"def scale(original_train, new_train):\\n # find magnitude original training data\\n o_mag = np.linalg.norm(np.stack(original_train[:,1]))\\n # find magnitude new data\\n n_mag = np.linalg.norm(np.stack(new_train[:,1]))\\n # scale new data\\n scale = o_mag / n_mag\\n return scale\",\n \"def scale(self):\\n return self._scale\",\n \"def scaling(self) -> Optional['outputs.AiFeatureStoreOnlineServingConfigScaling']:\\n return pulumi.get(self, \\\"scaling\\\")\",\n \"def compute_scaler(args):\\n workspace = args.workspace\\n data_type = args.data_type\\n dir_name = args.dir_name \\n # Load data. \\n t1 = time.time()\\n hdf5_path = os.path.join(workspace, \\\"packed_features\\\", \\\"spectrogram\\\", data_type, dir_name, \\\"data.h5\\\")\\n with h5py.File(hdf5_path, 'r') as hf:\\n x = hf.get('x') \\n x = np.array(x) # (n_segs, n_concat, n_freq)\\n \\n # Compute scaler. \\n (n_segs, n_concat, n_freq) = x.shape\\n x2d = x.reshape((n_segs * n_concat, n_freq))\\n scaler = preprocessing.StandardScaler(with_mean=True, with_std=True).fit(x2d)\\n print(scaler.mean_)\\n print(scaler.scale_)\\n \\n # Write out scaler. \\n out_path = os.path.join(workspace, \\\"packed_features\\\", \\\"spectrogram\\\", data_type, dir_name, \\\"scaler.p\\\")\\n create_folder(os.path.dirname(out_path))\\n pickle.dump(scaler, open(out_path, 'wb'))\\n \\n print(\\\"Save scaler to %s\\\" % out_path)\\n print(\\\"Compute scaler finished! %s s\\\" % (time.time() - t1,))\",\n \"def get_scaling_type(df_mfactors: pd.DataFrame, cmdl_args):\\n eps = 0.9\\n normalized_inst_ratio = 0\\n\\n # Check if there is only one trace\\n if len(df_mfactors.index) == 1:\\n return 'strong'\\n\\n for index, row in df_mfactors.iterrows():\\n inst_ratio = float(row[MOD_FACTORS_DOC['useful_ins']]) / float(df_mfactors[MOD_FACTORS_DOC['useful_ins']][0])\\n proc_ratio = float(row[MOD_FACTORS_DOC['num_processes']]) / float(\\n df_mfactors[MOD_FACTORS_DOC['num_processes']][0])\\n normalized_inst_ratio += inst_ratio / proc_ratio\\n\\n # Get the average inst increase. Ignore ratio of first trace (1.0)\\n normalized_inst_ratio = (normalized_inst_ratio - 1) / (len(df_mfactors.index) - 1)\\n\\n scaling_computed = ''\\n\\n if normalized_inst_ratio > eps:\\n scaling_computed = 'weak'\\n else:\\n scaling_computed = 'strong'\\n\\n if cmdl_args.scaling == 'auto':\\n if cmdl_args.debug:\\n print(f'==DEBUG== Detected {scaling_computed} scaling.')\\n print('')\\n return scaling_computed\\n\\n if cmdl_args.scaling == 'weak':\\n if scaling_computed == 'strong':\\n print('==WARNING== Scaling set to weak scaling but detected strong scaling.')\\n print('')\\n return 'strong'\\n\\n if cmdl_args.scaling == 'strong':\\n if scaling_computed == 'weak':\\n print('==WARNING== Scaling set to strong scaling but detected weak scaling.')\\n print('')\\n return 'strong'\\n\\n print('==ERROR== Reached undefined control flow state.')\\n sys.exit(1)\",\n \"def scale(requestContext, seriesList, factor):\\n for series in seriesList:\\n series.name = \\\"scale(%s,%g)\\\" % (series.name,float(factor))\\n series.pathExpression = series.name\\n for i,value in enumerate(series):\\n series[i] = safeMul(value,factor)\\n return seriesList\",\n \"def scale(inp, ab):\\n\\n return inp * ab[0] + ab[1]\",\n \"def find_scale(dataset, log_scales, path=_default_path):\\n fname = \\\"\\\".join([\\\"evaluate_\\\", dataset, \\\"_64x64.h5\\\"])\\n store = h5py.File(join(path, fname))\\n\\n rocs = dict()\\n dists = [\\\"L1\\\", \\\"L2\\\", \\\"COSINE\\\"]\\n norms = [\\\"l1\\\", \\\"l2\\\", \\\"id\\\"]\\n for scale in log_scales:\\n sift = \\\"\\\".join([\\\"evaluate_\\\", dataset, \\\"_sift_\\\", str(scale), \\\".h5\\\"])\\n sift = h5py.File(join(path, sift), \\\"w\\\")\\n store_as_sift(store, sift, scale=2**scale)\\n rocs[scale] = evaluate(sift, distances=dists, normalizations=norms)\\n sift.close()\\n return rocs\",\n \"def find_suggested_tonemap_scale(session):\\n avg_film_luminance = session.GetFilm().GetFilmY()\\n return (1.25 / avg_film_luminance * (118 / 255))\\n\\n # TODO\\n # measure this all the time, show a message to the user if\\n # abs(old - new) > threshold\\n # so the user can set the new value with one click\\n\\n # imagepipeline = scene.camera.data.luxcore.imagepipeline\\n # imagepipeline.tonemapper.linear_scale = suggested_linear_scale\\n # imagepipeline.tonemapper.use_autolinear = False\",\n \"def _scale_param(self, resid_us):\\n return((resid_us**2).sum().sum() / self.dof)\",\n \"def test_auto_scale_batch_size_trainer_arg(tmpdir, scale_arg):\\n tutils.reset_seed()\\n hparams = EvalModelTemplate.get_default_hparams()\\n model = EvalModelTemplate(**hparams)\\n before_batch_size = hparams.get('batch_size')\\n trainer = Trainer(default_root_dir=tmpdir,\\n max_epochs=1,\\n auto_scale_batch_size=scale_arg,\\n gpus=1)\\n trainer.tune(model)\\n after_batch_size = model.batch_size\\n assert before_batch_size != after_batch_size, \\\\\\n 'Batch size was not altered after running auto scaling of batch size'\",\n \"def price_user_scale_factor(self, new_price_scale_factor):\\n self._price_user_scale_factor = new_price_scale_factor\",\n \"def scale(self,bvp):\\n\\n sol = bvp.solution\\n # Additional aux entries for initial and terminal BCs\\n extras = [{'type':'initial','vars':self.problem_data['state_list']},\\n {'type':'terminal','vars':self.problem_data['state_list']}]\\n\\n # Scale the states and costates\\n for idx,state in enumerate(self.problem_data['state_list']):\\n sol.y[idx,:] /= self.scale_vals['states'][state]\\n\\n # Scale auxiliary variables\\n for aux in (self.problem_data['aux_list']+extras):\\n if aux['type'] not in Scaling.excluded_aux:\\n for var in aux['vars']:\\n sol.aux[aux['type']][var] /= self.scale_vals[aux['type']][var]\\n\\n # Scale parameters\\n for idx, param in enumerate(self.problem_data['parameter_list']):\\n sol.parameters[idx] /= self.scale_vals['parameters'][param]\",\n \"def __get_scale_units(self, device_type_name, channel_number):\\n if device_type_name == \\\"BPMS\\\":\\n if channel_number == 0:\\n fault_name = \\\"X\\\"\\n elif channel_number == 1:\\n fault_name = \\\"Y\\\"\\n elif channel_number == 2:\\n fault_name = \\\"TMIT\\\"\\n else:\\n raise ValueError(\\\"Function \\\\\\\"get_app_name(device_type_name={}, channel_number={})\\\\\\\". Invalid channel number for BPMS device type\\\"\\n .format(device_type_name, channel_number))\\n return self.__get_app_units(device_type_name, fault_name)\\n else:\\n return self.__get_app_units(device_type_name, \\\"\\\")\",\n \"def ConvertGpuToVendorName(gpu):\\n if not gpu:\\n return 'No GPU'\\n elif '8086' in gpu:\\n return 'Intel'\\n elif '10de' in gpu:\\n return 'NVIDIA'\\n elif '1002' in gpu:\\n return 'AMD'\\n return gpu\",\n \"def plane_scale(self, scale):\\n cmd = '{}testPlaneScale {}'.format(self.console, scale)\\n self.write_command(cmd)\",\n \"def scale(self, X_train, X_test):\\n\\n #X_train, X_test, y_train, y_test = self.split_X_y_sets()\\n self.scaler.fit(X_train)\\n X_train_sc = self.scaler.transform(X_train)\\n X_test_sc = self.scaler.transform(X_test)\\n\\n return X_train_sc, X_test_sc #, y_train, y_test\",\n \"def freqscale(self):\\n index = self._ordered_input_names.index('freqscale')\\n return self._inputs[index]\",\n \"def scale_matrix(self, matrix_df):\\n\\n print('scaling features...')\\n\\n originalkeys = matrix_df[['segment_id','date','time']].reset_index()\\n originalkeys['index'] = originalkeys.index\\n\\n columns = list(matrix_df)\\n columns.remove('segment_id')\\n columns.remove('date')\\n columns.remove('time')\\n data_to_scale = matrix_df[columns]\\n matrix_df_scaled_tmp = pd.DataFrame(preprocessing.scale(data_to_scale), columns = columns).reset_index()\\n matrix_df_scaled_tmp['index']=matrix_df_scaled_tmp.index\\n\\n matrix_df_scaled_labels_tmp = pd.merge(matrix_df_scaled_tmp, originalkeys, on=['index'])\\n matrix_df_scaled = matrix_df_scaled_tmp.drop(columns=['index'])\\n matrix_df_scaled_withlabels = matrix_df_scaled_labels_tmp.drop(columns=['index'])\\n \\n return matrix_df_scaled_withlabels\",\n \"def scale(image):\\n image = tf.cast(image, tf.float32)\\n image /= 255\\n return image\",\n \"def scaled_component(self, key):\\n\\n if key in self.components:\\n dat = self.components[key] \\n # Aliases\\n elif key in component_from_alias:\\n comp = component_from_alias[key]\\n if comp in self.components:\\n dat = self.components[comp] \\n else:\\n # Component not present, make zeros\\n return np.zeros(self.shape)\\n else:\\n raise ValueError(f'Component not available: {key}')\\n \\n # Multiply by scale factor\\n factor = self.factor \\n \\n if factor != 1:\\n return factor*dat\\n else:\\n return dat\",\n \"def test_patch_namespaced_scale_scale(self):\\n pass\",\n \"def config_from_pytorch_model(\\n model,\\n granularity='model',\\n backend=None,\\n default_precision='ap_fixed<16,6>',\\n default_reuse_factor=1,\\n inputs_channel_last=False,\\n transpose_outputs=True,\\n):\\n\\n config = {}\\n\\n model_config = {}\\n model_config['Precision'] = default_precision\\n model_config['ReuseFactor'] = default_reuse_factor\\n model_config['InputsChannelLast'] = inputs_channel_last\\n model_config['TransposeOutputs'] = transpose_outputs\\n model_config['Strategy'] = 'Latency'\\n\\n config['Model'] = model_config\\n\\n return config\",\n \"def scale(self):\\n return self.scale_factor / CONSTANTS.AU\",\n \"def getscales(self):\\n return self.scales\",\n \"def convert_scaling_to_form_factors(qz, scale):\\n apply_absorption_correction(qz, scale)\\n apply_Lorentz_correction(qz, scale)\\n for i in xrange(len(scale)):\\n scale[i] = np.sign(scale[i]) * math.sqrt(abs(scale[i]))\",\n \"def matScale(mat, scale):\\n shape=matShape(mat)\\n return [[matGet(mat,x,y)*scale for y in range(shape[1])] \\\\\\n for x in range(shape[0])]\",\n \"def scale_ticks_params(tick_scale='linear'):\\n if tick_scale == 'linear':\\n base = None\\n label_scale = 'Linear Scale'\\n else:\\n if tick_scale == 'log2':\\n base = 2\\n label_scale = 'Log2 Scale'\\n elif tick_scale == 'log10':\\n base = 10\\n label_scale = 'Log10 Scale'\\n else:\\n raise ValueError('The specified tick scale is not supported.')\\n return base, label_scale\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67240673","0.6272403","0.5994706","0.5941544","0.5833461","0.5681746","0.5676147","0.5382901","0.5348","0.53371793","0.5301205","0.52537143","0.52262044","0.51800525","0.51736313","0.5139863","0.512118","0.5083435","0.50683033","0.50677323","0.50584626","0.5052913","0.5050851","0.5048136","0.50284874","0.50180674","0.5017098","0.50109756","0.5006812","0.4985893","0.49858075","0.49794883","0.49584532","0.4958185","0.49545544","0.49530986","0.4952522","0.49500015","0.49393916","0.493769","0.4927588","0.49175602","0.49135134","0.4904828","0.4903294","0.4899042","0.4898761","0.48929045","0.48893055","0.48892927","0.48753592","0.48639697","0.48587188","0.4849531","0.48460126","0.4842077","0.48391664","0.48297128","0.48265994","0.48206714","0.48115358","0.48081496","0.4805505","0.4799054","0.4791941","0.4785347","0.47742388","0.47654986","0.47644013","0.47605643","0.47600996","0.47552285","0.47552136","0.475323","0.47503623","0.47493386","0.47468555","0.47452906","0.4732579","0.47198063","0.47178286","0.4713385","0.47104812","0.47053972","0.47023824","0.47014537","0.46941185","0.4693801","0.46932673","0.46926004","0.46917307","0.46898133","0.46876448","0.4674922","0.4670565","0.46699354","0.46675408","0.4667024","0.46642593","0.46607885"],"string":"[\n \"0.67240673\",\n \"0.6272403\",\n \"0.5994706\",\n \"0.5941544\",\n \"0.5833461\",\n \"0.5681746\",\n \"0.5676147\",\n \"0.5382901\",\n \"0.5348\",\n \"0.53371793\",\n \"0.5301205\",\n \"0.52537143\",\n \"0.52262044\",\n \"0.51800525\",\n \"0.51736313\",\n \"0.5139863\",\n \"0.512118\",\n \"0.5083435\",\n \"0.50683033\",\n \"0.50677323\",\n \"0.50584626\",\n \"0.5052913\",\n \"0.5050851\",\n \"0.5048136\",\n \"0.50284874\",\n \"0.50180674\",\n \"0.5017098\",\n \"0.50109756\",\n \"0.5006812\",\n \"0.4985893\",\n \"0.49858075\",\n \"0.49794883\",\n \"0.49584532\",\n \"0.4958185\",\n \"0.49545544\",\n \"0.49530986\",\n \"0.4952522\",\n \"0.49500015\",\n \"0.49393916\",\n \"0.493769\",\n \"0.4927588\",\n \"0.49175602\",\n \"0.49135134\",\n \"0.4904828\",\n \"0.4903294\",\n \"0.4899042\",\n \"0.4898761\",\n \"0.48929045\",\n \"0.48893055\",\n \"0.48892927\",\n \"0.48753592\",\n \"0.48639697\",\n \"0.48587188\",\n \"0.4849531\",\n \"0.48460126\",\n \"0.4842077\",\n \"0.48391664\",\n \"0.48297128\",\n \"0.48265994\",\n \"0.48206714\",\n \"0.48115358\",\n \"0.48081496\",\n \"0.4805505\",\n \"0.4799054\",\n \"0.4791941\",\n \"0.4785347\",\n \"0.47742388\",\n \"0.47654986\",\n \"0.47644013\",\n \"0.47605643\",\n \"0.47600996\",\n \"0.47552285\",\n \"0.47552136\",\n \"0.475323\",\n \"0.47503623\",\n \"0.47493386\",\n \"0.47468555\",\n \"0.47452906\",\n \"0.4732579\",\n \"0.47198063\",\n \"0.47178286\",\n \"0.4713385\",\n \"0.47104812\",\n \"0.47053972\",\n \"0.47023824\",\n \"0.47014537\",\n \"0.46941185\",\n \"0.4693801\",\n \"0.46932673\",\n \"0.46926004\",\n \"0.46917307\",\n \"0.46898133\",\n \"0.46876448\",\n \"0.4674922\",\n \"0.4670565\",\n \"0.46699354\",\n \"0.46675408\",\n \"0.4667024\",\n \"0.46642593\",\n \"0.46607885\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6472911"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":8479,"cells":{"query":{"kind":"string","value":"From all the data, it takes the columns TopicID and Question and for each topic, count the number of+ different SubTopic/Question"},"document":{"kind":"string","value":"def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:\n try:\n data_frame = data_frame \\\n .select(\"TopicID\", \"Question\") \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n except Py4JError:\n raise AnalysisException('One columns is incorrect')\n print(\"The following table represent the number of the type of each topic\")\n data_frame.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n tuple_result.append(each_tuple)\n\n # sort by the the second element, which is value, of each tuple in the list\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\n\n # separate the topic and count into two lists for graphing purpose\n topics = []\n counts = []\n\n for each_tuple in tuple_result:\n topics.append(each_tuple[0])\n counts.append(each_tuple[1])\n\n return counts, topics","def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Male\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of men group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def topic_stats(df_topic_sents_keywords):\n\n # Number of Documents for Each Topic\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n\n # Percentage of Documents for Each Topic\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n\n # Topic Number and Keywords\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n\n # Concatenate Column wise\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n\n # Change Column names\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n\n # Show\n df_dominant_topics","def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Black, non-Hispanic\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of black ethnicity people group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def trainCount(\n trainData, \n questionType,\n questionDict,\n questionIdict, \n objDict, \n objIdict,\n numAns):\n count_wa = np.zeros((len(objIdict), numAns))\n count_a = np.zeros((numAns))\n objIds = extractObjId(\n trainData[0], \n questionType, \n questionDict, \n questionIdict)\n for i in range(objIds.shape[0]):\n objId = objIds[i]\n obj = questionIdict[objId - 1]\n ansId = trainData[1][i, 0]\n objId2 = objDict[obj]\n count_wa[objId2, ansId] += 1\n count_a[ansId] += 1\n # Add UNK count\n count_a[-1] += 1\n return count_wa, count_a","def get_paper_counter_per_topic_id(all_topic_assignments):\n counter = {}\n for topic_assignment in all_topic_assignments:\n for topic_index, topic_value in topic_assignment:\n if topic_index not in counter:\n counter[topic_index] = 0\n\n counter[topic_index] += 1\n\n return counter","def count_topic_dist(self):\n if len(self.representants) == 0:\n self.log_writer(\"Representants not set. Cannot make topic dist.\")\n return\n for key, value in self.representants.items():\n self.topic_distributions.append(len(value)/len(self.training_docs))\n self.topic_numbers.append(key)","def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\n print(data_frame_pandas)\n return data_frame_pandas","def get_diseases(self):\n self.diseases = self.data.groupby('topic')['topic'].count()","def test_question_topics(self):\n p = ProductFactory()\n t1 = TopicFactory(slug='doesnotexist', product=p)\n t2 = TopicFactory(slug='cookies', product=p)\n t3 = TopicFactory(slug='sync', product=p)\n\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t3)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n )\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def test_extract_topics():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model = BERTopic()\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def test_topics_for_products(self):\n desktop_topics = topics_for(product=self.desktop)\n eq_(len(desktop_topics), 3)\n\n mobile_topics = topics_for(product=self.mobile)\n eq_(len(mobile_topics), 2)","def test_extract_topics(base_bertopic):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(documents)\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def assign_topics(self):\n\n # Load the dataset which has topic tags for each sentence\n dt, dt_less = self.assign_topics_to_sentences()\n \n dt_copy = dt\n \n # Minimum number of tags needed to tag the overall response\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\n \n # Final dataset with full survey response and its tags\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\n , 'num_sent': min\n , 'min_num_tags': min\n# , 'sentences': lambda x: \"%s\" % '. '.join(x)\n , self.col_name: min})\n final_dt.reset_index(level = 0, inplace = True)\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\n , axis = 1)\n\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\n\n return dt_copy, dt_less, final_dt, final_dt_less","def iterate_data(self):\n if \"single\" in self.dataset_name:\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for answer_id in self.data:\n summary = self.data[answer_id]['summary']\n articles = self.data[answer_id]['articles']\n question = self.data[answer_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(articles, 'article')\n self._get_token_cnts(question, 'question')\n self._write_stats(\"token_counts\")\n\n if \"multi\" in self.dataset_name:\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for q_id in self.data:\n summary = self.data[q_id]['summary']\n question = self.data[q_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(question, 'question')\n question = self.data[q_id]['question']\n for answer_id in self.data[q_id]['articles']:\n articles = self.data[q_id]['articles'][answer_id][0]\n if args.tokenize:\n self._get_token_cnts(articles, 'article')\n self._write_stats(\"token_counts\")\n\n if self.dataset_name == \"complete_dataset\":\n self.stat_dict = {'urls': [], 'sites': []}\n article_dict = {}\n print(\"Counting answers, sites, unique urls, and tokenized counts of unique articles\")\n answer_cnt = 0\n for q_id in self.data:\n for a_id in self.data[q_id]['answers']:\n answer_cnt += 1\n url = self.data[q_id]['answers'][a_id]['url']\n article = self.data[q_id]['answers'][a_id]['article']\n if url not in article_dict:\n article_dict[url] = article\n self.stat_dict['urls'].append(url)\n assert \"//\" in url, url\n site = url.split(\"//\")[1].split(\"/\")\n self.stat_dict['sites'].append(site[0])\n print(\"# of Answers:\", answer_cnt)\n print(\"Unique articles: \", len(article_dict)) # This should match up with count written to file\n self._write_stats(\"full collection\")\n\n # Get token/sent averages of unique articles\n if args.tokenize:\n self.stat_dict = {'article': [[], []]}\n for a in article_dict:\n self._get_token_cnts(article_dict[a], 'article')\n self._write_stats(\"token_counts\")","def so_data_statistics(data_file):\n with open(data_file, \"r\") as f:\n data = json.load(f)\n\n answer_to_num_questions = collections.Counter()\n comment_to_num_questions = collections.Counter()\n num_comments = 0\n num_answers = 0\n num_questions = len(data)\n\n for q in data:\n q = json.loads(q)\n q_comments = 0\n q_comments += len(q[\"comments\"])\n q_answers = len(q[\"answers\"])\n for a in q[\"answers\"]:\n q_comments += len(a[\"comments\"])\n\n answer_to_num_questions[q_answers] += 1\n comment_to_num_questions[q_comments] += 1\n\n num_comments += q_comments\n num_answers += q_answers\n\n print \"Num comments: {0}, Num answers: {1}, Num_questions: {2}\".format(\n num_comments, num_answers, num_questions)\n print \"-\" * 10\n print \"Answers map: \", answer_to_num_questions\n print \"Comments map: \", comment_to_num_questions\n\n return num_comments, num_answers, num_questions, answer_to_num_questions, \\\n comment_to_num_questions","def quiz_questions(topic):\n try:\n\n # returns size of quiz question\n current_list_size = len(topic)\n score = 0\n # loops through the passed topic questions list\n for i in copy.deepcopy(topic):\n print(i)\n user = input('Enter here:\\t').lower()\n # return correct if the given key question matches the its corresponding value i.e users answer\n if topic[i].lower() == user:\n print('correct\\n')\n score += 1\n else:\n print(\n f'Sorry that was incorrect. The correct answer I was looking for was {topic[i]}\\n') # if not correct display the correct answer\n # del questions[topic][i]\n\n if score == current_list_size: # check to see if they answered all the questions correctly\n print('Nice! you got all the answers correct!')\n else:\n # otherwise return score.\n print(f'You got {score}/{str(current_list_size)} correct!')\n except KeyError:\n print('Something went wrong in quiz_questions(), check logic')","def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\n\n for text_id, text in self.texts.items():\n # single-word topics act a bit different (no zips or comprehensions)\n # store data in self.topics, not zip_grams\n for word in text['doc']:\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\n\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\n continue\n\n if not (word.pos in self.nouns or word.ent_type in self.entities):\n continue\n\n if word_lemma in self.topics:\n self.topics[word_lemma][\"count\"] += 1\n self.topics[word_lemma][\"textIDs\"] |= {text_id}\n self.topics[word_lemma][\"verbatims\"] |= {word.text.lower()}\n else:\n self.topics[word_lemma] = {\"name\": word_lemma,\n \"count\": 1,\n \"textIDs\": {text_id},\n \"verbatims\": {word.text.lower()},\n \"subtopics\": {}}\n\n # Populate self.ngrams and self.topics\n for text_id, text in self.texts.items():\n doc = text['doc']\n\n # Find pentagrams - ngrams with 5 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\n self._ngram_counter(ngram, 5, text_id, doc)\n\n # Find pentagrams - ngrams with 4 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\n self._ngram_counter(ngram, 4, text_id, doc)\n\n for ngram in zip(doc, doc[1:], doc[2:]):\n self._ngram_counter(ngram, 3, text_id, doc)\n\n for ngram in zip(doc, doc[1:]):\n self._ngram_counter(ngram, 2, text_id, doc)\n\n\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\n # but it's only mentioned in 3 different texts, we'd show 3.\n for _, topic in self.topics.items():\n topic['textIDCount'] = len(topic['textIDs'])\n for _, ngram in self.ngrams.items():\n ngram['textIDCount'] = len(ngram['textIDs'])\n\n # Eliminate rarely occurring topics and ngrams.\n self.topics = {k: v for k, v in self.topics.items() if\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\n self.ngrams = {k: v for k, v in self.ngrams.items() if\n v['textIDCount'] >= min_text_id_count}\n\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\n for ngram_lemma, ngram in self.ngrams.items():\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\n # r'\\b' + ngram_lemma + r'\\b' --> does the ngram lemma fit in ngram_plus lemma (\\b is word boundary)\n if ngram['n'] + 1 != ngram_plus['n']:\n continue\n\n if not re.search(r'\\b' + ngram_lemma + r'\\b', ngram_plus_lemma):\n continue\n\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\n # instead of the other loop?\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\n ngram['count'] = -1\n\n # Eliminate newly demoted items\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}","def export_topics(self):\n\n # format as a list (for json output), then sort descending by textIDCount\n topics = [{'name': topic['name'], 'count': topic['count'],\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\n 'children': '' if 'children' not in topic else topic['children']}\n for topic_id, topic in self.topics.items()]\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\n\n for i, topic in enumerate(topics):\n # Note that 'rank' is from topic, not child.\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\n 'textIDCount': child['textIDCount']}\n for _, child in topic['children'].items()]\n\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\n\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\n child_count = sum([child['textIDCount'] for child in topic['children']])\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\n\n for child in topic['children']:\n child['size'] = child['textIDCount'] * child_count_multiplier\n\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\n\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\n self.model_output[\"children\"] = [topic for topic in topics]\n\n # Build file name and save\n if self.data_date:\n date = datetime.strptime(self.data_date, \"%Y-%m-%d\").strftime('%d') # from YYYY-MM-DD to DD\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\n else:\n file_name = '{}-Topics.txt'.format(self.corpus_name)\n\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\n json.dump(self.model_output, file)","def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = response.get(\"Items\")\n return questions","def get_n_questions(self):\n return self.df.question.nunique()","def test_topic_reduction(reduced_topics):\n model = BERTopic()\n nr_topics = reduced_topics + 2\n model.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents.copy())\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents.copy())\n new_freq = model.get_topic_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(model.mapped_topics, dict)\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\n assert model.mapped_topics","def check_intersections(db, topics, papers_by_topic):\n\n\t# Print the distribution of \"number of topics\"\n\tnum_subjects = []\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tnum_subjects.append(len(p.subject))\n\t\telse:\n\t\t\tnum_subjects.append(0)\n\tnum_subjects = np.array(num_subjects)\n\n\tfor i in range(np.max(num_subjects)+1):\n\t\tprint(\"Number of papers with\", i, \"topics:\", \n\t\t\tlen(np.where(num_subjects==i)[0]))\n\n\t# Figure out what's going on with triple-tagged guys (nothing weird)\n\t\"\"\"\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tif len(p.subject) > 2:\n\t\t\t\tprint(\"\\n\",p.title,\"\\n\\t\",p.container_title,\"\\n\\t\", p.subject)\n\t\t\t\t\n\t\t\t\tfor topic, topic_words in topics.items():\n\t\t\t\t\tprint(\"\\tCheck against '\" + topic + \"':\")\n\t\t\t\t\tfor journal in p.container_title:\n\t\t\t\t\t\tcheck_words(journal, topic_words, verbose=True)\n\t\"\"\"\n\n\t# Look in more detail at double-tagged guysfor p_hash, p in db.all_papers.items():\n\tcombos = defaultdict(int)\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tif len(p.subject) == 2:\n\t\t\t\tcombos[frozenset(p.subject)] += 1\n\t\t\t\t#print(\"\\n\",p.title,\"\\n\\t\",p.container_title,\"\\n\\t\", p.subject)\n\t\t\t\tif p.subject == {'Computer Science', 'Biology'}:\n\t\t\t\t\t#print(\"\\n\",p.title,\"\\n\\t\",p.container_title)#,\"\\n\\t\", p.subject)\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tbio_words = set()\n\t\t\t\t\tCS_words = set()\n\t\t\t\t\tfor journal in p.container_title:\n\t\t\t\t\t\tfor word in topics['Biology']:\n\t\t\t\t\t\t\tif journal.find(word) >= 0:\n\t\t\t\t\t\t\t\tbio_words.add(word)\n\t\t\t\t\t\tfor word in topics['Computer Science']:\n\t\t\t\t\t\t\tif journal.find(word) >= 0:\n\t\t\t\t\t\t\t\tCS_words.add(word)\n\n\t\t\t\t\t#print(\"\\tBiology words:\", bio_words)\n\t\t\t\t\t#print(\"\\tCS words:\", CS_words)\n\t\n\tfor k, v in combos.items():\n\t\tprint(k, v)","def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)","def assign_topics_to_sentences(self):\n \n\n # 10 topics\n topic_dict = {0: 'academics'\n , 1: 'career'\n , 2: 'commute'\n , 3: 'diversity'\n , 4: 'community'\n , 5: 'extracurricular'\n , 6: 'facilities'\n , 7: 'finance'\n , 8: 'housing'\n , 9: 'wellness'\n }\n\n # Some important words that should be included under each topic\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\n , 'degree', 'research', 'elective'\n , 'professor', 'project', 'scholarship', 'knowledge']\n , ['career', 'job', 'coop', 'employment']\n , ['commute', 'skytrain', 'transport', 'commuter']\n , ['diversity', 'diverse', 'background']\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\n , ['extracurricular', 'club', 'sport', 'activity']\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\n , ['finance', 'tuition', 'expensive']\n , ['housing', 'live', 'residence']\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\n\n # Read the data - id and reponse column\n dt = pd.read_csv(self.path_data_col\n , encoding = \"ISO-8859-1\"\n , usecols = [self.id_col_name, self.col_name])\n\n\n \n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n \n # Split into sentences\n dt['sentences'] = self.getSentences(dt[self.col_name])\n \n \n \n\n \n \n\n # Store number of sentences in each response as a column\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\n\n # Split each row into multiple rows - one row for each sentence\n dt = (dt\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\n .apply(pd.Series)\n .stack()\n .reset_index()\n .drop('level_3', axis = 1)\n .rename(columns = {0:'sentences'}))\n\n\n # Clean the sentences\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\n\n # Remove useless sentences\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\n\n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n\n # Tokenize words in the cleaned sentences\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\n\n\n # Call the lexicon function\n topic_lexicons = self.prepare_lexicons()\n\n # Lists to store results\n count_topic_all = []\n actual_topic_all = []\n\n # Tag each response into a topic\n for response in responses:\n\n count_topic = []\n actual_topic = []\n\n for topic in topic_lexicons:\n\n # Count occurance of each word in word stock in the response\n temp = sum(dict((x, response.count(x)) for x in topic).values())\n count_topic.append(temp)\n\n\n for index, value in enumerate(count_topic):\n\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\n if value > 0:\n actual_topic.append(topic_dict[index])\n\n\n # If more than 3 topics are tagged for single sentence, refine by increasing\n # cutoff to at least 2 words instead of 1\n if len(actual_topic) > 3:\n\n actual_topic = []\n for index, value in enumerate(count_topic):\n\n if value > 1: # Increase cutoff\n actual_topic.append(topic_dict[index])\n\n count_topic_all.append(count_topic)\n actual_topic_all.append(actual_topic)\n\n\n dt['tags'] = actual_topic_all\n dt['num_tags'] = count_topic_all\n\n\n # Select only the most important columns\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\n\n return dt, dt_less","def group_topics(sent_topics_sorteddf):\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0).sum()\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\n plt.title('Topic Share %');\n\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]","def __len__(self):\n size = self.HEADER_LEN + 2 + 4 + 4 # acks + timeout + len(topics)\n for topic, parts in iteritems(self.msets):\n # topic name\n size += 2 + len(topic) + 4 # topic name + len(parts)\n # partition + mset size + len(mset)\n size += sum(4 + 4 + len(mset) for mset in itervalues(parts))\n return size","def counts_table(data, attr):\n pd.options.mode.chained_assignment = None # default='warn'\n # gets class names for dataframe manipulation\n classes = attr.tail(1)['vars'].tolist()\n classlist = [classes[0][0], classes[0][1]]\n # expanding a table to have all variable options in a column with their \n # parent attribute\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\n allvariables.name='var'\n allvariables = attr.drop('vars', axis=1).join(allvariables)\n av = allvariables.drop(attr.index[-1])\n # populate the table with counts\n for c in classlist:\n clist = []\n count = 0\n for i, row in av.iterrows():\n att = row['attr']\n var = row['var']\n sub = data[[att,'class']]\n sub = sub[sub[att]==var]\n if not sub.empty:\n ssub = sub[sub['class']==c]\n if not ssub.empty:\n count = len(ssub)\n else:\n count = 0\n clist.append(count)\n av[c] = clist\n\n return av","def total_exs(dataset):\n total = 0\n for article in dataset['data']:\n for para in article['paragraphs']:\n total += len(para['qas'])\n return total","def document_distribution_per_topic(df_dominant_topic):\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\n\n\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\n\n for i, ax in enumerate(axes.flatten()):\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\n doc_lens = [len(d) for d in df_dominant_topic.Text]\n ax.hist(doc_lens, bins = 1000, color=cols[i])\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\n sns.kdeplot(doc_lens, color=\"black\", shade=False, ax=ax.twinx())\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\n ax.set_ylabel('Number of Documents', color=cols[i])\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\n\n fig.tight_layout()\n fig.subplots_adjust(top=0.90)\n plt.xticks(np.linspace(0,1000,9))\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\n plt.show()","def count_ngrams(self, corpus):\n \n self.unigramcounts = {} # might want to use defaultdict or Counter instead\n self.bigramcounts = {} \n self.trigramcounts = {} \n\n self.total = 2\n ##Your code here\n\n for sentence in corpus:\n temp_1 = get_ngrams(sentence,1)\n temp_2 = get_ngrams(sentence,2)\n temp_3 = get_ngrams(sentence,3)\n for i in range(len(temp_1)):\n if temp_1[i] in self.unigramcounts:\n self.unigramcounts[temp_1[i]] += 1\n else:\n self.unigramcounts[temp_1[i]] = 1\n self.total += 1\n\n for i in range(len(temp_2)):\n if temp_2[i] in self.bigramcounts:\n self.bigramcounts[temp_2[i]] += 1\n else:\n self.bigramcounts[temp_2[i]] = 1\n\n for i in range(len(temp_3)):\n if temp_3[i] in self.trigramcounts:\n self.trigramcounts[temp_3[i]] += 1\n else:\n self.trigramcounts[temp_3[i]] = 1\n return","def _initialize(self):\n for doc_index, doc in enumerate(self.document):\n temp_word_topic_matrix = []\n for word in doc:\n if word in self.word2id.keys():\n start_topic_index = np.random.randint(0, self.K)\n temp_word_topic_matrix.append(start_topic_index)\n self.doc_topic_matrix[doc_index, start_topic_index] += 1\n self.topic_word_matrix[start_topic_index, self.word2id[word]] += 1\n self.topic_matrix[start_topic_index] += 1\n self.current_word_topic_matrix.append(temp_word_topic_matrix)","def count_mentioned_pol_figures(data):\n figures_mentioned = {}\n figures = get_political_figures()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for figure in figures:\n if figure + \" \" in (subject_words + message_words):\n if figure in figures_mentioned:\n figures_mentioned[figure] += 1\n else:\n figures_mentioned[figure] = 0\n\n return pd.DataFrame(figures_mentioned)","def __len__(self):\n # Header + consumer group + len(topics)\n size = self.HEADER_LEN + 2 + len(self.consumer_group) + 4\n for topic, parts in iteritems(self._reqs):\n # topic name + len(parts)\n size += 2 + len(topic) + 4\n # partition => for each partition\n size += 4 * len(parts)\n return size","def __len__(self):\n # Header + replicaId + len(topics)\n size = self.HEADER_LEN + 4 + 4\n for topic, parts in iteritems(self._reqs):\n # topic name + len(parts)\n size += 2 + len(topic) + 4\n # partition + fetch offset + max bytes => for each partition\n size += (4 + 8 + 4) * len(parts)\n return size","def subtype_occurences(self):\n\n subtype_counts = Counter()\n\n for seqkey,seqs in self.seqs.iteritems():\n for seq,seqentry in seqs.iteritems():\n\n subtype_counts[seqentry['subtype']] += 1\n\n return subtype_counts","def analyze_count(data):\n\n dsct_vk = pd.unique(data['vk'])\n dsct_itemid = pd.unique(data['itemid'])\n\n print 'number of user:', dsct_vk.shape\n print 'number of items:', dsct_itemid.shape\n print 'the number of ratings:', data.shape\n\n print 'unique actions:', pd.unique(data['action'])\n print 'the number of action 0:', np.sum(data['action'] == 0)\n print 'the number of action 1:', np.sum(data['action'] == 1)\n print 'the number of action 2:', np.sum(data['action'] == 2)\n print 'the number of action 3:', np.sum(data['action'] == 3)\n print 'the number of action 4:', np.sum(data['action'] == 4)\n \n time_range_item = data.groupby('itemid')['real_time'].aggregate(sum_unique)\n print 'Max Range:', np.max(time_range_item)\n print 'Mean Range:', np.mean(time_range_item)\n print 'Median Range:', np.median(time_range_item)","def get_topics_count(khoros_object, user_settings=None, user_id=None, login=None, email=None):\n user_settings = _process_settings_and_user_id(khoros_object, user_settings, user_id, login, email)\n return _get_count(khoros_object, user_settings['id'], 'topics')","def test_topic_reduction(reduced_topics):\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\n nr_topics = reduced_topics + 2\n base_bertopic.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(old_documents)\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\n old_freq = base_bertopic.get_topics_freq()\n\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\n new_freq = base_bertopic.get_topics_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(base_bertopic.mapped_topics, dict)\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\n assert base_bertopic.mapped_topics","def uncategorized(df):\n\n counter = 0\n for movie in df.index:\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\n df.loc[movie, 'Political'] == 0:\n counter += 1\n\n return counter","def count_mentioned_countries(data):\n countries_mentioned = {}\n countries = get_countries()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for country in countries:\n if country in (subject_words + message_words):\n if country in countries_mentioned:\n countries_mentioned[country] += 1\n else:\n countries_mentioned[country] = 1\n\n return pd.DataFrame.from_dict(countries_mentioned, orient=\"index\")","def subtrace_count(self, trace, subtrace):\n if len(subtrace) == 0:\n return 0\n\n count = 0\n\n tr = list(map(lambda ac: self.activity_concept_name(ac), trace))\n\n for index in range(len(tr) - len(subtrace) + 1):\n slice = tr[index:index + len(subtrace)]\n\n if subtrace == slice:\n count += 1\n\n return count","def validate_new_curriculum_topics(self, curriculum_topics):\n\n for cur in curriculum_topics:\n # check to make sure its in the general topics table\n self.db_cursor.execute(\"\"\"SELECT COUNT(*) FROM Topic WHERE name = %s\"\"\", (cur,))\n ct = self.db_cursor.fetchone()\n ct = ct[0]\n if ct == 0:\n print(\"topic does not exist, we must create new one or cancel\") # todo\n\n return True","def compute_subj_metrics():\n\t# each participant does task 1,2,3 with method A,B = 3*2*N\n\t# likert data includes age, gender, Q1 - Q10 = 2+10\n\n\t# set up data structure\n\tsubj_metrics = {}\n\tfor ID in range(10):\n\t\tfor method in [\"A\",\"B\"]:\n\t\t\t# sanity checks\n\t\t\tif ID not in subj_metrics:\n\t\t\t\tsubj_metrics[ID] = {}\n\t\t\tif method not in subj_metrics[ID]:\n\t\t\t\tsubj_metrics[ID][method] = [None]*12\n\n\there = os.path.dirname(os.path.realpath(__file__))\n\tsubdir = \"/data/experimental/\"\n\tdatapath = here + subdir + \"Likert_Responses.csv\"\n\n\tdata = {}\n\tfirstline = True\n\twith open(datapath, 'r') as f:\n\t\tfor line in f:\n\t\t\tif firstline:\n\t\t\t\tfirstline = False\n\t\t\t\tcontinue\n\t\t\tvalues = line.split(',')\n\t\t\tinfo = values[1:len(values)]\n\t\t\tID = int(info[9])\n\t\t\tmethod = info[10]\n\t\t\tage = info[11]\n\t\t\tgender = info[12]\n\t\t\t# store age\n\t\t\tsubj_metrics[ID][method][0] = age\n\t\t\tsubj_metrics[ID][method][1] = gender\n\t\t\t# parse likert data\n\t\t\tfor i in range(8):\n\t\t\t\tsubj_metrics[ID][method][i+2] = info[i]\n\t\t\tsubj_metrics[ID][method][10] = info[14]\n\t\t\tsubj_metrics[ID][method][11] = info[15]\n\n\treturn subj_metrics","def count_score(self, docsInfo, avgdl, k1, b):\n\t\tdocScore = []\n\t\tfor doc in docsInfo:\n\t\t\tcurDocScore = 0\n\t\t\tfor queryWord in range(len(doc['meetCnt'])):\n\t\t\t\tTF = float(doc['meetCnt'][queryWord])\n\t\t\t\tfreaq = sum(1 for x in docsInfo if x['meetCnt'][queryWord])\n\t\t\t\tcurDocScore += self.count_IDF(len(docsInfo), freaq) * self.count_main_fraction(TF, k1, b, avgdl, doc['len'])\n\t\t\tdocScore.append(curDocScore)\n\t\treturn docScore","def _count_subscriptions(self):\n for partner in self:\n subscriptions = self.env['subscription.subscription']\n count = subscriptions.sudo().search_count([('partner_id', '=', partner.id)])\n for child in partner.child_ids:\n count += subscriptions.sudo().search_count([('partner_id', '=', child.id)])\n partner.subscriptions_count = count","def question_count_total(self, obj):\n return obj.questions.count()","def cubetest_per_topic(topic_truth, topic_result, gamma, max_height, cutoff):\n subtopic_num = topic_truth[1]\n topic_truth = topic_truth[0]\n\n subtopic_height = Counter() # current height of every subtopic\n subtopic_count = Counter() # #docs found relevant to every subtopic (nrels)\n\n weight_per_subtopic = 1.0 / subtopic_num\n\n def gain_per_doc(doc_no):\n if doc_no not in topic_truth:\n return 0\n gain = 0\n for subtopic_id, rating in topic_truth[doc_no].items():\n if subtopic_height[subtopic_id] < max_height:\n discount_height = (gamma ** (subtopic_count[subtopic_id] + 1)) * rating\n if discount_height + subtopic_height[subtopic_id] > max_height:\n discount_height = max_height - subtopic_height[subtopic_id]\n\n gain += weight_per_subtopic * discount_height\n # print(doc_no, subtopic_id,\"original_height\", rating, \"discount height\", discount_height)\n subtopic_height[subtopic_id] += discount_height\n subtopic_count[subtopic_id] += 1\n # print(doc_no, gain)\n return gain\n\n sorted_result = sorted(topic_result.items(), key=lambda x: x[0])\n time = 0.0\n total_gain = 0\n accu_gain = 0\n doc_num = 0\n for iter_num, doclist in sorted_result:\n if iter_num >= cutoff:\n break\n time += 1\n # gain_per_iteration = 0\n for doc_no in doclist:\n total_gain += gain_per_doc(doc_no)\n accu_gain += (total_gain / max_height / time)\n doc_num += 1\n\n # print(time)\n if time != 0:\n ct = total_gain / max_height / time\n else:\n ct = 0\n # print(doc_num)\n if doc_num > 0:\n act = accu_gain / doc_num\n else:\n act = 0\n # print( accu_gain , total_gain)\n return total_gain / max_height, ct, act","def part_one(answer_data):\n group_counts = []\n for answer_group in parse_groups(answer_data):\n group_counts.append(count_answer_set_union(answer_group))\n return sum(group_counts)","def _summarize_multiple_questions(self, data, id_prefix):\n type_info_dict = data['containedTypes']\n questions_list = []\n\n for instanceid, type_info in type_info_dict.iteritems():\n if isinstance(type_info, list):\n # This is a question group.\n mc_indices = [i for i in xrange(len(type_info))\n if type_info[i] == self.MC_QUESTION]\n questions_list += [{\n 'id': '%s.c.%s.i.%s' % (id_prefix, instanceid, index),\n 'score': data['individualScores'][instanceid][index],\n 'answers': data['answers'][instanceid][index]\n } for index in mc_indices if (\n data['answers'][instanceid][index])]\n\n elif (type_info == self.MC_QUESTION and\n data['answers'][instanceid]):\n # This is an individual multiple-choice question.\n questions_list += [{\n 'id': '%s.c.%s' % (id_prefix, instanceid),\n 'score': data['individualScores'][instanceid],\n 'answers': data['answers'][instanceid]\n }]\n\n return questions_list","def main_topic_doc(ldamodel, corpus=corpus): \n \n doc_topics = pd.DataFrame()\n\n for i, row in enumerate(ldamodel[corpus]):\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\n\n for j, (topic_num, prop_topic) in enumerate(row):\n if j == 0:\n wp = ldamodel.show_topic(topic_num)\n topic_keywords = \"' \".join([word for word, prop in wp])\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n else:\n break\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\n return doc_topics","def test_extract_topics_custom_cv():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n\n cv = CountVectorizer(ngram_range=(1, 2))\n model = BERTopic(vectorizer=cv)\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def count_ngrams(self, corpus):\n \n self.unigramcounts = defaultdict(int)\n self.bigramcounts = defaultdict(int)\n self.trigramcounts = defaultdict(int)\n\n self.sentence_counts = 0\n self.word_count = 0\n\n for line in corpus:\n sequence = line\n self.sentence_counts +=1\n\n unigrams = get_ngrams(sequence, n=1)\n for gram in unigrams:\n self.word_count += 1\n self.unigramcounts[gram] +=1\n\n bigrams = get_ngrams(sequence, n=2)\n for gram in bigrams:\n self.bigramcounts[gram] +=1\n\n trigrams = get_ngrams(sequence, n=3)\n for gram in trigrams:\n self.trigramcounts[gram] +=1\n\n #self.unigramcounts[('START')] = self.sentence_counts *2\n self.bigramcounts[('START', 'START')] = self.sentence_counts\n\n #return self","def __call__(self, example):\n para_counter = data.count_tokens(example['context_tokens'] if not self._iterate_over_example\n else [c for tkn in example['context_tokens'] for c in tkn])\n ques_counter = data.count_tokens(example['ques_tokens'] if not self._iterate_over_example\n else [c for tkn in example['ques_tokens'] for c in tkn])\n counter = para_counter + ques_counter\n return list(counter.items())","def test_topic_reduction_edge_cases():\n model = BERTopic()\n nr_topics = 5\n model.nr_topics = 100\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents)\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents)\n new_freq = model.get_topic_freq()\n\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\n pd.testing.assert_frame_equal(old_documents, new_documents)\n pd.testing.assert_frame_equal(old_freq, new_freq)","def build_intersection_matrix_of_subreddits(sub_count_list, top_sub_n=2000):\n sorted_keys = []\n for sub_counts in sub_count_list:\n sorted_keys.append(set(sorted(sub_counts, key=lambda x: sub_counts[x], reverse=True)[:top_sub_n]))\n \n int_list = []\n for i, s_key in enumerate(sorted_keys):\n temp_key_list = []\n temp_key_list.extend(sorted_keys[:i])\n temp_key_list.extend(sorted_keys[i+1:])\n temp_key_set = calculate_intersection(temp_key_list)\n sub_set = set(s_key)\n int_count = len(sub_set.intersection(temp_key_set))\n int_list.append(int_count)\n return int_list","def test_extract_topics_custom_cv(base_bertopic_custom_cv):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic_custom_cv._update_topic_size(documents)\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic_custom_cv.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def random_init(self, docs):\n for di in xrange(len(docs)):\n doc = docs[di]\n topics = np.random.randint(self.n_topic, size=len(doc))\n self.topic_assignment.append(topics)\n\n for wi in xrange(len(doc)):\n topic = topics[wi]\n word = doc[wi]\n self.TW[topic, word] += 1\n self.sum_T[topic] += 1\n self.DT[di, topic] += 1","def get_num_con_cat(con_by_sample, cat_by_sample):\r\n num_con_cat = defaultdict(float)\r\n num_con = 0\r\n for sample, connects in con_by_sample.items():\r\n sample_categories = cat_by_sample[sample]\r\n for s_con in connects:\r\n if s_con not in cat_by_sample.keys():\r\n continue\r\n for s_cat, con_cat in zip(sample_categories, cat_by_sample[s_con]):\r\n if s_cat == con_cat:\r\n num_con_cat[s_cat[0]] += 0.5\r\n num_con += 0.5\r\n\r\n return num_con_cat, num_con","def full_summarizer_word_comparison(sentences, topic_sentences, number_topics):\n\n word_counts = []\n\n for sentence in sentences:\n document_1_words = sentence.split()\n document_2_words = ''.join(topic_sentences).split()\n\n common_words = set(document_1_words).intersection(set(document_2_words))\n word_counts.append(len(common_words))\n\n return [j for i, j in sorted(list(zip(word_counts, sentences)), reverse=True)][0:number_topics]","def test_get_topics(self):\n\n for m in self.models:\n\n topics = m.topics\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertEqual(topics.num_rows(), 25)\n self.assertEqual(topics.num_columns(), 2)\n z = m.topics[\"topic_probabilities\"]\n for k in range(m.num_topics):\n self.assertTrue(\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\n \"Returned probabilities do not sum to 1.\",\n )\n\n # Make sure returned object is an SFrame of the right size\n topics = m.get_topics()\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertTrue(\n topics.num_columns() == 3,\n \"Returned SFrame should have a topic, word, and probs.\",\n )\n\n # Make sure that requesting a single topic returns only that topic\n num_words = 8\n topics = m.get_topics([5], num_words=num_words)\n self.assertTrue(\n all(topics[\"topic\"] == 5), \"Returned topics do not have the right id.\"\n )\n self.assertEqual(topics.num_rows(), num_words)\n topics = m.get_topics([2, 4], num_words=num_words)\n self.assertEqual(set(list(topics[\"topic\"])), set([2, 4]))\n self.assertEqual(topics.num_rows(), num_words + num_words)\n\n # Make sure the cumulative probability of the returned words is\n # is less than the cutoff we provided.\n # A cutoff of 1.0 should return num_words for every topic.\n cutoff = 1.0\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\n totals = topics.groupby(\n \"topic\", {\"total_score\": turicreate.aggregate.SUM(\"score\")}\n )\n self.assertTrue(\n all(totals[\"total_score\"] <= (cutoff + DELTA)),\n \"More words were returned than expected for this cutoff.\",\n )\n\n # Make sure we raise errors for bad input\n with self.assertRaises(ValueError):\n m.get_topics([-1])\n with self.assertRaises(ValueError):\n m.get_topics([10000])\n with self.assertRaises(ToolkitError):\n topics = m.get_topics(output_type=\"other\")\n\n # Test getting topic_words\n topic_words = m.get_topics(output_type=\"topic_words\", num_words=5)\n self.assertEqual(type(topic_words), turicreate.SFrame)\n\n # Test words are sorted correctly for the first topic\n # TODO: Make this more deterministic.\n\n # topic_probs = m.get_topics(num_words=5)\n # expected = [w for w in topic_probs['word'][:5]]\n # observed = topic_words['words'][0]\n # self.assertEqual(observed[0], expected[0])","def __len__(self):\n count = 0\n topics = set(six.iterkeys(self._topics))\n while topics:\n event_type = topics.pop()\n try:\n listeners = self._topics[event_type]\n count += len(listeners)\n except KeyError:\n pass\n return count","def get_n_per_feat(self, feat_subset=None):\n zipper = self.create_zipper(feat_subset)\n\n for key in zipper:\n # turns zipper values into lists storing the number of entries of the respective features per dataset\n zipper[key] = [len(z) for z in zipper[key]]\n\n counts = pd.DataFrame(zipper).transpose()\n counts.index.name = \"features\"\n counts.columns = self.df_names\n\n return counts","def listTopicRelevance(request):\n if request.method == 'GET':\n user = request.user;\n data = [];\n for topic in Topic.objects.all():\n row = {};\n\n topicSerializer = TopicNestedSerializer(topic)\n topicSerializer.Meta.depth = 1;\n #row['topic'] = topicSerializer.data;\n user_visits = topic.visits.filter(user=user)\n visitSerializer = VisitSerializer(user_visits, many=True)\n #visitSerializer.Meta.depth = 1;\n row['visit_count'] = len(user_visits);\n if row['visit_count'] > 0:\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\n else:\n row['last_visit'] = topic.created_at\n\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\n\n row['neighbor_visits_count'] = len(neighbor_visits);\n if row['neighbor_visits_count'] > 0:\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\n else:\n row['last_neighbor_visit'] = topic.created_at\n\n row['post_count'] = len(topic.posts.filter(user=user))\n row['like_count'] = len(topic.posts.filter(votes__user=user))\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\n row['recommendation'] = row['relevance_score'] + topic.hotness\n\n data.append(row)\n\n print(data)\n return Response(data)","def __rank_topics(self, found_topics, explanation):\n max_value = 0\n scores = []\n for _,topic in found_topics.items():\n topic[\"score\"] = topic[\"times\"] * len(topic['grams'].keys())\n scores.append(topic[\"score\"])\n if topic[\"score\"] > max_value:\n max_value = topic[\"score\"]\n\n for _,topic in found_topics.items():\n if \"syntactic\" in topic:\n topic[\"score\"] = max_value\n\n\n\n\n # Selection of unique topics\n unique_topics = {}\n for t_p,topic in found_topics.items():\n prim_label = self.cso.get_primary_label_wu(t_p)\n if prim_label in unique_topics:\n if unique_topics[prim_label] < topic[\"score\"]:\n unique_topics[prim_label] = topic[\"score\"]\n else:\n unique_topics[prim_label] = topic[\"score\"]\n\n # ranking topics by their score. High-scored topics go on top\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\n\n\n # perform\n vals = []\n for t_p in sort_t:\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\n\n\n #### suppressing some warnings that can be raised by the kneed library\n warnings.filterwarnings(\"ignore\")\n try:\n x_vals = range(1,len(vals)+1)\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\n if t_kn.knee is None:\n #print(\"I performed a different identification of knee\")\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\n except ValueError:\n pass\n\n ##################### Pruning\n\n try:\n knee = int(t_kn.knee)\n except TypeError:\n knee = 0\n except UnboundLocalError:\n knee = 0\n\n if knee > 5:\n try:\n knee += 0\n except TypeError:\n print(\"ERROR: \",t_kn.knee,\" \",knee, \" \", len(sort_t))\n\n else:\n try:\n if sort_t[0][1] == sort_t[4][1]:\n top = sort_t[0][1]\n test_topics = [item[1] for item in sort_t if item[1]==top]\n knee = len(test_topics)\n\n else:\n knee = 5\n except IndexError:\n knee = len(sort_t)\n\n final_topics = []\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\n self.reset_explanation()\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\n\n return final_topics","def subtype_counts(node_set, G, log=False):\n subtypes = Counter()\n for n in node_set:\n subtype = G.node[n]['subtype']\n subtypes[subtype] += 1\n\n if log:\n for k, v in subtypes.items():\n subtypes[k] = np.log10(v)\n \n return subtypes","def get_number_of_locations():\n count = 0\n tree = ET.parse('./devset_topics.xml')\n root = tree.getroot()\n for item in root.findall('./topic'):\n count = count + 1\n return count","def __find_topics(self, concepts):\n\n # Set up\n found_topics = dict() # to store the matched topics\n explanation = dict()\n\n # finding matches\n for concept in concepts:\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\n for grams in evgrams:\n gram = \"_\".join(grams)\n gram_without_underscore = \" \".join(grams)\n #### Finding similar words contained in the model\n\n list_of_matched_topics = []\n\n if self.fast_classification:\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\n else:\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\n\n\n for topic_item in list_of_matched_topics:\n\n topic = topic_item[\"topic\"]\n str_sim = topic_item[\"sim_t\"]\n wet = topic_item[\"wet\"]\n sim = topic_item[\"sim_w\"]\n\n\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\n\n\n if topic in found_topics:\n #tracking this match\n found_topics[topic][\"times\"] += 1\n\n found_topics[topic][\"gram_similarity\"].append(sim)\n\n #tracking the matched gram\n if gram in found_topics[topic][\"grams\"]:\n found_topics[topic][\"grams\"][gram] += 1\n else:\n found_topics[topic][\"grams\"][gram] = 1\n\n #tracking the most similar gram to the topic\n if str_sim > found_topics[topic][\"embedding_similarity\"]:\n found_topics[topic][\"embedding_similarity\"] = str_sim\n found_topics[topic][\"embedding_matched\"] = wet\n\n else:\n #creating new topic in the result set\n found_topics[topic] = {'grams': {gram:1},\n 'embedding_matched': wet,\n 'embedding_similarity': str_sim,\n 'gram_similarity':[sim],\n 'times': 1,\n 'topic':topic}\n\n\n\n if sim == 1:\n found_topics[topic][\"syntactic\"] = True\n\n\n\n primary_label_topic = self.cso.get_primary_label_wu(topic)\n if primary_label_topic not in explanation:\n explanation[primary_label_topic] = set()\n\n explanation[primary_label_topic].add(gram_without_underscore)\n\n return found_topics, explanation","def get_counts(data):\n\n word_count = {}\n syll_count = {}\n\n infile = data.corpus\n try:\n\n open_file = codecs.open(infile, 'r', encoding='utf-16')\n for line in open_file:\n line = line.lower()\n # Remove tablet indexing info and line numbers. Grab only text data\n line = line.split(',')\n text = clean_line(line[7])\n\n # Update the occurrences of the words in the line\n for word in text.split():\n count = word_count.setdefault(word, 0)\n word_count[word] = count + 1\n\n # Track occurrences of syllables\n update_syllable_count(word, syll_count)\n\n open_file.close()\n except IOError:\n print(\"Cannot open: \" + infile)\n\n return (word_count, syll_count)","def create_tag_frequencies(dataframe):\n from pyspark.sql.functions import desc\n from pyspark.sql.functions import col\n df_tags = dataframe.selectExpr(\"tag1 AS tag\").union(dataframe.selectExpr(\"tag2 AS tag\")).union(dataframe.selectExpr(\"tag3 AS tag\")) \\\n .union(dataframe.selectExpr(\"tag4 AS tag\")).union(dataframe.selectExpr(\"tag5 AS tag\"))\n df_tags = df_tags.na.drop(subset=[\"tag\"])\n tags_total_count = df_tags.count()\n print(\"Total number of tags used, including duplicates:\",tags_total_count)\n df_tag_freq = df_tags.groupBy(\"tag\").count().orderBy(desc(\"count\"))\n df_tag_freq = df_tag_freq.withColumn(\"frequency\", col(\"count\")/tags_total_count)\n df_tag_freq.orderBy(desc(\"frequency\")).show(20)","def connect_topic_id_to_topics(self, model):\n confidence = []\n for key, value in self.representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key, tp_num, val / len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n self.log_writer.add_log(\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\n conf[1],\n conf[2]))\n self.topic_indexes[conf[0]] = conf[1]\n\n for key, value in self.topic_indexes.items():\n self.topics_of_index[value] = [key]","def return_topic_figures(n_topics=5):\n\n ### import data ###\n\n data = return_keywords()\n data_for_topics = data[\"abstract_kw\"].apply(\n lambda x: list(ast.literal_eval(x).keys())\n )\n\n ### Build topic model ###\n\n # parameters\n n_topics = n_topics\n\n # Create Dictionary\n id2word = corpora.Dictionary(data_for_topics)\n\n # Create Corpus: Term Document Frequency\n corpus = [id2word.doc2bow(text) for text in data_for_topics]\n\n # Build LDA model\n lda_model = gensim.models.ldamodel.LdaModel(\n corpus=corpus,\n id2word=id2word,\n num_topics=n_topics,\n random_state=100,\n update_every=1,\n chunksize=10,\n passes=10,\n alpha=\"symmetric\",\n iterations=100,\n per_word_topics=True,\n )\n\n topics = lda_model.show_topics(formatted=False)\n data_flat = [w for w_list in data_for_topics for w in w_list]\n counter = Counter(data_flat)\n out = []\n for i, topic in topics:\n for word, weight in topic:\n out.append([word, i, weight, counter[word]])\n df = pd.DataFrame(out, columns=[\"word\", \"topic_id\", \"importance\", \"word_count\"])\n\n specs = np.full((ceil(n_topics / 2), 2), {\"secondary_y\": True})\n topic_bar_charts = make_subplots(\n rows=ceil(n_topics / 2),\n cols=2,\n specs=specs.tolist(),\n horizontal_spacing=0.1,\n vertical_spacing=0.15,\n )\n row, col = (0, 0)\n for topic in range(n_topics):\n if (topic % 2) != 0:\n col = 2\n else:\n col = 1\n row += 1\n color = px.colors.qualitative.Vivid[topic]\n topic_bar_charts.add_trace(\n go.Bar(\n x=df.loc[df.topic_id == topic, \"word\"],\n y=df.loc[df.topic_id == topic, \"word_count\"],\n width=0.5,\n opacity=0.3,\n marker_color=color,\n name=(\"Topic \" + str(topic) + \" word count\"),\n ),\n secondary_y=False,\n row=row,\n col=col,\n )\n topic_bar_charts.add_trace(\n go.Bar(\n x=df.loc[df.topic_id == topic, \"word\"],\n y=df.loc[df.topic_id == topic, \"importance\"],\n width=0.2,\n marker_color=color,\n name=(\"Topic \" + str(topic) + \" weight\"),\n ),\n secondary_y=True,\n row=row,\n col=col,\n )\n topic_bar_charts.update_layout(barmode=\"overlay\")\n\n topic_bar_charts.update_layout(\n height=800, width=1000, margin=dict(l=50, r=50, t=50, b=100)\n )\n\n # append all charts\n figures = [dict(data=topic_bar_charts)]\n\n return figures","def test_properties_count_group_by_group_by_and_sub_group_by_get(self):\n pass","def execQ2():\n # Put columns together\n frame = pan.DataFrame(data, columns=['Product', 'Amount'] )\n amount = frame.groupby(['Product']).count()\n return amount","def initialize(self):\n # Initializing the counter and distribution.\n for k in range(0, self.topic_number,1):\n self.topic_term_count_matrix[k]= [0.0] * self.term_number\n self.topic_distribution_over_term[k] = [0.0] * self.term_number\n self.sum_topic_by_term_count[k] = 0.0\n for m in range(0, self.document_number,1):\n self.document_topic_count_matrix[m] = [0.0] * self.topic_number\n self.document_distribution_over_topic[m] = [0.0] * self.topic_number\n self.sum_document_by_topic_count[m] = 0.0\n\n # Initializing topics assigned to all words of all documents.\n for m in range(0, self.document_number, 1):\n N = len(self.documents[m])\n self.word_topic_assignment[m] = [-1] * N\n for n in range(0, N,1):\n topic = int(random.uniform(0,1) * self.topic_number)\n self.document_topic_count_matrix[m][topic] += 1.0\n self.topic_term_count_matrix[topic][self.documents[m][n]] += 1.0\n self.sum_topic_by_term_count[topic] += 1.0\n self.word_topic_assignment[m][n] = topic\n self.sum_document_by_topic_count[m] = N","def solution_2(arr):\n total = 0\n for group in arr:\n group_list = []\n for person in group:\n group_list = group_list + person\n group_table = Counter(''.join(group_list))\n for k, v in group_table.items():\n if v == len(group):\n total += 1\n return total","def get_combined_topics(team):\n know1, know2 = team\n knowledge = int(know1, base=2) | int(know2, base=2)\n return bin(knowledge).count('1')","def count_by_tag(self, dataframe, tags):\r\n if tags and not dataframe['tags'].empty:\r\n data_to_return = []\r\n counter = 0\r\n for tag in tags:\r\n for datafield in dataframe['tags']:\r\n if tag in datafield:\r\n counter += 1\r\n data_to_return.append([tag, counter])\r\n counter = 0\r\n return pandas.DataFrame(data_to_return, columns=('TAG', 'TagCount'))","def initialize(self):\n self.n_words = len(self.vocab)\n self.n_docs = len(self.documents)\n\n # Initialize the three count matrices.\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\n self.nmz = np.zeros((self.n_docs, self.n_topics))\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\n self.nzw = np.zeros((self.n_topics, self.n_words))\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\n self.nz = np.zeros(self.n_topics)\n\n # Initialize the topic assignment dictionary.\n self.topics = {} # key-value pairs of form (m,i):z\n\n for m in range(self.n_docs):\n for i in self.documents[m]:\n # Get random topic assignment, i.e. z is a random integer in the range of topics\n z = np.random.randint(self.n_topics)\n # Increment count matrices\n self.nmz[m,z] += 1\n self.nzw[z,self.documents[m][i]] += 1\n self.nz[z] += 1\n # Store topic assignment\n self.topics[(m,i)] = z","def count_questions(txt):\n count = 0\n for c in txt:\n if c == '?':\n count += 1\n return count","def get_query_count(dataset_id, dataset_type, query_dataset_dict):\n if dataset_type == \"result_table\":\n return query_dataset_dict.get(dataset_id, 0)\n else:\n clean_rt_list = get_clean_rt(dataset_id)\n query_count = 0\n for each_rt in clean_rt_list:\n query_count += query_dataset_dict.get(each_rt, 0)\n return query_count","def getClassCounts(column, uniqueVal, decision, yes, no , total):\r\n dataDict = {} # a dictionary of labels\r\n for val in uniqueVal:\r\n label1 = val + '/Y'\r\n label2 = val + '/N'\r\n dataDict[label1] = 0; dataDict[label2] = 0\r\n for dec, at in zip(decision, column):\r\n if at == val and dec == 'No':\r\n dataDict[label2] += 1\r\n if at == val and dec == 'Yes':\r\n dataDict[label1] += 1\r\n dataDict[val] = (dataDict[label2]+ dataDict[label1])/ total\r\n dataDict[label2] = dataDict[label2] / no\r\n dataDict[label1] = dataDict[label1] / yes\r\n return dataDict","def run_compute_reread_counts(self):\n questions = []\n contexts = []\n student_data = self.responses[:]\n for response in student_data:\n if response.question.text not in questions:\n questions.append(response.question.text)\n if response.context.text not in contexts:\n contexts.append(response.context.text)\n\n compute_reread_counts_data = []\n\n for question in questions:\n for context in contexts:\n compute_reread_counts_data.append(self.compute_reread_counts(\n question, context))\n\n return compute_reread_counts_data","def cat_count(data, column_str, criteria):\r\n ct1 = []\r\n ct2 = []\r\n for i in range(len(find_cats_freq(data, column_str))):\r\n ct1.append(find_cats_freq(data[criteria], column_str)[i])\r\n ct2.append(find_cats_freq(data, column_str)[i])\r\n return np.array(ct1)/np.array(ct2)","def posts_per_topic_all(request, pk):\n #update is_expired in all posts\n update_posts_expiration()\n #get all posts with a certain topic\n posts = Post.objects.filter(topic=pk)\n serializer = ViewPostSerializer(posts, many=True)\n return Response(serializer.data)","def count(self) -> Tuple[groupable, pdarray]:\n repMsg = generic_msg(\n cmd=\"countReduction\",\n args={\"segments\": cast(pdarray, self.segments), \"size\": self.length},\n )\n self.logger.debug(repMsg)\n return self.unique_keys, create_pdarray(repMsg)","def display_topics(df, n_rows=10, n_cols=12):\n\n exemplar_scores, hovers = topic_exemplars(df)\n top_columns = sorted(range(len(exemplar_scores)),\n key=lambda i: exemplar_scores[i],\n reverse=True)[:n_cols]\n #I comented this line Im not 100% sure what was the purpuse of this\n # topics = df.pivot(index='pos', columns='topic',values='word*').replace([None], [''], regex=True)\n topics = df.pivot(index='pos', columns='topic',values='word*')\n\n topics_display = topics[top_columns].head(n_rows)\n\n return topics_display, top_columns","def __len__(self):\n # Header + string size + consumer group size\n size = self.HEADER_LEN + 2 + len(self.consumer_group)\n # + generation id + string size + consumer_id size + array length\n size += 4 + 2 + len(self.consumer_id) + 4\n for topic, parts in iteritems(self._reqs):\n # topic name + len(parts)\n size += 2 + len(topic) + 4\n # partition + offset + timestamp => for each partition\n size += (4 + 8 + 8) * len(parts)\n # metadata => for each partition\n for partition, (_, _, metadata) in iteritems(parts):\n size += 2 + len(metadata)\n return size","def get_num_cat(sample_by_cat, samples_in_otus):\r\n num_cat = defaultdict(int)\r\n for cat, samples in sample_by_cat.items():\r\n num_samples = len(set(samples_in_otus) & set(samples))\r\n num_cat[cat[0]] += (num_samples * (num_samples - 1)) / 2\r\n return num_cat","def get_total_partitions_for_topic(self, topic):\n return len(self.client.cluster.partitions_for_topic(topic))","def count_frequency(data: list) -> list:\n counts = [{'A': 0, 'C': 0, 'G': 0, 'T': 0} for _ in range(len(data[0][1]))]\n for i in range(len(data[0][1])):\n for datum in [datum[1] for datum in data]:\n counts[i][datum[i]] += 1\n return counts","def test_topic_reduction_edge_cases(base_bertopic):\n\n nr_topics = 5\n base_bertopic.nr_topics = 100\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(old_documents)\n c_tf_idf = base_bertopic._extract_topics(old_documents, topic_reduction=True)\n old_freq = base_bertopic.get_topics_freq()\n\n new_documents = base_bertopic._reduce_topics(old_documents, c_tf_idf)\n new_freq = base_bertopic.get_topics_freq()\n\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\n pd.testing.assert_frame_equal(old_documents, new_documents)\n pd.testing.assert_frame_equal(old_freq, new_freq)","def count_elements_in_dataset(dataset):\n return dataset.count()","def basic_statistics_of_email(data):\n word_counts = []\n character_count = 0\n\n for ind, row in data.iterrows():\n tokenizer = RegexpTokenizer(r'\\w+')\n real_words = tokenizer.tokenize(row[\"RawText\"].lower())\n\n character_count += sum(map(len, real_words))\n word_counts.append(len(real_words))\n\n return character_count, pd.Series(word_counts)","def part2(fname: dict) -> int:\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))","def get_topics(self):\n topics = self.word_topics\n return topics / topics.sum(axis=1)[:, None]","def analysis():\r\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\r\n data_frame.registerTempTable('business')\r\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\r\n\r\n def merge_count(a, b):\r\n r = {}\r\n for p, c in a.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n for p, c in b.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n return r\r\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\r\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\r\n\r\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\r\n print(result.take(10))\r\n print('----------------pron count-----------------')\r\n print(pron_count.collect())\r\n\r\n print(gd)","def _update_counts(self, msg, subtype, by):\n\n try:\n counts = self.get_local(msg, \"counts\")\n except KeyError:\n counts = defaultdict(int)\n\n counts['all'] += by\n counts[subtype] += by\n self.set_local(msg, \"counts\", counts)","def test_topic_model_generator_dimensions( ):\n N = 100\n D = 1000\n K = 10\n W = 100\n\n tm = TopicModel.generate( K, D )\n assert( tm.topics.shape == (D, K) )\n assert( tm.weights.shape == (K,) )\n\n docs = tm.sample( N, words = W )\n # Each document is a column\n assert( docs.shape == (N, D) ) \n # Each doc should have 100 words\n assert( sc.all(docs.sum(1) == W) )","def test_table_counts():\n number_of_test_run = 2 # Run the pipeline twice\n for i in range(number_of_test_run):\n dp = DataPipeline()\n dp.run()\n\n dp = DataPipeline()\n assert dp.get_product_count() == (500000,)\n assert dp.get_duplicate_count(from_table=\"products\") == (0,)\n assert dp.get_aggregate_table_result_count() == (222024, )\n 222024\n dp.close()"],"string":"[\n \"def topic_count():\\n # get the number topics and their counts as tuples: ('Topic', 123)\\n query = peewee.RawQuery(Post, \\\"select topic, count(topic) from post group by topic\\\").tuples()\\n\\n # turn the result of the query object into a list of tuples\\n tuple_result = []\\n for each_tuple in query:\\n tuple_result.append(each_tuple)\\n\\n # sort by the the second element, which is value, of each tuple in the list\\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\\n\\n # separate the topic and count into two lists for graphing purpose\\n topics = []\\n counts = []\\n\\n for each_tuple in tuple_result:\\n topics.append(each_tuple[0])\\n counts.append(each_tuple[1])\\n\\n return counts, topics\",\n \"def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\\n data_frame_topic = data_frame \\\\\\n .filter(data_frame[\\\"Stratification1\\\"].contains(\\\"Male\\\")) \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n\\n print(\\\"The following table represent the number of men group by the topic: \\\")\\n data_frame_topic.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def topic_stats(df_topic_sents_keywords):\\n\\n # Number of Documents for Each Topic\\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\\n\\n # Percentage of Documents for Each Topic\\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\\n\\n # Topic Number and Keywords\\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\\n\\n # Concatenate Column wise\\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\\n\\n # Change Column names\\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\\n\\n # Show\\n df_dominant_topics\",\n \"def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:\\n data_frame_topic = data_frame \\\\\\n .filter(data_frame[\\\"Stratification1\\\"].contains(\\\"Black, non-Hispanic\\\")) \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n\\n print(\\\"The following table represent the number of black ethnicity people group by the topic: \\\")\\n data_frame_topic.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def trainCount(\\n trainData, \\n questionType,\\n questionDict,\\n questionIdict, \\n objDict, \\n objIdict,\\n numAns):\\n count_wa = np.zeros((len(objIdict), numAns))\\n count_a = np.zeros((numAns))\\n objIds = extractObjId(\\n trainData[0], \\n questionType, \\n questionDict, \\n questionIdict)\\n for i in range(objIds.shape[0]):\\n objId = objIds[i]\\n obj = questionIdict[objId - 1]\\n ansId = trainData[1][i, 0]\\n objId2 = objDict[obj]\\n count_wa[objId2, ansId] += 1\\n count_a[ansId] += 1\\n # Add UNK count\\n count_a[-1] += 1\\n return count_wa, count_a\",\n \"def get_paper_counter_per_topic_id(all_topic_assignments):\\n counter = {}\\n for topic_assignment in all_topic_assignments:\\n for topic_index, topic_value in topic_assignment:\\n if topic_index not in counter:\\n counter[topic_index] = 0\\n\\n counter[topic_index] += 1\\n\\n return counter\",\n \"def count_topic_dist(self):\\n if len(self.representants) == 0:\\n self.log_writer(\\\"Representants not set. Cannot make topic dist.\\\")\\n return\\n for key, value in self.representants.items():\\n self.topic_distributions.append(len(value)/len(self.training_docs))\\n self.topic_numbers.append(key)\",\n \"def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\\n print(data_frame_pandas)\\n return data_frame_pandas\",\n \"def get_diseases(self):\\n self.diseases = self.data.groupby('topic')['topic'].count()\",\n \"def test_question_topics(self):\\n p = ProductFactory()\\n t1 = TopicFactory(slug='doesnotexist', product=p)\\n t2 = TopicFactory(slug='cookies', product=p)\\n t3 = TopicFactory(slug='sync', product=p)\\n\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t3)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n )\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def test_extract_topics():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model = BERTopic()\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def test_topics_for_products(self):\\n desktop_topics = topics_for(product=self.desktop)\\n eq_(len(desktop_topics), 3)\\n\\n mobile_topics = topics_for(product=self.mobile)\\n eq_(len(mobile_topics), 2)\",\n \"def test_extract_topics(base_bertopic):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(documents)\\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def assign_topics(self):\\n\\n # Load the dataset which has topic tags for each sentence\\n dt, dt_less = self.assign_topics_to_sentences()\\n \\n dt_copy = dt\\n \\n # Minimum number of tags needed to tag the overall response\\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\\n \\n # Final dataset with full survey response and its tags\\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\\n , 'num_sent': min\\n , 'min_num_tags': min\\n# , 'sentences': lambda x: \\\"%s\\\" % '. '.join(x)\\n , self.col_name: min})\\n final_dt.reset_index(level = 0, inplace = True)\\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\\n , axis = 1)\\n\\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\\n\\n return dt_copy, dt_less, final_dt, final_dt_less\",\n \"def iterate_data(self):\\n if \\\"single\\\" in self.dataset_name:\\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for answer_id in self.data:\\n summary = self.data[answer_id]['summary']\\n articles = self.data[answer_id]['articles']\\n question = self.data[answer_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(articles, 'article')\\n self._get_token_cnts(question, 'question')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if \\\"multi\\\" in self.dataset_name:\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for q_id in self.data:\\n summary = self.data[q_id]['summary']\\n question = self.data[q_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(question, 'question')\\n question = self.data[q_id]['question']\\n for answer_id in self.data[q_id]['articles']:\\n articles = self.data[q_id]['articles'][answer_id][0]\\n if args.tokenize:\\n self._get_token_cnts(articles, 'article')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if self.dataset_name == \\\"complete_dataset\\\":\\n self.stat_dict = {'urls': [], 'sites': []}\\n article_dict = {}\\n print(\\\"Counting answers, sites, unique urls, and tokenized counts of unique articles\\\")\\n answer_cnt = 0\\n for q_id in self.data:\\n for a_id in self.data[q_id]['answers']:\\n answer_cnt += 1\\n url = self.data[q_id]['answers'][a_id]['url']\\n article = self.data[q_id]['answers'][a_id]['article']\\n if url not in article_dict:\\n article_dict[url] = article\\n self.stat_dict['urls'].append(url)\\n assert \\\"//\\\" in url, url\\n site = url.split(\\\"//\\\")[1].split(\\\"/\\\")\\n self.stat_dict['sites'].append(site[0])\\n print(\\\"# of Answers:\\\", answer_cnt)\\n print(\\\"Unique articles: \\\", len(article_dict)) # This should match up with count written to file\\n self._write_stats(\\\"full collection\\\")\\n\\n # Get token/sent averages of unique articles\\n if args.tokenize:\\n self.stat_dict = {'article': [[], []]}\\n for a in article_dict:\\n self._get_token_cnts(article_dict[a], 'article')\\n self._write_stats(\\\"token_counts\\\")\",\n \"def so_data_statistics(data_file):\\n with open(data_file, \\\"r\\\") as f:\\n data = json.load(f)\\n\\n answer_to_num_questions = collections.Counter()\\n comment_to_num_questions = collections.Counter()\\n num_comments = 0\\n num_answers = 0\\n num_questions = len(data)\\n\\n for q in data:\\n q = json.loads(q)\\n q_comments = 0\\n q_comments += len(q[\\\"comments\\\"])\\n q_answers = len(q[\\\"answers\\\"])\\n for a in q[\\\"answers\\\"]:\\n q_comments += len(a[\\\"comments\\\"])\\n\\n answer_to_num_questions[q_answers] += 1\\n comment_to_num_questions[q_comments] += 1\\n\\n num_comments += q_comments\\n num_answers += q_answers\\n\\n print \\\"Num comments: {0}, Num answers: {1}, Num_questions: {2}\\\".format(\\n num_comments, num_answers, num_questions)\\n print \\\"-\\\" * 10\\n print \\\"Answers map: \\\", answer_to_num_questions\\n print \\\"Comments map: \\\", comment_to_num_questions\\n\\n return num_comments, num_answers, num_questions, answer_to_num_questions, \\\\\\n comment_to_num_questions\",\n \"def quiz_questions(topic):\\n try:\\n\\n # returns size of quiz question\\n current_list_size = len(topic)\\n score = 0\\n # loops through the passed topic questions list\\n for i in copy.deepcopy(topic):\\n print(i)\\n user = input('Enter here:\\\\t').lower()\\n # return correct if the given key question matches the its corresponding value i.e users answer\\n if topic[i].lower() == user:\\n print('correct\\\\n')\\n score += 1\\n else:\\n print(\\n f'Sorry that was incorrect. The correct answer I was looking for was {topic[i]}\\\\n') # if not correct display the correct answer\\n # del questions[topic][i]\\n\\n if score == current_list_size: # check to see if they answered all the questions correctly\\n print('Nice! you got all the answers correct!')\\n else:\\n # otherwise return score.\\n print(f'You got {score}/{str(current_list_size)} correct!')\\n except KeyError:\\n print('Something went wrong in quiz_questions(), check logic')\",\n \"def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\\n\\n for text_id, text in self.texts.items():\\n # single-word topics act a bit different (no zips or comprehensions)\\n # store data in self.topics, not zip_grams\\n for word in text['doc']:\\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\\n\\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\\n continue\\n\\n if not (word.pos in self.nouns or word.ent_type in self.entities):\\n continue\\n\\n if word_lemma in self.topics:\\n self.topics[word_lemma][\\\"count\\\"] += 1\\n self.topics[word_lemma][\\\"textIDs\\\"] |= {text_id}\\n self.topics[word_lemma][\\\"verbatims\\\"] |= {word.text.lower()}\\n else:\\n self.topics[word_lemma] = {\\\"name\\\": word_lemma,\\n \\\"count\\\": 1,\\n \\\"textIDs\\\": {text_id},\\n \\\"verbatims\\\": {word.text.lower()},\\n \\\"subtopics\\\": {}}\\n\\n # Populate self.ngrams and self.topics\\n for text_id, text in self.texts.items():\\n doc = text['doc']\\n\\n # Find pentagrams - ngrams with 5 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\\n self._ngram_counter(ngram, 5, text_id, doc)\\n\\n # Find pentagrams - ngrams with 4 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\\n self._ngram_counter(ngram, 4, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:], doc[2:]):\\n self._ngram_counter(ngram, 3, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:]):\\n self._ngram_counter(ngram, 2, text_id, doc)\\n\\n\\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\\n # but it's only mentioned in 3 different texts, we'd show 3.\\n for _, topic in self.topics.items():\\n topic['textIDCount'] = len(topic['textIDs'])\\n for _, ngram in self.ngrams.items():\\n ngram['textIDCount'] = len(ngram['textIDs'])\\n\\n # Eliminate rarely occurring topics and ngrams.\\n self.topics = {k: v for k, v in self.topics.items() if\\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\\n self.ngrams = {k: v for k, v in self.ngrams.items() if\\n v['textIDCount'] >= min_text_id_count}\\n\\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\\n for ngram_lemma, ngram in self.ngrams.items():\\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\\n # r'\\\\b' + ngram_lemma + r'\\\\b' --> does the ngram lemma fit in ngram_plus lemma (\\\\b is word boundary)\\n if ngram['n'] + 1 != ngram_plus['n']:\\n continue\\n\\n if not re.search(r'\\\\b' + ngram_lemma + r'\\\\b', ngram_plus_lemma):\\n continue\\n\\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\\n # instead of the other loop?\\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\\n ngram['count'] = -1\\n\\n # Eliminate newly demoted items\\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}\",\n \"def export_topics(self):\\n\\n # format as a list (for json output), then sort descending by textIDCount\\n topics = [{'name': topic['name'], 'count': topic['count'],\\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\\n 'children': '' if 'children' not in topic else topic['children']}\\n for topic_id, topic in self.topics.items()]\\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\\n\\n for i, topic in enumerate(topics):\\n # Note that 'rank' is from topic, not child.\\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\\n 'textIDCount': child['textIDCount']}\\n for _, child in topic['children'].items()]\\n\\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\\n\\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\\n child_count = sum([child['textIDCount'] for child in topic['children']])\\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\\n\\n for child in topic['children']:\\n child['size'] = child['textIDCount'] * child_count_multiplier\\n\\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\\n\\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\\n self.model_output[\\\"children\\\"] = [topic for topic in topics]\\n\\n # Build file name and save\\n if self.data_date:\\n date = datetime.strptime(self.data_date, \\\"%Y-%m-%d\\\").strftime('%d') # from YYYY-MM-DD to DD\\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\\n else:\\n file_name = '{}-Topics.txt'.format(self.corpus_name)\\n\\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\\n json.dump(self.model_output, file)\",\n \"def get_questions_of_topic(topic):\\n\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n fe = Attr(\\\"TopicId\\\").eq(topic.get(\\\"TopicId\\\"))\\n response = question_table.scan(FilterExpression=fe)\\n questions = response.get(\\\"Items\\\")\\n return questions\",\n \"def get_n_questions(self):\\n return self.df.question.nunique()\",\n \"def test_topic_reduction(reduced_topics):\\n model = BERTopic()\\n nr_topics = reduced_topics + 2\\n model.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents.copy())\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents.copy())\\n new_freq = model.get_topic_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(model.mapped_topics, dict)\\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\\n assert model.mapped_topics\",\n \"def check_intersections(db, topics, papers_by_topic):\\n\\n\\t# Print the distribution of \\\"number of topics\\\"\\n\\tnum_subjects = []\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tnum_subjects.append(len(p.subject))\\n\\t\\telse:\\n\\t\\t\\tnum_subjects.append(0)\\n\\tnum_subjects = np.array(num_subjects)\\n\\n\\tfor i in range(np.max(num_subjects)+1):\\n\\t\\tprint(\\\"Number of papers with\\\", i, \\\"topics:\\\", \\n\\t\\t\\tlen(np.where(num_subjects==i)[0]))\\n\\n\\t# Figure out what's going on with triple-tagged guys (nothing weird)\\n\\t\\\"\\\"\\\"\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tif len(p.subject) > 2:\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tfor topic, topic_words in topics.items():\\n\\t\\t\\t\\t\\tprint(\\\"\\\\tCheck against '\\\" + topic + \\\"':\\\")\\n\\t\\t\\t\\t\\tfor journal in p.container_title:\\n\\t\\t\\t\\t\\t\\tcheck_words(journal, topic_words, verbose=True)\\n\\t\\\"\\\"\\\"\\n\\n\\t# Look in more detail at double-tagged guysfor p_hash, p in db.all_papers.items():\\n\\tcombos = defaultdict(int)\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tif len(p.subject) == 2:\\n\\t\\t\\t\\tcombos[frozenset(p.subject)] += 1\\n\\t\\t\\t\\t#print(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\tif p.subject == {'Computer Science', 'Biology'}:\\n\\t\\t\\t\\t\\t#print(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title)#,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tbio_words = set()\\n\\t\\t\\t\\t\\tCS_words = set()\\n\\t\\t\\t\\t\\tfor journal in p.container_title:\\n\\t\\t\\t\\t\\t\\tfor word in topics['Biology']:\\n\\t\\t\\t\\t\\t\\t\\tif journal.find(word) >= 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tbio_words.add(word)\\n\\t\\t\\t\\t\\t\\tfor word in topics['Computer Science']:\\n\\t\\t\\t\\t\\t\\t\\tif journal.find(word) >= 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tCS_words.add(word)\\n\\n\\t\\t\\t\\t\\t#print(\\\"\\\\tBiology words:\\\", bio_words)\\n\\t\\t\\t\\t\\t#print(\\\"\\\\tCS words:\\\", CS_words)\\n\\t\\n\\tfor k, v in combos.items():\\n\\t\\tprint(k, v)\",\n \"def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)\",\n \"def assign_topics_to_sentences(self):\\n \\n\\n # 10 topics\\n topic_dict = {0: 'academics'\\n , 1: 'career'\\n , 2: 'commute'\\n , 3: 'diversity'\\n , 4: 'community'\\n , 5: 'extracurricular'\\n , 6: 'facilities'\\n , 7: 'finance'\\n , 8: 'housing'\\n , 9: 'wellness'\\n }\\n\\n # Some important words that should be included under each topic\\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\\n , 'degree', 'research', 'elective'\\n , 'professor', 'project', 'scholarship', 'knowledge']\\n , ['career', 'job', 'coop', 'employment']\\n , ['commute', 'skytrain', 'transport', 'commuter']\\n , ['diversity', 'diverse', 'background']\\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\\n , ['extracurricular', 'club', 'sport', 'activity']\\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\\n , ['finance', 'tuition', 'expensive']\\n , ['housing', 'live', 'residence']\\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\\n\\n # Read the data - id and reponse column\\n dt = pd.read_csv(self.path_data_col\\n , encoding = \\\"ISO-8859-1\\\"\\n , usecols = [self.id_col_name, self.col_name])\\n\\n\\n \\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n \\n # Split into sentences\\n dt['sentences'] = self.getSentences(dt[self.col_name])\\n \\n \\n \\n\\n \\n \\n\\n # Store number of sentences in each response as a column\\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\\n\\n # Split each row into multiple rows - one row for each sentence\\n dt = (dt\\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\\n .apply(pd.Series)\\n .stack()\\n .reset_index()\\n .drop('level_3', axis = 1)\\n .rename(columns = {0:'sentences'}))\\n\\n\\n # Clean the sentences\\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\\n\\n # Remove useless sentences\\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\\n\\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n\\n # Tokenize words in the cleaned sentences\\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\\n\\n\\n # Call the lexicon function\\n topic_lexicons = self.prepare_lexicons()\\n\\n # Lists to store results\\n count_topic_all = []\\n actual_topic_all = []\\n\\n # Tag each response into a topic\\n for response in responses:\\n\\n count_topic = []\\n actual_topic = []\\n\\n for topic in topic_lexicons:\\n\\n # Count occurance of each word in word stock in the response\\n temp = sum(dict((x, response.count(x)) for x in topic).values())\\n count_topic.append(temp)\\n\\n\\n for index, value in enumerate(count_topic):\\n\\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\\n if value > 0:\\n actual_topic.append(topic_dict[index])\\n\\n\\n # If more than 3 topics are tagged for single sentence, refine by increasing\\n # cutoff to at least 2 words instead of 1\\n if len(actual_topic) > 3:\\n\\n actual_topic = []\\n for index, value in enumerate(count_topic):\\n\\n if value > 1: # Increase cutoff\\n actual_topic.append(topic_dict[index])\\n\\n count_topic_all.append(count_topic)\\n actual_topic_all.append(actual_topic)\\n\\n\\n dt['tags'] = actual_topic_all\\n dt['num_tags'] = count_topic_all\\n\\n\\n # Select only the most important columns\\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\\n\\n return dt, dt_less\",\n \"def group_topics(sent_topics_sorteddf):\\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0).sum()\\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\\n plt.title('Topic Share %');\\n\\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]\",\n \"def __len__(self):\\n size = self.HEADER_LEN + 2 + 4 + 4 # acks + timeout + len(topics)\\n for topic, parts in iteritems(self.msets):\\n # topic name\\n size += 2 + len(topic) + 4 # topic name + len(parts)\\n # partition + mset size + len(mset)\\n size += sum(4 + 4 + len(mset) for mset in itervalues(parts))\\n return size\",\n \"def counts_table(data, attr):\\n pd.options.mode.chained_assignment = None # default='warn'\\n # gets class names for dataframe manipulation\\n classes = attr.tail(1)['vars'].tolist()\\n classlist = [classes[0][0], classes[0][1]]\\n # expanding a table to have all variable options in a column with their \\n # parent attribute\\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\\n allvariables.name='var'\\n allvariables = attr.drop('vars', axis=1).join(allvariables)\\n av = allvariables.drop(attr.index[-1])\\n # populate the table with counts\\n for c in classlist:\\n clist = []\\n count = 0\\n for i, row in av.iterrows():\\n att = row['attr']\\n var = row['var']\\n sub = data[[att,'class']]\\n sub = sub[sub[att]==var]\\n if not sub.empty:\\n ssub = sub[sub['class']==c]\\n if not ssub.empty:\\n count = len(ssub)\\n else:\\n count = 0\\n clist.append(count)\\n av[c] = clist\\n\\n return av\",\n \"def total_exs(dataset):\\n total = 0\\n for article in dataset['data']:\\n for para in article['paragraphs']:\\n total += len(para['qas'])\\n return total\",\n \"def document_distribution_per_topic(df_dominant_topic):\\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\\n\\n\\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\\n\\n for i, ax in enumerate(axes.flatten()):\\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\\n doc_lens = [len(d) for d in df_dominant_topic.Text]\\n ax.hist(doc_lens, bins = 1000, color=cols[i])\\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\\n sns.kdeplot(doc_lens, color=\\\"black\\\", shade=False, ax=ax.twinx())\\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\\n ax.set_ylabel('Number of Documents', color=cols[i])\\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\\n\\n fig.tight_layout()\\n fig.subplots_adjust(top=0.90)\\n plt.xticks(np.linspace(0,1000,9))\\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\\n plt.show()\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = {} # might want to use defaultdict or Counter instead\\n self.bigramcounts = {} \\n self.trigramcounts = {} \\n\\n self.total = 2\\n ##Your code here\\n\\n for sentence in corpus:\\n temp_1 = get_ngrams(sentence,1)\\n temp_2 = get_ngrams(sentence,2)\\n temp_3 = get_ngrams(sentence,3)\\n for i in range(len(temp_1)):\\n if temp_1[i] in self.unigramcounts:\\n self.unigramcounts[temp_1[i]] += 1\\n else:\\n self.unigramcounts[temp_1[i]] = 1\\n self.total += 1\\n\\n for i in range(len(temp_2)):\\n if temp_2[i] in self.bigramcounts:\\n self.bigramcounts[temp_2[i]] += 1\\n else:\\n self.bigramcounts[temp_2[i]] = 1\\n\\n for i in range(len(temp_3)):\\n if temp_3[i] in self.trigramcounts:\\n self.trigramcounts[temp_3[i]] += 1\\n else:\\n self.trigramcounts[temp_3[i]] = 1\\n return\",\n \"def _initialize(self):\\n for doc_index, doc in enumerate(self.document):\\n temp_word_topic_matrix = []\\n for word in doc:\\n if word in self.word2id.keys():\\n start_topic_index = np.random.randint(0, self.K)\\n temp_word_topic_matrix.append(start_topic_index)\\n self.doc_topic_matrix[doc_index, start_topic_index] += 1\\n self.topic_word_matrix[start_topic_index, self.word2id[word]] += 1\\n self.topic_matrix[start_topic_index] += 1\\n self.current_word_topic_matrix.append(temp_word_topic_matrix)\",\n \"def count_mentioned_pol_figures(data):\\n figures_mentioned = {}\\n figures = get_political_figures()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for figure in figures:\\n if figure + \\\" \\\" in (subject_words + message_words):\\n if figure in figures_mentioned:\\n figures_mentioned[figure] += 1\\n else:\\n figures_mentioned[figure] = 0\\n\\n return pd.DataFrame(figures_mentioned)\",\n \"def __len__(self):\\n # Header + consumer group + len(topics)\\n size = self.HEADER_LEN + 2 + len(self.consumer_group) + 4\\n for topic, parts in iteritems(self._reqs):\\n # topic name + len(parts)\\n size += 2 + len(topic) + 4\\n # partition => for each partition\\n size += 4 * len(parts)\\n return size\",\n \"def __len__(self):\\n # Header + replicaId + len(topics)\\n size = self.HEADER_LEN + 4 + 4\\n for topic, parts in iteritems(self._reqs):\\n # topic name + len(parts)\\n size += 2 + len(topic) + 4\\n # partition + fetch offset + max bytes => for each partition\\n size += (4 + 8 + 4) * len(parts)\\n return size\",\n \"def subtype_occurences(self):\\n\\n subtype_counts = Counter()\\n\\n for seqkey,seqs in self.seqs.iteritems():\\n for seq,seqentry in seqs.iteritems():\\n\\n subtype_counts[seqentry['subtype']] += 1\\n\\n return subtype_counts\",\n \"def analyze_count(data):\\n\\n dsct_vk = pd.unique(data['vk'])\\n dsct_itemid = pd.unique(data['itemid'])\\n\\n print 'number of user:', dsct_vk.shape\\n print 'number of items:', dsct_itemid.shape\\n print 'the number of ratings:', data.shape\\n\\n print 'unique actions:', pd.unique(data['action'])\\n print 'the number of action 0:', np.sum(data['action'] == 0)\\n print 'the number of action 1:', np.sum(data['action'] == 1)\\n print 'the number of action 2:', np.sum(data['action'] == 2)\\n print 'the number of action 3:', np.sum(data['action'] == 3)\\n print 'the number of action 4:', np.sum(data['action'] == 4)\\n \\n time_range_item = data.groupby('itemid')['real_time'].aggregate(sum_unique)\\n print 'Max Range:', np.max(time_range_item)\\n print 'Mean Range:', np.mean(time_range_item)\\n print 'Median Range:', np.median(time_range_item)\",\n \"def get_topics_count(khoros_object, user_settings=None, user_id=None, login=None, email=None):\\n user_settings = _process_settings_and_user_id(khoros_object, user_settings, user_id, login, email)\\n return _get_count(khoros_object, user_settings['id'], 'topics')\",\n \"def test_topic_reduction(reduced_topics):\\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\\n nr_topics = reduced_topics + 2\\n base_bertopic.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(old_documents)\\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\\n old_freq = base_bertopic.get_topics_freq()\\n\\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\\n new_freq = base_bertopic.get_topics_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(base_bertopic.mapped_topics, dict)\\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\\n assert base_bertopic.mapped_topics\",\n \"def uncategorized(df):\\n\\n counter = 0\\n for movie in df.index:\\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\\\\n df.loc[movie, 'Political'] == 0:\\n counter += 1\\n\\n return counter\",\n \"def count_mentioned_countries(data):\\n countries_mentioned = {}\\n countries = get_countries()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for country in countries:\\n if country in (subject_words + message_words):\\n if country in countries_mentioned:\\n countries_mentioned[country] += 1\\n else:\\n countries_mentioned[country] = 1\\n\\n return pd.DataFrame.from_dict(countries_mentioned, orient=\\\"index\\\")\",\n \"def subtrace_count(self, trace, subtrace):\\n if len(subtrace) == 0:\\n return 0\\n\\n count = 0\\n\\n tr = list(map(lambda ac: self.activity_concept_name(ac), trace))\\n\\n for index in range(len(tr) - len(subtrace) + 1):\\n slice = tr[index:index + len(subtrace)]\\n\\n if subtrace == slice:\\n count += 1\\n\\n return count\",\n \"def validate_new_curriculum_topics(self, curriculum_topics):\\n\\n for cur in curriculum_topics:\\n # check to make sure its in the general topics table\\n self.db_cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM Topic WHERE name = %s\\\"\\\"\\\", (cur,))\\n ct = self.db_cursor.fetchone()\\n ct = ct[0]\\n if ct == 0:\\n print(\\\"topic does not exist, we must create new one or cancel\\\") # todo\\n\\n return True\",\n \"def compute_subj_metrics():\\n\\t# each participant does task 1,2,3 with method A,B = 3*2*N\\n\\t# likert data includes age, gender, Q1 - Q10 = 2+10\\n\\n\\t# set up data structure\\n\\tsubj_metrics = {}\\n\\tfor ID in range(10):\\n\\t\\tfor method in [\\\"A\\\",\\\"B\\\"]:\\n\\t\\t\\t# sanity checks\\n\\t\\t\\tif ID not in subj_metrics:\\n\\t\\t\\t\\tsubj_metrics[ID] = {}\\n\\t\\t\\tif method not in subj_metrics[ID]:\\n\\t\\t\\t\\tsubj_metrics[ID][method] = [None]*12\\n\\n\\there = os.path.dirname(os.path.realpath(__file__))\\n\\tsubdir = \\\"/data/experimental/\\\"\\n\\tdatapath = here + subdir + \\\"Likert_Responses.csv\\\"\\n\\n\\tdata = {}\\n\\tfirstline = True\\n\\twith open(datapath, 'r') as f:\\n\\t\\tfor line in f:\\n\\t\\t\\tif firstline:\\n\\t\\t\\t\\tfirstline = False\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tvalues = line.split(',')\\n\\t\\t\\tinfo = values[1:len(values)]\\n\\t\\t\\tID = int(info[9])\\n\\t\\t\\tmethod = info[10]\\n\\t\\t\\tage = info[11]\\n\\t\\t\\tgender = info[12]\\n\\t\\t\\t# store age\\n\\t\\t\\tsubj_metrics[ID][method][0] = age\\n\\t\\t\\tsubj_metrics[ID][method][1] = gender\\n\\t\\t\\t# parse likert data\\n\\t\\t\\tfor i in range(8):\\n\\t\\t\\t\\tsubj_metrics[ID][method][i+2] = info[i]\\n\\t\\t\\tsubj_metrics[ID][method][10] = info[14]\\n\\t\\t\\tsubj_metrics[ID][method][11] = info[15]\\n\\n\\treturn subj_metrics\",\n \"def count_score(self, docsInfo, avgdl, k1, b):\\n\\t\\tdocScore = []\\n\\t\\tfor doc in docsInfo:\\n\\t\\t\\tcurDocScore = 0\\n\\t\\t\\tfor queryWord in range(len(doc['meetCnt'])):\\n\\t\\t\\t\\tTF = float(doc['meetCnt'][queryWord])\\n\\t\\t\\t\\tfreaq = sum(1 for x in docsInfo if x['meetCnt'][queryWord])\\n\\t\\t\\t\\tcurDocScore += self.count_IDF(len(docsInfo), freaq) * self.count_main_fraction(TF, k1, b, avgdl, doc['len'])\\n\\t\\t\\tdocScore.append(curDocScore)\\n\\t\\treturn docScore\",\n \"def _count_subscriptions(self):\\n for partner in self:\\n subscriptions = self.env['subscription.subscription']\\n count = subscriptions.sudo().search_count([('partner_id', '=', partner.id)])\\n for child in partner.child_ids:\\n count += subscriptions.sudo().search_count([('partner_id', '=', child.id)])\\n partner.subscriptions_count = count\",\n \"def question_count_total(self, obj):\\n return obj.questions.count()\",\n \"def cubetest_per_topic(topic_truth, topic_result, gamma, max_height, cutoff):\\n subtopic_num = topic_truth[1]\\n topic_truth = topic_truth[0]\\n\\n subtopic_height = Counter() # current height of every subtopic\\n subtopic_count = Counter() # #docs found relevant to every subtopic (nrels)\\n\\n weight_per_subtopic = 1.0 / subtopic_num\\n\\n def gain_per_doc(doc_no):\\n if doc_no not in topic_truth:\\n return 0\\n gain = 0\\n for subtopic_id, rating in topic_truth[doc_no].items():\\n if subtopic_height[subtopic_id] < max_height:\\n discount_height = (gamma ** (subtopic_count[subtopic_id] + 1)) * rating\\n if discount_height + subtopic_height[subtopic_id] > max_height:\\n discount_height = max_height - subtopic_height[subtopic_id]\\n\\n gain += weight_per_subtopic * discount_height\\n # print(doc_no, subtopic_id,\\\"original_height\\\", rating, \\\"discount height\\\", discount_height)\\n subtopic_height[subtopic_id] += discount_height\\n subtopic_count[subtopic_id] += 1\\n # print(doc_no, gain)\\n return gain\\n\\n sorted_result = sorted(topic_result.items(), key=lambda x: x[0])\\n time = 0.0\\n total_gain = 0\\n accu_gain = 0\\n doc_num = 0\\n for iter_num, doclist in sorted_result:\\n if iter_num >= cutoff:\\n break\\n time += 1\\n # gain_per_iteration = 0\\n for doc_no in doclist:\\n total_gain += gain_per_doc(doc_no)\\n accu_gain += (total_gain / max_height / time)\\n doc_num += 1\\n\\n # print(time)\\n if time != 0:\\n ct = total_gain / max_height / time\\n else:\\n ct = 0\\n # print(doc_num)\\n if doc_num > 0:\\n act = accu_gain / doc_num\\n else:\\n act = 0\\n # print( accu_gain , total_gain)\\n return total_gain / max_height, ct, act\",\n \"def part_one(answer_data):\\n group_counts = []\\n for answer_group in parse_groups(answer_data):\\n group_counts.append(count_answer_set_union(answer_group))\\n return sum(group_counts)\",\n \"def _summarize_multiple_questions(self, data, id_prefix):\\n type_info_dict = data['containedTypes']\\n questions_list = []\\n\\n for instanceid, type_info in type_info_dict.iteritems():\\n if isinstance(type_info, list):\\n # This is a question group.\\n mc_indices = [i for i in xrange(len(type_info))\\n if type_info[i] == self.MC_QUESTION]\\n questions_list += [{\\n 'id': '%s.c.%s.i.%s' % (id_prefix, instanceid, index),\\n 'score': data['individualScores'][instanceid][index],\\n 'answers': data['answers'][instanceid][index]\\n } for index in mc_indices if (\\n data['answers'][instanceid][index])]\\n\\n elif (type_info == self.MC_QUESTION and\\n data['answers'][instanceid]):\\n # This is an individual multiple-choice question.\\n questions_list += [{\\n 'id': '%s.c.%s' % (id_prefix, instanceid),\\n 'score': data['individualScores'][instanceid],\\n 'answers': data['answers'][instanceid]\\n }]\\n\\n return questions_list\",\n \"def main_topic_doc(ldamodel, corpus=corpus): \\n \\n doc_topics = pd.DataFrame()\\n\\n for i, row in enumerate(ldamodel[corpus]):\\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\\n\\n for j, (topic_num, prop_topic) in enumerate(row):\\n if j == 0:\\n wp = ldamodel.show_topic(topic_num)\\n topic_keywords = \\\"' \\\".join([word for word, prop in wp])\\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\\n else:\\n break\\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\\n return doc_topics\",\n \"def test_extract_topics_custom_cv():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n\\n cv = CountVectorizer(ngram_range=(1, 2))\\n model = BERTopic(vectorizer=cv)\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = defaultdict(int)\\n self.bigramcounts = defaultdict(int)\\n self.trigramcounts = defaultdict(int)\\n\\n self.sentence_counts = 0\\n self.word_count = 0\\n\\n for line in corpus:\\n sequence = line\\n self.sentence_counts +=1\\n\\n unigrams = get_ngrams(sequence, n=1)\\n for gram in unigrams:\\n self.word_count += 1\\n self.unigramcounts[gram] +=1\\n\\n bigrams = get_ngrams(sequence, n=2)\\n for gram in bigrams:\\n self.bigramcounts[gram] +=1\\n\\n trigrams = get_ngrams(sequence, n=3)\\n for gram in trigrams:\\n self.trigramcounts[gram] +=1\\n\\n #self.unigramcounts[('START')] = self.sentence_counts *2\\n self.bigramcounts[('START', 'START')] = self.sentence_counts\\n\\n #return self\",\n \"def __call__(self, example):\\n para_counter = data.count_tokens(example['context_tokens'] if not self._iterate_over_example\\n else [c for tkn in example['context_tokens'] for c in tkn])\\n ques_counter = data.count_tokens(example['ques_tokens'] if not self._iterate_over_example\\n else [c for tkn in example['ques_tokens'] for c in tkn])\\n counter = para_counter + ques_counter\\n return list(counter.items())\",\n \"def test_topic_reduction_edge_cases():\\n model = BERTopic()\\n nr_topics = 5\\n model.nr_topics = 100\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents)\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents)\\n new_freq = model.get_topic_freq()\\n\\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\\n pd.testing.assert_frame_equal(old_documents, new_documents)\\n pd.testing.assert_frame_equal(old_freq, new_freq)\",\n \"def build_intersection_matrix_of_subreddits(sub_count_list, top_sub_n=2000):\\n sorted_keys = []\\n for sub_counts in sub_count_list:\\n sorted_keys.append(set(sorted(sub_counts, key=lambda x: sub_counts[x], reverse=True)[:top_sub_n]))\\n \\n int_list = []\\n for i, s_key in enumerate(sorted_keys):\\n temp_key_list = []\\n temp_key_list.extend(sorted_keys[:i])\\n temp_key_list.extend(sorted_keys[i+1:])\\n temp_key_set = calculate_intersection(temp_key_list)\\n sub_set = set(s_key)\\n int_count = len(sub_set.intersection(temp_key_set))\\n int_list.append(int_count)\\n return int_list\",\n \"def test_extract_topics_custom_cv(base_bertopic_custom_cv):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic_custom_cv._update_topic_size(documents)\\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic_custom_cv.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def random_init(self, docs):\\n for di in xrange(len(docs)):\\n doc = docs[di]\\n topics = np.random.randint(self.n_topic, size=len(doc))\\n self.topic_assignment.append(topics)\\n\\n for wi in xrange(len(doc)):\\n topic = topics[wi]\\n word = doc[wi]\\n self.TW[topic, word] += 1\\n self.sum_T[topic] += 1\\n self.DT[di, topic] += 1\",\n \"def get_num_con_cat(con_by_sample, cat_by_sample):\\r\\n num_con_cat = defaultdict(float)\\r\\n num_con = 0\\r\\n for sample, connects in con_by_sample.items():\\r\\n sample_categories = cat_by_sample[sample]\\r\\n for s_con in connects:\\r\\n if s_con not in cat_by_sample.keys():\\r\\n continue\\r\\n for s_cat, con_cat in zip(sample_categories, cat_by_sample[s_con]):\\r\\n if s_cat == con_cat:\\r\\n num_con_cat[s_cat[0]] += 0.5\\r\\n num_con += 0.5\\r\\n\\r\\n return num_con_cat, num_con\",\n \"def full_summarizer_word_comparison(sentences, topic_sentences, number_topics):\\n\\n word_counts = []\\n\\n for sentence in sentences:\\n document_1_words = sentence.split()\\n document_2_words = ''.join(topic_sentences).split()\\n\\n common_words = set(document_1_words).intersection(set(document_2_words))\\n word_counts.append(len(common_words))\\n\\n return [j for i, j in sorted(list(zip(word_counts, sentences)), reverse=True)][0:number_topics]\",\n \"def test_get_topics(self):\\n\\n for m in self.models:\\n\\n topics = m.topics\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertEqual(topics.num_rows(), 25)\\n self.assertEqual(topics.num_columns(), 2)\\n z = m.topics[\\\"topic_probabilities\\\"]\\n for k in range(m.num_topics):\\n self.assertTrue(\\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\\n \\\"Returned probabilities do not sum to 1.\\\",\\n )\\n\\n # Make sure returned object is an SFrame of the right size\\n topics = m.get_topics()\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertTrue(\\n topics.num_columns() == 3,\\n \\\"Returned SFrame should have a topic, word, and probs.\\\",\\n )\\n\\n # Make sure that requesting a single topic returns only that topic\\n num_words = 8\\n topics = m.get_topics([5], num_words=num_words)\\n self.assertTrue(\\n all(topics[\\\"topic\\\"] == 5), \\\"Returned topics do not have the right id.\\\"\\n )\\n self.assertEqual(topics.num_rows(), num_words)\\n topics = m.get_topics([2, 4], num_words=num_words)\\n self.assertEqual(set(list(topics[\\\"topic\\\"])), set([2, 4]))\\n self.assertEqual(topics.num_rows(), num_words + num_words)\\n\\n # Make sure the cumulative probability of the returned words is\\n # is less than the cutoff we provided.\\n # A cutoff of 1.0 should return num_words for every topic.\\n cutoff = 1.0\\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\\n totals = topics.groupby(\\n \\\"topic\\\", {\\\"total_score\\\": turicreate.aggregate.SUM(\\\"score\\\")}\\n )\\n self.assertTrue(\\n all(totals[\\\"total_score\\\"] <= (cutoff + DELTA)),\\n \\\"More words were returned than expected for this cutoff.\\\",\\n )\\n\\n # Make sure we raise errors for bad input\\n with self.assertRaises(ValueError):\\n m.get_topics([-1])\\n with self.assertRaises(ValueError):\\n m.get_topics([10000])\\n with self.assertRaises(ToolkitError):\\n topics = m.get_topics(output_type=\\\"other\\\")\\n\\n # Test getting topic_words\\n topic_words = m.get_topics(output_type=\\\"topic_words\\\", num_words=5)\\n self.assertEqual(type(topic_words), turicreate.SFrame)\\n\\n # Test words are sorted correctly for the first topic\\n # TODO: Make this more deterministic.\\n\\n # topic_probs = m.get_topics(num_words=5)\\n # expected = [w for w in topic_probs['word'][:5]]\\n # observed = topic_words['words'][0]\\n # self.assertEqual(observed[0], expected[0])\",\n \"def __len__(self):\\n count = 0\\n topics = set(six.iterkeys(self._topics))\\n while topics:\\n event_type = topics.pop()\\n try:\\n listeners = self._topics[event_type]\\n count += len(listeners)\\n except KeyError:\\n pass\\n return count\",\n \"def get_n_per_feat(self, feat_subset=None):\\n zipper = self.create_zipper(feat_subset)\\n\\n for key in zipper:\\n # turns zipper values into lists storing the number of entries of the respective features per dataset\\n zipper[key] = [len(z) for z in zipper[key]]\\n\\n counts = pd.DataFrame(zipper).transpose()\\n counts.index.name = \\\"features\\\"\\n counts.columns = self.df_names\\n\\n return counts\",\n \"def listTopicRelevance(request):\\n if request.method == 'GET':\\n user = request.user;\\n data = [];\\n for topic in Topic.objects.all():\\n row = {};\\n\\n topicSerializer = TopicNestedSerializer(topic)\\n topicSerializer.Meta.depth = 1;\\n #row['topic'] = topicSerializer.data;\\n user_visits = topic.visits.filter(user=user)\\n visitSerializer = VisitSerializer(user_visits, many=True)\\n #visitSerializer.Meta.depth = 1;\\n row['visit_count'] = len(user_visits);\\n if row['visit_count'] > 0:\\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\\n else:\\n row['last_visit'] = topic.created_at\\n\\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\\n\\n row['neighbor_visits_count'] = len(neighbor_visits);\\n if row['neighbor_visits_count'] > 0:\\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\\n else:\\n row['last_neighbor_visit'] = topic.created_at\\n\\n row['post_count'] = len(topic.posts.filter(user=user))\\n row['like_count'] = len(topic.posts.filter(votes__user=user))\\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\\n row['recommendation'] = row['relevance_score'] + topic.hotness\\n\\n data.append(row)\\n\\n print(data)\\n return Response(data)\",\n \"def __rank_topics(self, found_topics, explanation):\\n max_value = 0\\n scores = []\\n for _,topic in found_topics.items():\\n topic[\\\"score\\\"] = topic[\\\"times\\\"] * len(topic['grams'].keys())\\n scores.append(topic[\\\"score\\\"])\\n if topic[\\\"score\\\"] > max_value:\\n max_value = topic[\\\"score\\\"]\\n\\n for _,topic in found_topics.items():\\n if \\\"syntactic\\\" in topic:\\n topic[\\\"score\\\"] = max_value\\n\\n\\n\\n\\n # Selection of unique topics\\n unique_topics = {}\\n for t_p,topic in found_topics.items():\\n prim_label = self.cso.get_primary_label_wu(t_p)\\n if prim_label in unique_topics:\\n if unique_topics[prim_label] < topic[\\\"score\\\"]:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n else:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n\\n # ranking topics by their score. High-scored topics go on top\\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\\n\\n\\n # perform\\n vals = []\\n for t_p in sort_t:\\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\\n\\n\\n #### suppressing some warnings that can be raised by the kneed library\\n warnings.filterwarnings(\\\"ignore\\\")\\n try:\\n x_vals = range(1,len(vals)+1)\\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\\n if t_kn.knee is None:\\n #print(\\\"I performed a different identification of knee\\\")\\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\\n except ValueError:\\n pass\\n\\n ##################### Pruning\\n\\n try:\\n knee = int(t_kn.knee)\\n except TypeError:\\n knee = 0\\n except UnboundLocalError:\\n knee = 0\\n\\n if knee > 5:\\n try:\\n knee += 0\\n except TypeError:\\n print(\\\"ERROR: \\\",t_kn.knee,\\\" \\\",knee, \\\" \\\", len(sort_t))\\n\\n else:\\n try:\\n if sort_t[0][1] == sort_t[4][1]:\\n top = sort_t[0][1]\\n test_topics = [item[1] for item in sort_t if item[1]==top]\\n knee = len(test_topics)\\n\\n else:\\n knee = 5\\n except IndexError:\\n knee = len(sort_t)\\n\\n final_topics = []\\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\\n self.reset_explanation()\\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\\n\\n return final_topics\",\n \"def subtype_counts(node_set, G, log=False):\\n subtypes = Counter()\\n for n in node_set:\\n subtype = G.node[n]['subtype']\\n subtypes[subtype] += 1\\n\\n if log:\\n for k, v in subtypes.items():\\n subtypes[k] = np.log10(v)\\n \\n return subtypes\",\n \"def get_number_of_locations():\\n count = 0\\n tree = ET.parse('./devset_topics.xml')\\n root = tree.getroot()\\n for item in root.findall('./topic'):\\n count = count + 1\\n return count\",\n \"def __find_topics(self, concepts):\\n\\n # Set up\\n found_topics = dict() # to store the matched topics\\n explanation = dict()\\n\\n # finding matches\\n for concept in concepts:\\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\\n for grams in evgrams:\\n gram = \\\"_\\\".join(grams)\\n gram_without_underscore = \\\" \\\".join(grams)\\n #### Finding similar words contained in the model\\n\\n list_of_matched_topics = []\\n\\n if self.fast_classification:\\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\\n else:\\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\\n\\n\\n for topic_item in list_of_matched_topics:\\n\\n topic = topic_item[\\\"topic\\\"]\\n str_sim = topic_item[\\\"sim_t\\\"]\\n wet = topic_item[\\\"wet\\\"]\\n sim = topic_item[\\\"sim_w\\\"]\\n\\n\\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\\n\\n\\n if topic in found_topics:\\n #tracking this match\\n found_topics[topic][\\\"times\\\"] += 1\\n\\n found_topics[topic][\\\"gram_similarity\\\"].append(sim)\\n\\n #tracking the matched gram\\n if gram in found_topics[topic][\\\"grams\\\"]:\\n found_topics[topic][\\\"grams\\\"][gram] += 1\\n else:\\n found_topics[topic][\\\"grams\\\"][gram] = 1\\n\\n #tracking the most similar gram to the topic\\n if str_sim > found_topics[topic][\\\"embedding_similarity\\\"]:\\n found_topics[topic][\\\"embedding_similarity\\\"] = str_sim\\n found_topics[topic][\\\"embedding_matched\\\"] = wet\\n\\n else:\\n #creating new topic in the result set\\n found_topics[topic] = {'grams': {gram:1},\\n 'embedding_matched': wet,\\n 'embedding_similarity': str_sim,\\n 'gram_similarity':[sim],\\n 'times': 1,\\n 'topic':topic}\\n\\n\\n\\n if sim == 1:\\n found_topics[topic][\\\"syntactic\\\"] = True\\n\\n\\n\\n primary_label_topic = self.cso.get_primary_label_wu(topic)\\n if primary_label_topic not in explanation:\\n explanation[primary_label_topic] = set()\\n\\n explanation[primary_label_topic].add(gram_without_underscore)\\n\\n return found_topics, explanation\",\n \"def get_counts(data):\\n\\n word_count = {}\\n syll_count = {}\\n\\n infile = data.corpus\\n try:\\n\\n open_file = codecs.open(infile, 'r', encoding='utf-16')\\n for line in open_file:\\n line = line.lower()\\n # Remove tablet indexing info and line numbers. Grab only text data\\n line = line.split(',')\\n text = clean_line(line[7])\\n\\n # Update the occurrences of the words in the line\\n for word in text.split():\\n count = word_count.setdefault(word, 0)\\n word_count[word] = count + 1\\n\\n # Track occurrences of syllables\\n update_syllable_count(word, syll_count)\\n\\n open_file.close()\\n except IOError:\\n print(\\\"Cannot open: \\\" + infile)\\n\\n return (word_count, syll_count)\",\n \"def create_tag_frequencies(dataframe):\\n from pyspark.sql.functions import desc\\n from pyspark.sql.functions import col\\n df_tags = dataframe.selectExpr(\\\"tag1 AS tag\\\").union(dataframe.selectExpr(\\\"tag2 AS tag\\\")).union(dataframe.selectExpr(\\\"tag3 AS tag\\\")) \\\\\\n .union(dataframe.selectExpr(\\\"tag4 AS tag\\\")).union(dataframe.selectExpr(\\\"tag5 AS tag\\\"))\\n df_tags = df_tags.na.drop(subset=[\\\"tag\\\"])\\n tags_total_count = df_tags.count()\\n print(\\\"Total number of tags used, including duplicates:\\\",tags_total_count)\\n df_tag_freq = df_tags.groupBy(\\\"tag\\\").count().orderBy(desc(\\\"count\\\"))\\n df_tag_freq = df_tag_freq.withColumn(\\\"frequency\\\", col(\\\"count\\\")/tags_total_count)\\n df_tag_freq.orderBy(desc(\\\"frequency\\\")).show(20)\",\n \"def connect_topic_id_to_topics(self, model):\\n confidence = []\\n for key, value in self.representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key, tp_num, val / len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n self.log_writer.add_log(\\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\\n conf[1],\\n conf[2]))\\n self.topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in self.topic_indexes.items():\\n self.topics_of_index[value] = [key]\",\n \"def return_topic_figures(n_topics=5):\\n\\n ### import data ###\\n\\n data = return_keywords()\\n data_for_topics = data[\\\"abstract_kw\\\"].apply(\\n lambda x: list(ast.literal_eval(x).keys())\\n )\\n\\n ### Build topic model ###\\n\\n # parameters\\n n_topics = n_topics\\n\\n # Create Dictionary\\n id2word = corpora.Dictionary(data_for_topics)\\n\\n # Create Corpus: Term Document Frequency\\n corpus = [id2word.doc2bow(text) for text in data_for_topics]\\n\\n # Build LDA model\\n lda_model = gensim.models.ldamodel.LdaModel(\\n corpus=corpus,\\n id2word=id2word,\\n num_topics=n_topics,\\n random_state=100,\\n update_every=1,\\n chunksize=10,\\n passes=10,\\n alpha=\\\"symmetric\\\",\\n iterations=100,\\n per_word_topics=True,\\n )\\n\\n topics = lda_model.show_topics(formatted=False)\\n data_flat = [w for w_list in data_for_topics for w in w_list]\\n counter = Counter(data_flat)\\n out = []\\n for i, topic in topics:\\n for word, weight in topic:\\n out.append([word, i, weight, counter[word]])\\n df = pd.DataFrame(out, columns=[\\\"word\\\", \\\"topic_id\\\", \\\"importance\\\", \\\"word_count\\\"])\\n\\n specs = np.full((ceil(n_topics / 2), 2), {\\\"secondary_y\\\": True})\\n topic_bar_charts = make_subplots(\\n rows=ceil(n_topics / 2),\\n cols=2,\\n specs=specs.tolist(),\\n horizontal_spacing=0.1,\\n vertical_spacing=0.15,\\n )\\n row, col = (0, 0)\\n for topic in range(n_topics):\\n if (topic % 2) != 0:\\n col = 2\\n else:\\n col = 1\\n row += 1\\n color = px.colors.qualitative.Vivid[topic]\\n topic_bar_charts.add_trace(\\n go.Bar(\\n x=df.loc[df.topic_id == topic, \\\"word\\\"],\\n y=df.loc[df.topic_id == topic, \\\"word_count\\\"],\\n width=0.5,\\n opacity=0.3,\\n marker_color=color,\\n name=(\\\"Topic \\\" + str(topic) + \\\" word count\\\"),\\n ),\\n secondary_y=False,\\n row=row,\\n col=col,\\n )\\n topic_bar_charts.add_trace(\\n go.Bar(\\n x=df.loc[df.topic_id == topic, \\\"word\\\"],\\n y=df.loc[df.topic_id == topic, \\\"importance\\\"],\\n width=0.2,\\n marker_color=color,\\n name=(\\\"Topic \\\" + str(topic) + \\\" weight\\\"),\\n ),\\n secondary_y=True,\\n row=row,\\n col=col,\\n )\\n topic_bar_charts.update_layout(barmode=\\\"overlay\\\")\\n\\n topic_bar_charts.update_layout(\\n height=800, width=1000, margin=dict(l=50, r=50, t=50, b=100)\\n )\\n\\n # append all charts\\n figures = [dict(data=topic_bar_charts)]\\n\\n return figures\",\n \"def test_properties_count_group_by_group_by_and_sub_group_by_get(self):\\n pass\",\n \"def execQ2():\\n # Put columns together\\n frame = pan.DataFrame(data, columns=['Product', 'Amount'] )\\n amount = frame.groupby(['Product']).count()\\n return amount\",\n \"def initialize(self):\\n # Initializing the counter and distribution.\\n for k in range(0, self.topic_number,1):\\n self.topic_term_count_matrix[k]= [0.0] * self.term_number\\n self.topic_distribution_over_term[k] = [0.0] * self.term_number\\n self.sum_topic_by_term_count[k] = 0.0\\n for m in range(0, self.document_number,1):\\n self.document_topic_count_matrix[m] = [0.0] * self.topic_number\\n self.document_distribution_over_topic[m] = [0.0] * self.topic_number\\n self.sum_document_by_topic_count[m] = 0.0\\n\\n # Initializing topics assigned to all words of all documents.\\n for m in range(0, self.document_number, 1):\\n N = len(self.documents[m])\\n self.word_topic_assignment[m] = [-1] * N\\n for n in range(0, N,1):\\n topic = int(random.uniform(0,1) * self.topic_number)\\n self.document_topic_count_matrix[m][topic] += 1.0\\n self.topic_term_count_matrix[topic][self.documents[m][n]] += 1.0\\n self.sum_topic_by_term_count[topic] += 1.0\\n self.word_topic_assignment[m][n] = topic\\n self.sum_document_by_topic_count[m] = N\",\n \"def solution_2(arr):\\n total = 0\\n for group in arr:\\n group_list = []\\n for person in group:\\n group_list = group_list + person\\n group_table = Counter(''.join(group_list))\\n for k, v in group_table.items():\\n if v == len(group):\\n total += 1\\n return total\",\n \"def get_combined_topics(team):\\n know1, know2 = team\\n knowledge = int(know1, base=2) | int(know2, base=2)\\n return bin(knowledge).count('1')\",\n \"def count_by_tag(self, dataframe, tags):\\r\\n if tags and not dataframe['tags'].empty:\\r\\n data_to_return = []\\r\\n counter = 0\\r\\n for tag in tags:\\r\\n for datafield in dataframe['tags']:\\r\\n if tag in datafield:\\r\\n counter += 1\\r\\n data_to_return.append([tag, counter])\\r\\n counter = 0\\r\\n return pandas.DataFrame(data_to_return, columns=('TAG', 'TagCount'))\",\n \"def initialize(self):\\n self.n_words = len(self.vocab)\\n self.n_docs = len(self.documents)\\n\\n # Initialize the three count matrices.\\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\\n self.nmz = np.zeros((self.n_docs, self.n_topics))\\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\\n self.nzw = np.zeros((self.n_topics, self.n_words))\\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\\n self.nz = np.zeros(self.n_topics)\\n\\n # Initialize the topic assignment dictionary.\\n self.topics = {} # key-value pairs of form (m,i):z\\n\\n for m in range(self.n_docs):\\n for i in self.documents[m]:\\n # Get random topic assignment, i.e. z is a random integer in the range of topics\\n z = np.random.randint(self.n_topics)\\n # Increment count matrices\\n self.nmz[m,z] += 1\\n self.nzw[z,self.documents[m][i]] += 1\\n self.nz[z] += 1\\n # Store topic assignment\\n self.topics[(m,i)] = z\",\n \"def count_questions(txt):\\n count = 0\\n for c in txt:\\n if c == '?':\\n count += 1\\n return count\",\n \"def get_query_count(dataset_id, dataset_type, query_dataset_dict):\\n if dataset_type == \\\"result_table\\\":\\n return query_dataset_dict.get(dataset_id, 0)\\n else:\\n clean_rt_list = get_clean_rt(dataset_id)\\n query_count = 0\\n for each_rt in clean_rt_list:\\n query_count += query_dataset_dict.get(each_rt, 0)\\n return query_count\",\n \"def getClassCounts(column, uniqueVal, decision, yes, no , total):\\r\\n dataDict = {} # a dictionary of labels\\r\\n for val in uniqueVal:\\r\\n label1 = val + '/Y'\\r\\n label2 = val + '/N'\\r\\n dataDict[label1] = 0; dataDict[label2] = 0\\r\\n for dec, at in zip(decision, column):\\r\\n if at == val and dec == 'No':\\r\\n dataDict[label2] += 1\\r\\n if at == val and dec == 'Yes':\\r\\n dataDict[label1] += 1\\r\\n dataDict[val] = (dataDict[label2]+ dataDict[label1])/ total\\r\\n dataDict[label2] = dataDict[label2] / no\\r\\n dataDict[label1] = dataDict[label1] / yes\\r\\n return dataDict\",\n \"def run_compute_reread_counts(self):\\n questions = []\\n contexts = []\\n student_data = self.responses[:]\\n for response in student_data:\\n if response.question.text not in questions:\\n questions.append(response.question.text)\\n if response.context.text not in contexts:\\n contexts.append(response.context.text)\\n\\n compute_reread_counts_data = []\\n\\n for question in questions:\\n for context in contexts:\\n compute_reread_counts_data.append(self.compute_reread_counts(\\n question, context))\\n\\n return compute_reread_counts_data\",\n \"def cat_count(data, column_str, criteria):\\r\\n ct1 = []\\r\\n ct2 = []\\r\\n for i in range(len(find_cats_freq(data, column_str))):\\r\\n ct1.append(find_cats_freq(data[criteria], column_str)[i])\\r\\n ct2.append(find_cats_freq(data, column_str)[i])\\r\\n return np.array(ct1)/np.array(ct2)\",\n \"def posts_per_topic_all(request, pk):\\n #update is_expired in all posts\\n update_posts_expiration()\\n #get all posts with a certain topic\\n posts = Post.objects.filter(topic=pk)\\n serializer = ViewPostSerializer(posts, many=True)\\n return Response(serializer.data)\",\n \"def count(self) -> Tuple[groupable, pdarray]:\\n repMsg = generic_msg(\\n cmd=\\\"countReduction\\\",\\n args={\\\"segments\\\": cast(pdarray, self.segments), \\\"size\\\": self.length},\\n )\\n self.logger.debug(repMsg)\\n return self.unique_keys, create_pdarray(repMsg)\",\n \"def display_topics(df, n_rows=10, n_cols=12):\\n\\n exemplar_scores, hovers = topic_exemplars(df)\\n top_columns = sorted(range(len(exemplar_scores)),\\n key=lambda i: exemplar_scores[i],\\n reverse=True)[:n_cols]\\n #I comented this line Im not 100% sure what was the purpuse of this\\n # topics = df.pivot(index='pos', columns='topic',values='word*').replace([None], [''], regex=True)\\n topics = df.pivot(index='pos', columns='topic',values='word*')\\n\\n topics_display = topics[top_columns].head(n_rows)\\n\\n return topics_display, top_columns\",\n \"def __len__(self):\\n # Header + string size + consumer group size\\n size = self.HEADER_LEN + 2 + len(self.consumer_group)\\n # + generation id + string size + consumer_id size + array length\\n size += 4 + 2 + len(self.consumer_id) + 4\\n for topic, parts in iteritems(self._reqs):\\n # topic name + len(parts)\\n size += 2 + len(topic) + 4\\n # partition + offset + timestamp => for each partition\\n size += (4 + 8 + 8) * len(parts)\\n # metadata => for each partition\\n for partition, (_, _, metadata) in iteritems(parts):\\n size += 2 + len(metadata)\\n return size\",\n \"def get_num_cat(sample_by_cat, samples_in_otus):\\r\\n num_cat = defaultdict(int)\\r\\n for cat, samples in sample_by_cat.items():\\r\\n num_samples = len(set(samples_in_otus) & set(samples))\\r\\n num_cat[cat[0]] += (num_samples * (num_samples - 1)) / 2\\r\\n return num_cat\",\n \"def get_total_partitions_for_topic(self, topic):\\n return len(self.client.cluster.partitions_for_topic(topic))\",\n \"def count_frequency(data: list) -> list:\\n counts = [{'A': 0, 'C': 0, 'G': 0, 'T': 0} for _ in range(len(data[0][1]))]\\n for i in range(len(data[0][1])):\\n for datum in [datum[1] for datum in data]:\\n counts[i][datum[i]] += 1\\n return counts\",\n \"def test_topic_reduction_edge_cases(base_bertopic):\\n\\n nr_topics = 5\\n base_bertopic.nr_topics = 100\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(old_documents)\\n c_tf_idf = base_bertopic._extract_topics(old_documents, topic_reduction=True)\\n old_freq = base_bertopic.get_topics_freq()\\n\\n new_documents = base_bertopic._reduce_topics(old_documents, c_tf_idf)\\n new_freq = base_bertopic.get_topics_freq()\\n\\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\\n pd.testing.assert_frame_equal(old_documents, new_documents)\\n pd.testing.assert_frame_equal(old_freq, new_freq)\",\n \"def count_elements_in_dataset(dataset):\\n return dataset.count()\",\n \"def basic_statistics_of_email(data):\\n word_counts = []\\n character_count = 0\\n\\n for ind, row in data.iterrows():\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\n real_words = tokenizer.tokenize(row[\\\"RawText\\\"].lower())\\n\\n character_count += sum(map(len, real_words))\\n word_counts.append(len(real_words))\\n\\n return character_count, pd.Series(word_counts)\",\n \"def part2(fname: dict) -> int:\\n return sum(len(set.intersection(*[set(pax) for pax in group])) for group in get_data(fname))\",\n \"def get_topics(self):\\n topics = self.word_topics\\n return topics / topics.sum(axis=1)[:, None]\",\n \"def analysis():\\r\\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\\r\\n data_frame.registerTempTable('business')\\r\\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\\r\\n\\r\\n def merge_count(a, b):\\r\\n r = {}\\r\\n for p, c in a.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n for p, c in b.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n return r\\r\\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\\r\\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\\r\\n\\r\\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\\r\\n print(result.take(10))\\r\\n print('----------------pron count-----------------')\\r\\n print(pron_count.collect())\\r\\n\\r\\n print(gd)\",\n \"def _update_counts(self, msg, subtype, by):\\n\\n try:\\n counts = self.get_local(msg, \\\"counts\\\")\\n except KeyError:\\n counts = defaultdict(int)\\n\\n counts['all'] += by\\n counts[subtype] += by\\n self.set_local(msg, \\\"counts\\\", counts)\",\n \"def test_topic_model_generator_dimensions( ):\\n N = 100\\n D = 1000\\n K = 10\\n W = 100\\n\\n tm = TopicModel.generate( K, D )\\n assert( tm.topics.shape == (D, K) )\\n assert( tm.weights.shape == (K,) )\\n\\n docs = tm.sample( N, words = W )\\n # Each document is a column\\n assert( docs.shape == (N, D) ) \\n # Each doc should have 100 words\\n assert( sc.all(docs.sum(1) == W) )\",\n \"def test_table_counts():\\n number_of_test_run = 2 # Run the pipeline twice\\n for i in range(number_of_test_run):\\n dp = DataPipeline()\\n dp.run()\\n\\n dp = DataPipeline()\\n assert dp.get_product_count() == (500000,)\\n assert dp.get_duplicate_count(from_table=\\\"products\\\") == (0,)\\n assert dp.get_aggregate_table_result_count() == (222024, )\\n 222024\\n dp.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6717123","0.63702446","0.61713773","0.5966875","0.5961095","0.5927321","0.5897049","0.579996","0.5752333","0.5736918","0.5653994","0.5621641","0.55750763","0.54705304","0.54477894","0.54441655","0.5436638","0.5420717","0.5412772","0.53985864","0.5367074","0.53649616","0.5307551","0.5292152","0.5262273","0.52395135","0.5210288","0.520255","0.51896495","0.5169957","0.5127032","0.5111573","0.510007","0.5087801","0.50854045","0.5079777","0.5072392","0.5065675","0.5036765","0.5035437","0.50350946","0.5033354","0.5031456","0.502549","0.501906","0.49957436","0.49928945","0.49774235","0.497683","0.49719694","0.49691325","0.49684167","0.49680936","0.49659014","0.49578416","0.49550122","0.4954171","0.49471876","0.49356496","0.49332508","0.49275005","0.49233413","0.49055085","0.48977187","0.48957607","0.48929524","0.48916018","0.4886652","0.48847088","0.4861437","0.48612806","0.48562106","0.48551533","0.485392","0.48475695","0.4847457","0.4846999","0.48432577","0.48335657","0.4831336","0.48305693","0.4823364","0.4819344","0.4813468","0.4810518","0.4810284","0.48072892","0.48056188","0.47954002","0.47933036","0.47869015","0.47766662","0.4774449","0.477056","0.47664762","0.47646415","0.4763438","0.47596654","0.47589552","0.4758853"],"string":"[\n \"0.6717123\",\n \"0.63702446\",\n \"0.61713773\",\n \"0.5966875\",\n \"0.5961095\",\n \"0.5927321\",\n \"0.5897049\",\n \"0.579996\",\n \"0.5752333\",\n \"0.5736918\",\n \"0.5653994\",\n \"0.5621641\",\n \"0.55750763\",\n \"0.54705304\",\n \"0.54477894\",\n \"0.54441655\",\n \"0.5436638\",\n \"0.5420717\",\n \"0.5412772\",\n \"0.53985864\",\n \"0.5367074\",\n \"0.53649616\",\n \"0.5307551\",\n \"0.5292152\",\n \"0.5262273\",\n \"0.52395135\",\n \"0.5210288\",\n \"0.520255\",\n \"0.51896495\",\n \"0.5169957\",\n \"0.5127032\",\n \"0.5111573\",\n \"0.510007\",\n \"0.5087801\",\n \"0.50854045\",\n \"0.5079777\",\n \"0.5072392\",\n \"0.5065675\",\n \"0.5036765\",\n \"0.5035437\",\n \"0.50350946\",\n \"0.5033354\",\n \"0.5031456\",\n \"0.502549\",\n \"0.501906\",\n \"0.49957436\",\n \"0.49928945\",\n \"0.49774235\",\n \"0.497683\",\n \"0.49719694\",\n \"0.49691325\",\n \"0.49684167\",\n \"0.49680936\",\n \"0.49659014\",\n \"0.49578416\",\n \"0.49550122\",\n \"0.4954171\",\n \"0.49471876\",\n \"0.49356496\",\n \"0.49332508\",\n \"0.49275005\",\n \"0.49233413\",\n \"0.49055085\",\n \"0.48977187\",\n \"0.48957607\",\n \"0.48929524\",\n \"0.48916018\",\n \"0.4886652\",\n \"0.48847088\",\n \"0.4861437\",\n \"0.48612806\",\n \"0.48562106\",\n \"0.48551533\",\n \"0.485392\",\n \"0.48475695\",\n \"0.4847457\",\n \"0.4846999\",\n \"0.48432577\",\n \"0.48335657\",\n \"0.4831336\",\n \"0.48305693\",\n \"0.4823364\",\n \"0.4819344\",\n \"0.4813468\",\n \"0.4810518\",\n \"0.4810284\",\n \"0.48072892\",\n \"0.48056188\",\n \"0.47954002\",\n \"0.47933036\",\n \"0.47869015\",\n \"0.47766662\",\n \"0.4774449\",\n \"0.477056\",\n \"0.47664762\",\n \"0.47646415\",\n \"0.4763438\",\n \"0.47596654\",\n \"0.47589552\",\n \"0.4758853\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.62771106"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":8480,"cells":{"query":{"kind":"string","value":"Take an specific list from rdd spark, which is formed as list of tuples (Topic, Question)"},"document":{"kind":"string","value":"def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\n print(data_frame_pandas)\n return data_frame_pandas"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = response.get(\"Items\")\n return questions","def get_words_from_tuples(examples):\n return [[t[0] for t in example] for example in examples]","def getSubmissionsByTopic(self, i):\r\n return [(ind,sub) for ind, sub in enumerate(self.submissions) if sub.topicId == i]","def to_simple_rdd(sc: SparkContext, features: np.array, labels: np.array):\n pairs = [(x, y) for x, y in zip(features, labels)]\n return sc.parallelize(pairs)","def get_users_movies(myRatings):\n #return [x[1] for x in myRatings]\n return list(myRatings.map(lambda x: x[1]).collect())","def partition(rows: list, question: Question) -> (list, list):\n true_rows = []\n false_rows = []\n for row in rows:\n if question.match(row): # True\n true_rows.append(row)\n else:\n false_rows.append(row)\n return true_rows, false_rows","def apply_transformations(rdd):\n ### BEGIN SOLUTION ###\n return rdd","def getPracticesRDD(sqlContext):\n from db import *\n return sqlContext \\\n .read \\\n .format(\"jdbc\") \\\n .options(\n driver=driver,\n url=url,\n dbtable=\"user_practice\",\n user=user,\n password=password\n ) \\\n .load() \\\n .rdd \\\n .map(lambda row: (row.id, row)) # We'll use the id to join with the users rdd defined above","def get_questions():\n data = pd.read_excel('Data/APNLP_QuestionsToUser.xlsx')\n df = pd.DataFrame(data, columns=['Questions'])\n lst = df.values.tolist()\n questions = []\n for q in lst:\n q = str(q)[2:-2]\n questions.append(q)\n return questions","def read_tuples_list(corpus_path):\n idx = 0\n data = []\n with open(corpus_path, encoding='utf-8') as fr:\n lines = fr.readlines()\n sent_, label_h_, pos_, ner_ = [], [], [], []\n\n for line in lines:\n idx += 1\n if line.find(\"DOC-ID\") < 0 and line != '\\n':\n try:\n [char, label_h, pos_tag, ner_tag] = line.strip().split()\n sent_.append(char)\n label_h_.append(label_h)\n pos_.append(pos_tag)\n ner_.append(ner_tag)\n except BaseException as e:\n print(e)\n print(line)\n else:\n # print(line)\n if idx > 1:\n data.append((sent_, label_h_, pos_, ner_))\n sent_, label_h_, pos_, ner_ = [], [], [], []\n\n return data","def processRDD(RDD, label):\n try:\n newRDD = RDD.map(lambda x: ((x[2], x[3]), (float(x[0]), float(x[1]), float(x[4]), label)))\n return newRDD\n except Exception as e:\n print e","def build_recommendations(sc, myRatings, model):\n #myRatedMovieIds = set([x[1] for x in myRatings])\n uid = get_uid_from_ratings(myRatings)\n #print \"uid:\", uid\n myRatedMovieIds = set([x[1] for x in myRatings.collect()])\n #print \"myRatedMovieIds:\", myRatedMovieIds\n candidates = sc.parallelize([m for m in movies if m not in myRatedMovieIds]).cache()\n #print candidates\n predictions = model.predictAll(candidates.map(lambda x: (uid, x))).collect()\n #print predictions\n recommendations = sorted(predictions, key = lambda x: x.product)\n return recommendations","def get_topic_quiz(topic_id):\n quiz_data = query_db(\"SELECT id, name FROM quizzes WHERE topic_id=?;\", [topic_id])\n quizzes = []\n for quiz in quiz_data:\n quiz_topic = {}\n quiz_topic[\"id\"] = quiz[0]\n quiz_topic[\"name\"] = quiz[1]\n quizzes.append(quiz_topic)\n return quizzes","def get_store_item_list(spark) -> list:\n sqlDF = spark.sql(\"SELECT DISTINCT SKU, Store FROM dfView\")\n store_item_list = sqlDF.rdd.map(tuple).collect()\n return store_item_list","def get_topic_of_question(question):\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n topic_table = dynamodb.Table(\"Topics\")\n\n topic_id = question.get(\"TopicId\")\n # query topic_id of the question\n try:\n response = topic_table.get_item(Key={\"TopicId\": topic_id})\n topic = response[\"Item\"]\n except:\n print(\"No topic found, returning None..\")\n return None\n return topic","def split_by_time(rdd, ratio):\n #(user_id, [(item_id, rating, time)])\n sort_by_time = rdd.map(lambda x:(x[0], (x[1], x[2], x[3])))\\\n .groupByKey().mapValues(list)\\\n .map(lambda x:(x[0],sorted(x[1], key=operator.itemgetter(2), reverse=False)))\n\n def _split_f(x, ratio):\n s = sum(ratio)\n b = 0\n l = []\n for r in ratio:\n e = int(float(r)/s*len(x))\n l.append(x[b:b+e])\n b = b+e\n return l\n\n #(user_id, [[(item_id, rating, time)]...[]])\n split_by_ratio = sort_by_time.map(lambda x: (x[0], _split_f(x[1], ratio)))\n print(\"split by ratio: \", split_by_ratio.take(1))\n\n split_list = []\n for i, _ in enumerate(ratio):\n split_list.append(split_by_ratio.map(lambda x:(x[0], x[1][i]))\\\n .flatMap(lambda x:[(x[0], y[0], y[1], y[2]) for y in x[1]]))\n print(\"i \", i)\n print(\"len \", split_list[i].count())\n\n return tuple(split_list)","def spark_df_to_records(df):\n return [tuple(r) for r in df.collect()]","def partition(self, rows, question):\n true_rows, false_rows = [], []\n for row in rows:\n if question.match(row):\n true_rows.append(row)\n else:\n false_rows.append(row)\n return true_rows, false_rows","def compute_topic_assignment(topic_model, abstracts, min_df=2, max_df=0, tf_matrix_dump_filename=None):\n if max_df == 0:\n max_df = 0.9 if len(abstracts) < 100 else 0.8\n\n\n # get the gensim representation of new papers in order to query the topic model\n papers_gensim = get_gensim_representation_new_docs_for_inference(abstracts, topic_model.id2word,\n use_lemmer=True, min_df=2,\n max_df=max_df,\n tf_matrix_dump_filename=tf_matrix_dump_filename)\n\n # for each paper compute the topics assignment\n topic_assignment = [None] * len(papers_gensim)\n for i, paper in enumerate(papers_gensim):\n topic_assignment[i] = topic_model[paper]\n\n return topic_assignment","def get_valid_phrases():\n return [x[0] for x in all_topics if x[1] == \"1\"]","def ex_list(data):\n return tuple(data)","def read_data(file_path, sparkContext):\n data_rdd = sparkContext \\\n .textFile(file_path) \\\n .map(eval) \\\n .map(lambda x: (x[0], x[1]))\n return data_rdd","def _topics_words(self, num_of_words):\n x = self.model.show_topics(-1, num_of_words, formatted=False)\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\n # where `topic` is a list of `(word, probability)` tuples.\n return [[i[0] for i in topic[1]] for topic in x]","def get_custom_phrases():\n return [x[0] for x in all_topics if x[2] == \"1\"]","def DatasetToTuple(sample):\n \n X_elem = []\n Y_elem = []\n for x,y in sample:\n X_elem.append(x if x.dim() > 0 else x.item())\n Y_elem.append(y if y.dim() > 0 else y.item()) \n return (torch.stack(X_elem),torch.stack(Y_elem))","def DatasetToTuple(sample):\n \n X_elem = []\n Y_elem = []\n for x,y in sample:\n X_elem.append(x if x.dim() > 0 else x.item())\n Y_elem.append(y if y.dim() > 0 else y.item()) \n return (torch.stack(X_elem),torch.stack(Y_elem))","def getUsersRDD(sqlContext):\n # Currently the id field ranges from '0' to '1000000'.\n # To avoid loading it all in memory, partition on the id field (100 partitions, about 10k records per partition).\n # Also setting fetch size to 10,000 to avoid multiple database calls per partition.\n # All records from a single partition will come in a single query.\n # If we need to use less memory, we can increase the # of partitions and decrease the lower/uppper bounds.\n # We are also relying on Spark to spill to disk if no memory is available.\n from db import *\n return sqlContext \\\n .read \\\n .format(\"jdbc\") \\\n .options(\n driver=driver,\n url=url,\n dbtable=\"user\",\n user=user,\n password=password,\n fetchSize=10000,\n numPartitions=100,\n partitionColumn=\"id\",\n lowerBound=0,\n upperBound=1000000\n ) \\\n .load() \\\n .rdd \\\n .persist(StorageLevel.MEMORY_AND_DISK) \\\n .map(lambda row: (row.practice_id, row)) # We are setting practice_id as the key here because we'll use that to join with user_practice table","def get_tweets_by_topic(topic, start_date, end_date):\n try:\n query = f\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\"\n logger.info(f'QUERY: {query}') \n with MysqlCursor() as cur:\n cur.execute(query)\n tweets = cur.fetchall()\n columns = [col[0] for col in cur.description]\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \n return tweets, columns\n \n except Exception as ex:\n logger.exception(ex)\n return f'Exception: {ex}'","def lp_to_simple_rdd(lp_rdd: RDD, categorical: bool = False, nb_classes: int = None):\n if categorical:\n if not nb_classes:\n labels = np.asarray(lp_rdd.map(\n lambda lp: lp.label).collect(), dtype='int32')\n nb_classes = np.max(labels) + 1\n rdd = lp_rdd.map(lambda lp: (from_vector(lp.features),\n encode_label(lp.label, nb_classes)))\n else:\n rdd = lp_rdd.map(lambda lp: (from_vector(lp.features), lp.label))\n return rdd","def get_training_data(self) -> List[Tuple[str, Optional[str]]]:\n return [(k, v) for k, v in sorted(self._clusters.items(), key=lambda x: (x[1] if x[1] else '', x[0]))]","def get_subscription_ids(self, topic: str) -> Tuple[int, ...]:\n try:\n return tuple(self.__sub_ids[topic])\n except KeyError:\n return ()","def convert_partitions_to_list(partition):\n parts = list()\n for part in partition:\n parts.append(part)\n return parts","def partition(rows, question):\n true_rows, false_rows = [], []\n for row in rows:\n if question.match(row):\n # the row's value of the column was greater than or equal to the questions value\n true_rows.append(row)\n else:\n false_rows.append(row)\n return true_rows, false_rows","def get_ped_topics(n_peds):\n ls = []\n for n in range(n_peds):\n ls += [coord.format(n) for coord in PED_TEMPL_TOPICS]\n return ls","def get_related_topics(self,keyword,cut=0.5):\n \n ret = []\n\n if type(keyword) == str:\n if keyword in self.topic_map.keys():\n ret = [(keyword,1.0)]\n keyword = \"\"\n else:\n _keyword = []\n for k in keyword:\n if k in self.topic_map.keys():\n ret.append((k,1.0))\n else:\n _keyword.append(k) \n keyword = _keyword\n\n keyword_rels = set(self.get_related_keywords(keyword,self.keyword_map_rel,_score=False))\n\n if len(keyword_rels) > 0:\n for topic,topic_rels in self.topic_map.items():\n alike = keyword_rels.intersection(topic_rels)\n score = (len(alike) * (100/len(keyword_rels)))/100\n ret.append((topic,round(score,3)))\n ret.sort(key=lambda x : x[1], reverse=True)\n ret = [t for t in ret if t[1] >= cut]\n \n return ret","def _topics_weights(self, num_of_words):\n topics = self.model.show_topics(-1, num_of_words, formatted=False)\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\n # where `topic` is a list of `(word, probability)` tuples.\n return [[i[1] for i in t[1]] for t in topics]","def get_most_relevant_topics(topics_list):\n topics_list.sort(cmp=lambda x, y: 1 if x[1] < y[1] else -1)\n topics_list = topics_list[:3] # ARBITRARY (at most 3 topic names)\n topics_id, _ = zip(*topics_list)\n return [topics_tools.lda_topic_names[tid] for tid in topics_id]","def tup_list_maker(tup_list):\n final_list = []\n for item in tup_list:\n index = item[0]\n sentences = item[1]\n for sentence in sentences:\n pair = (index, sentence)\n final_list.append(pair)\n return final_list","def get_user_list(dataset):\n res = dataset\\\n .map(lambda x: x[0])\\\n .collect()\n return list(set(res))","def select_essays_with_topic(conn, topic):\n cur = conn.cursor()\n cur.execute('SELECT link, title FROM essays WHERE \"{}\" > 0 ORDER BY \"{}\" DESC'.format(topic, topic))\n\n d = {}\n count = 0\n rows = cur.fetchall()\n\n print(\"Total: \", len(rows))\n for row in rows:\n d[count] = (list(row)[0], list(row)[1])\n count += 1\n # print(row)\n #print(list(rows[0]))\n return d","def schools_by_topic(mongo_collection, topic):\n res = []\n returned_values = mongo_collection.find({\"topics\": {\"$all\": [topic]}})\n for value in returned_values:\n res.append(value)\n return res","def get_topics(self):\r\n return [x[0] for x in get_published_topics()]","def get_teacher_topic_all():\n topic_data = query_db(\n \"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \"\n \"ON topics.class_id=classes.id WHERE teacher_id=?;\",\n [flask.session[\"id\"]],\n )\n topics = []\n for topic in topic_data:\n topic_dict_teacher = {}\n topic_dict_teacher[\"id\"] = topic[0]\n topic_dict_teacher[\"name\"] = flask.escape(str(topic[1]))\n topic_dict_teacher[\"class\"] = flask.escape(str(topic[2]))\n topics.append(topic_dict_teacher)\n return topics","def transform(self):\n result = []\n for item in self.doc_topic_matrix:\n result.append(item / np.sum(item))\n result = np.array(result)\n return result","def populate_weight_tuple_list(list_object):\n tuple_list = []\n\n for i in range(len(list_object[0])):\n weight_tuple = (list_object[0][i], float(list_object[1][i]))\n tuple_list.append(weight_tuple)\n \n return tuple_list","def _fix_examples(examples):\n if len(examples) > 0 and len(examples[0]) > 0 and len(examples[0][0]) > 2:\n # Assumes all elements use the same format of tuple of length 3 (so we only check [0][0])\n return [\n [(tuple_ex[0], tuple_ex[1]) for tuple_ex in example]\n for example in examples\n ]\n else:\n return examples","def get_valid_indices():\n return [i for i, val in enumerate(all_topics) if val[1] == \"1\"]","def partitionData(data, labels, partition):\n\treturn [s[partition] for s in data], labels[partition]","def to_sample_rdd(x, y, sc, num_slices=None):\n x_rdd = sc.parallelize(x, num_slices)\n y_rdd = sc.parallelize(y, num_slices)\n return x_rdd.zip(y_rdd).map(lambda item: Sample.from_ndarray(item[0], item[1]))","def combine_sub_blocks(rdd):\n ### BEGIN SOLUTION ###\n return rdd","def get_keypoint_tuples(eval_config):\n tuple_list = []\n kp_list = eval_config.keypoint_edge\n for edge in kp_list:\n tuple_list.append((edge.start, edge.end))\n return tuple_list","def kml_extract_RDD(xml_file):\n soup = BeautifulSoup(xml_file, \"lxml-xml\")\n return get_kml_content(soup)","def get_tuples(outputs) -> list:\n return list(map(get_tuples_helper, outputs))","def _process_data(rdd_entry, feature_list):\n events = []\n for event in rdd_entry:\n events.append(event[RDD_EVENT])\n return IptablesIngestor.vectorize_events(events, feature_list)","def generate_sub_blocks(rdd):\n ### BEGIN SOLUTION ###\n return rdd","def main_topic_doc(ldamodel, corpus=corpus): \n \n doc_topics = pd.DataFrame()\n\n for i, row in enumerate(ldamodel[corpus]):\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\n\n for j, (topic_num, prop_topic) in enumerate(row):\n if j == 0:\n wp = ldamodel.show_topic(topic_num)\n topic_keywords = \"' \".join([word for word, prop in wp])\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n else:\n break\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\n return doc_topics","def get_custom_indices():\n return [i for i, val in enumerate(all_topics) if val[2] == \"1\"]","def extract_records_for_nltk(iaa_df: pd.DataFrame) -> List[Tuple]:\n return [(b, c, d) for _, b, c, d in iaa_df.to_records()]","def get_score_book(self) -> List[Tuple[str, float]]:\n returned = []\n\n for item, size in self.score_book.items():\n my_tuple = item, size\n returned.append(my_tuple)\n\n return returned","def get_topic_words(self, topics):\n topic_words = []\n for topic, top_n_words in topics.items():\n words = [word for word, c_tf_idf in top_n_words]\n topic_words.append(words)\n return topic_words","def convert_shapely_points_to_tuples(list_of_points) -> list:\n return [(p.x, p.y) for p in list_of_points]","def process_rdd(time, rdd):\n print(\"----------- %s -----------\" % str(time))\n try:\n if rdd:\n # Get spark sql singleton context from the current context\n sql_context = get_sql_context_instance(rdd.context.getConf())\n # convert the RDD to Row RDD\n row_rdd = rdd.map(lambda w: Row(hashtag=w[0], hashtag_count=w[1]))\n # create a DF from the Row RDD\n hashtags_dataframe = sql_context.createDataFrame(row_rdd)\n # Register the dataframe as table\n hashtags_dataframe.createOrReplaceTempView(\"hashtags\")\n # get the top 10 hashtags from the table using SQL and print them\n hashtag_counts_dataframe = sql_context.sql(\n \"select hashtag, hashtag_count from hashtags order by hashtag_count desc limit 10\")\n hashtag_counts_dataframe.show()\n\n # call this method to prepare top 10 hashtags DF and send them\n\n def send_dataframe_to_dashboard(dataframe):\n \"\"\"\n Function to send DataFrame to the dashboard for visualization.\n :param dataframe: Spark DataFrame created by process_rdd().\n \"\"\"\n # extract the hashtags from dataframe and convert them into array\n top_tags = [str(t.hashtag) for t in dataframe.select(\"hashtag\").collect()]\n # extract the counts from dataframe and convert them into array\n tags_count = [p.hashtag_count for p in dataframe.select(\"hashtag_count\").collect()]\n # initialize and send the data through REST API\n request_data = {'label': str(top_tags), 'data': str(tags_count)}\n response = post(dashboard_url, data=request_data)\n\n send_dataframe_to_dashboard(hashtag_counts_dataframe)\n except:\n pass","def ID_Data(targ_driver, RDD, sc, K = 200):\n try:\n newID1 = [targ_driver] * K\n newID2 = np.arange(200, 201+K).astype(str)\n newID = zip(newID1, newID2)\n oldID = RDD.map(lambda x: (x[2],x[3])).distinct().collect()\n glossary = sc.parallelize(zip(oldID, newID))\n newRDD = RDD.map(lambda x: ((x[2],x[3]), ([x[0],x[1],x[4]]))).join(glossary)\n newID_RDD = newRDD.map(lambda x: (x[1][0][0], x[1][0][1], x[1][1][0], x[1][1][1], x[1][0][2]))\n return newID_RDD\n except Exception as e:\n print e","def answerer(embeddings, tuples: tf.Variable, scoring=multilinear):\n n_data, n_slots, rank = [d.value for d in embeddings.get_shape()]\n n_data, n_tuples, order = [d.value for d in tuples.get_shape()]\n\n shift_indices = tf.constant(np.reshape(\n np.outer(range(n_data), np.ones(n_tuples * n_slots)) * n_slots, (n_data, n_tuples, n_slots)), dtype='int64')\n questions_shifted = tuples + shift_indices\n\n preds = scoring(\n tf.reshape(embeddings, (n_data * n_slots, rank)),\n tf.reshape(questions_shifted, (n_data * n_tuples, order)))\n\n return tf.reshape(preds, (n_data, n_tuples))","def create_featseltuple(ids):\n newlist = []\n for part_id in ids:\n newlist.extend([part_id, part_id + 91])\n return tuple(sorted(newlist))","def _partition_rdd(self, rdd, function): \n\n N, buffer_tau, dom_mins, dom_maxs, symmetric = self.N, self.buffer_tau, \\\n self.dom_mins, self.dom_maxs, \\\n self.symmetric \n def partition_helper(iterator):\n for arr in iterator: \n res = function(arr,N,buffer_tau,symmetric,dom_mins,dom_maxs)\n for r in res: \n yield r\n return rdd.mapPartitions(partition_helper)","def toListOfTuple(self, df:pd.core.frame.DataFrame) -> List[Tuple]: \n df['TIME_STAMP'] = df['TIME_STAMP'].astype('str')\n records = df.to_records(index=False)\n listOfTuple = list(records)\n return listOfTuple","def get_topic_assign(topic_id):\n assignment_data = query_db(\n \"SELECT id, name, due_date FROM assignments WHERE topic_id=?;\", [topic_id]\n )\n assignments = []\n for assignment in assignment_data:\n topic_assign_dict = {}\n topic_assign_dict[\"id\"] = assignment[0]\n topic_assign_dict[\"name\"] = str(assignment[1])\n topic_assign_dict[\"due_date\"] = assignment[2]\n assignments.append(topic_assign_dict)\n return assignments","def get_topics(model, nlp_model, n_top_words):\n\n words = nlp_model.get_feature_names()\n\n return [convert_to_string([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]) for topic_idx, topic in enumerate(model.components_)]","def get_topics_for_alt(alt_list, pgm_dict):\n for epv in alt_list:\n name = epv.get('package', {}).get('name', [''])[0]\n if name:\n for pgm_pkg_key, pgm_list in pgm_dict.items():\n for pgm_epv in pgm_list:\n if name == pgm_epv.get('package_name', ''):\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\n\n return alt_list","def get_points_rdd(self, sc, parser_class=None):\n parser_class = parser_class or RowParser\n\n input_rdd = sc.textFile(self.output()['dataset'].path)\n\n # Build parser for dataset\n # maybe use csv module?\n fields = input_rdd.take(1)[0].split(',')\n feature_indices = [\n i\n for i, f in enumerate(fields)\n if f != 'id' and f != 'target'\n ]\n target_index = fields.index('target')\n id_index = fields.index('id')\n parser = parser_class(feature_indices, target_index, id_index)\n\n points = (\n input_rdd\n .map(parser)\n .filter(lambda x: x is not None)\n .persist(StorageLevel.DISK_ONLY)\n )\n return points","def get_topics(self):\n try:\n with self.__db_lock:\n sql = \"SELECT * FROM 'topics' ORDER BY 'name' ASC\"\n self.__cursor.execute(sql)\n topics = self.__cursor.fetchall()\n if topics is None or len(topics) == 0:\n return []\n return [topic[1] for topic in topics]\n except Exception as e:\n logging.error(\n \"Exception when trying to get topics list: {}\".format(e))\n return []","def get_dataset_tuple(Xtrain, cols_type_received, cols_ref):\n if len(cols_ref) <= 1 :\n return Xtrain\n\n Xtuple_train = []\n # cols_ref is the reference for types of cols groups (sparse/continuous)\n # This will result in deviding the dataset into many groups of features\n for cols_groupname in cols_ref :\n # Assert the group name is in the cols reference\n assert cols_groupname in cols_type_received, \"Error missing colgroup in config data_pars[cols_model_type] \"\n cols_i = cols_type_received[cols_groupname]\n # Add the columns of this group to the list\n Xtuple_train.append( Xtrain[cols_i] )\n\n if len(cols_ref) == 1 :\n return Xtuple_train[0] ### No tuple\n else :\n return Xtuple_train","def _covert_list_tensor_to_tuple_tensor(list_of_tensor):\n if isinstance(list_of_tensor, list):\n tuple_of_tensor = ()\n for tensor in list_of_tensor:\n tuple_of_tensor += (tensor,)\n return tuple_of_tensor\n return list_of_tensor","def get_variants(topic):\n return [topic['variant%s'%i] for i in range(1,3) if not pd.isnull(topic['variant%s'%i])]","def infertopics(self):\n\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\n for uid in self.scan(attribute=\"updated\"):\n # Remove updated attribute\n self.removeattribute(uid, \"updated\")\n\n # Get list of neighboring nodes\n ids = self.edges(uid)\n\n # Infer topic\n topic = Counter(self.attribute(x, \"topic\") for x in ids).most_common(1)[0][0] if ids else None\n if topic:\n # Add id to topic list and set topic attribute\n self.topics[topic].append(uid)\n self.addattribute(uid, \"topic\", topic)\n\n # Set topic rank\n self.addattribute(uid, \"topicrank\", len(self.topics[topic]) - 1)\n\n # Infer category\n category = Counter(self.attribute(x, \"category\") for x in ids).most_common(1)[0][0]\n self.addattribute(uid, \"category\", category)","def tuple_batch(l):\n def min_one(rev):\n if len(rev)==0:\n rev = [[1]]\n return rev\n\n\n _,review,rating = zip(*l)\n r_t = torch.Tensor(rating).long()\n \n list_rev = [min_one(x) for x in review]\n #word_len = [[len(w) for w in rev] for rev in list_rev ]\n chars = []\n lens = []\n len_c = 0\n #pour chaque tweet\n for rev in list_rev:\n chars_r = []\n #chaque mot\n for w in rev:\n #chaque lettre\n for c in w:\n chars_r.append(c)\n chars_r.append(2) #space character\n chars.append(chars_r)\n lens.append(len(chars_r))\n\n if len(chars_r) > len_c: #save max \n len_c = len(chars_r)\n\n tweets = torch.zeros(len(chars),len_c).long()\n len_s = torch.LongTensor(lens)\n\n for i,data in enumerate(chars):\n tweets[i,0:len(data)] = torch.LongTensor(data)\n \n\n return tweets, len_s, r_t","def tuple_to_list(tup):\n return [element for element in tup]","def get_data(self, topic, datetime_from=None, datetime_to=None):\n try:\n with self.__db_lock:\n time_column = \"timestamp\"\n sql = \"SELECT `{}`, `data`, `format_string` FROM `events` WHERE `topic` == ? ORDER BY `{}` ASC\".format(\n time_column, time_column)\n self.__cursor.execute(sql, (topic,))\n data = self.__cursor.fetchall()\n if data is None or len(data) == 0:\n return []\n\n # first column holds the datetime, second is the data (bytes), third is the format string, fourth is the timestamp\n data_decoded = []\n for d in data:\n timestamp = d[0]\n if d[2] == Database.__BYTES_DB_FORMAT_STRING:\n data = d[1]\n elif d[2] == Database.__UTF8_DB_FORMAT_STRING:\n data = d[1].decode('utf-8')\n else:\n data = struct.unpack(d[2], d[1])[0]\n data_decoded.append([timestamp, data])\n return data_decoded\n except Exception as e:\n logging.error(\n \"Exception when trying to get topics list: {}\".format(e))\n return []","def get_topics_for_comp(comp_list, pgm_list):\n for epv in comp_list:\n name = epv.get('package', {}).get('name', [''])[0]\n if name:\n for pgm_epv in pgm_list:\n if name == pgm_epv.get('package_name', ''):\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\n epv['package']['cooccurrence_probability'] = pgm_epv.get(\n 'cooccurrence_probability', 0)\n epv['package']['cooccurrence_count'] = pgm_epv.get(\n 'cooccurrence_count', 0)\n\n return comp_list","def connect_topic_id_to_topics(model, representants, log_writer):\n # t = model.get_topics()\n topic_indexes = {}\n topics_of_index = {}\n confidence = []\n for key, value in representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key,tp_num,val/len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2),reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n log_writer.add_log('Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],conf[1],conf[2]))\n topic_indexes[conf[0]] = conf[1]\n\n for key, value in topic_indexes.items():\n topics_of_index[value] = [key]\n print(topic_indexes)\n print(topics_of_index)\n return topic_indexes, topics_of_index","def select_data_from_record(self, record):\n x = record['input_ids']\n y = record['label_ids']\n return (x, y)","def gather(session, selected_clusters):\n doc_ids = []\n titles = []\n summaries = []\n links = []\n doc_vector_list = []\n tfidf_vector_list = []\n for i, label in enumerate(session['kmodel'].labels_):\n if str(label) in selected_clusters:\n doc_ids.append(i)\n titles.append(session['titles'][i])\n summaries.append(session['summaries'][i])\n links.append(session['links'][i])\n\n # Create a new topic space matrix by selecting only the vector\n # representations of the new scatter collection documents.\n doc_vector_list.append(session['vector_space'].getrow(i))\n tfidf_vector_list.append(session['tfidf'].getrow(i))\n\n vector_space = vstack(doc_vector_list, format='csr')\n tfidf = vstack(tfidf_vector_list, format='csr')\n\n return doc_ids, titles, summaries, links, vector_space, tfidf","def assign_topics_to_sentences(self):\n \n\n # 10 topics\n topic_dict = {0: 'academics'\n , 1: 'career'\n , 2: 'commute'\n , 3: 'diversity'\n , 4: 'community'\n , 5: 'extracurricular'\n , 6: 'facilities'\n , 7: 'finance'\n , 8: 'housing'\n , 9: 'wellness'\n }\n\n # Some important words that should be included under each topic\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\n , 'degree', 'research', 'elective'\n , 'professor', 'project', 'scholarship', 'knowledge']\n , ['career', 'job', 'coop', 'employment']\n , ['commute', 'skytrain', 'transport', 'commuter']\n , ['diversity', 'diverse', 'background']\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\n , ['extracurricular', 'club', 'sport', 'activity']\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\n , ['finance', 'tuition', 'expensive']\n , ['housing', 'live', 'residence']\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\n\n # Read the data - id and reponse column\n dt = pd.read_csv(self.path_data_col\n , encoding = \"ISO-8859-1\"\n , usecols = [self.id_col_name, self.col_name])\n\n\n \n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n \n # Split into sentences\n dt['sentences'] = self.getSentences(dt[self.col_name])\n \n \n \n\n \n \n\n # Store number of sentences in each response as a column\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\n\n # Split each row into multiple rows - one row for each sentence\n dt = (dt\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\n .apply(pd.Series)\n .stack()\n .reset_index()\n .drop('level_3', axis = 1)\n .rename(columns = {0:'sentences'}))\n\n\n # Clean the sentences\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\n\n # Remove useless sentences\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\n\n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n\n # Tokenize words in the cleaned sentences\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\n\n\n # Call the lexicon function\n topic_lexicons = self.prepare_lexicons()\n\n # Lists to store results\n count_topic_all = []\n actual_topic_all = []\n\n # Tag each response into a topic\n for response in responses:\n\n count_topic = []\n actual_topic = []\n\n for topic in topic_lexicons:\n\n # Count occurance of each word in word stock in the response\n temp = sum(dict((x, response.count(x)) for x in topic).values())\n count_topic.append(temp)\n\n\n for index, value in enumerate(count_topic):\n\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\n if value > 0:\n actual_topic.append(topic_dict[index])\n\n\n # If more than 3 topics are tagged for single sentence, refine by increasing\n # cutoff to at least 2 words instead of 1\n if len(actual_topic) > 3:\n\n actual_topic = []\n for index, value in enumerate(count_topic):\n\n if value > 1: # Increase cutoff\n actual_topic.append(topic_dict[index])\n\n count_topic_all.append(count_topic)\n actual_topic_all.append(actual_topic)\n\n\n dt['tags'] = actual_topic_all\n dt['num_tags'] = count_topic_all\n\n\n # Select only the most important columns\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\n\n return dt, dt_less","def muckrack_trending_topics():\n\tresponse = requests.get('http://muckrack.com')\n\thtml = response.text\n\tdom = pq(html)\n\ttrending_list = dom('.trending')\n\treturn [topic.text for topic in trending_list]","def get_matched_custom_feed(cls, fsid, topic_name): \n custom_feed_map = cls.get_custom_feed_map(fsid)\n if not custom_feed_map:\n return [], None\n if custom_feed_map.has_key(topic_name):\n return [], custom_feed_map.get(topic_name)\n wc_topic_name, required_keywords = cls.to_bracket_wild_card(topic_name)\n if wc_topic_name and custom_feed_map.has_key(wc_topic_name):\n return required_keywords, custom_feed_map.get(wc_topic_name)\n return [], None","def connect_topic_id_to_topics(self, model):\n confidence = []\n for key, value in self.representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key, tp_num, val / len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n self.log_writer.add_log(\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\n conf[1],\n conf[2]))\n self.topic_indexes[conf[0]] = conf[1]\n\n for key, value in self.topic_indexes.items():\n self.topics_of_index[value] = [key]","def getCategories(URIList, annotatedWords):\n \n L=[]\n wordByCategory=dict()\n i=0\n for URI in URIList:\n sparql = SPARQLWrapper(\"http://dbpedia.org/sparql\")\n sparql.setQuery(\"\"\"\n PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n PREFIX dc: <http://purl.org/dc/terms/>\n SELECT ?label\n WHERE { \"\"\"+ \"<\"+ URI + \"> dc:subject ?label }\"\n )\n sparql.setReturnFormat(JSON)\n results = sparql.query().convert()\n for result in results[\"results\"][\"bindings\"]:\n category=result[\"label\"][\"value\"].encode(\"UTF-8\").split(\"/\")[-1].replace(\"_\",\" \").replace(\"Category:\",\"\")\n L.append(category)\n if category in wordByCategory:\n if i>= len(annotatedWords):\n print \"getCategories is computing URI=\",URI\n print \"Trying to append element number\",i,\n print \"from a list having\",len(annotatedWords),\"elements.\"\n wordByCategory[category].append(annotatedWords[i])\n else:\n wordByCategory[category]=[annotatedWords[i]]\n i+=1\n return L, wordByCategory","def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n tuple_result.append(each_tuple)\n\n # sort by the the second element, which is value, of each tuple in the list\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\n\n # separate the topic and count into two lists for graphing purpose\n topics = []\n counts = []\n\n for each_tuple in tuple_result:\n topics.append(each_tuple[0])\n counts.append(each_tuple[1])\n\n return counts, topics","def __rank_topics(self, found_topics, explanation):\n max_value = 0\n scores = []\n for _,topic in found_topics.items():\n topic[\"score\"] = topic[\"times\"] * len(topic['grams'].keys())\n scores.append(topic[\"score\"])\n if topic[\"score\"] > max_value:\n max_value = topic[\"score\"]\n\n for _,topic in found_topics.items():\n if \"syntactic\" in topic:\n topic[\"score\"] = max_value\n\n\n\n\n # Selection of unique topics\n unique_topics = {}\n for t_p,topic in found_topics.items():\n prim_label = self.cso.get_primary_label_wu(t_p)\n if prim_label in unique_topics:\n if unique_topics[prim_label] < topic[\"score\"]:\n unique_topics[prim_label] = topic[\"score\"]\n else:\n unique_topics[prim_label] = topic[\"score\"]\n\n # ranking topics by their score. High-scored topics go on top\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\n\n\n # perform\n vals = []\n for t_p in sort_t:\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\n\n\n #### suppressing some warnings that can be raised by the kneed library\n warnings.filterwarnings(\"ignore\")\n try:\n x_vals = range(1,len(vals)+1)\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\n if t_kn.knee is None:\n #print(\"I performed a different identification of knee\")\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\n except ValueError:\n pass\n\n ##################### Pruning\n\n try:\n knee = int(t_kn.knee)\n except TypeError:\n knee = 0\n except UnboundLocalError:\n knee = 0\n\n if knee > 5:\n try:\n knee += 0\n except TypeError:\n print(\"ERROR: \",t_kn.knee,\" \",knee, \" \", len(sort_t))\n\n else:\n try:\n if sort_t[0][1] == sort_t[4][1]:\n top = sort_t[0][1]\n test_topics = [item[1] for item in sort_t if item[1]==top]\n knee = len(test_topics)\n\n else:\n knee = 5\n except IndexError:\n knee = len(sort_t)\n\n final_topics = []\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\n self.reset_explanation()\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\n\n return final_topics","def get_topic_data(bagFile, topic, return_t=False):\n\n all_msg = []\n if return_t:\n all_t = []\n\n\n # Initialize rosbag object\n bag = rosbag.Bag(bagFile)\n\n for topic, msg, t in bag.read_messages(topics=[topic]):\n all_msg = np.append(all_msg, msg)\n if return_t:\n all_t = np.append(all_t, t.to_sec())\n\n if return_t:\n return all_msg, all_t\n else:\n return all_msg","def extract_MSQs(line):\n \n tokens = sent_tokenize(line)\n questions = []\n \n for i in range(len(tokens)):\n if tokens[i].endswith('?'):\n q1 = tokens[i] # find first question\n sep = 0 # counter for separation between end of q1 and start q2\n for j in range(i+1, len(tokens)): # look for paired question\n if tokens[j].endswith('?'):\n q2 = tokens[j]\n questions.append((q1, q2, sep))\n break\n else:\n sep += len(tokens[j])\n\n return questions","def get_class_topic(class_id):\n topic_data = query_db(\"SELECT id, name FROM topics WHERE class_id=?\", [class_id])\n topics = []\n for topic in topic_data:\n topic_dict_class = {}\n topic_dict_class[\"id\"] = topic[0]\n topic_dict_class[\"name\"] = topic[1]\n topics.append(topic_dict_class)\n return topics","def resulttolist(result, feedback = 0):\n\n newlist = []\n\n if feedback == 2:\n for i in result:\n j = \" \".join(i)\n k = list(j.split(\" \"))\n newlist.append(k)\n elif feedback == 3:\n for i in result:\n j = \" \".join(i)\n k = list(j.split(\" \"))\n newlist.append(k)\n else:\n for i in result:\n j = \"\".join(i)\n newlist.append(j)\n\n return newlist","def get_list(vertices, partition):\n vertices.sort()\n return_array = list()\n for vertex in vertices:\n position = get_position(vertex, partition)\n return_array.append(position)\n return return_array","def _create_pairs_row(model, st_prefs, st_num):\n pairs_row = []\n simp_st_prefs, simp_st_ranks = _get_simple_pref_list_and_ranks(st_prefs)\n\n for i in range(len(simp_st_prefs)):\n pairs_row.append(Pair(st_num, simp_st_prefs[i], simp_st_ranks[i]))\n in_tie = True\n\n return pairs_row","def extract_queries(self, path_topics=\"../topics-rnd5.xml\"):\n \n topic_queries = []\n with open(path_topics, \"r\") as f:\n for line in f:\n match = re.match(\".*<query>([^<]*)<\\/query>.*\", line)\n if match:\n topic_queries.append(match.group(1))\n if len(topic_queries) != 50:\n sys.exit(\"There should be 50 topics, found {}\".format(\n len(topic_queries)))\n \n return topic_queries","def get_topic_labels(topics, num_labels=1, values=False):\n result = []\n for topic in topics:\n labels = get_label_for_topic(topic)\n sorted_labels = sorted(labels.items(), key=operator.itemgetter(1), reverse=False)\n if values:\n result.append(sorted_labels[:num_labels])\n else:\n result.append([h for h, s in sorted_labels][:num_labels])\n return result","def process(data_item, article_id):\n questions = []\n answers = []\n paragraph = [article_id, data_item['context']]\n\n for item in data_item['qas']:\n question = [item[\"id\"], item[\"question\"], item['is_impossible']]\n questions.append(question)\n if item['is_impossible']:\n continue\n answer_options = item[\"answers\"]\n answer_set = set()\n for option in answer_options:\n answer_tuple = (option['text'], option['answer_start'])\n answer_set.add(answer_tuple)\n for index, answer_tuple in enumerate(answer_set):\n answer = [\"{}_{}\".format(item[\"id\"], index+1), item[\"id\"], answer_tuple[0], answer_tuple[1]]\n answers.append(answer)\n return paragraph, questions, answers","def test_question_topics(self):\n p = ProductFactory()\n t1 = TopicFactory(slug='doesnotexist', product=p)\n t2 = TopicFactory(slug='cookies', product=p)\n t3 = TopicFactory(slug='sync', product=p)\n\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t3)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n )\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])"],"string":"[\n \"def get_questions_of_topic(topic):\\n\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n question_table = dynamodb.Table(\\\"Questions\\\")\\n\\n fe = Attr(\\\"TopicId\\\").eq(topic.get(\\\"TopicId\\\"))\\n response = question_table.scan(FilterExpression=fe)\\n questions = response.get(\\\"Items\\\")\\n return questions\",\n \"def get_words_from_tuples(examples):\\n return [[t[0] for t in example] for example in examples]\",\n \"def getSubmissionsByTopic(self, i):\\r\\n return [(ind,sub) for ind, sub in enumerate(self.submissions) if sub.topicId == i]\",\n \"def to_simple_rdd(sc: SparkContext, features: np.array, labels: np.array):\\n pairs = [(x, y) for x, y in zip(features, labels)]\\n return sc.parallelize(pairs)\",\n \"def get_users_movies(myRatings):\\n #return [x[1] for x in myRatings]\\n return list(myRatings.map(lambda x: x[1]).collect())\",\n \"def partition(rows: list, question: Question) -> (list, list):\\n true_rows = []\\n false_rows = []\\n for row in rows:\\n if question.match(row): # True\\n true_rows.append(row)\\n else:\\n false_rows.append(row)\\n return true_rows, false_rows\",\n \"def apply_transformations(rdd):\\n ### BEGIN SOLUTION ###\\n return rdd\",\n \"def getPracticesRDD(sqlContext):\\n from db import *\\n return sqlContext \\\\\\n .read \\\\\\n .format(\\\"jdbc\\\") \\\\\\n .options(\\n driver=driver,\\n url=url,\\n dbtable=\\\"user_practice\\\",\\n user=user,\\n password=password\\n ) \\\\\\n .load() \\\\\\n .rdd \\\\\\n .map(lambda row: (row.id, row)) # We'll use the id to join with the users rdd defined above\",\n \"def get_questions():\\n data = pd.read_excel('Data/APNLP_QuestionsToUser.xlsx')\\n df = pd.DataFrame(data, columns=['Questions'])\\n lst = df.values.tolist()\\n questions = []\\n for q in lst:\\n q = str(q)[2:-2]\\n questions.append(q)\\n return questions\",\n \"def read_tuples_list(corpus_path):\\n idx = 0\\n data = []\\n with open(corpus_path, encoding='utf-8') as fr:\\n lines = fr.readlines()\\n sent_, label_h_, pos_, ner_ = [], [], [], []\\n\\n for line in lines:\\n idx += 1\\n if line.find(\\\"DOC-ID\\\") < 0 and line != '\\\\n':\\n try:\\n [char, label_h, pos_tag, ner_tag] = line.strip().split()\\n sent_.append(char)\\n label_h_.append(label_h)\\n pos_.append(pos_tag)\\n ner_.append(ner_tag)\\n except BaseException as e:\\n print(e)\\n print(line)\\n else:\\n # print(line)\\n if idx > 1:\\n data.append((sent_, label_h_, pos_, ner_))\\n sent_, label_h_, pos_, ner_ = [], [], [], []\\n\\n return data\",\n \"def processRDD(RDD, label):\\n try:\\n newRDD = RDD.map(lambda x: ((x[2], x[3]), (float(x[0]), float(x[1]), float(x[4]), label)))\\n return newRDD\\n except Exception as e:\\n print e\",\n \"def build_recommendations(sc, myRatings, model):\\n #myRatedMovieIds = set([x[1] for x in myRatings])\\n uid = get_uid_from_ratings(myRatings)\\n #print \\\"uid:\\\", uid\\n myRatedMovieIds = set([x[1] for x in myRatings.collect()])\\n #print \\\"myRatedMovieIds:\\\", myRatedMovieIds\\n candidates = sc.parallelize([m for m in movies if m not in myRatedMovieIds]).cache()\\n #print candidates\\n predictions = model.predictAll(candidates.map(lambda x: (uid, x))).collect()\\n #print predictions\\n recommendations = sorted(predictions, key = lambda x: x.product)\\n return recommendations\",\n \"def get_topic_quiz(topic_id):\\n quiz_data = query_db(\\\"SELECT id, name FROM quizzes WHERE topic_id=?;\\\", [topic_id])\\n quizzes = []\\n for quiz in quiz_data:\\n quiz_topic = {}\\n quiz_topic[\\\"id\\\"] = quiz[0]\\n quiz_topic[\\\"name\\\"] = quiz[1]\\n quizzes.append(quiz_topic)\\n return quizzes\",\n \"def get_store_item_list(spark) -> list:\\n sqlDF = spark.sql(\\\"SELECT DISTINCT SKU, Store FROM dfView\\\")\\n store_item_list = sqlDF.rdd.map(tuple).collect()\\n return store_item_list\",\n \"def get_topic_of_question(question):\\n dynamodb = boto3.resource(\\\"dynamodb\\\", region_name=\\\"eu-central-1\\\")\\n topic_table = dynamodb.Table(\\\"Topics\\\")\\n\\n topic_id = question.get(\\\"TopicId\\\")\\n # query topic_id of the question\\n try:\\n response = topic_table.get_item(Key={\\\"TopicId\\\": topic_id})\\n topic = response[\\\"Item\\\"]\\n except:\\n print(\\\"No topic found, returning None..\\\")\\n return None\\n return topic\",\n \"def split_by_time(rdd, ratio):\\n #(user_id, [(item_id, rating, time)])\\n sort_by_time = rdd.map(lambda x:(x[0], (x[1], x[2], x[3])))\\\\\\n .groupByKey().mapValues(list)\\\\\\n .map(lambda x:(x[0],sorted(x[1], key=operator.itemgetter(2), reverse=False)))\\n\\n def _split_f(x, ratio):\\n s = sum(ratio)\\n b = 0\\n l = []\\n for r in ratio:\\n e = int(float(r)/s*len(x))\\n l.append(x[b:b+e])\\n b = b+e\\n return l\\n\\n #(user_id, [[(item_id, rating, time)]...[]])\\n split_by_ratio = sort_by_time.map(lambda x: (x[0], _split_f(x[1], ratio)))\\n print(\\\"split by ratio: \\\", split_by_ratio.take(1))\\n\\n split_list = []\\n for i, _ in enumerate(ratio):\\n split_list.append(split_by_ratio.map(lambda x:(x[0], x[1][i]))\\\\\\n .flatMap(lambda x:[(x[0], y[0], y[1], y[2]) for y in x[1]]))\\n print(\\\"i \\\", i)\\n print(\\\"len \\\", split_list[i].count())\\n\\n return tuple(split_list)\",\n \"def spark_df_to_records(df):\\n return [tuple(r) for r in df.collect()]\",\n \"def partition(self, rows, question):\\n true_rows, false_rows = [], []\\n for row in rows:\\n if question.match(row):\\n true_rows.append(row)\\n else:\\n false_rows.append(row)\\n return true_rows, false_rows\",\n \"def compute_topic_assignment(topic_model, abstracts, min_df=2, max_df=0, tf_matrix_dump_filename=None):\\n if max_df == 0:\\n max_df = 0.9 if len(abstracts) < 100 else 0.8\\n\\n\\n # get the gensim representation of new papers in order to query the topic model\\n papers_gensim = get_gensim_representation_new_docs_for_inference(abstracts, topic_model.id2word,\\n use_lemmer=True, min_df=2,\\n max_df=max_df,\\n tf_matrix_dump_filename=tf_matrix_dump_filename)\\n\\n # for each paper compute the topics assignment\\n topic_assignment = [None] * len(papers_gensim)\\n for i, paper in enumerate(papers_gensim):\\n topic_assignment[i] = topic_model[paper]\\n\\n return topic_assignment\",\n \"def get_valid_phrases():\\n return [x[0] for x in all_topics if x[1] == \\\"1\\\"]\",\n \"def ex_list(data):\\n return tuple(data)\",\n \"def read_data(file_path, sparkContext):\\n data_rdd = sparkContext \\\\\\n .textFile(file_path) \\\\\\n .map(eval) \\\\\\n .map(lambda x: (x[0], x[1]))\\n return data_rdd\",\n \"def _topics_words(self, num_of_words):\\n x = self.model.show_topics(-1, num_of_words, formatted=False)\\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\\n # where `topic` is a list of `(word, probability)` tuples.\\n return [[i[0] for i in topic[1]] for topic in x]\",\n \"def get_custom_phrases():\\n return [x[0] for x in all_topics if x[2] == \\\"1\\\"]\",\n \"def DatasetToTuple(sample):\\n \\n X_elem = []\\n Y_elem = []\\n for x,y in sample:\\n X_elem.append(x if x.dim() > 0 else x.item())\\n Y_elem.append(y if y.dim() > 0 else y.item()) \\n return (torch.stack(X_elem),torch.stack(Y_elem))\",\n \"def DatasetToTuple(sample):\\n \\n X_elem = []\\n Y_elem = []\\n for x,y in sample:\\n X_elem.append(x if x.dim() > 0 else x.item())\\n Y_elem.append(y if y.dim() > 0 else y.item()) \\n return (torch.stack(X_elem),torch.stack(Y_elem))\",\n \"def getUsersRDD(sqlContext):\\n # Currently the id field ranges from '0' to '1000000'.\\n # To avoid loading it all in memory, partition on the id field (100 partitions, about 10k records per partition).\\n # Also setting fetch size to 10,000 to avoid multiple database calls per partition.\\n # All records from a single partition will come in a single query.\\n # If we need to use less memory, we can increase the # of partitions and decrease the lower/uppper bounds.\\n # We are also relying on Spark to spill to disk if no memory is available.\\n from db import *\\n return sqlContext \\\\\\n .read \\\\\\n .format(\\\"jdbc\\\") \\\\\\n .options(\\n driver=driver,\\n url=url,\\n dbtable=\\\"user\\\",\\n user=user,\\n password=password,\\n fetchSize=10000,\\n numPartitions=100,\\n partitionColumn=\\\"id\\\",\\n lowerBound=0,\\n upperBound=1000000\\n ) \\\\\\n .load() \\\\\\n .rdd \\\\\\n .persist(StorageLevel.MEMORY_AND_DISK) \\\\\\n .map(lambda row: (row.practice_id, row)) # We are setting practice_id as the key here because we'll use that to join with user_practice table\",\n \"def get_tweets_by_topic(topic, start_date, end_date):\\n try:\\n query = f\\\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\\\"\\n logger.info(f'QUERY: {query}') \\n with MysqlCursor() as cur:\\n cur.execute(query)\\n tweets = cur.fetchall()\\n columns = [col[0] for col in cur.description]\\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \\n return tweets, columns\\n \\n except Exception as ex:\\n logger.exception(ex)\\n return f'Exception: {ex}'\",\n \"def lp_to_simple_rdd(lp_rdd: RDD, categorical: bool = False, nb_classes: int = None):\\n if categorical:\\n if not nb_classes:\\n labels = np.asarray(lp_rdd.map(\\n lambda lp: lp.label).collect(), dtype='int32')\\n nb_classes = np.max(labels) + 1\\n rdd = lp_rdd.map(lambda lp: (from_vector(lp.features),\\n encode_label(lp.label, nb_classes)))\\n else:\\n rdd = lp_rdd.map(lambda lp: (from_vector(lp.features), lp.label))\\n return rdd\",\n \"def get_training_data(self) -> List[Tuple[str, Optional[str]]]:\\n return [(k, v) for k, v in sorted(self._clusters.items(), key=lambda x: (x[1] if x[1] else '', x[0]))]\",\n \"def get_subscription_ids(self, topic: str) -> Tuple[int, ...]:\\n try:\\n return tuple(self.__sub_ids[topic])\\n except KeyError:\\n return ()\",\n \"def convert_partitions_to_list(partition):\\n parts = list()\\n for part in partition:\\n parts.append(part)\\n return parts\",\n \"def partition(rows, question):\\n true_rows, false_rows = [], []\\n for row in rows:\\n if question.match(row):\\n # the row's value of the column was greater than or equal to the questions value\\n true_rows.append(row)\\n else:\\n false_rows.append(row)\\n return true_rows, false_rows\",\n \"def get_ped_topics(n_peds):\\n ls = []\\n for n in range(n_peds):\\n ls += [coord.format(n) for coord in PED_TEMPL_TOPICS]\\n return ls\",\n \"def get_related_topics(self,keyword,cut=0.5):\\n \\n ret = []\\n\\n if type(keyword) == str:\\n if keyword in self.topic_map.keys():\\n ret = [(keyword,1.0)]\\n keyword = \\\"\\\"\\n else:\\n _keyword = []\\n for k in keyword:\\n if k in self.topic_map.keys():\\n ret.append((k,1.0))\\n else:\\n _keyword.append(k) \\n keyword = _keyword\\n\\n keyword_rels = set(self.get_related_keywords(keyword,self.keyword_map_rel,_score=False))\\n\\n if len(keyword_rels) > 0:\\n for topic,topic_rels in self.topic_map.items():\\n alike = keyword_rels.intersection(topic_rels)\\n score = (len(alike) * (100/len(keyword_rels)))/100\\n ret.append((topic,round(score,3)))\\n ret.sort(key=lambda x : x[1], reverse=True)\\n ret = [t for t in ret if t[1] >= cut]\\n \\n return ret\",\n \"def _topics_weights(self, num_of_words):\\n topics = self.model.show_topics(-1, num_of_words, formatted=False)\\n # `show_topics` method return a list of `(topic_number, topic)` tuples,\\n # where `topic` is a list of `(word, probability)` tuples.\\n return [[i[1] for i in t[1]] for t in topics]\",\n \"def get_most_relevant_topics(topics_list):\\n topics_list.sort(cmp=lambda x, y: 1 if x[1] < y[1] else -1)\\n topics_list = topics_list[:3] # ARBITRARY (at most 3 topic names)\\n topics_id, _ = zip(*topics_list)\\n return [topics_tools.lda_topic_names[tid] for tid in topics_id]\",\n \"def tup_list_maker(tup_list):\\n final_list = []\\n for item in tup_list:\\n index = item[0]\\n sentences = item[1]\\n for sentence in sentences:\\n pair = (index, sentence)\\n final_list.append(pair)\\n return final_list\",\n \"def get_user_list(dataset):\\n res = dataset\\\\\\n .map(lambda x: x[0])\\\\\\n .collect()\\n return list(set(res))\",\n \"def select_essays_with_topic(conn, topic):\\n cur = conn.cursor()\\n cur.execute('SELECT link, title FROM essays WHERE \\\"{}\\\" > 0 ORDER BY \\\"{}\\\" DESC'.format(topic, topic))\\n\\n d = {}\\n count = 0\\n rows = cur.fetchall()\\n\\n print(\\\"Total: \\\", len(rows))\\n for row in rows:\\n d[count] = (list(row)[0], list(row)[1])\\n count += 1\\n # print(row)\\n #print(list(rows[0]))\\n return d\",\n \"def schools_by_topic(mongo_collection, topic):\\n res = []\\n returned_values = mongo_collection.find({\\\"topics\\\": {\\\"$all\\\": [topic]}})\\n for value in returned_values:\\n res.append(value)\\n return res\",\n \"def get_topics(self):\\r\\n return [x[0] for x in get_published_topics()]\",\n \"def get_teacher_topic_all():\\n topic_data = query_db(\\n \\\"SELECT topics.id, topics.name, classes.name FROM topics JOIN classes \\\"\\n \\\"ON topics.class_id=classes.id WHERE teacher_id=?;\\\",\\n [flask.session[\\\"id\\\"]],\\n )\\n topics = []\\n for topic in topic_data:\\n topic_dict_teacher = {}\\n topic_dict_teacher[\\\"id\\\"] = topic[0]\\n topic_dict_teacher[\\\"name\\\"] = flask.escape(str(topic[1]))\\n topic_dict_teacher[\\\"class\\\"] = flask.escape(str(topic[2]))\\n topics.append(topic_dict_teacher)\\n return topics\",\n \"def transform(self):\\n result = []\\n for item in self.doc_topic_matrix:\\n result.append(item / np.sum(item))\\n result = np.array(result)\\n return result\",\n \"def populate_weight_tuple_list(list_object):\\n tuple_list = []\\n\\n for i in range(len(list_object[0])):\\n weight_tuple = (list_object[0][i], float(list_object[1][i]))\\n tuple_list.append(weight_tuple)\\n \\n return tuple_list\",\n \"def _fix_examples(examples):\\n if len(examples) > 0 and len(examples[0]) > 0 and len(examples[0][0]) > 2:\\n # Assumes all elements use the same format of tuple of length 3 (so we only check [0][0])\\n return [\\n [(tuple_ex[0], tuple_ex[1]) for tuple_ex in example]\\n for example in examples\\n ]\\n else:\\n return examples\",\n \"def get_valid_indices():\\n return [i for i, val in enumerate(all_topics) if val[1] == \\\"1\\\"]\",\n \"def partitionData(data, labels, partition):\\n\\treturn [s[partition] for s in data], labels[partition]\",\n \"def to_sample_rdd(x, y, sc, num_slices=None):\\n x_rdd = sc.parallelize(x, num_slices)\\n y_rdd = sc.parallelize(y, num_slices)\\n return x_rdd.zip(y_rdd).map(lambda item: Sample.from_ndarray(item[0], item[1]))\",\n \"def combine_sub_blocks(rdd):\\n ### BEGIN SOLUTION ###\\n return rdd\",\n \"def get_keypoint_tuples(eval_config):\\n tuple_list = []\\n kp_list = eval_config.keypoint_edge\\n for edge in kp_list:\\n tuple_list.append((edge.start, edge.end))\\n return tuple_list\",\n \"def kml_extract_RDD(xml_file):\\n soup = BeautifulSoup(xml_file, \\\"lxml-xml\\\")\\n return get_kml_content(soup)\",\n \"def get_tuples(outputs) -> list:\\n return list(map(get_tuples_helper, outputs))\",\n \"def _process_data(rdd_entry, feature_list):\\n events = []\\n for event in rdd_entry:\\n events.append(event[RDD_EVENT])\\n return IptablesIngestor.vectorize_events(events, feature_list)\",\n \"def generate_sub_blocks(rdd):\\n ### BEGIN SOLUTION ###\\n return rdd\",\n \"def main_topic_doc(ldamodel, corpus=corpus): \\n \\n doc_topics = pd.DataFrame()\\n\\n for i, row in enumerate(ldamodel[corpus]):\\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\\n\\n for j, (topic_num, prop_topic) in enumerate(row):\\n if j == 0:\\n wp = ldamodel.show_topic(topic_num)\\n topic_keywords = \\\"' \\\".join([word for word, prop in wp])\\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\\n else:\\n break\\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\\n return doc_topics\",\n \"def get_custom_indices():\\n return [i for i, val in enumerate(all_topics) if val[2] == \\\"1\\\"]\",\n \"def extract_records_for_nltk(iaa_df: pd.DataFrame) -> List[Tuple]:\\n return [(b, c, d) for _, b, c, d in iaa_df.to_records()]\",\n \"def get_score_book(self) -> List[Tuple[str, float]]:\\n returned = []\\n\\n for item, size in self.score_book.items():\\n my_tuple = item, size\\n returned.append(my_tuple)\\n\\n return returned\",\n \"def get_topic_words(self, topics):\\n topic_words = []\\n for topic, top_n_words in topics.items():\\n words = [word for word, c_tf_idf in top_n_words]\\n topic_words.append(words)\\n return topic_words\",\n \"def convert_shapely_points_to_tuples(list_of_points) -> list:\\n return [(p.x, p.y) for p in list_of_points]\",\n \"def process_rdd(time, rdd):\\n print(\\\"----------- %s -----------\\\" % str(time))\\n try:\\n if rdd:\\n # Get spark sql singleton context from the current context\\n sql_context = get_sql_context_instance(rdd.context.getConf())\\n # convert the RDD to Row RDD\\n row_rdd = rdd.map(lambda w: Row(hashtag=w[0], hashtag_count=w[1]))\\n # create a DF from the Row RDD\\n hashtags_dataframe = sql_context.createDataFrame(row_rdd)\\n # Register the dataframe as table\\n hashtags_dataframe.createOrReplaceTempView(\\\"hashtags\\\")\\n # get the top 10 hashtags from the table using SQL and print them\\n hashtag_counts_dataframe = sql_context.sql(\\n \\\"select hashtag, hashtag_count from hashtags order by hashtag_count desc limit 10\\\")\\n hashtag_counts_dataframe.show()\\n\\n # call this method to prepare top 10 hashtags DF and send them\\n\\n def send_dataframe_to_dashboard(dataframe):\\n \\\"\\\"\\\"\\n Function to send DataFrame to the dashboard for visualization.\\n :param dataframe: Spark DataFrame created by process_rdd().\\n \\\"\\\"\\\"\\n # extract the hashtags from dataframe and convert them into array\\n top_tags = [str(t.hashtag) for t in dataframe.select(\\\"hashtag\\\").collect()]\\n # extract the counts from dataframe and convert them into array\\n tags_count = [p.hashtag_count for p in dataframe.select(\\\"hashtag_count\\\").collect()]\\n # initialize and send the data through REST API\\n request_data = {'label': str(top_tags), 'data': str(tags_count)}\\n response = post(dashboard_url, data=request_data)\\n\\n send_dataframe_to_dashboard(hashtag_counts_dataframe)\\n except:\\n pass\",\n \"def ID_Data(targ_driver, RDD, sc, K = 200):\\n try:\\n newID1 = [targ_driver] * K\\n newID2 = np.arange(200, 201+K).astype(str)\\n newID = zip(newID1, newID2)\\n oldID = RDD.map(lambda x: (x[2],x[3])).distinct().collect()\\n glossary = sc.parallelize(zip(oldID, newID))\\n newRDD = RDD.map(lambda x: ((x[2],x[3]), ([x[0],x[1],x[4]]))).join(glossary)\\n newID_RDD = newRDD.map(lambda x: (x[1][0][0], x[1][0][1], x[1][1][0], x[1][1][1], x[1][0][2]))\\n return newID_RDD\\n except Exception as e:\\n print e\",\n \"def answerer(embeddings, tuples: tf.Variable, scoring=multilinear):\\n n_data, n_slots, rank = [d.value for d in embeddings.get_shape()]\\n n_data, n_tuples, order = [d.value for d in tuples.get_shape()]\\n\\n shift_indices = tf.constant(np.reshape(\\n np.outer(range(n_data), np.ones(n_tuples * n_slots)) * n_slots, (n_data, n_tuples, n_slots)), dtype='int64')\\n questions_shifted = tuples + shift_indices\\n\\n preds = scoring(\\n tf.reshape(embeddings, (n_data * n_slots, rank)),\\n tf.reshape(questions_shifted, (n_data * n_tuples, order)))\\n\\n return tf.reshape(preds, (n_data, n_tuples))\",\n \"def create_featseltuple(ids):\\n newlist = []\\n for part_id in ids:\\n newlist.extend([part_id, part_id + 91])\\n return tuple(sorted(newlist))\",\n \"def _partition_rdd(self, rdd, function): \\n\\n N, buffer_tau, dom_mins, dom_maxs, symmetric = self.N, self.buffer_tau, \\\\\\n self.dom_mins, self.dom_maxs, \\\\\\n self.symmetric \\n def partition_helper(iterator):\\n for arr in iterator: \\n res = function(arr,N,buffer_tau,symmetric,dom_mins,dom_maxs)\\n for r in res: \\n yield r\\n return rdd.mapPartitions(partition_helper)\",\n \"def toListOfTuple(self, df:pd.core.frame.DataFrame) -> List[Tuple]: \\n df['TIME_STAMP'] = df['TIME_STAMP'].astype('str')\\n records = df.to_records(index=False)\\n listOfTuple = list(records)\\n return listOfTuple\",\n \"def get_topic_assign(topic_id):\\n assignment_data = query_db(\\n \\\"SELECT id, name, due_date FROM assignments WHERE topic_id=?;\\\", [topic_id]\\n )\\n assignments = []\\n for assignment in assignment_data:\\n topic_assign_dict = {}\\n topic_assign_dict[\\\"id\\\"] = assignment[0]\\n topic_assign_dict[\\\"name\\\"] = str(assignment[1])\\n topic_assign_dict[\\\"due_date\\\"] = assignment[2]\\n assignments.append(topic_assign_dict)\\n return assignments\",\n \"def get_topics(model, nlp_model, n_top_words):\\n\\n words = nlp_model.get_feature_names()\\n\\n return [convert_to_string([words[i] for i in topic.argsort()[:-n_top_words - 1:-1]]) for topic_idx, topic in enumerate(model.components_)]\",\n \"def get_topics_for_alt(alt_list, pgm_dict):\\n for epv in alt_list:\\n name = epv.get('package', {}).get('name', [''])[0]\\n if name:\\n for pgm_pkg_key, pgm_list in pgm_dict.items():\\n for pgm_epv in pgm_list:\\n if name == pgm_epv.get('package_name', ''):\\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\\n\\n return alt_list\",\n \"def get_points_rdd(self, sc, parser_class=None):\\n parser_class = parser_class or RowParser\\n\\n input_rdd = sc.textFile(self.output()['dataset'].path)\\n\\n # Build parser for dataset\\n # maybe use csv module?\\n fields = input_rdd.take(1)[0].split(',')\\n feature_indices = [\\n i\\n for i, f in enumerate(fields)\\n if f != 'id' and f != 'target'\\n ]\\n target_index = fields.index('target')\\n id_index = fields.index('id')\\n parser = parser_class(feature_indices, target_index, id_index)\\n\\n points = (\\n input_rdd\\n .map(parser)\\n .filter(lambda x: x is not None)\\n .persist(StorageLevel.DISK_ONLY)\\n )\\n return points\",\n \"def get_topics(self):\\n try:\\n with self.__db_lock:\\n sql = \\\"SELECT * FROM 'topics' ORDER BY 'name' ASC\\\"\\n self.__cursor.execute(sql)\\n topics = self.__cursor.fetchall()\\n if topics is None or len(topics) == 0:\\n return []\\n return [topic[1] for topic in topics]\\n except Exception as e:\\n logging.error(\\n \\\"Exception when trying to get topics list: {}\\\".format(e))\\n return []\",\n \"def get_dataset_tuple(Xtrain, cols_type_received, cols_ref):\\n if len(cols_ref) <= 1 :\\n return Xtrain\\n\\n Xtuple_train = []\\n # cols_ref is the reference for types of cols groups (sparse/continuous)\\n # This will result in deviding the dataset into many groups of features\\n for cols_groupname in cols_ref :\\n # Assert the group name is in the cols reference\\n assert cols_groupname in cols_type_received, \\\"Error missing colgroup in config data_pars[cols_model_type] \\\"\\n cols_i = cols_type_received[cols_groupname]\\n # Add the columns of this group to the list\\n Xtuple_train.append( Xtrain[cols_i] )\\n\\n if len(cols_ref) == 1 :\\n return Xtuple_train[0] ### No tuple\\n else :\\n return Xtuple_train\",\n \"def _covert_list_tensor_to_tuple_tensor(list_of_tensor):\\n if isinstance(list_of_tensor, list):\\n tuple_of_tensor = ()\\n for tensor in list_of_tensor:\\n tuple_of_tensor += (tensor,)\\n return tuple_of_tensor\\n return list_of_tensor\",\n \"def get_variants(topic):\\n return [topic['variant%s'%i] for i in range(1,3) if not pd.isnull(topic['variant%s'%i])]\",\n \"def infertopics(self):\\n\\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\\n for uid in self.scan(attribute=\\\"updated\\\"):\\n # Remove updated attribute\\n self.removeattribute(uid, \\\"updated\\\")\\n\\n # Get list of neighboring nodes\\n ids = self.edges(uid)\\n\\n # Infer topic\\n topic = Counter(self.attribute(x, \\\"topic\\\") for x in ids).most_common(1)[0][0] if ids else None\\n if topic:\\n # Add id to topic list and set topic attribute\\n self.topics[topic].append(uid)\\n self.addattribute(uid, \\\"topic\\\", topic)\\n\\n # Set topic rank\\n self.addattribute(uid, \\\"topicrank\\\", len(self.topics[topic]) - 1)\\n\\n # Infer category\\n category = Counter(self.attribute(x, \\\"category\\\") for x in ids).most_common(1)[0][0]\\n self.addattribute(uid, \\\"category\\\", category)\",\n \"def tuple_batch(l):\\n def min_one(rev):\\n if len(rev)==0:\\n rev = [[1]]\\n return rev\\n\\n\\n _,review,rating = zip(*l)\\n r_t = torch.Tensor(rating).long()\\n \\n list_rev = [min_one(x) for x in review]\\n #word_len = [[len(w) for w in rev] for rev in list_rev ]\\n chars = []\\n lens = []\\n len_c = 0\\n #pour chaque tweet\\n for rev in list_rev:\\n chars_r = []\\n #chaque mot\\n for w in rev:\\n #chaque lettre\\n for c in w:\\n chars_r.append(c)\\n chars_r.append(2) #space character\\n chars.append(chars_r)\\n lens.append(len(chars_r))\\n\\n if len(chars_r) > len_c: #save max \\n len_c = len(chars_r)\\n\\n tweets = torch.zeros(len(chars),len_c).long()\\n len_s = torch.LongTensor(lens)\\n\\n for i,data in enumerate(chars):\\n tweets[i,0:len(data)] = torch.LongTensor(data)\\n \\n\\n return tweets, len_s, r_t\",\n \"def tuple_to_list(tup):\\n return [element for element in tup]\",\n \"def get_data(self, topic, datetime_from=None, datetime_to=None):\\n try:\\n with self.__db_lock:\\n time_column = \\\"timestamp\\\"\\n sql = \\\"SELECT `{}`, `data`, `format_string` FROM `events` WHERE `topic` == ? ORDER BY `{}` ASC\\\".format(\\n time_column, time_column)\\n self.__cursor.execute(sql, (topic,))\\n data = self.__cursor.fetchall()\\n if data is None or len(data) == 0:\\n return []\\n\\n # first column holds the datetime, second is the data (bytes), third is the format string, fourth is the timestamp\\n data_decoded = []\\n for d in data:\\n timestamp = d[0]\\n if d[2] == Database.__BYTES_DB_FORMAT_STRING:\\n data = d[1]\\n elif d[2] == Database.__UTF8_DB_FORMAT_STRING:\\n data = d[1].decode('utf-8')\\n else:\\n data = struct.unpack(d[2], d[1])[0]\\n data_decoded.append([timestamp, data])\\n return data_decoded\\n except Exception as e:\\n logging.error(\\n \\\"Exception when trying to get topics list: {}\\\".format(e))\\n return []\",\n \"def get_topics_for_comp(comp_list, pgm_list):\\n for epv in comp_list:\\n name = epv.get('package', {}).get('name', [''])[0]\\n if name:\\n for pgm_epv in pgm_list:\\n if name == pgm_epv.get('package_name', ''):\\n epv['package']['pgm_topics'] = pgm_epv.get('topic_list', [])\\n epv['package']['cooccurrence_probability'] = pgm_epv.get(\\n 'cooccurrence_probability', 0)\\n epv['package']['cooccurrence_count'] = pgm_epv.get(\\n 'cooccurrence_count', 0)\\n\\n return comp_list\",\n \"def connect_topic_id_to_topics(model, representants, log_writer):\\n # t = model.get_topics()\\n topic_indexes = {}\\n topics_of_index = {}\\n confidence = []\\n for key, value in representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key,tp_num,val/len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2),reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n log_writer.add_log('Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],conf[1],conf[2]))\\n topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in topic_indexes.items():\\n topics_of_index[value] = [key]\\n print(topic_indexes)\\n print(topics_of_index)\\n return topic_indexes, topics_of_index\",\n \"def select_data_from_record(self, record):\\n x = record['input_ids']\\n y = record['label_ids']\\n return (x, y)\",\n \"def gather(session, selected_clusters):\\n doc_ids = []\\n titles = []\\n summaries = []\\n links = []\\n doc_vector_list = []\\n tfidf_vector_list = []\\n for i, label in enumerate(session['kmodel'].labels_):\\n if str(label) in selected_clusters:\\n doc_ids.append(i)\\n titles.append(session['titles'][i])\\n summaries.append(session['summaries'][i])\\n links.append(session['links'][i])\\n\\n # Create a new topic space matrix by selecting only the vector\\n # representations of the new scatter collection documents.\\n doc_vector_list.append(session['vector_space'].getrow(i))\\n tfidf_vector_list.append(session['tfidf'].getrow(i))\\n\\n vector_space = vstack(doc_vector_list, format='csr')\\n tfidf = vstack(tfidf_vector_list, format='csr')\\n\\n return doc_ids, titles, summaries, links, vector_space, tfidf\",\n \"def assign_topics_to_sentences(self):\\n \\n\\n # 10 topics\\n topic_dict = {0: 'academics'\\n , 1: 'career'\\n , 2: 'commute'\\n , 3: 'diversity'\\n , 4: 'community'\\n , 5: 'extracurricular'\\n , 6: 'facilities'\\n , 7: 'finance'\\n , 8: 'housing'\\n , 9: 'wellness'\\n }\\n\\n # Some important words that should be included under each topic\\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\\n , 'degree', 'research', 'elective'\\n , 'professor', 'project', 'scholarship', 'knowledge']\\n , ['career', 'job', 'coop', 'employment']\\n , ['commute', 'skytrain', 'transport', 'commuter']\\n , ['diversity', 'diverse', 'background']\\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\\n , ['extracurricular', 'club', 'sport', 'activity']\\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\\n , ['finance', 'tuition', 'expensive']\\n , ['housing', 'live', 'residence']\\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\\n\\n # Read the data - id and reponse column\\n dt = pd.read_csv(self.path_data_col\\n , encoding = \\\"ISO-8859-1\\\"\\n , usecols = [self.id_col_name, self.col_name])\\n\\n\\n \\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n \\n # Split into sentences\\n dt['sentences'] = self.getSentences(dt[self.col_name])\\n \\n \\n \\n\\n \\n \\n\\n # Store number of sentences in each response as a column\\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\\n\\n # Split each row into multiple rows - one row for each sentence\\n dt = (dt\\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\\n .apply(pd.Series)\\n .stack()\\n .reset_index()\\n .drop('level_3', axis = 1)\\n .rename(columns = {0:'sentences'}))\\n\\n\\n # Clean the sentences\\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\\n\\n # Remove useless sentences\\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\\n\\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n\\n # Tokenize words in the cleaned sentences\\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\\n\\n\\n # Call the lexicon function\\n topic_lexicons = self.prepare_lexicons()\\n\\n # Lists to store results\\n count_topic_all = []\\n actual_topic_all = []\\n\\n # Tag each response into a topic\\n for response in responses:\\n\\n count_topic = []\\n actual_topic = []\\n\\n for topic in topic_lexicons:\\n\\n # Count occurance of each word in word stock in the response\\n temp = sum(dict((x, response.count(x)) for x in topic).values())\\n count_topic.append(temp)\\n\\n\\n for index, value in enumerate(count_topic):\\n\\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\\n if value > 0:\\n actual_topic.append(topic_dict[index])\\n\\n\\n # If more than 3 topics are tagged for single sentence, refine by increasing\\n # cutoff to at least 2 words instead of 1\\n if len(actual_topic) > 3:\\n\\n actual_topic = []\\n for index, value in enumerate(count_topic):\\n\\n if value > 1: # Increase cutoff\\n actual_topic.append(topic_dict[index])\\n\\n count_topic_all.append(count_topic)\\n actual_topic_all.append(actual_topic)\\n\\n\\n dt['tags'] = actual_topic_all\\n dt['num_tags'] = count_topic_all\\n\\n\\n # Select only the most important columns\\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\\n\\n return dt, dt_less\",\n \"def muckrack_trending_topics():\\n\\tresponse = requests.get('http://muckrack.com')\\n\\thtml = response.text\\n\\tdom = pq(html)\\n\\ttrending_list = dom('.trending')\\n\\treturn [topic.text for topic in trending_list]\",\n \"def get_matched_custom_feed(cls, fsid, topic_name): \\n custom_feed_map = cls.get_custom_feed_map(fsid)\\n if not custom_feed_map:\\n return [], None\\n if custom_feed_map.has_key(topic_name):\\n return [], custom_feed_map.get(topic_name)\\n wc_topic_name, required_keywords = cls.to_bracket_wild_card(topic_name)\\n if wc_topic_name and custom_feed_map.has_key(wc_topic_name):\\n return required_keywords, custom_feed_map.get(wc_topic_name)\\n return [], None\",\n \"def connect_topic_id_to_topics(self, model):\\n confidence = []\\n for key, value in self.representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key, tp_num, val / len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n self.log_writer.add_log(\\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\\n conf[1],\\n conf[2]))\\n self.topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in self.topic_indexes.items():\\n self.topics_of_index[value] = [key]\",\n \"def getCategories(URIList, annotatedWords):\\n \\n L=[]\\n wordByCategory=dict()\\n i=0\\n for URI in URIList:\\n sparql = SPARQLWrapper(\\\"http://dbpedia.org/sparql\\\")\\n sparql.setQuery(\\\"\\\"\\\"\\n PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\\n PREFIX dc: <http://purl.org/dc/terms/>\\n SELECT ?label\\n WHERE { \\\"\\\"\\\"+ \\\"<\\\"+ URI + \\\"> dc:subject ?label }\\\"\\n )\\n sparql.setReturnFormat(JSON)\\n results = sparql.query().convert()\\n for result in results[\\\"results\\\"][\\\"bindings\\\"]:\\n category=result[\\\"label\\\"][\\\"value\\\"].encode(\\\"UTF-8\\\").split(\\\"/\\\")[-1].replace(\\\"_\\\",\\\" \\\").replace(\\\"Category:\\\",\\\"\\\")\\n L.append(category)\\n if category in wordByCategory:\\n if i>= len(annotatedWords):\\n print \\\"getCategories is computing URI=\\\",URI\\n print \\\"Trying to append element number\\\",i,\\n print \\\"from a list having\\\",len(annotatedWords),\\\"elements.\\\"\\n wordByCategory[category].append(annotatedWords[i])\\n else:\\n wordByCategory[category]=[annotatedWords[i]]\\n i+=1\\n return L, wordByCategory\",\n \"def topic_count():\\n # get the number topics and their counts as tuples: ('Topic', 123)\\n query = peewee.RawQuery(Post, \\\"select topic, count(topic) from post group by topic\\\").tuples()\\n\\n # turn the result of the query object into a list of tuples\\n tuple_result = []\\n for each_tuple in query:\\n tuple_result.append(each_tuple)\\n\\n # sort by the the second element, which is value, of each tuple in the list\\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\\n\\n # separate the topic and count into two lists for graphing purpose\\n topics = []\\n counts = []\\n\\n for each_tuple in tuple_result:\\n topics.append(each_tuple[0])\\n counts.append(each_tuple[1])\\n\\n return counts, topics\",\n \"def __rank_topics(self, found_topics, explanation):\\n max_value = 0\\n scores = []\\n for _,topic in found_topics.items():\\n topic[\\\"score\\\"] = topic[\\\"times\\\"] * len(topic['grams'].keys())\\n scores.append(topic[\\\"score\\\"])\\n if topic[\\\"score\\\"] > max_value:\\n max_value = topic[\\\"score\\\"]\\n\\n for _,topic in found_topics.items():\\n if \\\"syntactic\\\" in topic:\\n topic[\\\"score\\\"] = max_value\\n\\n\\n\\n\\n # Selection of unique topics\\n unique_topics = {}\\n for t_p,topic in found_topics.items():\\n prim_label = self.cso.get_primary_label_wu(t_p)\\n if prim_label in unique_topics:\\n if unique_topics[prim_label] < topic[\\\"score\\\"]:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n else:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n\\n # ranking topics by their score. High-scored topics go on top\\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\\n\\n\\n # perform\\n vals = []\\n for t_p in sort_t:\\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\\n\\n\\n #### suppressing some warnings that can be raised by the kneed library\\n warnings.filterwarnings(\\\"ignore\\\")\\n try:\\n x_vals = range(1,len(vals)+1)\\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\\n if t_kn.knee is None:\\n #print(\\\"I performed a different identification of knee\\\")\\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\\n except ValueError:\\n pass\\n\\n ##################### Pruning\\n\\n try:\\n knee = int(t_kn.knee)\\n except TypeError:\\n knee = 0\\n except UnboundLocalError:\\n knee = 0\\n\\n if knee > 5:\\n try:\\n knee += 0\\n except TypeError:\\n print(\\\"ERROR: \\\",t_kn.knee,\\\" \\\",knee, \\\" \\\", len(sort_t))\\n\\n else:\\n try:\\n if sort_t[0][1] == sort_t[4][1]:\\n top = sort_t[0][1]\\n test_topics = [item[1] for item in sort_t if item[1]==top]\\n knee = len(test_topics)\\n\\n else:\\n knee = 5\\n except IndexError:\\n knee = len(sort_t)\\n\\n final_topics = []\\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\\n self.reset_explanation()\\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\\n\\n return final_topics\",\n \"def get_topic_data(bagFile, topic, return_t=False):\\n\\n all_msg = []\\n if return_t:\\n all_t = []\\n\\n\\n # Initialize rosbag object\\n bag = rosbag.Bag(bagFile)\\n\\n for topic, msg, t in bag.read_messages(topics=[topic]):\\n all_msg = np.append(all_msg, msg)\\n if return_t:\\n all_t = np.append(all_t, t.to_sec())\\n\\n if return_t:\\n return all_msg, all_t\\n else:\\n return all_msg\",\n \"def extract_MSQs(line):\\n \\n tokens = sent_tokenize(line)\\n questions = []\\n \\n for i in range(len(tokens)):\\n if tokens[i].endswith('?'):\\n q1 = tokens[i] # find first question\\n sep = 0 # counter for separation between end of q1 and start q2\\n for j in range(i+1, len(tokens)): # look for paired question\\n if tokens[j].endswith('?'):\\n q2 = tokens[j]\\n questions.append((q1, q2, sep))\\n break\\n else:\\n sep += len(tokens[j])\\n\\n return questions\",\n \"def get_class_topic(class_id):\\n topic_data = query_db(\\\"SELECT id, name FROM topics WHERE class_id=?\\\", [class_id])\\n topics = []\\n for topic in topic_data:\\n topic_dict_class = {}\\n topic_dict_class[\\\"id\\\"] = topic[0]\\n topic_dict_class[\\\"name\\\"] = topic[1]\\n topics.append(topic_dict_class)\\n return topics\",\n \"def resulttolist(result, feedback = 0):\\n\\n newlist = []\\n\\n if feedback == 2:\\n for i in result:\\n j = \\\" \\\".join(i)\\n k = list(j.split(\\\" \\\"))\\n newlist.append(k)\\n elif feedback == 3:\\n for i in result:\\n j = \\\" \\\".join(i)\\n k = list(j.split(\\\" \\\"))\\n newlist.append(k)\\n else:\\n for i in result:\\n j = \\\"\\\".join(i)\\n newlist.append(j)\\n\\n return newlist\",\n \"def get_list(vertices, partition):\\n vertices.sort()\\n return_array = list()\\n for vertex in vertices:\\n position = get_position(vertex, partition)\\n return_array.append(position)\\n return return_array\",\n \"def _create_pairs_row(model, st_prefs, st_num):\\n pairs_row = []\\n simp_st_prefs, simp_st_ranks = _get_simple_pref_list_and_ranks(st_prefs)\\n\\n for i in range(len(simp_st_prefs)):\\n pairs_row.append(Pair(st_num, simp_st_prefs[i], simp_st_ranks[i]))\\n in_tie = True\\n\\n return pairs_row\",\n \"def extract_queries(self, path_topics=\\\"../topics-rnd5.xml\\\"):\\n \\n topic_queries = []\\n with open(path_topics, \\\"r\\\") as f:\\n for line in f:\\n match = re.match(\\\".*<query>([^<]*)<\\\\/query>.*\\\", line)\\n if match:\\n topic_queries.append(match.group(1))\\n if len(topic_queries) != 50:\\n sys.exit(\\\"There should be 50 topics, found {}\\\".format(\\n len(topic_queries)))\\n \\n return topic_queries\",\n \"def get_topic_labels(topics, num_labels=1, values=False):\\n result = []\\n for topic in topics:\\n labels = get_label_for_topic(topic)\\n sorted_labels = sorted(labels.items(), key=operator.itemgetter(1), reverse=False)\\n if values:\\n result.append(sorted_labels[:num_labels])\\n else:\\n result.append([h for h, s in sorted_labels][:num_labels])\\n return result\",\n \"def process(data_item, article_id):\\n questions = []\\n answers = []\\n paragraph = [article_id, data_item['context']]\\n\\n for item in data_item['qas']:\\n question = [item[\\\"id\\\"], item[\\\"question\\\"], item['is_impossible']]\\n questions.append(question)\\n if item['is_impossible']:\\n continue\\n answer_options = item[\\\"answers\\\"]\\n answer_set = set()\\n for option in answer_options:\\n answer_tuple = (option['text'], option['answer_start'])\\n answer_set.add(answer_tuple)\\n for index, answer_tuple in enumerate(answer_set):\\n answer = [\\\"{}_{}\\\".format(item[\\\"id\\\"], index+1), item[\\\"id\\\"], answer_tuple[0], answer_tuple[1]]\\n answers.append(answer)\\n return paragraph, questions, answers\",\n \"def test_question_topics(self):\\n p = ProductFactory()\\n t1 = TopicFactory(slug='doesnotexist', product=p)\\n t2 = TopicFactory(slug='cookies', product=p)\\n t3 = TopicFactory(slug='sync', product=p)\\n\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t3)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n )\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.57467985","0.5711735","0.55085766","0.5506645","0.53411525","0.5329091","0.53050333","0.52999526","0.524876","0.5236957","0.5155814","0.51508963","0.514394","0.51368946","0.5046468","0.50216925","0.5004249","0.4996361","0.49733895","0.49623284","0.4962278","0.49580574","0.4954229","0.4952703","0.49385032","0.49385032","0.49313816","0.49104592","0.48869136","0.4878003","0.4869872","0.48559257","0.485005","0.4847874","0.48436883","0.4830317","0.48299396","0.48239222","0.48215353","0.48198238","0.48137048","0.4807396","0.4792458","0.47920457","0.47918186","0.47887972","0.47860616","0.47735697","0.47713384","0.4758382","0.4751026","0.47396493","0.47383827","0.47197968","0.47164208","0.47062388","0.4702375","0.46968347","0.46926036","0.46876323","0.468089","0.4664692","0.4662875","0.46528718","0.46502876","0.46495485","0.4633695","0.46248013","0.46162102","0.46160448","0.46156764","0.4609398","0.46062556","0.45938736","0.45918742","0.45850766","0.45815086","0.45694357","0.45679224","0.4566932","0.45614386","0.45576695","0.45471543","0.4538135","0.45335302","0.45270234","0.45236865","0.4519061","0.45159587","0.45152387","0.45140436","0.45087564","0.45027378","0.45015958","0.4496275","0.44917238","0.44875497","0.44862512","0.4480108","0.4479065"],"string":"[\n \"0.57467985\",\n \"0.5711735\",\n \"0.55085766\",\n \"0.5506645\",\n \"0.53411525\",\n \"0.5329091\",\n \"0.53050333\",\n \"0.52999526\",\n \"0.524876\",\n \"0.5236957\",\n \"0.5155814\",\n \"0.51508963\",\n \"0.514394\",\n \"0.51368946\",\n \"0.5046468\",\n \"0.50216925\",\n \"0.5004249\",\n \"0.4996361\",\n \"0.49733895\",\n \"0.49623284\",\n \"0.4962278\",\n \"0.49580574\",\n \"0.4954229\",\n \"0.4952703\",\n \"0.49385032\",\n \"0.49385032\",\n \"0.49313816\",\n \"0.49104592\",\n \"0.48869136\",\n \"0.4878003\",\n \"0.4869872\",\n \"0.48559257\",\n \"0.485005\",\n \"0.4847874\",\n \"0.48436883\",\n \"0.4830317\",\n \"0.48299396\",\n \"0.48239222\",\n \"0.48215353\",\n \"0.48198238\",\n \"0.48137048\",\n \"0.4807396\",\n \"0.4792458\",\n \"0.47920457\",\n \"0.47918186\",\n \"0.47887972\",\n \"0.47860616\",\n \"0.47735697\",\n \"0.47713384\",\n \"0.4758382\",\n \"0.4751026\",\n \"0.47396493\",\n \"0.47383827\",\n \"0.47197968\",\n \"0.47164208\",\n \"0.47062388\",\n \"0.4702375\",\n \"0.46968347\",\n \"0.46926036\",\n \"0.46876323\",\n \"0.468089\",\n \"0.4664692\",\n \"0.4662875\",\n \"0.46528718\",\n \"0.46502876\",\n \"0.46495485\",\n \"0.4633695\",\n \"0.46248013\",\n \"0.46162102\",\n \"0.46160448\",\n \"0.46156764\",\n \"0.4609398\",\n \"0.46062556\",\n \"0.45938736\",\n \"0.45918742\",\n \"0.45850766\",\n \"0.45815086\",\n \"0.45694357\",\n \"0.45679224\",\n \"0.4566932\",\n \"0.45614386\",\n \"0.45576695\",\n \"0.45471543\",\n \"0.4538135\",\n \"0.45335302\",\n \"0.45270234\",\n \"0.45236865\",\n \"0.4519061\",\n \"0.45159587\",\n \"0.45152387\",\n \"0.45140436\",\n \"0.45087564\",\n \"0.45027378\",\n \"0.45015958\",\n \"0.4496275\",\n \"0.44917238\",\n \"0.44875497\",\n \"0.44862512\",\n \"0.4480108\",\n \"0.4479065\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5424828"},"document_rank":{"kind":"string","value":"4"}}},{"rowIdx":8481,"cells":{"query":{"kind":"string","value":"From all the data, it takes the columns TopicID, and count the topic based on the gender"},"document":{"kind":"string","value":"def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Male\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of men group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_male_female_topicsDF(data_dict, gender):\n dataDF = pd.DataFrame.from_dict(data_dict[gender], orient='index')\n outlet_gender_topicsDF = pd.json_normalize(dataDF['topic_mean'])\n outlet_gender_topicsDF.index = dataDF.index\n outlet_gender_topicsDF = outlet_gender_topicsDF.sort_index()\n outlet_gender_topicsDF = outlet_gender_topicsDF.transpose()\n return outlet_gender_topicsDF","def construct_gender_df(data_dict):\n gender_dict = data_dict['perGenderTopics']\n topics = data_dict['topics']\n # Convert to Pandas DataFrame\n genderDF = pd.DataFrame.from_dict(gender_dict, orient='index').transpose()\n genderDF = genderDF[['female', 'male']]\n genderDF['diff'] = genderDF['female'] - genderDF['male']\n # Sort in order of the sum of mean values for each topic\n genderDF = genderDF.sort_values('diff')\n genderDF['topic'] = [f\"t{i}\" for i in genderDF.index]\n\n # Get a properly ordered list of topics based on prominence for axis labelling\n ordered_topics_dict = {idx: topics[idx] for idx in genderDF.index}\n ordered_names = [topics[idx]['name'] for idx in genderDF.index]\n top_5_words = get_top_n_words(ordered_topics_dict)\n # Get topic names for axis labels if they exist, otherwise return top-5 words per topic\n y_labels = [name if name else top_5_words[i] for i, name in enumerate(ordered_names)]\n genderDF['topic_names'] = y_labels\n return genderDF","def topic_stats(df_topic_sents_keywords):\n\n # Number of Documents for Each Topic\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n\n # Percentage of Documents for Each Topic\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n\n # Topic Number and Keywords\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n\n # Concatenate Column wise\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n\n # Change Column names\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n\n # Show\n df_dominant_topics","def user_gender_statistics(df):\n print('Count of gender \\n')\n gender_counts=df['Gender'].value_counts()\n #loop through to print the total number of gender\n for index, gender_count in enumerate(gender_counts):\n print(' {}: {}'.format(gender_counts.index[index],gender_count))\n \n print()","def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n tuple_result.append(each_tuple)\n\n # sort by the the second element, which is value, of each tuple in the list\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\n\n # separate the topic and count into two lists for graphing purpose\n topics = []\n counts = []\n\n for each_tuple in tuple_result:\n topics.append(each_tuple[0])\n counts.append(each_tuple[1])\n\n return counts, topics","def count_gender(data):\n data = column_to_list(data, -2)\n male = data.count(\"Male\")\n female = data.count(\"Female\")\n return [male, female]","def gender_word_counts(data):\n\n # We use the stopwords package from NLTK corpus.\n stop_words = set(stopwords.words('english'))\n data['tweet_words'] = data['text_cleaned'].str.split()\n # Ignoring all the stop words\n data['tweet_words'] = data['tweet_words'].apply(lambda tweet: [word for word in tweet if word not in stop_words])\n\n # Separating Male, Female and Brand profiles.\n male_profiles = data[data['gender'] == 'male']\n female_profiles = data[data['gender'] == 'female']\n brand_profiles = data[data['gender'] == 'brand']\n\n print(\"Top 20 most frequent words used by Men\")\n all_male_tweets = ' '.join(male_profiles['tweet_words'].astype(str))\n Male_words = pd.Series(all_male_tweets.split(\" \")).value_counts()[:20]\n print(Male_words)\n print()\n\n print(\"Top 20 most frequent words used by Women\")\n all_female_tweets = ' '.join(female_profiles['tweet_words'].astype(str))\n Female_words = pd.Series(all_female_tweets.split(\" \")).value_counts()[:20]\n print(Female_words)\n print()\n\n print(\"Top 20 most frequent words used by Brands\")\n all_brand_tweets = ' '.join(brand_profiles['tweet_words'].astype(str))\n Brand_words = pd.Series(all_brand_tweets.split(\" \")).value_counts()[:20]\n print(Brand_words)\n\n # Plotting horizontal bar graphs showing Top 20 tweet words used Vs. the word frequency.\n mp = Male_words.plot(kind='barh', stacked=True, colormap='plasma', title=\"Top 20 most frequently words used by Men\")\n mp.set_ylabel(\"Tweet words used by Males\")\n mp.set_xlabel(\"Word Frequency\")\n plt.show()\n\n fp = Female_words.plot(kind='barh', stacked=True, colormap='plasma',\n title=\"Top 20 most frequently words used by Women\")\n fp.set_ylabel(\"Tweet words used by Females\")\n fp.set_xlabel(\"Word Frequency\")\n plt.show()\n\n bp = Brand_words.plot(kind='barh', stacked=True, colormap='plasma',\n title=\"Top 20 most frequently words used by Brands\")\n bp.set_ylabel(\"Tweet words used by Brands\")\n bp.set_xlabel(\"Word Frequency\")\n plt.show()","def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Black, non-Hispanic\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of black ethnicity people group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def count_topic_dist(self):\n if len(self.representants) == 0:\n self.log_writer(\"Representants not set. Cannot make topic dist.\")\n return\n for key, value in self.representants.items():\n self.topic_distributions.append(len(value)/len(self.training_docs))\n self.topic_numbers.append(key)","def construct_outlet_gender_DF(data_dict):\n outlet_gender_dict = data_dict['perOutletGenderTopics']\n topics = data_dict['topics']\n male_outlet_topics = get_male_female_topicsDF(outlet_gender_dict, 'male')\n female_outlet_topics = get_male_female_topicsDF(outlet_gender_dict, 'female')\n # Plot the difference between the male-dominant and female-dominant topics\n diff = female_outlet_topics - male_outlet_topics\n # Calculate sum of all columns to decide sorting order\n diff['net'] = diff[diff.columns].sum(axis=1)\n diff = diff.sort_values('net').drop('net', axis=1)\n # Get a properly ordered list of topics based on prominence for axis labelling\n ordered_topics_dict = {idx: topics[idx] for idx in diff.index}\n ordered_names = [topics[idx]['name'] for idx in diff.index]\n top_5_words = get_top_n_words(ordered_topics_dict)\n # Get topic names for axis labels if they exist, otherwise return top-5 words per topic\n y_labels = [name if name else top_5_words[i] for i, name in enumerate(ordered_names)]\n return diff, y_labels","def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:\n try:\n data_frame = data_frame \\\n .select(\"TopicID\", \"Question\") \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n except Py4JError:\n raise AnalysisException('One columns is incorrect')\n print(\"The following table represent the number of the type of each topic\")\n data_frame.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def init():\n\n # Reading the data from the CSV file using the latin1 encoding.\n data_read = pd.read_csv(\"gender-classifier-DFE-791531.csv\", encoding='latin1') # Dataset Size = 20050\n\n # If all the attribute values are empty for any of the rows, we drop them.\n data = data_read.dropna(how='all') # After dropping, data set size is still 20050\n\n # Checking the names of the columns/attributes which contains at least one null value\n columns_containing_missing_values = data.columns[data.isnull().any()].tolist()\n print(\"Column names which has missing values\")\n print(columns_containing_missing_values)\n\n # Since 'gender' is our target variable, we would like to have values for it.\n # So, dropping all the rows which have no values for the 'gender' attribute.\n data = data[data['gender'].notnull()] # After dropping, dataset size = 19953 rows\n # Also, dropping all the rows which have values as 'unknown' for the 'gender' attribute\n data = data[data['gender'] != 'unknown'] # After dropping, dataset size = 18836 rows\n\n male_profile_count = len(data[data['gender'] == 'male'])\n print(\"Male Profile Count \" + str(male_profile_count))\n female_profile_count = len(data[data['gender'] == 'female'])\n print(\"Female Profile Count \" + str(female_profile_count))\n brand_profile_count = len(data[data['gender'] == 'brand'])\n print(\"Brand Profile Count \" + str(brand_profile_count))\n\n return data","def test_extract_topics():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model = BERTopic()\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def get_diseases(self):\n self.diseases = self.data.groupby('topic')['topic'].count()","def user_stats_gender(df):\n # Display counts of gender\n print(\"Counts of gender:\\n\")\n start_time = time.time()\n gender_counts = df['Gender'].value_counts()\n # iteratively print out the total numbers of genders \n # in this loop , it will iterative over the user_counts and its numbering\n for index,gender_count in enumerate(gender_counts):\n print(\" {}: {}\".format(gender_counts.index[index], gender_count))\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*50)","def count_mentioned_countries(data):\n countries_mentioned = {}\n countries = get_countries()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for country in countries:\n if country in (subject_words + message_words):\n if country in countries_mentioned:\n countries_mentioned[country] += 1\n else:\n countries_mentioned[country] = 1\n\n return pd.DataFrame.from_dict(countries_mentioned, orient=\"index\")","def count_gender(dictionary, gender_variable):\r\n boy = 0\r\n girl = 0\r\n for num in dictionary[gender_variable]:\r\n if num == 1:\r\n boy += 1\r\n elif num == 2:\r\n girl += 1\r\n return (boy, girl)","def count_authors_by_gender(self, gender):\n count = 0\n for document in self.documents:\n try:\n if document.author_gender.lower() == gender.lower():\n count += 1\n except AttributeError:\n raise MissingMetadataError(['author_gender'])\n\n return count","def test_extract_topics(base_bertopic):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(documents)\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def count_male_teams(self):\n return len(self.df['Adult male 11v11 (16-45)'].dropna())","def index():\n import numpy as np\n import random\n\n total_gender = {}\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\n\n groups = db(db.groups).select()\n freq_groups = {}\n grp_gender = {}\n for g in groups:\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\n grp_gender[g.code] = {}\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\n\n experiments = db(db.experiments).select()\n freq_experiments = {}\n exp_gender = {}\n for e in experiments:\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\n exp_gender[e.code] = {}\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\n\n grp_exp = {}\n for e in experiments:\n grp_exp[e.code] = {}\n for g in groups:\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\n\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)","def uncategorized(df):\n\n counter = 0\n for movie in df.index:\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\n df.loc[movie, 'Political'] == 0:\n counter += 1\n\n return counter","def summarize_corpus():\n\t\n\t# get metadata\n\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \"metadata\", md_mode)\n\t\n\t# visualize some metadata\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"author-continent\")\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"author-country\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"language\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_hist\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_x\")\n\tvisualize_metadata.plot_pie(wdir, md_csv, \"subgenre\")\n\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"subgenre\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"gender\")\n\t\n\t# make some counts\n\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\n\tnum_texts = len(md_table)\n\t#num_language = len(md_table.groupby([\"language\"]))\n\t#num_continent = len(md_table.groupby([\"author-continent\"]))\n\t#num_countries = len(md_table.groupby([\"author-country\"]))\n\t#num_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_subgenre = len(md_table.groupby([\"subgenre\"]))\n\t#num_subgenre_x = len(md_table.groupby([\"subgenre_x\"]))\n\t#fr_subgenre_hist = md_table.groupby([\"subgenre_hist\"]).count()\n\t#num_historical = fr_subgenre_hist[\"idno\"][\"historical\"]\n\t#num_not_historical = fr_subgenre_hist[\"idno\"][\"not_historical\"]\n\t\n\t\n\td = {\"texts\":[num_texts], \n\t#\"languages\":[num_language],\n\t#\"continents\":[num_continent],\n\t#\"countries\":[num_countries],\n\t\"authors\":[num_authors],\n\t#\"subgenre_x\":[num_subgenre_x],\n\t\"subgenre\":[num_subgenre]}\n\t#\"num_historical\":[num_historical],\n\t#\"num_not_historical\":[num_not_historical]}\n\t\n\t\n\t\n\tcount_fr = pd.DataFrame(d)\n\tcount_fr.to_csv(os.path.join(wdir, \"corpus-description.csv\"), sep=\",\", header=True)\n\tprint(\"Done: summarize corpus\")","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types\n print(\"Counts of user types:\\n\")\n user_counts = df['User Type'].value_counts()\n # printing out the total numbers of user types\n for index, user_count in enumerate(user_counts):\n print(\" {}: {}\".format(user_counts.index[index], user_count))\n\n # Display counts of gender","def test_extract_topics_custom_cv():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n\n cv = CountVectorizer(ngram_range=(1, 2))\n model = BERTopic(vectorizer=cv)\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def assign_topics_to_sentences(self):\n \n\n # 10 topics\n topic_dict = {0: 'academics'\n , 1: 'career'\n , 2: 'commute'\n , 3: 'diversity'\n , 4: 'community'\n , 5: 'extracurricular'\n , 6: 'facilities'\n , 7: 'finance'\n , 8: 'housing'\n , 9: 'wellness'\n }\n\n # Some important words that should be included under each topic\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\n , 'degree', 'research', 'elective'\n , 'professor', 'project', 'scholarship', 'knowledge']\n , ['career', 'job', 'coop', 'employment']\n , ['commute', 'skytrain', 'transport', 'commuter']\n , ['diversity', 'diverse', 'background']\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\n , ['extracurricular', 'club', 'sport', 'activity']\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\n , ['finance', 'tuition', 'expensive']\n , ['housing', 'live', 'residence']\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\n\n # Read the data - id and reponse column\n dt = pd.read_csv(self.path_data_col\n , encoding = \"ISO-8859-1\"\n , usecols = [self.id_col_name, self.col_name])\n\n\n \n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n \n # Split into sentences\n dt['sentences'] = self.getSentences(dt[self.col_name])\n \n \n \n\n \n \n\n # Store number of sentences in each response as a column\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\n\n # Split each row into multiple rows - one row for each sentence\n dt = (dt\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\n .apply(pd.Series)\n .stack()\n .reset_index()\n .drop('level_3', axis = 1)\n .rename(columns = {0:'sentences'}))\n\n\n # Clean the sentences\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\n\n # Remove useless sentences\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\n\n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n\n # Tokenize words in the cleaned sentences\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\n\n\n # Call the lexicon function\n topic_lexicons = self.prepare_lexicons()\n\n # Lists to store results\n count_topic_all = []\n actual_topic_all = []\n\n # Tag each response into a topic\n for response in responses:\n\n count_topic = []\n actual_topic = []\n\n for topic in topic_lexicons:\n\n # Count occurance of each word in word stock in the response\n temp = sum(dict((x, response.count(x)) for x in topic).values())\n count_topic.append(temp)\n\n\n for index, value in enumerate(count_topic):\n\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\n if value > 0:\n actual_topic.append(topic_dict[index])\n\n\n # If more than 3 topics are tagged for single sentence, refine by increasing\n # cutoff to at least 2 words instead of 1\n if len(actual_topic) > 3:\n\n actual_topic = []\n for index, value in enumerate(count_topic):\n\n if value > 1: # Increase cutoff\n actual_topic.append(topic_dict[index])\n\n count_topic_all.append(count_topic)\n actual_topic_all.append(actual_topic)\n\n\n dt['tags'] = actual_topic_all\n dt['num_tags'] = count_topic_all\n\n\n # Select only the most important columns\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\n\n return dt, dt_less","def calc_topic_mode_log_stats(user_exercise_graph, topic_id,\n just_earned_proficiency):\n topic = topic_models.Topic.get_by_id(topic_id)\n topic_exercises = topic.get_exercises()\n\n total_exercises = len(topic_exercises)\n count_proficient = len(set(ex.name for ex in topic_exercises) &\n set(user_exercise_graph.proficient_exercise_names()))\n just_completed = (just_earned_proficiency and total_exercises ==\n count_proficient)\n\n return {\n 'total_exercises': total_exercises,\n 'count_proficient': count_proficient,\n 'just_completed': just_completed,\n }","def pre_process_data(df):\n # setting `passengerID` as Index since it wont be necessary for the analysis\n df = df.set_index(\"PassengerId\")\n\n # convert 'Sex' values\n df['gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\n\n # We see that 2 passengers embarked data is missing, we fill those in as the most common Embarked value\n df.loc[df.Embarked.isnull(), 'Embarked'] = df['Embarked'].mode()[0]\n\n # Replace missing age values with median ages by gender\n for gender in df['gender'].unique():\n median_age = df[(df['gender'] == gender)].Age.median()\n df.loc[(df['Age'].isnull()) & (df['gender'] == gender), 'Age'] = median_age\n\n # convert 'gender' values to new columns\n df = pd.get_dummies(df, columns=['gender'])\n\n # convert 'Embarked' values to new columns\n df = pd.get_dummies(df, columns=['Embarked'])\n\n # bin Fare into five intervals with equal amount of values\n df['Fare-bin'] = pd.qcut(df['Fare'], 5, labels=[1, 2, 3, 4, 5]).astype(int)\n\n # bin Age into seven intervals with equal amount of values\n # ('baby','child','teenager','young','mid-age','over-50','senior')\n bins = [0, 4, 12, 18, 30, 50, 65, 100]\n age_index = (1, 2, 3, 4, 5, 6, 7)\n df['Age-bin'] = pd.cut(df['Age'], bins, labels=age_index).astype(int)\n\n # create a new column 'family' as a sum of 'SibSp' and 'Parch'\n df['family'] = df['SibSp'] + df['Parch'] + 1\n df['family'] = df['family'].map(lambda x: 4 if x > 4 else x)\n\n # create a new column 'FTicket' as the first character of the 'Ticket'\n df['FTicket'] = df['Ticket'].map(lambda x: x[0])\n # combine smaller categories into one\n df['FTicket'] = df['FTicket'].replace(['W', 'F', 'L', '5', '6', '7', '8', '9'], '4')\n # convert 'FTicket' values to new columns\n df = pd.get_dummies(df, columns=['FTicket'])\n\n # get titles from the name\n df['title'] = df.apply(lambda row: re.split('[,.]+', row['Name'])[1], axis=1)\n\n # convert titles to values\n df['title'] = df['title'].map({' Capt': 'Other', ' Master': 'Master', ' Mr': 'Mr', ' Don': 'Other',\n ' Dona': 'Other', ' Lady': 'Other', ' Col': 'Other', ' Miss': 'Miss',\n ' the Countess': 'Other', ' Dr': 'Other', ' Jonkheer': 'Other', ' Mlle': 'Other',\n ' Sir': 'Other', ' Rev': 'Other', ' Ms': 'Other', ' Mme': 'Other', ' Major': 'Other',\n ' Mrs': 'Mrs'})\n # convert 'title' values to new columns\n df = pd.get_dummies(df, columns=['title'])\n\n df = df.drop(['Name', 'Ticket', 'Cabin', 'Sex', 'Fare', 'Age'], axis=1)\n\n return df","def get_paper_counter_per_topic_id(all_topic_assignments):\n counter = {}\n for topic_assignment in all_topic_assignments:\n for topic_index, topic_value in topic_assignment:\n if topic_index not in counter:\n counter[topic_index] = 0\n\n counter[topic_index] += 1\n\n return counter","def topic(df, num_topics=5):\r\n# X, y = df[df.columns[:-1]], df[df.columns[-1]]\r\n lda = LatentDirichletAllocation(n_topics=num_topics,\r\n max_iter=5,\r\n learning_method='online',\r\n learning_offset=50.,\r\n random_state=0)\r\n return lda.fit_transform(df)","def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\n\n for text_id, text in self.texts.items():\n # single-word topics act a bit different (no zips or comprehensions)\n # store data in self.topics, not zip_grams\n for word in text['doc']:\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\n\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\n continue\n\n if not (word.pos in self.nouns or word.ent_type in self.entities):\n continue\n\n if word_lemma in self.topics:\n self.topics[word_lemma][\"count\"] += 1\n self.topics[word_lemma][\"textIDs\"] |= {text_id}\n self.topics[word_lemma][\"verbatims\"] |= {word.text.lower()}\n else:\n self.topics[word_lemma] = {\"name\": word_lemma,\n \"count\": 1,\n \"textIDs\": {text_id},\n \"verbatims\": {word.text.lower()},\n \"subtopics\": {}}\n\n # Populate self.ngrams and self.topics\n for text_id, text in self.texts.items():\n doc = text['doc']\n\n # Find pentagrams - ngrams with 5 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\n self._ngram_counter(ngram, 5, text_id, doc)\n\n # Find pentagrams - ngrams with 4 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\n self._ngram_counter(ngram, 4, text_id, doc)\n\n for ngram in zip(doc, doc[1:], doc[2:]):\n self._ngram_counter(ngram, 3, text_id, doc)\n\n for ngram in zip(doc, doc[1:]):\n self._ngram_counter(ngram, 2, text_id, doc)\n\n\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\n # but it's only mentioned in 3 different texts, we'd show 3.\n for _, topic in self.topics.items():\n topic['textIDCount'] = len(topic['textIDs'])\n for _, ngram in self.ngrams.items():\n ngram['textIDCount'] = len(ngram['textIDs'])\n\n # Eliminate rarely occurring topics and ngrams.\n self.topics = {k: v for k, v in self.topics.items() if\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\n self.ngrams = {k: v for k, v in self.ngrams.items() if\n v['textIDCount'] >= min_text_id_count}\n\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\n for ngram_lemma, ngram in self.ngrams.items():\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\n # r'\\b' + ngram_lemma + r'\\b' --> does the ngram lemma fit in ngram_plus lemma (\\b is word boundary)\n if ngram['n'] + 1 != ngram_plus['n']:\n continue\n\n if not re.search(r'\\b' + ngram_lemma + r'\\b', ngram_plus_lemma):\n continue\n\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\n # instead of the other loop?\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\n ngram['count'] = -1\n\n # Eliminate newly demoted items\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}","def prob4():\n#pass\n n_men = 0\n n_women = 0\n db = sql.connect('sql2')\n cur = db.cursor()\n cur.execute(\"SELECT Gender FROM ICD WHERE Age>=25 AND Age<=35\")\n for gen in cur :\n if gen[0] == 'M' :\n n_men += 1\n else :\n n_women += 1\n db.close()\n return n_men,n_women","def test_topic_reduction(reduced_topics):\n model = BERTopic()\n nr_topics = reduced_topics + 2\n model.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents.copy())\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents.copy())\n new_freq = model.get_topic_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(model.mapped_topics, dict)\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\n assert model.mapped_topics","def get_mutual_information(filename):\n categories = {} #{category: speakers of this category}\n features = {} #{feat: speakers who use this feature}\n pos_categories_features = {} #{category: {feat: speakers of category who use this feat}}\n neg_categories_features = {} #{category: {feat: speakers of category who do not use this feat}}\n users = set() #set of all users in data\n \n for line in open(filename):\n userid, c, date, statusid, rawtweet, toktweet, tagtweet = line.split('\\t')\n users.add(userid)\n \n if c not in categories:\n categories[c] = set()\n pos_categories_features[c] = {}\n categories[c].add(userid)\n \n feats = set(toktweet.lower().split()) #lowercase tweet and split into words\n\n for feat in feats:\n if feat not in pos_categories_features[c]:\n pos_categories_features[c][feat] = set()\n pos_categories_features[c][feat].add(userid)\n \n if feat not in features:\n features[feat] = set()\n features[feat].add(userid)\n\n print \"Parsed data\"\n\n numfeats = len(features) #num of features\n print numfeats, \"features\"\n numusers = len(users) #num of users \n print numusers, \"users\"\n\n #keep sizes of sets, not sets themselves\n for feat in features:\n features[feat] = len(features[feat])\n for c in categories:\n categories[c] = len(categories[c])\n for c in pos_categories_features:\n for feat in features:\n if feat in pos_categories_features[c]:\n pos_categories_features[c][feat] = len(pos_categories_features[c][feat])\n else:\n pos_categories_features[c][feat] = 0\n\n for c in categories:\n print c, categories[c], \"users\"\n\n print \"Computed counts\"\n \n mi = {}\n for feat in features:\n mi[feat] = 0.0\n for c in categories:\n #print c, feat, features[feat], pos_categories_features[c][feat]\n \n catprob = categories[c]/numusers\n\n #prob of speakers of category c using feat\n featprob = features[feat]/numusers\n jointprob = pos_categories_features[c][feat]/numusers\n if jointprob > 0 and featprob > 0:\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\n \n #prob of speakers of category c NOT using feat\n featprob = 1 - featprob\n jointprob = (categories[c] - pos_categories_features[c][feat])/numusers\n if jointprob > 0 and featprob > 0:\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\n\n print \"Computed mutual information\"\n\n feature_scores = sorted(mi.items(), key=lambda x:x[1], reverse=True)\n refcat = categories.keys()[0] #pick one of the categories\n print 'Feature\\tMI\\tP({0}|Feature)\\tNum. users'.format(refcat)\n for feat, score in feature_scores[:200]:\n prob = pos_categories_features[refcat][feat]/features[feat]\n print '{0}\\t{1:.3f}\\t{2:.3f}\\t{3}'.format(feat, score, prob, features[feat])","def topic(df, num_topics=5):\r\n\r\n lda = LatentDirichletAllocation(n_topics=num_topics,\r\n max_iter=5,\r\n learning_method='online',\r\n learning_offset=50.,\r\n random_state=0)\r\n return lda.fit_transform(df)","def analysis_1_result(primary_person_df,output_folder_path):\n male_death_count_df = primary_person_df\\\n .filter(col(\"PRSN_GNDR_ID\") == \"MALE\").agg(count(\"PRSN_GNDR_ID\").alias(\"MALE_DEATH_CNT\"))\n print(\"Analysis 1: \\nTotal number of crashes (accidents) in which number of persons killed are male is :\")\n male_death_count_df.show() #Dispalying result\n write_df_to_csv(male_death_count_df,output_folder_path+\"analysis_1_result\") #Writing to csv file","def topic_extraction(df, col_name):\n tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\n max_features=200,\n stop_words='english')\n tfidf = tfidf_vectorizer.fit_transform(df[col_name])\n\n tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,\n max_features=200,\n stop_words='english')\n tf = tf_vectorizer.fit_transform(df[col_name])\n nmf = NMF(n_components=20, random_state=1,\n alpha=.1, l1_ratio=.5)\n tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n nmf_w = nmf.fit_transform(tfidf)\n nmf_h = nmf.components_\n df['labels'] = nmf_w.argmax(axis=1) # this was the right code to get labels/clusters\n\n\n print(\"\\nTopics in NMF model:\")\n print_top_words(nmf, tfidf_feature_names)\n\n\n lda = LatentDirichletAllocation(n_topics=20, max_iter=5,\n learning_method='online',\n learning_offset=50.,\n random_state=0,\n n_jobs=-1)\n lda.fit(tf)\n doc_topic_distrib = lda.transform(tf)\n lda_labels = doc_topic_distrib.argmax(axis=1)\n print lda_labels[:100]\n df['lda_labels'] = lda_labels\n print(\"\\nTopics in LDA model:\")\n tf_feature_names = tf_vectorizer.get_feature_names()\n print_top_words(lda, tf_feature_names)\n return df","def calculate_statistics(city,df):\n \n #Total Trip duration per gender\n if 'Gender' in df.columns:\n viewData=df.groupby(['Gender'])['Trip Duration'].sum().reset_index(name='Trip Duration')\n print('Trip duration per gender\\n')\n print(viewData)\n #number of missing values in the entire sheet\n missing_values=np.count_nonzero(df.isnull())\n print('Number of missing values in the {} dataset:{}'.format(city,missing_values))","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def countCountry(self, docID, data):\n for entry in data.genData():\n if 'subject_doc_id' in entry and 'visitor_country' in entry and 'event_type' in entry:\n if entry['event_type'] == 'read':\n if str(entry['subject_doc_id']) == docID: # cast to string needed as input is string\n if entry['visitor_country'] in self.countryCounts:\n self.countryCounts[entry['visitor_country']] += 1\n else:\n self.countryCounts[entry['visitor_country']] = 1","def document_distribution_per_topic(df_dominant_topic):\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\n\n\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\n\n for i, ax in enumerate(axes.flatten()):\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\n doc_lens = [len(d) for d in df_dominant_topic.Text]\n ax.hist(doc_lens, bins = 1000, color=cols[i])\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\n sns.kdeplot(doc_lens, color=\"black\", shade=False, ax=ax.twinx())\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\n ax.set_ylabel('Number of Documents', color=cols[i])\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\n\n fig.tight_layout()\n fig.subplots_adjust(top=0.90)\n plt.xticks(np.linspace(0,1000,9))\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\n plt.show()","def main():\n\n # open links.csv in order to access IMDB id numbers\n ifile = open('movie-countries.csv', \"rb\")\n reader = csv.reader(ifile)\n \n # writer for csv with countries\n ofile = open('country_stats.csv', \"wb\")\n writer = csv.writer(ofile)\n\n # deal with headers\n reader.next() # skip first line\n writer.writerow(['country', 'number of movies', 'number of primary movies'])\n\n # one dictionary for all mention of a country, one dictionary for if the country was the first one listed\n country_count_dict = {}\n country_count_primary_dict= {}\n\n # iterate through data\n for row in reader:\n # get the countries column\n countries = row[3]\n\n # add to dicionary of countries\n for country in countries.split(\"|\"):\n country_count_dict[country] = country_count_dict.get(country, 0) + 1\n\n # if it's the primary country\n if country == countries.split(\"|\")[0]:\n country_count_primary_dict[country] = country_count_primary_dict.get(country, 0) + 1\n\n # write to the file\n for key, value in country_count_dict.iteritems():\n writer.writerow([key , str(value), country_count_primary_dict.get(key, \"0\")])\n\n ifile.close()\n ofile.close()","def posts_per_topic_all(request, pk):\n #update is_expired in all posts\n update_posts_expiration()\n #get all posts with a certain topic\n posts = Post.objects.filter(topic=pk)\n serializer = ViewPostSerializer(posts, many=True)\n return Response(serializer.data)","def main_topic_doc(ldamodel, corpus=corpus): \n \n doc_topics = pd.DataFrame()\n\n for i, row in enumerate(ldamodel[corpus]):\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\n\n for j, (topic_num, prop_topic) in enumerate(row):\n if j == 0:\n wp = ldamodel.show_topic(topic_num)\n topic_keywords = \"' \".join([word for word, prop in wp])\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n else:\n break\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\n return doc_topics","def check_confidence(data):\n gender_confident_data = data[data['gender:confidence'] == 1] # Dataset size = 13926\n return gender_confident_data","def get_titanic_fea(dataset):\n dataset['Name_length'] = dataset['Name'].apply(len)\n\n # Mapping Sex 不在map定义的 就是NaN\n dataset['Sex'] = dataset['Sex'].map({'female': 0, 'male': 1}).astype(int)\n\n dataset['Has_Cabin'] = dataset['Cabin'].apply(lambda x: 0 if type(x) == float else 1)\n dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n\n dataset['IsAlone'] = 0\n dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n\n # [Embarked]\n dataset['Embarked'] = dataset['Embarked'].fillna('0')\n dataset['Fare'] = dataset['Fare'].fillna(0)\n # Mapping Embarked\n dataset['Embarked'] = dataset['Embarked'].map({'0': 0, 'S': 1, 'C': 2, 'Q': 3}).astype(int)\n\n # [Fare]\n dataset['CategoricalFare'] = pd.qcut(dataset['Fare'], 4)\n # Mapping Fare\n dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0\n dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\n dataset.loc[dataset['Fare'] > 31, 'Fare'] = 3\n dataset['Fare'] = dataset['Fare'].astype(int)\n\n # [Age]\n age_avg = dataset['Age'].mean()\n age_std = dataset['Age'].std()\n age_null_count = dataset['Age'].isnull().sum()\n age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\n dataset['Age'] = dataset['Age'].astype(int)\n dataset['CategoricalAge'] = pd.cut(dataset['Age'], 5)\n # Mapping Age\n dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\n dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n dataset.loc[dataset['Age'] > 64, 'Age'] = 4\n\n # [Name]\n # 称谓 Mr 、Miss 等\n def get_title(name):\n title_search = re.search(' ([A-Za-z]+)\\.', name)\n # If the title exists, extract and return it.\n if title_search:\n return title_search.group(1)\n return \"\"\n dataset['Title'] = dataset['Name'].apply(get_title)\n\n # 只保留4类Title\n dataset['Title'] = dataset['Title'].replace(\n ['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n # Mapping titles\n title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5}\n dataset['Title'] = dataset['Title'].map(title_mapping)\n dataset['Title'] = dataset['Title'].fillna(0)\n\n # Feature selection\n drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\n dataset = dataset.drop(drop_elements, axis=1)\n dataset = dataset.drop(['CategoricalAge', 'CategoricalFare'], axis=1)\n\n return dataset","def test_extract_topics_custom_cv(base_bertopic_custom_cv):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic_custom_cv._update_topic_size(documents)\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic_custom_cv.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def export_topics(self):\n\n # format as a list (for json output), then sort descending by textIDCount\n topics = [{'name': topic['name'], 'count': topic['count'],\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\n 'children': '' if 'children' not in topic else topic['children']}\n for topic_id, topic in self.topics.items()]\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\n\n for i, topic in enumerate(topics):\n # Note that 'rank' is from topic, not child.\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\n 'textIDCount': child['textIDCount']}\n for _, child in topic['children'].items()]\n\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\n\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\n child_count = sum([child['textIDCount'] for child in topic['children']])\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\n\n for child in topic['children']:\n child['size'] = child['textIDCount'] * child_count_multiplier\n\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\n\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\n self.model_output[\"children\"] = [topic for topic in topics]\n\n # Build file name and save\n if self.data_date:\n date = datetime.strptime(self.data_date, \"%Y-%m-%d\").strftime('%d') # from YYYY-MM-DD to DD\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\n else:\n file_name = '{}-Topics.txt'.format(self.corpus_name)\n\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\n json.dump(self.model_output, file)","def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\n print(data_frame_pandas)\n return data_frame_pandas","def test_topic_model_generator_dimensions( ):\n N = 100\n D = 1000\n K = 10\n W = 100\n\n tm = TopicModel.generate( K, D )\n assert( tm.topics.shape == (D, K) )\n assert( tm.weights.shape == (K,) )\n\n docs = tm.sample( N, words = W )\n # Each document is a column\n assert( docs.shape == (N, D) ) \n # Each doc should have 100 words\n assert( sc.all(docs.sum(1) == W) )","def analysis_3_result(primary_person_df,output_folder_path):\n rankWindowSpec = Window.orderBy(col(\"MAX_FEMALE_COUNT\").desc())\n state_with_female_involved_df = primary_person_df \\\n .filter(col(\"PRSN_GNDR_ID\") == \"FEMALE\") \\\n .groupBy(\"DRVR_LIC_STATE_ID\", \"PRSN_GNDR_ID\") \\\n .agg(count(\"*\").alias(\"MAX_FEMALE_COUNT\"))\\\n .withColumn(\"RANK\", row_number().over(rankWindowSpec))\\\n .filter(\"RANK=1\") \\\n .drop(\"RANK\",\"PRSN_GNDR_ID\")\\\n .select(col(\"DRVR_LIC_STATE_ID\").alias(\"STATE\"),\"MAX_FEMALE_COUNT\")\n print(\"Analysis 3:\\nState with highest number of accidents in which females are involved is: \")\n state_with_female_involved_df.show()\n write_df_to_csv(state_with_female_involved_df,output_folder_path+\"analysis_3_result\")","def group_topics(sent_topics_sorteddf):\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0).sum()\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\n plt.title('Topic Share %');\n\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]","def iterate_data(self):\n if \"single\" in self.dataset_name:\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for answer_id in self.data:\n summary = self.data[answer_id]['summary']\n articles = self.data[answer_id]['articles']\n question = self.data[answer_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(articles, 'article')\n self._get_token_cnts(question, 'question')\n self._write_stats(\"token_counts\")\n\n if \"multi\" in self.dataset_name:\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for q_id in self.data:\n summary = self.data[q_id]['summary']\n question = self.data[q_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(question, 'question')\n question = self.data[q_id]['question']\n for answer_id in self.data[q_id]['articles']:\n articles = self.data[q_id]['articles'][answer_id][0]\n if args.tokenize:\n self._get_token_cnts(articles, 'article')\n self._write_stats(\"token_counts\")\n\n if self.dataset_name == \"complete_dataset\":\n self.stat_dict = {'urls': [], 'sites': []}\n article_dict = {}\n print(\"Counting answers, sites, unique urls, and tokenized counts of unique articles\")\n answer_cnt = 0\n for q_id in self.data:\n for a_id in self.data[q_id]['answers']:\n answer_cnt += 1\n url = self.data[q_id]['answers'][a_id]['url']\n article = self.data[q_id]['answers'][a_id]['article']\n if url not in article_dict:\n article_dict[url] = article\n self.stat_dict['urls'].append(url)\n assert \"//\" in url, url\n site = url.split(\"//\")[1].split(\"/\")\n self.stat_dict['sites'].append(site[0])\n print(\"# of Answers:\", answer_cnt)\n print(\"Unique articles: \", len(article_dict)) # This should match up with count written to file\n self._write_stats(\"full collection\")\n\n # Get token/sent averages of unique articles\n if args.tokenize:\n self.stat_dict = {'article': [[], []]}\n for a in article_dict:\n self._get_token_cnts(article_dict[a], 'article')\n self._write_stats(\"token_counts\")","def property_count_function(listOfProperties):\n\n property_count = {} # Empty dict, is gonna look like this: property_count{property : count}\n for lists in listOfProperties:\n try:\n for properties in lists:\n property_count[properties] = property_count.get(properties, 0) + 1\n except TypeError as e:\n print(e)\n\n # Converts the dictionary to a dataframe\n property_dataframe = pd.DataFrame(list(property_count.items()), columns=['Property', 'Frequency'])\n # property_dataframe = property_dataframe.set_index(\"Property\")\n property_dataframe = property_dataframe.sort_values(by=['Frequency'], ascending=False)\n\n return property_dataframe","def train(self, corpus):\n for sentence in corpus.corpus:\n for datum in sentence.data: \n self.unigramCounts[datum.word] += 1\n self.totalCount += 1","def run_gender_freq(corpus):\n novels = corpus._load_novels()\n c = len(novels)\n loops = c//10 + 1\n\n num = 0\n\n while num < loops:\n dictionary = {}\n for novel in novels[num * 10: min(c, num * 10 + 9)]:\n d = {'he': novel.get_word_freq('he'), 'she': novel.get_word_freq('she')}\n d = get_comparative_word_freq(d)\n lst = [d[\"he\"], d[\"she\"]]\n book = novel.title[0:20] + \"\\n\" + novel.author\n dictionary[book] = lst\n display_gender_freq(dictionary, str(num))\n num += 1","def process_family_frequencies(self, parent, family):\n if not family:\n return None\n counts = {}\n n_tokens = 0\n for description_array in family:\n seen = set([])\n for token in description_array:\n counts[token] = counts.get(token, 0) + 1\n n_tokens += 1\n\n self.word2tf[token] = self.word2tf.get(token, 0) + 1\n self.n_words += 1\n if token not in seen:\n self.word2df[token] = self.word2df.get(token, 0) + 1\n seen.add(token)\n\n self.family2tf[parent] = {tok: count/n_tokens for tok, count in counts.items()}\n self.data.extend(family)\n return True","def age_12_count() :\n\n import numpy as np\n import pandas as pd\n import matplotlib.pyplot as plt\n import itertools\n\n train = pd.read_csv('./data/train.csv')\n \n # Split data to contain ages only up to 12 months\n train_12_months = train.loc[train['Age'] < 13, ['State','Type', 'Age', 'AdoptionSpeed']]\n\n # Divide by dog (Type = 1) and cat (Type = 2)\n dog_df = train_12_months.loc[train_12_months['Type'] == 1, :]\n cat_df = train_12_months.loc[train_12_months['Type'] == 2, :]\n \n dog_max_age = max(dog_df.loc[:, 'Age'])\n dog_min_age = min(dog_df.loc[:, 'Age'])\n \n cat_max_age = max(cat_df.loc[:, 'Age'])\n cat_min_age = min(cat_df.loc[:, 'Age'])\n \n dog_age_labels = []\n dog_count = []\n \n cat_age_labels = []\n cat_count = []\n \n # Find dog count for each age\n for i in range(dog_min_age, dog_max_age + 1) :\n count = (dog_df.Age == i).sum()\n if(count > 0) :\n dog_count.append(count)\n dog_age_labels.append(i)\n\n # Find cat count for each age\n for i in range(cat_min_age, cat_max_age + 1) :\n count = (cat_df.Age == i).sum()\n if(count > 0) :\n cat_count.append(count)\n cat_age_labels.append(i)\n \n # Plot bar graphs\n plt.figure()\n index = np.arange(len(dog_age_labels))\n plt.bar(index, dog_count)\n plt.xlabel('Age in Months')\n plt.xticks(index, dog_age_labels)\n plt.ylabel('Count')\n plt.title('Count of Dogs Up to 12 Months of Age')\n plt.savefig('dog12.png', bbox_inches='tight')\n \n \n plt.figure()\n index = np.arange(len(cat_age_labels))\n plt.bar(index, cat_count)\n plt.xlabel('Age in Months')\n plt.xticks(index, cat_age_labels)\n plt.ylabel('Count')\n plt.title('Count of Cats Up to 12 Months of Age')\n plt.savefig('cat12.png', bbox_inches='tight')","def task2(self, doc) -> dict:\n country_count = {}\n match_records = []\n for entry in self.records:\n if (entry['event_type'] =='read'):\n if entry['subject_doc_id'] == doc:\n match_records.append(entry)\n for rec in match_records:\n if (rec['visitor_country'] in country_count):\n country_count[rec['visitor_country']] += 1\n else:\n country_count[rec['visitor_country']] = 1\n print(country_count)\n return country_count","def analyze_data(df, sentiment_col, tweet_col, path):\n\n # create empty dictionaries to store all encountered words and their frequencies\n all_dict = {}\n pos_dict = {}\n neg_dict = {}\n neu_dict = {}\n # initialize counters to counter total number of tweets based on their emotion\n pos_count = 0\n neg_count = 0\n neu_count = 0\n\n # iterate through each row of the df\n for index, row in df.iterrows():\n if row[sentiment_col] == \"positive\":\n pos_count = iterate_words(\n pos_count, row[tweet_col], all_dict, pos_dict)\n\n if row[sentiment_col] == \"negative\":\n neg_count = iterate_words(\n neg_count, row[tweet_col], all_dict, neg_dict)\n\n if row[sentiment_col] == \"neutral\":\n neu_count = iterate_words(\n neu_count, row[tweet_col], all_dict, neu_dict)\n\n # visualize statistics\n visualize_stats(all_dict, 'all_plot.png', 'all_cloud.png',\n 'Word frequency in all tweets', path)\n visualize_stats(pos_dict, 'pos_plot.png', 'pos_cloud.png',\n 'Word frequency in positive tweets', path)\n visualize_stats(neg_dict, 'neg_plot.png', 'neg_cloud.png',\n 'Word frequency in negative tweets', path)\n visualize_stats(neu_dict, 'neu_plot.png', 'neu_cloud.png',\n 'Word frequency in neutral tweets', path)\n\n # make plot for emotion frequency\n emotions = ('Positive', 'Negative', 'Neutral')\n freq = [pos_count, neg_count, neu_count]\n sns.set_style(\"darkgrid\")\n ax = plt.figure().gca()\n ax.xaxis.grid(False)\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n plt.bar(range(len(emotions)), freq, align='center',\n color=['forestgreen', 'firebrick', 'goldenrod'])\n plt.xticks(range(len(emotions)), emotions)\n plt.title('Tweet frequency based on emotion')\n plt.savefig(path + 'emotion_plot.png')\n plt.close()\n\n # make pie for emotion frequency\n sizes = [pos_count / len(df.index), neg_count /\n len(df.index), neu_count / len(df.index)]\n colors = ['forestgreen', 'firebrick', 'goldenrod']\n plt.pie(sizes, labels=emotions, colors=colors,\n autopct='%1.1f%%', startangle=140)\n plt.title('Tweet frequency based on emotion')\n plt.axis('equal')\n plt.savefig(path + 'emotion_pie.png')\n plt.close()","def trainCount(\n trainData, \n questionType,\n questionDict,\n questionIdict, \n objDict, \n objIdict,\n numAns):\n count_wa = np.zeros((len(objIdict), numAns))\n count_a = np.zeros((numAns))\n objIds = extractObjId(\n trainData[0], \n questionType, \n questionDict, \n questionIdict)\n for i in range(objIds.shape[0]):\n objId = objIds[i]\n obj = questionIdict[objId - 1]\n ansId = trainData[1][i, 0]\n objId2 = objDict[obj]\n count_wa[objId2, ansId] += 1\n count_a[ansId] += 1\n # Add UNK count\n count_a[-1] += 1\n return count_wa, count_a","def get_data(self):\n\n self.cur.execute('SELECT year, sex, education, score from vocabulary_scores;')\n scores = dict()\n education = dict()\n count = dict()\n\n for row in self.cur :\n if row[0] in scores:\n if row[1] in scores[row[0]]:\n scores[row[0]][row[1]] += int(row[3])\n education[row[0]][row[1]] += int(row[2])\n count[row[0]][row[1]] += 1\n else:\n scores[row[0]][row[1]] = int(row[3])\n education[row[0]][row[1]] = int(row[2])\n count[row[0]][row[1]] = 1\n else:\n # scores[year] = {gender: score}\n scores[row[0]] = {row[1]: int(row[3])}\n education[row[0]] = {row[1]: int(row[2])}\n count[row[0]] = {row[1]: 1}\n\n scores, education = self.average_scores(scores, education, count)\n\n return scores, education","def test_topics_for_products(self):\n desktop_topics = topics_for(product=self.desktop)\n eq_(len(desktop_topics), 3)\n\n mobile_topics = topics_for(product=self.mobile)\n eq_(len(mobile_topics), 2)","def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)","def num_words():\n # Load the GT.\n df = pd.read_csv(config.META_FQN, sep=\"\\t\")\n stats = {\n \"T\": {\"words\": [], \"duration\": []},\n \"P\": {\"words\": [], \"duration\": []},\n \"sess\": {\"words\": [], \"duration\": []},\n }\n\n for _, row in df.iterrows():\n if row[\"asr_test\"]:\n stats[\"P\"][\"words\"].append(float(row[\"gt_patient_num_words\"]))\n stats[\"T\"][\"words\"].append(float(row[\"gt_therapist_num_words\"]))\n stats[\"P\"][\"duration\"].append(float(row[\"gt_patient_time_spoken\"]))\n stats[\"T\"][\"duration\"].append(\n float(row[\"gt_therapist_time_spoken\"])\n )\n stats[\"sess\"][\"duration\"].append(float(row[\"sess_dur\"]))\n n_words = (\n row[\"gt_therapist_num_words\"] + row[\"gt_patient_num_words\"]\n )\n stats[\"sess\"][\"words\"].append(n_words)\n\n for speaker in stats:\n for metric in stats[speaker]:\n print(f\"------ {speaker} | {metric} ------\")\n print_stats(stats[speaker][metric])","def model_topics(df):\n\n data = df.text.values.tolist()\n data_words = list(sent_to_words(data))\n\n # Build the bigram and trigram models\n bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\n trigram = gensim.models.Phrases(bigram[data_words], threshold=100) \n\n # Faster way to get a sentence clubbed as a trigram/bigram\n bigram_mod = gensim.models.phrases.Phraser(bigram)\n trigram_mod = gensim.models.phrases.Phraser(trigram)\n\n # Remove Stop Words\n data_words_nostops = remove_stopwords(data_words)\n\n # Form Bigrams\n data_words_bigrams = make_bigrams(data_words_nostops,bigram_mod)\n\n # Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n nlp = spacy.load('en', disable=['parser', 'ner'])\n\n # Do lemmatization keeping only noun, adj, vb, adv\n data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n\n # Create Dictionary\n id2word = corpora.Dictionary(data_lemmatized)\n\n # Create Corpus\n texts = data_lemmatized\n\n # Term Document Frequency\n corpus = [id2word.doc2bow(text) for text in texts]\n\n # Perform Topic Modeling for number of topics ranging from 5 to 50 in steps of 5\n model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=5, limit=50, step=5)\n\n return model_list,coherence_values,corpus,id2word","def counts_table(data, attr):\n pd.options.mode.chained_assignment = None # default='warn'\n # gets class names for dataframe manipulation\n classes = attr.tail(1)['vars'].tolist()\n classlist = [classes[0][0], classes[0][1]]\n # expanding a table to have all variable options in a column with their \n # parent attribute\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\n allvariables.name='var'\n allvariables = attr.drop('vars', axis=1).join(allvariables)\n av = allvariables.drop(attr.index[-1])\n # populate the table with counts\n for c in classlist:\n clist = []\n count = 0\n for i, row in av.iterrows():\n att = row['attr']\n var = row['var']\n sub = data[[att,'class']]\n sub = sub[sub[att]==var]\n if not sub.empty:\n ssub = sub[sub['class']==c]\n if not ssub.empty:\n count = len(ssub)\n else:\n count = 0\n clist.append(count)\n av[c] = clist\n\n return av","def get_mentions(self, column, list_of_types, total=False, average=False):\n for mbti_type in list_of_types:\n self.df[mbti_type + '_mentions'] = [sum([x.casefold().count(mbti_type.casefold()) for x in post]) for post in self.df[column]]\n if total == True:\n mention_cols = [col for col in self.df.columns if 'mentions' in col]\n self.df['total_mentions'] = self.df.filter(mention_cols).sum(axis=1)\n if average == True:\n self.df['avg_mentions_per_post'] = self.df['total_mentions'] / self.df['count_posts']","def create_topic_columns(videos, topics):\n \n # Clear values\n videos['relevant'] = False\n\n # Create masks for each topic so we can later filter videos by topics\n topic_masks = []\n for _, topic in topics.iterrows():\n videos[topic['slug']] = False # Clear values\n pattern = get_pattern(topic)\n topic_mask = videos.apply(lambda video: is_relevant(video, pattern), axis=1)\n topic_masks.append(topic_mask)\n videos[topic['slug']] = topic_mask\n\n # Mark video as 'relevant' if it mentions any of the topics\n videos['relevant'] = np.any(np.column_stack(topic_masks), axis=1)","def user_stats(df):\n ##task4: user info\\type\\gender&birth year if available for the city\n ##user type data is avalable for all cities\n ## count of each type, frequency is printed in a table \"only i New York city 3 types!\n # TO DO: Display counts of user types\n user_counts = df['User Type'].value_counts().to_frame()\n user_percentage = df['User Type'].value_counts(normalize=True)*100\n user_count_stat=pd.concat([user_counts,user_percentage],axis=1,join=\"inner\")\n user_count_stat.columns=['User_Count', 'User_Frequency%'] \n print(\"There are {} types of users : \".format(len(user_counts)))\n for i in range (len(user_counts)):\n print(user_counts.index.values[i])\n print(\"\\n\\n\",\"*\"* 40)\n print(\"The count and percentage of each type is:\")\n print(\"*\"* 40)\n print(user_count_stat.round(2))\n print(\"*\"* 40)\n\n #Task 4.2: TO DO: Display counts of gender\n ##functions to calculate gender distriburion\"only for NY and washington\"\n ## start with if statement to check if the data is in dataseries\n ## if data is apended in the future no need to change the code\n ## display gender count and frequency in every dataset\n if 'Gender' in df.columns :\n print(\"\\n Gender data is available for this city\")\n user_gender = df['Gender'].value_counts()\n user_gender_percentage = df['Gender'].value_counts(normalize=True)*100\n gender_stat=pd.concat([user_gender,user_gender_percentage],axis=1,join=\"inner\")\n gender_stat.columns=['Gender_Count', 'Gender_Frequency%']\n print(\"The Number of users from each Gender and their percentage is given by\")\n print(\"#\"* 40)\n print(gender_stat.round(2))\n print(\"#\"* 40)\n \n else :\n print(\"\\n Gender data is not available for this city\")\n\n # task 4.2: TO DO: Display earliest, most recent, and most common year of birth\n ## functions to calculate year of birth statistics\n ## condition to check if data is available for the current city\n # calculate the most common city, earliest and latest\n if 'Birth Year' in df.columns :\n print(\"\\n Birth Year data is available for this city\")\n birth_year = df['Birth Year']\n popular_year =df['Birth Year'].mode()[0]\n print(\"Some statistics related to year of birth\")\n print(\"-\"* 40)\n print(\"The earliest year of birth is : ****\"+\"{:.0f}\".format(min(birth_year)),\"***\");\n print(\"The latest year of birth is : *** \"+\"{:.0f}\".format(max(birth_year)),\"***\");\n print(\"The most common year of birth is : ***\"+\"{:.0f}\".format(popular_year),\"***\");\n \n else :\n print(\"\\n Birth Year data is not available for this city\")\n start_time = time.time()\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)","def train_ngrams(dataset):\n trigram_counts = dict()\n bigram_counts = dict()\n unigram_counts = dict()\n token_count = 0\n ### YOUR CODE HERE\n raise NotImplementedError\n ### END YOUR CODE\n return trigram_counts, bigram_counts, unigram_counts, token_count","def test_get_topics(self):\n\n for m in self.models:\n\n topics = m.topics\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertEqual(topics.num_rows(), 25)\n self.assertEqual(topics.num_columns(), 2)\n z = m.topics[\"topic_probabilities\"]\n for k in range(m.num_topics):\n self.assertTrue(\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\n \"Returned probabilities do not sum to 1.\",\n )\n\n # Make sure returned object is an SFrame of the right size\n topics = m.get_topics()\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertTrue(\n topics.num_columns() == 3,\n \"Returned SFrame should have a topic, word, and probs.\",\n )\n\n # Make sure that requesting a single topic returns only that topic\n num_words = 8\n topics = m.get_topics([5], num_words=num_words)\n self.assertTrue(\n all(topics[\"topic\"] == 5), \"Returned topics do not have the right id.\"\n )\n self.assertEqual(topics.num_rows(), num_words)\n topics = m.get_topics([2, 4], num_words=num_words)\n self.assertEqual(set(list(topics[\"topic\"])), set([2, 4]))\n self.assertEqual(topics.num_rows(), num_words + num_words)\n\n # Make sure the cumulative probability of the returned words is\n # is less than the cutoff we provided.\n # A cutoff of 1.0 should return num_words for every topic.\n cutoff = 1.0\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\n totals = topics.groupby(\n \"topic\", {\"total_score\": turicreate.aggregate.SUM(\"score\")}\n )\n self.assertTrue(\n all(totals[\"total_score\"] <= (cutoff + DELTA)),\n \"More words were returned than expected for this cutoff.\",\n )\n\n # Make sure we raise errors for bad input\n with self.assertRaises(ValueError):\n m.get_topics([-1])\n with self.assertRaises(ValueError):\n m.get_topics([10000])\n with self.assertRaises(ToolkitError):\n topics = m.get_topics(output_type=\"other\")\n\n # Test getting topic_words\n topic_words = m.get_topics(output_type=\"topic_words\", num_words=5)\n self.assertEqual(type(topic_words), turicreate.SFrame)\n\n # Test words are sorted correctly for the first topic\n # TODO: Make this more deterministic.\n\n # topic_probs = m.get_topics(num_words=5)\n # expected = [w for w in topic_probs['word'][:5]]\n # observed = topic_words['words'][0]\n # self.assertEqual(observed[0], expected[0])","def test_topic_reduction_edge_cases():\n model = BERTopic()\n nr_topics = 5\n model.nr_topics = 100\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents)\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents)\n new_freq = model.get_topic_freq()\n\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\n pd.testing.assert_frame_equal(old_documents, new_documents)\n pd.testing.assert_frame_equal(old_freq, new_freq)","def get_initial_student_count(self):\n initial_date = self.start_date - timedelta(1)\n # date cannot be earlier than the school year start\n school_year_start_date = \\\n SchoolDB.models.SchoolYear.school_year_start_for_date()\n if (initial_date < school_year_start_date):\n initial_date = school_year_start_date\n section_roster_changes = self.section.get_section_roster_changes()\n student_dict = \\\n section_roster_changes.get_single_day_information(\n self.students, initial_date)\n male_count = 0\n female_count = 0\n for student in student_dict.values():\n if (student.gender == \"Male\"):\n male_count += 1\n else:\n female_count +=1\n #do not return 0 because it will generate a divide by zero in\n #the calling function\n if not male_count:\n male_count = 1\n if not female_count:\n female_count = 1\n return male_count, female_count","def listTopicRelevance(request):\n if request.method == 'GET':\n user = request.user;\n data = [];\n for topic in Topic.objects.all():\n row = {};\n\n topicSerializer = TopicNestedSerializer(topic)\n topicSerializer.Meta.depth = 1;\n #row['topic'] = topicSerializer.data;\n user_visits = topic.visits.filter(user=user)\n visitSerializer = VisitSerializer(user_visits, many=True)\n #visitSerializer.Meta.depth = 1;\n row['visit_count'] = len(user_visits);\n if row['visit_count'] > 0:\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\n else:\n row['last_visit'] = topic.created_at\n\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\n\n row['neighbor_visits_count'] = len(neighbor_visits);\n if row['neighbor_visits_count'] > 0:\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\n else:\n row['last_neighbor_visit'] = topic.created_at\n\n row['post_count'] = len(topic.posts.filter(user=user))\n row['like_count'] = len(topic.posts.filter(votes__user=user))\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\n row['recommendation'] = row['relevance_score'] + topic.hotness\n\n data.append(row)\n\n print(data)\n return Response(data)","def test_topic_reduction(reduced_topics):\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\n nr_topics = reduced_topics + 2\n base_bertopic.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(old_documents)\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\n old_freq = base_bertopic.get_topics_freq()\n\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\n new_freq = base_bertopic.get_topics_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(base_bertopic.mapped_topics, dict)\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\n assert base_bertopic.mapped_topics","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n #Display counts of user types\n counts_of_user_types = df['User Type'].value_counts()\n print('counts of user types :',counts_of_user_types )\n \n \n\n\n # Display counts of gender\n try:\n counts_of_gender = df['Gender'].value_counts()\n print('counts of gender :',counts_of_gender)\n except KeyError:\n print('counts of gender : no data available')\n \n\n \n #Display earliest, most recent, and most common year of birth\n try:\n earliest=df['Birth Year'].min()\n most_recent=df['Birth Year'].max()\n most_common_year_of_birth=df['Birth Year'].mode()[0]\n print('earliest : ', earliest)\n print('most recent : ', most_recent)\n print( 'most common year of birth : ', most_common_year_of_birth)\n except KeyError:\n print('counts of gender : no data available') \n \n \n \n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n \n\n #display_raw_data\n #please detrmine if you want to see some lines of raw data \n \n i=0\n while True:\n display_data = input('would you like to see (5) lines/next (5) lines of raw data ? , yes or no : ').lower()\n \n if display_data == 'yes':\n print(df[i:i+5])\n i+=5\n else:\n break","def count_ngrams(self, corpus):\n \n self.unigramcounts = {} # might want to use defaultdict or Counter instead\n self.bigramcounts = {} \n self.trigramcounts = {} \n\n self.total = 2\n ##Your code here\n\n for sentence in corpus:\n temp_1 = get_ngrams(sentence,1)\n temp_2 = get_ngrams(sentence,2)\n temp_3 = get_ngrams(sentence,3)\n for i in range(len(temp_1)):\n if temp_1[i] in self.unigramcounts:\n self.unigramcounts[temp_1[i]] += 1\n else:\n self.unigramcounts[temp_1[i]] = 1\n self.total += 1\n\n for i in range(len(temp_2)):\n if temp_2[i] in self.bigramcounts:\n self.bigramcounts[temp_2[i]] += 1\n else:\n self.bigramcounts[temp_2[i]] = 1\n\n for i in range(len(temp_3)):\n if temp_3[i] in self.trigramcounts:\n self.trigramcounts[temp_3[i]] += 1\n else:\n self.trigramcounts[temp_3[i]] = 1\n return","def infertopics(self):\n\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\n for uid in self.scan(attribute=\"updated\"):\n # Remove updated attribute\n self.removeattribute(uid, \"updated\")\n\n # Get list of neighboring nodes\n ids = self.edges(uid)\n\n # Infer topic\n topic = Counter(self.attribute(x, \"topic\") for x in ids).most_common(1)[0][0] if ids else None\n if topic:\n # Add id to topic list and set topic attribute\n self.topics[topic].append(uid)\n self.addattribute(uid, \"topic\", topic)\n\n # Set topic rank\n self.addattribute(uid, \"topicrank\", len(self.topics[topic]) - 1)\n\n # Infer category\n category = Counter(self.attribute(x, \"category\") for x in ids).most_common(1)[0][0]\n self.addattribute(uid, \"category\", category)","def filter_by_gender(self, gender):\n\n return self.subcorpus('author_gender', gender)","def process_frequencies(df_corpus, wdir, min_MFF, max_MFF, mode, names_MFF):\n # Normalization of the frequencies by the sum of the text\n df_corpus = df_corpus.loc[:].div(df_corpus.sum(axis='columns'), axis=\"index\")\n if mode == \"train\":\n # If we are doing a training corpus, it is easier\n \n # The dataframe gets a new summatory column that we use to order the df \n df_corpus = df_corpus.T\n df_corpus[\"sum\"]=df_corpus.sum(axis=\"columns\")\n df_corpus = df_corpus.sort_values(by=\"sum\", ascending=False)\n \n # Only a given amount of words is taken\n df_corpus = df_corpus[min_MFF:max_MFF]\n # Summatory column is deleted and the df goes back to its normal format\n del df_corpus['sum']\n df_corpus = df_corpus.T\n print(mode, \" last feature: \", df_corpus.columns[-1])\n \n elif mode == \"eval\" or mode == \"test\":\n # If we create the evaluation or the test corpus, we have to check first the features of the train corpus because the 5000 MFW of the train corpus are NOT the 5000 MFW of the test corpus.\n # TODO: I don't know if that is the best way to do it. Maybe we should calculate the total amount of features in the different corpora, get the list of the n MFF and then fill the diferent matrixs with this features.\n df_corpus = df_corpus.reindex_axis(names_MFF, axis=1)\n # Only a given amount of words is taken\n df_corpus = df_corpus.T\n df_corpus = df_corpus[min_MFF:max_MFF]\n df_corpus = df_corpus.T\n print(mode, \" last feature: \", df_corpus.columns[-1])\n\n df_corpus = df_corpus.fillna(0)\n \n # The table is saved as csv\n df_corpus.to_csv(wdir+\"freq_table.csv\", sep='\\t', encoding='utf-8', index=True)\n\n return df_corpus","def compute_subj_metrics():\n\t# each participant does task 1,2,3 with method A,B = 3*2*N\n\t# likert data includes age, gender, Q1 - Q10 = 2+10\n\n\t# set up data structure\n\tsubj_metrics = {}\n\tfor ID in range(10):\n\t\tfor method in [\"A\",\"B\"]:\n\t\t\t# sanity checks\n\t\t\tif ID not in subj_metrics:\n\t\t\t\tsubj_metrics[ID] = {}\n\t\t\tif method not in subj_metrics[ID]:\n\t\t\t\tsubj_metrics[ID][method] = [None]*12\n\n\there = os.path.dirname(os.path.realpath(__file__))\n\tsubdir = \"/data/experimental/\"\n\tdatapath = here + subdir + \"Likert_Responses.csv\"\n\n\tdata = {}\n\tfirstline = True\n\twith open(datapath, 'r') as f:\n\t\tfor line in f:\n\t\t\tif firstline:\n\t\t\t\tfirstline = False\n\t\t\t\tcontinue\n\t\t\tvalues = line.split(',')\n\t\t\tinfo = values[1:len(values)]\n\t\t\tID = int(info[9])\n\t\t\tmethod = info[10]\n\t\t\tage = info[11]\n\t\t\tgender = info[12]\n\t\t\t# store age\n\t\t\tsubj_metrics[ID][method][0] = age\n\t\t\tsubj_metrics[ID][method][1] = gender\n\t\t\t# parse likert data\n\t\t\tfor i in range(8):\n\t\t\t\tsubj_metrics[ID][method][i+2] = info[i]\n\t\t\tsubj_metrics[ID][method][10] = info[14]\n\t\t\tsubj_metrics[ID][method][11] = info[15]\n\n\treturn subj_metrics","def get_topics(self):\n topics = self.word_topics\n return topics / topics.sum(axis=1)[:, None]","def test_question_topics(self):\n p = ProductFactory()\n t1 = TopicFactory(slug='doesnotexist', product=p)\n t2 = TopicFactory(slug='cookies', product=p)\n t3 = TopicFactory(slug='sync', product=p)\n\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t3)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n )\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def user_stats(df):\n try: \n df = load_data(city, month, day)\n # TO DO: Display counts of user types\n count_user_type = df.groupby(['User Type']).count()\n print('Count of user type: ', count_user_type)\n\n # TO DO: Display counts of gender\n count_gender = df.groupby(['Gender']).count()\n print('Count of gender: ', count_gender)\n\n # TO DO: Display earliest, most recent, and most common year of birth\n earliest = df['Birth Year'].min()\n print('Earliest birth year: ', earliest)\n\n recent = df['Birth Year'].max()\n print('Most recent birth year: ', recent)\n\n common = df['Birth Year'].mode()[0]\n print('Most common birth year: ', common)\n \n except KeyError:\n df = load_data(city, month, day)\n # TO DO: Display counts of user types\n count_user_type = df.groupby(['User Type']).count()\n print('Count of user type: ', count_user_type)\n \n \n return user_stats","def query_transition_counts(ordered_pitch_types, query_filter):\n transition_counts = {}\n\n for first_pitch_type in ordered_pitch_types:\n transition_counts[first_pitch_type] = {}\n\n for second_pitch_type in ordered_pitch_types:\n count_query = PitchTransition.select().where(\n PitchTransition.first_pitch_type == first_pitch_type).where(\n PitchTransition.second_pitch_type == second_pitch_type)\n\n if query_filter:\n count_query = count_query.where(query_filter)\n\n transition_counts[first_pitch_type][second_pitch_type] = count_query.count()\n\n return transition_counts","def test_IsCorrectGender(self):\n \n # load data\n ilist, flist = load_data()\n\n # test case 01\n for fm in flist:\n result, msg = IsCorrectGender(fm.WifeID,fm.HusbandID, ilist)\n self.assertTrue(result)\n\n # test case 02\n ilist[0].Gender = 'F'\n ilist[1].Gender = 'M'\n result, msg = IsCorrectGender(flist[0].WifeID,flist[0].HusbandID, ilist)\n self.assertFalse(result)","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # TO DO: Display counts of user types\n user_type = df['User Type'].value_counts()\n print('The types of users are: \\n{}' .format(user_type))\n\n # TO DO: Display counts of gender\n while True:\n try:\n gender_type = df['Gender'].value_counts()\n print('The users classified by gender are: \\n{}' .format(gender_type))\n max_birth_year = df.groupby(['Gender'])['Birth Year'].max()\n min_birth_year = df.groupby(['Gender'])['Birth Year'].min()\n mode_birth_year = df['Birth Year'].mode()\n print ('1. The most recent year of birth is: {}\\n2. The most earliest year of birth is: {}\\n3. The most common year of birth is: {}'.format(max_birth_year, min_birth_year, mode_birth_year))\n\n # TO DO: Display earliest, most recent, and most common year of birth\n\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n break\n except:\n print('\\n Sorry, the genre field does not exist in the selected file, so no results are shown.')\n break","def assign_topics(self):\n\n # Load the dataset which has topic tags for each sentence\n dt, dt_less = self.assign_topics_to_sentences()\n \n dt_copy = dt\n \n # Minimum number of tags needed to tag the overall response\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\n \n # Final dataset with full survey response and its tags\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\n , 'num_sent': min\n , 'min_num_tags': min\n# , 'sentences': lambda x: \"%s\" % '. '.join(x)\n , self.col_name: min})\n final_dt.reset_index(level = 0, inplace = True)\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\n , axis = 1)\n\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\n\n return dt_copy, dt_less, final_dt, final_dt_less","def get_tops(name_file,number,gender):\r\n tops = []\r\n num = 0\r\n f = open(name_file)\r\n reader = csv.reader(f)\r\n for row in reader:\r\n if row.__contains__(gender.upper()):\r\n row[2] = int(row[2])\r\n tops.append(row)\r\n num = num + 1\r\n if num == number:\r\n break\r\n return tops","def count_persons(self):\n\n # full path of personData file\n path = self.pretty_print_path()\n\n data = self.person_data[[\"person_id\", \"telestatus\"]].copy()\n\n conditions = [(data[\"telestatus\"] == \"Not a Worker\"),\n (data[\"telestatus\"].isin([\"No Telecommute\",\n \"1 day a week\",\n \"2-3 days a week\",\n \"4+ days a week\",\n \"Work from Home\"]))]\n choices = [\"Non-worker\", \"Worker\"]\n data[\"telestatus\"] = np.select(conditions, choices, default=data[\"telestatus\"])\n\n counts = data.groupby([\"telestatus\"]).count()\n counts.loc['Total'] = counts.sum()\n counts.reset_index(inplace=True)\n counts['File'] = path # add file name\n\n results = counts.pivot(index=\"File\", columns=\"telestatus\", values=\"person_id\")\n\n # add percentage share\n counts[\"Total\"] = data.person_id.count()\n counts[\"share\"] = counts.person_id / counts.Total\n percent_share = counts.pivot(index=\"File\", columns=\"telestatus\", values=\"share\")\n\n results = results.append(percent_share, ignore_index=False, sort=True)\n\n cols = [\"Worker\",\n \"Non-worker\",\n \"Total\"]\n\n return results[cols]","def find_dominant_topic(df_topic_sents_keywords):\n\n # Format\n df_dominant_topic = df_topic_sents_keywords.reset_index()\n df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n\n # Group top 5 sentences under each topic\n sent_topics_sorteddf = pd.DataFrame()\n\n sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n start = time.time()\n for i, grp in sent_topics_outdf_grpd:\n sent_topics_sorteddf = pd.concat([sent_topics_sorteddf, \n grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n axis=0)\n print(f'Group done. Total time {time.time() - start} seconds.')\n\n # Reset Index \n sent_topics_sorteddf.reset_index(drop=True, inplace=True)\n\n # Format\n sent_topics_sorteddf.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n return sent_topics_sorteddf","def count_mentioned_pol_figures(data):\n figures_mentioned = {}\n figures = get_political_figures()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for figure in figures:\n if figure + \" \" in (subject_words + message_words):\n if figure in figures_mentioned:\n figures_mentioned[figure] += 1\n else:\n figures_mentioned[figure] = 0\n\n return pd.DataFrame(figures_mentioned)","def get_tweets_by_topic(topic, start_date, end_date):\n try:\n query = f\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\"\n logger.info(f'QUERY: {query}') \n with MysqlCursor() as cur:\n cur.execute(query)\n tweets = cur.fetchall()\n columns = [col[0] for col in cur.description]\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \n return tweets, columns\n \n except Exception as ex:\n logger.exception(ex)\n return f'Exception: {ex}'","def user_stats(df):\n\n print('\\nCalculating User Stats...\\n')\n start_time = time.time()\n\n # Display counts of user types\n user_count = df['User Type'].value_counts()\n print(f'{user_count}\\n')\n\n # Display counts of gender if only the data contains that column\n if 'Gender' in df:\n gender_count = df['Gender'].value_counts()\n print(gender_count, '\\n')\n\n # Display earliest, most recent, and most common year of birth if \"Birth Year\" column is found\n earliest = df['Birth Year'].min()\n print('Most earliest birth year:', int(earliest))\n recent = df['Birth Year'].max()\n print('Most recent birth year', int(recent))\n common = df['Birth Year'].value_counts().idxmax()\n print('Most common birth year', int(common))\n\n print(\"\\nTotal time taken: %s seconds.\" % (round(time.time() - start_time, 2)))\n print('-' * 40)","def topic_table(topic):\r\n table = Table(['property', 'value'])\r\n table.align['property'] = 'r'\r\n table.align['value'] = 'l'\r\n\r\n table.add_row(['name', topic['name']])\r\n table.add_row(['tags', listing(topic['tags'] or [])])\r\n return table","def split_dataset_for_one_attribute(gender_confident_data, attribute='text_cleaned'):\n\n # Converting text strings into a matrix of word token counts.\n cv = CountVectorizer()\n inputString = cv.fit_transform(gender_confident_data[attribute])\n\n # Encodes class labels from 0 to Num_of_classes-1\n le = LabelEncoder()\n outputString = le.fit_transform(gender_confident_data['gender'])\n\n # Splitting the data such that 66% of the data is assigned as training data and the rest as the test data set.\n input_train, input_test, output_train, output_test = train_test_split(inputString, outputString, train_size=0.66)\n\n return input_train, output_train, input_test, output_test","def expectation_step(self,number_of_topics):\n createdDataMat = np.zeros([self.number_of_documents, number_of_topics, self.vocabulary_size], dtype=np.float)\n print(\"E step:\")\n\t\t#print(\"Initial value of createdDataMat >>>>>\"+createdDataMat)\n \n \t\n for docValue in range(0, self.number_of_documents):\n for wordValue in range(0, self.vocabulary_size):\n recordSum = 0\n for topicValue in range(0, number_of_topics):\n self.topic_prob[docValue][topicValue][wordValue] = (self.document_topic_prob[docValue][topicValue] * self.topic_word_prob[topicValue][wordValue])\n recordSum = recordSum + self.topic_prob[docValue][topicValue][wordValue]\n for topicValue in range(0, number_of_topics):\n self.topic_prob[docValue][topicValue][wordValue] = self.topic_prob[docValue][topicValue][wordValue]/recordSum","def connect_topic_id_to_topics(self, model):\n confidence = []\n for key, value in self.representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key, tp_num, val / len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n self.log_writer.add_log(\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\n conf[1],\n conf[2]))\n self.topic_indexes[conf[0]] = conf[1]\n\n for key, value in self.topic_indexes.items():\n self.topics_of_index[value] = [key]"],"string":"[\n \"def get_male_female_topicsDF(data_dict, gender):\\n dataDF = pd.DataFrame.from_dict(data_dict[gender], orient='index')\\n outlet_gender_topicsDF = pd.json_normalize(dataDF['topic_mean'])\\n outlet_gender_topicsDF.index = dataDF.index\\n outlet_gender_topicsDF = outlet_gender_topicsDF.sort_index()\\n outlet_gender_topicsDF = outlet_gender_topicsDF.transpose()\\n return outlet_gender_topicsDF\",\n \"def construct_gender_df(data_dict):\\n gender_dict = data_dict['perGenderTopics']\\n topics = data_dict['topics']\\n # Convert to Pandas DataFrame\\n genderDF = pd.DataFrame.from_dict(gender_dict, orient='index').transpose()\\n genderDF = genderDF[['female', 'male']]\\n genderDF['diff'] = genderDF['female'] - genderDF['male']\\n # Sort in order of the sum of mean values for each topic\\n genderDF = genderDF.sort_values('diff')\\n genderDF['topic'] = [f\\\"t{i}\\\" for i in genderDF.index]\\n\\n # Get a properly ordered list of topics based on prominence for axis labelling\\n ordered_topics_dict = {idx: topics[idx] for idx in genderDF.index}\\n ordered_names = [topics[idx]['name'] for idx in genderDF.index]\\n top_5_words = get_top_n_words(ordered_topics_dict)\\n # Get topic names for axis labels if they exist, otherwise return top-5 words per topic\\n y_labels = [name if name else top_5_words[i] for i, name in enumerate(ordered_names)]\\n genderDF['topic_names'] = y_labels\\n return genderDF\",\n \"def topic_stats(df_topic_sents_keywords):\\n\\n # Number of Documents for Each Topic\\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\\n\\n # Percentage of Documents for Each Topic\\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\\n\\n # Topic Number and Keywords\\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\\n\\n # Concatenate Column wise\\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\\n\\n # Change Column names\\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\\n\\n # Show\\n df_dominant_topics\",\n \"def user_gender_statistics(df):\\n print('Count of gender \\\\n')\\n gender_counts=df['Gender'].value_counts()\\n #loop through to print the total number of gender\\n for index, gender_count in enumerate(gender_counts):\\n print(' {}: {}'.format(gender_counts.index[index],gender_count))\\n \\n print()\",\n \"def topic_count():\\n # get the number topics and their counts as tuples: ('Topic', 123)\\n query = peewee.RawQuery(Post, \\\"select topic, count(topic) from post group by topic\\\").tuples()\\n\\n # turn the result of the query object into a list of tuples\\n tuple_result = []\\n for each_tuple in query:\\n tuple_result.append(each_tuple)\\n\\n # sort by the the second element, which is value, of each tuple in the list\\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\\n\\n # separate the topic and count into two lists for graphing purpose\\n topics = []\\n counts = []\\n\\n for each_tuple in tuple_result:\\n topics.append(each_tuple[0])\\n counts.append(each_tuple[1])\\n\\n return counts, topics\",\n \"def count_gender(data):\\n data = column_to_list(data, -2)\\n male = data.count(\\\"Male\\\")\\n female = data.count(\\\"Female\\\")\\n return [male, female]\",\n \"def gender_word_counts(data):\\n\\n # We use the stopwords package from NLTK corpus.\\n stop_words = set(stopwords.words('english'))\\n data['tweet_words'] = data['text_cleaned'].str.split()\\n # Ignoring all the stop words\\n data['tweet_words'] = data['tweet_words'].apply(lambda tweet: [word for word in tweet if word not in stop_words])\\n\\n # Separating Male, Female and Brand profiles.\\n male_profiles = data[data['gender'] == 'male']\\n female_profiles = data[data['gender'] == 'female']\\n brand_profiles = data[data['gender'] == 'brand']\\n\\n print(\\\"Top 20 most frequent words used by Men\\\")\\n all_male_tweets = ' '.join(male_profiles['tweet_words'].astype(str))\\n Male_words = pd.Series(all_male_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Male_words)\\n print()\\n\\n print(\\\"Top 20 most frequent words used by Women\\\")\\n all_female_tweets = ' '.join(female_profiles['tweet_words'].astype(str))\\n Female_words = pd.Series(all_female_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Female_words)\\n print()\\n\\n print(\\\"Top 20 most frequent words used by Brands\\\")\\n all_brand_tweets = ' '.join(brand_profiles['tweet_words'].astype(str))\\n Brand_words = pd.Series(all_brand_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Brand_words)\\n\\n # Plotting horizontal bar graphs showing Top 20 tweet words used Vs. the word frequency.\\n mp = Male_words.plot(kind='barh', stacked=True, colormap='plasma', title=\\\"Top 20 most frequently words used by Men\\\")\\n mp.set_ylabel(\\\"Tweet words used by Males\\\")\\n mp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\\n\\n fp = Female_words.plot(kind='barh', stacked=True, colormap='plasma',\\n title=\\\"Top 20 most frequently words used by Women\\\")\\n fp.set_ylabel(\\\"Tweet words used by Females\\\")\\n fp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\\n\\n bp = Brand_words.plot(kind='barh', stacked=True, colormap='plasma',\\n title=\\\"Top 20 most frequently words used by Brands\\\")\\n bp.set_ylabel(\\\"Tweet words used by Brands\\\")\\n bp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\",\n \"def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:\\n data_frame_topic = data_frame \\\\\\n .filter(data_frame[\\\"Stratification1\\\"].contains(\\\"Black, non-Hispanic\\\")) \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n\\n print(\\\"The following table represent the number of black ethnicity people group by the topic: \\\")\\n data_frame_topic.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def count_topic_dist(self):\\n if len(self.representants) == 0:\\n self.log_writer(\\\"Representants not set. Cannot make topic dist.\\\")\\n return\\n for key, value in self.representants.items():\\n self.topic_distributions.append(len(value)/len(self.training_docs))\\n self.topic_numbers.append(key)\",\n \"def construct_outlet_gender_DF(data_dict):\\n outlet_gender_dict = data_dict['perOutletGenderTopics']\\n topics = data_dict['topics']\\n male_outlet_topics = get_male_female_topicsDF(outlet_gender_dict, 'male')\\n female_outlet_topics = get_male_female_topicsDF(outlet_gender_dict, 'female')\\n # Plot the difference between the male-dominant and female-dominant topics\\n diff = female_outlet_topics - male_outlet_topics\\n # Calculate sum of all columns to decide sorting order\\n diff['net'] = diff[diff.columns].sum(axis=1)\\n diff = diff.sort_values('net').drop('net', axis=1)\\n # Get a properly ordered list of topics based on prominence for axis labelling\\n ordered_topics_dict = {idx: topics[idx] for idx in diff.index}\\n ordered_names = [topics[idx]['name'] for idx in diff.index]\\n top_5_words = get_top_n_words(ordered_topics_dict)\\n # Get topic names for axis labels if they exist, otherwise return top-5 words per topic\\n y_labels = [name if name else top_5_words[i] for i, name in enumerate(ordered_names)]\\n return diff, y_labels\",\n \"def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:\\n try:\\n data_frame = data_frame \\\\\\n .select(\\\"TopicID\\\", \\\"Question\\\") \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n except Py4JError:\\n raise AnalysisException('One columns is incorrect')\\n print(\\\"The following table represent the number of the type of each topic\\\")\\n data_frame.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def init():\\n\\n # Reading the data from the CSV file using the latin1 encoding.\\n data_read = pd.read_csv(\\\"gender-classifier-DFE-791531.csv\\\", encoding='latin1') # Dataset Size = 20050\\n\\n # If all the attribute values are empty for any of the rows, we drop them.\\n data = data_read.dropna(how='all') # After dropping, data set size is still 20050\\n\\n # Checking the names of the columns/attributes which contains at least one null value\\n columns_containing_missing_values = data.columns[data.isnull().any()].tolist()\\n print(\\\"Column names which has missing values\\\")\\n print(columns_containing_missing_values)\\n\\n # Since 'gender' is our target variable, we would like to have values for it.\\n # So, dropping all the rows which have no values for the 'gender' attribute.\\n data = data[data['gender'].notnull()] # After dropping, dataset size = 19953 rows\\n # Also, dropping all the rows which have values as 'unknown' for the 'gender' attribute\\n data = data[data['gender'] != 'unknown'] # After dropping, dataset size = 18836 rows\\n\\n male_profile_count = len(data[data['gender'] == 'male'])\\n print(\\\"Male Profile Count \\\" + str(male_profile_count))\\n female_profile_count = len(data[data['gender'] == 'female'])\\n print(\\\"Female Profile Count \\\" + str(female_profile_count))\\n brand_profile_count = len(data[data['gender'] == 'brand'])\\n print(\\\"Brand Profile Count \\\" + str(brand_profile_count))\\n\\n return data\",\n \"def test_extract_topics():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model = BERTopic()\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def get_diseases(self):\\n self.diseases = self.data.groupby('topic')['topic'].count()\",\n \"def user_stats_gender(df):\\n # Display counts of gender\\n print(\\\"Counts of gender:\\\\n\\\")\\n start_time = time.time()\\n gender_counts = df['Gender'].value_counts()\\n # iteratively print out the total numbers of genders \\n # in this loop , it will iterative over the user_counts and its numbering\\n for index,gender_count in enumerate(gender_counts):\\n print(\\\" {}: {}\\\".format(gender_counts.index[index], gender_count))\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*50)\",\n \"def count_mentioned_countries(data):\\n countries_mentioned = {}\\n countries = get_countries()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for country in countries:\\n if country in (subject_words + message_words):\\n if country in countries_mentioned:\\n countries_mentioned[country] += 1\\n else:\\n countries_mentioned[country] = 1\\n\\n return pd.DataFrame.from_dict(countries_mentioned, orient=\\\"index\\\")\",\n \"def count_gender(dictionary, gender_variable):\\r\\n boy = 0\\r\\n girl = 0\\r\\n for num in dictionary[gender_variable]:\\r\\n if num == 1:\\r\\n boy += 1\\r\\n elif num == 2:\\r\\n girl += 1\\r\\n return (boy, girl)\",\n \"def count_authors_by_gender(self, gender):\\n count = 0\\n for document in self.documents:\\n try:\\n if document.author_gender.lower() == gender.lower():\\n count += 1\\n except AttributeError:\\n raise MissingMetadataError(['author_gender'])\\n\\n return count\",\n \"def test_extract_topics(base_bertopic):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(documents)\\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def count_male_teams(self):\\n return len(self.df['Adult male 11v11 (16-45)'].dropna())\",\n \"def index():\\n import numpy as np\\n import random\\n\\n total_gender = {}\\n total_gender['Male'] = db(db.patient.sex == 'Male').count()\\n total_gender['Female'] = db(db.patient.sex == 'Female').count()\\n total_gender['Undeclared'] = db(db.patient.sex == 'Undeclared').count()\\n\\n groups = db(db.groups).select()\\n freq_groups = {}\\n grp_gender = {}\\n for g in groups:\\n freq_groups[g.code] = db(db.patient.groups.contains(g.id)).count()\\n grp_gender[g.code] = {}\\n grp_gender[g.code]['Male'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Male')).count()\\n grp_gender[g.code]['Female'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Female')).count()\\n grp_gender[g.code]['Undeclared'] = db(db.patient.groups.contains(g.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n experiments = db(db.experiments).select()\\n freq_experiments = {}\\n exp_gender = {}\\n for e in experiments:\\n freq_experiments[e.code] = db(db.patient.experiments.contains(e.id)).count()\\n exp_gender[e.code] = {}\\n exp_gender[e.code]['Male'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Male')).count()\\n exp_gender[e.code]['Female'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Female')).count()\\n exp_gender[e.code]['Undeclared'] = db(db.patient.experiments.contains(e.id) & (db.patient.sex == 'Undeclared')).count()\\n\\n grp_exp = {}\\n for e in experiments:\\n grp_exp[e.code] = {}\\n for g in groups:\\n grp_exp[e.code][g.code] = db(db.patient.experiments.contains(e.id) & db.patient.groups.contains(g.id)).count()\\n\\n return dict(message=T('Pain Network: A web-based tool for diagnosis of the Chronic Pain.'),\\n freq_gender=total_gender,freq_groups=freq_groups,freq_experiments=freq_experiments,\\n exp_gender=exp_gender,grp_gender=grp_gender,grp_exp=grp_exp)\",\n \"def uncategorized(df):\\n\\n counter = 0\\n for movie in df.index:\\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\\\\n df.loc[movie, 'Political'] == 0:\\n counter += 1\\n\\n return counter\",\n \"def summarize_corpus():\\n\\t\\n\\t# get metadata\\n\\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \\\"metadata\\\", md_mode)\\n\\t\\n\\t# visualize some metadata\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"author-continent\\\")\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"author-country\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"language\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_hist\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_x\\\")\\n\\tvisualize_metadata.plot_pie(wdir, md_csv, \\\"subgenre\\\")\\n\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"gender\\\")\\n\\t\\n\\t# make some counts\\n\\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\\n\\tnum_texts = len(md_table)\\n\\t#num_language = len(md_table.groupby([\\\"language\\\"]))\\n\\t#num_continent = len(md_table.groupby([\\\"author-continent\\\"]))\\n\\t#num_countries = len(md_table.groupby([\\\"author-country\\\"]))\\n\\t#num_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_subgenre = len(md_table.groupby([\\\"subgenre\\\"]))\\n\\t#num_subgenre_x = len(md_table.groupby([\\\"subgenre_x\\\"]))\\n\\t#fr_subgenre_hist = md_table.groupby([\\\"subgenre_hist\\\"]).count()\\n\\t#num_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"historical\\\"]\\n\\t#num_not_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"not_historical\\\"]\\n\\t\\n\\t\\n\\td = {\\\"texts\\\":[num_texts], \\n\\t#\\\"languages\\\":[num_language],\\n\\t#\\\"continents\\\":[num_continent],\\n\\t#\\\"countries\\\":[num_countries],\\n\\t\\\"authors\\\":[num_authors],\\n\\t#\\\"subgenre_x\\\":[num_subgenre_x],\\n\\t\\\"subgenre\\\":[num_subgenre]}\\n\\t#\\\"num_historical\\\":[num_historical],\\n\\t#\\\"num_not_historical\\\":[num_not_historical]}\\n\\t\\n\\t\\n\\t\\n\\tcount_fr = pd.DataFrame(d)\\n\\tcount_fr.to_csv(os.path.join(wdir, \\\"corpus-description.csv\\\"), sep=\\\",\\\", header=True)\\n\\tprint(\\\"Done: summarize corpus\\\")\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # Display counts of user types\\n print(\\\"Counts of user types:\\\\n\\\")\\n user_counts = df['User Type'].value_counts()\\n # printing out the total numbers of user types\\n for index, user_count in enumerate(user_counts):\\n print(\\\" {}: {}\\\".format(user_counts.index[index], user_count))\\n\\n # Display counts of gender\",\n \"def test_extract_topics_custom_cv():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n\\n cv = CountVectorizer(ngram_range=(1, 2))\\n model = BERTopic(vectorizer=cv)\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def assign_topics_to_sentences(self):\\n \\n\\n # 10 topics\\n topic_dict = {0: 'academics'\\n , 1: 'career'\\n , 2: 'commute'\\n , 3: 'diversity'\\n , 4: 'community'\\n , 5: 'extracurricular'\\n , 6: 'facilities'\\n , 7: 'finance'\\n , 8: 'housing'\\n , 9: 'wellness'\\n }\\n\\n # Some important words that should be included under each topic\\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\\n , 'degree', 'research', 'elective'\\n , 'professor', 'project', 'scholarship', 'knowledge']\\n , ['career', 'job', 'coop', 'employment']\\n , ['commute', 'skytrain', 'transport', 'commuter']\\n , ['diversity', 'diverse', 'background']\\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\\n , ['extracurricular', 'club', 'sport', 'activity']\\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\\n , ['finance', 'tuition', 'expensive']\\n , ['housing', 'live', 'residence']\\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\\n\\n # Read the data - id and reponse column\\n dt = pd.read_csv(self.path_data_col\\n , encoding = \\\"ISO-8859-1\\\"\\n , usecols = [self.id_col_name, self.col_name])\\n\\n\\n \\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n \\n # Split into sentences\\n dt['sentences'] = self.getSentences(dt[self.col_name])\\n \\n \\n \\n\\n \\n \\n\\n # Store number of sentences in each response as a column\\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\\n\\n # Split each row into multiple rows - one row for each sentence\\n dt = (dt\\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\\n .apply(pd.Series)\\n .stack()\\n .reset_index()\\n .drop('level_3', axis = 1)\\n .rename(columns = {0:'sentences'}))\\n\\n\\n # Clean the sentences\\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\\n\\n # Remove useless sentences\\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\\n\\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n\\n # Tokenize words in the cleaned sentences\\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\\n\\n\\n # Call the lexicon function\\n topic_lexicons = self.prepare_lexicons()\\n\\n # Lists to store results\\n count_topic_all = []\\n actual_topic_all = []\\n\\n # Tag each response into a topic\\n for response in responses:\\n\\n count_topic = []\\n actual_topic = []\\n\\n for topic in topic_lexicons:\\n\\n # Count occurance of each word in word stock in the response\\n temp = sum(dict((x, response.count(x)) for x in topic).values())\\n count_topic.append(temp)\\n\\n\\n for index, value in enumerate(count_topic):\\n\\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\\n if value > 0:\\n actual_topic.append(topic_dict[index])\\n\\n\\n # If more than 3 topics are tagged for single sentence, refine by increasing\\n # cutoff to at least 2 words instead of 1\\n if len(actual_topic) > 3:\\n\\n actual_topic = []\\n for index, value in enumerate(count_topic):\\n\\n if value > 1: # Increase cutoff\\n actual_topic.append(topic_dict[index])\\n\\n count_topic_all.append(count_topic)\\n actual_topic_all.append(actual_topic)\\n\\n\\n dt['tags'] = actual_topic_all\\n dt['num_tags'] = count_topic_all\\n\\n\\n # Select only the most important columns\\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\\n\\n return dt, dt_less\",\n \"def calc_topic_mode_log_stats(user_exercise_graph, topic_id,\\n just_earned_proficiency):\\n topic = topic_models.Topic.get_by_id(topic_id)\\n topic_exercises = topic.get_exercises()\\n\\n total_exercises = len(topic_exercises)\\n count_proficient = len(set(ex.name for ex in topic_exercises) &\\n set(user_exercise_graph.proficient_exercise_names()))\\n just_completed = (just_earned_proficiency and total_exercises ==\\n count_proficient)\\n\\n return {\\n 'total_exercises': total_exercises,\\n 'count_proficient': count_proficient,\\n 'just_completed': just_completed,\\n }\",\n \"def pre_process_data(df):\\n # setting `passengerID` as Index since it wont be necessary for the analysis\\n df = df.set_index(\\\"PassengerId\\\")\\n\\n # convert 'Sex' values\\n df['gender'] = df['Sex'].map({'female': 0, 'male': 1}).astype(int)\\n\\n # We see that 2 passengers embarked data is missing, we fill those in as the most common Embarked value\\n df.loc[df.Embarked.isnull(), 'Embarked'] = df['Embarked'].mode()[0]\\n\\n # Replace missing age values with median ages by gender\\n for gender in df['gender'].unique():\\n median_age = df[(df['gender'] == gender)].Age.median()\\n df.loc[(df['Age'].isnull()) & (df['gender'] == gender), 'Age'] = median_age\\n\\n # convert 'gender' values to new columns\\n df = pd.get_dummies(df, columns=['gender'])\\n\\n # convert 'Embarked' values to new columns\\n df = pd.get_dummies(df, columns=['Embarked'])\\n\\n # bin Fare into five intervals with equal amount of values\\n df['Fare-bin'] = pd.qcut(df['Fare'], 5, labels=[1, 2, 3, 4, 5]).astype(int)\\n\\n # bin Age into seven intervals with equal amount of values\\n # ('baby','child','teenager','young','mid-age','over-50','senior')\\n bins = [0, 4, 12, 18, 30, 50, 65, 100]\\n age_index = (1, 2, 3, 4, 5, 6, 7)\\n df['Age-bin'] = pd.cut(df['Age'], bins, labels=age_index).astype(int)\\n\\n # create a new column 'family' as a sum of 'SibSp' and 'Parch'\\n df['family'] = df['SibSp'] + df['Parch'] + 1\\n df['family'] = df['family'].map(lambda x: 4 if x > 4 else x)\\n\\n # create a new column 'FTicket' as the first character of the 'Ticket'\\n df['FTicket'] = df['Ticket'].map(lambda x: x[0])\\n # combine smaller categories into one\\n df['FTicket'] = df['FTicket'].replace(['W', 'F', 'L', '5', '6', '7', '8', '9'], '4')\\n # convert 'FTicket' values to new columns\\n df = pd.get_dummies(df, columns=['FTicket'])\\n\\n # get titles from the name\\n df['title'] = df.apply(lambda row: re.split('[,.]+', row['Name'])[1], axis=1)\\n\\n # convert titles to values\\n df['title'] = df['title'].map({' Capt': 'Other', ' Master': 'Master', ' Mr': 'Mr', ' Don': 'Other',\\n ' Dona': 'Other', ' Lady': 'Other', ' Col': 'Other', ' Miss': 'Miss',\\n ' the Countess': 'Other', ' Dr': 'Other', ' Jonkheer': 'Other', ' Mlle': 'Other',\\n ' Sir': 'Other', ' Rev': 'Other', ' Ms': 'Other', ' Mme': 'Other', ' Major': 'Other',\\n ' Mrs': 'Mrs'})\\n # convert 'title' values to new columns\\n df = pd.get_dummies(df, columns=['title'])\\n\\n df = df.drop(['Name', 'Ticket', 'Cabin', 'Sex', 'Fare', 'Age'], axis=1)\\n\\n return df\",\n \"def get_paper_counter_per_topic_id(all_topic_assignments):\\n counter = {}\\n for topic_assignment in all_topic_assignments:\\n for topic_index, topic_value in topic_assignment:\\n if topic_index not in counter:\\n counter[topic_index] = 0\\n\\n counter[topic_index] += 1\\n\\n return counter\",\n \"def topic(df, num_topics=5):\\r\\n# X, y = df[df.columns[:-1]], df[df.columns[-1]]\\r\\n lda = LatentDirichletAllocation(n_topics=num_topics,\\r\\n max_iter=5,\\r\\n learning_method='online',\\r\\n learning_offset=50.,\\r\\n random_state=0)\\r\\n return lda.fit_transform(df)\",\n \"def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\\n\\n for text_id, text in self.texts.items():\\n # single-word topics act a bit different (no zips or comprehensions)\\n # store data in self.topics, not zip_grams\\n for word in text['doc']:\\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\\n\\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\\n continue\\n\\n if not (word.pos in self.nouns or word.ent_type in self.entities):\\n continue\\n\\n if word_lemma in self.topics:\\n self.topics[word_lemma][\\\"count\\\"] += 1\\n self.topics[word_lemma][\\\"textIDs\\\"] |= {text_id}\\n self.topics[word_lemma][\\\"verbatims\\\"] |= {word.text.lower()}\\n else:\\n self.topics[word_lemma] = {\\\"name\\\": word_lemma,\\n \\\"count\\\": 1,\\n \\\"textIDs\\\": {text_id},\\n \\\"verbatims\\\": {word.text.lower()},\\n \\\"subtopics\\\": {}}\\n\\n # Populate self.ngrams and self.topics\\n for text_id, text in self.texts.items():\\n doc = text['doc']\\n\\n # Find pentagrams - ngrams with 5 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\\n self._ngram_counter(ngram, 5, text_id, doc)\\n\\n # Find pentagrams - ngrams with 4 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\\n self._ngram_counter(ngram, 4, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:], doc[2:]):\\n self._ngram_counter(ngram, 3, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:]):\\n self._ngram_counter(ngram, 2, text_id, doc)\\n\\n\\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\\n # but it's only mentioned in 3 different texts, we'd show 3.\\n for _, topic in self.topics.items():\\n topic['textIDCount'] = len(topic['textIDs'])\\n for _, ngram in self.ngrams.items():\\n ngram['textIDCount'] = len(ngram['textIDs'])\\n\\n # Eliminate rarely occurring topics and ngrams.\\n self.topics = {k: v for k, v in self.topics.items() if\\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\\n self.ngrams = {k: v for k, v in self.ngrams.items() if\\n v['textIDCount'] >= min_text_id_count}\\n\\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\\n for ngram_lemma, ngram in self.ngrams.items():\\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\\n # r'\\\\b' + ngram_lemma + r'\\\\b' --> does the ngram lemma fit in ngram_plus lemma (\\\\b is word boundary)\\n if ngram['n'] + 1 != ngram_plus['n']:\\n continue\\n\\n if not re.search(r'\\\\b' + ngram_lemma + r'\\\\b', ngram_plus_lemma):\\n continue\\n\\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\\n # instead of the other loop?\\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\\n ngram['count'] = -1\\n\\n # Eliminate newly demoted items\\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}\",\n \"def prob4():\\n#pass\\n n_men = 0\\n n_women = 0\\n db = sql.connect('sql2')\\n cur = db.cursor()\\n cur.execute(\\\"SELECT Gender FROM ICD WHERE Age>=25 AND Age<=35\\\")\\n for gen in cur :\\n if gen[0] == 'M' :\\n n_men += 1\\n else :\\n n_women += 1\\n db.close()\\n return n_men,n_women\",\n \"def test_topic_reduction(reduced_topics):\\n model = BERTopic()\\n nr_topics = reduced_topics + 2\\n model.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents.copy())\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents.copy())\\n new_freq = model.get_topic_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(model.mapped_topics, dict)\\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\\n assert model.mapped_topics\",\n \"def get_mutual_information(filename):\\n categories = {} #{category: speakers of this category}\\n features = {} #{feat: speakers who use this feature}\\n pos_categories_features = {} #{category: {feat: speakers of category who use this feat}}\\n neg_categories_features = {} #{category: {feat: speakers of category who do not use this feat}}\\n users = set() #set of all users in data\\n \\n for line in open(filename):\\n userid, c, date, statusid, rawtweet, toktweet, tagtweet = line.split('\\\\t')\\n users.add(userid)\\n \\n if c not in categories:\\n categories[c] = set()\\n pos_categories_features[c] = {}\\n categories[c].add(userid)\\n \\n feats = set(toktweet.lower().split()) #lowercase tweet and split into words\\n\\n for feat in feats:\\n if feat not in pos_categories_features[c]:\\n pos_categories_features[c][feat] = set()\\n pos_categories_features[c][feat].add(userid)\\n \\n if feat not in features:\\n features[feat] = set()\\n features[feat].add(userid)\\n\\n print \\\"Parsed data\\\"\\n\\n numfeats = len(features) #num of features\\n print numfeats, \\\"features\\\"\\n numusers = len(users) #num of users \\n print numusers, \\\"users\\\"\\n\\n #keep sizes of sets, not sets themselves\\n for feat in features:\\n features[feat] = len(features[feat])\\n for c in categories:\\n categories[c] = len(categories[c])\\n for c in pos_categories_features:\\n for feat in features:\\n if feat in pos_categories_features[c]:\\n pos_categories_features[c][feat] = len(pos_categories_features[c][feat])\\n else:\\n pos_categories_features[c][feat] = 0\\n\\n for c in categories:\\n print c, categories[c], \\\"users\\\"\\n\\n print \\\"Computed counts\\\"\\n \\n mi = {}\\n for feat in features:\\n mi[feat] = 0.0\\n for c in categories:\\n #print c, feat, features[feat], pos_categories_features[c][feat]\\n \\n catprob = categories[c]/numusers\\n\\n #prob of speakers of category c using feat\\n featprob = features[feat]/numusers\\n jointprob = pos_categories_features[c][feat]/numusers\\n if jointprob > 0 and featprob > 0:\\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\\n \\n #prob of speakers of category c NOT using feat\\n featprob = 1 - featprob\\n jointprob = (categories[c] - pos_categories_features[c][feat])/numusers\\n if jointprob > 0 and featprob > 0:\\n mi[feat] += jointprob * log2(jointprob/(catprob * featprob))\\n\\n print \\\"Computed mutual information\\\"\\n\\n feature_scores = sorted(mi.items(), key=lambda x:x[1], reverse=True)\\n refcat = categories.keys()[0] #pick one of the categories\\n print 'Feature\\\\tMI\\\\tP({0}|Feature)\\\\tNum. users'.format(refcat)\\n for feat, score in feature_scores[:200]:\\n prob = pos_categories_features[refcat][feat]/features[feat]\\n print '{0}\\\\t{1:.3f}\\\\t{2:.3f}\\\\t{3}'.format(feat, score, prob, features[feat])\",\n \"def topic(df, num_topics=5):\\r\\n\\r\\n lda = LatentDirichletAllocation(n_topics=num_topics,\\r\\n max_iter=5,\\r\\n learning_method='online',\\r\\n learning_offset=50.,\\r\\n random_state=0)\\r\\n return lda.fit_transform(df)\",\n \"def analysis_1_result(primary_person_df,output_folder_path):\\n male_death_count_df = primary_person_df\\\\\\n .filter(col(\\\"PRSN_GNDR_ID\\\") == \\\"MALE\\\").agg(count(\\\"PRSN_GNDR_ID\\\").alias(\\\"MALE_DEATH_CNT\\\"))\\n print(\\\"Analysis 1: \\\\nTotal number of crashes (accidents) in which number of persons killed are male is :\\\")\\n male_death_count_df.show() #Dispalying result\\n write_df_to_csv(male_death_count_df,output_folder_path+\\\"analysis_1_result\\\") #Writing to csv file\",\n \"def topic_extraction(df, col_name):\\n tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\\n max_features=200,\\n stop_words='english')\\n tfidf = tfidf_vectorizer.fit_transform(df[col_name])\\n\\n tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,\\n max_features=200,\\n stop_words='english')\\n tf = tf_vectorizer.fit_transform(df[col_name])\\n nmf = NMF(n_components=20, random_state=1,\\n alpha=.1, l1_ratio=.5)\\n tfidf_feature_names = tfidf_vectorizer.get_feature_names()\\n nmf_w = nmf.fit_transform(tfidf)\\n nmf_h = nmf.components_\\n df['labels'] = nmf_w.argmax(axis=1) # this was the right code to get labels/clusters\\n\\n\\n print(\\\"\\\\nTopics in NMF model:\\\")\\n print_top_words(nmf, tfidf_feature_names)\\n\\n\\n lda = LatentDirichletAllocation(n_topics=20, max_iter=5,\\n learning_method='online',\\n learning_offset=50.,\\n random_state=0,\\n n_jobs=-1)\\n lda.fit(tf)\\n doc_topic_distrib = lda.transform(tf)\\n lda_labels = doc_topic_distrib.argmax(axis=1)\\n print lda_labels[:100]\\n df['lda_labels'] = lda_labels\\n print(\\\"\\\\nTopics in LDA model:\\\")\\n tf_feature_names = tf_vectorizer.get_feature_names()\\n print_top_words(lda, tf_feature_names)\\n return df\",\n \"def calculate_statistics(city,df):\\n \\n #Total Trip duration per gender\\n if 'Gender' in df.columns:\\n viewData=df.groupby(['Gender'])['Trip Duration'].sum().reset_index(name='Trip Duration')\\n print('Trip duration per gender\\\\n')\\n print(viewData)\\n #number of missing values in the entire sheet\\n missing_values=np.count_nonzero(df.isnull())\\n print('Number of missing values in the {} dataset:{}'.format(city,missing_values))\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def countCountry(self, docID, data):\\n for entry in data.genData():\\n if 'subject_doc_id' in entry and 'visitor_country' in entry and 'event_type' in entry:\\n if entry['event_type'] == 'read':\\n if str(entry['subject_doc_id']) == docID: # cast to string needed as input is string\\n if entry['visitor_country'] in self.countryCounts:\\n self.countryCounts[entry['visitor_country']] += 1\\n else:\\n self.countryCounts[entry['visitor_country']] = 1\",\n \"def document_distribution_per_topic(df_dominant_topic):\\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\\n\\n\\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\\n\\n for i, ax in enumerate(axes.flatten()):\\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\\n doc_lens = [len(d) for d in df_dominant_topic.Text]\\n ax.hist(doc_lens, bins = 1000, color=cols[i])\\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\\n sns.kdeplot(doc_lens, color=\\\"black\\\", shade=False, ax=ax.twinx())\\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\\n ax.set_ylabel('Number of Documents', color=cols[i])\\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\\n\\n fig.tight_layout()\\n fig.subplots_adjust(top=0.90)\\n plt.xticks(np.linspace(0,1000,9))\\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\\n plt.show()\",\n \"def main():\\n\\n # open links.csv in order to access IMDB id numbers\\n ifile = open('movie-countries.csv', \\\"rb\\\")\\n reader = csv.reader(ifile)\\n \\n # writer for csv with countries\\n ofile = open('country_stats.csv', \\\"wb\\\")\\n writer = csv.writer(ofile)\\n\\n # deal with headers\\n reader.next() # skip first line\\n writer.writerow(['country', 'number of movies', 'number of primary movies'])\\n\\n # one dictionary for all mention of a country, one dictionary for if the country was the first one listed\\n country_count_dict = {}\\n country_count_primary_dict= {}\\n\\n # iterate through data\\n for row in reader:\\n # get the countries column\\n countries = row[3]\\n\\n # add to dicionary of countries\\n for country in countries.split(\\\"|\\\"):\\n country_count_dict[country] = country_count_dict.get(country, 0) + 1\\n\\n # if it's the primary country\\n if country == countries.split(\\\"|\\\")[0]:\\n country_count_primary_dict[country] = country_count_primary_dict.get(country, 0) + 1\\n\\n # write to the file\\n for key, value in country_count_dict.iteritems():\\n writer.writerow([key , str(value), country_count_primary_dict.get(key, \\\"0\\\")])\\n\\n ifile.close()\\n ofile.close()\",\n \"def posts_per_topic_all(request, pk):\\n #update is_expired in all posts\\n update_posts_expiration()\\n #get all posts with a certain topic\\n posts = Post.objects.filter(topic=pk)\\n serializer = ViewPostSerializer(posts, many=True)\\n return Response(serializer.data)\",\n \"def main_topic_doc(ldamodel, corpus=corpus): \\n \\n doc_topics = pd.DataFrame()\\n\\n for i, row in enumerate(ldamodel[corpus]):\\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\\n\\n for j, (topic_num, prop_topic) in enumerate(row):\\n if j == 0:\\n wp = ldamodel.show_topic(topic_num)\\n topic_keywords = \\\"' \\\".join([word for word, prop in wp])\\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\\n else:\\n break\\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\\n return doc_topics\",\n \"def check_confidence(data):\\n gender_confident_data = data[data['gender:confidence'] == 1] # Dataset size = 13926\\n return gender_confident_data\",\n \"def get_titanic_fea(dataset):\\n dataset['Name_length'] = dataset['Name'].apply(len)\\n\\n # Mapping Sex 不在map定义的 就是NaN\\n dataset['Sex'] = dataset['Sex'].map({'female': 0, 'male': 1}).astype(int)\\n\\n dataset['Has_Cabin'] = dataset['Cabin'].apply(lambda x: 0 if type(x) == float else 1)\\n dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\\n\\n dataset['IsAlone'] = 0\\n dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\\n\\n # [Embarked]\\n dataset['Embarked'] = dataset['Embarked'].fillna('0')\\n dataset['Fare'] = dataset['Fare'].fillna(0)\\n # Mapping Embarked\\n dataset['Embarked'] = dataset['Embarked'].map({'0': 0, 'S': 1, 'C': 2, 'Q': 3}).astype(int)\\n\\n # [Fare]\\n dataset['CategoricalFare'] = pd.qcut(dataset['Fare'], 4)\\n # Mapping Fare\\n dataset.loc[dataset['Fare'] <= 7.91, 'Fare'] = 0\\n dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\\n dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare'] = 2\\n dataset.loc[dataset['Fare'] > 31, 'Fare'] = 3\\n dataset['Fare'] = dataset['Fare'].astype(int)\\n\\n # [Age]\\n age_avg = dataset['Age'].mean()\\n age_std = dataset['Age'].std()\\n age_null_count = dataset['Age'].isnull().sum()\\n age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\\n dataset['Age'][np.isnan(dataset['Age'])] = age_null_random_list\\n dataset['Age'] = dataset['Age'].astype(int)\\n dataset['CategoricalAge'] = pd.cut(dataset['Age'], 5)\\n # Mapping Age\\n dataset.loc[dataset['Age'] <= 16, 'Age'] = 0\\n dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\\n dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\\n dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\\n dataset.loc[dataset['Age'] > 64, 'Age'] = 4\\n\\n # [Name]\\n # 称谓 Mr 、Miss 等\\n def get_title(name):\\n title_search = re.search(' ([A-Za-z]+)\\\\.', name)\\n # If the title exists, extract and return it.\\n if title_search:\\n return title_search.group(1)\\n return \\\"\\\"\\n dataset['Title'] = dataset['Name'].apply(get_title)\\n\\n # 只保留4类Title\\n dataset['Title'] = dataset['Title'].replace(\\n ['Lady', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\\n dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\\n dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\\n dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\\n # Mapping titles\\n title_mapping = {\\\"Mr\\\": 1, \\\"Miss\\\": 2, \\\"Mrs\\\": 3, \\\"Master\\\": 4, \\\"Rare\\\": 5}\\n dataset['Title'] = dataset['Title'].map(title_mapping)\\n dataset['Title'] = dataset['Title'].fillna(0)\\n\\n # Feature selection\\n drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\\n dataset = dataset.drop(drop_elements, axis=1)\\n dataset = dataset.drop(['CategoricalAge', 'CategoricalFare'], axis=1)\\n\\n return dataset\",\n \"def test_extract_topics_custom_cv(base_bertopic_custom_cv):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic_custom_cv._update_topic_size(documents)\\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic_custom_cv.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def export_topics(self):\\n\\n # format as a list (for json output), then sort descending by textIDCount\\n topics = [{'name': topic['name'], 'count': topic['count'],\\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\\n 'children': '' if 'children' not in topic else topic['children']}\\n for topic_id, topic in self.topics.items()]\\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\\n\\n for i, topic in enumerate(topics):\\n # Note that 'rank' is from topic, not child.\\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\\n 'textIDCount': child['textIDCount']}\\n for _, child in topic['children'].items()]\\n\\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\\n\\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\\n child_count = sum([child['textIDCount'] for child in topic['children']])\\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\\n\\n for child in topic['children']:\\n child['size'] = child['textIDCount'] * child_count_multiplier\\n\\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\\n\\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\\n self.model_output[\\\"children\\\"] = [topic for topic in topics]\\n\\n # Build file name and save\\n if self.data_date:\\n date = datetime.strptime(self.data_date, \\\"%Y-%m-%d\\\").strftime('%d') # from YYYY-MM-DD to DD\\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\\n else:\\n file_name = '{}-Topics.txt'.format(self.corpus_name)\\n\\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\\n json.dump(self.model_output, file)\",\n \"def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\\n print(data_frame_pandas)\\n return data_frame_pandas\",\n \"def test_topic_model_generator_dimensions( ):\\n N = 100\\n D = 1000\\n K = 10\\n W = 100\\n\\n tm = TopicModel.generate( K, D )\\n assert( tm.topics.shape == (D, K) )\\n assert( tm.weights.shape == (K,) )\\n\\n docs = tm.sample( N, words = W )\\n # Each document is a column\\n assert( docs.shape == (N, D) ) \\n # Each doc should have 100 words\\n assert( sc.all(docs.sum(1) == W) )\",\n \"def analysis_3_result(primary_person_df,output_folder_path):\\n rankWindowSpec = Window.orderBy(col(\\\"MAX_FEMALE_COUNT\\\").desc())\\n state_with_female_involved_df = primary_person_df \\\\\\n .filter(col(\\\"PRSN_GNDR_ID\\\") == \\\"FEMALE\\\") \\\\\\n .groupBy(\\\"DRVR_LIC_STATE_ID\\\", \\\"PRSN_GNDR_ID\\\") \\\\\\n .agg(count(\\\"*\\\").alias(\\\"MAX_FEMALE_COUNT\\\"))\\\\\\n .withColumn(\\\"RANK\\\", row_number().over(rankWindowSpec))\\\\\\n .filter(\\\"RANK=1\\\") \\\\\\n .drop(\\\"RANK\\\",\\\"PRSN_GNDR_ID\\\")\\\\\\n .select(col(\\\"DRVR_LIC_STATE_ID\\\").alias(\\\"STATE\\\"),\\\"MAX_FEMALE_COUNT\\\")\\n print(\\\"Analysis 3:\\\\nState with highest number of accidents in which females are involved is: \\\")\\n state_with_female_involved_df.show()\\n write_df_to_csv(state_with_female_involved_df,output_folder_path+\\\"analysis_3_result\\\")\",\n \"def group_topics(sent_topics_sorteddf):\\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0).sum()\\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\\n plt.title('Topic Share %');\\n\\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]\",\n \"def iterate_data(self):\\n if \\\"single\\\" in self.dataset_name:\\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for answer_id in self.data:\\n summary = self.data[answer_id]['summary']\\n articles = self.data[answer_id]['articles']\\n question = self.data[answer_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(articles, 'article')\\n self._get_token_cnts(question, 'question')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if \\\"multi\\\" in self.dataset_name:\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for q_id in self.data:\\n summary = self.data[q_id]['summary']\\n question = self.data[q_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(question, 'question')\\n question = self.data[q_id]['question']\\n for answer_id in self.data[q_id]['articles']:\\n articles = self.data[q_id]['articles'][answer_id][0]\\n if args.tokenize:\\n self._get_token_cnts(articles, 'article')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if self.dataset_name == \\\"complete_dataset\\\":\\n self.stat_dict = {'urls': [], 'sites': []}\\n article_dict = {}\\n print(\\\"Counting answers, sites, unique urls, and tokenized counts of unique articles\\\")\\n answer_cnt = 0\\n for q_id in self.data:\\n for a_id in self.data[q_id]['answers']:\\n answer_cnt += 1\\n url = self.data[q_id]['answers'][a_id]['url']\\n article = self.data[q_id]['answers'][a_id]['article']\\n if url not in article_dict:\\n article_dict[url] = article\\n self.stat_dict['urls'].append(url)\\n assert \\\"//\\\" in url, url\\n site = url.split(\\\"//\\\")[1].split(\\\"/\\\")\\n self.stat_dict['sites'].append(site[0])\\n print(\\\"# of Answers:\\\", answer_cnt)\\n print(\\\"Unique articles: \\\", len(article_dict)) # This should match up with count written to file\\n self._write_stats(\\\"full collection\\\")\\n\\n # Get token/sent averages of unique articles\\n if args.tokenize:\\n self.stat_dict = {'article': [[], []]}\\n for a in article_dict:\\n self._get_token_cnts(article_dict[a], 'article')\\n self._write_stats(\\\"token_counts\\\")\",\n \"def property_count_function(listOfProperties):\\n\\n property_count = {} # Empty dict, is gonna look like this: property_count{property : count}\\n for lists in listOfProperties:\\n try:\\n for properties in lists:\\n property_count[properties] = property_count.get(properties, 0) + 1\\n except TypeError as e:\\n print(e)\\n\\n # Converts the dictionary to a dataframe\\n property_dataframe = pd.DataFrame(list(property_count.items()), columns=['Property', 'Frequency'])\\n # property_dataframe = property_dataframe.set_index(\\\"Property\\\")\\n property_dataframe = property_dataframe.sort_values(by=['Frequency'], ascending=False)\\n\\n return property_dataframe\",\n \"def train(self, corpus):\\n for sentence in corpus.corpus:\\n for datum in sentence.data: \\n self.unigramCounts[datum.word] += 1\\n self.totalCount += 1\",\n \"def run_gender_freq(corpus):\\n novels = corpus._load_novels()\\n c = len(novels)\\n loops = c//10 + 1\\n\\n num = 0\\n\\n while num < loops:\\n dictionary = {}\\n for novel in novels[num * 10: min(c, num * 10 + 9)]:\\n d = {'he': novel.get_word_freq('he'), 'she': novel.get_word_freq('she')}\\n d = get_comparative_word_freq(d)\\n lst = [d[\\\"he\\\"], d[\\\"she\\\"]]\\n book = novel.title[0:20] + \\\"\\\\n\\\" + novel.author\\n dictionary[book] = lst\\n display_gender_freq(dictionary, str(num))\\n num += 1\",\n \"def process_family_frequencies(self, parent, family):\\n if not family:\\n return None\\n counts = {}\\n n_tokens = 0\\n for description_array in family:\\n seen = set([])\\n for token in description_array:\\n counts[token] = counts.get(token, 0) + 1\\n n_tokens += 1\\n\\n self.word2tf[token] = self.word2tf.get(token, 0) + 1\\n self.n_words += 1\\n if token not in seen:\\n self.word2df[token] = self.word2df.get(token, 0) + 1\\n seen.add(token)\\n\\n self.family2tf[parent] = {tok: count/n_tokens for tok, count in counts.items()}\\n self.data.extend(family)\\n return True\",\n \"def age_12_count() :\\n\\n import numpy as np\\n import pandas as pd\\n import matplotlib.pyplot as plt\\n import itertools\\n\\n train = pd.read_csv('./data/train.csv')\\n \\n # Split data to contain ages only up to 12 months\\n train_12_months = train.loc[train['Age'] < 13, ['State','Type', 'Age', 'AdoptionSpeed']]\\n\\n # Divide by dog (Type = 1) and cat (Type = 2)\\n dog_df = train_12_months.loc[train_12_months['Type'] == 1, :]\\n cat_df = train_12_months.loc[train_12_months['Type'] == 2, :]\\n \\n dog_max_age = max(dog_df.loc[:, 'Age'])\\n dog_min_age = min(dog_df.loc[:, 'Age'])\\n \\n cat_max_age = max(cat_df.loc[:, 'Age'])\\n cat_min_age = min(cat_df.loc[:, 'Age'])\\n \\n dog_age_labels = []\\n dog_count = []\\n \\n cat_age_labels = []\\n cat_count = []\\n \\n # Find dog count for each age\\n for i in range(dog_min_age, dog_max_age + 1) :\\n count = (dog_df.Age == i).sum()\\n if(count > 0) :\\n dog_count.append(count)\\n dog_age_labels.append(i)\\n\\n # Find cat count for each age\\n for i in range(cat_min_age, cat_max_age + 1) :\\n count = (cat_df.Age == i).sum()\\n if(count > 0) :\\n cat_count.append(count)\\n cat_age_labels.append(i)\\n \\n # Plot bar graphs\\n plt.figure()\\n index = np.arange(len(dog_age_labels))\\n plt.bar(index, dog_count)\\n plt.xlabel('Age in Months')\\n plt.xticks(index, dog_age_labels)\\n plt.ylabel('Count')\\n plt.title('Count of Dogs Up to 12 Months of Age')\\n plt.savefig('dog12.png', bbox_inches='tight')\\n \\n \\n plt.figure()\\n index = np.arange(len(cat_age_labels))\\n plt.bar(index, cat_count)\\n plt.xlabel('Age in Months')\\n plt.xticks(index, cat_age_labels)\\n plt.ylabel('Count')\\n plt.title('Count of Cats Up to 12 Months of Age')\\n plt.savefig('cat12.png', bbox_inches='tight')\",\n \"def task2(self, doc) -> dict:\\n country_count = {}\\n match_records = []\\n for entry in self.records:\\n if (entry['event_type'] =='read'):\\n if entry['subject_doc_id'] == doc:\\n match_records.append(entry)\\n for rec in match_records:\\n if (rec['visitor_country'] in country_count):\\n country_count[rec['visitor_country']] += 1\\n else:\\n country_count[rec['visitor_country']] = 1\\n print(country_count)\\n return country_count\",\n \"def analyze_data(df, sentiment_col, tweet_col, path):\\n\\n # create empty dictionaries to store all encountered words and their frequencies\\n all_dict = {}\\n pos_dict = {}\\n neg_dict = {}\\n neu_dict = {}\\n # initialize counters to counter total number of tweets based on their emotion\\n pos_count = 0\\n neg_count = 0\\n neu_count = 0\\n\\n # iterate through each row of the df\\n for index, row in df.iterrows():\\n if row[sentiment_col] == \\\"positive\\\":\\n pos_count = iterate_words(\\n pos_count, row[tweet_col], all_dict, pos_dict)\\n\\n if row[sentiment_col] == \\\"negative\\\":\\n neg_count = iterate_words(\\n neg_count, row[tweet_col], all_dict, neg_dict)\\n\\n if row[sentiment_col] == \\\"neutral\\\":\\n neu_count = iterate_words(\\n neu_count, row[tweet_col], all_dict, neu_dict)\\n\\n # visualize statistics\\n visualize_stats(all_dict, 'all_plot.png', 'all_cloud.png',\\n 'Word frequency in all tweets', path)\\n visualize_stats(pos_dict, 'pos_plot.png', 'pos_cloud.png',\\n 'Word frequency in positive tweets', path)\\n visualize_stats(neg_dict, 'neg_plot.png', 'neg_cloud.png',\\n 'Word frequency in negative tweets', path)\\n visualize_stats(neu_dict, 'neu_plot.png', 'neu_cloud.png',\\n 'Word frequency in neutral tweets', path)\\n\\n # make plot for emotion frequency\\n emotions = ('Positive', 'Negative', 'Neutral')\\n freq = [pos_count, neg_count, neu_count]\\n sns.set_style(\\\"darkgrid\\\")\\n ax = plt.figure().gca()\\n ax.xaxis.grid(False)\\n ax.yaxis.set_major_locator(MaxNLocator(integer=True))\\n plt.bar(range(len(emotions)), freq, align='center',\\n color=['forestgreen', 'firebrick', 'goldenrod'])\\n plt.xticks(range(len(emotions)), emotions)\\n plt.title('Tweet frequency based on emotion')\\n plt.savefig(path + 'emotion_plot.png')\\n plt.close()\\n\\n # make pie for emotion frequency\\n sizes = [pos_count / len(df.index), neg_count /\\n len(df.index), neu_count / len(df.index)]\\n colors = ['forestgreen', 'firebrick', 'goldenrod']\\n plt.pie(sizes, labels=emotions, colors=colors,\\n autopct='%1.1f%%', startangle=140)\\n plt.title('Tweet frequency based on emotion')\\n plt.axis('equal')\\n plt.savefig(path + 'emotion_pie.png')\\n plt.close()\",\n \"def trainCount(\\n trainData, \\n questionType,\\n questionDict,\\n questionIdict, \\n objDict, \\n objIdict,\\n numAns):\\n count_wa = np.zeros((len(objIdict), numAns))\\n count_a = np.zeros((numAns))\\n objIds = extractObjId(\\n trainData[0], \\n questionType, \\n questionDict, \\n questionIdict)\\n for i in range(objIds.shape[0]):\\n objId = objIds[i]\\n obj = questionIdict[objId - 1]\\n ansId = trainData[1][i, 0]\\n objId2 = objDict[obj]\\n count_wa[objId2, ansId] += 1\\n count_a[ansId] += 1\\n # Add UNK count\\n count_a[-1] += 1\\n return count_wa, count_a\",\n \"def get_data(self):\\n\\n self.cur.execute('SELECT year, sex, education, score from vocabulary_scores;')\\n scores = dict()\\n education = dict()\\n count = dict()\\n\\n for row in self.cur :\\n if row[0] in scores:\\n if row[1] in scores[row[0]]:\\n scores[row[0]][row[1]] += int(row[3])\\n education[row[0]][row[1]] += int(row[2])\\n count[row[0]][row[1]] += 1\\n else:\\n scores[row[0]][row[1]] = int(row[3])\\n education[row[0]][row[1]] = int(row[2])\\n count[row[0]][row[1]] = 1\\n else:\\n # scores[year] = {gender: score}\\n scores[row[0]] = {row[1]: int(row[3])}\\n education[row[0]] = {row[1]: int(row[2])}\\n count[row[0]] = {row[1]: 1}\\n\\n scores, education = self.average_scores(scores, education, count)\\n\\n return scores, education\",\n \"def test_topics_for_products(self):\\n desktop_topics = topics_for(product=self.desktop)\\n eq_(len(desktop_topics), 3)\\n\\n mobile_topics = topics_for(product=self.mobile)\\n eq_(len(mobile_topics), 2)\",\n \"def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)\",\n \"def num_words():\\n # Load the GT.\\n df = pd.read_csv(config.META_FQN, sep=\\\"\\\\t\\\")\\n stats = {\\n \\\"T\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"P\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"sess\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n }\\n\\n for _, row in df.iterrows():\\n if row[\\\"asr_test\\\"]:\\n stats[\\\"P\\\"][\\\"words\\\"].append(float(row[\\\"gt_patient_num_words\\\"]))\\n stats[\\\"T\\\"][\\\"words\\\"].append(float(row[\\\"gt_therapist_num_words\\\"]))\\n stats[\\\"P\\\"][\\\"duration\\\"].append(float(row[\\\"gt_patient_time_spoken\\\"]))\\n stats[\\\"T\\\"][\\\"duration\\\"].append(\\n float(row[\\\"gt_therapist_time_spoken\\\"])\\n )\\n stats[\\\"sess\\\"][\\\"duration\\\"].append(float(row[\\\"sess_dur\\\"]))\\n n_words = (\\n row[\\\"gt_therapist_num_words\\\"] + row[\\\"gt_patient_num_words\\\"]\\n )\\n stats[\\\"sess\\\"][\\\"words\\\"].append(n_words)\\n\\n for speaker in stats:\\n for metric in stats[speaker]:\\n print(f\\\"------ {speaker} | {metric} ------\\\")\\n print_stats(stats[speaker][metric])\",\n \"def model_topics(df):\\n\\n data = df.text.values.tolist()\\n data_words = list(sent_to_words(data))\\n\\n # Build the bigram and trigram models\\n bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\\n trigram = gensim.models.Phrases(bigram[data_words], threshold=100) \\n\\n # Faster way to get a sentence clubbed as a trigram/bigram\\n bigram_mod = gensim.models.phrases.Phraser(bigram)\\n trigram_mod = gensim.models.phrases.Phraser(trigram)\\n\\n # Remove Stop Words\\n data_words_nostops = remove_stopwords(data_words)\\n\\n # Form Bigrams\\n data_words_bigrams = make_bigrams(data_words_nostops,bigram_mod)\\n\\n # Initialize spacy 'en' model, keeping only tagger component (for efficiency)\\n nlp = spacy.load('en', disable=['parser', 'ner'])\\n\\n # Do lemmatization keeping only noun, adj, vb, adv\\n data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\\n\\n # Create Dictionary\\n id2word = corpora.Dictionary(data_lemmatized)\\n\\n # Create Corpus\\n texts = data_lemmatized\\n\\n # Term Document Frequency\\n corpus = [id2word.doc2bow(text) for text in texts]\\n\\n # Perform Topic Modeling for number of topics ranging from 5 to 50 in steps of 5\\n model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=5, limit=50, step=5)\\n\\n return model_list,coherence_values,corpus,id2word\",\n \"def counts_table(data, attr):\\n pd.options.mode.chained_assignment = None # default='warn'\\n # gets class names for dataframe manipulation\\n classes = attr.tail(1)['vars'].tolist()\\n classlist = [classes[0][0], classes[0][1]]\\n # expanding a table to have all variable options in a column with their \\n # parent attribute\\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\\n allvariables.name='var'\\n allvariables = attr.drop('vars', axis=1).join(allvariables)\\n av = allvariables.drop(attr.index[-1])\\n # populate the table with counts\\n for c in classlist:\\n clist = []\\n count = 0\\n for i, row in av.iterrows():\\n att = row['attr']\\n var = row['var']\\n sub = data[[att,'class']]\\n sub = sub[sub[att]==var]\\n if not sub.empty:\\n ssub = sub[sub['class']==c]\\n if not ssub.empty:\\n count = len(ssub)\\n else:\\n count = 0\\n clist.append(count)\\n av[c] = clist\\n\\n return av\",\n \"def get_mentions(self, column, list_of_types, total=False, average=False):\\n for mbti_type in list_of_types:\\n self.df[mbti_type + '_mentions'] = [sum([x.casefold().count(mbti_type.casefold()) for x in post]) for post in self.df[column]]\\n if total == True:\\n mention_cols = [col for col in self.df.columns if 'mentions' in col]\\n self.df['total_mentions'] = self.df.filter(mention_cols).sum(axis=1)\\n if average == True:\\n self.df['avg_mentions_per_post'] = self.df['total_mentions'] / self.df['count_posts']\",\n \"def create_topic_columns(videos, topics):\\n \\n # Clear values\\n videos['relevant'] = False\\n\\n # Create masks for each topic so we can later filter videos by topics\\n topic_masks = []\\n for _, topic in topics.iterrows():\\n videos[topic['slug']] = False # Clear values\\n pattern = get_pattern(topic)\\n topic_mask = videos.apply(lambda video: is_relevant(video, pattern), axis=1)\\n topic_masks.append(topic_mask)\\n videos[topic['slug']] = topic_mask\\n\\n # Mark video as 'relevant' if it mentions any of the topics\\n videos['relevant'] = np.any(np.column_stack(topic_masks), axis=1)\",\n \"def user_stats(df):\\n ##task4: user info\\\\type\\\\gender&birth year if available for the city\\n ##user type data is avalable for all cities\\n ## count of each type, frequency is printed in a table \\\"only i New York city 3 types!\\n # TO DO: Display counts of user types\\n user_counts = df['User Type'].value_counts().to_frame()\\n user_percentage = df['User Type'].value_counts(normalize=True)*100\\n user_count_stat=pd.concat([user_counts,user_percentage],axis=1,join=\\\"inner\\\")\\n user_count_stat.columns=['User_Count', 'User_Frequency%'] \\n print(\\\"There are {} types of users : \\\".format(len(user_counts)))\\n for i in range (len(user_counts)):\\n print(user_counts.index.values[i])\\n print(\\\"\\\\n\\\\n\\\",\\\"*\\\"* 40)\\n print(\\\"The count and percentage of each type is:\\\")\\n print(\\\"*\\\"* 40)\\n print(user_count_stat.round(2))\\n print(\\\"*\\\"* 40)\\n\\n #Task 4.2: TO DO: Display counts of gender\\n ##functions to calculate gender distriburion\\\"only for NY and washington\\\"\\n ## start with if statement to check if the data is in dataseries\\n ## if data is apended in the future no need to change the code\\n ## display gender count and frequency in every dataset\\n if 'Gender' in df.columns :\\n print(\\\"\\\\n Gender data is available for this city\\\")\\n user_gender = df['Gender'].value_counts()\\n user_gender_percentage = df['Gender'].value_counts(normalize=True)*100\\n gender_stat=pd.concat([user_gender,user_gender_percentage],axis=1,join=\\\"inner\\\")\\n gender_stat.columns=['Gender_Count', 'Gender_Frequency%']\\n print(\\\"The Number of users from each Gender and their percentage is given by\\\")\\n print(\\\"#\\\"* 40)\\n print(gender_stat.round(2))\\n print(\\\"#\\\"* 40)\\n \\n else :\\n print(\\\"\\\\n Gender data is not available for this city\\\")\\n\\n # task 4.2: TO DO: Display earliest, most recent, and most common year of birth\\n ## functions to calculate year of birth statistics\\n ## condition to check if data is available for the current city\\n # calculate the most common city, earliest and latest\\n if 'Birth Year' in df.columns :\\n print(\\\"\\\\n Birth Year data is available for this city\\\")\\n birth_year = df['Birth Year']\\n popular_year =df['Birth Year'].mode()[0]\\n print(\\\"Some statistics related to year of birth\\\")\\n print(\\\"-\\\"* 40)\\n print(\\\"The earliest year of birth is : ****\\\"+\\\"{:.0f}\\\".format(min(birth_year)),\\\"***\\\");\\n print(\\\"The latest year of birth is : *** \\\"+\\\"{:.0f}\\\".format(max(birth_year)),\\\"***\\\");\\n print(\\\"The most common year of birth is : ***\\\"+\\\"{:.0f}\\\".format(popular_year),\\\"***\\\");\\n \\n else :\\n print(\\\"\\\\n Birth Year data is not available for this city\\\")\\n start_time = time.time()\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\",\n \"def train_ngrams(dataset):\\n trigram_counts = dict()\\n bigram_counts = dict()\\n unigram_counts = dict()\\n token_count = 0\\n ### YOUR CODE HERE\\n raise NotImplementedError\\n ### END YOUR CODE\\n return trigram_counts, bigram_counts, unigram_counts, token_count\",\n \"def test_get_topics(self):\\n\\n for m in self.models:\\n\\n topics = m.topics\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertEqual(topics.num_rows(), 25)\\n self.assertEqual(topics.num_columns(), 2)\\n z = m.topics[\\\"topic_probabilities\\\"]\\n for k in range(m.num_topics):\\n self.assertTrue(\\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\\n \\\"Returned probabilities do not sum to 1.\\\",\\n )\\n\\n # Make sure returned object is an SFrame of the right size\\n topics = m.get_topics()\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertTrue(\\n topics.num_columns() == 3,\\n \\\"Returned SFrame should have a topic, word, and probs.\\\",\\n )\\n\\n # Make sure that requesting a single topic returns only that topic\\n num_words = 8\\n topics = m.get_topics([5], num_words=num_words)\\n self.assertTrue(\\n all(topics[\\\"topic\\\"] == 5), \\\"Returned topics do not have the right id.\\\"\\n )\\n self.assertEqual(topics.num_rows(), num_words)\\n topics = m.get_topics([2, 4], num_words=num_words)\\n self.assertEqual(set(list(topics[\\\"topic\\\"])), set([2, 4]))\\n self.assertEqual(topics.num_rows(), num_words + num_words)\\n\\n # Make sure the cumulative probability of the returned words is\\n # is less than the cutoff we provided.\\n # A cutoff of 1.0 should return num_words for every topic.\\n cutoff = 1.0\\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\\n totals = topics.groupby(\\n \\\"topic\\\", {\\\"total_score\\\": turicreate.aggregate.SUM(\\\"score\\\")}\\n )\\n self.assertTrue(\\n all(totals[\\\"total_score\\\"] <= (cutoff + DELTA)),\\n \\\"More words were returned than expected for this cutoff.\\\",\\n )\\n\\n # Make sure we raise errors for bad input\\n with self.assertRaises(ValueError):\\n m.get_topics([-1])\\n with self.assertRaises(ValueError):\\n m.get_topics([10000])\\n with self.assertRaises(ToolkitError):\\n topics = m.get_topics(output_type=\\\"other\\\")\\n\\n # Test getting topic_words\\n topic_words = m.get_topics(output_type=\\\"topic_words\\\", num_words=5)\\n self.assertEqual(type(topic_words), turicreate.SFrame)\\n\\n # Test words are sorted correctly for the first topic\\n # TODO: Make this more deterministic.\\n\\n # topic_probs = m.get_topics(num_words=5)\\n # expected = [w for w in topic_probs['word'][:5]]\\n # observed = topic_words['words'][0]\\n # self.assertEqual(observed[0], expected[0])\",\n \"def test_topic_reduction_edge_cases():\\n model = BERTopic()\\n nr_topics = 5\\n model.nr_topics = 100\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents)\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents)\\n new_freq = model.get_topic_freq()\\n\\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\\n pd.testing.assert_frame_equal(old_documents, new_documents)\\n pd.testing.assert_frame_equal(old_freq, new_freq)\",\n \"def get_initial_student_count(self):\\n initial_date = self.start_date - timedelta(1)\\n # date cannot be earlier than the school year start\\n school_year_start_date = \\\\\\n SchoolDB.models.SchoolYear.school_year_start_for_date()\\n if (initial_date < school_year_start_date):\\n initial_date = school_year_start_date\\n section_roster_changes = self.section.get_section_roster_changes()\\n student_dict = \\\\\\n section_roster_changes.get_single_day_information(\\n self.students, initial_date)\\n male_count = 0\\n female_count = 0\\n for student in student_dict.values():\\n if (student.gender == \\\"Male\\\"):\\n male_count += 1\\n else:\\n female_count +=1\\n #do not return 0 because it will generate a divide by zero in\\n #the calling function\\n if not male_count:\\n male_count = 1\\n if not female_count:\\n female_count = 1\\n return male_count, female_count\",\n \"def listTopicRelevance(request):\\n if request.method == 'GET':\\n user = request.user;\\n data = [];\\n for topic in Topic.objects.all():\\n row = {};\\n\\n topicSerializer = TopicNestedSerializer(topic)\\n topicSerializer.Meta.depth = 1;\\n #row['topic'] = topicSerializer.data;\\n user_visits = topic.visits.filter(user=user)\\n visitSerializer = VisitSerializer(user_visits, many=True)\\n #visitSerializer.Meta.depth = 1;\\n row['visit_count'] = len(user_visits);\\n if row['visit_count'] > 0:\\n row['last_visit'] = user_visits.order_by('-visit_date')[0].visit_date\\n else:\\n row['last_visit'] = topic.created_at\\n\\n neighbor_visits = Visit.objects.filter(user=user, topic__relates_to__topic_to=topic)\\n\\n row['neighbor_visits_count'] = len(neighbor_visits);\\n if row['neighbor_visits_count'] > 0:\\n row['last_neighbor_visit'] = neighbor_visits.order_by('-visit_date')[0].visit_date;\\n else:\\n row['last_neighbor_visit'] = topic.created_at\\n\\n row['post_count'] = len(topic.posts.filter(user=user))\\n row['like_count'] = len(topic.posts.filter(votes__user=user))\\n row['relevance_score'] = 5*row['neighbor_visits_count'] - (timezone.now()-row['last_neighbor_visit']).total_seconds()/3600\\n row['recommendation'] = row['relevance_score'] + topic.hotness\\n\\n data.append(row)\\n\\n print(data)\\n return Response(data)\",\n \"def test_topic_reduction(reduced_topics):\\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\\n nr_topics = reduced_topics + 2\\n base_bertopic.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(old_documents)\\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\\n old_freq = base_bertopic.get_topics_freq()\\n\\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\\n new_freq = base_bertopic.get_topics_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(base_bertopic.mapped_topics, dict)\\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\\n assert base_bertopic.mapped_topics\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n #Display counts of user types\\n counts_of_user_types = df['User Type'].value_counts()\\n print('counts of user types :',counts_of_user_types )\\n \\n \\n\\n\\n # Display counts of gender\\n try:\\n counts_of_gender = df['Gender'].value_counts()\\n print('counts of gender :',counts_of_gender)\\n except KeyError:\\n print('counts of gender : no data available')\\n \\n\\n \\n #Display earliest, most recent, and most common year of birth\\n try:\\n earliest=df['Birth Year'].min()\\n most_recent=df['Birth Year'].max()\\n most_common_year_of_birth=df['Birth Year'].mode()[0]\\n print('earliest : ', earliest)\\n print('most recent : ', most_recent)\\n print( 'most common year of birth : ', most_common_year_of_birth)\\n except KeyError:\\n print('counts of gender : no data available') \\n \\n \\n \\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\\n\\n \\n\\n #display_raw_data\\n #please detrmine if you want to see some lines of raw data \\n \\n i=0\\n while True:\\n display_data = input('would you like to see (5) lines/next (5) lines of raw data ? , yes or no : ').lower()\\n \\n if display_data == 'yes':\\n print(df[i:i+5])\\n i+=5\\n else:\\n break\",\n \"def count_ngrams(self, corpus):\\n \\n self.unigramcounts = {} # might want to use defaultdict or Counter instead\\n self.bigramcounts = {} \\n self.trigramcounts = {} \\n\\n self.total = 2\\n ##Your code here\\n\\n for sentence in corpus:\\n temp_1 = get_ngrams(sentence,1)\\n temp_2 = get_ngrams(sentence,2)\\n temp_3 = get_ngrams(sentence,3)\\n for i in range(len(temp_1)):\\n if temp_1[i] in self.unigramcounts:\\n self.unigramcounts[temp_1[i]] += 1\\n else:\\n self.unigramcounts[temp_1[i]] = 1\\n self.total += 1\\n\\n for i in range(len(temp_2)):\\n if temp_2[i] in self.bigramcounts:\\n self.bigramcounts[temp_2[i]] += 1\\n else:\\n self.bigramcounts[temp_2[i]] = 1\\n\\n for i in range(len(temp_3)):\\n if temp_3[i] in self.trigramcounts:\\n self.trigramcounts[temp_3[i]] += 1\\n else:\\n self.trigramcounts[temp_3[i]] = 1\\n return\",\n \"def infertopics(self):\\n\\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\\n for uid in self.scan(attribute=\\\"updated\\\"):\\n # Remove updated attribute\\n self.removeattribute(uid, \\\"updated\\\")\\n\\n # Get list of neighboring nodes\\n ids = self.edges(uid)\\n\\n # Infer topic\\n topic = Counter(self.attribute(x, \\\"topic\\\") for x in ids).most_common(1)[0][0] if ids else None\\n if topic:\\n # Add id to topic list and set topic attribute\\n self.topics[topic].append(uid)\\n self.addattribute(uid, \\\"topic\\\", topic)\\n\\n # Set topic rank\\n self.addattribute(uid, \\\"topicrank\\\", len(self.topics[topic]) - 1)\\n\\n # Infer category\\n category = Counter(self.attribute(x, \\\"category\\\") for x in ids).most_common(1)[0][0]\\n self.addattribute(uid, \\\"category\\\", category)\",\n \"def filter_by_gender(self, gender):\\n\\n return self.subcorpus('author_gender', gender)\",\n \"def process_frequencies(df_corpus, wdir, min_MFF, max_MFF, mode, names_MFF):\\n # Normalization of the frequencies by the sum of the text\\n df_corpus = df_corpus.loc[:].div(df_corpus.sum(axis='columns'), axis=\\\"index\\\")\\n if mode == \\\"train\\\":\\n # If we are doing a training corpus, it is easier\\n \\n # The dataframe gets a new summatory column that we use to order the df \\n df_corpus = df_corpus.T\\n df_corpus[\\\"sum\\\"]=df_corpus.sum(axis=\\\"columns\\\")\\n df_corpus = df_corpus.sort_values(by=\\\"sum\\\", ascending=False)\\n \\n # Only a given amount of words is taken\\n df_corpus = df_corpus[min_MFF:max_MFF]\\n # Summatory column is deleted and the df goes back to its normal format\\n del df_corpus['sum']\\n df_corpus = df_corpus.T\\n print(mode, \\\" last feature: \\\", df_corpus.columns[-1])\\n \\n elif mode == \\\"eval\\\" or mode == \\\"test\\\":\\n # If we create the evaluation or the test corpus, we have to check first the features of the train corpus because the 5000 MFW of the train corpus are NOT the 5000 MFW of the test corpus.\\n # TODO: I don't know if that is the best way to do it. Maybe we should calculate the total amount of features in the different corpora, get the list of the n MFF and then fill the diferent matrixs with this features.\\n df_corpus = df_corpus.reindex_axis(names_MFF, axis=1)\\n # Only a given amount of words is taken\\n df_corpus = df_corpus.T\\n df_corpus = df_corpus[min_MFF:max_MFF]\\n df_corpus = df_corpus.T\\n print(mode, \\\" last feature: \\\", df_corpus.columns[-1])\\n\\n df_corpus = df_corpus.fillna(0)\\n \\n # The table is saved as csv\\n df_corpus.to_csv(wdir+\\\"freq_table.csv\\\", sep='\\\\t', encoding='utf-8', index=True)\\n\\n return df_corpus\",\n \"def compute_subj_metrics():\\n\\t# each participant does task 1,2,3 with method A,B = 3*2*N\\n\\t# likert data includes age, gender, Q1 - Q10 = 2+10\\n\\n\\t# set up data structure\\n\\tsubj_metrics = {}\\n\\tfor ID in range(10):\\n\\t\\tfor method in [\\\"A\\\",\\\"B\\\"]:\\n\\t\\t\\t# sanity checks\\n\\t\\t\\tif ID not in subj_metrics:\\n\\t\\t\\t\\tsubj_metrics[ID] = {}\\n\\t\\t\\tif method not in subj_metrics[ID]:\\n\\t\\t\\t\\tsubj_metrics[ID][method] = [None]*12\\n\\n\\there = os.path.dirname(os.path.realpath(__file__))\\n\\tsubdir = \\\"/data/experimental/\\\"\\n\\tdatapath = here + subdir + \\\"Likert_Responses.csv\\\"\\n\\n\\tdata = {}\\n\\tfirstline = True\\n\\twith open(datapath, 'r') as f:\\n\\t\\tfor line in f:\\n\\t\\t\\tif firstline:\\n\\t\\t\\t\\tfirstline = False\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tvalues = line.split(',')\\n\\t\\t\\tinfo = values[1:len(values)]\\n\\t\\t\\tID = int(info[9])\\n\\t\\t\\tmethod = info[10]\\n\\t\\t\\tage = info[11]\\n\\t\\t\\tgender = info[12]\\n\\t\\t\\t# store age\\n\\t\\t\\tsubj_metrics[ID][method][0] = age\\n\\t\\t\\tsubj_metrics[ID][method][1] = gender\\n\\t\\t\\t# parse likert data\\n\\t\\t\\tfor i in range(8):\\n\\t\\t\\t\\tsubj_metrics[ID][method][i+2] = info[i]\\n\\t\\t\\tsubj_metrics[ID][method][10] = info[14]\\n\\t\\t\\tsubj_metrics[ID][method][11] = info[15]\\n\\n\\treturn subj_metrics\",\n \"def get_topics(self):\\n topics = self.word_topics\\n return topics / topics.sum(axis=1)[:, None]\",\n \"def test_question_topics(self):\\n p = ProductFactory()\\n t1 = TopicFactory(slug='doesnotexist', product=p)\\n t2 = TopicFactory(slug='cookies', product=p)\\n t3 = TopicFactory(slug='sync', product=p)\\n\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t3)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n )\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def user_stats(df):\\n try: \\n df = load_data(city, month, day)\\n # TO DO: Display counts of user types\\n count_user_type = df.groupby(['User Type']).count()\\n print('Count of user type: ', count_user_type)\\n\\n # TO DO: Display counts of gender\\n count_gender = df.groupby(['Gender']).count()\\n print('Count of gender: ', count_gender)\\n\\n # TO DO: Display earliest, most recent, and most common year of birth\\n earliest = df['Birth Year'].min()\\n print('Earliest birth year: ', earliest)\\n\\n recent = df['Birth Year'].max()\\n print('Most recent birth year: ', recent)\\n\\n common = df['Birth Year'].mode()[0]\\n print('Most common birth year: ', common)\\n \\n except KeyError:\\n df = load_data(city, month, day)\\n # TO DO: Display counts of user types\\n count_user_type = df.groupby(['User Type']).count()\\n print('Count of user type: ', count_user_type)\\n \\n \\n return user_stats\",\n \"def query_transition_counts(ordered_pitch_types, query_filter):\\n transition_counts = {}\\n\\n for first_pitch_type in ordered_pitch_types:\\n transition_counts[first_pitch_type] = {}\\n\\n for second_pitch_type in ordered_pitch_types:\\n count_query = PitchTransition.select().where(\\n PitchTransition.first_pitch_type == first_pitch_type).where(\\n PitchTransition.second_pitch_type == second_pitch_type)\\n\\n if query_filter:\\n count_query = count_query.where(query_filter)\\n\\n transition_counts[first_pitch_type][second_pitch_type] = count_query.count()\\n\\n return transition_counts\",\n \"def test_IsCorrectGender(self):\\n \\n # load data\\n ilist, flist = load_data()\\n\\n # test case 01\\n for fm in flist:\\n result, msg = IsCorrectGender(fm.WifeID,fm.HusbandID, ilist)\\n self.assertTrue(result)\\n\\n # test case 02\\n ilist[0].Gender = 'F'\\n ilist[1].Gender = 'M'\\n result, msg = IsCorrectGender(flist[0].WifeID,flist[0].HusbandID, ilist)\\n self.assertFalse(result)\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # TO DO: Display counts of user types\\n user_type = df['User Type'].value_counts()\\n print('The types of users are: \\\\n{}' .format(user_type))\\n\\n # TO DO: Display counts of gender\\n while True:\\n try:\\n gender_type = df['Gender'].value_counts()\\n print('The users classified by gender are: \\\\n{}' .format(gender_type))\\n max_birth_year = df.groupby(['Gender'])['Birth Year'].max()\\n min_birth_year = df.groupby(['Gender'])['Birth Year'].min()\\n mode_birth_year = df['Birth Year'].mode()\\n print ('1. The most recent year of birth is: {}\\\\n2. The most earliest year of birth is: {}\\\\n3. The most common year of birth is: {}'.format(max_birth_year, min_birth_year, mode_birth_year))\\n\\n # TO DO: Display earliest, most recent, and most common year of birth\\n\\n\\n print(\\\"\\\\nThis took %s seconds.\\\" % (time.time() - start_time))\\n print('-'*40)\\n break\\n except:\\n print('\\\\n Sorry, the genre field does not exist in the selected file, so no results are shown.')\\n break\",\n \"def assign_topics(self):\\n\\n # Load the dataset which has topic tags for each sentence\\n dt, dt_less = self.assign_topics_to_sentences()\\n \\n dt_copy = dt\\n \\n # Minimum number of tags needed to tag the overall response\\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\\n \\n # Final dataset with full survey response and its tags\\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\\n , 'num_sent': min\\n , 'min_num_tags': min\\n# , 'sentences': lambda x: \\\"%s\\\" % '. '.join(x)\\n , self.col_name: min})\\n final_dt.reset_index(level = 0, inplace = True)\\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\\n , axis = 1)\\n\\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\\n\\n return dt_copy, dt_less, final_dt, final_dt_less\",\n \"def get_tops(name_file,number,gender):\\r\\n tops = []\\r\\n num = 0\\r\\n f = open(name_file)\\r\\n reader = csv.reader(f)\\r\\n for row in reader:\\r\\n if row.__contains__(gender.upper()):\\r\\n row[2] = int(row[2])\\r\\n tops.append(row)\\r\\n num = num + 1\\r\\n if num == number:\\r\\n break\\r\\n return tops\",\n \"def count_persons(self):\\n\\n # full path of personData file\\n path = self.pretty_print_path()\\n\\n data = self.person_data[[\\\"person_id\\\", \\\"telestatus\\\"]].copy()\\n\\n conditions = [(data[\\\"telestatus\\\"] == \\\"Not a Worker\\\"),\\n (data[\\\"telestatus\\\"].isin([\\\"No Telecommute\\\",\\n \\\"1 day a week\\\",\\n \\\"2-3 days a week\\\",\\n \\\"4+ days a week\\\",\\n \\\"Work from Home\\\"]))]\\n choices = [\\\"Non-worker\\\", \\\"Worker\\\"]\\n data[\\\"telestatus\\\"] = np.select(conditions, choices, default=data[\\\"telestatus\\\"])\\n\\n counts = data.groupby([\\\"telestatus\\\"]).count()\\n counts.loc['Total'] = counts.sum()\\n counts.reset_index(inplace=True)\\n counts['File'] = path # add file name\\n\\n results = counts.pivot(index=\\\"File\\\", columns=\\\"telestatus\\\", values=\\\"person_id\\\")\\n\\n # add percentage share\\n counts[\\\"Total\\\"] = data.person_id.count()\\n counts[\\\"share\\\"] = counts.person_id / counts.Total\\n percent_share = counts.pivot(index=\\\"File\\\", columns=\\\"telestatus\\\", values=\\\"share\\\")\\n\\n results = results.append(percent_share, ignore_index=False, sort=True)\\n\\n cols = [\\\"Worker\\\",\\n \\\"Non-worker\\\",\\n \\\"Total\\\"]\\n\\n return results[cols]\",\n \"def find_dominant_topic(df_topic_sents_keywords):\\n\\n # Format\\n df_dominant_topic = df_topic_sents_keywords.reset_index()\\n df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\\n\\n # Group top 5 sentences under each topic\\n sent_topics_sorteddf = pd.DataFrame()\\n\\n sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\\n start = time.time()\\n for i, grp in sent_topics_outdf_grpd:\\n sent_topics_sorteddf = pd.concat([sent_topics_sorteddf, \\n grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \\n axis=0)\\n print(f'Group done. Total time {time.time() - start} seconds.')\\n\\n # Reset Index \\n sent_topics_sorteddf.reset_index(drop=True, inplace=True)\\n\\n # Format\\n sent_topics_sorteddf.columns = ['Topic_Num', \\\"Topic_Perc_Contrib\\\", \\\"Keywords\\\", \\\"Text\\\"]\\n return sent_topics_sorteddf\",\n \"def count_mentioned_pol_figures(data):\\n figures_mentioned = {}\\n figures = get_political_figures()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for figure in figures:\\n if figure + \\\" \\\" in (subject_words + message_words):\\n if figure in figures_mentioned:\\n figures_mentioned[figure] += 1\\n else:\\n figures_mentioned[figure] = 0\\n\\n return pd.DataFrame(figures_mentioned)\",\n \"def get_tweets_by_topic(topic, start_date, end_date):\\n try:\\n query = f\\\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\\\"\\n logger.info(f'QUERY: {query}') \\n with MysqlCursor() as cur:\\n cur.execute(query)\\n tweets = cur.fetchall()\\n columns = [col[0] for col in cur.description]\\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \\n return tweets, columns\\n \\n except Exception as ex:\\n logger.exception(ex)\\n return f'Exception: {ex}'\",\n \"def user_stats(df):\\n\\n print('\\\\nCalculating User Stats...\\\\n')\\n start_time = time.time()\\n\\n # Display counts of user types\\n user_count = df['User Type'].value_counts()\\n print(f'{user_count}\\\\n')\\n\\n # Display counts of gender if only the data contains that column\\n if 'Gender' in df:\\n gender_count = df['Gender'].value_counts()\\n print(gender_count, '\\\\n')\\n\\n # Display earliest, most recent, and most common year of birth if \\\"Birth Year\\\" column is found\\n earliest = df['Birth Year'].min()\\n print('Most earliest birth year:', int(earliest))\\n recent = df['Birth Year'].max()\\n print('Most recent birth year', int(recent))\\n common = df['Birth Year'].value_counts().idxmax()\\n print('Most common birth year', int(common))\\n\\n print(\\\"\\\\nTotal time taken: %s seconds.\\\" % (round(time.time() - start_time, 2)))\\n print('-' * 40)\",\n \"def topic_table(topic):\\r\\n table = Table(['property', 'value'])\\r\\n table.align['property'] = 'r'\\r\\n table.align['value'] = 'l'\\r\\n\\r\\n table.add_row(['name', topic['name']])\\r\\n table.add_row(['tags', listing(topic['tags'] or [])])\\r\\n return table\",\n \"def split_dataset_for_one_attribute(gender_confident_data, attribute='text_cleaned'):\\n\\n # Converting text strings into a matrix of word token counts.\\n cv = CountVectorizer()\\n inputString = cv.fit_transform(gender_confident_data[attribute])\\n\\n # Encodes class labels from 0 to Num_of_classes-1\\n le = LabelEncoder()\\n outputString = le.fit_transform(gender_confident_data['gender'])\\n\\n # Splitting the data such that 66% of the data is assigned as training data and the rest as the test data set.\\n input_train, input_test, output_train, output_test = train_test_split(inputString, outputString, train_size=0.66)\\n\\n return input_train, output_train, input_test, output_test\",\n \"def expectation_step(self,number_of_topics):\\n createdDataMat = np.zeros([self.number_of_documents, number_of_topics, self.vocabulary_size], dtype=np.float)\\n print(\\\"E step:\\\")\\n\\t\\t#print(\\\"Initial value of createdDataMat >>>>>\\\"+createdDataMat)\\n \\n \\t\\n for docValue in range(0, self.number_of_documents):\\n for wordValue in range(0, self.vocabulary_size):\\n recordSum = 0\\n for topicValue in range(0, number_of_topics):\\n self.topic_prob[docValue][topicValue][wordValue] = (self.document_topic_prob[docValue][topicValue] * self.topic_word_prob[topicValue][wordValue])\\n recordSum = recordSum + self.topic_prob[docValue][topicValue][wordValue]\\n for topicValue in range(0, number_of_topics):\\n self.topic_prob[docValue][topicValue][wordValue] = self.topic_prob[docValue][topicValue][wordValue]/recordSum\",\n \"def connect_topic_id_to_topics(self, model):\\n confidence = []\\n for key, value in self.representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key, tp_num, val / len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n self.log_writer.add_log(\\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\\n conf[1],\\n conf[2]))\\n self.topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in self.topic_indexes.items():\\n self.topics_of_index[value] = [key]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6474987","0.63504976","0.6320572","0.62746453","0.627228","0.6258495","0.61637425","0.6114557","0.5977991","0.58644605","0.5836324","0.5831244","0.5673213","0.5647648","0.5610889","0.5574155","0.5492381","0.54264843","0.53719985","0.53717935","0.5295619","0.52562594","0.5255951","0.52543795","0.522742","0.52179205","0.519302","0.51659757","0.51119727","0.51042306","0.5091851","0.5090571","0.50634915","0.50474554","0.50215715","0.5015411","0.5008505","0.49809456","0.49774876","0.49774876","0.49541524","0.49456522","0.4938621","0.4927697","0.49162355","0.4905255","0.49034148","0.4901713","0.48947382","0.48945874","0.48576233","0.4853687","0.48180065","0.4796382","0.47956917","0.47933","0.47849742","0.47778904","0.4776961","0.47758076","0.47752246","0.47732186","0.47640452","0.47639966","0.475554","0.4753862","0.47459054","0.47447842","0.47386268","0.4736431","0.4734776","0.47332802","0.4733193","0.4702847","0.47012332","0.47006556","0.46998748","0.46925905","0.46923918","0.46828443","0.46812356","0.4676416","0.46734202","0.46649432","0.46560308","0.46494696","0.4647872","0.4646516","0.46460456","0.46407625","0.46205994","0.46184963","0.46179497","0.46134374","0.4610658","0.45986965","0.4596978","0.45967084","0.45942363","0.45926726"],"string":"[\n \"0.6474987\",\n \"0.63504976\",\n \"0.6320572\",\n \"0.62746453\",\n \"0.627228\",\n \"0.6258495\",\n \"0.61637425\",\n \"0.6114557\",\n \"0.5977991\",\n \"0.58644605\",\n \"0.5836324\",\n \"0.5831244\",\n \"0.5673213\",\n \"0.5647648\",\n \"0.5610889\",\n \"0.5574155\",\n \"0.5492381\",\n \"0.54264843\",\n \"0.53719985\",\n \"0.53717935\",\n \"0.5295619\",\n \"0.52562594\",\n \"0.5255951\",\n \"0.52543795\",\n \"0.522742\",\n \"0.52179205\",\n \"0.519302\",\n \"0.51659757\",\n \"0.51119727\",\n \"0.51042306\",\n \"0.5091851\",\n \"0.5090571\",\n \"0.50634915\",\n \"0.50474554\",\n \"0.50215715\",\n \"0.5015411\",\n \"0.5008505\",\n \"0.49809456\",\n \"0.49774876\",\n \"0.49774876\",\n \"0.49541524\",\n \"0.49456522\",\n \"0.4938621\",\n \"0.4927697\",\n \"0.49162355\",\n \"0.4905255\",\n \"0.49034148\",\n \"0.4901713\",\n \"0.48947382\",\n \"0.48945874\",\n \"0.48576233\",\n \"0.4853687\",\n \"0.48180065\",\n \"0.4796382\",\n \"0.47956917\",\n \"0.47933\",\n \"0.47849742\",\n \"0.47778904\",\n \"0.4776961\",\n \"0.47758076\",\n \"0.47752246\",\n \"0.47732186\",\n \"0.47640452\",\n \"0.47639966\",\n \"0.475554\",\n \"0.4753862\",\n \"0.47459054\",\n \"0.47447842\",\n \"0.47386268\",\n \"0.4736431\",\n \"0.4734776\",\n \"0.47332802\",\n \"0.4733193\",\n \"0.4702847\",\n \"0.47012332\",\n \"0.47006556\",\n \"0.46998748\",\n \"0.46925905\",\n \"0.46923918\",\n \"0.46828443\",\n \"0.46812356\",\n \"0.4676416\",\n \"0.46734202\",\n \"0.46649432\",\n \"0.46560308\",\n \"0.46494696\",\n \"0.4647872\",\n \"0.4646516\",\n \"0.46460456\",\n \"0.46407625\",\n \"0.46205994\",\n \"0.46184963\",\n \"0.46179497\",\n \"0.46134374\",\n \"0.4610658\",\n \"0.45986965\",\n \"0.4596978\",\n \"0.45967084\",\n \"0.45942363\",\n \"0.45926726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.80123305"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8482,"cells":{"query":{"kind":"string","value":"From all the data, it takes the columns TopicID, and count the topic based on the ethnicity"},"document":{"kind":"string","value":"def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Black, non-Hispanic\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of black ethnicity people group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Male\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print(\"The following table represent the number of men group by the topic: \")\n data_frame_topic.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n tuple_result.append(each_tuple)\n\n # sort by the the second element, which is value, of each tuple in the list\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\n\n # separate the topic and count into two lists for graphing purpose\n topics = []\n counts = []\n\n for each_tuple in tuple_result:\n topics.append(each_tuple[0])\n counts.append(each_tuple[1])\n\n return counts, topics","def topic_stats(df_topic_sents_keywords):\n\n # Number of Documents for Each Topic\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n\n # Percentage of Documents for Each Topic\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n\n # Topic Number and Keywords\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n\n # Concatenate Column wise\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n\n # Change Column names\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n\n # Show\n df_dominant_topics","def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:\n try:\n data_frame = data_frame \\\n .select(\"TopicID\", \"Question\") \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n except Py4JError:\n raise AnalysisException('One columns is incorrect')\n print(\"The following table represent the number of the type of each topic\")\n data_frame.show()\n data_frame_pandas = data_frame.toPandas()\n return data_frame_pandas","def count_topic_dist(self):\n if len(self.representants) == 0:\n self.log_writer(\"Representants not set. Cannot make topic dist.\")\n return\n for key, value in self.representants.items():\n self.topic_distributions.append(len(value)/len(self.training_docs))\n self.topic_numbers.append(key)","def test_extract_topics():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model = BERTopic()\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def get_diseases(self):\n self.diseases = self.data.groupby('topic')['topic'].count()","def get_paper_counter_per_topic_id(all_topic_assignments):\n counter = {}\n for topic_assignment in all_topic_assignments:\n for topic_index, topic_value in topic_assignment:\n if topic_index not in counter:\n counter[topic_index] = 0\n\n counter[topic_index] += 1\n\n return counter","def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\n print(data_frame_pandas)\n return data_frame_pandas","def test_extract_topics(base_bertopic):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(documents)\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def trainCount(\n trainData, \n questionType,\n questionDict,\n questionIdict, \n objDict, \n objIdict,\n numAns):\n count_wa = np.zeros((len(objIdict), numAns))\n count_a = np.zeros((numAns))\n objIds = extractObjId(\n trainData[0], \n questionType, \n questionDict, \n questionIdict)\n for i in range(objIds.shape[0]):\n objId = objIds[i]\n obj = questionIdict[objId - 1]\n ansId = trainData[1][i, 0]\n objId2 = objDict[obj]\n count_wa[objId2, ansId] += 1\n count_a[ansId] += 1\n # Add UNK count\n count_a[-1] += 1\n return count_wa, count_a","def connect_topic_id_to_topics(self, model):\n confidence = []\n for key, value in self.representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key, tp_num, val / len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n self.log_writer.add_log(\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\n conf[1],\n conf[2]))\n self.topic_indexes[conf[0]] = conf[1]\n\n for key, value in self.topic_indexes.items():\n self.topics_of_index[value] = [key]","def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\n\n for text_id, text in self.texts.items():\n # single-word topics act a bit different (no zips or comprehensions)\n # store data in self.topics, not zip_grams\n for word in text['doc']:\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\n\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\n continue\n\n if not (word.pos in self.nouns or word.ent_type in self.entities):\n continue\n\n if word_lemma in self.topics:\n self.topics[word_lemma][\"count\"] += 1\n self.topics[word_lemma][\"textIDs\"] |= {text_id}\n self.topics[word_lemma][\"verbatims\"] |= {word.text.lower()}\n else:\n self.topics[word_lemma] = {\"name\": word_lemma,\n \"count\": 1,\n \"textIDs\": {text_id},\n \"verbatims\": {word.text.lower()},\n \"subtopics\": {}}\n\n # Populate self.ngrams and self.topics\n for text_id, text in self.texts.items():\n doc = text['doc']\n\n # Find pentagrams - ngrams with 5 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\n self._ngram_counter(ngram, 5, text_id, doc)\n\n # Find pentagrams - ngrams with 4 words\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\n self._ngram_counter(ngram, 4, text_id, doc)\n\n for ngram in zip(doc, doc[1:], doc[2:]):\n self._ngram_counter(ngram, 3, text_id, doc)\n\n for ngram in zip(doc, doc[1:]):\n self._ngram_counter(ngram, 2, text_id, doc)\n\n\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\n # but it's only mentioned in 3 different texts, we'd show 3.\n for _, topic in self.topics.items():\n topic['textIDCount'] = len(topic['textIDs'])\n for _, ngram in self.ngrams.items():\n ngram['textIDCount'] = len(ngram['textIDs'])\n\n # Eliminate rarely occurring topics and ngrams.\n self.topics = {k: v for k, v in self.topics.items() if\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\n self.ngrams = {k: v for k, v in self.ngrams.items() if\n v['textIDCount'] >= min_text_id_count}\n\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\n for ngram_lemma, ngram in self.ngrams.items():\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\n # r'\\b' + ngram_lemma + r'\\b' --> does the ngram lemma fit in ngram_plus lemma (\\b is word boundary)\n if ngram['n'] + 1 != ngram_plus['n']:\n continue\n\n if not re.search(r'\\b' + ngram_lemma + r'\\b', ngram_plus_lemma):\n continue\n\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\n # instead of the other loop?\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\n ngram['count'] = -1\n\n # Eliminate newly demoted items\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}","def test_topics_for_products(self):\n desktop_topics = topics_for(product=self.desktop)\n eq_(len(desktop_topics), 3)\n\n mobile_topics = topics_for(product=self.mobile)\n eq_(len(mobile_topics), 2)","def assign_topics_to_sentences(self):\n \n\n # 10 topics\n topic_dict = {0: 'academics'\n , 1: 'career'\n , 2: 'commute'\n , 3: 'diversity'\n , 4: 'community'\n , 5: 'extracurricular'\n , 6: 'facilities'\n , 7: 'finance'\n , 8: 'housing'\n , 9: 'wellness'\n }\n\n # Some important words that should be included under each topic\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\n , 'degree', 'research', 'elective'\n , 'professor', 'project', 'scholarship', 'knowledge']\n , ['career', 'job', 'coop', 'employment']\n , ['commute', 'skytrain', 'transport', 'commuter']\n , ['diversity', 'diverse', 'background']\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\n , ['extracurricular', 'club', 'sport', 'activity']\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\n , ['finance', 'tuition', 'expensive']\n , ['housing', 'live', 'residence']\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\n\n # Read the data - id and reponse column\n dt = pd.read_csv(self.path_data_col\n , encoding = \"ISO-8859-1\"\n , usecols = [self.id_col_name, self.col_name])\n\n\n \n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n \n # Split into sentences\n dt['sentences'] = self.getSentences(dt[self.col_name])\n \n \n \n\n \n \n\n # Store number of sentences in each response as a column\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\n\n # Split each row into multiple rows - one row for each sentence\n dt = (dt\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\n .apply(pd.Series)\n .stack()\n .reset_index()\n .drop('level_3', axis = 1)\n .rename(columns = {0:'sentences'}))\n\n\n # Clean the sentences\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\n\n # Remove useless sentences\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\n\n # Remove rows with NA values\n dt = self.removeEmptyData(dt)\n\n # Tokenize words in the cleaned sentences\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\n\n\n # Call the lexicon function\n topic_lexicons = self.prepare_lexicons()\n\n # Lists to store results\n count_topic_all = []\n actual_topic_all = []\n\n # Tag each response into a topic\n for response in responses:\n\n count_topic = []\n actual_topic = []\n\n for topic in topic_lexicons:\n\n # Count occurance of each word in word stock in the response\n temp = sum(dict((x, response.count(x)) for x in topic).values())\n count_topic.append(temp)\n\n\n for index, value in enumerate(count_topic):\n\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\n if value > 0:\n actual_topic.append(topic_dict[index])\n\n\n # If more than 3 topics are tagged for single sentence, refine by increasing\n # cutoff to at least 2 words instead of 1\n if len(actual_topic) > 3:\n\n actual_topic = []\n for index, value in enumerate(count_topic):\n\n if value > 1: # Increase cutoff\n actual_topic.append(topic_dict[index])\n\n count_topic_all.append(count_topic)\n actual_topic_all.append(actual_topic)\n\n\n dt['tags'] = actual_topic_all\n dt['num_tags'] = count_topic_all\n\n\n # Select only the most important columns\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\n\n return dt, dt_less","def test_extract_topics_custom_cv():\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n\n cv = CountVectorizer(ngram_range=(1, 2))\n model = BERTopic(vectorizer=cv)\n model._update_topic_size(documents)\n model._extract_topics(documents)\n freq = model.get_topic_freq()\n\n assert model.c_tf_idf.shape[0] == 5\n assert model.c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def test_topic_reduction(reduced_topics):\n model = BERTopic()\n nr_topics = reduced_topics + 2\n model.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents.copy())\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents.copy())\n new_freq = model.get_topic_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(model.mapped_topics, dict)\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\n assert model.mapped_topics","def uncategorized(df):\n\n counter = 0\n for movie in df.index:\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\n df.loc[movie, 'Political'] == 0:\n counter += 1\n\n return counter","def group_topics(sent_topics_sorteddf):\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0).sum()\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\n plt.title('Topic Share %');\n\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\n topic_contribution.sort_index(),\n pd.Series(['Economy','Immigration','Environment','Event',\n 'Civil Rights','Civil Rights','Healthcare',\n 'Defense','Trump','Community','Event','Event',\n 'Thanks','Legislation','Trump','Community',\n 'Community','Trump','Defense',\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]","def test_get_topics(self):\n\n for m in self.models:\n\n topics = m.topics\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertEqual(topics.num_rows(), 25)\n self.assertEqual(topics.num_columns(), 2)\n z = m.topics[\"topic_probabilities\"]\n for k in range(m.num_topics):\n self.assertTrue(\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\n \"Returned probabilities do not sum to 1.\",\n )\n\n # Make sure returned object is an SFrame of the right size\n topics = m.get_topics()\n self.assertTrue(isinstance(topics, turicreate.SFrame))\n self.assertTrue(\n topics.num_columns() == 3,\n \"Returned SFrame should have a topic, word, and probs.\",\n )\n\n # Make sure that requesting a single topic returns only that topic\n num_words = 8\n topics = m.get_topics([5], num_words=num_words)\n self.assertTrue(\n all(topics[\"topic\"] == 5), \"Returned topics do not have the right id.\"\n )\n self.assertEqual(topics.num_rows(), num_words)\n topics = m.get_topics([2, 4], num_words=num_words)\n self.assertEqual(set(list(topics[\"topic\"])), set([2, 4]))\n self.assertEqual(topics.num_rows(), num_words + num_words)\n\n # Make sure the cumulative probability of the returned words is\n # is less than the cutoff we provided.\n # A cutoff of 1.0 should return num_words for every topic.\n cutoff = 1.0\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\n totals = topics.groupby(\n \"topic\", {\"total_score\": turicreate.aggregate.SUM(\"score\")}\n )\n self.assertTrue(\n all(totals[\"total_score\"] <= (cutoff + DELTA)),\n \"More words were returned than expected for this cutoff.\",\n )\n\n # Make sure we raise errors for bad input\n with self.assertRaises(ValueError):\n m.get_topics([-1])\n with self.assertRaises(ValueError):\n m.get_topics([10000])\n with self.assertRaises(ToolkitError):\n topics = m.get_topics(output_type=\"other\")\n\n # Test getting topic_words\n topic_words = m.get_topics(output_type=\"topic_words\", num_words=5)\n self.assertEqual(type(topic_words), turicreate.SFrame)\n\n # Test words are sorted correctly for the first topic\n # TODO: Make this more deterministic.\n\n # topic_probs = m.get_topics(num_words=5)\n # expected = [w for w in topic_probs['word'][:5]]\n # observed = topic_words['words'][0]\n # self.assertEqual(observed[0], expected[0])","def test_topic_reduction_edge_cases():\n model = BERTopic()\n nr_topics = 5\n model.nr_topics = 100\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n model._update_topic_size(old_documents)\n model._extract_topics(old_documents)\n old_freq = model.get_topic_freq()\n\n new_documents = model._reduce_topics(old_documents)\n new_freq = model.get_topic_freq()\n\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\n pd.testing.assert_frame_equal(old_documents, new_documents)\n pd.testing.assert_frame_equal(old_freq, new_freq)","def export_topics(self):\n\n # format as a list (for json output), then sort descending by textIDCount\n topics = [{'name': topic['name'], 'count': topic['count'],\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\n 'children': '' if 'children' not in topic else topic['children']}\n for topic_id, topic in self.topics.items()]\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\n\n for i, topic in enumerate(topics):\n # Note that 'rank' is from topic, not child.\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\n 'textIDCount': child['textIDCount']}\n for _, child in topic['children'].items()]\n\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\n\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\n child_count = sum([child['textIDCount'] for child in topic['children']])\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\n\n for child in topic['children']:\n child['size'] = child['textIDCount'] * child_count_multiplier\n\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\n\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\n self.model_output[\"children\"] = [topic for topic in topics]\n\n # Build file name and save\n if self.data_date:\n date = datetime.strptime(self.data_date, \"%Y-%m-%d\").strftime('%d') # from YYYY-MM-DD to DD\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\n else:\n file_name = '{}-Topics.txt'.format(self.corpus_name)\n\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\n json.dump(self.model_output, file)","def find_dominant_topic(df_topic_sents_keywords):\n\n # Format\n df_dominant_topic = df_topic_sents_keywords.reset_index()\n df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n\n # Group top 5 sentences under each topic\n sent_topics_sorteddf = pd.DataFrame()\n\n sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n start = time.time()\n for i, grp in sent_topics_outdf_grpd:\n sent_topics_sorteddf = pd.concat([sent_topics_sorteddf, \n grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n axis=0)\n print(f'Group done. Total time {time.time() - start} seconds.')\n\n # Reset Index \n sent_topics_sorteddf.reset_index(drop=True, inplace=True)\n\n # Format\n sent_topics_sorteddf.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n return sent_topics_sorteddf","def _collect_counts(self, instance_list):\n \"\"\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\n All \"rare words\" (those words that appear less than 3 times) are replaced by <UNK>.\n I also add label|START counts.\n \"\"\"\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\n for instance in instance_list: # Set of <(w, pos), l>\n index = 0\n for t in instance.data: # Tuple of (w, pos)\n index = instance.data.index(t)\n # print t[0] # word\n # print instance.label[index] # label\n if t in self.V:\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\n else:\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\n if index > 0:\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\n else:\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1","def summarize_corpus():\n\t\n\t# get metadata\n\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \"metadata\", md_mode)\n\t\n\t# visualize some metadata\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"author-continent\")\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"author-country\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"language\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_hist\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_x\")\n\tvisualize_metadata.plot_pie(wdir, md_csv, \"subgenre\")\n\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"subgenre\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"gender\")\n\t\n\t# make some counts\n\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\n\tnum_texts = len(md_table)\n\t#num_language = len(md_table.groupby([\"language\"]))\n\t#num_continent = len(md_table.groupby([\"author-continent\"]))\n\t#num_countries = len(md_table.groupby([\"author-country\"]))\n\t#num_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_subgenre = len(md_table.groupby([\"subgenre\"]))\n\t#num_subgenre_x = len(md_table.groupby([\"subgenre_x\"]))\n\t#fr_subgenre_hist = md_table.groupby([\"subgenre_hist\"]).count()\n\t#num_historical = fr_subgenre_hist[\"idno\"][\"historical\"]\n\t#num_not_historical = fr_subgenre_hist[\"idno\"][\"not_historical\"]\n\t\n\t\n\td = {\"texts\":[num_texts], \n\t#\"languages\":[num_language],\n\t#\"continents\":[num_continent],\n\t#\"countries\":[num_countries],\n\t\"authors\":[num_authors],\n\t#\"subgenre_x\":[num_subgenre_x],\n\t\"subgenre\":[num_subgenre]}\n\t#\"num_historical\":[num_historical],\n\t#\"num_not_historical\":[num_not_historical]}\n\t\n\t\n\t\n\tcount_fr = pd.DataFrame(d)\n\tcount_fr.to_csv(os.path.join(wdir, \"corpus-description.csv\"), sep=\",\", header=True)\n\tprint(\"Done: summarize corpus\")","def __rank_topics(self, found_topics, explanation):\n max_value = 0\n scores = []\n for _,topic in found_topics.items():\n topic[\"score\"] = topic[\"times\"] * len(topic['grams'].keys())\n scores.append(topic[\"score\"])\n if topic[\"score\"] > max_value:\n max_value = topic[\"score\"]\n\n for _,topic in found_topics.items():\n if \"syntactic\" in topic:\n topic[\"score\"] = max_value\n\n\n\n\n # Selection of unique topics\n unique_topics = {}\n for t_p,topic in found_topics.items():\n prim_label = self.cso.get_primary_label_wu(t_p)\n if prim_label in unique_topics:\n if unique_topics[prim_label] < topic[\"score\"]:\n unique_topics[prim_label] = topic[\"score\"]\n else:\n unique_topics[prim_label] = topic[\"score\"]\n\n # ranking topics by their score. High-scored topics go on top\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\n\n\n # perform\n vals = []\n for t_p in sort_t:\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\n\n\n #### suppressing some warnings that can be raised by the kneed library\n warnings.filterwarnings(\"ignore\")\n try:\n x_vals = range(1,len(vals)+1)\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\n if t_kn.knee is None:\n #print(\"I performed a different identification of knee\")\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\n except ValueError:\n pass\n\n ##################### Pruning\n\n try:\n knee = int(t_kn.knee)\n except TypeError:\n knee = 0\n except UnboundLocalError:\n knee = 0\n\n if knee > 5:\n try:\n knee += 0\n except TypeError:\n print(\"ERROR: \",t_kn.knee,\" \",knee, \" \", len(sort_t))\n\n else:\n try:\n if sort_t[0][1] == sort_t[4][1]:\n top = sort_t[0][1]\n test_topics = [item[1] for item in sort_t if item[1]==top]\n knee = len(test_topics)\n\n else:\n knee = 5\n except IndexError:\n knee = len(sort_t)\n\n final_topics = []\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\n self.reset_explanation()\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\n\n return final_topics","def get_result_table_and_info(cls):\n winning_dict = cls.get_winning_topics()\n winning_topics = winning_dict['winning_topics']\n runoff_poll_warning = winning_dict['runoff_poll_warning']\n\n # Create table\n result_table = []\n all_categories = sorted(Category.objects.all(), key=attrgetter('sum_of_votes', 'weight'), reverse=True)\n for category in all_categories:\n category_hoechstzahls = filter(lambda hoechstzahl: hoechstzahl.topic.category == category, cls.all_hoechstzahls)\n category_hoechstzahls.sort(key=lambda hoechstzahl: hoechstzahl.value, reverse=True)\n runoff_poll_warning = second_runoff_poll_check(runoff_poll_warning, category_hoechstzahls, winning_topics)\n category_hoechstzahls += (max(config['openslides_topicvoting_posts'], 3) - len(category_hoechstzahls)) * [None]\n result_table.append(category_hoechstzahls)\n\n # Return table and flags as dictionary\n return {'result_table': result_table,\n 'winning_topics': winning_topics,\n 'runoff_poll_warning': runoff_poll_warning,\n 'topic_post_warning': winning_dict['topic_post_warning']}","def test_extract_topics_custom_cv(base_bertopic_custom_cv):\n nr_topics = 5\n documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic_custom_cv._update_topic_size(documents)\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\n freq = base_bertopic_custom_cv.get_topics_freq()\n\n assert c_tf_idf.shape[0] == 5\n assert c_tf_idf.shape[1] > 100\n assert isinstance(freq, pd.DataFrame)\n assert nr_topics == len(freq.Topic.unique())\n assert freq.Count.sum() == len(documents)\n assert len(freq.Topic.unique()) == len(freq)","def display_topics(df, n_rows=10, n_cols=12):\n\n exemplar_scores, hovers = topic_exemplars(df)\n top_columns = sorted(range(len(exemplar_scores)),\n key=lambda i: exemplar_scores[i],\n reverse=True)[:n_cols]\n #I comented this line Im not 100% sure what was the purpuse of this\n # topics = df.pivot(index='pos', columns='topic',values='word*').replace([None], [''], regex=True)\n topics = df.pivot(index='pos', columns='topic',values='word*')\n\n topics_display = topics[top_columns].head(n_rows)\n\n return topics_display, top_columns","def count_mentioned_countries(data):\n countries_mentioned = {}\n countries = get_countries()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for country in countries:\n if country in (subject_words + message_words):\n if country in countries_mentioned:\n countries_mentioned[country] += 1\n else:\n countries_mentioned[country] = 1\n\n return pd.DataFrame.from_dict(countries_mentioned, orient=\"index\")","def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)","def num_words():\n # Load the GT.\n df = pd.read_csv(config.META_FQN, sep=\"\\t\")\n stats = {\n \"T\": {\"words\": [], \"duration\": []},\n \"P\": {\"words\": [], \"duration\": []},\n \"sess\": {\"words\": [], \"duration\": []},\n }\n\n for _, row in df.iterrows():\n if row[\"asr_test\"]:\n stats[\"P\"][\"words\"].append(float(row[\"gt_patient_num_words\"]))\n stats[\"T\"][\"words\"].append(float(row[\"gt_therapist_num_words\"]))\n stats[\"P\"][\"duration\"].append(float(row[\"gt_patient_time_spoken\"]))\n stats[\"T\"][\"duration\"].append(\n float(row[\"gt_therapist_time_spoken\"])\n )\n stats[\"sess\"][\"duration\"].append(float(row[\"sess_dur\"]))\n n_words = (\n row[\"gt_therapist_num_words\"] + row[\"gt_patient_num_words\"]\n )\n stats[\"sess\"][\"words\"].append(n_words)\n\n for speaker in stats:\n for metric in stats[speaker]:\n print(f\"------ {speaker} | {metric} ------\")\n print_stats(stats[speaker][metric])","def return_topic_figures(n_topics=5):\n\n ### import data ###\n\n data = return_keywords()\n data_for_topics = data[\"abstract_kw\"].apply(\n lambda x: list(ast.literal_eval(x).keys())\n )\n\n ### Build topic model ###\n\n # parameters\n n_topics = n_topics\n\n # Create Dictionary\n id2word = corpora.Dictionary(data_for_topics)\n\n # Create Corpus: Term Document Frequency\n corpus = [id2word.doc2bow(text) for text in data_for_topics]\n\n # Build LDA model\n lda_model = gensim.models.ldamodel.LdaModel(\n corpus=corpus,\n id2word=id2word,\n num_topics=n_topics,\n random_state=100,\n update_every=1,\n chunksize=10,\n passes=10,\n alpha=\"symmetric\",\n iterations=100,\n per_word_topics=True,\n )\n\n topics = lda_model.show_topics(formatted=False)\n data_flat = [w for w_list in data_for_topics for w in w_list]\n counter = Counter(data_flat)\n out = []\n for i, topic in topics:\n for word, weight in topic:\n out.append([word, i, weight, counter[word]])\n df = pd.DataFrame(out, columns=[\"word\", \"topic_id\", \"importance\", \"word_count\"])\n\n specs = np.full((ceil(n_topics / 2), 2), {\"secondary_y\": True})\n topic_bar_charts = make_subplots(\n rows=ceil(n_topics / 2),\n cols=2,\n specs=specs.tolist(),\n horizontal_spacing=0.1,\n vertical_spacing=0.15,\n )\n row, col = (0, 0)\n for topic in range(n_topics):\n if (topic % 2) != 0:\n col = 2\n else:\n col = 1\n row += 1\n color = px.colors.qualitative.Vivid[topic]\n topic_bar_charts.add_trace(\n go.Bar(\n x=df.loc[df.topic_id == topic, \"word\"],\n y=df.loc[df.topic_id == topic, \"word_count\"],\n width=0.5,\n opacity=0.3,\n marker_color=color,\n name=(\"Topic \" + str(topic) + \" word count\"),\n ),\n secondary_y=False,\n row=row,\n col=col,\n )\n topic_bar_charts.add_trace(\n go.Bar(\n x=df.loc[df.topic_id == topic, \"word\"],\n y=df.loc[df.topic_id == topic, \"importance\"],\n width=0.2,\n marker_color=color,\n name=(\"Topic \" + str(topic) + \" weight\"),\n ),\n secondary_y=True,\n row=row,\n col=col,\n )\n topic_bar_charts.update_layout(barmode=\"overlay\")\n\n topic_bar_charts.update_layout(\n height=800, width=1000, margin=dict(l=50, r=50, t=50, b=100)\n )\n\n # append all charts\n figures = [dict(data=topic_bar_charts)]\n\n return figures","def topic(df, num_topics=5):\r\n# X, y = df[df.columns[:-1]], df[df.columns[-1]]\r\n lda = LatentDirichletAllocation(n_topics=num_topics,\r\n max_iter=5,\r\n learning_method='online',\r\n learning_offset=50.,\r\n random_state=0)\r\n return lda.fit_transform(df)","def connect_topic_id_to_topics(model, representants, log_writer):\n # t = model.get_topics()\n topic_indexes = {}\n topics_of_index = {}\n confidence = []\n for key, value in representants.items():\n connection_results = {}\n for article in value:\n try:\n # get most possible index\n topic_index = max(model.analyse_text(article), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\") # TODO replace with if\n continue\n # add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n # find index that occured mostly\n print(connection_results)\n for tp_num, val in connection_results.items():\n confidence.append([key,tp_num,val/len(value)])\n confidence = sorted(confidence, key=operator.itemgetter(2),reverse=True)\n associated_indexes = []\n associated_topics = []\n for conf in confidence:\n if conf[1] in associated_indexes or conf[0] in associated_topics:\n continue\n associated_indexes.append(conf[1])\n associated_topics.append(conf[0])\n log_writer.add_log('Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],conf[1],conf[2]))\n topic_indexes[conf[0]] = conf[1]\n\n for key, value in topic_indexes.items():\n topics_of_index[value] = [key]\n print(topic_indexes)\n print(topics_of_index)\n return topic_indexes, topics_of_index","def check_intersections(db, topics, papers_by_topic):\n\n\t# Print the distribution of \"number of topics\"\n\tnum_subjects = []\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tnum_subjects.append(len(p.subject))\n\t\telse:\n\t\t\tnum_subjects.append(0)\n\tnum_subjects = np.array(num_subjects)\n\n\tfor i in range(np.max(num_subjects)+1):\n\t\tprint(\"Number of papers with\", i, \"topics:\", \n\t\t\tlen(np.where(num_subjects==i)[0]))\n\n\t# Figure out what's going on with triple-tagged guys (nothing weird)\n\t\"\"\"\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tif len(p.subject) > 2:\n\t\t\t\tprint(\"\\n\",p.title,\"\\n\\t\",p.container_title,\"\\n\\t\", p.subject)\n\t\t\t\t\n\t\t\t\tfor topic, topic_words in topics.items():\n\t\t\t\t\tprint(\"\\tCheck against '\" + topic + \"':\")\n\t\t\t\t\tfor journal in p.container_title:\n\t\t\t\t\t\tcheck_words(journal, topic_words, verbose=True)\n\t\"\"\"\n\n\t# Look in more detail at double-tagged guysfor p_hash, p in db.all_papers.items():\n\tcombos = defaultdict(int)\n\tfor p_hash, p in db.all_papers.items():\n\t\tif p.subject:\n\t\t\tif len(p.subject) == 2:\n\t\t\t\tcombos[frozenset(p.subject)] += 1\n\t\t\t\t#print(\"\\n\",p.title,\"\\n\\t\",p.container_title,\"\\n\\t\", p.subject)\n\t\t\t\tif p.subject == {'Computer Science', 'Biology'}:\n\t\t\t\t\t#print(\"\\n\",p.title,\"\\n\\t\",p.container_title)#,\"\\n\\t\", p.subject)\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tbio_words = set()\n\t\t\t\t\tCS_words = set()\n\t\t\t\t\tfor journal in p.container_title:\n\t\t\t\t\t\tfor word in topics['Biology']:\n\t\t\t\t\t\t\tif journal.find(word) >= 0:\n\t\t\t\t\t\t\t\tbio_words.add(word)\n\t\t\t\t\t\tfor word in topics['Computer Science']:\n\t\t\t\t\t\t\tif journal.find(word) >= 0:\n\t\t\t\t\t\t\t\tCS_words.add(word)\n\n\t\t\t\t\t#print(\"\\tBiology words:\", bio_words)\n\t\t\t\t\t#print(\"\\tCS words:\", CS_words)\n\t\n\tfor k, v in combos.items():\n\t\tprint(k, v)","def testArticleCount(self):\n\n self.articleCount(17)","def analysis():\r\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\r\n data_frame.registerTempTable('business')\r\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\r\n\r\n def merge_count(a, b):\r\n r = {}\r\n for p, c in a.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n for p, c in b.items():\r\n if p in r:\r\n r[p] += c\r\n else:\r\n r[p] = c\r\n return r\r\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\r\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\r\n\r\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\r\n print(result.take(10))\r\n print('----------------pron count-----------------')\r\n print(pron_count.collect())\r\n\r\n print(gd)","def connect_topic_id_to_topics_old(self, model):\n #t = model.get_topics()\n for key, value in self.representants.items():\n connection_results = {}\n for article in value:\n try:\n #get most possible index\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\n except ValueError:\n print(\"No topic index returned continuing\")#TODO replace with if\n continue\n #add most possible index for this article to counter\n if topic_index not in connection_results:\n connection_results[topic_index] = 1\n else:\n connection_results[topic_index] += 1\n #find index that occured mostly\n best_candidates = max(connection_results.items(), key=operator.itemgetter(1))\n print(best_candidates)\n self.log_writer.add_log(\"Best candidate with index {} is connected to topic {} with {}% accuracy\".format(best_candidates[0], key, (connection_results[best_candidates[0]]/len(value))*100))\n #create connection between topic id and model topic index\n self.topic_indexes[key] = best_candidates[0]\n #creat connection in opposite direction if there already is some connection add found index to that connection (some model topic index can represent more than one real topic)\n if best_candidates[0] not in self.topics_of_index:\n self.topics_of_index[best_candidates[0]] = [key]\n else:\n self.topics_of_index[best_candidates[0]].append(key)\n\n self.log_writer.add_log(\"Out of {} real topics only {} were learned\".format(len(self.representants), len(self.topics_of_index)))","def topic(df, num_topics=5):\r\n\r\n lda = LatentDirichletAllocation(n_topics=num_topics,\r\n max_iter=5,\r\n learning_method='online',\r\n learning_offset=50.,\r\n random_state=0)\r\n return lda.fit_transform(df)","def test_topic_reduction(reduced_topics):\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\n nr_topics = reduced_topics + 2\n base_bertopic.nr_topics = reduced_topics\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(old_documents)\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\n old_freq = base_bertopic.get_topics_freq()\n\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\n new_freq = base_bertopic.get_topics_freq()\n\n assert old_freq.Count.sum() == new_freq.Count.sum()\n assert len(old_freq.Topic.unique()) == len(old_freq)\n assert len(new_freq.Topic.unique()) == len(new_freq)\n assert isinstance(base_bertopic.mapped_topics, dict)\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\n assert base_bertopic.mapped_topics","def infertopics(self):\n\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\n for uid in self.scan(attribute=\"updated\"):\n # Remove updated attribute\n self.removeattribute(uid, \"updated\")\n\n # Get list of neighboring nodes\n ids = self.edges(uid)\n\n # Infer topic\n topic = Counter(self.attribute(x, \"topic\") for x in ids).most_common(1)[0][0] if ids else None\n if topic:\n # Add id to topic list and set topic attribute\n self.topics[topic].append(uid)\n self.addattribute(uid, \"topic\", topic)\n\n # Set topic rank\n self.addattribute(uid, \"topicrank\", len(self.topics[topic]) - 1)\n\n # Infer category\n category = Counter(self.attribute(x, \"category\") for x in ids).most_common(1)[0][0]\n self.addattribute(uid, \"category\", category)","def basic_statistics_of_email(data):\n word_counts = []\n character_count = 0\n\n for ind, row in data.iterrows():\n tokenizer = RegexpTokenizer(r'\\w+')\n real_words = tokenizer.tokenize(row[\"RawText\"].lower())\n\n character_count += sum(map(len, real_words))\n word_counts.append(len(real_words))\n\n return character_count, pd.Series(word_counts)","def assign_topics(self):\n\n # Load the dataset which has topic tags for each sentence\n dt, dt_less = self.assign_topics_to_sentences()\n \n dt_copy = dt\n \n # Minimum number of tags needed to tag the overall response\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\n \n # Final dataset with full survey response and its tags\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\n , 'num_sent': min\n , 'min_num_tags': min\n# , 'sentences': lambda x: \"%s\" % '. '.join(x)\n , self.col_name: min})\n final_dt.reset_index(level = 0, inplace = True)\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\n , axis = 1)\n\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\n\n return dt_copy, dt_less, final_dt, final_dt_less","def preprocess(df):\n \n # drop the following columns - irrelevant now\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\n , 'tmdbId', 'popularity', 'year']\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n # drop all of the language columns\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\"lan\"]\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n\n # loop through the columns we want to aggregate\n for col_type in [\n \"original_language_\"\n , \"prod_comp_cntry_\"\n , \"prod_comp_names_\"\n , \"writers_\"\n , \"actors_\"\n , \"genres_\"\n , \"director_\"\n ]:\n # create a dictionary of each unique value and its frequency\n val_freq = {}\n for col in df.columns:\n if col.startswith(col_type):\n val_freq[col] = df[col].sum()\n\n # create a dataframe from this dictionary; sort by count\n counts = pd.DataFrame.from_dict(\n val_freq\n , orient='index'\n , columns=['count']\n ).sort_values('count', ascending=False)\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\n\n # handle special case of production company country\n if col_type == \"prod_comp_cntry_\":\n DROP_COLUMNS = [col for col in counts.index][3:]\n\n # handle special case of directors\n elif col_type == \"director_\":\n DIRECTOR_COLS = [col for col in df.columns\n if col.startswith(\"director_\")\n and col!=\"director_pop\"]\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\n DROP_COLUMNS = DIRECTOR_COLS\n\n # handle special case of actors\n elif col_type == \"actors_\":\n ACTORS_COLS = [col for col in df.columns if \"actors\" in col]\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\n DROP_COLUMNS = ACTORS_COLS\n\n # handle all the other cases\n else:\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\n\n\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\n \n ##########################################################################\n # adjust the data for inflation\n CPI_tf = df['CPIAUCSL'].max()\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\n cpi_adjust\n , args=(CPI_tf ,)\n , axis=1\n )\n # no longer need CPI data\n df.drop('CPIAUCSL', axis=1, inplace=True)\n \n ########################################################################## \n # add in useful features about the cast and crew \n df['cast_crew_sum_pop'] = (\n df['director_pop']\n + df['avg_actor_pop']\n + df['avg_writer_pop']\n )\n df['cast_crew_product_pop'] = (\n df['director_pop']\n * df['avg_actor_pop']\n * df['avg_writer_pop']\n )\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\n df = df.query('10000 <= revenue').copy()\n df = df.query('100000 <= budget').copy()\n df.drop('sum_actor_pop', axis=1, inplace=True)\n df.drop('min_writer_pop', axis=1, inplace=True)\n\n # code to transform columns\n for col in [\n \"budget\", \"director_pop\", \"avg_writer_pop\"\n , \"max_writer_pop\", \"avg_actor_pop\", \"max_actor_pop\"\n , \"min_actor_pop\", 'cast_crew_sum_pop'\n , 'cast_crew_product_pop'\n ]:\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\n df.drop(col, axis=1, inplace=True)\n \n return df","def calc_topic_mode_log_stats(user_exercise_graph, topic_id,\n just_earned_proficiency):\n topic = topic_models.Topic.get_by_id(topic_id)\n topic_exercises = topic.get_exercises()\n\n total_exercises = len(topic_exercises)\n count_proficient = len(set(ex.name for ex in topic_exercises) &\n set(user_exercise_graph.proficient_exercise_names()))\n just_completed = (just_earned_proficiency and total_exercises ==\n count_proficient)\n\n return {\n 'total_exercises': total_exercises,\n 'count_proficient': count_proficient,\n 'just_completed': just_completed,\n }","def append_counting(dict):\n row_c = []\n # for nuc in NUC: #Scans all the elements and adds it to the table.\n # row_c.append(dict[nuc])\n for mot in MOT:\n row_c.append(dict[mot])\n for nuc_nr in NUC_NR :\n row_c.append(dict[nuc_nr + \"_NR\"])\n # #row.extend([dict[\"AA_NR\"], dict[\"TT_NR\"], dict[\"CC_NR\"], dict[\"GG_NR\"]])\n return row_c","def topic_extraction(df, col_name):\n tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\n max_features=200,\n stop_words='english')\n tfidf = tfidf_vectorizer.fit_transform(df[col_name])\n\n tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,\n max_features=200,\n stop_words='english')\n tf = tf_vectorizer.fit_transform(df[col_name])\n nmf = NMF(n_components=20, random_state=1,\n alpha=.1, l1_ratio=.5)\n tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n nmf_w = nmf.fit_transform(tfidf)\n nmf_h = nmf.components_\n df['labels'] = nmf_w.argmax(axis=1) # this was the right code to get labels/clusters\n\n\n print(\"\\nTopics in NMF model:\")\n print_top_words(nmf, tfidf_feature_names)\n\n\n lda = LatentDirichletAllocation(n_topics=20, max_iter=5,\n learning_method='online',\n learning_offset=50.,\n random_state=0,\n n_jobs=-1)\n lda.fit(tf)\n doc_topic_distrib = lda.transform(tf)\n lda_labels = doc_topic_distrib.argmax(axis=1)\n print lda_labels[:100]\n df['lda_labels'] = lda_labels\n print(\"\\nTopics in LDA model:\")\n tf_feature_names = tf_vectorizer.get_feature_names()\n print_top_words(lda, tf_feature_names)\n return df","def gender_word_counts(data):\n\n # We use the stopwords package from NLTK corpus.\n stop_words = set(stopwords.words('english'))\n data['tweet_words'] = data['text_cleaned'].str.split()\n # Ignoring all the stop words\n data['tweet_words'] = data['tweet_words'].apply(lambda tweet: [word for word in tweet if word not in stop_words])\n\n # Separating Male, Female and Brand profiles.\n male_profiles = data[data['gender'] == 'male']\n female_profiles = data[data['gender'] == 'female']\n brand_profiles = data[data['gender'] == 'brand']\n\n print(\"Top 20 most frequent words used by Men\")\n all_male_tweets = ' '.join(male_profiles['tweet_words'].astype(str))\n Male_words = pd.Series(all_male_tweets.split(\" \")).value_counts()[:20]\n print(Male_words)\n print()\n\n print(\"Top 20 most frequent words used by Women\")\n all_female_tweets = ' '.join(female_profiles['tweet_words'].astype(str))\n Female_words = pd.Series(all_female_tweets.split(\" \")).value_counts()[:20]\n print(Female_words)\n print()\n\n print(\"Top 20 most frequent words used by Brands\")\n all_brand_tweets = ' '.join(brand_profiles['tweet_words'].astype(str))\n Brand_words = pd.Series(all_brand_tweets.split(\" \")).value_counts()[:20]\n print(Brand_words)\n\n # Plotting horizontal bar graphs showing Top 20 tweet words used Vs. the word frequency.\n mp = Male_words.plot(kind='barh', stacked=True, colormap='plasma', title=\"Top 20 most frequently words used by Men\")\n mp.set_ylabel(\"Tweet words used by Males\")\n mp.set_xlabel(\"Word Frequency\")\n plt.show()\n\n fp = Female_words.plot(kind='barh', stacked=True, colormap='plasma',\n title=\"Top 20 most frequently words used by Women\")\n fp.set_ylabel(\"Tweet words used by Females\")\n fp.set_xlabel(\"Word Frequency\")\n plt.show()\n\n bp = Brand_words.plot(kind='barh', stacked=True, colormap='plasma',\n title=\"Top 20 most frequently words used by Brands\")\n bp.set_ylabel(\"Tweet words used by Brands\")\n bp.set_xlabel(\"Word Frequency\")\n plt.show()","def analyze_count(data):\n\n dsct_vk = pd.unique(data['vk'])\n dsct_itemid = pd.unique(data['itemid'])\n\n print 'number of user:', dsct_vk.shape\n print 'number of items:', dsct_itemid.shape\n print 'the number of ratings:', data.shape\n\n print 'unique actions:', pd.unique(data['action'])\n print 'the number of action 0:', np.sum(data['action'] == 0)\n print 'the number of action 1:', np.sum(data['action'] == 1)\n print 'the number of action 2:', np.sum(data['action'] == 2)\n print 'the number of action 3:', np.sum(data['action'] == 3)\n print 'the number of action 4:', np.sum(data['action'] == 4)\n \n time_range_item = data.groupby('itemid')['real_time'].aggregate(sum_unique)\n print 'Max Range:', np.max(time_range_item)\n print 'Mean Range:', np.mean(time_range_item)\n print 'Median Range:', np.median(time_range_item)","def test_mead_summary_length(self):\n topics = {'PUP1A': [Document('TST_ENG_20190101.0001'), Document('TST_ENG_20190101.0002'),\n Document('TST20190201.0001'), Document('TST20190201.0002')],\n 'WAR2A': [Document('TST_ENG_20190301.0001'), Document('TST_ENG_20190301.0002'),\n Document('TST20190401.0001'), Document('TST20190401.0002')]}\n WordMap.create_mapping()\n vec = Vectors()\n vec.create_freq_vectors(topics)\n idf = MeadSummaryGenerator(self.doc_list, MeadContentSelector(), self.args).get_idf_array()\n max_length = 100\n\n for topic_id, documents in topics.items():\n generator = MeadSummaryGenerator(documents, MeadContentSelector(), self.args)\n generator.select_content(idf)\n generator.order_information()\n realized_content = generator.realize_content()\n realized_content = [w for w in realized_content.split(\" \") if not \" \"]\n content_length = len(realized_content)\n self.assertLessEqual(content_length, max_length)","def metadata_summary(idx):\n tax_per_cluster = []\n genomes_per_tax = []\n genes_per_genome = []\n for cluster_id,v in idx.items():\n tax_per_cluster.append(len(v.keys()))\n for tax,vv in v.items():\n genomes_per_tax.append(len(vv.keys()))\n for genomeID,gene_ids in vv.items():\n genes_per_genome.append(len(set(gene_ids)))\n sum_stats(tax_per_cluster, 'Clades per cluster')\n sum_stats(genomes_per_tax, 'Gemomes per clade')\n sum_stats(genes_per_genome, 'Genes per genome')","def count_interests(rows: List[Row]) -> int:\n return len([row for row in rows if row[\"interest\"] is not None])","def reformat_countTable(\n self,analysis_id_I=None,sna2experimentID_I=None,\n sna2sns_I=None):\n if self.countTable: countTable = self.countTable[:];\n else: countTable = [];\n\n countTable_flat = self.reformat_countOrFPKMTable(\n countOrFPKMTable_I=countTable,\n analysis_id_I=analysis_id_I,\n sna2experimentID_I=sna2experimentID_I,\n sna2sns_I=sna2sns_I,\n count_or_FPKM = 'count');\n return countTable_flat;","def main_topic_doc(ldamodel, corpus=corpus): \n \n doc_topics = pd.DataFrame()\n\n for i, row in enumerate(ldamodel[corpus]):\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\n\n for j, (topic_num, prop_topic) in enumerate(row):\n if j == 0:\n wp = ldamodel.show_topic(topic_num)\n topic_keywords = \"' \".join([word for word, prop in wp])\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n else:\n break\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\n return doc_topics","def explore_topic_nouns(topic_number, topn=25, model=10):\n #\n if model==10:\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\n topicname=topic_names_10[topic_number]\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\n gensimSTR=str(gensimdic[topic_number])\n \n # \n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\n')\n \n dic={}\n j=0\n \n print('top 5 terms')\n for term, frequency in lda.show_topic(topic_number, topn):\n if dfff[dfff['nouns']==term].empty: ## dfff is loaded from pilot_path/bow_nouns.csv\n pass\n else:\n j=j+1\n if j<6:\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\n dic[term]=frequency\n dff=pd.DataFrame.from_dict(dic,orient='index')\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \n return(dff)","def model_topics(df):\n\n data = df.text.values.tolist()\n data_words = list(sent_to_words(data))\n\n # Build the bigram and trigram models\n bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\n trigram = gensim.models.Phrases(bigram[data_words], threshold=100) \n\n # Faster way to get a sentence clubbed as a trigram/bigram\n bigram_mod = gensim.models.phrases.Phraser(bigram)\n trigram_mod = gensim.models.phrases.Phraser(trigram)\n\n # Remove Stop Words\n data_words_nostops = remove_stopwords(data_words)\n\n # Form Bigrams\n data_words_bigrams = make_bigrams(data_words_nostops,bigram_mod)\n\n # Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n nlp = spacy.load('en', disable=['parser', 'ner'])\n\n # Do lemmatization keeping only noun, adj, vb, adv\n data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n\n # Create Dictionary\n id2word = corpora.Dictionary(data_lemmatized)\n\n # Create Corpus\n texts = data_lemmatized\n\n # Term Document Frequency\n corpus = [id2word.doc2bow(text) for text in texts]\n\n # Perform Topic Modeling for number of topics ranging from 5 to 50 in steps of 5\n model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=5, limit=50, step=5)\n\n return model_list,coherence_values,corpus,id2word","def explore_topic(topic_number, topn=25, model=10):\n #\n if model==25:\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_25'))\n topicname=topic_names_25[topic_number]\n gensimSTR=''\n elif model==15:\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_15'))\n topicname=topic_names_15[topic_number]\n gensimSTR=''\n elif model==10:\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\n topicname=topic_names_10[topic_number]\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\n gensimSTR=str(gensimdic[topic_number])\n \n \n # \n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\n')\n \n dic={}\n j=0\n \n print('top 5 terms')\n for term, frequency in lda.show_topic(topic_number, topn):\n j=j+1\n if j<6:\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\n dic[term]=frequency\n dff=pd.DataFrame.from_dict(dic,orient='index')\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \n return(dff)\n ##","def table_summary():\n \n t = dict()\n t['name'] = get_names()\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\n N = len(t['name'])\n \n # host\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\'itoi']\n \n # progenitor\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\n \n # progenitor type\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\n \n # metallicity\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\n \n # associations\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\n \n tout = Table(t)\n tout.pprint()\n tout.write('../data/stream_origin.fits', overwrite=True)","def get_tweets_by_topic(topic, start_date, end_date):\n try:\n query = f\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\"\n logger.info(f'QUERY: {query}') \n with MysqlCursor() as cur:\n cur.execute(query)\n tweets = cur.fetchall()\n columns = [col[0] for col in cur.description]\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \n return tweets, columns\n \n except Exception as ex:\n logger.exception(ex)\n return f'Exception: {ex}'","def property_count_function(listOfProperties):\n\n property_count = {} # Empty dict, is gonna look like this: property_count{property : count}\n for lists in listOfProperties:\n try:\n for properties in lists:\n property_count[properties] = property_count.get(properties, 0) + 1\n except TypeError as e:\n print(e)\n\n # Converts the dictionary to a dataframe\n property_dataframe = pd.DataFrame(list(property_count.items()), columns=['Property', 'Frequency'])\n # property_dataframe = property_dataframe.set_index(\"Property\")\n property_dataframe = property_dataframe.sort_values(by=['Frequency'], ascending=False)\n\n return property_dataframe","def categorize_data(data, top_count):\n sorted_by_tcp = sorted(\n data, key=lambda x: x['TCP Utilization'], reverse=True\n )[0:top_count]\n sorted_by_udp = sorted(\n data, key=lambda x: x['UDP Utilization'], reverse=True\n )[0:top_count]\n\n print(f\"\\nTOP-{top_count} port flooders by TCP\")\n print(tabulate(sorted_by_tcp, headers='keys', tablefmt=\"psql\"))\n print(f\"\\nTOP-{top_count} port flooders by UDP\")\n print(tabulate(sorted_by_udp, headers='keys', tablefmt=\"psql\"))","def get_num_topics(soup):\n try:\n #num = int(re.search(\"Suche ergab (\\\\d+)\", str(soup)).group(1)) \n num = int(re.search(\"(\\\\d+) Treffer\", str(soup)).group(1)) \n except: \n num = 0\n print(traceback.format_exc()) \n\n return num","def get_top_keywords_from_articles(self, kwords_list):\n _all_keywords = []\n for a in kwords_list:\n if a != []:\n for w in a:\n _all_keywords.append([w['keyword'],w['weight'],w['label']])\n _df_g = pd.DataFrame(_all_keywords, columns=[\"Keyword\", \"Count\",\"Label\"])\n _df_g.sort_values(by=\"Count\", inplace=True, ascending=False)\n _df_g.reset_index(drop=True, inplace=True)\n _df_g.to_csv('test.csv')\n print(len(_df_g))\n\n _df_g['Keyword'] = _df_g['Keyword'].apply(self.remove_repeat_words)\n _df_g.dropna(axis=0, inplace=True)\n p1,p2 = self.pos_taggers(_df_g)\n _df_g['c_POS'] = p1\n _df_g['s_POS'] = p2\n _df_g['c_POS_score'] = _df_g['c_POS'].apply(self.combine_pos_score)\n _df_g['s_POS_score'] = _df_g['s_POS'].apply(self.specific_pos_score)\n _df_g['Count'] = _df_g['Count'] + _df_g['c_POS_score'] + _df_g['s_POS_score'] \n print(len(_df_g))\n _df_g.sort_values(by='Count',inplace=True, ascending=False)\n print(len(_df_g))\n _df_g = _df_g.reset_index(drop=True)\n _df_g = _df_g[:10]\n response_dict = dict()\n response_dict['nc'] = \", \".join(_df_g['Keyword'].to_list())\n return response_dict","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\n\n if margin_column_row and len(margin_column_row) == 2:\n if all([isinstance(element, bool) for element in margin_column_row]):\n # margin_column_row = ((False, False),) * len(count_columns)\n margin_column_row = (margin_column_row, ) * len(count_columns)\n\n # by default keep summary row but remove summary column\n if not margin_column_row:\n margin_column_row = ((False, True),) * len(count_columns)\n if len(count_columns) != len(margin_column_row):\n print('\\n')\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\n exit()\n\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\n frequency_df = pd.DataFrame()\n\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\n if column in df.columns and df[column].notna().any():\n df[column].fillna(np.nan, inplace=True)\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\n current_df = current_df.sort_index()\n if any((margin_column, margin_row)):\n # drop column All\n if not margin_column:\n current_df.drop(columns=['All'], inplace=True)\n # drop row All\n if not margin_row:\n current_df.drop(index=['All'], inplace=True)\n if frequency_df.empty:\n frequency_df = current_df.copy()\n else:\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\n\n frequency_df.fillna(0, inplace=True) \n frequency_df.reset_index(inplace=True) \n return frequency_df","def get_topics(self):\n topics = self.word_topics\n return topics / topics.sum(axis=1)[:, None]","def get_topic_quality():\n model.eval() \n with torch.no_grad():\n alpha = model.mu_q_alpha\n beta = model.get_beta(alpha) \n print('beta: ', beta.size())\n\n print('\\n')\n print('#'*100)\n print('Get topic diversity...')\n num_tops = 25\n\n TD_all = _diversity_helper(beta, num_tops) \n \n TD = np.mean(TD_all)\n print('Topic Diversity is: {}'.format(TD))\n\n print('\\n')\n print('Get topic coherence...')\n print('train_tokens: ', train_tokens[0])\n \n TC_all, cnt_all = get_topic_coherence(beta.cpu().detach().numpy(), train_tokens, vocab)\n\n TC_all = torch.tensor(TC_all)\n cnt_all = torch.tensor(cnt_all)\n TC_all = TC_all / cnt_all\n TC_all[TC_all<0] = 0\n\n TC = TC_all.mean().item()\n print('Topic Coherence is: ', TC)\n print('\\n')\n\n print('Get topic quality...')\n TQ = TC * TD\n print('Topic Quality is: {}'.format(TQ))\n print('#'*100)\n\n return TQ, TC, TD","def get_topics_count(khoros_object, user_settings=None, user_id=None, login=None, email=None):\n user_settings = _process_settings_and_user_id(khoros_object, user_settings, user_id, login, email)\n return _get_count(khoros_object, user_settings['id'], 'topics')","def test_count_publications(self):\n pass","def test_question_topics(self):\n p = ProductFactory()\n t1 = TopicFactory(slug='doesnotexist', product=p)\n t2 = TopicFactory(slug='cookies', product=p)\n t3 = TopicFactory(slug='sync', product=p)\n\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t2)\n QuestionFactory(topic=t3)\n\n self.refresh()\n\n topic_vals = (\n (t1.slug, 0),\n (t2.slug, 2),\n (t3.slug, 1),\n )\n\n qs = {'a': 1, 'w': 2, 'format': 'json'}\n for topics, number in topic_vals:\n qs.update({'topics': topics})\n response = self.client.get(reverse('search.advanced'), qs)\n eq_(number, json.loads(response.content)['total'])","def query_transition_counts(ordered_pitch_types, query_filter):\n transition_counts = {}\n\n for first_pitch_type in ordered_pitch_types:\n transition_counts[first_pitch_type] = {}\n\n for second_pitch_type in ordered_pitch_types:\n count_query = PitchTransition.select().where(\n PitchTransition.first_pitch_type == first_pitch_type).where(\n PitchTransition.second_pitch_type == second_pitch_type)\n\n if query_filter:\n count_query = count_query.where(query_filter)\n\n transition_counts[first_pitch_type][second_pitch_type] = count_query.count()\n\n return transition_counts","def init():\n\n # Reading the data from the CSV file using the latin1 encoding.\n data_read = pd.read_csv(\"gender-classifier-DFE-791531.csv\", encoding='latin1') # Dataset Size = 20050\n\n # If all the attribute values are empty for any of the rows, we drop them.\n data = data_read.dropna(how='all') # After dropping, data set size is still 20050\n\n # Checking the names of the columns/attributes which contains at least one null value\n columns_containing_missing_values = data.columns[data.isnull().any()].tolist()\n print(\"Column names which has missing values\")\n print(columns_containing_missing_values)\n\n # Since 'gender' is our target variable, we would like to have values for it.\n # So, dropping all the rows which have no values for the 'gender' attribute.\n data = data[data['gender'].notnull()] # After dropping, dataset size = 19953 rows\n # Also, dropping all the rows which have values as 'unknown' for the 'gender' attribute\n data = data[data['gender'] != 'unknown'] # After dropping, dataset size = 18836 rows\n\n male_profile_count = len(data[data['gender'] == 'male'])\n print(\"Male Profile Count \" + str(male_profile_count))\n female_profile_count = len(data[data['gender'] == 'female'])\n print(\"Female Profile Count \" + str(female_profile_count))\n brand_profile_count = len(data[data['gender'] == 'brand'])\n print(\"Brand Profile Count \" + str(brand_profile_count))\n\n return data","def __find_topics(self, concepts):\n\n # Set up\n found_topics = dict() # to store the matched topics\n explanation = dict()\n\n # finding matches\n for concept in concepts:\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\n for grams in evgrams:\n gram = \"_\".join(grams)\n gram_without_underscore = \" \".join(grams)\n #### Finding similar words contained in the model\n\n list_of_matched_topics = []\n\n if self.fast_classification:\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\n else:\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\n\n\n for topic_item in list_of_matched_topics:\n\n topic = topic_item[\"topic\"]\n str_sim = topic_item[\"sim_t\"]\n wet = topic_item[\"wet\"]\n sim = topic_item[\"sim_w\"]\n\n\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\n\n\n if topic in found_topics:\n #tracking this match\n found_topics[topic][\"times\"] += 1\n\n found_topics[topic][\"gram_similarity\"].append(sim)\n\n #tracking the matched gram\n if gram in found_topics[topic][\"grams\"]:\n found_topics[topic][\"grams\"][gram] += 1\n else:\n found_topics[topic][\"grams\"][gram] = 1\n\n #tracking the most similar gram to the topic\n if str_sim > found_topics[topic][\"embedding_similarity\"]:\n found_topics[topic][\"embedding_similarity\"] = str_sim\n found_topics[topic][\"embedding_matched\"] = wet\n\n else:\n #creating new topic in the result set\n found_topics[topic] = {'grams': {gram:1},\n 'embedding_matched': wet,\n 'embedding_similarity': str_sim,\n 'gram_similarity':[sim],\n 'times': 1,\n 'topic':topic}\n\n\n\n if sim == 1:\n found_topics[topic][\"syntactic\"] = True\n\n\n\n primary_label_topic = self.cso.get_primary_label_wu(topic)\n if primary_label_topic not in explanation:\n explanation[primary_label_topic] = set()\n\n explanation[primary_label_topic].add(gram_without_underscore)\n\n return found_topics, explanation","def topic_table(topic):\r\n table = Table(['property', 'value'])\r\n table.align['property'] = 'r'\r\n table.align['value'] = 'l'\r\n\r\n table.add_row(['name', topic['name']])\r\n table.add_row(['tags', listing(topic['tags'] or [])])\r\n return table","def __len__(self):\n count = 0\n topics = set(six.iterkeys(self._topics))\n while topics:\n event_type = topics.pop()\n try:\n listeners = self._topics[event_type]\n count += len(listeners)\n except KeyError:\n pass\n return count","def test_data_counts(self):\n model = PoincareModel(self.data)\n self.assertEqual(len(model.all_relations), 5)\n self.assertEqual(len(model.node_relations[model.kv.vocab['kangaroo.n.01'].index]), 3)\n self.assertEqual(len(model.kv.vocab), 7)\n self.assertTrue('mammal.n.01' not in model.node_relations)","def contentcheck_categorical():\n filename = \"Analysis.txt\"\n temp_line = \"\"\n count = 0\n for line in open(filename, 'r'):\n temp_line = temp_line + line\n if \"VALUE COUNTS\" in temp_line:\n count = count + 1\n if \"DATA INFORMATION\" in temp_line:\n count = count + 1\n if \"MEAN, MEDIAN AND MODE:\" in temp_line:\n count = count + 1\n if \"Correlation\" in temp_line:\n count = count + 1\n if \"Normality Tests\" in temp_line:\n count = count + 1\n return count","def count_mentioned_pol_figures(data):\n figures_mentioned = {}\n figures = get_political_figures()\n\n for ind, row in data.iterrows():\n subject_words = row[\"MetadataSubject\"].lower()\n message_words = row[\"RawText\"].lower()\n\n for figure in figures:\n if figure + \" \" in (subject_words + message_words):\n if figure in figures_mentioned:\n figures_mentioned[figure] += 1\n else:\n figures_mentioned[figure] = 0\n\n return pd.DataFrame(figures_mentioned)","def counts_table(data, attr):\n pd.options.mode.chained_assignment = None # default='warn'\n # gets class names for dataframe manipulation\n classes = attr.tail(1)['vars'].tolist()\n classlist = [classes[0][0], classes[0][1]]\n # expanding a table to have all variable options in a column with their \n # parent attribute\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\n allvariables.name='var'\n allvariables = attr.drop('vars', axis=1).join(allvariables)\n av = allvariables.drop(attr.index[-1])\n # populate the table with counts\n for c in classlist:\n clist = []\n count = 0\n for i, row in av.iterrows():\n att = row['attr']\n var = row['var']\n sub = data[[att,'class']]\n sub = sub[sub[att]==var]\n if not sub.empty:\n ssub = sub[sub['class']==c]\n if not ssub.empty:\n count = len(ssub)\n else:\n count = 0\n clist.append(count)\n av[c] = clist\n\n return av","def _transform_idoc(df):\n global _SIMPLECOUNT_COLUMNS\n\n try:\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\n\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\n \n c_nc = df['admtypo3'] == 1\n c_tv = df['admtypo3'] == 2\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\n c_viol = df['offtype'] == 1\n c_male = df['sex'] == 'M'\n c_female = ~c_male\n\n c_first2 = [c_nc, c_tv]\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\n \n def helper(c, indicator_id, first2):\n df['fk_simplecount_indicator'] = indicator_id\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\n if first2:\n return df[c].groupby(g).size().reset_index(name='value')\n else:\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\n\n out = pd.DataFrame()\n for i in range(2):\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\n \n for i in range(len(c_others)):\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\n\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\n return out[_SIMPLECOUNT_COLUMNS]\n except:\n raise","def test_topic_reduction_edge_cases(base_bertopic):\n\n nr_topics = 5\n base_bertopic.nr_topics = 100\n old_documents = pd.DataFrame({\"Document\": newsgroup_docs,\n \"ID\": range(len(newsgroup_docs)),\n \"Topic\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\n base_bertopic._update_topic_size(old_documents)\n c_tf_idf = base_bertopic._extract_topics(old_documents, topic_reduction=True)\n old_freq = base_bertopic.get_topics_freq()\n\n new_documents = base_bertopic._reduce_topics(old_documents, c_tf_idf)\n new_freq = base_bertopic.get_topics_freq()\n\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\n pd.testing.assert_frame_equal(old_documents, new_documents)\n pd.testing.assert_frame_equal(old_freq, new_freq)","def get_winning_topics(cls):\n results_generator = cls.get_results()\n winning_hoechstzahls = []\n for i in range(config['openslides_topicvoting_posts']):\n try:\n winning_hoechstzahls.append(results_generator.next())\n except StopIteration:\n topic_post_warning = True\n runoff_poll_warning = False\n break\n else:\n topic_post_warning = False\n try:\n first_looser_hoechstzahl = results_generator.next()\n except StopIteration:\n runoff_poll_warning = False\n else:\n # First runoff poll check: Check equal hoechstzahls between the categories.\n if (first_looser_hoechstzahl.value == winning_hoechstzahls[-1].value and\n first_looser_hoechstzahl.topic.category.weight == winning_hoechstzahls[-1].topic.category.weight):\n runoff_poll_warning = True\n else:\n runoff_poll_warning = False\n winning_topics = map(lambda hoechstzahl: hoechstzahl.topic, winning_hoechstzahls)\n return {'winning_topics': winning_topics,\n 'topic_post_warning': topic_post_warning,\n 'runoff_poll_warning': runoff_poll_warning}","def manual_community_topic_labels(date='2019_01'):\n comm_size = get_blob(bucket_name=date, blob_name=f\"community_modularity.csv\", df=True)\n comm_size.name = 'size'\n comm_size.index.name = 'community'\n comm_size = comm_size.reset_index()\n labels = {\n 0:'images/discussion',\n 1:'discussion/tv',\n 2:'runescape',\n 3:'funny/images',\n 4:'gaming/tech',\n 5:'pol/geo',\n 6:'generalist',\n 7:'porn',\n 8:'music',\n 9:'sports',\n 10:'SE/DK',\n 11:'SP/IT/PT',\n 12:'NL'\n }\n\n membership = load_membership()\n order_keys = membership.drop_duplicates('community').set_index('community')['size_order'].to_dict()\n\n comm_size['size_order'] = comm_size['community'].map(lambda x: order_keys[x])\n comm_size = comm_size.sort_values('size_order').reset_index(drop=True)\n\n comm_size['labels']=comm_size['community'].map(lambda x: labels[x])\n comm_size.to_csv(tables_path(f\"{date}/comm_size.csv\"))","def create_topic_columns(videos, topics):\n \n # Clear values\n videos['relevant'] = False\n\n # Create masks for each topic so we can later filter videos by topics\n topic_masks = []\n for _, topic in topics.iterrows():\n videos[topic['slug']] = False # Clear values\n pattern = get_pattern(topic)\n topic_mask = videos.apply(lambda video: is_relevant(video, pattern), axis=1)\n topic_masks.append(topic_mask)\n videos[topic['slug']] = topic_mask\n\n # Mark video as 'relevant' if it mentions any of the topics\n videos['relevant'] = np.any(np.column_stack(topic_masks), axis=1)","def posts_per_topic_all(request, pk):\n #update is_expired in all posts\n update_posts_expiration()\n #get all posts with a certain topic\n posts = Post.objects.filter(topic=pk)\n serializer = ViewPostSerializer(posts, many=True)\n return Response(serializer.data)","def analysis_5_result(primary_person_df,units_df,output_folder_path):\n df1 = primary_person_df.distinct().select(\"CRASH_ID\",\"UNIT_NBR\",\"PRSN_ETHNICITY_ID\")\n df2 = units_df.distinct().select(\"CRASH_ID\",\"UNIT_NBR\",\"VEH_BODY_STYL_ID\")\n join_res_df = df1.join(broadcast(df2), [\"CRASH_ID\",\"UNIT_NBR\"])\n rank_window_spec = Window.partitionBy(\"VEH_BODY_STYL_ID\").orderBy(col(\"COUNT\").desc())\n top_ethnic_unique_body_df = join_res_df \\\n .groupBy(\"VEH_BODY_STYL_ID\",\"PRSN_ETHNICITY_ID\") \\\n .agg(count(\"*\").alias(\"COUNT\")) \\\n .withColumn(\"RANK\", dense_rank().over(rank_window_spec)) \\\n .filter(\"RANK == 1\")\\\n .drop(\"RANK\")\n\n print(\"Analysis 5: \\nMention the top ethnic user group of each unique body style\")\n top_ethnic_unique_body_df.show(truncate=False)\n write_df_to_csv(top_ethnic_unique_body_df, output_folder_path+\"analysis_5_result\")","def get_topic_measures(dict_clusters, dict_groundtruth, exact_match=True, cluster_n=None, coverage_percentage=None,\n coverage_n=None, keep_cluster_duplicates=False):\n total_tp = 0\n total_fp = 0\n total_n = get_total_time_frame_count(dict_groundtruth)\n\n dict_time_frame_measures = dict()\n for time_frame in dict_clusters.keys():\n tp = 0\n fp = 0\n keyword_n = 0\n if time_frame not in dict_groundtruth:\n fp += 1\n # total_fp += 1\n else:\n groundtruth = dict_groundtruth[time_frame]\n clusters = dict_clusters[time_frame]\n\n keyword_n, keyword_tp, keyword_fp, matched_events = eval_clusters(clusters, groundtruth, exact_match,\n cluster_n,\n keep_cluster_duplicates=keep_cluster_duplicates)\n\n temp_tp = 0\n temp_fp = 0\n for k in matched_events.keys():\n matched_event = matched_events[k]\n\n # if coverage percentage is given\n if coverage_percentage:\n if matched_event.event_TP / matched_event.event_n >= coverage_percentage:\n temp_tp += 1\n else:\n temp_fp += 1\n\n # if coverage count is given\n elif coverage_n:\n if matched_event.event_TP >= coverage_n:\n temp_tp += 1\n else:\n temp_fp += 1\n\n else:\n if matched_event.event_TP == matched_event.event_n:\n temp_tp += 1\n else:\n temp_fp += 1\n\n # if all events belong to the time frame are identified, count time frame as a TP\n if temp_tp == len(groundtruth):\n tp += 1\n else:\n fp += 1\n\n dict_time_frame_measures[time_frame] = [len(groundtruth), temp_tp, temp_fp]\n total_tp += tp\n total_fp += fp\n\n return total_n, total_tp, total_fp, dict_time_frame_measures","def filter_by_topic(tmp_df, keep_top_n_topics=0, min_count_threshold=0):\n\n topic_count_df = tmp_df[\"variety\"].value_counts()\n\n # filter by top n number of topics if specified\n if keep_top_n_topics is not None:\n topics_to_keep = topic_count_df.head(keep_top_n_topics).index\n tmp_df = tmp_df[tmp_df[\"variety\"].isin(topics_to_keep)]\n\n # filter out any topics that doesn't meet the minimum count threshold\n if min_count_threshold >= 0:\n topics_to_keep = topic_count_df[\n topic_count_df > min_count_threshold].index\n tmp_df = tmp_df[tmp_df[\"variety\"].isin(topics_to_keep)]\n\n return tmp_df","def get_word_frequencies(topic_description):\n frequencies = {w:f for w,f in topic_description}\n return frequencies","def get_stories(df):\n categories = df.get_categorical().columns\n continuous = df.get_numerical().columns\n\n stories = []\n cat_copy = list(categories)\n for col in categories:\n # Remove the current col\n if col in cat_copy:\n cat_copy.remove(col)\n try:\n # Get comparison variable\n x = cat_copy.pop()\n d = pd.pivot_table(df.data, index=(col), values=[x],\\\n aggfunc='count').reset_index().sort_values(by=x, ascending=False)\n stories.append({\n 'question': \"%s with high count of %s\" %(col, x),\n 'question_html': \"<span class='tag is-primary is-light'>%s</span>\\\n with high count of <span class='tag is-success is-light'>%s</span>\" % (col, x),\n 'answer': d[col].head(1).values[0],\n 'misc': d\n })\n except IndexError as e:\n pass\n \n for num in continuous:\n d = pd.pivot_table(df.data, index=[col], values=[num],\\\n aggfunc=np.sum).reset_index().sort_values(by=num, ascending=False)\n stories.append({\n 'question': \"%s with sum of %s\" % (col, num),\n 'question_html': \"<span class='tag is-primary is-light'>%s</span>\\\n with sum of <span class='tag is-success is-light'>%s</span>\" % (col, num),\n 'answer': round(d[num].head(1).values[0]),\n 'misc': d\n })\n\n return stories","def getTopicPmi(self, folderpath, numTopic):\n # get tweets\n # helper = Utility.Helper(self.rootpath)\n folderPath = os.path.join(folderpath, 'final')\n tweets = self.helper.getTweet(folderPath)\n with codecs.open(os.path.join(self.rootpath, folderPath,\n \"tweets_line.txt\"),\n \"w\", encoding='utf8') as fp:\n for tweet in tweets:\n # print (type(tweet.text.encode('utf8')))\n c1 = self.preprocessData.cleanTweet(tweet.text)\n fp.write(c1 + '\\n')\n\n # get topic with pmi\n corpus_path = os.path.join(self.rootpath, folderPath, \"tweets_line.txt\"\n )\n n_topics = numTopic\n n_top_words = 5\n preprocessing_steps = ['tag']\n n_cand_labels = 100\n label_min_df = 5\n label_tags = ['NN,NN', 'JJ,NN']\n n_labels = 3\n lda_random_state = 12345\n lda_n_iter = 10000\n\n labels, words, dist = label_topic.get_topic_labels(corpus_path,\n n_topics,\n n_top_words,\n preprocessing_steps,\n n_cand_labels,\n label_min_df,\n label_tags,\n n_labels,\n lda_random_state,\n lda_n_iter)\n print(\"\\nTopical labels:\")\n print(\"-\" * 20)\n topics = defaultdict(list)\n for i, labels in enumerate(labels):\n topics[i] = map(lambda l: ' '.join(l), labels)\n print(u\"Topic {}: {}\".format(i, ', '.join(map(lambda l:\n ' '.join(l),\n labels))\n )\n )\n self.helper.dumpJson(folderPath, \"tweets_label_words_pmi.json\",\n labels)\n print(\"tweets_topic_label_pmi.json has been saved.\")\n self.helper.dumpJson(folderPath, \"tweets_topic_words_pmi.json\",\n words)\n print(\"tweets_topic_words_pmi.json has been saved.\")\n return dist","def iterate_data(self):\n if \"single\" in self.dataset_name:\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for answer_id in self.data:\n summary = self.data[answer_id]['summary']\n articles = self.data[answer_id]['articles']\n question = self.data[answer_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(articles, 'article')\n self._get_token_cnts(question, 'question')\n self._write_stats(\"token_counts\")\n\n if \"multi\" in self.dataset_name:\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\n for q_id in self.data:\n summary = self.data[q_id]['summary']\n question = self.data[q_id]['question']\n if args.tokenize:\n self._get_token_cnts(summary, 'summary')\n self._get_token_cnts(question, 'question')\n question = self.data[q_id]['question']\n for answer_id in self.data[q_id]['articles']:\n articles = self.data[q_id]['articles'][answer_id][0]\n if args.tokenize:\n self._get_token_cnts(articles, 'article')\n self._write_stats(\"token_counts\")\n\n if self.dataset_name == \"complete_dataset\":\n self.stat_dict = {'urls': [], 'sites': []}\n article_dict = {}\n print(\"Counting answers, sites, unique urls, and tokenized counts of unique articles\")\n answer_cnt = 0\n for q_id in self.data:\n for a_id in self.data[q_id]['answers']:\n answer_cnt += 1\n url = self.data[q_id]['answers'][a_id]['url']\n article = self.data[q_id]['answers'][a_id]['article']\n if url not in article_dict:\n article_dict[url] = article\n self.stat_dict['urls'].append(url)\n assert \"//\" in url, url\n site = url.split(\"//\")[1].split(\"/\")\n self.stat_dict['sites'].append(site[0])\n print(\"# of Answers:\", answer_cnt)\n print(\"Unique articles: \", len(article_dict)) # This should match up with count written to file\n self._write_stats(\"full collection\")\n\n # Get token/sent averages of unique articles\n if args.tokenize:\n self.stat_dict = {'article': [[], []]}\n for a in article_dict:\n self._get_token_cnts(article_dict[a], 'article')\n self._write_stats(\"token_counts\")","def document_distribution_per_topic(df_dominant_topic):\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\n\n\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\n\n for i, ax in enumerate(axes.flatten()):\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\n doc_lens = [len(d) for d in df_dominant_topic.Text]\n ax.hist(doc_lens, bins = 1000, color=cols[i])\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\n sns.kdeplot(doc_lens, color=\"black\", shade=False, ax=ax.twinx())\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\n ax.set_ylabel('Number of Documents', color=cols[i])\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\n\n fig.tight_layout()\n fig.subplots_adjust(top=0.90)\n plt.xticks(np.linspace(0,1000,9))\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\n plt.show()","def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \n pass","def initialize(self):\n self.n_words = len(self.vocab)\n self.n_docs = len(self.documents)\n\n # Initialize the three count matrices.\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\n self.nmz = np.zeros((self.n_docs, self.n_topics))\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\n self.nzw = np.zeros((self.n_topics, self.n_words))\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\n self.nz = np.zeros(self.n_topics)\n\n # Initialize the topic assignment dictionary.\n self.topics = {} # key-value pairs of form (m,i):z\n\n for m in range(self.n_docs):\n for i in self.documents[m]:\n # Get random topic assignment, i.e. z is a random integer in the range of topics\n z = np.random.randint(self.n_topics)\n # Increment count matrices\n self.nmz[m,z] += 1\n self.nzw[z,self.documents[m][i]] += 1\n self.nz[z] += 1\n # Store topic assignment\n self.topics[(m,i)] = z","def validate_new_curriculum_topics(self, curriculum_topics):\n\n for cur in curriculum_topics:\n # check to make sure its in the general topics table\n self.db_cursor.execute(\"\"\"SELECT COUNT(*) FROM Topic WHERE name = %s\"\"\", (cur,))\n ct = self.db_cursor.fetchone()\n ct = ct[0]\n if ct == 0:\n print(\"topic does not exist, we must create new one or cancel\") # todo\n\n return True","def total_exs(dataset):\n total = 0\n for article in dataset['data']:\n for para in article['paragraphs']:\n total += len(para['qas'])\n return total","def prepare_titanic_data(df):\n\n df.embark_town.fillna('Other', inplace=True)\n\n # Drop deck and embarked_town\n df.drop(columns=['deck', 'embark_town'], inplace=True)\n\n # Encoding: Objects (Categorical Variables) to Numeric\n # Use sklearn's LabelEncoder\n encoder = LabelEncoder()\n\n # Set Unknown and encode Embarked column to numbers\n # 2 == \"S\" == Southampton == 644 people\n # 0 == \"C\" == Cherbourg == 168 people\n # 1 == \"Q\" == Queenstown == 77 people\n # 3 == \"Unknown\" == 2 people\n df.embarked.fillna('Unknown', inplace=True)\n encoder.fit(df.embarked)\n df.embarked = encoder.transform(df.embarked)\n\n # Encode the Class (first class, second, etc...)\n # First class == 0\n # Second class == 1\n # Third class == 2\n encoder.fit(df[\"class\"])\n df[\"class_encoded\"] = encoder.transform(df[\"class\"])\n\n # Encode gender\n # male == 1 == 577 records\n # female == 0 == 314 records\n encoder.fit(df.sex)\n df[\"sex_encoded\"] = encoder.transform(df.sex)\n\n # Handle the 177 records with missing age values\n average_age = df.age.mean()\n df.age.fillna(average_age, inplace=True)\n\n scaler = MinMaxScaler()\n scaler.fit(df[['fare']])\n df[\"fare_scaled\"] = scaler.transform(df[['fare']])\n\n scaler = MinMaxScaler()\n scaler.fit(df[['age']])\n df[\"age_scaled\"] = scaler.transform(df[['age']])\n\n # Set the index to the passenger id\n df = df.set_index(\"passenger_id\")\n return df","def tweet_df(n):\n # Retrieve the tweet contents\n first_tweet = get_value(df_1t, n)\n second_tweet = get_value(df_2t, n) \n third_tweet = get_value(df_3t, n)\n fourth_tweet = get_value(df_4t, n)\n fifth_tweet = get_value(df_5t, n)\n sixth_tweet = get_value(df_6t, n)\n seventh_tweet = get_value(df_7t, n)\n eighth_tweet = get_value(df_8t, n)\n nineth_tweet = get_value(df_9t, n)\n tenth_tweet = get_value(df_10t, n) \n \n # Sentiment of each tweet\n sa_first_tweet = sentiment_analyzer_scores(first_tweet)\n sa_second_tweet = sentiment_analyzer_scores(second_tweet)\n sa_third_tweet = sentiment_analyzer_scores(third_tweet)\n sa_fourth_tweet = sentiment_analyzer_scores(fourth_tweet)\n sa_fifth_tweet = sentiment_analyzer_scores(fifth_tweet)\n sa_sixth_tweet = sentiment_analyzer_scores(sixth_tweet)\n sa_seventh_tweet = sentiment_analyzer_scores(seventh_tweet)\n sa_eighth_tweet = sentiment_analyzer_scores(eighth_tweet)\n sa_nineth_tweet = sentiment_analyzer_scores(nineth_tweet)\n sa_tenth_tweet = sentiment_analyzer_scores(tenth_tweet)\n \n # Compute the compound score for obtaining a sentiment class\n compound_score_first_tweet = sentiment_logic((list(sa_first_tweet.values())[list(sa_first_tweet.keys()).index('compound')] ))\n compound_score_second_tweet = sentiment_logic((list(sa_second_tweet.values())[list(sa_second_tweet.keys()).index('compound')] )) \n compound_score_third_tweet = sentiment_logic((list(sa_third_tweet.values())[list(sa_third_tweet.keys()).index('compound')] ))\n compound_score_fourth_tweet = sentiment_logic((list(sa_fourth_tweet.values())[list(sa_fourth_tweet.keys()).index('compound')] ))\n compound_score_fifth_tweet = sentiment_logic((list(sa_fifth_tweet.values())[list(sa_fifth_tweet.keys()).index('compound')] ))\n compound_score_sixth_tweet = sentiment_logic((list(sa_sixth_tweet.values())[list(sa_sixth_tweet.keys()).index('compound')] ))\n compound_score_seventh_tweet = sentiment_logic((list(sa_seventh_tweet.values())[list(sa_seventh_tweet.keys()).index('compound')] ))\n compound_score_eighth_tweet = sentiment_logic((list(sa_eighth_tweet.values())[list(sa_eighth_tweet.keys()).index('compound')] ))\n compound_score_nineth_tweet = sentiment_logic((list(sa_nineth_tweet.values())[list(sa_nineth_tweet.keys()).index('compound')] ))\n compound_score_tenth_tweet = sentiment_logic((list(sa_tenth_tweet.values())[list(sa_tenth_tweet.keys()).index('compound')] ))\n \n # Create a new temporary dataframe for the tweet contents and sentiment\n compound_score_list = [compound_score_first_tweet, compound_score_second_tweet,\n compound_score_third_tweet, compound_score_fourth_tweet,\n compound_score_fifth_tweet, compound_score_sixth_tweet, \n compound_score_seventh_tweet, compound_score_eighth_tweet,\n compound_score_nineth_tweet, compound_score_tenth_tweet]\n \n \n first_col = [first_tweet, second_tweet,\n third_tweet, fourth_tweet,\n fifth_tweet, sixth_tweet,\n seventh_tweet, eighth_tweet,\n nineth_tweet, tenth_tweet]\n \n second_col = compound_score_list\n \n tmp_df = pd.DataFrame(data = {'Tweets' : first_col, \n 'Sentiment' : second_col})\n \n \n return tmp_df.to_json(date_format = 'iso', orient = 'split')"],"string":"[\n \"def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\\n data_frame_topic = data_frame \\\\\\n .filter(data_frame[\\\"Stratification1\\\"].contains(\\\"Male\\\")) \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n\\n print(\\\"The following table represent the number of men group by the topic: \\\")\\n data_frame_topic.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def topic_count():\\n # get the number topics and their counts as tuples: ('Topic', 123)\\n query = peewee.RawQuery(Post, \\\"select topic, count(topic) from post group by topic\\\").tuples()\\n\\n # turn the result of the query object into a list of tuples\\n tuple_result = []\\n for each_tuple in query:\\n tuple_result.append(each_tuple)\\n\\n # sort by the the second element, which is value, of each tuple in the list\\n tuple_result = sorted(tuple_result, key=lambda x: x[1], reverse=True)\\n\\n # separate the topic and count into two lists for graphing purpose\\n topics = []\\n counts = []\\n\\n for each_tuple in tuple_result:\\n topics.append(each_tuple[0])\\n counts.append(each_tuple[1])\\n\\n return counts, topics\",\n \"def topic_stats(df_topic_sents_keywords):\\n\\n # Number of Documents for Each Topic\\n topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\\n\\n # Percentage of Documents for Each Topic\\n topic_contribution = round(topic_counts/topic_counts.sum(), 4)\\n\\n # Topic Number and Keywords\\n topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\\n\\n # Concatenate Column wise\\n df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\\n\\n # Change Column names\\n df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\\n\\n # Show\\n df_dominant_topics\",\n \"def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:\\n try:\\n data_frame = data_frame \\\\\\n .select(\\\"TopicID\\\", \\\"Question\\\") \\\\\\n .distinct() \\\\\\n .groupBy(\\\"TopicID\\\") \\\\\\n .count() \\\\\\n .sort(\\\"TopicID\\\")\\n except Py4JError:\\n raise AnalysisException('One columns is incorrect')\\n print(\\\"The following table represent the number of the type of each topic\\\")\\n data_frame.show()\\n data_frame_pandas = data_frame.toPandas()\\n return data_frame_pandas\",\n \"def count_topic_dist(self):\\n if len(self.representants) == 0:\\n self.log_writer(\\\"Representants not set. Cannot make topic dist.\\\")\\n return\\n for key, value in self.representants.items():\\n self.topic_distributions.append(len(value)/len(self.training_docs))\\n self.topic_numbers.append(key)\",\n \"def test_extract_topics():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model = BERTopic()\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def get_diseases(self):\\n self.diseases = self.data.groupby('topic')['topic'].count()\",\n \"def get_paper_counter_per_topic_id(all_topic_assignments):\\n counter = {}\\n for topic_assignment in all_topic_assignments:\\n for topic_index, topic_value in topic_assignment:\\n if topic_index not in counter:\\n counter[topic_index] = 0\\n\\n counter[topic_index] += 1\\n\\n return counter\",\n \"def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:\\n data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])\\n print(data_frame_pandas)\\n return data_frame_pandas\",\n \"def test_extract_topics(base_bertopic):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(documents)\\n c_tf_idf = base_bertopic._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def trainCount(\\n trainData, \\n questionType,\\n questionDict,\\n questionIdict, \\n objDict, \\n objIdict,\\n numAns):\\n count_wa = np.zeros((len(objIdict), numAns))\\n count_a = np.zeros((numAns))\\n objIds = extractObjId(\\n trainData[0], \\n questionType, \\n questionDict, \\n questionIdict)\\n for i in range(objIds.shape[0]):\\n objId = objIds[i]\\n obj = questionIdict[objId - 1]\\n ansId = trainData[1][i, 0]\\n objId2 = objDict[obj]\\n count_wa[objId2, ansId] += 1\\n count_a[ansId] += 1\\n # Add UNK count\\n count_a[-1] += 1\\n return count_wa, count_a\",\n \"def connect_topic_id_to_topics(self, model):\\n confidence = []\\n for key, value in self.representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key, tp_num, val / len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2), reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n self.log_writer.add_log(\\n 'Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],\\n conf[1],\\n conf[2]))\\n self.topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in self.topic_indexes.items():\\n self.topics_of_index[value] = [key]\",\n \"def ngram_detection(self, min_topic_count=5, min_text_id_count=4):\\n\\n for text_id, text in self.texts.items():\\n # single-word topics act a bit different (no zips or comprehensions)\\n # store data in self.topics, not zip_grams\\n for word in text['doc']:\\n word_lemma = word.text.lower() if word.lemma_ == '-PRON-' else word.lemma_\\n\\n if {word.text}.intersection(self.punct) or {word.lemma_}.intersection(self.stop_words):\\n continue\\n\\n if not (word.pos in self.nouns or word.ent_type in self.entities):\\n continue\\n\\n if word_lemma in self.topics:\\n self.topics[word_lemma][\\\"count\\\"] += 1\\n self.topics[word_lemma][\\\"textIDs\\\"] |= {text_id}\\n self.topics[word_lemma][\\\"verbatims\\\"] |= {word.text.lower()}\\n else:\\n self.topics[word_lemma] = {\\\"name\\\": word_lemma,\\n \\\"count\\\": 1,\\n \\\"textIDs\\\": {text_id},\\n \\\"verbatims\\\": {word.text.lower()},\\n \\\"subtopics\\\": {}}\\n\\n # Populate self.ngrams and self.topics\\n for text_id, text in self.texts.items():\\n doc = text['doc']\\n\\n # Find pentagrams - ngrams with 5 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:], doc[4:]):\\n self._ngram_counter(ngram, 5, text_id, doc)\\n\\n # Find pentagrams - ngrams with 4 words\\n for ngram in zip(doc, doc[1:], doc[2:], doc[3:]):\\n self._ngram_counter(ngram, 4, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:], doc[2:]):\\n self._ngram_counter(ngram, 3, text_id, doc)\\n\\n for ngram in zip(doc, doc[1:]):\\n self._ngram_counter(ngram, 2, text_id, doc)\\n\\n\\n # Add text_id_count (the number of texts that the topic occurs in; so a topic might occur 50 times,\\n # but it's only mentioned in 3 different texts, we'd show 3.\\n for _, topic in self.topics.items():\\n topic['textIDCount'] = len(topic['textIDs'])\\n for _, ngram in self.ngrams.items():\\n ngram['textIDCount'] = len(ngram['textIDs'])\\n\\n # Eliminate rarely occurring topics and ngrams.\\n self.topics = {k: v for k, v in self.topics.items() if\\n v['textIDCount'] >= min_text_id_count and v['count'] >= min_topic_count}\\n self.ngrams = {k: v for k, v in self.ngrams.items() if\\n v['textIDCount'] >= min_text_id_count}\\n\\n # Loop through each ngram pair: outer loop is all ngrams, inner loop is all ngrams\\n for ngram_lemma, ngram in self.ngrams.items():\\n for ngram_plus_lemma, ngram_plus in self.ngrams.items():\\n # only stay in this loop if the inner ngram is one word longer than the outer loop and if the\\n # inner loop lemma contains the outer group lemma (avoid partial word matches like man in woman)\\n # r'\\\\b' + ngram_lemma + r'\\\\b' --> does the ngram lemma fit in ngram_plus lemma (\\\\b is word boundary)\\n if ngram['n'] + 1 != ngram_plus['n']:\\n continue\\n\\n if not re.search(r'\\\\b' + ngram_lemma + r'\\\\b', ngram_plus_lemma):\\n continue\\n\\n # Is the absolute count of occurrences and the count of text_id occurrences both big enough to use it\\n # instead of the other loop?\\n if ngram_plus['count'] + 3 >= ngram['count'] and ngram_plus['textIDCount'] + 3 >= ngram['textIDCount']:\\n # TODO: Is this the right action (deleting shorter, but not much more explanatory) phrase?\\n # TODO: Is this enough? Or will I end up double explaining things sometimes?\\n ngram['count'] = -1\\n\\n # Eliminate newly demoted items\\n self.ngrams = {ngram_lemma: ngram for ngram_lemma, ngram in self.ngrams.items() if ngram['count'] > 0}\",\n \"def test_topics_for_products(self):\\n desktop_topics = topics_for(product=self.desktop)\\n eq_(len(desktop_topics), 3)\\n\\n mobile_topics = topics_for(product=self.mobile)\\n eq_(len(mobile_topics), 2)\",\n \"def assign_topics_to_sentences(self):\\n \\n\\n # 10 topics\\n topic_dict = {0: 'academics'\\n , 1: 'career'\\n , 2: 'commute'\\n , 3: 'diversity'\\n , 4: 'community'\\n , 5: 'extracurricular'\\n , 6: 'facilities'\\n , 7: 'finance'\\n , 8: 'housing'\\n , 9: 'wellness'\\n }\\n\\n # Some important words that should be included under each topic\\n topics = [['academic', 'exam', 'study', 'learn', 'education', 'class', 'course', 'grade', 'assignment'\\n , 'degree', 'research', 'elective'\\n , 'professor', 'project', 'scholarship', 'knowledge']\\n , ['career', 'job', 'coop', 'employment']\\n , ['commute', 'skytrain', 'transport', 'commuter']\\n , ['diversity', 'diverse', 'background']\\n , ['community', 'welcome', 'support', 'social', 'friend', 'fun', 'network', 'home']\\n , ['extracurricular', 'club', 'sport', 'activity']\\n , ['facility', 'infrastructure', 'food', 'building', 'gym']\\n , ['finance', 'tuition', 'expensive']\\n , ['housing', 'live', 'residence']\\n , ['wellness', 'health', 'stress', 'depression', 'anxiety']]\\n\\n # Read the data - id and reponse column\\n dt = pd.read_csv(self.path_data_col\\n , encoding = \\\"ISO-8859-1\\\"\\n , usecols = [self.id_col_name, self.col_name])\\n\\n\\n \\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n \\n # Split into sentences\\n dt['sentences'] = self.getSentences(dt[self.col_name])\\n \\n \\n \\n\\n \\n \\n\\n # Store number of sentences in each response as a column\\n dt['num_sent'] = dt['sentences'].apply(lambda x: len(x))\\n\\n # Split each row into multiple rows - one row for each sentence\\n dt = (dt\\n .set_index([self.id_col_name, self.col_name, 'num_sent'])['sentences']\\n .apply(pd.Series)\\n .stack()\\n .reset_index()\\n .drop('level_3', axis = 1)\\n .rename(columns = {0:'sentences'}))\\n\\n\\n # Clean the sentences\\n dt['sentences_cleaned'] = self.cln.clean(dt['sentences'], typo = self.typo_ind)\\n\\n # Remove useless sentences\\n dt['sentences_cleaned'] = self.getValid(dt['sentences_cleaned'])\\n\\n # Remove rows with NA values\\n dt = self.removeEmptyData(dt)\\n\\n # Tokenize words in the cleaned sentences\\n responses = list(self.sent_to_words(dt['sentences_cleaned'].values.tolist()))\\n\\n\\n # Call the lexicon function\\n topic_lexicons = self.prepare_lexicons()\\n\\n # Lists to store results\\n count_topic_all = []\\n actual_topic_all = []\\n\\n # Tag each response into a topic\\n for response in responses:\\n\\n count_topic = []\\n actual_topic = []\\n\\n for topic in topic_lexicons:\\n\\n # Count occurance of each word in word stock in the response\\n temp = sum(dict((x, response.count(x)) for x in topic).values())\\n count_topic.append(temp)\\n\\n\\n for index, value in enumerate(count_topic):\\n\\n # Consider the topic if atleast one(?) word from its word-stock occurs in the response\\n if value > 0:\\n actual_topic.append(topic_dict[index])\\n\\n\\n # If more than 3 topics are tagged for single sentence, refine by increasing\\n # cutoff to at least 2 words instead of 1\\n if len(actual_topic) > 3:\\n\\n actual_topic = []\\n for index, value in enumerate(count_topic):\\n\\n if value > 1: # Increase cutoff\\n actual_topic.append(topic_dict[index])\\n\\n count_topic_all.append(count_topic)\\n actual_topic_all.append(actual_topic)\\n\\n\\n dt['tags'] = actual_topic_all\\n dt['num_tags'] = count_topic_all\\n\\n\\n # Select only the most important columns\\n dt_less = dt[[self.id_col_name, 'sentences', 'tags']]\\n\\n return dt, dt_less\",\n \"def test_extract_topics_custom_cv():\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n\\n cv = CountVectorizer(ngram_range=(1, 2))\\n model = BERTopic(vectorizer=cv)\\n model._update_topic_size(documents)\\n model._extract_topics(documents)\\n freq = model.get_topic_freq()\\n\\n assert model.c_tf_idf.shape[0] == 5\\n assert model.c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def test_topic_reduction(reduced_topics):\\n model = BERTopic()\\n nr_topics = reduced_topics + 2\\n model.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents.copy())\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents.copy())\\n new_freq = model.get_topic_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(model.mapped_topics, dict)\\n assert not set(model.get_topic_freq().Topic).difference(set(new_documents.Topic))\\n assert model.mapped_topics\",\n \"def uncategorized(df):\\n\\n counter = 0\\n for movie in df.index:\\n if len(df.loc[movie, 'imdbGenres']) == 1 and\\\\\\n df.loc[movie, 'Political'] == 0:\\n counter += 1\\n\\n return counter\",\n \"def group_topics(sent_topics_sorteddf):\\n new_topics=pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0).sum()\\n plt.pie(new_topics,labels=new_topics.index,autopct='%.0f',pctdistance=.8)\\n plt.title('Topic Share %');\\n\\n new_topic_words = pd.concat([sent_topics_sorteddf.groupby('Topic_Num').head()[['Keywords']],\\n topic_contribution.sort_index(),\\n pd.Series(['Economy','Immigration','Environment','Event',\\n 'Civil Rights','Civil Rights','Healthcare',\\n 'Defense','Trump','Community','Event','Event',\\n 'Thanks','Legislation','Trump','Community',\\n 'Community','Trump','Defense',\\n 'Legislation','Thanks','Economy','Thanks','Healthcare',\\n 'Legislation'])],axis=1).groupby(0)['Keywords'].sum()\\n [print(f'{topic}: ' + words) for topic,words in zip(new_topic_words.index,new_topic_words)]\",\n \"def test_get_topics(self):\\n\\n for m in self.models:\\n\\n topics = m.topics\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertEqual(topics.num_rows(), 25)\\n self.assertEqual(topics.num_columns(), 2)\\n z = m.topics[\\\"topic_probabilities\\\"]\\n for k in range(m.num_topics):\\n self.assertTrue(\\n abs(sum(z.vector_slice(k)) - 1) < DELTA,\\n \\\"Returned probabilities do not sum to 1.\\\",\\n )\\n\\n # Make sure returned object is an SFrame of the right size\\n topics = m.get_topics()\\n self.assertTrue(isinstance(topics, turicreate.SFrame))\\n self.assertTrue(\\n topics.num_columns() == 3,\\n \\\"Returned SFrame should have a topic, word, and probs.\\\",\\n )\\n\\n # Make sure that requesting a single topic returns only that topic\\n num_words = 8\\n topics = m.get_topics([5], num_words=num_words)\\n self.assertTrue(\\n all(topics[\\\"topic\\\"] == 5), \\\"Returned topics do not have the right id.\\\"\\n )\\n self.assertEqual(topics.num_rows(), num_words)\\n topics = m.get_topics([2, 4], num_words=num_words)\\n self.assertEqual(set(list(topics[\\\"topic\\\"])), set([2, 4]))\\n self.assertEqual(topics.num_rows(), num_words + num_words)\\n\\n # Make sure the cumulative probability of the returned words is\\n # is less than the cutoff we provided.\\n # A cutoff of 1.0 should return num_words for every topic.\\n cutoff = 1.0\\n topics = m.get_topics(cdf_cutoff=cutoff, num_words=len(m.vocabulary))\\n totals = topics.groupby(\\n \\\"topic\\\", {\\\"total_score\\\": turicreate.aggregate.SUM(\\\"score\\\")}\\n )\\n self.assertTrue(\\n all(totals[\\\"total_score\\\"] <= (cutoff + DELTA)),\\n \\\"More words were returned than expected for this cutoff.\\\",\\n )\\n\\n # Make sure we raise errors for bad input\\n with self.assertRaises(ValueError):\\n m.get_topics([-1])\\n with self.assertRaises(ValueError):\\n m.get_topics([10000])\\n with self.assertRaises(ToolkitError):\\n topics = m.get_topics(output_type=\\\"other\\\")\\n\\n # Test getting topic_words\\n topic_words = m.get_topics(output_type=\\\"topic_words\\\", num_words=5)\\n self.assertEqual(type(topic_words), turicreate.SFrame)\\n\\n # Test words are sorted correctly for the first topic\\n # TODO: Make this more deterministic.\\n\\n # topic_probs = m.get_topics(num_words=5)\\n # expected = [w for w in topic_probs['word'][:5]]\\n # observed = topic_words['words'][0]\\n # self.assertEqual(observed[0], expected[0])\",\n \"def test_topic_reduction_edge_cases():\\n model = BERTopic()\\n nr_topics = 5\\n model.nr_topics = 100\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n model._update_topic_size(old_documents)\\n model._extract_topics(old_documents)\\n old_freq = model.get_topic_freq()\\n\\n new_documents = model._reduce_topics(old_documents)\\n new_freq = model.get_topic_freq()\\n\\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\\n pd.testing.assert_frame_equal(old_documents, new_documents)\\n pd.testing.assert_frame_equal(old_freq, new_freq)\",\n \"def export_topics(self):\\n\\n # format as a list (for json output), then sort descending by textIDCount\\n topics = [{'name': topic['name'], 'count': topic['count'],\\n 'verbatims': list(topic['verbatims']), 'textIDs': list(topic['textIDs']),\\n 'textIDCount': topic['textIDCount'], 'rank': topic['rank'],\\n 'children': '' if 'children' not in topic else topic['children']}\\n for topic_id, topic in self.topics.items()]\\n topics = sorted(topics, key=lambda topic: topic['textIDCount'], reverse=True)\\n\\n for i, topic in enumerate(topics):\\n # Note that 'rank' is from topic, not child.\\n topic['children'] = [{'name': child['name'], 'count': child['count'], 'rank': topic['rank'],\\n 'verbatims': list(child['verbatims']), 'textIDs': list(child['textIDs']),\\n 'textIDCount': child['textIDCount']}\\n for _, child in topic['children'].items()]\\n\\n topic['children'] = sorted(topic['children'], key=lambda lemma: lemma['textIDCount'], reverse=True)\\n\\n # If the subtopic count is greater than the topic count, than calc a multiplier to size each subtopic\\n child_count = sum([child['textIDCount'] for child in topic['children']])\\n child_count_multiplier = 1 if child_count < topic['textIDCount'] else topic['textIDCount'] / child_count\\n\\n for child in topic['children']:\\n child['size'] = child['textIDCount'] * child_count_multiplier\\n\\n topic['size'] = topic['textIDCount'] - (child_count * child_count_multiplier)\\n\\n # Prune topics over max_topics (default ~40): we stopped calc'ing rank over the max_topics\\n self.model_output[\\\"children\\\"] = [topic for topic in topics]\\n\\n # Build file name and save\\n if self.data_date:\\n date = datetime.strptime(self.data_date, \\\"%Y-%m-%d\\\").strftime('%d') # from YYYY-MM-DD to DD\\n file_name = '{}-{}-Topics.txt'.format(self.corpus_name, date)\\n else:\\n file_name = '{}-Topics.txt'.format(self.corpus_name)\\n\\n with open(config.OUTPUT_DIR + file_name, 'w') as file:\\n json.dump(self.model_output, file)\",\n \"def find_dominant_topic(df_topic_sents_keywords):\\n\\n # Format\\n df_dominant_topic = df_topic_sents_keywords.reset_index()\\n df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\\n\\n # Group top 5 sentences under each topic\\n sent_topics_sorteddf = pd.DataFrame()\\n\\n sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\\n start = time.time()\\n for i, grp in sent_topics_outdf_grpd:\\n sent_topics_sorteddf = pd.concat([sent_topics_sorteddf, \\n grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \\n axis=0)\\n print(f'Group done. Total time {time.time() - start} seconds.')\\n\\n # Reset Index \\n sent_topics_sorteddf.reset_index(drop=True, inplace=True)\\n\\n # Format\\n sent_topics_sorteddf.columns = ['Topic_Num', \\\"Topic_Perc_Contrib\\\", \\\"Keywords\\\", \\\"Text\\\"]\\n return sent_topics_sorteddf\",\n \"def _collect_counts(self, instance_list):\\n \\\"\\\"\\\" Based on each instance, I augment empirical counts for every word and its BIO label in feature_count_table and for every transition from previous label to current label in transition_count_table.\\n All \\\"rare words\\\" (those words that appear less than 3 times) are replaced by <UNK>.\\n I also add label|START counts.\\n \\\"\\\"\\\"\\n # Build feature_count_table of V x labels and transition_count_table of labels x labels\\n for instance in instance_list: # Set of <(w, pos), l>\\n index = 0\\n for t in instance.data: # Tuple of (w, pos)\\n index = instance.data.index(t)\\n # print t[0] # word\\n # print instance.label[index] # label\\n if t in self.V:\\n self.feature_count_table[self.V.index(t)][self.labels.index(instance.label[index])] +=1\\n else:\\n self.feature_count_table[self.V.index('<UNK>')][self.labels.index(instance.label[index])] +=1\\n if index > 0:\\n self.transition_count_table[self.labels.index(instance.label[index-1])][self.labels.index(instance.label[index])] += 1\\n else:\\n self.transition_count_table[len(self.labels)][self.labels.index(instance.label[index])] += 1\",\n \"def summarize_corpus():\\n\\t\\n\\t# get metadata\\n\\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \\\"metadata\\\", md_mode)\\n\\t\\n\\t# visualize some metadata\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"author-continent\\\")\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"author-country\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"language\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_hist\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_x\\\")\\n\\tvisualize_metadata.plot_pie(wdir, md_csv, \\\"subgenre\\\")\\n\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"gender\\\")\\n\\t\\n\\t# make some counts\\n\\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\\n\\tnum_texts = len(md_table)\\n\\t#num_language = len(md_table.groupby([\\\"language\\\"]))\\n\\t#num_continent = len(md_table.groupby([\\\"author-continent\\\"]))\\n\\t#num_countries = len(md_table.groupby([\\\"author-country\\\"]))\\n\\t#num_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_subgenre = len(md_table.groupby([\\\"subgenre\\\"]))\\n\\t#num_subgenre_x = len(md_table.groupby([\\\"subgenre_x\\\"]))\\n\\t#fr_subgenre_hist = md_table.groupby([\\\"subgenre_hist\\\"]).count()\\n\\t#num_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"historical\\\"]\\n\\t#num_not_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"not_historical\\\"]\\n\\t\\n\\t\\n\\td = {\\\"texts\\\":[num_texts], \\n\\t#\\\"languages\\\":[num_language],\\n\\t#\\\"continents\\\":[num_continent],\\n\\t#\\\"countries\\\":[num_countries],\\n\\t\\\"authors\\\":[num_authors],\\n\\t#\\\"subgenre_x\\\":[num_subgenre_x],\\n\\t\\\"subgenre\\\":[num_subgenre]}\\n\\t#\\\"num_historical\\\":[num_historical],\\n\\t#\\\"num_not_historical\\\":[num_not_historical]}\\n\\t\\n\\t\\n\\t\\n\\tcount_fr = pd.DataFrame(d)\\n\\tcount_fr.to_csv(os.path.join(wdir, \\\"corpus-description.csv\\\"), sep=\\\",\\\", header=True)\\n\\tprint(\\\"Done: summarize corpus\\\")\",\n \"def __rank_topics(self, found_topics, explanation):\\n max_value = 0\\n scores = []\\n for _,topic in found_topics.items():\\n topic[\\\"score\\\"] = topic[\\\"times\\\"] * len(topic['grams'].keys())\\n scores.append(topic[\\\"score\\\"])\\n if topic[\\\"score\\\"] > max_value:\\n max_value = topic[\\\"score\\\"]\\n\\n for _,topic in found_topics.items():\\n if \\\"syntactic\\\" in topic:\\n topic[\\\"score\\\"] = max_value\\n\\n\\n\\n\\n # Selection of unique topics\\n unique_topics = {}\\n for t_p,topic in found_topics.items():\\n prim_label = self.cso.get_primary_label_wu(t_p)\\n if prim_label in unique_topics:\\n if unique_topics[prim_label] < topic[\\\"score\\\"]:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n else:\\n unique_topics[prim_label] = topic[\\\"score\\\"]\\n\\n # ranking topics by their score. High-scored topics go on top\\n sort_t = sorted(unique_topics.items(), key=lambda v: v[1], reverse=True)\\n #sort_t = sorted(found_topics.items(), key=lambda k: k[1]['score'], reverse=True)\\n\\n\\n # perform\\n vals = []\\n for t_p in sort_t:\\n vals.append(t_p[1]) #in 0, there is the topic, in 1 there is the info\\n\\n\\n #### suppressing some warnings that can be raised by the kneed library\\n warnings.filterwarnings(\\\"ignore\\\")\\n try:\\n x_vals = range(1,len(vals)+1)\\n t_kn = KneeLocator(x_vals, vals, direction='decreasing')\\n if t_kn.knee is None:\\n #print(\\\"I performed a different identification of knee\\\")\\n t_kn = KneeLocator(x_vals, vals, curve='convex', direction='decreasing')\\n except ValueError:\\n pass\\n\\n ##################### Pruning\\n\\n try:\\n knee = int(t_kn.knee)\\n except TypeError:\\n knee = 0\\n except UnboundLocalError:\\n knee = 0\\n\\n if knee > 5:\\n try:\\n knee += 0\\n except TypeError:\\n print(\\\"ERROR: \\\",t_kn.knee,\\\" \\\",knee, \\\" \\\", len(sort_t))\\n\\n else:\\n try:\\n if sort_t[0][1] == sort_t[4][1]:\\n top = sort_t[0][1]\\n test_topics = [item[1] for item in sort_t if item[1]==top]\\n knee = len(test_topics)\\n\\n else:\\n knee = 5\\n except IndexError:\\n knee = len(sort_t)\\n\\n final_topics = []\\n final_topics = [self.cso.get_topic_wu(sort_t[i][0]) for i in range(0,knee)]\\n self.reset_explanation()\\n self.explanation = {self.cso.topics_wu[sort_t[i][0]]: explanation[sort_t[i][0]] for i in range(0,knee)}\\n\\n return final_topics\",\n \"def get_result_table_and_info(cls):\\n winning_dict = cls.get_winning_topics()\\n winning_topics = winning_dict['winning_topics']\\n runoff_poll_warning = winning_dict['runoff_poll_warning']\\n\\n # Create table\\n result_table = []\\n all_categories = sorted(Category.objects.all(), key=attrgetter('sum_of_votes', 'weight'), reverse=True)\\n for category in all_categories:\\n category_hoechstzahls = filter(lambda hoechstzahl: hoechstzahl.topic.category == category, cls.all_hoechstzahls)\\n category_hoechstzahls.sort(key=lambda hoechstzahl: hoechstzahl.value, reverse=True)\\n runoff_poll_warning = second_runoff_poll_check(runoff_poll_warning, category_hoechstzahls, winning_topics)\\n category_hoechstzahls += (max(config['openslides_topicvoting_posts'], 3) - len(category_hoechstzahls)) * [None]\\n result_table.append(category_hoechstzahls)\\n\\n # Return table and flags as dictionary\\n return {'result_table': result_table,\\n 'winning_topics': winning_topics,\\n 'runoff_poll_warning': runoff_poll_warning,\\n 'topic_post_warning': winning_dict['topic_post_warning']}\",\n \"def test_extract_topics_custom_cv(base_bertopic_custom_cv):\\n nr_topics = 5\\n documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic_custom_cv._update_topic_size(documents)\\n c_tf_idf = base_bertopic_custom_cv._extract_topics(documents, topic_reduction=False)\\n freq = base_bertopic_custom_cv.get_topics_freq()\\n\\n assert c_tf_idf.shape[0] == 5\\n assert c_tf_idf.shape[1] > 100\\n assert isinstance(freq, pd.DataFrame)\\n assert nr_topics == len(freq.Topic.unique())\\n assert freq.Count.sum() == len(documents)\\n assert len(freq.Topic.unique()) == len(freq)\",\n \"def display_topics(df, n_rows=10, n_cols=12):\\n\\n exemplar_scores, hovers = topic_exemplars(df)\\n top_columns = sorted(range(len(exemplar_scores)),\\n key=lambda i: exemplar_scores[i],\\n reverse=True)[:n_cols]\\n #I comented this line Im not 100% sure what was the purpuse of this\\n # topics = df.pivot(index='pos', columns='topic',values='word*').replace([None], [''], regex=True)\\n topics = df.pivot(index='pos', columns='topic',values='word*')\\n\\n topics_display = topics[top_columns].head(n_rows)\\n\\n return topics_display, top_columns\",\n \"def count_mentioned_countries(data):\\n countries_mentioned = {}\\n countries = get_countries()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for country in countries:\\n if country in (subject_words + message_words):\\n if country in countries_mentioned:\\n countries_mentioned[country] += 1\\n else:\\n countries_mentioned[country] = 1\\n\\n return pd.DataFrame.from_dict(countries_mentioned, orient=\\\"index\\\")\",\n \"def get_paper_count_per_topic(topic_model, start_year, end_year, debug=False):\\n papers_count = get_papers_per_topic(topic_model, None, start_year, end_year, None, debug=debug)\\n return sorted(papers_count.items(), key=operator.itemgetter(1), reverse=True)\",\n \"def num_words():\\n # Load the GT.\\n df = pd.read_csv(config.META_FQN, sep=\\\"\\\\t\\\")\\n stats = {\\n \\\"T\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"P\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n \\\"sess\\\": {\\\"words\\\": [], \\\"duration\\\": []},\\n }\\n\\n for _, row in df.iterrows():\\n if row[\\\"asr_test\\\"]:\\n stats[\\\"P\\\"][\\\"words\\\"].append(float(row[\\\"gt_patient_num_words\\\"]))\\n stats[\\\"T\\\"][\\\"words\\\"].append(float(row[\\\"gt_therapist_num_words\\\"]))\\n stats[\\\"P\\\"][\\\"duration\\\"].append(float(row[\\\"gt_patient_time_spoken\\\"]))\\n stats[\\\"T\\\"][\\\"duration\\\"].append(\\n float(row[\\\"gt_therapist_time_spoken\\\"])\\n )\\n stats[\\\"sess\\\"][\\\"duration\\\"].append(float(row[\\\"sess_dur\\\"]))\\n n_words = (\\n row[\\\"gt_therapist_num_words\\\"] + row[\\\"gt_patient_num_words\\\"]\\n )\\n stats[\\\"sess\\\"][\\\"words\\\"].append(n_words)\\n\\n for speaker in stats:\\n for metric in stats[speaker]:\\n print(f\\\"------ {speaker} | {metric} ------\\\")\\n print_stats(stats[speaker][metric])\",\n \"def return_topic_figures(n_topics=5):\\n\\n ### import data ###\\n\\n data = return_keywords()\\n data_for_topics = data[\\\"abstract_kw\\\"].apply(\\n lambda x: list(ast.literal_eval(x).keys())\\n )\\n\\n ### Build topic model ###\\n\\n # parameters\\n n_topics = n_topics\\n\\n # Create Dictionary\\n id2word = corpora.Dictionary(data_for_topics)\\n\\n # Create Corpus: Term Document Frequency\\n corpus = [id2word.doc2bow(text) for text in data_for_topics]\\n\\n # Build LDA model\\n lda_model = gensim.models.ldamodel.LdaModel(\\n corpus=corpus,\\n id2word=id2word,\\n num_topics=n_topics,\\n random_state=100,\\n update_every=1,\\n chunksize=10,\\n passes=10,\\n alpha=\\\"symmetric\\\",\\n iterations=100,\\n per_word_topics=True,\\n )\\n\\n topics = lda_model.show_topics(formatted=False)\\n data_flat = [w for w_list in data_for_topics for w in w_list]\\n counter = Counter(data_flat)\\n out = []\\n for i, topic in topics:\\n for word, weight in topic:\\n out.append([word, i, weight, counter[word]])\\n df = pd.DataFrame(out, columns=[\\\"word\\\", \\\"topic_id\\\", \\\"importance\\\", \\\"word_count\\\"])\\n\\n specs = np.full((ceil(n_topics / 2), 2), {\\\"secondary_y\\\": True})\\n topic_bar_charts = make_subplots(\\n rows=ceil(n_topics / 2),\\n cols=2,\\n specs=specs.tolist(),\\n horizontal_spacing=0.1,\\n vertical_spacing=0.15,\\n )\\n row, col = (0, 0)\\n for topic in range(n_topics):\\n if (topic % 2) != 0:\\n col = 2\\n else:\\n col = 1\\n row += 1\\n color = px.colors.qualitative.Vivid[topic]\\n topic_bar_charts.add_trace(\\n go.Bar(\\n x=df.loc[df.topic_id == topic, \\\"word\\\"],\\n y=df.loc[df.topic_id == topic, \\\"word_count\\\"],\\n width=0.5,\\n opacity=0.3,\\n marker_color=color,\\n name=(\\\"Topic \\\" + str(topic) + \\\" word count\\\"),\\n ),\\n secondary_y=False,\\n row=row,\\n col=col,\\n )\\n topic_bar_charts.add_trace(\\n go.Bar(\\n x=df.loc[df.topic_id == topic, \\\"word\\\"],\\n y=df.loc[df.topic_id == topic, \\\"importance\\\"],\\n width=0.2,\\n marker_color=color,\\n name=(\\\"Topic \\\" + str(topic) + \\\" weight\\\"),\\n ),\\n secondary_y=True,\\n row=row,\\n col=col,\\n )\\n topic_bar_charts.update_layout(barmode=\\\"overlay\\\")\\n\\n topic_bar_charts.update_layout(\\n height=800, width=1000, margin=dict(l=50, r=50, t=50, b=100)\\n )\\n\\n # append all charts\\n figures = [dict(data=topic_bar_charts)]\\n\\n return figures\",\n \"def topic(df, num_topics=5):\\r\\n# X, y = df[df.columns[:-1]], df[df.columns[-1]]\\r\\n lda = LatentDirichletAllocation(n_topics=num_topics,\\r\\n max_iter=5,\\r\\n learning_method='online',\\r\\n learning_offset=50.,\\r\\n random_state=0)\\r\\n return lda.fit_transform(df)\",\n \"def connect_topic_id_to_topics(model, representants, log_writer):\\n # t = model.get_topics()\\n topic_indexes = {}\\n topics_of_index = {}\\n confidence = []\\n for key, value in representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n # get most possible index\\n topic_index = max(model.analyse_text(article), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\") # TODO replace with if\\n continue\\n # add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n # find index that occured mostly\\n print(connection_results)\\n for tp_num, val in connection_results.items():\\n confidence.append([key,tp_num,val/len(value)])\\n confidence = sorted(confidence, key=operator.itemgetter(2),reverse=True)\\n associated_indexes = []\\n associated_topics = []\\n for conf in confidence:\\n if conf[1] in associated_indexes or conf[0] in associated_topics:\\n continue\\n associated_indexes.append(conf[1])\\n associated_topics.append(conf[0])\\n log_writer.add_log('Connecting topic {} to model index {} based on highest unused confidence of {}'.format(conf[0],conf[1],conf[2]))\\n topic_indexes[conf[0]] = conf[1]\\n\\n for key, value in topic_indexes.items():\\n topics_of_index[value] = [key]\\n print(topic_indexes)\\n print(topics_of_index)\\n return topic_indexes, topics_of_index\",\n \"def check_intersections(db, topics, papers_by_topic):\\n\\n\\t# Print the distribution of \\\"number of topics\\\"\\n\\tnum_subjects = []\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tnum_subjects.append(len(p.subject))\\n\\t\\telse:\\n\\t\\t\\tnum_subjects.append(0)\\n\\tnum_subjects = np.array(num_subjects)\\n\\n\\tfor i in range(np.max(num_subjects)+1):\\n\\t\\tprint(\\\"Number of papers with\\\", i, \\\"topics:\\\", \\n\\t\\t\\tlen(np.where(num_subjects==i)[0]))\\n\\n\\t# Figure out what's going on with triple-tagged guys (nothing weird)\\n\\t\\\"\\\"\\\"\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tif len(p.subject) > 2:\\n\\t\\t\\t\\tprint(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tfor topic, topic_words in topics.items():\\n\\t\\t\\t\\t\\tprint(\\\"\\\\tCheck against '\\\" + topic + \\\"':\\\")\\n\\t\\t\\t\\t\\tfor journal in p.container_title:\\n\\t\\t\\t\\t\\t\\tcheck_words(journal, topic_words, verbose=True)\\n\\t\\\"\\\"\\\"\\n\\n\\t# Look in more detail at double-tagged guysfor p_hash, p in db.all_papers.items():\\n\\tcombos = defaultdict(int)\\n\\tfor p_hash, p in db.all_papers.items():\\n\\t\\tif p.subject:\\n\\t\\t\\tif len(p.subject) == 2:\\n\\t\\t\\t\\tcombos[frozenset(p.subject)] += 1\\n\\t\\t\\t\\t#print(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\tif p.subject == {'Computer Science', 'Biology'}:\\n\\t\\t\\t\\t\\t#print(\\\"\\\\n\\\",p.title,\\\"\\\\n\\\\t\\\",p.container_title)#,\\\"\\\\n\\\\t\\\", p.subject)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tbio_words = set()\\n\\t\\t\\t\\t\\tCS_words = set()\\n\\t\\t\\t\\t\\tfor journal in p.container_title:\\n\\t\\t\\t\\t\\t\\tfor word in topics['Biology']:\\n\\t\\t\\t\\t\\t\\t\\tif journal.find(word) >= 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tbio_words.add(word)\\n\\t\\t\\t\\t\\t\\tfor word in topics['Computer Science']:\\n\\t\\t\\t\\t\\t\\t\\tif journal.find(word) >= 0:\\n\\t\\t\\t\\t\\t\\t\\t\\tCS_words.add(word)\\n\\n\\t\\t\\t\\t\\t#print(\\\"\\\\tBiology words:\\\", bio_words)\\n\\t\\t\\t\\t\\t#print(\\\"\\\\tCS words:\\\", CS_words)\\n\\t\\n\\tfor k, v in combos.items():\\n\\t\\tprint(k, v)\",\n \"def testArticleCount(self):\\n\\n self.articleCount(17)\",\n \"def analysis():\\r\\n data_frame = load_from_mysql('core', 'BDFMHQAA_D')\\r\\n data_frame.registerTempTable('business')\\r\\n gd = data_frame.select('AA03CSNO', 'AA08PRON')\\r\\n\\r\\n def merge_count(a, b):\\r\\n r = {}\\r\\n for p, c in a.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n for p, c in b.items():\\r\\n if p in r:\\r\\n r[p] += c\\r\\n else:\\r\\n r[p] = c\\r\\n return r\\r\\n result = gd.map(lambda row: (row.AA03CSNO, {row.AA08PRON: 1})).reduceByKey(merge_count)\\r\\n pron_count = gd.map(lambda row: (row.AA08PRON, 1)).reduceByKey(lambda a, b: a + b)\\r\\n\\r\\n # result = gd.map(lambda row: (row.AA03CSNO, row.AA08PRON))\\r\\n print(result.take(10))\\r\\n print('----------------pron count-----------------')\\r\\n print(pron_count.collect())\\r\\n\\r\\n print(gd)\",\n \"def connect_topic_id_to_topics_old(self, model):\\n #t = model.get_topics()\\n for key, value in self.representants.items():\\n connection_results = {}\\n for article in value:\\n try:\\n #get most possible index\\n topic_index = max(model.analyse_text(article[1]), key=lambda item: item[1])[0]\\n except ValueError:\\n print(\\\"No topic index returned continuing\\\")#TODO replace with if\\n continue\\n #add most possible index for this article to counter\\n if topic_index not in connection_results:\\n connection_results[topic_index] = 1\\n else:\\n connection_results[topic_index] += 1\\n #find index that occured mostly\\n best_candidates = max(connection_results.items(), key=operator.itemgetter(1))\\n print(best_candidates)\\n self.log_writer.add_log(\\\"Best candidate with index {} is connected to topic {} with {}% accuracy\\\".format(best_candidates[0], key, (connection_results[best_candidates[0]]/len(value))*100))\\n #create connection between topic id and model topic index\\n self.topic_indexes[key] = best_candidates[0]\\n #creat connection in opposite direction if there already is some connection add found index to that connection (some model topic index can represent more than one real topic)\\n if best_candidates[0] not in self.topics_of_index:\\n self.topics_of_index[best_candidates[0]] = [key]\\n else:\\n self.topics_of_index[best_candidates[0]].append(key)\\n\\n self.log_writer.add_log(\\\"Out of {} real topics only {} were learned\\\".format(len(self.representants), len(self.topics_of_index)))\",\n \"def topic(df, num_topics=5):\\r\\n\\r\\n lda = LatentDirichletAllocation(n_topics=num_topics,\\r\\n max_iter=5,\\r\\n learning_method='online',\\r\\n learning_offset=50.,\\r\\n random_state=0)\\r\\n return lda.fit_transform(df)\",\n \"def test_topic_reduction(reduced_topics):\\n base_bertopic = BERTopic(bert_model='distilbert-base-nli-mean-tokens', verbose=False)\\n nr_topics = reduced_topics + 2\\n base_bertopic.nr_topics = reduced_topics\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(old_documents)\\n c_tf_idf = base_bertopic._extract_topics(old_documents.copy(), topic_reduction=True)\\n old_freq = base_bertopic.get_topics_freq()\\n\\n new_documents = base_bertopic._reduce_topics(old_documents.copy(), c_tf_idf)\\n new_freq = base_bertopic.get_topics_freq()\\n\\n assert old_freq.Count.sum() == new_freq.Count.sum()\\n assert len(old_freq.Topic.unique()) == len(old_freq)\\n assert len(new_freq.Topic.unique()) == len(new_freq)\\n assert isinstance(base_bertopic.mapped_topics, dict)\\n assert not set(base_bertopic.get_topics_freq().Topic).difference(set(new_documents.Topic))\\n assert base_bertopic.mapped_topics\",\n \"def infertopics(self):\\n\\n # Iterate over nodes missing topic attribute (only occurs for new nodes)\\n for uid in self.scan(attribute=\\\"updated\\\"):\\n # Remove updated attribute\\n self.removeattribute(uid, \\\"updated\\\")\\n\\n # Get list of neighboring nodes\\n ids = self.edges(uid)\\n\\n # Infer topic\\n topic = Counter(self.attribute(x, \\\"topic\\\") for x in ids).most_common(1)[0][0] if ids else None\\n if topic:\\n # Add id to topic list and set topic attribute\\n self.topics[topic].append(uid)\\n self.addattribute(uid, \\\"topic\\\", topic)\\n\\n # Set topic rank\\n self.addattribute(uid, \\\"topicrank\\\", len(self.topics[topic]) - 1)\\n\\n # Infer category\\n category = Counter(self.attribute(x, \\\"category\\\") for x in ids).most_common(1)[0][0]\\n self.addattribute(uid, \\\"category\\\", category)\",\n \"def basic_statistics_of_email(data):\\n word_counts = []\\n character_count = 0\\n\\n for ind, row in data.iterrows():\\n tokenizer = RegexpTokenizer(r'\\\\w+')\\n real_words = tokenizer.tokenize(row[\\\"RawText\\\"].lower())\\n\\n character_count += sum(map(len, real_words))\\n word_counts.append(len(real_words))\\n\\n return character_count, pd.Series(word_counts)\",\n \"def assign_topics(self):\\n\\n # Load the dataset which has topic tags for each sentence\\n dt, dt_less = self.assign_topics_to_sentences()\\n \\n dt_copy = dt\\n \\n # Minimum number of tags needed to tag the overall response\\n dt['min_num_tags'] = dt['num_sent'].apply(lambda x: math.ceil(0.3*x))\\n \\n # Final dataset with full survey response and its tags\\n final_dt = dt.groupby(self.id_col_name).agg({'tags': sum\\n , 'num_sent': min\\n , 'min_num_tags': min\\n# , 'sentences': lambda x: \\\"%s\\\" % '. '.join(x)\\n , self.col_name: min})\\n final_dt.reset_index(level = 0, inplace = True)\\n final_dt['topics'] = final_dt.apply(lambda x: set([i for i in x.tags if x.tags.count(i) >= x.min_num_tags])\\n , axis = 1)\\n\\n final_dt_less = final_dt[[self.id_col_name, self.col_name, 'topics']]\\n\\n return dt_copy, dt_less, final_dt, final_dt_less\",\n \"def preprocess(df):\\n \\n # drop the following columns - irrelevant now\\n DROP_COLUMNS = ['id', 'original_title', 'release_date'\\n , 'tmdbId', 'popularity', 'year']\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n # drop all of the language columns\\n DROP_COLUMNS = [col for col in df.columns if col[:3]==\\\"lan\\\"]\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n\\n # loop through the columns we want to aggregate\\n for col_type in [\\n \\\"original_language_\\\"\\n , \\\"prod_comp_cntry_\\\"\\n , \\\"prod_comp_names_\\\"\\n , \\\"writers_\\\"\\n , \\\"actors_\\\"\\n , \\\"genres_\\\"\\n , \\\"director_\\\"\\n ]:\\n # create a dictionary of each unique value and its frequency\\n val_freq = {}\\n for col in df.columns:\\n if col.startswith(col_type):\\n val_freq[col] = df[col].sum()\\n\\n # create a dataframe from this dictionary; sort by count\\n counts = pd.DataFrame.from_dict(\\n val_freq\\n , orient='index'\\n , columns=['count']\\n ).sort_values('count', ascending=False)\\n counts['frac'] = counts['count'].apply(lambda x: 100*x / df.shape[0])\\n\\n # handle special case of production company country\\n if col_type == \\\"prod_comp_cntry_\\\":\\n DROP_COLUMNS = [col for col in counts.index][3:]\\n\\n # handle special case of directors\\n elif col_type == \\\"director_\\\":\\n DIRECTOR_COLS = [col for col in df.columns\\n if col.startswith(\\\"director_\\\")\\n and col!=\\\"director_pop\\\"]\\n df['established_director'] = df[DIRECTOR_COLS].max(axis=1)\\n DROP_COLUMNS = DIRECTOR_COLS\\n\\n # handle special case of actors\\n elif col_type == \\\"actors_\\\":\\n ACTORS_COLS = [col for col in df.columns if \\\"actors\\\" in col]\\n df['num_top_100_actors'] = df[ACTORS_COLS].sum(axis=1)\\n DROP_COLUMNS = ACTORS_COLS\\n\\n # handle all the other cases\\n else:\\n DROP_COLUMNS = [col for col in counts.query('frac < 2').index]\\n\\n\\n df.drop(DROP_COLUMNS, axis=1, inplace=True)\\n \\n ##########################################################################\\n # adjust the data for inflation\\n CPI_tf = df['CPIAUCSL'].max()\\n df['budget'] = df[['budget', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n df['revenue'] = df[['revenue', 'CPIAUCSL']].apply(\\n cpi_adjust\\n , args=(CPI_tf ,)\\n , axis=1\\n )\\n # no longer need CPI data\\n df.drop('CPIAUCSL', axis=1, inplace=True)\\n \\n ########################################################################## \\n # add in useful features about the cast and crew \\n df['cast_crew_sum_pop'] = (\\n df['director_pop']\\n + df['avg_actor_pop']\\n + df['avg_writer_pop']\\n )\\n df['cast_crew_product_pop'] = (\\n df['director_pop']\\n * df['avg_actor_pop']\\n * df['avg_writer_pop']\\n )\\n df['runtime'].replace(to_replace=0, value=df['runtime'].median(), inplace=True)\\n df = df.query('10000 <= revenue').copy()\\n df = df.query('100000 <= budget').copy()\\n df.drop('sum_actor_pop', axis=1, inplace=True)\\n df.drop('min_writer_pop', axis=1, inplace=True)\\n\\n # code to transform columns\\n for col in [\\n \\\"budget\\\", \\\"director_pop\\\", \\\"avg_writer_pop\\\"\\n , \\\"max_writer_pop\\\", \\\"avg_actor_pop\\\", \\\"max_actor_pop\\\"\\n , \\\"min_actor_pop\\\", 'cast_crew_sum_pop'\\n , 'cast_crew_product_pop'\\n ]:\\n df['log10_'+col] = df[col].apply(lambda x: math.log10(x))\\n df.drop(col, axis=1, inplace=True)\\n \\n return df\",\n \"def calc_topic_mode_log_stats(user_exercise_graph, topic_id,\\n just_earned_proficiency):\\n topic = topic_models.Topic.get_by_id(topic_id)\\n topic_exercises = topic.get_exercises()\\n\\n total_exercises = len(topic_exercises)\\n count_proficient = len(set(ex.name for ex in topic_exercises) &\\n set(user_exercise_graph.proficient_exercise_names()))\\n just_completed = (just_earned_proficiency and total_exercises ==\\n count_proficient)\\n\\n return {\\n 'total_exercises': total_exercises,\\n 'count_proficient': count_proficient,\\n 'just_completed': just_completed,\\n }\",\n \"def append_counting(dict):\\n row_c = []\\n # for nuc in NUC: #Scans all the elements and adds it to the table.\\n # row_c.append(dict[nuc])\\n for mot in MOT:\\n row_c.append(dict[mot])\\n for nuc_nr in NUC_NR :\\n row_c.append(dict[nuc_nr + \\\"_NR\\\"])\\n # #row.extend([dict[\\\"AA_NR\\\"], dict[\\\"TT_NR\\\"], dict[\\\"CC_NR\\\"], dict[\\\"GG_NR\\\"]])\\n return row_c\",\n \"def topic_extraction(df, col_name):\\n tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\\n max_features=200,\\n stop_words='english')\\n tfidf = tfidf_vectorizer.fit_transform(df[col_name])\\n\\n tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,\\n max_features=200,\\n stop_words='english')\\n tf = tf_vectorizer.fit_transform(df[col_name])\\n nmf = NMF(n_components=20, random_state=1,\\n alpha=.1, l1_ratio=.5)\\n tfidf_feature_names = tfidf_vectorizer.get_feature_names()\\n nmf_w = nmf.fit_transform(tfidf)\\n nmf_h = nmf.components_\\n df['labels'] = nmf_w.argmax(axis=1) # this was the right code to get labels/clusters\\n\\n\\n print(\\\"\\\\nTopics in NMF model:\\\")\\n print_top_words(nmf, tfidf_feature_names)\\n\\n\\n lda = LatentDirichletAllocation(n_topics=20, max_iter=5,\\n learning_method='online',\\n learning_offset=50.,\\n random_state=0,\\n n_jobs=-1)\\n lda.fit(tf)\\n doc_topic_distrib = lda.transform(tf)\\n lda_labels = doc_topic_distrib.argmax(axis=1)\\n print lda_labels[:100]\\n df['lda_labels'] = lda_labels\\n print(\\\"\\\\nTopics in LDA model:\\\")\\n tf_feature_names = tf_vectorizer.get_feature_names()\\n print_top_words(lda, tf_feature_names)\\n return df\",\n \"def gender_word_counts(data):\\n\\n # We use the stopwords package from NLTK corpus.\\n stop_words = set(stopwords.words('english'))\\n data['tweet_words'] = data['text_cleaned'].str.split()\\n # Ignoring all the stop words\\n data['tweet_words'] = data['tweet_words'].apply(lambda tweet: [word for word in tweet if word not in stop_words])\\n\\n # Separating Male, Female and Brand profiles.\\n male_profiles = data[data['gender'] == 'male']\\n female_profiles = data[data['gender'] == 'female']\\n brand_profiles = data[data['gender'] == 'brand']\\n\\n print(\\\"Top 20 most frequent words used by Men\\\")\\n all_male_tweets = ' '.join(male_profiles['tweet_words'].astype(str))\\n Male_words = pd.Series(all_male_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Male_words)\\n print()\\n\\n print(\\\"Top 20 most frequent words used by Women\\\")\\n all_female_tweets = ' '.join(female_profiles['tweet_words'].astype(str))\\n Female_words = pd.Series(all_female_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Female_words)\\n print()\\n\\n print(\\\"Top 20 most frequent words used by Brands\\\")\\n all_brand_tweets = ' '.join(brand_profiles['tweet_words'].astype(str))\\n Brand_words = pd.Series(all_brand_tweets.split(\\\" \\\")).value_counts()[:20]\\n print(Brand_words)\\n\\n # Plotting horizontal bar graphs showing Top 20 tweet words used Vs. the word frequency.\\n mp = Male_words.plot(kind='barh', stacked=True, colormap='plasma', title=\\\"Top 20 most frequently words used by Men\\\")\\n mp.set_ylabel(\\\"Tweet words used by Males\\\")\\n mp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\\n\\n fp = Female_words.plot(kind='barh', stacked=True, colormap='plasma',\\n title=\\\"Top 20 most frequently words used by Women\\\")\\n fp.set_ylabel(\\\"Tweet words used by Females\\\")\\n fp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\\n\\n bp = Brand_words.plot(kind='barh', stacked=True, colormap='plasma',\\n title=\\\"Top 20 most frequently words used by Brands\\\")\\n bp.set_ylabel(\\\"Tweet words used by Brands\\\")\\n bp.set_xlabel(\\\"Word Frequency\\\")\\n plt.show()\",\n \"def analyze_count(data):\\n\\n dsct_vk = pd.unique(data['vk'])\\n dsct_itemid = pd.unique(data['itemid'])\\n\\n print 'number of user:', dsct_vk.shape\\n print 'number of items:', dsct_itemid.shape\\n print 'the number of ratings:', data.shape\\n\\n print 'unique actions:', pd.unique(data['action'])\\n print 'the number of action 0:', np.sum(data['action'] == 0)\\n print 'the number of action 1:', np.sum(data['action'] == 1)\\n print 'the number of action 2:', np.sum(data['action'] == 2)\\n print 'the number of action 3:', np.sum(data['action'] == 3)\\n print 'the number of action 4:', np.sum(data['action'] == 4)\\n \\n time_range_item = data.groupby('itemid')['real_time'].aggregate(sum_unique)\\n print 'Max Range:', np.max(time_range_item)\\n print 'Mean Range:', np.mean(time_range_item)\\n print 'Median Range:', np.median(time_range_item)\",\n \"def test_mead_summary_length(self):\\n topics = {'PUP1A': [Document('TST_ENG_20190101.0001'), Document('TST_ENG_20190101.0002'),\\n Document('TST20190201.0001'), Document('TST20190201.0002')],\\n 'WAR2A': [Document('TST_ENG_20190301.0001'), Document('TST_ENG_20190301.0002'),\\n Document('TST20190401.0001'), Document('TST20190401.0002')]}\\n WordMap.create_mapping()\\n vec = Vectors()\\n vec.create_freq_vectors(topics)\\n idf = MeadSummaryGenerator(self.doc_list, MeadContentSelector(), self.args).get_idf_array()\\n max_length = 100\\n\\n for topic_id, documents in topics.items():\\n generator = MeadSummaryGenerator(documents, MeadContentSelector(), self.args)\\n generator.select_content(idf)\\n generator.order_information()\\n realized_content = generator.realize_content()\\n realized_content = [w for w in realized_content.split(\\\" \\\") if not \\\" \\\"]\\n content_length = len(realized_content)\\n self.assertLessEqual(content_length, max_length)\",\n \"def metadata_summary(idx):\\n tax_per_cluster = []\\n genomes_per_tax = []\\n genes_per_genome = []\\n for cluster_id,v in idx.items():\\n tax_per_cluster.append(len(v.keys()))\\n for tax,vv in v.items():\\n genomes_per_tax.append(len(vv.keys()))\\n for genomeID,gene_ids in vv.items():\\n genes_per_genome.append(len(set(gene_ids)))\\n sum_stats(tax_per_cluster, 'Clades per cluster')\\n sum_stats(genomes_per_tax, 'Gemomes per clade')\\n sum_stats(genes_per_genome, 'Genes per genome')\",\n \"def count_interests(rows: List[Row]) -> int:\\n return len([row for row in rows if row[\\\"interest\\\"] is not None])\",\n \"def reformat_countTable(\\n self,analysis_id_I=None,sna2experimentID_I=None,\\n sna2sns_I=None):\\n if self.countTable: countTable = self.countTable[:];\\n else: countTable = [];\\n\\n countTable_flat = self.reformat_countOrFPKMTable(\\n countOrFPKMTable_I=countTable,\\n analysis_id_I=analysis_id_I,\\n sna2experimentID_I=sna2experimentID_I,\\n sna2sns_I=sna2sns_I,\\n count_or_FPKM = 'count');\\n return countTable_flat;\",\n \"def main_topic_doc(ldamodel, corpus=corpus): \\n \\n doc_topics = pd.DataFrame()\\n\\n for i, row in enumerate(ldamodel[corpus]):\\n row = sorted(row, key=lambda x: (x[1]), reverse=True)\\n\\n for j, (topic_num, prop_topic) in enumerate(row):\\n if j == 0:\\n wp = ldamodel.show_topic(topic_num)\\n topic_keywords = \\\"' \\\".join([word for word, prop in wp])\\n doc_topics = doc_topics.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\\n else:\\n break\\n doc_topics.columns = ['Dominant_Topic', 'Percent_Contrib', 'Topic_keywords']\\n return doc_topics\",\n \"def explore_topic_nouns(topic_number, topn=25, model=10):\\n #\\n if model==10:\\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\\n topicname=topic_names_10[topic_number]\\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\\n gensimSTR=str(gensimdic[topic_number])\\n \\n # \\n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\\\n')\\n \\n dic={}\\n j=0\\n \\n print('top 5 terms')\\n for term, frequency in lda.show_topic(topic_number, topn):\\n if dfff[dfff['nouns']==term].empty: ## dfff is loaded from pilot_path/bow_nouns.csv\\n pass\\n else:\\n j=j+1\\n if j<6:\\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\\n dic[term]=frequency\\n dff=pd.DataFrame.from_dict(dic,orient='index')\\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \\n return(dff)\",\n \"def model_topics(df):\\n\\n data = df.text.values.tolist()\\n data_words = list(sent_to_words(data))\\n\\n # Build the bigram and trigram models\\n bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\\n trigram = gensim.models.Phrases(bigram[data_words], threshold=100) \\n\\n # Faster way to get a sentence clubbed as a trigram/bigram\\n bigram_mod = gensim.models.phrases.Phraser(bigram)\\n trigram_mod = gensim.models.phrases.Phraser(trigram)\\n\\n # Remove Stop Words\\n data_words_nostops = remove_stopwords(data_words)\\n\\n # Form Bigrams\\n data_words_bigrams = make_bigrams(data_words_nostops,bigram_mod)\\n\\n # Initialize spacy 'en' model, keeping only tagger component (for efficiency)\\n nlp = spacy.load('en', disable=['parser', 'ner'])\\n\\n # Do lemmatization keeping only noun, adj, vb, adv\\n data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\\n\\n # Create Dictionary\\n id2word = corpora.Dictionary(data_lemmatized)\\n\\n # Create Corpus\\n texts = data_lemmatized\\n\\n # Term Document Frequency\\n corpus = [id2word.doc2bow(text) for text in texts]\\n\\n # Perform Topic Modeling for number of topics ranging from 5 to 50 in steps of 5\\n model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=5, limit=50, step=5)\\n\\n return model_list,coherence_values,corpus,id2word\",\n \"def explore_topic(topic_number, topn=25, model=10):\\n #\\n if model==25:\\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_25'))\\n topicname=topic_names_25[topic_number]\\n gensimSTR=''\\n elif model==15:\\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_15'))\\n topicname=topic_names_15[topic_number]\\n gensimSTR=''\\n elif model==10:\\n lda = LdaMulticore.load(joinp(pilot_path, 'lda_model_10'))\\n topicname=topic_names_10[topic_number]\\n gensimdic={0:9,1:8,2:6,3:7,4:3,5:10,6:5,7:1,8:2,9:4}\\n gensimSTR=str(gensimdic[topic_number])\\n \\n \\n # \\n print(u'{:20} {}'.format(u'term', u'frequency') + u'\\\\n')\\n \\n dic={}\\n j=0\\n \\n print('top 5 terms')\\n for term, frequency in lda.show_topic(topic_number, topn):\\n j=j+1\\n if j<6:\\n print (u'{:20} {:.3f}'.format(term, round(frequency, 3)))\\n dic[term]=frequency\\n dff=pd.DataFrame.from_dict(dic,orient='index')\\n dff.columns=[''.join(['topic:',topicname,' (gensim topic:',gensimSTR,')'])] \\n return(dff)\\n ##\",\n \"def table_summary():\\n \\n t = dict()\\n t['name'] = get_names()\\n t['Name'] = [get_properties(name)['label'] for name in t['name']]\\n N = len(t['name'])\\n \\n # host\\n t['host'] = ['Sagittarius', 'Sagittarius', 'none', 'Gaia-Sausage-Enceladus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Sequoia / Arjuna', np.nan, np.nan, 'Sequoia / Arjuna', 'Gaia-Sausage-Enceladus', 'Sequoia / Arjuna', 'Helmi / Wukong', 'Helmi / Wukong', 'Sagittarius', 'in situ / Helmi / Wukong', 'Helmi / Wukong', 'Cetus', 'Cetus', 'Sagittarius', 'Sequoia / Arjuna / I\\\\'itoi', 'Cetus', 'Sequoia / Arjuna / I\\\\'itoi']\\n \\n # progenitor\\n t['progenitor'] = [np.nan, np.nan, 'itself', 'NGC 5139', 'NGC 4590', np.nan, 'NGC 3201', '(Wukong / Helmi)', '(Wukong / Helmi)', np.nan, np.nan, np.nan, np.nan, 'NGC 5024', np.nan, 'NGC 5272', 'NGC 5024', 'NGC 5824', 'NGC 5824', np.nan, np.nan, np.nan, np.nan]\\n \\n # progenitor type\\n t['type'] = ['DG' if name in ['elqui', 'indus', 'jhelum'] else 'GC' for name in t['name']]\\n \\n # metallicity\\n t['feh'] = [-2.4, -2.4, -2.2, -1.5, -2.16, -2.3, -1.5, -2.1, -2.1, -1.6, -1.95, -1.6, -2.7, np.nan, -1.7, -1.1, -2.7, -1.9, np.nan, np.nan, -2.2, np.nan, -1.9]\\n \\n # associations\\n t['friends'] = ['ATLAS', 'Aliqa Uma', np.nan, np.nan, np.nan, np.nan, np.nan, 'Jhelum', 'Indus', np.nan, np.nan, np.nan, np.nan, 'Sylgr', np.nan, np.nan, 'Ravi', 'Turbio', 'Triangulum', np.nan, np.nan, np.nan, np.nan]\\n \\n tout = Table(t)\\n tout.pprint()\\n tout.write('../data/stream_origin.fits', overwrite=True)\",\n \"def get_tweets_by_topic(topic, start_date, end_date):\\n try:\\n query = f\\\"select tweet, sentence, polarity, subjectivity from {db_schema}.{db_table_tweet} t, {db_schema}.{db_table_pred} tp where t.id_tweet=tp.id_tweet and topic='{topic}' and tweet_date between str_to_date('{start_date}', '%Y-%m-%d') and str_to_date('{end_date}', '%Y-%m-%d')\\\"\\n logger.info(f'QUERY: {query}') \\n with MysqlCursor() as cur:\\n cur.execute(query)\\n tweets = cur.fetchall()\\n columns = [col[0] for col in cur.description]\\n logger.info(f'TOPIC: {topic}, N° TWEETS: {len(tweets)}') \\n return tweets, columns\\n \\n except Exception as ex:\\n logger.exception(ex)\\n return f'Exception: {ex}'\",\n \"def property_count_function(listOfProperties):\\n\\n property_count = {} # Empty dict, is gonna look like this: property_count{property : count}\\n for lists in listOfProperties:\\n try:\\n for properties in lists:\\n property_count[properties] = property_count.get(properties, 0) + 1\\n except TypeError as e:\\n print(e)\\n\\n # Converts the dictionary to a dataframe\\n property_dataframe = pd.DataFrame(list(property_count.items()), columns=['Property', 'Frequency'])\\n # property_dataframe = property_dataframe.set_index(\\\"Property\\\")\\n property_dataframe = property_dataframe.sort_values(by=['Frequency'], ascending=False)\\n\\n return property_dataframe\",\n \"def categorize_data(data, top_count):\\n sorted_by_tcp = sorted(\\n data, key=lambda x: x['TCP Utilization'], reverse=True\\n )[0:top_count]\\n sorted_by_udp = sorted(\\n data, key=lambda x: x['UDP Utilization'], reverse=True\\n )[0:top_count]\\n\\n print(f\\\"\\\\nTOP-{top_count} port flooders by TCP\\\")\\n print(tabulate(sorted_by_tcp, headers='keys', tablefmt=\\\"psql\\\"))\\n print(f\\\"\\\\nTOP-{top_count} port flooders by UDP\\\")\\n print(tabulate(sorted_by_udp, headers='keys', tablefmt=\\\"psql\\\"))\",\n \"def get_num_topics(soup):\\n try:\\n #num = int(re.search(\\\"Suche ergab (\\\\\\\\d+)\\\", str(soup)).group(1)) \\n num = int(re.search(\\\"(\\\\\\\\d+) Treffer\\\", str(soup)).group(1)) \\n except: \\n num = 0\\n print(traceback.format_exc()) \\n\\n return num\",\n \"def get_top_keywords_from_articles(self, kwords_list):\\n _all_keywords = []\\n for a in kwords_list:\\n if a != []:\\n for w in a:\\n _all_keywords.append([w['keyword'],w['weight'],w['label']])\\n _df_g = pd.DataFrame(_all_keywords, columns=[\\\"Keyword\\\", \\\"Count\\\",\\\"Label\\\"])\\n _df_g.sort_values(by=\\\"Count\\\", inplace=True, ascending=False)\\n _df_g.reset_index(drop=True, inplace=True)\\n _df_g.to_csv('test.csv')\\n print(len(_df_g))\\n\\n _df_g['Keyword'] = _df_g['Keyword'].apply(self.remove_repeat_words)\\n _df_g.dropna(axis=0, inplace=True)\\n p1,p2 = self.pos_taggers(_df_g)\\n _df_g['c_POS'] = p1\\n _df_g['s_POS'] = p2\\n _df_g['c_POS_score'] = _df_g['c_POS'].apply(self.combine_pos_score)\\n _df_g['s_POS_score'] = _df_g['s_POS'].apply(self.specific_pos_score)\\n _df_g['Count'] = _df_g['Count'] + _df_g['c_POS_score'] + _df_g['s_POS_score'] \\n print(len(_df_g))\\n _df_g.sort_values(by='Count',inplace=True, ascending=False)\\n print(len(_df_g))\\n _df_g = _df_g.reset_index(drop=True)\\n _df_g = _df_g[:10]\\n response_dict = dict()\\n response_dict['nc'] = \\\", \\\".join(_df_g['Keyword'].to_list())\\n return response_dict\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def count_frequency(df, count_columns: list, group_columns=['Fabric_name', 'Fabric_label'], margin_column_row:tuple=None):\\n\\n if margin_column_row and len(margin_column_row) == 2:\\n if all([isinstance(element, bool) for element in margin_column_row]):\\n # margin_column_row = ((False, False),) * len(count_columns)\\n margin_column_row = (margin_column_row, ) * len(count_columns)\\n\\n # by default keep summary row but remove summary column\\n if not margin_column_row:\\n margin_column_row = ((False, True),) * len(count_columns)\\n if len(count_columns) != len(margin_column_row):\\n print('\\\\n')\\n print('Parameters count_columns and margin_column_row in count_frequency function have different length')\\n exit()\\n\\n index_lst = [df[column] for column in group_columns if column in df.columns and df[column].notna().any()]\\n frequency_df = pd.DataFrame()\\n\\n for column, (margin_column, margin_row) in zip(count_columns, margin_column_row):\\n if column in df.columns and df[column].notna().any():\\n df[column].fillna(np.nan, inplace=True)\\n current_df = pd.crosstab(index=index_lst, columns=df[column], margins=any((margin_column, margin_row)))\\n current_df = current_df.sort_index()\\n if any((margin_column, margin_row)):\\n # drop column All\\n if not margin_column:\\n current_df.drop(columns=['All'], inplace=True)\\n # drop row All\\n if not margin_row:\\n current_df.drop(index=['All'], inplace=True)\\n if frequency_df.empty:\\n frequency_df = current_df.copy()\\n else:\\n frequency_df = frequency_df.merge(current_df, how='outer', on=group_columns)\\n\\n frequency_df.fillna(0, inplace=True) \\n frequency_df.reset_index(inplace=True) \\n return frequency_df\",\n \"def get_topics(self):\\n topics = self.word_topics\\n return topics / topics.sum(axis=1)[:, None]\",\n \"def get_topic_quality():\\n model.eval() \\n with torch.no_grad():\\n alpha = model.mu_q_alpha\\n beta = model.get_beta(alpha) \\n print('beta: ', beta.size())\\n\\n print('\\\\n')\\n print('#'*100)\\n print('Get topic diversity...')\\n num_tops = 25\\n\\n TD_all = _diversity_helper(beta, num_tops) \\n \\n TD = np.mean(TD_all)\\n print('Topic Diversity is: {}'.format(TD))\\n\\n print('\\\\n')\\n print('Get topic coherence...')\\n print('train_tokens: ', train_tokens[0])\\n \\n TC_all, cnt_all = get_topic_coherence(beta.cpu().detach().numpy(), train_tokens, vocab)\\n\\n TC_all = torch.tensor(TC_all)\\n cnt_all = torch.tensor(cnt_all)\\n TC_all = TC_all / cnt_all\\n TC_all[TC_all<0] = 0\\n\\n TC = TC_all.mean().item()\\n print('Topic Coherence is: ', TC)\\n print('\\\\n')\\n\\n print('Get topic quality...')\\n TQ = TC * TD\\n print('Topic Quality is: {}'.format(TQ))\\n print('#'*100)\\n\\n return TQ, TC, TD\",\n \"def get_topics_count(khoros_object, user_settings=None, user_id=None, login=None, email=None):\\n user_settings = _process_settings_and_user_id(khoros_object, user_settings, user_id, login, email)\\n return _get_count(khoros_object, user_settings['id'], 'topics')\",\n \"def test_count_publications(self):\\n pass\",\n \"def test_question_topics(self):\\n p = ProductFactory()\\n t1 = TopicFactory(slug='doesnotexist', product=p)\\n t2 = TopicFactory(slug='cookies', product=p)\\n t3 = TopicFactory(slug='sync', product=p)\\n\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t2)\\n QuestionFactory(topic=t3)\\n\\n self.refresh()\\n\\n topic_vals = (\\n (t1.slug, 0),\\n (t2.slug, 2),\\n (t3.slug, 1),\\n )\\n\\n qs = {'a': 1, 'w': 2, 'format': 'json'}\\n for topics, number in topic_vals:\\n qs.update({'topics': topics})\\n response = self.client.get(reverse('search.advanced'), qs)\\n eq_(number, json.loads(response.content)['total'])\",\n \"def query_transition_counts(ordered_pitch_types, query_filter):\\n transition_counts = {}\\n\\n for first_pitch_type in ordered_pitch_types:\\n transition_counts[first_pitch_type] = {}\\n\\n for second_pitch_type in ordered_pitch_types:\\n count_query = PitchTransition.select().where(\\n PitchTransition.first_pitch_type == first_pitch_type).where(\\n PitchTransition.second_pitch_type == second_pitch_type)\\n\\n if query_filter:\\n count_query = count_query.where(query_filter)\\n\\n transition_counts[first_pitch_type][second_pitch_type] = count_query.count()\\n\\n return transition_counts\",\n \"def init():\\n\\n # Reading the data from the CSV file using the latin1 encoding.\\n data_read = pd.read_csv(\\\"gender-classifier-DFE-791531.csv\\\", encoding='latin1') # Dataset Size = 20050\\n\\n # If all the attribute values are empty for any of the rows, we drop them.\\n data = data_read.dropna(how='all') # After dropping, data set size is still 20050\\n\\n # Checking the names of the columns/attributes which contains at least one null value\\n columns_containing_missing_values = data.columns[data.isnull().any()].tolist()\\n print(\\\"Column names which has missing values\\\")\\n print(columns_containing_missing_values)\\n\\n # Since 'gender' is our target variable, we would like to have values for it.\\n # So, dropping all the rows which have no values for the 'gender' attribute.\\n data = data[data['gender'].notnull()] # After dropping, dataset size = 19953 rows\\n # Also, dropping all the rows which have values as 'unknown' for the 'gender' attribute\\n data = data[data['gender'] != 'unknown'] # After dropping, dataset size = 18836 rows\\n\\n male_profile_count = len(data[data['gender'] == 'male'])\\n print(\\\"Male Profile Count \\\" + str(male_profile_count))\\n female_profile_count = len(data[data['gender'] == 'female'])\\n print(\\\"Female Profile Count \\\" + str(female_profile_count))\\n brand_profile_count = len(data[data['gender'] == 'brand'])\\n print(\\\"Brand Profile Count \\\" + str(brand_profile_count))\\n\\n return data\",\n \"def __find_topics(self, concepts):\\n\\n # Set up\\n found_topics = dict() # to store the matched topics\\n explanation = dict()\\n\\n # finding matches\\n for concept in concepts:\\n evgrams = everygrams(concept.split(), 1, 3) # list of unigrams, bigrams, trigrams\\n for grams in evgrams:\\n gram = \\\"_\\\".join(grams)\\n gram_without_underscore = \\\" \\\".join(grams)\\n #### Finding similar words contained in the model\\n\\n list_of_matched_topics = []\\n\\n if self.fast_classification:\\n list_of_matched_topics = self.__get_similar_words_from_cached_model(gram,grams)\\n else:\\n list_of_matched_topics = self.__get_similar_words_from_full_model(gram, grams)\\n\\n\\n for topic_item in list_of_matched_topics:\\n\\n topic = topic_item[\\\"topic\\\"]\\n str_sim = topic_item[\\\"sim_t\\\"]\\n wet = topic_item[\\\"wet\\\"]\\n sim = topic_item[\\\"sim_w\\\"]\\n\\n\\n if str_sim >= self.min_similarity and topic in self.cso.topics_wu:\\n\\n\\n if topic in found_topics:\\n #tracking this match\\n found_topics[topic][\\\"times\\\"] += 1\\n\\n found_topics[topic][\\\"gram_similarity\\\"].append(sim)\\n\\n #tracking the matched gram\\n if gram in found_topics[topic][\\\"grams\\\"]:\\n found_topics[topic][\\\"grams\\\"][gram] += 1\\n else:\\n found_topics[topic][\\\"grams\\\"][gram] = 1\\n\\n #tracking the most similar gram to the topic\\n if str_sim > found_topics[topic][\\\"embedding_similarity\\\"]:\\n found_topics[topic][\\\"embedding_similarity\\\"] = str_sim\\n found_topics[topic][\\\"embedding_matched\\\"] = wet\\n\\n else:\\n #creating new topic in the result set\\n found_topics[topic] = {'grams': {gram:1},\\n 'embedding_matched': wet,\\n 'embedding_similarity': str_sim,\\n 'gram_similarity':[sim],\\n 'times': 1,\\n 'topic':topic}\\n\\n\\n\\n if sim == 1:\\n found_topics[topic][\\\"syntactic\\\"] = True\\n\\n\\n\\n primary_label_topic = self.cso.get_primary_label_wu(topic)\\n if primary_label_topic not in explanation:\\n explanation[primary_label_topic] = set()\\n\\n explanation[primary_label_topic].add(gram_without_underscore)\\n\\n return found_topics, explanation\",\n \"def topic_table(topic):\\r\\n table = Table(['property', 'value'])\\r\\n table.align['property'] = 'r'\\r\\n table.align['value'] = 'l'\\r\\n\\r\\n table.add_row(['name', topic['name']])\\r\\n table.add_row(['tags', listing(topic['tags'] or [])])\\r\\n return table\",\n \"def __len__(self):\\n count = 0\\n topics = set(six.iterkeys(self._topics))\\n while topics:\\n event_type = topics.pop()\\n try:\\n listeners = self._topics[event_type]\\n count += len(listeners)\\n except KeyError:\\n pass\\n return count\",\n \"def test_data_counts(self):\\n model = PoincareModel(self.data)\\n self.assertEqual(len(model.all_relations), 5)\\n self.assertEqual(len(model.node_relations[model.kv.vocab['kangaroo.n.01'].index]), 3)\\n self.assertEqual(len(model.kv.vocab), 7)\\n self.assertTrue('mammal.n.01' not in model.node_relations)\",\n \"def contentcheck_categorical():\\n filename = \\\"Analysis.txt\\\"\\n temp_line = \\\"\\\"\\n count = 0\\n for line in open(filename, 'r'):\\n temp_line = temp_line + line\\n if \\\"VALUE COUNTS\\\" in temp_line:\\n count = count + 1\\n if \\\"DATA INFORMATION\\\" in temp_line:\\n count = count + 1\\n if \\\"MEAN, MEDIAN AND MODE:\\\" in temp_line:\\n count = count + 1\\n if \\\"Correlation\\\" in temp_line:\\n count = count + 1\\n if \\\"Normality Tests\\\" in temp_line:\\n count = count + 1\\n return count\",\n \"def count_mentioned_pol_figures(data):\\n figures_mentioned = {}\\n figures = get_political_figures()\\n\\n for ind, row in data.iterrows():\\n subject_words = row[\\\"MetadataSubject\\\"].lower()\\n message_words = row[\\\"RawText\\\"].lower()\\n\\n for figure in figures:\\n if figure + \\\" \\\" in (subject_words + message_words):\\n if figure in figures_mentioned:\\n figures_mentioned[figure] += 1\\n else:\\n figures_mentioned[figure] = 0\\n\\n return pd.DataFrame(figures_mentioned)\",\n \"def counts_table(data, attr):\\n pd.options.mode.chained_assignment = None # default='warn'\\n # gets class names for dataframe manipulation\\n classes = attr.tail(1)['vars'].tolist()\\n classlist = [classes[0][0], classes[0][1]]\\n # expanding a table to have all variable options in a column with their \\n # parent attribute\\n allvariables = attr.apply(lambda x: pd.Series(x['vars']),axis=1).stack().reset_index(level=1, drop=True)\\n allvariables.name='var'\\n allvariables = attr.drop('vars', axis=1).join(allvariables)\\n av = allvariables.drop(attr.index[-1])\\n # populate the table with counts\\n for c in classlist:\\n clist = []\\n count = 0\\n for i, row in av.iterrows():\\n att = row['attr']\\n var = row['var']\\n sub = data[[att,'class']]\\n sub = sub[sub[att]==var]\\n if not sub.empty:\\n ssub = sub[sub['class']==c]\\n if not ssub.empty:\\n count = len(ssub)\\n else:\\n count = 0\\n clist.append(count)\\n av[c] = clist\\n\\n return av\",\n \"def _transform_idoc(df):\\n global _SIMPLECOUNT_COLUMNS\\n\\n try:\\n df['comcnty'] = ((df['comcnty'] + 1) / 2).astype(int)\\n df.columns = ['year', 'fk_simplecount_county'] + df.columns.tolist()[2:]\\n\\n indicator_list = [1600, 1601, 1602, 1603, 1604, 1605, 1606, 1607, 1620, 1621]\\n \\n c_nc = df['admtypo3'] == 1\\n c_tv = df['admtypo3'] == 2\\n c_pers = df['offtype2'] == 1 # df['offtype'] == 1\\n c_prop = df['offtype2'] == 2 # df['offtype'] == 2\\n c_sex = df['offtype2'] == 4 # df['offtype'] == 4\\n c_drug = df['offtype2'].isin([3.1, 3.2, 3.3, 3.4, 3.5, 3.6]) # df['offtype'] == 3\\n c_other = df['offtype2'].isin([0, 3, 5, 7]) # df['offtype'] == 7\\n c_viol = df['offtype'] == 1\\n c_male = df['sex'] == 'M'\\n c_female = ~c_male\\n\\n c_first2 = [c_nc, c_tv]\\n c_others = [c_pers, c_prop, c_sex, c_drug, c_other, c_viol, c_male, c_female]\\n \\n def helper(c, indicator_id, first2):\\n df['fk_simplecount_indicator'] = indicator_id\\n g = ['fk_simplecount_indicator', 'year', 'fk_simplecount_county']\\n if first2:\\n return df[c].groupby(g).size().reset_index(name='value')\\n else:\\n return df[c_nc & c].groupby(g).size().reset_index(name='value')\\n\\n out = pd.DataFrame()\\n for i in range(2):\\n out = out.append(helper(c_first2[i], indicator_list[i], first2=True))\\n \\n for i in range(len(c_others)):\\n out = out.append(helper(c_others[i], indicator_list[i+2], first2=False))\\n\\n out = out.loc[out['fk_simplecount_county'].isin(range(1,102+1))]\\n return out[_SIMPLECOUNT_COLUMNS]\\n except:\\n raise\",\n \"def test_topic_reduction_edge_cases(base_bertopic):\\n\\n nr_topics = 5\\n base_bertopic.nr_topics = 100\\n old_documents = pd.DataFrame({\\\"Document\\\": newsgroup_docs,\\n \\\"ID\\\": range(len(newsgroup_docs)),\\n \\\"Topic\\\": np.random.randint(-1, nr_topics-1, len(newsgroup_docs))})\\n base_bertopic._update_topic_size(old_documents)\\n c_tf_idf = base_bertopic._extract_topics(old_documents, topic_reduction=True)\\n old_freq = base_bertopic.get_topics_freq()\\n\\n new_documents = base_bertopic._reduce_topics(old_documents, c_tf_idf)\\n new_freq = base_bertopic.get_topics_freq()\\n\\n assert not set(old_documents.Topic).difference(set(new_documents.Topic))\\n pd.testing.assert_frame_equal(old_documents, new_documents)\\n pd.testing.assert_frame_equal(old_freq, new_freq)\",\n \"def get_winning_topics(cls):\\n results_generator = cls.get_results()\\n winning_hoechstzahls = []\\n for i in range(config['openslides_topicvoting_posts']):\\n try:\\n winning_hoechstzahls.append(results_generator.next())\\n except StopIteration:\\n topic_post_warning = True\\n runoff_poll_warning = False\\n break\\n else:\\n topic_post_warning = False\\n try:\\n first_looser_hoechstzahl = results_generator.next()\\n except StopIteration:\\n runoff_poll_warning = False\\n else:\\n # First runoff poll check: Check equal hoechstzahls between the categories.\\n if (first_looser_hoechstzahl.value == winning_hoechstzahls[-1].value and\\n first_looser_hoechstzahl.topic.category.weight == winning_hoechstzahls[-1].topic.category.weight):\\n runoff_poll_warning = True\\n else:\\n runoff_poll_warning = False\\n winning_topics = map(lambda hoechstzahl: hoechstzahl.topic, winning_hoechstzahls)\\n return {'winning_topics': winning_topics,\\n 'topic_post_warning': topic_post_warning,\\n 'runoff_poll_warning': runoff_poll_warning}\",\n \"def manual_community_topic_labels(date='2019_01'):\\n comm_size = get_blob(bucket_name=date, blob_name=f\\\"community_modularity.csv\\\", df=True)\\n comm_size.name = 'size'\\n comm_size.index.name = 'community'\\n comm_size = comm_size.reset_index()\\n labels = {\\n 0:'images/discussion',\\n 1:'discussion/tv',\\n 2:'runescape',\\n 3:'funny/images',\\n 4:'gaming/tech',\\n 5:'pol/geo',\\n 6:'generalist',\\n 7:'porn',\\n 8:'music',\\n 9:'sports',\\n 10:'SE/DK',\\n 11:'SP/IT/PT',\\n 12:'NL'\\n }\\n\\n membership = load_membership()\\n order_keys = membership.drop_duplicates('community').set_index('community')['size_order'].to_dict()\\n\\n comm_size['size_order'] = comm_size['community'].map(lambda x: order_keys[x])\\n comm_size = comm_size.sort_values('size_order').reset_index(drop=True)\\n\\n comm_size['labels']=comm_size['community'].map(lambda x: labels[x])\\n comm_size.to_csv(tables_path(f\\\"{date}/comm_size.csv\\\"))\",\n \"def create_topic_columns(videos, topics):\\n \\n # Clear values\\n videos['relevant'] = False\\n\\n # Create masks for each topic so we can later filter videos by topics\\n topic_masks = []\\n for _, topic in topics.iterrows():\\n videos[topic['slug']] = False # Clear values\\n pattern = get_pattern(topic)\\n topic_mask = videos.apply(lambda video: is_relevant(video, pattern), axis=1)\\n topic_masks.append(topic_mask)\\n videos[topic['slug']] = topic_mask\\n\\n # Mark video as 'relevant' if it mentions any of the topics\\n videos['relevant'] = np.any(np.column_stack(topic_masks), axis=1)\",\n \"def posts_per_topic_all(request, pk):\\n #update is_expired in all posts\\n update_posts_expiration()\\n #get all posts with a certain topic\\n posts = Post.objects.filter(topic=pk)\\n serializer = ViewPostSerializer(posts, many=True)\\n return Response(serializer.data)\",\n \"def analysis_5_result(primary_person_df,units_df,output_folder_path):\\n df1 = primary_person_df.distinct().select(\\\"CRASH_ID\\\",\\\"UNIT_NBR\\\",\\\"PRSN_ETHNICITY_ID\\\")\\n df2 = units_df.distinct().select(\\\"CRASH_ID\\\",\\\"UNIT_NBR\\\",\\\"VEH_BODY_STYL_ID\\\")\\n join_res_df = df1.join(broadcast(df2), [\\\"CRASH_ID\\\",\\\"UNIT_NBR\\\"])\\n rank_window_spec = Window.partitionBy(\\\"VEH_BODY_STYL_ID\\\").orderBy(col(\\\"COUNT\\\").desc())\\n top_ethnic_unique_body_df = join_res_df \\\\\\n .groupBy(\\\"VEH_BODY_STYL_ID\\\",\\\"PRSN_ETHNICITY_ID\\\") \\\\\\n .agg(count(\\\"*\\\").alias(\\\"COUNT\\\")) \\\\\\n .withColumn(\\\"RANK\\\", dense_rank().over(rank_window_spec)) \\\\\\n .filter(\\\"RANK == 1\\\")\\\\\\n .drop(\\\"RANK\\\")\\n\\n print(\\\"Analysis 5: \\\\nMention the top ethnic user group of each unique body style\\\")\\n top_ethnic_unique_body_df.show(truncate=False)\\n write_df_to_csv(top_ethnic_unique_body_df, output_folder_path+\\\"analysis_5_result\\\")\",\n \"def get_topic_measures(dict_clusters, dict_groundtruth, exact_match=True, cluster_n=None, coverage_percentage=None,\\n coverage_n=None, keep_cluster_duplicates=False):\\n total_tp = 0\\n total_fp = 0\\n total_n = get_total_time_frame_count(dict_groundtruth)\\n\\n dict_time_frame_measures = dict()\\n for time_frame in dict_clusters.keys():\\n tp = 0\\n fp = 0\\n keyword_n = 0\\n if time_frame not in dict_groundtruth:\\n fp += 1\\n # total_fp += 1\\n else:\\n groundtruth = dict_groundtruth[time_frame]\\n clusters = dict_clusters[time_frame]\\n\\n keyword_n, keyword_tp, keyword_fp, matched_events = eval_clusters(clusters, groundtruth, exact_match,\\n cluster_n,\\n keep_cluster_duplicates=keep_cluster_duplicates)\\n\\n temp_tp = 0\\n temp_fp = 0\\n for k in matched_events.keys():\\n matched_event = matched_events[k]\\n\\n # if coverage percentage is given\\n if coverage_percentage:\\n if matched_event.event_TP / matched_event.event_n >= coverage_percentage:\\n temp_tp += 1\\n else:\\n temp_fp += 1\\n\\n # if coverage count is given\\n elif coverage_n:\\n if matched_event.event_TP >= coverage_n:\\n temp_tp += 1\\n else:\\n temp_fp += 1\\n\\n else:\\n if matched_event.event_TP == matched_event.event_n:\\n temp_tp += 1\\n else:\\n temp_fp += 1\\n\\n # if all events belong to the time frame are identified, count time frame as a TP\\n if temp_tp == len(groundtruth):\\n tp += 1\\n else:\\n fp += 1\\n\\n dict_time_frame_measures[time_frame] = [len(groundtruth), temp_tp, temp_fp]\\n total_tp += tp\\n total_fp += fp\\n\\n return total_n, total_tp, total_fp, dict_time_frame_measures\",\n \"def filter_by_topic(tmp_df, keep_top_n_topics=0, min_count_threshold=0):\\n\\n topic_count_df = tmp_df[\\\"variety\\\"].value_counts()\\n\\n # filter by top n number of topics if specified\\n if keep_top_n_topics is not None:\\n topics_to_keep = topic_count_df.head(keep_top_n_topics).index\\n tmp_df = tmp_df[tmp_df[\\\"variety\\\"].isin(topics_to_keep)]\\n\\n # filter out any topics that doesn't meet the minimum count threshold\\n if min_count_threshold >= 0:\\n topics_to_keep = topic_count_df[\\n topic_count_df > min_count_threshold].index\\n tmp_df = tmp_df[tmp_df[\\\"variety\\\"].isin(topics_to_keep)]\\n\\n return tmp_df\",\n \"def get_word_frequencies(topic_description):\\n frequencies = {w:f for w,f in topic_description}\\n return frequencies\",\n \"def get_stories(df):\\n categories = df.get_categorical().columns\\n continuous = df.get_numerical().columns\\n\\n stories = []\\n cat_copy = list(categories)\\n for col in categories:\\n # Remove the current col\\n if col in cat_copy:\\n cat_copy.remove(col)\\n try:\\n # Get comparison variable\\n x = cat_copy.pop()\\n d = pd.pivot_table(df.data, index=(col), values=[x],\\\\\\n aggfunc='count').reset_index().sort_values(by=x, ascending=False)\\n stories.append({\\n 'question': \\\"%s with high count of %s\\\" %(col, x),\\n 'question_html': \\\"<span class='tag is-primary is-light'>%s</span>\\\\\\n with high count of <span class='tag is-success is-light'>%s</span>\\\" % (col, x),\\n 'answer': d[col].head(1).values[0],\\n 'misc': d\\n })\\n except IndexError as e:\\n pass\\n \\n for num in continuous:\\n d = pd.pivot_table(df.data, index=[col], values=[num],\\\\\\n aggfunc=np.sum).reset_index().sort_values(by=num, ascending=False)\\n stories.append({\\n 'question': \\\"%s with sum of %s\\\" % (col, num),\\n 'question_html': \\\"<span class='tag is-primary is-light'>%s</span>\\\\\\n with sum of <span class='tag is-success is-light'>%s</span>\\\" % (col, num),\\n 'answer': round(d[num].head(1).values[0]),\\n 'misc': d\\n })\\n\\n return stories\",\n \"def getTopicPmi(self, folderpath, numTopic):\\n # get tweets\\n # helper = Utility.Helper(self.rootpath)\\n folderPath = os.path.join(folderpath, 'final')\\n tweets = self.helper.getTweet(folderPath)\\n with codecs.open(os.path.join(self.rootpath, folderPath,\\n \\\"tweets_line.txt\\\"),\\n \\\"w\\\", encoding='utf8') as fp:\\n for tweet in tweets:\\n # print (type(tweet.text.encode('utf8')))\\n c1 = self.preprocessData.cleanTweet(tweet.text)\\n fp.write(c1 + '\\\\n')\\n\\n # get topic with pmi\\n corpus_path = os.path.join(self.rootpath, folderPath, \\\"tweets_line.txt\\\"\\n )\\n n_topics = numTopic\\n n_top_words = 5\\n preprocessing_steps = ['tag']\\n n_cand_labels = 100\\n label_min_df = 5\\n label_tags = ['NN,NN', 'JJ,NN']\\n n_labels = 3\\n lda_random_state = 12345\\n lda_n_iter = 10000\\n\\n labels, words, dist = label_topic.get_topic_labels(corpus_path,\\n n_topics,\\n n_top_words,\\n preprocessing_steps,\\n n_cand_labels,\\n label_min_df,\\n label_tags,\\n n_labels,\\n lda_random_state,\\n lda_n_iter)\\n print(\\\"\\\\nTopical labels:\\\")\\n print(\\\"-\\\" * 20)\\n topics = defaultdict(list)\\n for i, labels in enumerate(labels):\\n topics[i] = map(lambda l: ' '.join(l), labels)\\n print(u\\\"Topic {}: {}\\\".format(i, ', '.join(map(lambda l:\\n ' '.join(l),\\n labels))\\n )\\n )\\n self.helper.dumpJson(folderPath, \\\"tweets_label_words_pmi.json\\\",\\n labels)\\n print(\\\"tweets_topic_label_pmi.json has been saved.\\\")\\n self.helper.dumpJson(folderPath, \\\"tweets_topic_words_pmi.json\\\",\\n words)\\n print(\\\"tweets_topic_words_pmi.json has been saved.\\\")\\n return dist\",\n \"def iterate_data(self):\\n if \\\"single\\\" in self.dataset_name:\\n # Index 0 for list of sentence lengths, index 1 for list of token lengths\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for answer_id in self.data:\\n summary = self.data[answer_id]['summary']\\n articles = self.data[answer_id]['articles']\\n question = self.data[answer_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(articles, 'article')\\n self._get_token_cnts(question, 'question')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if \\\"multi\\\" in self.dataset_name:\\n self.stat_dict = {'question': [[], []], 'summary': [[], []], 'article': [[], []]}\\n for q_id in self.data:\\n summary = self.data[q_id]['summary']\\n question = self.data[q_id]['question']\\n if args.tokenize:\\n self._get_token_cnts(summary, 'summary')\\n self._get_token_cnts(question, 'question')\\n question = self.data[q_id]['question']\\n for answer_id in self.data[q_id]['articles']:\\n articles = self.data[q_id]['articles'][answer_id][0]\\n if args.tokenize:\\n self._get_token_cnts(articles, 'article')\\n self._write_stats(\\\"token_counts\\\")\\n\\n if self.dataset_name == \\\"complete_dataset\\\":\\n self.stat_dict = {'urls': [], 'sites': []}\\n article_dict = {}\\n print(\\\"Counting answers, sites, unique urls, and tokenized counts of unique articles\\\")\\n answer_cnt = 0\\n for q_id in self.data:\\n for a_id in self.data[q_id]['answers']:\\n answer_cnt += 1\\n url = self.data[q_id]['answers'][a_id]['url']\\n article = self.data[q_id]['answers'][a_id]['article']\\n if url not in article_dict:\\n article_dict[url] = article\\n self.stat_dict['urls'].append(url)\\n assert \\\"//\\\" in url, url\\n site = url.split(\\\"//\\\")[1].split(\\\"/\\\")\\n self.stat_dict['sites'].append(site[0])\\n print(\\\"# of Answers:\\\", answer_cnt)\\n print(\\\"Unique articles: \\\", len(article_dict)) # This should match up with count written to file\\n self._write_stats(\\\"full collection\\\")\\n\\n # Get token/sent averages of unique articles\\n if args.tokenize:\\n self.stat_dict = {'article': [[], []]}\\n for a in article_dict:\\n self._get_token_cnts(article_dict[a], 'article')\\n self._write_stats(\\\"token_counts\\\")\",\n \"def document_distribution_per_topic(df_dominant_topic):\\n cols = [color for name, color in mcolors.TABLEAU_COLORS.items()] # more colors: 'mcolors.XKCD_COLORS'\\n\\n\\n fig, axes = plt.subplots(2,2,figsize=(16,14), dpi=160, sharex=True, sharey=True)\\n\\n for i, ax in enumerate(axes.flatten()):\\n df_dominant_topic_sub = df_dominant_topic.loc[df_dominant_topic.Dominant_Topic == i, :]\\n doc_lens = [len(d) for d in df_dominant_topic.Text]\\n ax.hist(doc_lens, bins = 1000, color=cols[i])\\n ax.tick_params(axis='y', labelcolor=cols[i], color=cols[i])\\n sns.kdeplot(doc_lens, color=\\\"black\\\", shade=False, ax=ax.twinx())\\n ax.set(xlim=(0, 1000), xlabel='Document Word Count')\\n ax.set_ylabel('Number of Documents', color=cols[i])\\n ax.set_title('Topic: '+str(i), fontdict=dict(size=16, color=cols[i]))\\n\\n fig.tight_layout()\\n fig.subplots_adjust(top=0.90)\\n plt.xticks(np.linspace(0,1000,9))\\n fig.suptitle('Distribution of Document Word Counts by Dominant Topic', fontsize=22)\\n plt.show()\",\n \"def scrapeTopic(self, topic, num_articles, sources=list(site.all_sites)): \\n pass\",\n \"def initialize(self):\\n self.n_words = len(self.vocab)\\n self.n_docs = len(self.documents)\\n\\n # Initialize the three count matrices.\\n # The (i,j) entry of self.nmz is the number of words in document i assigned to topic j.\\n self.nmz = np.zeros((self.n_docs, self.n_topics))\\n # The (i,j) entry of self.nzw is the number of times term j is assigned to topic i.\\n self.nzw = np.zeros((self.n_topics, self.n_words))\\n # The (i)-th entry is the number of times topic i is assigned in the corpus.\\n self.nz = np.zeros(self.n_topics)\\n\\n # Initialize the topic assignment dictionary.\\n self.topics = {} # key-value pairs of form (m,i):z\\n\\n for m in range(self.n_docs):\\n for i in self.documents[m]:\\n # Get random topic assignment, i.e. z is a random integer in the range of topics\\n z = np.random.randint(self.n_topics)\\n # Increment count matrices\\n self.nmz[m,z] += 1\\n self.nzw[z,self.documents[m][i]] += 1\\n self.nz[z] += 1\\n # Store topic assignment\\n self.topics[(m,i)] = z\",\n \"def validate_new_curriculum_topics(self, curriculum_topics):\\n\\n for cur in curriculum_topics:\\n # check to make sure its in the general topics table\\n self.db_cursor.execute(\\\"\\\"\\\"SELECT COUNT(*) FROM Topic WHERE name = %s\\\"\\\"\\\", (cur,))\\n ct = self.db_cursor.fetchone()\\n ct = ct[0]\\n if ct == 0:\\n print(\\\"topic does not exist, we must create new one or cancel\\\") # todo\\n\\n return True\",\n \"def total_exs(dataset):\\n total = 0\\n for article in dataset['data']:\\n for para in article['paragraphs']:\\n total += len(para['qas'])\\n return total\",\n \"def prepare_titanic_data(df):\\n\\n df.embark_town.fillna('Other', inplace=True)\\n\\n # Drop deck and embarked_town\\n df.drop(columns=['deck', 'embark_town'], inplace=True)\\n\\n # Encoding: Objects (Categorical Variables) to Numeric\\n # Use sklearn's LabelEncoder\\n encoder = LabelEncoder()\\n\\n # Set Unknown and encode Embarked column to numbers\\n # 2 == \\\"S\\\" == Southampton == 644 people\\n # 0 == \\\"C\\\" == Cherbourg == 168 people\\n # 1 == \\\"Q\\\" == Queenstown == 77 people\\n # 3 == \\\"Unknown\\\" == 2 people\\n df.embarked.fillna('Unknown', inplace=True)\\n encoder.fit(df.embarked)\\n df.embarked = encoder.transform(df.embarked)\\n\\n # Encode the Class (first class, second, etc...)\\n # First class == 0\\n # Second class == 1\\n # Third class == 2\\n encoder.fit(df[\\\"class\\\"])\\n df[\\\"class_encoded\\\"] = encoder.transform(df[\\\"class\\\"])\\n\\n # Encode gender\\n # male == 1 == 577 records\\n # female == 0 == 314 records\\n encoder.fit(df.sex)\\n df[\\\"sex_encoded\\\"] = encoder.transform(df.sex)\\n\\n # Handle the 177 records with missing age values\\n average_age = df.age.mean()\\n df.age.fillna(average_age, inplace=True)\\n\\n scaler = MinMaxScaler()\\n scaler.fit(df[['fare']])\\n df[\\\"fare_scaled\\\"] = scaler.transform(df[['fare']])\\n\\n scaler = MinMaxScaler()\\n scaler.fit(df[['age']])\\n df[\\\"age_scaled\\\"] = scaler.transform(df[['age']])\\n\\n # Set the index to the passenger id\\n df = df.set_index(\\\"passenger_id\\\")\\n return df\",\n \"def tweet_df(n):\\n # Retrieve the tweet contents\\n first_tweet = get_value(df_1t, n)\\n second_tweet = get_value(df_2t, n) \\n third_tweet = get_value(df_3t, n)\\n fourth_tweet = get_value(df_4t, n)\\n fifth_tweet = get_value(df_5t, n)\\n sixth_tweet = get_value(df_6t, n)\\n seventh_tweet = get_value(df_7t, n)\\n eighth_tweet = get_value(df_8t, n)\\n nineth_tweet = get_value(df_9t, n)\\n tenth_tweet = get_value(df_10t, n) \\n \\n # Sentiment of each tweet\\n sa_first_tweet = sentiment_analyzer_scores(first_tweet)\\n sa_second_tweet = sentiment_analyzer_scores(second_tweet)\\n sa_third_tweet = sentiment_analyzer_scores(third_tweet)\\n sa_fourth_tweet = sentiment_analyzer_scores(fourth_tweet)\\n sa_fifth_tweet = sentiment_analyzer_scores(fifth_tweet)\\n sa_sixth_tweet = sentiment_analyzer_scores(sixth_tweet)\\n sa_seventh_tweet = sentiment_analyzer_scores(seventh_tweet)\\n sa_eighth_tweet = sentiment_analyzer_scores(eighth_tweet)\\n sa_nineth_tweet = sentiment_analyzer_scores(nineth_tweet)\\n sa_tenth_tweet = sentiment_analyzer_scores(tenth_tweet)\\n \\n # Compute the compound score for obtaining a sentiment class\\n compound_score_first_tweet = sentiment_logic((list(sa_first_tweet.values())[list(sa_first_tweet.keys()).index('compound')] ))\\n compound_score_second_tweet = sentiment_logic((list(sa_second_tweet.values())[list(sa_second_tweet.keys()).index('compound')] )) \\n compound_score_third_tweet = sentiment_logic((list(sa_third_tweet.values())[list(sa_third_tweet.keys()).index('compound')] ))\\n compound_score_fourth_tweet = sentiment_logic((list(sa_fourth_tweet.values())[list(sa_fourth_tweet.keys()).index('compound')] ))\\n compound_score_fifth_tweet = sentiment_logic((list(sa_fifth_tweet.values())[list(sa_fifth_tweet.keys()).index('compound')] ))\\n compound_score_sixth_tweet = sentiment_logic((list(sa_sixth_tweet.values())[list(sa_sixth_tweet.keys()).index('compound')] ))\\n compound_score_seventh_tweet = sentiment_logic((list(sa_seventh_tweet.values())[list(sa_seventh_tweet.keys()).index('compound')] ))\\n compound_score_eighth_tweet = sentiment_logic((list(sa_eighth_tweet.values())[list(sa_eighth_tweet.keys()).index('compound')] ))\\n compound_score_nineth_tweet = sentiment_logic((list(sa_nineth_tweet.values())[list(sa_nineth_tweet.keys()).index('compound')] ))\\n compound_score_tenth_tweet = sentiment_logic((list(sa_tenth_tweet.values())[list(sa_tenth_tweet.keys()).index('compound')] ))\\n \\n # Create a new temporary dataframe for the tweet contents and sentiment\\n compound_score_list = [compound_score_first_tweet, compound_score_second_tweet,\\n compound_score_third_tweet, compound_score_fourth_tweet,\\n compound_score_fifth_tweet, compound_score_sixth_tweet, \\n compound_score_seventh_tweet, compound_score_eighth_tweet,\\n compound_score_nineth_tweet, compound_score_tenth_tweet]\\n \\n \\n first_col = [first_tweet, second_tweet,\\n third_tweet, fourth_tweet,\\n fifth_tweet, sixth_tweet,\\n seventh_tweet, eighth_tweet,\\n nineth_tweet, tenth_tweet]\\n \\n second_col = compound_score_list\\n \\n tmp_df = pd.DataFrame(data = {'Tweets' : first_col, \\n 'Sentiment' : second_col})\\n \\n \\n return tmp_df.to_json(date_format = 'iso', orient = 'split')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.670414","0.6682198","0.6473323","0.63052845","0.6284073","0.5883223","0.58540165","0.5768975","0.56314296","0.5595589","0.55580306","0.5541051","0.5484164","0.54825205","0.5471465","0.5443077","0.53968257","0.5374345","0.5362987","0.5333066","0.5320533","0.5308043","0.53065175","0.52841324","0.5240766","0.52356154","0.52329373","0.52273995","0.52240163","0.521626","0.5211933","0.5203348","0.5189752","0.5181925","0.5180661","0.5169465","0.515654","0.5126955","0.5111875","0.511152","0.51057273","0.5102154","0.50883985","0.5079168","0.507327","0.5064662","0.5062901","0.5061368","0.5060122","0.50509924","0.50500023","0.5049223","0.5037668","0.5037464","0.5037276","0.5029944","0.50166714","0.50112253","0.5011054","0.50106376","0.50070673","0.5005642","0.5005098","0.50037766","0.4995084","0.4995084","0.49873996","0.4977276","0.4969586","0.49690676","0.4965407","0.49626452","0.4960137","0.4946523","0.49440113","0.49387252","0.49378192","0.49370906","0.49369827","0.49368608","0.49324876","0.49308643","0.492595","0.49245626","0.49127477","0.4910924","0.4900158","0.4898201","0.48939243","0.48889497","0.48882478","0.4888174","0.4884081","0.4881955","0.48597437","0.4858623","0.48481187","0.48480734","0.48430032","0.48367956"],"string":"[\n \"0.670414\",\n \"0.6682198\",\n \"0.6473323\",\n \"0.63052845\",\n \"0.6284073\",\n \"0.5883223\",\n \"0.58540165\",\n \"0.5768975\",\n \"0.56314296\",\n \"0.5595589\",\n \"0.55580306\",\n \"0.5541051\",\n \"0.5484164\",\n \"0.54825205\",\n \"0.5471465\",\n \"0.5443077\",\n \"0.53968257\",\n \"0.5374345\",\n \"0.5362987\",\n \"0.5333066\",\n \"0.5320533\",\n \"0.5308043\",\n \"0.53065175\",\n \"0.52841324\",\n \"0.5240766\",\n \"0.52356154\",\n \"0.52329373\",\n \"0.52273995\",\n \"0.52240163\",\n \"0.521626\",\n \"0.5211933\",\n \"0.5203348\",\n \"0.5189752\",\n \"0.5181925\",\n \"0.5180661\",\n \"0.5169465\",\n \"0.515654\",\n \"0.5126955\",\n \"0.5111875\",\n \"0.511152\",\n \"0.51057273\",\n \"0.5102154\",\n \"0.50883985\",\n \"0.5079168\",\n \"0.507327\",\n \"0.5064662\",\n \"0.5062901\",\n \"0.5061368\",\n \"0.5060122\",\n \"0.50509924\",\n \"0.50500023\",\n \"0.5049223\",\n \"0.5037668\",\n \"0.5037464\",\n \"0.5037276\",\n \"0.5029944\",\n \"0.50166714\",\n \"0.50112253\",\n \"0.5011054\",\n \"0.50106376\",\n \"0.50070673\",\n \"0.5005642\",\n \"0.5005098\",\n \"0.50037766\",\n \"0.4995084\",\n \"0.4995084\",\n \"0.49873996\",\n \"0.4977276\",\n \"0.4969586\",\n \"0.49690676\",\n \"0.4965407\",\n \"0.49626452\",\n \"0.4960137\",\n \"0.4946523\",\n \"0.49440113\",\n \"0.49387252\",\n \"0.49378192\",\n \"0.49370906\",\n \"0.49369827\",\n \"0.49368608\",\n \"0.49324876\",\n \"0.49308643\",\n \"0.492595\",\n \"0.49245626\",\n \"0.49127477\",\n \"0.4910924\",\n \"0.4900158\",\n \"0.4898201\",\n \"0.48939243\",\n \"0.48889497\",\n \"0.48882478\",\n \"0.4888174\",\n \"0.4884081\",\n \"0.4881955\",\n \"0.48597437\",\n \"0.4858623\",\n \"0.48481187\",\n \"0.48480734\",\n \"0.48430032\",\n \"0.48367956\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7382751"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8483,"cells":{"query":{"kind":"string","value":"Plot a data frame with bar type"},"document":{"kind":"string","value":"def plot_type_of_topic(data_frame: pb.DataFrame) -> None:\n plt.interactive(False)\n plt.figure()\n data_frame.plot(kind='bar', x= data_frame['TopicID'])\n plt.show()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compte(df):\n\n df.value_counts()[:100].plot(kind='bar')\n plt.show()","def bar_plot(df, data_pt):\n \n x=df.loc[data_pt]\n y= df.columns.tolist()\n sorte=x.tolist()\n a=sorted(zip(sorte, y))[-10:]\n y=[y for _, y in a]\n ## soru burda yapıp altı ona göre duzeliyecegim birde\n \n x = df[y].loc[data_pt]\n \n # Here we modify the tickangle of the xaxis, resulting in rotated labels.\n #title={'text': \"<b>Comparing features with Golden for Cycle {}\".format(cycle),\n # 'y':0.9,'x':0.5,'xanchor': 'center','yanchor': 'top'}\n\n \n trace = {'type': 'bar',\n 'orientation':'h',\n 'x' : x,\n 'y' : y}\n data = Data([trace])\n layout = {'title' : \"<b>Reconstruction error in each dimension for cycle{}\".format(data_pt),\n 'titlefont':{'size' : 20},\n 'xaxis' : {'title': '<b>Reconstruction Error',\n 'titlefont':{'size' : 20},\n 'tickangle': -45, 'tickfont': {'size':15} ,},\n \n 'yaxis' : {'title': '<b>Features',\n 'titlefont':{'size' : 20},\n 'tickfont': {'size':15},},\n 'margin' : {'l':100, 'r' : 1, 'b': 200, 't': 100, 'pad' : 1},\n 'height' : 600, 'width' : 800,\n }\n \n fig = Figure(data = data, layout = layout)\n \n return pyo.iplot(fig)","def visualizeData(df):\n for column in df:\n df[column].value_counts().plot(kind = 'bar', rot = 'vertical', use_index = False)","def bar_plot(df_NP):\n cnt = Counter()\n for tax_list in df_NP.taxonomy:\n for tax in list(tax_list):\n if tax != 'no':\n cnt[tax] += 1\n plt.bar(cnt.keys(),cnt.values())\n plt.xlabel('taxonomic provenance')\n plt.ylabel('number of molecules')\n plt.title('number of aglycons with taxonomies')\n plt.savefig(\"output_data/Barplot.png\")\n print(\"BAR PLOT DONE\")","def bar_chart(self, df, n_groups, dict):\n fig, ax = plt.subplots()\n # choose bar width (standard 0.8 chosen)\n bar_width = 0.35\n # get an index to set the ticks for the x axis\n\n index = np.arange(n_groups)\n indexes = df.index.tolist()\n print(indexes)\n df[\"index\"] = indexes\n\n # make barchart for permutation test\n ax.bar(index, df[\"perm\"], bar_width, color='b', linewidth=4,\n label='Permutation test')\n # make barchart for t-test\n ax.bar(index + bar_width, df[\"t_test\"], bar_width, color='r',\n label='t-test')\n\n ax.set_xlabel(dict[\"xlabel\"])\n ax.set_ylabel(dict[\"ylabel\"])\n ax.set_title(dict[\"title\"])\n ax.set_xticks(index + bar_width / 2)\n ax.set_xticklabels(dict[\"xtickslabels\"])\n ax.legend()\n\n fig.tight_layout()\n plt.show()","def bar_plot(df, field_name, graph_title, threshold_value, x_axis_label, y_axis_label):\n\n x = df[field_name].value_counts().sort_values()\n x[x > threshold_value].plot(kind='barh', figsize=(12, 8), title=graph_title, x=x_axis_label, y=y_axis_label)\n return","def bar(\n df,\n x=None,\n y=\"value\",\n bars=\"variable\",\n order=None,\n bars_order=None,\n orient=\"v\",\n legend=True,\n title=True,\n ax=None,\n cmap=None,\n **kwargs,\n):\n\n # default x-axis to time-col attribute from an IamDataFrame, else use \"year\"\n x = x or time_col_or_year(df)\n\n # cast to DataFrame if necessary\n # TODO: select only relevant meta columns\n if not isinstance(df, pd.DataFrame):\n df = df.as_pandas()\n\n for col in set(SORT_IDX) - set([x, bars]):\n if len(df[col].unique()) > 1:\n msg = \"Can not plot multiple {}s in bar plot with x={}, bars={}\"\n raise ValueError(msg.format(col, x, bars))\n\n if ax is None:\n fig, ax = plt.subplots()\n\n # long form to one column per bar group\n _df = reshape_mpl(df, x, y, bars, **{x: order, bars: bars_order})\n\n # explicitly get colors\n defaults = default_props(reset=True, num_colors=len(_df.columns), colormap=cmap)[\n \"color\"\n ]\n rc = run_control()\n color = []\n for key in _df.columns:\n c = next(defaults)\n if \"color\" in rc and bars in rc[\"color\"] and key in rc[\"color\"][bars]:\n c = rc[\"color\"][bars][key]\n color.append(c)\n\n # change year to str to prevent pandas/matplotlib from auto-ordering (#474)\n if _df.index.name == \"year\":\n _df.index = map(str, _df.index)\n\n # plot data\n kind = \"bar\" if orient.startswith(\"v\") else \"barh\"\n _df.plot(kind=kind, color=color, ax=ax, **kwargs)\n\n # add legend\n ax.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n if not legend:\n ax.legend_.remove()\n\n # add default labels if possible\n if orient == \"v\":\n ax.set_xlabel(x.capitalize())\n else:\n ax.set_ylabel(x.capitalize())\n units = df[\"unit\"].unique()\n if len(units) == 1 and y == \"value\":\n if orient == \"v\":\n ax.set_ylabel(units[0])\n else:\n ax.set_xlabel(units[0])\n\n # build a default title if possible\n _title = []\n for var in [\"model\", \"scenario\", \"region\", \"variable\"]:\n values = df[var].unique()\n if len(values) == 1:\n _title.append(\"{}: {}\".format(var, values[0]))\n if title and _title:\n title = \" \".join(_title) if title is True else title\n ax.set_title(title)\n\n return ax","def barGraph(listOfWord, listOfFrequency):\r\n\r\n\tindex = np.arange(len(listOfWord))\r\n\r\n\tplt.title(\"Frekuensi Kemunculan Kata\")\r\n\tplt.barh(index, listOfFrequency)\r\n\tplt.xlabel('Frekuensi')\r\n\tplt.yticks(index, listOfWord, fontsize=6)\r\n\r\n\tplt.show()","def visualize_data(df):\n # Remove 'not available'\n genres = df.genre.unique().tolist()\n remove_index = genres.index('Not Available')\n genres.pop(remove_index)\n print('Genres: ', genres)\n\n # Extract number of songs in each genre\n genre_counts = df.genre.value_counts().tolist()\n genre_counts.pop(remove_index)\n print('Counts: ', genre_counts)\n\n # Plot bar graph\n plt.bar(genres, genre_counts)\n plt.xlabel('Genres')\n plt.ylabel('Count')\n plt.show()","def featuresBarPlot(barNames,barValues):\n plt.bar(range(0,len(barNames)),barValues)\n plt.xticks(range(0,len(barNames)), barNames,rotation='vertical')\n plt.show()","def plot_class_balances(df, col):\n\n ser_counts = df[col].value_counts()\n ser_counts.plot.bar()\n plt.title(col + ' Counts \\n(classes={})'.format(ser_counts.shape[0]))\n \n plt.show()","def plot_bv_bar(df, xcolname, ycolname, icol=0):\n # set plot size\n fig, ax = plt.subplots(figsize=(8,6))\n \n # plotting... box\n sns.barplot(ax=ax, data = df\n , x = str(xcolname)\n , y = str(ycolname)\n , color = sns.color_palette()[icol]);\n \n \n # title and labels\n plt.title(xcolname+' Vs '+ycolname, fontsize=20)\n plt.xlabel(xcolname+ ' (units)', fontsize=16)\n plt.ylabel(ycolname+ ' (units)', fontsize=16)\n \n return plt.show()","def BarPlot(data,colormap='Paired',ax=None,headers='show',value_max=None,x_ticklabels_rotation=90,**kws):\r\n if ax is None:\r\n ax=plt.subplot(111)\r\n\r\n if value_max is None:\r\n value_max=data.sum(1).max()\r\n\r\n data.plot(kind='bar', stacked=True,colormap=colormap, ax=ax,**kws)\r\n ax.set_ylim((0,value_max))\r\n\r\n\r\n #reverse legend order\r\n handles, labels = ax.get_legend_handles_labels()\r\n ax.legend(reversed(handles),reversed(data.columns),bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\r\n\r\n #AXES\r\n if (headers is None or headers=='hide'):\r\n ax.get_xaxis().set_visible(False)\r\n ax.get_xaxis().set_ticks([])\r\n elif headers=='show':\r\n plt.setp(ax.get_xticklabels(),rotation=x_ticklabels_rotation)\r\n ax.set_xlabel(None,visible=False)\r\n\r\n\r\n #plt.tight_layout()\r\n\r\n\r\n return ax","def bar_chart(\n df,\n orientation='v',\n bar_width=None,\n opacity=0.9,\n textpos=None,\n linewidth=1,\n linecolor='#2C3347',\n marker_color=None,\n **kwargs):\n\n traces = []\n rng = df.index.size if orientation == 'v' else df.columns.size\n otn = orientation\n for i in range(rng):\n x = [str(x) for x in df.columns] if otn == 'v' else df.iloc[:, i]\n y = df.iloc[i] if otn == 'v' else [str(x) for x in df.index]\n text = df.iloc[i] if otn == 'v' else df.iloc[:, i]\n name = df.iloc[i].name if otn == 'v' else df.columns[i]\n\n preset_args = dict(\n x=x,\n y=y,\n text=text,\n textposition=textpos,\n marker=dict(\n opacity=opacity,\n color=marker_color,\n line=dict(\n color=linecolor,\n width=linewidth)),\n name=name,\n width=bar_width,\n orientation=orientation\n )\n\n all_args = {**preset_args, **kwargs}\n bar = go.Bar(all_args)\n traces.append(bar)\n\n return traces","def plot_bar_chart(objects, data, title='', ylabel='', bar_color = 'blue'):\n y_pos = np.arange(len(objects))\n\n plt.bar(y_pos, data, align='center', alpha=0.5)\n plt.xticks(y_pos, objects, rotation='vertical')\n plt.ylabel(ylabel, fontsize=12)\n plt.title(title, fontsize=12)\n plt.ylim([0,1300])\n plt.bar(range(len(data)), data, color=bar_color)\n\n return plt.show()","def bar_plot(data, xtitle, title):\n label = list(set(data))\n height = count_elements(data)\n height = [height[i] for i in label]\n plt.bar(label, height=height, width=0.8)\n plt.ylabel('frequency')\n plt.xlabel(xtitle)\n plt.xticks(label)\n plt.savefig('./figures/{}.png'.format(title))\n plt.close()","def drawStackedBarPlot(df, column, hue):\n plt.style.use('default')\n plt.style.use('dark_background')\n p_table = pd.pivot_table(df, index=column, \n columns=hue, aggfunc='size')\n p_table = p_table.div(p_table.sum(axis=1), axis=0)\n p_table.plot.bar(stacked=True, figsize=(14,7))\n plt.xlabel('Spekraltyp')\n plt.ylabel('Anteil')\n plt.show()","def barplot(bars, title='', upColor='blue', downColor='red'):\n import pandas as pd\n import matplotlib.pyplot as plt\n from matplotlib.lines import Line2D\n from matplotlib.patches import Rectangle\n\n if isinstance(bars, pd.DataFrame):\n ohlcTups = [\n tuple(v) for v in bars[['open', 'high', 'low', 'close']].values]\n elif bars and hasattr(bars[0], 'open_'):\n ohlcTups = [(b.open_, b.high, b.low, b.close) for b in bars]\n else:\n ohlcTups = [(b.open, b.high, b.low, b.close) for b in bars]\n\n fig, ax = plt.subplots()\n ax.set_title(title)\n ax.grid(True)\n fig.set_size_inches(10, 6)\n for n, (open_, high, low, close) in enumerate(ohlcTups):\n if close >= open_:\n color = upColor\n bodyHi, bodyLo = close, open_\n else:\n color = downColor\n bodyHi, bodyLo = open_, close\n line = Line2D(\n xdata=(n, n),\n ydata=(low, bodyLo),\n color=color,\n linewidth=1)\n ax.add_line(line)\n line = Line2D(\n xdata=(n, n),\n ydata=(high, bodyHi),\n color=color,\n linewidth=1)\n ax.add_line(line)\n rect = Rectangle(\n xy=(n - 0.3, bodyLo),\n width=0.6,\n height=bodyHi - bodyLo,\n edgecolor=color,\n facecolor=color,\n alpha=0.4,\n antialiased=True\n )\n ax.add_patch(rect)\n\n ax.autoscale_view()\n return fig","def plot_data_stats(data):\n sns.set_style(\"dark\")\n f, ax = plt.subplots(figsize=(6, 15))\n\n ax = sns.barplot(x='tag', y='count', data=tags_freqs)\n\n ax.axes.set_title(\"POS Tags Frequencies\",fontsize=20)\n ax.set_xlabel(\"POS Tags\", fontsize=16)\n ax.set_ylabel(\"Counts\", fontsize=16)\n ax.tick_params(labelsize=12)\n\n plt.show()","def barplot(data, field_name, field_categories):\n\n\tcategories, counts = np.unique(data[field_name], return_counts=True)\n\n\tfig = plt.figure(figsize=(4, 3))\n\taxes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\n\taxes.bar(range(len(categories)), counts, fc=\"gray\") # fc is the face color\n\n\taxes.set_xlabel(\"\")\n\taxes.set_ylabel('Count')\n\taxes.set_title(field_name)\n\tfig.autofmt_xdate(rotation=45)\n\n\taxes.set_xticks(range(len(categories)))\n\taxes.set_xticklabels([field_categories[c] for c in categories]);","def _bar_example_4(quantity_by_fruit):\n ch = chartify.Chart(x_axis_type=\"categorical\", blank_labels=True)\n ch.set_title(\"Vertical bar plot with labels\")\n ch.set_subtitle(\"Hidden y-axis\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n color_column=\"fruit\",\n )\n ch.style.color_palette.reset_palette_order()\n ch.plot.text(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n text_column=\"quantity\",\n color_column=\"fruit\",\n )\n # Adjust the axis range to prevent clipping of the text labels.\n ch.axes.set_yaxis_range(0, 1200)\n ch.axes.hide_yaxis()\n ch.show(_OUTPUT_FORMAT)","def plotify_bar(title, data):\n\n x, y, z, labels = [], [], [], []\n\n for d in reversed(data[:len(data) - 1]):\n x.append(f\"{d['settimana_del']:%d-%b}\\n{d['settimana_fino_al']:%d-%b}\")\n y.append(d['nuovi_positivi'])\n z.append(\"lightgrey\" if d['giorni'] < 7 else 'green' if d['delta'] <= 0 else 'red' )\n labels.append(human_format(d['nuovi_positivi']) if d['giorni'] == 7 else f\"{human_format(d['nuovi_positivi'])}\\n(in corso)\" )\n\n x_pos = np.arange(len(x))\n\n # create a new figure\n plt.figure()\n\n plt.title(title)\n\n # Create bars with different colors\n plt.bar(x_pos, y, color=z)\n\n # Create names on the x-axis\n plt.xticks(x_pos, x, rotation=40)\n\n\n # Text on the top of each bar\n x_ticks = plt.gca().get_xticks()\n for i in range(len(y)):\n text = data[i]\n plt.text(x = x_ticks[i], y = y[i]+5, s = labels[i], size = 9, horizontalalignment='center', verticalalignment='bottom')\n\n # prettify y values\n current_values = plt.gca().get_yticks()\n plt.gca().set_yticklabels(['{:n}'.format(int(x)) for x in current_values])\n\n # responsive layout\n plt.tight_layout()\n\n\n\n buf = io.BytesIO()\n plt.savefig(buf, format='png')\n buf.seek(0)\n\n ### Release memory\n # Clear the current axes.\n plt.cla() \n # Clear the current figure.\n plt.clf() \n # Closes all the figure windows.\n plt.close('all') \n # plt.close(fig)\n gc.collect()\n\n return buf","def _bar_example_1(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, x_axis_type=\"categorical\")\n ch.set_title(\"Vertical bar plot\")\n ch.set_subtitle(\"Automatically sorts by value counts.\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n )\n ch.show(_OUTPUT_FORMAT)","def value_counts_plot(df):\n \n plt.figure(figsize=(15,10))\n \n #get rid of sort_index() to change the graph\n return df.value_counts().sort_index().plot(kind='bar')","def message_genre_bar_chart(df):\n genre_counts = df.groupby('genre').count()['message']\n genre_names = list(genre_counts.index)\n return {\n 'data': [\n Bar(\n x=genre_names,\n y=genre_counts\n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Message Genres',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"Genre\"\n }\n }\n }","def draw_bar(x_index, data_list, xticks, title, x_label, y_label):\n pyplot.bar(x_index, data_list)\n pyplot.xlabel(x_label)\n pyplot.ylabel(y_label)\n pyplot.xticks(x_index, xticks)\n pyplot.title(title)\n pyplot.show()\n pyplot.savefig()","def plot(*args, **params):\n if len(args) == 1: # only support data for now\n if isinstance(args[0], list):\n bar_2d = Bar2D(data=args[0])\n bar_2d.plot()\n bar_2d.fig.show()\n else:\n bar_2d = Bar2D(**params)\n bar_2d.plot()\n bar_2d.fig.show()","def _bar_example_3(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, y_axis_type=\"categorical\")\n ch.set_title(\"Horizontal bar plot\")\n ch.set_subtitle(\"Horizontal with color grouping\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n color_column=\"fruit\",\n )\n ch.show(_OUTPUT_FORMAT)","def BarOverview(data):\n return dcc.Graph(id=\"BarOverview\", className=\"bar\", figure=dict(\n data=[go.Bar(\n x=data[\"frequencies\"],\n y=data[\"names\"],\n orientation='h',\n marker={\n 'color': '#ff4058'\n },\n )],\n layout=dict(\n title=\"<b>Most common Persons</b>\",\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\n margin=dict(l=10, r=20, t=50, b=30),\n plot_bgcolor=\"rgba(0,0,0,0)\",\n paper_bgcolor=\"rgba(0,0,0,0)\",\n xaxis=dict(tick0=0, dtick=max(data[\"frequencies\"])),\n yaxis=dict(ticks='outside',\n showgrid=True,\n showline=False,\n showticklabels=False),\n annotations=[dict(xref='paper', yref='y',\n x=0, y=yd,\n font=dict(\n color=\"#000000\",\n size=19\n ),\n text=str(yd),\n showarrow=False) for xd, yd in zip(data[\"frequencies\"], data[\"names\"])]\n )\n ))","def plot_norm_bar(df, title, figsize=(12,7)):\n fig, ax = plt.subplots(ncols=1, figsize=figsize)\n fig.suptitle(title)\n cat_value_counts = df.fillna('missing').value_counts(normalize=True)\n sns.barplot(y = cat_value_counts.index, x= cat_value_counts.values*100)\n ax.set(xlabel= 'percentage', ylabel=str(df.name))\n \n plt.plot()\n\n return","def barplot(self, x = \"Predictor\", color = None, opacity = 1, template = \"ggplot2\", \n has_title = True, barmode=\"stack\", is_horizontal = False, title = None, is_percent = False,\n show_num = False):\n if color: #Produce either a stacked or grouped bar plot\n df_stack = self._df.groupby([x,color]).size().reset_index()\n df_stack['Percentage'] = self._df.groupby([x, color]).size().groupby(level = 0).apply(lambda \n x:100 * x/float(x.sum())).values\n df_stack.columns = [x, color, 'Count', 'Percentage']\n df_stack['Percentage'] = round(df_stack['Percentage'], 2)\n \n x_clean, df_clean = clean_varname(df_stack, var = x)\n color_clean, df_clean = clean_varname(df_clean, var = color)\n \n if has_title:\n if not title:\n title = f\"Bar Plot of {x_clean} and {color_clean}\"\n else:\n title = None\n \n \n # 8 different variations for how this graph can appear:\n if is_horizontal:\n if is_percent:\n if show_num: #Show percentages on stacked bar graph\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Percentage'])\n else:\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if show_num: #Show counts on stacked bar graph:\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Count'])\n else:\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if is_percent:\n if show_num:\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Percentage'])\n else:\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if show_num:\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Count'])\n else:\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title) \n \n return fig\n \n else: #Create a basic bar plot\n df_stack = self._df.groupby([x]).size().reset_index()\n df_stack['Percentage'] = self._df.groupby([x]).size().groupby(level = 0).apply(lambda","def bar( # pylint: disable=disallowed-name\n self, x: Hashable | None = None, y: Hashable | None = None, **kwargs\n ) -> PlotAccessor:\n return self(kind=\"bar\", x=x, y=y, **kwargs)","def bar(self, entry_type:str, x:str, labels:list=None, diff:bool=False, x_idx:int=-1):\n\n query = self._decode(x)\n\n data_points = []\n\n for idx, (log, name) in enumerate(zip(self.logs, self.log_names)):\n log = log[entry_type]\n\n candidates = []\n\n for entry in log:\n test = self._follow(entry, query)\n\n if type(test) == dict:\n candidates.append(test)\n elif type(test) == list:\n candidates.append({idx: v for idx, v in enumerate(test)})\n \n if len(candidates) > 0:\n data_points.append((name, candidates[x_idx]))\n \n if len(data_points) == 0:\n print('Warning: Nothing to show in bar chart!')\n return\n\n names = [x[0] for x in data_points]\n data_points = [x[1] for x in data_points]\n\n # Construct the labels for the data\n if labels is not None:\n data_labels = labels\n else:\n data_labels = set()\n for datum in data_points:\n for k in datum:\n data_labels.add(k)\n \n data_labels = list(data_labels)\n data_labels.sort()\n \n\n data_values = [[(datum[k] if k in datum else None) for k in data_labels] for datum in data_points]\n\n if diff:\n for idx in reversed(range(len(data_values))):\n for jdx in range(len(data_labels)):\n if data_values[0][jdx] is None or data_values[idx][jdx] is None:\n data_values[idx][jdx] = None\n else:\n data_values[idx][jdx] -= data_values[0][jdx]\n\n\n series_labels = names\n\n # Plot the graph now\n num_bars = len(series_labels)\n bar_width = 1 / (num_bars + 1)\n \n # Set position of bar on X axis\n positions = [np.arange(len(data_labels))]\n for _ in range(1, num_bars):\n positions.append([x + bar_width for x in positions[-1]])\n \n # Make the plot\n for idx, (series, data, pos) in enumerate(zip(series_labels, data_values, positions)):\n plt.bar(pos, data, color=self._color(idx), width=bar_width, edgecolor='white', label=series)\n \n # Add xticks on the middle of the group bars\n plt.title(x.replace('x.', entry_type + '.') + (' diff' if diff else ''))\n plt.xticks([r + bar_width for r in range(len(data_labels))], data_labels)\n \n # Create legend & Show graphic\n plt.legend()\n plt.show()","def bar_time_series(df, title, ylabel, report):\n for col in df:\n fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n plt.gcf().subplots_adjust(bottom=0.25)\n df[col].plot.bar();\n ax.set_xticklabels([v if i % 4 == 0 else '' for i, v in enumerate(df.index)])\n ax.xaxis.set_tick_params(rotation=45, length=0);\n ax.set_xlabel('Date')\n ax.set_ylabel(ylabel)\n full_title = title if df.shape[1] == 1 else '{} {}'.format(col, title)\n report.write_plot(full_title)\n plt.title(full_title)\n plt.show();\n plt.close();","def plot_uv_bar(df, colname, colorid=0):\n if (colname in list(df.columns)):\n \n # Set figure size \n fig, ax = plt.subplots(figsize=(8,6))\n \n # set colorid for bar plot\n base_color = sns.color_palette()[colorid]\n\n # variable counts to calculate percentage\n cdict_count = df[colname].value_counts().to_dict() \n total_count = df.shape[0]\n \n \n if (len(list(cdict_count.keys())) > 5):\n # max.count to position the %\n maxcount_pct= np.max(list(cdict_count.values()))*0.125\n # max. no. of categories Vs % rotation \n rottext_pct = 90 \n # font size for % display\n fontsiz_pct = 12\n else:\n # max.count to position the %\n maxcount_pct= np.max(list(cdict_count.values()))*0.075\n # max. no. of categories Vs % rotation \n rottext_pct = 0 \n # font size for % display\n fontsiz_pct = 16\n \n \n # plotting...\n sns.countplot(data = df, x = colname\n , order = list(cdict_count.keys())\n , color = base_color\n , saturation = 0.7)\n\n # title and labels\n plt.title('Order of '+ colname, fontsize=20)\n plt.xlabel(colname + ' Type', fontsize=16)\n plt.ylabel('Count', fontsize=16)\n \n # x-,y- ticks\n locs, labels = plt.xticks(fontsize=16)\n plt.yticks(fontsize=16)\n\n # display % count information on each tower of bar plot\n for loc, label in zip(locs, labels):\n count = cdict_count[label.get_text()]\n pct_string = '{:0.1f}%'.format(count*100/total_count)\n plt.text(loc, count-maxcount_pct, pct_string, ha='center', color='w', fontsize=fontsiz_pct, rotation=rottext_pct)\n\n return plt.show()\n\n else:\n \n print(' >>>Error:',colname,' is not in DataFrame')","def plot_df(data_frame):\n plt.figure(figsize = (10, 5))\n chart = sns.countplot(data_frame['label'], \n palette=\"Set1\"\n )\n plt.show()","def plot_bar(filename, data, std=None, xlab='x', ylab='y', ylim=[0,1],yticks=np.arange(0,1.1,0.1), title='Bar-Plot', methods=None, datasets=None, figwidth=8, figheight=6, colors=None, legend_loc=\"lower center\", xytick_fontsize=12, xylabel_fontsize=15, title_fontsize=15, legend_fontsize=12):\n import matplotlib as mpl\n mpl.use(\"pdf\")\n import matplotlib.pyplot as plt\n\n data=np.array(data)\n num_methods=len(data)\n \n # colors\n if colors is None:\n colors=['b','r','g','c','m','y','k','w'] # maximally 8 colors allowed so far\n\n ind = np.arange(num_methods) # the x locations of the bars\n width = 0.8 # the width of the bars\n fig=plt.figure(num=1,figsize=(figwidth,figheight))\n ax=fig.add_subplot(1,1,1)\n if std is None:\n ax.bar(ind,data,width,color=colors[0:num_methods],ecolor='k')\n else:\n ax.bar(ind,data,width,color=colors[0:num_methods],yerr=std,ecolor='k')\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel(ylab,fontsize=xylabel_fontsize)\n ax.set_xlabel(xlab,fontsize=xylabel_fontsize)\n ax.set_title(title,fontsize=title_fontsize)\n ax.set_xticks(ind+0.5*width)\n ax.set_xticklabels( methods )\n #if ylim is None:\n # yticks=np.arange(0,1.1,0.1)\n # ylim=[0,1]\n if yticks is not None:\n ax.set_yticks(yticks)\n if ylim is not None:\n ax.set_ylim(ylim[0],ylim[1])\n plt.setp(ax.get_xticklabels(), fontsize=xytick_fontsize)\n plt.setp(ax.get_yticklabels(), fontsize=xytick_fontsize)\n # shrink axis box \n #box = ax.get_position()\n #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n #ax.legend( method_bar, methods, loc='lower left', bbox_to_anchor=(1.0, 0.3), fontsize=legend_fontsize )\n #ax.legend(methods, loc=legend_loc, fontsize=legend_fontsize )\n #plt.show()\n fig.savefig(filename,bbox_inches='tight')\n plt.close(fig)","def category_bar_chart(df):\n label_names = df.drop(['message', 'original', 'genre', 'id'], axis=1).columns\n label_counts = []\n for column in label_names:\n label_counts.append(df[column].sum())\n return {\n 'data': [\n Bar(\n x=label_names,\n y=label_counts\n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Labelled Categories',\n 'yaxis': {\n 'title': \"Count\",\n 'type': 'log'\n },\n 'xaxis': {\n 'title': \"Category\"\n }\n }\n }","def bar(variable, name, data=None, x_v=None, color_set=custom_bw,\n ax_size=(20, 6), highlight=None, ax=None):\n\n common_set_up(ax_size) # Apply basic plot style\n\n fig = sns.barplot(x=x_v, y=variable, data=data, saturation=1, ax=ax,\n color=color_set[2], label=name,\n )\n\n sns.despine(offset=2, trim=True, left=True, bottom=True)\n\n # Set title and axes\n title_color = '#192231'\n font_colour = '#9099A2'\n if ax is None:\n fig.set_title('{0}'.format(name),\n fontsize=20, color=title_color)\n fig.set_ylabel('Frequency',\n color=font_colour)\n fig.set_xlabel('{0}'.format(name),\n color=font_colour)\n\n if highlight:\n bars = fig.patches\n bars[highlight].set_color(color_set[1])\n\n return fig","def barPlot2():\n n = 10\n X = np.arange(n)\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n\n for x,y in zip(X,Y1):\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n\n plt.ylim(0,1.25)\n plt.show()","def _bar_example_2(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, x_axis_type=\"categorical\")\n ch.set_title(\"Vertical bar plot - Label sort\")\n ch.set_subtitle(\"Set `categorical_order_by` to sort by labels\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n categorical_order_by=\"labels\",\n categorical_order_ascending=True,\n )\n ch.show(_OUTPUT_FORMAT)","def bar(self, row_id, col_id, label=None, offset=(350, 30), **kwargs):\n bar = BarGraph(label=label, **kwargs)\n self.pl[row_id, col_id].addItem(bar)\n\n bar.barClicked.connect(self.clickedBar)","def plot_results(t_val, mood):\r\n N = 8\r\n theta = np.linspace(0.0, 2 * np.pi , N, endpoint=False)\r\n the_stats = [t_val['number_words'], t_val['average_character_length'], \r\n t_val['signs'], t_val['multiple_signs'], t_val['question'],\r\n t_val['exclamation'], t_val['name'], mood] \r\n \r\n width = np.pi / N \r\n\r\n plt.figure()\r\n \r\n handle = plt.subplot(111, polar=True)\r\n handle.set_xticklabels(['Word', 'AvrChar', 'Signs', '2Signs', '?', '!', 'name', 'mood'])\r\n \r\n handle.bar(theta, the_stats, width=width, bottom=1.0)\r\n \r\n plt.show()","def img_gen_bar():\n data = pd.DataFrame(data=np.random.rand(5,1), index=range(1,6), columns=['Fred'])\n #m,n = np.shape(data)\n\n plt.clf()\n plt.bar(x=data.index.values, height=data.values.ravel(), color='k') # figsize=(10, 6))\n # Options for later from https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html\n # bar_width = 0.35\n # alpha = .3\n fig=plt.gcf()\n fig.set_size_inches(2.24, 2.24)\n plt.axis('off')\n fig.tight_layout()\n fig.canvas.draw()\n # grab the pixel buffer and dump it into a numpy array\n pixels = np.array(fig.canvas.renderer._renderer)[:,:,:3]\n #print(pixels.shape)\n return pixels, data.index.values + data.values.ravel()","def _bar2df(bars,data):\n tmp = list(map(lambda x: [x[0] for _ in range(x[1])],list(enumerate(bars))))\n labels = list(itertools.chain(*tmp)) # tmp is a list of list and this line flattens it\n data[\"labels\"] = labels\n data[\"index\"] = list(range(data.shape[0]))\n aggregation_condition = {'time': ['first', 'last'], \\\n 'index':['first', 'last'], \\\n 'price': ['min', 'max', 'first', 'last']}\n res = data.groupby(\"labels\",as_index=False).agg(aggregation_condition)\n res.columns = ['_'.join(col) for col in res.columns.values]\n res = res.drop([\"labels_\"],axis=1)\n res.columns = ['start_t', 'end_t', 'start_idx', 'end_idx','low', 'high', 'open', 'close']\n return res","def matplotlib_bar_chart() -> Tuple:\n df = read_dataset(Path('..', '..', 'iris.csv'))\n x = []\n\n for col in df.columns:\n try:\n max_val = get_column_max(df, col)\n x.append(max_val)\n except ValueError:\n pass\n \n fig, ax = a_libraries.matplotlib_bar_chart(np.array(x))\n\n return fig, ax","def plot_bar(label_array, acc_array, f1_array, width=0.5, axis_label=None, graph_title=None, file_name=\"\", dpi=100):\n plt.figure(figsize=plt.figaspect(1.), dpi=dpi)\n x = np.arange(len(label_array)) # the label locations\n plt.bar(x - 0.5 * width, acc_array, width, label='Accuracy')\n plt.bar(x + 0.5 * width, f1_array, width, label='F1 score')\n plt.ylim([0, 1.1])\n plt.xticks(x, labels=label_array)\n if axis_label is None:\n axis_label = ['Set', 'Values']\n plt.xlabel(axis_label[0])\n plt.ylabel(axis_label[1])\n if graph_title is None:\n graph_title = graph_title\n plt.title(graph_title)\n plt.tight_layout()\n plt.legend()\n plt.grid()\n if file_name:\n plt.savefig(file_name, bbox_inches='tight')\n plt.show()\n return","def PlotBayes( x=np.ones(1), bayes=np.ones(1), title=None, label=None, width=1.0, color='blue', show_values=False, ax=None, posterior=False ):\n\n if ax is None:\n fig, ax = plt.subplots( )\n ax.bar(x, bayes/bayes.max(), width, color=color )\n ax.set_title( title )\n ax.set_yscale('log')\n ax.set_xlabel( label )\n if posterior:\n ax.set_ylabel(r\"$P/P_{\\rm max}$\")\n else:\n ax.set_ylabel(r\"$\\mathcal{B}/\\mathcal{B}_{\\rm max}$\")\n# ax.set_ylabel(r\"$\\mathcal{B} = \\prod L / L_0$\")\n if show_values: ## print value on top of each bar, .... doesnt work ...\n shift = bayes.max()/bayes.min()/10\n for xx, b in zip( x, bayes ):\n ax.text( xx, b*shift, str(b), color=color, fontweight='bold' )\n\n ### assure that there are ticks at y axis\n lim = ax.get_ylim()\n ax.set_ylim(lim[0]*0.5, lim[1]*2)","def barh_plotter(data: pd.DataFrame, variable: str):\n fig, axs = plt.subplots(2,5, gridspec_kw={'wspace': 1, 'hspace': 0.2},\n figsize=(60, 40), sharex = False)\n\n for ax, dta in zip(axs.flatten(), data.values()) :\n ax.barh(dta['club'], dta[f'{variable}'])\n ax.set_xlabel(f'{variable}', fontsize=25)\n \n \n for ax, dta in zip(axs.flatten(), data.keys()):\n ax.set_title(dta, fontsize=30)\n\n return plt.show()","def barPlot1():\n n = 12\n X = np.arange(n)\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\n\n for x,y in zip(X,Y1):\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n\n for x,y in zip(X,Y2):\n plt.text(x+0.2, -y-0.1, '%.2f' % y, ha='center', va= 'bottom')\n\n plt.ylim(-1.25,+1.25)\n plt.show()","def graphy2():\n data = pd.read_csv(\"week2.csv\")\n plot_g = pygal.Bar(fill=True, interpolate='cubic', style=LightSolarizedStyle)\n plot_g.title = \"Top Fans in Week 2\"\n plot_g.x_labels = data.GENDER\n plot_g.y_labels = map(int, range(0, 80, 10))\n plot_g.add(\"Male\", data.COUNT)\n plot_g.add(\"Female\", data.COUNT2)\n plot_g.add(\"Total\", data.COUNT3)\n plot_g.render_to_file(\"plotweek2.svg\")","def draw_bar_plot(fig, x, y, labels):\r\n\r\n #Convert times to a displayable format\r\n (x_times, hour_mode) = times_to_axis(x)\r\n\r\n\r\n #Draw horizontal grid lines behind data\r\n fig.yaxis.grid(zorder=0)\r\n\r\n #Draw plot\r\n fig.bar(x_times, y, zorder=2)\r\n\r\n\r\n #If necessary, enable processing of \"datetime\" objects on the x-axis\r\n if not hour_mode:\r\n fig.xaxis_date()\r\n\r\n\r\n #Label and style plot\r\n set_axis_labels(fig, *labels)\r\n style_x_labels(fig)","def graph(df):\n df.plot()\n plt.show()","def plot_individual_bar_chart_graph(data_values, title,\r\n number_of_keys,\r\n max_val,\r\n vals_for_bar_chart,\r\n file_in):\r\n\r\n n_groups = len(vals_for_bar_chart)\r\n fig, ax = plt.subplots()\r\n index = np.arange(n_groups)\r\n bar_width = 0.9\r\n opacity = 0.4\r\n # print vals_for_bar_chart\r\n rects1 = plt.bar(index,\r\n vals_for_bar_chart,\r\n bar_width,\r\n alpha=opacity,\r\n color='b') # label='whatever'\r\n plt.xlabel('number in cluster')\r\n plt.ylabel('Count')\r\n plt.title(title+\"_barchart\")\r\n plt.legend()\r\n pylab.grid(True)\r\n ax.set_yscale('symlog')\r\n ax.set_xscale('symlog')\r\n plt.tight_layout()\r\n plt.show()\r\n pylab.savefig(file_in + \"_\" + title + '_barchart.png')\r\n plt.close()\r\n pylab.close()","def show_graph(d:dict):\n x = []\n y = []\n for key, value in d.items():\n x.append(str(key))\n y.append(value)\n\n x_pos = [i for i, _ in enumerate(x)]\n plt.figure()\n plt.bar(x_pos, y, color='green')\n plt.xlabel(\"Size\")\n plt.ylabel(\"Number of images\")\n plt.title(\"Count by size\")\n plt.xticks(x_pos, x)","def plot_type_of_two_topic(data_frame1: pb.DataFrame, data_frame2: pb.DataFrame) -> None:\n plt.interactive(False)\n plt.figure()\n data_frame1.plot(kind='bar', x= data_frame['TopicID'])\n data_frame2.plot(kind='bar', x= data_frame['TopicID'])\n plt.show()","def plotBarPlots(self, stage, strain, strainID, count, p, xm, ym, xn, yn):\n\t\t\n\t\ttimeList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n\t\t\n\t\tind = np.arange(len(timeList))\n\t\t\n\t\tplt.style.use('bmh')\n\t\tcolors = ['#b2182b', '#238b45', '#3690c0', '#023858']\n\t\t\n\t\tfig = plt.figure(figsize=(15,15), frameon=False)\n\t\tax1 = fig.add_subplot(111)\n\t\tfig.suptitle(strain + ' - ' + stage, fontsize=20, fontweight='bold')\n\t\n\t\tax1.spines['top'].set_visible(False)\n\t\tax1.spines['right'].set_visible(False)\n\t\tax1.spines['bottom'].set_visible(False)\n\t\tax1.spines['left'].set_visible(False)\n\t\tax1.yaxis.set_ticks_position('left')\n\t\tax1.xaxis.set_ticks_position('bottom')\n\t\twidth = 0.6\n\t\t\n\t\tax1.bar(ind+width/2., count[timeList,1], width, color=colors[0], edgecolor = \"none\")\n\t\t\n\t\tax1.plot(*p.linspace(), c='k', linewidth=3.0)\n\t\t\n\t\tfor point in range(len(xm)):\n\t\t\tax1.plot(xm[point], ym[point], marker='*', markersize=30, color=\"blue\")\n\t\tfor point in range(len(xn)):\n\t\t\tax1.plot(xn[point], yn[point], marker='*', markersize=30, color=\"blue\")\t\t\t\n\t\t\n\t\tax1.set_ylabel('Frequency as %', fontweight='bold', fontsize=20)\n\t\tax1.set_xlabel('Time', fontweight='bold', fontsize=20)\n\t\txTickMarks = ['%s' %str(j) for j in timeList]\n\t\tax1.set_xticks(ind+width/2)\n\t\txtickNames = ax1.set_xticklabels(xTickMarks, fontweight='bold')\n\t\t\n\t\tplt.setp(xtickNames, fontsize=15)\n\t\tax1.xaxis.set_ticks_position('none')\n\t\tax1.yaxis.set_ticks_position('none')\t\t\n\t\tax1.set_yticklabels(ax1.get_yticks(), fontweight='bold', fontsize=17)\n\t\tax1.set_xlim([6, len(timeList)-6])\n\t\t\t\t\t\n\t\tfname = 'strain%d' %strainID + stage + '.png' \n\t\t\n\t\tfig.savefig(fname, transparent=True, dpi=100)\n\t\tplt.close(fig)","def diagram_plugs(data_no,\r\n data_little,\r\n data_means,\r\n data_great,\r\n data_large_enough,\r\n data_super_large,\r\n er_no, er_little,\r\n er_means,\r\n er_great,\r\n er_large_enough,\r\n er_super_large):\r\n\r\n\r\n plt.bar(range(6), [data_no,\r\n data_little,\r\n data_means,\r\n data_great,\r\n data_large_enough,\r\n data_super_large],\r\n width=0.1, color='black',\r\n yerr=[er_no, er_little, er_means,\r\n er_great, er_large_enough,\r\n er_super_large],\r\n ecolor='black', capsize=10)\r\n\r\n\r\n plt.xticks(range(6), ['non', 'petit', 'moyen',\r\n 'grand', 'assez grand', 'tres grand'])\r\n\r\n\r\n plt.ylabel('Taux de pollution en AQI')\r\n plt.title(\"Taux de pollution selon les bouchons\")\r\n\r\n nouveau = new()\r\n print(nouveau)\r\n plt.savefig(nouveau, transparent=True)\r\n plt.clf()\r\n plt.close()\r\n\r\n shutil.move(nouveau, '/app/static/popo')\r\n\r\n return nouveau","def barplot(self, name: str, y_label: str, img_title: str):\n path = C.TEST_DIR\n\n sns.set(style='whitegrid')\n sns.set_palette(sns.color_palette(C.IRT_COLORS))\n df = pd.read_csv(path + name + '.csv')\n ax = sns.barplot(data=df)\n ax.set(ylabel=y_label, title=img_title)\n\n self.save_plot(name)\n plt.show()","def plot_bar_chart(log, frame_seconds=-1):\n\tif frame_seconds == -1:\n\t\tframe_seconds = (log.content[-1].timestamp - log.content[0].timestamp).total_seconds() / timedelta(seconds=100).total_seconds()\n\tlines,dates,areas = log.give_plot_data_bar(frame_seconds=frame_seconds)\n\tfig, ax = plt.subplots(figsize=(11,6))\n\tfig.autofmt_xdate()\n\tmyFmt = DateFormatter(\"%Y %d.%b %H:%M:%S\")\n\tax.xaxis.set_major_formatter(myFmt)\n\tax.set_xlabel('timestamps in UTC')\n\tax.set_ylabel('amount of entries')\n\tplt.title('Analysis of the file \\\"'+log.name+'\\\" \\n with frame size ' + str(frame_seconds) +' seconds')\n\n\twidth = timedelta(seconds=frame_seconds).total_seconds() / timedelta(days=1).total_seconds()\n\tnormal = ax.bar(dates, areas, width, label='entries', edgecolor='k')\n\n\tplt.legend()\n\tplt.subplots_adjust(left=0.15, bottom=0.2, right=0.9, top=0.9)\n\tfig.canvas.mpl_connect('key_press_event', _quit_figure)","def create_bar_graph(plot_df, title=\"\", x_title=\"\", y_title=\"\"):\n plot_df[\"quarter\"] = pd.PeriodIndex(pd.to_datetime(plot_df.iloc[:, 0]), freq=\"Q\")\n fig_data = [\n go.Bar(\n x=plot_df[\"quarter\"].astype(str),\n y=plot_df.iloc[:, 1],\n text=plot_df.iloc[:, 1],\n marker={\"color\": color_palette[0]},\n hoverinfo=\"x+y\",\n showlegend=False,\n )\n ]\n\n fiq_layout = build_bar_layout(title, x_title=x_title, y_title=y_title)\n\n return dict(data=fig_data, layout=fiq_layout)","def oneNumBar(df, colName):\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\n ax = bins.value_counts()\n bins = bins.cat.as_ordered()\n bins = bins.cat.categories\n bounds = bins.left \n bounds = list(bounds)\n bounds.append(bins[len(bounds)-1].right)\n texts = []\n for x,y in zip(bounds[0::],bounds[1::]):\n texts.append(\"(\" + str(x) + \", \" + str(y) + \"]\") \n barData = [go.Bar(x=texts, \n y=ax,\n marker=dict(\n color = '#92c5de',\n opacity=0.8)\n )] \n layout = go.Layout(\n title=\"Bar Plot Showing Count of Values for \" + str(colName[0]),\n xaxis=dict(\n title= colName[0]\n ),\n yaxis=dict(\n title= \"NUMBER OF RECORDS\", \n )\n )\n fig = go.Figure(data=barData, layout=layout)\n return {\"label\":\"Frequency\", \"plot\":fig}","def bar_plot(self,\n x: str,\n y: str,\n x_label: str=None,\n y_label: str=None,\n title: str='Bar Plot',\n **kwargs) -> Figure:\n x_label = x if x_label is None else x_label\n y_label = y if y_label is None else y_label\n fig = px.bar(self.df, x=x, y=y,\n labels={x: x_label, y: y_label},\n title=title, **kwargs)\n return fig","def setBarType(bartype='vertical'):\n dislin.bartyp(bardict[bartype])","def draw_bar(df=data):\n pt = {\n 1: 'Credit card',\n 2: 'Cash',\n 3: 'No charge',\n 4: 'Dispute',\n 5: 'Unknown',\n 6: 'Voided trip',\n }\n df['payment_type'] = df['payment_type'].replace(pt)\n gr = df.groupby(['payment_type', 'weekday']) \\\n .agg(total_amount=('total_amount', 'sum')) \\\n .reset_index(drop=False)\n return px.bar(gr, x='weekday', y='total_amount', color='payment_type', barmode='group') \\\n .update_layout(\n template='plotly_dark',\n plot_bgcolor='rgba(0, 0, 0, 0)',\n paper_bgcolor='rgba(0, 0, 0, 0)',\n )","def plot_dict_bar(aDictionary):\n\t# Convert strings to float\n\tfor key in aDictionary:\n\t\taDictionary[key] = float(aDictionary[key])\n\t\t\n\t# Plot the result\n\tplt.bar(range(len(aDictionary)), aDictionary.values(), align='center')\n\tplt.xticks(range(len(aDictionary)), aDictionary.keys(), rotation=90)\n\t\n\tplt.show()","def performanceBarCharts(): \n ##tauopathy HCS pearson\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null YFP Model\", \"Null DAPI Model\"]\n ml_model_perf = pickle.load(open(\"pickles/ml_model_perf.pkl\", \"rb\"))\n null_model_perf = pickle.load(open(\"pickles/null_model_perf.pkl\", \"rb\"))\n null_dapi_perf = pickle.load(open(\"pickles/single_channel_DAPI_null_model_perf.pkl\", \"rb\"))\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\n x = [1, 2, 3]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance\",fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=12)\n plt.yticks(fontname=\"Times New Roman\", fontsize=12)\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n plt.savefig(\"matplotlib_figures/tau_performance_pearson_special_HCS_model.png\", dpi=300)\n\n ##tauopathy HCS MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null YFP Model\", \"Null DAPI Model\"]\n ml_model_perf = pickle.load(open(\"pickles/ml_model_mse_perf.pkl\", \"rb\"))\n null_model_perf = pickle.load(open(\"pickles/null_model_mse_perf.pkl\", \"rb\"))\n null_dapi_perf = pickle.load(open(\"pickles/single_channel_DAPI_null_model_mse_perf.pkl\", \"rb\"))\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\n x = [1, 2, 3]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance\",fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=12)\n plt.yticks(fontname=\"Times New Roman\", fontsize=12)\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n plt.savefig(\"matplotlib_figures/tau_performance_mse_special_HCS_model.png\", dpi=300)\n\n ##osteosarcoma 3-fold (raw images) pearson\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ml_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_null_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [0.075, 0.1156] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with Raw Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.02)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_pearson_cross_val.png\", dpi=300)\n\n ##osteosarcoma 3-fold (raw images) MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ml_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_null_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [0.15, .2312] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance with Raw Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.01)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_mse.png\", dpi=300)\n\n ##osteosarcoma 3-fold (ablated image training) pearson\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ablated_ml_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_ablated_null_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [.1288, .1385] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with\\n95% Ablated Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.0)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_pearson_trained_ablation_model.png\", dpi=300)\n\n ##osteosarcoma 3-fold (ablated image training) MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ablated_ml_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_ablated_null_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [.2576, .2771] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance with\\n95% Ablated Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.0)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_MSE_trained_ablation_model.png\", dpi=300)\n\n ##supplemental single channel learning YFP and DAPI performance\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"YFP-tau to AT8-pTau\", \"DAPI to AT8-pTau\"]\n YFP_ml_model = pickle.load(open(\"pickles/single_channel_YFP_ml_model_perf.pkl\", \"rb\"))\n DAPI_ml_model = pickle.load(open(\"pickles/single_channel_DAPI_ml_model_perf.pkl\", \"rb\"))\n y = np.array([YFP_ml_model[0], DAPI_ml_model[0]]).round(decimals=2)\n stds = [YFP_ml_model[1], DAPI_ml_model[1]]\n x = [1, 2]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\"cornflowerblue\", zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with\\nSingle Channel Input Learning\",fontname=\"Times New Roman\", fontsize=17, y=1.01)\n ax.set_xlabel(\"Model\", fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=14)\n plt.yticks(fontname=\"Times New Roman\", fontsize=14)\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/supplemental_single_channel_learning.png\", dpi=300)\n\n ##supplemental single channel learning YFP and DAPI, input similarity to prediction\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"YFP-tau to AT8-pTau\", \"DAPI to AT8-pTau\"]\n y = np.array([0.94894628, 0.98718720]).round(decimals=2)\n stds = [0.1673864, 0.039042]\n x = [1, 2]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\"orange\", zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Similarity Between\\nInput Channel and Predicted Channel\",fontname=\"Times New Roman\", fontsize=17)\n ax.set_xlabel(\"Model\", fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=14)\n plt.yticks(fontname=\"Times New Roman\", fontsize=14)\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\"Times New Roman\")\n ax.set_ylim((0,1.13))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/supplemental_single_channel_learning_pearson_similarity_input_and_predicted.png\", dpi=300)","def visualize_type():\n \n data_file = parse(MY_FILE, ',')\n\n # num of incidents per category\n counter = Counter(item['Category'] for item in data_file)\n\n # Set the labels\n labels = tuple(counter.keys())\n\n # Set exactly where the labels hit the x-axis\n xlocations = na.array(range(len(labels))) + 0.5\n\n # Width of each bar\n width = 0.5\n\n # Assign data to a bar plot\n plt.bar(xlocations, counter.values(), width=width)\n\n # Assign labels and tick location to x-axis\n plt.xticks(xlocations + width / 2, labels, rotation=90)\n \n # Give some more room so the x-axis labels aren't cut off\n plt.subplots_adjust(bottom=0.4)\n\n # Make the overall graph/figure larger\n plt.rcParams['figure.figsize'] = 12, 8\n\n # save\n plt.savefig('Type.png')\n\n # close\n plt.clf()","def get_proba_plot(data_type):\n buffer = io.BytesIO()\n plt.subplots(figsize = (25,15))\n ax = sns.barplot(x='proba', y='type', data=data_type, palette = \"Blues_r\")\n ax.set_xlabel('Probability')\n plt.yticks(fontsize = 30)\n plt.xticks(fontsize = 30)\n plt.ylabel(\"Type\", fontsize = 38)\n plt.xlabel(\"Probability\", fontsize = 38);\n plt.title(\"Model Results\", fontsize = 50)\n plt.savefig(buffer, format='png')\n# plt.show()\n plt.close()\n buffer.seek(0)\n chart_probability= Image.open(buffer).resize((512+256,512))\n return chart_probability","def plot_bar(counter, title=\"\", filename=\"tmp.png\"):\n\n fig = plt.figure()\n axis = fig.add_subplot(111)\n\n if isinstance(counter, dict):\n frequencies = counter.values()\n names = counter.keys()\n elif isinstance(counter, list):\n frequencies = [x[1] for x in counter]\n names = [x[0] for x in counter]\n y_pos = np.arange(len(counter))\n axis.barh(y_pos, frequencies, align='center')\n axis.set_title(title)\n axis.set_yticks(y_pos)\n axis.set_yticklabels(list(names))\n axis.invert_yaxis()\n axis.set_xlabel('Frequency')\n print('going to save fig...')\n fig.savefig('png_files/' + filename.replace(\".csv\", \".png\"))\n\n return axis","def custom_barchart(ax, x, y, error, xlims, ylims, error_kw, color='lightblue', width=0.75):\n\n error = [np.zeros(len(error)), error]\n\n ax.bar(x, y, color=color, width=width, yerr=error, error_kw=error_kw, align='center')\n\n ax.set_xlim(xlims)\n ax.set_ylim(ylims)\n\n return ax","def bar_plot(trj: TrajaDataFrame, bins: Union[int, tuple] = None, **kwargs) -> Axes:\n # TODO: Add time component\n\n bins = traja.trajectory._bins_to_tuple(trj, bins)\n\n X, Y, U, V = coords_to_flow(trj, bins)\n\n hist, _ = trip_grid(trj, bins, hist_only=True)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=\"3d\")\n ax.set_aspect(\"equal\")\n X = X.flatten(\"F\")\n Y = Y.flatten(\"F\")\n ax.bar3d(\n X,\n Y,\n np.zeros_like(X),\n 1,\n 1,\n hist.flatten(),\n zsort=\"average\",\n shade=True,\n **kwargs,\n )\n ax.set(xlabel=\"x\", ylabel=\"y\", zlabel=\"Frames\")\n\n return ax","def _plot_dict_bar(d, xmin=None, label=None):\n xvals, yvals = _dict2lists(d)\n if xmin == None:\n xmin = min(xvals) - 1\n else:\n xmin = min(xmin, min(xvals) - 1)\n if label:\n pylab.bar(xvals, yvals, align='center', label=label)\n pylab.xlim([xmin, max(xvals)+1])\n else:\n pylab.bar(xvals, yvals, align='center')\n pylab.xlim([xmin, max(xvals)+1])","def plotBarChart(resultConfirmed, resultDeath, resultVaccinated):\n fig, ax = plt.subplots(3)\n\n ax[0].plot(resultConfirmed['Date'], resultConfirmed['Confirmed Cases'])\n ax[0].title.set_text('Confirmed Cases')\n \n ax[1].plot(resultDeath['Date'], resultDeath['Death Cases'])\n ax[1].title.set_text('Death Cases')\n \n ax[2].plot(resultVaccinated['Date'], resultVaccinated['Vaccinated Person'])\n ax[2].title.set_text('Vaccinated Cases')\n fig.tight_layout()\n plt.show()","def generate_barplot(predictions, labels):\n plot = figure(x_range=labels, plot_height=300, plot_width=400)\n plot.vbar(x=labels, top=predictions, width=0.8)\n # plot.xaxis.major_label_orientation = pi / 2.\n # plot.xaxis.axis_label_text_font_size = \"40pt\"\n # plot.yaxis.axis_label_text_font_size = \"40pt\"\n\n return components(plot)","def plot_balance_class(classes):\n unique, counts = np.unique(classes, return_counts=True)\n plt.bar(unique, counts)\n plt.title('Class Frequency')\n plt.xlabel('Class')\n plt.ylabel('Frequency')\n plt.show()","def make_bar_plot(x, y, title):\n return plotly.graph_objs.Figure(\n data=[plotly.graph_objs.Bar(x=list(x), y=list(y))],\n layout=plotly.graph_objs.Layout(title=title)\n )","def BarSpecific():\n return dcc.Graph(id=\"PersChart\", className=\"bar\", figure=dict(\n data=[go.Bar(\n x=[1],\n y=[\"Persons\"],\n orientation='h',\n marker={\n 'color': '#ff4058',\n },\n )],\n layout=dict(\n title=\"<b>Most similar Persons</b>\",\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\n margin=dict(l=100, r=20, t=50, b=30),\n plot_bgcolor=\"rgba(0,0,0,0)\",\n paper_bgcolor=\"rgba(0,0,0,0)\",\n xaxis=dict(tick0=0, dtick=1),\n yaxis=dict(ticks='outside')\n )\n ))","def plot(self):\n x = np.arange(5)\n # labels = ['temp', 'humi', 'mais', 'o2', 'co2']\n plt.bar(x - 0.35/2, self.data, 0.35, label='actual')\n plt.bar(x + 0.35/2, self.desired_values, 0.35, label='desired')\n plt.ylim(-5, 80)\n plt.legend()\n\n plt.draw()\n plt.pause(0.000001)\n plt.clf()","def simple_bar():\n\n # Make random discrete data\n discrete_a = np.zeros((8,2))\n discrete_b = np.zeros((8,2))\n discrete_c = np.zeros((8,2))\n discrete_a[:,0] = np.arange(8)\n discrete_b[:,0] = np.arange(8)\n discrete_c[:,0] = np.arange(8)\n discrete_a[:,1] = np.random.rand(8)*10\n discrete_b[:,1] = np.random.rand(8)*10\n discrete_c[:,1] = np.random.rand(8)*10\n\n # Make data sets, if using multiple bar_width must be the same\n dataset_a = DataSet(discrete_a,colour='pink',bar_width=0.8,plot='bar',label='A')\n dataset_b = DataSet(discrete_b,colour='violet',bar_width=0.8,plot='bar',label='B')\n dataset_c = DataSet(discrete_c,colour='darkviolet',bar_width=0.8,plot='bar',label='C')\n\n # Make plot object and add data sets\n plot = Plot()\n plot.add_dataset(dataset_a)\n plot.add_dataset(dataset_b)\n plot.add_dataset(dataset_c)\n plot.set_axes(xticks=(1,1),xlim=(-0.5,7.5),ylim=(0,12))\n plot.set_legend(legend=True,location='upper right')\n plot.set_text(legend=8)\n\n # Plot graph and display\n plot.plot()\n plot.save(name='./figures/2d_simple_bar',fmt='png')\n plot.display()","def bar(*args, **kwargs):\n ax, args, kwargs = maybe_get_ax(*args, **kwargs)\n color_cycle = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\n almost_black = '#262626'\n kwargs.setdefault('color', color_cycle[0])\n kwargs.setdefault('edgecolor', 'white')\n middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0\n\n # Check if data contains stacks\n stacked = kwargs.pop('stacked',False)\n # Check if stack text should be included\n stack_text = kwargs.pop('stack_text',False)\n # Get legend if available\n legend = kwargs.pop('legend',False)\n\n left = args[0]\n height = np.array(args[1])\n\n # Label each individual bar, if xticklabels is provided\n xtickabels = kwargs.pop('xticklabels', None)\n # left+0.4 is the center of the bar\n xticks = np.array(left) + middle\n\n # Whether or not to annotate each bar with the height value\n annotate = kwargs.pop('annotate', False)\n\n show_ticks = kwargs.pop('show_ticks', False)\n\n # If no grid specified, don't draw one.\n grid = kwargs.pop('grid', None)\n\n # Check if stacked and plot data accordingly\n if stacked:\n num_stacks, num_data = height.shape\n bottom = np.zeros(num_data)\n for i in np.arange(num_stacks):\n lst = list(args)\n lst[1] = height[i]\n args = tuple(lst)\n kwargs['color'] = set2[i]\n kwargs['bottom'] = bottom\n rectangles = ax.bar(*args, **kwargs)\n bottom += height[i]\n else:\n rectangles = ax.bar(*args, **kwargs)\n\n # add legend\n if isinstance(legend, collections.Iterable):\n ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)\n\n # add whitespace padding on left\n xmin, xmax = ax.get_xlim()\n xmin -= 0.2\n if stacked:\n xmax = num_data\n ax.set_xlim(xmin, xmax)\n\n # If the user is only plotting one bar, make it an iterable\n if not isinstance(height, collections.Iterable):\n height = [height]\n\n\n # If there are negative counts, remove the bottom axes\n # and add a line at y=0\n if any(h < 0 for h in height.tolist()):\n axes_to_remove = ['top', 'right', 'bottom']\n ax.hlines(y=0, xmin=xmin, xmax=xmax,\n linewidths=0.75)\n else:\n axes_to_remove = ['top', 'right']\n\n # Remove excess axes\n remove_chartjunk(ax, axes_to_remove, grid=grid, show_ticks=show_ticks)\n\n if stacked:\n data = height\n height = height.sum(axis=0)\n\n # Add the xticklabels if they are there\n if xtickabels is not None:\n ax.set_xticks(xticks)\n ax.set_xticklabels(xtickabels)\n\n if annotate or isinstance(annotate, collections.Iterable):\n annotate_yrange_factor = 0.025\n ymin, ymax = ax.get_ylim()\n yrange = ymax - ymin\n\n # Reset ymax and ymin so there's enough room to see the annotation of\n # the top-most\n if ymax > 0:\n ymax += yrange * 0.1\n if ymin < 0:\n ymin -= yrange * 0.1\n ax.set_ylim(ymin, ymax)\n yrange = ymax - ymin\n\n offset_ = math.log(yrange) + math.log(annotate_yrange_factor+1)\n print offset_\n print yrange * annotate_yrange_factor\n print math.log(yrange) + math.log(annotate_yrange_factor)\n if isinstance(annotate, collections.Iterable):\n annotations = map(str, annotate)\n else:\n annotations = ['%.3f' % h if type(h) is np.float_ else str(h)\n for h in height]\n\n for x, h, annotation in zip(xticks, height, annotations):\n # Adjust the offset to account for negative bars\n offset = offset_ if h >= 0 else -1 * offset_\n verticalalignment = 'bottom' if h >= 0 else 'top'\n\n # Finally, add the text to the axes\n ax.annotate(annotation, (x, h + annotate_yrange_factor), \n verticalalignment=verticalalignment,\n horizontalalignment='center',\n color=almost_black)\n\n # Text for each block of stack\n # This was partially inspired by the following article by Tableau software\n # http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812\n if stack_text:\n bottom = np.zeros(num_data)\n max_h = max(height)\n for i in np.arange(num_stacks):\n for x, d, b in zip(xticks, data[i], bottom):\n if (d*100.0/max_h) > 4.0:\n ax.text(x,b+d/2.0,d, ha='center', va='center', color=almost_black)\n bottom += data[i]\n return rectangles","def show_class_imbalance(df, title='Class Imbalance', PATH=None):\n ax = sns.barplot(x=[\"Normal\", \"Clickbait\"], y=df.groupby(['target']).target.count())\n ax.set_title(title, size=20)\n plt.xticks([0,1],[\"Normal\", \"Clickbait\"], size = 20)\n ax.set_ylabel(\"Document Count\", size=17)\n ax.set_xlabel(\"Article Class\", size=20)\n if PATH:\n plt.savefig(PATH, bbox_inches=\"tight\", transparent=True)\n return ax","def to_bar(self):\n factor = FactorData()\n return factor","def plot_what_if(df):\n fig = go.Figure()\n for col in df.columns:\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\n fig.update_xaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\n fig.update_yaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_layout(legend=dict(orientation=\"h\", yanchor=\"bottom\", y=-0.25, xanchor=\"center\", x=0.5),\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\n\n\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\n fig.write_html(join('..', 'docs', 'redistribution.html'))\n fig.show()","def draw_bar_plot():\n # Copy and modify data for monthly bar plot\n \n df_bar = df.copy()\n\n # Draw bar plot\n leglab = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n labels = [2016, 2017, 2018, 2019]\n months = np.zeros([12, 4])\n\n for i in range(12):\n for j, year in enumerate(labels):\n t = df[df.index.year == year]\n months[i][j] = t[t.index.month == i].value.mean()\n\n x = np.arange(len(labels))\n width = 0.7\n fig, ax = plt.subplots()\n fig.set_figwidth(10)\n fig.set_figheight(8)\n for i, month in enumerate(months):\n ax.bar(x - (width * (12 - i) / 12), months[i], width / 12, label=leglab[i])\n\n ax.set_ylabel(\"Average Page Views\")\n ax.set_xlabel(\"Years\")\n ax.set_xticks(x)\n ax.set_xticklabels(labels)\n ax.legend(title='Months')\n\n # Save image and return fig (don't change this part)\n fig.savefig('bar_plot.png')\n return fig","def plot_bar(source_files, column_ids, column_names, normalize, sort, plot_difference, freq_bound, title=None,\n dtype=int):\n\n def _filter_data(raw_data, numerical):\n \"\"\" Filters plot-able data. \"\"\"\n # Retain numeric information\n legal_count_inventory = digits + '.'\n # Retain POS tags, also\n legal_entry_inventory = ascii_uppercase + '$'\n filtered_data = list()\n for data_point in raw_data:\n skip = False\n for symbol in list(str(data_point)):\n if symbol not in legal_count_inventory and symbol not in legal_entry_inventory:\n skip = True\n if not skip:\n if numerical:\n filtered_data.append(dtype(data_point))\n else:\n filtered_data.append(data_point)\n # Optionally normalize count values, resulting in a proportion plot\n if numerical and normalize:\n filtered_data = filtered_data / np.sum(filtered_data)\n return np.array(filtered_data)\n\n # Set plot parameters\n sns.set_style('whitegrid')\n sns.set_context('paper')\n\n # Compile data to be plotted within a new dataframe\n # Not necessary, but convenient when plotting with seaborn\n source_dict = dict()\n # Read in data and sort alphanumeric features (e.g. POS tags) alphabetically\n df_features = pd.read_table(source_files[0], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\n df_features = df_features.sort_values('Tag', ascending=True)\n df_reference = pd.read_table(source_files[1], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\n df_reference = df_reference.sort_values('Tag', ascending=True)\n # Isolate columns to be plotted\n entries = _filter_data(df_features.iloc[:, column_ids[0]].values, False)\n counts = _filter_data(df_features.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus A\n reference_counts = _filter_data(df_reference.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus B\n # Construct dataframe to be visualized\n source_dict[column_names[0]] = entries\n source_dict['reference_counts'] = reference_counts\n # Generate frequency mask to exclude low-frequency features from the plot\n # Optional; results in a clearer, better readable visualization\n frequency_mask = np.array(\n [int(counts[i] >= freq_bound or reference_counts[i] >= freq_bound) for i in range(counts.shape[0])])\n source_dict['frequency_mask'] = frequency_mask\n # Calculate per-feature count differences (i.e. target counts vs. reference counts), if specified\n if plot_difference:\n diffs = counts - reference_counts\n source_dict[column_names[1]] = diffs\n else:\n source_dict[column_names[1]] = counts\n features = pd.DataFrame.from_dict(source_dict)\n # Sort by count value and apply frequency mask\n if sort:\n features = features.sort_values(column_names[0], ascending=True)\n if freq_bound > 0:\n features = features.drop(features[features.frequency_mask == 0].index)\n\n # Make plot\n fig, ax = plt.subplots()\n fig.set_size_inches(8, 6)\n if plot_difference:\n colors = ['coral' if feature >= 0 else 'skyblue' for feature in features[column_names[1]]]\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette=colors)\n else:\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette='Set2')\n sns.despine()\n if title is not None:\n plt.title(title)\n plt.show()","def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):\n\n # Check if colors where provided, otherwhise use the default color cycle\n if colors is None:\n colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n\n # Number of bars per group\n n_bars = len(data)\n\n # The width of a single bar\n bar_width = total_width / n_bars\n\n # List containing handles for the drawn bars, used for the legend\n bars = []\n\n # Iterate over all data\n for i, (name, values) in enumerate(data.items()):\n # The offset in x direction of that bar\n x_offset = (i - n_bars / 2) * bar_width + bar_width / 2\n\n # Draw a bar for every value of that type\n for x, y in enumerate(values):\n bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])\n\n # Add a handle to the last drawn bar, which we'll need for the legend\n bars.append(bar[0])\n\n # Draw legend if we need\n if legend:\n ax.legend(bars, data.keys())","def plot_featurewise_barplot(\n utr5_counts, cds_counts, utr3_counts, ax=None, saveto=None, **kwargs\n):\n fig = None\n if ax is None:\n fig, ax = plt.subplots()\n else:\n fig = ax.get_figure()\n barlist = ax.bar([0, 1, 2], [utr5_counts, cds_counts, utr3_counts])\n barlist[0].set_color(\"#1b9e77\")\n barlist[1].set_color(\"#d95f02\")\n barlist[2].set_color(\"#7570b3\")\n ax.set_xticks([0, 1, 2])\n ax.set_xticklabels([\"5'UTR\", \"CDS\", \"3'UTR\"])\n max_counts = np.max(np.hstack([utr5_counts, cds_counts, utr3_counts]))\n setup_axis(\n ax=ax, axis=\"y\", majorticks=max_counts // 10, minorticks=max_counts // 20\n )\n ax.set_ylabel(\"# RPFs\")\n # sns.despine(trim=True, offset=10)\n if saveto:\n fig.tight_layout()\n fig.savefig(saveto, dpi=DPI)\n return ax, fig","def bar_grapgh(dictionary, variable):\r\n plt.clf() # Deletes the previous plot \r\n plt.hist(dictionary[variable])\r\n plt.title('Histogram of ' + variable)\r\n plt.xlabel(variable)\r\n plt.ylabel('Frequency')\r\n plt.savefig(variable)","def visualize_type():\n\t\n\t#grab our parsed data\n\tdata_file = parse(MY_FILE, \",\")\n\t\n\t#make a new variable, counter, from iterating through each line of\n\t#data in parsed data, and count how many incidents happen by category\n\tcounter = Counter(item[\"Category\"] for item in data_file)\n\t\n\t#set the labels which are based on the keys of our counter\n\t#since order doesn't matter, we can just use counter.keys()\n\tlabels = tuple(counter.keys())\n\t\n\t#set exactly where the labels should hit the x-axis\n\txlocations = np.arange(len(labels)) + 0.5\n\t\n\t#width of each bar that will be plotted\n\twidth = 0.5\n\t\n\t#assign data to a bar plot\n\tplt.bar(xlocations, counter.values(), width=width)\n\t\n\t#assign labels and tick location to x-axis\n\tplt.xticks(xlocations + width /2, labels, rotation=90)\n\t\n\t#give more room to the x-axis so the labels aren't cut off\n\tplt.subplots_adjust(bottom=0.4)\n\t\n\t#make the overall graph/figure larger\n\tplt.rcParams['figure.figsize'] = 12, 8\n\t\n\t#save the graph\n\tplt.savefig(\"type.png\")\n\t\n\t#close the plot figure\n\tplt.clf()","def to_bar(self):\n group = GroupData()\n return group","def plot_bar_chart_quantum_vs_classical(\n df_bugs: pd.DataFrame,\n column_to_inspect: str,\n mapping_dict: Dict[str, str],\n categories_to_exclude: List[str] = [],\n categories_keep_only: List[str] = None,\n out_file_name: str = None,\n out_folder_path: str = None,\n horizontal: bool = False,\n map_value_since_beginning: bool = False,\n figsize: Tuple[int, int] = (10, 5),\n legend_placement: str = 'upper center'\n ):\n\n fig, ax = plt.subplots(figsize=figsize)\n\n df = expand_columns(df_bugs, column_to_inspect)\n df = df[~(df[column_to_inspect].isin(categories_to_exclude))]\n\n if categories_keep_only is not None:\n df = df[df[column_to_inspect].isin(categories_keep_only)]\n\n if map_value_since_beginning:\n df[column_to_inspect] = df[column_to_inspect].map(mapping_dict)\n\n categories_q_bugs = list(df[\n df['type'] == 'Quantum'].groupby(\n column_to_inspect).count().sort_values(\n by='type', ascending=False).index)\n\n for component in df[column_to_inspect].unique():\n if component not in categories_q_bugs:\n categories_q_bugs.append(component)\n\n args = {\n \"hue\": \"type\",\n \"data\": df,\n \"palette\": PALETTE,\n \"ax\": ax,\n \"order\": categories_q_bugs\n }\n\n if horizontal:\n sns.countplot(y=column_to_inspect, **args)\n ax.grid(axis='x')\n else:\n sns.countplot(x=column_to_inspect, **args)\n ax.grid(axis='y')\n\n if not map_value_since_beginning:\n # map the value at the latest stage, thus in the labels\n obj_labels = ax.get_xticklabels()\n for i, l in enumerate(obj_labels):\n obj_labels[i] = mapping_dict[l.get_text()]\n ax.set_xticklabels(obj_labels, rotation=60, ha='right')\n\n ax.set_xlabel(capitalize(column_to_inspect), fontsize=15)\n ax.set_ylabel(\"Count\", fontsize=15)\n plt.legend(title=\"Type of Bug\", loc=legend_placement)\n plt.tight_layout()\n\n if out_file_name is not None and out_folder_path is not None:\n fig.savefig(os.path.join(out_folder_path, out_file_name), format=\"pdf\")","def plot_bars(self, ax, bottom=None, log=False):\n if bottom is None and log:\n bottom = np.ones_like(self.values) * min(self.values[self.values > 0 ]) * .1\n return ax.bar(self.lefts, self.values, self.widths, color=self.color, label=self.label,edgecolor=self.color, bottom=bottom, log=log, **self.options)[0]","def _plot_group_bars(ax, xss, field, side):\n\n #translate side to index\n if(side == 'left'):\n side = 0\n else:\n side = 1\n #plot the bars\n x = range(len(xss))\n values = [xs.ROW_edge_fields[field].values[side] for xs in xss]\n ax.bar(x, values, color=[_colormap[i%len(_colormap)] for i in range(len(xss))],\n bottom=0.0, align='center', alpha=0.8, width=0.6)\n ax.set_xticks(x)\n ax.set_xticklabels([textwrap.fill(xs.sheet, 15) for xs in xss],\n rotation='vertical', fontsize=11)","def plot_bar_important_features(important_features, title, xlabel, ylabel, fname):\r\n plt.figure(figsize=(20, 21))\r\n plt.barh(important_features.index.astype(str).tolist(), important_features.values.tolist())\r\n plt.title(title)\r\n plt.xlabel(xlabel)\r\n plt.ylabel(ylabel)\r\n plt.savefig(fname, bbox_inches='tight')\r\n plt.close()","def plot_data(self):","def stopword_bar(df, stop_words, ax):\n df_test = df.copy()\n df_test['prop'] = df.title.apply(stopword_proportion)\n sns.barplot(data=df_test, x='target', y='prop', ax=ax, ci=False)\n ax.set_title(\"Ratio of Stopwords Between Classes\", size=20)\n ax.set_ylim([1,2])\n ax.set_ylabel(\"Ratio\", size=20)\n ax.set_xlabel(\"Article Class\", size=20)\n plt.xticks(ticks=range(2),labels=['Normal','Clickbait'], size=20)\n return ax","def plot_cat(df, cat_columns, hue = \"default_payment_next_month\"):\n fig = plt.figure(figsize = (20,(len(cat_columns)/2+1)*8))\n loc = 1\n for col in cat_columns:\n ax = fig.add_subplot(len(cat_columns)/2+1, 2, loc)\n df_plot = df[[col, hue, \"id\"]].groupby([col, hue]).count()\n df_plot.reset_index(inplace = True)\n sns.barplot(x=col, y= \"id\", hue = hue, data=df_plot, palette = \"GnBu_d\", ax = ax);\n plt.legend(title = \"default payment (1=yes, 0=no)\")\n plt.ylim([0.0001,15000])\n plt.ylabel(\"clients\");\n loc += 1","def plot_bar(self, nsingular=None, nbars=20):\n if nsingular is not None:\n self.get_identifiability_dataframe(nsingular)\n\n plot_obj = plots.IdentBar(self.ident_df, nsingular=nsingular, nbars=nbars)\n plot_obj.generate()\n plot_obj.draw()\n \n return plot_obj.fig, plot_obj.ax","def graph():\n fp = mpl.font_manager.FontProperties(family='JasmineUPC',size=24)\n x = np.arange(0,10)\n y = [386557057065, 368368395622, 242451971944, 225960095934, 161573560379, 107461232731, 89784502211, 73749349545, 54525219632, 52864743212]\n name = ['เชื้อเพลิงที่ได้จากแร่', 'เครื่องจักรและส่วนประกอบ', 'ยานยนต์และส่วนประกอบ', 'เครื่องอุปกรณ์ไฟฟ้าและส่วนประกอบ', 'เหล็กและเหล็กกล้า', 'พลาสติกและของทำด้วยพลาสติก', 'ของทำด้วยเหล็กหรือเหล็กกล้า', 'ทองแดงละของทำด้วยทองแดง', 'เคมีภัณฑ์เบ็ดเตล็ด', 'อุปกรณ์ที่ใช้ทางทัศนศาสตร์']\n ax = plt.gca(xticks=x)\n ax.set_xticklabels(name,rotation=1000,fontproperties=fp)\n plt.bar(x,y,color='g')\n plt.show()"],"string":"[\n \"def compte(df):\\n\\n df.value_counts()[:100].plot(kind='bar')\\n plt.show()\",\n \"def bar_plot(df, data_pt):\\n \\n x=df.loc[data_pt]\\n y= df.columns.tolist()\\n sorte=x.tolist()\\n a=sorted(zip(sorte, y))[-10:]\\n y=[y for _, y in a]\\n ## soru burda yapıp altı ona göre duzeliyecegim birde\\n \\n x = df[y].loc[data_pt]\\n \\n # Here we modify the tickangle of the xaxis, resulting in rotated labels.\\n #title={'text': \\\"<b>Comparing features with Golden for Cycle {}\\\".format(cycle),\\n # 'y':0.9,'x':0.5,'xanchor': 'center','yanchor': 'top'}\\n\\n \\n trace = {'type': 'bar',\\n 'orientation':'h',\\n 'x' : x,\\n 'y' : y}\\n data = Data([trace])\\n layout = {'title' : \\\"<b>Reconstruction error in each dimension for cycle{}\\\".format(data_pt),\\n 'titlefont':{'size' : 20},\\n 'xaxis' : {'title': '<b>Reconstruction Error',\\n 'titlefont':{'size' : 20},\\n 'tickangle': -45, 'tickfont': {'size':15} ,},\\n \\n 'yaxis' : {'title': '<b>Features',\\n 'titlefont':{'size' : 20},\\n 'tickfont': {'size':15},},\\n 'margin' : {'l':100, 'r' : 1, 'b': 200, 't': 100, 'pad' : 1},\\n 'height' : 600, 'width' : 800,\\n }\\n \\n fig = Figure(data = data, layout = layout)\\n \\n return pyo.iplot(fig)\",\n \"def visualizeData(df):\\n for column in df:\\n df[column].value_counts().plot(kind = 'bar', rot = 'vertical', use_index = False)\",\n \"def bar_plot(df_NP):\\n cnt = Counter()\\n for tax_list in df_NP.taxonomy:\\n for tax in list(tax_list):\\n if tax != 'no':\\n cnt[tax] += 1\\n plt.bar(cnt.keys(),cnt.values())\\n plt.xlabel('taxonomic provenance')\\n plt.ylabel('number of molecules')\\n plt.title('number of aglycons with taxonomies')\\n plt.savefig(\\\"output_data/Barplot.png\\\")\\n print(\\\"BAR PLOT DONE\\\")\",\n \"def bar_chart(self, df, n_groups, dict):\\n fig, ax = plt.subplots()\\n # choose bar width (standard 0.8 chosen)\\n bar_width = 0.35\\n # get an index to set the ticks for the x axis\\n\\n index = np.arange(n_groups)\\n indexes = df.index.tolist()\\n print(indexes)\\n df[\\\"index\\\"] = indexes\\n\\n # make barchart for permutation test\\n ax.bar(index, df[\\\"perm\\\"], bar_width, color='b', linewidth=4,\\n label='Permutation test')\\n # make barchart for t-test\\n ax.bar(index + bar_width, df[\\\"t_test\\\"], bar_width, color='r',\\n label='t-test')\\n\\n ax.set_xlabel(dict[\\\"xlabel\\\"])\\n ax.set_ylabel(dict[\\\"ylabel\\\"])\\n ax.set_title(dict[\\\"title\\\"])\\n ax.set_xticks(index + bar_width / 2)\\n ax.set_xticklabels(dict[\\\"xtickslabels\\\"])\\n ax.legend()\\n\\n fig.tight_layout()\\n plt.show()\",\n \"def bar_plot(df, field_name, graph_title, threshold_value, x_axis_label, y_axis_label):\\n\\n x = df[field_name].value_counts().sort_values()\\n x[x > threshold_value].plot(kind='barh', figsize=(12, 8), title=graph_title, x=x_axis_label, y=y_axis_label)\\n return\",\n \"def bar(\\n df,\\n x=None,\\n y=\\\"value\\\",\\n bars=\\\"variable\\\",\\n order=None,\\n bars_order=None,\\n orient=\\\"v\\\",\\n legend=True,\\n title=True,\\n ax=None,\\n cmap=None,\\n **kwargs,\\n):\\n\\n # default x-axis to time-col attribute from an IamDataFrame, else use \\\"year\\\"\\n x = x or time_col_or_year(df)\\n\\n # cast to DataFrame if necessary\\n # TODO: select only relevant meta columns\\n if not isinstance(df, pd.DataFrame):\\n df = df.as_pandas()\\n\\n for col in set(SORT_IDX) - set([x, bars]):\\n if len(df[col].unique()) > 1:\\n msg = \\\"Can not plot multiple {}s in bar plot with x={}, bars={}\\\"\\n raise ValueError(msg.format(col, x, bars))\\n\\n if ax is None:\\n fig, ax = plt.subplots()\\n\\n # long form to one column per bar group\\n _df = reshape_mpl(df, x, y, bars, **{x: order, bars: bars_order})\\n\\n # explicitly get colors\\n defaults = default_props(reset=True, num_colors=len(_df.columns), colormap=cmap)[\\n \\\"color\\\"\\n ]\\n rc = run_control()\\n color = []\\n for key in _df.columns:\\n c = next(defaults)\\n if \\\"color\\\" in rc and bars in rc[\\\"color\\\"] and key in rc[\\\"color\\\"][bars]:\\n c = rc[\\\"color\\\"][bars][key]\\n color.append(c)\\n\\n # change year to str to prevent pandas/matplotlib from auto-ordering (#474)\\n if _df.index.name == \\\"year\\\":\\n _df.index = map(str, _df.index)\\n\\n # plot data\\n kind = \\\"bar\\\" if orient.startswith(\\\"v\\\") else \\\"barh\\\"\\n _df.plot(kind=kind, color=color, ax=ax, **kwargs)\\n\\n # add legend\\n ax.legend(loc=\\\"center left\\\", bbox_to_anchor=(1.0, 0.5))\\n if not legend:\\n ax.legend_.remove()\\n\\n # add default labels if possible\\n if orient == \\\"v\\\":\\n ax.set_xlabel(x.capitalize())\\n else:\\n ax.set_ylabel(x.capitalize())\\n units = df[\\\"unit\\\"].unique()\\n if len(units) == 1 and y == \\\"value\\\":\\n if orient == \\\"v\\\":\\n ax.set_ylabel(units[0])\\n else:\\n ax.set_xlabel(units[0])\\n\\n # build a default title if possible\\n _title = []\\n for var in [\\\"model\\\", \\\"scenario\\\", \\\"region\\\", \\\"variable\\\"]:\\n values = df[var].unique()\\n if len(values) == 1:\\n _title.append(\\\"{}: {}\\\".format(var, values[0]))\\n if title and _title:\\n title = \\\" \\\".join(_title) if title is True else title\\n ax.set_title(title)\\n\\n return ax\",\n \"def barGraph(listOfWord, listOfFrequency):\\r\\n\\r\\n\\tindex = np.arange(len(listOfWord))\\r\\n\\r\\n\\tplt.title(\\\"Frekuensi Kemunculan Kata\\\")\\r\\n\\tplt.barh(index, listOfFrequency)\\r\\n\\tplt.xlabel('Frekuensi')\\r\\n\\tplt.yticks(index, listOfWord, fontsize=6)\\r\\n\\r\\n\\tplt.show()\",\n \"def visualize_data(df):\\n # Remove 'not available'\\n genres = df.genre.unique().tolist()\\n remove_index = genres.index('Not Available')\\n genres.pop(remove_index)\\n print('Genres: ', genres)\\n\\n # Extract number of songs in each genre\\n genre_counts = df.genre.value_counts().tolist()\\n genre_counts.pop(remove_index)\\n print('Counts: ', genre_counts)\\n\\n # Plot bar graph\\n plt.bar(genres, genre_counts)\\n plt.xlabel('Genres')\\n plt.ylabel('Count')\\n plt.show()\",\n \"def featuresBarPlot(barNames,barValues):\\n plt.bar(range(0,len(barNames)),barValues)\\n plt.xticks(range(0,len(barNames)), barNames,rotation='vertical')\\n plt.show()\",\n \"def plot_class_balances(df, col):\\n\\n ser_counts = df[col].value_counts()\\n ser_counts.plot.bar()\\n plt.title(col + ' Counts \\\\n(classes={})'.format(ser_counts.shape[0]))\\n \\n plt.show()\",\n \"def plot_bv_bar(df, xcolname, ycolname, icol=0):\\n # set plot size\\n fig, ax = plt.subplots(figsize=(8,6))\\n \\n # plotting... box\\n sns.barplot(ax=ax, data = df\\n , x = str(xcolname)\\n , y = str(ycolname)\\n , color = sns.color_palette()[icol]);\\n \\n \\n # title and labels\\n plt.title(xcolname+' Vs '+ycolname, fontsize=20)\\n plt.xlabel(xcolname+ ' (units)', fontsize=16)\\n plt.ylabel(ycolname+ ' (units)', fontsize=16)\\n \\n return plt.show()\",\n \"def BarPlot(data,colormap='Paired',ax=None,headers='show',value_max=None,x_ticklabels_rotation=90,**kws):\\r\\n if ax is None:\\r\\n ax=plt.subplot(111)\\r\\n\\r\\n if value_max is None:\\r\\n value_max=data.sum(1).max()\\r\\n\\r\\n data.plot(kind='bar', stacked=True,colormap=colormap, ax=ax,**kws)\\r\\n ax.set_ylim((0,value_max))\\r\\n\\r\\n\\r\\n #reverse legend order\\r\\n handles, labels = ax.get_legend_handles_labels()\\r\\n ax.legend(reversed(handles),reversed(data.columns),bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\\r\\n\\r\\n #AXES\\r\\n if (headers is None or headers=='hide'):\\r\\n ax.get_xaxis().set_visible(False)\\r\\n ax.get_xaxis().set_ticks([])\\r\\n elif headers=='show':\\r\\n plt.setp(ax.get_xticklabels(),rotation=x_ticklabels_rotation)\\r\\n ax.set_xlabel(None,visible=False)\\r\\n\\r\\n\\r\\n #plt.tight_layout()\\r\\n\\r\\n\\r\\n return ax\",\n \"def bar_chart(\\n df,\\n orientation='v',\\n bar_width=None,\\n opacity=0.9,\\n textpos=None,\\n linewidth=1,\\n linecolor='#2C3347',\\n marker_color=None,\\n **kwargs):\\n\\n traces = []\\n rng = df.index.size if orientation == 'v' else df.columns.size\\n otn = orientation\\n for i in range(rng):\\n x = [str(x) for x in df.columns] if otn == 'v' else df.iloc[:, i]\\n y = df.iloc[i] if otn == 'v' else [str(x) for x in df.index]\\n text = df.iloc[i] if otn == 'v' else df.iloc[:, i]\\n name = df.iloc[i].name if otn == 'v' else df.columns[i]\\n\\n preset_args = dict(\\n x=x,\\n y=y,\\n text=text,\\n textposition=textpos,\\n marker=dict(\\n opacity=opacity,\\n color=marker_color,\\n line=dict(\\n color=linecolor,\\n width=linewidth)),\\n name=name,\\n width=bar_width,\\n orientation=orientation\\n )\\n\\n all_args = {**preset_args, **kwargs}\\n bar = go.Bar(all_args)\\n traces.append(bar)\\n\\n return traces\",\n \"def plot_bar_chart(objects, data, title='', ylabel='', bar_color = 'blue'):\\n y_pos = np.arange(len(objects))\\n\\n plt.bar(y_pos, data, align='center', alpha=0.5)\\n plt.xticks(y_pos, objects, rotation='vertical')\\n plt.ylabel(ylabel, fontsize=12)\\n plt.title(title, fontsize=12)\\n plt.ylim([0,1300])\\n plt.bar(range(len(data)), data, color=bar_color)\\n\\n return plt.show()\",\n \"def bar_plot(data, xtitle, title):\\n label = list(set(data))\\n height = count_elements(data)\\n height = [height[i] for i in label]\\n plt.bar(label, height=height, width=0.8)\\n plt.ylabel('frequency')\\n plt.xlabel(xtitle)\\n plt.xticks(label)\\n plt.savefig('./figures/{}.png'.format(title))\\n plt.close()\",\n \"def drawStackedBarPlot(df, column, hue):\\n plt.style.use('default')\\n plt.style.use('dark_background')\\n p_table = pd.pivot_table(df, index=column, \\n columns=hue, aggfunc='size')\\n p_table = p_table.div(p_table.sum(axis=1), axis=0)\\n p_table.plot.bar(stacked=True, figsize=(14,7))\\n plt.xlabel('Spekraltyp')\\n plt.ylabel('Anteil')\\n plt.show()\",\n \"def barplot(bars, title='', upColor='blue', downColor='red'):\\n import pandas as pd\\n import matplotlib.pyplot as plt\\n from matplotlib.lines import Line2D\\n from matplotlib.patches import Rectangle\\n\\n if isinstance(bars, pd.DataFrame):\\n ohlcTups = [\\n tuple(v) for v in bars[['open', 'high', 'low', 'close']].values]\\n elif bars and hasattr(bars[0], 'open_'):\\n ohlcTups = [(b.open_, b.high, b.low, b.close) for b in bars]\\n else:\\n ohlcTups = [(b.open, b.high, b.low, b.close) for b in bars]\\n\\n fig, ax = plt.subplots()\\n ax.set_title(title)\\n ax.grid(True)\\n fig.set_size_inches(10, 6)\\n for n, (open_, high, low, close) in enumerate(ohlcTups):\\n if close >= open_:\\n color = upColor\\n bodyHi, bodyLo = close, open_\\n else:\\n color = downColor\\n bodyHi, bodyLo = open_, close\\n line = Line2D(\\n xdata=(n, n),\\n ydata=(low, bodyLo),\\n color=color,\\n linewidth=1)\\n ax.add_line(line)\\n line = Line2D(\\n xdata=(n, n),\\n ydata=(high, bodyHi),\\n color=color,\\n linewidth=1)\\n ax.add_line(line)\\n rect = Rectangle(\\n xy=(n - 0.3, bodyLo),\\n width=0.6,\\n height=bodyHi - bodyLo,\\n edgecolor=color,\\n facecolor=color,\\n alpha=0.4,\\n antialiased=True\\n )\\n ax.add_patch(rect)\\n\\n ax.autoscale_view()\\n return fig\",\n \"def plot_data_stats(data):\\n sns.set_style(\\\"dark\\\")\\n f, ax = plt.subplots(figsize=(6, 15))\\n\\n ax = sns.barplot(x='tag', y='count', data=tags_freqs)\\n\\n ax.axes.set_title(\\\"POS Tags Frequencies\\\",fontsize=20)\\n ax.set_xlabel(\\\"POS Tags\\\", fontsize=16)\\n ax.set_ylabel(\\\"Counts\\\", fontsize=16)\\n ax.tick_params(labelsize=12)\\n\\n plt.show()\",\n \"def barplot(data, field_name, field_categories):\\n\\n\\tcategories, counts = np.unique(data[field_name], return_counts=True)\\n\\n\\tfig = plt.figure(figsize=(4, 3))\\n\\taxes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\\n\\taxes.bar(range(len(categories)), counts, fc=\\\"gray\\\") # fc is the face color\\n\\n\\taxes.set_xlabel(\\\"\\\")\\n\\taxes.set_ylabel('Count')\\n\\taxes.set_title(field_name)\\n\\tfig.autofmt_xdate(rotation=45)\\n\\n\\taxes.set_xticks(range(len(categories)))\\n\\taxes.set_xticklabels([field_categories[c] for c in categories]);\",\n \"def _bar_example_4(quantity_by_fruit):\\n ch = chartify.Chart(x_axis_type=\\\"categorical\\\", blank_labels=True)\\n ch.set_title(\\\"Vertical bar plot with labels\\\")\\n ch.set_subtitle(\\\"Hidden y-axis\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n ch.style.color_palette.reset_palette_order()\\n ch.plot.text(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n text_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n # Adjust the axis range to prevent clipping of the text labels.\\n ch.axes.set_yaxis_range(0, 1200)\\n ch.axes.hide_yaxis()\\n ch.show(_OUTPUT_FORMAT)\",\n \"def plotify_bar(title, data):\\n\\n x, y, z, labels = [], [], [], []\\n\\n for d in reversed(data[:len(data) - 1]):\\n x.append(f\\\"{d['settimana_del']:%d-%b}\\\\n{d['settimana_fino_al']:%d-%b}\\\")\\n y.append(d['nuovi_positivi'])\\n z.append(\\\"lightgrey\\\" if d['giorni'] < 7 else 'green' if d['delta'] <= 0 else 'red' )\\n labels.append(human_format(d['nuovi_positivi']) if d['giorni'] == 7 else f\\\"{human_format(d['nuovi_positivi'])}\\\\n(in corso)\\\" )\\n\\n x_pos = np.arange(len(x))\\n\\n # create a new figure\\n plt.figure()\\n\\n plt.title(title)\\n\\n # Create bars with different colors\\n plt.bar(x_pos, y, color=z)\\n\\n # Create names on the x-axis\\n plt.xticks(x_pos, x, rotation=40)\\n\\n\\n # Text on the top of each bar\\n x_ticks = plt.gca().get_xticks()\\n for i in range(len(y)):\\n text = data[i]\\n plt.text(x = x_ticks[i], y = y[i]+5, s = labels[i], size = 9, horizontalalignment='center', verticalalignment='bottom')\\n\\n # prettify y values\\n current_values = plt.gca().get_yticks()\\n plt.gca().set_yticklabels(['{:n}'.format(int(x)) for x in current_values])\\n\\n # responsive layout\\n plt.tight_layout()\\n\\n\\n\\n buf = io.BytesIO()\\n plt.savefig(buf, format='png')\\n buf.seek(0)\\n\\n ### Release memory\\n # Clear the current axes.\\n plt.cla() \\n # Clear the current figure.\\n plt.clf() \\n # Closes all the figure windows.\\n plt.close('all') \\n # plt.close(fig)\\n gc.collect()\\n\\n return buf\",\n \"def _bar_example_1(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, x_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Vertical bar plot\\\")\\n ch.set_subtitle(\\\"Automatically sorts by value counts.\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def value_counts_plot(df):\\n \\n plt.figure(figsize=(15,10))\\n \\n #get rid of sort_index() to change the graph\\n return df.value_counts().sort_index().plot(kind='bar')\",\n \"def message_genre_bar_chart(df):\\n genre_counts = df.groupby('genre').count()['message']\\n genre_names = list(genre_counts.index)\\n return {\\n 'data': [\\n Bar(\\n x=genre_names,\\n y=genre_counts\\n )\\n ],\\n\\n 'layout': {\\n 'title': 'Distribution of Message Genres',\\n 'yaxis': {\\n 'title': \\\"Count\\\"\\n },\\n 'xaxis': {\\n 'title': \\\"Genre\\\"\\n }\\n }\\n }\",\n \"def draw_bar(x_index, data_list, xticks, title, x_label, y_label):\\n pyplot.bar(x_index, data_list)\\n pyplot.xlabel(x_label)\\n pyplot.ylabel(y_label)\\n pyplot.xticks(x_index, xticks)\\n pyplot.title(title)\\n pyplot.show()\\n pyplot.savefig()\",\n \"def plot(*args, **params):\\n if len(args) == 1: # only support data for now\\n if isinstance(args[0], list):\\n bar_2d = Bar2D(data=args[0])\\n bar_2d.plot()\\n bar_2d.fig.show()\\n else:\\n bar_2d = Bar2D(**params)\\n bar_2d.plot()\\n bar_2d.fig.show()\",\n \"def _bar_example_3(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, y_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Horizontal bar plot\\\")\\n ch.set_subtitle(\\\"Horizontal with color grouping\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def BarOverview(data):\\n return dcc.Graph(id=\\\"BarOverview\\\", className=\\\"bar\\\", figure=dict(\\n data=[go.Bar(\\n x=data[\\\"frequencies\\\"],\\n y=data[\\\"names\\\"],\\n orientation='h',\\n marker={\\n 'color': '#ff4058'\\n },\\n )],\\n layout=dict(\\n title=\\\"<b>Most common Persons</b>\\\",\\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\\n margin=dict(l=10, r=20, t=50, b=30),\\n plot_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n paper_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n xaxis=dict(tick0=0, dtick=max(data[\\\"frequencies\\\"])),\\n yaxis=dict(ticks='outside',\\n showgrid=True,\\n showline=False,\\n showticklabels=False),\\n annotations=[dict(xref='paper', yref='y',\\n x=0, y=yd,\\n font=dict(\\n color=\\\"#000000\\\",\\n size=19\\n ),\\n text=str(yd),\\n showarrow=False) for xd, yd in zip(data[\\\"frequencies\\\"], data[\\\"names\\\"])]\\n )\\n ))\",\n \"def plot_norm_bar(df, title, figsize=(12,7)):\\n fig, ax = plt.subplots(ncols=1, figsize=figsize)\\n fig.suptitle(title)\\n cat_value_counts = df.fillna('missing').value_counts(normalize=True)\\n sns.barplot(y = cat_value_counts.index, x= cat_value_counts.values*100)\\n ax.set(xlabel= 'percentage', ylabel=str(df.name))\\n \\n plt.plot()\\n\\n return\",\n \"def barplot(self, x = \\\"Predictor\\\", color = None, opacity = 1, template = \\\"ggplot2\\\", \\n has_title = True, barmode=\\\"stack\\\", is_horizontal = False, title = None, is_percent = False,\\n show_num = False):\\n if color: #Produce either a stacked or grouped bar plot\\n df_stack = self._df.groupby([x,color]).size().reset_index()\\n df_stack['Percentage'] = self._df.groupby([x, color]).size().groupby(level = 0).apply(lambda \\n x:100 * x/float(x.sum())).values\\n df_stack.columns = [x, color, 'Count', 'Percentage']\\n df_stack['Percentage'] = round(df_stack['Percentage'], 2)\\n \\n x_clean, df_clean = clean_varname(df_stack, var = x)\\n color_clean, df_clean = clean_varname(df_clean, var = color)\\n \\n if has_title:\\n if not title:\\n title = f\\\"Bar Plot of {x_clean} and {color_clean}\\\"\\n else:\\n title = None\\n \\n \\n # 8 different variations for how this graph can appear:\\n if is_horizontal:\\n if is_percent:\\n if show_num: #Show percentages on stacked bar graph\\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Percentage'])\\n else:\\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if show_num: #Show counts on stacked bar graph:\\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Count'])\\n else:\\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if is_percent:\\n if show_num:\\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Percentage'])\\n else:\\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if show_num:\\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Count'])\\n else:\\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title) \\n \\n return fig\\n \\n else: #Create a basic bar plot\\n df_stack = self._df.groupby([x]).size().reset_index()\\n df_stack['Percentage'] = self._df.groupby([x]).size().groupby(level = 0).apply(lambda\",\n \"def bar( # pylint: disable=disallowed-name\\n self, x: Hashable | None = None, y: Hashable | None = None, **kwargs\\n ) -> PlotAccessor:\\n return self(kind=\\\"bar\\\", x=x, y=y, **kwargs)\",\n \"def bar(self, entry_type:str, x:str, labels:list=None, diff:bool=False, x_idx:int=-1):\\n\\n query = self._decode(x)\\n\\n data_points = []\\n\\n for idx, (log, name) in enumerate(zip(self.logs, self.log_names)):\\n log = log[entry_type]\\n\\n candidates = []\\n\\n for entry in log:\\n test = self._follow(entry, query)\\n\\n if type(test) == dict:\\n candidates.append(test)\\n elif type(test) == list:\\n candidates.append({idx: v for idx, v in enumerate(test)})\\n \\n if len(candidates) > 0:\\n data_points.append((name, candidates[x_idx]))\\n \\n if len(data_points) == 0:\\n print('Warning: Nothing to show in bar chart!')\\n return\\n\\n names = [x[0] for x in data_points]\\n data_points = [x[1] for x in data_points]\\n\\n # Construct the labels for the data\\n if labels is not None:\\n data_labels = labels\\n else:\\n data_labels = set()\\n for datum in data_points:\\n for k in datum:\\n data_labels.add(k)\\n \\n data_labels = list(data_labels)\\n data_labels.sort()\\n \\n\\n data_values = [[(datum[k] if k in datum else None) for k in data_labels] for datum in data_points]\\n\\n if diff:\\n for idx in reversed(range(len(data_values))):\\n for jdx in range(len(data_labels)):\\n if data_values[0][jdx] is None or data_values[idx][jdx] is None:\\n data_values[idx][jdx] = None\\n else:\\n data_values[idx][jdx] -= data_values[0][jdx]\\n\\n\\n series_labels = names\\n\\n # Plot the graph now\\n num_bars = len(series_labels)\\n bar_width = 1 / (num_bars + 1)\\n \\n # Set position of bar on X axis\\n positions = [np.arange(len(data_labels))]\\n for _ in range(1, num_bars):\\n positions.append([x + bar_width for x in positions[-1]])\\n \\n # Make the plot\\n for idx, (series, data, pos) in enumerate(zip(series_labels, data_values, positions)):\\n plt.bar(pos, data, color=self._color(idx), width=bar_width, edgecolor='white', label=series)\\n \\n # Add xticks on the middle of the group bars\\n plt.title(x.replace('x.', entry_type + '.') + (' diff' if diff else ''))\\n plt.xticks([r + bar_width for r in range(len(data_labels))], data_labels)\\n \\n # Create legend & Show graphic\\n plt.legend()\\n plt.show()\",\n \"def bar_time_series(df, title, ylabel, report):\\n for col in df:\\n fig, ax = plt.subplots(1, 1, figsize=(12, 4))\\n plt.gcf().subplots_adjust(bottom=0.25)\\n df[col].plot.bar();\\n ax.set_xticklabels([v if i % 4 == 0 else '' for i, v in enumerate(df.index)])\\n ax.xaxis.set_tick_params(rotation=45, length=0);\\n ax.set_xlabel('Date')\\n ax.set_ylabel(ylabel)\\n full_title = title if df.shape[1] == 1 else '{} {}'.format(col, title)\\n report.write_plot(full_title)\\n plt.title(full_title)\\n plt.show();\\n plt.close();\",\n \"def plot_uv_bar(df, colname, colorid=0):\\n if (colname in list(df.columns)):\\n \\n # Set figure size \\n fig, ax = plt.subplots(figsize=(8,6))\\n \\n # set colorid for bar plot\\n base_color = sns.color_palette()[colorid]\\n\\n # variable counts to calculate percentage\\n cdict_count = df[colname].value_counts().to_dict() \\n total_count = df.shape[0]\\n \\n \\n if (len(list(cdict_count.keys())) > 5):\\n # max.count to position the %\\n maxcount_pct= np.max(list(cdict_count.values()))*0.125\\n # max. no. of categories Vs % rotation \\n rottext_pct = 90 \\n # font size for % display\\n fontsiz_pct = 12\\n else:\\n # max.count to position the %\\n maxcount_pct= np.max(list(cdict_count.values()))*0.075\\n # max. no. of categories Vs % rotation \\n rottext_pct = 0 \\n # font size for % display\\n fontsiz_pct = 16\\n \\n \\n # plotting...\\n sns.countplot(data = df, x = colname\\n , order = list(cdict_count.keys())\\n , color = base_color\\n , saturation = 0.7)\\n\\n # title and labels\\n plt.title('Order of '+ colname, fontsize=20)\\n plt.xlabel(colname + ' Type', fontsize=16)\\n plt.ylabel('Count', fontsize=16)\\n \\n # x-,y- ticks\\n locs, labels = plt.xticks(fontsize=16)\\n plt.yticks(fontsize=16)\\n\\n # display % count information on each tower of bar plot\\n for loc, label in zip(locs, labels):\\n count = cdict_count[label.get_text()]\\n pct_string = '{:0.1f}%'.format(count*100/total_count)\\n plt.text(loc, count-maxcount_pct, pct_string, ha='center', color='w', fontsize=fontsiz_pct, rotation=rottext_pct)\\n\\n return plt.show()\\n\\n else:\\n \\n print(' >>>Error:',colname,' is not in DataFrame')\",\n \"def plot_df(data_frame):\\n plt.figure(figsize = (10, 5))\\n chart = sns.countplot(data_frame['label'], \\n palette=\\\"Set1\\\"\\n )\\n plt.show()\",\n \"def plot_bar(filename, data, std=None, xlab='x', ylab='y', ylim=[0,1],yticks=np.arange(0,1.1,0.1), title='Bar-Plot', methods=None, datasets=None, figwidth=8, figheight=6, colors=None, legend_loc=\\\"lower center\\\", xytick_fontsize=12, xylabel_fontsize=15, title_fontsize=15, legend_fontsize=12):\\n import matplotlib as mpl\\n mpl.use(\\\"pdf\\\")\\n import matplotlib.pyplot as plt\\n\\n data=np.array(data)\\n num_methods=len(data)\\n \\n # colors\\n if colors is None:\\n colors=['b','r','g','c','m','y','k','w'] # maximally 8 colors allowed so far\\n\\n ind = np.arange(num_methods) # the x locations of the bars\\n width = 0.8 # the width of the bars\\n fig=plt.figure(num=1,figsize=(figwidth,figheight))\\n ax=fig.add_subplot(1,1,1)\\n if std is None:\\n ax.bar(ind,data,width,color=colors[0:num_methods],ecolor='k')\\n else:\\n ax.bar(ind,data,width,color=colors[0:num_methods],yerr=std,ecolor='k')\\n\\n # add some text for labels, title and axes ticks\\n ax.set_ylabel(ylab,fontsize=xylabel_fontsize)\\n ax.set_xlabel(xlab,fontsize=xylabel_fontsize)\\n ax.set_title(title,fontsize=title_fontsize)\\n ax.set_xticks(ind+0.5*width)\\n ax.set_xticklabels( methods )\\n #if ylim is None:\\n # yticks=np.arange(0,1.1,0.1)\\n # ylim=[0,1]\\n if yticks is not None:\\n ax.set_yticks(yticks)\\n if ylim is not None:\\n ax.set_ylim(ylim[0],ylim[1])\\n plt.setp(ax.get_xticklabels(), fontsize=xytick_fontsize)\\n plt.setp(ax.get_yticklabels(), fontsize=xytick_fontsize)\\n # shrink axis box \\n #box = ax.get_position()\\n #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\\n #ax.legend( method_bar, methods, loc='lower left', bbox_to_anchor=(1.0, 0.3), fontsize=legend_fontsize )\\n #ax.legend(methods, loc=legend_loc, fontsize=legend_fontsize )\\n #plt.show()\\n fig.savefig(filename,bbox_inches='tight')\\n plt.close(fig)\",\n \"def category_bar_chart(df):\\n label_names = df.drop(['message', 'original', 'genre', 'id'], axis=1).columns\\n label_counts = []\\n for column in label_names:\\n label_counts.append(df[column].sum())\\n return {\\n 'data': [\\n Bar(\\n x=label_names,\\n y=label_counts\\n )\\n ],\\n\\n 'layout': {\\n 'title': 'Distribution of Labelled Categories',\\n 'yaxis': {\\n 'title': \\\"Count\\\",\\n 'type': 'log'\\n },\\n 'xaxis': {\\n 'title': \\\"Category\\\"\\n }\\n }\\n }\",\n \"def bar(variable, name, data=None, x_v=None, color_set=custom_bw,\\n ax_size=(20, 6), highlight=None, ax=None):\\n\\n common_set_up(ax_size) # Apply basic plot style\\n\\n fig = sns.barplot(x=x_v, y=variable, data=data, saturation=1, ax=ax,\\n color=color_set[2], label=name,\\n )\\n\\n sns.despine(offset=2, trim=True, left=True, bottom=True)\\n\\n # Set title and axes\\n title_color = '#192231'\\n font_colour = '#9099A2'\\n if ax is None:\\n fig.set_title('{0}'.format(name),\\n fontsize=20, color=title_color)\\n fig.set_ylabel('Frequency',\\n color=font_colour)\\n fig.set_xlabel('{0}'.format(name),\\n color=font_colour)\\n\\n if highlight:\\n bars = fig.patches\\n bars[highlight].set_color(color_set[1])\\n\\n return fig\",\n \"def barPlot2():\\n n = 10\\n X = np.arange(n)\\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\\n\\n for x,y in zip(X,Y1):\\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\\n\\n plt.ylim(0,1.25)\\n plt.show()\",\n \"def _bar_example_2(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, x_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Vertical bar plot - Label sort\\\")\\n ch.set_subtitle(\\\"Set `categorical_order_by` to sort by labels\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n categorical_order_by=\\\"labels\\\",\\n categorical_order_ascending=True,\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def bar(self, row_id, col_id, label=None, offset=(350, 30), **kwargs):\\n bar = BarGraph(label=label, **kwargs)\\n self.pl[row_id, col_id].addItem(bar)\\n\\n bar.barClicked.connect(self.clickedBar)\",\n \"def plot_results(t_val, mood):\\r\\n N = 8\\r\\n theta = np.linspace(0.0, 2 * np.pi , N, endpoint=False)\\r\\n the_stats = [t_val['number_words'], t_val['average_character_length'], \\r\\n t_val['signs'], t_val['multiple_signs'], t_val['question'],\\r\\n t_val['exclamation'], t_val['name'], mood] \\r\\n \\r\\n width = np.pi / N \\r\\n\\r\\n plt.figure()\\r\\n \\r\\n handle = plt.subplot(111, polar=True)\\r\\n handle.set_xticklabels(['Word', 'AvrChar', 'Signs', '2Signs', '?', '!', 'name', 'mood'])\\r\\n \\r\\n handle.bar(theta, the_stats, width=width, bottom=1.0)\\r\\n \\r\\n plt.show()\",\n \"def img_gen_bar():\\n data = pd.DataFrame(data=np.random.rand(5,1), index=range(1,6), columns=['Fred'])\\n #m,n = np.shape(data)\\n\\n plt.clf()\\n plt.bar(x=data.index.values, height=data.values.ravel(), color='k') # figsize=(10, 6))\\n # Options for later from https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html\\n # bar_width = 0.35\\n # alpha = .3\\n fig=plt.gcf()\\n fig.set_size_inches(2.24, 2.24)\\n plt.axis('off')\\n fig.tight_layout()\\n fig.canvas.draw()\\n # grab the pixel buffer and dump it into a numpy array\\n pixels = np.array(fig.canvas.renderer._renderer)[:,:,:3]\\n #print(pixels.shape)\\n return pixels, data.index.values + data.values.ravel()\",\n \"def _bar2df(bars,data):\\n tmp = list(map(lambda x: [x[0] for _ in range(x[1])],list(enumerate(bars))))\\n labels = list(itertools.chain(*tmp)) # tmp is a list of list and this line flattens it\\n data[\\\"labels\\\"] = labels\\n data[\\\"index\\\"] = list(range(data.shape[0]))\\n aggregation_condition = {'time': ['first', 'last'], \\\\\\n 'index':['first', 'last'], \\\\\\n 'price': ['min', 'max', 'first', 'last']}\\n res = data.groupby(\\\"labels\\\",as_index=False).agg(aggregation_condition)\\n res.columns = ['_'.join(col) for col in res.columns.values]\\n res = res.drop([\\\"labels_\\\"],axis=1)\\n res.columns = ['start_t', 'end_t', 'start_idx', 'end_idx','low', 'high', 'open', 'close']\\n return res\",\n \"def matplotlib_bar_chart() -> Tuple:\\n df = read_dataset(Path('..', '..', 'iris.csv'))\\n x = []\\n\\n for col in df.columns:\\n try:\\n max_val = get_column_max(df, col)\\n x.append(max_val)\\n except ValueError:\\n pass\\n \\n fig, ax = a_libraries.matplotlib_bar_chart(np.array(x))\\n\\n return fig, ax\",\n \"def plot_bar(label_array, acc_array, f1_array, width=0.5, axis_label=None, graph_title=None, file_name=\\\"\\\", dpi=100):\\n plt.figure(figsize=plt.figaspect(1.), dpi=dpi)\\n x = np.arange(len(label_array)) # the label locations\\n plt.bar(x - 0.5 * width, acc_array, width, label='Accuracy')\\n plt.bar(x + 0.5 * width, f1_array, width, label='F1 score')\\n plt.ylim([0, 1.1])\\n plt.xticks(x, labels=label_array)\\n if axis_label is None:\\n axis_label = ['Set', 'Values']\\n plt.xlabel(axis_label[0])\\n plt.ylabel(axis_label[1])\\n if graph_title is None:\\n graph_title = graph_title\\n plt.title(graph_title)\\n plt.tight_layout()\\n plt.legend()\\n plt.grid()\\n if file_name:\\n plt.savefig(file_name, bbox_inches='tight')\\n plt.show()\\n return\",\n \"def PlotBayes( x=np.ones(1), bayes=np.ones(1), title=None, label=None, width=1.0, color='blue', show_values=False, ax=None, posterior=False ):\\n\\n if ax is None:\\n fig, ax = plt.subplots( )\\n ax.bar(x, bayes/bayes.max(), width, color=color )\\n ax.set_title( title )\\n ax.set_yscale('log')\\n ax.set_xlabel( label )\\n if posterior:\\n ax.set_ylabel(r\\\"$P/P_{\\\\rm max}$\\\")\\n else:\\n ax.set_ylabel(r\\\"$\\\\mathcal{B}/\\\\mathcal{B}_{\\\\rm max}$\\\")\\n# ax.set_ylabel(r\\\"$\\\\mathcal{B} = \\\\prod L / L_0$\\\")\\n if show_values: ## print value on top of each bar, .... doesnt work ...\\n shift = bayes.max()/bayes.min()/10\\n for xx, b in zip( x, bayes ):\\n ax.text( xx, b*shift, str(b), color=color, fontweight='bold' )\\n\\n ### assure that there are ticks at y axis\\n lim = ax.get_ylim()\\n ax.set_ylim(lim[0]*0.5, lim[1]*2)\",\n \"def barh_plotter(data: pd.DataFrame, variable: str):\\n fig, axs = plt.subplots(2,5, gridspec_kw={'wspace': 1, 'hspace': 0.2},\\n figsize=(60, 40), sharex = False)\\n\\n for ax, dta in zip(axs.flatten(), data.values()) :\\n ax.barh(dta['club'], dta[f'{variable}'])\\n ax.set_xlabel(f'{variable}', fontsize=25)\\n \\n \\n for ax, dta in zip(axs.flatten(), data.keys()):\\n ax.set_title(dta, fontsize=30)\\n\\n return plt.show()\",\n \"def barPlot1():\\n n = 12\\n X = np.arange(n)\\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n\\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\\n plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\\n\\n for x,y in zip(X,Y1):\\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\\n\\n for x,y in zip(X,Y2):\\n plt.text(x+0.2, -y-0.1, '%.2f' % y, ha='center', va= 'bottom')\\n\\n plt.ylim(-1.25,+1.25)\\n plt.show()\",\n \"def graphy2():\\n data = pd.read_csv(\\\"week2.csv\\\")\\n plot_g = pygal.Bar(fill=True, interpolate='cubic', style=LightSolarizedStyle)\\n plot_g.title = \\\"Top Fans in Week 2\\\"\\n plot_g.x_labels = data.GENDER\\n plot_g.y_labels = map(int, range(0, 80, 10))\\n plot_g.add(\\\"Male\\\", data.COUNT)\\n plot_g.add(\\\"Female\\\", data.COUNT2)\\n plot_g.add(\\\"Total\\\", data.COUNT3)\\n plot_g.render_to_file(\\\"plotweek2.svg\\\")\",\n \"def draw_bar_plot(fig, x, y, labels):\\r\\n\\r\\n #Convert times to a displayable format\\r\\n (x_times, hour_mode) = times_to_axis(x)\\r\\n\\r\\n\\r\\n #Draw horizontal grid lines behind data\\r\\n fig.yaxis.grid(zorder=0)\\r\\n\\r\\n #Draw plot\\r\\n fig.bar(x_times, y, zorder=2)\\r\\n\\r\\n\\r\\n #If necessary, enable processing of \\\"datetime\\\" objects on the x-axis\\r\\n if not hour_mode:\\r\\n fig.xaxis_date()\\r\\n\\r\\n\\r\\n #Label and style plot\\r\\n set_axis_labels(fig, *labels)\\r\\n style_x_labels(fig)\",\n \"def graph(df):\\n df.plot()\\n plt.show()\",\n \"def plot_individual_bar_chart_graph(data_values, title,\\r\\n number_of_keys,\\r\\n max_val,\\r\\n vals_for_bar_chart,\\r\\n file_in):\\r\\n\\r\\n n_groups = len(vals_for_bar_chart)\\r\\n fig, ax = plt.subplots()\\r\\n index = np.arange(n_groups)\\r\\n bar_width = 0.9\\r\\n opacity = 0.4\\r\\n # print vals_for_bar_chart\\r\\n rects1 = plt.bar(index,\\r\\n vals_for_bar_chart,\\r\\n bar_width,\\r\\n alpha=opacity,\\r\\n color='b') # label='whatever'\\r\\n plt.xlabel('number in cluster')\\r\\n plt.ylabel('Count')\\r\\n plt.title(title+\\\"_barchart\\\")\\r\\n plt.legend()\\r\\n pylab.grid(True)\\r\\n ax.set_yscale('symlog')\\r\\n ax.set_xscale('symlog')\\r\\n plt.tight_layout()\\r\\n plt.show()\\r\\n pylab.savefig(file_in + \\\"_\\\" + title + '_barchart.png')\\r\\n plt.close()\\r\\n pylab.close()\",\n \"def show_graph(d:dict):\\n x = []\\n y = []\\n for key, value in d.items():\\n x.append(str(key))\\n y.append(value)\\n\\n x_pos = [i for i, _ in enumerate(x)]\\n plt.figure()\\n plt.bar(x_pos, y, color='green')\\n plt.xlabel(\\\"Size\\\")\\n plt.ylabel(\\\"Number of images\\\")\\n plt.title(\\\"Count by size\\\")\\n plt.xticks(x_pos, x)\",\n \"def plot_type_of_two_topic(data_frame1: pb.DataFrame, data_frame2: pb.DataFrame) -> None:\\n plt.interactive(False)\\n plt.figure()\\n data_frame1.plot(kind='bar', x= data_frame['TopicID'])\\n data_frame2.plot(kind='bar', x= data_frame['TopicID'])\\n plt.show()\",\n \"def plotBarPlots(self, stage, strain, strainID, count, p, xm, ym, xn, yn):\\n\\t\\t\\n\\t\\ttimeList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\\n\\t\\t\\n\\t\\tind = np.arange(len(timeList))\\n\\t\\t\\n\\t\\tplt.style.use('bmh')\\n\\t\\tcolors = ['#b2182b', '#238b45', '#3690c0', '#023858']\\n\\t\\t\\n\\t\\tfig = plt.figure(figsize=(15,15), frameon=False)\\n\\t\\tax1 = fig.add_subplot(111)\\n\\t\\tfig.suptitle(strain + ' - ' + stage, fontsize=20, fontweight='bold')\\n\\t\\n\\t\\tax1.spines['top'].set_visible(False)\\n\\t\\tax1.spines['right'].set_visible(False)\\n\\t\\tax1.spines['bottom'].set_visible(False)\\n\\t\\tax1.spines['left'].set_visible(False)\\n\\t\\tax1.yaxis.set_ticks_position('left')\\n\\t\\tax1.xaxis.set_ticks_position('bottom')\\n\\t\\twidth = 0.6\\n\\t\\t\\n\\t\\tax1.bar(ind+width/2., count[timeList,1], width, color=colors[0], edgecolor = \\\"none\\\")\\n\\t\\t\\n\\t\\tax1.plot(*p.linspace(), c='k', linewidth=3.0)\\n\\t\\t\\n\\t\\tfor point in range(len(xm)):\\n\\t\\t\\tax1.plot(xm[point], ym[point], marker='*', markersize=30, color=\\\"blue\\\")\\n\\t\\tfor point in range(len(xn)):\\n\\t\\t\\tax1.plot(xn[point], yn[point], marker='*', markersize=30, color=\\\"blue\\\")\\t\\t\\t\\n\\t\\t\\n\\t\\tax1.set_ylabel('Frequency as %', fontweight='bold', fontsize=20)\\n\\t\\tax1.set_xlabel('Time', fontweight='bold', fontsize=20)\\n\\t\\txTickMarks = ['%s' %str(j) for j in timeList]\\n\\t\\tax1.set_xticks(ind+width/2)\\n\\t\\txtickNames = ax1.set_xticklabels(xTickMarks, fontweight='bold')\\n\\t\\t\\n\\t\\tplt.setp(xtickNames, fontsize=15)\\n\\t\\tax1.xaxis.set_ticks_position('none')\\n\\t\\tax1.yaxis.set_ticks_position('none')\\t\\t\\n\\t\\tax1.set_yticklabels(ax1.get_yticks(), fontweight='bold', fontsize=17)\\n\\t\\tax1.set_xlim([6, len(timeList)-6])\\n\\t\\t\\t\\t\\t\\n\\t\\tfname = 'strain%d' %strainID + stage + '.png' \\n\\t\\t\\n\\t\\tfig.savefig(fname, transparent=True, dpi=100)\\n\\t\\tplt.close(fig)\",\n \"def diagram_plugs(data_no,\\r\\n data_little,\\r\\n data_means,\\r\\n data_great,\\r\\n data_large_enough,\\r\\n data_super_large,\\r\\n er_no, er_little,\\r\\n er_means,\\r\\n er_great,\\r\\n er_large_enough,\\r\\n er_super_large):\\r\\n\\r\\n\\r\\n plt.bar(range(6), [data_no,\\r\\n data_little,\\r\\n data_means,\\r\\n data_great,\\r\\n data_large_enough,\\r\\n data_super_large],\\r\\n width=0.1, color='black',\\r\\n yerr=[er_no, er_little, er_means,\\r\\n er_great, er_large_enough,\\r\\n er_super_large],\\r\\n ecolor='black', capsize=10)\\r\\n\\r\\n\\r\\n plt.xticks(range(6), ['non', 'petit', 'moyen',\\r\\n 'grand', 'assez grand', 'tres grand'])\\r\\n\\r\\n\\r\\n plt.ylabel('Taux de pollution en AQI')\\r\\n plt.title(\\\"Taux de pollution selon les bouchons\\\")\\r\\n\\r\\n nouveau = new()\\r\\n print(nouveau)\\r\\n plt.savefig(nouveau, transparent=True)\\r\\n plt.clf()\\r\\n plt.close()\\r\\n\\r\\n shutil.move(nouveau, '/app/static/popo')\\r\\n\\r\\n return nouveau\",\n \"def barplot(self, name: str, y_label: str, img_title: str):\\n path = C.TEST_DIR\\n\\n sns.set(style='whitegrid')\\n sns.set_palette(sns.color_palette(C.IRT_COLORS))\\n df = pd.read_csv(path + name + '.csv')\\n ax = sns.barplot(data=df)\\n ax.set(ylabel=y_label, title=img_title)\\n\\n self.save_plot(name)\\n plt.show()\",\n \"def plot_bar_chart(log, frame_seconds=-1):\\n\\tif frame_seconds == -1:\\n\\t\\tframe_seconds = (log.content[-1].timestamp - log.content[0].timestamp).total_seconds() / timedelta(seconds=100).total_seconds()\\n\\tlines,dates,areas = log.give_plot_data_bar(frame_seconds=frame_seconds)\\n\\tfig, ax = plt.subplots(figsize=(11,6))\\n\\tfig.autofmt_xdate()\\n\\tmyFmt = DateFormatter(\\\"%Y %d.%b %H:%M:%S\\\")\\n\\tax.xaxis.set_major_formatter(myFmt)\\n\\tax.set_xlabel('timestamps in UTC')\\n\\tax.set_ylabel('amount of entries')\\n\\tplt.title('Analysis of the file \\\\\\\"'+log.name+'\\\\\\\" \\\\n with frame size ' + str(frame_seconds) +' seconds')\\n\\n\\twidth = timedelta(seconds=frame_seconds).total_seconds() / timedelta(days=1).total_seconds()\\n\\tnormal = ax.bar(dates, areas, width, label='entries', edgecolor='k')\\n\\n\\tplt.legend()\\n\\tplt.subplots_adjust(left=0.15, bottom=0.2, right=0.9, top=0.9)\\n\\tfig.canvas.mpl_connect('key_press_event', _quit_figure)\",\n \"def create_bar_graph(plot_df, title=\\\"\\\", x_title=\\\"\\\", y_title=\\\"\\\"):\\n plot_df[\\\"quarter\\\"] = pd.PeriodIndex(pd.to_datetime(plot_df.iloc[:, 0]), freq=\\\"Q\\\")\\n fig_data = [\\n go.Bar(\\n x=plot_df[\\\"quarter\\\"].astype(str),\\n y=plot_df.iloc[:, 1],\\n text=plot_df.iloc[:, 1],\\n marker={\\\"color\\\": color_palette[0]},\\n hoverinfo=\\\"x+y\\\",\\n showlegend=False,\\n )\\n ]\\n\\n fiq_layout = build_bar_layout(title, x_title=x_title, y_title=y_title)\\n\\n return dict(data=fig_data, layout=fiq_layout)\",\n \"def oneNumBar(df, colName):\\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\\n ax = bins.value_counts()\\n bins = bins.cat.as_ordered()\\n bins = bins.cat.categories\\n bounds = bins.left \\n bounds = list(bounds)\\n bounds.append(bins[len(bounds)-1].right)\\n texts = []\\n for x,y in zip(bounds[0::],bounds[1::]):\\n texts.append(\\\"(\\\" + str(x) + \\\", \\\" + str(y) + \\\"]\\\") \\n barData = [go.Bar(x=texts, \\n y=ax,\\n marker=dict(\\n color = '#92c5de',\\n opacity=0.8)\\n )] \\n layout = go.Layout(\\n title=\\\"Bar Plot Showing Count of Values for \\\" + str(colName[0]),\\n xaxis=dict(\\n title= colName[0]\\n ),\\n yaxis=dict(\\n title= \\\"NUMBER OF RECORDS\\\", \\n )\\n )\\n fig = go.Figure(data=barData, layout=layout)\\n return {\\\"label\\\":\\\"Frequency\\\", \\\"plot\\\":fig}\",\n \"def bar_plot(self,\\n x: str,\\n y: str,\\n x_label: str=None,\\n y_label: str=None,\\n title: str='Bar Plot',\\n **kwargs) -> Figure:\\n x_label = x if x_label is None else x_label\\n y_label = y if y_label is None else y_label\\n fig = px.bar(self.df, x=x, y=y,\\n labels={x: x_label, y: y_label},\\n title=title, **kwargs)\\n return fig\",\n \"def setBarType(bartype='vertical'):\\n dislin.bartyp(bardict[bartype])\",\n \"def draw_bar(df=data):\\n pt = {\\n 1: 'Credit card',\\n 2: 'Cash',\\n 3: 'No charge',\\n 4: 'Dispute',\\n 5: 'Unknown',\\n 6: 'Voided trip',\\n }\\n df['payment_type'] = df['payment_type'].replace(pt)\\n gr = df.groupby(['payment_type', 'weekday']) \\\\\\n .agg(total_amount=('total_amount', 'sum')) \\\\\\n .reset_index(drop=False)\\n return px.bar(gr, x='weekday', y='total_amount', color='payment_type', barmode='group') \\\\\\n .update_layout(\\n template='plotly_dark',\\n plot_bgcolor='rgba(0, 0, 0, 0)',\\n paper_bgcolor='rgba(0, 0, 0, 0)',\\n )\",\n \"def plot_dict_bar(aDictionary):\\n\\t# Convert strings to float\\n\\tfor key in aDictionary:\\n\\t\\taDictionary[key] = float(aDictionary[key])\\n\\t\\t\\n\\t# Plot the result\\n\\tplt.bar(range(len(aDictionary)), aDictionary.values(), align='center')\\n\\tplt.xticks(range(len(aDictionary)), aDictionary.keys(), rotation=90)\\n\\t\\n\\tplt.show()\",\n \"def performanceBarCharts(): \\n ##tauopathy HCS pearson\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null YFP Model\\\", \\\"Null DAPI Model\\\"]\\n ml_model_perf = pickle.load(open(\\\"pickles/ml_model_perf.pkl\\\", \\\"rb\\\"))\\n null_model_perf = pickle.load(open(\\\"pickles/null_model_perf.pkl\\\", \\\"rb\\\"))\\n null_dapi_perf = pickle.load(open(\\\"pickles/single_channel_DAPI_null_model_perf.pkl\\\", \\\"rb\\\"))\\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\\n x = [1, 2, 3]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance\\\",fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=12)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=12)\\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\\n plt.savefig(\\\"matplotlib_figures/tau_performance_pearson_special_HCS_model.png\\\", dpi=300)\\n\\n ##tauopathy HCS MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null YFP Model\\\", \\\"Null DAPI Model\\\"]\\n ml_model_perf = pickle.load(open(\\\"pickles/ml_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n null_model_perf = pickle.load(open(\\\"pickles/null_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n null_dapi_perf = pickle.load(open(\\\"pickles/single_channel_DAPI_null_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\\n x = [1, 2, 3]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance\\\",fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=12)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=12)\\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\\n plt.savefig(\\\"matplotlib_figures/tau_performance_mse_special_HCS_model.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (raw images) pearson\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ml_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_null_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [0.075, 0.1156] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with Raw Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.02)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_pearson_cross_val.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (raw images) MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ml_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_null_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [0.15, .2312] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance with Raw Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.01)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_mse.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (ablated image training) pearson\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ablated_ml_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_ablated_null_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [.1288, .1385] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with\\\\n95% Ablated Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.0)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_pearson_trained_ablation_model.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (ablated image training) MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ablated_ml_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_ablated_null_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [.2576, .2771] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance with\\\\n95% Ablated Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.0)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_MSE_trained_ablation_model.png\\\", dpi=300)\\n\\n ##supplemental single channel learning YFP and DAPI performance\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"YFP-tau to AT8-pTau\\\", \\\"DAPI to AT8-pTau\\\"]\\n YFP_ml_model = pickle.load(open(\\\"pickles/single_channel_YFP_ml_model_perf.pkl\\\", \\\"rb\\\"))\\n DAPI_ml_model = pickle.load(open(\\\"pickles/single_channel_DAPI_ml_model_perf.pkl\\\", \\\"rb\\\"))\\n y = np.array([YFP_ml_model[0], DAPI_ml_model[0]]).round(decimals=2)\\n stds = [YFP_ml_model[1], DAPI_ml_model[1]]\\n x = [1, 2]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\\\"cornflowerblue\\\", zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with\\\\nSingle Channel Input Learning\\\",fontname=\\\"Times New Roman\\\", fontsize=17, y=1.01)\\n ax.set_xlabel(\\\"Model\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/supplemental_single_channel_learning.png\\\", dpi=300)\\n\\n ##supplemental single channel learning YFP and DAPI, input similarity to prediction\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"YFP-tau to AT8-pTau\\\", \\\"DAPI to AT8-pTau\\\"]\\n y = np.array([0.94894628, 0.98718720]).round(decimals=2)\\n stds = [0.1673864, 0.039042]\\n x = [1, 2]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\\\"orange\\\", zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Similarity Between\\\\nInput Channel and Predicted Channel\\\",fontname=\\\"Times New Roman\\\", fontsize=17)\\n ax.set_xlabel(\\\"Model\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1.13))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/supplemental_single_channel_learning_pearson_similarity_input_and_predicted.png\\\", dpi=300)\",\n \"def visualize_type():\\n \\n data_file = parse(MY_FILE, ',')\\n\\n # num of incidents per category\\n counter = Counter(item['Category'] for item in data_file)\\n\\n # Set the labels\\n labels = tuple(counter.keys())\\n\\n # Set exactly where the labels hit the x-axis\\n xlocations = na.array(range(len(labels))) + 0.5\\n\\n # Width of each bar\\n width = 0.5\\n\\n # Assign data to a bar plot\\n plt.bar(xlocations, counter.values(), width=width)\\n\\n # Assign labels and tick location to x-axis\\n plt.xticks(xlocations + width / 2, labels, rotation=90)\\n \\n # Give some more room so the x-axis labels aren't cut off\\n plt.subplots_adjust(bottom=0.4)\\n\\n # Make the overall graph/figure larger\\n plt.rcParams['figure.figsize'] = 12, 8\\n\\n # save\\n plt.savefig('Type.png')\\n\\n # close\\n plt.clf()\",\n \"def get_proba_plot(data_type):\\n buffer = io.BytesIO()\\n plt.subplots(figsize = (25,15))\\n ax = sns.barplot(x='proba', y='type', data=data_type, palette = \\\"Blues_r\\\")\\n ax.set_xlabel('Probability')\\n plt.yticks(fontsize = 30)\\n plt.xticks(fontsize = 30)\\n plt.ylabel(\\\"Type\\\", fontsize = 38)\\n plt.xlabel(\\\"Probability\\\", fontsize = 38);\\n plt.title(\\\"Model Results\\\", fontsize = 50)\\n plt.savefig(buffer, format='png')\\n# plt.show()\\n plt.close()\\n buffer.seek(0)\\n chart_probability= Image.open(buffer).resize((512+256,512))\\n return chart_probability\",\n \"def plot_bar(counter, title=\\\"\\\", filename=\\\"tmp.png\\\"):\\n\\n fig = plt.figure()\\n axis = fig.add_subplot(111)\\n\\n if isinstance(counter, dict):\\n frequencies = counter.values()\\n names = counter.keys()\\n elif isinstance(counter, list):\\n frequencies = [x[1] for x in counter]\\n names = [x[0] for x in counter]\\n y_pos = np.arange(len(counter))\\n axis.barh(y_pos, frequencies, align='center')\\n axis.set_title(title)\\n axis.set_yticks(y_pos)\\n axis.set_yticklabels(list(names))\\n axis.invert_yaxis()\\n axis.set_xlabel('Frequency')\\n print('going to save fig...')\\n fig.savefig('png_files/' + filename.replace(\\\".csv\\\", \\\".png\\\"))\\n\\n return axis\",\n \"def custom_barchart(ax, x, y, error, xlims, ylims, error_kw, color='lightblue', width=0.75):\\n\\n error = [np.zeros(len(error)), error]\\n\\n ax.bar(x, y, color=color, width=width, yerr=error, error_kw=error_kw, align='center')\\n\\n ax.set_xlim(xlims)\\n ax.set_ylim(ylims)\\n\\n return ax\",\n \"def bar_plot(trj: TrajaDataFrame, bins: Union[int, tuple] = None, **kwargs) -> Axes:\\n # TODO: Add time component\\n\\n bins = traja.trajectory._bins_to_tuple(trj, bins)\\n\\n X, Y, U, V = coords_to_flow(trj, bins)\\n\\n hist, _ = trip_grid(trj, bins, hist_only=True)\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection=\\\"3d\\\")\\n ax.set_aspect(\\\"equal\\\")\\n X = X.flatten(\\\"F\\\")\\n Y = Y.flatten(\\\"F\\\")\\n ax.bar3d(\\n X,\\n Y,\\n np.zeros_like(X),\\n 1,\\n 1,\\n hist.flatten(),\\n zsort=\\\"average\\\",\\n shade=True,\\n **kwargs,\\n )\\n ax.set(xlabel=\\\"x\\\", ylabel=\\\"y\\\", zlabel=\\\"Frames\\\")\\n\\n return ax\",\n \"def _plot_dict_bar(d, xmin=None, label=None):\\n xvals, yvals = _dict2lists(d)\\n if xmin == None:\\n xmin = min(xvals) - 1\\n else:\\n xmin = min(xmin, min(xvals) - 1)\\n if label:\\n pylab.bar(xvals, yvals, align='center', label=label)\\n pylab.xlim([xmin, max(xvals)+1])\\n else:\\n pylab.bar(xvals, yvals, align='center')\\n pylab.xlim([xmin, max(xvals)+1])\",\n \"def plotBarChart(resultConfirmed, resultDeath, resultVaccinated):\\n fig, ax = plt.subplots(3)\\n\\n ax[0].plot(resultConfirmed['Date'], resultConfirmed['Confirmed Cases'])\\n ax[0].title.set_text('Confirmed Cases')\\n \\n ax[1].plot(resultDeath['Date'], resultDeath['Death Cases'])\\n ax[1].title.set_text('Death Cases')\\n \\n ax[2].plot(resultVaccinated['Date'], resultVaccinated['Vaccinated Person'])\\n ax[2].title.set_text('Vaccinated Cases')\\n fig.tight_layout()\\n plt.show()\",\n \"def generate_barplot(predictions, labels):\\n plot = figure(x_range=labels, plot_height=300, plot_width=400)\\n plot.vbar(x=labels, top=predictions, width=0.8)\\n # plot.xaxis.major_label_orientation = pi / 2.\\n # plot.xaxis.axis_label_text_font_size = \\\"40pt\\\"\\n # plot.yaxis.axis_label_text_font_size = \\\"40pt\\\"\\n\\n return components(plot)\",\n \"def plot_balance_class(classes):\\n unique, counts = np.unique(classes, return_counts=True)\\n plt.bar(unique, counts)\\n plt.title('Class Frequency')\\n plt.xlabel('Class')\\n plt.ylabel('Frequency')\\n plt.show()\",\n \"def make_bar_plot(x, y, title):\\n return plotly.graph_objs.Figure(\\n data=[plotly.graph_objs.Bar(x=list(x), y=list(y))],\\n layout=plotly.graph_objs.Layout(title=title)\\n )\",\n \"def BarSpecific():\\n return dcc.Graph(id=\\\"PersChart\\\", className=\\\"bar\\\", figure=dict(\\n data=[go.Bar(\\n x=[1],\\n y=[\\\"Persons\\\"],\\n orientation='h',\\n marker={\\n 'color': '#ff4058',\\n },\\n )],\\n layout=dict(\\n title=\\\"<b>Most similar Persons</b>\\\",\\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\\n margin=dict(l=100, r=20, t=50, b=30),\\n plot_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n paper_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n xaxis=dict(tick0=0, dtick=1),\\n yaxis=dict(ticks='outside')\\n )\\n ))\",\n \"def plot(self):\\n x = np.arange(5)\\n # labels = ['temp', 'humi', 'mais', 'o2', 'co2']\\n plt.bar(x - 0.35/2, self.data, 0.35, label='actual')\\n plt.bar(x + 0.35/2, self.desired_values, 0.35, label='desired')\\n plt.ylim(-5, 80)\\n plt.legend()\\n\\n plt.draw()\\n plt.pause(0.000001)\\n plt.clf()\",\n \"def simple_bar():\\n\\n # Make random discrete data\\n discrete_a = np.zeros((8,2))\\n discrete_b = np.zeros((8,2))\\n discrete_c = np.zeros((8,2))\\n discrete_a[:,0] = np.arange(8)\\n discrete_b[:,0] = np.arange(8)\\n discrete_c[:,0] = np.arange(8)\\n discrete_a[:,1] = np.random.rand(8)*10\\n discrete_b[:,1] = np.random.rand(8)*10\\n discrete_c[:,1] = np.random.rand(8)*10\\n\\n # Make data sets, if using multiple bar_width must be the same\\n dataset_a = DataSet(discrete_a,colour='pink',bar_width=0.8,plot='bar',label='A')\\n dataset_b = DataSet(discrete_b,colour='violet',bar_width=0.8,plot='bar',label='B')\\n dataset_c = DataSet(discrete_c,colour='darkviolet',bar_width=0.8,plot='bar',label='C')\\n\\n # Make plot object and add data sets\\n plot = Plot()\\n plot.add_dataset(dataset_a)\\n plot.add_dataset(dataset_b)\\n plot.add_dataset(dataset_c)\\n plot.set_axes(xticks=(1,1),xlim=(-0.5,7.5),ylim=(0,12))\\n plot.set_legend(legend=True,location='upper right')\\n plot.set_text(legend=8)\\n\\n # Plot graph and display\\n plot.plot()\\n plot.save(name='./figures/2d_simple_bar',fmt='png')\\n plot.display()\",\n \"def bar(*args, **kwargs):\\n ax, args, kwargs = maybe_get_ax(*args, **kwargs)\\n color_cycle = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\\n almost_black = '#262626'\\n kwargs.setdefault('color', color_cycle[0])\\n kwargs.setdefault('edgecolor', 'white')\\n middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0\\n\\n # Check if data contains stacks\\n stacked = kwargs.pop('stacked',False)\\n # Check if stack text should be included\\n stack_text = kwargs.pop('stack_text',False)\\n # Get legend if available\\n legend = kwargs.pop('legend',False)\\n\\n left = args[0]\\n height = np.array(args[1])\\n\\n # Label each individual bar, if xticklabels is provided\\n xtickabels = kwargs.pop('xticklabels', None)\\n # left+0.4 is the center of the bar\\n xticks = np.array(left) + middle\\n\\n # Whether or not to annotate each bar with the height value\\n annotate = kwargs.pop('annotate', False)\\n\\n show_ticks = kwargs.pop('show_ticks', False)\\n\\n # If no grid specified, don't draw one.\\n grid = kwargs.pop('grid', None)\\n\\n # Check if stacked and plot data accordingly\\n if stacked:\\n num_stacks, num_data = height.shape\\n bottom = np.zeros(num_data)\\n for i in np.arange(num_stacks):\\n lst = list(args)\\n lst[1] = height[i]\\n args = tuple(lst)\\n kwargs['color'] = set2[i]\\n kwargs['bottom'] = bottom\\n rectangles = ax.bar(*args, **kwargs)\\n bottom += height[i]\\n else:\\n rectangles = ax.bar(*args, **kwargs)\\n\\n # add legend\\n if isinstance(legend, collections.Iterable):\\n ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)\\n\\n # add whitespace padding on left\\n xmin, xmax = ax.get_xlim()\\n xmin -= 0.2\\n if stacked:\\n xmax = num_data\\n ax.set_xlim(xmin, xmax)\\n\\n # If the user is only plotting one bar, make it an iterable\\n if not isinstance(height, collections.Iterable):\\n height = [height]\\n\\n\\n # If there are negative counts, remove the bottom axes\\n # and add a line at y=0\\n if any(h < 0 for h in height.tolist()):\\n axes_to_remove = ['top', 'right', 'bottom']\\n ax.hlines(y=0, xmin=xmin, xmax=xmax,\\n linewidths=0.75)\\n else:\\n axes_to_remove = ['top', 'right']\\n\\n # Remove excess axes\\n remove_chartjunk(ax, axes_to_remove, grid=grid, show_ticks=show_ticks)\\n\\n if stacked:\\n data = height\\n height = height.sum(axis=0)\\n\\n # Add the xticklabels if they are there\\n if xtickabels is not None:\\n ax.set_xticks(xticks)\\n ax.set_xticklabels(xtickabels)\\n\\n if annotate or isinstance(annotate, collections.Iterable):\\n annotate_yrange_factor = 0.025\\n ymin, ymax = ax.get_ylim()\\n yrange = ymax - ymin\\n\\n # Reset ymax and ymin so there's enough room to see the annotation of\\n # the top-most\\n if ymax > 0:\\n ymax += yrange * 0.1\\n if ymin < 0:\\n ymin -= yrange * 0.1\\n ax.set_ylim(ymin, ymax)\\n yrange = ymax - ymin\\n\\n offset_ = math.log(yrange) + math.log(annotate_yrange_factor+1)\\n print offset_\\n print yrange * annotate_yrange_factor\\n print math.log(yrange) + math.log(annotate_yrange_factor)\\n if isinstance(annotate, collections.Iterable):\\n annotations = map(str, annotate)\\n else:\\n annotations = ['%.3f' % h if type(h) is np.float_ else str(h)\\n for h in height]\\n\\n for x, h, annotation in zip(xticks, height, annotations):\\n # Adjust the offset to account for negative bars\\n offset = offset_ if h >= 0 else -1 * offset_\\n verticalalignment = 'bottom' if h >= 0 else 'top'\\n\\n # Finally, add the text to the axes\\n ax.annotate(annotation, (x, h + annotate_yrange_factor), \\n verticalalignment=verticalalignment,\\n horizontalalignment='center',\\n color=almost_black)\\n\\n # Text for each block of stack\\n # This was partially inspired by the following article by Tableau software\\n # http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812\\n if stack_text:\\n bottom = np.zeros(num_data)\\n max_h = max(height)\\n for i in np.arange(num_stacks):\\n for x, d, b in zip(xticks, data[i], bottom):\\n if (d*100.0/max_h) > 4.0:\\n ax.text(x,b+d/2.0,d, ha='center', va='center', color=almost_black)\\n bottom += data[i]\\n return rectangles\",\n \"def show_class_imbalance(df, title='Class Imbalance', PATH=None):\\n ax = sns.barplot(x=[\\\"Normal\\\", \\\"Clickbait\\\"], y=df.groupby(['target']).target.count())\\n ax.set_title(title, size=20)\\n plt.xticks([0,1],[\\\"Normal\\\", \\\"Clickbait\\\"], size = 20)\\n ax.set_ylabel(\\\"Document Count\\\", size=17)\\n ax.set_xlabel(\\\"Article Class\\\", size=20)\\n if PATH:\\n plt.savefig(PATH, bbox_inches=\\\"tight\\\", transparent=True)\\n return ax\",\n \"def to_bar(self):\\n factor = FactorData()\\n return factor\",\n \"def plot_what_if(df):\\n fig = go.Figure()\\n for col in df.columns:\\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\\n fig.update_xaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\\n fig.update_yaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_layout(legend=dict(orientation=\\\"h\\\", yanchor=\\\"bottom\\\", y=-0.25, xanchor=\\\"center\\\", x=0.5),\\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\\n\\n\\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\\n fig.write_html(join('..', 'docs', 'redistribution.html'))\\n fig.show()\",\n \"def draw_bar_plot():\\n # Copy and modify data for monthly bar plot\\n \\n df_bar = df.copy()\\n\\n # Draw bar plot\\n leglab = [\\\"January\\\", \\\"February\\\", \\\"March\\\", \\\"April\\\", \\\"May\\\", \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\", \\\"November\\\", \\\"December\\\"]\\n labels = [2016, 2017, 2018, 2019]\\n months = np.zeros([12, 4])\\n\\n for i in range(12):\\n for j, year in enumerate(labels):\\n t = df[df.index.year == year]\\n months[i][j] = t[t.index.month == i].value.mean()\\n\\n x = np.arange(len(labels))\\n width = 0.7\\n fig, ax = plt.subplots()\\n fig.set_figwidth(10)\\n fig.set_figheight(8)\\n for i, month in enumerate(months):\\n ax.bar(x - (width * (12 - i) / 12), months[i], width / 12, label=leglab[i])\\n\\n ax.set_ylabel(\\\"Average Page Views\\\")\\n ax.set_xlabel(\\\"Years\\\")\\n ax.set_xticks(x)\\n ax.set_xticklabels(labels)\\n ax.legend(title='Months')\\n\\n # Save image and return fig (don't change this part)\\n fig.savefig('bar_plot.png')\\n return fig\",\n \"def plot_bar(source_files, column_ids, column_names, normalize, sort, plot_difference, freq_bound, title=None,\\n dtype=int):\\n\\n def _filter_data(raw_data, numerical):\\n \\\"\\\"\\\" Filters plot-able data. \\\"\\\"\\\"\\n # Retain numeric information\\n legal_count_inventory = digits + '.'\\n # Retain POS tags, also\\n legal_entry_inventory = ascii_uppercase + '$'\\n filtered_data = list()\\n for data_point in raw_data:\\n skip = False\\n for symbol in list(str(data_point)):\\n if symbol not in legal_count_inventory and symbol not in legal_entry_inventory:\\n skip = True\\n if not skip:\\n if numerical:\\n filtered_data.append(dtype(data_point))\\n else:\\n filtered_data.append(data_point)\\n # Optionally normalize count values, resulting in a proportion plot\\n if numerical and normalize:\\n filtered_data = filtered_data / np.sum(filtered_data)\\n return np.array(filtered_data)\\n\\n # Set plot parameters\\n sns.set_style('whitegrid')\\n sns.set_context('paper')\\n\\n # Compile data to be plotted within a new dataframe\\n # Not necessary, but convenient when plotting with seaborn\\n source_dict = dict()\\n # Read in data and sort alphanumeric features (e.g. POS tags) alphabetically\\n df_features = pd.read_table(source_files[0], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\\n df_features = df_features.sort_values('Tag', ascending=True)\\n df_reference = pd.read_table(source_files[1], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\\n df_reference = df_reference.sort_values('Tag', ascending=True)\\n # Isolate columns to be plotted\\n entries = _filter_data(df_features.iloc[:, column_ids[0]].values, False)\\n counts = _filter_data(df_features.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus A\\n reference_counts = _filter_data(df_reference.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus B\\n # Construct dataframe to be visualized\\n source_dict[column_names[0]] = entries\\n source_dict['reference_counts'] = reference_counts\\n # Generate frequency mask to exclude low-frequency features from the plot\\n # Optional; results in a clearer, better readable visualization\\n frequency_mask = np.array(\\n [int(counts[i] >= freq_bound or reference_counts[i] >= freq_bound) for i in range(counts.shape[0])])\\n source_dict['frequency_mask'] = frequency_mask\\n # Calculate per-feature count differences (i.e. target counts vs. reference counts), if specified\\n if plot_difference:\\n diffs = counts - reference_counts\\n source_dict[column_names[1]] = diffs\\n else:\\n source_dict[column_names[1]] = counts\\n features = pd.DataFrame.from_dict(source_dict)\\n # Sort by count value and apply frequency mask\\n if sort:\\n features = features.sort_values(column_names[0], ascending=True)\\n if freq_bound > 0:\\n features = features.drop(features[features.frequency_mask == 0].index)\\n\\n # Make plot\\n fig, ax = plt.subplots()\\n fig.set_size_inches(8, 6)\\n if plot_difference:\\n colors = ['coral' if feature >= 0 else 'skyblue' for feature in features[column_names[1]]]\\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette=colors)\\n else:\\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette='Set2')\\n sns.despine()\\n if title is not None:\\n plt.title(title)\\n plt.show()\",\n \"def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):\\n\\n # Check if colors where provided, otherwhise use the default color cycle\\n if colors is None:\\n colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\\n\\n # Number of bars per group\\n n_bars = len(data)\\n\\n # The width of a single bar\\n bar_width = total_width / n_bars\\n\\n # List containing handles for the drawn bars, used for the legend\\n bars = []\\n\\n # Iterate over all data\\n for i, (name, values) in enumerate(data.items()):\\n # The offset in x direction of that bar\\n x_offset = (i - n_bars / 2) * bar_width + bar_width / 2\\n\\n # Draw a bar for every value of that type\\n for x, y in enumerate(values):\\n bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])\\n\\n # Add a handle to the last drawn bar, which we'll need for the legend\\n bars.append(bar[0])\\n\\n # Draw legend if we need\\n if legend:\\n ax.legend(bars, data.keys())\",\n \"def plot_featurewise_barplot(\\n utr5_counts, cds_counts, utr3_counts, ax=None, saveto=None, **kwargs\\n):\\n fig = None\\n if ax is None:\\n fig, ax = plt.subplots()\\n else:\\n fig = ax.get_figure()\\n barlist = ax.bar([0, 1, 2], [utr5_counts, cds_counts, utr3_counts])\\n barlist[0].set_color(\\\"#1b9e77\\\")\\n barlist[1].set_color(\\\"#d95f02\\\")\\n barlist[2].set_color(\\\"#7570b3\\\")\\n ax.set_xticks([0, 1, 2])\\n ax.set_xticklabels([\\\"5'UTR\\\", \\\"CDS\\\", \\\"3'UTR\\\"])\\n max_counts = np.max(np.hstack([utr5_counts, cds_counts, utr3_counts]))\\n setup_axis(\\n ax=ax, axis=\\\"y\\\", majorticks=max_counts // 10, minorticks=max_counts // 20\\n )\\n ax.set_ylabel(\\\"# RPFs\\\")\\n # sns.despine(trim=True, offset=10)\\n if saveto:\\n fig.tight_layout()\\n fig.savefig(saveto, dpi=DPI)\\n return ax, fig\",\n \"def bar_grapgh(dictionary, variable):\\r\\n plt.clf() # Deletes the previous plot \\r\\n plt.hist(dictionary[variable])\\r\\n plt.title('Histogram of ' + variable)\\r\\n plt.xlabel(variable)\\r\\n plt.ylabel('Frequency')\\r\\n plt.savefig(variable)\",\n \"def visualize_type():\\n\\t\\n\\t#grab our parsed data\\n\\tdata_file = parse(MY_FILE, \\\",\\\")\\n\\t\\n\\t#make a new variable, counter, from iterating through each line of\\n\\t#data in parsed data, and count how many incidents happen by category\\n\\tcounter = Counter(item[\\\"Category\\\"] for item in data_file)\\n\\t\\n\\t#set the labels which are based on the keys of our counter\\n\\t#since order doesn't matter, we can just use counter.keys()\\n\\tlabels = tuple(counter.keys())\\n\\t\\n\\t#set exactly where the labels should hit the x-axis\\n\\txlocations = np.arange(len(labels)) + 0.5\\n\\t\\n\\t#width of each bar that will be plotted\\n\\twidth = 0.5\\n\\t\\n\\t#assign data to a bar plot\\n\\tplt.bar(xlocations, counter.values(), width=width)\\n\\t\\n\\t#assign labels and tick location to x-axis\\n\\tplt.xticks(xlocations + width /2, labels, rotation=90)\\n\\t\\n\\t#give more room to the x-axis so the labels aren't cut off\\n\\tplt.subplots_adjust(bottom=0.4)\\n\\t\\n\\t#make the overall graph/figure larger\\n\\tplt.rcParams['figure.figsize'] = 12, 8\\n\\t\\n\\t#save the graph\\n\\tplt.savefig(\\\"type.png\\\")\\n\\t\\n\\t#close the plot figure\\n\\tplt.clf()\",\n \"def to_bar(self):\\n group = GroupData()\\n return group\",\n \"def plot_bar_chart_quantum_vs_classical(\\n df_bugs: pd.DataFrame,\\n column_to_inspect: str,\\n mapping_dict: Dict[str, str],\\n categories_to_exclude: List[str] = [],\\n categories_keep_only: List[str] = None,\\n out_file_name: str = None,\\n out_folder_path: str = None,\\n horizontal: bool = False,\\n map_value_since_beginning: bool = False,\\n figsize: Tuple[int, int] = (10, 5),\\n legend_placement: str = 'upper center'\\n ):\\n\\n fig, ax = plt.subplots(figsize=figsize)\\n\\n df = expand_columns(df_bugs, column_to_inspect)\\n df = df[~(df[column_to_inspect].isin(categories_to_exclude))]\\n\\n if categories_keep_only is not None:\\n df = df[df[column_to_inspect].isin(categories_keep_only)]\\n\\n if map_value_since_beginning:\\n df[column_to_inspect] = df[column_to_inspect].map(mapping_dict)\\n\\n categories_q_bugs = list(df[\\n df['type'] == 'Quantum'].groupby(\\n column_to_inspect).count().sort_values(\\n by='type', ascending=False).index)\\n\\n for component in df[column_to_inspect].unique():\\n if component not in categories_q_bugs:\\n categories_q_bugs.append(component)\\n\\n args = {\\n \\\"hue\\\": \\\"type\\\",\\n \\\"data\\\": df,\\n \\\"palette\\\": PALETTE,\\n \\\"ax\\\": ax,\\n \\\"order\\\": categories_q_bugs\\n }\\n\\n if horizontal:\\n sns.countplot(y=column_to_inspect, **args)\\n ax.grid(axis='x')\\n else:\\n sns.countplot(x=column_to_inspect, **args)\\n ax.grid(axis='y')\\n\\n if not map_value_since_beginning:\\n # map the value at the latest stage, thus in the labels\\n obj_labels = ax.get_xticklabels()\\n for i, l in enumerate(obj_labels):\\n obj_labels[i] = mapping_dict[l.get_text()]\\n ax.set_xticklabels(obj_labels, rotation=60, ha='right')\\n\\n ax.set_xlabel(capitalize(column_to_inspect), fontsize=15)\\n ax.set_ylabel(\\\"Count\\\", fontsize=15)\\n plt.legend(title=\\\"Type of Bug\\\", loc=legend_placement)\\n plt.tight_layout()\\n\\n if out_file_name is not None and out_folder_path is not None:\\n fig.savefig(os.path.join(out_folder_path, out_file_name), format=\\\"pdf\\\")\",\n \"def plot_bars(self, ax, bottom=None, log=False):\\n if bottom is None and log:\\n bottom = np.ones_like(self.values) * min(self.values[self.values > 0 ]) * .1\\n return ax.bar(self.lefts, self.values, self.widths, color=self.color, label=self.label,edgecolor=self.color, bottom=bottom, log=log, **self.options)[0]\",\n \"def _plot_group_bars(ax, xss, field, side):\\n\\n #translate side to index\\n if(side == 'left'):\\n side = 0\\n else:\\n side = 1\\n #plot the bars\\n x = range(len(xss))\\n values = [xs.ROW_edge_fields[field].values[side] for xs in xss]\\n ax.bar(x, values, color=[_colormap[i%len(_colormap)] for i in range(len(xss))],\\n bottom=0.0, align='center', alpha=0.8, width=0.6)\\n ax.set_xticks(x)\\n ax.set_xticklabels([textwrap.fill(xs.sheet, 15) for xs in xss],\\n rotation='vertical', fontsize=11)\",\n \"def plot_bar_important_features(important_features, title, xlabel, ylabel, fname):\\r\\n plt.figure(figsize=(20, 21))\\r\\n plt.barh(important_features.index.astype(str).tolist(), important_features.values.tolist())\\r\\n plt.title(title)\\r\\n plt.xlabel(xlabel)\\r\\n plt.ylabel(ylabel)\\r\\n plt.savefig(fname, bbox_inches='tight')\\r\\n plt.close()\",\n \"def plot_data(self):\",\n \"def stopword_bar(df, stop_words, ax):\\n df_test = df.copy()\\n df_test['prop'] = df.title.apply(stopword_proportion)\\n sns.barplot(data=df_test, x='target', y='prop', ax=ax, ci=False)\\n ax.set_title(\\\"Ratio of Stopwords Between Classes\\\", size=20)\\n ax.set_ylim([1,2])\\n ax.set_ylabel(\\\"Ratio\\\", size=20)\\n ax.set_xlabel(\\\"Article Class\\\", size=20)\\n plt.xticks(ticks=range(2),labels=['Normal','Clickbait'], size=20)\\n return ax\",\n \"def plot_cat(df, cat_columns, hue = \\\"default_payment_next_month\\\"):\\n fig = plt.figure(figsize = (20,(len(cat_columns)/2+1)*8))\\n loc = 1\\n for col in cat_columns:\\n ax = fig.add_subplot(len(cat_columns)/2+1, 2, loc)\\n df_plot = df[[col, hue, \\\"id\\\"]].groupby([col, hue]).count()\\n df_plot.reset_index(inplace = True)\\n sns.barplot(x=col, y= \\\"id\\\", hue = hue, data=df_plot, palette = \\\"GnBu_d\\\", ax = ax);\\n plt.legend(title = \\\"default payment (1=yes, 0=no)\\\")\\n plt.ylim([0.0001,15000])\\n plt.ylabel(\\\"clients\\\");\\n loc += 1\",\n \"def plot_bar(self, nsingular=None, nbars=20):\\n if nsingular is not None:\\n self.get_identifiability_dataframe(nsingular)\\n\\n plot_obj = plots.IdentBar(self.ident_df, nsingular=nsingular, nbars=nbars)\\n plot_obj.generate()\\n plot_obj.draw()\\n \\n return plot_obj.fig, plot_obj.ax\",\n \"def graph():\\n fp = mpl.font_manager.FontProperties(family='JasmineUPC',size=24)\\n x = np.arange(0,10)\\n y = [386557057065, 368368395622, 242451971944, 225960095934, 161573560379, 107461232731, 89784502211, 73749349545, 54525219632, 52864743212]\\n name = ['เชื้อเพลิงที่ได้จากแร่', 'เครื่องจักรและส่วนประกอบ', 'ยานยนต์และส่วนประกอบ', 'เครื่องอุปกรณ์ไฟฟ้าและส่วนประกอบ', 'เหล็กและเหล็กกล้า', 'พลาสติกและของทำด้วยพลาสติก', 'ของทำด้วยเหล็กหรือเหล็กกล้า', 'ทองแดงละของทำด้วยทองแดง', 'เคมีภัณฑ์เบ็ดเตล็ด', 'อุปกรณ์ที่ใช้ทางทัศนศาสตร์']\\n ax = plt.gca(xticks=x)\\n ax.set_xticklabels(name,rotation=1000,fontproperties=fp)\\n plt.bar(x,y,color='g')\\n plt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72341543","0.7051117","0.7032588","0.702026","0.6910697","0.6906154","0.68671536","0.67882943","0.6782081","0.6735545","0.6699668","0.6685477","0.66498506","0.663799","0.6616477","0.6604195","0.6479067","0.6469803","0.64694345","0.64263964","0.6424513","0.6419261","0.640694","0.64018095","0.63064665","0.63012624","0.6273125","0.6266547","0.6259449","0.6240214","0.6228225","0.6225711","0.6224449","0.62193334","0.61900115","0.618825","0.6187623","0.61718714","0.61673653","0.615912","0.61552125","0.61546445","0.61543536","0.61523354","0.61441165","0.61416775","0.61191326","0.6104422","0.61014444","0.60894746","0.6074595","0.6069581","0.6053907","0.6037133","0.60273874","0.60265684","0.6025053","0.6024364","0.6023842","0.6023384","0.60215837","0.6020362","0.60189235","0.6016994","0.6011624","0.5997864","0.59918255","0.5982423","0.5979257","0.59706324","0.59666777","0.596615","0.59298617","0.5924291","0.591691","0.5916065","0.59147304","0.59119755","0.5911653","0.5911455","0.5908337","0.59053963","0.58876586","0.5880613","0.58777","0.58659816","0.58650124","0.58643466","0.5860246","0.58598906","0.585575","0.5855504","0.58311594","0.5823492","0.58207417","0.58191276","0.5806449","0.5797168","0.57748044","0.5772498"],"string":"[\n \"0.72341543\",\n \"0.7051117\",\n \"0.7032588\",\n \"0.702026\",\n \"0.6910697\",\n \"0.6906154\",\n \"0.68671536\",\n \"0.67882943\",\n \"0.6782081\",\n \"0.6735545\",\n \"0.6699668\",\n \"0.6685477\",\n \"0.66498506\",\n \"0.663799\",\n \"0.6616477\",\n \"0.6604195\",\n \"0.6479067\",\n \"0.6469803\",\n \"0.64694345\",\n \"0.64263964\",\n \"0.6424513\",\n \"0.6419261\",\n \"0.640694\",\n \"0.64018095\",\n \"0.63064665\",\n \"0.63012624\",\n \"0.6273125\",\n \"0.6266547\",\n \"0.6259449\",\n \"0.6240214\",\n \"0.6228225\",\n \"0.6225711\",\n \"0.6224449\",\n \"0.62193334\",\n \"0.61900115\",\n \"0.618825\",\n \"0.6187623\",\n \"0.61718714\",\n \"0.61673653\",\n \"0.615912\",\n \"0.61552125\",\n \"0.61546445\",\n \"0.61543536\",\n \"0.61523354\",\n \"0.61441165\",\n \"0.61416775\",\n \"0.61191326\",\n \"0.6104422\",\n \"0.61014444\",\n \"0.60894746\",\n \"0.6074595\",\n \"0.6069581\",\n \"0.6053907\",\n \"0.6037133\",\n \"0.60273874\",\n \"0.60265684\",\n \"0.6025053\",\n \"0.6024364\",\n \"0.6023842\",\n \"0.6023384\",\n \"0.60215837\",\n \"0.6020362\",\n \"0.60189235\",\n \"0.6016994\",\n \"0.6011624\",\n \"0.5997864\",\n \"0.59918255\",\n \"0.5982423\",\n \"0.5979257\",\n \"0.59706324\",\n \"0.59666777\",\n \"0.596615\",\n \"0.59298617\",\n \"0.5924291\",\n \"0.591691\",\n \"0.5916065\",\n \"0.59147304\",\n \"0.59119755\",\n \"0.5911653\",\n \"0.5911455\",\n \"0.5908337\",\n \"0.59053963\",\n \"0.58876586\",\n \"0.5880613\",\n \"0.58777\",\n \"0.58659816\",\n \"0.58650124\",\n \"0.58643466\",\n \"0.5860246\",\n \"0.58598906\",\n \"0.585575\",\n \"0.5855504\",\n \"0.58311594\",\n \"0.5823492\",\n \"0.58207417\",\n \"0.58191276\",\n \"0.5806449\",\n \"0.5797168\",\n \"0.57748044\",\n \"0.5772498\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7253609"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8484,"cells":{"query":{"kind":"string","value":"Plot a data frame with bar type"},"document":{"kind":"string","value":"def plot_type_of_two_topic(data_frame1: pb.DataFrame, data_frame2: pb.DataFrame) -> None:\n plt.interactive(False)\n plt.figure()\n data_frame1.plot(kind='bar', x= data_frame['TopicID'])\n data_frame2.plot(kind='bar', x= data_frame['TopicID'])\n plt.show()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def plot_type_of_topic(data_frame: pb.DataFrame) -> None:\n plt.interactive(False)\n plt.figure()\n data_frame.plot(kind='bar', x= data_frame['TopicID'])\n plt.show()","def compte(df):\n\n df.value_counts()[:100].plot(kind='bar')\n plt.show()","def bar_plot(df, data_pt):\n \n x=df.loc[data_pt]\n y= df.columns.tolist()\n sorte=x.tolist()\n a=sorted(zip(sorte, y))[-10:]\n y=[y for _, y in a]\n ## soru burda yapıp altı ona göre duzeliyecegim birde\n \n x = df[y].loc[data_pt]\n \n # Here we modify the tickangle of the xaxis, resulting in rotated labels.\n #title={'text': \"<b>Comparing features with Golden for Cycle {}\".format(cycle),\n # 'y':0.9,'x':0.5,'xanchor': 'center','yanchor': 'top'}\n\n \n trace = {'type': 'bar',\n 'orientation':'h',\n 'x' : x,\n 'y' : y}\n data = Data([trace])\n layout = {'title' : \"<b>Reconstruction error in each dimension for cycle{}\".format(data_pt),\n 'titlefont':{'size' : 20},\n 'xaxis' : {'title': '<b>Reconstruction Error',\n 'titlefont':{'size' : 20},\n 'tickangle': -45, 'tickfont': {'size':15} ,},\n \n 'yaxis' : {'title': '<b>Features',\n 'titlefont':{'size' : 20},\n 'tickfont': {'size':15},},\n 'margin' : {'l':100, 'r' : 1, 'b': 200, 't': 100, 'pad' : 1},\n 'height' : 600, 'width' : 800,\n }\n \n fig = Figure(data = data, layout = layout)\n \n return pyo.iplot(fig)","def visualizeData(df):\n for column in df:\n df[column].value_counts().plot(kind = 'bar', rot = 'vertical', use_index = False)","def bar_plot(df_NP):\n cnt = Counter()\n for tax_list in df_NP.taxonomy:\n for tax in list(tax_list):\n if tax != 'no':\n cnt[tax] += 1\n plt.bar(cnt.keys(),cnt.values())\n plt.xlabel('taxonomic provenance')\n plt.ylabel('number of molecules')\n plt.title('number of aglycons with taxonomies')\n plt.savefig(\"output_data/Barplot.png\")\n print(\"BAR PLOT DONE\")","def bar_chart(self, df, n_groups, dict):\n fig, ax = plt.subplots()\n # choose bar width (standard 0.8 chosen)\n bar_width = 0.35\n # get an index to set the ticks for the x axis\n\n index = np.arange(n_groups)\n indexes = df.index.tolist()\n print(indexes)\n df[\"index\"] = indexes\n\n # make barchart for permutation test\n ax.bar(index, df[\"perm\"], bar_width, color='b', linewidth=4,\n label='Permutation test')\n # make barchart for t-test\n ax.bar(index + bar_width, df[\"t_test\"], bar_width, color='r',\n label='t-test')\n\n ax.set_xlabel(dict[\"xlabel\"])\n ax.set_ylabel(dict[\"ylabel\"])\n ax.set_title(dict[\"title\"])\n ax.set_xticks(index + bar_width / 2)\n ax.set_xticklabels(dict[\"xtickslabels\"])\n ax.legend()\n\n fig.tight_layout()\n plt.show()","def bar_plot(df, field_name, graph_title, threshold_value, x_axis_label, y_axis_label):\n\n x = df[field_name].value_counts().sort_values()\n x[x > threshold_value].plot(kind='barh', figsize=(12, 8), title=graph_title, x=x_axis_label, y=y_axis_label)\n return","def bar(\n df,\n x=None,\n y=\"value\",\n bars=\"variable\",\n order=None,\n bars_order=None,\n orient=\"v\",\n legend=True,\n title=True,\n ax=None,\n cmap=None,\n **kwargs,\n):\n\n # default x-axis to time-col attribute from an IamDataFrame, else use \"year\"\n x = x or time_col_or_year(df)\n\n # cast to DataFrame if necessary\n # TODO: select only relevant meta columns\n if not isinstance(df, pd.DataFrame):\n df = df.as_pandas()\n\n for col in set(SORT_IDX) - set([x, bars]):\n if len(df[col].unique()) > 1:\n msg = \"Can not plot multiple {}s in bar plot with x={}, bars={}\"\n raise ValueError(msg.format(col, x, bars))\n\n if ax is None:\n fig, ax = plt.subplots()\n\n # long form to one column per bar group\n _df = reshape_mpl(df, x, y, bars, **{x: order, bars: bars_order})\n\n # explicitly get colors\n defaults = default_props(reset=True, num_colors=len(_df.columns), colormap=cmap)[\n \"color\"\n ]\n rc = run_control()\n color = []\n for key in _df.columns:\n c = next(defaults)\n if \"color\" in rc and bars in rc[\"color\"] and key in rc[\"color\"][bars]:\n c = rc[\"color\"][bars][key]\n color.append(c)\n\n # change year to str to prevent pandas/matplotlib from auto-ordering (#474)\n if _df.index.name == \"year\":\n _df.index = map(str, _df.index)\n\n # plot data\n kind = \"bar\" if orient.startswith(\"v\") else \"barh\"\n _df.plot(kind=kind, color=color, ax=ax, **kwargs)\n\n # add legend\n ax.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\n if not legend:\n ax.legend_.remove()\n\n # add default labels if possible\n if orient == \"v\":\n ax.set_xlabel(x.capitalize())\n else:\n ax.set_ylabel(x.capitalize())\n units = df[\"unit\"].unique()\n if len(units) == 1 and y == \"value\":\n if orient == \"v\":\n ax.set_ylabel(units[0])\n else:\n ax.set_xlabel(units[0])\n\n # build a default title if possible\n _title = []\n for var in [\"model\", \"scenario\", \"region\", \"variable\"]:\n values = df[var].unique()\n if len(values) == 1:\n _title.append(\"{}: {}\".format(var, values[0]))\n if title and _title:\n title = \" \".join(_title) if title is True else title\n ax.set_title(title)\n\n return ax","def barGraph(listOfWord, listOfFrequency):\r\n\r\n\tindex = np.arange(len(listOfWord))\r\n\r\n\tplt.title(\"Frekuensi Kemunculan Kata\")\r\n\tplt.barh(index, listOfFrequency)\r\n\tplt.xlabel('Frekuensi')\r\n\tplt.yticks(index, listOfWord, fontsize=6)\r\n\r\n\tplt.show()","def visualize_data(df):\n # Remove 'not available'\n genres = df.genre.unique().tolist()\n remove_index = genres.index('Not Available')\n genres.pop(remove_index)\n print('Genres: ', genres)\n\n # Extract number of songs in each genre\n genre_counts = df.genre.value_counts().tolist()\n genre_counts.pop(remove_index)\n print('Counts: ', genre_counts)\n\n # Plot bar graph\n plt.bar(genres, genre_counts)\n plt.xlabel('Genres')\n plt.ylabel('Count')\n plt.show()","def featuresBarPlot(barNames,barValues):\n plt.bar(range(0,len(barNames)),barValues)\n plt.xticks(range(0,len(barNames)), barNames,rotation='vertical')\n plt.show()","def plot_class_balances(df, col):\n\n ser_counts = df[col].value_counts()\n ser_counts.plot.bar()\n plt.title(col + ' Counts \\n(classes={})'.format(ser_counts.shape[0]))\n \n plt.show()","def plot_bv_bar(df, xcolname, ycolname, icol=0):\n # set plot size\n fig, ax = plt.subplots(figsize=(8,6))\n \n # plotting... box\n sns.barplot(ax=ax, data = df\n , x = str(xcolname)\n , y = str(ycolname)\n , color = sns.color_palette()[icol]);\n \n \n # title and labels\n plt.title(xcolname+' Vs '+ycolname, fontsize=20)\n plt.xlabel(xcolname+ ' (units)', fontsize=16)\n plt.ylabel(ycolname+ ' (units)', fontsize=16)\n \n return plt.show()","def BarPlot(data,colormap='Paired',ax=None,headers='show',value_max=None,x_ticklabels_rotation=90,**kws):\r\n if ax is None:\r\n ax=plt.subplot(111)\r\n\r\n if value_max is None:\r\n value_max=data.sum(1).max()\r\n\r\n data.plot(kind='bar', stacked=True,colormap=colormap, ax=ax,**kws)\r\n ax.set_ylim((0,value_max))\r\n\r\n\r\n #reverse legend order\r\n handles, labels = ax.get_legend_handles_labels()\r\n ax.legend(reversed(handles),reversed(data.columns),bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\r\n\r\n #AXES\r\n if (headers is None or headers=='hide'):\r\n ax.get_xaxis().set_visible(False)\r\n ax.get_xaxis().set_ticks([])\r\n elif headers=='show':\r\n plt.setp(ax.get_xticklabels(),rotation=x_ticklabels_rotation)\r\n ax.set_xlabel(None,visible=False)\r\n\r\n\r\n #plt.tight_layout()\r\n\r\n\r\n return ax","def bar_chart(\n df,\n orientation='v',\n bar_width=None,\n opacity=0.9,\n textpos=None,\n linewidth=1,\n linecolor='#2C3347',\n marker_color=None,\n **kwargs):\n\n traces = []\n rng = df.index.size if orientation == 'v' else df.columns.size\n otn = orientation\n for i in range(rng):\n x = [str(x) for x in df.columns] if otn == 'v' else df.iloc[:, i]\n y = df.iloc[i] if otn == 'v' else [str(x) for x in df.index]\n text = df.iloc[i] if otn == 'v' else df.iloc[:, i]\n name = df.iloc[i].name if otn == 'v' else df.columns[i]\n\n preset_args = dict(\n x=x,\n y=y,\n text=text,\n textposition=textpos,\n marker=dict(\n opacity=opacity,\n color=marker_color,\n line=dict(\n color=linecolor,\n width=linewidth)),\n name=name,\n width=bar_width,\n orientation=orientation\n )\n\n all_args = {**preset_args, **kwargs}\n bar = go.Bar(all_args)\n traces.append(bar)\n\n return traces","def plot_bar_chart(objects, data, title='', ylabel='', bar_color = 'blue'):\n y_pos = np.arange(len(objects))\n\n plt.bar(y_pos, data, align='center', alpha=0.5)\n plt.xticks(y_pos, objects, rotation='vertical')\n plt.ylabel(ylabel, fontsize=12)\n plt.title(title, fontsize=12)\n plt.ylim([0,1300])\n plt.bar(range(len(data)), data, color=bar_color)\n\n return plt.show()","def bar_plot(data, xtitle, title):\n label = list(set(data))\n height = count_elements(data)\n height = [height[i] for i in label]\n plt.bar(label, height=height, width=0.8)\n plt.ylabel('frequency')\n plt.xlabel(xtitle)\n plt.xticks(label)\n plt.savefig('./figures/{}.png'.format(title))\n plt.close()","def drawStackedBarPlot(df, column, hue):\n plt.style.use('default')\n plt.style.use('dark_background')\n p_table = pd.pivot_table(df, index=column, \n columns=hue, aggfunc='size')\n p_table = p_table.div(p_table.sum(axis=1), axis=0)\n p_table.plot.bar(stacked=True, figsize=(14,7))\n plt.xlabel('Spekraltyp')\n plt.ylabel('Anteil')\n plt.show()","def barplot(bars, title='', upColor='blue', downColor='red'):\n import pandas as pd\n import matplotlib.pyplot as plt\n from matplotlib.lines import Line2D\n from matplotlib.patches import Rectangle\n\n if isinstance(bars, pd.DataFrame):\n ohlcTups = [\n tuple(v) for v in bars[['open', 'high', 'low', 'close']].values]\n elif bars and hasattr(bars[0], 'open_'):\n ohlcTups = [(b.open_, b.high, b.low, b.close) for b in bars]\n else:\n ohlcTups = [(b.open, b.high, b.low, b.close) for b in bars]\n\n fig, ax = plt.subplots()\n ax.set_title(title)\n ax.grid(True)\n fig.set_size_inches(10, 6)\n for n, (open_, high, low, close) in enumerate(ohlcTups):\n if close >= open_:\n color = upColor\n bodyHi, bodyLo = close, open_\n else:\n color = downColor\n bodyHi, bodyLo = open_, close\n line = Line2D(\n xdata=(n, n),\n ydata=(low, bodyLo),\n color=color,\n linewidth=1)\n ax.add_line(line)\n line = Line2D(\n xdata=(n, n),\n ydata=(high, bodyHi),\n color=color,\n linewidth=1)\n ax.add_line(line)\n rect = Rectangle(\n xy=(n - 0.3, bodyLo),\n width=0.6,\n height=bodyHi - bodyLo,\n edgecolor=color,\n facecolor=color,\n alpha=0.4,\n antialiased=True\n )\n ax.add_patch(rect)\n\n ax.autoscale_view()\n return fig","def plot_data_stats(data):\n sns.set_style(\"dark\")\n f, ax = plt.subplots(figsize=(6, 15))\n\n ax = sns.barplot(x='tag', y='count', data=tags_freqs)\n\n ax.axes.set_title(\"POS Tags Frequencies\",fontsize=20)\n ax.set_xlabel(\"POS Tags\", fontsize=16)\n ax.set_ylabel(\"Counts\", fontsize=16)\n ax.tick_params(labelsize=12)\n\n plt.show()","def barplot(data, field_name, field_categories):\n\n\tcategories, counts = np.unique(data[field_name], return_counts=True)\n\n\tfig = plt.figure(figsize=(4, 3))\n\taxes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\n\taxes.bar(range(len(categories)), counts, fc=\"gray\") # fc is the face color\n\n\taxes.set_xlabel(\"\")\n\taxes.set_ylabel('Count')\n\taxes.set_title(field_name)\n\tfig.autofmt_xdate(rotation=45)\n\n\taxes.set_xticks(range(len(categories)))\n\taxes.set_xticklabels([field_categories[c] for c in categories]);","def _bar_example_4(quantity_by_fruit):\n ch = chartify.Chart(x_axis_type=\"categorical\", blank_labels=True)\n ch.set_title(\"Vertical bar plot with labels\")\n ch.set_subtitle(\"Hidden y-axis\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n color_column=\"fruit\",\n )\n ch.style.color_palette.reset_palette_order()\n ch.plot.text(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n text_column=\"quantity\",\n color_column=\"fruit\",\n )\n # Adjust the axis range to prevent clipping of the text labels.\n ch.axes.set_yaxis_range(0, 1200)\n ch.axes.hide_yaxis()\n ch.show(_OUTPUT_FORMAT)","def plotify_bar(title, data):\n\n x, y, z, labels = [], [], [], []\n\n for d in reversed(data[:len(data) - 1]):\n x.append(f\"{d['settimana_del']:%d-%b}\\n{d['settimana_fino_al']:%d-%b}\")\n y.append(d['nuovi_positivi'])\n z.append(\"lightgrey\" if d['giorni'] < 7 else 'green' if d['delta'] <= 0 else 'red' )\n labels.append(human_format(d['nuovi_positivi']) if d['giorni'] == 7 else f\"{human_format(d['nuovi_positivi'])}\\n(in corso)\" )\n\n x_pos = np.arange(len(x))\n\n # create a new figure\n plt.figure()\n\n plt.title(title)\n\n # Create bars with different colors\n plt.bar(x_pos, y, color=z)\n\n # Create names on the x-axis\n plt.xticks(x_pos, x, rotation=40)\n\n\n # Text on the top of each bar\n x_ticks = plt.gca().get_xticks()\n for i in range(len(y)):\n text = data[i]\n plt.text(x = x_ticks[i], y = y[i]+5, s = labels[i], size = 9, horizontalalignment='center', verticalalignment='bottom')\n\n # prettify y values\n current_values = plt.gca().get_yticks()\n plt.gca().set_yticklabels(['{:n}'.format(int(x)) for x in current_values])\n\n # responsive layout\n plt.tight_layout()\n\n\n\n buf = io.BytesIO()\n plt.savefig(buf, format='png')\n buf.seek(0)\n\n ### Release memory\n # Clear the current axes.\n plt.cla() \n # Clear the current figure.\n plt.clf() \n # Closes all the figure windows.\n plt.close('all') \n # plt.close(fig)\n gc.collect()\n\n return buf","def _bar_example_1(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, x_axis_type=\"categorical\")\n ch.set_title(\"Vertical bar plot\")\n ch.set_subtitle(\"Automatically sorts by value counts.\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n )\n ch.show(_OUTPUT_FORMAT)","def value_counts_plot(df):\n \n plt.figure(figsize=(15,10))\n \n #get rid of sort_index() to change the graph\n return df.value_counts().sort_index().plot(kind='bar')","def message_genre_bar_chart(df):\n genre_counts = df.groupby('genre').count()['message']\n genre_names = list(genre_counts.index)\n return {\n 'data': [\n Bar(\n x=genre_names,\n y=genre_counts\n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Message Genres',\n 'yaxis': {\n 'title': \"Count\"\n },\n 'xaxis': {\n 'title': \"Genre\"\n }\n }\n }","def draw_bar(x_index, data_list, xticks, title, x_label, y_label):\n pyplot.bar(x_index, data_list)\n pyplot.xlabel(x_label)\n pyplot.ylabel(y_label)\n pyplot.xticks(x_index, xticks)\n pyplot.title(title)\n pyplot.show()\n pyplot.savefig()","def plot(*args, **params):\n if len(args) == 1: # only support data for now\n if isinstance(args[0], list):\n bar_2d = Bar2D(data=args[0])\n bar_2d.plot()\n bar_2d.fig.show()\n else:\n bar_2d = Bar2D(**params)\n bar_2d.plot()\n bar_2d.fig.show()","def _bar_example_3(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, y_axis_type=\"categorical\")\n ch.set_title(\"Horizontal bar plot\")\n ch.set_subtitle(\"Horizontal with color grouping\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n color_column=\"fruit\",\n )\n ch.show(_OUTPUT_FORMAT)","def BarOverview(data):\n return dcc.Graph(id=\"BarOverview\", className=\"bar\", figure=dict(\n data=[go.Bar(\n x=data[\"frequencies\"],\n y=data[\"names\"],\n orientation='h',\n marker={\n 'color': '#ff4058'\n },\n )],\n layout=dict(\n title=\"<b>Most common Persons</b>\",\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\n margin=dict(l=10, r=20, t=50, b=30),\n plot_bgcolor=\"rgba(0,0,0,0)\",\n paper_bgcolor=\"rgba(0,0,0,0)\",\n xaxis=dict(tick0=0, dtick=max(data[\"frequencies\"])),\n yaxis=dict(ticks='outside',\n showgrid=True,\n showline=False,\n showticklabels=False),\n annotations=[dict(xref='paper', yref='y',\n x=0, y=yd,\n font=dict(\n color=\"#000000\",\n size=19\n ),\n text=str(yd),\n showarrow=False) for xd, yd in zip(data[\"frequencies\"], data[\"names\"])]\n )\n ))","def plot_norm_bar(df, title, figsize=(12,7)):\n fig, ax = plt.subplots(ncols=1, figsize=figsize)\n fig.suptitle(title)\n cat_value_counts = df.fillna('missing').value_counts(normalize=True)\n sns.barplot(y = cat_value_counts.index, x= cat_value_counts.values*100)\n ax.set(xlabel= 'percentage', ylabel=str(df.name))\n \n plt.plot()\n\n return","def barplot(self, x = \"Predictor\", color = None, opacity = 1, template = \"ggplot2\", \n has_title = True, barmode=\"stack\", is_horizontal = False, title = None, is_percent = False,\n show_num = False):\n if color: #Produce either a stacked or grouped bar plot\n df_stack = self._df.groupby([x,color]).size().reset_index()\n df_stack['Percentage'] = self._df.groupby([x, color]).size().groupby(level = 0).apply(lambda \n x:100 * x/float(x.sum())).values\n df_stack.columns = [x, color, 'Count', 'Percentage']\n df_stack['Percentage'] = round(df_stack['Percentage'], 2)\n \n x_clean, df_clean = clean_varname(df_stack, var = x)\n color_clean, df_clean = clean_varname(df_clean, var = color)\n \n if has_title:\n if not title:\n title = f\"Bar Plot of {x_clean} and {color_clean}\"\n else:\n title = None\n \n \n # 8 different variations for how this graph can appear:\n if is_horizontal:\n if is_percent:\n if show_num: #Show percentages on stacked bar graph\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Percentage'])\n else:\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if show_num: #Show counts on stacked bar graph:\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Count'])\n else:\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if is_percent:\n if show_num:\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Percentage'])\n else:\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title)\n else:\n if show_num:\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title, text = df_clean['Count'])\n else:\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \n color = color_clean, template = template, barmode=barmode, \n opacity = opacity, title = title) \n \n return fig\n \n else: #Create a basic bar plot\n df_stack = self._df.groupby([x]).size().reset_index()\n df_stack['Percentage'] = self._df.groupby([x]).size().groupby(level = 0).apply(lambda","def bar( # pylint: disable=disallowed-name\n self, x: Hashable | None = None, y: Hashable | None = None, **kwargs\n ) -> PlotAccessor:\n return self(kind=\"bar\", x=x, y=y, **kwargs)","def bar(self, entry_type:str, x:str, labels:list=None, diff:bool=False, x_idx:int=-1):\n\n query = self._decode(x)\n\n data_points = []\n\n for idx, (log, name) in enumerate(zip(self.logs, self.log_names)):\n log = log[entry_type]\n\n candidates = []\n\n for entry in log:\n test = self._follow(entry, query)\n\n if type(test) == dict:\n candidates.append(test)\n elif type(test) == list:\n candidates.append({idx: v for idx, v in enumerate(test)})\n \n if len(candidates) > 0:\n data_points.append((name, candidates[x_idx]))\n \n if len(data_points) == 0:\n print('Warning: Nothing to show in bar chart!')\n return\n\n names = [x[0] for x in data_points]\n data_points = [x[1] for x in data_points]\n\n # Construct the labels for the data\n if labels is not None:\n data_labels = labels\n else:\n data_labels = set()\n for datum in data_points:\n for k in datum:\n data_labels.add(k)\n \n data_labels = list(data_labels)\n data_labels.sort()\n \n\n data_values = [[(datum[k] if k in datum else None) for k in data_labels] for datum in data_points]\n\n if diff:\n for idx in reversed(range(len(data_values))):\n for jdx in range(len(data_labels)):\n if data_values[0][jdx] is None or data_values[idx][jdx] is None:\n data_values[idx][jdx] = None\n else:\n data_values[idx][jdx] -= data_values[0][jdx]\n\n\n series_labels = names\n\n # Plot the graph now\n num_bars = len(series_labels)\n bar_width = 1 / (num_bars + 1)\n \n # Set position of bar on X axis\n positions = [np.arange(len(data_labels))]\n for _ in range(1, num_bars):\n positions.append([x + bar_width for x in positions[-1]])\n \n # Make the plot\n for idx, (series, data, pos) in enumerate(zip(series_labels, data_values, positions)):\n plt.bar(pos, data, color=self._color(idx), width=bar_width, edgecolor='white', label=series)\n \n # Add xticks on the middle of the group bars\n plt.title(x.replace('x.', entry_type + '.') + (' diff' if diff else ''))\n plt.xticks([r + bar_width for r in range(len(data_labels))], data_labels)\n \n # Create legend & Show graphic\n plt.legend()\n plt.show()","def bar_time_series(df, title, ylabel, report):\n for col in df:\n fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n plt.gcf().subplots_adjust(bottom=0.25)\n df[col].plot.bar();\n ax.set_xticklabels([v if i % 4 == 0 else '' for i, v in enumerate(df.index)])\n ax.xaxis.set_tick_params(rotation=45, length=0);\n ax.set_xlabel('Date')\n ax.set_ylabel(ylabel)\n full_title = title if df.shape[1] == 1 else '{} {}'.format(col, title)\n report.write_plot(full_title)\n plt.title(full_title)\n plt.show();\n plt.close();","def plot_uv_bar(df, colname, colorid=0):\n if (colname in list(df.columns)):\n \n # Set figure size \n fig, ax = plt.subplots(figsize=(8,6))\n \n # set colorid for bar plot\n base_color = sns.color_palette()[colorid]\n\n # variable counts to calculate percentage\n cdict_count = df[colname].value_counts().to_dict() \n total_count = df.shape[0]\n \n \n if (len(list(cdict_count.keys())) > 5):\n # max.count to position the %\n maxcount_pct= np.max(list(cdict_count.values()))*0.125\n # max. no. of categories Vs % rotation \n rottext_pct = 90 \n # font size for % display\n fontsiz_pct = 12\n else:\n # max.count to position the %\n maxcount_pct= np.max(list(cdict_count.values()))*0.075\n # max. no. of categories Vs % rotation \n rottext_pct = 0 \n # font size for % display\n fontsiz_pct = 16\n \n \n # plotting...\n sns.countplot(data = df, x = colname\n , order = list(cdict_count.keys())\n , color = base_color\n , saturation = 0.7)\n\n # title and labels\n plt.title('Order of '+ colname, fontsize=20)\n plt.xlabel(colname + ' Type', fontsize=16)\n plt.ylabel('Count', fontsize=16)\n \n # x-,y- ticks\n locs, labels = plt.xticks(fontsize=16)\n plt.yticks(fontsize=16)\n\n # display % count information on each tower of bar plot\n for loc, label in zip(locs, labels):\n count = cdict_count[label.get_text()]\n pct_string = '{:0.1f}%'.format(count*100/total_count)\n plt.text(loc, count-maxcount_pct, pct_string, ha='center', color='w', fontsize=fontsiz_pct, rotation=rottext_pct)\n\n return plt.show()\n\n else:\n \n print(' >>>Error:',colname,' is not in DataFrame')","def plot_df(data_frame):\n plt.figure(figsize = (10, 5))\n chart = sns.countplot(data_frame['label'], \n palette=\"Set1\"\n )\n plt.show()","def plot_bar(filename, data, std=None, xlab='x', ylab='y', ylim=[0,1],yticks=np.arange(0,1.1,0.1), title='Bar-Plot', methods=None, datasets=None, figwidth=8, figheight=6, colors=None, legend_loc=\"lower center\", xytick_fontsize=12, xylabel_fontsize=15, title_fontsize=15, legend_fontsize=12):\n import matplotlib as mpl\n mpl.use(\"pdf\")\n import matplotlib.pyplot as plt\n\n data=np.array(data)\n num_methods=len(data)\n \n # colors\n if colors is None:\n colors=['b','r','g','c','m','y','k','w'] # maximally 8 colors allowed so far\n\n ind = np.arange(num_methods) # the x locations of the bars\n width = 0.8 # the width of the bars\n fig=plt.figure(num=1,figsize=(figwidth,figheight))\n ax=fig.add_subplot(1,1,1)\n if std is None:\n ax.bar(ind,data,width,color=colors[0:num_methods],ecolor='k')\n else:\n ax.bar(ind,data,width,color=colors[0:num_methods],yerr=std,ecolor='k')\n\n # add some text for labels, title and axes ticks\n ax.set_ylabel(ylab,fontsize=xylabel_fontsize)\n ax.set_xlabel(xlab,fontsize=xylabel_fontsize)\n ax.set_title(title,fontsize=title_fontsize)\n ax.set_xticks(ind+0.5*width)\n ax.set_xticklabels( methods )\n #if ylim is None:\n # yticks=np.arange(0,1.1,0.1)\n # ylim=[0,1]\n if yticks is not None:\n ax.set_yticks(yticks)\n if ylim is not None:\n ax.set_ylim(ylim[0],ylim[1])\n plt.setp(ax.get_xticklabels(), fontsize=xytick_fontsize)\n plt.setp(ax.get_yticklabels(), fontsize=xytick_fontsize)\n # shrink axis box \n #box = ax.get_position()\n #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n #ax.legend( method_bar, methods, loc='lower left', bbox_to_anchor=(1.0, 0.3), fontsize=legend_fontsize )\n #ax.legend(methods, loc=legend_loc, fontsize=legend_fontsize )\n #plt.show()\n fig.savefig(filename,bbox_inches='tight')\n plt.close(fig)","def category_bar_chart(df):\n label_names = df.drop(['message', 'original', 'genre', 'id'], axis=1).columns\n label_counts = []\n for column in label_names:\n label_counts.append(df[column].sum())\n return {\n 'data': [\n Bar(\n x=label_names,\n y=label_counts\n )\n ],\n\n 'layout': {\n 'title': 'Distribution of Labelled Categories',\n 'yaxis': {\n 'title': \"Count\",\n 'type': 'log'\n },\n 'xaxis': {\n 'title': \"Category\"\n }\n }\n }","def bar(variable, name, data=None, x_v=None, color_set=custom_bw,\n ax_size=(20, 6), highlight=None, ax=None):\n\n common_set_up(ax_size) # Apply basic plot style\n\n fig = sns.barplot(x=x_v, y=variable, data=data, saturation=1, ax=ax,\n color=color_set[2], label=name,\n )\n\n sns.despine(offset=2, trim=True, left=True, bottom=True)\n\n # Set title and axes\n title_color = '#192231'\n font_colour = '#9099A2'\n if ax is None:\n fig.set_title('{0}'.format(name),\n fontsize=20, color=title_color)\n fig.set_ylabel('Frequency',\n color=font_colour)\n fig.set_xlabel('{0}'.format(name),\n color=font_colour)\n\n if highlight:\n bars = fig.patches\n bars[highlight].set_color(color_set[1])\n\n return fig","def barPlot2():\n n = 10\n X = np.arange(n)\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n\n for x,y in zip(X,Y1):\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n\n plt.ylim(0,1.25)\n plt.show()","def _bar_example_2(quantity_by_fruit):\n ch = chartify.Chart(blank_labels=True, x_axis_type=\"categorical\")\n ch.set_title(\"Vertical bar plot - Label sort\")\n ch.set_subtitle(\"Set `categorical_order_by` to sort by labels\")\n ch.plot.bar(\n data_frame=quantity_by_fruit,\n categorical_columns=\"fruit\",\n numeric_column=\"quantity\",\n categorical_order_by=\"labels\",\n categorical_order_ascending=True,\n )\n ch.show(_OUTPUT_FORMAT)","def bar(self, row_id, col_id, label=None, offset=(350, 30), **kwargs):\n bar = BarGraph(label=label, **kwargs)\n self.pl[row_id, col_id].addItem(bar)\n\n bar.barClicked.connect(self.clickedBar)","def plot_results(t_val, mood):\r\n N = 8\r\n theta = np.linspace(0.0, 2 * np.pi , N, endpoint=False)\r\n the_stats = [t_val['number_words'], t_val['average_character_length'], \r\n t_val['signs'], t_val['multiple_signs'], t_val['question'],\r\n t_val['exclamation'], t_val['name'], mood] \r\n \r\n width = np.pi / N \r\n\r\n plt.figure()\r\n \r\n handle = plt.subplot(111, polar=True)\r\n handle.set_xticklabels(['Word', 'AvrChar', 'Signs', '2Signs', '?', '!', 'name', 'mood'])\r\n \r\n handle.bar(theta, the_stats, width=width, bottom=1.0)\r\n \r\n plt.show()","def img_gen_bar():\n data = pd.DataFrame(data=np.random.rand(5,1), index=range(1,6), columns=['Fred'])\n #m,n = np.shape(data)\n\n plt.clf()\n plt.bar(x=data.index.values, height=data.values.ravel(), color='k') # figsize=(10, 6))\n # Options for later from https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html\n # bar_width = 0.35\n # alpha = .3\n fig=plt.gcf()\n fig.set_size_inches(2.24, 2.24)\n plt.axis('off')\n fig.tight_layout()\n fig.canvas.draw()\n # grab the pixel buffer and dump it into a numpy array\n pixels = np.array(fig.canvas.renderer._renderer)[:,:,:3]\n #print(pixels.shape)\n return pixels, data.index.values + data.values.ravel()","def _bar2df(bars,data):\n tmp = list(map(lambda x: [x[0] for _ in range(x[1])],list(enumerate(bars))))\n labels = list(itertools.chain(*tmp)) # tmp is a list of list and this line flattens it\n data[\"labels\"] = labels\n data[\"index\"] = list(range(data.shape[0]))\n aggregation_condition = {'time': ['first', 'last'], \\\n 'index':['first', 'last'], \\\n 'price': ['min', 'max', 'first', 'last']}\n res = data.groupby(\"labels\",as_index=False).agg(aggregation_condition)\n res.columns = ['_'.join(col) for col in res.columns.values]\n res = res.drop([\"labels_\"],axis=1)\n res.columns = ['start_t', 'end_t', 'start_idx', 'end_idx','low', 'high', 'open', 'close']\n return res","def matplotlib_bar_chart() -> Tuple:\n df = read_dataset(Path('..', '..', 'iris.csv'))\n x = []\n\n for col in df.columns:\n try:\n max_val = get_column_max(df, col)\n x.append(max_val)\n except ValueError:\n pass\n \n fig, ax = a_libraries.matplotlib_bar_chart(np.array(x))\n\n return fig, ax","def plot_bar(label_array, acc_array, f1_array, width=0.5, axis_label=None, graph_title=None, file_name=\"\", dpi=100):\n plt.figure(figsize=plt.figaspect(1.), dpi=dpi)\n x = np.arange(len(label_array)) # the label locations\n plt.bar(x - 0.5 * width, acc_array, width, label='Accuracy')\n plt.bar(x + 0.5 * width, f1_array, width, label='F1 score')\n plt.ylim([0, 1.1])\n plt.xticks(x, labels=label_array)\n if axis_label is None:\n axis_label = ['Set', 'Values']\n plt.xlabel(axis_label[0])\n plt.ylabel(axis_label[1])\n if graph_title is None:\n graph_title = graph_title\n plt.title(graph_title)\n plt.tight_layout()\n plt.legend()\n plt.grid()\n if file_name:\n plt.savefig(file_name, bbox_inches='tight')\n plt.show()\n return","def PlotBayes( x=np.ones(1), bayes=np.ones(1), title=None, label=None, width=1.0, color='blue', show_values=False, ax=None, posterior=False ):\n\n if ax is None:\n fig, ax = plt.subplots( )\n ax.bar(x, bayes/bayes.max(), width, color=color )\n ax.set_title( title )\n ax.set_yscale('log')\n ax.set_xlabel( label )\n if posterior:\n ax.set_ylabel(r\"$P/P_{\\rm max}$\")\n else:\n ax.set_ylabel(r\"$\\mathcal{B}/\\mathcal{B}_{\\rm max}$\")\n# ax.set_ylabel(r\"$\\mathcal{B} = \\prod L / L_0$\")\n if show_values: ## print value on top of each bar, .... doesnt work ...\n shift = bayes.max()/bayes.min()/10\n for xx, b in zip( x, bayes ):\n ax.text( xx, b*shift, str(b), color=color, fontweight='bold' )\n\n ### assure that there are ticks at y axis\n lim = ax.get_ylim()\n ax.set_ylim(lim[0]*0.5, lim[1]*2)","def barh_plotter(data: pd.DataFrame, variable: str):\n fig, axs = plt.subplots(2,5, gridspec_kw={'wspace': 1, 'hspace': 0.2},\n figsize=(60, 40), sharex = False)\n\n for ax, dta in zip(axs.flatten(), data.values()) :\n ax.barh(dta['club'], dta[f'{variable}'])\n ax.set_xlabel(f'{variable}', fontsize=25)\n \n \n for ax, dta in zip(axs.flatten(), data.keys()):\n ax.set_title(dta, fontsize=30)\n\n return plt.show()","def barPlot1():\n n = 12\n X = np.arange(n)\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\n\n for x,y in zip(X,Y1):\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n\n for x,y in zip(X,Y2):\n plt.text(x+0.2, -y-0.1, '%.2f' % y, ha='center', va= 'bottom')\n\n plt.ylim(-1.25,+1.25)\n plt.show()","def graphy2():\n data = pd.read_csv(\"week2.csv\")\n plot_g = pygal.Bar(fill=True, interpolate='cubic', style=LightSolarizedStyle)\n plot_g.title = \"Top Fans in Week 2\"\n plot_g.x_labels = data.GENDER\n plot_g.y_labels = map(int, range(0, 80, 10))\n plot_g.add(\"Male\", data.COUNT)\n plot_g.add(\"Female\", data.COUNT2)\n plot_g.add(\"Total\", data.COUNT3)\n plot_g.render_to_file(\"plotweek2.svg\")","def draw_bar_plot(fig, x, y, labels):\r\n\r\n #Convert times to a displayable format\r\n (x_times, hour_mode) = times_to_axis(x)\r\n\r\n\r\n #Draw horizontal grid lines behind data\r\n fig.yaxis.grid(zorder=0)\r\n\r\n #Draw plot\r\n fig.bar(x_times, y, zorder=2)\r\n\r\n\r\n #If necessary, enable processing of \"datetime\" objects on the x-axis\r\n if not hour_mode:\r\n fig.xaxis_date()\r\n\r\n\r\n #Label and style plot\r\n set_axis_labels(fig, *labels)\r\n style_x_labels(fig)","def graph(df):\n df.plot()\n plt.show()","def plot_individual_bar_chart_graph(data_values, title,\r\n number_of_keys,\r\n max_val,\r\n vals_for_bar_chart,\r\n file_in):\r\n\r\n n_groups = len(vals_for_bar_chart)\r\n fig, ax = plt.subplots()\r\n index = np.arange(n_groups)\r\n bar_width = 0.9\r\n opacity = 0.4\r\n # print vals_for_bar_chart\r\n rects1 = plt.bar(index,\r\n vals_for_bar_chart,\r\n bar_width,\r\n alpha=opacity,\r\n color='b') # label='whatever'\r\n plt.xlabel('number in cluster')\r\n plt.ylabel('Count')\r\n plt.title(title+\"_barchart\")\r\n plt.legend()\r\n pylab.grid(True)\r\n ax.set_yscale('symlog')\r\n ax.set_xscale('symlog')\r\n plt.tight_layout()\r\n plt.show()\r\n pylab.savefig(file_in + \"_\" + title + '_barchart.png')\r\n plt.close()\r\n pylab.close()","def show_graph(d:dict):\n x = []\n y = []\n for key, value in d.items():\n x.append(str(key))\n y.append(value)\n\n x_pos = [i for i, _ in enumerate(x)]\n plt.figure()\n plt.bar(x_pos, y, color='green')\n plt.xlabel(\"Size\")\n plt.ylabel(\"Number of images\")\n plt.title(\"Count by size\")\n plt.xticks(x_pos, x)","def plotBarPlots(self, stage, strain, strainID, count, p, xm, ym, xn, yn):\n\t\t\n\t\ttimeList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n\t\t\n\t\tind = np.arange(len(timeList))\n\t\t\n\t\tplt.style.use('bmh')\n\t\tcolors = ['#b2182b', '#238b45', '#3690c0', '#023858']\n\t\t\n\t\tfig = plt.figure(figsize=(15,15), frameon=False)\n\t\tax1 = fig.add_subplot(111)\n\t\tfig.suptitle(strain + ' - ' + stage, fontsize=20, fontweight='bold')\n\t\n\t\tax1.spines['top'].set_visible(False)\n\t\tax1.spines['right'].set_visible(False)\n\t\tax1.spines['bottom'].set_visible(False)\n\t\tax1.spines['left'].set_visible(False)\n\t\tax1.yaxis.set_ticks_position('left')\n\t\tax1.xaxis.set_ticks_position('bottom')\n\t\twidth = 0.6\n\t\t\n\t\tax1.bar(ind+width/2., count[timeList,1], width, color=colors[0], edgecolor = \"none\")\n\t\t\n\t\tax1.plot(*p.linspace(), c='k', linewidth=3.0)\n\t\t\n\t\tfor point in range(len(xm)):\n\t\t\tax1.plot(xm[point], ym[point], marker='*', markersize=30, color=\"blue\")\n\t\tfor point in range(len(xn)):\n\t\t\tax1.plot(xn[point], yn[point], marker='*', markersize=30, color=\"blue\")\t\t\t\n\t\t\n\t\tax1.set_ylabel('Frequency as %', fontweight='bold', fontsize=20)\n\t\tax1.set_xlabel('Time', fontweight='bold', fontsize=20)\n\t\txTickMarks = ['%s' %str(j) for j in timeList]\n\t\tax1.set_xticks(ind+width/2)\n\t\txtickNames = ax1.set_xticklabels(xTickMarks, fontweight='bold')\n\t\t\n\t\tplt.setp(xtickNames, fontsize=15)\n\t\tax1.xaxis.set_ticks_position('none')\n\t\tax1.yaxis.set_ticks_position('none')\t\t\n\t\tax1.set_yticklabels(ax1.get_yticks(), fontweight='bold', fontsize=17)\n\t\tax1.set_xlim([6, len(timeList)-6])\n\t\t\t\t\t\n\t\tfname = 'strain%d' %strainID + stage + '.png' \n\t\t\n\t\tfig.savefig(fname, transparent=True, dpi=100)\n\t\tplt.close(fig)","def diagram_plugs(data_no,\r\n data_little,\r\n data_means,\r\n data_great,\r\n data_large_enough,\r\n data_super_large,\r\n er_no, er_little,\r\n er_means,\r\n er_great,\r\n er_large_enough,\r\n er_super_large):\r\n\r\n\r\n plt.bar(range(6), [data_no,\r\n data_little,\r\n data_means,\r\n data_great,\r\n data_large_enough,\r\n data_super_large],\r\n width=0.1, color='black',\r\n yerr=[er_no, er_little, er_means,\r\n er_great, er_large_enough,\r\n er_super_large],\r\n ecolor='black', capsize=10)\r\n\r\n\r\n plt.xticks(range(6), ['non', 'petit', 'moyen',\r\n 'grand', 'assez grand', 'tres grand'])\r\n\r\n\r\n plt.ylabel('Taux de pollution en AQI')\r\n plt.title(\"Taux de pollution selon les bouchons\")\r\n\r\n nouveau = new()\r\n print(nouveau)\r\n plt.savefig(nouveau, transparent=True)\r\n plt.clf()\r\n plt.close()\r\n\r\n shutil.move(nouveau, '/app/static/popo')\r\n\r\n return nouveau","def barplot(self, name: str, y_label: str, img_title: str):\n path = C.TEST_DIR\n\n sns.set(style='whitegrid')\n sns.set_palette(sns.color_palette(C.IRT_COLORS))\n df = pd.read_csv(path + name + '.csv')\n ax = sns.barplot(data=df)\n ax.set(ylabel=y_label, title=img_title)\n\n self.save_plot(name)\n plt.show()","def plot_bar_chart(log, frame_seconds=-1):\n\tif frame_seconds == -1:\n\t\tframe_seconds = (log.content[-1].timestamp - log.content[0].timestamp).total_seconds() / timedelta(seconds=100).total_seconds()\n\tlines,dates,areas = log.give_plot_data_bar(frame_seconds=frame_seconds)\n\tfig, ax = plt.subplots(figsize=(11,6))\n\tfig.autofmt_xdate()\n\tmyFmt = DateFormatter(\"%Y %d.%b %H:%M:%S\")\n\tax.xaxis.set_major_formatter(myFmt)\n\tax.set_xlabel('timestamps in UTC')\n\tax.set_ylabel('amount of entries')\n\tplt.title('Analysis of the file \\\"'+log.name+'\\\" \\n with frame size ' + str(frame_seconds) +' seconds')\n\n\twidth = timedelta(seconds=frame_seconds).total_seconds() / timedelta(days=1).total_seconds()\n\tnormal = ax.bar(dates, areas, width, label='entries', edgecolor='k')\n\n\tplt.legend()\n\tplt.subplots_adjust(left=0.15, bottom=0.2, right=0.9, top=0.9)\n\tfig.canvas.mpl_connect('key_press_event', _quit_figure)","def create_bar_graph(plot_df, title=\"\", x_title=\"\", y_title=\"\"):\n plot_df[\"quarter\"] = pd.PeriodIndex(pd.to_datetime(plot_df.iloc[:, 0]), freq=\"Q\")\n fig_data = [\n go.Bar(\n x=plot_df[\"quarter\"].astype(str),\n y=plot_df.iloc[:, 1],\n text=plot_df.iloc[:, 1],\n marker={\"color\": color_palette[0]},\n hoverinfo=\"x+y\",\n showlegend=False,\n )\n ]\n\n fiq_layout = build_bar_layout(title, x_title=x_title, y_title=y_title)\n\n return dict(data=fig_data, layout=fiq_layout)","def oneNumBar(df, colName):\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\n ax = bins.value_counts()\n bins = bins.cat.as_ordered()\n bins = bins.cat.categories\n bounds = bins.left \n bounds = list(bounds)\n bounds.append(bins[len(bounds)-1].right)\n texts = []\n for x,y in zip(bounds[0::],bounds[1::]):\n texts.append(\"(\" + str(x) + \", \" + str(y) + \"]\") \n barData = [go.Bar(x=texts, \n y=ax,\n marker=dict(\n color = '#92c5de',\n opacity=0.8)\n )] \n layout = go.Layout(\n title=\"Bar Plot Showing Count of Values for \" + str(colName[0]),\n xaxis=dict(\n title= colName[0]\n ),\n yaxis=dict(\n title= \"NUMBER OF RECORDS\", \n )\n )\n fig = go.Figure(data=barData, layout=layout)\n return {\"label\":\"Frequency\", \"plot\":fig}","def bar_plot(self,\n x: str,\n y: str,\n x_label: str=None,\n y_label: str=None,\n title: str='Bar Plot',\n **kwargs) -> Figure:\n x_label = x if x_label is None else x_label\n y_label = y if y_label is None else y_label\n fig = px.bar(self.df, x=x, y=y,\n labels={x: x_label, y: y_label},\n title=title, **kwargs)\n return fig","def setBarType(bartype='vertical'):\n dislin.bartyp(bardict[bartype])","def draw_bar(df=data):\n pt = {\n 1: 'Credit card',\n 2: 'Cash',\n 3: 'No charge',\n 4: 'Dispute',\n 5: 'Unknown',\n 6: 'Voided trip',\n }\n df['payment_type'] = df['payment_type'].replace(pt)\n gr = df.groupby(['payment_type', 'weekday']) \\\n .agg(total_amount=('total_amount', 'sum')) \\\n .reset_index(drop=False)\n return px.bar(gr, x='weekday', y='total_amount', color='payment_type', barmode='group') \\\n .update_layout(\n template='plotly_dark',\n plot_bgcolor='rgba(0, 0, 0, 0)',\n paper_bgcolor='rgba(0, 0, 0, 0)',\n )","def plot_dict_bar(aDictionary):\n\t# Convert strings to float\n\tfor key in aDictionary:\n\t\taDictionary[key] = float(aDictionary[key])\n\t\t\n\t# Plot the result\n\tplt.bar(range(len(aDictionary)), aDictionary.values(), align='center')\n\tplt.xticks(range(len(aDictionary)), aDictionary.keys(), rotation=90)\n\t\n\tplt.show()","def performanceBarCharts(): \n ##tauopathy HCS pearson\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null YFP Model\", \"Null DAPI Model\"]\n ml_model_perf = pickle.load(open(\"pickles/ml_model_perf.pkl\", \"rb\"))\n null_model_perf = pickle.load(open(\"pickles/null_model_perf.pkl\", \"rb\"))\n null_dapi_perf = pickle.load(open(\"pickles/single_channel_DAPI_null_model_perf.pkl\", \"rb\"))\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\n x = [1, 2, 3]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance\",fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=12)\n plt.yticks(fontname=\"Times New Roman\", fontsize=12)\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n plt.savefig(\"matplotlib_figures/tau_performance_pearson_special_HCS_model.png\", dpi=300)\n\n ##tauopathy HCS MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null YFP Model\", \"Null DAPI Model\"]\n ml_model_perf = pickle.load(open(\"pickles/ml_model_mse_perf.pkl\", \"rb\"))\n null_model_perf = pickle.load(open(\"pickles/null_model_mse_perf.pkl\", \"rb\"))\n null_dapi_perf = pickle.load(open(\"pickles/single_channel_DAPI_null_model_mse_perf.pkl\", \"rb\"))\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\n x = [1, 2, 3]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance\",fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=12)\n plt.yticks(fontname=\"Times New Roman\", fontsize=12)\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\n plt.savefig(\"matplotlib_figures/tau_performance_mse_special_HCS_model.png\", dpi=300)\n\n ##osteosarcoma 3-fold (raw images) pearson\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ml_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_null_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [0.075, 0.1156] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with Raw Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.02)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_pearson_cross_val.png\", dpi=300)\n\n ##osteosarcoma 3-fold (raw images) MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ml_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_null_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [0.15, .2312] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance with Raw Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.01)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_mse.png\", dpi=300)\n\n ##osteosarcoma 3-fold (ablated image training) pearson\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ablated_ml_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_ablated_null_model_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [.1288, .1385] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with\\n95% Ablated Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.0)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_pearson_trained_ablation_model.png\", dpi=300)\n\n ##osteosarcoma 3-fold (ablated image training) MSE\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"ML Model\", \"Null Model\"]\n x = [1, 2]\n ys = []\n nulls = []\n for fold in [1,2,3]:\n osteo_ml_perf = pickle.load(open(\"pickles/osteo_ablated_ml_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n osteo_null_perf = pickle.load(open(\"pickles/osteo_ablated_null_model_mse_perf_fold_{}.pkl\".format(fold), \"rb\"))\n ys.append(osteo_ml_perf)\n nulls.append(osteo_null_perf) \n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\n stds = [.2576, .2771] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\"Times New Roman\")\n plt.title(\"MSE Performance with\\n95% Ablated Hoechst Images\",fontname=\"Times New Roman\", fontsize=20, y=1.0)\n ax.set_ylabel(\"MSE\", fontname=\"Times New Roman\", fontsize=18)\n plt.yticks(fontname=\"Times New Roman\", fontsize=18)\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\"Times New Roman\")\n ax.set_ylim((0,2))\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/osteosarcoma_performance_MSE_trained_ablation_model.png\", dpi=300)\n\n ##supplemental single channel learning YFP and DAPI performance\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"YFP-tau to AT8-pTau\", \"DAPI to AT8-pTau\"]\n YFP_ml_model = pickle.load(open(\"pickles/single_channel_YFP_ml_model_perf.pkl\", \"rb\"))\n DAPI_ml_model = pickle.load(open(\"pickles/single_channel_DAPI_ml_model_perf.pkl\", \"rb\"))\n y = np.array([YFP_ml_model[0], DAPI_ml_model[0]]).round(decimals=2)\n stds = [YFP_ml_model[1], DAPI_ml_model[1]]\n x = [1, 2]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\"cornflowerblue\", zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Performance with\\nSingle Channel Input Learning\",fontname=\"Times New Roman\", fontsize=17, y=1.01)\n ax.set_xlabel(\"Model\", fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=14)\n plt.yticks(fontname=\"Times New Roman\", fontsize=14)\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\"Times New Roman\")\n ax.set_ylim((0,1))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/supplemental_single_channel_learning.png\", dpi=300)\n\n ##supplemental single channel learning YFP and DAPI, input similarity to prediction\n plt.cla()\n plt.clf()\n width = .50\n fig, ax = plt.subplots()\n xlabels = [\"null\", \"YFP-tau to AT8-pTau\", \"DAPI to AT8-pTau\"]\n y = np.array([0.94894628, 0.98718720]).round(decimals=2)\n stds = [0.1673864, 0.039042]\n x = [1, 2]\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\"orange\", zorder=3)\n for i,j in zip(x, y):\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\"Times New Roman\")\n plt.title(\"Pearson Similarity Between\\nInput Channel and Predicted Channel\",fontname=\"Times New Roman\", fontsize=17)\n ax.set_xlabel(\"Model\", fontname=\"Times New Roman\", fontsize=14)\n ax.set_ylabel(\"Pearson Correlation Coefficient\", fontname=\"Times New Roman\", fontsize=14)\n plt.yticks(fontname=\"Times New Roman\", fontsize=14)\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\"Times New Roman\")\n ax.set_ylim((0,1.13))\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\n plt.savefig(\"matplotlib_figures/supplemental_single_channel_learning_pearson_similarity_input_and_predicted.png\", dpi=300)","def visualize_type():\n \n data_file = parse(MY_FILE, ',')\n\n # num of incidents per category\n counter = Counter(item['Category'] for item in data_file)\n\n # Set the labels\n labels = tuple(counter.keys())\n\n # Set exactly where the labels hit the x-axis\n xlocations = na.array(range(len(labels))) + 0.5\n\n # Width of each bar\n width = 0.5\n\n # Assign data to a bar plot\n plt.bar(xlocations, counter.values(), width=width)\n\n # Assign labels and tick location to x-axis\n plt.xticks(xlocations + width / 2, labels, rotation=90)\n \n # Give some more room so the x-axis labels aren't cut off\n plt.subplots_adjust(bottom=0.4)\n\n # Make the overall graph/figure larger\n plt.rcParams['figure.figsize'] = 12, 8\n\n # save\n plt.savefig('Type.png')\n\n # close\n plt.clf()","def get_proba_plot(data_type):\n buffer = io.BytesIO()\n plt.subplots(figsize = (25,15))\n ax = sns.barplot(x='proba', y='type', data=data_type, palette = \"Blues_r\")\n ax.set_xlabel('Probability')\n plt.yticks(fontsize = 30)\n plt.xticks(fontsize = 30)\n plt.ylabel(\"Type\", fontsize = 38)\n plt.xlabel(\"Probability\", fontsize = 38);\n plt.title(\"Model Results\", fontsize = 50)\n plt.savefig(buffer, format='png')\n# plt.show()\n plt.close()\n buffer.seek(0)\n chart_probability= Image.open(buffer).resize((512+256,512))\n return chart_probability","def plot_bar(counter, title=\"\", filename=\"tmp.png\"):\n\n fig = plt.figure()\n axis = fig.add_subplot(111)\n\n if isinstance(counter, dict):\n frequencies = counter.values()\n names = counter.keys()\n elif isinstance(counter, list):\n frequencies = [x[1] for x in counter]\n names = [x[0] for x in counter]\n y_pos = np.arange(len(counter))\n axis.barh(y_pos, frequencies, align='center')\n axis.set_title(title)\n axis.set_yticks(y_pos)\n axis.set_yticklabels(list(names))\n axis.invert_yaxis()\n axis.set_xlabel('Frequency')\n print('going to save fig...')\n fig.savefig('png_files/' + filename.replace(\".csv\", \".png\"))\n\n return axis","def custom_barchart(ax, x, y, error, xlims, ylims, error_kw, color='lightblue', width=0.75):\n\n error = [np.zeros(len(error)), error]\n\n ax.bar(x, y, color=color, width=width, yerr=error, error_kw=error_kw, align='center')\n\n ax.set_xlim(xlims)\n ax.set_ylim(ylims)\n\n return ax","def bar_plot(trj: TrajaDataFrame, bins: Union[int, tuple] = None, **kwargs) -> Axes:\n # TODO: Add time component\n\n bins = traja.trajectory._bins_to_tuple(trj, bins)\n\n X, Y, U, V = coords_to_flow(trj, bins)\n\n hist, _ = trip_grid(trj, bins, hist_only=True)\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=\"3d\")\n ax.set_aspect(\"equal\")\n X = X.flatten(\"F\")\n Y = Y.flatten(\"F\")\n ax.bar3d(\n X,\n Y,\n np.zeros_like(X),\n 1,\n 1,\n hist.flatten(),\n zsort=\"average\",\n shade=True,\n **kwargs,\n )\n ax.set(xlabel=\"x\", ylabel=\"y\", zlabel=\"Frames\")\n\n return ax","def _plot_dict_bar(d, xmin=None, label=None):\n xvals, yvals = _dict2lists(d)\n if xmin == None:\n xmin = min(xvals) - 1\n else:\n xmin = min(xmin, min(xvals) - 1)\n if label:\n pylab.bar(xvals, yvals, align='center', label=label)\n pylab.xlim([xmin, max(xvals)+1])\n else:\n pylab.bar(xvals, yvals, align='center')\n pylab.xlim([xmin, max(xvals)+1])","def plotBarChart(resultConfirmed, resultDeath, resultVaccinated):\n fig, ax = plt.subplots(3)\n\n ax[0].plot(resultConfirmed['Date'], resultConfirmed['Confirmed Cases'])\n ax[0].title.set_text('Confirmed Cases')\n \n ax[1].plot(resultDeath['Date'], resultDeath['Death Cases'])\n ax[1].title.set_text('Death Cases')\n \n ax[2].plot(resultVaccinated['Date'], resultVaccinated['Vaccinated Person'])\n ax[2].title.set_text('Vaccinated Cases')\n fig.tight_layout()\n plt.show()","def generate_barplot(predictions, labels):\n plot = figure(x_range=labels, plot_height=300, plot_width=400)\n plot.vbar(x=labels, top=predictions, width=0.8)\n # plot.xaxis.major_label_orientation = pi / 2.\n # plot.xaxis.axis_label_text_font_size = \"40pt\"\n # plot.yaxis.axis_label_text_font_size = \"40pt\"\n\n return components(plot)","def plot_balance_class(classes):\n unique, counts = np.unique(classes, return_counts=True)\n plt.bar(unique, counts)\n plt.title('Class Frequency')\n plt.xlabel('Class')\n plt.ylabel('Frequency')\n plt.show()","def make_bar_plot(x, y, title):\n return plotly.graph_objs.Figure(\n data=[plotly.graph_objs.Bar(x=list(x), y=list(y))],\n layout=plotly.graph_objs.Layout(title=title)\n )","def BarSpecific():\n return dcc.Graph(id=\"PersChart\", className=\"bar\", figure=dict(\n data=[go.Bar(\n x=[1],\n y=[\"Persons\"],\n orientation='h',\n marker={\n 'color': '#ff4058',\n },\n )],\n layout=dict(\n title=\"<b>Most similar Persons</b>\",\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\n margin=dict(l=100, r=20, t=50, b=30),\n plot_bgcolor=\"rgba(0,0,0,0)\",\n paper_bgcolor=\"rgba(0,0,0,0)\",\n xaxis=dict(tick0=0, dtick=1),\n yaxis=dict(ticks='outside')\n )\n ))","def plot(self):\n x = np.arange(5)\n # labels = ['temp', 'humi', 'mais', 'o2', 'co2']\n plt.bar(x - 0.35/2, self.data, 0.35, label='actual')\n plt.bar(x + 0.35/2, self.desired_values, 0.35, label='desired')\n plt.ylim(-5, 80)\n plt.legend()\n\n plt.draw()\n plt.pause(0.000001)\n plt.clf()","def simple_bar():\n\n # Make random discrete data\n discrete_a = np.zeros((8,2))\n discrete_b = np.zeros((8,2))\n discrete_c = np.zeros((8,2))\n discrete_a[:,0] = np.arange(8)\n discrete_b[:,0] = np.arange(8)\n discrete_c[:,0] = np.arange(8)\n discrete_a[:,1] = np.random.rand(8)*10\n discrete_b[:,1] = np.random.rand(8)*10\n discrete_c[:,1] = np.random.rand(8)*10\n\n # Make data sets, if using multiple bar_width must be the same\n dataset_a = DataSet(discrete_a,colour='pink',bar_width=0.8,plot='bar',label='A')\n dataset_b = DataSet(discrete_b,colour='violet',bar_width=0.8,plot='bar',label='B')\n dataset_c = DataSet(discrete_c,colour='darkviolet',bar_width=0.8,plot='bar',label='C')\n\n # Make plot object and add data sets\n plot = Plot()\n plot.add_dataset(dataset_a)\n plot.add_dataset(dataset_b)\n plot.add_dataset(dataset_c)\n plot.set_axes(xticks=(1,1),xlim=(-0.5,7.5),ylim=(0,12))\n plot.set_legend(legend=True,location='upper right')\n plot.set_text(legend=8)\n\n # Plot graph and display\n plot.plot()\n plot.save(name='./figures/2d_simple_bar',fmt='png')\n plot.display()","def bar(*args, **kwargs):\n ax, args, kwargs = maybe_get_ax(*args, **kwargs)\n color_cycle = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\n almost_black = '#262626'\n kwargs.setdefault('color', color_cycle[0])\n kwargs.setdefault('edgecolor', 'white')\n middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0\n\n # Check if data contains stacks\n stacked = kwargs.pop('stacked',False)\n # Check if stack text should be included\n stack_text = kwargs.pop('stack_text',False)\n # Get legend if available\n legend = kwargs.pop('legend',False)\n\n left = args[0]\n height = np.array(args[1])\n\n # Label each individual bar, if xticklabels is provided\n xtickabels = kwargs.pop('xticklabels', None)\n # left+0.4 is the center of the bar\n xticks = np.array(left) + middle\n\n # Whether or not to annotate each bar with the height value\n annotate = kwargs.pop('annotate', False)\n\n show_ticks = kwargs.pop('show_ticks', False)\n\n # If no grid specified, don't draw one.\n grid = kwargs.pop('grid', None)\n\n # Check if stacked and plot data accordingly\n if stacked:\n num_stacks, num_data = height.shape\n bottom = np.zeros(num_data)\n for i in np.arange(num_stacks):\n lst = list(args)\n lst[1] = height[i]\n args = tuple(lst)\n kwargs['color'] = set2[i]\n kwargs['bottom'] = bottom\n rectangles = ax.bar(*args, **kwargs)\n bottom += height[i]\n else:\n rectangles = ax.bar(*args, **kwargs)\n\n # add legend\n if isinstance(legend, collections.Iterable):\n ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)\n\n # add whitespace padding on left\n xmin, xmax = ax.get_xlim()\n xmin -= 0.2\n if stacked:\n xmax = num_data\n ax.set_xlim(xmin, xmax)\n\n # If the user is only plotting one bar, make it an iterable\n if not isinstance(height, collections.Iterable):\n height = [height]\n\n\n # If there are negative counts, remove the bottom axes\n # and add a line at y=0\n if any(h < 0 for h in height.tolist()):\n axes_to_remove = ['top', 'right', 'bottom']\n ax.hlines(y=0, xmin=xmin, xmax=xmax,\n linewidths=0.75)\n else:\n axes_to_remove = ['top', 'right']\n\n # Remove excess axes\n remove_chartjunk(ax, axes_to_remove, grid=grid, show_ticks=show_ticks)\n\n if stacked:\n data = height\n height = height.sum(axis=0)\n\n # Add the xticklabels if they are there\n if xtickabels is not None:\n ax.set_xticks(xticks)\n ax.set_xticklabels(xtickabels)\n\n if annotate or isinstance(annotate, collections.Iterable):\n annotate_yrange_factor = 0.025\n ymin, ymax = ax.get_ylim()\n yrange = ymax - ymin\n\n # Reset ymax and ymin so there's enough room to see the annotation of\n # the top-most\n if ymax > 0:\n ymax += yrange * 0.1\n if ymin < 0:\n ymin -= yrange * 0.1\n ax.set_ylim(ymin, ymax)\n yrange = ymax - ymin\n\n offset_ = math.log(yrange) + math.log(annotate_yrange_factor+1)\n print offset_\n print yrange * annotate_yrange_factor\n print math.log(yrange) + math.log(annotate_yrange_factor)\n if isinstance(annotate, collections.Iterable):\n annotations = map(str, annotate)\n else:\n annotations = ['%.3f' % h if type(h) is np.float_ else str(h)\n for h in height]\n\n for x, h, annotation in zip(xticks, height, annotations):\n # Adjust the offset to account for negative bars\n offset = offset_ if h >= 0 else -1 * offset_\n verticalalignment = 'bottom' if h >= 0 else 'top'\n\n # Finally, add the text to the axes\n ax.annotate(annotation, (x, h + annotate_yrange_factor), \n verticalalignment=verticalalignment,\n horizontalalignment='center',\n color=almost_black)\n\n # Text for each block of stack\n # This was partially inspired by the following article by Tableau software\n # http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812\n if stack_text:\n bottom = np.zeros(num_data)\n max_h = max(height)\n for i in np.arange(num_stacks):\n for x, d, b in zip(xticks, data[i], bottom):\n if (d*100.0/max_h) > 4.0:\n ax.text(x,b+d/2.0,d, ha='center', va='center', color=almost_black)\n bottom += data[i]\n return rectangles","def show_class_imbalance(df, title='Class Imbalance', PATH=None):\n ax = sns.barplot(x=[\"Normal\", \"Clickbait\"], y=df.groupby(['target']).target.count())\n ax.set_title(title, size=20)\n plt.xticks([0,1],[\"Normal\", \"Clickbait\"], size = 20)\n ax.set_ylabel(\"Document Count\", size=17)\n ax.set_xlabel(\"Article Class\", size=20)\n if PATH:\n plt.savefig(PATH, bbox_inches=\"tight\", transparent=True)\n return ax","def to_bar(self):\n factor = FactorData()\n return factor","def plot_what_if(df):\n fig = go.Figure()\n for col in df.columns:\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\n fig.update_xaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\n fig.update_yaxes(title_text=\"Gains/Losses ($ USD)\",\n showline=True, mirror=True, linewidth=1, linecolor='black',\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\n fig.update_layout(legend=dict(orientation=\"h\", yanchor=\"bottom\", y=-0.25, xanchor=\"center\", x=0.5),\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\n\n\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\n fig.write_html(join('..', 'docs', 'redistribution.html'))\n fig.show()","def draw_bar_plot():\n # Copy and modify data for monthly bar plot\n \n df_bar = df.copy()\n\n # Draw bar plot\n leglab = [\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n labels = [2016, 2017, 2018, 2019]\n months = np.zeros([12, 4])\n\n for i in range(12):\n for j, year in enumerate(labels):\n t = df[df.index.year == year]\n months[i][j] = t[t.index.month == i].value.mean()\n\n x = np.arange(len(labels))\n width = 0.7\n fig, ax = plt.subplots()\n fig.set_figwidth(10)\n fig.set_figheight(8)\n for i, month in enumerate(months):\n ax.bar(x - (width * (12 - i) / 12), months[i], width / 12, label=leglab[i])\n\n ax.set_ylabel(\"Average Page Views\")\n ax.set_xlabel(\"Years\")\n ax.set_xticks(x)\n ax.set_xticklabels(labels)\n ax.legend(title='Months')\n\n # Save image and return fig (don't change this part)\n fig.savefig('bar_plot.png')\n return fig","def plot_bar(source_files, column_ids, column_names, normalize, sort, plot_difference, freq_bound, title=None,\n dtype=int):\n\n def _filter_data(raw_data, numerical):\n \"\"\" Filters plot-able data. \"\"\"\n # Retain numeric information\n legal_count_inventory = digits + '.'\n # Retain POS tags, also\n legal_entry_inventory = ascii_uppercase + '$'\n filtered_data = list()\n for data_point in raw_data:\n skip = False\n for symbol in list(str(data_point)):\n if symbol not in legal_count_inventory and symbol not in legal_entry_inventory:\n skip = True\n if not skip:\n if numerical:\n filtered_data.append(dtype(data_point))\n else:\n filtered_data.append(data_point)\n # Optionally normalize count values, resulting in a proportion plot\n if numerical and normalize:\n filtered_data = filtered_data / np.sum(filtered_data)\n return np.array(filtered_data)\n\n # Set plot parameters\n sns.set_style('whitegrid')\n sns.set_context('paper')\n\n # Compile data to be plotted within a new dataframe\n # Not necessary, but convenient when plotting with seaborn\n source_dict = dict()\n # Read in data and sort alphanumeric features (e.g. POS tags) alphabetically\n df_features = pd.read_table(source_files[0], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\n df_features = df_features.sort_values('Tag', ascending=True)\n df_reference = pd.read_table(source_files[1], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\n df_reference = df_reference.sort_values('Tag', ascending=True)\n # Isolate columns to be plotted\n entries = _filter_data(df_features.iloc[:, column_ids[0]].values, False)\n counts = _filter_data(df_features.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus A\n reference_counts = _filter_data(df_reference.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus B\n # Construct dataframe to be visualized\n source_dict[column_names[0]] = entries\n source_dict['reference_counts'] = reference_counts\n # Generate frequency mask to exclude low-frequency features from the plot\n # Optional; results in a clearer, better readable visualization\n frequency_mask = np.array(\n [int(counts[i] >= freq_bound or reference_counts[i] >= freq_bound) for i in range(counts.shape[0])])\n source_dict['frequency_mask'] = frequency_mask\n # Calculate per-feature count differences (i.e. target counts vs. reference counts), if specified\n if plot_difference:\n diffs = counts - reference_counts\n source_dict[column_names[1]] = diffs\n else:\n source_dict[column_names[1]] = counts\n features = pd.DataFrame.from_dict(source_dict)\n # Sort by count value and apply frequency mask\n if sort:\n features = features.sort_values(column_names[0], ascending=True)\n if freq_bound > 0:\n features = features.drop(features[features.frequency_mask == 0].index)\n\n # Make plot\n fig, ax = plt.subplots()\n fig.set_size_inches(8, 6)\n if plot_difference:\n colors = ['coral' if feature >= 0 else 'skyblue' for feature in features[column_names[1]]]\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette=colors)\n else:\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette='Set2')\n sns.despine()\n if title is not None:\n plt.title(title)\n plt.show()","def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):\n\n # Check if colors where provided, otherwhise use the default color cycle\n if colors is None:\n colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n\n # Number of bars per group\n n_bars = len(data)\n\n # The width of a single bar\n bar_width = total_width / n_bars\n\n # List containing handles for the drawn bars, used for the legend\n bars = []\n\n # Iterate over all data\n for i, (name, values) in enumerate(data.items()):\n # The offset in x direction of that bar\n x_offset = (i - n_bars / 2) * bar_width + bar_width / 2\n\n # Draw a bar for every value of that type\n for x, y in enumerate(values):\n bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])\n\n # Add a handle to the last drawn bar, which we'll need for the legend\n bars.append(bar[0])\n\n # Draw legend if we need\n if legend:\n ax.legend(bars, data.keys())","def plot_featurewise_barplot(\n utr5_counts, cds_counts, utr3_counts, ax=None, saveto=None, **kwargs\n):\n fig = None\n if ax is None:\n fig, ax = plt.subplots()\n else:\n fig = ax.get_figure()\n barlist = ax.bar([0, 1, 2], [utr5_counts, cds_counts, utr3_counts])\n barlist[0].set_color(\"#1b9e77\")\n barlist[1].set_color(\"#d95f02\")\n barlist[2].set_color(\"#7570b3\")\n ax.set_xticks([0, 1, 2])\n ax.set_xticklabels([\"5'UTR\", \"CDS\", \"3'UTR\"])\n max_counts = np.max(np.hstack([utr5_counts, cds_counts, utr3_counts]))\n setup_axis(\n ax=ax, axis=\"y\", majorticks=max_counts // 10, minorticks=max_counts // 20\n )\n ax.set_ylabel(\"# RPFs\")\n # sns.despine(trim=True, offset=10)\n if saveto:\n fig.tight_layout()\n fig.savefig(saveto, dpi=DPI)\n return ax, fig","def bar_grapgh(dictionary, variable):\r\n plt.clf() # Deletes the previous plot \r\n plt.hist(dictionary[variable])\r\n plt.title('Histogram of ' + variable)\r\n plt.xlabel(variable)\r\n plt.ylabel('Frequency')\r\n plt.savefig(variable)","def visualize_type():\n\t\n\t#grab our parsed data\n\tdata_file = parse(MY_FILE, \",\")\n\t\n\t#make a new variable, counter, from iterating through each line of\n\t#data in parsed data, and count how many incidents happen by category\n\tcounter = Counter(item[\"Category\"] for item in data_file)\n\t\n\t#set the labels which are based on the keys of our counter\n\t#since order doesn't matter, we can just use counter.keys()\n\tlabels = tuple(counter.keys())\n\t\n\t#set exactly where the labels should hit the x-axis\n\txlocations = np.arange(len(labels)) + 0.5\n\t\n\t#width of each bar that will be plotted\n\twidth = 0.5\n\t\n\t#assign data to a bar plot\n\tplt.bar(xlocations, counter.values(), width=width)\n\t\n\t#assign labels and tick location to x-axis\n\tplt.xticks(xlocations + width /2, labels, rotation=90)\n\t\n\t#give more room to the x-axis so the labels aren't cut off\n\tplt.subplots_adjust(bottom=0.4)\n\t\n\t#make the overall graph/figure larger\n\tplt.rcParams['figure.figsize'] = 12, 8\n\t\n\t#save the graph\n\tplt.savefig(\"type.png\")\n\t\n\t#close the plot figure\n\tplt.clf()","def to_bar(self):\n group = GroupData()\n return group","def plot_bar_chart_quantum_vs_classical(\n df_bugs: pd.DataFrame,\n column_to_inspect: str,\n mapping_dict: Dict[str, str],\n categories_to_exclude: List[str] = [],\n categories_keep_only: List[str] = None,\n out_file_name: str = None,\n out_folder_path: str = None,\n horizontal: bool = False,\n map_value_since_beginning: bool = False,\n figsize: Tuple[int, int] = (10, 5),\n legend_placement: str = 'upper center'\n ):\n\n fig, ax = plt.subplots(figsize=figsize)\n\n df = expand_columns(df_bugs, column_to_inspect)\n df = df[~(df[column_to_inspect].isin(categories_to_exclude))]\n\n if categories_keep_only is not None:\n df = df[df[column_to_inspect].isin(categories_keep_only)]\n\n if map_value_since_beginning:\n df[column_to_inspect] = df[column_to_inspect].map(mapping_dict)\n\n categories_q_bugs = list(df[\n df['type'] == 'Quantum'].groupby(\n column_to_inspect).count().sort_values(\n by='type', ascending=False).index)\n\n for component in df[column_to_inspect].unique():\n if component not in categories_q_bugs:\n categories_q_bugs.append(component)\n\n args = {\n \"hue\": \"type\",\n \"data\": df,\n \"palette\": PALETTE,\n \"ax\": ax,\n \"order\": categories_q_bugs\n }\n\n if horizontal:\n sns.countplot(y=column_to_inspect, **args)\n ax.grid(axis='x')\n else:\n sns.countplot(x=column_to_inspect, **args)\n ax.grid(axis='y')\n\n if not map_value_since_beginning:\n # map the value at the latest stage, thus in the labels\n obj_labels = ax.get_xticklabels()\n for i, l in enumerate(obj_labels):\n obj_labels[i] = mapping_dict[l.get_text()]\n ax.set_xticklabels(obj_labels, rotation=60, ha='right')\n\n ax.set_xlabel(capitalize(column_to_inspect), fontsize=15)\n ax.set_ylabel(\"Count\", fontsize=15)\n plt.legend(title=\"Type of Bug\", loc=legend_placement)\n plt.tight_layout()\n\n if out_file_name is not None and out_folder_path is not None:\n fig.savefig(os.path.join(out_folder_path, out_file_name), format=\"pdf\")","def plot_bars(self, ax, bottom=None, log=False):\n if bottom is None and log:\n bottom = np.ones_like(self.values) * min(self.values[self.values > 0 ]) * .1\n return ax.bar(self.lefts, self.values, self.widths, color=self.color, label=self.label,edgecolor=self.color, bottom=bottom, log=log, **self.options)[0]","def _plot_group_bars(ax, xss, field, side):\n\n #translate side to index\n if(side == 'left'):\n side = 0\n else:\n side = 1\n #plot the bars\n x = range(len(xss))\n values = [xs.ROW_edge_fields[field].values[side] for xs in xss]\n ax.bar(x, values, color=[_colormap[i%len(_colormap)] for i in range(len(xss))],\n bottom=0.0, align='center', alpha=0.8, width=0.6)\n ax.set_xticks(x)\n ax.set_xticklabels([textwrap.fill(xs.sheet, 15) for xs in xss],\n rotation='vertical', fontsize=11)","def plot_bar_important_features(important_features, title, xlabel, ylabel, fname):\r\n plt.figure(figsize=(20, 21))\r\n plt.barh(important_features.index.astype(str).tolist(), important_features.values.tolist())\r\n plt.title(title)\r\n plt.xlabel(xlabel)\r\n plt.ylabel(ylabel)\r\n plt.savefig(fname, bbox_inches='tight')\r\n plt.close()","def plot_data(self):","def stopword_bar(df, stop_words, ax):\n df_test = df.copy()\n df_test['prop'] = df.title.apply(stopword_proportion)\n sns.barplot(data=df_test, x='target', y='prop', ax=ax, ci=False)\n ax.set_title(\"Ratio of Stopwords Between Classes\", size=20)\n ax.set_ylim([1,2])\n ax.set_ylabel(\"Ratio\", size=20)\n ax.set_xlabel(\"Article Class\", size=20)\n plt.xticks(ticks=range(2),labels=['Normal','Clickbait'], size=20)\n return ax","def plot_cat(df, cat_columns, hue = \"default_payment_next_month\"):\n fig = plt.figure(figsize = (20,(len(cat_columns)/2+1)*8))\n loc = 1\n for col in cat_columns:\n ax = fig.add_subplot(len(cat_columns)/2+1, 2, loc)\n df_plot = df[[col, hue, \"id\"]].groupby([col, hue]).count()\n df_plot.reset_index(inplace = True)\n sns.barplot(x=col, y= \"id\", hue = hue, data=df_plot, palette = \"GnBu_d\", ax = ax);\n plt.legend(title = \"default payment (1=yes, 0=no)\")\n plt.ylim([0.0001,15000])\n plt.ylabel(\"clients\");\n loc += 1","def plot_bar(self, nsingular=None, nbars=20):\n if nsingular is not None:\n self.get_identifiability_dataframe(nsingular)\n\n plot_obj = plots.IdentBar(self.ident_df, nsingular=nsingular, nbars=nbars)\n plot_obj.generate()\n plot_obj.draw()\n \n return plot_obj.fig, plot_obj.ax","def graph():\n fp = mpl.font_manager.FontProperties(family='JasmineUPC',size=24)\n x = np.arange(0,10)\n y = [386557057065, 368368395622, 242451971944, 225960095934, 161573560379, 107461232731, 89784502211, 73749349545, 54525219632, 52864743212]\n name = ['เชื้อเพลิงที่ได้จากแร่', 'เครื่องจักรและส่วนประกอบ', 'ยานยนต์และส่วนประกอบ', 'เครื่องอุปกรณ์ไฟฟ้าและส่วนประกอบ', 'เหล็กและเหล็กกล้า', 'พลาสติกและของทำด้วยพลาสติก', 'ของทำด้วยเหล็กหรือเหล็กกล้า', 'ทองแดงละของทำด้วยทองแดง', 'เคมีภัณฑ์เบ็ดเตล็ด', 'อุปกรณ์ที่ใช้ทางทัศนศาสตร์']\n ax = plt.gca(xticks=x)\n ax.set_xticklabels(name,rotation=1000,fontproperties=fp)\n plt.bar(x,y,color='g')\n plt.show()"],"string":"[\n \"def plot_type_of_topic(data_frame: pb.DataFrame) -> None:\\n plt.interactive(False)\\n plt.figure()\\n data_frame.plot(kind='bar', x= data_frame['TopicID'])\\n plt.show()\",\n \"def compte(df):\\n\\n df.value_counts()[:100].plot(kind='bar')\\n plt.show()\",\n \"def bar_plot(df, data_pt):\\n \\n x=df.loc[data_pt]\\n y= df.columns.tolist()\\n sorte=x.tolist()\\n a=sorted(zip(sorte, y))[-10:]\\n y=[y for _, y in a]\\n ## soru burda yapıp altı ona göre duzeliyecegim birde\\n \\n x = df[y].loc[data_pt]\\n \\n # Here we modify the tickangle of the xaxis, resulting in rotated labels.\\n #title={'text': \\\"<b>Comparing features with Golden for Cycle {}\\\".format(cycle),\\n # 'y':0.9,'x':0.5,'xanchor': 'center','yanchor': 'top'}\\n\\n \\n trace = {'type': 'bar',\\n 'orientation':'h',\\n 'x' : x,\\n 'y' : y}\\n data = Data([trace])\\n layout = {'title' : \\\"<b>Reconstruction error in each dimension for cycle{}\\\".format(data_pt),\\n 'titlefont':{'size' : 20},\\n 'xaxis' : {'title': '<b>Reconstruction Error',\\n 'titlefont':{'size' : 20},\\n 'tickangle': -45, 'tickfont': {'size':15} ,},\\n \\n 'yaxis' : {'title': '<b>Features',\\n 'titlefont':{'size' : 20},\\n 'tickfont': {'size':15},},\\n 'margin' : {'l':100, 'r' : 1, 'b': 200, 't': 100, 'pad' : 1},\\n 'height' : 600, 'width' : 800,\\n }\\n \\n fig = Figure(data = data, layout = layout)\\n \\n return pyo.iplot(fig)\",\n \"def visualizeData(df):\\n for column in df:\\n df[column].value_counts().plot(kind = 'bar', rot = 'vertical', use_index = False)\",\n \"def bar_plot(df_NP):\\n cnt = Counter()\\n for tax_list in df_NP.taxonomy:\\n for tax in list(tax_list):\\n if tax != 'no':\\n cnt[tax] += 1\\n plt.bar(cnt.keys(),cnt.values())\\n plt.xlabel('taxonomic provenance')\\n plt.ylabel('number of molecules')\\n plt.title('number of aglycons with taxonomies')\\n plt.savefig(\\\"output_data/Barplot.png\\\")\\n print(\\\"BAR PLOT DONE\\\")\",\n \"def bar_chart(self, df, n_groups, dict):\\n fig, ax = plt.subplots()\\n # choose bar width (standard 0.8 chosen)\\n bar_width = 0.35\\n # get an index to set the ticks for the x axis\\n\\n index = np.arange(n_groups)\\n indexes = df.index.tolist()\\n print(indexes)\\n df[\\\"index\\\"] = indexes\\n\\n # make barchart for permutation test\\n ax.bar(index, df[\\\"perm\\\"], bar_width, color='b', linewidth=4,\\n label='Permutation test')\\n # make barchart for t-test\\n ax.bar(index + bar_width, df[\\\"t_test\\\"], bar_width, color='r',\\n label='t-test')\\n\\n ax.set_xlabel(dict[\\\"xlabel\\\"])\\n ax.set_ylabel(dict[\\\"ylabel\\\"])\\n ax.set_title(dict[\\\"title\\\"])\\n ax.set_xticks(index + bar_width / 2)\\n ax.set_xticklabels(dict[\\\"xtickslabels\\\"])\\n ax.legend()\\n\\n fig.tight_layout()\\n plt.show()\",\n \"def bar_plot(df, field_name, graph_title, threshold_value, x_axis_label, y_axis_label):\\n\\n x = df[field_name].value_counts().sort_values()\\n x[x > threshold_value].plot(kind='barh', figsize=(12, 8), title=graph_title, x=x_axis_label, y=y_axis_label)\\n return\",\n \"def bar(\\n df,\\n x=None,\\n y=\\\"value\\\",\\n bars=\\\"variable\\\",\\n order=None,\\n bars_order=None,\\n orient=\\\"v\\\",\\n legend=True,\\n title=True,\\n ax=None,\\n cmap=None,\\n **kwargs,\\n):\\n\\n # default x-axis to time-col attribute from an IamDataFrame, else use \\\"year\\\"\\n x = x or time_col_or_year(df)\\n\\n # cast to DataFrame if necessary\\n # TODO: select only relevant meta columns\\n if not isinstance(df, pd.DataFrame):\\n df = df.as_pandas()\\n\\n for col in set(SORT_IDX) - set([x, bars]):\\n if len(df[col].unique()) > 1:\\n msg = \\\"Can not plot multiple {}s in bar plot with x={}, bars={}\\\"\\n raise ValueError(msg.format(col, x, bars))\\n\\n if ax is None:\\n fig, ax = plt.subplots()\\n\\n # long form to one column per bar group\\n _df = reshape_mpl(df, x, y, bars, **{x: order, bars: bars_order})\\n\\n # explicitly get colors\\n defaults = default_props(reset=True, num_colors=len(_df.columns), colormap=cmap)[\\n \\\"color\\\"\\n ]\\n rc = run_control()\\n color = []\\n for key in _df.columns:\\n c = next(defaults)\\n if \\\"color\\\" in rc and bars in rc[\\\"color\\\"] and key in rc[\\\"color\\\"][bars]:\\n c = rc[\\\"color\\\"][bars][key]\\n color.append(c)\\n\\n # change year to str to prevent pandas/matplotlib from auto-ordering (#474)\\n if _df.index.name == \\\"year\\\":\\n _df.index = map(str, _df.index)\\n\\n # plot data\\n kind = \\\"bar\\\" if orient.startswith(\\\"v\\\") else \\\"barh\\\"\\n _df.plot(kind=kind, color=color, ax=ax, **kwargs)\\n\\n # add legend\\n ax.legend(loc=\\\"center left\\\", bbox_to_anchor=(1.0, 0.5))\\n if not legend:\\n ax.legend_.remove()\\n\\n # add default labels if possible\\n if orient == \\\"v\\\":\\n ax.set_xlabel(x.capitalize())\\n else:\\n ax.set_ylabel(x.capitalize())\\n units = df[\\\"unit\\\"].unique()\\n if len(units) == 1 and y == \\\"value\\\":\\n if orient == \\\"v\\\":\\n ax.set_ylabel(units[0])\\n else:\\n ax.set_xlabel(units[0])\\n\\n # build a default title if possible\\n _title = []\\n for var in [\\\"model\\\", \\\"scenario\\\", \\\"region\\\", \\\"variable\\\"]:\\n values = df[var].unique()\\n if len(values) == 1:\\n _title.append(\\\"{}: {}\\\".format(var, values[0]))\\n if title and _title:\\n title = \\\" \\\".join(_title) if title is True else title\\n ax.set_title(title)\\n\\n return ax\",\n \"def barGraph(listOfWord, listOfFrequency):\\r\\n\\r\\n\\tindex = np.arange(len(listOfWord))\\r\\n\\r\\n\\tplt.title(\\\"Frekuensi Kemunculan Kata\\\")\\r\\n\\tplt.barh(index, listOfFrequency)\\r\\n\\tplt.xlabel('Frekuensi')\\r\\n\\tplt.yticks(index, listOfWord, fontsize=6)\\r\\n\\r\\n\\tplt.show()\",\n \"def visualize_data(df):\\n # Remove 'not available'\\n genres = df.genre.unique().tolist()\\n remove_index = genres.index('Not Available')\\n genres.pop(remove_index)\\n print('Genres: ', genres)\\n\\n # Extract number of songs in each genre\\n genre_counts = df.genre.value_counts().tolist()\\n genre_counts.pop(remove_index)\\n print('Counts: ', genre_counts)\\n\\n # Plot bar graph\\n plt.bar(genres, genre_counts)\\n plt.xlabel('Genres')\\n plt.ylabel('Count')\\n plt.show()\",\n \"def featuresBarPlot(barNames,barValues):\\n plt.bar(range(0,len(barNames)),barValues)\\n plt.xticks(range(0,len(barNames)), barNames,rotation='vertical')\\n plt.show()\",\n \"def plot_class_balances(df, col):\\n\\n ser_counts = df[col].value_counts()\\n ser_counts.plot.bar()\\n plt.title(col + ' Counts \\\\n(classes={})'.format(ser_counts.shape[0]))\\n \\n plt.show()\",\n \"def plot_bv_bar(df, xcolname, ycolname, icol=0):\\n # set plot size\\n fig, ax = plt.subplots(figsize=(8,6))\\n \\n # plotting... box\\n sns.barplot(ax=ax, data = df\\n , x = str(xcolname)\\n , y = str(ycolname)\\n , color = sns.color_palette()[icol]);\\n \\n \\n # title and labels\\n plt.title(xcolname+' Vs '+ycolname, fontsize=20)\\n plt.xlabel(xcolname+ ' (units)', fontsize=16)\\n plt.ylabel(ycolname+ ' (units)', fontsize=16)\\n \\n return plt.show()\",\n \"def BarPlot(data,colormap='Paired',ax=None,headers='show',value_max=None,x_ticklabels_rotation=90,**kws):\\r\\n if ax is None:\\r\\n ax=plt.subplot(111)\\r\\n\\r\\n if value_max is None:\\r\\n value_max=data.sum(1).max()\\r\\n\\r\\n data.plot(kind='bar', stacked=True,colormap=colormap, ax=ax,**kws)\\r\\n ax.set_ylim((0,value_max))\\r\\n\\r\\n\\r\\n #reverse legend order\\r\\n handles, labels = ax.get_legend_handles_labels()\\r\\n ax.legend(reversed(handles),reversed(data.columns),bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\\r\\n\\r\\n #AXES\\r\\n if (headers is None or headers=='hide'):\\r\\n ax.get_xaxis().set_visible(False)\\r\\n ax.get_xaxis().set_ticks([])\\r\\n elif headers=='show':\\r\\n plt.setp(ax.get_xticklabels(),rotation=x_ticklabels_rotation)\\r\\n ax.set_xlabel(None,visible=False)\\r\\n\\r\\n\\r\\n #plt.tight_layout()\\r\\n\\r\\n\\r\\n return ax\",\n \"def bar_chart(\\n df,\\n orientation='v',\\n bar_width=None,\\n opacity=0.9,\\n textpos=None,\\n linewidth=1,\\n linecolor='#2C3347',\\n marker_color=None,\\n **kwargs):\\n\\n traces = []\\n rng = df.index.size if orientation == 'v' else df.columns.size\\n otn = orientation\\n for i in range(rng):\\n x = [str(x) for x in df.columns] if otn == 'v' else df.iloc[:, i]\\n y = df.iloc[i] if otn == 'v' else [str(x) for x in df.index]\\n text = df.iloc[i] if otn == 'v' else df.iloc[:, i]\\n name = df.iloc[i].name if otn == 'v' else df.columns[i]\\n\\n preset_args = dict(\\n x=x,\\n y=y,\\n text=text,\\n textposition=textpos,\\n marker=dict(\\n opacity=opacity,\\n color=marker_color,\\n line=dict(\\n color=linecolor,\\n width=linewidth)),\\n name=name,\\n width=bar_width,\\n orientation=orientation\\n )\\n\\n all_args = {**preset_args, **kwargs}\\n bar = go.Bar(all_args)\\n traces.append(bar)\\n\\n return traces\",\n \"def plot_bar_chart(objects, data, title='', ylabel='', bar_color = 'blue'):\\n y_pos = np.arange(len(objects))\\n\\n plt.bar(y_pos, data, align='center', alpha=0.5)\\n plt.xticks(y_pos, objects, rotation='vertical')\\n plt.ylabel(ylabel, fontsize=12)\\n plt.title(title, fontsize=12)\\n plt.ylim([0,1300])\\n plt.bar(range(len(data)), data, color=bar_color)\\n\\n return plt.show()\",\n \"def bar_plot(data, xtitle, title):\\n label = list(set(data))\\n height = count_elements(data)\\n height = [height[i] for i in label]\\n plt.bar(label, height=height, width=0.8)\\n plt.ylabel('frequency')\\n plt.xlabel(xtitle)\\n plt.xticks(label)\\n plt.savefig('./figures/{}.png'.format(title))\\n plt.close()\",\n \"def drawStackedBarPlot(df, column, hue):\\n plt.style.use('default')\\n plt.style.use('dark_background')\\n p_table = pd.pivot_table(df, index=column, \\n columns=hue, aggfunc='size')\\n p_table = p_table.div(p_table.sum(axis=1), axis=0)\\n p_table.plot.bar(stacked=True, figsize=(14,7))\\n plt.xlabel('Spekraltyp')\\n plt.ylabel('Anteil')\\n plt.show()\",\n \"def barplot(bars, title='', upColor='blue', downColor='red'):\\n import pandas as pd\\n import matplotlib.pyplot as plt\\n from matplotlib.lines import Line2D\\n from matplotlib.patches import Rectangle\\n\\n if isinstance(bars, pd.DataFrame):\\n ohlcTups = [\\n tuple(v) for v in bars[['open', 'high', 'low', 'close']].values]\\n elif bars and hasattr(bars[0], 'open_'):\\n ohlcTups = [(b.open_, b.high, b.low, b.close) for b in bars]\\n else:\\n ohlcTups = [(b.open, b.high, b.low, b.close) for b in bars]\\n\\n fig, ax = plt.subplots()\\n ax.set_title(title)\\n ax.grid(True)\\n fig.set_size_inches(10, 6)\\n for n, (open_, high, low, close) in enumerate(ohlcTups):\\n if close >= open_:\\n color = upColor\\n bodyHi, bodyLo = close, open_\\n else:\\n color = downColor\\n bodyHi, bodyLo = open_, close\\n line = Line2D(\\n xdata=(n, n),\\n ydata=(low, bodyLo),\\n color=color,\\n linewidth=1)\\n ax.add_line(line)\\n line = Line2D(\\n xdata=(n, n),\\n ydata=(high, bodyHi),\\n color=color,\\n linewidth=1)\\n ax.add_line(line)\\n rect = Rectangle(\\n xy=(n - 0.3, bodyLo),\\n width=0.6,\\n height=bodyHi - bodyLo,\\n edgecolor=color,\\n facecolor=color,\\n alpha=0.4,\\n antialiased=True\\n )\\n ax.add_patch(rect)\\n\\n ax.autoscale_view()\\n return fig\",\n \"def plot_data_stats(data):\\n sns.set_style(\\\"dark\\\")\\n f, ax = plt.subplots(figsize=(6, 15))\\n\\n ax = sns.barplot(x='tag', y='count', data=tags_freqs)\\n\\n ax.axes.set_title(\\\"POS Tags Frequencies\\\",fontsize=20)\\n ax.set_xlabel(\\\"POS Tags\\\", fontsize=16)\\n ax.set_ylabel(\\\"Counts\\\", fontsize=16)\\n ax.tick_params(labelsize=12)\\n\\n plt.show()\",\n \"def barplot(data, field_name, field_categories):\\n\\n\\tcategories, counts = np.unique(data[field_name], return_counts=True)\\n\\n\\tfig = plt.figure(figsize=(4, 3))\\n\\taxes = fig.add_axes([0, 0, 1, 1]) # left, bottom, width, height (range 0 to 1)\\n\\taxes.bar(range(len(categories)), counts, fc=\\\"gray\\\") # fc is the face color\\n\\n\\taxes.set_xlabel(\\\"\\\")\\n\\taxes.set_ylabel('Count')\\n\\taxes.set_title(field_name)\\n\\tfig.autofmt_xdate(rotation=45)\\n\\n\\taxes.set_xticks(range(len(categories)))\\n\\taxes.set_xticklabels([field_categories[c] for c in categories]);\",\n \"def _bar_example_4(quantity_by_fruit):\\n ch = chartify.Chart(x_axis_type=\\\"categorical\\\", blank_labels=True)\\n ch.set_title(\\\"Vertical bar plot with labels\\\")\\n ch.set_subtitle(\\\"Hidden y-axis\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n ch.style.color_palette.reset_palette_order()\\n ch.plot.text(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n text_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n # Adjust the axis range to prevent clipping of the text labels.\\n ch.axes.set_yaxis_range(0, 1200)\\n ch.axes.hide_yaxis()\\n ch.show(_OUTPUT_FORMAT)\",\n \"def plotify_bar(title, data):\\n\\n x, y, z, labels = [], [], [], []\\n\\n for d in reversed(data[:len(data) - 1]):\\n x.append(f\\\"{d['settimana_del']:%d-%b}\\\\n{d['settimana_fino_al']:%d-%b}\\\")\\n y.append(d['nuovi_positivi'])\\n z.append(\\\"lightgrey\\\" if d['giorni'] < 7 else 'green' if d['delta'] <= 0 else 'red' )\\n labels.append(human_format(d['nuovi_positivi']) if d['giorni'] == 7 else f\\\"{human_format(d['nuovi_positivi'])}\\\\n(in corso)\\\" )\\n\\n x_pos = np.arange(len(x))\\n\\n # create a new figure\\n plt.figure()\\n\\n plt.title(title)\\n\\n # Create bars with different colors\\n plt.bar(x_pos, y, color=z)\\n\\n # Create names on the x-axis\\n plt.xticks(x_pos, x, rotation=40)\\n\\n\\n # Text on the top of each bar\\n x_ticks = plt.gca().get_xticks()\\n for i in range(len(y)):\\n text = data[i]\\n plt.text(x = x_ticks[i], y = y[i]+5, s = labels[i], size = 9, horizontalalignment='center', verticalalignment='bottom')\\n\\n # prettify y values\\n current_values = plt.gca().get_yticks()\\n plt.gca().set_yticklabels(['{:n}'.format(int(x)) for x in current_values])\\n\\n # responsive layout\\n plt.tight_layout()\\n\\n\\n\\n buf = io.BytesIO()\\n plt.savefig(buf, format='png')\\n buf.seek(0)\\n\\n ### Release memory\\n # Clear the current axes.\\n plt.cla() \\n # Clear the current figure.\\n plt.clf() \\n # Closes all the figure windows.\\n plt.close('all') \\n # plt.close(fig)\\n gc.collect()\\n\\n return buf\",\n \"def _bar_example_1(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, x_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Vertical bar plot\\\")\\n ch.set_subtitle(\\\"Automatically sorts by value counts.\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def value_counts_plot(df):\\n \\n plt.figure(figsize=(15,10))\\n \\n #get rid of sort_index() to change the graph\\n return df.value_counts().sort_index().plot(kind='bar')\",\n \"def message_genre_bar_chart(df):\\n genre_counts = df.groupby('genre').count()['message']\\n genre_names = list(genre_counts.index)\\n return {\\n 'data': [\\n Bar(\\n x=genre_names,\\n y=genre_counts\\n )\\n ],\\n\\n 'layout': {\\n 'title': 'Distribution of Message Genres',\\n 'yaxis': {\\n 'title': \\\"Count\\\"\\n },\\n 'xaxis': {\\n 'title': \\\"Genre\\\"\\n }\\n }\\n }\",\n \"def draw_bar(x_index, data_list, xticks, title, x_label, y_label):\\n pyplot.bar(x_index, data_list)\\n pyplot.xlabel(x_label)\\n pyplot.ylabel(y_label)\\n pyplot.xticks(x_index, xticks)\\n pyplot.title(title)\\n pyplot.show()\\n pyplot.savefig()\",\n \"def plot(*args, **params):\\n if len(args) == 1: # only support data for now\\n if isinstance(args[0], list):\\n bar_2d = Bar2D(data=args[0])\\n bar_2d.plot()\\n bar_2d.fig.show()\\n else:\\n bar_2d = Bar2D(**params)\\n bar_2d.plot()\\n bar_2d.fig.show()\",\n \"def _bar_example_3(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, y_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Horizontal bar plot\\\")\\n ch.set_subtitle(\\\"Horizontal with color grouping\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n color_column=\\\"fruit\\\",\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def BarOverview(data):\\n return dcc.Graph(id=\\\"BarOverview\\\", className=\\\"bar\\\", figure=dict(\\n data=[go.Bar(\\n x=data[\\\"frequencies\\\"],\\n y=data[\\\"names\\\"],\\n orientation='h',\\n marker={\\n 'color': '#ff4058'\\n },\\n )],\\n layout=dict(\\n title=\\\"<b>Most common Persons</b>\\\",\\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\\n margin=dict(l=10, r=20, t=50, b=30),\\n plot_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n paper_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n xaxis=dict(tick0=0, dtick=max(data[\\\"frequencies\\\"])),\\n yaxis=dict(ticks='outside',\\n showgrid=True,\\n showline=False,\\n showticklabels=False),\\n annotations=[dict(xref='paper', yref='y',\\n x=0, y=yd,\\n font=dict(\\n color=\\\"#000000\\\",\\n size=19\\n ),\\n text=str(yd),\\n showarrow=False) for xd, yd in zip(data[\\\"frequencies\\\"], data[\\\"names\\\"])]\\n )\\n ))\",\n \"def plot_norm_bar(df, title, figsize=(12,7)):\\n fig, ax = plt.subplots(ncols=1, figsize=figsize)\\n fig.suptitle(title)\\n cat_value_counts = df.fillna('missing').value_counts(normalize=True)\\n sns.barplot(y = cat_value_counts.index, x= cat_value_counts.values*100)\\n ax.set(xlabel= 'percentage', ylabel=str(df.name))\\n \\n plt.plot()\\n\\n return\",\n \"def barplot(self, x = \\\"Predictor\\\", color = None, opacity = 1, template = \\\"ggplot2\\\", \\n has_title = True, barmode=\\\"stack\\\", is_horizontal = False, title = None, is_percent = False,\\n show_num = False):\\n if color: #Produce either a stacked or grouped bar plot\\n df_stack = self._df.groupby([x,color]).size().reset_index()\\n df_stack['Percentage'] = self._df.groupby([x, color]).size().groupby(level = 0).apply(lambda \\n x:100 * x/float(x.sum())).values\\n df_stack.columns = [x, color, 'Count', 'Percentage']\\n df_stack['Percentage'] = round(df_stack['Percentage'], 2)\\n \\n x_clean, df_clean = clean_varname(df_stack, var = x)\\n color_clean, df_clean = clean_varname(df_clean, var = color)\\n \\n if has_title:\\n if not title:\\n title = f\\\"Bar Plot of {x_clean} and {color_clean}\\\"\\n else:\\n title = None\\n \\n \\n # 8 different variations for how this graph can appear:\\n if is_horizontal:\\n if is_percent:\\n if show_num: #Show percentages on stacked bar graph\\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Percentage'])\\n else:\\n fig = px.bar(df_clean, y = x_clean, x = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if show_num: #Show counts on stacked bar graph:\\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Count'])\\n else:\\n fig = px.bar(df_clean, y = x_clean, x = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if is_percent:\\n if show_num:\\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Percentage'])\\n else:\\n fig = px.bar(df_clean, x = x_clean, y = 'Percentage', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title)\\n else:\\n if show_num:\\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title, text = df_clean['Count'])\\n else:\\n fig = px.bar(df_clean, x = x_clean, y = 'Count', \\n color = color_clean, template = template, barmode=barmode, \\n opacity = opacity, title = title) \\n \\n return fig\\n \\n else: #Create a basic bar plot\\n df_stack = self._df.groupby([x]).size().reset_index()\\n df_stack['Percentage'] = self._df.groupby([x]).size().groupby(level = 0).apply(lambda\",\n \"def bar( # pylint: disable=disallowed-name\\n self, x: Hashable | None = None, y: Hashable | None = None, **kwargs\\n ) -> PlotAccessor:\\n return self(kind=\\\"bar\\\", x=x, y=y, **kwargs)\",\n \"def bar(self, entry_type:str, x:str, labels:list=None, diff:bool=False, x_idx:int=-1):\\n\\n query = self._decode(x)\\n\\n data_points = []\\n\\n for idx, (log, name) in enumerate(zip(self.logs, self.log_names)):\\n log = log[entry_type]\\n\\n candidates = []\\n\\n for entry in log:\\n test = self._follow(entry, query)\\n\\n if type(test) == dict:\\n candidates.append(test)\\n elif type(test) == list:\\n candidates.append({idx: v for idx, v in enumerate(test)})\\n \\n if len(candidates) > 0:\\n data_points.append((name, candidates[x_idx]))\\n \\n if len(data_points) == 0:\\n print('Warning: Nothing to show in bar chart!')\\n return\\n\\n names = [x[0] for x in data_points]\\n data_points = [x[1] for x in data_points]\\n\\n # Construct the labels for the data\\n if labels is not None:\\n data_labels = labels\\n else:\\n data_labels = set()\\n for datum in data_points:\\n for k in datum:\\n data_labels.add(k)\\n \\n data_labels = list(data_labels)\\n data_labels.sort()\\n \\n\\n data_values = [[(datum[k] if k in datum else None) for k in data_labels] for datum in data_points]\\n\\n if diff:\\n for idx in reversed(range(len(data_values))):\\n for jdx in range(len(data_labels)):\\n if data_values[0][jdx] is None or data_values[idx][jdx] is None:\\n data_values[idx][jdx] = None\\n else:\\n data_values[idx][jdx] -= data_values[0][jdx]\\n\\n\\n series_labels = names\\n\\n # Plot the graph now\\n num_bars = len(series_labels)\\n bar_width = 1 / (num_bars + 1)\\n \\n # Set position of bar on X axis\\n positions = [np.arange(len(data_labels))]\\n for _ in range(1, num_bars):\\n positions.append([x + bar_width for x in positions[-1]])\\n \\n # Make the plot\\n for idx, (series, data, pos) in enumerate(zip(series_labels, data_values, positions)):\\n plt.bar(pos, data, color=self._color(idx), width=bar_width, edgecolor='white', label=series)\\n \\n # Add xticks on the middle of the group bars\\n plt.title(x.replace('x.', entry_type + '.') + (' diff' if diff else ''))\\n plt.xticks([r + bar_width for r in range(len(data_labels))], data_labels)\\n \\n # Create legend & Show graphic\\n plt.legend()\\n plt.show()\",\n \"def bar_time_series(df, title, ylabel, report):\\n for col in df:\\n fig, ax = plt.subplots(1, 1, figsize=(12, 4))\\n plt.gcf().subplots_adjust(bottom=0.25)\\n df[col].plot.bar();\\n ax.set_xticklabels([v if i % 4 == 0 else '' for i, v in enumerate(df.index)])\\n ax.xaxis.set_tick_params(rotation=45, length=0);\\n ax.set_xlabel('Date')\\n ax.set_ylabel(ylabel)\\n full_title = title if df.shape[1] == 1 else '{} {}'.format(col, title)\\n report.write_plot(full_title)\\n plt.title(full_title)\\n plt.show();\\n plt.close();\",\n \"def plot_uv_bar(df, colname, colorid=0):\\n if (colname in list(df.columns)):\\n \\n # Set figure size \\n fig, ax = plt.subplots(figsize=(8,6))\\n \\n # set colorid for bar plot\\n base_color = sns.color_palette()[colorid]\\n\\n # variable counts to calculate percentage\\n cdict_count = df[colname].value_counts().to_dict() \\n total_count = df.shape[0]\\n \\n \\n if (len(list(cdict_count.keys())) > 5):\\n # max.count to position the %\\n maxcount_pct= np.max(list(cdict_count.values()))*0.125\\n # max. no. of categories Vs % rotation \\n rottext_pct = 90 \\n # font size for % display\\n fontsiz_pct = 12\\n else:\\n # max.count to position the %\\n maxcount_pct= np.max(list(cdict_count.values()))*0.075\\n # max. no. of categories Vs % rotation \\n rottext_pct = 0 \\n # font size for % display\\n fontsiz_pct = 16\\n \\n \\n # plotting...\\n sns.countplot(data = df, x = colname\\n , order = list(cdict_count.keys())\\n , color = base_color\\n , saturation = 0.7)\\n\\n # title and labels\\n plt.title('Order of '+ colname, fontsize=20)\\n plt.xlabel(colname + ' Type', fontsize=16)\\n plt.ylabel('Count', fontsize=16)\\n \\n # x-,y- ticks\\n locs, labels = plt.xticks(fontsize=16)\\n plt.yticks(fontsize=16)\\n\\n # display % count information on each tower of bar plot\\n for loc, label in zip(locs, labels):\\n count = cdict_count[label.get_text()]\\n pct_string = '{:0.1f}%'.format(count*100/total_count)\\n plt.text(loc, count-maxcount_pct, pct_string, ha='center', color='w', fontsize=fontsiz_pct, rotation=rottext_pct)\\n\\n return plt.show()\\n\\n else:\\n \\n print(' >>>Error:',colname,' is not in DataFrame')\",\n \"def plot_df(data_frame):\\n plt.figure(figsize = (10, 5))\\n chart = sns.countplot(data_frame['label'], \\n palette=\\\"Set1\\\"\\n )\\n plt.show()\",\n \"def plot_bar(filename, data, std=None, xlab='x', ylab='y', ylim=[0,1],yticks=np.arange(0,1.1,0.1), title='Bar-Plot', methods=None, datasets=None, figwidth=8, figheight=6, colors=None, legend_loc=\\\"lower center\\\", xytick_fontsize=12, xylabel_fontsize=15, title_fontsize=15, legend_fontsize=12):\\n import matplotlib as mpl\\n mpl.use(\\\"pdf\\\")\\n import matplotlib.pyplot as plt\\n\\n data=np.array(data)\\n num_methods=len(data)\\n \\n # colors\\n if colors is None:\\n colors=['b','r','g','c','m','y','k','w'] # maximally 8 colors allowed so far\\n\\n ind = np.arange(num_methods) # the x locations of the bars\\n width = 0.8 # the width of the bars\\n fig=plt.figure(num=1,figsize=(figwidth,figheight))\\n ax=fig.add_subplot(1,1,1)\\n if std is None:\\n ax.bar(ind,data,width,color=colors[0:num_methods],ecolor='k')\\n else:\\n ax.bar(ind,data,width,color=colors[0:num_methods],yerr=std,ecolor='k')\\n\\n # add some text for labels, title and axes ticks\\n ax.set_ylabel(ylab,fontsize=xylabel_fontsize)\\n ax.set_xlabel(xlab,fontsize=xylabel_fontsize)\\n ax.set_title(title,fontsize=title_fontsize)\\n ax.set_xticks(ind+0.5*width)\\n ax.set_xticklabels( methods )\\n #if ylim is None:\\n # yticks=np.arange(0,1.1,0.1)\\n # ylim=[0,1]\\n if yticks is not None:\\n ax.set_yticks(yticks)\\n if ylim is not None:\\n ax.set_ylim(ylim[0],ylim[1])\\n plt.setp(ax.get_xticklabels(), fontsize=xytick_fontsize)\\n plt.setp(ax.get_yticklabels(), fontsize=xytick_fontsize)\\n # shrink axis box \\n #box = ax.get_position()\\n #ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\\n #ax.legend( method_bar, methods, loc='lower left', bbox_to_anchor=(1.0, 0.3), fontsize=legend_fontsize )\\n #ax.legend(methods, loc=legend_loc, fontsize=legend_fontsize )\\n #plt.show()\\n fig.savefig(filename,bbox_inches='tight')\\n plt.close(fig)\",\n \"def category_bar_chart(df):\\n label_names = df.drop(['message', 'original', 'genre', 'id'], axis=1).columns\\n label_counts = []\\n for column in label_names:\\n label_counts.append(df[column].sum())\\n return {\\n 'data': [\\n Bar(\\n x=label_names,\\n y=label_counts\\n )\\n ],\\n\\n 'layout': {\\n 'title': 'Distribution of Labelled Categories',\\n 'yaxis': {\\n 'title': \\\"Count\\\",\\n 'type': 'log'\\n },\\n 'xaxis': {\\n 'title': \\\"Category\\\"\\n }\\n }\\n }\",\n \"def bar(variable, name, data=None, x_v=None, color_set=custom_bw,\\n ax_size=(20, 6), highlight=None, ax=None):\\n\\n common_set_up(ax_size) # Apply basic plot style\\n\\n fig = sns.barplot(x=x_v, y=variable, data=data, saturation=1, ax=ax,\\n color=color_set[2], label=name,\\n )\\n\\n sns.despine(offset=2, trim=True, left=True, bottom=True)\\n\\n # Set title and axes\\n title_color = '#192231'\\n font_colour = '#9099A2'\\n if ax is None:\\n fig.set_title('{0}'.format(name),\\n fontsize=20, color=title_color)\\n fig.set_ylabel('Frequency',\\n color=font_colour)\\n fig.set_xlabel('{0}'.format(name),\\n color=font_colour)\\n\\n if highlight:\\n bars = fig.patches\\n bars[highlight].set_color(color_set[1])\\n\\n return fig\",\n \"def barPlot2():\\n n = 10\\n X = np.arange(n)\\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\\n\\n for x,y in zip(X,Y1):\\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\\n\\n plt.ylim(0,1.25)\\n plt.show()\",\n \"def _bar_example_2(quantity_by_fruit):\\n ch = chartify.Chart(blank_labels=True, x_axis_type=\\\"categorical\\\")\\n ch.set_title(\\\"Vertical bar plot - Label sort\\\")\\n ch.set_subtitle(\\\"Set `categorical_order_by` to sort by labels\\\")\\n ch.plot.bar(\\n data_frame=quantity_by_fruit,\\n categorical_columns=\\\"fruit\\\",\\n numeric_column=\\\"quantity\\\",\\n categorical_order_by=\\\"labels\\\",\\n categorical_order_ascending=True,\\n )\\n ch.show(_OUTPUT_FORMAT)\",\n \"def bar(self, row_id, col_id, label=None, offset=(350, 30), **kwargs):\\n bar = BarGraph(label=label, **kwargs)\\n self.pl[row_id, col_id].addItem(bar)\\n\\n bar.barClicked.connect(self.clickedBar)\",\n \"def plot_results(t_val, mood):\\r\\n N = 8\\r\\n theta = np.linspace(0.0, 2 * np.pi , N, endpoint=False)\\r\\n the_stats = [t_val['number_words'], t_val['average_character_length'], \\r\\n t_val['signs'], t_val['multiple_signs'], t_val['question'],\\r\\n t_val['exclamation'], t_val['name'], mood] \\r\\n \\r\\n width = np.pi / N \\r\\n\\r\\n plt.figure()\\r\\n \\r\\n handle = plt.subplot(111, polar=True)\\r\\n handle.set_xticklabels(['Word', 'AvrChar', 'Signs', '2Signs', '?', '!', 'name', 'mood'])\\r\\n \\r\\n handle.bar(theta, the_stats, width=width, bottom=1.0)\\r\\n \\r\\n plt.show()\",\n \"def img_gen_bar():\\n data = pd.DataFrame(data=np.random.rand(5,1), index=range(1,6), columns=['Fred'])\\n #m,n = np.shape(data)\\n\\n plt.clf()\\n plt.bar(x=data.index.values, height=data.values.ravel(), color='k') # figsize=(10, 6))\\n # Options for later from https://matplotlib.org/api/_as_gen/matplotlib.pyplot.bar.html\\n # bar_width = 0.35\\n # alpha = .3\\n fig=plt.gcf()\\n fig.set_size_inches(2.24, 2.24)\\n plt.axis('off')\\n fig.tight_layout()\\n fig.canvas.draw()\\n # grab the pixel buffer and dump it into a numpy array\\n pixels = np.array(fig.canvas.renderer._renderer)[:,:,:3]\\n #print(pixels.shape)\\n return pixels, data.index.values + data.values.ravel()\",\n \"def _bar2df(bars,data):\\n tmp = list(map(lambda x: [x[0] for _ in range(x[1])],list(enumerate(bars))))\\n labels = list(itertools.chain(*tmp)) # tmp is a list of list and this line flattens it\\n data[\\\"labels\\\"] = labels\\n data[\\\"index\\\"] = list(range(data.shape[0]))\\n aggregation_condition = {'time': ['first', 'last'], \\\\\\n 'index':['first', 'last'], \\\\\\n 'price': ['min', 'max', 'first', 'last']}\\n res = data.groupby(\\\"labels\\\",as_index=False).agg(aggregation_condition)\\n res.columns = ['_'.join(col) for col in res.columns.values]\\n res = res.drop([\\\"labels_\\\"],axis=1)\\n res.columns = ['start_t', 'end_t', 'start_idx', 'end_idx','low', 'high', 'open', 'close']\\n return res\",\n \"def matplotlib_bar_chart() -> Tuple:\\n df = read_dataset(Path('..', '..', 'iris.csv'))\\n x = []\\n\\n for col in df.columns:\\n try:\\n max_val = get_column_max(df, col)\\n x.append(max_val)\\n except ValueError:\\n pass\\n \\n fig, ax = a_libraries.matplotlib_bar_chart(np.array(x))\\n\\n return fig, ax\",\n \"def plot_bar(label_array, acc_array, f1_array, width=0.5, axis_label=None, graph_title=None, file_name=\\\"\\\", dpi=100):\\n plt.figure(figsize=plt.figaspect(1.), dpi=dpi)\\n x = np.arange(len(label_array)) # the label locations\\n plt.bar(x - 0.5 * width, acc_array, width, label='Accuracy')\\n plt.bar(x + 0.5 * width, f1_array, width, label='F1 score')\\n plt.ylim([0, 1.1])\\n plt.xticks(x, labels=label_array)\\n if axis_label is None:\\n axis_label = ['Set', 'Values']\\n plt.xlabel(axis_label[0])\\n plt.ylabel(axis_label[1])\\n if graph_title is None:\\n graph_title = graph_title\\n plt.title(graph_title)\\n plt.tight_layout()\\n plt.legend()\\n plt.grid()\\n if file_name:\\n plt.savefig(file_name, bbox_inches='tight')\\n plt.show()\\n return\",\n \"def PlotBayes( x=np.ones(1), bayes=np.ones(1), title=None, label=None, width=1.0, color='blue', show_values=False, ax=None, posterior=False ):\\n\\n if ax is None:\\n fig, ax = plt.subplots( )\\n ax.bar(x, bayes/bayes.max(), width, color=color )\\n ax.set_title( title )\\n ax.set_yscale('log')\\n ax.set_xlabel( label )\\n if posterior:\\n ax.set_ylabel(r\\\"$P/P_{\\\\rm max}$\\\")\\n else:\\n ax.set_ylabel(r\\\"$\\\\mathcal{B}/\\\\mathcal{B}_{\\\\rm max}$\\\")\\n# ax.set_ylabel(r\\\"$\\\\mathcal{B} = \\\\prod L / L_0$\\\")\\n if show_values: ## print value on top of each bar, .... doesnt work ...\\n shift = bayes.max()/bayes.min()/10\\n for xx, b in zip( x, bayes ):\\n ax.text( xx, b*shift, str(b), color=color, fontweight='bold' )\\n\\n ### assure that there are ticks at y axis\\n lim = ax.get_ylim()\\n ax.set_ylim(lim[0]*0.5, lim[1]*2)\",\n \"def barh_plotter(data: pd.DataFrame, variable: str):\\n fig, axs = plt.subplots(2,5, gridspec_kw={'wspace': 1, 'hspace': 0.2},\\n figsize=(60, 40), sharex = False)\\n\\n for ax, dta in zip(axs.flatten(), data.values()) :\\n ax.barh(dta['club'], dta[f'{variable}'])\\n ax.set_xlabel(f'{variable}', fontsize=25)\\n \\n \\n for ax, dta in zip(axs.flatten(), data.keys()):\\n ax.set_title(dta, fontsize=30)\\n\\n return plt.show()\",\n \"def barPlot1():\\n n = 12\\n X = np.arange(n)\\n Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\\n\\n plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\\n plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\\n\\n for x,y in zip(X,Y1):\\n plt.text(x+0.2, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\\n\\n for x,y in zip(X,Y2):\\n plt.text(x+0.2, -y-0.1, '%.2f' % y, ha='center', va= 'bottom')\\n\\n plt.ylim(-1.25,+1.25)\\n plt.show()\",\n \"def graphy2():\\n data = pd.read_csv(\\\"week2.csv\\\")\\n plot_g = pygal.Bar(fill=True, interpolate='cubic', style=LightSolarizedStyle)\\n plot_g.title = \\\"Top Fans in Week 2\\\"\\n plot_g.x_labels = data.GENDER\\n plot_g.y_labels = map(int, range(0, 80, 10))\\n plot_g.add(\\\"Male\\\", data.COUNT)\\n plot_g.add(\\\"Female\\\", data.COUNT2)\\n plot_g.add(\\\"Total\\\", data.COUNT3)\\n plot_g.render_to_file(\\\"plotweek2.svg\\\")\",\n \"def draw_bar_plot(fig, x, y, labels):\\r\\n\\r\\n #Convert times to a displayable format\\r\\n (x_times, hour_mode) = times_to_axis(x)\\r\\n\\r\\n\\r\\n #Draw horizontal grid lines behind data\\r\\n fig.yaxis.grid(zorder=0)\\r\\n\\r\\n #Draw plot\\r\\n fig.bar(x_times, y, zorder=2)\\r\\n\\r\\n\\r\\n #If necessary, enable processing of \\\"datetime\\\" objects on the x-axis\\r\\n if not hour_mode:\\r\\n fig.xaxis_date()\\r\\n\\r\\n\\r\\n #Label and style plot\\r\\n set_axis_labels(fig, *labels)\\r\\n style_x_labels(fig)\",\n \"def graph(df):\\n df.plot()\\n plt.show()\",\n \"def plot_individual_bar_chart_graph(data_values, title,\\r\\n number_of_keys,\\r\\n max_val,\\r\\n vals_for_bar_chart,\\r\\n file_in):\\r\\n\\r\\n n_groups = len(vals_for_bar_chart)\\r\\n fig, ax = plt.subplots()\\r\\n index = np.arange(n_groups)\\r\\n bar_width = 0.9\\r\\n opacity = 0.4\\r\\n # print vals_for_bar_chart\\r\\n rects1 = plt.bar(index,\\r\\n vals_for_bar_chart,\\r\\n bar_width,\\r\\n alpha=opacity,\\r\\n color='b') # label='whatever'\\r\\n plt.xlabel('number in cluster')\\r\\n plt.ylabel('Count')\\r\\n plt.title(title+\\\"_barchart\\\")\\r\\n plt.legend()\\r\\n pylab.grid(True)\\r\\n ax.set_yscale('symlog')\\r\\n ax.set_xscale('symlog')\\r\\n plt.tight_layout()\\r\\n plt.show()\\r\\n pylab.savefig(file_in + \\\"_\\\" + title + '_barchart.png')\\r\\n plt.close()\\r\\n pylab.close()\",\n \"def show_graph(d:dict):\\n x = []\\n y = []\\n for key, value in d.items():\\n x.append(str(key))\\n y.append(value)\\n\\n x_pos = [i for i, _ in enumerate(x)]\\n plt.figure()\\n plt.bar(x_pos, y, color='green')\\n plt.xlabel(\\\"Size\\\")\\n plt.ylabel(\\\"Number of images\\\")\\n plt.title(\\\"Count by size\\\")\\n plt.xticks(x_pos, x)\",\n \"def plotBarPlots(self, stage, strain, strainID, count, p, xm, ym, xn, yn):\\n\\t\\t\\n\\t\\ttimeList = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\\n\\t\\t\\n\\t\\tind = np.arange(len(timeList))\\n\\t\\t\\n\\t\\tplt.style.use('bmh')\\n\\t\\tcolors = ['#b2182b', '#238b45', '#3690c0', '#023858']\\n\\t\\t\\n\\t\\tfig = plt.figure(figsize=(15,15), frameon=False)\\n\\t\\tax1 = fig.add_subplot(111)\\n\\t\\tfig.suptitle(strain + ' - ' + stage, fontsize=20, fontweight='bold')\\n\\t\\n\\t\\tax1.spines['top'].set_visible(False)\\n\\t\\tax1.spines['right'].set_visible(False)\\n\\t\\tax1.spines['bottom'].set_visible(False)\\n\\t\\tax1.spines['left'].set_visible(False)\\n\\t\\tax1.yaxis.set_ticks_position('left')\\n\\t\\tax1.xaxis.set_ticks_position('bottom')\\n\\t\\twidth = 0.6\\n\\t\\t\\n\\t\\tax1.bar(ind+width/2., count[timeList,1], width, color=colors[0], edgecolor = \\\"none\\\")\\n\\t\\t\\n\\t\\tax1.plot(*p.linspace(), c='k', linewidth=3.0)\\n\\t\\t\\n\\t\\tfor point in range(len(xm)):\\n\\t\\t\\tax1.plot(xm[point], ym[point], marker='*', markersize=30, color=\\\"blue\\\")\\n\\t\\tfor point in range(len(xn)):\\n\\t\\t\\tax1.plot(xn[point], yn[point], marker='*', markersize=30, color=\\\"blue\\\")\\t\\t\\t\\n\\t\\t\\n\\t\\tax1.set_ylabel('Frequency as %', fontweight='bold', fontsize=20)\\n\\t\\tax1.set_xlabel('Time', fontweight='bold', fontsize=20)\\n\\t\\txTickMarks = ['%s' %str(j) for j in timeList]\\n\\t\\tax1.set_xticks(ind+width/2)\\n\\t\\txtickNames = ax1.set_xticklabels(xTickMarks, fontweight='bold')\\n\\t\\t\\n\\t\\tplt.setp(xtickNames, fontsize=15)\\n\\t\\tax1.xaxis.set_ticks_position('none')\\n\\t\\tax1.yaxis.set_ticks_position('none')\\t\\t\\n\\t\\tax1.set_yticklabels(ax1.get_yticks(), fontweight='bold', fontsize=17)\\n\\t\\tax1.set_xlim([6, len(timeList)-6])\\n\\t\\t\\t\\t\\t\\n\\t\\tfname = 'strain%d' %strainID + stage + '.png' \\n\\t\\t\\n\\t\\tfig.savefig(fname, transparent=True, dpi=100)\\n\\t\\tplt.close(fig)\",\n \"def diagram_plugs(data_no,\\r\\n data_little,\\r\\n data_means,\\r\\n data_great,\\r\\n data_large_enough,\\r\\n data_super_large,\\r\\n er_no, er_little,\\r\\n er_means,\\r\\n er_great,\\r\\n er_large_enough,\\r\\n er_super_large):\\r\\n\\r\\n\\r\\n plt.bar(range(6), [data_no,\\r\\n data_little,\\r\\n data_means,\\r\\n data_great,\\r\\n data_large_enough,\\r\\n data_super_large],\\r\\n width=0.1, color='black',\\r\\n yerr=[er_no, er_little, er_means,\\r\\n er_great, er_large_enough,\\r\\n er_super_large],\\r\\n ecolor='black', capsize=10)\\r\\n\\r\\n\\r\\n plt.xticks(range(6), ['non', 'petit', 'moyen',\\r\\n 'grand', 'assez grand', 'tres grand'])\\r\\n\\r\\n\\r\\n plt.ylabel('Taux de pollution en AQI')\\r\\n plt.title(\\\"Taux de pollution selon les bouchons\\\")\\r\\n\\r\\n nouveau = new()\\r\\n print(nouveau)\\r\\n plt.savefig(nouveau, transparent=True)\\r\\n plt.clf()\\r\\n plt.close()\\r\\n\\r\\n shutil.move(nouveau, '/app/static/popo')\\r\\n\\r\\n return nouveau\",\n \"def barplot(self, name: str, y_label: str, img_title: str):\\n path = C.TEST_DIR\\n\\n sns.set(style='whitegrid')\\n sns.set_palette(sns.color_palette(C.IRT_COLORS))\\n df = pd.read_csv(path + name + '.csv')\\n ax = sns.barplot(data=df)\\n ax.set(ylabel=y_label, title=img_title)\\n\\n self.save_plot(name)\\n plt.show()\",\n \"def plot_bar_chart(log, frame_seconds=-1):\\n\\tif frame_seconds == -1:\\n\\t\\tframe_seconds = (log.content[-1].timestamp - log.content[0].timestamp).total_seconds() / timedelta(seconds=100).total_seconds()\\n\\tlines,dates,areas = log.give_plot_data_bar(frame_seconds=frame_seconds)\\n\\tfig, ax = plt.subplots(figsize=(11,6))\\n\\tfig.autofmt_xdate()\\n\\tmyFmt = DateFormatter(\\\"%Y %d.%b %H:%M:%S\\\")\\n\\tax.xaxis.set_major_formatter(myFmt)\\n\\tax.set_xlabel('timestamps in UTC')\\n\\tax.set_ylabel('amount of entries')\\n\\tplt.title('Analysis of the file \\\\\\\"'+log.name+'\\\\\\\" \\\\n with frame size ' + str(frame_seconds) +' seconds')\\n\\n\\twidth = timedelta(seconds=frame_seconds).total_seconds() / timedelta(days=1).total_seconds()\\n\\tnormal = ax.bar(dates, areas, width, label='entries', edgecolor='k')\\n\\n\\tplt.legend()\\n\\tplt.subplots_adjust(left=0.15, bottom=0.2, right=0.9, top=0.9)\\n\\tfig.canvas.mpl_connect('key_press_event', _quit_figure)\",\n \"def create_bar_graph(plot_df, title=\\\"\\\", x_title=\\\"\\\", y_title=\\\"\\\"):\\n plot_df[\\\"quarter\\\"] = pd.PeriodIndex(pd.to_datetime(plot_df.iloc[:, 0]), freq=\\\"Q\\\")\\n fig_data = [\\n go.Bar(\\n x=plot_df[\\\"quarter\\\"].astype(str),\\n y=plot_df.iloc[:, 1],\\n text=plot_df.iloc[:, 1],\\n marker={\\\"color\\\": color_palette[0]},\\n hoverinfo=\\\"x+y\\\",\\n showlegend=False,\\n )\\n ]\\n\\n fiq_layout = build_bar_layout(title, x_title=x_title, y_title=y_title)\\n\\n return dict(data=fig_data, layout=fiq_layout)\",\n \"def oneNumBar(df, colName):\\n bins = pd.qcut(x=df[colName[0]], q=15, duplicates='drop')\\n ax = bins.value_counts()\\n bins = bins.cat.as_ordered()\\n bins = bins.cat.categories\\n bounds = bins.left \\n bounds = list(bounds)\\n bounds.append(bins[len(bounds)-1].right)\\n texts = []\\n for x,y in zip(bounds[0::],bounds[1::]):\\n texts.append(\\\"(\\\" + str(x) + \\\", \\\" + str(y) + \\\"]\\\") \\n barData = [go.Bar(x=texts, \\n y=ax,\\n marker=dict(\\n color = '#92c5de',\\n opacity=0.8)\\n )] \\n layout = go.Layout(\\n title=\\\"Bar Plot Showing Count of Values for \\\" + str(colName[0]),\\n xaxis=dict(\\n title= colName[0]\\n ),\\n yaxis=dict(\\n title= \\\"NUMBER OF RECORDS\\\", \\n )\\n )\\n fig = go.Figure(data=barData, layout=layout)\\n return {\\\"label\\\":\\\"Frequency\\\", \\\"plot\\\":fig}\",\n \"def bar_plot(self,\\n x: str,\\n y: str,\\n x_label: str=None,\\n y_label: str=None,\\n title: str='Bar Plot',\\n **kwargs) -> Figure:\\n x_label = x if x_label is None else x_label\\n y_label = y if y_label is None else y_label\\n fig = px.bar(self.df, x=x, y=y,\\n labels={x: x_label, y: y_label},\\n title=title, **kwargs)\\n return fig\",\n \"def setBarType(bartype='vertical'):\\n dislin.bartyp(bardict[bartype])\",\n \"def draw_bar(df=data):\\n pt = {\\n 1: 'Credit card',\\n 2: 'Cash',\\n 3: 'No charge',\\n 4: 'Dispute',\\n 5: 'Unknown',\\n 6: 'Voided trip',\\n }\\n df['payment_type'] = df['payment_type'].replace(pt)\\n gr = df.groupby(['payment_type', 'weekday']) \\\\\\n .agg(total_amount=('total_amount', 'sum')) \\\\\\n .reset_index(drop=False)\\n return px.bar(gr, x='weekday', y='total_amount', color='payment_type', barmode='group') \\\\\\n .update_layout(\\n template='plotly_dark',\\n plot_bgcolor='rgba(0, 0, 0, 0)',\\n paper_bgcolor='rgba(0, 0, 0, 0)',\\n )\",\n \"def plot_dict_bar(aDictionary):\\n\\t# Convert strings to float\\n\\tfor key in aDictionary:\\n\\t\\taDictionary[key] = float(aDictionary[key])\\n\\t\\t\\n\\t# Plot the result\\n\\tplt.bar(range(len(aDictionary)), aDictionary.values(), align='center')\\n\\tplt.xticks(range(len(aDictionary)), aDictionary.keys(), rotation=90)\\n\\t\\n\\tplt.show()\",\n \"def performanceBarCharts(): \\n ##tauopathy HCS pearson\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null YFP Model\\\", \\\"Null DAPI Model\\\"]\\n ml_model_perf = pickle.load(open(\\\"pickles/ml_model_perf.pkl\\\", \\\"rb\\\"))\\n null_model_perf = pickle.load(open(\\\"pickles/null_model_perf.pkl\\\", \\\"rb\\\"))\\n null_dapi_perf = pickle.load(open(\\\"pickles/single_channel_DAPI_null_model_perf.pkl\\\", \\\"rb\\\"))\\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\\n x = [1, 2, 3]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance\\\",fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=12)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=12)\\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\\n plt.savefig(\\\"matplotlib_figures/tau_performance_pearson_special_HCS_model.png\\\", dpi=300)\\n\\n ##tauopathy HCS MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null YFP Model\\\", \\\"Null DAPI Model\\\"]\\n ml_model_perf = pickle.load(open(\\\"pickles/ml_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n null_model_perf = pickle.load(open(\\\"pickles/null_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n null_dapi_perf = pickle.load(open(\\\"pickles/single_channel_DAPI_null_model_mse_perf.pkl\\\", \\\"rb\\\"))\\n y= np.array([ml_model_perf[0], null_model_perf[0], null_dapi_perf[0]]).round(decimals=2)\\n stds = [ml_model_perf[1], null_model_perf[1], null_dapi_perf[1]]\\n x = [1, 2, 3]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'gold', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance\\\",fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=12)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=12)\\n ax.set_xticklabels(xlabels,fontsize=12, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(3))\\n plt.savefig(\\\"matplotlib_figures/tau_performance_mse_special_HCS_model.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (raw images) pearson\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ml_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_null_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [0.075, 0.1156] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with Raw Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.02)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_pearson_cross_val.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (raw images) MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ml_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_null_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [0.15, .2312] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance with Raw Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.01)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_mse.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (ablated image training) pearson\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ablated_ml_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_ablated_null_model_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [.1288, .1385] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with\\\\n95% Ablated Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.0)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_pearson_trained_ablation_model.png\\\", dpi=300)\\n\\n ##osteosarcoma 3-fold (ablated image training) MSE\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"ML Model\\\", \\\"Null Model\\\"]\\n x = [1, 2]\\n ys = []\\n nulls = []\\n for fold in [1,2,3]:\\n osteo_ml_perf = pickle.load(open(\\\"pickles/osteo_ablated_ml_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n osteo_null_perf = pickle.load(open(\\\"pickles/osteo_ablated_null_model_mse_perf_fold_{}.pkl\\\".format(fold), \\\"rb\\\"))\\n ys.append(osteo_ml_perf)\\n nulls.append(osteo_null_perf) \\n y = np.array([np.mean([result[0] for result in ys]), np.mean([result[0] for result in nulls])]).round(decimals=2)\\n stds = [.2576, .2771] ##see https://www.statstodo.com/CombineMeansSDs_Pgm.php\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=['red', 'blue'], zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .16, j +.03),fontsize=16, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"MSE Performance with\\\\n95% Ablated Hoechst Images\\\",fontname=\\\"Times New Roman\\\", fontsize=20, y=1.0)\\n ax.set_ylabel(\\\"MSE\\\", fontname=\\\"Times New Roman\\\", fontsize=18)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=18)\\n ax.set_xticklabels(xlabels,fontsize=18, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,2))\\n ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/osteosarcoma_performance_MSE_trained_ablation_model.png\\\", dpi=300)\\n\\n ##supplemental single channel learning YFP and DAPI performance\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"YFP-tau to AT8-pTau\\\", \\\"DAPI to AT8-pTau\\\"]\\n YFP_ml_model = pickle.load(open(\\\"pickles/single_channel_YFP_ml_model_perf.pkl\\\", \\\"rb\\\"))\\n DAPI_ml_model = pickle.load(open(\\\"pickles/single_channel_DAPI_ml_model_perf.pkl\\\", \\\"rb\\\"))\\n y = np.array([YFP_ml_model[0], DAPI_ml_model[0]]).round(decimals=2)\\n stds = [YFP_ml_model[1], DAPI_ml_model[1]]\\n x = [1, 2]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\\\"cornflowerblue\\\", zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Performance with\\\\nSingle Channel Input Learning\\\",fontname=\\\"Times New Roman\\\", fontsize=17, y=1.01)\\n ax.set_xlabel(\\\"Model\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/supplemental_single_channel_learning.png\\\", dpi=300)\\n\\n ##supplemental single channel learning YFP and DAPI, input similarity to prediction\\n plt.cla()\\n plt.clf()\\n width = .50\\n fig, ax = plt.subplots()\\n xlabels = [\\\"null\\\", \\\"YFP-tau to AT8-pTau\\\", \\\"DAPI to AT8-pTau\\\"]\\n y = np.array([0.94894628, 0.98718720]).round(decimals=2)\\n stds = [0.1673864, 0.039042]\\n x = [1, 2]\\n rects = ax.bar(x, y, width, yerr=stds, capsize=3, error_kw=dict(lw=1, capsize=3, capthick=1), color=\\\"orange\\\", zorder=3)\\n for i,j in zip(x, y):\\n ax.annotate(str(j)[0:4],xy=(i - .20, j +.03),fontsize=12, fontname=\\\"Times New Roman\\\")\\n plt.title(\\\"Pearson Similarity Between\\\\nInput Channel and Predicted Channel\\\",fontname=\\\"Times New Roman\\\", fontsize=17)\\n ax.set_xlabel(\\\"Model\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_ylabel(\\\"Pearson Correlation Coefficient\\\", fontname=\\\"Times New Roman\\\", fontsize=14)\\n plt.yticks(fontname=\\\"Times New Roman\\\", fontsize=14)\\n ax.set_xticklabels(xlabels,fontsize=14, fontname=\\\"Times New Roman\\\")\\n ax.set_ylim((0,1.13))\\n ax.yaxis.grid(True, linestyle='-', which='major', color='grey', alpha=.25, zorder=0)\\n ax.xaxis.set_major_locator(plt.MaxNLocator(2))\\n plt.savefig(\\\"matplotlib_figures/supplemental_single_channel_learning_pearson_similarity_input_and_predicted.png\\\", dpi=300)\",\n \"def visualize_type():\\n \\n data_file = parse(MY_FILE, ',')\\n\\n # num of incidents per category\\n counter = Counter(item['Category'] for item in data_file)\\n\\n # Set the labels\\n labels = tuple(counter.keys())\\n\\n # Set exactly where the labels hit the x-axis\\n xlocations = na.array(range(len(labels))) + 0.5\\n\\n # Width of each bar\\n width = 0.5\\n\\n # Assign data to a bar plot\\n plt.bar(xlocations, counter.values(), width=width)\\n\\n # Assign labels and tick location to x-axis\\n plt.xticks(xlocations + width / 2, labels, rotation=90)\\n \\n # Give some more room so the x-axis labels aren't cut off\\n plt.subplots_adjust(bottom=0.4)\\n\\n # Make the overall graph/figure larger\\n plt.rcParams['figure.figsize'] = 12, 8\\n\\n # save\\n plt.savefig('Type.png')\\n\\n # close\\n plt.clf()\",\n \"def get_proba_plot(data_type):\\n buffer = io.BytesIO()\\n plt.subplots(figsize = (25,15))\\n ax = sns.barplot(x='proba', y='type', data=data_type, palette = \\\"Blues_r\\\")\\n ax.set_xlabel('Probability')\\n plt.yticks(fontsize = 30)\\n plt.xticks(fontsize = 30)\\n plt.ylabel(\\\"Type\\\", fontsize = 38)\\n plt.xlabel(\\\"Probability\\\", fontsize = 38);\\n plt.title(\\\"Model Results\\\", fontsize = 50)\\n plt.savefig(buffer, format='png')\\n# plt.show()\\n plt.close()\\n buffer.seek(0)\\n chart_probability= Image.open(buffer).resize((512+256,512))\\n return chart_probability\",\n \"def plot_bar(counter, title=\\\"\\\", filename=\\\"tmp.png\\\"):\\n\\n fig = plt.figure()\\n axis = fig.add_subplot(111)\\n\\n if isinstance(counter, dict):\\n frequencies = counter.values()\\n names = counter.keys()\\n elif isinstance(counter, list):\\n frequencies = [x[1] for x in counter]\\n names = [x[0] for x in counter]\\n y_pos = np.arange(len(counter))\\n axis.barh(y_pos, frequencies, align='center')\\n axis.set_title(title)\\n axis.set_yticks(y_pos)\\n axis.set_yticklabels(list(names))\\n axis.invert_yaxis()\\n axis.set_xlabel('Frequency')\\n print('going to save fig...')\\n fig.savefig('png_files/' + filename.replace(\\\".csv\\\", \\\".png\\\"))\\n\\n return axis\",\n \"def custom_barchart(ax, x, y, error, xlims, ylims, error_kw, color='lightblue', width=0.75):\\n\\n error = [np.zeros(len(error)), error]\\n\\n ax.bar(x, y, color=color, width=width, yerr=error, error_kw=error_kw, align='center')\\n\\n ax.set_xlim(xlims)\\n ax.set_ylim(ylims)\\n\\n return ax\",\n \"def bar_plot(trj: TrajaDataFrame, bins: Union[int, tuple] = None, **kwargs) -> Axes:\\n # TODO: Add time component\\n\\n bins = traja.trajectory._bins_to_tuple(trj, bins)\\n\\n X, Y, U, V = coords_to_flow(trj, bins)\\n\\n hist, _ = trip_grid(trj, bins, hist_only=True)\\n fig = plt.figure()\\n ax = fig.add_subplot(111, projection=\\\"3d\\\")\\n ax.set_aspect(\\\"equal\\\")\\n X = X.flatten(\\\"F\\\")\\n Y = Y.flatten(\\\"F\\\")\\n ax.bar3d(\\n X,\\n Y,\\n np.zeros_like(X),\\n 1,\\n 1,\\n hist.flatten(),\\n zsort=\\\"average\\\",\\n shade=True,\\n **kwargs,\\n )\\n ax.set(xlabel=\\\"x\\\", ylabel=\\\"y\\\", zlabel=\\\"Frames\\\")\\n\\n return ax\",\n \"def _plot_dict_bar(d, xmin=None, label=None):\\n xvals, yvals = _dict2lists(d)\\n if xmin == None:\\n xmin = min(xvals) - 1\\n else:\\n xmin = min(xmin, min(xvals) - 1)\\n if label:\\n pylab.bar(xvals, yvals, align='center', label=label)\\n pylab.xlim([xmin, max(xvals)+1])\\n else:\\n pylab.bar(xvals, yvals, align='center')\\n pylab.xlim([xmin, max(xvals)+1])\",\n \"def plotBarChart(resultConfirmed, resultDeath, resultVaccinated):\\n fig, ax = plt.subplots(3)\\n\\n ax[0].plot(resultConfirmed['Date'], resultConfirmed['Confirmed Cases'])\\n ax[0].title.set_text('Confirmed Cases')\\n \\n ax[1].plot(resultDeath['Date'], resultDeath['Death Cases'])\\n ax[1].title.set_text('Death Cases')\\n \\n ax[2].plot(resultVaccinated['Date'], resultVaccinated['Vaccinated Person'])\\n ax[2].title.set_text('Vaccinated Cases')\\n fig.tight_layout()\\n plt.show()\",\n \"def generate_barplot(predictions, labels):\\n plot = figure(x_range=labels, plot_height=300, plot_width=400)\\n plot.vbar(x=labels, top=predictions, width=0.8)\\n # plot.xaxis.major_label_orientation = pi / 2.\\n # plot.xaxis.axis_label_text_font_size = \\\"40pt\\\"\\n # plot.yaxis.axis_label_text_font_size = \\\"40pt\\\"\\n\\n return components(plot)\",\n \"def plot_balance_class(classes):\\n unique, counts = np.unique(classes, return_counts=True)\\n plt.bar(unique, counts)\\n plt.title('Class Frequency')\\n plt.xlabel('Class')\\n plt.ylabel('Frequency')\\n plt.show()\",\n \"def make_bar_plot(x, y, title):\\n return plotly.graph_objs.Figure(\\n data=[plotly.graph_objs.Bar(x=list(x), y=list(y))],\\n layout=plotly.graph_objs.Layout(title=title)\\n )\",\n \"def BarSpecific():\\n return dcc.Graph(id=\\\"PersChart\\\", className=\\\"bar\\\", figure=dict(\\n data=[go.Bar(\\n x=[1],\\n y=[\\\"Persons\\\"],\\n orientation='h',\\n marker={\\n 'color': '#ff4058',\\n },\\n )],\\n layout=dict(\\n title=\\\"<b>Most similar Persons</b>\\\",\\n font=dict(family='Soria, Times New Roman, Times, serif', color='#002C77', size=19),\\n margin=dict(l=100, r=20, t=50, b=30),\\n plot_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n paper_bgcolor=\\\"rgba(0,0,0,0)\\\",\\n xaxis=dict(tick0=0, dtick=1),\\n yaxis=dict(ticks='outside')\\n )\\n ))\",\n \"def plot(self):\\n x = np.arange(5)\\n # labels = ['temp', 'humi', 'mais', 'o2', 'co2']\\n plt.bar(x - 0.35/2, self.data, 0.35, label='actual')\\n plt.bar(x + 0.35/2, self.desired_values, 0.35, label='desired')\\n plt.ylim(-5, 80)\\n plt.legend()\\n\\n plt.draw()\\n plt.pause(0.000001)\\n plt.clf()\",\n \"def simple_bar():\\n\\n # Make random discrete data\\n discrete_a = np.zeros((8,2))\\n discrete_b = np.zeros((8,2))\\n discrete_c = np.zeros((8,2))\\n discrete_a[:,0] = np.arange(8)\\n discrete_b[:,0] = np.arange(8)\\n discrete_c[:,0] = np.arange(8)\\n discrete_a[:,1] = np.random.rand(8)*10\\n discrete_b[:,1] = np.random.rand(8)*10\\n discrete_c[:,1] = np.random.rand(8)*10\\n\\n # Make data sets, if using multiple bar_width must be the same\\n dataset_a = DataSet(discrete_a,colour='pink',bar_width=0.8,plot='bar',label='A')\\n dataset_b = DataSet(discrete_b,colour='violet',bar_width=0.8,plot='bar',label='B')\\n dataset_c = DataSet(discrete_c,colour='darkviolet',bar_width=0.8,plot='bar',label='C')\\n\\n # Make plot object and add data sets\\n plot = Plot()\\n plot.add_dataset(dataset_a)\\n plot.add_dataset(dataset_b)\\n plot.add_dataset(dataset_c)\\n plot.set_axes(xticks=(1,1),xlim=(-0.5,7.5),ylim=(0,12))\\n plot.set_legend(legend=True,location='upper right')\\n plot.set_text(legend=8)\\n\\n # Plot graph and display\\n plot.plot()\\n plot.save(name='./figures/2d_simple_bar',fmt='png')\\n plot.display()\",\n \"def bar(*args, **kwargs):\\n ax, args, kwargs = maybe_get_ax(*args, **kwargs)\\n color_cycle = brewer2mpl.get_map('Set2', 'qualitative', 8).mpl_colors\\n almost_black = '#262626'\\n kwargs.setdefault('color', color_cycle[0])\\n kwargs.setdefault('edgecolor', 'white')\\n middle = 0.4 if 'width' not in kwargs else kwargs['width']/2.0\\n\\n # Check if data contains stacks\\n stacked = kwargs.pop('stacked',False)\\n # Check if stack text should be included\\n stack_text = kwargs.pop('stack_text',False)\\n # Get legend if available\\n legend = kwargs.pop('legend',False)\\n\\n left = args[0]\\n height = np.array(args[1])\\n\\n # Label each individual bar, if xticklabels is provided\\n xtickabels = kwargs.pop('xticklabels', None)\\n # left+0.4 is the center of the bar\\n xticks = np.array(left) + middle\\n\\n # Whether or not to annotate each bar with the height value\\n annotate = kwargs.pop('annotate', False)\\n\\n show_ticks = kwargs.pop('show_ticks', False)\\n\\n # If no grid specified, don't draw one.\\n grid = kwargs.pop('grid', None)\\n\\n # Check if stacked and plot data accordingly\\n if stacked:\\n num_stacks, num_data = height.shape\\n bottom = np.zeros(num_data)\\n for i in np.arange(num_stacks):\\n lst = list(args)\\n lst[1] = height[i]\\n args = tuple(lst)\\n kwargs['color'] = set2[i]\\n kwargs['bottom'] = bottom\\n rectangles = ax.bar(*args, **kwargs)\\n bottom += height[i]\\n else:\\n rectangles = ax.bar(*args, **kwargs)\\n\\n # add legend\\n if isinstance(legend, collections.Iterable):\\n ax.legend(legend,loc='upper center',bbox_to_anchor=(0.5,1.11), ncol=5)\\n\\n # add whitespace padding on left\\n xmin, xmax = ax.get_xlim()\\n xmin -= 0.2\\n if stacked:\\n xmax = num_data\\n ax.set_xlim(xmin, xmax)\\n\\n # If the user is only plotting one bar, make it an iterable\\n if not isinstance(height, collections.Iterable):\\n height = [height]\\n\\n\\n # If there are negative counts, remove the bottom axes\\n # and add a line at y=0\\n if any(h < 0 for h in height.tolist()):\\n axes_to_remove = ['top', 'right', 'bottom']\\n ax.hlines(y=0, xmin=xmin, xmax=xmax,\\n linewidths=0.75)\\n else:\\n axes_to_remove = ['top', 'right']\\n\\n # Remove excess axes\\n remove_chartjunk(ax, axes_to_remove, grid=grid, show_ticks=show_ticks)\\n\\n if stacked:\\n data = height\\n height = height.sum(axis=0)\\n\\n # Add the xticklabels if they are there\\n if xtickabels is not None:\\n ax.set_xticks(xticks)\\n ax.set_xticklabels(xtickabels)\\n\\n if annotate or isinstance(annotate, collections.Iterable):\\n annotate_yrange_factor = 0.025\\n ymin, ymax = ax.get_ylim()\\n yrange = ymax - ymin\\n\\n # Reset ymax and ymin so there's enough room to see the annotation of\\n # the top-most\\n if ymax > 0:\\n ymax += yrange * 0.1\\n if ymin < 0:\\n ymin -= yrange * 0.1\\n ax.set_ylim(ymin, ymax)\\n yrange = ymax - ymin\\n\\n offset_ = math.log(yrange) + math.log(annotate_yrange_factor+1)\\n print offset_\\n print yrange * annotate_yrange_factor\\n print math.log(yrange) + math.log(annotate_yrange_factor)\\n if isinstance(annotate, collections.Iterable):\\n annotations = map(str, annotate)\\n else:\\n annotations = ['%.3f' % h if type(h) is np.float_ else str(h)\\n for h in height]\\n\\n for x, h, annotation in zip(xticks, height, annotations):\\n # Adjust the offset to account for negative bars\\n offset = offset_ if h >= 0 else -1 * offset_\\n verticalalignment = 'bottom' if h >= 0 else 'top'\\n\\n # Finally, add the text to the axes\\n ax.annotate(annotation, (x, h + annotate_yrange_factor), \\n verticalalignment=verticalalignment,\\n horizontalalignment='center',\\n color=almost_black)\\n\\n # Text for each block of stack\\n # This was partially inspired by the following article by Tableau software\\n # http://www.tableausoftware.com/about/blog/2014/1/new-whitepaper-survey-data-less-ugly-more-understandable-27812\\n if stack_text:\\n bottom = np.zeros(num_data)\\n max_h = max(height)\\n for i in np.arange(num_stacks):\\n for x, d, b in zip(xticks, data[i], bottom):\\n if (d*100.0/max_h) > 4.0:\\n ax.text(x,b+d/2.0,d, ha='center', va='center', color=almost_black)\\n bottom += data[i]\\n return rectangles\",\n \"def show_class_imbalance(df, title='Class Imbalance', PATH=None):\\n ax = sns.barplot(x=[\\\"Normal\\\", \\\"Clickbait\\\"], y=df.groupby(['target']).target.count())\\n ax.set_title(title, size=20)\\n plt.xticks([0,1],[\\\"Normal\\\", \\\"Clickbait\\\"], size = 20)\\n ax.set_ylabel(\\\"Document Count\\\", size=17)\\n ax.set_xlabel(\\\"Article Class\\\", size=20)\\n if PATH:\\n plt.savefig(PATH, bbox_inches=\\\"tight\\\", transparent=True)\\n return ax\",\n \"def to_bar(self):\\n factor = FactorData()\\n return factor\",\n \"def plot_what_if(df):\\n fig = go.Figure()\\n for col in df.columns:\\n fig.add_trace(go.Bar(x=df[col].index, y=df[col].values, name=col))\\n fig.update_xaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=False, zerolinewidth=1, zerolinecolor='lightgrey',\\n showgrid=False, gridwidth=1, gridcolor='lightgrey')\\n fig.update_yaxes(title_text=\\\"Gains/Losses ($ USD)\\\",\\n showline=True, mirror=True, linewidth=1, linecolor='black',\\n zeroline=True, zerolinewidth=2, zerolinecolor='grey',\\n showgrid=True, gridwidth=1, gridcolor='lightgrey')\\n fig.update_layout(legend=dict(orientation=\\\"h\\\", yanchor=\\\"bottom\\\", y=-0.25, xanchor=\\\"center\\\", x=0.5),\\n font=dict(family='Times New Roman', size=15), plot_bgcolor='rgba(0,0,0,0)',\\n margin_l=20, margin_r=20, margin_t=20, margin_b=20,\\n xaxis=dict(tickmode='linear', tick0=1, dtick=1,))\\n\\n\\n fig.write_image(join('..', 'docs', 'redistribution.png'), height=700, width=900, engine='kaleido')\\n fig.write_html(join('..', 'docs', 'redistribution.html'))\\n fig.show()\",\n \"def draw_bar_plot():\\n # Copy and modify data for monthly bar plot\\n \\n df_bar = df.copy()\\n\\n # Draw bar plot\\n leglab = [\\\"January\\\", \\\"February\\\", \\\"March\\\", \\\"April\\\", \\\"May\\\", \\\"June\\\", \\\"July\\\", \\\"August\\\", \\\"September\\\", \\\"October\\\", \\\"November\\\", \\\"December\\\"]\\n labels = [2016, 2017, 2018, 2019]\\n months = np.zeros([12, 4])\\n\\n for i in range(12):\\n for j, year in enumerate(labels):\\n t = df[df.index.year == year]\\n months[i][j] = t[t.index.month == i].value.mean()\\n\\n x = np.arange(len(labels))\\n width = 0.7\\n fig, ax = plt.subplots()\\n fig.set_figwidth(10)\\n fig.set_figheight(8)\\n for i, month in enumerate(months):\\n ax.bar(x - (width * (12 - i) / 12), months[i], width / 12, label=leglab[i])\\n\\n ax.set_ylabel(\\\"Average Page Views\\\")\\n ax.set_xlabel(\\\"Years\\\")\\n ax.set_xticks(x)\\n ax.set_xticklabels(labels)\\n ax.legend(title='Months')\\n\\n # Save image and return fig (don't change this part)\\n fig.savefig('bar_plot.png')\\n return fig\",\n \"def plot_bar(source_files, column_ids, column_names, normalize, sort, plot_difference, freq_bound, title=None,\\n dtype=int):\\n\\n def _filter_data(raw_data, numerical):\\n \\\"\\\"\\\" Filters plot-able data. \\\"\\\"\\\"\\n # Retain numeric information\\n legal_count_inventory = digits + '.'\\n # Retain POS tags, also\\n legal_entry_inventory = ascii_uppercase + '$'\\n filtered_data = list()\\n for data_point in raw_data:\\n skip = False\\n for symbol in list(str(data_point)):\\n if symbol not in legal_count_inventory and symbol not in legal_entry_inventory:\\n skip = True\\n if not skip:\\n if numerical:\\n filtered_data.append(dtype(data_point))\\n else:\\n filtered_data.append(data_point)\\n # Optionally normalize count values, resulting in a proportion plot\\n if numerical and normalize:\\n filtered_data = filtered_data / np.sum(filtered_data)\\n return np.array(filtered_data)\\n\\n # Set plot parameters\\n sns.set_style('whitegrid')\\n sns.set_context('paper')\\n\\n # Compile data to be plotted within a new dataframe\\n # Not necessary, but convenient when plotting with seaborn\\n source_dict = dict()\\n # Read in data and sort alphanumeric features (e.g. POS tags) alphabetically\\n df_features = pd.read_table(source_files[0], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\\n df_features = df_features.sort_values('Tag', ascending=True)\\n df_reference = pd.read_table(source_files[1], header=None, names=['Tag', 'Count'], skip_blank_lines=True)\\n df_reference = df_reference.sort_values('Tag', ascending=True)\\n # Isolate columns to be plotted\\n entries = _filter_data(df_features.iloc[:, column_ids[0]].values, False)\\n counts = _filter_data(df_features.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus A\\n reference_counts = _filter_data(df_reference.iloc[:, column_ids[1]].values, True) # e.g. counts from corpus B\\n # Construct dataframe to be visualized\\n source_dict[column_names[0]] = entries\\n source_dict['reference_counts'] = reference_counts\\n # Generate frequency mask to exclude low-frequency features from the plot\\n # Optional; results in a clearer, better readable visualization\\n frequency_mask = np.array(\\n [int(counts[i] >= freq_bound or reference_counts[i] >= freq_bound) for i in range(counts.shape[0])])\\n source_dict['frequency_mask'] = frequency_mask\\n # Calculate per-feature count differences (i.e. target counts vs. reference counts), if specified\\n if plot_difference:\\n diffs = counts - reference_counts\\n source_dict[column_names[1]] = diffs\\n else:\\n source_dict[column_names[1]] = counts\\n features = pd.DataFrame.from_dict(source_dict)\\n # Sort by count value and apply frequency mask\\n if sort:\\n features = features.sort_values(column_names[0], ascending=True)\\n if freq_bound > 0:\\n features = features.drop(features[features.frequency_mask == 0].index)\\n\\n # Make plot\\n fig, ax = plt.subplots()\\n fig.set_size_inches(8, 6)\\n if plot_difference:\\n colors = ['coral' if feature >= 0 else 'skyblue' for feature in features[column_names[1]]]\\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette=colors)\\n else:\\n sns.barplot(x=column_names[1], y=column_names[0], data=features, ax=ax, palette='Set2')\\n sns.despine()\\n if title is not None:\\n plt.title(title)\\n plt.show()\",\n \"def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):\\n\\n # Check if colors where provided, otherwhise use the default color cycle\\n if colors is None:\\n colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\\n\\n # Number of bars per group\\n n_bars = len(data)\\n\\n # The width of a single bar\\n bar_width = total_width / n_bars\\n\\n # List containing handles for the drawn bars, used for the legend\\n bars = []\\n\\n # Iterate over all data\\n for i, (name, values) in enumerate(data.items()):\\n # The offset in x direction of that bar\\n x_offset = (i - n_bars / 2) * bar_width + bar_width / 2\\n\\n # Draw a bar for every value of that type\\n for x, y in enumerate(values):\\n bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])\\n\\n # Add a handle to the last drawn bar, which we'll need for the legend\\n bars.append(bar[0])\\n\\n # Draw legend if we need\\n if legend:\\n ax.legend(bars, data.keys())\",\n \"def plot_featurewise_barplot(\\n utr5_counts, cds_counts, utr3_counts, ax=None, saveto=None, **kwargs\\n):\\n fig = None\\n if ax is None:\\n fig, ax = plt.subplots()\\n else:\\n fig = ax.get_figure()\\n barlist = ax.bar([0, 1, 2], [utr5_counts, cds_counts, utr3_counts])\\n barlist[0].set_color(\\\"#1b9e77\\\")\\n barlist[1].set_color(\\\"#d95f02\\\")\\n barlist[2].set_color(\\\"#7570b3\\\")\\n ax.set_xticks([0, 1, 2])\\n ax.set_xticklabels([\\\"5'UTR\\\", \\\"CDS\\\", \\\"3'UTR\\\"])\\n max_counts = np.max(np.hstack([utr5_counts, cds_counts, utr3_counts]))\\n setup_axis(\\n ax=ax, axis=\\\"y\\\", majorticks=max_counts // 10, minorticks=max_counts // 20\\n )\\n ax.set_ylabel(\\\"# RPFs\\\")\\n # sns.despine(trim=True, offset=10)\\n if saveto:\\n fig.tight_layout()\\n fig.savefig(saveto, dpi=DPI)\\n return ax, fig\",\n \"def bar_grapgh(dictionary, variable):\\r\\n plt.clf() # Deletes the previous plot \\r\\n plt.hist(dictionary[variable])\\r\\n plt.title('Histogram of ' + variable)\\r\\n plt.xlabel(variable)\\r\\n plt.ylabel('Frequency')\\r\\n plt.savefig(variable)\",\n \"def visualize_type():\\n\\t\\n\\t#grab our parsed data\\n\\tdata_file = parse(MY_FILE, \\\",\\\")\\n\\t\\n\\t#make a new variable, counter, from iterating through each line of\\n\\t#data in parsed data, and count how many incidents happen by category\\n\\tcounter = Counter(item[\\\"Category\\\"] for item in data_file)\\n\\t\\n\\t#set the labels which are based on the keys of our counter\\n\\t#since order doesn't matter, we can just use counter.keys()\\n\\tlabels = tuple(counter.keys())\\n\\t\\n\\t#set exactly where the labels should hit the x-axis\\n\\txlocations = np.arange(len(labels)) + 0.5\\n\\t\\n\\t#width of each bar that will be plotted\\n\\twidth = 0.5\\n\\t\\n\\t#assign data to a bar plot\\n\\tplt.bar(xlocations, counter.values(), width=width)\\n\\t\\n\\t#assign labels and tick location to x-axis\\n\\tplt.xticks(xlocations + width /2, labels, rotation=90)\\n\\t\\n\\t#give more room to the x-axis so the labels aren't cut off\\n\\tplt.subplots_adjust(bottom=0.4)\\n\\t\\n\\t#make the overall graph/figure larger\\n\\tplt.rcParams['figure.figsize'] = 12, 8\\n\\t\\n\\t#save the graph\\n\\tplt.savefig(\\\"type.png\\\")\\n\\t\\n\\t#close the plot figure\\n\\tplt.clf()\",\n \"def to_bar(self):\\n group = GroupData()\\n return group\",\n \"def plot_bar_chart_quantum_vs_classical(\\n df_bugs: pd.DataFrame,\\n column_to_inspect: str,\\n mapping_dict: Dict[str, str],\\n categories_to_exclude: List[str] = [],\\n categories_keep_only: List[str] = None,\\n out_file_name: str = None,\\n out_folder_path: str = None,\\n horizontal: bool = False,\\n map_value_since_beginning: bool = False,\\n figsize: Tuple[int, int] = (10, 5),\\n legend_placement: str = 'upper center'\\n ):\\n\\n fig, ax = plt.subplots(figsize=figsize)\\n\\n df = expand_columns(df_bugs, column_to_inspect)\\n df = df[~(df[column_to_inspect].isin(categories_to_exclude))]\\n\\n if categories_keep_only is not None:\\n df = df[df[column_to_inspect].isin(categories_keep_only)]\\n\\n if map_value_since_beginning:\\n df[column_to_inspect] = df[column_to_inspect].map(mapping_dict)\\n\\n categories_q_bugs = list(df[\\n df['type'] == 'Quantum'].groupby(\\n column_to_inspect).count().sort_values(\\n by='type', ascending=False).index)\\n\\n for component in df[column_to_inspect].unique():\\n if component not in categories_q_bugs:\\n categories_q_bugs.append(component)\\n\\n args = {\\n \\\"hue\\\": \\\"type\\\",\\n \\\"data\\\": df,\\n \\\"palette\\\": PALETTE,\\n \\\"ax\\\": ax,\\n \\\"order\\\": categories_q_bugs\\n }\\n\\n if horizontal:\\n sns.countplot(y=column_to_inspect, **args)\\n ax.grid(axis='x')\\n else:\\n sns.countplot(x=column_to_inspect, **args)\\n ax.grid(axis='y')\\n\\n if not map_value_since_beginning:\\n # map the value at the latest stage, thus in the labels\\n obj_labels = ax.get_xticklabels()\\n for i, l in enumerate(obj_labels):\\n obj_labels[i] = mapping_dict[l.get_text()]\\n ax.set_xticklabels(obj_labels, rotation=60, ha='right')\\n\\n ax.set_xlabel(capitalize(column_to_inspect), fontsize=15)\\n ax.set_ylabel(\\\"Count\\\", fontsize=15)\\n plt.legend(title=\\\"Type of Bug\\\", loc=legend_placement)\\n plt.tight_layout()\\n\\n if out_file_name is not None and out_folder_path is not None:\\n fig.savefig(os.path.join(out_folder_path, out_file_name), format=\\\"pdf\\\")\",\n \"def plot_bars(self, ax, bottom=None, log=False):\\n if bottom is None and log:\\n bottom = np.ones_like(self.values) * min(self.values[self.values > 0 ]) * .1\\n return ax.bar(self.lefts, self.values, self.widths, color=self.color, label=self.label,edgecolor=self.color, bottom=bottom, log=log, **self.options)[0]\",\n \"def _plot_group_bars(ax, xss, field, side):\\n\\n #translate side to index\\n if(side == 'left'):\\n side = 0\\n else:\\n side = 1\\n #plot the bars\\n x = range(len(xss))\\n values = [xs.ROW_edge_fields[field].values[side] for xs in xss]\\n ax.bar(x, values, color=[_colormap[i%len(_colormap)] for i in range(len(xss))],\\n bottom=0.0, align='center', alpha=0.8, width=0.6)\\n ax.set_xticks(x)\\n ax.set_xticklabels([textwrap.fill(xs.sheet, 15) for xs in xss],\\n rotation='vertical', fontsize=11)\",\n \"def plot_bar_important_features(important_features, title, xlabel, ylabel, fname):\\r\\n plt.figure(figsize=(20, 21))\\r\\n plt.barh(important_features.index.astype(str).tolist(), important_features.values.tolist())\\r\\n plt.title(title)\\r\\n plt.xlabel(xlabel)\\r\\n plt.ylabel(ylabel)\\r\\n plt.savefig(fname, bbox_inches='tight')\\r\\n plt.close()\",\n \"def plot_data(self):\",\n \"def stopword_bar(df, stop_words, ax):\\n df_test = df.copy()\\n df_test['prop'] = df.title.apply(stopword_proportion)\\n sns.barplot(data=df_test, x='target', y='prop', ax=ax, ci=False)\\n ax.set_title(\\\"Ratio of Stopwords Between Classes\\\", size=20)\\n ax.set_ylim([1,2])\\n ax.set_ylabel(\\\"Ratio\\\", size=20)\\n ax.set_xlabel(\\\"Article Class\\\", size=20)\\n plt.xticks(ticks=range(2),labels=['Normal','Clickbait'], size=20)\\n return ax\",\n \"def plot_cat(df, cat_columns, hue = \\\"default_payment_next_month\\\"):\\n fig = plt.figure(figsize = (20,(len(cat_columns)/2+1)*8))\\n loc = 1\\n for col in cat_columns:\\n ax = fig.add_subplot(len(cat_columns)/2+1, 2, loc)\\n df_plot = df[[col, hue, \\\"id\\\"]].groupby([col, hue]).count()\\n df_plot.reset_index(inplace = True)\\n sns.barplot(x=col, y= \\\"id\\\", hue = hue, data=df_plot, palette = \\\"GnBu_d\\\", ax = ax);\\n plt.legend(title = \\\"default payment (1=yes, 0=no)\\\")\\n plt.ylim([0.0001,15000])\\n plt.ylabel(\\\"clients\\\");\\n loc += 1\",\n \"def plot_bar(self, nsingular=None, nbars=20):\\n if nsingular is not None:\\n self.get_identifiability_dataframe(nsingular)\\n\\n plot_obj = plots.IdentBar(self.ident_df, nsingular=nsingular, nbars=nbars)\\n plot_obj.generate()\\n plot_obj.draw()\\n \\n return plot_obj.fig, plot_obj.ax\",\n \"def graph():\\n fp = mpl.font_manager.FontProperties(family='JasmineUPC',size=24)\\n x = np.arange(0,10)\\n y = [386557057065, 368368395622, 242451971944, 225960095934, 161573560379, 107461232731, 89784502211, 73749349545, 54525219632, 52864743212]\\n name = ['เชื้อเพลิงที่ได้จากแร่', 'เครื่องจักรและส่วนประกอบ', 'ยานยนต์และส่วนประกอบ', 'เครื่องอุปกรณ์ไฟฟ้าและส่วนประกอบ', 'เหล็กและเหล็กกล้า', 'พลาสติกและของทำด้วยพลาสติก', 'ของทำด้วยเหล็กหรือเหล็กกล้า', 'ทองแดงละของทำด้วยทองแดง', 'เคมีภัณฑ์เบ็ดเตล็ด', 'อุปกรณ์ที่ใช้ทางทัศนศาสตร์']\\n ax = plt.gca(xticks=x)\\n ax.set_xticklabels(name,rotation=1000,fontproperties=fp)\\n plt.bar(x,y,color='g')\\n plt.show()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7253609","0.72341543","0.7051117","0.7032588","0.702026","0.6910697","0.6906154","0.68671536","0.67882943","0.6782081","0.6735545","0.6699668","0.6685477","0.66498506","0.663799","0.6616477","0.6604195","0.6479067","0.6469803","0.64694345","0.64263964","0.6424513","0.6419261","0.640694","0.64018095","0.63064665","0.63012624","0.6273125","0.6266547","0.6259449","0.6240214","0.6228225","0.6225711","0.6224449","0.62193334","0.61900115","0.618825","0.6187623","0.61718714","0.61673653","0.615912","0.61552125","0.61546445","0.61543536","0.61523354","0.61441165","0.61416775","0.61191326","0.6104422","0.61014444","0.60894746","0.6074595","0.6069581","0.6053907","0.6037133","0.60273874","0.6025053","0.6024364","0.6023842","0.6023384","0.60215837","0.6020362","0.60189235","0.6016994","0.6011624","0.5997864","0.59918255","0.5982423","0.5979257","0.59706324","0.59666777","0.596615","0.59298617","0.5924291","0.591691","0.5916065","0.59147304","0.59119755","0.5911653","0.5911455","0.5908337","0.59053963","0.58876586","0.5880613","0.58777","0.58659816","0.58650124","0.58643466","0.5860246","0.58598906","0.585575","0.5855504","0.58311594","0.5823492","0.58207417","0.58191276","0.5806449","0.5797168","0.57748044","0.5772498"],"string":"[\n \"0.7253609\",\n \"0.72341543\",\n \"0.7051117\",\n \"0.7032588\",\n \"0.702026\",\n \"0.6910697\",\n \"0.6906154\",\n \"0.68671536\",\n \"0.67882943\",\n \"0.6782081\",\n \"0.6735545\",\n \"0.6699668\",\n \"0.6685477\",\n \"0.66498506\",\n \"0.663799\",\n \"0.6616477\",\n \"0.6604195\",\n \"0.6479067\",\n \"0.6469803\",\n \"0.64694345\",\n \"0.64263964\",\n \"0.6424513\",\n \"0.6419261\",\n \"0.640694\",\n \"0.64018095\",\n \"0.63064665\",\n \"0.63012624\",\n \"0.6273125\",\n \"0.6266547\",\n \"0.6259449\",\n \"0.6240214\",\n \"0.6228225\",\n \"0.6225711\",\n \"0.6224449\",\n \"0.62193334\",\n \"0.61900115\",\n \"0.618825\",\n \"0.6187623\",\n \"0.61718714\",\n \"0.61673653\",\n \"0.615912\",\n \"0.61552125\",\n \"0.61546445\",\n \"0.61543536\",\n \"0.61523354\",\n \"0.61441165\",\n \"0.61416775\",\n \"0.61191326\",\n \"0.6104422\",\n \"0.61014444\",\n \"0.60894746\",\n \"0.6074595\",\n \"0.6069581\",\n \"0.6053907\",\n \"0.6037133\",\n \"0.60273874\",\n \"0.6025053\",\n \"0.6024364\",\n \"0.6023842\",\n \"0.6023384\",\n \"0.60215837\",\n \"0.6020362\",\n \"0.60189235\",\n \"0.6016994\",\n \"0.6011624\",\n \"0.5997864\",\n \"0.59918255\",\n \"0.5982423\",\n \"0.5979257\",\n \"0.59706324\",\n \"0.59666777\",\n \"0.596615\",\n \"0.59298617\",\n \"0.5924291\",\n \"0.591691\",\n \"0.5916065\",\n \"0.59147304\",\n \"0.59119755\",\n \"0.5911653\",\n \"0.5911455\",\n \"0.5908337\",\n \"0.59053963\",\n \"0.58876586\",\n \"0.5880613\",\n \"0.58777\",\n \"0.58659816\",\n \"0.58650124\",\n \"0.58643466\",\n \"0.5860246\",\n \"0.58598906\",\n \"0.585575\",\n \"0.5855504\",\n \"0.58311594\",\n \"0.5823492\",\n \"0.58207417\",\n \"0.58191276\",\n \"0.5806449\",\n \"0.5797168\",\n \"0.57748044\",\n \"0.5772498\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60265684"},"document_rank":{"kind":"string","value":"56"}}},{"rowIdx":8485,"cells":{"query":{"kind":"string","value":"Return the project name when printed."},"document":{"kind":"string","value":"def __str__(self):\n return self.project_name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def project_name(self):\n pass","def getProjectName():","def full_name(self):\n if not self.project_id:\n raise ValueError('Missing project ID.')\n return 'projects/%s' % (self.project_id)","def get_project_name(self):\n return self.line_edit.text()","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def project_name(self) -> typing.Optional[str]:\n return self._values.get(\"project_name\")","def get_project_display_name(self, package):\n with self._conn.begin():\n return self._conn.execute(\n \"VALUES (get_project_display_name(%s))\",\n (package, )\n ).scalar()","def getProjectName(self, projectId: int) -> str:\n query = f\"SELECT name FROM projects WHERE id = {projectId}\"\n result = sql.executeAndReadQuery(self.connection, query)\n return result[0][0]","def _get_project_name(self, context, project_id):\n return project_id","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def project(self) -> str:\n return pulumi.get(self, \"project\")","def get_project_name(self, project_id):\n return self.project_names.get(project_id)","def log_project(self) -> str:\n return pulumi.get(self, \"log_project\")","def __str__(self):\n return_string = \"Project: {}-{}\".\\\n format(self.public_information[\"project_id\"],\n self.public_information[\"title\"])\n\n return return_string","def _project_name(self):\n name = getattr(self._req.req, 'project_name', '')\n if name:\n return name\n raise ValueError('Requirement has no project_name.')","def fullname(self):\n return \"{project}/{version}\".format(\n project=self.project.name, version=self.name\n )","def get_project_name(self):\n remote = self.get_gitlab_remote()\n return self.get_project_name_from_url(remote.url)","def name(self):\r\n return self.setuptools_requirement.project_name","def fullname(cls): # pylint: disable=no-self-argument\n return func.concat(\n (select([Project.name]).where(Project.id == cls.project_id).as_scalar()),\n \" \",\n cls.name,\n )","def get_current_project(self):\n\n try:\n command = self._oc_command([\"project\", \"-q\"])\n output = run_cmd(command, return_output=True)\n except subprocess.CalledProcessError as ex:\n raise ConuException(\"Failed to obtain current project name : %s\" % ex)\n\n try:\n return output.rstrip() # remove '\\n'\n except IndexError:\n raise ConuException(\"Failed to obtain project name\")","def get_project_name(working_dir):\n return path.path(working_dir).name","def log_project(self) -> Optional[str]:\n return pulumi.get(self, \"log_project\")","def get_project_name_from_id(project_id: int) -> str:\n session = konfuzio_session()\n url = get_project_url(project_id)\n r = session.get(url=url)\n return r.json()['name']","def show():\n info(str(Project))","def getTopicName(nd_proj):\n # does not line &\n return '-'.join(nd_proj.generateProjectInfo())","def project_name() -> str:\n fake = Faker()\n raw_name: str = (\n fake.name_female().lower().replace(\" \", \"_\").replace(\"-\", \"_\").replace(\".\", \"_\")\n )\n return re.sub(\"_+\", \"_\", raw_name).strip(\"_\")","def project(self) -> str:\n return self._db_data.project","def displayname(self):\n if self.path.is_dir():\n if (is_uuid(self.path.parts[-1])):\n self.is_uuid_folder = True\n return self.path.name + '/'\n elif is_proj(self.path.parts[-1]):\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\n return self.path.name","def project(self) -> str:\n return self.proto.project","def __str__(self):\r\n proj_string = \" Project Name: \" + self.__name\r\n proj_string += \"\\n Cover Photo: \" + self.__cover_photo\r\n proj_string += \"\\n Links: \" + self.__links\r\n proj_string += \" Note: \" + self.__note\r\n proj_string += \" Photos: \" + list_str(self.__photos)\r\n\r\n return proj_string","def project_id(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"project_id\")","def get_project_name(projects, project_id):\n for project in projects:\n if project['id'] == project_id:\n return project['name']","def __str__(self):\n return \"{}: {}\".format(self.project, self.id)","def display_project_info(project_name):\n\n # project = request.args.get('project')\n\n title, description, max_grade = hackbright.get_project_by_title(project_name)\n\n grades = hackbright.get_grades_by_title(project_name)\n\n return render_template(\"project_info.html\",\n title=title,\n description=description,\n grade=max_grade,\n grades=grades)","def get_project_name(self, project_id):\n test = \"\"\"SELECT EXISTS(\n SELECT 1\n FROM barcodes.project\n WHERE project_id=%s\n )\"\"\"\n query = \"\"\"SELECT project\n FROM barcodes.project\n WHERE project_id=%s\"\"\"\n\n with self._transaction.cursor() as cur:\n cur.execute(test, [project_id, ])\n if not cur.fetchone()[0]:\n raise NotFound(f\"Project f'{project_id}' not found\")\n else:\n cur.execute(query, [project_id, ])\n return cur.fetchone()[0]","def project(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"project\")","def get_project_name(name: str) -> str:\n if is_shortcut_name(name):\n return name.split(config.name_separator)[1]\n raise CHCShortCutNameError(name)","def get_project_name(build_rules_list, working_directory,\n verbose=False, project_name=None):\n\n if not project_name:\n # Check build_rules.py\n project_name = getattr_build_rules_list(\n build_rules_list, \"PROJECT_NAME\", None)\n if not project_name:\n project_name = os.path.basename(working_directory)\n\n # Print if needed.\n if verbose:\n print(\"Project name is {}\".format(project_name))\n return project_name","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[str]:\n return pulumi.get(self, \"display_name\")","def full_name(self) -> str:\n inherited_from = (\n [self.inherited_from]\n if isinstance(self.inherited_from, str)\n else self.inherited_from\n )\n\n return \".\".join([*inherited_from, self.project_name])","def base_name(self):\n return self._project.path","def get_project():\n\n title = request.args.get('title')\n if not title:\n return \"Please enter a title!\"\n\n project = hackbright.get_project_by_title(title)\n\n grades = hackbright.get_grades_by_title(title)\n\n if not project:\n return \"There is no project with title \\\"{}\\\".\".format(title)\n\n title, description, max_grade = project\n return render_template(\"project_info.html\",\n title=title,\n description=description,\n max_grade=max_grade,\n grades=grades)","def displaySummary(self):\r\n print('Project Name:' + self.project['name'])\r\n print('Project chip:' + self.project['chip'])\r\n print('Project includes: ' + ' '.join(self.project['incs']))\r\n print('Project defines: ' + ' '.join(self.project['defs']))\r\n print('Project srcs: ' + ' '.join(self.project['srcs']))","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"project\")","def project_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"project_id\")","def project_id(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"project_id\")","def get_and_display_project():\n\n project = request.args.get('project')\n\n title, description, max_grade = hackbright.get_project_by_title(project)\n\n\n github_grade_list = hackbright.get_grades_by_title(project)\n\n return render_template(\"project_info.html\",\n title=title,\n description=description,\n max_grade=max_grade,\n github_grade_list=github_grade_list)","def do_project_show(cs, args):\n key = args.project\n if cs.projects.is_id(key):\n id = key\n else:\n id = cs.projects.get_id_by_name(key)\n _, project = cs.projects.get(id)\n utils.print_dict(project)","def display_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"display_name\")","def display_name(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"display_name\")"],"string":"[\n \"def project_name(self):\\n pass\",\n \"def getProjectName():\",\n \"def full_name(self):\\n if not self.project_id:\\n raise ValueError('Missing project ID.')\\n return 'projects/%s' % (self.project_id)\",\n \"def get_project_name(self):\\n return self.line_edit.text()\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def project_name(self) -> typing.Optional[str]:\\n return self._values.get(\\\"project_name\\\")\",\n \"def get_project_display_name(self, package):\\n with self._conn.begin():\\n return self._conn.execute(\\n \\\"VALUES (get_project_display_name(%s))\\\",\\n (package, )\\n ).scalar()\",\n \"def getProjectName(self, projectId: int) -> str:\\n query = f\\\"SELECT name FROM projects WHERE id = {projectId}\\\"\\n result = sql.executeAndReadQuery(self.connection, query)\\n return result[0][0]\",\n \"def _get_project_name(self, context, project_id):\\n return project_id\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> str:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def get_project_name(self, project_id):\\n return self.project_names.get(project_id)\",\n \"def log_project(self) -> str:\\n return pulumi.get(self, \\\"log_project\\\")\",\n \"def __str__(self):\\n return_string = \\\"Project: {}-{}\\\".\\\\\\n format(self.public_information[\\\"project_id\\\"],\\n self.public_information[\\\"title\\\"])\\n\\n return return_string\",\n \"def _project_name(self):\\n name = getattr(self._req.req, 'project_name', '')\\n if name:\\n return name\\n raise ValueError('Requirement has no project_name.')\",\n \"def fullname(self):\\n return \\\"{project}/{version}\\\".format(\\n project=self.project.name, version=self.name\\n )\",\n \"def get_project_name(self):\\n remote = self.get_gitlab_remote()\\n return self.get_project_name_from_url(remote.url)\",\n \"def name(self):\\r\\n return self.setuptools_requirement.project_name\",\n \"def fullname(cls): # pylint: disable=no-self-argument\\n return func.concat(\\n (select([Project.name]).where(Project.id == cls.project_id).as_scalar()),\\n \\\" \\\",\\n cls.name,\\n )\",\n \"def get_current_project(self):\\n\\n try:\\n command = self._oc_command([\\\"project\\\", \\\"-q\\\"])\\n output = run_cmd(command, return_output=True)\\n except subprocess.CalledProcessError as ex:\\n raise ConuException(\\\"Failed to obtain current project name : %s\\\" % ex)\\n\\n try:\\n return output.rstrip() # remove '\\\\n'\\n except IndexError:\\n raise ConuException(\\\"Failed to obtain project name\\\")\",\n \"def get_project_name(working_dir):\\n return path.path(working_dir).name\",\n \"def log_project(self) -> Optional[str]:\\n return pulumi.get(self, \\\"log_project\\\")\",\n \"def get_project_name_from_id(project_id: int) -> str:\\n session = konfuzio_session()\\n url = get_project_url(project_id)\\n r = session.get(url=url)\\n return r.json()['name']\",\n \"def show():\\n info(str(Project))\",\n \"def getTopicName(nd_proj):\\n # does not line &\\n return '-'.join(nd_proj.generateProjectInfo())\",\n \"def project_name() -> str:\\n fake = Faker()\\n raw_name: str = (\\n fake.name_female().lower().replace(\\\" \\\", \\\"_\\\").replace(\\\"-\\\", \\\"_\\\").replace(\\\".\\\", \\\"_\\\")\\n )\\n return re.sub(\\\"_+\\\", \\\"_\\\", raw_name).strip(\\\"_\\\")\",\n \"def project(self) -> str:\\n return self._db_data.project\",\n \"def displayname(self):\\n if self.path.is_dir():\\n if (is_uuid(self.path.parts[-1])):\\n self.is_uuid_folder = True\\n return self.path.name + '/'\\n elif is_proj(self.path.parts[-1]):\\n return f'{bcolors.BOLD}' + self.path.name + f'{bcolors.ENDC}'\\n return self.path.name\",\n \"def project(self) -> str:\\n return self.proto.project\",\n \"def __str__(self):\\r\\n proj_string = \\\" Project Name: \\\" + self.__name\\r\\n proj_string += \\\"\\\\n Cover Photo: \\\" + self.__cover_photo\\r\\n proj_string += \\\"\\\\n Links: \\\" + self.__links\\r\\n proj_string += \\\" Note: \\\" + self.__note\\r\\n proj_string += \\\" Photos: \\\" + list_str(self.__photos)\\r\\n\\r\\n return proj_string\",\n \"def project_id(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"project_id\\\")\",\n \"def get_project_name(projects, project_id):\\n for project in projects:\\n if project['id'] == project_id:\\n return project['name']\",\n \"def __str__(self):\\n return \\\"{}: {}\\\".format(self.project, self.id)\",\n \"def display_project_info(project_name):\\n\\n # project = request.args.get('project')\\n\\n title, description, max_grade = hackbright.get_project_by_title(project_name)\\n\\n grades = hackbright.get_grades_by_title(project_name)\\n\\n return render_template(\\\"project_info.html\\\",\\n title=title,\\n description=description,\\n grade=max_grade,\\n grades=grades)\",\n \"def get_project_name(self, project_id):\\n test = \\\"\\\"\\\"SELECT EXISTS(\\n SELECT 1\\n FROM barcodes.project\\n WHERE project_id=%s\\n )\\\"\\\"\\\"\\n query = \\\"\\\"\\\"SELECT project\\n FROM barcodes.project\\n WHERE project_id=%s\\\"\\\"\\\"\\n\\n with self._transaction.cursor() as cur:\\n cur.execute(test, [project_id, ])\\n if not cur.fetchone()[0]:\\n raise NotFound(f\\\"Project f'{project_id}' not found\\\")\\n else:\\n cur.execute(query, [project_id, ])\\n return cur.fetchone()[0]\",\n \"def project(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def get_project_name(name: str) -> str:\\n if is_shortcut_name(name):\\n return name.split(config.name_separator)[1]\\n raise CHCShortCutNameError(name)\",\n \"def get_project_name(build_rules_list, working_directory,\\n verbose=False, project_name=None):\\n\\n if not project_name:\\n # Check build_rules.py\\n project_name = getattr_build_rules_list(\\n build_rules_list, \\\"PROJECT_NAME\\\", None)\\n if not project_name:\\n project_name = os.path.basename(working_directory)\\n\\n # Print if needed.\\n if verbose:\\n print(\\\"Project name is {}\\\".format(project_name))\\n return project_name\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[str]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def full_name(self) -> str:\\n inherited_from = (\\n [self.inherited_from]\\n if isinstance(self.inherited_from, str)\\n else self.inherited_from\\n )\\n\\n return \\\".\\\".join([*inherited_from, self.project_name])\",\n \"def base_name(self):\\n return self._project.path\",\n \"def get_project():\\n\\n title = request.args.get('title')\\n if not title:\\n return \\\"Please enter a title!\\\"\\n\\n project = hackbright.get_project_by_title(title)\\n\\n grades = hackbright.get_grades_by_title(title)\\n\\n if not project:\\n return \\\"There is no project with title \\\\\\\"{}\\\\\\\".\\\".format(title)\\n\\n title, description, max_grade = project\\n return render_template(\\\"project_info.html\\\",\\n title=title,\\n description=description,\\n max_grade=max_grade,\\n grades=grades)\",\n \"def displaySummary(self):\\r\\n print('Project Name:' + self.project['name'])\\r\\n print('Project chip:' + self.project['chip'])\\r\\n print('Project includes: ' + ' '.join(self.project['incs']))\\r\\n print('Project defines: ' + ' '.join(self.project['defs']))\\r\\n print('Project srcs: ' + ' '.join(self.project['srcs']))\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"project\\\")\",\n \"def project_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"project_id\\\")\",\n \"def project_id(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"project_id\\\")\",\n \"def get_and_display_project():\\n\\n project = request.args.get('project')\\n\\n title, description, max_grade = hackbright.get_project_by_title(project)\\n\\n\\n github_grade_list = hackbright.get_grades_by_title(project)\\n\\n return render_template(\\\"project_info.html\\\",\\n title=title,\\n description=description,\\n max_grade=max_grade,\\n github_grade_list=github_grade_list)\",\n \"def do_project_show(cs, args):\\n key = args.project\\n if cs.projects.is_id(key):\\n id = key\\n else:\\n id = cs.projects.get_id_by_name(key)\\n _, project = cs.projects.get(id)\\n utils.print_dict(project)\",\n \"def display_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"display_name\\\")\",\n \"def display_name(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"display_name\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8392448","0.82995224","0.7878728","0.78781843","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.7737","0.76813203","0.76813203","0.76813203","0.76813203","0.76813203","0.76813203","0.76414037","0.7613182","0.755784","0.75346524","0.75346524","0.75346524","0.75346524","0.75346524","0.75346524","0.7491989","0.7464459","0.74489504","0.7402116","0.73303837","0.73093045","0.7301804","0.7220941","0.7211046","0.7171814","0.7135486","0.7084348","0.70739794","0.70434344","0.7018383","0.6946094","0.6939117","0.69063085","0.6849107","0.6827202","0.68258435","0.682146","0.6802679","0.6769938","0.6739797","0.67281663","0.66794527","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6675128","0.6645449","0.6625041","0.6608102","0.65734327","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65722334","0.65361696","0.65361696","0.6506469","0.649929","0.64889365","0.64889365"],"string":"[\n \"0.8392448\",\n \"0.82995224\",\n \"0.7878728\",\n \"0.78781843\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.7737\",\n \"0.76813203\",\n \"0.76813203\",\n \"0.76813203\",\n \"0.76813203\",\n \"0.76813203\",\n \"0.76813203\",\n \"0.76414037\",\n \"0.7613182\",\n \"0.755784\",\n \"0.75346524\",\n \"0.75346524\",\n \"0.75346524\",\n \"0.75346524\",\n \"0.75346524\",\n \"0.75346524\",\n \"0.7491989\",\n \"0.7464459\",\n \"0.74489504\",\n \"0.7402116\",\n \"0.73303837\",\n \"0.73093045\",\n \"0.7301804\",\n \"0.7220941\",\n \"0.7211046\",\n \"0.7171814\",\n \"0.7135486\",\n \"0.7084348\",\n \"0.70739794\",\n \"0.70434344\",\n \"0.7018383\",\n \"0.6946094\",\n \"0.6939117\",\n \"0.69063085\",\n \"0.6849107\",\n \"0.6827202\",\n \"0.68258435\",\n \"0.682146\",\n \"0.6802679\",\n \"0.6769938\",\n \"0.6739797\",\n \"0.67281663\",\n \"0.66794527\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6675128\",\n \"0.6645449\",\n \"0.6625041\",\n \"0.6608102\",\n \"0.65734327\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65722334\",\n \"0.65361696\",\n \"0.65361696\",\n \"0.6506469\",\n \"0.649929\",\n \"0.64889365\",\n \"0.64889365\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77389073"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":8486,"cells":{"query":{"kind":"string","value":"Return the skill name."},"document":{"kind":"string","value":"def __str__(self):\n return self.skill"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def skill(self):\n return self._get(\"skill\")","def test_get_skill_name(self):\n result = self.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"config\", \"get\", \"skills.dummy.name\"],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n assert result.output == \"dummy\\n\"","def display_skill(self):\n return ', '.join([skill.name for skill in self.skill.all()[:3]])","def getSkill(self, skillName):\r\n if self.__contains__(skillName):\r\n return self.skills[skillName]\r\n return None","def get_name(self):\n return '-'.join(self._name_parts +\n [self.role.name, self.scenario.name])","def get_name() -> str:","def get_name() -> str:\n pass","def alexa_skill_id(self) -> pulumi.Input[str]:\n return pulumi.get(self, \"alexa_skill_id\")","def get_name(self) -> str:\n return self.name","def get_name(self) -> str:\n return self.name","def get_name(self) -> str:\n return self.name","def get_name(self) -> str:\n pass","def getName(self):\n return _libsbml.Objective_getName(self)","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return 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\"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def name(self) -> str:\n return pulumi.get(self, \"name\")","def 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\"def getSkill(self, skillName):\\r\\n if self.__contains__(skillName):\\r\\n return self.skills[skillName]\\r\\n return None\",\n \"def get_name(self):\\n return '-'.join(self._name_parts +\\n [self.role.name, self.scenario.name])\",\n \"def get_name() -> str:\",\n \"def get_name() -> str:\\n pass\",\n \"def alexa_skill_id(self) -> pulumi.Input[str]:\\n return pulumi.get(self, \\\"alexa_skill_id\\\")\",\n \"def get_name(self) -> str:\\n return self.name\",\n \"def get_name(self) -> str:\\n return self.name\",\n \"def get_name(self) -> str:\\n return self.name\",\n \"def get_name(self) -> str:\\n pass\",\n \"def getName(self):\\n return _libsbml.Objective_getName(self)\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n 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pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\",\n \"def name(self) -> str:\\n return pulumi.get(self, \\\"name\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list 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If it does not exist a new one is created and returned"},"document":{"kind":"string","value":"def get_account(self, client: int):\n try:\n return self.accounts[client]\n except KeyError:\n return self._create_account(client)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_client(self, clientname):\n client = self.dbsession.query(Client).filter_by(clientname=clientname).all()\n if not client:\n return self.create_client({'clientname': clientname})\n else:\n return client[0]","def get_client(self, user_id: int, client_name: str) -> Client:\n return self.clients[user_id][client_name]","def client(self,context,params):\n url = f\"https://api.freshbooks.com/accounting/account/{params['account_id']}/users/clients/{params['id']}\"\n result = json.loads(util.rest(\"GET\", url, {}, context[\"headers\"][\"access_token\"]).text)\n client = result[\"response\"][\"result\"][\"client\"]\n client_obj = FreshbooksClient(\n accounting_systemid=client['accounting_systemid'], \n first_name=client['fname'],\n last_name=client['lname'],\n email=client['email'],\n vat_name=client['vat_name'],\n vat_number=client['vat_number'],\n home_phone=client['home_phone'],\n organization=client['organization'],\n username=client['username']\n )\n return client_obj.__dict__","def get_account_for_user(cls, user):\n email = user.email()\n assert email\n key = '<%s>' % email\n # Since usually the account already exists, first try getting it\n # without the transaction implied by get_or_insert().\n account = cls.get_by_key_name(key)\n if account is not None:\n return account\n nickname = cls.create_nickname_for_user(user)\n return cls.get_or_insert(key, user=user, email=email, nickname=nickname,\n fresh=True)","def account(self, account_code):\r\n return acc.Account(self, account_code)","def get_client_by_id(self, client_id=None):\n # search client_id in list and return the client object\n for client in self.client_list:\n if client_id == client.client_id:\n return client.copy()\n\n # return empty client otherwise\n return Client()","def get_account():\n\n bus = session_bus()\n\n goa_manager = bus.get_object(GOA_NAME, GOA_PATH)\n\n goa_objects = goa_manager.GetManagedObjects(dbus_interface=OBJECT_MANAGER)\n\n accounts = [\n obj for obj in goa_objects\n if obj != GOA_MANAGER_PATH\n ]\n\n if len(accounts) > 1:\n sys.exit(\"More than one account found.\")\n\n (account_path,) = accounts\n\n return bus.get_object(GOA_NAME, account_path)","def get_account(self, account):\n \n pass","def account(self, account_id):\r\n return resources.Account(self, account_id)","def _GetAccountFromUser(self):\n name = self._GetAccountNameFromUser()\n number = self._GetAccountNumberFromUser()\n # Validate that the number is a number (assumes no alphabet characters in\n # the account number).\n if re.match(\"^[0-9]*$\", number) is None:\n raise ValueError(\"Account number is invalid: %r\" % number)\n return accounts_lib.Account(name, int(number))","def getaccount(self, vergeaddress):\n return self.proxy.getaccount(vergeaddress)","def get_account(self):\n return self._account","def get_account(self):\n return self._account","def client(self, id):\n return self.query(Client).filter(Client.id == id).one()","def get_account(self, *args):\n\n account_data = api.get_account(\n *args,\n api_key=self.__creds.api_key_v2)\n\n return en.Account(creds=self.__creds, **account_data)","def get_account(self, name):\n return self._accounts[name]","def account(self, account_id: str):\n return get_from_list(self.accounts, \"id\", account_id)","def get_cached_account(username, registry):\n cache_key = get_account_cache_key(username, registry)\n cache = registry.cache\n cached_account = cache.get(cache_key)\n return cached_account","def account(cls, key, code):\n\n if key and code:\n utable = current.auth.settings.table_user\n query = (utable.registration_key == cls.keyhash(key, code))\n account = current.db(query).select(utable.ALL, limitby=(0, 1)).first()\n else:\n account = None\n\n return account","def account_id():\n return client.get_caller_identity()['Account']","def account(self, acct):\n aMgr = self.acctManager\n if len(aMgr.accounts) <= acct:\n raise Exception(\"requested unknown account number %i\" % acct)\n return aMgr.account(acct)","def get_account():\n\n # get user\n user = g.user\n\n # response\n return jsonify({'user_account': UserAccountAdminSchema().dump(user)}), 200","def __get_account(self, address):\n\t\tfor acct in self.wallet:\n\t\t\tif acct[\"address\"] == address:\n\t\t\t\treturn acct\n\t\traise ValueError(\"The given address does not exist in the bunkr-wallet\")","def get_account_by_name(self, account_name):\n accounts = self.service_old.management().accounts().list().execute()\n\n account = None\n if accounts.get('items'):\n account = next(acnt for acnt in accounts.get('items') if acnt[\"name\"] == account_name)\n\n if account is None:\n log_msg = \"The account named \" + account_name + \" does not exist!\"\n print(log_msg)\n\n return account","def get_client(self, name):\n return self.get_clients(as_dict=True).get(name)","def get_client(self, service, region, account):\n\n client = AwsApi.CLIENTS_CACHE.get((service, region, account))\n if client:\n return client # from cache\n\n if region == '*':\n eprint(\"warn: unknown region ('*'), using the default ('{}')\", self.default_region)\n region = self.default_region\n\n if account == '*':\n eprint(\"warn: unknown account ('*'), using default session\")\n client = self.session.client(\n service,\n region_name=region\n )\n elif account == self.default_account:\n client = self.session.client(\n service,\n region_name=region\n )\n elif self.args.no_input:\n eprint(\"warn: unknown account ('{}') and --no-input set, using default session\", account)\n client = self.session.client(\n service,\n region_name=region\n )\n else:\n account_config = self.config.setdefault('aws', {}).setdefault('accounts', {}).setdefault(account, {})\n if not 'profile' in account_config:\n account_config['profile'] = input(\"Enter configured AWS profile for {}: \".format(account))\n client = boto3.Session(profile_name=account_config['profile']).client(service, region_name=region)\n\n AwsApi.CLIENTS_CACHE[(service, region, account)] = client\n return client","def get_client_id():\n\n return str(get_account().Get(GOA_ACCOUNT_OAUTH2, 'ClientId',\n dbus_interface=PROPERTIES))","def get_or_create(account, account_name):\n if account.account == account_name:\n return account\n return realization.get_or_create(account, account_name)","def account(self, account_id):\r\n return Account(self, account_id)","def account(self):\n return Account(self)","def get_client(self, ip_address):\n\n self.cur.execute(\n 'select * from authenticated_clients where ip_address=%s',\n (ip_address, )\n )\n return self.cur.fetchone()","def account(self, sid):\r\n return accounts.Account(self, sid)","def account(self):\r\n return Account(self)","def get_dependent_accounts(client):\n\n PAGE_SIZE = 500\n managed_customer_service = client.GetService(\n 'ManagedCustomerService', version=settings.API_VERSION)\n\n offset = 0\n selector = {\n 'fields': ['CustomerId', 'Name'],\n 'predicates': {\n 'field': 'CanManageClients',\n 'operator': 'EQUALS',\n 'values': 'False'\n },\n 'paging': {\n 'startIndex': str(offset),\n 'numberResults': str(PAGE_SIZE)\n }\n }\n more_pages = True\n accounts = {}\n while more_pages:\n page = managed_customer_service.get(selector)\n\n if 'entries' in page and page['entries']:\n for account in page['entries']:\n accounts[account['customerId']] = str(account['name']) \\\n if hasattr(account, 'name') else 'None'\n\n offset += PAGE_SIZE\n selector['paging']['startIndex'] = str(offset)\n more_pages = offset < int(page['totalNumEntries'])\n\n return accounts","def get_user_reference(client):\n role = None\n try:\n role = client.hr.get_user_role()['userrole'][0]\n except Exception, exc:\n logging.error('Failed to get role: %r' % (exc,))\n return role\n return role['user__reference']","async def get_current_account(pub_key: str = Depends(get_current_pub_key)):\n account = await Account.query.where(Account.pub_key == pub_key).gino.first()\n if account is None:\n raise HTTPException(status_code=403, detail=\"Account doesn't exist\")\n return account","def logged_in_client(client):\n user = UserFactory()\n client.force_login(user)\n return client, user","def get(self, account_id):\n self.client.get_account(account_id)","def auth_by_pbid(self):\n self.console.debug(\"Auth by FSA: %r\", self.pbid)\n clients_matching_pbid = self.console.storage.getClientsMatching(dict(pbid=self.pbid))\n if len(clients_matching_pbid) > 1:\n self.console.warning(\"Found %s client having FSA '%s'\", len(clients_matching_pbid), self.pbid)\n return self.auth_by_pbid_and_guid()\n elif len(clients_matching_pbid) == 1:\n self.id = clients_matching_pbid[0].id\n # we may have a second client entry in database with current guid.\n # we want to update our current client guid only if it is not the case.\n try:\n client_by_guid = self.console.storage.getClient(Iourt42Client(guid=self.guid))\n except KeyError:\n pass\n else:\n if client_by_guid.id != self.id:\n # so storage.getClient is able to overwrite the value which will make\n # it remain unchanged in database when .save() will be called later on\n self._guid = None\n return self.console.storage.getClient(self)\n else:\n self.console.debug('Frozen Sand account [%s] unknown in database', self.pbid)\n return False","def get_client_by_id(self, client_id):\r\n cursor = self.conn.cursor()\r\n cursor.execute(\"\"\"SELECT * FROM CLIENT WHERE id={}\"\"\".format(client_id))\r\n return cursor.fetchall()","def get_client_by_nick(self, nick):\n for client in self.clients.values():\n if client.ident.nick == nick:\n return client\n return None","def _get_client(self):\n try:\n client = boto3_cached_conn(\n 'iam', **self.conn_details)\n\n if not client:\n raise ValueError(f\"boto3_cached_conn returned null IAM client for {self.account_number}\")\n\n return client\n\n except Exception as e:\n self.on_failure.send(self, error=e)\n self.current_app.logger.exception(f\"Failed to obtain boto3 IAM client for account {self.account_number}.\", exc_info=False)\n raise e","def getCustomerAccount(self):\n return self._CustomerAccount","def getCustomerAccount(self):\n return self._CustomerAccount","def find_account_by_name(cls, application_name):\n \n for credential in cls.credentials_list:\n if credential.application_name == application_name:\n return credential","def get_user_details(client):\n\n try:\n return client.user(user_id='me').get(fields=['login'])\n # print(f\"The email of the user is: {me['login']}\")\n\n except Exception as e:\n print(f\"Error has occurred: {e}\")\n return None","def get_token(client):\n # Begin by looking in token cache, first arg is for scopes,\n # because token is for app rather than user, second arg is None.\n result = client.acquire_token_silent(\n [\"https://graph.microsoft.com/.default\"], account=None\n )\n\n if not result:\n logger.info(\"No suitable token exists in cache. Get new one from Azure AD\")\n result = client.acquire_token_for_client(\n scopes=[\"https://graph.microsoft.com/.default\"]\n )\n\n # If we can't get access token, see what went wrong, otherwise return it.\n if \"access_token\" not in result:\n logger.exception(f'{result[\"error_description\"]} - {result[\"correlation_id\"]}')\n else:\n return result[\"access_token\"]","def query_client(self, client_id):\n try:\n return self.client_model.objects.get(client_id=client_id)\n except self.client_model.DoesNotExist:\n return None","def get_by_key_name(cls, key, **kwds):\n if not kwds and cls.current_user_account is not None:\n if key == cls.current_user_account.key().name():\n return cls.current_user_account\n return super(Account, cls).get_by_key_name(key, **kwds)","def retrieve(cls, account):\n requested_acct = None\n try:\n requested_acct = BankAccount.__acct_store[account]\n except KeyError:\n return False\n finally:\n return requested_acct","def get_account(self, accountid):\n payload = {'appkey': self._lr_object._get_api_key(), 'appsecret': self._lr_object._get_api_secret(),\n 'accountid': accountid}\n url = SECURE_API_URL + \"raas/v1/account\"\n return self._lr_object._get_json(url, payload)","def get_mcc_accounts(client):\n\n PAGE_SIZE = 500\n managed_customer_service = client.GetService(\n 'ManagedCustomerService', version=settings.API_VERSION)\n\n offset = 0\n selector = {\n 'fields': ['CustomerId', 'Name'],\n 'predicates': {\n 'field': 'CanManageClients',\n 'operator': 'EQUALS',\n 'values': 'True'\n },\n 'paging': {\n 'startIndex': str(offset),\n 'numberResults': str(PAGE_SIZE)\n }\n }\n more_pages = True\n mcc_accounts = {}\n while more_pages:\n page = managed_customer_service.get(selector)\n\n if 'entries' in page and page['entries']:\n for account in page['entries']:\n mcc_accounts[account['customerId']] = unicode(account['name']) \\\n if hasattr(account, 'name') else 'None'\n\n offset += PAGE_SIZE\n selector['paging']['startIndex'] = str(offset)\n more_pages = offset < int(page['totalNumEntries'])\n\n return mcc_accounts","def get_client_instance(cls, session, client_config, create=False):\n client = None\n if cls.SESSION_ID_KEY in session:\n client = session[cls.SESSION_ID_KEY]\n log.debug(\"Found OAuth client in session.\")\n if client is None and create:\n client = cls(client_config)\n session[cls.SESSION_ID_KEY] = client\n session.save()\n log.debug(\"No OAuth client in session - created new one.\")\n return client","def _get_user_client(self):\n return api.OAuthClient(settings.CLIENT_ID, settings.CLIENT_SECRET, settings.USER, settings.PASSWORD)","def GetAccount(host):\n return FetchUrlJson(host, 'accounts/self')","async def get_user_account(self):\n ts = tools.get_cur_timestamp_ms()\n params = {\n \"timestamp\": str(ts)\n }\n success, error = await self.request(\"GET\", \"/api/v3/account\", params, auth=True)\n return success, error","def get_account_for_tenant(test_auth, tenant_id):\n return '%s%s' % (test_auth.reseller_prefixes[0], tenant_id)","def get_account(self, account_id, **kwargs):\r\n\r\n if 'mask' not in kwargs:\r\n kwargs['mask'] = 'status'\r\n\r\n return self.account.getObject(id=account_id, **kwargs)","def get_client(self, mac_address: str) -> Union[Any, None]:\n if mac_address in self._clients:\n return self._clients[mac_address]","def bank_account():\n return BankAccount()","def __fetch_user_account(self, guid):\n\n try:\n user_account = UserAccount.objects.get(guid=guid)\n except Exception as e:\n logger.exception(e)\n else:\n return user_account","def get_stencila_account(cls) -> \"Account\":\n if not hasattr(cls, \"_stencila_account\"):\n cls._stencila_account = Account.objects.get(name=\"stencila\")\n return cls._stencila_account","def get_wim_account_by(self, wim=None, tenant=None, uuid=None, **kwargs):\n kwargs.setdefault('error_if_multiple', True)\n return self.get_wim_accounts_by(wim, tenant, uuid, **kwargs)[0]","def create_client(name):\n client = Client(name=name)\n print(client.client_secret)\n db.session.add(client)\n db.session.commit()\n return client","def find_credential(account):\n return Credentials.find_by_username(account)","def _account(self) -> Account:\n if isinstance(self._node_cached_account, Account):\n return self._node_cached_account\n account = Account.retrieve(\n session=self.entity.session,\n entity=self.entity,\n account_id=self.account_id\n )\n self._node_cached_account = account\n return account","def get_clientid(self):\n\n url = f'https://{self.__api}/v1/objects/client'\n body = {\"filter\": {}}\n with requests.post(url, json=body,\n headers={'X-WallarmAPI-UUID': self.__uuid,\n 'X-WallarmAPI-Secret': self.__secret}) as response:\n if response.status_code not in [200, 201, 202, 204, 304]:\n raise NonSuccessResponse(response.status_code, response.content)\n return response.json().get('body')[0].get('id')","def get_client_data(client_name):\n log.debug('starting get_client_data')\n clients = wf.cached_data('clients', None, max_age=0)\n\n # Loop through clients and return client with a match\n for client in clients:\n if client['name'] == client_name:\n log.debug('get_client_id finished, client_data: ' + str(client))\n return client","def get_by_id(self, id):\n accts = [acct for acct in self.accounts if UUID(acct.uuid) == UUID(id)]\n assert len(accts) <= 1\n if len(accts) == 0:\n raise KeyError('account with id {} unknown'.format(id))\n elif len(accts) > 1:\n log.warning('multiple accounts with same UUID found', uuid=id)\n return accts[0]","def get_object(self):\n account = Account.get_account_with_admins(account.id)\n\n return account[0] if account else None","def lookup_client(self, ip_addr: str):\n try:\n conn_obj = self.client_list[ip_addr]\n except KeyError:\n raise Networking.Host.ClientNotFoundException\n\n if conn_obj is not None:\n return conn_obj\n else:\n raise Networking.Host.ClientNotFoundException","def get_wim_account(self, uuid_or_name, **kwargs):\n kwargs.setdefault('postprocess', _postprocess_wim_account)\n kwargs.setdefault('SELECT', _WIM_ACCOUNT_SELECT)\n return self.get_by_name_or_uuid('wim_accounts', uuid_or_name, **kwargs)","def get_account(self, account_number):\n\n if not isinstance(account_number, str):\n raise ValueError('Invalid type <{}> for account number'.format(\n type(account_number)))\n\n try:\n if self.di is not None:\n result = self.di.get(account_number)\n else:\n result = self.accounts.get(account_number, None)\n\n except DBConnectionError:\n result = \"Connection error occurred. Try Again.\"\n return result","def account():\n\n bank_test = Bank.objects.create(name='R-Bank')\n company_test = Company.objects.create(name='Tre Belarus', country='Belarus')\n account = Account.objects.create(iban_number='TEEdddddddfs', swift_code='tertrefdsf',\n bank=bank_test, company=company_test)\n return account","def get_account_for_nickname(cls, nickname):\n assert nickname\n assert '@' not in nickname\n return cls.all().filter('lower_nickname =', nickname.lower()).get()","def find_by_account_name(cls, account_name):\n for account in cls.credentials_list:\n if account.account_name == account_name:\n return account","async def get_user_account(self):\n uri = \"/fapi/v1/account\"\n ts = tools.get_cur_timestamp_ms()\n params = {\n \"timestamp\": str(ts)\n }\n success, error = await self.request(\"GET\", uri, params, auth=True)\n return success, error","def get_account_for_email(cls, email):\n assert email\n key = '<%s>' % email\n return cls.get_by_key_name(key)","def account(request: Request) -> Dict:\n # Get account\n account_id: int = request.matchdict.get(\"account_id\")\n account_obj: Optional[Account] = get_account_by_id(\n session=request.dbsession,\n account_id=account_id,\n )\n # TODO: Check access\n\n\n return {\n \"account\": account_obj,\n }","def get_client(self):\n return self.client","def get_account_by_id(self, id_):\n return next((account for account in self.accounts\n if account.id == id_), None)","def ClientCreateAccount(account_name,\n chap,\n strict,\n client_ips,\n client_user,\n client_pass,\n mvip,\n username,\n password):\n log = GetLogger()\n\n log.info(\"Searching for accounts\")\n cluster = SFCluster(mvip, username, password)\n\n try:\n svip = cluster.GetClusterInfo()[\"svip\"]\n except SolidFireError as e:\n log.error(\"Failed to get cluster info: {}\".format(e))\n return False\n\n # Get a list of accounts from the cluster\n try:\n allaccounts = SFCluster(mvip, username, password).ListAccounts()\n except SolidFireError as e:\n log.error(\"Failed to list accounts: {}\".format(e))\n return False\n\n # Run all of the client operations in parallel\n allgood = True\n results = []\n pool = threadutil.GlobalPool()\n for client_ip in client_ips:\n results.append(pool.Post(_ClientThread, mvip, username, password, client_ip, client_user, client_pass, account_name, svip, allaccounts, chap, strict))\n\n for idx, client_ip in enumerate(client_ips):\n try:\n results[idx].Get()\n except SolidFireError as e:\n log.error(\" {}: Error creating account: {}\".format(client_ip, e))\n allgood = False\n continue\n\n if allgood:\n log.passed(\"Successfully created accounts for all clients\")\n return True\n else:\n log.error(\"Could not create accounts for all clients\")\n return False","def find_credential(account):\n return Credentials.find_credential(account)","def get_account():\n account_id = request.json['id']\n account = [account for account in accounts if account['id'] == account_id]\n if len(account) == 0:\n abort(404, 'Account not found')\n\n return json.dumps(account[0], ensure_ascii=False), 200, {'Content-Type': 'text/css; charset=utf-8'}","def auth_by_pbid_and_guid(self):\n self.console.debug(\"Auth by both guid and FSA: %r, %r\", self.guid, self.pbid)\n clients_matching_pbid = self.console.storage.getClientsMatching({'pbid': self.pbid, 'guid': self.guid})\n if len(clients_matching_pbid):\n self.id = clients_matching_pbid[0].id\n return self.console.storage.getClient(self)\n else:\n self.console.debug(\"Frozen Sand account [%s] with guid '%s' unknown in database\", self.pbid, self.guid)\n return False","def _get_billing_account_id():\n org_client = boto3.client(\"organizations\")\n response = org_client.describe_organization()\n return response[\"Organization\"][\"MasterAccountId\"]","def service_account(self) -> str:\n return pulumi.get(self, \"service_account\")","def get_user(self, user_id):\n try:\n return Account.objects.get(pk=user_id)\n except Account.DoesNotExist:\n return None","def get(self, id: int) -> Client:\n\n return self.__clients[id]","def get_client_key(self, client, channel):\n result = None\n if channel in self.clients:\n for service in self.clients[channel]:\n if service[0] != client:\n continue\n result = service[1]\n return result","def account(request):\r\n # if auth fails, it'll raise an HTTPForbidden exception\r\n with ReqAuthorize(request):\r\n user = UserMgr.get(username=request.user.username)\r\n\r\n return {\r\n 'user': user,\r\n 'username': user.username,\r\n }","def get_by_address(self, address):\n assert len(address) == 20\n accounts = [account for account in self.accounts if account.address == address]\n if len(accounts) == 0:\n raise KeyError('account with address {} not found'.format(encode_hex(address)))\n elif len(accounts) > 1:\n log.warning('multiple accounts with same address found', address=encode_hex(address))\n return accounts[0]","def get_account(self, account_id=None, account_name=None, search=False):\n if not (account_id or account_name):\n aliases = self.get_account_aliases()\n if aliases:\n account_name = aliases[0]\n else:\n raise ValueError('get_account(). Account id, name, or alias not found')\n accounts = self.get_all_accounts(account_id=account_id, account_name=account_name,\n search=search)\n if accounts:\n if len(accounts) > 1:\n raise ValueError('get_account matched more than a single account with the '\n 'provided criteria: account_id=\"{0}\", account_name=\"{1}\". '\n 'Matched:{2}'\n .format(account_id, account_name,\n \", \".join(str(x) for x in accounts)))\n else:\n return accounts[0]\n return None","async def get_client(\n self,\n request: Request,\n client_id: str,\n client_secret: Optional[str] = None,\n ) -> Optional[OAuth2Client]:\n\n client_record = await self._db.query_one(\n Client.select(*OAuth2Client._fields, filters=\".id = <uuid>$id\"),\n id=client_id,\n )\n client_record = Client.from_obj(client_record)\n\n if client_record is not None:\n return OAuth2Client(\n client_id=client_record.client_id,\n client_secret=client_record.client_secret,\n grant_types=client_record.grant_types,\n response_types=client_record.response_types,\n redirect_uris=client_record.redirect_uris,\n scope=client_record.scope,\n )","def service_account(self) -> Optional[str]:\n return pulumi.get(self, \"service_account\")","def get_settings_from_client(client):\r\n settings = {\r\n 'username': '',\r\n 'api_key': '',\r\n 'timeout': client.timeout or '',\r\n 'endpoint_url': client.endpoint_url,\r\n }\r\n try:\r\n settings['username'] = client.auth.username\r\n settings['api_key'] = client.auth.api_key\r\n except AttributeError:\r\n pass\r\n\r\n return settings","def get_client_data(self, client_id):\n query = \"\"\"SELECT id,\n secret\n FROM clients\n WHERE active = 1\n AND id = %s\"\"\"\n self._execute(query, (client_id,))\n return self._dictfetchone()","def customer_get_one(user_id):\n return customer_get(user_id)","def account_id(self):\n return self.get('/accounts')[0]['Id']","def get_account_by_name(self, name):\n return next((account for account in self.accounts\n if account.name.lower() == name.lower()), None)"],"string":"[\n \"def get_client(self, clientname):\\n client = self.dbsession.query(Client).filter_by(clientname=clientname).all()\\n if not client:\\n return self.create_client({'clientname': clientname})\\n else:\\n return client[0]\",\n \"def get_client(self, user_id: int, client_name: str) -> Client:\\n return self.clients[user_id][client_name]\",\n \"def client(self,context,params):\\n url = f\\\"https://api.freshbooks.com/accounting/account/{params['account_id']}/users/clients/{params['id']}\\\"\\n result = json.loads(util.rest(\\\"GET\\\", url, {}, context[\\\"headers\\\"][\\\"access_token\\\"]).text)\\n client = result[\\\"response\\\"][\\\"result\\\"][\\\"client\\\"]\\n client_obj = FreshbooksClient(\\n accounting_systemid=client['accounting_systemid'], \\n first_name=client['fname'],\\n last_name=client['lname'],\\n email=client['email'],\\n vat_name=client['vat_name'],\\n vat_number=client['vat_number'],\\n home_phone=client['home_phone'],\\n organization=client['organization'],\\n username=client['username']\\n )\\n return client_obj.__dict__\",\n \"def get_account_for_user(cls, user):\\n email = user.email()\\n assert email\\n key = '<%s>' % email\\n # Since usually the account already exists, first try getting it\\n # without the transaction implied by get_or_insert().\\n account = cls.get_by_key_name(key)\\n if account is not None:\\n return account\\n nickname = cls.create_nickname_for_user(user)\\n return cls.get_or_insert(key, user=user, email=email, nickname=nickname,\\n fresh=True)\",\n \"def account(self, account_code):\\r\\n return acc.Account(self, account_code)\",\n \"def get_client_by_id(self, client_id=None):\\n # search client_id in list and return the client object\\n for client in self.client_list:\\n if client_id == client.client_id:\\n return client.copy()\\n\\n # return empty client otherwise\\n return Client()\",\n \"def get_account():\\n\\n bus = session_bus()\\n\\n goa_manager = bus.get_object(GOA_NAME, GOA_PATH)\\n\\n goa_objects = goa_manager.GetManagedObjects(dbus_interface=OBJECT_MANAGER)\\n\\n accounts = [\\n obj for obj in goa_objects\\n if obj != GOA_MANAGER_PATH\\n ]\\n\\n if len(accounts) > 1:\\n sys.exit(\\\"More than one account found.\\\")\\n\\n (account_path,) = accounts\\n\\n return bus.get_object(GOA_NAME, account_path)\",\n \"def get_account(self, account):\\n \\n pass\",\n \"def account(self, account_id):\\r\\n return resources.Account(self, account_id)\",\n \"def _GetAccountFromUser(self):\\n name = self._GetAccountNameFromUser()\\n number = self._GetAccountNumberFromUser()\\n # Validate that the number is a number (assumes no alphabet characters in\\n # the account number).\\n if re.match(\\\"^[0-9]*$\\\", number) is None:\\n raise ValueError(\\\"Account number is invalid: %r\\\" % number)\\n return accounts_lib.Account(name, int(number))\",\n \"def getaccount(self, vergeaddress):\\n return self.proxy.getaccount(vergeaddress)\",\n \"def get_account(self):\\n return self._account\",\n \"def get_account(self):\\n return self._account\",\n \"def client(self, id):\\n return self.query(Client).filter(Client.id == id).one()\",\n \"def get_account(self, *args):\\n\\n account_data = api.get_account(\\n *args,\\n api_key=self.__creds.api_key_v2)\\n\\n return en.Account(creds=self.__creds, **account_data)\",\n \"def get_account(self, name):\\n return self._accounts[name]\",\n \"def account(self, account_id: str):\\n return get_from_list(self.accounts, \\\"id\\\", account_id)\",\n \"def get_cached_account(username, registry):\\n cache_key = get_account_cache_key(username, registry)\\n cache = registry.cache\\n cached_account = cache.get(cache_key)\\n return cached_account\",\n \"def account(cls, key, code):\\n\\n if key and code:\\n utable = current.auth.settings.table_user\\n query = (utable.registration_key == cls.keyhash(key, code))\\n account = current.db(query).select(utable.ALL, limitby=(0, 1)).first()\\n else:\\n account = None\\n\\n return account\",\n \"def account_id():\\n return client.get_caller_identity()['Account']\",\n \"def account(self, acct):\\n aMgr = self.acctManager\\n if len(aMgr.accounts) <= acct:\\n raise Exception(\\\"requested unknown account number %i\\\" % acct)\\n return aMgr.account(acct)\",\n \"def get_account():\\n\\n # get user\\n user = g.user\\n\\n # response\\n return jsonify({'user_account': UserAccountAdminSchema().dump(user)}), 200\",\n \"def __get_account(self, address):\\n\\t\\tfor acct in self.wallet:\\n\\t\\t\\tif acct[\\\"address\\\"] == address:\\n\\t\\t\\t\\treturn acct\\n\\t\\traise ValueError(\\\"The given address does not exist in the bunkr-wallet\\\")\",\n \"def get_account_by_name(self, account_name):\\n accounts = self.service_old.management().accounts().list().execute()\\n\\n account = None\\n if accounts.get('items'):\\n account = next(acnt for acnt in accounts.get('items') if acnt[\\\"name\\\"] == account_name)\\n\\n if account is None:\\n log_msg = \\\"The account named \\\" + account_name + \\\" does not exist!\\\"\\n print(log_msg)\\n\\n return account\",\n \"def get_client(self, name):\\n return self.get_clients(as_dict=True).get(name)\",\n \"def get_client(self, service, region, account):\\n\\n client = AwsApi.CLIENTS_CACHE.get((service, region, account))\\n if client:\\n return client # from cache\\n\\n if region == '*':\\n eprint(\\\"warn: unknown region ('*'), using the default ('{}')\\\", self.default_region)\\n region = self.default_region\\n\\n if account == '*':\\n eprint(\\\"warn: unknown account ('*'), using default session\\\")\\n client = self.session.client(\\n service,\\n region_name=region\\n )\\n elif account == self.default_account:\\n client = self.session.client(\\n service,\\n region_name=region\\n )\\n elif self.args.no_input:\\n eprint(\\\"warn: unknown account ('{}') and --no-input set, using default session\\\", account)\\n client = self.session.client(\\n service,\\n region_name=region\\n )\\n else:\\n account_config = self.config.setdefault('aws', {}).setdefault('accounts', {}).setdefault(account, {})\\n if not 'profile' in account_config:\\n account_config['profile'] = input(\\\"Enter configured AWS profile for {}: \\\".format(account))\\n client = boto3.Session(profile_name=account_config['profile']).client(service, region_name=region)\\n\\n AwsApi.CLIENTS_CACHE[(service, region, account)] = client\\n return client\",\n \"def get_client_id():\\n\\n return str(get_account().Get(GOA_ACCOUNT_OAUTH2, 'ClientId',\\n dbus_interface=PROPERTIES))\",\n \"def get_or_create(account, account_name):\\n if account.account == account_name:\\n return account\\n return realization.get_or_create(account, account_name)\",\n \"def account(self, account_id):\\r\\n return Account(self, account_id)\",\n \"def account(self):\\n return Account(self)\",\n \"def get_client(self, ip_address):\\n\\n self.cur.execute(\\n 'select * from authenticated_clients where ip_address=%s',\\n (ip_address, )\\n )\\n return self.cur.fetchone()\",\n \"def account(self, sid):\\r\\n return accounts.Account(self, sid)\",\n \"def account(self):\\r\\n return Account(self)\",\n \"def get_dependent_accounts(client):\\n\\n PAGE_SIZE = 500\\n managed_customer_service = client.GetService(\\n 'ManagedCustomerService', version=settings.API_VERSION)\\n\\n offset = 0\\n selector = {\\n 'fields': ['CustomerId', 'Name'],\\n 'predicates': {\\n 'field': 'CanManageClients',\\n 'operator': 'EQUALS',\\n 'values': 'False'\\n },\\n 'paging': {\\n 'startIndex': str(offset),\\n 'numberResults': str(PAGE_SIZE)\\n }\\n }\\n more_pages = True\\n accounts = {}\\n while more_pages:\\n page = managed_customer_service.get(selector)\\n\\n if 'entries' in page and page['entries']:\\n for account in page['entries']:\\n accounts[account['customerId']] = str(account['name']) \\\\\\n if hasattr(account, 'name') else 'None'\\n\\n offset += PAGE_SIZE\\n selector['paging']['startIndex'] = str(offset)\\n more_pages = offset < int(page['totalNumEntries'])\\n\\n return accounts\",\n \"def get_user_reference(client):\\n role = None\\n try:\\n role = client.hr.get_user_role()['userrole'][0]\\n except Exception, exc:\\n logging.error('Failed to get role: %r' % (exc,))\\n return role\\n return role['user__reference']\",\n \"async def get_current_account(pub_key: str = Depends(get_current_pub_key)):\\n account = await Account.query.where(Account.pub_key == pub_key).gino.first()\\n if account is None:\\n raise HTTPException(status_code=403, detail=\\\"Account doesn't exist\\\")\\n return account\",\n \"def logged_in_client(client):\\n user = UserFactory()\\n client.force_login(user)\\n return client, user\",\n \"def get(self, account_id):\\n self.client.get_account(account_id)\",\n \"def auth_by_pbid(self):\\n self.console.debug(\\\"Auth by FSA: %r\\\", self.pbid)\\n clients_matching_pbid = self.console.storage.getClientsMatching(dict(pbid=self.pbid))\\n if len(clients_matching_pbid) > 1:\\n self.console.warning(\\\"Found %s client having FSA '%s'\\\", len(clients_matching_pbid), self.pbid)\\n return self.auth_by_pbid_and_guid()\\n elif len(clients_matching_pbid) == 1:\\n self.id = clients_matching_pbid[0].id\\n # we may have a second client entry in database with current guid.\\n # we want to update our current client guid only if it is not the case.\\n try:\\n client_by_guid = self.console.storage.getClient(Iourt42Client(guid=self.guid))\\n except KeyError:\\n pass\\n else:\\n if client_by_guid.id != self.id:\\n # so storage.getClient is able to overwrite the value which will make\\n # it remain unchanged in database when .save() will be called later on\\n self._guid = None\\n return self.console.storage.getClient(self)\\n else:\\n self.console.debug('Frozen Sand account [%s] unknown in database', self.pbid)\\n return False\",\n \"def get_client_by_id(self, client_id):\\r\\n cursor = self.conn.cursor()\\r\\n cursor.execute(\\\"\\\"\\\"SELECT * FROM CLIENT WHERE id={}\\\"\\\"\\\".format(client_id))\\r\\n return cursor.fetchall()\",\n \"def get_client_by_nick(self, nick):\\n for client in self.clients.values():\\n if client.ident.nick == nick:\\n return client\\n return None\",\n \"def _get_client(self):\\n try:\\n client = boto3_cached_conn(\\n 'iam', **self.conn_details)\\n\\n if not client:\\n raise ValueError(f\\\"boto3_cached_conn returned null IAM client for {self.account_number}\\\")\\n\\n return client\\n\\n except Exception as e:\\n self.on_failure.send(self, error=e)\\n self.current_app.logger.exception(f\\\"Failed to obtain boto3 IAM client for account {self.account_number}.\\\", exc_info=False)\\n raise e\",\n \"def getCustomerAccount(self):\\n return self._CustomerAccount\",\n \"def getCustomerAccount(self):\\n return self._CustomerAccount\",\n \"def find_account_by_name(cls, application_name):\\n \\n for credential in cls.credentials_list:\\n if credential.application_name == application_name:\\n return credential\",\n \"def get_user_details(client):\\n\\n try:\\n return client.user(user_id='me').get(fields=['login'])\\n # print(f\\\"The email of the user is: {me['login']}\\\")\\n\\n except Exception as e:\\n print(f\\\"Error has occurred: {e}\\\")\\n return None\",\n \"def get_token(client):\\n # Begin by looking in token cache, first arg is for scopes,\\n # because token is for app rather than user, second arg is None.\\n result = client.acquire_token_silent(\\n [\\\"https://graph.microsoft.com/.default\\\"], account=None\\n )\\n\\n if not result:\\n logger.info(\\\"No suitable token exists in cache. Get new one from Azure AD\\\")\\n result = client.acquire_token_for_client(\\n scopes=[\\\"https://graph.microsoft.com/.default\\\"]\\n )\\n\\n # If we can't get access token, see what went wrong, otherwise return it.\\n if \\\"access_token\\\" not in result:\\n logger.exception(f'{result[\\\"error_description\\\"]} - {result[\\\"correlation_id\\\"]}')\\n else:\\n return result[\\\"access_token\\\"]\",\n \"def query_client(self, client_id):\\n try:\\n return self.client_model.objects.get(client_id=client_id)\\n except self.client_model.DoesNotExist:\\n return None\",\n \"def get_by_key_name(cls, key, **kwds):\\n if not kwds and cls.current_user_account is not None:\\n if key == cls.current_user_account.key().name():\\n return cls.current_user_account\\n return super(Account, cls).get_by_key_name(key, **kwds)\",\n \"def retrieve(cls, account):\\n requested_acct = None\\n try:\\n requested_acct = BankAccount.__acct_store[account]\\n except KeyError:\\n return False\\n finally:\\n return requested_acct\",\n \"def get_account(self, accountid):\\n payload = {'appkey': self._lr_object._get_api_key(), 'appsecret': self._lr_object._get_api_secret(),\\n 'accountid': accountid}\\n url = SECURE_API_URL + \\\"raas/v1/account\\\"\\n return self._lr_object._get_json(url, payload)\",\n \"def get_mcc_accounts(client):\\n\\n PAGE_SIZE = 500\\n managed_customer_service = client.GetService(\\n 'ManagedCustomerService', version=settings.API_VERSION)\\n\\n offset = 0\\n selector = {\\n 'fields': ['CustomerId', 'Name'],\\n 'predicates': {\\n 'field': 'CanManageClients',\\n 'operator': 'EQUALS',\\n 'values': 'True'\\n },\\n 'paging': {\\n 'startIndex': str(offset),\\n 'numberResults': str(PAGE_SIZE)\\n }\\n }\\n more_pages = True\\n mcc_accounts = {}\\n while more_pages:\\n page = managed_customer_service.get(selector)\\n\\n if 'entries' in page and page['entries']:\\n for account in page['entries']:\\n mcc_accounts[account['customerId']] = unicode(account['name']) \\\\\\n if hasattr(account, 'name') else 'None'\\n\\n offset += PAGE_SIZE\\n selector['paging']['startIndex'] = str(offset)\\n more_pages = offset < int(page['totalNumEntries'])\\n\\n return mcc_accounts\",\n \"def get_client_instance(cls, session, client_config, create=False):\\n client = None\\n if cls.SESSION_ID_KEY in session:\\n client = session[cls.SESSION_ID_KEY]\\n log.debug(\\\"Found OAuth client in session.\\\")\\n if client is None and create:\\n client = cls(client_config)\\n session[cls.SESSION_ID_KEY] = client\\n session.save()\\n log.debug(\\\"No OAuth client in session - created new one.\\\")\\n return client\",\n \"def _get_user_client(self):\\n return api.OAuthClient(settings.CLIENT_ID, settings.CLIENT_SECRET, settings.USER, settings.PASSWORD)\",\n \"def GetAccount(host):\\n return FetchUrlJson(host, 'accounts/self')\",\n \"async def get_user_account(self):\\n ts = tools.get_cur_timestamp_ms()\\n params = {\\n \\\"timestamp\\\": str(ts)\\n }\\n success, error = await self.request(\\\"GET\\\", \\\"/api/v3/account\\\", params, auth=True)\\n return success, error\",\n \"def get_account_for_tenant(test_auth, tenant_id):\\n return '%s%s' % (test_auth.reseller_prefixes[0], tenant_id)\",\n \"def get_account(self, account_id, **kwargs):\\r\\n\\r\\n if 'mask' not in kwargs:\\r\\n kwargs['mask'] = 'status'\\r\\n\\r\\n return self.account.getObject(id=account_id, **kwargs)\",\n \"def get_client(self, mac_address: str) -> Union[Any, None]:\\n if mac_address in self._clients:\\n return self._clients[mac_address]\",\n \"def bank_account():\\n return BankAccount()\",\n \"def __fetch_user_account(self, guid):\\n\\n try:\\n user_account = UserAccount.objects.get(guid=guid)\\n except Exception as e:\\n logger.exception(e)\\n else:\\n return user_account\",\n \"def get_stencila_account(cls) -> \\\"Account\\\":\\n if not hasattr(cls, \\\"_stencila_account\\\"):\\n cls._stencila_account = Account.objects.get(name=\\\"stencila\\\")\\n return cls._stencila_account\",\n \"def get_wim_account_by(self, wim=None, tenant=None, uuid=None, **kwargs):\\n kwargs.setdefault('error_if_multiple', True)\\n return self.get_wim_accounts_by(wim, tenant, uuid, **kwargs)[0]\",\n \"def create_client(name):\\n client = Client(name=name)\\n print(client.client_secret)\\n db.session.add(client)\\n db.session.commit()\\n return client\",\n \"def find_credential(account):\\n return Credentials.find_by_username(account)\",\n \"def _account(self) -> Account:\\n if isinstance(self._node_cached_account, Account):\\n return self._node_cached_account\\n account = Account.retrieve(\\n session=self.entity.session,\\n entity=self.entity,\\n account_id=self.account_id\\n )\\n self._node_cached_account = account\\n return account\",\n \"def get_clientid(self):\\n\\n url = f'https://{self.__api}/v1/objects/client'\\n body = {\\\"filter\\\": {}}\\n with requests.post(url, json=body,\\n headers={'X-WallarmAPI-UUID': self.__uuid,\\n 'X-WallarmAPI-Secret': self.__secret}) as response:\\n if response.status_code not in [200, 201, 202, 204, 304]:\\n raise NonSuccessResponse(response.status_code, response.content)\\n return response.json().get('body')[0].get('id')\",\n \"def get_client_data(client_name):\\n log.debug('starting get_client_data')\\n clients = wf.cached_data('clients', None, max_age=0)\\n\\n # Loop through clients and return client with a match\\n for client in clients:\\n if client['name'] == client_name:\\n log.debug('get_client_id finished, client_data: ' + str(client))\\n return client\",\n \"def get_by_id(self, id):\\n accts = [acct for acct in self.accounts if UUID(acct.uuid) == UUID(id)]\\n assert len(accts) <= 1\\n if len(accts) == 0:\\n raise KeyError('account with id {} unknown'.format(id))\\n elif len(accts) > 1:\\n log.warning('multiple accounts with same UUID found', uuid=id)\\n return accts[0]\",\n \"def get_object(self):\\n account = Account.get_account_with_admins(account.id)\\n\\n return account[0] if account else None\",\n \"def lookup_client(self, ip_addr: str):\\n try:\\n conn_obj = self.client_list[ip_addr]\\n except KeyError:\\n raise Networking.Host.ClientNotFoundException\\n\\n if conn_obj is not None:\\n return conn_obj\\n else:\\n raise Networking.Host.ClientNotFoundException\",\n \"def get_wim_account(self, uuid_or_name, **kwargs):\\n kwargs.setdefault('postprocess', _postprocess_wim_account)\\n kwargs.setdefault('SELECT', _WIM_ACCOUNT_SELECT)\\n return self.get_by_name_or_uuid('wim_accounts', uuid_or_name, **kwargs)\",\n \"def get_account(self, account_number):\\n\\n if not isinstance(account_number, str):\\n raise ValueError('Invalid type <{}> for account number'.format(\\n type(account_number)))\\n\\n try:\\n if self.di is not None:\\n result = self.di.get(account_number)\\n else:\\n result = self.accounts.get(account_number, None)\\n\\n except DBConnectionError:\\n result = \\\"Connection error occurred. Try Again.\\\"\\n return result\",\n \"def account():\\n\\n bank_test = Bank.objects.create(name='R-Bank')\\n company_test = Company.objects.create(name='Tre Belarus', country='Belarus')\\n account = Account.objects.create(iban_number='TEEdddddddfs', swift_code='tertrefdsf',\\n bank=bank_test, company=company_test)\\n return account\",\n \"def get_account_for_nickname(cls, nickname):\\n assert nickname\\n assert '@' not in nickname\\n return cls.all().filter('lower_nickname =', nickname.lower()).get()\",\n \"def find_by_account_name(cls, account_name):\\n for account in cls.credentials_list:\\n if account.account_name == account_name:\\n return account\",\n \"async def get_user_account(self):\\n uri = \\\"/fapi/v1/account\\\"\\n ts = tools.get_cur_timestamp_ms()\\n params = {\\n \\\"timestamp\\\": str(ts)\\n }\\n success, error = await self.request(\\\"GET\\\", uri, params, auth=True)\\n return success, error\",\n \"def get_account_for_email(cls, email):\\n assert email\\n key = '<%s>' % email\\n return cls.get_by_key_name(key)\",\n \"def account(request: Request) -> Dict:\\n # Get account\\n account_id: int = request.matchdict.get(\\\"account_id\\\")\\n account_obj: Optional[Account] = get_account_by_id(\\n session=request.dbsession,\\n account_id=account_id,\\n )\\n # TODO: Check access\\n\\n\\n return {\\n \\\"account\\\": account_obj,\\n }\",\n \"def get_client(self):\\n return self.client\",\n \"def get_account_by_id(self, id_):\\n return next((account for account in self.accounts\\n if account.id == id_), None)\",\n \"def ClientCreateAccount(account_name,\\n chap,\\n strict,\\n client_ips,\\n client_user,\\n client_pass,\\n mvip,\\n username,\\n password):\\n log = GetLogger()\\n\\n log.info(\\\"Searching for accounts\\\")\\n cluster = SFCluster(mvip, username, password)\\n\\n try:\\n svip = cluster.GetClusterInfo()[\\\"svip\\\"]\\n except SolidFireError as e:\\n log.error(\\\"Failed to get cluster info: {}\\\".format(e))\\n return False\\n\\n # Get a list of accounts from the cluster\\n try:\\n allaccounts = SFCluster(mvip, username, password).ListAccounts()\\n except SolidFireError as e:\\n log.error(\\\"Failed to list accounts: {}\\\".format(e))\\n return False\\n\\n # Run all of the client operations in parallel\\n allgood = True\\n results = []\\n pool = threadutil.GlobalPool()\\n for client_ip in client_ips:\\n results.append(pool.Post(_ClientThread, mvip, username, password, client_ip, client_user, client_pass, account_name, svip, allaccounts, chap, strict))\\n\\n for idx, client_ip in enumerate(client_ips):\\n try:\\n results[idx].Get()\\n except SolidFireError as e:\\n log.error(\\\" {}: Error creating account: {}\\\".format(client_ip, e))\\n allgood = False\\n continue\\n\\n if allgood:\\n log.passed(\\\"Successfully created accounts for all clients\\\")\\n return True\\n else:\\n log.error(\\\"Could not create accounts for all clients\\\")\\n return False\",\n \"def find_credential(account):\\n return Credentials.find_credential(account)\",\n \"def get_account():\\n account_id = request.json['id']\\n account = [account for account in accounts if account['id'] == account_id]\\n if len(account) == 0:\\n abort(404, 'Account not found')\\n\\n return json.dumps(account[0], ensure_ascii=False), 200, {'Content-Type': 'text/css; charset=utf-8'}\",\n \"def auth_by_pbid_and_guid(self):\\n self.console.debug(\\\"Auth by both guid and FSA: %r, %r\\\", self.guid, self.pbid)\\n clients_matching_pbid = self.console.storage.getClientsMatching({'pbid': self.pbid, 'guid': self.guid})\\n if len(clients_matching_pbid):\\n self.id = clients_matching_pbid[0].id\\n return self.console.storage.getClient(self)\\n else:\\n self.console.debug(\\\"Frozen Sand account [%s] with guid '%s' unknown in database\\\", self.pbid, self.guid)\\n return False\",\n \"def _get_billing_account_id():\\n org_client = boto3.client(\\\"organizations\\\")\\n response = org_client.describe_organization()\\n return response[\\\"Organization\\\"][\\\"MasterAccountId\\\"]\",\n \"def service_account(self) -> str:\\n return pulumi.get(self, \\\"service_account\\\")\",\n \"def get_user(self, user_id):\\n try:\\n return Account.objects.get(pk=user_id)\\n except Account.DoesNotExist:\\n return None\",\n \"def get(self, id: int) -> Client:\\n\\n return self.__clients[id]\",\n \"def get_client_key(self, client, channel):\\n result = None\\n if channel in self.clients:\\n for service in self.clients[channel]:\\n if service[0] != client:\\n continue\\n result = service[1]\\n return result\",\n \"def account(request):\\r\\n # if auth fails, it'll raise an HTTPForbidden exception\\r\\n with ReqAuthorize(request):\\r\\n user = UserMgr.get(username=request.user.username)\\r\\n\\r\\n return {\\r\\n 'user': user,\\r\\n 'username': user.username,\\r\\n }\",\n \"def get_by_address(self, address):\\n assert len(address) == 20\\n accounts = [account for account in self.accounts if account.address == address]\\n if len(accounts) == 0:\\n raise KeyError('account with address {} not found'.format(encode_hex(address)))\\n elif len(accounts) > 1:\\n log.warning('multiple accounts with same address found', address=encode_hex(address))\\n return accounts[0]\",\n \"def get_account(self, account_id=None, account_name=None, search=False):\\n if not (account_id or account_name):\\n aliases = self.get_account_aliases()\\n if aliases:\\n account_name = aliases[0]\\n else:\\n raise ValueError('get_account(). Account id, name, or alias not found')\\n accounts = self.get_all_accounts(account_id=account_id, account_name=account_name,\\n search=search)\\n if accounts:\\n if len(accounts) > 1:\\n raise ValueError('get_account matched more than a single account with the '\\n 'provided criteria: account_id=\\\"{0}\\\", account_name=\\\"{1}\\\". '\\n 'Matched:{2}'\\n .format(account_id, account_name,\\n \\\", \\\".join(str(x) for x in accounts)))\\n else:\\n return accounts[0]\\n return None\",\n \"async def get_client(\\n self,\\n request: Request,\\n client_id: str,\\n client_secret: Optional[str] = None,\\n ) -> Optional[OAuth2Client]:\\n\\n client_record = await self._db.query_one(\\n Client.select(*OAuth2Client._fields, filters=\\\".id = <uuid>$id\\\"),\\n id=client_id,\\n )\\n client_record = Client.from_obj(client_record)\\n\\n if client_record is not None:\\n return OAuth2Client(\\n client_id=client_record.client_id,\\n client_secret=client_record.client_secret,\\n grant_types=client_record.grant_types,\\n response_types=client_record.response_types,\\n redirect_uris=client_record.redirect_uris,\\n scope=client_record.scope,\\n )\",\n \"def service_account(self) -> Optional[str]:\\n return pulumi.get(self, \\\"service_account\\\")\",\n \"def get_settings_from_client(client):\\r\\n settings = {\\r\\n 'username': '',\\r\\n 'api_key': '',\\r\\n 'timeout': client.timeout or '',\\r\\n 'endpoint_url': client.endpoint_url,\\r\\n }\\r\\n try:\\r\\n settings['username'] = client.auth.username\\r\\n settings['api_key'] = client.auth.api_key\\r\\n except AttributeError:\\r\\n pass\\r\\n\\r\\n return settings\",\n \"def get_client_data(self, client_id):\\n query = \\\"\\\"\\\"SELECT id,\\n secret\\n FROM clients\\n WHERE active = 1\\n AND id = %s\\\"\\\"\\\"\\n self._execute(query, (client_id,))\\n return self._dictfetchone()\",\n \"def customer_get_one(user_id):\\n return customer_get(user_id)\",\n \"def account_id(self):\\n return self.get('/accounts')[0]['Id']\",\n \"def get_account_by_name(self, name):\\n return next((account for account in self.accounts\\n if account.name.lower() == name.lower()), None)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66133344","0.6345957","0.6296915","0.62183553","0.6195077","0.61447585","0.6143067","0.61124474","0.6101005","0.6082482","0.6080354","0.60321856","0.60321856","0.60195756","0.5999535","0.59913605","0.5953246","0.5937958","0.5926711","0.59228164","0.58934146","0.5786306","0.575765","0.57551455","0.573389","0.56322896","0.5619452","0.55649114","0.55547","0.55461085","0.5523594","0.5516546","0.5511032","0.55092376","0.550592","0.5489547","0.5481052","0.5458719","0.5438777","0.5429854","0.54263484","0.54209274","0.54155385","0.54155385","0.5415127","0.5402789","0.5391464","0.53897667","0.53745735","0.5373628","0.53604907","0.5347643","0.5340946","0.534042","0.53269655","0.53265125","0.53243315","0.5319333","0.53187835","0.53056556","0.5294829","0.5289865","0.52851474","0.5283333","0.5282494","0.52701205","0.5267372","0.52561164","0.52554905","0.52525395","0.52521753","0.5239201","0.52382994","0.5225702","0.5223156","0.52143705","0.5213595","0.521035","0.52094674","0.5194261","0.5192971","0.5191183","0.51853245","0.5181834","0.5180524","0.51775134","0.51774114","0.51767874","0.5171143","0.5169232","0.51661587","0.51647186","0.51628786","0.5158746","0.51424193","0.5140492","0.5132355","0.51233375","0.51098603","0.50999725"],"string":"[\n \"0.66133344\",\n \"0.6345957\",\n \"0.6296915\",\n \"0.62183553\",\n \"0.6195077\",\n \"0.61447585\",\n \"0.6143067\",\n \"0.61124474\",\n \"0.6101005\",\n \"0.6082482\",\n \"0.6080354\",\n \"0.60321856\",\n \"0.60321856\",\n \"0.60195756\",\n \"0.5999535\",\n \"0.59913605\",\n \"0.5953246\",\n \"0.5937958\",\n \"0.5926711\",\n \"0.59228164\",\n \"0.58934146\",\n \"0.5786306\",\n \"0.575765\",\n \"0.57551455\",\n \"0.573389\",\n \"0.56322896\",\n \"0.5619452\",\n \"0.55649114\",\n \"0.55547\",\n \"0.55461085\",\n \"0.5523594\",\n \"0.5516546\",\n \"0.5511032\",\n \"0.55092376\",\n \"0.550592\",\n \"0.5489547\",\n \"0.5481052\",\n \"0.5458719\",\n \"0.5438777\",\n \"0.5429854\",\n \"0.54263484\",\n \"0.54209274\",\n \"0.54155385\",\n \"0.54155385\",\n \"0.5415127\",\n \"0.5402789\",\n \"0.5391464\",\n \"0.53897667\",\n \"0.53745735\",\n \"0.5373628\",\n \"0.53604907\",\n \"0.5347643\",\n \"0.5340946\",\n \"0.534042\",\n \"0.53269655\",\n \"0.53265125\",\n \"0.53243315\",\n \"0.5319333\",\n \"0.53187835\",\n \"0.53056556\",\n \"0.5294829\",\n \"0.5289865\",\n \"0.52851474\",\n \"0.5283333\",\n \"0.5282494\",\n \"0.52701205\",\n \"0.5267372\",\n \"0.52561164\",\n \"0.52554905\",\n \"0.52525395\",\n \"0.52521753\",\n \"0.5239201\",\n \"0.52382994\",\n \"0.5225702\",\n \"0.5223156\",\n \"0.52143705\",\n \"0.5213595\",\n \"0.521035\",\n \"0.52094674\",\n \"0.5194261\",\n \"0.5192971\",\n \"0.5191183\",\n \"0.51853245\",\n \"0.5181834\",\n \"0.5180524\",\n \"0.51775134\",\n \"0.51774114\",\n \"0.51767874\",\n \"0.5171143\",\n \"0.5169232\",\n \"0.51661587\",\n \"0.51647186\",\n \"0.51628786\",\n \"0.5158746\",\n \"0.51424193\",\n \"0.5140492\",\n \"0.5132355\",\n \"0.51233375\",\n \"0.51098603\",\n \"0.50999725\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.9063625"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8488,"cells":{"query":{"kind":"string","value":"Write a volume to a file path."},"document":{"kind":"string","value":"def write(img, path):\n create_directories_for_file_name(path)\n writer = sitk.ImageFileWriter()\n writer.Execute(img, path, True)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def write(self, path, **kwargs):\n client = self.connect(VAULT_TOKEN)\n client.write(path, **kwargs)","def write_to_path(self, path):\n assert not path.exists()\n fout = path.open(\"wb\")\n fout.write(self.to_string())\n assert not fout.close()\n path.setdata()","def writable(path):","def setVolume(value):\n client = connect()\n setVolumeInternal(client, value)\n disconnect(client)","def update_volume(VolumeId=None, Name=None, MountPoint=None):\n pass","def put(self, filename, data, **kw):\n\n file_path = os.path.join(self.storage_path, filename)\n file_obj = open(file_path, \"w\")\n file_obj.write(data)","def add_writable_file_volume(self,\n runtime, # type: List[Text]\n volume, # type: MapperEnt\n host_outdir_tgt, # type: Optional[Text]\n tmpdir_prefix # type: Text\n ):\n if self.inplace_update:\n self._add_volume_binding(volume.resolved, volume.target, writable=True)\n else:\n if host_outdir_tgt:\n # shortcut, just copy to the output directory\n # which is already going to be mounted\n log.debug('shutil.copy({}, {})'.format(volume.resolved, host_outdir_tgt))\n shutil.copy(volume.resolved, host_outdir_tgt)\n else:\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\n tmpdir = tempfile.mkdtemp(dir=self.tmpdir)\n file_copy = os.path.join(\n tmpdir, os.path.basename(volume.resolved))\n log.debug('shutil.copy({}, {})'.format(volume.resolved, file_copy))\n shutil.copy(volume.resolved, file_copy)\n self._add_volume_binding(file_copy, volume.target, writable=True)\n ensure_writable(host_outdir_tgt or file_copy)","def save(self, filepath: str | Path) -> None:\n extension = Path(filepath).suffix\n if extension.lower() in VIDEO_TYPES:\n video_writer = imageio.get_writer(filepath, macro_block_size=None)\n for slice in self.volume:\n slice = slice.astype(\"uint8\")\n video_writer.append_data(slice)\n video_writer.close()\n elif extension.lower() in IMAGE_TYPES:\n base = Path(filepath).stem\n print(\n \"Saving OCT as sequential slices {}_[1..{}]{}\".format(\n base, len(self.volume), extension\n )\n )\n full_base = Path(filepath).with_suffix(\"\")\n self.volume = np.array(self.volume).astype(\"float64\")\n self.volume *= 255.0 / self.volume.max()\n for index, slice in enumerate(self.volume):\n filename = \"{}_{}{}\".format(full_base, index, extension)\n cv2.imwrite(filename, slice)\n elif extension.lower() == \".npy\":\n np.save(filepath, self.volume)\n else:\n raise NotImplementedError(\n \"Saving with file extension {} not supported\".format(extension)\n )","def write_inventory_file(inventory_item):\n try:\n with open('inventory', 'w') as file:\n file.write(inventory_item)\n except OSError:\n pass","def write_file(path, data):\n # opens file\n try:\n os.makedirs(os.path.dirname(path), exist_ok=True)\n f = open(str(path), \"w\")\n f.write(data)\n f.close()\n except Exception as e:\n print(\"Error writing file: \", e)\n sys.exit(1)","def write_binary(self, path):\n return","def write(self,path,content):\n file_path = os.path.join( self.directory, path)\n with open(file_path, \"w\") as file:\n file.write( content )","def volume(self, value):\n self._volume = value\n self._sendCommand('%03dVL' % value)","async def async_set_volume(self, volume):\n self._volume = volume","def store_volume(volume_name):\n class store(argparse.Action):\n def __call__(self, parser, namespace, values, option_strings = None):\n # Add the new volume to the list of volumes\n volumes = getattr(namespace, \"volumes\", [])\n new_volume = NamedVolume(volume_name, Path(values)) if values else None\n setattr(namespace, \"volumes\", [*volumes, new_volume])\n\n # Allow the new volume to be found by name on the opts object\n setattr(namespace, volume_name.replace('/', '_'), new_volume)\n\n return store","def add_file_or_directory_volume(self,\n runtime, # type: List[Text]\n volume, # type: MapperEnt\n host_outdir_tgt # type: Optional[Text]\n ):\n if not volume.resolved.startswith(\"_:\"):\n self._add_volume_binding(volume.resolved, volume.target) # this one defaults to read_only","def write(self, path, key):\n raise NotImplementedError","def setVolume(self, *args):\n return _libsbml.Compartment_setVolume(self, *args)","def nv_write(self, path: Union[bytes, str], data: Union[bytes, str]) -> None:\n path = _to_bytes_or_null(path)\n data = _to_bytes_or_null(data)\n ret = lib.Fapi_NvWrite(self._ctx, path, data, len(data))\n _chkrc(ret)","def saveOnFile(self, path, data):\n with open(path, \"w\") as f:\n f.write(data)","def write(self, filename, data):\n owner_rw = 0600\n fd = os.open(filename, os.O_WRONLY | os.O_CREAT, owner_rw)\n # In case file existed already with wrong permissions, fix them.\n os.chmod(filename, owner_rw)\n os.write(fd, data)\n os.close(fd)","def volume(self, volume):\n\n self._volume = volume","def volume(self, volume):\n\n self._volume = volume","def volume(self, volume):\n\n self._volume = volume","def write(cls, path, text):\n with cls.open(path, 'wt') as fd:\n return fd.write(text)","def write(self, filename):\n pass","def write(self, filename):\n pass","def set_volume(self, volume):\n self.get(COMMAND_UIC, 'SetVolume', [('volume', int(volume))])","async def write_file(self, directory: str, name: str, file: bytes):\n pass","def _save(self, name, content):\n full_path = self.path(name)\n with caches['default'].lock('{}_{}'.format(full_path, 'reader')):\n with caches['default'].lock('{}_{}'.format(full_path, 'writer')):\n if cache.islocked(full_path) is False:\n with cache.lock(full_path):\n cache.set(full_path, 'storage')\n try:\n directory = os.path.dirname(full_path)\n\n # Create any intermediate directories that do not exist.\n if self.__volume.exists(directory) is False:\n try:\n if self.directory_permissions_mode is not None:\n # os.makedirs applies the global umask, so we reset it,\n # for consistency with file_permissions_mode behavior.\n self.volume.makedirs(directory, self.directory_permissions_mode)\n else:\n self.volume.makedirs(directory)\n except FileNotFoundError:\n # There's a race between os.path.exists() and os.makedirs().\n # If os.makedirs() fails with FileNotFoundError, the directory\n # was created concurrently.\n pass\n if not os.path.isdir(directory):\n raise IOError(\"%s exists and is not a directory.\" % directory)\n\n # There's a potential race condition between get_available_name and\n # saving the file; it's possible that two threads might return the\n # same name, at which point all sorts of fun happens. So we need to\n # try to create the file, but if it already exists we have to go back\n # to get_available_name() and try again.\n\n while True:\n try:\n # This file has a file path that we can move.\n if hasattr(content, 'temporary_file_path'):\n file_move_safe(content.temporary_file_path(), full_path)\n\n # This is a normal uploadedfile that we can stream.\n else:\n # The current umask value is masked out by os.open!\n fd = self.__volume.open(full_path, self.OS_OPEN_FLAGS, 0o666)\n _file = None\n try:\n for chunk in content.chunks():\n if _file is None:\n _file = fd.dup()\n _file.write(chunk)\n finally:\n if _file is not None:\n _file.close()\n fd.close()\n except FileExistsError:\n # A new name is needed if the file exists.\n name = self.get_available_name(name)\n full_path = self.path(name)\n else:\n # OK, the file save worked. Break out of the loop.\n break\n\n if self.file_permissions_mode is not None:\n self.__volume.chmod(full_path, self.file_permissions_mode)\n finally:\n cache.delete(full_path)\n # Store filenames with forward slashes, even on Windows.\n return (True, name.replace('\\\\', '/'))\n return (False, cache.get(full_path))","def write(self, filename, data):\n\t\t# create the path if it doesn't exists\n\t\tdir = os.path.dirname(filename)\n\t\tif not os.path.isdir(dir):\n\t\t\tos.mkdir(dir)\n\t\t\n\t\t# write data\n\t\tfile = codecs.open(filename, 'w', 'utf8')\n\t\tfile.write(data)\n\t\tfile.close()","def write_file(path, data):\n with open_local_or_gcs(path, 'w') as h_dest:\n h_dest.write(data) # pylint: disable=no-member","def create_volume(self, volume):\n LOG.debug('SPDK create volume')\n\n return self._create_volume(volume)","def spew(path, data):\n with open(path, 'w+') as f:\n f.write(data)","def _write_to_file(self, string):\n with open(self.p.base_dir + '/' + self.p.filename, 'w') as f:\n f.write(string)","def set_volume(self, volume):\n self._volume = volume\n self._update_volume()","def attach_volume(self, instance_name, device_path, mountpoint):\n return True","def do_create_volume(self, arg):\n args = self.parse_arguments(arg)\n if len(args) == 0:\n self.perror(\"No name given.\")\n return\n if len(args) == 1:\n self.perror(\"No path given.\")\n return\n if not os.path.isabs(args[1]):\n print(\"Path must be absolute: \" + args[1])\n return\n self.do_coroutine(self._localStorageRoutines.create_volume_routine(args[0], args[1]))","def write_to_file(self, filename: str) -> None:","def on_volume(self, _instance, volume):\n self._set_volume(volume)","def attach_volume(self, host_path: str, container_path: str, mode: str = None):\n self.volumes[host_path] = {\n \"bind\": container_path,\n \"mode\": mode or \"Z\"\n }","def write(self, path, overwrite=False):\r\n\r\n self.data.write(path, format='ascii.fixed_width', delimiter='|', overwrite=overwrite)","def create_volume_string(host_dir, container_dir, read_only = True):\n access = \"ro\" if read_only else \"rw\"\n return \":\".join([os.path.abspath(host_dir), container_dir, access])","def write_to_disk(self):\n\n\t\t# print \"--------------------------------------------------------WRITING PIECE %r TO DISK\" %self.index\n\t\ttry:\n\t\t\tos.makedirs(PATH)\n\t\texcept:\n\t\t\tpass\n\t\tself.piece_file_name = os.path.join(PATH, self.torrent.name+'.'+'00'+str(self.index))\n\t\t# print \"Saving piece to file name: \", self.piece_file_name\n\t\tpiece_file = open(self.piece_file_name, 'w')\n\t\tpiece_file.write(self.data)\n\t\tpiece_file.close()","def write(self, path, content):\n this_file = open(path, 'w')\n this_file.write(content)\n this_file.close()","def test_volumes_simple_volume(self):\n with open(\".scuba.yml\", \"w\") as f:\n f.write(\n r\"\"\"\n image: na\n volumes:\n /cpath: /hpath\n \"\"\"\n )\n\n config = scuba.config.load_config(\".scuba.yml\")\n assert len(config.volumes) == 1\n\n v = config.volumes[\"/cpath\"]\n assert v.container_path == \"/cpath\"\n assert v.host_path == \"/hpath\"","def write(path, data):\r\n path = encode(path)\r\n if path.lower().startswith(\"smb://\"):\r\n from sambatools.smb.smb_structs import OperationFailure\r\n try:\r\n samba.store_file(os.path.basename(path), data, os.path.dirname(path))\r\n except OperationFailure:\r\n logger.info(\"deportesalacarta.core.filetools write: Error al guardar el archivo: {0}\".format(path))\r\n return False\r\n else:\r\n return True\r\n\r\n else:\r\n try:\r\n f = open(path, \"wb\")\r\n f.write(data)\r\n f.close()\r\n\r\n # except EnvironmentError:\r\n except Exception, ex:\r\n logger.info(\"filetools.write: Error al guardar el archivo: \")\r\n template = \"An exception of type {0} occured. Arguments:\\n{1!r}\"\r\n message = template.format(type(ex).__name__, ex.args)\r\n logger.info(message)\r\n # logger.info(\"deportesalacarta.core.filetools write: Error al guardar el archivo: {0}\".format(path))\r\n return False\r\n else:\r\n return True","def test_edit_volume(self, volume, volumes_steps):\n new_name = volume.name + ' (updated)'\n with volume.put(name=new_name):\n volumes_steps.edit_volume(volume_name=volume.name,\n new_volume_name=new_name)","def write_img_to_fs(name, data):\n with open(name, \"wb\") as fout:\n fout.write(data)","def add_volume_info(self, vi):\n vol_num = vi.volume_number\n self.volume_info_dict[vol_num] = vi\n if self.fh:\n self.fh.write(vi.to_string() + \"\\n\")","def write(message):\n\n with open(str(path), 'a') as fp:\n fp.write(message)","def write(path):\n return mac_slideshow.preferences.write(KEY, path)","def Set(*args):\n return _XCAFDoc.XCAFDoc_Volume_Set(*args)","def update_volume( opencloud_volume ):\n\n client = connect_syndicate()\n\n vol_name = opencloud_volume.name\n vol_description = opencloud_volume.description\n vol_private = opencloud_volume.private\n vol_archive = opencloud_volume.archive\n vol_default_gateway_caps = opencloud_caps_to_syndicate_caps( opencloud_volume.cap_read_data, opencloud_volume.cap_write_data, opencloud_volume.cap_host_data )\n\n try:\n rc = client.update_volume( vol_name,\n description=vol_description,\n private=vol_private,\n archive=vol_archive,\n default_gateway_caps=vol_default_gateway_caps )\n\n if not rc:\n raise Exception(\"update_volume(%s) failed!\" % vol_name )\n\n except Exception, e:\n # transort or method error \n logger.exception(e)\n return False\n\n else:\n return True","def fs_write(obj, file_path):\n try:\n with open(str(file_path), 'w') as f:\n f.write(obj)\n return obj\n except TypeError as e:\n raise e","def put_file(container, filepath, content):\n return put_files(container, [(filepath, content)])","def attach_volume(self, context, connection_info, instance, mountpoint,\n disk_bus=None, device_type=None, encryption=None):","def write(self, content, mode='wb'):\r\n self.localpath.write(content, mode)","def touch(path):\n with open(path, 'wt') as f:\n pass","def copy_file_out(self, path, callback=None):\n try:\n self.copy_volume(path, self.device, callback=callback)\n except IOError, e:\n logger.exception(\"copy_file_out failed with '%s'\" % e)\n raise ISCSICopyFailed()","def journal_write(session, k, v):\n entry = models.VppEtcdJournal(k=k, v=v)\n session.add(entry)\n session.flush()","def write(self, filepath, data, encoding=None):\n if isinstance(filepath, basestring):\n filepath = filepath.split('/')\n if len(filepath) > 1:\n dirpath = self._join(filepath[:-1])\n if not os.path.exists(dirpath):\n os.makedirs(dirpath)\n thepath = self._join(filepath)\n encoding = encoding or self.encoding\n if encoding is not None:\n data = data.encode(encoding)\n with open(thepath, 'wb') as f:\n f.write(data)\n return thepath","def write(s, path, encoding=\"utf-8\"):\n with open(path, \"wb\") as f:\n f.write(s.encode(encoding))","def writefile(path: Union[str, Path], txt: str) -> None:\n with open(path, 'w') as outfile:\n outfile.write(txt)","def write(self, cw, message):\n if cw in self.location:\n fn = self.location[str(cw)]\n try:\n swf = open(fn, \"w\")\n except Exception:\n logmsg.update(\"Error writing to file \" + fn + \"!\", 'E')\n else:\n swf.write(str(message))\n swf.close()\n else:\n logmsg.update(\"Wrong target [\" + str(cw) + \"] for saving file!\", 'E')","def write(self, block_no, value):\n with open(self.file_path, 'r+') as f:\n f.seek(block_no * config.block_size)\n f.write(value)","def fwrite(filename, text):\n basedir = os.path.dirname(filename)\n if not os.path.isdir(basedir):\n os.makedirs(basedir)\n\n with open(filename, 'w') as f:\n f.write(text)","def writetofile(self,direction,value):\r\n output = str(\"{},{} \\n\".format(direction,value))\r\n self.new_file.write(output)","def add_volume(self, volume: 'Volume'):\n self.volumes.append(volume)","def path_to_volume(path):\n gFile = gio.File(path)\n try:\n mount = gFile.find_enclosing_mount()\n except gio.Error:\n return None\n else:\n if mount != None:\n volume = mount.get_volume()\n return volume\n return None","def write_to_file(filepath, data):\n\n with open(filepath, 'w') as f:\n f.write(str(data))","def write_remote_file(sid, path, data):\n with slycat.web.server.remote.get_session(sid) as session:\n return session.write_file(path, data)","async def async_set_volume_level(self, volume: float) -> None:\n await self._client.set_volume(round(volume * 100))\n self.async_write_ha_state()","def write(filePath, data, mkdir=False):\r\n __dir, __name = os.path.split(filePath)\r\n\r\n if not os.path.isdir(__dir):\r\n if mkdir:\r\n preparedir(__dir)\r\n else:\r\n raise ValueError(\"Target dir doesn't exist: %s\" % __dir)\r\n\r\n with open(filePath, \"w\") as outf:\r\n try:\r\n outf.write(str(data))\r\n return filePath\r\n except Exception as e:\r\n print \"Failed to write points to file:\\n%s\" % e\r\n return False","def write(self, filename, data):\n raise NotImplementedError","def write_file(data, file_path):\n try:\n with open(file_path, \"w\") as file_obj:\n file_obj.write(data)\n\n except OSError:\n writer(f\"\\nwarning: Unable to write backup file {file_path}\\n\", FORMAT[\"WARNING\"])","def write(self, string):\n self.__file.write(string)","def save_video_to_volume(\n filename_in, \n filename_out,\n color_diff=20,\n crop_threshold=0,\n interpolator=Interpolator(),\n ):\n try:\n video = read_video_into_numpy(filename_in)\n volume = video_to_volume(video, \n color_diff=color_diff, \n crop_threshold=crop_threshold, \n interpolator=interpolator)\n np.save(filename_out, volume)\n return True\n except:\n print(\"Error:\", filename_in, filename_out)\n return False","async def volume(self, ctx, volume: int):\n\n if ctx.voice_client is None:\n return await ctx.send(\"Not connected to a voice channel.\")\n\n ctx.voice_client.source.volume = volume / 100\n await ctx.send(\"Changed volume to {}%\".format(volume),delete_after=15)","def write(cls, file, data):\n file.write(data)","def write_to_binary_file(self, loc: str, data: bytes):\n try:\n os.mkdir(\"../\" + self.uri)\n except FileExistsError:\n pass\n\n f = open(\"../\" + self.uri + loc, \"wb\")\n f.write(data)\n print(\"[WRITE] written to binary file loc\")\n f.close()","def touch(path):\n open(path, 'wb').close()","def volume(name, map, ramp=\"rainbow2\"):\r\n return f'\\ncmd.volume(name=\"{name}\", map=\"{map}\", ramp=\"{ramp}\")\\n'","def write_file(self, path, data):\n _url = (\n f\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\"\n f\"/files/{path}\"\n )\n\n self.connector.http_call(\"post\", _url, data=data)","def w(self, value):\n self.oFile.write(value)","def write(\n path: Union[Path, str],\n image: np.ndarray) -> None:\n raise NotImplementedError()","def write_file(path: str, content: Union[str, bytes], mode: str = 'w') -> None:\n from peltak.core import context, log\n\n if context.get('pretend', False):\n log.info(\"Would overwrite <34>{path}<32> with:\\n<90>{content}\",\n path=path,\n content=content)\n else:\n with open(path, mode) as fp:\n fp.write(content)","def FileWrite(offset, buf):\r\n return _hiew.HiewGate_FileWrite(offset, buf)","def _write_value(value, path):\n base_command = \"echo '{0}' > {1}\"\n # There is no common method for redirecting stderr to a null sink, so the\n # command string is platform-dependent\n if platform == 'win32':\n command = \"{0} > NUL\".format(base_command)\n else:\n command = \"exec 2> /dev/null; {0}\".format(base_command)\n os.system(command.format(value, path))","def add_writable_directory_volume(self,\n runtime, # type: List[Text]\n volume, # type: MapperEnt\n host_outdir_tgt, # type: Optional[Text]\n tmpdir_prefix # type: Text\n ):\n if volume.resolved.startswith(\"_:\"):\n # Synthetic directory that needs creating first\n if not host_outdir_tgt:\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\n new_dir = os.path.join(\n tempfile.mkdtemp(dir=self.tmpdir),\n os.path.basename(volume.target))\n self._add_volume_binding(new_dir, volume.target, writable=True)\n elif not os.path.exists(host_outdir_tgt):\n log.debug('os.makedirs({}, 0o0755)'.format(host_outdir_tgt))\n os.makedirs(host_outdir_tgt, 0o0755)\n else:\n if self.inplace_update:\n self._add_volume_binding(volume.resolved, volume.target, writable=True)\n else:\n if not host_outdir_tgt:\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\n tmpdir = tempfile.mkdtemp(dir=self.tmpdir)\n new_dir = os.path.join(\n tmpdir, os.path.basename(volume.resolved))\n log.debug('shutil.copytree({}, {})'.format(volume.resolved, new_dir))\n shutil.copytree(volume.resolved, new_dir)\n self._add_volume_binding(new_dir, volume.target, writable=True)\n else:\n log.debug('shutil.copytree({}, {})'.format(volume.resolved, host_outdir_tgt))\n shutil.copytree(volume.resolved, host_outdir_tgt)\n ensure_writable(host_outdir_tgt or new_dir)","def send_channel_volume(self, value=127, ch=None):\n self.send_control_change(CHANNEL_VOLUME, value, ch=ch)","def set_volume_level(self, volume):\n self._volume = volume","def set_volume_level(self, volume):\n self._volume = volume","def savedata(data, path):\n if path.endswith(\".tiff\") or path.endswith('.tif'):\n try:\n from vigra.impex import writeVolume\n except ImportError:\n raise ImportError(\"Vigra is needed to read/write TIFF volumes, but could not be imported.\")\n\n writeVolume(data, path, '', dtype='UINT8')\n\n elif path.endswith(\".h5\"):\n try:\n from vigra.impex import writeHDF5\n vigra_available = True\n except ImportError:\n vigra_available = False\n import h5py\n\n if vigra_available:\n writeHDF5(data, path, \"/data\")\n else:\n with h5py.File(path, mode='w') as hf:\n hf.create_dataset(name='data', data=data)\n\n else:\n raise NotImplementedError(\"Can't save: unsupported format. Supported formats are .tiff and .h5\")","def set_volume(self):\n import fcntl\n import struct\n try:\n knob = struct.pack(\"III\", 0, 0, self.volume) # VOLUME_DEVICE_ID, VOLUME_KNOB_ID, volume_level\n fcntl.ioctl(self.mixer_fd, 3, knob)\n except:\n pass","def filewrite(self, filename):\n io.write(self, filename)","def write(self, path):\n with open(path, \"w\") as fh_:\n fh_.write(self.config())","def write(self, path):\n with open(path, \"w\") as fh_:\n fh_.write(self.config())","def write(self, arg, **kwargs):\r\n if hasattr(arg, 'seek'):\r\n self._tofile(arg, **kwargs)\r\n else:\r\n with open(arg, 'wb') as fid:\r\n self._tofile(fid, **kwargs)","def write_to_local(path, data_rec):\n path, filename = os.path.split(path)\n with open(filename, 'wb') as f:\n f.write(data_rec)\n f.close()"],"string":"[\n \"def write(self, path, **kwargs):\\n client = self.connect(VAULT_TOKEN)\\n client.write(path, **kwargs)\",\n \"def write_to_path(self, path):\\n assert not path.exists()\\n fout = path.open(\\\"wb\\\")\\n fout.write(self.to_string())\\n assert not fout.close()\\n path.setdata()\",\n \"def writable(path):\",\n \"def setVolume(value):\\n client = connect()\\n setVolumeInternal(client, value)\\n disconnect(client)\",\n \"def update_volume(VolumeId=None, Name=None, MountPoint=None):\\n pass\",\n \"def put(self, filename, data, **kw):\\n\\n file_path = os.path.join(self.storage_path, filename)\\n file_obj = open(file_path, \\\"w\\\")\\n file_obj.write(data)\",\n \"def add_writable_file_volume(self,\\n runtime, # type: List[Text]\\n volume, # type: MapperEnt\\n host_outdir_tgt, # type: Optional[Text]\\n tmpdir_prefix # type: Text\\n ):\\n if self.inplace_update:\\n self._add_volume_binding(volume.resolved, volume.target, writable=True)\\n else:\\n if host_outdir_tgt:\\n # shortcut, just copy to the output directory\\n # which is already going to be mounted\\n log.debug('shutil.copy({}, {})'.format(volume.resolved, host_outdir_tgt))\\n shutil.copy(volume.resolved, host_outdir_tgt)\\n else:\\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\\n tmpdir = tempfile.mkdtemp(dir=self.tmpdir)\\n file_copy = os.path.join(\\n tmpdir, os.path.basename(volume.resolved))\\n log.debug('shutil.copy({}, {})'.format(volume.resolved, file_copy))\\n shutil.copy(volume.resolved, file_copy)\\n self._add_volume_binding(file_copy, volume.target, writable=True)\\n ensure_writable(host_outdir_tgt or file_copy)\",\n \"def save(self, filepath: str | Path) -> None:\\n extension = Path(filepath).suffix\\n if extension.lower() in VIDEO_TYPES:\\n video_writer = imageio.get_writer(filepath, macro_block_size=None)\\n for slice in self.volume:\\n slice = slice.astype(\\\"uint8\\\")\\n video_writer.append_data(slice)\\n video_writer.close()\\n elif extension.lower() in IMAGE_TYPES:\\n base = Path(filepath).stem\\n print(\\n \\\"Saving OCT as sequential slices {}_[1..{}]{}\\\".format(\\n base, len(self.volume), extension\\n )\\n )\\n full_base = Path(filepath).with_suffix(\\\"\\\")\\n self.volume = np.array(self.volume).astype(\\\"float64\\\")\\n self.volume *= 255.0 / self.volume.max()\\n for index, slice in enumerate(self.volume):\\n filename = \\\"{}_{}{}\\\".format(full_base, index, extension)\\n cv2.imwrite(filename, slice)\\n elif extension.lower() == \\\".npy\\\":\\n np.save(filepath, self.volume)\\n else:\\n raise NotImplementedError(\\n \\\"Saving with file extension {} not supported\\\".format(extension)\\n )\",\n \"def write_inventory_file(inventory_item):\\n try:\\n with open('inventory', 'w') as file:\\n file.write(inventory_item)\\n except OSError:\\n pass\",\n \"def write_file(path, data):\\n # opens file\\n try:\\n os.makedirs(os.path.dirname(path), exist_ok=True)\\n f = open(str(path), \\\"w\\\")\\n f.write(data)\\n f.close()\\n except Exception as e:\\n print(\\\"Error writing file: \\\", e)\\n sys.exit(1)\",\n \"def write_binary(self, path):\\n return\",\n \"def write(self,path,content):\\n file_path = os.path.join( self.directory, path)\\n with open(file_path, \\\"w\\\") as file:\\n file.write( content )\",\n \"def volume(self, value):\\n self._volume = value\\n self._sendCommand('%03dVL' % value)\",\n \"async def async_set_volume(self, volume):\\n self._volume = volume\",\n \"def store_volume(volume_name):\\n class store(argparse.Action):\\n def __call__(self, parser, namespace, values, option_strings = None):\\n # Add the new volume to the list of volumes\\n volumes = getattr(namespace, \\\"volumes\\\", [])\\n new_volume = NamedVolume(volume_name, Path(values)) if values else None\\n setattr(namespace, \\\"volumes\\\", [*volumes, new_volume])\\n\\n # Allow the new volume to be found by name on the opts object\\n setattr(namespace, volume_name.replace('/', '_'), new_volume)\\n\\n return store\",\n \"def add_file_or_directory_volume(self,\\n runtime, # type: List[Text]\\n volume, # type: MapperEnt\\n host_outdir_tgt # type: Optional[Text]\\n ):\\n if not volume.resolved.startswith(\\\"_:\\\"):\\n self._add_volume_binding(volume.resolved, volume.target) # this one defaults to read_only\",\n \"def write(self, path, key):\\n raise NotImplementedError\",\n \"def setVolume(self, *args):\\n return _libsbml.Compartment_setVolume(self, *args)\",\n \"def nv_write(self, path: Union[bytes, str], data: Union[bytes, str]) -> None:\\n path = _to_bytes_or_null(path)\\n data = _to_bytes_or_null(data)\\n ret = lib.Fapi_NvWrite(self._ctx, path, data, len(data))\\n _chkrc(ret)\",\n \"def saveOnFile(self, path, data):\\n with open(path, \\\"w\\\") as f:\\n f.write(data)\",\n \"def write(self, filename, data):\\n owner_rw = 0600\\n fd = os.open(filename, os.O_WRONLY | os.O_CREAT, owner_rw)\\n # In case file existed already with wrong permissions, fix them.\\n os.chmod(filename, owner_rw)\\n os.write(fd, data)\\n os.close(fd)\",\n \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"def volume(self, volume):\\n\\n self._volume = volume\",\n \"def write(cls, path, text):\\n with cls.open(path, 'wt') as fd:\\n return fd.write(text)\",\n \"def write(self, filename):\\n pass\",\n \"def write(self, filename):\\n pass\",\n \"def set_volume(self, volume):\\n self.get(COMMAND_UIC, 'SetVolume', [('volume', int(volume))])\",\n \"async def write_file(self, directory: str, name: str, file: bytes):\\n pass\",\n \"def _save(self, name, content):\\n full_path = self.path(name)\\n with caches['default'].lock('{}_{}'.format(full_path, 'reader')):\\n with caches['default'].lock('{}_{}'.format(full_path, 'writer')):\\n if cache.islocked(full_path) is False:\\n with cache.lock(full_path):\\n cache.set(full_path, 'storage')\\n try:\\n directory = os.path.dirname(full_path)\\n\\n # Create any intermediate directories that do not exist.\\n if self.__volume.exists(directory) is False:\\n try:\\n if self.directory_permissions_mode is not None:\\n # os.makedirs applies the global umask, so we reset it,\\n # for consistency with file_permissions_mode behavior.\\n self.volume.makedirs(directory, self.directory_permissions_mode)\\n else:\\n self.volume.makedirs(directory)\\n except FileNotFoundError:\\n # There's a race between os.path.exists() and os.makedirs().\\n # If os.makedirs() fails with FileNotFoundError, the directory\\n # was created concurrently.\\n pass\\n if not os.path.isdir(directory):\\n raise IOError(\\\"%s exists and is not a directory.\\\" % directory)\\n\\n # There's a potential race condition between get_available_name and\\n # saving the file; it's possible that two threads might return the\\n # same name, at which point all sorts of fun happens. So we need to\\n # try to create the file, but if it already exists we have to go back\\n # to get_available_name() and try again.\\n\\n while True:\\n try:\\n # This file has a file path that we can move.\\n if hasattr(content, 'temporary_file_path'):\\n file_move_safe(content.temporary_file_path(), full_path)\\n\\n # This is a normal uploadedfile that we can stream.\\n else:\\n # The current umask value is masked out by os.open!\\n fd = self.__volume.open(full_path, self.OS_OPEN_FLAGS, 0o666)\\n _file = None\\n try:\\n for chunk in content.chunks():\\n if _file is None:\\n _file = fd.dup()\\n _file.write(chunk)\\n finally:\\n if _file is not None:\\n _file.close()\\n fd.close()\\n except FileExistsError:\\n # A new name is needed if the file exists.\\n name = self.get_available_name(name)\\n full_path = self.path(name)\\n else:\\n # OK, the file save worked. Break out of the loop.\\n break\\n\\n if self.file_permissions_mode is not None:\\n self.__volume.chmod(full_path, self.file_permissions_mode)\\n finally:\\n cache.delete(full_path)\\n # Store filenames with forward slashes, even on Windows.\\n return (True, name.replace('\\\\\\\\', '/'))\\n return (False, cache.get(full_path))\",\n \"def write(self, filename, data):\\n\\t\\t# create the path if it doesn't exists\\n\\t\\tdir = os.path.dirname(filename)\\n\\t\\tif not os.path.isdir(dir):\\n\\t\\t\\tos.mkdir(dir)\\n\\t\\t\\n\\t\\t# write data\\n\\t\\tfile = codecs.open(filename, 'w', 'utf8')\\n\\t\\tfile.write(data)\\n\\t\\tfile.close()\",\n \"def write_file(path, data):\\n with open_local_or_gcs(path, 'w') as h_dest:\\n h_dest.write(data) # pylint: disable=no-member\",\n \"def create_volume(self, volume):\\n LOG.debug('SPDK create volume')\\n\\n return self._create_volume(volume)\",\n \"def spew(path, data):\\n with open(path, 'w+') as f:\\n f.write(data)\",\n \"def _write_to_file(self, string):\\n with open(self.p.base_dir + '/' + self.p.filename, 'w') as f:\\n f.write(string)\",\n \"def set_volume(self, volume):\\n self._volume = volume\\n self._update_volume()\",\n \"def attach_volume(self, instance_name, device_path, mountpoint):\\n return True\",\n \"def do_create_volume(self, arg):\\n args = self.parse_arguments(arg)\\n if len(args) == 0:\\n self.perror(\\\"No name given.\\\")\\n return\\n if len(args) == 1:\\n self.perror(\\\"No path given.\\\")\\n return\\n if not os.path.isabs(args[1]):\\n print(\\\"Path must be absolute: \\\" + args[1])\\n return\\n self.do_coroutine(self._localStorageRoutines.create_volume_routine(args[0], args[1]))\",\n \"def write_to_file(self, filename: str) -> None:\",\n \"def on_volume(self, _instance, volume):\\n self._set_volume(volume)\",\n \"def attach_volume(self, host_path: str, container_path: str, mode: str = None):\\n self.volumes[host_path] = {\\n \\\"bind\\\": container_path,\\n \\\"mode\\\": mode or \\\"Z\\\"\\n }\",\n \"def write(self, path, overwrite=False):\\r\\n\\r\\n self.data.write(path, format='ascii.fixed_width', delimiter='|', overwrite=overwrite)\",\n \"def create_volume_string(host_dir, container_dir, read_only = True):\\n access = \\\"ro\\\" if read_only else \\\"rw\\\"\\n return \\\":\\\".join([os.path.abspath(host_dir), container_dir, access])\",\n \"def write_to_disk(self):\\n\\n\\t\\t# print \\\"--------------------------------------------------------WRITING PIECE %r TO DISK\\\" %self.index\\n\\t\\ttry:\\n\\t\\t\\tos.makedirs(PATH)\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\t\\tself.piece_file_name = os.path.join(PATH, self.torrent.name+'.'+'00'+str(self.index))\\n\\t\\t# print \\\"Saving piece to file name: \\\", self.piece_file_name\\n\\t\\tpiece_file = open(self.piece_file_name, 'w')\\n\\t\\tpiece_file.write(self.data)\\n\\t\\tpiece_file.close()\",\n \"def write(self, path, content):\\n this_file = open(path, 'w')\\n this_file.write(content)\\n this_file.close()\",\n \"def test_volumes_simple_volume(self):\\n with open(\\\".scuba.yml\\\", \\\"w\\\") as f:\\n f.write(\\n r\\\"\\\"\\\"\\n image: na\\n volumes:\\n /cpath: /hpath\\n \\\"\\\"\\\"\\n )\\n\\n config = scuba.config.load_config(\\\".scuba.yml\\\")\\n assert len(config.volumes) == 1\\n\\n v = config.volumes[\\\"/cpath\\\"]\\n assert v.container_path == \\\"/cpath\\\"\\n assert v.host_path == \\\"/hpath\\\"\",\n \"def write(path, data):\\r\\n path = encode(path)\\r\\n if path.lower().startswith(\\\"smb://\\\"):\\r\\n from sambatools.smb.smb_structs import OperationFailure\\r\\n try:\\r\\n samba.store_file(os.path.basename(path), data, os.path.dirname(path))\\r\\n except OperationFailure:\\r\\n logger.info(\\\"deportesalacarta.core.filetools write: Error al guardar el archivo: {0}\\\".format(path))\\r\\n return False\\r\\n else:\\r\\n return True\\r\\n\\r\\n else:\\r\\n try:\\r\\n f = open(path, \\\"wb\\\")\\r\\n f.write(data)\\r\\n f.close()\\r\\n\\r\\n # except EnvironmentError:\\r\\n except Exception, ex:\\r\\n logger.info(\\\"filetools.write: Error al guardar el archivo: \\\")\\r\\n template = \\\"An exception of type {0} occured. Arguments:\\\\n{1!r}\\\"\\r\\n message = template.format(type(ex).__name__, ex.args)\\r\\n logger.info(message)\\r\\n # logger.info(\\\"deportesalacarta.core.filetools write: Error al guardar el archivo: {0}\\\".format(path))\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def test_edit_volume(self, volume, volumes_steps):\\n new_name = volume.name + ' (updated)'\\n with volume.put(name=new_name):\\n volumes_steps.edit_volume(volume_name=volume.name,\\n new_volume_name=new_name)\",\n \"def write_img_to_fs(name, data):\\n with open(name, \\\"wb\\\") as fout:\\n fout.write(data)\",\n \"def add_volume_info(self, vi):\\n vol_num = vi.volume_number\\n self.volume_info_dict[vol_num] = vi\\n if self.fh:\\n self.fh.write(vi.to_string() + \\\"\\\\n\\\")\",\n \"def write(message):\\n\\n with open(str(path), 'a') as fp:\\n fp.write(message)\",\n \"def write(path):\\n return mac_slideshow.preferences.write(KEY, path)\",\n \"def Set(*args):\\n return _XCAFDoc.XCAFDoc_Volume_Set(*args)\",\n \"def update_volume( opencloud_volume ):\\n\\n client = connect_syndicate()\\n\\n vol_name = opencloud_volume.name\\n vol_description = opencloud_volume.description\\n vol_private = opencloud_volume.private\\n vol_archive = opencloud_volume.archive\\n vol_default_gateway_caps = opencloud_caps_to_syndicate_caps( opencloud_volume.cap_read_data, opencloud_volume.cap_write_data, opencloud_volume.cap_host_data )\\n\\n try:\\n rc = client.update_volume( vol_name,\\n description=vol_description,\\n private=vol_private,\\n archive=vol_archive,\\n default_gateway_caps=vol_default_gateway_caps )\\n\\n if not rc:\\n raise Exception(\\\"update_volume(%s) failed!\\\" % vol_name )\\n\\n except Exception, e:\\n # transort or method error \\n logger.exception(e)\\n return False\\n\\n else:\\n return True\",\n \"def fs_write(obj, file_path):\\n try:\\n with open(str(file_path), 'w') as f:\\n f.write(obj)\\n return obj\\n except TypeError as e:\\n raise e\",\n \"def put_file(container, filepath, content):\\n return put_files(container, [(filepath, content)])\",\n \"def attach_volume(self, context, connection_info, instance, mountpoint,\\n disk_bus=None, device_type=None, encryption=None):\",\n \"def write(self, content, mode='wb'):\\r\\n self.localpath.write(content, mode)\",\n \"def touch(path):\\n with open(path, 'wt') as f:\\n pass\",\n \"def copy_file_out(self, path, callback=None):\\n try:\\n self.copy_volume(path, self.device, callback=callback)\\n except IOError, e:\\n logger.exception(\\\"copy_file_out failed with '%s'\\\" % e)\\n raise ISCSICopyFailed()\",\n \"def journal_write(session, k, v):\\n entry = models.VppEtcdJournal(k=k, v=v)\\n session.add(entry)\\n session.flush()\",\n \"def write(self, filepath, data, encoding=None):\\n if isinstance(filepath, basestring):\\n filepath = filepath.split('/')\\n if len(filepath) > 1:\\n dirpath = self._join(filepath[:-1])\\n if not os.path.exists(dirpath):\\n os.makedirs(dirpath)\\n thepath = self._join(filepath)\\n encoding = encoding or self.encoding\\n if encoding is not None:\\n data = data.encode(encoding)\\n with open(thepath, 'wb') as f:\\n f.write(data)\\n return thepath\",\n \"def write(s, path, encoding=\\\"utf-8\\\"):\\n with open(path, \\\"wb\\\") as f:\\n f.write(s.encode(encoding))\",\n \"def writefile(path: Union[str, Path], txt: str) -> None:\\n with open(path, 'w') as outfile:\\n outfile.write(txt)\",\n \"def write(self, cw, message):\\n if cw in self.location:\\n fn = self.location[str(cw)]\\n try:\\n swf = open(fn, \\\"w\\\")\\n except Exception:\\n logmsg.update(\\\"Error writing to file \\\" + fn + \\\"!\\\", 'E')\\n else:\\n swf.write(str(message))\\n swf.close()\\n else:\\n logmsg.update(\\\"Wrong target [\\\" + str(cw) + \\\"] for saving file!\\\", 'E')\",\n \"def write(self, block_no, value):\\n with open(self.file_path, 'r+') as f:\\n f.seek(block_no * config.block_size)\\n f.write(value)\",\n \"def fwrite(filename, text):\\n basedir = os.path.dirname(filename)\\n if not os.path.isdir(basedir):\\n os.makedirs(basedir)\\n\\n with open(filename, 'w') as f:\\n f.write(text)\",\n \"def writetofile(self,direction,value):\\r\\n output = str(\\\"{},{} \\\\n\\\".format(direction,value))\\r\\n self.new_file.write(output)\",\n \"def add_volume(self, volume: 'Volume'):\\n self.volumes.append(volume)\",\n \"def path_to_volume(path):\\n gFile = gio.File(path)\\n try:\\n mount = gFile.find_enclosing_mount()\\n except gio.Error:\\n return None\\n else:\\n if mount != None:\\n volume = mount.get_volume()\\n return volume\\n return None\",\n \"def write_to_file(filepath, data):\\n\\n with open(filepath, 'w') as f:\\n f.write(str(data))\",\n \"def write_remote_file(sid, path, data):\\n with slycat.web.server.remote.get_session(sid) as session:\\n return session.write_file(path, data)\",\n \"async def async_set_volume_level(self, volume: float) -> None:\\n await self._client.set_volume(round(volume * 100))\\n self.async_write_ha_state()\",\n \"def write(filePath, data, mkdir=False):\\r\\n __dir, __name = os.path.split(filePath)\\r\\n\\r\\n if not os.path.isdir(__dir):\\r\\n if mkdir:\\r\\n preparedir(__dir)\\r\\n else:\\r\\n raise ValueError(\\\"Target dir doesn't exist: %s\\\" % __dir)\\r\\n\\r\\n with open(filePath, \\\"w\\\") as outf:\\r\\n try:\\r\\n outf.write(str(data))\\r\\n return filePath\\r\\n except Exception as e:\\r\\n print \\\"Failed to write points to file:\\\\n%s\\\" % e\\r\\n return False\",\n \"def write(self, filename, data):\\n raise NotImplementedError\",\n \"def write_file(data, file_path):\\n try:\\n with open(file_path, \\\"w\\\") as file_obj:\\n file_obj.write(data)\\n\\n except OSError:\\n writer(f\\\"\\\\nwarning: Unable to write backup file {file_path}\\\\n\\\", FORMAT[\\\"WARNING\\\"])\",\n \"def write(self, string):\\n self.__file.write(string)\",\n \"def save_video_to_volume(\\n filename_in, \\n filename_out,\\n color_diff=20,\\n crop_threshold=0,\\n interpolator=Interpolator(),\\n ):\\n try:\\n video = read_video_into_numpy(filename_in)\\n volume = video_to_volume(video, \\n color_diff=color_diff, \\n crop_threshold=crop_threshold, \\n interpolator=interpolator)\\n np.save(filename_out, volume)\\n return True\\n except:\\n print(\\\"Error:\\\", filename_in, filename_out)\\n return False\",\n \"async def volume(self, ctx, volume: int):\\n\\n if ctx.voice_client is None:\\n return await ctx.send(\\\"Not connected to a voice channel.\\\")\\n\\n ctx.voice_client.source.volume = volume / 100\\n await ctx.send(\\\"Changed volume to {}%\\\".format(volume),delete_after=15)\",\n \"def write(cls, file, data):\\n file.write(data)\",\n \"def write_to_binary_file(self, loc: str, data: bytes):\\n try:\\n os.mkdir(\\\"../\\\" + self.uri)\\n except FileExistsError:\\n pass\\n\\n f = open(\\\"../\\\" + self.uri + loc, \\\"wb\\\")\\n f.write(data)\\n print(\\\"[WRITE] written to binary file loc\\\")\\n f.close()\",\n \"def touch(path):\\n open(path, 'wb').close()\",\n \"def volume(name, map, ramp=\\\"rainbow2\\\"):\\r\\n return f'\\\\ncmd.volume(name=\\\"{name}\\\", map=\\\"{map}\\\", ramp=\\\"{ramp}\\\")\\\\n'\",\n \"def write_file(self, path, data):\\n _url = (\\n f\\\"{self.connector.base_url}/projects/{self.project_id}/nodes/{self.node_id}\\\"\\n f\\\"/files/{path}\\\"\\n )\\n\\n self.connector.http_call(\\\"post\\\", _url, data=data)\",\n \"def w(self, value):\\n self.oFile.write(value)\",\n \"def write(\\n path: Union[Path, str],\\n image: np.ndarray) -> None:\\n raise NotImplementedError()\",\n \"def write_file(path: str, content: Union[str, bytes], mode: str = 'w') -> None:\\n from peltak.core import context, log\\n\\n if context.get('pretend', False):\\n log.info(\\\"Would overwrite <34>{path}<32> with:\\\\n<90>{content}\\\",\\n path=path,\\n content=content)\\n else:\\n with open(path, mode) as fp:\\n fp.write(content)\",\n \"def FileWrite(offset, buf):\\r\\n return _hiew.HiewGate_FileWrite(offset, buf)\",\n \"def _write_value(value, path):\\n base_command = \\\"echo '{0}' > {1}\\\"\\n # There is no common method for redirecting stderr to a null sink, so the\\n # command string is platform-dependent\\n if platform == 'win32':\\n command = \\\"{0} > NUL\\\".format(base_command)\\n else:\\n command = \\\"exec 2> /dev/null; {0}\\\".format(base_command)\\n os.system(command.format(value, path))\",\n \"def add_writable_directory_volume(self,\\n runtime, # type: List[Text]\\n volume, # type: MapperEnt\\n host_outdir_tgt, # type: Optional[Text]\\n tmpdir_prefix # type: Text\\n ):\\n if volume.resolved.startswith(\\\"_:\\\"):\\n # Synthetic directory that needs creating first\\n if not host_outdir_tgt:\\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\\n new_dir = os.path.join(\\n tempfile.mkdtemp(dir=self.tmpdir),\\n os.path.basename(volume.target))\\n self._add_volume_binding(new_dir, volume.target, writable=True)\\n elif not os.path.exists(host_outdir_tgt):\\n log.debug('os.makedirs({}, 0o0755)'.format(host_outdir_tgt))\\n os.makedirs(host_outdir_tgt, 0o0755)\\n else:\\n if self.inplace_update:\\n self._add_volume_binding(volume.resolved, volume.target, writable=True)\\n else:\\n if not host_outdir_tgt:\\n log.debug('tempfile.mkdtemp(dir={})'.format(self.tmpdir))\\n tmpdir = tempfile.mkdtemp(dir=self.tmpdir)\\n new_dir = os.path.join(\\n tmpdir, os.path.basename(volume.resolved))\\n log.debug('shutil.copytree({}, {})'.format(volume.resolved, new_dir))\\n shutil.copytree(volume.resolved, new_dir)\\n self._add_volume_binding(new_dir, volume.target, writable=True)\\n else:\\n log.debug('shutil.copytree({}, {})'.format(volume.resolved, host_outdir_tgt))\\n shutil.copytree(volume.resolved, host_outdir_tgt)\\n ensure_writable(host_outdir_tgt or new_dir)\",\n \"def send_channel_volume(self, value=127, ch=None):\\n self.send_control_change(CHANNEL_VOLUME, value, ch=ch)\",\n \"def set_volume_level(self, volume):\\n self._volume = volume\",\n \"def set_volume_level(self, volume):\\n self._volume = volume\",\n \"def savedata(data, path):\\n if path.endswith(\\\".tiff\\\") or path.endswith('.tif'):\\n try:\\n from vigra.impex import writeVolume\\n except ImportError:\\n raise ImportError(\\\"Vigra is needed to read/write TIFF volumes, but could not be imported.\\\")\\n\\n writeVolume(data, path, '', dtype='UINT8')\\n\\n elif path.endswith(\\\".h5\\\"):\\n try:\\n from vigra.impex import writeHDF5\\n vigra_available = True\\n except ImportError:\\n vigra_available = False\\n import h5py\\n\\n if vigra_available:\\n writeHDF5(data, path, \\\"/data\\\")\\n else:\\n with h5py.File(path, mode='w') as hf:\\n hf.create_dataset(name='data', data=data)\\n\\n else:\\n raise NotImplementedError(\\\"Can't save: unsupported format. Supported formats are .tiff and .h5\\\")\",\n \"def set_volume(self):\\n import fcntl\\n import struct\\n try:\\n knob = struct.pack(\\\"III\\\", 0, 0, self.volume) # VOLUME_DEVICE_ID, VOLUME_KNOB_ID, volume_level\\n fcntl.ioctl(self.mixer_fd, 3, knob)\\n except:\\n pass\",\n \"def filewrite(self, filename):\\n io.write(self, filename)\",\n \"def write(self, path):\\n with open(path, \\\"w\\\") as fh_:\\n fh_.write(self.config())\",\n \"def write(self, path):\\n with open(path, \\\"w\\\") as fh_:\\n fh_.write(self.config())\",\n \"def write(self, arg, **kwargs):\\r\\n if hasattr(arg, 'seek'):\\r\\n self._tofile(arg, **kwargs)\\r\\n else:\\r\\n with open(arg, 'wb') as fid:\\r\\n self._tofile(fid, **kwargs)\",\n \"def write_to_local(path, data_rec):\\n path, filename = os.path.split(path)\\n with open(filename, 'wb') as f:\\n f.write(data_rec)\\n f.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6251699","0.60698074","0.60018075","0.5975603","0.5835609","0.5832426","0.5821404","0.5796797","0.5752071","0.5750823","0.57148486","0.5701186","0.5690606","0.56724447","0.565024","0.56472427","0.5638502","0.56315726","0.56258756","0.5602037","0.5573271","0.5560137","0.5560137","0.5560137","0.55437773","0.55258846","0.55258846","0.55229485","0.5505212","0.54960054","0.5494361","0.549311","0.54913056","0.54909086","0.54846764","0.5462422","0.54529774","0.5452756","0.54433125","0.5434171","0.54327095","0.54264665","0.5422272","0.54011554","0.53961647","0.5395942","0.5394504","0.53702563","0.5368564","0.5366436","0.5366093","0.536576","0.5358981","0.53566825","0.5353158","0.53517795","0.53340876","0.53106564","0.52969366","0.5292138","0.5278205","0.5274248","0.52556074","0.52530044","0.5248171","0.52385545","0.52234846","0.5223051","0.5220737","0.5210948","0.5208342","0.52075857","0.5199806","0.5198602","0.51896757","0.5186363","0.51824176","0.51721704","0.51695484","0.5164829","0.51643246","0.51640207","0.5162911","0.5155831","0.51528525","0.51487803","0.51396585","0.51382613","0.5138009","0.51369387","0.5132596","0.5130728","0.5130728","0.5126496","0.51262647","0.51092046","0.5105601","0.5105601","0.5100507","0.50987977"],"string":"[\n \"0.6251699\",\n \"0.60698074\",\n \"0.60018075\",\n \"0.5975603\",\n \"0.5835609\",\n \"0.5832426\",\n \"0.5821404\",\n \"0.5796797\",\n \"0.5752071\",\n \"0.5750823\",\n \"0.57148486\",\n \"0.5701186\",\n \"0.5690606\",\n \"0.56724447\",\n \"0.565024\",\n \"0.56472427\",\n \"0.5638502\",\n \"0.56315726\",\n \"0.56258756\",\n \"0.5602037\",\n \"0.5573271\",\n \"0.5560137\",\n \"0.5560137\",\n \"0.5560137\",\n \"0.55437773\",\n \"0.55258846\",\n \"0.55258846\",\n \"0.55229485\",\n \"0.5505212\",\n \"0.54960054\",\n \"0.5494361\",\n \"0.549311\",\n \"0.54913056\",\n \"0.54909086\",\n \"0.54846764\",\n \"0.5462422\",\n \"0.54529774\",\n \"0.5452756\",\n \"0.54433125\",\n \"0.5434171\",\n \"0.54327095\",\n \"0.54264665\",\n \"0.5422272\",\n \"0.54011554\",\n \"0.53961647\",\n \"0.5395942\",\n \"0.5394504\",\n \"0.53702563\",\n \"0.5368564\",\n \"0.5366436\",\n \"0.5366093\",\n \"0.536576\",\n \"0.5358981\",\n \"0.53566825\",\n \"0.5353158\",\n \"0.53517795\",\n \"0.53340876\",\n \"0.53106564\",\n \"0.52969366\",\n \"0.5292138\",\n \"0.5278205\",\n \"0.5274248\",\n \"0.52556074\",\n \"0.52530044\",\n \"0.5248171\",\n \"0.52385545\",\n \"0.52234846\",\n \"0.5223051\",\n \"0.5220737\",\n \"0.5210948\",\n \"0.5208342\",\n \"0.52075857\",\n \"0.5199806\",\n \"0.5198602\",\n \"0.51896757\",\n \"0.5186363\",\n \"0.51824176\",\n \"0.51721704\",\n \"0.51695484\",\n \"0.5164829\",\n \"0.51643246\",\n \"0.51640207\",\n \"0.5162911\",\n \"0.5155831\",\n \"0.51528525\",\n \"0.51487803\",\n \"0.51396585\",\n \"0.51382613\",\n \"0.5138009\",\n \"0.51369387\",\n \"0.5132596\",\n \"0.5130728\",\n \"0.5130728\",\n \"0.5126496\",\n \"0.51262647\",\n \"0.51092046\",\n \"0.5105601\",\n \"0.5105601\",\n \"0.5100507\",\n \"0.50987977\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5269131"},"document_rank":{"kind":"string","value":"62"}}},{"rowIdx":8489,"cells":{"query":{"kind":"string","value":"r\"\"\" Calculates precipitable water (cm) from ambient air temperature (C) and relatively humidity (%) using an empirical model. The accuracy of this method is approximately 20% for moderate PW (13 cm) and less accurate otherwise."},"document":{"kind":"string","value":"def gueymard94_pw(temp_air, relative_humidity):\n\n T = temp_air + 273.15 # Convert to Kelvin # noqa: N806\n RH = relative_humidity # noqa: N806\n\n theta = T / 273.15\n\n # Eq. 1 from Keogh and Blakers\n pw = (\n 0.1 *\n (0.4976 + 1.5265*theta + np.exp(13.6897*theta - 14.9188*(theta)**3)) *\n (216.7*RH/(100*T)*np.exp(22.330 - 49.140*(100/T) -\n 10.922*(100/T)**2 - 0.39015*T/100)))\n\n pw = np.maximum(pw, 0.1)\n\n return pw"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_compensated_temperature() -> float:\n comp_factor = 2.25\n cpu_temp = get_cpu_temperature()\n raw_temp = bme280.get_temperature()\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\n # print(\"\"\"\n # Compensated_Temperature: {:05.2f} *C\n # Pressure: {:05.2f} hPa\n # Relative humidity: {:05.2f} %\n # \"\"\".format(temperature, pressure, humidity))\n return comp_temp","def thermal_conductivity_of_air(self) -> float:\n\n # This more accurate equation is not used by the paper.\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\n # )\n\n # The reference suggests this equation is accurate to 1%.\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646","def get_cape(temp,pres,dewpt,hght,startp,startt,startdp,totalcape=False): \n\n # Check units\n # Init temp is startt in C, Init dew point is stwrtdp,\n # pressure levels are in hPa \n temp = temp - 273.15 # convert temperature to celsius\n dewpt = dewpt - 273.15 # convert dewpoint to celsius\n pres = pres/100 # convert pressure to hPa\n \n \n inds = np.where( (pres < startp) ) \n tmp = pres[inds]\n del pres\n #pres = tmp[::-1]\n pres = tmp[:]\n del tmp \n startp = startp/100\n \n tmp = temp[inds]\n del temp\n #temp = tmp[::-1]\n temp = tmp[:]\n del tmp \n\n tmp = dewpt[inds]\n del dewpt\n #dewpt = tmp[::-1]\n dewpt = tmp[:]\n del tmp \n\n tmp = hght[inds]\n del hght\n #hght = tmp[::-1]\n hght = tmp[:]\n del tmp \n\n \n # Get Sub-LCL traces \n presdry,tempdry,tempiso=dry_ascent(startp,startt-degCtoK,startdp-degCtoK) \n \n\n # make lcl variables explicit\n P_lcl=presdry[-1]\n T_lcl=tempdry[-1]\n\n # Now lift a wet parcel from the intersection point\n # preswet=linspace(P_lcl,100,101)\n preswet,tempwet=moist_ascent(P_lcl,T_lcl)\n\n # tparcel is the concatenation of tempdry and \n # tempwet, and so on.\n \n tparcel=np.concatenate((tempdry,tempwet[1:]))\n pparcel=np.concatenate((presdry,preswet[1:]))\n\n # Interpolating the environmental profile onto the \n # parcel pressure coordinate\n # tempenv=interp(preswet,pres[::-1],temp[::-1])\n ## NEW, for total column:\n tempenv=interp(pparcel,pres[::-1],temp[::-1])\n\n\n # now solve for the equlibrium levels above LCL\n # (all of them, including unstable ones)\n # eqlev,stab=solve_eq(preswet[::-1],(tempwet-tempenv)[::-1])\n # NEW, for total column:\n # On second thought, we don't really want/need\n # any equilibrium levels below LCL\n # eqlev,stab=solve_eq(pparcel[::-1],(tparcel-tempenv)[::-1])\n # This is equivalent to the old statement :\n eqlev,stab=solve_eq(pparcel[pparcel<=P_lcl][::-1],\\\n (tparcel-tempenv)[pparcel<=P_lcl][::-1])\n\n aa = tparcel-tempenv\n\n # Sorting index by decreasing pressure\n I=np.argsort(eqlev)[::-1]\n eqlev=eqlev[I]; stab=stab[I]\n\n # temperatures at the equilibrium level\n # tempeq=interp(eqlev,preswet[::-1],tempenv[::-1])\n ## NEW, for total column:\n tempeq=interp(eqlev,pparcel[::-1],tparcel[::-1])\n\n # This helps with debugging\n # for ii,eq in enumerate(eqlev):\n # print \"%5.2f %5.2f %2d\"%(eq,tempeq[ii],stab[ii])\n\n # need environmental temperature at LCL\n tenv_lcl=interp(P_lcl,pparcel[::-1],tempenv[::-1])\n\n isstab=np.where(stab==1.,True,False)\n unstab=np.where(stab==1.,False,True) \n\n if eqlev.shape[0]==0:\n # no unstable layers in entire profile\n # because the parcel never crosses the tenv\n P_lfc=float('NaN')\n P_el=float('NaN')\n elif T_lcl>tenv_lcl:\n # check LCL to see if this is unstable\n P_lfc=P_lcl\n if totalcape:\n P_el=eqlev[isstab][-1]\n else:\n P_el=eqlev[isstab][0]\n elif eqlev.shape[0]>1:\n # Parcel is stable at LCL so LFC is the \n # first unstable equilibrium level and \n # \"EQ\" level is the first stable equilibrium \n # level\n P_lfc=eqlev[unstab][0]\n if totalcape:\n P_el=eqlev[isstab][-1]\n else:\n P_el=eqlev[isstab][0]\n else:\n # catch a problem... if there is only\n # one eqlev and it's stable (this is \n # unphysical), then it could be a vertical\n # resolution thing. This is a kind of \n # \"null\" option\n try:\n\t P_el=eqlev[isstab][0]\n P_lfc=eqlev[isstab][0]\n except:\n\t P_el=eqlev[unstab][0]\n P_lfc=eqlev[unstab][0]\t\n\t\n if np.isnan(P_lfc):\n return P_lcl,P_lfc,P_el,0,0\n\n # need to handle case where dwpt is not available \n # above a certain level for any reason. Most simplest \n # thing to do is set it to a reasonably low value; \n # this should be a conservative approach!\n \n #dwpt=dewpt.copy().soften_mask()\n [inds] = np.where(np.isnan(dewpt))\n dwpt = dewpt\n dwpt[inds] = dwpt.min()\n \n # raise ValueError\n #if dwpt[(pres>=P_el).data*(pres<P_lfc).data].mask.any():\n # print \"WARNING: substituting dwpt.min() for masked values of DWPT in this sounding\"\n #dwpt[dwpt.mask]=dwpt.min()\n # dwptenv=interp(preswet,pres[::-1],dwpt[::-1])\n # NEW:\n\n dwptenv=interp(pparcel,pres[::-1],dwpt[::-1])\n\n\n \n #if hght[(pres>=P_el).data].mask.any():\n # raise NotImplementedError, \"TODO: Implement standard atmosphere to substitute missing heights\"\n # hghtenv=interp(preswet,pres[::-1],self.soundingdata['hght'][::-1])\n # NEW:\n hghtenv=interp(pparcel,pres[::-1],hght[::-1])\n \n\n # Areas of POSITIVE Bouyancy\n # cond1=(tempwet>=tempenv)*(preswet<=P_lfc)*(preswet>P_el)\n # NEW:\n cond1=(tparcel>=tempenv)*(pparcel<=P_lfc)*(pparcel>P_el)\n # Areas of NEGATIVE Bouyancy\n # cond2=(tempwet<tempenv)*(preswet<=P_lcl)*(preswet>P_el)\n # NEW:\n if totalcape:\n cond2=(tparcel<tempenv)*(pparcel>P_el)\n else:\n cond2=(tparcel<tempenv)*(pparcel>P_lfc)\n # Do CAPE calculation\n # 1. Virtual temperature of parcel... remember it's saturated above LCL.\n # e_parcel=SatVap(tempwet)\n # Tv_parcel=VirtualTemp(tempwet+degCtoK,preswet*100.,e_parcel)\n # e_env=SatVap(dwptenv)\n # Tv_env=VirtualTemp(tempenv+degCtoK,preswet*100.,e_env)\n # NEW:\n e_parcel=SatVap(tparcel)\n Tv_parcel=VirtualTemp(tparcel+degCtoK,pparcel*100.,e_parcel)\n e_env=SatVap(dwptenv)\n Tv_env=VirtualTemp(tempenv+degCtoK,pparcel*100.,e_env)\n\n CAPE=trapz(9.81*(Tv_parcel[cond1]-Tv_env[cond1])/Tv_env[cond1],hghtenv[cond1])\n CIN=trapz(9.81*(Tv_parcel[cond2]-Tv_env[cond2])/Tv_env[cond2],hghtenv[cond2])\n\n return P_lcl,P_lfc,P_el,CAPE,CIN","def calculate_water_vapour_pressure(self, T=None, units='atm'): # temp in Kelvin\n A,B,C = self.get_ABC(T=T)\n \n if A is not None and B is not None and C is not None:\n # bar \n p_vap_bar = math.pow(10, (A-B/(C+T)))\n if units=='bar':\n return p_vap_bar\n \n # atm\n elif units=='atm': \n p_vap_atm = convertor.convert(\n p_vap_bar, \n currentUnits='bar', \n newUnits='atm')\n return p_vap_atm\n \n else:\n return None\n else:\n return None","def Latentc(tempc):\n \n return 1000*(2500.8 - 2.36*tempc + 0.0016*tempc**2 - 0.00006*tempc**3)","def calculateTemperature(self):\n \n # CIE XYZ space\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\n\n # CIE Chromaticities xy\n self.x = self.X/(self.X + self.Y + self.Z)\n self.y = self.Y/(self.X + self.Y + self.Z)\n \n # CIE Chromaticities uv\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\n \n # constant for McCamy's/Hernandez-Andrés formula\n n = (self.x - self.x_e)/(self.y - self.y_e)\n \n # Correlated color temperature according to Hernández-Andrés (1999)\n self.color_temp = ( self.A_0 + \n self.A_1*np.exp(-n/self.t_1) + \n self.A_2*np.exp(-n/self.t_2) + \n self.A_3*np.exp(-n/self.t_3) )\n \n # Delete too high values\n self.color_temp[self.color_temp > 30000] = 0\n \n # Affichage de la CCT\n self.mean_temp = int(round(self.color_temp.mean()))\n self.mean_temp_label.setText(\"Temperature moyenne = \"+str(self.mean_temp))\n self.mean_temp_label.adjustSize()\n \t\n # Affichage de l'illuminance (Y)\n self.mean_illu = int(round((self.Y.mean())))\n self.illuminance_label.setText(\"Illuminance moyenne = \"+str(self.mean_illu))\n self.illuminance_label.adjustSize()","def compute_dewpoint(temperature, humidity):\n\n temp_C = (temperature - 32) * 5 / 9 # Convert temperature from deg F to deg C\n rh = humidity / 100\n\n b = 18.678\n c = 257.14 # deg C\n\n gamma = math.log(rh) + (b * temp_C) / (c + temp_C)\n tdp = c * gamma / (b -gamma)\n\n tdp_F = 9 / 5 * tdp + 32 # Convert temperature from deg C to deg F\n return tdp_F;","def get_corrected_ppm(self, temperature, humidity):\n return self.PARA * math.pow((self.get_corrected_resistance(temperature, humidity)/ self.RZERO), -self.PARB)","def get_corrected_ppm(self, temperature, humidity):\n return self.PARA * math.pow((self.get_corrected_resistance(temperature, humidity)/ self.RZERO), -self.PARB)","def tempWater(sample):\n sample *= .0009\n sample *= 1000\n celsius = (sample - 20.5128) * 0.0512\n return round(celsius,2)","def get_cooling_output_for_supply_air_estimation(\n a_a: float, q: float, mu_c: float, v_vent: np.ndarray,\n theta_ex: np.ndarray, x_ex: np.ndarray, j: np.ndarray,\n hc_period: np.ndarray, n_p: np.ndarray, q_gen: np.ndarray, w_gen: np.ndarray, v_local: np.ndarray,\n theta_set_c: float, x_set_c: float):\n\n # specific heat of air, J/kgK\n c = get_specific_heat()\n\n # air density, kg/m3\n rho = get_air_density()\n\n # latent heat of evaporation, kJ/kg\n l_wtr = get_evaporation_latent_heat()\n\n q_d_hs_cs = np.maximum(\n (((q - 0.35 * 0.5 * 2.4) * a_a + c * rho * (v_local + sum(v_vent)) / 3600) * (\n theta_ex - theta_set_c)\n + mu_c * a_a * j + q_gen + n_p * 51.0) * 3600 * 10 ** (-6),\n 0.0) * (hc_period == 'c')\n\n q_d_hs_cl = np.maximum(\n (((v_local + sum(v_vent)) * rho * (x_ex - x_set_c) * 10 ** 3 + w_gen) * l_wtr\n + n_p * 40.0 * 3600) * 10 ** (-6), 0.0) * (hc_period == 'c')\n\n return q_d_hs_cs + q_d_hs_cl","def _calculate_temperature(c, h):\n\n return (c - 331.4 - 0.0124 * h) / 0.6","def thermal_conductivity(temperature):\n a0 = -4.1236\n a1 = 13.788\n a2 = -26.068\n a3 = 26.272\n a4 = -14.663\n a5 = 4.4954\n a6 = -0.6905\n a7 = 0.0397\n log_t = math.log10(temperature)\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\n g10_thermal_conductivity = 10.0**f_exp\n return g10_thermal_conductivity","def C_P(self):\n return self.generic_getter(\n get_heat_capacity_pressure, \"C_P\", \"convert_heat_capacity\"\n )","def compute_water_charge(self, volume, time, units_volume='ml'):\n if units_volume == 'ml':\n volume_iu = volume / 1e6\n else:\n raise NotImplementedError\n \n return volume_iu / time","def get_decided_outlet_supply_air_absolute_humidity_for_cooling(\n x_req_c: np.ndarray, v_supply: np.ndarray, x_hs_out_min_c: np.ndarray) -> np.ndarray:\n\n return np.maximum(np.sum(x_req_c * v_supply / v_supply.sum(axis=0), axis=0), x_hs_out_min_c)","def get_capacitive_rain_sensor_temp(\n self, rain_sensor_temp: Optional[int] = None\n ) -> float:\n # TODO: these values were hardcoded but now are taken from the CW.\n # Check which way is the \"true\" way based on the sensor type (capacitive vs Hydredon)\n # rain_pull_up_resistance = 1\n # rain_res_at_25 = 1\n # rain_beta = 3450\n absolute_zero = 273.15\n\n if rain_sensor_temp is None:\n rain_sensor_temp = self.raw_rain_sensor_temp\n\n if rain_sensor_temp < 1:\n rain_sensor_temp = 1\n elif rain_sensor_temp > 1022:\n rain_sensor_temp = 1022\n\n r = self.rain_pull_up_resistance / ((1023 / rain_sensor_temp) - 1)\n r = math.log(r / self.rain_res_at_25)\n\n return 1 / (r / self.rain_beta + 1 / (absolute_zero + 25)) - absolute_zero","def ambient_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"ambient_temperature_c\"))\r\n return kelvin_to_celsius(self._ambient_temperature)","def ionization_constant_water(temperature=298.15, density=None):\n import numpy as np\n\n # using Model II from Bandura etal\n # model parameters\n n = 6\n alpha_0 = -0.864671\n alpha_1 = 8659.19\n alpha_2 = -22786.2\n beta_0 = 0.642044\n beta_1 = -56.8534\n beta_2 = -0.375754\n\n # Water parameters\n Mw = 18.01528\n\n # temperature\n T = temperature\n\n # density\n if density:\n D = density\n else:\n D = density_water(T)\n\n pKWG = 0.61415 \\\n + 48251.33 / T \\\n - 67707.93 / T**2.0 \\\n + 10102100.0 / T**3.0\n\n Z = D * np.exp(alpha_0 \\\n + alpha_1/T \\\n + alpha_2/T**2 *np.power(D,2.0/3.0)\n )\n\n pKw = -2*n*(\n np.log10(1 + Z) - (Z/(Z + 1)) * D * (\n beta_0 + beta_1/T + beta_2*D\n )\n ) + pKWG + 2 * np.log10(Mw/1000.0)\n\n return np.power(10, -pKw)","def cp(wair,pres,entr=None,temp=None,airf=None,dhum=None,chkvals=False,\n chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,chkbnd=False,\n mathargs=None):\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\n h_s = temp\n h_ss = iceair_h(0,2,0,wair,pres,temp=temp,airf=airf,dhum=dhum)\n cp = h_s/h_ss\n return cp","def thermal_expansivity_of_air(self) -> float:\n\n return 1 / self.ambient_temperature","def get_air_conditioned_room_absolute_humidity(\n hc_period: np.ndarray, x_ex: np.ndarray, x_set_c: float) -> np.ndarray:\n\n return x_set_c * (hc_period == 'c') + x_ex * (hc_period != 'c')","def _calculate_temp_in_c(temp):\r\n return str((temp * 9 / 5.0 + 32) if temp else \"\")","def temperature() -> float:","def dielectric_constant_water(temperature=298.15):\n tabulated_data = np.array([[263.15, 92.10],\n [268.15, 89.96],\n [273.15, 87.90],\n [278.15, 85.90],\n [283.15, 83.96],\n [288.15, 82.06],\n [293.15, 80.20],\n [298.15, 78.38],\n [303.15, 76.60],\n [308.15, 74.86],\n [313.15, 73.17],\n [318.15, 71.50],\n [323.15, 69.88],\n [328.15, 68.29],\n [333.15, 66.74],\n [338.15, 65.22],\n [343.15, 63.73],\n [348.15, 62.28],\n [353.15, 60.87],\n [358.15, 59.48],\n [363.15, 58.13],\n [368.15, 56.81],\n [373.15, 55.51]])\n polynomal_degree = 5\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\n polynomal_degree)\n fitfunction = np.poly1d(fitdata)\n return fitfunction(temperature)","def tempAir(sample):\n sample *= 1.0\n sample /= 1000\n celsius = (sample - 0.5) * 100\n return round(celsius,2)","def eco_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_low_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_low)","def get_pressure(self): # This function implements the equations needed to convert the digital data into mbars\n self.digital_pressure_data()\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\n temperature, dT=self.get_temperature()\n OFF = ((C_2 * (2**16)) + ((C_4 * dT)/2**7))\n SENS = (C_1 * (2**15)) + ((C_3 * dT)/(2**8))\n pressure=(((self.presadc*(SENS/(2**21)))-OFF)/(2**15))/100\n return pressure, temperature","def get_actual_air_conditioned_temperature(\n hc_period: np.ndarray,\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\n\n rho = get_air_density()\n c = get_specific_heat()\n\n a_prt = a_prt.reshape(1, 5).T\n a_hcz = a_hcz[0:5].reshape(1, 5).T\n\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\n\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')","def calculate_dew_point(temp, hum):\n return temp - (100 - hum) / 5","def partial_pressure(fraction=3, tem=283.15, pre=1.21325):\n pwater = np.exp(77.345 + 0.0057 * tem - 7235 / tem) / (tem ** 8.2) / 100000\n # partial pressure of H2O in air by relation, [Bar]\n p_hcl = fraction * 10 ** -5 * pre\n # firstly use 3ppm concentration to do estimation [Bar]\n return tem, pre, pwater, p_hcl","def heat_capacity_of_air(self) -> float:\n\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2","def test_ccl_with_ml():\n pressure = np.array([992.0, 990.0, 983.0, 967.0, 950.0, 944.0, 928.0, 925.0, 922.0,\n 883.0, 877.7, 858.0, 853.0, 850.0, 835.0, 830.0, 827.0, 826.0,\n 813.6, 808.0, 799.0, 784.0, 783.3, 769.0, 760.0, 758.0, 754.0,\n 753.0, 738.0, 725.7, 711.0, 704.0, 700.0, 685.0, 672.0, 646.6,\n 598.6, 596.0, 587.0, 582.0, 567.0, 560.0, 555.0, 553.3, 537.0,\n 526.0, 521.0, 519.0, 515.0, 500.0]) * units.mbar\n temperature = np.array([6.8, 6.2, 7.8, 7.6, 7.2, 7.6, 6.6, 6.4, 6.2, 3.2, 2.8, 1.2,\n 1.0, 0.8, -0.3, -0.1, 0.4, 0.6, 0.9, 1.0, 0.6, -0.3, -0.3,\n -0.7, -1.5, -1.3, 0.2, 0.2, -1.1, -2.1, -3.3, -2.3, -1.7, 0.2,\n -0.9, -3.0, -7.3, -7.5, -8.1, -8.3, -9.5, -10.1, -10.7,\n -10.8, -12.1, -12.5, -12.7, -12.9, -13.5, -15.5]) * units.degC\n dewpoint = np.array([5.1, 5.0, 4.2, 2.7, 2.2, 0.6, -2.4, -2.6, -2.8, -3.8, -3.6,\n -3.1, -5.0, -4.2, -1.8, -4.3, -7.6, -6.4, -8.2, -9.0, -10.4,\n -9.3, -9.6, -14.7, -11.5, -12.3, -25.8, -25.8, -19.1, -19.6,\n -20.3, -42.3, -39.7, -46.8, -46.8, -46.7, -46.5, -46.5,\n -52.1, -36.3, -47.5, -30.1, -29.7, -30.4, -37.1, -49.5,\n -36.7, -28.9, -28.5, -22.5]) * units.degC\n\n ccl_p, ccl_t, t_c = ccl(pressure, temperature, dewpoint,\n mixed_layer_depth=500 * units.m, which='all')\n\n assert_array_almost_equal(ccl_p, np.array(\n [850.600930, 784.325312, 737.767377, 648.076147]) * units.mbar, 5)\n assert_array_almost_equal(ccl_t, np.array(\n [0.840118, -0.280299, -1.118757, -2.875716]) * units.degC, 5)\n assert_array_almost_equal(t_c, np.array(\n [13.146845, 18.661621, 22.896152, 32.081388]) * units.degC, 5)","def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\n return 0.1 * lumped_cover_heat_capacity","def get_air_conditioned_room_temperature(\n hc_period: np.ndarray,\n theta_ex: np.ndarray, theta_set_h: float, theta_set_c: float) -> np.ndarray:\n\n theta_ac_m = np.clip(theta_ex, theta_set_h, theta_set_c)\n\n return theta_set_h * (hc_period == 'h') + theta_set_c * (hc_period == 'c') + theta_ac_m * (hc_period == 'm')","def target_temperature_low_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_low_c\"))\r\n return kelvin_to_celsius(self._target_temperature_low)","def cooled_surface_temp(T:np.ndarray) -> float:\n \n return T.dot(cs_temp_weights)","def CMB_spec(eng, temp):\n if np.issubdtype(type(eng), float):\n eng = np.array([eng])\n prefactor = 8*np.pi*(eng**2)/((ele_compton*me)**3)\n phot_spec_density = prefactor*[1/(np.exp(photeng/temp) - 1) if photeng/temp < 500 else 0 for photeng in eng]\n\n if np.issubdtype(type(eng), float): \n return phot_spec_density[0] \n else: \n return phot_spec_density","def thermalConductivity(self, Tk=None, Tc=None):\n Tk = getTk(Tc, Tk)\n self.checkPropertyTempRange(\"thermal conductivity\", Tk)\n thermalConductivity = (\n 2.13014e-08 * Tk**3\n - 6.31916e-05 * Tk**2\n + 1.11629e-01 * Tk\n - 2.00043e00\n )\n return thermalConductivity * 1e-3","def water_vapour(t):\n T_0 = 273.15\n T_rw = 35.86 # over water\n a = 17.269\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))","def test_wet_psychrometric_rh():\n p = 1013.25 * units.mbar\n dry_bulb_temperature = 20. * units.degC\n wet_bulb_temperature = 18. * units.degC\n psychrometric_rh = relative_humidity_wet_psychrometric(p, dry_bulb_temperature,\n wet_bulb_temperature)\n assert_almost_equal(psychrometric_rh, 82.8747 * units.percent, 3)","def get_supply_air_absolute_humidity_for_cooling(\n x_hs_out_c: np.ndarray, x_ac: np.ndarray, operation: np.ndarray) -> np.ndarray:\n\n return np.where(operation == 'c', x_hs_out_c, x_ac)","def calc_supply_temp(tr, Q, m, cp, case):\n if m > 0:\n if case == \"DH\":\n ts = tr + Q / (m * cp)\n else:\n ts = tr - Q / (m * cp)\n else:\n ts = 0\n return ts","def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\n self.digital_temp_data() \n dT = self.tempadc-(C_5*(2**8))\n temperature=(2000+(dT*(C_6/(2**23))))/100\n return temperature, dT","def target_temperature_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_c\"))\r\n return kelvin_to_celsius(self._target_temperature)","def plc_temp(coil_df):","def get_chiller_temperature(self) -> float:\n\n return self.send(self.cmd.GET_COOLING_ACT)","def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\n\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\n\n return h_kJ_kg","def initialize_thermal_prediction(self, config_file):\n conf_pred = config_file['prediction']['heat']\n conf_powr = config_file['prediction']['power']\n # conf_pred\n n_day = conf_pred['n_day']\n n_values = conf_pred['n_values_per_day']\n precision_in_h = conf_pred['precision_in_h']\n use_predef_loads = conf_pred['use_predef_loads']\n predef_loads_file_path = conf_pred['path_loads']\n # heating curve\n conf_hk = config_file['components']['heating_curve']\n hk_ta = conf_hk['design_ambient_temperature_oC']\n hk_ti = conf_hk['design_indoor_temperature_oC']\n hk_tv = conf_hk['design_supply_temperature_oC']\n hk_tr = conf_hk['design_return_temperature_oC']\n hk_n = conf_hk['radiator_coefficient_n']\n hk_m = conf_hk['radiator_coefficient_m']\n hk_qn = conf_hk['design_heat_load_in_kW']\n # chp unit\n patm = utils.get_pressure_in_MPa()\n calcopt = utils.get_calc_option()\n eps_el_chp = config_file['components']['chp_unit']['electrical_efficiency']\n eps_th_chp = config_file['components']['chp_unit']['thermal_efficiency']\n qel_n_chp = config_file['components']['chp_unit']['max_electric_power_in_kW']\n chp_tinp = config_file['components']['chp_unit']['design_input_temperature_oC']\n chp_tmax = config_file['components']['chp_unit']['design_output_temperature_oC']\n qth_n_chp = eps_th_chp * qel_n_chp / eps_el_chp # in kW\n mstr_chp = qth_n_chp / (utils.cp_fluid_water(0.5 * (chp_tmax + chp_tinp), patm, calcopt) * (chp_tmax - chp_tinp)) # in kg/s = kW / (kJ/kg/K * K)\n # gas boiler\n qth_n_gb = config_file['components']['gas_boiler']['max_thermal_power_in_kW']\n gb_tinp = config_file['components']['gas_boiler']['design_input_temperature_oC']\n gb_tmax = config_file['components']['gas_boiler']['design_output_temperature_oC']\n mstr_gb = qth_n_gb / (utils.cp_fluid_water(0.5 * (gb_tinp + gb_tmax), patm, calcopt) * (gb_tmax - gb_tinp)) # in kg/s = kW / (kJ/kg/K * K) # in kg/s = kW / (kJ/kg/K * K)\n # storage tank\n effective_height = config_file['components']['storage_tank']['effective_heigth_in_m']\n inner_radius = config_file['components']['storage_tank']['inner_radius_tank_in_m']\n effective_pipe_volume = config_file['components']['storage_tank']['effective_coil_volume_in_m3'] # in m3 - water volume of the pipes of inner heat exchanger\n effective_volume = config_file['components']['storage_tank']['effective_volume_in_m3']\n if (effective_volume <= 0.0):\n effective_volume = math.pi * inner_radius * inner_radius * effective_height - effective_pipe_volume # in m3\n nr_calc = 20\n slice_volume = effective_volume / nr_calc # in m3\n qmax_rod_el = config_file['components']['storage_tank']['power_heating_rod_in_kW']\n open_weather_map_active = config_file['calculation']['platform_mode']['open_weather_map_active']\n # conf_powr\n #print('\\n initialize_thermal_prediction')\n #print('use_predef_loads = {}; {}'.format(use_predef_loads,type(use_predef_loads)))\n #print('predef_loads_file_path = {}; {}'.format(predef_loads_file_path,type(predef_loads_file_path)))\n return predict_thermal.predict_Q(n_day, n_values, precision_in_h, predef_loads_file_path, use_predef_loads, \n self.output_horizon_in_h, self.output_resolution_in_s, conf_powr, hk_tv, hk_tr, hk_ti, hk_ta, \n hk_qn, hk_n, hk_m, chp_tmax, gb_tmax, slice_volume, mstr_chp, mstr_gb, qmax_rod_el, eps_th_chp, \n eps_el_chp, open_weather_map_active)","def target_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"target_temperature_high_c\"))\r\n return kelvin_to_celsius(self._target_temperature_high)","def calc_VPD(t_celsius, rel_humidity):\n # according to Licor LI-6400 manual pg 14-10\n # and Buck AL (1981). New equations for computing vapor pressure and\n # enhancement factor. J Appl Meteor 20:1527-1532\n vp_sat = 0.61365 * math.exp((17.502 * t_celsius) / (240.97 + t_celsius))\n\n vp_air = vp_sat * rel_humidity\n return vp_sat - vp_air # or vp_sat * (1 - rel_humidity)","def internal_temp_c(self) -> int:\n return int(self._device_info[\"Temperature\"])","def convert_f_to_c(temp_in_farenheit): ## ##\n celsiustemp = round((temp_in_farenheit - 32) * 5/9, 1) ##\n return celsiustemp ##","def get_decided_outlet_supply_air_temperature_for_cooling(\n vav_system: bool, theta_req_c: np.ndarray, v_d_supply: np.ndarray,\n theta_hs_out_min_c: np.ndarray) -> np.ndarray:\n\n if vav_system:\n return np.maximum(np.min(theta_req_c, axis=0), theta_hs_out_min_c)\n else:\n return np.maximum(np.sum(theta_req_c * v_d_supply / v_d_supply.sum(axis=0), axis=0), theta_hs_out_min_c)","def eco_temperature_high_c(self) -> float:\r\n self._logger.debug(log_message_formatter(\r\n \"get\", f\"{self}\", \"eco_temperature_high_c\"))\r\n return kelvin_to_celsius(self._eco_temperature_high)","def get_percent_wet():\n # Create an ADS1115 ADC (16-bit) instance.\n adc = Adafruit_ADS1x15.ADS1115()\n\n GAIN = 1\n DRY = 20280 # 100% Dry\n WET = 10140 # 100% Wet\n\n value = adc.read_adc(0, gain=GAIN)\n \n # print \"value: %d\" % value\n \n percent_dry = ((value - WET)*100)/(DRY-WET)\n percent_wet = 100 - percent_dry\n\n return percent_wet","def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\n \n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\n for mi in range(pm.size):\n for xi in range(px.size):\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\n z = pmx_c.sum(axis=0)\n pmx_c /= z #Normalize\n \n \t\n return pmx_c, z","def get_requested_supply_air_absolute_humidity_for_cooling(\n x_ac: np.ndarray, l_d_cl: np.ndarray, v_d_supply: np.ndarray) -> np.ndarray:\n\n # air density, kg/m3\n rho = get_air_density()\n\n # latent heat of evaporation, kJ/kg\n l_wtr = get_evaporation_latent_heat()\n\n return x_ac - l_d_cl * 10 ** 3 / (v_d_supply * rho * l_wtr)","def convert_f_to_c(temp_in_farenheit):\n \n temp=round((float(temp_in_farenheit)-32)*5/9,1)\n \n return (temp)","def initialize_thermal_prediction(self, config_file):\n conf_pred = config_file['prediction']['heat']\n conf_powr = config_file['prediction']['power']\n # config_json\n n_day = conf_pred['n_day']\n n_values = conf_pred['n_values_per_day']\n precision_in_h = conf_pred['precision_in_h']\n use_predef_loads = conf_pred['use_predef_loads']\n predef_loads_file_path = conf_pred['path_loads']\n # heating curve\n conf_hk = config_file['components']['heating_curve']\n hk_ta = conf_hk['design_ambient_temperature_oC']\n hk_ti = conf_hk['design_indoor_temperature_oC']\n hk_tv = conf_hk['design_supply_temperature_oC']\n hk_tr = conf_hk['design_return_temperature_oC']\n hk_n = conf_hk['radiator_coefficient_n']\n hk_m = conf_hk['radiator_coefficient_m']\n hk_qn = conf_hk['design_heat_load_in_kW']\n # chp unit\n patm = utils.get_pressure_in_MPa()\n calcopt = utils.get_calc_option()\n eps_el_chp = config_file['components']['chp_unit']['electrical_efficiency']\n eps_th_chp = config_file['components']['chp_unit']['thermal_efficiency']\n qel_n_chp = config_file['components']['chp_unit']['max_electric_power_in_kW']\n chp_tinp = config_file['components']['chp_unit']['design_input_temperature_oC']\n chp_tmax = config_file['components']['chp_unit']['design_output_temperature_oC']\n qth_n_chp = eps_th_chp * qel_n_chp / eps_el_chp # in kW\n mstr_chp = qth_n_chp / (utils.cp_fluid_water(0.5 * (chp_tmax + chp_tinp), patm, calcopt) * (chp_tmax - chp_tinp)) # in kg/s = kW / (kJ/kg/K * K)\n # gas boiler\n qth_n_gb = config_file['components']['gas_boiler']['max_thermal_power_in_kW']\n gb_tinp = config_file['components']['gas_boiler']['design_input_temperature_oC']\n gb_tmax = config_file['components']['gas_boiler']['design_output_temperature_oC']\n mstr_gb = qth_n_gb / (utils.cp_fluid_water(0.5 * (gb_tinp + gb_tmax), patm, calcopt) * (gb_tmax - gb_tinp)) # in kg/s = kW / (kJ/kg/K * K) # in kg/s = kW / (kJ/kg/K * K)\n # storage tank\n effective_height = config_file['components']['storage_tank']['effective_heigth_in_m']\n inner_radius = config_file['components']['storage_tank']['inner_radius_tank_in_m']\n effective_pipe_volume = config_file['components']['storage_tank']['effective_coil_volume_in_m3']\n effective_volume = config_file['components']['storage_tank']['effective_volume_in_m3']\n if (effective_volume <= 0.0):\n effective_volume = math.pi * inner_radius * inner_radius * effective_height - effective_pipe_volume # in m3\n nr_calc = 20\n slice_volume = effective_volume / nr_calc # in m3\n qmax_rod_el = config_file['components']['storage_tank']['power_heating_rod_in_kW']\n open_weather_map_active = config_file['calculation']['platform_mode']['open_weather_map_active']\n # conf_powr\n #print('\\n initialize_thermal_prediction')\n #print('use_predef_loads = {}; {}'.format(use_predef_loads,type(use_predef_loads)))\n #print('predef_loads_file_path = {}; {}'.format(predef_loads_file_path,type(predef_loads_file_path)))\n return predict_thermal.predict_Q(n_day, n_values, precision_in_h, predef_loads_file_path, use_predef_loads, self.output_horizon_in_h, \n self.output_resolution_in_s, conf_powr, hk_tv, hk_tr, hk_ti, hk_ta, hk_qn, hk_n, hk_m, chp_tmax, gb_tmax, slice_volume, \n mstr_chp, mstr_gb, qmax_rod_el, eps_th_chp, eps_el_chp, open_weather_map_active)","def calc_temp_withlosses(t0, Q, m, cp, case):\n if m > 0:\n if case == \"positive\":\n t1 = t0 + Q / (m * cp)\n else:\n t1 = t0 - Q / (m * cp)\n else:\n t1 = 0\n return t1","def get_hc_external(self, weather, surface, h_surface, terrain):\r\n roughness = surface.construction[0].roughness_unit # Change back to this line...left as below to match Na's\r\n if roughness == \"VeryRough\":\r\n D = 11.58\r\n E = 5.894\r\n F = 0\r\n elif roughness == \"Rough\":\r\n D = 12.49\r\n E = 4.065\r\n F = 0.028\r\n elif roughness == \"MediumRough\":\r\n D = 10.79\r\n E = 4.192\r\n F = 0.0\r\n elif roughness == \"MediumSmooth\":\r\n D = 8.23\r\n E = 4.0\r\n F = -0.057\r\n elif roughness == \"Smooth\":\r\n D = 10.22\r\n E = 3.1\r\n F = 0.0\r\n elif roughness == \"VerySmooth\":\r\n D = 8.23\r\n E = 3.33\r\n F = -0.036\r\n else:\r\n D = 8.23\r\n E = 4.0\r\n F = -0.057\r\n print \"No Roughness Value Found so Set Default Values of 8.23,4.0,-0.057\"\r\n\r\n wind_speed_temp = weather[\"wind_speed\"]\r\n # Terrain Lookup Table\r\n if terrain == 'Flat or Open Countryside':\r\n sigma = 270\r\n a = 0.14\r\n elif terrain == 'Rough or Wooded Country':\r\n sigma = 370\r\n a = 0.22\r\n elif terrain == 'Towns and City Scapes':\r\n sigma = 460\r\n a = 0.33\r\n elif terrain == 'Ocean Front Areas':\r\n sigma = 210\r\n a = 0.10\r\n elif terrain == 'Urban, Industrial, or Forest':\r\n sigma = 370\r\n a = 0.22\r\n else:\r\n sigma = 370\r\n a = 0.22\r\n print \"No Terrain Type Found so Set Default Values of 370,0.22\"\r\n terrain_sigma = sigma\r\n terrain_cof = a\r\n\r\n # Adjust the wind speed...Stable air above human inhabited areas:\r\n #wind_speed = wind_speed_temp * ((h_surface / 10) ** 0.5) # This was the line used to get wind_speed before terrain was added\r\n # Wind speed corrected for terrain differences;\r\n wind_speed = wind_speed_temp * ((270/10) ** 0.14) * (h_surface/terrain_sigma) ** terrain_cof\r\n #print wind_speed\r\n # Calculate the hc_external\r\n # hc_external= D+E*Wind_speed+F*Wind_speed^2\r\n hc_external = D + (E * wind_speed) + (F * wind_speed ** 2)\r\n\r\n # depending on the direction of the wind adjust the hc_external...as of versions 3 and 4 this part seems omitted\r\n #x = abs(wind_speed_dir - azimuth)\r\n #if x > 100:\r\n # if x < 260:\r\n # hc_external *= 0.5\r\n #print \"hc_external : \", hc_external, D, E, F\r\n\r\n return round(hc_external, 5)","def cmm(self) -> Optional[np.ndarray]:\n if self.sensorsz is None:\n return None\n return self.c * self.sensorsz / self.imgsz","def __we_c(cls, calib, tc, temp, we_v, ae_v):\n we_t = we_v - (calib.we_elc_mv / 1000.0) # remove electronic we zero\n ae_t = ae_v - (calib.ae_elc_mv / 1000.0) # remove electronic ae zero\n\n we_c = tc.correct(calib, temp, we_t, ae_t)\n\n # print(\"A4Datum__we_c: we_t:%f ae_t:%f we_c:%s\" % (we_t, ae_t, we_c), file=sys.stderr)\n\n return we_c","def _approx_wep(wair,entr,pres):\n pvmax = pres * (1-wair) / (1-wair + _EPSW*wair)\n if pvmax >= _PTPE:\n # Saturation would start at _TTP; use saturated heat capacity at _TTP\n a_t = (pres - _PTPE)/(pres - _PTPE + _EPSW*_PTPE)\n s_t = (wair*_CDRY*numpy.log(_TTP/_TCELS) - (1-wair)*_LILTP/_TTP\n - wair*_RDRY*numpy.log((pres-_PTPE)/_PATM)\n + wair*_RWAT*_EPSW*_PTPE/(pres-_PTPE)*_AVI)\n c_t = (wair*_CDRY + wair*(1-a_t)/a_t*_CVAP + (1-wair/a_t)*_CICE\n + wair*_RWAT*(1-a_t)*(_EPSW*a_t + 1-a_t)/_EPSW/a_t**2 * _AVI**2)\n temp = _TTP * numpy.exp(-(s_t-entr)/c_t)\n else:\n # Get approximate saturation temperature\n v = numpy.log(pres*(1-wair)/(_PTPE*(_EPSW*wair + 1-wair)))/_BVI\n r = _AVI/_BVI\n x = maths4.lamb2(v,r)\n tsat = _TTP/x\n ssat = (wair * (_CDRY*numpy.log(tsat/_TCELS)\n - _RDRY*numpy.log((pres-pvmax)/_PATM))\n + (1-wair) * (_CVAP*numpy.log(tsat/_TTP) + _LLVTP/_TTP\n - _RWAT*numpy.log(pvmax/_PTPE)))\n \n if entr >= ssat:\n ceff = wair*_CDRY + (1-wair)*_CVAP\n temp = _TTP * numpy.exp((entr-ssat)/ceff)\n else:\n csat = (wair*_CDRY + (1-wair)*_CVAP\n + (1-wair)*_RWAT*pres/(pres-pvmax)\n * ((_AVI+_BVI)*_TTP/tsat - _BVI)**2)\n temp = tsat * numpy.exp(-(ssat-entr)/csat)\n pvap = _PTPE * numpy.exp((_AVI+_BVI)*(1 - _TTP/temp)\n - _BVI*numpy.log(temp/_TTP))\n airf = (pres - pvap) / (pres - pvap + _EPSW*pvap)\n dhum = pres/(_RDRY*temp) / (airf + (1-airf)/_EPSW)\n return airf, temp, dhum","def get_specific_heat() -> float:\n return 1006.0","def pressure(z: tf.Tensor) -> tf.Tensor:\n return self._p_thermal * tf.math.exp(\n -(z + self._height * delta_t_frac *\n (tf.math.log(1.0 - delta_t_frac * tf.math.tanh(z / self._height)) -\n tf.math.log(1.0 + tf.math.tanh(z / self._height)) +\n z / self._height)) / h_sfc / (1.0 - delta_t_frac**2))","def convert_f_to_c(temp_in_farenheit):\n temp_in_celcius = ((temp_in_farenheit - 32) * 5) / 9\n temp_in_celcius = round(temp_in_celcius, 1)\n return temp_in_celcius","def lookup_effective_mass_area_factor(self, cm):\n\n if cm == 0.0:\n return 0.0\n elif 0.0 < cm <= 165000.0:\n return 2.5\n else:\n return 3.2","def water_vapour_saturation_pressure(T, isCelsius = True):\n if isCelsius:\n T += 273.15\n\n if T < 273.15: \n warnings.warn(\"Temperature {:} K is below freezing. This function is not valid.\".format(T))\n elif T > 647.096:\n warnings.warn(\"Temperature {:} K is above the critical temperature. This function is not valid.\".format(T))\n \n Tc = 647.096 # critical temperature, K\n Pc = 22.064e6 # critical pressure Pa\n C1 = -7.85951783\n C2 = 1.84408259\n C3 = -11.7866497\n C4 = 22.6807411\n C5 = -15.9618719\n C6 = 1.80122502\n v = 1 - T / Tc # vartheta = transformed temperature\n \n a = C1 * v + C2 * v**1.5 + C3 * v**3 + C4 * v**3.5 + C5 * v**4 + C6 * v**7.5\n Pws = Pc * numpy.exp(Tc * a / T)\n return Pws","def farenheit(ctemp):\n return round(9.0/5.0 * ctemp + 32)","def pressure(self):\r\n self._read_temperature()\r\n\r\n # Algorithm from the BME280 driver\r\n # https://github.com/BoschSensortec/BME280_driver/blob/master/bme280.c\r\n adc = self._read24(_BME280_REGISTER_PRESSUREDATA) / 16 # lowest 4 bits get dropped\r\n var1 = float(self._t_fine) / 2.0 - 64000.0\r\n var2 = var1 * var1 * self._pressure_calib[5] / 32768.0\r\n var2 = var2 + var1 * self._pressure_calib[4] * 2.0\r\n var2 = var2 / 4.0 + self._pressure_calib[3] * 65536.0\r\n var3 = self._pressure_calib[2] * var1 * var1 / 524288.0\r\n var1 = (var3 + self._pressure_calib[1] * var1) / 524288.0\r\n var1 = (1.0 + var1 / 32768.0) * self._pressure_calib[0]\r\n if var1 == 0:\r\n return 0\r\n if var1:\r\n pressure = 1048576.0 - adc\r\n pressure = ((pressure - var2 / 4096.0) * 6250.0) / var1\r\n var1 = self._pressure_calib[8] * pressure * pressure / 2147483648.0\r\n var2 = pressure * self._pressure_calib[7] / 32768.0\r\n pressure = pressure + (var1 + var2 + self._pressure_calib[6]) / 16.0\r\n\r\n pressure /= 100\r\n if pressure < _BME280_PRESSURE_MIN_HPA:\r\n return _BME280_PRESSURE_MIN_HPA\r\n if pressure > _BME280_PRESSURE_MAX_HPA:\r\n return _BME280_PRESSURE_MAX_HPA\r\n return pressure\r\n else:\r\n return _BME280_PRESSURE_MIN_HPA","def conductivity(self):\n m = 1.67296736e-02 # Determined from optimisation\n c = 8.54665149e-05 # Determined from optimisation\n return m * self.concentration + c","def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\n hotTemp = 700.0\n dLL = self.component.material.linearExpansionFactor(\n Tc=hotTemp, T0=self._coldTemp\n )\n ref = 1.0 + dLL\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\n self.assertAlmostEqual(cur, ref)","def compute_cm_fm(illuminant, gains, ccm, cal):\n\n ###########################################################################\n # Standard matrices.\n\n # W is the matrix that maps sRGB to XYZ.\n # See: http://www.brucelindbloom.com/\n W = numpy.array([\n [ 0.4124564, 0.3575761, 0.1804375],\n [ 0.2126729, 0.7151522, 0.0721750],\n [ 0.0193339, 0.1191920, 0.9503041]])\n\n # HH is the chromatic adaptation matrix from D65 (since sRGB's ref white is\n # D65) to D50 (since CIE XYZ's ref white is D50).\n HH = numpy.array([\n [ 1.0478112, 0.0228866, -0.0501270],\n [ 0.0295424, 0.9904844, -0.0170491],\n [-0.0092345, 0.0150436, 0.7521316]])\n\n # H is a chromatic adaptation matrix from D65 (because sRGB's reference\n # white is D65) to the calibration illuminant (which is a standard matrix\n # depending on the illuminant). For a D65 illuminant, the matrix is the\n # identity. For the A illuminant, the matrix uses the linear Bradford\n # adaptation method to map from D65 to A.\n # See: http://www.brucelindbloom.com/\n H_D65 = numpy.array([\n [ 1.0, 0.0, 0.0],\n [ 0.0, 1.0, 0.0],\n [ 0.0, 0.0, 1.0]])\n H_A = numpy.array([\n [ 1.2164557, 0.1109905, -0.1549325],\n [ 0.1533326, 0.9152313, -0.0559953],\n [-0.0239469, 0.0358984, 0.3147529]])\n H = [H_A, H_D65][illuminant]\n\n ###########################################################################\n # Per-model matrices (that should be the same for all units of a particular\n # phone/camera. These are statics in the HAL camera properties.\n\n # G is formed by taking the r,g,b gains and putting them into a\n # diagonal matrix.\n G = numpy.array([[gains[0],0,0], [0,gains[1],0], [0,0,gains[3]]])\n\n # S is just the CCM.\n S = numpy.array([ccm[0:3], ccm[3:6], ccm[6:9]])\n\n ###########################################################################\n # Per-unit matrices.\n\n # The per-unit calibration matrix for the given illuminant.\n CC = numpy.array([cal[0:3],cal[3:6],cal[6:9]])\n\n ###########################################################################\n # Derived matrices. These should match up with DNG-related matrices\n # provided by the HAL.\n\n # The color matrix and forward matrix are computed as follows:\n # CM = inv(H * W * S * G * CC)\n # FM = HH * W * S\n CM = numpy.linalg.inv(\n numpy.dot(numpy.dot(numpy.dot(numpy.dot(H, W), S), G), CC))\n FM = numpy.dot(numpy.dot(HH, W), S)\n\n # The color matrix is normalized so that it maps the D50 (PCS) white\n # point to a maximum component value of 1.\n CM = CM / max(numpy.dot(CM, (0.9642957, 1.0, 0.8251046)))\n\n return CM, FM","def volumetric_heat_capacity(temperature):\n a = -2.4083\n b = 7.6006\n c = -8.2982\n d = 7.3301\n e = -4.2386\n f = 1.4294\n g = -0.24396\n h = 0.015236\n i = 0.0\n log_t = math.log10(temperature)\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\n h*log_t**7.0 + i*log_t**8.0\n g10_cp = 10.0**f_exp\n return g10_cp * G10NISTMaterialProperties.density","def P(self, energy, newEnergy, temperature):\n \"\"\" This is the decision-rule, adapted from Nascimento, et al., 2009 (See references) \"\"\"\n \n delta = self.calcDelta(newEnergy, energy)\n\n minTemp = 0.00001 # use minimum to avoid div/0 and buffer overflow\n if temperature == 0:\n return minTemp\n elif temperature > minTemp:\n try:\n return math.exp(-1 * round(delta, 4) / round(temperature, 4))\n except OverflowError as detail:\n return minTemp\n else:\n return 1","def partial_pressure(num_moles, ideal_gas_law, temp, volume, is_temp_kelvin=True):\n ATM = 0.0821\n KPA = 8.314\n MMHG = 62.4\n if ideal_gas_law == \"atm\":\n ideal_gas_law_number = ATM\n if ideal_gas_law == \"kpa\":\n ideal_gas_law_number = KPA\n if ideal_gas_law == \"mmhg\":\n ideal_gas_law_number = MMHG\n if not is_temp_kelvin:\n temp += 273\n nrt = (num_moles) * (ideal_gas_law_number) * (temp)\n final_pressure = nrt / volume\n return final_pressure","def get_cold_junction_temperature(self):\n return self._mcp9600.get('COLD_JUNCTION').temperature","def temperature(self):\n return float(self._current_observation['temp_c'])","def read_ambient_temperatureC(self, ):\n return self._read_temperature(MLX90614_TA)","def _pwr_std_temp(rpm, MP, altitude):\n # get the power at sea level (i.e. point B on the left side of the Lycoming power chart)\n \n # get pwr at two even hundreds of rpm, and then interpolate\n if rpm >= 2600:\n rpm1 = 2600\n elif rpm <= 1800:\n rpm1 = 1800\n else:\n rpm1 = rpm - rpm % 100\n\n rpm2 = rpm1 + 100\n \n pwr_SL1 = _pwr_sl(rpm1, MP)\n pwr_SL2 = _pwr_sl(rpm2, MP)\n # print \"SL Pwr 1=\", pwr_SL1\n # print \"SL Pwr 2=\", pwr_SL2\n \n # get power at full throttle at this rpm and MP at altitude (i.e. point A on the right side of the Lycoming power chart)\n # density ratio at point A on the right side of the Lycoming power chart)\n pwr_FT1, DR_FT1 = _hp_at_MP_and_altitude(rpm1, MP)\n pwr_FT2, DR_FT2 = _hp_at_MP_and_altitude(rpm2, MP)\n # print \"FT pwr 1=\", pwr_FT1\n # print \"FT pwr 2=\", pwr_FT2\n # print \"DR FT 1=\", DR_FT1\n # print \"DR FT 2=\", DR_FT2\n \n # density ratio at sea level\n DR_sl = 1\n \n # density ratio for the actual conditions (i.e. point D on the right side of the Lycoming power chart)\n DR_test = SA.alt2density_ratio(altitude)\n # print \"DR_test=\", DR_test\n \n # function is unstable if the DR at FT is close to 1. This sends the slope off to unpredictable values.\n slope1=(pwr_FT1 - pwr_SL1) / (DR_FT1 - DR_sl)\n slope2=(pwr_FT2 - pwr_SL2) / (DR_FT2 - DR_sl)\n \n if MP > 28:\n if slope1 < -80:\n slope1=-62\n elif slope1> -60:\n slope1=-62\n if slope2< -80:\n slope2 = -62\n elif slope2> -60:\n slope2=-62\n \n # print \"slope1=\", slope1\n # print \"slope2=\", slope2\n \n pwr_std_temp1 = pwr_SL1 + (DR_test - DR_sl) * slope1\n pwr_std_temp2 = pwr_SL2 + (DR_test - DR_sl) * slope2\n # print \"Pwr Std Temp 1=\", pwr_std_temp1\n # print \"Pwr Std Temp 2=\", pwr_std_temp2\n pwr_std_temp = pwr_std_temp1 + (rpm - rpm1) * (pwr_std_temp2 - pwr_std_temp1) / (rpm2 - rpm1)\n\n return pwr_std_temp","def set_temp_compensation(self, temp: int = 25) -> str:\n if type(temp) == float or int:\n response = self.query(f'T,{temp}')\n if temp < 10 or temp > 40:\n print(f'\\nNOTE: Unusual ocean temperature set: {temp} C.')\n else:\n print('Temp compensation factor should be a decimal/integer!')\n return response","def air_humidity_method_qsat26air(air_temperature,surface_air_pressure,relative_humdity):\n es = vapor_pressure(air_temperature,surface_air_pressure)\n em = 0.01*relative_humdity*es\n air_humidity = 622.*em/(surface_air_pressure-0.378*em)\n return air_humidity","def get_D_C3H8_air_eff(self, T):\n\n Kn = self.get_Kn(T)\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\n\n if np.isscalar(Kn):\n if Kn <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n else:\n if Kn.any() <= 1.:\n D_C3H8_air_eff = (\n self.porosity / self.tortuosity * self.D_C3H8_air\n )\n else:\n D_C3H8_air_eff = (\n 2. * self.porosity / self.tortuosity *\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\n D_C3H8_air_Kn)\n )\n\n self.D_C3H8_air_eff = D_C3H8_air_eff\n\n return D_C3H8_air_eff","def convert_f_to_c(temp_in_farenheit):\n celcius_temp = round(float((temp_in_farenheit) - 32)*(5/9),1)\n return(celcius_temp)","def molar_mass_dry_air():\n return 28.9647","def distribute(self, date_time, air_temp, vapor_pressure=None,\n dew_point=None, cloud_factor=None):\n\n self._logger.debug('%s Distributing thermal' % date_time)\n\n # calculate clear sky thermal\n if self.clear_sky_method == 'marks1979':\n cth = np.zeros_like(air_temp, dtype=np.float64)\n envphys_c.ctopotherm(\n air_temp, dew_point,\n self.dem,\n self.sky_view_factor,\n cth,\n self.config['marks1979_nthreads'])\n\n elif self.clear_sky_method == 'dilley1998':\n cth = clear_sky.Dilly1998(air_temp, vapor_pressure/1000)\n\n elif self.clear_sky_method == 'prata1996':\n cth = clear_sky.Prata1996(air_temp, vapor_pressure/1000)\n\n elif self.clear_sky_method == 'angstrom1918':\n cth = clear_sky.Angstrom1918(air_temp, vapor_pressure/1000)\n\n # terrain factor correction\n if (self.sky_view_factor is not None) and \\\n (self.clear_sky_method != 'marks1979'):\n # apply (emiss * skvfac) + (1.0 - skvfac) to the longwave\n cth = cth * self.sky_view_factor + (1.0 - self.sky_view_factor) * \\\n STEF_BOLTZ * air_temp**4\n\n # make output variable\n self.thermal_clear = cth.copy()\n\n # correct for the cloud factor\n # ratio of measured/modeled solar indicates the thermal correction\n if self.correct_cloud:\n if self.cloud_method == 'garen2005':\n cth = cloud.Garen2005(cth,\n cloud_factor)\n\n elif self.cloud_method == 'unsworth1975':\n cth = cloud.Unsworth1975(cth,\n air_temp,\n cloud_factor)\n\n elif self.cloud_method == 'kimball1982':\n cth = cloud.Kimball1982(cth,\n air_temp,\n vapor_pressure/1000,\n cloud_factor)\n\n elif self.cloud_method == 'crawford1999':\n cth = cloud.Crawford1999(cth,\n air_temp,\n cloud_factor)\n\n # make output variable\n self.thermal_cloud = cth.copy()\n\n # correct for vegetation\n if self.correct_veg:\n cth = vegetation.thermal_correct_canopy(cth,\n air_temp,\n self.veg_tau,\n self.veg_height)\n\n # make output variable\n self.thermal_veg = cth.copy()\n\n self.thermal = utils.set_min_max(cth, self.min, self.max)","def test_cape_cin_value_error():\n pressure = np.array([1012.0, 1009.0, 1002.0, 1000.0, 925.0, 896.0, 855.0, 850.0, 849.0,\n 830.0, 775.0, 769.0, 758.0, 747.0, 741.0, 731.0, 712.0, 700.0, 691.0,\n 671.0, 636.0, 620.0, 610.0, 601.0, 594.0, 587.0, 583.0, 580.0, 571.0,\n 569.0, 554.0, 530.0, 514.0, 506.0, 502.0, 500.0, 492.0, 484.0, 475.0,\n 456.0, 449.0, 442.0, 433.0, 427.0, 400.0, 395.0, 390.0, 351.0, 300.0,\n 298.0, 294.0, 274.0, 250.0]) * units.hPa\n temperature = np.array([27.8, 25.8, 24.2, 24, 18.8, 16, 13, 12.6, 12.6, 11.6, 9.2, 8.6,\n 8.4, 9.2, 10, 9.4, 7.4, 6.2, 5.2, 3.2, -0.3, -2.3, -3.3, -4.5,\n -5.5, -6.1, -6.1, -6.1, -6.3, -6.3, -7.7, -9.5, -9.9, -10.3,\n -10.9, -11.1, -11.9, -12.7, -13.7, -16.1, -16.9, -17.9, -19.1,\n -19.9, -23.9, -24.7, -25.3, -29.5, -39.3, -39.7, -40.5, -44.3,\n -49.3]) * units.degC\n dewpoint = np.array([19.8, 16.8, 16.2, 16, 13.8, 12.8, 10.1, 9.7, 9.7,\n 8.6, 4.2, 3.9, 0.4, -5.8, -32, -34.6, -35.6, -34.8,\n -32.8, -10.8, -9.3, -10.3, -9.3, -10.5, -10.5, -10, -16.1,\n -19.1, -23.3, -18.3, -17.7, -20.5, -27.9, -32.3, -33.9, -34.1,\n -35.9, -26.7, -37.7, -43.1, -33.9, -40.9, -46.1, -34.9, -33.9,\n -33.7, -33.3, -42.5, -50.3, -49.7, -49.5, -58.3, -61.3]) * units.degC\n cape, cin = surface_based_cape_cin(pressure, temperature, dewpoint)\n expected_cape, expected_cin = 2098.688061 * units('joules/kg'), 0.0 * units('joules/kg')\n assert_almost_equal(cape, expected_cape, 3)\n assert_almost_equal(cin, expected_cin, 3)","def conduct_heat(self, delta_time, external_power):\n\t\tself.temperature_container = self.temperature_container+self.area*external_power*delta_time/(self.heat_capacity_container*self.mass_container)#https://en.wikipedia.org/wiki/Heat_capacity\n\t\t\n\t\tinternal_power = 0.591*(self.temperature_container-self.temperature)/0.01#No idea of this is right. Mainly the devides by its length bit. https://en.wikipedia.org/wiki/Thermal_conduction#Fourier's_law\n\t\t\n\t\tif (self.heat_capacity*self.mass())!=0:\n\t\t\tself.temperature = self.temperature+internal_power*delta_time/(self.heat_capacity*self.mass())\n\t\t\t#self.temperature_container=self.temperature_container-internal_power*delta_time/(self.heat_capacity_container*self.mass_container)#Als je dit toevoegd lijkt de simulatie niet goed meer te werken dus nog even uitzoeken heo dat zit.","def get_pressure_coefficient(self):\n depth = self.params[\"Measured_Pressure\"][\"depth\"]\n coef = self.params[\"Measured_Pressure\"][\"coef\"]\n pres = self.params[\"Measured_Pressure\"][\"data\"]\n if depth and not coef and pres:\n hydro = hydrostatic_pressure(self.depth,\n kelly_bushing=self.kelly_bushing,\n depth_w=self.water_depth)\n coef_data = list()\n for dp, pr in zip(depth, pres):\n idx = np.searchsorted(self.depth, dp)\n coef_data.append(pr / hydro[idx])\n log = Log()\n log.depth = depth\n log.data = coef_data\n return log\n else:\n log = Log()\n log.depth = depth\n log.data = coef\n return log","def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\n k = get_kappa(K_medium, rho_medium, c_medium)\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial","def get_temperature(elevation, sea_level):\n if elevation <= sea_level:\n return 0.8\n else:\n return (-1.0 / (1.0 - sea_level)) * (elevation - sea_level) + 1.0","def _temperature(self, p_input:float) -> float:\n if self._unit_in == 'R':\n temp_K = p_input*5.0/9.0\n elif self._unit_in == 'F':\n temp_K = (p_input+459.67)/9.0*5.0\n elif self._unit_in == 'C':\n temp_K = p_input+273.15\n elif self._unit_in == 'K':\n temp_K = p_input\n \n if self._unit_out == 'R':\n return (temp_K*9.0/5.0)\n elif self._unit_out == 'F':\n return (temp_K*9.0/5.0-459.67) \n elif self._unit_out == 'C':\n return (temp_K-273.15)\n elif self._unit_out == 'K':\n return temp_K","def p_ym_c(pm,px,py,pyx_c,pmx_c):\n pym_c = np.zeros((py.size,pm.size))\n for yi in range(py.size):\n for mi in range(pm.size):\n for xi in range(px.size):\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\n return pym_c","def get_requested_supply_air_temperature_for_cooling(\n theta_sur_c: np.ndarray, theta_ac: np.ndarray, l_d_cs: np.ndarray, v_d_supply: np.ndarray,\n psi: float, l_duct: np.ndarray) -> np.ndarray:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n l_duct = np.array(l_duct).reshape(1,5).T\n\n theta_req_c = theta_sur_c - (theta_sur_c - theta_ac + l_d_cs * 10 ** 6 / (v_d_supply * c * rho)) \\\n * np.exp(psi * l_duct * 3600 / (v_d_supply * c * rho))\n\n return np.minimum(theta_req_c, theta_ac)","def get_Cm(self, K0, K1):\n # if(self.weight>=1.5):\n # raise ValueError,\" Error bounds only accurate for k<1.5! got k=%s\" % self.weight\n twominusk = mp2 - self._weight\n tmp = mpmath.mpf(len(self.multiplier().weil_module().D()))\n tmp1 = mppi * mp2\n tmp1 = mpmath.power(tmp1, twominusk)\n tmp3 = mpmath.zeta(twominusk)\n if(K0 == 0):\n tmp4 = 1\n else:\n tmp4 = mpmath.power(K0, 1 - self._weight)\n g1 = mpmath.gamma(1 - self._weight)\n g2 = mpmath.gamma(2 - self._weight)\n\n Cm = mp2 / g1 + mp4 * tmp1 / g1 / g2 * tmp * tmp3 * tmp4\n return Cm","def calculate_economics(\n irradiance: pd.DataFrame, temperature: pd.DataFrame, wind_speed: pd.DataFrame,\n CECMod: pd.DataFrame, configuration: float = 1\n ):\n p_out = calculate_dc_output(irradiance, temperature, wind_speed, CECMod=CECMod)\n\n # convert dc to AC - considering a flat loss of 14%\n # we have to improve this in the future\n p_out = [v * 0.86 for v in p_out]\n\n day_count = [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n monthly_electricity = []\n\n for month in range(12):\n st_index = sum(day_count[:month + 1]) * 24\n end_index = sum(day_count[:month + 2]) * 24\n data = p_out[st_index: end_index]\n # Note: division by 50 is to match the values - remove it later!\n monthly_electricity.append(sum(data) / len(data) / 50)\n\n total_ac_energy = sum(p_out)\n monthly_ac_energy = pd.DataFrame(\n zip(calendar.month_abbr[1:], monthly_electricity),\n columns=['month', 'Thousand kWh']\n )\n\n # Based on the example here: https://nrel-pysam.readthedocs.io/en/master/Import.html\n\n grid = Grid.default(\"PVWattsCommercial\")\n ur = UtilityRate.from_existing(grid, \"PVWattsCommercial\")\n cl = Cashloan.from_existing(grid,\"PVWattsCommercial\")\n\n sam_data = read_sam_data(configuration)\n for module, data in zip([grid, ur, cl], sam_data[:-1]):\n for k, v in data.items():\n if k == 'number_inputs':\n continue\n try:\n module.value(k, v)\n except AttributeError:\n print(module, k, v)\n\n\n grid.SystemOutput.gen = p_out\n\n grid.execute()\n ur.execute()\n cl.execute()\n\n # list possible outputs here\n adjusted_installed_cost = cl.Outputs.adjusted_installed_cost\n payback_cash_flow = [-1 * x for x in cl.Outputs.cf_discounted_payback]\n\n return total_ac_energy, monthly_ac_energy, adjusted_installed_cost, payback_cash_flow","def get_heat_capacity_pressure(vv, tt, chemical_potential, gbar=2.0, *args, **kwargs):\n # type: (np.ndarray, np.ndarray, np.ndarray, float, list, dict) -> np.ndarray\n y = chemical_potential / tt\n # There is a precision problem with \"-\" (minus) operator\n # We'll use asymptotic formula for high temperatures to avoid that problem\n y_low = y[y < THRESHOLD]\n vv_low, vv_high = vv[y < THRESHOLD], vv[y >= THRESHOLD]\n tt_low, tt_high = tt[y < THRESHOLD], tt[y >= THRESHOLD]\n # high temperatures - low numbers\n C_P_low = 5 * gbar * np.sqrt(2) / (36 * np.pi ** 2) * tt_low ** (3 / 2) * vv_low\n C_P_low *= (\n 5 * _1d_call(_fdk, y_low, k=-1 / 2) * _1d_call(_fdk, y_low, k=3 / 2)\n - 9 * _1d_call(_fdk, y_low, k=1 / 2) ** 2\n )\n C_P_low *= _1d_call(_fdk, y_low, k=3 / 2) / _1d_call(_fdk, y_low, k=1 / 2) ** 2\n # low temperatures - high numbers\n C_P_high = (gbar * np.pi / 6) ** (2 / 3) * tt_high * vv_high ** (2 / 3)\n return np.concatenate((C_P_low, C_P_high)).reshape(y.shape)","def getCMean(inp):\n\tinp = sorted(inp, key = lambda x: x[0])\n\treturn 1 - getClearWaterDepth(inp) / getY90(inp)"],"string":"[\n \"def get_compensated_temperature() -> float:\\n comp_factor = 2.25\\n cpu_temp = get_cpu_temperature()\\n raw_temp = bme280.get_temperature()\\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\\n # print(\\\"\\\"\\\"\\n # Compensated_Temperature: {:05.2f} *C\\n # Pressure: {:05.2f} hPa\\n # Relative humidity: {:05.2f} %\\n # \\\"\\\"\\\".format(temperature, pressure, humidity))\\n return comp_temp\",\n \"def thermal_conductivity_of_air(self) -> float:\\n\\n # This more accurate equation is not used by the paper.\\n # return (0.02646 * self.ambient_temperature ** 1.5) / (\\n # self.ambient_temperature + 254.4 * (10 ** (-12 / self.ambient_temperature))\\n # )\\n\\n # The reference suggests this equation is accurate to 1%.\\n return 0.02646 * (self.ambient_temperature / 300) ** 0.8646\",\n \"def get_cape(temp,pres,dewpt,hght,startp,startt,startdp,totalcape=False): \\n\\n # Check units\\n # Init temp is startt in C, Init dew point is stwrtdp,\\n # pressure levels are in hPa \\n temp = temp - 273.15 # convert temperature to celsius\\n dewpt = dewpt - 273.15 # convert dewpoint to celsius\\n pres = pres/100 # convert pressure to hPa\\n \\n \\n inds = np.where( (pres < startp) ) \\n tmp = pres[inds]\\n del pres\\n #pres = tmp[::-1]\\n pres = tmp[:]\\n del tmp \\n startp = startp/100\\n \\n tmp = temp[inds]\\n del temp\\n #temp = tmp[::-1]\\n temp = tmp[:]\\n del tmp \\n\\n tmp = dewpt[inds]\\n del dewpt\\n #dewpt = tmp[::-1]\\n dewpt = tmp[:]\\n del tmp \\n\\n tmp = hght[inds]\\n del hght\\n #hght = tmp[::-1]\\n hght = tmp[:]\\n del tmp \\n\\n \\n # Get Sub-LCL traces \\n presdry,tempdry,tempiso=dry_ascent(startp,startt-degCtoK,startdp-degCtoK) \\n \\n\\n # make lcl variables explicit\\n P_lcl=presdry[-1]\\n T_lcl=tempdry[-1]\\n\\n # Now lift a wet parcel from the intersection point\\n # preswet=linspace(P_lcl,100,101)\\n preswet,tempwet=moist_ascent(P_lcl,T_lcl)\\n\\n # tparcel is the concatenation of tempdry and \\n # tempwet, and so on.\\n \\n tparcel=np.concatenate((tempdry,tempwet[1:]))\\n pparcel=np.concatenate((presdry,preswet[1:]))\\n\\n # Interpolating the environmental profile onto the \\n # parcel pressure coordinate\\n # tempenv=interp(preswet,pres[::-1],temp[::-1])\\n ## NEW, for total column:\\n tempenv=interp(pparcel,pres[::-1],temp[::-1])\\n\\n\\n # now solve for the equlibrium levels above LCL\\n # (all of them, including unstable ones)\\n # eqlev,stab=solve_eq(preswet[::-1],(tempwet-tempenv)[::-1])\\n # NEW, for total column:\\n # On second thought, we don't really want/need\\n # any equilibrium levels below LCL\\n # eqlev,stab=solve_eq(pparcel[::-1],(tparcel-tempenv)[::-1])\\n # This is equivalent to the old statement :\\n eqlev,stab=solve_eq(pparcel[pparcel<=P_lcl][::-1],\\\\\\n (tparcel-tempenv)[pparcel<=P_lcl][::-1])\\n\\n aa = tparcel-tempenv\\n\\n # Sorting index by decreasing pressure\\n I=np.argsort(eqlev)[::-1]\\n eqlev=eqlev[I]; stab=stab[I]\\n\\n # temperatures at the equilibrium level\\n # tempeq=interp(eqlev,preswet[::-1],tempenv[::-1])\\n ## NEW, for total column:\\n tempeq=interp(eqlev,pparcel[::-1],tparcel[::-1])\\n\\n # This helps with debugging\\n # for ii,eq in enumerate(eqlev):\\n # print \\\"%5.2f %5.2f %2d\\\"%(eq,tempeq[ii],stab[ii])\\n\\n # need environmental temperature at LCL\\n tenv_lcl=interp(P_lcl,pparcel[::-1],tempenv[::-1])\\n\\n isstab=np.where(stab==1.,True,False)\\n unstab=np.where(stab==1.,False,True) \\n\\n if eqlev.shape[0]==0:\\n # no unstable layers in entire profile\\n # because the parcel never crosses the tenv\\n P_lfc=float('NaN')\\n P_el=float('NaN')\\n elif T_lcl>tenv_lcl:\\n # check LCL to see if this is unstable\\n P_lfc=P_lcl\\n if totalcape:\\n P_el=eqlev[isstab][-1]\\n else:\\n P_el=eqlev[isstab][0]\\n elif eqlev.shape[0]>1:\\n # Parcel is stable at LCL so LFC is the \\n # first unstable equilibrium level and \\n # \\\"EQ\\\" level is the first stable equilibrium \\n # level\\n P_lfc=eqlev[unstab][0]\\n if totalcape:\\n P_el=eqlev[isstab][-1]\\n else:\\n P_el=eqlev[isstab][0]\\n else:\\n # catch a problem... if there is only\\n # one eqlev and it's stable (this is \\n # unphysical), then it could be a vertical\\n # resolution thing. This is a kind of \\n # \\\"null\\\" option\\n try:\\n\\t P_el=eqlev[isstab][0]\\n P_lfc=eqlev[isstab][0]\\n except:\\n\\t P_el=eqlev[unstab][0]\\n P_lfc=eqlev[unstab][0]\\t\\n\\t\\n if np.isnan(P_lfc):\\n return P_lcl,P_lfc,P_el,0,0\\n\\n # need to handle case where dwpt is not available \\n # above a certain level for any reason. Most simplest \\n # thing to do is set it to a reasonably low value; \\n # this should be a conservative approach!\\n \\n #dwpt=dewpt.copy().soften_mask()\\n [inds] = np.where(np.isnan(dewpt))\\n dwpt = dewpt\\n dwpt[inds] = dwpt.min()\\n \\n # raise ValueError\\n #if dwpt[(pres>=P_el).data*(pres<P_lfc).data].mask.any():\\n # print \\\"WARNING: substituting dwpt.min() for masked values of DWPT in this sounding\\\"\\n #dwpt[dwpt.mask]=dwpt.min()\\n # dwptenv=interp(preswet,pres[::-1],dwpt[::-1])\\n # NEW:\\n\\n dwptenv=interp(pparcel,pres[::-1],dwpt[::-1])\\n\\n\\n \\n #if hght[(pres>=P_el).data].mask.any():\\n # raise NotImplementedError, \\\"TODO: Implement standard atmosphere to substitute missing heights\\\"\\n # hghtenv=interp(preswet,pres[::-1],self.soundingdata['hght'][::-1])\\n # NEW:\\n hghtenv=interp(pparcel,pres[::-1],hght[::-1])\\n \\n\\n # Areas of POSITIVE Bouyancy\\n # cond1=(tempwet>=tempenv)*(preswet<=P_lfc)*(preswet>P_el)\\n # NEW:\\n cond1=(tparcel>=tempenv)*(pparcel<=P_lfc)*(pparcel>P_el)\\n # Areas of NEGATIVE Bouyancy\\n # cond2=(tempwet<tempenv)*(preswet<=P_lcl)*(preswet>P_el)\\n # NEW:\\n if totalcape:\\n cond2=(tparcel<tempenv)*(pparcel>P_el)\\n else:\\n cond2=(tparcel<tempenv)*(pparcel>P_lfc)\\n # Do CAPE calculation\\n # 1. Virtual temperature of parcel... remember it's saturated above LCL.\\n # e_parcel=SatVap(tempwet)\\n # Tv_parcel=VirtualTemp(tempwet+degCtoK,preswet*100.,e_parcel)\\n # e_env=SatVap(dwptenv)\\n # Tv_env=VirtualTemp(tempenv+degCtoK,preswet*100.,e_env)\\n # NEW:\\n e_parcel=SatVap(tparcel)\\n Tv_parcel=VirtualTemp(tparcel+degCtoK,pparcel*100.,e_parcel)\\n e_env=SatVap(dwptenv)\\n Tv_env=VirtualTemp(tempenv+degCtoK,pparcel*100.,e_env)\\n\\n CAPE=trapz(9.81*(Tv_parcel[cond1]-Tv_env[cond1])/Tv_env[cond1],hghtenv[cond1])\\n CIN=trapz(9.81*(Tv_parcel[cond2]-Tv_env[cond2])/Tv_env[cond2],hghtenv[cond2])\\n\\n return P_lcl,P_lfc,P_el,CAPE,CIN\",\n \"def calculate_water_vapour_pressure(self, T=None, units='atm'): # temp in Kelvin\\n A,B,C = self.get_ABC(T=T)\\n \\n if A is not None and B is not None and C is not None:\\n # bar \\n p_vap_bar = math.pow(10, (A-B/(C+T)))\\n if units=='bar':\\n return p_vap_bar\\n \\n # atm\\n elif units=='atm': \\n p_vap_atm = convertor.convert(\\n p_vap_bar, \\n currentUnits='bar', \\n newUnits='atm')\\n return p_vap_atm\\n \\n else:\\n return None\\n else:\\n return None\",\n \"def Latentc(tempc):\\n \\n return 1000*(2500.8 - 2.36*tempc + 0.0016*tempc**2 - 0.00006*tempc**3)\",\n \"def calculateTemperature(self):\\n \\n # CIE XYZ space\\n self.X = (1/0.17697)*((0.49)*self.R + (0.31)*self.G + (0.2)*self.B)\\n self.Y = (1/0.17697)*((0.17697)*self.R + (0.81240)*self.G + (0.01063)*self.B)\\n self.Z = (1/0.17697)*((0)*self.R + (0.010)*self.G + (0.99)*self.B)\\n\\n # CIE Chromaticities xy\\n self.x = self.X/(self.X + self.Y + self.Z)\\n self.y = self.Y/(self.X + self.Y + self.Z)\\n \\n # CIE Chromaticities uv\\n #self.u = (0.4661*self.x + 0.1593*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n #self.v = (0.6581*self.y)/(self.y - 0.15735*self.x + 0.2424)\\n \\n # constant for McCamy's/Hernandez-Andrés formula\\n n = (self.x - self.x_e)/(self.y - self.y_e)\\n \\n # Correlated color temperature according to Hernández-Andrés (1999)\\n self.color_temp = ( self.A_0 + \\n self.A_1*np.exp(-n/self.t_1) + \\n self.A_2*np.exp(-n/self.t_2) + \\n self.A_3*np.exp(-n/self.t_3) )\\n \\n # Delete too high values\\n self.color_temp[self.color_temp > 30000] = 0\\n \\n # Affichage de la CCT\\n self.mean_temp = int(round(self.color_temp.mean()))\\n self.mean_temp_label.setText(\\\"Temperature moyenne = \\\"+str(self.mean_temp))\\n self.mean_temp_label.adjustSize()\\n \\t\\n # Affichage de l'illuminance (Y)\\n self.mean_illu = int(round((self.Y.mean())))\\n self.illuminance_label.setText(\\\"Illuminance moyenne = \\\"+str(self.mean_illu))\\n self.illuminance_label.adjustSize()\",\n \"def compute_dewpoint(temperature, humidity):\\n\\n temp_C = (temperature - 32) * 5 / 9 # Convert temperature from deg F to deg C\\n rh = humidity / 100\\n\\n b = 18.678\\n c = 257.14 # deg C\\n\\n gamma = math.log(rh) + (b * temp_C) / (c + temp_C)\\n tdp = c * gamma / (b -gamma)\\n\\n tdp_F = 9 / 5 * tdp + 32 # Convert temperature from deg C to deg F\\n return tdp_F;\",\n \"def get_corrected_ppm(self, temperature, humidity):\\n return self.PARA * math.pow((self.get_corrected_resistance(temperature, humidity)/ self.RZERO), -self.PARB)\",\n \"def get_corrected_ppm(self, temperature, humidity):\\n return self.PARA * math.pow((self.get_corrected_resistance(temperature, humidity)/ self.RZERO), -self.PARB)\",\n \"def tempWater(sample):\\n sample *= .0009\\n sample *= 1000\\n celsius = (sample - 20.5128) * 0.0512\\n return round(celsius,2)\",\n \"def get_cooling_output_for_supply_air_estimation(\\n a_a: float, q: float, mu_c: float, v_vent: np.ndarray,\\n theta_ex: np.ndarray, x_ex: np.ndarray, j: np.ndarray,\\n hc_period: np.ndarray, n_p: np.ndarray, q_gen: np.ndarray, w_gen: np.ndarray, v_local: np.ndarray,\\n theta_set_c: float, x_set_c: float):\\n\\n # specific heat of air, J/kgK\\n c = get_specific_heat()\\n\\n # air density, kg/m3\\n rho = get_air_density()\\n\\n # latent heat of evaporation, kJ/kg\\n l_wtr = get_evaporation_latent_heat()\\n\\n q_d_hs_cs = np.maximum(\\n (((q - 0.35 * 0.5 * 2.4) * a_a + c * rho * (v_local + sum(v_vent)) / 3600) * (\\n theta_ex - theta_set_c)\\n + mu_c * a_a * j + q_gen + n_p * 51.0) * 3600 * 10 ** (-6),\\n 0.0) * (hc_period == 'c')\\n\\n q_d_hs_cl = np.maximum(\\n (((v_local + sum(v_vent)) * rho * (x_ex - x_set_c) * 10 ** 3 + w_gen) * l_wtr\\n + n_p * 40.0 * 3600) * 10 ** (-6), 0.0) * (hc_period == 'c')\\n\\n return q_d_hs_cs + q_d_hs_cl\",\n \"def _calculate_temperature(c, h):\\n\\n return (c - 331.4 - 0.0124 * h) / 0.6\",\n \"def thermal_conductivity(temperature):\\n a0 = -4.1236\\n a1 = 13.788\\n a2 = -26.068\\n a3 = 26.272\\n a4 = -14.663\\n a5 = 4.4954\\n a6 = -0.6905\\n a7 = 0.0397\\n log_t = math.log10(temperature)\\n f_exp = a0 + a1*log_t + a2*log_t**2.0 + a3*log_t**3.0 + a4*log_t**4.0 + \\\\\\n a5*log_t**5.0 + a6*log_t**6.0 + a7*log_t**7\\n g10_thermal_conductivity = 10.0**f_exp\\n return g10_thermal_conductivity\",\n \"def C_P(self):\\n return self.generic_getter(\\n get_heat_capacity_pressure, \\\"C_P\\\", \\\"convert_heat_capacity\\\"\\n )\",\n \"def compute_water_charge(self, volume, time, units_volume='ml'):\\n if units_volume == 'ml':\\n volume_iu = volume / 1e6\\n else:\\n raise NotImplementedError\\n \\n return volume_iu / time\",\n \"def get_decided_outlet_supply_air_absolute_humidity_for_cooling(\\n x_req_c: np.ndarray, v_supply: np.ndarray, x_hs_out_min_c: np.ndarray) -> np.ndarray:\\n\\n return np.maximum(np.sum(x_req_c * v_supply / v_supply.sum(axis=0), axis=0), x_hs_out_min_c)\",\n \"def get_capacitive_rain_sensor_temp(\\n self, rain_sensor_temp: Optional[int] = None\\n ) -> float:\\n # TODO: these values were hardcoded but now are taken from the CW.\\n # Check which way is the \\\"true\\\" way based on the sensor type (capacitive vs Hydredon)\\n # rain_pull_up_resistance = 1\\n # rain_res_at_25 = 1\\n # rain_beta = 3450\\n absolute_zero = 273.15\\n\\n if rain_sensor_temp is None:\\n rain_sensor_temp = self.raw_rain_sensor_temp\\n\\n if rain_sensor_temp < 1:\\n rain_sensor_temp = 1\\n elif rain_sensor_temp > 1022:\\n rain_sensor_temp = 1022\\n\\n r = self.rain_pull_up_resistance / ((1023 / rain_sensor_temp) - 1)\\n r = math.log(r / self.rain_res_at_25)\\n\\n return 1 / (r / self.rain_beta + 1 / (absolute_zero + 25)) - absolute_zero\",\n \"def ambient_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"ambient_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._ambient_temperature)\",\n \"def ionization_constant_water(temperature=298.15, density=None):\\n import numpy as np\\n\\n # using Model II from Bandura etal\\n # model parameters\\n n = 6\\n alpha_0 = -0.864671\\n alpha_1 = 8659.19\\n alpha_2 = -22786.2\\n beta_0 = 0.642044\\n beta_1 = -56.8534\\n beta_2 = -0.375754\\n\\n # Water parameters\\n Mw = 18.01528\\n\\n # temperature\\n T = temperature\\n\\n # density\\n if density:\\n D = density\\n else:\\n D = density_water(T)\\n\\n pKWG = 0.61415 \\\\\\n + 48251.33 / T \\\\\\n - 67707.93 / T**2.0 \\\\\\n + 10102100.0 / T**3.0\\n\\n Z = D * np.exp(alpha_0 \\\\\\n + alpha_1/T \\\\\\n + alpha_2/T**2 *np.power(D,2.0/3.0)\\n )\\n\\n pKw = -2*n*(\\n np.log10(1 + Z) - (Z/(Z + 1)) * D * (\\n beta_0 + beta_1/T + beta_2*D\\n )\\n ) + pKWG + 2 * np.log10(Mw/1000.0)\\n\\n return np.power(10, -pKw)\",\n \"def cp(wair,pres,entr=None,temp=None,airf=None,dhum=None,chkvals=False,\\n chktol=_CHKTOL,airf0=None,temp0=None,dhum0=None,chkbnd=False,\\n mathargs=None):\\n airf, temp, dhum = eq_wpte(wair,pres,entr=entr,temp=temp,airf=airf,\\n dhum=dhum,chkvals=chkvals,chktol=chktol,airf0=airf0,temp0=temp0,\\n dhum0=dhum0,chkbnd=chkbnd,mathargs=mathargs)\\n h_s = temp\\n h_ss = iceair_h(0,2,0,wair,pres,temp=temp,airf=airf,dhum=dhum)\\n cp = h_s/h_ss\\n return cp\",\n \"def thermal_expansivity_of_air(self) -> float:\\n\\n return 1 / self.ambient_temperature\",\n \"def get_air_conditioned_room_absolute_humidity(\\n hc_period: np.ndarray, x_ex: np.ndarray, x_set_c: float) -> np.ndarray:\\n\\n return x_set_c * (hc_period == 'c') + x_ex * (hc_period != 'c')\",\n \"def _calculate_temp_in_c(temp):\\r\\n return str((temp * 9 / 5.0 + 32) if temp else \\\"\\\")\",\n \"def temperature() -> float:\",\n \"def dielectric_constant_water(temperature=298.15):\\n tabulated_data = np.array([[263.15, 92.10],\\n [268.15, 89.96],\\n [273.15, 87.90],\\n [278.15, 85.90],\\n [283.15, 83.96],\\n [288.15, 82.06],\\n [293.15, 80.20],\\n [298.15, 78.38],\\n [303.15, 76.60],\\n [308.15, 74.86],\\n [313.15, 73.17],\\n [318.15, 71.50],\\n [323.15, 69.88],\\n [328.15, 68.29],\\n [333.15, 66.74],\\n [338.15, 65.22],\\n [343.15, 63.73],\\n [348.15, 62.28],\\n [353.15, 60.87],\\n [358.15, 59.48],\\n [363.15, 58.13],\\n [368.15, 56.81],\\n [373.15, 55.51]])\\n polynomal_degree = 5\\n fitdata = np.polyfit(tabulated_data[:, 0], tabulated_data[:, 1],\\n polynomal_degree)\\n fitfunction = np.poly1d(fitdata)\\n return fitfunction(temperature)\",\n \"def tempAir(sample):\\n sample *= 1.0\\n sample /= 1000\\n celsius = (sample - 0.5) * 100\\n return round(celsius,2)\",\n \"def eco_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_low)\",\n \"def get_pressure(self): # This function implements the equations needed to convert the digital data into mbars\\n self.digital_pressure_data()\\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\\n temperature, dT=self.get_temperature()\\n OFF = ((C_2 * (2**16)) + ((C_4 * dT)/2**7))\\n SENS = (C_1 * (2**15)) + ((C_3 * dT)/(2**8))\\n pressure=(((self.presadc*(SENS/(2**21)))-OFF)/(2**15))/100\\n return pressure, temperature\",\n \"def get_actual_air_conditioned_temperature(\\n hc_period: np.ndarray,\\n theta_ac: np.ndarray, v_supply: np.ndarray, theta_supply_h: np.ndarray, theta_supply_c: np.ndarray,\\n l_d_h: np.ndarray, l_d_cs: np.ndarray,\\n u_prt: float, a_prt: np.ndarray, a_hcz: np.ndarray, q: float) -> np.ndarray:\\n\\n rho = get_air_density()\\n c = get_specific_heat()\\n\\n a_prt = a_prt.reshape(1, 5).T\\n a_hcz = a_hcz[0:5].reshape(1, 5).T\\n\\n theta_ac_act_h = np.maximum(theta_ac + (c * rho * v_supply * (theta_supply_h - theta_ac) - l_d_h * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n theta_ac_act_c = np.minimum(theta_ac - (c * rho * v_supply * (theta_ac - theta_supply_c) - l_d_cs * 10 ** 6)\\n / (c * rho * v_supply + (u_prt * a_prt + q * a_hcz) * 3600), theta_ac)\\n\\n return theta_ac_act_h * (hc_period == 'h') + theta_ac_act_c * (hc_period == 'c') + theta_ac * (hc_period == 'm')\",\n \"def calculate_dew_point(temp, hum):\\n return temp - (100 - hum) / 5\",\n \"def partial_pressure(fraction=3, tem=283.15, pre=1.21325):\\n pwater = np.exp(77.345 + 0.0057 * tem - 7235 / tem) / (tem ** 8.2) / 100000\\n # partial pressure of H2O in air by relation, [Bar]\\n p_hcl = fraction * 10 ** -5 * pre\\n # firstly use 3ppm concentration to do estimation [Bar]\\n return tem, pre, pwater, p_hcl\",\n \"def heat_capacity_of_air(self) -> float:\\n\\n return 1002.5 + 275 * (10 ** (-6)) * (self.ambient_temperature - 200) ** 2\",\n \"def test_ccl_with_ml():\\n pressure = np.array([992.0, 990.0, 983.0, 967.0, 950.0, 944.0, 928.0, 925.0, 922.0,\\n 883.0, 877.7, 858.0, 853.0, 850.0, 835.0, 830.0, 827.0, 826.0,\\n 813.6, 808.0, 799.0, 784.0, 783.3, 769.0, 760.0, 758.0, 754.0,\\n 753.0, 738.0, 725.7, 711.0, 704.0, 700.0, 685.0, 672.0, 646.6,\\n 598.6, 596.0, 587.0, 582.0, 567.0, 560.0, 555.0, 553.3, 537.0,\\n 526.0, 521.0, 519.0, 515.0, 500.0]) * units.mbar\\n temperature = np.array([6.8, 6.2, 7.8, 7.6, 7.2, 7.6, 6.6, 6.4, 6.2, 3.2, 2.8, 1.2,\\n 1.0, 0.8, -0.3, -0.1, 0.4, 0.6, 0.9, 1.0, 0.6, -0.3, -0.3,\\n -0.7, -1.5, -1.3, 0.2, 0.2, -1.1, -2.1, -3.3, -2.3, -1.7, 0.2,\\n -0.9, -3.0, -7.3, -7.5, -8.1, -8.3, -9.5, -10.1, -10.7,\\n -10.8, -12.1, -12.5, -12.7, -12.9, -13.5, -15.5]) * units.degC\\n dewpoint = np.array([5.1, 5.0, 4.2, 2.7, 2.2, 0.6, -2.4, -2.6, -2.8, -3.8, -3.6,\\n -3.1, -5.0, -4.2, -1.8, -4.3, -7.6, -6.4, -8.2, -9.0, -10.4,\\n -9.3, -9.6, -14.7, -11.5, -12.3, -25.8, -25.8, -19.1, -19.6,\\n -20.3, -42.3, -39.7, -46.8, -46.8, -46.7, -46.5, -46.5,\\n -52.1, -36.3, -47.5, -30.1, -29.7, -30.4, -37.1, -49.5,\\n -36.7, -28.9, -28.5, -22.5]) * units.degC\\n\\n ccl_p, ccl_t, t_c = ccl(pressure, temperature, dewpoint,\\n mixed_layer_depth=500 * units.m, which='all')\\n\\n assert_array_almost_equal(ccl_p, np.array(\\n [850.600930, 784.325312, 737.767377, 648.076147]) * units.mbar, 5)\\n assert_array_almost_equal(ccl_t, np.array(\\n [0.840118, -0.280299, -1.118757, -2.875716]) * units.degC, 5)\\n assert_array_almost_equal(t_c, np.array(\\n [13.146845, 18.661621, 22.896152, 32.081388]) * units.degC, 5)\",\n \"def internal_external_canopy_heat_capacity(lumped_cover_heat_capacity: float) -> float:\\n return 0.1 * lumped_cover_heat_capacity\",\n \"def get_air_conditioned_room_temperature(\\n hc_period: np.ndarray,\\n theta_ex: np.ndarray, theta_set_h: float, theta_set_c: float) -> np.ndarray:\\n\\n theta_ac_m = np.clip(theta_ex, theta_set_h, theta_set_c)\\n\\n return theta_set_h * (hc_period == 'h') + theta_set_c * (hc_period == 'c') + theta_ac_m * (hc_period == 'm')\",\n \"def target_temperature_low_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_low_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_low)\",\n \"def cooled_surface_temp(T:np.ndarray) -> float:\\n \\n return T.dot(cs_temp_weights)\",\n \"def CMB_spec(eng, temp):\\n if np.issubdtype(type(eng), float):\\n eng = np.array([eng])\\n prefactor = 8*np.pi*(eng**2)/((ele_compton*me)**3)\\n phot_spec_density = prefactor*[1/(np.exp(photeng/temp) - 1) if photeng/temp < 500 else 0 for photeng in eng]\\n\\n if np.issubdtype(type(eng), float): \\n return phot_spec_density[0] \\n else: \\n return phot_spec_density\",\n \"def thermalConductivity(self, Tk=None, Tc=None):\\n Tk = getTk(Tc, Tk)\\n self.checkPropertyTempRange(\\\"thermal conductivity\\\", Tk)\\n thermalConductivity = (\\n 2.13014e-08 * Tk**3\\n - 6.31916e-05 * Tk**2\\n + 1.11629e-01 * Tk\\n - 2.00043e00\\n )\\n return thermalConductivity * 1e-3\",\n \"def water_vapour(t):\\n T_0 = 273.15\\n T_rw = 35.86 # over water\\n a = 17.269\\n # cdo -mulc,610.78 -exp -div -mulc,17.5 -subc,273.15 a\\n return 610.78 * np.exp(a * (t - T_0) / (t - T_rw))\",\n \"def test_wet_psychrometric_rh():\\n p = 1013.25 * units.mbar\\n dry_bulb_temperature = 20. * units.degC\\n wet_bulb_temperature = 18. * units.degC\\n psychrometric_rh = relative_humidity_wet_psychrometric(p, dry_bulb_temperature,\\n wet_bulb_temperature)\\n assert_almost_equal(psychrometric_rh, 82.8747 * units.percent, 3)\",\n \"def get_supply_air_absolute_humidity_for_cooling(\\n x_hs_out_c: np.ndarray, x_ac: np.ndarray, operation: np.ndarray) -> np.ndarray:\\n\\n return np.where(operation == 'c', x_hs_out_c, x_ac)\",\n \"def calc_supply_temp(tr, Q, m, cp, case):\\n if m > 0:\\n if case == \\\"DH\\\":\\n ts = tr + Q / (m * cp)\\n else:\\n ts = tr - Q / (m * cp)\\n else:\\n ts = 0\\n return ts\",\n \"def get_temperature(self): # This function implements the equations needed to convert the digital data to degrees celsius\\n C_1, C_2, C_3, C_4, C_5, C_6=self.calibration_constants()\\n self.digital_temp_data() \\n dT = self.tempadc-(C_5*(2**8))\\n temperature=(2000+(dT*(C_6/(2**23))))/100\\n return temperature, dT\",\n \"def target_temperature_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature)\",\n \"def plc_temp(coil_df):\",\n \"def get_chiller_temperature(self) -> float:\\n\\n return self.send(self.cmd.GET_COOLING_ACT)\",\n \"def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\\n\\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\\n\\n return h_kJ_kg\",\n \"def initialize_thermal_prediction(self, config_file):\\n conf_pred = config_file['prediction']['heat']\\n conf_powr = config_file['prediction']['power']\\n # conf_pred\\n n_day = conf_pred['n_day']\\n n_values = conf_pred['n_values_per_day']\\n precision_in_h = conf_pred['precision_in_h']\\n use_predef_loads = conf_pred['use_predef_loads']\\n predef_loads_file_path = conf_pred['path_loads']\\n # heating curve\\n conf_hk = config_file['components']['heating_curve']\\n hk_ta = conf_hk['design_ambient_temperature_oC']\\n hk_ti = conf_hk['design_indoor_temperature_oC']\\n hk_tv = conf_hk['design_supply_temperature_oC']\\n hk_tr = conf_hk['design_return_temperature_oC']\\n hk_n = conf_hk['radiator_coefficient_n']\\n hk_m = conf_hk['radiator_coefficient_m']\\n hk_qn = conf_hk['design_heat_load_in_kW']\\n # chp unit\\n patm = utils.get_pressure_in_MPa()\\n calcopt = utils.get_calc_option()\\n eps_el_chp = config_file['components']['chp_unit']['electrical_efficiency']\\n eps_th_chp = config_file['components']['chp_unit']['thermal_efficiency']\\n qel_n_chp = config_file['components']['chp_unit']['max_electric_power_in_kW']\\n chp_tinp = config_file['components']['chp_unit']['design_input_temperature_oC']\\n chp_tmax = config_file['components']['chp_unit']['design_output_temperature_oC']\\n qth_n_chp = eps_th_chp * qel_n_chp / eps_el_chp # in kW\\n mstr_chp = qth_n_chp / (utils.cp_fluid_water(0.5 * (chp_tmax + chp_tinp), patm, calcopt) * (chp_tmax - chp_tinp)) # in kg/s = kW / (kJ/kg/K * K)\\n # gas boiler\\n qth_n_gb = config_file['components']['gas_boiler']['max_thermal_power_in_kW']\\n gb_tinp = config_file['components']['gas_boiler']['design_input_temperature_oC']\\n gb_tmax = config_file['components']['gas_boiler']['design_output_temperature_oC']\\n mstr_gb = qth_n_gb / (utils.cp_fluid_water(0.5 * (gb_tinp + gb_tmax), patm, calcopt) * (gb_tmax - gb_tinp)) # in kg/s = kW / (kJ/kg/K * K) # in kg/s = kW / (kJ/kg/K * K)\\n # storage tank\\n effective_height = config_file['components']['storage_tank']['effective_heigth_in_m']\\n inner_radius = config_file['components']['storage_tank']['inner_radius_tank_in_m']\\n effective_pipe_volume = config_file['components']['storage_tank']['effective_coil_volume_in_m3'] # in m3 - water volume of the pipes of inner heat exchanger\\n effective_volume = config_file['components']['storage_tank']['effective_volume_in_m3']\\n if (effective_volume <= 0.0):\\n effective_volume = math.pi * inner_radius * inner_radius * effective_height - effective_pipe_volume # in m3\\n nr_calc = 20\\n slice_volume = effective_volume / nr_calc # in m3\\n qmax_rod_el = config_file['components']['storage_tank']['power_heating_rod_in_kW']\\n open_weather_map_active = config_file['calculation']['platform_mode']['open_weather_map_active']\\n # conf_powr\\n #print('\\\\n initialize_thermal_prediction')\\n #print('use_predef_loads = {}; {}'.format(use_predef_loads,type(use_predef_loads)))\\n #print('predef_loads_file_path = {}; {}'.format(predef_loads_file_path,type(predef_loads_file_path)))\\n return predict_thermal.predict_Q(n_day, n_values, precision_in_h, predef_loads_file_path, use_predef_loads, \\n self.output_horizon_in_h, self.output_resolution_in_s, conf_powr, hk_tv, hk_tr, hk_ti, hk_ta, \\n hk_qn, hk_n, hk_m, chp_tmax, gb_tmax, slice_volume, mstr_chp, mstr_gb, qmax_rod_el, eps_th_chp, \\n eps_el_chp, open_weather_map_active)\",\n \"def target_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"target_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._target_temperature_high)\",\n \"def calc_VPD(t_celsius, rel_humidity):\\n # according to Licor LI-6400 manual pg 14-10\\n # and Buck AL (1981). New equations for computing vapor pressure and\\n # enhancement factor. J Appl Meteor 20:1527-1532\\n vp_sat = 0.61365 * math.exp((17.502 * t_celsius) / (240.97 + t_celsius))\\n\\n vp_air = vp_sat * rel_humidity\\n return vp_sat - vp_air # or vp_sat * (1 - rel_humidity)\",\n \"def internal_temp_c(self) -> int:\\n return int(self._device_info[\\\"Temperature\\\"])\",\n \"def convert_f_to_c(temp_in_farenheit): ## ##\\n celsiustemp = round((temp_in_farenheit - 32) * 5/9, 1) ##\\n return celsiustemp ##\",\n \"def get_decided_outlet_supply_air_temperature_for_cooling(\\n vav_system: bool, theta_req_c: np.ndarray, v_d_supply: np.ndarray,\\n theta_hs_out_min_c: np.ndarray) -> np.ndarray:\\n\\n if vav_system:\\n return np.maximum(np.min(theta_req_c, axis=0), theta_hs_out_min_c)\\n else:\\n return np.maximum(np.sum(theta_req_c * v_d_supply / v_d_supply.sum(axis=0), axis=0), theta_hs_out_min_c)\",\n \"def eco_temperature_high_c(self) -> float:\\r\\n self._logger.debug(log_message_formatter(\\r\\n \\\"get\\\", f\\\"{self}\\\", \\\"eco_temperature_high_c\\\"))\\r\\n return kelvin_to_celsius(self._eco_temperature_high)\",\n \"def get_percent_wet():\\n # Create an ADS1115 ADC (16-bit) instance.\\n adc = Adafruit_ADS1x15.ADS1115()\\n\\n GAIN = 1\\n DRY = 20280 # 100% Dry\\n WET = 10140 # 100% Wet\\n\\n value = adc.read_adc(0, gain=GAIN)\\n \\n # print \\\"value: %d\\\" % value\\n \\n percent_dry = ((value - WET)*100)/(DRY-WET)\\n percent_wet = 100 - percent_dry\\n\\n return percent_wet\",\n \"def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\\n \\n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\\n z = pmx_c.sum(axis=0)\\n pmx_c /= z #Normalize\\n \\n \\t\\n return pmx_c, z\",\n \"def get_requested_supply_air_absolute_humidity_for_cooling(\\n x_ac: np.ndarray, l_d_cl: np.ndarray, v_d_supply: np.ndarray) -> np.ndarray:\\n\\n # air density, kg/m3\\n rho = get_air_density()\\n\\n # latent heat of evaporation, kJ/kg\\n l_wtr = get_evaporation_latent_heat()\\n\\n return x_ac - l_d_cl * 10 ** 3 / (v_d_supply * rho * l_wtr)\",\n \"def convert_f_to_c(temp_in_farenheit):\\n \\n temp=round((float(temp_in_farenheit)-32)*5/9,1)\\n \\n return (temp)\",\n \"def initialize_thermal_prediction(self, config_file):\\n conf_pred = config_file['prediction']['heat']\\n conf_powr = config_file['prediction']['power']\\n # config_json\\n n_day = conf_pred['n_day']\\n n_values = conf_pred['n_values_per_day']\\n precision_in_h = conf_pred['precision_in_h']\\n use_predef_loads = conf_pred['use_predef_loads']\\n predef_loads_file_path = conf_pred['path_loads']\\n # heating curve\\n conf_hk = config_file['components']['heating_curve']\\n hk_ta = conf_hk['design_ambient_temperature_oC']\\n hk_ti = conf_hk['design_indoor_temperature_oC']\\n hk_tv = conf_hk['design_supply_temperature_oC']\\n hk_tr = conf_hk['design_return_temperature_oC']\\n hk_n = conf_hk['radiator_coefficient_n']\\n hk_m = conf_hk['radiator_coefficient_m']\\n hk_qn = conf_hk['design_heat_load_in_kW']\\n # chp unit\\n patm = utils.get_pressure_in_MPa()\\n calcopt = utils.get_calc_option()\\n eps_el_chp = config_file['components']['chp_unit']['electrical_efficiency']\\n eps_th_chp = config_file['components']['chp_unit']['thermal_efficiency']\\n qel_n_chp = config_file['components']['chp_unit']['max_electric_power_in_kW']\\n chp_tinp = config_file['components']['chp_unit']['design_input_temperature_oC']\\n chp_tmax = config_file['components']['chp_unit']['design_output_temperature_oC']\\n qth_n_chp = eps_th_chp * qel_n_chp / eps_el_chp # in kW\\n mstr_chp = qth_n_chp / (utils.cp_fluid_water(0.5 * (chp_tmax + chp_tinp), patm, calcopt) * (chp_tmax - chp_tinp)) # in kg/s = kW / (kJ/kg/K * K)\\n # gas boiler\\n qth_n_gb = config_file['components']['gas_boiler']['max_thermal_power_in_kW']\\n gb_tinp = config_file['components']['gas_boiler']['design_input_temperature_oC']\\n gb_tmax = config_file['components']['gas_boiler']['design_output_temperature_oC']\\n mstr_gb = qth_n_gb / (utils.cp_fluid_water(0.5 * (gb_tinp + gb_tmax), patm, calcopt) * (gb_tmax - gb_tinp)) # in kg/s = kW / (kJ/kg/K * K) # in kg/s = kW / (kJ/kg/K * K)\\n # storage tank\\n effective_height = config_file['components']['storage_tank']['effective_heigth_in_m']\\n inner_radius = config_file['components']['storage_tank']['inner_radius_tank_in_m']\\n effective_pipe_volume = config_file['components']['storage_tank']['effective_coil_volume_in_m3']\\n effective_volume = config_file['components']['storage_tank']['effective_volume_in_m3']\\n if (effective_volume <= 0.0):\\n effective_volume = math.pi * inner_radius * inner_radius * effective_height - effective_pipe_volume # in m3\\n nr_calc = 20\\n slice_volume = effective_volume / nr_calc # in m3\\n qmax_rod_el = config_file['components']['storage_tank']['power_heating_rod_in_kW']\\n open_weather_map_active = config_file['calculation']['platform_mode']['open_weather_map_active']\\n # conf_powr\\n #print('\\\\n initialize_thermal_prediction')\\n #print('use_predef_loads = {}; {}'.format(use_predef_loads,type(use_predef_loads)))\\n #print('predef_loads_file_path = {}; {}'.format(predef_loads_file_path,type(predef_loads_file_path)))\\n return predict_thermal.predict_Q(n_day, n_values, precision_in_h, predef_loads_file_path, use_predef_loads, self.output_horizon_in_h, \\n self.output_resolution_in_s, conf_powr, hk_tv, hk_tr, hk_ti, hk_ta, hk_qn, hk_n, hk_m, chp_tmax, gb_tmax, slice_volume, \\n mstr_chp, mstr_gb, qmax_rod_el, eps_th_chp, eps_el_chp, open_weather_map_active)\",\n \"def calc_temp_withlosses(t0, Q, m, cp, case):\\n if m > 0:\\n if case == \\\"positive\\\":\\n t1 = t0 + Q / (m * cp)\\n else:\\n t1 = t0 - Q / (m * cp)\\n else:\\n t1 = 0\\n return t1\",\n \"def get_hc_external(self, weather, surface, h_surface, terrain):\\r\\n roughness = surface.construction[0].roughness_unit # Change back to this line...left as below to match Na's\\r\\n if roughness == \\\"VeryRough\\\":\\r\\n D = 11.58\\r\\n E = 5.894\\r\\n F = 0\\r\\n elif roughness == \\\"Rough\\\":\\r\\n D = 12.49\\r\\n E = 4.065\\r\\n F = 0.028\\r\\n elif roughness == \\\"MediumRough\\\":\\r\\n D = 10.79\\r\\n E = 4.192\\r\\n F = 0.0\\r\\n elif roughness == \\\"MediumSmooth\\\":\\r\\n D = 8.23\\r\\n E = 4.0\\r\\n F = -0.057\\r\\n elif roughness == \\\"Smooth\\\":\\r\\n D = 10.22\\r\\n E = 3.1\\r\\n F = 0.0\\r\\n elif roughness == \\\"VerySmooth\\\":\\r\\n D = 8.23\\r\\n E = 3.33\\r\\n F = -0.036\\r\\n else:\\r\\n D = 8.23\\r\\n E = 4.0\\r\\n F = -0.057\\r\\n print \\\"No Roughness Value Found so Set Default Values of 8.23,4.0,-0.057\\\"\\r\\n\\r\\n wind_speed_temp = weather[\\\"wind_speed\\\"]\\r\\n # Terrain Lookup Table\\r\\n if terrain == 'Flat or Open Countryside':\\r\\n sigma = 270\\r\\n a = 0.14\\r\\n elif terrain == 'Rough or Wooded Country':\\r\\n sigma = 370\\r\\n a = 0.22\\r\\n elif terrain == 'Towns and City Scapes':\\r\\n sigma = 460\\r\\n a = 0.33\\r\\n elif terrain == 'Ocean Front Areas':\\r\\n sigma = 210\\r\\n a = 0.10\\r\\n elif terrain == 'Urban, Industrial, or Forest':\\r\\n sigma = 370\\r\\n a = 0.22\\r\\n else:\\r\\n sigma = 370\\r\\n a = 0.22\\r\\n print \\\"No Terrain Type Found so Set Default Values of 370,0.22\\\"\\r\\n terrain_sigma = sigma\\r\\n terrain_cof = a\\r\\n\\r\\n # Adjust the wind speed...Stable air above human inhabited areas:\\r\\n #wind_speed = wind_speed_temp * ((h_surface / 10) ** 0.5) # This was the line used to get wind_speed before terrain was added\\r\\n # Wind speed corrected for terrain differences;\\r\\n wind_speed = wind_speed_temp * ((270/10) ** 0.14) * (h_surface/terrain_sigma) ** terrain_cof\\r\\n #print wind_speed\\r\\n # Calculate the hc_external\\r\\n # hc_external= D+E*Wind_speed+F*Wind_speed^2\\r\\n hc_external = D + (E * wind_speed) + (F * wind_speed ** 2)\\r\\n\\r\\n # depending on the direction of the wind adjust the hc_external...as of versions 3 and 4 this part seems omitted\\r\\n #x = abs(wind_speed_dir - azimuth)\\r\\n #if x > 100:\\r\\n # if x < 260:\\r\\n # hc_external *= 0.5\\r\\n #print \\\"hc_external : \\\", hc_external, D, E, F\\r\\n\\r\\n return round(hc_external, 5)\",\n \"def cmm(self) -> Optional[np.ndarray]:\\n if self.sensorsz is None:\\n return None\\n return self.c * self.sensorsz / self.imgsz\",\n \"def __we_c(cls, calib, tc, temp, we_v, ae_v):\\n we_t = we_v - (calib.we_elc_mv / 1000.0) # remove electronic we zero\\n ae_t = ae_v - (calib.ae_elc_mv / 1000.0) # remove electronic ae zero\\n\\n we_c = tc.correct(calib, temp, we_t, ae_t)\\n\\n # print(\\\"A4Datum__we_c: we_t:%f ae_t:%f we_c:%s\\\" % (we_t, ae_t, we_c), file=sys.stderr)\\n\\n return we_c\",\n \"def _approx_wep(wair,entr,pres):\\n pvmax = pres * (1-wair) / (1-wair + _EPSW*wair)\\n if pvmax >= _PTPE:\\n # Saturation would start at _TTP; use saturated heat capacity at _TTP\\n a_t = (pres - _PTPE)/(pres - _PTPE + _EPSW*_PTPE)\\n s_t = (wair*_CDRY*numpy.log(_TTP/_TCELS) - (1-wair)*_LILTP/_TTP\\n - wair*_RDRY*numpy.log((pres-_PTPE)/_PATM)\\n + wair*_RWAT*_EPSW*_PTPE/(pres-_PTPE)*_AVI)\\n c_t = (wair*_CDRY + wair*(1-a_t)/a_t*_CVAP + (1-wair/a_t)*_CICE\\n + wair*_RWAT*(1-a_t)*(_EPSW*a_t + 1-a_t)/_EPSW/a_t**2 * _AVI**2)\\n temp = _TTP * numpy.exp(-(s_t-entr)/c_t)\\n else:\\n # Get approximate saturation temperature\\n v = numpy.log(pres*(1-wair)/(_PTPE*(_EPSW*wair + 1-wair)))/_BVI\\n r = _AVI/_BVI\\n x = maths4.lamb2(v,r)\\n tsat = _TTP/x\\n ssat = (wair * (_CDRY*numpy.log(tsat/_TCELS)\\n - _RDRY*numpy.log((pres-pvmax)/_PATM))\\n + (1-wair) * (_CVAP*numpy.log(tsat/_TTP) + _LLVTP/_TTP\\n - _RWAT*numpy.log(pvmax/_PTPE)))\\n \\n if entr >= ssat:\\n ceff = wair*_CDRY + (1-wair)*_CVAP\\n temp = _TTP * numpy.exp((entr-ssat)/ceff)\\n else:\\n csat = (wair*_CDRY + (1-wair)*_CVAP\\n + (1-wair)*_RWAT*pres/(pres-pvmax)\\n * ((_AVI+_BVI)*_TTP/tsat - _BVI)**2)\\n temp = tsat * numpy.exp(-(ssat-entr)/csat)\\n pvap = _PTPE * numpy.exp((_AVI+_BVI)*(1 - _TTP/temp)\\n - _BVI*numpy.log(temp/_TTP))\\n airf = (pres - pvap) / (pres - pvap + _EPSW*pvap)\\n dhum = pres/(_RDRY*temp) / (airf + (1-airf)/_EPSW)\\n return airf, temp, dhum\",\n \"def get_specific_heat() -> float:\\n return 1006.0\",\n \"def pressure(z: tf.Tensor) -> tf.Tensor:\\n return self._p_thermal * tf.math.exp(\\n -(z + self._height * delta_t_frac *\\n (tf.math.log(1.0 - delta_t_frac * tf.math.tanh(z / self._height)) -\\n tf.math.log(1.0 + tf.math.tanh(z / self._height)) +\\n z / self._height)) / h_sfc / (1.0 - delta_t_frac**2))\",\n \"def convert_f_to_c(temp_in_farenheit):\\n temp_in_celcius = ((temp_in_farenheit - 32) * 5) / 9\\n temp_in_celcius = round(temp_in_celcius, 1)\\n return temp_in_celcius\",\n \"def lookup_effective_mass_area_factor(self, cm):\\n\\n if cm == 0.0:\\n return 0.0\\n elif 0.0 < cm <= 165000.0:\\n return 2.5\\n else:\\n return 3.2\",\n \"def water_vapour_saturation_pressure(T, isCelsius = True):\\n if isCelsius:\\n T += 273.15\\n\\n if T < 273.15: \\n warnings.warn(\\\"Temperature {:} K is below freezing. This function is not valid.\\\".format(T))\\n elif T > 647.096:\\n warnings.warn(\\\"Temperature {:} K is above the critical temperature. This function is not valid.\\\".format(T))\\n \\n Tc = 647.096 # critical temperature, K\\n Pc = 22.064e6 # critical pressure Pa\\n C1 = -7.85951783\\n C2 = 1.84408259\\n C3 = -11.7866497\\n C4 = 22.6807411\\n C5 = -15.9618719\\n C6 = 1.80122502\\n v = 1 - T / Tc # vartheta = transformed temperature\\n \\n a = C1 * v + C2 * v**1.5 + C3 * v**3 + C4 * v**3.5 + C5 * v**4 + C6 * v**7.5\\n Pws = Pc * numpy.exp(Tc * a / T)\\n return Pws\",\n \"def farenheit(ctemp):\\n return round(9.0/5.0 * ctemp + 32)\",\n \"def pressure(self):\\r\\n self._read_temperature()\\r\\n\\r\\n # Algorithm from the BME280 driver\\r\\n # https://github.com/BoschSensortec/BME280_driver/blob/master/bme280.c\\r\\n adc = self._read24(_BME280_REGISTER_PRESSUREDATA) / 16 # lowest 4 bits get dropped\\r\\n var1 = float(self._t_fine) / 2.0 - 64000.0\\r\\n var2 = var1 * var1 * self._pressure_calib[5] / 32768.0\\r\\n var2 = var2 + var1 * self._pressure_calib[4] * 2.0\\r\\n var2 = var2 / 4.0 + self._pressure_calib[3] * 65536.0\\r\\n var3 = self._pressure_calib[2] * var1 * var1 / 524288.0\\r\\n var1 = (var3 + self._pressure_calib[1] * var1) / 524288.0\\r\\n var1 = (1.0 + var1 / 32768.0) * self._pressure_calib[0]\\r\\n if var1 == 0:\\r\\n return 0\\r\\n if var1:\\r\\n pressure = 1048576.0 - adc\\r\\n pressure = ((pressure - var2 / 4096.0) * 6250.0) / var1\\r\\n var1 = self._pressure_calib[8] * pressure * pressure / 2147483648.0\\r\\n var2 = pressure * self._pressure_calib[7] / 32768.0\\r\\n pressure = pressure + (var1 + var2 + self._pressure_calib[6]) / 16.0\\r\\n\\r\\n pressure /= 100\\r\\n if pressure < _BME280_PRESSURE_MIN_HPA:\\r\\n return _BME280_PRESSURE_MIN_HPA\\r\\n if pressure > _BME280_PRESSURE_MAX_HPA:\\r\\n return _BME280_PRESSURE_MAX_HPA\\r\\n return pressure\\r\\n else:\\r\\n return _BME280_PRESSURE_MIN_HPA\",\n \"def conductivity(self):\\n m = 1.67296736e-02 # Determined from optimisation\\n c = 8.54665149e-05 # Determined from optimisation\\n return m * self.concentration + c\",\n \"def test_getThermalExpansionFactorConservedMassByLinearExpansionPercent(self):\\n hotTemp = 700.0\\n dLL = self.component.material.linearExpansionFactor(\\n Tc=hotTemp, T0=self._coldTemp\\n )\\n ref = 1.0 + dLL\\n cur = self.component.getThermalExpansionFactor(Tc=hotTemp)\\n self.assertAlmostEqual(cur, ref)\",\n \"def compute_cm_fm(illuminant, gains, ccm, cal):\\n\\n ###########################################################################\\n # Standard matrices.\\n\\n # W is the matrix that maps sRGB to XYZ.\\n # See: http://www.brucelindbloom.com/\\n W = numpy.array([\\n [ 0.4124564, 0.3575761, 0.1804375],\\n [ 0.2126729, 0.7151522, 0.0721750],\\n [ 0.0193339, 0.1191920, 0.9503041]])\\n\\n # HH is the chromatic adaptation matrix from D65 (since sRGB's ref white is\\n # D65) to D50 (since CIE XYZ's ref white is D50).\\n HH = numpy.array([\\n [ 1.0478112, 0.0228866, -0.0501270],\\n [ 0.0295424, 0.9904844, -0.0170491],\\n [-0.0092345, 0.0150436, 0.7521316]])\\n\\n # H is a chromatic adaptation matrix from D65 (because sRGB's reference\\n # white is D65) to the calibration illuminant (which is a standard matrix\\n # depending on the illuminant). For a D65 illuminant, the matrix is the\\n # identity. For the A illuminant, the matrix uses the linear Bradford\\n # adaptation method to map from D65 to A.\\n # See: http://www.brucelindbloom.com/\\n H_D65 = numpy.array([\\n [ 1.0, 0.0, 0.0],\\n [ 0.0, 1.0, 0.0],\\n [ 0.0, 0.0, 1.0]])\\n H_A = numpy.array([\\n [ 1.2164557, 0.1109905, -0.1549325],\\n [ 0.1533326, 0.9152313, -0.0559953],\\n [-0.0239469, 0.0358984, 0.3147529]])\\n H = [H_A, H_D65][illuminant]\\n\\n ###########################################################################\\n # Per-model matrices (that should be the same for all units of a particular\\n # phone/camera. These are statics in the HAL camera properties.\\n\\n # G is formed by taking the r,g,b gains and putting them into a\\n # diagonal matrix.\\n G = numpy.array([[gains[0],0,0], [0,gains[1],0], [0,0,gains[3]]])\\n\\n # S is just the CCM.\\n S = numpy.array([ccm[0:3], ccm[3:6], ccm[6:9]])\\n\\n ###########################################################################\\n # Per-unit matrices.\\n\\n # The per-unit calibration matrix for the given illuminant.\\n CC = numpy.array([cal[0:3],cal[3:6],cal[6:9]])\\n\\n ###########################################################################\\n # Derived matrices. These should match up with DNG-related matrices\\n # provided by the HAL.\\n\\n # The color matrix and forward matrix are computed as follows:\\n # CM = inv(H * W * S * G * CC)\\n # FM = HH * W * S\\n CM = numpy.linalg.inv(\\n numpy.dot(numpy.dot(numpy.dot(numpy.dot(H, W), S), G), CC))\\n FM = numpy.dot(numpy.dot(HH, W), S)\\n\\n # The color matrix is normalized so that it maps the D50 (PCS) white\\n # point to a maximum component value of 1.\\n CM = CM / max(numpy.dot(CM, (0.9642957, 1.0, 0.8251046)))\\n\\n return CM, FM\",\n \"def volumetric_heat_capacity(temperature):\\n a = -2.4083\\n b = 7.6006\\n c = -8.2982\\n d = 7.3301\\n e = -4.2386\\n f = 1.4294\\n g = -0.24396\\n h = 0.015236\\n i = 0.0\\n log_t = math.log10(temperature)\\n f_exp = a + b*log_t + c*log_t**2.0 + d*log_t**3.0 + e*log_t**4.0 + f*log_t**5.0 + g*log_t**6.0 + \\\\\\n h*log_t**7.0 + i*log_t**8.0\\n g10_cp = 10.0**f_exp\\n return g10_cp * G10NISTMaterialProperties.density\",\n \"def P(self, energy, newEnergy, temperature):\\n \\\"\\\"\\\" This is the decision-rule, adapted from Nascimento, et al., 2009 (See references) \\\"\\\"\\\"\\n \\n delta = self.calcDelta(newEnergy, energy)\\n\\n minTemp = 0.00001 # use minimum to avoid div/0 and buffer overflow\\n if temperature == 0:\\n return minTemp\\n elif temperature > minTemp:\\n try:\\n return math.exp(-1 * round(delta, 4) / round(temperature, 4))\\n except OverflowError as detail:\\n return minTemp\\n else:\\n return 1\",\n \"def partial_pressure(num_moles, ideal_gas_law, temp, volume, is_temp_kelvin=True):\\n ATM = 0.0821\\n KPA = 8.314\\n MMHG = 62.4\\n if ideal_gas_law == \\\"atm\\\":\\n ideal_gas_law_number = ATM\\n if ideal_gas_law == \\\"kpa\\\":\\n ideal_gas_law_number = KPA\\n if ideal_gas_law == \\\"mmhg\\\":\\n ideal_gas_law_number = MMHG\\n if not is_temp_kelvin:\\n temp += 273\\n nrt = (num_moles) * (ideal_gas_law_number) * (temp)\\n final_pressure = nrt / volume\\n return final_pressure\",\n \"def get_cold_junction_temperature(self):\\n return self._mcp9600.get('COLD_JUNCTION').temperature\",\n \"def temperature(self):\\n return float(self._current_observation['temp_c'])\",\n \"def read_ambient_temperatureC(self, ):\\n return self._read_temperature(MLX90614_TA)\",\n \"def _pwr_std_temp(rpm, MP, altitude):\\n # get the power at sea level (i.e. point B on the left side of the Lycoming power chart)\\n \\n # get pwr at two even hundreds of rpm, and then interpolate\\n if rpm >= 2600:\\n rpm1 = 2600\\n elif rpm <= 1800:\\n rpm1 = 1800\\n else:\\n rpm1 = rpm - rpm % 100\\n\\n rpm2 = rpm1 + 100\\n \\n pwr_SL1 = _pwr_sl(rpm1, MP)\\n pwr_SL2 = _pwr_sl(rpm2, MP)\\n # print \\\"SL Pwr 1=\\\", pwr_SL1\\n # print \\\"SL Pwr 2=\\\", pwr_SL2\\n \\n # get power at full throttle at this rpm and MP at altitude (i.e. point A on the right side of the Lycoming power chart)\\n # density ratio at point A on the right side of the Lycoming power chart)\\n pwr_FT1, DR_FT1 = _hp_at_MP_and_altitude(rpm1, MP)\\n pwr_FT2, DR_FT2 = _hp_at_MP_and_altitude(rpm2, MP)\\n # print \\\"FT pwr 1=\\\", pwr_FT1\\n # print \\\"FT pwr 2=\\\", pwr_FT2\\n # print \\\"DR FT 1=\\\", DR_FT1\\n # print \\\"DR FT 2=\\\", DR_FT2\\n \\n # density ratio at sea level\\n DR_sl = 1\\n \\n # density ratio for the actual conditions (i.e. point D on the right side of the Lycoming power chart)\\n DR_test = SA.alt2density_ratio(altitude)\\n # print \\\"DR_test=\\\", DR_test\\n \\n # function is unstable if the DR at FT is close to 1. This sends the slope off to unpredictable values.\\n slope1=(pwr_FT1 - pwr_SL1) / (DR_FT1 - DR_sl)\\n slope2=(pwr_FT2 - pwr_SL2) / (DR_FT2 - DR_sl)\\n \\n if MP > 28:\\n if slope1 < -80:\\n slope1=-62\\n elif slope1> -60:\\n slope1=-62\\n if slope2< -80:\\n slope2 = -62\\n elif slope2> -60:\\n slope2=-62\\n \\n # print \\\"slope1=\\\", slope1\\n # print \\\"slope2=\\\", slope2\\n \\n pwr_std_temp1 = pwr_SL1 + (DR_test - DR_sl) * slope1\\n pwr_std_temp2 = pwr_SL2 + (DR_test - DR_sl) * slope2\\n # print \\\"Pwr Std Temp 1=\\\", pwr_std_temp1\\n # print \\\"Pwr Std Temp 2=\\\", pwr_std_temp2\\n pwr_std_temp = pwr_std_temp1 + (rpm - rpm1) * (pwr_std_temp2 - pwr_std_temp1) / (rpm2 - rpm1)\\n\\n return pwr_std_temp\",\n \"def set_temp_compensation(self, temp: int = 25) -> str:\\n if type(temp) == float or int:\\n response = self.query(f'T,{temp}')\\n if temp < 10 or temp > 40:\\n print(f'\\\\nNOTE: Unusual ocean temperature set: {temp} C.')\\n else:\\n print('Temp compensation factor should be a decimal/integer!')\\n return response\",\n \"def air_humidity_method_qsat26air(air_temperature,surface_air_pressure,relative_humdity):\\n es = vapor_pressure(air_temperature,surface_air_pressure)\\n em = 0.01*relative_humdity*es\\n air_humidity = 622.*em/(surface_air_pressure-0.378*em)\\n return air_humidity\",\n \"def get_D_C3H8_air_eff(self, T):\\n\\n Kn = self.get_Kn(T)\\n D_C3H8_air_Kn = self.get_D_C3H8_air_Kn(T)\\n\\n if np.isscalar(Kn):\\n if Kn <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n else:\\n if Kn.any() <= 1.:\\n D_C3H8_air_eff = (\\n self.porosity / self.tortuosity * self.D_C3H8_air\\n )\\n else:\\n D_C3H8_air_eff = (\\n 2. * self.porosity / self.tortuosity *\\n (self.D_C3H8_air * D_C3H8_air_Kn) / (self.D_C3H8_air +\\n D_C3H8_air_Kn)\\n )\\n\\n self.D_C3H8_air_eff = D_C3H8_air_eff\\n\\n return D_C3H8_air_eff\",\n \"def convert_f_to_c(temp_in_farenheit):\\n celcius_temp = round(float((temp_in_farenheit) - 32)*(5/9),1)\\n return(celcius_temp)\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def distribute(self, date_time, air_temp, vapor_pressure=None,\\n dew_point=None, cloud_factor=None):\\n\\n self._logger.debug('%s Distributing thermal' % date_time)\\n\\n # calculate clear sky thermal\\n if self.clear_sky_method == 'marks1979':\\n cth = np.zeros_like(air_temp, dtype=np.float64)\\n envphys_c.ctopotherm(\\n air_temp, dew_point,\\n self.dem,\\n self.sky_view_factor,\\n cth,\\n self.config['marks1979_nthreads'])\\n\\n elif self.clear_sky_method == 'dilley1998':\\n cth = clear_sky.Dilly1998(air_temp, vapor_pressure/1000)\\n\\n elif self.clear_sky_method == 'prata1996':\\n cth = clear_sky.Prata1996(air_temp, vapor_pressure/1000)\\n\\n elif self.clear_sky_method == 'angstrom1918':\\n cth = clear_sky.Angstrom1918(air_temp, vapor_pressure/1000)\\n\\n # terrain factor correction\\n if (self.sky_view_factor is not None) and \\\\\\n (self.clear_sky_method != 'marks1979'):\\n # apply (emiss * skvfac) + (1.0 - skvfac) to the longwave\\n cth = cth * self.sky_view_factor + (1.0 - self.sky_view_factor) * \\\\\\n STEF_BOLTZ * air_temp**4\\n\\n # make output variable\\n self.thermal_clear = cth.copy()\\n\\n # correct for the cloud factor\\n # ratio of measured/modeled solar indicates the thermal correction\\n if self.correct_cloud:\\n if self.cloud_method == 'garen2005':\\n cth = cloud.Garen2005(cth,\\n cloud_factor)\\n\\n elif self.cloud_method == 'unsworth1975':\\n cth = cloud.Unsworth1975(cth,\\n air_temp,\\n cloud_factor)\\n\\n elif self.cloud_method == 'kimball1982':\\n cth = cloud.Kimball1982(cth,\\n air_temp,\\n vapor_pressure/1000,\\n cloud_factor)\\n\\n elif self.cloud_method == 'crawford1999':\\n cth = cloud.Crawford1999(cth,\\n air_temp,\\n cloud_factor)\\n\\n # make output variable\\n self.thermal_cloud = cth.copy()\\n\\n # correct for vegetation\\n if self.correct_veg:\\n cth = vegetation.thermal_correct_canopy(cth,\\n air_temp,\\n self.veg_tau,\\n self.veg_height)\\n\\n # make output variable\\n self.thermal_veg = cth.copy()\\n\\n self.thermal = utils.set_min_max(cth, self.min, self.max)\",\n \"def test_cape_cin_value_error():\\n pressure = np.array([1012.0, 1009.0, 1002.0, 1000.0, 925.0, 896.0, 855.0, 850.0, 849.0,\\n 830.0, 775.0, 769.0, 758.0, 747.0, 741.0, 731.0, 712.0, 700.0, 691.0,\\n 671.0, 636.0, 620.0, 610.0, 601.0, 594.0, 587.0, 583.0, 580.0, 571.0,\\n 569.0, 554.0, 530.0, 514.0, 506.0, 502.0, 500.0, 492.0, 484.0, 475.0,\\n 456.0, 449.0, 442.0, 433.0, 427.0, 400.0, 395.0, 390.0, 351.0, 300.0,\\n 298.0, 294.0, 274.0, 250.0]) * units.hPa\\n temperature = np.array([27.8, 25.8, 24.2, 24, 18.8, 16, 13, 12.6, 12.6, 11.6, 9.2, 8.6,\\n 8.4, 9.2, 10, 9.4, 7.4, 6.2, 5.2, 3.2, -0.3, -2.3, -3.3, -4.5,\\n -5.5, -6.1, -6.1, -6.1, -6.3, -6.3, -7.7, -9.5, -9.9, -10.3,\\n -10.9, -11.1, -11.9, -12.7, -13.7, -16.1, -16.9, -17.9, -19.1,\\n -19.9, -23.9, -24.7, -25.3, -29.5, -39.3, -39.7, -40.5, -44.3,\\n -49.3]) * units.degC\\n dewpoint = np.array([19.8, 16.8, 16.2, 16, 13.8, 12.8, 10.1, 9.7, 9.7,\\n 8.6, 4.2, 3.9, 0.4, -5.8, -32, -34.6, -35.6, -34.8,\\n -32.8, -10.8, -9.3, -10.3, -9.3, -10.5, -10.5, -10, -16.1,\\n -19.1, -23.3, -18.3, -17.7, -20.5, -27.9, -32.3, -33.9, -34.1,\\n -35.9, -26.7, -37.7, -43.1, -33.9, -40.9, -46.1, -34.9, -33.9,\\n -33.7, -33.3, -42.5, -50.3, -49.7, -49.5, -58.3, -61.3]) * units.degC\\n cape, cin = surface_based_cape_cin(pressure, temperature, dewpoint)\\n expected_cape, expected_cin = 2098.688061 * units('joules/kg'), 0.0 * units('joules/kg')\\n assert_almost_equal(cape, expected_cape, 3)\\n assert_almost_equal(cin, expected_cin, 3)\",\n \"def conduct_heat(self, delta_time, external_power):\\n\\t\\tself.temperature_container = self.temperature_container+self.area*external_power*delta_time/(self.heat_capacity_container*self.mass_container)#https://en.wikipedia.org/wiki/Heat_capacity\\n\\t\\t\\n\\t\\tinternal_power = 0.591*(self.temperature_container-self.temperature)/0.01#No idea of this is right. Mainly the devides by its length bit. https://en.wikipedia.org/wiki/Thermal_conduction#Fourier's_law\\n\\t\\t\\n\\t\\tif (self.heat_capacity*self.mass())!=0:\\n\\t\\t\\tself.temperature = self.temperature+internal_power*delta_time/(self.heat_capacity*self.mass())\\n\\t\\t\\t#self.temperature_container=self.temperature_container-internal_power*delta_time/(self.heat_capacity_container*self.mass_container)#Als je dit toevoegd lijkt de simulatie niet goed meer te werken dus nog even uitzoeken heo dat zit.\",\n \"def get_pressure_coefficient(self):\\n depth = self.params[\\\"Measured_Pressure\\\"][\\\"depth\\\"]\\n coef = self.params[\\\"Measured_Pressure\\\"][\\\"coef\\\"]\\n pres = self.params[\\\"Measured_Pressure\\\"][\\\"data\\\"]\\n if depth and not coef and pres:\\n hydro = hydrostatic_pressure(self.depth,\\n kelly_bushing=self.kelly_bushing,\\n depth_w=self.water_depth)\\n coef_data = list()\\n for dp, pr in zip(depth, pres):\\n idx = np.searchsorted(self.depth, dp)\\n coef_data.append(pr / hydro[idx])\\n log = Log()\\n log.depth = depth\\n log.data = coef_data\\n return log\\n else:\\n log = Log()\\n log.depth = depth\\n log.data = coef\\n return log\",\n \"def prescribed_surface_temperature(x, t, K_medium, rho_medium, c_medium, T_medium_initial, T_external_applied):\\n k = get_kappa(K_medium, rho_medium, c_medium)\\n return (T_external_applied - T_medium_initial)*erfc(x/(2*np.sqrt(k*t))) + T_medium_initial\",\n \"def get_temperature(elevation, sea_level):\\n if elevation <= sea_level:\\n return 0.8\\n else:\\n return (-1.0 / (1.0 - sea_level)) * (elevation - sea_level) + 1.0\",\n \"def _temperature(self, p_input:float) -> float:\\n if self._unit_in == 'R':\\n temp_K = p_input*5.0/9.0\\n elif self._unit_in == 'F':\\n temp_K = (p_input+459.67)/9.0*5.0\\n elif self._unit_in == 'C':\\n temp_K = p_input+273.15\\n elif self._unit_in == 'K':\\n temp_K = p_input\\n \\n if self._unit_out == 'R':\\n return (temp_K*9.0/5.0)\\n elif self._unit_out == 'F':\\n return (temp_K*9.0/5.0-459.67) \\n elif self._unit_out == 'C':\\n return (temp_K-273.15)\\n elif self._unit_out == 'K':\\n return temp_K\",\n \"def p_ym_c(pm,px,py,pyx_c,pmx_c):\\n pym_c = np.zeros((py.size,pm.size))\\n for yi in range(py.size):\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\\n return pym_c\",\n \"def get_requested_supply_air_temperature_for_cooling(\\n theta_sur_c: np.ndarray, theta_ac: np.ndarray, l_d_cs: np.ndarray, v_d_supply: np.ndarray,\\n psi: float, l_duct: np.ndarray) -> np.ndarray:\\n\\n c = get_specific_heat()\\n rho = get_air_density()\\n\\n l_duct = np.array(l_duct).reshape(1,5).T\\n\\n theta_req_c = theta_sur_c - (theta_sur_c - theta_ac + l_d_cs * 10 ** 6 / (v_d_supply * c * rho)) \\\\\\n * np.exp(psi * l_duct * 3600 / (v_d_supply * c * rho))\\n\\n return np.minimum(theta_req_c, theta_ac)\",\n \"def get_Cm(self, K0, K1):\\n # if(self.weight>=1.5):\\n # raise ValueError,\\\" Error bounds only accurate for k<1.5! got k=%s\\\" % self.weight\\n twominusk = mp2 - self._weight\\n tmp = mpmath.mpf(len(self.multiplier().weil_module().D()))\\n tmp1 = mppi * mp2\\n tmp1 = mpmath.power(tmp1, twominusk)\\n tmp3 = mpmath.zeta(twominusk)\\n if(K0 == 0):\\n tmp4 = 1\\n else:\\n tmp4 = mpmath.power(K0, 1 - self._weight)\\n g1 = mpmath.gamma(1 - self._weight)\\n g2 = mpmath.gamma(2 - self._weight)\\n\\n Cm = mp2 / g1 + mp4 * tmp1 / g1 / g2 * tmp * tmp3 * tmp4\\n return Cm\",\n \"def calculate_economics(\\n irradiance: pd.DataFrame, temperature: pd.DataFrame, wind_speed: pd.DataFrame,\\n CECMod: pd.DataFrame, configuration: float = 1\\n ):\\n p_out = calculate_dc_output(irradiance, temperature, wind_speed, CECMod=CECMod)\\n\\n # convert dc to AC - considering a flat loss of 14%\\n # we have to improve this in the future\\n p_out = [v * 0.86 for v in p_out]\\n\\n day_count = [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\\n monthly_electricity = []\\n\\n for month in range(12):\\n st_index = sum(day_count[:month + 1]) * 24\\n end_index = sum(day_count[:month + 2]) * 24\\n data = p_out[st_index: end_index]\\n # Note: division by 50 is to match the values - remove it later!\\n monthly_electricity.append(sum(data) / len(data) / 50)\\n\\n total_ac_energy = sum(p_out)\\n monthly_ac_energy = pd.DataFrame(\\n zip(calendar.month_abbr[1:], monthly_electricity),\\n columns=['month', 'Thousand kWh']\\n )\\n\\n # Based on the example here: https://nrel-pysam.readthedocs.io/en/master/Import.html\\n\\n grid = Grid.default(\\\"PVWattsCommercial\\\")\\n ur = UtilityRate.from_existing(grid, \\\"PVWattsCommercial\\\")\\n cl = Cashloan.from_existing(grid,\\\"PVWattsCommercial\\\")\\n\\n sam_data = read_sam_data(configuration)\\n for module, data in zip([grid, ur, cl], sam_data[:-1]):\\n for k, v in data.items():\\n if k == 'number_inputs':\\n continue\\n try:\\n module.value(k, v)\\n except AttributeError:\\n print(module, k, v)\\n\\n\\n grid.SystemOutput.gen = p_out\\n\\n grid.execute()\\n ur.execute()\\n cl.execute()\\n\\n # list possible outputs here\\n adjusted_installed_cost = cl.Outputs.adjusted_installed_cost\\n payback_cash_flow = [-1 * x for x in cl.Outputs.cf_discounted_payback]\\n\\n return total_ac_energy, monthly_ac_energy, adjusted_installed_cost, payback_cash_flow\",\n \"def get_heat_capacity_pressure(vv, tt, chemical_potential, gbar=2.0, *args, **kwargs):\\n # type: (np.ndarray, np.ndarray, np.ndarray, float, list, dict) -> np.ndarray\\n y = chemical_potential / tt\\n # There is a precision problem with \\\"-\\\" (minus) operator\\n # We'll use asymptotic formula for high temperatures to avoid that problem\\n y_low = y[y < THRESHOLD]\\n vv_low, vv_high = vv[y < THRESHOLD], vv[y >= THRESHOLD]\\n tt_low, tt_high = tt[y < THRESHOLD], tt[y >= THRESHOLD]\\n # high temperatures - low numbers\\n C_P_low = 5 * gbar * np.sqrt(2) / (36 * np.pi ** 2) * tt_low ** (3 / 2) * vv_low\\n C_P_low *= (\\n 5 * _1d_call(_fdk, y_low, k=-1 / 2) * _1d_call(_fdk, y_low, k=3 / 2)\\n - 9 * _1d_call(_fdk, y_low, k=1 / 2) ** 2\\n )\\n C_P_low *= _1d_call(_fdk, y_low, k=3 / 2) / _1d_call(_fdk, y_low, k=1 / 2) ** 2\\n # low temperatures - high numbers\\n C_P_high = (gbar * np.pi / 6) ** (2 / 3) * tt_high * vv_high ** (2 / 3)\\n return np.concatenate((C_P_low, C_P_high)).reshape(y.shape)\",\n \"def getCMean(inp):\\n\\tinp = sorted(inp, key = lambda x: x[0])\\n\\treturn 1 - getClearWaterDepth(inp) / getY90(inp)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6271697","0.61329305","0.61147255","0.60481554","0.5957173","0.59200716","0.58812517","0.5873447","0.5873447","0.58698595","0.58185816","0.57858187","0.5785377","0.572641","0.56684095","0.56592894","0.56545794","0.56407183","0.5634677","0.56337875","0.5617559","0.56123394","0.5606336","0.5598867","0.5584177","0.5552192","0.5533172","0.55303067","0.55013573","0.5483794","0.54750246","0.5474339","0.54675716","0.5433305","0.54293674","0.5429006","0.5412294","0.54104203","0.5402218","0.539962","0.53971815","0.53942996","0.5385547","0.5371968","0.53715336","0.5359355","0.53377676","0.5329969","0.5321873","0.53110546","0.53047395","0.5301873","0.53006583","0.52958083","0.5290615","0.52904224","0.5290135","0.5284513","0.52772313","0.5266409","0.5255935","0.52553815","0.52493906","0.5248162","0.5246704","0.5243548","0.5243156","0.5237512","0.5225367","0.5220558","0.52204484","0.52195525","0.5218908","0.52172214","0.52123225","0.5211388","0.52075976","0.52060854","0.51964647","0.5194749","0.51944846","0.5193705","0.5192458","0.51921517","0.5191625","0.51893294","0.5187028","0.51843154","0.5178343","0.5176842","0.51748","0.51651376","0.5161176","0.51561844","0.5155146","0.5151945","0.5150044","0.5149724","0.5144883","0.5141581"],"string":"[\n \"0.6271697\",\n \"0.61329305\",\n \"0.61147255\",\n \"0.60481554\",\n \"0.5957173\",\n \"0.59200716\",\n \"0.58812517\",\n \"0.5873447\",\n \"0.5873447\",\n \"0.58698595\",\n \"0.58185816\",\n \"0.57858187\",\n \"0.5785377\",\n \"0.572641\",\n \"0.56684095\",\n \"0.56592894\",\n \"0.56545794\",\n \"0.56407183\",\n \"0.5634677\",\n \"0.56337875\",\n \"0.5617559\",\n \"0.56123394\",\n \"0.5606336\",\n \"0.5598867\",\n \"0.5584177\",\n \"0.5552192\",\n \"0.5533172\",\n \"0.55303067\",\n \"0.55013573\",\n \"0.5483794\",\n \"0.54750246\",\n \"0.5474339\",\n \"0.54675716\",\n \"0.5433305\",\n \"0.54293674\",\n \"0.5429006\",\n \"0.5412294\",\n \"0.54104203\",\n \"0.5402218\",\n \"0.539962\",\n \"0.53971815\",\n \"0.53942996\",\n \"0.5385547\",\n \"0.5371968\",\n \"0.53715336\",\n \"0.5359355\",\n \"0.53377676\",\n \"0.5329969\",\n \"0.5321873\",\n \"0.53110546\",\n \"0.53047395\",\n \"0.5301873\",\n \"0.53006583\",\n \"0.52958083\",\n \"0.5290615\",\n \"0.52904224\",\n \"0.5290135\",\n \"0.5284513\",\n \"0.52772313\",\n \"0.5266409\",\n \"0.5255935\",\n \"0.52553815\",\n \"0.52493906\",\n \"0.5248162\",\n \"0.5246704\",\n \"0.5243548\",\n \"0.5243156\",\n \"0.5237512\",\n \"0.5225367\",\n \"0.5220558\",\n \"0.52204484\",\n \"0.52195525\",\n \"0.5218908\",\n \"0.52172214\",\n \"0.52123225\",\n \"0.5211388\",\n \"0.52075976\",\n \"0.52060854\",\n \"0.51964647\",\n \"0.5194749\",\n \"0.51944846\",\n \"0.5193705\",\n \"0.5192458\",\n \"0.51921517\",\n \"0.5191625\",\n \"0.51893294\",\n \"0.5187028\",\n \"0.51843154\",\n \"0.5178343\",\n \"0.5176842\",\n \"0.51748\",\n \"0.51651376\",\n \"0.5161176\",\n \"0.51561844\",\n \"0.5155146\",\n \"0.5151945\",\n \"0.5150044\",\n \"0.5149724\",\n \"0.5144883\",\n \"0.5141581\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.542909"},"document_rank":{"kind":"string","value":"35"}}},{"rowIdx":8490,"cells":{"query":{"kind":"string","value":"Approximate broadband aerosol optical depth. Bird and Hulstrom developed a correlation for broadband aerosol optical depth (AOD) using two wavelengths, 380 nm and 500 nm."},"document":{"kind":"string","value":"def bird_hulstrom80_aod_bb(aod380, aod500):\n # approximate broadband AOD using (Bird-Hulstrom 1980)\n return 0.27583 * aod380 + 0.35 * aod500"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculate_blue_haze_TOA(d,sza,L):\n rho_blue_TOA=np.pi*(d**2)*L[:,5,:]/(2.16*np.cos(sza))\n return rho_blue_TOA","def ead(o2, depth):\n fraction_o2 = percentage_to_fraction(o2)\n fraction_n2 = 1.0 - fraction_o2\n return math.ceil(((depth + 10.0) * (fraction_n2 / 0.79)) - 10.0)","def optical_depth(self,z0,ETeV,\n #OmegaM = cosmo.Om0, OmegaL = 1. - cosmo.Om0, \n OmegaM = 0.3, OmegaL = 0.7, \n H0 = 70., #cosmo.H0.value, \n steps_z = 50,\n steps_e = 50,\n egamma_LIV = True,\n LIV_scale = 0., nLIV=1):\n if np.isscalar(ETeV):\n ETeV = np.array([ETeV])\n elif type(ETeV) == list or type(ETeV) == tuple:\n ETeV = np.array(ETeV)\n\n\n z_array = np.linspace(0.,z0,steps_z)\n result = self.mean_free_path(z_array,ETeV,\n LIV_scale = LIV_scale,\n nLIV = nLIV, \n egamma_LIV = egamma_LIV,\n steps_e = steps_e)\n\n\n zz,ee = np.meshgrid(z_array, ETeV, indexing = 'ij')\n result = 1. / (result.T * u.Mpc).to(u.cm).value # this is in cm^-1\n result *= 1./ ( (1. + zz ) * np.sqrt((1.+ zz )**3. * OmegaM + OmegaL) ) # dt / dz for a flat universe\n\n result = simps(result,zz, axis = 0)\n\n H0 = (H0 * cosmo.H0.unit).to('1 / s').value # convert from km / Mpc / s to 1 / s\n\n return result * c.c.to('cm / s').value / H0","def calculate_blue_TOA(d,sza,L):\n rho_blue_TOA=np.pi*(d**2)*L[:,0,:]/(1.57*np.cos(sza))\n return rho_blue_TOA","def optical_depth(self, dispersion):\n\n from ..calculations import optical_depth, tau_peak\n\n if isinstance(dispersion, Quantity):\n dispersion = dispersion.to(angstrom)\n\n elif hasattr(dispersion, 'unit'):\n if dispersion.unit is not None:\n dispersion = dispersion.to(angstrom)\n\n else:\n dispersion = Quantity(dispersion, unit=angstrom)\n\n velocity_range = ([-20000, 20000] * km / s if self.logn > 18\n else [-1000, 1000] * km / s)\n\n # Select only transitions with redshifted central wavelengths inside\n # `dispersion` +/- 500 km/s:\n rest_wavelengths = Quantity([t.wavelength for t in self.transitions])\n observed_wavelengths = rest_wavelengths * (1 + self.redshift)\n\n wmin = dispersion[0] * (1 - 500 * km / s / c_kms)\n wmax = dispersion[-1] * (1 - 500 * km / s / c_kms)\n\n in_range = ((observed_wavelengths >= wmin) &\n (observed_wavelengths <= wmax))\n transitions = np.array(self.transitions)[in_range]\n\n tau = np.zeros_like(dispersion.value)\n\n for i, transition in enumerate(transitions):\n\n tau_max = tau_peak(transition, self.logn, self.b)\n\n if 1 - exp(-tau_max) < 1e-3:\n continue\n\n observed_wavelength = transition.wavelength * (1 + self.redshift)\n dv = ((dispersion - observed_wavelength) /\n observed_wavelength * c_kms)\n\n i0, i1 = dv.searchsorted(velocity_range)\n tau0 = optical_depth(dv[i0:i1], transition, self.logn, self.b)\n tau[i0:i1] += tau0\n\n return tau","def one_transition_spectrum_cd(self,tr):\n \n\n ta = tr[\"ta\"] # TimeAxis\n rr = tr[\"rr\"] # transition dipole strength\n om = tr[\"om\"] # frequency - rwa\n gg = tr[\"gg\"] # natural broadening (constant or time dependent)\n fwhm = tr[\"fwhm\"] # Additional gaussian broadening of the spectra\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\n \n # CD and fluorescence can be calculated in this step\n # TODO if rotatory strength defined calculate also circular dichroism spectra\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \n \n \n if self.system._has_system_bath_coupling:\n# ct = tr[\"ct\"] # correlation function\n \n # convert correlation function to lineshape function\n #gt = self._c2g(ta,ct.data)\n gt = tr[\"gt\"]\n # calculate time dependent response\n at = numpy.exp(-gt -1j*om*ta.data)\n else:\n # calculate time dependent response\n at = numpy.exp(-1j*om*ta.data) \n# plt.figure()\n# plt.title(\"Absorption\")\n# plt.plot(ta.data,numpy.real(at))\n# plt.plot(ta.data,numpy.imag(at))\n \n \n if len(gg) == 1:\n gam = gg[0]\n rt = numpy.exp(gam*ta.data)\n at *= rt\n #print(\"Constant: \", rt[20], len(at))\n else:\n rt = numpy.exp((gg)*ta.data) \n at *= rt\n #print(\"Time dependent: len = \", rt[20], len(rt))\n \n if fwhm!=0.0:\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\n at *= gauss\n \n # Fourier transform the result\n ft = rr*numpy.fft.hfft(at)*ta.step\n ft = numpy.fft.fftshift(ft)\n # invert the order because hfft is a transform with -i\n ft = numpy.flipud(ft) \n # cut the center of the spectrum\n Nt = ta.length #len(ta.data) \n return ft[Nt//2:Nt+Nt//2]","def calculate_relative_soma_depth(data: Data) -> float:\n\n return data.relative_soma_depth","def depth_to_ata(depth):\n return (depth / 10.0) + 1.0","def Get_AASR(DopplerBandwidth, Frequencies, NumAmbiguities, FreqSteps, Prf,\n TxAntennaPattern, RxAntennaPattern, TxGain=1, RxGain=1):\n _BWDop = DopplerBandwidth\n _NA = NumAmbiguities\n _step = _BWDop / FreqSteps\n \n _denom = 0\n for f in np.arange(start=-_BWDop/2, stop=_BWDop/2, step=_step):\n # find the nearest frequency and magnitude pair for the TX and RX patterns\n _idx, _ = Utils.find_nearest(Frequencies, f)\n _txAzPat = TxAntennaPattern[_idx] * TxGain\n _rxAzPat = RxAntennaPattern[_idx] * RxGain\n _azPat = _txAzPat * _rxAzPat\n _denom += ((math.pow(_azPat, 2)) * _step)\n\n # _txFreq, _txAzPat = Utils.find_nearest(list(TxAntennaPattern.keys()), f)\n # _rxFreq, _rxAzPat = Utils.find_nearest(list(RxAntennaPattern.keys()), f)\n\n # # _txAzPat = Get_AntennaPatternWithDopplerFrequency(f, txDimension, velocity, txGain)\n # # _rxAzPat = Get_AntennaPatternWithDopplerFrequency(f, rxDimension, velocity, rxGain)\n # # _denom += ((math.pow(_azPat, 2)) * _step)\n # _denom += ((_txAzPat * _rxAzPat) * _step)\n \n _num = 0\n _azPat = 0\n for m in range(-_NA, _NA+1, 1):\n if m != 0:\n for f in np.arange(start=-_BWDop/2, stop=_BWDop/2, step=_step):\n # find the nearest frequency and magnitude pair for the TX and RX patterns\n _idx, _ = Utils.find_nearest(Frequencies, f + (m*Prf))\n _txAzPat = TxAntennaPattern[_idx] * TxGain\n _rxAzPat = RxAntennaPattern[_idx] * RxGain\n _azPat = _txAzPat * _rxAzPat\n _num += ((math.pow(_azPat, 2)) * _step)\n\n # _txFreq, _txAzPat = Utils.find_nearest(list(TxAntennaPattern.keys()), f + (m*Prf))\n # _rxFreq, _rxAzPat = Utils.find_nearest(list(RxAntennaPattern.keys()), f + (m*Prf))\n\n # # _txAzPat = Get_AntennaPatternWithDopplerFrequency((f + (m*prf)), txDimension, velocity, txGain)\n # # _rxAzPat = Get_AntennaPatternWithDopplerFrequency((f + (m*prf)), rxDimension, velocity, rxGain)\n # # #_num += ((math.pow(_azPat, 2)) * _step)\n # _num += ((_txAzPat * _rxAzPat) * _step)\n \n return (_num / _denom)","def BoatEarMoon():\n D=1\n alpha=math.radians(83)\n beta=math.radians(42) \n phi=math.radians(70) \n mu=math.radians(10) \n omega=math.radians(30) \n A=25, a=12, b=20, L=0, P=0, W1=0, W2=0, N=0\n \n resMode()","def GetConcBeer(Abs, epsilon, pathLength):\n return Abs / (epsilon * pathLength)","def rayonDeCourbur(**kwargs):\n a = 0\n b = 0\n try:\n if kwargs[\"ref\"] == \"local\":\n a = 6378249.145\n b = 6356515\n elif kwargs[\"ref\"] == \"global\":\n a = 6378137\n b = 6356752.314\n elif kwargs[\"a\"] and kwargs[\"b\"]:\n a = kwargs[\"a\"]\n b = kwargs[\"b\"]\n except KeyError:\n return {\"erreur\": \"params a and b is required, you can use ref too which has two possible value: local and global\"}\n if a != 0 and b != 0:\n try:\n phi = math.radians(kwargs[\"phi\"])\n e2 = 1-(b/a)**2\n w = math.sqrt(1-e2*math.sin(phi)**2)\n M = a*(1-e2)/w**3\n N = a/w\n if \"alpha\" in kwargs.keys():\n alpha = math.radians(kwargs[\"alpha\"])\n rAlpha = (M*N)/(M*math.sin(alpha)**2+N*math.cos(alpha)**2)\n return{\"M\": M, \"N\": N, \"rAlpha\": rAlpha, \"1/R\": 1/rAlpha}\n elif kwargs[\"radius\"] == \"M\":\n return {\"M\": M}\n elif kwargs[\"radius\"] == \"N\":\n return {\"N\": N}\n except KeyError as err:\n return {\"erreur\": f\"{format(err)} is required!\"}","def GetAbsBeer(epsilon, conc, pathLength):\n return epsilon * conc * pathLength","def one_transition_spectrum_abs(self,tr):\n \n\n ta = tr[\"ta\"] # TimeAxis\n dd = tr[\"dd\"] # transition dipole strength\n om = tr[\"om\"] # frequency - rwa\n gg = tr[\"gg\"] # natural broadening (constant or time dependent)\n fwhm = tr[\"fwhm\"] # Additional gaussian broadening of the spectra\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\n \n # CD and fluorescence can be calculated in this step\n # TODO if rotatory strength defined calculate also circular dichroism spectra\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \n \n \n if self.system._has_system_bath_coupling:\n# ct = tr[\"ct\"] # correlation function\n \n # convert correlation function to lineshape function\n #gt = self._c2g(ta,ct.data)\n gt = tr[\"gt\"]\n # calculate time dependent response\n at = numpy.exp(-gt -1j*om*ta.data)\n else:\n # calculate time dependent response\n at = numpy.exp(-1j*om*ta.data) \n# plt.figure()\n# plt.title(\"Absorption\")\n# plt.plot(ta.data,numpy.real(at))\n# plt.plot(ta.data,numpy.imag(at))\n \n \n if len(gg) == 1:\n gam = gg[0]\n rt = numpy.exp(gam*ta.data)\n at *= rt\n #print(\"Constant: \", rt[20], len(at))\n else:\n rt = numpy.exp((gg)*ta.data) \n at *= rt\n #print(\"Time dependent: len = \", rt[20], len(rt))\n \n if fwhm!=0.0:\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\n at *= gauss\n \n # Fourier transform the result\n ft = dd*numpy.fft.hfft(at)*ta.step\n ft = numpy.fft.fftshift(ft)\n # invert the order because hfft is a transform with -i\n ft = numpy.flipud(ft) \n # cut the center of the spectrum\n Nt = ta.length #len(ta.data) \n return ft[Nt//2:Nt+Nt//2]","def doctored_depth_1852_adhoc(self, longitude, latitude, dip):\n # set up sample point and fault array\n p1 = np.array([longitude, latitude])\n fault_file = './InputData/fault_array.npy'\n fault_array = np.load(fault_file)\n # will store haversine distances for comparison\n #dist_array = np.zeros(0.5 * len(fault_array))\n dist_array = np.zeros(len(fault_array)//2)\n for i in range(len(dist_array)):\n x = fault_array[2 * i]\n y = fault_array[2 * i + 1]\n p2 = np.array([x, y])\n dist_array[i] = self.haversine_distance(p1, p2)\n\n dist = np.amin(dist_array)\n\n # need to add trig correction\n return (20000 + dist * np.tan(20 * np.pi / 180))","def calcDispCorrandR(aaconst, aacorr, caliConst, AAdata, outName):\n k=caliConst[3]\n n=caliConst[2]\n #Be sure to change this appropriately to the fixed dye conc\n x=k*((fixed_dye_conc-n)/55.5)\n n = n\n print(aaconst)\n perr2=np.sqrt(np.diag(aacorr))\n print(perr2)\n corrmat2=np.zeros([len(aaconst),len(aaconst)])\n for i in range(len(aacorr)):\n for j in range(len(aacorr)):\n ele=aacorr[i,j]\n diele=ele/(perr2[i]*perr2[j])\n corrmat2[i,j]=round(diele,3)\n print(corrmat2)\n #calculate the r^2 value\n AAss_res = 0\n AAss_total = 0\n residuals = np.zeros([len(AAdata[:,0]), 1])\n for i in range(len(AAdata[:,0])):\n residuals[i] = (DispCurve(AAdata[i,0],x,aaconst[0],aaconst[1],n,aaconst[2]) - AAdata[i,1])\n AAss_res += np.square(residuals[i])\n AAss_total += np.square((AAdata[i,1] - np.average(AAdata[:,1])))\n print(AAss_res)\n print(AAss_total)\n AAr_sq = 1 - (AAss_res/AAss_total)\n print(AAr_sq)\n #write out the fit results\n f = open(outName + \"_disp_constants.txt\", 'w')\n f.write(\"B\\ta\\tN\\tK\\n\")\n for i in range(len(aaconst)):\n f.write('%.9f' %aaconst[i] + \"\\t\")\n f.write(\"\\n\")\n for i in range(len(aacorr)):\n f.write('%.9f' %perr2[i] + \"\\t\")\n f.write(\"\\n\\n\")\n f.write(\"Correlation matrix :\\n\\n\")\n for i in range(len(aacorr)):\n for j in range(len(aacorr)):\n f.write('%.9f' %corrmat2[i,j]+'\\t')\n f.write(\"\\n\\n\")\n f.write(\"R^2 value : \\t\" + '%.9f' %AAr_sq)\n f.close()","def test_GBL_tau_star():\n z = 1.0\n\n # Fully ionized H and He\n x_ionH = 1.0\n x_ionHe = 2.0\n\n cosmo = {}\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\n cosmo['h'] = 0.65\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\n cosmo['Y_He'] = 0.24\n cd.set_omega_k_0(cosmo)\n\n tau_inst, tau_star = cr.optical_depth_instant(z, \n x_ionH=x_ionH, \n x_ionHe=x_ionHe, \n return_tau_star=True,\n **cosmo)\n print(\"tau_star = %.7f\" % (tau_star))\n print(\"tau_star/(h Omega_b) = %.7f =? 0.061\" % \n (tau_star / (cosmo['h'] * cosmo['omega_b_0'])))\n\n ntest.assert_approx_equal(tau_star / (cosmo['h'] * cosmo['omega_b_0']),\n 0.061,\n 2)\n\n print(\"(1 - Y_He/2) = %.3f =? 0.88\" % (1. - (cosmo['Y_He']/2.)))\n ntest.assert_approx_equal((1. - (cosmo['Y_He']/2.)),\n 0.88,\n 7)\n\n H_0 = cc.H100_s * cosmo['h']\n\n # s^-1 * Mpc s^-1 * Mpc^2 / Mpc^3 msun^-1 s^-2 / Msun -> \n tau_star_explicit = ((1. - (cosmo['Y_He']/2.)) * \n ((3. * H_0 * cosmo['omega_b_0'] * cc.c_light_Mpc_s *\n cc.sigma_T_Mpc) / \n (8. * math.pi * cc.G_const_Mpc_Msun_s * \n (cc.m_p_g/cc.M_sun_g))))\n\n print(\"tau_star_explicit = %.7f =? tau_star\" % (tau_star_explicit))\n ntest.assert_approx_equal(tau_star, tau_star_explicit, 3)","def Build_quadrant(self) :\n\n self.omega = np.zeros((self.n_dir,3))\n self.weight = np.zeros((self.n_dir))\n\n if self.sn==2 :\n direction = 0.577350269189625764509149\n weight = 1.\n\n self.omega[0,0] = direction\n self.omega[0,1] = direction\n self.omega[0,2] = direction\n \n self.weight[0] = weight\n \n elif self.sn==4 :\n direction_1 = 0.350021174581540677777041\n direction_2 = 0.868890300722201205229788\n weight = 1./3.\n\n self.omega[0,0] = direction_2\n self.omega[0,1] = direction_1\n self.omega[0,2] = direction_1\n \n self.omega[1,0] = direction_1\n self.omega[1,1] = direction_2\n self.omega[1,2] = direction_1\n\n self.omega[2,0] = direction_1\n self.omega[2,1] = direction_1\n self.omega[2,2] = direction_2\n\n self.weight[0] = weight\n self.weight[1] = weight\n self.weight[2] = weight\n\n elif self.sn==6 :\n direction_1 = 0.266635401516704720331535\n direction_2 = 0.681507726536546927403750\n direction_3 = 0.926180935517489107558380\n weight_1 = 0.176126130863383433783565\n weight_2 = 0.157207202469949899549768\n\n self.omega[0,0] = direction_3\n self.omega[0,1] = direction_1\n self.omega[0,2] = direction_1\n\n self.omega[1,0] = direction_2\n self.omega[1,1] = direction_2\n self.omega[1,2] = direction_1\n\n self.omega[2,0] = direction_1\n self.omega[2,1] = direction_3\n self.omega[2,2] = direction_1\n\n self.omega[3,0] = direction_2\n self.omega[3,1] = direction_1\n self.omega[3,2] = direction_2\n \n self.omega[4,0] = direction_1\n self.omega[4,1] = direction_2\n self.omega[4,2] = direction_2\n\n self.omega[5,0] = direction_1\n self.omega[5,1] = direction_1\n self.omega[5,2] = direction_3\n\n self.weight[0] = weight_1\n self.weight[1] = weight_2\n self.weight[2] = weight_1\n self.weight[3] = weight_2\n self.weight[4] = weight_2\n self.weight[5] = weight_1\n\n elif self.sn==8 :\n direction_1 = 0.218217890235992381266097\n direction_2 = 0.577350269189625764509149\n direction_3 = 0.786795792469443145800830\n direction_4 = 0.951189731211341853132399\n\n weight_1 = 0.120987654320987654320988\n weight_2 = 0.0907407407407407407407407\n weight_3 = 0.0925925925925925925925926\n\n self.omega[0,0] = direction_4\n self.omega[0,1] = direction_1\n self.omega[0,2] = direction_1\n\n self.omega[1,0] = direction_3\n self.omega[1,1] = direction_2\n self.omega[1,2] = direction_1\n \n self.omega[2,0] = direction_2\n self.omega[2,1] = direction_3\n self.omega[2,2] = direction_1\n\n self.omega[3,0] = direction_1\n self.omega[3,1] = direction_4\n self.omega[3,2] = direction_1\n\n self.omega[4,0] = direction_3\n self.omega[4,1] = direction_1\n self.omega[4,2] = direction_2\n\n self.omega[5,0] = direction_2\n self.omega[5,1] = direction_2\n self.omega[5,2] = direction_2\n\n self.omega[6,0] = direction_1\n self.omega[6,1] = direction_3\n self.omega[6,2] = direction_2\n\n self.omega[7,0] = direction_2\n self.omega[7,1] = direction_1\n self.omega[7,2] = direction_3\n\n self.omega[8,0] = direction_1\n self.omega[8,1] = direction_2\n self.omega[8,2] = direction_3\n\n self.omega[9,0] = direction_1\n self.omega[9,1] = direction_1\n self.omega[9,2] = direction_4\n\n self.weight[0] = weight_1\n self.weight[1] = weight_2\n self.weight[2] = weight_2\n self.weight[3] = weight_1\n self.weight[4] = weight_2\n self.weight[5] = weight_3\n self.weight[6] = weight_2\n self.weight[7] = weight_2\n self.weight[8] = weight_2\n self.weight[9] = weight_1\n\n elif self.sn==10 :\n direction_1 = 0.189321326478010476671494\n direction_2 = 0.508881755582618974382711\n direction_3 = 0.694318887594384317279217\n direction_4 = 0.839759962236684758403029\n direction_5 = 0.963490981110468484701598\n\n weight_1 = 0.0893031479843567214704325\n weight_2 = 0.0725291517123655242296233\n weight_3 = 0.0450437674364086390490892\n weight_4 = 0.0539281144878369243545650\n\n self.omega[0,0] = direction_5\n self.omega[0,1] = direction_1\n self.omega[0,2] = direction_1\n \n self.omega[1,0] = direction_4\n self.omega[1,1] = direction_2\n self.omega[1,2] = direction_1\n \n self.omega[2,0] = direction_3\n self.omega[2,1] = direction_3\n self.omega[2,2] = direction_1\n \n self.omega[3,0] = direction_2\n self.omega[3,1] = direction_4\n self.omega[3,2] = direction_1\n\n self.omega[4,0] = direction_1\n self.omega[4,1] = direction_5\n self.omega[4,2] = direction_1\n\n self.omega[5,0] = direction_4\n self.omega[5,1] = direction_1\n self.omega[5,2] = direction_2\n\n self.omega[6,0] = direction_3\n self.omega[6,1] = direction_2\n self.omega[6,2] = direction_2\n\n self.omega[7,0] = direction_2\n self.omega[7,1] = direction_3\n self.omega[7,2] = direction_2\n\n self.omega[8,0] = direction_1\n self.omega[8,1] = direction_4\n self.omega[8,2] = direction_2\n\n self.omega[9,0] = direction_3\n self.omega[9,1] = direction_1\n self.omega[9,2] = direction_3\n\n self.omega[10,0] = direction_2\n self.omega[10,1] = direction_2\n self.omega[10,2] = direction_3\n\n self.omega[11,0] = direction_1\n self.omega[11,1] = direction_3\n self.omega[11,2] = direction_3\n\n self.omega[12,0] = direction_2\n self.omega[12,1] = direction_1\n self.omega[12,2] = direction_4\n\n self.omega[13,0] = direction_1\n self.omega[13,1] = direction_2\n self.omega[13,2] = direction_4\n\n self.weight[0] = weight_1\n self.weight[1] = weight_2\n self.weight[2] = weight_3\n self.weight[3] = weight_2\n self.weight[4] = weight_1\n self.weight[5] = weight_2\n self.weight[6] = weight_4\n self.weight[7] = weight_4\n self.weight[8] = weight_2\n self.weight[9] = weight_3\n self.weight[10] = weight_4\n self.weight[11] = weight_3\n self.weight[12] = weight_2\n self.weight[13] = weight_2\n self.weight[14] = weight_1\n\n elif self.sn==12 :\n direction = np.zeros((6,1))\n\n direction[0] = 0.167212652822713264084504\n direction[1] = 0.459547634642594690016761\n direction[2] = 0.628019096642130901034766\n direction[3] = 0.760021014833664062877138\n direction[4] = 0.872270543025721502340662\n direction[5] = 0.971637719251358378302376\n\n weight_1 = 0.0707625899700910439766549\n weight_2 = 0.0558811015648888075828962\n weight_3 = 0.0373376737588285824652402\n weight_4 = 0.0502819010600571181385765\n weight_5 = 0.0258512916557503911218290\n\n for i in xrange(0,6) :\n self.omega[i,0] = direction[5-i]\n self.omega[i,1] = direction[i]\n self.omega[i,2] = direction[0]\n \n offset = 6\n for i in xrange(0,5) :\n self.omega[offset+i,0] = direction[4-i]\n self.omega[offset+i,1] = direction[i]\n self.omega[offset+i,2] = direction[1]\n\n offset += 5\n for i in xrange(0,4) :\n self.omega[offset+i,0] = direction[3-i]\n self.omega[offset+i,1] = direction[i]\n self.omega[offset+i,2] = direction[2]\n \n offset += 4\n for i in xrange(0,3) :\n self.omega[offset+i,0] = direction[2-i]\n self.omega[offset+i,1] = direction[i]\n self.omega[offset+i,2] = direction[3]\n\n offset += 3\n for i in xrange(0,2) :\n self.omega[offset+i,0] = direction[1-i]\n self.omega[offset+i,1] = direction[i]\n self.omega[offset+i,2] = direction[4]\n \n offset += 2\n self.omega[offset+i,0] = direction[0]\n self.omega[offset+i,1] = direction[1]\n self.omega[offset+i,2] = direction[5]\n\n self.weight[0] = weigth_1\n self.weight[1] = weight_2\n self.weight[2] = weight_3\n self.weight[3] = weight_3\n self.weight[4] = weight_2\n self.weight[5] = weight_1\n self.weight[6] = weight_2\n self.weight[7] = weight_4\n self.weight[8] = weight_5\n self.weight[9] = weight_4\n self.weight[10] = weight_2\n self.weight[11] = weight_3\n self.weight[12] = weight_5\n self.weight[13] = weight_5\n self.weight[14] = weight_3\n self.weight[15] = weight_3\n self.weight[16] = weight_4\n self.weight[17] = weight_3\n self.weight[18] = weight_2\n self.weight[19] = weight_2\n self.weight[20] = weight_1","def comp_angle_opening_magnet(self):\n\n if self.W1 > 0:\n Rbo = self.get_Rbo()\n return float(2 * arcsin(self.W1 / (2 * Rbo)))\n else:\n return self.comp_angle_magnet()","def Agl2ArcLen(self,agl):\r\n\r\n return (self.distance_between_wheels/2)*agl","def ant_max_direction(freq, fit='Lebedev_model'):\n if fit == 'Lebedev':\n freqkHz = freq * 1e3 # to kHz, to float\n dep = 0.2816\n ep = 185634.9983 \\\n - 4.877867876 * freqkHz \\\n + 4.803667396E-005 * freqkHz**2 \\\n - 2.102600271E-010 * freqkHz**3 \\\n + 3.453540782E-016 * freqkHz**4\n ep += dep\n return np.deg2rad(ep)\n elif fit == 'Lebedev_model':\n return ant_max_direction_model_c_wrapped(freq * 1000.0)\n elif fit in ['Vasiliev_old', 'Vasiliev']:\n freqHz = freq * 1e6 # to kHz, to float\n d = 0.87\n a = 0.136\n b = 0.014\n h = 0.384\n wavelength = speed_of_light / freqHz\n k = 2.0 * np.pi / wavelength\n\n if fit == 'Vasiliev_old':\n hi_f = -32.3802499 \\\n + 6.329699841E-007 * freqHz \\\n - 4.001034124E-015 * freqHz ** 2 \\\n + 8.374351252E-024 * freqHz ** 3\n else:\n hi_f = 13.5066804030779 \\\n - 23.894031675887E-008 * freqHz \\\n + 15.2068384591317E-016 * freqHz ** 2 \\\n - 3.28019314316317E-024 * freqHz ** 3\n\n g = hi_f * np.sqrt(1 + ((a / (a + b)) * np.tan(k * h)) ** 2)\n theta = g - wavelength / d\n return theta\n else:\n raise ValueError('Incorrect fit: {}'.format(fit))","def ASS_theo(D, k):\n\ta1b = k[\"A1B1\"]\n\tba1 = k[\"B1A1\"]\n\tca1 = k[\"C1A1\"]\n\tcb = k[\"B1C1\"]\n\tnum = (ca1*cb + ba1*ca1 + ba1*cb) + ca1*ca1*D\n\tden = (ba1*cb + ba1*ca1 + ca1*cb) + (2*ca1*ca1+ba1*cb+ca1*cb)*D + ba1*ca1*D*D\n\treturn num/den","def wavelength(energy):\r\n return 2 * np.pi * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy","def mamajek08_logRpHK_Ro_edge():\n Ro_edge = 0.31935816876122064\n return Ro_edge","def one_transition_spectrum_fluor(self,tr):\n \n\n ta = tr[\"ta\"] # TimeAxis\n dd = tr[\"dd\"] # transition dipole strength\n om = tr[\"om\"] # frequency - rwa\n gg = tr[\"gg\"] # natural broadening (constant or time dependent)\n fwhm = tr[\"fwhm\"] # Additional gaussian broadening of the spectra\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\n \n # CD and fluorescence can be calculated in this step\n # TODO if rotatory strength defined calculate also circular dichroism spectra\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \n \n \n if self.system._has_system_bath_coupling:\n# ct = tr[\"ct\"] # correlation function\n re = tr[\"re\"] # reorganisation energy\n \n # convert correlation function to lineshape function\n #gt = self._c2g(ta,ct.data)\n gt = tr[\"gt\"]\n # calculate time dependent response\n at = numpy.exp(-numpy.conjugate(gt) -1j*om*ta.data + 2j*re*ta.data)\n else:\n # calculate time dependent response\n at = numpy.exp(-1j*om*ta.data) \n# plt.figure()\n# plt.title(\"Absorption\")\n# plt.plot(ta.data,numpy.real(at))\n# plt.plot(ta.data,numpy.imag(at))\n \n \n if len(gg) == 1:\n gam = gg[0]\n rt = numpy.exp(gam*ta.data)\n at *= rt\n #print(\"Constant: \", rt[20], len(at))\n else:\n rt = numpy.exp((gg)*ta.data) \n at *= rt\n #print(\"Time dependent: len = \", rt[20], len(rt))\n \n if fwhm!=0.0:\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\n at *= gauss\n \n # Fourier transform the result\n ft = dd*numpy.fft.hfft(at)*ta.step\n ft = numpy.fft.fftshift(ft)\n # invert the order because hfft is a transform with -i\n ft = numpy.flipud(ft) \n # cut the center of the spectrum\n Nt = ta.length #len(ta.data) \n return ft[Nt//2:Nt+Nt//2]","def get_sn2005ek(colorplt=False):\n z = 0.016551\n ebv = 0.210\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_max = 53639.9\n print (\"adopt r band t_max from Drout+13\")\n \n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\t')\n # tb = tb.drop(columns=[\"Unnamed: 6\"])\n \n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\n 53654.2, 53655.2, 53656.2, 53657.2])\n \n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\n 21.74, np.nan, np.nan, np.nan])\n \n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\n 0.12, np.nan, np.nan, np.nan])\n \n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\n 20.60, 20.74, 20.88, 21.22])\n \n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \n 0.08, 0.10, 0.08, 0.13])\n \n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\n 20.08, np.nan, 20.47, np.nan])\n \n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\n 0.05, np.nan, 0.08, np.nan])\n\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \n 19.01, np.nan, 19.47, np.nan])\n \n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\n 0.05, np.nan, 0.06, np.nan])\n \n mymjds = np.hstack([mjds, mjds, mjds, mjds])\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\n myfilts = np.hstack([ np.repeat(\"B\", len(Bmags)),\n np.repeat(\"V\", len(Bmags)),\n np.repeat(\"R\", len(Rmags)),\n np.repeat(\"I\", len(Imags)) ])\n ix = ~np.isnan(mymags)\n tb = pd.DataFrame({'mjd': mymjds[ix],\n 'mag': mymags[ix],\n 'emag': myemags[ix],\n \"filter\": myfilts[ix]})\n \n ixB = tb['filter'].values==\"B\"\n ixV = tb['filter'].values==\"V\"\n ixR = tb['filter'].values==\"R\"\n ixI = tb['filter'].values==\"I\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixV] = 5430\n tb['wave'].values[ixR] = 6349\n tb['wave'].values[ixI] = 8797\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['B', 'R', 'I']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"R\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"B\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"R\"]\n itb = tbsub[tbsub[\"filter\"].values==\"I\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"BmR\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"RmI\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def getArea(antenna):\n diameter = getDiameter(antenna)\n if antenna >=1 and antenna <= 6: \n efficiency = 0.5\n elif antenna >=7 and antenna <= 15: \n efficiency = 0.6\n elif antenna >=16 and antenna <= 23: \n efficiency = 0.6\n else:\n raise Exception,'Unregonized antenna type'\n area = math.pi * diameter*diameter / 4.0 * efficiency\n\n return area","def anl_solution(self):\r\n\r\n m = float(self.mass) / self.nu_m\r\n qe = 1 / self.nu_m * (self.nu_t * self.nu_t / self.nu_x) * 1.0 \\\r\n / float(self.size_tick * self.size_tick)\r\n print 'qE=', qe\r\n c = self.light_vel\r\n for i in range(0, len(self.obs.obt_g)):\r\n ddt = float(self.obs.obt[i] - self.obs.obt[i - 1])\r\n x = m * c ** 2 / qe * (math.sqrt(1.0 + (qe * self.t[i] / (m\r\n * c)) ** 2) - 1.0)\r\n self.xa_track.append(x)\r\n p = qe * self.t[i]\r\n self.pa.append(p)\r\n v = p / math.sqrt(m ** 2 + (p / c) ** 2)\r\n jv = self.t[i] * qe / (m * c)\r\n v = math.sqrt(jv * jv / (1 + jv * jv)) * c\r\n self.va.append(v)\r\n print 'Analytical solution of the differential equation of motion'","def get_caravan_depth(self):\n return self.caravan_depth","def fringes_ABCD_phase(opd, fringesAr, fringesBr,\n fringesCr, fringesDr,\n wavelength=2.1e-6, plot=False):\n # define boundaries\n n_samples_per_fringes = wavelength/np.abs(np.diff(opd).mean())\n\n # compute Morlet's transform\n dopd = np.abs(np.diff(opd).mean())\n\n wavA = morlet.CWT(fringesAr, dopd, scale_min=1/6e-6,\n scale_max=1/2.e-6, n_per_octave=32)\n wavB = morlet.CWT(fringesBr, dopd, scale_min=1/6e-6,\n scale_max=1/2.e-6, n_per_octave=32)\n wavC = morlet.CWT(fringesCr, dopd, scale_min=1/6e-6,\n scale_max=1/2.e-6, n_per_octave=32)\n wavD = morlet.CWT(fringesDr, dopd, scale_min=1/6e-6,\n scale_max=1/2.e-6, n_per_octave=32)\n\n data = [wavA, wavB, wavC, wavD]\n scale=[]\n for wav in data:\n weight = np.sum(np.abs(wav.cwt)**2, axis=1)\n i0 = weight.argmax()\n i0 = min(max(i0, len(weight)-5), 5)\n print i0, len(weight)\n c = np.polyfit(wav.frequencies[i0-2:i0+2],\\\n np.log(weight[i0-2:i0+2]), 2)\n scale.append(-c[1]/(2*c[0]))\n scale = np.array(scale)\n wlABCD = 1/scale*1e6\n print 'wavelength (WT peak) [A B C D]:'\n print wlABCD\n print 100*(scale/scale.mean()-1), '(% / mean)'\n if plot:\n opd0 = (opd-opd.mean())*1e6\n Xp, Yp = np.meshgrid(opd0, 1/wavA.frequencies*1e6)\n\n plt.figure(4)\n plt.clf()\n ax = plt.subplot(211)\n plt.plot(opd0, fringesAr-fringesCr, 'k')\n plt.subplot(212, sharex=ax)\n plt.plot(opd0, fringesBr-fringesDr, 'k')\n\n plt.figure(3)\n plt.clf()\n plt.subplot(241, sharex=ax)\n plt.plot(opd0, fringesAr, 'k')\n plt.ylabel('fringes')\n plt.title('A')\n plt.subplot(245, sharex=ax)\n plt.pcolormesh(Xp,Yp, abs(wavA.cwt))\n #plt.hlines(wlABCD[0], opd0.min(), opd0.max(),\\\n # color='y')\n plt.ylabel('wavelength (um)')\n plt.subplot(242, sharex=ax)\n plt.plot(opd0, fringesBr, 'k')\n plt.title('B')\n plt.subplot(246, sharex=ax)\n plt.pcolormesh(Xp,Yp, abs(wavB.cwt))\n #plt.hlines(wlABCD[1], opd0.min(), opd0.max(),\\\n # color='y')\n plt.subplot(243, sharex=ax)\n plt.plot(opd0, fringesCr, 'k')\n plt.title('C')\n plt.subplot(247, sharex=ax)\n plt.pcolormesh(Xp,Yp, abs(wavC.cwt))\n #plt.hlines(wlABCD[2], opd0.min(), opd0.max(),\\\n # color='y')\n plt.subplot(244, sharex=ax)\n plt.plot(opd0, fringesDr, 'k')\n plt.title('D')\n plt.subplot(248, sharex=ax)\n plt.pcolormesh(Xp,Yp, abs(wavD.cwt))\n #plt.hlines(wlABCD[3], opd0.min(), opd0.max(),\\\n # color='y')\n\n # compute phases as function of OPD\n phiAB = fringes_morlet_phase(wavA, wavB)\n phiAC = fringes_morlet_phase(wavA, wavC, quasi_pi=True)\n phiDA = fringes_morlet_phase(wavD, wavA)\n phiBC = fringes_morlet_phase(wavB, wavC)\n phiBD = fringes_morlet_phase(wavB, wavD, quasi_pi=True)\n phiCD = fringes_morlet_phase(wavC, wavD)\n weightABCD = np.sum(abs(wavA.cwt)*abs(wavB.cwt)*\\\n abs(wavC.cwt)*abs(wavD.cwt),axis=0)\n weightABCD = weightABCD**(0.25)\n return phiAB, phiBC, phiCD, phiDA, phiAC, phiBD, weightABCD, wlABCD\n\n wp = np.where(weightABCD>\n 3*np.median(weightABCD))\n print 'WP=', wp\n phi_abcd = np.arctan2(fringesAr-fringesCr, \\\n fringesBr-fringesDr)\n phi_abcd = np.unwrap(phi_abcd)\n slopeAB = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiAB[wp], 1)\n slopeBC = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiBC[wp], 1)\n slopeCD = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiCD[wp], 1)\n slopeDA = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiDA[wp], 1)\n slopeAC = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiAC[wp], 1)\n slopeBD = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\n phiBD[wp], 1)\n print 'phases: slope (rad/um of OPD), phi0/pi'\n print 'A-B %6.3f %6.3f' % (slopeAB[0], slopeAB[1]/np.pi)\n print 'B-C %6.3f %6.3f' % (slopeBC[0], slopeBC[1]/np.pi)\n print 'C-D %6.3f %6.3f' % (slopeCD[0], slopeCD[1]/np.pi)\n print 'D-A %6.3f %6.3f' % (slopeDA[0], slopeDA[1]/np.pi)\n slopes = [slopeAB[0], slopeBC[0], slopeCD[0], slopeDA[0]]\n slopes = np.array(slopes)\n wldiff = [wlABCD[0]-wlABCD[1], wlABCD[1]-wlABCD[2], \\\n wlABCD[2]-wlABCD[3], wlABCD[3]-wlABCD[0]]\n wldiff = np.array(wldiff)\n #------- FIGURE ----------\n if plot:\n plt.figure(1)\n plt.clf()\n plt.subplot(211)\n plt.plot(opd, fringesBr, 'k')\n plt.subplot(212)\n plt.hlines(np.pi*np.arange(-1,1.5,0.5), \\\n opd.min(), opd.max(), color='y',\\\n linestyles='dotted')\n plt.plot(opd[wp], phiAB[wp], 'r', linewidth=2)\n plt.plot(opd[wp], phiBC[wp], 'g', linewidth=2)\n plt.plot(opd[wp], phiCD[wp], 'b', linewidth=2)\n plt.plot(opd[wp], phiDA[wp], 'm', linewidth=2)\n plt.plot(opd[wp], np.unwrap(phiAC[wp]),\\\n color=((1,0.5,0,0)), linewidth=2)\n plt.plot(opd[wp], np.unwrap(phiBD[wp]),\\\n color=((0.3,0.2,1,0)), linewidth=2)\n plt.ylabel('phase (radian)')\n plt.xlabel('OPD (m)')\n plt.legend(('A-B', 'B-C', 'C-D', 'D-A',\\\n 'A-C','B-D'))\n plt.ylim(0, 4)\n return phiAB, phiBC, phiCD, phiDA, phiAC, phiBD, weightABCD, wlABCD","def airydisk(unit_r, fno, wavelength):\n u_eff = unit_r * np.pi / wavelength / fno\n return abs(2 * jinc(u_eff)) ** 2","def isothermal_depth_wyrtki1964(da_PT):\n\n # interpolate to finer vertical resolution (2.5m)\n da_interp = da_PT.interp(z=np.arange(0,da_PT.z.max(),2.5))\n\n # make land mask based on surface layer\n da_mask = da_PT.isel(z=0)*0.+1.\n\n # calculate rho-rho0\n da_diff = np.abs(da_interp-da_PT.isel(z=0))\n\n # remove values shallower than critcal value\n da_diff = da_diff.where(da_diff>0.5,other=99999)\n\n # find first index that have value larger than critical value\n z_ind = da_diff.argmin(dim='z',skipna=True)\n\n # used 2d index to find 2d depth map\n da_itd = da_diff.z[z_ind]*da_mask\n\n return da_itd","def merdianArcLength(**kwargs):\n a = 0\n b = 0\n kwargs[\"radius\"] = \"M\"\n try:\n if kwargs[\"ref\"] == \"local\":\n a = 6378249.145\n b = 6356515\n elif kwargs[\"ref\"] == \"global\":\n a = 6378137\n b = 6356752.314\n elif kwargs[\"a\"] and kwargs[\"b\"]:\n a = kwargs[\"a\"]\n b = kwargs[\"b\"]\n except KeyError:\n return {\"erreur\": \"params a and b is required, you can use ref too which has two possible value: local and global\"}\n try:\n kwargs[\"phi\"] = kwargs[\"phi1\"]\n M1 = rayonDeCourbur(**kwargs)[\"M\"]\n kwargs[\"phi\"] = kwargs[\"phi2\"]\n M2 = rayonDeCourbur(**kwargs)[\"M\"]\n deltaPhi = abs(kwargs[\"phi2\"]-kwargs[\"phi1\"])\n if 2 <= deltaPhi < 5:\n e2 = 1-(b/a)**2\n Mm = (M1+M2)/2\n phiM = (kwargs[\"phi1\"]+kwargs[\"phi2\"])/2\n S = ((M1+M2+4*Mm)*deltaPhi +\n ((math.cos(math.radians(2*phiM))*deltaPhi**5)*(a*e2)/240))/6\n return {\"S\": S}\n elif deltaPhi < 2:\n e2 = 1-(b/a)**2\n Mm = (M1+M2)/2\n phiM = (kwargs[\"phi1\"]+kwargs[\"phi2\"])/2\n S = Mm*deltaPhi+(a*e2*math.cos(2*phiM)*deltaPhi**3)/3\n return {\"S\": S}\n except KeyError:\n return {\"error\": \"the function required 3 basics params phi1,phi2 and ref or a&b\"}","def GetEpsilonBeer(Abs, conc, pathLength):\n return Abs / (conc * pathLength)","def find_omega(filename_free,filename_close,filename_far,TI=0.11,wind_speed=8.):\n\n \"\"\"free FAST\"\"\"\n lines = np.loadtxt(filename_free,skiprows=8)\n Omega_free = np.mean(lines[:,6])\n\n \"\"\"waked FAST CLOSE\"\"\"\n lines = np.loadtxt(filename_close,skiprows=8)\n Omega_close = np.mean(lines[:,6])\n\n \"\"\"waked FAST FAR\"\"\"\n lines = np.loadtxt(filename_far,skiprows=8)\n Omega_far = np.mean(lines[:,6])\n\n\n \"\"\"setup the CCBlade loads\"\"\"\n turbineX_close = np.array([0.,126.4])*4.\n turbineX_far = np.array([0.,126.4])*10.\n\n turbineY_waked = np.array([0.,0.])\n\n hub_height = 90.\n\n free_speed = wind_speed\n close_speed = get_eff_turbine_speeds(turbineX_close, turbineY_waked, wind_speed,TI=TI)[1]\n far_speed = get_eff_turbine_speeds(turbineX_far, turbineY_waked, wind_speed,TI=TI)[1]\n\n print 'tip speed ratios'\n print 'free: ', Omega_free*126.4/2./free_speed\n print 'close: ', Omega_close*126.4/2./close_speed\n print 'far: ', Omega_far*126.4/2./far_speed\n\n # return flap_free_atm, edge_free_atm, Omega_free, free_speed, flap_close_atm, edge_close_atm, Omega_close, close_speed, flap_far_atm, edge_far_atm, Omega_far, far_speed\n return Omega_free, free_speed, Omega_close, close_speed, Omega_far, far_speed","def distcalc(z,h=0.70,omegalambda=0.7,omegam=0.3,omegak=0.0):\n\n H0 = 100 * h # this is in units of km/s/Mpc\n\n H0freq = H0 * constants.kilo/(constants.mega * constants.parsec) # this is H0 is units of Hz\n \n hubbletime = 1.0/H0freq # in seconds\n hubbletimeyr = hubbletime / constants.year\n\n #hubble distance\n dh = constants.c / H0freq # in meters\n\n #now i can calculate the comoving distance (line of sight) using hogg eqn 15\n dc = dh * integrate.quad(dcintegrand,0,z,(omegalambda,omegam,omegak))[0]\n\n #now i can find the transverse comoving distance using hogg eqn 16\n if omegak == 0:\n dm = dc\n elif omegak > 0:\n dm = dh/np.sqrt(omegak) * np.sinh(dc * np.sqrt(omegak) / dh)\n else:\n dm = dh/np.sqrt(abs(omegak)) * np.sin(dc * np.sqrt(abs(omegak)) / dh)\n\n\n #now i will calculate the angular diameter distance (hogg eqn 18)\n da = dm/(1+z)\n \n #now i will calculate scale in kpc/arcsec, since this is commonly used\n scale = da * constants.arcsec / (constants.kilo * constants.parsec)\n\n #now i will calculate the luminosity distance (hog eqn 21)\n dl = (1+z)*dm\n \n #now i will calculate lookback time and \n #time from the begining of the universe to that redshift using hogg eqn 30\n \n tlookback = hubbletimeyr * integrate.quad(timeintegrand,0,z,(omegalambda,omegam,omegak))[0]\n \n tz = hubbletimeyr * integrate.quad(timeintegrand,z,np.inf,(omegalambda,omegam,omegak))[0]\n \n #all sky co-moving volume out to redshift z (hogg eqn 30)\n if omegak == 0:\n vc = 4 * np.pi * dm**3 / 3\n elif omegak > 0:\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \n np.arcsinh( np.sqrt(omegak) * dm / dh ) / np.sqrt(omegak) )\n else:\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \n np.arcsin( np.sqrt(abs(omegak)) * dm / dh ) / np.sqrt(abs(omegak)) )\n\n #for output, i will make a dictionary\n output = dict(dh=dh, dc=dc, dm=dm, da=da, scale=scale, dl=dl, tlookback = tlookback, tz=tz, vc=vc)\n\n return output","def update():\n # TODO: Park the car 30 cm away from the closest orange cone.\n # Use both color and depth information to handle cones of multiple sizes.\n # You may wish to copy some of your code from lab2b.py\n global speed\n global angle\n global curState\n # Search for contours in the current color image\n update_contour()\n\n imgX = rc.camera.get_width()\n\n depth_image = rc.camera.get_depth_image()\n depth_image_adjust = (depth_image - 0.01) % 9999\n depth_image_adjust_blur = cv.GaussianBlur(depth_image_adjust, (11,11), 0)\n\n contour_x = contour_center[1]\n contour_y = contour_center[0]\n\n if contour_center is not None:\n angle = rc_utils.remap_range(contour_center[1],0,imgX,-1,1)\n\n contour_distance = depth_image_adjust_blur[contour_y][contour_x]\n\n print(contour_distance)\n # TODO: Park the car 30 cm away from the closest orange cone\n if curState == State.search:\n rc.drive.set_speed_angle(0.5, 1)\n \n if contour_center is not None:\n curState = State.approach\n\n elif curState == State.approach:\n # rc.drive.set_speed_angle(0.5, angle)\n\n if contour_distance > 50:\n rc.drive.set_speed_angle(0.3,angle)\n elif contour_distance > 32:\n rc.drive.set_speed_angle(0.1,angle)\n elif contour_distance == 32:\n rc.drive.set_speed_angle(-0.1,angle)\n elif contour_distance < 32:\n curState = State.stop\n print(\"stop\")\n\n elif curState == State.stop:\n rc.drive.set_speed_angle(0,0)\n\n pass","def perfectrefl(wavelength):\n return 1.0","def wavelength(energy):\n return 2 * PI * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy","def rod(lam, co2=400., lat=45., z=0., P=1013.25):\n Avogadro = codata.value('Avogadro constant')\n zs = 0.73737 * z + 5517.56 # effective mass-weighted altitude\n G = g(lat, zs)\n # air pressure at the pixel (i.e. at altitude) in hPa\n Psurf = (P * (1. - 0.0065 * z / 288.15) ** 5.255) * 1000. # air pressure at pixel location in dyn / cm2, which is hPa * 1000\n return raycrs(lam, co2) * Psurf * Avogadro/ma(co2)/G","def derive_Damineli16(wavelength):\n # From their eq 19\n x = np.log10(2.159 / wavelength)\n log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4.\n\n # Now to convert this back to linear space\n A_AKs_at_wave = 10**log_A_AKs \n\n return A_AKs_at_wave","def albedo_separation(albedo, Rs_1, F, fc, aleafv, aleafn, aleafl, adeadv,\n adeadn, adeadl, zs, iterations=10):\n # DAYTIME\n # Calculate potential (clear-sky) VIS and NIR solar components\n\n rad2deg = 180.0 / math.pi\n # deg2rad = math.pi / 180.0\n\n # Correct for curvature of atmos in airmas\n airmas = zs.expression(\n '(sqrt(cos(zs) ** 2 + 0.0025) - cos(zs)) / 0.00125', {'zs': zs})\n\n # Correct for refraction(good up to 89.5 deg.)\n airmas = airmas.where(\n zs.multiply(rad2deg).lt(89.5),\n zs.expression(\n 'airmas - (2.8 / (90.0 - zs_temp) ** 2)',\n {'airmas': airmas, 'zs_temp': zs.multiply(rad2deg)}))\n\n potbm1 = zs.expression(\n '600.0 * exp(-0.160 * airmas)', {'airmas': airmas})\n potvis = zs.expression(\n '(potbm1 + (600.0 - potbm1) * 0.4) * cos(zs)',\n {'potbm1': potbm1, 'zs': zs})\n # CGM - Not used\n potdif = zs.expression(\n '(600.0 - potbm1) * 0.4 * cos(zs)', {'potbm1': potbm1, 'zs': zs})\n uu = zs.expression('1.0 / cos(zs)', {'zs': zs}) \\\n .max(0.01)\n a = zs.expression(\n '10 ** (-1.195 + 0.4459 * axlog - 0.0345 * axlog * axlog)',\n {'axlog': uu.log10()})\n watabs = zs.expression('1320.0 * a', {'a': a})\n potbm2 = zs.expression(\n '720.0 * exp(-0.05 * airmas) - watabs',\n {'airmas': airmas, 'watabs': watabs})\n evaL = zs.expression(\n '(720.0 - potbm2 - watabs) * 0.54 * cos(zs)',\n {'potbm2': potbm2, 'watabs': watabs, 'zs': zs})\n potnir = zs.expression(\n 'evaL + potbm2 * cos(zs)',\n {'evaL': evaL, 'potbm2': potbm2, 'zs': zs})\n\n fclear = zs \\\n .expression(\n 'Rs_1 / (potvis + potnir)',\n {'potvis': potvis, 'potnir': potnir, 'Rs_1': Rs_1}) \\\n .clamp(0.01, 1.0) \\\n .where(zs.cos().lte(0.01), 1)\n \n # Partition SDN into VIS and NIR\n fvis = zs.expression(\n 'potvis / (potvis + potnir)', {'potvis': potvis, 'potnir': potnir})\n fnir = zs.expression(\n 'potnir / (potvis + potnir)', {'potvis': potvis, 'potnir': potnir})\n \n # Estimate direct beam and diffuse fraction in VIS and NIR wavebands\n fb1 = zs.expression(\n 'potbm1 * cos(zs) / potvis',\n {'potbm1': potbm1, 'potvis': potvis, 'zs': zs})\n fb2 = zs.expression(\n 'potbm2 * cos(zs) / potnir',\n {'potbm2': potbm2, 'potnir': potnir, 'zs': zs})\n\n dirvis = zs \\\n .expression(\n 'fb1 * (1.0 - ((0.9 - ratiox) / 0.7) ** 0.6667)',\n {'fb1': fb1, 'ratiox': fclear.min(0.9)}) \\\n .min(fb1)\n dirnir = zs \\\n .expression(\n 'fb1 * (1.0 - ((0.88 - ratiox) / 0.68) ** 0.6667)',\n {'fb1': fb1, 'ratiox': fclear.min(0.88)}) \\\n .min(fb1)\n\n dirvis = dirvis.where(dirvis.lt(0.01).And(dirnir.gt(0.01)), 0.011)\n dirnir = dirnir.where(dirnir.lt(0.01).And(dirvis.gt(0.01)), 0.011)\n\n difvis = zs.expression('1.0 - dirvis', {'dirvis': dirvis})\n difnir = zs.expression('1.0 - dirnir', {'dirnir': dirnir})\n \n # Correction for NIGHTIME\n ind = zs.cos().lte(0.01)\n fvis = fvis.where(ind, 0.5)\n fnir = fnir.where(ind, 0.5)\n difvis = difvis.where(ind, 0.0)\n difnir = difnir.where(ind, 0.0)\n\n\n # CGM - Not used anymore in function since e_atm is not computed\n # Rs0 = zs \\\n # .expression('potvis + potnir', {'potnir': potnir, 'potvis': potvis}) \\\n # .where(zs.cos().lte(0.01), 0.0)\n\n #**********************************************\n # Compute Albedo\n ratio_soil = 2.\n\n # CGM - Initialize rsoilv and fg from F and albedo\n rsoilv = F.multiply(0).add(0.12)\n fg = albedo.multiply(0).add(1)\n # rsoilv = ee.Image.constant(0.12)\n # fg = ee.Image.constant(1.0)\n\n # print('\\nairmas: {:>20.14f}'.format(utils.image_value(airmas).values()[0]))\n # print('airmas: {:>30.24f}'.format(utils.image_value(airmas).values()[0]))\n # print('potbm1: {:>20.14f}'.format(utils.image_value(potbm1).values()[0]))\n # print('potvis: {:>20.14f}'.format(utils.image_value(potvis).values()[0]))\n # print('potbm2: {:>20.14f}'.format(utils.image_value(potbm2).values()[0]))\n # print('potnir: {:>20.14f}'.format(utils.image_value(potnir).values()[0]))\n # print('fclear: {:>20.14f}'.format(utils.image_value(fclear).values()[0]))\n # print('fvis: {:>20.14f}'.format(utils.image_value(fvis).values()[0]))\n # print('fnir: {:>20.14f}'.format(utils.image_value(fnir).values()[0]))\n # print('fb1: {:>20.14f}'.format(utils.image_value(fb1).values()[0]))\n # print('dirvis: {:>20.14f}'.format(utils.image_value(dirvis).values()[0]))\n # print('dirnir: {:>20.14f}'.format(utils.image_value(dirnir).values()[0]))\n # print('difvis: {:>20.14f}'.format(utils.image_value(difvis).values()[0]))\n # print('difnir: {:>20.14f}'.format(utils.image_value(difnir).values()[0]))\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\n # print('fg: {:>20.14f}'.format(utils.image_value(fg).values()[0]))\n # print('aleafv: {:>20.14f}'.format(utils.image_value(aleafv).values()[0]))\n # print('aleafn: {:>20.14f}'.format(utils.image_value(aleafn).values()[0]))\n # print('adeadv: {:>20.14f}'.format(utils.image_value(adeadv).values()[0]))\n # print('adeadn: {:>20.14f}'.format(utils.image_value(adeadn).values()[0]))\n\n # CGM - Switched to an iterate call\n def iter_func(n, prev):\n # Extract inputs from previous iteration\n # CGM - Variables that are commented out only need to be returned\n # akb = ee.Image(ee.Dictionary(prev).get('akb'));\n # albedo_c = ee.Image(ee.Dictionary(prev).get('albedo_c'));\n # albedo_s = ee.Image(ee.Dictionary(prev).get('albedo_s'));\n # ameann = ee.Image(ee.Dictionary(prev).get('ameann'));\n # ameanv = ee.Image(ee.Dictionary(prev).get('ameanv'));\n # diff = ee.Image(ee.Dictionary(prev).get('diff'));\n fg_iter = ee.Image(ee.Dictionary(prev).get('fg'));\n # rbcpyn = ee.Image(ee.Dictionary(prev).get('rbcpyn'));\n # rbcpyv = ee.Image(ee.Dictionary(prev).get('rbcpyv'));\n rsoilv_iter = ee.Image(ee.Dictionary(prev).get('rsoilv'));\n # taudn = ee.Image(ee.Dictionary(prev).get('taudn'));\n # taudv = ee.Image(ee.Dictionary(prev).get('taudv'));\n\n rsoiln = rsoilv_iter.multiply(ratio_soil)\n # rsoiln = .expression(\n # 'rsoilv * ratio_soil',\n # {'rsoilv': rsoilv, 'ratio_soil': ratio_soil})\n\n # Weighted live/dead leaf average properties\n ameanv = aleafv.expression(\n 'aleafv * fg + adeadv * (1.0 - fg)',\n {'adeadv': adeadv, 'aleafv': aleafv, 'fg': fg_iter})\n ameann = aleafn.expression(\n 'aleafn * fg + adeadn * (1.0 - fg)',\n {'adeadn': adeadn, 'aleafn': aleafn, 'fg': fg_iter})\n ameanl = aleafl.expression(\n 'aleafl * fg + adeadl * (1.0 - fg)',\n {'adeadl': adeadl, 'aleafl': aleafl, 'fg': fg_iter})\n\n # DIFFUSE COMPONENT\n #*******************************\n # Canopy reflection (deep canopy)\n # Fit to Fig 15.4 for x=1\n akd = F.expression('-0.0683 * log(F) + 0.804', {'F': F})\n\n # Eq 15.7\n rcpyn = ameann.expression(\n '(1.0 - sqrt(ameann)) / (1.0 + sqrt(ameann))', {'ameann': ameann})\n rcpyv = ameanv.expression(\n '(1.0 - sqrt(ameanv)) / (1.0 + sqrt(ameanv))', {'ameanv': ameanv})\n # rcpyl = ameanl.expression(\n # '(1.0 - sqrt(ameanl)) / (1.0 + sqrt(ameanl))', {'ameanl': ameanl})\n\n # Eq 15.8\n rdcpyn = akd.expression(\n '2.0 * akd * rcpyn / (akd + 1.0)', {'akd': akd, 'rcpyn': rcpyn})\n rdcpyv = akd.expression(\n '2.0 * akd * rcpyv / (akd + 1.0)', {'akd': akd, 'rcpyv': rcpyv})\n # rdcpyl = akd.expression(\n # '2.0 * akd * rcpyl / (akd + 1.0)', {'akd': akd, 'rcpyl': rcpyl})\n\n # Canopy transmission (VIS)\n expfac = F.expression(\n 'sqrt(ameanv) * akd * F', {'akd': akd, 'ameanv': ameanv, 'F': F})\n expfac = expfac.max(0.001)\n # expfac = expfac.where(expfac.lt(0.001), 0.001)\n xnum = F.expression(\n '(rdcpyv * rdcpyv - 1.0) * exp(-expfac)',\n {'rdcpyv': rdcpyv, 'expfac': expfac})\n xden = F.expression(\n '(rdcpyv * rsoilv - 1.0) + '\n 'rdcpyv * (rdcpyv - rsoilv) * exp(-2.0 * expfac)',\n {'expfac': expfac, 'rdcpyv': rdcpyv, 'rsoilv': rsoilv_iter})\n # Eq 15.11\n taudv = F.expression('xnum / xden', {'xden': xden, 'xnum': xnum})\n # taudv = xnum.divide(xden)\n\n # Canopy transmission (NIR)\n expfac = F.expression(\n 'sqrt(ameann) * akd * F', {'akd': akd, 'ameann': ameann, 'F': F})\n expfac = expfac.max(0.001)\n # expfac = expfac.where(expfac.lt(0.001), 0.001)\n xnum = F.expression(\n '(rdcpyn * rdcpyn - 1.0) * exp(-expfac)',\n {'expfac': expfac, 'rdcpyn': rdcpyn})\n xden = F.expression(\n '(rdcpyn * rsoiln - 1.0) + '\n 'rdcpyn * (rdcpyn - rsoiln) * exp(-2.0 * expfac)',\n {'expfac': expfac, 'rdcpyn': rdcpyn, 'rsoiln': rsoiln})\n # Eq 15.11\n taudn = F.expression('xnum / xden', {'xden': xden, 'xnum': xnum})\n # taudn = xnum.divide(nden)\n\n # Canopy transmission (LW)\n taudl = F.expression(\n 'exp(-sqrt(ameanl) * akd * F)',\n {'akd': akd, 'ameanl': ameanl, 'F': F})\n\n # Diffuse albedo for generic canopy\n # Eq 15.9\n fact = F.expression(\n '((rdcpyn - rsoiln) / (rdcpyn * rsoiln - 1.0)) * '\n 'exp(-2.0 * sqrt(ameann) * akd * F)',\n {'akd': akd, 'ameann': ameann, 'F': F, 'rdcpyn': rdcpyn,\n 'rsoiln': rsoiln})\n albdn = F.expression(\n '(rdcpyn + fact) / (1.0 + rdcpyn * fact)',\n {'fact': fact, 'rdcpyn': rdcpyn})\n\n # Eq 15.9\n fact = F.expression(\n '((rdcpyv - rsoilv) / (rdcpyv * rsoilv - 1.0)) * '\n 'exp(-2.0 * sqrt(ameanv) * akd * F)',\n {'akd': akd, 'ameanv': ameanv, 'F': F, 'rdcpyv': rdcpyv,\n 'rsoilv': rsoilv_iter})\n albdv = F.expression(\n '(rdcpyv + fact) / (1.0 + rdcpyv * fact)',\n {'fact': fact, 'rdcpyv': rdcpyv})\n\n # BEAM COMPONENT\n #*******************************\n # Canopy reflection (deep canopy)\n akb = zs.expression('0.5 / cos(zs)', {'zs': zs})\n akb = akb.where(zs.cos().lte(0.01), 0.5)\n\n # Eq 15.7\n rcpyn = ameann.expression(\n '(1.0 - sqrt(ameann)) / (1.0 + sqrt(ameann))',\n {'ameann': ameann})\n rcpyv = ameanv.expression(\n '(1.0 - sqrt(ameanv)) / (1.0 + sqrt(ameanv))',\n {'ameanv': ameanv})\n\n # Eq 15.8\n rbcpyn = rcpyn.expression(\n '2.0 * akb * rcpyn / (akb + 1.0)', {'akb': akb, 'rcpyn': rcpyn})\n rbcpyv = rcpyv.expression(\n '2.0 * akb * rcpyv / (akb + 1.0)', {'akb': akb, 'rcpyv': rcpyv})\n\n # Beam albedo for generic canopy\n # Eq 15.9\n fact = F.expression(\n '((rbcpyn - rsoiln) / (rbcpyn * rsoiln - 1.0)) * '\n 'exp(-2.0 * sqrt(ameann) * akb * F)',\n {'akb': akb, 'ameann': ameann, 'F': F, 'rbcpyn': rbcpyn,\n 'rsoiln': rsoiln})\n albbn = F.expression(\n '(rbcpyn + fact) / (1.0 + rbcpyn * fact)',\n {'fact': fact, 'rbcpyn': rbcpyn})\n\n # Eq 15.9\n fact = F.expression(\n '((rbcpyv - rsoilv) / (rbcpyv * rsoilv - 1.0)) * '\n 'exp(-2.0 * sqrt(ameanv) * akb * F)',\n {'akb': akb, 'ameanv': ameanv, 'F': F, 'rbcpyv': rbcpyv,\n 'rsoilv': rsoilv_iter})\n albbv = F.expression(\n '(rbcpyv + fact) / (1.0 + rbcpyv * fact)',\n {'fact': fact, 'rbcpyv': rbcpyv})\n\n # CGM - finish\n # Weighted albedo (canopy)\n albedo_c = F.expression(\n 'fvis * (dirvis * albbv + difvis * albdv) + '\n 'fnir * (dirnir * albbn + difnir * albdn)',\n {'albbn': albbn, 'albbv': albbv, 'albdn': albdn, 'albdv': albdv,\n 'difnir': difnir, 'difvis': difvis, 'dirvis': dirvis,\n 'dirnir': dirnir, 'fnir': fnir, 'fvis': fvis, })\n albedo_c = albedo_c.where(\n zs.cos().lte(0.01),\n F.expression(\n 'fvis * (difvis * albdv) + fnir * (difnir * albdn)',\n {'albdn': albdn, 'albdv': albdv, 'difnir': difnir,\n 'difvis': difvis, 'fnir': fnir, 'fvis': fvis}))\n\n albedo_s = rsoilv.expression(\n 'fvis * rsoilv + fnir * rsoiln',\n {'fnir': fnir, 'fvis': fvis, 'rsoiln': rsoiln, 'rsoilv': rsoilv_iter})\n\n albedo_avg = fc.expression(\n '(fc * albedo_c) + ((1 - fc) * albedo_s)',\n {'albedo_c': albedo_c, 'albedo_s': albedo_s, 'fc': fc})\n diff = albedo_avg.subtract(albedo)\n # diff = albedo_avg.expression(\n # 'albedo_avg - albedo',\n # {'albedo_avg': albedo_avg, 'albedo': albedo})\n\n # CGM - Check what this is doing\n # Extra select call is needed if LAI is multiband\n # Added fc_mask call\n fc_mask = fc.select([0]).lt(0.75)\n rsoilv_iter = rsoilv_iter \\\n .where(fc_mask.And(diff.lte(-0.01)), rsoilv_iter.add(0.01)) \\\n .where(fc_mask.And(diff.gt(0.01)), rsoilv_iter.add(-0.01))\n # # CGM - IDL function\n # rsoilv = ((fc lt 0.75) * (\n # ((abs(diff) le 0.01) * rsoilv) +\n # ((diff le -0.01)*(rsoilv + 0.01)) +\n # ((diff gt 0.01)*(rsoilv - 0.01))))+\n # ((fc ge 0.75) * rsoilv)\n\n # CGM - Extra select call is needed since fc is multiband\n fc_mask = fc.select([0]).gte(0.75)\n fg_iter = fg_iter \\\n .where(fc_mask.And(diff.lte(-0.01)), fg_iter.subtract(0.05)) \\\n .where(fc_mask.And(diff.gt(0.01)), fg_iter.add(0.05)) \\\n .clamp(0.01, 1)\n # # CGM - IDL function\n # fg = ((fc ge 0.75) * (\n # ((abs(diff) le 0.01)*fg) +\n # ((diff le -0.01) * (fg - 0.05d0)) +\n # ((diff gt 0.01) * (fg + 0.05d0)))) +\n # ((fc lt 0.75) * fg)\n\n return ee.Dictionary({\n 'akb': akb, 'albedo_c': albedo_c, 'albedo_s': albedo_s,\n 'ameann': ameann, 'ameanv': ameanv, 'diff': diff, 'fg': fg_iter,\n 'rbcpyn': rbcpyn, 'rbcpyv': rbcpyv,\n 'rsoiln': rsoiln, 'rsoilv': rsoilv_iter,\n 'taudn': taudn, 'taudv': taudv\n })\n\n # Iterate the function n times\n input_images = ee.Dictionary({\n 'akb': None, 'albedo_c': None, 'albedo_s': None,\n 'ameann': None, 'ameanv': None, 'diff': None, 'fg': fg,\n 'rbcpyn': None, 'rbcpyv': None,\n 'rsoiln': None, 'rsoilv': rsoilv,\n 'taudn': None, 'taudv': None\n })\n iter_output = ee.Dictionary(\n # ee.List.sequence(1, iterations) \\\n ee.List.repeat(input_images, iterations) \\\n .iterate(iter_func, input_images))\n\n # Unpack the iteration output\n akb = ee.Image(iter_output.get('akb'))\n albedo_c = ee.Image(iter_output.get('albedo_c'))\n albedo_s = ee.Image(iter_output.get('albedo_s'))\n ameann = ee.Image(iter_output.get('ameann'))\n ameanv = ee.Image(iter_output.get('ameanv'))\n diff = ee.Image(iter_output.get('diff'))\n rbcpyn = ee.Image(iter_output.get('rbcpyn'))\n rbcpyv = ee.Image(iter_output.get('rbcpyv'))\n rsoilv = ee.Image(iter_output.get('rsoilv'))\n rsoiln = ee.Image(iter_output.get('rsoiln'))\n # rsoiln = rsoilv.multiply(ratio_soil)\n taudn = ee.Image(iter_output.get('taudn'))\n taudv = ee.Image(iter_output.get('taudv'))\n # print('\\nakb: {:>20.14f}'.format(utils.image_value(akb).values()[0]))\n # print('albedo_c: {:>20.14f}'.format(utils.image_value(albedo_c).values()[0]))\n # print('albedo_s: {:>20.14f}'.format(utils.image_value(albedo_s).values()[0]))\n # print('ameann: {:>20.14f}'.format(utils.image_value(ameann).values()[0]))\n # print('ameanv: {:>20.14f}'.format(utils.image_value(ameanv).values()[0]))\n # print('diff: {:>20.14f}'.format(utils.image_value(diff).values()[0]))\n # print('rbcpyn: {:>20.14f}'.format(utils.image_value(rbcpyn).values()[0]))\n # print('rbcpyv: {:>20.14f}'.format(utils.image_value(rbcpyv).values()[0]))\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\n # print('rsoiln: {:>20.14f}'.format(utils.image_value(rsoiln).values()[0]))\n # print('taudv: {:>20.14f}'.format(utils.image_value(taudv).values()[0]))\n # print('taudn: {:>20.14f}'.format(utils.image_value(taudn).values()[0]))\n\n # if a solution is not reached, alb_c=alb_s=alb\n albedo_c = albedo_c.where(diff.abs().gt(0.05), albedo)\n albedo_s = albedo_s.where(diff.abs().gt(0.05), albedo)\n\n # Direct beam+scattered canopy transmission coefficient (visible)\n expfac = F.expression(\n 'sqrt(ameanv) * akb * F',\n {'ameanv': ameanv, 'akb': akb, 'F': F})\n xnum = F.expression(\n '(rbcpyv * rbcpyv - 1.0) * exp(-expfac)',\n {'rbcpyv': rbcpyv, 'expfac': expfac})\n xden = F.expression(\n '(rbcpyv * rsoilv - 1.0) + '\n 'rbcpyv * (rbcpyv - rsoilv) * exp(-2.0 * expfac)',\n {'rbcpyv': rbcpyv, 'rsoilv': rsoilv, 'expfac': expfac})\n # Eq 15.11\n taubtv = F.expression('xnum / xden', {'xnum': xnum, 'xden': xden})\n # print('\\nexpfac: {:>20.14f}'.format(utils.image_value(expfac).values()[0]))\n # print('rbcpyv: {:>20.14f}'.format(utils.image_value(rbcpyv).values()[0]))\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\n # print('xnum: {:>20.14f}'.format(utils.image_value(xnum).values()[0]))\n # print('xden: {:>20.14f}'.format(utils.image_value(xden).values()[0]))\n # print('taubtv: {:>20.14f}'.format(utils.image_value(taubtv).values()[0]))\n \n # Direct beam+scattered canopy transmission coefficient (NIR)\n expfac = F.expression(\n 'sqrt(ameann) * akb * F',\n {'ameann': ameann, 'akb': akb, 'F': F})\n xnum = F.expression(\n '(rbcpyn * rbcpyn - 1.0) * exp(-expfac)',\n {'rbcpyn': rbcpyn, 'expfac': expfac})\n xden = F.expression(\n '(rbcpyn * rsoiln - 1.0) + '\n 'rbcpyn * (rbcpyn - rsoiln) * exp(-2.0 * expfac)',\n {'rbcpyn': rbcpyn, 'rsoiln': rsoiln, 'expfac': expfac})\n # Eq 15.11\n taubtn = F.expression('xnum / xden', {'xnum': xnum, 'xden': xden})\n # print('\\nexpfac: {:>20.14f}'.format(utils.image_value(expfac).values()[0]))\n # print('rbcpyn: {:>20.14f}'.format(utils.image_value(rbcpyn).values()[0]))\n # print('rsoiln: {:>20.14f}'.format(utils.image_value(rsoiln).values()[0]))\n # print('xnum: {:>20.14f}'.format(utils.image_value(xnum).values()[0]))\n # print('xden: {:>20.14f}'.format(utils.image_value(xden).values()[0]))\n # print('taubtn: {:>20.14f}'.format(utils.image_value(taubtn).values()[0]))\n \n # Shortwave radiation components\n tausolar = F.expression(\n 'fvis * (difvis * taudv + dirvis * taubtv) + '\n 'fnir * (difnir * taudn + dirnir * taubtn)',\n {'difnir': difnir, 'difvis': difvis,\n 'dirnir': dirnir, 'dirvis': dirvis,\n 'fnir': fnir, 'fvis': fvis,\n 'taubtn': taubtn, 'taubtv': taubtv,\n 'taudn': taudn, 'taudv': taudv})\n # print('tausolar: {}'.format(utils.image_value(tausolar).values()[0]))\n # print('Rs_1: {}'.format(utils.image_value(Rs_1).values()[0]))\n Rs_c = Rs_1.expression(\n 'Rs_1 * (1.0 - tausolar)', {'Rs_1': Rs_1, 'tausolar': tausolar})\n Rs_s = Rs_1.expression(\n 'Rs_1 * tausolar', {'Rs_1': Rs_1, 'tausolar': tausolar})\n\n # print('\\nRs_c: {:>20.14f}'.format(utils.image_value(Rs_c).values()[0]))\n # print('Rs_s: {:>20.14f}'.format(utils.image_value(Rs_s).values()[0]))\n # print('albedo_c: {:>20.14f}'.format(utils.image_value(albedo_c).values()[0]))\n # print('albedo_s: {:>20.14f}'.format(utils.image_value(albedo_s).values()[0]))\n\n return Rs_c, Rs_s, albedo_c, albedo_s","def orthopyroxene():\n\n rho = 3304.\n\n C = np.zeros((6,6), dtype=float)\n C[0,0] = 236.9; C[0,1] = 79.6; C[0,2] = 63.2; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\n C[1,0] = C[0,1]; C[1,1] = 180.5; C[1,2] = 56.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 84.3; C[3,4] = 0.; C[3,5] = 0.\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 79.4; C[4,5] = 0.\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 80.1\n\n return C, rho","def get_depth_pwm(self, goal):\n ddiff = goal.target_depth - self.curr_depth\n if self.curr_depth < -1:\n rospy.loginfo('Depth sensor is not initialized')\n return self.pwm_center\n zout = ddiff * self.depth_p\n # limit output if necassary\n if abs(zout) > self.depth_pmax:\n if zout < 0:\n zout = -self.depth_pmax\n else:\n zout = self.depth_pmax\n zout += self.pwm_center\n return zout","def ApproxConc280(Abs, pathLength = 10):\n pass","def get_wl_band(radar_frequency):\n return 0 if (30 < radar_frequency < 40) else 1","def depth_estimation(x_left, x_right, f=33.4, d=114):\n depth = abs(f * d / ((x_left - x_right) / 72 * 2.54)) / 100 # - 0.418879\n return depth","def DR(R,Pc):\n return r1*R*(K1**B1/(K1**B1 + (A/R)**B1))*(S/(S + R*Pc + Pc)) \\\n - gwt*A - r2*R*(K2**B2/(K2**B2 + (A)**B2))*(S/(S + R*Pc + Pc) ) \\\n *(R*M)/(K3 + R*M) + R*gc","def one_transition_spectrum_ld(self,tr):\n \n\n ta = tr[\"ta\"] # TimeAxis\n ld = tr[\"ld\"] # linear dichroism strength\n om = tr[\"om\"] # frequency - rwa\n gg = tr[\"gg\"] # natural broadening (constant or time dependent)\n fwhm = tr[\"fwhm\"] # Additional gaussian broadening of the spectra\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\n \n if self.system._has_system_bath_coupling:\n# ct = tr[\"ct\"] # correlation function\n \n # convert correlation function to lineshape function\n #gt = self._c2g(ta,ct.data)\n gt = tr[\"gt\"]\n # calculate time dependent response\n at = numpy.exp(-gt -1j*om*ta.data)\n else:\n # calculate time dependent response\n at = numpy.exp(-1j*om*ta.data) \n \n \n if len(gg) == 1:\n gam = gg[0]\n rt = numpy.exp(gam*ta.data)\n at *= rt\n #print(\"Constant: \", rt[20], len(at))\n else:\n rt = numpy.exp((gg)*ta.data) \n at *= rt\n #print(\"Time dependent: len = \", rt[20], len(rt))\n \n if fwhm!=0.0:\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\n at *= gauss\n \n # Fourier transform the result\n ft = ld*numpy.fft.hfft(at)*ta.step\n ft = numpy.fft.fftshift(ft)\n # invert the order because hfft is a transform with -i\n ft = numpy.flipud(ft) \n # cut the center of the spectrum\n Nt = ta.length #len(ta.data) \n return ft[Nt//2:Nt+Nt//2]","def find_large_separation(self):\n\n x = self.modes['n'] # radial order\n y = self.modes['freq'] # frequency\n wid = (0.66*self.numax**0.88)/2/np.sqrt(2*np.log(2.0))\n w = (np.exp((-(y-self.numax)**2)/(2*wid**2))) # weight\n\n mN = np.sum(w)*np.sum(w*x*y) - np.sum(w*x)*np.sum(w*y)\n D = np.sum(w)*np.sum(w*x**2) - np.sum(w*x)**2\n Dn = mN/D\n #print Dn\n\n return Dn","def albedoc(solin, fsntoa):\n var = (solin - fsntoa) / solin\n var.units = \"dimensionless\"\n var.long_name = \"TOA albedo clear-sky\"\n return var","def rayleigh(th,r,wl,a,n1,n2):\n k = 2*np.pi/wl\n n_2 = n2**2/n1**2\n return ((k**2)*(a**3)*((n_2-1)/(n_2+2))/r)*np.array([[np.cos(th), 0],[0,1]])","def best_deriv_drops_arclengths_r(fname):\r\n dat = sio.loadmat(fname,struct_as_record=False,squeeze_me=True)\r\n output = dat['output']\r\n y = dat['y'].ravel()\r\n cbool = dat['cbool'].astype('bool').ravel()\r\n R = {}\r\n inputs_list =[]\r\n arclength_list =[]\r\n kernels=[]\r\n for model in output._fieldnames:\r\n print('\\t'+model)\r\n arclength = output.__getattribute__(model).arclengths\r\n if len(arclength)==0 or arclength == 'all':\r\n compare_bool = cbool\r\n else:\r\n arclength_bool = dat['arclengths'].__getattribute__(arclength).astype('bool')\r\n compare_bool = np.logical_and(arclength_bool,cbool)\r\n\r\n inputs_list.append(output.__getattribute__(model).inputs)\r\n arclength_list.append(arclength)\r\n yhat = output.__getattribute__(model).yhat\r\n R[model],_,kernels = get_corr(y,yhat,compare_bool)\r\n\r\n R['kernels'] = kernels\r\n df = pd.DataFrame(R)\r\n df['id'] = os.path.basename(fname)[:10]\r\n # df['inputs'] = inputs_list\r\n # df['arclengths'] = arclength_list\r\n return(df)","def calculate_wavelength(period, depth, gravity):\r\n return geometry.gmCalculateWavelength(period, depth, gravity)","def mercier(self):\n\n # See Overleaf note \"Mercier criterion near the magnetic axis- detailed notes\".\n # See also \"20200604-02 Checking sign in Mercier DGeod near axis.docx\"\n\n # Shorthand:\n d_l_d_phi = self.d_l_d_phi\n B0 = self.B0\n G0 = self.G0\n p2 = self.p2\n etabar = self.etabar\n curvature = self.curvature\n sigma = self.sigma\n iotaN = self.iotaN\n iota = self.iota\n pi = np.pi\n\n #integrand = d_l_d_phi * (Y1c * Y1c + X1c * (X1c + Y1s)) / (Y1c * Y1c + (X1c + Y1s) * (X1c + Y1s))\n integrand = d_l_d_phi * (etabar*etabar*etabar*etabar + curvature*curvature*curvature*curvature*sigma*sigma + etabar*etabar*curvature*curvature) \\\n / (etabar*etabar*etabar*etabar + curvature*curvature*curvature*curvature*(1+sigma*sigma) + 2*etabar*etabar*curvature*curvature)\n\n integral = np.sum(integrand) * self.d_phi * self.nfp * 2 * pi / self.axis_length\n\n #DGeod_times_r2 = -(2 * sG * spsi * mu0 * mu0 * p2 * p2 * G0 * G0 * G0 * G0 * etabar * etabar &\n self.DGeod_times_r2 = -(2 * mu0 * mu0 * p2 * p2 * G0 * G0 * G0 * G0 * etabar * etabar \\\n / (pi * pi * pi * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * iotaN * iotaN)) \\\n * integral\n\n self.d2_volume_d_psi2 = 4*pi*pi*abs(G0)/(B0*B0*B0)*(3*etabar*etabar - 4*self.B20_mean/B0 + 2 * (self.G2 + iota * self.I2)/G0)\n\n self.DWell_times_r2 = (mu0 * p2 * abs(G0) / (8 * pi * pi * pi * pi * B0 * B0 * B0)) * \\\n (self.d2_volume_d_psi2 - 8 * pi * pi * mu0 * p2 * abs(G0) / (B0 * B0 * B0 * B0 * B0))\n\n self.DMerc_times_r2 = self.DWell_times_r2 + self.DGeod_times_r2","def isothermal_depth_wyrtki1964_gradient(da_PT):\n\n # make land mask based on surface layer\n da_mask = da_PT.isel(z=0)*0.+1.\n\n # calculate drho/dz\n da_PT_dz = da_PT.differentiate('z') # kg/m^4\n\n # interpolate to finer vertical resolution (2.5m)\n da_interp = da_PT_dz.interp(z=np.arange(0,da_PT_dz.z.max(),2.5))\n\n # remove values shallower than critcal value\n da_interp_masked = da_interp.where(da_interp>0.02,other=99999)\n\n # find first index that have value larger than critical value\n z_ind = da_interp_masked.argmin(dim='z',skipna=True)\n\n # used 2d index to find 2d depth map\n da_itd = da_interp.z[z_ind]*da_mask\n\n return da_itd","def isothermal_depth_wyrtki1964_gradient(da_PT):\n\n # make land mask based on surface layer\n da_mask = da_PT.isel(z=0)*0.+1.\n\n # calculate drho/dz\n da_PT_dz = da_PT.differentiate('z') # kg/m^4\n\n # interpolate to finer vertical resolution (2.5m)\n da_interp = da_PT_dz.interp(z=np.arange(0,da_PT_dz.z.max(),2.5))\n\n # remove values shallower than critcal value\n da_interp_masked = da_interp.where(da_interp>0.02,other=99999)\n\n # find first index that have value larger than critical value\n z_ind = da_interp_masked.argmin(dim='z',skipna=True)\n\n # used 2d index to find 2d depth map\n da_itd = da_interp.z[z_ind]*da_mask\n\n return da_itd","def computeA(diameter):\n radius = diameter / 2.0\n return np.pi * (radius**2)","def complete_spectrum_time(cn, Ahr_flat = 0.2, wantSNR = 10.0, bandwidth = 0.2, architecture = \"A\",\n plot = False, verbose = False):\n\n # If the coronagraph model has already been run...\n if cn._computed:\n # Use the existing stellar flux\n fstar = cn.solhr\n else:\n # Otherwise use the solar flux\n fstar = FSTAR\n\n if plot: fig, ax = plt.subplots()\n\n cc = [\"C0\", \"C2\", \"C3\"]\n t_chan = np.zeros(len(CHANNELS))\n Nbands_per_chan = np.zeros(len(CHANNELS))\n t_per_band_per_chan = []\n full_lam = []\n full_dlam = []\n full_Cobs = []\n full_Cratio = []\n full_Csig = []\n pct_obs_iwa = []\n lammax_obs_iwa = []\n lam_extrema = []\n\n ibp = 0\n\n # Loop over telescope channels\n for j, channel in enumerate(CHANNELS):\n\n t_tmp = []\n\n # Get the channel specific telescope parameters\n luvoir = default_luvoir(channel=channel, architecture = architecture)\n cn.telescope = luvoir\n\n if verbose: print(channel, luvoir.lammin, luvoir.lammax)\n\n lam_extrema.append(luvoir.lammin)\n lam_extrema.append(luvoir.lammax)\n\n # Calculate the bandpass edges\n edges = calculate_bandpass_edges(luvoir.lammin, luvoir.lammax, bandwidth = bandwidth)\n\n # Calculate the number of bandpasses\n Nbands = len(edges) - 1\n Nbands_per_chan[j] = Nbands\n\n # Run count rates (necessary to generate new wavelength grid)\n #cn.run_count_rates(spectroscopy.AHR, spectroscopy.LAMHR, spectroscopy.FSTAR)\n\n # Get new wavelength grid\n #l_grid, dl_grid = get_lam_dlam(cn)\n\n # Calculate how much of the spectrum is observable\n cnc = copy.deepcopy(cn)\n cnc.run_count_rates(AHR, LAMHR, FSTAR)\n pct, lammax_obs = calc_observable_spectrum(cnc)\n pct_obs_iwa.append(pct)\n lammax_obs_iwa.append(lammax_obs)\n\n # Loop over bandpasses\n for i in range(Nbands):\n\n if (type(wantSNR) is float) or (type(wantSNR) is int):\n wSNR = wantSNR\n else:\n wSNR = wantSNR[ibp]\n\n # Get the max, min, and middle wavelenths for this bandpass\n lammin = edges[i]\n lammax = edges[i+1]\n lammid = 0.5*(lammax + lammin)\n\n # Set telescope wavelength range\n cn.telescope.lammin = lammin\n cn.telescope.lammax = lammax\n\n if channel == \"UV\":\n cn.telescope.lam = np.array([lammid])\n cn.telescope.dlam = np.array([lammax - lammin])\n\n # Set spectrum to use for exposure time calcs\n # Using flat spectrum so not biased by bottom of bands\n Ahr_flat = Ahr_flat * np.ones(len(LAMHR))\n\n # Run count rates (necessary to generate new wavelength grid)\n cn.run_count_rates(Ahr_flat, LAMHR, fstar)\n\n # Calculate exposure times to wantSNR\n etimes = determine_exposure_time(cn, None, plot_snr_curves=False,\n plot_spectrum=False, wantSNR=wSNR, ref_lam = lammid)\n t_ref_lam = etimes[-1]\n\n # Re-do count rate calcs for true Earth spectrum\n cn.run_count_rates(AHR, LAMHR, fstar)\n\n # Draw random samples of data for a plot\n cn.make_fake_data(texp=t_ref_lam)\n\n if verbose: print(lammid, t_ref_lam)\n\n # Plot\n if plot:\n ax.axvspan(lammin, lammax, alpha = 0.2, color = cc[j])\n #ax.plot(cn.lam, cn.Cratio, ls = \"steps-mid\", color = \"grey\", zorder = 100)\n ax.plot(cn.lam, cn.Cobs, \"o\", ms = 3.0, alpha = 1.0, color = \"w\", zorder = 70)\n ax.errorbar(cn.lam, cn.Cobs, yerr=cn.Csig, fmt = \"o\", ms = 2.0, alpha = 0.7, color = \"k\", zorder = 70)\n ax.set_xlabel(\"Wavelength [$\\mu$m]\")\n ax.set_ylabel(\"$F_p / F_s$\")\n\n # Save values\n t_tmp.append(t_ref_lam)\n full_lam.append(cn.lam)\n full_dlam.append(cn.dlam)\n full_Cratio.append(cn.Cratio)\n full_Cobs.append(cn.Cobs)\n full_Csig.append(cn.Csig)\n\n # Add time\n if np.isfinite(t_ref_lam):\n t_chan[j] += t_ref_lam\n\n ibp += 1\n\n # Save tmp times per band\n t_per_band_per_chan.append(t_tmp)\n\n # Deal with the \"two channels at a time\" thing\n t_tot = apply_two_channels(t_chan)\n\n spectrum = (np.array(full_lam),\n np.array(full_dlam),\n np.array(full_Cratio),\n np.array(full_Cobs),\n np.array(full_Csig))\n iwa = (pct_obs_iwa, lammax_obs_iwa)\n\n if plot:\n lam_extrema = np.array(lam_extrema)\n cn.telescope.lammin = np.min(lam_extrema)\n cn.telescope.lammax = np.max(lam_extrema)\n cn.telescope.resolution = 140.\n # Re-do count rate calcs for true Earth spectrum\n cn.run_count_rates(AHR, LAMHR, FSTAR)\n ax.plot(cn.lam, cn.Cratio, color = \"grey\", zorder = 80, lw = 3.0)\n ax.plot(cn.lam, cn.Cratio, color = \"w\", zorder = 80, lw = 2.0)\n\n return t_tot, t_per_band_per_chan, spectrum, iwa","def nrefrac(wavelength, density=1.0):\n\n # The IAU standard for conversion from air to vacuum wavelengths is given\n # in Morton (1991, ApJS, 77, 119). For vacuum wavelengths (VAC) in\n # Angstroms, convert to air wavelength (AIR) via: \n\n # AIR = VAC / (1.0 + 2.735182E-4 + 131.4182 / VAC^2 + 2.76249E8 / VAC^4)\n\n try:\n wl = np.array(wavelength)\n except TypeError:\n return None\n\n wl2inv = (1e4/wl)**2\n refracstp = 272.643 + 1.2288 * wl2inv + 3.555e-2 * wl2inv**2\n return density * refracstp","def Ag():\n # return load_material(miepy.__path__[0] + \"/materials/ag.npy\")\n\n wp = 9.01\n sig = [1.01889808, 0.62834151]\n f = [0,5.05635462]\n gam = [0.01241231, 0.54965831]\n wav = np.linspace(300,1100,1000)\n return drude_lorentz(wp,sig,f,gam,wav)","def molar_mass_dry_air():\n return 28.9647","def breathIO(self, bdry, dmap):\n cur_bdry = np.array([bdry[0][1], bdry[3][1], bdry[1][0], bdry[2][0]])\n if len(self.ref_bdry) == 0:\n # setup reference frame's boundary (up, down, left and right)\n self.ref_bdry = cur_bdry\n self.ref_dmap = dmap\n else:\n ubdry = np.array([int(min(cur_bdry[0], self.ref_bdry[0])),\n int(max(cur_bdry[1], self.ref_bdry[1])),\n int(max(cur_bdry[2], self.ref_bdry[2])),\n int(min(cur_bdry[3], self.ref_bdry[3]))])\n blk_diff = gf_2D((dmap-self.ref_dmap)[ubdry[1]:ubdry[0], ubdry[2]:ubdry[3]], 5)\n self.breath_list.append(np.mean(blk_diff))","def retrieve_EchoDepth(\n ds,\n threshold,\n variable=\"zFactorFinal\",\n radar_frequency=\"Ku\",\n min_threshold=0,\n mask_liquid_phase=False,\n):\n # Retrieve required DataArrays\n da = get_variable_dataarray(ds, variable=variable)\n if len(da[\"radar_frequency\"].data) != 1:\n da = da.sel({\"radar_frequency\": radar_frequency})\n da_height = ds[\"height\"].copy()\n # Mask height bin where not raining\n da_mask_3d_rain = da > min_threshold\n da_height = da_height.where(da_mask_3d_rain)\n\n # Mask heights where Z is not above threshold\n da_mask_3d = da > threshold\n da_height_masked = da_height.where(da_mask_3d)\n\n # Mask liquid phase\n if mask_liquid_phase:\n da_liquid_mask = get_liquid_phase_mask(ds)\n da_height_masked = da_height_masked.where(~da_liquid_mask)\n\n # Retrieve min and max echo height\n da_max_height = da_height_masked.max(dim=\"range\")\n da_min_height = da_height_masked.min(dim=\"range\")\n\n # OLD MASKING\n # if mask_liquid_phase:\n # da_isnan = np.isnan(da_min_height)\n # da_height_melting = ds[\"heightZeroDeg\"]\n # da_height_melting = da_height_melting.where(~da_isnan)\n # # If max is below the 0 °C isotherm --> set to nan\n # da_max_height = da_max_height.where(da_max_height > da_height_melting)\n # # If min below the 0 °C isoterm --> set the isotherm height\n # da_min_height = da_min_height.where(da_min_height > da_height_melting, da_height_melting)\n\n # Compute depth\n da_depth = da_max_height - da_min_height\n\n # Add attributes\n da_depth.name = f\"EchoDepth{threshold}dBZ\"\n da_depth.attrs[\"units\"] = \"m\"\n return da_depth","def far_fields(horn_width, horn_height, eplane_effective_length, hplane_effective_length, frequency, r, theta, phi):\n # Calculate the wavenumber\n k = 2.0 * pi * frequency / c\n\n # Calculate the wave impedance\n eta = sqrt(mu_0 / epsilon_0)\n\n # Define the radial-component of the electric field\n e_r = 0.0\n\n # Define the theta-component of the electric field\n e_theta = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * sin(phi) * (1.0 + cos(theta)) * \\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\n I2(k, horn_height, eplane_effective_length, theta, phi)\n\n # Define the phi-component of the electric field\n e_phi = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * cos(phi) * (1.0 + cos(theta)) * \\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\n I2(k, horn_height, eplane_effective_length, theta, phi)\n\n # Define the radial-component of the magnetic field\n h_r = 0.0\n\n # Define the theta-component of the magnetic field\n h_theta = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * -cos(phi) * (1.0 + cos(theta)) / eta * \\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\n I2(k, horn_height, eplane_effective_length, theta, phi)\n\n # Define the phi-component of the magnetic field\n h_phi = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * sin(phi) * (1.0 + cos(theta)) / eta * \\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\n I2(k, horn_height, eplane_effective_length, theta, phi)\n\n # Return all six components of the far field\n return e_r, e_theta, e_phi, h_r, h_theta, h_phi","def geometric_tortuosity(maze):\n pathsTotal = []\n path_star_list = findPoints(maze, \"S\")\n\n total_caminos = []\n unit_caminos = 0\n array_path = np.array(maze)\n line = (array_path.shape)[2]\n global path\n i = 0\n path_star_list = endPoints(maze[0], 'S')\n # print(path_star_list)\n listEndPoints = endPoints(maze[-1], \"E\")\n # print(path_star_list)\n # print(listEndPoints)\n toTal = len(listEndPoints)*len(path_star_list)\n\n for star in path_star_list:\n caminos = []\n for end in listEndPoints:\n print(\"camino:\"+str(i+1)+\"/\"+str(toTal))\n\n path = astar(maze, star, end)\n\n if path != None:\n pathsTotal.append(path)\n\n i += 1\n result = 0\n # caminos.append(path)\n # total_caminos.append(caminos)\n try:\n x = map(valuepath, path)\n result = sum(x)\n except:\n pass\n\n caminos.append(result)\n unit_caminos += 1\n\n total_caminos.append(min(caminos))\n\n valor = (np.mean(np.array(total_caminos)))\n geometric_tortusity = valor/(int(line)-1)\n return geometric_tortusity, pathsTotal\n # return \"f\",\"f\"","def compute_angle(map_id,padding_ratio=a.padding_ratio,map_size=a.map_size,sep=a.sep,freq=a.freq,\\\n f_dust=a.f_dust,lMax=a.lMax,lMin=a.lMin,l_step=a.l_step,FWHM=a.FWHM,noise_power=a.noise_power,\\\n slope=a.slope,delensing_fraction=a.delensing_fraction,useQU=a.useQU,N_bias=a.N_bias):\n\n # Step 1, create actual B-mode map\n lCut=int(1.35*lMax) # maximum ell for Fourier space maps\n\n # First compute B-mode map from padded-real space map with desired padding ratio. Also compute the padded window function for later use\n from hades.PaddedPower import MakePowerAndFourierMaps,DegradeMap,DegradeFourier\n fBdust,padded_window,unpadded_window=MakePowerAndFourierMaps(map_id,padding_ratio=padding_ratio,map_size=map_size,sep=sep,freq=freq,fourier=True,power=False,returnMasks=True,flipU=a.flipU)\n\n # Also compute unpadded map to give binning values without bias\n unpadded_fBdust=MakePowerAndFourierMaps(map_id,padding_ratio=1.,map_size=map_size,freq=freq,fourier=True,power=False,returnMasks=False,flipU=a.flipU)\n unpadded_fBdust=DegradeFourier(unpadded_fBdust,lCut) # remove high ell pixels\n\n fBdust=DegradeFourier(fBdust,lCut) # discard high-ell pixels\n padded_window=DegradeMap(padded_window.copy(),lCut) # remove high-ell data\n unpadded_window=DegradeMap(unpadded_window.copy(),lCut)\n\n unpadded_fBdust.kMap*=f_dust\n fBdust.kMap*=f_dust\n\n wCorrection = np.mean(padded_window.data**2.)**2./np.mean(padded_window.data**4.)\n\n from hades.NoisePower import noise_model,lensed_Cl,r_Cl\n Cl_lens_func=lensed_Cl(delensing_fraction=delensing_fraction) # function for lensed Cl\n\n def total_Cl_noise(l):\n return Cl_lens_func(l)+noise_model(l,FWHM=FWHM,noise_power=noise_power)\n\n from hades.PaddedPower import fourier_noise_map\n ellNoise=np.arange(5,lCut) # ell range for noise spectrum\n\n from hades.RandomField import fill_from_model\n #fourierNoise=fourier_noise_map\n\n from hades.PaddedPower import fourier_noise_test\n fourierNoise,unpadded_noise=fourier_noise_test(padded_window,unpadded_window,ellNoise,total_Cl_noise(ellNoise),padding_ratio=padding_ratio,unpadded=False,log=True)\n\n totFmap=fBdust.copy()\n totFmap.kMap+=fourierNoise.kMap# for total B modes\n unpadded_totFmap=unpadded_fBdust.copy()\n unpadded_totFmap.kMap+=unpadded_noise.kMap\n\n fBtrue=totFmap.copy()\n\n # Step 2: Compute the I map\n inDir=a.root_dir+'%sdeg%s/' %(map_size,sep)\n Tmap=liteMap.liteMapFromFits(inDir+'fvsmapT_'+str(map_id).zfill(5)+'.fits')\n Qmap=liteMap.liteMapFromFits(inDir+'fvsmapQ_'+str(map_id).zfill(5)+'.fits')\n Umap=liteMap.liteMapFromFits(inDir+'fvsmapU_'+str(map_id).zfill(5)+'.fits')\n Umap.data*=-1.\n QUmap=Qmap.copy()\n QUmap.data=np.sqrt(Qmap.data**2.+Umap.data**2.)\n if useQU:\n \tscaling=np.mean(QUmap.data**4.)\n else:\n \tscaling=np.mean(Tmap.data**4.)\n \n maskMap=liteMap.liteMapFromFits(inDir+'fvsmapMaskSmoothed_'+str(map_id).zfill(5)+'.fits')\n from hades.PaddedPower import zero_padding\n zTmap=zero_padding(Tmap,padding_ratio)\n zQUmap=zero_padding(QUmap,padding_ratio)\n zWindow=zero_padding(maskMap,padding_ratio)\n # Compute window factor <W^2> for padded window (since this is only region with data)\n windowFactor=np.mean(zWindow.data**2.)\n\n # Define mod(l) and ang(l) maps needed for fourier transforms\n modL,angL=fp.fftPol.makeEllandAngCoordinate(zTmap) # choice of map is arbitary\n # Create pure T,E,B maps using 'hybrid' method to minimize E->B leakage\n zTmap.data*=zWindow.data\n zQUmap.data*=zWindow.data\n fT=fftTools.fftFromLiteMap(zTmap)\n fQU=fftTools.fftFromLiteMap(zQUmap)\n\n # Rescale to correct amplitude using dust SED\n from hades.PowerMap import dust_emission_ratio\n dust_intensity_ratio=dust_emission_ratio(freq)\n\n fT.kMap*=dust_intensity_ratio # apply dust-reduction factor \n fT.kMap/=np.sqrt(windowFactor)\n fQU.kMap*=dust_intensity_ratio\n fQU.kMap/=np.sqrt(windowFactor)\n fImap=DegradeFourier(fT,lCut)\n fQUmap=DegradeFourier(fQU,lCut)\n\n # Step 3: Compute angle estimate\n powBtrue=fftTools.powerFromFFT(fBtrue)\n unpadded_powBtrue=fftTools.powerFromFFT(unpadded_totFmap)\n from hades.KKdebiased import derotated_estimator\n output=derotated_estimator(powBtrue,map_id,lMin=lMin,lMax=lMax,FWHM=FWHM,noise_power=noise_power,delensing_fraction=delensing_fraction,slope=slope)\n A,fs,fc,Afs,Afc,_=output\n HexPow2=Afs**2.+Afc**2.\n \n if a.debias_dedust:\n \tfrom .RandomField import padded_fill_from_Cell\n\tbias_data=np.zeros(N_bias)\n\t\n\tdef analytic_model(ell,A_est,slope):\n\t\t\"\"\"Use the estimate for A to construct analytic model.\n\t\tNB: This is just used for finding the centres of the actual binned data.\n\t\t\"\"\"\n\t\treturn total_Cl_noise(ell)+A_est*ell**(-slope)\n\t\n\tfrom .PowerMap import oneD_binning\n\tl_cen,mean_pow = oneD_binning(unpadded_powBtrue.copy(),lMin*padding_ratio,lCut,l_step*padding_ratio,binErr=False,exactCen=a.exactCen,\\\n\t\t\t\t\tC_ell_model=analytic_model,params=[A,slope]) \n\t#l_cen,mean_pow=oneD_binning(totPow.copy(),lMin,lCut,l_step,binErr=False,exactCen=a.exactCen,C_ell_model=analytic_model,params=[A_est,slope])\n\t# gives central binning l and mean power in annulus using window function corrections \n\t\n\t# Create spline fit\n\tfrom scipy.interpolate import UnivariateSpline\n\tspl=UnivariateSpline(l_cen,np.log(mean_pow),k=5)\n\tdef spline(ell):\n\t\treturn np.exp(spl(ell))\n\t#del l_cen,mean_pow\n\t\n\t# Precompute useful data:\n\tfrom hades.RandomField import precompute\n\tprecomp=precompute(padded_window.copy(),spline,lMin=lMin,lMax=lMax)\n\t\n\tfor n in range(N_bias):\n\t\tif n%100==0:\n\t\t\tprint 'Computing bias sim %s of %s' %(n+1,N_bias)\n\t\tfBias=padded_fill_from_Cell(padded_window.copy(),l_cen,mean_pow,lMin=lMin,unPadded=a.unPadded,precomp=precomp)#,padding_ratio=padding_ratio)\n\t\tbias_cross=fftTools.powerFromFFT(fBias.copy(),totFmap.copy()) # cross map\n\t\tbias_self=fftTools.powerFromFFT(fBias.copy()) # self map\n\t\t# First compute estimators on cross-spectrum\n\t\tcross_ests=derotated_estimator(bias_cross.copy(),map_id,lMin=lMin,lMax=lMax,slope=slope,\\\n\t\t\t\t\t\tfactor=A,FWHM=FWHM,noise_power=noise_power,\\\n\t\t\t\t\t\trot=a.rot,delensing_fraction=delensing_fraction,useTensors=a.useTensors,\\\n\t\t\t\t\t\tdebiasAmplitude=False,rot_average=a.rot_average,KKdebiasH2=False) # NB: CHANGE DEBIAS_AMPLITUDE parameter here\n\t\tself_ests=derotated_estimator(bias_self.copy(),map_id,lMin=lMin,lMax=lMax,slope=slope,\\\n\t\t\t\t\t\tfactor=A,FWHM=FWHM,noise_power=noise_power,\\\n\t\t\t\t\t\trot=a.rot,delensing_fraction=delensing_fraction,useTensors=a.useTensors,\\\n\t\t\t\t\t\tdebiasAmplitude=True,rot_average=a.rot_average,KKdebiasH2=a.KKdebiasH2)\n\t\tbias_data[n]=(-1.*(self_ests[3]**4.+self_ests[4]**2.)+4.*(cross_ests[3]**2.+cross_ests[4]**2.))*wCorrection\n\t# Now compute the mean bias - this debiases the DATA only\n\tbias=np.mean(bias_data)\n\tdel bias_self,bias_cross\n\t\n HexPow2-=bias\t\n \n norm=np.sqrt(Afs**2.+Afc**2.)\n fsbar,fcbar=Afs/norm,Afc/norm\n\n sin2a=fsbar/np.sqrt(2.*(fcbar+1.))\n cos2a=np.sqrt((1.+fcbar)/2.)\n\n # Step 4: Compute B estimate\n angleMap=fImap.thetaMap*np.pi/180.\n fB_est=fImap.copy()\n if useQU:\n \tbaseMap=fQUmap.copy()\n else:\n \tbaseMap=fImap.copy()\n fB_est.kMap=baseMap.kMap*(sin2a*np.cos(2.*angleMap)-cos2a*np.sin(2.*angleMap))\n\n # Step 5: Now compute cross coefficient\n crossPow=fftTools.powerFromFFT(fB_est,fBtrue)\n estPow=fftTools.powerFromFFT(fB_est,fB_est)\n\n from hades.PowerMap import oneD_binning\n lC,pC=oneD_binning(crossPow,lMin,lMax/2.,l_step,exactCen=False)\n lE,pE=oneD_binning(estPow,lMin,lMax/2.,l_step,exactCen=False)\n lB,pB=oneD_binning(powBtrue,lMin,lMax/2.,l_step,exactCen=False)\n #rho=np.array(pC)/np.sqrt(np.array(pB)*np.array(pE))\n ratio=np.array(pC)/np.array(pE)\n sign=np.sign(np.mean(ratio))\n\n # Step 6: Now compute the actual angle\n alpha0=0.25*np.arctan2(fsbar,fcbar) # range is [-pi/4,pi/4]\n if sign==-1.0:\n alpha0+=np.pi/2.\n \n # Step 7: Compute the ratio of H^2/<I^4> for rescaling\n ratio=(np.abs(HexPow2)/scaling)**0.25\n \n alpha_deg=alpha0*180./np.pi\n print 'MapID: %s Angle: %.2f Ratio: %.2e' %(map_id,alpha_deg,ratio)\n \n return alpha_deg,ratio","def get_displacement(self, color, depth):\n displacement = None\n keypoints, descriptors = self._feature_detector.detectAndCompute(color, None)\n \n if descriptors is not None and self._last_descriptors is not None:\n matches = self._matcher.match(descriptors, self._last_descriptors)\n matches = self._select_matches(matches) \n points, last_points = self._get_points_from_matches(keypoints, self._last_keypoints, matches)\n t = []\n horizontal = 0\n for i in range(len(matches)):\n d1 = depth[int(points[i][1]), int(points[i][0])]\n d2 = self._last_depth[int(last_points[i][1]), int(points[i][0])]\n #print(d1, d2)\n \n if d1 != 0 and abs(d1-d2) < 10:\n t.append(matches[i])\n horizontal += (points[i][0] - last_points[i][0]) * d1\n \n matches = t\n horizontal /= len(t) \n \n if len(matches) > 0:\n if self._config['show_matches']:\n matches_preview = cv2.drawMatches(color, keypoints, self._last_color, self._last_keypoints, matches, None, flags=2)\n cv2.imshow('Basic_Odometry_matches_preview', matches_preview)\n \n points, last_points = self._get_points_from_matches(keypoints, self._last_keypoints, matches)\n #It uses default values for parameters - there must be here because of broken python bindings I suppose\n tf_mat = cv2.estimateRigidTransform(points, last_points, False, 500, 0.5, 3)\n \n if tf_mat is not None:\n a11, a12, b1 = tf_mat[0][0], tf_mat[0][1], tf_mat[0][2]\n a21, a22, b2 = -tf_mat[1][0], tf_mat[1][1], tf_mat[1][2]\n roll = math.atan2(-a12, a11) \n \n displacement = {\n 'roll': roll,\n 'horizontal': horizontal,#b1,\n 'vertical': b2,\n 'depth': math.sqrt(a11**2+a12**2) * (-1 if a11 < 0 else 1)\n }\n \n if descriptors is not None: \n self._last_keypoints = keypoints\n self._last_descriptors = descriptors\n self._last_color = color\n self._last_depth = depth\n \n return displacement","def test_objectbased_depth(layout, gain_calc):\n bf_no_depth = AudioBlockFormatObjects(position=dict(azimuth=0, elevation=0, distance=1),\n width=360, height=360, depth=0)\n bf_with_depth = AudioBlockFormatObjects(position=dict(azimuth=0, elevation=0, distance=0.5),\n width=0, height=0, depth=1)\n gains_no_depth = gain_calc.render(ObjectTypeMetadata(bf_no_depth)).direct\n gains_with_depth = gain_calc.render(ObjectTypeMetadata(bf_with_depth)).direct\n\n npt.assert_allclose(np.linalg.norm(gains_no_depth), 1.0)\n npt.assert_allclose(np.linalg.norm(gains_with_depth), 1.0)\n\n front_idx = layout.channel_names.index(\"M+000\")\n assert np.all(gains_with_depth[~layout.is_lfe]) > 0 and gains_with_depth[front_idx] > gains_no_depth[front_idx]","def find_all_ORFs(dna):\n #these functions offset to analyze each each open frame reference\n zero_offset=find_all_ORFs_oneframe(dna[0:])\n \n one_offset=find_all_ORFs_oneframe(dna[1:])\n \n two_offset=find_all_ORFs_oneframe(dna[2:])\n \n return zero_offset+one_offset+two_offset\n \n # YOUR IMPLEMENTATION HERE","def approximate_betweenness(graph, max_depth):\n ###TODO\n pass","def colourMagnitudeDiagram(lens_r_list, lens_gr_list, lens_gi_list, source_r_list, source_gr_list,\n source_gi_list, positive_path):\n\n int_lens_r_list = []\n int_lens_gr_list = []\n int_lens_gi_list = []\n int_source_r_list = []\n int_source_gr_list = []\n int_source_gi_list = []\n\n # creating lists containing floats and integrs that can be used in the graph\n for i in range(0, len(lens_r_list)):\n int_lens_r = float(lens_r_list[i])\n int_lens_r_list.append(int_lens_r)\n int_lens_gr = float(lens_gr_list[i])\n int_lens_gr_list.append(int_lens_gr)\n int_lens_gi = float(lens_gi_list[i])\n int_lens_gi_list.append(int_lens_gi)\n int_source_r = float(source_r_list[i])\n int_source_r_list.append(int_source_r)\n int_source_gr = float(source_gr_list[i])\n int_source_gr_list.append(int_source_gr)\n int_source_gi = float(source_gi_list[i])\n int_source_gi_list.append(int_source_gi)\n\n # Getting the min and max fro the axees\n lens_r_min = min(int_lens_r_list)\n lens_r_max = max(int_lens_r_list)\n lens_gr_min = min(int_lens_gr_list)\n lens_gr_max = max(int_lens_gr_list)\n lens_gi_min = min(int_lens_gi_list)\n lens_gi_max = max(int_lens_gi_list)\n source_r_min = min(int_source_r_list)\n source_r_max = max(int_source_r_list)\n source_gr_min = min(int_source_gr_list)\n source_gr_max = max(int_source_gr_list)\n source_gi_min = min(int_source_gi_list)\n source_gi_max = max(int_source_gi_list)\n\n r_min = min(lens_r_min, source_r_min)\n r_max = max(lens_r_max, source_r_max)\n gr_min = min(lens_gr_min, source_gr_min)\n gr_max = max(lens_gr_max, source_gr_max)\n gi_min = min(lens_gi_min, source_gi_min)\n gi_max = max(lens_gi_max, source_gi_max)\n\n y_min = gr_min - 0.2\n y_max = gr_max + 0.2\n x_min = r_min - 0.2\n x_max = r_max + 0.2\n a_min = gi_min -0.2\n a_max = gi_max+0.2\n\n x = int_lens_r_list\n y = int_lens_gr_list\n x2 = int_source_r_list\n y2 = int_source_gr_list\n gi_source = int_source_gi_list\n gi_lens = int_lens_gi_list\n\n # plotting the colour magnitude graph\n fig1 = plt.figure()\n plt.scatter(x, y, color='b', marker='.', label='Lenses')\n plt.scatter(x2, y2, color='r', marker='.', label='Sources')\n plt.xlabel('r')\n plt.ylabel('g-r')\n\n axes = plt.axes()\n axes.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\n\n if positive_path == 'Training/g_r_PositiveAll':\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs r) Diagram')\n elif positive_path == 'Testing/g_r_PositiveAll':\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs r) Diagram')\n elif positive_path == 'Training/g_r_AllData':\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs r) Diagram')\n plt.legend()\n plt.show()\n fig1.savefig('%s_g_r_colourMagnitudeDiagram.png' % positive_path)\n\n fig2 = plt.figure()\n plt.scatter(gi_lens, y, color = 'b', marker= '.', label = 'Lenses')\n plt.scatter(gi_source, y2, color ='r', marker='.', label = 'Sources')\n if positive_path == 'Training/g_r_PositiveAll':\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs g-i ) Diagram')\n elif positive_path == 'Testing/g_r_PositiveAll':\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs g-i) Diagram')\n elif positive_path == 'Training/g_r_AllData':\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs g-i) Diagram')\n\n axes = plt.axes()\n axes.set(xlim=(a_min, a_max), ylim=(y_min, y_max))\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\n\n plt.xlabel('g-i')\n plt.ylabel('g-r')\n plt.legend()\n plt.show()\n fig2.savefig('%s_g_i_colourMagnitudeDiagram.png' % positive_path)","def antenny_calibrate(self):\n if self.antenny_config.get(\"use_bno08x_rvc\"):\n t = .25\n d = .5\n us = 50\n else:\n t = .1\n d = .5\n us = 100\n self.platform.auto_calibrate_elevation_servo(us=us, t=t, d=d)\n self.platform.auto_calibrate_azimuth_servo(us=us, t=t, d=d)\n if self.antenny_config.get(\"use_bno055\"):\n self.platform.auto_calibrate_magnetometer()\n self.platform.auto_calibrate_gyroscope()\n self.platform.auto_calibrate_accelerometer()","def optical_depth_height(path_to_netcdf, opt_variable='upopt', tau=1.):\n data = nc.Dataset(path_to_netcdf)\n tau = data[opt_variable][:]\n z = data['z'][:]\n t1height = []\n decreasing = True\n # check if optical depth is increasing or decreasing\n if tau.transpose()[0][1] - tau.transpose()[0][0] > 0.:\n decreasing = False\n for i, t in enumerate(tau.transpose()):\n for j, opt in enumerate(t):\n if decreasing:\n # record first tau <= 1\n if opt <= 1.:\n t1height.append(z[j])\n t1index.append(j)\n break\n else:\n # increasing, so record first tau >= 1\n if opt >= 1.:\n t1height.append(z[j])\n t1index.append(j)\n break\n return t1height","def e_c_b97_polarized(rhoa,\n rhob,\n sigma_aa,\n sigma_ab,\n sigma_bb,\n power_series_ss=(\n (0, 0.1737), (1, 2.3487), (2, -2.4868)\n ),\n power_series_os=(\n (0, 0.9454), (1, 0.7471), (2, -4.5961)\n ),\n gamma_ss=0.2,\n gamma_os=0.006):\n del sigma_ab\n\n e_c_lda_aa, e_c_lda_bb, e_c_lda_ab = lda.decomposed_e_c_lda_polarized(\n rhoa, rhob)\n\n xa = get_reduced_density_gradient(rhoa, sigma_aa)\n xb = get_reduced_density_gradient(rhob, sigma_bb)\n xave = jnp.sqrt(0.5 * (xa ** 2 + xb ** 2))\n\n f_c_aa = f_b97(\n xa, power_series=power_series_ss, gamma=gamma_ss, polarized=True)\n f_c_bb = f_b97(\n xb, power_series=power_series_ss, gamma=gamma_ss, polarized=True)\n f_c_ab = f_b97(\n xave, power_series=power_series_os, gamma=gamma_os, polarized=True)\n\n return e_c_lda_aa * f_c_aa + e_c_lda_bb * f_c_bb + e_c_lda_ab * f_c_ab","def calc_elv_spectra(self, red, comp, src):\n if ((src in red.data.keys())\n & (src in red.data.keys())):\n # check that the wavelenth grids are identical\n delt_wave = red.data[src].waves - comp.data[src].waves\n if np.sum(np.absolute(delt_wave)) > 0.01*u.micron:\n warnings.warn(\"wavelength grids not equal for %s\" % src,\n UserWarning)\n else:\n # reference band\n red_V = red.data['BAND'].get_band_mag('V')\n comp_V = comp.data['BAND'].get_band_mag('V')\n\n # setup the needed variables\n self.waves[src] = red.data[src].waves\n n_waves = len(self.waves[src])\n self.exts[src] = np.zeros(n_waves)\n self.uncs[src] = np.zeros(n_waves)\n self.npts[src] = np.zeros(n_waves)\n\n # only compute the extinction for good, positive fluxes\n print(comp.data[src].npts)\n print(comp.data[src].fluxes)\n indxs, = np.where((red.data[src].npts > 0)\n & (comp.data[src].npts > 0)\n & (red.data[src].fluxes.value > 0)\n & (comp.data[src].fluxes.value > 0))\n self.exts[src][indxs] = \\\n (-2.5*np.log10(red.data[src].fluxes[indxs]\n / comp.data[src].fluxes[indxs])\n + (comp_V[0] - red_V[0]))\n self.uncs[src][indxs] = np.sqrt(\n np.square(_flux_unc_as_mags(red.data[src].fluxes[indxs],\n red.data[src].uncs[indxs]))\n + np.square(_flux_unc_as_mags(comp.data[src].fluxes[indxs],\n comp.data[src].uncs[indxs]))\n + np.square(red_V[1])\n + np.square(comp_V[1]))\n self.npts[src][indxs] = np.full(len(indxs), 1)","def distance_23(self, alphas, motor_positions):\n\n self.roof_vertex_z[1] = motor_positions[1] * math.cos(alphas[1])\n s2 = motor_positions[1] * math.sin(alphas[1])\n self.roof_vertex_x[1] = self.base_point[1][0] - (s2 * 0.5) # sin 30° = 1/2\n self.roof_vertex_y[1] = self.base_point[1][1] - (s2 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\n\n self.roof_vertex_z[2] = motor_positions[2] * math.cos(alphas[2])\n s3 = motor_positions[2] * math.sin(alphas[2])\n self.roof_vertex_x[2] = self.base_point[2][0] - (s3 * 0.5) # sin 30° = 1/2\n self.roof_vertex_y[2] = self.base_point[2][1] + (s3 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\n\n return math.sqrt(\n ((self.roof_vertex_x[2] - self.roof_vertex_x[1]) ** 2) +\n ((self.roof_vertex_y[1] - self.roof_vertex_y[2]) ** 2) +\n ((self.roof_vertex_z[2] - self.roof_vertex_z[1]) ** 2))","def shadeToDepth(color):\n minDistance = 0.7\n maxDistance = 20\n maxColor = 255\n color = np.average(color, axis=None)\n # depth = mx + b\n # m = (minDistance - maxDistance)/maxColor\n # x = color\n # b = maxDistance\n m = (maxDistance - minDistance)/maxColor\n x = color\n b = minDistance\n return m * x + b","def alpha_crit_fromEarth(a_p): #OK\n a_earth = 1. #in AU\n if a_p > a_earth:\n alpha_max = np.arcsin(a_earth/a_p) #occurs at quadrature\n else: #if a_p < a_earth:\n alpha_max = np.pi #0 deg when opposite side of sta180 deg on same side of star\n return alpha_max","def test_GBL_tau_inst():\n dz = 0.05\n z = numpy.arange(0., 80. + 1.5*dz, dz)\n\n # Fully ionized H and He\n x_ionH = 1.0\n x_ionHe = 2.0\n\n cosmo = {}\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\n cosmo['h'] = 0.65\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\n cosmo['Y_He'] = 0.24\n cd.set_omega_k_0(cosmo)\n\n tau_inst = cr.optical_depth_instant(z, x_ionH=x_ionH, x_ionHe=x_ionHe, \n **cosmo)\n tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)\n\n linestyle = ['-', ':', '--']\n \n pylab.figure()\n pylab.subplot(2,1,1)\n pylab.title(\"Compare to GB&L fig. 1 (astro-ph/9812125v3.)\")\n for i in range(len(linestyle)):\n pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')\n pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')\n\n pylab.xlim(0,80)\n pylab.ylim(0,1)\n pylab.xlabel(r\"$\\mathrm{z_{ion}}$\")\n pylab.ylabel(r\"$\\tau$\")\n \n pylab.subplot(2,1,2)\n for i in range(len(linestyle)):\n pylab.plot(z, \n 1.e4 * (tau_int[i] - tau_inst[i])/tau_inst[i], \n ls=linestyle[i], color='k')\n diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]\n diff[numpy.isnan(diff)] = 0.0\n print(\"max fractional error in num. int. = %.3g\" % \n numpy.max(numpy.abs(diff))\n )\n ntest.assert_array_less(numpy.abs(diff), \n numpy.zeros(diff.shape) + 2.e-4)\n\n pylab.xlim(0,40)\n pylab.xlabel(r\"$\\mathrm{z_{ion}}$\")\n pylab.ylabel(r\"$\\mathrm{10^4 \\times (num.\\tau - ana.\\tau)/ana.\\tau}$\")","def getDiameter(antenna):\n if antenna >=1 and antenna <= 6: \n diameter = 10.4\n elif antenna >=7 and antenna <= 15: \n diameter = 6.1\n elif antenna >=16 and antenna <= 23: \n diameter = 3.5\n else:\n raise Exception,'Invalid antenna number : '+str(antenna)\n return diameter","def comp_angle_magnet(self):\n Rbo = self.get_Rbo()\n W0 = self.comp_W0m()\n Harc = self.comp_H_arc()\n if self.is_outwards():\n return float(2 * arctan(W0 / (2 * (Rbo + self.H1 - Harc))))\n else:\n return float(2 * arctan(W0 / (2 * (Rbo - self.H1 - Harc))))\n\n # if self.W0_is_rad:\n # return self.W0\n # else: # Convert W0 from m to rad\n # Rbo = self.get_Rbo()\n # return float(2 * arcsin(self.W0 / (2 * Rbo)))","def MiyamotoNagaiAccel(self, M, rd, r):\n R = np.sqrt(r[0]**2 + r[1]**2) #Finding magnitude of x and y compnets\n zd = rd/5. #Calculating \"zd\"\n B = rd + np.sqrt(r[2]**2 + zd**2) #Calclating \"B\"\n zstuff = 1/np.sqrt(r[2]**2 + zd**2) #Calculating stuff that only appears in z componet\n MNa = -self.G*M/(R**2+B**2)**1.5 * r * np.array([1,1,zstuff]) #Putting it all together\n\n return MNa","def calculate_SWIR2_TOA(d,sza,L):\n rho_SWIR2_TOA=np.pi*(d**2)*L[:,178,:]/(0.0825*np.cos(sza))\n return rho_SWIR2_TOA","def get_re(r, cl, aoa, arctan, rel_v, blade_number):\n chord = 8*math.pi*r*(1-math.cos(math.radians(arctan-aoa))) / blade_number / cl\n Re = round(rel_v * chord / 0.00001511 / 100) * 100\n return Re, chord","def _estimate_bearing_(self):\n fname = \"data/sim/{dn}/{rad}/bearing.mat\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), rad=self.rad)\n m = {}\n lat, lon, bearing = utils.get_sd_radar(self.rad)\n p = (lat, lon)\n gc = GC(p, p)\n dist = np.linspace(0,self.mrange,self.nmrange)\n lats, lons = [], []\n for d in dist:\n x = gc.destination(p, bearing, distance=d)\n lats.append(x[0])\n lons.append(x[1])\n rinc = dist[1]-dist[0]\n m[\"dist\"], m[\"lat\"], m[\"lon\"] = dist, np.array(lats), np.array(lons)\n m[\"olat\"], m[\"olon\"], m[\"rb\"], m[\"num_range\"], m[\"max_range\"], m[\"range_inc\"] = lat, lon, bearing, float(self.nmrange),\\\n float(self.mrange), float(rinc)\n m[\"start_height\"], m[\"height_inc\"], m[\"num_heights\"] = float(self.sheight), float(self.hinc),\\\n float(len(np.arange(self.sheight,self.eheight,self.hinc)))\n m[\"ht\"] = np.arange(self.sheight,self.eheight,self.hinc)\n m[\"freq\"], m[\"tol\"], m[\"nhops\"] = float(self.frequency), float(1e-7), float(self.nhops)\n m[\"elev_s\"], m[\"elev_i\"], m[\"elev_e\"] = float(self.selev), float(self.ielev), float(self.eelev)\n m[\"radius_earth\"] = 6371.0\n m[\"d_ratio\"], m[\"d_start\"], m[\"d_end\"], m[\"d_rtime\"] = float(self.d_ratio), float(self.d_start),\\\n float(self.d_end), float(self.d_rtime)\n m[\"f_ratio\"], m[\"f_start\"], m[\"f_end\"], m[\"f_rtime\"] = float(self.f_ratio), float(self.f_start),\\\n float(self.f_end), float(self.f_rtime)\n m[\"e_ratio\"] = float(self.e_ratio)\n savemat(fname, m)\n self.m, self.lat, self.lon, self.ht = m, m[\"lat\"], m[\"lon\"], m[\"ht\"]\n return","def depth_heading_rc(self, goal):\n # initialize empty RC command\n channels = [1500] * 8\n # compute heading and depth changes\n zout = self.get_depth_pwm(goal)\n hout = self.get_heading_pwm(goal)\n\n channels[self.zchannel] = zout\n channels[self.rchannel] = hout\n return channels","def _estimate_bearing_(self):\n fname = \"data/sim/{dn}/{rad}/bearing.mat\".format(dn=self.event.strftime(\"%Y.%m.%d.%H.%M\"), rad=self.rad)\n m = {}\n lat, lon, bearing = utils.get_sd_radar(self.rad)\n p = (lat, lon)\n gc = GC(p, p)\n dist = np.linspace(0,self.mrange,self.nmrange)\n lats, lons = [], []\n for d in dist:\n x = gc.destination(p, bearing, distance=d)\n lats.append(x[0])\n lons.append(x[1])\n rinc = dist[1]-dist[0]\n m[\"dist\"], m[\"lat\"], m[\"lon\"] = dist, np.array(lats), np.array(lons)\n m[\"olat\"], m[\"olon\"], m[\"rb\"], m[\"num_range\"], m[\"max_range\"], m[\"range_inc\"] = lat, lon, bearing, float(self.nmrange),\\\n float(self.mrange), float(rinc)\n m[\"start_height\"], m[\"height_inc\"], m[\"num_heights\"] = float(self.sheight), float(self.hinc),\\\n float(len(np.arange(self.sheight,self.eheight,self.hinc)))\n m[\"ht\"] = np.arange(self.sheight,self.eheight,self.hinc)\n m[\"freq\"], m[\"tol\"], m[\"nhops\"] = float(self.frequency), float(1e-7), float(self.nhops)\n m[\"elev_s\"], m[\"elev_i\"], m[\"elev_e\"] = float(self.selev), float(self.ielev), float(self.eelev)\n m[\"radius_earth\"] = 6371.0\n m[\"d_ratio\"], m[\"d_start\"], m[\"d_end\"] = float(self.d_ratio), 10., 35.\n m[\"f_ratio\"], m[\"f_start\"], m[\"f_end\"] = float(self.f_ratio), 130., 240.\n m[\"e_ratio\"], m[\"e_start\"], m[\"e_end\"] = float(self.e_ratio), 50., 70.\n savemat(fname, m)\n self.m, self.lat, self.lon, self.ht = m, m[\"lat\"], m[\"lon\"], m[\"ht\"]\n return","def distance_13(self, alphas, motor_positions):\n\n self.roof_vertex_z[0] = motor_positions[0] * math.cos(alphas[0])\n self.roof_vertex_x[0] = motor_positions[0] * math.sin(alphas[0])\n self.roof_vertex_z[2] = motor_positions[2] * math.cos(alphas[2])\n\n s3 = motor_positions[2] * math.sin(alphas[2])\n self.roof_vertex_x[2] = self.base_point[2][0] - (s3 / 2) # sin 30° = 1/2\n self.roof_vertex_y[2] = self.base_point[2][1] + (s3 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\n\n return math.sqrt(\n ((self.roof_vertex_x[2] - self.roof_vertex_x[0]) ** 2) +\n (self.roof_vertex_y[2] ** 2) +\n ((self.roof_vertex_z[2] - self.roof_vertex_z[0]) ** 2))","def radialApproxEffect(hubdist1,hubdist2,width,length):\n #Grating coordinates\n x,y = np.meshgrid(np.linspace(-width,width,1000),\\\n np.linspace(-length,length,1000))\n y1 = y + hubdist1\n y2 = y + hubdist2\n\n #Convert to period and yaw angle\n period1 = np.sqrt(x**2+y1**2)/hubdist1*160. #nm\n period2 = np.sqrt(x**2+y2**2)/hubdist2*160. #nm\n yaw = blazeYaw(1.5*np.pi/180,2.4,3,160.)\n yaw1 = np.pi/2 - np.arctan(x/y1) + yaw\n yaw2 = np.pi/2 - np.arctan(x/y2) + yaw\n\n #Determine alpha and beta\n beta0,alpha0 = litBetaAlpha(1.5*np.pi/180,2.4,3,160.)\n alpha1 = alpha0 + 3*2.4/period1*np.sin(yaw1)\n alpha2 = alpha0 + 3*2.4/period2*np.sin(yaw2)\n beta1 = beta0 + (3*2.4/period1)*np.cos(yaw1)\n beta2 = beta0 + (3*2.4/period2)*np.cos(yaw2)\n\n #Determine spot shifts\n x1 = hubdist2*(alpha1/beta1)\n x2 = hubdist2*(alpha2/beta2)\n \n\n pdb.set_trace()\n \n return x1,x2","def _calculate_a_value(self, bval, nvalue, nyr, cmag, ref_mag):\n\n denominator = np.sum(nyr * np.exp(-bval * (cmag - ref_mag)))\n return nvalue / denominator","def getTheta(self, trackWidth):\n leftDist = leftUS.sensor_detect()\n print(\"LEFT US: \" + str(leftDist))\n rightDist = rightUS.sensor_detect()\n print(\"RIGHT US: \" + str(rightDist))\n #totalWidth (hypotenuse) = leftUS + rightUS + robotWidth\n totalWidth = leftDist + rightDist + 6\n try:\n print(math.acos(trackWidth/totalWidth))\n return math.acos(trackWidth/totalWidth)\n except ValueError:\n return 0","def calMeasuredContactAngle(self):\n #account the base\n bottomLength = 0\n arrayHeight = np.empty([0, ], dtype = 'int64')\n for i in sp.arange(self.nx):\n if (self.densityFluid1[1, i] >= 0.485):\n bottomLength += 1\n #account the height\n for m in sp.arange(self.nx):\n tmpHeight = 0\n for n in sp.arange(1, self.ny - 1):\n if (self.densityFluid1[n, m] >= 0.485):\n tmpHeight += 1\n arrayHeight = np.append(arrayHeight, tmpHeight)\n heightH = np.amax(arrayHeight)\n #radius of droplet\n radiusD = (4. * np.power(heightH, 2.) + np.power(bottomLength, 2.)) / \\\n (8. * heightH)\n contactAngle = np.arctan((bottomLength) / (2. * (radiusD - heightH))) \n return contactAngle","def computeRjb(self, lon, lat, depth):\n pass","def bv_to_radius(b_minus_v):\n # Boyajian 2012\n X = b_minus_v\n a0 = 0.3830\n a1 = 0.9907\n a2 = -0.6038\n Y = 0\n # Ignore metallicity\n a3 = 0\n a4 = 0\n a5 = 0\n return (a0 + a1 * X + a2 * X ** 2 + a3 * X * Y +\n a4 * Y + a5 * Y ** 2) * R_sun","def computeRhyp(self, lon, lat, depth):\n origin = self._origin\n oldshape = lon.shape\n\n\n rhyp = geodetic.distance(origin.lon, origin.lat, origin.depth,\n lon, lat, depth)\n rhyp = rhyp.reshape(oldshape)\n return rhyp","def calculateDetectorArea(self):\n area = 0.0\n r = self.geoParam['CylinderLightGuideRadius']\n while(r + self.geoParam['DetectorThickness'] < self.geoParam['CylinderRadius']):\n area -= math.pow(r,2)\n r += self.geoParam['DetectorThickness']\n area += math.pow(r,2)\n r += self.geoParam['DetectorSpacing']\n return math.pi*area","def rayleigh(th,r,wl,a,n1,n2):\n c = np.cos(th)\n c2,s2 = c**2, np.sin(th)**2\n k = 2*np.pi/wl\n n_2 = n2**2/n1**2\n m = (k**4)*(a**6)*(abs(n_2-1)**2) / ((abs(n_2+2)**2) * 2 * (r**2))\n return m*np.array([[1+c2 , -s2 , 0 , 0],\n [-s2 , 1+c2 , 0 , 0],\n [0 , 0 , 2*c , 0],\n [0 , 0 , 0 , 2*c]])","def paf_o_r(rr_o_r, alpha):\n return ((rr_o_r - 1) * (1 - alpha)) / ((rr_o_r - 1) * (1 - alpha) + 1)","def myDihedralFunctionAirliner(Epsilon):\n BaseDihedral = 7\n\n # A simple model of a loaded wing shape:\n return BaseDihedral + Epsilon*Epsilon*10"],"string":"[\n \"def calculate_blue_haze_TOA(d,sza,L):\\n rho_blue_TOA=np.pi*(d**2)*L[:,5,:]/(2.16*np.cos(sza))\\n return rho_blue_TOA\",\n \"def ead(o2, depth):\\n fraction_o2 = percentage_to_fraction(o2)\\n fraction_n2 = 1.0 - fraction_o2\\n return math.ceil(((depth + 10.0) * (fraction_n2 / 0.79)) - 10.0)\",\n \"def optical_depth(self,z0,ETeV,\\n #OmegaM = cosmo.Om0, OmegaL = 1. - cosmo.Om0, \\n OmegaM = 0.3, OmegaL = 0.7, \\n H0 = 70., #cosmo.H0.value, \\n steps_z = 50,\\n steps_e = 50,\\n egamma_LIV = True,\\n LIV_scale = 0., nLIV=1):\\n if np.isscalar(ETeV):\\n ETeV = np.array([ETeV])\\n elif type(ETeV) == list or type(ETeV) == tuple:\\n ETeV = np.array(ETeV)\\n\\n\\n z_array = np.linspace(0.,z0,steps_z)\\n result = self.mean_free_path(z_array,ETeV,\\n LIV_scale = LIV_scale,\\n nLIV = nLIV, \\n egamma_LIV = egamma_LIV,\\n steps_e = steps_e)\\n\\n\\n zz,ee = np.meshgrid(z_array, ETeV, indexing = 'ij')\\n result = 1. / (result.T * u.Mpc).to(u.cm).value # this is in cm^-1\\n result *= 1./ ( (1. + zz ) * np.sqrt((1.+ zz )**3. * OmegaM + OmegaL) ) # dt / dz for a flat universe\\n\\n result = simps(result,zz, axis = 0)\\n\\n H0 = (H0 * cosmo.H0.unit).to('1 / s').value # convert from km / Mpc / s to 1 / s\\n\\n return result * c.c.to('cm / s').value / H0\",\n \"def calculate_blue_TOA(d,sza,L):\\n rho_blue_TOA=np.pi*(d**2)*L[:,0,:]/(1.57*np.cos(sza))\\n return rho_blue_TOA\",\n \"def optical_depth(self, dispersion):\\n\\n from ..calculations import optical_depth, tau_peak\\n\\n if isinstance(dispersion, Quantity):\\n dispersion = dispersion.to(angstrom)\\n\\n elif hasattr(dispersion, 'unit'):\\n if dispersion.unit is not None:\\n dispersion = dispersion.to(angstrom)\\n\\n else:\\n dispersion = Quantity(dispersion, unit=angstrom)\\n\\n velocity_range = ([-20000, 20000] * km / s if self.logn > 18\\n else [-1000, 1000] * km / s)\\n\\n # Select only transitions with redshifted central wavelengths inside\\n # `dispersion` +/- 500 km/s:\\n rest_wavelengths = Quantity([t.wavelength for t in self.transitions])\\n observed_wavelengths = rest_wavelengths * (1 + self.redshift)\\n\\n wmin = dispersion[0] * (1 - 500 * km / s / c_kms)\\n wmax = dispersion[-1] * (1 - 500 * km / s / c_kms)\\n\\n in_range = ((observed_wavelengths >= wmin) &\\n (observed_wavelengths <= wmax))\\n transitions = np.array(self.transitions)[in_range]\\n\\n tau = np.zeros_like(dispersion.value)\\n\\n for i, transition in enumerate(transitions):\\n\\n tau_max = tau_peak(transition, self.logn, self.b)\\n\\n if 1 - exp(-tau_max) < 1e-3:\\n continue\\n\\n observed_wavelength = transition.wavelength * (1 + self.redshift)\\n dv = ((dispersion - observed_wavelength) /\\n observed_wavelength * c_kms)\\n\\n i0, i1 = dv.searchsorted(velocity_range)\\n tau0 = optical_depth(dv[i0:i1], transition, self.logn, self.b)\\n tau[i0:i1] += tau0\\n\\n return tau\",\n \"def one_transition_spectrum_cd(self,tr):\\n \\n\\n ta = tr[\\\"ta\\\"] # TimeAxis\\n rr = tr[\\\"rr\\\"] # transition dipole strength\\n om = tr[\\\"om\\\"] # frequency - rwa\\n gg = tr[\\\"gg\\\"] # natural broadening (constant or time dependent)\\n fwhm = tr[\\\"fwhm\\\"] # Additional gaussian broadening of the spectra\\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\\n \\n # CD and fluorescence can be calculated in this step\\n # TODO if rotatory strength defined calculate also circular dichroism spectra\\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \\n \\n \\n if self.system._has_system_bath_coupling:\\n# ct = tr[\\\"ct\\\"] # correlation function\\n \\n # convert correlation function to lineshape function\\n #gt = self._c2g(ta,ct.data)\\n gt = tr[\\\"gt\\\"]\\n # calculate time dependent response\\n at = numpy.exp(-gt -1j*om*ta.data)\\n else:\\n # calculate time dependent response\\n at = numpy.exp(-1j*om*ta.data) \\n# plt.figure()\\n# plt.title(\\\"Absorption\\\")\\n# plt.plot(ta.data,numpy.real(at))\\n# plt.plot(ta.data,numpy.imag(at))\\n \\n \\n if len(gg) == 1:\\n gam = gg[0]\\n rt = numpy.exp(gam*ta.data)\\n at *= rt\\n #print(\\\"Constant: \\\", rt[20], len(at))\\n else:\\n rt = numpy.exp((gg)*ta.data) \\n at *= rt\\n #print(\\\"Time dependent: len = \\\", rt[20], len(rt))\\n \\n if fwhm!=0.0:\\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\\n at *= gauss\\n \\n # Fourier transform the result\\n ft = rr*numpy.fft.hfft(at)*ta.step\\n ft = numpy.fft.fftshift(ft)\\n # invert the order because hfft is a transform with -i\\n ft = numpy.flipud(ft) \\n # cut the center of the spectrum\\n Nt = ta.length #len(ta.data) \\n return ft[Nt//2:Nt+Nt//2]\",\n \"def calculate_relative_soma_depth(data: Data) -> float:\\n\\n return data.relative_soma_depth\",\n \"def depth_to_ata(depth):\\n return (depth / 10.0) + 1.0\",\n \"def Get_AASR(DopplerBandwidth, Frequencies, NumAmbiguities, FreqSteps, Prf,\\n TxAntennaPattern, RxAntennaPattern, TxGain=1, RxGain=1):\\n _BWDop = DopplerBandwidth\\n _NA = NumAmbiguities\\n _step = _BWDop / FreqSteps\\n \\n _denom = 0\\n for f in np.arange(start=-_BWDop/2, stop=_BWDop/2, step=_step):\\n # find the nearest frequency and magnitude pair for the TX and RX patterns\\n _idx, _ = Utils.find_nearest(Frequencies, f)\\n _txAzPat = TxAntennaPattern[_idx] * TxGain\\n _rxAzPat = RxAntennaPattern[_idx] * RxGain\\n _azPat = _txAzPat * _rxAzPat\\n _denom += ((math.pow(_azPat, 2)) * _step)\\n\\n # _txFreq, _txAzPat = Utils.find_nearest(list(TxAntennaPattern.keys()), f)\\n # _rxFreq, _rxAzPat = Utils.find_nearest(list(RxAntennaPattern.keys()), f)\\n\\n # # _txAzPat = Get_AntennaPatternWithDopplerFrequency(f, txDimension, velocity, txGain)\\n # # _rxAzPat = Get_AntennaPatternWithDopplerFrequency(f, rxDimension, velocity, rxGain)\\n # # _denom += ((math.pow(_azPat, 2)) * _step)\\n # _denom += ((_txAzPat * _rxAzPat) * _step)\\n \\n _num = 0\\n _azPat = 0\\n for m in range(-_NA, _NA+1, 1):\\n if m != 0:\\n for f in np.arange(start=-_BWDop/2, stop=_BWDop/2, step=_step):\\n # find the nearest frequency and magnitude pair for the TX and RX patterns\\n _idx, _ = Utils.find_nearest(Frequencies, f + (m*Prf))\\n _txAzPat = TxAntennaPattern[_idx] * TxGain\\n _rxAzPat = RxAntennaPattern[_idx] * RxGain\\n _azPat = _txAzPat * _rxAzPat\\n _num += ((math.pow(_azPat, 2)) * _step)\\n\\n # _txFreq, _txAzPat = Utils.find_nearest(list(TxAntennaPattern.keys()), f + (m*Prf))\\n # _rxFreq, _rxAzPat = Utils.find_nearest(list(RxAntennaPattern.keys()), f + (m*Prf))\\n\\n # # _txAzPat = Get_AntennaPatternWithDopplerFrequency((f + (m*prf)), txDimension, velocity, txGain)\\n # # _rxAzPat = Get_AntennaPatternWithDopplerFrequency((f + (m*prf)), rxDimension, velocity, rxGain)\\n # # #_num += ((math.pow(_azPat, 2)) * _step)\\n # _num += ((_txAzPat * _rxAzPat) * _step)\\n \\n return (_num / _denom)\",\n \"def BoatEarMoon():\\n D=1\\n alpha=math.radians(83)\\n beta=math.radians(42) \\n phi=math.radians(70) \\n mu=math.radians(10) \\n omega=math.radians(30) \\n A=25, a=12, b=20, L=0, P=0, W1=0, W2=0, N=0\\n \\n resMode()\",\n \"def GetConcBeer(Abs, epsilon, pathLength):\\n return Abs / (epsilon * pathLength)\",\n \"def rayonDeCourbur(**kwargs):\\n a = 0\\n b = 0\\n try:\\n if kwargs[\\\"ref\\\"] == \\\"local\\\":\\n a = 6378249.145\\n b = 6356515\\n elif kwargs[\\\"ref\\\"] == \\\"global\\\":\\n a = 6378137\\n b = 6356752.314\\n elif kwargs[\\\"a\\\"] and kwargs[\\\"b\\\"]:\\n a = kwargs[\\\"a\\\"]\\n b = kwargs[\\\"b\\\"]\\n except KeyError:\\n return {\\\"erreur\\\": \\\"params a and b is required, you can use ref too which has two possible value: local and global\\\"}\\n if a != 0 and b != 0:\\n try:\\n phi = math.radians(kwargs[\\\"phi\\\"])\\n e2 = 1-(b/a)**2\\n w = math.sqrt(1-e2*math.sin(phi)**2)\\n M = a*(1-e2)/w**3\\n N = a/w\\n if \\\"alpha\\\" in kwargs.keys():\\n alpha = math.radians(kwargs[\\\"alpha\\\"])\\n rAlpha = (M*N)/(M*math.sin(alpha)**2+N*math.cos(alpha)**2)\\n return{\\\"M\\\": M, \\\"N\\\": N, \\\"rAlpha\\\": rAlpha, \\\"1/R\\\": 1/rAlpha}\\n elif kwargs[\\\"radius\\\"] == \\\"M\\\":\\n return {\\\"M\\\": M}\\n elif kwargs[\\\"radius\\\"] == \\\"N\\\":\\n return {\\\"N\\\": N}\\n except KeyError as err:\\n return {\\\"erreur\\\": f\\\"{format(err)} is required!\\\"}\",\n \"def GetAbsBeer(epsilon, conc, pathLength):\\n return epsilon * conc * pathLength\",\n \"def one_transition_spectrum_abs(self,tr):\\n \\n\\n ta = tr[\\\"ta\\\"] # TimeAxis\\n dd = tr[\\\"dd\\\"] # transition dipole strength\\n om = tr[\\\"om\\\"] # frequency - rwa\\n gg = tr[\\\"gg\\\"] # natural broadening (constant or time dependent)\\n fwhm = tr[\\\"fwhm\\\"] # Additional gaussian broadening of the spectra\\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\\n \\n # CD and fluorescence can be calculated in this step\\n # TODO if rotatory strength defined calculate also circular dichroism spectra\\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \\n \\n \\n if self.system._has_system_bath_coupling:\\n# ct = tr[\\\"ct\\\"] # correlation function\\n \\n # convert correlation function to lineshape function\\n #gt = self._c2g(ta,ct.data)\\n gt = tr[\\\"gt\\\"]\\n # calculate time dependent response\\n at = numpy.exp(-gt -1j*om*ta.data)\\n else:\\n # calculate time dependent response\\n at = numpy.exp(-1j*om*ta.data) \\n# plt.figure()\\n# plt.title(\\\"Absorption\\\")\\n# plt.plot(ta.data,numpy.real(at))\\n# plt.plot(ta.data,numpy.imag(at))\\n \\n \\n if len(gg) == 1:\\n gam = gg[0]\\n rt = numpy.exp(gam*ta.data)\\n at *= rt\\n #print(\\\"Constant: \\\", rt[20], len(at))\\n else:\\n rt = numpy.exp((gg)*ta.data) \\n at *= rt\\n #print(\\\"Time dependent: len = \\\", rt[20], len(rt))\\n \\n if fwhm!=0.0:\\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\\n at *= gauss\\n \\n # Fourier transform the result\\n ft = dd*numpy.fft.hfft(at)*ta.step\\n ft = numpy.fft.fftshift(ft)\\n # invert the order because hfft is a transform with -i\\n ft = numpy.flipud(ft) \\n # cut the center of the spectrum\\n Nt = ta.length #len(ta.data) \\n return ft[Nt//2:Nt+Nt//2]\",\n \"def doctored_depth_1852_adhoc(self, longitude, latitude, dip):\\n # set up sample point and fault array\\n p1 = np.array([longitude, latitude])\\n fault_file = './InputData/fault_array.npy'\\n fault_array = np.load(fault_file)\\n # will store haversine distances for comparison\\n #dist_array = np.zeros(0.5 * len(fault_array))\\n dist_array = np.zeros(len(fault_array)//2)\\n for i in range(len(dist_array)):\\n x = fault_array[2 * i]\\n y = fault_array[2 * i + 1]\\n p2 = np.array([x, y])\\n dist_array[i] = self.haversine_distance(p1, p2)\\n\\n dist = np.amin(dist_array)\\n\\n # need to add trig correction\\n return (20000 + dist * np.tan(20 * np.pi / 180))\",\n \"def calcDispCorrandR(aaconst, aacorr, caliConst, AAdata, outName):\\n k=caliConst[3]\\n n=caliConst[2]\\n #Be sure to change this appropriately to the fixed dye conc\\n x=k*((fixed_dye_conc-n)/55.5)\\n n = n\\n print(aaconst)\\n perr2=np.sqrt(np.diag(aacorr))\\n print(perr2)\\n corrmat2=np.zeros([len(aaconst),len(aaconst)])\\n for i in range(len(aacorr)):\\n for j in range(len(aacorr)):\\n ele=aacorr[i,j]\\n diele=ele/(perr2[i]*perr2[j])\\n corrmat2[i,j]=round(diele,3)\\n print(corrmat2)\\n #calculate the r^2 value\\n AAss_res = 0\\n AAss_total = 0\\n residuals = np.zeros([len(AAdata[:,0]), 1])\\n for i in range(len(AAdata[:,0])):\\n residuals[i] = (DispCurve(AAdata[i,0],x,aaconst[0],aaconst[1],n,aaconst[2]) - AAdata[i,1])\\n AAss_res += np.square(residuals[i])\\n AAss_total += np.square((AAdata[i,1] - np.average(AAdata[:,1])))\\n print(AAss_res)\\n print(AAss_total)\\n AAr_sq = 1 - (AAss_res/AAss_total)\\n print(AAr_sq)\\n #write out the fit results\\n f = open(outName + \\\"_disp_constants.txt\\\", 'w')\\n f.write(\\\"B\\\\ta\\\\tN\\\\tK\\\\n\\\")\\n for i in range(len(aaconst)):\\n f.write('%.9f' %aaconst[i] + \\\"\\\\t\\\")\\n f.write(\\\"\\\\n\\\")\\n for i in range(len(aacorr)):\\n f.write('%.9f' %perr2[i] + \\\"\\\\t\\\")\\n f.write(\\\"\\\\n\\\\n\\\")\\n f.write(\\\"Correlation matrix :\\\\n\\\\n\\\")\\n for i in range(len(aacorr)):\\n for j in range(len(aacorr)):\\n f.write('%.9f' %corrmat2[i,j]+'\\\\t')\\n f.write(\\\"\\\\n\\\\n\\\")\\n f.write(\\\"R^2 value : \\\\t\\\" + '%.9f' %AAr_sq)\\n f.close()\",\n \"def test_GBL_tau_star():\\n z = 1.0\\n\\n # Fully ionized H and He\\n x_ionH = 1.0\\n x_ionHe = 2.0\\n\\n cosmo = {}\\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\\n cosmo['h'] = 0.65\\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\\n cosmo['Y_He'] = 0.24\\n cd.set_omega_k_0(cosmo)\\n\\n tau_inst, tau_star = cr.optical_depth_instant(z, \\n x_ionH=x_ionH, \\n x_ionHe=x_ionHe, \\n return_tau_star=True,\\n **cosmo)\\n print(\\\"tau_star = %.7f\\\" % (tau_star))\\n print(\\\"tau_star/(h Omega_b) = %.7f =? 0.061\\\" % \\n (tau_star / (cosmo['h'] * cosmo['omega_b_0'])))\\n\\n ntest.assert_approx_equal(tau_star / (cosmo['h'] * cosmo['omega_b_0']),\\n 0.061,\\n 2)\\n\\n print(\\\"(1 - Y_He/2) = %.3f =? 0.88\\\" % (1. - (cosmo['Y_He']/2.)))\\n ntest.assert_approx_equal((1. - (cosmo['Y_He']/2.)),\\n 0.88,\\n 7)\\n\\n H_0 = cc.H100_s * cosmo['h']\\n\\n # s^-1 * Mpc s^-1 * Mpc^2 / Mpc^3 msun^-1 s^-2 / Msun -> \\n tau_star_explicit = ((1. - (cosmo['Y_He']/2.)) * \\n ((3. * H_0 * cosmo['omega_b_0'] * cc.c_light_Mpc_s *\\n cc.sigma_T_Mpc) / \\n (8. * math.pi * cc.G_const_Mpc_Msun_s * \\n (cc.m_p_g/cc.M_sun_g))))\\n\\n print(\\\"tau_star_explicit = %.7f =? tau_star\\\" % (tau_star_explicit))\\n ntest.assert_approx_equal(tau_star, tau_star_explicit, 3)\",\n \"def Build_quadrant(self) :\\n\\n self.omega = np.zeros((self.n_dir,3))\\n self.weight = np.zeros((self.n_dir))\\n\\n if self.sn==2 :\\n direction = 0.577350269189625764509149\\n weight = 1.\\n\\n self.omega[0,0] = direction\\n self.omega[0,1] = direction\\n self.omega[0,2] = direction\\n \\n self.weight[0] = weight\\n \\n elif self.sn==4 :\\n direction_1 = 0.350021174581540677777041\\n direction_2 = 0.868890300722201205229788\\n weight = 1./3.\\n\\n self.omega[0,0] = direction_2\\n self.omega[0,1] = direction_1\\n self.omega[0,2] = direction_1\\n \\n self.omega[1,0] = direction_1\\n self.omega[1,1] = direction_2\\n self.omega[1,2] = direction_1\\n\\n self.omega[2,0] = direction_1\\n self.omega[2,1] = direction_1\\n self.omega[2,2] = direction_2\\n\\n self.weight[0] = weight\\n self.weight[1] = weight\\n self.weight[2] = weight\\n\\n elif self.sn==6 :\\n direction_1 = 0.266635401516704720331535\\n direction_2 = 0.681507726536546927403750\\n direction_3 = 0.926180935517489107558380\\n weight_1 = 0.176126130863383433783565\\n weight_2 = 0.157207202469949899549768\\n\\n self.omega[0,0] = direction_3\\n self.omega[0,1] = direction_1\\n self.omega[0,2] = direction_1\\n\\n self.omega[1,0] = direction_2\\n self.omega[1,1] = direction_2\\n self.omega[1,2] = direction_1\\n\\n self.omega[2,0] = direction_1\\n self.omega[2,1] = direction_3\\n self.omega[2,2] = direction_1\\n\\n self.omega[3,0] = direction_2\\n self.omega[3,1] = direction_1\\n self.omega[3,2] = direction_2\\n \\n self.omega[4,0] = direction_1\\n self.omega[4,1] = direction_2\\n self.omega[4,2] = direction_2\\n\\n self.omega[5,0] = direction_1\\n self.omega[5,1] = direction_1\\n self.omega[5,2] = direction_3\\n\\n self.weight[0] = weight_1\\n self.weight[1] = weight_2\\n self.weight[2] = weight_1\\n self.weight[3] = weight_2\\n self.weight[4] = weight_2\\n self.weight[5] = weight_1\\n\\n elif self.sn==8 :\\n direction_1 = 0.218217890235992381266097\\n direction_2 = 0.577350269189625764509149\\n direction_3 = 0.786795792469443145800830\\n direction_4 = 0.951189731211341853132399\\n\\n weight_1 = 0.120987654320987654320988\\n weight_2 = 0.0907407407407407407407407\\n weight_3 = 0.0925925925925925925925926\\n\\n self.omega[0,0] = direction_4\\n self.omega[0,1] = direction_1\\n self.omega[0,2] = direction_1\\n\\n self.omega[1,0] = direction_3\\n self.omega[1,1] = direction_2\\n self.omega[1,2] = direction_1\\n \\n self.omega[2,0] = direction_2\\n self.omega[2,1] = direction_3\\n self.omega[2,2] = direction_1\\n\\n self.omega[3,0] = direction_1\\n self.omega[3,1] = direction_4\\n self.omega[3,2] = direction_1\\n\\n self.omega[4,0] = direction_3\\n self.omega[4,1] = direction_1\\n self.omega[4,2] = direction_2\\n\\n self.omega[5,0] = direction_2\\n self.omega[5,1] = direction_2\\n self.omega[5,2] = direction_2\\n\\n self.omega[6,0] = direction_1\\n self.omega[6,1] = direction_3\\n self.omega[6,2] = direction_2\\n\\n self.omega[7,0] = direction_2\\n self.omega[7,1] = direction_1\\n self.omega[7,2] = direction_3\\n\\n self.omega[8,0] = direction_1\\n self.omega[8,1] = direction_2\\n self.omega[8,2] = direction_3\\n\\n self.omega[9,0] = direction_1\\n self.omega[9,1] = direction_1\\n self.omega[9,2] = direction_4\\n\\n self.weight[0] = weight_1\\n self.weight[1] = weight_2\\n self.weight[2] = weight_2\\n self.weight[3] = weight_1\\n self.weight[4] = weight_2\\n self.weight[5] = weight_3\\n self.weight[6] = weight_2\\n self.weight[7] = weight_2\\n self.weight[8] = weight_2\\n self.weight[9] = weight_1\\n\\n elif self.sn==10 :\\n direction_1 = 0.189321326478010476671494\\n direction_2 = 0.508881755582618974382711\\n direction_3 = 0.694318887594384317279217\\n direction_4 = 0.839759962236684758403029\\n direction_5 = 0.963490981110468484701598\\n\\n weight_1 = 0.0893031479843567214704325\\n weight_2 = 0.0725291517123655242296233\\n weight_3 = 0.0450437674364086390490892\\n weight_4 = 0.0539281144878369243545650\\n\\n self.omega[0,0] = direction_5\\n self.omega[0,1] = direction_1\\n self.omega[0,2] = direction_1\\n \\n self.omega[1,0] = direction_4\\n self.omega[1,1] = direction_2\\n self.omega[1,2] = direction_1\\n \\n self.omega[2,0] = direction_3\\n self.omega[2,1] = direction_3\\n self.omega[2,2] = direction_1\\n \\n self.omega[3,0] = direction_2\\n self.omega[3,1] = direction_4\\n self.omega[3,2] = direction_1\\n\\n self.omega[4,0] = direction_1\\n self.omega[4,1] = direction_5\\n self.omega[4,2] = direction_1\\n\\n self.omega[5,0] = direction_4\\n self.omega[5,1] = direction_1\\n self.omega[5,2] = direction_2\\n\\n self.omega[6,0] = direction_3\\n self.omega[6,1] = direction_2\\n self.omega[6,2] = direction_2\\n\\n self.omega[7,0] = direction_2\\n self.omega[7,1] = direction_3\\n self.omega[7,2] = direction_2\\n\\n self.omega[8,0] = direction_1\\n self.omega[8,1] = direction_4\\n self.omega[8,2] = direction_2\\n\\n self.omega[9,0] = direction_3\\n self.omega[9,1] = direction_1\\n self.omega[9,2] = direction_3\\n\\n self.omega[10,0] = direction_2\\n self.omega[10,1] = direction_2\\n self.omega[10,2] = direction_3\\n\\n self.omega[11,0] = direction_1\\n self.omega[11,1] = direction_3\\n self.omega[11,2] = direction_3\\n\\n self.omega[12,0] = direction_2\\n self.omega[12,1] = direction_1\\n self.omega[12,2] = direction_4\\n\\n self.omega[13,0] = direction_1\\n self.omega[13,1] = direction_2\\n self.omega[13,2] = direction_4\\n\\n self.weight[0] = weight_1\\n self.weight[1] = weight_2\\n self.weight[2] = weight_3\\n self.weight[3] = weight_2\\n self.weight[4] = weight_1\\n self.weight[5] = weight_2\\n self.weight[6] = weight_4\\n self.weight[7] = weight_4\\n self.weight[8] = weight_2\\n self.weight[9] = weight_3\\n self.weight[10] = weight_4\\n self.weight[11] = weight_3\\n self.weight[12] = weight_2\\n self.weight[13] = weight_2\\n self.weight[14] = weight_1\\n\\n elif self.sn==12 :\\n direction = np.zeros((6,1))\\n\\n direction[0] = 0.167212652822713264084504\\n direction[1] = 0.459547634642594690016761\\n direction[2] = 0.628019096642130901034766\\n direction[3] = 0.760021014833664062877138\\n direction[4] = 0.872270543025721502340662\\n direction[5] = 0.971637719251358378302376\\n\\n weight_1 = 0.0707625899700910439766549\\n weight_2 = 0.0558811015648888075828962\\n weight_3 = 0.0373376737588285824652402\\n weight_4 = 0.0502819010600571181385765\\n weight_5 = 0.0258512916557503911218290\\n\\n for i in xrange(0,6) :\\n self.omega[i,0] = direction[5-i]\\n self.omega[i,1] = direction[i]\\n self.omega[i,2] = direction[0]\\n \\n offset = 6\\n for i in xrange(0,5) :\\n self.omega[offset+i,0] = direction[4-i]\\n self.omega[offset+i,1] = direction[i]\\n self.omega[offset+i,2] = direction[1]\\n\\n offset += 5\\n for i in xrange(0,4) :\\n self.omega[offset+i,0] = direction[3-i]\\n self.omega[offset+i,1] = direction[i]\\n self.omega[offset+i,2] = direction[2]\\n \\n offset += 4\\n for i in xrange(0,3) :\\n self.omega[offset+i,0] = direction[2-i]\\n self.omega[offset+i,1] = direction[i]\\n self.omega[offset+i,2] = direction[3]\\n\\n offset += 3\\n for i in xrange(0,2) :\\n self.omega[offset+i,0] = direction[1-i]\\n self.omega[offset+i,1] = direction[i]\\n self.omega[offset+i,2] = direction[4]\\n \\n offset += 2\\n self.omega[offset+i,0] = direction[0]\\n self.omega[offset+i,1] = direction[1]\\n self.omega[offset+i,2] = direction[5]\\n\\n self.weight[0] = weigth_1\\n self.weight[1] = weight_2\\n self.weight[2] = weight_3\\n self.weight[3] = weight_3\\n self.weight[4] = weight_2\\n self.weight[5] = weight_1\\n self.weight[6] = weight_2\\n self.weight[7] = weight_4\\n self.weight[8] = weight_5\\n self.weight[9] = weight_4\\n self.weight[10] = weight_2\\n self.weight[11] = weight_3\\n self.weight[12] = weight_5\\n self.weight[13] = weight_5\\n self.weight[14] = weight_3\\n self.weight[15] = weight_3\\n self.weight[16] = weight_4\\n self.weight[17] = weight_3\\n self.weight[18] = weight_2\\n self.weight[19] = weight_2\\n self.weight[20] = weight_1\",\n \"def comp_angle_opening_magnet(self):\\n\\n if self.W1 > 0:\\n Rbo = self.get_Rbo()\\n return float(2 * arcsin(self.W1 / (2 * Rbo)))\\n else:\\n return self.comp_angle_magnet()\",\n \"def Agl2ArcLen(self,agl):\\r\\n\\r\\n return (self.distance_between_wheels/2)*agl\",\n \"def ant_max_direction(freq, fit='Lebedev_model'):\\n if fit == 'Lebedev':\\n freqkHz = freq * 1e3 # to kHz, to float\\n dep = 0.2816\\n ep = 185634.9983 \\\\\\n - 4.877867876 * freqkHz \\\\\\n + 4.803667396E-005 * freqkHz**2 \\\\\\n - 2.102600271E-010 * freqkHz**3 \\\\\\n + 3.453540782E-016 * freqkHz**4\\n ep += dep\\n return np.deg2rad(ep)\\n elif fit == 'Lebedev_model':\\n return ant_max_direction_model_c_wrapped(freq * 1000.0)\\n elif fit in ['Vasiliev_old', 'Vasiliev']:\\n freqHz = freq * 1e6 # to kHz, to float\\n d = 0.87\\n a = 0.136\\n b = 0.014\\n h = 0.384\\n wavelength = speed_of_light / freqHz\\n k = 2.0 * np.pi / wavelength\\n\\n if fit == 'Vasiliev_old':\\n hi_f = -32.3802499 \\\\\\n + 6.329699841E-007 * freqHz \\\\\\n - 4.001034124E-015 * freqHz ** 2 \\\\\\n + 8.374351252E-024 * freqHz ** 3\\n else:\\n hi_f = 13.5066804030779 \\\\\\n - 23.894031675887E-008 * freqHz \\\\\\n + 15.2068384591317E-016 * freqHz ** 2 \\\\\\n - 3.28019314316317E-024 * freqHz ** 3\\n\\n g = hi_f * np.sqrt(1 + ((a / (a + b)) * np.tan(k * h)) ** 2)\\n theta = g - wavelength / d\\n return theta\\n else:\\n raise ValueError('Incorrect fit: {}'.format(fit))\",\n \"def ASS_theo(D, k):\\n\\ta1b = k[\\\"A1B1\\\"]\\n\\tba1 = k[\\\"B1A1\\\"]\\n\\tca1 = k[\\\"C1A1\\\"]\\n\\tcb = k[\\\"B1C1\\\"]\\n\\tnum = (ca1*cb + ba1*ca1 + ba1*cb) + ca1*ca1*D\\n\\tden = (ba1*cb + ba1*ca1 + ca1*cb) + (2*ca1*ca1+ba1*cb+ca1*cb)*D + ba1*ca1*D*D\\n\\treturn num/den\",\n \"def wavelength(energy):\\r\\n return 2 * np.pi * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy\",\n \"def mamajek08_logRpHK_Ro_edge():\\n Ro_edge = 0.31935816876122064\\n return Ro_edge\",\n \"def one_transition_spectrum_fluor(self,tr):\\n \\n\\n ta = tr[\\\"ta\\\"] # TimeAxis\\n dd = tr[\\\"dd\\\"] # transition dipole strength\\n om = tr[\\\"om\\\"] # frequency - rwa\\n gg = tr[\\\"gg\\\"] # natural broadening (constant or time dependent)\\n fwhm = tr[\\\"fwhm\\\"] # Additional gaussian broadening of the spectra\\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\\n \\n # CD and fluorescence can be calculated in this step\\n # TODO if rotatory strength defined calculate also circular dichroism spectra\\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \\n \\n \\n if self.system._has_system_bath_coupling:\\n# ct = tr[\\\"ct\\\"] # correlation function\\n re = tr[\\\"re\\\"] # reorganisation energy\\n \\n # convert correlation function to lineshape function\\n #gt = self._c2g(ta,ct.data)\\n gt = tr[\\\"gt\\\"]\\n # calculate time dependent response\\n at = numpy.exp(-numpy.conjugate(gt) -1j*om*ta.data + 2j*re*ta.data)\\n else:\\n # calculate time dependent response\\n at = numpy.exp(-1j*om*ta.data) \\n# plt.figure()\\n# plt.title(\\\"Absorption\\\")\\n# plt.plot(ta.data,numpy.real(at))\\n# plt.plot(ta.data,numpy.imag(at))\\n \\n \\n if len(gg) == 1:\\n gam = gg[0]\\n rt = numpy.exp(gam*ta.data)\\n at *= rt\\n #print(\\\"Constant: \\\", rt[20], len(at))\\n else:\\n rt = numpy.exp((gg)*ta.data) \\n at *= rt\\n #print(\\\"Time dependent: len = \\\", rt[20], len(rt))\\n \\n if fwhm!=0.0:\\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\\n at *= gauss\\n \\n # Fourier transform the result\\n ft = dd*numpy.fft.hfft(at)*ta.step\\n ft = numpy.fft.fftshift(ft)\\n # invert the order because hfft is a transform with -i\\n ft = numpy.flipud(ft) \\n # cut the center of the spectrum\\n Nt = ta.length #len(ta.data) \\n return ft[Nt//2:Nt+Nt//2]\",\n \"def get_sn2005ek(colorplt=False):\\n z = 0.016551\\n ebv = 0.210\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_max = 53639.9\\n print (\\\"adopt r band t_max from Drout+13\\\")\\n \\n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\\\t')\\n # tb = tb.drop(columns=[\\\"Unnamed: 6\\\"])\\n \\n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\\n 53654.2, 53655.2, 53656.2, 53657.2])\\n \\n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\\n 21.74, np.nan, np.nan, np.nan])\\n \\n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \\n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\\n 0.12, np.nan, np.nan, np.nan])\\n \\n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\\n 20.60, 20.74, 20.88, 21.22])\\n \\n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \\n 0.08, 0.10, 0.08, 0.13])\\n \\n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \\n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\\n 20.08, np.nan, 20.47, np.nan])\\n \\n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\\n 0.05, np.nan, 0.08, np.nan])\\n\\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \\n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \\n 19.01, np.nan, 19.47, np.nan])\\n \\n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\\n 0.05, np.nan, 0.06, np.nan])\\n \\n mymjds = np.hstack([mjds, mjds, mjds, mjds])\\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\\n myfilts = np.hstack([ np.repeat(\\\"B\\\", len(Bmags)),\\n np.repeat(\\\"V\\\", len(Bmags)),\\n np.repeat(\\\"R\\\", len(Rmags)),\\n np.repeat(\\\"I\\\", len(Imags)) ])\\n ix = ~np.isnan(mymags)\\n tb = pd.DataFrame({'mjd': mymjds[ix],\\n 'mag': mymags[ix],\\n 'emag': myemags[ix],\\n \\\"filter\\\": myfilts[ix]})\\n \\n ixB = tb['filter'].values==\\\"B\\\"\\n ixV = tb['filter'].values==\\\"V\\\"\\n ixR = tb['filter'].values==\\\"R\\\"\\n ixI = tb['filter'].values==\\\"I\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixV] = 5430\\n tb['wave'].values[ixR] = 6349\\n tb['wave'].values[ixI] = 8797\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['B', 'R', 'I']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"R\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"B\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"R\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"I\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"BmR\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"RmI\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def getArea(antenna):\\n diameter = getDiameter(antenna)\\n if antenna >=1 and antenna <= 6: \\n efficiency = 0.5\\n elif antenna >=7 and antenna <= 15: \\n efficiency = 0.6\\n elif antenna >=16 and antenna <= 23: \\n efficiency = 0.6\\n else:\\n raise Exception,'Unregonized antenna type'\\n area = math.pi * diameter*diameter / 4.0 * efficiency\\n\\n return area\",\n \"def anl_solution(self):\\r\\n\\r\\n m = float(self.mass) / self.nu_m\\r\\n qe = 1 / self.nu_m * (self.nu_t * self.nu_t / self.nu_x) * 1.0 \\\\\\r\\n / float(self.size_tick * self.size_tick)\\r\\n print 'qE=', qe\\r\\n c = self.light_vel\\r\\n for i in range(0, len(self.obs.obt_g)):\\r\\n ddt = float(self.obs.obt[i] - self.obs.obt[i - 1])\\r\\n x = m * c ** 2 / qe * (math.sqrt(1.0 + (qe * self.t[i] / (m\\r\\n * c)) ** 2) - 1.0)\\r\\n self.xa_track.append(x)\\r\\n p = qe * self.t[i]\\r\\n self.pa.append(p)\\r\\n v = p / math.sqrt(m ** 2 + (p / c) ** 2)\\r\\n jv = self.t[i] * qe / (m * c)\\r\\n v = math.sqrt(jv * jv / (1 + jv * jv)) * c\\r\\n self.va.append(v)\\r\\n print 'Analytical solution of the differential equation of motion'\",\n \"def get_caravan_depth(self):\\n return self.caravan_depth\",\n \"def fringes_ABCD_phase(opd, fringesAr, fringesBr,\\n fringesCr, fringesDr,\\n wavelength=2.1e-6, plot=False):\\n # define boundaries\\n n_samples_per_fringes = wavelength/np.abs(np.diff(opd).mean())\\n\\n # compute Morlet's transform\\n dopd = np.abs(np.diff(opd).mean())\\n\\n wavA = morlet.CWT(fringesAr, dopd, scale_min=1/6e-6,\\n scale_max=1/2.e-6, n_per_octave=32)\\n wavB = morlet.CWT(fringesBr, dopd, scale_min=1/6e-6,\\n scale_max=1/2.e-6, n_per_octave=32)\\n wavC = morlet.CWT(fringesCr, dopd, scale_min=1/6e-6,\\n scale_max=1/2.e-6, n_per_octave=32)\\n wavD = morlet.CWT(fringesDr, dopd, scale_min=1/6e-6,\\n scale_max=1/2.e-6, n_per_octave=32)\\n\\n data = [wavA, wavB, wavC, wavD]\\n scale=[]\\n for wav in data:\\n weight = np.sum(np.abs(wav.cwt)**2, axis=1)\\n i0 = weight.argmax()\\n i0 = min(max(i0, len(weight)-5), 5)\\n print i0, len(weight)\\n c = np.polyfit(wav.frequencies[i0-2:i0+2],\\\\\\n np.log(weight[i0-2:i0+2]), 2)\\n scale.append(-c[1]/(2*c[0]))\\n scale = np.array(scale)\\n wlABCD = 1/scale*1e6\\n print 'wavelength (WT peak) [A B C D]:'\\n print wlABCD\\n print 100*(scale/scale.mean()-1), '(% / mean)'\\n if plot:\\n opd0 = (opd-opd.mean())*1e6\\n Xp, Yp = np.meshgrid(opd0, 1/wavA.frequencies*1e6)\\n\\n plt.figure(4)\\n plt.clf()\\n ax = plt.subplot(211)\\n plt.plot(opd0, fringesAr-fringesCr, 'k')\\n plt.subplot(212, sharex=ax)\\n plt.plot(opd0, fringesBr-fringesDr, 'k')\\n\\n plt.figure(3)\\n plt.clf()\\n plt.subplot(241, sharex=ax)\\n plt.plot(opd0, fringesAr, 'k')\\n plt.ylabel('fringes')\\n plt.title('A')\\n plt.subplot(245, sharex=ax)\\n plt.pcolormesh(Xp,Yp, abs(wavA.cwt))\\n #plt.hlines(wlABCD[0], opd0.min(), opd0.max(),\\\\\\n # color='y')\\n plt.ylabel('wavelength (um)')\\n plt.subplot(242, sharex=ax)\\n plt.plot(opd0, fringesBr, 'k')\\n plt.title('B')\\n plt.subplot(246, sharex=ax)\\n plt.pcolormesh(Xp,Yp, abs(wavB.cwt))\\n #plt.hlines(wlABCD[1], opd0.min(), opd0.max(),\\\\\\n # color='y')\\n plt.subplot(243, sharex=ax)\\n plt.plot(opd0, fringesCr, 'k')\\n plt.title('C')\\n plt.subplot(247, sharex=ax)\\n plt.pcolormesh(Xp,Yp, abs(wavC.cwt))\\n #plt.hlines(wlABCD[2], opd0.min(), opd0.max(),\\\\\\n # color='y')\\n plt.subplot(244, sharex=ax)\\n plt.plot(opd0, fringesDr, 'k')\\n plt.title('D')\\n plt.subplot(248, sharex=ax)\\n plt.pcolormesh(Xp,Yp, abs(wavD.cwt))\\n #plt.hlines(wlABCD[3], opd0.min(), opd0.max(),\\\\\\n # color='y')\\n\\n # compute phases as function of OPD\\n phiAB = fringes_morlet_phase(wavA, wavB)\\n phiAC = fringes_morlet_phase(wavA, wavC, quasi_pi=True)\\n phiDA = fringes_morlet_phase(wavD, wavA)\\n phiBC = fringes_morlet_phase(wavB, wavC)\\n phiBD = fringes_morlet_phase(wavB, wavD, quasi_pi=True)\\n phiCD = fringes_morlet_phase(wavC, wavD)\\n weightABCD = np.sum(abs(wavA.cwt)*abs(wavB.cwt)*\\\\\\n abs(wavC.cwt)*abs(wavD.cwt),axis=0)\\n weightABCD = weightABCD**(0.25)\\n return phiAB, phiBC, phiCD, phiDA, phiAC, phiBD, weightABCD, wlABCD\\n\\n wp = np.where(weightABCD>\\n 3*np.median(weightABCD))\\n print 'WP=', wp\\n phi_abcd = np.arctan2(fringesAr-fringesCr, \\\\\\n fringesBr-fringesDr)\\n phi_abcd = np.unwrap(phi_abcd)\\n slopeAB = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiAB[wp], 1)\\n slopeBC = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiBC[wp], 1)\\n slopeCD = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiCD[wp], 1)\\n slopeDA = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiDA[wp], 1)\\n slopeAC = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiAC[wp], 1)\\n slopeBD = np.polyfit((opd[wp]-opd[wp].mean())*1e6,\\\\\\n phiBD[wp], 1)\\n print 'phases: slope (rad/um of OPD), phi0/pi'\\n print 'A-B %6.3f %6.3f' % (slopeAB[0], slopeAB[1]/np.pi)\\n print 'B-C %6.3f %6.3f' % (slopeBC[0], slopeBC[1]/np.pi)\\n print 'C-D %6.3f %6.3f' % (slopeCD[0], slopeCD[1]/np.pi)\\n print 'D-A %6.3f %6.3f' % (slopeDA[0], slopeDA[1]/np.pi)\\n slopes = [slopeAB[0], slopeBC[0], slopeCD[0], slopeDA[0]]\\n slopes = np.array(slopes)\\n wldiff = [wlABCD[0]-wlABCD[1], wlABCD[1]-wlABCD[2], \\\\\\n wlABCD[2]-wlABCD[3], wlABCD[3]-wlABCD[0]]\\n wldiff = np.array(wldiff)\\n #------- FIGURE ----------\\n if plot:\\n plt.figure(1)\\n plt.clf()\\n plt.subplot(211)\\n plt.plot(opd, fringesBr, 'k')\\n plt.subplot(212)\\n plt.hlines(np.pi*np.arange(-1,1.5,0.5), \\\\\\n opd.min(), opd.max(), color='y',\\\\\\n linestyles='dotted')\\n plt.plot(opd[wp], phiAB[wp], 'r', linewidth=2)\\n plt.plot(opd[wp], phiBC[wp], 'g', linewidth=2)\\n plt.plot(opd[wp], phiCD[wp], 'b', linewidth=2)\\n plt.plot(opd[wp], phiDA[wp], 'm', linewidth=2)\\n plt.plot(opd[wp], np.unwrap(phiAC[wp]),\\\\\\n color=((1,0.5,0,0)), linewidth=2)\\n plt.plot(opd[wp], np.unwrap(phiBD[wp]),\\\\\\n color=((0.3,0.2,1,0)), linewidth=2)\\n plt.ylabel('phase (radian)')\\n plt.xlabel('OPD (m)')\\n plt.legend(('A-B', 'B-C', 'C-D', 'D-A',\\\\\\n 'A-C','B-D'))\\n plt.ylim(0, 4)\\n return phiAB, phiBC, phiCD, phiDA, phiAC, phiBD, weightABCD, wlABCD\",\n \"def airydisk(unit_r, fno, wavelength):\\n u_eff = unit_r * np.pi / wavelength / fno\\n return abs(2 * jinc(u_eff)) ** 2\",\n \"def isothermal_depth_wyrtki1964(da_PT):\\n\\n # interpolate to finer vertical resolution (2.5m)\\n da_interp = da_PT.interp(z=np.arange(0,da_PT.z.max(),2.5))\\n\\n # make land mask based on surface layer\\n da_mask = da_PT.isel(z=0)*0.+1.\\n\\n # calculate rho-rho0\\n da_diff = np.abs(da_interp-da_PT.isel(z=0))\\n\\n # remove values shallower than critcal value\\n da_diff = da_diff.where(da_diff>0.5,other=99999)\\n\\n # find first index that have value larger than critical value\\n z_ind = da_diff.argmin(dim='z',skipna=True)\\n\\n # used 2d index to find 2d depth map\\n da_itd = da_diff.z[z_ind]*da_mask\\n\\n return da_itd\",\n \"def merdianArcLength(**kwargs):\\n a = 0\\n b = 0\\n kwargs[\\\"radius\\\"] = \\\"M\\\"\\n try:\\n if kwargs[\\\"ref\\\"] == \\\"local\\\":\\n a = 6378249.145\\n b = 6356515\\n elif kwargs[\\\"ref\\\"] == \\\"global\\\":\\n a = 6378137\\n b = 6356752.314\\n elif kwargs[\\\"a\\\"] and kwargs[\\\"b\\\"]:\\n a = kwargs[\\\"a\\\"]\\n b = kwargs[\\\"b\\\"]\\n except KeyError:\\n return {\\\"erreur\\\": \\\"params a and b is required, you can use ref too which has two possible value: local and global\\\"}\\n try:\\n kwargs[\\\"phi\\\"] = kwargs[\\\"phi1\\\"]\\n M1 = rayonDeCourbur(**kwargs)[\\\"M\\\"]\\n kwargs[\\\"phi\\\"] = kwargs[\\\"phi2\\\"]\\n M2 = rayonDeCourbur(**kwargs)[\\\"M\\\"]\\n deltaPhi = abs(kwargs[\\\"phi2\\\"]-kwargs[\\\"phi1\\\"])\\n if 2 <= deltaPhi < 5:\\n e2 = 1-(b/a)**2\\n Mm = (M1+M2)/2\\n phiM = (kwargs[\\\"phi1\\\"]+kwargs[\\\"phi2\\\"])/2\\n S = ((M1+M2+4*Mm)*deltaPhi +\\n ((math.cos(math.radians(2*phiM))*deltaPhi**5)*(a*e2)/240))/6\\n return {\\\"S\\\": S}\\n elif deltaPhi < 2:\\n e2 = 1-(b/a)**2\\n Mm = (M1+M2)/2\\n phiM = (kwargs[\\\"phi1\\\"]+kwargs[\\\"phi2\\\"])/2\\n S = Mm*deltaPhi+(a*e2*math.cos(2*phiM)*deltaPhi**3)/3\\n return {\\\"S\\\": S}\\n except KeyError:\\n return {\\\"error\\\": \\\"the function required 3 basics params phi1,phi2 and ref or a&b\\\"}\",\n \"def GetEpsilonBeer(Abs, conc, pathLength):\\n return Abs / (conc * pathLength)\",\n \"def find_omega(filename_free,filename_close,filename_far,TI=0.11,wind_speed=8.):\\n\\n \\\"\\\"\\\"free FAST\\\"\\\"\\\"\\n lines = np.loadtxt(filename_free,skiprows=8)\\n Omega_free = np.mean(lines[:,6])\\n\\n \\\"\\\"\\\"waked FAST CLOSE\\\"\\\"\\\"\\n lines = np.loadtxt(filename_close,skiprows=8)\\n Omega_close = np.mean(lines[:,6])\\n\\n \\\"\\\"\\\"waked FAST FAR\\\"\\\"\\\"\\n lines = np.loadtxt(filename_far,skiprows=8)\\n Omega_far = np.mean(lines[:,6])\\n\\n\\n \\\"\\\"\\\"setup the CCBlade loads\\\"\\\"\\\"\\n turbineX_close = np.array([0.,126.4])*4.\\n turbineX_far = np.array([0.,126.4])*10.\\n\\n turbineY_waked = np.array([0.,0.])\\n\\n hub_height = 90.\\n\\n free_speed = wind_speed\\n close_speed = get_eff_turbine_speeds(turbineX_close, turbineY_waked, wind_speed,TI=TI)[1]\\n far_speed = get_eff_turbine_speeds(turbineX_far, turbineY_waked, wind_speed,TI=TI)[1]\\n\\n print 'tip speed ratios'\\n print 'free: ', Omega_free*126.4/2./free_speed\\n print 'close: ', Omega_close*126.4/2./close_speed\\n print 'far: ', Omega_far*126.4/2./far_speed\\n\\n # return flap_free_atm, edge_free_atm, Omega_free, free_speed, flap_close_atm, edge_close_atm, Omega_close, close_speed, flap_far_atm, edge_far_atm, Omega_far, far_speed\\n return Omega_free, free_speed, Omega_close, close_speed, Omega_far, far_speed\",\n \"def distcalc(z,h=0.70,omegalambda=0.7,omegam=0.3,omegak=0.0):\\n\\n H0 = 100 * h # this is in units of km/s/Mpc\\n\\n H0freq = H0 * constants.kilo/(constants.mega * constants.parsec) # this is H0 is units of Hz\\n \\n hubbletime = 1.0/H0freq # in seconds\\n hubbletimeyr = hubbletime / constants.year\\n\\n #hubble distance\\n dh = constants.c / H0freq # in meters\\n\\n #now i can calculate the comoving distance (line of sight) using hogg eqn 15\\n dc = dh * integrate.quad(dcintegrand,0,z,(omegalambda,omegam,omegak))[0]\\n\\n #now i can find the transverse comoving distance using hogg eqn 16\\n if omegak == 0:\\n dm = dc\\n elif omegak > 0:\\n dm = dh/np.sqrt(omegak) * np.sinh(dc * np.sqrt(omegak) / dh)\\n else:\\n dm = dh/np.sqrt(abs(omegak)) * np.sin(dc * np.sqrt(abs(omegak)) / dh)\\n\\n\\n #now i will calculate the angular diameter distance (hogg eqn 18)\\n da = dm/(1+z)\\n \\n #now i will calculate scale in kpc/arcsec, since this is commonly used\\n scale = da * constants.arcsec / (constants.kilo * constants.parsec)\\n\\n #now i will calculate the luminosity distance (hog eqn 21)\\n dl = (1+z)*dm\\n \\n #now i will calculate lookback time and \\n #time from the begining of the universe to that redshift using hogg eqn 30\\n \\n tlookback = hubbletimeyr * integrate.quad(timeintegrand,0,z,(omegalambda,omegam,omegak))[0]\\n \\n tz = hubbletimeyr * integrate.quad(timeintegrand,z,np.inf,(omegalambda,omegam,omegak))[0]\\n \\n #all sky co-moving volume out to redshift z (hogg eqn 30)\\n if omegak == 0:\\n vc = 4 * np.pi * dm**3 / 3\\n elif omegak > 0:\\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \\n np.arcsinh( np.sqrt(omegak) * dm / dh ) / np.sqrt(omegak) )\\n else:\\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \\n np.arcsin( np.sqrt(abs(omegak)) * dm / dh ) / np.sqrt(abs(omegak)) )\\n\\n #for output, i will make a dictionary\\n output = dict(dh=dh, dc=dc, dm=dm, da=da, scale=scale, dl=dl, tlookback = tlookback, tz=tz, vc=vc)\\n\\n return output\",\n \"def update():\\n # TODO: Park the car 30 cm away from the closest orange cone.\\n # Use both color and depth information to handle cones of multiple sizes.\\n # You may wish to copy some of your code from lab2b.py\\n global speed\\n global angle\\n global curState\\n # Search for contours in the current color image\\n update_contour()\\n\\n imgX = rc.camera.get_width()\\n\\n depth_image = rc.camera.get_depth_image()\\n depth_image_adjust = (depth_image - 0.01) % 9999\\n depth_image_adjust_blur = cv.GaussianBlur(depth_image_adjust, (11,11), 0)\\n\\n contour_x = contour_center[1]\\n contour_y = contour_center[0]\\n\\n if contour_center is not None:\\n angle = rc_utils.remap_range(contour_center[1],0,imgX,-1,1)\\n\\n contour_distance = depth_image_adjust_blur[contour_y][contour_x]\\n\\n print(contour_distance)\\n # TODO: Park the car 30 cm away from the closest orange cone\\n if curState == State.search:\\n rc.drive.set_speed_angle(0.5, 1)\\n \\n if contour_center is not None:\\n curState = State.approach\\n\\n elif curState == State.approach:\\n # rc.drive.set_speed_angle(0.5, angle)\\n\\n if contour_distance > 50:\\n rc.drive.set_speed_angle(0.3,angle)\\n elif contour_distance > 32:\\n rc.drive.set_speed_angle(0.1,angle)\\n elif contour_distance == 32:\\n rc.drive.set_speed_angle(-0.1,angle)\\n elif contour_distance < 32:\\n curState = State.stop\\n print(\\\"stop\\\")\\n\\n elif curState == State.stop:\\n rc.drive.set_speed_angle(0,0)\\n\\n pass\",\n \"def perfectrefl(wavelength):\\n return 1.0\",\n \"def wavelength(energy):\\n return 2 * PI * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy\",\n \"def rod(lam, co2=400., lat=45., z=0., P=1013.25):\\n Avogadro = codata.value('Avogadro constant')\\n zs = 0.73737 * z + 5517.56 # effective mass-weighted altitude\\n G = g(lat, zs)\\n # air pressure at the pixel (i.e. at altitude) in hPa\\n Psurf = (P * (1. - 0.0065 * z / 288.15) ** 5.255) * 1000. # air pressure at pixel location in dyn / cm2, which is hPa * 1000\\n return raycrs(lam, co2) * Psurf * Avogadro/ma(co2)/G\",\n \"def derive_Damineli16(wavelength):\\n # From their eq 19\\n x = np.log10(2.159 / wavelength)\\n log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4.\\n\\n # Now to convert this back to linear space\\n A_AKs_at_wave = 10**log_A_AKs \\n\\n return A_AKs_at_wave\",\n \"def albedo_separation(albedo, Rs_1, F, fc, aleafv, aleafn, aleafl, adeadv,\\n adeadn, adeadl, zs, iterations=10):\\n # DAYTIME\\n # Calculate potential (clear-sky) VIS and NIR solar components\\n\\n rad2deg = 180.0 / math.pi\\n # deg2rad = math.pi / 180.0\\n\\n # Correct for curvature of atmos in airmas\\n airmas = zs.expression(\\n '(sqrt(cos(zs) ** 2 + 0.0025) - cos(zs)) / 0.00125', {'zs': zs})\\n\\n # Correct for refraction(good up to 89.5 deg.)\\n airmas = airmas.where(\\n zs.multiply(rad2deg).lt(89.5),\\n zs.expression(\\n 'airmas - (2.8 / (90.0 - zs_temp) ** 2)',\\n {'airmas': airmas, 'zs_temp': zs.multiply(rad2deg)}))\\n\\n potbm1 = zs.expression(\\n '600.0 * exp(-0.160 * airmas)', {'airmas': airmas})\\n potvis = zs.expression(\\n '(potbm1 + (600.0 - potbm1) * 0.4) * cos(zs)',\\n {'potbm1': potbm1, 'zs': zs})\\n # CGM - Not used\\n potdif = zs.expression(\\n '(600.0 - potbm1) * 0.4 * cos(zs)', {'potbm1': potbm1, 'zs': zs})\\n uu = zs.expression('1.0 / cos(zs)', {'zs': zs}) \\\\\\n .max(0.01)\\n a = zs.expression(\\n '10 ** (-1.195 + 0.4459 * axlog - 0.0345 * axlog * axlog)',\\n {'axlog': uu.log10()})\\n watabs = zs.expression('1320.0 * a', {'a': a})\\n potbm2 = zs.expression(\\n '720.0 * exp(-0.05 * airmas) - watabs',\\n {'airmas': airmas, 'watabs': watabs})\\n evaL = zs.expression(\\n '(720.0 - potbm2 - watabs) * 0.54 * cos(zs)',\\n {'potbm2': potbm2, 'watabs': watabs, 'zs': zs})\\n potnir = zs.expression(\\n 'evaL + potbm2 * cos(zs)',\\n {'evaL': evaL, 'potbm2': potbm2, 'zs': zs})\\n\\n fclear = zs \\\\\\n .expression(\\n 'Rs_1 / (potvis + potnir)',\\n {'potvis': potvis, 'potnir': potnir, 'Rs_1': Rs_1}) \\\\\\n .clamp(0.01, 1.0) \\\\\\n .where(zs.cos().lte(0.01), 1)\\n \\n # Partition SDN into VIS and NIR\\n fvis = zs.expression(\\n 'potvis / (potvis + potnir)', {'potvis': potvis, 'potnir': potnir})\\n fnir = zs.expression(\\n 'potnir / (potvis + potnir)', {'potvis': potvis, 'potnir': potnir})\\n \\n # Estimate direct beam and diffuse fraction in VIS and NIR wavebands\\n fb1 = zs.expression(\\n 'potbm1 * cos(zs) / potvis',\\n {'potbm1': potbm1, 'potvis': potvis, 'zs': zs})\\n fb2 = zs.expression(\\n 'potbm2 * cos(zs) / potnir',\\n {'potbm2': potbm2, 'potnir': potnir, 'zs': zs})\\n\\n dirvis = zs \\\\\\n .expression(\\n 'fb1 * (1.0 - ((0.9 - ratiox) / 0.7) ** 0.6667)',\\n {'fb1': fb1, 'ratiox': fclear.min(0.9)}) \\\\\\n .min(fb1)\\n dirnir = zs \\\\\\n .expression(\\n 'fb1 * (1.0 - ((0.88 - ratiox) / 0.68) ** 0.6667)',\\n {'fb1': fb1, 'ratiox': fclear.min(0.88)}) \\\\\\n .min(fb1)\\n\\n dirvis = dirvis.where(dirvis.lt(0.01).And(dirnir.gt(0.01)), 0.011)\\n dirnir = dirnir.where(dirnir.lt(0.01).And(dirvis.gt(0.01)), 0.011)\\n\\n difvis = zs.expression('1.0 - dirvis', {'dirvis': dirvis})\\n difnir = zs.expression('1.0 - dirnir', {'dirnir': dirnir})\\n \\n # Correction for NIGHTIME\\n ind = zs.cos().lte(0.01)\\n fvis = fvis.where(ind, 0.5)\\n fnir = fnir.where(ind, 0.5)\\n difvis = difvis.where(ind, 0.0)\\n difnir = difnir.where(ind, 0.0)\\n\\n\\n # CGM - Not used anymore in function since e_atm is not computed\\n # Rs0 = zs \\\\\\n # .expression('potvis + potnir', {'potnir': potnir, 'potvis': potvis}) \\\\\\n # .where(zs.cos().lte(0.01), 0.0)\\n\\n #**********************************************\\n # Compute Albedo\\n ratio_soil = 2.\\n\\n # CGM - Initialize rsoilv and fg from F and albedo\\n rsoilv = F.multiply(0).add(0.12)\\n fg = albedo.multiply(0).add(1)\\n # rsoilv = ee.Image.constant(0.12)\\n # fg = ee.Image.constant(1.0)\\n\\n # print('\\\\nairmas: {:>20.14f}'.format(utils.image_value(airmas).values()[0]))\\n # print('airmas: {:>30.24f}'.format(utils.image_value(airmas).values()[0]))\\n # print('potbm1: {:>20.14f}'.format(utils.image_value(potbm1).values()[0]))\\n # print('potvis: {:>20.14f}'.format(utils.image_value(potvis).values()[0]))\\n # print('potbm2: {:>20.14f}'.format(utils.image_value(potbm2).values()[0]))\\n # print('potnir: {:>20.14f}'.format(utils.image_value(potnir).values()[0]))\\n # print('fclear: {:>20.14f}'.format(utils.image_value(fclear).values()[0]))\\n # print('fvis: {:>20.14f}'.format(utils.image_value(fvis).values()[0]))\\n # print('fnir: {:>20.14f}'.format(utils.image_value(fnir).values()[0]))\\n # print('fb1: {:>20.14f}'.format(utils.image_value(fb1).values()[0]))\\n # print('dirvis: {:>20.14f}'.format(utils.image_value(dirvis).values()[0]))\\n # print('dirnir: {:>20.14f}'.format(utils.image_value(dirnir).values()[0]))\\n # print('difvis: {:>20.14f}'.format(utils.image_value(difvis).values()[0]))\\n # print('difnir: {:>20.14f}'.format(utils.image_value(difnir).values()[0]))\\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\\n # print('fg: {:>20.14f}'.format(utils.image_value(fg).values()[0]))\\n # print('aleafv: {:>20.14f}'.format(utils.image_value(aleafv).values()[0]))\\n # print('aleafn: {:>20.14f}'.format(utils.image_value(aleafn).values()[0]))\\n # print('adeadv: {:>20.14f}'.format(utils.image_value(adeadv).values()[0]))\\n # print('adeadn: {:>20.14f}'.format(utils.image_value(adeadn).values()[0]))\\n\\n # CGM - Switched to an iterate call\\n def iter_func(n, prev):\\n # Extract inputs from previous iteration\\n # CGM - Variables that are commented out only need to be returned\\n # akb = ee.Image(ee.Dictionary(prev).get('akb'));\\n # albedo_c = ee.Image(ee.Dictionary(prev).get('albedo_c'));\\n # albedo_s = ee.Image(ee.Dictionary(prev).get('albedo_s'));\\n # ameann = ee.Image(ee.Dictionary(prev).get('ameann'));\\n # ameanv = ee.Image(ee.Dictionary(prev).get('ameanv'));\\n # diff = ee.Image(ee.Dictionary(prev).get('diff'));\\n fg_iter = ee.Image(ee.Dictionary(prev).get('fg'));\\n # rbcpyn = ee.Image(ee.Dictionary(prev).get('rbcpyn'));\\n # rbcpyv = ee.Image(ee.Dictionary(prev).get('rbcpyv'));\\n rsoilv_iter = ee.Image(ee.Dictionary(prev).get('rsoilv'));\\n # taudn = ee.Image(ee.Dictionary(prev).get('taudn'));\\n # taudv = ee.Image(ee.Dictionary(prev).get('taudv'));\\n\\n rsoiln = rsoilv_iter.multiply(ratio_soil)\\n # rsoiln = .expression(\\n # 'rsoilv * ratio_soil',\\n # {'rsoilv': rsoilv, 'ratio_soil': ratio_soil})\\n\\n # Weighted live/dead leaf average properties\\n ameanv = aleafv.expression(\\n 'aleafv * fg + adeadv * (1.0 - fg)',\\n {'adeadv': adeadv, 'aleafv': aleafv, 'fg': fg_iter})\\n ameann = aleafn.expression(\\n 'aleafn * fg + adeadn * (1.0 - fg)',\\n {'adeadn': adeadn, 'aleafn': aleafn, 'fg': fg_iter})\\n ameanl = aleafl.expression(\\n 'aleafl * fg + adeadl * (1.0 - fg)',\\n {'adeadl': adeadl, 'aleafl': aleafl, 'fg': fg_iter})\\n\\n # DIFFUSE COMPONENT\\n #*******************************\\n # Canopy reflection (deep canopy)\\n # Fit to Fig 15.4 for x=1\\n akd = F.expression('-0.0683 * log(F) + 0.804', {'F': F})\\n\\n # Eq 15.7\\n rcpyn = ameann.expression(\\n '(1.0 - sqrt(ameann)) / (1.0 + sqrt(ameann))', {'ameann': ameann})\\n rcpyv = ameanv.expression(\\n '(1.0 - sqrt(ameanv)) / (1.0 + sqrt(ameanv))', {'ameanv': ameanv})\\n # rcpyl = ameanl.expression(\\n # '(1.0 - sqrt(ameanl)) / (1.0 + sqrt(ameanl))', {'ameanl': ameanl})\\n\\n # Eq 15.8\\n rdcpyn = akd.expression(\\n '2.0 * akd * rcpyn / (akd + 1.0)', {'akd': akd, 'rcpyn': rcpyn})\\n rdcpyv = akd.expression(\\n '2.0 * akd * rcpyv / (akd + 1.0)', {'akd': akd, 'rcpyv': rcpyv})\\n # rdcpyl = akd.expression(\\n # '2.0 * akd * rcpyl / (akd + 1.0)', {'akd': akd, 'rcpyl': rcpyl})\\n\\n # Canopy transmission (VIS)\\n expfac = F.expression(\\n 'sqrt(ameanv) * akd * F', {'akd': akd, 'ameanv': ameanv, 'F': F})\\n expfac = expfac.max(0.001)\\n # expfac = expfac.where(expfac.lt(0.001), 0.001)\\n xnum = F.expression(\\n '(rdcpyv * rdcpyv - 1.0) * exp(-expfac)',\\n {'rdcpyv': rdcpyv, 'expfac': expfac})\\n xden = F.expression(\\n '(rdcpyv * rsoilv - 1.0) + '\\n 'rdcpyv * (rdcpyv - rsoilv) * exp(-2.0 * expfac)',\\n {'expfac': expfac, 'rdcpyv': rdcpyv, 'rsoilv': rsoilv_iter})\\n # Eq 15.11\\n taudv = F.expression('xnum / xden', {'xden': xden, 'xnum': xnum})\\n # taudv = xnum.divide(xden)\\n\\n # Canopy transmission (NIR)\\n expfac = F.expression(\\n 'sqrt(ameann) * akd * F', {'akd': akd, 'ameann': ameann, 'F': F})\\n expfac = expfac.max(0.001)\\n # expfac = expfac.where(expfac.lt(0.001), 0.001)\\n xnum = F.expression(\\n '(rdcpyn * rdcpyn - 1.0) * exp(-expfac)',\\n {'expfac': expfac, 'rdcpyn': rdcpyn})\\n xden = F.expression(\\n '(rdcpyn * rsoiln - 1.0) + '\\n 'rdcpyn * (rdcpyn - rsoiln) * exp(-2.0 * expfac)',\\n {'expfac': expfac, 'rdcpyn': rdcpyn, 'rsoiln': rsoiln})\\n # Eq 15.11\\n taudn = F.expression('xnum / xden', {'xden': xden, 'xnum': xnum})\\n # taudn = xnum.divide(nden)\\n\\n # Canopy transmission (LW)\\n taudl = F.expression(\\n 'exp(-sqrt(ameanl) * akd * F)',\\n {'akd': akd, 'ameanl': ameanl, 'F': F})\\n\\n # Diffuse albedo for generic canopy\\n # Eq 15.9\\n fact = F.expression(\\n '((rdcpyn - rsoiln) / (rdcpyn * rsoiln - 1.0)) * '\\n 'exp(-2.0 * sqrt(ameann) * akd * F)',\\n {'akd': akd, 'ameann': ameann, 'F': F, 'rdcpyn': rdcpyn,\\n 'rsoiln': rsoiln})\\n albdn = F.expression(\\n '(rdcpyn + fact) / (1.0 + rdcpyn * fact)',\\n {'fact': fact, 'rdcpyn': rdcpyn})\\n\\n # Eq 15.9\\n fact = F.expression(\\n '((rdcpyv - rsoilv) / (rdcpyv * rsoilv - 1.0)) * '\\n 'exp(-2.0 * sqrt(ameanv) * akd * F)',\\n {'akd': akd, 'ameanv': ameanv, 'F': F, 'rdcpyv': rdcpyv,\\n 'rsoilv': rsoilv_iter})\\n albdv = F.expression(\\n '(rdcpyv + fact) / (1.0 + rdcpyv * fact)',\\n {'fact': fact, 'rdcpyv': rdcpyv})\\n\\n # BEAM COMPONENT\\n #*******************************\\n # Canopy reflection (deep canopy)\\n akb = zs.expression('0.5 / cos(zs)', {'zs': zs})\\n akb = akb.where(zs.cos().lte(0.01), 0.5)\\n\\n # Eq 15.7\\n rcpyn = ameann.expression(\\n '(1.0 - sqrt(ameann)) / (1.0 + sqrt(ameann))',\\n {'ameann': ameann})\\n rcpyv = ameanv.expression(\\n '(1.0 - sqrt(ameanv)) / (1.0 + sqrt(ameanv))',\\n {'ameanv': ameanv})\\n\\n # Eq 15.8\\n rbcpyn = rcpyn.expression(\\n '2.0 * akb * rcpyn / (akb + 1.0)', {'akb': akb, 'rcpyn': rcpyn})\\n rbcpyv = rcpyv.expression(\\n '2.0 * akb * rcpyv / (akb + 1.0)', {'akb': akb, 'rcpyv': rcpyv})\\n\\n # Beam albedo for generic canopy\\n # Eq 15.9\\n fact = F.expression(\\n '((rbcpyn - rsoiln) / (rbcpyn * rsoiln - 1.0)) * '\\n 'exp(-2.0 * sqrt(ameann) * akb * F)',\\n {'akb': akb, 'ameann': ameann, 'F': F, 'rbcpyn': rbcpyn,\\n 'rsoiln': rsoiln})\\n albbn = F.expression(\\n '(rbcpyn + fact) / (1.0 + rbcpyn * fact)',\\n {'fact': fact, 'rbcpyn': rbcpyn})\\n\\n # Eq 15.9\\n fact = F.expression(\\n '((rbcpyv - rsoilv) / (rbcpyv * rsoilv - 1.0)) * '\\n 'exp(-2.0 * sqrt(ameanv) * akb * F)',\\n {'akb': akb, 'ameanv': ameanv, 'F': F, 'rbcpyv': rbcpyv,\\n 'rsoilv': rsoilv_iter})\\n albbv = F.expression(\\n '(rbcpyv + fact) / (1.0 + rbcpyv * fact)',\\n {'fact': fact, 'rbcpyv': rbcpyv})\\n\\n # CGM - finish\\n # Weighted albedo (canopy)\\n albedo_c = F.expression(\\n 'fvis * (dirvis * albbv + difvis * albdv) + '\\n 'fnir * (dirnir * albbn + difnir * albdn)',\\n {'albbn': albbn, 'albbv': albbv, 'albdn': albdn, 'albdv': albdv,\\n 'difnir': difnir, 'difvis': difvis, 'dirvis': dirvis,\\n 'dirnir': dirnir, 'fnir': fnir, 'fvis': fvis, })\\n albedo_c = albedo_c.where(\\n zs.cos().lte(0.01),\\n F.expression(\\n 'fvis * (difvis * albdv) + fnir * (difnir * albdn)',\\n {'albdn': albdn, 'albdv': albdv, 'difnir': difnir,\\n 'difvis': difvis, 'fnir': fnir, 'fvis': fvis}))\\n\\n albedo_s = rsoilv.expression(\\n 'fvis * rsoilv + fnir * rsoiln',\\n {'fnir': fnir, 'fvis': fvis, 'rsoiln': rsoiln, 'rsoilv': rsoilv_iter})\\n\\n albedo_avg = fc.expression(\\n '(fc * albedo_c) + ((1 - fc) * albedo_s)',\\n {'albedo_c': albedo_c, 'albedo_s': albedo_s, 'fc': fc})\\n diff = albedo_avg.subtract(albedo)\\n # diff = albedo_avg.expression(\\n # 'albedo_avg - albedo',\\n # {'albedo_avg': albedo_avg, 'albedo': albedo})\\n\\n # CGM - Check what this is doing\\n # Extra select call is needed if LAI is multiband\\n # Added fc_mask call\\n fc_mask = fc.select([0]).lt(0.75)\\n rsoilv_iter = rsoilv_iter \\\\\\n .where(fc_mask.And(diff.lte(-0.01)), rsoilv_iter.add(0.01)) \\\\\\n .where(fc_mask.And(diff.gt(0.01)), rsoilv_iter.add(-0.01))\\n # # CGM - IDL function\\n # rsoilv = ((fc lt 0.75) * (\\n # ((abs(diff) le 0.01) * rsoilv) +\\n # ((diff le -0.01)*(rsoilv + 0.01)) +\\n # ((diff gt 0.01)*(rsoilv - 0.01))))+\\n # ((fc ge 0.75) * rsoilv)\\n\\n # CGM - Extra select call is needed since fc is multiband\\n fc_mask = fc.select([0]).gte(0.75)\\n fg_iter = fg_iter \\\\\\n .where(fc_mask.And(diff.lte(-0.01)), fg_iter.subtract(0.05)) \\\\\\n .where(fc_mask.And(diff.gt(0.01)), fg_iter.add(0.05)) \\\\\\n .clamp(0.01, 1)\\n # # CGM - IDL function\\n # fg = ((fc ge 0.75) * (\\n # ((abs(diff) le 0.01)*fg) +\\n # ((diff le -0.01) * (fg - 0.05d0)) +\\n # ((diff gt 0.01) * (fg + 0.05d0)))) +\\n # ((fc lt 0.75) * fg)\\n\\n return ee.Dictionary({\\n 'akb': akb, 'albedo_c': albedo_c, 'albedo_s': albedo_s,\\n 'ameann': ameann, 'ameanv': ameanv, 'diff': diff, 'fg': fg_iter,\\n 'rbcpyn': rbcpyn, 'rbcpyv': rbcpyv,\\n 'rsoiln': rsoiln, 'rsoilv': rsoilv_iter,\\n 'taudn': taudn, 'taudv': taudv\\n })\\n\\n # Iterate the function n times\\n input_images = ee.Dictionary({\\n 'akb': None, 'albedo_c': None, 'albedo_s': None,\\n 'ameann': None, 'ameanv': None, 'diff': None, 'fg': fg,\\n 'rbcpyn': None, 'rbcpyv': None,\\n 'rsoiln': None, 'rsoilv': rsoilv,\\n 'taudn': None, 'taudv': None\\n })\\n iter_output = ee.Dictionary(\\n # ee.List.sequence(1, iterations) \\\\\\n ee.List.repeat(input_images, iterations) \\\\\\n .iterate(iter_func, input_images))\\n\\n # Unpack the iteration output\\n akb = ee.Image(iter_output.get('akb'))\\n albedo_c = ee.Image(iter_output.get('albedo_c'))\\n albedo_s = ee.Image(iter_output.get('albedo_s'))\\n ameann = ee.Image(iter_output.get('ameann'))\\n ameanv = ee.Image(iter_output.get('ameanv'))\\n diff = ee.Image(iter_output.get('diff'))\\n rbcpyn = ee.Image(iter_output.get('rbcpyn'))\\n rbcpyv = ee.Image(iter_output.get('rbcpyv'))\\n rsoilv = ee.Image(iter_output.get('rsoilv'))\\n rsoiln = ee.Image(iter_output.get('rsoiln'))\\n # rsoiln = rsoilv.multiply(ratio_soil)\\n taudn = ee.Image(iter_output.get('taudn'))\\n taudv = ee.Image(iter_output.get('taudv'))\\n # print('\\\\nakb: {:>20.14f}'.format(utils.image_value(akb).values()[0]))\\n # print('albedo_c: {:>20.14f}'.format(utils.image_value(albedo_c).values()[0]))\\n # print('albedo_s: {:>20.14f}'.format(utils.image_value(albedo_s).values()[0]))\\n # print('ameann: {:>20.14f}'.format(utils.image_value(ameann).values()[0]))\\n # print('ameanv: {:>20.14f}'.format(utils.image_value(ameanv).values()[0]))\\n # print('diff: {:>20.14f}'.format(utils.image_value(diff).values()[0]))\\n # print('rbcpyn: {:>20.14f}'.format(utils.image_value(rbcpyn).values()[0]))\\n # print('rbcpyv: {:>20.14f}'.format(utils.image_value(rbcpyv).values()[0]))\\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\\n # print('rsoiln: {:>20.14f}'.format(utils.image_value(rsoiln).values()[0]))\\n # print('taudv: {:>20.14f}'.format(utils.image_value(taudv).values()[0]))\\n # print('taudn: {:>20.14f}'.format(utils.image_value(taudn).values()[0]))\\n\\n # if a solution is not reached, alb_c=alb_s=alb\\n albedo_c = albedo_c.where(diff.abs().gt(0.05), albedo)\\n albedo_s = albedo_s.where(diff.abs().gt(0.05), albedo)\\n\\n # Direct beam+scattered canopy transmission coefficient (visible)\\n expfac = F.expression(\\n 'sqrt(ameanv) * akb * F',\\n {'ameanv': ameanv, 'akb': akb, 'F': F})\\n xnum = F.expression(\\n '(rbcpyv * rbcpyv - 1.0) * exp(-expfac)',\\n {'rbcpyv': rbcpyv, 'expfac': expfac})\\n xden = F.expression(\\n '(rbcpyv * rsoilv - 1.0) + '\\n 'rbcpyv * (rbcpyv - rsoilv) * exp(-2.0 * expfac)',\\n {'rbcpyv': rbcpyv, 'rsoilv': rsoilv, 'expfac': expfac})\\n # Eq 15.11\\n taubtv = F.expression('xnum / xden', {'xnum': xnum, 'xden': xden})\\n # print('\\\\nexpfac: {:>20.14f}'.format(utils.image_value(expfac).values()[0]))\\n # print('rbcpyv: {:>20.14f}'.format(utils.image_value(rbcpyv).values()[0]))\\n # print('rsoilv: {:>20.14f}'.format(utils.image_value(rsoilv).values()[0]))\\n # print('xnum: {:>20.14f}'.format(utils.image_value(xnum).values()[0]))\\n # print('xden: {:>20.14f}'.format(utils.image_value(xden).values()[0]))\\n # print('taubtv: {:>20.14f}'.format(utils.image_value(taubtv).values()[0]))\\n \\n # Direct beam+scattered canopy transmission coefficient (NIR)\\n expfac = F.expression(\\n 'sqrt(ameann) * akb * F',\\n {'ameann': ameann, 'akb': akb, 'F': F})\\n xnum = F.expression(\\n '(rbcpyn * rbcpyn - 1.0) * exp(-expfac)',\\n {'rbcpyn': rbcpyn, 'expfac': expfac})\\n xden = F.expression(\\n '(rbcpyn * rsoiln - 1.0) + '\\n 'rbcpyn * (rbcpyn - rsoiln) * exp(-2.0 * expfac)',\\n {'rbcpyn': rbcpyn, 'rsoiln': rsoiln, 'expfac': expfac})\\n # Eq 15.11\\n taubtn = F.expression('xnum / xden', {'xnum': xnum, 'xden': xden})\\n # print('\\\\nexpfac: {:>20.14f}'.format(utils.image_value(expfac).values()[0]))\\n # print('rbcpyn: {:>20.14f}'.format(utils.image_value(rbcpyn).values()[0]))\\n # print('rsoiln: {:>20.14f}'.format(utils.image_value(rsoiln).values()[0]))\\n # print('xnum: {:>20.14f}'.format(utils.image_value(xnum).values()[0]))\\n # print('xden: {:>20.14f}'.format(utils.image_value(xden).values()[0]))\\n # print('taubtn: {:>20.14f}'.format(utils.image_value(taubtn).values()[0]))\\n \\n # Shortwave radiation components\\n tausolar = F.expression(\\n 'fvis * (difvis * taudv + dirvis * taubtv) + '\\n 'fnir * (difnir * taudn + dirnir * taubtn)',\\n {'difnir': difnir, 'difvis': difvis,\\n 'dirnir': dirnir, 'dirvis': dirvis,\\n 'fnir': fnir, 'fvis': fvis,\\n 'taubtn': taubtn, 'taubtv': taubtv,\\n 'taudn': taudn, 'taudv': taudv})\\n # print('tausolar: {}'.format(utils.image_value(tausolar).values()[0]))\\n # print('Rs_1: {}'.format(utils.image_value(Rs_1).values()[0]))\\n Rs_c = Rs_1.expression(\\n 'Rs_1 * (1.0 - tausolar)', {'Rs_1': Rs_1, 'tausolar': tausolar})\\n Rs_s = Rs_1.expression(\\n 'Rs_1 * tausolar', {'Rs_1': Rs_1, 'tausolar': tausolar})\\n\\n # print('\\\\nRs_c: {:>20.14f}'.format(utils.image_value(Rs_c).values()[0]))\\n # print('Rs_s: {:>20.14f}'.format(utils.image_value(Rs_s).values()[0]))\\n # print('albedo_c: {:>20.14f}'.format(utils.image_value(albedo_c).values()[0]))\\n # print('albedo_s: {:>20.14f}'.format(utils.image_value(albedo_s).values()[0]))\\n\\n return Rs_c, Rs_s, albedo_c, albedo_s\",\n \"def orthopyroxene():\\n\\n rho = 3304.\\n\\n C = np.zeros((6,6), dtype=float)\\n C[0,0] = 236.9; C[0,1] = 79.6; C[0,2] = 63.2; C[0,3] = 0.; C[0,4] = 0.; C[0,5] = 0.\\n C[1,0] = C[0,1]; C[1,1] = 180.5; C[1,2] = 56.8; C[1,3] = 0.; C[1,4] = 0.; C[1,5] = 0.\\n C[2,0] = C[0,2]; C[2,1] = C[1,2]; C[2,2] = 230.4; C[2,3] = 0.; C[2,4] = 0.; C[2,5] = 0.\\n C[3,0] = C[0,3]; C[3,1] = C[1,3]; C[3,2] = C[2,3]; C[3,3] = 84.3; C[3,4] = 0.; C[3,5] = 0.\\n C[4,0] = C[0,4]; C[4,1] = C[1,4]; C[4,2] = C[2,4]; C[4,3] = C[3,4]; C[4,4] = 79.4; C[4,5] = 0.\\n C[5,0] = C[0,5]; C[5,1] = C[1,5]; C[5,2] = C[2,5]; C[5,3] = C[3,5]; C[5,4] = C[4,5]; C[5,5] = 80.1\\n\\n return C, rho\",\n \"def get_depth_pwm(self, goal):\\n ddiff = goal.target_depth - self.curr_depth\\n if self.curr_depth < -1:\\n rospy.loginfo('Depth sensor is not initialized')\\n return self.pwm_center\\n zout = ddiff * self.depth_p\\n # limit output if necassary\\n if abs(zout) > self.depth_pmax:\\n if zout < 0:\\n zout = -self.depth_pmax\\n else:\\n zout = self.depth_pmax\\n zout += self.pwm_center\\n return zout\",\n \"def ApproxConc280(Abs, pathLength = 10):\\n pass\",\n \"def get_wl_band(radar_frequency):\\n return 0 if (30 < radar_frequency < 40) else 1\",\n \"def depth_estimation(x_left, x_right, f=33.4, d=114):\\n depth = abs(f * d / ((x_left - x_right) / 72 * 2.54)) / 100 # - 0.418879\\n return depth\",\n \"def DR(R,Pc):\\n return r1*R*(K1**B1/(K1**B1 + (A/R)**B1))*(S/(S + R*Pc + Pc)) \\\\\\n - gwt*A - r2*R*(K2**B2/(K2**B2 + (A)**B2))*(S/(S + R*Pc + Pc) ) \\\\\\n *(R*M)/(K3 + R*M) + R*gc\",\n \"def one_transition_spectrum_ld(self,tr):\\n \\n\\n ta = tr[\\\"ta\\\"] # TimeAxis\\n ld = tr[\\\"ld\\\"] # linear dichroism strength\\n om = tr[\\\"om\\\"] # frequency - rwa\\n gg = tr[\\\"gg\\\"] # natural broadening (constant or time dependent)\\n fwhm = tr[\\\"fwhm\\\"] # Additional gaussian broadening of the spectra\\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\\n \\n if self.system._has_system_bath_coupling:\\n# ct = tr[\\\"ct\\\"] # correlation function\\n \\n # convert correlation function to lineshape function\\n #gt = self._c2g(ta,ct.data)\\n gt = tr[\\\"gt\\\"]\\n # calculate time dependent response\\n at = numpy.exp(-gt -1j*om*ta.data)\\n else:\\n # calculate time dependent response\\n at = numpy.exp(-1j*om*ta.data) \\n \\n \\n if len(gg) == 1:\\n gam = gg[0]\\n rt = numpy.exp(gam*ta.data)\\n at *= rt\\n #print(\\\"Constant: \\\", rt[20], len(at))\\n else:\\n rt = numpy.exp((gg)*ta.data) \\n at *= rt\\n #print(\\\"Time dependent: len = \\\", rt[20], len(rt))\\n \\n if fwhm!=0.0:\\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\\n at *= gauss\\n \\n # Fourier transform the result\\n ft = ld*numpy.fft.hfft(at)*ta.step\\n ft = numpy.fft.fftshift(ft)\\n # invert the order because hfft is a transform with -i\\n ft = numpy.flipud(ft) \\n # cut the center of the spectrum\\n Nt = ta.length #len(ta.data) \\n return ft[Nt//2:Nt+Nt//2]\",\n \"def find_large_separation(self):\\n\\n x = self.modes['n'] # radial order\\n y = self.modes['freq'] # frequency\\n wid = (0.66*self.numax**0.88)/2/np.sqrt(2*np.log(2.0))\\n w = (np.exp((-(y-self.numax)**2)/(2*wid**2))) # weight\\n\\n mN = np.sum(w)*np.sum(w*x*y) - np.sum(w*x)*np.sum(w*y)\\n D = np.sum(w)*np.sum(w*x**2) - np.sum(w*x)**2\\n Dn = mN/D\\n #print Dn\\n\\n return Dn\",\n \"def albedoc(solin, fsntoa):\\n var = (solin - fsntoa) / solin\\n var.units = \\\"dimensionless\\\"\\n var.long_name = \\\"TOA albedo clear-sky\\\"\\n return var\",\n \"def rayleigh(th,r,wl,a,n1,n2):\\n k = 2*np.pi/wl\\n n_2 = n2**2/n1**2\\n return ((k**2)*(a**3)*((n_2-1)/(n_2+2))/r)*np.array([[np.cos(th), 0],[0,1]])\",\n \"def best_deriv_drops_arclengths_r(fname):\\r\\n dat = sio.loadmat(fname,struct_as_record=False,squeeze_me=True)\\r\\n output = dat['output']\\r\\n y = dat['y'].ravel()\\r\\n cbool = dat['cbool'].astype('bool').ravel()\\r\\n R = {}\\r\\n inputs_list =[]\\r\\n arclength_list =[]\\r\\n kernels=[]\\r\\n for model in output._fieldnames:\\r\\n print('\\\\t'+model)\\r\\n arclength = output.__getattribute__(model).arclengths\\r\\n if len(arclength)==0 or arclength == 'all':\\r\\n compare_bool = cbool\\r\\n else:\\r\\n arclength_bool = dat['arclengths'].__getattribute__(arclength).astype('bool')\\r\\n compare_bool = np.logical_and(arclength_bool,cbool)\\r\\n\\r\\n inputs_list.append(output.__getattribute__(model).inputs)\\r\\n arclength_list.append(arclength)\\r\\n yhat = output.__getattribute__(model).yhat\\r\\n R[model],_,kernels = get_corr(y,yhat,compare_bool)\\r\\n\\r\\n R['kernels'] = kernels\\r\\n df = pd.DataFrame(R)\\r\\n df['id'] = os.path.basename(fname)[:10]\\r\\n # df['inputs'] = inputs_list\\r\\n # df['arclengths'] = arclength_list\\r\\n return(df)\",\n \"def calculate_wavelength(period, depth, gravity):\\r\\n return geometry.gmCalculateWavelength(period, depth, gravity)\",\n \"def mercier(self):\\n\\n # See Overleaf note \\\"Mercier criterion near the magnetic axis- detailed notes\\\".\\n # See also \\\"20200604-02 Checking sign in Mercier DGeod near axis.docx\\\"\\n\\n # Shorthand:\\n d_l_d_phi = self.d_l_d_phi\\n B0 = self.B0\\n G0 = self.G0\\n p2 = self.p2\\n etabar = self.etabar\\n curvature = self.curvature\\n sigma = self.sigma\\n iotaN = self.iotaN\\n iota = self.iota\\n pi = np.pi\\n\\n #integrand = d_l_d_phi * (Y1c * Y1c + X1c * (X1c + Y1s)) / (Y1c * Y1c + (X1c + Y1s) * (X1c + Y1s))\\n integrand = d_l_d_phi * (etabar*etabar*etabar*etabar + curvature*curvature*curvature*curvature*sigma*sigma + etabar*etabar*curvature*curvature) \\\\\\n / (etabar*etabar*etabar*etabar + curvature*curvature*curvature*curvature*(1+sigma*sigma) + 2*etabar*etabar*curvature*curvature)\\n\\n integral = np.sum(integrand) * self.d_phi * self.nfp * 2 * pi / self.axis_length\\n\\n #DGeod_times_r2 = -(2 * sG * spsi * mu0 * mu0 * p2 * p2 * G0 * G0 * G0 * G0 * etabar * etabar &\\n self.DGeod_times_r2 = -(2 * mu0 * mu0 * p2 * p2 * G0 * G0 * G0 * G0 * etabar * etabar \\\\\\n / (pi * pi * pi * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * B0 * iotaN * iotaN)) \\\\\\n * integral\\n\\n self.d2_volume_d_psi2 = 4*pi*pi*abs(G0)/(B0*B0*B0)*(3*etabar*etabar - 4*self.B20_mean/B0 + 2 * (self.G2 + iota * self.I2)/G0)\\n\\n self.DWell_times_r2 = (mu0 * p2 * abs(G0) / (8 * pi * pi * pi * pi * B0 * B0 * B0)) * \\\\\\n (self.d2_volume_d_psi2 - 8 * pi * pi * mu0 * p2 * abs(G0) / (B0 * B0 * B0 * B0 * B0))\\n\\n self.DMerc_times_r2 = self.DWell_times_r2 + self.DGeod_times_r2\",\n \"def isothermal_depth_wyrtki1964_gradient(da_PT):\\n\\n # make land mask based on surface layer\\n da_mask = da_PT.isel(z=0)*0.+1.\\n\\n # calculate drho/dz\\n da_PT_dz = da_PT.differentiate('z') # kg/m^4\\n\\n # interpolate to finer vertical resolution (2.5m)\\n da_interp = da_PT_dz.interp(z=np.arange(0,da_PT_dz.z.max(),2.5))\\n\\n # remove values shallower than critcal value\\n da_interp_masked = da_interp.where(da_interp>0.02,other=99999)\\n\\n # find first index that have value larger than critical value\\n z_ind = da_interp_masked.argmin(dim='z',skipna=True)\\n\\n # used 2d index to find 2d depth map\\n da_itd = da_interp.z[z_ind]*da_mask\\n\\n return da_itd\",\n \"def isothermal_depth_wyrtki1964_gradient(da_PT):\\n\\n # make land mask based on surface layer\\n da_mask = da_PT.isel(z=0)*0.+1.\\n\\n # calculate drho/dz\\n da_PT_dz = da_PT.differentiate('z') # kg/m^4\\n\\n # interpolate to finer vertical resolution (2.5m)\\n da_interp = da_PT_dz.interp(z=np.arange(0,da_PT_dz.z.max(),2.5))\\n\\n # remove values shallower than critcal value\\n da_interp_masked = da_interp.where(da_interp>0.02,other=99999)\\n\\n # find first index that have value larger than critical value\\n z_ind = da_interp_masked.argmin(dim='z',skipna=True)\\n\\n # used 2d index to find 2d depth map\\n da_itd = da_interp.z[z_ind]*da_mask\\n\\n return da_itd\",\n \"def computeA(diameter):\\n radius = diameter / 2.0\\n return np.pi * (radius**2)\",\n \"def complete_spectrum_time(cn, Ahr_flat = 0.2, wantSNR = 10.0, bandwidth = 0.2, architecture = \\\"A\\\",\\n plot = False, verbose = False):\\n\\n # If the coronagraph model has already been run...\\n if cn._computed:\\n # Use the existing stellar flux\\n fstar = cn.solhr\\n else:\\n # Otherwise use the solar flux\\n fstar = FSTAR\\n\\n if plot: fig, ax = plt.subplots()\\n\\n cc = [\\\"C0\\\", \\\"C2\\\", \\\"C3\\\"]\\n t_chan = np.zeros(len(CHANNELS))\\n Nbands_per_chan = np.zeros(len(CHANNELS))\\n t_per_band_per_chan = []\\n full_lam = []\\n full_dlam = []\\n full_Cobs = []\\n full_Cratio = []\\n full_Csig = []\\n pct_obs_iwa = []\\n lammax_obs_iwa = []\\n lam_extrema = []\\n\\n ibp = 0\\n\\n # Loop over telescope channels\\n for j, channel in enumerate(CHANNELS):\\n\\n t_tmp = []\\n\\n # Get the channel specific telescope parameters\\n luvoir = default_luvoir(channel=channel, architecture = architecture)\\n cn.telescope = luvoir\\n\\n if verbose: print(channel, luvoir.lammin, luvoir.lammax)\\n\\n lam_extrema.append(luvoir.lammin)\\n lam_extrema.append(luvoir.lammax)\\n\\n # Calculate the bandpass edges\\n edges = calculate_bandpass_edges(luvoir.lammin, luvoir.lammax, bandwidth = bandwidth)\\n\\n # Calculate the number of bandpasses\\n Nbands = len(edges) - 1\\n Nbands_per_chan[j] = Nbands\\n\\n # Run count rates (necessary to generate new wavelength grid)\\n #cn.run_count_rates(spectroscopy.AHR, spectroscopy.LAMHR, spectroscopy.FSTAR)\\n\\n # Get new wavelength grid\\n #l_grid, dl_grid = get_lam_dlam(cn)\\n\\n # Calculate how much of the spectrum is observable\\n cnc = copy.deepcopy(cn)\\n cnc.run_count_rates(AHR, LAMHR, FSTAR)\\n pct, lammax_obs = calc_observable_spectrum(cnc)\\n pct_obs_iwa.append(pct)\\n lammax_obs_iwa.append(lammax_obs)\\n\\n # Loop over bandpasses\\n for i in range(Nbands):\\n\\n if (type(wantSNR) is float) or (type(wantSNR) is int):\\n wSNR = wantSNR\\n else:\\n wSNR = wantSNR[ibp]\\n\\n # Get the max, min, and middle wavelenths for this bandpass\\n lammin = edges[i]\\n lammax = edges[i+1]\\n lammid = 0.5*(lammax + lammin)\\n\\n # Set telescope wavelength range\\n cn.telescope.lammin = lammin\\n cn.telescope.lammax = lammax\\n\\n if channel == \\\"UV\\\":\\n cn.telescope.lam = np.array([lammid])\\n cn.telescope.dlam = np.array([lammax - lammin])\\n\\n # Set spectrum to use for exposure time calcs\\n # Using flat spectrum so not biased by bottom of bands\\n Ahr_flat = Ahr_flat * np.ones(len(LAMHR))\\n\\n # Run count rates (necessary to generate new wavelength grid)\\n cn.run_count_rates(Ahr_flat, LAMHR, fstar)\\n\\n # Calculate exposure times to wantSNR\\n etimes = determine_exposure_time(cn, None, plot_snr_curves=False,\\n plot_spectrum=False, wantSNR=wSNR, ref_lam = lammid)\\n t_ref_lam = etimes[-1]\\n\\n # Re-do count rate calcs for true Earth spectrum\\n cn.run_count_rates(AHR, LAMHR, fstar)\\n\\n # Draw random samples of data for a plot\\n cn.make_fake_data(texp=t_ref_lam)\\n\\n if verbose: print(lammid, t_ref_lam)\\n\\n # Plot\\n if plot:\\n ax.axvspan(lammin, lammax, alpha = 0.2, color = cc[j])\\n #ax.plot(cn.lam, cn.Cratio, ls = \\\"steps-mid\\\", color = \\\"grey\\\", zorder = 100)\\n ax.plot(cn.lam, cn.Cobs, \\\"o\\\", ms = 3.0, alpha = 1.0, color = \\\"w\\\", zorder = 70)\\n ax.errorbar(cn.lam, cn.Cobs, yerr=cn.Csig, fmt = \\\"o\\\", ms = 2.0, alpha = 0.7, color = \\\"k\\\", zorder = 70)\\n ax.set_xlabel(\\\"Wavelength [$\\\\mu$m]\\\")\\n ax.set_ylabel(\\\"$F_p / F_s$\\\")\\n\\n # Save values\\n t_tmp.append(t_ref_lam)\\n full_lam.append(cn.lam)\\n full_dlam.append(cn.dlam)\\n full_Cratio.append(cn.Cratio)\\n full_Cobs.append(cn.Cobs)\\n full_Csig.append(cn.Csig)\\n\\n # Add time\\n if np.isfinite(t_ref_lam):\\n t_chan[j] += t_ref_lam\\n\\n ibp += 1\\n\\n # Save tmp times per band\\n t_per_band_per_chan.append(t_tmp)\\n\\n # Deal with the \\\"two channels at a time\\\" thing\\n t_tot = apply_two_channels(t_chan)\\n\\n spectrum = (np.array(full_lam),\\n np.array(full_dlam),\\n np.array(full_Cratio),\\n np.array(full_Cobs),\\n np.array(full_Csig))\\n iwa = (pct_obs_iwa, lammax_obs_iwa)\\n\\n if plot:\\n lam_extrema = np.array(lam_extrema)\\n cn.telescope.lammin = np.min(lam_extrema)\\n cn.telescope.lammax = np.max(lam_extrema)\\n cn.telescope.resolution = 140.\\n # Re-do count rate calcs for true Earth spectrum\\n cn.run_count_rates(AHR, LAMHR, FSTAR)\\n ax.plot(cn.lam, cn.Cratio, color = \\\"grey\\\", zorder = 80, lw = 3.0)\\n ax.plot(cn.lam, cn.Cratio, color = \\\"w\\\", zorder = 80, lw = 2.0)\\n\\n return t_tot, t_per_band_per_chan, spectrum, iwa\",\n \"def nrefrac(wavelength, density=1.0):\\n\\n # The IAU standard for conversion from air to vacuum wavelengths is given\\n # in Morton (1991, ApJS, 77, 119). For vacuum wavelengths (VAC) in\\n # Angstroms, convert to air wavelength (AIR) via: \\n\\n # AIR = VAC / (1.0 + 2.735182E-4 + 131.4182 / VAC^2 + 2.76249E8 / VAC^4)\\n\\n try:\\n wl = np.array(wavelength)\\n except TypeError:\\n return None\\n\\n wl2inv = (1e4/wl)**2\\n refracstp = 272.643 + 1.2288 * wl2inv + 3.555e-2 * wl2inv**2\\n return density * refracstp\",\n \"def Ag():\\n # return load_material(miepy.__path__[0] + \\\"/materials/ag.npy\\\")\\n\\n wp = 9.01\\n sig = [1.01889808, 0.62834151]\\n f = [0,5.05635462]\\n gam = [0.01241231, 0.54965831]\\n wav = np.linspace(300,1100,1000)\\n return drude_lorentz(wp,sig,f,gam,wav)\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def breathIO(self, bdry, dmap):\\n cur_bdry = np.array([bdry[0][1], bdry[3][1], bdry[1][0], bdry[2][0]])\\n if len(self.ref_bdry) == 0:\\n # setup reference frame's boundary (up, down, left and right)\\n self.ref_bdry = cur_bdry\\n self.ref_dmap = dmap\\n else:\\n ubdry = np.array([int(min(cur_bdry[0], self.ref_bdry[0])),\\n int(max(cur_bdry[1], self.ref_bdry[1])),\\n int(max(cur_bdry[2], self.ref_bdry[2])),\\n int(min(cur_bdry[3], self.ref_bdry[3]))])\\n blk_diff = gf_2D((dmap-self.ref_dmap)[ubdry[1]:ubdry[0], ubdry[2]:ubdry[3]], 5)\\n self.breath_list.append(np.mean(blk_diff))\",\n \"def retrieve_EchoDepth(\\n ds,\\n threshold,\\n variable=\\\"zFactorFinal\\\",\\n radar_frequency=\\\"Ku\\\",\\n min_threshold=0,\\n mask_liquid_phase=False,\\n):\\n # Retrieve required DataArrays\\n da = get_variable_dataarray(ds, variable=variable)\\n if len(da[\\\"radar_frequency\\\"].data) != 1:\\n da = da.sel({\\\"radar_frequency\\\": radar_frequency})\\n da_height = ds[\\\"height\\\"].copy()\\n # Mask height bin where not raining\\n da_mask_3d_rain = da > min_threshold\\n da_height = da_height.where(da_mask_3d_rain)\\n\\n # Mask heights where Z is not above threshold\\n da_mask_3d = da > threshold\\n da_height_masked = da_height.where(da_mask_3d)\\n\\n # Mask liquid phase\\n if mask_liquid_phase:\\n da_liquid_mask = get_liquid_phase_mask(ds)\\n da_height_masked = da_height_masked.where(~da_liquid_mask)\\n\\n # Retrieve min and max echo height\\n da_max_height = da_height_masked.max(dim=\\\"range\\\")\\n da_min_height = da_height_masked.min(dim=\\\"range\\\")\\n\\n # OLD MASKING\\n # if mask_liquid_phase:\\n # da_isnan = np.isnan(da_min_height)\\n # da_height_melting = ds[\\\"heightZeroDeg\\\"]\\n # da_height_melting = da_height_melting.where(~da_isnan)\\n # # If max is below the 0 °C isotherm --> set to nan\\n # da_max_height = da_max_height.where(da_max_height > da_height_melting)\\n # # If min below the 0 °C isoterm --> set the isotherm height\\n # da_min_height = da_min_height.where(da_min_height > da_height_melting, da_height_melting)\\n\\n # Compute depth\\n da_depth = da_max_height - da_min_height\\n\\n # Add attributes\\n da_depth.name = f\\\"EchoDepth{threshold}dBZ\\\"\\n da_depth.attrs[\\\"units\\\"] = \\\"m\\\"\\n return da_depth\",\n \"def far_fields(horn_width, horn_height, eplane_effective_length, hplane_effective_length, frequency, r, theta, phi):\\n # Calculate the wavenumber\\n k = 2.0 * pi * frequency / c\\n\\n # Calculate the wave impedance\\n eta = sqrt(mu_0 / epsilon_0)\\n\\n # Define the radial-component of the electric field\\n e_r = 0.0\\n\\n # Define the theta-component of the electric field\\n e_theta = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * sin(phi) * (1.0 + cos(theta)) * \\\\\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\\\\n I2(k, horn_height, eplane_effective_length, theta, phi)\\n\\n # Define the phi-component of the electric field\\n e_phi = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * cos(phi) * (1.0 + cos(theta)) * \\\\\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\\\\n I2(k, horn_height, eplane_effective_length, theta, phi)\\n\\n # Define the radial-component of the magnetic field\\n h_r = 0.0\\n\\n # Define the theta-component of the magnetic field\\n h_theta = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * -cos(phi) * (1.0 + cos(theta)) / eta * \\\\\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\\\\n I2(k, horn_height, eplane_effective_length, theta, phi)\\n\\n # Define the phi-component of the magnetic field\\n h_phi = 1j * k / (4.0 * pi * r) * exp(-1j * k * r) * sin(phi) * (1.0 + cos(theta)) / eta * \\\\\\n I1(k, horn_width, hplane_effective_length, theta, phi) * \\\\\\n I2(k, horn_height, eplane_effective_length, theta, phi)\\n\\n # Return all six components of the far field\\n return e_r, e_theta, e_phi, h_r, h_theta, h_phi\",\n \"def geometric_tortuosity(maze):\\n pathsTotal = []\\n path_star_list = findPoints(maze, \\\"S\\\")\\n\\n total_caminos = []\\n unit_caminos = 0\\n array_path = np.array(maze)\\n line = (array_path.shape)[2]\\n global path\\n i = 0\\n path_star_list = endPoints(maze[0], 'S')\\n # print(path_star_list)\\n listEndPoints = endPoints(maze[-1], \\\"E\\\")\\n # print(path_star_list)\\n # print(listEndPoints)\\n toTal = len(listEndPoints)*len(path_star_list)\\n\\n for star in path_star_list:\\n caminos = []\\n for end in listEndPoints:\\n print(\\\"camino:\\\"+str(i+1)+\\\"/\\\"+str(toTal))\\n\\n path = astar(maze, star, end)\\n\\n if path != None:\\n pathsTotal.append(path)\\n\\n i += 1\\n result = 0\\n # caminos.append(path)\\n # total_caminos.append(caminos)\\n try:\\n x = map(valuepath, path)\\n result = sum(x)\\n except:\\n pass\\n\\n caminos.append(result)\\n unit_caminos += 1\\n\\n total_caminos.append(min(caminos))\\n\\n valor = (np.mean(np.array(total_caminos)))\\n geometric_tortusity = valor/(int(line)-1)\\n return geometric_tortusity, pathsTotal\\n # return \\\"f\\\",\\\"f\\\"\",\n \"def compute_angle(map_id,padding_ratio=a.padding_ratio,map_size=a.map_size,sep=a.sep,freq=a.freq,\\\\\\n f_dust=a.f_dust,lMax=a.lMax,lMin=a.lMin,l_step=a.l_step,FWHM=a.FWHM,noise_power=a.noise_power,\\\\\\n slope=a.slope,delensing_fraction=a.delensing_fraction,useQU=a.useQU,N_bias=a.N_bias):\\n\\n # Step 1, create actual B-mode map\\n lCut=int(1.35*lMax) # maximum ell for Fourier space maps\\n\\n # First compute B-mode map from padded-real space map with desired padding ratio. Also compute the padded window function for later use\\n from hades.PaddedPower import MakePowerAndFourierMaps,DegradeMap,DegradeFourier\\n fBdust,padded_window,unpadded_window=MakePowerAndFourierMaps(map_id,padding_ratio=padding_ratio,map_size=map_size,sep=sep,freq=freq,fourier=True,power=False,returnMasks=True,flipU=a.flipU)\\n\\n # Also compute unpadded map to give binning values without bias\\n unpadded_fBdust=MakePowerAndFourierMaps(map_id,padding_ratio=1.,map_size=map_size,freq=freq,fourier=True,power=False,returnMasks=False,flipU=a.flipU)\\n unpadded_fBdust=DegradeFourier(unpadded_fBdust,lCut) # remove high ell pixels\\n\\n fBdust=DegradeFourier(fBdust,lCut) # discard high-ell pixels\\n padded_window=DegradeMap(padded_window.copy(),lCut) # remove high-ell data\\n unpadded_window=DegradeMap(unpadded_window.copy(),lCut)\\n\\n unpadded_fBdust.kMap*=f_dust\\n fBdust.kMap*=f_dust\\n\\n wCorrection = np.mean(padded_window.data**2.)**2./np.mean(padded_window.data**4.)\\n\\n from hades.NoisePower import noise_model,lensed_Cl,r_Cl\\n Cl_lens_func=lensed_Cl(delensing_fraction=delensing_fraction) # function for lensed Cl\\n\\n def total_Cl_noise(l):\\n return Cl_lens_func(l)+noise_model(l,FWHM=FWHM,noise_power=noise_power)\\n\\n from hades.PaddedPower import fourier_noise_map\\n ellNoise=np.arange(5,lCut) # ell range for noise spectrum\\n\\n from hades.RandomField import fill_from_model\\n #fourierNoise=fourier_noise_map\\n\\n from hades.PaddedPower import fourier_noise_test\\n fourierNoise,unpadded_noise=fourier_noise_test(padded_window,unpadded_window,ellNoise,total_Cl_noise(ellNoise),padding_ratio=padding_ratio,unpadded=False,log=True)\\n\\n totFmap=fBdust.copy()\\n totFmap.kMap+=fourierNoise.kMap# for total B modes\\n unpadded_totFmap=unpadded_fBdust.copy()\\n unpadded_totFmap.kMap+=unpadded_noise.kMap\\n\\n fBtrue=totFmap.copy()\\n\\n # Step 2: Compute the I map\\n inDir=a.root_dir+'%sdeg%s/' %(map_size,sep)\\n Tmap=liteMap.liteMapFromFits(inDir+'fvsmapT_'+str(map_id).zfill(5)+'.fits')\\n Qmap=liteMap.liteMapFromFits(inDir+'fvsmapQ_'+str(map_id).zfill(5)+'.fits')\\n Umap=liteMap.liteMapFromFits(inDir+'fvsmapU_'+str(map_id).zfill(5)+'.fits')\\n Umap.data*=-1.\\n QUmap=Qmap.copy()\\n QUmap.data=np.sqrt(Qmap.data**2.+Umap.data**2.)\\n if useQU:\\n \\tscaling=np.mean(QUmap.data**4.)\\n else:\\n \\tscaling=np.mean(Tmap.data**4.)\\n \\n maskMap=liteMap.liteMapFromFits(inDir+'fvsmapMaskSmoothed_'+str(map_id).zfill(5)+'.fits')\\n from hades.PaddedPower import zero_padding\\n zTmap=zero_padding(Tmap,padding_ratio)\\n zQUmap=zero_padding(QUmap,padding_ratio)\\n zWindow=zero_padding(maskMap,padding_ratio)\\n # Compute window factor <W^2> for padded window (since this is only region with data)\\n windowFactor=np.mean(zWindow.data**2.)\\n\\n # Define mod(l) and ang(l) maps needed for fourier transforms\\n modL,angL=fp.fftPol.makeEllandAngCoordinate(zTmap) # choice of map is arbitary\\n # Create pure T,E,B maps using 'hybrid' method to minimize E->B leakage\\n zTmap.data*=zWindow.data\\n zQUmap.data*=zWindow.data\\n fT=fftTools.fftFromLiteMap(zTmap)\\n fQU=fftTools.fftFromLiteMap(zQUmap)\\n\\n # Rescale to correct amplitude using dust SED\\n from hades.PowerMap import dust_emission_ratio\\n dust_intensity_ratio=dust_emission_ratio(freq)\\n\\n fT.kMap*=dust_intensity_ratio # apply dust-reduction factor \\n fT.kMap/=np.sqrt(windowFactor)\\n fQU.kMap*=dust_intensity_ratio\\n fQU.kMap/=np.sqrt(windowFactor)\\n fImap=DegradeFourier(fT,lCut)\\n fQUmap=DegradeFourier(fQU,lCut)\\n\\n # Step 3: Compute angle estimate\\n powBtrue=fftTools.powerFromFFT(fBtrue)\\n unpadded_powBtrue=fftTools.powerFromFFT(unpadded_totFmap)\\n from hades.KKdebiased import derotated_estimator\\n output=derotated_estimator(powBtrue,map_id,lMin=lMin,lMax=lMax,FWHM=FWHM,noise_power=noise_power,delensing_fraction=delensing_fraction,slope=slope)\\n A,fs,fc,Afs,Afc,_=output\\n HexPow2=Afs**2.+Afc**2.\\n \\n if a.debias_dedust:\\n \\tfrom .RandomField import padded_fill_from_Cell\\n\\tbias_data=np.zeros(N_bias)\\n\\t\\n\\tdef analytic_model(ell,A_est,slope):\\n\\t\\t\\\"\\\"\\\"Use the estimate for A to construct analytic model.\\n\\t\\tNB: This is just used for finding the centres of the actual binned data.\\n\\t\\t\\\"\\\"\\\"\\n\\t\\treturn total_Cl_noise(ell)+A_est*ell**(-slope)\\n\\t\\n\\tfrom .PowerMap import oneD_binning\\n\\tl_cen,mean_pow = oneD_binning(unpadded_powBtrue.copy(),lMin*padding_ratio,lCut,l_step*padding_ratio,binErr=False,exactCen=a.exactCen,\\\\\\n\\t\\t\\t\\t\\tC_ell_model=analytic_model,params=[A,slope]) \\n\\t#l_cen,mean_pow=oneD_binning(totPow.copy(),lMin,lCut,l_step,binErr=False,exactCen=a.exactCen,C_ell_model=analytic_model,params=[A_est,slope])\\n\\t# gives central binning l and mean power in annulus using window function corrections \\n\\t\\n\\t# Create spline fit\\n\\tfrom scipy.interpolate import UnivariateSpline\\n\\tspl=UnivariateSpline(l_cen,np.log(mean_pow),k=5)\\n\\tdef spline(ell):\\n\\t\\treturn np.exp(spl(ell))\\n\\t#del l_cen,mean_pow\\n\\t\\n\\t# Precompute useful data:\\n\\tfrom hades.RandomField import precompute\\n\\tprecomp=precompute(padded_window.copy(),spline,lMin=lMin,lMax=lMax)\\n\\t\\n\\tfor n in range(N_bias):\\n\\t\\tif n%100==0:\\n\\t\\t\\tprint 'Computing bias sim %s of %s' %(n+1,N_bias)\\n\\t\\tfBias=padded_fill_from_Cell(padded_window.copy(),l_cen,mean_pow,lMin=lMin,unPadded=a.unPadded,precomp=precomp)#,padding_ratio=padding_ratio)\\n\\t\\tbias_cross=fftTools.powerFromFFT(fBias.copy(),totFmap.copy()) # cross map\\n\\t\\tbias_self=fftTools.powerFromFFT(fBias.copy()) # self map\\n\\t\\t# First compute estimators on cross-spectrum\\n\\t\\tcross_ests=derotated_estimator(bias_cross.copy(),map_id,lMin=lMin,lMax=lMax,slope=slope,\\\\\\n\\t\\t\\t\\t\\t\\tfactor=A,FWHM=FWHM,noise_power=noise_power,\\\\\\n\\t\\t\\t\\t\\t\\trot=a.rot,delensing_fraction=delensing_fraction,useTensors=a.useTensors,\\\\\\n\\t\\t\\t\\t\\t\\tdebiasAmplitude=False,rot_average=a.rot_average,KKdebiasH2=False) # NB: CHANGE DEBIAS_AMPLITUDE parameter here\\n\\t\\tself_ests=derotated_estimator(bias_self.copy(),map_id,lMin=lMin,lMax=lMax,slope=slope,\\\\\\n\\t\\t\\t\\t\\t\\tfactor=A,FWHM=FWHM,noise_power=noise_power,\\\\\\n\\t\\t\\t\\t\\t\\trot=a.rot,delensing_fraction=delensing_fraction,useTensors=a.useTensors,\\\\\\n\\t\\t\\t\\t\\t\\tdebiasAmplitude=True,rot_average=a.rot_average,KKdebiasH2=a.KKdebiasH2)\\n\\t\\tbias_data[n]=(-1.*(self_ests[3]**4.+self_ests[4]**2.)+4.*(cross_ests[3]**2.+cross_ests[4]**2.))*wCorrection\\n\\t# Now compute the mean bias - this debiases the DATA only\\n\\tbias=np.mean(bias_data)\\n\\tdel bias_self,bias_cross\\n\\t\\n HexPow2-=bias\\t\\n \\n norm=np.sqrt(Afs**2.+Afc**2.)\\n fsbar,fcbar=Afs/norm,Afc/norm\\n\\n sin2a=fsbar/np.sqrt(2.*(fcbar+1.))\\n cos2a=np.sqrt((1.+fcbar)/2.)\\n\\n # Step 4: Compute B estimate\\n angleMap=fImap.thetaMap*np.pi/180.\\n fB_est=fImap.copy()\\n if useQU:\\n \\tbaseMap=fQUmap.copy()\\n else:\\n \\tbaseMap=fImap.copy()\\n fB_est.kMap=baseMap.kMap*(sin2a*np.cos(2.*angleMap)-cos2a*np.sin(2.*angleMap))\\n\\n # Step 5: Now compute cross coefficient\\n crossPow=fftTools.powerFromFFT(fB_est,fBtrue)\\n estPow=fftTools.powerFromFFT(fB_est,fB_est)\\n\\n from hades.PowerMap import oneD_binning\\n lC,pC=oneD_binning(crossPow,lMin,lMax/2.,l_step,exactCen=False)\\n lE,pE=oneD_binning(estPow,lMin,lMax/2.,l_step,exactCen=False)\\n lB,pB=oneD_binning(powBtrue,lMin,lMax/2.,l_step,exactCen=False)\\n #rho=np.array(pC)/np.sqrt(np.array(pB)*np.array(pE))\\n ratio=np.array(pC)/np.array(pE)\\n sign=np.sign(np.mean(ratio))\\n\\n # Step 6: Now compute the actual angle\\n alpha0=0.25*np.arctan2(fsbar,fcbar) # range is [-pi/4,pi/4]\\n if sign==-1.0:\\n alpha0+=np.pi/2.\\n \\n # Step 7: Compute the ratio of H^2/<I^4> for rescaling\\n ratio=(np.abs(HexPow2)/scaling)**0.25\\n \\n alpha_deg=alpha0*180./np.pi\\n print 'MapID: %s Angle: %.2f Ratio: %.2e' %(map_id,alpha_deg,ratio)\\n \\n return alpha_deg,ratio\",\n \"def get_displacement(self, color, depth):\\n displacement = None\\n keypoints, descriptors = self._feature_detector.detectAndCompute(color, None)\\n \\n if descriptors is not None and self._last_descriptors is not None:\\n matches = self._matcher.match(descriptors, self._last_descriptors)\\n matches = self._select_matches(matches) \\n points, last_points = self._get_points_from_matches(keypoints, self._last_keypoints, matches)\\n t = []\\n horizontal = 0\\n for i in range(len(matches)):\\n d1 = depth[int(points[i][1]), int(points[i][0])]\\n d2 = self._last_depth[int(last_points[i][1]), int(points[i][0])]\\n #print(d1, d2)\\n \\n if d1 != 0 and abs(d1-d2) < 10:\\n t.append(matches[i])\\n horizontal += (points[i][0] - last_points[i][0]) * d1\\n \\n matches = t\\n horizontal /= len(t) \\n \\n if len(matches) > 0:\\n if self._config['show_matches']:\\n matches_preview = cv2.drawMatches(color, keypoints, self._last_color, self._last_keypoints, matches, None, flags=2)\\n cv2.imshow('Basic_Odometry_matches_preview', matches_preview)\\n \\n points, last_points = self._get_points_from_matches(keypoints, self._last_keypoints, matches)\\n #It uses default values for parameters - there must be here because of broken python bindings I suppose\\n tf_mat = cv2.estimateRigidTransform(points, last_points, False, 500, 0.5, 3)\\n \\n if tf_mat is not None:\\n a11, a12, b1 = tf_mat[0][0], tf_mat[0][1], tf_mat[0][2]\\n a21, a22, b2 = -tf_mat[1][0], tf_mat[1][1], tf_mat[1][2]\\n roll = math.atan2(-a12, a11) \\n \\n displacement = {\\n 'roll': roll,\\n 'horizontal': horizontal,#b1,\\n 'vertical': b2,\\n 'depth': math.sqrt(a11**2+a12**2) * (-1 if a11 < 0 else 1)\\n }\\n \\n if descriptors is not None: \\n self._last_keypoints = keypoints\\n self._last_descriptors = descriptors\\n self._last_color = color\\n self._last_depth = depth\\n \\n return displacement\",\n \"def test_objectbased_depth(layout, gain_calc):\\n bf_no_depth = AudioBlockFormatObjects(position=dict(azimuth=0, elevation=0, distance=1),\\n width=360, height=360, depth=0)\\n bf_with_depth = AudioBlockFormatObjects(position=dict(azimuth=0, elevation=0, distance=0.5),\\n width=0, height=0, depth=1)\\n gains_no_depth = gain_calc.render(ObjectTypeMetadata(bf_no_depth)).direct\\n gains_with_depth = gain_calc.render(ObjectTypeMetadata(bf_with_depth)).direct\\n\\n npt.assert_allclose(np.linalg.norm(gains_no_depth), 1.0)\\n npt.assert_allclose(np.linalg.norm(gains_with_depth), 1.0)\\n\\n front_idx = layout.channel_names.index(\\\"M+000\\\")\\n assert np.all(gains_with_depth[~layout.is_lfe]) > 0 and gains_with_depth[front_idx] > gains_no_depth[front_idx]\",\n \"def find_all_ORFs(dna):\\n #these functions offset to analyze each each open frame reference\\n zero_offset=find_all_ORFs_oneframe(dna[0:])\\n \\n one_offset=find_all_ORFs_oneframe(dna[1:])\\n \\n two_offset=find_all_ORFs_oneframe(dna[2:])\\n \\n return zero_offset+one_offset+two_offset\\n \\n # YOUR IMPLEMENTATION HERE\",\n \"def approximate_betweenness(graph, max_depth):\\n ###TODO\\n pass\",\n \"def colourMagnitudeDiagram(lens_r_list, lens_gr_list, lens_gi_list, source_r_list, source_gr_list,\\n source_gi_list, positive_path):\\n\\n int_lens_r_list = []\\n int_lens_gr_list = []\\n int_lens_gi_list = []\\n int_source_r_list = []\\n int_source_gr_list = []\\n int_source_gi_list = []\\n\\n # creating lists containing floats and integrs that can be used in the graph\\n for i in range(0, len(lens_r_list)):\\n int_lens_r = float(lens_r_list[i])\\n int_lens_r_list.append(int_lens_r)\\n int_lens_gr = float(lens_gr_list[i])\\n int_lens_gr_list.append(int_lens_gr)\\n int_lens_gi = float(lens_gi_list[i])\\n int_lens_gi_list.append(int_lens_gi)\\n int_source_r = float(source_r_list[i])\\n int_source_r_list.append(int_source_r)\\n int_source_gr = float(source_gr_list[i])\\n int_source_gr_list.append(int_source_gr)\\n int_source_gi = float(source_gi_list[i])\\n int_source_gi_list.append(int_source_gi)\\n\\n # Getting the min and max fro the axees\\n lens_r_min = min(int_lens_r_list)\\n lens_r_max = max(int_lens_r_list)\\n lens_gr_min = min(int_lens_gr_list)\\n lens_gr_max = max(int_lens_gr_list)\\n lens_gi_min = min(int_lens_gi_list)\\n lens_gi_max = max(int_lens_gi_list)\\n source_r_min = min(int_source_r_list)\\n source_r_max = max(int_source_r_list)\\n source_gr_min = min(int_source_gr_list)\\n source_gr_max = max(int_source_gr_list)\\n source_gi_min = min(int_source_gi_list)\\n source_gi_max = max(int_source_gi_list)\\n\\n r_min = min(lens_r_min, source_r_min)\\n r_max = max(lens_r_max, source_r_max)\\n gr_min = min(lens_gr_min, source_gr_min)\\n gr_max = max(lens_gr_max, source_gr_max)\\n gi_min = min(lens_gi_min, source_gi_min)\\n gi_max = max(lens_gi_max, source_gi_max)\\n\\n y_min = gr_min - 0.2\\n y_max = gr_max + 0.2\\n x_min = r_min - 0.2\\n x_max = r_max + 0.2\\n a_min = gi_min -0.2\\n a_max = gi_max+0.2\\n\\n x = int_lens_r_list\\n y = int_lens_gr_list\\n x2 = int_source_r_list\\n y2 = int_source_gr_list\\n gi_source = int_source_gi_list\\n gi_lens = int_lens_gi_list\\n\\n # plotting the colour magnitude graph\\n fig1 = plt.figure()\\n plt.scatter(x, y, color='b', marker='.', label='Lenses')\\n plt.scatter(x2, y2, color='r', marker='.', label='Sources')\\n plt.xlabel('r')\\n plt.ylabel('g-r')\\n\\n axes = plt.axes()\\n axes.set(xlim=(x_min, x_max), ylim=(y_min, y_max))\\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\\n\\n if positive_path == 'Training/g_r_PositiveAll':\\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs r) Diagram')\\n elif positive_path == 'Testing/g_r_PositiveAll':\\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs r) Diagram')\\n elif positive_path == 'Training/g_r_AllData':\\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs r) Diagram')\\n plt.legend()\\n plt.show()\\n fig1.savefig('%s_g_r_colourMagnitudeDiagram.png' % positive_path)\\n\\n fig2 = plt.figure()\\n plt.scatter(gi_lens, y, color = 'b', marker= '.', label = 'Lenses')\\n plt.scatter(gi_source, y2, color ='r', marker='.', label = 'Sources')\\n if positive_path == 'Training/g_r_PositiveAll':\\n plt.title('Positively Simulated Training Data Colour Magnitude (g-r vs g-i ) Diagram')\\n elif positive_path == 'Testing/g_r_PositiveAll':\\n plt.title('Positively Simulated Testing Data Colour Magnitude (g-r vs g-i) Diagram')\\n elif positive_path == 'Training/g_r_AllData':\\n plt.title('All Positively Simulated Data Colour Magnitude (g-r vs g-i) Diagram')\\n\\n axes = plt.axes()\\n axes.set(xlim=(a_min, a_max), ylim=(y_min, y_max))\\n axes.get_yaxis().set_major_locator(LinearLocator(numticks=12))\\n axes.get_xaxis().set_major_locator(LinearLocator(numticks=12))\\n\\n plt.xlabel('g-i')\\n plt.ylabel('g-r')\\n plt.legend()\\n plt.show()\\n fig2.savefig('%s_g_i_colourMagnitudeDiagram.png' % positive_path)\",\n \"def antenny_calibrate(self):\\n if self.antenny_config.get(\\\"use_bno08x_rvc\\\"):\\n t = .25\\n d = .5\\n us = 50\\n else:\\n t = .1\\n d = .5\\n us = 100\\n self.platform.auto_calibrate_elevation_servo(us=us, t=t, d=d)\\n self.platform.auto_calibrate_azimuth_servo(us=us, t=t, d=d)\\n if self.antenny_config.get(\\\"use_bno055\\\"):\\n self.platform.auto_calibrate_magnetometer()\\n self.platform.auto_calibrate_gyroscope()\\n self.platform.auto_calibrate_accelerometer()\",\n \"def optical_depth_height(path_to_netcdf, opt_variable='upopt', tau=1.):\\n data = nc.Dataset(path_to_netcdf)\\n tau = data[opt_variable][:]\\n z = data['z'][:]\\n t1height = []\\n decreasing = True\\n # check if optical depth is increasing or decreasing\\n if tau.transpose()[0][1] - tau.transpose()[0][0] > 0.:\\n decreasing = False\\n for i, t in enumerate(tau.transpose()):\\n for j, opt in enumerate(t):\\n if decreasing:\\n # record first tau <= 1\\n if opt <= 1.:\\n t1height.append(z[j])\\n t1index.append(j)\\n break\\n else:\\n # increasing, so record first tau >= 1\\n if opt >= 1.:\\n t1height.append(z[j])\\n t1index.append(j)\\n break\\n return t1height\",\n \"def e_c_b97_polarized(rhoa,\\n rhob,\\n sigma_aa,\\n sigma_ab,\\n sigma_bb,\\n power_series_ss=(\\n (0, 0.1737), (1, 2.3487), (2, -2.4868)\\n ),\\n power_series_os=(\\n (0, 0.9454), (1, 0.7471), (2, -4.5961)\\n ),\\n gamma_ss=0.2,\\n gamma_os=0.006):\\n del sigma_ab\\n\\n e_c_lda_aa, e_c_lda_bb, e_c_lda_ab = lda.decomposed_e_c_lda_polarized(\\n rhoa, rhob)\\n\\n xa = get_reduced_density_gradient(rhoa, sigma_aa)\\n xb = get_reduced_density_gradient(rhob, sigma_bb)\\n xave = jnp.sqrt(0.5 * (xa ** 2 + xb ** 2))\\n\\n f_c_aa = f_b97(\\n xa, power_series=power_series_ss, gamma=gamma_ss, polarized=True)\\n f_c_bb = f_b97(\\n xb, power_series=power_series_ss, gamma=gamma_ss, polarized=True)\\n f_c_ab = f_b97(\\n xave, power_series=power_series_os, gamma=gamma_os, polarized=True)\\n\\n return e_c_lda_aa * f_c_aa + e_c_lda_bb * f_c_bb + e_c_lda_ab * f_c_ab\",\n \"def calc_elv_spectra(self, red, comp, src):\\n if ((src in red.data.keys())\\n & (src in red.data.keys())):\\n # check that the wavelenth grids are identical\\n delt_wave = red.data[src].waves - comp.data[src].waves\\n if np.sum(np.absolute(delt_wave)) > 0.01*u.micron:\\n warnings.warn(\\\"wavelength grids not equal for %s\\\" % src,\\n UserWarning)\\n else:\\n # reference band\\n red_V = red.data['BAND'].get_band_mag('V')\\n comp_V = comp.data['BAND'].get_band_mag('V')\\n\\n # setup the needed variables\\n self.waves[src] = red.data[src].waves\\n n_waves = len(self.waves[src])\\n self.exts[src] = np.zeros(n_waves)\\n self.uncs[src] = np.zeros(n_waves)\\n self.npts[src] = np.zeros(n_waves)\\n\\n # only compute the extinction for good, positive fluxes\\n print(comp.data[src].npts)\\n print(comp.data[src].fluxes)\\n indxs, = np.where((red.data[src].npts > 0)\\n & (comp.data[src].npts > 0)\\n & (red.data[src].fluxes.value > 0)\\n & (comp.data[src].fluxes.value > 0))\\n self.exts[src][indxs] = \\\\\\n (-2.5*np.log10(red.data[src].fluxes[indxs]\\n / comp.data[src].fluxes[indxs])\\n + (comp_V[0] - red_V[0]))\\n self.uncs[src][indxs] = np.sqrt(\\n np.square(_flux_unc_as_mags(red.data[src].fluxes[indxs],\\n red.data[src].uncs[indxs]))\\n + np.square(_flux_unc_as_mags(comp.data[src].fluxes[indxs],\\n comp.data[src].uncs[indxs]))\\n + np.square(red_V[1])\\n + np.square(comp_V[1]))\\n self.npts[src][indxs] = np.full(len(indxs), 1)\",\n \"def distance_23(self, alphas, motor_positions):\\n\\n self.roof_vertex_z[1] = motor_positions[1] * math.cos(alphas[1])\\n s2 = motor_positions[1] * math.sin(alphas[1])\\n self.roof_vertex_x[1] = self.base_point[1][0] - (s2 * 0.5) # sin 30° = 1/2\\n self.roof_vertex_y[1] = self.base_point[1][1] - (s2 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\\n\\n self.roof_vertex_z[2] = motor_positions[2] * math.cos(alphas[2])\\n s3 = motor_positions[2] * math.sin(alphas[2])\\n self.roof_vertex_x[2] = self.base_point[2][0] - (s3 * 0.5) # sin 30° = 1/2\\n self.roof_vertex_y[2] = self.base_point[2][1] + (s3 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\\n\\n return math.sqrt(\\n ((self.roof_vertex_x[2] - self.roof_vertex_x[1]) ** 2) +\\n ((self.roof_vertex_y[1] - self.roof_vertex_y[2]) ** 2) +\\n ((self.roof_vertex_z[2] - self.roof_vertex_z[1]) ** 2))\",\n \"def shadeToDepth(color):\\n minDistance = 0.7\\n maxDistance = 20\\n maxColor = 255\\n color = np.average(color, axis=None)\\n # depth = mx + b\\n # m = (minDistance - maxDistance)/maxColor\\n # x = color\\n # b = maxDistance\\n m = (maxDistance - minDistance)/maxColor\\n x = color\\n b = minDistance\\n return m * x + b\",\n \"def alpha_crit_fromEarth(a_p): #OK\\n a_earth = 1. #in AU\\n if a_p > a_earth:\\n alpha_max = np.arcsin(a_earth/a_p) #occurs at quadrature\\n else: #if a_p < a_earth:\\n alpha_max = np.pi #0 deg when opposite side of sta180 deg on same side of star\\n return alpha_max\",\n \"def test_GBL_tau_inst():\\n dz = 0.05\\n z = numpy.arange(0., 80. + 1.5*dz, dz)\\n\\n # Fully ionized H and He\\n x_ionH = 1.0\\n x_ionHe = 2.0\\n\\n cosmo = {}\\n cosmo['omega_M_0'] = numpy.array([[0.3],[0.6],[1.0]])\\n cosmo['omega_lambda_0'] = 1. - cosmo['omega_M_0']\\n cosmo['h'] = 0.65\\n cosmo['omega_b_0'] = 0.02 / cosmo['h']**2.\\n cosmo['Y_He'] = 0.24\\n cd.set_omega_k_0(cosmo)\\n\\n tau_inst = cr.optical_depth_instant(z, x_ionH=x_ionH, x_ionHe=x_ionHe, \\n **cosmo)\\n tau_int = cr.integrate_optical_depth(x_ionH, x_ionHe, z, **cosmo)\\n\\n linestyle = ['-', ':', '--']\\n \\n pylab.figure()\\n pylab.subplot(2,1,1)\\n pylab.title(\\\"Compare to GB&L fig. 1 (astro-ph/9812125v3.)\\\")\\n for i in range(len(linestyle)):\\n pylab.plot(z, tau_inst[i], ls=linestyle[i], color='b')\\n pylab.plot(z, tau_int[i], ls=linestyle[i], color='r')\\n\\n pylab.xlim(0,80)\\n pylab.ylim(0,1)\\n pylab.xlabel(r\\\"$\\\\mathrm{z_{ion}}$\\\")\\n pylab.ylabel(r\\\"$\\\\tau$\\\")\\n \\n pylab.subplot(2,1,2)\\n for i in range(len(linestyle)):\\n pylab.plot(z, \\n 1.e4 * (tau_int[i] - tau_inst[i])/tau_inst[i], \\n ls=linestyle[i], color='k')\\n diff = (tau_int[i] - tau_inst[i]) / tau_inst[i]\\n diff[numpy.isnan(diff)] = 0.0\\n print(\\\"max fractional error in num. int. = %.3g\\\" % \\n numpy.max(numpy.abs(diff))\\n )\\n ntest.assert_array_less(numpy.abs(diff), \\n numpy.zeros(diff.shape) + 2.e-4)\\n\\n pylab.xlim(0,40)\\n pylab.xlabel(r\\\"$\\\\mathrm{z_{ion}}$\\\")\\n pylab.ylabel(r\\\"$\\\\mathrm{10^4 \\\\times (num.\\\\tau - ana.\\\\tau)/ana.\\\\tau}$\\\")\",\n \"def getDiameter(antenna):\\n if antenna >=1 and antenna <= 6: \\n diameter = 10.4\\n elif antenna >=7 and antenna <= 15: \\n diameter = 6.1\\n elif antenna >=16 and antenna <= 23: \\n diameter = 3.5\\n else:\\n raise Exception,'Invalid antenna number : '+str(antenna)\\n return diameter\",\n \"def comp_angle_magnet(self):\\n Rbo = self.get_Rbo()\\n W0 = self.comp_W0m()\\n Harc = self.comp_H_arc()\\n if self.is_outwards():\\n return float(2 * arctan(W0 / (2 * (Rbo + self.H1 - Harc))))\\n else:\\n return float(2 * arctan(W0 / (2 * (Rbo - self.H1 - Harc))))\\n\\n # if self.W0_is_rad:\\n # return self.W0\\n # else: # Convert W0 from m to rad\\n # Rbo = self.get_Rbo()\\n # return float(2 * arcsin(self.W0 / (2 * Rbo)))\",\n \"def MiyamotoNagaiAccel(self, M, rd, r):\\n R = np.sqrt(r[0]**2 + r[1]**2) #Finding magnitude of x and y compnets\\n zd = rd/5. #Calculating \\\"zd\\\"\\n B = rd + np.sqrt(r[2]**2 + zd**2) #Calclating \\\"B\\\"\\n zstuff = 1/np.sqrt(r[2]**2 + zd**2) #Calculating stuff that only appears in z componet\\n MNa = -self.G*M/(R**2+B**2)**1.5 * r * np.array([1,1,zstuff]) #Putting it all together\\n\\n return MNa\",\n \"def calculate_SWIR2_TOA(d,sza,L):\\n rho_SWIR2_TOA=np.pi*(d**2)*L[:,178,:]/(0.0825*np.cos(sza))\\n return rho_SWIR2_TOA\",\n \"def get_re(r, cl, aoa, arctan, rel_v, blade_number):\\n chord = 8*math.pi*r*(1-math.cos(math.radians(arctan-aoa))) / blade_number / cl\\n Re = round(rel_v * chord / 0.00001511 / 100) * 100\\n return Re, chord\",\n \"def _estimate_bearing_(self):\\n fname = \\\"data/sim/{dn}/{rad}/bearing.mat\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), rad=self.rad)\\n m = {}\\n lat, lon, bearing = utils.get_sd_radar(self.rad)\\n p = (lat, lon)\\n gc = GC(p, p)\\n dist = np.linspace(0,self.mrange,self.nmrange)\\n lats, lons = [], []\\n for d in dist:\\n x = gc.destination(p, bearing, distance=d)\\n lats.append(x[0])\\n lons.append(x[1])\\n rinc = dist[1]-dist[0]\\n m[\\\"dist\\\"], m[\\\"lat\\\"], m[\\\"lon\\\"] = dist, np.array(lats), np.array(lons)\\n m[\\\"olat\\\"], m[\\\"olon\\\"], m[\\\"rb\\\"], m[\\\"num_range\\\"], m[\\\"max_range\\\"], m[\\\"range_inc\\\"] = lat, lon, bearing, float(self.nmrange),\\\\\\n float(self.mrange), float(rinc)\\n m[\\\"start_height\\\"], m[\\\"height_inc\\\"], m[\\\"num_heights\\\"] = float(self.sheight), float(self.hinc),\\\\\\n float(len(np.arange(self.sheight,self.eheight,self.hinc)))\\n m[\\\"ht\\\"] = np.arange(self.sheight,self.eheight,self.hinc)\\n m[\\\"freq\\\"], m[\\\"tol\\\"], m[\\\"nhops\\\"] = float(self.frequency), float(1e-7), float(self.nhops)\\n m[\\\"elev_s\\\"], m[\\\"elev_i\\\"], m[\\\"elev_e\\\"] = float(self.selev), float(self.ielev), float(self.eelev)\\n m[\\\"radius_earth\\\"] = 6371.0\\n m[\\\"d_ratio\\\"], m[\\\"d_start\\\"], m[\\\"d_end\\\"], m[\\\"d_rtime\\\"] = float(self.d_ratio), float(self.d_start),\\\\\\n float(self.d_end), float(self.d_rtime)\\n m[\\\"f_ratio\\\"], m[\\\"f_start\\\"], m[\\\"f_end\\\"], m[\\\"f_rtime\\\"] = float(self.f_ratio), float(self.f_start),\\\\\\n float(self.f_end), float(self.f_rtime)\\n m[\\\"e_ratio\\\"] = float(self.e_ratio)\\n savemat(fname, m)\\n self.m, self.lat, self.lon, self.ht = m, m[\\\"lat\\\"], m[\\\"lon\\\"], m[\\\"ht\\\"]\\n return\",\n \"def depth_heading_rc(self, goal):\\n # initialize empty RC command\\n channels = [1500] * 8\\n # compute heading and depth changes\\n zout = self.get_depth_pwm(goal)\\n hout = self.get_heading_pwm(goal)\\n\\n channels[self.zchannel] = zout\\n channels[self.rchannel] = hout\\n return channels\",\n \"def _estimate_bearing_(self):\\n fname = \\\"data/sim/{dn}/{rad}/bearing.mat\\\".format(dn=self.event.strftime(\\\"%Y.%m.%d.%H.%M\\\"), rad=self.rad)\\n m = {}\\n lat, lon, bearing = utils.get_sd_radar(self.rad)\\n p = (lat, lon)\\n gc = GC(p, p)\\n dist = np.linspace(0,self.mrange,self.nmrange)\\n lats, lons = [], []\\n for d in dist:\\n x = gc.destination(p, bearing, distance=d)\\n lats.append(x[0])\\n lons.append(x[1])\\n rinc = dist[1]-dist[0]\\n m[\\\"dist\\\"], m[\\\"lat\\\"], m[\\\"lon\\\"] = dist, np.array(lats), np.array(lons)\\n m[\\\"olat\\\"], m[\\\"olon\\\"], m[\\\"rb\\\"], m[\\\"num_range\\\"], m[\\\"max_range\\\"], m[\\\"range_inc\\\"] = lat, lon, bearing, float(self.nmrange),\\\\\\n float(self.mrange), float(rinc)\\n m[\\\"start_height\\\"], m[\\\"height_inc\\\"], m[\\\"num_heights\\\"] = float(self.sheight), float(self.hinc),\\\\\\n float(len(np.arange(self.sheight,self.eheight,self.hinc)))\\n m[\\\"ht\\\"] = np.arange(self.sheight,self.eheight,self.hinc)\\n m[\\\"freq\\\"], m[\\\"tol\\\"], m[\\\"nhops\\\"] = float(self.frequency), float(1e-7), float(self.nhops)\\n m[\\\"elev_s\\\"], m[\\\"elev_i\\\"], m[\\\"elev_e\\\"] = float(self.selev), float(self.ielev), float(self.eelev)\\n m[\\\"radius_earth\\\"] = 6371.0\\n m[\\\"d_ratio\\\"], m[\\\"d_start\\\"], m[\\\"d_end\\\"] = float(self.d_ratio), 10., 35.\\n m[\\\"f_ratio\\\"], m[\\\"f_start\\\"], m[\\\"f_end\\\"] = float(self.f_ratio), 130., 240.\\n m[\\\"e_ratio\\\"], m[\\\"e_start\\\"], m[\\\"e_end\\\"] = float(self.e_ratio), 50., 70.\\n savemat(fname, m)\\n self.m, self.lat, self.lon, self.ht = m, m[\\\"lat\\\"], m[\\\"lon\\\"], m[\\\"ht\\\"]\\n return\",\n \"def distance_13(self, alphas, motor_positions):\\n\\n self.roof_vertex_z[0] = motor_positions[0] * math.cos(alphas[0])\\n self.roof_vertex_x[0] = motor_positions[0] * math.sin(alphas[0])\\n self.roof_vertex_z[2] = motor_positions[2] * math.cos(alphas[2])\\n\\n s3 = motor_positions[2] * math.sin(alphas[2])\\n self.roof_vertex_x[2] = self.base_point[2][0] - (s3 / 2) # sin 30° = 1/2\\n self.roof_vertex_y[2] = self.base_point[2][1] + (s3 * 0.8660254037844386) # cos 30° = sqrt(3) / 2\\n\\n return math.sqrt(\\n ((self.roof_vertex_x[2] - self.roof_vertex_x[0]) ** 2) +\\n (self.roof_vertex_y[2] ** 2) +\\n ((self.roof_vertex_z[2] - self.roof_vertex_z[0]) ** 2))\",\n \"def radialApproxEffect(hubdist1,hubdist2,width,length):\\n #Grating coordinates\\n x,y = np.meshgrid(np.linspace(-width,width,1000),\\\\\\n np.linspace(-length,length,1000))\\n y1 = y + hubdist1\\n y2 = y + hubdist2\\n\\n #Convert to period and yaw angle\\n period1 = np.sqrt(x**2+y1**2)/hubdist1*160. #nm\\n period2 = np.sqrt(x**2+y2**2)/hubdist2*160. #nm\\n yaw = blazeYaw(1.5*np.pi/180,2.4,3,160.)\\n yaw1 = np.pi/2 - np.arctan(x/y1) + yaw\\n yaw2 = np.pi/2 - np.arctan(x/y2) + yaw\\n\\n #Determine alpha and beta\\n beta0,alpha0 = litBetaAlpha(1.5*np.pi/180,2.4,3,160.)\\n alpha1 = alpha0 + 3*2.4/period1*np.sin(yaw1)\\n alpha2 = alpha0 + 3*2.4/period2*np.sin(yaw2)\\n beta1 = beta0 + (3*2.4/period1)*np.cos(yaw1)\\n beta2 = beta0 + (3*2.4/period2)*np.cos(yaw2)\\n\\n #Determine spot shifts\\n x1 = hubdist2*(alpha1/beta1)\\n x2 = hubdist2*(alpha2/beta2)\\n \\n\\n pdb.set_trace()\\n \\n return x1,x2\",\n \"def _calculate_a_value(self, bval, nvalue, nyr, cmag, ref_mag):\\n\\n denominator = np.sum(nyr * np.exp(-bval * (cmag - ref_mag)))\\n return nvalue / denominator\",\n \"def getTheta(self, trackWidth):\\n leftDist = leftUS.sensor_detect()\\n print(\\\"LEFT US: \\\" + str(leftDist))\\n rightDist = rightUS.sensor_detect()\\n print(\\\"RIGHT US: \\\" + str(rightDist))\\n #totalWidth (hypotenuse) = leftUS + rightUS + robotWidth\\n totalWidth = leftDist + rightDist + 6\\n try:\\n print(math.acos(trackWidth/totalWidth))\\n return math.acos(trackWidth/totalWidth)\\n except ValueError:\\n return 0\",\n \"def calMeasuredContactAngle(self):\\n #account the base\\n bottomLength = 0\\n arrayHeight = np.empty([0, ], dtype = 'int64')\\n for i in sp.arange(self.nx):\\n if (self.densityFluid1[1, i] >= 0.485):\\n bottomLength += 1\\n #account the height\\n for m in sp.arange(self.nx):\\n tmpHeight = 0\\n for n in sp.arange(1, self.ny - 1):\\n if (self.densityFluid1[n, m] >= 0.485):\\n tmpHeight += 1\\n arrayHeight = np.append(arrayHeight, tmpHeight)\\n heightH = np.amax(arrayHeight)\\n #radius of droplet\\n radiusD = (4. * np.power(heightH, 2.) + np.power(bottomLength, 2.)) / \\\\\\n (8. * heightH)\\n contactAngle = np.arctan((bottomLength) / (2. * (radiusD - heightH))) \\n return contactAngle\",\n \"def computeRjb(self, lon, lat, depth):\\n pass\",\n \"def bv_to_radius(b_minus_v):\\n # Boyajian 2012\\n X = b_minus_v\\n a0 = 0.3830\\n a1 = 0.9907\\n a2 = -0.6038\\n Y = 0\\n # Ignore metallicity\\n a3 = 0\\n a4 = 0\\n a5 = 0\\n return (a0 + a1 * X + a2 * X ** 2 + a3 * X * Y +\\n a4 * Y + a5 * Y ** 2) * R_sun\",\n \"def computeRhyp(self, lon, lat, depth):\\n origin = self._origin\\n oldshape = lon.shape\\n\\n\\n rhyp = geodetic.distance(origin.lon, origin.lat, origin.depth,\\n lon, lat, depth)\\n rhyp = rhyp.reshape(oldshape)\\n return rhyp\",\n \"def calculateDetectorArea(self):\\n area = 0.0\\n r = self.geoParam['CylinderLightGuideRadius']\\n while(r + self.geoParam['DetectorThickness'] < self.geoParam['CylinderRadius']):\\n area -= math.pow(r,2)\\n r += self.geoParam['DetectorThickness']\\n area += math.pow(r,2)\\n r += self.geoParam['DetectorSpacing']\\n return math.pi*area\",\n \"def rayleigh(th,r,wl,a,n1,n2):\\n c = np.cos(th)\\n c2,s2 = c**2, np.sin(th)**2\\n k = 2*np.pi/wl\\n n_2 = n2**2/n1**2\\n m = (k**4)*(a**6)*(abs(n_2-1)**2) / ((abs(n_2+2)**2) * 2 * (r**2))\\n return m*np.array([[1+c2 , -s2 , 0 , 0],\\n [-s2 , 1+c2 , 0 , 0],\\n [0 , 0 , 2*c , 0],\\n [0 , 0 , 0 , 2*c]])\",\n \"def paf_o_r(rr_o_r, alpha):\\n return ((rr_o_r - 1) * (1 - alpha)) / ((rr_o_r - 1) * (1 - alpha) + 1)\",\n \"def myDihedralFunctionAirliner(Epsilon):\\n BaseDihedral = 7\\n\\n # A simple model of a loaded wing shape:\\n return BaseDihedral + Epsilon*Epsilon*10\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5567054","0.55453414","0.5525464","0.5403252","0.539342","0.53296256","0.53177613","0.52844834","0.52782506","0.524908","0.52308244","0.52085507","0.5208206","0.5199287","0.5178122","0.51560545","0.51540154","0.51226914","0.51127833","0.5078825","0.5057937","0.5048884","0.50282073","0.5016557","0.49781042","0.4977122","0.49544078","0.49530238","0.49478027","0.49472052","0.49209085","0.49140793","0.49038708","0.48967668","0.48903805","0.488919","0.48789334","0.48715436","0.48710847","0.48707694","0.4867667","0.48640135","0.4851743","0.48509294","0.48500252","0.48411572","0.48265257","0.48225453","0.48111543","0.48071784","0.48034796","0.4801928","0.4801597","0.4799918","0.47858912","0.4779709","0.4779709","0.47790226","0.4778265","0.47772378","0.47747242","0.4773483","0.47710076","0.476825","0.47618446","0.4758449","0.474737","0.47425744","0.47421792","0.47263193","0.47199884","0.47175983","0.4710021","0.47099128","0.47085398","0.47040403","0.46939683","0.46932667","0.4690595","0.46883106","0.46842098","0.46803418","0.46753258","0.4675066","0.4674817","0.467167","0.4670782","0.46669513","0.46668252","0.46667317","0.46665388","0.46640545","0.4660455","0.46579146","0.46521062","0.46471214","0.4646077","0.46442637","0.4634683","0.4633802"],"string":"[\n \"0.5567054\",\n \"0.55453414\",\n \"0.5525464\",\n \"0.5403252\",\n \"0.539342\",\n \"0.53296256\",\n \"0.53177613\",\n \"0.52844834\",\n \"0.52782506\",\n \"0.524908\",\n \"0.52308244\",\n \"0.52085507\",\n \"0.5208206\",\n \"0.5199287\",\n \"0.5178122\",\n \"0.51560545\",\n \"0.51540154\",\n \"0.51226914\",\n \"0.51127833\",\n \"0.5078825\",\n \"0.5057937\",\n \"0.5048884\",\n \"0.50282073\",\n \"0.5016557\",\n \"0.49781042\",\n \"0.4977122\",\n \"0.49544078\",\n \"0.49530238\",\n \"0.49478027\",\n \"0.49472052\",\n \"0.49209085\",\n \"0.49140793\",\n \"0.49038708\",\n \"0.48967668\",\n \"0.48903805\",\n \"0.488919\",\n \"0.48789334\",\n \"0.48715436\",\n \"0.48710847\",\n \"0.48707694\",\n \"0.4867667\",\n \"0.48640135\",\n \"0.4851743\",\n \"0.48509294\",\n \"0.48500252\",\n \"0.48411572\",\n \"0.48265257\",\n \"0.48225453\",\n \"0.48111543\",\n \"0.48071784\",\n \"0.48034796\",\n \"0.4801928\",\n \"0.4801597\",\n \"0.4799918\",\n \"0.47858912\",\n \"0.4779709\",\n \"0.4779709\",\n \"0.47790226\",\n \"0.4778265\",\n \"0.47772378\",\n \"0.47747242\",\n \"0.4773483\",\n \"0.47710076\",\n \"0.476825\",\n \"0.47618446\",\n \"0.4758449\",\n \"0.474737\",\n \"0.47425744\",\n \"0.47421792\",\n \"0.47263193\",\n \"0.47199884\",\n \"0.47175983\",\n \"0.4710021\",\n \"0.47099128\",\n \"0.47085398\",\n \"0.47040403\",\n \"0.46939683\",\n \"0.46932667\",\n \"0.4690595\",\n \"0.46883106\",\n \"0.46842098\",\n \"0.46803418\",\n \"0.46753258\",\n \"0.4675066\",\n \"0.4674817\",\n \"0.467167\",\n \"0.4670782\",\n \"0.46669513\",\n \"0.46668252\",\n \"0.46667317\",\n \"0.46665388\",\n \"0.46640545\",\n \"0.4660455\",\n \"0.46579146\",\n \"0.46521062\",\n \"0.46471214\",\n \"0.4646077\",\n \"0.46442637\",\n \"0.4634683\",\n \"0.4633802\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53029495"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":8491,"cells":{"query":{"kind":"string","value":"Calculate Linke turbidity using Kasten pyrheliometric formula. Note that broadband aerosol optical depth (AOD) can be approximated by AOD measured at 700 nm according to Molineaux [4] . Bird and Hulstrom offer an alternate approximation using AOD measured at 380 nm and 500 nm. Based on original implementation by Armel Oumbe."},"document":{"kind":"string","value":"def kasten96_lt(airmass_absolute, precipitable_water, aod_bb):\n # \"From numerically integrated spectral simulations done with Modtran\n # (Berk, 1989), Molineaux (1998) obtained for the broadband optical depth\n # of a clean and dry atmospshere (fictitious atmosphere that comprises only\n # the effects of Rayleigh scattering and absorption by the atmosphere gases\n # other than the water vapor) the following expression\"\n # - P. Ineichen (2008)\n delta_cda = -0.101 + 0.235 * airmass_absolute ** (-0.16)\n # \"and the broadband water vapor optical depth where pwat is the integrated\n # precipitable water vapor content of the atmosphere expressed in cm and am\n # the optical air mass. The precision of these fits is better than 1% when\n # compared with Modtran simulations in the range 1 < am < 5 and\n # 0 < pwat < 5 cm at sea level\" - P. Ineichen (2008)\n delta_w = 0.112 * airmass_absolute ** (-0.55) * precipitable_water ** 0.34\n # broadband AOD\n delta_a = aod_bb\n # \"Then using the Kasten pyrheliometric formula (1980, 1996), the Linke\n # turbidity at am = 2 can be written. The extension of the Linke turbidity\n # coefficient to other values of air mass was published by Ineichen and\n # Perez (2002)\" - P. Ineichen (2008)\n lt = -(9.4 + 0.9 * airmass_absolute) * np.log(\n np.exp(-airmass_absolute * (delta_cda + delta_w + delta_a))\n ) / airmass_absolute\n # filter out of extrapolated values\n return lt"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def estimate_lwdown(tairK, rh):\n zeroC = 273.15\n\n sat_vapress = 611.2 * np.exp(17.67 * ((tairK - zeroC) / (tairK - 29.65)))\n vapress = np.maximum(5.0, rh) / 100. * sat_vapress\n lw_down = 2.648 * tairK + 0.0346 * vapress - 474.0\n\n return lw_down","def derive_RiekeLebofsky(wavelength):\n filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', \n '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', \n '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']\n #wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.25, 1.635, 2.2, \n # 3.77, 4.68, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\n # 11.5, 12.0, 12.5, 13.0])\n \n # Wavelengths from Nishiyama+09 plot of RL+85 law...slightly different than standard, \n # drop N filter\n wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.17, 1.57, 2.12, \n 3.40, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\n 11.5, 12.0, 12.5, 13.0])\n A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,\n 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,\n 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])\n # Want to change this from A/Av to A/AK\n k_ind = np.where(np.array(filters) == 'K')\n Ak_Av = A_Av[k_ind]\n Av_Ak = 1.0 / Ak_Av\n\n A_Ak = A_Av * Av_Ak\n \n # Interpolate over the curve\n spline_interp = interpolate.splrep(wave, A_Ak, k=3, s=0)\n A_Ak_at_wave = interpolate.splev(wavelength, spline_interp)\n\n return A_Ak_at_wave","def getTauLos(losdens, kap):\n rhocell = losdens['valcell']\n nzs =len(rhocell)\n nz = nzs + 1\n zlos = losdens['zlos'] # cell\n zlosi = losdens['zlosi'] # wall\n\n tau, dtaustg, taustg = getTauz(zlosi, zlos, rhocell, kap)\n\n\n# dzlos = zlosi[1:] - zlosi[:-1] # zlos is in the same direction as tau\n# dtaustg = kap * rhocell * dzlos\n\n# tau = np.zeros(nz, dtype=np.float64)\n# for iz in range(nzs):\n# tau[iz+1] = sum(dtaustg[:iz+1])\n\n# taustg = 0.5*(tau[1:] + tau[:-1])\n\n return tau, dtaustg, taustg","def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\n\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\n\n return h_kJ_kg","def planckwavelen(wavel,Temp):\n wavel=wavel*1.e-6 #convert to meters\n c1=2.*h*c**2.\n c2=h*c/kb\n Blambda=1.e-6*c1/(wavel**5.*(np.exp(c2/(wavel*Temp)) -1))\n return Blambda","def make_stehle(self):\n\n temp_k = self.temp * e / k # temperature in K\n dens_cm = self.e_dens * 1.e-6 # electronic density in cm-3\n prefix = 'n_' + str(self.n_upper) + '_' + str(self.n_lower) + '_'\n\n # extract raw tabulated tabulated_data\n tab_temp_k = np.array(pystark.nc.variables[prefix + 'tempe'].data) # tabulated electron temperatures (K)\n olam0 = pystark.nc.variables[prefix + 'olam0'].data # line centre wavelength (A)\n num_tab_dens = pystark.nc.variables[prefix + 'id_max'].data\n fainom = pystark.nc.variables[prefix + 'fainom'].data\n tab_dens_cm = np.array(pystark.nc.variables[prefix + 'dense'].data) # tabulated electron densities (cm ** -3)\n f00 = np.array(pystark.nc.variables[prefix + 'f00'].data) # normal Holtsmark field strength (30 kV / m)\n dl12 = np.array(pystark.nc.variables[prefix + 'dl12'].data)\n dl12s = np.array(pystark.nc.variables[prefix + 'dl12s'].data)\n fainu = pystark.nc.variables[\n prefix + 'fainu'].data # Asymptotic value of iStark * (alpha ** 2.5) (\"wings factor in alfa units\")\n pr0 = np.array(pystark.nc.variables[\n prefix + 'pr0'].data) # Ratio of the mean interelectronic distance to the electronic Debye length\n jtot = np.array(pystark.nc.variables[prefix + 'jtot'].data,\n dtype=np.int) # \"number of wave lengths for the couple (T,Ne)\"\n dom = np.array(pystark.nc.variables[prefix + 'dom'].data) # frequency detunings in units (rad / (s*ues)\n d1om = np.array(pystark.nc.variables[prefix + 'd1om'].data)\n o1line = np.array(pystark.nc.variables[prefix + 'o1line'].data)\n o1lines = np.array(pystark.nc.variables[prefix + 'o1lines'].data)\n\n # ensure given temperature + density falls within tabulated values\n # change sligtly the value of the input density\n # dens_cm in order to remain , as far as possible, inside the tabulation\n # JSA: this first step seems bogus!\n\n if np.abs(dens_cm - tab_dens_cm[0]) / dens_cm <= 1.0E-3:\n dens_cm = tab_dens_cm[0] * 1.001\n\n for id in np.arange(1, num_tab_dens + 1):\n if np.abs(dens_cm - tab_dens_cm[id]) / dens_cm <= 1.0E-3:\n dens_cm = tab_dens_cm[id] * 0.999\n\n if dens_cm >= 2.0 * tab_dens_cm[num_tab_dens]:\n raise Exception(\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\n\n if dens_cm <= tab_dens_cm[0]:\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\n\n if temp_k >= tab_temp_k[9]:\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\n\n if temp_k <= tab_temp_k[0]:\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\n\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\n\n # calculate line centre wavelength and frequency using Rydberg formula\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\n\n wl_centre_angst = self.wl_centre * 1e10\n\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\n\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\n\n olines = o1lines / np.abs(otrans)\n oline = o1line / np.abs(otrans)\n\n # Limit analysis_tools to uncorrelated plasmas.\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\n if PR0_exp > 1.:\n raise Exception('The plasma is too strongly correlated\\ni.e. r0/debye=0.1\\nthe line cannot be computed.')\n\n # fainom_exp=fainom*(F00_exp**1.5)\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\n\n # ========================\n # TABULATION Format CDS\n # si on veut ecrire\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\n\n # IN_cds= N+0.01\n # INP_cds = NP+0.01\n\n # ***********************************************************\n # Don't edit the CDS format...\n # ***********************************************************\n\n # Skipped the code in the IF statement starting at line 470, since it\n # isn't used, if (.FALSE.) ...\n\n # ==============================================\n # define an unique detunings grid - domm - for the tabulated\n # profiles ( various temperatures , densities)\n # calculate all the line shapes for this common grid\n # units used at this points are Domega_new= Delta(omega)/F00\n # in rd/(s-1 ues)\n\n max_num_dens = 30 # Maximum number of densities\n max_num_tab_temp = 10\n max_num_detunings = 60 # Maximum number of detunings\n jtot = jtot.astype(np.int)\n domm = np.zeros(100000)\n dom0 = np.zeros(10000)\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\n uprof = np.zeros([max_num_dens, 10000])\n uprofs = np.zeros([max_num_dens, 10000])\n\n inc = 0\n domm[inc] = 0.0\n # ---- Look to replace this loop\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\n for i in np.arange(1, jtot[id, j]):\n inc += 1\n dom0[inc] = dom[id, j, i]\n\n inc = np.count_nonzero(dom)\n npik = inc + 1\n # nut=10000\n\n # Calling numpy sort instead of piksrt\n tmp = np.sort(dom0[0:npik])\n dom0[0:npik] = tmp[0:npik]\n # dom0 seems to agree with the FORTRAN version\n\n inc = 0\n domm[0] = 0.0\n # print 'npik',npik\n # ---- Look to replace this loop\n for i in np.arange(1, npik):\n dif = (dom0[i] - dom0[i - 1])\n if dif <= 1.0E-6:\n continue\n if dif / np.abs(dom0[i]) <= 0.1:\n continue\n inc = inc + 1\n domm[inc] = dom0[i]\n\n jdom = inc + 1 # One line after marker 35\n\n for id in np.arange(num_tab_dens):\n for j in np.arange(10):\n if pr0[id, j] > 1.0:\n continue\n\n tprof[id, j, 0] = oline[id, j, 0]\n tprofs[id, j, 0] = olines[id, j, 0]\n\n if jtot[id, j] == 0:\n continue\n\n for i in np.arange(1, jdom + 1):\n skip1 = False\n skip2 = False\n # print 'i',i\n domeg = domm[i]\n ij_max = jtot[id, j]\n # print 'domeg,ij_max',domeg,ij_max\n for ij in np.arange(1, ij_max - 1):\n # print 'ij',ij\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\n # print 'test1:',test\n if test <= 0.0:\n # print 'triggered test1'\n x1 = dom[id, j, ij - 1]\n x2 = dom[id, j, ij]\n x3 = dom[id, j, ij + 1]\n y1 = oline[id, j, ij - 1]\n y2 = oline[id, j, ij]\n y3 = oline[id, j, ij + 1]\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij - 1]\n y2 = olines[id, j, ij]\n y3 = olines[id, j, ij + 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n skip1 = True\n skip2 = True\n break\n\n if skip1 is False:\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\n # print 'test2:',test\n # print 'domeg',domeg\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\n if test <= 0.0:\n # print 'triggered test2'\n x1 = dom[id, j, ij_max - 3]\n x2 = dom[id, j, ij_max - 2]\n x3 = dom[id, j, ij_max - 1]\n y1 = oline[id, j, ij_max - 3]\n y2 = oline[id, j, ij_max - 2]\n y3 = oline[id, j, ij_max - 1]\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n y1 = olines[id, j, ij_max - 3]\n y2 = olines[id, j, ij_max - 2]\n y3 = olines[id, j, ij_max - 1]\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\n skip2 = True\n # print 'x1,x2,x3',x1,x2,x3\n # print 'y1,y2,y3',y1,y2,y3\n # print 'tprof[id,j,i]',tprof[id,j,i]\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\n continue\n\n if skip2 is False:\n if domeg > dom[id, j, ij_max]:\n # print 'triggered test3'\n tprof[id, j, i] = fainom / (domeg ** 2.5)\n tprofs[id, j, i] = tprof[id, j, i]\n continue\n\n # We can skip writing the intermediate file\n\n\n for id in np.arange(num_tab_dens):\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\n if otest_dens <= 0.0:\n dense1 = tab_dens_cm[id]\n dense2 = tab_dens_cm[id + 1]\n id1 = id\n id2 = id + 1\n break\n\n if dens_cm >= tab_dens_cm[num_tab_dens]:\n dense1 = tab_dens_cm[num_tab_dens - 1]\n dense2 = tab_dens_cm[num_tab_dens]\n id1 = num_tab_dens - 1\n id2 = num_tab_dens\n\n for it in np.arange(10):\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\n if otest <= 0.0:\n it1 = it\n it2 = it + 1\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\n tempe1 = tab_temp_k[it]\n tempe2 = tab_temp_k[it + 1]\n break\n\n # interpolation in temperature\n for id in np.arange(id1, id2 + 1):\n for i in np.arange(jdom):\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\n tempe2 - tempe1)\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\n tempe2 - tempe1)\n\n delta_lambda = np.zeros(jdom)\n delta_nu = np.zeros(jdom)\n wprof_nu = np.zeros(jdom)\n wprofs_nu = np.zeros(jdom)\n\n for i in np.arange(jdom):\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\n delta_omega = domm[i] * normal_holtsmark_field\n delta_nu[i] = delta_omega / (2 * np.pi)\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\n # print(delta_lambda[i])\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\n\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\n\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\n # output and manually convolve it with the Doppler profile -- not sure why...\n ls_sd = wprofs_nu2\n\n # interpolate onto frequency axis\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\n\n return ls_sd","def k_Wa92(wind_second_moment, temp_C):\n\n U2 = wind_second_moment\n\n Sc = schmidt_number(temp_C)\n k = (0.31 * U2) * (660 / Sc) ** 0.5\n\n return k","def abbott_steam():\n per_klb = 20 # dollars per klb of steam\n kwh_eq = to_kwh(1) # kwh equivalent of steam\n per_kwh = per_klb / kwh_eq\n return per_kwh","def test_klauder(self):\n ideal = np.array([0.14899879, -0.16633309, -0.42806931, 0.16605633,\n 0.70769336, 0.16605633, -0.42806931, -0.16633309])\n actual = misc.klauder(8)\n np.testing.assert_allclose(ideal, actual, atol=1e-8, rtol=1e-8)","def calc_TEB(Imap_name='HFI_SkyMap_353_2048_R2.02_full.fits',\n Pmap_name='HFI_SkyMap_353_2048_R2.02_full.fits',\n nus=None, fwhm=0.063, nside=16, lmax=100,\n lmaps_only=False, filename=None):\n\n # read from file if it's there\n if filename is None:\n filename = 'bispectrum_lmax{}'.format(lmax)\n if nus is not None:\n filename += '_{}-{}-{}GHz.npy'.format(nus[0],nus[1],nus[1])\n else:\n filename += '_{}'.format(Imap_name[-5])\n if Imap_name != Pmap_name:\n filename += '_{}.npy'.format(Pmap_name[-5])\n else:\n filename += '.npy'\n print 'looking for {} ...'.format(filename)\n if os.path.exists(filename) and not lmaps_only:\n bispectrum = np.load(filename, 'r')\n return bispectrum\n\n # compute it, if the file doesn't exist\n if nus is not None:\n Imap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[0])\n Pmap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[1])\n title = '$I_{%i} P^2_{%i}$ (equilateral)' % (nus[0],nus[1])\n \n Imap = prepare_map( Imap_name, field=0,\n nside_out=nside, fwhm=fwhm )\n Tlm = hp.map2alm( Imap, lmax=lmax )\n\n Qmap, Umap = prepare_map( Pmap_name, field=(1,2),\n nside_out=nside, fwhm=fwhm )\n \n \n Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2, lmax=lmax )\n\n if lmax is None:\n lmax = hp.sphtfunc.Alm.getlmax(len(Tlm))\n ls, ms = hp.sphtfunc.Alm.getlm(lmax,np.arange(len(Tlm)))\n lmin = ls.min()\n mapsize = len(Imap)\n pixelsize = hp.pixelfunc.nside2pixarea(nside)\n \n Ylm = calc_Ylm(Imap, ls, ms)\n\n\n #return Ylm, Tlm, ls, ms\n print 'calculating Tl,El,Bl ...'\n \n Tl = sum_over_m(Tlm, Ylm, ls,\n lmax=lmax, lmin=lmin, mapsize=mapsize)\n El = sum_over_m(Elm, Ylm, ls,\n lmax=lmax, lmin=lmin, mapsize=mapsize)\n Bl = sum_over_m(Blm, Ylm, ls,\n lmax=lmax, lmin=lmin, mapsize=mapsize)\n\n if lmaps_only:\n return Tl,El,Bl\n \n hs = get_hs(lmin=lmin, lmax=lmax)\n\n print 'calculating bispectrum ...'\n bispectrum = calc_bispectrum(Tl, El, Bl, hs,\n pixelsize,\n lmax=lmax, lmin=lmin,\n mapsize=mapsize)\n clean_bispectrum_of_naninf(bispectrum, hs, inplace=True)\n np.save(filename, bispectrum)\n return bispectrum","def PlankFunction(wavelen,T=5778.):\n\n c1=1.191042E8\n c2=1.4387752E4\n L=c1/(wavelen**5*(np.exp(c2/(wavelen*T))-1))\n return L","def k_Li86(wind_ms, temp_C):\n from numpy import zeros_like\n\n U = wind_ms\n T = temp_C\n\n Sc = schmidt_number(T)\n k = zeros_like(temp_C)\n\n i1 = U <= 3.6\n i2 = (U > 3.6) & (U < 13.0)\n i3 = U >= 13.0\n\n k[i1] = (0.17 * U[i1]) * (Sc[i1] / 600) ** (-2.0 / 3.0)\n k[i2] = ((U[i2] - 3.4) * 2.8) * (600 / Sc[i2]) ** 0.5\n k[i3] = ((U[i3] - 8.4) * 5.9) * (600 / Sc[i3]) ** 0.5\n\n return k","def wind_chill(T_a, v):\r\n return 13.12 + 0.6215*(T_a) - 11.37*(v)**0.16 + 0.3965*(T_a)*(v)**0.16","def moonlongitude(time):\n B0 = 481267.8809\n C0 = 218.3162\n # fmt: off\n A = np.array([62888.e-4, 12740.e-4, 6583.e-4, 2136.e-4, 1851.e-4, \\\n 1144.e-4, 588.e-4, 571.e-4, 533.e-4, 458.e-4, 409.e-4, \\\n 347.e-4, 304.e-4, 154.e-4, 125.e-4, 110.e-4, 107.e-4, \\\n 100.e-4, 85.e-4, 79.e-4, 68.e-4, 52.e-4, 50.e-4, 40.e-4, \\\n 40.e-4, 40.e-4, 38.e-4, 37.e-4, 28.e-4, 27.e-4, 26.e-4, \\\n 24.e-4, 23.e-4, 22.e-4, 21.e-4, 21.e-4, 21.e-4, 18.e-4, \\\n 16.e-4, 12.e-4, 11.e-4, 9.e-4, 8.e-4, 7.e-4, 7.e-4, \\\n 7.e-4, 7.e-4, 6.e-4, 6.e-4, 5.e-4, 5.e-4, 5.e-4, \\\n 4.e-4, 4.e-4, 3.e-4, 3.e-4, 3.e-4, 3.e-4, 3.e-4, \\\n 3.e-4, 3.e-4])\n B = np.array([477198.868, 413335.35, 890534.22, 954397.74, \\\n 35999.05, 966404.0, 63863.5, 377336.3, \\\n 1367733.1, 854535.2, 441199.8, 445267.1, \\\n 513197.9, 75870, 1443603, 489205, 1303870, \\\n 1431597, 826671, 449334, 926533, 31932, \\\n 481266, 1331734, 1844932, 133, 1781068, \\\n 541062, 1934, 918399, 1379739, 99863, \\\n 922466, 818536, 990397, 71998, 341337, \\\n 401329, 1856938, 1267871, 1920802, 858602, \\\n 1403732, 790672, 405201, 485333, 27864, \\\n 111869, 2258267, 1908795, 1745069, 509131, \\\n 39871, 12006, 958465, 381404, 349472, \\\n 1808933, 549197, 4067, 2322131.])\n C = np.array([44.963, 10.74, 145.70, 179.93, 87.53, 276.5, \\\n 124.2, 13.2, 280.7, 148.2, 47.4, 27.9, 222.5, \\\n 41, 52, 142, 246, 315, 111, 188, \\\n 323, 107, 205, 283, 56, 29, 21, \\\n 259, 145, 182, 17, 122, 163, 151, \\\n 357, 85, 16, 274, 152, 249, 186, \\\n 129, 98, 114, 50, 186, 127, 38, \\\n 156, 90, 24, 242, 223, 187, 340, \\\n 354, 337, 58, 220, 70, 191])\n # fmt: on\n RAD = 0.0174532925199433\n tempb = (B * time + C) * RAD\n amp = A * np.cos(tempb)\n moonlon = np.sum(amp)\n moonlon = (moonlon + B0 * time + C0) * RAD\n return moonlon","def ReadTinker():\n # Total Potential Energy : {f} Kcal/mole\n total_line = \" Total Potential Energy :\"\n with open('LICHM_TINKEREnergy_0.log') as f:\n for line in f:\n if line.startswith(total_line):\n # print(line)\n TinkE = re.findall(r'\\-\\d+\\.*\\d*', line)\n TinkE = float(TinkE[0])\n # if AMOEBA == True:\n # if line.startswith(\"Polarization\"):\n # Epol = re.findall(r'\\-\\d+\\.*\\d*', line)\n # Epol = float(Epol[0])\n # elif line.startswith(\"Implicit Solvation\")\n # Esolv = re.findall(r'\\-\\d+\\.*\\d*', line)\n # Esolv = float(Esolv[0])\n f.close()\n # if AMOEBA == True:\n # TINKERPolForces = EPol + ESolv\n # TinkE += TINKERPolForces\n #\n TinkE *= kcal2ev\n return TinkE","def earth_tide(theta, lamda, gtime):\n\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist # bpos common block\n global h, k, l # love common block\n h = [0.6114, 0.2891, 0.175]\n k = [0.304, 0.09421, 0.043]\n l = [0.0832, 0.0145, 0.0103]\n\n global azt, azs # azimut common block\n global etmut # tdiff common block\n global moon # sunny common block\n moon = 0\n # hardwire these - you can only send it ONE droptime\n deltat = 1\n NPT = 1\n\n temp_time = num2date(gtime)\n\n YY = temp_time.year\n MO = temp_time.month\n DD = temp_time.day\n HH = temp_time.hour\n MM = temp_time.minute\n SS = temp_time.second\n # Initialize variables\n irl = 1\n iflag = 0\n ntotl = 1\n iget = [0, 0, 0, 0, 0, 0, 0] # ' !!!\n ispc = [0, 0, 0, 0] # ' !!!\n ntw = [1, 0, 0] # ' !!!\n ioptn = 't'\n ielement = 0\n # \tdata statements for input and output unit numbers (on terminal I/O)\n inun = 5\n ioun = 6\n nptpb = 6\n\n yr1 = YY - 1900\n day1 = date2num(datetime(YY, MO, DD))\n # \tfind times in hours from 0 hr, 1 jan 1900\n # matlab:\n ts = (\n SS / 3600\n + MM / 60\n + HH\n + 24 * (day1 - 1)\n + 8760 * yr1\n + 24 * np.fix((yr1 - 1) / 4)\n )\n # python:\n dj = date_to_julian_day(datetime(YY, MO, DD))\n djref = date_to_julian_day(datetime(1899, 12, 31, 0, 0, 0))\n delta_dj = (\n dj - djref\n ) # difference in days from current date (0hr) to 0hr, 1 jan 1900\n delta_djhr = float(delta_dj) * 24.0 + HH - 12.0 + MM / 60.0 + SS / 3600.0\n te = ts + (NPT - 1) * deltat / 3600\n d = deltat / 3600\n # terms=(te-ts)/d + 1\n terms = NPT\n\n # done asking questions - begin execution\n i = 1\n tt = ts\n sph(theta, lamda, 0)\n etmut = 41.184 + yr1 - 70\n # matlab:\n # t = (tt+12 + (etmut/3600))/876600\n t = (delta_djhr + etmut / 3600) / 876600\n # t is ephemeris time in julian centuries from 12 hr 0 jan 1900\n ephem(t)\n\n # calculate normalized gravity tides\n [grav, tilt, strain, gdc] = elastd(ntw)\n\n gravtide = 1.0e5 * grav\n # convert m/s² to mgal: 1m/s² = 100 gal = 100 000 mgal\n\n iflag = 1\n\n iterms = np.fix(terms)\n i = 1\n return gravtide","def derive_Hosek18b(wavelength):\n # Extinction law definition\n wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])\n A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50])\n \n # Following Hosek+18, Interpolate over the curve with cubic spline interpolation\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\n A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)\n\n # This curve already assumes A_Ks = 1.0, so we can go straight to\n # output \n return A_AKs_at_wave","def halflife(self, humidity: _VectorisedFloat, inside_temp: _VectorisedFloat) -> _VectorisedFloat:\n # Updated to use the formula from Dabish et al. with correction https://doi.org/10.1080/02786826.2020.1829536\n # with a maximum at hl = 6.43 (compensate for the negative decay values in the paper). \n # Note that humidity is in percentage and inside_temp in °C.\n # factor np.log(2) -> decay rate to half-life; factor 60 -> minutes to hours\n hl_calc = ((np.log(2)/((0.16030 + 0.04018*(((inside_temp-273.15)-20.615)/10.585)\n +0.02176*(((humidity*100)-45.235)/28.665)\n -0.14369\n -0.02636*((inside_temp-273.15)-20.615)/10.585)))/60)\n \n return np.where(hl_calc <= 0, 6.43, np.minimum(6.43, hl_calc))","def kth_func(Th, ThS, lbd, ksat):\n if Th < 0.0:\n # rwarn(\"water content < 0 IN kth_func\")\n Th = 0.0\n kth = ksat * (Th / ThS) ** (3 + (2 / lbd))\n\n return kth","def calc_h_sen(dry_bulb_C):\n\n h_kJ_kg = dry_bulb_C * CPA_kJ_kgC\n\n return h_kJ_kg","def ftlan_E1c(hop, v0, T, m=50, Min_b=10e-10, Min_m=5, kB=1, norm = np.linalg.norm):\n# def Tri_diag(a1, b1):\n# mat = np.diag(b1, -1) + np.diag(a1, 0) + np.diag(b1, 1)\n# e, w = np.linalg.eigh(mat)\n# return e, w\n\n beta = 1./(T * kB)\n E = 0.\n a, b = [], []\n v0 = v0/norm(v0)\n Hv = hop(v0)\n a.append(v0.dot(Hv))\n v1 = Hv - a[0] * v0\n b.append(norm(v1))\n if b[0] < Min_b:\n return 0\n\n v1 = v1/b[0]\n Hv = hop(v1)\n a.append(v1.dot(Hv))\n\n for i in range(1, m - 1):\n v2 = Hv - b[i - 1] * v0 - a[i] * v1\n b.append(norm(v2))\n if abs(b[i]) < Min_b:\n b.pop()\n break\n\n v2 = v2/b[i]\n Hv = hop(v2)\n a.append(v2.dot(Hv))\n v0 = v1.copy()\n v1 = v2.copy()\n \n a = np.asarray(a)\n b = np.asarray(b)\n\n eps, phi = Tri_diag(a, b)\n l = len(eps)\n# Eo = eps[0]\n# eps = eps-Eo\n exp_eps = np.exp(-beta * eps)\n E = np.sum(exp_eps * eps * phi[0, :]**2.)\n Z = np.sum(exp_eps * phi[0, :]**2.)\n# for i in range(len(eps)):\n# E += exp_eps[i] * eps[i] * phi[0, i]**2\n\n# E = E + Eo\n# de = eps[:, np.newaxis] - eps\n# for i in range(l):\n# E += eps[i] * phi[0, i]**2./np.sum(np.exp(-beta*de[:l, i])*(phi[0, :l]**2.))\n return E, Z","def stockdon2006(H,L,B):\n \n # Make sure parameters are double\n H = np.double(H)\n L = np.double(L)\n B = np.double(B)\n \n # Compute incident swash (equation 11) \n incSwash = 1.1 / 2 * 0.75 * B * (H*L)**0.5\n \n # Infragravity swash (Equation 12)\n igSwash = 1.1 / 2 * 0.06 * (H*L)**0.5\n \n # Compute R2% (Equation 19)\n setup = 1.1 * 0.35 * B * ((H * L)**0.5)\n swash = 1.1 / 2.0 * (H*L * (0.563 * B**2 + 0.004))**0.5 \n r2 = setup + swash\n \n return r2,setup,incSwash,igSwash","def planck_w(lam, T):\n return ((2*h*c**2)/(lam**5))*(1./(np.exp((h*c)/(lam*k*T))-1))","def get_sn2005ek(colorplt=False):\n z = 0.016551\n ebv = 0.210\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_max = 53639.9\n print (\"adopt r band t_max from Drout+13\")\n \n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\t')\n # tb = tb.drop(columns=[\"Unnamed: 6\"])\n \n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\n 53654.2, 53655.2, 53656.2, 53657.2])\n \n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\n 21.74, np.nan, np.nan, np.nan])\n \n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\n 0.12, np.nan, np.nan, np.nan])\n \n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\n 20.60, 20.74, 20.88, 21.22])\n \n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \n 0.08, 0.10, 0.08, 0.13])\n \n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\n 20.08, np.nan, 20.47, np.nan])\n \n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\n 0.05, np.nan, 0.08, np.nan])\n\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \n 19.01, np.nan, 19.47, np.nan])\n \n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\n 0.05, np.nan, 0.06, np.nan])\n \n mymjds = np.hstack([mjds, mjds, mjds, mjds])\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\n myfilts = np.hstack([ np.repeat(\"B\", len(Bmags)),\n np.repeat(\"V\", len(Bmags)),\n np.repeat(\"R\", len(Rmags)),\n np.repeat(\"I\", len(Imags)) ])\n ix = ~np.isnan(mymags)\n tb = pd.DataFrame({'mjd': mymjds[ix],\n 'mag': mymags[ix],\n 'emag': myemags[ix],\n \"filter\": myfilts[ix]})\n \n ixB = tb['filter'].values==\"B\"\n ixV = tb['filter'].values==\"V\"\n ixR = tb['filter'].values==\"R\"\n ixI = tb['filter'].values==\"I\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixV] = 5430\n tb['wave'].values[ixR] = 6349\n tb['wave'].values[ixI] = 8797\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['B', 'R', 'I']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"R\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"B\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"R\"]\n itb = tbsub[tbsub[\"filter\"].values==\"I\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"BmR\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"RmI\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def Tloken(self, x):\n return 11.2 * (self.r500*0.7)**2. * ( 1. + 0.75*x)**(-1.6)","def B_func(Th33, Th1500):\n\n D = ln(Th33) - ln(Th1500)\n B = (ln(1500) - ln(33)) / D\n\n def lbd_func(C):\n \"\"\"return the slope of logarithmic tension-moisture curve\"\"\"\n if C == 0:\n return 0.0\n lbd = 1 / C\n return lbd\n\n return lbd_func(B)","def planckian(temp, wavelength):\n if wavelength==560: return 100.0\n if temp<60: temp=60 # For simplicity, in very low temperature\n num = wavelength**(-5)\n try:\n v=num / (math.exp(0.0143877687750393/(wavelength*(10**(-9))*temp)) - 1)\n except:\n print(temp)\n print(wavelength)\n raise ValueError\n v2=(560.0**(-5)) / (math.exp(0.0143877687750393/(560.0*(10**(-9))*temp)) - 1)\n return v*100.0/v2","def kervella(magB=None, magV=None, magK=None):\n if magB is None or np.isnan(magB) or magB > 49:\n magB = np.nan\n if magV is None or np.isnan(magV) or magV > 49:\n magV = np.nan\n if magK is None or np.isnan(magK) or magK > 49:\n magK = np.nan\n const1 = np.array([0.0755, 0.0535])\n const2 = np.array([0.5170, 0.5159])\n mag = np.array([magV, magB])\n vals = 10**(const1*(mag-magK)+const2-0.2*magK)\n diameter = {}\n if not np.isnan(vals[0]):\n diameter['V'] = vals[0]*u.mas\n if not np.isnan(vals[1]):\n diameter['B'] = vals[1]*u.mas\n return diameter","def ka_tf(Y,t,voltage_clamp_func,voltage_clamp_params):\n # g = gbar * n * h\n v = voltage_clamp_func(t,voltage_clamp_params)\n n = Y[0]\n h = Y[1]\n q10 = 1.0#3.3 # Preserved in case it is useful but disabled\n \n ninf = (1.0/(1.0 + np.exp(-(v+5.4+15)/16.4)))**4\n ntau = 0.25 + 10.04*np.exp((-(v+24.67)**2)/(2*34.8**2))*q10\n\t\t\n hinf = 1.0/(1.0 + np.exp((v+49.9 + 15.0)/4.6))\n htau = 20.0 + 50.0 * np.exp((-(v+40.0)**2)/(2.0*40.0**2))*q10\n \n # Trap for htau following Sheets /ChoiWaxman/Tigerholm - set it to 5 ms if less than 5 ms\n if htau < 5.0:\n htau = 5.0\n\n dn = (ninf-n)/ntau\n dh = (hinf-h)/htau\n \n return [dn,dh]","async def wetbulb(self, temp, humidity, pressure):\n t = float(temp)\n rh = float(humidity)\n p = float(pressure)\n\n # Variables\n edifference = 1\n twguess = 0\n previoussign = 1\n incr = 10\n es = 6.112 * math.exp(17.67 * t / (t + 243.5))\n e2 = es * (rh / 100)\n\n while (abs(edifference) > 0.005):\n ewguess = 6.112 * math.exp((17.67 * twguess) / (twguess + 243.5))\n eguess = ewguess - p * (t - twguess) * 0.00066 * (1 + (0.00115 * twguess))\n edifference = e2 - eguess\n if edifference == 0:\n break\n\n if edifference < 0:\n cursign = -1\n if (cursign != previoussign):\n previoussign = cursign\n incr = incr / 10\n else:\n incr = incr\n else:\n cursign = 1\n if (cursign != previoussign):\n previoussign = cursign\n incr = incr / 10\n else:\n incr = incr\n\n twguess = twguess + incr * previoussign\n\n return await self.temperature(twguess)","def fahrenheitToKelvin(fahrenheit:float, ndigits = 2)->float:\n return round(((float(fahrenheit) - 32) * 5 / 9) + 273.5, ndigits)","def compute_kinoshita(s):\n length = int(NBENDS*LAMBDA/DS) + 1\n x = np.zeros(length)\n y = np.zeros(length)\n cur = np.zeros(length+1)\n theta = THETA0*np.sin(2*np.pi*s/LAMBDA) \\\n + THETA0**3*(JS*np.cos(6*np.pi*s/LAMBDA) \\\n - JF*np.sin(6*np.pi*s/LAMBDA))\n theta[np.abs(theta)<ZERO] = 0\n for i in range(length):\n cossum, sinsum = 0, 0\n for j in range(i):\n cossum += DS*np.cos(theta[j])\n sinsum += DS*np.sin(theta[j])\n x[i] = 0 if np.abs(cossum) < ZERO else cossum\n y[i] = 0 if np.abs(sinsum) < ZERO else sinsum\n x = np.concatenate((x, np.array([x[-1]+x[1]-x[0]])))\n y = np.concatenate((y, np.array([y[-1]+y[1]-y[0]])))\n s = np.concatenate((s, np.array([s[-1]+DS])))\n theta = np.concatenate((theta, np.array([theta[-1]])))\n if FLIPSTRM:\n x = x[::-1]\n y = y[::-1]\n theta = np.concatenate((theta[::-1][1:], np.array([theta[0]])))\n for i in range(1, length):\n cur[i] = (theta[i]-theta[i-1])/DS\n cur[i] = 0 if np.abs(cur[i]) < ZERO else cur[i]\n cur[0], cur[-1] = cur[-2], cur[1]\n return s, x, y, cur, theta","def get_hbls_hbbl(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n z_u_r = self.grid_dict['z_u_r']\n u = self.u\n v = self.v\n \n v_upts = TTTW_func.v2u(v)\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\n\n\n\n # CALCULATE swr_frac\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\n\n\n # WHOLE THING HAPPENS IN j loop through y-indices\n \n # INITIALIZE ARRAYS\n self.kmo = np.zeros([Ly])\n self.Cr = np.zeros([Ly])\n self.kbl = np.empty([Ly],dtype='int')\n self.C_h_MO = np.zeros([Ly])\n self.Cr = np.zeros([Ly,N+1]) # sum term\n self.FC = np.zeros([Ly,N+1])\n self.swdk_r = np.zeros([Ly,N+1])\n \n self.zscale = np.zeros([Ly,N])\n self.Kern = np.zeros([Ly,N])\n\n \n # --> LOOP THROUGH Y-INDICES\n for j in range(Ly):\n if self.LIMIT_MO_DEPTH:\n self.kmo[j] = 0\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\n \n self.kbl[j] = 0\n self.Cr[j,-1] = 0 # set top Cr\n self.Cr[j,0] = 0 # set bottom Cr\n \n # SEARCH FOR MIXED LAYER DEPTH\n self.FC[j,-1] = 0.\n\n\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\n for k in range(N-1,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n \n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n self.zscale[j,k_w] = zscale\n if self.LMD_KPP:\n if self.LMD_BKPP:\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\n else:\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\n else:\n Kern = 1.\n \n\n\n self.Kern[j,k_w] = Kern\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\n\n\n #\t\tLOOP THAT FINDS BL DEPTH ##\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \n \n if self.LMD_KPP:\n #swdk_r only used in this function so don't need to be class attribute\n # but for testing make it an attribute to see what it is\n \n # fortran equivlanet ===> k=N,1,-1 \n for k in range(N,0,-1):\n # INDEX MAP\n k_r = k-1\n k_w = k\n\n ###################################################################### \n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\n \n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\n \n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\n \t\t\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\n \n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\n \n\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\n \n\n #######################################################################\n \n # SEARCH FOR hbls vertical level #\n '''\n kbl is specified at vertical w-level (via Cr which is at\n vertical w-levels)\n '''\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\n self.kbl[j] = k_w\n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\n self.kmo[j] = k_w\n\n \n #--> still in j-loop\n #######################################################\n \n # \t\tGET SURFACE BOUNDARY LAYER DEPTH # \n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\n if self.kbl[j] > 0:\n k_w = self.kbl[j]\n k_r = k_w - 1 \n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\n \n else:\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\n \n if self.LIMIT_MO_DEPTH:\n if self.kmo[j] > 0:\n k_w = self.kmo[j]\n k_r = k_w-1\n if k_w == N:\n z_up = z_u_w[j,N]\n cff_up = np.max([0,Bo[j]])\n else:\n z_up = z_r[j,k_w+1]\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\n \n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\n + self.C_h_MO[j] * (cff_dn - cff_up)\n\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\n\n\n\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\n if self.LMD_BKPP:\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\n self.Cr[j,0] = 0.\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\n \n #---> LOOP BOTTOM TO TOP\n # FIND kbl for BBL\n for k in range(1,N+1):\n k_r = k-1\n k_w = k \n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\n \n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\n self.kbl[j] = k_w \n \n\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\n if self.kbl[j] > 0 :\n k_w = self.kbl[j] \n k_r = k_w -1\n if k_w == 1: # NO BBL CASE\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\n else:\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]","def ksat_func(ThS, Th33, lbd):\n\n # assert ThS > Th33, \"sat <= fc IN ksat_func\"\n ksat = 1930 * (ThS - Th33) ** (3 - lbd)\n return ksat","def test_thermal_relaxation_error_kraus(self):\n t1, t2, time, p1 = (1, 2, 1, 0.3)\n error = thermal_relaxation_error(t1, t2, time, p1)\n circ, p = error.error_term(0)\n self.assertEqual(p, 1)\n self.assertEqual(circ[0]['name'], 'kraus')\n self.assertEqual(circ[0]['qubits'], [0])","def rad(tx,K,w,e,T0,Vo,P):\r\n\r\n M=2*np.pi*(tx-T0)/P #Mean anomaly\r\n E=np.pi\r\n for j in range(0,25):\r\n E=(M-e*(E*np.cos(E)-np.sin(E)))/(1-e*np.cos(E))\r\n th=2*np.arctan(((1+e)/(1-e))**0.5*np.tan(E/2))\r\n return K*(np.cos(th+w)+e*np.cos(w))+Vo","def kelvinToRankie(kelvin:float, ndigits = 2)->float:\n return round(float(fahrenheit)+ 459, ndigits)","def molar_mass_dry_air():\n return 28.9647","def whc_tot(mukey, layers=''):\n #read appropriate soils.in content to a python list\n mukey = str(mukey)\n soil_path = \"/data/paustian/ernie/SSURGO_master_script/soil_test2/\"\n soil_fpath = soil_path+mukey[:-3]+\"/\"+mukey+\".in\"\n cont = [[]]\n data_input = open(soil_fpath, 'r')\n for line in data_input:\n cont.append(line.split())\n del cont[0]\n\n #convert all entries in the 2D list to float format where possible, or zero in the case\n #of very small numbers recorded in scientific notation\n for k in range(len(cont)):\n for l in range(len(cont[k])):\n cont[k][l] = float(cont[k][l])\n\n #loop through list and compute the water holding capacity increment represented in \n #each line\n min_h2o_evap = 0\n min_h2o = 0\n max_h2o = 0\n whc = 0\n for i in range(len(cont)):\n if not layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n else:\n if 1+i <= layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n if layers:\n if layers > len(cont):\n print \"NOTE: specified layer limit exceeds number of layers found in soils.in file\"\n\n return whc, min_h2o, max_h2o","def flattop_risefall(t, params):\n risefall = tf.cast(params['risefall'].get_value(), dtype=tf.float64)\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\n t_up = risefall\n t_down = t_final - risefall\n return (1 + tf.math.erf((t - t_up) / risefall)) / 2 * \\\n (1 + tf.math.erf((-t + t_down) / risefall)) / 2","def hz2mel(hz):\r\n return 2595 * np.log10(1+hz/700.0)","def derive_Hosek18(wavelength):\n # Extinction law definition\n wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])\n A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50])\n \n\n # Following Hosek+18, Interpolate over the curve with cubic spline interpolation\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\n A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)\n\n # This curve already assumes A_Ks = 1.0, so we can go straight to\n # output \n return A_AKs_at_wave","def compute_windchill(t,v):\n a = 35.74\n b = 0.6215\n c = 35.75\n d = 0.4275\n v16 = v**0.16\n wci = a+(b*t)-(c*v16)+(d*t*v16)\n return wci","def KendallTau_calc(TP, FP, FN, TN):\n try:\n n = TP + FP + FN + TN\n return (2 * (TP + TN - FP - FN)) / (n * (n - 1))\n except Exception:\n return \"None\"","def T_naught(z, h, OM, OB):\n\n T0 = 28.5 * ((1.0+z)/10.0)**(0.5) * OB/0.042 * h/0.73 * (0.24/OM)**(0.5)\n return T0","def uL( wavelen, **kwargs ):\n Cc = C.c * 1e6 # speed of light um s^-1\n\n Gamma = kwargs.get('Gamma', 2*np.pi *5.9e6 ) # linewidth s^-1\n lambda0 = kwargs.get('lambda0', 0.671 ) # transition wavelength in microns \n \n omega0 = 2*np.pi*Cc / lambda0\n omegaL = 2*np.pi*Cc / wavelen\n intensity = 1.0 \n depthJ = (intensity)* -3*np.pi* Cc**2*Gamma / ( 2*omega0**3) * \\\n ( 1/(omega0 - omegaL ) + 1/(omega0 + omegaL ) ) # Joule\n depthuK = depthJ / C.k *1e6 # C.k is Boltzmann's constant\n return depthuK","def water_uptake_campbell(self, soil):\r\n daily_ref_evap_transp = soil.daily_ref_evap_transp\r\n root_hydr_cond = np.zeros(soil.total_layers)\r\n shoot_hydr_cond = np.zeros(soil.total_layers)\r\n plant_hydr_cond = np.zeros(soil.total_layers)\r\n root_activity = np.zeros(soil.total_layers)\r\n root_cond_adj = np.zeros(soil.total_layers)\r\n tot_root_cond_adj = 0\r\n salinity_factor = np.zeros(soil.total_layers)\r\n soil_water_pot_avg = 0\r\n WAT_POT_FIELD_CAP = -33\r\n\r\n # Transpiration\r\n self.pot_transp = daily_ref_evap_transp * self.light_intercpt\r\n self.max_pot_transp = (self.campbell_max_daily_transp *\r\n self.light_intercpt)\r\n self.expect_transp = min(self.pot_transp, self.max_pot_transp) # mm/day\r\n\r\n # Plant hydraulic conductance (kg s m-4)\r\n tot_plant_hydr_cond = (self.max_pot_transp /\r\n (WAT_POT_FIELD_CAP -\r\n self.leaf_water_pot_stress_onset))\r\n # assumption of 2/3 of plant hydraulic conductance is from roots\r\n tot_root_hydr_cond = tot_plant_hydr_cond / 0.65\r\n # assumption of 1/3 of plant hydraulic conductivity is from shoots\r\n tot_shoot_hydr_cond = tot_plant_hydr_cond / 0.35\r\n\r\n for lyr in soil.layers:\r\n root_activity[lyr] = 1\r\n salinity_factor[lyr] = 1\r\n root_cond_adj[lyr] = (root_activity[lyr] * self.root_fraction[lyr]\r\n * salinity_factor[lyr])\r\n root_hydr_cond[lyr] = tot_root_hydr_cond * root_cond_adj[lyr]\r\n tot_root_cond_adj += root_cond_adj[lyr]\r\n\r\n # Root, shoot and plant hydraulic conductance(kg s m-4)\r\n for lyr in soil.layers:\r\n if root_cond_adj[lyr] > 0:\r\n shoot_hydr_cond[lyr] = (tot_shoot_hydr_cond *\r\n root_cond_adj[lyr] / tot_root_cond_adj)\r\n plant_hydr_cond[lyr] = (root_hydr_cond[lyr] *\r\n shoot_hydr_cond[lyr] /\r\n (root_hydr_cond[lyr] +\r\n shoot_hydr_cond[lyr]))\r\n else:\r\n plant_hydr_cond[lyr] = 0\r\n\r\n tot_root_hydr_cond *= tot_root_cond_adj\r\n tot_plant_hydr_cond = ((tot_root_hydr_cond * tot_shoot_hydr_cond) /\r\n (tot_root_hydr_cond + tot_shoot_hydr_cond))\r\n\r\n if tot_plant_hydr_cond > 0:\r\n for lyr in soil.layers:\r\n soil_water_pot_avg += (soil.water_potential[lyr] *\r\n root_cond_adj[lyr])\r\n leaf_water_pot = (soil_water_pot_avg - self.expect_transp /\r\n tot_plant_hydr_cond)\r\n if leaf_water_pot < self.leaf_water_pot_stress_onset:\r\n leaf_water_pot = ((tot_plant_hydr_cond * soil_water_pot_avg *\r\n (self.leaf_water_pot_stress_onset -\r\n self.leaf_water_pot_wilt_point) +\r\n self.leaf_water_pot_wilt_point *\r\n self.expect_transp)\r\n / (tot_plant_hydr_cond *\r\n (self.leaf_water_pot_stress_onset -\r\n self.leaf_water_pot_wilt_point) +\r\n self.expect_transp))\r\n if leaf_water_pot < self.leaf_water_pot_wilt_point:\r\n leaf_water_pot = self.leaf_water_pot_wilt_point\r\n self.att_transp = 0\r\n transp_ratio = self.att_transp / self.expect_transp\r\n\r\n elif leaf_water_pot < self.leaf_water_pot_stress_onset:\r\n self.att_transp = (self.expect_transp * (leaf_water_pot -\r\n self.leaf_water_pot_wilt_point) / (\r\n self.leaf_water_pot_stress_onset -\r\n self.leaf_water_pot_wilt_point))\r\n transp_ratio = self.att_transp / self.expect_transp\r\n\r\n else:\r\n self.att_transp = self.expect_transp\r\n transp_ratio = 1\r\n # crop water uptake (kg/m2/d = mm/d)\r\n for lyr in soil.layers:\r\n self.water_uptake[lyr] = (plant_hydr_cond[lyr] *\r\n (soil.water_potential[lyr] -\r\n leaf_water_pot) * transp_ratio)\r\n if self.water_uptake[lyr] < 0:\r\n self.water_uptake[lyr] = 0\r\n self.crop_transp = self.water_uptake.sum() # mm/day\r\n self.cum_transp += self.crop_transp\r\n self.cum_pot_transp += self.expect_transp\r\n self.transp_ratio = self.crop_transp / self.expect_transp","def k_Ni00(wind_ms, temp_C):\n\n U = wind_ms\n\n Sc = schmidt_number(temp_C)\n k = (0.333 * U + 0.222 * U ** 2) * (600 / Sc) ** 0.5\n\n return k","def calc_equil(sst, ft_qv, use_NT=False):\n \n run_main(sst, ft_qv, use_NT)\n \n # grab csv file\n with open('dumpmodel.csv','r') as f:\n df_result=pd.read_csv(f)\n\n # last time step into named tupple\n out=df_result.iloc[-1]\n steady_state=make_tuple(out.to_dict())\n steady_state\n \n # obtain steady-state values\n dth=steady_state.deltheta\n dqt=steady_state.delqv\n thetal_m=steady_state.theta\n qt_m=steady_state.qv\n h=steady_state.h\n press=tf.find_press(steady_state.h) #kPa\n thetal_ft = steady_state.theta + dth\n qt_ft = steady_state.qv + dqt\n zb = steady_state.LCL\n zi = steady_state.h\n we = steady_state.went\n \n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\n gamma = 6e-3 \n thetal_3000 = thetal_ft + gamma*(3000-h)\n LTS = thetal_3000 - steady_state.theta\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\n\n # calculate LWP\n rho = 1.\n LWP = 0.5*rho*(zi-zb)**2\n \n # put all required variables into output array\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\n \n return out_array","def rt60_eyring(S, V, a, m, c):\n\n return -(24 * np.log(10) / c) * V / (S * np.log(1 - a) + 4 * m * V)","def bet(P_1,V0_1,Vi_1,Vflap_1,J_a,Twist,Pitch,Chord,PLE,Polar,rho,dt):\n Phi = np.zeros_like(Twist)\n V_4 = np.zeros_like(P_1)\n dV_4 = np.zeros_like(P_1)\n F_5 = np.zeros_like(P_1)\n F_4 = np.zeros_like(P_1)\n F_1 = np.zeros_like(P_1)\n l = np.zeros_like(Twist)\n d = np.zeros_like(Twist)\n m = np.zeros_like(Twist)\n aoa = np.zeros_like(Twist)\n daoa = np.zeros_like(Twist)\n cl = np.zeros_like(Twist)\n cd = np.zeros_like(Twist)\n cm = np.zeros_like(Twist)\n w = np.zeros_like(Chord)\n wy = np.zeros_like(Twist)\n S = np.zeros_like(Twist)\n \n Vflap_1[np.abs(Vflap_1) < 1e-8] = 0\n \n for i in range(len(P_1)):\n \n for j in range(len(P_1[i])):\n \n # Calculate wing element elevation angle\n \n if j <= J_a:\n \n dz = P_1[i,J_a,2]\n dy = P_1[i,J_a,1]\n\n Phi[i,j] = np.arctan(dz/dy)\n \n else:\n \n dz = P_1[i,-1,2]-P_1[i,J_a+1,2]\n dy = P_1[i,-1,1]-P_1[i,J_a+1,1]\n \n Phi[i,j] = np.arctan(dz/dy)\n \n # Calculate local flow velocity\n V_4[i,j] = roty((Twist[i,j]+Pitch[j]), rotx(-Phi[i,j],(V0_1 + Vi_1 + Vflap_1[i,j]))) \n V_4[np.abs(V_4) < 1e-12] = 0\n\n # Calculate angle of attack\n aoa[i,j] = np.arctan(-V_4[i,j,2]/V_4[i,j,0])\n\n # Find cl,cd,cm from polars with linear interpolation\n cl[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,1])\n cd[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,2])\n cm[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,3])\n\n # Calculate element width\n if j < len(P_1[i])-1:\n wy[i,j] = P_1[i,j+1,1]-P_1[i,j,1]\n else:\n wy[i,j] = P_1[i,j,1]-P_1[i,j-1,1]\n \n # Calculate element surface area\n S[i,j] = Chord[j] * wy[i,j]\n \n # Calculate aerodynamic forces\n l[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cl[i,j]\n d[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cd[i,j]\n m[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cm[i,j]\n \n # Force vector in Blade Element local axes\n F_5[i,j] = np.array([-d[i,j], 0, l[i,j]])\n \n # Force vector in Blade local axes\n F_4[i,j] = roty(aoa[i,j],F_5[i,j])\n \n \n \n \"\"\"\n Add mass effect and rotate to stroke plane axes\n \"\"\"\n for i in range(len(P_1)):\n \n for j in range(len(P_1[i])):\n # Add mass effects\n if i==0:\n daoa[i,j] = (aoa[1,j]-aoa[0,j])/dt\n dV_4[i,j] = (V_4[1,j]-V_4[0,j])/dt\n \n elif i < len(P_1)-1:\n daoa[i,j] = (aoa[i+1,j]-aoa[i-1,j])/(2*dt)\n dV_4[i,j] = (V_4[i+1,j]-V_4[i-1,j])/(2*dt)\n \n else:\n daoa[i,j] = (aoa[i,j]-aoa[i-1,j])/dt\n dV_4[i,j] = (V_4[i,j]-V_4[i-1,j])/dt\n \n \n F_4[i,j,0] = F_4[i,j,0] - 0.25 * rho * np.pi * Chord[j] * S[i,j] * V_4[i,j,2] * daoa[i,j]\n F_4[i,j,2] = F_4[i,j,2] + 0.25 * rho * np.pi * Chord[j] * S[i,j] * dV_4[i,j,2]\n \n # Tranform to Stroke Plane Axes\n F_1[i,j] = rotx(Phi[i,j],roty(-(Twist[i,j]+Pitch[j]),F_4[i,j]))\n\n return F_1","def eq_br_mo(k, K, kr, theta_eq, bist_ang_az, eq_azi, wind_dir, u_10, fetch, div, polarization):\r\n\r\n nk = k.shape[2]\r\n\r\n # Set azimuth to compute transfer function\r\n azimuth = np.linspace(-np.pi, np.pi, 37)\r\n\r\n # # Sea surface slope in the direction of incidence angle\r\n ni2 = GoM_ni2_func(kr, k, K, u_10, fetch, azimuth, div, wind_dir)\r\n\r\n nn = 89 * 2 * np.pi / 180\r\n ni = (np.arange(nk) * nn / nk).reshape(1, nk) - nn / 2\r\n ni = ni.reshape(nk, 1)\r\n ni = np.tan(ni)\r\n\r\n Br = np.zeros([div.shape[0], div.shape[1]])\r\n\r\n for ii in np.arange(ni2.shape[0]):\r\n for jj in np.arange(ni2.shape[1]):\r\n P = np.exp(-0.5 * (ni - np.mean(ni)) ** 2 / ni2[ii, jj]) / np.sqrt(2 * np.pi * ni2[ii, jj])\r\n # the range of the sea surface slope\r\n angle_index = np.logical_and(-3 * 180 * np.arctan(np.sqrt(ni2[ii,jj])) / np.pi < np.arctan(ni) * 180 / np.pi, np.arctan(ni) * 180 / np.pi < 3 * 180 * np.arctan(np.sqrt(ni2[ii,jj])) / np.pi)\r\n P = P[angle_index]\r\n nini = ni[angle_index]\r\n nnk = nini.shape[0]\r\n nini = nini.reshape(nnk, 1)\r\n # local incidence angle\r\n theta_l = np.abs(theta_eq[jj] - np.arctan(nini).reshape(nnk, 1))\r\n # geometric scattering coefficients [Plant 1997] equation 5,6\r\n eps_sin = np.sqrt(const.epsilon_sw-np.sin(theta_l)**2)\r\n # compute the wave number for bistatic geometry\r\n kbr = 2 * kr * np.sin(theta_l) * np.cos(bist_ang_az[jj]/2)\r\n kkbr = np.sort(kbr[:,0])\r\n T = Trans_func(kkbr, K[ii, jj], u_10[ii, jj], fetch, azimuth, div[ii, jj])[np.argsort(kbr[:,0])].reshape(nnk,1)\r\n Skb = B_int_single(kkbr.reshape(nnk, 1), u_10[ii, jj], fetch, eq_azi[jj])[np.argsort(kbr[:,0]), :] * (1+abs(T)) / kbr.reshape(nnk,1) ** 4\r\n Skb_pi = B_int_single(kkbr.reshape(nnk, 1), u_10[ii, jj], fetch, np.pi + eq_azi[jj])[np.argsort(kbr[:,0]), :] * (1+abs(T)) / kbr.reshape(nnk, 1) ** 4\r\n Skb_r = (Skb + Skb_pi) / 2 # Kudryavtsev 2003a equation 2\r\n if polarization == 'VV':\r\n G = np.cos(theta_l) ** 2 * (const.epsilon_sw - 1) * (\r\n const.epsilon_sw * (1 + np.sin(theta_l) ** 2) - np.sin(theta_l) ** 2) / (\r\n const.epsilon_sw * np.cos(theta_l) + eps_sin) ** 2\r\n G = np.abs(G) ** 2\r\n else:\r\n G = np.cos(theta_l) ** 2 * (const.epsilon_sw - 1) / (np.cos(theta_l) + eps_sin) ** 2\r\n G = np.abs(G) ** 2\r\n # pure Bragg scattering NRCS\r\n br0 = 16 * np.pi * kr ** 4 * G * Skb_r\r\n # Bragg scattering composite model\r\n BR = br0 * P.reshape(nnk, 1)\r\n # integral over kbr >= kd\r\n a = np.tan(theta_eq[jj] - const.d / (2 * np.cos(bist_ang_az[jj]/2)))\r\n b = np.tan(theta_eq[jj] + const.d / (2 * np.cos(bist_ang_az[jj]/2)))\r\n Br[ii, jj] = np.trapz(BR[nini <= a], nini[nini <= a]) + np.trapz(BR[nini >= b], nini[nini >= b])\r\n return Br","def calc_lhv(self):\n hf = {}\n hf['hydrogen'] = 0\n hf['methane'] = -74.85\n hf['ethane'] = -84.68\n hf['propane'] = -103.8\n hf['butane'] = -124.51\n hf['O2'] = 0\n hf['CO2'] = -393.5\n # water (gaseous)\n hf['H2O'] = -241.8\n\n lhv = 0\n\n for f, x in self.fuel.val.items():\n molar_masses[f] = CP.PropsSI('M', f)\n fl = set(list(hf.keys())).intersection(\n set([a.replace(' ', '') for a in CP.get_aliases(f)]))\n if len(fl) == 0:\n continue\n\n if list(fl)[0] in self.fuels():\n structure = fluid_structure(f)\n\n n = {}\n for el in ['C', 'H', 'O']:\n if el in structure:\n n[el] = structure[el]\n else:\n n[el] = 0\n\n lhv += (-(n['H'] / 2 * hf['H2O'] + n['C'] * hf['CO2'] -\n ((n['C'] + n['H'] / 4) * hf['O2'] +\n hf[list(fl)[0]])) / molar_masses[f] * 1000) * x\n\n return lhv","def ts_bt_func(thermal_rad, k1, k2):\n ts_bt = np.copy(thermal_rad).astype(np.float64)\n ts_bt[ts_bt <= 0] = np.nan\n np.reciprocal(ts_bt, out=ts_bt)\n ts_bt *= k1\n ts_bt += 1.0\n np.log(ts_bt, out=ts_bt)\n np.reciprocal(ts_bt, out=ts_bt)\n ts_bt *= k2\n return ts_bt.astype(np.float32)","def valley(self, h=2, b1=3, b2=3, b3=9):\n mua = 2 * h / self.sk\n mub = 2 * b3 / (b1+b2)\n muc = 5\n self.mu_valley = mu = min(mua, mub, muc)\n self.s_valley = self.mu_valley * self.Ce * self.Ct * self.sk\n print(f'The valley snow shape coefficient = {self.mu_valley :.2f}')\n print(f'The peak valley snow load = {self.s_valley :.2f}kPa')","def flattop_risefall_1ns(t, params):\n params['risefall'] = 1e-9\n return flattop_risefall(t, params)","def hz2mel(hz):\n return 2595 * np.log10(1+hz/700.)","def jarrow_rudd(s, k, t, v, rf, cp, am=False, n=100):\n\n\t# Basic Calculations\n\th = t / n \n\tu = math.exp((rf - 0.5 * math.pow(v, 2)) * h + v * math.sqrt(h))\n\td = math.exp((rf - 0.5 * math.pow(v, 2)) * h - v * math.sqrt(h))\n\tdrift = math.exp(rf * h)\n\tq = (drift - d) / (u - d)\n\n\t#Process the terminal stock price\n\tstkval = np.zeros((n+1, n+1))\n\toptval = np.zeros((n+1, n+1))\n\tstkval[0, 0] = s\n\n\tfor i in range(1, n+1):\n\t\tstkval[i, 0] = stkval[i - 1, 0] * u\n\t\tfor j in range(1, i + 1):\n\t\t\tstkval[i, j] = stkval[i - 1, j - 1] * d\n\n\t# Backwards recursion for option price\n\tfor j in range(n + 1):\n\t\toptval[n, j] = max(0, cp * (stkval[n, j] - k))\n\tfor i in range(n - 1, -1, -1):\n\t\tfor j in range(i + 1):\n\t\t\toptval[i, j] = (q * optval[i + 1, j] + (1 - q) * optval[i + 1, j + 1]) / drift\n\t\t\tif am:\n\t\t\t\toptval[i, j] = max(optval[i, j], cp * (stkval[i, j] - k))\n\treturn optval[0, 0]","def compute_thermo(E,dos,TT):\n if (len(dos)<3):\n print (\"Not enough points in the phonon DOS!\")\n return None\n \n ZPE = 0.5*dos_integral(E,dos,1)\n modes = dos_integral(E,dos)\n \n EvibT = np.zeros(len(TT))\n SvibT = np.zeros(len(TT))\n CvibT = np.zeros(len(TT))\n FvibT = np.zeros(len(TT))\n for i in range(0,len(TT)):\n h = 0.5*(E[2]-E[0])\n arg = K_BOLTZMANN_RY*TT[i]\n arg2 = 2.0 * arg\n Evib = 0.0\n Svib = 0.0\n Cvib = 0.0\n for j in range(0,len(dos)-3,3):\n\n Evib += 3.0*E[j]/tanh(E[j]/(arg2))*dos[j]+\\\n 3.0*E[j+1]/tanh(E[j+1]/(arg2))*dos[j+1]+\\\n 2.0*E[j+2]/tanh(E[j+2]/(arg2))*dos[j+2]\n \n Svib += 3.0*(E[j]/arg2/tanh(E[j]/arg2)-log(2.0*sinh(E[j]/arg2)))*dos[j]+\\\n 3.0*(E[j+1]/arg2/tanh(E[j+1]/arg2)-log(2.0*sinh(E[j+1]/arg2)))*dos[j+1]+\\\n 2.0*(E[j+2]/arg2/tanh(E[j+2]/arg2)-log(2.0*sinh(E[j+2]/arg2)))*dos[j+2]\n\n try: # avoid overflow error for arg very small\n Cvib += 3.0*pow(E[j]/arg,2)/( 4.0*pow(sinh(E[j]/(arg2)),2) )*dos[j]+\\\n 3.0*pow(E[j+1]/arg,2)/( 4.0*pow(sinh(E[j+1]/(arg2)),2) )*dos[j+1]+\\\n 2.0*pow(E[j+2]/arg,2)/( 4.0*pow(sinh(E[j+2]/(arg2)),2) )*dos[j+2]\n except:\n Cvib += 0.0\n\n EvibT[i] = h*0.5*Evib*3.0/8.0 # h is the integration step, 0.5 comes from the equation for E,\n # the factor 3.0/8.0 comes from the Simpson 3/8 rule\n SvibT[i] = h*K_BOLTZMANN_RY*Svib*3.0/8.0\n CvibT[i] = h*K_BOLTZMANN_RY*Cvib*3.0/8.0\n FvibT = EvibT - SvibT * TT\n\n print ()\n return TT, EvibT, SvibT, CvibT, FvibT, ZPE, modes","def overheads(NPT, DIT, NDIT):\n ov = 360. + 120. + NPT*NDIT*(DIT + 80. + 15.)\n print 'Telescope time in h = ', ov/3600.","def test_calculate_enthalpy(self):\n expected = [454734.6, 807677.3, 1171053.1]\n result = WetBulbTemperature()._calculate_enthalpy(\n self.mixing_ratio, self.specific_heat, self.latent_heat, self.temperature\n )\n self.assertArrayAlmostEqual(result, expected, decimal=1)","def lunarperigee(time):\n dtor = np.pi / 180\n t1 = 1 + time\n t2 = t1 * t1\n t3 = t2 * t1\n perigee = (\n 334.329653 * dtor\n + 4069.0340329575 * dtor * t1\n - 0.010325 * dtor * t2\n - 1.2e-5 * dtor * t3\n )\n return perigee","def hz2mel(hz):\n return 1127 * np.log(1 + hz / 700)","def slewEstimate(source1,source2=None,ant=1,returnUnit='time') :\n azel1 = azel(source1)\n azel1[0] = _wrapFix(azel1[0], ant)\n if source2 == None: \n azel2 = currentAzel(ant)\n else:\n azel2 = azel(source2)\n azel2[0] = _wrapFix(azel2[0], ant)\n az1 = [azel1[0], _wrapChoice(azel1[0], ant)]\n az2 = [azel2[0], _wrapChoice(azel2[0], ant)]\n print az1, az2\n deltaAz = 360\n deltaAz = min(deltaAz, abs(az1[0]-az2[0]))\n deltaAz = min(deltaAz, abs(az1[0]-az2[1]))\n deltaAz = min(deltaAz, abs(az1[1]-az2[0]))\n deltaAz = min(deltaAz, abs(az1[1]-az2[1]))\n delta = [deltaAz, abs(azel1[1]-azel2[1])]\n print delta\n if ant < 7:\n azRate = 60.0\n elRate = 30.0\n settleTime = 5.0\n elif ant < 16: \n azRate = 120.0\n elRate = 60.0\n settleTime = 10.0\n else: \n azRate = 60.0\n elRate = 30.0\n settleTime = 8.0\n if returnUnit == 'time': \n return max(delta[0]/azRate,delta[1]/elRate) + settleTime/60.0\n return delta","def hz2mel(hz):\n\treturn 2595 * numpy.log10(1 + hz / 700.0)","def derive_Fritz11(wavelength):\n # Extinction law definition\n wave = np.array([1.282, 1.736, 2.166, 2.625, 2.758, 2.873, 3.039, 3.297, 3.74, 3.819, 3.907, 4.052,\n 4.376, 5.128, 5.908, 6.772, 7.459, 7.502, 8.76, 12.371, 19.062])\n A_AKs = np.array([7.91, 4.30, 2.49, 1.83, 1.51, 1.84, 2.07, 1.66, 1.19, 1.19, 1.09, 1.01, 1.09, 0.99,\n 1.04, 0.84, 0.81, 0.79, 2.04, 1.34, 1.34])\n\n\n # Interpolate over the curve\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\n A_at_wave = interpolate.splev(wavelength, spline_interp)\n\n # We'll call 2.14 microns the K-band\n idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )\n A_AKs_at_wave = A_at_wave / A_at_wave[idx] \n\n return A_AKs_at_wave","def internal_heat_gain(dwelling):\n losses_gain = -40 * dwelling.Nocc\n water_heating_gains = (1000. / 24.) * dwelling.heat_gains_from_hw / DAYS_PER_MONTH\n\n mean_appliance_energy = 207.8 * (dwelling.GFA * dwelling.Nocc) ** 0.4714\n appliance_consumption_per_day = (mean_appliance_energy / 365.) * (\n 1 + 0.157 * numpy.cos((2. * math.pi / 12.) * (numpy.arange(12) - .78)))\n\n appliance_consumption = appliance_consumption_per_day * DAYS_PER_MONTH\n\n if dwelling.reduced_gains:\n met_gain = 50 * dwelling.Nocc\n cooking_gain = 23 + 5 * dwelling.Nocc\n appliance_gain = (0.67 * 1000. / 24) * appliance_consumption_per_day\n light_gain = 0.4 * dwelling.full_light_gain\n else:\n met_gain = 60 * dwelling.Nocc\n cooking_gain = 35 + 7 * dwelling.Nocc\n appliance_gain = (1000. / 24) * appliance_consumption_per_day\n light_gain = dwelling.full_light_gain\n\n total_internal_gains = (met_gain\n + light_gain\n + appliance_gain\n + cooking_gain\n + water_heating_gains\n + dwelling.pump_gain\n + losses_gain)\n\n if dwelling.reduced_gains:\n summer_met_gain = 60 * dwelling.Nocc\n summer_cooking_gain = 35 + 7 * dwelling.Nocc\n summer_appliance_gain = (1000. / 24) * appliance_consumption_per_day\n summer_light_gain = dwelling.full_light_gain\n total_internal_gains_summer = (summer_met_gain +\n water_heating_gains +\n summer_light_gain +\n summer_appliance_gain +\n summer_cooking_gain +\n dwelling.pump_gain +\n losses_gain\n - dwelling.heating_system_pump_gain)\n else:\n total_internal_gains_summer = total_internal_gains - dwelling.heating_system_pump_gain\n\n # Apply results to dwelling\n return dict(appliance_consumption=appliance_consumption,\n met_gain=met_gain,\n cooking_gain=cooking_gain,\n appliance_gain=appliance_gain,\n light_gain=light_gain,\n water_heating_gains=water_heating_gains,\n losses_gain=losses_gain,\n total_internal_gains=total_internal_gains,\n total_internal_gains_summer=total_internal_gains_summer)","def twostr_func(wavelength, F_s, solarzenithangle,albedo_dif, \n\t\t\talbedo_dir, temp_ground, w_0, g, tau_n, temp_c):\n\t\n\t########################\n\t###Import useful libraries\n\t########################\n\timport numpy as np\n\timport pdb\n\timport scipy.linalg\n\n\n\n\n\t########################\n\t###Define model parameters\n\t########################\n\t#Properties of the ground\n\temissivity_ground=1.-albedo_dif #emissivity of ground. 1=perfect BB emitter.\n\n\t#Optical depth structure\n\tNlayer=len(tau_n) #number of layers in the atmospheric model.\n\t\n\ttau_c=np.zeros(Nlayer+1)# tau_c[n] is the cumulative optical depth at the upper edge of layer n. So tau_c[0]=0, and tau_c[N] is the maximum possible.\n\tfor n in range(0, Nlayer):\n\t\ttau_c[n+1]=tau_c[n]+tau_n[n] \n\n\t#In the Toon formalism, j=0 corresponds to space, and j=N+1 corresponds to the planet surface.\n\t#These points in wavelength space define the edges of the bins in tau space. \n\t#Other terminology:\n\t#\ttau_c=cumulative optical depth of layers *above* layer n. \n\t#\ttau_n=total optical depth of the layer n\n\t#\ttau=total optical depth at any point within a layer n, hence satisfying 0<tau<tau_n\n\n\tmu_0=np.cos(solarzenithangle) #\"incident direction of solar beam\"\n\n\n\t########################\n\t###Determine the two-stream approximation coefficients.\n\t########################\n\t#Eddington and quadrature are good at solar wavelengths (i.e., not thermal blackbody dominated). delta scalings of Joseph et al (1976) recommended to replace w_0, g, tau in this case. However, when dominated by internal isotropic sources like the Planck function, hemispheric mean approximation is preferable. When w_0=0, quadrature case has problems. This happens esp at thermal wavelengths. Again this favors using hemispheric mean at these wavelengths\n\t\n\t#We use quadrature because 1) we are at solar wavelengths for this UV work and 2) that's what twostr.f does (which is our comparison case)\n\tgamma_1= np.sqrt(3.)*(2.-w_0*(1.+g))/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_1\n\tgamma_2=np.sqrt(3.)*w_0*(1.-g)/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_2\n\tgamma_3=(1.-np.sqrt(3.)*g*mu_0)/2. #consistent with Toon et al; equal to the Pierrehumbert gamma_plus/w_0\n\tgamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\n\tmu_1=1./np.sqrt(3.)+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\n\n\t##Eddington\n\t#gamma_1= (7.-w_0*(4.+3.*g))/4.\n\t#gamma_2=-1.*(1.-w_0*(4.-3.*g))/4.\n\t#gamma_3=(2.-3.*g*mu_0)/4.\n\t#gamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\n\t#mu_1=1./2.+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\n\n\talambda=np.sqrt(np.abs(gamma_1*gamma_1-gamma_2*gamma_2)) #this is the lower-case lambda, from eqn 21 of Toon et al\n\t\t\t\t\t\t\t\t #The absolute value was added based on the code Toon just sent us. This corresponds to his AK(L,J) parameter. But it should not matter since gamma_1>gamma_2 for w_0<1.\n\tclambda=(gamma_1-alambda)/(gamma_2) #this is the upper-case lambda, from eqn 22 of Toon et al\n\n\tEMLT=np.exp(-alambda*tau_n) #this appears to be a prefactor used to facilitate computation of eqn 44 of Toon et al\n\te1=1.+clambda*EMLT\n\te2=1.-clambda*EMLT\n\te3=clambda+EMLT\n\te4=clambda-EMLT\n\n\t########################\n\t###Set up calculation\n\t########################\n\t\"\"\"\n\tThe fundamental equation we are solving is of form:\n\tA_{l}*Y_{l-1}+B_{l}*Y_{l}+D{l+1}=E_{l} (equation 39 of Toon et al)\n\tHere, A_l, B_l, D_l, E_l are quantities we determine, and the Y_l is what we solve for.\n\tHence, we can summarize that we are solving a matrix equation that takes form:\n\tPY=E\n\twhere Y[l]=Y_l\n\t E[l]=E_l\n\t P[l, l-1]=A_l [row, column]\n\t P[l, l]=B_l\n\t P[l, l+1]=D_l\n\t P[i,j]=0 else\n\tToon et al use 1-indexing. Hence n runs from 1 to N, l runs from 1 to 2N, where N is the number of layers, and they have:\n\tY_l=Y_{1n} for l=1,3,5,...2n-1...2N-1\n\tY_l=Y_{2n} for l=2,4,6,...2n...2N\n\n\tHowever, we use Python, which has 0-indexing. Hence *we* choose that n runs from 0 to N-1, l runs from 0 to 2N-1, and:\n\tY_l=Y_{1n} for l=0,2,4...2n...2N-2\n\tY_l=Y_{2n} for l=1,3,5...2n+1...2N-1\n\n\tThe Y_{1n} and Y_{2n} are related to F^+_n and F^-_n via equations 31 and 32 of Toon et al.\n\tThis parametrization has been done to remove exponentials with positive operands (ie ones that could grow large and lead to numerical instabilities) from the matrix.\n\n\tNote: The mapping of this PQ=R to the F+ and F- is unclear because of 1) this parametrization in terms of Y_l (done to eliminate numerical instabilities) and 2)further linear combinations done to convert a pentagiagonal matrix to an even simpler tridiagonal matrix. Hence intuitive checks are hard.\n\t\"\"\"\n\n\t########################\n\t###Set up surface flux\n\t########################\n\tS_sfc=albedo_dir*mu_0*np.exp(-tau_c[-1]/mu_0)*np.pi*F_s+emissivity_ground*np.pi*Planck(temp_ground, wavelength)\n\t#Surface emission. Formed by adding blackbody emission from the ground to the reflected energy from the direct beam. The direct beam's reflected energy is assumed to be purely diffuse. This corresponds to equations 37 and 38 of Toon et al. Note that this does NOT match equation 5.31 of Pierrehumbert because it does not include the reflected diffuse radiation. So, this implicitly assumes the diffuse albedo to be 0. \n\n\t########################\n\t###Set up C-values\n\t########################\n\t#In the reshuffled set of parameters used in this formalism, these seem analagous to the forcing term in Pierrehumbert. All the added radiation is contained in here.\n\n\tdef C_plus(n, tau): #implementation of superposition of eqns 23 and 27 from Toon et al\n\t\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\n\t\tsolarrad_prefactor=w_0[n]*F_s*np.pi\n\t\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\n\t\tsolarrad_factor=((gamma_1[n]-1./mu_0)*gamma_3[n]+gamma_4[n]*gamma_2[n])\n\t\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\n\t\t\n\t\tblackbody_prefactor=2*np.pi*mu_1[n]\n\t\tB0n=Planck(temp_c[n], wavelength)\n\t\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\n\t\tblackbody_factor=B0n+B1n*(tau+1./(gamma_1[n]+gamma_2[n]))\n\t\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \n\t\t\n\t\tresult=solarrad+blackbody\n\t\treturn result\n\n\tdef C_minus(n, tau): #implementation of superposition of eqns 24 and 27 from Toon et al\n\t\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\n\t\tsolarrad_prefactor=w_0[n]*F_s*np.pi\n\t\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\n\t\tsolarrad_factor=((gamma_1[n]+1./mu_0)*gamma_4[n]+gamma_3[n]*gamma_2[n])\n\t\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\n\t\t\n\t\tblackbody_prefactor=2*np.pi*mu_1[n]\n\t\tB0n=Planck(temp_c[n], wavelength)\n\t\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\n\t\tblackbody_factor=B0n+B1n*(tau-1./(gamma_1[n]+gamma_2[n]))\n\t\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \n\t\t\n\t\tresult=solarrad+blackbody\n\t\treturn result\n\n\t########################\n\t###Calculate matrix coefficients\n\t#########################\n\t#initialize the A, B, D, and E.\n\tA=np.zeros(Nlayer*2)\n\tB=np.zeros(np.shape(A))\n\tD=np.zeros(np.shape(A))\n\tE=np.zeros(np.shape(A))\n\n\n\t#For l=0 (n=0) we have the boundary condition that the downward diffuse flux at the top of the first layer is equal to any incident diffuse downward flux. We set this to be zero.\n\tA[0]=0.\n\tB[0]=e1[0]\n\tD[0]=-1.*e2[0]\n\tE[0]=0.-1*C_minus(0,0) #This is really F_minus[0,0], i.e. we are assuming there is no downward diffuse flux from the top of the atmosphere.\n\n\t#for l=2N-1 (n=N-1), we have the boundary condition that the upward flux at the surface is the sume of the reflected downward diffuse flux and energy from any other sources (e.g. reflected direct beam, BB emission of the ground)/np.sqrt(3.)\n\tA[2*Nlayer-1]=e1[Nlayer-1]-albedo_dif*e3[Nlayer-1]\n\tB[2*Nlayer-1]=e2[Nlayer-1]-albedo_dif*e4[Nlayer-1]\n\tD[2*Nlayer-1]=0.\n\tE[2*Nlayer-1]=S_sfc-C_plus(Nlayer-1, tau_n[Nlayer-1])+albedo_dif*C_minus(Nlayer-1, tau_n[Nlayer-1])\n\n\t#There is a problem in the Toon paper. As written, the l=2n depends on e_n+1, running over the array edge. twostr.f resolves this by adopting a different mapping: their definition reduces to defining l=2(n+1) and running n from 0 to N-1. In this case, l=2 (The third value in the list of ls) depends on n=0 and n=1. This eliminates the overflow problem. We have implemented this below.\n\t\n\t##For n=1,2,3...N-1, l=2,4,6,...2N-2:\n\tfor n in range(0, Nlayer-1):\n\t\tl=2*(n+1)\n\t\tA[l]=e2[n]*e3[n]-e4[n]*e1[n]\n\t\tB[l]=e1[n]*e1[n+1]-e3[n]*e3[n+1]\n\t\tD[l]=e3[n]*e4[n+1]-e1[n]*e2[n+1]\n\t\t\n\t\tE[l]=e3[n]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))+e1[n]*(C_minus(n,tau_n[n])-C_minus(n+1,0.))\n\n\n\t#For n=0...N-2, l=1,3...2N-3:\n\tfor n in range(0, Nlayer-1):\n\t\tl=2*n+1\n\t\tA[l]=e2[n+1]*e1[n]-e3[n]*e4[n+1]\n\t\tB[l]=e2[n]*e2[n+1]-e4[n]*e4[n+1]\n\t\tD[l]=e1[n+1]*e4[n+1]-e2[n+1]*e3[n+1]\n\t\t\n\t\tE[l]=e2[n+1]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))-e4[n+1]*(C_minus(n+1, 0)-C_minus(n, tau_n[n])) #twostr.f has a -1*e_{4,n+1}. We have applied the same even though this is NOT what is written in the Toon et al paper. We have done this because Toon told us (6/26/2015) that there are some sign errors in the coefficients, and we currently trust the validated CLIMA code over the paper we know has errors in it. EDIT: Looking at the code Toon shared with us, he does the same. \n\n\n\t########################\n\t###Assemble matrix equation components\n\t#########################\n\tP=np.zeros([Nlayer*2,Nlayer*2])\n\n\t#l=0: no \"A\" coefficient b/c l-1 has no meaning\n\tP[0,0]=B[0]\n\tP[0,1]=D[0]\n\n\t#l=2N-1: no \"D\" coefficient b/c l+1 has no meaning\n\tP[2*Nlayer-1,2*Nlayer-1-1]=A[2*Nlayer-1]\n\tP[2*Nlayer-1,2*Nlayer-1]=B[2*Nlayer-1]\n\n\tfor l in range(1, Nlayer*2-1): #This populates the matrix P in PY=E. \n\t\tP[l, l-1]=A[l]\n\t\tP[l,l]=B[l]\n\t\tP[l,l+1]=D[l]\n\n\t########################\n\t###Invert matrix\n\t#########################\n\t#Y=np.linalg.solve(P, E) #this is the Y_l\n\t\n\t#try using a specialized solver\n\tab=np.zeros([3,2*Nlayer])\n\tab[0,:]=np.append(0.0, np.diag(P, k=1))\n\tab[1,:]=np.diag(P, k=0)\n\tab[2,:]=np.append(np.diag(P, k=-1),0.0)\n\t#pdb.set_trace()\n\tY=scipy.linalg.solve_banded((1,1), ab, E) #this is the Y_l\n\n\n\t########################\n\t###Convert from Y_l to Y_1n, Y_2n\n\t#########################\n\t#The Y_1n as defined in Toon et al correspond to l=1,3, 5...2N-1. Adjusting for the zero-indexing of Python as we have done, they instead correspond to l=0,2,...2N-2\n\t#The Y_2n as defined in Toon et al correspond to l=2,4,6...2N. Adjusting for Python zero-indexing as we have done, they instead correspond to l=1,3,5...2N-1.\n\t#For detail, see eq. 40.\n\tY_1=np.zeros(Nlayer)\n\tY_2=np.zeros(Nlayer)\n\tfor n in range(0, Nlayer):\n\t\tY_1[n]=Y[2*n]\n\t\tY_2[n]=Y[2*n+1] \n\t\t#last number called is Nlayer-1=N-1, so is consistent.\n\t\n\t########################\n\t###Convert from Y_1n, Y_2n to F_plus, F_minus\n\t#########################\n\tdef F_plus(n,tau): #defined from Eqn 31 of Toon et al.\n\t\tterm1=Y_1[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))+clambda[n]*np.exp(-alambda[n]*tau))\n\t\tterm2=Y_2[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))-clambda[n]*np.exp(-alambda[n]*tau))\n\t\tterm3=C_plus(n,tau)\n\t\t\n\t\tresult=term1+term2+term3\n\t\treturn result\n\n\tdef F_minus(n, tau): #defined from Eqn 32 of Toon et al.\n\t\tterm1=Y_1[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))+np.exp(-alambda[n]*tau))\n\t\tterm2=Y_2[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))-np.exp(-alambda[n]*tau))\n\t\tterm3=C_minus(n,tau)\n\t\t\n\t\tresult=term1+term2+term3\n\t\treturn result\n\t\n\t########################\n\t###Evaluate F_plus, F_minus at boundary edges\n\t#########################\n\tF_plus_tau0=np.zeros(np.shape(tau_n))\n\tF_plus_taumax=np.zeros(np.shape(tau_n))\n\tF_minus_tau0=np.zeros(np.shape(tau_n))\n\tF_minus_taumax=np.zeros(np.shape(tau_n))\n\n\tfor n in range(0, Nlayer):\n\t\tF_plus_tau0[n]=F_plus(n, 0.)\n\t\tF_plus_taumax[n]=F_plus(n, tau_n[n])\n\t\tF_minus_tau0[n]=F_minus(n, 0.)\n\t\tF_minus_taumax[n]=F_minus(n, tau_n[n])\n\n\n\t########################\n\t###Convert from Y_1n, Y_2n to F_net, mean intensity.\n\t#########################\n\t#test if diffuse flux dominates over direct flux. If direct flux dominant, instead set mu_1=mu_0\n\t\n\t#if F_minus_taumax[-1]<mu_0*np.pi*F_s*np.exp(-tau_c[-1]/mu_0):\n\t\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\n\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\n\t\n\tF_net=np.zeros(np.shape(tau_n)) #defined from Eqn 48 of Toon et al. This quantity is the net flux at the BASE of layer n.\n\tfor n in range(0, Nlayer):\n\t\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\n\n\t\tterm1=Y_1[n]*(e1[n]-e3[n])\n\t\tterm2=Y_2[n]*(e2[n]-e4[n])\n\t\tterm3=C_plus(n, tau_n[n])-C_minus(n, tau_n[n])\n\t\t\n\t\tF_net[n]=term1+term2+term3 -direct\n\n\tAMEAN=np.zeros(np.shape(tau_n)) #defined from Eqn 49 of Toon et al. This is the equivalent of the quantity AMEAN in the twostr.f code. It is equal to 4*np.pi*J_n, where J_n is the mean intensity at the base of layer n. Hence this quantity AMEAN should be equal to the total intensity received by a point at the base of layer n. \n\tfor n in range(0, Nlayer):\n\t\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\n\t\n\t\tterm1=Y_1[n]*(e1[n]+e3[n])\n\t\tterm2=Y_2[n]*(e2[n]+e4[n])\n\t\tterm3=C_plus(n, tau_n[n])+C_minus(n, tau_n[n])\n\t\t\n\t\t#AMEAN[n]=(1./mu_1[n])*(term1+term2+term3)+direct/mu_0\t\n\t\tAMEAN[n]=(1./mu_1[n])*(F_plus_taumax[n]+F_minus_taumax[n])+direct/mu_0\t\n\t\n\t########################\n\t###Compute \"surface intensity\"\n\t#########################\t\n\t#\"Surface intensity\" refers to the total intensity that would be intercepted by a particle at the surface of the planet. Whereas the total intensity is equal to (F_plus[-1]+F_minus[-1])/mu_1+direct[-1]/mu_0, the surface intensity is instead equal to (F_minus[-1])/mu_1+direct[-1]/mu_0, i.e. the downwelling diffuse intensity (since the bottom intensity is cut out due to there being a planet there) plus the direct intensity\n\t\n\tsurface_intensity=(F_minus_taumax[-1]/mu_1[-1])+(np.pi*F_s)*np.exp(-(tau_c[-1])/mu_0)\n\t\n\t########################\n\t###Return Result\n\t#########################\n\t#F_minus_tau0\n\t#np.max(np.abs((F_minus_taumax[:-1]-F_minus_tau0[1:]))/F_minus_tau0[1:])\n\t#np.max(np.abs((F_plus_taumax[:-1]-F_plus_tau0[1:]))/F_plus_tau0[1:])\n\t\n\treturn (F_plus_tau0, F_plus_taumax, F_minus_tau0, F_minus_taumax, F_net, AMEAN, surface_intensity)","def hz2mel(hz):\n return 1127 * numpy.log(1+hz/700.0)","def DeltaT_EspenakMeeus(ut):\n # Fred Espenak writes about Delta-T generically here:\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltaT.html\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltat2004.html\n # He provides polynomial approximations for distant years here:\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltatpoly2004.html\n # They start with a year value 'y' such that y=2000 corresponds\n # to the UTC Date 15-January-2000. Convert difference in days\n # to mean tropical years.\n\n y = 2000 + ((ut - 14) / _DAYS_PER_TROPICAL_YEAR)\n\n if y < -500:\n u = (y - 1820) / 100\n return -20 + (32 * u*u)\n\n if y < 500:\n u = y / 100\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3\n return 10583.6 - 1014.41*u + 33.78311*u2 - 5.952053*u3 - 0.1798452*u4 + 0.022174192*u5 + 0.0090316521*u6\n\n if y < 1600:\n u = (y - 1000) / 100\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3\n return 1574.2 - 556.01*u + 71.23472*u2 + 0.319781*u3 - 0.8503463*u4 - 0.005050998*u5 + 0.0083572073*u6\n\n if y < 1700:\n u = y - 1600\n u2 = u*u; u3 = u*u2\n return 120 - 0.9808*u - 0.01532*u2 + u3/7129.0\n\n if y < 1800:\n u = y - 1700\n u2 = u*u; u3 = u*u2; u4 = u2*u2\n return 8.83 + 0.1603*u - 0.0059285*u2 + 0.00013336*u3 - u4/1174000\n\n if y < 1860:\n u = y - 1800\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3; u7 = u3*u4\n return 13.72 - 0.332447*u + 0.0068612*u2 + 0.0041116*u3 - 0.00037436*u4 + 0.0000121272*u5 - 0.0000001699*u6 + 0.000000000875*u7\n\n if y < 1900:\n u = y - 1860\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3\n return 7.62 + 0.5737*u - 0.251754*u2 + 0.01680668*u3 - 0.0004473624*u4 + u5/233174\n\n if y < 1920:\n u = y - 1900\n u2 = u*u; u3 = u*u2; u4 = u2*u2\n return -2.79 + 1.494119*u - 0.0598939*u2 + 0.0061966*u3 - 0.000197*u4\n\n if y < 1941:\n u = y - 1920\n u2 = u*u; u3 = u*u2\n return 21.20 + 0.84493*u - 0.076100*u2 + 0.0020936*u3\n\n if y < 1961:\n u = y - 1950\n u2 = u*u; u3 = u*u2\n return 29.07 + 0.407*u - u2/233 + u3/2547\n\n if y < 1986:\n u = y - 1975\n u2 = u*u; u3 = u*u2\n return 45.45 + 1.067*u - u2/260 - u3/718\n\n if y < 2005:\n u = y - 2000\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3\n return 63.86 + 0.3345*u - 0.060374*u2 + 0.0017275*u3 + 0.000651814*u4 + 0.00002373599*u5\n\n if y < 2050:\n u = y - 2000\n return 62.92 + 0.32217*u + 0.005589*u*u\n\n if y < 2150:\n u = (y-1820)/100\n return -20 + 32*u*u - 0.5628*(2150 - y)\n\n # all years after 2150\n u = (y - 1820) / 100\n return -20 + (32 * u*u)","def _microstrip_v_with_Lk(wire_width, dielectric_thickness, eps_r, Lk_per_sq):\n L_m, C_m = _microstrip_LC_per_meter(wire_width,\n dielectric_thickness,\n eps_r)\n Lk_m = Lk_per_sq * (1.0/wire_width)\n v = 1 / sqrt((L_m+Lk_m) * C_m)\n return v","def isDeboutHandCoded( sk, bOnlyTorso = False, bVerbose = False ):\n \n neck = sk.listPoints[Skeleton.getNeckIndex()]\n \n if bVerbose: print(\"neck: %s\" % str(neck))\n \n legsInfo = sk.getLegs()\n if bVerbose: print(\"legs: %s\" % str(legsInfo))\n \n bb = sk.getBB_Size()\n sto = sk.getStomach()\n \n\n \n rh,rk,ra = legsInfo[0] # hip, knee, ankle\n lh,lk,la = legsInfo[1]\n \n avgFeets = [ ra[0]+la[0],ra[1]+la[1],ra[2]+la[2] ]\n div2(avgFeets)\n \n lal = sk.getArms()\n if bVerbose: print(\"arms: %s\" % str(lal))\n rs,re,rw = lal[0] #shoulder, elbow, wrist\n ls,le,lw = lal[1]\n \n rThreshold = 0.2\n \n #~ # si les pieds sont plus bas que les hanches\n # a essayer: orientation cou/(estomac ou moyenne des hanches): vertical => debout; sinon couche\n # a essayer: quand les fesses sont sur le sol\n \n # NB: on n'arrivera jamais a voir que quelqu'un qui est assis ou couche' oriente' vers la camera est tombe'\n \n # si les mains ou a defaut les coudes sont plus hautes que les pieds ou a defaut les hanches\n bDeboutFromArmsLegsHeight = None\n \n if rw[2] > rThreshold:\n rHi = rw[:2]\n elif re[2] > rThreshold:\n rHi = re[:2]\n else:\n rHi = None\n\n if lw[2] > rThreshold:\n lHi = lw[:2]\n elif le[2] > rThreshold:\n lHi = le[:2]\n else:\n # check le neck\n if neck[2] > rThreshold:\n lHi = neck[:2]\n else:\n lHi = None\n \n if lHi != None or rHi != None:\n \n if lHi == None:\n hi = rHi\n elif rHi == None:\n hi = lHi\n else:\n hi = avg2(rHi,lHi)\n \n \n \n if ra[2] > rThreshold:\n rLo = ra[:2]\n elif rk[2] > rThreshold:\n rLo = rk[:2]\n else:\n rLo = None\n\n if la[2] > rThreshold:\n lLo = la[:2]\n elif lk[2] > rThreshold:\n lLo = lk[:2]\n else:\n lLo = None\n \n if lLo != None or rLo != None:\n\n if lLo == None:\n lo = rLo\n elif rLo == None:\n lo = lLo\n else:\n lo = avg2(rLo,lLo)\n \n if bVerbose: print(\"rLo:%s,lLo:%s\" % (rLo,lLo) )\n \n if bVerbose: print(\"hi:%s,lo:%s\" % (hi,lo) )\n \n #~ return hi[1]<lo[1] # add a margin ?\n \n bb = sk.getBB_Size()\n rMargin = bb[1]/4\n\n \n bDeboutFromArmsLegsHeight = hi[1]+rMargin<lo[1] # WRN: pixel Y are inverted (high pixel are smaller than lower)\n \n if bVerbose: print(\"rMargin:%5.2f, bDeboutFromArmsLegsHeight: %s\"% (rMargin,bDeboutFromArmsLegsHeight) )\n\n bDeboutFromTorsoAngle = None\n if (rh[2] > rThreshold or lh[2] > rThreshold) and neck[2] > rThreshold:\n if (rh[2] > rThreshold and lh[2] > rThreshold):\n avg_hip = avg2(rh,lh)\n elif rh[2] > rThreshold:\n avg_hip = rh\n else:\n avg_hip = lh\n dx = avg_hip[0]-neck[0]\n dy = avg_hip[1]-neck[1]\n if abs(dx) < 0.1:\n coef = dy*10\n else:\n coef = dy/dx\n bDeboutFromTorsoAngle = abs(coef) > 1. # 1: diagonal\n if bVerbose: print(\"coef: %5.1f (dy:%3.1f,dx:%3.1f), bDeboutFromTorsoAngle: %s\" % (coef,dy, dx, bDeboutFromTorsoAngle) )\n #~ else:\n #~ return None\n \n # fesses sur le sol\n bNotBumOnGround = None\n if rh[2] > rThreshold and lh[2] > rThreshold:\n avg_hip = avg2(rh,lh)\n elif rh[2] > rThreshold:\n avg_hip = rh\n elif lh[2] > rThreshold:\n avg_hip = lh\n else:\n avg_hip = None\n if avg_hip != None:\n # look for lower point in legs, but not hip:\n rLowest = -10000\n #~ for i in range(cv2_openpose.Skeleton.NBR_POINTS):\n for i in [cv2_openpose.Skeleton.RKNEE,cv2_openpose.Skeleton.LKNEE,cv2_openpose.Skeleton.RANKLE,cv2_openpose.Skeleton.LANKLE]:\n if i == cv2_openpose.Skeleton.RHIP or i == cv2_openpose.Skeleton.LHIP:\n continue\n if sk.listPoints[i][2] < rThreshold:\n continue\n if sk.listPoints[i][1] > rLowest:\n rLowest = sk.listPoints[i][1]\n if rLowest >= 0:\n #~ lenLimbs = sk.getLenLimbs()\n #~ if bVerbose: print(\"lenLimbs: %s\" % str(lenLimbs) )\n #~ rLenLegs = (lenLimbs[0][0] +lenLimbs[1][0]) / 2\n rLenLegs = sk.getAvgLenLeg()\n if rLenLegs != None:\n bNotBumOnGround = (avg_hip[1] + (rLenLegs*0.75)) < rLowest\n if bVerbose: print(\"avg hip: %5.1f, lowest: %5.1f, rLenLegs: %5.1f, bNotBum: %s\" % (avg_hip[1],rLowest,rLenLegs, bNotBumOnGround) )\n if legsInfo[0][2][2] < rThreshold and legsInfo[1][2][2] < rThreshold:\n if bVerbose: print(\"INF: no foot seen, reseting bNotBumOnGround\" )\n # on ne voit aucun pied, soit ils ne sont pas a l'ecran soit il sont derriere, dans le doute, on prefere dire None\n bNotBumOnGround = None\n #~ return bNotBumOnGround\n \n # on veut etre sur => si hesitation, ne se prononces pas\n if 0:\n if not bOnlyTorso:\n if bDeboutFromArmsLegsHeight != bDeboutFromTorsoAngle:\n return None\n\n \n #~ if bDeboutFromArmsLegsHeight:\n if bDeboutFromTorsoAngle == None and bNotBumOnGround:\n return 1\n \n if bDeboutFromTorsoAngle and bNotBumOnGround:\n return 1\n\n \n if bDeboutFromTorsoAngle == None and bNotBumOnGround == None and bDeboutFromArmsLegsHeight != None:\n return bDeboutFromArmsLegsHeight\n \n if bDeboutFromTorsoAngle != None and bNotBumOnGround == None:\n return bDeboutFromTorsoAngle\n \n return 0\n \n \n else:\n if bDeboutFromTorsoAngle == bNotBumOnGround and bDeboutFromTorsoAngle == bDeboutFromArmsLegsHeight:\n return bDeboutFromTorsoAngle\n if bNotBumOnGround != None:\n return bNotBumOnGround\n if bDeboutFromTorsoAngle != None:\n return bDeboutFromTorsoAngle \n if bDeboutFromArmsLegsHeight != None:\n return bDeboutFromArmsLegsHeight\n return None","def BraggEnergy(ID,hkl,twotheta):\n ID=goodID(ID)\n d=dSpace(ID,hkl)\n l=2*d*sind(twotheta/2.0)\n E=lam2E(l)\n return E","def getWNSA1(ChargeSA):\n temp = 0.0\n for i in ChargeSA:\n temp = temp+i[2]\n if temp == 0.0:\n return 0.0\n\n return getPNSA1(ChargeSA)*temp/1000","def le_calibration_func(etr, kc, ts):\n return etr * kc * (2.501 - 2.361E-3 * (ts - 273)) * 2500 / 9","def RiekeLebofsky85(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def leff(self):\n with Vega() as v:\n s = self.reinterp(v.wavelength)\n w = s._wavelength\n if s.transmit.max() > 0:\n leff = np.trapz(w * s.transmit * v.flux.value, w, axis=-1)\n leff /= np.trapz(s.transmit * v.flux.value, w, axis=-1)\n else:\n leff = float('nan')\n if s.wavelength_unit is not None:\n leff = leff * Unit(s.wavelength_unit)\n if self.wavelength_unit is not None:\n return leff.to(self.wavelength_unit)\n return leff\n else:\n return leff","def test_double_ended_ols_wls_estimate_synthetic():\n from dtscalibration import DataStore\n import numpy as np\n\n np.random.seed(0)\n\n cable_len = 100.\n nt = 50\n time = np.arange(nt)\n x = np.linspace(0., cable_len, 100)\n ts_cold = np.ones(nt) * 4.\n ts_warm = np.ones(nt) * 20.\n\n C_p = 15246\n C_m = 2400.\n dalpha_r = 0.0005284\n dalpha_m = 0.0004961\n dalpha_p = 0.0005607\n gamma = 482.6\n cold_mask = x < 0.5 * cable_len\n warm_mask = np.invert(cold_mask) # == False\n temp_real = np.ones((len(x), nt))\n temp_real[cold_mask] *= ts_cold + 273.15\n temp_real[warm_mask] *= ts_warm + 273.15\n\n st = C_p * np.exp(-dalpha_r * x[:, None]) * \\\n np.exp(-dalpha_p * x[:, None]) * np.exp(gamma / temp_real) / \\\n (np.exp(-gamma / temp_real) - 1)\n ast = C_m * np.exp(-dalpha_r * x[:, None]) * \\\n np.exp(-dalpha_m * x[:, None]) / (np.exp(-gamma / temp_real) - 1)\n rst = C_p * np.exp(-dalpha_r * (-x[:, None] + cable_len)) * \\\n np.exp(-dalpha_p * (-x[:, None] + cable_len)) * \\\n np.exp(gamma / temp_real) / (np.exp(-gamma / temp_real) - 1)\n rast = C_m * np.exp(-dalpha_r * (-x[:, None] + cable_len)) * np.exp(\n -dalpha_m * (-x[:, None] + cable_len)) / \\\n (np.exp(-gamma / temp_real) - 1)\n\n alpha = np.mean(np.log(rst / rast) - np.log(st / ast), axis=1) / 2\n\n ds = DataStore({\n 'st': (['x', 'time'], st),\n 'ast': (['x', 'time'], ast),\n 'rst': (['x', 'time'], rst),\n 'rast': (['x', 'time'], rast),\n 'userAcquisitionTimeFW': (['time'], np.ones(nt)),\n 'userAcquisitionTimeBW': (['time'], np.ones(nt)),\n 'cold': (['time'], ts_cold),\n 'warm': (['time'], ts_warm)\n },\n coords={\n 'x': x,\n 'time': time},\n attrs={\n 'isDoubleEnded': '1'})\n\n sections = {\n 'cold': [slice(0., 0.5 * cable_len)],\n 'warm': [slice(0.5 * cable_len, cable_len)]}\n\n # OLS\n ds.calibration_double_ended(sections=sections,\n st_label='st',\n ast_label='ast',\n rst_label='rst',\n rast_label='rast',\n method='ols',\n solver='sparse')\n\n np.testing.assert_almost_equal(\n ds.gamma.values, gamma, decimal=6)\n np.testing.assert_almost_equal(\n ds.alpha.values, alpha, decimal=7)\n np.testing.assert_almost_equal(\n ds.TMPF.values, temp_real - 273.15, decimal=4)\n np.testing.assert_almost_equal(\n ds.TMPB.values, temp_real - 273.15, decimal=4)\n np.testing.assert_almost_equal(\n ds.TMPW.values, temp_real - 273.15, decimal=4)\n\n # WLS\n ds.calibration_double_ended(sections=sections,\n st_label='st',\n ast_label='ast',\n rst_label='rst',\n rast_label='rast',\n st_var=1e-7,\n ast_var=1e-7,\n rst_var=1e-7,\n rast_var=1e-7,\n method='wls',\n solver='sparse',\n tmpw_mc_size=5)\n\n np.testing.assert_almost_equal(\n ds.gamma.values, gamma, decimal=5)\n np.testing.assert_almost_equal(\n ds.alpha.values, alpha, decimal=6)\n np.testing.assert_almost_equal(\n ds.TMPF.values, temp_real - 273.15, decimal=4)\n np.testing.assert_almost_equal(\n ds.TMPB.values, temp_real - 273.15, decimal=4)\n np.testing.assert_almost_equal(\n ds.TMPW.values, temp_real - 273.15, decimal=4)","def closest_cruising_altitude(altitude):\n return 1000 * ((altitude + 500) // 1000)","def _get_tb(I, nu, beam):\n from astropy import units as u\n return (1222.0*I/(nu**2*(beam.minor/1.0).to(u.arcsecond)*(beam.major/1.0).to(u.arcsecond))).value","def Frischknecht16_net(self):\n import numpy.lib.recfunctions as rcfuncs\n import os\n \n # Define metallicites \n self.metallicities = [0.0134,1e-3,1e-5] # First is solar value\n \n # Define masses\n self.masses= np.array((15,20,25,40))\n \n # Define isotope indexing. For radioactive isotopes with half-lives << Chempy time_step they are assigned to their daughter element\n # NB: we only use elements up to Ge here, as in the paper\n indexing={}\n indexing['H']=['p','d']\n indexing['He'] = ['he3','he4']\n indexing['Li'] = ['li6','li7']\n indexing['Be'] = ['be9']\n indexing['B'] = ['b10','b11']\n indexing['C'] = ['c12','c13']\n indexing['N'] = ['n14','n15']\n indexing['O'] = ['o16','o17','o18']\n indexing['F'] = ['f19']\n indexing['Ne'] = ['ne20','ne21','ne22']\n indexing['Na'] = ['na23']\n indexing['Mg'] = ['mg24','mg25','mg26','al26']\n indexing['Al'] = ['al27']\n indexing['Si'] = ['si28','si29','si30']\n indexing['P'] = ['p31']\n indexing['S'] = ['s32','s33','s34','s36']\n indexing['Cl'] = ['cl35','cl37']\n indexing['Ar'] = ['ar36','ar38','ar40']\n indexing['K'] = ['k39','k41']\n indexing['Ca'] = ['ca40','ca42','ca43','ca44','ca46','ca48']\n indexing['Sc'] = ['sc45']\n indexing['Ti'] = ['ti46','ti47','ti48','ti49','ti50']\n indexing['V'] = ['v50','v51']\n indexing['Cr'] = ['cr50','cr52','cr53','cr54']\n indexing['Mn'] = ['mn55']\n indexing['Fe'] = ['fe54', 'fe56','fe57','fe58']\n indexing['Co'] = ['fe60', 'co59']\n indexing['Ni'] = ['ni58','ni60','ni61','ni62','ni64']\n indexing['Cu'] = ['cu63','cu65']\n indexing['Zn'] = ['zn64','zn66','zn67','zn68','zn70']\n indexing['Ga'] = ['ga69','ga71']\n indexing['Ge'] = ['ge70','ge72','ge73','ge74','ge76']\n\n # Define indexed elements \n self.elements = list(indexing.keys())\n \n \n # Define data types\n dt = np.dtype('U8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8')\n \n # Initialise yield table\n yield_table = {}\n \n \n # Import full table with correct rows and data-types\n z = np.genfromtxt(localpath+'input/yields/Frischknecht16/yields_total.txt',skip_header=62,dtype=dt)\n \n \n \n # Create model dictionary indexed by metallicity, giving relevant model number for each choice of mass\n # See Frischknecht info_yields.txt file for model information\n model_dict = {}\n model_dict[0.0134] = [2,8,14,27]\n model_dict[1e-3]=[4,10,16,28]\n model_dict[1e-5]=[6,12,18,29]\n \n # Import list of remnant masses for each model (from row 32-60, column 6 of .txt file) \n # NB: these are in solar masses\n rem_mass_table = np.loadtxt(localpath+'input/yields/Frischknecht16/yields_total.txt',skiprows=31,usecols=6)[:29]\n\n # Create one subtable for each metallicity \n for metallicity in self.metallicities:\n additional_keys = ['Mass', 'mass_in_remnants','unprocessed_mass_in_winds'] # List of keys for table\n names = additional_keys + self.elements\n \n # Initialise table and arrays \n base = np.zeros(len(self.masses))\n list_of_arrays = []\n for i in range(len(names)):\n list_of_arrays.append(base)\n yield_subtable = np.core.records.fromarrays(list_of_arrays,names=names)\n mass_in_remnants = np.zeros(len(self.masses))\n total_mass_fraction = np.zeros(len(self.masses))\n element_mass = np.zeros(len(self.masses))\n \n # Add masses to table\n yield_subtable['Mass'] = self.masses\n \n \n # Extract remnant masses (in solar masses) for each model:\t\t\t\n for mass_index,model_index in enumerate(model_dict[metallicity]):\n mass_in_remnants[mass_index] = rem_mass_table[model_index-1] \n \n # Iterate over all elements \n for element in self.elements:\n element_mass = np.zeros(len(self.masses))\n for isotope in indexing[element]: # Iterate over isotopes of each element\n for mass_index,model_index in enumerate(model_dict[metallicity]): # Iterate over masses \n for row in z: # Find required row in table \n if row[0] == isotope:\n element_mass[mass_index]+=row[model_index] # Compute cumulative mass for all isotopes\n yield_subtable[element]=np.divide(element_mass,self.masses) # Add entry to subtable\n \n all_fractions = [row[model_index] for row in z] # This lists all elements (not just up to Ge)\n total_mass_fraction[mass_index] = np.sum(all_fractions) # Compute total net mass fraction (sums to approximately 0)\n \n # Add fields for remnant mass (now as a mass fraction) and unprocessed mass fraction\t\t\t\n yield_subtable['mass_in_remnants']=np.divide(mass_in_remnants,self.masses) \n yield_subtable['unprocessed_mass_in_winds'] = 1.-(yield_subtable['mass_in_remnants']+total_mass_fraction) # This is all mass not from yields/remnants\n \n # Add subtable to full table\n yield_table[metallicity]=yield_subtable\n\n # Define final yield table for output\n self.table = yield_table","def b_Kjeldsen2008(self):\n return 10.0**(-3.16*self.string_to_param(\"log_Teff\") + 0.184*self.string_to_param(\"log_g\")+11.7)","def T_L(Td, taue):\n return np.sqrt(np.pi)/2.0 * taue * np.exp(-(np.pi*taue/(4*Td))*(np.pi*taue/(4*Td)))","def blackbody( wave, T, waveunit='Angstrom' ):\n \n if waveunit=='Angstrom':\n # convert wavelength from angstroms to cm\n wave = wave / 1e10 * 100.\n elif waveunit=='nm':\n # convert wavelength from angstroms to cm\n wave = wave / 1e9 * 100.\n\n return( ((2 * h * c* c)/wave**5 ) / (exp(h*c/(wave*k*T))-1) )","def calculate_wrist_ee(self):\n\n data = [i * (30 / 75) for i in self.df_epoch[\"LWrist\"]]\n\n # Modified equation from Powell et al. 2017. Removed resting component (constant = 1.15451)\n mets = [.022261 * i for i in data]\n\n # Converts METs to relative VO2 (mL O2/kg/min)\n r_vo2 = [3.5 * m for m in mets]\n\n # Converts relative VO2 to absolute VO2 (L O2/kg/min)\n a_vo2 = [i * self.weight / 1000 for i in r_vo2]\n\n # Converts absolute VO2 to kcal/min (assumes 1 L O2 -> 4.825 kcal)\n kcal_min = [a * 4.825 for a in a_vo2]\n\n # Calculates kcal/epoch\n kcal_epoch = [k * (15 / 60) for k in kcal_min]\n\n total_ee = sum([i for i in kcal_epoch if not np.isnan(i)])\n print(\"-Total energy expenditure estimated from Wrist is {} kcal.\".format(int(total_ee)))\n\n self.df_epoch[\"Wrist_EE\"] = kcal_min","def abbott_elec():\n per_kwh = 0.08 # [$/kWh]\n return per_kwh","def adjust_for_speed_of_light_in_water(df, tide_level):\n speed_of_light_air = 300000\n speed_of_light_water = 225000\n coef = speed_of_light_water / speed_of_light_air\n df['Height'] = (df['Height']- tide_level) * coef\n return df","def convert_ha_to_km2(nb):\n return nb / 100","def calc_relhum(dewpt,t):\n\n relhum=100.*(np.exp((const.es_Abolton*dewpt)/(const.es_Bbolton+dewpt))/np.exp((const.es_Abolton*t)/(const.es_Bbolton+t)))\n return relhum","def getWNSA2(ChargeSA):\n temp = 0.0\n for i in ChargeSA:\n temp = temp+i[2]\n if temp == 0.0:\n return 0.0\n\n return getPNSA2(ChargeSA)*temp/1000.0","def get_D_C3H8_air_Kn(self, T):\n\n Kn = self.get_Kn(T)\n D_C3H8_air = self.get_D_C3H8_air(T)\n\n self.D_C3H8_air_Kn = D_C3H8_air / Kn\n\n return self.D_C3H8_air_Kn","def stiffenerBuckle(dim):\n bst = dim[0]\n tst = dim[1]\n tsk = dim[2]\n\n epsilont = kt * ((tst / bst)) ** 2\n Et = (Esk * tsk) + (Est * ((bst * tst) / bsk))\n Nst = Et*epsilont # Critical Load\n rsf = Nst/Nx\n return rsf - 1 # Using a target Reserve Factor of 1","def bjerrum_length_water(temperature=298.15):\n bjerrum = np.power(ELECTRON_CHARGE, 2.0) / \\\n (4.0 * np.pi *\n ELECTRIC_CONSTANT *\n dielectric_constant_water(temperature) *\n BOLTZMANN_CONSTANT *\n temperature\n )\n return bjerrum","def UTMtoLL(northing, easting, zone, isSouthernHemisphere=True,\r\n ReferenceEllipsoid=23):\r\n k0 = 0.9996\r\n a = _ellipsoid[ReferenceEllipsoid][_EquatorialRadius]\r\n eccSquared = _ellipsoid[ReferenceEllipsoid][_eccentricitySquared]\r\n e1 = (1-sqrt(1-eccSquared))/(1+sqrt(1-eccSquared))\r\n\r\n x = easting - 500000.0 #remove 500,000 meter offset for longitude\r\n y = northing\r\n\r\n ZoneNumber = int(zone)\r\n if isSouthernHemisphere:\r\n y -= 10000000.0 # remove 10,000,000 meter offset used\r\n # for southern hemisphere\r\n\r\n LongOrigin = (ZoneNumber - 1)*6 - 180 + 3 # +3 puts origin in middle of zone\r\n\r\n eccPrimeSquared = (eccSquared)/(1-eccSquared)\r\n\r\n M = y / k0\r\n mu = M/(a*(1-eccSquared/4-3*eccSquared*eccSquared/64-5*eccSquared*eccSquared*eccSquared/256))\r\n\r\n phi1Rad = (mu + (3*e1/2-27*e1*e1*e1/32)*sin(2*mu)\r\n + (21*e1*e1/16-55*e1*e1*e1*e1/32)*sin(4*mu)\r\n +(151*e1*e1*e1/96)*sin(6*mu))\r\n phi1 = phi1Rad*_rad2deg;\r\n\r\n N1 = a/sqrt(1-eccSquared*sin(phi1Rad)*sin(phi1Rad))\r\n T1 = tan(phi1Rad)*tan(phi1Rad)\r\n C1 = eccPrimeSquared*cos(phi1Rad)*cos(phi1Rad)\r\n R1 = a*(1-eccSquared)/pow(1-eccSquared*sin(phi1Rad)*sin(phi1Rad), 1.5)\r\n D = x/(N1*k0)\r\n\r\n Lat = phi1Rad - (N1*tan(phi1Rad)/R1)*(D*D/2-(5+3*T1+10*C1-4*C1*C1-9*eccPrimeSquared)*D*D*D*D/24\r\n +(61+90*T1+298*C1+45*T1*T1-252*eccPrimeSquared-3*C1*C1)*D*D*D*D*D*D/720)\r\n Lat = Lat * _rad2deg\r\n\r\n Long = (D-(1+2*T1+C1)*D*D*D/6+(5-2*C1+28*T1-3*C1*C1+8*eccPrimeSquared+24*T1*T1)\r\n *D*D*D*D*D/120)/cos(phi1Rad)\r\n Long = LongOrigin + Long * _rad2deg\r\n return (Lat, Long)","def calc_ti(self):\n m = 0\n for i in self.inl:\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\n\n for o in self.outl:\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\n\n return m * self.lhv","def get_evaporation_latent_heat() -> float:\n theta = 28.0\n return 2500.8 - 2.3668 * theta","def fuel_cond(T):\n\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\n \n return kc","def SSt_theo_old(D, k):\n\ta1b = k[\"A1B1\"]\n\tba1 = k[\"B1A1\"]\n\tca1 = k[\"C1A1\"]\n\tcb = k[\"B1C1\"]\n\tnum = a1b*ba1*ca1*ca1 + ba1*ba1*ca1*ca1 + 3*a1b*ba1*ca1*cb + 2*ba1*ba1*ca1*cb + \\\n\t\t\ta1b*ca1*ca1*cb + 2*ba1*ca1*ca1*cb + 2*a1b*ba1*cb*cb + ba1*ba1*cb*cb + \\\n\t\t\t2*a1b*ca1*cb*cb + 2*ba1*ca1*cb*cb + ca1*ca1*cb*cb + \\\n\t\t\t\\\n\t\t\t(a1b*ba1*ba1*ca1 + ba1*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + a1b*ca1*ca1*ca1 + \\\n\t\t\t3*ba1*ca1*ca1*ca1 + 2*a1b*ba1*ba1*cb + ba1*ba1*ba1*cb + 2*a1b*ba1*ca1*cb + \\\n\t\t\t3*ba1*ba1*ca1*cb + 4*a1b*ca1*ca1*cb + 5*ba1*ca1*ca1*cb + 3*ca1*ca1*ca1*cb + \\\n\t\t\t2*a1b*ba1*cb*cb + 2*ba1*ba1*cb*cb + 2*a1b*ca1*cb*cb + 4*ba1*ca1*cb*cb + \\\n\t\t\t2*ca1*ca1*cb*cb) * D + \\\n\t\t\t\\\n\t\t\t(a1b*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + 4*ba1*ba1*ca1*ca1 + a1b*ca1*ca1*ca1 + \\\n\t\t\t2*ca1*ca1*ca1*ca1 + ba1*ba1*ba1*cb + 3*a1b*ba1*ca1*cb + 3*ba1*ba1*ca1*cb + \\\n\t\t\ta1b*ca1*ca1*cb + 5*ba1*ca1*ca1*cb + 3*ca1*ca1*ca1*cb + ba1*ba1*cb*cb + \\\n\t\t\t2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) * D*D + \\\n\t\t\t\\\n\t\t\t(ba1*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + 3*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + \\\n\t\t\t2*ba1*ca1*ca1*cb) * D*D*D + \\\n\t\t\t\\\n\t\t\tba1*ba1*ca1*ca1 * D*D*D*D\n\t##\n\tden = a1b*(ba1*ba1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 2*ba1*ca1*ca1*cb + ba1*ba1*cb*cb + \n\t\t\t2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) + \\\n\t\t\t\\\n\t\t\ta1b*(4*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 6*ba1*ca1*ca1*cb + 4*ca1*ca1*ca1*cb + \n\t\t\t2*ba1*ba1*cb*cb + 4*ba1*ca1*cb*cb + 2*ca1*ca1*cb*cb) * D + \\\n\t\t\t\\\n\t\t\ta1b*(2*ba1*ba1*ca1*ca1 + 4*ca1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 6*ba1*ca1*ca1*cb + \n\t\t\t4*ca1*ca1*ca1*cb + ba1*ba1*cb*cb + 2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) * D*D + \\\n\t\t\t\\\n\t\t\ta1b*(4*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 2*ba1*ca1*ca1*cb) * D*D*D + \\\n\t\t\t\\\n\t\t\ta1b*ba1*ba1*ca1*ca1 * D*D*D*D\n\t##\n\ttau = num/den\n\t##\n\treturn tau*np.log(20)","def celciusToKelvin(celcius: float, ndigits: int = 2)->float:\n return round(float(celcius) + 273.15, ndigits)","def bern_metric(pipe_diameter, delta_p, pipe_length):\n fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper\n fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation\n rho = 1000 # density of water @ 4 deg celsius (kg/m**3)\n\n v = math.sqrt((2 * delta_p) / (rho * (4 * fr_reyn * (pipe_length / pipe_diameter) - 1)))\n flow_rate_turb = v * ((math.pi / 4) * (pipe_diameter ** 2))\n\n return flow_rate_turb, v"],"string":"[\n \"def estimate_lwdown(tairK, rh):\\n zeroC = 273.15\\n\\n sat_vapress = 611.2 * np.exp(17.67 * ((tairK - zeroC) / (tairK - 29.65)))\\n vapress = np.maximum(5.0, rh) / 100. * sat_vapress\\n lw_down = 2.648 * tairK + 0.0346 * vapress - 474.0\\n\\n return lw_down\",\n \"def derive_RiekeLebofsky(wavelength):\\n filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', \\n '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', \\n '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']\\n #wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.25, 1.635, 2.2, \\n # 3.77, 4.68, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\\n # 11.5, 12.0, 12.5, 13.0])\\n \\n # Wavelengths from Nishiyama+09 plot of RL+85 law...slightly different than standard, \\n # drop N filter\\n wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.17, 1.57, 2.12, \\n 3.40, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\\n 11.5, 12.0, 12.5, 13.0])\\n A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,\\n 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,\\n 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])\\n # Want to change this from A/Av to A/AK\\n k_ind = np.where(np.array(filters) == 'K')\\n Ak_Av = A_Av[k_ind]\\n Av_Ak = 1.0 / Ak_Av\\n\\n A_Ak = A_Av * Av_Ak\\n \\n # Interpolate over the curve\\n spline_interp = interpolate.splrep(wave, A_Ak, k=3, s=0)\\n A_Ak_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n return A_Ak_at_wave\",\n \"def getTauLos(losdens, kap):\\n rhocell = losdens['valcell']\\n nzs =len(rhocell)\\n nz = nzs + 1\\n zlos = losdens['zlos'] # cell\\n zlosi = losdens['zlosi'] # wall\\n\\n tau, dtaustg, taustg = getTauz(zlosi, zlos, rhocell, kap)\\n\\n\\n# dzlos = zlosi[1:] - zlosi[:-1] # zlos is in the same direction as tau\\n# dtaustg = kap * rhocell * dzlos\\n\\n# tau = np.zeros(nz, dtype=np.float64)\\n# for iz in range(nzs):\\n# tau[iz+1] = sum(dtaustg[:iz+1])\\n\\n# taustg = 0.5*(tau[1:] + tau[:-1])\\n\\n return tau, dtaustg, taustg\",\n \"def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\\n\\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\\n\\n return h_kJ_kg\",\n \"def planckwavelen(wavel,Temp):\\n wavel=wavel*1.e-6 #convert to meters\\n c1=2.*h*c**2.\\n c2=h*c/kb\\n Blambda=1.e-6*c1/(wavel**5.*(np.exp(c2/(wavel*Temp)) -1))\\n return Blambda\",\n \"def make_stehle(self):\\n\\n temp_k = self.temp * e / k # temperature in K\\n dens_cm = self.e_dens * 1.e-6 # electronic density in cm-3\\n prefix = 'n_' + str(self.n_upper) + '_' + str(self.n_lower) + '_'\\n\\n # extract raw tabulated tabulated_data\\n tab_temp_k = np.array(pystark.nc.variables[prefix + 'tempe'].data) # tabulated electron temperatures (K)\\n olam0 = pystark.nc.variables[prefix + 'olam0'].data # line centre wavelength (A)\\n num_tab_dens = pystark.nc.variables[prefix + 'id_max'].data\\n fainom = pystark.nc.variables[prefix + 'fainom'].data\\n tab_dens_cm = np.array(pystark.nc.variables[prefix + 'dense'].data) # tabulated electron densities (cm ** -3)\\n f00 = np.array(pystark.nc.variables[prefix + 'f00'].data) # normal Holtsmark field strength (30 kV / m)\\n dl12 = np.array(pystark.nc.variables[prefix + 'dl12'].data)\\n dl12s = np.array(pystark.nc.variables[prefix + 'dl12s'].data)\\n fainu = pystark.nc.variables[\\n prefix + 'fainu'].data # Asymptotic value of iStark * (alpha ** 2.5) (\\\"wings factor in alfa units\\\")\\n pr0 = np.array(pystark.nc.variables[\\n prefix + 'pr0'].data) # Ratio of the mean interelectronic distance to the electronic Debye length\\n jtot = np.array(pystark.nc.variables[prefix + 'jtot'].data,\\n dtype=np.int) # \\\"number of wave lengths for the couple (T,Ne)\\\"\\n dom = np.array(pystark.nc.variables[prefix + 'dom'].data) # frequency detunings in units (rad / (s*ues)\\n d1om = np.array(pystark.nc.variables[prefix + 'd1om'].data)\\n o1line = np.array(pystark.nc.variables[prefix + 'o1line'].data)\\n o1lines = np.array(pystark.nc.variables[prefix + 'o1lines'].data)\\n\\n # ensure given temperature + density falls within tabulated values\\n # change sligtly the value of the input density\\n # dens_cm in order to remain , as far as possible, inside the tabulation\\n # JSA: this first step seems bogus!\\n\\n if np.abs(dens_cm - tab_dens_cm[0]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[0] * 1.001\\n\\n for id in np.arange(1, num_tab_dens + 1):\\n if np.abs(dens_cm - tab_dens_cm[id]) / dens_cm <= 1.0E-3:\\n dens_cm = tab_dens_cm[id] * 0.999\\n\\n if dens_cm >= 2.0 * tab_dens_cm[num_tab_dens]:\\n raise Exception(\\n 'Your input density is higher than the largest tabulated value %f' % tab_dens_cm[num_tab_dens])\\n\\n if dens_cm <= tab_dens_cm[0]:\\n raise Exception('Your input density is smaller than the smallest tabulated value %f' % tab_dens_cm[0])\\n\\n if temp_k >= tab_temp_k[9]:\\n raise Exception('Your input temperature is higher than the largest tabulated value %f' % tab_temp_k[9])\\n\\n if temp_k <= tab_temp_k[0]:\\n raise Exception('Your input temperature is lower than the smallest tabulated value %f' % tab_temp_k[0])\\n\\n normal_holtsmark_field = 1.25e-9 * (dens_cm ** (2. / 3.)) # normal field value in ues\\n\\n # calculate line centre wavelength and frequency using Rydberg formula\\n # JSA: I have made this step clearer and corrected for deuteron mass in the Rydberg constant (though the effect is small)\\n # TODO make sure this matches olam0 parameter above -- why were there two variables in the first place?!\\n # rydberg_m = Rydberg / (1. + (electron_mass / physical_constants['deuteron mass'][0]))\\n # wl_0_angst = 1e10 * (rydberg_m * (1 / n_lower ** 2 - 1 / n_upper ** 2)) ** -1\\n\\n wl_centre_angst = self.wl_centre * 1e10\\n\\n c_angst = c * 1e10 # velocity of light in Ansgtroms / s\\n angular_freq_0 = 2 * np.pi * c_angst / wl_centre_angst # rad / s\\n\\n otrans = -2 * np.pi * c_angst / wl_centre_angst ** 2\\n\\n olines = o1lines / np.abs(otrans)\\n oline = o1line / np.abs(otrans)\\n\\n # Limit analysis_tools to uncorrelated plasmas.\\n # check that mean interelectronic distance is smaller than the electronic Debye length (equ. 10)\\n PR0_exp = 0.0898 * (dens_cm ** (1. / 6.)) / np.sqrt(temp_k) # = (r0 / debye)\\n if PR0_exp > 1.:\\n raise Exception('The plasma is too strongly correlated\\\\ni.e. r0/debye=0.1\\\\nthe line cannot be computed.')\\n\\n # fainom_exp=fainom*(F00_exp**1.5)\\n # fainum_exp=fainom_exp/( (OPI*2.)**1.5)\\n\\n # ========================\\n # TABULATION Format CDS\\n # si on veut ecrire\\n # n -np lambda0 kalpha Ne E0 T R0/Debye Dalpha iDoppler iStark\\n\\n # IN_cds= N+0.01\\n # INP_cds = NP+0.01\\n\\n # ***********************************************************\\n # Don't edit the CDS format...\\n # ***********************************************************\\n\\n # Skipped the code in the IF statement starting at line 470, since it\\n # isn't used, if (.FALSE.) ...\\n\\n # ==============================================\\n # define an unique detunings grid - domm - for the tabulated\\n # profiles ( various temperatures , densities)\\n # calculate all the line shapes for this common grid\\n # units used at this points are Domega_new= Delta(omega)/F00\\n # in rd/(s-1 ues)\\n\\n max_num_dens = 30 # Maximum number of densities\\n max_num_tab_temp = 10\\n max_num_detunings = 60 # Maximum number of detunings\\n jtot = jtot.astype(np.int)\\n domm = np.zeros(100000)\\n dom0 = np.zeros(10000)\\n tprof = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n tprofs = np.zeros([max_num_dens, max_num_tab_temp, 10000])\\n uprof = np.zeros([max_num_dens, 10000])\\n uprofs = np.zeros([max_num_dens, 10000])\\n\\n inc = 0\\n domm[inc] = 0.0\\n # ---- Look to replace this loop\\n for id in np.arange(num_tab_dens + 1): # loop over tab densities\\n for j in np.arange(max_num_tab_temp): # loop over tab temperatures (?)\\n for i in np.arange(1, jtot[id, j]):\\n inc += 1\\n dom0[inc] = dom[id, j, i]\\n\\n inc = np.count_nonzero(dom)\\n npik = inc + 1\\n # nut=10000\\n\\n # Calling numpy sort instead of piksrt\\n tmp = np.sort(dom0[0:npik])\\n dom0[0:npik] = tmp[0:npik]\\n # dom0 seems to agree with the FORTRAN version\\n\\n inc = 0\\n domm[0] = 0.0\\n # print 'npik',npik\\n # ---- Look to replace this loop\\n for i in np.arange(1, npik):\\n dif = (dom0[i] - dom0[i - 1])\\n if dif <= 1.0E-6:\\n continue\\n if dif / np.abs(dom0[i]) <= 0.1:\\n continue\\n inc = inc + 1\\n domm[inc] = dom0[i]\\n\\n jdom = inc + 1 # One line after marker 35\\n\\n for id in np.arange(num_tab_dens):\\n for j in np.arange(10):\\n if pr0[id, j] > 1.0:\\n continue\\n\\n tprof[id, j, 0] = oline[id, j, 0]\\n tprofs[id, j, 0] = olines[id, j, 0]\\n\\n if jtot[id, j] == 0:\\n continue\\n\\n for i in np.arange(1, jdom + 1):\\n skip1 = False\\n skip2 = False\\n # print 'i',i\\n domeg = domm[i]\\n ij_max = jtot[id, j]\\n # print 'domeg,ij_max',domeg,ij_max\\n for ij in np.arange(1, ij_max - 1):\\n # print 'ij',ij\\n test = (domeg - dom[id, j, ij]) * (domeg - dom[id, j, ij - 1])\\n # print 'test1:',test\\n if test <= 0.0:\\n # print 'triggered test1'\\n x1 = dom[id, j, ij - 1]\\n x2 = dom[id, j, ij]\\n x3 = dom[id, j, ij + 1]\\n y1 = oline[id, j, ij - 1]\\n y2 = oline[id, j, ij]\\n y3 = oline[id, j, ij + 1]\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij - 1]\\n y2 = olines[id, j, ij]\\n y3 = olines[id, j, ij + 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n skip1 = True\\n skip2 = True\\n break\\n\\n if skip1 is False:\\n test = (domeg - dom[id, j, ij_max - 2]) * (domeg - dom[id, j, ij_max - 1])\\n # print 'test2:',test\\n # print 'domeg',domeg\\n # print 'dom[id,j,ij_max-1]',dom[id,j,ij_max-2]\\n # print 'dom[id,j,ij_max]',dom[id,j,ij_max-1]\\n if test <= 0.0:\\n # print 'triggered test2'\\n x1 = dom[id, j, ij_max - 3]\\n x2 = dom[id, j, ij_max - 2]\\n x3 = dom[id, j, ij_max - 1]\\n y1 = oline[id, j, ij_max - 3]\\n y2 = oline[id, j, ij_max - 2]\\n y3 = oline[id, j, ij_max - 1]\\n tprof[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n y1 = olines[id, j, ij_max - 3]\\n y2 = olines[id, j, ij_max - 2]\\n y3 = olines[id, j, ij_max - 1]\\n tprofs[id, j, i] = pystark.FINTRP(x1, x2, x3, y1, y2, y3, domeg)\\n skip2 = True\\n # print 'x1,x2,x3',x1,x2,x3\\n # print 'y1,y2,y3',y1,y2,y3\\n # print 'tprof[id,j,i]',tprof[id,j,i]\\n # print 'tprofs[id,j,i]',tprofs[id,j,i]\\n continue\\n\\n if skip2 is False:\\n if domeg > dom[id, j, ij_max]:\\n # print 'triggered test3'\\n tprof[id, j, i] = fainom / (domeg ** 2.5)\\n tprofs[id, j, i] = tprof[id, j, i]\\n continue\\n\\n # We can skip writing the intermediate file\\n\\n\\n for id in np.arange(num_tab_dens):\\n otest_dens = (dens_cm - tab_dens_cm[id]) * (dens_cm - tab_dens_cm[id + 1])\\n if otest_dens <= 0.0:\\n dense1 = tab_dens_cm[id]\\n dense2 = tab_dens_cm[id + 1]\\n id1 = id\\n id2 = id + 1\\n break\\n\\n if dens_cm >= tab_dens_cm[num_tab_dens]:\\n dense1 = tab_dens_cm[num_tab_dens - 1]\\n dense2 = tab_dens_cm[num_tab_dens]\\n id1 = num_tab_dens - 1\\n id2 = num_tab_dens\\n\\n for it in np.arange(10):\\n otest = (temp_k - tab_temp_k[it]) * (temp_k - tab_temp_k[it + 1])\\n if otest <= 0.0:\\n it1 = it\\n it2 = it + 1\\n # pr01 = pr0[id2,it1] # max value of pr0 for T1,T2,dense1,dense2\\n tempe1 = tab_temp_k[it]\\n tempe2 = tab_temp_k[it + 1]\\n break\\n\\n # interpolation in temperature\\n for id in np.arange(id1, id2 + 1):\\n for i in np.arange(jdom):\\n uprof[id, i] = tprof[id, it1, i] + (temp_k - tempe1) * (tprof[id, it2, i] - tprof[id, it1, i]) / (\\n tempe2 - tempe1)\\n uprofs[id, i] = tprofs[id, it1, i] + (temp_k - tempe1) * (tprofs[id, it2, i] - tprofs[id, it1, i]) / (\\n tempe2 - tempe1)\\n\\n delta_lambda = np.zeros(jdom)\\n delta_nu = np.zeros(jdom)\\n wprof_nu = np.zeros(jdom)\\n wprofs_nu = np.zeros(jdom)\\n\\n for i in np.arange(jdom):\\n wprof = uprof[id1, i] + (dens_cm - dense1) * (uprof[id2, i] - uprof[id1, i]) / (dense2 - dense1)\\n wprofs = uprofs[id1, i] + (dens_cm - dense1) * (uprofs[id2, i] - uprofs[id1, i]) / (dense2 - dense1)\\n delta_omega = domm[i] * normal_holtsmark_field\\n delta_nu[i] = delta_omega / (2 * np.pi)\\n delta_lambda[i] = wl_centre_angst * delta_omega / (angular_freq_0 + delta_omega)\\n # print(delta_lambda[i])\\n wprof_nu[i] = (wprof / normal_holtsmark_field) * (2. * np.pi)\\n wprofs_nu[i] = (wprofs / normal_holtsmark_field) * (2. * np.pi)\\n # print '%e %e %e %e' %(delta_lambda[i],delta_nu[i],wprof_nu[i],wprofs_nu[i])\\n\\n delta_lambda2 = np.concatenate((-delta_lambda[::-1], delta_lambda)) + wl_centre_angst # + olam0\\n delta_nu2 = np.concatenate((-delta_nu[::-1], delta_nu))\\n wprof_nu2 = np.concatenate((wprof_nu[::-1], wprof_nu))\\n wprofs_nu2 = np.concatenate((wprofs_nu[::-1], wprofs_nu))\\n\\n # for some reason, i only get a good agreement with the other models if i take the pure Stark broadened Stehle\\n # output and manually convolve it with the Doppler profile -- not sure why...\\n ls_sd = wprofs_nu2\\n\\n # interpolate onto frequency axis\\n ls_sd = np.interp(self.freq_axis, delta_nu2 + self.freq_centre, ls_sd)\\n\\n return ls_sd\",\n \"def k_Wa92(wind_second_moment, temp_C):\\n\\n U2 = wind_second_moment\\n\\n Sc = schmidt_number(temp_C)\\n k = (0.31 * U2) * (660 / Sc) ** 0.5\\n\\n return k\",\n \"def abbott_steam():\\n per_klb = 20 # dollars per klb of steam\\n kwh_eq = to_kwh(1) # kwh equivalent of steam\\n per_kwh = per_klb / kwh_eq\\n return per_kwh\",\n \"def test_klauder(self):\\n ideal = np.array([0.14899879, -0.16633309, -0.42806931, 0.16605633,\\n 0.70769336, 0.16605633, -0.42806931, -0.16633309])\\n actual = misc.klauder(8)\\n np.testing.assert_allclose(ideal, actual, atol=1e-8, rtol=1e-8)\",\n \"def calc_TEB(Imap_name='HFI_SkyMap_353_2048_R2.02_full.fits',\\n Pmap_name='HFI_SkyMap_353_2048_R2.02_full.fits',\\n nus=None, fwhm=0.063, nside=16, lmax=100,\\n lmaps_only=False, filename=None):\\n\\n # read from file if it's there\\n if filename is None:\\n filename = 'bispectrum_lmax{}'.format(lmax)\\n if nus is not None:\\n filename += '_{}-{}-{}GHz.npy'.format(nus[0],nus[1],nus[1])\\n else:\\n filename += '_{}'.format(Imap_name[-5])\\n if Imap_name != Pmap_name:\\n filename += '_{}.npy'.format(Pmap_name[-5])\\n else:\\n filename += '.npy'\\n print 'looking for {} ...'.format(filename)\\n if os.path.exists(filename) and not lmaps_only:\\n bispectrum = np.load(filename, 'r')\\n return bispectrum\\n\\n # compute it, if the file doesn't exist\\n if nus is not None:\\n Imap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[0])\\n Pmap_name = 'HFI_SkyMap_{}_2048_R2.02_full.fits'.format(nus[1])\\n title = '$I_{%i} P^2_{%i}$ (equilateral)' % (nus[0],nus[1])\\n \\n Imap = prepare_map( Imap_name, field=0,\\n nside_out=nside, fwhm=fwhm )\\n Tlm = hp.map2alm( Imap, lmax=lmax )\\n\\n Qmap, Umap = prepare_map( Pmap_name, field=(1,2),\\n nside_out=nside, fwhm=fwhm )\\n \\n \\n Elm,Blm = hp.map2alm_spin( (Qmap,Umap), 2, lmax=lmax )\\n\\n if lmax is None:\\n lmax = hp.sphtfunc.Alm.getlmax(len(Tlm))\\n ls, ms = hp.sphtfunc.Alm.getlm(lmax,np.arange(len(Tlm)))\\n lmin = ls.min()\\n mapsize = len(Imap)\\n pixelsize = hp.pixelfunc.nside2pixarea(nside)\\n \\n Ylm = calc_Ylm(Imap, ls, ms)\\n\\n\\n #return Ylm, Tlm, ls, ms\\n print 'calculating Tl,El,Bl ...'\\n \\n Tl = sum_over_m(Tlm, Ylm, ls,\\n lmax=lmax, lmin=lmin, mapsize=mapsize)\\n El = sum_over_m(Elm, Ylm, ls,\\n lmax=lmax, lmin=lmin, mapsize=mapsize)\\n Bl = sum_over_m(Blm, Ylm, ls,\\n lmax=lmax, lmin=lmin, mapsize=mapsize)\\n\\n if lmaps_only:\\n return Tl,El,Bl\\n \\n hs = get_hs(lmin=lmin, lmax=lmax)\\n\\n print 'calculating bispectrum ...'\\n bispectrum = calc_bispectrum(Tl, El, Bl, hs,\\n pixelsize,\\n lmax=lmax, lmin=lmin,\\n mapsize=mapsize)\\n clean_bispectrum_of_naninf(bispectrum, hs, inplace=True)\\n np.save(filename, bispectrum)\\n return bispectrum\",\n \"def PlankFunction(wavelen,T=5778.):\\n\\n c1=1.191042E8\\n c2=1.4387752E4\\n L=c1/(wavelen**5*(np.exp(c2/(wavelen*T))-1))\\n return L\",\n \"def k_Li86(wind_ms, temp_C):\\n from numpy import zeros_like\\n\\n U = wind_ms\\n T = temp_C\\n\\n Sc = schmidt_number(T)\\n k = zeros_like(temp_C)\\n\\n i1 = U <= 3.6\\n i2 = (U > 3.6) & (U < 13.0)\\n i3 = U >= 13.0\\n\\n k[i1] = (0.17 * U[i1]) * (Sc[i1] / 600) ** (-2.0 / 3.0)\\n k[i2] = ((U[i2] - 3.4) * 2.8) * (600 / Sc[i2]) ** 0.5\\n k[i3] = ((U[i3] - 8.4) * 5.9) * (600 / Sc[i3]) ** 0.5\\n\\n return k\",\n \"def wind_chill(T_a, v):\\r\\n return 13.12 + 0.6215*(T_a) - 11.37*(v)**0.16 + 0.3965*(T_a)*(v)**0.16\",\n \"def moonlongitude(time):\\n B0 = 481267.8809\\n C0 = 218.3162\\n # fmt: off\\n A = np.array([62888.e-4, 12740.e-4, 6583.e-4, 2136.e-4, 1851.e-4, \\\\\\n 1144.e-4, 588.e-4, 571.e-4, 533.e-4, 458.e-4, 409.e-4, \\\\\\n 347.e-4, 304.e-4, 154.e-4, 125.e-4, 110.e-4, 107.e-4, \\\\\\n 100.e-4, 85.e-4, 79.e-4, 68.e-4, 52.e-4, 50.e-4, 40.e-4, \\\\\\n 40.e-4, 40.e-4, 38.e-4, 37.e-4, 28.e-4, 27.e-4, 26.e-4, \\\\\\n 24.e-4, 23.e-4, 22.e-4, 21.e-4, 21.e-4, 21.e-4, 18.e-4, \\\\\\n 16.e-4, 12.e-4, 11.e-4, 9.e-4, 8.e-4, 7.e-4, 7.e-4, \\\\\\n 7.e-4, 7.e-4, 6.e-4, 6.e-4, 5.e-4, 5.e-4, 5.e-4, \\\\\\n 4.e-4, 4.e-4, 3.e-4, 3.e-4, 3.e-4, 3.e-4, 3.e-4, \\\\\\n 3.e-4, 3.e-4])\\n B = np.array([477198.868, 413335.35, 890534.22, 954397.74, \\\\\\n 35999.05, 966404.0, 63863.5, 377336.3, \\\\\\n 1367733.1, 854535.2, 441199.8, 445267.1, \\\\\\n 513197.9, 75870, 1443603, 489205, 1303870, \\\\\\n 1431597, 826671, 449334, 926533, 31932, \\\\\\n 481266, 1331734, 1844932, 133, 1781068, \\\\\\n 541062, 1934, 918399, 1379739, 99863, \\\\\\n 922466, 818536, 990397, 71998, 341337, \\\\\\n 401329, 1856938, 1267871, 1920802, 858602, \\\\\\n 1403732, 790672, 405201, 485333, 27864, \\\\\\n 111869, 2258267, 1908795, 1745069, 509131, \\\\\\n 39871, 12006, 958465, 381404, 349472, \\\\\\n 1808933, 549197, 4067, 2322131.])\\n C = np.array([44.963, 10.74, 145.70, 179.93, 87.53, 276.5, \\\\\\n 124.2, 13.2, 280.7, 148.2, 47.4, 27.9, 222.5, \\\\\\n 41, 52, 142, 246, 315, 111, 188, \\\\\\n 323, 107, 205, 283, 56, 29, 21, \\\\\\n 259, 145, 182, 17, 122, 163, 151, \\\\\\n 357, 85, 16, 274, 152, 249, 186, \\\\\\n 129, 98, 114, 50, 186, 127, 38, \\\\\\n 156, 90, 24, 242, 223, 187, 340, \\\\\\n 354, 337, 58, 220, 70, 191])\\n # fmt: on\\n RAD = 0.0174532925199433\\n tempb = (B * time + C) * RAD\\n amp = A * np.cos(tempb)\\n moonlon = np.sum(amp)\\n moonlon = (moonlon + B0 * time + C0) * RAD\\n return moonlon\",\n \"def ReadTinker():\\n # Total Potential Energy : {f} Kcal/mole\\n total_line = \\\" Total Potential Energy :\\\"\\n with open('LICHM_TINKEREnergy_0.log') as f:\\n for line in f:\\n if line.startswith(total_line):\\n # print(line)\\n TinkE = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n TinkE = float(TinkE[0])\\n # if AMOEBA == True:\\n # if line.startswith(\\\"Polarization\\\"):\\n # Epol = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n # Epol = float(Epol[0])\\n # elif line.startswith(\\\"Implicit Solvation\\\")\\n # Esolv = re.findall(r'\\\\-\\\\d+\\\\.*\\\\d*', line)\\n # Esolv = float(Esolv[0])\\n f.close()\\n # if AMOEBA == True:\\n # TINKERPolForces = EPol + ESolv\\n # TinkE += TINKERPolForces\\n #\\n TinkE *= kcal2ev\\n return TinkE\",\n \"def earth_tide(theta, lamda, gtime):\\n\\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist # bpos common block\\n global h, k, l # love common block\\n h = [0.6114, 0.2891, 0.175]\\n k = [0.304, 0.09421, 0.043]\\n l = [0.0832, 0.0145, 0.0103]\\n\\n global azt, azs # azimut common block\\n global etmut # tdiff common block\\n global moon # sunny common block\\n moon = 0\\n # hardwire these - you can only send it ONE droptime\\n deltat = 1\\n NPT = 1\\n\\n temp_time = num2date(gtime)\\n\\n YY = temp_time.year\\n MO = temp_time.month\\n DD = temp_time.day\\n HH = temp_time.hour\\n MM = temp_time.minute\\n SS = temp_time.second\\n # Initialize variables\\n irl = 1\\n iflag = 0\\n ntotl = 1\\n iget = [0, 0, 0, 0, 0, 0, 0] # ' !!!\\n ispc = [0, 0, 0, 0] # ' !!!\\n ntw = [1, 0, 0] # ' !!!\\n ioptn = 't'\\n ielement = 0\\n # \\tdata statements for input and output unit numbers (on terminal I/O)\\n inun = 5\\n ioun = 6\\n nptpb = 6\\n\\n yr1 = YY - 1900\\n day1 = date2num(datetime(YY, MO, DD))\\n # \\tfind times in hours from 0 hr, 1 jan 1900\\n # matlab:\\n ts = (\\n SS / 3600\\n + MM / 60\\n + HH\\n + 24 * (day1 - 1)\\n + 8760 * yr1\\n + 24 * np.fix((yr1 - 1) / 4)\\n )\\n # python:\\n dj = date_to_julian_day(datetime(YY, MO, DD))\\n djref = date_to_julian_day(datetime(1899, 12, 31, 0, 0, 0))\\n delta_dj = (\\n dj - djref\\n ) # difference in days from current date (0hr) to 0hr, 1 jan 1900\\n delta_djhr = float(delta_dj) * 24.0 + HH - 12.0 + MM / 60.0 + SS / 3600.0\\n te = ts + (NPT - 1) * deltat / 3600\\n d = deltat / 3600\\n # terms=(te-ts)/d + 1\\n terms = NPT\\n\\n # done asking questions - begin execution\\n i = 1\\n tt = ts\\n sph(theta, lamda, 0)\\n etmut = 41.184 + yr1 - 70\\n # matlab:\\n # t = (tt+12 + (etmut/3600))/876600\\n t = (delta_djhr + etmut / 3600) / 876600\\n # t is ephemeris time in julian centuries from 12 hr 0 jan 1900\\n ephem(t)\\n\\n # calculate normalized gravity tides\\n [grav, tilt, strain, gdc] = elastd(ntw)\\n\\n gravtide = 1.0e5 * grav\\n # convert m/s² to mgal: 1m/s² = 100 gal = 100 000 mgal\\n\\n iflag = 1\\n\\n iterms = np.fix(terms)\\n i = 1\\n return gravtide\",\n \"def derive_Hosek18b(wavelength):\\n # Extinction law definition\\n wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])\\n A_AKs = np.array([7.943, 5.715, 3.142, 2.04, 1.0, 0.50])\\n \\n # Following Hosek+18, Interpolate over the curve with cubic spline interpolation\\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\\n A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n # This curve already assumes A_Ks = 1.0, so we can go straight to\\n # output \\n return A_AKs_at_wave\",\n \"def halflife(self, humidity: _VectorisedFloat, inside_temp: _VectorisedFloat) -> _VectorisedFloat:\\n # Updated to use the formula from Dabish et al. with correction https://doi.org/10.1080/02786826.2020.1829536\\n # with a maximum at hl = 6.43 (compensate for the negative decay values in the paper). \\n # Note that humidity is in percentage and inside_temp in °C.\\n # factor np.log(2) -> decay rate to half-life; factor 60 -> minutes to hours\\n hl_calc = ((np.log(2)/((0.16030 + 0.04018*(((inside_temp-273.15)-20.615)/10.585)\\n +0.02176*(((humidity*100)-45.235)/28.665)\\n -0.14369\\n -0.02636*((inside_temp-273.15)-20.615)/10.585)))/60)\\n \\n return np.where(hl_calc <= 0, 6.43, np.minimum(6.43, hl_calc))\",\n \"def kth_func(Th, ThS, lbd, ksat):\\n if Th < 0.0:\\n # rwarn(\\\"water content < 0 IN kth_func\\\")\\n Th = 0.0\\n kth = ksat * (Th / ThS) ** (3 + (2 / lbd))\\n\\n return kth\",\n \"def calc_h_sen(dry_bulb_C):\\n\\n h_kJ_kg = dry_bulb_C * CPA_kJ_kgC\\n\\n return h_kJ_kg\",\n \"def ftlan_E1c(hop, v0, T, m=50, Min_b=10e-10, Min_m=5, kB=1, norm = np.linalg.norm):\\n# def Tri_diag(a1, b1):\\n# mat = np.diag(b1, -1) + np.diag(a1, 0) + np.diag(b1, 1)\\n# e, w = np.linalg.eigh(mat)\\n# return e, w\\n\\n beta = 1./(T * kB)\\n E = 0.\\n a, b = [], []\\n v0 = v0/norm(v0)\\n Hv = hop(v0)\\n a.append(v0.dot(Hv))\\n v1 = Hv - a[0] * v0\\n b.append(norm(v1))\\n if b[0] < Min_b:\\n return 0\\n\\n v1 = v1/b[0]\\n Hv = hop(v1)\\n a.append(v1.dot(Hv))\\n\\n for i in range(1, m - 1):\\n v2 = Hv - b[i - 1] * v0 - a[i] * v1\\n b.append(norm(v2))\\n if abs(b[i]) < Min_b:\\n b.pop()\\n break\\n\\n v2 = v2/b[i]\\n Hv = hop(v2)\\n a.append(v2.dot(Hv))\\n v0 = v1.copy()\\n v1 = v2.copy()\\n \\n a = np.asarray(a)\\n b = np.asarray(b)\\n\\n eps, phi = Tri_diag(a, b)\\n l = len(eps)\\n# Eo = eps[0]\\n# eps = eps-Eo\\n exp_eps = np.exp(-beta * eps)\\n E = np.sum(exp_eps * eps * phi[0, :]**2.)\\n Z = np.sum(exp_eps * phi[0, :]**2.)\\n# for i in range(len(eps)):\\n# E += exp_eps[i] * eps[i] * phi[0, i]**2\\n\\n# E = E + Eo\\n# de = eps[:, np.newaxis] - eps\\n# for i in range(l):\\n# E += eps[i] * phi[0, i]**2./np.sum(np.exp(-beta*de[:l, i])*(phi[0, :l]**2.))\\n return E, Z\",\n \"def stockdon2006(H,L,B):\\n \\n # Make sure parameters are double\\n H = np.double(H)\\n L = np.double(L)\\n B = np.double(B)\\n \\n # Compute incident swash (equation 11) \\n incSwash = 1.1 / 2 * 0.75 * B * (H*L)**0.5\\n \\n # Infragravity swash (Equation 12)\\n igSwash = 1.1 / 2 * 0.06 * (H*L)**0.5\\n \\n # Compute R2% (Equation 19)\\n setup = 1.1 * 0.35 * B * ((H * L)**0.5)\\n swash = 1.1 / 2.0 * (H*L * (0.563 * B**2 + 0.004))**0.5 \\n r2 = setup + swash\\n \\n return r2,setup,incSwash,igSwash\",\n \"def planck_w(lam, T):\\n return ((2*h*c**2)/(lam**5))*(1./(np.exp((h*c)/(lam*k*T))-1))\",\n \"def get_sn2005ek(colorplt=False):\\n z = 0.016551\\n ebv = 0.210\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_max = 53639.9\\n print (\\\"adopt r band t_max from Drout+13\\\")\\n \\n # tb = pd.read_csv('/Users/yuhanyao/Desktop/ZTF18abfcmjw/data/Drout2013/table1', sep='\\\\t')\\n # tb = tb.drop(columns=[\\\"Unnamed: 6\\\"])\\n \\n mjds = np.array([53639.3, 53640.3, 53641.3, 53642.2, 53643.2, 53645.3,\\n 53646.5, 53648.0, 53649.2, 53650.4, 53651.3, 53652.5,\\n 53654.2, 53655.2, 53656.2, 53657.2])\\n \\n Bmags = np.array([18.25, 18.38, 18.65, np.nan, 19.10, 19.71,\\n 20.07, np.nan, 20.67, 20.90, 21.05, np.nan,\\n 21.74, np.nan, np.nan, np.nan])\\n \\n Bmag_uncs = np.array([0.02, 0.03, 0.02, np.nan, 0.05, 0.07, \\n 0.07, np.nan, 0.04, 0.04, 0.04, np.nan,\\n 0.12, np.nan, np.nan, np.nan])\\n \\n Vmags = np.array([17.83, 18.03, 17.92, np.nan, 18.24, 18.66,\\n 18.93, 19.48, 19.63, 19.86, 19.98, 20.35,\\n 20.60, 20.74, 20.88, 21.22])\\n \\n Vmag_uncs = np.array([0.02, 0.03, 0.01, np.nan, 0.02, 0.02,\\n 0.02, 0.06, 0.03, 0.03, 0.04, 0.05, \\n 0.08, 0.10, 0.08, 0.13])\\n \\n Rmags = np.array([17.46, 17.41, 17.60, 17.69, 17.86, 18.18, \\n np.nan, 18.83, 19.03, 19.26, 19.48, 19.75,\\n 20.08, np.nan, 20.47, np.nan])\\n \\n Rmag_uncs = np.array([0.01, 0.02, 0.01, 0.02, 0.01, 0.01,\\n np.nan, 0.03, 0.02, 0.02, 0.02, 0.04,\\n 0.05, np.nan, 0.08, np.nan])\\n\\n Imags = np.array([17.20, 17.13, 17.18, np.nan, 17.47, 17.71, \\n np.nan, 18.13, 18.26, 18.51, 18.61, 18.74, \\n 19.01, np.nan, 19.47, np.nan])\\n \\n Imag_uncs = np.array([0.02, 0.04, 0.02, np.nan, 0.03, 0.02,\\n np.nan, 0.06, 0.02, 0.02, 0.02, 0.03,\\n 0.05, np.nan, 0.06, np.nan])\\n \\n mymjds = np.hstack([mjds, mjds, mjds, mjds])\\n mymags = np.hstack([Bmags, Vmags, Rmags, Imags])\\n myemags = np.hstack([Bmag_uncs, Vmag_uncs, Rmag_uncs, Imag_uncs])\\n myfilts = np.hstack([ np.repeat(\\\"B\\\", len(Bmags)),\\n np.repeat(\\\"V\\\", len(Bmags)),\\n np.repeat(\\\"R\\\", len(Rmags)),\\n np.repeat(\\\"I\\\", len(Imags)) ])\\n ix = ~np.isnan(mymags)\\n tb = pd.DataFrame({'mjd': mymjds[ix],\\n 'mag': mymags[ix],\\n 'emag': myemags[ix],\\n \\\"filter\\\": myfilts[ix]})\\n \\n ixB = tb['filter'].values==\\\"B\\\"\\n ixV = tb['filter'].values==\\\"V\\\"\\n ixR = tb['filter'].values==\\\"R\\\"\\n ixI = tb['filter'].values==\\\"I\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixV] = 5430\\n tb['wave'].values[ixR] = 6349\\n tb['wave'].values[ixI] = 8797\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['B', 'R', 'I']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"R\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"B\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"R\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"I\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"BmR\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"RmI\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def Tloken(self, x):\\n return 11.2 * (self.r500*0.7)**2. * ( 1. + 0.75*x)**(-1.6)\",\n \"def B_func(Th33, Th1500):\\n\\n D = ln(Th33) - ln(Th1500)\\n B = (ln(1500) - ln(33)) / D\\n\\n def lbd_func(C):\\n \\\"\\\"\\\"return the slope of logarithmic tension-moisture curve\\\"\\\"\\\"\\n if C == 0:\\n return 0.0\\n lbd = 1 / C\\n return lbd\\n\\n return lbd_func(B)\",\n \"def planckian(temp, wavelength):\\n if wavelength==560: return 100.0\\n if temp<60: temp=60 # For simplicity, in very low temperature\\n num = wavelength**(-5)\\n try:\\n v=num / (math.exp(0.0143877687750393/(wavelength*(10**(-9))*temp)) - 1)\\n except:\\n print(temp)\\n print(wavelength)\\n raise ValueError\\n v2=(560.0**(-5)) / (math.exp(0.0143877687750393/(560.0*(10**(-9))*temp)) - 1)\\n return v*100.0/v2\",\n \"def kervella(magB=None, magV=None, magK=None):\\n if magB is None or np.isnan(magB) or magB > 49:\\n magB = np.nan\\n if magV is None or np.isnan(magV) or magV > 49:\\n magV = np.nan\\n if magK is None or np.isnan(magK) or magK > 49:\\n magK = np.nan\\n const1 = np.array([0.0755, 0.0535])\\n const2 = np.array([0.5170, 0.5159])\\n mag = np.array([magV, magB])\\n vals = 10**(const1*(mag-magK)+const2-0.2*magK)\\n diameter = {}\\n if not np.isnan(vals[0]):\\n diameter['V'] = vals[0]*u.mas\\n if not np.isnan(vals[1]):\\n diameter['B'] = vals[1]*u.mas\\n return diameter\",\n \"def ka_tf(Y,t,voltage_clamp_func,voltage_clamp_params):\\n # g = gbar * n * h\\n v = voltage_clamp_func(t,voltage_clamp_params)\\n n = Y[0]\\n h = Y[1]\\n q10 = 1.0#3.3 # Preserved in case it is useful but disabled\\n \\n ninf = (1.0/(1.0 + np.exp(-(v+5.4+15)/16.4)))**4\\n ntau = 0.25 + 10.04*np.exp((-(v+24.67)**2)/(2*34.8**2))*q10\\n\\t\\t\\n hinf = 1.0/(1.0 + np.exp((v+49.9 + 15.0)/4.6))\\n htau = 20.0 + 50.0 * np.exp((-(v+40.0)**2)/(2.0*40.0**2))*q10\\n \\n # Trap for htau following Sheets /ChoiWaxman/Tigerholm - set it to 5 ms if less than 5 ms\\n if htau < 5.0:\\n htau = 5.0\\n\\n dn = (ninf-n)/ntau\\n dh = (hinf-h)/htau\\n \\n return [dn,dh]\",\n \"async def wetbulb(self, temp, humidity, pressure):\\n t = float(temp)\\n rh = float(humidity)\\n p = float(pressure)\\n\\n # Variables\\n edifference = 1\\n twguess = 0\\n previoussign = 1\\n incr = 10\\n es = 6.112 * math.exp(17.67 * t / (t + 243.5))\\n e2 = es * (rh / 100)\\n\\n while (abs(edifference) > 0.005):\\n ewguess = 6.112 * math.exp((17.67 * twguess) / (twguess + 243.5))\\n eguess = ewguess - p * (t - twguess) * 0.00066 * (1 + (0.00115 * twguess))\\n edifference = e2 - eguess\\n if edifference == 0:\\n break\\n\\n if edifference < 0:\\n cursign = -1\\n if (cursign != previoussign):\\n previoussign = cursign\\n incr = incr / 10\\n else:\\n incr = incr\\n else:\\n cursign = 1\\n if (cursign != previoussign):\\n previoussign = cursign\\n incr = incr / 10\\n else:\\n incr = incr\\n\\n twguess = twguess + incr * previoussign\\n\\n return await self.temperature(twguess)\",\n \"def fahrenheitToKelvin(fahrenheit:float, ndigits = 2)->float:\\n return round(((float(fahrenheit) - 32) * 5 / 9) + 273.5, ndigits)\",\n \"def compute_kinoshita(s):\\n length = int(NBENDS*LAMBDA/DS) + 1\\n x = np.zeros(length)\\n y = np.zeros(length)\\n cur = np.zeros(length+1)\\n theta = THETA0*np.sin(2*np.pi*s/LAMBDA) \\\\\\n + THETA0**3*(JS*np.cos(6*np.pi*s/LAMBDA) \\\\\\n - JF*np.sin(6*np.pi*s/LAMBDA))\\n theta[np.abs(theta)<ZERO] = 0\\n for i in range(length):\\n cossum, sinsum = 0, 0\\n for j in range(i):\\n cossum += DS*np.cos(theta[j])\\n sinsum += DS*np.sin(theta[j])\\n x[i] = 0 if np.abs(cossum) < ZERO else cossum\\n y[i] = 0 if np.abs(sinsum) < ZERO else sinsum\\n x = np.concatenate((x, np.array([x[-1]+x[1]-x[0]])))\\n y = np.concatenate((y, np.array([y[-1]+y[1]-y[0]])))\\n s = np.concatenate((s, np.array([s[-1]+DS])))\\n theta = np.concatenate((theta, np.array([theta[-1]])))\\n if FLIPSTRM:\\n x = x[::-1]\\n y = y[::-1]\\n theta = np.concatenate((theta[::-1][1:], np.array([theta[0]])))\\n for i in range(1, length):\\n cur[i] = (theta[i]-theta[i-1])/DS\\n cur[i] = 0 if np.abs(cur[i]) < ZERO else cur[i]\\n cur[0], cur[-1] = cur[-2], cur[1]\\n return s, x, y, cur, theta\",\n \"def get_hbls_hbbl(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n z_u_r = self.grid_dict['z_u_r']\\n u = self.u\\n v = self.v\\n \\n v_upts = TTTW_func.v2u(v)\\n Hz = z_u_w[:,1:] - z_u_w[:,:-1]\\n\\n\\n\\n # CALCULATE swr_frac\\n self.swr_frac = TTTW_func.lmd_swr_frac(self.grid_dict)\\n\\n\\n # WHOLE THING HAPPENS IN j loop through y-indices\\n \\n # INITIALIZE ARRAYS\\n self.kmo = np.zeros([Ly])\\n self.Cr = np.zeros([Ly])\\n self.kbl = np.empty([Ly],dtype='int')\\n self.C_h_MO = np.zeros([Ly])\\n self.Cr = np.zeros([Ly,N+1]) # sum term\\n self.FC = np.zeros([Ly,N+1])\\n self.swdk_r = np.zeros([Ly,N+1])\\n \\n self.zscale = np.zeros([Ly,N])\\n self.Kern = np.zeros([Ly,N])\\n\\n \\n # --> LOOP THROUGH Y-INDICES\\n for j in range(Ly):\\n if self.LIMIT_MO_DEPTH:\\n self.kmo[j] = 0\\n self.C_h_MO[j] = self.C_MO *self.ustar[j]**3/self.vonKar\\n \\n self.kbl[j] = 0\\n self.Cr[j,-1] = 0 # set top Cr\\n self.Cr[j,0] = 0 # set bottom Cr\\n \\n # SEARCH FOR MIXED LAYER DEPTH\\n self.FC[j,-1] = 0.\\n\\n\\n # ---> LOOP TOP TO BOTTOM (FORTRAN ==> k=N-1,1,-1)\\n for k in range(N-1,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n \\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n self.zscale[j,k_w] = zscale\\n if self.LMD_KPP:\\n if self.LMD_BKPP:\\n zscaleb = z_u_r[j,k_r] - z_u_w[j,0]\\n Kern = zscale * zscaleb**2 / ( (zscale + self.epssfcs*self.hbls_old[j]) * (zscaleb**2+(self.epssfcb**2*self.hbbl_old[j]**2)))\\n else:\\n Kern = zscale / (zscale + (self.epssfcs*self.hbls_old[j]))\\n else:\\n Kern = 1.\\n \\n\\n\\n self.Kern[j,k_w] = Kern\\n self.FC[j,k_w] = self.FC[j,k_w+1] + Kern * (\\\\\\n ( ( u[j,k_r+1] - u[j,k_r] )**2 + ( v_upts[j,k_r+1] - v_upts[j,k_r])**2 ) \\\\\\n / (Hz[j,k_r] + Hz[j,k_r+1]) \\\\\\n - 0.5 * ( Hz[j,k_r] + Hz[j,k_r+1]) * (self.Ri_inv * self.bvf[j,k_w] + self.C_Ek*self.f[j]*self.f[j]))\\n\\n\\n #\\t\\tLOOP THAT FINDS BL DEPTH ##\\n #----> LOOP TOP TO BOTTOM (start at free surface, w-level surface) \\n \\n if self.LMD_KPP:\\n #swdk_r only used in this function so don't need to be class attribute\\n # but for testing make it an attribute to see what it is\\n \\n # fortran equivlanet ===> k=N,1,-1 \\n for k in range(N,0,-1):\\n # INDEX MAP\\n k_r = k-1\\n k_w = k\\n\\n ###################################################################### \\n self.swdk_r[j,k_w] = np.sqrt( self.swr_frac[j,k_w] * self.swr_frac[j,k_w-1])\\n zscale = z_u_w[j,N] - z_u_r[j,k_r]\\n Bfsfc = self.Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])\\n \\n self.bvf_max = np.sqrt(np.max([0,self.bvf[j,k_w-1]]))\\n \\n # CALCULATE TURBULENT VELOCITY SCALE FOR TRACERS\\n \\t\\t\\t self.ws = self.lmd_wscale_ws_only(Bfsfc, zscale,self.hbls_old[j],self.ustar[j])\\n \\n self.Vtsq = self.Vtc * self.ws* self.bvf_max + self.V0\\n \\n\\n self.Cr[j,k_w] = self.FC[j,k_w] + self.Vtsq\\n \\n\\n #######################################################################\\n \\n # SEARCH FOR hbls vertical level #\\n '''\\n kbl is specified at vertical w-level (via Cr which is at\\n vertical w-levels)\\n '''\\n if self.kbl[j] == 0 and self.Cr[j,k_w] < 0:\\n self.kbl[j] = k_w\\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] == 0 and Bfsfc*(z_u_w[j,N] - z_u_r[j,k_r]) > self.C_h_MO[j]:\\n self.kmo[j] = k_w\\n\\n \\n #--> still in j-loop\\n #######################################################\\n \\n # \\t\\tGET SURFACE BOUNDARY LAYER DEPTH # \\n self.hbls[j] = z_u_w[j,N] - z_u_w[j,0] + self.eps # set hbls as depth of entire water column\\n if self.kbl[j] > 0:\\n k_w = self.kbl[j]\\n k_r = k_w - 1 \\n if k_w == N: # set hbls at the surface btwn w- and rho-levels at surface\\n self.hbls[j] = z_u_w[j,N] - z_u_r[j,N-1]\\n \\n else:\\n self.hbls[j] = z_u_w[j,N] - ( z_u_r[j,k_r] * self.Cr[j,k_w+1] - z_u_r[j,k_r+1] * self.Cr[j,k_w]) / \\\\\\n (self.Cr[j,k_w+1] - self.Cr[j,k_w])\\n \\n if self.LIMIT_MO_DEPTH:\\n if self.kmo[j] > 0:\\n k_w = self.kmo[j]\\n k_r = k_w-1\\n if k_w == N:\\n z_up = z_u_w[j,N]\\n cff_up = np.max([0,Bo[j]])\\n else:\\n z_up = z_r[j,k_w+1]\\n cff_up = np.max([0, Bo[j] + self.Bosol[j]*(1-self.swdk_r[j,(k_w-1)+1])])\\n \\n cff_dn = np.max([0,Bo[j] + self.Bosol[j] * (1-self.swdk_r[j,k_w])]) \\n h_MO = z_u_w[j,N] + self.C_h_MO[j] * ( cff_up*z_up - cff_dn * z_u_r[j,k_r] ) \\\\\\n / ( cff_up * cff_dn * (z_up - z_u_r[j,k_r]) ) \\\\\\n + self.C_h_MO[j] * (cff_dn - cff_up)\\n\\n self.hbls[j] = np.min([self.hbls[j],np.max([h_MO,0])])\\n\\n\\n\\n #### GET BOTTOM BOUNDARY LAYER DEPTH #######\\n if self.LMD_BKPP:\\n self.kbl[j] = 0 # reset Cr at bottom and kbl for BKPP\\n self.Cr[j,0] = 0.\\n self.FC[j,0] = 1.5 * self.FC[j,1] - 0.5 * self.FC[j,2] # linear extrapolation\\n \\n #---> LOOP BOTTOM TO TOP\\n # FIND kbl for BBL\\n for k in range(1,N+1):\\n k_r = k-1\\n k_w = k \\n self.Cr[j,k_w] = self.FC[j,k_w] - self.FC[j,0]\\n \\n # LOOK FOR FIRST ZERO CROSSING FROM BOTTOM UP\\n if self.kbl[j] == 0 and self.Cr[j,k_w] > 0:\\n self.kbl[j] = k_w \\n \\n\\n self.hbbl[j] = z_u_w[j,N] - z_u_w[j,0] # total depth\\n if self.kbl[j] > 0 :\\n k_w = self.kbl[j] \\n k_r = k_w -1\\n if k_w == 1: # NO BBL CASE\\n self.hbbl[j] = z_u_r[j,0] - z_u_w[j,0] #in between bottom rho and w-level\\n else:\\n self.hbbl[j] = ( z_u_r[j,k_r-1] * self.Cr[j,k_w] - z_u_r[j,k_r] * self.Cr[j,k_w-1]) / \\\\\\n (self.Cr[j,k_w] - self.Cr[j,k_w-1]) - z_u_w[j,0]\",\n \"def ksat_func(ThS, Th33, lbd):\\n\\n # assert ThS > Th33, \\\"sat <= fc IN ksat_func\\\"\\n ksat = 1930 * (ThS - Th33) ** (3 - lbd)\\n return ksat\",\n \"def test_thermal_relaxation_error_kraus(self):\\n t1, t2, time, p1 = (1, 2, 1, 0.3)\\n error = thermal_relaxation_error(t1, t2, time, p1)\\n circ, p = error.error_term(0)\\n self.assertEqual(p, 1)\\n self.assertEqual(circ[0]['name'], 'kraus')\\n self.assertEqual(circ[0]['qubits'], [0])\",\n \"def rad(tx,K,w,e,T0,Vo,P):\\r\\n\\r\\n M=2*np.pi*(tx-T0)/P #Mean anomaly\\r\\n E=np.pi\\r\\n for j in range(0,25):\\r\\n E=(M-e*(E*np.cos(E)-np.sin(E)))/(1-e*np.cos(E))\\r\\n th=2*np.arctan(((1+e)/(1-e))**0.5*np.tan(E/2))\\r\\n return K*(np.cos(th+w)+e*np.cos(w))+Vo\",\n \"def kelvinToRankie(kelvin:float, ndigits = 2)->float:\\n return round(float(fahrenheit)+ 459, ndigits)\",\n \"def molar_mass_dry_air():\\n return 28.9647\",\n \"def whc_tot(mukey, layers=''):\\n #read appropriate soils.in content to a python list\\n mukey = str(mukey)\\n soil_path = \\\"/data/paustian/ernie/SSURGO_master_script/soil_test2/\\\"\\n soil_fpath = soil_path+mukey[:-3]+\\\"/\\\"+mukey+\\\".in\\\"\\n cont = [[]]\\n data_input = open(soil_fpath, 'r')\\n for line in data_input:\\n cont.append(line.split())\\n del cont[0]\\n\\n #convert all entries in the 2D list to float format where possible, or zero in the case\\n #of very small numbers recorded in scientific notation\\n for k in range(len(cont)):\\n for l in range(len(cont[k])):\\n cont[k][l] = float(cont[k][l])\\n\\n #loop through list and compute the water holding capacity increment represented in \\n #each line\\n min_h2o_evap = 0\\n min_h2o = 0\\n max_h2o = 0\\n whc = 0\\n for i in range(len(cont)):\\n if not layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n else:\\n if 1+i <= layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n if layers:\\n if layers > len(cont):\\n print \\\"NOTE: specified layer limit exceeds number of layers found in soils.in file\\\"\\n\\n return whc, min_h2o, max_h2o\",\n \"def flattop_risefall(t, params):\\n risefall = tf.cast(params['risefall'].get_value(), dtype=tf.float64)\\n t_final = tf.cast(params['t_final'].get_value(), dtype=tf.float64)\\n t_up = risefall\\n t_down = t_final - risefall\\n return (1 + tf.math.erf((t - t_up) / risefall)) / 2 * \\\\\\n (1 + tf.math.erf((-t + t_down) / risefall)) / 2\",\n \"def hz2mel(hz):\\r\\n return 2595 * np.log10(1+hz/700.0)\",\n \"def derive_Hosek18(wavelength):\\n # Extinction law definition\\n wave = np.array([0.8059, 0.962, 1.25, 1.53, 2.14, 3.545])\\n A_AKs = np.array([9.66, 6.29, 3.56, 2.33, 1.0, 0.50])\\n \\n\\n # Following Hosek+18, Interpolate over the curve with cubic spline interpolation\\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\\n A_AKs_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n # This curve already assumes A_Ks = 1.0, so we can go straight to\\n # output \\n return A_AKs_at_wave\",\n \"def compute_windchill(t,v):\\n a = 35.74\\n b = 0.6215\\n c = 35.75\\n d = 0.4275\\n v16 = v**0.16\\n wci = a+(b*t)-(c*v16)+(d*t*v16)\\n return wci\",\n \"def KendallTau_calc(TP, FP, FN, TN):\\n try:\\n n = TP + FP + FN + TN\\n return (2 * (TP + TN - FP - FN)) / (n * (n - 1))\\n except Exception:\\n return \\\"None\\\"\",\n \"def T_naught(z, h, OM, OB):\\n\\n T0 = 28.5 * ((1.0+z)/10.0)**(0.5) * OB/0.042 * h/0.73 * (0.24/OM)**(0.5)\\n return T0\",\n \"def uL( wavelen, **kwargs ):\\n Cc = C.c * 1e6 # speed of light um s^-1\\n\\n Gamma = kwargs.get('Gamma', 2*np.pi *5.9e6 ) # linewidth s^-1\\n lambda0 = kwargs.get('lambda0', 0.671 ) # transition wavelength in microns \\n \\n omega0 = 2*np.pi*Cc / lambda0\\n omegaL = 2*np.pi*Cc / wavelen\\n intensity = 1.0 \\n depthJ = (intensity)* -3*np.pi* Cc**2*Gamma / ( 2*omega0**3) * \\\\\\n ( 1/(omega0 - omegaL ) + 1/(omega0 + omegaL ) ) # Joule\\n depthuK = depthJ / C.k *1e6 # C.k is Boltzmann's constant\\n return depthuK\",\n \"def water_uptake_campbell(self, soil):\\r\\n daily_ref_evap_transp = soil.daily_ref_evap_transp\\r\\n root_hydr_cond = np.zeros(soil.total_layers)\\r\\n shoot_hydr_cond = np.zeros(soil.total_layers)\\r\\n plant_hydr_cond = np.zeros(soil.total_layers)\\r\\n root_activity = np.zeros(soil.total_layers)\\r\\n root_cond_adj = np.zeros(soil.total_layers)\\r\\n tot_root_cond_adj = 0\\r\\n salinity_factor = np.zeros(soil.total_layers)\\r\\n soil_water_pot_avg = 0\\r\\n WAT_POT_FIELD_CAP = -33\\r\\n\\r\\n # Transpiration\\r\\n self.pot_transp = daily_ref_evap_transp * self.light_intercpt\\r\\n self.max_pot_transp = (self.campbell_max_daily_transp *\\r\\n self.light_intercpt)\\r\\n self.expect_transp = min(self.pot_transp, self.max_pot_transp) # mm/day\\r\\n\\r\\n # Plant hydraulic conductance (kg s m-4)\\r\\n tot_plant_hydr_cond = (self.max_pot_transp /\\r\\n (WAT_POT_FIELD_CAP -\\r\\n self.leaf_water_pot_stress_onset))\\r\\n # assumption of 2/3 of plant hydraulic conductance is from roots\\r\\n tot_root_hydr_cond = tot_plant_hydr_cond / 0.65\\r\\n # assumption of 1/3 of plant hydraulic conductivity is from shoots\\r\\n tot_shoot_hydr_cond = tot_plant_hydr_cond / 0.35\\r\\n\\r\\n for lyr in soil.layers:\\r\\n root_activity[lyr] = 1\\r\\n salinity_factor[lyr] = 1\\r\\n root_cond_adj[lyr] = (root_activity[lyr] * self.root_fraction[lyr]\\r\\n * salinity_factor[lyr])\\r\\n root_hydr_cond[lyr] = tot_root_hydr_cond * root_cond_adj[lyr]\\r\\n tot_root_cond_adj += root_cond_adj[lyr]\\r\\n\\r\\n # Root, shoot and plant hydraulic conductance(kg s m-4)\\r\\n for lyr in soil.layers:\\r\\n if root_cond_adj[lyr] > 0:\\r\\n shoot_hydr_cond[lyr] = (tot_shoot_hydr_cond *\\r\\n root_cond_adj[lyr] / tot_root_cond_adj)\\r\\n plant_hydr_cond[lyr] = (root_hydr_cond[lyr] *\\r\\n shoot_hydr_cond[lyr] /\\r\\n (root_hydr_cond[lyr] +\\r\\n shoot_hydr_cond[lyr]))\\r\\n else:\\r\\n plant_hydr_cond[lyr] = 0\\r\\n\\r\\n tot_root_hydr_cond *= tot_root_cond_adj\\r\\n tot_plant_hydr_cond = ((tot_root_hydr_cond * tot_shoot_hydr_cond) /\\r\\n (tot_root_hydr_cond + tot_shoot_hydr_cond))\\r\\n\\r\\n if tot_plant_hydr_cond > 0:\\r\\n for lyr in soil.layers:\\r\\n soil_water_pot_avg += (soil.water_potential[lyr] *\\r\\n root_cond_adj[lyr])\\r\\n leaf_water_pot = (soil_water_pot_avg - self.expect_transp /\\r\\n tot_plant_hydr_cond)\\r\\n if leaf_water_pot < self.leaf_water_pot_stress_onset:\\r\\n leaf_water_pot = ((tot_plant_hydr_cond * soil_water_pot_avg *\\r\\n (self.leaf_water_pot_stress_onset -\\r\\n self.leaf_water_pot_wilt_point) +\\r\\n self.leaf_water_pot_wilt_point *\\r\\n self.expect_transp)\\r\\n / (tot_plant_hydr_cond *\\r\\n (self.leaf_water_pot_stress_onset -\\r\\n self.leaf_water_pot_wilt_point) +\\r\\n self.expect_transp))\\r\\n if leaf_water_pot < self.leaf_water_pot_wilt_point:\\r\\n leaf_water_pot = self.leaf_water_pot_wilt_point\\r\\n self.att_transp = 0\\r\\n transp_ratio = self.att_transp / self.expect_transp\\r\\n\\r\\n elif leaf_water_pot < self.leaf_water_pot_stress_onset:\\r\\n self.att_transp = (self.expect_transp * (leaf_water_pot -\\r\\n self.leaf_water_pot_wilt_point) / (\\r\\n self.leaf_water_pot_stress_onset -\\r\\n self.leaf_water_pot_wilt_point))\\r\\n transp_ratio = self.att_transp / self.expect_transp\\r\\n\\r\\n else:\\r\\n self.att_transp = self.expect_transp\\r\\n transp_ratio = 1\\r\\n # crop water uptake (kg/m2/d = mm/d)\\r\\n for lyr in soil.layers:\\r\\n self.water_uptake[lyr] = (plant_hydr_cond[lyr] *\\r\\n (soil.water_potential[lyr] -\\r\\n leaf_water_pot) * transp_ratio)\\r\\n if self.water_uptake[lyr] < 0:\\r\\n self.water_uptake[lyr] = 0\\r\\n self.crop_transp = self.water_uptake.sum() # mm/day\\r\\n self.cum_transp += self.crop_transp\\r\\n self.cum_pot_transp += self.expect_transp\\r\\n self.transp_ratio = self.crop_transp / self.expect_transp\",\n \"def k_Ni00(wind_ms, temp_C):\\n\\n U = wind_ms\\n\\n Sc = schmidt_number(temp_C)\\n k = (0.333 * U + 0.222 * U ** 2) * (600 / Sc) ** 0.5\\n\\n return k\",\n \"def calc_equil(sst, ft_qv, use_NT=False):\\n \\n run_main(sst, ft_qv, use_NT)\\n \\n # grab csv file\\n with open('dumpmodel.csv','r') as f:\\n df_result=pd.read_csv(f)\\n\\n # last time step into named tupple\\n out=df_result.iloc[-1]\\n steady_state=make_tuple(out.to_dict())\\n steady_state\\n \\n # obtain steady-state values\\n dth=steady_state.deltheta\\n dqt=steady_state.delqv\\n thetal_m=steady_state.theta\\n qt_m=steady_state.qv\\n h=steady_state.h\\n press=tf.find_press(steady_state.h) #kPa\\n thetal_ft = steady_state.theta + dth\\n qt_ft = steady_state.qv + dqt\\n zb = steady_state.LCL\\n zi = steady_state.h\\n we = steady_state.went\\n \\n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\\n gamma = 6e-3 \\n thetal_3000 = thetal_ft + gamma*(3000-h)\\n LTS = thetal_3000 - steady_state.theta\\n \\n # calculate delta_Fr\\n delta_Frstar = 82.0 # Wm^-2\\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\\n\\n # calculate LWP\\n rho = 1.\\n LWP = 0.5*rho*(zi-zb)**2\\n \\n # put all required variables into output array\\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\\n \\n return out_array\",\n \"def rt60_eyring(S, V, a, m, c):\\n\\n return -(24 * np.log(10) / c) * V / (S * np.log(1 - a) + 4 * m * V)\",\n \"def bet(P_1,V0_1,Vi_1,Vflap_1,J_a,Twist,Pitch,Chord,PLE,Polar,rho,dt):\\n Phi = np.zeros_like(Twist)\\n V_4 = np.zeros_like(P_1)\\n dV_4 = np.zeros_like(P_1)\\n F_5 = np.zeros_like(P_1)\\n F_4 = np.zeros_like(P_1)\\n F_1 = np.zeros_like(P_1)\\n l = np.zeros_like(Twist)\\n d = np.zeros_like(Twist)\\n m = np.zeros_like(Twist)\\n aoa = np.zeros_like(Twist)\\n daoa = np.zeros_like(Twist)\\n cl = np.zeros_like(Twist)\\n cd = np.zeros_like(Twist)\\n cm = np.zeros_like(Twist)\\n w = np.zeros_like(Chord)\\n wy = np.zeros_like(Twist)\\n S = np.zeros_like(Twist)\\n \\n Vflap_1[np.abs(Vflap_1) < 1e-8] = 0\\n \\n for i in range(len(P_1)):\\n \\n for j in range(len(P_1[i])):\\n \\n # Calculate wing element elevation angle\\n \\n if j <= J_a:\\n \\n dz = P_1[i,J_a,2]\\n dy = P_1[i,J_a,1]\\n\\n Phi[i,j] = np.arctan(dz/dy)\\n \\n else:\\n \\n dz = P_1[i,-1,2]-P_1[i,J_a+1,2]\\n dy = P_1[i,-1,1]-P_1[i,J_a+1,1]\\n \\n Phi[i,j] = np.arctan(dz/dy)\\n \\n # Calculate local flow velocity\\n V_4[i,j] = roty((Twist[i,j]+Pitch[j]), rotx(-Phi[i,j],(V0_1 + Vi_1 + Vflap_1[i,j]))) \\n V_4[np.abs(V_4) < 1e-12] = 0\\n\\n # Calculate angle of attack\\n aoa[i,j] = np.arctan(-V_4[i,j,2]/V_4[i,j,0])\\n\\n # Find cl,cd,cm from polars with linear interpolation\\n cl[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,1])\\n cd[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,2])\\n cm[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,3])\\n\\n # Calculate element width\\n if j < len(P_1[i])-1:\\n wy[i,j] = P_1[i,j+1,1]-P_1[i,j,1]\\n else:\\n wy[i,j] = P_1[i,j,1]-P_1[i,j-1,1]\\n \\n # Calculate element surface area\\n S[i,j] = Chord[j] * wy[i,j]\\n \\n # Calculate aerodynamic forces\\n l[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cl[i,j]\\n d[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cd[i,j]\\n m[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cm[i,j]\\n \\n # Force vector in Blade Element local axes\\n F_5[i,j] = np.array([-d[i,j], 0, l[i,j]])\\n \\n # Force vector in Blade local axes\\n F_4[i,j] = roty(aoa[i,j],F_5[i,j])\\n \\n \\n \\n \\\"\\\"\\\"\\n Add mass effect and rotate to stroke plane axes\\n \\\"\\\"\\\"\\n for i in range(len(P_1)):\\n \\n for j in range(len(P_1[i])):\\n # Add mass effects\\n if i==0:\\n daoa[i,j] = (aoa[1,j]-aoa[0,j])/dt\\n dV_4[i,j] = (V_4[1,j]-V_4[0,j])/dt\\n \\n elif i < len(P_1)-1:\\n daoa[i,j] = (aoa[i+1,j]-aoa[i-1,j])/(2*dt)\\n dV_4[i,j] = (V_4[i+1,j]-V_4[i-1,j])/(2*dt)\\n \\n else:\\n daoa[i,j] = (aoa[i,j]-aoa[i-1,j])/dt\\n dV_4[i,j] = (V_4[i,j]-V_4[i-1,j])/dt\\n \\n \\n F_4[i,j,0] = F_4[i,j,0] - 0.25 * rho * np.pi * Chord[j] * S[i,j] * V_4[i,j,2] * daoa[i,j]\\n F_4[i,j,2] = F_4[i,j,2] + 0.25 * rho * np.pi * Chord[j] * S[i,j] * dV_4[i,j,2]\\n \\n # Tranform to Stroke Plane Axes\\n F_1[i,j] = rotx(Phi[i,j],roty(-(Twist[i,j]+Pitch[j]),F_4[i,j]))\\n\\n return F_1\",\n \"def eq_br_mo(k, K, kr, theta_eq, bist_ang_az, eq_azi, wind_dir, u_10, fetch, div, polarization):\\r\\n\\r\\n nk = k.shape[2]\\r\\n\\r\\n # Set azimuth to compute transfer function\\r\\n azimuth = np.linspace(-np.pi, np.pi, 37)\\r\\n\\r\\n # # Sea surface slope in the direction of incidence angle\\r\\n ni2 = GoM_ni2_func(kr, k, K, u_10, fetch, azimuth, div, wind_dir)\\r\\n\\r\\n nn = 89 * 2 * np.pi / 180\\r\\n ni = (np.arange(nk) * nn / nk).reshape(1, nk) - nn / 2\\r\\n ni = ni.reshape(nk, 1)\\r\\n ni = np.tan(ni)\\r\\n\\r\\n Br = np.zeros([div.shape[0], div.shape[1]])\\r\\n\\r\\n for ii in np.arange(ni2.shape[0]):\\r\\n for jj in np.arange(ni2.shape[1]):\\r\\n P = np.exp(-0.5 * (ni - np.mean(ni)) ** 2 / ni2[ii, jj]) / np.sqrt(2 * np.pi * ni2[ii, jj])\\r\\n # the range of the sea surface slope\\r\\n angle_index = np.logical_and(-3 * 180 * np.arctan(np.sqrt(ni2[ii,jj])) / np.pi < np.arctan(ni) * 180 / np.pi, np.arctan(ni) * 180 / np.pi < 3 * 180 * np.arctan(np.sqrt(ni2[ii,jj])) / np.pi)\\r\\n P = P[angle_index]\\r\\n nini = ni[angle_index]\\r\\n nnk = nini.shape[0]\\r\\n nini = nini.reshape(nnk, 1)\\r\\n # local incidence angle\\r\\n theta_l = np.abs(theta_eq[jj] - np.arctan(nini).reshape(nnk, 1))\\r\\n # geometric scattering coefficients [Plant 1997] equation 5,6\\r\\n eps_sin = np.sqrt(const.epsilon_sw-np.sin(theta_l)**2)\\r\\n # compute the wave number for bistatic geometry\\r\\n kbr = 2 * kr * np.sin(theta_l) * np.cos(bist_ang_az[jj]/2)\\r\\n kkbr = np.sort(kbr[:,0])\\r\\n T = Trans_func(kkbr, K[ii, jj], u_10[ii, jj], fetch, azimuth, div[ii, jj])[np.argsort(kbr[:,0])].reshape(nnk,1)\\r\\n Skb = B_int_single(kkbr.reshape(nnk, 1), u_10[ii, jj], fetch, eq_azi[jj])[np.argsort(kbr[:,0]), :] * (1+abs(T)) / kbr.reshape(nnk,1) ** 4\\r\\n Skb_pi = B_int_single(kkbr.reshape(nnk, 1), u_10[ii, jj], fetch, np.pi + eq_azi[jj])[np.argsort(kbr[:,0]), :] * (1+abs(T)) / kbr.reshape(nnk, 1) ** 4\\r\\n Skb_r = (Skb + Skb_pi) / 2 # Kudryavtsev 2003a equation 2\\r\\n if polarization == 'VV':\\r\\n G = np.cos(theta_l) ** 2 * (const.epsilon_sw - 1) * (\\r\\n const.epsilon_sw * (1 + np.sin(theta_l) ** 2) - np.sin(theta_l) ** 2) / (\\r\\n const.epsilon_sw * np.cos(theta_l) + eps_sin) ** 2\\r\\n G = np.abs(G) ** 2\\r\\n else:\\r\\n G = np.cos(theta_l) ** 2 * (const.epsilon_sw - 1) / (np.cos(theta_l) + eps_sin) ** 2\\r\\n G = np.abs(G) ** 2\\r\\n # pure Bragg scattering NRCS\\r\\n br0 = 16 * np.pi * kr ** 4 * G * Skb_r\\r\\n # Bragg scattering composite model\\r\\n BR = br0 * P.reshape(nnk, 1)\\r\\n # integral over kbr >= kd\\r\\n a = np.tan(theta_eq[jj] - const.d / (2 * np.cos(bist_ang_az[jj]/2)))\\r\\n b = np.tan(theta_eq[jj] + const.d / (2 * np.cos(bist_ang_az[jj]/2)))\\r\\n Br[ii, jj] = np.trapz(BR[nini <= a], nini[nini <= a]) + np.trapz(BR[nini >= b], nini[nini >= b])\\r\\n return Br\",\n \"def calc_lhv(self):\\n hf = {}\\n hf['hydrogen'] = 0\\n hf['methane'] = -74.85\\n hf['ethane'] = -84.68\\n hf['propane'] = -103.8\\n hf['butane'] = -124.51\\n hf['O2'] = 0\\n hf['CO2'] = -393.5\\n # water (gaseous)\\n hf['H2O'] = -241.8\\n\\n lhv = 0\\n\\n for f, x in self.fuel.val.items():\\n molar_masses[f] = CP.PropsSI('M', f)\\n fl = set(list(hf.keys())).intersection(\\n set([a.replace(' ', '') for a in CP.get_aliases(f)]))\\n if len(fl) == 0:\\n continue\\n\\n if list(fl)[0] in self.fuels():\\n structure = fluid_structure(f)\\n\\n n = {}\\n for el in ['C', 'H', 'O']:\\n if el in structure:\\n n[el] = structure[el]\\n else:\\n n[el] = 0\\n\\n lhv += (-(n['H'] / 2 * hf['H2O'] + n['C'] * hf['CO2'] -\\n ((n['C'] + n['H'] / 4) * hf['O2'] +\\n hf[list(fl)[0]])) / molar_masses[f] * 1000) * x\\n\\n return lhv\",\n \"def ts_bt_func(thermal_rad, k1, k2):\\n ts_bt = np.copy(thermal_rad).astype(np.float64)\\n ts_bt[ts_bt <= 0] = np.nan\\n np.reciprocal(ts_bt, out=ts_bt)\\n ts_bt *= k1\\n ts_bt += 1.0\\n np.log(ts_bt, out=ts_bt)\\n np.reciprocal(ts_bt, out=ts_bt)\\n ts_bt *= k2\\n return ts_bt.astype(np.float32)\",\n \"def valley(self, h=2, b1=3, b2=3, b3=9):\\n mua = 2 * h / self.sk\\n mub = 2 * b3 / (b1+b2)\\n muc = 5\\n self.mu_valley = mu = min(mua, mub, muc)\\n self.s_valley = self.mu_valley * self.Ce * self.Ct * self.sk\\n print(f'The valley snow shape coefficient = {self.mu_valley :.2f}')\\n print(f'The peak valley snow load = {self.s_valley :.2f}kPa')\",\n \"def flattop_risefall_1ns(t, params):\\n params['risefall'] = 1e-9\\n return flattop_risefall(t, params)\",\n \"def hz2mel(hz):\\n return 2595 * np.log10(1+hz/700.)\",\n \"def jarrow_rudd(s, k, t, v, rf, cp, am=False, n=100):\\n\\n\\t# Basic Calculations\\n\\th = t / n \\n\\tu = math.exp((rf - 0.5 * math.pow(v, 2)) * h + v * math.sqrt(h))\\n\\td = math.exp((rf - 0.5 * math.pow(v, 2)) * h - v * math.sqrt(h))\\n\\tdrift = math.exp(rf * h)\\n\\tq = (drift - d) / (u - d)\\n\\n\\t#Process the terminal stock price\\n\\tstkval = np.zeros((n+1, n+1))\\n\\toptval = np.zeros((n+1, n+1))\\n\\tstkval[0, 0] = s\\n\\n\\tfor i in range(1, n+1):\\n\\t\\tstkval[i, 0] = stkval[i - 1, 0] * u\\n\\t\\tfor j in range(1, i + 1):\\n\\t\\t\\tstkval[i, j] = stkval[i - 1, j - 1] * d\\n\\n\\t# Backwards recursion for option price\\n\\tfor j in range(n + 1):\\n\\t\\toptval[n, j] = max(0, cp * (stkval[n, j] - k))\\n\\tfor i in range(n - 1, -1, -1):\\n\\t\\tfor j in range(i + 1):\\n\\t\\t\\toptval[i, j] = (q * optval[i + 1, j] + (1 - q) * optval[i + 1, j + 1]) / drift\\n\\t\\t\\tif am:\\n\\t\\t\\t\\toptval[i, j] = max(optval[i, j], cp * (stkval[i, j] - k))\\n\\treturn optval[0, 0]\",\n \"def compute_thermo(E,dos,TT):\\n if (len(dos)<3):\\n print (\\\"Not enough points in the phonon DOS!\\\")\\n return None\\n \\n ZPE = 0.5*dos_integral(E,dos,1)\\n modes = dos_integral(E,dos)\\n \\n EvibT = np.zeros(len(TT))\\n SvibT = np.zeros(len(TT))\\n CvibT = np.zeros(len(TT))\\n FvibT = np.zeros(len(TT))\\n for i in range(0,len(TT)):\\n h = 0.5*(E[2]-E[0])\\n arg = K_BOLTZMANN_RY*TT[i]\\n arg2 = 2.0 * arg\\n Evib = 0.0\\n Svib = 0.0\\n Cvib = 0.0\\n for j in range(0,len(dos)-3,3):\\n\\n Evib += 3.0*E[j]/tanh(E[j]/(arg2))*dos[j]+\\\\\\n 3.0*E[j+1]/tanh(E[j+1]/(arg2))*dos[j+1]+\\\\\\n 2.0*E[j+2]/tanh(E[j+2]/(arg2))*dos[j+2]\\n \\n Svib += 3.0*(E[j]/arg2/tanh(E[j]/arg2)-log(2.0*sinh(E[j]/arg2)))*dos[j]+\\\\\\n 3.0*(E[j+1]/arg2/tanh(E[j+1]/arg2)-log(2.0*sinh(E[j+1]/arg2)))*dos[j+1]+\\\\\\n 2.0*(E[j+2]/arg2/tanh(E[j+2]/arg2)-log(2.0*sinh(E[j+2]/arg2)))*dos[j+2]\\n\\n try: # avoid overflow error for arg very small\\n Cvib += 3.0*pow(E[j]/arg,2)/( 4.0*pow(sinh(E[j]/(arg2)),2) )*dos[j]+\\\\\\n 3.0*pow(E[j+1]/arg,2)/( 4.0*pow(sinh(E[j+1]/(arg2)),2) )*dos[j+1]+\\\\\\n 2.0*pow(E[j+2]/arg,2)/( 4.0*pow(sinh(E[j+2]/(arg2)),2) )*dos[j+2]\\n except:\\n Cvib += 0.0\\n\\n EvibT[i] = h*0.5*Evib*3.0/8.0 # h is the integration step, 0.5 comes from the equation for E,\\n # the factor 3.0/8.0 comes from the Simpson 3/8 rule\\n SvibT[i] = h*K_BOLTZMANN_RY*Svib*3.0/8.0\\n CvibT[i] = h*K_BOLTZMANN_RY*Cvib*3.0/8.0\\n FvibT = EvibT - SvibT * TT\\n\\n print ()\\n return TT, EvibT, SvibT, CvibT, FvibT, ZPE, modes\",\n \"def overheads(NPT, DIT, NDIT):\\n ov = 360. + 120. + NPT*NDIT*(DIT + 80. + 15.)\\n print 'Telescope time in h = ', ov/3600.\",\n \"def test_calculate_enthalpy(self):\\n expected = [454734.6, 807677.3, 1171053.1]\\n result = WetBulbTemperature()._calculate_enthalpy(\\n self.mixing_ratio, self.specific_heat, self.latent_heat, self.temperature\\n )\\n self.assertArrayAlmostEqual(result, expected, decimal=1)\",\n \"def lunarperigee(time):\\n dtor = np.pi / 180\\n t1 = 1 + time\\n t2 = t1 * t1\\n t3 = t2 * t1\\n perigee = (\\n 334.329653 * dtor\\n + 4069.0340329575 * dtor * t1\\n - 0.010325 * dtor * t2\\n - 1.2e-5 * dtor * t3\\n )\\n return perigee\",\n \"def hz2mel(hz):\\n return 1127 * np.log(1 + hz / 700)\",\n \"def slewEstimate(source1,source2=None,ant=1,returnUnit='time') :\\n azel1 = azel(source1)\\n azel1[0] = _wrapFix(azel1[0], ant)\\n if source2 == None: \\n azel2 = currentAzel(ant)\\n else:\\n azel2 = azel(source2)\\n azel2[0] = _wrapFix(azel2[0], ant)\\n az1 = [azel1[0], _wrapChoice(azel1[0], ant)]\\n az2 = [azel2[0], _wrapChoice(azel2[0], ant)]\\n print az1, az2\\n deltaAz = 360\\n deltaAz = min(deltaAz, abs(az1[0]-az2[0]))\\n deltaAz = min(deltaAz, abs(az1[0]-az2[1]))\\n deltaAz = min(deltaAz, abs(az1[1]-az2[0]))\\n deltaAz = min(deltaAz, abs(az1[1]-az2[1]))\\n delta = [deltaAz, abs(azel1[1]-azel2[1])]\\n print delta\\n if ant < 7:\\n azRate = 60.0\\n elRate = 30.0\\n settleTime = 5.0\\n elif ant < 16: \\n azRate = 120.0\\n elRate = 60.0\\n settleTime = 10.0\\n else: \\n azRate = 60.0\\n elRate = 30.0\\n settleTime = 8.0\\n if returnUnit == 'time': \\n return max(delta[0]/azRate,delta[1]/elRate) + settleTime/60.0\\n return delta\",\n \"def hz2mel(hz):\\n\\treturn 2595 * numpy.log10(1 + hz / 700.0)\",\n \"def derive_Fritz11(wavelength):\\n # Extinction law definition\\n wave = np.array([1.282, 1.736, 2.166, 2.625, 2.758, 2.873, 3.039, 3.297, 3.74, 3.819, 3.907, 4.052,\\n 4.376, 5.128, 5.908, 6.772, 7.459, 7.502, 8.76, 12.371, 19.062])\\n A_AKs = np.array([7.91, 4.30, 2.49, 1.83, 1.51, 1.84, 2.07, 1.66, 1.19, 1.19, 1.09, 1.01, 1.09, 0.99,\\n 1.04, 0.84, 0.81, 0.79, 2.04, 1.34, 1.34])\\n\\n\\n # Interpolate over the curve\\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\\n A_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n # We'll call 2.14 microns the K-band\\n idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )\\n A_AKs_at_wave = A_at_wave / A_at_wave[idx] \\n\\n return A_AKs_at_wave\",\n \"def internal_heat_gain(dwelling):\\n losses_gain = -40 * dwelling.Nocc\\n water_heating_gains = (1000. / 24.) * dwelling.heat_gains_from_hw / DAYS_PER_MONTH\\n\\n mean_appliance_energy = 207.8 * (dwelling.GFA * dwelling.Nocc) ** 0.4714\\n appliance_consumption_per_day = (mean_appliance_energy / 365.) * (\\n 1 + 0.157 * numpy.cos((2. * math.pi / 12.) * (numpy.arange(12) - .78)))\\n\\n appliance_consumption = appliance_consumption_per_day * DAYS_PER_MONTH\\n\\n if dwelling.reduced_gains:\\n met_gain = 50 * dwelling.Nocc\\n cooking_gain = 23 + 5 * dwelling.Nocc\\n appliance_gain = (0.67 * 1000. / 24) * appliance_consumption_per_day\\n light_gain = 0.4 * dwelling.full_light_gain\\n else:\\n met_gain = 60 * dwelling.Nocc\\n cooking_gain = 35 + 7 * dwelling.Nocc\\n appliance_gain = (1000. / 24) * appliance_consumption_per_day\\n light_gain = dwelling.full_light_gain\\n\\n total_internal_gains = (met_gain\\n + light_gain\\n + appliance_gain\\n + cooking_gain\\n + water_heating_gains\\n + dwelling.pump_gain\\n + losses_gain)\\n\\n if dwelling.reduced_gains:\\n summer_met_gain = 60 * dwelling.Nocc\\n summer_cooking_gain = 35 + 7 * dwelling.Nocc\\n summer_appliance_gain = (1000. / 24) * appliance_consumption_per_day\\n summer_light_gain = dwelling.full_light_gain\\n total_internal_gains_summer = (summer_met_gain +\\n water_heating_gains +\\n summer_light_gain +\\n summer_appliance_gain +\\n summer_cooking_gain +\\n dwelling.pump_gain +\\n losses_gain\\n - dwelling.heating_system_pump_gain)\\n else:\\n total_internal_gains_summer = total_internal_gains - dwelling.heating_system_pump_gain\\n\\n # Apply results to dwelling\\n return dict(appliance_consumption=appliance_consumption,\\n met_gain=met_gain,\\n cooking_gain=cooking_gain,\\n appliance_gain=appliance_gain,\\n light_gain=light_gain,\\n water_heating_gains=water_heating_gains,\\n losses_gain=losses_gain,\\n total_internal_gains=total_internal_gains,\\n total_internal_gains_summer=total_internal_gains_summer)\",\n \"def twostr_func(wavelength, F_s, solarzenithangle,albedo_dif, \\n\\t\\t\\talbedo_dir, temp_ground, w_0, g, tau_n, temp_c):\\n\\t\\n\\t########################\\n\\t###Import useful libraries\\n\\t########################\\n\\timport numpy as np\\n\\timport pdb\\n\\timport scipy.linalg\\n\\n\\n\\n\\n\\t########################\\n\\t###Define model parameters\\n\\t########################\\n\\t#Properties of the ground\\n\\temissivity_ground=1.-albedo_dif #emissivity of ground. 1=perfect BB emitter.\\n\\n\\t#Optical depth structure\\n\\tNlayer=len(tau_n) #number of layers in the atmospheric model.\\n\\t\\n\\ttau_c=np.zeros(Nlayer+1)# tau_c[n] is the cumulative optical depth at the upper edge of layer n. So tau_c[0]=0, and tau_c[N] is the maximum possible.\\n\\tfor n in range(0, Nlayer):\\n\\t\\ttau_c[n+1]=tau_c[n]+tau_n[n] \\n\\n\\t#In the Toon formalism, j=0 corresponds to space, and j=N+1 corresponds to the planet surface.\\n\\t#These points in wavelength space define the edges of the bins in tau space. \\n\\t#Other terminology:\\n\\t#\\ttau_c=cumulative optical depth of layers *above* layer n. \\n\\t#\\ttau_n=total optical depth of the layer n\\n\\t#\\ttau=total optical depth at any point within a layer n, hence satisfying 0<tau<tau_n\\n\\n\\tmu_0=np.cos(solarzenithangle) #\\\"incident direction of solar beam\\\"\\n\\n\\n\\t########################\\n\\t###Determine the two-stream approximation coefficients.\\n\\t########################\\n\\t#Eddington and quadrature are good at solar wavelengths (i.e., not thermal blackbody dominated). delta scalings of Joseph et al (1976) recommended to replace w_0, g, tau in this case. However, when dominated by internal isotropic sources like the Planck function, hemispheric mean approximation is preferable. When w_0=0, quadrature case has problems. This happens esp at thermal wavelengths. Again this favors using hemispheric mean at these wavelengths\\n\\t\\n\\t#We use quadrature because 1) we are at solar wavelengths for this UV work and 2) that's what twostr.f does (which is our comparison case)\\n\\tgamma_1= np.sqrt(3.)*(2.-w_0*(1.+g))/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_1\\n\\tgamma_2=np.sqrt(3.)*w_0*(1.-g)/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_2\\n\\tgamma_3=(1.-np.sqrt(3.)*g*mu_0)/2. #consistent with Toon et al; equal to the Pierrehumbert gamma_plus/w_0\\n\\tgamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\\n\\tmu_1=1./np.sqrt(3.)+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\\n\\n\\t##Eddington\\n\\t#gamma_1= (7.-w_0*(4.+3.*g))/4.\\n\\t#gamma_2=-1.*(1.-w_0*(4.-3.*g))/4.\\n\\t#gamma_3=(2.-3.*g*mu_0)/4.\\n\\t#gamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\\n\\t#mu_1=1./2.+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\\n\\n\\talambda=np.sqrt(np.abs(gamma_1*gamma_1-gamma_2*gamma_2)) #this is the lower-case lambda, from eqn 21 of Toon et al\\n\\t\\t\\t\\t\\t\\t\\t\\t #The absolute value was added based on the code Toon just sent us. This corresponds to his AK(L,J) parameter. But it should not matter since gamma_1>gamma_2 for w_0<1.\\n\\tclambda=(gamma_1-alambda)/(gamma_2) #this is the upper-case lambda, from eqn 22 of Toon et al\\n\\n\\tEMLT=np.exp(-alambda*tau_n) #this appears to be a prefactor used to facilitate computation of eqn 44 of Toon et al\\n\\te1=1.+clambda*EMLT\\n\\te2=1.-clambda*EMLT\\n\\te3=clambda+EMLT\\n\\te4=clambda-EMLT\\n\\n\\t########################\\n\\t###Set up calculation\\n\\t########################\\n\\t\\\"\\\"\\\"\\n\\tThe fundamental equation we are solving is of form:\\n\\tA_{l}*Y_{l-1}+B_{l}*Y_{l}+D{l+1}=E_{l} (equation 39 of Toon et al)\\n\\tHere, A_l, B_l, D_l, E_l are quantities we determine, and the Y_l is what we solve for.\\n\\tHence, we can summarize that we are solving a matrix equation that takes form:\\n\\tPY=E\\n\\twhere Y[l]=Y_l\\n\\t E[l]=E_l\\n\\t P[l, l-1]=A_l [row, column]\\n\\t P[l, l]=B_l\\n\\t P[l, l+1]=D_l\\n\\t P[i,j]=0 else\\n\\tToon et al use 1-indexing. Hence n runs from 1 to N, l runs from 1 to 2N, where N is the number of layers, and they have:\\n\\tY_l=Y_{1n} for l=1,3,5,...2n-1...2N-1\\n\\tY_l=Y_{2n} for l=2,4,6,...2n...2N\\n\\n\\tHowever, we use Python, which has 0-indexing. Hence *we* choose that n runs from 0 to N-1, l runs from 0 to 2N-1, and:\\n\\tY_l=Y_{1n} for l=0,2,4...2n...2N-2\\n\\tY_l=Y_{2n} for l=1,3,5...2n+1...2N-1\\n\\n\\tThe Y_{1n} and Y_{2n} are related to F^+_n and F^-_n via equations 31 and 32 of Toon et al.\\n\\tThis parametrization has been done to remove exponentials with positive operands (ie ones that could grow large and lead to numerical instabilities) from the matrix.\\n\\n\\tNote: The mapping of this PQ=R to the F+ and F- is unclear because of 1) this parametrization in terms of Y_l (done to eliminate numerical instabilities) and 2)further linear combinations done to convert a pentagiagonal matrix to an even simpler tridiagonal matrix. Hence intuitive checks are hard.\\n\\t\\\"\\\"\\\"\\n\\n\\t########################\\n\\t###Set up surface flux\\n\\t########################\\n\\tS_sfc=albedo_dir*mu_0*np.exp(-tau_c[-1]/mu_0)*np.pi*F_s+emissivity_ground*np.pi*Planck(temp_ground, wavelength)\\n\\t#Surface emission. Formed by adding blackbody emission from the ground to the reflected energy from the direct beam. The direct beam's reflected energy is assumed to be purely diffuse. This corresponds to equations 37 and 38 of Toon et al. Note that this does NOT match equation 5.31 of Pierrehumbert because it does not include the reflected diffuse radiation. So, this implicitly assumes the diffuse albedo to be 0. \\n\\n\\t########################\\n\\t###Set up C-values\\n\\t########################\\n\\t#In the reshuffled set of parameters used in this formalism, these seem analagous to the forcing term in Pierrehumbert. All the added radiation is contained in here.\\n\\n\\tdef C_plus(n, tau): #implementation of superposition of eqns 23 and 27 from Toon et al\\n\\t\\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\\n\\t\\tsolarrad_prefactor=w_0[n]*F_s*np.pi\\n\\t\\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\\n\\t\\tsolarrad_factor=((gamma_1[n]-1./mu_0)*gamma_3[n]+gamma_4[n]*gamma_2[n])\\n\\t\\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\\n\\t\\t\\n\\t\\tblackbody_prefactor=2*np.pi*mu_1[n]\\n\\t\\tB0n=Planck(temp_c[n], wavelength)\\n\\t\\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\\n\\t\\tblackbody_factor=B0n+B1n*(tau+1./(gamma_1[n]+gamma_2[n]))\\n\\t\\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \\n\\t\\t\\n\\t\\tresult=solarrad+blackbody\\n\\t\\treturn result\\n\\n\\tdef C_minus(n, tau): #implementation of superposition of eqns 24 and 27 from Toon et al\\n\\t\\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\\n\\t\\tsolarrad_prefactor=w_0[n]*F_s*np.pi\\n\\t\\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\\n\\t\\tsolarrad_factor=((gamma_1[n]+1./mu_0)*gamma_4[n]+gamma_3[n]*gamma_2[n])\\n\\t\\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\\n\\t\\t\\n\\t\\tblackbody_prefactor=2*np.pi*mu_1[n]\\n\\t\\tB0n=Planck(temp_c[n], wavelength)\\n\\t\\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\\n\\t\\tblackbody_factor=B0n+B1n*(tau-1./(gamma_1[n]+gamma_2[n]))\\n\\t\\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \\n\\t\\t\\n\\t\\tresult=solarrad+blackbody\\n\\t\\treturn result\\n\\n\\t########################\\n\\t###Calculate matrix coefficients\\n\\t#########################\\n\\t#initialize the A, B, D, and E.\\n\\tA=np.zeros(Nlayer*2)\\n\\tB=np.zeros(np.shape(A))\\n\\tD=np.zeros(np.shape(A))\\n\\tE=np.zeros(np.shape(A))\\n\\n\\n\\t#For l=0 (n=0) we have the boundary condition that the downward diffuse flux at the top of the first layer is equal to any incident diffuse downward flux. We set this to be zero.\\n\\tA[0]=0.\\n\\tB[0]=e1[0]\\n\\tD[0]=-1.*e2[0]\\n\\tE[0]=0.-1*C_minus(0,0) #This is really F_minus[0,0], i.e. we are assuming there is no downward diffuse flux from the top of the atmosphere.\\n\\n\\t#for l=2N-1 (n=N-1), we have the boundary condition that the upward flux at the surface is the sume of the reflected downward diffuse flux and energy from any other sources (e.g. reflected direct beam, BB emission of the ground)/np.sqrt(3.)\\n\\tA[2*Nlayer-1]=e1[Nlayer-1]-albedo_dif*e3[Nlayer-1]\\n\\tB[2*Nlayer-1]=e2[Nlayer-1]-albedo_dif*e4[Nlayer-1]\\n\\tD[2*Nlayer-1]=0.\\n\\tE[2*Nlayer-1]=S_sfc-C_plus(Nlayer-1, tau_n[Nlayer-1])+albedo_dif*C_minus(Nlayer-1, tau_n[Nlayer-1])\\n\\n\\t#There is a problem in the Toon paper. As written, the l=2n depends on e_n+1, running over the array edge. twostr.f resolves this by adopting a different mapping: their definition reduces to defining l=2(n+1) and running n from 0 to N-1. In this case, l=2 (The third value in the list of ls) depends on n=0 and n=1. This eliminates the overflow problem. We have implemented this below.\\n\\t\\n\\t##For n=1,2,3...N-1, l=2,4,6,...2N-2:\\n\\tfor n in range(0, Nlayer-1):\\n\\t\\tl=2*(n+1)\\n\\t\\tA[l]=e2[n]*e3[n]-e4[n]*e1[n]\\n\\t\\tB[l]=e1[n]*e1[n+1]-e3[n]*e3[n+1]\\n\\t\\tD[l]=e3[n]*e4[n+1]-e1[n]*e2[n+1]\\n\\t\\t\\n\\t\\tE[l]=e3[n]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))+e1[n]*(C_minus(n,tau_n[n])-C_minus(n+1,0.))\\n\\n\\n\\t#For n=0...N-2, l=1,3...2N-3:\\n\\tfor n in range(0, Nlayer-1):\\n\\t\\tl=2*n+1\\n\\t\\tA[l]=e2[n+1]*e1[n]-e3[n]*e4[n+1]\\n\\t\\tB[l]=e2[n]*e2[n+1]-e4[n]*e4[n+1]\\n\\t\\tD[l]=e1[n+1]*e4[n+1]-e2[n+1]*e3[n+1]\\n\\t\\t\\n\\t\\tE[l]=e2[n+1]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))-e4[n+1]*(C_minus(n+1, 0)-C_minus(n, tau_n[n])) #twostr.f has a -1*e_{4,n+1}. We have applied the same even though this is NOT what is written in the Toon et al paper. We have done this because Toon told us (6/26/2015) that there are some sign errors in the coefficients, and we currently trust the validated CLIMA code over the paper we know has errors in it. EDIT: Looking at the code Toon shared with us, he does the same. \\n\\n\\n\\t########################\\n\\t###Assemble matrix equation components\\n\\t#########################\\n\\tP=np.zeros([Nlayer*2,Nlayer*2])\\n\\n\\t#l=0: no \\\"A\\\" coefficient b/c l-1 has no meaning\\n\\tP[0,0]=B[0]\\n\\tP[0,1]=D[0]\\n\\n\\t#l=2N-1: no \\\"D\\\" coefficient b/c l+1 has no meaning\\n\\tP[2*Nlayer-1,2*Nlayer-1-1]=A[2*Nlayer-1]\\n\\tP[2*Nlayer-1,2*Nlayer-1]=B[2*Nlayer-1]\\n\\n\\tfor l in range(1, Nlayer*2-1): #This populates the matrix P in PY=E. \\n\\t\\tP[l, l-1]=A[l]\\n\\t\\tP[l,l]=B[l]\\n\\t\\tP[l,l+1]=D[l]\\n\\n\\t########################\\n\\t###Invert matrix\\n\\t#########################\\n\\t#Y=np.linalg.solve(P, E) #this is the Y_l\\n\\t\\n\\t#try using a specialized solver\\n\\tab=np.zeros([3,2*Nlayer])\\n\\tab[0,:]=np.append(0.0, np.diag(P, k=1))\\n\\tab[1,:]=np.diag(P, k=0)\\n\\tab[2,:]=np.append(np.diag(P, k=-1),0.0)\\n\\t#pdb.set_trace()\\n\\tY=scipy.linalg.solve_banded((1,1), ab, E) #this is the Y_l\\n\\n\\n\\t########################\\n\\t###Convert from Y_l to Y_1n, Y_2n\\n\\t#########################\\n\\t#The Y_1n as defined in Toon et al correspond to l=1,3, 5...2N-1. Adjusting for the zero-indexing of Python as we have done, they instead correspond to l=0,2,...2N-2\\n\\t#The Y_2n as defined in Toon et al correspond to l=2,4,6...2N. Adjusting for Python zero-indexing as we have done, they instead correspond to l=1,3,5...2N-1.\\n\\t#For detail, see eq. 40.\\n\\tY_1=np.zeros(Nlayer)\\n\\tY_2=np.zeros(Nlayer)\\n\\tfor n in range(0, Nlayer):\\n\\t\\tY_1[n]=Y[2*n]\\n\\t\\tY_2[n]=Y[2*n+1] \\n\\t\\t#last number called is Nlayer-1=N-1, so is consistent.\\n\\t\\n\\t########################\\n\\t###Convert from Y_1n, Y_2n to F_plus, F_minus\\n\\t#########################\\n\\tdef F_plus(n,tau): #defined from Eqn 31 of Toon et al.\\n\\t\\tterm1=Y_1[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))+clambda[n]*np.exp(-alambda[n]*tau))\\n\\t\\tterm2=Y_2[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))-clambda[n]*np.exp(-alambda[n]*tau))\\n\\t\\tterm3=C_plus(n,tau)\\n\\t\\t\\n\\t\\tresult=term1+term2+term3\\n\\t\\treturn result\\n\\n\\tdef F_minus(n, tau): #defined from Eqn 32 of Toon et al.\\n\\t\\tterm1=Y_1[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))+np.exp(-alambda[n]*tau))\\n\\t\\tterm2=Y_2[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))-np.exp(-alambda[n]*tau))\\n\\t\\tterm3=C_minus(n,tau)\\n\\t\\t\\n\\t\\tresult=term1+term2+term3\\n\\t\\treturn result\\n\\t\\n\\t########################\\n\\t###Evaluate F_plus, F_minus at boundary edges\\n\\t#########################\\n\\tF_plus_tau0=np.zeros(np.shape(tau_n))\\n\\tF_plus_taumax=np.zeros(np.shape(tau_n))\\n\\tF_minus_tau0=np.zeros(np.shape(tau_n))\\n\\tF_minus_taumax=np.zeros(np.shape(tau_n))\\n\\n\\tfor n in range(0, Nlayer):\\n\\t\\tF_plus_tau0[n]=F_plus(n, 0.)\\n\\t\\tF_plus_taumax[n]=F_plus(n, tau_n[n])\\n\\t\\tF_minus_tau0[n]=F_minus(n, 0.)\\n\\t\\tF_minus_taumax[n]=F_minus(n, tau_n[n])\\n\\n\\n\\t########################\\n\\t###Convert from Y_1n, Y_2n to F_net, mean intensity.\\n\\t#########################\\n\\t#test if diffuse flux dominates over direct flux. If direct flux dominant, instead set mu_1=mu_0\\n\\t\\n\\t#if F_minus_taumax[-1]<mu_0*np.pi*F_s*np.exp(-tau_c[-1]/mu_0):\\n\\t\\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\\n\\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\\n\\t\\n\\tF_net=np.zeros(np.shape(tau_n)) #defined from Eqn 48 of Toon et al. This quantity is the net flux at the BASE of layer n.\\n\\tfor n in range(0, Nlayer):\\n\\t\\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\\n\\n\\t\\tterm1=Y_1[n]*(e1[n]-e3[n])\\n\\t\\tterm2=Y_2[n]*(e2[n]-e4[n])\\n\\t\\tterm3=C_plus(n, tau_n[n])-C_minus(n, tau_n[n])\\n\\t\\t\\n\\t\\tF_net[n]=term1+term2+term3 -direct\\n\\n\\tAMEAN=np.zeros(np.shape(tau_n)) #defined from Eqn 49 of Toon et al. This is the equivalent of the quantity AMEAN in the twostr.f code. It is equal to 4*np.pi*J_n, where J_n is the mean intensity at the base of layer n. Hence this quantity AMEAN should be equal to the total intensity received by a point at the base of layer n. \\n\\tfor n in range(0, Nlayer):\\n\\t\\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\\n\\t\\n\\t\\tterm1=Y_1[n]*(e1[n]+e3[n])\\n\\t\\tterm2=Y_2[n]*(e2[n]+e4[n])\\n\\t\\tterm3=C_plus(n, tau_n[n])+C_minus(n, tau_n[n])\\n\\t\\t\\n\\t\\t#AMEAN[n]=(1./mu_1[n])*(term1+term2+term3)+direct/mu_0\\t\\n\\t\\tAMEAN[n]=(1./mu_1[n])*(F_plus_taumax[n]+F_minus_taumax[n])+direct/mu_0\\t\\n\\t\\n\\t########################\\n\\t###Compute \\\"surface intensity\\\"\\n\\t#########################\\t\\n\\t#\\\"Surface intensity\\\" refers to the total intensity that would be intercepted by a particle at the surface of the planet. Whereas the total intensity is equal to (F_plus[-1]+F_minus[-1])/mu_1+direct[-1]/mu_0, the surface intensity is instead equal to (F_minus[-1])/mu_1+direct[-1]/mu_0, i.e. the downwelling diffuse intensity (since the bottom intensity is cut out due to there being a planet there) plus the direct intensity\\n\\t\\n\\tsurface_intensity=(F_minus_taumax[-1]/mu_1[-1])+(np.pi*F_s)*np.exp(-(tau_c[-1])/mu_0)\\n\\t\\n\\t########################\\n\\t###Return Result\\n\\t#########################\\n\\t#F_minus_tau0\\n\\t#np.max(np.abs((F_minus_taumax[:-1]-F_minus_tau0[1:]))/F_minus_tau0[1:])\\n\\t#np.max(np.abs((F_plus_taumax[:-1]-F_plus_tau0[1:]))/F_plus_tau0[1:])\\n\\t\\n\\treturn (F_plus_tau0, F_plus_taumax, F_minus_tau0, F_minus_taumax, F_net, AMEAN, surface_intensity)\",\n \"def hz2mel(hz):\\n return 1127 * numpy.log(1+hz/700.0)\",\n \"def DeltaT_EspenakMeeus(ut):\\n # Fred Espenak writes about Delta-T generically here:\\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltaT.html\\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltat2004.html\\n # He provides polynomial approximations for distant years here:\\n # https://eclipse.gsfc.nasa.gov/SEhelp/deltatpoly2004.html\\n # They start with a year value 'y' such that y=2000 corresponds\\n # to the UTC Date 15-January-2000. Convert difference in days\\n # to mean tropical years.\\n\\n y = 2000 + ((ut - 14) / _DAYS_PER_TROPICAL_YEAR)\\n\\n if y < -500:\\n u = (y - 1820) / 100\\n return -20 + (32 * u*u)\\n\\n if y < 500:\\n u = y / 100\\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3\\n return 10583.6 - 1014.41*u + 33.78311*u2 - 5.952053*u3 - 0.1798452*u4 + 0.022174192*u5 + 0.0090316521*u6\\n\\n if y < 1600:\\n u = (y - 1000) / 100\\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3\\n return 1574.2 - 556.01*u + 71.23472*u2 + 0.319781*u3 - 0.8503463*u4 - 0.005050998*u5 + 0.0083572073*u6\\n\\n if y < 1700:\\n u = y - 1600\\n u2 = u*u; u3 = u*u2\\n return 120 - 0.9808*u - 0.01532*u2 + u3/7129.0\\n\\n if y < 1800:\\n u = y - 1700\\n u2 = u*u; u3 = u*u2; u4 = u2*u2\\n return 8.83 + 0.1603*u - 0.0059285*u2 + 0.00013336*u3 - u4/1174000\\n\\n if y < 1860:\\n u = y - 1800\\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3; u6 = u3*u3; u7 = u3*u4\\n return 13.72 - 0.332447*u + 0.0068612*u2 + 0.0041116*u3 - 0.00037436*u4 + 0.0000121272*u5 - 0.0000001699*u6 + 0.000000000875*u7\\n\\n if y < 1900:\\n u = y - 1860\\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3\\n return 7.62 + 0.5737*u - 0.251754*u2 + 0.01680668*u3 - 0.0004473624*u4 + u5/233174\\n\\n if y < 1920:\\n u = y - 1900\\n u2 = u*u; u3 = u*u2; u4 = u2*u2\\n return -2.79 + 1.494119*u - 0.0598939*u2 + 0.0061966*u3 - 0.000197*u4\\n\\n if y < 1941:\\n u = y - 1920\\n u2 = u*u; u3 = u*u2\\n return 21.20 + 0.84493*u - 0.076100*u2 + 0.0020936*u3\\n\\n if y < 1961:\\n u = y - 1950\\n u2 = u*u; u3 = u*u2\\n return 29.07 + 0.407*u - u2/233 + u3/2547\\n\\n if y < 1986:\\n u = y - 1975\\n u2 = u*u; u3 = u*u2\\n return 45.45 + 1.067*u - u2/260 - u3/718\\n\\n if y < 2005:\\n u = y - 2000\\n u2 = u*u; u3 = u*u2; u4 = u2*u2; u5 = u2*u3\\n return 63.86 + 0.3345*u - 0.060374*u2 + 0.0017275*u3 + 0.000651814*u4 + 0.00002373599*u5\\n\\n if y < 2050:\\n u = y - 2000\\n return 62.92 + 0.32217*u + 0.005589*u*u\\n\\n if y < 2150:\\n u = (y-1820)/100\\n return -20 + 32*u*u - 0.5628*(2150 - y)\\n\\n # all years after 2150\\n u = (y - 1820) / 100\\n return -20 + (32 * u*u)\",\n \"def _microstrip_v_with_Lk(wire_width, dielectric_thickness, eps_r, Lk_per_sq):\\n L_m, C_m = _microstrip_LC_per_meter(wire_width,\\n dielectric_thickness,\\n eps_r)\\n Lk_m = Lk_per_sq * (1.0/wire_width)\\n v = 1 / sqrt((L_m+Lk_m) * C_m)\\n return v\",\n \"def isDeboutHandCoded( sk, bOnlyTorso = False, bVerbose = False ):\\n \\n neck = sk.listPoints[Skeleton.getNeckIndex()]\\n \\n if bVerbose: print(\\\"neck: %s\\\" % str(neck))\\n \\n legsInfo = sk.getLegs()\\n if bVerbose: print(\\\"legs: %s\\\" % str(legsInfo))\\n \\n bb = sk.getBB_Size()\\n sto = sk.getStomach()\\n \\n\\n \\n rh,rk,ra = legsInfo[0] # hip, knee, ankle\\n lh,lk,la = legsInfo[1]\\n \\n avgFeets = [ ra[0]+la[0],ra[1]+la[1],ra[2]+la[2] ]\\n div2(avgFeets)\\n \\n lal = sk.getArms()\\n if bVerbose: print(\\\"arms: %s\\\" % str(lal))\\n rs,re,rw = lal[0] #shoulder, elbow, wrist\\n ls,le,lw = lal[1]\\n \\n rThreshold = 0.2\\n \\n #~ # si les pieds sont plus bas que les hanches\\n # a essayer: orientation cou/(estomac ou moyenne des hanches): vertical => debout; sinon couche\\n # a essayer: quand les fesses sont sur le sol\\n \\n # NB: on n'arrivera jamais a voir que quelqu'un qui est assis ou couche' oriente' vers la camera est tombe'\\n \\n # si les mains ou a defaut les coudes sont plus hautes que les pieds ou a defaut les hanches\\n bDeboutFromArmsLegsHeight = None\\n \\n if rw[2] > rThreshold:\\n rHi = rw[:2]\\n elif re[2] > rThreshold:\\n rHi = re[:2]\\n else:\\n rHi = None\\n\\n if lw[2] > rThreshold:\\n lHi = lw[:2]\\n elif le[2] > rThreshold:\\n lHi = le[:2]\\n else:\\n # check le neck\\n if neck[2] > rThreshold:\\n lHi = neck[:2]\\n else:\\n lHi = None\\n \\n if lHi != None or rHi != None:\\n \\n if lHi == None:\\n hi = rHi\\n elif rHi == None:\\n hi = lHi\\n else:\\n hi = avg2(rHi,lHi)\\n \\n \\n \\n if ra[2] > rThreshold:\\n rLo = ra[:2]\\n elif rk[2] > rThreshold:\\n rLo = rk[:2]\\n else:\\n rLo = None\\n\\n if la[2] > rThreshold:\\n lLo = la[:2]\\n elif lk[2] > rThreshold:\\n lLo = lk[:2]\\n else:\\n lLo = None\\n \\n if lLo != None or rLo != None:\\n\\n if lLo == None:\\n lo = rLo\\n elif rLo == None:\\n lo = lLo\\n else:\\n lo = avg2(rLo,lLo)\\n \\n if bVerbose: print(\\\"rLo:%s,lLo:%s\\\" % (rLo,lLo) )\\n \\n if bVerbose: print(\\\"hi:%s,lo:%s\\\" % (hi,lo) )\\n \\n #~ return hi[1]<lo[1] # add a margin ?\\n \\n bb = sk.getBB_Size()\\n rMargin = bb[1]/4\\n\\n \\n bDeboutFromArmsLegsHeight = hi[1]+rMargin<lo[1] # WRN: pixel Y are inverted (high pixel are smaller than lower)\\n \\n if bVerbose: print(\\\"rMargin:%5.2f, bDeboutFromArmsLegsHeight: %s\\\"% (rMargin,bDeboutFromArmsLegsHeight) )\\n\\n bDeboutFromTorsoAngle = None\\n if (rh[2] > rThreshold or lh[2] > rThreshold) and neck[2] > rThreshold:\\n if (rh[2] > rThreshold and lh[2] > rThreshold):\\n avg_hip = avg2(rh,lh)\\n elif rh[2] > rThreshold:\\n avg_hip = rh\\n else:\\n avg_hip = lh\\n dx = avg_hip[0]-neck[0]\\n dy = avg_hip[1]-neck[1]\\n if abs(dx) < 0.1:\\n coef = dy*10\\n else:\\n coef = dy/dx\\n bDeboutFromTorsoAngle = abs(coef) > 1. # 1: diagonal\\n if bVerbose: print(\\\"coef: %5.1f (dy:%3.1f,dx:%3.1f), bDeboutFromTorsoAngle: %s\\\" % (coef,dy, dx, bDeboutFromTorsoAngle) )\\n #~ else:\\n #~ return None\\n \\n # fesses sur le sol\\n bNotBumOnGround = None\\n if rh[2] > rThreshold and lh[2] > rThreshold:\\n avg_hip = avg2(rh,lh)\\n elif rh[2] > rThreshold:\\n avg_hip = rh\\n elif lh[2] > rThreshold:\\n avg_hip = lh\\n else:\\n avg_hip = None\\n if avg_hip != None:\\n # look for lower point in legs, but not hip:\\n rLowest = -10000\\n #~ for i in range(cv2_openpose.Skeleton.NBR_POINTS):\\n for i in [cv2_openpose.Skeleton.RKNEE,cv2_openpose.Skeleton.LKNEE,cv2_openpose.Skeleton.RANKLE,cv2_openpose.Skeleton.LANKLE]:\\n if i == cv2_openpose.Skeleton.RHIP or i == cv2_openpose.Skeleton.LHIP:\\n continue\\n if sk.listPoints[i][2] < rThreshold:\\n continue\\n if sk.listPoints[i][1] > rLowest:\\n rLowest = sk.listPoints[i][1]\\n if rLowest >= 0:\\n #~ lenLimbs = sk.getLenLimbs()\\n #~ if bVerbose: print(\\\"lenLimbs: %s\\\" % str(lenLimbs) )\\n #~ rLenLegs = (lenLimbs[0][0] +lenLimbs[1][0]) / 2\\n rLenLegs = sk.getAvgLenLeg()\\n if rLenLegs != None:\\n bNotBumOnGround = (avg_hip[1] + (rLenLegs*0.75)) < rLowest\\n if bVerbose: print(\\\"avg hip: %5.1f, lowest: %5.1f, rLenLegs: %5.1f, bNotBum: %s\\\" % (avg_hip[1],rLowest,rLenLegs, bNotBumOnGround) )\\n if legsInfo[0][2][2] < rThreshold and legsInfo[1][2][2] < rThreshold:\\n if bVerbose: print(\\\"INF: no foot seen, reseting bNotBumOnGround\\\" )\\n # on ne voit aucun pied, soit ils ne sont pas a l'ecran soit il sont derriere, dans le doute, on prefere dire None\\n bNotBumOnGround = None\\n #~ return bNotBumOnGround\\n \\n # on veut etre sur => si hesitation, ne se prononces pas\\n if 0:\\n if not bOnlyTorso:\\n if bDeboutFromArmsLegsHeight != bDeboutFromTorsoAngle:\\n return None\\n\\n \\n #~ if bDeboutFromArmsLegsHeight:\\n if bDeboutFromTorsoAngle == None and bNotBumOnGround:\\n return 1\\n \\n if bDeboutFromTorsoAngle and bNotBumOnGround:\\n return 1\\n\\n \\n if bDeboutFromTorsoAngle == None and bNotBumOnGround == None and bDeboutFromArmsLegsHeight != None:\\n return bDeboutFromArmsLegsHeight\\n \\n if bDeboutFromTorsoAngle != None and bNotBumOnGround == None:\\n return bDeboutFromTorsoAngle\\n \\n return 0\\n \\n \\n else:\\n if bDeboutFromTorsoAngle == bNotBumOnGround and bDeboutFromTorsoAngle == bDeboutFromArmsLegsHeight:\\n return bDeboutFromTorsoAngle\\n if bNotBumOnGround != None:\\n return bNotBumOnGround\\n if bDeboutFromTorsoAngle != None:\\n return bDeboutFromTorsoAngle \\n if bDeboutFromArmsLegsHeight != None:\\n return bDeboutFromArmsLegsHeight\\n return None\",\n \"def BraggEnergy(ID,hkl,twotheta):\\n ID=goodID(ID)\\n d=dSpace(ID,hkl)\\n l=2*d*sind(twotheta/2.0)\\n E=lam2E(l)\\n return E\",\n \"def getWNSA1(ChargeSA):\\n temp = 0.0\\n for i in ChargeSA:\\n temp = temp+i[2]\\n if temp == 0.0:\\n return 0.0\\n\\n return getPNSA1(ChargeSA)*temp/1000\",\n \"def le_calibration_func(etr, kc, ts):\\n return etr * kc * (2.501 - 2.361E-3 * (ts - 273)) * 2500 / 9\",\n \"def RiekeLebofsky85(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def leff(self):\\n with Vega() as v:\\n s = self.reinterp(v.wavelength)\\n w = s._wavelength\\n if s.transmit.max() > 0:\\n leff = np.trapz(w * s.transmit * v.flux.value, w, axis=-1)\\n leff /= np.trapz(s.transmit * v.flux.value, w, axis=-1)\\n else:\\n leff = float('nan')\\n if s.wavelength_unit is not None:\\n leff = leff * Unit(s.wavelength_unit)\\n if self.wavelength_unit is not None:\\n return leff.to(self.wavelength_unit)\\n return leff\\n else:\\n return leff\",\n \"def test_double_ended_ols_wls_estimate_synthetic():\\n from dtscalibration import DataStore\\n import numpy as np\\n\\n np.random.seed(0)\\n\\n cable_len = 100.\\n nt = 50\\n time = np.arange(nt)\\n x = np.linspace(0., cable_len, 100)\\n ts_cold = np.ones(nt) * 4.\\n ts_warm = np.ones(nt) * 20.\\n\\n C_p = 15246\\n C_m = 2400.\\n dalpha_r = 0.0005284\\n dalpha_m = 0.0004961\\n dalpha_p = 0.0005607\\n gamma = 482.6\\n cold_mask = x < 0.5 * cable_len\\n warm_mask = np.invert(cold_mask) # == False\\n temp_real = np.ones((len(x), nt))\\n temp_real[cold_mask] *= ts_cold + 273.15\\n temp_real[warm_mask] *= ts_warm + 273.15\\n\\n st = C_p * np.exp(-dalpha_r * x[:, None]) * \\\\\\n np.exp(-dalpha_p * x[:, None]) * np.exp(gamma / temp_real) / \\\\\\n (np.exp(-gamma / temp_real) - 1)\\n ast = C_m * np.exp(-dalpha_r * x[:, None]) * \\\\\\n np.exp(-dalpha_m * x[:, None]) / (np.exp(-gamma / temp_real) - 1)\\n rst = C_p * np.exp(-dalpha_r * (-x[:, None] + cable_len)) * \\\\\\n np.exp(-dalpha_p * (-x[:, None] + cable_len)) * \\\\\\n np.exp(gamma / temp_real) / (np.exp(-gamma / temp_real) - 1)\\n rast = C_m * np.exp(-dalpha_r * (-x[:, None] + cable_len)) * np.exp(\\n -dalpha_m * (-x[:, None] + cable_len)) / \\\\\\n (np.exp(-gamma / temp_real) - 1)\\n\\n alpha = np.mean(np.log(rst / rast) - np.log(st / ast), axis=1) / 2\\n\\n ds = DataStore({\\n 'st': (['x', 'time'], st),\\n 'ast': (['x', 'time'], ast),\\n 'rst': (['x', 'time'], rst),\\n 'rast': (['x', 'time'], rast),\\n 'userAcquisitionTimeFW': (['time'], np.ones(nt)),\\n 'userAcquisitionTimeBW': (['time'], np.ones(nt)),\\n 'cold': (['time'], ts_cold),\\n 'warm': (['time'], ts_warm)\\n },\\n coords={\\n 'x': x,\\n 'time': time},\\n attrs={\\n 'isDoubleEnded': '1'})\\n\\n sections = {\\n 'cold': [slice(0., 0.5 * cable_len)],\\n 'warm': [slice(0.5 * cable_len, cable_len)]}\\n\\n # OLS\\n ds.calibration_double_ended(sections=sections,\\n st_label='st',\\n ast_label='ast',\\n rst_label='rst',\\n rast_label='rast',\\n method='ols',\\n solver='sparse')\\n\\n np.testing.assert_almost_equal(\\n ds.gamma.values, gamma, decimal=6)\\n np.testing.assert_almost_equal(\\n ds.alpha.values, alpha, decimal=7)\\n np.testing.assert_almost_equal(\\n ds.TMPF.values, temp_real - 273.15, decimal=4)\\n np.testing.assert_almost_equal(\\n ds.TMPB.values, temp_real - 273.15, decimal=4)\\n np.testing.assert_almost_equal(\\n ds.TMPW.values, temp_real - 273.15, decimal=4)\\n\\n # WLS\\n ds.calibration_double_ended(sections=sections,\\n st_label='st',\\n ast_label='ast',\\n rst_label='rst',\\n rast_label='rast',\\n st_var=1e-7,\\n ast_var=1e-7,\\n rst_var=1e-7,\\n rast_var=1e-7,\\n method='wls',\\n solver='sparse',\\n tmpw_mc_size=5)\\n\\n np.testing.assert_almost_equal(\\n ds.gamma.values, gamma, decimal=5)\\n np.testing.assert_almost_equal(\\n ds.alpha.values, alpha, decimal=6)\\n np.testing.assert_almost_equal(\\n ds.TMPF.values, temp_real - 273.15, decimal=4)\\n np.testing.assert_almost_equal(\\n ds.TMPB.values, temp_real - 273.15, decimal=4)\\n np.testing.assert_almost_equal(\\n ds.TMPW.values, temp_real - 273.15, decimal=4)\",\n \"def closest_cruising_altitude(altitude):\\n return 1000 * ((altitude + 500) // 1000)\",\n \"def _get_tb(I, nu, beam):\\n from astropy import units as u\\n return (1222.0*I/(nu**2*(beam.minor/1.0).to(u.arcsecond)*(beam.major/1.0).to(u.arcsecond))).value\",\n \"def Frischknecht16_net(self):\\n import numpy.lib.recfunctions as rcfuncs\\n import os\\n \\n # Define metallicites \\n self.metallicities = [0.0134,1e-3,1e-5] # First is solar value\\n \\n # Define masses\\n self.masses= np.array((15,20,25,40))\\n \\n # Define isotope indexing. For radioactive isotopes with half-lives << Chempy time_step they are assigned to their daughter element\\n # NB: we only use elements up to Ge here, as in the paper\\n indexing={}\\n indexing['H']=['p','d']\\n indexing['He'] = ['he3','he4']\\n indexing['Li'] = ['li6','li7']\\n indexing['Be'] = ['be9']\\n indexing['B'] = ['b10','b11']\\n indexing['C'] = ['c12','c13']\\n indexing['N'] = ['n14','n15']\\n indexing['O'] = ['o16','o17','o18']\\n indexing['F'] = ['f19']\\n indexing['Ne'] = ['ne20','ne21','ne22']\\n indexing['Na'] = ['na23']\\n indexing['Mg'] = ['mg24','mg25','mg26','al26']\\n indexing['Al'] = ['al27']\\n indexing['Si'] = ['si28','si29','si30']\\n indexing['P'] = ['p31']\\n indexing['S'] = ['s32','s33','s34','s36']\\n indexing['Cl'] = ['cl35','cl37']\\n indexing['Ar'] = ['ar36','ar38','ar40']\\n indexing['K'] = ['k39','k41']\\n indexing['Ca'] = ['ca40','ca42','ca43','ca44','ca46','ca48']\\n indexing['Sc'] = ['sc45']\\n indexing['Ti'] = ['ti46','ti47','ti48','ti49','ti50']\\n indexing['V'] = ['v50','v51']\\n indexing['Cr'] = ['cr50','cr52','cr53','cr54']\\n indexing['Mn'] = ['mn55']\\n indexing['Fe'] = ['fe54', 'fe56','fe57','fe58']\\n indexing['Co'] = ['fe60', 'co59']\\n indexing['Ni'] = ['ni58','ni60','ni61','ni62','ni64']\\n indexing['Cu'] = ['cu63','cu65']\\n indexing['Zn'] = ['zn64','zn66','zn67','zn68','zn70']\\n indexing['Ga'] = ['ga69','ga71']\\n indexing['Ge'] = ['ge70','ge72','ge73','ge74','ge76']\\n\\n # Define indexed elements \\n self.elements = list(indexing.keys())\\n \\n \\n # Define data types\\n dt = np.dtype('U8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8,f8')\\n \\n # Initialise yield table\\n yield_table = {}\\n \\n \\n # Import full table with correct rows and data-types\\n z = np.genfromtxt(localpath+'input/yields/Frischknecht16/yields_total.txt',skip_header=62,dtype=dt)\\n \\n \\n \\n # Create model dictionary indexed by metallicity, giving relevant model number for each choice of mass\\n # See Frischknecht info_yields.txt file for model information\\n model_dict = {}\\n model_dict[0.0134] = [2,8,14,27]\\n model_dict[1e-3]=[4,10,16,28]\\n model_dict[1e-5]=[6,12,18,29]\\n \\n # Import list of remnant masses for each model (from row 32-60, column 6 of .txt file) \\n # NB: these are in solar masses\\n rem_mass_table = np.loadtxt(localpath+'input/yields/Frischknecht16/yields_total.txt',skiprows=31,usecols=6)[:29]\\n\\n # Create one subtable for each metallicity \\n for metallicity in self.metallicities:\\n additional_keys = ['Mass', 'mass_in_remnants','unprocessed_mass_in_winds'] # List of keys for table\\n names = additional_keys + self.elements\\n \\n # Initialise table and arrays \\n base = np.zeros(len(self.masses))\\n list_of_arrays = []\\n for i in range(len(names)):\\n list_of_arrays.append(base)\\n yield_subtable = np.core.records.fromarrays(list_of_arrays,names=names)\\n mass_in_remnants = np.zeros(len(self.masses))\\n total_mass_fraction = np.zeros(len(self.masses))\\n element_mass = np.zeros(len(self.masses))\\n \\n # Add masses to table\\n yield_subtable['Mass'] = self.masses\\n \\n \\n # Extract remnant masses (in solar masses) for each model:\\t\\t\\t\\n for mass_index,model_index in enumerate(model_dict[metallicity]):\\n mass_in_remnants[mass_index] = rem_mass_table[model_index-1] \\n \\n # Iterate over all elements \\n for element in self.elements:\\n element_mass = np.zeros(len(self.masses))\\n for isotope in indexing[element]: # Iterate over isotopes of each element\\n for mass_index,model_index in enumerate(model_dict[metallicity]): # Iterate over masses \\n for row in z: # Find required row in table \\n if row[0] == isotope:\\n element_mass[mass_index]+=row[model_index] # Compute cumulative mass for all isotopes\\n yield_subtable[element]=np.divide(element_mass,self.masses) # Add entry to subtable\\n \\n all_fractions = [row[model_index] for row in z] # This lists all elements (not just up to Ge)\\n total_mass_fraction[mass_index] = np.sum(all_fractions) # Compute total net mass fraction (sums to approximately 0)\\n \\n # Add fields for remnant mass (now as a mass fraction) and unprocessed mass fraction\\t\\t\\t\\n yield_subtable['mass_in_remnants']=np.divide(mass_in_remnants,self.masses) \\n yield_subtable['unprocessed_mass_in_winds'] = 1.-(yield_subtable['mass_in_remnants']+total_mass_fraction) # This is all mass not from yields/remnants\\n \\n # Add subtable to full table\\n yield_table[metallicity]=yield_subtable\\n\\n # Define final yield table for output\\n self.table = yield_table\",\n \"def b_Kjeldsen2008(self):\\n return 10.0**(-3.16*self.string_to_param(\\\"log_Teff\\\") + 0.184*self.string_to_param(\\\"log_g\\\")+11.7)\",\n \"def T_L(Td, taue):\\n return np.sqrt(np.pi)/2.0 * taue * np.exp(-(np.pi*taue/(4*Td))*(np.pi*taue/(4*Td)))\",\n \"def blackbody( wave, T, waveunit='Angstrom' ):\\n \\n if waveunit=='Angstrom':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e10 * 100.\\n elif waveunit=='nm':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e9 * 100.\\n\\n return( ((2 * h * c* c)/wave**5 ) / (exp(h*c/(wave*k*T))-1) )\",\n \"def calculate_wrist_ee(self):\\n\\n data = [i * (30 / 75) for i in self.df_epoch[\\\"LWrist\\\"]]\\n\\n # Modified equation from Powell et al. 2017. Removed resting component (constant = 1.15451)\\n mets = [.022261 * i for i in data]\\n\\n # Converts METs to relative VO2 (mL O2/kg/min)\\n r_vo2 = [3.5 * m for m in mets]\\n\\n # Converts relative VO2 to absolute VO2 (L O2/kg/min)\\n a_vo2 = [i * self.weight / 1000 for i in r_vo2]\\n\\n # Converts absolute VO2 to kcal/min (assumes 1 L O2 -> 4.825 kcal)\\n kcal_min = [a * 4.825 for a in a_vo2]\\n\\n # Calculates kcal/epoch\\n kcal_epoch = [k * (15 / 60) for k in kcal_min]\\n\\n total_ee = sum([i for i in kcal_epoch if not np.isnan(i)])\\n print(\\\"-Total energy expenditure estimated from Wrist is {} kcal.\\\".format(int(total_ee)))\\n\\n self.df_epoch[\\\"Wrist_EE\\\"] = kcal_min\",\n \"def abbott_elec():\\n per_kwh = 0.08 # [$/kWh]\\n return per_kwh\",\n \"def adjust_for_speed_of_light_in_water(df, tide_level):\\n speed_of_light_air = 300000\\n speed_of_light_water = 225000\\n coef = speed_of_light_water / speed_of_light_air\\n df['Height'] = (df['Height']- tide_level) * coef\\n return df\",\n \"def convert_ha_to_km2(nb):\\n return nb / 100\",\n \"def calc_relhum(dewpt,t):\\n\\n relhum=100.*(np.exp((const.es_Abolton*dewpt)/(const.es_Bbolton+dewpt))/np.exp((const.es_Abolton*t)/(const.es_Bbolton+t)))\\n return relhum\",\n \"def getWNSA2(ChargeSA):\\n temp = 0.0\\n for i in ChargeSA:\\n temp = temp+i[2]\\n if temp == 0.0:\\n return 0.0\\n\\n return getPNSA2(ChargeSA)*temp/1000.0\",\n \"def get_D_C3H8_air_Kn(self, T):\\n\\n Kn = self.get_Kn(T)\\n D_C3H8_air = self.get_D_C3H8_air(T)\\n\\n self.D_C3H8_air_Kn = D_C3H8_air / Kn\\n\\n return self.D_C3H8_air_Kn\",\n \"def stiffenerBuckle(dim):\\n bst = dim[0]\\n tst = dim[1]\\n tsk = dim[2]\\n\\n epsilont = kt * ((tst / bst)) ** 2\\n Et = (Esk * tsk) + (Est * ((bst * tst) / bsk))\\n Nst = Et*epsilont # Critical Load\\n rsf = Nst/Nx\\n return rsf - 1 # Using a target Reserve Factor of 1\",\n \"def bjerrum_length_water(temperature=298.15):\\n bjerrum = np.power(ELECTRON_CHARGE, 2.0) / \\\\\\n (4.0 * np.pi *\\n ELECTRIC_CONSTANT *\\n dielectric_constant_water(temperature) *\\n BOLTZMANN_CONSTANT *\\n temperature\\n )\\n return bjerrum\",\n \"def UTMtoLL(northing, easting, zone, isSouthernHemisphere=True,\\r\\n ReferenceEllipsoid=23):\\r\\n k0 = 0.9996\\r\\n a = _ellipsoid[ReferenceEllipsoid][_EquatorialRadius]\\r\\n eccSquared = _ellipsoid[ReferenceEllipsoid][_eccentricitySquared]\\r\\n e1 = (1-sqrt(1-eccSquared))/(1+sqrt(1-eccSquared))\\r\\n\\r\\n x = easting - 500000.0 #remove 500,000 meter offset for longitude\\r\\n y = northing\\r\\n\\r\\n ZoneNumber = int(zone)\\r\\n if isSouthernHemisphere:\\r\\n y -= 10000000.0 # remove 10,000,000 meter offset used\\r\\n # for southern hemisphere\\r\\n\\r\\n LongOrigin = (ZoneNumber - 1)*6 - 180 + 3 # +3 puts origin in middle of zone\\r\\n\\r\\n eccPrimeSquared = (eccSquared)/(1-eccSquared)\\r\\n\\r\\n M = y / k0\\r\\n mu = M/(a*(1-eccSquared/4-3*eccSquared*eccSquared/64-5*eccSquared*eccSquared*eccSquared/256))\\r\\n\\r\\n phi1Rad = (mu + (3*e1/2-27*e1*e1*e1/32)*sin(2*mu)\\r\\n + (21*e1*e1/16-55*e1*e1*e1*e1/32)*sin(4*mu)\\r\\n +(151*e1*e1*e1/96)*sin(6*mu))\\r\\n phi1 = phi1Rad*_rad2deg;\\r\\n\\r\\n N1 = a/sqrt(1-eccSquared*sin(phi1Rad)*sin(phi1Rad))\\r\\n T1 = tan(phi1Rad)*tan(phi1Rad)\\r\\n C1 = eccPrimeSquared*cos(phi1Rad)*cos(phi1Rad)\\r\\n R1 = a*(1-eccSquared)/pow(1-eccSquared*sin(phi1Rad)*sin(phi1Rad), 1.5)\\r\\n D = x/(N1*k0)\\r\\n\\r\\n Lat = phi1Rad - (N1*tan(phi1Rad)/R1)*(D*D/2-(5+3*T1+10*C1-4*C1*C1-9*eccPrimeSquared)*D*D*D*D/24\\r\\n +(61+90*T1+298*C1+45*T1*T1-252*eccPrimeSquared-3*C1*C1)*D*D*D*D*D*D/720)\\r\\n Lat = Lat * _rad2deg\\r\\n\\r\\n Long = (D-(1+2*T1+C1)*D*D*D/6+(5-2*C1+28*T1-3*C1*C1+8*eccPrimeSquared+24*T1*T1)\\r\\n *D*D*D*D*D/120)/cos(phi1Rad)\\r\\n Long = LongOrigin + Long * _rad2deg\\r\\n return (Lat, Long)\",\n \"def calc_ti(self):\\n m = 0\\n for i in self.inl:\\n m += i.m.val_SI * i.fluid.val[self.fuel_alias.val]\\n\\n for o in self.outl:\\n m -= o.m.val_SI * o.fluid.val[self.fuel_alias.val]\\n\\n return m * self.lhv\",\n \"def get_evaporation_latent_heat() -> float:\\n theta = 28.0\\n return 2500.8 - 2.3668 * theta\",\n \"def fuel_cond(T):\\n\\n kc = 1.841e-19*math.pow(T,6) - 2.097e-15*math.pow(T,5) +\\\\\\n 9.721e-12*math.pow(T,4) - 2.369e-8*math.pow(T,3) +\\\\\\n 3.283e-5*math.pow(T,2) - 0.0267*T + 63.18\\n \\n return kc\",\n \"def SSt_theo_old(D, k):\\n\\ta1b = k[\\\"A1B1\\\"]\\n\\tba1 = k[\\\"B1A1\\\"]\\n\\tca1 = k[\\\"C1A1\\\"]\\n\\tcb = k[\\\"B1C1\\\"]\\n\\tnum = a1b*ba1*ca1*ca1 + ba1*ba1*ca1*ca1 + 3*a1b*ba1*ca1*cb + 2*ba1*ba1*ca1*cb + \\\\\\n\\t\\t\\ta1b*ca1*ca1*cb + 2*ba1*ca1*ca1*cb + 2*a1b*ba1*cb*cb + ba1*ba1*cb*cb + \\\\\\n\\t\\t\\t2*a1b*ca1*cb*cb + 2*ba1*ca1*cb*cb + ca1*ca1*cb*cb + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\t(a1b*ba1*ba1*ca1 + ba1*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + a1b*ca1*ca1*ca1 + \\\\\\n\\t\\t\\t3*ba1*ca1*ca1*ca1 + 2*a1b*ba1*ba1*cb + ba1*ba1*ba1*cb + 2*a1b*ba1*ca1*cb + \\\\\\n\\t\\t\\t3*ba1*ba1*ca1*cb + 4*a1b*ca1*ca1*cb + 5*ba1*ca1*ca1*cb + 3*ca1*ca1*ca1*cb + \\\\\\n\\t\\t\\t2*a1b*ba1*cb*cb + 2*ba1*ba1*cb*cb + 2*a1b*ca1*cb*cb + 4*ba1*ca1*cb*cb + \\\\\\n\\t\\t\\t2*ca1*ca1*cb*cb) * D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\t(a1b*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + 4*ba1*ba1*ca1*ca1 + a1b*ca1*ca1*ca1 + \\\\\\n\\t\\t\\t2*ca1*ca1*ca1*ca1 + ba1*ba1*ba1*cb + 3*a1b*ba1*ca1*cb + 3*ba1*ba1*ca1*cb + \\\\\\n\\t\\t\\ta1b*ca1*ca1*cb + 5*ba1*ca1*ca1*cb + 3*ca1*ca1*ca1*cb + ba1*ba1*cb*cb + \\\\\\n\\t\\t\\t2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) * D*D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\t(ba1*ba1*ba1*ca1 + a1b*ba1*ca1*ca1 + 3*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + \\\\\\n\\t\\t\\t2*ba1*ca1*ca1*cb) * D*D*D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\tba1*ba1*ca1*ca1 * D*D*D*D\\n\\t##\\n\\tden = a1b*(ba1*ba1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 2*ba1*ca1*ca1*cb + ba1*ba1*cb*cb + \\n\\t\\t\\t2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\ta1b*(4*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 6*ba1*ca1*ca1*cb + 4*ca1*ca1*ca1*cb + \\n\\t\\t\\t2*ba1*ba1*cb*cb + 4*ba1*ca1*cb*cb + 2*ca1*ca1*cb*cb) * D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\ta1b*(2*ba1*ba1*ca1*ca1 + 4*ca1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 6*ba1*ca1*ca1*cb + \\n\\t\\t\\t4*ca1*ca1*ca1*cb + ba1*ba1*cb*cb + 2*ba1*ca1*cb*cb + ca1*ca1*cb*cb) * D*D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\ta1b*(4*ba1*ca1*ca1*ca1 + 2*ba1*ba1*ca1*cb + 2*ba1*ca1*ca1*cb) * D*D*D + \\\\\\n\\t\\t\\t\\\\\\n\\t\\t\\ta1b*ba1*ba1*ca1*ca1 * D*D*D*D\\n\\t##\\n\\ttau = num/den\\n\\t##\\n\\treturn tau*np.log(20)\",\n \"def celciusToKelvin(celcius: float, ndigits: int = 2)->float:\\n return round(float(celcius) + 273.15, ndigits)\",\n \"def bern_metric(pipe_diameter, delta_p, pipe_length):\\n fr_c = 0.003 # assuming Reynolds number is 10**5 and pipe material is smooth copper\\n fr_reyn = 0.046 / (reynolds_num(pipe_diameter, delta_p, pipe_length) ** 0.2) # Taitel and Dukler approximation\\n rho = 1000 # density of water @ 4 deg celsius (kg/m**3)\\n\\n v = math.sqrt((2 * delta_p) / (rho * (4 * fr_reyn * (pipe_length / pipe_diameter) - 1)))\\n flow_rate_turb = v * ((math.pi / 4) * (pipe_diameter ** 2))\\n\\n return flow_rate_turb, v\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.6074756\",\n \"0.60489684\",\n \"0.6021683\",\n \"0.58620036\",\n \"0.5819184\",\n \"0.5817391\",\n \"0.5701168\",\n \"0.56542647\",\n \"0.5622918\",\n \"0.5620226\",\n \"0.5618999\",\n \"0.55917037\",\n \"0.55595964\",\n \"0.5550017\",\n \"0.5537136\",\n \"0.5530169\",\n \"0.5526478\",\n \"0.5487843\",\n \"0.5477624\",\n \"0.5460396\",\n \"0.54503703\",\n \"0.54500014\",\n \"0.54438895\",\n \"0.54244804\",\n \"0.54152185\",\n \"0.5406566\",\n \"0.54061073\",\n \"0.53903854\",\n \"0.5387455\",\n \"0.5368951\",\n \"0.5346727\",\n \"0.53384906\",\n \"0.5325162\",\n \"0.53191954\",\n \"0.5314231\",\n \"0.53136086\",\n \"0.5313077\",\n \"0.5311422\",\n \"0.53073394\",\n \"0.530346\",\n \"0.53003144\",\n \"0.52962947\",\n \"0.52958554\",\n \"0.52827436\",\n \"0.5282719\",\n \"0.52772045\",\n \"0.52708966\",\n \"0.5266472\",\n \"0.5264123\",\n \"0.5263611\",\n \"0.5249507\",\n \"0.5246597\",\n \"0.52375174\",\n \"0.5234523\",\n \"0.5220995\",\n \"0.52207047\",\n \"0.5215768\",\n \"0.5214564\",\n \"0.5214163\",\n \"0.52097803\",\n \"0.5203122\",\n \"0.5202069\",\n \"0.5200833\",\n \"0.51967096\",\n \"0.5193933\",\n \"0.5193548\",\n \"0.519249\",\n \"0.519136\",\n \"0.51818526\",\n \"0.5181353\",\n \"0.517518\",\n \"0.51728415\",\n \"0.5172196\",\n \"0.5167219\",\n \"0.51639974\",\n \"0.51602536\",\n \"0.5159262\",\n \"0.51495284\",\n \"0.5149191\",\n \"0.51454246\",\n \"0.51407903\",\n \"0.51385975\",\n \"0.5137425\",\n \"0.51349795\",\n \"0.51336193\",\n \"0.5132459\",\n \"0.51317227\",\n \"0.513127\",\n \"0.5126381\",\n \"0.5125836\",\n \"0.5123143\",\n \"0.5117155\",\n \"0.5116209\",\n \"0.51135945\",\n \"0.5110454\",\n \"0.51063466\",\n \"0.51031625\",\n \"0.50993836\",\n \"0.50989574\",\n \"0.50924534\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64853966"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8492,"cells":{"query":{"kind":"string","value":"r\"\"\" Get AOD at specified wavelength using Angstrom turbidity model."},"document":{"kind":"string","value":"def angstrom_aod_at_lambda(aod0, lambda0, alpha=1.14, lambda1=700.0):\n return aod0 * ((lambda1 / lambda0) ** (-alpha))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_experimental_spectra(mol):\n\n data = pd.read_csv(mol, sep=',')\n wavelength = data.values[:, 0]\n\n absorption = data.values[:, 1]\n\n func = interp1d(wavelength, absorption, kind='quadratic')\n wavelength_new = 1. / np.linspace(1. / wavelength.max(), 1. / wavelength.min(), 100)\n absorption_new = func(wavelength_new)\n absorption_new *= 100. / absorption_new.max()\n\n return wavelength_new, absorption_new","def referenceIllum(temp, wavelength):\n ct=temp\n if ct <= 0:\n return 0\n if ct < 4000:\n return planckian(ct, wavelength)\n if ct < 5000:\n p=planckian(ct, wavelength)\n d=dseries(ct, wavelength)\n return p+(d-p)*(ct-4000)/1500.0\n return dseries(ct, wavelength)","def wavelength(refractive_index, omega):\n return 2 * np.pi * cgs.c / (refractive_index * omega)","def airydisk(unit_r, fno, wavelength):\n u_eff = unit_r * np.pi / wavelength / fno\n return abs(2 * jinc(u_eff)) ** 2","def return_obs_RA_DEC():\n return SkyCoord('03h 32m 30s', '10d 00m 24s')","def get(self, wave, flux, **kwargs):\n if hasUnit(wave):\n _w = wave.to('AA').magnitude\n else:\n print(\"Warning: assuming units are in Angstroms\")\n _w = _drop_units(wave)\n _f = _drop_units(flux)\n\n blue = self._get_wavelength_attrs_with_units('blue').magnitude\n red = self._get_wavelength_attrs_with_units('red').magnitude\n band = self._get_wavelength_attrs_with_units('band').magnitude\n \n nocheck = kwargs.pop('nocheck', False)\n not_covered = (blue[0] < _w[0]) | (red[-1] > _w[-1])\n if (not_covered):\n if (not nocheck):\n raise ValueError(\"Spectrum does not cover this index.\")\n else:\n return np.zeros(_f.shape[0]) * float('nan') \n else:\n return self._get_indice(_w, _f, blue, red, band, self.index_unit, **kwargs)","def aIllum(wavelength):\n return planckian(2856, wavelength)","def RiekeLebofsky85(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def _get(self, wave, flux, **kwargs):\n if hasUnit(wave):\n _w = wave.to('AA').value\n else:\n print(\"Warning: assuming units are in Angstroms\")\n _w = _drop_units(wave)\n _f = _drop_units(flux)\n\n blue = self._get_wavelength_attrs_with_units('blue').value\n red = self._get_wavelength_attrs_with_units('red').value\n band = self._get_wavelength_attrs_with_units('band').value\n\n nocheck = kwargs.pop('nocheck', False)\n not_covered = (blue[0] < _w[0]) | (red[-1] > _w[-1])\n if (not_covered):\n if (not nocheck):\n raise ValueError(\"Spectrum does not cover this index.\")\n else:\n return np.zeros(_f.shape[0]) * float('nan')\n else:\n return self._get_indice(_w, _f, blue, red, band, self.index_unit,\n **kwargs)","def search(self, wavelength, format=None):\n q = self.create_query(wavelength, format)\n return q.execute()","def wavelength(energy):\r\n return 2 * np.pi * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy","def Damineli16(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def read_aeronet_data_main(station_name, month, year, plot_results):\n # Load AERONET file of month-year\n station = gs.Station(station_name)\n\n monthdays = (date(year, month + 1, 1) - date(year, month, 1)).days\n start_day = datetime(year, month, 1, 0, 0)\n end_day = datetime(year, month, monthdays, 0, 0)\n wavelengths = [355, 532, 1064]\n\n base_name = f\"{start_day.strftime('%Y%m%d')}_{end_day.strftime('%Y%m%d')}_{station.aeronet_name}\"\n file_name = os.path.join(station.aeronet_folder, base_name, base_name + '.lev20')\n # TODO : add automatic download of `.lev20' file from AERONET in case a file is missing.\n aeronet_data = pd.read_csv(file_name, skiprows=6).dropna()\n\n # Parse data and rename columns for easier extrapolation of AOD values\n df_dt = pd.to_datetime(aeronet_data['Date(dd:mm:yyyy)'] + aeronet_data['Time(hh:mm:ss)'], format=\"%d:%m:%Y%H:%M:%S\")\n columns = ['AOD_1640nm', 'AOD_1020nm', 'AOD_675nm', 'AOD_500nm', 'AOD_380nm', 'AOD_340nm']\n df_AOD_ANGSTROM = aeronet_data[columns].copy(deep=True)\n df_AOD_ANGSTROM.index = df_dt\n for col in sorted(columns):\n col_new = int(col.split('_')[1].replace('nm', ''))\n df_AOD_ANGSTROM.rename(columns={col: col_new}, inplace=True)\n\n cols = df_AOD_ANGSTROM.columns.values.tolist()\n cols.extend(wavelengths)\n df_AOD_ANGSTROM = df_AOD_ANGSTROM.reindex(cols, axis='columns').sort_index(axis=1)\n\n # Calculate AOD for missing wavelengths as $355,532,1064$\n # by interpolation values from the nearest existing measured wavelengths.\n cols = df_AOD_ANGSTROM.columns.values.tolist()\n for wavelength in wavelengths:\n col_ind = df_AOD_ANGSTROM.columns.get_loc(wavelength)\n ratio = (cols[col_ind + 1] - cols[col_ind]) / (cols[col_ind + 1] - cols[col_ind - 1])\n df_AOD_ANGSTROM[wavelength] = df_AOD_ANGSTROM.iloc[:, col_ind - 1] * \\\n ratio + (1 - ratio) * \\\n df_AOD_ANGSTROM.iloc[:, col_ind + 1]\n\n # Create dataset of AOD per wavelength\n ds_chans = []\n for wavelength in wavelengths:\n aeronet_ds_chan = xr.Dataset(\n data_vars={'aod': ('Time', df_AOD_ANGSTROM[wavelength]),\n 'lambda_nm': ('Wavelength', [wavelength])\n },\n coords={'Time': df_AOD_ANGSTROM.index.tolist(),\n 'Wavelength': [wavelength]\n })\n ds_chans.append(aeronet_ds_chan)\n ds_aod = xr.concat(ds_chans, dim='Wavelength')\n\n ds_aod.aod.attrs['long_name'] = r'$\\tau$'\n ds_aod = ds_aod.aod.where(ds_aod >= 0, drop=True)\n ds_aod.attrs = {'info': 'Aerosol Optical Depth - generated from AERONET - level 2.0',\n 'location': station.name, 'source_file': file_name,\n 'start_time': start_day.strftime(\"%Y-%d-%m\"), 'end_time': end_day.strftime(\"%Y-%d-%m\")}\n\n # Calculate Angstrom Exponent\n couples = [(355, 532), (355, 1064), (532, 1064)]\n angstrom_daily = []\n for lambda_1, lambda_2 in couples:\n angstrom_couple = xr.apply_ufunc(lambda x, y: misc_lidar.angstrom(ds_aod.sel(Wavelength=x).aod,\n ds_aod.sel(Wavelength=y).aod, x, y), lambda_1, lambda_2,\n keep_attrs=True).rename('angstrom')\n angstrom_ds_chan = xr.Dataset(\n data_vars={'angstrom': ('Time', angstrom_couple.values),\n 'lambda_nm': ('Wavelengths', [f\"{lambda_1}-{lambda_2}\"])\n },\n coords={'Time': df_AOD_ANGSTROM.index.tolist(),\n 'Wavelengths': [f\"{lambda_1}-{lambda_2}\"]\n })\n\n angstrom_daily.append(angstrom_ds_chan)\n ds_ang = xr.concat(angstrom_daily, dim='Wavelengths')\n ds_ang.angstrom.attrs['long_name'] = r'$\\AA$'\n ds_ang.attrs = {'info': 'Angstrom Exponent - generated from AERONET AOD',\n 'location': station.name, 'source_file': file_name,\n 'start_time': start_day.strftime(\"%Y-%d-%m\"), 'end_time': end_day.strftime(\"%Y-%d-%m\")}\n\n # Show AOD and Angstrom Exponent for a period\n if plot_results:\n t_slice = slice(start_day, start_day + timedelta(days=30) - timedelta(seconds=30))\n\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 8))\n ax = axes.ravel()\n for wavelength in wavelengths:\n aod_mean = ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).mean().item()\n aod_std = ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).std().item()\n textstr = ' '.join((\n r'$\\mu=%.2f$, ' % (aod_mean,),\n r'$\\sigma=%.2f$' % (aod_std,)))\n ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).plot(label=fr\"{wavelength}, \" + textstr, ax=ax[0])\n ax[0].set_title(ds_aod.attrs['info'])\n ax[0].legend()\n ax[0].set_ylabel(r'$\\tau$')\n\n for lambda_1, lambda_2 in couples:\n angstrom_mean = ds_ang.angstrom.sel(Wavelengths=f\"{lambda_1}-{lambda_2}\", Time=t_slice).mean().item()\n angstrom_std = ds_ang.angstrom.sel(Wavelengths=f\"{lambda_1}-{lambda_2}\", Time=t_slice).std().item()\n textstr = ' '.join((\n r'$\\mu=%.2f$, ' % (angstrom_mean,),\n r'$\\sigma=%.2f$' % (angstrom_std,)))\n ds_ang.angstrom.sel(Wavelengths=f\"{lambda_1}-{lambda_2}\", Time=t_slice).plot(x='Time',\n label=fr\"$ \\AA \\, {lambda_1},{lambda_2}$, \" + textstr\n , ax=ax[1])\n ax[1].legend()\n ax[1].set_title('Angstrom Exponent')\n plt.tight_layout()\n plt.show()\n\n # Angstrom Exponent distribution of a month\n couple_0 = f\"{355}-{532}\"\n couple_1 = f\"{532}-{1064}\"\n\n x = ds_ang.angstrom.sel(Time=t_slice, Wavelengths=couple_0).values\n y = ds_ang.angstrom.sel(Time=t_slice, Wavelengths=couple_1).values\n\n fig, ax = plt.subplots(nrows=1, ncols=1)\n ax.scatter(x=x, y=y)\n ax.set_ylabel(couple_0)\n ax.set_xlabel(couple_1)\n ax.set_title(f\"Angstrom Exponent distribution {t_slice.start.strftime('%Y-%m')}\")\n plt.tight_layout()\n plt.show()\n\n # Save AOD and Angstrom Exponent datasets\n nc_base_name = f\"{start_day.strftime('%Y%m%d')}_{end_day.strftime('%Y%m%d')}_{station.name}\"\n\n xr_utils.save_dataset(ds_aod, folder_name=station.aeronet_folder, nc_name=nc_base_name+\"_aod.nc\")\n xr_utils.save_dataset(ds_ang, folder_name=station.aeronet_folder, nc_name=nc_base_name+\"_ang.nc\")","def wavelength_ex(hdulist):\n wave = hdulist[1].data['loglam']\n wave = 10**wave\n\n return wave","def Hosek18b(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def wavelength(energy):\n return 2 * PI * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy","def Nishiyama09(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def find_nearest_wav(self, wavelength):\n\n idx = np.searchsorted(self.wavelengths, wavelength, side=\"left\")\n if idx > 0 and (idx == len(self.wavelengths) or math.fabs(wavelength - self.wavelengths[idx-1]) < math.fabs(wavelength - self.wavelengths[idx])):\n return self.wavelengths[idx-1]\n else:\n return self.wavelengths[idx]","def get_wavelength_location(headers, wavelength):\n start = headers['CRVAL1']\n step = headers['CDELT1']\n distance = wavelength - start\n number = round(distance / step)\n return number","def search(self, wavelength, format=None, **keywords):\n q = self.create_query(wavelength, format)\n return q.execute()","def wvac2air(w):\n scalar = False\n if isinstance(w, (int, float)):\n w = [w]\n scalar = True\n w = np.array([w])\n wair = w.copy()\n\n mask = w > 2000. # Modify only wavelength above 2000 A\n\n s2 = (1e4/w[mask])**2\n f = 1.+0.05792105/(238.0185-s2)+0.00167917/(57.362-s2)\n wair[mask] = w[mask]/f\n return wair[0][0] if scalar else wair[0]","def Hosek18(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def NoguerasLara18(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) \n\n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def get_wave(q):\n\n approximant = 'SEOBNRv4'\n chi1 = [0,0,0]\n chi2 = [0,0,0]\n deltaTOverM = 0.1\n omega0 = 2e-2\n\n t, h = LALPy.generate_LAL_waveform(approximant, q, chi1, chi2, deltaTOverM, omega0)\n\n Amp = np.abs(h)\n peakIdx = np.argmax(Amp)\n\n t -= t[peakIdx]\n\n tmin = -500\n if min(t) > tmin:\n raise Exception('Data not long enough, decrease omega0.')\n keepIdx = t - tmin > -1e-3 # simple hack to ensure t_vec is always nearly the same\n t = t[keepIdx]\n h = h[keepIdx]\n\n tmax = 100\n keepIdx = t - tmax < 1e-3\n t = t[keepIdx]\n h = h[keepIdx]\n\n return t, h","def GetWavelengths (self) :\n\t\treturn self.run(\"GetWavelengths\")","def Fritz11(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def Cardelli89(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def OxygenTransmission(T,P,n_wv,wavelength,dr,freq_lim=np.array([lp.c/770e-9,lp.c/768e-9]),sim_nu=np.array([]),spec_file=''):\n # fraction of O2 by number density\n fO2 = (32*0.2320+28.02*0.7547+44.01*0.00046+39.94*0.0128+20.18*0.000012+4.0*0.0000007+83.8*0.000003+131.29*0.00004)*0.2320/32.0\n \n if len(spec_file) == 0:\n spec_file = '/Users/mhayman/Documents/DIAL/O2_HITRAN2012_760_781.txt'\n \n if sim_nu.size==0:\n sim_nu = np.arange(-3e9,3e9,20e6)\n \n# inu0 = np.argmin(np.abs(sim_nu)) # index to center of frequency array\n \n n_o2=fO2*(P/(lp.kB*T)-n_wv) # to convert atm to Pa use *101325\n ext_o2 = rb.ExtinctionFromHITRAN(lp.c/wavelength+sim_nu,T,P,(mO2*1e-3)/lp.N_A,nuLim=freq_lim,freqnorm=True,filename=spec_file).T\n T_o2 = np.exp(-np.cumsum(n_o2[np.newaxis,:]*ext_o2,axis=1)*dr)\n \n return T_o2,sim_nu","def add_wavelength(filename, model, std_tol, overwrite=False, plot_path=None):\n hdulist = fits.open(filename)\n\n # read both hdu's\n logger.debug(\"\\tObject: {}\".format(hdulist[0].header['OBJECT']))\n\n # extract just the middle part of the CCD (we only really care about Halpha)\n tbl = Table(hdulist[1].data)\n\n if 'wavelength' in tbl.colnames and not overwrite:\n logger.debug(\"\\tTable already contains wavelength values!\")\n return\n\n # compute wavelength array for the pixels\n wavelength, var = model.gp.predict(model.y, tbl['pix']-model.x_shift,\n return_var=True)\n bad_idx = np.sqrt(var) > std_tol.to(u.angstrom).value\n wavelength[bad_idx] = np.nan\n\n tbl['wavelength'] = wavelength\n tbl['wavelength_err'] = np.sqrt(var)\n\n new_hdu1 = fits.table_to_hdu(tbl)\n new_hdulist = fits.HDUList([hdulist[0], new_hdu1])\n\n logger.debug(\"\\tWriting out file with wavelength array.\")\n new_hdulist.writeto(filename, overwrite=True)\n\n if plot_path is not None:\n # plot the spectrum vs. wavelength\n fig,axes = plt.subplots(2, 1, figsize=(12,8), sharex=True)\n\n axes[0].plot(tbl['wavelength'], tbl['source_flux'],\n marker='', drawstyle='steps-mid', linewidth=1.)\n axes[0].errorbar(tbl['wavelength'], tbl['source_flux'], 1/np.sqrt(tbl['source_ivar']),\n linestyle='none', marker='', ecolor='#666666', alpha=1., zorder=-10)\n axes[0].set_ylim(tbl['source_flux'][200]/4, np.nanmax(tbl['source_flux']))\n axes[0].set_yscale('log')\n\n axes[1].plot(tbl['wavelength'], tbl['background_flux'],\n marker='', drawstyle='steps-mid', linewidth=1.)\n axes[1].errorbar(tbl['wavelength'], tbl['background_flux'], 1/np.sqrt(tbl['background_ivar']),\n linestyle='none', marker='', ecolor='#666666', alpha=1., zorder=-10)\n axes[1].set_ylim(1e-1, np.nanmax(tbl['background_flux']))\n axes[1].set_yscale('log')\n\n fig.tight_layout()\n _filename_base = path.splitext(path.basename(filename))[0]\n fig.savefig(path.join(plot_path, '{0}_1d_wvln.png'\n .format(_filename_base)))\n\n plt.close(fig)","def calc_impedance(self) -> (Ohm, None):\n power: ComType = complex(0)\n power_unit: str = ''\n amp: NumType = 0\n amp_unit: str = ''\n volt: NumType = 0\n volt_unit: str = ''\n\n if self._volt_exists and self._volt_exists:\n if hasattr(self._obj1, 'volts'):\n volt, volt_unit = self._obj1.volts, self._obj1.volt_unit\n elif hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n if hasattr(self._obj2, 'volts'):\n volt, volt_unit = self._obj2.volts, self._obj2.volt_unit\n elif hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n z = volt / amp\n z_unit: str = f'Ohms ({volt_unit}/{amp_unit})'\n\n elif self._amp_exists and self._power_exists:\n if hasattr(self._obj1, 'amps'):\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\n elif hasattr(self._obj1, 'power'):\n power, power_unit = self._obj1.power, self._obj1.power_unit\n if hasattr(self._obj2, 'amps'):\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\n elif hasattr(self._obj2, 'power'):\n power, power_unit = self._obj2.power, self._obj2.power_unit\n z = power / amp**2\n z_unit: str = f'Ohms ({power_unit}/{amp_unit}^2)'\n\n else:\n return None\n\n return Ohm(z, z_unit, self._obj1.frequency, self._obj1.freq_unit)","def getOrmsby(f,t):\n assert len(f) == 4, 'Ormsby wavelet needs 4 frequencies as input'\n f = np.sort(f) #Ormsby wavelet frequencies must be in increasing order\n pif = pi*f\n den1 = pif[3] - pif[2]\n den2 = pif[1] - pif[0]\n term1 = (pif[3]*np.sinc(pif[3]*t))**2 - (pif[2]*np.sinc(pif[2]))**2\n term2 = (pif[1]*np.sinc(pif[1]*t))**2 - (pif[0]*np.sinc(pif[0]))**2\n\n wav = term1/den1 - term2/den2;\n return wav","def wavelength(self):\n return wavelength(energy)","def bird_hulstrom80_aod_bb(aod380, aod500):\n # approximate broadband AOD using (Bird-Hulstrom 1980)\n return 0.27583 * aod380 + 0.35 * aod500","def get(self):\n try:\n print('jwlee-test-observation-get, self.level:', self.level)\n if self.level is not None:\n data_obs = self._obs_file.get(\n self.var, level=self.level, region=self.region\n )\n else:\n data_obs = self._obs_file.get(self.var, region=self.region)\n return data_obs\n except Exception as e:\n if self.level is not None:\n logging.getLogger(\"pcmdi_metrics\").error(\n \"{} {} {} {}\".format(\n \"Failed opening 4D OBS\", self.var, self.obs_or_model, e\n )\n )\n else:\n logging.getLogger(\"pcmdi_metrics\").error(\n \"{} {} {} {}\".format(\n \"Failed opening 3D OBS\", self.var, self.obs_or_model, e\n )\n )","def DeMarchi16(self, wavelength, AK):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AK (since law assumes AK = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AK\n\n return A_at_wave","def air_to_vac(self, wave):\n #Convert to um\n wave_um = wave*.001\n ohm2 = (1./wave_um)**(2)\n\n #Calculate index at every wavelength\n nn = []\n for x in ohm2:\n n = 1+10**(-8)*(8342.13 + (2406030/float(130.-x)) + (15997/float(389-x)))\n nn.append(n)\n \n #Get new wavelength by multiplying by index of refraction\n vac_wave = nn*wave\n return vac_wave","def _get_omega(self, vehicle_id):\n pos = self.positions[vehicle_id]\n omega = self.frenets[vehicle_id].get_omega(\n pos[0], pos[1], pos[2], pos[3])\n\n return omega","def derive_Damineli16(wavelength):\n # From their eq 19\n x = np.log10(2.159 / wavelength)\n log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4.\n\n # Now to convert this back to linear space\n A_AKs_at_wave = 10**log_A_AKs \n\n return A_AKs_at_wave","def bragg_law(self, d_list, wavelength):\r\n new_twotheta = []\r\n for d in d_list:\r\n new_twotheta.append(2*math.degrees(np.arcsin(wavelength/(2*d))))\r\n return new_twotheta","def efftau_madau(rwl, z):\n\n from numpy import array, where, exp\n\n la = 1215.67 ## Lyman alpha. Angstroms. \n lb = 1026. ## Lyman beta. Angstroms. \n lg = 973.\n ld = 950.\n le = 938.\n ll = 912. ## Lyman limit.\n\n ## Redshifted from restframe to (definitely) observed. \n wl = rwl*(1. + z)\n \n n = len(wl)\n c = array([3.6e-3, 1.7e-3, 1.2e-3, 9.3e-4])\n l = array([ la, lb, lg, ld])\n\n tau = np.zeros_like(wl)\n xe = 1. + z\n\n ## Lyman series\n for i in range(len(l) ):\n indices = where(wl <= l[i]*xe) ## Note: no lower wavelength limit is correct as 'broadcasted' corrections. \n tau[indices] += c[i]*(wl[indices]/l[i])**3.46\n\n ## Photoelectric absorption\n xc = wl/ll\n xc3 = xc**3\n \n tau = where(wl <= ll*xe, tau + 0.25*xc3*(xe**.46 - xc**0.46) \\\n + 9.4*xc**1.5*(xe**0.18 - xc**0.18) \\\n - 0.7*xc3*(xc**(-1.32) - xe**(-1.32)) \\\n - 0.023*(xe**1.68-xc**1.68), tau)\n \n '''\n min_tau = tau.min()\n index = np.where(tau == min_tau)\n tau[:index] = min_tau \n \n tau = where(tau < 0.0, 0.0, tau)\n '''\n\n return where(tau > 700., 0., exp(-tau))","def powerlaw(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def get_signal(self, audio, gain, phase):\n max_delay_ms = self.center_ms + self.depth_ms\n max_length_samples = int(self.sample_rate / 1000.0 * max_delay_ms)\n\n depth_phase = self.depth_ms / max_delay_ms\n center_phase = self.center_ms / max_delay_ms\n phase = phase * depth_phase + center_phase\n wet_audio = core.variable_length_delay(\n audio=audio, phase=phase, max_length=max_length_samples\n )\n # Remove channel dimension.\n if gain.dim() == 3:\n gain = gain[:, 0, :]\n\n wet_audio *= gain\n return (wet_audio + audio) if self.add_dry else wet_audio","def Schlafly16(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4):\n sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity)\n\n # Do some error checking\n idx = np.where(sp.flux != 0)[0]\n if len(idx) == 0:\n print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for')\n print( ' temperature = %d' % temperature)\n print( ' metallicity = %.1f' % metallicity)\n print( ' log gravity = %.1f' % gravity)\n\n return sp","def _get_wavelength_attrs_with_units(self, attrname, units='AA'):\n attr = self._lick[attrname]\n if self.wavelength_unit is not None:\n if units is None:\n return attr * Unit(self.wavelength_unit)\n else:\n return (attr * Unit(self.wavelength_unit)).to(units)\n else:\n return attr","def antennaResponse(peak, xoff, yoff, fwhm):\n return peak * np.exp(CNST / (fwhm*fwhm) * (xoff*xoff + yoff*yoff))","def convertToOpd(self, wavelength):\n\n if wavelength is None:\n scale = 1.\n else:\n scale = 2. * math.pi / wavelength\n self.opd = self.pupil * np.exp(1.j * self.phase * scale)","def Fitzpactrick09(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def absorbance( self, lmin=0, lmax=0 ):\n A = self.prop[\"SPEC\"][:,1]\n if lmax>0:\n m = np.vstack( (self.wavelength(), A) ).T # matrix w lambda and absorbance\n m = m[ m[:,0] >= lmin ] # slice by wavelength...\n m = m[ m[:,0] <= lmax ]\n return np.average( m[:,1] ) # scalar\n return A # array","def search(url, wavelength, **keywords):\n service = SLAService(url)\n return service.search(wavelength, **keywords)","def derive_RiekeLebofsky(wavelength):\n filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', \n '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', \n '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']\n #wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.25, 1.635, 2.2, \n # 3.77, 4.68, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\n # 11.5, 12.0, 12.5, 13.0])\n \n # Wavelengths from Nishiyama+09 plot of RL+85 law...slightly different than standard, \n # drop N filter\n wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.17, 1.57, 2.12, \n 3.40, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\n 11.5, 12.0, 12.5, 13.0])\n A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,\n 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,\n 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])\n # Want to change this from A/Av to A/AK\n k_ind = np.where(np.array(filters) == 'K')\n Ak_Av = A_Av[k_ind]\n Av_Ak = 1.0 / Ak_Av\n\n A_Ak = A_Av * Av_Ak\n \n # Interpolate over the curve\n spline_interp = interpolate.splrep(wave, A_Ak, k=3, s=0)\n A_Ak_at_wave = interpolate.splev(wavelength, spline_interp)\n\n return A_Ak_at_wave","def get_wave_unit(tag, hdulist, idx=None):\n from astropy.units import Unit\n if idx is None:\n idx = 1\n # Use Table\n if isinstance(hdulist[idx],BinTableHDU):\n tab = Table(hdulist[idx].data)\n header = hdulist[idx].header\n else:\n # NEED HEADER INFO\n return None\n # Try table header (following VLT/X-Shooter here)\n keys = list(header) # Python 3\n values = list(itervalues(header)) # Python 3\n hidx = values.index(tag)\n if keys[hidx][0:5] == 'TTYPE':\n try:\n tunit = header[keys[hidx].replace('TYPE','UNIT')]\n except KeyError:\n return None\n else:\n if tunit in ['Angstroem', 'Angstroms', 'ANGSTROMS']:\n tunit = 'Angstrom'\n unit = Unit(tunit)\n return unit\n else:\n return None","def wavelength(self,freq):\n return self.phase_velocity()/freq","def simulate_UVSPEC(file, config):\n\n wavelength = config['wavelength']\n\n # Coordenates from position of the station Hannover\n latitude = 52.39 # positive in the northern hemisphere\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\n\n # Read name of the file (correct time)\n name = os.path.split(file)\n time_n = name[1][0:15]\n print(\"Time name\", time_n)\n # convert time to datetime format\n time = datetime.datetime.strptime(time_n,\n '%Y%m%d_%H%M%S')\n # Calculate the azimuth and zenith angles in function of the date\n elev = ps.GetAltitude(latitude, longitud, time)\n azi = ps.GetAzimuth(latitude, longitud, time)\n zenith = 90 - elev\n\n # Correction between the sign and real azimuth for plot of radiance\n if -180 <= azi < 0:\n azi = 180 - azi\n elif -360 <= azi < -180:\n azi = -azi - 180\n else:\n pass\n\n print(\"Azimuth: {:5.1f}\".format(azi),\n \"\\nZenith: {:5.1f}\".format(zenith))\n\n # Change the value of zenith and azimuth angles in function of the time and\n # position in the UVSPEC file\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\n data = file.readlines()\n\n data[14] = \"day_of_year \" + str(time.timetuple().tm_yday) + \" \" + \"\\n\"\n data[15] = \"wavelength \" + str(\"{}\".format(wavelength)) + \" \" + \\\n str(\"{}\".format(wavelength)) + \\\n \" # wavelength to calcule [nm] \\n\"\n data[17] = \"sza \" + str(\"{:2.3f}\".format(zenith)) + \\\n \" # Solar zenith angle \\n\"\n data[18] = \"phi0 \" + str(\"{:2.3f}\".format(azi)) + \\\n \" #Azimuth angle with zenith position \\n\"\n\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\n file.writelines(data)\n\n # Create the directory to save the results\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\n wavelength)),\n exist_ok=True)\n\n # Run the program UVSPEC in the terminal\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\n '.txt')","def get_evwma(data):\n if data is None:\n raise EmptyDataError('[!] Invalid data value')\n\n result = TA.EVWMA(data)\n if result is None:\n raise IndicatorException\n return result","def _get_wavelength_attrs_with_units(self, attrname, units='AA'):\n attr = self._lick[attrname]\n if self.wavelength_unit is not None:\n if units is None:\n return attr * unit[self.wavelength_unit]\n else:\n return (attr * unit[self.wavelength_unit]).to(units)\n else:\n return attr","def convert_nasa_unit_to_astropy(unit_str):\n\n print('Unit str: ' + repr(unit_str))\n if unit_str in ['days', 'hrs']:\n unit_str = unit_str[:-1]\n elif unit_str == 'decimal degrees':\n unit_str = 'degree'\n elif (\n unit_str in ['dex', 'Earth flux', 'sexagesimal']\n or\n unit_str.startswith('log10(')\n or\n unit_str.startswith('log(')\n ):\n return None\n elif unit_str == 'Solar mass':\n unit_str = 'solMass'\n elif unit_str == 'Solar radii':\n unit_str = 'solRad'\n elif unit_str.endswith(' mass'):\n unit_str = unit_str.split()[0].lower() + 'Mass'\n elif unit_str.endswith(' radii'):\n unit_str = unit_str.split()[0].lower() + 'Rad'\n elif unit_str.startswith('percent'):\n return 0.01\n\n print('Converted to: ' + repr(unit_str))\n\n return Unit(unit_str)","def readAmesDustySpectrum(fname=''):\n print('Reading : ', fname)\n\n # Get the effective temperature, logg and metallicity from the file name\n ind = fname.find('lte')\n fname_tags = fname[ind+3:ind+13].split('-')\n teff = np.float(fname_tags[0]) * 100.\n logg = np.float(fname_tags[1]) * 100.\n mph = np.float(fname_tags[2]) * 100.\n\n wav = []\n inu = []\n bnu = []\n with open(fname, 'r') as rfile:\n dum = rfile.readline()\n while dum != '':\n dum = str(dum).replace('D', 'E')\n sdum = dum.split()\n wav.append(np.float(sdum[0]))\n inu.append(np.float(sdum[1]))\n bnu.append(np.float(sdum[2]))\n dum = rfile.readline()\n\n wav = np.array(wav)\n inu = np.array(inu)\n bnu = np.array(bnu)\n ii = wav.argsort()\n\n wav = wav[ii]\n inu = inu[ii]\n bnu = bnu[ii]\n\n # \"Decode\" the intensity arrays\n inu = 10.**(inu - 8.0) * wav\n bnu = 10.**(bnu - 8.0) * wav\n\n # Convert the wavelength to micron from Angstrom\n wav /= 1e4\n nwav = wav.shape[0]\n\n return {'teff': teff, 'logg': logg, 'mph': mph, 'nwav': nwav, 'wav': wav, 'inu': inu, 'bnu': bnu}","def dseries(temp, wavelength):\n if wavelength < 300 or wavelength > 830:\n return 0\n mm=wavelength%10\n s=_dseriesd(temp, wavelength-mm)\n if mm==0:\n return s\n m=mm*0.1\n e=_dseriesd(temp, (wavelength-mm)+10)\n return s+(e-s)*m","def wavelength(self):\n return self.get(self._names[\"wavelength\"])","def wavelength(self):\n return self.getparam(\"WAVELENGTH\")","def wavelength(self):\n return self.getparam(\"WAVELENGTH\")","def wavelength_to_wavenumber(wavelength):\n return 1. / wavelength","def speed_of_sound(altitude):\n t = temperature(altitude) # R\n a = sqrt(gamma*gas_constant*t) # [ft/s]\n return a","def get(self, wave, flux, **kwargs):\n return self._get(wave, flux.to('flam').value, **kwargs)","def AcquiredData (self, arguments=None) :\n\t\tself.OODriver.Wrapper_getSpectrum(self.wrapperHandle,self.spectrometerIndex,self.bufferHandle)\n\t\t\n\t\tif self.OODriver.Wrapper_isSaturated(self.wrapperHandle,self.spectrometerIndex) :\n\t\t\tprint \"Warning: OcenOptics spectrometer is saturated!\"\n\t\t\t\n\t\ttry : return self.buffer[self.spectral_interval]\n\t\texcept AttributeError : return self.buffer","def blackbody( wave, T, waveunit='Angstrom' ):\n \n if waveunit=='Angstrom':\n # convert wavelength from angstroms to cm\n wave = wave / 1e10 * 100.\n elif waveunit=='nm':\n # convert wavelength from angstroms to cm\n wave = wave / 1e9 * 100.\n\n return( ((2 * h * c* c)/wave**5 ) / (exp(h*c/(wave*k*T))-1) )","def get_pulse_value(object = pulse_value_req):\n try:\n response = urllib2.urlopen(object).read()\n pulse_value = json.loads(response)\n return pulse_value['GetSensorValue'][0]\n except URLError, e:\n print 'Error: No Heartrate Value.'","def measure_wavelength_avg(self, from_time, to_time, print_n=False):\n from_idx = self._find_index_for_time(from_time)\n to_idx = self._find_index_for_time(to_time)\n \n # print number of measurements\n if print_n:\n n_measurements = to_idx - from_idx + 1\n print 'n measurements:', n_measurements\n \n # calculate overlap\n overlap = self.overlap()[from_idx:to_idx+1,:]\n \n # intitialize wavelength storage\n wavelengths = np.zeros(overlap.shape[0])\n \n for i in range(overlap.shape[0]):\n this_overlap = overlap[i,:]\n \n # set all overlap entries below 0.1% of the maximum overlap to zero\n this_overlap[this_overlap<0.001*np.max(this_overlap)] = 0.0\n \n nonzero = np.nonzero(this_overlap)\n \n # find connected section of chain with nonzero overlap\n consecutives = np.split(nonzero, np.where(np.diff(nonzero) != 1)[0]+1)\n \n if len(consecutives) != 1:\n warnings.warn('Wavelength could not be determined unambiguously.')\n return np.nan\n else:\n # add 1 since overlap involves two beads each\n wavelengths[i] = float( len(consecutives[0][0]) ) + 1\n \n return np.mean( wavelengths )","def calculate_flux(self, band):\n\n if (self.wavelength[0] > band.wavelength[0] or\n self.wavelength[-1] < band.wavelength[-1]):\n\n warn('Spectrum does not cover the whole bandpass, '\n 'extrapolating...')\n dw = np.median(np.diff(self.wavelength.value))\n spec_wavelength = np.arange(\n band.wavelength.value[0],\n band.wavelength.value[-1] + dw, dw) * angstrom\n spec_flux = np.interp(spec_wavelength, self.wavelength,\n self.flux.value)\n\n else:\n spec_wavelength = self.wavelength\n spec_flux = self.flux.value\n\n i, j = spec_wavelength.searchsorted(\n Quantity([band.wavelength[0], band.wavelength[-1]]))\n wavelength = spec_wavelength[i:j]\n flux = spec_flux[i:j]\n\n dw_band = np.median(np.diff(band.wavelength))\n dw_spec = np.median(np.diff(wavelength))\n\n if dw_spec.value > dw_band.value > 20:\n\n warn('Spectrum wavelength sampling interval {0:.2f}, but bandpass'\n 'sampling interval {1:.2f}'.format(dw_spec, dw_band))\n\n # Interpolate the spectrum to the passband wavelengths:\n flux = np.interp(band.wavelength, wavelength, flux)\n band_transmission = band.transmission\n wavelength = band.wavelength\n\n else:\n # Interpolate the band transmission to the spectrum wavelengths:\n band_transmission = np.interp(\n wavelength, band.wavelength, band.transmission)\n\n # Weight by the response and wavelength, appropriate when we're\n # counting the number of photons within the band:\n flux = (np.trapz(band_transmission * flux * wavelength, wavelength) /\n np.trapz(band_transmission * wavelength, wavelength))\n flux *= erg / s / cm ** 2 / angstrom\n\n return flux","def wair2vac(w):\n scalar = False\n if isinstance(w, (int, float)):\n w = [w]\n scalar = True\n w = np.array([w])\n wvac = w.copy()\n\n mask = w > 2000. # Modify only wavelength above 2000 A\n\n s2 = (1e4/w[mask])**2\n f = 1.+0.05792105/(238.0185-s2)+0.00167917/(57.362-s2)\n wvac[mask] = w[mask]*f\n return wvac[0][0] if scalar else wvac[0]","def _get_url_for_timerange(self, timerange, **kwargs):\n base_url = \"https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/goes{goes_number}/\"\n supported_waves = [94, 131, 171, 195, 284, 304]\n supported_levels = (\"2\", \"1b\")\n\n # these are optional requirements so if not provided assume defaults\n # if wavelength is not provided assuming all of them\n if \"wavelength\" in kwargs.keys():\n wavelength_input = kwargs.get(\"wavelength\")\n if isinstance(wavelength_input, u.Quantity): # not a range\n if int(wavelength_input.to_value('Angstrom')) not in supported_waves:\n raise ValueError(f\"Wavelength {kwargs.get('wavelength')} not supported.\")\n else:\n wavelength = [kwargs.get(\"wavelength\")]\n else: # Range was provided\n compress_index = [wavelength_input.wavemin <= this_wave <=\n wavelength_input.wavemax for this_wave in (supported_waves * u.Angstrom)]\n if not any(compress_index):\n raise ValueError(\n f\"Wavelength {wavelength_input} not supported.\")\n else:\n wavelength = list(compress(supported_waves, compress_index)) * u.Angstrom\n else: # no wavelength provided return all of them\n wavelength = supported_waves * u.Angstrom\n # check that the input wavelength can be converted to angstrom\n waves = [int(this_wave.to_value('angstrom', equivalencies=u.spectral()))\n for this_wave in wavelength]\n # use the given satellite number or choose the best one\n satellitenumber = int(kwargs.get(\n \"satellitenumber\", self._get_goes_sat_num(timerange.start)))\n if satellitenumber < 16:\n raise ValueError(f\"Satellite number {satellitenumber} not supported.\")\n # default to the highest level of data\n level = str(kwargs.get(\"level\", \"2\")) # make string in case the input is a number\n\n if level not in supported_levels:\n raise ValueError(f\"Level {level} is not supported.\")\n\n results = []\n for this_wave in waves:\n if level == \"2\":\n search_pattern = base_url + \\\n r'l{level}/data/suvi-l{level}-ci{wave:03}/%Y/%m/%d/dr_suvi-l{level}-ci{wave:03}_g{goes_number}_s%Y%m%dT%H%M%SZ_.*\\.fits'\n elif level == \"1b\":\n if this_wave in [131, 171, 195, 284]:\n search_pattern = base_url + \\\n r'l{level}/suvi-l{level}-fe{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-Fe{wave:03}_G{goes_number}_s%Y%j%H%M%S.*\\.fits.gz'\n elif this_wave == 304:\n search_pattern = base_url + \\\n r'l{level}/suvi-l{level}-he{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-He{wave_minus1:03}_G{goes_number}_s%Y%j%H%M%S.*\\.fits.gz'\n elif this_wave == 94:\n search_pattern = base_url + \\\n r'l{level}/suvi-l{level}-fe{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-Fe{wave_minus1:03}_G{goes_number}_s%Y%j%H%M%S.*\\.fits.gz'\n\n if search_pattern.count('wave_minus1'):\n scraper = Scraper(search_pattern, level=level, wave=this_wave,\n goes_number=satellitenumber, wave_minus1=this_wave-1)\n else:\n scraper = Scraper(search_pattern, level=level, wave=this_wave,\n goes_number=satellitenumber)\n results.extend(scraper.filelist(timerange))\n return results","def d65Illum(wavelength):\n return _d65Illum.calc(wavelength)","def read_data(self, uv, tave=False):\n self.freqs = uv.freq_array[0]\n a1 = uv.ant_1_array[:uv.Nbls]\n a2 = uv.ant_2_array[:uv.Nbls]\n for a in uv.antenna_numbers:\n if not a in a1 and not a in a2:\n if not a in self.dead: self.dead.append(a)\n pid = np.where(uv.polarization_array == pol_lookup[self.pol])[0][0]\n data = uv.data_array[:,0,:,pid].reshape(uv.Ntimes,uv.Nbls,uv.Nfreqs)\n flag = uv.flag_array[:,0,:,pid].reshape(uv.Ntimes,uv.Nbls,uv.Nfreqs)\n ind = np.where(a1!=a2)[0]\n self.mask = output_mask_array(flag[:,ind])\n self.shape_waterfall = (uv.Ntimes, uv.Nfreqs)\n def creat_dict(ii):\n if a1[ii] < 57 or a2[ii] < 57 or a1[ii] == a2[ii]: return # hard coded for MWA Phase II\n if a1[ii] in self.dead or a2[ii] in self.dead: return\n bl = (a1[ii],a2[ii])\n md = np.ma.masked_array(data[:,ii],flag[:,ii])\n diff = md[1:] - md[:-1]\n self.noise[bl] = np.var(diff,axis=0).data/2\n zerofq = np.where(np.sum(np.logical_not(diff.mask),axis=0) < 3)[0]\n md.mask[:,zerofq] = True\n self.data_backup[bl] = {self.pol: np.complex64(md.data)}\n self.flag_backup[bl] = {self.pol: np.copy(md.mask)}\n if tave:\n md = np.mean(md,axis=0,keepdims=True)\n self.data[bl] = {self.pol: np.complex64(md.data)}\n self.flag[bl] = {self.pol: md.mask}\n map(creat_dict, np.arange(uv.Nbls))\n if tave: self.mask= np.product(self.mask, axis=0, keepdims=True).astype(bool)\n mask = np.copy(self.mask)\n if mask.ndim == 2: mask = np.product(self.mask, axis=0).astype(bool)\n self.gains = RedGain(freqs=self.freqs, mask=mask)\n self.gains.get_auto(uv)","def RomanZuniga07(self, wavelength, AKs):\n # If input entry is a single float, turn it into an array\n try:\n len(wavelength)\n except:\n wavelength = [wavelength]\n\n # Return error if any wavelength is beyond interpolation range of\n # extinction law\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\n \n # Extract wave and A/AKs from law, turning wave into micron units\n wave = self.wave * (10**-4)\n law = self.obscuration\n\n # Find the value of the law at the closest points\n # to wavelength\n A_AKs_at_wave = []\n for ii in wavelength:\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\n A_AKs_at_wave.append(law[idx][0])\n\n # Now multiply by AKs (since law assumes AKs = 1)\n A_at_wave = np.array(A_AKs_at_wave) * AKs\n\n return A_at_wave","def wind(\n da_model: Union[xr.DataArray, xr.Dataset],\n wind: xr.DataArray = None,\n wind_u: xr.DataArray = None,\n wind_v: xr.DataArray = None,\n altitude: float = 10,\n altitude_correction: bool = False,\n freq: pd.Timedelta = None,\n reproj_method: str = \"nearest_index\",\n resample_kwargs: dict = {},\n logger=logger,\n):\n if wind_u is not None and wind_v is not None:\n wind = np.sqrt(np.power(wind_u, 2) + np.power(wind_v, 2))\n elif wind is None:\n raise ValueError(\"Either wind or wind_u and wind_v varibales must be supplied.\")\n\n if wind.raster.dim0 != \"time\":\n raise ValueError(f'First wind dim should be \"time\", not {wind.raster.dim0}')\n\n # compute wind at 2 meters altitude\n if altitude_correction:\n wind = wind * (4.87 / np.log((67.8 * altitude) - 5.42))\n # downscale wind (lazy)\n wind_out = wind.raster.reproject_like(da_model, method=reproj_method)\n # resample time\n wind_out.name = \"wind\"\n wind_out.attrs.update(unit=\"m s-1\")\n if freq is not None:\n resample_kwargs.update(upsampling=\"bfill\", downsampling=\"mean\", logger=logger)\n wind_out = resample_time(wind_out, freq, conserve_mass=False, **resample_kwargs)\n return wind_out","def calc(self, wavelength):\n if wavelength < self.minWavelength or wavelength > self.maxWavelength:\n return 0\n mm=wavelength%self.interval\n s=self._calcd(wavelength-mm)\n if mm==0:\n return s\n m=mm*1.0/self.interval\n e=self._calcd((wavelength-mm)+self.interval)\n return s+(e-s)*m","def test_filt_abmag(self):\n sun = Sun.from_builtin('E490_2014')\n V = get_bandpass('johnson v')\n wave, fluxd = sun.filt(V, unit=u.ABmag)\n assert np.isclose(fluxd.value, -26.77, atol=0.007)","def bommer_stafford_alarcon_ds_2009(magnitude=7.0, distance=10.0, vs30=760.0, ztor=0.0, duration_type='DS575H'):\n\n # duration type map\n dur_map = {'DS575H':0, 'DS595H': 1}\n dur_tag = dur_map.get(duration_type.upper(), None)\n if dur_tag is None:\n print(\"SignificantDurationModel.bommer_stafford_alarcon_ds_2009: duration_type='DS575H','DS595H'?\")\n return None, None, None, None\n \n # modeling coefficients\n c0 = [-5.6298, -2.2393]\n m1 = [1.2619, 0.9368]\n r1 = [2.0063, 1.5686]\n r2 = [-0.2520, -0.1953]\n h1 = [-2.3316, 2.5000]\n v1 = [-0.2900, -0.3478]\n z1 = [-0.0522, -0.0365]\n tauCoeff = [0.3527, 0.3252]\n phiCoeff = [0.4304, 0.3460]\n sigma_c = [0.1729, 0.1114]\n sigma_Tgm = [0.5289, 0.4616]\n\n # median\n ds_median = np.exp(c0[dur_tag]+m1[dur_tag]*magnitude+(r1[dur_tag]+ \\\n r2[dur_tag]*magnitude)*np.log(np.sqrt(distance**2+h1[dur_tag]**2))+ \\\n v1[dur_tag]*np.log(vs30)+z1[dur_tag]*ztor)\n # standard deviations\n ds_sigma = sigma_Tgm[dur_tag]\n ds_tau = tauCoeff[dur_tag]\n ds_phi = phiCoeff[dur_tag]\n\n # return\n return np.log(ds_median), ds_sigma, ds_tau, ds_phi","def read300yrh(period):\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\n file300 = 'U10_1700-2000.nc'\n filename = directory300 + file300\n \n data = Dataset(filename)\n lat = data.variables['latitude'][:]\n lon = data.variables['longitude'][:]\n u10q = data.variables['U10'][:]\n data.close()\n \n ### Reshape in year/month\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\n \n ### Calculate over particular months\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\n \n ### Slice U10 at 65N\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\n lat = lat[latq].squeeze()\n u10 = u10[:,latq,:].squeeze()\n \n ### Take zonal mean \n u10z = np.nanmean(u10,axis=1)\n \n ### Remove missing data\n mask = np.where(u10z > -1e5)[0]\n \n ### Detrend\n u10zdt = sss.detrend(u10z[mask],type='linear')\n \n return lat,lon,u10zdt","def derive_Fritz11(wavelength):\n # Extinction law definition\n wave = np.array([1.282, 1.736, 2.166, 2.625, 2.758, 2.873, 3.039, 3.297, 3.74, 3.819, 3.907, 4.052,\n 4.376, 5.128, 5.908, 6.772, 7.459, 7.502, 8.76, 12.371, 19.062])\n A_AKs = np.array([7.91, 4.30, 2.49, 1.83, 1.51, 1.84, 2.07, 1.66, 1.19, 1.19, 1.09, 1.01, 1.09, 0.99,\n 1.04, 0.84, 0.81, 0.79, 2.04, 1.34, 1.34])\n\n\n # Interpolate over the curve\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\n A_at_wave = interpolate.splev(wavelength, spline_interp)\n\n # We'll call 2.14 microns the K-band\n idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )\n A_AKs_at_wave = A_at_wave / A_at_wave[idx] \n\n return A_AKs_at_wave","def getFWHM(antenna, freq):\n diameter = getDiameter(antenna)\n lam = 299792458.0 / (freq * 1e9)\n fwhmo = lam / math.pi * 180.0 * 60.0\n fwhm = 1.22 * fwhmo / diameter\n return fwhm","def __init__(self, downsampling=False, wl_range = False, spectra_path = '/Users/kyleturner/Dropbox/My_Box/Code/M_Files/process_svc_v2p1/rrs_model_3C-master/spectra/'):\n\n\t\t\ta_ph = pd.read_csv(spectra_path + 'phyto.A', skiprows=11, sep='\\t', index_col=0).iloc[:,0]\n\t\t\n\t\t\ta_w = pd.read_csv(spectra_path + 'water.A', skiprows=10, sep='\\t', index_col=0).iloc[:,0]\n\t\t\tdaw_dT = pd.read_csv(spectra_path + 'daWdT.txt', skiprows=10, sep='\\t', index_col=0).iloc[:,0]\n\t\t\tastar_y = pd.read_csv(spectra_path + 'Y.A', skiprows=11, delimiter=' ', index_col=0).loc[350:].iloc[:,0]\n\n\t\t\td = pd.DataFrame(index = a_ph.index)\n\t\t\td.index.name = 'Wavelength, [nm]'\n\t\t\td['astar_ph'] = a_ph\n\t\t\td['astar_y'] = astar_y \n\t\t\td['a_w'] = a_w \n\t\t\td['daw_dT'] = daw_dT\n\n\t\t\tif downsampling == False:\n\t\t\t\tself.spectra = d\n\t\t\telse: \n\t\t\t\tself.spectra = d.apply(lambda x: pd.rolling_mean(x, downsampling, center=True))\n\n\t\t\tif wl_range != False:\n\t\t\t\tself.spectra = self.spectra.loc[wl_range[0]:wl_range[1]].dropna()\n\n\t\t\tself.wl = np.array(self.spectra.index)\n\t\t\t\t\n\t\t\tself.model = self._compile_model()","def turbulence(*args, attenuation: Union[float, bool]=0.0, frequency: Union[float, bool]=0.0,\n magnitude: Union[float, bool]=0.0, maxDistance: Union[float, bool]=0.0, name:\n Union[AnyStr, bool]=\"\", noiseLevel: Union[int, bool]=0, noiseRatio: Union[float,\n bool]=0.0, perVertex: bool=True, phase: Union[float, bool]=0.0, phaseX:\n Union[float, bool]=0.0, phaseY: Union[float, bool]=0.0, phaseZ: Union[float,\n bool]=0.0, position: Union[List[float, float, float], List[List[float, float,\n float]], bool]=None, torusSectionRadius: Union[float, bool]=0.0,\n volumeExclusion: bool=True, volumeOffset: Union[List[float, float, float],\n bool]=None, volumeShape: Union[AnyStr, bool]=\"\", volumeSweep: Union[float,\n bool]=0.0, q=True, query=True, e=True, edit=True, **kwargs)->Union[AnyStr, Any]:\n pass","def twostr_func(wavelength, F_s, solarzenithangle,albedo_dif, \n\t\t\talbedo_dir, temp_ground, w_0, g, tau_n, temp_c):\n\t\n\t########################\n\t###Import useful libraries\n\t########################\n\timport numpy as np\n\timport pdb\n\timport scipy.linalg\n\n\n\n\n\t########################\n\t###Define model parameters\n\t########################\n\t#Properties of the ground\n\temissivity_ground=1.-albedo_dif #emissivity of ground. 1=perfect BB emitter.\n\n\t#Optical depth structure\n\tNlayer=len(tau_n) #number of layers in the atmospheric model.\n\t\n\ttau_c=np.zeros(Nlayer+1)# tau_c[n] is the cumulative optical depth at the upper edge of layer n. So tau_c[0]=0, and tau_c[N] is the maximum possible.\n\tfor n in range(0, Nlayer):\n\t\ttau_c[n+1]=tau_c[n]+tau_n[n] \n\n\t#In the Toon formalism, j=0 corresponds to space, and j=N+1 corresponds to the planet surface.\n\t#These points in wavelength space define the edges of the bins in tau space. \n\t#Other terminology:\n\t#\ttau_c=cumulative optical depth of layers *above* layer n. \n\t#\ttau_n=total optical depth of the layer n\n\t#\ttau=total optical depth at any point within a layer n, hence satisfying 0<tau<tau_n\n\n\tmu_0=np.cos(solarzenithangle) #\"incident direction of solar beam\"\n\n\n\t########################\n\t###Determine the two-stream approximation coefficients.\n\t########################\n\t#Eddington and quadrature are good at solar wavelengths (i.e., not thermal blackbody dominated). delta scalings of Joseph et al (1976) recommended to replace w_0, g, tau in this case. However, when dominated by internal isotropic sources like the Planck function, hemispheric mean approximation is preferable. When w_0=0, quadrature case has problems. This happens esp at thermal wavelengths. Again this favors using hemispheric mean at these wavelengths\n\t\n\t#We use quadrature because 1) we are at solar wavelengths for this UV work and 2) that's what twostr.f does (which is our comparison case)\n\tgamma_1= np.sqrt(3.)*(2.-w_0*(1.+g))/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_1\n\tgamma_2=np.sqrt(3.)*w_0*(1.-g)/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_2\n\tgamma_3=(1.-np.sqrt(3.)*g*mu_0)/2. #consistent with Toon et al; equal to the Pierrehumbert gamma_plus/w_0\n\tgamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\n\tmu_1=1./np.sqrt(3.)+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\n\n\t##Eddington\n\t#gamma_1= (7.-w_0*(4.+3.*g))/4.\n\t#gamma_2=-1.*(1.-w_0*(4.-3.*g))/4.\n\t#gamma_3=(2.-3.*g*mu_0)/4.\n\t#gamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\n\t#mu_1=1./2.+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\n\n\talambda=np.sqrt(np.abs(gamma_1*gamma_1-gamma_2*gamma_2)) #this is the lower-case lambda, from eqn 21 of Toon et al\n\t\t\t\t\t\t\t\t #The absolute value was added based on the code Toon just sent us. This corresponds to his AK(L,J) parameter. But it should not matter since gamma_1>gamma_2 for w_0<1.\n\tclambda=(gamma_1-alambda)/(gamma_2) #this is the upper-case lambda, from eqn 22 of Toon et al\n\n\tEMLT=np.exp(-alambda*tau_n) #this appears to be a prefactor used to facilitate computation of eqn 44 of Toon et al\n\te1=1.+clambda*EMLT\n\te2=1.-clambda*EMLT\n\te3=clambda+EMLT\n\te4=clambda-EMLT\n\n\t########################\n\t###Set up calculation\n\t########################\n\t\"\"\"\n\tThe fundamental equation we are solving is of form:\n\tA_{l}*Y_{l-1}+B_{l}*Y_{l}+D{l+1}=E_{l} (equation 39 of Toon et al)\n\tHere, A_l, B_l, D_l, E_l are quantities we determine, and the Y_l is what we solve for.\n\tHence, we can summarize that we are solving a matrix equation that takes form:\n\tPY=E\n\twhere Y[l]=Y_l\n\t E[l]=E_l\n\t P[l, l-1]=A_l [row, column]\n\t P[l, l]=B_l\n\t P[l, l+1]=D_l\n\t P[i,j]=0 else\n\tToon et al use 1-indexing. Hence n runs from 1 to N, l runs from 1 to 2N, where N is the number of layers, and they have:\n\tY_l=Y_{1n} for l=1,3,5,...2n-1...2N-1\n\tY_l=Y_{2n} for l=2,4,6,...2n...2N\n\n\tHowever, we use Python, which has 0-indexing. Hence *we* choose that n runs from 0 to N-1, l runs from 0 to 2N-1, and:\n\tY_l=Y_{1n} for l=0,2,4...2n...2N-2\n\tY_l=Y_{2n} for l=1,3,5...2n+1...2N-1\n\n\tThe Y_{1n} and Y_{2n} are related to F^+_n and F^-_n via equations 31 and 32 of Toon et al.\n\tThis parametrization has been done to remove exponentials with positive operands (ie ones that could grow large and lead to numerical instabilities) from the matrix.\n\n\tNote: The mapping of this PQ=R to the F+ and F- is unclear because of 1) this parametrization in terms of Y_l (done to eliminate numerical instabilities) and 2)further linear combinations done to convert a pentagiagonal matrix to an even simpler tridiagonal matrix. Hence intuitive checks are hard.\n\t\"\"\"\n\n\t########################\n\t###Set up surface flux\n\t########################\n\tS_sfc=albedo_dir*mu_0*np.exp(-tau_c[-1]/mu_0)*np.pi*F_s+emissivity_ground*np.pi*Planck(temp_ground, wavelength)\n\t#Surface emission. Formed by adding blackbody emission from the ground to the reflected energy from the direct beam. The direct beam's reflected energy is assumed to be purely diffuse. This corresponds to equations 37 and 38 of Toon et al. Note that this does NOT match equation 5.31 of Pierrehumbert because it does not include the reflected diffuse radiation. So, this implicitly assumes the diffuse albedo to be 0. \n\n\t########################\n\t###Set up C-values\n\t########################\n\t#In the reshuffled set of parameters used in this formalism, these seem analagous to the forcing term in Pierrehumbert. All the added radiation is contained in here.\n\n\tdef C_plus(n, tau): #implementation of superposition of eqns 23 and 27 from Toon et al\n\t\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\n\t\tsolarrad_prefactor=w_0[n]*F_s*np.pi\n\t\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\n\t\tsolarrad_factor=((gamma_1[n]-1./mu_0)*gamma_3[n]+gamma_4[n]*gamma_2[n])\n\t\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\n\t\t\n\t\tblackbody_prefactor=2*np.pi*mu_1[n]\n\t\tB0n=Planck(temp_c[n], wavelength)\n\t\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\n\t\tblackbody_factor=B0n+B1n*(tau+1./(gamma_1[n]+gamma_2[n]))\n\t\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \n\t\t\n\t\tresult=solarrad+blackbody\n\t\treturn result\n\n\tdef C_minus(n, tau): #implementation of superposition of eqns 24 and 27 from Toon et al\n\t\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\n\t\tsolarrad_prefactor=w_0[n]*F_s*np.pi\n\t\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\n\t\tsolarrad_factor=((gamma_1[n]+1./mu_0)*gamma_4[n]+gamma_3[n]*gamma_2[n])\n\t\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\n\t\t\n\t\tblackbody_prefactor=2*np.pi*mu_1[n]\n\t\tB0n=Planck(temp_c[n], wavelength)\n\t\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\n\t\tblackbody_factor=B0n+B1n*(tau-1./(gamma_1[n]+gamma_2[n]))\n\t\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \n\t\t\n\t\tresult=solarrad+blackbody\n\t\treturn result\n\n\t########################\n\t###Calculate matrix coefficients\n\t#########################\n\t#initialize the A, B, D, and E.\n\tA=np.zeros(Nlayer*2)\n\tB=np.zeros(np.shape(A))\n\tD=np.zeros(np.shape(A))\n\tE=np.zeros(np.shape(A))\n\n\n\t#For l=0 (n=0) we have the boundary condition that the downward diffuse flux at the top of the first layer is equal to any incident diffuse downward flux. We set this to be zero.\n\tA[0]=0.\n\tB[0]=e1[0]\n\tD[0]=-1.*e2[0]\n\tE[0]=0.-1*C_minus(0,0) #This is really F_minus[0,0], i.e. we are assuming there is no downward diffuse flux from the top of the atmosphere.\n\n\t#for l=2N-1 (n=N-1), we have the boundary condition that the upward flux at the surface is the sume of the reflected downward diffuse flux and energy from any other sources (e.g. reflected direct beam, BB emission of the ground)/np.sqrt(3.)\n\tA[2*Nlayer-1]=e1[Nlayer-1]-albedo_dif*e3[Nlayer-1]\n\tB[2*Nlayer-1]=e2[Nlayer-1]-albedo_dif*e4[Nlayer-1]\n\tD[2*Nlayer-1]=0.\n\tE[2*Nlayer-1]=S_sfc-C_plus(Nlayer-1, tau_n[Nlayer-1])+albedo_dif*C_minus(Nlayer-1, tau_n[Nlayer-1])\n\n\t#There is a problem in the Toon paper. As written, the l=2n depends on e_n+1, running over the array edge. twostr.f resolves this by adopting a different mapping: their definition reduces to defining l=2(n+1) and running n from 0 to N-1. In this case, l=2 (The third value in the list of ls) depends on n=0 and n=1. This eliminates the overflow problem. We have implemented this below.\n\t\n\t##For n=1,2,3...N-1, l=2,4,6,...2N-2:\n\tfor n in range(0, Nlayer-1):\n\t\tl=2*(n+1)\n\t\tA[l]=e2[n]*e3[n]-e4[n]*e1[n]\n\t\tB[l]=e1[n]*e1[n+1]-e3[n]*e3[n+1]\n\t\tD[l]=e3[n]*e4[n+1]-e1[n]*e2[n+1]\n\t\t\n\t\tE[l]=e3[n]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))+e1[n]*(C_minus(n,tau_n[n])-C_minus(n+1,0.))\n\n\n\t#For n=0...N-2, l=1,3...2N-3:\n\tfor n in range(0, Nlayer-1):\n\t\tl=2*n+1\n\t\tA[l]=e2[n+1]*e1[n]-e3[n]*e4[n+1]\n\t\tB[l]=e2[n]*e2[n+1]-e4[n]*e4[n+1]\n\t\tD[l]=e1[n+1]*e4[n+1]-e2[n+1]*e3[n+1]\n\t\t\n\t\tE[l]=e2[n+1]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))-e4[n+1]*(C_minus(n+1, 0)-C_minus(n, tau_n[n])) #twostr.f has a -1*e_{4,n+1}. We have applied the same even though this is NOT what is written in the Toon et al paper. We have done this because Toon told us (6/26/2015) that there are some sign errors in the coefficients, and we currently trust the validated CLIMA code over the paper we know has errors in it. EDIT: Looking at the code Toon shared with us, he does the same. \n\n\n\t########################\n\t###Assemble matrix equation components\n\t#########################\n\tP=np.zeros([Nlayer*2,Nlayer*2])\n\n\t#l=0: no \"A\" coefficient b/c l-1 has no meaning\n\tP[0,0]=B[0]\n\tP[0,1]=D[0]\n\n\t#l=2N-1: no \"D\" coefficient b/c l+1 has no meaning\n\tP[2*Nlayer-1,2*Nlayer-1-1]=A[2*Nlayer-1]\n\tP[2*Nlayer-1,2*Nlayer-1]=B[2*Nlayer-1]\n\n\tfor l in range(1, Nlayer*2-1): #This populates the matrix P in PY=E. \n\t\tP[l, l-1]=A[l]\n\t\tP[l,l]=B[l]\n\t\tP[l,l+1]=D[l]\n\n\t########################\n\t###Invert matrix\n\t#########################\n\t#Y=np.linalg.solve(P, E) #this is the Y_l\n\t\n\t#try using a specialized solver\n\tab=np.zeros([3,2*Nlayer])\n\tab[0,:]=np.append(0.0, np.diag(P, k=1))\n\tab[1,:]=np.diag(P, k=0)\n\tab[2,:]=np.append(np.diag(P, k=-1),0.0)\n\t#pdb.set_trace()\n\tY=scipy.linalg.solve_banded((1,1), ab, E) #this is the Y_l\n\n\n\t########################\n\t###Convert from Y_l to Y_1n, Y_2n\n\t#########################\n\t#The Y_1n as defined in Toon et al correspond to l=1,3, 5...2N-1. Adjusting for the zero-indexing of Python as we have done, they instead correspond to l=0,2,...2N-2\n\t#The Y_2n as defined in Toon et al correspond to l=2,4,6...2N. Adjusting for Python zero-indexing as we have done, they instead correspond to l=1,3,5...2N-1.\n\t#For detail, see eq. 40.\n\tY_1=np.zeros(Nlayer)\n\tY_2=np.zeros(Nlayer)\n\tfor n in range(0, Nlayer):\n\t\tY_1[n]=Y[2*n]\n\t\tY_2[n]=Y[2*n+1] \n\t\t#last number called is Nlayer-1=N-1, so is consistent.\n\t\n\t########################\n\t###Convert from Y_1n, Y_2n to F_plus, F_minus\n\t#########################\n\tdef F_plus(n,tau): #defined from Eqn 31 of Toon et al.\n\t\tterm1=Y_1[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))+clambda[n]*np.exp(-alambda[n]*tau))\n\t\tterm2=Y_2[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))-clambda[n]*np.exp(-alambda[n]*tau))\n\t\tterm3=C_plus(n,tau)\n\t\t\n\t\tresult=term1+term2+term3\n\t\treturn result\n\n\tdef F_minus(n, tau): #defined from Eqn 32 of Toon et al.\n\t\tterm1=Y_1[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))+np.exp(-alambda[n]*tau))\n\t\tterm2=Y_2[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))-np.exp(-alambda[n]*tau))\n\t\tterm3=C_minus(n,tau)\n\t\t\n\t\tresult=term1+term2+term3\n\t\treturn result\n\t\n\t########################\n\t###Evaluate F_plus, F_minus at boundary edges\n\t#########################\n\tF_plus_tau0=np.zeros(np.shape(tau_n))\n\tF_plus_taumax=np.zeros(np.shape(tau_n))\n\tF_minus_tau0=np.zeros(np.shape(tau_n))\n\tF_minus_taumax=np.zeros(np.shape(tau_n))\n\n\tfor n in range(0, Nlayer):\n\t\tF_plus_tau0[n]=F_plus(n, 0.)\n\t\tF_plus_taumax[n]=F_plus(n, tau_n[n])\n\t\tF_minus_tau0[n]=F_minus(n, 0.)\n\t\tF_minus_taumax[n]=F_minus(n, tau_n[n])\n\n\n\t########################\n\t###Convert from Y_1n, Y_2n to F_net, mean intensity.\n\t#########################\n\t#test if diffuse flux dominates over direct flux. If direct flux dominant, instead set mu_1=mu_0\n\t\n\t#if F_minus_taumax[-1]<mu_0*np.pi*F_s*np.exp(-tau_c[-1]/mu_0):\n\t\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\n\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\n\t\n\tF_net=np.zeros(np.shape(tau_n)) #defined from Eqn 48 of Toon et al. This quantity is the net flux at the BASE of layer n.\n\tfor n in range(0, Nlayer):\n\t\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\n\n\t\tterm1=Y_1[n]*(e1[n]-e3[n])\n\t\tterm2=Y_2[n]*(e2[n]-e4[n])\n\t\tterm3=C_plus(n, tau_n[n])-C_minus(n, tau_n[n])\n\t\t\n\t\tF_net[n]=term1+term2+term3 -direct\n\n\tAMEAN=np.zeros(np.shape(tau_n)) #defined from Eqn 49 of Toon et al. This is the equivalent of the quantity AMEAN in the twostr.f code. It is equal to 4*np.pi*J_n, where J_n is the mean intensity at the base of layer n. Hence this quantity AMEAN should be equal to the total intensity received by a point at the base of layer n. \n\tfor n in range(0, Nlayer):\n\t\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\n\t\n\t\tterm1=Y_1[n]*(e1[n]+e3[n])\n\t\tterm2=Y_2[n]*(e2[n]+e4[n])\n\t\tterm3=C_plus(n, tau_n[n])+C_minus(n, tau_n[n])\n\t\t\n\t\t#AMEAN[n]=(1./mu_1[n])*(term1+term2+term3)+direct/mu_0\t\n\t\tAMEAN[n]=(1./mu_1[n])*(F_plus_taumax[n]+F_minus_taumax[n])+direct/mu_0\t\n\t\n\t########################\n\t###Compute \"surface intensity\"\n\t#########################\t\n\t#\"Surface intensity\" refers to the total intensity that would be intercepted by a particle at the surface of the planet. Whereas the total intensity is equal to (F_plus[-1]+F_minus[-1])/mu_1+direct[-1]/mu_0, the surface intensity is instead equal to (F_minus[-1])/mu_1+direct[-1]/mu_0, i.e. the downwelling diffuse intensity (since the bottom intensity is cut out due to there being a planet there) plus the direct intensity\n\t\n\tsurface_intensity=(F_minus_taumax[-1]/mu_1[-1])+(np.pi*F_s)*np.exp(-(tau_c[-1])/mu_0)\n\t\n\t########################\n\t###Return Result\n\t#########################\n\t#F_minus_tau0\n\t#np.max(np.abs((F_minus_taumax[:-1]-F_minus_tau0[1:]))/F_minus_tau0[1:])\n\t#np.max(np.abs((F_plus_taumax[:-1]-F_plus_tau0[1:]))/F_plus_tau0[1:])\n\t\n\treturn (F_plus_tau0, F_plus_taumax, F_minus_tau0, F_minus_taumax, F_net, AMEAN, surface_intensity)","def vacuum_to_air(wavelength):\n # Following the vacuum to air conversion the formula from Donald Morton (2000, ApJ. Suppl., 130, 403) which is also a IAU standard\n # - More info: http://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion\n s_square = np.power(1.e4 / wavelength, 2)\n n = 1 + 0.0000834254 + 0.02406147 / (130 - s_square) + 0.00015998 / (38.9 - s_square)\n return wavelength/n # Angstroms","def refractive_index_fused_silica(wavelength):\n wavelength_um = wavelength / 1000\n\n A0 = 2.104025406E+00\n A1 = -1.456000330E-04\n A2 = -9.049135390E-03\n A3 = 8.801830992E-03\n A4 = 8.435237228E-05\n A5 = 1.681656789E-06\n A6 = -1.675425449E-08\n A7 = 8.326602461E-10\n\n n = np.sqrt( A0 + A1 * wavelength_um ** 4 + A2 * wavelength_um ** 2 + A3 * wavelength_um ** -2 + \\\n A4 * wavelength_um ** -4 + A5 * wavelength_um ** -6 + A6 * wavelength_um ** -8 + A7 * wavelength_um ** -10 )\n\n return n","def d50Illum(wavelength):\n return _d50Illum.calc(wavelength)","def test_ulaw(self):\n duration = 1\n num_channels = 1\n sample_rate = 8000\n path = self.get_temp_path(\"data.wav\")\n sox_utils.gen_audio_file(\n path, sample_rate=sample_rate, num_channels=num_channels, bit_depth=8, encoding=\"u-law\", duration=duration\n )\n info = self._info(path)\n assert info.sample_rate == sample_rate\n assert info.num_frames == sample_rate * duration\n assert info.num_channels == num_channels\n assert info.bits_per_sample == 8\n assert info.encoding == \"ULAW\"","def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\n if ldr_voltage is None:\n ldr_voltage = self.raw_ldr_voltage\n\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\n # It goes \"to infinity\" as ldr_voltage nears 1023 (hence the clamp, I guess)\n # Clarify.\n if ldr_voltage > 1022:\n ldr_voltage = 1022\n if ldr_voltage < 1:\n ldr_voltage = 1\n\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)","def _frequency_to_wavelength(freq):\n return ifc.SPEED_OF_LIGHT_METRES_PER_SECOND / freq","def set_wavelength(self, wavelength):\n print('Setting Santec wavelength to %.4f nm' % wavelength)\n\n # We need to select which of the 4 lasers to select depending on\n # the desired wavelength\n\n if 1530.0 < wavelength < 1630.000001:\n self.santec1.write(\"SW 4\")\n self.santec4.write(\"WA %.4f\" % wavelength)\n if self.active_module != 4:\n self.active_module = 4\n time.sleep(5.00)\n else:\n time.sleep(0.01)\n\n elif 1440.0 < wavelength < 1530.1:\n self.santec1.write(\"SW 3\")\n self.santec3.write(\"WA %.4f\" % wavelength)\n if self.active_module != 3:\n self.active_module = 3\n time.sleep(5.00)\n else:\n time.sleep(0.01)\n\n elif 1355 < wavelength < 1440.1:\n self.santec1.write(\"SW 2\")\n self.santec2.write(\"WA %.4f\" % wavelength)\n if self.active_module != 2:\n self.active_module = 2\n time.sleep(5.00)\n else:\n time.sleep(0.01)\n\n elif 1259.999999 < wavelength < 1355.1:\n self.santec1.write(\"SW 1\")\n self.santec1.write(\"WA %.4f\" % wavelength)\n if self.active_module != 1:\n self.active_module = 1\n time.sleep(5.00)\n else:\n time.sleep(0.01)\n\n else:\n print(\"Wavelength out of range. No change will be made\")","def get_iPTF16asu():\n z = 0.187\n ebv = 0.0\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n \n tb = asci.read('../data/otherSN/Whitesides2017/table1.txt')\n tb = tb.to_pandas()\n tb = tb[tb[\"col4\"].values!=\">\"]\n \n tb = tb.rename(columns={'col1' : 'mjd',\n 'col2': 'tmax_rf',\n 'col3': 'filter',\n \"col4\": 'mag',\n 'col5': 'emag',\n 'col6': 'instrument'})\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb[\"mag\"] = np.array(tb[\"mag\"].values, dtype = np.float)\n #tb[\"emag\"] = np.array(tb[\"emag\"].values, dtype = np.float)\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb = tb[tb.wave!=0]\n return tb","def gen_OSA(self, time_window_ps, center_wavelength_nm, power, \n power_is_epp = False,\n fileloc = 'O:\\\\OFM\\\\Maser\\\\Dual-Comb 100 MHz System\\\\Pump spectrum-Yb-101614.csv',\n log = True, rows = 30): # Yb spectrum\n \n try:\n self.fileloc = fileloc\n \n self.set_time_window_ps(time_window_ps)\n \n self.center_wl = center_wavelength_nm # reference wavelength (nm) \n \n self.w0 = (2. * np.pi * self.c) / self.center_wl # reference angular frequency \n \n self.setup_grids()\n \n if not power_is_epp: \n power = power / self.frep\n \n # Read in OSA data\n osa_data = np.genfromtxt(self.fileloc, delimiter = ',', skiprows = rows) \n \n wavelengths = osa_data[:,0]# (nm)\n wavelengths = self.internal_wl_from_nm(wavelengths)\n \n intensity = osa_data[:,1]# (arb. units)\n\n if log:\n intensity = 10.**(intensity / 10.)\n \n freq_abs = self.c/wavelengths\n freq_abs = np.sort(freq_abs)\n \n self.freq_rel = freq_abs - self.c / self.center_wl\n \n pulse_envelope = interp1d(self.freq_rel, intensity, kind='linear',\n bounds_error = False, fill_value=0)\n \n self.gridded_intensity = pulse_envelope(self.V / (2*np.pi))\n \n # Calculate time domain complex electric field A\n self.A = IFFT_t(self.gridded_intensity)\n # Calculate normalization factor to achieve requested \n # pulse energy\n e_scale = np.sqrt(power / self.calc_epp() )\n self.A = self.A * e_scale\n \n except IOError:\n print ('File not found.')","def read300yr(period):\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\n file300 = 'U10_1700-2000.nc'\n filename = directory300 + file300\n \n data = Dataset(filename)\n lat = data.variables['latitude'][:]\n lon = data.variables['longitude'][:]\n u10q = data.variables['U10'][:]\n data.close()\n \n ### Reshape in year/month\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\n \n ### Calculate over particular months\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\n \n ### Slice U10 at 65N\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\n lat = lat[latq].squeeze()\n u10 = u10[:,latq,:].squeeze()\n \n ### Take zonal mean \n u10z = np.nanmean(u10,axis=1)\n \n ### Remove missing data\n mask = np.where(u10z > -1e5)[0]\n \n ### Detrend\n u10zdt = sss.detrend(u10z[mask],type='linear')\n \n return lat,lon,u10zdt","def get_tod(self, unit=None):\n tod = Tod.empty((self.get_ndetectors(), np.sum(self.get_nsamples())))\n sizeofpmatrix = self.info.npixels_per_sample * tod.size\n pmatrix = np.zeros(sizeofpmatrix, dtype=int)\n status = tmf.madmap1_read_tod(self.info.todfile, self.info.invnttfile,\n self.info.convert, self.info.npixels_per_sample, 0, tod.T, pmatrix)\n if status != 0: raise RuntimeError()\n if unit is not None:\n tod.unit = unit\n return tod","def getWavelength(self, inAngle, outAngle, wavelengths = [0.25,1.0]):\n\n # Get prism point and angle of input at Unit3d\n #\n pt = self.getInputPoint()\n u = Unit3d(Angle(inAngle))\n\n # Guess at initial wavelngth\n wave = (wavelengths[1] - wavelengths[0])/2\n # Make input ray at guess wavelength\n ray = IntensityRay(pt,u,wave)\n\n # Parameters for seaerch\n delta = 0.1\n forward = True\n na = float(\"inf\") # New angle\n\n while abs(na - outAngle) > 1.0e-9/abs(outAngle) :\n nray = ray*self # New Ray through prism\n na = nray.getAngle()\n na = na.theta*math.cos(na.psi) # In radians\n if na < outAngle: # Less that target\n wave += delta\n forward = True\n else:\n if forward: # Half step\n delta *= 0.5\n forward = False\n wave -= delta\n if wave < wavelengths[0] or wave > wavelengths[1]:\n print(\"Out of wavelength range :\")\n return float(\"nan\")\n\n ray.wavelength = wave # Update the wavelength of ray\n\n return ray.getWavelength() # End of loop, so success, return value","def getCalibration(self):\n self.a0 = float(self.getParameter(index=1))\n self.a1 = float(self.getParameter(index=2))\n self.a2 = float(self.getParameter(index=3))\n self.a3 = float(self.getParameter(index=4))\n status = self.getStatus()\n self.wavelength = [ self.a0 + self.a1*x + self.a2*x*x + self.a3*x*x*x \n for x in range(status.pixels)]\n if self.discardTrailingSamples > 0:\n self.wavelength = self.wavelength[:-self.discardTrailingSamples]\n if self.discardLeadingSamples > 0:\n self.wavelength = self.wavelength[self.discardLeadingSamples:]","def get_observation(observation_id: str):\n pass","def spectrum(self, wl: Union[float, ndarray]) -> Union[float, ndarray]:\n wlm = wl * 1e-9 # Wavelength to meters\n return 3.74183e-16 * wlm ** -5. / (np.exp(0.014388 / (wlm * self.temp)) - 1.)","def test_regression_determine_wavelength_solution(\n ad, params, caplog, change_working_dir, path_to_refs, request):\n caplog.set_level(logging.INFO, logger=\"geminidr\")\n\n with change_working_dir():\n logutils.config(file_name='log_regress_{:s}.txt'.format(ad.data_label()))\n p = GNIRSLongslit([ad])\n p.viewer = geminidr.dormantViewer(p, None)\n\n p.determineWavelengthSolution(**{**determine_wavelength_solution_parameters,\n **params})\n\n wcalibrated_ad = p.streams[\"main\"][0]\n\n for record in caplog.records:\n if record.levelname == \"WARNING\":\n assert \"No acceptable wavelength solution found\" not in record.message\n\n ref_ad = astrodata.open(os.path.join(path_to_refs, wcalibrated_ad.filename))\n model = am.get_named_submodel(wcalibrated_ad[0].wcs.forward_transform, \"WAVE\")\n ref_model = am.get_named_submodel(ref_ad[0].wcs.forward_transform, \"WAVE\")\n\n x = np.arange(wcalibrated_ad[0].shape[1])\n wavelength = model(x)\n ref_wavelength = ref_model(x)\n\n pixel_scale = wcalibrated_ad[0].pixel_scale() # arcsec / px\n slit_size_in_arcsec = float(wcalibrated_ad[0].focal_plane_mask(pretty=True).replace('arcsec', ''))\n slit_size_in_px = slit_size_in_arcsec / pixel_scale\n dispersion = abs(wcalibrated_ad[0].dispersion(asNanometers=True)) # nm / px\n\n # We don't care about what the wavelength solution is doing at\n # wavelengths outside where we've matched lines\n lines = ref_ad[0].WAVECAL[\"wavelengths\"].data\n indices = np.where(np.logical_and(ref_wavelength > lines.min(),\n ref_wavelength < lines.max()))\n tolerance = 0.5 * (slit_size_in_px * dispersion)\n\n write_report = request.config.getoption('--do-report', False)\n failed = False\n try:\n np.testing.assert_allclose(wavelength[indices], ref_wavelength[indices],\n atol=tolerance)\n except AssertionError:\n failed = True\n raise\n finally:\n if write_report:\n do_report(wcalibrated_ad, ref_ad, failed=failed)\n\n if request.config.getoption(\"--do-plots\"):\n do_plots(wcalibrated_ad)"],"string":"[\n \"def get_experimental_spectra(mol):\\n\\n data = pd.read_csv(mol, sep=',')\\n wavelength = data.values[:, 0]\\n\\n absorption = data.values[:, 1]\\n\\n func = interp1d(wavelength, absorption, kind='quadratic')\\n wavelength_new = 1. / np.linspace(1. / wavelength.max(), 1. / wavelength.min(), 100)\\n absorption_new = func(wavelength_new)\\n absorption_new *= 100. / absorption_new.max()\\n\\n return wavelength_new, absorption_new\",\n \"def referenceIllum(temp, wavelength):\\n ct=temp\\n if ct <= 0:\\n return 0\\n if ct < 4000:\\n return planckian(ct, wavelength)\\n if ct < 5000:\\n p=planckian(ct, wavelength)\\n d=dseries(ct, wavelength)\\n return p+(d-p)*(ct-4000)/1500.0\\n return dseries(ct, wavelength)\",\n \"def wavelength(refractive_index, omega):\\n return 2 * np.pi * cgs.c / (refractive_index * omega)\",\n \"def airydisk(unit_r, fno, wavelength):\\n u_eff = unit_r * np.pi / wavelength / fno\\n return abs(2 * jinc(u_eff)) ** 2\",\n \"def return_obs_RA_DEC():\\n return SkyCoord('03h 32m 30s', '10d 00m 24s')\",\n \"def get(self, wave, flux, **kwargs):\\n if hasUnit(wave):\\n _w = wave.to('AA').magnitude\\n else:\\n print(\\\"Warning: assuming units are in Angstroms\\\")\\n _w = _drop_units(wave)\\n _f = _drop_units(flux)\\n\\n blue = self._get_wavelength_attrs_with_units('blue').magnitude\\n red = self._get_wavelength_attrs_with_units('red').magnitude\\n band = self._get_wavelength_attrs_with_units('band').magnitude\\n \\n nocheck = kwargs.pop('nocheck', False)\\n not_covered = (blue[0] < _w[0]) | (red[-1] > _w[-1])\\n if (not_covered):\\n if (not nocheck):\\n raise ValueError(\\\"Spectrum does not cover this index.\\\")\\n else:\\n return np.zeros(_f.shape[0]) * float('nan') \\n else:\\n return self._get_indice(_w, _f, blue, red, band, self.index_unit, **kwargs)\",\n \"def aIllum(wavelength):\\n return planckian(2856, wavelength)\",\n \"def RiekeLebofsky85(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def _get(self, wave, flux, **kwargs):\\n if hasUnit(wave):\\n _w = wave.to('AA').value\\n else:\\n print(\\\"Warning: assuming units are in Angstroms\\\")\\n _w = _drop_units(wave)\\n _f = _drop_units(flux)\\n\\n blue = self._get_wavelength_attrs_with_units('blue').value\\n red = self._get_wavelength_attrs_with_units('red').value\\n band = self._get_wavelength_attrs_with_units('band').value\\n\\n nocheck = kwargs.pop('nocheck', False)\\n not_covered = (blue[0] < _w[0]) | (red[-1] > _w[-1])\\n if (not_covered):\\n if (not nocheck):\\n raise ValueError(\\\"Spectrum does not cover this index.\\\")\\n else:\\n return np.zeros(_f.shape[0]) * float('nan')\\n else:\\n return self._get_indice(_w, _f, blue, red, band, self.index_unit,\\n **kwargs)\",\n \"def search(self, wavelength, format=None):\\n q = self.create_query(wavelength, format)\\n return q.execute()\",\n \"def wavelength(energy):\\r\\n return 2 * np.pi * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy\",\n \"def Damineli16(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def read_aeronet_data_main(station_name, month, year, plot_results):\\n # Load AERONET file of month-year\\n station = gs.Station(station_name)\\n\\n monthdays = (date(year, month + 1, 1) - date(year, month, 1)).days\\n start_day = datetime(year, month, 1, 0, 0)\\n end_day = datetime(year, month, monthdays, 0, 0)\\n wavelengths = [355, 532, 1064]\\n\\n base_name = f\\\"{start_day.strftime('%Y%m%d')}_{end_day.strftime('%Y%m%d')}_{station.aeronet_name}\\\"\\n file_name = os.path.join(station.aeronet_folder, base_name, base_name + '.lev20')\\n # TODO : add automatic download of `.lev20' file from AERONET in case a file is missing.\\n aeronet_data = pd.read_csv(file_name, skiprows=6).dropna()\\n\\n # Parse data and rename columns for easier extrapolation of AOD values\\n df_dt = pd.to_datetime(aeronet_data['Date(dd:mm:yyyy)'] + aeronet_data['Time(hh:mm:ss)'], format=\\\"%d:%m:%Y%H:%M:%S\\\")\\n columns = ['AOD_1640nm', 'AOD_1020nm', 'AOD_675nm', 'AOD_500nm', 'AOD_380nm', 'AOD_340nm']\\n df_AOD_ANGSTROM = aeronet_data[columns].copy(deep=True)\\n df_AOD_ANGSTROM.index = df_dt\\n for col in sorted(columns):\\n col_new = int(col.split('_')[1].replace('nm', ''))\\n df_AOD_ANGSTROM.rename(columns={col: col_new}, inplace=True)\\n\\n cols = df_AOD_ANGSTROM.columns.values.tolist()\\n cols.extend(wavelengths)\\n df_AOD_ANGSTROM = df_AOD_ANGSTROM.reindex(cols, axis='columns').sort_index(axis=1)\\n\\n # Calculate AOD for missing wavelengths as $355,532,1064$\\n # by interpolation values from the nearest existing measured wavelengths.\\n cols = df_AOD_ANGSTROM.columns.values.tolist()\\n for wavelength in wavelengths:\\n col_ind = df_AOD_ANGSTROM.columns.get_loc(wavelength)\\n ratio = (cols[col_ind + 1] - cols[col_ind]) / (cols[col_ind + 1] - cols[col_ind - 1])\\n df_AOD_ANGSTROM[wavelength] = df_AOD_ANGSTROM.iloc[:, col_ind - 1] * \\\\\\n ratio + (1 - ratio) * \\\\\\n df_AOD_ANGSTROM.iloc[:, col_ind + 1]\\n\\n # Create dataset of AOD per wavelength\\n ds_chans = []\\n for wavelength in wavelengths:\\n aeronet_ds_chan = xr.Dataset(\\n data_vars={'aod': ('Time', df_AOD_ANGSTROM[wavelength]),\\n 'lambda_nm': ('Wavelength', [wavelength])\\n },\\n coords={'Time': df_AOD_ANGSTROM.index.tolist(),\\n 'Wavelength': [wavelength]\\n })\\n ds_chans.append(aeronet_ds_chan)\\n ds_aod = xr.concat(ds_chans, dim='Wavelength')\\n\\n ds_aod.aod.attrs['long_name'] = r'$\\\\tau$'\\n ds_aod = ds_aod.aod.where(ds_aod >= 0, drop=True)\\n ds_aod.attrs = {'info': 'Aerosol Optical Depth - generated from AERONET - level 2.0',\\n 'location': station.name, 'source_file': file_name,\\n 'start_time': start_day.strftime(\\\"%Y-%d-%m\\\"), 'end_time': end_day.strftime(\\\"%Y-%d-%m\\\")}\\n\\n # Calculate Angstrom Exponent\\n couples = [(355, 532), (355, 1064), (532, 1064)]\\n angstrom_daily = []\\n for lambda_1, lambda_2 in couples:\\n angstrom_couple = xr.apply_ufunc(lambda x, y: misc_lidar.angstrom(ds_aod.sel(Wavelength=x).aod,\\n ds_aod.sel(Wavelength=y).aod, x, y), lambda_1, lambda_2,\\n keep_attrs=True).rename('angstrom')\\n angstrom_ds_chan = xr.Dataset(\\n data_vars={'angstrom': ('Time', angstrom_couple.values),\\n 'lambda_nm': ('Wavelengths', [f\\\"{lambda_1}-{lambda_2}\\\"])\\n },\\n coords={'Time': df_AOD_ANGSTROM.index.tolist(),\\n 'Wavelengths': [f\\\"{lambda_1}-{lambda_2}\\\"]\\n })\\n\\n angstrom_daily.append(angstrom_ds_chan)\\n ds_ang = xr.concat(angstrom_daily, dim='Wavelengths')\\n ds_ang.angstrom.attrs['long_name'] = r'$\\\\AA$'\\n ds_ang.attrs = {'info': 'Angstrom Exponent - generated from AERONET AOD',\\n 'location': station.name, 'source_file': file_name,\\n 'start_time': start_day.strftime(\\\"%Y-%d-%m\\\"), 'end_time': end_day.strftime(\\\"%Y-%d-%m\\\")}\\n\\n # Show AOD and Angstrom Exponent for a period\\n if plot_results:\\n t_slice = slice(start_day, start_day + timedelta(days=30) - timedelta(seconds=30))\\n\\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 8))\\n ax = axes.ravel()\\n for wavelength in wavelengths:\\n aod_mean = ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).mean().item()\\n aod_std = ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).std().item()\\n textstr = ' '.join((\\n r'$\\\\mu=%.2f$, ' % (aod_mean,),\\n r'$\\\\sigma=%.2f$' % (aod_std,)))\\n ds_aod.aod.sel(Wavelength=wavelength, Time=t_slice).plot(label=fr\\\"{wavelength}, \\\" + textstr, ax=ax[0])\\n ax[0].set_title(ds_aod.attrs['info'])\\n ax[0].legend()\\n ax[0].set_ylabel(r'$\\\\tau$')\\n\\n for lambda_1, lambda_2 in couples:\\n angstrom_mean = ds_ang.angstrom.sel(Wavelengths=f\\\"{lambda_1}-{lambda_2}\\\", Time=t_slice).mean().item()\\n angstrom_std = ds_ang.angstrom.sel(Wavelengths=f\\\"{lambda_1}-{lambda_2}\\\", Time=t_slice).std().item()\\n textstr = ' '.join((\\n r'$\\\\mu=%.2f$, ' % (angstrom_mean,),\\n r'$\\\\sigma=%.2f$' % (angstrom_std,)))\\n ds_ang.angstrom.sel(Wavelengths=f\\\"{lambda_1}-{lambda_2}\\\", Time=t_slice).plot(x='Time',\\n label=fr\\\"$ \\\\AA \\\\, {lambda_1},{lambda_2}$, \\\" + textstr\\n , ax=ax[1])\\n ax[1].legend()\\n ax[1].set_title('Angstrom Exponent')\\n plt.tight_layout()\\n plt.show()\\n\\n # Angstrom Exponent distribution of a month\\n couple_0 = f\\\"{355}-{532}\\\"\\n couple_1 = f\\\"{532}-{1064}\\\"\\n\\n x = ds_ang.angstrom.sel(Time=t_slice, Wavelengths=couple_0).values\\n y = ds_ang.angstrom.sel(Time=t_slice, Wavelengths=couple_1).values\\n\\n fig, ax = plt.subplots(nrows=1, ncols=1)\\n ax.scatter(x=x, y=y)\\n ax.set_ylabel(couple_0)\\n ax.set_xlabel(couple_1)\\n ax.set_title(f\\\"Angstrom Exponent distribution {t_slice.start.strftime('%Y-%m')}\\\")\\n plt.tight_layout()\\n plt.show()\\n\\n # Save AOD and Angstrom Exponent datasets\\n nc_base_name = f\\\"{start_day.strftime('%Y%m%d')}_{end_day.strftime('%Y%m%d')}_{station.name}\\\"\\n\\n xr_utils.save_dataset(ds_aod, folder_name=station.aeronet_folder, nc_name=nc_base_name+\\\"_aod.nc\\\")\\n xr_utils.save_dataset(ds_ang, folder_name=station.aeronet_folder, nc_name=nc_base_name+\\\"_ang.nc\\\")\",\n \"def wavelength_ex(hdulist):\\n wave = hdulist[1].data['loglam']\\n wave = 10**wave\\n\\n return wave\",\n \"def Hosek18b(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def wavelength(energy):\\n return 2 * PI * PLANCK_CONSTANT * SPEED_OF_LIGHT / energy\",\n \"def Nishiyama09(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def find_nearest_wav(self, wavelength):\\n\\n idx = np.searchsorted(self.wavelengths, wavelength, side=\\\"left\\\")\\n if idx > 0 and (idx == len(self.wavelengths) or math.fabs(wavelength - self.wavelengths[idx-1]) < math.fabs(wavelength - self.wavelengths[idx])):\\n return self.wavelengths[idx-1]\\n else:\\n return self.wavelengths[idx]\",\n \"def get_wavelength_location(headers, wavelength):\\n start = headers['CRVAL1']\\n step = headers['CDELT1']\\n distance = wavelength - start\\n number = round(distance / step)\\n return number\",\n \"def search(self, wavelength, format=None, **keywords):\\n q = self.create_query(wavelength, format)\\n return q.execute()\",\n \"def wvac2air(w):\\n scalar = False\\n if isinstance(w, (int, float)):\\n w = [w]\\n scalar = True\\n w = np.array([w])\\n wair = w.copy()\\n\\n mask = w > 2000. # Modify only wavelength above 2000 A\\n\\n s2 = (1e4/w[mask])**2\\n f = 1.+0.05792105/(238.0185-s2)+0.00167917/(57.362-s2)\\n wair[mask] = w[mask]/f\\n return wair[0][0] if scalar else wair[0]\",\n \"def Hosek18(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def NoguerasLara18(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self)) \\n\\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def get_wave(q):\\n\\n approximant = 'SEOBNRv4'\\n chi1 = [0,0,0]\\n chi2 = [0,0,0]\\n deltaTOverM = 0.1\\n omega0 = 2e-2\\n\\n t, h = LALPy.generate_LAL_waveform(approximant, q, chi1, chi2, deltaTOverM, omega0)\\n\\n Amp = np.abs(h)\\n peakIdx = np.argmax(Amp)\\n\\n t -= t[peakIdx]\\n\\n tmin = -500\\n if min(t) > tmin:\\n raise Exception('Data not long enough, decrease omega0.')\\n keepIdx = t - tmin > -1e-3 # simple hack to ensure t_vec is always nearly the same\\n t = t[keepIdx]\\n h = h[keepIdx]\\n\\n tmax = 100\\n keepIdx = t - tmax < 1e-3\\n t = t[keepIdx]\\n h = h[keepIdx]\\n\\n return t, h\",\n \"def GetWavelengths (self) :\\n\\t\\treturn self.run(\\\"GetWavelengths\\\")\",\n \"def Fritz11(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def Cardelli89(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def OxygenTransmission(T,P,n_wv,wavelength,dr,freq_lim=np.array([lp.c/770e-9,lp.c/768e-9]),sim_nu=np.array([]),spec_file=''):\\n # fraction of O2 by number density\\n fO2 = (32*0.2320+28.02*0.7547+44.01*0.00046+39.94*0.0128+20.18*0.000012+4.0*0.0000007+83.8*0.000003+131.29*0.00004)*0.2320/32.0\\n \\n if len(spec_file) == 0:\\n spec_file = '/Users/mhayman/Documents/DIAL/O2_HITRAN2012_760_781.txt'\\n \\n if sim_nu.size==0:\\n sim_nu = np.arange(-3e9,3e9,20e6)\\n \\n# inu0 = np.argmin(np.abs(sim_nu)) # index to center of frequency array\\n \\n n_o2=fO2*(P/(lp.kB*T)-n_wv) # to convert atm to Pa use *101325\\n ext_o2 = rb.ExtinctionFromHITRAN(lp.c/wavelength+sim_nu,T,P,(mO2*1e-3)/lp.N_A,nuLim=freq_lim,freqnorm=True,filename=spec_file).T\\n T_o2 = np.exp(-np.cumsum(n_o2[np.newaxis,:]*ext_o2,axis=1)*dr)\\n \\n return T_o2,sim_nu\",\n \"def add_wavelength(filename, model, std_tol, overwrite=False, plot_path=None):\\n hdulist = fits.open(filename)\\n\\n # read both hdu's\\n logger.debug(\\\"\\\\tObject: {}\\\".format(hdulist[0].header['OBJECT']))\\n\\n # extract just the middle part of the CCD (we only really care about Halpha)\\n tbl = Table(hdulist[1].data)\\n\\n if 'wavelength' in tbl.colnames and not overwrite:\\n logger.debug(\\\"\\\\tTable already contains wavelength values!\\\")\\n return\\n\\n # compute wavelength array for the pixels\\n wavelength, var = model.gp.predict(model.y, tbl['pix']-model.x_shift,\\n return_var=True)\\n bad_idx = np.sqrt(var) > std_tol.to(u.angstrom).value\\n wavelength[bad_idx] = np.nan\\n\\n tbl['wavelength'] = wavelength\\n tbl['wavelength_err'] = np.sqrt(var)\\n\\n new_hdu1 = fits.table_to_hdu(tbl)\\n new_hdulist = fits.HDUList([hdulist[0], new_hdu1])\\n\\n logger.debug(\\\"\\\\tWriting out file with wavelength array.\\\")\\n new_hdulist.writeto(filename, overwrite=True)\\n\\n if plot_path is not None:\\n # plot the spectrum vs. wavelength\\n fig,axes = plt.subplots(2, 1, figsize=(12,8), sharex=True)\\n\\n axes[0].plot(tbl['wavelength'], tbl['source_flux'],\\n marker='', drawstyle='steps-mid', linewidth=1.)\\n axes[0].errorbar(tbl['wavelength'], tbl['source_flux'], 1/np.sqrt(tbl['source_ivar']),\\n linestyle='none', marker='', ecolor='#666666', alpha=1., zorder=-10)\\n axes[0].set_ylim(tbl['source_flux'][200]/4, np.nanmax(tbl['source_flux']))\\n axes[0].set_yscale('log')\\n\\n axes[1].plot(tbl['wavelength'], tbl['background_flux'],\\n marker='', drawstyle='steps-mid', linewidth=1.)\\n axes[1].errorbar(tbl['wavelength'], tbl['background_flux'], 1/np.sqrt(tbl['background_ivar']),\\n linestyle='none', marker='', ecolor='#666666', alpha=1., zorder=-10)\\n axes[1].set_ylim(1e-1, np.nanmax(tbl['background_flux']))\\n axes[1].set_yscale('log')\\n\\n fig.tight_layout()\\n _filename_base = path.splitext(path.basename(filename))[0]\\n fig.savefig(path.join(plot_path, '{0}_1d_wvln.png'\\n .format(_filename_base)))\\n\\n plt.close(fig)\",\n \"def calc_impedance(self) -> (Ohm, None):\\n power: ComType = complex(0)\\n power_unit: str = ''\\n amp: NumType = 0\\n amp_unit: str = ''\\n volt: NumType = 0\\n volt_unit: str = ''\\n\\n if self._volt_exists and self._volt_exists:\\n if hasattr(self._obj1, 'volts'):\\n volt, volt_unit = self._obj1.volts, self._obj1.volt_unit\\n elif hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n if hasattr(self._obj2, 'volts'):\\n volt, volt_unit = self._obj2.volts, self._obj2.volt_unit\\n elif hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n z = volt / amp\\n z_unit: str = f'Ohms ({volt_unit}/{amp_unit})'\\n\\n elif self._amp_exists and self._power_exists:\\n if hasattr(self._obj1, 'amps'):\\n amp, amp_unit = self._obj1.amps, self._obj1.amp_unit\\n elif hasattr(self._obj1, 'power'):\\n power, power_unit = self._obj1.power, self._obj1.power_unit\\n if hasattr(self._obj2, 'amps'):\\n amp, amp_unit = self._obj2.amps, self._obj2.amp_unit\\n elif hasattr(self._obj2, 'power'):\\n power, power_unit = self._obj2.power, self._obj2.power_unit\\n z = power / amp**2\\n z_unit: str = f'Ohms ({power_unit}/{amp_unit}^2)'\\n\\n else:\\n return None\\n\\n return Ohm(z, z_unit, self._obj1.frequency, self._obj1.freq_unit)\",\n \"def getOrmsby(f,t):\\n assert len(f) == 4, 'Ormsby wavelet needs 4 frequencies as input'\\n f = np.sort(f) #Ormsby wavelet frequencies must be in increasing order\\n pif = pi*f\\n den1 = pif[3] - pif[2]\\n den2 = pif[1] - pif[0]\\n term1 = (pif[3]*np.sinc(pif[3]*t))**2 - (pif[2]*np.sinc(pif[2]))**2\\n term2 = (pif[1]*np.sinc(pif[1]*t))**2 - (pif[0]*np.sinc(pif[0]))**2\\n\\n wav = term1/den1 - term2/den2;\\n return wav\",\n \"def wavelength(self):\\n return wavelength(energy)\",\n \"def bird_hulstrom80_aod_bb(aod380, aod500):\\n # approximate broadband AOD using (Bird-Hulstrom 1980)\\n return 0.27583 * aod380 + 0.35 * aod500\",\n \"def get(self):\\n try:\\n print('jwlee-test-observation-get, self.level:', self.level)\\n if self.level is not None:\\n data_obs = self._obs_file.get(\\n self.var, level=self.level, region=self.region\\n )\\n else:\\n data_obs = self._obs_file.get(self.var, region=self.region)\\n return data_obs\\n except Exception as e:\\n if self.level is not None:\\n logging.getLogger(\\\"pcmdi_metrics\\\").error(\\n \\\"{} {} {} {}\\\".format(\\n \\\"Failed opening 4D OBS\\\", self.var, self.obs_or_model, e\\n )\\n )\\n else:\\n logging.getLogger(\\\"pcmdi_metrics\\\").error(\\n \\\"{} {} {} {}\\\".format(\\n \\\"Failed opening 3D OBS\\\", self.var, self.obs_or_model, e\\n )\\n )\",\n \"def DeMarchi16(self, wavelength, AK):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AK (since law assumes AK = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AK\\n\\n return A_at_wave\",\n \"def air_to_vac(self, wave):\\n #Convert to um\\n wave_um = wave*.001\\n ohm2 = (1./wave_um)**(2)\\n\\n #Calculate index at every wavelength\\n nn = []\\n for x in ohm2:\\n n = 1+10**(-8)*(8342.13 + (2406030/float(130.-x)) + (15997/float(389-x)))\\n nn.append(n)\\n \\n #Get new wavelength by multiplying by index of refraction\\n vac_wave = nn*wave\\n return vac_wave\",\n \"def _get_omega(self, vehicle_id):\\n pos = self.positions[vehicle_id]\\n omega = self.frenets[vehicle_id].get_omega(\\n pos[0], pos[1], pos[2], pos[3])\\n\\n return omega\",\n \"def derive_Damineli16(wavelength):\\n # From their eq 19\\n x = np.log10(2.159 / wavelength)\\n log_A_AKs = -0.015 + 2.33*x + 0.522*x**2. - 3.001*x**3. + 2.034*x**4.\\n\\n # Now to convert this back to linear space\\n A_AKs_at_wave = 10**log_A_AKs \\n\\n return A_AKs_at_wave\",\n \"def bragg_law(self, d_list, wavelength):\\r\\n new_twotheta = []\\r\\n for d in d_list:\\r\\n new_twotheta.append(2*math.degrees(np.arcsin(wavelength/(2*d))))\\r\\n return new_twotheta\",\n \"def efftau_madau(rwl, z):\\n\\n from numpy import array, where, exp\\n\\n la = 1215.67 ## Lyman alpha. Angstroms. \\n lb = 1026. ## Lyman beta. Angstroms. \\n lg = 973.\\n ld = 950.\\n le = 938.\\n ll = 912. ## Lyman limit.\\n\\n ## Redshifted from restframe to (definitely) observed. \\n wl = rwl*(1. + z)\\n \\n n = len(wl)\\n c = array([3.6e-3, 1.7e-3, 1.2e-3, 9.3e-4])\\n l = array([ la, lb, lg, ld])\\n\\n tau = np.zeros_like(wl)\\n xe = 1. + z\\n\\n ## Lyman series\\n for i in range(len(l) ):\\n indices = where(wl <= l[i]*xe) ## Note: no lower wavelength limit is correct as 'broadcasted' corrections. \\n tau[indices] += c[i]*(wl[indices]/l[i])**3.46\\n\\n ## Photoelectric absorption\\n xc = wl/ll\\n xc3 = xc**3\\n \\n tau = where(wl <= ll*xe, tau + 0.25*xc3*(xe**.46 - xc**0.46) \\\\\\n + 9.4*xc**1.5*(xe**0.18 - xc**0.18) \\\\\\n - 0.7*xc3*(xc**(-1.32) - xe**(-1.32)) \\\\\\n - 0.023*(xe**1.68-xc**1.68), tau)\\n \\n '''\\n min_tau = tau.min()\\n index = np.where(tau == min_tau)\\n tau[:index] = min_tau \\n \\n tau = where(tau < 0.0, 0.0, tau)\\n '''\\n\\n return where(tau > 700., 0., exp(-tau))\",\n \"def powerlaw(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def get_signal(self, audio, gain, phase):\\n max_delay_ms = self.center_ms + self.depth_ms\\n max_length_samples = int(self.sample_rate / 1000.0 * max_delay_ms)\\n\\n depth_phase = self.depth_ms / max_delay_ms\\n center_phase = self.center_ms / max_delay_ms\\n phase = phase * depth_phase + center_phase\\n wet_audio = core.variable_length_delay(\\n audio=audio, phase=phase, max_length=max_length_samples\\n )\\n # Remove channel dimension.\\n if gain.dim() == 3:\\n gain = gain[:, 0, :]\\n\\n wet_audio *= gain\\n return (wet_audio + audio) if self.add_dry else wet_audio\",\n \"def Schlafly16(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def get_amesdusty_atmosphere(metallicity=0, temperature=5000, gravity=4):\\n sp = pysynphot.Icat('AMESdusty', temperature, metallicity, gravity)\\n\\n # Do some error checking\\n idx = np.where(sp.flux != 0)[0]\\n if len(idx) == 0:\\n print( 'Could not find AMESdusty Allard+ 2000 atmosphere model for')\\n print( ' temperature = %d' % temperature)\\n print( ' metallicity = %.1f' % metallicity)\\n print( ' log gravity = %.1f' % gravity)\\n\\n return sp\",\n \"def _get_wavelength_attrs_with_units(self, attrname, units='AA'):\\n attr = self._lick[attrname]\\n if self.wavelength_unit is not None:\\n if units is None:\\n return attr * Unit(self.wavelength_unit)\\n else:\\n return (attr * Unit(self.wavelength_unit)).to(units)\\n else:\\n return attr\",\n \"def antennaResponse(peak, xoff, yoff, fwhm):\\n return peak * np.exp(CNST / (fwhm*fwhm) * (xoff*xoff + yoff*yoff))\",\n \"def convertToOpd(self, wavelength):\\n\\n if wavelength is None:\\n scale = 1.\\n else:\\n scale = 2. * math.pi / wavelength\\n self.opd = self.pupil * np.exp(1.j * self.phase * scale)\",\n \"def Fitzpactrick09(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def absorbance( self, lmin=0, lmax=0 ):\\n A = self.prop[\\\"SPEC\\\"][:,1]\\n if lmax>0:\\n m = np.vstack( (self.wavelength(), A) ).T # matrix w lambda and absorbance\\n m = m[ m[:,0] >= lmin ] # slice by wavelength...\\n m = m[ m[:,0] <= lmax ]\\n return np.average( m[:,1] ) # scalar\\n return A # array\",\n \"def search(url, wavelength, **keywords):\\n service = SLAService(url)\\n return service.search(wavelength, **keywords)\",\n \"def derive_RiekeLebofsky(wavelength):\\n filters = ['U', 'B', 'V', 'R', 'I', 'J', 'H', 'K', 'L', 'M', \\n '[8.0]', '[8.5]', '[9.0]', '[9.5]', '[10.0]', '[10.5]', \\n '[11.0]', '[11.5]', '[12.0]', '[12.5]', '[13.0]']\\n #wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.25, 1.635, 2.2, \\n # 3.77, 4.68, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\\n # 11.5, 12.0, 12.5, 13.0])\\n \\n # Wavelengths from Nishiyama+09 plot of RL+85 law...slightly different than standard, \\n # drop N filter\\n wave = np.array([0.365, 0.445, 0.551, 0.658, 0.806, 1.17, 1.57, 2.12, \\n 3.40, 4.75, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0,\\n 11.5, 12.0, 12.5, 13.0])\\n A_Av = np.array([1.531, 1.324, 1.00, 0.748, 0.482, 0.282, 0.175, 0.112,\\n 0.058, 0.023, 0.02, 0.043, 0.074, 0.087, 0.083,\\n 0.074, 0.060, 0.047, 0.037, 0.030, 0.027])\\n # Want to change this from A/Av to A/AK\\n k_ind = np.where(np.array(filters) == 'K')\\n Ak_Av = A_Av[k_ind]\\n Av_Ak = 1.0 / Ak_Av\\n\\n A_Ak = A_Av * Av_Ak\\n \\n # Interpolate over the curve\\n spline_interp = interpolate.splrep(wave, A_Ak, k=3, s=0)\\n A_Ak_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n return A_Ak_at_wave\",\n \"def get_wave_unit(tag, hdulist, idx=None):\\n from astropy.units import Unit\\n if idx is None:\\n idx = 1\\n # Use Table\\n if isinstance(hdulist[idx],BinTableHDU):\\n tab = Table(hdulist[idx].data)\\n header = hdulist[idx].header\\n else:\\n # NEED HEADER INFO\\n return None\\n # Try table header (following VLT/X-Shooter here)\\n keys = list(header) # Python 3\\n values = list(itervalues(header)) # Python 3\\n hidx = values.index(tag)\\n if keys[hidx][0:5] == 'TTYPE':\\n try:\\n tunit = header[keys[hidx].replace('TYPE','UNIT')]\\n except KeyError:\\n return None\\n else:\\n if tunit in ['Angstroem', 'Angstroms', 'ANGSTROMS']:\\n tunit = 'Angstrom'\\n unit = Unit(tunit)\\n return unit\\n else:\\n return None\",\n \"def wavelength(self,freq):\\n return self.phase_velocity()/freq\",\n \"def simulate_UVSPEC(file, config):\\n\\n wavelength = config['wavelength']\\n\\n # Coordenates from position of the station Hannover\\n latitude = 52.39 # positive in the northern hemisphere\\n longitud = 9.7 # negative reckoning west from prime meridian in Greenwich,\\n\\n # Read name of the file (correct time)\\n name = os.path.split(file)\\n time_n = name[1][0:15]\\n print(\\\"Time name\\\", time_n)\\n # convert time to datetime format\\n time = datetime.datetime.strptime(time_n,\\n '%Y%m%d_%H%M%S')\\n # Calculate the azimuth and zenith angles in function of the date\\n elev = ps.GetAltitude(latitude, longitud, time)\\n azi = ps.GetAzimuth(latitude, longitud, time)\\n zenith = 90 - elev\\n\\n # Correction between the sign and real azimuth for plot of radiance\\n if -180 <= azi < 0:\\n azi = 180 - azi\\n elif -360 <= azi < -180:\\n azi = -azi - 180\\n else:\\n pass\\n\\n print(\\\"Azimuth: {:5.1f}\\\".format(azi),\\n \\\"\\\\nZenith: {:5.1f}\\\".format(zenith))\\n\\n # Change the value of zenith and azimuth angles in function of the time and\\n # position in the UVSPEC file\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'r') as file:\\n data = file.readlines()\\n\\n data[14] = \\\"day_of_year \\\" + str(time.timetuple().tm_yday) + \\\" \\\" + \\\"\\\\n\\\"\\n data[15] = \\\"wavelength \\\" + str(\\\"{}\\\".format(wavelength)) + \\\" \\\" + \\\\\\n str(\\\"{}\\\".format(wavelength)) + \\\\\\n \\\" # wavelength to calcule [nm] \\\\n\\\"\\n data[17] = \\\"sza \\\" + str(\\\"{:2.3f}\\\".format(zenith)) + \\\\\\n \\\" # Solar zenith angle \\\\n\\\"\\n data[18] = \\\"phi0 \\\" + str(\\\"{:2.3f}\\\".format(azi)) + \\\\\\n \\\" #Azimuth angle with zenith position \\\\n\\\"\\n\\n with open(config['personal_libraries'] + 'MUDIS_HDF5/MUDIS_radiance_Input.txt', 'w') as file:\\n file.writelines(data)\\n\\n # Create the directory to save the results\\n os.makedirs(os.path.dirname(config['str_dir'] + '/simulation/' + '{}/{}nm/txt_files/'.format(time_n[0:8],\\n wavelength)),\\n exist_ok=True)\\n\\n # Run the program UVSPEC in the terminal\\n os.system(config['UVSPEC_path'] + 'uvspec < ' + config['personal_libraries'] +\\n 'MUDIS_HDF5/MUDIS_radiance_Input.txt> ' + config['str_dir'] + '/simulation/' +\\n '{}/{}nm/txt_files/'.format(time_n[0:8], wavelength) + time_n +\\n '.txt')\",\n \"def get_evwma(data):\\n if data is None:\\n raise EmptyDataError('[!] Invalid data value')\\n\\n result = TA.EVWMA(data)\\n if result is None:\\n raise IndicatorException\\n return result\",\n \"def _get_wavelength_attrs_with_units(self, attrname, units='AA'):\\n attr = self._lick[attrname]\\n if self.wavelength_unit is not None:\\n if units is None:\\n return attr * unit[self.wavelength_unit]\\n else:\\n return (attr * unit[self.wavelength_unit]).to(units)\\n else:\\n return attr\",\n \"def convert_nasa_unit_to_astropy(unit_str):\\n\\n print('Unit str: ' + repr(unit_str))\\n if unit_str in ['days', 'hrs']:\\n unit_str = unit_str[:-1]\\n elif unit_str == 'decimal degrees':\\n unit_str = 'degree'\\n elif (\\n unit_str in ['dex', 'Earth flux', 'sexagesimal']\\n or\\n unit_str.startswith('log10(')\\n or\\n unit_str.startswith('log(')\\n ):\\n return None\\n elif unit_str == 'Solar mass':\\n unit_str = 'solMass'\\n elif unit_str == 'Solar radii':\\n unit_str = 'solRad'\\n elif unit_str.endswith(' mass'):\\n unit_str = unit_str.split()[0].lower() + 'Mass'\\n elif unit_str.endswith(' radii'):\\n unit_str = unit_str.split()[0].lower() + 'Rad'\\n elif unit_str.startswith('percent'):\\n return 0.01\\n\\n print('Converted to: ' + repr(unit_str))\\n\\n return Unit(unit_str)\",\n \"def readAmesDustySpectrum(fname=''):\\n print('Reading : ', fname)\\n\\n # Get the effective temperature, logg and metallicity from the file name\\n ind = fname.find('lte')\\n fname_tags = fname[ind+3:ind+13].split('-')\\n teff = np.float(fname_tags[0]) * 100.\\n logg = np.float(fname_tags[1]) * 100.\\n mph = np.float(fname_tags[2]) * 100.\\n\\n wav = []\\n inu = []\\n bnu = []\\n with open(fname, 'r') as rfile:\\n dum = rfile.readline()\\n while dum != '':\\n dum = str(dum).replace('D', 'E')\\n sdum = dum.split()\\n wav.append(np.float(sdum[0]))\\n inu.append(np.float(sdum[1]))\\n bnu.append(np.float(sdum[2]))\\n dum = rfile.readline()\\n\\n wav = np.array(wav)\\n inu = np.array(inu)\\n bnu = np.array(bnu)\\n ii = wav.argsort()\\n\\n wav = wav[ii]\\n inu = inu[ii]\\n bnu = bnu[ii]\\n\\n # \\\"Decode\\\" the intensity arrays\\n inu = 10.**(inu - 8.0) * wav\\n bnu = 10.**(bnu - 8.0) * wav\\n\\n # Convert the wavelength to micron from Angstrom\\n wav /= 1e4\\n nwav = wav.shape[0]\\n\\n return {'teff': teff, 'logg': logg, 'mph': mph, 'nwav': nwav, 'wav': wav, 'inu': inu, 'bnu': bnu}\",\n \"def dseries(temp, wavelength):\\n if wavelength < 300 or wavelength > 830:\\n return 0\\n mm=wavelength%10\\n s=_dseriesd(temp, wavelength-mm)\\n if mm==0:\\n return s\\n m=mm*0.1\\n e=_dseriesd(temp, (wavelength-mm)+10)\\n return s+(e-s)*m\",\n \"def wavelength(self):\\n return self.get(self._names[\\\"wavelength\\\"])\",\n \"def wavelength(self):\\n return self.getparam(\\\"WAVELENGTH\\\")\",\n \"def wavelength(self):\\n return self.getparam(\\\"WAVELENGTH\\\")\",\n \"def wavelength_to_wavenumber(wavelength):\\n return 1. / wavelength\",\n \"def speed_of_sound(altitude):\\n t = temperature(altitude) # R\\n a = sqrt(gamma*gas_constant*t) # [ft/s]\\n return a\",\n \"def get(self, wave, flux, **kwargs):\\n return self._get(wave, flux.to('flam').value, **kwargs)\",\n \"def AcquiredData (self, arguments=None) :\\n\\t\\tself.OODriver.Wrapper_getSpectrum(self.wrapperHandle,self.spectrometerIndex,self.bufferHandle)\\n\\t\\t\\n\\t\\tif self.OODriver.Wrapper_isSaturated(self.wrapperHandle,self.spectrometerIndex) :\\n\\t\\t\\tprint \\\"Warning: OcenOptics spectrometer is saturated!\\\"\\n\\t\\t\\t\\n\\t\\ttry : return self.buffer[self.spectral_interval]\\n\\t\\texcept AttributeError : return self.buffer\",\n \"def blackbody( wave, T, waveunit='Angstrom' ):\\n \\n if waveunit=='Angstrom':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e10 * 100.\\n elif waveunit=='nm':\\n # convert wavelength from angstroms to cm\\n wave = wave / 1e9 * 100.\\n\\n return( ((2 * h * c* c)/wave**5 ) / (exp(h*c/(wave*k*T))-1) )\",\n \"def get_pulse_value(object = pulse_value_req):\\n try:\\n response = urllib2.urlopen(object).read()\\n pulse_value = json.loads(response)\\n return pulse_value['GetSensorValue'][0]\\n except URLError, e:\\n print 'Error: No Heartrate Value.'\",\n \"def measure_wavelength_avg(self, from_time, to_time, print_n=False):\\n from_idx = self._find_index_for_time(from_time)\\n to_idx = self._find_index_for_time(to_time)\\n \\n # print number of measurements\\n if print_n:\\n n_measurements = to_idx - from_idx + 1\\n print 'n measurements:', n_measurements\\n \\n # calculate overlap\\n overlap = self.overlap()[from_idx:to_idx+1,:]\\n \\n # intitialize wavelength storage\\n wavelengths = np.zeros(overlap.shape[0])\\n \\n for i in range(overlap.shape[0]):\\n this_overlap = overlap[i,:]\\n \\n # set all overlap entries below 0.1% of the maximum overlap to zero\\n this_overlap[this_overlap<0.001*np.max(this_overlap)] = 0.0\\n \\n nonzero = np.nonzero(this_overlap)\\n \\n # find connected section of chain with nonzero overlap\\n consecutives = np.split(nonzero, np.where(np.diff(nonzero) != 1)[0]+1)\\n \\n if len(consecutives) != 1:\\n warnings.warn('Wavelength could not be determined unambiguously.')\\n return np.nan\\n else:\\n # add 1 since overlap involves two beads each\\n wavelengths[i] = float( len(consecutives[0][0]) ) + 1\\n \\n return np.mean( wavelengths )\",\n \"def calculate_flux(self, band):\\n\\n if (self.wavelength[0] > band.wavelength[0] or\\n self.wavelength[-1] < band.wavelength[-1]):\\n\\n warn('Spectrum does not cover the whole bandpass, '\\n 'extrapolating...')\\n dw = np.median(np.diff(self.wavelength.value))\\n spec_wavelength = np.arange(\\n band.wavelength.value[0],\\n band.wavelength.value[-1] + dw, dw) * angstrom\\n spec_flux = np.interp(spec_wavelength, self.wavelength,\\n self.flux.value)\\n\\n else:\\n spec_wavelength = self.wavelength\\n spec_flux = self.flux.value\\n\\n i, j = spec_wavelength.searchsorted(\\n Quantity([band.wavelength[0], band.wavelength[-1]]))\\n wavelength = spec_wavelength[i:j]\\n flux = spec_flux[i:j]\\n\\n dw_band = np.median(np.diff(band.wavelength))\\n dw_spec = np.median(np.diff(wavelength))\\n\\n if dw_spec.value > dw_band.value > 20:\\n\\n warn('Spectrum wavelength sampling interval {0:.2f}, but bandpass'\\n 'sampling interval {1:.2f}'.format(dw_spec, dw_band))\\n\\n # Interpolate the spectrum to the passband wavelengths:\\n flux = np.interp(band.wavelength, wavelength, flux)\\n band_transmission = band.transmission\\n wavelength = band.wavelength\\n\\n else:\\n # Interpolate the band transmission to the spectrum wavelengths:\\n band_transmission = np.interp(\\n wavelength, band.wavelength, band.transmission)\\n\\n # Weight by the response and wavelength, appropriate when we're\\n # counting the number of photons within the band:\\n flux = (np.trapz(band_transmission * flux * wavelength, wavelength) /\\n np.trapz(band_transmission * wavelength, wavelength))\\n flux *= erg / s / cm ** 2 / angstrom\\n\\n return flux\",\n \"def wair2vac(w):\\n scalar = False\\n if isinstance(w, (int, float)):\\n w = [w]\\n scalar = True\\n w = np.array([w])\\n wvac = w.copy()\\n\\n mask = w > 2000. # Modify only wavelength above 2000 A\\n\\n s2 = (1e4/w[mask])**2\\n f = 1.+0.05792105/(238.0185-s2)+0.00167917/(57.362-s2)\\n wvac[mask] = w[mask]*f\\n return wvac[0][0] if scalar else wvac[0]\",\n \"def _get_url_for_timerange(self, timerange, **kwargs):\\n base_url = \\\"https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/goes{goes_number}/\\\"\\n supported_waves = [94, 131, 171, 195, 284, 304]\\n supported_levels = (\\\"2\\\", \\\"1b\\\")\\n\\n # these are optional requirements so if not provided assume defaults\\n # if wavelength is not provided assuming all of them\\n if \\\"wavelength\\\" in kwargs.keys():\\n wavelength_input = kwargs.get(\\\"wavelength\\\")\\n if isinstance(wavelength_input, u.Quantity): # not a range\\n if int(wavelength_input.to_value('Angstrom')) not in supported_waves:\\n raise ValueError(f\\\"Wavelength {kwargs.get('wavelength')} not supported.\\\")\\n else:\\n wavelength = [kwargs.get(\\\"wavelength\\\")]\\n else: # Range was provided\\n compress_index = [wavelength_input.wavemin <= this_wave <=\\n wavelength_input.wavemax for this_wave in (supported_waves * u.Angstrom)]\\n if not any(compress_index):\\n raise ValueError(\\n f\\\"Wavelength {wavelength_input} not supported.\\\")\\n else:\\n wavelength = list(compress(supported_waves, compress_index)) * u.Angstrom\\n else: # no wavelength provided return all of them\\n wavelength = supported_waves * u.Angstrom\\n # check that the input wavelength can be converted to angstrom\\n waves = [int(this_wave.to_value('angstrom', equivalencies=u.spectral()))\\n for this_wave in wavelength]\\n # use the given satellite number or choose the best one\\n satellitenumber = int(kwargs.get(\\n \\\"satellitenumber\\\", self._get_goes_sat_num(timerange.start)))\\n if satellitenumber < 16:\\n raise ValueError(f\\\"Satellite number {satellitenumber} not supported.\\\")\\n # default to the highest level of data\\n level = str(kwargs.get(\\\"level\\\", \\\"2\\\")) # make string in case the input is a number\\n\\n if level not in supported_levels:\\n raise ValueError(f\\\"Level {level} is not supported.\\\")\\n\\n results = []\\n for this_wave in waves:\\n if level == \\\"2\\\":\\n search_pattern = base_url + \\\\\\n r'l{level}/data/suvi-l{level}-ci{wave:03}/%Y/%m/%d/dr_suvi-l{level}-ci{wave:03}_g{goes_number}_s%Y%m%dT%H%M%SZ_.*\\\\.fits'\\n elif level == \\\"1b\\\":\\n if this_wave in [131, 171, 195, 284]:\\n search_pattern = base_url + \\\\\\n r'l{level}/suvi-l{level}-fe{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-Fe{wave:03}_G{goes_number}_s%Y%j%H%M%S.*\\\\.fits.gz'\\n elif this_wave == 304:\\n search_pattern = base_url + \\\\\\n r'l{level}/suvi-l{level}-he{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-He{wave_minus1:03}_G{goes_number}_s%Y%j%H%M%S.*\\\\.fits.gz'\\n elif this_wave == 94:\\n search_pattern = base_url + \\\\\\n r'l{level}/suvi-l{level}-fe{wave:03}/%Y/%m/%d/OR_SUVI-L{level}-Fe{wave_minus1:03}_G{goes_number}_s%Y%j%H%M%S.*\\\\.fits.gz'\\n\\n if search_pattern.count('wave_minus1'):\\n scraper = Scraper(search_pattern, level=level, wave=this_wave,\\n goes_number=satellitenumber, wave_minus1=this_wave-1)\\n else:\\n scraper = Scraper(search_pattern, level=level, wave=this_wave,\\n goes_number=satellitenumber)\\n results.extend(scraper.filelist(timerange))\\n return results\",\n \"def d65Illum(wavelength):\\n return _d65Illum.calc(wavelength)\",\n \"def read_data(self, uv, tave=False):\\n self.freqs = uv.freq_array[0]\\n a1 = uv.ant_1_array[:uv.Nbls]\\n a2 = uv.ant_2_array[:uv.Nbls]\\n for a in uv.antenna_numbers:\\n if not a in a1 and not a in a2:\\n if not a in self.dead: self.dead.append(a)\\n pid = np.where(uv.polarization_array == pol_lookup[self.pol])[0][0]\\n data = uv.data_array[:,0,:,pid].reshape(uv.Ntimes,uv.Nbls,uv.Nfreqs)\\n flag = uv.flag_array[:,0,:,pid].reshape(uv.Ntimes,uv.Nbls,uv.Nfreqs)\\n ind = np.where(a1!=a2)[0]\\n self.mask = output_mask_array(flag[:,ind])\\n self.shape_waterfall = (uv.Ntimes, uv.Nfreqs)\\n def creat_dict(ii):\\n if a1[ii] < 57 or a2[ii] < 57 or a1[ii] == a2[ii]: return # hard coded for MWA Phase II\\n if a1[ii] in self.dead or a2[ii] in self.dead: return\\n bl = (a1[ii],a2[ii])\\n md = np.ma.masked_array(data[:,ii],flag[:,ii])\\n diff = md[1:] - md[:-1]\\n self.noise[bl] = np.var(diff,axis=0).data/2\\n zerofq = np.where(np.sum(np.logical_not(diff.mask),axis=0) < 3)[0]\\n md.mask[:,zerofq] = True\\n self.data_backup[bl] = {self.pol: np.complex64(md.data)}\\n self.flag_backup[bl] = {self.pol: np.copy(md.mask)}\\n if tave:\\n md = np.mean(md,axis=0,keepdims=True)\\n self.data[bl] = {self.pol: np.complex64(md.data)}\\n self.flag[bl] = {self.pol: md.mask}\\n map(creat_dict, np.arange(uv.Nbls))\\n if tave: self.mask= np.product(self.mask, axis=0, keepdims=True).astype(bool)\\n mask = np.copy(self.mask)\\n if mask.ndim == 2: mask = np.product(self.mask, axis=0).astype(bool)\\n self.gains = RedGain(freqs=self.freqs, mask=mask)\\n self.gains.get_auto(uv)\",\n \"def RomanZuniga07(self, wavelength, AKs):\\n # If input entry is a single float, turn it into an array\\n try:\\n len(wavelength)\\n except:\\n wavelength = [wavelength]\\n\\n # Return error if any wavelength is beyond interpolation range of\\n # extinction law\\n if ((min(wavelength) < (self.low_lim*10**-4)) | (max(wavelength) > (self.high_lim*10**-4))):\\n return ValueError('{0}: wavelength values beyond interpolation range'.format(self))\\n \\n # Extract wave and A/AKs from law, turning wave into micron units\\n wave = self.wave * (10**-4)\\n law = self.obscuration\\n\\n # Find the value of the law at the closest points\\n # to wavelength\\n A_AKs_at_wave = []\\n for ii in wavelength:\\n idx = np.where( abs(wave - ii) == min(abs(wave - ii)) )\\n A_AKs_at_wave.append(law[idx][0])\\n\\n # Now multiply by AKs (since law assumes AKs = 1)\\n A_at_wave = np.array(A_AKs_at_wave) * AKs\\n\\n return A_at_wave\",\n \"def wind(\\n da_model: Union[xr.DataArray, xr.Dataset],\\n wind: xr.DataArray = None,\\n wind_u: xr.DataArray = None,\\n wind_v: xr.DataArray = None,\\n altitude: float = 10,\\n altitude_correction: bool = False,\\n freq: pd.Timedelta = None,\\n reproj_method: str = \\\"nearest_index\\\",\\n resample_kwargs: dict = {},\\n logger=logger,\\n):\\n if wind_u is not None and wind_v is not None:\\n wind = np.sqrt(np.power(wind_u, 2) + np.power(wind_v, 2))\\n elif wind is None:\\n raise ValueError(\\\"Either wind or wind_u and wind_v varibales must be supplied.\\\")\\n\\n if wind.raster.dim0 != \\\"time\\\":\\n raise ValueError(f'First wind dim should be \\\"time\\\", not {wind.raster.dim0}')\\n\\n # compute wind at 2 meters altitude\\n if altitude_correction:\\n wind = wind * (4.87 / np.log((67.8 * altitude) - 5.42))\\n # downscale wind (lazy)\\n wind_out = wind.raster.reproject_like(da_model, method=reproj_method)\\n # resample time\\n wind_out.name = \\\"wind\\\"\\n wind_out.attrs.update(unit=\\\"m s-1\\\")\\n if freq is not None:\\n resample_kwargs.update(upsampling=\\\"bfill\\\", downsampling=\\\"mean\\\", logger=logger)\\n wind_out = resample_time(wind_out, freq, conserve_mass=False, **resample_kwargs)\\n return wind_out\",\n \"def calc(self, wavelength):\\n if wavelength < self.minWavelength or wavelength > self.maxWavelength:\\n return 0\\n mm=wavelength%self.interval\\n s=self._calcd(wavelength-mm)\\n if mm==0:\\n return s\\n m=mm*1.0/self.interval\\n e=self._calcd((wavelength-mm)+self.interval)\\n return s+(e-s)*m\",\n \"def test_filt_abmag(self):\\n sun = Sun.from_builtin('E490_2014')\\n V = get_bandpass('johnson v')\\n wave, fluxd = sun.filt(V, unit=u.ABmag)\\n assert np.isclose(fluxd.value, -26.77, atol=0.007)\",\n \"def bommer_stafford_alarcon_ds_2009(magnitude=7.0, distance=10.0, vs30=760.0, ztor=0.0, duration_type='DS575H'):\\n\\n # duration type map\\n dur_map = {'DS575H':0, 'DS595H': 1}\\n dur_tag = dur_map.get(duration_type.upper(), None)\\n if dur_tag is None:\\n print(\\\"SignificantDurationModel.bommer_stafford_alarcon_ds_2009: duration_type='DS575H','DS595H'?\\\")\\n return None, None, None, None\\n \\n # modeling coefficients\\n c0 = [-5.6298, -2.2393]\\n m1 = [1.2619, 0.9368]\\n r1 = [2.0063, 1.5686]\\n r2 = [-0.2520, -0.1953]\\n h1 = [-2.3316, 2.5000]\\n v1 = [-0.2900, -0.3478]\\n z1 = [-0.0522, -0.0365]\\n tauCoeff = [0.3527, 0.3252]\\n phiCoeff = [0.4304, 0.3460]\\n sigma_c = [0.1729, 0.1114]\\n sigma_Tgm = [0.5289, 0.4616]\\n\\n # median\\n ds_median = np.exp(c0[dur_tag]+m1[dur_tag]*magnitude+(r1[dur_tag]+ \\\\\\n r2[dur_tag]*magnitude)*np.log(np.sqrt(distance**2+h1[dur_tag]**2))+ \\\\\\n v1[dur_tag]*np.log(vs30)+z1[dur_tag]*ztor)\\n # standard deviations\\n ds_sigma = sigma_Tgm[dur_tag]\\n ds_tau = tauCoeff[dur_tag]\\n ds_phi = phiCoeff[dur_tag]\\n\\n # return\\n return np.log(ds_median), ds_sigma, ds_tau, ds_phi\",\n \"def read300yrh(period):\\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\\n file300 = 'U10_1700-2000.nc'\\n filename = directory300 + file300\\n \\n data = Dataset(filename)\\n lat = data.variables['latitude'][:]\\n lon = data.variables['longitude'][:]\\n u10q = data.variables['U10'][:]\\n data.close()\\n \\n ### Reshape in year/month\\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\\n \\n ### Calculate over particular months\\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\\n \\n ### Slice U10 at 65N\\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\\n lat = lat[latq].squeeze()\\n u10 = u10[:,latq,:].squeeze()\\n \\n ### Take zonal mean \\n u10z = np.nanmean(u10,axis=1)\\n \\n ### Remove missing data\\n mask = np.where(u10z > -1e5)[0]\\n \\n ### Detrend\\n u10zdt = sss.detrend(u10z[mask],type='linear')\\n \\n return lat,lon,u10zdt\",\n \"def derive_Fritz11(wavelength):\\n # Extinction law definition\\n wave = np.array([1.282, 1.736, 2.166, 2.625, 2.758, 2.873, 3.039, 3.297, 3.74, 3.819, 3.907, 4.052,\\n 4.376, 5.128, 5.908, 6.772, 7.459, 7.502, 8.76, 12.371, 19.062])\\n A_AKs = np.array([7.91, 4.30, 2.49, 1.83, 1.51, 1.84, 2.07, 1.66, 1.19, 1.19, 1.09, 1.01, 1.09, 0.99,\\n 1.04, 0.84, 0.81, 0.79, 2.04, 1.34, 1.34])\\n\\n\\n # Interpolate over the curve\\n spline_interp = interpolate.splrep(wave, A_AKs, k=3, s=0)\\n A_at_wave = interpolate.splev(wavelength, spline_interp)\\n\\n # We'll call 2.14 microns the K-band\\n idx = np.where( abs(wavelength - 2.14) == min(abs(wavelength - 2.14)) )\\n A_AKs_at_wave = A_at_wave / A_at_wave[idx] \\n\\n return A_AKs_at_wave\",\n \"def getFWHM(antenna, freq):\\n diameter = getDiameter(antenna)\\n lam = 299792458.0 / (freq * 1e9)\\n fwhmo = lam / math.pi * 180.0 * 60.0\\n fwhm = 1.22 * fwhmo / diameter\\n return fwhm\",\n \"def __init__(self, downsampling=False, wl_range = False, spectra_path = '/Users/kyleturner/Dropbox/My_Box/Code/M_Files/process_svc_v2p1/rrs_model_3C-master/spectra/'):\\n\\n\\t\\t\\ta_ph = pd.read_csv(spectra_path + 'phyto.A', skiprows=11, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\n\\t\\t\\ta_w = pd.read_csv(spectra_path + 'water.A', skiprows=10, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\tdaw_dT = pd.read_csv(spectra_path + 'daWdT.txt', skiprows=10, sep='\\\\t', index_col=0).iloc[:,0]\\n\\t\\t\\tastar_y = pd.read_csv(spectra_path + 'Y.A', skiprows=11, delimiter=' ', index_col=0).loc[350:].iloc[:,0]\\n\\n\\t\\t\\td = pd.DataFrame(index = a_ph.index)\\n\\t\\t\\td.index.name = 'Wavelength, [nm]'\\n\\t\\t\\td['astar_ph'] = a_ph\\n\\t\\t\\td['astar_y'] = astar_y \\n\\t\\t\\td['a_w'] = a_w \\n\\t\\t\\td['daw_dT'] = daw_dT\\n\\n\\t\\t\\tif downsampling == False:\\n\\t\\t\\t\\tself.spectra = d\\n\\t\\t\\telse: \\n\\t\\t\\t\\tself.spectra = d.apply(lambda x: pd.rolling_mean(x, downsampling, center=True))\\n\\n\\t\\t\\tif wl_range != False:\\n\\t\\t\\t\\tself.spectra = self.spectra.loc[wl_range[0]:wl_range[1]].dropna()\\n\\n\\t\\t\\tself.wl = np.array(self.spectra.index)\\n\\t\\t\\t\\t\\n\\t\\t\\tself.model = self._compile_model()\",\n \"def turbulence(*args, attenuation: Union[float, bool]=0.0, frequency: Union[float, bool]=0.0,\\n magnitude: Union[float, bool]=0.0, maxDistance: Union[float, bool]=0.0, name:\\n Union[AnyStr, bool]=\\\"\\\", noiseLevel: Union[int, bool]=0, noiseRatio: Union[float,\\n bool]=0.0, perVertex: bool=True, phase: Union[float, bool]=0.0, phaseX:\\n Union[float, bool]=0.0, phaseY: Union[float, bool]=0.0, phaseZ: Union[float,\\n bool]=0.0, position: Union[List[float, float, float], List[List[float, float,\\n float]], bool]=None, torusSectionRadius: Union[float, bool]=0.0,\\n volumeExclusion: bool=True, volumeOffset: Union[List[float, float, float],\\n bool]=None, volumeShape: Union[AnyStr, bool]=\\\"\\\", volumeSweep: Union[float,\\n bool]=0.0, q=True, query=True, e=True, edit=True, **kwargs)->Union[AnyStr, Any]:\\n pass\",\n \"def twostr_func(wavelength, F_s, solarzenithangle,albedo_dif, \\n\\t\\t\\talbedo_dir, temp_ground, w_0, g, tau_n, temp_c):\\n\\t\\n\\t########################\\n\\t###Import useful libraries\\n\\t########################\\n\\timport numpy as np\\n\\timport pdb\\n\\timport scipy.linalg\\n\\n\\n\\n\\n\\t########################\\n\\t###Define model parameters\\n\\t########################\\n\\t#Properties of the ground\\n\\temissivity_ground=1.-albedo_dif #emissivity of ground. 1=perfect BB emitter.\\n\\n\\t#Optical depth structure\\n\\tNlayer=len(tau_n) #number of layers in the atmospheric model.\\n\\t\\n\\ttau_c=np.zeros(Nlayer+1)# tau_c[n] is the cumulative optical depth at the upper edge of layer n. So tau_c[0]=0, and tau_c[N] is the maximum possible.\\n\\tfor n in range(0, Nlayer):\\n\\t\\ttau_c[n+1]=tau_c[n]+tau_n[n] \\n\\n\\t#In the Toon formalism, j=0 corresponds to space, and j=N+1 corresponds to the planet surface.\\n\\t#These points in wavelength space define the edges of the bins in tau space. \\n\\t#Other terminology:\\n\\t#\\ttau_c=cumulative optical depth of layers *above* layer n. \\n\\t#\\ttau_n=total optical depth of the layer n\\n\\t#\\ttau=total optical depth at any point within a layer n, hence satisfying 0<tau<tau_n\\n\\n\\tmu_0=np.cos(solarzenithangle) #\\\"incident direction of solar beam\\\"\\n\\n\\n\\t########################\\n\\t###Determine the two-stream approximation coefficients.\\n\\t########################\\n\\t#Eddington and quadrature are good at solar wavelengths (i.e., not thermal blackbody dominated). delta scalings of Joseph et al (1976) recommended to replace w_0, g, tau in this case. However, when dominated by internal isotropic sources like the Planck function, hemispheric mean approximation is preferable. When w_0=0, quadrature case has problems. This happens esp at thermal wavelengths. Again this favors using hemispheric mean at these wavelengths\\n\\t\\n\\t#We use quadrature because 1) we are at solar wavelengths for this UV work and 2) that's what twostr.f does (which is our comparison case)\\n\\tgamma_1= np.sqrt(3.)*(2.-w_0*(1.+g))/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_1\\n\\tgamma_2=np.sqrt(3.)*w_0*(1.-g)/2. #consistent with Toon et al; consistent with Pierrehumbert gamma_2\\n\\tgamma_3=(1.-np.sqrt(3.)*g*mu_0)/2. #consistent with Toon et al; equal to the Pierrehumbert gamma_plus/w_0\\n\\tgamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\\n\\tmu_1=1./np.sqrt(3.)+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\\n\\n\\t##Eddington\\n\\t#gamma_1= (7.-w_0*(4.+3.*g))/4.\\n\\t#gamma_2=-1.*(1.-w_0*(4.-3.*g))/4.\\n\\t#gamma_3=(2.-3.*g*mu_0)/4.\\n\\t#gamma_4=1.-gamma_3 #consistent with Toon et al; equal to the Pierrehumbert gamma_minus/w_0\\n\\t#mu_1=1./2.+np.zeros(np.shape(gamma_1))#In Toon paper (eqn 18), this is given by: (1.-w_0)/(gamma_1-gamma_2). For the quadrature approximation, it is 1./np.sqrt(3.). Given its use, it seems to relate most closely to gamma_B from Pierrehumbert (see eqs 5.27, 5.30)\\n\\n\\talambda=np.sqrt(np.abs(gamma_1*gamma_1-gamma_2*gamma_2)) #this is the lower-case lambda, from eqn 21 of Toon et al\\n\\t\\t\\t\\t\\t\\t\\t\\t #The absolute value was added based on the code Toon just sent us. This corresponds to his AK(L,J) parameter. But it should not matter since gamma_1>gamma_2 for w_0<1.\\n\\tclambda=(gamma_1-alambda)/(gamma_2) #this is the upper-case lambda, from eqn 22 of Toon et al\\n\\n\\tEMLT=np.exp(-alambda*tau_n) #this appears to be a prefactor used to facilitate computation of eqn 44 of Toon et al\\n\\te1=1.+clambda*EMLT\\n\\te2=1.-clambda*EMLT\\n\\te3=clambda+EMLT\\n\\te4=clambda-EMLT\\n\\n\\t########################\\n\\t###Set up calculation\\n\\t########################\\n\\t\\\"\\\"\\\"\\n\\tThe fundamental equation we are solving is of form:\\n\\tA_{l}*Y_{l-1}+B_{l}*Y_{l}+D{l+1}=E_{l} (equation 39 of Toon et al)\\n\\tHere, A_l, B_l, D_l, E_l are quantities we determine, and the Y_l is what we solve for.\\n\\tHence, we can summarize that we are solving a matrix equation that takes form:\\n\\tPY=E\\n\\twhere Y[l]=Y_l\\n\\t E[l]=E_l\\n\\t P[l, l-1]=A_l [row, column]\\n\\t P[l, l]=B_l\\n\\t P[l, l+1]=D_l\\n\\t P[i,j]=0 else\\n\\tToon et al use 1-indexing. Hence n runs from 1 to N, l runs from 1 to 2N, where N is the number of layers, and they have:\\n\\tY_l=Y_{1n} for l=1,3,5,...2n-1...2N-1\\n\\tY_l=Y_{2n} for l=2,4,6,...2n...2N\\n\\n\\tHowever, we use Python, which has 0-indexing. Hence *we* choose that n runs from 0 to N-1, l runs from 0 to 2N-1, and:\\n\\tY_l=Y_{1n} for l=0,2,4...2n...2N-2\\n\\tY_l=Y_{2n} for l=1,3,5...2n+1...2N-1\\n\\n\\tThe Y_{1n} and Y_{2n} are related to F^+_n and F^-_n via equations 31 and 32 of Toon et al.\\n\\tThis parametrization has been done to remove exponentials with positive operands (ie ones that could grow large and lead to numerical instabilities) from the matrix.\\n\\n\\tNote: The mapping of this PQ=R to the F+ and F- is unclear because of 1) this parametrization in terms of Y_l (done to eliminate numerical instabilities) and 2)further linear combinations done to convert a pentagiagonal matrix to an even simpler tridiagonal matrix. Hence intuitive checks are hard.\\n\\t\\\"\\\"\\\"\\n\\n\\t########################\\n\\t###Set up surface flux\\n\\t########################\\n\\tS_sfc=albedo_dir*mu_0*np.exp(-tau_c[-1]/mu_0)*np.pi*F_s+emissivity_ground*np.pi*Planck(temp_ground, wavelength)\\n\\t#Surface emission. Formed by adding blackbody emission from the ground to the reflected energy from the direct beam. The direct beam's reflected energy is assumed to be purely diffuse. This corresponds to equations 37 and 38 of Toon et al. Note that this does NOT match equation 5.31 of Pierrehumbert because it does not include the reflected diffuse radiation. So, this implicitly assumes the diffuse albedo to be 0. \\n\\n\\t########################\\n\\t###Set up C-values\\n\\t########################\\n\\t#In the reshuffled set of parameters used in this formalism, these seem analagous to the forcing term in Pierrehumbert. All the added radiation is contained in here.\\n\\n\\tdef C_plus(n, tau): #implementation of superposition of eqns 23 and 27 from Toon et al\\n\\t\\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\\n\\t\\tsolarrad_prefactor=w_0[n]*F_s*np.pi\\n\\t\\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\\n\\t\\tsolarrad_factor=((gamma_1[n]-1./mu_0)*gamma_3[n]+gamma_4[n]*gamma_2[n])\\n\\t\\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\\n\\t\\t\\n\\t\\tblackbody_prefactor=2*np.pi*mu_1[n]\\n\\t\\tB0n=Planck(temp_c[n], wavelength)\\n\\t\\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\\n\\t\\tblackbody_factor=B0n+B1n*(tau+1./(gamma_1[n]+gamma_2[n]))\\n\\t\\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \\n\\t\\t\\n\\t\\tresult=solarrad+blackbody\\n\\t\\treturn result\\n\\n\\tdef C_minus(n, tau): #implementation of superposition of eqns 24 and 27 from Toon et al\\n\\t\\tsolarrad_denominator=alambda[n]**2.-1./mu_0**2.\\n\\t\\tsolarrad_prefactor=w_0[n]*F_s*np.pi\\n\\t\\tsolarrad_exponential=np.exp(-1.*(tau_c[n]+tau)/mu_0)\\n\\t\\tsolarrad_factor=((gamma_1[n]+1./mu_0)*gamma_4[n]+gamma_3[n]*gamma_2[n])\\n\\t\\tsolarrad=solarrad_prefactor*solarrad_factor*solarrad_exponential/solarrad_denominator #units of flux: erg/s/cm2/nm\\n\\t\\t\\n\\t\\tblackbody_prefactor=2*np.pi*mu_1[n]\\n\\t\\tB0n=Planck(temp_c[n], wavelength)\\n\\t\\tB1n=(Planck(temp_c[n+1], wavelength)-B0n)/tau_n[n] #this is effectively a slope\\n\\t\\tblackbody_factor=B0n+B1n*(tau-1./(gamma_1[n]+gamma_2[n]))\\n\\t\\tblackbody=blackbody_prefactor*blackbody_factor #start with units of the Planck function, which are: erg/s/cm2/nm/sr. But multiplying by 2pi sr restores the units of flux. So can safely add them. \\n\\t\\t\\n\\t\\tresult=solarrad+blackbody\\n\\t\\treturn result\\n\\n\\t########################\\n\\t###Calculate matrix coefficients\\n\\t#########################\\n\\t#initialize the A, B, D, and E.\\n\\tA=np.zeros(Nlayer*2)\\n\\tB=np.zeros(np.shape(A))\\n\\tD=np.zeros(np.shape(A))\\n\\tE=np.zeros(np.shape(A))\\n\\n\\n\\t#For l=0 (n=0) we have the boundary condition that the downward diffuse flux at the top of the first layer is equal to any incident diffuse downward flux. We set this to be zero.\\n\\tA[0]=0.\\n\\tB[0]=e1[0]\\n\\tD[0]=-1.*e2[0]\\n\\tE[0]=0.-1*C_minus(0,0) #This is really F_minus[0,0], i.e. we are assuming there is no downward diffuse flux from the top of the atmosphere.\\n\\n\\t#for l=2N-1 (n=N-1), we have the boundary condition that the upward flux at the surface is the sume of the reflected downward diffuse flux and energy from any other sources (e.g. reflected direct beam, BB emission of the ground)/np.sqrt(3.)\\n\\tA[2*Nlayer-1]=e1[Nlayer-1]-albedo_dif*e3[Nlayer-1]\\n\\tB[2*Nlayer-1]=e2[Nlayer-1]-albedo_dif*e4[Nlayer-1]\\n\\tD[2*Nlayer-1]=0.\\n\\tE[2*Nlayer-1]=S_sfc-C_plus(Nlayer-1, tau_n[Nlayer-1])+albedo_dif*C_minus(Nlayer-1, tau_n[Nlayer-1])\\n\\n\\t#There is a problem in the Toon paper. As written, the l=2n depends on e_n+1, running over the array edge. twostr.f resolves this by adopting a different mapping: their definition reduces to defining l=2(n+1) and running n from 0 to N-1. In this case, l=2 (The third value in the list of ls) depends on n=0 and n=1. This eliminates the overflow problem. We have implemented this below.\\n\\t\\n\\t##For n=1,2,3...N-1, l=2,4,6,...2N-2:\\n\\tfor n in range(0, Nlayer-1):\\n\\t\\tl=2*(n+1)\\n\\t\\tA[l]=e2[n]*e3[n]-e4[n]*e1[n]\\n\\t\\tB[l]=e1[n]*e1[n+1]-e3[n]*e3[n+1]\\n\\t\\tD[l]=e3[n]*e4[n+1]-e1[n]*e2[n+1]\\n\\t\\t\\n\\t\\tE[l]=e3[n]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))+e1[n]*(C_minus(n,tau_n[n])-C_minus(n+1,0.))\\n\\n\\n\\t#For n=0...N-2, l=1,3...2N-3:\\n\\tfor n in range(0, Nlayer-1):\\n\\t\\tl=2*n+1\\n\\t\\tA[l]=e2[n+1]*e1[n]-e3[n]*e4[n+1]\\n\\t\\tB[l]=e2[n]*e2[n+1]-e4[n]*e4[n+1]\\n\\t\\tD[l]=e1[n+1]*e4[n+1]-e2[n+1]*e3[n+1]\\n\\t\\t\\n\\t\\tE[l]=e2[n+1]*(C_plus(n+1, 0.)-C_plus(n, tau_n[n]))-e4[n+1]*(C_minus(n+1, 0)-C_minus(n, tau_n[n])) #twostr.f has a -1*e_{4,n+1}. We have applied the same even though this is NOT what is written in the Toon et al paper. We have done this because Toon told us (6/26/2015) that there are some sign errors in the coefficients, and we currently trust the validated CLIMA code over the paper we know has errors in it. EDIT: Looking at the code Toon shared with us, he does the same. \\n\\n\\n\\t########################\\n\\t###Assemble matrix equation components\\n\\t#########################\\n\\tP=np.zeros([Nlayer*2,Nlayer*2])\\n\\n\\t#l=0: no \\\"A\\\" coefficient b/c l-1 has no meaning\\n\\tP[0,0]=B[0]\\n\\tP[0,1]=D[0]\\n\\n\\t#l=2N-1: no \\\"D\\\" coefficient b/c l+1 has no meaning\\n\\tP[2*Nlayer-1,2*Nlayer-1-1]=A[2*Nlayer-1]\\n\\tP[2*Nlayer-1,2*Nlayer-1]=B[2*Nlayer-1]\\n\\n\\tfor l in range(1, Nlayer*2-1): #This populates the matrix P in PY=E. \\n\\t\\tP[l, l-1]=A[l]\\n\\t\\tP[l,l]=B[l]\\n\\t\\tP[l,l+1]=D[l]\\n\\n\\t########################\\n\\t###Invert matrix\\n\\t#########################\\n\\t#Y=np.linalg.solve(P, E) #this is the Y_l\\n\\t\\n\\t#try using a specialized solver\\n\\tab=np.zeros([3,2*Nlayer])\\n\\tab[0,:]=np.append(0.0, np.diag(P, k=1))\\n\\tab[1,:]=np.diag(P, k=0)\\n\\tab[2,:]=np.append(np.diag(P, k=-1),0.0)\\n\\t#pdb.set_trace()\\n\\tY=scipy.linalg.solve_banded((1,1), ab, E) #this is the Y_l\\n\\n\\n\\t########################\\n\\t###Convert from Y_l to Y_1n, Y_2n\\n\\t#########################\\n\\t#The Y_1n as defined in Toon et al correspond to l=1,3, 5...2N-1. Adjusting for the zero-indexing of Python as we have done, they instead correspond to l=0,2,...2N-2\\n\\t#The Y_2n as defined in Toon et al correspond to l=2,4,6...2N. Adjusting for Python zero-indexing as we have done, they instead correspond to l=1,3,5...2N-1.\\n\\t#For detail, see eq. 40.\\n\\tY_1=np.zeros(Nlayer)\\n\\tY_2=np.zeros(Nlayer)\\n\\tfor n in range(0, Nlayer):\\n\\t\\tY_1[n]=Y[2*n]\\n\\t\\tY_2[n]=Y[2*n+1] \\n\\t\\t#last number called is Nlayer-1=N-1, so is consistent.\\n\\t\\n\\t########################\\n\\t###Convert from Y_1n, Y_2n to F_plus, F_minus\\n\\t#########################\\n\\tdef F_plus(n,tau): #defined from Eqn 31 of Toon et al.\\n\\t\\tterm1=Y_1[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))+clambda[n]*np.exp(-alambda[n]*tau))\\n\\t\\tterm2=Y_2[n]*(np.exp(-alambda[n]*(tau_n[n]-tau))-clambda[n]*np.exp(-alambda[n]*tau))\\n\\t\\tterm3=C_plus(n,tau)\\n\\t\\t\\n\\t\\tresult=term1+term2+term3\\n\\t\\treturn result\\n\\n\\tdef F_minus(n, tau): #defined from Eqn 32 of Toon et al.\\n\\t\\tterm1=Y_1[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))+np.exp(-alambda[n]*tau))\\n\\t\\tterm2=Y_2[n]*(clambda[n]*np.exp(-alambda[n]*(tau_n[n]-tau))-np.exp(-alambda[n]*tau))\\n\\t\\tterm3=C_minus(n,tau)\\n\\t\\t\\n\\t\\tresult=term1+term2+term3\\n\\t\\treturn result\\n\\t\\n\\t########################\\n\\t###Evaluate F_plus, F_minus at boundary edges\\n\\t#########################\\n\\tF_plus_tau0=np.zeros(np.shape(tau_n))\\n\\tF_plus_taumax=np.zeros(np.shape(tau_n))\\n\\tF_minus_tau0=np.zeros(np.shape(tau_n))\\n\\tF_minus_taumax=np.zeros(np.shape(tau_n))\\n\\n\\tfor n in range(0, Nlayer):\\n\\t\\tF_plus_tau0[n]=F_plus(n, 0.)\\n\\t\\tF_plus_taumax[n]=F_plus(n, tau_n[n])\\n\\t\\tF_minus_tau0[n]=F_minus(n, 0.)\\n\\t\\tF_minus_taumax[n]=F_minus(n, tau_n[n])\\n\\n\\n\\t########################\\n\\t###Convert from Y_1n, Y_2n to F_net, mean intensity.\\n\\t#########################\\n\\t#test if diffuse flux dominates over direct flux. If direct flux dominant, instead set mu_1=mu_0\\n\\t\\n\\t#if F_minus_taumax[-1]<mu_0*np.pi*F_s*np.exp(-tau_c[-1]/mu_0):\\n\\t\\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\\n\\t#mu_1=np.zeros(np.shape(mu_1))+mu_0\\n\\t\\n\\tF_net=np.zeros(np.shape(tau_n)) #defined from Eqn 48 of Toon et al. This quantity is the net flux at the BASE of layer n.\\n\\tfor n in range(0, Nlayer):\\n\\t\\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\\n\\n\\t\\tterm1=Y_1[n]*(e1[n]-e3[n])\\n\\t\\tterm2=Y_2[n]*(e2[n]-e4[n])\\n\\t\\tterm3=C_plus(n, tau_n[n])-C_minus(n, tau_n[n])\\n\\t\\t\\n\\t\\tF_net[n]=term1+term2+term3 -direct\\n\\n\\tAMEAN=np.zeros(np.shape(tau_n)) #defined from Eqn 49 of Toon et al. This is the equivalent of the quantity AMEAN in the twostr.f code. It is equal to 4*np.pi*J_n, where J_n is the mean intensity at the base of layer n. Hence this quantity AMEAN should be equal to the total intensity received by a point at the base of layer n. \\n\\tfor n in range(0, Nlayer):\\n\\t\\tdirect=mu_0*np.pi*F_s*np.exp(-(tau_c[n]+tau_n[n])/mu_0) #eqn 50 of Toon et al\\n\\t\\n\\t\\tterm1=Y_1[n]*(e1[n]+e3[n])\\n\\t\\tterm2=Y_2[n]*(e2[n]+e4[n])\\n\\t\\tterm3=C_plus(n, tau_n[n])+C_minus(n, tau_n[n])\\n\\t\\t\\n\\t\\t#AMEAN[n]=(1./mu_1[n])*(term1+term2+term3)+direct/mu_0\\t\\n\\t\\tAMEAN[n]=(1./mu_1[n])*(F_plus_taumax[n]+F_minus_taumax[n])+direct/mu_0\\t\\n\\t\\n\\t########################\\n\\t###Compute \\\"surface intensity\\\"\\n\\t#########################\\t\\n\\t#\\\"Surface intensity\\\" refers to the total intensity that would be intercepted by a particle at the surface of the planet. Whereas the total intensity is equal to (F_plus[-1]+F_minus[-1])/mu_1+direct[-1]/mu_0, the surface intensity is instead equal to (F_minus[-1])/mu_1+direct[-1]/mu_0, i.e. the downwelling diffuse intensity (since the bottom intensity is cut out due to there being a planet there) plus the direct intensity\\n\\t\\n\\tsurface_intensity=(F_minus_taumax[-1]/mu_1[-1])+(np.pi*F_s)*np.exp(-(tau_c[-1])/mu_0)\\n\\t\\n\\t########################\\n\\t###Return Result\\n\\t#########################\\n\\t#F_minus_tau0\\n\\t#np.max(np.abs((F_minus_taumax[:-1]-F_minus_tau0[1:]))/F_minus_tau0[1:])\\n\\t#np.max(np.abs((F_plus_taumax[:-1]-F_plus_tau0[1:]))/F_plus_tau0[1:])\\n\\t\\n\\treturn (F_plus_tau0, F_plus_taumax, F_minus_tau0, F_minus_taumax, F_net, AMEAN, surface_intensity)\",\n \"def vacuum_to_air(wavelength):\\n # Following the vacuum to air conversion the formula from Donald Morton (2000, ApJ. Suppl., 130, 403) which is also a IAU standard\\n # - More info: http://www.astro.uu.se/valdwiki/Air-to-vacuum%20conversion\\n s_square = np.power(1.e4 / wavelength, 2)\\n n = 1 + 0.0000834254 + 0.02406147 / (130 - s_square) + 0.00015998 / (38.9 - s_square)\\n return wavelength/n # Angstroms\",\n \"def refractive_index_fused_silica(wavelength):\\n wavelength_um = wavelength / 1000\\n\\n A0 = 2.104025406E+00\\n A1 = -1.456000330E-04\\n A2 = -9.049135390E-03\\n A3 = 8.801830992E-03\\n A4 = 8.435237228E-05\\n A5 = 1.681656789E-06\\n A6 = -1.675425449E-08\\n A7 = 8.326602461E-10\\n\\n n = np.sqrt( A0 + A1 * wavelength_um ** 4 + A2 * wavelength_um ** 2 + A3 * wavelength_um ** -2 + \\\\\\n A4 * wavelength_um ** -4 + A5 * wavelength_um ** -6 + A6 * wavelength_um ** -8 + A7 * wavelength_um ** -10 )\\n\\n return n\",\n \"def d50Illum(wavelength):\\n return _d50Illum.calc(wavelength)\",\n \"def test_ulaw(self):\\n duration = 1\\n num_channels = 1\\n sample_rate = 8000\\n path = self.get_temp_path(\\\"data.wav\\\")\\n sox_utils.gen_audio_file(\\n path, sample_rate=sample_rate, num_channels=num_channels, bit_depth=8, encoding=\\\"u-law\\\", duration=duration\\n )\\n info = self._info(path)\\n assert info.sample_rate == sample_rate\\n assert info.num_frames == sample_rate * duration\\n assert info.num_channels == num_channels\\n assert info.bits_per_sample == 8\\n assert info.encoding == \\\"ULAW\\\"\",\n \"def get_ambient_light(self, ldr_voltage: Optional[int] = None) -> float:\\n if ldr_voltage is None:\\n ldr_voltage = self.raw_ldr_voltage\\n\\n # TODO: this conversion algorithm is straight from the manual but it seems odd.\\n # It goes \\\"to infinity\\\" as ldr_voltage nears 1023 (hence the clamp, I guess)\\n # Clarify.\\n if ldr_voltage > 1022:\\n ldr_voltage = 1022\\n if ldr_voltage < 1:\\n ldr_voltage = 1\\n\\n return self.ldr_pull_up_resistance / ((1023 / ldr_voltage) - 1)\",\n \"def _frequency_to_wavelength(freq):\\n return ifc.SPEED_OF_LIGHT_METRES_PER_SECOND / freq\",\n \"def set_wavelength(self, wavelength):\\n print('Setting Santec wavelength to %.4f nm' % wavelength)\\n\\n # We need to select which of the 4 lasers to select depending on\\n # the desired wavelength\\n\\n if 1530.0 < wavelength < 1630.000001:\\n self.santec1.write(\\\"SW 4\\\")\\n self.santec4.write(\\\"WA %.4f\\\" % wavelength)\\n if self.active_module != 4:\\n self.active_module = 4\\n time.sleep(5.00)\\n else:\\n time.sleep(0.01)\\n\\n elif 1440.0 < wavelength < 1530.1:\\n self.santec1.write(\\\"SW 3\\\")\\n self.santec3.write(\\\"WA %.4f\\\" % wavelength)\\n if self.active_module != 3:\\n self.active_module = 3\\n time.sleep(5.00)\\n else:\\n time.sleep(0.01)\\n\\n elif 1355 < wavelength < 1440.1:\\n self.santec1.write(\\\"SW 2\\\")\\n self.santec2.write(\\\"WA %.4f\\\" % wavelength)\\n if self.active_module != 2:\\n self.active_module = 2\\n time.sleep(5.00)\\n else:\\n time.sleep(0.01)\\n\\n elif 1259.999999 < wavelength < 1355.1:\\n self.santec1.write(\\\"SW 1\\\")\\n self.santec1.write(\\\"WA %.4f\\\" % wavelength)\\n if self.active_module != 1:\\n self.active_module = 1\\n time.sleep(5.00)\\n else:\\n time.sleep(0.01)\\n\\n else:\\n print(\\\"Wavelength out of range. No change will be made\\\")\",\n \"def get_iPTF16asu():\\n z = 0.187\\n ebv = 0.0\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n \\n tb = asci.read('../data/otherSN/Whitesides2017/table1.txt')\\n tb = tb.to_pandas()\\n tb = tb[tb[\\\"col4\\\"].values!=\\\">\\\"]\\n \\n tb = tb.rename(columns={'col1' : 'mjd',\\n 'col2': 'tmax_rf',\\n 'col3': 'filter',\\n \\\"col4\\\": 'mag',\\n 'col5': 'emag',\\n 'col6': 'instrument'})\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb[\\\"mag\\\"] = np.array(tb[\\\"mag\\\"].values, dtype = np.float)\\n #tb[\\\"emag\\\"] = np.array(tb[\\\"emag\\\"].values, dtype = np.float)\\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb = tb[tb.wave!=0]\\n return tb\",\n \"def gen_OSA(self, time_window_ps, center_wavelength_nm, power, \\n power_is_epp = False,\\n fileloc = 'O:\\\\\\\\OFM\\\\\\\\Maser\\\\\\\\Dual-Comb 100 MHz System\\\\\\\\Pump spectrum-Yb-101614.csv',\\n log = True, rows = 30): # Yb spectrum\\n \\n try:\\n self.fileloc = fileloc\\n \\n self.set_time_window_ps(time_window_ps)\\n \\n self.center_wl = center_wavelength_nm # reference wavelength (nm) \\n \\n self.w0 = (2. * np.pi * self.c) / self.center_wl # reference angular frequency \\n \\n self.setup_grids()\\n \\n if not power_is_epp: \\n power = power / self.frep\\n \\n # Read in OSA data\\n osa_data = np.genfromtxt(self.fileloc, delimiter = ',', skiprows = rows) \\n \\n wavelengths = osa_data[:,0]# (nm)\\n wavelengths = self.internal_wl_from_nm(wavelengths)\\n \\n intensity = osa_data[:,1]# (arb. units)\\n\\n if log:\\n intensity = 10.**(intensity / 10.)\\n \\n freq_abs = self.c/wavelengths\\n freq_abs = np.sort(freq_abs)\\n \\n self.freq_rel = freq_abs - self.c / self.center_wl\\n \\n pulse_envelope = interp1d(self.freq_rel, intensity, kind='linear',\\n bounds_error = False, fill_value=0)\\n \\n self.gridded_intensity = pulse_envelope(self.V / (2*np.pi))\\n \\n # Calculate time domain complex electric field A\\n self.A = IFFT_t(self.gridded_intensity)\\n # Calculate normalization factor to achieve requested \\n # pulse energy\\n e_scale = np.sqrt(power / self.calc_epp() )\\n self.A = self.A * e_scale\\n \\n except IOError:\\n print ('File not found.')\",\n \"def read300yr(period):\\n directory300 = '/seley/ypeings/simu/PAMIP-1.1-QBO-300yr/monthly/'\\n file300 = 'U10_1700-2000.nc'\\n filename = directory300 + file300\\n \\n data = Dataset(filename)\\n lat = data.variables['latitude'][:]\\n lon = data.variables['longitude'][:]\\n u10q = data.variables['U10'][:]\\n data.close()\\n \\n ### Reshape in year/month\\n u10n = np.reshape(u10q,(u10q.shape[0]//12,12,lat.shape[0],lon.shape[0]))\\n \\n ### Calculate over particular months\\n u10 = UT.calcDecJanFeb(u10n,lat,lon,'surface',1)\\n \\n ### Slice U10 at 65N\\n latq = np.where((lat >= 64.5) & (lat <= 65.5))[0]\\n lat = lat[latq].squeeze()\\n u10 = u10[:,latq,:].squeeze()\\n \\n ### Take zonal mean \\n u10z = np.nanmean(u10,axis=1)\\n \\n ### Remove missing data\\n mask = np.where(u10z > -1e5)[0]\\n \\n ### Detrend\\n u10zdt = sss.detrend(u10z[mask],type='linear')\\n \\n return lat,lon,u10zdt\",\n \"def get_tod(self, unit=None):\\n tod = Tod.empty((self.get_ndetectors(), np.sum(self.get_nsamples())))\\n sizeofpmatrix = self.info.npixels_per_sample * tod.size\\n pmatrix = np.zeros(sizeofpmatrix, dtype=int)\\n status = tmf.madmap1_read_tod(self.info.todfile, self.info.invnttfile,\\n self.info.convert, self.info.npixels_per_sample, 0, tod.T, pmatrix)\\n if status != 0: raise RuntimeError()\\n if unit is not None:\\n tod.unit = unit\\n return tod\",\n \"def getWavelength(self, inAngle, outAngle, wavelengths = [0.25,1.0]):\\n\\n # Get prism point and angle of input at Unit3d\\n #\\n pt = self.getInputPoint()\\n u = Unit3d(Angle(inAngle))\\n\\n # Guess at initial wavelngth\\n wave = (wavelengths[1] - wavelengths[0])/2\\n # Make input ray at guess wavelength\\n ray = IntensityRay(pt,u,wave)\\n\\n # Parameters for seaerch\\n delta = 0.1\\n forward = True\\n na = float(\\\"inf\\\") # New angle\\n\\n while abs(na - outAngle) > 1.0e-9/abs(outAngle) :\\n nray = ray*self # New Ray through prism\\n na = nray.getAngle()\\n na = na.theta*math.cos(na.psi) # In radians\\n if na < outAngle: # Less that target\\n wave += delta\\n forward = True\\n else:\\n if forward: # Half step\\n delta *= 0.5\\n forward = False\\n wave -= delta\\n if wave < wavelengths[0] or wave > wavelengths[1]:\\n print(\\\"Out of wavelength range :\\\")\\n return float(\\\"nan\\\")\\n\\n ray.wavelength = wave # Update the wavelength of ray\\n\\n return ray.getWavelength() # End of loop, so success, return value\",\n \"def getCalibration(self):\\n self.a0 = float(self.getParameter(index=1))\\n self.a1 = float(self.getParameter(index=2))\\n self.a2 = float(self.getParameter(index=3))\\n self.a3 = float(self.getParameter(index=4))\\n status = self.getStatus()\\n self.wavelength = [ self.a0 + self.a1*x + self.a2*x*x + self.a3*x*x*x \\n for x in range(status.pixels)]\\n if self.discardTrailingSamples > 0:\\n self.wavelength = self.wavelength[:-self.discardTrailingSamples]\\n if self.discardLeadingSamples > 0:\\n self.wavelength = self.wavelength[self.discardLeadingSamples:]\",\n \"def get_observation(observation_id: str):\\n pass\",\n \"def spectrum(self, wl: Union[float, ndarray]) -> Union[float, ndarray]:\\n wlm = wl * 1e-9 # Wavelength to meters\\n return 3.74183e-16 * wlm ** -5. / (np.exp(0.014388 / (wlm * self.temp)) - 1.)\",\n \"def test_regression_determine_wavelength_solution(\\n ad, params, caplog, change_working_dir, path_to_refs, request):\\n caplog.set_level(logging.INFO, logger=\\\"geminidr\\\")\\n\\n with change_working_dir():\\n logutils.config(file_name='log_regress_{:s}.txt'.format(ad.data_label()))\\n p = GNIRSLongslit([ad])\\n p.viewer = geminidr.dormantViewer(p, None)\\n\\n p.determineWavelengthSolution(**{**determine_wavelength_solution_parameters,\\n **params})\\n\\n wcalibrated_ad = p.streams[\\\"main\\\"][0]\\n\\n for record in caplog.records:\\n if record.levelname == \\\"WARNING\\\":\\n assert \\\"No acceptable wavelength solution found\\\" not in record.message\\n\\n ref_ad = astrodata.open(os.path.join(path_to_refs, wcalibrated_ad.filename))\\n model = am.get_named_submodel(wcalibrated_ad[0].wcs.forward_transform, \\\"WAVE\\\")\\n ref_model = am.get_named_submodel(ref_ad[0].wcs.forward_transform, \\\"WAVE\\\")\\n\\n x = np.arange(wcalibrated_ad[0].shape[1])\\n wavelength = model(x)\\n ref_wavelength = ref_model(x)\\n\\n pixel_scale = wcalibrated_ad[0].pixel_scale() # arcsec / px\\n slit_size_in_arcsec = float(wcalibrated_ad[0].focal_plane_mask(pretty=True).replace('arcsec', ''))\\n slit_size_in_px = slit_size_in_arcsec / pixel_scale\\n dispersion = abs(wcalibrated_ad[0].dispersion(asNanometers=True)) # nm / px\\n\\n # We don't care about what the wavelength solution is doing at\\n # wavelengths outside where we've matched lines\\n lines = ref_ad[0].WAVECAL[\\\"wavelengths\\\"].data\\n indices = np.where(np.logical_and(ref_wavelength > lines.min(),\\n ref_wavelength < lines.max()))\\n tolerance = 0.5 * (slit_size_in_px * dispersion)\\n\\n write_report = request.config.getoption('--do-report', False)\\n failed = False\\n try:\\n np.testing.assert_allclose(wavelength[indices], ref_wavelength[indices],\\n atol=tolerance)\\n except AssertionError:\\n failed = True\\n raise\\n finally:\\n if write_report:\\n do_report(wcalibrated_ad, ref_ad, failed=failed)\\n\\n if request.config.getoption(\\\"--do-plots\\\"):\\n do_plots(wcalibrated_ad)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5712585","0.5581919","0.5486108","0.53831595","0.53533727","0.53258014","0.5305268","0.526969","0.52618587","0.52508855","0.52357584","0.51796144","0.516192","0.5157981","0.5145479","0.5129892","0.50970304","0.50838137","0.5080797","0.5045471","0.5044165","0.50435984","0.50404006","0.5017592","0.49941468","0.49887577","0.49881014","0.49863347","0.49751332","0.49611792","0.49575964","0.49547914","0.4938535","0.49320897","0.49053022","0.48991328","0.48884866","0.48695815","0.48341852","0.4829966","0.48170704","0.48146194","0.48130408","0.48050964","0.48024154","0.48015058","0.4800287","0.47913882","0.47849107","0.47846746","0.4778462","0.47762427","0.476735","0.4762327","0.47537616","0.47494623","0.47297296","0.47169808","0.47160786","0.47144374","0.470919","0.470919","0.47044158","0.46978906","0.46942973","0.46867839","0.46839","0.46835375","0.46805042","0.46799946","0.46629068","0.4659726","0.4649702","0.4648093","0.46471384","0.4640122","0.46289173","0.46147165","0.4609321","0.46053845","0.4598725","0.45923424","0.45877036","0.45812902","0.45766076","0.45701858","0.45607302","0.45580047","0.45323497","0.4523522","0.45233053","0.45223525","0.45193225","0.45183045","0.45174035","0.4514776","0.45126286","0.45121214","0.4510876","0.45090827","0.45070258"],"string":"[\n \"0.5712585\",\n \"0.5581919\",\n \"0.5486108\",\n \"0.53831595\",\n \"0.53533727\",\n \"0.53258014\",\n \"0.5305268\",\n \"0.526969\",\n \"0.52618587\",\n \"0.52508855\",\n \"0.52357584\",\n \"0.51796144\",\n \"0.516192\",\n \"0.5157981\",\n \"0.5145479\",\n \"0.5129892\",\n \"0.50970304\",\n \"0.50838137\",\n \"0.5080797\",\n \"0.5045471\",\n \"0.5044165\",\n \"0.50435984\",\n \"0.50404006\",\n \"0.5017592\",\n \"0.49941468\",\n \"0.49887577\",\n \"0.49881014\",\n \"0.49863347\",\n \"0.49751332\",\n \"0.49611792\",\n \"0.49575964\",\n \"0.49547914\",\n \"0.4938535\",\n \"0.49320897\",\n \"0.49053022\",\n \"0.48991328\",\n \"0.48884866\",\n \"0.48695815\",\n \"0.48341852\",\n \"0.4829966\",\n \"0.48170704\",\n \"0.48146194\",\n \"0.48130408\",\n \"0.48050964\",\n \"0.48024154\",\n \"0.48015058\",\n \"0.4800287\",\n \"0.47913882\",\n \"0.47849107\",\n \"0.47846746\",\n \"0.4778462\",\n \"0.47762427\",\n \"0.476735\",\n \"0.4762327\",\n \"0.47537616\",\n \"0.47494623\",\n \"0.47297296\",\n \"0.47169808\",\n \"0.47160786\",\n \"0.47144374\",\n \"0.470919\",\n \"0.470919\",\n \"0.47044158\",\n \"0.46978906\",\n \"0.46942973\",\n \"0.46867839\",\n \"0.46839\",\n \"0.46835375\",\n \"0.46805042\",\n \"0.46799946\",\n \"0.46629068\",\n \"0.4659726\",\n \"0.4649702\",\n \"0.4648093\",\n \"0.46471384\",\n \"0.4640122\",\n \"0.46289173\",\n \"0.46147165\",\n \"0.4609321\",\n \"0.46053845\",\n \"0.4598725\",\n \"0.45923424\",\n \"0.45877036\",\n \"0.45812902\",\n \"0.45766076\",\n \"0.45701858\",\n \"0.45607302\",\n \"0.45580047\",\n \"0.45323497\",\n \"0.4523522\",\n \"0.45233053\",\n \"0.45223525\",\n \"0.45193225\",\n \"0.45183045\",\n \"0.45174035\",\n \"0.4514776\",\n \"0.45126286\",\n \"0.45121214\",\n \"0.4510876\",\n \"0.45090827\",\n \"0.45070258\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":8493,"cells":{"query":{"kind":"string","value":"r\"\"\" Calculate Angstrom alpha exponent."},"document":{"kind":"string","value":"def angstrom_alpha(aod1, lambda1, aod2, lambda2):\n return - np.log(aod1 / aod2) / np.log(lambda1 / lambda2)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculate_exponent():\n pass","def powerlaw(E,alpha,A):\n\n\treturn A*E**alpha","def calc_alpha(epsilon): \n return float(0.5 * np.log((1-epsilon)/epsilon))","def encode_exponent(e: int) -> int:\n assert 0 <= e <= MAX_EXPONENT\n return DECODING_TABLE[e]","def test_exp_decay(self, alpha: float):\n x = np.linspace(0, 1, 100)\n y = np.exp(alpha * x)\n\n alpha_guess = guess.exp_decay(x, y)\n\n self.assertAlmostEqualAbsolute(alpha_guess, alpha)","def exp(self, num, zf=2):\n return str(num).zfill(zf)","def superobl_alpha(k):\n if k%2==0:\n return 2**(-(k - 1))*(1 - 1/(k + 1)**2)**(k/2)\n else:\n return 2**(-(k - 1))*(1 - 1/k**2)**((k - 1)/2)","def schechter(l,alpha):\n return exp(-l)*(l**alpha)","def myExp(base,exponent,modulus):\n result = 1\n while exponent > 0:\n if exponent & 1 == 1:\n result = (result * base) % modulus\n exponent = exponent >> 1\n base = (base * base) % modulus\n return result","def correctalpha(desiredalpha, level):\n \n correctedalpha = 1 - (1 - desiredalpha) ** (1.0 / level)\n \n return correctedalpha","def _alpha(self):\n return _handle_ab(self.solution, self.use_const)[0]","def power_normalize(xx, alpha = 0.5):\r\n\treturn np.sign(xx) * np.abs(xx) ** alpha","def calculateCrypt(asci: int, e: int, n: int) -> int:\n return pow(int(asci),e,n)","def get_ralpha(self, alpha, xalpha):\n A = self.A\n b = self.b\n ralpha = dot(A, xalpha - b)\n return ralpha","def _se_alpha(self):\n return _handle_ab(self._se_all, self.use_const)[0]","def inverse_exponential(x):\n return math.exp(-x)","def negative_exponent():\n print(\"Problem: Negative exponent\")\n\n a = float(input())\n n = int(input())\n\n result = power(a, n)\n print(result)","def exponent(num,power=2):\n return num ** power","def decode_exponent(e: int) -> int:\n assert 0 <= e <= MAX_EXPONENT\n return ENCODING_TABLE[e]","def rdf_deriv(alpha):\n\n ALPHA_MAX = 0.59999\n\n ALPHA0 = 0.6\n\n numpy.where(alpha > ALPHA_MAX, ALPHA_MAX, alpha)\n\n return -1.0/(3.0*ALPHA0)*(alpha/ALPHA0)**(-2.0/3.0)/(1.0-(alpha/ALPHA0)**(1.0/3.0))**2","def alpha_number(alpha):\r\n if alpha.isupper() == False:\r\n num = ord(alpha) - 96\r\n return num\r\n elif alpha.isupper() == True:\r\n num = ord(alpha) - 64\r\n return num","def exponential(value):\n return math.exp(value)","def reg(letter,number):\n v=ord(letter.lower())-ord('a')\n v=v<<10\n v+=number\n return v","def exponent(self):\n return self.__exponent","def nalpha(self) -> int:\n return self._core.nalpha()","def dd_xpowalpha(cls,grid,alpha,cutoff=False):\n grid.l.info('bc.hom: Setting initial data to (-x)^alpha.')\n grid.l.debug('bc.hom: Parameters to dd_xpowalpha: alpha={},cutoff={}'.format(alpha,cutoff))\n if alpha is 0:\n def tmp(x): return float(x[1]<=0)\n return cls._tpl(grid, tmp) \n\n if cutoff:\n def tmp(x):\n return sum(pow(-1*float(x[i]<0)*x[i],alpha) for i in range(0,grid.u.ndim))\n else:\n def tmp(x):\n return sum(pow(float(x[i]>=0)*x[i],alpha)-pow(-1*float(x[i]<0)*x[i],alpha) for i in range(0,grid.u.ndim))\n return cls._tpl(grid, tmp)","def alpha_from_R(R):\n return 1-np.abs(R)**2","def se_alpha(self):\n return self._se_alpha","def powAlpha( n ):\n return (1-betaval)*Fib(n) + Fib(n-1)\n #return Fib(n+1) - Fib(n) * betaval","def private_exponent(primes, e): \n return pow(e, -1, math.lcm((primes[0]-1),(primes[1]-1)))","def getExponent(number):\n exponent = np.floor(np.log10(number))\n return(exponent)","def exponentiation():\n print(\"Problem: Exponentiation\")\n\n a = float(input())\n n = int(input())\n\n result = power_v2(a, n)\n print(result)","def calc_alpha_init(alpha, decay):\n\tif not decay or decay <= 0:\n\t\treturn alpha\n\telse:\n\t\treturn float(alpha * decay)","def Exp(num):\n return math.exp(float(num))","def alpha(self):\n return self._alpha","def alpha(self):\n return self._alpha","def alpha(self):\n return self._alpha","def _get_alpha(cls, cl: float, tail: str):\n alpha = (1 - cl) / 2 if tail == \"two\" else (1 - cl)\n return round(alpha, 3)","def getExponent(self):\n return _libsbml.ASTNode_getExponent(self)","def alpha(self) -> float:\n return self._alpha","def _g(self, x):\n e_x = math.exp(-self._alpha * x)\n return (1.0 / (1 + e_x))","def get_alpha(self, error_rate, func='default'):\n return 0.5 * np.log((1. - error_rate) / error_rate)","def num_alpha(self) -> int:\n return self._num_alpha","def calculate_alphabeta(self, p, prec):\n var('x')\n sqrtdelta = None\n try:\n M = Qp(p, prec).extension(x ** 2 - self.constants.delta, names=\"padicroot\")\n sqrtdelta = M.gen(0)\n except NotImplementedError:\n try:\n M = Qp(p, prec)\n sqrtdelta = M(self.constants.delta).sqrt()\n except NotImplementedError:\n # Exceptional case.\n M = Qp(p, prec).extension(x ** 2 - self.constants.A * x - self.constants.B, names=\"padicroot\")\n alpha = M.gen(0)\n beta = self.constants.A - alpha\n return (alpha, beta)\n\n alpha = (self.constants.A + sqrtdelta) / 2\n beta = self.constants.A - alpha\n return (alpha, beta)","def test_check_digits_with_wrong_alphabet(self, _, alpha):\n with self.assertRaises(exceptions.WrongArgumentValueError):\n positional.encode(42, 10, alphabet=alpha)","def exponential(image: np.ndarray) -> np.ndarray:\n return np.power(image, 0.75).astype('uint8')","def power(base, exponent):\n return base ** exponent","def getExponent(self):\n return _libsbml.Unit_getExponent(self)","def erfcinv(a):","def modExponent(self, base, power):\n result = 1\n power = int(power)\n base = base % self.mod\n while power > 0:\n if power & 1:\n # self.modReduce(result * base)\n result = result * base % self.mod\n base = base * base % self.mod # self.modReduce(base * base)\n power = power >> 1\n return result","def optimal_alpha():\n\n # When I checked all of alphas, -0.01 was the best\n alpha = -0.01\n # np.random.choice([-0.06, -0.01, 0.04, 0.1])\n return alpha","def alpha(self):\n if self._alpha is None:\n df = self.fdist\n alpha_ = -self._powerlaw(df.index, df.freq)\n self._alpha = alpha_\n\n # return\n return self._alpha","def check_alpha(a):\n\n a = check_1d(a, \"alpha\")\n if any(map(lambda d: d <= 0, a)):\n raise Exception('Alpha cannot be 0 or negative')\n\n return a","def Alpha(self):\r\n return self._alpha","def alpha(self):\r\n return self.unif[17]","def power(num, exponent):\n return num ** exponent","def comp_alpha(self):\n pass","def alpha(self):\n return tf.clip_by_value(tf.exp(self.log_alpha), *SCALE_ALPHA_MIN_MAX)","def exp(a: Decimal, b: Decimal) -> Decimal:\n return a ** b","def calculateDeCrypt(asci: int, d: int, n: int) -> int:\n return pow(int(asci),d,n)","def exponents(num1, num2):\n product = num1 ** num2\n return product","def alpha_0(r): # r in um\n r1 = r / (1e6) # meters\n epsilonr = 3.\n return 3. * epsilon0 * ((epsilonr - 1)/(epsilonr + 2)) * (4. * np.pi / 3.) * (r1 ** 3)","def _enc(x: int) -> float:\n return 2 + x + (29 / (x ** 2 + (1 - x) ** 2))","def idecibel(x):\n return 10.0 ** (x / 10.0)","def letter2num(letters, zbase=True):\n\n letters = letters.upper()\n res = 0\n weight = len(letters) - 1\n for i, ch in enumerate(letters):\n res += (ord(ch) - 64) * 26 ** (weight - i)\n if not zbase:\n return res\n return res - 1","def gamma_natural(A):\n return 2.6544188e-12*A","def letter_code(letter):\n value = ord(letter.lower()) - ord('a') + 10\n return value + value // 11","def fill_alpha(self) -> Number:\r\n from apysc.type import value_util\r\n self._initialize_fill_alpha_if_not_initialized()\r\n fill_alpha: Number = value_util.get_copy(value=self._fill_alpha)\r\n return fill_alpha","def expexp(x,y,z,p):\n\n \"\"\"\n Fermat's little theorem can be exploited to handle large values.\n This theorem states that:\n (a^p) is equivalent to (a mod p)\n This is the same as:\n (a^(p - 1)) is equivalent to (1 mod p)\n Thus, modular exponentiation can be done with (p - 1) to get\n (y^z mod (p - 1)), which is stored as b.\n For each test, the b values are:\n Test 1: b = 0\n Test 2: b = 4\n Test 3: b = 72\n Test 4: b = 72\n As shown, these values are much smaller to handle. Now, \n perform modular exponentiation again, this time with (p),\n to get (x^(y^z) mod p), store as a, and return.\n For each test, the a values are:\n Test 1: a = 1\n Test 2: a = 16\n Test 3: a = 1\n Test 4: a = 4\n Each return value matches the expected values in the test,\n therefore the algorithm is correct.\n \"\"\"\n b = pow(y, z, p - 1)\n a = pow(x, b, p)\n return a","def calculate_alpha(color, F, B):\n return np.dot(color - B, F - B) / np.dot(F - B, F - B)","def addExponent(self):\n\t\t# if the exponent part is not set and this number is allowed an exponent\n\t\tif(self.exponent == None and self.allowExponent):\n\t\t\t# set the exponent to another number (disallowing exponents since we can't\n\t\t\t# have an exponent with an exponent\n\t\t\tself.exponent = Number(allowExponent = False)","def _pvalue_alpha(self):\n return _handle_ab(self._pvalues_all, self.use_const)[0]","def letter_num(num: int):\n if abs(num) > 26 or num == 0:\n let = ord('a') + 26 - 1\n else:\n let = ord('a') + abs(num) - 1\n return chr(let)","def Es_case_E(z, x, gamma):\n \n if z == 0 and x == 0:\n return 0\n \n beta2 = 1-1/gamma**2\n beta = sqrt(beta2)\n \n L = (z + beta*sqrt(x**2*(1-beta2) + z**2))/(1-beta2)\n \n S = sqrt(x**2 + L**2)\n N1 = L - beta * S\n D = S-beta*L\n \n return N1/D**3","def exp(x):\n raise NotImplementedError","def power(num, exponent):\n power = num ** exponent\n return power","def fast_exp(a, x, n):\n x_2 = int2bin(x)\n vprint(\"{} = [{}]_2\".format(str(x), x_2))\n powers = [a % n]\n vprint(\"{}^(2^0) = {}^1 = {} \\\\equiv {}\".format(a, a, a, (a % n)))\n i = 1\n while i < len(x_2):\n # This (hilariously ugly) print statement prints the\n # intermediary operations in a format that can be easily\n # exported to LaTeX. TODO: Split it up into sane chunks.\n vprint(\"{}^{{ {}^{} }} = {}^{{ {} }} = {}^{{ {} }} * {}^{{ {} }} = {}*{} = {} \\\\equiv {}\".format(\n a, 2, i,\n a, pow(2, i),\n a, pow(2, i-1),\n a, pow(2, i-1),\n powers[-1], powers[-1],\n powers[-1] * powers[-1],\n (powers[-1] * powers[-1]) % n))\n next_power = (powers[-1] * powers[-1]) % n\n powers.append(next_power)\n i += 1\n\n vprint(\"{}^{{ {} }} = ...\".format(a, x))\n rpowers = list(reversed(powers))\n prod = 1\n i = 0\n while i < len(x_2):\n bit = x_2[i]\n power = rpowers[i]\n if bit == \"1\":\n vprint(\"* {} \\t== {}^{{ 2^{{ {} }} }}\\n\".format(power, a, len(x_2) - i - 1))\n prod *= power\n i += 1\n result = prod % n\n vprint(\"= {} \\\\equiv {}\".format(prod, result))\n return result","def alphahighz(self, z):\n return self.alphaMe(3.8,self.r_vect[0],self.alpha0_vect[0]) - 0.018*(z-3.8)","def alpha_evol( redshift, richness):\n\treturn function_eq10( redshift, richness, alpha_z[0], alpha_z[1], alpha_z[2] )","def Ernst_T1(TR, alpha_e):\n return -TR / np.log(np.cos(alpha_e))","def truncated_power(self, exponent, degs = None):\n a = exponent\n return self.truncated_fun(\n fun_der = lambda k, t: binom(a, k) * factorial(k) * t**(a-k),\n degs = degs\n )","def _get_a(b):\n\t\tbval = (b - 6.0)\n\t\t_a = np.exp(-1.6805 - 1.7139*bval + 0.8155*bval**2 - 0.6667*bval**3)*r0\n\t\treturn _a","def _get_alpha(self, m_t, v_t):\n return max(0, ((-m_t * self._psi \n + math.sqrt((m_t ** 2 * self._phi ** 4) \n / 4 + v_t * self._phi ** 2 * self._xi)) \n / (v_t * self._xi)))","def pvalue_alpha(self):\n return self._pvalue_alpha","def power(base, exp):\n\tans = [1]\n\twhile exp > 0:\n\t\tcarry = 0\n\t\tfor i in xrange(len(ans)):\n\t\t\tmult = ans[i] * base + carry\n\t\t\tans[i] = mult % 10\n\t\t\tcarry = mult / 10\n\t\twhile carry > 0:\n\t\t\tans.append(carry % 10)\n\t\t\tcarry /= 10\n\t\texp -= 1\n\treturn ans","def encode(num, alphabet=BASE62):\n if num == 0:\n return alphabet[0]\n arr = []\n base = len(alphabet)\n while num:\n num, rem = divmod(num, base)\n arr.append(alphabet[rem])\n arr.reverse()\n return ''.join(arr)","def exponential(gp_link=None):\r\n if gp_link is None:\r\n gp_link = noise_models.gp_transformations.Log_ex_1()\r\n\r\n analytical_mean = False\r\n analytical_variance = False\r\n return noise_models.exponential_noise.Exponential(gp_link,analytical_mean,analytical_variance)","def gamma_function(\n a: ArrayLike,\n exponent: ArrayLike = 1,\n negative_number_handling: Literal[\n \"Clamp\", \"Indeterminate\", \"Mirror\", \"Preserve\"\n ]\n | str = \"Indeterminate\",\n) -> NDArrayFloat:\n\n a = as_float_array(a)\n exponent = as_float_array(exponent)\n negative_number_handling = validate_method(\n negative_number_handling,\n (\"Indeterminate\", \"Mirror\", \"Preserve\", \"Clamp\"),\n '\"{0}\" negative number handling is invalid, it must be one of {1}!',\n )\n\n if negative_number_handling == \"indeterminate\":\n return as_float(a**exponent)\n elif negative_number_handling == \"mirror\":\n return spow(a, exponent)\n elif negative_number_handling == \"preserve\":\n return as_float(np.where(a <= 0, a, a**exponent))\n else: # negative_number_handling == 'clamp':\n return as_float(np.where(a <= 0, 0, a**exponent))","def phase_Earth(alpha):\n phase = 10.**(-0.4*(- 1.060e-3*alpha + 2.054e-4*alpha**2.))\n return phase","def modular_exponentiation(x, y, n):\r\n result = 1\r\n while y > 0:\r\n if y & 1 == 1:\r\n result = (result * x) % n\r\n\r\n y = y >> 1\r\n x = (x * x) % n\r\n return result","def __call__(self, epoch):\n decay = (1 - (epoch / float(self.maxEpochs))) ** self.power\n alpha = self.initAlpha * decay\n \n # return alpha\n return float(alpha)","def mod_exp(val, exp, modulus):\n return pow(int(val), int(exp), int(modulus))","def _calculate_norm_alpha(sr: int, hop_size: int, tau: float):\n dt = hop_size / sr\n return math.exp(-dt / tau)","def elevation_to_a_degree(numb1, numb2):\r\n return f\"Your result: {numb1**numb2}\"","def power(a, b):\n \n return a**b","def int_to_alpha(num):\n remainder = num\n text = []\n if num >= 26:\n major = remainder // 26\n text.append(ascii_lowercase[remainder // 26 - 1])\n remainder -= major * 26\n text.append(ascii_lowercase[remainder])\n return \"\".join(text)","def alpha_exact(z, x, beta):\n f = lambda a: a - beta/2 * sqrt(x**2 + 4*(1+x) * sin(a)**2 ) - z\n \n res = scipy.optimize.root_scalar(f, bracket=(-1,1))\n \n return res.root","def base_alphabet_to_10(letters):\r\n\r\n return sum(\r\n (ord(letter) - A_UPPERCASE + 1) * ALPHABET_SIZE ** i\r\n for i, letter in enumerate(reversed(letters.upper()))\r\n )","def __pow__(self, a: float) -> np.ndarray:\n return np.e**(a*self.logarithm)","def encryptAffine(letter, a, b):\n if(gcd(7, 26) != 1):\n return \"Error, not a bijection\"\n else:\n encrypted_letter = \"\"\n for i in range(len(letter)):\n if(ord(letter[i]) == 32):\n encrypted_letter += chr(ord(letter[i]))\n else:\n let = letter[i].lower()\n let = ord(let) - 97\n new_let = (((let* a) + b) % 26) + 97\n encrypted_letter += chr(new_let)\n return encrypted_letter"],"string":"[\n \"def calculate_exponent():\\n pass\",\n \"def powerlaw(E,alpha,A):\\n\\n\\treturn A*E**alpha\",\n \"def calc_alpha(epsilon): \\n return float(0.5 * np.log((1-epsilon)/epsilon))\",\n \"def encode_exponent(e: int) -> int:\\n assert 0 <= e <= MAX_EXPONENT\\n return DECODING_TABLE[e]\",\n \"def test_exp_decay(self, alpha: float):\\n x = np.linspace(0, 1, 100)\\n y = np.exp(alpha * x)\\n\\n alpha_guess = guess.exp_decay(x, y)\\n\\n self.assertAlmostEqualAbsolute(alpha_guess, alpha)\",\n \"def exp(self, num, zf=2):\\n return str(num).zfill(zf)\",\n \"def superobl_alpha(k):\\n if k%2==0:\\n return 2**(-(k - 1))*(1 - 1/(k + 1)**2)**(k/2)\\n else:\\n return 2**(-(k - 1))*(1 - 1/k**2)**((k - 1)/2)\",\n \"def schechter(l,alpha):\\n return exp(-l)*(l**alpha)\",\n \"def myExp(base,exponent,modulus):\\n result = 1\\n while exponent > 0:\\n if exponent & 1 == 1:\\n result = (result * base) % modulus\\n exponent = exponent >> 1\\n base = (base * base) % modulus\\n return result\",\n \"def correctalpha(desiredalpha, level):\\n \\n correctedalpha = 1 - (1 - desiredalpha) ** (1.0 / level)\\n \\n return correctedalpha\",\n \"def _alpha(self):\\n return _handle_ab(self.solution, self.use_const)[0]\",\n \"def power_normalize(xx, alpha = 0.5):\\r\\n\\treturn np.sign(xx) * np.abs(xx) ** alpha\",\n \"def calculateCrypt(asci: int, e: int, n: int) -> int:\\n return pow(int(asci),e,n)\",\n \"def get_ralpha(self, alpha, xalpha):\\n A = self.A\\n b = self.b\\n ralpha = dot(A, xalpha - b)\\n return ralpha\",\n \"def _se_alpha(self):\\n return _handle_ab(self._se_all, self.use_const)[0]\",\n \"def inverse_exponential(x):\\n return math.exp(-x)\",\n \"def negative_exponent():\\n print(\\\"Problem: Negative exponent\\\")\\n\\n a = float(input())\\n n = int(input())\\n\\n result = power(a, n)\\n print(result)\",\n \"def exponent(num,power=2):\\n return num ** power\",\n \"def decode_exponent(e: int) -> int:\\n assert 0 <= e <= MAX_EXPONENT\\n return ENCODING_TABLE[e]\",\n \"def rdf_deriv(alpha):\\n\\n ALPHA_MAX = 0.59999\\n\\n ALPHA0 = 0.6\\n\\n numpy.where(alpha > ALPHA_MAX, ALPHA_MAX, alpha)\\n\\n return -1.0/(3.0*ALPHA0)*(alpha/ALPHA0)**(-2.0/3.0)/(1.0-(alpha/ALPHA0)**(1.0/3.0))**2\",\n \"def alpha_number(alpha):\\r\\n if alpha.isupper() == False:\\r\\n num = ord(alpha) - 96\\r\\n return num\\r\\n elif alpha.isupper() == True:\\r\\n num = ord(alpha) - 64\\r\\n return num\",\n \"def exponential(value):\\n return math.exp(value)\",\n \"def reg(letter,number):\\n v=ord(letter.lower())-ord('a')\\n v=v<<10\\n v+=number\\n return v\",\n \"def exponent(self):\\n return self.__exponent\",\n \"def nalpha(self) -> int:\\n return self._core.nalpha()\",\n \"def dd_xpowalpha(cls,grid,alpha,cutoff=False):\\n grid.l.info('bc.hom: Setting initial data to (-x)^alpha.')\\n grid.l.debug('bc.hom: Parameters to dd_xpowalpha: alpha={},cutoff={}'.format(alpha,cutoff))\\n if alpha is 0:\\n def tmp(x): return float(x[1]<=0)\\n return cls._tpl(grid, tmp) \\n\\n if cutoff:\\n def tmp(x):\\n return sum(pow(-1*float(x[i]<0)*x[i],alpha) for i in range(0,grid.u.ndim))\\n else:\\n def tmp(x):\\n return sum(pow(float(x[i]>=0)*x[i],alpha)-pow(-1*float(x[i]<0)*x[i],alpha) for i in range(0,grid.u.ndim))\\n return cls._tpl(grid, tmp)\",\n \"def alpha_from_R(R):\\n return 1-np.abs(R)**2\",\n \"def se_alpha(self):\\n return self._se_alpha\",\n \"def powAlpha( n ):\\n return (1-betaval)*Fib(n) + Fib(n-1)\\n #return Fib(n+1) - Fib(n) * betaval\",\n \"def private_exponent(primes, e): \\n return pow(e, -1, math.lcm((primes[0]-1),(primes[1]-1)))\",\n \"def getExponent(number):\\n exponent = np.floor(np.log10(number))\\n return(exponent)\",\n \"def exponentiation():\\n print(\\\"Problem: Exponentiation\\\")\\n\\n a = float(input())\\n n = int(input())\\n\\n result = power_v2(a, n)\\n print(result)\",\n \"def calc_alpha_init(alpha, decay):\\n\\tif not decay or decay <= 0:\\n\\t\\treturn alpha\\n\\telse:\\n\\t\\treturn float(alpha * decay)\",\n \"def Exp(num):\\n return math.exp(float(num))\",\n \"def alpha(self):\\n return self._alpha\",\n \"def alpha(self):\\n return self._alpha\",\n \"def alpha(self):\\n return self._alpha\",\n \"def _get_alpha(cls, cl: float, tail: str):\\n alpha = (1 - cl) / 2 if tail == \\\"two\\\" else (1 - cl)\\n return round(alpha, 3)\",\n \"def getExponent(self):\\n return _libsbml.ASTNode_getExponent(self)\",\n \"def alpha(self) -> float:\\n return self._alpha\",\n \"def _g(self, x):\\n e_x = math.exp(-self._alpha * x)\\n return (1.0 / (1 + e_x))\",\n \"def get_alpha(self, error_rate, func='default'):\\n return 0.5 * np.log((1. - error_rate) / error_rate)\",\n \"def num_alpha(self) -> int:\\n return self._num_alpha\",\n \"def calculate_alphabeta(self, p, prec):\\n var('x')\\n sqrtdelta = None\\n try:\\n M = Qp(p, prec).extension(x ** 2 - self.constants.delta, names=\\\"padicroot\\\")\\n sqrtdelta = M.gen(0)\\n except NotImplementedError:\\n try:\\n M = Qp(p, prec)\\n sqrtdelta = M(self.constants.delta).sqrt()\\n except NotImplementedError:\\n # Exceptional case.\\n M = Qp(p, prec).extension(x ** 2 - self.constants.A * x - self.constants.B, names=\\\"padicroot\\\")\\n alpha = M.gen(0)\\n beta = self.constants.A - alpha\\n return (alpha, beta)\\n\\n alpha = (self.constants.A + sqrtdelta) / 2\\n beta = self.constants.A - alpha\\n return (alpha, beta)\",\n \"def test_check_digits_with_wrong_alphabet(self, _, alpha):\\n with self.assertRaises(exceptions.WrongArgumentValueError):\\n positional.encode(42, 10, alphabet=alpha)\",\n \"def exponential(image: np.ndarray) -> np.ndarray:\\n return np.power(image, 0.75).astype('uint8')\",\n \"def power(base, exponent):\\n return base ** exponent\",\n \"def getExponent(self):\\n return _libsbml.Unit_getExponent(self)\",\n \"def erfcinv(a):\",\n \"def modExponent(self, base, power):\\n result = 1\\n power = int(power)\\n base = base % self.mod\\n while power > 0:\\n if power & 1:\\n # self.modReduce(result * base)\\n result = result * base % self.mod\\n base = base * base % self.mod # self.modReduce(base * base)\\n power = power >> 1\\n return result\",\n \"def optimal_alpha():\\n\\n # When I checked all of alphas, -0.01 was the best\\n alpha = -0.01\\n # np.random.choice([-0.06, -0.01, 0.04, 0.1])\\n return alpha\",\n \"def alpha(self):\\n if self._alpha is None:\\n df = self.fdist\\n alpha_ = -self._powerlaw(df.index, df.freq)\\n self._alpha = alpha_\\n\\n # return\\n return self._alpha\",\n \"def check_alpha(a):\\n\\n a = check_1d(a, \\\"alpha\\\")\\n if any(map(lambda d: d <= 0, a)):\\n raise Exception('Alpha cannot be 0 or negative')\\n\\n return a\",\n \"def Alpha(self):\\r\\n return self._alpha\",\n \"def alpha(self):\\r\\n return self.unif[17]\",\n \"def power(num, exponent):\\n return num ** exponent\",\n \"def comp_alpha(self):\\n pass\",\n \"def alpha(self):\\n return tf.clip_by_value(tf.exp(self.log_alpha), *SCALE_ALPHA_MIN_MAX)\",\n \"def exp(a: Decimal, b: Decimal) -> Decimal:\\n return a ** b\",\n \"def calculateDeCrypt(asci: int, d: int, n: int) -> int:\\n return pow(int(asci),d,n)\",\n \"def exponents(num1, num2):\\n product = num1 ** num2\\n return product\",\n \"def alpha_0(r): # r in um\\n r1 = r / (1e6) # meters\\n epsilonr = 3.\\n return 3. * epsilon0 * ((epsilonr - 1)/(epsilonr + 2)) * (4. * np.pi / 3.) * (r1 ** 3)\",\n \"def _enc(x: int) -> float:\\n return 2 + x + (29 / (x ** 2 + (1 - x) ** 2))\",\n \"def idecibel(x):\\n return 10.0 ** (x / 10.0)\",\n \"def letter2num(letters, zbase=True):\\n\\n letters = letters.upper()\\n res = 0\\n weight = len(letters) - 1\\n for i, ch in enumerate(letters):\\n res += (ord(ch) - 64) * 26 ** (weight - i)\\n if not zbase:\\n return res\\n return res - 1\",\n \"def gamma_natural(A):\\n return 2.6544188e-12*A\",\n \"def letter_code(letter):\\n value = ord(letter.lower()) - ord('a') + 10\\n return value + value // 11\",\n \"def fill_alpha(self) -> Number:\\r\\n from apysc.type import value_util\\r\\n self._initialize_fill_alpha_if_not_initialized()\\r\\n fill_alpha: Number = value_util.get_copy(value=self._fill_alpha)\\r\\n return fill_alpha\",\n \"def expexp(x,y,z,p):\\n\\n \\\"\\\"\\\"\\n Fermat's little theorem can be exploited to handle large values.\\n This theorem states that:\\n (a^p) is equivalent to (a mod p)\\n This is the same as:\\n (a^(p - 1)) is equivalent to (1 mod p)\\n Thus, modular exponentiation can be done with (p - 1) to get\\n (y^z mod (p - 1)), which is stored as b.\\n For each test, the b values are:\\n Test 1: b = 0\\n Test 2: b = 4\\n Test 3: b = 72\\n Test 4: b = 72\\n As shown, these values are much smaller to handle. Now, \\n perform modular exponentiation again, this time with (p),\\n to get (x^(y^z) mod p), store as a, and return.\\n For each test, the a values are:\\n Test 1: a = 1\\n Test 2: a = 16\\n Test 3: a = 1\\n Test 4: a = 4\\n Each return value matches the expected values in the test,\\n therefore the algorithm is correct.\\n \\\"\\\"\\\"\\n b = pow(y, z, p - 1)\\n a = pow(x, b, p)\\n return a\",\n \"def calculate_alpha(color, F, B):\\n return np.dot(color - B, F - B) / np.dot(F - B, F - B)\",\n \"def addExponent(self):\\n\\t\\t# if the exponent part is not set and this number is allowed an exponent\\n\\t\\tif(self.exponent == None and self.allowExponent):\\n\\t\\t\\t# set the exponent to another number (disallowing exponents since we can't\\n\\t\\t\\t# have an exponent with an exponent\\n\\t\\t\\tself.exponent = Number(allowExponent = False)\",\n \"def _pvalue_alpha(self):\\n return _handle_ab(self._pvalues_all, self.use_const)[0]\",\n \"def letter_num(num: int):\\n if abs(num) > 26 or num == 0:\\n let = ord('a') + 26 - 1\\n else:\\n let = ord('a') + abs(num) - 1\\n return chr(let)\",\n \"def Es_case_E(z, x, gamma):\\n \\n if z == 0 and x == 0:\\n return 0\\n \\n beta2 = 1-1/gamma**2\\n beta = sqrt(beta2)\\n \\n L = (z + beta*sqrt(x**2*(1-beta2) + z**2))/(1-beta2)\\n \\n S = sqrt(x**2 + L**2)\\n N1 = L - beta * S\\n D = S-beta*L\\n \\n return N1/D**3\",\n \"def exp(x):\\n raise NotImplementedError\",\n \"def power(num, exponent):\\n power = num ** exponent\\n return power\",\n \"def fast_exp(a, x, n):\\n x_2 = int2bin(x)\\n vprint(\\\"{} = [{}]_2\\\".format(str(x), x_2))\\n powers = [a % n]\\n vprint(\\\"{}^(2^0) = {}^1 = {} \\\\\\\\equiv {}\\\".format(a, a, a, (a % n)))\\n i = 1\\n while i < len(x_2):\\n # This (hilariously ugly) print statement prints the\\n # intermediary operations in a format that can be easily\\n # exported to LaTeX. TODO: Split it up into sane chunks.\\n vprint(\\\"{}^{{ {}^{} }} = {}^{{ {} }} = {}^{{ {} }} * {}^{{ {} }} = {}*{} = {} \\\\\\\\equiv {}\\\".format(\\n a, 2, i,\\n a, pow(2, i),\\n a, pow(2, i-1),\\n a, pow(2, i-1),\\n powers[-1], powers[-1],\\n powers[-1] * powers[-1],\\n (powers[-1] * powers[-1]) % n))\\n next_power = (powers[-1] * powers[-1]) % n\\n powers.append(next_power)\\n i += 1\\n\\n vprint(\\\"{}^{{ {} }} = ...\\\".format(a, x))\\n rpowers = list(reversed(powers))\\n prod = 1\\n i = 0\\n while i < len(x_2):\\n bit = x_2[i]\\n power = rpowers[i]\\n if bit == \\\"1\\\":\\n vprint(\\\"* {} \\\\t== {}^{{ 2^{{ {} }} }}\\\\n\\\".format(power, a, len(x_2) - i - 1))\\n prod *= power\\n i += 1\\n result = prod % n\\n vprint(\\\"= {} \\\\\\\\equiv {}\\\".format(prod, result))\\n return result\",\n \"def alphahighz(self, z):\\n return self.alphaMe(3.8,self.r_vect[0],self.alpha0_vect[0]) - 0.018*(z-3.8)\",\n \"def alpha_evol( redshift, richness):\\n\\treturn function_eq10( redshift, richness, alpha_z[0], alpha_z[1], alpha_z[2] )\",\n \"def Ernst_T1(TR, alpha_e):\\n return -TR / np.log(np.cos(alpha_e))\",\n \"def truncated_power(self, exponent, degs = None):\\n a = exponent\\n return self.truncated_fun(\\n fun_der = lambda k, t: binom(a, k) * factorial(k) * t**(a-k),\\n degs = degs\\n )\",\n \"def _get_a(b):\\n\\t\\tbval = (b - 6.0)\\n\\t\\t_a = np.exp(-1.6805 - 1.7139*bval + 0.8155*bval**2 - 0.6667*bval**3)*r0\\n\\t\\treturn _a\",\n \"def _get_alpha(self, m_t, v_t):\\n return max(0, ((-m_t * self._psi \\n + math.sqrt((m_t ** 2 * self._phi ** 4) \\n / 4 + v_t * self._phi ** 2 * self._xi)) \\n / (v_t * self._xi)))\",\n \"def pvalue_alpha(self):\\n return self._pvalue_alpha\",\n \"def power(base, exp):\\n\\tans = [1]\\n\\twhile exp > 0:\\n\\t\\tcarry = 0\\n\\t\\tfor i in xrange(len(ans)):\\n\\t\\t\\tmult = ans[i] * base + carry\\n\\t\\t\\tans[i] = mult % 10\\n\\t\\t\\tcarry = mult / 10\\n\\t\\twhile carry > 0:\\n\\t\\t\\tans.append(carry % 10)\\n\\t\\t\\tcarry /= 10\\n\\t\\texp -= 1\\n\\treturn ans\",\n \"def encode(num, alphabet=BASE62):\\n if num == 0:\\n return alphabet[0]\\n arr = []\\n base = len(alphabet)\\n while num:\\n num, rem = divmod(num, base)\\n arr.append(alphabet[rem])\\n arr.reverse()\\n return ''.join(arr)\",\n \"def exponential(gp_link=None):\\r\\n if gp_link is None:\\r\\n gp_link = noise_models.gp_transformations.Log_ex_1()\\r\\n\\r\\n analytical_mean = False\\r\\n analytical_variance = False\\r\\n return noise_models.exponential_noise.Exponential(gp_link,analytical_mean,analytical_variance)\",\n \"def gamma_function(\\n a: ArrayLike,\\n exponent: ArrayLike = 1,\\n negative_number_handling: Literal[\\n \\\"Clamp\\\", \\\"Indeterminate\\\", \\\"Mirror\\\", \\\"Preserve\\\"\\n ]\\n | str = \\\"Indeterminate\\\",\\n) -> NDArrayFloat:\\n\\n a = as_float_array(a)\\n exponent = as_float_array(exponent)\\n negative_number_handling = validate_method(\\n negative_number_handling,\\n (\\\"Indeterminate\\\", \\\"Mirror\\\", \\\"Preserve\\\", \\\"Clamp\\\"),\\n '\\\"{0}\\\" negative number handling is invalid, it must be one of {1}!',\\n )\\n\\n if negative_number_handling == \\\"indeterminate\\\":\\n return as_float(a**exponent)\\n elif negative_number_handling == \\\"mirror\\\":\\n return spow(a, exponent)\\n elif negative_number_handling == \\\"preserve\\\":\\n return as_float(np.where(a <= 0, a, a**exponent))\\n else: # negative_number_handling == 'clamp':\\n return as_float(np.where(a <= 0, 0, a**exponent))\",\n \"def phase_Earth(alpha):\\n phase = 10.**(-0.4*(- 1.060e-3*alpha + 2.054e-4*alpha**2.))\\n return phase\",\n \"def modular_exponentiation(x, y, n):\\r\\n result = 1\\r\\n while y > 0:\\r\\n if y & 1 == 1:\\r\\n result = (result * x) % n\\r\\n\\r\\n y = y >> 1\\r\\n x = (x * x) % n\\r\\n return result\",\n \"def __call__(self, epoch):\\n decay = (1 - (epoch / float(self.maxEpochs))) ** self.power\\n alpha = self.initAlpha * decay\\n \\n # return alpha\\n return float(alpha)\",\n \"def mod_exp(val, exp, modulus):\\n return pow(int(val), int(exp), int(modulus))\",\n \"def _calculate_norm_alpha(sr: int, hop_size: int, tau: float):\\n dt = hop_size / sr\\n return math.exp(-dt / tau)\",\n \"def elevation_to_a_degree(numb1, numb2):\\r\\n return f\\\"Your result: {numb1**numb2}\\\"\",\n \"def power(a, b):\\n \\n return a**b\",\n \"def int_to_alpha(num):\\n remainder = num\\n text = []\\n if num >= 26:\\n major = remainder // 26\\n text.append(ascii_lowercase[remainder // 26 - 1])\\n remainder -= major * 26\\n text.append(ascii_lowercase[remainder])\\n return \\\"\\\".join(text)\",\n \"def alpha_exact(z, x, beta):\\n f = lambda a: a - beta/2 * sqrt(x**2 + 4*(1+x) * sin(a)**2 ) - z\\n \\n res = scipy.optimize.root_scalar(f, bracket=(-1,1))\\n \\n return res.root\",\n \"def base_alphabet_to_10(letters):\\r\\n\\r\\n return sum(\\r\\n (ord(letter) - A_UPPERCASE + 1) * ALPHABET_SIZE ** i\\r\\n for i, letter in enumerate(reversed(letters.upper()))\\r\\n )\",\n \"def __pow__(self, a: float) -> np.ndarray:\\n return np.e**(a*self.logarithm)\",\n \"def encryptAffine(letter, a, b):\\n if(gcd(7, 26) != 1):\\n return \\\"Error, not a bijection\\\"\\n else:\\n encrypted_letter = \\\"\\\"\\n for i in range(len(letter)):\\n if(ord(letter[i]) == 32):\\n encrypted_letter += chr(ord(letter[i]))\\n else:\\n let = letter[i].lower()\\n let = ord(let) - 97\\n new_let = (((let* a) + b) % 26) + 97\\n encrypted_letter += chr(new_let)\\n return encrypted_letter\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71915054","0.6592303","0.64282143","0.6347429","0.63466847","0.63178813","0.63117254","0.6303169","0.6258958","0.62512463","0.6246484","0.62283045","0.62211376","0.62090594","0.61820316","0.6104411","0.61022353","0.608179","0.60706884","0.60041946","0.59448314","0.5888297","0.58719623","0.5861893","0.5858459","0.5854756","0.5851482","0.58491886","0.58405995","0.5835575","0.58299726","0.58090067","0.5783786","0.578117","0.5771549","0.5771549","0.5771549","0.57703805","0.5764625","0.57486844","0.570689","0.57017004","0.5681861","0.5679434","0.5675041","0.5666225","0.5664334","0.56580395","0.56492186","0.56377244","0.5591701","0.55829555","0.55766803","0.55760914","0.55748713","0.5574117","0.55592847","0.55543405","0.5543285","0.55401224","0.5538213","0.55374604","0.55361956","0.55235755","0.5522784","0.5504261","0.5486094","0.5484698","0.5484379","0.5480815","0.54704595","0.5446941","0.543953","0.54390705","0.54312414","0.54311013","0.54097974","0.5408075","0.5407888","0.54076636","0.54063493","0.5399118","0.53974146","0.53936756","0.5382479","0.5373985","0.5368054","0.5358558","0.5357504","0.5356759","0.53524786","0.53478545","0.53436005","0.53248584","0.53226775","0.53216183","0.5316805","0.5316458","0.53039277","0.5301133"],"string":"[\n \"0.71915054\",\n \"0.6592303\",\n \"0.64282143\",\n \"0.6347429\",\n \"0.63466847\",\n \"0.63178813\",\n \"0.63117254\",\n \"0.6303169\",\n \"0.6258958\",\n \"0.62512463\",\n \"0.6246484\",\n \"0.62283045\",\n \"0.62211376\",\n \"0.62090594\",\n \"0.61820316\",\n \"0.6104411\",\n \"0.61022353\",\n \"0.608179\",\n \"0.60706884\",\n \"0.60041946\",\n \"0.59448314\",\n \"0.5888297\",\n \"0.58719623\",\n \"0.5861893\",\n \"0.5858459\",\n \"0.5854756\",\n \"0.5851482\",\n \"0.58491886\",\n \"0.58405995\",\n \"0.5835575\",\n \"0.58299726\",\n \"0.58090067\",\n \"0.5783786\",\n \"0.578117\",\n \"0.5771549\",\n \"0.5771549\",\n \"0.5771549\",\n \"0.57703805\",\n \"0.5764625\",\n \"0.57486844\",\n \"0.570689\",\n \"0.57017004\",\n \"0.5681861\",\n \"0.5679434\",\n \"0.5675041\",\n \"0.5666225\",\n \"0.5664334\",\n \"0.56580395\",\n \"0.56492186\",\n \"0.56377244\",\n \"0.5591701\",\n \"0.55829555\",\n \"0.55766803\",\n \"0.55760914\",\n \"0.55748713\",\n \"0.5574117\",\n \"0.55592847\",\n \"0.55543405\",\n \"0.5543285\",\n \"0.55401224\",\n \"0.5538213\",\n \"0.55374604\",\n \"0.55361956\",\n \"0.55235755\",\n \"0.5522784\",\n \"0.5504261\",\n \"0.5486094\",\n \"0.5484698\",\n \"0.5484379\",\n \"0.5480815\",\n \"0.54704595\",\n \"0.5446941\",\n \"0.543953\",\n \"0.54390705\",\n \"0.54312414\",\n \"0.54311013\",\n \"0.54097974\",\n \"0.5408075\",\n \"0.5407888\",\n \"0.54076636\",\n \"0.54063493\",\n \"0.5399118\",\n \"0.53974146\",\n \"0.53936756\",\n \"0.5382479\",\n \"0.5373985\",\n \"0.5368054\",\n \"0.5358558\",\n \"0.5357504\",\n \"0.5356759\",\n \"0.53524786\",\n \"0.53478545\",\n \"0.53436005\",\n \"0.53248584\",\n \"0.53226775\",\n \"0.53216183\",\n \"0.5316805\",\n \"0.5316458\",\n \"0.53039277\",\n \"0.5301133\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.56222945"},"document_rank":{"kind":"string","value":"50"}}},{"rowIdx":8494,"cells":{"query":{"kind":"string","value":"Given a file name for baby.html, returns a list starting with the year string followed by the namerank strings in alphabetical order. ['2006', 'Aaliyah 91', Aaron 57', 'Abagail 895', ' ...]"},"document":{"kind":"string","value":"def extract_names(filename):\n f = open(filename,'rU') \n name_data = f.read()\n year_data= re.search(r'Popularity\\sin\\s(\\d\\d\\d\\d)', name_data)\n if not year_data :\n print ' no year found '\n sys.exit(1)\n name_year=year_data.group(1) \n #print 'year :'\n #print name_year\n tuples=re.findall(r'<td>(\\d+)</td><td>(\\w+)</td><td>(\\w+)</td>',name_data)\n #print 'tuples'\n #print tuples\n dict_name = {}\n for a,b,c in tuples :\n #print a + ' boy name: ' + b + ' , girl name : ' + c\n if b not in dict_name :\n dict_name[b] = a\n if c not in dict_name :\n dict_name[c] = a \n #print dict_name \n lst_names = sorted(dict_name.keys()) \n result_names_sorted = []\n result_names_sorted.append(name_year)\n for name in lst_names :\n #print name + \" : \" + dict_name[name]\n result_names_sorted.append(name + ' ' + dict_name[name])\n #print result_names_sorted \n\n return result_names_sorted"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def extract_names(filename):\n\n # Extracting the year\n year_match = re.search(r'\\d\\d\\d\\d', filename)\n if not year_match:\n sys.stderr.write('Could not find a year!\\n')\n sys.exit()\n year = year_match.group()\n\n # Opening the file\n try:\n with open(filename) as file:\n data = file.read()\n except FileNotFoundError:\n sys.stderr.write('There is no such file in the directory!\\n')\n sys.exit()\n\n # Finding patterns\n regex = re.compile(r'<td>\\w+')\n names = regex.findall(data)\n for i in range(len(names)):\n names[i] = names[i].replace('<td>', '')\n\n # Creating a dictionary with names data\n names_dict = {}\n for i in range(0, len(names) - 2, 3):\n key = names[i]\n names_dict[key] = [names[i + 1], names[i + 2]]\n\n # Creating a list with result\n boy_names = []\n girl_names = []\n result = [year]\n for key, value in names_dict.items():\n if value[0] not in boy_names:\n result.append(value[0] + ' ' + key)\n boy_names.append(value[0])\n if value[1] not in girl_names:\n result.append(value[1] + ' ' + key)\n girl_names.append(value[1])\n\n result.sort()\n # result.insert(0, year)\n\n return result","def name_extractor(file):\n \n import os\n import re\n \n name_list = []\n rank_dict = {}\n \n year = re.search(r'(\\d+)\\.html$', file) \n current = open(file) \n match = re.findall(r'<tr\\salign=\"right\"><td>(\\d+).*?>(\\w+).*?>(\\w+)', current.read())\n current.close\n\n \n for one_touple in match: #Check for existing match, only accept lower rank value into dictionary\n \n for index in range(1,2):\n \n if one_touple[index] in rank_dict:\n if rank_dict[one_touple[index]] < one_touple[0]:\n continue\n rank_dict[one_touple[index]] = one_touple[0]\n \n for one_item in rank_dict:\n \n ranking = rank_dict[one_item] #Build target list from dictionary formatted as \"Name rank\"\n name_list.append(f\"{one_item} {ranking}\") \n \n name_list = sorted(name_list)\n name_list.insert(0,year.group(1))\n \n return name_list","def extract_names(filename):\n raw_text = read_html(filename) \n \n #searching for the year\n year = re.search('(<h3 align=\"center\">Popularity in )(\\d\\d\\d\\d)',raw_text).group(2)\n \n #searching for the list of names\n list_of_names = re.findall('<td>(\\d+)</td><td>(\\w+)</td><td>(\\w+)</td>',raw_text)\n \n #pair each name with it's rank\n name_and_rank = [] \n for line in list_of_names:\n name_and_rank.append((line[1], line[0]))\n name_and_rank.append((line[2], line[0]))\n \n # sort the list alphabetically\n name_and_rank = sorted(name_and_rank, key = lambda x:x[0])\n name_and_rank = dict(name_and_rank)\n\n return year, name_and_rank[:20]","def getFileNames():\n input_path = \"/Users/tim/OneDrive/Master/Text_Mining/project/texts/glenarvon_html/\"\n temp_list = os.listdir(input_path)\n name_list = [i for i in temp_list if i[-4:] == \"html\"]\n name_list.sort(key=natural_keys) # see http://stackoverflow.com/questions/5967500/how-to-correctly-sort-a-string-with-a-number-inside\n return name_list, input_path","def extract_names(filename):\n # +++your code here+++\n f = open(filename, 'r')\n fl = read_file(filename)\n\n l = []\n lFiltFinal = []\n\n year_match = re.search(r'Popularity\\sin\\s(\\d\\d\\d\\d)', f.read())\n year = year_match.group(1)\n\n for line in fl:\n #if '<h3 align=\"center\">Popularity in' in line:\n #year = line[-10:-6]\n if '<tr align=\"right\"><td>' in line:\n rank = line[line.find('<td>')+len('<td>'):line.find('</td>')]\n boys = line[line.index('</td><td>')+len('</td><td>'):line.index('</td><td>',line.index('</td><td>')+1)]\n girls = line[line.index('</td><td>',line.index('</td><td>')+1)+len('</td><td>'):-6]\n l.append([boys,rank])\n l.append([girls,rank])\n\n lFilt = list(unique_by_first_n(1, l))\n\n lFiltFinal.append(year)\n for key in lFilt:\n lFiltFinal.append( key[0] + ' ' + key[1])\n\n lFiltFinal.sort()\n return lFiltFinal","def extract_names(filename):\n # +++your code here+++\n # Opening the file\n f = open(filename, 'rU')\n # Reading all of the lines\n lines = f.readlines()\n # Empty list to hold the year, names, and ranks\n ranks_names = []\n for line in lines:\n # search for the year\n year = re.search(r'\\s(\\d\\d\\d\\d)</h3>', line)\n # if the year is found, append it to the list\n if year: \n ranks_names.append(year.group(1))\n # search for the rank, male name, and female name\n rank_male_female = re.search(r'(\\d+)</td><td>(\\w+)</td><td>(\\w+)</td>', line)\n # If they are found then append the male name plus its rank, as well as the \n # female name plus its rank\n if rank_male_female:\n ranks_names.append(rank_male_female.group(2) + ' ' + rank_male_female.group(1))\n ranks_names.append(rank_male_female.group(3) + ' ' + rank_male_female.group(1))\n # Sort the list alphabetically\n ranks_names.sort()\n # Return the list\n return ranks_names","def extract_names(filename):\n f = open(filename, 'rU')\n file_text = f.read()\n f.close()\n \n year = re.search(r'Popularity in \\d\\d\\d\\d', file_text)\n if year:\n year = year.group()[-4:]\n else:\n print 'ERROR: year not found in ' + filename\n \n html_rank_names = re.findall(r'<tr align=\"right\"><td>\\d+</td><td>\\w+</td><td>\\w+</td>', file_text)\n name_ranks = {}\n i = 0\n while i < len(html_rank_names):\n line = html_rank_names[i]\n first_tag = line.find('<td>')\n first_end_tag = line.find('</td>')\n rank = line[first_tag + 4 : first_end_tag]\n \n second_tag = first_end_tag + 9\n second_end_tag = line.find('</td>', second_tag)\n name1 = line[second_tag : second_end_tag]\n \n third_tag = second_end_tag + 9\n third_end_tag = len(line) - 5\n name2 = line[third_tag : third_end_tag]\n \n # if the names already are in the dict, skip them because they have a larger number than what is already in the dict\n if name1 not in name_ranks: name_ranks[name1] = rank\n if name2 not in name_ranks: name_ranks[name2] = rank\n i = i + 1\n \n year_name_ranks = []\n year_name_ranks.append(year)\n for name, rank in name_ranks.iteritems():\n year_name_ranks.append(name + ' ' + rank)\n year_name_ranks.sort()\n return year_name_ranks","def name_list(file_name):\n \n li = open(file_name)\n list_of_names = []\n\n for name in li:\n (first,last) = str.split(name,' ')\n list_of_names.append(Name(first,last))\n return list_of_names","def parse_year(txt):\n\n txt = txt.strip()\n if \"-\" in txt:\n res = re.sub('[^0-9]', '', txt)\n return [res[0:4], res[4:8]]\n else:\n return [txt, txt]","def get_imdb_list():\n list_file = 'imdb.txt'\n name_column = 26\n f = open(list_file, 'r')\n film_list = []\n pos = 0\n\n for line in f:\n pos += 1\n words = line.split()\n name = line[name_column:-1]\n # could be problematic is there are brackets in the film name\n year = name[name.find('(') + 1:name.find(')')]\n name = name.replace('(' + year + ')', '')\n film = {\n 'pos': pos,\n 'score': Decimal(words[2]),\n 'name': name.strip(),\n 'year': year\n }\n film_list.append(film)\n f.close()\n return film_list","def list_names(text):\n with open(text, 'r') as names:\n list_of_names = list(names)\n name_list = [name.strip() for name in list_of_names]\n return name_list","def getYears():\n url = \"http://www.boxofficemojo.com/weekend/\"\n src = urllib.request.urlopen(url).read()\n soup = BeautifulSoup(src, 'html.parser')\n year_header = soup.find_all(name = \"b\")[1]\n year_elems = year_header.find_all([\"a\", \"font\"])\n years = [int(year.get_text()) for year in year_elems]\n return years","def year_tracker(words):\n new_words = []\n for w in words:\n new_word = re.sub(r\"^[1][789][0-9]{2}$\", \"jahreszahl\", w) # for 1700-1999\n new_word = re.sub(r\"^[2][01][0-9]{2}$\", \"jahreszahl\", new_word) # for 2000-2199\n new_words += [new_word]\n return new_words","def getFile(filename):\n f = open(filename)\n lines = f.readlines()\n f.close()\n names = []\n names.append(\"\")\n for name in lines[0].split(','):\n \n names.append(name[1:-1])\n names = sorted(names)\n return names","def load_names(file_name: str) -> List[str]:\n full_path_name = os.path.join(os.getcwd(), 'names', file_name)\n with open(full_path_name, 'r') as file:\n names = file.read().rstrip('\\n').split(',')\n return sorted(names)","def get_names(url):\n\t# get html element tree\n\ttree = get_tree(url)\n\t# Names are text within <a> elements in this list\n\t# xpath returns a list with alternating last and first names as elements\n\t# Concatenate each last name and first name pair and put in new list as full name\n\tnames = tree.xpath('//*[@id=\"research-teachinglist\"]/li//a//text()')\n\tfull_names = []\n\tfor i in range(0, len(names)-1, 2):\n\t\tfull_names.append(names[i] + names[i+1])\n\n\treturn full_names","def get_filenames(start_year = 1980, end_year = 2009, path = 'ucr_offenses_known_monthly_1960_2016_dta/'):\n filenames = []\n for filename in os.listdir(path):\n #ignore pdf files in folder, filter out dta file names that contain 1980-2009\n if filename.endswith('.dta'):\n for years in range(start_year, end_year + 1):\n if str(years) in filename:\n filenames.append(filename)\n return(filenames)","def get_surnames(filename):\n result = []\n with open(filename, \"r\") as file:\n for line in file.readlines():\n surname = line.split('\\t')[1]\n result.append(surname)\n return result","def filter_years(text):\n months = ['january', 'february', 'march', 'april', 'may', 'june',\n 'july', 'august', 'september', 'october', 'november', 'december']\n prepositions = ['around', 'after', 'at', 'as',\n 'approximately', 'before', 'between', 'by',\n 'during', 'from', 'in', 'near', 'past',\n 'since', 'until', 'within'] # removed: about, on\n conjugations = ['and']\n articles = ['the']\n times = ['early', 'mid', 'late']\n patterns = months + prepositions + conjugations + articles + times\n re_string = r'\\b(' + '|'.join(patterns) + r')\\b(\\s|-)\\b([0-9]{3,4})s?\\b(?i)(?!\\sMYA)\\s?(BCE|BC)?'\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\s?(BCE|BC)?'\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\n for match in re.finditer(re_string, text, re.IGNORECASE)]\n years += centuries\n years = [y for y in years if y<Dump.MAX_YEAR]\n return sorted(years + centuries)","def getnames(f):\n # Assumes file is sorted with girl names first, boy names second, and the\n # most popular name at the top of each list.\n\n lineoftext = f.readline()\n girlname,sex,count = processline(lineoftext)\n\n while sex != \"M\":\n name,sex,count = processline(f.readline())\n boyname=name\n\n return girlname,boyname","def get_team_names(self, year1, year2):\n\n base_url = 'http://www.sports-reference.com/cbb/schools/'\n response = urllib2.urlopen(base_url)\n content = response.read()\n soup = BeautifulSoup(content, 'html.parser')\n soup_results = soup.findAll('tr', {'class':''})\n extract_name = lambda name: name.split('/')[3]\n team_names = []\n \n for result in soup_results[1::]:\n year_span = result.findAll('td', {'align':'center'}) \n year_span = map(int, [year.string for year in year_span])\n\n if year_span[0] <= year1 and year_span[1] >= year2:\n team_name = result.find('a', href = True).get('href')\n team_name = extract_name(team_name)\n team_names.append(str(team_name))\n\n self.team_names = team_names","def get_titles(filename):\n\n with open(filename) as f:\n reader = csv.DictReader(f, fieldnames = ['title', 'year'])\n titles = list(reader)\n return titles","def extract_names(elements, year):\r\n milers = []\r\n groupings = _miler_grouper(elements)\r\n # import pdb; pdb.set_trace()\r\n for miler in groupings:\r\n person = {'last_name': miler[0],\r\n 'first_name': miler[1],\r\n 'trail_name': miler[2],\r\n 'year': year}\r\n milers.append(person)\r\n\r\n return milers","def baby_search_engine(name):\r\n \r\n name_ranking = []\r\n \r\n for publication_name, name_list in baby_names.items():\r\n publication = {}\r\n if name.capitalize() in name_list:\r\n publication['list'] = publication_name\r\n publication['rank'] = name_list.index(name.capitalize()) + 1\r\n name_ranking.append(publication)\r\n\r\n \r\n return sorted(name_ranking, key=lambda k: k['rank'])","def get_names(path='names.txt'):\n if not os.path.exists(path):\n logging.info('data not found, downloading.')\n url = 'https://www.sec.gov/rules/other/4-460list.htm'\n r = requests.get(url)\n tree = html.fromstring(r.content)\n names = tree.xpath('//tr/td[2]')\n names = [name.text.strip()\n for name in names if name.text is not None]\n\n url = 'http://www.nasdaq.com/screening/companies-by-industry.aspx?industry=Technology&render=download'\n r = requests.get(url)\n newnames = [line.strip().split(',')[1].replace('\"', '')\n for line in r.text.split('\\n')[:-1]]\n names.extend(newnames)\n with open(path, 'w') as txtfile:\n txtfile.write('\\n'.join(names))\n else:\n with open(path, 'r') as txtfile:\n names = txtfile.read().split('\\n')\n return names","def get_dates_list() -> List[str]:\n dates = listdir(\"hansard_gathering/processed_hansard_data\")\n return sorted([_file for _file in dates if not _file.endswith(\"_num\")])","def process_file(file_name):\n f = open(\"babynames/\" + file_name, 'r')\n reader = csv.reader(f)\n return list(reader)","def get_oscars_best_picture_list():\n list_file = 'oscar_best_picture_list.txt'\n f = open(list_file, 'r')\n film_list = []\n\n for line in f:\n words = line.split('-')\n film = {\n 'year': words[0][:-1],\n 'name': words[1][2:-2]\n }\n film_list.append(film)\n f.close()\n # Reverse as we want newest first not last\n film_list.reverse()\n return film_list","def get_names(filename):\n\n with open(filename, \"r\") as readFile:\n names = [each.split(',') for each in readFile] #Split at the commas\n names = list(names[0]) #Retrieves the list of names within the list names\n names = [each.strip(\"\\\"\") for each in names] #Strips each name of the double quotes\n return names","def extract(fichier):\r\n names = []\r\n for line in open(fichier, 'r'):\r\n for almost_name in line.split(','):\r\n names.append(almost_name[1:len(almost_name)-1]) \r\n return [i for i in names if i != '']","def get_html_files_for_candidates(f_name):\n candidates = ['Joe Biden','Kamala Harris','Elizabeth Warren','Bernie Sanders']\n candidates_files = ['{}.html'.format(candidate) for candidate in candidates]\n results = []\n files = sorted([os.path.join(f_name, f) for f in os.listdir(f_name) if os.path.isfile(os.path.join(f_name, f))]) #extra check if is file unnecessary\n for file in files:\n for c in candidates_files:\n if c in file:\n results.append(file)\n print(results)\n return results","def get_people(filename):\n\n lines = [line.rstrip('\\n').rstrip('\\r') for line in open(filename)]\n return lines","def read_file(file_name):\n with open(file_name) as f:\n content = f.readlines()\n names = []\n dnas = []\n dna = \"\"\n name = \"\"\n for line in content:\n line = line.strip()\n if line[0] == \">\":\n names.append(name)\n dnas.append(dna)\n name = line[1:]\n dna = \"\"\n else:\n dna += line\n names.append(name)\n dnas.append(dna)\n\n return (names[1:], dnas[1:])","def get_bfi_list():\n list_file = 'bfi_sight_and_sound_2012.txt'\n f = open(list_file, 'r')\n film_list = []\n\n for line in f:\n words = line.split(' ')\n #NOTE: pos is not the position in the pyton list but in the original\n # list so is not always an integer due to joint places\n film = {'pos': words[0], 'name': words[1][:-1]}\n film_list.append(film)\n f.close()\n return film_list","def read_all_names(start,end,gender,datadir):\r\n names = []\r\n name_dict = {}\r\n\r\n for year in range(start,end+1):\r\n year_names = []\r\n year = str(year)\r\n try:\r\n path = r\"{0}/yob{1}.txt\".format(datadir,year)\r\n f = open(path)\r\n reader = csv.reader(f)\r\n rank = 1\r\n for name in reader:\r\n if name[1].upper() == gender.upper():\r\n #first time name appears\r\n if name[0] not in name_dict:\r\n name_dict[name[0]] = {}\r\n name_dict[name[0]].update({'gender':gender.upper()})\r\n name_dict[name[0]].update({'intro_year_pop':int(name[pop_index])})\r\n name_dict[name[0]].update({'intro_year':year})\r\n name_dict[name[0]].update({'intro_year_rank':rank})\r\n name_dict[name[0]].update({'pops':{year:int(name[pop_index])}})\r\n name_dict[name[0]].update({'ranks':{year:rank}})\r\n #every time the name appears\r\n name_dict[name[0]]['pops'].update({year:int(name[pop_index])})\r\n name_dict[name[0]]['ranks'].update({year:rank})\r\n rank = rank + 1\r\n year_names.append(name[name_index])\r\n names.extend(year_names)\r\n f.close()\r\n except IOError:\r\n \tprint(\"{0} not found\".format(path))\r\n return name_dict","def display_get_date_ordered():\n ordered_games = reports.get_date_ordered(filename)\n print(\"Ordered games by date and by alphabet:\")\n for title in ordered_games:\n print(title)\n print()","def get_word_list(file_name):\n file_ = open(file_name, 'r')\n lines = file_.readlines()\n\n start_line = 0\n while lines[start_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\n start_line += 1\n\n lines = lines[start_line+1:]\n\n end_line = 0\n while lines[end_line].find('END OF THIS PROJECT GUTENBERG EBOOK') == -1:\n end_line += 1\n\n lines = lines[:end_line-3]\n\n list_ = ' '.join(lines)\n list_ = str.lower(list_)\n list_ = list_.translate(None, string.punctuation)\n list_ = list_.split()\n\n return list_","def get_word_list(file_name):\n\tbook = get_file_text(file_name)\n\tbook = strip_header(book)\n\tbook = strip_punctuation(book)\n\tbook = book.lower()\n\twords = re.split(r'\\s+', book)\n\treturn words","def get_word_list(file_name):\n\tnew_list = []\n\n\tf = open(file_name,'r')\n\tlines = f.readlines()\n\tcurr_line = 0\n\tend_line = 0\n\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\n\t\tcurr_line += 1\n\twhile lines[end_line].find('End of the Project Gutenberg EBook') == -1:\n\t\tend_line -= 1\n\tlines = lines[curr_line + 1:end_line]\n\n\tlong_lines = ''.join(str(e) for e in lines)\n\tlong_lines = long_lines.lower()\n\tlong_lines = long_lines.translate(None, punctuation)\n\n\twords = long_lines.split()\n\tfor item in words:\n\t\tnew_list.append(item)\n\n\treturn new_list","def get_word_list(file_name):\n\n\tstoryEdit = []\n\n\t#Reads the file starting after the beginning\t\n\tf = open(file_name,'r')\n\tlines = f.readlines()\n\tcurr_line = 0\n\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\n\t\tcurr_line += 1\n\tlines = lines[curr_line+1:]\n\n\n\t#Loops through each row, making everything lowercase and replacing all punctuation\n\tfor row in lines:\n\t \trow = row.lower()\n\t \trow = row.translate(string.maketrans(\"\",\"\"), string.punctuation)\n\t \tstoryEdit += row.split()\n\n\n\t#Returns the final list as \n\treturn storyEdit","def get_teams_from_text(file_name, index):\n\n\tteams = []\n\tList = open(\"teams/\" + file_name + \".txt\",'r').read().splitlines()\n\t\n\tj = 0\n\tfor i in range(0,len(List)):\n\t\tif j < index:\n\t\t\tdummy_team = Team(List[i], \"Wappen\")\n\t\t\tteams.append(dummy_team)\n\t\tj = j+1\n\treturn teams","def read_drama_names(drama_file):\n with open(drama_file, 'rb') as f:\n drama_list = [d.decode('utf-8').strip() for d in f.readlines()]\n return drama_list","def format_birthday(birth_list):\r\n # Remove space and tab from the left and right part of the string\r\n stripped_text = birth_list.strip(\"\\n\\t\")\r\n # Split the date and count\r\n split_text = [s.split(\"\\t\") for s in stripped_text.split(\"\\n\")[6:]] \r\n split_date = [map(int, s[0].split(\"/\")) for s in split_text] \r\n split_count = map(int, [s[1] for s in split_text])\r\n # Concatenate the date and count \r\n merge = [split_date[i] + [split_count[i]] for i in xrange(365)]\r\n return merge","def sort_by_surname_desc(names):\n names = dedup_and_title_case_names(names)\n names1 = []\n for n in names:\n x = n.split(\" \")\n names1.append(x[1] + \" \" + x[0])\n return names1\n # ...","def main(html_file):\n \n biglist = name_extractor(html_file)\n text = '\\n'.join(biglist)\n print (text)","def fasta_headers(file_name):\n list = []\n with open('../test_files/' + file_name, 'r') as infile:\n text = infile.read()\n seqs = text.split('>')\n for seq in seqs:\n try:\n x = seq.split('\\n', 1)\n if x[0] != '':\n #x[0] contains only headers\n list.append(x[0])\n except:\n pass\n return list","def extract_bird_name(filename, split=None):\n description = ''\n if split is None:\n words = filename.split()\n else:\n words = filename.split(split)\n for word in words:\n if is_number(word):\n break\n if description is '':\n description += word\n else:\n description += ' ' + word\n return description","def find_yearly_playlist(data):\n\n if data['year'] and str(data['year']).isdigit():\n median = round(int(data['year']), -1)\n return 'years-{}-{}.m3u'.format((int(median) - 5), (int(median) + 4))\n\n return '00-without-year.m3u'","def filename_to_file_list_key(filename):\n pattern = r'\\.\\d\\d\\d\\d-'\n index = re.search(pattern, filename)\n if index:\n year = int(filename[index.start() + 1: index.start() + 5])\n else:\n return '0-0'\n # the YYYY field is 4 characters long, the month is two\n year_offset = index.start() + 5\n # two characters for the month, and one for the - between year and month\n month_offset = year_offset + 3\n month = int(filename[year_offset + 1: month_offset])\n key = \"{year}-{month}\".format(year=year, month=month)\n return key","def get_list(file_name):\n with open(file_name, \"r\", encoding=\"latin-1\") as file:\n text = file.read()\n text = text.lower() # Make everything lowercase\n text = text.split(\"\\n\")\n return text","def parse(filename):\n file_map = {\n '1995-1996.html': ninety_six,\n '2005-2006.html': twenty_six,\n '2014-2015.html': twenty_fifteen\n }\n func = file_map.get(filename, lambda: \"Invalid File\")\n func(filename)","def read_names(path):\n return SortedSet([os.path.basename(n) for n in glob.glob(path + os.sep + '*')])","def getFilmTitles(checkFolder):\n\n files = [str(x) for x in Path(checkFolder).rglob(\"*\")]\n libFilmFiles = list(map(os.path.basename,files)) # Remove the path\n libFilmTitles = [os.path.splitext(x)[0] for x in libFilmFiles]\n return libFilmTitles","def get_nifty_list(root_dir, name=\"\"):\n \n file_list = glob(root_dir + \"/**/*.nii.gz\", recursive=True)\n file_list = sorted([file for file in file_list if name in file])\n return file_list","def read_name_file(filename):\n with open(filename, 'r') as f:\n names = f.read()\n names = names.split('\\n')\n names = list(filter(None, names))\n return names","def _get_authors_list():\n\n articles = os.listdir(\"../data/\")\n authors = []\n for article in articles:\n with open(\"../data/\" + article, 'r') as file:\n lines = file.readlines()\n author = tuple(\n line.replace(\"\\n\", \"\").split()[1] for line in lines\n if \"Автор:\" in line\n )[0]\n authors.append(author)\n\n return authors","def getlistofpossibletitles(fileitem,shows):\n title = []\n title.append(fileitem)\n lookfor = fileitem.replace(\".\",\" \")\n title.append(lookfor)\n lookfor = fileitem.replace('-',\" \")\n title.append(lookfor)\n return title","def get_file_two_years(file):\n f = '_' + '(\\d+)' + '_' + '(\\d+)'\n match = re.search(f, file)\n if match:\n return int(match.group(1)), int(match.group(2))","def get_names():\n only_links = SoupStrainer(\"a\")\n names = set()\n doc = requests.get(NAMES_URL).content\n links = BeautifulSoup(doc, \"html.parser\", parse_only=only_links)\n pokemon = links.find_all(title=re.compile(\"(\\w+)(\\s){1}(\\(Pokémon\\))\"))\n for cell in pokemon:\n names.add(str(cell.string))\n \n\n return names","def get_word_list(file_name):\n # Read the file specified\n f = open(file_name,'r')\n lines = f.readlines()\n \n # Remove header text from lines\n curr_line = 0\n while lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\n curr_line += 1\n lines = lines[curr_line + 1:]\n\n # Remove footer text from lines\n curr_line = -1\n while lines[curr_line].find('END OF THIS PROJECT GUTENBERG EBOOK') == -1:\n curr_line -= 1\n lines = lines[: curr_line]\n\n # Strip lines into words\n words = []\n for i in range(len(lines)):\n # Remove punctuation\n next_line = lines[i].translate(string.maketrans(\"\",\"\"), string.punctuation)\n next_line = next_line.lower()\n words += next_line.split()\n \n return words","def get_names_url(i):\n urls = list()\n with open('./urls/fall11_urls_train_'+str(i)+'.txt','r',encoding=\"Latin-1\") as f:\n for line in f:\n urls.append(line)\n urls = [url.strip('\\n') for url in urls]\n urls1 = [url.split('\\t')[1] for url in urls]\n names = [url.split('\\t')[0] for url in urls]\n return urls1,names","def format_finder(files):\r\n\r\n file_format = ['%Y', '%m', '%d']\r\n year = ''\r\n month = ''\r\n da = ''\r\n\r\n for file in files:\r\n\r\n index = file.find('_')\r\n date = file[: index]\r\n if '-' in date:\r\n separator = '-'\r\n else:\r\n separator = '_'\r\n date = date.split(separator)\r\n\r\n for d in range(len(date)):\r\n\r\n # If the date doesn't contain decimal (Eg: August) then it would return None\r\n if not date[d].isdecimal():\r\n return None\r\n\r\n # If the element in the date is of length greater then 2 then it would be a year (Eg: 2020)\r\n # And that value is set as the index of year\r\n if len(date[d]) > 2:\r\n year = d\r\n\r\n # If the integer of element in the date is of length greater then 12 then it would be a date (Eg: 25)\r\n # And that value is set as the index of date\r\n elif int(date[d]) > 12:\r\n da = d\r\n\r\n # If Both year and date are set, then the correct index for the month would be 3- (year+date)\r\n # Eg: 3 -(0+1)\r\n if (year != '') and (da != ''):\r\n month = 3 - (year + da)\r\n break\r\n\r\n # If Month is set, then we change the format according to their set value\r\n # Eg: format = ['%Y', '%m', '%d'], and year = 1, da = 0, month = 2\r\n # Then format[year=1] = '%Y'\r\n # Then format[da=0] = '%d'\r\n # Then format[month=2] = '%m'\r\n # format = ['%d', '%Y', '%m']\r\n if month:\r\n file_format[year] = '%Y'\r\n file_format[month] = '%m'\r\n file_format[da] = '%d'\r\n break\r\n else:\r\n # The script executes this only if none of the files had an date element( Which is not year)\r\n # That was greater than 12, Eg: 2020-06-10\r\n # Meaning that we cannot know for sure which element represents the date/month\r\n # Hence we arbitrarily assign one element as date and another as month\r\n if year != 0:\r\n # If the index of year is zero, we let the format to be same as it was assigned first\r\n # Else we arbitrarily assign '0' th index to month\r\n file_format[year] = '%Y'\r\n file_format[0] = '%m'\r\n file_format[3 - year] = '%d'\r\n return f'{file_format[0]}-{file_format[1]}-{file_format[2]}'","def list_assay_files(path):\n if rank != 0:\n print(\"Rank {} tried scanning the directory.\".format(rank))\n sys.exit()\n\n valid_names = []\n for root, dirs, files in os.walk(path):\n for f in files:\n if f.startswith(\"NENE\") and f.endswith((\"A.txt\", \"B.txt\")):\n fullpath = os.path.join(root, f)\n valid_names.append(fullpath)\n\n return valid_names","def read_screen_names(filename):\n flist = []\n f = open('candidates.txt')\n for line in f:\n \tflist.append(line.strip('\\n'))\t\n return flist","def read_names_list(file_path):\r\n\tnames_list = []\r\n\twith open(file_path) as file:\r\n\t for line in file:\r\n\t cline = line.rstrip().split()\r\n\t #row_id = cline[0]\r\n\t row_name = cline[1:]\r\n\t #names_list.append((row_id, \" \".join(row_name)))\r\n\t names_list.append(\" \".join(row_name))\r\n\treturn names_list","def get_oscars_list():\n file = 'oscars.html'\n handler = open(file).read()\n soup = BeautifulSoup(handler)\n film_list = []\n\n for row in soup.findAll('tr'):\n #TODO: add more elegant check instead of dirty try catch\n try:\n links = row.findAll('a')\n cells = row.findAll('td')\n awards = int(cells[2].contents[0])\n\n # Filter only nominations\n if awards > 0:\n film = {\n 'name': links[0].contents[0],\n 'year': links[1].contents[0],\n 'awards': awards,\n 'nominations': cells[3].contents\n }\n film_list.append(film)\n except Exception, e:\n pass\n\n film_list = sorted(film_list, key=lambda k: k['awards'])\n film_list.reverse()\n return film_list","def GetColumnistNames():\n global columnistnames\n if not columnistnames:\n columnistnames = []\n html = ukmedia.FetchURL( columnistmainpage )\n soup = BeautifulSoup( html )\n for a in soup.findAll( 'a', {'class':'author'} ):\n n = a.renderContents(None).strip()\n if not n in columnistnames:\n columnistnames.append( n )\n return columnistnames","def test_get_teams_in_year_names():\n assert sorted(gtiy(2008)) == sorted(team_2008)\n assert sorted(gtiy(2009)) == sorted(team_2009)\n assert sorted(gtiy(2010)) == sorted(team_2010)\n assert sorted(gtiy(2011)) == sorted(team_2011)\n assert sorted(gtiy(2012)) == sorted(team_2012)\n assert sorted(gtiy(2013)) == sorted(team_2013)\n assert sorted(gtiy(2014)) == sorted(team_2014)\n assert sorted(gtiy(2015)) == sorted(team_2015)\n assert sorted(gtiy(2016)) == sorted(team_2016)\n assert sorted(gtiy(2017)) == sorted(team_2017)","def list_titles(genre):\n text = genre_html(genre)\n num_titles = text.count('title=')\n\n titles = []\n for i in range(num_titles):\n start = text.find('title=')\n end = text[start+7:].find('\">')\n title = text[start+7:start+end]\n titles.append(title)\n text = text[start+7:]\n\n return titles","def get_html_files(f_name):\n files = sorted([os.path.join(f_name, f) for f in os.listdir(f_name) if os.path.isfile(os.path.join(f_name, f))]) #extra check if is file unnecessary\n return files","def filelist(basedir):\n day_files = []\n for root, dirs, files in os.walk(basedir):\n for file in files:\n if file.endswith(\".png\"):\n day_files.append(os.path.join(file))\n dates_files = []\n\n for i in day_files:\n year = i.split('_')[1]\n day = i.split('_')[2]\n mounth = i.split('_')[3]\n hour = i.split('_')[4]\n dates_files.append(UTCDateTime(year+'-'+mounth+'-'+day+'T'+hour)-3)\n return sorted(dates_files)","def sort_by_version(compiled_re, names):\n annotated_names = [([int(n) for n in compiled_re.match(name).groups()], name) for name in names]\n annotated_names.sort()\n return [annotated_name[1] for annotated_name in reversed(annotated_names)]","def getlistofpossibletitles(fileitem,fname):\n title = []\n oddtitles = open(\"oddtitles.txt\", 'r')\n content = oddtitles.read()\n oddtitles.close()\n\n content = content.split(\"\\n\")\n for line in content:\n elements = line.split(',')\n if fileitem in elements[0]:\n #print(elements[1])\n title.append(elements[1].title())\n\n \n title.append(fileitem)\n title.append(fileitem.title())\n lookfor = fileitem.replace(\".\",\" \")\n title.append(lookfor)\n title.append(lookfor.title())\n lookfor = fileitem.replace('-',\" \")\n title.append(lookfor)\n title.append(lookfor.title())\n with open(fname, \"r\") as dataf:\n for line in dataf:\n if lookfor.upper() in line.upper():\n line = line.replace(\"\\n\",\"\")\n title.append(line)\n title.append(line.title())\n return title","def get_data(url):\n\n request = requests.get(url)\n\n soup = BeautifulSoup(request.text, \"lxml\")\n\n ol_tags = soup.find_all('ol')\n\n names_list = []\n\n for li_tags in ol_tags:\n for names in li_tags:\n names_list.append(names.text)\n\n return names_list","def sort_slide_names(l): \n convert = lambda text: int(text) if text.isdigit() else text \n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key) ] \n return sorted(l, key = alphanum_key)","def get_links(names, html):\n ###TODO\n people = []\n readweb = BeautifulSoup(html, 'html.parser')\n for a in readweb.find_all('a'):\n person = os.path.basename(str(a.get('href')))\n if person in names:\n people.append(person)\n return SortedSet(people)\n pass","def get_year_desc():\n return Year.objects.all().order_by('-name')","def report_name_sort(rpt_name: str):\n rptn = rpt_name.split(\" \")\n try:\n rpta = rptn[0].split(\"-\")\n returned = (rpta[0], rpta[1])\n except IndexError:\n returned = (rptn[0], \"\")\n\n return returned","def _possible_names(self, filename):\n names = [filename]\n if not self._iszip(filename):\n for zipext in _file_openers.keys():\n if zipext:\n names.append(filename+zipext)\n return names","def create_dropdown_list_years(movies):\n dropdown = ''\n movies.sort(key=lambda x: x.year, reverse=True)\n y = []\n for movie in movies:\n y.append(movie.year)\n y = list(set(y))\n for x in y:\n dropdown += dropdown_tile_year.format(id_year=x,\n year=x)\n \n return dropdown","def test_iter_all_years(self):\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\n self.assertEqual([ary.year for ary in ar], [2008,2009])","def get_animals_list(filename):\n\n f = open(filename, 'r')\n text = f.read()\n f.close()\n animals = text.split('\\n')\n animals = [animal for animal in animals if animal != '' and len(animal) > 1]\n return sorted(animals)","def loadFiles():\n all_chapters = []\n for name in names:\n f = open(file_path + name, \"r\", encoding=\"utf-8\")\n html_file = f.read()\n f.close()\n chap_text = extractText(html_file)\n new_text = cleanText(chap_text)\n all_chapters.append(new_text)\n concatenated_chapters = \" \".join(all_chapters)\n return concatenated_chapters","def parse_bib_from_list(filename):\n\tentry_regex = r\"TITEL: .*\\s*AUTOR: .*\"\n\tparse_func = make_parse_func(r\"AUTOR: (.*)\", r\"TITEL: (.*)\", None)\n\treturn parse_bib(filename, entry_regex, parse_func)","def readFile(fileName):\n\tf = open(fileName, 'r')\n\tnames = map(lambda s: s[1:-1], f.read().split(','))\n\tnames.sort()\n\treturn names","def get_patient_names(self):\n\t# use pre-defined patient names\n\tif (self.data_names is not None):\n\t\tassert (os.path.isfile(self.data_names))\n\t\twith open(self.data_names) as f:\n\t\t\tcontent = f.readlines()\n\t\tpatient_names = [x.strip() for x in content]\n\t# use all the patient names in data_root\n\telse:\n\t\tpatient_names = os.listdir(self.data_root[0])\n\t\tpatient_names = [name for name in patient_names if 'brats' in name.lower()]\n\treturn patient_names","def __file_sorter(self, filename: str) -> int:\n filename = filename.split('/')[-1]\n year = int(filename[-3:-1]) # extracts YY from ssssdddh.YYo\n day_info = filename[4:8] # extracts dddh from ssssdddh.YYo\n day_of_year = int(day_info[:3]) # ddd\n hour_block = day_info[-1] # h\n if hour_block.isdigit():\n return (year, day_of_year)\n return (year, day_of_year + ord(hour_block))","def get_image_names(file_path: str) -> List:\n name_list = []\n f = open(file_path, \"r\")\n lines = f.readlines()\n f.close()\n for line in lines:\n name = line.split(\"/\")[-1].replace(\"\\n\", \"\")\n name_list.append(name)\n return name_list","def parse_bibtex(file, build_dir):\n\n parser = BibTexParser()\n parser.customization = customizations\n years = []\n with open(file, 'r') as f: \n bibtex = bibtexparser.load(f, parser=parser)\n for i in range (len(bibtex.entries)):\n for key, value in bibtex.entries[i].items():\n if key == 'year':\n years.append(int(value))\n years.sort()\n years.reverse()\n years_no_repeat = []\n for i in range(len(years)):\n if years_no_repeat.count(years[i]) == 0:\n years_no_repeat.append(years[i])\n \n for i in range(len(years_no_repeat)):\n bibtext = copy.deepcopy(bibtex)\n array = []\n for j in range (len(bibtex.entries)):\n for key, value in bibtex.entries[j].items():\n if key == 'year':\n if int(value) == years_no_repeat[i]:\n array.append(bibtex.entries[j])\n bibtext.entries = array\n parse_file = os.path.join(build_dir, str(years_no_repeat[i]) + 'parsed.bib')\n writer = BibTexWriter()\n writer.order_entries_by = ('ENTRYTYPE', )\n with open(parse_file, 'w') as f:\n f.write(writer.write(bibtext))","def get_page_name(self,en_code):\n files_and_names = {}\n for files_named in self.find_enc(en_code):\n search_in_file = open(self.file_location+\"/\"+files_named)\n for line in search_in_file:\n if '# LINKNAME:' in line:\n #print(line)\n new_line = line.split('# LINKNAME:')\n for nl in new_line:\n fnl = nl.strip()\n if fnl is not None:\n files_and_names[files_named] = fnl\n search_in_file.close()\n return files_and_names","def sortednameslist(nameslist):\n sortednames = sorted(nameslist, key=lambda x: x[1])\n return sortednames","def top_ten_movies(path):\n content = open(path, \"r\")\n topten = []\n for x in content:\n topten.append(x) \n return topten","def parse_authors():\n import subprocess\n try:\n output = subprocess.check_output(['git', 'shortlog', '-s'],\n universal_newlines=True)\n except Exception as ex:\n print('ex = {!r}'.format(ex))\n return []\n else:\n striped_lines = (l.strip() for l in output.split('\\n'))\n freq_authors = [line.split(None, 1) for line in striped_lines if line]\n freq_authors = sorted((int(f), a) for f, a in freq_authors)[::-1]\n # keep authors with uppercase letters\n authors = [a for f, a in freq_authors if a.lower() != a]\n return authors","def scrape_all_course_names(filename, verbose):\n \n\tsoup =\tBeautifulSoup(open(filename, 'r'), 'html.parser')\n\t#print(soup)\n\tcourses = []\n\t# all the courses are stored in a div w/class=view-content\n\t\t\n\th4_field_content = soup.find('h4', 'field-content')\n\t#print(h4_field_content)\n\tall_course_content = soup.find(\"div\", \"view-content\") # this contains ALL the classes...\n\tif all_course_content == None:\n\t\tprint(\"There are no courses on this page. Try a smaller page number!\")\n\t\treturn []\n\t\n\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\n\tclass_list=[]\n course_code_and_number = {}\n\tfor c in candidate_classes:\n\t\t#print(c.text, \"\\n\")\n\t\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\n\t\ttext = text.split(\" \")# split on the space\n\t\tcourse_id = \" \".join(text[:2]).replace('\\n', '') # the first 2 are the course id\n\t\tcourse_name = \" \".join(text[2:-2]).replace('\\n', '')\n\t\tnum_credits = text[-2].replace(\"(\", \"\")# just get the course number, replace the ( with nothing \n\t\n\t\n\t\t#print(f\"{course_id}\\n{course_name}\\n{num_credits}\\n\")\n\t\ttry:# Check that the course number is a digit bc sometimes it is something weird\n\t\t\tfloat(num_credits)\n\t\texcept ValueError:\n\t\t\t#print(f\"Wrong course format. Ignoring {c}\")\n\t\t\tif(verbose):\n\t\t\t\tprint(f\"Wrong course format. Ignoring course: {c.text}\")\n\t\t\tcontinue\n\t\tclass_list.append(course_id)\n\treturn class_list","def _get_wordlist(file_name):\n ifile = codecs.open(file_name, 'r', encoding='utf-8')\n for _ in range(int(ifile.__next__())):\n yield (ifile.__next__().strip() for _ in range(int(ifile.__next__())))","def get_word_list(file_name, n):\n f = open(file_name, 'r')\n text = f.read()\n words = re.compile('\\w+').findall(text)\n return get_top_n_words(words, n)","def read_str_name(path):\r\n name = []\r\n name_stru = {}\r\n with open(path, \"r+\") as f:\r\n line = f.readlines()\r\n \r\n # to load the name to list. files\r\n for i in range(len(line)):\r\n \r\n if line[i][:-1] != '':\r\n \r\n name.append(line[i][:-1])\r\n else:\r\n \r\n name.append(line[i-1][:-1] + str())\r\n \r\n line[i] = line[i-1]\r\n \r\n # to remark the structure name\r\n for s in name:\r\n \r\n name_stru[s] = (name.count(s),name.index(s))\r\n \r\n for key,values in name_stru.items():\r\n \r\n if values[0] != 1:\r\n for i in range(values[0]):\r\n name[values[1]+i] = name[values[1]+i] + str(i+1)\r\n \r\n return name","def get_title_name_year(self) -> Tuple[str, str]:\n r = self.session.get(f\"https://www.imdb.com/title/{self.imdb}\")\n if r.status_code != 200:\n raise ValueError(f\"An unexpected error occurred getting IMDB Title Page [{r.status_code}]\")\n imdb_page = html.unescape(r.text)\n imdb_title = re.search(\n # testing ground: https://regex101.com/r/bEoEDn/1\n r\"<title>(?P<name>.+) \\(((?P<type>TV (Movie|Series|Mini[- ]Series|Short|Episode) |Video |Short |)\"\n r\"(?P<year>(\\d{4})(|– |–\\d{4})))\\) - IMDb</title>\",\n imdb_page\n )\n if not imdb_title:\n raise ValueError(f\"Could not scrape Movie Title or Year for {self.imdb}...\")\n return imdb_title.group(\"name\").strip(), imdb_title.group(\"year\").strip()","def createFileNames(nFileNames, seqPrefix):\n nameList = []\n nameList = [seqPrefix+str(i)+\".txt\" for i in range(0, nFileNames)]\n return nameList","def get_labyrinth_from_file_name(self, file_name):\n\n excel_file = xlrd.open_workbook(self.file_name)\n labyrinth = list()\n for sheet in excel_file.sheets():\n for i in range(sheet.nrows):\n row = list()\n for j in range(sheet.ncols):\n row.append(sheet.cell_value(i, j))\n labyrinth.append(row)\n\n return labyrinth"],"string":"[\n \"def extract_names(filename):\\n\\n # Extracting the year\\n year_match = re.search(r'\\\\d\\\\d\\\\d\\\\d', filename)\\n if not year_match:\\n sys.stderr.write('Could not find a year!\\\\n')\\n sys.exit()\\n year = year_match.group()\\n\\n # Opening the file\\n try:\\n with open(filename) as file:\\n data = file.read()\\n except FileNotFoundError:\\n sys.stderr.write('There is no such file in the directory!\\\\n')\\n sys.exit()\\n\\n # Finding patterns\\n regex = re.compile(r'<td>\\\\w+')\\n names = regex.findall(data)\\n for i in range(len(names)):\\n names[i] = names[i].replace('<td>', '')\\n\\n # Creating a dictionary with names data\\n names_dict = {}\\n for i in range(0, len(names) - 2, 3):\\n key = names[i]\\n names_dict[key] = [names[i + 1], names[i + 2]]\\n\\n # Creating a list with result\\n boy_names = []\\n girl_names = []\\n result = [year]\\n for key, value in names_dict.items():\\n if value[0] not in boy_names:\\n result.append(value[0] + ' ' + key)\\n boy_names.append(value[0])\\n if value[1] not in girl_names:\\n result.append(value[1] + ' ' + key)\\n girl_names.append(value[1])\\n\\n result.sort()\\n # result.insert(0, year)\\n\\n return result\",\n \"def name_extractor(file):\\n \\n import os\\n import re\\n \\n name_list = []\\n rank_dict = {}\\n \\n year = re.search(r'(\\\\d+)\\\\.html$', file) \\n current = open(file) \\n match = re.findall(r'<tr\\\\salign=\\\"right\\\"><td>(\\\\d+).*?>(\\\\w+).*?>(\\\\w+)', current.read())\\n current.close\\n\\n \\n for one_touple in match: #Check for existing match, only accept lower rank value into dictionary\\n \\n for index in range(1,2):\\n \\n if one_touple[index] in rank_dict:\\n if rank_dict[one_touple[index]] < one_touple[0]:\\n continue\\n rank_dict[one_touple[index]] = one_touple[0]\\n \\n for one_item in rank_dict:\\n \\n ranking = rank_dict[one_item] #Build target list from dictionary formatted as \\\"Name rank\\\"\\n name_list.append(f\\\"{one_item} {ranking}\\\") \\n \\n name_list = sorted(name_list)\\n name_list.insert(0,year.group(1))\\n \\n return name_list\",\n \"def extract_names(filename):\\n raw_text = read_html(filename) \\n \\n #searching for the year\\n year = re.search('(<h3 align=\\\"center\\\">Popularity in )(\\\\d\\\\d\\\\d\\\\d)',raw_text).group(2)\\n \\n #searching for the list of names\\n list_of_names = re.findall('<td>(\\\\d+)</td><td>(\\\\w+)</td><td>(\\\\w+)</td>',raw_text)\\n \\n #pair each name with it's rank\\n name_and_rank = [] \\n for line in list_of_names:\\n name_and_rank.append((line[1], line[0]))\\n name_and_rank.append((line[2], line[0]))\\n \\n # sort the list alphabetically\\n name_and_rank = sorted(name_and_rank, key = lambda x:x[0])\\n name_and_rank = dict(name_and_rank)\\n\\n return year, name_and_rank[:20]\",\n \"def getFileNames():\\n input_path = \\\"/Users/tim/OneDrive/Master/Text_Mining/project/texts/glenarvon_html/\\\"\\n temp_list = os.listdir(input_path)\\n name_list = [i for i in temp_list if i[-4:] == \\\"html\\\"]\\n name_list.sort(key=natural_keys) # see http://stackoverflow.com/questions/5967500/how-to-correctly-sort-a-string-with-a-number-inside\\n return name_list, input_path\",\n \"def extract_names(filename):\\n # +++your code here+++\\n f = open(filename, 'r')\\n fl = read_file(filename)\\n\\n l = []\\n lFiltFinal = []\\n\\n year_match = re.search(r'Popularity\\\\sin\\\\s(\\\\d\\\\d\\\\d\\\\d)', f.read())\\n year = year_match.group(1)\\n\\n for line in fl:\\n #if '<h3 align=\\\"center\\\">Popularity in' in line:\\n #year = line[-10:-6]\\n if '<tr align=\\\"right\\\"><td>' in line:\\n rank = line[line.find('<td>')+len('<td>'):line.find('</td>')]\\n boys = line[line.index('</td><td>')+len('</td><td>'):line.index('</td><td>',line.index('</td><td>')+1)]\\n girls = line[line.index('</td><td>',line.index('</td><td>')+1)+len('</td><td>'):-6]\\n l.append([boys,rank])\\n l.append([girls,rank])\\n\\n lFilt = list(unique_by_first_n(1, l))\\n\\n lFiltFinal.append(year)\\n for key in lFilt:\\n lFiltFinal.append( key[0] + ' ' + key[1])\\n\\n lFiltFinal.sort()\\n return lFiltFinal\",\n \"def extract_names(filename):\\n # +++your code here+++\\n # Opening the file\\n f = open(filename, 'rU')\\n # Reading all of the lines\\n lines = f.readlines()\\n # Empty list to hold the year, names, and ranks\\n ranks_names = []\\n for line in lines:\\n # search for the year\\n year = re.search(r'\\\\s(\\\\d\\\\d\\\\d\\\\d)</h3>', line)\\n # if the year is found, append it to the list\\n if year: \\n ranks_names.append(year.group(1))\\n # search for the rank, male name, and female name\\n rank_male_female = re.search(r'(\\\\d+)</td><td>(\\\\w+)</td><td>(\\\\w+)</td>', line)\\n # If they are found then append the male name plus its rank, as well as the \\n # female name plus its rank\\n if rank_male_female:\\n ranks_names.append(rank_male_female.group(2) + ' ' + rank_male_female.group(1))\\n ranks_names.append(rank_male_female.group(3) + ' ' + rank_male_female.group(1))\\n # Sort the list alphabetically\\n ranks_names.sort()\\n # Return the list\\n return ranks_names\",\n \"def extract_names(filename):\\n f = open(filename, 'rU')\\n file_text = f.read()\\n f.close()\\n \\n year = re.search(r'Popularity in \\\\d\\\\d\\\\d\\\\d', file_text)\\n if year:\\n year = year.group()[-4:]\\n else:\\n print 'ERROR: year not found in ' + filename\\n \\n html_rank_names = re.findall(r'<tr align=\\\"right\\\"><td>\\\\d+</td><td>\\\\w+</td><td>\\\\w+</td>', file_text)\\n name_ranks = {}\\n i = 0\\n while i < len(html_rank_names):\\n line = html_rank_names[i]\\n first_tag = line.find('<td>')\\n first_end_tag = line.find('</td>')\\n rank = line[first_tag + 4 : first_end_tag]\\n \\n second_tag = first_end_tag + 9\\n second_end_tag = line.find('</td>', second_tag)\\n name1 = line[second_tag : second_end_tag]\\n \\n third_tag = second_end_tag + 9\\n third_end_tag = len(line) - 5\\n name2 = line[third_tag : third_end_tag]\\n \\n # if the names already are in the dict, skip them because they have a larger number than what is already in the dict\\n if name1 not in name_ranks: name_ranks[name1] = rank\\n if name2 not in name_ranks: name_ranks[name2] = rank\\n i = i + 1\\n \\n year_name_ranks = []\\n year_name_ranks.append(year)\\n for name, rank in name_ranks.iteritems():\\n year_name_ranks.append(name + ' ' + rank)\\n year_name_ranks.sort()\\n return year_name_ranks\",\n \"def name_list(file_name):\\n \\n li = open(file_name)\\n list_of_names = []\\n\\n for name in li:\\n (first,last) = str.split(name,' ')\\n list_of_names.append(Name(first,last))\\n return list_of_names\",\n \"def parse_year(txt):\\n\\n txt = txt.strip()\\n if \\\"-\\\" in txt:\\n res = re.sub('[^0-9]', '', txt)\\n return [res[0:4], res[4:8]]\\n else:\\n return [txt, txt]\",\n \"def get_imdb_list():\\n list_file = 'imdb.txt'\\n name_column = 26\\n f = open(list_file, 'r')\\n film_list = []\\n pos = 0\\n\\n for line in f:\\n pos += 1\\n words = line.split()\\n name = line[name_column:-1]\\n # could be problematic is there are brackets in the film name\\n year = name[name.find('(') + 1:name.find(')')]\\n name = name.replace('(' + year + ')', '')\\n film = {\\n 'pos': pos,\\n 'score': Decimal(words[2]),\\n 'name': name.strip(),\\n 'year': year\\n }\\n film_list.append(film)\\n f.close()\\n return film_list\",\n \"def list_names(text):\\n with open(text, 'r') as names:\\n list_of_names = list(names)\\n name_list = [name.strip() for name in list_of_names]\\n return name_list\",\n \"def getYears():\\n url = \\\"http://www.boxofficemojo.com/weekend/\\\"\\n src = urllib.request.urlopen(url).read()\\n soup = BeautifulSoup(src, 'html.parser')\\n year_header = soup.find_all(name = \\\"b\\\")[1]\\n year_elems = year_header.find_all([\\\"a\\\", \\\"font\\\"])\\n years = [int(year.get_text()) for year in year_elems]\\n return years\",\n \"def year_tracker(words):\\n new_words = []\\n for w in words:\\n new_word = re.sub(r\\\"^[1][789][0-9]{2}$\\\", \\\"jahreszahl\\\", w) # for 1700-1999\\n new_word = re.sub(r\\\"^[2][01][0-9]{2}$\\\", \\\"jahreszahl\\\", new_word) # for 2000-2199\\n new_words += [new_word]\\n return new_words\",\n \"def getFile(filename):\\n f = open(filename)\\n lines = f.readlines()\\n f.close()\\n names = []\\n names.append(\\\"\\\")\\n for name in lines[0].split(','):\\n \\n names.append(name[1:-1])\\n names = sorted(names)\\n return names\",\n \"def load_names(file_name: str) -> List[str]:\\n full_path_name = os.path.join(os.getcwd(), 'names', file_name)\\n with open(full_path_name, 'r') as file:\\n names = file.read().rstrip('\\\\n').split(',')\\n return sorted(names)\",\n \"def get_names(url):\\n\\t# get html element tree\\n\\ttree = get_tree(url)\\n\\t# Names are text within <a> elements in this list\\n\\t# xpath returns a list with alternating last and first names as elements\\n\\t# Concatenate each last name and first name pair and put in new list as full name\\n\\tnames = tree.xpath('//*[@id=\\\"research-teachinglist\\\"]/li//a//text()')\\n\\tfull_names = []\\n\\tfor i in range(0, len(names)-1, 2):\\n\\t\\tfull_names.append(names[i] + names[i+1])\\n\\n\\treturn full_names\",\n \"def get_filenames(start_year = 1980, end_year = 2009, path = 'ucr_offenses_known_monthly_1960_2016_dta/'):\\n filenames = []\\n for filename in os.listdir(path):\\n #ignore pdf files in folder, filter out dta file names that contain 1980-2009\\n if filename.endswith('.dta'):\\n for years in range(start_year, end_year + 1):\\n if str(years) in filename:\\n filenames.append(filename)\\n return(filenames)\",\n \"def get_surnames(filename):\\n result = []\\n with open(filename, \\\"r\\\") as file:\\n for line in file.readlines():\\n surname = line.split('\\\\t')[1]\\n result.append(surname)\\n return result\",\n \"def filter_years(text):\\n months = ['january', 'february', 'march', 'april', 'may', 'june',\\n 'july', 'august', 'september', 'october', 'november', 'december']\\n prepositions = ['around', 'after', 'at', 'as',\\n 'approximately', 'before', 'between', 'by',\\n 'during', 'from', 'in', 'near', 'past',\\n 'since', 'until', 'within'] # removed: about, on\\n conjugations = ['and']\\n articles = ['the']\\n times = ['early', 'mid', 'late']\\n patterns = months + prepositions + conjugations + articles + times\\n re_string = r'\\\\b(' + '|'.join(patterns) + r')\\\\b(\\\\s|-)\\\\b([0-9]{3,4})s?\\\\b(?i)(?!\\\\sMYA)\\\\s?(BCE|BC)?'\\n years = [int(match.group(3)) * (-2*bool(match.group(4))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n re_string = r'([0-9]{1,2})(st|nd|rd|th) century\\\\s?(BCE|BC)?'\\n centuries = [(int(match.group(1)) * 100 - 100) * (-2*bool(match.group(2))+1)\\n for match in re.finditer(re_string, text, re.IGNORECASE)]\\n years += centuries\\n years = [y for y in years if y<Dump.MAX_YEAR]\\n return sorted(years + centuries)\",\n \"def getnames(f):\\n # Assumes file is sorted with girl names first, boy names second, and the\\n # most popular name at the top of each list.\\n\\n lineoftext = f.readline()\\n girlname,sex,count = processline(lineoftext)\\n\\n while sex != \\\"M\\\":\\n name,sex,count = processline(f.readline())\\n boyname=name\\n\\n return girlname,boyname\",\n \"def get_team_names(self, year1, year2):\\n\\n base_url = 'http://www.sports-reference.com/cbb/schools/'\\n response = urllib2.urlopen(base_url)\\n content = response.read()\\n soup = BeautifulSoup(content, 'html.parser')\\n soup_results = soup.findAll('tr', {'class':''})\\n extract_name = lambda name: name.split('/')[3]\\n team_names = []\\n \\n for result in soup_results[1::]:\\n year_span = result.findAll('td', {'align':'center'}) \\n year_span = map(int, [year.string for year in year_span])\\n\\n if year_span[0] <= year1 and year_span[1] >= year2:\\n team_name = result.find('a', href = True).get('href')\\n team_name = extract_name(team_name)\\n team_names.append(str(team_name))\\n\\n self.team_names = team_names\",\n \"def get_titles(filename):\\n\\n with open(filename) as f:\\n reader = csv.DictReader(f, fieldnames = ['title', 'year'])\\n titles = list(reader)\\n return titles\",\n \"def extract_names(elements, year):\\r\\n milers = []\\r\\n groupings = _miler_grouper(elements)\\r\\n # import pdb; pdb.set_trace()\\r\\n for miler in groupings:\\r\\n person = {'last_name': miler[0],\\r\\n 'first_name': miler[1],\\r\\n 'trail_name': miler[2],\\r\\n 'year': year}\\r\\n milers.append(person)\\r\\n\\r\\n return milers\",\n \"def baby_search_engine(name):\\r\\n \\r\\n name_ranking = []\\r\\n \\r\\n for publication_name, name_list in baby_names.items():\\r\\n publication = {}\\r\\n if name.capitalize() in name_list:\\r\\n publication['list'] = publication_name\\r\\n publication['rank'] = name_list.index(name.capitalize()) + 1\\r\\n name_ranking.append(publication)\\r\\n\\r\\n \\r\\n return sorted(name_ranking, key=lambda k: k['rank'])\",\n \"def get_names(path='names.txt'):\\n if not os.path.exists(path):\\n logging.info('data not found, downloading.')\\n url = 'https://www.sec.gov/rules/other/4-460list.htm'\\n r = requests.get(url)\\n tree = html.fromstring(r.content)\\n names = tree.xpath('//tr/td[2]')\\n names = [name.text.strip()\\n for name in names if name.text is not None]\\n\\n url = 'http://www.nasdaq.com/screening/companies-by-industry.aspx?industry=Technology&render=download'\\n r = requests.get(url)\\n newnames = [line.strip().split(',')[1].replace('\\\"', '')\\n for line in r.text.split('\\\\n')[:-1]]\\n names.extend(newnames)\\n with open(path, 'w') as txtfile:\\n txtfile.write('\\\\n'.join(names))\\n else:\\n with open(path, 'r') as txtfile:\\n names = txtfile.read().split('\\\\n')\\n return names\",\n \"def get_dates_list() -> List[str]:\\n dates = listdir(\\\"hansard_gathering/processed_hansard_data\\\")\\n return sorted([_file for _file in dates if not _file.endswith(\\\"_num\\\")])\",\n \"def process_file(file_name):\\n f = open(\\\"babynames/\\\" + file_name, 'r')\\n reader = csv.reader(f)\\n return list(reader)\",\n \"def get_oscars_best_picture_list():\\n list_file = 'oscar_best_picture_list.txt'\\n f = open(list_file, 'r')\\n film_list = []\\n\\n for line in f:\\n words = line.split('-')\\n film = {\\n 'year': words[0][:-1],\\n 'name': words[1][2:-2]\\n }\\n film_list.append(film)\\n f.close()\\n # Reverse as we want newest first not last\\n film_list.reverse()\\n return film_list\",\n \"def get_names(filename):\\n\\n with open(filename, \\\"r\\\") as readFile:\\n names = [each.split(',') for each in readFile] #Split at the commas\\n names = list(names[0]) #Retrieves the list of names within the list names\\n names = [each.strip(\\\"\\\\\\\"\\\") for each in names] #Strips each name of the double quotes\\n return names\",\n \"def extract(fichier):\\r\\n names = []\\r\\n for line in open(fichier, 'r'):\\r\\n for almost_name in line.split(','):\\r\\n names.append(almost_name[1:len(almost_name)-1]) \\r\\n return [i for i in names if i != '']\",\n \"def get_html_files_for_candidates(f_name):\\n candidates = ['Joe Biden','Kamala Harris','Elizabeth Warren','Bernie Sanders']\\n candidates_files = ['{}.html'.format(candidate) for candidate in candidates]\\n results = []\\n files = sorted([os.path.join(f_name, f) for f in os.listdir(f_name) if os.path.isfile(os.path.join(f_name, f))]) #extra check if is file unnecessary\\n for file in files:\\n for c in candidates_files:\\n if c in file:\\n results.append(file)\\n print(results)\\n return results\",\n \"def get_people(filename):\\n\\n lines = [line.rstrip('\\\\n').rstrip('\\\\r') for line in open(filename)]\\n return lines\",\n \"def read_file(file_name):\\n with open(file_name) as f:\\n content = f.readlines()\\n names = []\\n dnas = []\\n dna = \\\"\\\"\\n name = \\\"\\\"\\n for line in content:\\n line = line.strip()\\n if line[0] == \\\">\\\":\\n names.append(name)\\n dnas.append(dna)\\n name = line[1:]\\n dna = \\\"\\\"\\n else:\\n dna += line\\n names.append(name)\\n dnas.append(dna)\\n\\n return (names[1:], dnas[1:])\",\n \"def get_bfi_list():\\n list_file = 'bfi_sight_and_sound_2012.txt'\\n f = open(list_file, 'r')\\n film_list = []\\n\\n for line in f:\\n words = line.split(' ')\\n #NOTE: pos is not the position in the pyton list but in the original\\n # list so is not always an integer due to joint places\\n film = {'pos': words[0], 'name': words[1][:-1]}\\n film_list.append(film)\\n f.close()\\n return film_list\",\n \"def read_all_names(start,end,gender,datadir):\\r\\n names = []\\r\\n name_dict = {}\\r\\n\\r\\n for year in range(start,end+1):\\r\\n year_names = []\\r\\n year = str(year)\\r\\n try:\\r\\n path = r\\\"{0}/yob{1}.txt\\\".format(datadir,year)\\r\\n f = open(path)\\r\\n reader = csv.reader(f)\\r\\n rank = 1\\r\\n for name in reader:\\r\\n if name[1].upper() == gender.upper():\\r\\n #first time name appears\\r\\n if name[0] not in name_dict:\\r\\n name_dict[name[0]] = {}\\r\\n name_dict[name[0]].update({'gender':gender.upper()})\\r\\n name_dict[name[0]].update({'intro_year_pop':int(name[pop_index])})\\r\\n name_dict[name[0]].update({'intro_year':year})\\r\\n name_dict[name[0]].update({'intro_year_rank':rank})\\r\\n name_dict[name[0]].update({'pops':{year:int(name[pop_index])}})\\r\\n name_dict[name[0]].update({'ranks':{year:rank}})\\r\\n #every time the name appears\\r\\n name_dict[name[0]]['pops'].update({year:int(name[pop_index])})\\r\\n name_dict[name[0]]['ranks'].update({year:rank})\\r\\n rank = rank + 1\\r\\n year_names.append(name[name_index])\\r\\n names.extend(year_names)\\r\\n f.close()\\r\\n except IOError:\\r\\n \\tprint(\\\"{0} not found\\\".format(path))\\r\\n return name_dict\",\n \"def display_get_date_ordered():\\n ordered_games = reports.get_date_ordered(filename)\\n print(\\\"Ordered games by date and by alphabet:\\\")\\n for title in ordered_games:\\n print(title)\\n print()\",\n \"def get_word_list(file_name):\\n file_ = open(file_name, 'r')\\n lines = file_.readlines()\\n\\n start_line = 0\\n while lines[start_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n start_line += 1\\n\\n lines = lines[start_line+1:]\\n\\n end_line = 0\\n while lines[end_line].find('END OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n end_line += 1\\n\\n lines = lines[:end_line-3]\\n\\n list_ = ' '.join(lines)\\n list_ = str.lower(list_)\\n list_ = list_.translate(None, string.punctuation)\\n list_ = list_.split()\\n\\n return list_\",\n \"def get_word_list(file_name):\\n\\tbook = get_file_text(file_name)\\n\\tbook = strip_header(book)\\n\\tbook = strip_punctuation(book)\\n\\tbook = book.lower()\\n\\twords = re.split(r'\\\\s+', book)\\n\\treturn words\",\n \"def get_word_list(file_name):\\n\\tnew_list = []\\n\\n\\tf = open(file_name,'r')\\n\\tlines = f.readlines()\\n\\tcurr_line = 0\\n\\tend_line = 0\\n\\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n\\t\\tcurr_line += 1\\n\\twhile lines[end_line].find('End of the Project Gutenberg EBook') == -1:\\n\\t\\tend_line -= 1\\n\\tlines = lines[curr_line + 1:end_line]\\n\\n\\tlong_lines = ''.join(str(e) for e in lines)\\n\\tlong_lines = long_lines.lower()\\n\\tlong_lines = long_lines.translate(None, punctuation)\\n\\n\\twords = long_lines.split()\\n\\tfor item in words:\\n\\t\\tnew_list.append(item)\\n\\n\\treturn new_list\",\n \"def get_word_list(file_name):\\n\\n\\tstoryEdit = []\\n\\n\\t#Reads the file starting after the beginning\\t\\n\\tf = open(file_name,'r')\\n\\tlines = f.readlines()\\n\\tcurr_line = 0\\n\\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n\\t\\tcurr_line += 1\\n\\tlines = lines[curr_line+1:]\\n\\n\\n\\t#Loops through each row, making everything lowercase and replacing all punctuation\\n\\tfor row in lines:\\n\\t \\trow = row.lower()\\n\\t \\trow = row.translate(string.maketrans(\\\"\\\",\\\"\\\"), string.punctuation)\\n\\t \\tstoryEdit += row.split()\\n\\n\\n\\t#Returns the final list as \\n\\treturn storyEdit\",\n \"def get_teams_from_text(file_name, index):\\n\\n\\tteams = []\\n\\tList = open(\\\"teams/\\\" + file_name + \\\".txt\\\",'r').read().splitlines()\\n\\t\\n\\tj = 0\\n\\tfor i in range(0,len(List)):\\n\\t\\tif j < index:\\n\\t\\t\\tdummy_team = Team(List[i], \\\"Wappen\\\")\\n\\t\\t\\tteams.append(dummy_team)\\n\\t\\tj = j+1\\n\\treturn teams\",\n \"def read_drama_names(drama_file):\\n with open(drama_file, 'rb') as f:\\n drama_list = [d.decode('utf-8').strip() for d in f.readlines()]\\n return drama_list\",\n \"def format_birthday(birth_list):\\r\\n # Remove space and tab from the left and right part of the string\\r\\n stripped_text = birth_list.strip(\\\"\\\\n\\\\t\\\")\\r\\n # Split the date and count\\r\\n split_text = [s.split(\\\"\\\\t\\\") for s in stripped_text.split(\\\"\\\\n\\\")[6:]] \\r\\n split_date = [map(int, s[0].split(\\\"/\\\")) for s in split_text] \\r\\n split_count = map(int, [s[1] for s in split_text])\\r\\n # Concatenate the date and count \\r\\n merge = [split_date[i] + [split_count[i]] for i in xrange(365)]\\r\\n return merge\",\n \"def sort_by_surname_desc(names):\\n names = dedup_and_title_case_names(names)\\n names1 = []\\n for n in names:\\n x = n.split(\\\" \\\")\\n names1.append(x[1] + \\\" \\\" + x[0])\\n return names1\\n # ...\",\n \"def main(html_file):\\n \\n biglist = name_extractor(html_file)\\n text = '\\\\n'.join(biglist)\\n print (text)\",\n \"def fasta_headers(file_name):\\n list = []\\n with open('../test_files/' + file_name, 'r') as infile:\\n text = infile.read()\\n seqs = text.split('>')\\n for seq in seqs:\\n try:\\n x = seq.split('\\\\n', 1)\\n if x[0] != '':\\n #x[0] contains only headers\\n list.append(x[0])\\n except:\\n pass\\n return list\",\n \"def extract_bird_name(filename, split=None):\\n description = ''\\n if split is None:\\n words = filename.split()\\n else:\\n words = filename.split(split)\\n for word in words:\\n if is_number(word):\\n break\\n if description is '':\\n description += word\\n else:\\n description += ' ' + word\\n return description\",\n \"def find_yearly_playlist(data):\\n\\n if data['year'] and str(data['year']).isdigit():\\n median = round(int(data['year']), -1)\\n return 'years-{}-{}.m3u'.format((int(median) - 5), (int(median) + 4))\\n\\n return '00-without-year.m3u'\",\n \"def filename_to_file_list_key(filename):\\n pattern = r'\\\\.\\\\d\\\\d\\\\d\\\\d-'\\n index = re.search(pattern, filename)\\n if index:\\n year = int(filename[index.start() + 1: index.start() + 5])\\n else:\\n return '0-0'\\n # the YYYY field is 4 characters long, the month is two\\n year_offset = index.start() + 5\\n # two characters for the month, and one for the - between year and month\\n month_offset = year_offset + 3\\n month = int(filename[year_offset + 1: month_offset])\\n key = \\\"{year}-{month}\\\".format(year=year, month=month)\\n return key\",\n \"def get_list(file_name):\\n with open(file_name, \\\"r\\\", encoding=\\\"latin-1\\\") as file:\\n text = file.read()\\n text = text.lower() # Make everything lowercase\\n text = text.split(\\\"\\\\n\\\")\\n return text\",\n \"def parse(filename):\\n file_map = {\\n '1995-1996.html': ninety_six,\\n '2005-2006.html': twenty_six,\\n '2014-2015.html': twenty_fifteen\\n }\\n func = file_map.get(filename, lambda: \\\"Invalid File\\\")\\n func(filename)\",\n \"def read_names(path):\\n return SortedSet([os.path.basename(n) for n in glob.glob(path + os.sep + '*')])\",\n \"def getFilmTitles(checkFolder):\\n\\n files = [str(x) for x in Path(checkFolder).rglob(\\\"*\\\")]\\n libFilmFiles = list(map(os.path.basename,files)) # Remove the path\\n libFilmTitles = [os.path.splitext(x)[0] for x in libFilmFiles]\\n return libFilmTitles\",\n \"def get_nifty_list(root_dir, name=\\\"\\\"):\\n \\n file_list = glob(root_dir + \\\"/**/*.nii.gz\\\", recursive=True)\\n file_list = sorted([file for file in file_list if name in file])\\n return file_list\",\n \"def read_name_file(filename):\\n with open(filename, 'r') as f:\\n names = f.read()\\n names = names.split('\\\\n')\\n names = list(filter(None, names))\\n return names\",\n \"def _get_authors_list():\\n\\n articles = os.listdir(\\\"../data/\\\")\\n authors = []\\n for article in articles:\\n with open(\\\"../data/\\\" + article, 'r') as file:\\n lines = file.readlines()\\n author = tuple(\\n line.replace(\\\"\\\\n\\\", \\\"\\\").split()[1] for line in lines\\n if \\\"Автор:\\\" in line\\n )[0]\\n authors.append(author)\\n\\n return authors\",\n \"def getlistofpossibletitles(fileitem,shows):\\n title = []\\n title.append(fileitem)\\n lookfor = fileitem.replace(\\\".\\\",\\\" \\\")\\n title.append(lookfor)\\n lookfor = fileitem.replace('-',\\\" \\\")\\n title.append(lookfor)\\n return title\",\n \"def get_file_two_years(file):\\n f = '_' + '(\\\\d+)' + '_' + '(\\\\d+)'\\n match = re.search(f, file)\\n if match:\\n return int(match.group(1)), int(match.group(2))\",\n \"def get_names():\\n only_links = SoupStrainer(\\\"a\\\")\\n names = set()\\n doc = requests.get(NAMES_URL).content\\n links = BeautifulSoup(doc, \\\"html.parser\\\", parse_only=only_links)\\n pokemon = links.find_all(title=re.compile(\\\"(\\\\w+)(\\\\s){1}(\\\\(Pokémon\\\\))\\\"))\\n for cell in pokemon:\\n names.add(str(cell.string))\\n \\n\\n return names\",\n \"def get_word_list(file_name):\\n # Read the file specified\\n f = open(file_name,'r')\\n lines = f.readlines()\\n \\n # Remove header text from lines\\n curr_line = 0\\n while lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n curr_line += 1\\n lines = lines[curr_line + 1:]\\n\\n # Remove footer text from lines\\n curr_line = -1\\n while lines[curr_line].find('END OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n curr_line -= 1\\n lines = lines[: curr_line]\\n\\n # Strip lines into words\\n words = []\\n for i in range(len(lines)):\\n # Remove punctuation\\n next_line = lines[i].translate(string.maketrans(\\\"\\\",\\\"\\\"), string.punctuation)\\n next_line = next_line.lower()\\n words += next_line.split()\\n \\n return words\",\n \"def get_names_url(i):\\n urls = list()\\n with open('./urls/fall11_urls_train_'+str(i)+'.txt','r',encoding=\\\"Latin-1\\\") as f:\\n for line in f:\\n urls.append(line)\\n urls = [url.strip('\\\\n') for url in urls]\\n urls1 = [url.split('\\\\t')[1] for url in urls]\\n names = [url.split('\\\\t')[0] for url in urls]\\n return urls1,names\",\n \"def format_finder(files):\\r\\n\\r\\n file_format = ['%Y', '%m', '%d']\\r\\n year = ''\\r\\n month = ''\\r\\n da = ''\\r\\n\\r\\n for file in files:\\r\\n\\r\\n index = file.find('_')\\r\\n date = file[: index]\\r\\n if '-' in date:\\r\\n separator = '-'\\r\\n else:\\r\\n separator = '_'\\r\\n date = date.split(separator)\\r\\n\\r\\n for d in range(len(date)):\\r\\n\\r\\n # If the date doesn't contain decimal (Eg: August) then it would return None\\r\\n if not date[d].isdecimal():\\r\\n return None\\r\\n\\r\\n # If the element in the date is of length greater then 2 then it would be a year (Eg: 2020)\\r\\n # And that value is set as the index of year\\r\\n if len(date[d]) > 2:\\r\\n year = d\\r\\n\\r\\n # If the integer of element in the date is of length greater then 12 then it would be a date (Eg: 25)\\r\\n # And that value is set as the index of date\\r\\n elif int(date[d]) > 12:\\r\\n da = d\\r\\n\\r\\n # If Both year and date are set, then the correct index for the month would be 3- (year+date)\\r\\n # Eg: 3 -(0+1)\\r\\n if (year != '') and (da != ''):\\r\\n month = 3 - (year + da)\\r\\n break\\r\\n\\r\\n # If Month is set, then we change the format according to their set value\\r\\n # Eg: format = ['%Y', '%m', '%d'], and year = 1, da = 0, month = 2\\r\\n # Then format[year=1] = '%Y'\\r\\n # Then format[da=0] = '%d'\\r\\n # Then format[month=2] = '%m'\\r\\n # format = ['%d', '%Y', '%m']\\r\\n if month:\\r\\n file_format[year] = '%Y'\\r\\n file_format[month] = '%m'\\r\\n file_format[da] = '%d'\\r\\n break\\r\\n else:\\r\\n # The script executes this only if none of the files had an date element( Which is not year)\\r\\n # That was greater than 12, Eg: 2020-06-10\\r\\n # Meaning that we cannot know for sure which element represents the date/month\\r\\n # Hence we arbitrarily assign one element as date and another as month\\r\\n if year != 0:\\r\\n # If the index of year is zero, we let the format to be same as it was assigned first\\r\\n # Else we arbitrarily assign '0' th index to month\\r\\n file_format[year] = '%Y'\\r\\n file_format[0] = '%m'\\r\\n file_format[3 - year] = '%d'\\r\\n return f'{file_format[0]}-{file_format[1]}-{file_format[2]}'\",\n \"def list_assay_files(path):\\n if rank != 0:\\n print(\\\"Rank {} tried scanning the directory.\\\".format(rank))\\n sys.exit()\\n\\n valid_names = []\\n for root, dirs, files in os.walk(path):\\n for f in files:\\n if f.startswith(\\\"NENE\\\") and f.endswith((\\\"A.txt\\\", \\\"B.txt\\\")):\\n fullpath = os.path.join(root, f)\\n valid_names.append(fullpath)\\n\\n return valid_names\",\n \"def read_screen_names(filename):\\n flist = []\\n f = open('candidates.txt')\\n for line in f:\\n \\tflist.append(line.strip('\\\\n'))\\t\\n return flist\",\n \"def read_names_list(file_path):\\r\\n\\tnames_list = []\\r\\n\\twith open(file_path) as file:\\r\\n\\t for line in file:\\r\\n\\t cline = line.rstrip().split()\\r\\n\\t #row_id = cline[0]\\r\\n\\t row_name = cline[1:]\\r\\n\\t #names_list.append((row_id, \\\" \\\".join(row_name)))\\r\\n\\t names_list.append(\\\" \\\".join(row_name))\\r\\n\\treturn names_list\",\n \"def get_oscars_list():\\n file = 'oscars.html'\\n handler = open(file).read()\\n soup = BeautifulSoup(handler)\\n film_list = []\\n\\n for row in soup.findAll('tr'):\\n #TODO: add more elegant check instead of dirty try catch\\n try:\\n links = row.findAll('a')\\n cells = row.findAll('td')\\n awards = int(cells[2].contents[0])\\n\\n # Filter only nominations\\n if awards > 0:\\n film = {\\n 'name': links[0].contents[0],\\n 'year': links[1].contents[0],\\n 'awards': awards,\\n 'nominations': cells[3].contents\\n }\\n film_list.append(film)\\n except Exception, e:\\n pass\\n\\n film_list = sorted(film_list, key=lambda k: k['awards'])\\n film_list.reverse()\\n return film_list\",\n \"def GetColumnistNames():\\n global columnistnames\\n if not columnistnames:\\n columnistnames = []\\n html = ukmedia.FetchURL( columnistmainpage )\\n soup = BeautifulSoup( html )\\n for a in soup.findAll( 'a', {'class':'author'} ):\\n n = a.renderContents(None).strip()\\n if not n in columnistnames:\\n columnistnames.append( n )\\n return columnistnames\",\n \"def test_get_teams_in_year_names():\\n assert sorted(gtiy(2008)) == sorted(team_2008)\\n assert sorted(gtiy(2009)) == sorted(team_2009)\\n assert sorted(gtiy(2010)) == sorted(team_2010)\\n assert sorted(gtiy(2011)) == sorted(team_2011)\\n assert sorted(gtiy(2012)) == sorted(team_2012)\\n assert sorted(gtiy(2013)) == sorted(team_2013)\\n assert sorted(gtiy(2014)) == sorted(team_2014)\\n assert sorted(gtiy(2015)) == sorted(team_2015)\\n assert sorted(gtiy(2016)) == sorted(team_2016)\\n assert sorted(gtiy(2017)) == sorted(team_2017)\",\n \"def list_titles(genre):\\n text = genre_html(genre)\\n num_titles = text.count('title=')\\n\\n titles = []\\n for i in range(num_titles):\\n start = text.find('title=')\\n end = text[start+7:].find('\\\">')\\n title = text[start+7:start+end]\\n titles.append(title)\\n text = text[start+7:]\\n\\n return titles\",\n \"def get_html_files(f_name):\\n files = sorted([os.path.join(f_name, f) for f in os.listdir(f_name) if os.path.isfile(os.path.join(f_name, f))]) #extra check if is file unnecessary\\n return files\",\n \"def filelist(basedir):\\n day_files = []\\n for root, dirs, files in os.walk(basedir):\\n for file in files:\\n if file.endswith(\\\".png\\\"):\\n day_files.append(os.path.join(file))\\n dates_files = []\\n\\n for i in day_files:\\n year = i.split('_')[1]\\n day = i.split('_')[2]\\n mounth = i.split('_')[3]\\n hour = i.split('_')[4]\\n dates_files.append(UTCDateTime(year+'-'+mounth+'-'+day+'T'+hour)-3)\\n return sorted(dates_files)\",\n \"def sort_by_version(compiled_re, names):\\n annotated_names = [([int(n) for n in compiled_re.match(name).groups()], name) for name in names]\\n annotated_names.sort()\\n return [annotated_name[1] for annotated_name in reversed(annotated_names)]\",\n \"def getlistofpossibletitles(fileitem,fname):\\n title = []\\n oddtitles = open(\\\"oddtitles.txt\\\", 'r')\\n content = oddtitles.read()\\n oddtitles.close()\\n\\n content = content.split(\\\"\\\\n\\\")\\n for line in content:\\n elements = line.split(',')\\n if fileitem in elements[0]:\\n #print(elements[1])\\n title.append(elements[1].title())\\n\\n \\n title.append(fileitem)\\n title.append(fileitem.title())\\n lookfor = fileitem.replace(\\\".\\\",\\\" \\\")\\n title.append(lookfor)\\n title.append(lookfor.title())\\n lookfor = fileitem.replace('-',\\\" \\\")\\n title.append(lookfor)\\n title.append(lookfor.title())\\n with open(fname, \\\"r\\\") as dataf:\\n for line in dataf:\\n if lookfor.upper() in line.upper():\\n line = line.replace(\\\"\\\\n\\\",\\\"\\\")\\n title.append(line)\\n title.append(line.title())\\n return title\",\n \"def get_data(url):\\n\\n request = requests.get(url)\\n\\n soup = BeautifulSoup(request.text, \\\"lxml\\\")\\n\\n ol_tags = soup.find_all('ol')\\n\\n names_list = []\\n\\n for li_tags in ol_tags:\\n for names in li_tags:\\n names_list.append(names.text)\\n\\n return names_list\",\n \"def sort_slide_names(l): \\n convert = lambda text: int(text) if text.isdigit() else text \\n alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key) ] \\n return sorted(l, key = alphanum_key)\",\n \"def get_links(names, html):\\n ###TODO\\n people = []\\n readweb = BeautifulSoup(html, 'html.parser')\\n for a in readweb.find_all('a'):\\n person = os.path.basename(str(a.get('href')))\\n if person in names:\\n people.append(person)\\n return SortedSet(people)\\n pass\",\n \"def get_year_desc():\\n return Year.objects.all().order_by('-name')\",\n \"def report_name_sort(rpt_name: str):\\n rptn = rpt_name.split(\\\" \\\")\\n try:\\n rpta = rptn[0].split(\\\"-\\\")\\n returned = (rpta[0], rpta[1])\\n except IndexError:\\n returned = (rptn[0], \\\"\\\")\\n\\n return returned\",\n \"def _possible_names(self, filename):\\n names = [filename]\\n if not self._iszip(filename):\\n for zipext in _file_openers.keys():\\n if zipext:\\n names.append(filename+zipext)\\n return names\",\n \"def create_dropdown_list_years(movies):\\n dropdown = ''\\n movies.sort(key=lambda x: x.year, reverse=True)\\n y = []\\n for movie in movies:\\n y.append(movie.year)\\n y = list(set(y))\\n for x in y:\\n dropdown += dropdown_tile_year.format(id_year=x,\\n year=x)\\n \\n return dropdown\",\n \"def test_iter_all_years(self):\\n ar = awstats_reader.AwstatsReader(test_file_dir, 'jjncj.com')\\n self.assertEqual([ary.year for ary in ar], [2008,2009])\",\n \"def get_animals_list(filename):\\n\\n f = open(filename, 'r')\\n text = f.read()\\n f.close()\\n animals = text.split('\\\\n')\\n animals = [animal for animal in animals if animal != '' and len(animal) > 1]\\n return sorted(animals)\",\n \"def loadFiles():\\n all_chapters = []\\n for name in names:\\n f = open(file_path + name, \\\"r\\\", encoding=\\\"utf-8\\\")\\n html_file = f.read()\\n f.close()\\n chap_text = extractText(html_file)\\n new_text = cleanText(chap_text)\\n all_chapters.append(new_text)\\n concatenated_chapters = \\\" \\\".join(all_chapters)\\n return concatenated_chapters\",\n \"def parse_bib_from_list(filename):\\n\\tentry_regex = r\\\"TITEL: .*\\\\s*AUTOR: .*\\\"\\n\\tparse_func = make_parse_func(r\\\"AUTOR: (.*)\\\", r\\\"TITEL: (.*)\\\", None)\\n\\treturn parse_bib(filename, entry_regex, parse_func)\",\n \"def readFile(fileName):\\n\\tf = open(fileName, 'r')\\n\\tnames = map(lambda s: s[1:-1], f.read().split(','))\\n\\tnames.sort()\\n\\treturn names\",\n \"def get_patient_names(self):\\n\\t# use pre-defined patient names\\n\\tif (self.data_names is not None):\\n\\t\\tassert (os.path.isfile(self.data_names))\\n\\t\\twith open(self.data_names) as f:\\n\\t\\t\\tcontent = f.readlines()\\n\\t\\tpatient_names = [x.strip() for x in content]\\n\\t# use all the patient names in data_root\\n\\telse:\\n\\t\\tpatient_names = os.listdir(self.data_root[0])\\n\\t\\tpatient_names = [name for name in patient_names if 'brats' in name.lower()]\\n\\treturn patient_names\",\n \"def __file_sorter(self, filename: str) -> int:\\n filename = filename.split('/')[-1]\\n year = int(filename[-3:-1]) # extracts YY from ssssdddh.YYo\\n day_info = filename[4:8] # extracts dddh from ssssdddh.YYo\\n day_of_year = int(day_info[:3]) # ddd\\n hour_block = day_info[-1] # h\\n if hour_block.isdigit():\\n return (year, day_of_year)\\n return (year, day_of_year + ord(hour_block))\",\n \"def get_image_names(file_path: str) -> List:\\n name_list = []\\n f = open(file_path, \\\"r\\\")\\n lines = f.readlines()\\n f.close()\\n for line in lines:\\n name = line.split(\\\"/\\\")[-1].replace(\\\"\\\\n\\\", \\\"\\\")\\n name_list.append(name)\\n return name_list\",\n \"def parse_bibtex(file, build_dir):\\n\\n parser = BibTexParser()\\n parser.customization = customizations\\n years = []\\n with open(file, 'r') as f: \\n bibtex = bibtexparser.load(f, parser=parser)\\n for i in range (len(bibtex.entries)):\\n for key, value in bibtex.entries[i].items():\\n if key == 'year':\\n years.append(int(value))\\n years.sort()\\n years.reverse()\\n years_no_repeat = []\\n for i in range(len(years)):\\n if years_no_repeat.count(years[i]) == 0:\\n years_no_repeat.append(years[i])\\n \\n for i in range(len(years_no_repeat)):\\n bibtext = copy.deepcopy(bibtex)\\n array = []\\n for j in range (len(bibtex.entries)):\\n for key, value in bibtex.entries[j].items():\\n if key == 'year':\\n if int(value) == years_no_repeat[i]:\\n array.append(bibtex.entries[j])\\n bibtext.entries = array\\n parse_file = os.path.join(build_dir, str(years_no_repeat[i]) + 'parsed.bib')\\n writer = BibTexWriter()\\n writer.order_entries_by = ('ENTRYTYPE', )\\n with open(parse_file, 'w') as f:\\n f.write(writer.write(bibtext))\",\n \"def get_page_name(self,en_code):\\n files_and_names = {}\\n for files_named in self.find_enc(en_code):\\n search_in_file = open(self.file_location+\\\"/\\\"+files_named)\\n for line in search_in_file:\\n if '# LINKNAME:' in line:\\n #print(line)\\n new_line = line.split('# LINKNAME:')\\n for nl in new_line:\\n fnl = nl.strip()\\n if fnl is not None:\\n files_and_names[files_named] = fnl\\n search_in_file.close()\\n return files_and_names\",\n \"def sortednameslist(nameslist):\\n sortednames = sorted(nameslist, key=lambda x: x[1])\\n return sortednames\",\n \"def top_ten_movies(path):\\n content = open(path, \\\"r\\\")\\n topten = []\\n for x in content:\\n topten.append(x) \\n return topten\",\n \"def parse_authors():\\n import subprocess\\n try:\\n output = subprocess.check_output(['git', 'shortlog', '-s'],\\n universal_newlines=True)\\n except Exception as ex:\\n print('ex = {!r}'.format(ex))\\n return []\\n else:\\n striped_lines = (l.strip() for l in output.split('\\\\n'))\\n freq_authors = [line.split(None, 1) for line in striped_lines if line]\\n freq_authors = sorted((int(f), a) for f, a in freq_authors)[::-1]\\n # keep authors with uppercase letters\\n authors = [a for f, a in freq_authors if a.lower() != a]\\n return authors\",\n \"def scrape_all_course_names(filename, verbose):\\n \\n\\tsoup =\\tBeautifulSoup(open(filename, 'r'), 'html.parser')\\n\\t#print(soup)\\n\\tcourses = []\\n\\t# all the courses are stored in a div w/class=view-content\\n\\t\\t\\n\\th4_field_content = soup.find('h4', 'field-content')\\n\\t#print(h4_field_content)\\n\\tall_course_content = soup.find(\\\"div\\\", \\\"view-content\\\") # this contains ALL the classes...\\n\\tif all_course_content == None:\\n\\t\\tprint(\\\"There are no courses on this page. Try a smaller page number!\\\")\\n\\t\\treturn []\\n\\t\\n\\tcandidate_classes = all_course_content.find_all('a') # we want all the 'a' tags within\\n\\tclass_list=[]\\n course_code_and_number = {}\\n\\tfor c in candidate_classes:\\n\\t\\t#print(c.text, \\\"\\\\n\\\")\\n\\t\\ttext = c.text # ex. AEMA 611 Experimental Designs 1 (3 credits)\\n\\t\\ttext = text.split(\\\" \\\")# split on the space\\n\\t\\tcourse_id = \\\" \\\".join(text[:2]).replace('\\\\n', '') # the first 2 are the course id\\n\\t\\tcourse_name = \\\" \\\".join(text[2:-2]).replace('\\\\n', '')\\n\\t\\tnum_credits = text[-2].replace(\\\"(\\\", \\\"\\\")# just get the course number, replace the ( with nothing \\n\\t\\n\\t\\n\\t\\t#print(f\\\"{course_id}\\\\n{course_name}\\\\n{num_credits}\\\\n\\\")\\n\\t\\ttry:# Check that the course number is a digit bc sometimes it is something weird\\n\\t\\t\\tfloat(num_credits)\\n\\t\\texcept ValueError:\\n\\t\\t\\t#print(f\\\"Wrong course format. Ignoring {c}\\\")\\n\\t\\t\\tif(verbose):\\n\\t\\t\\t\\tprint(f\\\"Wrong course format. Ignoring course: {c.text}\\\")\\n\\t\\t\\tcontinue\\n\\t\\tclass_list.append(course_id)\\n\\treturn class_list\",\n \"def _get_wordlist(file_name):\\n ifile = codecs.open(file_name, 'r', encoding='utf-8')\\n for _ in range(int(ifile.__next__())):\\n yield (ifile.__next__().strip() for _ in range(int(ifile.__next__())))\",\n \"def get_word_list(file_name, n):\\n f = open(file_name, 'r')\\n text = f.read()\\n words = re.compile('\\\\w+').findall(text)\\n return get_top_n_words(words, n)\",\n \"def read_str_name(path):\\r\\n name = []\\r\\n name_stru = {}\\r\\n with open(path, \\\"r+\\\") as f:\\r\\n line = f.readlines()\\r\\n \\r\\n # to load the name to list. files\\r\\n for i in range(len(line)):\\r\\n \\r\\n if line[i][:-1] != '':\\r\\n \\r\\n name.append(line[i][:-1])\\r\\n else:\\r\\n \\r\\n name.append(line[i-1][:-1] + str())\\r\\n \\r\\n line[i] = line[i-1]\\r\\n \\r\\n # to remark the structure name\\r\\n for s in name:\\r\\n \\r\\n name_stru[s] = (name.count(s),name.index(s))\\r\\n \\r\\n for key,values in name_stru.items():\\r\\n \\r\\n if values[0] != 1:\\r\\n for i in range(values[0]):\\r\\n name[values[1]+i] = name[values[1]+i] + str(i+1)\\r\\n \\r\\n return name\",\n \"def get_title_name_year(self) -> Tuple[str, str]:\\n r = self.session.get(f\\\"https://www.imdb.com/title/{self.imdb}\\\")\\n if r.status_code != 200:\\n raise ValueError(f\\\"An unexpected error occurred getting IMDB Title Page [{r.status_code}]\\\")\\n imdb_page = html.unescape(r.text)\\n imdb_title = re.search(\\n # testing ground: https://regex101.com/r/bEoEDn/1\\n r\\\"<title>(?P<name>.+) \\\\(((?P<type>TV (Movie|Series|Mini[- ]Series|Short|Episode) |Video |Short |)\\\"\\n r\\\"(?P<year>(\\\\d{4})(|– |–\\\\d{4})))\\\\) - IMDb</title>\\\",\\n imdb_page\\n )\\n if not imdb_title:\\n raise ValueError(f\\\"Could not scrape Movie Title or Year for {self.imdb}...\\\")\\n return imdb_title.group(\\\"name\\\").strip(), imdb_title.group(\\\"year\\\").strip()\",\n \"def createFileNames(nFileNames, seqPrefix):\\n nameList = []\\n nameList = [seqPrefix+str(i)+\\\".txt\\\" for i in range(0, nFileNames)]\\n return nameList\",\n \"def get_labyrinth_from_file_name(self, file_name):\\n\\n excel_file = xlrd.open_workbook(self.file_name)\\n labyrinth = list()\\n for sheet in excel_file.sheets():\\n for i in range(sheet.nrows):\\n row = list()\\n for j in range(sheet.ncols):\\n row.append(sheet.cell_value(i, j))\\n labyrinth.append(row)\\n\\n return labyrinth\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7748594","0.74819154","0.73469853","0.7098531","0.70823675","0.6961224","0.67496955","0.6549014","0.6204802","0.6009291","0.5994891","0.59557843","0.59324557","0.591772","0.5879463","0.5868835","0.5803098","0.5757769","0.57187426","0.57022166","0.5698993","0.56967515","0.566333","0.56408554","0.5637905","0.5633162","0.55702776","0.55400187","0.5537733","0.5537317","0.55194575","0.5513656","0.5502073","0.54979014","0.5492763","0.54882896","0.54785943","0.5449284","0.5447175","0.5423578","0.54224414","0.5414946","0.54095227","0.5392193","0.53748566","0.53620976","0.5356559","0.53563696","0.53461087","0.53336346","0.5329907","0.53074235","0.52998954","0.5280197","0.5265009","0.525049","0.5248914","0.52479285","0.5245172","0.52428025","0.5225552","0.522313","0.5215354","0.52116287","0.5202964","0.5200536","0.519765","0.51941496","0.51904005","0.5177675","0.51672155","0.5159661","0.51533175","0.5152566","0.5148155","0.5144185","0.5141968","0.51391196","0.5137758","0.5136683","0.51214767","0.511978","0.5116746","0.51150167","0.51062936","0.51006734","0.5100279","0.5093798","0.5087557","0.5087244","0.5066502","0.5060116","0.50574976","0.5050647","0.50425404","0.5040403","0.5039778","0.50362396","0.5034267","0.5031472"],"string":"[\n \"0.7748594\",\n \"0.74819154\",\n \"0.73469853\",\n \"0.7098531\",\n \"0.70823675\",\n \"0.6961224\",\n \"0.67496955\",\n \"0.6549014\",\n \"0.6204802\",\n \"0.6009291\",\n \"0.5994891\",\n \"0.59557843\",\n \"0.59324557\",\n \"0.591772\",\n \"0.5879463\",\n \"0.5868835\",\n \"0.5803098\",\n \"0.5757769\",\n \"0.57187426\",\n \"0.57022166\",\n \"0.5698993\",\n \"0.56967515\",\n \"0.566333\",\n \"0.56408554\",\n \"0.5637905\",\n \"0.5633162\",\n \"0.55702776\",\n \"0.55400187\",\n \"0.5537733\",\n \"0.5537317\",\n \"0.55194575\",\n \"0.5513656\",\n \"0.5502073\",\n \"0.54979014\",\n \"0.5492763\",\n \"0.54882896\",\n \"0.54785943\",\n \"0.5449284\",\n \"0.5447175\",\n \"0.5423578\",\n \"0.54224414\",\n \"0.5414946\",\n \"0.54095227\",\n \"0.5392193\",\n \"0.53748566\",\n \"0.53620976\",\n \"0.5356559\",\n \"0.53563696\",\n \"0.53461087\",\n \"0.53336346\",\n \"0.5329907\",\n \"0.53074235\",\n \"0.52998954\",\n \"0.5280197\",\n \"0.5265009\",\n \"0.525049\",\n \"0.5248914\",\n \"0.52479285\",\n \"0.5245172\",\n \"0.52428025\",\n \"0.5225552\",\n \"0.522313\",\n \"0.5215354\",\n \"0.52116287\",\n \"0.5202964\",\n \"0.5200536\",\n \"0.519765\",\n \"0.51941496\",\n \"0.51904005\",\n \"0.5177675\",\n \"0.51672155\",\n \"0.5159661\",\n \"0.51533175\",\n \"0.5152566\",\n \"0.5148155\",\n \"0.5144185\",\n \"0.5141968\",\n \"0.51391196\",\n \"0.5137758\",\n \"0.5136683\",\n \"0.51214767\",\n \"0.511978\",\n \"0.5116746\",\n \"0.51150167\",\n \"0.51062936\",\n \"0.51006734\",\n \"0.5100279\",\n \"0.5093798\",\n \"0.5087557\",\n \"0.5087244\",\n \"0.5066502\",\n \"0.5060116\",\n \"0.50574976\",\n \"0.5050647\",\n \"0.50425404\",\n \"0.5040403\",\n \"0.5039778\",\n \"0.50362396\",\n \"0.5034267\",\n \"0.5031472\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7415442"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":8495,"cells":{"query":{"kind":"string","value":"input h (meters) and the coefficients for the linear profile for the free troposphere theta (ft_intercept (K) and slope gamma (K/m)) return the free tropospher theta at height h"},"document":{"kind":"string","value":"def theta_ft(h,ft_intercept,gamma):\n theta_top = ft_intercept + h*gamma\n return theta_top"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_theta_surf_hex_h(theta_hs_in_h: float, theta_hs_out_h: float, v_hs: float) -> float:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n # sensible heating capacity of heat source for heating, W\n q_hs_h = (theta_hs_out_h - theta_hs_in_h) * c * rho * v_hs / 3600\n\n # sensible heat transfer coefficient on the surface of the heat exchanger of the internal unit, W/m2K\n alpha_c_hex_h = get_alpha_c_hex_h(v_hs)\n\n # effective area for heat exchange of the surface area of heat exchanger or the internal unit, m2\n a_e_hex = get_a_e_hex()\n\n return (theta_hs_in_h + theta_hs_out_h) / 2 + q_hs_h / (a_e_hex * alpha_c_hex_h)","def trapezoidal(f, x0, h):\n return (h/2.0 * (f(x0)+f(x0+h)))","def get_theta_surf_hex_test_h(theta_hs_in_h: float, q_hs_h: float, v_hs: float) -> float:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n theta_hs_out_h = theta_hs_in_h + q_hs_h / (c * rho * v_hs) * 3600\n\n # sensible heat transfer coefficient on the surface of the heat exchanger of the internal unit, W/m2K\n alpha_c_hex_h = get_alpha_c_hex_h(v_hs)\n\n # effective area for heat exchange of the surface area of heat exchanger or the internal unit, m2\n a_e_hex = get_a_e_hex()\n\n return (theta_hs_in_h + theta_hs_out_h) / 2 + q_hs_h / (a_e_hex * alpha_c_hex_h)","def two_theta_hkl(self, H, K, L):\n return self.unit_cell.two_theta((H, K, L), self.wavelength, deg=True)","def curve_with_hillcoef(ph, pka, hillcoef):\n# return hillcoef * ph - pka\n return 1/(1+10**(hillcoef*(pka-ph)))","def get_metrics(H):\n theta = np.arctan2(H[0,1], H[0,0])\n scale = H[0,0] / np.cos(theta)\n tx = H[0,2]\n ty = H[1,2]\t\n return tx,ty,theta","def phitheta(loc):\n x = loc[0]\n y = loc[1]\n z = loc[2]\n r = sqrt(x**2 + y**2 + z**2)\n theta = arcsin(z/r)\n phi = arctan2(y,x)\n return(phi, theta)","def _step(self, t, y, h):\n # We must use solvers / implicit form\n f_pn1 = lambda a_n1: (y + h*self.v + (h**2 / 2.0) * \\\n ((1.0 - 2.*self.beta)*self.a + 2.*self.beta*a_n1))\n f_vn1 = lambda a_n1: (self.v + h*((1.0-self.gamma)*self.a + self.gamma*a_n1))\n def f_an1(a_n1):\n f_n1 = self.f(t+h,f_pn1(a_n1),f_vn1(a_n1))\n f_n = self.f(t,y,self.v,)\n return a_n1 - ((1.0+self.alpha)*f_n1 - self.alpha*f_n)\n\n a = self.solver(f_an1, self.a)\n y = f_pn1(a) # Calculate and store new variables. \n self.v = f_vn1(a)\n self.a = a\n return t+h, y","def __s_polynomial(g, h):\n\n deg_g = __multidegree(g)\n deg_h = __multidegree(h)\n max_deg = map(max, zip(deg_g, deg_h))\n R = g.parent()\n\n # Builds a polynomial with the variables raised to max_deg, in order\n vars = map(R, R.variable_names())\n x_pow_max_deg = reduce(operator.mul, [x ** d for (d, x) in zip(max_deg, vars)], R(1))\n\n quo_g, _ = x_pow_max_deg.quo_rem(g.lt())\n quo_h, _ = x_pow_max_deg.quo_rem(h.lt())\n return quo_g * g - quo_h * h","def ab2rhotheta(a, b):\n \"\"\" also : y - ax - b = 0 \"\"\"\n \"\"\" y*sin(theta) + x*cos(theta) - rho = 0 \"\"\"\n #print(\"a: %f b: %f\" % (a, b))\n theta = math.atan(a) + math.pi/2.0\n rho = b*math.sin(theta)\n #print(\"a: %f b: %f rho: %f theta: %f\" % (a, b, rho, theta))\n return (rho, theta)","def alkTphosfac(hguess,ks):\n #mick - first estimate of contribution from phosphate\n #mick based on Dickson and Goyet\n h3po4g,h2po4g,hpo4g,po4g = phosfracs(hguess,ks)\n return h3po4g-hpo4g-2*po4g","def get_desired_heading(self, h, p):\n heading_vector = [self.alpha*h[0] + self.beta*p[0],\n self.alpha*h[1] + self.beta*p[1]]\n a = [(heading_vector[0]/linalg.norm(heading_vector)),\n (heading_vector[1]/linalg.norm(heading_vector))]\n return a","def H1(self,kx,ky):\n return -2.*self.t2*np.cos(self.phi)*(np.cos(3.*kx/2.+np.sqrt(3.)*ky/2.)+np.cos(-3.*kx/2.+np.sqrt(3.)*ky/2.)+np.cos(-np.sqrt(3.)*ky))","def get_theta_hs_out_max_h(\n theta_d_hs_in: np.ndarray, q_hs_max_h: np.ndarray, v_d_supply: np.ndarray) -> np.ndarray:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n return np.minimum(theta_d_hs_in + q_hs_max_h / (c * rho * np.sum(v_d_supply, axis=0)) * 10 ** 6, 45.0)","def cost(self, h_theta, y):\n \n return -y*np.log(h_theta) - (1. - y)*np.log(1. - h_theta)","def mort(self, h):\n return(self.mu_bg +\\\n (1.0 - self.mu_bg) * self.pr_P * self.p_att *\\\n (1 - h**self.ap))","def forward(self, h, r, t):\n h_e, r_e, t_e = self.embed(h, r, t)\n\n norm_h_e = F.normalize(h_e, p=2, dim=-1)\n norm_r_e = F.normalize(r_e, p=2, dim=-1)\n norm_t_e = F.normalize(t_e, p=2, dim=-1)\n\n r_theta = self.theta[r]\n\n if self.l1_flag:\n return r_theta*torch.norm(norm_h_e + norm_r_e - norm_t_e, p=1, dim=-1)\n\n return r_theta*torch.norm(norm_h_e + norm_r_e - norm_t_e, p=2, dim=-1)","def h( self , x , u , t ):\n \n #y = np.zeros(self.p) # Output vector\n \n # Using u = ubar to avoid algeabric loops\n \n y = self.plant.h( x , self.plant.ubar , t )\n \n return y","def lambert_eqarea(khi,phi):\n r = 2 * np.sin(khi/2.)\n th = phi\n return r, th","def get_h0(self, t):\n return self.h0 * np.sin(2 * np.pi * t / self.Pmod + self.Pmod_phi)","def Psi(l,m,theta,phi):\n if numpy.isscalar(theta): \n theta=numpy.array([[theta]])\n phi=numpy.array([[phi]])\n Psilm_th=numpy.zeros(theta.shape,dtype=complex)\n Psilm_ph=numpy.zeros(theta.shape,dtype=complex)\n x=numpy.cos(theta)\n thetaNonZerosIdx=numpy.where(theta!=0.0)\n if len(thetaNonZerosIdx[0]) != 0:\n Ylm=scipy.special.sph_harm(m,l,phi[thetaNonZerosIdx],theta[thetaNonZerosIdx])\n #Compute derivative of sphrHarm function w.r.t. theta:\n if l>=numpy.abs(m):\n Plmpo=legendreLM(l,m+1,x[thetaNonZerosIdx])\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/float(math.factorial(l+m)))*Plmpo*numpy.exp(1j*m*phi[thetaNonZerosIdx])\n #YlmPmpo=sqrt((l-m)*(l+m+1))*spharm(l,m+1,theta,phi)*exp(-i*phi) %Should be equivalent to above formula.\n dtYlm=+YlmPmpo+m*x[thetaNonZerosIdx]*Ylm/numpy.sin(theta[thetaNonZerosIdx])\n # thetZerInd=[find(theta==0); find(theta==pi)]\n # dtYlm(thetZerInd)=0; %This is a fudge to remove NaNs\n else:\n dtYlm=numpy.zeros(theta[thetaNonZerosIdx].shape,dtype=complex)\n\n #dtYlm=spharmDtheta(l,m,theta,phi)\n\n Psilm_ph[thetaNonZerosIdx]=+1j*m/numpy.sin(theta[thetaNonZerosIdx])*Ylm\n Psilm_th[thetaNonZerosIdx]=+dtYlm\n #Ref: http://mathworld.wolfram.com/VectorSphericalHarmonic.html\n\n thetaZerosIdx=numpy.where(theta==0.0)\n if len(thetaZerosIdx[0]) != 0:\n if numpy.abs(m)==1:\n Yl1B=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*PBl1(l,m)*numpy.exp(1j*m*phi[thetaZerosIdx])\n Plmpo=legendreLM(l,m+1,x[thetaZerosIdx])\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*Plmpo*numpy.exp(1j*m*phi[thetaZerosIdx])\n dtYlm=+YlmPmpo+m*Yl1B\n Psilm_ph[thetaZerosIdx]=+1j*m*Yl1B\n Psilm_th[thetaZerosIdx]=+dtYlm\n else:\n Plmpo=legendreLM(l,m+1,x[thetaZerosIdx])\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*Plmpo*numpy.exp(1j*m*phi[thetaZerosIdx])\n dtYlm=+YlmPmpo+0\n Psilm_ph[thetaZerosIdx]=0\n Psilm_th[thetaZerosIdx]=+dtYlm\n return Psilm_th,Psilm_ph","def phireturn(xhat0, tof):\n\t\n\t\tstoptime = tof\n\t\tnumpoints = 2\n\t\t#Integration time array:\n\t\tt = [stoptime * float(i) / (numpoints - 1) for i in range(numpoints)]\n\t\t\n\t\txsol = twomode.intfull(xhat0, t, abserror=1.0e-14, relerror=1.0e-12)\n\t\t#Phase of the first mode is the slice phase\n\t\tphi = np.angle(xsol[1,0] + 1j*xsol[1,1]) \t\n\t\t\n\t\treturn -phi","def statsi(h):\n\n # Define constants\n zsa = np.array([0.0, 11000.0, 20000.0, 32000.0, 47000.0, 52000.0, 61000.0, 79000.0, 9.9e20])\n Tsa = np.array([288.15, 216.65, 216.65, 228.65, 270.65, 270.65,252.65, 180.65, 180.65])\n g = 9.80665\n R = 287.0528\n Re = 6346766.0\n Psa = 101325.0\n\n # Calculate geopotential altitude\n z = Re*h/(Re+h)\n\n # Loop through atmosphere layers\n for i in range(8):\n \n # Calculate layer temperature gradient\n Lt = -(Tsa[i+1]-Tsa[i])/(zsa[i+1]-zsa[i])\n\n # If no temperature gradient\n if Lt == 0.0:\n\n # Are we in this layer of the atmosphere?\n if z <= zsa[i+1]:\n t = Tsa[i] # Temp isn't changing\n p = Psa*np.exp(-g*(z-zsa[i])/R/Tsa[i])\n d = p/R/t\n break\n\n # We need to go higher\n else:\n Psa *= np.exp(-g*(zsa[i+1]-zsa[i])/R/Tsa[i])\n\n # Temperature gradient\n else:\n ex = g/R/Lt\n if z <= zsa[i+1]:\n t = Tsa[i]-Lt*(z-zsa[i])\n p = Psa*(t/Tsa[i])**ex\n d = p/R/t\n break\n else:\n Psa *= (Tsa[i+1]/Tsa[i])**ex\n\n # We have left the atmosphere...\n else:\n t = Tsa[-1]\n p = 0.0\n d = 0.0\n\n return z, t, p, d","def calcTrh(N, rh, m, G, gamma=0.02):\n return 0.138*N**0.5*rh**1.5/(m**0.5*np.log(gamma*N)*G**0.5)","def Hstep_cost_function(H): \n U = Wold - Yold\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \n return cost","def ISA_trop(h):\n\tT = 288.15 - 0.0065*h;\n\tp = 101325*(T/288.15)**(-g/(-0.0065*287));\n\trho = 1.225*(T/288.15)**(-g/(-0.0065*287) - 1);\n\ta = np.sqrt(1.4*287*T);\n\treturn T, p, rho, a;","def get_hessian(phi, pred, t, dot_product, reg= 1, regression= \"logistic\"):\n R = np.eye(pred.shape[0])\n if regression == \"logistic\":\n for i in range(pred.shape[0]):\n R[i,i] = pred[i,0] * (1- pred[i,0])\n elif regression == \"probit\":\n for i in range(pred.shape[0]):\n y_n = pred[i,0]\n t_n = t[i,0] \n dotp = dot_product[i, 0]\n pdf = norm.pdf(dotp)\n\n term1 = 1/ (y_n * (1- y_n) + TOLERANCE)\n term2 = (y_n - t_n)/(y_n**2 * (1- y_n) + TOLERANCE)\n term3 = (y_n - t_n)/((1- y_n)**2 * y_n + TOLERANCE)\n term4 = (y_n - t_n)* dotp/(y_n * (1- y_n) * pdf + TOLERANCE)\n\n R[i,i] = (term1 - term2 + term3 - term4)*(pdf**2)\n\n # Add regularization\t\t\t\n hessian = np.matmul(np.matmul(phi.T, R), phi) + np.eye(phi.shape[1])/reg\n return hessian","def sph_harm_diff_theta(m, l, theta, phi):\n check_degree_and_order(m, l)\n return (m * _sph_harm(m, l, theta, phi) / np.tan(theta) +\n np.sqrt( (l - m) * (l + m + 1) ) * np.exp( -1j*phi ) *\n _sph_harm(m+1, l, theta, phi))","def qFelder(h):\n\treturn (0.92 + 0.153 * h/1.01) * math.sqrt(9.8 * (2/3.0 * h)**3)","def decode(self, h):\n return self.tanh(self.linearD(h))","def decode(self, h):\n return self.tanh(self.linearD(h))","def decode(self, h):\n return self.tanh(self.linearD(h))","def vsh2(m, l, theta, phi):\n c_theta = 1/np.sin(phi) * sph_harm_diff_theta(m, l, theta, phi)\n c_phi = sph_harm_diff_phi(m, l, theta, phi)\n return np.array((0, c_theta, c_phi))","def curve_no_hillcoef(ph, pka):\n# return ph - pka\n return 1/(10**(pka-ph)+1)","def h_spec(k, He, h):\r\n return np.array(((k**3/np.pi**2) * h))","def forward(self, t, h):\n if self.i == 0:\n self.A = self.beta * (self.B - self.B.transpose(1, 0)) + (\n 1 - self.beta) * (self.B +\n self.B.transpose(1, 0)) - self.gamma * self.I\n self.W = self.beta * (self.C - self.C.transpose(1, 0)) + (\n 1 - self.beta) * (self.C +\n self.C.transpose(1, 0)) - self.gamma * self.I\n\n return torch.matmul(\n h, self.A) + self.tanh(torch.matmul(h, self.W) + self.z)","def bidimensional_map_nonlin_6(h, t, x, y, x_0, y_0):\n gamma = 3 * np.pi\n r = np.sqrt(\n np.square(x - x_0 + 0.01 * x_0 * np.sin(gamma * t))\n + np.square(y - y_0 + 0.01 * y_0 * np.sin(2 * gamma * t))\n )\n\n f = lambda r: gamma * r + (gamma / 2) * r**3 + np.sqrt(r)\n\n return h(t - f(r))","def phs(x, y, rbfParam) :\n return (x**2 + y**2) ** (rbfParam/2)","def theta_rule(a, u, h, t, n, th):\n \n Dt = t[n+1] - t[n]\n num = (1.0 - (1-th)*a*Dt)*u[n] + (1-th)*a*Dt*h(t[n]) + th*a*Dt*h(t[n+1])\n den = 1 + th*a*Dt\n \n return num/den","def test_H_hat(self):\n\t\tposition = [0.0, 1.57079, 3.14159, 4.71238, 6.28318, 7.85398, 9.42477]\n\t\tpotential = [0.0, 6.0, 0.0, -6.0, 0.0, 6.0, 0.0]\n\t\tc = 1\n\t\tposition = tf.constant(position, shape = [1, len(position)], dtype = tf.float32)\n\t\tpotential = tf.constant(potential, shape = [1, len(potential)], dtype = tf.float32)\n\t\tbasis = schrodinger.create_basis(5)\n\t\tv = schrodinger.v0(position, potential, basis)\n\t\tcoeff = schrodinger.coefficient(position, basis)\n\t\tv0_hat = tf.linalg.solve(coeff, v)\n\t\tH = schrodinger.H_hat(c, len(basis), v0_hat)\n\t\tself.assertEqual(coeff.get_shape(), [len(basis), len(basis)])","def bidimensional_map_nonlin_3(h, t, x, y, x_0, y_0):\n gamma = 3 * np.pi\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\n\n f = lambda r: gamma * r + (gamma / 2) * r**3 + np.sqrt(r)\n\n return h(t - f(r))","def Tanh(z):\n return 1.7159 * np.tanh(2 / 3.0 * z)","def rh(self, h):\n sez=self.getSect(h)\n area=self.area(sez)\n wetborder = self.wetBorder(sez)\n return area/wetborder","def geofun(self, h, hK, cK):\n if (h < hK):\n # Wave is rarefaction wave (or depression)\n c = math.sqrt(9.81 * h) # we use math because is faster\n return 2 * (c-cK), 9.81/c\n else:\n # Wave is shock wave (or bore)\n ges = math.sqrt(0.5*9.81 * ( h + hK )/( h * hK ) )\n return ( h - hK ) * ges, ges - 0.25*9.81 * (h-hK)/(ges * h*h)","def discretizar_funcion(a, I, h, t, th):\n \n u = np.zeros(len(t), dtype=float)\n u[0] = I\n for n in range(0, len(t) - 1):\n u[n+1] = theta_rule(a, u, h, t, n, th)\n return u","def phi(t, *args):\n # Unpacking data\n mu_1, pi_mu_2, distance, affine_transfo = args\n A, b = get_Ab(t)\n N = len(mu_1)\n assert len(mu_1) == len(pi_mu_2)\n # Computing value of objective function\n r = 0.\n for i in np.arange(N):\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\n return r","def solver(self, theta):\n\n m = len(self.x)\n n = len(self.t[0])\n h = self.step_x\n k = self.step_t\n lamb = k / (h * h)\n w = np.zeros(m + 1)\n l = np.zeros(m + 1)\n u = np.zeros(m + 1)\n\n uK = np.zeros(self.x.shape)\n print('comecei para k= ({},{})'.format(theta[0], theta[1]))\n startTime = time.clock()\n if self.first:\n self.uR = np.zeros(self.x.shape)\n self.first = False\n error = 0\n errorCurve = np.zeros(self.x.shape)\n z = np.zeros(m + 1)\n w[m] = 0 # following the initial condition u(0,t) = u(l,t) = 0. If needed, change this.\n for i in range(1, m - 1):\n w[i] = self.g(i * h)\n\n l[1] = 1 + lamb\n u[1] = -lamb / (2 * l[1])\n for i in range(2, m - 1):\n l[i] = 1 + lamb + lamb * u[i - 1] / 2\n u[i] = -lamb / (2 * l[i])\n\n l[m - 1] = 1 + lamb + lamb * u[m - 2] / 2\n for j in range(1, n + 1):\n t = j * k # current t\n z[1] = ((1 - lamb) * w[1] + lamb / 2 * w[2] + self.f(t, theta)) / l[1]\n for i in range(2, m):\n z[i] = ((1 - lamb) * w[i] + lamb / 2 * (w[i + 1] + w[i - 1] + z[i - 1]) + self.f(t, theta)) / l[i]\n w[m - 1] = z[m - 1]\n for i in range(m - 2, 0, -1):\n w[i] = z[i] - u[i] * w[i + 1]\n\n for i in range(0, m + 1):\n x = i * h\n # print(x, w[i])\n # print('oi')\n uK[i - 1, j - 1] = w[i]\n self.t[i - 1, j - 1] = t\n self.x[i - 1, j - 1] = x\n error += pow(w[i] - self.uR[i - 1, j - 1], 2) / uK.size\n errorCurve[i - 1, j - 1] = (pow(w[i] - self.uR[i - 1, j - 1], 2)) / uK.size\n print('acabei para k= ({},{}) em {} segundos'.format(theta[0], theta[1], time.clock() - startTime))\n return (uK, error, errorCurve, theta)","def h(X, theta, n_hidden_layers=1):\n _, a = feed_forward(X, theta, n_hidden_layers)\n L = n_hidden_layers + 1 # last layer\n\n hypothesis = a[L]\n return hypothesis","def bidimensional_map_nonlin_2(h, t, x, y, x_0, y_0):\n\n gamma = 3 * np.pi\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\n\n f = lambda r: gamma * r\n\n return h(t - f(r))","def refine_theta_parabolic_peak(\n request_recon_h,\n varphi_estimate,\n deg,\n n_pts = 3,\n alpha_fidelity = 1,\n):\n if n_pts < 3:\n raise ValueError(\n f\"The number of sample points {n_pts} is not enough to fit a parabola\"\n )\n half_window_width = np.pi / (2 * deg)\n angles = np.linspace(\n varphi_estimate - half_window_width,\n varphi_estimate + half_window_width,\n n_pts,\n )\n vals = np.array([request_recon_h(omega) for omega in angles], dtype=complex)\n # impose correction when the circuit error is not coherent\n vals = (vals + (1 - alpha_fidelity) * (1 + 1j) / 4) / alpha_fidelity\n p = np.polyfit(angles, np.abs(vals), 2)\n if p[0] >= 0:\n theta_estimate_mle = -np.inf\n varphi_estimate_mle = -np.inf\n return (theta_estimate_mle, varphi_estimate_mle), (angles, vals)\n else:\n peak_loc = -p[1] / (2 * p[0])\n peak_val = p[2] - p[1] ** 2 / (4 * p[0])\n theta_estimate_mle = peak_val / deg\n varphi_estimate_mle = peak_loc\n return (theta_estimate_mle, varphi_estimate_mle), (angles, vals)","def calcT(self, theta, phi, T_ss):\n return T_ss * np.maximum(0.0, np.cos(theta) * np.cos(phi))**0.25","def H(lattice, i, j, h, T):\n\n gamma = np.random.rand()\n delta_E = -2*lattice[i, j] * (lattice[i, j - 1] + lattice[i - 1, j] + lattice[i, j + 1] + lattice[i + 1, j]) - 2*h * lattice[i, j]\n\n return not (delta_E > 0 and np.exp(-(delta_E)/(kb * T)) > gamma)","def bidimensional_map_nonlin_1(h, t, x, y, x_0, y_0):\n\n gamma = 3 * np.pi\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\n\n f = lambda r: gamma * r + (gamma / 2) * r**2 + np.sqrt(r)\n\n return h(t - f(r))","def z_eq(self):\n theta = self.T_cmb/2.7\n return 25000.*self.Omega_m*self.h**2.*theta**-4.","def calc_theta(U, V, quad, h_length, radians):\n import numpy as np\n theta = np.arcsin(U / h_length)\n import numpy as np\n if quad == 1:\n theta = theta\n elif quad == 2:\n theta = -theta + np.pi / 2\n elif quad - - 3:\n theta = np.pi / 2 + theta + np.pi\n elif quad == 4:\n theta = 3 * np.pi / 2\n theta = 2 * np.pi - theta\n if not radians:\n theta = theta * 180 / np.pi\n\n return (theta)","def h_T(self, z0):\n # Get the governing variables\n (B, N, u_slip, u_inf) = self.get_variables(z0, 0.)\n \n # Compute U_N\n U_N = u_slip / (B * N)**(1./4.)\n \n # Compute the correlation equation\n return 2.9 * np.exp(-(U_N - 1.0)**2 / 28.09) * (B / N**3)**(1./4.)","def ph(self,k,z=0):\n return self.p(k*self.h,z)*self.h**3","def _h(W):\r\n # E = slin.expm(W * W)\r\n # h = np.trace(E) - d\r\n M = np.eye(d) + W * W / d\r\n E = np.linalg.matrix_power(M, d - 1)\r\n h = (E.T * M).sum() - d\r\n G_h = E.T * W * 2\r\n return h, G_h","def func1(y, j, h, add_u = 0):\n y_temp = y[j] + add_u\n N = xsize\n k = np.zeros(xsize)\n for i in range(xsize):\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2) - 0.5/(h**3)*(y_temp[(i+2)%N]-2*y_temp[(i+1)%N]+2*y_temp[(i-1)%N]-y_temp[(i-2)%N])\n return k","def one_step(self, x, h):\n concatHX = torch.cat((x, h), 1)\n zt = self.sigmoid(self.linearZ(concatHX))\n rt = self.sigmoid(self.linearR(concatHX))\n ht = (1-zt)*h + zt* self.tanh(self.linearH(rt*concatHX))\n return ht","def layer(self, h, t):\n mr1h = torch.matmul(h, self.mr1.weight) # h => [m, d], self.mr1 => [d, k]\n mr2t = torch.matmul(t, self.mr2.weight) # t => [m, d], self.mr2 => [d, k]\n return torch.tanh(mr1h + mr2t)","def func2(y, j, h, add_u = 0):\n y_temp = y[j] + add_u\n N = xsize\n k = np.zeros(xsize)\n for i in range(xsize):\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2)\n return k","def theta():\n pass","def forward(self, z_t_1, h_x, phi_table):\n z_category = self.gen_z_t_dist_now(z_t_1, h_x)\n \n if self.use_gumbel_softmax:\n \n# device = z_category.device\n \n averaged_z_t = 0\n \n log_prob = Variable(torch.log(z_category))\n \n for k in range(self.sampling_times): \n curr_z_t = F.gumbel_softmax(log_prob, tau = 0.1)\n \n averaged_z_t += curr_z_t\n \n del curr_z_t\n \n# averaged_z_t = averaged_z_t.to(device)\n \n z_t = averaged_z_t/self.sampling_times\n else:\n z_t = z_category\n \n phi_z = torch.mm(z_t, torch.t(phi_table))\n# mu = self.h_to_mu(h_combined)\n# logvar = self.h_to_logvar(h_combined)\n# std = torch.exp(0.5 * logvar) \n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\n return z_t, z_category, phi_z","def eval_costh_phi_fold_pt_HX(data):\n return data.costh_HX_fold, data.phi_HX_fold, data.JpsiPt","def __call__(self, h):\n\n Wh = self.W(h)\n p_yt = F.log_softmax(Wh) # should be (B x V)\n\n return p_yt","def spherical_harmonics_vec(th,ph, lmax):\n Y = []\n lm = []\n n = lmax*(lmax+2)+1\n sph_it = spherical_harmonics_it(th,ph)\n for i in range(0, n):\n Ylm,l,m = sph_it.next()\n Y.append(Ylm)\n lm.append((l,m))\n assert l == lmax\n assert m == -lmax\n\n return Y, lm","def get_heat_source_heating_output(\n theta_hs_out_h: np.ndarray, theta_hs_in: np.ndarray, v_supply: np.ndarray, operation: np.ndarray) -> np.ndarray:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n q_hs_h = np.maximum((theta_hs_out_h - theta_hs_in) * c * rho * np.sum(v_supply, axis=0) * 10 ** (-6), 0.0)\n\n return np.where(operation == 'h', q_hs_h, 0.0)","def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\n\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\n\n return h_kJ_kg","def h(x, theta):\n # ... dopolnite (naloga 1)\n\n power = x.dot(-theta.T)\n\n return 1 / (1 + np.exp(power))","def get_f_h_gas_comp_in(p: float, theta: float) -> float:\n\n return -1.00110355 * 10 ** (-1) * p ** 3 \\\n - 1.184450639 * 10 * p ** 2 \\\n - 2.052740252 * 10 ** 2 * p \\\n + 3.20391 * 10 ** (-6) * (theta + 273.15) ** 3 \\\n - 2.24685 * 10 ** (-3) * (theta + 273.15) ** 2 \\\n + 1.279436909 * (theta + 273.15) \\\n + 3.1271238 * 10 ** (-2) * p ** 2 * (theta + 273.15) \\\n - 1.415359 * 10 ** (-3) * p * (theta + 273.15) ** 2 \\\n + 1.05553912 * p * (theta + 273.15)+1.949505039 * 10 ** 2","def parang (hourangle, declination, latitude):\n\n return -np.arctan2 (-np.sin (hourangle),\n np.cos (declination) * np.tan (latitude)\n - np.sin (declination) * np.cos (hourangle))","def sph_harm_diff_phi(m, l, theta, phi):\n check_degree_and_order(m, l)\n return 1j * m * _sph_harm(m, l, theta, phi)","def compute_tsky_hot( xv, yv, hv, thot, tcold):\n\n nData = len(yv) \n epsilons = np.full( nData, EPSILON)\n tsys = np.zeros(nData) # initialize arrays\n\n Z = np.zeros(nData)\n oneMZ = np.zeros(nData)\n # For full Temp calibration, a spectrum taken at high elevation away from \n # The galactic plan is used. For this program the cold spectrum must be\n # the spectrum being calibrated. See the M command for comparision\n epsilons = np.full( nData, EPSILON)\n yv = np.maximum( yv, epsilons)\n hv = np.maximum( hv, epsilons)\n # comput the cold/hot ratio\n Z = yv/hv\n oneMZ = np.full( nData, 1.) - Z\n oneMZ = np.maximum( oneMZ, epsilons)\n\n # the cold, receiver, temperature is this function\n tsys = ((Z*thot) - tcold)/oneMZ\n \n n6 = int(nData/6)\n n56 = 5*n6\n\n tsysmedian = np.median( tsys[n6:n56])\n\n tsky = np.zeros(nData) # initialize arrays\n S = np.zeros(nData) # initialize arrays\n\n # The system gain S is computed assuming a tsys is the cold load\n S = np.full( nData, tsysmedian+thot)/hv\n # scale the observed instensity in counts to Kelvins.\n tsky = S*yv\n\n return tsky","def hrf_fit_err(theta, z, y, t_r, hrf_dur):\n h, _ = spm_hrf(theta, t_r, hrf_dur, False)\n return 0.5 * np.sum(np.square(y - spectral_convolve(h, z)))","def sph(grlat, elong, ht):\n\n # Initialize Variables\n global cth, sth, clg, slg, dif, radn, gl # common/obs/\n gn = 9.798277692\n ae = 6378140.0\n f = 0.00335281\n rm = 0.00344978\n dr = 0.01745329252\n\n clong = np.cos(elong * dr)\n slong = np.sin(elong * dr)\n # latitude difference\n dvert = f * (1.0 + 0.5 * f) * np.sin(2.0 * grlat * dr) - 0.5 * f * f * np.sin(\n 4.0 * grlat * dr\n )\n gcclat = (3.1415926535898 / 2.0) - (grlat * dr - dvert)\n cthet = np.cos(gcclat)\n sthet = np.sin(gcclat)\n # geocentric radius\n radn = 1 - f * (cthet ** 2) * (1 + 1.5 * f * (sthet ** 2))\n # formulae for g are from jeffreys, 4.022 and 4.023\n g = gn * (\n 1\n + f\n - 1.5 * rm\n + f * (f - (27 / 14) * rm)\n + (2.5 * rm - f - f * (f - (39 / 14) * rm)) * (cthet ** 2)\n - (f / 2) * (7 * f - 15.0 * rm) * ((cthet * sthet) ** 2)\n )\n # free air correction\n g = g - g * (2.0 * ht * (1.0 + f + rm - 2.0 * f * (cthet ** 2)) / ae)\n\n # Conversion Here for Globals\n cth = cthet\n sth = sthet\n clg = clong\n slg = slong\n dif = dvert\n gl = g","def isa(h):\n if np.any((h<0)|(h>11000)):\n print (\"The altitude must be between 0m and 11000m\")\n return \"Returning a string to cause errors later\"\n T = T0 + -0.0065*h\n p = p0*(T/T0)**(-g0/(-0.0065*R))\n rho = p/(R*T)\n return T,p,rho","def Spin(phi,theta):\n return 1/2*(cos(phi)*sin(theta)*xhat + sin(phi)*sin(theta)*yhat + cos(theta)*zhat)","def H(lattice, i, j, h, T):\n\n gamma = np.random.rand()\n delta_E = 2*lattice[i, j] * (lattice[i, j - 1] + lattice[i - 1, j] + lattice[i, j + 1] + lattice[i + 1, j]) + 2*h * lattice[i, j]\n\n return delta_E < 0 or gamma < np.exp(-(delta_E)/(kb * T))","def h(self, z):\n # See definition at end of Section 1, p2 of Arnaud et al.\n return np.sqrt(self.Om*(1.+z)**3. + self.Ol)","def get_z(theta, phi):\n return math.cos(phi)/math.tan(theta/2) + 1j*math.sin(phi)/math.tan(theta/2)","def compute_stability_fm_h(H, t0, u_attr, r_air, hc, d0, z0m, cp=1004.16):\n L_ob = H.expression(\n '-(r_air * cp * t0 * (u_attr ** 3.0) / 0.41 / 9.806 / H)',\n {'cp': cp, 'H': H, 'r_air': r_air, 't0': t0, 'u_attr': u_attr})\n L_ob = L_ob.where(L_ob.gte(0), -99.0)\n mm_h = H \\\n .expression(\n '((1 - (16.0 * (hc - d0) / L_ob)) ** 0.25)',\n {'d0': d0, 'hc': hc, 'L_ob': L_ob}) \\\n .where(L_ob.eq(-99.0), 0.0)\n fm_h = H \\\n .expression(\n '2.0 * log((1.0 + mm_h) / 2.0) + log((1.0 + (mm_h ** 2)) / 2.0) - '\n '2.0 * atan(mm_h) + (pi / 2)',\n {'mm_h': mm_h, 'pi': math.pi}) \\\n .where(L_ob.lte(-100).Or(L_ob.gte(100)), 0)\n\n # CGM - Swapped order of calc since d0 is an image compute from hc and\n # z_u is being set as a constant number (for now).\n fm_h = fm_h.where(fm_h.eq(hc.subtract(d0).divide(z0m).log()), fm_h.add(1.0))\n # fm_h = fm_h.where(fm_h.eq(hc.subtract(d0).divide(z0m).log()), fm_h.add(1.0))\n return fm_h","def cost(h, y):\n\tm = y.shape[0]\n\tcost = (-1/m) * (y.T @ np.log(h) + (1 - y).T @ np.log(1 - h))\n\treturn cost","def guidance_target(self, state, final_x, y_profile, time_horizon, time_step, polyfit=None):\n current_x, current_y, current_theta, current_y_dot = state[XX], state[YY], state[THETA], state[Y_DOT]\n y_index = np.where(y_profile == np.extract(y_profile <= current_y, y_profile)[0])[0][0]\n\n # m, c = polyfit\n beta = 3\n gamma = 0.5\n\n delta_t = [t * time_step for t in range(0, time_horizon)]\n y = y_profile[y_index : y_index+time_horizon]\n y = np.clip(y, y_profile[-1], 26)\n # x_adjust = final_x * (1 - np.exp(-0.05 * np.array(delta_t)))\n # x = current_x + x_adjust * (final_x - current_x)\n x = current_x + (final_x - current_x) / (1 + np.exp(-np.linspace(-4, 4, num=time_horizon)))\n if current_x > final_x:\n x = np.clip(x, final_x, 33)\n else:\n x = np.clip(x, 0, final_x)\n\n y_dot = current_y_dot * np.exp(-np.linspace(0, 2, time_horizon)) # -2 * np.exp(-1/abs(y))\n x_dot = (x - final_x)\n\n theta = current_theta * np.exp(-beta * np.array(delta_t)) + current_theta * 0.3\n if theta[0] > 0:\n theta = np.clip(theta, 0, 15 * DEGTORAD)\n else:\n theta = np.clip(theta, -15 * DEGTORAD, 0)\n theta_dot = gamma * theta\n\n targets = [x, y, x_dot, y_dot, theta, theta_dot]\n return targets","def infilCapaHorton(f0, fc, k, t):\n ft = fc + (f0 - fc)*np.exp(-k*t)\n return ft","def gha(self):\n return np.mod(self.gmst*self.turndeg +\n self.turndeg*self.T*self.century +\n self.turndeg/2.0, self.turndeg)","def func3(y, j, h, add_u = 0):\n y_temp = y[j] + add_u\n N = xsize\n k = np.zeros(xsize)\n for i in range(xsize):\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2) + (1/2.)*(1./h**2)*(y_temp[(i+1)%N]-2*y_temp[i%N]+y_temp[(i-1)%N])\n return k","def vsh1(m, l, theta, phi):\n check_degree_and_order(m, l)\n return np.array((_sph_harm(m, l, theta, phi), 0, 0))","def vsh3(m, l, theta, phi):\n if l==0: return np.array([0, 0, 0])\n\n r = 1\n R = np.array([r, 0, 0])\n gradY = sph_harm_gradient(m, l, r, theta, phi)\n return -1j * np.cross(R, gradY) / np.sqrt( l * (l + 1))","def get_thetaRHT_hat(self, sample_psi0, rht_data):\n QRHT = np.sum(np.cos(2*sample_psi0)*rht_data)\n URHT = np.sum(np.sin(2*sample_psi0)*rht_data)\n theta_rht = np.mod(0.5*np.arctan2(URHT, QRHT), np.pi)\n\n return theta_rht","def eval_costh_phi_fold_HX(data):\n return data.costh_HX_fold, data.phi_HX_fold","def one_step(self, x, h):\n concatHX = torch.cat((x, h), 1)\n ft = self.sigmoid(self.linearF(concatHX))\n it = self.sigmoid(self.linearI(concatHX))\n newCt = ft*self.ct.clone() + it*self.tanh(self.linearC(concatHX))\n #self.ct = ft*self.ct.clone() + it*self.tanh(self.linearC(concatHX))\n ot = self.sigmoid(self.linearO(concatHX))\n ht = ot*self.tanh(newCt)\n self.ct = newCt\n \n return ht","def H(t, args):\n\n f0 = args['f0']\n n = args['n']\n omega = args['omega']\n omegaDt = args['omegaDt']\n omegaArgs = args['omegaArgs']\n\n ad = create(n)\n a = destroy(n)\n # H0, for the first two terms see Silveri 2017 Quantum_systems_under_frequency_modulation\n ham = omega(t, omegaArgs)*(ad*a+0.5*qeye(n))\n # additional term because of w(t) not constant\n ham += 1j/4*omegaDt(t, omegaArgs)/omega(t, omegaArgs)*(a*a-ad*ad)\n # Force term (9**10^-9 = x0, extent of ground state wave function), see Wittmann diss\n # with compensation term -f0/w0^2 (e.g. no force in the case of no modulation)\n ham += 9*(f0/(omega(t, omegaArgs)**2) - f0/(omegaArgs[0]**2))*(ad + a)\n # ham += (9*10**-9)/(10**6)*(f0/(omega(t, omegaArgs)**2))*(ad + a)\n return(ham)","def update_H(self):\n gamma = self.get_gamma()\n delta = self.get_delta()\n summand2 = ((1 + (gamma.transpose().dot(self.H).dot(gamma) /\n delta.transpose().dot(gamma))) *\n delta.dot(delta.transpose()) / delta.transpose().dot(gamma)\n )\n summand3 = - ((delta.dot(gamma.transpose()).dot(self.H) +\n self.H.dot(gamma).dot(delta.transpose())) /\n delta.transpose().dot(gamma))\n self.H = self.H + summand2 + summand3","def _sph_harm(m, n, theta, phi):\n return sph_harm(m, n, phi, theta)","def hrf_estim(z, y, t_r, dur, verbose=0):\n args = (z, y, t_r, dur)\n bounds = [(MIN_DELTA + 1.0e-1, MAX_DELTA - 1.0e-1)]\n f_cost = Tracker(hrf_fit_err, args, verbose)\n\n theta, _, _ = fmin_l_bfgs_b(\n func=hrf_fit_err, x0=MAX_DELTA, args=args,\n bounds=bounds, approx_grad=True, callback=f_cost,\n maxiter=99999, pgtol=1.0e-12)\n J = f_cost.J\n h, _ = spm_hrf(theta, t_r, dur, False)\n\n return h, J","def _prob_v_given_h(self, h):\n return tf.sigmoid(tf.add(self.a, tf.matmul(h, tf.transpose(self.W))))","def rk4(self, t, h,G) :\r\n k1 = h*self.calc_diff_eqn(t, self.quant_vec,G,self.mass_vec)\r\n k2 = h*self.calc_diff_eqn(t + 0.5*h , self.quant_vec + 0.5*k1 ,G, self.mass_vec)\r\n k3 = h*self.calc_diff_eqn(t + 0.5*h , self.quant_vec + 0.5*k2 ,G, self.mass_vec)\r\n k4 = h*self.calc_diff_eqn(t + h , self.quant_vec + k3 ,G, self.mass_vec)\r\n y_new = self.quant_vec + ((k1 + 2*k2 + 2*k3 + k4)/6)\r\n return y_new","def theta_v_time():\n pass","def T_naught(z, h, OM, OB):\n\n T0 = 28.5 * ((1.0+z)/10.0)**(0.5) * OB/0.042 * h/0.73 * (0.24/OM)**(0.5)\n return T0"],"string":"[\n \"def get_theta_surf_hex_h(theta_hs_in_h: float, theta_hs_out_h: float, v_hs: float) -> float:\\n\\n c = get_specific_heat()\\n rho = get_air_density()\\n\\n # sensible heating capacity of heat source for heating, W\\n q_hs_h = (theta_hs_out_h - theta_hs_in_h) * c * rho * v_hs / 3600\\n\\n # sensible heat transfer coefficient on the surface of the heat exchanger of the internal unit, W/m2K\\n alpha_c_hex_h = get_alpha_c_hex_h(v_hs)\\n\\n # effective area for heat exchange of the surface area of heat exchanger or the internal unit, m2\\n a_e_hex = get_a_e_hex()\\n\\n return (theta_hs_in_h + theta_hs_out_h) / 2 + q_hs_h / (a_e_hex * alpha_c_hex_h)\",\n \"def trapezoidal(f, x0, h):\\n return (h/2.0 * (f(x0)+f(x0+h)))\",\n \"def get_theta_surf_hex_test_h(theta_hs_in_h: float, q_hs_h: float, v_hs: float) -> float:\\n\\n c = get_specific_heat()\\n rho = get_air_density()\\n\\n theta_hs_out_h = theta_hs_in_h + q_hs_h / (c * rho * v_hs) * 3600\\n\\n # sensible heat transfer coefficient on the surface of the heat exchanger of the internal unit, W/m2K\\n alpha_c_hex_h = get_alpha_c_hex_h(v_hs)\\n\\n # effective area for heat exchange of the surface area of heat exchanger or the internal unit, m2\\n a_e_hex = get_a_e_hex()\\n\\n return (theta_hs_in_h + theta_hs_out_h) / 2 + q_hs_h / (a_e_hex * alpha_c_hex_h)\",\n \"def two_theta_hkl(self, H, K, L):\\n return self.unit_cell.two_theta((H, K, L), self.wavelength, deg=True)\",\n \"def curve_with_hillcoef(ph, pka, hillcoef):\\n# return hillcoef * ph - pka\\n return 1/(1+10**(hillcoef*(pka-ph)))\",\n \"def get_metrics(H):\\n theta = np.arctan2(H[0,1], H[0,0])\\n scale = H[0,0] / np.cos(theta)\\n tx = H[0,2]\\n ty = H[1,2]\\t\\n return tx,ty,theta\",\n \"def phitheta(loc):\\n x = loc[0]\\n y = loc[1]\\n z = loc[2]\\n r = sqrt(x**2 + y**2 + z**2)\\n theta = arcsin(z/r)\\n phi = arctan2(y,x)\\n return(phi, theta)\",\n \"def _step(self, t, y, h):\\n # We must use solvers / implicit form\\n f_pn1 = lambda a_n1: (y + h*self.v + (h**2 / 2.0) * \\\\\\n ((1.0 - 2.*self.beta)*self.a + 2.*self.beta*a_n1))\\n f_vn1 = lambda a_n1: (self.v + h*((1.0-self.gamma)*self.a + self.gamma*a_n1))\\n def f_an1(a_n1):\\n f_n1 = self.f(t+h,f_pn1(a_n1),f_vn1(a_n1))\\n f_n = self.f(t,y,self.v,)\\n return a_n1 - ((1.0+self.alpha)*f_n1 - self.alpha*f_n)\\n\\n a = self.solver(f_an1, self.a)\\n y = f_pn1(a) # Calculate and store new variables. \\n self.v = f_vn1(a)\\n self.a = a\\n return t+h, y\",\n \"def __s_polynomial(g, h):\\n\\n deg_g = __multidegree(g)\\n deg_h = __multidegree(h)\\n max_deg = map(max, zip(deg_g, deg_h))\\n R = g.parent()\\n\\n # Builds a polynomial with the variables raised to max_deg, in order\\n vars = map(R, R.variable_names())\\n x_pow_max_deg = reduce(operator.mul, [x ** d for (d, x) in zip(max_deg, vars)], R(1))\\n\\n quo_g, _ = x_pow_max_deg.quo_rem(g.lt())\\n quo_h, _ = x_pow_max_deg.quo_rem(h.lt())\\n return quo_g * g - quo_h * h\",\n \"def ab2rhotheta(a, b):\\n \\\"\\\"\\\" also : y - ax - b = 0 \\\"\\\"\\\"\\n \\\"\\\"\\\" y*sin(theta) + x*cos(theta) - rho = 0 \\\"\\\"\\\"\\n #print(\\\"a: %f b: %f\\\" % (a, b))\\n theta = math.atan(a) + math.pi/2.0\\n rho = b*math.sin(theta)\\n #print(\\\"a: %f b: %f rho: %f theta: %f\\\" % (a, b, rho, theta))\\n return (rho, theta)\",\n \"def alkTphosfac(hguess,ks):\\n #mick - first estimate of contribution from phosphate\\n #mick based on Dickson and Goyet\\n h3po4g,h2po4g,hpo4g,po4g = phosfracs(hguess,ks)\\n return h3po4g-hpo4g-2*po4g\",\n \"def get_desired_heading(self, h, p):\\n heading_vector = [self.alpha*h[0] + self.beta*p[0],\\n self.alpha*h[1] + self.beta*p[1]]\\n a = [(heading_vector[0]/linalg.norm(heading_vector)),\\n (heading_vector[1]/linalg.norm(heading_vector))]\\n return a\",\n \"def H1(self,kx,ky):\\n return -2.*self.t2*np.cos(self.phi)*(np.cos(3.*kx/2.+np.sqrt(3.)*ky/2.)+np.cos(-3.*kx/2.+np.sqrt(3.)*ky/2.)+np.cos(-np.sqrt(3.)*ky))\",\n \"def get_theta_hs_out_max_h(\\n theta_d_hs_in: np.ndarray, q_hs_max_h: np.ndarray, v_d_supply: np.ndarray) -> np.ndarray:\\n\\n c = get_specific_heat()\\n rho = get_air_density()\\n\\n return np.minimum(theta_d_hs_in + q_hs_max_h / (c * rho * np.sum(v_d_supply, axis=0)) * 10 ** 6, 45.0)\",\n \"def cost(self, h_theta, y):\\n \\n return -y*np.log(h_theta) - (1. - y)*np.log(1. - h_theta)\",\n \"def mort(self, h):\\n return(self.mu_bg +\\\\\\n (1.0 - self.mu_bg) * self.pr_P * self.p_att *\\\\\\n (1 - h**self.ap))\",\n \"def forward(self, h, r, t):\\n h_e, r_e, t_e = self.embed(h, r, t)\\n\\n norm_h_e = F.normalize(h_e, p=2, dim=-1)\\n norm_r_e = F.normalize(r_e, p=2, dim=-1)\\n norm_t_e = F.normalize(t_e, p=2, dim=-1)\\n\\n r_theta = self.theta[r]\\n\\n if self.l1_flag:\\n return r_theta*torch.norm(norm_h_e + norm_r_e - norm_t_e, p=1, dim=-1)\\n\\n return r_theta*torch.norm(norm_h_e + norm_r_e - norm_t_e, p=2, dim=-1)\",\n \"def h( self , x , u , t ):\\n \\n #y = np.zeros(self.p) # Output vector\\n \\n # Using u = ubar to avoid algeabric loops\\n \\n y = self.plant.h( x , self.plant.ubar , t )\\n \\n return y\",\n \"def lambert_eqarea(khi,phi):\\n r = 2 * np.sin(khi/2.)\\n th = phi\\n return r, th\",\n \"def get_h0(self, t):\\n return self.h0 * np.sin(2 * np.pi * t / self.Pmod + self.Pmod_phi)\",\n \"def Psi(l,m,theta,phi):\\n if numpy.isscalar(theta): \\n theta=numpy.array([[theta]])\\n phi=numpy.array([[phi]])\\n Psilm_th=numpy.zeros(theta.shape,dtype=complex)\\n Psilm_ph=numpy.zeros(theta.shape,dtype=complex)\\n x=numpy.cos(theta)\\n thetaNonZerosIdx=numpy.where(theta!=0.0)\\n if len(thetaNonZerosIdx[0]) != 0:\\n Ylm=scipy.special.sph_harm(m,l,phi[thetaNonZerosIdx],theta[thetaNonZerosIdx])\\n #Compute derivative of sphrHarm function w.r.t. theta:\\n if l>=numpy.abs(m):\\n Plmpo=legendreLM(l,m+1,x[thetaNonZerosIdx])\\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/float(math.factorial(l+m)))*Plmpo*numpy.exp(1j*m*phi[thetaNonZerosIdx])\\n #YlmPmpo=sqrt((l-m)*(l+m+1))*spharm(l,m+1,theta,phi)*exp(-i*phi) %Should be equivalent to above formula.\\n dtYlm=+YlmPmpo+m*x[thetaNonZerosIdx]*Ylm/numpy.sin(theta[thetaNonZerosIdx])\\n # thetZerInd=[find(theta==0); find(theta==pi)]\\n # dtYlm(thetZerInd)=0; %This is a fudge to remove NaNs\\n else:\\n dtYlm=numpy.zeros(theta[thetaNonZerosIdx].shape,dtype=complex)\\n\\n #dtYlm=spharmDtheta(l,m,theta,phi)\\n\\n Psilm_ph[thetaNonZerosIdx]=+1j*m/numpy.sin(theta[thetaNonZerosIdx])*Ylm\\n Psilm_th[thetaNonZerosIdx]=+dtYlm\\n #Ref: http://mathworld.wolfram.com/VectorSphericalHarmonic.html\\n\\n thetaZerosIdx=numpy.where(theta==0.0)\\n if len(thetaZerosIdx[0]) != 0:\\n if numpy.abs(m)==1:\\n Yl1B=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*PBl1(l,m)*numpy.exp(1j*m*phi[thetaZerosIdx])\\n Plmpo=legendreLM(l,m+1,x[thetaZerosIdx])\\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*Plmpo*numpy.exp(1j*m*phi[thetaZerosIdx])\\n dtYlm=+YlmPmpo+m*Yl1B\\n Psilm_ph[thetaZerosIdx]=+1j*m*Yl1B\\n Psilm_th[thetaZerosIdx]=+dtYlm\\n else:\\n Plmpo=legendreLM(l,m+1,x[thetaZerosIdx])\\n YlmPmpo=math.sqrt((2*l+1)/(4*math.pi)*math.factorial(l-m)/math.factorial(l+m))*Plmpo*numpy.exp(1j*m*phi[thetaZerosIdx])\\n dtYlm=+YlmPmpo+0\\n Psilm_ph[thetaZerosIdx]=0\\n Psilm_th[thetaZerosIdx]=+dtYlm\\n return Psilm_th,Psilm_ph\",\n \"def phireturn(xhat0, tof):\\n\\t\\n\\t\\tstoptime = tof\\n\\t\\tnumpoints = 2\\n\\t\\t#Integration time array:\\n\\t\\tt = [stoptime * float(i) / (numpoints - 1) for i in range(numpoints)]\\n\\t\\t\\n\\t\\txsol = twomode.intfull(xhat0, t, abserror=1.0e-14, relerror=1.0e-12)\\n\\t\\t#Phase of the first mode is the slice phase\\n\\t\\tphi = np.angle(xsol[1,0] + 1j*xsol[1,1]) \\t\\n\\t\\t\\n\\t\\treturn -phi\",\n \"def statsi(h):\\n\\n # Define constants\\n zsa = np.array([0.0, 11000.0, 20000.0, 32000.0, 47000.0, 52000.0, 61000.0, 79000.0, 9.9e20])\\n Tsa = np.array([288.15, 216.65, 216.65, 228.65, 270.65, 270.65,252.65, 180.65, 180.65])\\n g = 9.80665\\n R = 287.0528\\n Re = 6346766.0\\n Psa = 101325.0\\n\\n # Calculate geopotential altitude\\n z = Re*h/(Re+h)\\n\\n # Loop through atmosphere layers\\n for i in range(8):\\n \\n # Calculate layer temperature gradient\\n Lt = -(Tsa[i+1]-Tsa[i])/(zsa[i+1]-zsa[i])\\n\\n # If no temperature gradient\\n if Lt == 0.0:\\n\\n # Are we in this layer of the atmosphere?\\n if z <= zsa[i+1]:\\n t = Tsa[i] # Temp isn't changing\\n p = Psa*np.exp(-g*(z-zsa[i])/R/Tsa[i])\\n d = p/R/t\\n break\\n\\n # We need to go higher\\n else:\\n Psa *= np.exp(-g*(zsa[i+1]-zsa[i])/R/Tsa[i])\\n\\n # Temperature gradient\\n else:\\n ex = g/R/Lt\\n if z <= zsa[i+1]:\\n t = Tsa[i]-Lt*(z-zsa[i])\\n p = Psa*(t/Tsa[i])**ex\\n d = p/R/t\\n break\\n else:\\n Psa *= (Tsa[i+1]/Tsa[i])**ex\\n\\n # We have left the atmosphere...\\n else:\\n t = Tsa[-1]\\n p = 0.0\\n d = 0.0\\n\\n return z, t, p, d\",\n \"def calcTrh(N, rh, m, G, gamma=0.02):\\n return 0.138*N**0.5*rh**1.5/(m**0.5*np.log(gamma*N)*G**0.5)\",\n \"def Hstep_cost_function(H): \\n U = Wold - Yold\\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \\n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \\n return cost\",\n \"def ISA_trop(h):\\n\\tT = 288.15 - 0.0065*h;\\n\\tp = 101325*(T/288.15)**(-g/(-0.0065*287));\\n\\trho = 1.225*(T/288.15)**(-g/(-0.0065*287) - 1);\\n\\ta = np.sqrt(1.4*287*T);\\n\\treturn T, p, rho, a;\",\n \"def get_hessian(phi, pred, t, dot_product, reg= 1, regression= \\\"logistic\\\"):\\n R = np.eye(pred.shape[0])\\n if regression == \\\"logistic\\\":\\n for i in range(pred.shape[0]):\\n R[i,i] = pred[i,0] * (1- pred[i,0])\\n elif regression == \\\"probit\\\":\\n for i in range(pred.shape[0]):\\n y_n = pred[i,0]\\n t_n = t[i,0] \\n dotp = dot_product[i, 0]\\n pdf = norm.pdf(dotp)\\n\\n term1 = 1/ (y_n * (1- y_n) + TOLERANCE)\\n term2 = (y_n - t_n)/(y_n**2 * (1- y_n) + TOLERANCE)\\n term3 = (y_n - t_n)/((1- y_n)**2 * y_n + TOLERANCE)\\n term4 = (y_n - t_n)* dotp/(y_n * (1- y_n) * pdf + TOLERANCE)\\n\\n R[i,i] = (term1 - term2 + term3 - term4)*(pdf**2)\\n\\n # Add regularization\\t\\t\\t\\n hessian = np.matmul(np.matmul(phi.T, R), phi) + np.eye(phi.shape[1])/reg\\n return hessian\",\n \"def sph_harm_diff_theta(m, l, theta, phi):\\n check_degree_and_order(m, l)\\n return (m * _sph_harm(m, l, theta, phi) / np.tan(theta) +\\n np.sqrt( (l - m) * (l + m + 1) ) * np.exp( -1j*phi ) *\\n _sph_harm(m+1, l, theta, phi))\",\n \"def qFelder(h):\\n\\treturn (0.92 + 0.153 * h/1.01) * math.sqrt(9.8 * (2/3.0 * h)**3)\",\n \"def decode(self, h):\\n return self.tanh(self.linearD(h))\",\n \"def decode(self, h):\\n return self.tanh(self.linearD(h))\",\n \"def decode(self, h):\\n return self.tanh(self.linearD(h))\",\n \"def vsh2(m, l, theta, phi):\\n c_theta = 1/np.sin(phi) * sph_harm_diff_theta(m, l, theta, phi)\\n c_phi = sph_harm_diff_phi(m, l, theta, phi)\\n return np.array((0, c_theta, c_phi))\",\n \"def curve_no_hillcoef(ph, pka):\\n# return ph - pka\\n return 1/(10**(pka-ph)+1)\",\n \"def h_spec(k, He, h):\\r\\n return np.array(((k**3/np.pi**2) * h))\",\n \"def forward(self, t, h):\\n if self.i == 0:\\n self.A = self.beta * (self.B - self.B.transpose(1, 0)) + (\\n 1 - self.beta) * (self.B +\\n self.B.transpose(1, 0)) - self.gamma * self.I\\n self.W = self.beta * (self.C - self.C.transpose(1, 0)) + (\\n 1 - self.beta) * (self.C +\\n self.C.transpose(1, 0)) - self.gamma * self.I\\n\\n return torch.matmul(\\n h, self.A) + self.tanh(torch.matmul(h, self.W) + self.z)\",\n \"def bidimensional_map_nonlin_6(h, t, x, y, x_0, y_0):\\n gamma = 3 * np.pi\\n r = np.sqrt(\\n np.square(x - x_0 + 0.01 * x_0 * np.sin(gamma * t))\\n + np.square(y - y_0 + 0.01 * y_0 * np.sin(2 * gamma * t))\\n )\\n\\n f = lambda r: gamma * r + (gamma / 2) * r**3 + np.sqrt(r)\\n\\n return h(t - f(r))\",\n \"def phs(x, y, rbfParam) :\\n return (x**2 + y**2) ** (rbfParam/2)\",\n \"def theta_rule(a, u, h, t, n, th):\\n \\n Dt = t[n+1] - t[n]\\n num = (1.0 - (1-th)*a*Dt)*u[n] + (1-th)*a*Dt*h(t[n]) + th*a*Dt*h(t[n+1])\\n den = 1 + th*a*Dt\\n \\n return num/den\",\n \"def test_H_hat(self):\\n\\t\\tposition = [0.0, 1.57079, 3.14159, 4.71238, 6.28318, 7.85398, 9.42477]\\n\\t\\tpotential = [0.0, 6.0, 0.0, -6.0, 0.0, 6.0, 0.0]\\n\\t\\tc = 1\\n\\t\\tposition = tf.constant(position, shape = [1, len(position)], dtype = tf.float32)\\n\\t\\tpotential = tf.constant(potential, shape = [1, len(potential)], dtype = tf.float32)\\n\\t\\tbasis = schrodinger.create_basis(5)\\n\\t\\tv = schrodinger.v0(position, potential, basis)\\n\\t\\tcoeff = schrodinger.coefficient(position, basis)\\n\\t\\tv0_hat = tf.linalg.solve(coeff, v)\\n\\t\\tH = schrodinger.H_hat(c, len(basis), v0_hat)\\n\\t\\tself.assertEqual(coeff.get_shape(), [len(basis), len(basis)])\",\n \"def bidimensional_map_nonlin_3(h, t, x, y, x_0, y_0):\\n gamma = 3 * np.pi\\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\\n\\n f = lambda r: gamma * r + (gamma / 2) * r**3 + np.sqrt(r)\\n\\n return h(t - f(r))\",\n \"def Tanh(z):\\n return 1.7159 * np.tanh(2 / 3.0 * z)\",\n \"def rh(self, h):\\n sez=self.getSect(h)\\n area=self.area(sez)\\n wetborder = self.wetBorder(sez)\\n return area/wetborder\",\n \"def geofun(self, h, hK, cK):\\n if (h < hK):\\n # Wave is rarefaction wave (or depression)\\n c = math.sqrt(9.81 * h) # we use math because is faster\\n return 2 * (c-cK), 9.81/c\\n else:\\n # Wave is shock wave (or bore)\\n ges = math.sqrt(0.5*9.81 * ( h + hK )/( h * hK ) )\\n return ( h - hK ) * ges, ges - 0.25*9.81 * (h-hK)/(ges * h*h)\",\n \"def discretizar_funcion(a, I, h, t, th):\\n \\n u = np.zeros(len(t), dtype=float)\\n u[0] = I\\n for n in range(0, len(t) - 1):\\n u[n+1] = theta_rule(a, u, h, t, n, th)\\n return u\",\n \"def phi(t, *args):\\n # Unpacking data\\n mu_1, pi_mu_2, distance, affine_transfo = args\\n A, b = get_Ab(t)\\n N = len(mu_1)\\n assert len(mu_1) == len(pi_mu_2)\\n # Computing value of objective function\\n r = 0.\\n for i in np.arange(N):\\n r += distance(affine_transfo(A, b, mu_1[i]), pi_mu_2[i]) ** 2\\n return r\",\n \"def solver(self, theta):\\n\\n m = len(self.x)\\n n = len(self.t[0])\\n h = self.step_x\\n k = self.step_t\\n lamb = k / (h * h)\\n w = np.zeros(m + 1)\\n l = np.zeros(m + 1)\\n u = np.zeros(m + 1)\\n\\n uK = np.zeros(self.x.shape)\\n print('comecei para k= ({},{})'.format(theta[0], theta[1]))\\n startTime = time.clock()\\n if self.first:\\n self.uR = np.zeros(self.x.shape)\\n self.first = False\\n error = 0\\n errorCurve = np.zeros(self.x.shape)\\n z = np.zeros(m + 1)\\n w[m] = 0 # following the initial condition u(0,t) = u(l,t) = 0. If needed, change this.\\n for i in range(1, m - 1):\\n w[i] = self.g(i * h)\\n\\n l[1] = 1 + lamb\\n u[1] = -lamb / (2 * l[1])\\n for i in range(2, m - 1):\\n l[i] = 1 + lamb + lamb * u[i - 1] / 2\\n u[i] = -lamb / (2 * l[i])\\n\\n l[m - 1] = 1 + lamb + lamb * u[m - 2] / 2\\n for j in range(1, n + 1):\\n t = j * k # current t\\n z[1] = ((1 - lamb) * w[1] + lamb / 2 * w[2] + self.f(t, theta)) / l[1]\\n for i in range(2, m):\\n z[i] = ((1 - lamb) * w[i] + lamb / 2 * (w[i + 1] + w[i - 1] + z[i - 1]) + self.f(t, theta)) / l[i]\\n w[m - 1] = z[m - 1]\\n for i in range(m - 2, 0, -1):\\n w[i] = z[i] - u[i] * w[i + 1]\\n\\n for i in range(0, m + 1):\\n x = i * h\\n # print(x, w[i])\\n # print('oi')\\n uK[i - 1, j - 1] = w[i]\\n self.t[i - 1, j - 1] = t\\n self.x[i - 1, j - 1] = x\\n error += pow(w[i] - self.uR[i - 1, j - 1], 2) / uK.size\\n errorCurve[i - 1, j - 1] = (pow(w[i] - self.uR[i - 1, j - 1], 2)) / uK.size\\n print('acabei para k= ({},{}) em {} segundos'.format(theta[0], theta[1], time.clock() - startTime))\\n return (uK, error, errorCurve, theta)\",\n \"def h(X, theta, n_hidden_layers=1):\\n _, a = feed_forward(X, theta, n_hidden_layers)\\n L = n_hidden_layers + 1 # last layer\\n\\n hypothesis = a[L]\\n return hypothesis\",\n \"def bidimensional_map_nonlin_2(h, t, x, y, x_0, y_0):\\n\\n gamma = 3 * np.pi\\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\\n\\n f = lambda r: gamma * r\\n\\n return h(t - f(r))\",\n \"def refine_theta_parabolic_peak(\\n request_recon_h,\\n varphi_estimate,\\n deg,\\n n_pts = 3,\\n alpha_fidelity = 1,\\n):\\n if n_pts < 3:\\n raise ValueError(\\n f\\\"The number of sample points {n_pts} is not enough to fit a parabola\\\"\\n )\\n half_window_width = np.pi / (2 * deg)\\n angles = np.linspace(\\n varphi_estimate - half_window_width,\\n varphi_estimate + half_window_width,\\n n_pts,\\n )\\n vals = np.array([request_recon_h(omega) for omega in angles], dtype=complex)\\n # impose correction when the circuit error is not coherent\\n vals = (vals + (1 - alpha_fidelity) * (1 + 1j) / 4) / alpha_fidelity\\n p = np.polyfit(angles, np.abs(vals), 2)\\n if p[0] >= 0:\\n theta_estimate_mle = -np.inf\\n varphi_estimate_mle = -np.inf\\n return (theta_estimate_mle, varphi_estimate_mle), (angles, vals)\\n else:\\n peak_loc = -p[1] / (2 * p[0])\\n peak_val = p[2] - p[1] ** 2 / (4 * p[0])\\n theta_estimate_mle = peak_val / deg\\n varphi_estimate_mle = peak_loc\\n return (theta_estimate_mle, varphi_estimate_mle), (angles, vals)\",\n \"def calcT(self, theta, phi, T_ss):\\n return T_ss * np.maximum(0.0, np.cos(theta) * np.cos(phi))**0.25\",\n \"def H(lattice, i, j, h, T):\\n\\n gamma = np.random.rand()\\n delta_E = -2*lattice[i, j] * (lattice[i, j - 1] + lattice[i - 1, j] + lattice[i, j + 1] + lattice[i + 1, j]) - 2*h * lattice[i, j]\\n\\n return not (delta_E > 0 and np.exp(-(delta_E)/(kb * T)) > gamma)\",\n \"def bidimensional_map_nonlin_1(h, t, x, y, x_0, y_0):\\n\\n gamma = 3 * np.pi\\n r = np.sqrt(np.square(x - x_0) + np.square(y - y_0))\\n\\n f = lambda r: gamma * r + (gamma / 2) * r**2 + np.sqrt(r)\\n\\n return h(t - f(r))\",\n \"def z_eq(self):\\n theta = self.T_cmb/2.7\\n return 25000.*self.Omega_m*self.h**2.*theta**-4.\",\n \"def calc_theta(U, V, quad, h_length, radians):\\n import numpy as np\\n theta = np.arcsin(U / h_length)\\n import numpy as np\\n if quad == 1:\\n theta = theta\\n elif quad == 2:\\n theta = -theta + np.pi / 2\\n elif quad - - 3:\\n theta = np.pi / 2 + theta + np.pi\\n elif quad == 4:\\n theta = 3 * np.pi / 2\\n theta = 2 * np.pi - theta\\n if not radians:\\n theta = theta * 180 / np.pi\\n\\n return (theta)\",\n \"def h_T(self, z0):\\n # Get the governing variables\\n (B, N, u_slip, u_inf) = self.get_variables(z0, 0.)\\n \\n # Compute U_N\\n U_N = u_slip / (B * N)**(1./4.)\\n \\n # Compute the correlation equation\\n return 2.9 * np.exp(-(U_N - 1.0)**2 / 28.09) * (B / N**3)**(1./4.)\",\n \"def ph(self,k,z=0):\\n return self.p(k*self.h,z)*self.h**3\",\n \"def _h(W):\\r\\n # E = slin.expm(W * W)\\r\\n # h = np.trace(E) - d\\r\\n M = np.eye(d) + W * W / d\\r\\n E = np.linalg.matrix_power(M, d - 1)\\r\\n h = (E.T * M).sum() - d\\r\\n G_h = E.T * W * 2\\r\\n return h, G_h\",\n \"def func1(y, j, h, add_u = 0):\\n y_temp = y[j] + add_u\\n N = xsize\\n k = np.zeros(xsize)\\n for i in range(xsize):\\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2) - 0.5/(h**3)*(y_temp[(i+2)%N]-2*y_temp[(i+1)%N]+2*y_temp[(i-1)%N]-y_temp[(i-2)%N])\\n return k\",\n \"def one_step(self, x, h):\\n concatHX = torch.cat((x, h), 1)\\n zt = self.sigmoid(self.linearZ(concatHX))\\n rt = self.sigmoid(self.linearR(concatHX))\\n ht = (1-zt)*h + zt* self.tanh(self.linearH(rt*concatHX))\\n return ht\",\n \"def layer(self, h, t):\\n mr1h = torch.matmul(h, self.mr1.weight) # h => [m, d], self.mr1 => [d, k]\\n mr2t = torch.matmul(t, self.mr2.weight) # t => [m, d], self.mr2 => [d, k]\\n return torch.tanh(mr1h + mr2t)\",\n \"def func2(y, j, h, add_u = 0):\\n y_temp = y[j] + add_u\\n N = xsize\\n k = np.zeros(xsize)\\n for i in range(xsize):\\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2)\\n return k\",\n \"def theta():\\n pass\",\n \"def forward(self, z_t_1, h_x, phi_table):\\n z_category = self.gen_z_t_dist_now(z_t_1, h_x)\\n \\n if self.use_gumbel_softmax:\\n \\n# device = z_category.device\\n \\n averaged_z_t = 0\\n \\n log_prob = Variable(torch.log(z_category))\\n \\n for k in range(self.sampling_times): \\n curr_z_t = F.gumbel_softmax(log_prob, tau = 0.1)\\n \\n averaged_z_t += curr_z_t\\n \\n del curr_z_t\\n \\n# averaged_z_t = averaged_z_t.to(device)\\n \\n z_t = averaged_z_t/self.sampling_times\\n else:\\n z_t = z_category\\n \\n phi_z = torch.mm(z_t, torch.t(phi_table))\\n# mu = self.h_to_mu(h_combined)\\n# logvar = self.h_to_logvar(h_combined)\\n# std = torch.exp(0.5 * logvar) \\n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\\n return z_t, z_category, phi_z\",\n \"def eval_costh_phi_fold_pt_HX(data):\\n return data.costh_HX_fold, data.phi_HX_fold, data.JpsiPt\",\n \"def __call__(self, h):\\n\\n Wh = self.W(h)\\n p_yt = F.log_softmax(Wh) # should be (B x V)\\n\\n return p_yt\",\n \"def spherical_harmonics_vec(th,ph, lmax):\\n Y = []\\n lm = []\\n n = lmax*(lmax+2)+1\\n sph_it = spherical_harmonics_it(th,ph)\\n for i in range(0, n):\\n Ylm,l,m = sph_it.next()\\n Y.append(Ylm)\\n lm.append((l,m))\\n assert l == lmax\\n assert m == -lmax\\n\\n return Y, lm\",\n \"def get_heat_source_heating_output(\\n theta_hs_out_h: np.ndarray, theta_hs_in: np.ndarray, v_supply: np.ndarray, operation: np.ndarray) -> np.ndarray:\\n\\n c = get_specific_heat()\\n rho = get_air_density()\\n\\n q_hs_h = np.maximum((theta_hs_out_h - theta_hs_in) * c * rho * np.sum(v_supply, axis=0) * 10 ** (-6), 0.0)\\n\\n return np.where(operation == 'h', q_hs_h, 0.0)\",\n \"def calc_h_lat(dry_bulb_C, humidity_ratio_out_kgperkg):\\n\\n h_kJ_kg = humidity_ratio_out_kgperkg * (dry_bulb_C * CPW_kJ_kgC + h_we_kJ_kg)\\n\\n return h_kJ_kg\",\n \"def h(x, theta):\\n # ... dopolnite (naloga 1)\\n\\n power = x.dot(-theta.T)\\n\\n return 1 / (1 + np.exp(power))\",\n \"def get_f_h_gas_comp_in(p: float, theta: float) -> float:\\n\\n return -1.00110355 * 10 ** (-1) * p ** 3 \\\\\\n - 1.184450639 * 10 * p ** 2 \\\\\\n - 2.052740252 * 10 ** 2 * p \\\\\\n + 3.20391 * 10 ** (-6) * (theta + 273.15) ** 3 \\\\\\n - 2.24685 * 10 ** (-3) * (theta + 273.15) ** 2 \\\\\\n + 1.279436909 * (theta + 273.15) \\\\\\n + 3.1271238 * 10 ** (-2) * p ** 2 * (theta + 273.15) \\\\\\n - 1.415359 * 10 ** (-3) * p * (theta + 273.15) ** 2 \\\\\\n + 1.05553912 * p * (theta + 273.15)+1.949505039 * 10 ** 2\",\n \"def parang (hourangle, declination, latitude):\\n\\n return -np.arctan2 (-np.sin (hourangle),\\n np.cos (declination) * np.tan (latitude)\\n - np.sin (declination) * np.cos (hourangle))\",\n \"def sph_harm_diff_phi(m, l, theta, phi):\\n check_degree_and_order(m, l)\\n return 1j * m * _sph_harm(m, l, theta, phi)\",\n \"def compute_tsky_hot( xv, yv, hv, thot, tcold):\\n\\n nData = len(yv) \\n epsilons = np.full( nData, EPSILON)\\n tsys = np.zeros(nData) # initialize arrays\\n\\n Z = np.zeros(nData)\\n oneMZ = np.zeros(nData)\\n # For full Temp calibration, a spectrum taken at high elevation away from \\n # The galactic plan is used. For this program the cold spectrum must be\\n # the spectrum being calibrated. See the M command for comparision\\n epsilons = np.full( nData, EPSILON)\\n yv = np.maximum( yv, epsilons)\\n hv = np.maximum( hv, epsilons)\\n # comput the cold/hot ratio\\n Z = yv/hv\\n oneMZ = np.full( nData, 1.) - Z\\n oneMZ = np.maximum( oneMZ, epsilons)\\n\\n # the cold, receiver, temperature is this function\\n tsys = ((Z*thot) - tcold)/oneMZ\\n \\n n6 = int(nData/6)\\n n56 = 5*n6\\n\\n tsysmedian = np.median( tsys[n6:n56])\\n\\n tsky = np.zeros(nData) # initialize arrays\\n S = np.zeros(nData) # initialize arrays\\n\\n # The system gain S is computed assuming a tsys is the cold load\\n S = np.full( nData, tsysmedian+thot)/hv\\n # scale the observed instensity in counts to Kelvins.\\n tsky = S*yv\\n\\n return tsky\",\n \"def hrf_fit_err(theta, z, y, t_r, hrf_dur):\\n h, _ = spm_hrf(theta, t_r, hrf_dur, False)\\n return 0.5 * np.sum(np.square(y - spectral_convolve(h, z)))\",\n \"def sph(grlat, elong, ht):\\n\\n # Initialize Variables\\n global cth, sth, clg, slg, dif, radn, gl # common/obs/\\n gn = 9.798277692\\n ae = 6378140.0\\n f = 0.00335281\\n rm = 0.00344978\\n dr = 0.01745329252\\n\\n clong = np.cos(elong * dr)\\n slong = np.sin(elong * dr)\\n # latitude difference\\n dvert = f * (1.0 + 0.5 * f) * np.sin(2.0 * grlat * dr) - 0.5 * f * f * np.sin(\\n 4.0 * grlat * dr\\n )\\n gcclat = (3.1415926535898 / 2.0) - (grlat * dr - dvert)\\n cthet = np.cos(gcclat)\\n sthet = np.sin(gcclat)\\n # geocentric radius\\n radn = 1 - f * (cthet ** 2) * (1 + 1.5 * f * (sthet ** 2))\\n # formulae for g are from jeffreys, 4.022 and 4.023\\n g = gn * (\\n 1\\n + f\\n - 1.5 * rm\\n + f * (f - (27 / 14) * rm)\\n + (2.5 * rm - f - f * (f - (39 / 14) * rm)) * (cthet ** 2)\\n - (f / 2) * (7 * f - 15.0 * rm) * ((cthet * sthet) ** 2)\\n )\\n # free air correction\\n g = g - g * (2.0 * ht * (1.0 + f + rm - 2.0 * f * (cthet ** 2)) / ae)\\n\\n # Conversion Here for Globals\\n cth = cthet\\n sth = sthet\\n clg = clong\\n slg = slong\\n dif = dvert\\n gl = g\",\n \"def isa(h):\\n if np.any((h<0)|(h>11000)):\\n print (\\\"The altitude must be between 0m and 11000m\\\")\\n return \\\"Returning a string to cause errors later\\\"\\n T = T0 + -0.0065*h\\n p = p0*(T/T0)**(-g0/(-0.0065*R))\\n rho = p/(R*T)\\n return T,p,rho\",\n \"def Spin(phi,theta):\\n return 1/2*(cos(phi)*sin(theta)*xhat + sin(phi)*sin(theta)*yhat + cos(theta)*zhat)\",\n \"def H(lattice, i, j, h, T):\\n\\n gamma = np.random.rand()\\n delta_E = 2*lattice[i, j] * (lattice[i, j - 1] + lattice[i - 1, j] + lattice[i, j + 1] + lattice[i + 1, j]) + 2*h * lattice[i, j]\\n\\n return delta_E < 0 or gamma < np.exp(-(delta_E)/(kb * T))\",\n \"def h(self, z):\\n # See definition at end of Section 1, p2 of Arnaud et al.\\n return np.sqrt(self.Om*(1.+z)**3. + self.Ol)\",\n \"def get_z(theta, phi):\\n return math.cos(phi)/math.tan(theta/2) + 1j*math.sin(phi)/math.tan(theta/2)\",\n \"def compute_stability_fm_h(H, t0, u_attr, r_air, hc, d0, z0m, cp=1004.16):\\n L_ob = H.expression(\\n '-(r_air * cp * t0 * (u_attr ** 3.0) / 0.41 / 9.806 / H)',\\n {'cp': cp, 'H': H, 'r_air': r_air, 't0': t0, 'u_attr': u_attr})\\n L_ob = L_ob.where(L_ob.gte(0), -99.0)\\n mm_h = H \\\\\\n .expression(\\n '((1 - (16.0 * (hc - d0) / L_ob)) ** 0.25)',\\n {'d0': d0, 'hc': hc, 'L_ob': L_ob}) \\\\\\n .where(L_ob.eq(-99.0), 0.0)\\n fm_h = H \\\\\\n .expression(\\n '2.0 * log((1.0 + mm_h) / 2.0) + log((1.0 + (mm_h ** 2)) / 2.0) - '\\n '2.0 * atan(mm_h) + (pi / 2)',\\n {'mm_h': mm_h, 'pi': math.pi}) \\\\\\n .where(L_ob.lte(-100).Or(L_ob.gte(100)), 0)\\n\\n # CGM - Swapped order of calc since d0 is an image compute from hc and\\n # z_u is being set as a constant number (for now).\\n fm_h = fm_h.where(fm_h.eq(hc.subtract(d0).divide(z0m).log()), fm_h.add(1.0))\\n # fm_h = fm_h.where(fm_h.eq(hc.subtract(d0).divide(z0m).log()), fm_h.add(1.0))\\n return fm_h\",\n \"def cost(h, y):\\n\\tm = y.shape[0]\\n\\tcost = (-1/m) * (y.T @ np.log(h) + (1 - y).T @ np.log(1 - h))\\n\\treturn cost\",\n \"def guidance_target(self, state, final_x, y_profile, time_horizon, time_step, polyfit=None):\\n current_x, current_y, current_theta, current_y_dot = state[XX], state[YY], state[THETA], state[Y_DOT]\\n y_index = np.where(y_profile == np.extract(y_profile <= current_y, y_profile)[0])[0][0]\\n\\n # m, c = polyfit\\n beta = 3\\n gamma = 0.5\\n\\n delta_t = [t * time_step for t in range(0, time_horizon)]\\n y = y_profile[y_index : y_index+time_horizon]\\n y = np.clip(y, y_profile[-1], 26)\\n # x_adjust = final_x * (1 - np.exp(-0.05 * np.array(delta_t)))\\n # x = current_x + x_adjust * (final_x - current_x)\\n x = current_x + (final_x - current_x) / (1 + np.exp(-np.linspace(-4, 4, num=time_horizon)))\\n if current_x > final_x:\\n x = np.clip(x, final_x, 33)\\n else:\\n x = np.clip(x, 0, final_x)\\n\\n y_dot = current_y_dot * np.exp(-np.linspace(0, 2, time_horizon)) # -2 * np.exp(-1/abs(y))\\n x_dot = (x - final_x)\\n\\n theta = current_theta * np.exp(-beta * np.array(delta_t)) + current_theta * 0.3\\n if theta[0] > 0:\\n theta = np.clip(theta, 0, 15 * DEGTORAD)\\n else:\\n theta = np.clip(theta, -15 * DEGTORAD, 0)\\n theta_dot = gamma * theta\\n\\n targets = [x, y, x_dot, y_dot, theta, theta_dot]\\n return targets\",\n \"def infilCapaHorton(f0, fc, k, t):\\n ft = fc + (f0 - fc)*np.exp(-k*t)\\n return ft\",\n \"def gha(self):\\n return np.mod(self.gmst*self.turndeg +\\n self.turndeg*self.T*self.century +\\n self.turndeg/2.0, self.turndeg)\",\n \"def func3(y, j, h, add_u = 0):\\n y_temp = y[j] + add_u\\n N = xsize\\n k = np.zeros(xsize)\\n for i in range(xsize):\\n k[i] = -(1/4.)*(1./h)*(y_temp[(i+1)%N]**2-y_temp[(i-1)%N]**2) + (1/2.)*(1./h**2)*(y_temp[(i+1)%N]-2*y_temp[i%N]+y_temp[(i-1)%N])\\n return k\",\n \"def vsh1(m, l, theta, phi):\\n check_degree_and_order(m, l)\\n return np.array((_sph_harm(m, l, theta, phi), 0, 0))\",\n \"def vsh3(m, l, theta, phi):\\n if l==0: return np.array([0, 0, 0])\\n\\n r = 1\\n R = np.array([r, 0, 0])\\n gradY = sph_harm_gradient(m, l, r, theta, phi)\\n return -1j * np.cross(R, gradY) / np.sqrt( l * (l + 1))\",\n \"def get_thetaRHT_hat(self, sample_psi0, rht_data):\\n QRHT = np.sum(np.cos(2*sample_psi0)*rht_data)\\n URHT = np.sum(np.sin(2*sample_psi0)*rht_data)\\n theta_rht = np.mod(0.5*np.arctan2(URHT, QRHT), np.pi)\\n\\n return theta_rht\",\n \"def eval_costh_phi_fold_HX(data):\\n return data.costh_HX_fold, data.phi_HX_fold\",\n \"def one_step(self, x, h):\\n concatHX = torch.cat((x, h), 1)\\n ft = self.sigmoid(self.linearF(concatHX))\\n it = self.sigmoid(self.linearI(concatHX))\\n newCt = ft*self.ct.clone() + it*self.tanh(self.linearC(concatHX))\\n #self.ct = ft*self.ct.clone() + it*self.tanh(self.linearC(concatHX))\\n ot = self.sigmoid(self.linearO(concatHX))\\n ht = ot*self.tanh(newCt)\\n self.ct = newCt\\n \\n return ht\",\n \"def H(t, args):\\n\\n f0 = args['f0']\\n n = args['n']\\n omega = args['omega']\\n omegaDt = args['omegaDt']\\n omegaArgs = args['omegaArgs']\\n\\n ad = create(n)\\n a = destroy(n)\\n # H0, for the first two terms see Silveri 2017 Quantum_systems_under_frequency_modulation\\n ham = omega(t, omegaArgs)*(ad*a+0.5*qeye(n))\\n # additional term because of w(t) not constant\\n ham += 1j/4*omegaDt(t, omegaArgs)/omega(t, omegaArgs)*(a*a-ad*ad)\\n # Force term (9**10^-9 = x0, extent of ground state wave function), see Wittmann diss\\n # with compensation term -f0/w0^2 (e.g. no force in the case of no modulation)\\n ham += 9*(f0/(omega(t, omegaArgs)**2) - f0/(omegaArgs[0]**2))*(ad + a)\\n # ham += (9*10**-9)/(10**6)*(f0/(omega(t, omegaArgs)**2))*(ad + a)\\n return(ham)\",\n \"def update_H(self):\\n gamma = self.get_gamma()\\n delta = self.get_delta()\\n summand2 = ((1 + (gamma.transpose().dot(self.H).dot(gamma) /\\n delta.transpose().dot(gamma))) *\\n delta.dot(delta.transpose()) / delta.transpose().dot(gamma)\\n )\\n summand3 = - ((delta.dot(gamma.transpose()).dot(self.H) +\\n self.H.dot(gamma).dot(delta.transpose())) /\\n delta.transpose().dot(gamma))\\n self.H = self.H + summand2 + summand3\",\n \"def _sph_harm(m, n, theta, phi):\\n return sph_harm(m, n, phi, theta)\",\n \"def hrf_estim(z, y, t_r, dur, verbose=0):\\n args = (z, y, t_r, dur)\\n bounds = [(MIN_DELTA + 1.0e-1, MAX_DELTA - 1.0e-1)]\\n f_cost = Tracker(hrf_fit_err, args, verbose)\\n\\n theta, _, _ = fmin_l_bfgs_b(\\n func=hrf_fit_err, x0=MAX_DELTA, args=args,\\n bounds=bounds, approx_grad=True, callback=f_cost,\\n maxiter=99999, pgtol=1.0e-12)\\n J = f_cost.J\\n h, _ = spm_hrf(theta, t_r, dur, False)\\n\\n return h, J\",\n \"def _prob_v_given_h(self, h):\\n return tf.sigmoid(tf.add(self.a, tf.matmul(h, tf.transpose(self.W))))\",\n \"def rk4(self, t, h,G) :\\r\\n k1 = h*self.calc_diff_eqn(t, self.quant_vec,G,self.mass_vec)\\r\\n k2 = h*self.calc_diff_eqn(t + 0.5*h , self.quant_vec + 0.5*k1 ,G, self.mass_vec)\\r\\n k3 = h*self.calc_diff_eqn(t + 0.5*h , self.quant_vec + 0.5*k2 ,G, self.mass_vec)\\r\\n k4 = h*self.calc_diff_eqn(t + h , self.quant_vec + k3 ,G, self.mass_vec)\\r\\n y_new = self.quant_vec + ((k1 + 2*k2 + 2*k3 + k4)/6)\\r\\n return y_new\",\n \"def theta_v_time():\\n pass\",\n \"def T_naught(z, h, OM, OB):\\n\\n T0 = 28.5 * ((1.0+z)/10.0)**(0.5) * OB/0.042 * h/0.73 * (0.24/OM)**(0.5)\\n return T0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5862929","0.5850773","0.57733494","0.5686483","0.56282175","0.5592743","0.55332625","0.55067706","0.55043215","0.5491298","0.5484844","0.5470747","0.5449616","0.5431975","0.5429191","0.5417069","0.54054326","0.5379685","0.53791565","0.5375629","0.5361891","0.53573406","0.5336242","0.532041","0.5293915","0.52920234","0.5254426","0.5241588","0.5235897","0.5231546","0.5231546","0.5231546","0.52311575","0.51916766","0.5190566","0.5189652","0.5180938","0.5161224","0.5159144","0.51512045","0.51433146","0.5132367","0.51217246","0.5121117","0.51159626","0.5099102","0.5086923","0.50843567","0.50836533","0.50732934","0.5071702","0.5069259","0.50681096","0.50654995","0.5064698","0.5062462","0.50561285","0.5053195","0.505197","0.5047961","0.50362456","0.5025721","0.50241864","0.50150687","0.5012761","0.50125396","0.5012422","0.5011258","0.50103176","0.50068533","0.49983248","0.4992054","0.49916747","0.4988998","0.497834","0.49760893","0.49728727","0.49716958","0.49706614","0.49693283","0.49603072","0.49540213","0.49522707","0.49486235","0.49431026","0.49425134","0.49415436","0.49372387","0.4935343","0.49320382","0.4928524","0.49270064","0.49229106","0.49174798","0.49158025","0.49047348","0.48805198","0.48761627","0.4867689","0.48641452"],"string":"[\n \"0.5862929\",\n \"0.5850773\",\n \"0.57733494\",\n \"0.5686483\",\n \"0.56282175\",\n \"0.5592743\",\n \"0.55332625\",\n \"0.55067706\",\n \"0.55043215\",\n \"0.5491298\",\n \"0.5484844\",\n \"0.5470747\",\n \"0.5449616\",\n \"0.5431975\",\n \"0.5429191\",\n \"0.5417069\",\n \"0.54054326\",\n \"0.5379685\",\n \"0.53791565\",\n \"0.5375629\",\n \"0.5361891\",\n \"0.53573406\",\n \"0.5336242\",\n \"0.532041\",\n \"0.5293915\",\n \"0.52920234\",\n \"0.5254426\",\n \"0.5241588\",\n \"0.5235897\",\n \"0.5231546\",\n \"0.5231546\",\n \"0.5231546\",\n \"0.52311575\",\n \"0.51916766\",\n \"0.5190566\",\n \"0.5189652\",\n \"0.5180938\",\n \"0.5161224\",\n \"0.5159144\",\n \"0.51512045\",\n \"0.51433146\",\n \"0.5132367\",\n \"0.51217246\",\n \"0.5121117\",\n \"0.51159626\",\n \"0.5099102\",\n \"0.5086923\",\n \"0.50843567\",\n \"0.50836533\",\n \"0.50732934\",\n \"0.5071702\",\n \"0.5069259\",\n \"0.50681096\",\n \"0.50654995\",\n \"0.5064698\",\n \"0.5062462\",\n \"0.50561285\",\n \"0.5053195\",\n \"0.505197\",\n \"0.5047961\",\n \"0.50362456\",\n \"0.5025721\",\n \"0.50241864\",\n \"0.50150687\",\n \"0.5012761\",\n \"0.50125396\",\n \"0.5012422\",\n \"0.5011258\",\n \"0.50103176\",\n \"0.50068533\",\n \"0.49983248\",\n \"0.4992054\",\n \"0.49916747\",\n \"0.4988998\",\n \"0.497834\",\n \"0.49760893\",\n \"0.49728727\",\n \"0.49716958\",\n \"0.49706614\",\n \"0.49693283\",\n \"0.49603072\",\n \"0.49540213\",\n \"0.49522707\",\n \"0.49486235\",\n \"0.49431026\",\n \"0.49425134\",\n \"0.49415436\",\n \"0.49372387\",\n \"0.4935343\",\n \"0.49320382\",\n \"0.4928524\",\n \"0.49270064\",\n \"0.49229106\",\n \"0.49174798\",\n \"0.49158025\",\n \"0.49047348\",\n \"0.48805198\",\n \"0.48761627\",\n \"0.4867689\",\n \"0.48641452\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75179046"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8496,"cells":{"query":{"kind":"string","value":"the_vars[0]= thetabar the_vars[1] = h the_vars[2] = qv surface flux from drag law with subsidence and diagnosed deltheta"},"document":{"kind":"string","value":"def dmixed_vars(the_vars,tstep,coeffs):\n\n deltheta = theta_ft(the_vars[1],coeffs.ft_intercept,coeffs.ft_gamma) - the_vars[0]\n F0 = coeffs.U*coeffs.Cd*(coeffs.sst - the_vars[0]) #surface heat flux\n Fqv0 = coeffs.U*coeffs.Cd*(coeffs.qsfc - the_vars[2]) #surface vapor flux\n Fint = -coeffs.k*F0 #entrainment heat flux\n \n if coeffs.use_NT: # use NT parameterization by calculating we using function\n went = calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0) # Nicholls-Turton parameterization\n \n else: # use simple we parameterization\n went = -Fint/deltheta #simple entrainment parameterization\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*coeffs.ft_qv*1000 # convert qt_ft to g kg^-1\n \n Fqvent = -went*( coeffs.ft_qv - the_vars[2])\n wsubs = -coeffs.D*the_vars[1]\n rho=1.\n cp=1004.\n \n derivs=np.empty_like(the_vars)\n \n # higher delta_Fr from drier air at mixed-layer top...hence cloudy air results in less radiative cooling\n derivs[0]=(F0 - Fint)/(the_vars[1]*rho) - delta_Fr/1004./the_vars[1] \n derivs[1] = went + wsubs\n derivs[2] = (Fqv0 - Fqvent)/the_vars[1]\n return derivs"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ha(env, cstate=0):\n T1 = 10\n T2 = 10\n thM = 20\n thm = 5\n vr = 10.5\n v1 = -1.3\n v2 = -2.7\n assert(T1 == T2)\n\n delta = None # None to cause failure\n # The continous variables used in this ha\n x = T1 # clock1 variable\n y = T2 # clock2 variable\n th = 11.5 # The reactor temperature\n\n # You need vtol here, because of floating point error.\n loc0_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc0_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc0_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(vr),\n ttol=10**-3, iterations=100, vtol=10**-10)\n loc0_FT = False\n\n loc1_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc1_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc1_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(v1),\n ttol=10**-3, iterations=100, vtol=10**-10)\n loc1_FT = False\n\n loc2_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc2_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\n ttol=10**-3, iterations=100)\n loc2_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(v2),\n ttol=10**-3, iterations=100, vtol=10**-10)\n loc2_FT = False\n\n # Location 3 is reactor shutdown\n loc3_FT = False\n\n # Location 0\n def location0(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\n vals = {S.sympify('x(t)'): x,\n S.sympify('y(t)'): y,\n S.sympify('th(t)'): th}\n curr_time = env.now\n # The edge guard takes preference\n if th == thM and x >= T1:\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n return 1, 0, x, y, th, None, True, None, None, curr_time\n elif th == thM and y >= T2:\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n return 2, 0, x, y, th, None, None, True, None, curr_time\n elif th == thM and x < T1 and y < T2:\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n return 3, 0, x, y, th, None, None, None, True, curr_time\n # The invariant\n elif th <= thM:\n if not loc0_FT:\n x = loc0_ode_x.compute(vals, curr_time-prev_time)\n y = loc0_ode_y.compute(vals, curr_time-prev_time)\n th = loc0_ode_th.compute(vals, curr_time-prev_time)\n loc0_FT = True\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n if abs(th-thM) > loc0_ode_th.vtol:\n deltath = loc0_ode_th.delta(vals, quanta=(thM-th))\n else:\n th = thM\n deltath = 0\n return 0, deltath, x, y, th, False, None, None, None, curr_time\n else:\n # print('th:', th)\n raise RuntimeError('Reached unreachable branch'\n ' in location 0')\n\n def location1(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\n vals = {S.sympify('x(t)'): x,\n S.sympify('y(t)'): y,\n S.sympify('th(t)'): th}\n curr_time = env.now\n # The edge guard takes preference\n if th == thm:\n x = 0 # Reset\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n return 0, 0, x, y, th, True, None, None, None, curr_time\n # The invariant\n elif th >= thm:\n if not loc1_FT:\n x = loc1_ode_x.compute(vals, curr_time-prev_time)\n y = loc1_ode_y.compute(vals, curr_time-prev_time)\n th = loc1_ode_th.compute(vals, curr_time-prev_time)\n loc1_FT = True\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n if abs(th-thm) > loc1_ode_th.vtol:\n deltath = loc1_ode_th.delta(vals, quanta=(thm-th))\n else:\n th = thm\n deltath = 0\n return 1, deltath, x, y, th, False, None, None, None, curr_time\n else:\n raise RuntimeError('Reached unreachable branch'\n ' in location 1')\n\n def location2(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\n vals = {S.sympify('x(t)'): x,\n S.sympify('y(t)'): y,\n S.sympify('th(t)'): th}\n curr_time = env.now\n # The edge guard takes preference\n if th == thm:\n y = 0 # Reset\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n return 0, 0, x, y, th, True, None, None, None, curr_time\n # The invariant\n elif th >= thm:\n if not loc2_FT:\n x = loc2_ode_x.compute(vals, curr_time-prev_time)\n y = loc2_ode_y.compute(vals, curr_time-prev_time)\n th = loc2_ode_th.compute(vals, curr_time-prev_time)\n loc2_FT = True\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\n if abs(th-thm) > loc2_ode_th.vtol:\n deltath = loc2_ode_th.delta(vals, quanta=(thm-th))\n else:\n th = thm\n deltath = 0\n return 2, deltath, x, y, th, False, None, None, None, curr_time\n else:\n raise RuntimeError('Reached unreachable branch'\n ' in location 2')\n\n def location3(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\n global step\n # print('total steps: ', step)\n # Done\n print(time.time()-start)\n sys.exit(1)\n\n # The dictionary for the switch statement.\n switch_case = {\n 0: location0,\n 1: location1,\n 2: location2,\n 3: location3\n }\n\n prev_time = env.now\n while(True):\n (cstate, delta, x, y, th,\n loc0_FT, loc1_FT, loc2_FT, loc3_FT,\n prev_time) = switch_case[cstate](x, y, th,\n loc0_FT,\n loc1_FT,\n loc2_FT,\n loc3_FT,\n prev_time)\n # This should always be the final statement in this function\n global step\n step += 1\n yield env.timeout(delta)","def calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0):\n thetal_m = the_vars[0]\n qt_m = the_vars[2]\n zi = the_vars[1]\n dth = deltheta\n \n thetal_ft = thetal_m + dth\n qt_ft = coeffs.ft_qv\n \n dqt = qt_ft - qt_m\n \n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\n gamma = 6e-3 \n thetal_3000 = thetal_ft + gamma*(3000-zi)\n LTS = thetal_3000 - coeffs.sst # lower tropospheric stability\n\n # calculate coefficients\n press=tf.find_press(zi)\n Ad,Bd,issat = tf.calc_ABcoeffs(thetal_ft,qt_ft,press)\n Aw,Bw,issat = tf.calc_ABcoeffs(thetal_m,qt_m,press)\n \n invert= tf.t_uos_thetal(thetal_m,qt_m,press)\n T_0 = invert.temp\n lv=tf.L_t(invert.temp)\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\n del_thv_dry = Ad * dth + Bd * dqt\n del_thv_sat = Aw * dth + Bw * dqt\n \n # account for evaporative cooling (increases we)\n ql_max = invert.ql\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\n Del_thv = del_thv_dry - Cl * ql_max\n \n # calculate buoyancy integral terms\n rho = 1.\n lcl_press=tf.LCL_thetal(thetal_m,qt_m)\n zb=tf.find_height(lcl_press)\n\n T1 = zb/zi\n T2 = 0.5 * zb**2 / zi**2\n T3 = (zi-zb)/zi\n T4 = 0.5 * (zi**2 - zb**2) / zi**2\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\n\n wtl_0=F0\n wqt_0=Fqv0\n Del_F = delta_Fr/(tc.CPD*rho) # use sensitivity to radiation a la Gesso Fig. 3\n term1 = wtl_0 * (Ad * (T1-T2) + Aw * (T3-T4))\n term2 = wqt_0 * (Bd * (T1-T2) + Bw * (T3-T4))\n term3 = Del_F * (Ad * T2 + Aw * T4)\n\n Theta_NE = term1 + term2 + term3\n \n # calculate w*\n wstar=(2.5*9.8/T_0*zi*Theta_NE)**(1/3.)\n \n # calculate chi*\n chi_star = Cl * ql_max / (del_thv_dry - del_thv_sat)\n \n # calculate del_m\n Del_m = del_thv_dry + chi_star * (2. - chi_star) * (del_thv_sat - del_thv_dry)\n \n # calculate we\n a2=15.\n Del_thv_NT = Del_thv / (1. + a2 * (1. - Del_m/Del_thv))\n \n A_NT = 0.2\n fac_NT = 2.5\n\n term4 = Del_thv_NT\n term5 = A_NT * fac_NT * (T2 * del_thv_dry + T4 * del_thv_sat)\n denominator = term4 + term5\n\n we = A_NT * fac_NT * Theta_NE / denominator\n \n return we","def dewT_2_q_magnus(ds, var):\n A1, B1, C1 = 17.625, 243.04, 610.94\n vpsl = C1 * np.exp(A1 * (ds[var['dew']] - 273.15) / (B1 + (ds[var['dew']] - 273.15)))\n wsl = eps0 * vpsl / (ds[var['pressure']] - vpsl)\n ds[var['spec_h']] = wsl / (1 + wsl)\n return ds","def _redef_sp1_vars(self):\r\n\r\n if len(self.fq_list) == 0:\r\n no_rad = True\r\n lst_tmp = np.matrix(np.reshape(self.lst_tmp, \r\n (self.lst_tmp.size, 1)))\r\n else: no_rad = False\r\n # The practically constants...\r\n # Big Epsilon:\r\n if self.cond == True:\r\n self.Epsilon = self.d_T * self.thermal_conductivity\r\n else:\r\n self.Epsilon = (self.diff_scale ** 2) / \\\r\n (3.0 * self.absorb_coeffs[self.rad] ** 2)\r\n # Beta:\r\n if self.cond == True:\r\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\r\n (self.convect_coeff)\r\n else:\r\n self.Beta = (1.0 + 3.0 * self.r2) * (2.0 * self.diff_scale) / \\\r\n ((1.0 - 2.0 * self.r1) * (\r\n 3.0 * self.absorb_coeffs[self.rad]))\r\n\r\n # The feild solutions at the last timestep.\r\n # The integral vF:\r\n if self.cond == True:\r\n # The horrifically complicated F:\r\n def F_func_cond(elem, eta):\r\n F = 0.0\r\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n F += Tn\r\n for k in range(0, len(self.fq_list)):\r\n vk = self.fq_list[k]\r\n try:\r\n vk_m = self.fq_list[k - 1]\r\n except:\r\n vk_m = self.v0_frequency\r\n absorbtion = self.absorb_coeffs[k]\r\n phi = elem.eval_elem(self.node_map, self.lst_rad[k],\r\n [eta])[0]\r\n inter1 = phi - 4.0 * sconst.pi * \\\r\n self.B_int_function(Tn, self.refr_idx_vol,\r\n vk, vk_m)\r\n inter2 = absorbtion * self.d_T / (self.diff_scale ** 2)\r\n F += inter2 * inter1\r\n return elem.funcs(eta) * F\r\n if not no_rad:\r\n # We're integrating something non-linear for SP1\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func_cond,\r\n self.node_map)\r\n else:\r\n # Or something easier if we're only looking at heat.\r\n self.vF_vect_vol = np.array(self.uv_vol * lst_tmp).reshape(-1)\r\n else:\r\n def F_func_radiative(elem, eta):\r\n T = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n vk = self.fq_list[self.rad]\r\n try:\r\n vk_minus = self.fq_list[self.rad - 1]\r\n except:\r\n vk_minus = self.v0_frequency\r\n n = self.refr_idx_vol\r\n F = 4.0 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\r\n return elem.funcs(eta) * F\r\n\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func_radiative,\r\n self.node_map)\r\n # The path integral vf:\r\n if self.cond == True:\r\n def f_func_cond(elem, eta):\r\n Tb = self.background_temperature\r\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n n = self.refr_idx_background\r\n vk = self.v0_frequency\r\n vk_minus = 0\r\n Bb0 = self.B_int_function(Tb, n, vk, vk_minus)\r\n Bn0 = self.B_int_function(Tn, n, vk, vk_minus)\r\n B_coeff = (self.alpha * sconst.pi) / self.convect_coeff\r\n f = Tb + B_coeff * (Bb0 - Bn0)\r\n return elem.funcs(eta) * f\r\n if not no_rad:\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func_cond,\r\n self.node_map)\r\n else:\r\n try:\r\n self.vf_vect_bound = self.cache_tb_integral_array\r\n except AttributeError:\r\n def elem_functor(elem, eta): return elem.funcs(eta)\r\n self.cache_tb_integral_array = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n elem_functor,\r\n self.node_map)\r\n self.cache_tb_integral_array *= self.background_temperature\r\n self.vf_vect_bound = self.cache_tb_integral_array\r\n \r\n else:\r\n # Radiation f = 4*pi*B^{(k)}(T_b, n_g)\r\n def f_func_radiative(elem, eta):\r\n T = self.background_temperature\r\n vk = self.fq_list[self.rad]\r\n try:\r\n vk_minus = self.fq_list[self.rad - 1]\r\n except:\r\n vk_minus = self.v0_frequency\r\n n = self.refr_idx_vol\r\n f = 4 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\r\n return elem.funcs(eta) * f\r\n\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func_radiative,\r\n self.node_map)\r\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\r\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\r\n assert (self.vf_vect_bound.shape[0] == \\\r\n self.vF_vect_vol.shape[0])","def getdelta(self):\n\t\tmyhmag.initializehelmholtz()\n\t\tabar = 13.714285714285715\n\t\tzbar = abar/2.0\n\t\tself.data[\"delta\"] = np.zeros(len(self.data[\"rho\"]))\n\t\tfor i in range(len(self.data[\"rho\"])):\n\t\t\tadgradred,hydrograd,my_nu,my_alpha,self.data[\"delta\"][i],my_gamma1,my_cp,my_cph,my_c_s,failtrig = myhmag.gethelmgrads(self.data[\"T\"][i], self.data[\"rho\"][i], 0.,abar,zbar,True)","def __init__(self,b,u,v,hbls_old,hbbl_old,Kv_old,Kt_old,srflx,sustr,svstr,f,grid_dict,tstep_mode,dt):\n \n # INPUTS FROM TTTW SYSTEM\n self.b = b #buoyancy field: [Ly,N]\n self.u = u # x-component of velocity [Ly,N]\n self.v = v # y-component of velocity [Ly+1,N]\n self.hbls_old = hbls_old #boundary layer depth from previous time step [Ly]\n self.hbbl_old = hbbl_old # bottom boundary layer depth from previous time step [Ly]\n self.Kv_old = Kv_old # momentum mixing coefficeint from previous time step [Ly,N+1]\n self.Kt_old = Kt_old # tracer mixing coefficient from previous time step [Ly,N+1]\n self.srflx = srflx #solar heat flux [Ly] (degC * (m/s))\n self.sustr = sustr # x-component surface wind stress [Ly] (N/m^2) \n self.svstr = svstr # y-component surface wind stress [Ly+1] (N/m^2)\n self.grid_dict = grid_dict #gridded data\n self.f = f #coriolis parameter\n # KPP-SPECIFIC VARIABLES \n self.hbls = np.zeros([self.b.shape[0]])\n self.hbbl = np.zeros([self.b.shape[0]])\n self.ustar = []\n self.bvf = [] \n self.kmo = []\n self.C_h_MO = []\n self.kbl = []\n self.Cr = [] \n self.Fc = []\n self.ghat = [] #NONLOCAL TERM: TO BE USED IN TIME STEPPING\n self.tstep_mode = tstep_mode# if in time steppign mode, turn on HBL_RATE_LIMIT\n self.dt = dt","def CalcForce_aeroframe_DEP(V, CoefMatrix, x, rho, g):\r\n\r\n #Compute aero forces\r\n # here x must be of the form (alpha, beta, p, q, r, da, dr, de) (last one punctualy used)\r\n # set non dim for p,q,r\r\n nonDim=np.ones(7)\r\n nonDim[2]=g.b/(2*V)\r\n nonDim[3]=g.c/(2*V)\r\n nonDim[4]=g.b/(2*V)\r\n # F=np.dot(CoefMatrix,x[0:7]) # commented form, modification to account for symmetric drag increase of side slip\r\n F=np.zeros((3))\r\n M=np.zeros((3))\r\n xsym=np.copy(x[0:-1])\r\n xsym[1]=abs(xsym[1]) # make beta always positive since derivatives have already correct sign for drag and lift only\r\n xsym[-3]=abs(xsym[-3]) # make ailerons deflection always positive for drag increase and lift decrease\r\n xsym[-1]=abs(xsym[-1]) # make rudder deflection always positive for drag increase and lift decrease\r\n F[0]=np.dot(CoefMatrix[0],xsym)\r\n F[1]=np.dot(CoefMatrix[1],x[0:-1]) #side force\r\n F[2]=np.dot(CoefMatrix[2],xsym)\r\n M=np.dot(CoefMatrix[3:6,:],x[0:-1])\r\n# print(\"Printing moment coeff\")\r\n# print(M)\r\n\r\n \r\n #No need to project\r\n# alpha=x[0]\r\n# beta=x[1]\r\n# H=np.array([[math.cos(alpha)*math.sin(beta), -math.cos(alpha)*math.sin(beta), -math.sin(alpha)],[math.sin(beta), math.cos(beta), 0],[math.sin(alpha)*math.cos(beta), -math.sin(alpha)*math.sin(beta), math.cos(alpha)]])\r\n if V<=71 :\r\n Fbody=np.array([-F[0]-g.Cd0_fl,F[1],-F[2]-g.CL0_fl]) # add alpha=0 coefficients\r\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0_fl,0])\r\n else:\r\n Fbody=np.array([-F[0]-g.Cd0,F[1],-F[2]-g.CL0]) # add alpha=0 coefficients\r\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0,0])\r\n \r\n\r\n Fbody=0.5*V**2.0*rho*g.S*Fbody\r\n Moment=0.5*V**2.0*rho*g.S*g.b*Moment\r\n \r\n return np.append(Fbody, Moment)","def driftRHS_3D(field,drift_velocity,t,x):\n f = field.getValue(x)\n fs = np.sqrt(f[0]**2 + f[1]**2 + f[2]**2)\n f = f/fs\n return -f*drift_velocity(fs)","def dynamics(self,eta,nu,u_actual,u_control,sampleTime): \n \n # Current velocities\n u_c = self.V_c * math.cos(self.beta_c - eta[5]) # current surge velocity\n v_c = self.V_c * math.sin(self.beta_c - eta[5]) # current sway velocity \n \n nu_c = np.array([u_c,v_c,0,0,0,0],float) # current velocity vector\n nu_r = nu - nu_c # relative velocity vector\n \n U_r = math.sqrt( nu_r[0]**2 + nu_r[1]**2 ) # relative speed\n \n # Rudder command and actual rudder angle\n delta_c = u_control[0]\n delta = u_actual[0]\n \n # Rudder forces and moment (Fossen 2021, Chapter 9.5.1)\n b = 0.7 * self.T # rudder height\n AR = b**2 / self.Lambda # aspect ratio: Lamdba = b**2/AR \n CN = 6.13 * self.Lambda / ( self.Lambda + 2.25 ) # normal coefficient\n t_R = 1 - 0.28 * self.Cb - 0.55\n a_H = 0.4\n x_R = -0.45 * self.L\n x_H = -1.0 * self.L\n\n Xdd = -0.5 * ( 1 - t_R ) * self.rho * U_r**2 * AR * CN\n Yd = -0.25 * ( 1 + a_H ) * self.rho * U_r**2 * AR * CN \n Nd = -0.25 * ( x_R + a_H * x_H ) * self.rho * U_r**2 * AR * CN \n \n # Control forces and moment\n delta_R = -delta # physical rudder angle (rad)\n T = self.tau_X # thrust (N)\n t_deduction = 0.1 # thrust deduction number\n tau1 = ( 1 - t_deduction ) * T - Xdd * math.sin( delta_R )**2 \n tau2 = -Yd * math.sin( 2 * delta_R ) \n tau6 = -Nd * math.sin( 2 * delta_R ) \n tau = np.array( [ tau1, tau2, tau6 ],float) \n \n # Linear maneuvering model\n T_surge = self.L # approx. time constant in surge (s)\n xg = 0 # approx. x-coordinate, CG (m) \n \n # 3-DOF ship model\n [M,N] = clarke83(U_r,self.L, self.B, self.T,self.Cb,self.R66,xg,T_surge)\n Minv = np.linalg.inv(M)\n nu3 = np.array( [ nu_r[0], nu_r[1], nu_r[5] ]) \n nu3_dot = np.matmul( Minv, tau - np.matmul(N,nu3) ) \n \n # 6-DOF ship model\n nu_dot = np.array( [ nu3_dot[0],nu3_dot[1],0,0,0,nu3_dot[2] ]) \n\n # Rudder angle saturation\n if ( abs(delta) >= self.deltaMax * math.pi / 180 ):\n delta = np.sign(delta) * self.deltaMax * math.pi / 180\n \n # Rudder dynamics\n delta_dot = (delta_c - delta) / self.T_delta \n\n # Forward Euler integration [k+1]\n nu = nu + sampleTime * nu_dot\n delta = delta + sampleTime * delta_dot\n\n u_actual = np.array([delta],float) \n\n return nu, u_actual","def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\n rad = dia/2.\n tsr = rad*fabs(rot)/Vinf\n solidity = (chord*B)/rad\n\n # Translating the turbine position\n x0t = x0 - xt\n y0t = y0 - yt\n\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\n\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\n if param is None:\n loc1 = _parameterval(tsr,solidity,coef0)\n loc2 = _parameterval(tsr,solidity,coef1)\n loc3 = _parameterval(tsr,solidity,coef2)\n spr1 = _parameterval(tsr,solidity,coef3)\n spr2 = _parameterval(tsr,solidity,coef4)\n skw1 = _parameterval(tsr,solidity,coef5)\n skw2 = _parameterval(tsr,solidity,coef6)\n scl1 = _parameterval(tsr,solidity,coef7)\n scl2 = _parameterval(tsr,solidity,coef8)\n scl3 = _parameterval(tsr,solidity,coef9)\n\n else:\n # Reading in EMG distribution parameters\n loc1 = param[0]\n loc2 = param[1]\n loc3 = param[2]\n spr1 = param[3]\n spr2 = param[4]\n skw1 = param[5]\n skw2 = param[6]\n scl1 = param[7]\n scl2 = param[8]\n scl3 = param[9]\n\n ###################################\n if veltype == 'vort':\n # VORTICITY CALCULATION (NO INTEGRATION)\n if x0t < 0.:\n vel = 0.\n else:\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\n ###################################\n else:\n # Integration of the vorticity profile to calculate velocity\n if integration == 'simp':\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\n inte = 1 # Simpson's Rule\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\n\n if param is not None:\n print \"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\"\n\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\n\n if veltype == 'all':\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs*Vinf + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])\n ###################################\n elif integration == 'gskr':\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\n xbound = (scl3+5.)*dia\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\n\n if veltype == 'all':\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\n elif veltype == 'x':\n vel = (vel_xs + Vinf)/Vinf\n elif veltype == 'y':\n vel = vel_ys/Vinf\n elif veltype == 'ind':\n vel = np.array([vel_xs,vel_ys])/Vinf\n ###################################\n\n return vel","def fluid_reynolds(uu, param, grid, lnrho=list(), shock=list(), nghost=3,\n lmix=True):\n #viscous forces\n th2 = 2./3\n th1 = 1./3\n fvisc = np.zeros_like(uu)\n #molecular viscosity contribution\n ldel2, lshock, lhyper3 = False, False, False\n for ivisc in param.ivisc:\n if not 'shock' in ivisc and not 'hyper' in ivisc\\\n and not '\\n' in ivisc:\n ldel2 = True\n if 'shock' in ivisc:\n lshock = True\n if 'hyper3' in ivisc:\n lhyper3 = True\n \n if ldel2:\n if lhyper3:\n lhyper3 = lhyper3==lmix\n del2u = np.zeros_like(uu)\n for j in range(0,3):\n del2u[j] = del2(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n del2u[j, : nghost, nghost:-nghost, nghost:-nghost] = del2u[j,-2*nghost: -nghost, nghost: -nghost, nghost: -nghost]\n del2u[j,-nghost: , nghost:-nghost, nghost:-nghost] = del2u[j, nghost:2*nghost, nghost: -nghost, nghost: -nghost]\n del2u[j, nghost:-nghost, : nghost, nghost:-nghost] = del2u[j, nghost: -nghost,-2*nghost: -nghost, nghost: -nghost]\n del2u[j, nghost:-nghost,-nghost: , nghost:-nghost] = del2u[j, nghost: -nghost, nghost:2*nghost, nghost: -nghost]\n del2u[j, nghost:-nghost, nghost:-nghost, : nghost] = del2u[j, nghost: -nghost, nghost: -nghost,-2*nghost: -nghost]\n del2u[j, nghost:-nghost, nghost:-nghost,-nghost: ] = del2u[j, nghost: -nghost, nghost: -nghost, nghost:2*nghost]\n for ivisc in param.ivisc:\n ivisc = str.strip(ivisc,'\\n')\n if 'nu-const' not in ivisc and 'shock' not in ivisc\\\n and 'hyper' not in ivisc and len(ivisc) > 0:\n print('fluid_reynolds WARNING: '+ivisc+' not implemented\\n'+\n 'terms may be missing from the standard rate of strain tensor')\n fvisc = fvisc + param.nu*del2u\n del(del2u)\n tmp0 = grad(uu[0],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp0[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp0[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp0[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp0[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp0[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp0[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp0[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp0[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp0[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n tmp1 = grad(uu[1],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp1[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp1[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp1[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp1[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp1[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp1[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp1[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp1[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp1[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n tmp2 = grad(uu[2],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp2[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp2[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp2[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp2[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp2[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp2[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp2[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp2[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp2[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n #effect of compressibility \n if len(lnrho) > 0:\n divu = div(uu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n divu[ :nghost,nghost:-nghost,nghost:-nghost] = divu[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n divu[-nghost:,nghost:-nghost,nghost:-nghost] = divu[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n divu[nghost:-nghost, :nghost,nghost:-nghost] = divu[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n divu[nghost:-nghost,-nghost:,nghost:-nghost] = divu[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n divu[nghost:-nghost,nghost:-nghost, :nghost] = divu[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n divu[nghost:-nghost,nghost:-nghost,-nghost:] = divu[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n gradlnrho = grad(lnrho,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n gradlnrho[j, :nghost,nghost:-nghost,nghost:-nghost] = gradlnrho[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n gradlnrho[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradlnrho[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost, :nghost,nghost:-nghost] = gradlnrho[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradlnrho[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost,nghost:-nghost, :nghost] = gradlnrho[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n gradlnrho[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradlnrho[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n Sglnrho = np.zeros_like(uu)\n Sglnrho[0] = dot(tmp0,gradlnrho) +\\\n (tmp0[0]+tmp1[0]+tmp2[0]-th2*divu)*gradlnrho[0] \n Sglnrho[1] = dot(tmp1,gradlnrho) +\\\n (tmp0[1]+tmp1[1]+tmp2[1]-th2*divu)*gradlnrho[1]\n Sglnrho[2] = dot(tmp2,gradlnrho) +\\\n (tmp0[2]+tmp1[2]+tmp2[2]-th2*divu)*gradlnrho[2]\n graddivu = grad(divu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n graddivu[j, :nghost,nghost:-nghost,nghost:-nghost] = graddivu[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n graddivu[j,-nghost:,nghost:-nghost,nghost:-nghost] = graddivu[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost, :nghost,nghost:-nghost] = graddivu[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost,-nghost:,nghost:-nghost] = graddivu[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost,nghost:-nghost, :nghost] = graddivu[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n graddivu[j,nghost:-nghost,nghost:-nghost,-nghost:] = graddivu[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n fvisc = fvisc + param.nu*(th1*graddivu+Sglnrho)\n del(Sglnrho)\n elif param.ldensity:\n print('fluid_reynolds WARNING: no lnrho provided\\n'+\n 'rate of strain tensor likely incomplete')\n #shock contribution\n if lshock:\n if len(shock) == 0:\n print('fluid_reynolds WARNING: no shock provided\\n'+\n 'rate of strain tensor likely incomplete')\n else:\n shock[ :nghost,nghost:-nghost,nghost:-nghost] = shock[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n shock[-nghost:,nghost:-nghost,nghost:-nghost] = shock[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n shock[nghost:-nghost, :nghost,nghost:-nghost] = shock[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n shock[nghost:-nghost,-nghost:,nghost:-nghost] = shock[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n shock[nghost:-nghost,nghost:-nghost, :nghost] = shock[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n shock[nghost:-nghost,nghost:-nghost,-nghost:] = shock[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n divugradlnrho = np.zeros_like(uu)\n gradshock = grad(shock,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n gradshock[j, :nghost,nghost:-nghost,nghost:-nghost] = gradshock[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n gradshock[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradshock[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost, :nghost,nghost:-nghost] = gradshock[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradshock[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost,nghost:-nghost, :nghost] = gradshock[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n gradshock[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradshock[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n for j in range(0,3):\n divugradlnrho[j] = param.nu_shock*divu*gradshock[j] +\\\n param.nu_shock*shock*(divu*gradlnrho[j] + graddivu[j])\n del(divu,gradshock,gradlnrho,graddivu)\n fvisc = fvisc + divugradlnrho\n del(divugradlnrho)\n if lhyper3:\n #deluij5 = np.zeros_like([uu,uu,uu])\n #uij5glnrho to be included\n del6u = np.zeros_like(uu)\n for j in range(0,3):\n del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz)\n del6u[j, :nghost,nghost:-nghost,nghost:-nghost] = del6u[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n del6u[j,-nghost:,nghost:-nghost,nghost:-nghost] = del6u[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n del6u[j,nghost:-nghost, :nghost,nghost:-nghost] = del6u[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n del6u[j,nghost:-nghost,-nghost:,nghost:-nghost] = del6u[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n del6u[j,nghost:-nghost,nghost:-nghost, :nghost] = del6u[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n del6u[j,nghost:-nghost,nghost:-nghost,-nghost:] = del6u[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n #del6 for non-cartesian tba\n #del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n # coordinate_system=param.coord_system)\n fvisc = fvisc + param.nu_hyper3*del6u\n del(del6u)\n fvisc2 = np.sqrt(dot2(fvisc))\n #advective forces\n advec = np.zeros_like(uu)\n advec[0] = dot(uu,tmp0)\n advec[1] = dot(uu,tmp1)\n advec[0] = dot(uu,tmp2)\n del(tmp0,tmp1,tmp2)\n advec2 = np.sqrt(dot2(advec))\n del(advec)\n #avoid division by zero\n if fvisc2.max() > 0:\n fvisc2[np.where(fvisc2==0)] = fvisc2[np.where(fvisc2>0)].min()\n Re = advec2/fvisc2\n #set minimum floor to exclude zero-valued Re \n Re[np.where(Re==0)] = Re[np.where(Re>0)].min()\n else:\n Re = advec2\n print('Re undefined')\n return Re","def calculate_ft(self):\n \n # Create a function which is able to evaluate B**2\n ffunc = scipy.interpolate.interp1d(self.psigrid, self.e.getF()[self.tind])\n def b2_func(R, Z, psi):\n bt = ffunc(psi)/R\n br = -self.psifunc.ev(R, Z, dy=1)/R\n bz = self.psifunc.ev(R, Z, dx=1)/R\n \n return bt**2 + br**2 + bz**2\n \n\n def b_bmax2(R,Z,psi):\n b2 = b2_func(R,Z,psi)\n return b2 / np.max(b2)\n \n def b_bmax(R,Z,psi):\n return np.sqrt(b_bmax2(R,Z,psi))\n \n # Evaluate the flux-surface averaged h^2 and h, as required\n fsa_h2 = self.fs_average(b_bmax2)\n fsa_h = self.fs_average(b_bmax)\n \n # This is the function which gets flux-surface averaged in equation (7)\n def ftl_func(R,Z,psi):\n h = b_bmax(R,Z,psi)\n h2 = b_bmax2(R,Z,psi)\n \n return (1 - (np.sqrt(1 - h) * (1 + 0.5 * h)))/h2\n \n \n # Equation 6, 7 in Lin-Liu\n fs_ftu = 1 - fsa_h2 / fsa_h**2 * (1 - np.sqrt(1 - fsa_h) * (1 + 0.5 * fsa_h))\n fs_ftl = 1 - fsa_h2 * self.fs_average(ftl_func)\n # Equation 18, 19 \n om = 0.75\n self.fs_ft = om*fs_ftu + (1-om)*fs_ftl","def trajectories(t_upper=3600*24*687, h=100, m1=5.972e+24, m2=6.417e+23,\n m3=1.989e+30, a1=1.0*1.496e+11, a2=1.52*1.496e+11):\n\n # We check if parameters are all positive\n\n list_parameters = [t_upper, h, m1, m2, m3,\n a1, a2]\n\n for parameters in list_parameters:\n\n if parameters < 0:\n print(f'You have entered a negative parameter')\n\n # initial values for planet 1 in x, y and z direction\n x_i1 = a1\n y_i1 = 0\n v_x1i = 0\n v_y1i = 29779.301841746023\n z_i1 = 0\n v_z1i = 0\n\n # initial values for planet 2 in x, y and z direction\n x_i2 = a2\n y_i2 = 0\n v_x2i = 0\n v_y2i = 24154.203325249873\n z_i2 = 0\n v_z2i = 0\n\n # initial values for Sun in x, y and z direction\n x_i3 = 0\n y_i3 = 0\n v_x3i = 0\n v_y3i = 0\n z_i3 = 0\n v_z3i = 0\n\n# Initial positions and velocities\n r = np.array([x_i1, y_i1, v_x1i, v_y1i, x_i2,\n y_i2, v_x2i, v_y2i, x_i3, y_i3, v_x3i, v_y3i,\n z_i1, z_i2, z_i3, v_z1i, v_z2i, v_z3i])\n\n # We create vectors which will contains the trajectories\n # and velocities of each bodies\n x_pnts1 = [x_i1]\n y_pnts1 = [y_i1]\n v_x_pnts1 = [v_x1i]\n v_y_pnts1 = [v_y1i]\n\n x_pnts2 = [x_i2]\n y_pnts2 = [y_i2]\n v_x_pnts2 = [v_x2i]\n v_y_pnts2 = [v_y2i]\n\n x_pnts3 = [x_i3]\n y_pnts3 = [y_i3]\n v_x_pnts3 = [v_x3i]\n v_y_pnts3 = [v_y3i]\n\n x_pnts3 = [x_i3]\n y_pnts3 = [y_i3]\n v_x_pnts3 = [v_x3i]\n v_y_pnts3 = [v_y3i]\n\n z_pnts1 = [z_i1]\n z_pnts2 = [z_i2]\n z_pnts3 = [z_i3]\n\n v_z_pnts1 = [v_z1i]\n v_z_pnts2 = [v_z2i]\n v_z_pnts3 = [v_z3i]\n\n m1 = m1\n m2 = m2\n m3 = m3\n a1 = a1\n a2 = a2\n\n # We create a vector which will contain the time\n # Initial value\n t_i = 0.0\n t_values = [t_i]\n\n for t in range(0, t_upper, h):\n\n # We used the RK4 formula here\n k1 = h*derivative(r=r, t=0, m1=5.972e+24, m2=m2, m3=1.989e+30,\n a1=a1, a2=1.52*1.496e+11)\n k2 = h*derivative(r=r + 0.5*k1, t=t + (h/2), m1=5.972e+24,\n m2=6.417e+23, m3=1.989e+30, a1=1.0*1.496e+11,\n a2=1.52*1.496e+11)\n k3 = h*derivative(r=r + 0.5*k2, t=t + (h/2), m1=5.972e+24,\n m2=6.417e+23, m3=1.989e+30, a1=1.0*1.496e+11,\n a2=1.52*1.496e+11)\n k4 = h*derivative(r=r + h*k3, t=t+h, m1=5.972e+24, m2=6.417e+23,\n m3=1.989e+30, a1=1.0*1.496e+11, a2=1.52*1.496e+11)\n\n # We calculate the new vector r\n r += (k1 + 2*k2 + 2*k3 + k4)*(1.0/6.0)\n\n # We add the new points calculated\n x_pnts1.append(r[0])\n y_pnts1.append(r[1])\n\n v_x_pnts1.append(r[2])\n v_y_pnts1.append(r[3])\n\n x_pnts2.append(r[4])\n y_pnts2.append(r[5])\n v_x_pnts2.append(r[6])\n v_y_pnts2.append(r[7])\n\n x_pnts3.append(r[8])\n y_pnts3.append(r[9])\n v_x_pnts3.append(r[10])\n v_y_pnts3.append(r[11])\n\n z_pnts1.append(r[12])\n z_pnts2.append(r[13])\n z_pnts3.append(r[14])\n\n v_z_pnts1.append(r[15])\n v_z_pnts2.append(r[16])\n v_z_pnts3.append(r[17])\n\n t_values.append(t)\n\n # We return all the trajectories\n return x_pnts1, y_pnts1, x_pnts2, y_pnts2, x_pnts3, y_pnts3, z_pnts1, z_pnts2, z_pnts3","def rhs(y, t, l, m, g):\n # Unpack the states so you can use the variable names in the\n # sympy.physics.mechanics equations\n q1 = y[0]\n q2 = y[1]\n u1 = y[2]\n u2 = y[3]\n # or you can make use of python's tuple unpacking for a one liner\n # q1, q2, u1, u2 = y\n\n # Initialize a vector for the derivatives.\n dydt = zeros((len(y)))\n\n # Compute the derivatives, these are pasted in from the\n # sympy.physics.mechanics results.\n dydt[0] = u1\n dydt[1] = u2\n dydt[2] = (-g*sin(q1)*sin(q2)**2 + 2*g*sin(q1) -\n g*sin(q2)*cos(q1)*cos(q2) + 2*l*u1**2*sin(q1)*cos(q1)*cos(q2)**2 -\n l*u1**2*sin(q1)*cos(q1) - 2*l*u1**2*sin(q2)*cos(q1)**2*cos(q2) +\n l*u1**2*sin(q2)*cos(q2) + l*u2**2*sin(q1)*cos(q2) -\n l*u2**2*sin(q2)*cos(q1))/(l*(sin(q1)**2*sin(q2)**2 +\n 2*sin(q1)*sin(q2)*cos(q1)*cos(q2) + cos(q1)**2*cos(q2)**2 - 2))\n dydt[3] = (-sin(q1)*sin(q2)/2 - cos(q1)*cos(q2)/2)*(2*g*l*m*sin(q1) -\n l**2*m*(-sin(q1)*cos(q2) +\n sin(q2)*cos(q1))*u2**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2)) -\n l**2*m) + (g*l*m*sin(q2) - l**2*m*(sin(q1)*cos(q2) -\n sin(q2)*cos(q1))*u1**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2))\n - l**2*m)\n\n # Return the derivatives.\n return dydt","def main():\r\n #Drag Force Equation: 1/2 * rho * Cd * A * v^2\r\n\r\n #User-Defined Constants\r\n global m\r\n global v0\r\n global theta\r\n global rho #Fluid Density\r\n global A #Cross-sectional Area\r\n global Cd #Drag coefficient\r\n global tStep\r\n global g\r\n\r\n m = 1\r\n v0 = 30\r\n theta = math.radians(45)\r\n rho = 1.225\r\n A = 0.05\r\n Cd = 0.5 #A ball is approx. 0.5\r\n tStep = 0.005\r\n g = 9.8\r\n\r\n\r\n #Data Structures\r\n global tHist\r\n global xHist\r\n global yHist\r\n global thetaHist\r\n global vHist\r\n global vXHist\r\n global vYHist\r\n tHist = [] #list for all time steps\r\n xHist = [] #list for all x position steps\r\n yHist = [] #list for all y position steps\r\n thetaHist = [] #List for all theta at every time step\r\n vHist = [] #list for all velocities at every time step\r\n vXHist = [] #list for all x-axis velocities at every time step\r\n vYHist = [] #list for all y-axis velocities at every time step\r\n\r\n #Initialize intial values\r\n tHist.append(0.0)\r\n xHist.append(0.0)\r\n yHist.append(0.0)\r\n thetaHist.append(theta)\r\n vHist.append(v0)\r\n vXHist.append(v0 * math.cos(theta))\r\n vYHist.append(v0 * math.sin(theta))\r\n vTheta = math.atan(vYHist[0] / vXHist[0])\r\n # print(\"t: \" + str(tHist[0]))\r\n # print(\"x: \" + str(xHist[0]))\r\n # print(\"y: \" + str(yHist[0]))\r\n # print(\"v: \" + str(vHist[0]))\r\n # print(\"Vx: \" + str(vXHist[0]))\r\n # print(\"Vy: \" + str(vYHist[0]))\r\n\r\n #Convenience variables\r\n global k\r\n\r\n counter = 1\r\n #Loop until the y-displacement becomes negative (projectile reaches ground again)\r\n while True:\r\n tHist.append(counter * tStep) #increment time\r\n print(\"t: \" + str(tHist[counter]))\r\n\r\n #This large hunk is the solution to the net force differential equation in the x-axis\r\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep)) #STABLE\r\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd)/(2*m))*(tStep))\r\n # oneOverVX = (1/vHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep))\r\n oneOverVX = (1/vXHist[counter-1]) + ((rho*A*Cd)/(2*m*math.cos(thetaHist[counter-1]))*(tStep)) #This is one over the solution for velocity in the x-axis net force differential equation\r\n vXHist.append(1 / oneOverVX) #Adding the velocity to the list of velocities\r\n\r\n vY0 = vYHist[counter-1] #Convenience variable\r\n # k = 0.5 * rho * A * Cd * math.sin(abs(thetaHist[counter-1])) #STABLE\r\n # k = 0.5 * rho * A * Cd\r\n k = (rho * A * Cd) / (2 * math.sin(abs(thetaHist[counter-1]))) #Convenience variable\r\n print(\"k: \" + str(k))\r\n print(\"vX: \" + str(vXHist[counter]))\r\n rootGMK = math.sqrt(g*m*k) #Convenience variable\r\n if vYHist[counter-1] > 0.0: #If the projectile is going upwards\r\n #Solving the y-axis differential equation for velocity\r\n equationRight = -rootGMK * ((tStep/m) - (math.atan((k*vY0)/(rootGMK))/rootGMK))\r\n vYHist.append((math.tan(equationRight) * rootGMK) / k)\r\n elif vYHist[counter-1] < 0.0: #If the projectile is going downwards\r\n #Solving the y-axis differential equation for velocity\r\n\r\n # Hand-solved integral\r\n # exponent = -(2*tStep*rootGMK)/m\r\n # numerator = g*m*math.exp(exponent) - math.exp(exponent)*vY0*rootGMK - vY0*rootGMK - g*m\r\n # denominator = math.exp(exponent)*(vY0-rootGMK) - vY0*k - rootGMK\r\n # vYHist.append(numerator / denominator)\r\n\r\n #Wolfram Alpha arctanh integral\r\n arctanh =(vY0*math.sqrt(k))/(math.sqrt(g*m))\r\n print(\"arctanh: \" + str(arctanh))\r\n equationRight = (np.arctanh(arctanh))/(rootGMK) - (tStep/m)\r\n vYHist.append(np.tanh(rootGMK * equationRight) * ((math.sqrt(g*m))/(math.sqrt(k))))\r\n else: #If current y velocity is 0\r\n vYHist.append(vY0 - g*tStep)\r\n print(\"vY: \" + str(vYHist[counter]))\r\n\r\n vHist.append(math.hypot(vXHist[counter], vYHist[counter])) #Calculate the net velocity and add it to the velocities list\r\n print(\"v: \" + str(vHist[counter]))\r\n thetaHist.append(math.atan(vYHist[counter]/vXHist[counter])) #Calculate the current angle based on the velocities and add it to the theta list\r\n print(\"0: \" + str(math.degrees(thetaHist[counter])))\r\n\r\n x0 = xHist[counter-1]\r\n y0 = yHist[counter-1]\r\n\r\n # yIntegral = trigintegrate()\r\n\r\n \"\"\"\r\n Note: What I wanted to do here was to integrate the velocity functions over the time interval to find the exact\r\n changes in position. Unfortunately, I was running short of time and decided it was not worth it to move forward with\r\n this final step, and instead worked on the presentation and testing different cases.\r\n \"\"\"\r\n xHist.append(x0 + vXHist[counter]*tStep) #Calculate new x position using x = x0 + vt\r\n yHist.append(y0 + vYHist[counter]*tStep) #Calculate new y position using y = y0 + vt\r\n print(\"x: \" + str(xHist[counter]))\r\n print(\"y: \" + str(yHist[counter]))\r\n print()\r\n\r\n # xHist.append(xHist[counter-1] + vXHist[counter-1]*tStep + 0.5*aXHist[counter-1]*tStep**2)\r\n # yHist.append(yHist[counter-1] + vYHist[counter-1]*tStep + 0.5*aYHist[counter-1]*tStep**2)\r\n # vXHist.append(vXHist[counter-1] + aXHist[counter-1]*tStep)\r\n # vYHist.append(vYHist[counter-1] + aYHist[counter-1]*tStep)\r\n # vHist.append(math.hypot(vXHist[counter], vYHist[counter]))\r\n #\r\n # vTheta = math.atan(vYHist[counter] / vXHist[counter])\r\n # xDragAccel = -0.5*rho*Cd*A*vHist[counter]**2*math.cos(vTheta) / m\r\n # yDragAccel = -math.copysign(0.5*rho*Cd*A*vHist[counter]**2*math.sin(vTheta) / m, vYHist[counter])\r\n #\r\n # aXHist.append(xDragAccel)\r\n # aYHist.append(-g*tStep + yDragAccel)\r\n\r\n if vYHist[counter-1] > 0.0 and vYHist[counter] < 0.0: #Check if the projectile has reached it's peak by checking for a critical point\r\n print(\"max height reached at time=\" + str(tHist[counter]))\r\n # break\r\n\r\n # print(\"t: \" + str(tHist[counter]))\r\n # print(\"x: \" + str(xHist[counter]))\r\n # print(\"y: \" + str(yHist[counter]))\r\n # print(\"Vx: \" + str(vXHist[counter]))\r\n # print(\"Vy: \" + str(vYHist[counter]))\r\n # print(\"Ax: \" + str(aXHist[counter]))\r\n # print(\"Ay: \" + str(aYHist[counter]))\r\n\r\n if yHist[counter] < 0 or counter > 99999: #End the loop if the projectile has reached the ground (or limit the number of iterations to avoid computer death)\r\n break\r\n\r\n counter += 1\r\n\r\n plotData()","def hydro_solver(self):\n u_dx = self.central_x(self.u)\n w_dy = self.central_y(self.w)\n P_dx = self.central_x(self.P)\n P_dy = self.central_y(self.P)\n\n rho_dx_upwind = self.upwind_x(self.rho, self.u)\n rho_dy_upwwind = self.upwind_y(self.rho, self.w)\n rho_udx_upwind = self.upwind_x(self.rho * self.u, self.u)\n rho_udy_upwind = self.upwind_y(self.rho * self.u, self.w)\n rho_wdx_upwind = self.upwind_x(self.rho * self.w, self.u)\n rho_wdy_upwind = self.upwind_y(self.rho * self.w, self.w)\n u_dx_uu = self.upwind_x(self.u, self.u)\n u_dx_uw = self.upwind_x(self.u, self.w)\n w_dy_uu = self.upwind_y(self.w, self.u)\n w_dy_uw = self.upwind_y(self.w, self.w)\n e_dx = self.upwind_x(self.e, self.u)\n e_dy = self.upwind_y(self.e, self.w)\n\n self.rho_dt = (\n -self.rho * (u_dx + w_dy)\n - self.u * rho_dx_upwind\n - self.w * rho_dy_upwwind\n )\n self.e_dt = (\n -(self.e + self.P) * (u_dx + w_dy) - self.u * e_dx - self.w * e_dy\n )\n self.rho_udt = (\n -self.rho * self.u * (u_dx_uu + w_dy_uu)\n - self.u * rho_udx_upwind\n - self.w * rho_udy_upwind\n - P_dx\n )\n self.rho_wdt = (\n -self.rho * self.w * (u_dx_uw + w_dy_uw)\n - self.u * rho_wdx_upwind\n - self.w * rho_wdy_upwind\n - P_dy\n + self.rho * self.g\n )\n\n self.time_step()\n rho_previous = np.zeros_like(self.rho)\n rho_previous[:, :] = self.rho\n self.rho[:, :] = self.rho + self.rho_dt * self.dt\n self.e[:, :] = self.e + self.e_dt * self.dt\n self.u[:, :] = (\n rho_previous * self.u + self.rho_udt * self.dt\n ) / self.rho\n self.w[:, :] = (\n rho_previous * self.w + self.rho_wdt * self.dt\n ) / self.rho\n\n self.boundary_conditions()\n self.T[:, :] = (\n (self.Y - 1) * self.e * self.mu * self.m_u / (self.kb * self.rho)\n )\n self.P[:, :] = (self.Y - 1) * self.e\n uw = (self.u, self.w)\n v = np.linalg.norm(uw)\n dt = self.dt\n\n return dt","def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\n beta = np.float32(beta)\n mu = np.float32(mu)\n \n du1, du0 = du[:2]\n dv1, dv0 = dv[:2]\n dn0 = dn[0]\n \n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\n \n n1 = n + ( dn0 )*dt\n \n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n \n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\n \n n, u, v = n1, u1, v1\n \n du = [du1, du0, du0, du0]\n dv = [dv1, dv0, dv0, dv0]\n dn = [dn0, dn0, dn0]\n return n1, u1, v1, du, dv, dn","def t_rh_2_dewT(ds, var):\n ds['dew'] = 243.04 * (np.log(ds[var['rh']] / 100) + ((17.625 * ds[var['temp']]) / (243.04 + ds[var['temp']])))/\\\n (17.625-np.log(ds[var['rh']] / 100) - ((17.625 * ds[var['temp']]) / (243.04 + ds[var['temp']])))\n return ds","def main():\n\n varList = {'beta': 6., 'convSpeed': 1.2, 'Mark': 0., 'axi': 1, 'acModes': 4, 'Nr': 801, 'Tf': 600., 'xf': 0.51}\n\n # Solve steady flame.\n # BC1: I have the attachment BC at r = 1, always\n # BC2: I need to set dF/dr = 0 at r = 0 iff Mark != 0\n [qMean, r, FMean] = steady_flame_area_FD3(varList['Mark'], varList['beta'], varList['axi'], varList['Nr'])\n r = r * varList['beta']\n\n # Calculate mean flame derivatives\n dFMeanDr = derivsnew.FD1_CT2_D(FMean, r[1] - r[0])\n d2FMeanDr2 = derivsnew.FD2_CT2_D(FMean, r[1] - r[0])\n\n #Apply BC smooth tip:\n if(varList['Mark']!=0.0):\n dFMeanDr[-1] = 0.0\n\n # Use correct number of points. Remember that the extrems need to be set depending on the BC!\n # The attach BC (first point) is always assumed to be true and removed from the vector list\n if(varList['Mark']==0):\n Nr = varList['Nr'] / 2\n dFMeanDr = dFMeanDr[1:]\n d2FMeanDr2 = d2FMeanDr2[1:]\n r = r[1:]\n # The smooth BC holds only if Mark!=0 (second derivatives appear): remove also the last point\n else:\n Nr = varList['Nr'] / 2 - 1\n dFMeanDr = dFMeanDr[1:-1]\n d2FMeanDr2 = d2FMeanDr2[1:-1]\n r = r[1:-1]\n\n # Calculate geometric values\n den = 1 + varList['beta'] * varList['beta'] * dFMeanDr * dFMeanDr\n dR = r[1] - r[0]\n # Set Nx equal to Nr for now.\n # The implementation is more complicated if they differ, and need to interpolate between values.\n Nx = Nr\n\n # Nonuniform grid spacing along x!\n # Nx = length(dx) has to hold.\n dx = np.empty(len(FMean) - 1)\n for ii in range(1, len(FMean)):\n dx[ii - 1] = FMean[ii] - FMean[ii - 1]\n\n [A, B, C, tau] = loadAcoustics(varList['xf'], varList['Tf'], varList['acModes'], varList['beta'])\n\n Matrix = buildMatrix(Nr, dR, varList['beta'], den, r, FMean, dFMeanDr, d2FMeanDr2, varList['Mark'], varList['acModes'], A,\n B, C, Nx, dx, tau, qMean, varList['convSpeed'])\n\n [d, W, V] = eigProblem.solveEigProb(Matrix)\n [dnew, Wnew, Vnew] = eigProblem.selectUnstable(d, W, V)\n\n print dnew / (2. * np.pi)","def main(data, setup):\n # input check \n varnames = ('vm_raw', 'vm_raw_theo')\n for varname in varnames:\n if varname not in data.keys():\n raise LookupError('data must contain variable %s.' %s)\n\n # display info message\n chrono = setup['chrono']\n chrono.issue('target velocity: correct for sensor motion...')\n\n # retrieve varialbes\n vnys = data['nqv']\n v_sensor_r = data['v_sensor_r']\n\n # ========== main =================================== #\n for key_raw in ('vm_raw', 'vm_raw_theo'):\n key_c = key_raw.replace('raw', 'raw_c')\n\n # sum\n vm_raw = data[key_raw]\n v_sum = (vm_raw + np.expand_dims(v_sensor_r, 1))\n\n # mod\n data[key_c] = symmod(v_sum, vnys)\n # ==================================================== #\n\n return data","def update_variables(self):\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\n # aquire transformation matrices and tips for segment 1 and 2\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\n # Frenet frames\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\n # Bishop frames\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]","def build_rhs():\n\n def div(\n coeff_rho,\n momentum_x,\n momentum_y,\n momentum_z,\n ):\n \"\"\"Computes the divergence of the velocity field.\"\"\"\n # Compute the fourth order derivative of the pressure for the face\n # velocity correction.\n p_corr = (\n states['p']\n if self._params.enable_rhie_chow_correction else states['dp'])\n d4p_dx4 = self._kernel_op.apply_kernel_op_x(p_corr, 'k4d2x')\n d4p_dy4 = self._kernel_op.apply_kernel_op_y(p_corr, 'k4d2y')\n d4p_dz4 = self._kernel_op.apply_kernel_op_z(p_corr, 'k4d2z',\n 'k4d2zsh')\n\n # Compute velocity gradient based on interpolated values on cell faces.\n coeff_x = dt / (4. * coeff_rho * dx**2)\n du = self._kernel_op.apply_kernel_op_x(momentum_x, 'kDx')\n du_dx = [\n du_i / (2. * dx) + coeff_x * d4p_dx4_i\n for du_i, d4p_dx4_i in zip(du, d4p_dx4)\n ]\n\n coeff_y = dt / (4. * coeff_rho * dy**2)\n dv = self._kernel_op.apply_kernel_op_y(momentum_y, 'kDy')\n dv_dy = [\n dv_i / (2. * dy) + coeff_y * d4p_dy4_i\n for dv_i, d4p_dy4_i in zip(dv, d4p_dy4)\n ]\n\n coeff_z = dt / (4. * coeff_rho * dz**2)\n dw = self._kernel_op.apply_kernel_op_z(momentum_z, 'kDz', 'kDzsh')\n dw_dz = [\n dw_i / (2. * dz) + coeff_z * d4p_dz4_i\n for dw_i, d4p_dz4_i in zip(dw, d4p_dz4)\n ]\n\n return [\n du_dx_i + dv_dy_i + dw_dz_i\n for du_dx_i, dv_dy_i, dw_dz_i in zip(du_dx, dv_dy, dw_dz)\n ]\n\n def add_factor(\n v,\n factor,\n ):\n return [factor * v_i for v_i in v]\n\n b_terms = {\n _B_TERM_SOURCE_RHO: add_factor(src_rho, inv_dt),\n }\n if isinstance(rho_info, ConstantDensityInfo):\n b_terms.update({\n _B_TERM_DIV:\n add_factor(\n div(rho_info.rho, states['u'], states['v'], states['w']),\n inv_dt * rho_info.rho),\n _B_TERM_DRHO_DT: [\n tf.zeros_like(src_rho_i) for src_rho_i in src_rho\n ],\n })\n\n elif isinstance(rho_info, VariableDensityInfo):\n b_terms.update({\n _B_TERM_DIV:\n add_factor(\n div(1.0, states['rho_u'], states['rho_v'], states['rho_w']),\n inv_dt),\n _B_TERM_DRHO_DT:\n add_factor(rho_info.drho_dt, inv_dt),\n })\n\n else:\n raise ValueError('`rho_info` has to be either `ConstantDensityInfo` or '\n '`VariableDensityInfo`.')\n\n # pylint: disable=g-complex-comprehension\n return [(div_i + drho_dt_i - src_rho_i)\n for div_i, drho_dt_i, src_rho_i in zip(\n b_terms[_B_TERM_DIV],\n b_terms[_B_TERM_DRHO_DT],\n b_terms[_B_TERM_SOURCE_RHO],\n )], b_terms\n # pylint: enable=g-complex-comprehension","def manipulate_heat_data(self): \n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\n self.exh.T_outlet_array) + 273.15)\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\n self.exh.T_outlet_array )\n \n self.cool.delta_T_array = ( self.cool.T_inlet_array -\n self.cool.T_outlet_array )\n self.cool.C = self.cool.mdot * self.cool.c_p","def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\n self.A0 = A_phi[0]\n self.phi0 = A_phi[1]\n self.sim = sim\n self.V0_frac = V0_frac\n self.t = t\n self.x_eq0 = self.sim.x_eq([0,0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\n self.sol = integrate.odeint(sim, self.x0, t=t)\n self.z = sim.zLI(self.sol, t)\n self.phi = np.unwrap(np.angle(self.z))\n self.t_filt = t_filt = t[15:] \n self.i0 = i0 = np.argmin(abs(self.t_filt)) \n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\n self.t_wide = t_filt[::Ndec_response]\n self.respRePts = self.sim.responseReVec(self.t_wide)\n self.Ht = lambda t: np.interp(t, self.t_wide, self.respRePts)\n \n \n \n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act","def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\n self.A0 = A_phi[0]\n self.phi0 = A_phi[1]\n self.sim = sim\n self.V0_frac = V0_frac\n self.t = t\n self.x_eq0 = self.sim.x_eq([0, 0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\n self.sol = integrate.odeint(sim, self.x0, t=t)\n self.z = sim.zLI(self.sol, t)\n self.phi = np.unwrap(np.angle(self.z))\n self.t_filt = t_filt = t[15:] \n self.i0 = i0 = np.argmin(abs(self.t_filt)) \n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\n self.t_wide = t_filt[::Ndec_response]\n self.respRePts = self.sim.responseReVec(self.t_wide)\n self.Ht = lambda tt: np.interp(tt, self.t_wide, self.respRePts)\n \n \n \n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act","def forward(self, z_t_1, h_x, phi_table):\n z_category = self.gen_z_t_dist_now(z_t_1, h_x)\n \n if self.use_gumbel_softmax:\n \n# device = z_category.device\n \n averaged_z_t = 0\n \n log_prob = Variable(torch.log(z_category))\n \n for k in range(self.sampling_times): \n curr_z_t = F.gumbel_softmax(log_prob, tau = 0.1)\n \n averaged_z_t += curr_z_t\n \n del curr_z_t\n \n# averaged_z_t = averaged_z_t.to(device)\n \n z_t = averaged_z_t/self.sampling_times\n else:\n z_t = z_category\n \n phi_z = torch.mm(z_t, torch.t(phi_table))\n# mu = self.h_to_mu(h_combined)\n# logvar = self.h_to_logvar(h_combined)\n# std = torch.exp(0.5 * logvar) \n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\n return z_t, z_category, phi_z","def get_observations(self):\n joint_states = self.joints_state\n self.force = self.wrench_stamped.wrench.force\n self.torque = self.wrench_stamped.wrench.torque\n self.static_taxel = self.tactile_static.taxels\n# dynamic_taxel= tactile_dynamic\n\n# print(\"[force]\", self.force.x, self.force.y, self.force.z)\n# print(\"[torque]\", self.torque.x, self.torque.y, self.torque.z)\n shp_joint_ang = joint_states.position[0]\n shl_joint_ang = joint_states.position[1]\n elb_joint_ang = joint_states.position[2]\n wr1_joint_ang = joint_states.position[3]\n wr2_joint_ang = joint_states.position[4]\n wr3_joint_ang = joint_states.position[5]\n\n shp_joint_vel = joint_states.velocity[0]\n shl_joint_vel = joint_states.velocity[1]\n elb_joint_vel = joint_states.velocity[2]\n wr1_joint_vel = joint_states.velocity[3]\n wr2_joint_vel = joint_states.velocity[4]\n wr3_joint_vel = joint_states.velocity[5]\n\n q = [shp_joint_ang, shl_joint_ang, elb_joint_ang, wr1_joint_ang, wr2_joint_ang, wr3_joint_ang]\n# print(\"q(observation):\", q)\n eef_x, eef_y, eef_z = self.get_xyz(q)\n self.end_effector = self.get_xyz(q)\n eef_x_ini, eef_y_ini, eef_z_ini = self.get_xyz(self.init_joint_pose2) \n\n delta_image_r, delta_image_l = self.get_image()\n self.cnn_image_r = agent.update_cnn(delta_image_r)\n self.cnn_image_l = agent.update_cnn(delta_image_l)\n self.cnn_image_r_list = self.cnn_image_r.tolist()\n self.cnn_image_l_list = self.cnn_image_l.tolist()\n print(\"r_list\", self.cnn_image_r_list)\n print(\"l_list\", self.cnn_image_l_list)\n\n observation = []\n# rospy.logdebug(\"List of Observations==>\"+str(self.observations))\n for obs_name in self.observations:\n if obs_name == \"shp_joint_ang\":\n observation.append((shp_joint_ang - self.init_joint_pose2[0]) * self.joint_n)\n elif obs_name == \"shl_joint_ang\":\n observation.append((shl_joint_ang - self.init_joint_pose2[1]) * self.joint_n)\n elif obs_name == \"elb_joint_ang\":\n observation.append((elb_joint_ang - self.init_joint_pose2[2]) * self.joint_n)\n elif obs_name == \"wr1_joint_ang\":\n observation.append((wr1_joint_ang - self.init_joint_pose2[3]) * self.joint_n)\n elif obs_name == \"wr2_joint_ang\":\n observation.append((wr2_joint_ang - self.init_joint_pose2[4]) * self.joint_n)\n elif obs_name == \"wr3_joint_ang\":\n observation.append((wr3_joint_ang - self.init_joint_pose2[5]) * self.joint_n)\n elif obs_name == \"shp_joint_vel\":\n observation.append(shp_joint_vel)\n elif obs_name == \"shl_joint_vel\":\n observation.append(shl_joint_vel)\n elif obs_name == \"elb_joint_vel\":\n observation.append(elb_joint_vel)\n elif obs_name == \"wr1_joint_vel\":\n observation.append(wr1_joint_vel)\n elif obs_name == \"wr2_joint_vel\":\n observation.append(wr2_joint_vel)\n elif obs_name == \"wr3_joint_vel\":\n observation.append(wr3_joint_vel)\n elif obs_name == \"eef_x\":\n observation.append((eef_x - eef_x_ini) * self.eef_n)\n elif obs_name == \"eef_y\":\n observation.append((eef_y - eef_y_ini) * self.eef_n)\n elif obs_name == \"eef_z\":\n observation.append((eef_z - eef_z_ini) * self.eef_n)\n elif obs_name == \"force_x\":\n observation.append((self.force.x - self.force_ini.x) / self.force_limit1 * self.force_n)\n elif obs_name == \"force_y\":\n observation.append((self.force.y - self.force_ini.y) / self.force_limit1 * self.force_n)\n elif obs_name == \"force_z\":\n observation.append((self.force.z - self.force_ini.z) / self.force_limit1 * self.force_n)\n elif obs_name == \"torque_x\":\n observation.append((self.torque.x - self.torque_ini.x) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"torque_y\":\n observation.append((self.torque.y - self.torque_ini.y) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"torque_z\":\n observation.append((self.torque.z - self.torque_ini.z) / self.torque_limit1 * self.torque_n)\n elif obs_name == \"image_cnn\":\n for x in range(0, 10):\n observation.append(self.cnn_image_r_list[0][x])\n# print(\"r_list\", self.cnn_image_r_list[0][x])\n for x in range(0, 10):\n observation.append(self.cnn_image_l_list[0][x])\n# print(\"l_list\", self.cnn_image_l_list[0][x])\n elif obs_name == \"static_taxel\":\n for x in range(0, 28):\n observation.append((self.static_taxel[0].values[x] - self.static_taxel_ini[0].values[x]) * self.taxel_n)\n for x in range(0, 28):\n observation.append((self.static_taxel[1].values[x] - self.static_taxel_ini[1].values[x]) * self.taxel_n)\n# elif obs_name == \"dynamic_taxel\":\n# observation.append(dynamic_taxel[0].values) * self.taxel_n\n# observation.append(dynamic_taxel[1].values) * self.taxel_n\n else:\n raise NameError('Observation Asked does not exist=='+str(obs_name))\n\n print(\"observation\", list(map(round, observation, [3]*len(observation))))\n# print(\"observation\", observation)\n\n return observation","def get_variables(self, z0, u_inf):\n # Get the ambient data from the CTD profile\n Ta, Sa, P = self.profile.get_values(z0, ['temperature', 'salinity',\n 'pressure'])\n rho = seawater.density(Ta, Sa, P)\n \n # Compute the properties of each dispersed-phase particle\n us = np.zeros(len(self.particles))\n rho_p = np.zeros(len(self.particles))\n m_p = np.zeros(len(self.particles))\n B_p = np.zeros(len(self.particles))\n for i in range(len(self.particles)):\n m0 = self.particles[i].m0\n T0 = self.particles[i].T0\n m_p[i] = np.sum(m0) * self.particles[i].nb0\n if m_p[i] > 0.:\n # Particles exist, get properties. Make sure the algorithm \n # uses the dirty bubble properties since this is supposed\n # to be the rise velocity averaged over the whole plume.\n us[i], rho_p[i]= self.particles[i].properties(m0, T0, P, Sa, \n Ta, np.inf)[0:2]\n B_p[i] = (rho - rho_p[i]) / rho * 9.81 * (m_p[i] / rho_p[i])\n else:\n # Particles dissolved, set to ambient conditions\n us[i] = 0.\n rho_p[i] = rho\n B_p[i] = 0.\n \n # Select the correct slip velocity\n u_slip = us[0]\n for i in range(len(self.particles) - 1):\n if B_p[i+1] > B_p[i]:\n u_slip = us[i+1]\n \n # Compute the total buoyancy flux\n B = np.sum(B_p)\n \n # Get the ambient buoyancy frequency\n N = self.profile.buoyancy_frequency(z0)\n \n # Return the governing parameters\n return (B, N, u_slip, u_inf)","def dynamics(x,Earth):\r\n\r\n # precompute a few terms to reduce number of operations\r\n r = norm(x[0:3])\r\n Re_r_sqr = 1.5*Earth.J2*(Earth.R/r)**2\r\n five_z_sqr = 5*x[2]**2/(r**2)\r\n\r\n # two body and J2 acceleration together\r\n accel = (-Earth.mu/(r**3))*np.array([x[0]*(1 - Re_r_sqr*(five_z_sqr - 1)),\r\n x[1]*(1 - Re_r_sqr*(five_z_sqr - 1)),\r\n x[2]*(1 - Re_r_sqr*(five_z_sqr - 3))])\r\n\r\n return np.array([x[3],x[4],x[5],accel[0],accel[1],accel[2]])","def main():\n \n # Particle in SHO - c.f. Mocz & Succi (2015) Fig. 2\n # parameters\n n = 100 # number of particles\n dt = 0.02 # timestep\n nt = 100 # number of timesteps\n nt_setup = 400 # number of timesteps to set up simulation\n n_out = 25 # plot solution every nout steps\n b = 4 # velocity damping for acquiring initial condition\n m = 1/n # mass of SPH particle ( m * n = 1 normalizes |wavefunction|^2 to 1)\n h = 40/n # smoothing length\n t = 0. # time\n\n # plot potential\n xx = np.linspace(-4.0, 4.0, num=400)\n xx = np.reshape(xx,(xx.size,1))\n fig = plt.plot(xx, 0.5*xx**2, linewidth=5, color=[0.7, 0.7, 0.9])\n \n # initialize\n x = np.linspace(-3.0, 3.0, num=n)\n x = np.reshape(x,(n,1))\n u = np.zeros((n,1))\n \n rho = density( x, m, h )\n P = pressure( x, rho, m, h )\n a = acceleration( x, u, m, rho, P, b, h )\n\n # get v at t=-0.5*dt for the leap frog integrator using Euler's method\n u_mhalf = u - 0.5 * dt * a\n\n # main loop (time evolution)\n for i in np.arange(-nt_setup, nt): # negative time (t<0, i<0) is used to set up initial conditions\n\n # leap frog\n u_phalf = u_mhalf + a*dt\n x = x + u_phalf*dt\n u = 0.5*(u_mhalf+u_phalf)\n u_mhalf = u_phalf\n if (i >= 0):\n t = t + dt\n print(\"%.2f\" % t)\n \n if (i == -1 ): # switch off damping before t=0\n u = np.zeros((n,1)) + 1.0\n u_mhalf = u\n b = 0 # switch off damping at time t=0\n \n # update densities, pressures, accelerations\n rho = density( x, m, h )\n P = pressure( x, rho, m, h )\n a = acceleration( x, u, m, rho, P, b, h)\n \n # plot solution every n_out steps\n if( (i >= 0) and (i % n_out) == 0 ):\n xx = np.linspace(-4.0, 4.0, num=400)\n xx = np.reshape(xx,(xx.size,1))\n rr = probeDensity(x, m, h, xx)\n rr_exact = 1./np.sqrt(np.pi) * np.exp(-(xx-np.sin(t))**2/2.)**2\n fig = plt.plot(xx, rr_exact, linewidth=2, color=[.6, .6, .6])\n fig = plt.plot(xx, rr, linewidth=2, color=[1.*i/nt, 0, 1.-1.*i/nt], label='$t='+\"%.2f\" % t +'$')\n # plot the t<0 damping process for fun\n if( i==-nt_setup or i==-nt_setup*3/4 or i==-nt_setup/2 ):\n xx = np.linspace(-4.0, 4.0, num=400)\n xx = np.reshape(xx,(xx.size,1))\n rr = probeDensity(x, m, h, xx)\n fig = plt.plot(xx, rr, linewidth=1, color=[0.9, 0.9, 0.9])\n \n plt.legend()\n plt.xlabel('$x$')\n plt.ylabel('$|\\psi|^2$')\n plt.axis([-2, 4, 0, 0.8])\n plt.savefig('solution.pdf', aspect = 'normal', bbox_inches='tight', pad_inches = 0)\n plt.close()","def solve_amps(self, h, a, g):\n\n # Apply n_body symmetry, which builds all\n # other spin parts of the unitary group residual\n g3s = (+ g.t3\n - g.t3.transpose([0, 1, 2, 3, 5, 4])\n + g.t3.transpose([0, 1, 2, 4, 5, 3]))\n g3 = ((+ g.t3\n + g.t3.transpose([1, 2, 0, 4, 5, 3])\n + g.t3.transpose([2, 0, 1, 5, 3, 4])\n + g.t3.transpose([0, 2, 1, 3, 5, 4])\n + g.t3.transpose([2, 1, 0, 5, 4, 3])\n + g.t3.transpose([1, 0, 2, 4, 3, 5])\n + 2 * g3s) / 12)\n\n g2 = 1 / 2 * (g.t2 + g.t2.transpose([1, 0, 3, 2]))\n\n t2 = g2 * (- cc_denom(h.f, g.t2.ndim, 'dir', 'full'))\n t3 = g3 * (- cc_denom(h.f, g.t3.ndim, 'dir', 'full'))\n\n # Symmetrize\n t2 = 1 / 2 * (t2 + t2.transpose([1, 0, 3, 2]))\n t3 = ((+ t3\n + t3.transpose([1, 2, 0, 4, 5, 3])\n + t3.transpose([2, 0, 1, 5, 3, 4])\n + t3.transpose([0, 2, 1, 3, 5, 4])\n + t3.transpose([2, 1, 0, 5, 4, 3])\n + t3.transpose([1, 0, 2, 4, 3, 5])) / 6)\n\n return Tensors(\n t1=g.t1 * (- cc_denom(h.f, g.t1.ndim, 'dir', 'full')),\n t2=t2,\n t3=t3)","def update_rhs(self, h, a, r):\n return Tensors(\n t1=r.t1 - a.t1 / cc_denom(h.f, 2, 'dir', 'full'),\n t2=r.t2 - a.t2 / cc_denom(h.f, 4, 'dir', 'full'),\n t3=r.t3 - (a.t3 - a.t3.transpose([0, 1, 2, 4, 3, 5])) /\n cc_denom(h.f, 6, 'dir', 'full')\n )","def update_rhs(self, h, a, r):\n return Tensors(\n t1=r.t1 - a.t1 / cc_denom(h.f, 2, 'dir', 'full'),\n t2=r.t2 - a.t2 / cc_denom(h.f, 4, 'dir', 'full'),\n t3=r.t3 - (a.t3 - a.t3.transpose([0, 1, 2, 4, 3, 5])) /\n cc_denom(h.f, 6, 'dir', 'full')\n )","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H1 + sigma_H1\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def bet(P_1,V0_1,Vi_1,Vflap_1,J_a,Twist,Pitch,Chord,PLE,Polar,rho,dt):\n Phi = np.zeros_like(Twist)\n V_4 = np.zeros_like(P_1)\n dV_4 = np.zeros_like(P_1)\n F_5 = np.zeros_like(P_1)\n F_4 = np.zeros_like(P_1)\n F_1 = np.zeros_like(P_1)\n l = np.zeros_like(Twist)\n d = np.zeros_like(Twist)\n m = np.zeros_like(Twist)\n aoa = np.zeros_like(Twist)\n daoa = np.zeros_like(Twist)\n cl = np.zeros_like(Twist)\n cd = np.zeros_like(Twist)\n cm = np.zeros_like(Twist)\n w = np.zeros_like(Chord)\n wy = np.zeros_like(Twist)\n S = np.zeros_like(Twist)\n \n Vflap_1[np.abs(Vflap_1) < 1e-8] = 0\n \n for i in range(len(P_1)):\n \n for j in range(len(P_1[i])):\n \n # Calculate wing element elevation angle\n \n if j <= J_a:\n \n dz = P_1[i,J_a,2]\n dy = P_1[i,J_a,1]\n\n Phi[i,j] = np.arctan(dz/dy)\n \n else:\n \n dz = P_1[i,-1,2]-P_1[i,J_a+1,2]\n dy = P_1[i,-1,1]-P_1[i,J_a+1,1]\n \n Phi[i,j] = np.arctan(dz/dy)\n \n # Calculate local flow velocity\n V_4[i,j] = roty((Twist[i,j]+Pitch[j]), rotx(-Phi[i,j],(V0_1 + Vi_1 + Vflap_1[i,j]))) \n V_4[np.abs(V_4) < 1e-12] = 0\n\n # Calculate angle of attack\n aoa[i,j] = np.arctan(-V_4[i,j,2]/V_4[i,j,0])\n\n # Find cl,cd,cm from polars with linear interpolation\n cl[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,1])\n cd[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,2])\n cm[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,3])\n\n # Calculate element width\n if j < len(P_1[i])-1:\n wy[i,j] = P_1[i,j+1,1]-P_1[i,j,1]\n else:\n wy[i,j] = P_1[i,j,1]-P_1[i,j-1,1]\n \n # Calculate element surface area\n S[i,j] = Chord[j] * wy[i,j]\n \n # Calculate aerodynamic forces\n l[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cl[i,j]\n d[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cd[i,j]\n m[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cm[i,j]\n \n # Force vector in Blade Element local axes\n F_5[i,j] = np.array([-d[i,j], 0, l[i,j]])\n \n # Force vector in Blade local axes\n F_4[i,j] = roty(aoa[i,j],F_5[i,j])\n \n \n \n \"\"\"\n Add mass effect and rotate to stroke plane axes\n \"\"\"\n for i in range(len(P_1)):\n \n for j in range(len(P_1[i])):\n # Add mass effects\n if i==0:\n daoa[i,j] = (aoa[1,j]-aoa[0,j])/dt\n dV_4[i,j] = (V_4[1,j]-V_4[0,j])/dt\n \n elif i < len(P_1)-1:\n daoa[i,j] = (aoa[i+1,j]-aoa[i-1,j])/(2*dt)\n dV_4[i,j] = (V_4[i+1,j]-V_4[i-1,j])/(2*dt)\n \n else:\n daoa[i,j] = (aoa[i,j]-aoa[i-1,j])/dt\n dV_4[i,j] = (V_4[i,j]-V_4[i-1,j])/dt\n \n \n F_4[i,j,0] = F_4[i,j,0] - 0.25 * rho * np.pi * Chord[j] * S[i,j] * V_4[i,j,2] * daoa[i,j]\n F_4[i,j,2] = F_4[i,j,2] + 0.25 * rho * np.pi * Chord[j] * S[i,j] * dV_4[i,j,2]\n \n # Tranform to Stroke Plane Axes\n F_1[i,j] = rotx(Phi[i,j],roty(-(Twist[i,j]+Pitch[j]),F_4[i,j]))\n\n return F_1","def _redef_via_predef_eqn(self):\r\n time = self.current_T # + self.d_T\r\n\r\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\r\n (self.convect_coeff) \r\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\r\n (self.density * self.heat_capacity)\r\n\r\n # Source term.\r\n def F_func(elem, eta):\r\n x = elem.local_to_global(eta)\r\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\r\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * F\r\n\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func,\r\n self.node_map,\r\n gauss_mult=2) # Use double gp_1D\r\n\r\n # Boundary term.\r\n def f_func(elem, eta):\r\n n = elem.guess_normal_vector_global(eta)\r\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n x = elem.local_to_global(eta)\r\n # Evaluate our boundary term.\r\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\r\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * f\r\n\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func,\r\n self.node_map,\r\n gauss_mult=2)","def F_trans(self):\n rho_H1 = self.edp_par['rho_H1'].value\n Z_H1 = self.edp_par['Z_H1'].value\n sigma_H1 = self.edp_par['sigma_H1'].value\n rho_H2 = self.edp_par['rho_H2'].value\n Z_H2 = self.edp_par['Z_H2'].value\n sigma_H2 = self.edp_par['sigma_H2'].value\n rho_M = self.edp_par['rho_M'].value\n sigma_M = self.edp_par['sigma_M'].value\n psi = self.edp_par['psi'].value \n common_scale = self.edp_par['common_scale'].value\n \n \n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\n if Z_H1 > Z_H2:\n Z_H1, Z_H2 = Z_H2, Z_H1\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\n rho_H1, rho_H2 = rho_H2, rho_H1\n \n # Calculate the intermediate variables\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\n Z_CH2 = Z_H1 - sigma_H1\n Z_W = Z_H2 + sigma_H2\n DeltaZ_H = Z_W - Z_CH2\n \n # Calculate the Gaussian part \n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\n FG *= np.sqrt(2*pi)\n \n # Calculate the strip part\n FS = -2 * sin(alpha*Z_CH2) / alpha\n \n # Calculate the bridging part\n FB = 1 / (alpha + pi/DeltaZ_H)\n FB += 1 / (alpha - pi/DeltaZ_H)\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\n FB *= 0.5\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\n \n return common_scale * (FG + FS + FB)","def R_term(\n enst, # enstrophy field\n omega1, # vorticity-1 component\n omega2, # vorticity-2 component\n omega3, # vorticity-3 component\n s11, # strain rate-11 component\n s12, # strain rate-12 component\n s13, # strain rate-13 component\n s22, # strain rate-22 component\n s23, # strain rate-23 component\n s33, # strain rate-33 component\n diff = False): # differentiation flag\n #---------------------------------------------------------------------#\n # Defining domain variables #\n #---------------------------------------------------------------------#\n pi = np.pi # pi\n dx = (2.0*pi)/64.0 # spatial step\n nu = 0.000185 # default viscosity\n #---------------------------------------------------------------------#\n # Spectral differentiation variables #\n #---------------------------------------------------------------------#\n dim = 64\n kspec = np.fft.fftfreq(dim) * dim\n Kfield = np.array(np.meshgrid(kspec, kspec, kspec, indexing='ij'))\n #---------------------------------------------------------------------#\n # Spectral differentiation variables #\n #---------------------------------------------------------------------#\n term1 = np.zeros((dim, dim, dim))\n term2 = np.zeros((dim, dim, dim))\n term3 = np.zeros((dim, dim, dim))\n #---------------------------------------------------------------------#\n # Numerator (numpy gradient tool) #\n #---------------------------------------------------------------------#\n if diff is not False:\n term1 = np.gradient(enst,dx, edge_order=2)[0]\n term2 = np.gradient(enst,dx, edge_order=2)[1]\n term3 = np.gradient(enst,dx, edge_order=2)[2]\n #---------------------------------------------------------------------#\n # Numerator (spectral differentiation) #\n #---------------------------------------------------------------------#\n else:\n term1 = 0.5*np.fft.ifftn(1j*Kfield[0]*np.fft.fftn(enst) +\\\n 1j*Kfield[0]*np.fft.fftn(enst)).real\n term2 = 0.5*np.fft.ifftn(1j*Kfield[1]*np.fft.fftn(enst) +\\\n 1j*Kfield[1]*np.fft.fftn(enst)).real\n term3 = 0.5*np.fft.ifftn(1j*Kfield[2]*np.fft.fftn(enst) +\\\n 1j*Kfield[2]*np.fft.fftn(enst)).real\n #---------------------------------------------------------------------#\n # Numerator #\n #---------------------------------------------------------------------#\n num = nu*(term1**2.0+ term2**2.0 + term3**2.0)\n #---------------------------------------------------------------------#\n # Denominator #\n #---------------------------------------------------------------------#\n den = omega1*s11*omega1 + omega1*s12*omega2 + omega1*s13*omega3 +\\\n omega2*s12*omega1 + omega2*s22*omega2 + omega2*s23*omega3+\\\n omega3*s13*omega1 + omega3*s23*omega2 + omega3*s33*omega3\n #---------------------------------------------------------------------#\n # R calculation #\n #---------------------------------------------------------------------#\n R = num/den\n\n return R","def calculate_forces(v0, mu, density_m, CD, diameter_b, \\\n area_b, volume_b, density_b, \\\n dt, T):\n \n # Gravitational const. m/s^2\n g = 9.81 \n # Proportionality constant for\n # Reynolds number\n Re_const = diameter_b*density_m/mu\n \n a_s = 3*math.pi*diameter_b*mu/(density_b*volume_b)\n a_q = 0.5*CD*density_m*area_b/(density_b*volume_b)\n b = g*(density_m/density_b - 1.0)\n \n # Numerical solution gives velocity as \n # a function of time.\n v, t = vm.solver(v0, a_s, a_q, b, Re_const, T, dt) \n\n # Initialize vectors\n Fg = zeros(len(v))\n Fb = zeros(len(v))\n Fd = zeros(len(v))\n\n # Loop over time steps\n for n in range(0, len(v)):\n # Evaluate Reynolds number\n Re = Re_const*v[n] \n \n # Gravity force\n Fg[n] = -density_b*volume_b*g\n # Bouyancy force\n Fb[n] = density_m*g*volume_b\n \n # Drag force\n if abs(Re) < 1:\n # If Re < 1, use Stokes' drag force \n Fd[n] = -3.0*math.pi*diameter_b*mu*v[n]\n else:\n # If Re >= 1, use the quadratic\n # drag force\n Fd[n] = -0.5*CD*density_m*area_b*abs(v[n])*v[n]\n\n \n return Fg, Fb, Fd, t","def calculate_surface_heatflux(self, weather, spaces_dict, surface, temp_record, Coeff, space, h_surface, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, Aflag, terrain, areaDict, areaWinDict, shadowRatios, shadowRatioIndex):\r\n #print \"Reaching Surface function...\"\r\n\r\n # First get the As\r\n A_total = self.get_A(surface, areaDict, areaWinDict)\r\n if Aflag == 0:\r\n # If it is the first surface of the space, label the space ID in the log file:\r\n la = str(surface.obj_id)\r\n lb = str(surface.obj_type)\r\n #TM_user.info(\"%s,surface area,%s,%s\" % (la, A_total, lb))\r\n A_noWin = self.get_A_noWin(surface, areaDict, areaWinDict)\r\n A_noOp = self.get_A_noOp(surface, areaDict, areaWinDict)\r\n T_space = spaces_dict[space.obj_id][1]\r\n T1 = weather[\"t_outside\"]\r\n hc_external = float(self.get_hc_external(weather, surface, h_surface, terrain))\r\n transmitted_win = 0\r\n Q_flux = 0\r\n\r\n # need the surface related information, T_space, U, R3\r\n U = self.get_U_surface_e(A_total, A_noOp, surface, areaWinDict) # U = Infor_surface{11,i_surface}; Defined Below\r\n #print U\r\n R3 = 1/U\r\n # Using calculations from: self.surface.constr.layer.C # Infor_surface{10, i_surface} ; from gbXML\r\n C = self.get_C_surface(A_total, A_noOp, surface, Coeff, areaWinDict) # need to pass surface and opening ids\r\n #print C\r\n\r\n temperature = Temperature()\r\n\r\n #Sub-routines for each wall type based on the returned hc_external\r\n # This hc is different for each surface type so moved under this sub-routine area\r\n #hc = 3.076 sent this to the Temperature Object\r\n if surface.obj_type == \"ExteriorWall\":\r\n transmitted_win, Q_flux = temperature.exterior_wall(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\r\n #print Q_flux\r\n if surface.obj_type == \"Roof\":\r\n transmitted_win, Q_flux = temperature.roof(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\r\n #print Q_flux # Matches for Four Room\r\n if surface.obj_type == \"InteriorWall\":\r\n transmitted_win, Q_flux = temperature.interior_wall(surface, A_total, R3, C, spaces_dict, T_space, temp_record)\r\n #print Q_flux # Matches for Four Room\r\n if surface.obj_type == \"UndergroundWall\":\r\n transmitted_win, Q_flux = temperature.underground_wall(surface, A_total, R3, C, T_space, temp_record) # No instance of yet to test\r\n if surface.obj_type == \"RaisedFloor\":\r\n # This will eventually need some values when we start using raised floors\r\n transmitted_win, Q_flux = temperature.raised_floor(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record) # Not instance of yet to test\r\n\r\n return transmitted_win, Q_flux","def Hstep_cost_function(H): \n U = Wold - Yold\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \n return cost","def get_synth(obswvl, obsflux, ivar, dlam, synth=None, temp=None, logg=None, fe=None, alpha=None):\n\n\t# Get synthetic spectrum from grid\n\tif synth is None:\n\n\t\t# Use modified version of interpolateAtm to get blue synthetic spectrum from Ivanna's grid\n\t\tsynthflux_blue = 1. - interpolateAtm(temp,logg,fe,alpha,griddir='/raid/gridie/bin/')\n\t\twvl_range_blue = np.arange(4100., 6300.+0.14, 0.14)\n\t\tsynthwvl_blue = 0.5*(wvl_range_blue[1:] + wvl_range_blue[:-1])\n\n\t\t# Also get synthetic spectrum for redder part from Evan's grid\n\t\tsynthflux_red = 1. - interpolateAtm(temp,logg,fe,alpha,griddir='/raid/grid7/bin/')\n\t\tsynthwvl_red = np.fromfile('/raid/grid7/bin/lambda.bin')\n\t\tsynthwvl_red = np.around(synthwvl_red,2)\n\n\t\t# Splice blue + red parts together\n\t\tsynthflux = np.hstack((synthflux_blue, synthflux_red))\n\t\tsynthwvl = np.hstack((synthwvl_blue, synthwvl_red))\n\n\t# Else, use input synthetic spectrum\n\telse:\n\t\tsynthflux = synth[0]\n\t\tsynthwvl = synth[1]\n\n\t#print(synthwvl, obswvl)\n\n\t# Clip synthetic spectrum so it's within range of obs spectrum\n\tmask = np.where((synthwvl > obswvl[0]) & (synthwvl < obswvl[-1]))\n\tsynthwvl = synthwvl[mask]\n\tsynthflux = synthflux[mask]\n\n\t# Interpolate and smooth the synthetic spectrum onto the observed wavelength array\n\tsynthfluxnew = smooth_gauss_wrapper(synthwvl, synthflux, obswvl, dlam)\n\n\t# For testing purposes\n\t'''\n\tplt.figure()\n\tplt.plot(synthwvl, synthflux, 'k-', label='Synthetic')\n\tplt.plot(obswvl, synthfluxnew, 'r-', label='Synthetic (smoothed)')\n\t#plt.xlim(5000, 5250)\n\t#plt.ylim(0.9,1.0)\n\tplt.legend(loc='best')\n\tplt.savefig('synth_cont.png')\n\t#plt.show()\n\t'''\n\n\treturn synthfluxnew","def distcalc(z,h=0.70,omegalambda=0.7,omegam=0.3,omegak=0.0):\n\n H0 = 100 * h # this is in units of km/s/Mpc\n\n H0freq = H0 * constants.kilo/(constants.mega * constants.parsec) # this is H0 is units of Hz\n \n hubbletime = 1.0/H0freq # in seconds\n hubbletimeyr = hubbletime / constants.year\n\n #hubble distance\n dh = constants.c / H0freq # in meters\n\n #now i can calculate the comoving distance (line of sight) using hogg eqn 15\n dc = dh * integrate.quad(dcintegrand,0,z,(omegalambda,omegam,omegak))[0]\n\n #now i can find the transverse comoving distance using hogg eqn 16\n if omegak == 0:\n dm = dc\n elif omegak > 0:\n dm = dh/np.sqrt(omegak) * np.sinh(dc * np.sqrt(omegak) / dh)\n else:\n dm = dh/np.sqrt(abs(omegak)) * np.sin(dc * np.sqrt(abs(omegak)) / dh)\n\n\n #now i will calculate the angular diameter distance (hogg eqn 18)\n da = dm/(1+z)\n \n #now i will calculate scale in kpc/arcsec, since this is commonly used\n scale = da * constants.arcsec / (constants.kilo * constants.parsec)\n\n #now i will calculate the luminosity distance (hog eqn 21)\n dl = (1+z)*dm\n \n #now i will calculate lookback time and \n #time from the begining of the universe to that redshift using hogg eqn 30\n \n tlookback = hubbletimeyr * integrate.quad(timeintegrand,0,z,(omegalambda,omegam,omegak))[0]\n \n tz = hubbletimeyr * integrate.quad(timeintegrand,z,np.inf,(omegalambda,omegam,omegak))[0]\n \n #all sky co-moving volume out to redshift z (hogg eqn 30)\n if omegak == 0:\n vc = 4 * np.pi * dm**3 / 3\n elif omegak > 0:\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \n np.arcsinh( np.sqrt(omegak) * dm / dh ) / np.sqrt(omegak) )\n else:\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \n np.arcsin( np.sqrt(abs(omegak)) * dm / dh ) / np.sqrt(abs(omegak)) )\n\n #for output, i will make a dictionary\n output = dict(dh=dh, dc=dc, dm=dm, da=da, scale=scale, dl=dl, tlookback = tlookback, tz=tz, vc=vc)\n\n return output","def define_ufl_equations_diff(self):\n\n # Derivatives of velocity integration equation.\n if self.f1 != 0:\n self.df1_du = dlf.derivative(self.f1, self.displacement, self.trial_vector)\n self.df1_dv = dlf.derivative(self.f1, self.velocity, self.trial_vector)\n else:\n self.df1_du = 0\n self.df1_dv = 0\n self.df1_dp = 0 # This is always zero.\n\n # Derivatives of momentum equation.\n if self.displacement != 0:\n self.df2_du = dlf.derivative(self.f2, self.displacement, self.trial_vector)\n else:\n self.df2_du = 0\n\n if self.velocity != 0:\n self.df2_dv = dlf.derivative(self.f2, self.velocity, self.trial_vector)\n else:\n self.df2_dv = 0\n\n if self.pressure != 0:\n self.df2_dp = dlf.derivative(self.f2, self.pressure, self.trial_scalar)\n else:\n self.df2_dp = 0\n\n # Derivatives of incompressibility equation.\n if self.f3 != 0:\n if self.displacement != 0:\n self.df3_du = dlf.derivative(self.f3, self.displacement, self.trial_vector)\n else:\n self.df3_du = 0\n\n if self.velocity != 0:\n self.df3_dv = dlf.derivative(self.f3, self.velocity, self.trial_vector)\n else:\n self.df3_dv = 0\n\n self.df3_dp = dlf.derivative(self.f3, self.pressure, self.trial_scalar)\n else:\n self.df3_du = 0\n self.df3_dv = 0\n self.df3_dp = 0\n\n return None","def derivative(r, t, G=6.67e-11, AU=1.496e+11,\n m1=5.972e+24, m2=6.417e+23, m3=1.989e+30,\n a1=1.0*1.496e+11, a2=1.52*1.496e+11):\n\n if G < 0:\n print(f\"The gravitational constant is negative\")\n\n if AU < 0:\n print(f\"The Astronomical unit is negative\")\n\n if m1 < 0:\n print(f\"The mass of the first body is negative\")\n\n if m2 < 0:\n print(f\"The mass of the second body is negative\")\n\n if m3 < 0:\n print(f\"The mass of the third body is negative\")\n\n if a1 < 0:\n print(f\"The distance of body 1 from the body center is negative\")\n\n if a2 < 0:\n print(f\"The distance of body 2 from the body center is negative\")\n\n x1 = r[0]\n y1 = r[1]\n v_x1 = r[2]\n v_y1 = r[3]\n\n x2 = r[4]\n y2 = r[5]\n v_x2 = r[6]\n v_y2 = r[7]\n\n x3 = r[8]\n y3 = r[9]\n v_x3 = r[10]\n v_y3 = r[11]\n\n z1 = r[12]\n z2 = r[13]\n z3 = r[14]\n\n v_z1 = r[15]\n v_z2 = r[16]\n v_z3 = r[17]\n\n r1 = np.array([x1, y1, z1])\n r2 = np.array([x2, y2, z2])\n r3 = np.array([x3, y3, z3])\n\n dr1 = v_x1\n dr2 = v_y1\n\n dr3 = (G*m2/distance(r1, r2)**3)*(x2-x1) + (G*m3/distance(r1, r3)**3)*(x3-x1)\n dr4 = (G*m2/distance(r1, r2)**3)*(y2-y1) + (G*m3/distance(r1, r3)**3)*(y3-y1)\n\n dr5 = v_x2\n dr6 = v_y2\n\n dr7 = (G*m1/distance(r1, r2)**3)*(x1-x2) + (G*m3/distance(r2, r3)**3)*(x3-x2)\n dr8 = (G*m1/distance(r1, r2)**3)*(y1-y2) + (G*m3/distance(r2, r3)**3)*(y3-y2)\n\n dr9 = v_x3\n dr10 = v_y3\n\n dr11 = (G*m1/distance(r1, r3)**3)*(x1-x3) + (G*m2/distance(r2, r3)**3)*(x2-x3)\n dr12 = (G*m1/distance(r1, r3)**3)*(y1-y3) + (G*m2/distance(r2, r3)**3)*(y2-y3)\n\n dr13 = v_z1\n dr14 = v_z2\n dr15 = v_z3\n\n dr16 = (G*m2/distance(r1, r2)**3)*(z2-z2) + (G*m3/distance(r1, r3)**3)*(z3-z1)\n dr17 = (G*m3/distance(r2, r3)**3)*(z1-z2) + (G*m1/distance(r2, r1)**3)*(z1-z2)\n dr18 = (G*m1/distance(r1, r3)**3)*(z1-z3) + (G*m2/distance(r2, r3)**3)*(z2-z3)\n\n dr = np.array([dr1, dr2, dr3, dr4, dr5, dr6,\n dr7, dr8, dr9, dr10, dr11, dr12,\n dr13, dr14, dr15, dr16, dr17, dr18])\n\n return dr","def one_transition_spectrum_fluor(self,tr):\n \n\n ta = tr[\"ta\"] # TimeAxis\n dd = tr[\"dd\"] # transition dipole strength\n om = tr[\"om\"] # frequency - rwa\n gg = tr[\"gg\"] # natural broadening (constant or time dependent)\n fwhm = tr[\"fwhm\"] # Additional gaussian broadening of the spectra\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\n \n # CD and fluorescence can be calculated in this step\n # TODO if rotatory strength defined calculate also circular dichroism spectra\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \n \n \n if self.system._has_system_bath_coupling:\n# ct = tr[\"ct\"] # correlation function\n re = tr[\"re\"] # reorganisation energy\n \n # convert correlation function to lineshape function\n #gt = self._c2g(ta,ct.data)\n gt = tr[\"gt\"]\n # calculate time dependent response\n at = numpy.exp(-numpy.conjugate(gt) -1j*om*ta.data + 2j*re*ta.data)\n else:\n # calculate time dependent response\n at = numpy.exp(-1j*om*ta.data) \n# plt.figure()\n# plt.title(\"Absorption\")\n# plt.plot(ta.data,numpy.real(at))\n# plt.plot(ta.data,numpy.imag(at))\n \n \n if len(gg) == 1:\n gam = gg[0]\n rt = numpy.exp(gam*ta.data)\n at *= rt\n #print(\"Constant: \", rt[20], len(at))\n else:\n rt = numpy.exp((gg)*ta.data) \n at *= rt\n #print(\"Time dependent: len = \", rt[20], len(rt))\n \n if fwhm!=0.0:\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\n at *= gauss\n \n # Fourier transform the result\n ft = dd*numpy.fft.hfft(at)*ta.step\n ft = numpy.fft.fftshift(ft)\n # invert the order because hfft is a transform with -i\n ft = numpy.flipud(ft) \n # cut the center of the spectrum\n Nt = ta.length #len(ta.data) \n return ft[Nt//2:Nt+Nt//2]","def velocity_field(xt,yt,x0,y0,velf,dia,tsr,solidity):\n rad = dia/2.\n rot = tsr*velf/rad\n\n # Calculating EMG distribution parameters\n loc,spr,skw,scl = vorticity(tsr,solidity)\n \n # Translating the turbine position\n x0t = x0 - xt\n y0t = y0 - yt\n \n # Integration of the vorticity profile using Fortran code (vorticity.f90; _vortrun.so)\n vel_vs = dblquad(_vortmodel.integrand,0.,35.*dia,lambda x: -4.*dia,lambda x: 4.*dia, args=(x0t,y0t,dia,loc[0],loc[1],loc[2],spr[0],spr[1],skw[0],skw[1],scl[0],scl[1],scl[2]))\n \n # Calculating velocity deficit\n vel = (vel_vs[0]*(rot))/(2.*pi)\n vel = (vel + velf)/velf # normalization of velocity\n \n return vel","def efSolver2(self):\n dx = self.dh[0] # dx\n dy = self.dh[1] # dy\n dz = self.dh[2] # dz\n \n \"\"\"\n for i in np.arange(0, self.ni):\n for j in np.arange(0, self.nj):\n for k in np.arange(0, self.nk):\n \"\"\"\n\n ##x-component#\n #if i==0: \n #x-component#\n \"\"\"\n if i==0: \n # forward\n self.ef[i][j][k][0] = -(-3*self.phi[i][j][k]+\\\n 4*self.phi[i+1][j][k]-\\\n self.phi[i+2][j][k])/(2*dx)\n \"\"\"\n \n # forward\n self.ef[0,0:self.nj,0:self.nk,0] = -(-3*self.phi[0,0:self.nj,0:self.nk]+\\\n 4*self.phi[1,0:self.nj,0:self.nk]-\\\n self.phi[2,0:self.nj,0:self.nk])/(2*dx)\n \n #elif i==self.ni-1: \n \"\"\"\n elif i==self.ni-1: \n # backward\n self.ef[i][j][k][0] = -(self.phi[i-2][j][k]-\\\n 4*self.phi[i-1][j][k]+\\\n 3*self.phi[i][j][k])/(2*dx)\n \"\"\" \n # backward\n self.ef[self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[self.ni-3,0:self.nj,0:self.nk]-\\\n 4*self.phi[self.ni-2,0:self.nj,0:self.nk]+\\\n 3*self.phi[self.ni-1,0:self.nj,0:self.nk])/(2*dx)\n \"\"\"\n else: \n #central\n self.ef[i][j][k][0] = -(self.phi[i+1][j][k] - \\\n self.phi[i-1][j][k])/(2*dx)\n \"\"\" \n #central\n self.ef[1:self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[2:self.ni,0:self.nj,0:self.nk] - \\\n self.phi[0:self.ni-2,0:self.nj,0:self.nk])/(2*dx)\n\n\n #y-component\n #if j==0:\n \"\"\"\n if j==0:\n self.ef[i][j][k][1] = -(-3*self.phi[i][j][k] + \\\n 4*self.phi[i][j+1][k]-\\\n self.phi[i][j+2][k])/(2*dy)\n \n \"\"\"\n self.ef[0:self.ni,0,0:self.nk,1] = -(-3*self.phi[0:self.ni,0,0:self.nk] + \\\n 4*self.phi[0:self.ni,1,0:self.nk]-\\\n self.phi[0:self.ni,2,0:self.nk])/(2*dy)\n #elif j==self.nj-1:\n \"\"\"\n elif j==self.nj-1:\n self.ef[i][j][k][1] = -(self.phi[i][j-2][k] - \\\n 4*self.phi[i][j-1][k] +\\\n 3*self.phi[i][j][k])/(2*dy)\n \n \"\"\"\n self.ef[0:self.ni,self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,self.nj-3,0:self.nk] - \\\n 4*self.phi[0:self.ni,self.nj-2,0:self.nk] +\\\n 3*self.phi[0:self.ni,self.nj-1,0:self.nk])/(2*dy)\n #else:\n \"\"\"\n else:\n self.ef[i][j][k][1] = -(self.phi[i][j+1][k] - \\\n self.phi[i][j-1][k])/(2*dy)\n\n \"\"\"\n self.ef[0:self.ni,1:self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,2:self.nj,0:self.nk] - \\\n self.phi[0:self.ni,0:self.nj-2,0:self.nk])/(2*dy)\n\n #z-component\n '''\n if k==0:\n self.ef[i][j][k][2] = -(-3*self.phi[i][j][k] + \\\n 4*self.phi[i][j][k+1]-\n self.phi[i][j][k+2])/(2*dz)\n \n '''\n #z-component\n #if k==0:\n self.ef[0:self.ni,0:self.nj,0,2] = -(-3*self.phi[0:self.ni,0:self.nj,0] + \\\n 4*self.phi[0:self.ni,0:self.nj,1]-\n self.phi[0:self.ni,0:self.nj,2])/(2*dz)\n\n \"\"\"\n elif k==self.nk-1:\n self.ef[i][j][k][2] = -(self.phi[i][j][k-2] - \\\n 4*self.phi[i][j][k-1] + \\\n 3*self.phi[i][j][k])/(2*dz)\n \"\"\"\n \n #elif k==self.nk-1:\n self.ef[0:self.ni,0:self.nj,self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,self.nk-3] - \\\n 4*self.phi[0:self.ni,0:self.nj,self.nk-2] + \\\n 3*self.phi[0:self.ni,0:self.nj,self.nk-1])/(2*dz) \n \"\"\"\n else:\n self.ef[i][j][k][2] = -(self.phi[i][j][k+1] - \\\n self.phi[i][j][k-1])/(2*dz)\n \"\"\"\n #else:\n self.ef[0:self.ni,0:self.nj,1:self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,2:self.nk] - \\\n self.phi[0:self.ni,0:self.nj,0:self.nk-2])/(2*dz)","def dvdt(self, args: Dict) -> float:\n if self.channel_bool['leak']:\n i_leak: float = self.leak.i(args['v'])\n else:\n i_leak: float = 0.\n \n if self.channel_bool['nav']:\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\n else:\n i_nav: float = 0.\n\n if self.channel_bool['kvhh']:\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\n else:\n i_kvhh: float = 0.\n\n if self.channel_bool['kva']:\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\n else:\n i_kva: float = 0.\n\n if self.channel_bool['kvsi']:\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\n else:\n i_kvsi: float = 0.\n\n if self.channel_bool['cav']:\n i_cav: float = self.cav.i(args['v'])\n else:\n i_cav: float = 0.\n\n if self.channel_bool['kca']:\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\n else:\n i_kca: float = 0.\n \n if self.channel_bool['nap']:\n i_nap: float = self.nap.i(args['v'])\n else:\n i_nap: float = 0.\n\n if self.channel_bool['kir']:\n i_kir: float = self.kir.i(args['v'])\n else:\n i_kir: float = 0.\n\n if self.channel_bool['ampar']:\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\n else:\n i_ampar: float = 0.\n\n if self.channel_bool['nmdar']:\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\n else:\n i_nmdar: float = 0.\n\n if self.channel_bool['gabar']:\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\n else:\n i_gabar: float = 0.\n\n return ((-10.0*self.params.area \n * (i_leak\n + i_nav \n + i_kvhh \n + i_kva \n + i_kvsi \n + i_cav \n + i_kca \n + i_nap \n + i_kir) \n - (i_ampar \n + i_nmdar \n + i_gabar))\n / (10.0*self.params.cm*self.params.area))","def solver(T, dt, v0, Cd, rho, A, m, Source=None):\n a = Cd*rho*A/(2*m) # Set up the constant a for compact code\n v = zeros(int(T/dt) + 1) # Create the velocity mesh\n v[0] = v0; g = 9.81;#Initial velocity and the value of gravity acceleration\n \n # Description of the functions X(t) and Y(t) is given in the PDF file\n def X(t): \n if(Source == None):\n return -g\n else:\n return -g + Source(t+dt/2.)/m\n \n def Y(t):\n return a\n \n #Calculate the velocity at each meshpoint\n for i in range(1,len(v)):\n v[i] = skydiving_iterate(v[i-1], dt*(i-1), dt, X, Y)\n return v, linspace(0, T, T/dt +1)","def calc_variables ( ):\n\n # In this example we simulate using the shifted-force potential only\n # The values of < p_sf >, < e_sf > and density should be consistent (for this potential)\n # There are no long-range or delta corrections\n\n from averages_module import VariableType\n \n # Preliminary calculations\n vol = box**3 # Volume\n rho = n / vol # Density\n\n # Variables of interest, of class VariableType, containing three attributes:\n # .val: the instantaneous value\n # .nam: used for headings\n # .method: indicating averaging method\n # If not set below, .method adopts its default value of avg\n # The .nam and some other attributes need only be defined once, at the start of the program,\n # but for clarity and readability we assign all the values together below\n\n # Move acceptance ratio\n m_r = VariableType ( nam = 'Move ratio', val = m_ratio, instant = False )\n\n # Internal energy per molecule (shifted-force potential)\n # Ideal gas contribution (assuming nonlinear molecules) plus total PE divided by N\n e_sf = VariableType ( nam = 'E/N shifted force', val = 3.0*temperature + total.pot/n )\n\n # Pressure (shifted-force potential)\n # Ideal gas contribution plus total virial divided by V\n p_sf = VariableType ( nam = 'P shifted force', val = rho*temperature + total.vir/vol )\n\n # Collect together into a list for averaging\n return [ m_r, e_sf, p_sf ]","def elastd(ntw):\n # Simulated common block obs with observatory information\n global cth, sth, clg, slg, dif, radn, gl\n # Simulated common block love with love numbers\n global h, k, l\n # Simulated common block azimut with strainmeter and tiltmeter azimuths\n global azt, azs\n # Simulated common block bpos with lunar and solar colat and long, lunar sine parallax,\n # and solar distance\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist\n\n coor = [dsz, dcz, dsl, dcl, ssz, scz, ssl, scl]\n par = dpar\n # Data for mean parallaxes, a times m over m(earth), equatorial radius.\n rbor = [1.6592496e-2, 4.2635233e-5]\n amrat = [78451.25, 2.1235762e12]\n a = 6.37814e6\n g1 = 9.79828\n g2 = 9.82022\n ppp = [3, 0, 0]\n iflag = 0\n strain = [0]\n dele = [0, 0, 0]\n dim = [0, 0, 0]\n # On first call compute factors for gravity and tilt, and dc tides\n # at the given latitude.\n if iflag != 1:\n iflag = 1\n for i in range(3):\n # browse 0,1,2\n dele[i] = 1.0 + (2.0 / (i + 2.0)) * h[i] - ((i + 3.0) / (i + 2.0)) * k[i]\n dim[i] = 1.0 + k[i] - h[i]\n # dc gravity tide is also known as the Honkasalo correction\n # **note that the love numbers for an elastic earth are used\n # in computing the dc tide as well.eq\n gdc = -3.0481e-7 * (3 * cth ** 2 - 1.0) * dele[0] * radn\n tnsdc = -9.1445e-7 * cth * sth * dim[0] * radn / gl\n etdc = -1.555e-8 * (\n h[0] * (3.0 * cth ** 2 - 1.0) - 6.0 * l[0] * (2.0 * cth ** 2 - 1.0)\n )\n eldc = -1.555e-8 * (h[0] * (3.0 * cth ** 2 - 1.0) - 6.0 * l[0] * cth ** 2)\n potdc = 0.0992064 * (1.0 - 3 * cth ** 2)\n re = 1.0 / (radn * a)\n\n # zero out arrays\n tilt = [0, 0]\n e = [0, 0, 0]\n tltcor = [0, 0]\n grav = 0\n gnth = 0\n # compute normalized parallax\n pa = [par / 3422.45, 1 / sdist]\n\n # in outer loop, ii = 0 for moon, 1 for sun (basile)\n for ii in [0, 1]:\n id = 3\n if ii == 1:\n id = 1\n ir = 4 * (ii)\n # find cosine of zenith angle, potential constants, legendre polynomials\n # and their derivatives, and derivatives of the cosine of the zenith angle.\n cll = clg * coor[ir + 3] + slg * coor[ir + 2]\n sll = slg * coor[ir + 3] - clg * coor[ir + 2]\n cz = coor[ir + 1]\n sz = coor[ir]\n cu = cth * cz + sth * sz * cll\n xi = rbor[ii] * pa[ii] * radn\n cc = amrat[ii] * rbor[ii] * pa[ii]\n rkr = [cc * xi * xi, 0, 0]\n rkr[1] = rkr[0] * xi\n rkr[2] = rkr[1] * xi\n\n p = [0.5 * (3 * cu * cu - 1.0), 0, 0]\n pp = [3 * cu, 0, 0]\n if ii != 1:\n p[1] = 0.5 * cu * (5.0 * cu * cu - 3.0)\n p[2] = 0.25 * (7.0 * cu * p[1] - 3.0 * p[0])\n pp[1] = 1.5 * (5.0 * cu * cu - 1.0)\n pp[2] = 0.25 * (7.0 * p[1] + cu * pp[1]) - 3.0 * pp[0]\n ppp[1] = 15.0 * cu\n ppp[2] = 7.5 * (7.0 * cu * cu - 1.0)\n\n cut = -sth * cz + cth * sz * cll\n cutt = -cu\n cul = -sth * sz * sll\n cull = -sth * sz * cll\n cutl = -cth * sz * sll\n # for j = 1:id:\n for j in range(id):\n if ntw[0] == 1:\n grav = grav + dele[j] * (j + 2) * rkr[j] * p[j] * g1 * re\n gnth = gnth - dim[0] * rkr[0] * pp[0] * g1 * cut * re\n # ellipticity corrections, convert strains to strainmeter\n if ntw[0] == 1:\n grav = grav + gnth * dif - gdc\n\n return [grav, tilt, strain, gdc]","def fwd_model(Ti_samples,To_samples, dw_samples, kw_samples,hi_samples,ho_samples,TA_samples):\n\t#Determine number of samples (totquat)\n\ttotquat=len(Ti_samples)\n\t# List to store values of Q (assuming no radiative heat transfer) calculated from\n\t# the random samples of the parameters\n\tQ_samples_4PCE=[]\n\t# List to store values of Q assuming radiative heat transfer occurs\n\t#Q_r_samples_4PCE=[]\n\t# Calculate values of heat flux Q (assuming no radiative heat transfer)\n\t# for the different sample values and append to the list\n\tfor i in range(totquat):\n\t\t(Q,T1,T2)=compute_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i],\\\n\t\t\tkw_samples[i], hi_samples[i], ho_samples[i])\n\t\tQ_samples_4PCE.append(Q)\n\t\t# Calculate values of heat flux Q assuming radiative heat transfer to atmosphere and append to list\n\t\t# For the required estimates of Q,T1, and T2 needed to solve the nonlinear system,\n\t\t# we use the values obtained by solving the system assuming no radiative heat transfer\n\t\t\"\"\"Q2=r_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i], kw_samples[i],\\\n\t\t\thi_samples[i], ho_samples[i], TA_samples[i], (Q,T1,T2))\n\t\tQ_r_samples_4PCE.append(Q2)\n\t# Convert Q_r_samples_4PCE to numpy array\n\tQ_evals = np.array(Q_r_samples_4PCE)\n\treturn Q_evals\"\"\"\n\t\tConvert Q_samples_4PCE to numpy array\n\t\tQ_evals = np.array(Q_samples_4PCE)\n\t\treturn Q_evals\"\"\"\n\n\ndef KDE(fcn_evals):\n\t\"\"\"\n\tPerforms kernel density estimation\n\tInput:\n\t\tfcn_evals: numpy array of evaluations of the forward model (values of heat flux Q)\n\tOutput:\n\t\txpts_pce: numpy array of points at which the PDF is estimated.\n\t\tPDF_data_pce: numpy array of estimated PDF values.\n\t\"\"\"\n\t# Perform KDE on fcn_evals\n\tkern_pce=stats.kde.gaussian_kde(fcn_evals)\n\t# Generate points at which to evaluate the PDF\n\txpts_pce=np.linspace(fcn_evals.min(),fcn_evals.max(),200)\n\t# Evaluate the estimated PDF at these points\n\tPDF_data_pce=kern_pce(xpts_pce)\n\treturn xpts_pce, PDF_data_pce","def get_variables(self):\n return [self.g_t, self.m_t]","def finite_diff(F, x0, v0, dt, M, K, C, T):\r\n\r\n ### INITIAL PARAMETERS ####\r\n\r\n # defining the number of steps of analysis = Ns\r\n Ns = int(T/dt)+1\r\n # step t0 (initial acceleration)\r\n ngl = np.shape(F)[0] # captures the number of degrees of freedom\r\n\r\n ### MODELLING THE DISPLACEMENTS ###\r\n\r\n x_before = np.zeros((ngl,1))\r\n # matrix that indicates the displacements, in each degree of freedom, along the time of \r\n # duration of analysis. Each column is a time step\r\n x = np.zeros((ngl, Ns))\r\n x[:,0] = x0[:,0]\r\n\r\n ### SOLVING INITIAL STEP ###\r\n\r\n # initial Force F0 is equivalent to the first column of the matrix of load vectors F along time\r\n aux1 = np.zeros((ngl,1))\r\n aux1[:,0] = np.copy(F[:,0])\r\n aux2 = aux1 - np.dot(C,v0) - np.dot(K,x0)\r\n a0 = np.dot(la.inv(M),aux2)\r\n # step t-1 (before initial condition)\r\n x_before = dt*dt*a0/2 - dt*v0 + x0 \r\n # step t+1 (after initial condition)\r\n C1 = M / (dt*dt) + C / (2*dt)\r\n C2 = K - 2*M / (dt*dt)\r\n C3 = M / (dt*dt) - C / (2*dt)\r\n aux3 = aux1 - np.dot(C2, x0) - np.dot(C3, x_before)\r\n x[:,1] = np.dot(la.inv(C1), aux3[:,0])\r\n\r\n ### INTEGRATING ALONG THE DURATION OS ANALYSIS ###\r\n\r\n i = 0\r\n aux4 = np.zeros((ngl,1))\r\n aux5 = np.zeros((ngl,1))\r\n aux6 = np.zeros((ngl,1))\r\n aux7 = np.zeros((ngl,1))\r\n for i in range(1,Ns-1):\r\n aux4[:,0] = np.copy(F[:,i])\r\n aux5[:,0] = np.copy(x[:,i])\r\n aux6[:,0] = np.copy(x[:,i-1])\r\n aux7[:,0] = np.copy(x[:,i+1])\r\n aux7 = np.dot(la.inv(C1), aux4 - np.dot(C2,aux5) - np.dot(C3,aux6))\r\n x[:,i+1] = np.copy(aux7[:,0])\r\n return x","def _compute_H(self, t, index, t2, index2, update_derivatives=False, stationary=False):\r\n\r\n if stationary:\r\n raise NotImplementedError, \"Error, stationary version of this covariance not yet implemented.\"\r\n # Vector of decays and delays associated with each output.\r\n Decay = self.decay[index]\r\n Decay2 = self.decay[index2]\r\n t_mat = t[:, None]\r\n t2_mat = t2[None, :]\r\n if self.delay is not None:\r\n Delay = self.delay[index]\r\n Delay2 = self.delay[index2]\r\n t_mat-=Delay[:, None]\r\n t2_mat-=Delay2[None, :]\r\n\r\n diff_t = (t_mat - t2_mat)\r\n inv_sigma_diff_t = 1./self.sigma*diff_t\r\n half_sigma_decay_i = 0.5*self.sigma*Decay[:, None]\r\n\r\n ln_part_1, sign1 = ln_diff_erfs(half_sigma_decay_i + t2_mat/self.sigma, \r\n half_sigma_decay_i - inv_sigma_diff_t,\r\n return_sign=True)\r\n ln_part_2, sign2 = ln_diff_erfs(half_sigma_decay_i,\r\n half_sigma_decay_i - t_mat/self.sigma,\r\n return_sign=True)\r\n\r\n h = sign1*np.exp(half_sigma_decay_i\r\n *half_sigma_decay_i\r\n -Decay[:, None]*diff_t+ln_part_1\r\n -np.log(Decay[:, None] + Decay2[None, :]))\r\n h -= sign2*np.exp(half_sigma_decay_i*half_sigma_decay_i\r\n -Decay[:, None]*t_mat-Decay2[None, :]*t2_mat+ln_part_2\r\n -np.log(Decay[:, None] + Decay2[None, :]))\r\n\r\n if update_derivatives:\r\n sigma2 = self.sigma*self.sigma\r\n # Update ith decay gradient\r\n\r\n dh_ddecay = ((0.5*Decay[:, None]*sigma2*(Decay[:, None] + Decay2[None, :])-1)*h\r\n + (-diff_t*sign1*np.exp(\r\n half_sigma_decay_i*half_sigma_decay_i-Decay[:, None]*diff_t+ln_part_1\r\n )\r\n +t_mat*sign2*np.exp(\r\n half_sigma_decay_i*half_sigma_decay_i-Decay[:, None]*t_mat\r\n - Decay2*t2_mat+ln_part_2))\r\n +self.sigma/np.sqrt(np.pi)*(\r\n -np.exp(\r\n -diff_t*diff_t/sigma2\r\n )+np.exp(\r\n -t2_mat*t2_mat/sigma2-Decay[:, None]*t_mat\r\n )+np.exp(\r\n -t_mat*t_mat/sigma2-Decay2[None, :]*t2_mat\r\n )-np.exp(\r\n -(Decay[:, None]*t_mat + Decay2[None, :]*t2_mat)\r\n )\r\n ))\r\n self._dh_ddecay = (dh_ddecay/(Decay[:, None]+Decay2[None, :])).real\r\n \r\n # Update jth decay gradient\r\n dh_ddecay2 = (t2_mat*sign2\r\n *np.exp(\r\n half_sigma_decay_i*half_sigma_decay_i\r\n -(Decay[:, None]*t_mat + Decay2[None, :]*t2_mat)\r\n +ln_part_2\r\n )\r\n -h)\r\n self._dh_ddecay2 = (dh_ddecay/(Decay[:, None] + Decay2[None, :])).real\r\n \r\n # Update sigma gradient\r\n self._dh_dsigma = (half_sigma_decay_i*Decay[:, None]*h\r\n + 2/(np.sqrt(np.pi)\r\n *(Decay[:, None]+Decay2[None, :]))\r\n *((-diff_t/sigma2-Decay[:, None]/2)\r\n *np.exp(-diff_t*diff_t/sigma2)\r\n + (-t2_mat/sigma2+Decay[:, None]/2)\r\n *np.exp(-t2_mat*t2_mat/sigma2-Decay[:, None]*t_mat) \r\n - (-t_mat/sigma2-Decay[:, None]/2) \r\n *np.exp(-t_mat*t_mat/sigma2-Decay2[None, :]*t2_mat) \r\n - Decay[:, None]/2\r\n *np.exp(-(Decay[:, None]*t_mat+Decay2[None, :]*t2_mat))))\r\n \r\n return h","def calc(self):\n\n # the following if query ensures that volume- and interaction-terms\n # are only calculated if tau > 0.\n # (to avoid nan-values from invalid function-evaluations)\n\n if self.V.tau.shape == (1,):\n Isurf = self.surface()\n # differentiation for non-existing canopy, as otherwise NAN values\n if self.V.tau > 0.:\n Ivol = self.volume()\n if self.int_Q is True:\n Iint = self.interaction()\n else:\n Iint = np.array([0.])\n else:\n Ivol = np.array([0.])\n Iint = np.array([0.])\n else:\n # calculate surface-term (valid for any tau-value)\n Isurf = self.surface()\n\n # store initial parameter-values\n old_t_0 = self.t_0\n old_p_0 = self.p_0\n old_t_ex = self.t_ex\n old_p_ex = self.p_ex\n\n old_tau = self.V._get_tau()\n old_omega = self.V._get_omega()\n old_NN = self.SRF._get_NormBRDF()\n\n # set mask for tau > 0.\n mask = old_tau > 0.\n valid_index = np.where(mask)\n inval_index = np.where(~mask)\n\n # set parameter-values to valid values for calculation\n self.t_0 = old_t_0[valid_index[0]]\n self.p_0 = old_p_0[valid_index[0]]\n self.t_ex = old_t_ex[valid_index[0]]\n self.p_ex = old_p_ex[valid_index[0]]\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\n if np.array(self.V.omega).size != 1:\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\n if np.array(self.SRF.NormBRDF).size != 1:\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\n\n # calculate volume and interaction term where tau-values are valid\n _Ivol = self.volume()\n if self.int_Q is True:\n _Iint = self.interaction()\n else:\n _Iint = np.full_like(self.t_0, 0.)\n\n # reset parameter values to old values\n self.t_0 = old_t_0\n self.p_0 = old_p_0\n self.t_ex = old_t_ex\n self.p_ex = old_p_ex\n\n # squeezing the arrays is necessary since the setter-function for\n # tau, omega and NormBRDF automatically add an axis to the arrays!\n self.V.tau = np.squeeze(old_tau)\n self.V.omega = np.squeeze(old_omega)\n self.SRF.NormBRDF = np.squeeze(old_NN)\n\n # combine calculated volume-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n Ivol = np.ones_like(self.t_0)\n Ivol[valid_index[0]] = _Ivol\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\n\n # combine calculated interaction-contributions for valid tau-values\n # with zero-arrays for invalid tau-values\n if self.int_Q is True:\n Iint = np.ones_like(self.t_0)\n Iint[valid_index[0]] = _Iint\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\n else:\n Iint = np.full_like(self.t_0, 0.)\n\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint","def grad_phi_mesh(self):\n fy = -0.5 * (np.roll(self.phi, 1, axis = 1) - np.roll(self.phi, -1, axis=1)) \n fx = -0.5 * (np.roll(self.phi, 1, axis = 0) - np.roll(self.phi, -1, axis=0))\n return fx,fy","def compute_mixing_coefficients_surf(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n\n # SET UP NEW MIXING COEFFICIENT ARRAYS\n self.Kv_surf = np.zeros([Ly,N+1])\n self.Kt_surf = np.zeros([Ly,N+1])\n \n self.ghat = np.zeros([Ly,N+1])\n \n\n #################################\n # \tSURFACE KPP\n ################################\n #---> j-loop\n \n self.wm2 = []\n self.ws2 = []\n self.sigma_y = []\n for j in range(Ly):\n #--> k-loop (top to kbl[j])\n # in fortran k=N-1,kbl(j),-1\n for k in range(N-1,self.kbl[j]-1,-1):\n k_w = k\n k_r = k-1\n\n Bfsfc = self.Bfsfc_bl[j]\n zscale = z_u_w[j,N] - z_u_w[j,k_w]\n \n # CALCULATE TURBULENT VELOCITY SCALES\n wm,ws = self.lmd_wscale_wm_and_ws(Bfsfc,zscale,self.ustar[j],self.hbls[j])\n self.wm2.append(wm)\n self.ws2.append(ws)\n # COMPUTE VERTICAL MIXING COEFFICIENTS\n sigma = (z_u_w[j,N] - z_u_w[j,k_w]) / np.max([self.hbls[j],self.eps])\n self.sigma1 = sigma #for debugging\n if j == 25: \n self.sigma_y.append(sigma)\n a1 = sigma - 2.\n a2 = 3.-2.*sigma\n a3 = sigma - 1.\n\n if sigma < 0.07:\n cff = 0.5 * (sigma-0.07)**2/0.07\n else:\n cff = 0\n \n \n if k == N-1: \n self.wm_debug = wm\n self.hbls_debug = self.hbls[j]\n self.cff_debug = cff\n self.sigma_debug = sigma\n self.a1_debug = a1\n self.a2_debug = a2\n self.a3_debug = a3\n\n self.Kv_surf[j,k_w] = wm * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\n a1 + a2*self.Gm1[j]+a3*self.dGm1_dS[j])))\n\n if k == N-1:\n self.ws_debug = ws\n self.hbls_debug = self.hbls[j]\n self.cff_debug = cff\n self.sigma_debug = sigma\n self.a1_debug = a1\n self.a2_debug = a2\n self.a3_debug = a3\n \n self.Kt_surf[j,k_w] = ws * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\n a1 + a2*self.Gt1[j]+a3*self.dGt1_dS[j])))\n #---> end k-loop \n if self.LMD_NONLOCAL:\n if Bfsfc < 0:\n self.ghat[j,k_w] = 0\n self.ghat[j,k_w] = self.Cg * sigma * (1.-sigma)**2\n else:\n self.ghat[j,k_w] = 0.\n\n # ADD CONVECTIVE ADJUSTMENT IN SURFACE MIXED LAYER \n if self.LMD_CONVEC and self.MLCONVEC: \n for k in range(N-1,int(self.kbl[j]-1),-1):\n k_w = k\n k_r = k -1\n\n if self.bvf[j,k_w] < 0:\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.ffac*self.nu0c\n\n # ADD CONVECTIVE ADJUSTMENT BELOW SURFACE MIXED LAYER\n # IF BKPP IS SWITCHED OFF!!\n for k in range(int(self.kbl[j]-1),-1,-1):\n k_w = k\n k_r = k -1\n if self.LMD_NONLOCAL:\n self.ghat[j,k_w] = 0\n if self.LMD_CONVEC and self.LMD_BKPP == False:\n if self.bvf[j,k_w] < 0:\n self.Kv_surf[j,k_w] = self.Kv_surf[j,k_w] + self.nu0c\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.nu0c\n \n\n #---> end j-loop","def _forces_moments(self, delta):\n # assert delta.shape == (4,1)\n da = delta[0]\n de = delta[1]\n dt = delta[2]\n dr = delta[3]\n\n e0 = self._state[3]\n e1 = self._state[4]\n e2 = self._state[5]\n e3 = self._state[6]\n u = self._state[7]\n v = self._state[8]\n w = self._state[9]\n p = self._state[10]\n q = self._state[11]\n r = self._state[12]\n\n self._Va = np.sqrt(u**2 + v**2 + w**2)\n self._alpha = np.arctan(1.0*w/u)\n self._beta = np.arcsin(1.0*v/self._Va)\n\n\n\n Fg = self.mass*self.gravity*np.array([2*(e1*e3-e2*e0),\n 2*(e2*e3 + e1*e0),\n e3**2 + e0**2 - e1**2 - e2**2,\n ])\n\n # Fg = self.mass*self.gravity*np.array([2*(e1*e3 - e2*e0),\n # 2*(e2*e3 + e1*e0),\n # e3**2 + e0**2 - e1**2 - e2**2,\n # ])\n\n M_e = 25\n sig = lambda a: (1+np.exp(-M_e*(a-self.alpha0))+np.exp(M_e*(a+self.alpha0)))/((1+np.exp(-M_e*(a-self.alpha0)))*(1+np.exp(M_e*(a+self.alpha0))))\n cla = lambda a: (1-sig(a))*(self.C_L_0+self.C_L_alpha*a)+sig(a)*(2*np.sign(a)*np.sin(a)**2*np.cos(a))\n cda = lambda a: self.C_D_p + (self.C_L_0+self.C_L_alpha*a)**2/(np.pi*self.e*self.AR)\n\n cxa = lambda a: -(cda(a)) * np.cos(a) + (cla(a)) * np.sin(a)\n\n cxq = lambda a: -self.C_D_q * np.cos(a) +self.C_L_q * np.sin(a)\n\n cxde = lambda a: -self.C_D_delta_e * np.cos(a) + self.C_L_delta_e * np.sin(a)\n\n cza = lambda a: -(cda(a)) * np.sin(a) - (cla(a)) * np.cos(a)\n\n czq = lambda a: -self.C_D_q * np.sin(a) - self.C_L_q * np.cos(a)\n\n czde = lambda a: -self.C_D_delta_e * np.sin(a) - self.C_L_delta_e * np.cos(a)\n\n c = self.c/(2.0*self._Va)\n b = self.b/(2.0*self._Va)\n\n\n\n one = 0.5*self.rho*self._Va**2*self.S_wing\n # two = np.array([[1,0,0],[0,1,0],[0,0,1]])\n three = np.array([[cxa(self._alpha)+cxq(self._alpha)*c*q+cxde(self._alpha)*de],\n [self.C_Y_0+self.C_Y_beta*self._beta+self.C_Y_p*b*p+self.C_Y_r*b*r+self.C_Y_delta_a*da+self.C_Y_delta_r*dr],\n [cza(self._alpha)+czq(self._alpha)*c*q+czde(self._alpha)*de]])\n\n Fa = np.squeeze(three) * one\n # pdb.set_trace()\n Fa = Fa.reshape((3,-1))\n\n F = Fg + Fa\n #\n # print(\"Fa:\",Fa)\n\n Fp = 0.5*self.rho*self.S_prop*self.C_prop*((self.k_motor*dt)**2-self._Va**2)\n\n # print(\"FP:\", Fp)\n\n fx = F[0] + Fp\n # + 0.5*MAV.rho*self._Va**2*MAV.S_wing*(\\\n # +cxa(self._alpha)\\\n # + cxq(self._alpha)*c*q\\\n # + cxde(self._alpha)*de\n # )\n\n fy = F[1]\n fz = F[2]\n\n # Moment time!!!\n one = 0.5*self.rho*self._Va**2*self.S_wing\n two = np.array([\\\n [self.b*(self.C_ell_0+self.C_ell_beta*self._beta+self.C_ell_p*b*p+self.C_ell_r*b*r+self.C_ell_delta_a*da+self.C_ell_delta_r*dr)],\n [self.c*(self.C_m_0+(self.C_m_alpha*self._alpha)+(self.C_m_q*c*q)+(self.C_m_delta_e*de))],\n [self.b*(self.C_n_0+(self.C_n_beta*self._beta)+(self.C_n_p*b*p)+(self.C_n_r*b*r)+(self.C_n_delta_a*da)+(self.C_n_delta_r*dr))]\n ])\n Ma = one * np.squeeze(two)\n # print(\"\\nMa:\", Ma)\n # pdb.set_trace()\n Ma = Ma.reshape((3,-1))\n\n size = Ma.shape[1]\n\n Mp = np.block([[np.ones(size)*-self.kTp*(self.kOmega*dt)**2],\n [np.zeros(size)],\n [np.zeros(size)]\n ])\n\n M = Mp + Ma\n\n Mx = M[0]\n My = M[1]\n Mz = M[2]\n\n # self._forces[0] = fx\n # self._forces[1] = fy\n # self._forces[2] = fz\n # pdb.set_trace()\n # print(fx, fy, fz, Mx, My, Mz)\n\n return np.array([fx, fy, fz, Mx, My, Mz])","def _F_qt(vh_comp,conn,q,wind=5):\n\n # get the van Hove data\n (vanHove,temp,dtime) = extract_vanHove_all(vh_comp,conn,wind)\n \n Fqt = [_Fqt_comp(vh,q) for vh in vanHove]\n\n return Fqt,temp,dtime","def evolve(self):\n\n rho = self.cc_data.get_var(\"density\")\n u = self.cc_data.get_var(\"x-velocity\")\n v = self.cc_data.get_var(\"y-velocity\")\n\n gradp_x = self.cc_data.get_var(\"gradp_x\")\n gradp_y = self.cc_data.get_var(\"gradp_y\")\n\n # note: the base state quantities do not have valid ghost cells\n beta0 = self.base[\"beta0\"]\n beta0_edges = self.base[\"beta0-edges\"]\n\n rho0 = self.base[\"rho0\"]\n\n phi = self.cc_data.get_var(\"phi\")\n\n myg = self.cc_data.grid\n\n # ---------------------------------------------------------------------\n # create the limited slopes of rho, u and v (in both directions)\n # ---------------------------------------------------------------------\n limiter = self.rp.get_param(\"lm-atmosphere.limiter\")\n\n ldelta_rx = reconstruction.limit(rho, myg, 1, limiter)\n ldelta_ux = reconstruction.limit(u, myg, 1, limiter)\n ldelta_vx = reconstruction.limit(v, myg, 1, limiter)\n\n ldelta_ry = reconstruction.limit(rho, myg, 2, limiter)\n ldelta_uy = reconstruction.limit(u, myg, 2, limiter)\n ldelta_vy = reconstruction.limit(v, myg, 2, limiter)\n\n # ---------------------------------------------------------------------\n # get the advective velocities\n # ---------------------------------------------------------------------\n\n \"\"\"\n the advective velocities are the normal velocity through each cell\n interface, and are defined on the cell edges, in a MAC type\n staggered form\n\n n+1/2\n v\n i,j+1/2\n +------+------+\n | |\n n+1/2 | | n+1/2\n u + U + u\n i-1/2,j | i,j | i+1/2,j\n | |\n +------+------+\n n+1/2\n v\n i,j-1/2\n\n \"\"\"\n\n # this returns u on x-interfaces and v on y-interfaces. These\n # constitute the MAC grid\n if self.verbose > 0:\n print(\" making MAC velocities\")\n\n # create the coefficient to the grad (pi/beta) term\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 1.0/rho.v()\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n # create the source term\n source = self.aux_data.get_var(\"source_y\")\n\n g = self.rp.get_param(\"lm-atmosphere.grav\")\n rhoprime = self.make_prime(rho, rho0)\n source.v()[:, :] = rhoprime.v()*g/rho.v()\n self.aux_data.fill_BC(\"source_y\")\n\n _um, _vm = lm_interface.mac_vels(myg.ng, myg.dx, myg.dy, self.dt,\n u, v,\n ldelta_ux, ldelta_vx,\n ldelta_uy, ldelta_vy,\n coeff*gradp_x, coeff*gradp_y,\n source)\n\n u_MAC = ai.ArrayIndexer(d=_um, grid=myg)\n v_MAC = ai.ArrayIndexer(d=_vm, grid=myg)\n\n # ---------------------------------------------------------------------\n # do a MAC projection to make the advective velocities divergence\n # free\n # ---------------------------------------------------------------------\n\n # we will solve D (beta_0^2/rho) G phi = D (beta_0 U^MAC), where\n # phi is cell centered, and U^MAC is the MAC-type staggered\n # grid of the advective velocities.\n\n if self.verbose > 0:\n print(\" MAC projection\")\n\n # create the coefficient array: beta0**2/rho\n # MZ!!!! probably don't need the buf here\n coeff.v(buf=1)[:, :] = 1.0/rho.v(buf=1)\n coeff.v(buf=1)[:, :] = coeff.v(buf=1)*beta0.v2d(buf=1)**2\n\n # create the multigrid object\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\n xl_BC_type=self.cc_data.BCs[\"phi-MAC\"].xlb,\n xr_BC_type=self.cc_data.BCs[\"phi-MAC\"].xrb,\n yl_BC_type=self.cc_data.BCs[\"phi-MAC\"].ylb,\n yr_BC_type=self.cc_data.BCs[\"phi-MAC\"].yrb,\n xmin=myg.xmin, xmax=myg.xmax,\n ymin=myg.ymin, ymax=myg.ymax,\n coeffs=coeff,\n coeffs_bc=self.cc_data.BCs[\"density\"],\n verbose=0)\n\n # first compute div{beta_0 U}\n div_beta_U = mg.soln_grid.scratch_array()\n\n # MAC velocities are edge-centered. div{beta_0 U} is cell-centered.\n div_beta_U.v()[:, :] = \\\n beta0.v2d()*(u_MAC.ip(1) - u_MAC.v())/myg.dx + \\\n (beta0_edges.v2dp(1)*v_MAC.jp(1) -\n beta0_edges.v2d()*v_MAC.v())/myg.dy\n\n # solve the Poisson problem\n mg.init_RHS(div_beta_U)\n mg.solve(rtol=1.e-12)\n\n # update the normal velocities with the pressure gradient -- these\n # constitute our advective velocities. Note that what we actually\n # solved for here is phi/beta_0\n phi_MAC = self.cc_data.get_var(\"phi-MAC\")\n phi_MAC[:, :] = mg.get_solution(grid=myg)\n\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 1.0/rho.v()\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n coeff_x = myg.scratch_array()\n b = (3, 1, 0, 0) # this seems more than we need\n coeff_x.v(buf=b)[:, :] = 0.5*(coeff.ip(-1, buf=b) + coeff.v(buf=b))\n\n coeff_y = myg.scratch_array()\n b = (0, 0, 3, 1)\n coeff_y.v(buf=b)[:, :] = 0.5*(coeff.jp(-1, buf=b) + coeff.v(buf=b))\n\n # we need the MAC velocities on all edges of the computational domain\n # here we do U = U - (beta_0/rho) grad (phi/beta_0)\n b = (0, 1, 0, 0)\n u_MAC.v(buf=b)[:, :] -= \\\n coeff_x.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.ip(-1, buf=b))/myg.dx\n\n b = (0, 0, 0, 1)\n v_MAC.v(buf=b)[:, :] -= \\\n coeff_y.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.jp(-1, buf=b))/myg.dy\n\n # ---------------------------------------------------------------------\n # predict rho to the edges and do its conservative update\n # ---------------------------------------------------------------------\n _rx, _ry = lm_interface.rho_states(myg.ng, myg.dx, myg.dy, self.dt,\n rho, u_MAC, v_MAC,\n ldelta_rx, ldelta_ry)\n\n rho_xint = ai.ArrayIndexer(d=_rx, grid=myg)\n rho_yint = ai.ArrayIndexer(d=_ry, grid=myg)\n\n rho_old = rho.copy()\n\n rho.v()[:, :] -= self.dt*(\n # (rho u)_x\n (rho_xint.ip(1)*u_MAC.ip(1) - rho_xint.v()*u_MAC.v())/myg.dx +\n # (rho v)_y\n (rho_yint.jp(1)*v_MAC.jp(1) - rho_yint.v()*v_MAC.v())/myg.dy)\n\n self.cc_data.fill_BC(\"density\")\n\n # update eint as a diagnostic\n eint = self.cc_data.get_var(\"eint\")\n gamma = self.rp.get_param(\"eos.gamma\")\n eint.v()[:, :] = self.base[\"p0\"].v2d()/(gamma - 1.0)/rho.v()\n\n # ---------------------------------------------------------------------\n # recompute the interface states, using the advective velocity\n # from above\n # ---------------------------------------------------------------------\n if self.verbose > 0:\n print(\" making u, v edge states\")\n\n coeff = self.aux_data.get_var(\"coeff\")\n coeff.v()[:, :] = 2.0/(rho.v() + rho_old.v())\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n self.aux_data.fill_BC(\"coeff\")\n\n _ux, _vx, _uy, _vy = \\\n lm_interface.states(myg.ng, myg.dx, myg.dy, self.dt,\n u, v,\n ldelta_ux, ldelta_vx,\n ldelta_uy, ldelta_vy,\n coeff*gradp_x, coeff*gradp_y,\n source,\n u_MAC, v_MAC)\n\n u_xint = ai.ArrayIndexer(d=_ux, grid=myg)\n v_xint = ai.ArrayIndexer(d=_vx, grid=myg)\n u_yint = ai.ArrayIndexer(d=_uy, grid=myg)\n v_yint = ai.ArrayIndexer(d=_vy, grid=myg)\n\n # ---------------------------------------------------------------------\n # update U to get the provisional velocity field\n # ---------------------------------------------------------------------\n if self.verbose > 0:\n print(\" doing provisional update of u, v\")\n\n # compute (U.grad)U\n\n # we want u_MAC U_x + v_MAC U_y\n advect_x = myg.scratch_array()\n advect_y = myg.scratch_array()\n\n advect_x.v()[:, :] = \\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(u_xint.ip(1) - u_xint.v())/myg.dx +\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(u_yint.jp(1) - u_yint.v())/myg.dy\n\n advect_y.v()[:, :] = \\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(v_xint.ip(1) - v_xint.v())/myg.dx +\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(v_yint.jp(1) - v_yint.v())/myg.dy\n\n proj_type = self.rp.get_param(\"lm-atmosphere.proj_type\")\n\n if proj_type == 1:\n u.v()[:, :] -= (self.dt*advect_x.v() + self.dt*gradp_x.v())\n v.v()[:, :] -= (self.dt*advect_y.v() + self.dt*gradp_y.v())\n\n elif proj_type == 2:\n u.v()[:, :] -= self.dt*advect_x.v()\n v.v()[:, :] -= self.dt*advect_y.v()\n\n # add the gravitational source\n rho_half = 0.5*(rho + rho_old)\n rhoprime = self.make_prime(rho_half, rho0)\n source[:, :] = rhoprime*g/rho_half\n self.aux_data.fill_BC(\"source_y\")\n\n v[:, :] += self.dt*source\n\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n if self.verbose > 0:\n print(\"min/max rho = {}, {}\".format(self.cc_data.min(\"density\"), self.cc_data.max(\"density\")))\n print(\"min/max u = {}, {}\".format(self.cc_data.min(\"x-velocity\"), self.cc_data.max(\"x-velocity\")))\n print(\"min/max v = {}, {}\".format(self.cc_data.min(\"y-velocity\"), self.cc_data.max(\"y-velocity\")))\n\n # ---------------------------------------------------------------------\n # project the final velocity\n # ---------------------------------------------------------------------\n\n # now we solve L phi = D (U* /dt)\n if self.verbose > 0:\n print(\" final projection\")\n\n # create the coefficient array: beta0**2/rho\n coeff = 1.0/rho\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\n\n # create the multigrid object\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\n xl_BC_type=self.cc_data.BCs[\"phi\"].xlb,\n xr_BC_type=self.cc_data.BCs[\"phi\"].xrb,\n yl_BC_type=self.cc_data.BCs[\"phi\"].ylb,\n yr_BC_type=self.cc_data.BCs[\"phi\"].yrb,\n xmin=myg.xmin, xmax=myg.xmax,\n ymin=myg.ymin, ymax=myg.ymax,\n coeffs=coeff,\n coeffs_bc=self.cc_data.BCs[\"density\"],\n verbose=0)\n\n # first compute div{beta_0 U}\n\n # u/v are cell-centered, divU is cell-centered\n div_beta_U.v()[:, :] = \\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\n\n mg.init_RHS(div_beta_U/self.dt)\n\n # use the old phi as our initial guess\n phiGuess = mg.soln_grid.scratch_array()\n phiGuess.v(buf=1)[:, :] = phi.v(buf=1)\n mg.init_solution(phiGuess)\n\n # solve\n mg.solve(rtol=1.e-12)\n\n # store the solution in our self.cc_data object -- include a single\n # ghostcell\n phi[:, :] = mg.get_solution(grid=myg)\n\n # get the cell-centered gradient of p and update the velocities\n # this differs depending on what we projected.\n gradphi_x, gradphi_y = mg.get_solution_gradient(grid=myg)\n\n # U = U - (beta_0/rho) grad (phi/beta_0)\n coeff = 1.0/rho\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\n\n u.v()[:, :] -= self.dt*coeff.v()*gradphi_x.v()\n v.v()[:, :] -= self.dt*coeff.v()*gradphi_y.v()\n\n # store gradp for the next step\n\n if proj_type == 1:\n gradp_x.v()[:, :] += gradphi_x.v()\n gradp_y.v()[:, :] += gradphi_y.v()\n\n elif proj_type == 2:\n gradp_x.v()[:, :] = gradphi_x.v()\n gradp_y.v()[:, :] = gradphi_y.v()\n\n self.cc_data.fill_BC(\"x-velocity\")\n self.cc_data.fill_BC(\"y-velocity\")\n\n self.cc_data.fill_BC(\"gradp_x\")\n self.cc_data.fill_BC(\"gradp_y\")\n\n # increment the time\n if not self.in_preevolve:\n self.cc_data.t += self.dt\n self.n += 1","def enthalpy(temp,pres):\n g = liq_g(0,0,temp,pres)\n g_t = liq_g(1,0,temp,pres)\n h = g - temp*g_t\n return h","def dynamics(state,t):\n global M,m\n f = control_upright(state)\n # f = 0\n dydx = np.zeros_like(state)\n x,x_dot,th,th_dot = state #unpacking the state\n dydx[0] = x_dot\n dydx[2] = th_dot\n\n den1 = M + (m*sin(th)*sin(th))\n dydx[1] = (f + (m*g*sin(th)*cos(th)) + m*L*th_dot*th_dot*sin(th) + (b/L)*(th_dot*cos(th)))/den1\n den2 = L*den1\n dydx[3] = (((M+m)*g*sin(th) + f*cos(th) + m*L*th_dot*th_dot*sin(th)*cos(th))/den2) + (b/(m*L*L))*th_dot\n dydx[3] = -dydx[3]\n\n return dydx","def VerletHope2(r, v, beta,dt,R_dust,M_dust):\n # Deceptively simple (read about Velocity Verlet on wikipedia)\n r_new = r + v*dt + calculate_acceleration2(r,v,beta,omega,R_dust,M_dust)*dt**2/2\n v_new = v + (calculate_acceleration2(r,v,beta,omega,R_dust,M_dust) + calculate_acceleration2(r_new,v,beta,omega,R_dust,M_dust))/2 * dt\n \n return (r_new, v_new)","def _diff_pot(a2,t2,d2,wair,temp,pres,ppot,airf,dhum):\n ph2 = _eq_pressure(0,0,0,a2,t2,d2)\n gi2 = _ice_g(0,0,t2,ppot)\n gv2 = _eq_vappot(0,0,0,a2,t2,d2)\n sh1 = -_air_f(0,1,0,airf,temp,dhum)\n si1 = -_ice_g(1,0,temp,pres)\n s1 = wair/airf*sh1 + (1-wair/airf)*si1\n sh2 = -_air_f(0,1,0,a2,t2,d2)\n si2 = -_ice_g(1,0,t2,ppot)\n s2 = wair/a2*sh2 + (1-wair/a2)*si2\n lhs = numpy.array([ppot, gi2, s1])\n rhs = numpy.array([ph2, gv2, s2])\n \n ph2_a = _eq_pressure(1,0,0,a2,t2,d2)\n ph2_t = _eq_pressure(0,1,0,a2,t2,d2)\n ph2_d = _eq_pressure(0,0,1,a2,t2,d2)\n gi2_t = _ice_g(1,0,t2,ppot)\n gv2_a = _eq_vappot(1,0,0,a2,t2,d2)\n gv2_t = _eq_vappot(0,1,0,a2,t2,d2)\n gv2_d = _eq_vappot(0,0,1,a2,t2,d2)\n sh2_a = -_air_f(1,1,0,a2,t2,d2)\n sh2_t = -_air_f(0,2,0,a2,t2,d2)\n sh2_d = -_air_f(0,1,1,a2,t2,d2)\n si2_t = -_ice_g(2,0,t2,ppot)\n s2_a = -wair/a2**2*(sh2 - a2*sh2_a - si2)\n s2_t = wair/a2*sh2_t + (1-wair/a2)*si2_t\n s2_d = wair/a2*sh2_d\n dlhs = numpy.array([[0.,0.,0.], [0.,gi2_t,0.], [0.,0.,0.]])\n drhs = numpy.array([[ph2_a,ph2_t,ph2_d], [gv2_a,gv2_t,gv2_d],\n [s2_a,s2_t,s2_d]])\n return lhs, rhs, dlhs, drhs","def __init__(self, trap=2.5*10**16, Keq=1.0*10**17,\n EHdecay=1.0*10**-10, Etrap=2.0*10**-10, FHloss=8.0*10**-12,\n G3decay = 0, step=200*ps, pretime=2, reprate=80000000,\n verbose=False, trackQ=False, scalar=1, Gdecay=0, GHdecay=0,\n tolerance=0.005, G2decay=0. ,Gescape=1., Gform=1., G3loss=0.):\n # Some other variables used\n self.tolerance = tolerance\n self.scalar = scalar\n self.verbose = verbose\n self.reprate = reprate\n self.duration = 1.00 / reprate\n self.step = step\n self.steps = int(self.duration / self.step)\n self.powers = []\n self.pretime = pretime\n # Variables which hold state densities\n self.exciton = []\n self.hole = []\n self.electron = []\n self.trap = (trap) # Total number of traps\n self.filled = [] # Filled traps\n self.signal = []\n self.xsignal = []\n self.ehsignal = []\n self.xloss = []\n self.tloss = []\n self.pulses = []\n self.qk = []\n self.trackQ = trackQ\n # Rate and equilibrium constants, corrected for time step size\n self.Keq = Gescape/Gform # Equilibrium constant for X<-->e+h\n self.EHdecay = (EHdecay * step) # e+h->ground\n self.Etrap = (Etrap * step) # e+trap->filled\n self.FHloss = (FHloss * step) # filled+h->ground\n self.Gdecay = Gdecay * step\n self.G2decay = G2decay * step\n self.G3decay = G3decay * step\n self.GHdecay = GHdecay * step\n self.Gescape = Gescape * step\n self.G3loss = G3loss * step\n self.Gform = Gform * step","def term_1(\n omega1, # vorticity-1\n omega2, # vorticity-2\n omega3, # vorticity-3\n enst, # enstrophy\n nu_sgs, # turbulent viscosity\n h = True): # spatial step size\n #---------------------------------------------------------------------#\n # Setting default values #\n #---------------------------------------------------------------------#\n if h is True:\n h = 2.0*np.pi/64.0\n #---------------------------------------------------------------------#\n # Preallocating space #\n #---------------------------------------------------------------------#\n term = np.zeros((64,64,64))\n #---------------------------------------------------------------------#\n # Enstrophy term #\n #---------------------------------------------------------------------#\n term += np.gradient(\\\n np.gradient(enst, h, edge_order=2)[2], h, edge_order=2)[2]\n term += np.gradient(\\\n np.gradient(enst, h, edge_order=2)[1], h, edge_order=2)[1]\n term += np.gradient(\\\n np.gradient(enst, h, edge_order=2)[0], h, edge_order=2)[0]\n #---------------------------------------------------------------------#\n # Dissipation #\n #---------------------------------------------------------------------#\n omega1_grad = np.gradient(omega1, h, edge_order=2)\n omega2_grad = np.gradient(omega2, h, edge_order=2)\n omega3_grad = np.gradient(omega3, h, edge_order=2)\n term -= np.square(omega1_grad[2])\n term -= np.square(omega1_grad[1])\n term -= np.square(omega1_grad[0])\n term -= np.square(omega2_grad[2])\n term -= np.square(omega2_grad[1])\n term -= np.square(omega2_grad[0])\n term -= np.square(omega3_grad[2])\n term -= np.square(omega3_grad[1])\n term -= np.square(omega3_grad[0])\n #---------------------------------------------------------------------#\n # Applying the subgrid stress #\n #---------------------------------------------------------------------#\n term *= nu_sgs\n\n return term","def update(self, t, dt):\n # update boundary values\n self._apply_boundary_conditions(t,dt)\n #update current empirical wind velocity\n corner_mean_index = int(scipy.floor(t/self.wind_update_period))\n self._empirical_velocity = self.u_av[corner_mean_index], self.v_av[corner_mean_index]\n # initialise wind speed derivative arrays\n du_dt = scipy.zeros((self.nx, self.ny))\n dv_dt = scipy.zeros((self.nx, self.ny))\n # approximate spatial first derivatives with centred finite difference\n # equations for both components of wind field\n du_dx, du_dy = self._centred_first_derivs(self._u)\n dv_dx, dv_dy = self._centred_first_derivs(self._v)\n # calculate centred first sums i.e. sf_x = f(x+dx,y)+f(x-dx,y) and\n # sf_y = f(x,y+dy)-f(x,y-dy) as first step in approximating spatial\n # second derivatives with second order finite difference equations\n # d2f/dx2 ~ [f(x+dx,y)-2f(x,y)+f(x-dx,y)] / (dx*dx)\n # = [sf_x-2f(x,y)] / (dx*dx)\n # d2f/dy2 ~ [f(x,y+dy)-2f(x,y)+f(x,y-dy)] / (dy*dy)\n # = [sf_y-2f(x,y)] / (dy*dy)\n # second finite differences are not computed in full as the common\n # f(x,y) term in both expressions can be extracted out to reduce\n # the number of +/- operations required\n su_x, su_y = self._centred_first_sums(self._u)\n sv_x, sv_y = self._centred_first_sums(self._v)\n # use finite difference method to approximate time derivatives across\n # simulation region interior from defining PDEs\n # du/dt = -(u*du/dx + v*du/dy) + 0.5*Kx*d2u/dx2 + 0.5*Ky*d2u/dy2\n # dv/dt = -(u*dv/dx + v*dv/dy) + 0.5*Kx*d2v/dx2 + 0.5*Ky*d2v/dy2\n du_dt = (-self._u_int * du_dx - self._v_int * du_dy +\n self._Bx * su_x + self._By * su_y -\n self._C * self._u_int)\n dv_dt = (-self._u_int * dv_dx - self._v_int * dv_dy +\n self._Bx * sv_x + self._By * sv_y -\n self._C * self._v_int)\n # perform update with Euler integration\n self._u_int += du_dt * dt\n self._v_int += dv_dt * dt\n # update spline interpolators\n self._set_interpolators()","def __init__(self, temperatures, daytypes, consumptions, nb_days, nb_particles, sigma2, kappa, u_heat):\n self.temperatures = temperatures\n self.daytypes = daytypes\n self.consumptions = consumptions\n self.nb_days = nb_days\n self.nb_particles = nb_particles\n self.sigma2 = sigma2\n self.kappa = kappa\n self.u_heat = u_heat\n #Var init\n self.s = np.zeros((nb_days, nb_particles)) \n self.g_heat = np.zeros((nb_days, nb_particles))\n #sigma_s and sigma_g are fixed\n self.sigma_s_star_2 = np.zeros((1, nb_particles)) \n self.sigma_g_star_2 = np.zeros((1, nb_particles))\n self.x_season = np.zeros((1, nb_particles))\n self.x_heat = np.zeros((1, nb_particles))\n self.x = np.zeros((1, nb_particles))\n self.w = np.zeros((1, nb_particles))","def rhs_fenics(y,t):\n #print \"time: \",t\n uprev.vector()[:]=y\n f.t = t #dolfin needs to know the current time for cos(t)\n uprime_solver.solve()\n return uprime_solution.vector().array()","def __getitem__(self, i):\n T0, S0 = get_surface_ts(self.nc, i)\n \n # average the variables if we got multiple time elements\n if isinstance(i, slice):\n T0, S0, = T0.mean(axis=0), S0.mean(axis=0)\n if self.p == 0.:\n rho, drhodT, drhodS = jmd95.eos.state_surface(T0, S0)\n else:\n rho, drhodT, drhodS = jmd95.eos.state(self.p, T0, S0)\n return rho","def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\n\n # def Q_gen(I, R):\n # w = I * I * R\n # return w\n\n # def Q_rad_in(I_sun, A_s, a_s):\n # w = I_sun * D * a_s\n # return w\n\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\n # w = htcoeff * A_s * (T_line - T_amb)\n # return w\n\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\n # return w\n\n def reynolds(V, D, v, Mair=1.103):\n r = V * D / v\n return r\n\n def nusselt(Re, Pr):\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\n c = (Re / 282000) ** (5.0/8.0)\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\n return n\n\n def air_prop(T_amb):\n # temp v k Pr\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\n [250, 11.44e-6, 22.3e-3, 0.720],\n [300, 15.89e-6, 26.3e-3, 0.707],\n [350, 20.92e-6, 30.0e-3, 0.700],\n [400, 26.41e-6, 33.8e-3, 0.690],\n [450, 32.39e-6, 37.3e-3, 0.686],\n [500, 38.79e-6, 40.7e-3, 0.684],\n [550, 45.57e-6, 43.9e-3, 0.683],\n [600, 52.69e-6, 46.9e-3, 0.685]])\n\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\n 0, air_prop[:,1:])\n return v, k, Pr\n\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\n if 273 <= T_line <= 323:\n R = ((R_lo + \n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\n *(T_line - T_range[0]))/N_cond)\n elif T_line > 323:\n R = ((R_mid + \n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\n *(T_line - T_range[1]))/N_cond)\n else:\n R = R_lo\n print('Out of bounds')\n return R\n\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\n temp_factor = 1\n wind_factor = 1\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\n\n T_amb = T_amb*temp_factor\n V = V*wind_factor\n\n v, k, Pr = air_prop(T_amb)\n Re = reynolds(V, D, v)\n htcoeff = nusselt(Re, Pr) * k / D\n\n def T_line(T_init):\n \n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\n print R\n\n C4 = e_s * sigma * D * math.pi\n C3 = 0.0\n C2 = 0.0\n C1 = htcoeff * D * math.pi\n C0 = - ( I ** 2 * R\n + I_sun * a_s * D\n + htcoeff * D * math.pi * T_amb\n + e_s * D * math.pi * sigma * (T_amb ** 4))\n\n return np.roots([C4, C3, C2, C1, C0])\n\n T_c = T_amb\n \n for i in range(n_iter):\n T_arr = T_line(T_c)\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\n print T_c\n\n return T_c","def dvdt(self, args: List[float]) -> float:\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, _, s_nmdar, s_gabar, ca = args\n return ((-10.0*self.params.area \n * (self.leak.i(v)\n + self.nav.i(v, h=h_nav) \n + self.kvhh.i(v, n=n_kvhh)\n + self.kva.i(v, h=h_kva)\n + self.kvsi.i(v, m=m_kvsi)\n + self.cav.i(v)\n + self.kca.i(v, ca=ca)\n + self.nap.i(v)\n + self.kir.i(v))\n - (self.ampar.i(v, s=s_ampar)\n + self.nmdar.i(v, s=s_nmdar)\n + self.gabar.i(v, s=s_gabar))) \n / (10.0*self.params.cm*self.params.area))","def derivatives(self):\n self.rdot = self.v\n self.vdot[:,:] = 0.0\n self.udot[:] = 0.0\n\n t = time()\n for nl in self.nlists: \n nl.separations()\n #nl.apply_minimum_image()\n self.timing['pairsep time'] = (time() - t)\n\n t = time()\n if SPROPS:\n properties.spam_properties(self,self.nl_default \\\n ,self.h[0:self.n],self.hlr[0:self.n])\n self.timing['SPAM time'] = time() - t\n \n t = time()\n for force in self.forces:\n force.apply()\n self.timing['force time'] = time() - t\n \n if ADVECTIVE:\n self.rdot[:,:] = 0.0","def forward(self, z_t_1, h_x, phi_table, t, temp=0):\n \n# sparsemax.device = z_t_1.device\n \n z_category, z_category_sparse = self.gen_z_t_dist_now(z_t_1, h_x)\n \n# if t > self.t_thres:\n# \n# if self.use_gumbel_softmax:\n# # print(t, 'inference here')\n# # device = z_category.device\n# \n# averaged_z_t = 0\n# \n# log_prob = Variable(torch.log(z_category))\n# \n# for k in range(self.sampling_times): \n# curr_z_t = F.gumbel_softmax(log_prob, tau = 0.05)\n# \n# # curr_z_t = sparsemax(log_prob)\n# \n# \n# averaged_z_t += curr_z_t\n# \n# del curr_z_t\n# \n# # averaged_z_t = averaged_z_t.to(device)\n# \n# z_t = averaged_z_t/self.sampling_times\n# \n# # print('diff::', torch.norm(z_t - z_category))\n# # \n# # print()\n# else:\n# z_t = z_category\n# \n# else:\n z_t = z_category\n \n if len(z_t.shape) == 2:\n phi_z = torch.mm(z_t, torch.t(phi_table))\n else:\n \n phi_table_full = (torch.t(phi_table)).view(1, phi_table.shape[1], phi_table.shape[0])\n \n phi_table_full = phi_table_full.repeat(phi_table.shape[1], 1, 1)\n \n phi_z = torch.bmm(z_t, phi_table_full)\n# mu = self.h_to_mu(h_combined)\n# logvar = self.h_to_logvar(h_combined)\n# std = torch.exp(0.5 * logvar) \n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\n return z_t, z_category, phi_z, z_category_sparse","def addPhotonVariables(hf, event, data_temp, pho):\n # data_temp[ 0, column_names.index( 'pho_truthPdgId_egam') ] = hf[ 'pho_truthPdgId_egam' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_truthPdgId_atlas') ] = hf[ 'pho_truthPdgId_atlas' ][ event][ pho ]\n # data_temp[ 0, column_names.index( 'pho_egamTruthParticle') ] = hf[ 'pho_egamTruthParticle' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_truthType') ] = hf[ 'pho_truthType' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_truthOrigin') ] = hf[ 'pho_truthOrigin' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_isPhotonEMLoose') ] = hf[ 'pho_isPhotonEMLoose' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_isPhotonEMTight') ] = hf[ 'pho_isPhotonEMTight' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_e') ] = hf[ 'pho_e' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_eta') ] = hf[ 'pho_eta' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_phi') ] = hf[ 'pho_phi' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_et') ] = hf[ 'pho_et' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_Rhad1') ] = hf[ 'pho_Rhad1' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_Rhad') ] = hf[ 'pho_Rhad' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_weta2') ] = hf[ 'pho_weta2' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_Rphi') ] = hf[ 'pho_Rphi' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_Reta') ] = hf[ 'pho_Reta' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_Eratio') ] = hf[ 'pho_Eratio' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_f1') ] = hf[ 'pho_f1' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_wtots1') ] = hf[ 'pho_wtots1' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_DeltaE') ] = hf[ 'pho_DeltaE' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_weta1') ] = hf[ 'pho_weta1' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_fracs1') ] = hf[ 'pho_fracs1' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_ConversionType') ] = hf[ 'pho_ConversionType' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_ConversionRadius') ] = hf[ 'pho_ConversionRadius' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_VertexConvEtOverPt') ] = hf[ 'pho_VertexConvEtOverPt' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_VertexConvPtRatio') ] = hf[ 'pho_VertexConvPtRatio' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_topoetcone20') ] = hf[ 'pho_topoetcone20' ][ event][ pho ]\n # data_temp[ 0, column_names.index( 'pho_topoetcone30') ] = hf[ 'pho_topoetcone30' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_topoetcone40') ] = hf[ 'pho_topoetcone40' ][ event][ pho ]\n data_temp[ 0, column_names.index( 'pho_ptvarcone20') ] = hf[ 'pho_ptvarcone20' ][ event][ pho ]\n # data_temp[ 0, column_names.index( 'pho_ptvarcone30') ] = hf[ 'pho_ptvarcone30' ][ event][ pho ]\n # data_temp[ 0, column_names.index( 'pho_ptvarcone40') ] = hf[ 'pho_ptvarcone40' ][ event][ pho ]","def __init__(self,\n uDict,\n phiDict,\n testSpaceDict,\n matType,\n dofBoundaryConditionsDict,\n dofBoundaryConditionsSetterDict,\n coefficients,\n elementQuadrature,\n elementBoundaryQuadrature,\n fluxBoundaryConditionsDict=None,\n advectiveFluxBoundaryConditionsSetterDict=None,\n diffusiveFluxBoundaryConditionsSetterDictDict=None,\n stressTraceBoundaryConditionsSetterDict=None,\n stabilization=None,\n shockCapturing=None,\n conservativeFluxDict=None,\n numericalFluxType=None,\n TimeIntegrationClass=None,\n massLumping=False,\n reactionLumping=False,\n options=None,\n name='defaultName',\n reuse_trial_and_test_quadrature=True,\n sd = True,\n movingDomain=False):\n #\n #set the objects describing the method and boundary conditions\n #\n self.movingDomain=movingDomain\n self.tLast_mesh=None\n #\n self.name=name\n self.sd=sd\n self.Hess=False\n self.lowmem=True\n self.timeTerm=True#allow turning off the time derivative\n #self.lowmem=False\n self.testIsTrial=True\n self.phiTrialIsTrial=True\n self.u = uDict\n self.ua = {}#analytical solutions\n self.phi = phiDict\n self.dphi={}\n for ck,phi in phiDict.iteritems():\n if coefficients.potential.has_key(ck):\n for cj in coefficients.potential[ck].keys():\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\n else:\n self.dphi[(ck,ck)] = FiniteElementFunction(phi.femSpace)\n #check for nonlinearities in the diffusion coefficient that don't match the potential\n for ci,ckDict in coefficients.diffusion.iteritems():\n #for ck,cjDict in coefficients.diffusion.iteritems(): #cek: bug?\n for ck,cjDict in ckDict.iteritems():\n for cj in cjDict.keys():\n if not self.dphi.has_key((ck,cj)):\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\n self.matType = matType\n #try to reuse test and trial information across components if spaces are the same\n self.reuse_test_trial_quadrature = reuse_trial_and_test_quadrature#True#False\n if self.reuse_test_trial_quadrature:\n for ci in range(1,coefficients.nc):\n assert self.u[ci].femSpace.__class__.__name__ == self.u[0].femSpace.__class__.__name__, \"to reuse_test_trial_quad all femSpaces must be the same!\"\n ## Simplicial Mesh\n self.mesh = self.u[0].femSpace.mesh #assume the same mesh for all components for now\n self.testSpace = testSpaceDict\n self.dirichletConditions = dofBoundaryConditionsDict\n self.dirichletNodeSetList=None #explicit Dirichlet conditions for now, no Dirichlet BC constraints\n self.coefficients = coefficients\n self.coefficients.initializeMesh(self.mesh)\n self.nc = self.coefficients.nc\n self.stabilization = stabilization\n self.shockCapturing = shockCapturing\n self.conservativeFlux = conservativeFluxDict #no velocity post-processing for now\n self.fluxBoundaryConditions=fluxBoundaryConditionsDict\n self.advectiveFluxBoundaryConditionsSetterDict=advectiveFluxBoundaryConditionsSetterDict\n self.diffusiveFluxBoundaryConditionsSetterDictDict = diffusiveFluxBoundaryConditionsSetterDictDict\n #determine whether the stabilization term is nonlinear\n self.stabilizationIsNonlinear = False\n #cek come back\n if self.stabilization != None:\n for ci in range(self.nc):\n if coefficients.mass.has_key(ci):\n for flag in coefficients.mass[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.advection.has_key(ci):\n for flag in coefficients.advection[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.diffusion.has_key(ci):\n for diffusionDict in coefficients.diffusion[ci].values():\n for flag in diffusionDict.values():\n if flag != 'constant':\n self.stabilizationIsNonlinear=True\n if coefficients.potential.has_key(ci):\n for flag in coefficients.potential[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.reaction.has_key(ci):\n for flag in coefficients.reaction[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.hamiltonian.has_key(ci):\n for flag in coefficients.hamiltonian[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n #determine if we need element boundary storage\n self.elementBoundaryIntegrals = {}\n for ci in range(self.nc):\n self.elementBoundaryIntegrals[ci] = ((self.conservativeFlux != None) or\n (numericalFluxType != None) or\n (self.fluxBoundaryConditions[ci] == 'outFlow') or\n (self.fluxBoundaryConditions[ci] == 'mixedFlow') or\n (self.fluxBoundaryConditions[ci] == 'setFlow'))\n #\n #calculate some dimensions\n #\n self.nSpace_global = self.u[0].femSpace.nSpace_global #assume same space dim for all variables\n self.nDOF_trial_element = [u_j.femSpace.max_nDOF_element for u_j in self.u.values()]\n self.nDOF_phi_trial_element = [phi_k.femSpace.max_nDOF_element for phi_k in self.phi.values()]\n self.n_phi_ip_element = [phi_k.femSpace.referenceFiniteElement.interpolationConditions.nQuadraturePoints for phi_k in self.phi.values()]\n self.nDOF_test_element = [femSpace.max_nDOF_element for femSpace in self.testSpace.values()]\n self.nFreeDOF_global = [dc.nFreeDOF_global for dc in self.dirichletConditions.values()]\n self.nVDOF_element = sum(self.nDOF_trial_element)\n self.nFreeVDOF_global = sum(self.nFreeDOF_global)\n #\n NonlinearEquation.__init__(self,self.nFreeVDOF_global)\n #\n #build the quadrature point dictionaries from the input (this\n #is just for convenience so that the input doesn't have to be\n #complete)\n #\n elementQuadratureDict={}\n elemQuadIsDict = isinstance(elementQuadrature,dict)\n if elemQuadIsDict: #set terms manually\n for I in self.coefficients.elementIntegralKeys:\n if elementQuadrature.has_key(I):\n elementQuadratureDict[I] = elementQuadrature[I]\n else:\n elementQuadratureDict[I] = elementQuadrature['default']\n else:\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[I] = elementQuadrature\n if self.stabilization != None:\n for I in self.coefficients.elementIntegralKeys:\n if elemQuadIsDict:\n if elementQuadrature.has_key(I):\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature[I]\n else:\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature['default']\n else:\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature\n if self.shockCapturing != None:\n for ci in self.shockCapturing.components:\n if elemQuadIsDict:\n if elementQuadrature.has_key(('numDiff',ci,ci)):\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature[('numDiff',ci,ci)]\n else:\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature['default']\n else:\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature\n if massLumping:\n for ci in self.coefficients.mass.keys():\n elementQuadratureDict[('m',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n if reactionLumping:\n for ci in self.coefficients.mass.keys():\n elementQuadratureDict[('r',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n elementBoundaryQuadratureDict={}\n if isinstance(elementBoundaryQuadrature,dict): #set terms manually\n for I in self.coefficients.elementBoundaryIntegralKeys:\n if elementBoundaryQuadrature.has_key(I):\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature[I]\n else:\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature['default']\n else:\n for I in self.coefficients.elementBoundaryIntegralKeys:\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature\n #\n # find the union of all element quadrature points and\n # build a quadrature rule for each integral that has a\n # weight at each point in the union\n #mwf include tag telling me which indices are which quadrature rule?\n (self.elementQuadraturePoints,self.elementQuadratureWeights,\n self.elementQuadratureRuleIndeces) = Quadrature.buildUnion(elementQuadratureDict)\n self.nQuadraturePoints_element = self.elementQuadraturePoints.shape[0]\n self.nQuadraturePoints_global = self.nQuadraturePoints_element*self.mesh.nElements_global\n #\n #Repeat the same thing for the element boundary quadrature\n #\n (self.elementBoundaryQuadraturePoints,\n self.elementBoundaryQuadratureWeights,\n self.elementBoundaryQuadratureRuleIndeces) = Quadrature.buildUnion(elementBoundaryQuadratureDict)\n self.nElementBoundaryQuadraturePoints_elementBoundary = self.elementBoundaryQuadraturePoints.shape[0]\n self.nElementBoundaryQuadraturePoints_global = (self.mesh.nElements_global*\n self.mesh.nElementBoundaries_element*\n self.nElementBoundaryQuadraturePoints_elementBoundary)\n\n #\n #storage dictionaries\n self.scalars_element = set()\n #\n #simplified allocations for test==trial and also check if space is mixed or not\n #\n self.q={}\n self.ebq={}\n self.ebq_global={}\n self.ebqe={}\n self.phi_ip={}\n #mesh\n self.q['x'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,3),'d')\n self.q['det(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q['abs(det(J))'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q['J'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\n self.q['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\n self.ebqe['x'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['g'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n max(1,self.nSpace_global-1),\n max(1,self.nSpace_global-1)),\n 'd')\n self.ebqe['inverse(J)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global,self.nSpace_global),'d')\n self.ebqe['hat(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['bar(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['sqrt(det(g))'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n self.ebqe[('n')] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n #shape\n self.q[('v',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.q[('w',0)] = self.q[('v',0)]\n self.q[('grad(v)',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.q[('grad(w)',0)] = self.q[('grad(v)',0)]\n self.q[('grad(w)*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.q[('grad(w)*dV_f',0)] = self.q[('grad(w)*dV',0)]\n #todo get rid of dV_{f,a}, etc\n self.q[('w*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.q[('w*dV_m',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n #assume all components are the same space for now\n shapeKeysForAlias = ['v','w','grad(v)','grad(w)*dV','grad(w)*dV_f','w*dV','w*dV_m']\n for ci in range(1,self.nc):\n for key in shapeKeysForAlias:\n key_ci = (key,ci)\n key_0 = (key,0)\n self.q[key_ci] = self.q[key_0]\n #ELLAM weights stiffness, body integrals by dt\n for ci in range(self.nc):\n self.q[('dt*grad(w)*dV',ci)]= numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[ci],self.nSpace_global),'d')\n #\n self.ebqe[('v',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\n self.ebqe[('w',0)] = self.ebqe[('v',0)]\n self.ebqe[('grad(v)',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.ebqe[('w*dS_f',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\n #assume all components are the same space for now\n shapeKeysForAlias = ['v','w','grad(v)','w*dS_f']\n for ci in range(1,self.nc):\n for key in shapeKeysForAlias:\n key_ci = (key,ci)\n key_0 = (key,0)\n self.ebqe[key_ci] = self.ebqe[key_0]\n\n for ci in range(self.nc):\n self.q[('u',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('grad(u)',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n #f\n for ci in self.coefficients.advection.keys():\n self.q[('f',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n for cj in self.coefficients.advection[ci].keys():\n self.q[('df',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n self.ebqe[('f',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n for cj in self.coefficients.advection[ci].keys():\n self.ebqe[('df',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n\n #a, linear dispersion single component\n\n for ci,ckDict in self.coefficients.diffusion.iteritems():\n for ck,cjDict in ckDict.iteritems():\n for flag in cjDict.values():\n assert flag == 'constant', \"Error potential %s LADRellam does not handle diffusion = %s yet\" % (ck,flag)\n\n if self.coefficients.sdInfo != None and (ci,ck) in self.coefficients.sdInfo.keys():\n self.q[('a',ci,ck)] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n for cj in cjDict.keys():\n self.q[('da',ci,ck,cj)] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n self.ebqe[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n for cj in cjDict.keys():\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n\n else:\n self.q[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n for cj in cjDict.keys():\n self.q[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n self.ebqe[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n for cj in cjDict.keys():\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n #dense storage\n self.q[('grad(w)*dV_a',ci,ck)] = self.q[('grad(w)*dV_f',ci)]\n self.q[('dt*grad(w)*dV_a',ci,ck)]= self.q[('dt*grad(w)*dV',ci)]\n #ci,ckDict\n #linear potential only for now, need to change for e.g., Buckley Leverett\n for ck in self.phi.keys():\n self.phi[ck].dof[:]=self.u[ck].dof\n self.q[('grad(phi)',ck)] = self.q[('grad(u)',ck)]\n for key in self.dphi.keys():\n self.dphi[key].dof.fill(1.0)\n self.q[('dphi',key[0],key[1])] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n\n\n\n# if self.coefficients.diffusion.has_key(0):\n# for ck,flag in self.coefficients.diffusion[0][0].iteritems():\n# assert self.coefficients.diffusion[0][0][ck] == 'constant', \"Error potential %s LADRellam does not handle diffusion = %s yet\" % (ck,flag)\n# if self.coefficients.sdInfo != None and (0,0) in self.coefficients.sdInfo.keys():\n# self.q[('a',0,0)] = numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.q[('da',0,0,0)] = numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.ebqe[('a',0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n\n# else:\n# self.q[('a',0,0)]=numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.q[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.ebqe[('a',0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# #\n# self.phi[0].dof[:]=self.u[0].dof\n# self.dphi[(0,0)].dof.fill(1.0)\n# self.q[('grad(phi)',0)] = self.q[('grad(u)',0)]\n# self.q[('dphi',0,0)] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n\n# self.q[('grad(w)*dV_a',0,0)] = self.q[('grad(w)*dV_f',0)]\n# self.q[('dt*grad(w)*dV_a',0,0)]= self.q[('dt*grad(w)*dV',0)]\n\n #r 'constant' ie not a function of solution but go ahead and include dr for now\n for ci,cjDict in self.coefficients.reaction.iteritems():\n self.q[('r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n for cj in cjDict.keys():\n self.q[('dr',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('w*dV_r',ci)] = self.q[('w*dV',ci)]\n self.q[('dt*w*dV_r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.ebqe[('r',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n #m\n for ci,cjDict in self.coefficients.mass.iteritems():\n self.q[('m',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n for cj in cjDict.keys():\n self.q[('dm',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('mt',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('m_last',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('m_tmp',ci)] = self.q[('m',ci)]\n self.q[('cfl',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('numDiff',ci,ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.ebqe[('m',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n for cj in cjDict.keys():\n self.ebqe[('dm',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n\n ###ellam specific options with defauls here\n self.ellamDiscretization = ELLAMtools.ELLAMdiscretization(self,options)\n\n #\n self.needEBQ = options.needEBQ #could need for analytical velocity evaluation with RT0,BDM\n\n #beg normal stuff allocating things\n self.points_elementBoundaryQuadrature= set()\n self.scalars_elementBoundaryQuadrature= set([('u',ci) for ci in range(self.nc)])\n self.vectors_elementBoundaryQuadrature= set()\n self.tensors_elementBoundaryQuadrature= set()\n\n if self.needEBQ:\n for k in ['x','hat(x)']:\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n 3),'d')\n self.ebq['n'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global),'d')\n self.ebq['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),'d')\n #allocate the metric tensor\n self.ebq['g'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n max(1,self.nSpace_global-1),\n max(1,self.nSpace_global-1)),\n 'd')\n log(memory(\"element boundary quadrature\",\"LADRellam\"),level=4)\n ebq_keys = ['sqrt(det(g))']\n ebq_keys.extend([('u',ci) for ci in range(self.nc)])\n for k in ebq_keys:\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n #test and trial info\n self.ebq[('w',0)] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nDOF_trial_element[0]),'d')\n for ci in range(1,self.nc):\n self.ebq[('w',ci)] = self.ebq[('w',0)]\n for ci in range(self.nc):\n self.ebq[('v',ci)] = self.ebq[('w',0)]\n\n #ebq_global info\n self.ebq_global['x'] = numpy.zeros((self.mesh.nElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n 3),'d')\n self.ebq_global['n'] = numpy.zeros((self.mesh.nElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global),'d')\n #\n # allocate residual and Jacobian storage\n #\n self.elementResidual = [numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci]),\n 'd') for ci in range(self.nc)]\n self.elementSpatialResidual = [numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci]),\n 'd') for ci in range(self.nc)]\n self.elementJacobian = {}\n for ci in range(self.nc):\n self.elementJacobian[ci]={}\n for cj in range(self.nc):\n if cj in self.coefficients.stencil[ci]:\n self.elementJacobian[ci][cj] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci],\n self.nDOF_trial_element[cj]),\n 'd')\n #\n self.fluxJacobian_exterior = {}\n for ci in range(self.nc):\n self.fluxJacobian_exterior[ci]={}\n for cj in self.coefficients.stencil[ci]:\n self.fluxJacobian_exterior[ci][cj] = numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nDOF_trial_element[cj]),\n 'd')\n\n #\n #\n #\n #\n log(memory(\"element and element boundary Jacobians\",\"OneLevelTransport\"),level=4)\n self.inflowBoundaryBC = {}\n self.inflowBoundaryBC_values = {}\n self.inflowFlux = {}\n for cj in range(self.nc):\n self.inflowBoundaryBC[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,),'i')\n self.inflowBoundaryBC_values[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nDOF_trial_element[cj]),'d')\n self.inflowFlux[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n self.internalNodes = set(range(self.mesh.nNodes_global))\n #identify the internal nodes this is ought to be in mesh\n ##\\todo move this to mesh\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\n eN_global = self.mesh.elementBoundaryElementsArray[ebN,0]\n ebN_element = self.mesh.elementBoundaryLocalElementBoundariesArray[ebN,0]\n for i in range(self.mesh.nNodes_element):\n if i != ebN_element:\n I = self.mesh.elementNodesArray[eN_global,i]\n self.internalNodes -= set([I])\n self.nNodes_internal = len(self.internalNodes)\n self.internalNodesArray=numpy.zeros((self.nNodes_internal,),'i')\n for nI,n in enumerate(self.internalNodes):\n self.internalNodesArray[nI]=n\n #\n del self.internalNodes\n self.internalNodes = None\n log(\"Updating local to global mappings\",2)\n self.updateLocal2Global()\n log(\"Building time integration object\",2)\n log(memory(\"inflowBC, internalNodes,updateLocal2Global\",\"OneLevelTransport\"),level=4)\n #mwf for interpolating subgrid error for gradients etc\n if self.stabilization and self.stabilization.usesGradientStabilization:\n self.timeIntegration = TimeIntegrationClass(self,integrateInterpolationPoints=True)\n else:\n self.timeIntegration = TimeIntegrationClass(self)\n\n if options != None:\n self.timeIntegration.setFromOptions(options)\n log(memory(\"TimeIntegration\",\"OneLevelTransport\"),level=4)\n log(\"Calculating numerical quadrature formulas\",2)\n self.calculateQuadrature()\n #lay out components/equations contiguously for now\n self.offset = [0]\n for ci in range(1,self.nc):\n self.offset += [self.offset[ci-1]+self.nFreeDOF_global[ci-1]]\n self.stride = [1 for ci in range(self.nc)]\n #use contiguous layout of components for parallel, requires weak DBC's\n comm = Comm.get()\n self.comm=comm\n if comm.size() > 1:\n assert numericalFluxType != None and numericalFluxType.useWeakDirichletConditions,\"You must use a numerical flux to apply weak boundary conditions for parallel runs\"\n self.offset = [0]\n for ci in range(1,self.nc):\n self.offset += [ci]\n self.stride = [self.nc for ci in range(self.nc)]\n #\n log(memory(\"stride+offset\",\"OneLevelTransport\"),level=4)\n if numericalFluxType != None:\n if options == None or options.periodicDirichletConditions == None:\n self.numericalFlux = numericalFluxType(self,\n dofBoundaryConditionsSetterDict,\n advectiveFluxBoundaryConditionsSetterDict,\n diffusiveFluxBoundaryConditionsSetterDictDict)\n else:\n self.numericalFlux = numericalFluxType(self,\n dofBoundaryConditionsSetterDict,\n advectiveFluxBoundaryConditionsSetterDict,\n diffusiveFluxBoundaryConditionsSetterDictDict,\n options.periodicDirichletConditions)\n else:\n self.numericalFlux = None\n #set penalty terms\n #cek todo move into numerical flux initialization\n if self.ebq_global.has_key('penalty'):\n for ebN in range(self.mesh.nElementBoundaries_global):\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\n self.ebq_global['penalty'][ebN,k] = self.numericalFlux.penalty_constant/(self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power)\n #penalty term\n #cek move to Numerical flux initialization\n if self.ebqe.has_key('penalty'):\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\n self.ebqe['penalty'][ebNE,k] = self.numericalFlux.penalty_constant/self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power\n log(memory(\"numericalFlux\",\"OneLevelTransport\"),level=4)\n self.elementEffectiveDiametersArray = self.mesh.elementInnerDiametersArray\n #use post processing tools to get conservative fluxes, None by default\n import PostProcessingTools\n self.velocityPostProcessor = PostProcessingTools.VelocityPostProcessingChooser(self)\n log(memory(\"velocity postprocessor\",\"OneLevelTransport\"),level=4)\n #helper for writing out data storage\n import Archiver\n self.elementQuadratureDictionaryWriter = Archiver.XdmfWriter()\n self.elementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\n self.exteriorElementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\n #TODO get rid of this\n for ci,fbcObject in self.fluxBoundaryConditionsObjectsDict.iteritems():\n self.ebqe[('advectiveFlux_bc_flag',ci)] = numpy.zeros(self.ebqe[('advectiveFlux_bc',ci)].shape,'i')\n for t,g in fbcObject.advectiveFluxBoundaryConditionsDict.iteritems():\n if self.coefficients.advection.has_key(ci):\n self.ebqe[('advectiveFlux_bc',ci)][t[0],t[1]] = g(self.ebqe[('x')][t[0],t[1]],self.timeIntegration.t)\n self.ebqe[('advectiveFlux_bc_flag',ci)][t[0],t[1]] = 1\n\n if hasattr(self.numericalFlux,'setDirichletValues'):\n self.numericalFlux.setDirichletValues(self.ebqe)\n if not hasattr(self.numericalFlux,'isDOFBoundary'):\n self.numericalFlux.isDOFBoundary = {}\n for ci in range(self.nc):\n self.numericalFlux.isDOFBoundary[ci]= numpy.zeros(self.ebqe[('u',ci)].shape,'i')\n if not hasattr(self.numericalFlux,'ebqe'):\n self.numericalFlux.ebqe = {}\n for ci in range(self.nc):\n self.numericalFlux.ebqe[('u',ci)]= numpy.zeros(self.ebqe[('u',ci)].shape,'d')","def forward(self,y,xt,D,h,param):\n \n \n h_flip=h.flip(2,3)\n a=conv(h,xt)-y \n b=xt-self.learning_rate*conv(h_flip,a)\n u=tor.matmul(D,tor.matmul(b,D.transpose(2,3)))\n u_=rho(u,param)\n xt1=tor.matmul(D.transpose(2,3),tor.matmul(u_,D))\n \n return xt1","def __init__(self, parameters, mesh_name, facet_name,\n bc_dict={\"obstacle\": 2, \"channel_walls\": 1, \"inlet\": 3,\n \"outlet\": 4}):\n self.bc_dict = bc_dict\n self.mesh = df.Mesh()\n with df.XDMFFile(mesh_name) as infile:\n infile.read(self.mesh)\n\n mvc = df.MeshValueCollection(\"size_t\", self.mesh,\n self.mesh.topology().dim() - 1)\n with df.XDMFFile(facet_name) as infile:\n infile.read(mvc, \"name_to_read\")\n self.mf = mf = df.cpp.mesh.MeshFunctionSizet(self.mesh, mvc)\n\n self.V = V = df.VectorFunctionSpace(self.mesh, 'P',\n parameters[\"degree velocity\"])\n self.Q = Q = df.FunctionSpace(self.mesh, 'P',\n parameters[\"degree pressure\"])\n self.rho = df.Constant(parameters[\"density [kg/m3]\"])\n self.mu = df.Constant(parameters[\"viscosity [Pa*s]\"])\n self.dt = df.Constant(parameters[\"dt [s]\"])\n self.g = df.Constant((0, 0))\n self.vu, self.vp = df.TestFunction(V), df.TestFunction(Q)\n self.u_, self.p_ = df.Function(V), df.Function(Q)\n self.u_1, self.p_1 = df.Function(V), df.Function(Q)\n self.u_k, self.p_k = df.Function(V), df.Function(Q)\n self.u, self.p = df.TrialFunction(V), df.TrialFunction(Q) # unknown!\n\n self.U_m = U_m = parameters[\"velocity [m/s]\"]\n x = [0, .41 / 2] # center of the channel\n Ucenter = 4.*U_m*x[1]*(.41-x[1])/(.41*.41)\n U0_str = \"4.*U_m*x[1]*(.41-x[1])/(.41*.41)\"\n self.U_mean = np.mean(2 / 3 * Ucenter)\n\n U0 = df.Expression((U0_str, \"0\"), U_m=U_m, degree=2)\n bc0 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\"obstacle\"])\n bc1 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\"channel_walls\"])\n bc2 = df.DirichletBC(V, U0, mf, bc_dict[\"inlet\"])\n bc3 = df.DirichletBC(Q, df.Constant(0), mf, bc_dict[\"outlet\"])\n self.bcu = [bc0, bc1, bc2]\n self.bcp = [bc3]\n self.ds_ = df.Measure(\"ds\", domain=self.mesh, subdomain_data=mf)\n return","def tde(ax, col, legend):\n z = 0.354\n d = Planck15.luminosity_distance(z=z).cgs.value\n\n # In the Eftekhari paper, it says that although the event was first\n # triggered by Swift/BAT on 2011 March 28.55 UT, subsequent\n # analysis of the BAT data revealed discernible emission as early as\n # 2011 March 25. All times should therefore be shifted relative to Mar 25.5\n\n # Need to add 3.04 to the Zauderer points\n nu, dt, f, ef, islim = zauderer()\n t = (dt+3.04)/(1+z)\n\n # Low frequency\n nu_plt = 4.9E9\n choose = np.logical_and(~islim, nu == nu_plt/1E9)\n dt_all = t[choose]\n nufnu_all = nu_plt*f[choose]\n\n # adding the set from Berger2012\n # and making the same correction as above\n # this is 4.9 GHz\n t = (np.array([3.87, 4.76, 5.00, 5.79, 6.78, 7.77, 9.79, 14.98, 22.78,\n 35.86, 50.65, 67.61, 94.64, 111.62, 126.51, 143.62, 164.38, 174.47,\n 197.41, 213.32])) / (1+z)\n f = np.array([0.25, 0.34, 0.34, 0.61, 0.82, 1.48, 1.47, 1.80, 2.10, 4.62,\n 4.84, 5.86, 9.06, 9.10, 9.10, 11.71, 12.93, 12.83, 13.29, 12.43])\n\n # Berger 2012: use the 8.4 GHz light curve, since that's closest in freq\n #t = (np.array([14.97, 127.69, 159.77, 174.47, 177.50, 197.41, 213.32, 219.22]))/(1+z)\n #f = np.array([5.49, 19.03, 22.15, 23.19, 23.65, 22.42, 22.04, 21.52])\n #dt_all = np.append(dt_all, t)\n #nufnu_all = np.append(nufnu_all, f*nu_plt)\n\n # adding the set from Zauderer2013\n # they also say it's relative to March 25.5...\n # so I think I need to subtract 3.04 days from here too\n t = (np.array([245.23, 302.95, 383.92, 453.66, 582.31]))/(1+z)\n f = np.array([12.17, 12.05, 12.24, 11.12, 8.90])\n dt_all = np.append(dt_all, t)\n nufnu_all = np.append(nufnu_all, f*nu_plt)\n\n # adding the set from Eftekhari 2018\n t = np.array([645, 651.1, 787.6, 1032, 1105, 1373, 1894])\n f = np.array([8.24, 8.63, 6.23, 4.21, 3.52, 2.34, 1.47])\n dt_all = np.append(dt_all, t)\n nufnu_all = np.append(nufnu_all, f*nu_plt)\n\n order = np.argsort(dt_all)\n lum = plot_line(\n ax, d, dt_all[order], nufnu_all[order], \n 'SwiftJ1644+57', 'TDE', col, legend)\n ax.text(dt_all[order][10], lum[10]*1.1, 'Swift J1644+57', fontsize=11,\n verticalalignment='bottom',\n horizontalalignment='left')","def TM_fluid(layer, kx, om):\n\n h = layer.d\n rho = layer.medium.rho\n K = layer.medium.K\n k = om*np.sqrt(rho/K)\n ky = np.sqrt(k**2-kx**2)\n T = np.zeros((2, 2), dtype=complex)\n T[0, 0] = np.cos(ky*h)\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\n T[1, 1] = np.cos(ky*h)\n return T","def initialize_variables(self):\n super(D2Model, self).initialize_variables()\n\n s = \"::: initializing 2D variables :::\"\n print_text(s, cls=self)\n\n # Depth below sea level :\n class Depth(Expression):\n def eval(self, values, x):\n values[0] = abs(min(0, x[2]))\n self.D = Depth(element=self.Q.ufl_element())\n \n # Enthalpy model\n self.theta_surface = Function(self.Q, name='theta_surface')\n self.theta_float = Function(self.Q, name='theta_float')\n self.theta_app = Function(self.Q, name='theta_app')\n self.theta = Function(self.Q, name='theta')\n self.theta0 = Function(self.Q, name='theta0')\n self.W0 = Function(self.Q, name='W0')\n self.thetahat = Function(self.Q, name='thetahat')\n self.uhat = Function(self.Q, name='uhat')\n self.vhat = Function(self.Q, name='vhat')\n self.what = Function(self.Q, name='what')\n self.mhat = Function(self.Q, name='mhat')\n self.rho_b = Function(self.Q, name='rho_b')\n\n # Age model \n self.age = Function(self.Q, name='age')\n self.a0 = Function(self.Q, name='a0')\n\n # Surface climate model\n self.precip = Function(self.Q, name='precip')\n\n # Stokes-balance model :\n self.u_s = Function(self.Q, name='u_s')\n self.u_t = Function(self.Q, name='u_t')\n self.F_id = Function(self.Q, name='F_id')\n self.F_jd = Function(self.Q, name='F_jd')\n self.F_ib = Function(self.Q, name='F_ib')\n self.F_jb = Function(self.Q, name='F_jb')\n self.F_ip = Function(self.Q, name='F_ip')\n self.F_jp = Function(self.Q, name='F_jp')\n self.F_ii = Function(self.Q, name='F_ii')\n self.F_ij = Function(self.Q, name='F_ij')\n self.F_iz = Function(self.Q, name='F_iz')\n self.F_ji = Function(self.Q, name='F_ji')\n self.F_jj = Function(self.Q, name='F_jj')\n self.F_jz = Function(self.Q, name='F_jz')\n self.tau_iz = Function(self.Q, name='tau_iz')\n self.tau_jz = Function(self.Q, name='tau_jz')","def car_dynamics(self,x, t, u, p):\n # f = vehicle_dynamics_ks(x, u, p)\n f = vehicle_dynamics_st(x, u, p)\n # f = vehicle_dynamics_std(x, u, p)\n # f = vehicle_dynamics_mb(x, u, p)\n return f","def __init__(self):\n # Set constants\n self.fromHztoeV = 6.58e-16\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\n self.mtoev = 1/(1.97 * 1e-7) \n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\n self.Om0 = cosmo.Om0 #total matter \n self.OLambda0 = cosmo.Ode0 # cosmological constant\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\n PSgal1h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\")\n PSgal2h = np.loadtxt(\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\")\n self.Mpc = 1e3 * const.kpc.value\n self.zmin = 0.001\n self.zmax = 30.001\n self.zbins = 301\n self.h = cosmo.h\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\n self.k_vect = PSgal1h[:,0]* self.h\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\n self.Power = self.Power1h + self.Power2h\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))","def run_main(sst, ft_qv, use_NT):\n\n dtout=10. #minutes\n end_time=8*24. #hours\n del_time=dtout*60. #seconds\n end_time=end_time*3600. #seconds\n #sst=297\n D=5.e-6 #s-1\n U=7 #m/s\n psfc=100. #kPa\n qsfc=tf.qs_tp(sst,psfc)\n ft_intercept = 292 #K\n ft_gamma = 6.e-3 #K/m\n #ft_qv = 2.e-3\n k=0.2 #entrainment efficiency\n Cd = 1.e-3 #drag coefficient\n tspan = np.arange(0.,end_time,del_time)\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\n the_tup=make_tuple(the_tup,'coeffs')\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\n\n # save time/computation by only doing calculations for the last timestep (equilibrium)\n result['time']=tspan[-1]/3600./24. #days\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\n \n # decide how to calculate entrainment\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\n if use_NT:\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\n else:\n result['went']=calc_went(result.iloc[-1],the_tup)\n\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\n\n with open('dumpmodel.csv','w') as f:\n result.to_csv(f,index=False)\n \n return None","def solve_amps(self, h, a, g):\n\n # Symmetrize T3 RHS\n g3 = ((+ g.t3\n + g.t3.transpose([1, 2, 0, 4, 5, 3])\n + g.t3.transpose([2, 0, 1, 5, 3, 4])\n + g.t3.transpose([0, 2, 1, 3, 5, 4])\n + g.t3.transpose([2, 1, 0, 5, 4, 3])\n + g.t3.transpose([1, 0, 2, 4, 3, 5])\n ) / 12)\n\n # Symmetrize T2 RHS\n g2 = 1 / 2 * (g.t2 + g.t2.transpose([1, 0, 3, 2]))\n\n # Solve\n t2 = g2 * (- cc_denom(h.f, g.t2.ndim, 'dir', 'full'))\n t3 = g3 * (- cc_denom(h.f, g.t3.ndim, 'dir', 'full'))\n\n # Symmetrize amplitudes\n t2 = 1 / 2 * (t2 + t2.transpose([1, 0, 3, 2]))\n t3 = ((+ t3\n + t3.transpose([1, 2, 0, 4, 5, 3])\n + t3.transpose([2, 0, 1, 5, 3, 4])\n + t3.transpose([0, 2, 1, 3, 5, 4])\n + t3.transpose([2, 1, 0, 5, 4, 3])\n + t3.transpose([1, 0, 2, 4, 3, 5])) / 6)\n\n return Tensors(\n t1=g.t1 * (- cc_denom(h.f, g.t1.ndim, 'dir', 'full')),\n t2=t2,\n t3=t3)","def variable_costs(dh: DataHandler):\n print(\"PtHydrogen not implemented\")\n\n scen_hor_map = dh.scenarios.horizon\n\n cost_var = dh.get(\"i_cost\").xs(\"varcost\", level=\"par_cost\")\n cost_var = cost_var.groupby([\"alltec\"]).apply(\n extract_horizon_specific_cost, scen_hor_map\n )\n cost_var = add_dimension(cost_var, dh.merge_stored_sets(\"r\"), \"r\")\n cost_var = cost_var.reorder_levels([\"alltec\", \"r\"])\n\n h2_price = dh.get(\"o_h2price_buy\")\n h2_price = add_dimension(h2_price, dh.merge_stored_sets(\"tec_h2g\"), \"alltec\")\n\n elec_price = dh.get(\"o_prices\")\n\n cost_fuel = dh.get(\"cost_fuel\")\n cost_fuel = add_dimension(cost_fuel, dh.merge_stored_sets(\"r\"), \"r\")\n cost_fuel = cost_fuel.reorder_levels([\"alltec\", \"r\"])\n\n cost_fuel.loc[h2_price.index, :] = h2_price\n\n eff = dh.get(\"eff\")\n\n co2_int = dh.get(\"co2_int\").div(1000)\n\n co2_price = dh.get(\"o_co2price\")\n\n co2_costs = co2_int * co2_price\n co2_costs.index.names = [\"alltec\", \"r\"]\n\n var_cost = (\n cost_fuel.add(co2_costs, fill_value=0).div(eff).add(cost_var, fill_value=0)\n )\n\n return var_cost","def vorticity(self):\n \n ux,_ = np.gradient(self._obj['u'],self._obj['x'],self._obj['y'],axis=(0,1))\n _,vy = np.gradient(self._obj['v'],self._obj['x'],self._obj['y'],axis=(0,1))\n # self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y'])\n self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y','t'])\n \n if len(self._obj.attrs['units']) == 4:\n vel_units = self._obj.attrs['units'][-1]\n self._obj.attrs['units'].append('1/dt')\n else:\n vel_units = self._obj.attrs['units'][-2]\n self._obj.attrs['units'][-1] = ('1/dt')\n\n\n return self._obj","def velocityVerlet(XY, yh, yt, h, n):\n for l in range(n):\n yt_temp = yt + (0.5 * h * XY.grad_log_density(yh))\n yhp1 = yh + (h * yt_temp)\n ytp1 = yt_temp + (0.5 * h * XY.grad_log_density(yhp1))\n yh = yhp1\n yt = ytp1\n\n return [yhp1, ytp1]","def ADT_QCD_LEPTON():\n\n # As input for the quark-mass ratios, we use the quark masses at MZ and the lepton masses\n ip = Num_input()\n\n mu = ip.mu_at_MZ\n md = ip.md_at_MZ\n ms = ip.ms_at_MZ\n me = ip.me\n mmu = ip.mmu\n mtau = ip.mtau\n\n # Create the ADT:\n\n gamma_hat_P63eu_Q81u = np.hstack((-16 * me**2/mu**2, np.zeros(5)))\n gamma_hat_P63muu_Q81u = np.hstack((np.zeros(1), -16 * mmu**2/mu**2, np.zeros(4)))\n gamma_hat_P63tauu_Q81u = np.hstack((np.zeros(2), -16 * mtau**2/mu**2, np.zeros(3)))\n\n gamma_hat_P63ed_Q81d = np.hstack((-16 * me**2/md**2, np.zeros(5)))\n gamma_hat_P63mud_Q81d = np.hstack((np.zeros(1), -16 * mmu**2/md**2, np.zeros(4)))\n gamma_hat_P63taud_Q81d = np.hstack((np.zeros(2), -16 * mtau**2/md**2, np.zeros(3)))\n\n gamma_hat_P63es_Q81s = np.hstack((-16 * me**2/ms**2, np.zeros(5)))\n gamma_hat_P63mus_Q81s = np.hstack((np.zeros(1), -16 * mmu**2/ms**2, np.zeros(4)))\n gamma_hat_P63taus_Q81s = np.hstack((np.zeros(2), -16 * mtau**2/ms**2, np.zeros(3)))\n\n\n\n gamma_hat_P63eu_Q82u = np.hstack((np.zeros(3), -16 * me**2/mu**2, np.zeros(2)))\n gamma_hat_P63muu_Q82u = np.hstack((np.zeros(4), -16 * mmu**2/mu**2, np.zeros(1)))\n gamma_hat_P63tauu_Q82u = np.hstack((np.zeros(5), -16 * mtau**2/mu**2))\n\n gamma_hat_P63ed_Q82d = np.hstack((np.zeros(3), -16 * me**2/md**2, np.zeros(2)))\n gamma_hat_P63mud_Q82d = np.hstack((np.zeros(4), -16 * mmu**2/md**2, np.zeros(1)))\n gamma_hat_P63taud_Q82d = np.hstack((np.zeros(5), -16 * mtau**2/md**2))\n\n gamma_hat_P63es_Q82s = np.hstack((np.zeros(3), -16 * me**2/ms**2, np.zeros(2)))\n gamma_hat_P63mus_Q82s = np.hstack((np.zeros(4), -16 * mmu**2/ms**2, np.zeros(1)))\n gamma_hat_P63taus_Q82s = np.hstack((np.zeros(5), -16 * mtau**2/ms**2))\n\n\n\n gamma_hat_P62ue_Q83u = np.hstack((-16 * me**2/mu**2, np.zeros(5)))\n gamma_hat_P62umu_Q83u = np.hstack((np.zeros(1), -16 * mmu**2/mu**2, np.zeros(4)))\n gamma_hat_P62utau_Q83u = np.hstack((np.zeros(2), -16 * mtau**2/mu**2, np.zeros(3)))\n\n gamma_hat_P62de_Q83d = np.hstack((-16 * me**2/md**2, np.zeros(5)))\n gamma_hat_P62dmu_Q83d = np.hstack((np.zeros(1), -16 * mmu**2/md**2, np.zeros(4)))\n gamma_hat_P62dtau_Q83d = np.hstack((np.zeros(2), -16 * mtau**2/md**2, np.zeros(3)))\n\n gamma_hat_P62se_Q83s = np.hstack((-16 * me**2/ms**2, np.zeros(5)))\n gamma_hat_P62smu_Q83s = np.hstack((np.zeros(1), -16 * mmu**2/ms**2, np.zeros(4)))\n gamma_hat_P62stau_Q83s = np.hstack((np.zeros(2), -16 * mtau**2/ms**2, np.zeros(3)))\n\n\n\n gamma_hat_P62ue_Q84u = np.hstack((np.zeros(3), -16 * me**2/mu**2, np.zeros(2)))\n gamma_hat_P62umu_Q84u = np.hstack((np.zeros(4), -16 * mmu**2/mu**2, np.zeros(1)))\n gamma_hat_P62utau_Q84u = np.hstack((np.zeros(5), -16 * mtau**2/mu**2))\n\n gamma_hat_P62de_Q84d = np.hstack((np.zeros(3), -16 * me**2/md**2, np.zeros(2)))\n gamma_hat_P62dmu_Q84d = np.hstack((np.zeros(4), -16 * mmu**2/md**2, np.zeros(1)))\n gamma_hat_P62dtau_Q84d = np.hstack((np.zeros(5), -16 * mtau**2/md**2))\n\n gamma_hat_P62se_Q84s = np.hstack((np.zeros(3), -16 * me**2/ms**2, np.zeros(2)))\n gamma_hat_P62smu_Q84s = np.hstack((np.zeros(4), -16 * mmu**2/ms**2, np.zeros(1)))\n gamma_hat_P62stau_Q84s = np.hstack((np.zeros(5), -16 * mtau**2/ms**2))\n\n\n\n gamma_hat_Q81u = np.vstack((gamma_hat_P63eu_Q81u, gamma_hat_P63muu_Q81u, gamma_hat_P63tauu_Q81u, np.zeros((15,6))))\n gamma_hat_Q81d = np.vstack((np.zeros((3,6)), gamma_hat_P63ed_Q81d, gamma_hat_P63mud_Q81d, gamma_hat_P63taud_Q81d, np.zeros((12,6))))\n gamma_hat_Q81s = np.vstack((np.zeros((6,6)), gamma_hat_P63es_Q81s, gamma_hat_P63mus_Q81s, gamma_hat_P63taus_Q81s, np.zeros((9,6))))\n\n gamma_hat_Q82u = np.vstack((gamma_hat_P63eu_Q82u, gamma_hat_P63muu_Q82u, gamma_hat_P63tauu_Q82u, np.zeros((15,6))))\n gamma_hat_Q82d = np.vstack((np.zeros((3,6)), gamma_hat_P63ed_Q82d, gamma_hat_P63mud_Q82d, gamma_hat_P63taud_Q82d, np.zeros((12,6))))\n gamma_hat_Q82s = np.vstack((np.zeros((6,6)), gamma_hat_P63es_Q82s, gamma_hat_P63mus_Q82s, gamma_hat_P63taus_Q82s, np.zeros((9,6))))\n\n gamma_hat_Q83u = np.vstack((np.zeros((9,6)), gamma_hat_P62ue_Q83u, gamma_hat_P62umu_Q83u, gamma_hat_P62utau_Q83u, np.zeros((6,6))))\n gamma_hat_Q83d = np.vstack((np.zeros((12,6)), gamma_hat_P62de_Q83d, gamma_hat_P62dmu_Q83d, gamma_hat_P62dtau_Q83d, np.zeros((3,6))))\n gamma_hat_Q83s = np.vstack((np.zeros((15,6)), gamma_hat_P62se_Q83s, gamma_hat_P62smu_Q83s, gamma_hat_P62stau_Q83s))\n\n gamma_hat_Q84u = np.vstack((np.zeros((9,6)), gamma_hat_P62ue_Q84u, gamma_hat_P62umu_Q84u, gamma_hat_P62utau_Q84u, np.zeros((6,6))))\n gamma_hat_Q84d = np.vstack((np.zeros((12,6)), gamma_hat_P62de_Q84d, gamma_hat_P62dmu_Q84d, gamma_hat_P62dtau_Q84d, np.zeros((3,6))))\n gamma_hat_Q84s = np.vstack((np.zeros((15,6)), gamma_hat_P62se_Q84s, gamma_hat_P62smu_Q84s, gamma_hat_P62stau_Q84s))\n\n\n\n\n gamma_hat = np.array([gamma_hat_Q81u, gamma_hat_Q81d, gamma_hat_Q81s, gamma_hat_Q82u, gamma_hat_Q82d, gamma_hat_Q82s,\n gamma_hat_Q83u, gamma_hat_Q83d, gamma_hat_Q83s, gamma_hat_Q84u, gamma_hat_Q84d, gamma_hat_Q84s])\n\n\n # Return the tensor\n\n # tensor, zeile, spalte\n\n return gamma_hat","def create_flux_vector_pms_gr(self):\n soma_prod = 0\n soma_inj = 0\n lim4 = 1e-4\n store_velocity = {}\n store_flux = {}\n for primal in self.primals:\n #1\n primal_id = self.mb.tag_get_data(self.primal_id_tag, primal, flat=True)[0]\n primal_id = self.ident_primal[primal_id]\n fine_elems_in_primal = self.mb.get_entities_by_handle(primal)\n for volume in fine_elems_in_primal:\n #2\n flux = {}\n velocity = {}\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n centroid_volume = self.mesh_topo_util.get_average_position([volume])\n z_vol = self.tz - centroid_volume[2]\n adjs_vol = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\n gid_vol = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n for adj in adjs_vol:\n #3\n gid_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\n if adj not in fine_elems_in_primal:\n #4\n pvol = self.mb.tag_get_data(self.pms_tag, volume, flat=True)[0]\n padj = self.mb.tag_get_data(self.pms_tag, adj, flat=True)[0]\n #3\n else:\n #4\n pvol = self.mb.tag_get_data(self.pcorr_tag, volume, flat=True)[0]\n padj = self.mb.tag_get_data(self.pcorr_tag, adj, flat=True)[0]\n #3\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\n centroid_adj = self.mesh_topo_util.get_average_position([adj])\n z_adj = self.tz - centroid_adj[2]\n direction = centroid_adj - centroid_volume\n unit = direction/np.linalg.norm(direction)\n #unit = vetor unitario na direcao de direction\n uni = self.unitary(direction)\n # uni = valor positivo do vetor unitario\n kvol = np.dot(np.dot(kvol,uni),uni)\n kadj = np.dot(np.dot(kadj,uni),uni)\n keq = self.kequiv(kvol, kadj)/(self.mi)\n keq2 = keq\n keq = keq*(np.dot(self.A, uni))\n pvol2 = self.mb.tag_get_data(self.pms_tag, volume, flat=True)[0]\n padj2 = self.mb.tag_get_data(self.pms_tag, adj, flat=True)[0]\n grad_p = (padj - pvol)/float(abs(np.dot(direction, uni)))\n grad_z = (z_adj - z_vol)/float(abs(np.dot(direction, uni)))\n grad_p2 = (padj2 - pvol2)/float(abs(np.dot(direction, uni)))\n q = (grad_p)*keq - grad_z*keq*self.gama\n print((grad_p)*keq)\n print(- grad_z*keq*self.gama)\n print(q)\n print(self.store_flux_pf_gr[volume][tuple(unit)])\n print('\\n')\n import pdb; pdb.set_trace()\n\n if gid_adj > gid_vol:\n v = -((grad_p2)*keq2 - grad_z*self.gama*keq2)\n else:\n v = -((grad_p2)*keq2 - grad_z*self.gama*keq2)\n\n flux[tuple(unit)] = q\n velocity[tuple(unit)] = v\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\n\n #2\n # print(gid_vol)\n # print(velocity)\n # print('\\n')\n # import pdb; pdb.set_trace()\n store_flux[volume] = flux\n self.mb.tag_set_data(self.flux_fine_pms_tag, volume, sum(flux.values()))\n # flt = sum(flux.values())\n # if volume not in self.wells_inj and volume not in self.wells_prod:\n # lim4 = 1e-7\n # if abs(flt) > lim4:\n # print(gid_vol)\n # print(flt)\n # import pdb; pdb.set_trace()\n # flt = sum(flux.values())\n store_velocity[volume] = velocity\n\n for volume in set(self.all_fine_vols) - set(self.wells):\n gid = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\n values = store_flux[volume].values()\n if sum(values) > lim4:\n print('fluxo multiescala nao esta dando conservativo')\n print('gid:{0}'.format(gid))\n print(sum(values))\n import pdb; pdb.set_trace()\n\n with open('fluxo_multiescala_gr.txt', 'w') as arq:\n for volume in self.wells:\n gid = self.mb.tag_get_data(self.global_id_tag, volume, flat= True)[0]\n values = store_flux[volume].values()\n if volume in self.wells_inj:\n soma_inj += sum(values)\n else:\n soma_prod += sum(values)\n arq.write('gid:{0} , fluxo:{1}\\n'.format(gid, sum(values)))\n arq.write('\\n')\n arq.write('soma_inj:{0}\\n'.format(soma_inj))\n arq.write('soma_prod:{0}\\n'.format(soma_prod))\n\n return store_flux","def _step(self, t, y, h):\n # We must use solvers / implicit form\n f_pn1 = lambda a_n1: (y + h*self.v + (h**2 / 2.0) * \\\n ((1.0 - 2.*self.beta)*self.a + 2.*self.beta*a_n1))\n f_vn1 = lambda a_n1: (self.v + h*((1.0-self.gamma)*self.a + self.gamma*a_n1))\n def f_an1(a_n1):\n f_n1 = self.f(t+h,f_pn1(a_n1),f_vn1(a_n1))\n f_n = self.f(t,y,self.v,)\n return a_n1 - ((1.0+self.alpha)*f_n1 - self.alpha*f_n)\n\n a = self.solver(f_an1, self.a)\n y = f_pn1(a) # Calculate and store new variables. \n self.v = f_vn1(a)\n self.a = a\n return t+h, y","def prepare_rhs(self, simulation):\n\n nv = simulation.container.nv\n sorder = simulation.container.sorder\n nspace = [1] * (len(sorder) - 1)\n v = self.stencil.get_all_velocities()\n\n gpu_support = simulation.container.gpu_support\n\n for key, value in self.value_bc.items():\n if value is not None:\n indices = np.where(self.ilabel == key)\n # TODO: check the index in sorder to be the most contiguous\n nspace[0] = indices[0].size\n k = self.istore[0, indices]\n\n s = 1 - self.distance[indices]\n coords = tuple()\n for i in range(simulation.domain.dim):\n x = simulation.domain.coords_halo[i][self.istore[i + 1, indices]]\n x += s * v[k, i] * simulation.domain.dx\n x = x.ravel()\n for j in range(\n 1, simulation.domain.dim\n ): # pylint: disable=unused-variable\n x = x[:, np.newaxis]\n coords += (x,)\n\n m = Array(nv, nspace, 0, sorder, gpu_support=gpu_support)\n m.set_conserved_moments(simulation.scheme.consm)\n\n f = Array(nv, nspace, 0, sorder, gpu_support=gpu_support)\n f.set_conserved_moments(simulation.scheme.consm)\n\n args = coords\n if isinstance(value, types.FunctionType):\n func = value\n elif isinstance(value, tuple):\n func = value[0]\n args += value[1]\n\n if self.time_bc[key]:\n func(f, m, 0, *args)\n else:\n func(f, m, *args)\n\n simulation.equilibrium(m)\n simulation.m2f(m, f)\n\n if self.generator.backend.upper() == \"LOOPY\":\n f.array_cpu[...] = f.array.get()\n\n self.feq[:, indices[0]] = f.swaparray.reshape((nv, indices[0].size))\n\n if self.time_bc[key]:\n self.func.append(func)\n self.args.append(args)\n self.f.append(f)\n self.m.append(m)\n self.indices.append(indices[0])","def kelvin_effect(pres, surft, temp, mw_ba, dcell):\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))","def trc_fgen_prefb(self,trc,dt,nspad=200,hwin=150,vlen=51):\n output=np.zeros((len(trc),((11*(vlen))+1)))\n pad=np.random.rand(nspad)/100\n trc_norm=trc/np.amax(np.abs(trc))\n trc_norm_padded=np.hstack((pad,trc_norm))\n trc_entropy=self.entropy(trc_norm_padded,50)\n trc_fdm=self.fdm(trc_norm_padded,50,np.arange(1,4),15)\n trc_slta=trigger.classic_sta_lta(trc_norm_padded,2,100)\n trc_fq_win_sum=self.fq_win_sum(trc_norm_padded,hwin,dt)\n hwin2=50\n trc_kurtosis_skew=self.kurtosis_skewness(trc_norm_padded,hwin2)\n for i,j in enumerate(trc):\n ftrc=[]\n fb=i*dt\n ftrc=np.append(ftrc,trc_norm_padded[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(np.gradient(np.abs(trc_norm_padded)))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(trc_entropy)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_entropy))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1]) \n ftrc=np.append(ftrc,self.norm(trc_fdm)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_fdm))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1]) \n ftrc=np.append(ftrc,self.norm(trc_slta)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(trc_fq_win_sum)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_fq_win_sum))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(trc_kurtosis_skew[0])[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,self.norm(trc_kurtosis_skew[1])[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\n ftrc=np.append(ftrc,1)\n output[i,:]=ftrc\n return output","def _calc_var(self, f, a, b, z):\n\n c_1 = (t.exp(a[...,0] * f[...,0] + b[...,0]) *\n (a[...,0]**2 * f[...,0]**2\n - 2 * a[...,0] * f[...,0] + 2)\n ) / a[...,0]**3\n c_2 = ((t.exp(a[...,1:-1] * f[...,1:] + b[...,1:-1]) *\n (a[...,1:-1]**2 * f[...,1:]**2 - 2 * a[...,1:-1] * f[...,1:] + 2) -\n t.exp(a[...,1:-1] * f[...,:-1] + b[...,1:-1]) *\n (a[...,1:-1]**2 * f[...,:-1]**2 - 2 * a[...,1:-1] * f[...,:-1] + 2)\n ) / a[...,1:-1]**3).sum(-1)\n c_3 = (t.exp(a[...,-1] * f[...,-1] + b[...,-1]) *\n (a[...,-1]**2 * f[...,-1]**2 - 2 * a[...,-1] * f[...,-1] + 2)\n ) / a[...,-1]**3\n\n return 1/z * (c_1 + c_2 + c_3)","def _diff_wep(a,t,dh,wair,entr,pres):\n ph = _eq_pressure(0,0,0,a,t,dh)\n gi = _ice_g(0,0,t,pres)\n gv = _eq_vappot(0,0,0,a,t,dh)\n sh = -_air_f(0,1,0,a,t,dh)\n si = -_ice_g(1,0,t,pres)\n stot = wair/a*sh + (1-wair/a)*si\n lhs = numpy.array([pres, gi, entr])\n rhs = numpy.array([ph, gv, stot])\n \n ph_a = _eq_pressure(1,0,0,a,t,dh)\n ph_t = _eq_pressure(0,1,0,a,t,dh)\n ph_d = _eq_pressure(0,0,1,a,t,dh)\n gi_t = _ice_g(1,0,t,pres)\n gv_a = _eq_vappot(1,0,0,a,t,dh)\n gv_t = _eq_vappot(0,1,0,a,t,dh)\n gv_d = _eq_vappot(0,0,1,a,t,dh)\n sh_a = -_air_f(1,1,0,a,t,dh)\n sh_t = -_air_f(0,2,0,a,t,dh)\n sh_d = -_air_f(0,1,1,a,t,dh)\n si_t = -_ice_g(2,0,t,pres)\n s_a = -wair/a**2 * (sh - a*sh_a - si)\n s_t = wair/a*sh_t + (1-wair/a)*si_t\n s_d = wair/a*sh_d\n dlhs = numpy.array([[0.,0.,0.], [0.,gi_t,0.], [0.,0.,0.]])\n drhs = numpy.array([[ph_a,ph_t,ph_d], [gv_a,gv_t,gv_d], [s_a,s_t,s_d]])\n return lhs, rhs, dlhs, drhs","def init_amplitudes(self, ham):\n e_ai = cc_denom(ham.f, 2, 'dir', 'full')\n e_abij = cc_denom(ham.f, 4, 'dir', 'full')\n nocc = self.mos.nocc\n nvir = self.mos.nvir\n\n t1 = ham.f.ov.transpose().conj() * (- e_ai)\n v_vovo = einsum(\"pia,pjb->aibj\", ham.l.pov, ham.l.pov).conj()\n\n t2 = v_vovo.transpose([0, 2, 1, 3]) * (- e_abij)\n t3 = np.zeros((nvir,) * 3 + (nocc,) * 3)\n\n return Tensors(t1=t1, t2=t2, t3=t3)","def init_amplitudes(self, ham):\n e_ai = cc_denom(ham.f, 2, 'dir', 'full')\n e_abij = cc_denom(ham.f, 4, 'dir', 'full')\n nocc = self.mos.nocc\n nvir = self.mos.nvir\n\n t1 = ham.f.ov.transpose().conj() * (- e_ai)\n v_vovo = einsum(\"pia,pjb->aibj\", ham.l.pov, ham.l.pov).conj()\n\n t2 = v_vovo.transpose([0, 2, 1, 3]) * (- e_abij)\n t3 = np.zeros((nvir,) * 3 + (nocc,) * 3)\n\n return Tensors(t1=t1, t2=t2, t3=t3)"],"string":"[\n \"def ha(env, cstate=0):\\n T1 = 10\\n T2 = 10\\n thM = 20\\n thm = 5\\n vr = 10.5\\n v1 = -1.3\\n v2 = -2.7\\n assert(T1 == T2)\\n\\n delta = None # None to cause failure\\n # The continous variables used in this ha\\n x = T1 # clock1 variable\\n y = T2 # clock2 variable\\n th = 11.5 # The reactor temperature\\n\\n # You need vtol here, because of floating point error.\\n loc0_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc0_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc0_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(vr),\\n ttol=10**-3, iterations=100, vtol=10**-10)\\n loc0_FT = False\\n\\n loc1_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc1_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc1_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(v1),\\n ttol=10**-3, iterations=100, vtol=10**-10)\\n loc1_FT = False\\n\\n loc2_ode_x = ODE(env, S.sympify('diff(x(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc2_ode_y = ODE(env, S.sympify('diff(y(t))'), S.sympify('1.0'),\\n ttol=10**-3, iterations=100)\\n loc2_ode_th = ODE(env, S.sympify('diff(th(t))'), S.sympify(v2),\\n ttol=10**-3, iterations=100, vtol=10**-10)\\n loc2_FT = False\\n\\n # Location 3 is reactor shutdown\\n loc3_FT = False\\n\\n # Location 0\\n def location0(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\\n vals = {S.sympify('x(t)'): x,\\n S.sympify('y(t)'): y,\\n S.sympify('th(t)'): th}\\n curr_time = env.now\\n # The edge guard takes preference\\n if th == thM and x >= T1:\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n return 1, 0, x, y, th, None, True, None, None, curr_time\\n elif th == thM and y >= T2:\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n return 2, 0, x, y, th, None, None, True, None, curr_time\\n elif th == thM and x < T1 and y < T2:\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n return 3, 0, x, y, th, None, None, None, True, curr_time\\n # The invariant\\n elif th <= thM:\\n if not loc0_FT:\\n x = loc0_ode_x.compute(vals, curr_time-prev_time)\\n y = loc0_ode_y.compute(vals, curr_time-prev_time)\\n th = loc0_ode_th.compute(vals, curr_time-prev_time)\\n loc0_FT = True\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n if abs(th-thM) > loc0_ode_th.vtol:\\n deltath = loc0_ode_th.delta(vals, quanta=(thM-th))\\n else:\\n th = thM\\n deltath = 0\\n return 0, deltath, x, y, th, False, None, None, None, curr_time\\n else:\\n # print('th:', th)\\n raise RuntimeError('Reached unreachable branch'\\n ' in location 0')\\n\\n def location1(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\\n vals = {S.sympify('x(t)'): x,\\n S.sympify('y(t)'): y,\\n S.sympify('th(t)'): th}\\n curr_time = env.now\\n # The edge guard takes preference\\n if th == thm:\\n x = 0 # Reset\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n return 0, 0, x, y, th, True, None, None, None, curr_time\\n # The invariant\\n elif th >= thm:\\n if not loc1_FT:\\n x = loc1_ode_x.compute(vals, curr_time-prev_time)\\n y = loc1_ode_y.compute(vals, curr_time-prev_time)\\n th = loc1_ode_th.compute(vals, curr_time-prev_time)\\n loc1_FT = True\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n if abs(th-thm) > loc1_ode_th.vtol:\\n deltath = loc1_ode_th.delta(vals, quanta=(thm-th))\\n else:\\n th = thm\\n deltath = 0\\n return 1, deltath, x, y, th, False, None, None, None, curr_time\\n else:\\n raise RuntimeError('Reached unreachable branch'\\n ' in location 1')\\n\\n def location2(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\\n vals = {S.sympify('x(t)'): x,\\n S.sympify('y(t)'): y,\\n S.sympify('th(t)'): th}\\n curr_time = env.now\\n # The edge guard takes preference\\n if th == thm:\\n y = 0 # Reset\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n return 0, 0, x, y, th, True, None, None, None, curr_time\\n # The invariant\\n elif th >= thm:\\n if not loc2_FT:\\n x = loc2_ode_x.compute(vals, curr_time-prev_time)\\n y = loc2_ode_y.compute(vals, curr_time-prev_time)\\n th = loc2_ode_th.compute(vals, curr_time-prev_time)\\n loc2_FT = True\\n # print('%7.4f %7.4f %7.4f %7.4f' % (curr_time, x, y, th))\\n if abs(th-thm) > loc2_ode_th.vtol:\\n deltath = loc2_ode_th.delta(vals, quanta=(thm-th))\\n else:\\n th = thm\\n deltath = 0\\n return 2, deltath, x, y, th, False, None, None, None, curr_time\\n else:\\n raise RuntimeError('Reached unreachable branch'\\n ' in location 2')\\n\\n def location3(x, y, th, loc0_FT, loc1_FT, loc2_FT, loc3_FT, prev_time):\\n global step\\n # print('total steps: ', step)\\n # Done\\n print(time.time()-start)\\n sys.exit(1)\\n\\n # The dictionary for the switch statement.\\n switch_case = {\\n 0: location0,\\n 1: location1,\\n 2: location2,\\n 3: location3\\n }\\n\\n prev_time = env.now\\n while(True):\\n (cstate, delta, x, y, th,\\n loc0_FT, loc1_FT, loc2_FT, loc3_FT,\\n prev_time) = switch_case[cstate](x, y, th,\\n loc0_FT,\\n loc1_FT,\\n loc2_FT,\\n loc3_FT,\\n prev_time)\\n # This should always be the final statement in this function\\n global step\\n step += 1\\n yield env.timeout(delta)\",\n \"def calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0):\\n thetal_m = the_vars[0]\\n qt_m = the_vars[2]\\n zi = the_vars[1]\\n dth = deltheta\\n \\n thetal_ft = thetal_m + dth\\n qt_ft = coeffs.ft_qv\\n \\n dqt = qt_ft - qt_m\\n \\n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\\n gamma = 6e-3 \\n thetal_3000 = thetal_ft + gamma*(3000-zi)\\n LTS = thetal_3000 - coeffs.sst # lower tropospheric stability\\n\\n # calculate coefficients\\n press=tf.find_press(zi)\\n Ad,Bd,issat = tf.calc_ABcoeffs(thetal_ft,qt_ft,press)\\n Aw,Bw,issat = tf.calc_ABcoeffs(thetal_m,qt_m,press)\\n \\n invert= tf.t_uos_thetal(thetal_m,qt_m,press)\\n T_0 = invert.temp\\n lv=tf.L_t(invert.temp)\\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\\n del_thv_dry = Ad * dth + Bd * dqt\\n del_thv_sat = Aw * dth + Bw * dqt\\n \\n # account for evaporative cooling (increases we)\\n ql_max = invert.ql\\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\\n Del_thv = del_thv_dry - Cl * ql_max\\n \\n # calculate buoyancy integral terms\\n rho = 1.\\n lcl_press=tf.LCL_thetal(thetal_m,qt_m)\\n zb=tf.find_height(lcl_press)\\n\\n T1 = zb/zi\\n T2 = 0.5 * zb**2 / zi**2\\n T3 = (zi-zb)/zi\\n T4 = 0.5 * (zi**2 - zb**2) / zi**2\\n \\n # calculate delta_Fr\\n delta_Frstar = 82.0 # Wm^-2\\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\\n\\n wtl_0=F0\\n wqt_0=Fqv0\\n Del_F = delta_Fr/(tc.CPD*rho) # use sensitivity to radiation a la Gesso Fig. 3\\n term1 = wtl_0 * (Ad * (T1-T2) + Aw * (T3-T4))\\n term2 = wqt_0 * (Bd * (T1-T2) + Bw * (T3-T4))\\n term3 = Del_F * (Ad * T2 + Aw * T4)\\n\\n Theta_NE = term1 + term2 + term3\\n \\n # calculate w*\\n wstar=(2.5*9.8/T_0*zi*Theta_NE)**(1/3.)\\n \\n # calculate chi*\\n chi_star = Cl * ql_max / (del_thv_dry - del_thv_sat)\\n \\n # calculate del_m\\n Del_m = del_thv_dry + chi_star * (2. - chi_star) * (del_thv_sat - del_thv_dry)\\n \\n # calculate we\\n a2=15.\\n Del_thv_NT = Del_thv / (1. + a2 * (1. - Del_m/Del_thv))\\n \\n A_NT = 0.2\\n fac_NT = 2.5\\n\\n term4 = Del_thv_NT\\n term5 = A_NT * fac_NT * (T2 * del_thv_dry + T4 * del_thv_sat)\\n denominator = term4 + term5\\n\\n we = A_NT * fac_NT * Theta_NE / denominator\\n \\n return we\",\n \"def dewT_2_q_magnus(ds, var):\\n A1, B1, C1 = 17.625, 243.04, 610.94\\n vpsl = C1 * np.exp(A1 * (ds[var['dew']] - 273.15) / (B1 + (ds[var['dew']] - 273.15)))\\n wsl = eps0 * vpsl / (ds[var['pressure']] - vpsl)\\n ds[var['spec_h']] = wsl / (1 + wsl)\\n return ds\",\n \"def _redef_sp1_vars(self):\\r\\n\\r\\n if len(self.fq_list) == 0:\\r\\n no_rad = True\\r\\n lst_tmp = np.matrix(np.reshape(self.lst_tmp, \\r\\n (self.lst_tmp.size, 1)))\\r\\n else: no_rad = False\\r\\n # The practically constants...\\r\\n # Big Epsilon:\\r\\n if self.cond == True:\\r\\n self.Epsilon = self.d_T * self.thermal_conductivity\\r\\n else:\\r\\n self.Epsilon = (self.diff_scale ** 2) / \\\\\\r\\n (3.0 * self.absorb_coeffs[self.rad] ** 2)\\r\\n # Beta:\\r\\n if self.cond == True:\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff)\\r\\n else:\\r\\n self.Beta = (1.0 + 3.0 * self.r2) * (2.0 * self.diff_scale) / \\\\\\r\\n ((1.0 - 2.0 * self.r1) * (\\r\\n 3.0 * self.absorb_coeffs[self.rad]))\\r\\n\\r\\n # The feild solutions at the last timestep.\\r\\n # The integral vF:\\r\\n if self.cond == True:\\r\\n # The horrifically complicated F:\\r\\n def F_func_cond(elem, eta):\\r\\n F = 0.0\\r\\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F += Tn\\r\\n for k in range(0, len(self.fq_list)):\\r\\n vk = self.fq_list[k]\\r\\n try:\\r\\n vk_m = self.fq_list[k - 1]\\r\\n except:\\r\\n vk_m = self.v0_frequency\\r\\n absorbtion = self.absorb_coeffs[k]\\r\\n phi = elem.eval_elem(self.node_map, self.lst_rad[k],\\r\\n [eta])[0]\\r\\n inter1 = phi - 4.0 * sconst.pi * \\\\\\r\\n self.B_int_function(Tn, self.refr_idx_vol,\\r\\n vk, vk_m)\\r\\n inter2 = absorbtion * self.d_T / (self.diff_scale ** 2)\\r\\n F += inter2 * inter1\\r\\n return elem.funcs(eta) * F\\r\\n if not no_rad:\\r\\n # We're integrating something non-linear for SP1\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func_cond,\\r\\n self.node_map)\\r\\n else:\\r\\n # Or something easier if we're only looking at heat.\\r\\n self.vF_vect_vol = np.array(self.uv_vol * lst_tmp).reshape(-1)\\r\\n else:\\r\\n def F_func_radiative(elem, eta):\\r\\n T = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n vk = self.fq_list[self.rad]\\r\\n try:\\r\\n vk_minus = self.fq_list[self.rad - 1]\\r\\n except:\\r\\n vk_minus = self.v0_frequency\\r\\n n = self.refr_idx_vol\\r\\n F = 4.0 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func_radiative,\\r\\n self.node_map)\\r\\n # The path integral vf:\\r\\n if self.cond == True:\\r\\n def f_func_cond(elem, eta):\\r\\n Tb = self.background_temperature\\r\\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n n = self.refr_idx_background\\r\\n vk = self.v0_frequency\\r\\n vk_minus = 0\\r\\n Bb0 = self.B_int_function(Tb, n, vk, vk_minus)\\r\\n Bn0 = self.B_int_function(Tn, n, vk, vk_minus)\\r\\n B_coeff = (self.alpha * sconst.pi) / self.convect_coeff\\r\\n f = Tb + B_coeff * (Bb0 - Bn0)\\r\\n return elem.funcs(eta) * f\\r\\n if not no_rad:\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func_cond,\\r\\n self.node_map)\\r\\n else:\\r\\n try:\\r\\n self.vf_vect_bound = self.cache_tb_integral_array\\r\\n except AttributeError:\\r\\n def elem_functor(elem, eta): return elem.funcs(eta)\\r\\n self.cache_tb_integral_array = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n elem_functor,\\r\\n self.node_map)\\r\\n self.cache_tb_integral_array *= self.background_temperature\\r\\n self.vf_vect_bound = self.cache_tb_integral_array\\r\\n \\r\\n else:\\r\\n # Radiation f = 4*pi*B^{(k)}(T_b, n_g)\\r\\n def f_func_radiative(elem, eta):\\r\\n T = self.background_temperature\\r\\n vk = self.fq_list[self.rad]\\r\\n try:\\r\\n vk_minus = self.fq_list[self.rad - 1]\\r\\n except:\\r\\n vk_minus = self.v0_frequency\\r\\n n = self.refr_idx_vol\\r\\n f = 4 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func_radiative,\\r\\n self.node_map)\\r\\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\\r\\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\\r\\n assert (self.vf_vect_bound.shape[0] == \\\\\\r\\n self.vF_vect_vol.shape[0])\",\n \"def getdelta(self):\\n\\t\\tmyhmag.initializehelmholtz()\\n\\t\\tabar = 13.714285714285715\\n\\t\\tzbar = abar/2.0\\n\\t\\tself.data[\\\"delta\\\"] = np.zeros(len(self.data[\\\"rho\\\"]))\\n\\t\\tfor i in range(len(self.data[\\\"rho\\\"])):\\n\\t\\t\\tadgradred,hydrograd,my_nu,my_alpha,self.data[\\\"delta\\\"][i],my_gamma1,my_cp,my_cph,my_c_s,failtrig = myhmag.gethelmgrads(self.data[\\\"T\\\"][i], self.data[\\\"rho\\\"][i], 0.,abar,zbar,True)\",\n \"def __init__(self,b,u,v,hbls_old,hbbl_old,Kv_old,Kt_old,srflx,sustr,svstr,f,grid_dict,tstep_mode,dt):\\n \\n # INPUTS FROM TTTW SYSTEM\\n self.b = b #buoyancy field: [Ly,N]\\n self.u = u # x-component of velocity [Ly,N]\\n self.v = v # y-component of velocity [Ly+1,N]\\n self.hbls_old = hbls_old #boundary layer depth from previous time step [Ly]\\n self.hbbl_old = hbbl_old # bottom boundary layer depth from previous time step [Ly]\\n self.Kv_old = Kv_old # momentum mixing coefficeint from previous time step [Ly,N+1]\\n self.Kt_old = Kt_old # tracer mixing coefficient from previous time step [Ly,N+1]\\n self.srflx = srflx #solar heat flux [Ly] (degC * (m/s))\\n self.sustr = sustr # x-component surface wind stress [Ly] (N/m^2) \\n self.svstr = svstr # y-component surface wind stress [Ly+1] (N/m^2)\\n self.grid_dict = grid_dict #gridded data\\n self.f = f #coriolis parameter\\n # KPP-SPECIFIC VARIABLES \\n self.hbls = np.zeros([self.b.shape[0]])\\n self.hbbl = np.zeros([self.b.shape[0]])\\n self.ustar = []\\n self.bvf = [] \\n self.kmo = []\\n self.C_h_MO = []\\n self.kbl = []\\n self.Cr = [] \\n self.Fc = []\\n self.ghat = [] #NONLOCAL TERM: TO BE USED IN TIME STEPPING\\n self.tstep_mode = tstep_mode# if in time steppign mode, turn on HBL_RATE_LIMIT\\n self.dt = dt\",\n \"def CalcForce_aeroframe_DEP(V, CoefMatrix, x, rho, g):\\r\\n\\r\\n #Compute aero forces\\r\\n # here x must be of the form (alpha, beta, p, q, r, da, dr, de) (last one punctualy used)\\r\\n # set non dim for p,q,r\\r\\n nonDim=np.ones(7)\\r\\n nonDim[2]=g.b/(2*V)\\r\\n nonDim[3]=g.c/(2*V)\\r\\n nonDim[4]=g.b/(2*V)\\r\\n # F=np.dot(CoefMatrix,x[0:7]) # commented form, modification to account for symmetric drag increase of side slip\\r\\n F=np.zeros((3))\\r\\n M=np.zeros((3))\\r\\n xsym=np.copy(x[0:-1])\\r\\n xsym[1]=abs(xsym[1]) # make beta always positive since derivatives have already correct sign for drag and lift only\\r\\n xsym[-3]=abs(xsym[-3]) # make ailerons deflection always positive for drag increase and lift decrease\\r\\n xsym[-1]=abs(xsym[-1]) # make rudder deflection always positive for drag increase and lift decrease\\r\\n F[0]=np.dot(CoefMatrix[0],xsym)\\r\\n F[1]=np.dot(CoefMatrix[1],x[0:-1]) #side force\\r\\n F[2]=np.dot(CoefMatrix[2],xsym)\\r\\n M=np.dot(CoefMatrix[3:6,:],x[0:-1])\\r\\n# print(\\\"Printing moment coeff\\\")\\r\\n# print(M)\\r\\n\\r\\n \\r\\n #No need to project\\r\\n# alpha=x[0]\\r\\n# beta=x[1]\\r\\n# H=np.array([[math.cos(alpha)*math.sin(beta), -math.cos(alpha)*math.sin(beta), -math.sin(alpha)],[math.sin(beta), math.cos(beta), 0],[math.sin(alpha)*math.cos(beta), -math.sin(alpha)*math.sin(beta), math.cos(alpha)]])\\r\\n if V<=71 :\\r\\n Fbody=np.array([-F[0]-g.Cd0_fl,F[1],-F[2]-g.CL0_fl]) # add alpha=0 coefficients\\r\\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0_fl,0])\\r\\n else:\\r\\n Fbody=np.array([-F[0]-g.Cd0,F[1],-F[2]-g.CL0]) # add alpha=0 coefficients\\r\\n Moment=M+np.array([0,x[-1]*g.Cm_de+g.Cm0,0])\\r\\n \\r\\n\\r\\n Fbody=0.5*V**2.0*rho*g.S*Fbody\\r\\n Moment=0.5*V**2.0*rho*g.S*g.b*Moment\\r\\n \\r\\n return np.append(Fbody, Moment)\",\n \"def driftRHS_3D(field,drift_velocity,t,x):\\n f = field.getValue(x)\\n fs = np.sqrt(f[0]**2 + f[1]**2 + f[2]**2)\\n f = f/fs\\n return -f*drift_velocity(fs)\",\n \"def dynamics(self,eta,nu,u_actual,u_control,sampleTime): \\n \\n # Current velocities\\n u_c = self.V_c * math.cos(self.beta_c - eta[5]) # current surge velocity\\n v_c = self.V_c * math.sin(self.beta_c - eta[5]) # current sway velocity \\n \\n nu_c = np.array([u_c,v_c,0,0,0,0],float) # current velocity vector\\n nu_r = nu - nu_c # relative velocity vector\\n \\n U_r = math.sqrt( nu_r[0]**2 + nu_r[1]**2 ) # relative speed\\n \\n # Rudder command and actual rudder angle\\n delta_c = u_control[0]\\n delta = u_actual[0]\\n \\n # Rudder forces and moment (Fossen 2021, Chapter 9.5.1)\\n b = 0.7 * self.T # rudder height\\n AR = b**2 / self.Lambda # aspect ratio: Lamdba = b**2/AR \\n CN = 6.13 * self.Lambda / ( self.Lambda + 2.25 ) # normal coefficient\\n t_R = 1 - 0.28 * self.Cb - 0.55\\n a_H = 0.4\\n x_R = -0.45 * self.L\\n x_H = -1.0 * self.L\\n\\n Xdd = -0.5 * ( 1 - t_R ) * self.rho * U_r**2 * AR * CN\\n Yd = -0.25 * ( 1 + a_H ) * self.rho * U_r**2 * AR * CN \\n Nd = -0.25 * ( x_R + a_H * x_H ) * self.rho * U_r**2 * AR * CN \\n \\n # Control forces and moment\\n delta_R = -delta # physical rudder angle (rad)\\n T = self.tau_X # thrust (N)\\n t_deduction = 0.1 # thrust deduction number\\n tau1 = ( 1 - t_deduction ) * T - Xdd * math.sin( delta_R )**2 \\n tau2 = -Yd * math.sin( 2 * delta_R ) \\n tau6 = -Nd * math.sin( 2 * delta_R ) \\n tau = np.array( [ tau1, tau2, tau6 ],float) \\n \\n # Linear maneuvering model\\n T_surge = self.L # approx. time constant in surge (s)\\n xg = 0 # approx. x-coordinate, CG (m) \\n \\n # 3-DOF ship model\\n [M,N] = clarke83(U_r,self.L, self.B, self.T,self.Cb,self.R66,xg,T_surge)\\n Minv = np.linalg.inv(M)\\n nu3 = np.array( [ nu_r[0], nu_r[1], nu_r[5] ]) \\n nu3_dot = np.matmul( Minv, tau - np.matmul(N,nu3) ) \\n \\n # 6-DOF ship model\\n nu_dot = np.array( [ nu3_dot[0],nu3_dot[1],0,0,0,nu3_dot[2] ]) \\n\\n # Rudder angle saturation\\n if ( abs(delta) >= self.deltaMax * math.pi / 180 ):\\n delta = np.sign(delta) * self.deltaMax * math.pi / 180\\n \\n # Rudder dynamics\\n delta_dot = (delta_c - delta) / self.T_delta \\n\\n # Forward Euler integration [k+1]\\n nu = nu + sampleTime * nu_dot\\n delta = delta + sampleTime * delta_dot\\n\\n u_actual = np.array([delta],float) \\n\\n return nu, u_actual\",\n \"def velocity_field(xt,yt,x0,y0,Vinf,dia,rot,chord,B,param=None,veltype='all',integration='simp',m=220,n=200):\\n rad = dia/2.\\n tsr = rad*fabs(rot)/Vinf\\n solidity = (chord*B)/rad\\n\\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n\\n coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9 = coef_val()\\n\\n # Calculating EMG distribution parameters (based on polynomial surface fitting)\\n if param is None:\\n loc1 = _parameterval(tsr,solidity,coef0)\\n loc2 = _parameterval(tsr,solidity,coef1)\\n loc3 = _parameterval(tsr,solidity,coef2)\\n spr1 = _parameterval(tsr,solidity,coef3)\\n spr2 = _parameterval(tsr,solidity,coef4)\\n skw1 = _parameterval(tsr,solidity,coef5)\\n skw2 = _parameterval(tsr,solidity,coef6)\\n scl1 = _parameterval(tsr,solidity,coef7)\\n scl2 = _parameterval(tsr,solidity,coef8)\\n scl3 = _parameterval(tsr,solidity,coef9)\\n\\n else:\\n # Reading in EMG distribution parameters\\n loc1 = param[0]\\n loc2 = param[1]\\n loc3 = param[2]\\n spr1 = param[3]\\n spr2 = param[4]\\n skw1 = param[5]\\n skw2 = param[6]\\n scl1 = param[7]\\n scl2 = param[8]\\n scl3 = param[9]\\n\\n ###################################\\n if veltype == 'vort':\\n # VORTICITY CALCULATION (NO INTEGRATION)\\n if x0t < 0.:\\n vel = 0.\\n else:\\n vel = _vawtwake.vorticitystrength(x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)/rot\\n ###################################\\n else:\\n # Integration of the vorticity profile to calculate velocity\\n if integration == 'simp':\\n # SIMPSON'S RULE INTEGRATION (must use polynomial surface coefficients from VAWTPolySurfaceCoef.csv)\\n inte = 1 # Simpson's Rule\\n # inte = 2 # Trapezoidal Rule (optional ability of the code-- faster but less accurate)\\n\\n if param is not None:\\n print \\\"**** Using polynomial surface coefficients from VAWTPolySurfaceCoef.csv for Simpson's rule integration ****\\\"\\n\\n vel_xs,vel_ys = _vawtwake.vel_field(xt,yt,x0,y0,dia,rot,chord,B,Vinf,coef0,coef1,coef2,coef3,coef4,coef5,coef6,coef7,coef8,coef9,m,n,inte)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs*Vinf + Vinf)**2 + (vel_ys*Vinf)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs*Vinf + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])\\n ###################################\\n elif integration == 'gskr':\\n # 21-POINT GAUSS-KRONROD RULE QUADRATURE INTEGRATION\\n xbound = (scl3+5.)*dia\\n argval = (x0t,y0t,dia,loc1,loc2,loc3,spr1,spr2,skw1,skw2,scl1,scl2,scl3)\\n if veltype == 'all' or veltype == 'x' or veltype == 'ind':\\n vel_x = _dblquad(_vawtwake.integrandx,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_xs = (vel_x[0]*fabs(rot))/(2.*pi)\\n if veltype == 'all' or veltype == 'y' or veltype == 'ind':\\n vel_y = _dblquad(_vawtwake.integrandy,0.,xbound,lambda x: -1.*dia,lambda x: 1.*dia,args=argval)\\n vel_ys = (vel_y[0]*fabs(rot))/(2.*pi)\\n\\n if veltype == 'all':\\n vel = sqrt((vel_xs + Vinf)**2 + (vel_ys)**2)/Vinf\\n elif veltype == 'x':\\n vel = (vel_xs + Vinf)/Vinf\\n elif veltype == 'y':\\n vel = vel_ys/Vinf\\n elif veltype == 'ind':\\n vel = np.array([vel_xs,vel_ys])/Vinf\\n ###################################\\n\\n return vel\",\n \"def fluid_reynolds(uu, param, grid, lnrho=list(), shock=list(), nghost=3,\\n lmix=True):\\n #viscous forces\\n th2 = 2./3\\n th1 = 1./3\\n fvisc = np.zeros_like(uu)\\n #molecular viscosity contribution\\n ldel2, lshock, lhyper3 = False, False, False\\n for ivisc in param.ivisc:\\n if not 'shock' in ivisc and not 'hyper' in ivisc\\\\\\n and not '\\\\n' in ivisc:\\n ldel2 = True\\n if 'shock' in ivisc:\\n lshock = True\\n if 'hyper3' in ivisc:\\n lhyper3 = True\\n \\n if ldel2:\\n if lhyper3:\\n lhyper3 = lhyper3==lmix\\n del2u = np.zeros_like(uu)\\n for j in range(0,3):\\n del2u[j] = del2(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n del2u[j, : nghost, nghost:-nghost, nghost:-nghost] = del2u[j,-2*nghost: -nghost, nghost: -nghost, nghost: -nghost]\\n del2u[j,-nghost: , nghost:-nghost, nghost:-nghost] = del2u[j, nghost:2*nghost, nghost: -nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost, : nghost, nghost:-nghost] = del2u[j, nghost: -nghost,-2*nghost: -nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost,-nghost: , nghost:-nghost] = del2u[j, nghost: -nghost, nghost:2*nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost, nghost:-nghost, : nghost] = del2u[j, nghost: -nghost, nghost: -nghost,-2*nghost: -nghost]\\n del2u[j, nghost:-nghost, nghost:-nghost,-nghost: ] = del2u[j, nghost: -nghost, nghost: -nghost, nghost:2*nghost]\\n for ivisc in param.ivisc:\\n ivisc = str.strip(ivisc,'\\\\n')\\n if 'nu-const' not in ivisc and 'shock' not in ivisc\\\\\\n and 'hyper' not in ivisc and len(ivisc) > 0:\\n print('fluid_reynolds WARNING: '+ivisc+' not implemented\\\\n'+\\n 'terms may be missing from the standard rate of strain tensor')\\n fvisc = fvisc + param.nu*del2u\\n del(del2u)\\n tmp0 = grad(uu[0],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp0[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp0[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp0[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp0[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp0[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp0[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp0[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp0[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp0[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n tmp1 = grad(uu[1],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp1[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp1[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp1[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp1[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp1[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp1[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp1[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp1[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp1[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n tmp2 = grad(uu[2],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp2[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp2[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp2[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp2[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp2[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp2[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp2[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp2[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp2[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n #effect of compressibility \\n if len(lnrho) > 0:\\n divu = div(uu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n divu[ :nghost,nghost:-nghost,nghost:-nghost] = divu[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n divu[-nghost:,nghost:-nghost,nghost:-nghost] = divu[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n divu[nghost:-nghost, :nghost,nghost:-nghost] = divu[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n divu[nghost:-nghost,-nghost:,nghost:-nghost] = divu[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n divu[nghost:-nghost,nghost:-nghost, :nghost] = divu[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n divu[nghost:-nghost,nghost:-nghost,-nghost:] = divu[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n gradlnrho = grad(lnrho,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n gradlnrho[j, :nghost,nghost:-nghost,nghost:-nghost] = gradlnrho[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradlnrho[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost, :nghost,nghost:-nghost] = gradlnrho[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradlnrho[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,nghost:-nghost, :nghost] = gradlnrho[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradlnrho[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n Sglnrho = np.zeros_like(uu)\\n Sglnrho[0] = dot(tmp0,gradlnrho) +\\\\\\n (tmp0[0]+tmp1[0]+tmp2[0]-th2*divu)*gradlnrho[0] \\n Sglnrho[1] = dot(tmp1,gradlnrho) +\\\\\\n (tmp0[1]+tmp1[1]+tmp2[1]-th2*divu)*gradlnrho[1]\\n Sglnrho[2] = dot(tmp2,gradlnrho) +\\\\\\n (tmp0[2]+tmp1[2]+tmp2[2]-th2*divu)*gradlnrho[2]\\n graddivu = grad(divu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n graddivu[j, :nghost,nghost:-nghost,nghost:-nghost] = graddivu[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n graddivu[j,-nghost:,nghost:-nghost,nghost:-nghost] = graddivu[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost, :nghost,nghost:-nghost] = graddivu[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost,-nghost:,nghost:-nghost] = graddivu[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost,nghost:-nghost, :nghost] = graddivu[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n graddivu[j,nghost:-nghost,nghost:-nghost,-nghost:] = graddivu[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n fvisc = fvisc + param.nu*(th1*graddivu+Sglnrho)\\n del(Sglnrho)\\n elif param.ldensity:\\n print('fluid_reynolds WARNING: no lnrho provided\\\\n'+\\n 'rate of strain tensor likely incomplete')\\n #shock contribution\\n if lshock:\\n if len(shock) == 0:\\n print('fluid_reynolds WARNING: no shock provided\\\\n'+\\n 'rate of strain tensor likely incomplete')\\n else:\\n shock[ :nghost,nghost:-nghost,nghost:-nghost] = shock[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n shock[-nghost:,nghost:-nghost,nghost:-nghost] = shock[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n shock[nghost:-nghost, :nghost,nghost:-nghost] = shock[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n shock[nghost:-nghost,-nghost:,nghost:-nghost] = shock[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n shock[nghost:-nghost,nghost:-nghost, :nghost] = shock[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n shock[nghost:-nghost,nghost:-nghost,-nghost:] = shock[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n divugradlnrho = np.zeros_like(uu)\\n gradshock = grad(shock,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n gradshock[j, :nghost,nghost:-nghost,nghost:-nghost] = gradshock[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n gradshock[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradshock[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost, :nghost,nghost:-nghost] = gradshock[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradshock[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost,nghost:-nghost, :nghost] = gradshock[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n gradshock[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradshock[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n for j in range(0,3):\\n divugradlnrho[j] = param.nu_shock*divu*gradshock[j] +\\\\\\n param.nu_shock*shock*(divu*gradlnrho[j] + graddivu[j])\\n del(divu,gradshock,gradlnrho,graddivu)\\n fvisc = fvisc + divugradlnrho\\n del(divugradlnrho)\\n if lhyper3:\\n #deluij5 = np.zeros_like([uu,uu,uu])\\n #uij5glnrho to be included\\n del6u = np.zeros_like(uu)\\n for j in range(0,3):\\n del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz)\\n del6u[j, :nghost,nghost:-nghost,nghost:-nghost] = del6u[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n del6u[j,-nghost:,nghost:-nghost,nghost:-nghost] = del6u[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost, :nghost,nghost:-nghost] = del6u[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost,-nghost:,nghost:-nghost] = del6u[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost,nghost:-nghost, :nghost] = del6u[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n del6u[j,nghost:-nghost,nghost:-nghost,-nghost:] = del6u[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n #del6 for non-cartesian tba\\n #del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n # coordinate_system=param.coord_system)\\n fvisc = fvisc + param.nu_hyper3*del6u\\n del(del6u)\\n fvisc2 = np.sqrt(dot2(fvisc))\\n #advective forces\\n advec = np.zeros_like(uu)\\n advec[0] = dot(uu,tmp0)\\n advec[1] = dot(uu,tmp1)\\n advec[0] = dot(uu,tmp2)\\n del(tmp0,tmp1,tmp2)\\n advec2 = np.sqrt(dot2(advec))\\n del(advec)\\n #avoid division by zero\\n if fvisc2.max() > 0:\\n fvisc2[np.where(fvisc2==0)] = fvisc2[np.where(fvisc2>0)].min()\\n Re = advec2/fvisc2\\n #set minimum floor to exclude zero-valued Re \\n Re[np.where(Re==0)] = Re[np.where(Re>0)].min()\\n else:\\n Re = advec2\\n print('Re undefined')\\n return Re\",\n \"def calculate_ft(self):\\n \\n # Create a function which is able to evaluate B**2\\n ffunc = scipy.interpolate.interp1d(self.psigrid, self.e.getF()[self.tind])\\n def b2_func(R, Z, psi):\\n bt = ffunc(psi)/R\\n br = -self.psifunc.ev(R, Z, dy=1)/R\\n bz = self.psifunc.ev(R, Z, dx=1)/R\\n \\n return bt**2 + br**2 + bz**2\\n \\n\\n def b_bmax2(R,Z,psi):\\n b2 = b2_func(R,Z,psi)\\n return b2 / np.max(b2)\\n \\n def b_bmax(R,Z,psi):\\n return np.sqrt(b_bmax2(R,Z,psi))\\n \\n # Evaluate the flux-surface averaged h^2 and h, as required\\n fsa_h2 = self.fs_average(b_bmax2)\\n fsa_h = self.fs_average(b_bmax)\\n \\n # This is the function which gets flux-surface averaged in equation (7)\\n def ftl_func(R,Z,psi):\\n h = b_bmax(R,Z,psi)\\n h2 = b_bmax2(R,Z,psi)\\n \\n return (1 - (np.sqrt(1 - h) * (1 + 0.5 * h)))/h2\\n \\n \\n # Equation 6, 7 in Lin-Liu\\n fs_ftu = 1 - fsa_h2 / fsa_h**2 * (1 - np.sqrt(1 - fsa_h) * (1 + 0.5 * fsa_h))\\n fs_ftl = 1 - fsa_h2 * self.fs_average(ftl_func)\\n # Equation 18, 19 \\n om = 0.75\\n self.fs_ft = om*fs_ftu + (1-om)*fs_ftl\",\n \"def trajectories(t_upper=3600*24*687, h=100, m1=5.972e+24, m2=6.417e+23,\\n m3=1.989e+30, a1=1.0*1.496e+11, a2=1.52*1.496e+11):\\n\\n # We check if parameters are all positive\\n\\n list_parameters = [t_upper, h, m1, m2, m3,\\n a1, a2]\\n\\n for parameters in list_parameters:\\n\\n if parameters < 0:\\n print(f'You have entered a negative parameter')\\n\\n # initial values for planet 1 in x, y and z direction\\n x_i1 = a1\\n y_i1 = 0\\n v_x1i = 0\\n v_y1i = 29779.301841746023\\n z_i1 = 0\\n v_z1i = 0\\n\\n # initial values for planet 2 in x, y and z direction\\n x_i2 = a2\\n y_i2 = 0\\n v_x2i = 0\\n v_y2i = 24154.203325249873\\n z_i2 = 0\\n v_z2i = 0\\n\\n # initial values for Sun in x, y and z direction\\n x_i3 = 0\\n y_i3 = 0\\n v_x3i = 0\\n v_y3i = 0\\n z_i3 = 0\\n v_z3i = 0\\n\\n# Initial positions and velocities\\n r = np.array([x_i1, y_i1, v_x1i, v_y1i, x_i2,\\n y_i2, v_x2i, v_y2i, x_i3, y_i3, v_x3i, v_y3i,\\n z_i1, z_i2, z_i3, v_z1i, v_z2i, v_z3i])\\n\\n # We create vectors which will contains the trajectories\\n # and velocities of each bodies\\n x_pnts1 = [x_i1]\\n y_pnts1 = [y_i1]\\n v_x_pnts1 = [v_x1i]\\n v_y_pnts1 = [v_y1i]\\n\\n x_pnts2 = [x_i2]\\n y_pnts2 = [y_i2]\\n v_x_pnts2 = [v_x2i]\\n v_y_pnts2 = [v_y2i]\\n\\n x_pnts3 = [x_i3]\\n y_pnts3 = [y_i3]\\n v_x_pnts3 = [v_x3i]\\n v_y_pnts3 = [v_y3i]\\n\\n x_pnts3 = [x_i3]\\n y_pnts3 = [y_i3]\\n v_x_pnts3 = [v_x3i]\\n v_y_pnts3 = [v_y3i]\\n\\n z_pnts1 = [z_i1]\\n z_pnts2 = [z_i2]\\n z_pnts3 = [z_i3]\\n\\n v_z_pnts1 = [v_z1i]\\n v_z_pnts2 = [v_z2i]\\n v_z_pnts3 = [v_z3i]\\n\\n m1 = m1\\n m2 = m2\\n m3 = m3\\n a1 = a1\\n a2 = a2\\n\\n # We create a vector which will contain the time\\n # Initial value\\n t_i = 0.0\\n t_values = [t_i]\\n\\n for t in range(0, t_upper, h):\\n\\n # We used the RK4 formula here\\n k1 = h*derivative(r=r, t=0, m1=5.972e+24, m2=m2, m3=1.989e+30,\\n a1=a1, a2=1.52*1.496e+11)\\n k2 = h*derivative(r=r + 0.5*k1, t=t + (h/2), m1=5.972e+24,\\n m2=6.417e+23, m3=1.989e+30, a1=1.0*1.496e+11,\\n a2=1.52*1.496e+11)\\n k3 = h*derivative(r=r + 0.5*k2, t=t + (h/2), m1=5.972e+24,\\n m2=6.417e+23, m3=1.989e+30, a1=1.0*1.496e+11,\\n a2=1.52*1.496e+11)\\n k4 = h*derivative(r=r + h*k3, t=t+h, m1=5.972e+24, m2=6.417e+23,\\n m3=1.989e+30, a1=1.0*1.496e+11, a2=1.52*1.496e+11)\\n\\n # We calculate the new vector r\\n r += (k1 + 2*k2 + 2*k3 + k4)*(1.0/6.0)\\n\\n # We add the new points calculated\\n x_pnts1.append(r[0])\\n y_pnts1.append(r[1])\\n\\n v_x_pnts1.append(r[2])\\n v_y_pnts1.append(r[3])\\n\\n x_pnts2.append(r[4])\\n y_pnts2.append(r[5])\\n v_x_pnts2.append(r[6])\\n v_y_pnts2.append(r[7])\\n\\n x_pnts3.append(r[8])\\n y_pnts3.append(r[9])\\n v_x_pnts3.append(r[10])\\n v_y_pnts3.append(r[11])\\n\\n z_pnts1.append(r[12])\\n z_pnts2.append(r[13])\\n z_pnts3.append(r[14])\\n\\n v_z_pnts1.append(r[15])\\n v_z_pnts2.append(r[16])\\n v_z_pnts3.append(r[17])\\n\\n t_values.append(t)\\n\\n # We return all the trajectories\\n return x_pnts1, y_pnts1, x_pnts2, y_pnts2, x_pnts3, y_pnts3, z_pnts1, z_pnts2, z_pnts3\",\n \"def rhs(y, t, l, m, g):\\n # Unpack the states so you can use the variable names in the\\n # sympy.physics.mechanics equations\\n q1 = y[0]\\n q2 = y[1]\\n u1 = y[2]\\n u2 = y[3]\\n # or you can make use of python's tuple unpacking for a one liner\\n # q1, q2, u1, u2 = y\\n\\n # Initialize a vector for the derivatives.\\n dydt = zeros((len(y)))\\n\\n # Compute the derivatives, these are pasted in from the\\n # sympy.physics.mechanics results.\\n dydt[0] = u1\\n dydt[1] = u2\\n dydt[2] = (-g*sin(q1)*sin(q2)**2 + 2*g*sin(q1) -\\n g*sin(q2)*cos(q1)*cos(q2) + 2*l*u1**2*sin(q1)*cos(q1)*cos(q2)**2 -\\n l*u1**2*sin(q1)*cos(q1) - 2*l*u1**2*sin(q2)*cos(q1)**2*cos(q2) +\\n l*u1**2*sin(q2)*cos(q2) + l*u2**2*sin(q1)*cos(q2) -\\n l*u2**2*sin(q2)*cos(q1))/(l*(sin(q1)**2*sin(q2)**2 +\\n 2*sin(q1)*sin(q2)*cos(q1)*cos(q2) + cos(q1)**2*cos(q2)**2 - 2))\\n dydt[3] = (-sin(q1)*sin(q2)/2 - cos(q1)*cos(q2)/2)*(2*g*l*m*sin(q1) -\\n l**2*m*(-sin(q1)*cos(q2) +\\n sin(q2)*cos(q1))*u2**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2)) -\\n l**2*m) + (g*l*m*sin(q2) - l**2*m*(sin(q1)*cos(q2) -\\n sin(q2)*cos(q1))*u1**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2))\\n - l**2*m)\\n\\n # Return the derivatives.\\n return dydt\",\n \"def main():\\r\\n #Drag Force Equation: 1/2 * rho * Cd * A * v^2\\r\\n\\r\\n #User-Defined Constants\\r\\n global m\\r\\n global v0\\r\\n global theta\\r\\n global rho #Fluid Density\\r\\n global A #Cross-sectional Area\\r\\n global Cd #Drag coefficient\\r\\n global tStep\\r\\n global g\\r\\n\\r\\n m = 1\\r\\n v0 = 30\\r\\n theta = math.radians(45)\\r\\n rho = 1.225\\r\\n A = 0.05\\r\\n Cd = 0.5 #A ball is approx. 0.5\\r\\n tStep = 0.005\\r\\n g = 9.8\\r\\n\\r\\n\\r\\n #Data Structures\\r\\n global tHist\\r\\n global xHist\\r\\n global yHist\\r\\n global thetaHist\\r\\n global vHist\\r\\n global vXHist\\r\\n global vYHist\\r\\n tHist = [] #list for all time steps\\r\\n xHist = [] #list for all x position steps\\r\\n yHist = [] #list for all y position steps\\r\\n thetaHist = [] #List for all theta at every time step\\r\\n vHist = [] #list for all velocities at every time step\\r\\n vXHist = [] #list for all x-axis velocities at every time step\\r\\n vYHist = [] #list for all y-axis velocities at every time step\\r\\n\\r\\n #Initialize intial values\\r\\n tHist.append(0.0)\\r\\n xHist.append(0.0)\\r\\n yHist.append(0.0)\\r\\n thetaHist.append(theta)\\r\\n vHist.append(v0)\\r\\n vXHist.append(v0 * math.cos(theta))\\r\\n vYHist.append(v0 * math.sin(theta))\\r\\n vTheta = math.atan(vYHist[0] / vXHist[0])\\r\\n # print(\\\"t: \\\" + str(tHist[0]))\\r\\n # print(\\\"x: \\\" + str(xHist[0]))\\r\\n # print(\\\"y: \\\" + str(yHist[0]))\\r\\n # print(\\\"v: \\\" + str(vHist[0]))\\r\\n # print(\\\"Vx: \\\" + str(vXHist[0]))\\r\\n # print(\\\"Vy: \\\" + str(vYHist[0]))\\r\\n\\r\\n #Convenience variables\\r\\n global k\\r\\n\\r\\n counter = 1\\r\\n #Loop until the y-displacement becomes negative (projectile reaches ground again)\\r\\n while True:\\r\\n tHist.append(counter * tStep) #increment time\\r\\n print(\\\"t: \\\" + str(tHist[counter]))\\r\\n\\r\\n #This large hunk is the solution to the net force differential equation in the x-axis\\r\\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep)) #STABLE\\r\\n # oneOverVX = (1/vXHist[counter-1]) + (((rho*A*Cd)/(2*m))*(tStep))\\r\\n # oneOverVX = (1/vHist[counter-1]) + (((rho*A*Cd*math.cos(thetaHist[counter-1]))/(2*m))*(tStep))\\r\\n oneOverVX = (1/vXHist[counter-1]) + ((rho*A*Cd)/(2*m*math.cos(thetaHist[counter-1]))*(tStep)) #This is one over the solution for velocity in the x-axis net force differential equation\\r\\n vXHist.append(1 / oneOverVX) #Adding the velocity to the list of velocities\\r\\n\\r\\n vY0 = vYHist[counter-1] #Convenience variable\\r\\n # k = 0.5 * rho * A * Cd * math.sin(abs(thetaHist[counter-1])) #STABLE\\r\\n # k = 0.5 * rho * A * Cd\\r\\n k = (rho * A * Cd) / (2 * math.sin(abs(thetaHist[counter-1]))) #Convenience variable\\r\\n print(\\\"k: \\\" + str(k))\\r\\n print(\\\"vX: \\\" + str(vXHist[counter]))\\r\\n rootGMK = math.sqrt(g*m*k) #Convenience variable\\r\\n if vYHist[counter-1] > 0.0: #If the projectile is going upwards\\r\\n #Solving the y-axis differential equation for velocity\\r\\n equationRight = -rootGMK * ((tStep/m) - (math.atan((k*vY0)/(rootGMK))/rootGMK))\\r\\n vYHist.append((math.tan(equationRight) * rootGMK) / k)\\r\\n elif vYHist[counter-1] < 0.0: #If the projectile is going downwards\\r\\n #Solving the y-axis differential equation for velocity\\r\\n\\r\\n # Hand-solved integral\\r\\n # exponent = -(2*tStep*rootGMK)/m\\r\\n # numerator = g*m*math.exp(exponent) - math.exp(exponent)*vY0*rootGMK - vY0*rootGMK - g*m\\r\\n # denominator = math.exp(exponent)*(vY0-rootGMK) - vY0*k - rootGMK\\r\\n # vYHist.append(numerator / denominator)\\r\\n\\r\\n #Wolfram Alpha arctanh integral\\r\\n arctanh =(vY0*math.sqrt(k))/(math.sqrt(g*m))\\r\\n print(\\\"arctanh: \\\" + str(arctanh))\\r\\n equationRight = (np.arctanh(arctanh))/(rootGMK) - (tStep/m)\\r\\n vYHist.append(np.tanh(rootGMK * equationRight) * ((math.sqrt(g*m))/(math.sqrt(k))))\\r\\n else: #If current y velocity is 0\\r\\n vYHist.append(vY0 - g*tStep)\\r\\n print(\\\"vY: \\\" + str(vYHist[counter]))\\r\\n\\r\\n vHist.append(math.hypot(vXHist[counter], vYHist[counter])) #Calculate the net velocity and add it to the velocities list\\r\\n print(\\\"v: \\\" + str(vHist[counter]))\\r\\n thetaHist.append(math.atan(vYHist[counter]/vXHist[counter])) #Calculate the current angle based on the velocities and add it to the theta list\\r\\n print(\\\"0: \\\" + str(math.degrees(thetaHist[counter])))\\r\\n\\r\\n x0 = xHist[counter-1]\\r\\n y0 = yHist[counter-1]\\r\\n\\r\\n # yIntegral = trigintegrate()\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Note: What I wanted to do here was to integrate the velocity functions over the time interval to find the exact\\r\\n changes in position. Unfortunately, I was running short of time and decided it was not worth it to move forward with\\r\\n this final step, and instead worked on the presentation and testing different cases.\\r\\n \\\"\\\"\\\"\\r\\n xHist.append(x0 + vXHist[counter]*tStep) #Calculate new x position using x = x0 + vt\\r\\n yHist.append(y0 + vYHist[counter]*tStep) #Calculate new y position using y = y0 + vt\\r\\n print(\\\"x: \\\" + str(xHist[counter]))\\r\\n print(\\\"y: \\\" + str(yHist[counter]))\\r\\n print()\\r\\n\\r\\n # xHist.append(xHist[counter-1] + vXHist[counter-1]*tStep + 0.5*aXHist[counter-1]*tStep**2)\\r\\n # yHist.append(yHist[counter-1] + vYHist[counter-1]*tStep + 0.5*aYHist[counter-1]*tStep**2)\\r\\n # vXHist.append(vXHist[counter-1] + aXHist[counter-1]*tStep)\\r\\n # vYHist.append(vYHist[counter-1] + aYHist[counter-1]*tStep)\\r\\n # vHist.append(math.hypot(vXHist[counter], vYHist[counter]))\\r\\n #\\r\\n # vTheta = math.atan(vYHist[counter] / vXHist[counter])\\r\\n # xDragAccel = -0.5*rho*Cd*A*vHist[counter]**2*math.cos(vTheta) / m\\r\\n # yDragAccel = -math.copysign(0.5*rho*Cd*A*vHist[counter]**2*math.sin(vTheta) / m, vYHist[counter])\\r\\n #\\r\\n # aXHist.append(xDragAccel)\\r\\n # aYHist.append(-g*tStep + yDragAccel)\\r\\n\\r\\n if vYHist[counter-1] > 0.0 and vYHist[counter] < 0.0: #Check if the projectile has reached it's peak by checking for a critical point\\r\\n print(\\\"max height reached at time=\\\" + str(tHist[counter]))\\r\\n # break\\r\\n\\r\\n # print(\\\"t: \\\" + str(tHist[counter]))\\r\\n # print(\\\"x: \\\" + str(xHist[counter]))\\r\\n # print(\\\"y: \\\" + str(yHist[counter]))\\r\\n # print(\\\"Vx: \\\" + str(vXHist[counter]))\\r\\n # print(\\\"Vy: \\\" + str(vYHist[counter]))\\r\\n # print(\\\"Ax: \\\" + str(aXHist[counter]))\\r\\n # print(\\\"Ay: \\\" + str(aYHist[counter]))\\r\\n\\r\\n if yHist[counter] < 0 or counter > 99999: #End the loop if the projectile has reached the ground (or limit the number of iterations to avoid computer death)\\r\\n break\\r\\n\\r\\n counter += 1\\r\\n\\r\\n plotData()\",\n \"def hydro_solver(self):\\n u_dx = self.central_x(self.u)\\n w_dy = self.central_y(self.w)\\n P_dx = self.central_x(self.P)\\n P_dy = self.central_y(self.P)\\n\\n rho_dx_upwind = self.upwind_x(self.rho, self.u)\\n rho_dy_upwwind = self.upwind_y(self.rho, self.w)\\n rho_udx_upwind = self.upwind_x(self.rho * self.u, self.u)\\n rho_udy_upwind = self.upwind_y(self.rho * self.u, self.w)\\n rho_wdx_upwind = self.upwind_x(self.rho * self.w, self.u)\\n rho_wdy_upwind = self.upwind_y(self.rho * self.w, self.w)\\n u_dx_uu = self.upwind_x(self.u, self.u)\\n u_dx_uw = self.upwind_x(self.u, self.w)\\n w_dy_uu = self.upwind_y(self.w, self.u)\\n w_dy_uw = self.upwind_y(self.w, self.w)\\n e_dx = self.upwind_x(self.e, self.u)\\n e_dy = self.upwind_y(self.e, self.w)\\n\\n self.rho_dt = (\\n -self.rho * (u_dx + w_dy)\\n - self.u * rho_dx_upwind\\n - self.w * rho_dy_upwwind\\n )\\n self.e_dt = (\\n -(self.e + self.P) * (u_dx + w_dy) - self.u * e_dx - self.w * e_dy\\n )\\n self.rho_udt = (\\n -self.rho * self.u * (u_dx_uu + w_dy_uu)\\n - self.u * rho_udx_upwind\\n - self.w * rho_udy_upwind\\n - P_dx\\n )\\n self.rho_wdt = (\\n -self.rho * self.w * (u_dx_uw + w_dy_uw)\\n - self.u * rho_wdx_upwind\\n - self.w * rho_wdy_upwind\\n - P_dy\\n + self.rho * self.g\\n )\\n\\n self.time_step()\\n rho_previous = np.zeros_like(self.rho)\\n rho_previous[:, :] = self.rho\\n self.rho[:, :] = self.rho + self.rho_dt * self.dt\\n self.e[:, :] = self.e + self.e_dt * self.dt\\n self.u[:, :] = (\\n rho_previous * self.u + self.rho_udt * self.dt\\n ) / self.rho\\n self.w[:, :] = (\\n rho_previous * self.w + self.rho_wdt * self.dt\\n ) / self.rho\\n\\n self.boundary_conditions()\\n self.T[:, :] = (\\n (self.Y - 1) * self.e * self.mu * self.m_u / (self.kb * self.rho)\\n )\\n self.P[:, :] = (self.Y - 1) * self.e\\n uw = (self.u, self.w)\\n v = np.linalg.norm(uw)\\n dt = self.dt\\n\\n return dt\",\n \"def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\\n beta = np.float32(beta)\\n mu = np.float32(mu)\\n \\n du1, du0 = du[:2]\\n dv1, dv0 = dv[:2]\\n dn0 = dn[0]\\n \\n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\\n \\n n1 = n + ( dn0 )*dt\\n \\n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n \\n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\\n \\n n, u, v = n1, u1, v1\\n \\n du = [du1, du0, du0, du0]\\n dv = [dv1, dv0, dv0, dv0]\\n dn = [dn0, dn0, dn0]\\n return n1, u1, v1, du, dv, dn\",\n \"def t_rh_2_dewT(ds, var):\\n ds['dew'] = 243.04 * (np.log(ds[var['rh']] / 100) + ((17.625 * ds[var['temp']]) / (243.04 + ds[var['temp']])))/\\\\\\n (17.625-np.log(ds[var['rh']] / 100) - ((17.625 * ds[var['temp']]) / (243.04 + ds[var['temp']])))\\n return ds\",\n \"def main():\\n\\n varList = {'beta': 6., 'convSpeed': 1.2, 'Mark': 0., 'axi': 1, 'acModes': 4, 'Nr': 801, 'Tf': 600., 'xf': 0.51}\\n\\n # Solve steady flame.\\n # BC1: I have the attachment BC at r = 1, always\\n # BC2: I need to set dF/dr = 0 at r = 0 iff Mark != 0\\n [qMean, r, FMean] = steady_flame_area_FD3(varList['Mark'], varList['beta'], varList['axi'], varList['Nr'])\\n r = r * varList['beta']\\n\\n # Calculate mean flame derivatives\\n dFMeanDr = derivsnew.FD1_CT2_D(FMean, r[1] - r[0])\\n d2FMeanDr2 = derivsnew.FD2_CT2_D(FMean, r[1] - r[0])\\n\\n #Apply BC smooth tip:\\n if(varList['Mark']!=0.0):\\n dFMeanDr[-1] = 0.0\\n\\n # Use correct number of points. Remember that the extrems need to be set depending on the BC!\\n # The attach BC (first point) is always assumed to be true and removed from the vector list\\n if(varList['Mark']==0):\\n Nr = varList['Nr'] / 2\\n dFMeanDr = dFMeanDr[1:]\\n d2FMeanDr2 = d2FMeanDr2[1:]\\n r = r[1:]\\n # The smooth BC holds only if Mark!=0 (second derivatives appear): remove also the last point\\n else:\\n Nr = varList['Nr'] / 2 - 1\\n dFMeanDr = dFMeanDr[1:-1]\\n d2FMeanDr2 = d2FMeanDr2[1:-1]\\n r = r[1:-1]\\n\\n # Calculate geometric values\\n den = 1 + varList['beta'] * varList['beta'] * dFMeanDr * dFMeanDr\\n dR = r[1] - r[0]\\n # Set Nx equal to Nr for now.\\n # The implementation is more complicated if they differ, and need to interpolate between values.\\n Nx = Nr\\n\\n # Nonuniform grid spacing along x!\\n # Nx = length(dx) has to hold.\\n dx = np.empty(len(FMean) - 1)\\n for ii in range(1, len(FMean)):\\n dx[ii - 1] = FMean[ii] - FMean[ii - 1]\\n\\n [A, B, C, tau] = loadAcoustics(varList['xf'], varList['Tf'], varList['acModes'], varList['beta'])\\n\\n Matrix = buildMatrix(Nr, dR, varList['beta'], den, r, FMean, dFMeanDr, d2FMeanDr2, varList['Mark'], varList['acModes'], A,\\n B, C, Nx, dx, tau, qMean, varList['convSpeed'])\\n\\n [d, W, V] = eigProblem.solveEigProb(Matrix)\\n [dnew, Wnew, Vnew] = eigProblem.selectUnstable(d, W, V)\\n\\n print dnew / (2. * np.pi)\",\n \"def main(data, setup):\\n # input check \\n varnames = ('vm_raw', 'vm_raw_theo')\\n for varname in varnames:\\n if varname not in data.keys():\\n raise LookupError('data must contain variable %s.' %s)\\n\\n # display info message\\n chrono = setup['chrono']\\n chrono.issue('target velocity: correct for sensor motion...')\\n\\n # retrieve varialbes\\n vnys = data['nqv']\\n v_sensor_r = data['v_sensor_r']\\n\\n # ========== main =================================== #\\n for key_raw in ('vm_raw', 'vm_raw_theo'):\\n key_c = key_raw.replace('raw', 'raw_c')\\n\\n # sum\\n vm_raw = data[key_raw]\\n v_sum = (vm_raw + np.expand_dims(v_sensor_r, 1))\\n\\n # mod\\n data[key_c] = symmod(v_sum, vnys)\\n # ==================================================== #\\n\\n return data\",\n \"def update_variables(self):\\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\\n # aquire transformation matrices and tips for segment 1 and 2\\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\\n # Frenet frames\\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\\n # Bishop frames\\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]\",\n \"def build_rhs():\\n\\n def div(\\n coeff_rho,\\n momentum_x,\\n momentum_y,\\n momentum_z,\\n ):\\n \\\"\\\"\\\"Computes the divergence of the velocity field.\\\"\\\"\\\"\\n # Compute the fourth order derivative of the pressure for the face\\n # velocity correction.\\n p_corr = (\\n states['p']\\n if self._params.enable_rhie_chow_correction else states['dp'])\\n d4p_dx4 = self._kernel_op.apply_kernel_op_x(p_corr, 'k4d2x')\\n d4p_dy4 = self._kernel_op.apply_kernel_op_y(p_corr, 'k4d2y')\\n d4p_dz4 = self._kernel_op.apply_kernel_op_z(p_corr, 'k4d2z',\\n 'k4d2zsh')\\n\\n # Compute velocity gradient based on interpolated values on cell faces.\\n coeff_x = dt / (4. * coeff_rho * dx**2)\\n du = self._kernel_op.apply_kernel_op_x(momentum_x, 'kDx')\\n du_dx = [\\n du_i / (2. * dx) + coeff_x * d4p_dx4_i\\n for du_i, d4p_dx4_i in zip(du, d4p_dx4)\\n ]\\n\\n coeff_y = dt / (4. * coeff_rho * dy**2)\\n dv = self._kernel_op.apply_kernel_op_y(momentum_y, 'kDy')\\n dv_dy = [\\n dv_i / (2. * dy) + coeff_y * d4p_dy4_i\\n for dv_i, d4p_dy4_i in zip(dv, d4p_dy4)\\n ]\\n\\n coeff_z = dt / (4. * coeff_rho * dz**2)\\n dw = self._kernel_op.apply_kernel_op_z(momentum_z, 'kDz', 'kDzsh')\\n dw_dz = [\\n dw_i / (2. * dz) + coeff_z * d4p_dz4_i\\n for dw_i, d4p_dz4_i in zip(dw, d4p_dz4)\\n ]\\n\\n return [\\n du_dx_i + dv_dy_i + dw_dz_i\\n for du_dx_i, dv_dy_i, dw_dz_i in zip(du_dx, dv_dy, dw_dz)\\n ]\\n\\n def add_factor(\\n v,\\n factor,\\n ):\\n return [factor * v_i for v_i in v]\\n\\n b_terms = {\\n _B_TERM_SOURCE_RHO: add_factor(src_rho, inv_dt),\\n }\\n if isinstance(rho_info, ConstantDensityInfo):\\n b_terms.update({\\n _B_TERM_DIV:\\n add_factor(\\n div(rho_info.rho, states['u'], states['v'], states['w']),\\n inv_dt * rho_info.rho),\\n _B_TERM_DRHO_DT: [\\n tf.zeros_like(src_rho_i) for src_rho_i in src_rho\\n ],\\n })\\n\\n elif isinstance(rho_info, VariableDensityInfo):\\n b_terms.update({\\n _B_TERM_DIV:\\n add_factor(\\n div(1.0, states['rho_u'], states['rho_v'], states['rho_w']),\\n inv_dt),\\n _B_TERM_DRHO_DT:\\n add_factor(rho_info.drho_dt, inv_dt),\\n })\\n\\n else:\\n raise ValueError('`rho_info` has to be either `ConstantDensityInfo` or '\\n '`VariableDensityInfo`.')\\n\\n # pylint: disable=g-complex-comprehension\\n return [(div_i + drho_dt_i - src_rho_i)\\n for div_i, drho_dt_i, src_rho_i in zip(\\n b_terms[_B_TERM_DIV],\\n b_terms[_B_TERM_DRHO_DT],\\n b_terms[_B_TERM_SOURCE_RHO],\\n )], b_terms\\n # pylint: enable=g-complex-comprehension\",\n \"def manipulate_heat_data(self): \\n self.exh.T_array = ( 0.5 * (self.exh.T_inlet_array +\\n self.exh.T_outlet_array) + 273.15)\\n self.exh.delta_T_array = ( self.exh.T_inlet_array -\\n self.exh.T_outlet_array )\\n \\n self.cool.delta_T_array = ( self.cool.T_inlet_array -\\n self.cool.T_outlet_array )\\n self.cool.C = self.cool.mdot * self.cool.c_p\",\n \"def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\\n self.A0 = A_phi[0]\\n self.phi0 = A_phi[1]\\n self.sim = sim\\n self.V0_frac = V0_frac\\n self.t = t\\n self.x_eq0 = self.sim.x_eq([0,0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\\n self.sol = integrate.odeint(sim, self.x0, t=t)\\n self.z = sim.zLI(self.sol, t)\\n self.phi = np.unwrap(np.angle(self.z))\\n self.t_filt = t_filt = t[15:] \\n self.i0 = i0 = np.argmin(abs(self.t_filt)) \\n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\\n self.t_wide = t_filt[::Ndec_response]\\n self.respRePts = self.sim.responseReVec(self.t_wide)\\n self.Ht = lambda t: np.interp(t, self.t_wide, self.respRePts)\\n \\n \\n \\n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act\",\n \"def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\\n self.A0 = A_phi[0]\\n self.phi0 = A_phi[1]\\n self.sim = sim\\n self.V0_frac = V0_frac\\n self.t = t\\n self.x_eq0 = self.sim.x_eq([0, 0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\\n self.sol = integrate.odeint(sim, self.x0, t=t)\\n self.z = sim.zLI(self.sol, t)\\n self.phi = np.unwrap(np.angle(self.z))\\n self.t_filt = t_filt = t[15:] \\n self.i0 = i0 = np.argmin(abs(self.t_filt)) \\n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\\n self.t_wide = t_filt[::Ndec_response]\\n self.respRePts = self.sim.responseReVec(self.t_wide)\\n self.Ht = lambda tt: np.interp(tt, self.t_wide, self.respRePts)\\n \\n \\n \\n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act\",\n \"def forward(self, z_t_1, h_x, phi_table):\\n z_category = self.gen_z_t_dist_now(z_t_1, h_x)\\n \\n if self.use_gumbel_softmax:\\n \\n# device = z_category.device\\n \\n averaged_z_t = 0\\n \\n log_prob = Variable(torch.log(z_category))\\n \\n for k in range(self.sampling_times): \\n curr_z_t = F.gumbel_softmax(log_prob, tau = 0.1)\\n \\n averaged_z_t += curr_z_t\\n \\n del curr_z_t\\n \\n# averaged_z_t = averaged_z_t.to(device)\\n \\n z_t = averaged_z_t/self.sampling_times\\n else:\\n z_t = z_category\\n \\n phi_z = torch.mm(z_t, torch.t(phi_table))\\n# mu = self.h_to_mu(h_combined)\\n# logvar = self.h_to_logvar(h_combined)\\n# std = torch.exp(0.5 * logvar) \\n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\\n return z_t, z_category, phi_z\",\n \"def get_observations(self):\\n joint_states = self.joints_state\\n self.force = self.wrench_stamped.wrench.force\\n self.torque = self.wrench_stamped.wrench.torque\\n self.static_taxel = self.tactile_static.taxels\\n# dynamic_taxel= tactile_dynamic\\n\\n# print(\\\"[force]\\\", self.force.x, self.force.y, self.force.z)\\n# print(\\\"[torque]\\\", self.torque.x, self.torque.y, self.torque.z)\\n shp_joint_ang = joint_states.position[0]\\n shl_joint_ang = joint_states.position[1]\\n elb_joint_ang = joint_states.position[2]\\n wr1_joint_ang = joint_states.position[3]\\n wr2_joint_ang = joint_states.position[4]\\n wr3_joint_ang = joint_states.position[5]\\n\\n shp_joint_vel = joint_states.velocity[0]\\n shl_joint_vel = joint_states.velocity[1]\\n elb_joint_vel = joint_states.velocity[2]\\n wr1_joint_vel = joint_states.velocity[3]\\n wr2_joint_vel = joint_states.velocity[4]\\n wr3_joint_vel = joint_states.velocity[5]\\n\\n q = [shp_joint_ang, shl_joint_ang, elb_joint_ang, wr1_joint_ang, wr2_joint_ang, wr3_joint_ang]\\n# print(\\\"q(observation):\\\", q)\\n eef_x, eef_y, eef_z = self.get_xyz(q)\\n self.end_effector = self.get_xyz(q)\\n eef_x_ini, eef_y_ini, eef_z_ini = self.get_xyz(self.init_joint_pose2) \\n\\n delta_image_r, delta_image_l = self.get_image()\\n self.cnn_image_r = agent.update_cnn(delta_image_r)\\n self.cnn_image_l = agent.update_cnn(delta_image_l)\\n self.cnn_image_r_list = self.cnn_image_r.tolist()\\n self.cnn_image_l_list = self.cnn_image_l.tolist()\\n print(\\\"r_list\\\", self.cnn_image_r_list)\\n print(\\\"l_list\\\", self.cnn_image_l_list)\\n\\n observation = []\\n# rospy.logdebug(\\\"List of Observations==>\\\"+str(self.observations))\\n for obs_name in self.observations:\\n if obs_name == \\\"shp_joint_ang\\\":\\n observation.append((shp_joint_ang - self.init_joint_pose2[0]) * self.joint_n)\\n elif obs_name == \\\"shl_joint_ang\\\":\\n observation.append((shl_joint_ang - self.init_joint_pose2[1]) * self.joint_n)\\n elif obs_name == \\\"elb_joint_ang\\\":\\n observation.append((elb_joint_ang - self.init_joint_pose2[2]) * self.joint_n)\\n elif obs_name == \\\"wr1_joint_ang\\\":\\n observation.append((wr1_joint_ang - self.init_joint_pose2[3]) * self.joint_n)\\n elif obs_name == \\\"wr2_joint_ang\\\":\\n observation.append((wr2_joint_ang - self.init_joint_pose2[4]) * self.joint_n)\\n elif obs_name == \\\"wr3_joint_ang\\\":\\n observation.append((wr3_joint_ang - self.init_joint_pose2[5]) * self.joint_n)\\n elif obs_name == \\\"shp_joint_vel\\\":\\n observation.append(shp_joint_vel)\\n elif obs_name == \\\"shl_joint_vel\\\":\\n observation.append(shl_joint_vel)\\n elif obs_name == \\\"elb_joint_vel\\\":\\n observation.append(elb_joint_vel)\\n elif obs_name == \\\"wr1_joint_vel\\\":\\n observation.append(wr1_joint_vel)\\n elif obs_name == \\\"wr2_joint_vel\\\":\\n observation.append(wr2_joint_vel)\\n elif obs_name == \\\"wr3_joint_vel\\\":\\n observation.append(wr3_joint_vel)\\n elif obs_name == \\\"eef_x\\\":\\n observation.append((eef_x - eef_x_ini) * self.eef_n)\\n elif obs_name == \\\"eef_y\\\":\\n observation.append((eef_y - eef_y_ini) * self.eef_n)\\n elif obs_name == \\\"eef_z\\\":\\n observation.append((eef_z - eef_z_ini) * self.eef_n)\\n elif obs_name == \\\"force_x\\\":\\n observation.append((self.force.x - self.force_ini.x) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"force_y\\\":\\n observation.append((self.force.y - self.force_ini.y) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"force_z\\\":\\n observation.append((self.force.z - self.force_ini.z) / self.force_limit1 * self.force_n)\\n elif obs_name == \\\"torque_x\\\":\\n observation.append((self.torque.x - self.torque_ini.x) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"torque_y\\\":\\n observation.append((self.torque.y - self.torque_ini.y) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"torque_z\\\":\\n observation.append((self.torque.z - self.torque_ini.z) / self.torque_limit1 * self.torque_n)\\n elif obs_name == \\\"image_cnn\\\":\\n for x in range(0, 10):\\n observation.append(self.cnn_image_r_list[0][x])\\n# print(\\\"r_list\\\", self.cnn_image_r_list[0][x])\\n for x in range(0, 10):\\n observation.append(self.cnn_image_l_list[0][x])\\n# print(\\\"l_list\\\", self.cnn_image_l_list[0][x])\\n elif obs_name == \\\"static_taxel\\\":\\n for x in range(0, 28):\\n observation.append((self.static_taxel[0].values[x] - self.static_taxel_ini[0].values[x]) * self.taxel_n)\\n for x in range(0, 28):\\n observation.append((self.static_taxel[1].values[x] - self.static_taxel_ini[1].values[x]) * self.taxel_n)\\n# elif obs_name == \\\"dynamic_taxel\\\":\\n# observation.append(dynamic_taxel[0].values) * self.taxel_n\\n# observation.append(dynamic_taxel[1].values) * self.taxel_n\\n else:\\n raise NameError('Observation Asked does not exist=='+str(obs_name))\\n\\n print(\\\"observation\\\", list(map(round, observation, [3]*len(observation))))\\n# print(\\\"observation\\\", observation)\\n\\n return observation\",\n \"def get_variables(self, z0, u_inf):\\n # Get the ambient data from the CTD profile\\n Ta, Sa, P = self.profile.get_values(z0, ['temperature', 'salinity',\\n 'pressure'])\\n rho = seawater.density(Ta, Sa, P)\\n \\n # Compute the properties of each dispersed-phase particle\\n us = np.zeros(len(self.particles))\\n rho_p = np.zeros(len(self.particles))\\n m_p = np.zeros(len(self.particles))\\n B_p = np.zeros(len(self.particles))\\n for i in range(len(self.particles)):\\n m0 = self.particles[i].m0\\n T0 = self.particles[i].T0\\n m_p[i] = np.sum(m0) * self.particles[i].nb0\\n if m_p[i] > 0.:\\n # Particles exist, get properties. Make sure the algorithm \\n # uses the dirty bubble properties since this is supposed\\n # to be the rise velocity averaged over the whole plume.\\n us[i], rho_p[i]= self.particles[i].properties(m0, T0, P, Sa, \\n Ta, np.inf)[0:2]\\n B_p[i] = (rho - rho_p[i]) / rho * 9.81 * (m_p[i] / rho_p[i])\\n else:\\n # Particles dissolved, set to ambient conditions\\n us[i] = 0.\\n rho_p[i] = rho\\n B_p[i] = 0.\\n \\n # Select the correct slip velocity\\n u_slip = us[0]\\n for i in range(len(self.particles) - 1):\\n if B_p[i+1] > B_p[i]:\\n u_slip = us[i+1]\\n \\n # Compute the total buoyancy flux\\n B = np.sum(B_p)\\n \\n # Get the ambient buoyancy frequency\\n N = self.profile.buoyancy_frequency(z0)\\n \\n # Return the governing parameters\\n return (B, N, u_slip, u_inf)\",\n \"def dynamics(x,Earth):\\r\\n\\r\\n # precompute a few terms to reduce number of operations\\r\\n r = norm(x[0:3])\\r\\n Re_r_sqr = 1.5*Earth.J2*(Earth.R/r)**2\\r\\n five_z_sqr = 5*x[2]**2/(r**2)\\r\\n\\r\\n # two body and J2 acceleration together\\r\\n accel = (-Earth.mu/(r**3))*np.array([x[0]*(1 - Re_r_sqr*(five_z_sqr - 1)),\\r\\n x[1]*(1 - Re_r_sqr*(five_z_sqr - 1)),\\r\\n x[2]*(1 - Re_r_sqr*(five_z_sqr - 3))])\\r\\n\\r\\n return np.array([x[3],x[4],x[5],accel[0],accel[1],accel[2]])\",\n \"def main():\\n \\n # Particle in SHO - c.f. Mocz & Succi (2015) Fig. 2\\n # parameters\\n n = 100 # number of particles\\n dt = 0.02 # timestep\\n nt = 100 # number of timesteps\\n nt_setup = 400 # number of timesteps to set up simulation\\n n_out = 25 # plot solution every nout steps\\n b = 4 # velocity damping for acquiring initial condition\\n m = 1/n # mass of SPH particle ( m * n = 1 normalizes |wavefunction|^2 to 1)\\n h = 40/n # smoothing length\\n t = 0. # time\\n\\n # plot potential\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n fig = plt.plot(xx, 0.5*xx**2, linewidth=5, color=[0.7, 0.7, 0.9])\\n \\n # initialize\\n x = np.linspace(-3.0, 3.0, num=n)\\n x = np.reshape(x,(n,1))\\n u = np.zeros((n,1))\\n \\n rho = density( x, m, h )\\n P = pressure( x, rho, m, h )\\n a = acceleration( x, u, m, rho, P, b, h )\\n\\n # get v at t=-0.5*dt for the leap frog integrator using Euler's method\\n u_mhalf = u - 0.5 * dt * a\\n\\n # main loop (time evolution)\\n for i in np.arange(-nt_setup, nt): # negative time (t<0, i<0) is used to set up initial conditions\\n\\n # leap frog\\n u_phalf = u_mhalf + a*dt\\n x = x + u_phalf*dt\\n u = 0.5*(u_mhalf+u_phalf)\\n u_mhalf = u_phalf\\n if (i >= 0):\\n t = t + dt\\n print(\\\"%.2f\\\" % t)\\n \\n if (i == -1 ): # switch off damping before t=0\\n u = np.zeros((n,1)) + 1.0\\n u_mhalf = u\\n b = 0 # switch off damping at time t=0\\n \\n # update densities, pressures, accelerations\\n rho = density( x, m, h )\\n P = pressure( x, rho, m, h )\\n a = acceleration( x, u, m, rho, P, b, h)\\n \\n # plot solution every n_out steps\\n if( (i >= 0) and (i % n_out) == 0 ):\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n rr = probeDensity(x, m, h, xx)\\n rr_exact = 1./np.sqrt(np.pi) * np.exp(-(xx-np.sin(t))**2/2.)**2\\n fig = plt.plot(xx, rr_exact, linewidth=2, color=[.6, .6, .6])\\n fig = plt.plot(xx, rr, linewidth=2, color=[1.*i/nt, 0, 1.-1.*i/nt], label='$t='+\\\"%.2f\\\" % t +'$')\\n # plot the t<0 damping process for fun\\n if( i==-nt_setup or i==-nt_setup*3/4 or i==-nt_setup/2 ):\\n xx = np.linspace(-4.0, 4.0, num=400)\\n xx = np.reshape(xx,(xx.size,1))\\n rr = probeDensity(x, m, h, xx)\\n fig = plt.plot(xx, rr, linewidth=1, color=[0.9, 0.9, 0.9])\\n \\n plt.legend()\\n plt.xlabel('$x$')\\n plt.ylabel('$|\\\\psi|^2$')\\n plt.axis([-2, 4, 0, 0.8])\\n plt.savefig('solution.pdf', aspect = 'normal', bbox_inches='tight', pad_inches = 0)\\n plt.close()\",\n \"def solve_amps(self, h, a, g):\\n\\n # Apply n_body symmetry, which builds all\\n # other spin parts of the unitary group residual\\n g3s = (+ g.t3\\n - g.t3.transpose([0, 1, 2, 3, 5, 4])\\n + g.t3.transpose([0, 1, 2, 4, 5, 3]))\\n g3 = ((+ g.t3\\n + g.t3.transpose([1, 2, 0, 4, 5, 3])\\n + g.t3.transpose([2, 0, 1, 5, 3, 4])\\n + g.t3.transpose([0, 2, 1, 3, 5, 4])\\n + g.t3.transpose([2, 1, 0, 5, 4, 3])\\n + g.t3.transpose([1, 0, 2, 4, 3, 5])\\n + 2 * g3s) / 12)\\n\\n g2 = 1 / 2 * (g.t2 + g.t2.transpose([1, 0, 3, 2]))\\n\\n t2 = g2 * (- cc_denom(h.f, g.t2.ndim, 'dir', 'full'))\\n t3 = g3 * (- cc_denom(h.f, g.t3.ndim, 'dir', 'full'))\\n\\n # Symmetrize\\n t2 = 1 / 2 * (t2 + t2.transpose([1, 0, 3, 2]))\\n t3 = ((+ t3\\n + t3.transpose([1, 2, 0, 4, 5, 3])\\n + t3.transpose([2, 0, 1, 5, 3, 4])\\n + t3.transpose([0, 2, 1, 3, 5, 4])\\n + t3.transpose([2, 1, 0, 5, 4, 3])\\n + t3.transpose([1, 0, 2, 4, 3, 5])) / 6)\\n\\n return Tensors(\\n t1=g.t1 * (- cc_denom(h.f, g.t1.ndim, 'dir', 'full')),\\n t2=t2,\\n t3=t3)\",\n \"def update_rhs(self, h, a, r):\\n return Tensors(\\n t1=r.t1 - a.t1 / cc_denom(h.f, 2, 'dir', 'full'),\\n t2=r.t2 - a.t2 / cc_denom(h.f, 4, 'dir', 'full'),\\n t3=r.t3 - (a.t3 - a.t3.transpose([0, 1, 2, 4, 3, 5])) /\\n cc_denom(h.f, 6, 'dir', 'full')\\n )\",\n \"def update_rhs(self, h, a, r):\\n return Tensors(\\n t1=r.t1 - a.t1 / cc_denom(h.f, 2, 'dir', 'full'),\\n t2=r.t2 - a.t2 / cc_denom(h.f, 4, 'dir', 'full'),\\n t3=r.t3 - (a.t3 - a.t3.transpose([0, 1, 2, 4, 3, 5])) /\\n cc_denom(h.f, 6, 'dir', 'full')\\n )\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H1 + sigma_H1\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def bet(P_1,V0_1,Vi_1,Vflap_1,J_a,Twist,Pitch,Chord,PLE,Polar,rho,dt):\\n Phi = np.zeros_like(Twist)\\n V_4 = np.zeros_like(P_1)\\n dV_4 = np.zeros_like(P_1)\\n F_5 = np.zeros_like(P_1)\\n F_4 = np.zeros_like(P_1)\\n F_1 = np.zeros_like(P_1)\\n l = np.zeros_like(Twist)\\n d = np.zeros_like(Twist)\\n m = np.zeros_like(Twist)\\n aoa = np.zeros_like(Twist)\\n daoa = np.zeros_like(Twist)\\n cl = np.zeros_like(Twist)\\n cd = np.zeros_like(Twist)\\n cm = np.zeros_like(Twist)\\n w = np.zeros_like(Chord)\\n wy = np.zeros_like(Twist)\\n S = np.zeros_like(Twist)\\n \\n Vflap_1[np.abs(Vflap_1) < 1e-8] = 0\\n \\n for i in range(len(P_1)):\\n \\n for j in range(len(P_1[i])):\\n \\n # Calculate wing element elevation angle\\n \\n if j <= J_a:\\n \\n dz = P_1[i,J_a,2]\\n dy = P_1[i,J_a,1]\\n\\n Phi[i,j] = np.arctan(dz/dy)\\n \\n else:\\n \\n dz = P_1[i,-1,2]-P_1[i,J_a+1,2]\\n dy = P_1[i,-1,1]-P_1[i,J_a+1,1]\\n \\n Phi[i,j] = np.arctan(dz/dy)\\n \\n # Calculate local flow velocity\\n V_4[i,j] = roty((Twist[i,j]+Pitch[j]), rotx(-Phi[i,j],(V0_1 + Vi_1 + Vflap_1[i,j]))) \\n V_4[np.abs(V_4) < 1e-12] = 0\\n\\n # Calculate angle of attack\\n aoa[i,j] = np.arctan(-V_4[i,j,2]/V_4[i,j,0])\\n\\n # Find cl,cd,cm from polars with linear interpolation\\n cl[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,1])\\n cd[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,2])\\n cm[i,j] = np.interp(np.degrees(aoa[i,j]),Polar[j,0],Polar[j,3])\\n\\n # Calculate element width\\n if j < len(P_1[i])-1:\\n wy[i,j] = P_1[i,j+1,1]-P_1[i,j,1]\\n else:\\n wy[i,j] = P_1[i,j,1]-P_1[i,j-1,1]\\n \\n # Calculate element surface area\\n S[i,j] = Chord[j] * wy[i,j]\\n \\n # Calculate aerodynamic forces\\n l[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cl[i,j]\\n d[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cd[i,j]\\n m[i,j] = 0.5 * rho * np.linalg.norm(V_4[i,j])**2 * S[i,j] * cm[i,j]\\n \\n # Force vector in Blade Element local axes\\n F_5[i,j] = np.array([-d[i,j], 0, l[i,j]])\\n \\n # Force vector in Blade local axes\\n F_4[i,j] = roty(aoa[i,j],F_5[i,j])\\n \\n \\n \\n \\\"\\\"\\\"\\n Add mass effect and rotate to stroke plane axes\\n \\\"\\\"\\\"\\n for i in range(len(P_1)):\\n \\n for j in range(len(P_1[i])):\\n # Add mass effects\\n if i==0:\\n daoa[i,j] = (aoa[1,j]-aoa[0,j])/dt\\n dV_4[i,j] = (V_4[1,j]-V_4[0,j])/dt\\n \\n elif i < len(P_1)-1:\\n daoa[i,j] = (aoa[i+1,j]-aoa[i-1,j])/(2*dt)\\n dV_4[i,j] = (V_4[i+1,j]-V_4[i-1,j])/(2*dt)\\n \\n else:\\n daoa[i,j] = (aoa[i,j]-aoa[i-1,j])/dt\\n dV_4[i,j] = (V_4[i,j]-V_4[i-1,j])/dt\\n \\n \\n F_4[i,j,0] = F_4[i,j,0] - 0.25 * rho * np.pi * Chord[j] * S[i,j] * V_4[i,j,2] * daoa[i,j]\\n F_4[i,j,2] = F_4[i,j,2] + 0.25 * rho * np.pi * Chord[j] * S[i,j] * dV_4[i,j,2]\\n \\n # Tranform to Stroke Plane Axes\\n F_1[i,j] = rotx(Phi[i,j],roty(-(Twist[i,j]+Pitch[j]),F_4[i,j]))\\n\\n return F_1\",\n \"def _redef_via_predef_eqn(self):\\r\\n time = self.current_T # + self.d_T\\r\\n\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff) \\r\\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\\\\r\\n (self.density * self.heat_capacity)\\r\\n\\r\\n # Source term.\\r\\n def F_func(elem, eta):\\r\\n x = elem.local_to_global(eta)\\r\\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\\r\\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func,\\r\\n self.node_map,\\r\\n gauss_mult=2) # Use double gp_1D\\r\\n\\r\\n # Boundary term.\\r\\n def f_func(elem, eta):\\r\\n n = elem.guess_normal_vector_global(eta)\\r\\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n x = elem.local_to_global(eta)\\r\\n # Evaluate our boundary term.\\r\\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\\r\\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func,\\r\\n self.node_map,\\r\\n gauss_mult=2)\",\n \"def F_trans(self):\\n rho_H1 = self.edp_par['rho_H1'].value\\n Z_H1 = self.edp_par['Z_H1'].value\\n sigma_H1 = self.edp_par['sigma_H1'].value\\n rho_H2 = self.edp_par['rho_H2'].value\\n Z_H2 = self.edp_par['Z_H2'].value\\n sigma_H2 = self.edp_par['sigma_H2'].value\\n rho_M = self.edp_par['rho_M'].value\\n sigma_M = self.edp_par['sigma_M'].value\\n psi = self.edp_par['psi'].value \\n common_scale = self.edp_par['common_scale'].value\\n \\n \\n # Make sure Z_H2 > Z_H1. If Z_H2 < Z_H1, swap them\\n if Z_H1 > Z_H2:\\n Z_H1, Z_H2 = Z_H2, Z_H1\\n sigma_H1, sigma_H2 = sigma_H2, sigma_H1\\n rho_H1, rho_H2 = rho_H2, rho_H1\\n \\n # Calculate the intermediate variables\\n alpha = self.qz*cos(psi) - self.qx*sin(psi)\\n Z_CH2 = Z_H1 - sigma_H1\\n Z_W = Z_H2 + sigma_H2\\n DeltaZ_H = Z_W - Z_CH2\\n \\n # Calculate the Gaussian part \\n FG = -rho_M*sigma_M * exp(-0.5*(alpha*sigma_M)**2)\\n FG += 2*rho_H1*sigma_H1 * cos(alpha*Z_H1) * exp(-0.5*(alpha*sigma_H1)**2)\\n FG += 2*rho_H2*sigma_H2 * cos(alpha*Z_H2) * exp(-0.5*(alpha*sigma_H2)**2)\\n FG *= np.sqrt(2*pi)\\n \\n # Calculate the strip part\\n FS = -2 * sin(alpha*Z_CH2) / alpha\\n \\n # Calculate the bridging part\\n FB = 1 / (alpha + pi/DeltaZ_H)\\n FB += 1 / (alpha - pi/DeltaZ_H)\\n FB *= sin(alpha*Z_W) + sin(alpha*Z_CH2)\\n FB *= 0.5\\n FB -= (sin(alpha*Z_W)-sin(alpha*Z_CH2)) / alpha\\n \\n return common_scale * (FG + FS + FB)\",\n \"def R_term(\\n enst, # enstrophy field\\n omega1, # vorticity-1 component\\n omega2, # vorticity-2 component\\n omega3, # vorticity-3 component\\n s11, # strain rate-11 component\\n s12, # strain rate-12 component\\n s13, # strain rate-13 component\\n s22, # strain rate-22 component\\n s23, # strain rate-23 component\\n s33, # strain rate-33 component\\n diff = False): # differentiation flag\\n #---------------------------------------------------------------------#\\n # Defining domain variables #\\n #---------------------------------------------------------------------#\\n pi = np.pi # pi\\n dx = (2.0*pi)/64.0 # spatial step\\n nu = 0.000185 # default viscosity\\n #---------------------------------------------------------------------#\\n # Spectral differentiation variables #\\n #---------------------------------------------------------------------#\\n dim = 64\\n kspec = np.fft.fftfreq(dim) * dim\\n Kfield = np.array(np.meshgrid(kspec, kspec, kspec, indexing='ij'))\\n #---------------------------------------------------------------------#\\n # Spectral differentiation variables #\\n #---------------------------------------------------------------------#\\n term1 = np.zeros((dim, dim, dim))\\n term2 = np.zeros((dim, dim, dim))\\n term3 = np.zeros((dim, dim, dim))\\n #---------------------------------------------------------------------#\\n # Numerator (numpy gradient tool) #\\n #---------------------------------------------------------------------#\\n if diff is not False:\\n term1 = np.gradient(enst,dx, edge_order=2)[0]\\n term2 = np.gradient(enst,dx, edge_order=2)[1]\\n term3 = np.gradient(enst,dx, edge_order=2)[2]\\n #---------------------------------------------------------------------#\\n # Numerator (spectral differentiation) #\\n #---------------------------------------------------------------------#\\n else:\\n term1 = 0.5*np.fft.ifftn(1j*Kfield[0]*np.fft.fftn(enst) +\\\\\\n 1j*Kfield[0]*np.fft.fftn(enst)).real\\n term2 = 0.5*np.fft.ifftn(1j*Kfield[1]*np.fft.fftn(enst) +\\\\\\n 1j*Kfield[1]*np.fft.fftn(enst)).real\\n term3 = 0.5*np.fft.ifftn(1j*Kfield[2]*np.fft.fftn(enst) +\\\\\\n 1j*Kfield[2]*np.fft.fftn(enst)).real\\n #---------------------------------------------------------------------#\\n # Numerator #\\n #---------------------------------------------------------------------#\\n num = nu*(term1**2.0+ term2**2.0 + term3**2.0)\\n #---------------------------------------------------------------------#\\n # Denominator #\\n #---------------------------------------------------------------------#\\n den = omega1*s11*omega1 + omega1*s12*omega2 + omega1*s13*omega3 +\\\\\\n omega2*s12*omega1 + omega2*s22*omega2 + omega2*s23*omega3+\\\\\\n omega3*s13*omega1 + omega3*s23*omega2 + omega3*s33*omega3\\n #---------------------------------------------------------------------#\\n # R calculation #\\n #---------------------------------------------------------------------#\\n R = num/den\\n\\n return R\",\n \"def calculate_forces(v0, mu, density_m, CD, diameter_b, \\\\\\n area_b, volume_b, density_b, \\\\\\n dt, T):\\n \\n # Gravitational const. m/s^2\\n g = 9.81 \\n # Proportionality constant for\\n # Reynolds number\\n Re_const = diameter_b*density_m/mu\\n \\n a_s = 3*math.pi*diameter_b*mu/(density_b*volume_b)\\n a_q = 0.5*CD*density_m*area_b/(density_b*volume_b)\\n b = g*(density_m/density_b - 1.0)\\n \\n # Numerical solution gives velocity as \\n # a function of time.\\n v, t = vm.solver(v0, a_s, a_q, b, Re_const, T, dt) \\n\\n # Initialize vectors\\n Fg = zeros(len(v))\\n Fb = zeros(len(v))\\n Fd = zeros(len(v))\\n\\n # Loop over time steps\\n for n in range(0, len(v)):\\n # Evaluate Reynolds number\\n Re = Re_const*v[n] \\n \\n # Gravity force\\n Fg[n] = -density_b*volume_b*g\\n # Bouyancy force\\n Fb[n] = density_m*g*volume_b\\n \\n # Drag force\\n if abs(Re) < 1:\\n # If Re < 1, use Stokes' drag force \\n Fd[n] = -3.0*math.pi*diameter_b*mu*v[n]\\n else:\\n # If Re >= 1, use the quadratic\\n # drag force\\n Fd[n] = -0.5*CD*density_m*area_b*abs(v[n])*v[n]\\n\\n \\n return Fg, Fb, Fd, t\",\n \"def calculate_surface_heatflux(self, weather, spaces_dict, surface, temp_record, Coeff, space, h_surface, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, Aflag, terrain, areaDict, areaWinDict, shadowRatios, shadowRatioIndex):\\r\\n #print \\\"Reaching Surface function...\\\"\\r\\n\\r\\n # First get the As\\r\\n A_total = self.get_A(surface, areaDict, areaWinDict)\\r\\n if Aflag == 0:\\r\\n # If it is the first surface of the space, label the space ID in the log file:\\r\\n la = str(surface.obj_id)\\r\\n lb = str(surface.obj_type)\\r\\n #TM_user.info(\\\"%s,surface area,%s,%s\\\" % (la, A_total, lb))\\r\\n A_noWin = self.get_A_noWin(surface, areaDict, areaWinDict)\\r\\n A_noOp = self.get_A_noOp(surface, areaDict, areaWinDict)\\r\\n T_space = spaces_dict[space.obj_id][1]\\r\\n T1 = weather[\\\"t_outside\\\"]\\r\\n hc_external = float(self.get_hc_external(weather, surface, h_surface, terrain))\\r\\n transmitted_win = 0\\r\\n Q_flux = 0\\r\\n\\r\\n # need the surface related information, T_space, U, R3\\r\\n U = self.get_U_surface_e(A_total, A_noOp, surface, areaWinDict) # U = Infor_surface{11,i_surface}; Defined Below\\r\\n #print U\\r\\n R3 = 1/U\\r\\n # Using calculations from: self.surface.constr.layer.C # Infor_surface{10, i_surface} ; from gbXML\\r\\n C = self.get_C_surface(A_total, A_noOp, surface, Coeff, areaWinDict) # need to pass surface and opening ids\\r\\n #print C\\r\\n\\r\\n temperature = Temperature()\\r\\n\\r\\n #Sub-routines for each wall type based on the returned hc_external\\r\\n # This hc is different for each surface type so moved under this sub-routine area\\r\\n #hc = 3.076 sent this to the Temperature Object\\r\\n if surface.obj_type == \\\"ExteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.exterior_wall(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux\\r\\n if surface.obj_type == \\\"Roof\\\":\\r\\n transmitted_win, Q_flux = temperature.roof(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"InteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.interior_wall(surface, A_total, R3, C, spaces_dict, T_space, temp_record)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"UndergroundWall\\\":\\r\\n transmitted_win, Q_flux = temperature.underground_wall(surface, A_total, R3, C, T_space, temp_record) # No instance of yet to test\\r\\n if surface.obj_type == \\\"RaisedFloor\\\":\\r\\n # This will eventually need some values when we start using raised floors\\r\\n transmitted_win, Q_flux = temperature.raised_floor(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record) # Not instance of yet to test\\r\\n\\r\\n return transmitted_win, Q_flux\",\n \"def Hstep_cost_function(H): \\n U = Wold - Yold\\n #cost = -np.trace(H.T@K@H) + (self.admm_rho/2)*(norm(H.T@D - Wold + self.Y, 'fro')**2) \\n cost = -np.trace(H.T@K@H)/nsamples + (rho/2)*np.trace((H.T@D - U)@(H.T@D-U).T) \\n return cost\",\n \"def get_synth(obswvl, obsflux, ivar, dlam, synth=None, temp=None, logg=None, fe=None, alpha=None):\\n\\n\\t# Get synthetic spectrum from grid\\n\\tif synth is None:\\n\\n\\t\\t# Use modified version of interpolateAtm to get blue synthetic spectrum from Ivanna's grid\\n\\t\\tsynthflux_blue = 1. - interpolateAtm(temp,logg,fe,alpha,griddir='/raid/gridie/bin/')\\n\\t\\twvl_range_blue = np.arange(4100., 6300.+0.14, 0.14)\\n\\t\\tsynthwvl_blue = 0.5*(wvl_range_blue[1:] + wvl_range_blue[:-1])\\n\\n\\t\\t# Also get synthetic spectrum for redder part from Evan's grid\\n\\t\\tsynthflux_red = 1. - interpolateAtm(temp,logg,fe,alpha,griddir='/raid/grid7/bin/')\\n\\t\\tsynthwvl_red = np.fromfile('/raid/grid7/bin/lambda.bin')\\n\\t\\tsynthwvl_red = np.around(synthwvl_red,2)\\n\\n\\t\\t# Splice blue + red parts together\\n\\t\\tsynthflux = np.hstack((synthflux_blue, synthflux_red))\\n\\t\\tsynthwvl = np.hstack((synthwvl_blue, synthwvl_red))\\n\\n\\t# Else, use input synthetic spectrum\\n\\telse:\\n\\t\\tsynthflux = synth[0]\\n\\t\\tsynthwvl = synth[1]\\n\\n\\t#print(synthwvl, obswvl)\\n\\n\\t# Clip synthetic spectrum so it's within range of obs spectrum\\n\\tmask = np.where((synthwvl > obswvl[0]) & (synthwvl < obswvl[-1]))\\n\\tsynthwvl = synthwvl[mask]\\n\\tsynthflux = synthflux[mask]\\n\\n\\t# Interpolate and smooth the synthetic spectrum onto the observed wavelength array\\n\\tsynthfluxnew = smooth_gauss_wrapper(synthwvl, synthflux, obswvl, dlam)\\n\\n\\t# For testing purposes\\n\\t'''\\n\\tplt.figure()\\n\\tplt.plot(synthwvl, synthflux, 'k-', label='Synthetic')\\n\\tplt.plot(obswvl, synthfluxnew, 'r-', label='Synthetic (smoothed)')\\n\\t#plt.xlim(5000, 5250)\\n\\t#plt.ylim(0.9,1.0)\\n\\tplt.legend(loc='best')\\n\\tplt.savefig('synth_cont.png')\\n\\t#plt.show()\\n\\t'''\\n\\n\\treturn synthfluxnew\",\n \"def distcalc(z,h=0.70,omegalambda=0.7,omegam=0.3,omegak=0.0):\\n\\n H0 = 100 * h # this is in units of km/s/Mpc\\n\\n H0freq = H0 * constants.kilo/(constants.mega * constants.parsec) # this is H0 is units of Hz\\n \\n hubbletime = 1.0/H0freq # in seconds\\n hubbletimeyr = hubbletime / constants.year\\n\\n #hubble distance\\n dh = constants.c / H0freq # in meters\\n\\n #now i can calculate the comoving distance (line of sight) using hogg eqn 15\\n dc = dh * integrate.quad(dcintegrand,0,z,(omegalambda,omegam,omegak))[0]\\n\\n #now i can find the transverse comoving distance using hogg eqn 16\\n if omegak == 0:\\n dm = dc\\n elif omegak > 0:\\n dm = dh/np.sqrt(omegak) * np.sinh(dc * np.sqrt(omegak) / dh)\\n else:\\n dm = dh/np.sqrt(abs(omegak)) * np.sin(dc * np.sqrt(abs(omegak)) / dh)\\n\\n\\n #now i will calculate the angular diameter distance (hogg eqn 18)\\n da = dm/(1+z)\\n \\n #now i will calculate scale in kpc/arcsec, since this is commonly used\\n scale = da * constants.arcsec / (constants.kilo * constants.parsec)\\n\\n #now i will calculate the luminosity distance (hog eqn 21)\\n dl = (1+z)*dm\\n \\n #now i will calculate lookback time and \\n #time from the begining of the universe to that redshift using hogg eqn 30\\n \\n tlookback = hubbletimeyr * integrate.quad(timeintegrand,0,z,(omegalambda,omegam,omegak))[0]\\n \\n tz = hubbletimeyr * integrate.quad(timeintegrand,z,np.inf,(omegalambda,omegam,omegak))[0]\\n \\n #all sky co-moving volume out to redshift z (hogg eqn 30)\\n if omegak == 0:\\n vc = 4 * np.pi * dm**3 / 3\\n elif omegak > 0:\\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \\n np.arcsinh( np.sqrt(omegak) * dm / dh ) / np.sqrt(omegak) )\\n else:\\n vc = ( 4 * np.pi * dh**3 / (2 * omegak) ) * ( dm * np.sqrt(1 + omegak * dm**2 / dh**2) / dh - \\n np.arcsin( np.sqrt(abs(omegak)) * dm / dh ) / np.sqrt(abs(omegak)) )\\n\\n #for output, i will make a dictionary\\n output = dict(dh=dh, dc=dc, dm=dm, da=da, scale=scale, dl=dl, tlookback = tlookback, tz=tz, vc=vc)\\n\\n return output\",\n \"def define_ufl_equations_diff(self):\\n\\n # Derivatives of velocity integration equation.\\n if self.f1 != 0:\\n self.df1_du = dlf.derivative(self.f1, self.displacement, self.trial_vector)\\n self.df1_dv = dlf.derivative(self.f1, self.velocity, self.trial_vector)\\n else:\\n self.df1_du = 0\\n self.df1_dv = 0\\n self.df1_dp = 0 # This is always zero.\\n\\n # Derivatives of momentum equation.\\n if self.displacement != 0:\\n self.df2_du = dlf.derivative(self.f2, self.displacement, self.trial_vector)\\n else:\\n self.df2_du = 0\\n\\n if self.velocity != 0:\\n self.df2_dv = dlf.derivative(self.f2, self.velocity, self.trial_vector)\\n else:\\n self.df2_dv = 0\\n\\n if self.pressure != 0:\\n self.df2_dp = dlf.derivative(self.f2, self.pressure, self.trial_scalar)\\n else:\\n self.df2_dp = 0\\n\\n # Derivatives of incompressibility equation.\\n if self.f3 != 0:\\n if self.displacement != 0:\\n self.df3_du = dlf.derivative(self.f3, self.displacement, self.trial_vector)\\n else:\\n self.df3_du = 0\\n\\n if self.velocity != 0:\\n self.df3_dv = dlf.derivative(self.f3, self.velocity, self.trial_vector)\\n else:\\n self.df3_dv = 0\\n\\n self.df3_dp = dlf.derivative(self.f3, self.pressure, self.trial_scalar)\\n else:\\n self.df3_du = 0\\n self.df3_dv = 0\\n self.df3_dp = 0\\n\\n return None\",\n \"def derivative(r, t, G=6.67e-11, AU=1.496e+11,\\n m1=5.972e+24, m2=6.417e+23, m3=1.989e+30,\\n a1=1.0*1.496e+11, a2=1.52*1.496e+11):\\n\\n if G < 0:\\n print(f\\\"The gravitational constant is negative\\\")\\n\\n if AU < 0:\\n print(f\\\"The Astronomical unit is negative\\\")\\n\\n if m1 < 0:\\n print(f\\\"The mass of the first body is negative\\\")\\n\\n if m2 < 0:\\n print(f\\\"The mass of the second body is negative\\\")\\n\\n if m3 < 0:\\n print(f\\\"The mass of the third body is negative\\\")\\n\\n if a1 < 0:\\n print(f\\\"The distance of body 1 from the body center is negative\\\")\\n\\n if a2 < 0:\\n print(f\\\"The distance of body 2 from the body center is negative\\\")\\n\\n x1 = r[0]\\n y1 = r[1]\\n v_x1 = r[2]\\n v_y1 = r[3]\\n\\n x2 = r[4]\\n y2 = r[5]\\n v_x2 = r[6]\\n v_y2 = r[7]\\n\\n x3 = r[8]\\n y3 = r[9]\\n v_x3 = r[10]\\n v_y3 = r[11]\\n\\n z1 = r[12]\\n z2 = r[13]\\n z3 = r[14]\\n\\n v_z1 = r[15]\\n v_z2 = r[16]\\n v_z3 = r[17]\\n\\n r1 = np.array([x1, y1, z1])\\n r2 = np.array([x2, y2, z2])\\n r3 = np.array([x3, y3, z3])\\n\\n dr1 = v_x1\\n dr2 = v_y1\\n\\n dr3 = (G*m2/distance(r1, r2)**3)*(x2-x1) + (G*m3/distance(r1, r3)**3)*(x3-x1)\\n dr4 = (G*m2/distance(r1, r2)**3)*(y2-y1) + (G*m3/distance(r1, r3)**3)*(y3-y1)\\n\\n dr5 = v_x2\\n dr6 = v_y2\\n\\n dr7 = (G*m1/distance(r1, r2)**3)*(x1-x2) + (G*m3/distance(r2, r3)**3)*(x3-x2)\\n dr8 = (G*m1/distance(r1, r2)**3)*(y1-y2) + (G*m3/distance(r2, r3)**3)*(y3-y2)\\n\\n dr9 = v_x3\\n dr10 = v_y3\\n\\n dr11 = (G*m1/distance(r1, r3)**3)*(x1-x3) + (G*m2/distance(r2, r3)**3)*(x2-x3)\\n dr12 = (G*m1/distance(r1, r3)**3)*(y1-y3) + (G*m2/distance(r2, r3)**3)*(y2-y3)\\n\\n dr13 = v_z1\\n dr14 = v_z2\\n dr15 = v_z3\\n\\n dr16 = (G*m2/distance(r1, r2)**3)*(z2-z2) + (G*m3/distance(r1, r3)**3)*(z3-z1)\\n dr17 = (G*m3/distance(r2, r3)**3)*(z1-z2) + (G*m1/distance(r2, r1)**3)*(z1-z2)\\n dr18 = (G*m1/distance(r1, r3)**3)*(z1-z3) + (G*m2/distance(r2, r3)**3)*(z2-z3)\\n\\n dr = np.array([dr1, dr2, dr3, dr4, dr5, dr6,\\n dr7, dr8, dr9, dr10, dr11, dr12,\\n dr13, dr14, dr15, dr16, dr17, dr18])\\n\\n return dr\",\n \"def one_transition_spectrum_fluor(self,tr):\\n \\n\\n ta = tr[\\\"ta\\\"] # TimeAxis\\n dd = tr[\\\"dd\\\"] # transition dipole strength\\n om = tr[\\\"om\\\"] # frequency - rwa\\n gg = tr[\\\"gg\\\"] # natural broadening (constant or time dependent)\\n fwhm = tr[\\\"fwhm\\\"] # Additional gaussian broadening of the spectra\\n sgm = fwhm/(2*numpy.sqrt(2*numpy.log(2)))\\n \\n # CD and fluorescence can be calculated in this step\\n # TODO if rotatory strength defined calculate also circular dichroism spectra\\n # TOOD calculate fluorescence spectra (for fluorescence there should be a switch because it should be calculated only for the first transition) \\n \\n \\n if self.system._has_system_bath_coupling:\\n# ct = tr[\\\"ct\\\"] # correlation function\\n re = tr[\\\"re\\\"] # reorganisation energy\\n \\n # convert correlation function to lineshape function\\n #gt = self._c2g(ta,ct.data)\\n gt = tr[\\\"gt\\\"]\\n # calculate time dependent response\\n at = numpy.exp(-numpy.conjugate(gt) -1j*om*ta.data + 2j*re*ta.data)\\n else:\\n # calculate time dependent response\\n at = numpy.exp(-1j*om*ta.data) \\n# plt.figure()\\n# plt.title(\\\"Absorption\\\")\\n# plt.plot(ta.data,numpy.real(at))\\n# plt.plot(ta.data,numpy.imag(at))\\n \\n \\n if len(gg) == 1:\\n gam = gg[0]\\n rt = numpy.exp(gam*ta.data)\\n at *= rt\\n #print(\\\"Constant: \\\", rt[20], len(at))\\n else:\\n rt = numpy.exp((gg)*ta.data) \\n at *= rt\\n #print(\\\"Time dependent: len = \\\", rt[20], len(rt))\\n \\n if fwhm!=0.0:\\n gauss = numpy.exp(-2*(numpy.pi**2)*(sgm**2)*(ta.data**2))\\n at *= gauss\\n \\n # Fourier transform the result\\n ft = dd*numpy.fft.hfft(at)*ta.step\\n ft = numpy.fft.fftshift(ft)\\n # invert the order because hfft is a transform with -i\\n ft = numpy.flipud(ft) \\n # cut the center of the spectrum\\n Nt = ta.length #len(ta.data) \\n return ft[Nt//2:Nt+Nt//2]\",\n \"def velocity_field(xt,yt,x0,y0,velf,dia,tsr,solidity):\\n rad = dia/2.\\n rot = tsr*velf/rad\\n\\n # Calculating EMG distribution parameters\\n loc,spr,skw,scl = vorticity(tsr,solidity)\\n \\n # Translating the turbine position\\n x0t = x0 - xt\\n y0t = y0 - yt\\n \\n # Integration of the vorticity profile using Fortran code (vorticity.f90; _vortrun.so)\\n vel_vs = dblquad(_vortmodel.integrand,0.,35.*dia,lambda x: -4.*dia,lambda x: 4.*dia, args=(x0t,y0t,dia,loc[0],loc[1],loc[2],spr[0],spr[1],skw[0],skw[1],scl[0],scl[1],scl[2]))\\n \\n # Calculating velocity deficit\\n vel = (vel_vs[0]*(rot))/(2.*pi)\\n vel = (vel + velf)/velf # normalization of velocity\\n \\n return vel\",\n \"def efSolver2(self):\\n dx = self.dh[0] # dx\\n dy = self.dh[1] # dy\\n dz = self.dh[2] # dz\\n \\n \\\"\\\"\\\"\\n for i in np.arange(0, self.ni):\\n for j in np.arange(0, self.nj):\\n for k in np.arange(0, self.nk):\\n \\\"\\\"\\\"\\n\\n ##x-component#\\n #if i==0: \\n #x-component#\\n \\\"\\\"\\\"\\n if i==0: \\n # forward\\n self.ef[i][j][k][0] = -(-3*self.phi[i][j][k]+\\\\\\n 4*self.phi[i+1][j][k]-\\\\\\n self.phi[i+2][j][k])/(2*dx)\\n \\\"\\\"\\\"\\n \\n # forward\\n self.ef[0,0:self.nj,0:self.nk,0] = -(-3*self.phi[0,0:self.nj,0:self.nk]+\\\\\\n 4*self.phi[1,0:self.nj,0:self.nk]-\\\\\\n self.phi[2,0:self.nj,0:self.nk])/(2*dx)\\n \\n #elif i==self.ni-1: \\n \\\"\\\"\\\"\\n elif i==self.ni-1: \\n # backward\\n self.ef[i][j][k][0] = -(self.phi[i-2][j][k]-\\\\\\n 4*self.phi[i-1][j][k]+\\\\\\n 3*self.phi[i][j][k])/(2*dx)\\n \\\"\\\"\\\" \\n # backward\\n self.ef[self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[self.ni-3,0:self.nj,0:self.nk]-\\\\\\n 4*self.phi[self.ni-2,0:self.nj,0:self.nk]+\\\\\\n 3*self.phi[self.ni-1,0:self.nj,0:self.nk])/(2*dx)\\n \\\"\\\"\\\"\\n else: \\n #central\\n self.ef[i][j][k][0] = -(self.phi[i+1][j][k] - \\\\\\n self.phi[i-1][j][k])/(2*dx)\\n \\\"\\\"\\\" \\n #central\\n self.ef[1:self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[2:self.ni,0:self.nj,0:self.nk] - \\\\\\n self.phi[0:self.ni-2,0:self.nj,0:self.nk])/(2*dx)\\n\\n\\n #y-component\\n #if j==0:\\n \\\"\\\"\\\"\\n if j==0:\\n self.ef[i][j][k][1] = -(-3*self.phi[i][j][k] + \\\\\\n 4*self.phi[i][j+1][k]-\\\\\\n self.phi[i][j+2][k])/(2*dy)\\n \\n \\\"\\\"\\\"\\n self.ef[0:self.ni,0,0:self.nk,1] = -(-3*self.phi[0:self.ni,0,0:self.nk] + \\\\\\n 4*self.phi[0:self.ni,1,0:self.nk]-\\\\\\n self.phi[0:self.ni,2,0:self.nk])/(2*dy)\\n #elif j==self.nj-1:\\n \\\"\\\"\\\"\\n elif j==self.nj-1:\\n self.ef[i][j][k][1] = -(self.phi[i][j-2][k] - \\\\\\n 4*self.phi[i][j-1][k] +\\\\\\n 3*self.phi[i][j][k])/(2*dy)\\n \\n \\\"\\\"\\\"\\n self.ef[0:self.ni,self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,self.nj-3,0:self.nk] - \\\\\\n 4*self.phi[0:self.ni,self.nj-2,0:self.nk] +\\\\\\n 3*self.phi[0:self.ni,self.nj-1,0:self.nk])/(2*dy)\\n #else:\\n \\\"\\\"\\\"\\n else:\\n self.ef[i][j][k][1] = -(self.phi[i][j+1][k] - \\\\\\n self.phi[i][j-1][k])/(2*dy)\\n\\n \\\"\\\"\\\"\\n self.ef[0:self.ni,1:self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,2:self.nj,0:self.nk] - \\\\\\n self.phi[0:self.ni,0:self.nj-2,0:self.nk])/(2*dy)\\n\\n #z-component\\n '''\\n if k==0:\\n self.ef[i][j][k][2] = -(-3*self.phi[i][j][k] + \\\\\\n 4*self.phi[i][j][k+1]-\\n self.phi[i][j][k+2])/(2*dz)\\n \\n '''\\n #z-component\\n #if k==0:\\n self.ef[0:self.ni,0:self.nj,0,2] = -(-3*self.phi[0:self.ni,0:self.nj,0] + \\\\\\n 4*self.phi[0:self.ni,0:self.nj,1]-\\n self.phi[0:self.ni,0:self.nj,2])/(2*dz)\\n\\n \\\"\\\"\\\"\\n elif k==self.nk-1:\\n self.ef[i][j][k][2] = -(self.phi[i][j][k-2] - \\\\\\n 4*self.phi[i][j][k-1] + \\\\\\n 3*self.phi[i][j][k])/(2*dz)\\n \\\"\\\"\\\"\\n \\n #elif k==self.nk-1:\\n self.ef[0:self.ni,0:self.nj,self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,self.nk-3] - \\\\\\n 4*self.phi[0:self.ni,0:self.nj,self.nk-2] + \\\\\\n 3*self.phi[0:self.ni,0:self.nj,self.nk-1])/(2*dz) \\n \\\"\\\"\\\"\\n else:\\n self.ef[i][j][k][2] = -(self.phi[i][j][k+1] - \\\\\\n self.phi[i][j][k-1])/(2*dz)\\n \\\"\\\"\\\"\\n #else:\\n self.ef[0:self.ni,0:self.nj,1:self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,2:self.nk] - \\\\\\n self.phi[0:self.ni,0:self.nj,0:self.nk-2])/(2*dz)\",\n \"def dvdt(self, args: Dict) -> float:\\n if self.channel_bool['leak']:\\n i_leak: float = self.leak.i(args['v'])\\n else:\\n i_leak: float = 0.\\n \\n if self.channel_bool['nav']:\\n i_nav: float = self.nav.i(args['v'], h=args['h_nav'])\\n else:\\n i_nav: float = 0.\\n\\n if self.channel_bool['kvhh']:\\n i_kvhh: float = self.kvhh.i(args['v'], n=args['n_kvhh'])\\n else:\\n i_kvhh: float = 0.\\n\\n if self.channel_bool['kva']:\\n i_kva: float = self.kva.i(args['v'], h=args['h_kva'])\\n else:\\n i_kva: float = 0.\\n\\n if self.channel_bool['kvsi']:\\n i_kvsi: float = self.kvsi.i(args['v'], m=args['m_kvsi'])\\n else:\\n i_kvsi: float = 0.\\n\\n if self.channel_bool['cav']:\\n i_cav: float = self.cav.i(args['v'])\\n else:\\n i_cav: float = 0.\\n\\n if self.channel_bool['kca']:\\n i_kca: float = self.kca.i(args['v'], ca=args['ca'])\\n else:\\n i_kca: float = 0.\\n \\n if self.channel_bool['nap']:\\n i_nap: float = self.nap.i(args['v'])\\n else:\\n i_nap: float = 0.\\n\\n if self.channel_bool['kir']:\\n i_kir: float = self.kir.i(args['v'])\\n else:\\n i_kir: float = 0.\\n\\n if self.channel_bool['ampar']:\\n i_ampar: float = self.ampar.i(args['v'], s=args['s_ampar'])\\n else:\\n i_ampar: float = 0.\\n\\n if self.channel_bool['nmdar']:\\n i_nmdar: float = self.nmdar.i(args['v'], s=args['s_nmdar'])\\n else:\\n i_nmdar: float = 0.\\n\\n if self.channel_bool['gabar']:\\n i_gabar: float = self.gabar.i(args['v'], s=args['s_gabar'])\\n else:\\n i_gabar: float = 0.\\n\\n return ((-10.0*self.params.area \\n * (i_leak\\n + i_nav \\n + i_kvhh \\n + i_kva \\n + i_kvsi \\n + i_cav \\n + i_kca \\n + i_nap \\n + i_kir) \\n - (i_ampar \\n + i_nmdar \\n + i_gabar))\\n / (10.0*self.params.cm*self.params.area))\",\n \"def solver(T, dt, v0, Cd, rho, A, m, Source=None):\\n a = Cd*rho*A/(2*m) # Set up the constant a for compact code\\n v = zeros(int(T/dt) + 1) # Create the velocity mesh\\n v[0] = v0; g = 9.81;#Initial velocity and the value of gravity acceleration\\n \\n # Description of the functions X(t) and Y(t) is given in the PDF file\\n def X(t): \\n if(Source == None):\\n return -g\\n else:\\n return -g + Source(t+dt/2.)/m\\n \\n def Y(t):\\n return a\\n \\n #Calculate the velocity at each meshpoint\\n for i in range(1,len(v)):\\n v[i] = skydiving_iterate(v[i-1], dt*(i-1), dt, X, Y)\\n return v, linspace(0, T, T/dt +1)\",\n \"def calc_variables ( ):\\n\\n # In this example we simulate using the shifted-force potential only\\n # The values of < p_sf >, < e_sf > and density should be consistent (for this potential)\\n # There are no long-range or delta corrections\\n\\n from averages_module import VariableType\\n \\n # Preliminary calculations\\n vol = box**3 # Volume\\n rho = n / vol # Density\\n\\n # Variables of interest, of class VariableType, containing three attributes:\\n # .val: the instantaneous value\\n # .nam: used for headings\\n # .method: indicating averaging method\\n # If not set below, .method adopts its default value of avg\\n # The .nam and some other attributes need only be defined once, at the start of the program,\\n # but for clarity and readability we assign all the values together below\\n\\n # Move acceptance ratio\\n m_r = VariableType ( nam = 'Move ratio', val = m_ratio, instant = False )\\n\\n # Internal energy per molecule (shifted-force potential)\\n # Ideal gas contribution (assuming nonlinear molecules) plus total PE divided by N\\n e_sf = VariableType ( nam = 'E/N shifted force', val = 3.0*temperature + total.pot/n )\\n\\n # Pressure (shifted-force potential)\\n # Ideal gas contribution plus total virial divided by V\\n p_sf = VariableType ( nam = 'P shifted force', val = rho*temperature + total.vir/vol )\\n\\n # Collect together into a list for averaging\\n return [ m_r, e_sf, p_sf ]\",\n \"def elastd(ntw):\\n # Simulated common block obs with observatory information\\n global cth, sth, clg, slg, dif, radn, gl\\n # Simulated common block love with love numbers\\n global h, k, l\\n # Simulated common block azimut with strainmeter and tiltmeter azimuths\\n global azt, azs\\n # Simulated common block bpos with lunar and solar colat and long, lunar sine parallax,\\n # and solar distance\\n global dsz, dcz, dsl, dcl, ssz, scz, ssl, scl, dpar, sdist\\n\\n coor = [dsz, dcz, dsl, dcl, ssz, scz, ssl, scl]\\n par = dpar\\n # Data for mean parallaxes, a times m over m(earth), equatorial radius.\\n rbor = [1.6592496e-2, 4.2635233e-5]\\n amrat = [78451.25, 2.1235762e12]\\n a = 6.37814e6\\n g1 = 9.79828\\n g2 = 9.82022\\n ppp = [3, 0, 0]\\n iflag = 0\\n strain = [0]\\n dele = [0, 0, 0]\\n dim = [0, 0, 0]\\n # On first call compute factors for gravity and tilt, and dc tides\\n # at the given latitude.\\n if iflag != 1:\\n iflag = 1\\n for i in range(3):\\n # browse 0,1,2\\n dele[i] = 1.0 + (2.0 / (i + 2.0)) * h[i] - ((i + 3.0) / (i + 2.0)) * k[i]\\n dim[i] = 1.0 + k[i] - h[i]\\n # dc gravity tide is also known as the Honkasalo correction\\n # **note that the love numbers for an elastic earth are used\\n # in computing the dc tide as well.eq\\n gdc = -3.0481e-7 * (3 * cth ** 2 - 1.0) * dele[0] * radn\\n tnsdc = -9.1445e-7 * cth * sth * dim[0] * radn / gl\\n etdc = -1.555e-8 * (\\n h[0] * (3.0 * cth ** 2 - 1.0) - 6.0 * l[0] * (2.0 * cth ** 2 - 1.0)\\n )\\n eldc = -1.555e-8 * (h[0] * (3.0 * cth ** 2 - 1.0) - 6.0 * l[0] * cth ** 2)\\n potdc = 0.0992064 * (1.0 - 3 * cth ** 2)\\n re = 1.0 / (radn * a)\\n\\n # zero out arrays\\n tilt = [0, 0]\\n e = [0, 0, 0]\\n tltcor = [0, 0]\\n grav = 0\\n gnth = 0\\n # compute normalized parallax\\n pa = [par / 3422.45, 1 / sdist]\\n\\n # in outer loop, ii = 0 for moon, 1 for sun (basile)\\n for ii in [0, 1]:\\n id = 3\\n if ii == 1:\\n id = 1\\n ir = 4 * (ii)\\n # find cosine of zenith angle, potential constants, legendre polynomials\\n # and their derivatives, and derivatives of the cosine of the zenith angle.\\n cll = clg * coor[ir + 3] + slg * coor[ir + 2]\\n sll = slg * coor[ir + 3] - clg * coor[ir + 2]\\n cz = coor[ir + 1]\\n sz = coor[ir]\\n cu = cth * cz + sth * sz * cll\\n xi = rbor[ii] * pa[ii] * radn\\n cc = amrat[ii] * rbor[ii] * pa[ii]\\n rkr = [cc * xi * xi, 0, 0]\\n rkr[1] = rkr[0] * xi\\n rkr[2] = rkr[1] * xi\\n\\n p = [0.5 * (3 * cu * cu - 1.0), 0, 0]\\n pp = [3 * cu, 0, 0]\\n if ii != 1:\\n p[1] = 0.5 * cu * (5.0 * cu * cu - 3.0)\\n p[2] = 0.25 * (7.0 * cu * p[1] - 3.0 * p[0])\\n pp[1] = 1.5 * (5.0 * cu * cu - 1.0)\\n pp[2] = 0.25 * (7.0 * p[1] + cu * pp[1]) - 3.0 * pp[0]\\n ppp[1] = 15.0 * cu\\n ppp[2] = 7.5 * (7.0 * cu * cu - 1.0)\\n\\n cut = -sth * cz + cth * sz * cll\\n cutt = -cu\\n cul = -sth * sz * sll\\n cull = -sth * sz * cll\\n cutl = -cth * sz * sll\\n # for j = 1:id:\\n for j in range(id):\\n if ntw[0] == 1:\\n grav = grav + dele[j] * (j + 2) * rkr[j] * p[j] * g1 * re\\n gnth = gnth - dim[0] * rkr[0] * pp[0] * g1 * cut * re\\n # ellipticity corrections, convert strains to strainmeter\\n if ntw[0] == 1:\\n grav = grav + gnth * dif - gdc\\n\\n return [grav, tilt, strain, gdc]\",\n \"def fwd_model(Ti_samples,To_samples, dw_samples, kw_samples,hi_samples,ho_samples,TA_samples):\\n\\t#Determine number of samples (totquat)\\n\\ttotquat=len(Ti_samples)\\n\\t# List to store values of Q (assuming no radiative heat transfer) calculated from\\n\\t# the random samples of the parameters\\n\\tQ_samples_4PCE=[]\\n\\t# List to store values of Q assuming radiative heat transfer occurs\\n\\t#Q_r_samples_4PCE=[]\\n\\t# Calculate values of heat flux Q (assuming no radiative heat transfer)\\n\\t# for the different sample values and append to the list\\n\\tfor i in range(totquat):\\n\\t\\t(Q,T1,T2)=compute_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i],\\\\\\n\\t\\t\\tkw_samples[i], hi_samples[i], ho_samples[i])\\n\\t\\tQ_samples_4PCE.append(Q)\\n\\t\\t# Calculate values of heat flux Q assuming radiative heat transfer to atmosphere and append to list\\n\\t\\t# For the required estimates of Q,T1, and T2 needed to solve the nonlinear system,\\n\\t\\t# we use the values obtained by solving the system assuming no radiative heat transfer\\n\\t\\t\\\"\\\"\\\"Q2=r_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i], kw_samples[i],\\\\\\n\\t\\t\\thi_samples[i], ho_samples[i], TA_samples[i], (Q,T1,T2))\\n\\t\\tQ_r_samples_4PCE.append(Q2)\\n\\t# Convert Q_r_samples_4PCE to numpy array\\n\\tQ_evals = np.array(Q_r_samples_4PCE)\\n\\treturn Q_evals\\\"\\\"\\\"\\n\\t\\tConvert Q_samples_4PCE to numpy array\\n\\t\\tQ_evals = np.array(Q_samples_4PCE)\\n\\t\\treturn Q_evals\\\"\\\"\\\"\\n\\n\\ndef KDE(fcn_evals):\\n\\t\\\"\\\"\\\"\\n\\tPerforms kernel density estimation\\n\\tInput:\\n\\t\\tfcn_evals: numpy array of evaluations of the forward model (values of heat flux Q)\\n\\tOutput:\\n\\t\\txpts_pce: numpy array of points at which the PDF is estimated.\\n\\t\\tPDF_data_pce: numpy array of estimated PDF values.\\n\\t\\\"\\\"\\\"\\n\\t# Perform KDE on fcn_evals\\n\\tkern_pce=stats.kde.gaussian_kde(fcn_evals)\\n\\t# Generate points at which to evaluate the PDF\\n\\txpts_pce=np.linspace(fcn_evals.min(),fcn_evals.max(),200)\\n\\t# Evaluate the estimated PDF at these points\\n\\tPDF_data_pce=kern_pce(xpts_pce)\\n\\treturn xpts_pce, PDF_data_pce\",\n \"def get_variables(self):\\n return [self.g_t, self.m_t]\",\n \"def finite_diff(F, x0, v0, dt, M, K, C, T):\\r\\n\\r\\n ### INITIAL PARAMETERS ####\\r\\n\\r\\n # defining the number of steps of analysis = Ns\\r\\n Ns = int(T/dt)+1\\r\\n # step t0 (initial acceleration)\\r\\n ngl = np.shape(F)[0] # captures the number of degrees of freedom\\r\\n\\r\\n ### MODELLING THE DISPLACEMENTS ###\\r\\n\\r\\n x_before = np.zeros((ngl,1))\\r\\n # matrix that indicates the displacements, in each degree of freedom, along the time of \\r\\n # duration of analysis. Each column is a time step\\r\\n x = np.zeros((ngl, Ns))\\r\\n x[:,0] = x0[:,0]\\r\\n\\r\\n ### SOLVING INITIAL STEP ###\\r\\n\\r\\n # initial Force F0 is equivalent to the first column of the matrix of load vectors F along time\\r\\n aux1 = np.zeros((ngl,1))\\r\\n aux1[:,0] = np.copy(F[:,0])\\r\\n aux2 = aux1 - np.dot(C,v0) - np.dot(K,x0)\\r\\n a0 = np.dot(la.inv(M),aux2)\\r\\n # step t-1 (before initial condition)\\r\\n x_before = dt*dt*a0/2 - dt*v0 + x0 \\r\\n # step t+1 (after initial condition)\\r\\n C1 = M / (dt*dt) + C / (2*dt)\\r\\n C2 = K - 2*M / (dt*dt)\\r\\n C3 = M / (dt*dt) - C / (2*dt)\\r\\n aux3 = aux1 - np.dot(C2, x0) - np.dot(C3, x_before)\\r\\n x[:,1] = np.dot(la.inv(C1), aux3[:,0])\\r\\n\\r\\n ### INTEGRATING ALONG THE DURATION OS ANALYSIS ###\\r\\n\\r\\n i = 0\\r\\n aux4 = np.zeros((ngl,1))\\r\\n aux5 = np.zeros((ngl,1))\\r\\n aux6 = np.zeros((ngl,1))\\r\\n aux7 = np.zeros((ngl,1))\\r\\n for i in range(1,Ns-1):\\r\\n aux4[:,0] = np.copy(F[:,i])\\r\\n aux5[:,0] = np.copy(x[:,i])\\r\\n aux6[:,0] = np.copy(x[:,i-1])\\r\\n aux7[:,0] = np.copy(x[:,i+1])\\r\\n aux7 = np.dot(la.inv(C1), aux4 - np.dot(C2,aux5) - np.dot(C3,aux6))\\r\\n x[:,i+1] = np.copy(aux7[:,0])\\r\\n return x\",\n \"def _compute_H(self, t, index, t2, index2, update_derivatives=False, stationary=False):\\r\\n\\r\\n if stationary:\\r\\n raise NotImplementedError, \\\"Error, stationary version of this covariance not yet implemented.\\\"\\r\\n # Vector of decays and delays associated with each output.\\r\\n Decay = self.decay[index]\\r\\n Decay2 = self.decay[index2]\\r\\n t_mat = t[:, None]\\r\\n t2_mat = t2[None, :]\\r\\n if self.delay is not None:\\r\\n Delay = self.delay[index]\\r\\n Delay2 = self.delay[index2]\\r\\n t_mat-=Delay[:, None]\\r\\n t2_mat-=Delay2[None, :]\\r\\n\\r\\n diff_t = (t_mat - t2_mat)\\r\\n inv_sigma_diff_t = 1./self.sigma*diff_t\\r\\n half_sigma_decay_i = 0.5*self.sigma*Decay[:, None]\\r\\n\\r\\n ln_part_1, sign1 = ln_diff_erfs(half_sigma_decay_i + t2_mat/self.sigma, \\r\\n half_sigma_decay_i - inv_sigma_diff_t,\\r\\n return_sign=True)\\r\\n ln_part_2, sign2 = ln_diff_erfs(half_sigma_decay_i,\\r\\n half_sigma_decay_i - t_mat/self.sigma,\\r\\n return_sign=True)\\r\\n\\r\\n h = sign1*np.exp(half_sigma_decay_i\\r\\n *half_sigma_decay_i\\r\\n -Decay[:, None]*diff_t+ln_part_1\\r\\n -np.log(Decay[:, None] + Decay2[None, :]))\\r\\n h -= sign2*np.exp(half_sigma_decay_i*half_sigma_decay_i\\r\\n -Decay[:, None]*t_mat-Decay2[None, :]*t2_mat+ln_part_2\\r\\n -np.log(Decay[:, None] + Decay2[None, :]))\\r\\n\\r\\n if update_derivatives:\\r\\n sigma2 = self.sigma*self.sigma\\r\\n # Update ith decay gradient\\r\\n\\r\\n dh_ddecay = ((0.5*Decay[:, None]*sigma2*(Decay[:, None] + Decay2[None, :])-1)*h\\r\\n + (-diff_t*sign1*np.exp(\\r\\n half_sigma_decay_i*half_sigma_decay_i-Decay[:, None]*diff_t+ln_part_1\\r\\n )\\r\\n +t_mat*sign2*np.exp(\\r\\n half_sigma_decay_i*half_sigma_decay_i-Decay[:, None]*t_mat\\r\\n - Decay2*t2_mat+ln_part_2))\\r\\n +self.sigma/np.sqrt(np.pi)*(\\r\\n -np.exp(\\r\\n -diff_t*diff_t/sigma2\\r\\n )+np.exp(\\r\\n -t2_mat*t2_mat/sigma2-Decay[:, None]*t_mat\\r\\n )+np.exp(\\r\\n -t_mat*t_mat/sigma2-Decay2[None, :]*t2_mat\\r\\n )-np.exp(\\r\\n -(Decay[:, None]*t_mat + Decay2[None, :]*t2_mat)\\r\\n )\\r\\n ))\\r\\n self._dh_ddecay = (dh_ddecay/(Decay[:, None]+Decay2[None, :])).real\\r\\n \\r\\n # Update jth decay gradient\\r\\n dh_ddecay2 = (t2_mat*sign2\\r\\n *np.exp(\\r\\n half_sigma_decay_i*half_sigma_decay_i\\r\\n -(Decay[:, None]*t_mat + Decay2[None, :]*t2_mat)\\r\\n +ln_part_2\\r\\n )\\r\\n -h)\\r\\n self._dh_ddecay2 = (dh_ddecay/(Decay[:, None] + Decay2[None, :])).real\\r\\n \\r\\n # Update sigma gradient\\r\\n self._dh_dsigma = (half_sigma_decay_i*Decay[:, None]*h\\r\\n + 2/(np.sqrt(np.pi)\\r\\n *(Decay[:, None]+Decay2[None, :]))\\r\\n *((-diff_t/sigma2-Decay[:, None]/2)\\r\\n *np.exp(-diff_t*diff_t/sigma2)\\r\\n + (-t2_mat/sigma2+Decay[:, None]/2)\\r\\n *np.exp(-t2_mat*t2_mat/sigma2-Decay[:, None]*t_mat) \\r\\n - (-t_mat/sigma2-Decay[:, None]/2) \\r\\n *np.exp(-t_mat*t_mat/sigma2-Decay2[None, :]*t2_mat) \\r\\n - Decay[:, None]/2\\r\\n *np.exp(-(Decay[:, None]*t_mat+Decay2[None, :]*t2_mat))))\\r\\n \\r\\n return h\",\n \"def calc(self):\\n\\n # the following if query ensures that volume- and interaction-terms\\n # are only calculated if tau > 0.\\n # (to avoid nan-values from invalid function-evaluations)\\n\\n if self.V.tau.shape == (1,):\\n Isurf = self.surface()\\n # differentiation for non-existing canopy, as otherwise NAN values\\n if self.V.tau > 0.:\\n Ivol = self.volume()\\n if self.int_Q is True:\\n Iint = self.interaction()\\n else:\\n Iint = np.array([0.])\\n else:\\n Ivol = np.array([0.])\\n Iint = np.array([0.])\\n else:\\n # calculate surface-term (valid for any tau-value)\\n Isurf = self.surface()\\n\\n # store initial parameter-values\\n old_t_0 = self.t_0\\n old_p_0 = self.p_0\\n old_t_ex = self.t_ex\\n old_p_ex = self.p_ex\\n\\n old_tau = self.V._get_tau()\\n old_omega = self.V._get_omega()\\n old_NN = self.SRF._get_NormBRDF()\\n\\n # set mask for tau > 0.\\n mask = old_tau > 0.\\n valid_index = np.where(mask)\\n inval_index = np.where(~mask)\\n\\n # set parameter-values to valid values for calculation\\n self.t_0 = old_t_0[valid_index[0]]\\n self.p_0 = old_p_0[valid_index[0]]\\n self.t_ex = old_t_ex[valid_index[0]]\\n self.p_ex = old_p_ex[valid_index[0]]\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically adds an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau[valid_index[0]])\\n if np.array(self.V.omega).size != 1:\\n self.V.omega = np.squeeze(old_omega[valid_index[0]])\\n if np.array(self.SRF.NormBRDF).size != 1:\\n self.SRF.NormBRDF = np.squeeze(old_NN[valid_index[0]])\\n\\n # calculate volume and interaction term where tau-values are valid\\n _Ivol = self.volume()\\n if self.int_Q is True:\\n _Iint = self.interaction()\\n else:\\n _Iint = np.full_like(self.t_0, 0.)\\n\\n # reset parameter values to old values\\n self.t_0 = old_t_0\\n self.p_0 = old_p_0\\n self.t_ex = old_t_ex\\n self.p_ex = old_p_ex\\n\\n # squeezing the arrays is necessary since the setter-function for\\n # tau, omega and NormBRDF automatically add an axis to the arrays!\\n self.V.tau = np.squeeze(old_tau)\\n self.V.omega = np.squeeze(old_omega)\\n self.SRF.NormBRDF = np.squeeze(old_NN)\\n\\n # combine calculated volume-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n Ivol = np.ones_like(self.t_0)\\n Ivol[valid_index[0]] = _Ivol\\n Ivol[inval_index[0]] = np.ones_like(Ivol[inval_index[0]]) * 0.\\n\\n # combine calculated interaction-contributions for valid tau-values\\n # with zero-arrays for invalid tau-values\\n if self.int_Q is True:\\n Iint = np.ones_like(self.t_0)\\n Iint[valid_index[0]] = _Iint\\n Iint[inval_index[0]] = np.ones_like(Iint[inval_index[0]]) * 0.\\n else:\\n Iint = np.full_like(self.t_0, 0.)\\n\\n return Isurf + Ivol + Iint, Isurf, Ivol, Iint\",\n \"def grad_phi_mesh(self):\\n fy = -0.5 * (np.roll(self.phi, 1, axis = 1) - np.roll(self.phi, -1, axis=1)) \\n fx = -0.5 * (np.roll(self.phi, 1, axis = 0) - np.roll(self.phi, -1, axis=0))\\n return fx,fy\",\n \"def compute_mixing_coefficients_surf(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n\\n # SET UP NEW MIXING COEFFICIENT ARRAYS\\n self.Kv_surf = np.zeros([Ly,N+1])\\n self.Kt_surf = np.zeros([Ly,N+1])\\n \\n self.ghat = np.zeros([Ly,N+1])\\n \\n\\n #################################\\n # \\tSURFACE KPP\\n ################################\\n #---> j-loop\\n \\n self.wm2 = []\\n self.ws2 = []\\n self.sigma_y = []\\n for j in range(Ly):\\n #--> k-loop (top to kbl[j])\\n # in fortran k=N-1,kbl(j),-1\\n for k in range(N-1,self.kbl[j]-1,-1):\\n k_w = k\\n k_r = k-1\\n\\n Bfsfc = self.Bfsfc_bl[j]\\n zscale = z_u_w[j,N] - z_u_w[j,k_w]\\n \\n # CALCULATE TURBULENT VELOCITY SCALES\\n wm,ws = self.lmd_wscale_wm_and_ws(Bfsfc,zscale,self.ustar[j],self.hbls[j])\\n self.wm2.append(wm)\\n self.ws2.append(ws)\\n # COMPUTE VERTICAL MIXING COEFFICIENTS\\n sigma = (z_u_w[j,N] - z_u_w[j,k_w]) / np.max([self.hbls[j],self.eps])\\n self.sigma1 = sigma #for debugging\\n if j == 25: \\n self.sigma_y.append(sigma)\\n a1 = sigma - 2.\\n a2 = 3.-2.*sigma\\n a3 = sigma - 1.\\n\\n if sigma < 0.07:\\n cff = 0.5 * (sigma-0.07)**2/0.07\\n else:\\n cff = 0\\n \\n \\n if k == N-1: \\n self.wm_debug = wm\\n self.hbls_debug = self.hbls[j]\\n self.cff_debug = cff\\n self.sigma_debug = sigma\\n self.a1_debug = a1\\n self.a2_debug = a2\\n self.a3_debug = a3\\n\\n self.Kv_surf[j,k_w] = wm * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\\\\n a1 + a2*self.Gm1[j]+a3*self.dGm1_dS[j])))\\n\\n if k == N-1:\\n self.ws_debug = ws\\n self.hbls_debug = self.hbls[j]\\n self.cff_debug = cff\\n self.sigma_debug = sigma\\n self.a1_debug = a1\\n self.a2_debug = a2\\n self.a3_debug = a3\\n \\n self.Kt_surf[j,k_w] = ws * self.hbls[j] * ( cff + sigma * (1. + sigma * (\\\\\\n a1 + a2*self.Gt1[j]+a3*self.dGt1_dS[j])))\\n #---> end k-loop \\n if self.LMD_NONLOCAL:\\n if Bfsfc < 0:\\n self.ghat[j,k_w] = 0\\n self.ghat[j,k_w] = self.Cg * sigma * (1.-sigma)**2\\n else:\\n self.ghat[j,k_w] = 0.\\n\\n # ADD CONVECTIVE ADJUSTMENT IN SURFACE MIXED LAYER \\n if self.LMD_CONVEC and self.MLCONVEC: \\n for k in range(N-1,int(self.kbl[j]-1),-1):\\n k_w = k\\n k_r = k -1\\n\\n if self.bvf[j,k_w] < 0:\\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.ffac*self.nu0c\\n\\n # ADD CONVECTIVE ADJUSTMENT BELOW SURFACE MIXED LAYER\\n # IF BKPP IS SWITCHED OFF!!\\n for k in range(int(self.kbl[j]-1),-1,-1):\\n k_w = k\\n k_r = k -1\\n if self.LMD_NONLOCAL:\\n self.ghat[j,k_w] = 0\\n if self.LMD_CONVEC and self.LMD_BKPP == False:\\n if self.bvf[j,k_w] < 0:\\n self.Kv_surf[j,k_w] = self.Kv_surf[j,k_w] + self.nu0c\\n self.Kt_surf[j,k_w] = self.Kt_surf[j,k_w] + self.nu0c\\n \\n\\n #---> end j-loop\",\n \"def _forces_moments(self, delta):\\n # assert delta.shape == (4,1)\\n da = delta[0]\\n de = delta[1]\\n dt = delta[2]\\n dr = delta[3]\\n\\n e0 = self._state[3]\\n e1 = self._state[4]\\n e2 = self._state[5]\\n e3 = self._state[6]\\n u = self._state[7]\\n v = self._state[8]\\n w = self._state[9]\\n p = self._state[10]\\n q = self._state[11]\\n r = self._state[12]\\n\\n self._Va = np.sqrt(u**2 + v**2 + w**2)\\n self._alpha = np.arctan(1.0*w/u)\\n self._beta = np.arcsin(1.0*v/self._Va)\\n\\n\\n\\n Fg = self.mass*self.gravity*np.array([2*(e1*e3-e2*e0),\\n 2*(e2*e3 + e1*e0),\\n e3**2 + e0**2 - e1**2 - e2**2,\\n ])\\n\\n # Fg = self.mass*self.gravity*np.array([2*(e1*e3 - e2*e0),\\n # 2*(e2*e3 + e1*e0),\\n # e3**2 + e0**2 - e1**2 - e2**2,\\n # ])\\n\\n M_e = 25\\n sig = lambda a: (1+np.exp(-M_e*(a-self.alpha0))+np.exp(M_e*(a+self.alpha0)))/((1+np.exp(-M_e*(a-self.alpha0)))*(1+np.exp(M_e*(a+self.alpha0))))\\n cla = lambda a: (1-sig(a))*(self.C_L_0+self.C_L_alpha*a)+sig(a)*(2*np.sign(a)*np.sin(a)**2*np.cos(a))\\n cda = lambda a: self.C_D_p + (self.C_L_0+self.C_L_alpha*a)**2/(np.pi*self.e*self.AR)\\n\\n cxa = lambda a: -(cda(a)) * np.cos(a) + (cla(a)) * np.sin(a)\\n\\n cxq = lambda a: -self.C_D_q * np.cos(a) +self.C_L_q * np.sin(a)\\n\\n cxde = lambda a: -self.C_D_delta_e * np.cos(a) + self.C_L_delta_e * np.sin(a)\\n\\n cza = lambda a: -(cda(a)) * np.sin(a) - (cla(a)) * np.cos(a)\\n\\n czq = lambda a: -self.C_D_q * np.sin(a) - self.C_L_q * np.cos(a)\\n\\n czde = lambda a: -self.C_D_delta_e * np.sin(a) - self.C_L_delta_e * np.cos(a)\\n\\n c = self.c/(2.0*self._Va)\\n b = self.b/(2.0*self._Va)\\n\\n\\n\\n one = 0.5*self.rho*self._Va**2*self.S_wing\\n # two = np.array([[1,0,0],[0,1,0],[0,0,1]])\\n three = np.array([[cxa(self._alpha)+cxq(self._alpha)*c*q+cxde(self._alpha)*de],\\n [self.C_Y_0+self.C_Y_beta*self._beta+self.C_Y_p*b*p+self.C_Y_r*b*r+self.C_Y_delta_a*da+self.C_Y_delta_r*dr],\\n [cza(self._alpha)+czq(self._alpha)*c*q+czde(self._alpha)*de]])\\n\\n Fa = np.squeeze(three) * one\\n # pdb.set_trace()\\n Fa = Fa.reshape((3,-1))\\n\\n F = Fg + Fa\\n #\\n # print(\\\"Fa:\\\",Fa)\\n\\n Fp = 0.5*self.rho*self.S_prop*self.C_prop*((self.k_motor*dt)**2-self._Va**2)\\n\\n # print(\\\"FP:\\\", Fp)\\n\\n fx = F[0] + Fp\\n # + 0.5*MAV.rho*self._Va**2*MAV.S_wing*(\\\\\\n # +cxa(self._alpha)\\\\\\n # + cxq(self._alpha)*c*q\\\\\\n # + cxde(self._alpha)*de\\n # )\\n\\n fy = F[1]\\n fz = F[2]\\n\\n # Moment time!!!\\n one = 0.5*self.rho*self._Va**2*self.S_wing\\n two = np.array([\\\\\\n [self.b*(self.C_ell_0+self.C_ell_beta*self._beta+self.C_ell_p*b*p+self.C_ell_r*b*r+self.C_ell_delta_a*da+self.C_ell_delta_r*dr)],\\n [self.c*(self.C_m_0+(self.C_m_alpha*self._alpha)+(self.C_m_q*c*q)+(self.C_m_delta_e*de))],\\n [self.b*(self.C_n_0+(self.C_n_beta*self._beta)+(self.C_n_p*b*p)+(self.C_n_r*b*r)+(self.C_n_delta_a*da)+(self.C_n_delta_r*dr))]\\n ])\\n Ma = one * np.squeeze(two)\\n # print(\\\"\\\\nMa:\\\", Ma)\\n # pdb.set_trace()\\n Ma = Ma.reshape((3,-1))\\n\\n size = Ma.shape[1]\\n\\n Mp = np.block([[np.ones(size)*-self.kTp*(self.kOmega*dt)**2],\\n [np.zeros(size)],\\n [np.zeros(size)]\\n ])\\n\\n M = Mp + Ma\\n\\n Mx = M[0]\\n My = M[1]\\n Mz = M[2]\\n\\n # self._forces[0] = fx\\n # self._forces[1] = fy\\n # self._forces[2] = fz\\n # pdb.set_trace()\\n # print(fx, fy, fz, Mx, My, Mz)\\n\\n return np.array([fx, fy, fz, Mx, My, Mz])\",\n \"def _F_qt(vh_comp,conn,q,wind=5):\\n\\n # get the van Hove data\\n (vanHove,temp,dtime) = extract_vanHove_all(vh_comp,conn,wind)\\n \\n Fqt = [_Fqt_comp(vh,q) for vh in vanHove]\\n\\n return Fqt,temp,dtime\",\n \"def evolve(self):\\n\\n rho = self.cc_data.get_var(\\\"density\\\")\\n u = self.cc_data.get_var(\\\"x-velocity\\\")\\n v = self.cc_data.get_var(\\\"y-velocity\\\")\\n\\n gradp_x = self.cc_data.get_var(\\\"gradp_x\\\")\\n gradp_y = self.cc_data.get_var(\\\"gradp_y\\\")\\n\\n # note: the base state quantities do not have valid ghost cells\\n beta0 = self.base[\\\"beta0\\\"]\\n beta0_edges = self.base[\\\"beta0-edges\\\"]\\n\\n rho0 = self.base[\\\"rho0\\\"]\\n\\n phi = self.cc_data.get_var(\\\"phi\\\")\\n\\n myg = self.cc_data.grid\\n\\n # ---------------------------------------------------------------------\\n # create the limited slopes of rho, u and v (in both directions)\\n # ---------------------------------------------------------------------\\n limiter = self.rp.get_param(\\\"lm-atmosphere.limiter\\\")\\n\\n ldelta_rx = reconstruction.limit(rho, myg, 1, limiter)\\n ldelta_ux = reconstruction.limit(u, myg, 1, limiter)\\n ldelta_vx = reconstruction.limit(v, myg, 1, limiter)\\n\\n ldelta_ry = reconstruction.limit(rho, myg, 2, limiter)\\n ldelta_uy = reconstruction.limit(u, myg, 2, limiter)\\n ldelta_vy = reconstruction.limit(v, myg, 2, limiter)\\n\\n # ---------------------------------------------------------------------\\n # get the advective velocities\\n # ---------------------------------------------------------------------\\n\\n \\\"\\\"\\\"\\n the advective velocities are the normal velocity through each cell\\n interface, and are defined on the cell edges, in a MAC type\\n staggered form\\n\\n n+1/2\\n v\\n i,j+1/2\\n +------+------+\\n | |\\n n+1/2 | | n+1/2\\n u + U + u\\n i-1/2,j | i,j | i+1/2,j\\n | |\\n +------+------+\\n n+1/2\\n v\\n i,j-1/2\\n\\n \\\"\\\"\\\"\\n\\n # this returns u on x-interfaces and v on y-interfaces. These\\n # constitute the MAC grid\\n if self.verbose > 0:\\n print(\\\" making MAC velocities\\\")\\n\\n # create the coefficient to the grad (pi/beta) term\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 1.0/rho.v()\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n # create the source term\\n source = self.aux_data.get_var(\\\"source_y\\\")\\n\\n g = self.rp.get_param(\\\"lm-atmosphere.grav\\\")\\n rhoprime = self.make_prime(rho, rho0)\\n source.v()[:, :] = rhoprime.v()*g/rho.v()\\n self.aux_data.fill_BC(\\\"source_y\\\")\\n\\n _um, _vm = lm_interface.mac_vels(myg.ng, myg.dx, myg.dy, self.dt,\\n u, v,\\n ldelta_ux, ldelta_vx,\\n ldelta_uy, ldelta_vy,\\n coeff*gradp_x, coeff*gradp_y,\\n source)\\n\\n u_MAC = ai.ArrayIndexer(d=_um, grid=myg)\\n v_MAC = ai.ArrayIndexer(d=_vm, grid=myg)\\n\\n # ---------------------------------------------------------------------\\n # do a MAC projection to make the advective velocities divergence\\n # free\\n # ---------------------------------------------------------------------\\n\\n # we will solve D (beta_0^2/rho) G phi = D (beta_0 U^MAC), where\\n # phi is cell centered, and U^MAC is the MAC-type staggered\\n # grid of the advective velocities.\\n\\n if self.verbose > 0:\\n print(\\\" MAC projection\\\")\\n\\n # create the coefficient array: beta0**2/rho\\n # MZ!!!! probably don't need the buf here\\n coeff.v(buf=1)[:, :] = 1.0/rho.v(buf=1)\\n coeff.v(buf=1)[:, :] = coeff.v(buf=1)*beta0.v2d(buf=1)**2\\n\\n # create the multigrid object\\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\\n xl_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].xlb,\\n xr_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].xrb,\\n yl_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].ylb,\\n yr_BC_type=self.cc_data.BCs[\\\"phi-MAC\\\"].yrb,\\n xmin=myg.xmin, xmax=myg.xmax,\\n ymin=myg.ymin, ymax=myg.ymax,\\n coeffs=coeff,\\n coeffs_bc=self.cc_data.BCs[\\\"density\\\"],\\n verbose=0)\\n\\n # first compute div{beta_0 U}\\n div_beta_U = mg.soln_grid.scratch_array()\\n\\n # MAC velocities are edge-centered. div{beta_0 U} is cell-centered.\\n div_beta_U.v()[:, :] = \\\\\\n beta0.v2d()*(u_MAC.ip(1) - u_MAC.v())/myg.dx + \\\\\\n (beta0_edges.v2dp(1)*v_MAC.jp(1) -\\n beta0_edges.v2d()*v_MAC.v())/myg.dy\\n\\n # solve the Poisson problem\\n mg.init_RHS(div_beta_U)\\n mg.solve(rtol=1.e-12)\\n\\n # update the normal velocities with the pressure gradient -- these\\n # constitute our advective velocities. Note that what we actually\\n # solved for here is phi/beta_0\\n phi_MAC = self.cc_data.get_var(\\\"phi-MAC\\\")\\n phi_MAC[:, :] = mg.get_solution(grid=myg)\\n\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 1.0/rho.v()\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n coeff_x = myg.scratch_array()\\n b = (3, 1, 0, 0) # this seems more than we need\\n coeff_x.v(buf=b)[:, :] = 0.5*(coeff.ip(-1, buf=b) + coeff.v(buf=b))\\n\\n coeff_y = myg.scratch_array()\\n b = (0, 0, 3, 1)\\n coeff_y.v(buf=b)[:, :] = 0.5*(coeff.jp(-1, buf=b) + coeff.v(buf=b))\\n\\n # we need the MAC velocities on all edges of the computational domain\\n # here we do U = U - (beta_0/rho) grad (phi/beta_0)\\n b = (0, 1, 0, 0)\\n u_MAC.v(buf=b)[:, :] -= \\\\\\n coeff_x.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.ip(-1, buf=b))/myg.dx\\n\\n b = (0, 0, 0, 1)\\n v_MAC.v(buf=b)[:, :] -= \\\\\\n coeff_y.v(buf=b)*(phi_MAC.v(buf=b) - phi_MAC.jp(-1, buf=b))/myg.dy\\n\\n # ---------------------------------------------------------------------\\n # predict rho to the edges and do its conservative update\\n # ---------------------------------------------------------------------\\n _rx, _ry = lm_interface.rho_states(myg.ng, myg.dx, myg.dy, self.dt,\\n rho, u_MAC, v_MAC,\\n ldelta_rx, ldelta_ry)\\n\\n rho_xint = ai.ArrayIndexer(d=_rx, grid=myg)\\n rho_yint = ai.ArrayIndexer(d=_ry, grid=myg)\\n\\n rho_old = rho.copy()\\n\\n rho.v()[:, :] -= self.dt*(\\n # (rho u)_x\\n (rho_xint.ip(1)*u_MAC.ip(1) - rho_xint.v()*u_MAC.v())/myg.dx +\\n # (rho v)_y\\n (rho_yint.jp(1)*v_MAC.jp(1) - rho_yint.v()*v_MAC.v())/myg.dy)\\n\\n self.cc_data.fill_BC(\\\"density\\\")\\n\\n # update eint as a diagnostic\\n eint = self.cc_data.get_var(\\\"eint\\\")\\n gamma = self.rp.get_param(\\\"eos.gamma\\\")\\n eint.v()[:, :] = self.base[\\\"p0\\\"].v2d()/(gamma - 1.0)/rho.v()\\n\\n # ---------------------------------------------------------------------\\n # recompute the interface states, using the advective velocity\\n # from above\\n # ---------------------------------------------------------------------\\n if self.verbose > 0:\\n print(\\\" making u, v edge states\\\")\\n\\n coeff = self.aux_data.get_var(\\\"coeff\\\")\\n coeff.v()[:, :] = 2.0/(rho.v() + rho_old.v())\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n self.aux_data.fill_BC(\\\"coeff\\\")\\n\\n _ux, _vx, _uy, _vy = \\\\\\n lm_interface.states(myg.ng, myg.dx, myg.dy, self.dt,\\n u, v,\\n ldelta_ux, ldelta_vx,\\n ldelta_uy, ldelta_vy,\\n coeff*gradp_x, coeff*gradp_y,\\n source,\\n u_MAC, v_MAC)\\n\\n u_xint = ai.ArrayIndexer(d=_ux, grid=myg)\\n v_xint = ai.ArrayIndexer(d=_vx, grid=myg)\\n u_yint = ai.ArrayIndexer(d=_uy, grid=myg)\\n v_yint = ai.ArrayIndexer(d=_vy, grid=myg)\\n\\n # ---------------------------------------------------------------------\\n # update U to get the provisional velocity field\\n # ---------------------------------------------------------------------\\n if self.verbose > 0:\\n print(\\\" doing provisional update of u, v\\\")\\n\\n # compute (U.grad)U\\n\\n # we want u_MAC U_x + v_MAC U_y\\n advect_x = myg.scratch_array()\\n advect_y = myg.scratch_array()\\n\\n advect_x.v()[:, :] = \\\\\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(u_xint.ip(1) - u_xint.v())/myg.dx +\\\\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(u_yint.jp(1) - u_yint.v())/myg.dy\\n\\n advect_y.v()[:, :] = \\\\\\n 0.5*(u_MAC.v() + u_MAC.ip(1))*(v_xint.ip(1) - v_xint.v())/myg.dx +\\\\\\n 0.5*(v_MAC.v() + v_MAC.jp(1))*(v_yint.jp(1) - v_yint.v())/myg.dy\\n\\n proj_type = self.rp.get_param(\\\"lm-atmosphere.proj_type\\\")\\n\\n if proj_type == 1:\\n u.v()[:, :] -= (self.dt*advect_x.v() + self.dt*gradp_x.v())\\n v.v()[:, :] -= (self.dt*advect_y.v() + self.dt*gradp_y.v())\\n\\n elif proj_type == 2:\\n u.v()[:, :] -= self.dt*advect_x.v()\\n v.v()[:, :] -= self.dt*advect_y.v()\\n\\n # add the gravitational source\\n rho_half = 0.5*(rho + rho_old)\\n rhoprime = self.make_prime(rho_half, rho0)\\n source[:, :] = rhoprime*g/rho_half\\n self.aux_data.fill_BC(\\\"source_y\\\")\\n\\n v[:, :] += self.dt*source\\n\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n if self.verbose > 0:\\n print(\\\"min/max rho = {}, {}\\\".format(self.cc_data.min(\\\"density\\\"), self.cc_data.max(\\\"density\\\")))\\n print(\\\"min/max u = {}, {}\\\".format(self.cc_data.min(\\\"x-velocity\\\"), self.cc_data.max(\\\"x-velocity\\\")))\\n print(\\\"min/max v = {}, {}\\\".format(self.cc_data.min(\\\"y-velocity\\\"), self.cc_data.max(\\\"y-velocity\\\")))\\n\\n # ---------------------------------------------------------------------\\n # project the final velocity\\n # ---------------------------------------------------------------------\\n\\n # now we solve L phi = D (U* /dt)\\n if self.verbose > 0:\\n print(\\\" final projection\\\")\\n\\n # create the coefficient array: beta0**2/rho\\n coeff = 1.0/rho\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()**2\\n\\n # create the multigrid object\\n mg = vcMG.VarCoeffCCMG2d(myg.nx, myg.ny,\\n xl_BC_type=self.cc_data.BCs[\\\"phi\\\"].xlb,\\n xr_BC_type=self.cc_data.BCs[\\\"phi\\\"].xrb,\\n yl_BC_type=self.cc_data.BCs[\\\"phi\\\"].ylb,\\n yr_BC_type=self.cc_data.BCs[\\\"phi\\\"].yrb,\\n xmin=myg.xmin, xmax=myg.xmax,\\n ymin=myg.ymin, ymax=myg.ymax,\\n coeffs=coeff,\\n coeffs_bc=self.cc_data.BCs[\\\"density\\\"],\\n verbose=0)\\n\\n # first compute div{beta_0 U}\\n\\n # u/v are cell-centered, divU is cell-centered\\n div_beta_U.v()[:, :] = \\\\\\n 0.5*beta0.v2d()*(u.ip(1) - u.ip(-1))/myg.dx + \\\\\\n 0.5*(beta0.v2dp(1)*v.jp(1) - beta0.v2dp(-1)*v.jp(-1))/myg.dy\\n\\n mg.init_RHS(div_beta_U/self.dt)\\n\\n # use the old phi as our initial guess\\n phiGuess = mg.soln_grid.scratch_array()\\n phiGuess.v(buf=1)[:, :] = phi.v(buf=1)\\n mg.init_solution(phiGuess)\\n\\n # solve\\n mg.solve(rtol=1.e-12)\\n\\n # store the solution in our self.cc_data object -- include a single\\n # ghostcell\\n phi[:, :] = mg.get_solution(grid=myg)\\n\\n # get the cell-centered gradient of p and update the velocities\\n # this differs depending on what we projected.\\n gradphi_x, gradphi_y = mg.get_solution_gradient(grid=myg)\\n\\n # U = U - (beta_0/rho) grad (phi/beta_0)\\n coeff = 1.0/rho\\n coeff.v()[:, :] = coeff.v()*beta0.v2d()\\n\\n u.v()[:, :] -= self.dt*coeff.v()*gradphi_x.v()\\n v.v()[:, :] -= self.dt*coeff.v()*gradphi_y.v()\\n\\n # store gradp for the next step\\n\\n if proj_type == 1:\\n gradp_x.v()[:, :] += gradphi_x.v()\\n gradp_y.v()[:, :] += gradphi_y.v()\\n\\n elif proj_type == 2:\\n gradp_x.v()[:, :] = gradphi_x.v()\\n gradp_y.v()[:, :] = gradphi_y.v()\\n\\n self.cc_data.fill_BC(\\\"x-velocity\\\")\\n self.cc_data.fill_BC(\\\"y-velocity\\\")\\n\\n self.cc_data.fill_BC(\\\"gradp_x\\\")\\n self.cc_data.fill_BC(\\\"gradp_y\\\")\\n\\n # increment the time\\n if not self.in_preevolve:\\n self.cc_data.t += self.dt\\n self.n += 1\",\n \"def enthalpy(temp,pres):\\n g = liq_g(0,0,temp,pres)\\n g_t = liq_g(1,0,temp,pres)\\n h = g - temp*g_t\\n return h\",\n \"def dynamics(state,t):\\n global M,m\\n f = control_upright(state)\\n # f = 0\\n dydx = np.zeros_like(state)\\n x,x_dot,th,th_dot = state #unpacking the state\\n dydx[0] = x_dot\\n dydx[2] = th_dot\\n\\n den1 = M + (m*sin(th)*sin(th))\\n dydx[1] = (f + (m*g*sin(th)*cos(th)) + m*L*th_dot*th_dot*sin(th) + (b/L)*(th_dot*cos(th)))/den1\\n den2 = L*den1\\n dydx[3] = (((M+m)*g*sin(th) + f*cos(th) + m*L*th_dot*th_dot*sin(th)*cos(th))/den2) + (b/(m*L*L))*th_dot\\n dydx[3] = -dydx[3]\\n\\n return dydx\",\n \"def VerletHope2(r, v, beta,dt,R_dust,M_dust):\\n # Deceptively simple (read about Velocity Verlet on wikipedia)\\n r_new = r + v*dt + calculate_acceleration2(r,v,beta,omega,R_dust,M_dust)*dt**2/2\\n v_new = v + (calculate_acceleration2(r,v,beta,omega,R_dust,M_dust) + calculate_acceleration2(r_new,v,beta,omega,R_dust,M_dust))/2 * dt\\n \\n return (r_new, v_new)\",\n \"def _diff_pot(a2,t2,d2,wair,temp,pres,ppot,airf,dhum):\\n ph2 = _eq_pressure(0,0,0,a2,t2,d2)\\n gi2 = _ice_g(0,0,t2,ppot)\\n gv2 = _eq_vappot(0,0,0,a2,t2,d2)\\n sh1 = -_air_f(0,1,0,airf,temp,dhum)\\n si1 = -_ice_g(1,0,temp,pres)\\n s1 = wair/airf*sh1 + (1-wair/airf)*si1\\n sh2 = -_air_f(0,1,0,a2,t2,d2)\\n si2 = -_ice_g(1,0,t2,ppot)\\n s2 = wair/a2*sh2 + (1-wair/a2)*si2\\n lhs = numpy.array([ppot, gi2, s1])\\n rhs = numpy.array([ph2, gv2, s2])\\n \\n ph2_a = _eq_pressure(1,0,0,a2,t2,d2)\\n ph2_t = _eq_pressure(0,1,0,a2,t2,d2)\\n ph2_d = _eq_pressure(0,0,1,a2,t2,d2)\\n gi2_t = _ice_g(1,0,t2,ppot)\\n gv2_a = _eq_vappot(1,0,0,a2,t2,d2)\\n gv2_t = _eq_vappot(0,1,0,a2,t2,d2)\\n gv2_d = _eq_vappot(0,0,1,a2,t2,d2)\\n sh2_a = -_air_f(1,1,0,a2,t2,d2)\\n sh2_t = -_air_f(0,2,0,a2,t2,d2)\\n sh2_d = -_air_f(0,1,1,a2,t2,d2)\\n si2_t = -_ice_g(2,0,t2,ppot)\\n s2_a = -wair/a2**2*(sh2 - a2*sh2_a - si2)\\n s2_t = wair/a2*sh2_t + (1-wair/a2)*si2_t\\n s2_d = wair/a2*sh2_d\\n dlhs = numpy.array([[0.,0.,0.], [0.,gi2_t,0.], [0.,0.,0.]])\\n drhs = numpy.array([[ph2_a,ph2_t,ph2_d], [gv2_a,gv2_t,gv2_d],\\n [s2_a,s2_t,s2_d]])\\n return lhs, rhs, dlhs, drhs\",\n \"def __init__(self, trap=2.5*10**16, Keq=1.0*10**17,\\n EHdecay=1.0*10**-10, Etrap=2.0*10**-10, FHloss=8.0*10**-12,\\n G3decay = 0, step=200*ps, pretime=2, reprate=80000000,\\n verbose=False, trackQ=False, scalar=1, Gdecay=0, GHdecay=0,\\n tolerance=0.005, G2decay=0. ,Gescape=1., Gform=1., G3loss=0.):\\n # Some other variables used\\n self.tolerance = tolerance\\n self.scalar = scalar\\n self.verbose = verbose\\n self.reprate = reprate\\n self.duration = 1.00 / reprate\\n self.step = step\\n self.steps = int(self.duration / self.step)\\n self.powers = []\\n self.pretime = pretime\\n # Variables which hold state densities\\n self.exciton = []\\n self.hole = []\\n self.electron = []\\n self.trap = (trap) # Total number of traps\\n self.filled = [] # Filled traps\\n self.signal = []\\n self.xsignal = []\\n self.ehsignal = []\\n self.xloss = []\\n self.tloss = []\\n self.pulses = []\\n self.qk = []\\n self.trackQ = trackQ\\n # Rate and equilibrium constants, corrected for time step size\\n self.Keq = Gescape/Gform # Equilibrium constant for X<-->e+h\\n self.EHdecay = (EHdecay * step) # e+h->ground\\n self.Etrap = (Etrap * step) # e+trap->filled\\n self.FHloss = (FHloss * step) # filled+h->ground\\n self.Gdecay = Gdecay * step\\n self.G2decay = G2decay * step\\n self.G3decay = G3decay * step\\n self.GHdecay = GHdecay * step\\n self.Gescape = Gescape * step\\n self.G3loss = G3loss * step\\n self.Gform = Gform * step\",\n \"def term_1(\\n omega1, # vorticity-1\\n omega2, # vorticity-2\\n omega3, # vorticity-3\\n enst, # enstrophy\\n nu_sgs, # turbulent viscosity\\n h = True): # spatial step size\\n #---------------------------------------------------------------------#\\n # Setting default values #\\n #---------------------------------------------------------------------#\\n if h is True:\\n h = 2.0*np.pi/64.0\\n #---------------------------------------------------------------------#\\n # Preallocating space #\\n #---------------------------------------------------------------------#\\n term = np.zeros((64,64,64))\\n #---------------------------------------------------------------------#\\n # Enstrophy term #\\n #---------------------------------------------------------------------#\\n term += np.gradient(\\\\\\n np.gradient(enst, h, edge_order=2)[2], h, edge_order=2)[2]\\n term += np.gradient(\\\\\\n np.gradient(enst, h, edge_order=2)[1], h, edge_order=2)[1]\\n term += np.gradient(\\\\\\n np.gradient(enst, h, edge_order=2)[0], h, edge_order=2)[0]\\n #---------------------------------------------------------------------#\\n # Dissipation #\\n #---------------------------------------------------------------------#\\n omega1_grad = np.gradient(omega1, h, edge_order=2)\\n omega2_grad = np.gradient(omega2, h, edge_order=2)\\n omega3_grad = np.gradient(omega3, h, edge_order=2)\\n term -= np.square(omega1_grad[2])\\n term -= np.square(omega1_grad[1])\\n term -= np.square(omega1_grad[0])\\n term -= np.square(omega2_grad[2])\\n term -= np.square(omega2_grad[1])\\n term -= np.square(omega2_grad[0])\\n term -= np.square(omega3_grad[2])\\n term -= np.square(omega3_grad[1])\\n term -= np.square(omega3_grad[0])\\n #---------------------------------------------------------------------#\\n # Applying the subgrid stress #\\n #---------------------------------------------------------------------#\\n term *= nu_sgs\\n\\n return term\",\n \"def update(self, t, dt):\\n # update boundary values\\n self._apply_boundary_conditions(t,dt)\\n #update current empirical wind velocity\\n corner_mean_index = int(scipy.floor(t/self.wind_update_period))\\n self._empirical_velocity = self.u_av[corner_mean_index], self.v_av[corner_mean_index]\\n # initialise wind speed derivative arrays\\n du_dt = scipy.zeros((self.nx, self.ny))\\n dv_dt = scipy.zeros((self.nx, self.ny))\\n # approximate spatial first derivatives with centred finite difference\\n # equations for both components of wind field\\n du_dx, du_dy = self._centred_first_derivs(self._u)\\n dv_dx, dv_dy = self._centred_first_derivs(self._v)\\n # calculate centred first sums i.e. sf_x = f(x+dx,y)+f(x-dx,y) and\\n # sf_y = f(x,y+dy)-f(x,y-dy) as first step in approximating spatial\\n # second derivatives with second order finite difference equations\\n # d2f/dx2 ~ [f(x+dx,y)-2f(x,y)+f(x-dx,y)] / (dx*dx)\\n # = [sf_x-2f(x,y)] / (dx*dx)\\n # d2f/dy2 ~ [f(x,y+dy)-2f(x,y)+f(x,y-dy)] / (dy*dy)\\n # = [sf_y-2f(x,y)] / (dy*dy)\\n # second finite differences are not computed in full as the common\\n # f(x,y) term in both expressions can be extracted out to reduce\\n # the number of +/- operations required\\n su_x, su_y = self._centred_first_sums(self._u)\\n sv_x, sv_y = self._centred_first_sums(self._v)\\n # use finite difference method to approximate time derivatives across\\n # simulation region interior from defining PDEs\\n # du/dt = -(u*du/dx + v*du/dy) + 0.5*Kx*d2u/dx2 + 0.5*Ky*d2u/dy2\\n # dv/dt = -(u*dv/dx + v*dv/dy) + 0.5*Kx*d2v/dx2 + 0.5*Ky*d2v/dy2\\n du_dt = (-self._u_int * du_dx - self._v_int * du_dy +\\n self._Bx * su_x + self._By * su_y -\\n self._C * self._u_int)\\n dv_dt = (-self._u_int * dv_dx - self._v_int * dv_dy +\\n self._Bx * sv_x + self._By * sv_y -\\n self._C * self._v_int)\\n # perform update with Euler integration\\n self._u_int += du_dt * dt\\n self._v_int += dv_dt * dt\\n # update spline interpolators\\n self._set_interpolators()\",\n \"def __init__(self, temperatures, daytypes, consumptions, nb_days, nb_particles, sigma2, kappa, u_heat):\\n self.temperatures = temperatures\\n self.daytypes = daytypes\\n self.consumptions = consumptions\\n self.nb_days = nb_days\\n self.nb_particles = nb_particles\\n self.sigma2 = sigma2\\n self.kappa = kappa\\n self.u_heat = u_heat\\n #Var init\\n self.s = np.zeros((nb_days, nb_particles)) \\n self.g_heat = np.zeros((nb_days, nb_particles))\\n #sigma_s and sigma_g are fixed\\n self.sigma_s_star_2 = np.zeros((1, nb_particles)) \\n self.sigma_g_star_2 = np.zeros((1, nb_particles))\\n self.x_season = np.zeros((1, nb_particles))\\n self.x_heat = np.zeros((1, nb_particles))\\n self.x = np.zeros((1, nb_particles))\\n self.w = np.zeros((1, nb_particles))\",\n \"def rhs_fenics(y,t):\\n #print \\\"time: \\\",t\\n uprev.vector()[:]=y\\n f.t = t #dolfin needs to know the current time for cos(t)\\n uprime_solver.solve()\\n return uprime_solution.vector().array()\",\n \"def __getitem__(self, i):\\n T0, S0 = get_surface_ts(self.nc, i)\\n \\n # average the variables if we got multiple time elements\\n if isinstance(i, slice):\\n T0, S0, = T0.mean(axis=0), S0.mean(axis=0)\\n if self.p == 0.:\\n rho, drhodT, drhodS = jmd95.eos.state_surface(T0, S0)\\n else:\\n rho, drhodT, drhodS = jmd95.eos.state(self.p, T0, S0)\\n return rho\",\n \"def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\\n\\n # def Q_gen(I, R):\\n # w = I * I * R\\n # return w\\n\\n # def Q_rad_in(I_sun, A_s, a_s):\\n # w = I_sun * D * a_s\\n # return w\\n\\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\\n # w = htcoeff * A_s * (T_line - T_amb)\\n # return w\\n\\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\\n # return w\\n\\n def reynolds(V, D, v, Mair=1.103):\\n r = V * D / v\\n return r\\n\\n def nusselt(Re, Pr):\\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\\n c = (Re / 282000) ** (5.0/8.0)\\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\\n return n\\n\\n def air_prop(T_amb):\\n # temp v k Pr\\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\\n [250, 11.44e-6, 22.3e-3, 0.720],\\n [300, 15.89e-6, 26.3e-3, 0.707],\\n [350, 20.92e-6, 30.0e-3, 0.700],\\n [400, 26.41e-6, 33.8e-3, 0.690],\\n [450, 32.39e-6, 37.3e-3, 0.686],\\n [500, 38.79e-6, 40.7e-3, 0.684],\\n [550, 45.57e-6, 43.9e-3, 0.683],\\n [600, 52.69e-6, 46.9e-3, 0.685]])\\n\\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\\n 0, air_prop[:,1:])\\n return v, k, Pr\\n\\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\\n if 273 <= T_line <= 323:\\n R = ((R_lo + \\n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\\n *(T_line - T_range[0]))/N_cond)\\n elif T_line > 323:\\n R = ((R_mid + \\n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\\n *(T_line - T_range[1]))/N_cond)\\n else:\\n R = R_lo\\n print('Out of bounds')\\n return R\\n\\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\\n temp_factor = 1\\n wind_factor = 1\\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\\n\\n T_amb = T_amb*temp_factor\\n V = V*wind_factor\\n\\n v, k, Pr = air_prop(T_amb)\\n Re = reynolds(V, D, v)\\n htcoeff = nusselt(Re, Pr) * k / D\\n\\n def T_line(T_init):\\n \\n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\\n print R\\n\\n C4 = e_s * sigma * D * math.pi\\n C3 = 0.0\\n C2 = 0.0\\n C1 = htcoeff * D * math.pi\\n C0 = - ( I ** 2 * R\\n + I_sun * a_s * D\\n + htcoeff * D * math.pi * T_amb\\n + e_s * D * math.pi * sigma * (T_amb ** 4))\\n\\n return np.roots([C4, C3, C2, C1, C0])\\n\\n T_c = T_amb\\n \\n for i in range(n_iter):\\n T_arr = T_line(T_c)\\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\\n print T_c\\n\\n return T_c\",\n \"def dvdt(self, args: List[float]) -> float:\\n v, h_nav, n_kvhh, h_kva, m_kvsi, s_ampar, _, s_nmdar, s_gabar, ca = args\\n return ((-10.0*self.params.area \\n * (self.leak.i(v)\\n + self.nav.i(v, h=h_nav) \\n + self.kvhh.i(v, n=n_kvhh)\\n + self.kva.i(v, h=h_kva)\\n + self.kvsi.i(v, m=m_kvsi)\\n + self.cav.i(v)\\n + self.kca.i(v, ca=ca)\\n + self.nap.i(v)\\n + self.kir.i(v))\\n - (self.ampar.i(v, s=s_ampar)\\n + self.nmdar.i(v, s=s_nmdar)\\n + self.gabar.i(v, s=s_gabar))) \\n / (10.0*self.params.cm*self.params.area))\",\n \"def derivatives(self):\\n self.rdot = self.v\\n self.vdot[:,:] = 0.0\\n self.udot[:] = 0.0\\n\\n t = time()\\n for nl in self.nlists: \\n nl.separations()\\n #nl.apply_minimum_image()\\n self.timing['pairsep time'] = (time() - t)\\n\\n t = time()\\n if SPROPS:\\n properties.spam_properties(self,self.nl_default \\\\\\n ,self.h[0:self.n],self.hlr[0:self.n])\\n self.timing['SPAM time'] = time() - t\\n \\n t = time()\\n for force in self.forces:\\n force.apply()\\n self.timing['force time'] = time() - t\\n \\n if ADVECTIVE:\\n self.rdot[:,:] = 0.0\",\n \"def forward(self, z_t_1, h_x, phi_table, t, temp=0):\\n \\n# sparsemax.device = z_t_1.device\\n \\n z_category, z_category_sparse = self.gen_z_t_dist_now(z_t_1, h_x)\\n \\n# if t > self.t_thres:\\n# \\n# if self.use_gumbel_softmax:\\n# # print(t, 'inference here')\\n# # device = z_category.device\\n# \\n# averaged_z_t = 0\\n# \\n# log_prob = Variable(torch.log(z_category))\\n# \\n# for k in range(self.sampling_times): \\n# curr_z_t = F.gumbel_softmax(log_prob, tau = 0.05)\\n# \\n# # curr_z_t = sparsemax(log_prob)\\n# \\n# \\n# averaged_z_t += curr_z_t\\n# \\n# del curr_z_t\\n# \\n# # averaged_z_t = averaged_z_t.to(device)\\n# \\n# z_t = averaged_z_t/self.sampling_times\\n# \\n# # print('diff::', torch.norm(z_t - z_category))\\n# # \\n# # print()\\n# else:\\n# z_t = z_category\\n# \\n# else:\\n z_t = z_category\\n \\n if len(z_t.shape) == 2:\\n phi_z = torch.mm(z_t, torch.t(phi_table))\\n else:\\n \\n phi_table_full = (torch.t(phi_table)).view(1, phi_table.shape[1], phi_table.shape[0])\\n \\n phi_table_full = phi_table_full.repeat(phi_table.shape[1], 1, 1)\\n \\n phi_z = torch.bmm(z_t, phi_table_full)\\n# mu = self.h_to_mu(h_combined)\\n# logvar = self.h_to_logvar(h_combined)\\n# std = torch.exp(0.5 * logvar) \\n# epsilon = torch.randn(z_t_1.size(), device=z_t_1.device) # sampling z by re-parameterization\\n# z_t = epsilon * std + mu # [batch_sz x z_sz]\\n return z_t, z_category, phi_z, z_category_sparse\",\n \"def addPhotonVariables(hf, event, data_temp, pho):\\n # data_temp[ 0, column_names.index( 'pho_truthPdgId_egam') ] = hf[ 'pho_truthPdgId_egam' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_truthPdgId_atlas') ] = hf[ 'pho_truthPdgId_atlas' ][ event][ pho ]\\n # data_temp[ 0, column_names.index( 'pho_egamTruthParticle') ] = hf[ 'pho_egamTruthParticle' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_truthType') ] = hf[ 'pho_truthType' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_truthOrigin') ] = hf[ 'pho_truthOrigin' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_isPhotonEMLoose') ] = hf[ 'pho_isPhotonEMLoose' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_isPhotonEMTight') ] = hf[ 'pho_isPhotonEMTight' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_e') ] = hf[ 'pho_e' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_eta') ] = hf[ 'pho_eta' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_phi') ] = hf[ 'pho_phi' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_et') ] = hf[ 'pho_et' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_Rhad1') ] = hf[ 'pho_Rhad1' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_Rhad') ] = hf[ 'pho_Rhad' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_weta2') ] = hf[ 'pho_weta2' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_Rphi') ] = hf[ 'pho_Rphi' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_Reta') ] = hf[ 'pho_Reta' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_Eratio') ] = hf[ 'pho_Eratio' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_f1') ] = hf[ 'pho_f1' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_wtots1') ] = hf[ 'pho_wtots1' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_DeltaE') ] = hf[ 'pho_DeltaE' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_weta1') ] = hf[ 'pho_weta1' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_fracs1') ] = hf[ 'pho_fracs1' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_ConversionType') ] = hf[ 'pho_ConversionType' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_ConversionRadius') ] = hf[ 'pho_ConversionRadius' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_VertexConvEtOverPt') ] = hf[ 'pho_VertexConvEtOverPt' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_VertexConvPtRatio') ] = hf[ 'pho_VertexConvPtRatio' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_topoetcone20') ] = hf[ 'pho_topoetcone20' ][ event][ pho ]\\n # data_temp[ 0, column_names.index( 'pho_topoetcone30') ] = hf[ 'pho_topoetcone30' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_topoetcone40') ] = hf[ 'pho_topoetcone40' ][ event][ pho ]\\n data_temp[ 0, column_names.index( 'pho_ptvarcone20') ] = hf[ 'pho_ptvarcone20' ][ event][ pho ]\\n # data_temp[ 0, column_names.index( 'pho_ptvarcone30') ] = hf[ 'pho_ptvarcone30' ][ event][ pho ]\\n # data_temp[ 0, column_names.index( 'pho_ptvarcone40') ] = hf[ 'pho_ptvarcone40' ][ event][ pho ]\",\n \"def __init__(self,\\n uDict,\\n phiDict,\\n testSpaceDict,\\n matType,\\n dofBoundaryConditionsDict,\\n dofBoundaryConditionsSetterDict,\\n coefficients,\\n elementQuadrature,\\n elementBoundaryQuadrature,\\n fluxBoundaryConditionsDict=None,\\n advectiveFluxBoundaryConditionsSetterDict=None,\\n diffusiveFluxBoundaryConditionsSetterDictDict=None,\\n stressTraceBoundaryConditionsSetterDict=None,\\n stabilization=None,\\n shockCapturing=None,\\n conservativeFluxDict=None,\\n numericalFluxType=None,\\n TimeIntegrationClass=None,\\n massLumping=False,\\n reactionLumping=False,\\n options=None,\\n name='defaultName',\\n reuse_trial_and_test_quadrature=True,\\n sd = True,\\n movingDomain=False):\\n #\\n #set the objects describing the method and boundary conditions\\n #\\n self.movingDomain=movingDomain\\n self.tLast_mesh=None\\n #\\n self.name=name\\n self.sd=sd\\n self.Hess=False\\n self.lowmem=True\\n self.timeTerm=True#allow turning off the time derivative\\n #self.lowmem=False\\n self.testIsTrial=True\\n self.phiTrialIsTrial=True\\n self.u = uDict\\n self.ua = {}#analytical solutions\\n self.phi = phiDict\\n self.dphi={}\\n for ck,phi in phiDict.iteritems():\\n if coefficients.potential.has_key(ck):\\n for cj in coefficients.potential[ck].keys():\\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\\n else:\\n self.dphi[(ck,ck)] = FiniteElementFunction(phi.femSpace)\\n #check for nonlinearities in the diffusion coefficient that don't match the potential\\n for ci,ckDict in coefficients.diffusion.iteritems():\\n #for ck,cjDict in coefficients.diffusion.iteritems(): #cek: bug?\\n for ck,cjDict in ckDict.iteritems():\\n for cj in cjDict.keys():\\n if not self.dphi.has_key((ck,cj)):\\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\\n self.matType = matType\\n #try to reuse test and trial information across components if spaces are the same\\n self.reuse_test_trial_quadrature = reuse_trial_and_test_quadrature#True#False\\n if self.reuse_test_trial_quadrature:\\n for ci in range(1,coefficients.nc):\\n assert self.u[ci].femSpace.__class__.__name__ == self.u[0].femSpace.__class__.__name__, \\\"to reuse_test_trial_quad all femSpaces must be the same!\\\"\\n ## Simplicial Mesh\\n self.mesh = self.u[0].femSpace.mesh #assume the same mesh for all components for now\\n self.testSpace = testSpaceDict\\n self.dirichletConditions = dofBoundaryConditionsDict\\n self.dirichletNodeSetList=None #explicit Dirichlet conditions for now, no Dirichlet BC constraints\\n self.coefficients = coefficients\\n self.coefficients.initializeMesh(self.mesh)\\n self.nc = self.coefficients.nc\\n self.stabilization = stabilization\\n self.shockCapturing = shockCapturing\\n self.conservativeFlux = conservativeFluxDict #no velocity post-processing for now\\n self.fluxBoundaryConditions=fluxBoundaryConditionsDict\\n self.advectiveFluxBoundaryConditionsSetterDict=advectiveFluxBoundaryConditionsSetterDict\\n self.diffusiveFluxBoundaryConditionsSetterDictDict = diffusiveFluxBoundaryConditionsSetterDictDict\\n #determine whether the stabilization term is nonlinear\\n self.stabilizationIsNonlinear = False\\n #cek come back\\n if self.stabilization != None:\\n for ci in range(self.nc):\\n if coefficients.mass.has_key(ci):\\n for flag in coefficients.mass[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.advection.has_key(ci):\\n for flag in coefficients.advection[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.diffusion.has_key(ci):\\n for diffusionDict in coefficients.diffusion[ci].values():\\n for flag in diffusionDict.values():\\n if flag != 'constant':\\n self.stabilizationIsNonlinear=True\\n if coefficients.potential.has_key(ci):\\n for flag in coefficients.potential[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.reaction.has_key(ci):\\n for flag in coefficients.reaction[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.hamiltonian.has_key(ci):\\n for flag in coefficients.hamiltonian[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n #determine if we need element boundary storage\\n self.elementBoundaryIntegrals = {}\\n for ci in range(self.nc):\\n self.elementBoundaryIntegrals[ci] = ((self.conservativeFlux != None) or\\n (numericalFluxType != None) or\\n (self.fluxBoundaryConditions[ci] == 'outFlow') or\\n (self.fluxBoundaryConditions[ci] == 'mixedFlow') or\\n (self.fluxBoundaryConditions[ci] == 'setFlow'))\\n #\\n #calculate some dimensions\\n #\\n self.nSpace_global = self.u[0].femSpace.nSpace_global #assume same space dim for all variables\\n self.nDOF_trial_element = [u_j.femSpace.max_nDOF_element for u_j in self.u.values()]\\n self.nDOF_phi_trial_element = [phi_k.femSpace.max_nDOF_element for phi_k in self.phi.values()]\\n self.n_phi_ip_element = [phi_k.femSpace.referenceFiniteElement.interpolationConditions.nQuadraturePoints for phi_k in self.phi.values()]\\n self.nDOF_test_element = [femSpace.max_nDOF_element for femSpace in self.testSpace.values()]\\n self.nFreeDOF_global = [dc.nFreeDOF_global for dc in self.dirichletConditions.values()]\\n self.nVDOF_element = sum(self.nDOF_trial_element)\\n self.nFreeVDOF_global = sum(self.nFreeDOF_global)\\n #\\n NonlinearEquation.__init__(self,self.nFreeVDOF_global)\\n #\\n #build the quadrature point dictionaries from the input (this\\n #is just for convenience so that the input doesn't have to be\\n #complete)\\n #\\n elementQuadratureDict={}\\n elemQuadIsDict = isinstance(elementQuadrature,dict)\\n if elemQuadIsDict: #set terms manually\\n for I in self.coefficients.elementIntegralKeys:\\n if elementQuadrature.has_key(I):\\n elementQuadratureDict[I] = elementQuadrature[I]\\n else:\\n elementQuadratureDict[I] = elementQuadrature['default']\\n else:\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[I] = elementQuadrature\\n if self.stabilization != None:\\n for I in self.coefficients.elementIntegralKeys:\\n if elemQuadIsDict:\\n if elementQuadrature.has_key(I):\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature[I]\\n else:\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature['default']\\n else:\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature\\n if self.shockCapturing != None:\\n for ci in self.shockCapturing.components:\\n if elemQuadIsDict:\\n if elementQuadrature.has_key(('numDiff',ci,ci)):\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature[('numDiff',ci,ci)]\\n else:\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature['default']\\n else:\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature\\n if massLumping:\\n for ci in self.coefficients.mass.keys():\\n elementQuadratureDict[('m',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n if reactionLumping:\\n for ci in self.coefficients.mass.keys():\\n elementQuadratureDict[('r',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n elementBoundaryQuadratureDict={}\\n if isinstance(elementBoundaryQuadrature,dict): #set terms manually\\n for I in self.coefficients.elementBoundaryIntegralKeys:\\n if elementBoundaryQuadrature.has_key(I):\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature[I]\\n else:\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature['default']\\n else:\\n for I in self.coefficients.elementBoundaryIntegralKeys:\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature\\n #\\n # find the union of all element quadrature points and\\n # build a quadrature rule for each integral that has a\\n # weight at each point in the union\\n #mwf include tag telling me which indices are which quadrature rule?\\n (self.elementQuadraturePoints,self.elementQuadratureWeights,\\n self.elementQuadratureRuleIndeces) = Quadrature.buildUnion(elementQuadratureDict)\\n self.nQuadraturePoints_element = self.elementQuadraturePoints.shape[0]\\n self.nQuadraturePoints_global = self.nQuadraturePoints_element*self.mesh.nElements_global\\n #\\n #Repeat the same thing for the element boundary quadrature\\n #\\n (self.elementBoundaryQuadraturePoints,\\n self.elementBoundaryQuadratureWeights,\\n self.elementBoundaryQuadratureRuleIndeces) = Quadrature.buildUnion(elementBoundaryQuadratureDict)\\n self.nElementBoundaryQuadraturePoints_elementBoundary = self.elementBoundaryQuadraturePoints.shape[0]\\n self.nElementBoundaryQuadraturePoints_global = (self.mesh.nElements_global*\\n self.mesh.nElementBoundaries_element*\\n self.nElementBoundaryQuadraturePoints_elementBoundary)\\n\\n #\\n #storage dictionaries\\n self.scalars_element = set()\\n #\\n #simplified allocations for test==trial and also check if space is mixed or not\\n #\\n self.q={}\\n self.ebq={}\\n self.ebq_global={}\\n self.ebqe={}\\n self.phi_ip={}\\n #mesh\\n self.q['x'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,3),'d')\\n self.q['det(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q['abs(det(J))'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q['J'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\\n self.q['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\\n self.ebqe['x'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['g'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n max(1,self.nSpace_global-1),\\n max(1,self.nSpace_global-1)),\\n 'd')\\n self.ebqe['inverse(J)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global,self.nSpace_global),'d')\\n self.ebqe['hat(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['bar(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['sqrt(det(g))'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n self.ebqe[('n')] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n #shape\\n self.q[('v',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.q[('w',0)] = self.q[('v',0)]\\n self.q[('grad(v)',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.q[('grad(w)',0)] = self.q[('grad(v)',0)]\\n self.q[('grad(w)*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.q[('grad(w)*dV_f',0)] = self.q[('grad(w)*dV',0)]\\n #todo get rid of dV_{f,a}, etc\\n self.q[('w*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.q[('w*dV_m',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n #assume all components are the same space for now\\n shapeKeysForAlias = ['v','w','grad(v)','grad(w)*dV','grad(w)*dV_f','w*dV','w*dV_m']\\n for ci in range(1,self.nc):\\n for key in shapeKeysForAlias:\\n key_ci = (key,ci)\\n key_0 = (key,0)\\n self.q[key_ci] = self.q[key_0]\\n #ELLAM weights stiffness, body integrals by dt\\n for ci in range(self.nc):\\n self.q[('dt*grad(w)*dV',ci)]= numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[ci],self.nSpace_global),'d')\\n #\\n self.ebqe[('v',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\\n self.ebqe[('w',0)] = self.ebqe[('v',0)]\\n self.ebqe[('grad(v)',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.ebqe[('w*dS_f',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\\n #assume all components are the same space for now\\n shapeKeysForAlias = ['v','w','grad(v)','w*dS_f']\\n for ci in range(1,self.nc):\\n for key in shapeKeysForAlias:\\n key_ci = (key,ci)\\n key_0 = (key,0)\\n self.ebqe[key_ci] = self.ebqe[key_0]\\n\\n for ci in range(self.nc):\\n self.q[('u',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('grad(u)',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n #f\\n for ci in self.coefficients.advection.keys():\\n self.q[('f',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n for cj in self.coefficients.advection[ci].keys():\\n self.q[('df',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n self.ebqe[('f',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n for cj in self.coefficients.advection[ci].keys():\\n self.ebqe[('df',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n\\n #a, linear dispersion single component\\n\\n for ci,ckDict in self.coefficients.diffusion.iteritems():\\n for ck,cjDict in ckDict.iteritems():\\n for flag in cjDict.values():\\n assert flag == 'constant', \\\"Error potential %s LADRellam does not handle diffusion = %s yet\\\" % (ck,flag)\\n\\n if self.coefficients.sdInfo != None and (ci,ck) in self.coefficients.sdInfo.keys():\\n self.q[('a',ci,ck)] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n for cj in cjDict.keys():\\n self.q[('da',ci,ck,cj)] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n self.ebqe[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n for cj in cjDict.keys():\\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n\\n else:\\n self.q[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n for cj in cjDict.keys():\\n self.q[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n self.ebqe[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n for cj in cjDict.keys():\\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n #dense storage\\n self.q[('grad(w)*dV_a',ci,ck)] = self.q[('grad(w)*dV_f',ci)]\\n self.q[('dt*grad(w)*dV_a',ci,ck)]= self.q[('dt*grad(w)*dV',ci)]\\n #ci,ckDict\\n #linear potential only for now, need to change for e.g., Buckley Leverett\\n for ck in self.phi.keys():\\n self.phi[ck].dof[:]=self.u[ck].dof\\n self.q[('grad(phi)',ck)] = self.q[('grad(u)',ck)]\\n for key in self.dphi.keys():\\n self.dphi[key].dof.fill(1.0)\\n self.q[('dphi',key[0],key[1])] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n\\n\\n\\n# if self.coefficients.diffusion.has_key(0):\\n# for ck,flag in self.coefficients.diffusion[0][0].iteritems():\\n# assert self.coefficients.diffusion[0][0][ck] == 'constant', \\\"Error potential %s LADRellam does not handle diffusion = %s yet\\\" % (ck,flag)\\n# if self.coefficients.sdInfo != None and (0,0) in self.coefficients.sdInfo.keys():\\n# self.q[('a',0,0)] = numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.q[('da',0,0,0)] = numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.ebqe[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n\\n# else:\\n# self.q[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.q[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.ebqe[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# #\\n# self.phi[0].dof[:]=self.u[0].dof\\n# self.dphi[(0,0)].dof.fill(1.0)\\n# self.q[('grad(phi)',0)] = self.q[('grad(u)',0)]\\n# self.q[('dphi',0,0)] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n\\n# self.q[('grad(w)*dV_a',0,0)] = self.q[('grad(w)*dV_f',0)]\\n# self.q[('dt*grad(w)*dV_a',0,0)]= self.q[('dt*grad(w)*dV',0)]\\n\\n #r 'constant' ie not a function of solution but go ahead and include dr for now\\n for ci,cjDict in self.coefficients.reaction.iteritems():\\n self.q[('r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n for cj in cjDict.keys():\\n self.q[('dr',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('w*dV_r',ci)] = self.q[('w*dV',ci)]\\n self.q[('dt*w*dV_r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.ebqe[('r',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n #m\\n for ci,cjDict in self.coefficients.mass.iteritems():\\n self.q[('m',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n for cj in cjDict.keys():\\n self.q[('dm',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('mt',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('m_last',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('m_tmp',ci)] = self.q[('m',ci)]\\n self.q[('cfl',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('numDiff',ci,ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.ebqe[('m',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n for cj in cjDict.keys():\\n self.ebqe[('dm',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n\\n ###ellam specific options with defauls here\\n self.ellamDiscretization = ELLAMtools.ELLAMdiscretization(self,options)\\n\\n #\\n self.needEBQ = options.needEBQ #could need for analytical velocity evaluation with RT0,BDM\\n\\n #beg normal stuff allocating things\\n self.points_elementBoundaryQuadrature= set()\\n self.scalars_elementBoundaryQuadrature= set([('u',ci) for ci in range(self.nc)])\\n self.vectors_elementBoundaryQuadrature= set()\\n self.tensors_elementBoundaryQuadrature= set()\\n\\n if self.needEBQ:\\n for k in ['x','hat(x)']:\\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n 3),'d')\\n self.ebq['n'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global),'d')\\n self.ebq['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),'d')\\n #allocate the metric tensor\\n self.ebq['g'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n max(1,self.nSpace_global-1),\\n max(1,self.nSpace_global-1)),\\n 'd')\\n log(memory(\\\"element boundary quadrature\\\",\\\"LADRellam\\\"),level=4)\\n ebq_keys = ['sqrt(det(g))']\\n ebq_keys.extend([('u',ci) for ci in range(self.nc)])\\n for k in ebq_keys:\\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n #test and trial info\\n self.ebq[('w',0)] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nDOF_trial_element[0]),'d')\\n for ci in range(1,self.nc):\\n self.ebq[('w',ci)] = self.ebq[('w',0)]\\n for ci in range(self.nc):\\n self.ebq[('v',ci)] = self.ebq[('w',0)]\\n\\n #ebq_global info\\n self.ebq_global['x'] = numpy.zeros((self.mesh.nElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n 3),'d')\\n self.ebq_global['n'] = numpy.zeros((self.mesh.nElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global),'d')\\n #\\n # allocate residual and Jacobian storage\\n #\\n self.elementResidual = [numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci]),\\n 'd') for ci in range(self.nc)]\\n self.elementSpatialResidual = [numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci]),\\n 'd') for ci in range(self.nc)]\\n self.elementJacobian = {}\\n for ci in range(self.nc):\\n self.elementJacobian[ci]={}\\n for cj in range(self.nc):\\n if cj in self.coefficients.stencil[ci]:\\n self.elementJacobian[ci][cj] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci],\\n self.nDOF_trial_element[cj]),\\n 'd')\\n #\\n self.fluxJacobian_exterior = {}\\n for ci in range(self.nc):\\n self.fluxJacobian_exterior[ci]={}\\n for cj in self.coefficients.stencil[ci]:\\n self.fluxJacobian_exterior[ci][cj] = numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nDOF_trial_element[cj]),\\n 'd')\\n\\n #\\n #\\n #\\n #\\n log(memory(\\\"element and element boundary Jacobians\\\",\\\"OneLevelTransport\\\"),level=4)\\n self.inflowBoundaryBC = {}\\n self.inflowBoundaryBC_values = {}\\n self.inflowFlux = {}\\n for cj in range(self.nc):\\n self.inflowBoundaryBC[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,),'i')\\n self.inflowBoundaryBC_values[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nDOF_trial_element[cj]),'d')\\n self.inflowFlux[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n self.internalNodes = set(range(self.mesh.nNodes_global))\\n #identify the internal nodes this is ought to be in mesh\\n ##\\\\todo move this to mesh\\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\\n eN_global = self.mesh.elementBoundaryElementsArray[ebN,0]\\n ebN_element = self.mesh.elementBoundaryLocalElementBoundariesArray[ebN,0]\\n for i in range(self.mesh.nNodes_element):\\n if i != ebN_element:\\n I = self.mesh.elementNodesArray[eN_global,i]\\n self.internalNodes -= set([I])\\n self.nNodes_internal = len(self.internalNodes)\\n self.internalNodesArray=numpy.zeros((self.nNodes_internal,),'i')\\n for nI,n in enumerate(self.internalNodes):\\n self.internalNodesArray[nI]=n\\n #\\n del self.internalNodes\\n self.internalNodes = None\\n log(\\\"Updating local to global mappings\\\",2)\\n self.updateLocal2Global()\\n log(\\\"Building time integration object\\\",2)\\n log(memory(\\\"inflowBC, internalNodes,updateLocal2Global\\\",\\\"OneLevelTransport\\\"),level=4)\\n #mwf for interpolating subgrid error for gradients etc\\n if self.stabilization and self.stabilization.usesGradientStabilization:\\n self.timeIntegration = TimeIntegrationClass(self,integrateInterpolationPoints=True)\\n else:\\n self.timeIntegration = TimeIntegrationClass(self)\\n\\n if options != None:\\n self.timeIntegration.setFromOptions(options)\\n log(memory(\\\"TimeIntegration\\\",\\\"OneLevelTransport\\\"),level=4)\\n log(\\\"Calculating numerical quadrature formulas\\\",2)\\n self.calculateQuadrature()\\n #lay out components/equations contiguously for now\\n self.offset = [0]\\n for ci in range(1,self.nc):\\n self.offset += [self.offset[ci-1]+self.nFreeDOF_global[ci-1]]\\n self.stride = [1 for ci in range(self.nc)]\\n #use contiguous layout of components for parallel, requires weak DBC's\\n comm = Comm.get()\\n self.comm=comm\\n if comm.size() > 1:\\n assert numericalFluxType != None and numericalFluxType.useWeakDirichletConditions,\\\"You must use a numerical flux to apply weak boundary conditions for parallel runs\\\"\\n self.offset = [0]\\n for ci in range(1,self.nc):\\n self.offset += [ci]\\n self.stride = [self.nc for ci in range(self.nc)]\\n #\\n log(memory(\\\"stride+offset\\\",\\\"OneLevelTransport\\\"),level=4)\\n if numericalFluxType != None:\\n if options == None or options.periodicDirichletConditions == None:\\n self.numericalFlux = numericalFluxType(self,\\n dofBoundaryConditionsSetterDict,\\n advectiveFluxBoundaryConditionsSetterDict,\\n diffusiveFluxBoundaryConditionsSetterDictDict)\\n else:\\n self.numericalFlux = numericalFluxType(self,\\n dofBoundaryConditionsSetterDict,\\n advectiveFluxBoundaryConditionsSetterDict,\\n diffusiveFluxBoundaryConditionsSetterDictDict,\\n options.periodicDirichletConditions)\\n else:\\n self.numericalFlux = None\\n #set penalty terms\\n #cek todo move into numerical flux initialization\\n if self.ebq_global.has_key('penalty'):\\n for ebN in range(self.mesh.nElementBoundaries_global):\\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\\n self.ebq_global['penalty'][ebN,k] = self.numericalFlux.penalty_constant/(self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power)\\n #penalty term\\n #cek move to Numerical flux initialization\\n if self.ebqe.has_key('penalty'):\\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\\n self.ebqe['penalty'][ebNE,k] = self.numericalFlux.penalty_constant/self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power\\n log(memory(\\\"numericalFlux\\\",\\\"OneLevelTransport\\\"),level=4)\\n self.elementEffectiveDiametersArray = self.mesh.elementInnerDiametersArray\\n #use post processing tools to get conservative fluxes, None by default\\n import PostProcessingTools\\n self.velocityPostProcessor = PostProcessingTools.VelocityPostProcessingChooser(self)\\n log(memory(\\\"velocity postprocessor\\\",\\\"OneLevelTransport\\\"),level=4)\\n #helper for writing out data storage\\n import Archiver\\n self.elementQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n self.elementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n self.exteriorElementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n #TODO get rid of this\\n for ci,fbcObject in self.fluxBoundaryConditionsObjectsDict.iteritems():\\n self.ebqe[('advectiveFlux_bc_flag',ci)] = numpy.zeros(self.ebqe[('advectiveFlux_bc',ci)].shape,'i')\\n for t,g in fbcObject.advectiveFluxBoundaryConditionsDict.iteritems():\\n if self.coefficients.advection.has_key(ci):\\n self.ebqe[('advectiveFlux_bc',ci)][t[0],t[1]] = g(self.ebqe[('x')][t[0],t[1]],self.timeIntegration.t)\\n self.ebqe[('advectiveFlux_bc_flag',ci)][t[0],t[1]] = 1\\n\\n if hasattr(self.numericalFlux,'setDirichletValues'):\\n self.numericalFlux.setDirichletValues(self.ebqe)\\n if not hasattr(self.numericalFlux,'isDOFBoundary'):\\n self.numericalFlux.isDOFBoundary = {}\\n for ci in range(self.nc):\\n self.numericalFlux.isDOFBoundary[ci]= numpy.zeros(self.ebqe[('u',ci)].shape,'i')\\n if not hasattr(self.numericalFlux,'ebqe'):\\n self.numericalFlux.ebqe = {}\\n for ci in range(self.nc):\\n self.numericalFlux.ebqe[('u',ci)]= numpy.zeros(self.ebqe[('u',ci)].shape,'d')\",\n \"def forward(self,y,xt,D,h,param):\\n \\n \\n h_flip=h.flip(2,3)\\n a=conv(h,xt)-y \\n b=xt-self.learning_rate*conv(h_flip,a)\\n u=tor.matmul(D,tor.matmul(b,D.transpose(2,3)))\\n u_=rho(u,param)\\n xt1=tor.matmul(D.transpose(2,3),tor.matmul(u_,D))\\n \\n return xt1\",\n \"def __init__(self, parameters, mesh_name, facet_name,\\n bc_dict={\\\"obstacle\\\": 2, \\\"channel_walls\\\": 1, \\\"inlet\\\": 3,\\n \\\"outlet\\\": 4}):\\n self.bc_dict = bc_dict\\n self.mesh = df.Mesh()\\n with df.XDMFFile(mesh_name) as infile:\\n infile.read(self.mesh)\\n\\n mvc = df.MeshValueCollection(\\\"size_t\\\", self.mesh,\\n self.mesh.topology().dim() - 1)\\n with df.XDMFFile(facet_name) as infile:\\n infile.read(mvc, \\\"name_to_read\\\")\\n self.mf = mf = df.cpp.mesh.MeshFunctionSizet(self.mesh, mvc)\\n\\n self.V = V = df.VectorFunctionSpace(self.mesh, 'P',\\n parameters[\\\"degree velocity\\\"])\\n self.Q = Q = df.FunctionSpace(self.mesh, 'P',\\n parameters[\\\"degree pressure\\\"])\\n self.rho = df.Constant(parameters[\\\"density [kg/m3]\\\"])\\n self.mu = df.Constant(parameters[\\\"viscosity [Pa*s]\\\"])\\n self.dt = df.Constant(parameters[\\\"dt [s]\\\"])\\n self.g = df.Constant((0, 0))\\n self.vu, self.vp = df.TestFunction(V), df.TestFunction(Q)\\n self.u_, self.p_ = df.Function(V), df.Function(Q)\\n self.u_1, self.p_1 = df.Function(V), df.Function(Q)\\n self.u_k, self.p_k = df.Function(V), df.Function(Q)\\n self.u, self.p = df.TrialFunction(V), df.TrialFunction(Q) # unknown!\\n\\n self.U_m = U_m = parameters[\\\"velocity [m/s]\\\"]\\n x = [0, .41 / 2] # center of the channel\\n Ucenter = 4.*U_m*x[1]*(.41-x[1])/(.41*.41)\\n U0_str = \\\"4.*U_m*x[1]*(.41-x[1])/(.41*.41)\\\"\\n self.U_mean = np.mean(2 / 3 * Ucenter)\\n\\n U0 = df.Expression((U0_str, \\\"0\\\"), U_m=U_m, degree=2)\\n bc0 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\\\"obstacle\\\"])\\n bc1 = df.DirichletBC(V, df.Constant((0, 0)), mf, bc_dict[\\\"channel_walls\\\"])\\n bc2 = df.DirichletBC(V, U0, mf, bc_dict[\\\"inlet\\\"])\\n bc3 = df.DirichletBC(Q, df.Constant(0), mf, bc_dict[\\\"outlet\\\"])\\n self.bcu = [bc0, bc1, bc2]\\n self.bcp = [bc3]\\n self.ds_ = df.Measure(\\\"ds\\\", domain=self.mesh, subdomain_data=mf)\\n return\",\n \"def tde(ax, col, legend):\\n z = 0.354\\n d = Planck15.luminosity_distance(z=z).cgs.value\\n\\n # In the Eftekhari paper, it says that although the event was first\\n # triggered by Swift/BAT on 2011 March 28.55 UT, subsequent\\n # analysis of the BAT data revealed discernible emission as early as\\n # 2011 March 25. All times should therefore be shifted relative to Mar 25.5\\n\\n # Need to add 3.04 to the Zauderer points\\n nu, dt, f, ef, islim = zauderer()\\n t = (dt+3.04)/(1+z)\\n\\n # Low frequency\\n nu_plt = 4.9E9\\n choose = np.logical_and(~islim, nu == nu_plt/1E9)\\n dt_all = t[choose]\\n nufnu_all = nu_plt*f[choose]\\n\\n # adding the set from Berger2012\\n # and making the same correction as above\\n # this is 4.9 GHz\\n t = (np.array([3.87, 4.76, 5.00, 5.79, 6.78, 7.77, 9.79, 14.98, 22.78,\\n 35.86, 50.65, 67.61, 94.64, 111.62, 126.51, 143.62, 164.38, 174.47,\\n 197.41, 213.32])) / (1+z)\\n f = np.array([0.25, 0.34, 0.34, 0.61, 0.82, 1.48, 1.47, 1.80, 2.10, 4.62,\\n 4.84, 5.86, 9.06, 9.10, 9.10, 11.71, 12.93, 12.83, 13.29, 12.43])\\n\\n # Berger 2012: use the 8.4 GHz light curve, since that's closest in freq\\n #t = (np.array([14.97, 127.69, 159.77, 174.47, 177.50, 197.41, 213.32, 219.22]))/(1+z)\\n #f = np.array([5.49, 19.03, 22.15, 23.19, 23.65, 22.42, 22.04, 21.52])\\n #dt_all = np.append(dt_all, t)\\n #nufnu_all = np.append(nufnu_all, f*nu_plt)\\n\\n # adding the set from Zauderer2013\\n # they also say it's relative to March 25.5...\\n # so I think I need to subtract 3.04 days from here too\\n t = (np.array([245.23, 302.95, 383.92, 453.66, 582.31]))/(1+z)\\n f = np.array([12.17, 12.05, 12.24, 11.12, 8.90])\\n dt_all = np.append(dt_all, t)\\n nufnu_all = np.append(nufnu_all, f*nu_plt)\\n\\n # adding the set from Eftekhari 2018\\n t = np.array([645, 651.1, 787.6, 1032, 1105, 1373, 1894])\\n f = np.array([8.24, 8.63, 6.23, 4.21, 3.52, 2.34, 1.47])\\n dt_all = np.append(dt_all, t)\\n nufnu_all = np.append(nufnu_all, f*nu_plt)\\n\\n order = np.argsort(dt_all)\\n lum = plot_line(\\n ax, d, dt_all[order], nufnu_all[order], \\n 'SwiftJ1644+57', 'TDE', col, legend)\\n ax.text(dt_all[order][10], lum[10]*1.1, 'Swift J1644+57', fontsize=11,\\n verticalalignment='bottom',\\n horizontalalignment='left')\",\n \"def TM_fluid(layer, kx, om):\\n\\n h = layer.d\\n rho = layer.medium.rho\\n K = layer.medium.K\\n k = om*np.sqrt(rho/K)\\n ky = np.sqrt(k**2-kx**2)\\n T = np.zeros((2, 2), dtype=complex)\\n T[0, 0] = np.cos(ky*h)\\n T[1, 0] = (om**2*rho/ky)*np.sin(ky*h)\\n T[0, 1] = -(ky/(om**2*rho))*np.sin(ky*h)\\n T[1, 1] = np.cos(ky*h)\\n return T\",\n \"def initialize_variables(self):\\n super(D2Model, self).initialize_variables()\\n\\n s = \\\"::: initializing 2D variables :::\\\"\\n print_text(s, cls=self)\\n\\n # Depth below sea level :\\n class Depth(Expression):\\n def eval(self, values, x):\\n values[0] = abs(min(0, x[2]))\\n self.D = Depth(element=self.Q.ufl_element())\\n \\n # Enthalpy model\\n self.theta_surface = Function(self.Q, name='theta_surface')\\n self.theta_float = Function(self.Q, name='theta_float')\\n self.theta_app = Function(self.Q, name='theta_app')\\n self.theta = Function(self.Q, name='theta')\\n self.theta0 = Function(self.Q, name='theta0')\\n self.W0 = Function(self.Q, name='W0')\\n self.thetahat = Function(self.Q, name='thetahat')\\n self.uhat = Function(self.Q, name='uhat')\\n self.vhat = Function(self.Q, name='vhat')\\n self.what = Function(self.Q, name='what')\\n self.mhat = Function(self.Q, name='mhat')\\n self.rho_b = Function(self.Q, name='rho_b')\\n\\n # Age model \\n self.age = Function(self.Q, name='age')\\n self.a0 = Function(self.Q, name='a0')\\n\\n # Surface climate model\\n self.precip = Function(self.Q, name='precip')\\n\\n # Stokes-balance model :\\n self.u_s = Function(self.Q, name='u_s')\\n self.u_t = Function(self.Q, name='u_t')\\n self.F_id = Function(self.Q, name='F_id')\\n self.F_jd = Function(self.Q, name='F_jd')\\n self.F_ib = Function(self.Q, name='F_ib')\\n self.F_jb = Function(self.Q, name='F_jb')\\n self.F_ip = Function(self.Q, name='F_ip')\\n self.F_jp = Function(self.Q, name='F_jp')\\n self.F_ii = Function(self.Q, name='F_ii')\\n self.F_ij = Function(self.Q, name='F_ij')\\n self.F_iz = Function(self.Q, name='F_iz')\\n self.F_ji = Function(self.Q, name='F_ji')\\n self.F_jj = Function(self.Q, name='F_jj')\\n self.F_jz = Function(self.Q, name='F_jz')\\n self.tau_iz = Function(self.Q, name='tau_iz')\\n self.tau_jz = Function(self.Q, name='tau_jz')\",\n \"def car_dynamics(self,x, t, u, p):\\n # f = vehicle_dynamics_ks(x, u, p)\\n f = vehicle_dynamics_st(x, u, p)\\n # f = vehicle_dynamics_std(x, u, p)\\n # f = vehicle_dynamics_mb(x, u, p)\\n return f\",\n \"def __init__(self):\\n # Set constants\\n self.fromHztoeV = 6.58e-16\\n self.gramstoeV = 1 / ( 1.78 * 1e-33)\\n self.mtoev = 1/(1.97 * 1e-7) \\n self.H0 = cosmo.H(0).value * 1e3 / (1e3 * const.kpc.value) #expressed in 1/s\\n self.rhocritical = cosmo.critical_density(0).value * self.gramstoeV /(1e-2)**3 # eV/m**3\\n self.Om0 = cosmo.Om0 #total matter \\n self.OLambda0 = cosmo.Ode0 # cosmological constant\\n self.DM0 = self.Om0 - cosmo.Ob0 # dark matter\\n self.evtonJoule = 1.60218 * 1e-10 # from eV to nJ\\n self.evtoJoule = 1.60218 * 1e-19 # from eV to J\\n PSgal1h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_1h.dat\\\")\\n PSgal2h = np.loadtxt(\\\"/Users/andreacaputo/Desktop/Phd/AxionDecayCrossCorr/Codes/NIRB_PS/PS_GALl_2h.dat\\\")\\n self.Mpc = 1e3 * const.kpc.value\\n self.zmin = 0.001\\n self.zmax = 30.001\\n self.zbins = 301\\n self.h = cosmo.h\\n self.z_vect = np.linspace(self.zmin, self.zmax, self.zbins)\\n self.k_vect = PSgal1h[:,0]* self.h\\n self.Power1h = PSgal1h[:,1:]/(self.h**3)\\n self.Power2h = PSgal2h[:,1:]/(self.h**3)\\n self.Power = self.Power1h + self.Power2h\\n self.Praw_prova1h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power1h))\\n self.Praw_prova2h = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power2h))\\n self.Praw_prova = interp2d(self.k_vect, self.z_vect, np.transpose(self.Power))\",\n \"def run_main(sst, ft_qv, use_NT):\\n\\n dtout=10. #minutes\\n end_time=8*24. #hours\\n del_time=dtout*60. #seconds\\n end_time=end_time*3600. #seconds\\n #sst=297\\n D=5.e-6 #s-1\\n U=7 #m/s\\n psfc=100. #kPa\\n qsfc=tf.qs_tp(sst,psfc)\\n ft_intercept = 292 #K\\n ft_gamma = 6.e-3 #K/m\\n #ft_qv = 2.e-3\\n k=0.2 #entrainment efficiency\\n Cd = 1.e-3 #drag coefficient\\n tspan = np.arange(0.,end_time,del_time)\\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\\n the_tup=make_tuple(the_tup,'coeffs')\\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\\n\\n # save time/computation by only doing calculations for the last timestep (equilibrium)\\n result['time']=tspan[-1]/3600./24. #days\\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\\n \\n # decide how to calculate entrainment\\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\\n if use_NT:\\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \\n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\\n else:\\n result['went']=calc_went(result.iloc[-1],the_tup)\\n\\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\\n\\n with open('dumpmodel.csv','w') as f:\\n result.to_csv(f,index=False)\\n \\n return None\",\n \"def solve_amps(self, h, a, g):\\n\\n # Symmetrize T3 RHS\\n g3 = ((+ g.t3\\n + g.t3.transpose([1, 2, 0, 4, 5, 3])\\n + g.t3.transpose([2, 0, 1, 5, 3, 4])\\n + g.t3.transpose([0, 2, 1, 3, 5, 4])\\n + g.t3.transpose([2, 1, 0, 5, 4, 3])\\n + g.t3.transpose([1, 0, 2, 4, 3, 5])\\n ) / 12)\\n\\n # Symmetrize T2 RHS\\n g2 = 1 / 2 * (g.t2 + g.t2.transpose([1, 0, 3, 2]))\\n\\n # Solve\\n t2 = g2 * (- cc_denom(h.f, g.t2.ndim, 'dir', 'full'))\\n t3 = g3 * (- cc_denom(h.f, g.t3.ndim, 'dir', 'full'))\\n\\n # Symmetrize amplitudes\\n t2 = 1 / 2 * (t2 + t2.transpose([1, 0, 3, 2]))\\n t3 = ((+ t3\\n + t3.transpose([1, 2, 0, 4, 5, 3])\\n + t3.transpose([2, 0, 1, 5, 3, 4])\\n + t3.transpose([0, 2, 1, 3, 5, 4])\\n + t3.transpose([2, 1, 0, 5, 4, 3])\\n + t3.transpose([1, 0, 2, 4, 3, 5])) / 6)\\n\\n return Tensors(\\n t1=g.t1 * (- cc_denom(h.f, g.t1.ndim, 'dir', 'full')),\\n t2=t2,\\n t3=t3)\",\n \"def variable_costs(dh: DataHandler):\\n print(\\\"PtHydrogen not implemented\\\")\\n\\n scen_hor_map = dh.scenarios.horizon\\n\\n cost_var = dh.get(\\\"i_cost\\\").xs(\\\"varcost\\\", level=\\\"par_cost\\\")\\n cost_var = cost_var.groupby([\\\"alltec\\\"]).apply(\\n extract_horizon_specific_cost, scen_hor_map\\n )\\n cost_var = add_dimension(cost_var, dh.merge_stored_sets(\\\"r\\\"), \\\"r\\\")\\n cost_var = cost_var.reorder_levels([\\\"alltec\\\", \\\"r\\\"])\\n\\n h2_price = dh.get(\\\"o_h2price_buy\\\")\\n h2_price = add_dimension(h2_price, dh.merge_stored_sets(\\\"tec_h2g\\\"), \\\"alltec\\\")\\n\\n elec_price = dh.get(\\\"o_prices\\\")\\n\\n cost_fuel = dh.get(\\\"cost_fuel\\\")\\n cost_fuel = add_dimension(cost_fuel, dh.merge_stored_sets(\\\"r\\\"), \\\"r\\\")\\n cost_fuel = cost_fuel.reorder_levels([\\\"alltec\\\", \\\"r\\\"])\\n\\n cost_fuel.loc[h2_price.index, :] = h2_price\\n\\n eff = dh.get(\\\"eff\\\")\\n\\n co2_int = dh.get(\\\"co2_int\\\").div(1000)\\n\\n co2_price = dh.get(\\\"o_co2price\\\")\\n\\n co2_costs = co2_int * co2_price\\n co2_costs.index.names = [\\\"alltec\\\", \\\"r\\\"]\\n\\n var_cost = (\\n cost_fuel.add(co2_costs, fill_value=0).div(eff).add(cost_var, fill_value=0)\\n )\\n\\n return var_cost\",\n \"def vorticity(self):\\n \\n ux,_ = np.gradient(self._obj['u'],self._obj['x'],self._obj['y'],axis=(0,1))\\n _,vy = np.gradient(self._obj['v'],self._obj['x'],self._obj['y'],axis=(0,1))\\n # self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y'])\\n self._obj['w'] = xr.DataArray(vy - ux, dims=['x', 'y','t'])\\n \\n if len(self._obj.attrs['units']) == 4:\\n vel_units = self._obj.attrs['units'][-1]\\n self._obj.attrs['units'].append('1/dt')\\n else:\\n vel_units = self._obj.attrs['units'][-2]\\n self._obj.attrs['units'][-1] = ('1/dt')\\n\\n\\n return self._obj\",\n \"def velocityVerlet(XY, yh, yt, h, n):\\n for l in range(n):\\n yt_temp = yt + (0.5 * h * XY.grad_log_density(yh))\\n yhp1 = yh + (h * yt_temp)\\n ytp1 = yt_temp + (0.5 * h * XY.grad_log_density(yhp1))\\n yh = yhp1\\n yt = ytp1\\n\\n return [yhp1, ytp1]\",\n \"def ADT_QCD_LEPTON():\\n\\n # As input for the quark-mass ratios, we use the quark masses at MZ and the lepton masses\\n ip = Num_input()\\n\\n mu = ip.mu_at_MZ\\n md = ip.md_at_MZ\\n ms = ip.ms_at_MZ\\n me = ip.me\\n mmu = ip.mmu\\n mtau = ip.mtau\\n\\n # Create the ADT:\\n\\n gamma_hat_P63eu_Q81u = np.hstack((-16 * me**2/mu**2, np.zeros(5)))\\n gamma_hat_P63muu_Q81u = np.hstack((np.zeros(1), -16 * mmu**2/mu**2, np.zeros(4)))\\n gamma_hat_P63tauu_Q81u = np.hstack((np.zeros(2), -16 * mtau**2/mu**2, np.zeros(3)))\\n\\n gamma_hat_P63ed_Q81d = np.hstack((-16 * me**2/md**2, np.zeros(5)))\\n gamma_hat_P63mud_Q81d = np.hstack((np.zeros(1), -16 * mmu**2/md**2, np.zeros(4)))\\n gamma_hat_P63taud_Q81d = np.hstack((np.zeros(2), -16 * mtau**2/md**2, np.zeros(3)))\\n\\n gamma_hat_P63es_Q81s = np.hstack((-16 * me**2/ms**2, np.zeros(5)))\\n gamma_hat_P63mus_Q81s = np.hstack((np.zeros(1), -16 * mmu**2/ms**2, np.zeros(4)))\\n gamma_hat_P63taus_Q81s = np.hstack((np.zeros(2), -16 * mtau**2/ms**2, np.zeros(3)))\\n\\n\\n\\n gamma_hat_P63eu_Q82u = np.hstack((np.zeros(3), -16 * me**2/mu**2, np.zeros(2)))\\n gamma_hat_P63muu_Q82u = np.hstack((np.zeros(4), -16 * mmu**2/mu**2, np.zeros(1)))\\n gamma_hat_P63tauu_Q82u = np.hstack((np.zeros(5), -16 * mtau**2/mu**2))\\n\\n gamma_hat_P63ed_Q82d = np.hstack((np.zeros(3), -16 * me**2/md**2, np.zeros(2)))\\n gamma_hat_P63mud_Q82d = np.hstack((np.zeros(4), -16 * mmu**2/md**2, np.zeros(1)))\\n gamma_hat_P63taud_Q82d = np.hstack((np.zeros(5), -16 * mtau**2/md**2))\\n\\n gamma_hat_P63es_Q82s = np.hstack((np.zeros(3), -16 * me**2/ms**2, np.zeros(2)))\\n gamma_hat_P63mus_Q82s = np.hstack((np.zeros(4), -16 * mmu**2/ms**2, np.zeros(1)))\\n gamma_hat_P63taus_Q82s = np.hstack((np.zeros(5), -16 * mtau**2/ms**2))\\n\\n\\n\\n gamma_hat_P62ue_Q83u = np.hstack((-16 * me**2/mu**2, np.zeros(5)))\\n gamma_hat_P62umu_Q83u = np.hstack((np.zeros(1), -16 * mmu**2/mu**2, np.zeros(4)))\\n gamma_hat_P62utau_Q83u = np.hstack((np.zeros(2), -16 * mtau**2/mu**2, np.zeros(3)))\\n\\n gamma_hat_P62de_Q83d = np.hstack((-16 * me**2/md**2, np.zeros(5)))\\n gamma_hat_P62dmu_Q83d = np.hstack((np.zeros(1), -16 * mmu**2/md**2, np.zeros(4)))\\n gamma_hat_P62dtau_Q83d = np.hstack((np.zeros(2), -16 * mtau**2/md**2, np.zeros(3)))\\n\\n gamma_hat_P62se_Q83s = np.hstack((-16 * me**2/ms**2, np.zeros(5)))\\n gamma_hat_P62smu_Q83s = np.hstack((np.zeros(1), -16 * mmu**2/ms**2, np.zeros(4)))\\n gamma_hat_P62stau_Q83s = np.hstack((np.zeros(2), -16 * mtau**2/ms**2, np.zeros(3)))\\n\\n\\n\\n gamma_hat_P62ue_Q84u = np.hstack((np.zeros(3), -16 * me**2/mu**2, np.zeros(2)))\\n gamma_hat_P62umu_Q84u = np.hstack((np.zeros(4), -16 * mmu**2/mu**2, np.zeros(1)))\\n gamma_hat_P62utau_Q84u = np.hstack((np.zeros(5), -16 * mtau**2/mu**2))\\n\\n gamma_hat_P62de_Q84d = np.hstack((np.zeros(3), -16 * me**2/md**2, np.zeros(2)))\\n gamma_hat_P62dmu_Q84d = np.hstack((np.zeros(4), -16 * mmu**2/md**2, np.zeros(1)))\\n gamma_hat_P62dtau_Q84d = np.hstack((np.zeros(5), -16 * mtau**2/md**2))\\n\\n gamma_hat_P62se_Q84s = np.hstack((np.zeros(3), -16 * me**2/ms**2, np.zeros(2)))\\n gamma_hat_P62smu_Q84s = np.hstack((np.zeros(4), -16 * mmu**2/ms**2, np.zeros(1)))\\n gamma_hat_P62stau_Q84s = np.hstack((np.zeros(5), -16 * mtau**2/ms**2))\\n\\n\\n\\n gamma_hat_Q81u = np.vstack((gamma_hat_P63eu_Q81u, gamma_hat_P63muu_Q81u, gamma_hat_P63tauu_Q81u, np.zeros((15,6))))\\n gamma_hat_Q81d = np.vstack((np.zeros((3,6)), gamma_hat_P63ed_Q81d, gamma_hat_P63mud_Q81d, gamma_hat_P63taud_Q81d, np.zeros((12,6))))\\n gamma_hat_Q81s = np.vstack((np.zeros((6,6)), gamma_hat_P63es_Q81s, gamma_hat_P63mus_Q81s, gamma_hat_P63taus_Q81s, np.zeros((9,6))))\\n\\n gamma_hat_Q82u = np.vstack((gamma_hat_P63eu_Q82u, gamma_hat_P63muu_Q82u, gamma_hat_P63tauu_Q82u, np.zeros((15,6))))\\n gamma_hat_Q82d = np.vstack((np.zeros((3,6)), gamma_hat_P63ed_Q82d, gamma_hat_P63mud_Q82d, gamma_hat_P63taud_Q82d, np.zeros((12,6))))\\n gamma_hat_Q82s = np.vstack((np.zeros((6,6)), gamma_hat_P63es_Q82s, gamma_hat_P63mus_Q82s, gamma_hat_P63taus_Q82s, np.zeros((9,6))))\\n\\n gamma_hat_Q83u = np.vstack((np.zeros((9,6)), gamma_hat_P62ue_Q83u, gamma_hat_P62umu_Q83u, gamma_hat_P62utau_Q83u, np.zeros((6,6))))\\n gamma_hat_Q83d = np.vstack((np.zeros((12,6)), gamma_hat_P62de_Q83d, gamma_hat_P62dmu_Q83d, gamma_hat_P62dtau_Q83d, np.zeros((3,6))))\\n gamma_hat_Q83s = np.vstack((np.zeros((15,6)), gamma_hat_P62se_Q83s, gamma_hat_P62smu_Q83s, gamma_hat_P62stau_Q83s))\\n\\n gamma_hat_Q84u = np.vstack((np.zeros((9,6)), gamma_hat_P62ue_Q84u, gamma_hat_P62umu_Q84u, gamma_hat_P62utau_Q84u, np.zeros((6,6))))\\n gamma_hat_Q84d = np.vstack((np.zeros((12,6)), gamma_hat_P62de_Q84d, gamma_hat_P62dmu_Q84d, gamma_hat_P62dtau_Q84d, np.zeros((3,6))))\\n gamma_hat_Q84s = np.vstack((np.zeros((15,6)), gamma_hat_P62se_Q84s, gamma_hat_P62smu_Q84s, gamma_hat_P62stau_Q84s))\\n\\n\\n\\n\\n gamma_hat = np.array([gamma_hat_Q81u, gamma_hat_Q81d, gamma_hat_Q81s, gamma_hat_Q82u, gamma_hat_Q82d, gamma_hat_Q82s,\\n gamma_hat_Q83u, gamma_hat_Q83d, gamma_hat_Q83s, gamma_hat_Q84u, gamma_hat_Q84d, gamma_hat_Q84s])\\n\\n\\n # Return the tensor\\n\\n # tensor, zeile, spalte\\n\\n return gamma_hat\",\n \"def create_flux_vector_pms_gr(self):\\n soma_prod = 0\\n soma_inj = 0\\n lim4 = 1e-4\\n store_velocity = {}\\n store_flux = {}\\n for primal in self.primals:\\n #1\\n primal_id = self.mb.tag_get_data(self.primal_id_tag, primal, flat=True)[0]\\n primal_id = self.ident_primal[primal_id]\\n fine_elems_in_primal = self.mb.get_entities_by_handle(primal)\\n for volume in fine_elems_in_primal:\\n #2\\n flux = {}\\n velocity = {}\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n centroid_volume = self.mesh_topo_util.get_average_position([volume])\\n z_vol = self.tz - centroid_volume[2]\\n adjs_vol = self.mesh_topo_util.get_bridge_adjacencies(volume, 2, 3)\\n gid_vol = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n for adj in adjs_vol:\\n #3\\n gid_adj = self.mb.tag_get_data(self.global_id_tag, adj, flat=True)[0]\\n if adj not in fine_elems_in_primal:\\n #4\\n pvol = self.mb.tag_get_data(self.pms_tag, volume, flat=True)[0]\\n padj = self.mb.tag_get_data(self.pms_tag, adj, flat=True)[0]\\n #3\\n else:\\n #4\\n pvol = self.mb.tag_get_data(self.pcorr_tag, volume, flat=True)[0]\\n padj = self.mb.tag_get_data(self.pcorr_tag, adj, flat=True)[0]\\n #3\\n kadj = self.mb.tag_get_data(self.perm_tag, adj).reshape([3, 3])\\n centroid_adj = self.mesh_topo_util.get_average_position([adj])\\n z_adj = self.tz - centroid_adj[2]\\n direction = centroid_adj - centroid_volume\\n unit = direction/np.linalg.norm(direction)\\n #unit = vetor unitario na direcao de direction\\n uni = self.unitary(direction)\\n # uni = valor positivo do vetor unitario\\n kvol = np.dot(np.dot(kvol,uni),uni)\\n kadj = np.dot(np.dot(kadj,uni),uni)\\n keq = self.kequiv(kvol, kadj)/(self.mi)\\n keq2 = keq\\n keq = keq*(np.dot(self.A, uni))\\n pvol2 = self.mb.tag_get_data(self.pms_tag, volume, flat=True)[0]\\n padj2 = self.mb.tag_get_data(self.pms_tag, adj, flat=True)[0]\\n grad_p = (padj - pvol)/float(abs(np.dot(direction, uni)))\\n grad_z = (z_adj - z_vol)/float(abs(np.dot(direction, uni)))\\n grad_p2 = (padj2 - pvol2)/float(abs(np.dot(direction, uni)))\\n q = (grad_p)*keq - grad_z*keq*self.gama\\n print((grad_p)*keq)\\n print(- grad_z*keq*self.gama)\\n print(q)\\n print(self.store_flux_pf_gr[volume][tuple(unit)])\\n print('\\\\n')\\n import pdb; pdb.set_trace()\\n\\n if gid_adj > gid_vol:\\n v = -((grad_p2)*keq2 - grad_z*self.gama*keq2)\\n else:\\n v = -((grad_p2)*keq2 - grad_z*self.gama*keq2)\\n\\n flux[tuple(unit)] = q\\n velocity[tuple(unit)] = v\\n kvol = self.mb.tag_get_data(self.perm_tag, volume).reshape([3, 3])\\n\\n #2\\n # print(gid_vol)\\n # print(velocity)\\n # print('\\\\n')\\n # import pdb; pdb.set_trace()\\n store_flux[volume] = flux\\n self.mb.tag_set_data(self.flux_fine_pms_tag, volume, sum(flux.values()))\\n # flt = sum(flux.values())\\n # if volume not in self.wells_inj and volume not in self.wells_prod:\\n # lim4 = 1e-7\\n # if abs(flt) > lim4:\\n # print(gid_vol)\\n # print(flt)\\n # import pdb; pdb.set_trace()\\n # flt = sum(flux.values())\\n store_velocity[volume] = velocity\\n\\n for volume in set(self.all_fine_vols) - set(self.wells):\\n gid = self.mb.tag_get_data(self.global_id_tag, volume, flat=True)[0]\\n values = store_flux[volume].values()\\n if sum(values) > lim4:\\n print('fluxo multiescala nao esta dando conservativo')\\n print('gid:{0}'.format(gid))\\n print(sum(values))\\n import pdb; pdb.set_trace()\\n\\n with open('fluxo_multiescala_gr.txt', 'w') as arq:\\n for volume in self.wells:\\n gid = self.mb.tag_get_data(self.global_id_tag, volume, flat= True)[0]\\n values = store_flux[volume].values()\\n if volume in self.wells_inj:\\n soma_inj += sum(values)\\n else:\\n soma_prod += sum(values)\\n arq.write('gid:{0} , fluxo:{1}\\\\n'.format(gid, sum(values)))\\n arq.write('\\\\n')\\n arq.write('soma_inj:{0}\\\\n'.format(soma_inj))\\n arq.write('soma_prod:{0}\\\\n'.format(soma_prod))\\n\\n return store_flux\",\n \"def _step(self, t, y, h):\\n # We must use solvers / implicit form\\n f_pn1 = lambda a_n1: (y + h*self.v + (h**2 / 2.0) * \\\\\\n ((1.0 - 2.*self.beta)*self.a + 2.*self.beta*a_n1))\\n f_vn1 = lambda a_n1: (self.v + h*((1.0-self.gamma)*self.a + self.gamma*a_n1))\\n def f_an1(a_n1):\\n f_n1 = self.f(t+h,f_pn1(a_n1),f_vn1(a_n1))\\n f_n = self.f(t,y,self.v,)\\n return a_n1 - ((1.0+self.alpha)*f_n1 - self.alpha*f_n)\\n\\n a = self.solver(f_an1, self.a)\\n y = f_pn1(a) # Calculate and store new variables. \\n self.v = f_vn1(a)\\n self.a = a\\n return t+h, y\",\n \"def prepare_rhs(self, simulation):\\n\\n nv = simulation.container.nv\\n sorder = simulation.container.sorder\\n nspace = [1] * (len(sorder) - 1)\\n v = self.stencil.get_all_velocities()\\n\\n gpu_support = simulation.container.gpu_support\\n\\n for key, value in self.value_bc.items():\\n if value is not None:\\n indices = np.where(self.ilabel == key)\\n # TODO: check the index in sorder to be the most contiguous\\n nspace[0] = indices[0].size\\n k = self.istore[0, indices]\\n\\n s = 1 - self.distance[indices]\\n coords = tuple()\\n for i in range(simulation.domain.dim):\\n x = simulation.domain.coords_halo[i][self.istore[i + 1, indices]]\\n x += s * v[k, i] * simulation.domain.dx\\n x = x.ravel()\\n for j in range(\\n 1, simulation.domain.dim\\n ): # pylint: disable=unused-variable\\n x = x[:, np.newaxis]\\n coords += (x,)\\n\\n m = Array(nv, nspace, 0, sorder, gpu_support=gpu_support)\\n m.set_conserved_moments(simulation.scheme.consm)\\n\\n f = Array(nv, nspace, 0, sorder, gpu_support=gpu_support)\\n f.set_conserved_moments(simulation.scheme.consm)\\n\\n args = coords\\n if isinstance(value, types.FunctionType):\\n func = value\\n elif isinstance(value, tuple):\\n func = value[0]\\n args += value[1]\\n\\n if self.time_bc[key]:\\n func(f, m, 0, *args)\\n else:\\n func(f, m, *args)\\n\\n simulation.equilibrium(m)\\n simulation.m2f(m, f)\\n\\n if self.generator.backend.upper() == \\\"LOOPY\\\":\\n f.array_cpu[...] = f.array.get()\\n\\n self.feq[:, indices[0]] = f.swaparray.reshape((nv, indices[0].size))\\n\\n if self.time_bc[key]:\\n self.func.append(func)\\n self.args.append(args)\\n self.f.append(f)\\n self.m.append(m)\\n self.indices.append(indices[0])\",\n \"def kelvin_effect(pres, surft, temp, mw_ba, dcell):\\n volm = mw_ba/1e3 # approximation: using density 1000 kg/m3\\n return pres*exp(-4*surft*volm/(dcell*gas_constant*temp))\",\n \"def trc_fgen_prefb(self,trc,dt,nspad=200,hwin=150,vlen=51):\\n output=np.zeros((len(trc),((11*(vlen))+1)))\\n pad=np.random.rand(nspad)/100\\n trc_norm=trc/np.amax(np.abs(trc))\\n trc_norm_padded=np.hstack((pad,trc_norm))\\n trc_entropy=self.entropy(trc_norm_padded,50)\\n trc_fdm=self.fdm(trc_norm_padded,50,np.arange(1,4),15)\\n trc_slta=trigger.classic_sta_lta(trc_norm_padded,2,100)\\n trc_fq_win_sum=self.fq_win_sum(trc_norm_padded,hwin,dt)\\n hwin2=50\\n trc_kurtosis_skew=self.kurtosis_skewness(trc_norm_padded,hwin2)\\n for i,j in enumerate(trc):\\n ftrc=[]\\n fb=i*dt\\n ftrc=np.append(ftrc,trc_norm_padded[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(np.gradient(np.abs(trc_norm_padded)))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(trc_entropy)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_entropy))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1]) \\n ftrc=np.append(ftrc,self.norm(trc_fdm)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_fdm))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1]) \\n ftrc=np.append(ftrc,self.norm(trc_slta)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(trc_fq_win_sum)[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(np.gradient(trc_fq_win_sum))[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(trc_kurtosis_skew[0])[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,self.norm(trc_kurtosis_skew[1])[np.int(nspad+fb/dt)-vlen+1:np.int(nspad+fb/dt)+1])\\n ftrc=np.append(ftrc,1)\\n output[i,:]=ftrc\\n return output\",\n \"def _calc_var(self, f, a, b, z):\\n\\n c_1 = (t.exp(a[...,0] * f[...,0] + b[...,0]) *\\n (a[...,0]**2 * f[...,0]**2\\n - 2 * a[...,0] * f[...,0] + 2)\\n ) / a[...,0]**3\\n c_2 = ((t.exp(a[...,1:-1] * f[...,1:] + b[...,1:-1]) *\\n (a[...,1:-1]**2 * f[...,1:]**2 - 2 * a[...,1:-1] * f[...,1:] + 2) -\\n t.exp(a[...,1:-1] * f[...,:-1] + b[...,1:-1]) *\\n (a[...,1:-1]**2 * f[...,:-1]**2 - 2 * a[...,1:-1] * f[...,:-1] + 2)\\n ) / a[...,1:-1]**3).sum(-1)\\n c_3 = (t.exp(a[...,-1] * f[...,-1] + b[...,-1]) *\\n (a[...,-1]**2 * f[...,-1]**2 - 2 * a[...,-1] * f[...,-1] + 2)\\n ) / a[...,-1]**3\\n\\n return 1/z * (c_1 + c_2 + c_3)\",\n \"def _diff_wep(a,t,dh,wair,entr,pres):\\n ph = _eq_pressure(0,0,0,a,t,dh)\\n gi = _ice_g(0,0,t,pres)\\n gv = _eq_vappot(0,0,0,a,t,dh)\\n sh = -_air_f(0,1,0,a,t,dh)\\n si = -_ice_g(1,0,t,pres)\\n stot = wair/a*sh + (1-wair/a)*si\\n lhs = numpy.array([pres, gi, entr])\\n rhs = numpy.array([ph, gv, stot])\\n \\n ph_a = _eq_pressure(1,0,0,a,t,dh)\\n ph_t = _eq_pressure(0,1,0,a,t,dh)\\n ph_d = _eq_pressure(0,0,1,a,t,dh)\\n gi_t = _ice_g(1,0,t,pres)\\n gv_a = _eq_vappot(1,0,0,a,t,dh)\\n gv_t = _eq_vappot(0,1,0,a,t,dh)\\n gv_d = _eq_vappot(0,0,1,a,t,dh)\\n sh_a = -_air_f(1,1,0,a,t,dh)\\n sh_t = -_air_f(0,2,0,a,t,dh)\\n sh_d = -_air_f(0,1,1,a,t,dh)\\n si_t = -_ice_g(2,0,t,pres)\\n s_a = -wair/a**2 * (sh - a*sh_a - si)\\n s_t = wair/a*sh_t + (1-wair/a)*si_t\\n s_d = wair/a*sh_d\\n dlhs = numpy.array([[0.,0.,0.], [0.,gi_t,0.], [0.,0.,0.]])\\n drhs = numpy.array([[ph_a,ph_t,ph_d], [gv_a,gv_t,gv_d], [s_a,s_t,s_d]])\\n return lhs, rhs, dlhs, drhs\",\n \"def init_amplitudes(self, ham):\\n e_ai = cc_denom(ham.f, 2, 'dir', 'full')\\n e_abij = cc_denom(ham.f, 4, 'dir', 'full')\\n nocc = self.mos.nocc\\n nvir = self.mos.nvir\\n\\n t1 = ham.f.ov.transpose().conj() * (- e_ai)\\n v_vovo = einsum(\\\"pia,pjb->aibj\\\", ham.l.pov, ham.l.pov).conj()\\n\\n t2 = v_vovo.transpose([0, 2, 1, 3]) * (- e_abij)\\n t3 = np.zeros((nvir,) * 3 + (nocc,) * 3)\\n\\n return Tensors(t1=t1, t2=t2, t3=t3)\",\n \"def init_amplitudes(self, ham):\\n e_ai = cc_denom(ham.f, 2, 'dir', 'full')\\n e_abij = cc_denom(ham.f, 4, 'dir', 'full')\\n nocc = self.mos.nocc\\n nvir = self.mos.nvir\\n\\n t1 = ham.f.ov.transpose().conj() * (- e_ai)\\n v_vovo = einsum(\\\"pia,pjb->aibj\\\", ham.l.pov, ham.l.pov).conj()\\n\\n t2 = v_vovo.transpose([0, 2, 1, 3]) * (- e_abij)\\n t3 = np.zeros((nvir,) * 3 + (nocc,) * 3)\\n\\n return Tensors(t1=t1, t2=t2, t3=t3)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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inputs into dmixed_vars deltheta, F0, Fqv0 are calculated in dmixed_vars"},"document":{"kind":"string","value":"def calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0):\n thetal_m = the_vars[0]\n qt_m = the_vars[2]\n zi = the_vars[1]\n dth = deltheta\n \n thetal_ft = thetal_m + dth\n qt_ft = coeffs.ft_qv\n \n dqt = qt_ft - qt_m\n \n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\n gamma = 6e-3 \n thetal_3000 = thetal_ft + gamma*(3000-zi)\n LTS = thetal_3000 - coeffs.sst # lower tropospheric stability\n\n # calculate coefficients\n press=tf.find_press(zi)\n Ad,Bd,issat = tf.calc_ABcoeffs(thetal_ft,qt_ft,press)\n Aw,Bw,issat = tf.calc_ABcoeffs(thetal_m,qt_m,press)\n \n invert= tf.t_uos_thetal(thetal_m,qt_m,press)\n T_0 = invert.temp\n lv=tf.L_t(invert.temp)\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\n del_thv_dry = Ad * dth + Bd * dqt\n del_thv_sat = Aw * dth + Bw * dqt\n \n # account for evaporative cooling (increases we)\n ql_max = invert.ql\n Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)\n Del_thv = del_thv_dry - Cl * ql_max\n \n # calculate buoyancy integral terms\n rho = 1.\n lcl_press=tf.LCL_thetal(thetal_m,qt_m)\n zb=tf.find_height(lcl_press)\n\n T1 = zb/zi\n T2 = 0.5 * zb**2 / zi**2\n T3 = (zi-zb)/zi\n T4 = 0.5 * (zi**2 - zb**2) / zi**2\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\n\n wtl_0=F0\n wqt_0=Fqv0\n Del_F = delta_Fr/(tc.CPD*rho) # use sensitivity to radiation a la Gesso Fig. 3\n term1 = wtl_0 * (Ad * (T1-T2) + Aw * (T3-T4))\n term2 = wqt_0 * (Bd * (T1-T2) + Bw * (T3-T4))\n term3 = Del_F * (Ad * T2 + Aw * T4)\n\n Theta_NE = term1 + term2 + term3\n \n # calculate w*\n wstar=(2.5*9.8/T_0*zi*Theta_NE)**(1/3.)\n \n # calculate chi*\n chi_star = Cl * ql_max / (del_thv_dry - del_thv_sat)\n \n # calculate del_m\n Del_m = del_thv_dry + chi_star * (2. - chi_star) * (del_thv_sat - del_thv_dry)\n \n # calculate we\n a2=15.\n Del_thv_NT = Del_thv / (1. + a2 * (1. - Del_m/Del_thv))\n \n A_NT = 0.2\n fac_NT = 2.5\n\n term4 = Del_thv_NT\n term5 = A_NT * fac_NT * (T2 * del_thv_dry + T4 * del_thv_sat)\n denominator = term4 + term5\n\n we = A_NT * fac_NT * Theta_NE / denominator\n \n return we"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dmixed_vars(the_vars,tstep,coeffs):\n\n deltheta = theta_ft(the_vars[1],coeffs.ft_intercept,coeffs.ft_gamma) - the_vars[0]\n F0 = coeffs.U*coeffs.Cd*(coeffs.sst - the_vars[0]) #surface heat flux\n Fqv0 = coeffs.U*coeffs.Cd*(coeffs.qsfc - the_vars[2]) #surface vapor flux\n Fint = -coeffs.k*F0 #entrainment heat flux\n \n if coeffs.use_NT: # use NT parameterization by calculating we using function\n went = calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0) # Nicholls-Turton parameterization\n \n else: # use simple we parameterization\n went = -Fint/deltheta #simple entrainment parameterization\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*coeffs.ft_qv*1000 # convert qt_ft to g kg^-1\n \n Fqvent = -went*( coeffs.ft_qv - the_vars[2])\n wsubs = -coeffs.D*the_vars[1]\n rho=1.\n cp=1004.\n \n derivs=np.empty_like(the_vars)\n \n # higher delta_Fr from drier air at mixed-layer top...hence cloudy air results in less radiative cooling\n derivs[0]=(F0 - Fint)/(the_vars[1]*rho) - delta_Fr/1004./the_vars[1] \n derivs[1] = went + wsubs\n derivs[2] = (Fqv0 - Fqvent)/the_vars[1]\n return derivs","def update_model_parameters(phi, T, nz, coord, SWVD, form=\"Calonne\"):\r\n D_eff = np.ones(nz)\r\n\r\n if form == \"Hansen\": # Hansen and Foslien (2015)\r\n D_eff = phi * (1 - phi) * D0 + D0\r\n elif form == \"Calonne\": # Calonne et al. (2014)\r\n x = 2 / 3 - phi\r\n b = np.heaviside(x, 1)\r\n D_eff = D0 * (1 - 3 / 2 * phi) * b\r\n else:\r\n print(\"requested method not available, check input\")\r\n\r\n ## effective thermal conductivity W/m/K\r\n k_eff = np.ones(nz)\r\n\r\n if form == \"Hansen\": # Hansen and Foslien (2015)\r\n k_eff = phi * ((1 - phi) * k_a + phi * k_i) + k_a\r\n elif form == \"Calonne\": # Calonne et al. (2011)\r\n k_eff = ka0 + ka1 * (rho_i * phi) + ka2 * (rho_i * phi) ** 2\r\n else:\r\n print(\"requested method not available, check input\")\r\n\r\n ## effective heat capacity - similar forumla in Hansen and Foslien (2015) and Löwe et al. (2019)\r\n rhoC_eff = np.zeros(nz)\r\n rhoC_eff = phi * rho_i * C_i + (np.ones(nz) - phi) * rho_a * C_a\r\n\r\n ## Water Vapor density rho_v and its derivative rho_v_dT:\r\n [rho_v, rho_v_dT] = sat_vap_dens(nz, T, SWVD)\r\n\r\n return D_eff, k_eff, rhoC_eff, rho_v, rho_v_dT","def solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,P,N,RHS=3):\n #I have added the variables P the transport matrix \n #and N the network size because they are needed\n #in the RHS.\n #I have added the variable RHS to be able to \n #choose which RHS method we want to use when running\n #solveFluNet\n \n #add input variables to RHS functions if needed\n def RHSnet(y,t,a,b0,b1,g,k,w):\n \"\"\"RHS used by odeint to solve Flu model\"\"\"\n S = y[:N]\n E = y[N:2*N]\n C = y[2*N:3*N]\n b = b0 + b1*(1+np.cos(2*np.pi*t))\n dy = np.zeros(3*N)\n dy[:N]= k*(1-S)-b*C*S+w*np.dot(P,S)-w*S\n dy[N:2*N]= b*C*S-(k+a)*E+w*np.dot(P,E)-w*E\n dy[2*N:3*N]= a*E-(g+k)*C+w*np.dot(P,C)-w*C\n return dy\n \n def RHSnetF(y,t,a,b0,b1,g,k,w):\n \"\"\"RHS used by odeint to solve Flu model\"\n Calculations carried out by fn.rhs\n \"\"\"\n dy = fn.rhs(P,y,t,a,b0,b1,g,k,w)\n return dy\n \n def RHSnetFomp(y,t,a,b0,b1,g,k,w):\n \"\"\"RHS used by odeint to solve Flu model\n Calculations carried out by fn.rhs_omp\n \"\"\"\n dy = fn.rhs_omp(P,y,t,a,b0,b1,g,k,w,2)\n return dy\n\n #Add code here and to RHS functions above to simulate network flu model\n t = np.linspace(0,T,Ntime)\n if (RHS==1):\n sol = odeint(RHSnet,y0,t,args=(a,b0,b1,g,k,w))\n if (RHS==2):\n sol = odeint(RHSnetF,y0,t,args=(a,b0,b1,g,k,w))\n if (RHS==3):\n sol = odeint(RHSnetFomp,y0,t,args=(a,b0,b1,g,k,w))\n S = sol[:,:N]\n E = sol[:,N:2*N]\n C = sol[:,2*N:3*N]\n return t,S,E,C","def taylor_expansion(self,g_temp,g_step,var):\n A=np.zeros([self.n+1,self.n])\n for i in range(self.n):\n A[self.n][i]=1\n for j in range(self.n):\n if(i==j): A[i][j]=2.*var[i]+2.+g_temp*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\n else: A[i][j]=-g_temp*self.XXZ.Z(j,i)\n #First derivative\n B1=np.zeros(self.n+1)\n for i in range(self.n): \n B1[i]=self.gamma*2.*g_temp*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\n Ainv=np.linalg.pinv(A)\n der1=np.dot(Ainv,B1)\n #Second derivative\n B2=np.zeros(self.n+1)\n for k in range(self.n):\n B2[k]=self.gamma*2.*self.N*(self.n-self.N) -2.*der1[k]**2+2.*np.sum([self.XXZ.Z(l,k)*(der1[l]-der1[k]) for l in range(self.n) if k!=l])\n der2=np.dot(Ainv,B2)\n #Third derivative\n B3=np.zeros(self.n+1)\n for k in range(self.n):\n B3[k]=-6*der1[k]*der2[k]+3.*np.sum([self.XXZ.Z(l,k)*(der2[l]-der2[k]) for l in range(self.n) if k!=l])\n der3=np.dot(Ainv,B3)\n #Fourth derivative\n B4=np.zeros(self.n+1)\n for k in range(self.n):\n B4[k]=-8.*der3[k]*der1[k]-6.*der2[k]*der2[k]+4.*np.sum([self.XXZ.Z(l,k)*(der3[l]-der3[k]) for l in range(self.n) if k!=l])\n der4=np.dot(Ainv,B4)\n \n return var+g_step*der1+g_step**2*der2/2.+g_step**3*der3/6.+g_step**4*der4/24.","def _derivatives(self, state, forces_moments):\n # extract the states\n pn = state[0]\n pe = state[1]\n pd = state[2]\n e0 = state[3]\n e1 = state[4]\n e2 = state[5]\n e3 = state[6]\n u = state[7]\n v = state[8]\n w = state[9]\n # state[6:10] = normalize(state[6:10])\n p = state[10]\n q = state[11]\n r = state[12]\n # extract forces/moments\n fx = forces_moments[0]\n fy = forces_moments[1]\n fz = forces_moments[2]\n l = forces_moments[3]\n m = forces_moments[4]\n n = forces_moments[5]\n\n\n # with warnings.catch_warnings():\n # warnings.filterwarnings('error')\n # try:\n # # position kinematics\n # except Warning as e:\n # pdb.set_trace()\n # print(e)\n\n pn_dot = (e1**2+e0**2-e2**2-e3**2)*u + 2*(e1*e2-e3*e0)*v + 2*(e1*e3+e2*e0)*w\n pe_dot = 2*(e1*e2+e3*e0)*u + (e2**2+e0**2-e1**2-e3**2)*v + 2*(e2*e3-e1*e0)*w\n pd_dot = 2*(e1*e3-e2*e0)*u + 2*(e2*e3+e1*e0)*v + (e3**2+e0**2-e1**2-e2**2)*w\n\n # pn_dot = (e0**2+e1**2-e2**2-e3**2)*u + 2*(e1*e2+e3*e0)*v + 2*(e1*e3-e2*e0)*w\n # pe_dot = 2*(e1*e2-e3*e0)*u + (e0**2-e1**2+e2**2-e3**2)*v + 2*(e2*e3+e1*e0)*w\n # pd_dot = 2*(e1*e3+e2*e0)*u + 2*(e2*e3-e1*e0)*v + (e0**2-e1**2-e2**2+e3**2)*w\n\n # pdb.set_trace()\n\n # position dynamics\n mass = self.mass\n u_dot = (r*v-q*w)+fx/mass\n v_dot = (p*w-r*u)+fy/mass\n w_dot = (q*u-p*v)+fz/mass\n\n # rotational kinematics\n e0_dot = 0.5*(-p*e1-q*e2-r*e3)\n e1_dot = 0.5*(p*e0+r*e2-q*e3)\n e2_dot = 0.5*(q*e0-r*e1+p*e3)\n e3_dot = 0.5*(r*e0+q*e1-p*e2)\n\n # rotatonal dynamics\n p_dot = self.gamma1*p*q - self.gamma2*q*r + self.gamma3*l + self.gamma4*n\n q_dot = self.gamma5*p*r - self.gamma6*(p**2-r**2) + m/self.Jy\n r_dot = self.gamma7*p*q - self.gamma1*q*r + self.gamma4*l + self.gamma8*n\n\n # collect the derivative of the states\n x_dot = np.array([pn_dot, pe_dot, pd_dot, e0_dot, e1_dot, e2_dot, e3_dot,\n u_dot, v_dot, w_dot, p_dot, q_dot, r_dot])\n # pdb.set_trace()\n\n\n # print(x_dot)\n return x_dot","def niv_variable_selection(x, y, t, max_vars):\n y1_t = (y == 1) & (t == 1)\n y0_t = (y == 0) & (t == 1)\n y1_c = (y == 1) & (t == 0)\n y0_c = (y == 0) & (t == 0)\n\n sum_y1_t = sum(y1_t)\n sum_y0_t = sum(y0_t)\n sum_y1_c = sum(y1_c)\n sum_y0_c = sum(y0_c)\n\n niv_dict = {}\n for col in x.columns:\n df = pd.concat([x[col].rename(col), y1_t.rename('y1_t'), y0_t.rename('y0_t'),\n y1_c.rename('y1_c'), y0_c.rename('y0_c')], axis=1)\n x_group = df.groupby(x[col])\n x_sum = x_group.sum()\n\n if sum_y0_t == 0 or sum_y1_t == 0:\n woe_t = 0\n else:\n woe_t = x_sum.apply(lambda r: np.log((r['y1_t'] * sum_y0_t) / (r['y0_t'] * sum_y1_t))\n if r['y1_t'] > 0 and r['y0_t'] > 0 else 0, axis=1)\n\n if sum_y0_c == 0 or sum_y1_c == 0:\n woe_c = 0\n else:\n woe_c = x_sum.apply(lambda r: np.log((r['y1_c'] * sum_y0_c) / (r['y0_c'] * sum_y1_c))\n if r['y1_c'] > 0 and r['y0_c'] > 0 else 0, axis=1)\n\n nwoe = woe_t - woe_c\n\n p_x_y1_t = x_sum['y1_t'] / sum_y1_t if sum_y1_t > 0 else 0\n p_x_y0_t = x_sum['y0_t'] / sum_y0_t if sum_y0_t > 0 else 0\n p_x_y1_c = x_sum['y1_c'] / sum_y1_c if sum_y1_c > 0 else 0\n p_x_y0_c = x_sum['y0_c'] / sum_y0_c if sum_y0_c > 0 else 0\n niv_weight = (p_x_y1_t * p_x_y0_c - p_x_y0_t * p_x_y1_c)\n\n niv_row = 100 * nwoe * niv_weight\n niv = niv_row.sum()\n niv_dict[col] = niv\n\n s_niv = pd.Series(niv_dict)\n s_selected_niv = s_niv.sort_values(ascending=False)[: max_vars]\n\n return s_selected_niv.index","def undetermined_coefficients(gensols: List[Symbol], func_coeffs: List[Symbol], gt: Symbol, t: Symbol = t) -> Tuple[Symbol, Procedure]:\n\n Y = Function('Y', real=True)(t)\n\n coeffs = numbered_symbols('A', cls=Dummy)\n coefflist = []\n\n trialset = _undetermined_coefficients_match(gt, t)['trialset']\n\n notneedset = set()\n\n mult = 0\n for i, sol in enumerate(gensols):\n check = sol\n if check in trialset:\n # If an element of the trial function is already part of the\n # homogeneous solution, we need to multiply by sufficient x to\n # make it linearly independent. We also don't need to bother\n # checking for the coefficients on those elements, since we\n # already know it will be 0.\n while True:\n if check*t**mult in trialset:\n mult += 1\n else:\n break\n trialset.add(check*t**mult)\n notneedset.add(check)\n\n newtrialset = trialset - notneedset\n\n # while True:\n # dependent = False\n # for trial in newtrialset:\n # if trial in gensols:\n # dependent = True\n # break\n # if not dependent:\n # break\n # newtrialset = set([t*trial for trial in trialset])\n\n # trialset = trialset.union(newtrialset)\n\n trialfunc = sympy.Number(0)\n for i in newtrialset:\n c = next(coeffs)\n coefflist.append(c)\n trialfunc += c*i\n\n derivatives = []\n\n eqs = 0\n for order, coeff in enumerate(func_coeffs[::-1]):\n deriv = simplify(trialfunc.diff(t, order))\n derivatives.append(\n Eq(Derivative(Y, t, order), deriv, evaluate=False))\n eqs += coeff * deriv\n\n coeffsdict = dict(list(zip(trialset, [0]*(len(trialset) + 1))))\n\n eqs_lhs = eqs\n\n eqs = _mexpand(simplify(eqs - gt).expand())\n\n for i in Add.make_args(eqs):\n s = separatevars(i, dict=True, symbols=[t])\n coeffsdict[s[t]] += s['coeff']\n\n coeffvals = solve(list(coeffsdict.values()), coefflist)\n\n if not coeffvals:\n print(\n \"Could not solve `%s` using the \"\n \"method of undetermined coefficients \"\n \"(unable to solve for coefficients).\" % eqs)\n\n psol = trialfunc.subs(coeffvals)\n\n procedure = Procedure()\n procedure\\\n .text('Find ').latex('Y(t)').text(' that mimics the form of ').latex('g(t)', nl=True)\\\n .eq(Eq(Y, trialfunc, evaluate=False))\\\n .text('Compute successive derivatives of ').latex('Y(t)', nl=True)\\\n .equlist(derivatives)\\\n .text('Plug the derivatives into the LHS and equate coefficients', nl=True)\\\n .equlist([Eq(eqs_lhs, gt, evaluate=False),\n Eq(simplify(eqs_lhs).expand().collect(t), gt, evaluate=False)])\\\n .equarr([Eq(a, 0, evaluate=False) for a in coeffsdict.values()])\\\n .text('Solve for the undetermined coefficients', nl=True)\\\n .equarr([Eq(k, v, evaluate=False)\n for k, v in coeffvals.items() if k != 0] if len(coeffvals) > 0 else [])\\\n .text('Substitute the coefficients to get the particular solution', nl=True)\\\n .eq(Eq(Dummy('y_p'), psol, evaluate=False))\n\n return psol, procedure","def _redef_via_predef_eqn(self):\r\n time = self.current_T # + self.d_T\r\n\r\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\r\n (self.convect_coeff) \r\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\r\n (self.density * self.heat_capacity)\r\n\r\n # Source term.\r\n def F_func(elem, eta):\r\n x = elem.local_to_global(eta)\r\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\r\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * F\r\n\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func,\r\n self.node_map,\r\n gauss_mult=2) # Use double gp_1D\r\n\r\n # Boundary term.\r\n def f_func(elem, eta):\r\n n = elem.guess_normal_vector_global(eta)\r\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n x = elem.local_to_global(eta)\r\n # Evaluate our boundary term.\r\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\r\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\r\n return elem.funcs(eta) * f\r\n\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func,\r\n self.node_map,\r\n gauss_mult=2)","def variational_distribution(self):\n activation = tf.nn.relu\n if self.linear:\n activation = None\n\n #q(z | x, s)\n if self.log_variational:\n x = tf.log(1 + self.expression)\n else:\n x = self.expression\n\n h = dense(x, self.n_hidden, activation=activation, \\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\n for layer in range(2, self.n_layers + 1):\n h = dense(h, self.n_hidden, activation=activation, \\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\n\n \n self.qz_m = dense(h, self.n_latent, activation=None, \\\n bn=False, keep_prob=None, phase=self.training_phase)\n self.qz_v = dense(h, self.n_latent, activation=tf.exp, \\\n bn=False, keep_prob=None, phase=self.training_phase)\n \n if self.scalings:\n # q(l | x, s)\n h = dense(x, self.n_hidden, activation=activation, \\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\n self.ql_m = dense(h, 1, activation=None, \\\n bn=False, keep_prob=None, phase=self.training_phase)\n self.ql_v = dense(h, 1, activation=tf.exp, \\\n bn=False, keep_prob=None, phase=self.training_phase)","def fluid_deriv(self):\n deriv = np.zeros((self.fluid_constraints['num_eq'],\n 2 * self.num_i + self.num_vars,\n self.num_nw_vars))\n for i in range(self.num_i):\n for j in range(self.num_nw_fluids):\n deriv[i * self.num_nw_fluids + j, i, j + 3] = 1\n deriv[i * self.num_nw_fluids + j, self.num_i + i, j + 3] = -1\n return deriv","def variable_costs(dh: DataHandler):\n print(\"PtHydrogen not implemented\")\n\n scen_hor_map = dh.scenarios.horizon\n\n cost_var = dh.get(\"i_cost\").xs(\"varcost\", level=\"par_cost\")\n cost_var = cost_var.groupby([\"alltec\"]).apply(\n extract_horizon_specific_cost, scen_hor_map\n )\n cost_var = add_dimension(cost_var, dh.merge_stored_sets(\"r\"), \"r\")\n cost_var = cost_var.reorder_levels([\"alltec\", \"r\"])\n\n h2_price = dh.get(\"o_h2price_buy\")\n h2_price = add_dimension(h2_price, dh.merge_stored_sets(\"tec_h2g\"), \"alltec\")\n\n elec_price = dh.get(\"o_prices\")\n\n cost_fuel = dh.get(\"cost_fuel\")\n cost_fuel = add_dimension(cost_fuel, dh.merge_stored_sets(\"r\"), \"r\")\n cost_fuel = cost_fuel.reorder_levels([\"alltec\", \"r\"])\n\n cost_fuel.loc[h2_price.index, :] = h2_price\n\n eff = dh.get(\"eff\")\n\n co2_int = dh.get(\"co2_int\").div(1000)\n\n co2_price = dh.get(\"o_co2price\")\n\n co2_costs = co2_int * co2_price\n co2_costs.index.names = [\"alltec\", \"r\"]\n\n var_cost = (\n cost_fuel.add(co2_costs, fill_value=0).div(eff).add(cost_var, fill_value=0)\n )\n\n return var_cost","def taylor_exp_3(y0, t, f, jac, hess, df_dt=None, d2f_dt2=None, d2f_dtdu=None, verbose=True, krylov_subspace_dim=None,\n **_):\n try:\n n, d = len(t), len(y0)\n y = np.zeros((n, d))\n except TypeError:\n n, d = len(t), 1\n y = np.zeros((n,))\n if verbose is False:\n count = Counter('', 0)\n elif verbose is True:\n count = Counter('Taylor Exp 3', n)\n else:\n count = Counter(verbose, n)\n if df_dt is None:\n def df_dt(*_): return np.zeros((d,))\n if d2f_dt2 is None:\n def d2f_dt2(*_): return np.zeros((d,))\n if d2f_dtdu is None:\n def d2f_dtdu(*_): return np.zeros((d, d))\n y[0] = y0\n j = jac(y[0], t[0])\n w = np.zeros((d, 3))\n expanded_vector = np.zeros((d + 3,))\n expanded_vector[-1] = 1\n expanded_matrix = np.zeros((d + 3, d + 3))\n expanded_matrix[-3:-1, -2:] = np.eye(2)\n expanded_matrix[:d, :d] = j\n for i in range(n - 1):\n h = t[i + 1] - t[i]\n w[:, -1] = f(y[i], t[i]) - np.dot(j, y[i])\n w[:, -2] = np.dot(jac(y[i], t[i]) - j, f(y[i], t[i])) + df_dt(y[i], t[i])\n w[:, -3] = np.dot(np.dot(hess(y[i], t[i]), f(y[i], t[i])), f(y[i], t[i])) \\\n + np.dot(jac(y[i], t[i]) - j, np.dot(jac(y[i], t[i]), y[i])) \\\n + np.dot(jac(y[i], t[i]) - j, df_dt(y[i], t[i])) \\\n + 2 * np.dot(d2f_dtdu(y[i], t[i]), f(y[i], t[i])) \\\n + d2f_dt2(y[i], t[i])\n expanded_vector[:d] = y[i]\n expanded_matrix[:d, -3:] = w\n if krylov_subspace_dim is None:\n y[i + 1] = np.dot(expm_sp(h * expanded_matrix), expanded_vector)[:d]\n else:\n y[i + 1] = expm_krylov(h * expanded_matrix, expanded_vector, krylov_subspace_dim)[:d]\n count(i + 1)\n return y","def f1d(t,y,float_params,sigmaI): #sigmastep is an array\n \n ## y is Ntot0 ##\n\n # unpack parameters\n Nbar, Nstar, sigma0, nu_kin_mlyperus, DoverdeltaX2 = float_params \n\n # Ntot is passed in, Fqll calculated from Ntot\n Ntot0 = np.ascontiguousarray(y)\n Nqll0 = Nbar - Nstar * np.sin(2*np.pi*(Ntot0))\n\n # Calc surface deposition, dNtot_dt before diffusion\n m = (Nqll0 - (Nbar - Nstar))/(2*Nstar)\n sigmaM = (sigmaI - m * sigma0)/(1+m*sigma0)\n depsurf = nu_kin_mlyperus * sigmaM\n dNtot_dt = depsurf\n\n # Diffusion\n dy = diffuse_1d(Nqll0,DoverdeltaX2)\n dNtot_dt += dy \n\n # Package for output, only values of dNtot\n derivs = dNtot_dt\n return derivs","def variation_of_parameters(y: List[Symbol], gt: Symbol, t: Symbol = t, do_integral=True) -> Tuple[Symbol, Procedure]:\n W, w = Wronskian(y, t)\n goW = simplify(gt / W)\n\n yp = 0\n\n Wdets = []\n integrals = []\n\n col = [0] * len(y)\n col[-1] = 1\n for i in range(len(y)):\n Wi = w.copy()\n Wi[:, i] = col.copy()\n\n # reduce cos^2 t + sin^2 t to 1\n Wi_det = trigsimp(simplify(Wi.det()), deep=True, recursive=True)\n\n integrand = (Wi_det * goW).expand()\n integral = integrate(\n integrand, t) if do_integral else Integral(integrand, t)\n yp += y[i] * integral\n\n if do_integral:\n integrals.append(\n Eq(Dummy('mu_{}'.format(i + 1)),\n Eq(Integral(integrand, t), integral, evaluate=False), evaluate=False)\n )\n else:\n integrals.append(Eq(Dummy('mu_{}'.format(i)),\n Integral(integrand, t), evaluate=False))\n\n Wdets.append(\n Eq(Symbol('W{}'.format(i+1)), Eq(Determinant(Wi), Wi_det, evaluate=False), evaluate=False))\n\n yps = logcombine(simplify(yp))\n\n procedure = Procedure()\n procedure\\\n .text('Compute the Wronskian determinant', nl=True)\\\n .eq(Eq(Dummy('W'), Eq(Determinant(w), W, evaluate=False), evaluate=False))\\\n .text('Compute ').latex('W_i', nl=True)\\\n .equlist(Wdets)\\\n .text('Calculate and simplify ').latex('\\\\frac{g(t)}{W(t)}', nl=True)\\\n .eq(Eq(sympy.Mul(gt, sympy.Pow(W, -1, evaluate=False), evaluate=False), goW, evaluate=False))\\\n .text('Compute ').latex('\\\\mu_i = \\\\int \\\\frac{g(t)W_i(t)}{W(t)} dt', nl=True)\\\n .equlist(integrals)\\\n .text('Compute the sum ').latex('\\\\sum_{i=1}^{k} y_i \\\\int \\\\frac{g(t)W_i(t)}{W(t)} dt', nl=True)\\\n .equlist([\n Eq(Dummy('y_p'), yp, evaluate=False),\n Eq(Dummy('y_p'), yps, evaluate=False)\n ])\\\n .text('Complementray + particular = general', nl=True)\\\n .eq(Eq(Dummy('y'), to_general(y, yps)[0], evaluate=False))\n\n return yps, procedure","def hexapodZernikeMultiLinearModel_hexapodcoordinate():\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\n Vfile = '/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_validate.cp'\n b=p.load(open(Tfile))\n vb=p.load(open(Vfile))\n nobs = len(b)\n x = b[:,0]\n y = b[:,1]\n z = b[:,2]\n theta = b[:,3]\n phi = b[:,4]\n fwhm = b[:,5]\n e1 = b[:,6]\n e2 = b[:,7]\n thetax = theta*np.cos(np.deg2rad(phi))\n thetay = theta*np.sin(np.deg2rad(phi))\n xh = x*1000 # convert to hexapod coordinate\n yh = -y*1000\n zh = -z*1000\n xtilth = - thetay\n ytilth = - thetax\n dataX = b[:,8:68]\n coeff_xh = sm.WLS(xh,dataX).fit().params\n coeff_yh = sm.WLS(yh,dataX).fit().params\n coeff_zh = sm.WLS(zh,dataX).fit().params\n coeff_xtilth = sm.WLS(xtilth,dataX).fit().params\n coeff_ytilth = sm.WLS(ytilth,dataX).fit().params\n coeff = np.array([coeff_xh,coeff_yh,coeff_zh,coeff_xtilth,coeff_ytilth])\n vx = vb[:,0]\n vy = vb[:,1]\n vz = vb[:,2]\n vtheta = vb[:,3]\n vphi = vb[:,4]\n vfwhm = vb[:,5]\n ve1 = vb[:,6]\n ve2 = vb[:,7]\n vthetax = vtheta*np.cos(np.deg2rad(vphi))\n vthetay = vtheta*np.sin(np.deg2rad(vphi))\n vxh = vx*1000 # convert to hexapod coordinate\n vyh = -vy*1000\n vzh = -vz*1000\n vxtilth = - vthetay\n vytilth = - vthetax\n vdataX = vb[:,8:68]\n fit = np.dot(vdataX,coeff.T)\n bp.bin_scatter(vxh,fit[:,0],nbins=20,fmt='bo',scatter=True)\n bp.bin_scatter(vyh,fit[:,1],nbins=20,fmt='bo',scatter=True)\n bp.bin_scatter(vzh,fit[:,2],nbins=20,fmt='bo',scatter=True)\n bp.bin_scatter(vxtilth,fit[:,3],nbins=20,fmt='bo',scatter=True)\n bp.bin_scatter(vytilth,fit[:,4],nbins=20,fmt='bo',scatter=True)","def fwd_model(Ti_samples,To_samples, dw_samples, kw_samples,hi_samples,ho_samples,TA_samples):\n\t#Determine number of samples (totquat)\n\ttotquat=len(Ti_samples)\n\t# List to store values of Q (assuming no radiative heat transfer) calculated from\n\t# the random samples of the parameters\n\tQ_samples_4PCE=[]\n\t# List to store values of Q assuming radiative heat transfer occurs\n\t#Q_r_samples_4PCE=[]\n\t# Calculate values of heat flux Q (assuming no radiative heat transfer)\n\t# for the different sample values and append to the list\n\tfor i in range(totquat):\n\t\t(Q,T1,T2)=compute_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i],\\\n\t\t\tkw_samples[i], hi_samples[i], ho_samples[i])\n\t\tQ_samples_4PCE.append(Q)\n\t\t# Calculate values of heat flux Q assuming radiative heat transfer to atmosphere and append to list\n\t\t# For the required estimates of Q,T1, and T2 needed to solve the nonlinear system,\n\t\t# we use the values obtained by solving the system assuming no radiative heat transfer\n\t\t\"\"\"Q2=r_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i], kw_samples[i],\\\n\t\t\thi_samples[i], ho_samples[i], TA_samples[i], (Q,T1,T2))\n\t\tQ_r_samples_4PCE.append(Q2)\n\t# Convert Q_r_samples_4PCE to numpy array\n\tQ_evals = np.array(Q_r_samples_4PCE)\n\treturn Q_evals\"\"\"\n\t\tConvert Q_samples_4PCE to numpy array\n\t\tQ_evals = np.array(Q_samples_4PCE)\n\t\treturn Q_evals\"\"\"\n\n\ndef KDE(fcn_evals):\n\t\"\"\"\n\tPerforms kernel density estimation\n\tInput:\n\t\tfcn_evals: numpy array of evaluations of the forward model (values of heat flux Q)\n\tOutput:\n\t\txpts_pce: numpy array of points at which the PDF is estimated.\n\t\tPDF_data_pce: numpy array of estimated PDF values.\n\t\"\"\"\n\t# Perform KDE on fcn_evals\n\tkern_pce=stats.kde.gaussian_kde(fcn_evals)\n\t# Generate points at which to evaluate the PDF\n\txpts_pce=np.linspace(fcn_evals.min(),fcn_evals.max(),200)\n\t# Evaluate the estimated PDF at these points\n\tPDF_data_pce=kern_pce(xpts_pce)\n\treturn xpts_pce, PDF_data_pce","def run_main(sst, ft_qv, use_NT):\n\n dtout=10. #minutes\n end_time=8*24. #hours\n del_time=dtout*60. #seconds\n end_time=end_time*3600. #seconds\n #sst=297\n D=5.e-6 #s-1\n U=7 #m/s\n psfc=100. #kPa\n qsfc=tf.qs_tp(sst,psfc)\n ft_intercept = 292 #K\n ft_gamma = 6.e-3 #K/m\n #ft_qv = 2.e-3\n k=0.2 #entrainment efficiency\n Cd = 1.e-3 #drag coefficient\n tspan = np.arange(0.,end_time,del_time)\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\n the_tup=make_tuple(the_tup,'coeffs')\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\n\n # save time/computation by only doing calculations for the last timestep (equilibrium)\n result['time']=tspan[-1]/3600./24. #days\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\n \n # decide how to calculate entrainment\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\n if use_NT:\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\n else:\n result['went']=calc_went(result.iloc[-1],the_tup)\n\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\n\n with open('dumpmodel.csv','w') as f:\n result.to_csv(f,index=False)\n \n return None","def doParametersOfInterest(self):\n \n self.modelBuilder.doVar('expr::cosW(\"0.87681811112\",)')\n self.modelBuilder.doVar('expr::sinW(\"0.48082221247\",)')\n self.modelBuilder.doVar('expr::mZ(\"91.2\",)')\n self.modelBuilder.doVar('expr::Lambda1(\"100.0\",)')\n self.modelBuilder.doVar('expr::e2(\"0.0917\",)')\n self.modelBuilder.doVar('expr::gs2(\"1.533\",)')\n\n # EFT Higgs basis couplings\n\n self.modelBuilder.doVar('cZ[0,-1,1]') \n self.modelBuilder.doVar(\"cZZ[0,-2,2]\") \n self.modelBuilder.doVar(\"cZZt[0,-2,2]\") \n self.modelBuilder.doVar(\"cZB[0,-6,6]\") \n\n poi='cZ,cZZ,cZZt,cZB'\n\n # Amplitude couplings from EFT couplings \n\n self.modelBuilder.doVar('expr::a1(\"@0+1\",cZ)') # (\"2*(@0+1)\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\n self.modelBuilder.doVar('expr::a2(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZ,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::a3(\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\",cZZt,e2,sinW,cosW)')\n self.modelBuilder.doVar('expr::k1(\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\",cZB,e2,Lambda1,sinW,mZ)')\n self.modelBuilder.doVar('expr::k1L1(\"@0/pow(@1,2)\",k1,Lambda1)')\n\n ###### gamma_H ########\n\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\n\n self.modelBuilder.doVar('expr::kappa(\"1.0\",)')\n self.modelBuilder.doVar('expr::kappa_tilde(\"0.0\",)') \n\n self.modelBuilder.doVar('expr::a1_WW(\"@0\",a1)')\n self.modelBuilder.doVar('expr::a2_WW(\"@0*@0*@1\",cosW,a2)')\n self.modelBuilder.doVar('expr::a3_WW(\"@0*@0*@1\",cosW,a3)')\n self.modelBuilder.doVar('expr::k1_WW(\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\",cosW,sinW,k1,a2,Lambda1,mZ)')\n self.modelBuilder.doVar('expr::k2_k1(\"2*@0*@1*@2/(@0*@0 - @1*@1)\",cosW,sinW,k1)')\n self.modelBuilder.doVar('expr::k2_a2(\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\",cosW,sinW,mZ,a2,Lambda1)')\n self.modelBuilder.doVar('expr::k2(\"@0 + @1\",k2_k1,k2_a2)')\n\n # Determine gamma_H from VV couplings\n\n zz_expr = '\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \",a1,a2,a3,k1,k2'\n ww_expr = '\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \",a1_WW,a2_WW,a3_WW,k1_WW'\n zgamma_expr = '\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\",a1_WW,kappa,kappa_tilde'\n gg_expr = '\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\",kappa,kappa_tilde'\n bb_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n cc_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n tautau_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n mumu_expr = '\"(@0*@0 + @1*@1)\",kappa,kappa_tilde'\n gmgm_expr = '\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\",a1_WW,kappa,kappa_tilde'\n \n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\n\n self.modelBuilder.doVar('expr::gammaH(\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \n\n ###########################\n\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\"1V\") # Compensate for scaling of k1 templates \n self.g2V = GetCoupTerms(1,1,1,-0.0001,\"2V\") \n \n self.modelBuilder.doVar(\"expr::g2V_1(\\\"\"+str(self.g2V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1(\\\"((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T1_Neg(\\\"-1*((pow(@0,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\") \n self.modelBuilder.doVar(\"expr::g2V_2(\\\"\"+str(self.g2V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2(\\\"((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T2_Neg(\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\") \n self.modelBuilder.doVar(\"expr::g2V_3(\\\"\"+str(self.g2V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3(\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T3_Neg(\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\") \n self.modelBuilder.doVar(\"expr::g2V_4(\\\"\"+str(self.g2V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4(\\\"((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T4_Neg(\\\"-1*((@0*pow(@1,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\") \n self.modelBuilder.doVar(\"expr::g2V_5(\\\"\"+str(self.g2V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5(\\\"((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T5_Neg(\\\"-1*((pow(@1,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\") \n self.modelBuilder.doVar(\"expr::g2V_6(\\\"\"+str(self.g2V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6(\\\"((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T6_Neg(\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\") \n self.modelBuilder.doVar(\"expr::g2V_7(\\\"\"+str(self.g2V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7(\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T7_Neg(\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\") \n self.modelBuilder.doVar(\"expr::g2V_8(\\\"\"+str(self.g2V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8(\\\"((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T8_Neg(\\\"-1*((@0*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\") \n self.modelBuilder.doVar(\"expr::g2V_9(\\\"\"+str(self.g2V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9(\\\"((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T9_Neg(\\\"-1*((pow(@2,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\") \n self.modelBuilder.doVar(\"expr::g2V_10(\\\"\"+str(self.g2V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10(\\\"((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T10_Neg(\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\") \n self.modelBuilder.doVar(\"expr::g2V_11(\\\"\"+str(self.g2V[10])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11(\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T11_Neg(\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\") \n self.modelBuilder.doVar(\"expr::g2V_12(\\\"\"+str(self.g2V[11])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12(\\\"((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T12_Neg(\\\"-1*((@0*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\") \n self.modelBuilder.doVar(\"expr::g2V_13(\\\"\"+str(self.g2V[12])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13(\\\"((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T13_Neg(\\\"-1*((pow(@3,4))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\") \n self.modelBuilder.doVar(\"expr::g2V_14(\\\"\"+str(self.g2V[13])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14(\\\"((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T14_Neg(\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\") \n self.modelBuilder.doVar(\"expr::g2V_15(\\\"\"+str(self.g2V[14])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15(\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T15_Neg(\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\") \n self.modelBuilder.doVar(\"expr::g2V_16(\\\"\"+str(self.g2V[15])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16(\\\"((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T16_Neg(\\\"-1*((@1*pow(@2,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\") \n self.modelBuilder.doVar(\"expr::g2V_17(\\\"\"+str(self.g2V[16])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17(\\\"((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T17_Neg(\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\") \n self.modelBuilder.doVar(\"expr::g2V_18(\\\"\"+str(self.g2V[17])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18(\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T18_Neg(\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\") \n self.modelBuilder.doVar(\"expr::g2V_19(\\\"\"+str(self.g2V[18])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19(\\\"((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T19_Neg(\\\"-1*((@1*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\") \n self.modelBuilder.doVar(\"expr::g2V_20(\\\"\"+str(self.g2V[19])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20(\\\"((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T20_Neg(\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\") \n self.modelBuilder.doVar(\"expr::g2V_21(\\\"\"+str(self.g2V[20])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21(\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T21_Neg(\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\") \n self.modelBuilder.doVar(\"expr::g2V_22(\\\"\"+str(self.g2V[21])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22(\\\"((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T22_Neg(\\\"-1*((@2*pow(@3,3))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\") \n self.modelBuilder.doVar(\"expr::g2V_23(\\\"\"+str(self.g2V[22])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23(\\\"((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T23_Neg(\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\") \n self.modelBuilder.doVar(\"expr::g2V_24(\\\"\"+str(self.g2V[23])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24(\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T24_Neg(\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\") \n self.modelBuilder.doVar(\"expr::g2V_25(\\\"\"+str(self.g2V[24])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25(\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T25_Neg(\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\") \n self.modelBuilder.doVar(\"expr::g2V_26(\\\"\"+str(self.g2V[25])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26(\\\"((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T26_Neg(\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\") \n self.modelBuilder.doVar(\"expr::g2V_27(\\\"\"+str(self.g2V[26])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27(\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T27_Neg(\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\") \n self.modelBuilder.doVar(\"expr::g2V_28(\\\"\"+str(self.g2V[27])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28(\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T28_Neg(\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\") \n self.modelBuilder.doVar(\"expr::g2V_29(\\\"\"+str(self.g2V[28])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29(\\\"((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T29_Neg(\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\") \n self.modelBuilder.doVar(\"expr::g2V_30(\\\"\"+str(self.g2V[29])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30(\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T30_Neg(\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\") \n self.modelBuilder.doVar(\"expr::g2V_31(\\\"\"+str(self.g2V[30])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31(\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T31_Neg(\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\") \n self.modelBuilder.doVar(\"expr::g2V_32(\\\"\"+str(self.g2V[31])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32(\\\"((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T32_Neg(\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\") \n self.modelBuilder.doVar(\"expr::g2V_33(\\\"\"+str(self.g2V[32])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33(\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T33_Neg(\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\") \n self.modelBuilder.doVar(\"expr::g2V_34(\\\"\"+str(self.g2V[33])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34(\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T34_Neg(\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\") \n self.modelBuilder.doVar(\"expr::g2V_35(\\\"\"+str(self.g2V[34])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35(\\\"((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n self.modelBuilder.factory_(\"expr::scale_Ewk_T35_Neg(\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\") \n \n self.modelBuilder.doVar(\"expr::g1V_1(\\\"\"+str(self.g1V[0])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1(\\\"((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T1_Neg(\\\"-1*((pow(@0,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\") \n self.modelBuilder.doVar(\"expr::g1V_2(\\\"\"+str(self.g1V[1])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2(\\\"((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T2_Neg(\\\"-1*((@0*@1)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\") \n self.modelBuilder.doVar(\"expr::g1V_3(\\\"\"+str(self.g1V[2])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3(\\\"((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T3_Neg(\\\"-1*((pow(@1,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\") \n self.modelBuilder.doVar(\"expr::g1V_4(\\\"\"+str(self.g1V[3])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4(\\\"((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T4_Neg(\\\"-1*((@0*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\") \n self.modelBuilder.doVar(\"expr::g1V_5(\\\"\"+str(self.g1V[4])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5(\\\"((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T5_Neg(\\\"-1*((pow(@2,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\") \n self.modelBuilder.doVar(\"expr::g1V_6(\\\"\"+str(self.g1V[5])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6(\\\"((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T6_Neg(\\\"-1*((@0*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\") \n self.modelBuilder.doVar(\"expr::g1V_7(\\\"\"+str(self.g1V[6])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7(\\\"((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T7_Neg(\\\"-1*((pow(@3,2))/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\") \n self.modelBuilder.doVar(\"expr::g1V_8(\\\"\"+str(self.g1V[7])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8(\\\"((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T8_Neg(\\\"-1*((@1*@2)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\") \n self.modelBuilder.doVar(\"expr::g1V_9(\\\"\"+str(self.g1V[8])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9(\\\"((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T9_Neg(\\\"-1*((@1*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\") \n self.modelBuilder.doVar(\"expr::g1V_10(\\\"\"+str(self.g1V[9])+\"\\\",)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10(\\\"((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n self.modelBuilder.factory_(\"expr::scale_ggH_T10_Neg(\\\"-1*((@2*@3)/@4)*@5\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\") \n \n self.modelBuilder.doSet(\"POI\",poi)","def function_PLD_Ntransits(coeffs, t_list, Pns_list, lc_list,\n coeffs_dict_list,\n coeffs_tuple, fix_coeffs, fix_coeffs_channels,\n batman_params_list, PLD_params_list):\n\n residuals = []\n\n x1 = len([key for key in coeffs_tuple[0:9] if key not in fix_coeffs\n and key not in fix_coeffs_channels])\n x2 = len([key for key in coeffs_tuple[0:9] if key in fix_coeffs_channels])\n x3 = len([key for key in coeffs_tuple if key not in fix_coeffs\n and key not in fix_coeffs_channels])\n\n for i in range(len(coeffs_dict_list)):\n\n coeffs_fit = np.concatenate((coeffs[i*x3:i*x3 + x1],\n coeffs[-x2:],\n coeffs[i*x3 + x1 :(i+1)*x3 ]))\n\n new_flux = model_PLD(coeffs_fit, t_list[i], Pns_list[i], coeffs_dict_list[i],\n coeffs_tuple, fix_coeffs, batman_params_list[i], PLD_params_list[i])\n\n residuals.append(lc_list[i]-new_flux)\n\n return np.array(np.concatenate(residuals))","def derivatives(self, increment_filter):\n ######################################################################\n # derivatives fluid and mass balance are static\n k = self.num_nw_fluids + 1\n\n ######################################################################\n # derivatives for specified heat transfer\n if self.Q.is_set:\n self.jacobian[k, 0, 0] = (\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\n self.jacobian[k, 0, 2] = -self.inl[0].m.val_SI\n self.jacobian[k, 1, 2] = self.inl[0].m.val_SI\n # custom variable Q\n if self.Q.is_var:\n self.jacobian[k, 2 + self.Q.var_pos, 0] = -1\n k += 1\n\n ######################################################################\n # derivatives for specified pressure ratio\n if self.pr.is_set:\n self.jacobian[k, 0, 1] = self.pr.val\n self.jacobian[k, 1, 1] = -1\n # custom variable pr\n if self.pr.is_var:\n self.jacobian[k, 2 + self.pr.var_pos, 0] = (\n self.inl[0].p.val_SI)\n k += 1\n\n ######################################################################\n # derivatives for specified zeta\n if self.zeta.is_set:\n f = self.zeta_func\n if not increment_filter[0, 0]:\n self.jacobian[k, 0, 0] = self.numeric_deriv(\n f, 'm', 0, zeta='zeta')\n if not increment_filter[0, 2]:\n self.jacobian[k, 0, 1] = self.numeric_deriv(\n f, 'p', 0, zeta='zeta')\n if not increment_filter[0, 2]:\n self.jacobian[k, 0, 2] = self.numeric_deriv(\n f, 'h', 0, zeta='zeta')\n if not increment_filter[1, 1]:\n self.jacobian[k, 1, 1] = self.numeric_deriv(\n f, 'p', 1, zeta='zeta')\n if not increment_filter[1, 2]:\n self.jacobian[k, 1, 2] = self.numeric_deriv(\n f, 'h', 1, zeta='zeta')\n # custom variable zeta\n if self.zeta.is_var:\n self.jacobian[k, 2 + self.zeta.var_pos, 0] = (\n self.numeric_deriv(f, 'zeta', 2, zeta='zeta'))\n k += 1\n\n ######################################################################\n # derivatives for specified hydro-group parameters\n if self.hydro_group.is_set:\n # hazen williams equation\n if self.hydro_group.method == 'HW':\n func = self.hw_func\n # darcy friction factor\n else:\n func = self.darcy_func\n\n if not increment_filter[0, 0]:\n self.jacobian[k, 0, 0] = self.numeric_deriv(func, 'm', 0)\n if not increment_filter[0, 1]:\n self.jacobian[k, 0, 1] = self.numeric_deriv(func, 'p', 0)\n if not increment_filter[0, 2]:\n self.jacobian[k, 0, 2] = self.numeric_deriv(func, 'h', 0)\n if not increment_filter[1, 1]:\n self.jacobian[k, 1, 1] = self.numeric_deriv(func, 'p', 1)\n if not increment_filter[1, 2]:\n self.jacobian[k, 1, 2] = self.numeric_deriv(func, 'h', 1)\n # custom variables of hydro group\n for var in self.hydro_group.elements:\n if var.is_var:\n self.jacobian[k, 2 + var.var_pos, 0] = (\n self.numeric_deriv(func, self.vars[var], 2))\n k += 1\n\n ######################################################################\n # derivatives for additional equations\n self.additional_derivatives(increment_filter, k)","def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\n beta = np.float32(beta)\n mu = np.float32(mu)\n \n du1, du0 = du[:2]\n dv1, dv0 = dv[:2]\n dn0 = dn[0]\n \n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\n \n n1 = n + ( dn0 )*dt\n \n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\n \n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\n \n n, u, v = n1, u1, v1\n \n du = [du1, du0, du0, du0]\n dv = [dv1, dv0, dv0, dv0]\n dn = [dn0, dn0, dn0]\n return n1, u1, v1, du, dv, dn","def dfegn17(mod96file, wavetype, nmod, freq, pertu = 0.05, tmpdir = None, cleanup = True, fig = None):\n \n #create the main temporary directory\n if tmpdir is None: \n tmpdir = \"/tmp/tmpdir_dfegn17_%10d\" % (np.random.rand() * 1e10)\n while os.path.isdir(tmpdir):\n tmpdir += \"_%10d\" % (np.random.rand() * 1e10)\n os.mkdir(tmpdir)\n\n #read starting model\n Z, H, VP, VS, RHO, QP, QS, ETAP, ETAS, FREFP, FREFS = readmod96(mod96file)\n model0 = np.concatenate((H, VP, VS, RHO))\n nlayer = len(H)\n IH = np.arange(nlayer) #index of thickness parameters\n IVP = np.arange(1 * nlayer, 2 * nlayer) #index of Vp parameters\n IVS = np.arange(2 * nlayer, 3 * nlayer) \n IRH = np.arange(3 * nlayer, 4 * nlayer) \n\n\n\n #compute eigenfunctions for starting model\n out0 = fegn17(mod96file, wavetype, nmod, freq, tmpdir = \"%s/%s\" % (tmpdir, \"startingmodel\"), fig = None, cleanup = False)\n\n #initiate kernels\n if wavetype == \"R\":\n DURDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n DUZDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n DTRDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n DTZDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n elif wavetype == \"L\":\n DUTDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n DTTDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\n\n\n #perturbate each parameter subsequently\n for nparam in xrange(len(model0)):\n if nparam == nlayer - 1 :\n #thickness of the half space: meaningless\n continue\n\n #prepare the perturbated model and determine the sub-directory name\n modeln = model0.copy()\n modeln[nparam] *= (1.0 + pertu)\n tmpdir_n = \"%s/param%06d\" % (tmpdir, nparam)\n mod96file_n = \"%s/model_param%06d.mod96\" % (tmpdir, nparam) #write the perturbated file in the main temporary directory\n\n #write perturbated model\n writemod96(mod96file_n, \n H = modeln[IH], \n VP = modeln[IVP], \n VS = modeln[IVS], \n RHO = modeln[IRH], \n QP = QP, QS = QS, ETAP=ETAP, ETAS=ETAS, FREFP=FREFP, FREFS=FREFS) #keep attenuation untouched\n\n #call fegn17 with perturbated model\n out_n = fegn17(mod96file_n, wavetype, nmod, freq, tmpdir = tmpdir_n, fig = None, cleanup = False)\n dm = (modeln[nparam] - model0[nparam])\n\n for modnum in xrange(nmod+1):\n key = \"%s%d\" % (wavetype, modnum)\n\n if wavetype == \"R\":\n DURDN[modnum, :, nparam] = (out_n[key]['UR'] - out0[key]['UR']) / dm\n DUZDN[modnum, :, nparam] = (out_n[key]['UZ'] - out0[key]['UZ']) / dm\n DTRDN[modnum, :, nparam] = (out_n[key]['TR'] - out0[key]['TR']) / dm\n DTZDN[modnum, :, nparam] = (out_n[key]['TZ'] - out0[key]['TZ']) / dm\n elif wavetype == \"L\":\n\n DUTDN[modnum, :, nparam] = (out_n[key]['UT'] - out0[key]['UT']) / dm\n DTTDN[modnum, :, nparam] = (out_n[key]['TT'] - out0[key]['TT']) / dm\n\n\n #-------------------\n dout = {\"Z\" : out0['model'][\"Z\"], \"T\" : 1. / freq}\n if wavetype == \"R\":\n dout['DURDH'] = DURDN[:, :, IH ]\n dout['DURDVP'] = DURDN[:, :, IVP]\n dout['DURDVS'] = DURDN[:, :, IVS]\n dout['DURDRH'] = DURDN[:, :, IRH]\n\n dout['DUZDH'] = DUZDN[:, :, IH ]\n dout['DUZDVP'] = DUZDN[:, :, IVP]\n dout['DUZDVS'] = DUZDN[:, :, IVS]\n dout['DUZDRH'] = DUZDN[:, :, IRH]\n\n dout['DTRDH'] = DTRDN[:, :, IH ]\n dout['DTRDVP'] = DTRDN[:, :, IVP]\n dout['DTRDVS'] = DTRDN[:, :, IVS]\n dout['DTRDRH'] = DTRDN[:, :, IRH]\n\n dout['DTZDH'] = DTZDN[:, :, IH ]\n dout['DTZDVP'] = DTZDN[:, :, IVP]\n dout['DTZDVS'] = DTZDN[:, :, IVS]\n dout['DTZDRH'] = DTZDN[:, :, IRH]\n \n elif wavetype == \"L\": \n dout['DUTDH'] = DUTDN[:, :, IH ]\n dout['DUTDVP'] = DUTDN[:, :, IVP]\n dout['DUTDVS'] = DUTDN[:, :, IVS]\n dout['DUTDRH'] = DUTDN[:, :, IRH]\n \n dout['DTTDH'] = DTTDN[:, :, IH ]\n dout['DTTDVP'] = DTTDN[:, :, IVP]\n dout['DTTDVS'] = DTTDN[:, :, IVS]\n dout['DTTDRH'] = DTTDN[:, :, IRH]\n\n if cleanup:\n #remove temporary directory\n execbash('rm -rf %s' % tmpdir, \".\")\n\n if fig is not None:\n\n ax2 = fig.add_subplot(224)\n ax1 = fig.add_subplot(223, sharey = ax2)\n ax3 = fig.add_subplot(222, sharex = ax2)\n\n #------------------\n ax1.invert_yaxis()\n #------------------\n z = np.concatenate((np.repeat(out0['model'][\"Z\"], 2)[1:], [sum(out0['model'][\"H\"]) * 1.1]))\n vp = np.repeat(out0['model'][\"Vp\"], 2)\n vs = np.repeat(out0['model'][\"Vs\"], 2)\n rh = np.repeat(out0['model'][\"Rh\"], 2)\n ax1.plot(vp, z, label = \"Vp\")\n ax1.plot(vs, z, label = \"Vs\")\n ax1.plot(rh, z, label = \"Rh\")\n ax1.legend()\n ax1.grid(True)\n ax1.set_ylabel('model depth (km)')\n\n #------------------\n if wavetype == \"R\":\n vmax = abs(dout['DUZDVS'][nmod, :, :]).max()\n ax3.plot(dout[\"Z\"], out0[\"%s%d\" % (wavetype, nmod)]['UZ'], label = \"UZ\")\n Y = dout['DUZDVS'][nmod, :, :]\n Y = np.ma.masked_where(np.isnan(Y), Y)\n ax2.pcolormesh(dout[\"Z\"], dout[\"Z\"], Y, vmin = -vmax, vmax = vmax)\n ax2.set_title(\"DUZ/DVS, T = %f, mode %d\" % (1. / freq, nmod))\n ax3.set_xlabel('eigenfunction depth (km)')\n elif wavetype == \"L\":\n vmax = abs(dout['DUTDVS'][nmod, :, :]).max()\n ax3.plot(dout[\"Z\"], out0[\"%s%d\" % (wavetype, nmod)]['UT'], label = \"UT\")\n Y = dout['DUTDVS'][nmod, :, :]\n Y = np.ma.masked_where(np.isnan(Y), Y)\n ax2.pcolormesh(dout[\"Z\"], dout[\"Z\"], Y, vmin = -vmax, vmax = vmax)\n ax2.set_title(\"DUT/DVS, T = %f, mode %d\" % (1. / freq, nmod))\n ax3.set_xlabel('eigenfunction depth (km)')\n\n ax3.xaxis.set_label_position(\"top\") \n ax3.legend()\n ax2.grid(True)\n ax3.grid(True)\n\n return dout","def dynstall_mhh_dxdt_simple(t, x, U, U_dot, omega, alpha_34, p):\n # States\n x1=x[0] # Downwash memory term 1\n x2=x[1] # Downwash memory term 2\n x3=x[2] # Clp', Lift coefficient with a time lag to the attached lift coeff\n x4=x[3] # f'' , Final separation point function\n # Parameters\n alpha0 = p['alpha0']\n Cla = p['Cla']\n c = p['chord']\n A1 = p['A1']\n A2 = p['A2']\n b1 = p['b1']\n b2 = p['b2']\n F_st = p['F_st']\n # Variables derived from inputs\n U = max(U, 0.01)\n Tu = max(c/(2*U), 1e-4) # Eq. 23\n Tf = p['Tf0']*Tu # OLD was twice: Tf = p['Tf0']*c/U\n Tp = p['Tp0']*Tu # OLD was twice: Tp = p['Tp0']*c/U\n # Variables derived from states\n if p['alpha0_in_x1x2']:\n alphaE = alpha_34*(1-A1-A2)+ x1 + x2 # Eq. 12\n else:\n alphaE = (alpha_34-alpha0)*(1-A1-A2)+ x1 + x2 + alpha0 # Eq. 12\n\n# alphaE = u['alphaE'](t) # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HACK HACK TODO TODO TODO TODO TODO\n\n Clp = Cla * (alphaE-alpha0) + np.pi * Tu * omega # Eq. 13\n alphaF = x3/Cla+alpha0 # p. 13\n fs_aF = F_st(alphaF) # p. 13\n if(fs_aF<0):\n print('Problematic fs:',fs_aF)\n x4 = np.clip(x4, 1e-16, 1.0) # Constraining x4 between 0 and 1 increases numerical stability\n # State equation\n xdot = [0]*4\n if p['alpha0_in_x1x2']:\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * alpha_34\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * alpha_34\n else:\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * (alpha_34-alpha0)\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * (alpha_34-alpha0)\n xdot[2] = -1/Tp * x3 + 1/Tp * Clp\n xdot[3] = -1/Tf * x4 + 1/Tf * fs_aF\n return xdot","def DyEvo(x, t, T0, r1, r2, K_co, K_ch, alpha, n):\n y=np.zeros([np.size(x)])\n D=np.zeros([2]) \n #define fitnss\n D[0]=dmax*x[0]**n/(K_co**n+x[0]**n) #cooperator\n D[1]=dmax*x[0]**n/(K_ch**n+x[0]**n) #cheater \n #degradation\n deg=fmax*x[1]/(x[1]+Kd) \n #ODE of eco-evo dynamics\n y[0]=alpha*T0-deg*x[0]-alpha*x[0] #dt/dt\n y[1]=x[1]*(r1*(1-x[1]-x[2])-D[0]-alpha)#d Co/dt\n y[2]=x[2]*(r2*(1-x[1]-x[2])-D[1]-alpha) #d Ch/dt\n \n return y","def dE_mdn(self, x, y, t, w1 = None, w2 = None):\n if w2 == None:\n w2 = self.w2\n M = int(self.M)\n # avoid underrun\n \n alpha, sigma, mu = self.getMixtureParams(y.T)\n #import pdb; pdb.set_trace()\n \n #T = t.T[None, None, :] # note: np.tile is slower than this notation\n T = t.T[None, :]\n \n phi = self._phi(T, mu, sigma)\n aphi = alpha*phi\n pi = aphi / np.sum(aphi, 0)\n \n # derivatives of E with respect to the output variables (s. Bishop 1995, chp. 6.4)\n dE_dy_alpha = alpha - pi\n dE_dy_sigma = - 0.5 * pi * ((np.sum((T-mu)**2 , 1) / sigma) - self.c)\n dE_dy_mu = pi[:,np.newaxis,:] * (mu - T) / sigma[:,np.newaxis,:]\n\n dk = np.zeros([self.ny, x.shape[0]])\n dk[0:M,:] = dE_dy_alpha\n dk[M:2*M,:] = dE_dy_sigma\n \n dk[2*M:] = np.reshape(dE_dy_mu, [M*self.c, x.shape[0]])\n \n # back-propagate the dks\n #t0=datetime.now()\n dEnw1, dEnw2 = self.backward(x, dk, None, w2)\n #print 'eval of dE_mdn:' + str((datetime.now()-t0))\n #dj = (1 - self.z[1:]**2) * np.dot(w2[:,1:].T, dk)\n # evaluate derivatives with respect to the weights\n #dEnw1 = (dj[:,:,np.newaxis]*x[np.newaxis,:,:]).transpose(1,0,2)\n #dEnw2 = (dk[:,:,np.newaxis]*self.z.T[np.newaxis,:,:]).transpose(1,0,2)\n return dEnw1, dEnw2","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n z = unknowns['z']\n znew = z\n\n iter = 0\n eps = 1.0e99\n while iter < self.maxiter and abs(eps) > self.atol:\n z = znew\n znew = 4.0 - x*z\n\n eps = x*znew + znew - 4.0\n\n unknowns['z'] = znew\n unknowns['y'] = x + 2.0*znew\n\n resids['z'] = eps\n #print(unknowns['y'], unknowns['z'])","def _func_pen(self, coeffs_ext):\n l_elastic_net = self.l_elastic_net\n eta = self.eta\n n_features = self.n_features\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\n return l_elastic_net * ((1. - eta) * coeffs_ext.sum()\n + 0.5 * eta * np.linalg.norm(coeffs) ** 2)","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n a = self.a\n b = self.b\n c = self.c\n\n unknowns['y'] = a*x**2 + b*x + c","def test_adding_three_variables():\n a = fwd.Variable()\n b = fwd.Variable()\n c = fwd.Variable()\n f = fwd.exp(a-b+c)\n assert equals(f.evaluation_at({a: 1.0, b: 2.0, c: 3.0}), np.exp(2.0))\n assert equals(f.derivative_at(b, {a: 1.0, b: 2.0, c: 3.0}), -np.exp(2.0))\n assert equals(f.derivative_at(a, {a: 1.0, b: 2.0, c: 3.0}), np.exp(2.0))","def fluid_deriv(self, increment_filter, k):\n i = 0\n for fluid in self.nw_fluids:\n j = 0\n for o in self.outl:\n self.jacobian[k, j + 1, 0] = -o.fluid.val[fluid]\n self.jacobian[k, j + 1, i + 3] = -o.m.val_SI\n j += 1\n self.jacobian[k, 0, 0] = self.inl[0].fluid.val[fluid]\n self.jacobian[k, 0, i + 3] = self.inl[0].m.val_SI\n k += 1\n i += 1","def nonlinear_model(t, q, u, p, calcOutput=False):\n # Compute variables useful to several other functions\n m = calcMisc(t, q, u, p)\n\n # Aerodynamic Force (length 3)\n Fa, aeroOut = Faero(t, q, u, p, calcOutput=calcOutput, m=m)\n\n # Inertial force (length 3)\n Fs = Fstruct(t, q, u, p, calcOutput=calcOutput, m=m)\n\n # Total force\n F = np.zeros(3)\n F[0] = Fa[0] + Fs[0]\n F[1] = Fa[1] + Fs[1]\n F[2] =-Fa[2] - Fs[2]\n\n # Derivative of states (structural and aero)\n dqxd = dxd_dt(t, m['qx'], m['qxd'], F[p['Ix']], p)\n dxa = dxa_dt(t, m['x'], m['xd'], m['qxa_ua'], m['qxa_di'], p, u)\n dq = np.concatenate((m['qxd'], dqxd, dxa)) # NOTE: assumed order here\n\n if calcOutput:\n dq_full, xd, xdd = inflate_q(dq, Iq=p['Iq'])\n d = dict()\n ## Structural states\n d['x'] = m['x'][0]\n d['y'] = m['x'][1]\n d['thetat'] = m['x'][2]\n d['xd'] = m['xd'][0]\n d['yd'] = m['xd'][1]\n d['thetad'] = m['xd'][2]\n d['xdd'] = xdd[0]\n d['ydd'] = xdd[1]\n d['thetadd'] = xdd[2]\n # Inputs\n # (NOTE: computed with \"calc='u')\n #m['Ux'], m['Uy'], m['theta_p'] = inputsAtTime(t, u)\n # Struct\n d['theta'] = m['theta']\n d['rho_x'] = m['rho_x']\n d['rho_y'] = m['rho_y']\n ## Aero\n d.update(aeroOut)\n if p['dynamicStallModel'] == 'oye':\n d['fs'] = m['qxa_ua'][0]\n d['dfs'] = dxa [0]\n elif p['dynamicStallModel'] == 'mhh':\n d['x1_ds'] = m['qxa_ua'][0]\n d['x2_ds'] = m['qxa_ua'][1]\n d['x3_ds'] = m['qxa_ua'][2]\n d['x4_ds'] = m['qxa_ua'][3]\n return pd.Series(d)\n else:\n return dq","def _compute_GP_variables(self):\r\n Wi = 1.0/self.W\r\n self.Sigma_tilde = np.diagflat(Wi)\r\n\r\n Y_tilde = Wi*self.Ki_f + self.f_hat\r\n\r\n self.Wi_K_i = self.W12BiW12\r\n ln_det_Wi_K = pddet(self.Sigma_tilde + self.K)\r\n lik = self.noise_model.logpdf(self.f_hat, self.data, extra_data=self.extra_data)\r\n y_Wi_K_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)\r\n\r\n Z_tilde = (+ lik\r\n - 0.5*self.ln_B_det\r\n + 0.5*ln_det_Wi_K\r\n - 0.5*self.f_Ki_f\r\n + 0.5*y_Wi_K_i_y\r\n + self.NORMAL_CONST\r\n )\r\n\r\n #Convert to float as its (1, 1) and Z must be a scalar\r\n self.Z = np.float64(Z_tilde)\r\n self.Y = Y_tilde\r\n self.YYT = np.dot(self.Y, self.Y.T)\r\n self.covariance_matrix = self.Sigma_tilde\r\n self.precision = 1.0 / np.diag(self.covariance_matrix)[:, None]\r\n\r\n #Compute dZ_dK which is how the approximated distributions gradients differ from the dL_dK computed for other likelihoods\r\n self.dZ_dK = self._Kgradients()\r\n #+ 0.5*self.Wi_K_i - 0.5*np.dot(self.Ki_f, self.Ki_f.T) #since we are not adding the K gradients explicit part theres no need to compute this again\r","def coeffs(f):\n return dmp_coeffs(f.rep, f.lev, f.dom)","def _fv(self):\n return self.beta * (self.x ** self.c)","def rhs(y, t, l, m, g):\n # Unpack the states so you can use the variable names in the\n # sympy.physics.mechanics equations\n q1 = y[0]\n q2 = y[1]\n u1 = y[2]\n u2 = y[3]\n # or you can make use of python's tuple unpacking for a one liner\n # q1, q2, u1, u2 = y\n\n # Initialize a vector for the derivatives.\n dydt = zeros((len(y)))\n\n # Compute the derivatives, these are pasted in from the\n # sympy.physics.mechanics results.\n dydt[0] = u1\n dydt[1] = u2\n dydt[2] = (-g*sin(q1)*sin(q2)**2 + 2*g*sin(q1) -\n g*sin(q2)*cos(q1)*cos(q2) + 2*l*u1**2*sin(q1)*cos(q1)*cos(q2)**2 -\n l*u1**2*sin(q1)*cos(q1) - 2*l*u1**2*sin(q2)*cos(q1)**2*cos(q2) +\n l*u1**2*sin(q2)*cos(q2) + l*u2**2*sin(q1)*cos(q2) -\n l*u2**2*sin(q2)*cos(q1))/(l*(sin(q1)**2*sin(q2)**2 +\n 2*sin(q1)*sin(q2)*cos(q1)*cos(q2) + cos(q1)**2*cos(q2)**2 - 2))\n dydt[3] = (-sin(q1)*sin(q2)/2 - cos(q1)*cos(q2)/2)*(2*g*l*m*sin(q1) -\n l**2*m*(-sin(q1)*cos(q2) +\n sin(q2)*cos(q1))*u2**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2)) -\n l**2*m) + (g*l*m*sin(q2) - l**2*m*(sin(q1)*cos(q2) -\n sin(q2)*cos(q1))*u1**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2))\n - l**2*m)\n\n # Return the derivatives.\n return dydt","def equations(self):\n k = 0\n ######################################################################\n # equations for fluid balance\n self.residual[k:k + self.num_nw_fluids * 2] = self.fluid_func()\n k += self.num_nw_fluids * 2\n\n ######################################################################\n # equations for mass flow balance\n self.residual[k:k + 2] = self.mass_flow_func()\n k += 2\n\n ######################################################################\n # equations for energy balance\n self.residual[k] = self.energy_func()\n k += 1\n\n ######################################################################\n # equations for specified heat transfer\n if self.Q.is_set:\n self.residual[k] = (\n self.inl[0].m.val_SI * (\n self.outl[0].h.val_SI - self.inl[0].h.val_SI) - self.Q.val)\n k += 1\n\n ######################################################################\n # equations for specified heat transfer coefficient\n if self.kA.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.kA_func()\n k += 1\n\n ######################################################################\n # equations for specified heat transfer coefficient characteristic\n if self.kA_char.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.kA_char_func()\n k += 1\n\n ######################################################################\n # equations for specified upper terminal temperature difference\n if self.ttd_u.is_set:\n self.residual[k] = self.ttd_u_func()\n k += 1\n\n ######################################################################\n # equations for specified lower terminal temperature difference\n if self.ttd_l.is_set:\n self.residual[k] = self.ttd_l_func()\n k += 1\n\n ######################################################################\n # equations for specified pressure ratio at hot side\n if self.pr1.is_set:\n self.residual[k] = (\n self.pr1.val * self.inl[0].p.val_SI - self.outl[0].p.val_SI)\n k += 1\n\n ######################################################################\n # equations for specified pressure ratio at cold side\n if self.pr2.is_set:\n self.residual[k] = (\n self.pr2.val * self.inl[1].p.val_SI - self.outl[1].p.val_SI)\n k += 1\n\n ######################################################################\n # equations for specified zeta at hot side\n if self.zeta1.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.zeta_func(\n zeta='zeta1', inconn=0, outconn=0)\n k += 1\n\n ######################################################################\n # equations for specified zeta at cold side\n if self.zeta2.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.zeta_func(\n zeta='zeta2', inconn=1, outconn=1)\n k += 1\n\n ######################################################################\n # additional equations\n self.additional_equations(k)","def define_ufl_equations_diff(self):\n\n # Derivatives of velocity integration equation.\n if self.f1 != 0:\n self.df1_du = dlf.derivative(self.f1, self.displacement, self.trial_vector)\n self.df1_dv = dlf.derivative(self.f1, self.velocity, self.trial_vector)\n else:\n self.df1_du = 0\n self.df1_dv = 0\n self.df1_dp = 0 # This is always zero.\n\n # Derivatives of momentum equation.\n if self.displacement != 0:\n self.df2_du = dlf.derivative(self.f2, self.displacement, self.trial_vector)\n else:\n self.df2_du = 0\n\n if self.velocity != 0:\n self.df2_dv = dlf.derivative(self.f2, self.velocity, self.trial_vector)\n else:\n self.df2_dv = 0\n\n if self.pressure != 0:\n self.df2_dp = dlf.derivative(self.f2, self.pressure, self.trial_scalar)\n else:\n self.df2_dp = 0\n\n # Derivatives of incompressibility equation.\n if self.f3 != 0:\n if self.displacement != 0:\n self.df3_du = dlf.derivative(self.f3, self.displacement, self.trial_vector)\n else:\n self.df3_du = 0\n\n if self.velocity != 0:\n self.df3_dv = dlf.derivative(self.f3, self.velocity, self.trial_vector)\n else:\n self.df3_dv = 0\n\n self.df3_dp = dlf.derivative(self.f3, self.pressure, self.trial_scalar)\n else:\n self.df3_du = 0\n self.df3_dv = 0\n self.df3_dp = 0\n\n return None","def tsne(X = Math.array([]), no_dims = 2, initial_dims = 50, perplexity = 30.0):\n \n # Check inputs\n if X.dtype != \"float64\":\n raise ValueError(\"array X should have type float64.\")\n #if no_dims.__class__ != \"<type 'int'>\": # doesn't work yet!\n # print \"Error: number of dimensions should be an integer.\";\n # return -1;\n \n # Initialize variables\n if X.shape[1] > initial_dims:\n print \"Reducing the dimensionality to %d\" %initial_dims\n X = pca(X, initial_dims);\n (n, d) = X.shape;\n max_iter = 1000;\n initial_momentum = 0.5;\n final_momentum = 0.8;\n eta = 500;\n min_gain = 0.01;\n Y = Math.random.randn(n, no_dims);\n dY = Math.zeros((n, no_dims));\n iY = Math.zeros((n, no_dims));\n gains = Math.ones((n, no_dims));\n \n # Compute P-values\n P = x2p(X, 1e-5, perplexity);\n P = P + Math.transpose(P);\n P = P / Math.sum(P);\n P = P * 4; # early exaggeration\n P = Math.maximum(P, 1e-12);\n \n # Run iterations\n for iter in range(max_iter):\n \n # Compute pairwise affinities\n sum_Y = Math.sum(Math.square(Y), 1); \n num = 1 / (1 + Math.add(Math.add(-2 * Math.dot(Y, Y.T), sum_Y).T, sum_Y));\n num[range(n), range(n)] = 0;\n Q = num / Math.sum(num);\n Q = Math.maximum(Q, 1e-12);\n \n # Compute gradient\n PQ = P - Q;\n for i in range(n):\n dY[i,:] = Math.sum(Math.tile(PQ[:,i] * num[:,i], (no_dims, 1)).T * (Y[i,:] - Y), 0);\n \n # Perform the update\n if iter < 20:\n momentum = initial_momentum\n else:\n momentum = final_momentum\n gains = (gains + 0.2) * ((dY > 0) != (iY > 0)) + (gains * 0.8) * ((dY > 0) == (iY > 0));\n gains[gains < min_gain] = min_gain;\n iY = momentum * iY - eta * (gains * dY);\n Y = Y + iY;\n Y = Y - Math.tile(Math.mean(Y, 0), (n, 1));\n \n # Compute current value of cost function\n if (iter + 1) % 10 == 0:\n C = Math.sum(P * Math.log(P / Q));\n print \"Iteration \", (iter + 1), \": error is \", C\n \n # Stop lying about P-values\n if iter == 100:\n P = P / 4;\n \n # Return solution\n return Y;","def ti_deriv(self, increment_filter, k):\n fuel = self.fuel_alias.val\n pos = 3 + self.nw_fluids.index(fuel)\n\n for i in range(2):\n self.jacobian[k, i, 0] = -self.inl[i].fluid.val[fuel]\n self.jacobian[k, i, pos] = -self.inl[i].m.val_SI\n self.jacobian[k, 2, 0] = self.outl[0].fluid.val[fuel]\n self.jacobian[k, 2, pos] = self.outl[0].m.val_SI\n self.jacobian[k] *= self.lhv\n k += 1","def run_optimization(file, sess, Hx_final, Hy_final, w_ij, ops):\n\n sub_patch = read_patch(file)\n # all_plot_params = []\n verbose = True\n\n # Prepare input, randomness in fv is achieved by adding a noise\n vi, Ti, fv_o, subTi = input_prep(sub_patch, do_show=False, avoid_triangle_flip=True)\n B_inv_new = None\n N = 8\n BATCH = 32\n type = 'NN'\n for type in ('NN', 'GD', 'BFGS'):\n\n fvi = fv_o.copy()\n print(\"*\" * 1000)\n print(fvi)\n print(\"&\" * 1000)\n all_plot_params = []\n\n def get_nn_hessians(fv, v, subt):\n inputs_b = pack_batch(fv, v, subt, BATCH)\n feed_dict = gen_feed_dict_inference(inputs_b, is_training=False, ops=ops)\n (Hx_final_out, Hy_final_out, w_ij_out) = sess.run((Hx_final, Hy_final, w_ij), feed_dict=feed_dict)\n (Hx, Hy) = extract_Hs(Hx_final_out, Hy_final_out)\n return (Hx, Hy)\n\n for i in range(N):\n\n params = dirichlet_sym_gd_bfgs_nn_weak_armijo_optimizer_step(line_search=line_search_armijio,\n get_nn_hessians=get_nn_hessians,\n source=vi,\n t=Ti,\n target=fvi,\n subt=subTi,\n verbose=verbose,\n # Back feed the next estimate of Hessian\n B_inv_new=B_inv_new,\n type=type)\n\n optimization_res = do_optimization_result_unpack(params=params, t=Ti, v=vi, verbose=verbose)\n\n (displacement,\n p_k, # optimization_res['step_fv'],\n p_k_n, # optimization_res['step_fv_n'],\n _, # optimization_res['subvertices'],\n eng_xnew_first_order_approx,\n eng_xnew_x0_diff_sub,\n source,\n t,\n source_dim,\n t_dim,\n eng_at_x0_sub,\n eng_at_xnew,\n eng_at_xnew_sub,\n deng_x_at_x0_sub,\n alpha,\n edges_sub,\n epsilon,\n sub_source_dim, # nv_sub\n edges_sub_dim,\n sv_at_x0,\n sv_at_x0_sub,\n sv_at_xnew,\n sv_at_xnew_sub,\n fv_at_xnew,\n eng_first_order_approx_xnew_x0_diff,\n t_flip,\n x0,\n eng_first_order_approx_xnew_x0_diff_sub,\n ray_deng_x_at_x0,\n ray_deng_x_at_xnew,\n eng_plot,\n ray_deng_x_at_x0_sub,\n ray_deng_x_at_xnew_sub,\n eng_plot_sub,\n span,\n type,\n B_inv_new,\n eng_xnew_x0_diff) = optim_res_unpack(optimization_res)\n\n plot_params = (ray_deng_x_at_x0,\n ray_deng_x_at_xnew,\n eng_plot,\n ray_deng_x_at_x0_sub,\n ray_deng_x_at_xnew_sub,\n eng_plot_sub,\n alpha,\n span,\n type)\n\n do_verbose(\"linear_diff\", eng_first_order_approx_xnew_x0_diff, verbose)\n all_plot_params.append(plot_params)\n fvi = fv_at_xnew\n\n sequence_params = concatinate_optimization_sequence_params(all_plot_params=all_plot_params, do_show=True)\n plot_optimization_sequence(sequence_params=sequence_params, do_show=True, type=type)\n\n plt.show()\n return 0","def energy_function(self, x):\n \n return -T.dot(T.transpose(x), T.dot(self.W, x)) -\\\n T.dot(T.transpose(self.b), x)","def TDGradientFunction(Prof,x,Trx,rb_spec,abs_spec,dr,inu0,bsrMult,base_T,base_P,r0,lam=[0,0,0,0,0,0]): \n \n iR = Prof['WV Online'].size # range index for a profile into 1D x array\n x2 = np.reshape(x,(iR+1,6))\n xK = x2[0,:] # constants [HSRL Mol HSRL Comb, WV On, WV Off, O2 On ,O2 Off]\n xS = x2[1:,:] # state vector [T, nWV, BSR, phi_HSRL, phi_WV, phi_O2]\n \n grad2 = np.zeros(x2.shape) \n \n #N,dNdB,dNdT = HSRLDerivative(T,BSR,phi,rb_spec,Trx,inu0,K,base_T,base_P)\n HSRL_mol,dHmdB,dHmdT = HSRLDerivative(xS[:,0],xS[:,2],xS[:,3],rb_spec['HSRL'],Trx['HSRL Mol'],inu0['HSRL'],xK[0],base_T,base_P)\n HSRL_comb,dHcdB,dHcdT = HSRLDerivative(xS[:,0],xS[:,2],xS[:,3],rb_spec['HSRL'],Trx['HSRL Comb'],inu0['HSRL'],xK[1],base_T,base_P)\n \n # N,dNdB,dNdnWV,dNdT = WVDIALDerivative(T,nWV,BSR,phi,rb_spec,abs_spec,Trx,inu0,K,base_T,base_P,dr)\n WV_on,dWVndB,dWVndnWV,dWVndT = WVDIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['WV'],xS[:,4],rb_spec['WV Online'],abs_spec['WV Online'],Trx['WV Online'],inu0['WV Online'],xK[2],base_T,base_P,dr,r0)\n WV_off,dWVfdB,dWVfdnWV,dWVfdT = WVDIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['WV'],xS[:,4],rb_spec['WV Offline'],abs_spec['WV Offline'],Trx['WV Offline'],inu0['WV Offline'],xK[3],base_T,base_P,dr,r0) \n \n # N,dNdB,dNdnWV,dNdT = O2DIALDerivative(T,nWV,BSR,phi,rb_spec,abs_spec,Trx,inu0,K,base_T,base_P,dr)\n O2_on,dO2ndB,dO2ndnWV,dO2ndT = O2DIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['O2'],xS[:,5],rb_spec['O2 Online'],abs_spec['O2 Online'],Trx['O2 Online'],inu0['O2 Online'],xK[4],base_T,base_P,dr,r0)\n O2_off,dO2fdB,dO2fdnWV,dO2fdT = O2DIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['O2'],xS[:,5],rb_spec['O2 Offline'],abs_spec['O2 Offline'],Trx['O2 Offline'],inu0['O2 Offline'],xK[5],base_T,base_P,dr,r0)\n \n# HSRLModel,dHSdB,dHSdT = HSRLProfileRatioDeriv(xS[:,0],P,xS[:,2], \\\n# Trx['HSRL Mol'],Trx['HSRL Comb'], \\\n# rb_spec['HSRL'],inu0['HSRL'],GainRatio=xK[0])\n#\n# WVModel,dWVdB,dWVdnWV,dWVdT = WaterVaporProfileRatioDeriv(xS[:,0],P,xS[:,1],xS[:,2]*bsrMult['WV'],\n# Trx['WV Online'], Trx['WV Offline'], \\\n# rb_spec['WV Online'],rb_spec['WV Offline'], \\\n# abs_spec['WV Online'],abs_spec['WV Offline'],dr, \\\n# inu0['WV Online'],inu0['WV Offline'],GainRatio=xK[1])\n# \n# O2Model,dO2dB,dO2dnWV,dO2dT = OxygenProfileRatioDeriv(xS[:,0],P,xS[:,1],xS[:,2]*bsrMult['O2'],\n# Trx['O2 Online'], Trx['O2 Offline'], \\\n# rb_spec['O2 Online'],rb_spec['O2 Offline'], \\\n# abs_spec['O2 Online'],abs_spec['O2 Offline'],dr, \\\n# inu0['O2 Online'],inu0['O2 Offline'],GainRatio=xK[2])\n \n HSRLmolBase = 1-(Prof['HSRL Mol'])/(HSRL_mol+Prof['HSRL Mol BG'])\n HSRLcombBase = 1-(Prof['HSRL Comb'])/(HSRL_comb+Prof['HSRL Comb BG'])\n WVonBase = 1-(Prof['WV Online'])/(WV_on+Prof['WV Online BG'])\n WVoffBase = 1-(Prof['WV Offline'])/(WV_off+Prof['WV Offline BG'])\n O2onBase = 1-(Prof['O2 Online'])/(O2_on+Prof['O2 Online BG'])\n O2offBase = 1-(Prof['O2 Offline'])/(O2_off+Prof['O2 Offline BG'])\n \n \n# HSRLbase = 2*(HSRLModel-Prof['HSRL'])/ProfVar['HSRL']\n# WVbase = 2*(WVModel-Prof['WV'])/ProfVar['WV']\n# O2base = 2*(O2Model-Prof['O2'])/ProfVar['O2']\n \n # temperature gradient\n grad2[1:,0] = np.nansum(HSRLmolBase[np.newaxis]*dHmdT,axis=1) \\\n + np.nansum(HSRLcombBase[np.newaxis]*dHcdT,axis=1) \\\n + np.nansum(WVonBase[np.newaxis]*dWVndT,axis=1) \\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdT,axis=1) \\\n + np.nansum(O2onBase[np.newaxis]*dO2ndT,axis=1) \\\n + np.nansum(O2offBase[np.newaxis]*dO2fdT,axis=1)\n# # piece wise penalty function \n# gradpen = lam[0]*np.sign(np.diff(xS[:,0]))\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\n# grad2[2:,0] = grad2[2:,0] + gradpen\n# grad2[1:-1,0] = grad2[1:-1,0] - gradpen\n# piece wise slope penalty function \n gradpen = lam[0]*np.sign(np.diff(np.diff(xS[:,0])))\n gradpen[np.nonzero(np.isnan(gradpen))] = 0\n grad2[3:,0] = grad2[3:,0] + gradpen\n grad2[2:-1,0] = grad2[2:-1,0] - 2*gradpen\n grad2[1:-2,0] = grad2[1:-2,0] + gradpen\n \n # water vapor gradient\n grad2[1:,1] = np.nansum(WVonBase[np.newaxis]*dWVndnWV,axis=1) \\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdnWV,axis=1) \\\n + np.nansum(O2onBase[np.newaxis]*dO2ndnWV,axis=1) \\\n + np.nansum(O2offBase[np.newaxis]*dO2fdnWV,axis=1)\n # piecewise penalty function\n gradpen = lam[1]*np.sign(np.diff(xS[:,1]))\n gradpen[np.nonzero(np.isnan(gradpen))] = 0\n grad2[2:,1] = grad2[2:,1] + gradpen\n grad2[1:-1,1] = grad2[1:-1,1] - gradpen\n \n # backscatter gradient\n grad2[1:,2] = np.nansum(HSRLmolBase[np.newaxis]*dHmdB,axis=1) \\\n + np.nansum(HSRLcombBase[np.newaxis]*dHcdB,axis=1) \\\n + np.nansum(WVonBase[np.newaxis]*dWVndB,axis=1) \\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdB,axis=1) \\\n + np.nansum(O2onBase[np.newaxis]*dO2ndB,axis=1) \\\n + np.nansum(O2offBase[np.newaxis]*dO2fdB,axis=1) \n# #piecewise penalty function\n# gradpen = lam[2]*np.sign(np.diff(xS[:,2]))\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\n# grad2[2:,2] = grad2[2:,2] + gradpen\n# grad2[1:-1,2] = grad2[1:-1,2] - gradpen\n \n\n # *bsrMult['WV']\n # *bsrMult['WV']\n # *bsrMult['O2']\n # *bsrMult['O2']\n\n # HSRL Common terms\n grad2[1:,3] = np.nansum(HSRLmolBase[np.newaxis]*HSRL_mol,axis=0) + np.nansum(HSRLcombBase[np.newaxis]*HSRL_comb,axis=0)\n# # piece wise penalty function \n# gradpen = lam[3]*np.sign(np.diff(xS[:,3]))\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\n# grad2[2:,3] = grad2[2:,3] + gradpen\n# grad2[1:-1,3] = grad2[1:-1,3] - gradpen\n \n # WV Common terms\n grad2[1:,4] = np.nansum(WVonBase[np.newaxis]*WV_on,axis=0) + np.nansum(WVoffBase[np.newaxis]*WV_off,axis=0)\n# # piece wise penalty function \n# gradpen = lam[4]*np.sign(np.diff(xS[:,4]))\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\n# grad2[2:,4] = grad2[2:,4] + gradpen\n# grad2[1:-1,4] = grad2[1:-1,4] - gradpen\n \n # O2 Common terms\n grad2[1:,5] = np.nansum(O2onBase[np.newaxis]*O2_on,axis=0) + np.nansum(O2offBase[np.newaxis]*O2_off,axis=0)\n# # piece wise penalty function \n# gradpen = lam[5]*np.sign(np.diff(xS[:,5]))\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\n# grad2[2:,5] = grad2[2:,5] + gradpen\n# grad2[1:-1,5] = grad2[1:-1,5] - gradpen\n\n grad2[0,0] = np.nansum(HSRLmolBase*HSRL_mol/xK[0])\n grad2[0,1] = np.nansum(HSRLcombBase*HSRL_comb/xK[1])\n grad2[0,2] = np.nansum(WVonBase*WV_on/xK[2])\n grad2[0,3] = np.nansum(WVoffBase*WV_off/xK[3])\n grad2[0,4] = np.nansum(O2onBase*O2_on/xK[4])\n grad2[0,5] = np.nansum(O2offBase*O2_off/xK[5])\n \n# grad2[0,1] = np.nansum(WVbase*WVModel/xK[1])\n# grad2[0,2] = np.nansum(O2base*O2Model/xK[2])\n \n# OptError = np.nansum(2*(HSRLModel-Prof['HSRL'])/ProfVar['HSRL']*) \\\n# +np.nansum((WVModel-Prof['WV'])**2/ProfVar['WV']) \\\n# +np.sum((O2Model-Prof['O2'])**2/ProfVar['O2'])\n \n return grad2.flatten()","def change_variables((a,b,c,d), (n,r,m)): \n return ( n*a**2 + r*a*b + m*b**2, 2*(n*a*c + m*b*d) + r*(a*d + c*b), \\\n n*c**2 + r*c*d + m*d**2 )","def f(self,y,psi):\r\n\r\n #1. check that number of params is consistent\r\n # assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'\r\n # assert psi.shape[1] == 4, 'inconsistent parameter dimensions'\r\n mpsi = psi.copy()\r\n d = psi[-1]\r\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\r\n\r\n #3. transform data\r\n z = d*y.copy()\r\n for i in range(len(mpsi)):\r\n a,b,c = mpsi[i]\r\n z += a*np.tanh(b*(y+c))\r\n return z","def F(cst, x):\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\n [u, v, g1, g2, g3] = x\n a = g1*u1 - u0\n b = g2*u2 - u0\n c = g3*u - u0\n l = g1*v1 - v0 \n m = g2*v2 - v0\n n = g3*v - v0\n r = g1 - 1\n s = g2 - 1\n t = g3 - 1\n return np.array([\n coeffs[0]*(a**2-l**2) + 2*coeffs[1]*(a*b-l*m) + coeffs[2]*(b**2-m**2) + 2*coeffs[3]*(a*c-l*n) + 2*coeffs[4]*(b*c-m*n) + c**2 - n**2,\n coeffs[0]*(l**2-r**2) + 2*coeffs[1]*(l*m-r*s) + coeffs[2]*(m**2-s**2) + 2*coeffs[3]*(l*n-r*t) + 2*coeffs[4]*(m*n-s*t) + n**2 - t**2,\n coeffs[0]*a*l + coeffs[1]*(l*b+m*a) + coeffs[2]*m*b + coeffs[3]*(l*c+n*a) + coeffs[4]*(m*c+b*n) + c*n,\n coeffs[0]*a*r + coeffs[1]*(r*b+s*a) + coeffs[2]*s*b + coeffs[3]*(r*c+t*a) + coeffs[4]*(s*c+b*t) + c*t,\n coeffs[0]*r*l + coeffs[1]*(l*s+m*r) + coeffs[2]*m*s + coeffs[3]*(l*t+n*r) + coeffs[4]*(m*t+s*n) + t*n \n ])","def independent_variables(self, indep_vars):\n self.set(independent_variables=indep_vars)","def derivatives(self, increment_filter):\n ######################################################################\n # derivatives fluid and mass balance are static\n k = self.num_nw_fluids * 2 + 2\n\n ######################################################################\n # derivatives for energy balance equation\n for i in range(2):\n self.jacobian[k, i, 0] = (\n self.outl[i].h.val_SI - self.inl[i].h.val_SI)\n self.jacobian[k, i, 2] = -self.inl[i].m.val_SI\n\n self.jacobian[k, 2, 2] = self.inl[0].m.val_SI\n self.jacobian[k, 3, 2] = self.inl[1].m.val_SI\n k += 1\n\n ######################################################################\n # derivatives for specified heat transfer\n if self.Q.is_set:\n self.jacobian[k, 0, 0] = (\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\n self.jacobian[k, 0, 2] = -self.inl[0].m.val_SI\n self.jacobian[k, 2, 2] = self.inl[0].m.val_SI\n k += 1\n\n ######################################################################\n # derivatives for specified heat transfer coefficient\n if self.kA.is_set:\n f = self.kA_func\n self.jacobian[k, 0, 0] = (\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\n for i in range(4):\n if not increment_filter[i, 1]:\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\n if not increment_filter[i, 2]:\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\n k += 1\n\n ######################################################################\n # derivatives for specified heat transfer coefficient\n if self.kA_char.is_set:\n f = self.kA_char_func\n if not increment_filter[0, 0]:\n self.jacobian[k, 0, 0] = self.numeric_deriv(f, 'm', 0)\n if not increment_filter[1, 0]:\n self.jacobian[k, 1, 0] = self.numeric_deriv(f, 'm', 1)\n for i in range(4):\n if not increment_filter[i, 1]:\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\n if not increment_filter[i, 2]:\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\n k += 1\n\n ######################################################################\n # derivatives for specified upper terminal temperature difference\n if self.ttd_u.is_set:\n f = self.ttd_u_func\n for i in [0, 3]:\n if not increment_filter[i, 1]:\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\n if not increment_filter[i, 2]:\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\n k += 1\n\n ######################################################################\n # derivatives for specified lower terminal temperature difference\n if self.ttd_l.is_set:\n f = self.ttd_l_func\n for i in [1, 2]:\n if not increment_filter[i, 1]:\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\n if not increment_filter[i, 2]:\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\n k += 1\n\n ######################################################################\n # derivatives for specified pressure ratio at hot side\n if self.pr1.is_set:\n self.jacobian[k, 0, 1] = self.pr1.val\n self.jacobian[k, 2, 1] = -1\n k += 1\n\n ######################################################################\n # derivatives for specified pressure ratio at cold side\n if self.pr2.is_set:\n self.jacobian[k, 1, 1] = self.pr2.val\n self.jacobian[k, 3, 1] = -1\n k += 1\n\n ######################################################################\n # derivatives for specified zeta at hot side\n if self.zeta1.is_set:\n f = self.zeta_func\n if not increment_filter[0, 0]:\n self.jacobian[k, 0, 0] = self.numeric_deriv(\n f, 'm', 0, zeta='zeta1', inconn=0, outconn=0)\n if not increment_filter[0, 1]:\n self.jacobian[k, 0, 1] = self.numeric_deriv(\n f, 'p', 0, zeta='zeta1', inconn=0, outconn=0)\n if not increment_filter[0, 2]:\n self.jacobian[k, 0, 2] = self.numeric_deriv(\n f, 'h', 0, zeta='zeta1', inconn=0, outconn=0)\n if not increment_filter[2, 1]:\n self.jacobian[k, 2, 1] = self.numeric_deriv(\n f, 'p', 2, zeta='zeta1', inconn=0, outconn=0)\n if not increment_filter[2, 2]:\n self.jacobian[k, 2, 2] = self.numeric_deriv(\n f, 'h', 2, zeta='zeta1', inconn=0, outconn=0)\n k += 1\n\n ######################################################################\n # derivatives for specified zeta at cold side\n if self.zeta2.is_set:\n f = self.zeta_func\n if not increment_filter[1, 0]:\n self.jacobian[k, 1, 0] = self.numeric_deriv(\n f, 'm', 1, zeta='zeta2', inconn=1, outconn=1)\n if not increment_filter[1, 1]:\n self.jacobian[k, 1, 1] = self.numeric_deriv(\n f, 'p', 1, zeta='zeta2', inconn=1, outconn=1)\n if not increment_filter[1, 2]:\n self.jacobian[k, 1, 2] = self.numeric_deriv(\n f, 'h', 1, zeta='zeta2', inconn=1, outconn=1)\n if not increment_filter[3, 1]:\n self.jacobian[k, 3, 1] = self.numeric_deriv(\n f, 'p', 3, zeta='zeta2', inconn=1, outconn=1)\n if not increment_filter[3, 2]:\n self.jacobian[k, 3, 2] = self.numeric_deriv(\n f, 'h', 3, zeta='zeta2', inconn=1, outconn=1)\n k += 1\n\n ######################################################################\n # derivatives for additional equations\n self.additional_derivatives(increment_filter, k)","def calc_equil(sst, ft_qv, use_NT=False):\n \n run_main(sst, ft_qv, use_NT)\n \n # grab csv file\n with open('dumpmodel.csv','r') as f:\n df_result=pd.read_csv(f)\n\n # last time step into named tupple\n out=df_result.iloc[-1]\n steady_state=make_tuple(out.to_dict())\n steady_state\n \n # obtain steady-state values\n dth=steady_state.deltheta\n dqt=steady_state.delqv\n thetal_m=steady_state.theta\n qt_m=steady_state.qv\n h=steady_state.h\n press=tf.find_press(steady_state.h) #kPa\n thetal_ft = steady_state.theta + dth\n qt_ft = steady_state.qv + dqt\n zb = steady_state.LCL\n zi = steady_state.h\n we = steady_state.went\n \n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\n gamma = 6e-3 \n thetal_3000 = thetal_ft + gamma*(3000-h)\n LTS = thetal_3000 - steady_state.theta\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\n\n # calculate LWP\n rho = 1.\n LWP = 0.5*rho*(zi-zb)**2\n \n # put all required variables into output array\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\n \n return out_array","def DynamicsCo(x, t, T0, alpha, cost_co, cost_ch, K_co, K_ch, n, r):\n y=np.zeros([np.size(x)])\n D=np.zeros([2]) \n #define fitnss\n D[0]=dmax*x[0]**n/(K_co**n+x[0]**n) #cooperator\n D[1]=dmax*x[0]**n/(K_ch**n+x[0]**n) #cheater \n #degradation\n deg=fmax*x[1]/(x[1]+Kd) \n #ODE of eco-evo dynamics\n y[0]=alpha*T0-deg*x[0]-alpha*x[0] #dt/dt\n y[1]=x[1]*(r*(1-cost_co)*(1-x[1]-x[2])-D[0]-alpha)#d Co/dt\n y[2]=x[2]*(r*(1-cost_ch)*(1-x[1]-x[2])-D[1]-alpha) #d Ch/dt\n \n return y","def solve_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n y = params['y']\n\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0","def nonhomo_system_variation_of_parameters(xc: List[Symbol], gt, t: Symbol = Symbol('t', real=True)):\n fund_matrix = eye(len(xc))\n for i, x in enumerate(xc):\n fund_matrix[:, i] = x\n\n procedure = Procedure()\n procedure.text('Fundamental matrix ').latex('\\\\Psi', nl=True)\\\n .eq(fund_matrix)\n\n gt = Matrix(gt)\n fund_inv = fund_matrix**(-1)\n procedure.text('Calculate the inverse of the fundamental matrix ').latex('\\\\Psi^{-1}', nl=True)\\\n .latex('\\\\Psi^{-1} = ').eq(fund_inv)\n\n fund_inv_gt = expand(simplify(fund_inv * gt))\n procedure.text('Compute ').latex('\\\\Psi^{-1} g(t)', nl=True)\\\n .latex('\\\\Psi^{-1} g(t) = ').eq(fund_inv_gt)\n\n procedure.text('Compute the integral', nl=True)\n fund_inv_gt_int = expand(simplify(integrate(fund_inv_gt)))\n procedure.latex('\\\\int \\\\Psi^{-1} g(t) =').eq(fund_inv_gt_int)\n\n procedure.text('Finally, ').latex(\n '\\\\vec{\\\\mathbf{x_p}} = \\\\Psi \\\\int \\\\Psi^{-1} g(t)', nl=True)\n sol = expand(fund_matrix * fund_inv_gt_int)\n procedure.latex('\\\\vec{\\\\mathbf{x_p}} =').eq(sol)\n return sol, procedure","def temp_func(self,var):\n y = var['intercept']\n for p,c in zip(var['powers'],var['coef']):\n # Exp the 4 inputs to the power for that coef\n #to plug them into the equation, un-scale them:\n# a = np.multiply(self.ins,(1/self.scale_var))**p\n a = self.ins**p\n y += c* np.prod(a)\n\n #to fit this into the environment, re-scale:\n y = y #* self.scale_var\n\n # Noise is a random number (positive or negative), scaled by self.noise\n noise = random.randint(-1,1) * random.random() * self.noise * cfg['thresh1'] * self.scale_var #scales noise\n y += noise\n return y","def efSolver2(self):\n dx = self.dh[0] # dx\n dy = self.dh[1] # dy\n dz = self.dh[2] # dz\n \n \"\"\"\n for i in np.arange(0, self.ni):\n for j in np.arange(0, self.nj):\n for k in np.arange(0, self.nk):\n \"\"\"\n\n ##x-component#\n #if i==0: \n #x-component#\n \"\"\"\n if i==0: \n # forward\n self.ef[i][j][k][0] = -(-3*self.phi[i][j][k]+\\\n 4*self.phi[i+1][j][k]-\\\n self.phi[i+2][j][k])/(2*dx)\n \"\"\"\n \n # forward\n self.ef[0,0:self.nj,0:self.nk,0] = -(-3*self.phi[0,0:self.nj,0:self.nk]+\\\n 4*self.phi[1,0:self.nj,0:self.nk]-\\\n self.phi[2,0:self.nj,0:self.nk])/(2*dx)\n \n #elif i==self.ni-1: \n \"\"\"\n elif i==self.ni-1: \n # backward\n self.ef[i][j][k][0] = -(self.phi[i-2][j][k]-\\\n 4*self.phi[i-1][j][k]+\\\n 3*self.phi[i][j][k])/(2*dx)\n \"\"\" \n # backward\n self.ef[self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[self.ni-3,0:self.nj,0:self.nk]-\\\n 4*self.phi[self.ni-2,0:self.nj,0:self.nk]+\\\n 3*self.phi[self.ni-1,0:self.nj,0:self.nk])/(2*dx)\n \"\"\"\n else: \n #central\n self.ef[i][j][k][0] = -(self.phi[i+1][j][k] - \\\n self.phi[i-1][j][k])/(2*dx)\n \"\"\" \n #central\n self.ef[1:self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[2:self.ni,0:self.nj,0:self.nk] - \\\n self.phi[0:self.ni-2,0:self.nj,0:self.nk])/(2*dx)\n\n\n #y-component\n #if j==0:\n \"\"\"\n if j==0:\n self.ef[i][j][k][1] = -(-3*self.phi[i][j][k] + \\\n 4*self.phi[i][j+1][k]-\\\n self.phi[i][j+2][k])/(2*dy)\n \n \"\"\"\n self.ef[0:self.ni,0,0:self.nk,1] = -(-3*self.phi[0:self.ni,0,0:self.nk] + \\\n 4*self.phi[0:self.ni,1,0:self.nk]-\\\n self.phi[0:self.ni,2,0:self.nk])/(2*dy)\n #elif j==self.nj-1:\n \"\"\"\n elif j==self.nj-1:\n self.ef[i][j][k][1] = -(self.phi[i][j-2][k] - \\\n 4*self.phi[i][j-1][k] +\\\n 3*self.phi[i][j][k])/(2*dy)\n \n \"\"\"\n self.ef[0:self.ni,self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,self.nj-3,0:self.nk] - \\\n 4*self.phi[0:self.ni,self.nj-2,0:self.nk] +\\\n 3*self.phi[0:self.ni,self.nj-1,0:self.nk])/(2*dy)\n #else:\n \"\"\"\n else:\n self.ef[i][j][k][1] = -(self.phi[i][j+1][k] - \\\n self.phi[i][j-1][k])/(2*dy)\n\n \"\"\"\n self.ef[0:self.ni,1:self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,2:self.nj,0:self.nk] - \\\n self.phi[0:self.ni,0:self.nj-2,0:self.nk])/(2*dy)\n\n #z-component\n '''\n if k==0:\n self.ef[i][j][k][2] = -(-3*self.phi[i][j][k] + \\\n 4*self.phi[i][j][k+1]-\n self.phi[i][j][k+2])/(2*dz)\n \n '''\n #z-component\n #if k==0:\n self.ef[0:self.ni,0:self.nj,0,2] = -(-3*self.phi[0:self.ni,0:self.nj,0] + \\\n 4*self.phi[0:self.ni,0:self.nj,1]-\n self.phi[0:self.ni,0:self.nj,2])/(2*dz)\n\n \"\"\"\n elif k==self.nk-1:\n self.ef[i][j][k][2] = -(self.phi[i][j][k-2] - \\\n 4*self.phi[i][j][k-1] + \\\n 3*self.phi[i][j][k])/(2*dz)\n \"\"\"\n \n #elif k==self.nk-1:\n self.ef[0:self.ni,0:self.nj,self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,self.nk-3] - \\\n 4*self.phi[0:self.ni,0:self.nj,self.nk-2] + \\\n 3*self.phi[0:self.ni,0:self.nj,self.nk-1])/(2*dz) \n \"\"\"\n else:\n self.ef[i][j][k][2] = -(self.phi[i][j][k+1] - \\\n self.phi[i][j][k-1])/(2*dz)\n \"\"\"\n #else:\n self.ef[0:self.ni,0:self.nj,1:self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,2:self.nk] - \\\n self.phi[0:self.ni,0:self.nj,0:self.nk-2])/(2*dz)","def function_PLD(coeffs, t, Pns, lc, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, PLD_params, eclipse = False):\n new_flux = model_PLD(coeffs, t, Pns, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, PLD_params, eclipse = eclipse)\n return lc - new_flux","def __init__(self,\n uDict,\n phiDict,\n testSpaceDict,\n matType,\n dofBoundaryConditionsDict,\n dofBoundaryConditionsSetterDict,\n coefficients,\n elementQuadrature,\n elementBoundaryQuadrature,\n fluxBoundaryConditionsDict=None,\n advectiveFluxBoundaryConditionsSetterDict=None,\n diffusiveFluxBoundaryConditionsSetterDictDict=None,\n stressTraceBoundaryConditionsSetterDict=None,\n stabilization=None,\n shockCapturing=None,\n conservativeFluxDict=None,\n numericalFluxType=None,\n TimeIntegrationClass=None,\n massLumping=False,\n reactionLumping=False,\n options=None,\n name='defaultName',\n reuse_trial_and_test_quadrature=True,\n sd = True,\n movingDomain=False):\n #\n #set the objects describing the method and boundary conditions\n #\n self.movingDomain=movingDomain\n self.tLast_mesh=None\n #\n self.name=name\n self.sd=sd\n self.Hess=False\n self.lowmem=True\n self.timeTerm=True#allow turning off the time derivative\n #self.lowmem=False\n self.testIsTrial=True\n self.phiTrialIsTrial=True\n self.u = uDict\n self.ua = {}#analytical solutions\n self.phi = phiDict\n self.dphi={}\n for ck,phi in phiDict.iteritems():\n if coefficients.potential.has_key(ck):\n for cj in coefficients.potential[ck].keys():\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\n else:\n self.dphi[(ck,ck)] = FiniteElementFunction(phi.femSpace)\n #check for nonlinearities in the diffusion coefficient that don't match the potential\n for ci,ckDict in coefficients.diffusion.iteritems():\n #for ck,cjDict in coefficients.diffusion.iteritems(): #cek: bug?\n for ck,cjDict in ckDict.iteritems():\n for cj in cjDict.keys():\n if not self.dphi.has_key((ck,cj)):\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\n self.matType = matType\n #try to reuse test and trial information across components if spaces are the same\n self.reuse_test_trial_quadrature = reuse_trial_and_test_quadrature#True#False\n if self.reuse_test_trial_quadrature:\n for ci in range(1,coefficients.nc):\n assert self.u[ci].femSpace.__class__.__name__ == self.u[0].femSpace.__class__.__name__, \"to reuse_test_trial_quad all femSpaces must be the same!\"\n ## Simplicial Mesh\n self.mesh = self.u[0].femSpace.mesh #assume the same mesh for all components for now\n self.testSpace = testSpaceDict\n self.dirichletConditions = dofBoundaryConditionsDict\n self.dirichletNodeSetList=None #explicit Dirichlet conditions for now, no Dirichlet BC constraints\n self.coefficients = coefficients\n self.coefficients.initializeMesh(self.mesh)\n self.nc = self.coefficients.nc\n self.stabilization = stabilization\n self.shockCapturing = shockCapturing\n self.conservativeFlux = conservativeFluxDict #no velocity post-processing for now\n self.fluxBoundaryConditions=fluxBoundaryConditionsDict\n self.advectiveFluxBoundaryConditionsSetterDict=advectiveFluxBoundaryConditionsSetterDict\n self.diffusiveFluxBoundaryConditionsSetterDictDict = diffusiveFluxBoundaryConditionsSetterDictDict\n #determine whether the stabilization term is nonlinear\n self.stabilizationIsNonlinear = False\n #cek come back\n if self.stabilization != None:\n for ci in range(self.nc):\n if coefficients.mass.has_key(ci):\n for flag in coefficients.mass[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.advection.has_key(ci):\n for flag in coefficients.advection[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.diffusion.has_key(ci):\n for diffusionDict in coefficients.diffusion[ci].values():\n for flag in diffusionDict.values():\n if flag != 'constant':\n self.stabilizationIsNonlinear=True\n if coefficients.potential.has_key(ci):\n for flag in coefficients.potential[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.reaction.has_key(ci):\n for flag in coefficients.reaction[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n if coefficients.hamiltonian.has_key(ci):\n for flag in coefficients.hamiltonian[ci].values():\n if flag == 'nonlinear':\n self.stabilizationIsNonlinear=True\n #determine if we need element boundary storage\n self.elementBoundaryIntegrals = {}\n for ci in range(self.nc):\n self.elementBoundaryIntegrals[ci] = ((self.conservativeFlux != None) or\n (numericalFluxType != None) or\n (self.fluxBoundaryConditions[ci] == 'outFlow') or\n (self.fluxBoundaryConditions[ci] == 'mixedFlow') or\n (self.fluxBoundaryConditions[ci] == 'setFlow'))\n #\n #calculate some dimensions\n #\n self.nSpace_global = self.u[0].femSpace.nSpace_global #assume same space dim for all variables\n self.nDOF_trial_element = [u_j.femSpace.max_nDOF_element for u_j in self.u.values()]\n self.nDOF_phi_trial_element = [phi_k.femSpace.max_nDOF_element for phi_k in self.phi.values()]\n self.n_phi_ip_element = [phi_k.femSpace.referenceFiniteElement.interpolationConditions.nQuadraturePoints for phi_k in self.phi.values()]\n self.nDOF_test_element = [femSpace.max_nDOF_element for femSpace in self.testSpace.values()]\n self.nFreeDOF_global = [dc.nFreeDOF_global for dc in self.dirichletConditions.values()]\n self.nVDOF_element = sum(self.nDOF_trial_element)\n self.nFreeVDOF_global = sum(self.nFreeDOF_global)\n #\n NonlinearEquation.__init__(self,self.nFreeVDOF_global)\n #\n #build the quadrature point dictionaries from the input (this\n #is just for convenience so that the input doesn't have to be\n #complete)\n #\n elementQuadratureDict={}\n elemQuadIsDict = isinstance(elementQuadrature,dict)\n if elemQuadIsDict: #set terms manually\n for I in self.coefficients.elementIntegralKeys:\n if elementQuadrature.has_key(I):\n elementQuadratureDict[I] = elementQuadrature[I]\n else:\n elementQuadratureDict[I] = elementQuadrature['default']\n else:\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[I] = elementQuadrature\n if self.stabilization != None:\n for I in self.coefficients.elementIntegralKeys:\n if elemQuadIsDict:\n if elementQuadrature.has_key(I):\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature[I]\n else:\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature['default']\n else:\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature\n if self.shockCapturing != None:\n for ci in self.shockCapturing.components:\n if elemQuadIsDict:\n if elementQuadrature.has_key(('numDiff',ci,ci)):\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature[('numDiff',ci,ci)]\n else:\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature['default']\n else:\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature\n if massLumping:\n for ci in self.coefficients.mass.keys():\n elementQuadratureDict[('m',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n if reactionLumping:\n for ci in self.coefficients.mass.keys():\n elementQuadratureDict[('r',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n for I in self.coefficients.elementIntegralKeys:\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\n elementBoundaryQuadratureDict={}\n if isinstance(elementBoundaryQuadrature,dict): #set terms manually\n for I in self.coefficients.elementBoundaryIntegralKeys:\n if elementBoundaryQuadrature.has_key(I):\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature[I]\n else:\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature['default']\n else:\n for I in self.coefficients.elementBoundaryIntegralKeys:\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature\n #\n # find the union of all element quadrature points and\n # build a quadrature rule for each integral that has a\n # weight at each point in the union\n #mwf include tag telling me which indices are which quadrature rule?\n (self.elementQuadraturePoints,self.elementQuadratureWeights,\n self.elementQuadratureRuleIndeces) = Quadrature.buildUnion(elementQuadratureDict)\n self.nQuadraturePoints_element = self.elementQuadraturePoints.shape[0]\n self.nQuadraturePoints_global = self.nQuadraturePoints_element*self.mesh.nElements_global\n #\n #Repeat the same thing for the element boundary quadrature\n #\n (self.elementBoundaryQuadraturePoints,\n self.elementBoundaryQuadratureWeights,\n self.elementBoundaryQuadratureRuleIndeces) = Quadrature.buildUnion(elementBoundaryQuadratureDict)\n self.nElementBoundaryQuadraturePoints_elementBoundary = self.elementBoundaryQuadraturePoints.shape[0]\n self.nElementBoundaryQuadraturePoints_global = (self.mesh.nElements_global*\n self.mesh.nElementBoundaries_element*\n self.nElementBoundaryQuadraturePoints_elementBoundary)\n\n #\n #storage dictionaries\n self.scalars_element = set()\n #\n #simplified allocations for test==trial and also check if space is mixed or not\n #\n self.q={}\n self.ebq={}\n self.ebq_global={}\n self.ebqe={}\n self.phi_ip={}\n #mesh\n self.q['x'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,3),'d')\n self.q['det(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q['abs(det(J))'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q['J'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\n self.q['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\n self.ebqe['x'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['g'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n max(1,self.nSpace_global-1),\n max(1,self.nSpace_global-1)),\n 'd')\n self.ebqe['inverse(J)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global,self.nSpace_global),'d')\n self.ebqe['hat(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['bar(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\n self.ebqe['sqrt(det(g))'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n self.ebqe[('n')] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n #shape\n self.q[('v',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.q[('w',0)] = self.q[('v',0)]\n self.q[('grad(v)',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.q[('grad(w)',0)] = self.q[('grad(v)',0)]\n self.q[('grad(w)*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.q[('grad(w)*dV_f',0)] = self.q[('grad(w)*dV',0)]\n #todo get rid of dV_{f,a}, etc\n self.q[('w*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.q[('w*dV_m',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n #assume all components are the same space for now\n shapeKeysForAlias = ['v','w','grad(v)','grad(w)*dV','grad(w)*dV_f','w*dV','w*dV_m']\n for ci in range(1,self.nc):\n for key in shapeKeysForAlias:\n key_ci = (key,ci)\n key_0 = (key,0)\n self.q[key_ci] = self.q[key_0]\n #ELLAM weights stiffness, body integrals by dt\n for ci in range(self.nc):\n self.q[('dt*grad(w)*dV',ci)]= numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[ci],self.nSpace_global),'d')\n #\n self.ebqe[('v',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\n self.ebqe[('w',0)] = self.ebqe[('v',0)]\n self.ebqe[('grad(v)',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0],self.nSpace_global),'d')\n self.ebqe[('w*dS_f',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\n #assume all components are the same space for now\n shapeKeysForAlias = ['v','w','grad(v)','w*dS_f']\n for ci in range(1,self.nc):\n for key in shapeKeysForAlias:\n key_ci = (key,ci)\n key_0 = (key,0)\n self.ebqe[key_ci] = self.ebqe[key_0]\n\n for ci in range(self.nc):\n self.q[('u',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('grad(u)',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n #f\n for ci in self.coefficients.advection.keys():\n self.q[('f',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n for cj in self.coefficients.advection[ci].keys():\n self.q[('df',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\n self.ebqe[('f',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n for cj in self.coefficients.advection[ci].keys():\n self.ebqe[('df',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\n\n #a, linear dispersion single component\n\n for ci,ckDict in self.coefficients.diffusion.iteritems():\n for ck,cjDict in ckDict.iteritems():\n for flag in cjDict.values():\n assert flag == 'constant', \"Error potential %s LADRellam does not handle diffusion = %s yet\" % (ck,flag)\n\n if self.coefficients.sdInfo != None and (ci,ck) in self.coefficients.sdInfo.keys():\n self.q[('a',ci,ck)] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n for cj in cjDict.keys():\n self.q[('da',ci,ck,cj)] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n self.ebqe[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n for cj in cjDict.keys():\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\n 'd')\n\n else:\n self.q[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n for cj in cjDict.keys():\n self.q[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nElements_global,\n self.nQuadraturePoints_element,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n self.ebqe[('a',ci,ck)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n for cj in cjDict.keys():\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),\n 'd')\n #dense storage\n self.q[('grad(w)*dV_a',ci,ck)] = self.q[('grad(w)*dV_f',ci)]\n self.q[('dt*grad(w)*dV_a',ci,ck)]= self.q[('dt*grad(w)*dV',ci)]\n #ci,ckDict\n #linear potential only for now, need to change for e.g., Buckley Leverett\n for ck in self.phi.keys():\n self.phi[ck].dof[:]=self.u[ck].dof\n self.q[('grad(phi)',ck)] = self.q[('grad(u)',ck)]\n for key in self.dphi.keys():\n self.dphi[key].dof.fill(1.0)\n self.q[('dphi',key[0],key[1])] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n\n\n\n# if self.coefficients.diffusion.has_key(0):\n# for ck,flag in self.coefficients.diffusion[0][0].iteritems():\n# assert self.coefficients.diffusion[0][0][ck] == 'constant', \"Error potential %s LADRellam does not handle diffusion = %s yet\" % (ck,flag)\n# if self.coefficients.sdInfo != None and (0,0) in self.coefficients.sdInfo.keys():\n# self.q[('a',0,0)] = numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.q[('da',0,0,0)] = numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.ebqe[('a',0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\n# 'd')\n\n# else:\n# self.q[('a',0,0)]=numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.q[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nElements_global,\n# self.nQuadraturePoints_element,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.ebqe[('a',0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\n# (self.mesh.nExteriorElementBoundaries_global,\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\n# self.nSpace_global,\n# self.nSpace_global),\n# 'd')\n# #\n# self.phi[0].dof[:]=self.u[0].dof\n# self.dphi[(0,0)].dof.fill(1.0)\n# self.q[('grad(phi)',0)] = self.q[('grad(u)',0)]\n# self.q[('dphi',0,0)] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n\n# self.q[('grad(w)*dV_a',0,0)] = self.q[('grad(w)*dV_f',0)]\n# self.q[('dt*grad(w)*dV_a',0,0)]= self.q[('dt*grad(w)*dV',0)]\n\n #r 'constant' ie not a function of solution but go ahead and include dr for now\n for ci,cjDict in self.coefficients.reaction.iteritems():\n self.q[('r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n for cj in cjDict.keys():\n self.q[('dr',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('w*dV_r',ci)] = self.q[('w*dV',ci)]\n self.q[('dt*w*dV_r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\n self.ebqe[('r',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n #m\n for ci,cjDict in self.coefficients.mass.iteritems():\n self.q[('m',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n for cj in cjDict.keys():\n self.q[('dm',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('mt',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('m_last',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('m_tmp',ci)] = self.q[('m',ci)]\n self.q[('cfl',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.q[('numDiff',ci,ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\n self.ebqe[('m',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n for cj in cjDict.keys():\n self.ebqe[('dm',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n\n ###ellam specific options with defauls here\n self.ellamDiscretization = ELLAMtools.ELLAMdiscretization(self,options)\n\n #\n self.needEBQ = options.needEBQ #could need for analytical velocity evaluation with RT0,BDM\n\n #beg normal stuff allocating things\n self.points_elementBoundaryQuadrature= set()\n self.scalars_elementBoundaryQuadrature= set([('u',ci) for ci in range(self.nc)])\n self.vectors_elementBoundaryQuadrature= set()\n self.tensors_elementBoundaryQuadrature= set()\n\n if self.needEBQ:\n for k in ['x','hat(x)']:\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n 3),'d')\n self.ebq['n'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global),'d')\n self.ebq['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global,\n self.nSpace_global),'d')\n #allocate the metric tensor\n self.ebq['g'] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n max(1,self.nSpace_global-1),\n max(1,self.nSpace_global-1)),\n 'd')\n log(memory(\"element boundary quadrature\",\"LADRellam\"),level=4)\n ebq_keys = ['sqrt(det(g))']\n ebq_keys.extend([('u',ci) for ci in range(self.nc)])\n for k in ebq_keys:\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n\n #test and trial info\n self.ebq[('w',0)] = numpy.zeros((self.mesh.nElements_global,\n self.mesh.nElementBoundaries_element,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nDOF_trial_element[0]),'d')\n for ci in range(1,self.nc):\n self.ebq[('w',ci)] = self.ebq[('w',0)]\n for ci in range(self.nc):\n self.ebq[('v',ci)] = self.ebq[('w',0)]\n\n #ebq_global info\n self.ebq_global['x'] = numpy.zeros((self.mesh.nElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n 3),'d')\n self.ebq_global['n'] = numpy.zeros((self.mesh.nElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nSpace_global),'d')\n #\n # allocate residual and Jacobian storage\n #\n self.elementResidual = [numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci]),\n 'd') for ci in range(self.nc)]\n self.elementSpatialResidual = [numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci]),\n 'd') for ci in range(self.nc)]\n self.elementJacobian = {}\n for ci in range(self.nc):\n self.elementJacobian[ci]={}\n for cj in range(self.nc):\n if cj in self.coefficients.stencil[ci]:\n self.elementJacobian[ci][cj] = numpy.zeros(\n (self.mesh.nElements_global,\n self.nDOF_test_element[ci],\n self.nDOF_trial_element[cj]),\n 'd')\n #\n self.fluxJacobian_exterior = {}\n for ci in range(self.nc):\n self.fluxJacobian_exterior[ci]={}\n for cj in self.coefficients.stencil[ci]:\n self.fluxJacobian_exterior[ci][cj] = numpy.zeros(\n (self.mesh.nExteriorElementBoundaries_global,\n self.nElementBoundaryQuadraturePoints_elementBoundary,\n self.nDOF_trial_element[cj]),\n 'd')\n\n #\n #\n #\n #\n log(memory(\"element and element boundary Jacobians\",\"OneLevelTransport\"),level=4)\n self.inflowBoundaryBC = {}\n self.inflowBoundaryBC_values = {}\n self.inflowFlux = {}\n for cj in range(self.nc):\n self.inflowBoundaryBC[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,),'i')\n self.inflowBoundaryBC_values[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nDOF_trial_element[cj]),'d')\n self.inflowFlux[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\n self.internalNodes = set(range(self.mesh.nNodes_global))\n #identify the internal nodes this is ought to be in mesh\n ##\\todo move this to mesh\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\n eN_global = self.mesh.elementBoundaryElementsArray[ebN,0]\n ebN_element = self.mesh.elementBoundaryLocalElementBoundariesArray[ebN,0]\n for i in range(self.mesh.nNodes_element):\n if i != ebN_element:\n I = self.mesh.elementNodesArray[eN_global,i]\n self.internalNodes -= set([I])\n self.nNodes_internal = len(self.internalNodes)\n self.internalNodesArray=numpy.zeros((self.nNodes_internal,),'i')\n for nI,n in enumerate(self.internalNodes):\n self.internalNodesArray[nI]=n\n #\n del self.internalNodes\n self.internalNodes = None\n log(\"Updating local to global mappings\",2)\n self.updateLocal2Global()\n log(\"Building time integration object\",2)\n log(memory(\"inflowBC, internalNodes,updateLocal2Global\",\"OneLevelTransport\"),level=4)\n #mwf for interpolating subgrid error for gradients etc\n if self.stabilization and self.stabilization.usesGradientStabilization:\n self.timeIntegration = TimeIntegrationClass(self,integrateInterpolationPoints=True)\n else:\n self.timeIntegration = TimeIntegrationClass(self)\n\n if options != None:\n self.timeIntegration.setFromOptions(options)\n log(memory(\"TimeIntegration\",\"OneLevelTransport\"),level=4)\n log(\"Calculating numerical quadrature formulas\",2)\n self.calculateQuadrature()\n #lay out components/equations contiguously for now\n self.offset = [0]\n for ci in range(1,self.nc):\n self.offset += [self.offset[ci-1]+self.nFreeDOF_global[ci-1]]\n self.stride = [1 for ci in range(self.nc)]\n #use contiguous layout of components for parallel, requires weak DBC's\n comm = Comm.get()\n self.comm=comm\n if comm.size() > 1:\n assert numericalFluxType != None and numericalFluxType.useWeakDirichletConditions,\"You must use a numerical flux to apply weak boundary conditions for parallel runs\"\n self.offset = [0]\n for ci in range(1,self.nc):\n self.offset += [ci]\n self.stride = [self.nc for ci in range(self.nc)]\n #\n log(memory(\"stride+offset\",\"OneLevelTransport\"),level=4)\n if numericalFluxType != None:\n if options == None or options.periodicDirichletConditions == None:\n self.numericalFlux = numericalFluxType(self,\n dofBoundaryConditionsSetterDict,\n advectiveFluxBoundaryConditionsSetterDict,\n diffusiveFluxBoundaryConditionsSetterDictDict)\n else:\n self.numericalFlux = numericalFluxType(self,\n dofBoundaryConditionsSetterDict,\n advectiveFluxBoundaryConditionsSetterDict,\n diffusiveFluxBoundaryConditionsSetterDictDict,\n options.periodicDirichletConditions)\n else:\n self.numericalFlux = None\n #set penalty terms\n #cek todo move into numerical flux initialization\n if self.ebq_global.has_key('penalty'):\n for ebN in range(self.mesh.nElementBoundaries_global):\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\n self.ebq_global['penalty'][ebN,k] = self.numericalFlux.penalty_constant/(self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power)\n #penalty term\n #cek move to Numerical flux initialization\n if self.ebqe.has_key('penalty'):\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\n self.ebqe['penalty'][ebNE,k] = self.numericalFlux.penalty_constant/self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power\n log(memory(\"numericalFlux\",\"OneLevelTransport\"),level=4)\n self.elementEffectiveDiametersArray = self.mesh.elementInnerDiametersArray\n #use post processing tools to get conservative fluxes, None by default\n import PostProcessingTools\n self.velocityPostProcessor = PostProcessingTools.VelocityPostProcessingChooser(self)\n log(memory(\"velocity postprocessor\",\"OneLevelTransport\"),level=4)\n #helper for writing out data storage\n import Archiver\n self.elementQuadratureDictionaryWriter = Archiver.XdmfWriter()\n self.elementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\n self.exteriorElementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\n #TODO get rid of this\n for ci,fbcObject in self.fluxBoundaryConditionsObjectsDict.iteritems():\n self.ebqe[('advectiveFlux_bc_flag',ci)] = numpy.zeros(self.ebqe[('advectiveFlux_bc',ci)].shape,'i')\n for t,g in fbcObject.advectiveFluxBoundaryConditionsDict.iteritems():\n if self.coefficients.advection.has_key(ci):\n self.ebqe[('advectiveFlux_bc',ci)][t[0],t[1]] = g(self.ebqe[('x')][t[0],t[1]],self.timeIntegration.t)\n self.ebqe[('advectiveFlux_bc_flag',ci)][t[0],t[1]] = 1\n\n if hasattr(self.numericalFlux,'setDirichletValues'):\n self.numericalFlux.setDirichletValues(self.ebqe)\n if not hasattr(self.numericalFlux,'isDOFBoundary'):\n self.numericalFlux.isDOFBoundary = {}\n for ci in range(self.nc):\n self.numericalFlux.isDOFBoundary[ci]= numpy.zeros(self.ebqe[('u',ci)].shape,'i')\n if not hasattr(self.numericalFlux,'ebqe'):\n self.numericalFlux.ebqe = {}\n for ci in range(self.nc):\n self.numericalFlux.ebqe[('u',ci)]= numpy.zeros(self.ebqe[('u',ci)].shape,'d')","def objective(trial):\n # The parameters that we will calibrate the model for are shown here.\n # Optuna trial i\n BOD = trial.suggest_uniform(\"BOD\", 0, 1) #Review ranges here\n k_r = trial.suggest_uniform(\"k_r\", 0, 1) #Review Ranges here \n \n def ChLa(t):\n return 1 # Need to link to data\n\n def I(x):\n return 1 # Need to link to data\n\n K_z = 2 * 10**(-5) # p.51\n a = K_z\n k_b = 0.1 # Table 5\n th_b = 1.047 # Table 5\n k_r = 0.1 # Table 5\n YCHO2 = 0.0083 # Table 5\n th_p = 1.036 # Table 5\n th_s = 1.065 # Table 5\n th_r = 1.047 # Table 5\n\n def Temp(t):\n \"\"\"\n Function that maps time to temperature\n \"\"\"\n return 20 # Need to link to data\n\n def P_max(t):\n return 9.6 * 1.036 **(Temp(t) - 20) # Eq. 4\n\n def L_min(t):\n I = 1 # Need to link to PAR data\n K_1 = 0.687 * 1.086**(Temp(t) - 20)\n K_2 = 15\n return I * (1 + 2 * np.sqrt(K_1 / K_2)) / (I + K_1 + I**2 / K_2) # Eq. 5\n \n # f deals with sink and source terms \n def f(x, t):\n return -1 / YCHO2 * k_r * th_r**(Temp(t) - 20) * ChLa(t) + P_max(t) * L_min(t) * ChLa(t) - k_b * th_b**(Temp(t)-20) * BOD \n\n L = 200 # Length of domain\n dt = 1 / 48 # Mesh spacing in t\n F = a * dt # a * dt / dx**2\n T = 100 # Simulation time stop\n\n # Solving the PDE\n DO, x, t, _ = solver_FE_simple(I, a, f, L, dt, F, T)\n \n # Creating some bogus targets while database errors are happening\n DO_data = DO + np.random.random(len(DO))\n\n # Using mean squared error as the measure of fit, where we want\n # to minimize this number\n return ((DO - DO_data)**2).mean()","def fluid_reynolds(uu, param, grid, lnrho=list(), shock=list(), nghost=3,\n lmix=True):\n #viscous forces\n th2 = 2./3\n th1 = 1./3\n fvisc = np.zeros_like(uu)\n #molecular viscosity contribution\n ldel2, lshock, lhyper3 = False, False, False\n for ivisc in param.ivisc:\n if not 'shock' in ivisc and not 'hyper' in ivisc\\\n and not '\\n' in ivisc:\n ldel2 = True\n if 'shock' in ivisc:\n lshock = True\n if 'hyper3' in ivisc:\n lhyper3 = True\n \n if ldel2:\n if lhyper3:\n lhyper3 = lhyper3==lmix\n del2u = np.zeros_like(uu)\n for j in range(0,3):\n del2u[j] = del2(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n del2u[j, : nghost, nghost:-nghost, nghost:-nghost] = del2u[j,-2*nghost: -nghost, nghost: -nghost, nghost: -nghost]\n del2u[j,-nghost: , nghost:-nghost, nghost:-nghost] = del2u[j, nghost:2*nghost, nghost: -nghost, nghost: -nghost]\n del2u[j, nghost:-nghost, : nghost, nghost:-nghost] = del2u[j, nghost: -nghost,-2*nghost: -nghost, nghost: -nghost]\n del2u[j, nghost:-nghost,-nghost: , nghost:-nghost] = del2u[j, nghost: -nghost, nghost:2*nghost, nghost: -nghost]\n del2u[j, nghost:-nghost, nghost:-nghost, : nghost] = del2u[j, nghost: -nghost, nghost: -nghost,-2*nghost: -nghost]\n del2u[j, nghost:-nghost, nghost:-nghost,-nghost: ] = del2u[j, nghost: -nghost, nghost: -nghost, nghost:2*nghost]\n for ivisc in param.ivisc:\n ivisc = str.strip(ivisc,'\\n')\n if 'nu-const' not in ivisc and 'shock' not in ivisc\\\n and 'hyper' not in ivisc and len(ivisc) > 0:\n print('fluid_reynolds WARNING: '+ivisc+' not implemented\\n'+\n 'terms may be missing from the standard rate of strain tensor')\n fvisc = fvisc + param.nu*del2u\n del(del2u)\n tmp0 = grad(uu[0],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp0[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp0[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp0[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp0[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp0[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp0[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp0[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp0[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp0[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp0[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n tmp1 = grad(uu[1],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp1[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp1[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp1[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp1[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp1[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp1[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp1[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp1[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp1[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp1[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n tmp2 = grad(uu[2],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n tmp2[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp2[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n tmp2[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp2[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp2[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp2[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n tmp2[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp2[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n tmp2[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp2[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n #effect of compressibility \n if len(lnrho) > 0:\n divu = div(uu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n divu[ :nghost,nghost:-nghost,nghost:-nghost] = divu[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n divu[-nghost:,nghost:-nghost,nghost:-nghost] = divu[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n divu[nghost:-nghost, :nghost,nghost:-nghost] = divu[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n divu[nghost:-nghost,-nghost:,nghost:-nghost] = divu[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n divu[nghost:-nghost,nghost:-nghost, :nghost] = divu[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n divu[nghost:-nghost,nghost:-nghost,-nghost:] = divu[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n gradlnrho = grad(lnrho,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n gradlnrho[j, :nghost,nghost:-nghost,nghost:-nghost] = gradlnrho[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n gradlnrho[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradlnrho[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost, :nghost,nghost:-nghost] = gradlnrho[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradlnrho[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n gradlnrho[j,nghost:-nghost,nghost:-nghost, :nghost] = gradlnrho[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n gradlnrho[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradlnrho[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n Sglnrho = np.zeros_like(uu)\n Sglnrho[0] = dot(tmp0,gradlnrho) +\\\n (tmp0[0]+tmp1[0]+tmp2[0]-th2*divu)*gradlnrho[0] \n Sglnrho[1] = dot(tmp1,gradlnrho) +\\\n (tmp0[1]+tmp1[1]+tmp2[1]-th2*divu)*gradlnrho[1]\n Sglnrho[2] = dot(tmp2,gradlnrho) +\\\n (tmp0[2]+tmp1[2]+tmp2[2]-th2*divu)*gradlnrho[2]\n graddivu = grad(divu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n graddivu[j, :nghost,nghost:-nghost,nghost:-nghost] = graddivu[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n graddivu[j,-nghost:,nghost:-nghost,nghost:-nghost] = graddivu[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost, :nghost,nghost:-nghost] = graddivu[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost,-nghost:,nghost:-nghost] = graddivu[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n graddivu[j,nghost:-nghost,nghost:-nghost, :nghost] = graddivu[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n graddivu[j,nghost:-nghost,nghost:-nghost,-nghost:] = graddivu[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n fvisc = fvisc + param.nu*(th1*graddivu+Sglnrho)\n del(Sglnrho)\n elif param.ldensity:\n print('fluid_reynolds WARNING: no lnrho provided\\n'+\n 'rate of strain tensor likely incomplete')\n #shock contribution\n if lshock:\n if len(shock) == 0:\n print('fluid_reynolds WARNING: no shock provided\\n'+\n 'rate of strain tensor likely incomplete')\n else:\n shock[ :nghost,nghost:-nghost,nghost:-nghost] = shock[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n shock[-nghost:,nghost:-nghost,nghost:-nghost] = shock[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n shock[nghost:-nghost, :nghost,nghost:-nghost] = shock[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n shock[nghost:-nghost,-nghost:,nghost:-nghost] = shock[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n shock[nghost:-nghost,nghost:-nghost, :nghost] = shock[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n shock[nghost:-nghost,nghost:-nghost,-nghost:] = shock[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n divugradlnrho = np.zeros_like(uu)\n gradshock = grad(shock,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n coordinate_system=param.coord_system)\n for j in range(0,3):\n gradshock[j, :nghost,nghost:-nghost,nghost:-nghost] = gradshock[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n gradshock[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradshock[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost, :nghost,nghost:-nghost] = gradshock[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradshock[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n gradshock[j,nghost:-nghost,nghost:-nghost, :nghost] = gradshock[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n gradshock[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradshock[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n for j in range(0,3):\n divugradlnrho[j] = param.nu_shock*divu*gradshock[j] +\\\n param.nu_shock*shock*(divu*gradlnrho[j] + graddivu[j])\n del(divu,gradshock,gradlnrho,graddivu)\n fvisc = fvisc + divugradlnrho\n del(divugradlnrho)\n if lhyper3:\n #deluij5 = np.zeros_like([uu,uu,uu])\n #uij5glnrho to be included\n del6u = np.zeros_like(uu)\n for j in range(0,3):\n del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz)\n del6u[j, :nghost,nghost:-nghost,nghost:-nghost] = del6u[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\n del6u[j,-nghost:,nghost:-nghost,nghost:-nghost] = del6u[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\n del6u[j,nghost:-nghost, :nghost,nghost:-nghost] = del6u[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\n del6u[j,nghost:-nghost,-nghost:,nghost:-nghost] = del6u[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\n del6u[j,nghost:-nghost,nghost:-nghost, :nghost] = del6u[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\n del6u[j,nghost:-nghost,nghost:-nghost,-nghost:] = del6u[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\n #del6 for non-cartesian tba\n #del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\n # coordinate_system=param.coord_system)\n fvisc = fvisc + param.nu_hyper3*del6u\n del(del6u)\n fvisc2 = np.sqrt(dot2(fvisc))\n #advective forces\n advec = np.zeros_like(uu)\n advec[0] = dot(uu,tmp0)\n advec[1] = dot(uu,tmp1)\n advec[0] = dot(uu,tmp2)\n del(tmp0,tmp1,tmp2)\n advec2 = np.sqrt(dot2(advec))\n del(advec)\n #avoid division by zero\n if fvisc2.max() > 0:\n fvisc2[np.where(fvisc2==0)] = fvisc2[np.where(fvisc2>0)].min()\n Re = advec2/fvisc2\n #set minimum floor to exclude zero-valued Re \n Re[np.where(Re==0)] = Re[np.where(Re>0)].min()\n else:\n Re = advec2\n print('Re undefined')\n return Re","def gather_derivatives(self):\n self.xdot[0,0:self.n] = self.mdot[0:self.n] \n self.xdot[1,0:self.n] = self.rdot[0:self.n,0]\n self.xdot[2,0:self.n] = self.rdot[0:self.n,1]\n self.xdot[3,0:self.n] = self.rdot[0:self.n,2]\n self.xdot[4,0:self.n] = self.vdot[0:self.n,0]\n self.xdot[5,0:self.n] = self.vdot[0:self.n,1]\n self.xdot[6,0:self.n] = self.vdot[0:self.n,2]\n self.xdot[7,0:self.n] = self.rhodot[0:self.n] \n self.xdot[8,0:self.n] = 0\n self.xdot[9,0:self.n] = 0\n self.xdot[10,0:self.n] = self.udot[0:self.n]\n return self.xdot","def exocytosis_model(rate,twin,N_syn,\n pars,N_var,\n recompile=0,\n model='spiking',\n solver_options=None,\n stimulus='poisson',spikes_pre=None):\n\n # First check that N_var is compatible\n assert N_var<3 and N_var>=1, \"Number of variables of exocytosis model must be either 1 (x) or 2 (x,u)\"\n\n # Assures that twin is Numpy array for later handling\n twin = np.asarray(twin,dtype=float)\n\n # Also convert make sure to recast N_eq in a way that is suitable for C\n N_var = np.intc(N_var)\n if not N_syn: N_syn = 0 # Dummy value of N_syn in the case of the mean-field model\n N_syn = np.intc(N_syn)\n\n if model=='spiking':\n # Create input_spikes\n if twin.size == 2:\n # One rate or multiple ones in the same interval\n spikes = spg.input_spikes(N_syn, twin[1], rate, 0, stimulus=stimulus, spikes_pre=spikes_pre)\n else:\n # Multiple rates in different intervals\n spikes = spg.input_spikes(N_syn, twin, rate, 0, stimulus=stimulus, spikes_pre=spikes_pre)\n twin = np.r_[0, np.sum(twin)]\n N_spk = int(np.shape(spikes)[1])\n\n # Check that ICs are of size N_var x N_syn\n # NOTE: you will have to pass the whole vector of ICs to compute solutions from last point\n if pars['ICs'].size != (N_var+1)*N_syn:\n pars['ICs'] = np.tile(pars['ICs'][:N_var+1],(1,N_syn))[0]\n\n support_code = \"\"\"\n #include \"gliotransmission_models.h\"\n \"\"\"\n source_files = [os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/pycapi_utils.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/solver_options.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/stochastic_solvers.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/code/gliotransmission_models.cpp')]\n code = \"\"\"\n // Version\n double version = 1.0;\n \n // Define astrocyte model\n release synapse(N_var,N_syn,N_spk,pars);\n synapse.set_ics(pars);\n \n // Declare output structure\n out_release out(N_spk,N_var);\n \n // Simulator\n out = synapse.simulate(spikes.data());\n \n //Output \n return_val = out.make_PyDict();\n \"\"\"\n libs = ['gsl', 'gslcblas', 'm']\n dirs = [os.path.join(os.path.expanduser('~'), base_dir + '/code/'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers')]\n vars = ['pars', 'spikes','N_spk','N_var','N_syn']\n otm = weave.inline(code,\n vars,\n support_code=support_code,\n sources=source_files,\n libraries=libs,\n library_dirs=dirs,\n include_dirs=dirs,\n runtime_library_dirs=dirs,\n type_converters=converters.blitz,\n compiler='gcc',\n extra_compile_args=['-std=c++11'],\n force=recompile)\n # Post-stimulus processing\n otm['spk'] = spikes[0] # Spike instants\n otm['is'] = spikes[-1] # Synapse indexes in the spike train\n otm['ICs'] = pars['ICs']\n if N_var>1:\n u_ = otm['u']\n else:\n u_ = None\n otm['LCs'] = last_point(pars,otm['spk'],twin[-1],otm['x'],otm['y'],otm['is'],uval=u_)\n elif model=='average':\n # Check that rate is scalar\n assert isscalar(rate), \"Mean-field rate must be scalar\"\n\n support_code = \"\"\"\n #include \"gliotransmission_models.h\"\n \"\"\"\n source_files = [os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/pycapi_utils.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/solver_options.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/stochastic_solvers.cpp'),\n os.path.join(os.path.expanduser('~'), base_dir + '/code/gliotransmission_models.cpp')]\n code = \"\"\"\n // Version\n double version = 0.0;\n\n // Define astrocyte model\n release_ave synapse(N_var);\n\n // Declare output structure\n out_release out;\n\n // Simulator\n out = synapse.simulate(rate,pars,solver_options);\n\n //Output \n return_val = out.make_PyDict();\n \"\"\"\n libs = ['gsl', 'gslcblas', 'm']\n dirs = [os.path.join(os.path.expanduser('~'), base_dir + '/code/'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules'),\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers')]\n vars = ['rate', 'pars', 'solver_options', 'N_var']\n otm = weave.inline(code,\n vars,\n support_code=support_code,\n sources=source_files,\n libraries=libs,\n library_dirs=dirs,\n include_dirs=dirs,\n runtime_library_dirs=dirs,\n type_converters=converters.blitz,\n compiler='gcc',\n extra_compile_args=['-std=c++11'],\n force=recompile)\n\n # Post-stimulus processing\n otm['twin'] = twin # Simulate interval\n # Add released resources\n if (N_var < 2):\n otm['r'] = pars['u0'] * otm['x']\n else:\n otm['r'] = np.multiply(otm['x'], otm['u'])\n return otm","def apply_nonlinear(self, params, unknowns, resids):\n\n x = params['x']\n z = unknowns['z']\n resids['z'] = x*z + z - 4.0\n\n # Output equations need to evaluate a residual just like an explicit comp.\n resids['y'] = x + 2.0*z - unknowns['y']\n #print(x, unknowns['y'], z, resids['z'], resids['y'])","def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\n self.A0 = A_phi[0]\n self.phi0 = A_phi[1]\n self.sim = sim\n self.V0_frac = V0_frac\n self.t = t\n self.x_eq0 = self.sim.x_eq([0,0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\n self.sol = integrate.odeint(sim, self.x0, t=t)\n self.z = sim.zLI(self.sol, t)\n self.phi = np.unwrap(np.angle(self.z))\n self.t_filt = t_filt = t[15:] \n self.i0 = i0 = np.argmin(abs(self.t_filt)) \n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\n self.t_wide = t_filt[::Ndec_response]\n self.respRePts = self.sim.responseReVec(self.t_wide)\n self.Ht = lambda t: np.interp(t, self.t_wide, self.respRePts)\n \n \n \n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act","def pt_operators(N, L, J, theta, f, phi, band_idxs, deltaE, exact=False, qs=None, verbose=False, **kwargs):\n if qs is None:\n qs = range(N)\n BandIdxs = band_idxs\n DeltaE = deltaE\n if 'theta0' in kwargs:\n theta0 = kwargs['theta0']\n else:\n theta0 = theta\n H0Op = pf.ParafermionicChainOp(N, L, J, theta0, 0.0, 0.0, q=0) # we get MPO object for full unperturbed Hamiltonian (note the same in each sector)\n HfOps = []\n for q in qs:\n if 'exclude_side' in kwargs:\n if kwargs['exclude_side'] == 'left':\n fs = np.ones(L)*f\n fs[0] = 0.0\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\n elif kwargs['exclude_side'] == 'right':\n fs = np.ones(L)*f\n fs[-1] = 0.0\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\n elif kwargs['exclude_side'] == 'neither':\n fs = np.ones(L)*f\n fs[0] = 0.0\n fs[-1] = 0.0\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\n else:\n raise Exception('\\'exlude_side\\' argument should be either left or right')\n else:\n Hf = pf.ParafermionicChainOp(N, L, J, theta, f, phi, q=q)\n Hf.add(H0Op, c1=1.0, c2=-1.0, inplace=True, compress=False)\n HfOps.append(Hf)\n [Partitions, H0Energies] = H0Op.get_bands_and_energies() # get all the partitions and energies of each\n BandEnergy = H0Energies[BandIdxs[0]] # get the energy of the band we start from, this is E0\n BandPartitions = list(map(lambda x: Partitions[x], BandIdxs)) # get the\n FullBand = np.vstack(list(map(lambda x: pfcy.GetFullBandDW(BandPartitions[x]), range(len(BandIdxs)))))\n FullBandDim = len(FullBand)\n [NeighbouringBands,] = np.where(np.abs(H0Energies - BandEnergy) < DeltaE) # find other bands within deltaE in energy\n FullSubspace = np.copy(FullBand)\n for NeighbouringBand in NeighbouringBands:\n if NeighbouringBand not in BandIdxs:\n FullSubspace = np.vstack((FullSubspace, pfcy.GetFullBandDW(Partitions[NeighbouringBand])))\n FullSubspaceDim = FullSubspace.shape[0]\n if verbose: print('Full subspace dim: ' + str(FullSubspaceDim) + '.')\n x = np.arange(FullSubspaceDim)\n I = sps.diags(np.ones(FullSubspaceDim), 0)\n P0 = sps.diags(np.piecewise(x, [x < FullBandDim, x >= FullBandDim], [1.0, 0.0]), 0)\n Q0 = sps.diags(np.piecewise(x, [x < FullBandDim, x >= FullBandDim], [0.0, 1.0]), 0)\n s = time.time()\n H0 = H0Op.mats_subspace(FullSubspace)\n e = time.time()\n if verbose: print('Time taken to calculate H0 matrix: ' + str(e-s) + ' seconds.')\n\n s = time.time()\n Hfs = list(map(lambda x : HfOps[x].mats_subspace(FullSubspace), qs))\n e = time.time()\n if verbose: print('Time taken to calculate V matrices: ' + str(e-s) + ' seconds.')\n denominators = (BandEnergy - H0.diagonal()[FullBandDim:])\n if len(np.where(denominators == 0)[0]) > 0:\n return None\n Q = sps.diags(np.hstack([np.zeros(FullBandDim),np.ones(FullSubspaceDim-FullBandDim)/denominators]), 0)\n\n if exact:\n Offset = np.sum(map(lambda x: len(pfcy.GetFullBandDW(Partitions[x])), range(min(BandIdxs))))\n # for debugging purposes, calculate some of full spectrum exactly, can be time consuming\n FullEs = list(map(lambda x: pf.Diagonalise(N, L, J, theta, f, phi, q=x, k=Offset + FullBandDim), qs))\n FullEs = list(map(lambda x: FullEs[x][0][Offset:(Offset+FullBandDim)], qs))\n else:\n FullEs = None\n\n return [P0, Q, H0, Hfs, FullBandDim, BandEnergy, FullEs]","def variables(self, *args, **kwargs):\n return self._optimizer.variables(*args, **kwargs)","def f(self,y,psi):\r\n\r\n #1. check that number of params is consistent\r\n assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'\r\n assert psi.shape[1] == 3, 'inconsistent parameter dimensions'\r\n\r\n #2. exponentiate the a and b (positive!)\r\n mpsi = psi.copy()\r\n\r\n #3. transform data\r\n z = y.copy()\r\n for i in range(len(mpsi)):\r\n a,b,c = mpsi[i]\r\n z += a*np.tanh(b*(y+c))\r\n return z","def set_coeffs(self, sol):\n # TODO: look for bugs here!\n self.log_debug(\"Set spline coefficients\")\n\n # task: find which of the free parameters (coeffs) belong to which spline object\n sol_bak = sol.copy()\n subs = dict()\n\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\n for k, v in list(self.indep_vars.items()):\n i = len(v)\n # TODO: improve comment\n subs[k] = sol[:i] # set numerical value to symbolical value\n sol = sol[i:] ##:: sol = []\n \n if self._parameters['use_chains']:\n for var in self.sys.states + self.sys.inputs:\n for ic in self._chains:\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\n \n # set numerical coefficients for each spline and derivative\n # TODO: handle `!!`-comments after talking to yx \n ##!! spline_key_plus_k = self.splines.keys().append('k')\n for k in list(self.splines.keys()): ##:: ['x1','x3']\n self.splines[k].set_coefficients(free_coeffs=subs[k])\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\n \n # yet another dictionary for solution and coeffs\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\n# ##!! self.indep_vars = indep_vars\n\n coeffs_sol = OrderedDict()\n\n # used for indexing\n i = 0\n j = 0\n\n for k, v in list(self.indep_vars.items()):\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\n j += len(v)\n coeffs_sol[k] = sol_bak[i:j]\n i = j\n\n self.coeffs_sol = coeffs_sol\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\n\n ##!! return self.coeffs_sol['z_par'].tolist()","def compute_plume(self, full_nondim=False):\n def compute_nondimensional(x):\n \"\"\" both nondim melt rate and circulation \"\"\"\n M = self.nondim_M(x)\n M.attrs = {'long_name':'dimensionless meltrate; eqn. (26)'}\n phi0 = self.phi0(x)\n phi0.attrs = {'long_name':'dimensionless circulation; eqn. (25)'}\n return M, phi0\n\n # calculations\n self.dp['M'], self.dp['phi0'] = compute_nondimensional(self.dp.dgrl_)\n\n self.dp['m'] = self.dim_M().where(self.dp.mask==3)*3600*24*365 # [m/s] -> [m/yr]\n self.dp.m.attrs = {'long_name':'dimensional meltrates; eqn. (28a)', 'units':'m/yr'}\n \n self.dp['Phi'] = self.Phi().where(self.dp.mask==3)\n self.dp.Phi.attrs = {'long_name':'dimensional circulation; eqn. (29)', 'units':'m^3/s'}\n\n if full_nondim: # compute non-dimensional 1D melt curve for full [0,1] interval\n self.dp = self.dp.assign_coords({'x_':np.linspace(0,1,51)})\n self.dp.x_.attrs['long_name'] = 'non-dimensional coordinates in [0,1]'\n self.dp['M_full'], self.dp['phi0_full'] = compute_nondimensional(self.dp.coords['x_'])\n \n return self.dp","def dynstall_oye_dxdt(t,fs,u,p):\n alpha = u['alpha'](t)\n f_st = p['F_st'](alpha)\n return 1/p['tau'] * (f_st - fs)","def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\n self.A0 = A_phi[0]\n self.phi0 = A_phi[1]\n self.sim = sim\n self.V0_frac = V0_frac\n self.t = t\n self.x_eq0 = self.sim.x_eq([0, 0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\n self.sol = integrate.odeint(sim, self.x0, t=t)\n self.z = sim.zLI(self.sol, t)\n self.phi = np.unwrap(np.angle(self.z))\n self.t_filt = t_filt = t[15:] \n self.i0 = i0 = np.argmin(abs(self.t_filt)) \n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\n self.t_wide = t_filt[::Ndec_response]\n self.respRePts = self.sim.responseReVec(self.t_wide)\n self.Ht = lambda tt: np.interp(tt, self.t_wide, self.respRePts)\n \n \n \n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act","def _redef_sp1_vars(self):\r\n\r\n if len(self.fq_list) == 0:\r\n no_rad = True\r\n lst_tmp = np.matrix(np.reshape(self.lst_tmp, \r\n (self.lst_tmp.size, 1)))\r\n else: no_rad = False\r\n # The practically constants...\r\n # Big Epsilon:\r\n if self.cond == True:\r\n self.Epsilon = self.d_T * self.thermal_conductivity\r\n else:\r\n self.Epsilon = (self.diff_scale ** 2) / \\\r\n (3.0 * self.absorb_coeffs[self.rad] ** 2)\r\n # Beta:\r\n if self.cond == True:\r\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\r\n (self.convect_coeff)\r\n else:\r\n self.Beta = (1.0 + 3.0 * self.r2) * (2.0 * self.diff_scale) / \\\r\n ((1.0 - 2.0 * self.r1) * (\r\n 3.0 * self.absorb_coeffs[self.rad]))\r\n\r\n # The feild solutions at the last timestep.\r\n # The integral vF:\r\n if self.cond == True:\r\n # The horrifically complicated F:\r\n def F_func_cond(elem, eta):\r\n F = 0.0\r\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n F += Tn\r\n for k in range(0, len(self.fq_list)):\r\n vk = self.fq_list[k]\r\n try:\r\n vk_m = self.fq_list[k - 1]\r\n except:\r\n vk_m = self.v0_frequency\r\n absorbtion = self.absorb_coeffs[k]\r\n phi = elem.eval_elem(self.node_map, self.lst_rad[k],\r\n [eta])[0]\r\n inter1 = phi - 4.0 * sconst.pi * \\\r\n self.B_int_function(Tn, self.refr_idx_vol,\r\n vk, vk_m)\r\n inter2 = absorbtion * self.d_T / (self.diff_scale ** 2)\r\n F += inter2 * inter1\r\n return elem.funcs(eta) * F\r\n if not no_rad:\r\n # We're integrating something non-linear for SP1\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func_cond,\r\n self.node_map)\r\n else:\r\n # Or something easier if we're only looking at heat.\r\n self.vF_vect_vol = np.array(self.uv_vol * lst_tmp).reshape(-1)\r\n else:\r\n def F_func_radiative(elem, eta):\r\n T = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n vk = self.fq_list[self.rad]\r\n try:\r\n vk_minus = self.fq_list[self.rad - 1]\r\n except:\r\n vk_minus = self.v0_frequency\r\n n = self.refr_idx_vol\r\n F = 4.0 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\r\n return elem.funcs(eta) * F\r\n\r\n self.vF_vect_vol = et.elems_2_array(self.mesh,\r\n F_func_radiative,\r\n self.node_map)\r\n # The path integral vf:\r\n if self.cond == True:\r\n def f_func_cond(elem, eta):\r\n Tb = self.background_temperature\r\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\r\n n = self.refr_idx_background\r\n vk = self.v0_frequency\r\n vk_minus = 0\r\n Bb0 = self.B_int_function(Tb, n, vk, vk_minus)\r\n Bn0 = self.B_int_function(Tn, n, vk, vk_minus)\r\n B_coeff = (self.alpha * sconst.pi) / self.convect_coeff\r\n f = Tb + B_coeff * (Bb0 - Bn0)\r\n return elem.funcs(eta) * f\r\n if not no_rad:\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func_cond,\r\n self.node_map)\r\n else:\r\n try:\r\n self.vf_vect_bound = self.cache_tb_integral_array\r\n except AttributeError:\r\n def elem_functor(elem, eta): return elem.funcs(eta)\r\n self.cache_tb_integral_array = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n elem_functor,\r\n self.node_map)\r\n self.cache_tb_integral_array *= self.background_temperature\r\n self.vf_vect_bound = self.cache_tb_integral_array\r\n \r\n else:\r\n # Radiation f = 4*pi*B^{(k)}(T_b, n_g)\r\n def f_func_radiative(elem, eta):\r\n T = self.background_temperature\r\n vk = self.fq_list[self.rad]\r\n try:\r\n vk_minus = self.fq_list[self.rad - 1]\r\n except:\r\n vk_minus = self.v0_frequency\r\n n = self.refr_idx_vol\r\n f = 4 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\r\n return elem.funcs(eta) * f\r\n\r\n self.vf_vect_bound = et.edge_2_array(self.mesh,\r\n \"Boundary\",\r\n f_func_radiative,\r\n self.node_map)\r\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\r\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\r\n assert (self.vf_vect_bound.shape[0] == \\\r\n self.vF_vect_vol.shape[0])","def solve(self):\n \n # Definition of the parameters\n Q_pc = self.parameters.getParam(\"Q_pc\")\n V_c = self.parameters.getParam(\"V_c\")\n V_p = self.parameters.getParam(\"V_p\")\n CL = self.parameters.getParam(\"CL\")\n initial_conditions = [\n self.parameters.getParam(\"q_c0\"),\n self.parameters.getParam(\"q_p0\"),\n ]\n t_eval = np.linspace(0, self.timespan, self.nsteps)\n\n # Definition of the model ODEs\n def pk_iv_model(t, y, Q_pc, V_c, V_p, CL):\n \"\"\"Defines the differential equations for the PK IV model.\n\n Parameters:\n :param t: time (h)\n :param y: list of the state variables of the ODEs system, in the\n form [q_c, q_p]\n :param Q_pc: transition rate between central and peripheral\n compartments (mL/h)\n :param V_c: volume of central compartment (mL)\n :param V_p: volume of peripheral compartment (mL)\n :param CL: clearance/elimination rate from the central\n compartment (mL/h)\n\n The parameters (except for t and y) are extracted from the\n Parameter class, using getParam method.\n\n Returns list containing the differential equations, in the form:\n [dqc_dt, dqp_dt]\n \"\"\"\n q_c, q_p = y\n transfer = Q_pc * (q_c / V_c - q_p / V_p)\n dqc_dt = self.dosefunction(t) - q_c / V_c * CL - transfer\n dqp_dt = transfer\n return [dqc_dt, dqp_dt]\n\n # Solving the model\n sol = scipy.integrate.solve_ivp(\n fun=lambda t, y: pk_iv_model(t, y, Q_pc, V_c, V_p, CL),\n t_span=[t_eval[0], t_eval[-1]],\n y0=initial_conditions,\n t_eval=t_eval,\n )\n\n # Feeding the solution line by line to solution class\n t = sol.t\n y = sol.y\n N = t.shape[0]\n columnNames = [\"t\", \"dose\", \"q_c\", \"q_p\"]\n self.solution.begin(columnNames, N)\n for i in range(N):\n arr = np.zeros((len(columnNames), 1))\n arr[0] = t[i]\n arr[1] = self.dosefunction(t[i])\n arr[2:, 0] = y[:, i]\n self.solution.report(arr)","def variational_expectations(self, Fmu, Fvar, Y):\n\n gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)\n gh_x = gh_x.reshape(1, -1)\n gh_w = gh_w.reshape(-1, 1) / np.sqrt(np.pi)\n shape = tf.shape(Fmu)\n Fmu, Fvar, Y = [tf.reshape(e, (-1, 1)) for e in (Fmu, Fvar, Y)]\n X = gh_x * tf.sqrt(2.0 * Fvar) + Fmu\n Y = tf.tile(Y, [1, self.num_gauss_hermite_points]) # broadcast Y to match X\n\n logp = self.logp(X, Y)\n return tf.reshape(tf.matmul(logp, gh_w), shape)","def test_var_exp(self):\n with self.test_context() as session:\n test_setups, F, feed = self.prepare()\n for test_setup in test_setups:\n l = test_setup.likelihood\n y = test_setup.Y\n l.compile()\n r1 = session.run(l.logp(F, y), feed_dict=feed)\n zero = F * 0.\n r2 = session.run(\n l.variational_expectations(F, zero, test_setup.Y), feed_dict=feed)\n assert_allclose(r1, r2, atol=test_setup.tolerance, rtol=test_setup.tolerance)","def f_obs(x, *args):\n sslm, word_counts, totals, mean_deriv_mtx, word, deriv = args\n # flag\n init_mult = 1000\n\n T = len(x)\n val = 0\n term1 = 0\n term2 = 0\n\n # term 3 and 4 for DIM\n term3 = 0\n term4 = 0\n\n sslm.obs[word] = x\n sslm.mean[word], sslm.fwd_mean[word] = sslm.compute_post_mean(word, sslm.chain_variance)\n\n mean = sslm.mean[word]\n variance = sslm.variance[word]\n\n # only used for DIM mode\n # w_phi_l = sslm.w_phi_l[word]\n # m_update_coeff = sslm.m_update_coeff[word]\n\n for t in range(1, T + 1):\n mean_t = mean[t]\n mean_t_prev = mean[t - 1]\n\n val = mean_t - mean_t_prev\n term1 += val * val\n term2 += word_counts[t - 1] * mean_t - totals[t - 1] * np.exp(mean_t + variance[t] / 2) / sslm.zeta[t - 1]\n\n model = \"DTM\"\n if model == \"DIM\":\n # stuff happens\n pass\n\n if sslm.chain_variance > 0.0:\n\n term1 = - (term1 / (2 * sslm.chain_variance))\n term1 = term1 - mean[0] * mean[0] / (2 * init_mult * sslm.chain_variance)\n else:\n term1 = 0.0\n\n final = -(term1 + term2 + term3 + term4)\n\n return final","def tsne(x, no_dims=2, perplexity=30.0, max_iter=1000):\n\n # Check inputs\n if isinstance(no_dims, float):\n print(\"Error: array x should have type float.\")\n return -1\n\n (n, d) = x.shape\n\n # 动量\n initial_momentum = 0.5\n final_momentum = 0.8\n eta = 500\n min_gain = 0.01\n # 随机初始化Y\n y = np.random.randn(n, no_dims)\n # dy梯度\n dy = np.zeros((n, no_dims))\n # iy是什么\n iy = np.zeros((n, no_dims))\n\n gains = np.ones((n, no_dims))\n\n # 对称化\n P = seach_prob(x, 1e-5, perplexity)\n P = P + np.transpose(P)\n P = P / np.sum(P) #pij\n # early exaggeration\n # pi\\j，提前夸大\n print (\"T-SNE DURING:%s\" % time.clock())\n P = P * 4\n P = np.maximum(P, 1e-12)\n\n # Run iterations\n for iter in range(max_iter):\n # Compute pairwise affinities\n sum_y = np.sum(np.square(y), 1)\n num = 1 / (1 + np.add(np.add(-2 * np.dot(y, y.T), sum_y).T, sum_y))\n num[range(n), range(n)] = 0\n Q = num / np.sum(num) #qij\n Q = np.maximum(Q, 1e-12) #X与Y逐位比较取其大者\n\n # Compute gradient\n # np.tile(A,N) 重复数组AN次 [1],5 [1,1,1,1,1]\n # pij-qij\n PQ = P - Q\n # 梯度dy\n for i in range(n):\n dy[i,:] = np.sum(np.tile(PQ[:,i] * num[:,i], (no_dims, 1)).T * (y[i,:] - y), 0)\n\n # Perform the update\n if iter < 20:\n momentum = initial_momentum\n else:\n momentum = final_momentum\n\n gains = (gains + 0.2) * ((dy > 0) != (iy > 0)) + (gains * 0.8) * ((dy > 0) == (iy > 0))\n gains[gains < min_gain] = min_gain\n # 迭代\n iy = momentum * iy - eta * (gains * dy)\n y = y + iy\n y = y - np.tile(np.mean(y, 0), (n, 1))\n # Compute current value of cost function\\\n if (iter + 1) % 100 == 0:\n C = np.sum(P * np.log(P / Q))\n print(\"Iteration \", (iter + 1), \": error is \", C)\n if (iter+1) != 100:\n ratio = C/oldC\n print(\"ratio \", ratio)\n if ratio >= 0.95:\n break\n oldC = C\n # Stop lying about P-values\n if iter == 100:\n P = P / 4\n print(\"finished training!\")\n return y","def forward(self,y,xt,D,h,param):\n \n \n h_flip=h.flip(2,3)\n a=conv(h,xt)-y \n b=xt-self.learning_rate*conv(h_flip,a)\n u=tor.matmul(D,tor.matmul(b,D.transpose(2,3)))\n u_=rho(u,param)\n xt1=tor.matmul(D.transpose(2,3),tor.matmul(u_,D))\n \n return xt1","def den_evolve(self, delt, txp, src):\n self.ne += (-txp.dfluxe + src.se)*delt\n self.ni += (-txp.dfluxi + src.si)*delt","def equations(self):\n k = 0\n ######################################################################\n # equations for fluid balance\n self.residual[k:k + self.num_nw_fluids] = self.fluid_func()\n k += self.num_nw_fluids\n\n ######################################################################\n # equations for mass flow balance\n self.residual[k] = self.mass_flow_func()\n k += 1\n\n ######################################################################\n # equations for specified heta transfer\n if self.Q.is_set:\n self.residual[k] = self.inl[0].m.val_SI * (\n self.outl[0].h.val_SI - self.inl[0].h.val_SI) - self.Q.val\n k += 1\n\n ######################################################################\n # equations for specified pressure ratio\n if self.pr.is_set:\n self.residual[k] = (\n self.inl[0].p.val_SI * self.pr.val - self.outl[0].p.val_SI)\n k += 1\n\n ######################################################################\n # equations for specified zeta\n if self.zeta.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n self.residual[k] = self.zeta_func(zeta='zeta')\n k += 1\n\n ######################################################################\n # equation for specified hydro-group paremeters\n if self.hydro_group.is_set:\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\n # hazen williams equation\n if self.hydro_group.method == 'HW':\n func = self.hw_func\n # darcy friction factor\n else:\n func = self.darcy_func\n self.residual[k] = func()\n k += 1\n\n ######################################################################\n # additional equations\n self.additional_equations(k)","def cubic_evolve(self,nt=1):\n #loop through time steps\n for l in range(nt):\n # temporary array\n y_temp = np.zeros(self.y.shape[0])\n # loop through array\n for i in range(self.y.shape[0]):\n # idx left to departure point\n x_dep = self.x[i]-self.u[i]*self.dt\n j = int(np.floor(x_dep/self.dx))\n # alpha\n a = (self.x[i]-self.u[i]*self.dt - j*self.dx)/self.dx\n # calculate next time step\n f = lambda x: x % self.y.shape[0] if x >= self.y.shape[0] else x\n y_temp[i] = - a * (1-a)*(2-a)/6 * self.y[f(j-1)]\n y_temp[i] += (1-a**2)*(2-a)/2 * self.y[f(j)]\n y_temp[i] += a*(1+a)*(2-a)/2 * self.y[f(j+1)]\n y_temp[i] -= a*(1-a**2)/6 * self.y[f(j+2)]\n self.y = np.copy(y_temp)\n return self.y","def forward(self, inputs):\n inputs = inputs.view(-1, self.nVar)\n x, y, z = inputs[:, 0], inputs[:, 1], inputs[:, 2]\n output = torch.zeros_like(inputs)\n s = self.sigma\n r = self.r\n b = self.b\n output[:, 0] = s * (y-x)\n output[:, 1] = x * (r-z) - y\n output[:, 2] = x*y - b*z\n return output","def _sesolve_func_td(H_func, psi0, tlist, e_ops, args, opt, progress_bar):\n if debug:\n print(inspect.stack()[0][3])\n\n #\n # check initial state or oper\n #\n if psi0.isket:\n oper_evo = False\n elif psi0.isunitary:\n oper_evo = True\n else:\n raise TypeError(\"The unitary solver requires psi0 to be\"\n \" a ket as initial state\"\n \" or a unitary as initial operator.\")\n\n if opt.moving_mode_indices is None:\n n_modes = len(psi0.dims[0])\n opt.moving_mode_indices = np.arange(n_modes)\n\n #\n # setup integrator\n #\n new_args = None\n\n if type(args) is dict:\n new_args = {}\n for key in args:\n if isinstance(args[key], Qobj):\n new_args[key] = args[key].data\n else:\n new_args[key] = args[key]\n\n elif type(args) is list or type(args) is tuple:\n new_args = []\n for arg in args:\n if isinstance(arg, Qobj):\n new_args.append(arg.data)\n else:\n new_args.append(arg)\n\n if type(args) is tuple:\n new_args = tuple(new_args)\n else:\n if isinstance(args, Qobj):\n new_args = args.data\n else:\n new_args = args\n\n if oper_evo:\n initial_vector = operator_to_vector(psi0).full().ravel()\n # Check that function returns superoperator\n if H_func(0, args).issuper:\n L_func = H_func\n else:\n L_func = lambda t, args: spre(H_func(t, args))\n\n else:\n new_psi0 = psi0\n initial_vector = new_psi0.full().ravel()\n L_func = H_func\n if opt.moving_basis:\n initial_mode_displacements = np.zeros(len(opt.moving_mode_indices), dtype=complex)\n for mode_idx in opt.moving_mode_indices:\n identities = [qeye(dim) for dim in psi0.dims[0]]\n a_op_components = identities\n a_op_components[mode_idx] = destroy(psi0.dims[0][mode_idx])\n a_op = tensor(a_op_components)\n a_expect = expect(a_op, psi0)\n initial_mode_displacements[mode_idx] = a_expect\n displacement_components = identities\n displacement_components[mode_idx] = displace(psi0.dims[0][mode_idx], -a_expect)\n displacement_op = tensor(displacement_components)\n new_psi0 = displacement_op * new_psi0\n initial_vector = np.hstack([initial_vector, initial_mode_displacements])\n\n if not opt.rhs_with_state:\n print('Using cython function.')\n r = scipy.integrate.ode(cy_ode_psi_func_td)\n else:\n if opt.moving_basis:\n r = scipy.integrate.ode(_ode_psi_func_td_with_state_moving_basis)\n else:\n r = scipy.integrate.ode(_ode_psi_func_td_with_state)\n\n r.set_integrator('zvode', method=opt.method, order=opt.order,\n atol=opt.atol, rtol=opt.rtol, nsteps=opt.nsteps,\n first_step=opt.first_step, min_step=opt.min_step,\n max_step=opt.max_step)\n r.set_initial_value(initial_vector, tlist[0])\n if opt.moving_basis:\n r.set_f_params(L_func, new_args, opt, psi0.dims)\n else:\n r.set_f_params(L_func, new_args)\n\n #\n # call generic ODE code\n #\n return _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar,\n dims=psi0.dims)","def gather_derivatives(self):\n self.xdot[0,0:self.n] = self.mdot[0:self.n] \n self.xdot[1,0:self.n] = self.rdot[0:self.n,0]\n self.xdot[2,0:self.n] = self.rdot[0:self.n,1]\n self.xdot[3,0:self.n] = self.rdot[0:self.n,2]\n self.xdot[4,0:self.n] = self.vdot[0:self.n,0]\n self.xdot[5,0:self.n] = self.vdot[0:self.n,1]\n self.xdot[6,0:self.n] = self.vdot[0:self.n,2]\n return self.xdot","def obj_u_opt_N_opt(u, T, alpha, B, N, num_tumor_voxels, Td = 2):\n x = T.dot(u)\n alpha_tilde = alpha #np.repeat(N, num_tumor_voxels)*alpha\n B_tilde = B #np.repeat(N, num_tumor_voxels)*B\n #Note that all modalities must have the same number of tumor voxels:\n return alpha_tilde.T.dot(x) - x.T.dot(B_tilde*x) + num_tumor_voxels*(np.sum(N)-1)*(np.log(2)/Td)","def du(self):\n du = setup_nonlinear_model_du() # Ux, Uy, theta_p\n return du","def compute_hessian(self, dw, trn_X, trn_y, epsilon: float = 0.01):\n norm = torch.cat([w.view(-1) for w in dw]).norm()\n eps = epsilon / norm\n\n dalpha_pos = self.finite_difference(dw, trn_X, trn_y, eps, wrt='alpha')\n dalpha_neg = self.finite_difference(dw, trn_X, trn_y, -eps, wrt='alpha')\n hessian = [(p - n) / (2. * eps) for p, n in zip(dalpha_pos, dalpha_neg)]\n return hessian","def J(cst, x):\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\n [u, v, g1, g2, g3] = x\n df1du = 2*u*g3**2 - 2*g3*u0 + 2*g3*coeffs[3]*(g1*u1-u0) + 2*g3*coeffs[4]*(g2*u2-u0)\n df1dv = -2*v*g3**2 + 2*g3*v0 - 2*g3*coeffs[3]*(g1*v1-v0) - 2*g3*coeffs[4]*(g2*v2-v0)\n df1dg1 = 2*g1*coeffs[0]*(u1**2-v1**2) + 2*(v1*v0-u1*u0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[3]*(u1*u-v1*v)\n df1dg2 = 2*g2*coeffs[2]*(u2**2-v2**2) + 2*(v2*v0-u2*u0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[4]*(u2*u-v2*v)\n df1dg3 = 2*g3*(u**2-v**2) + 2*(v*v0-u*u0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(u1*u-v1*v) + 2*g2*coeffs[4]*(u2*u-v2*v)\n\n df2du = 0\n df2dv = 2*v*g3**2 + 2*g3*(-v0 + coeffs[3]*(g1*v1-v0) + coeffs[4]*(g2*v2-v0))\n df2dg1 = 2*g1*coeffs[0]*(v1**2-1) + 2*(1-v1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[3]*(v1*v-1)\n df2dg2 = 2*g2*coeffs[2]*(v2**2-1) + 2*(1-v2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[4]*(v2*v-1)\n df2dg3 = 2*g3*(v**2-1) + 2*(1-v*v0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(v1*v-1) + 2*g2*coeffs[4]*(v2*v-1)\n\n df3du = g3*coeffs[3]*(g1*v1-v0) + g3*coeffs[4]*(g2*v2-v0) + g3*(g3*v-v0)\n df3dv = g3*coeffs[3]*(g1*u1-u0) + g3*coeffs[4]*(g2*u2-u0) + g3*(g3*u-u0)\n df3dg1 = 2*g1*coeffs[0]*u1*v1 - (v1*u0+u1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[3]*(v1*u+u1*v)\n df3dg2 = 2*g2*coeffs[2]*u2*v2 - (v2*u0+u2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[4]*(v2*u+u2*v)\n df3dg3 = 2*g3*u*v - (u*v0+v*u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1*u+u1*v) + g2*coeffs[4]*(v2*u+u2*v)\n\n df4du = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\n df4dv = 0\n df4dg1 = 2*g1*coeffs[0]*u1 - (u0+u1)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1+u2) + g3*coeffs[3]*(u+u1)\n df4dg2 = 2*g2*coeffs[2]*u2 - (u0+u2)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1+u2) + g3*coeffs[4]*(u+u2)\n df4dg3 = 2*g3*u - (u+u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(u+u1) + g2*coeffs[4]*(u+u2)\n\n df5du = 0\n df5dv = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\n df5dg1 = 2*g1*coeffs[0]*v1 - (v1+v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(v2+v1) + g3*coeffs[3]*(v1+v)\n df5dg2 = 2*g2*coeffs[2]*v2 - (v2+v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(v2+v1) + g3*coeffs[4]*(v2+v)\n df5dg3 = 2*g3*v - (v0+v)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1+v) + g2*coeffs[4]*(v2+v)\n\n return np.array([\n [df1du, df1dv, df1dg1, df1dg2, df1dg3],\n [df2du, df2dv, df2dg1, df2dg2, df2dg3],\n [df3du, df3dv, df3dg1, df3dg2, df3dg3],\n [df4du, df4dv, df4dg1, df4dg2, df4dg3],\n [df5du, df5dv, df5dg1, df5dg2, df5dg3],\n ])","def LiftCoeff(h,Vc,Temp_m,W,S):\n T,p,rho = isa(h)\n return W*g0/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)","def newtonian_profile(PSI):\n\n U = dot(MDX, PSI)\n V = - dot(MDY, PSI)\n VGRAD = dot(U,MDX) + dot(V,MDY)\n\n BPFEQNS = zeros((3*vecLen, 3*vecLen), dtype='D')\n # Cxx eqn\n # Cxx\n BPFEQNS[0:vecLen, 0:vecLen] = Nu*MDX - VGRAD \\\n + 2*tsm.c_prod_mat(dot(MDX,U)) - oneOverWi*II\n # Cyy\n BPFEQNS[0:vecLen, vecLen:2*vecLen] = 0\n # Cxy\n BPFEQNS[0:vecLen, 2*vecLen:3*vecLen] = 2*tsm.c_prod_mat(dot(MDY, U))\n # Cyy eqn\n # Cxx\n BPFEQNS[vecLen:2*vecLen, 0:vecLen] = 0\n # Cyy\n BPFEQNS[vecLen:2*vecLen, vecLen:2*vecLen] = Nu*MDX - VGRAD - oneOverWi*II\\\n + 2.*tsm.c_prod_mat(dot(MDY, V))\n # Cxy\n BPFEQNS[vecLen:2*vecLen, 2*vecLen:3*vecLen] = 2.*tsm.c_prod_mat(dot(MDX, V))\n #Cxy eqn\n # Cxx\n BPFEQNS[2*vecLen:3*vecLen, 0:vecLen] = tsm.c_prod_mat(dot(MDX, V))\n # Cyy \n BPFEQNS[2*vecLen:3*vecLen, vecLen:2*vecLen] = tsm.c_prod_mat(dot(MDY, U))\n # Cxy\n BPFEQNS[2*vecLen:3*vecLen, 2*vecLen:3*vecLen] = Nu*MDX - VGRAD - oneOverWi*II \n\n RHS = zeros(3*vecLen, dtype='D')\n RHS[0] = -oneOverWi\n RHS[vecLen] = -oneOverWi\n RHS[2*vecLen:3*vecLen] = 0\n\n soln = linalg.solve(BPFEQNS, RHS)\n\n Cxx = soln[0:vecLen]\n Cyy = soln[vecLen:2*vecLen]\n Cxy = soln[2*vecLen:3*vecLen]\n\n return Cxx, Cyy, Cxy","def variational_objective(params, t, num_samples, beta=1.):\n\n # 1. draw samples from the variational posterior, eps ~ N(0,I)\n zs, ldet_sums = draw_variational_samples(params, num_samples)\n\n # 1.5 negative entropy of z0 --- likely we need this for KL though\n # not needed for optimization\n\n # 2. compute expected value of the sum of jacobian terms\n E_ldet_sum = np.mean(ldet_sums)\n\n # 3. compute data term\n lls = logprob(zs, t)\n E_logprob = np.mean(lls)\n\n if debug_print:\n print \"entropy term: \", E_ldet_sum\n print \"data term : \", E_logprob, \" (+/- \", np.std(lls), \")\", \" min = \", np.min(lls)\n\n # return lower bound\n beta = 1. if t >= len(beta_schedule) else beta_schedule[t]\n lower_bound = beta * E_logprob + E_ldet_sum\n return -lower_bound","def dewT_2_q_magnus(ds, var):\n A1, B1, C1 = 17.625, 243.04, 610.94\n vpsl = C1 * np.exp(A1 * (ds[var['dew']] - 273.15) / (B1 + (ds[var['dew']] - 273.15)))\n wsl = eps0 * vpsl / (ds[var['pressure']] - vpsl)\n ds[var['spec_h']] = wsl / (1 + wsl)\n return ds","def ev(knotvec, coeffs, u):\n assert len(coeffs) == knotvec.numdofs, 'Wrong size of coefficient vector'\n return scipy.interpolate.splev(u, (knotvec.kv, coeffs, knotvec.p))","def linear_evolve(self,nt=1):\n for l in range(nt):\n y_temp = np.empty(self.y.shape[0])\n for i in range(self.y.shape[0]):\n \n # idx left to the departure point\n j = int(np.floor((self.x[i]-self.u[i]*self.dt)/self.dx))\n # idx right to the departure point\n k = j+1\n print i, j, k\n # linear interpolation\n alpha = (self.x[i]-self.u[i]*self.dt - j*self.dx)/self.dx\n y_temp[i] = (1-alpha)*self.y[j] + alpha*self.y[k]\n # copy array to current time\n self.y = np.copy(y_temp)\n stop\n #return current varibale\n return self.y","def V_phi(self, x):\n\n x = self.featureExtractor.getFeatures(x)\n\n x = torch.tensor(x).float()\n\n x = F.relu(self.linear(x))\n\n v = self.linear_v(x)\n\n return v","def none_model_vars():\n obj_fun = None\n time_sol = None\n gap = None\n s_pos = None\n b_target = None\n threads = None\n\n return obj_fun, time_sol, gap, s_pos, b_target, threads","def update_variables(self):\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\n # aquire transformation matrices and tips for segment 1 and 2\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\n # Frenet frames\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\n # Bishop frames\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]","def get_dphi_dx_fast(self, sess, x_train):\n dphi_dx = tf.gradients(self.featurizations, self.x)\n feed_dict = {self.x: x_train}\n dphi_dx_np = sess.run(dphi_dx, feed_dict = feed_dict)\n return dphi_dx_np","def get_dphi_dx(self, sess, x_test):\n self.logger.debug(\"Finding dphi dx\")\n n, d = x_test.shape\n\n #TODO: Remove the hardcoding from here\n featurization_sliced = self.slice_tensor_one_d(self.featurizations, 32)\n num_hidden_last_layer = len(featurization_sliced)\n self.logger.debug(\"Completed slicing\")\n\n run_list = []\n for i in range(len(featurization_sliced)):\n dphi_i_dx = tf.gradients(featurization_sliced[i], self.x)\n run_list.append(dphi_i_dx)\n\n self.logger.debug(\"Found run list\")\n feed_dict = {self.x: x_test}\n dphi_dx_np = sess.run(run_list, feed_dict = feed_dict)\n dphi_dx_np = np.array(dphi_dx_np).squeeze()\n\n self.logger.debug(\"Found grads\")\n #dphi_dx_np = dphi_dx_np.reshape((n, num_hidden_last_layer, d))\n dphi_dx_np = dphi_dx_np.swapaxes(0, 1)\n return dphi_dx_np","def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\n\n f=lambda y, t : gamma*y\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\n return Y","def additional_derivatives(self, increment_filter, k):\n ######################################################################\n # derivatives for saturated liquid at hot side outlet equation\n if self.subcooling.val is False:\n o1 = self.outl[0].to_flow()\n self.jacobian[k, 2, 1] = -dh_mix_dpQ(o1, 0)\n self.jacobian[k, 2, 2] = 1\n k += 1","def main():\n\n varList = {'beta': 6., 'convSpeed': 1.2, 'Mark': 0., 'axi': 1, 'acModes': 4, 'Nr': 801, 'Tf': 600., 'xf': 0.51}\n\n # Solve steady flame.\n # BC1: I have the attachment BC at r = 1, always\n # BC2: I need to set dF/dr = 0 at r = 0 iff Mark != 0\n [qMean, r, FMean] = steady_flame_area_FD3(varList['Mark'], varList['beta'], varList['axi'], varList['Nr'])\n r = r * varList['beta']\n\n # Calculate mean flame derivatives\n dFMeanDr = derivsnew.FD1_CT2_D(FMean, r[1] - r[0])\n d2FMeanDr2 = derivsnew.FD2_CT2_D(FMean, r[1] - r[0])\n\n #Apply BC smooth tip:\n if(varList['Mark']!=0.0):\n dFMeanDr[-1] = 0.0\n\n # Use correct number of points. Remember that the extrems need to be set depending on the BC!\n # The attach BC (first point) is always assumed to be true and removed from the vector list\n if(varList['Mark']==0):\n Nr = varList['Nr'] / 2\n dFMeanDr = dFMeanDr[1:]\n d2FMeanDr2 = d2FMeanDr2[1:]\n r = r[1:]\n # The smooth BC holds only if Mark!=0 (second derivatives appear): remove also the last point\n else:\n Nr = varList['Nr'] / 2 - 1\n dFMeanDr = dFMeanDr[1:-1]\n d2FMeanDr2 = d2FMeanDr2[1:-1]\n r = r[1:-1]\n\n # Calculate geometric values\n den = 1 + varList['beta'] * varList['beta'] * dFMeanDr * dFMeanDr\n dR = r[1] - r[0]\n # Set Nx equal to Nr for now.\n # The implementation is more complicated if they differ, and need to interpolate between values.\n Nx = Nr\n\n # Nonuniform grid spacing along x!\n # Nx = length(dx) has to hold.\n dx = np.empty(len(FMean) - 1)\n for ii in range(1, len(FMean)):\n dx[ii - 1] = FMean[ii] - FMean[ii - 1]\n\n [A, B, C, tau] = loadAcoustics(varList['xf'], varList['Tf'], varList['acModes'], varList['beta'])\n\n Matrix = buildMatrix(Nr, dR, varList['beta'], den, r, FMean, dFMeanDr, d2FMeanDr2, varList['Mark'], varList['acModes'], A,\n B, C, Nx, dx, tau, qMean, varList['convSpeed'])\n\n [d, W, V] = eigProblem.solveEigProb(Matrix)\n [dnew, Wnew, Vnew] = eigProblem.selectUnstable(d, W, V)\n\n print dnew / (2. * np.pi)","def set_solver_parameters(m, gamma, horizon, my_vars, timeout=30 * 60, pre_solve=-1):\n h = ext.get_set_time(horizon)\n beta = get_var(my_vars, 'beta')\n m.setObjective(quicksum((gamma ** t) * beta[0, t] for t in h), GRB.MAXIMIZE)\n\n m.setParam('TimeLimit', timeout)\n m.setParam('Threads', 8)\n m.setParam('Presolve', pre_solve)\n m.setParam('OutputFlag', 0)"],"string":"[\n \"def dmixed_vars(the_vars,tstep,coeffs):\\n\\n deltheta = theta_ft(the_vars[1],coeffs.ft_intercept,coeffs.ft_gamma) - the_vars[0]\\n F0 = coeffs.U*coeffs.Cd*(coeffs.sst - the_vars[0]) #surface heat flux\\n Fqv0 = coeffs.U*coeffs.Cd*(coeffs.qsfc - the_vars[2]) #surface vapor flux\\n Fint = -coeffs.k*F0 #entrainment heat flux\\n \\n if coeffs.use_NT: # use NT parameterization by calculating we using function\\n went = calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0) # Nicholls-Turton parameterization\\n \\n else: # use simple we parameterization\\n went = -Fint/deltheta #simple entrainment parameterization\\n \\n # calculate delta_Fr\\n delta_Frstar = 82.0 # Wm^-2\\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\\n delta_Fr = delta_Frstar - Frlambda*coeffs.ft_qv*1000 # convert qt_ft to g kg^-1\\n \\n Fqvent = -went*( coeffs.ft_qv - the_vars[2])\\n wsubs = -coeffs.D*the_vars[1]\\n rho=1.\\n cp=1004.\\n \\n derivs=np.empty_like(the_vars)\\n \\n # higher delta_Fr from drier air at mixed-layer top...hence cloudy air results in less radiative cooling\\n derivs[0]=(F0 - Fint)/(the_vars[1]*rho) - delta_Fr/1004./the_vars[1] \\n derivs[1] = went + wsubs\\n derivs[2] = (Fqv0 - Fqvent)/the_vars[1]\\n return derivs\",\n \"def update_model_parameters(phi, T, nz, coord, SWVD, form=\\\"Calonne\\\"):\\r\\n D_eff = np.ones(nz)\\r\\n\\r\\n if form == \\\"Hansen\\\": # Hansen and Foslien (2015)\\r\\n D_eff = phi * (1 - phi) * D0 + D0\\r\\n elif form == \\\"Calonne\\\": # Calonne et al. (2014)\\r\\n x = 2 / 3 - phi\\r\\n b = np.heaviside(x, 1)\\r\\n D_eff = D0 * (1 - 3 / 2 * phi) * b\\r\\n else:\\r\\n print(\\\"requested method not available, check input\\\")\\r\\n\\r\\n ## effective thermal conductivity W/m/K\\r\\n k_eff = np.ones(nz)\\r\\n\\r\\n if form == \\\"Hansen\\\": # Hansen and Foslien (2015)\\r\\n k_eff = phi * ((1 - phi) * k_a + phi * k_i) + k_a\\r\\n elif form == \\\"Calonne\\\": # Calonne et al. (2011)\\r\\n k_eff = ka0 + ka1 * (rho_i * phi) + ka2 * (rho_i * phi) ** 2\\r\\n else:\\r\\n print(\\\"requested method not available, check input\\\")\\r\\n\\r\\n ## effective heat capacity - similar forumla in Hansen and Foslien (2015) and Löwe et al. (2019)\\r\\n rhoC_eff = np.zeros(nz)\\r\\n rhoC_eff = phi * rho_i * C_i + (np.ones(nz) - phi) * rho_a * C_a\\r\\n\\r\\n ## Water Vapor density rho_v and its derivative rho_v_dT:\\r\\n [rho_v, rho_v_dT] = sat_vap_dens(nz, T, SWVD)\\r\\n\\r\\n return D_eff, k_eff, rhoC_eff, rho_v, rho_v_dT\",\n \"def solveFluNet(T,Ntime,a,b0,b1,g,k,w,y0,P,N,RHS=3):\\n #I have added the variables P the transport matrix \\n #and N the network size because they are needed\\n #in the RHS.\\n #I have added the variable RHS to be able to \\n #choose which RHS method we want to use when running\\n #solveFluNet\\n \\n #add input variables to RHS functions if needed\\n def RHSnet(y,t,a,b0,b1,g,k,w):\\n \\\"\\\"\\\"RHS used by odeint to solve Flu model\\\"\\\"\\\"\\n S = y[:N]\\n E = y[N:2*N]\\n C = y[2*N:3*N]\\n b = b0 + b1*(1+np.cos(2*np.pi*t))\\n dy = np.zeros(3*N)\\n dy[:N]= k*(1-S)-b*C*S+w*np.dot(P,S)-w*S\\n dy[N:2*N]= b*C*S-(k+a)*E+w*np.dot(P,E)-w*E\\n dy[2*N:3*N]= a*E-(g+k)*C+w*np.dot(P,C)-w*C\\n return dy\\n \\n def RHSnetF(y,t,a,b0,b1,g,k,w):\\n \\\"\\\"\\\"RHS used by odeint to solve Flu model\\\"\\n Calculations carried out by fn.rhs\\n \\\"\\\"\\\"\\n dy = fn.rhs(P,y,t,a,b0,b1,g,k,w)\\n return dy\\n \\n def RHSnetFomp(y,t,a,b0,b1,g,k,w):\\n \\\"\\\"\\\"RHS used by odeint to solve Flu model\\n Calculations carried out by fn.rhs_omp\\n \\\"\\\"\\\"\\n dy = fn.rhs_omp(P,y,t,a,b0,b1,g,k,w,2)\\n return dy\\n\\n #Add code here and to RHS functions above to simulate network flu model\\n t = np.linspace(0,T,Ntime)\\n if (RHS==1):\\n sol = odeint(RHSnet,y0,t,args=(a,b0,b1,g,k,w))\\n if (RHS==2):\\n sol = odeint(RHSnetF,y0,t,args=(a,b0,b1,g,k,w))\\n if (RHS==3):\\n sol = odeint(RHSnetFomp,y0,t,args=(a,b0,b1,g,k,w))\\n S = sol[:,:N]\\n E = sol[:,N:2*N]\\n C = sol[:,2*N:3*N]\\n return t,S,E,C\",\n \"def taylor_expansion(self,g_temp,g_step,var):\\n A=np.zeros([self.n+1,self.n])\\n for i in range(self.n):\\n A[self.n][i]=1\\n for j in range(self.n):\\n if(i==j): A[i][j]=2.*var[i]+2.+g_temp*np.sum([self.XXZ.Z(k,i) for k in range(self.n) if k!=i])\\n else: A[i][j]=-g_temp*self.XXZ.Z(j,i)\\n #First derivative\\n B1=np.zeros(self.n+1)\\n for i in range(self.n): \\n B1[i]=self.gamma*2.*g_temp*self.N*(self.n-self.N)+np.sum([self.XXZ.Z(k,i)*(var[k]-var[i]) for k in range(self.n) if k!=i])\\n Ainv=np.linalg.pinv(A)\\n der1=np.dot(Ainv,B1)\\n #Second derivative\\n B2=np.zeros(self.n+1)\\n for k in range(self.n):\\n B2[k]=self.gamma*2.*self.N*(self.n-self.N) -2.*der1[k]**2+2.*np.sum([self.XXZ.Z(l,k)*(der1[l]-der1[k]) for l in range(self.n) if k!=l])\\n der2=np.dot(Ainv,B2)\\n #Third derivative\\n B3=np.zeros(self.n+1)\\n for k in range(self.n):\\n B3[k]=-6*der1[k]*der2[k]+3.*np.sum([self.XXZ.Z(l,k)*(der2[l]-der2[k]) for l in range(self.n) if k!=l])\\n der3=np.dot(Ainv,B3)\\n #Fourth derivative\\n B4=np.zeros(self.n+1)\\n for k in range(self.n):\\n B4[k]=-8.*der3[k]*der1[k]-6.*der2[k]*der2[k]+4.*np.sum([self.XXZ.Z(l,k)*(der3[l]-der3[k]) for l in range(self.n) if k!=l])\\n der4=np.dot(Ainv,B4)\\n \\n return var+g_step*der1+g_step**2*der2/2.+g_step**3*der3/6.+g_step**4*der4/24.\",\n \"def _derivatives(self, state, forces_moments):\\n # extract the states\\n pn = state[0]\\n pe = state[1]\\n pd = state[2]\\n e0 = state[3]\\n e1 = state[4]\\n e2 = state[5]\\n e3 = state[6]\\n u = state[7]\\n v = state[8]\\n w = state[9]\\n # state[6:10] = normalize(state[6:10])\\n p = state[10]\\n q = state[11]\\n r = state[12]\\n # extract forces/moments\\n fx = forces_moments[0]\\n fy = forces_moments[1]\\n fz = forces_moments[2]\\n l = forces_moments[3]\\n m = forces_moments[4]\\n n = forces_moments[5]\\n\\n\\n # with warnings.catch_warnings():\\n # warnings.filterwarnings('error')\\n # try:\\n # # position kinematics\\n # except Warning as e:\\n # pdb.set_trace()\\n # print(e)\\n\\n pn_dot = (e1**2+e0**2-e2**2-e3**2)*u + 2*(e1*e2-e3*e0)*v + 2*(e1*e3+e2*e0)*w\\n pe_dot = 2*(e1*e2+e3*e0)*u + (e2**2+e0**2-e1**2-e3**2)*v + 2*(e2*e3-e1*e0)*w\\n pd_dot = 2*(e1*e3-e2*e0)*u + 2*(e2*e3+e1*e0)*v + (e3**2+e0**2-e1**2-e2**2)*w\\n\\n # pn_dot = (e0**2+e1**2-e2**2-e3**2)*u + 2*(e1*e2+e3*e0)*v + 2*(e1*e3-e2*e0)*w\\n # pe_dot = 2*(e1*e2-e3*e0)*u + (e0**2-e1**2+e2**2-e3**2)*v + 2*(e2*e3+e1*e0)*w\\n # pd_dot = 2*(e1*e3+e2*e0)*u + 2*(e2*e3-e1*e0)*v + (e0**2-e1**2-e2**2+e3**2)*w\\n\\n # pdb.set_trace()\\n\\n # position dynamics\\n mass = self.mass\\n u_dot = (r*v-q*w)+fx/mass\\n v_dot = (p*w-r*u)+fy/mass\\n w_dot = (q*u-p*v)+fz/mass\\n\\n # rotational kinematics\\n e0_dot = 0.5*(-p*e1-q*e2-r*e3)\\n e1_dot = 0.5*(p*e0+r*e2-q*e3)\\n e2_dot = 0.5*(q*e0-r*e1+p*e3)\\n e3_dot = 0.5*(r*e0+q*e1-p*e2)\\n\\n # rotatonal dynamics\\n p_dot = self.gamma1*p*q - self.gamma2*q*r + self.gamma3*l + self.gamma4*n\\n q_dot = self.gamma5*p*r - self.gamma6*(p**2-r**2) + m/self.Jy\\n r_dot = self.gamma7*p*q - self.gamma1*q*r + self.gamma4*l + self.gamma8*n\\n\\n # collect the derivative of the states\\n x_dot = np.array([pn_dot, pe_dot, pd_dot, e0_dot, e1_dot, e2_dot, e3_dot,\\n u_dot, v_dot, w_dot, p_dot, q_dot, r_dot])\\n # pdb.set_trace()\\n\\n\\n # print(x_dot)\\n return x_dot\",\n \"def niv_variable_selection(x, y, t, max_vars):\\n y1_t = (y == 1) & (t == 1)\\n y0_t = (y == 0) & (t == 1)\\n y1_c = (y == 1) & (t == 0)\\n y0_c = (y == 0) & (t == 0)\\n\\n sum_y1_t = sum(y1_t)\\n sum_y0_t = sum(y0_t)\\n sum_y1_c = sum(y1_c)\\n sum_y0_c = sum(y0_c)\\n\\n niv_dict = {}\\n for col in x.columns:\\n df = pd.concat([x[col].rename(col), y1_t.rename('y1_t'), y0_t.rename('y0_t'),\\n y1_c.rename('y1_c'), y0_c.rename('y0_c')], axis=1)\\n x_group = df.groupby(x[col])\\n x_sum = x_group.sum()\\n\\n if sum_y0_t == 0 or sum_y1_t == 0:\\n woe_t = 0\\n else:\\n woe_t = x_sum.apply(lambda r: np.log((r['y1_t'] * sum_y0_t) / (r['y0_t'] * sum_y1_t))\\n if r['y1_t'] > 0 and r['y0_t'] > 0 else 0, axis=1)\\n\\n if sum_y0_c == 0 or sum_y1_c == 0:\\n woe_c = 0\\n else:\\n woe_c = x_sum.apply(lambda r: np.log((r['y1_c'] * sum_y0_c) / (r['y0_c'] * sum_y1_c))\\n if r['y1_c'] > 0 and r['y0_c'] > 0 else 0, axis=1)\\n\\n nwoe = woe_t - woe_c\\n\\n p_x_y1_t = x_sum['y1_t'] / sum_y1_t if sum_y1_t > 0 else 0\\n p_x_y0_t = x_sum['y0_t'] / sum_y0_t if sum_y0_t > 0 else 0\\n p_x_y1_c = x_sum['y1_c'] / sum_y1_c if sum_y1_c > 0 else 0\\n p_x_y0_c = x_sum['y0_c'] / sum_y0_c if sum_y0_c > 0 else 0\\n niv_weight = (p_x_y1_t * p_x_y0_c - p_x_y0_t * p_x_y1_c)\\n\\n niv_row = 100 * nwoe * niv_weight\\n niv = niv_row.sum()\\n niv_dict[col] = niv\\n\\n s_niv = pd.Series(niv_dict)\\n s_selected_niv = s_niv.sort_values(ascending=False)[: max_vars]\\n\\n return s_selected_niv.index\",\n \"def undetermined_coefficients(gensols: List[Symbol], func_coeffs: List[Symbol], gt: Symbol, t: Symbol = t) -> Tuple[Symbol, Procedure]:\\n\\n Y = Function('Y', real=True)(t)\\n\\n coeffs = numbered_symbols('A', cls=Dummy)\\n coefflist = []\\n\\n trialset = _undetermined_coefficients_match(gt, t)['trialset']\\n\\n notneedset = set()\\n\\n mult = 0\\n for i, sol in enumerate(gensols):\\n check = sol\\n if check in trialset:\\n # If an element of the trial function is already part of the\\n # homogeneous solution, we need to multiply by sufficient x to\\n # make it linearly independent. We also don't need to bother\\n # checking for the coefficients on those elements, since we\\n # already know it will be 0.\\n while True:\\n if check*t**mult in trialset:\\n mult += 1\\n else:\\n break\\n trialset.add(check*t**mult)\\n notneedset.add(check)\\n\\n newtrialset = trialset - notneedset\\n\\n # while True:\\n # dependent = False\\n # for trial in newtrialset:\\n # if trial in gensols:\\n # dependent = True\\n # break\\n # if not dependent:\\n # break\\n # newtrialset = set([t*trial for trial in trialset])\\n\\n # trialset = trialset.union(newtrialset)\\n\\n trialfunc = sympy.Number(0)\\n for i in newtrialset:\\n c = next(coeffs)\\n coefflist.append(c)\\n trialfunc += c*i\\n\\n derivatives = []\\n\\n eqs = 0\\n for order, coeff in enumerate(func_coeffs[::-1]):\\n deriv = simplify(trialfunc.diff(t, order))\\n derivatives.append(\\n Eq(Derivative(Y, t, order), deriv, evaluate=False))\\n eqs += coeff * deriv\\n\\n coeffsdict = dict(list(zip(trialset, [0]*(len(trialset) + 1))))\\n\\n eqs_lhs = eqs\\n\\n eqs = _mexpand(simplify(eqs - gt).expand())\\n\\n for i in Add.make_args(eqs):\\n s = separatevars(i, dict=True, symbols=[t])\\n coeffsdict[s[t]] += s['coeff']\\n\\n coeffvals = solve(list(coeffsdict.values()), coefflist)\\n\\n if not coeffvals:\\n print(\\n \\\"Could not solve `%s` using the \\\"\\n \\\"method of undetermined coefficients \\\"\\n \\\"(unable to solve for coefficients).\\\" % eqs)\\n\\n psol = trialfunc.subs(coeffvals)\\n\\n procedure = Procedure()\\n procedure\\\\\\n .text('Find ').latex('Y(t)').text(' that mimics the form of ').latex('g(t)', nl=True)\\\\\\n .eq(Eq(Y, trialfunc, evaluate=False))\\\\\\n .text('Compute successive derivatives of ').latex('Y(t)', nl=True)\\\\\\n .equlist(derivatives)\\\\\\n .text('Plug the derivatives into the LHS and equate coefficients', nl=True)\\\\\\n .equlist([Eq(eqs_lhs, gt, evaluate=False),\\n Eq(simplify(eqs_lhs).expand().collect(t), gt, evaluate=False)])\\\\\\n .equarr([Eq(a, 0, evaluate=False) for a in coeffsdict.values()])\\\\\\n .text('Solve for the undetermined coefficients', nl=True)\\\\\\n .equarr([Eq(k, v, evaluate=False)\\n for k, v in coeffvals.items() if k != 0] if len(coeffvals) > 0 else [])\\\\\\n .text('Substitute the coefficients to get the particular solution', nl=True)\\\\\\n .eq(Eq(Dummy('y_p'), psol, evaluate=False))\\n\\n return psol, procedure\",\n \"def _redef_via_predef_eqn(self):\\r\\n time = self.current_T # + self.d_T\\r\\n\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff) \\r\\n self.Epsilon = self.d_T * self.thermal_conductivity / \\\\\\r\\n (self.density * self.heat_capacity)\\r\\n\\r\\n # Source term.\\r\\n def F_func(elem, eta):\\r\\n x = elem.local_to_global(eta)\\r\\n F = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F -= self.Epsilon * self.redef_F_laplacian(x[0], x[1], time)\\r\\n F += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func,\\r\\n self.node_map,\\r\\n gauss_mult=2) # Use double gp_1D\\r\\n\\r\\n # Boundary term.\\r\\n def f_func(elem, eta):\\r\\n n = elem.guess_normal_vector_global(eta)\\r\\n f = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n x = elem.local_to_global(eta)\\r\\n # Evaluate our boundary term.\\r\\n f += self.Beta * self.redef_f_norm_grad(x[0], x[1], time, n)\\r\\n f += self.redef_dTdt(x[0], x[1], time) * self.d_T\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func,\\r\\n self.node_map,\\r\\n gauss_mult=2)\",\n \"def variational_distribution(self):\\n activation = tf.nn.relu\\n if self.linear:\\n activation = None\\n\\n #q(z | x, s)\\n if self.log_variational:\\n x = tf.log(1 + self.expression)\\n else:\\n x = self.expression\\n\\n h = dense(x, self.n_hidden, activation=activation, \\\\\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\\n for layer in range(2, self.n_layers + 1):\\n h = dense(h, self.n_hidden, activation=activation, \\\\\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\\n\\n \\n self.qz_m = dense(h, self.n_latent, activation=None, \\\\\\n bn=False, keep_prob=None, phase=self.training_phase)\\n self.qz_v = dense(h, self.n_latent, activation=tf.exp, \\\\\\n bn=False, keep_prob=None, phase=self.training_phase)\\n \\n if self.scalings:\\n # q(l | x, s)\\n h = dense(x, self.n_hidden, activation=activation, \\\\\\n bn=True, keep_prob=self.dropout_rate, phase=self.training_phase)\\n self.ql_m = dense(h, 1, activation=None, \\\\\\n bn=False, keep_prob=None, phase=self.training_phase)\\n self.ql_v = dense(h, 1, activation=tf.exp, \\\\\\n bn=False, keep_prob=None, phase=self.training_phase)\",\n \"def fluid_deriv(self):\\n deriv = np.zeros((self.fluid_constraints['num_eq'],\\n 2 * self.num_i + self.num_vars,\\n self.num_nw_vars))\\n for i in range(self.num_i):\\n for j in range(self.num_nw_fluids):\\n deriv[i * self.num_nw_fluids + j, i, j + 3] = 1\\n deriv[i * self.num_nw_fluids + j, self.num_i + i, j + 3] = -1\\n return deriv\",\n \"def variable_costs(dh: DataHandler):\\n print(\\\"PtHydrogen not implemented\\\")\\n\\n scen_hor_map = dh.scenarios.horizon\\n\\n cost_var = dh.get(\\\"i_cost\\\").xs(\\\"varcost\\\", level=\\\"par_cost\\\")\\n cost_var = cost_var.groupby([\\\"alltec\\\"]).apply(\\n extract_horizon_specific_cost, scen_hor_map\\n )\\n cost_var = add_dimension(cost_var, dh.merge_stored_sets(\\\"r\\\"), \\\"r\\\")\\n cost_var = cost_var.reorder_levels([\\\"alltec\\\", \\\"r\\\"])\\n\\n h2_price = dh.get(\\\"o_h2price_buy\\\")\\n h2_price = add_dimension(h2_price, dh.merge_stored_sets(\\\"tec_h2g\\\"), \\\"alltec\\\")\\n\\n elec_price = dh.get(\\\"o_prices\\\")\\n\\n cost_fuel = dh.get(\\\"cost_fuel\\\")\\n cost_fuel = add_dimension(cost_fuel, dh.merge_stored_sets(\\\"r\\\"), \\\"r\\\")\\n cost_fuel = cost_fuel.reorder_levels([\\\"alltec\\\", \\\"r\\\"])\\n\\n cost_fuel.loc[h2_price.index, :] = h2_price\\n\\n eff = dh.get(\\\"eff\\\")\\n\\n co2_int = dh.get(\\\"co2_int\\\").div(1000)\\n\\n co2_price = dh.get(\\\"o_co2price\\\")\\n\\n co2_costs = co2_int * co2_price\\n co2_costs.index.names = [\\\"alltec\\\", \\\"r\\\"]\\n\\n var_cost = (\\n cost_fuel.add(co2_costs, fill_value=0).div(eff).add(cost_var, fill_value=0)\\n )\\n\\n return var_cost\",\n \"def taylor_exp_3(y0, t, f, jac, hess, df_dt=None, d2f_dt2=None, d2f_dtdu=None, verbose=True, krylov_subspace_dim=None,\\n **_):\\n try:\\n n, d = len(t), len(y0)\\n y = np.zeros((n, d))\\n except TypeError:\\n n, d = len(t), 1\\n y = np.zeros((n,))\\n if verbose is False:\\n count = Counter('', 0)\\n elif verbose is True:\\n count = Counter('Taylor Exp 3', n)\\n else:\\n count = Counter(verbose, n)\\n if df_dt is None:\\n def df_dt(*_): return np.zeros((d,))\\n if d2f_dt2 is None:\\n def d2f_dt2(*_): return np.zeros((d,))\\n if d2f_dtdu is None:\\n def d2f_dtdu(*_): return np.zeros((d, d))\\n y[0] = y0\\n j = jac(y[0], t[0])\\n w = np.zeros((d, 3))\\n expanded_vector = np.zeros((d + 3,))\\n expanded_vector[-1] = 1\\n expanded_matrix = np.zeros((d + 3, d + 3))\\n expanded_matrix[-3:-1, -2:] = np.eye(2)\\n expanded_matrix[:d, :d] = j\\n for i in range(n - 1):\\n h = t[i + 1] - t[i]\\n w[:, -1] = f(y[i], t[i]) - np.dot(j, y[i])\\n w[:, -2] = np.dot(jac(y[i], t[i]) - j, f(y[i], t[i])) + df_dt(y[i], t[i])\\n w[:, -3] = np.dot(np.dot(hess(y[i], t[i]), f(y[i], t[i])), f(y[i], t[i])) \\\\\\n + np.dot(jac(y[i], t[i]) - j, np.dot(jac(y[i], t[i]), y[i])) \\\\\\n + np.dot(jac(y[i], t[i]) - j, df_dt(y[i], t[i])) \\\\\\n + 2 * np.dot(d2f_dtdu(y[i], t[i]), f(y[i], t[i])) \\\\\\n + d2f_dt2(y[i], t[i])\\n expanded_vector[:d] = y[i]\\n expanded_matrix[:d, -3:] = w\\n if krylov_subspace_dim is None:\\n y[i + 1] = np.dot(expm_sp(h * expanded_matrix), expanded_vector)[:d]\\n else:\\n y[i + 1] = expm_krylov(h * expanded_matrix, expanded_vector, krylov_subspace_dim)[:d]\\n count(i + 1)\\n return y\",\n \"def f1d(t,y,float_params,sigmaI): #sigmastep is an array\\n \\n ## y is Ntot0 ##\\n\\n # unpack parameters\\n Nbar, Nstar, sigma0, nu_kin_mlyperus, DoverdeltaX2 = float_params \\n\\n # Ntot is passed in, Fqll calculated from Ntot\\n Ntot0 = np.ascontiguousarray(y)\\n Nqll0 = Nbar - Nstar * np.sin(2*np.pi*(Ntot0))\\n\\n # Calc surface deposition, dNtot_dt before diffusion\\n m = (Nqll0 - (Nbar - Nstar))/(2*Nstar)\\n sigmaM = (sigmaI - m * sigma0)/(1+m*sigma0)\\n depsurf = nu_kin_mlyperus * sigmaM\\n dNtot_dt = depsurf\\n\\n # Diffusion\\n dy = diffuse_1d(Nqll0,DoverdeltaX2)\\n dNtot_dt += dy \\n\\n # Package for output, only values of dNtot\\n derivs = dNtot_dt\\n return derivs\",\n \"def variation_of_parameters(y: List[Symbol], gt: Symbol, t: Symbol = t, do_integral=True) -> Tuple[Symbol, Procedure]:\\n W, w = Wronskian(y, t)\\n goW = simplify(gt / W)\\n\\n yp = 0\\n\\n Wdets = []\\n integrals = []\\n\\n col = [0] * len(y)\\n col[-1] = 1\\n for i in range(len(y)):\\n Wi = w.copy()\\n Wi[:, i] = col.copy()\\n\\n # reduce cos^2 t + sin^2 t to 1\\n Wi_det = trigsimp(simplify(Wi.det()), deep=True, recursive=True)\\n\\n integrand = (Wi_det * goW).expand()\\n integral = integrate(\\n integrand, t) if do_integral else Integral(integrand, t)\\n yp += y[i] * integral\\n\\n if do_integral:\\n integrals.append(\\n Eq(Dummy('mu_{}'.format(i + 1)),\\n Eq(Integral(integrand, t), integral, evaluate=False), evaluate=False)\\n )\\n else:\\n integrals.append(Eq(Dummy('mu_{}'.format(i)),\\n Integral(integrand, t), evaluate=False))\\n\\n Wdets.append(\\n Eq(Symbol('W{}'.format(i+1)), Eq(Determinant(Wi), Wi_det, evaluate=False), evaluate=False))\\n\\n yps = logcombine(simplify(yp))\\n\\n procedure = Procedure()\\n procedure\\\\\\n .text('Compute the Wronskian determinant', nl=True)\\\\\\n .eq(Eq(Dummy('W'), Eq(Determinant(w), W, evaluate=False), evaluate=False))\\\\\\n .text('Compute ').latex('W_i', nl=True)\\\\\\n .equlist(Wdets)\\\\\\n .text('Calculate and simplify ').latex('\\\\\\\\frac{g(t)}{W(t)}', nl=True)\\\\\\n .eq(Eq(sympy.Mul(gt, sympy.Pow(W, -1, evaluate=False), evaluate=False), goW, evaluate=False))\\\\\\n .text('Compute ').latex('\\\\\\\\mu_i = \\\\\\\\int \\\\\\\\frac{g(t)W_i(t)}{W(t)} dt', nl=True)\\\\\\n .equlist(integrals)\\\\\\n .text('Compute the sum ').latex('\\\\\\\\sum_{i=1}^{k} y_i \\\\\\\\int \\\\\\\\frac{g(t)W_i(t)}{W(t)} dt', nl=True)\\\\\\n .equlist([\\n Eq(Dummy('y_p'), yp, evaluate=False),\\n Eq(Dummy('y_p'), yps, evaluate=False)\\n ])\\\\\\n .text('Complementray + particular = general', nl=True)\\\\\\n .eq(Eq(Dummy('y'), to_general(y, yps)[0], evaluate=False))\\n\\n return yps, procedure\",\n \"def hexapodZernikeMultiLinearModel_hexapodcoordinate():\\n Tfile='/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_training.cp'\\n Vfile = '/home/jghao/research/decamFocus/psf_withseeing/finerGrid_coeff_matrix/zernike_coeff_finerGrid_validate.cp'\\n b=p.load(open(Tfile))\\n vb=p.load(open(Vfile))\\n nobs = len(b)\\n x = b[:,0]\\n y = b[:,1]\\n z = b[:,2]\\n theta = b[:,3]\\n phi = b[:,4]\\n fwhm = b[:,5]\\n e1 = b[:,6]\\n e2 = b[:,7]\\n thetax = theta*np.cos(np.deg2rad(phi))\\n thetay = theta*np.sin(np.deg2rad(phi))\\n xh = x*1000 # convert to hexapod coordinate\\n yh = -y*1000\\n zh = -z*1000\\n xtilth = - thetay\\n ytilth = - thetax\\n dataX = b[:,8:68]\\n coeff_xh = sm.WLS(xh,dataX).fit().params\\n coeff_yh = sm.WLS(yh,dataX).fit().params\\n coeff_zh = sm.WLS(zh,dataX).fit().params\\n coeff_xtilth = sm.WLS(xtilth,dataX).fit().params\\n coeff_ytilth = sm.WLS(ytilth,dataX).fit().params\\n coeff = np.array([coeff_xh,coeff_yh,coeff_zh,coeff_xtilth,coeff_ytilth])\\n vx = vb[:,0]\\n vy = vb[:,1]\\n vz = vb[:,2]\\n vtheta = vb[:,3]\\n vphi = vb[:,4]\\n vfwhm = vb[:,5]\\n ve1 = vb[:,6]\\n ve2 = vb[:,7]\\n vthetax = vtheta*np.cos(np.deg2rad(vphi))\\n vthetay = vtheta*np.sin(np.deg2rad(vphi))\\n vxh = vx*1000 # convert to hexapod coordinate\\n vyh = -vy*1000\\n vzh = -vz*1000\\n vxtilth = - vthetay\\n vytilth = - vthetax\\n vdataX = vb[:,8:68]\\n fit = np.dot(vdataX,coeff.T)\\n bp.bin_scatter(vxh,fit[:,0],nbins=20,fmt='bo',scatter=True)\\n bp.bin_scatter(vyh,fit[:,1],nbins=20,fmt='bo',scatter=True)\\n bp.bin_scatter(vzh,fit[:,2],nbins=20,fmt='bo',scatter=True)\\n bp.bin_scatter(vxtilth,fit[:,3],nbins=20,fmt='bo',scatter=True)\\n bp.bin_scatter(vytilth,fit[:,4],nbins=20,fmt='bo',scatter=True)\",\n \"def fwd_model(Ti_samples,To_samples, dw_samples, kw_samples,hi_samples,ho_samples,TA_samples):\\n\\t#Determine number of samples (totquat)\\n\\ttotquat=len(Ti_samples)\\n\\t# List to store values of Q (assuming no radiative heat transfer) calculated from\\n\\t# the random samples of the parameters\\n\\tQ_samples_4PCE=[]\\n\\t# List to store values of Q assuming radiative heat transfer occurs\\n\\t#Q_r_samples_4PCE=[]\\n\\t# Calculate values of heat flux Q (assuming no radiative heat transfer)\\n\\t# for the different sample values and append to the list\\n\\tfor i in range(totquat):\\n\\t\\t(Q,T1,T2)=compute_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i],\\\\\\n\\t\\t\\tkw_samples[i], hi_samples[i], ho_samples[i])\\n\\t\\tQ_samples_4PCE.append(Q)\\n\\t\\t# Calculate values of heat flux Q assuming radiative heat transfer to atmosphere and append to list\\n\\t\\t# For the required estimates of Q,T1, and T2 needed to solve the nonlinear system,\\n\\t\\t# we use the values obtained by solving the system assuming no radiative heat transfer\\n\\t\\t\\\"\\\"\\\"Q2=r_heat_flux(Ti_samples[i], To_samples[i], dw_samples[i], kw_samples[i],\\\\\\n\\t\\t\\thi_samples[i], ho_samples[i], TA_samples[i], (Q,T1,T2))\\n\\t\\tQ_r_samples_4PCE.append(Q2)\\n\\t# Convert Q_r_samples_4PCE to numpy array\\n\\tQ_evals = np.array(Q_r_samples_4PCE)\\n\\treturn Q_evals\\\"\\\"\\\"\\n\\t\\tConvert Q_samples_4PCE to numpy array\\n\\t\\tQ_evals = np.array(Q_samples_4PCE)\\n\\t\\treturn Q_evals\\\"\\\"\\\"\\n\\n\\ndef KDE(fcn_evals):\\n\\t\\\"\\\"\\\"\\n\\tPerforms kernel density estimation\\n\\tInput:\\n\\t\\tfcn_evals: numpy array of evaluations of the forward model (values of heat flux Q)\\n\\tOutput:\\n\\t\\txpts_pce: numpy array of points at which the PDF is estimated.\\n\\t\\tPDF_data_pce: numpy array of estimated PDF values.\\n\\t\\\"\\\"\\\"\\n\\t# Perform KDE on fcn_evals\\n\\tkern_pce=stats.kde.gaussian_kde(fcn_evals)\\n\\t# Generate points at which to evaluate the PDF\\n\\txpts_pce=np.linspace(fcn_evals.min(),fcn_evals.max(),200)\\n\\t# Evaluate the estimated PDF at these points\\n\\tPDF_data_pce=kern_pce(xpts_pce)\\n\\treturn xpts_pce, PDF_data_pce\",\n \"def run_main(sst, ft_qv, use_NT):\\n\\n dtout=10. #minutes\\n end_time=8*24. #hours\\n del_time=dtout*60. #seconds\\n end_time=end_time*3600. #seconds\\n #sst=297\\n D=5.e-6 #s-1\\n U=7 #m/s\\n psfc=100. #kPa\\n qsfc=tf.qs_tp(sst,psfc)\\n ft_intercept = 292 #K\\n ft_gamma = 6.e-3 #K/m\\n #ft_qv = 2.e-3\\n k=0.2 #entrainment efficiency\\n Cd = 1.e-3 #drag coefficient\\n tspan = np.arange(0.,end_time,del_time)\\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\\n the_tup=make_tuple(the_tup,'coeffs')\\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\\n\\n # save time/computation by only doing calculations for the last timestep (equilibrium)\\n result['time']=tspan[-1]/3600./24. #days\\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\\n \\n # decide how to calculate entrainment\\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\\n if use_NT:\\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \\n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\\n else:\\n result['went']=calc_went(result.iloc[-1],the_tup)\\n\\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\\n\\n with open('dumpmodel.csv','w') as f:\\n result.to_csv(f,index=False)\\n \\n return None\",\n \"def doParametersOfInterest(self):\\n \\n self.modelBuilder.doVar('expr::cosW(\\\"0.87681811112\\\",)')\\n self.modelBuilder.doVar('expr::sinW(\\\"0.48082221247\\\",)')\\n self.modelBuilder.doVar('expr::mZ(\\\"91.2\\\",)')\\n self.modelBuilder.doVar('expr::Lambda1(\\\"100.0\\\",)')\\n self.modelBuilder.doVar('expr::e2(\\\"0.0917\\\",)')\\n self.modelBuilder.doVar('expr::gs2(\\\"1.533\\\",)')\\n\\n # EFT Higgs basis couplings\\n\\n self.modelBuilder.doVar('cZ[0,-1,1]') \\n self.modelBuilder.doVar(\\\"cZZ[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZZt[0,-2,2]\\\") \\n self.modelBuilder.doVar(\\\"cZB[0,-6,6]\\\") \\n\\n poi='cZ,cZZ,cZZt,cZB'\\n\\n # Amplitude couplings from EFT couplings \\n\\n self.modelBuilder.doVar('expr::a1(\\\"@0+1\\\",cZ)') # (\\\"2*(@0+1)\\\",cZ) in AN/Paper but a1 = 1 for signal model and width calculation\\n self.modelBuilder.doVar('expr::a2(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZ,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::a3(\\\"-1*@0*(@1/(2*pow(@2,2)*pow(@3,2)))\\\",cZZt,e2,sinW,cosW)')\\n self.modelBuilder.doVar('expr::k1(\\\"@0*(@1*pow(@2,2)/(pow(@3,2)*pow(@4,2)))\\\",cZB,e2,Lambda1,sinW,mZ)')\\n self.modelBuilder.doVar('expr::k1L1(\\\"@0/pow(@1,2)\\\",k1,Lambda1)')\\n\\n ###### gamma_H ########\\n\\n # SMEFT relationships for VV couplings (Expressed using amplitude couplings)\\n\\n self.modelBuilder.doVar('expr::kappa(\\\"1.0\\\",)')\\n self.modelBuilder.doVar('expr::kappa_tilde(\\\"0.0\\\",)') \\n\\n self.modelBuilder.doVar('expr::a1_WW(\\\"@0\\\",a1)')\\n self.modelBuilder.doVar('expr::a2_WW(\\\"@0*@0*@1\\\",cosW,a2)')\\n self.modelBuilder.doVar('expr::a3_WW(\\\"@0*@0*@1\\\",cosW,a3)')\\n self.modelBuilder.doVar('expr::k1_WW(\\\"(@2 / (@0*@0 - @1*@1) - 2*@1*@1*@3*@4*@4 /(@5*@5*(@0*@0 - @1*@1)))\\\",cosW,sinW,k1,a2,Lambda1,mZ)')\\n self.modelBuilder.doVar('expr::k2_k1(\\\"2*@0*@1*@2/(@0*@0 - @1*@1)\\\",cosW,sinW,k1)')\\n self.modelBuilder.doVar('expr::k2_a2(\\\"-2*@0*@1*@3*@4*@4/((@2*@2)*(@0*@0 - @1*@1))\\\",cosW,sinW,mZ,a2,Lambda1)')\\n self.modelBuilder.doVar('expr::k2(\\\"@0 + @1\\\",k2_k1,k2_a2)')\\n\\n # Determine gamma_H from VV couplings\\n\\n zz_expr = '\\\"4*(@0*@0/4. + 0.1695*@3*@3 + 0.09076*@1*@1 + 0.03809*@2*@2 + 0.8095*@0*@3/2. + 0.5046*@0*@1/2. + 0.2092*@1*@3 + 0.1023*@4*@4 + 0.1901*@0*@4/2. + 0.07429*@3*@4 + 0.04710*@1*@4) \\\",a1,a2,a3,k1,k2'\\n ww_expr = '\\\"4*(@0*@0/4. + 0.1320*@3*@3 + 0.1944*@1*@1 + 0.08075*@2*@2 + 0.7204*@0*@3/2. + 0.7437*@0*@1/2. + 0.2774*@3*@1) \\\",a1_WW,a2_WW,a3_WW,k1_WW'\\n zgamma_expr = '\\\"4*(1.118600*@0*@0/4. +0.0035*@1*@1 - 0.125010*@0*@1/2. + 0.000003*@1*@1 - 0.00018*@1*@1 + 0.003100*@0*@1/2. +0.00126*@2*@2 + 0.000005*@2*@2 -0.00047*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n gg_expr = '\\\"(1.1068*@0*@0 + 0.0082*@0*@0 - 0.1150*@0*@0 + 2.5717*@1*@1 + 0.0091*@1*@1 - 0.1982*@1*@1)\\\",kappa,kappa_tilde'\\n bb_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n cc_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n tautau_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n mumu_expr = '\\\"(@0*@0 + @1*@1)\\\",kappa,kappa_tilde'\\n gmgm_expr = '\\\"4*(1.6054*@0*@0/4. + 0.07312*@1*@1 - 0.6854*@0*@1/2. + 0.00002*@1*@1 - 0.0018*@1*@1 + 0.0085*@0*@1/2. + 0.1699*@2*@2 + 0.00002*@2*@2 - 0.0031*@2*@2)\\\",a1_WW,kappa,kappa_tilde'\\n \\n self.modelBuilder.doVar('expr::R_WW('+str(ww_expr)+')')\\n self.modelBuilder.doVar('expr::R_ZZ('+str(zz_expr)+')')\\n self.modelBuilder.doVar('expr::R_Zgamma('+str(zgamma_expr)+')')\\n self.modelBuilder.doVar('expr::R_gg('+str(gg_expr)+')')\\n self.modelBuilder.doVar('expr::R_bb('+str(bb_expr)+')')\\n self.modelBuilder.doVar('expr::R_cc('+str(cc_expr)+')')\\n self.modelBuilder.doVar('expr::R_tautau('+str(tautau_expr)+')')\\n self.modelBuilder.doVar('expr::R_mumu('+str(mumu_expr)+')')\\n self.modelBuilder.doVar('expr:R_gammagamma('+str(gmgm_expr)+')')\\n\\n self.modelBuilder.doVar('expr::gammaH(\\\"(0.5824*@0 + 0.2137*@1 + 0.08187*@2 + 0.06272*@3 + 0.02891*@4 + 0.02619*@5 + 0.002270*@6 + 0.001533*@7 + 0.0002176*@8 )/0.9998\\\",R_bb,R_WW,R_gg,R_tautau,R_cc,R_ZZ,R_gammagamma,R_Zgamma,R_mumu)') \\n\\n ###########################\\n\\n self.g1V = GetCoupTerms(1,1,1,-0.0001,\\\"1V\\\") # Compensate for scaling of k1 templates \\n self.g2V = GetCoupTerms(1,1,1,-0.0001,\\\"2V\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g2V_1(\\\\\\\"\\\"+str(self.g2V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1(\\\\\\\"((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T1_Neg(\\\\\\\"-1*((pow(@0,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_2(\\\\\\\"\\\"+str(self.g2V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2(\\\\\\\"((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T2_Neg(\\\\\\\"-1*((pow(@0,3)*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_3(\\\\\\\"\\\"+str(self.g2V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3(\\\\\\\"((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T3_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_4(\\\\\\\"\\\"+str(self.g2V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4(\\\\\\\"((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T4_Neg(\\\\\\\"-1*((@0*pow(@1,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_5(\\\\\\\"\\\"+str(self.g2V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5(\\\\\\\"((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T5_Neg(\\\\\\\"-1*((pow(@1,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_6(\\\\\\\"\\\"+str(self.g2V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6(\\\\\\\"((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T6_Neg(\\\\\\\"-1*((pow(@0,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_7(\\\\\\\"\\\"+str(self.g2V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7(\\\\\\\"((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T7_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_8(\\\\\\\"\\\"+str(self.g2V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8(\\\\\\\"((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T8_Neg(\\\\\\\"-1*((@0*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_9(\\\\\\\"\\\"+str(self.g2V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9(\\\\\\\"((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T9_Neg(\\\\\\\"-1*((pow(@2,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_10(\\\\\\\"\\\"+str(self.g2V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10(\\\\\\\"((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T10_Neg(\\\\\\\"-1*((pow(@0,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_10)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_11(\\\\\\\"\\\"+str(self.g2V[10])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11(\\\\\\\"((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T11_Neg(\\\\\\\"-1*((pow(@0,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_11)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_12(\\\\\\\"\\\"+str(self.g2V[11])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12(\\\\\\\"((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T12_Neg(\\\\\\\"-1*((@0*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_12)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_13(\\\\\\\"\\\"+str(self.g2V[12])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13(\\\\\\\"((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T13_Neg(\\\\\\\"-1*((pow(@3,4))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_13)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_14(\\\\\\\"\\\"+str(self.g2V[13])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14(\\\\\\\"((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T14_Neg(\\\\\\\"-1*((pow(@1,3)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_14)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_15(\\\\\\\"\\\"+str(self.g2V[14])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15(\\\\\\\"((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T15_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_15)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_16(\\\\\\\"\\\"+str(self.g2V[15])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16(\\\\\\\"((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T16_Neg(\\\\\\\"-1*((@1*pow(@2,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_16)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_17(\\\\\\\"\\\"+str(self.g2V[16])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17(\\\\\\\"((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T17_Neg(\\\\\\\"-1*((pow(@1,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_17)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_18(\\\\\\\"\\\"+str(self.g2V[17])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18(\\\\\\\"((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T18_Neg(\\\\\\\"-1*((pow(@1,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_18)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_19(\\\\\\\"\\\"+str(self.g2V[18])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19(\\\\\\\"((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T19_Neg(\\\\\\\"-1*((@1*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_19)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_20(\\\\\\\"\\\"+str(self.g2V[19])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20(\\\\\\\"((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T20_Neg(\\\\\\\"-1*((pow(@2,3)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_20)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_21(\\\\\\\"\\\"+str(self.g2V[20])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21(\\\\\\\"((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T21_Neg(\\\\\\\"-1*((pow(@2,2)*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_21)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_22(\\\\\\\"\\\"+str(self.g2V[21])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22(\\\\\\\"((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T22_Neg(\\\\\\\"-1*((@2*pow(@3,3))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_22)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_23(\\\\\\\"\\\"+str(self.g2V[22])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23(\\\\\\\"((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T23_Neg(\\\\\\\"-1*((@0*@1*pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_23)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_24(\\\\\\\"\\\"+str(self.g2V[23])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24(\\\\\\\"((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T24_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_24)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_25(\\\\\\\"\\\"+str(self.g2V[24])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25(\\\\\\\"((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T25_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_25)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_26(\\\\\\\"\\\"+str(self.g2V[25])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26(\\\\\\\"((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T26_Neg(\\\\\\\"-1*((@0*@1*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_26)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_27(\\\\\\\"\\\"+str(self.g2V[26])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27(\\\\\\\"((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T27_Neg(\\\\\\\"-1*((@0*pow(@1,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_27)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_28(\\\\\\\"\\\"+str(self.g2V[27])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28(\\\\\\\"((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T28_Neg(\\\\\\\"-1*((pow(@0,2)*@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_28)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_29(\\\\\\\"\\\"+str(self.g2V[28])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29(\\\\\\\"((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T29_Neg(\\\\\\\"-1*((@0*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_29)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_30(\\\\\\\"\\\"+str(self.g2V[29])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30(\\\\\\\"((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T30_Neg(\\\\\\\"-1*((@0*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_30)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_31(\\\\\\\"\\\"+str(self.g2V[30])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31(\\\\\\\"((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T31_Neg(\\\\\\\"-1*((pow(@0,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_31)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_32(\\\\\\\"\\\"+str(self.g2V[31])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32(\\\\\\\"((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T32_Neg(\\\\\\\"-1*((@1*@2*pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_32)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_33(\\\\\\\"\\\"+str(self.g2V[32])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33(\\\\\\\"((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T33_Neg(\\\\\\\"-1*((@1*pow(@2,2)*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_33)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_34(\\\\\\\"\\\"+str(self.g2V[33])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34(\\\\\\\"((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T34_Neg(\\\\\\\"-1*((pow(@1,2)*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_34)\\\") \\n self.modelBuilder.doVar(\\\"expr::g2V_35(\\\\\\\"\\\"+str(self.g2V[34])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35(\\\\\\\"((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_Ewk_T35_Neg(\\\\\\\"-1*((@0*@1*@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g2V_35)\\\") \\n \\n self.modelBuilder.doVar(\\\"expr::g1V_1(\\\\\\\"\\\"+str(self.g1V[0])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1(\\\\\\\"((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T1_Neg(\\\\\\\"-1*((pow(@0,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_1)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_2(\\\\\\\"\\\"+str(self.g1V[1])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2(\\\\\\\"((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T2_Neg(\\\\\\\"-1*((@0*@1)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_2)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_3(\\\\\\\"\\\"+str(self.g1V[2])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3(\\\\\\\"((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T3_Neg(\\\\\\\"-1*((pow(@1,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_3)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_4(\\\\\\\"\\\"+str(self.g1V[3])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4(\\\\\\\"((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T4_Neg(\\\\\\\"-1*((@0*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_4)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_5(\\\\\\\"\\\"+str(self.g1V[4])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5(\\\\\\\"((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T5_Neg(\\\\\\\"-1*((pow(@2,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_5)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_6(\\\\\\\"\\\"+str(self.g1V[5])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6(\\\\\\\"((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T6_Neg(\\\\\\\"-1*((@0*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_6)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_7(\\\\\\\"\\\"+str(self.g1V[6])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7(\\\\\\\"((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T7_Neg(\\\\\\\"-1*((pow(@3,2))/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_7)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_8(\\\\\\\"\\\"+str(self.g1V[7])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8(\\\\\\\"((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T8_Neg(\\\\\\\"-1*((@1*@2)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_8)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_9(\\\\\\\"\\\"+str(self.g1V[8])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9(\\\\\\\"((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T9_Neg(\\\\\\\"-1*((@1*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_9)\\\") \\n self.modelBuilder.doVar(\\\"expr::g1V_10(\\\\\\\"\\\"+str(self.g1V[9])+\\\"\\\\\\\",)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10(\\\\\\\"((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n self.modelBuilder.factory_(\\\"expr::scale_ggH_T10_Neg(\\\\\\\"-1*((@2*@3)/@4)*@5\\\\\\\", a1, a2, a3, k1L1, gammaH, g1V_10)\\\") \\n \\n self.modelBuilder.doSet(\\\"POI\\\",poi)\",\n \"def function_PLD_Ntransits(coeffs, t_list, Pns_list, lc_list,\\n coeffs_dict_list,\\n coeffs_tuple, fix_coeffs, fix_coeffs_channels,\\n batman_params_list, PLD_params_list):\\n\\n residuals = []\\n\\n x1 = len([key for key in coeffs_tuple[0:9] if key not in fix_coeffs\\n and key not in fix_coeffs_channels])\\n x2 = len([key for key in coeffs_tuple[0:9] if key in fix_coeffs_channels])\\n x3 = len([key for key in coeffs_tuple if key not in fix_coeffs\\n and key not in fix_coeffs_channels])\\n\\n for i in range(len(coeffs_dict_list)):\\n\\n coeffs_fit = np.concatenate((coeffs[i*x3:i*x3 + x1],\\n coeffs[-x2:],\\n coeffs[i*x3 + x1 :(i+1)*x3 ]))\\n\\n new_flux = model_PLD(coeffs_fit, t_list[i], Pns_list[i], coeffs_dict_list[i],\\n coeffs_tuple, fix_coeffs, batman_params_list[i], PLD_params_list[i])\\n\\n residuals.append(lc_list[i]-new_flux)\\n\\n return np.array(np.concatenate(residuals))\",\n \"def derivatives(self, increment_filter):\\n ######################################################################\\n # derivatives fluid and mass balance are static\\n k = self.num_nw_fluids + 1\\n\\n ######################################################################\\n # derivatives for specified heat transfer\\n if self.Q.is_set:\\n self.jacobian[k, 0, 0] = (\\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\\n self.jacobian[k, 0, 2] = -self.inl[0].m.val_SI\\n self.jacobian[k, 1, 2] = self.inl[0].m.val_SI\\n # custom variable Q\\n if self.Q.is_var:\\n self.jacobian[k, 2 + self.Q.var_pos, 0] = -1\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified pressure ratio\\n if self.pr.is_set:\\n self.jacobian[k, 0, 1] = self.pr.val\\n self.jacobian[k, 1, 1] = -1\\n # custom variable pr\\n if self.pr.is_var:\\n self.jacobian[k, 2 + self.pr.var_pos, 0] = (\\n self.inl[0].p.val_SI)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified zeta\\n if self.zeta.is_set:\\n f = self.zeta_func\\n if not increment_filter[0, 0]:\\n self.jacobian[k, 0, 0] = self.numeric_deriv(\\n f, 'm', 0, zeta='zeta')\\n if not increment_filter[0, 2]:\\n self.jacobian[k, 0, 1] = self.numeric_deriv(\\n f, 'p', 0, zeta='zeta')\\n if not increment_filter[0, 2]:\\n self.jacobian[k, 0, 2] = self.numeric_deriv(\\n f, 'h', 0, zeta='zeta')\\n if not increment_filter[1, 1]:\\n self.jacobian[k, 1, 1] = self.numeric_deriv(\\n f, 'p', 1, zeta='zeta')\\n if not increment_filter[1, 2]:\\n self.jacobian[k, 1, 2] = self.numeric_deriv(\\n f, 'h', 1, zeta='zeta')\\n # custom variable zeta\\n if self.zeta.is_var:\\n self.jacobian[k, 2 + self.zeta.var_pos, 0] = (\\n self.numeric_deriv(f, 'zeta', 2, zeta='zeta'))\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified hydro-group parameters\\n if self.hydro_group.is_set:\\n # hazen williams equation\\n if self.hydro_group.method == 'HW':\\n func = self.hw_func\\n # darcy friction factor\\n else:\\n func = self.darcy_func\\n\\n if not increment_filter[0, 0]:\\n self.jacobian[k, 0, 0] = self.numeric_deriv(func, 'm', 0)\\n if not increment_filter[0, 1]:\\n self.jacobian[k, 0, 1] = self.numeric_deriv(func, 'p', 0)\\n if not increment_filter[0, 2]:\\n self.jacobian[k, 0, 2] = self.numeric_deriv(func, 'h', 0)\\n if not increment_filter[1, 1]:\\n self.jacobian[k, 1, 1] = self.numeric_deriv(func, 'p', 1)\\n if not increment_filter[1, 2]:\\n self.jacobian[k, 1, 2] = self.numeric_deriv(func, 'h', 1)\\n # custom variables of hydro group\\n for var in self.hydro_group.elements:\\n if var.is_var:\\n self.jacobian[k, 2 + var.var_pos, 0] = (\\n self.numeric_deriv(func, self.vars[var], 2))\\n k += 1\\n\\n ######################################################################\\n # derivatives for additional equations\\n self.additional_derivatives(increment_filter, k)\",\n \"def forward(h, n, u, v, f, dt, dx, dy, du, dv, dn, beta=0, eps=0, gamma=0, mu=0.3, nu=0, dudt_x=dudt, dvdt_x=dvdt, dndt_x=dndt, grav=True, cori=True, advx=True, advy=True, attn=True): # forward euler and forward/backward timestep\\n beta = np.float32(beta)\\n mu = np.float32(mu)\\n \\n du1, du0 = du[:2]\\n dv1, dv0 = dv[:2]\\n dn0 = dn[0]\\n \\n dndt_x(h, n, u, v, dx, dy, dn0) # calculate dndt and put it into dn0\\n \\n n1 = n + ( dn0 )*dt\\n \\n dudt_x(h, n, f, u, v, dx, dy, du0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n, f, u, v, dx, dy, dv0, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dudt_x(h, n1, f, u, v, dx, dy, du1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n dvdt_x(h, n1, f, u, v, dx, dy, dv1, grav=grav, cori=cori, advx=advx, advy=advy, attn=attn,nu=nu,mu=mu)\\n \\n u1 = u + ( beta*du1 + (one-beta)*du0 )*dt\\n v1 = v + ( beta*dv1 + (one-beta)*dv0 )*dt\\n \\n n, u, v = n1, u1, v1\\n \\n du = [du1, du0, du0, du0]\\n dv = [dv1, dv0, dv0, dv0]\\n dn = [dn0, dn0, dn0]\\n return n1, u1, v1, du, dv, dn\",\n \"def dfegn17(mod96file, wavetype, nmod, freq, pertu = 0.05, tmpdir = None, cleanup = True, fig = None):\\n \\n #create the main temporary directory\\n if tmpdir is None: \\n tmpdir = \\\"/tmp/tmpdir_dfegn17_%10d\\\" % (np.random.rand() * 1e10)\\n while os.path.isdir(tmpdir):\\n tmpdir += \\\"_%10d\\\" % (np.random.rand() * 1e10)\\n os.mkdir(tmpdir)\\n\\n #read starting model\\n Z, H, VP, VS, RHO, QP, QS, ETAP, ETAS, FREFP, FREFS = readmod96(mod96file)\\n model0 = np.concatenate((H, VP, VS, RHO))\\n nlayer = len(H)\\n IH = np.arange(nlayer) #index of thickness parameters\\n IVP = np.arange(1 * nlayer, 2 * nlayer) #index of Vp parameters\\n IVS = np.arange(2 * nlayer, 3 * nlayer) \\n IRH = np.arange(3 * nlayer, 4 * nlayer) \\n\\n\\n\\n #compute eigenfunctions for starting model\\n out0 = fegn17(mod96file, wavetype, nmod, freq, tmpdir = \\\"%s/%s\\\" % (tmpdir, \\\"startingmodel\\\"), fig = None, cleanup = False)\\n\\n #initiate kernels\\n if wavetype == \\\"R\\\":\\n DURDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n DUZDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n DTRDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n DTZDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n elif wavetype == \\\"L\\\":\\n DUTDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n DTTDN = np.zeros((nmod + 1, nlayer, len(model0)), float) * np.nan\\n\\n\\n #perturbate each parameter subsequently\\n for nparam in xrange(len(model0)):\\n if nparam == nlayer - 1 :\\n #thickness of the half space: meaningless\\n continue\\n\\n #prepare the perturbated model and determine the sub-directory name\\n modeln = model0.copy()\\n modeln[nparam] *= (1.0 + pertu)\\n tmpdir_n = \\\"%s/param%06d\\\" % (tmpdir, nparam)\\n mod96file_n = \\\"%s/model_param%06d.mod96\\\" % (tmpdir, nparam) #write the perturbated file in the main temporary directory\\n\\n #write perturbated model\\n writemod96(mod96file_n, \\n H = modeln[IH], \\n VP = modeln[IVP], \\n VS = modeln[IVS], \\n RHO = modeln[IRH], \\n QP = QP, QS = QS, ETAP=ETAP, ETAS=ETAS, FREFP=FREFP, FREFS=FREFS) #keep attenuation untouched\\n\\n #call fegn17 with perturbated model\\n out_n = fegn17(mod96file_n, wavetype, nmod, freq, tmpdir = tmpdir_n, fig = None, cleanup = False)\\n dm = (modeln[nparam] - model0[nparam])\\n\\n for modnum in xrange(nmod+1):\\n key = \\\"%s%d\\\" % (wavetype, modnum)\\n\\n if wavetype == \\\"R\\\":\\n DURDN[modnum, :, nparam] = (out_n[key]['UR'] - out0[key]['UR']) / dm\\n DUZDN[modnum, :, nparam] = (out_n[key]['UZ'] - out0[key]['UZ']) / dm\\n DTRDN[modnum, :, nparam] = (out_n[key]['TR'] - out0[key]['TR']) / dm\\n DTZDN[modnum, :, nparam] = (out_n[key]['TZ'] - out0[key]['TZ']) / dm\\n elif wavetype == \\\"L\\\":\\n\\n DUTDN[modnum, :, nparam] = (out_n[key]['UT'] - out0[key]['UT']) / dm\\n DTTDN[modnum, :, nparam] = (out_n[key]['TT'] - out0[key]['TT']) / dm\\n\\n\\n #-------------------\\n dout = {\\\"Z\\\" : out0['model'][\\\"Z\\\"], \\\"T\\\" : 1. / freq}\\n if wavetype == \\\"R\\\":\\n dout['DURDH'] = DURDN[:, :, IH ]\\n dout['DURDVP'] = DURDN[:, :, IVP]\\n dout['DURDVS'] = DURDN[:, :, IVS]\\n dout['DURDRH'] = DURDN[:, :, IRH]\\n\\n dout['DUZDH'] = DUZDN[:, :, IH ]\\n dout['DUZDVP'] = DUZDN[:, :, IVP]\\n dout['DUZDVS'] = DUZDN[:, :, IVS]\\n dout['DUZDRH'] = DUZDN[:, :, IRH]\\n\\n dout['DTRDH'] = DTRDN[:, :, IH ]\\n dout['DTRDVP'] = DTRDN[:, :, IVP]\\n dout['DTRDVS'] = DTRDN[:, :, IVS]\\n dout['DTRDRH'] = DTRDN[:, :, IRH]\\n\\n dout['DTZDH'] = DTZDN[:, :, IH ]\\n dout['DTZDVP'] = DTZDN[:, :, IVP]\\n dout['DTZDVS'] = DTZDN[:, :, IVS]\\n dout['DTZDRH'] = DTZDN[:, :, IRH]\\n \\n elif wavetype == \\\"L\\\": \\n dout['DUTDH'] = DUTDN[:, :, IH ]\\n dout['DUTDVP'] = DUTDN[:, :, IVP]\\n dout['DUTDVS'] = DUTDN[:, :, IVS]\\n dout['DUTDRH'] = DUTDN[:, :, IRH]\\n \\n dout['DTTDH'] = DTTDN[:, :, IH ]\\n dout['DTTDVP'] = DTTDN[:, :, IVP]\\n dout['DTTDVS'] = DTTDN[:, :, IVS]\\n dout['DTTDRH'] = DTTDN[:, :, IRH]\\n\\n if cleanup:\\n #remove temporary directory\\n execbash('rm -rf %s' % tmpdir, \\\".\\\")\\n\\n if fig is not None:\\n\\n ax2 = fig.add_subplot(224)\\n ax1 = fig.add_subplot(223, sharey = ax2)\\n ax3 = fig.add_subplot(222, sharex = ax2)\\n\\n #------------------\\n ax1.invert_yaxis()\\n #------------------\\n z = np.concatenate((np.repeat(out0['model'][\\\"Z\\\"], 2)[1:], [sum(out0['model'][\\\"H\\\"]) * 1.1]))\\n vp = np.repeat(out0['model'][\\\"Vp\\\"], 2)\\n vs = np.repeat(out0['model'][\\\"Vs\\\"], 2)\\n rh = np.repeat(out0['model'][\\\"Rh\\\"], 2)\\n ax1.plot(vp, z, label = \\\"Vp\\\")\\n ax1.plot(vs, z, label = \\\"Vs\\\")\\n ax1.plot(rh, z, label = \\\"Rh\\\")\\n ax1.legend()\\n ax1.grid(True)\\n ax1.set_ylabel('model depth (km)')\\n\\n #------------------\\n if wavetype == \\\"R\\\":\\n vmax = abs(dout['DUZDVS'][nmod, :, :]).max()\\n ax3.plot(dout[\\\"Z\\\"], out0[\\\"%s%d\\\" % (wavetype, nmod)]['UZ'], label = \\\"UZ\\\")\\n Y = dout['DUZDVS'][nmod, :, :]\\n Y = np.ma.masked_where(np.isnan(Y), Y)\\n ax2.pcolormesh(dout[\\\"Z\\\"], dout[\\\"Z\\\"], Y, vmin = -vmax, vmax = vmax)\\n ax2.set_title(\\\"DUZ/DVS, T = %f, mode %d\\\" % (1. / freq, nmod))\\n ax3.set_xlabel('eigenfunction depth (km)')\\n elif wavetype == \\\"L\\\":\\n vmax = abs(dout['DUTDVS'][nmod, :, :]).max()\\n ax3.plot(dout[\\\"Z\\\"], out0[\\\"%s%d\\\" % (wavetype, nmod)]['UT'], label = \\\"UT\\\")\\n Y = dout['DUTDVS'][nmod, :, :]\\n Y = np.ma.masked_where(np.isnan(Y), Y)\\n ax2.pcolormesh(dout[\\\"Z\\\"], dout[\\\"Z\\\"], Y, vmin = -vmax, vmax = vmax)\\n ax2.set_title(\\\"DUT/DVS, T = %f, mode %d\\\" % (1. / freq, nmod))\\n ax3.set_xlabel('eigenfunction depth (km)')\\n\\n ax3.xaxis.set_label_position(\\\"top\\\") \\n ax3.legend()\\n ax2.grid(True)\\n ax3.grid(True)\\n\\n return dout\",\n \"def dynstall_mhh_dxdt_simple(t, x, U, U_dot, omega, alpha_34, p):\\n # States\\n x1=x[0] # Downwash memory term 1\\n x2=x[1] # Downwash memory term 2\\n x3=x[2] # Clp', Lift coefficient with a time lag to the attached lift coeff\\n x4=x[3] # f'' , Final separation point function\\n # Parameters\\n alpha0 = p['alpha0']\\n Cla = p['Cla']\\n c = p['chord']\\n A1 = p['A1']\\n A2 = p['A2']\\n b1 = p['b1']\\n b2 = p['b2']\\n F_st = p['F_st']\\n # Variables derived from inputs\\n U = max(U, 0.01)\\n Tu = max(c/(2*U), 1e-4) # Eq. 23\\n Tf = p['Tf0']*Tu # OLD was twice: Tf = p['Tf0']*c/U\\n Tp = p['Tp0']*Tu # OLD was twice: Tp = p['Tp0']*c/U\\n # Variables derived from states\\n if p['alpha0_in_x1x2']:\\n alphaE = alpha_34*(1-A1-A2)+ x1 + x2 # Eq. 12\\n else:\\n alphaE = (alpha_34-alpha0)*(1-A1-A2)+ x1 + x2 + alpha0 # Eq. 12\\n\\n# alphaE = u['alphaE'](t) # <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< HACK HACK TODO TODO TODO TODO TODO\\n\\n Clp = Cla * (alphaE-alpha0) + np.pi * Tu * omega # Eq. 13\\n alphaF = x3/Cla+alpha0 # p. 13\\n fs_aF = F_st(alphaF) # p. 13\\n if(fs_aF<0):\\n print('Problematic fs:',fs_aF)\\n x4 = np.clip(x4, 1e-16, 1.0) # Constraining x4 between 0 and 1 increases numerical stability\\n # State equation\\n xdot = [0]*4\\n if p['alpha0_in_x1x2']:\\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * alpha_34\\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * alpha_34\\n else:\\n xdot[0] = -1/Tu * (b1 + c * U_dot/(2*U**2)) * x1 + b1 * A1 / Tu * (alpha_34-alpha0)\\n xdot[1] = -1/Tu * (b2 + c * U_dot/(2*U**2)) * x2 + b2 * A2 / Tu * (alpha_34-alpha0)\\n xdot[2] = -1/Tp * x3 + 1/Tp * Clp\\n xdot[3] = -1/Tf * x4 + 1/Tf * fs_aF\\n return xdot\",\n \"def DyEvo(x, t, T0, r1, r2, K_co, K_ch, alpha, n):\\n y=np.zeros([np.size(x)])\\n D=np.zeros([2]) \\n #define fitnss\\n D[0]=dmax*x[0]**n/(K_co**n+x[0]**n) #cooperator\\n D[1]=dmax*x[0]**n/(K_ch**n+x[0]**n) #cheater \\n #degradation\\n deg=fmax*x[1]/(x[1]+Kd) \\n #ODE of eco-evo dynamics\\n y[0]=alpha*T0-deg*x[0]-alpha*x[0] #dt/dt\\n y[1]=x[1]*(r1*(1-x[1]-x[2])-D[0]-alpha)#d Co/dt\\n y[2]=x[2]*(r2*(1-x[1]-x[2])-D[1]-alpha) #d Ch/dt\\n \\n return y\",\n \"def dE_mdn(self, x, y, t, w1 = None, w2 = None):\\n if w2 == None:\\n w2 = self.w2\\n M = int(self.M)\\n # avoid underrun\\n \\n alpha, sigma, mu = self.getMixtureParams(y.T)\\n #import pdb; pdb.set_trace()\\n \\n #T = t.T[None, None, :] # note: np.tile is slower than this notation\\n T = t.T[None, :]\\n \\n phi = self._phi(T, mu, sigma)\\n aphi = alpha*phi\\n pi = aphi / np.sum(aphi, 0)\\n \\n # derivatives of E with respect to the output variables (s. Bishop 1995, chp. 6.4)\\n dE_dy_alpha = alpha - pi\\n dE_dy_sigma = - 0.5 * pi * ((np.sum((T-mu)**2 , 1) / sigma) - self.c)\\n dE_dy_mu = pi[:,np.newaxis,:] * (mu - T) / sigma[:,np.newaxis,:]\\n\\n dk = np.zeros([self.ny, x.shape[0]])\\n dk[0:M,:] = dE_dy_alpha\\n dk[M:2*M,:] = dE_dy_sigma\\n \\n dk[2*M:] = np.reshape(dE_dy_mu, [M*self.c, x.shape[0]])\\n \\n # back-propagate the dks\\n #t0=datetime.now()\\n dEnw1, dEnw2 = self.backward(x, dk, None, w2)\\n #print 'eval of dE_mdn:' + str((datetime.now()-t0))\\n #dj = (1 - self.z[1:]**2) * np.dot(w2[:,1:].T, dk)\\n # evaluate derivatives with respect to the weights\\n #dEnw1 = (dj[:,:,np.newaxis]*x[np.newaxis,:,:]).transpose(1,0,2)\\n #dEnw2 = (dk[:,:,np.newaxis]*self.z.T[np.newaxis,:,:]).transpose(1,0,2)\\n return dEnw1, dEnw2\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n z = unknowns['z']\\n znew = z\\n\\n iter = 0\\n eps = 1.0e99\\n while iter < self.maxiter and abs(eps) > self.atol:\\n z = znew\\n znew = 4.0 - x*z\\n\\n eps = x*znew + znew - 4.0\\n\\n unknowns['z'] = znew\\n unknowns['y'] = x + 2.0*znew\\n\\n resids['z'] = eps\\n #print(unknowns['y'], unknowns['z'])\",\n \"def _func_pen(self, coeffs_ext):\\n l_elastic_net = self.l_elastic_net\\n eta = self.eta\\n n_features = self.n_features\\n coeffs = coeffs_ext[:n_features] - coeffs_ext[n_features:]\\n return l_elastic_net * ((1. - eta) * coeffs_ext.sum()\\n + 0.5 * eta * np.linalg.norm(coeffs) ** 2)\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n a = self.a\\n b = self.b\\n c = self.c\\n\\n unknowns['y'] = a*x**2 + b*x + c\",\n \"def test_adding_three_variables():\\n a = fwd.Variable()\\n b = fwd.Variable()\\n c = fwd.Variable()\\n f = fwd.exp(a-b+c)\\n assert equals(f.evaluation_at({a: 1.0, b: 2.0, c: 3.0}), np.exp(2.0))\\n assert equals(f.derivative_at(b, {a: 1.0, b: 2.0, c: 3.0}), -np.exp(2.0))\\n assert equals(f.derivative_at(a, {a: 1.0, b: 2.0, c: 3.0}), np.exp(2.0))\",\n \"def fluid_deriv(self, increment_filter, k):\\n i = 0\\n for fluid in self.nw_fluids:\\n j = 0\\n for o in self.outl:\\n self.jacobian[k, j + 1, 0] = -o.fluid.val[fluid]\\n self.jacobian[k, j + 1, i + 3] = -o.m.val_SI\\n j += 1\\n self.jacobian[k, 0, 0] = self.inl[0].fluid.val[fluid]\\n self.jacobian[k, 0, i + 3] = self.inl[0].m.val_SI\\n k += 1\\n i += 1\",\n \"def nonlinear_model(t, q, u, p, calcOutput=False):\\n # Compute variables useful to several other functions\\n m = calcMisc(t, q, u, p)\\n\\n # Aerodynamic Force (length 3)\\n Fa, aeroOut = Faero(t, q, u, p, calcOutput=calcOutput, m=m)\\n\\n # Inertial force (length 3)\\n Fs = Fstruct(t, q, u, p, calcOutput=calcOutput, m=m)\\n\\n # Total force\\n F = np.zeros(3)\\n F[0] = Fa[0] + Fs[0]\\n F[1] = Fa[1] + Fs[1]\\n F[2] =-Fa[2] - Fs[2]\\n\\n # Derivative of states (structural and aero)\\n dqxd = dxd_dt(t, m['qx'], m['qxd'], F[p['Ix']], p)\\n dxa = dxa_dt(t, m['x'], m['xd'], m['qxa_ua'], m['qxa_di'], p, u)\\n dq = np.concatenate((m['qxd'], dqxd, dxa)) # NOTE: assumed order here\\n\\n if calcOutput:\\n dq_full, xd, xdd = inflate_q(dq, Iq=p['Iq'])\\n d = dict()\\n ## Structural states\\n d['x'] = m['x'][0]\\n d['y'] = m['x'][1]\\n d['thetat'] = m['x'][2]\\n d['xd'] = m['xd'][0]\\n d['yd'] = m['xd'][1]\\n d['thetad'] = m['xd'][2]\\n d['xdd'] = xdd[0]\\n d['ydd'] = xdd[1]\\n d['thetadd'] = xdd[2]\\n # Inputs\\n # (NOTE: computed with \\\"calc='u')\\n #m['Ux'], m['Uy'], m['theta_p'] = inputsAtTime(t, u)\\n # Struct\\n d['theta'] = m['theta']\\n d['rho_x'] = m['rho_x']\\n d['rho_y'] = m['rho_y']\\n ## Aero\\n d.update(aeroOut)\\n if p['dynamicStallModel'] == 'oye':\\n d['fs'] = m['qxa_ua'][0]\\n d['dfs'] = dxa [0]\\n elif p['dynamicStallModel'] == 'mhh':\\n d['x1_ds'] = m['qxa_ua'][0]\\n d['x2_ds'] = m['qxa_ua'][1]\\n d['x3_ds'] = m['qxa_ua'][2]\\n d['x4_ds'] = m['qxa_ua'][3]\\n return pd.Series(d)\\n else:\\n return dq\",\n \"def _compute_GP_variables(self):\\r\\n Wi = 1.0/self.W\\r\\n self.Sigma_tilde = np.diagflat(Wi)\\r\\n\\r\\n Y_tilde = Wi*self.Ki_f + self.f_hat\\r\\n\\r\\n self.Wi_K_i = self.W12BiW12\\r\\n ln_det_Wi_K = pddet(self.Sigma_tilde + self.K)\\r\\n lik = self.noise_model.logpdf(self.f_hat, self.data, extra_data=self.extra_data)\\r\\n y_Wi_K_i_y = mdot(Y_tilde.T, self.Wi_K_i, Y_tilde)\\r\\n\\r\\n Z_tilde = (+ lik\\r\\n - 0.5*self.ln_B_det\\r\\n + 0.5*ln_det_Wi_K\\r\\n - 0.5*self.f_Ki_f\\r\\n + 0.5*y_Wi_K_i_y\\r\\n + self.NORMAL_CONST\\r\\n )\\r\\n\\r\\n #Convert to float as its (1, 1) and Z must be a scalar\\r\\n self.Z = np.float64(Z_tilde)\\r\\n self.Y = Y_tilde\\r\\n self.YYT = np.dot(self.Y, self.Y.T)\\r\\n self.covariance_matrix = self.Sigma_tilde\\r\\n self.precision = 1.0 / np.diag(self.covariance_matrix)[:, None]\\r\\n\\r\\n #Compute dZ_dK which is how the approximated distributions gradients differ from the dL_dK computed for other likelihoods\\r\\n self.dZ_dK = self._Kgradients()\\r\\n #+ 0.5*self.Wi_K_i - 0.5*np.dot(self.Ki_f, self.Ki_f.T) #since we are not adding the K gradients explicit part theres no need to compute this again\\r\",\n \"def coeffs(f):\\n return dmp_coeffs(f.rep, f.lev, f.dom)\",\n \"def _fv(self):\\n return self.beta * (self.x ** self.c)\",\n \"def rhs(y, t, l, m, g):\\n # Unpack the states so you can use the variable names in the\\n # sympy.physics.mechanics equations\\n q1 = y[0]\\n q2 = y[1]\\n u1 = y[2]\\n u2 = y[3]\\n # or you can make use of python's tuple unpacking for a one liner\\n # q1, q2, u1, u2 = y\\n\\n # Initialize a vector for the derivatives.\\n dydt = zeros((len(y)))\\n\\n # Compute the derivatives, these are pasted in from the\\n # sympy.physics.mechanics results.\\n dydt[0] = u1\\n dydt[1] = u2\\n dydt[2] = (-g*sin(q1)*sin(q2)**2 + 2*g*sin(q1) -\\n g*sin(q2)*cos(q1)*cos(q2) + 2*l*u1**2*sin(q1)*cos(q1)*cos(q2)**2 -\\n l*u1**2*sin(q1)*cos(q1) - 2*l*u1**2*sin(q2)*cos(q1)**2*cos(q2) +\\n l*u1**2*sin(q2)*cos(q2) + l*u2**2*sin(q1)*cos(q2) -\\n l*u2**2*sin(q2)*cos(q1))/(l*(sin(q1)**2*sin(q2)**2 +\\n 2*sin(q1)*sin(q2)*cos(q1)*cos(q2) + cos(q1)**2*cos(q2)**2 - 2))\\n dydt[3] = (-sin(q1)*sin(q2)/2 - cos(q1)*cos(q2)/2)*(2*g*l*m*sin(q1) -\\n l**2*m*(-sin(q1)*cos(q2) +\\n sin(q2)*cos(q1))*u2**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2)) -\\n l**2*m) + (g*l*m*sin(q2) - l**2*m*(sin(q1)*cos(q2) -\\n sin(q2)*cos(q1))*u1**2)/(l**2*m*(sin(q1)*sin(q2)/2 +\\n cos(q1)*cos(q2)/2)*(sin(q1)*sin(q2) + cos(q1)*cos(q2))\\n - l**2*m)\\n\\n # Return the derivatives.\\n return dydt\",\n \"def equations(self):\\n k = 0\\n ######################################################################\\n # equations for fluid balance\\n self.residual[k:k + self.num_nw_fluids * 2] = self.fluid_func()\\n k += self.num_nw_fluids * 2\\n\\n ######################################################################\\n # equations for mass flow balance\\n self.residual[k:k + 2] = self.mass_flow_func()\\n k += 2\\n\\n ######################################################################\\n # equations for energy balance\\n self.residual[k] = self.energy_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified heat transfer\\n if self.Q.is_set:\\n self.residual[k] = (\\n self.inl[0].m.val_SI * (\\n self.outl[0].h.val_SI - self.inl[0].h.val_SI) - self.Q.val)\\n k += 1\\n\\n ######################################################################\\n # equations for specified heat transfer coefficient\\n if self.kA.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.kA_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified heat transfer coefficient characteristic\\n if self.kA_char.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.kA_char_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified upper terminal temperature difference\\n if self.ttd_u.is_set:\\n self.residual[k] = self.ttd_u_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified lower terminal temperature difference\\n if self.ttd_l.is_set:\\n self.residual[k] = self.ttd_l_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified pressure ratio at hot side\\n if self.pr1.is_set:\\n self.residual[k] = (\\n self.pr1.val * self.inl[0].p.val_SI - self.outl[0].p.val_SI)\\n k += 1\\n\\n ######################################################################\\n # equations for specified pressure ratio at cold side\\n if self.pr2.is_set:\\n self.residual[k] = (\\n self.pr2.val * self.inl[1].p.val_SI - self.outl[1].p.val_SI)\\n k += 1\\n\\n ######################################################################\\n # equations for specified zeta at hot side\\n if self.zeta1.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.zeta_func(\\n zeta='zeta1', inconn=0, outconn=0)\\n k += 1\\n\\n ######################################################################\\n # equations for specified zeta at cold side\\n if self.zeta2.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.zeta_func(\\n zeta='zeta2', inconn=1, outconn=1)\\n k += 1\\n\\n ######################################################################\\n # additional equations\\n self.additional_equations(k)\",\n \"def define_ufl_equations_diff(self):\\n\\n # Derivatives of velocity integration equation.\\n if self.f1 != 0:\\n self.df1_du = dlf.derivative(self.f1, self.displacement, self.trial_vector)\\n self.df1_dv = dlf.derivative(self.f1, self.velocity, self.trial_vector)\\n else:\\n self.df1_du = 0\\n self.df1_dv = 0\\n self.df1_dp = 0 # This is always zero.\\n\\n # Derivatives of momentum equation.\\n if self.displacement != 0:\\n self.df2_du = dlf.derivative(self.f2, self.displacement, self.trial_vector)\\n else:\\n self.df2_du = 0\\n\\n if self.velocity != 0:\\n self.df2_dv = dlf.derivative(self.f2, self.velocity, self.trial_vector)\\n else:\\n self.df2_dv = 0\\n\\n if self.pressure != 0:\\n self.df2_dp = dlf.derivative(self.f2, self.pressure, self.trial_scalar)\\n else:\\n self.df2_dp = 0\\n\\n # Derivatives of incompressibility equation.\\n if self.f3 != 0:\\n if self.displacement != 0:\\n self.df3_du = dlf.derivative(self.f3, self.displacement, self.trial_vector)\\n else:\\n self.df3_du = 0\\n\\n if self.velocity != 0:\\n self.df3_dv = dlf.derivative(self.f3, self.velocity, self.trial_vector)\\n else:\\n self.df3_dv = 0\\n\\n self.df3_dp = dlf.derivative(self.f3, self.pressure, self.trial_scalar)\\n else:\\n self.df3_du = 0\\n self.df3_dv = 0\\n self.df3_dp = 0\\n\\n return None\",\n \"def tsne(X = Math.array([]), no_dims = 2, initial_dims = 50, perplexity = 30.0):\\n \\n # Check inputs\\n if X.dtype != \\\"float64\\\":\\n raise ValueError(\\\"array X should have type float64.\\\")\\n #if no_dims.__class__ != \\\"<type 'int'>\\\": # doesn't work yet!\\n # print \\\"Error: number of dimensions should be an integer.\\\";\\n # return -1;\\n \\n # Initialize variables\\n if X.shape[1] > initial_dims:\\n print \\\"Reducing the dimensionality to %d\\\" %initial_dims\\n X = pca(X, initial_dims);\\n (n, d) = X.shape;\\n max_iter = 1000;\\n initial_momentum = 0.5;\\n final_momentum = 0.8;\\n eta = 500;\\n min_gain = 0.01;\\n Y = Math.random.randn(n, no_dims);\\n dY = Math.zeros((n, no_dims));\\n iY = Math.zeros((n, no_dims));\\n gains = Math.ones((n, no_dims));\\n \\n # Compute P-values\\n P = x2p(X, 1e-5, perplexity);\\n P = P + Math.transpose(P);\\n P = P / Math.sum(P);\\n P = P * 4; # early exaggeration\\n P = Math.maximum(P, 1e-12);\\n \\n # Run iterations\\n for iter in range(max_iter):\\n \\n # Compute pairwise affinities\\n sum_Y = Math.sum(Math.square(Y), 1); \\n num = 1 / (1 + Math.add(Math.add(-2 * Math.dot(Y, Y.T), sum_Y).T, sum_Y));\\n num[range(n), range(n)] = 0;\\n Q = num / Math.sum(num);\\n Q = Math.maximum(Q, 1e-12);\\n \\n # Compute gradient\\n PQ = P - Q;\\n for i in range(n):\\n dY[i,:] = Math.sum(Math.tile(PQ[:,i] * num[:,i], (no_dims, 1)).T * (Y[i,:] - Y), 0);\\n \\n # Perform the update\\n if iter < 20:\\n momentum = initial_momentum\\n else:\\n momentum = final_momentum\\n gains = (gains + 0.2) * ((dY > 0) != (iY > 0)) + (gains * 0.8) * ((dY > 0) == (iY > 0));\\n gains[gains < min_gain] = min_gain;\\n iY = momentum * iY - eta * (gains * dY);\\n Y = Y + iY;\\n Y = Y - Math.tile(Math.mean(Y, 0), (n, 1));\\n \\n # Compute current value of cost function\\n if (iter + 1) % 10 == 0:\\n C = Math.sum(P * Math.log(P / Q));\\n print \\\"Iteration \\\", (iter + 1), \\\": error is \\\", C\\n \\n # Stop lying about P-values\\n if iter == 100:\\n P = P / 4;\\n \\n # Return solution\\n return Y;\",\n \"def ti_deriv(self, increment_filter, k):\\n fuel = self.fuel_alias.val\\n pos = 3 + self.nw_fluids.index(fuel)\\n\\n for i in range(2):\\n self.jacobian[k, i, 0] = -self.inl[i].fluid.val[fuel]\\n self.jacobian[k, i, pos] = -self.inl[i].m.val_SI\\n self.jacobian[k, 2, 0] = self.outl[0].fluid.val[fuel]\\n self.jacobian[k, 2, pos] = self.outl[0].m.val_SI\\n self.jacobian[k] *= self.lhv\\n k += 1\",\n \"def run_optimization(file, sess, Hx_final, Hy_final, w_ij, ops):\\n\\n sub_patch = read_patch(file)\\n # all_plot_params = []\\n verbose = True\\n\\n # Prepare input, randomness in fv is achieved by adding a noise\\n vi, Ti, fv_o, subTi = input_prep(sub_patch, do_show=False, avoid_triangle_flip=True)\\n B_inv_new = None\\n N = 8\\n BATCH = 32\\n type = 'NN'\\n for type in ('NN', 'GD', 'BFGS'):\\n\\n fvi = fv_o.copy()\\n print(\\\"*\\\" * 1000)\\n print(fvi)\\n print(\\\"&\\\" * 1000)\\n all_plot_params = []\\n\\n def get_nn_hessians(fv, v, subt):\\n inputs_b = pack_batch(fv, v, subt, BATCH)\\n feed_dict = gen_feed_dict_inference(inputs_b, is_training=False, ops=ops)\\n (Hx_final_out, Hy_final_out, w_ij_out) = sess.run((Hx_final, Hy_final, w_ij), feed_dict=feed_dict)\\n (Hx, Hy) = extract_Hs(Hx_final_out, Hy_final_out)\\n return (Hx, Hy)\\n\\n for i in range(N):\\n\\n params = dirichlet_sym_gd_bfgs_nn_weak_armijo_optimizer_step(line_search=line_search_armijio,\\n get_nn_hessians=get_nn_hessians,\\n source=vi,\\n t=Ti,\\n target=fvi,\\n subt=subTi,\\n verbose=verbose,\\n # Back feed the next estimate of Hessian\\n B_inv_new=B_inv_new,\\n type=type)\\n\\n optimization_res = do_optimization_result_unpack(params=params, t=Ti, v=vi, verbose=verbose)\\n\\n (displacement,\\n p_k, # optimization_res['step_fv'],\\n p_k_n, # optimization_res['step_fv_n'],\\n _, # optimization_res['subvertices'],\\n eng_xnew_first_order_approx,\\n eng_xnew_x0_diff_sub,\\n source,\\n t,\\n source_dim,\\n t_dim,\\n eng_at_x0_sub,\\n eng_at_xnew,\\n eng_at_xnew_sub,\\n deng_x_at_x0_sub,\\n alpha,\\n edges_sub,\\n epsilon,\\n sub_source_dim, # nv_sub\\n edges_sub_dim,\\n sv_at_x0,\\n sv_at_x0_sub,\\n sv_at_xnew,\\n sv_at_xnew_sub,\\n fv_at_xnew,\\n eng_first_order_approx_xnew_x0_diff,\\n t_flip,\\n x0,\\n eng_first_order_approx_xnew_x0_diff_sub,\\n ray_deng_x_at_x0,\\n ray_deng_x_at_xnew,\\n eng_plot,\\n ray_deng_x_at_x0_sub,\\n ray_deng_x_at_xnew_sub,\\n eng_plot_sub,\\n span,\\n type,\\n B_inv_new,\\n eng_xnew_x0_diff) = optim_res_unpack(optimization_res)\\n\\n plot_params = (ray_deng_x_at_x0,\\n ray_deng_x_at_xnew,\\n eng_plot,\\n ray_deng_x_at_x0_sub,\\n ray_deng_x_at_xnew_sub,\\n eng_plot_sub,\\n alpha,\\n span,\\n type)\\n\\n do_verbose(\\\"linear_diff\\\", eng_first_order_approx_xnew_x0_diff, verbose)\\n all_plot_params.append(plot_params)\\n fvi = fv_at_xnew\\n\\n sequence_params = concatinate_optimization_sequence_params(all_plot_params=all_plot_params, do_show=True)\\n plot_optimization_sequence(sequence_params=sequence_params, do_show=True, type=type)\\n\\n plt.show()\\n return 0\",\n \"def energy_function(self, x):\\n \\n return -T.dot(T.transpose(x), T.dot(self.W, x)) -\\\\\\n T.dot(T.transpose(self.b), x)\",\n \"def TDGradientFunction(Prof,x,Trx,rb_spec,abs_spec,dr,inu0,bsrMult,base_T,base_P,r0,lam=[0,0,0,0,0,0]): \\n \\n iR = Prof['WV Online'].size # range index for a profile into 1D x array\\n x2 = np.reshape(x,(iR+1,6))\\n xK = x2[0,:] # constants [HSRL Mol HSRL Comb, WV On, WV Off, O2 On ,O2 Off]\\n xS = x2[1:,:] # state vector [T, nWV, BSR, phi_HSRL, phi_WV, phi_O2]\\n \\n grad2 = np.zeros(x2.shape) \\n \\n #N,dNdB,dNdT = HSRLDerivative(T,BSR,phi,rb_spec,Trx,inu0,K,base_T,base_P)\\n HSRL_mol,dHmdB,dHmdT = HSRLDerivative(xS[:,0],xS[:,2],xS[:,3],rb_spec['HSRL'],Trx['HSRL Mol'],inu0['HSRL'],xK[0],base_T,base_P)\\n HSRL_comb,dHcdB,dHcdT = HSRLDerivative(xS[:,0],xS[:,2],xS[:,3],rb_spec['HSRL'],Trx['HSRL Comb'],inu0['HSRL'],xK[1],base_T,base_P)\\n \\n # N,dNdB,dNdnWV,dNdT = WVDIALDerivative(T,nWV,BSR,phi,rb_spec,abs_spec,Trx,inu0,K,base_T,base_P,dr)\\n WV_on,dWVndB,dWVndnWV,dWVndT = WVDIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['WV'],xS[:,4],rb_spec['WV Online'],abs_spec['WV Online'],Trx['WV Online'],inu0['WV Online'],xK[2],base_T,base_P,dr,r0)\\n WV_off,dWVfdB,dWVfdnWV,dWVfdT = WVDIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['WV'],xS[:,4],rb_spec['WV Offline'],abs_spec['WV Offline'],Trx['WV Offline'],inu0['WV Offline'],xK[3],base_T,base_P,dr,r0) \\n \\n # N,dNdB,dNdnWV,dNdT = O2DIALDerivative(T,nWV,BSR,phi,rb_spec,abs_spec,Trx,inu0,K,base_T,base_P,dr)\\n O2_on,dO2ndB,dO2ndnWV,dO2ndT = O2DIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['O2'],xS[:,5],rb_spec['O2 Online'],abs_spec['O2 Online'],Trx['O2 Online'],inu0['O2 Online'],xK[4],base_T,base_P,dr,r0)\\n O2_off,dO2fdB,dO2fdnWV,dO2fdT = O2DIALDerivative(xS[:,0],xS[:,1],xS[:,2]+bsrMult['O2'],xS[:,5],rb_spec['O2 Offline'],abs_spec['O2 Offline'],Trx['O2 Offline'],inu0['O2 Offline'],xK[5],base_T,base_P,dr,r0)\\n \\n# HSRLModel,dHSdB,dHSdT = HSRLProfileRatioDeriv(xS[:,0],P,xS[:,2], \\\\\\n# Trx['HSRL Mol'],Trx['HSRL Comb'], \\\\\\n# rb_spec['HSRL'],inu0['HSRL'],GainRatio=xK[0])\\n#\\n# WVModel,dWVdB,dWVdnWV,dWVdT = WaterVaporProfileRatioDeriv(xS[:,0],P,xS[:,1],xS[:,2]*bsrMult['WV'],\\n# Trx['WV Online'], Trx['WV Offline'], \\\\\\n# rb_spec['WV Online'],rb_spec['WV Offline'], \\\\\\n# abs_spec['WV Online'],abs_spec['WV Offline'],dr, \\\\\\n# inu0['WV Online'],inu0['WV Offline'],GainRatio=xK[1])\\n# \\n# O2Model,dO2dB,dO2dnWV,dO2dT = OxygenProfileRatioDeriv(xS[:,0],P,xS[:,1],xS[:,2]*bsrMult['O2'],\\n# Trx['O2 Online'], Trx['O2 Offline'], \\\\\\n# rb_spec['O2 Online'],rb_spec['O2 Offline'], \\\\\\n# abs_spec['O2 Online'],abs_spec['O2 Offline'],dr, \\\\\\n# inu0['O2 Online'],inu0['O2 Offline'],GainRatio=xK[2])\\n \\n HSRLmolBase = 1-(Prof['HSRL Mol'])/(HSRL_mol+Prof['HSRL Mol BG'])\\n HSRLcombBase = 1-(Prof['HSRL Comb'])/(HSRL_comb+Prof['HSRL Comb BG'])\\n WVonBase = 1-(Prof['WV Online'])/(WV_on+Prof['WV Online BG'])\\n WVoffBase = 1-(Prof['WV Offline'])/(WV_off+Prof['WV Offline BG'])\\n O2onBase = 1-(Prof['O2 Online'])/(O2_on+Prof['O2 Online BG'])\\n O2offBase = 1-(Prof['O2 Offline'])/(O2_off+Prof['O2 Offline BG'])\\n \\n \\n# HSRLbase = 2*(HSRLModel-Prof['HSRL'])/ProfVar['HSRL']\\n# WVbase = 2*(WVModel-Prof['WV'])/ProfVar['WV']\\n# O2base = 2*(O2Model-Prof['O2'])/ProfVar['O2']\\n \\n # temperature gradient\\n grad2[1:,0] = np.nansum(HSRLmolBase[np.newaxis]*dHmdT,axis=1) \\\\\\n + np.nansum(HSRLcombBase[np.newaxis]*dHcdT,axis=1) \\\\\\n + np.nansum(WVonBase[np.newaxis]*dWVndT,axis=1) \\\\\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdT,axis=1) \\\\\\n + np.nansum(O2onBase[np.newaxis]*dO2ndT,axis=1) \\\\\\n + np.nansum(O2offBase[np.newaxis]*dO2fdT,axis=1)\\n# # piece wise penalty function \\n# gradpen = lam[0]*np.sign(np.diff(xS[:,0]))\\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n# grad2[2:,0] = grad2[2:,0] + gradpen\\n# grad2[1:-1,0] = grad2[1:-1,0] - gradpen\\n# piece wise slope penalty function \\n gradpen = lam[0]*np.sign(np.diff(np.diff(xS[:,0])))\\n gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n grad2[3:,0] = grad2[3:,0] + gradpen\\n grad2[2:-1,0] = grad2[2:-1,0] - 2*gradpen\\n grad2[1:-2,0] = grad2[1:-2,0] + gradpen\\n \\n # water vapor gradient\\n grad2[1:,1] = np.nansum(WVonBase[np.newaxis]*dWVndnWV,axis=1) \\\\\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdnWV,axis=1) \\\\\\n + np.nansum(O2onBase[np.newaxis]*dO2ndnWV,axis=1) \\\\\\n + np.nansum(O2offBase[np.newaxis]*dO2fdnWV,axis=1)\\n # piecewise penalty function\\n gradpen = lam[1]*np.sign(np.diff(xS[:,1]))\\n gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n grad2[2:,1] = grad2[2:,1] + gradpen\\n grad2[1:-1,1] = grad2[1:-1,1] - gradpen\\n \\n # backscatter gradient\\n grad2[1:,2] = np.nansum(HSRLmolBase[np.newaxis]*dHmdB,axis=1) \\\\\\n + np.nansum(HSRLcombBase[np.newaxis]*dHcdB,axis=1) \\\\\\n + np.nansum(WVonBase[np.newaxis]*dWVndB,axis=1) \\\\\\n + np.nansum(WVoffBase[np.newaxis]*dWVfdB,axis=1) \\\\\\n + np.nansum(O2onBase[np.newaxis]*dO2ndB,axis=1) \\\\\\n + np.nansum(O2offBase[np.newaxis]*dO2fdB,axis=1) \\n# #piecewise penalty function\\n# gradpen = lam[2]*np.sign(np.diff(xS[:,2]))\\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n# grad2[2:,2] = grad2[2:,2] + gradpen\\n# grad2[1:-1,2] = grad2[1:-1,2] - gradpen\\n \\n\\n # *bsrMult['WV']\\n # *bsrMult['WV']\\n # *bsrMult['O2']\\n # *bsrMult['O2']\\n\\n # HSRL Common terms\\n grad2[1:,3] = np.nansum(HSRLmolBase[np.newaxis]*HSRL_mol,axis=0) + np.nansum(HSRLcombBase[np.newaxis]*HSRL_comb,axis=0)\\n# # piece wise penalty function \\n# gradpen = lam[3]*np.sign(np.diff(xS[:,3]))\\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n# grad2[2:,3] = grad2[2:,3] + gradpen\\n# grad2[1:-1,3] = grad2[1:-1,3] - gradpen\\n \\n # WV Common terms\\n grad2[1:,4] = np.nansum(WVonBase[np.newaxis]*WV_on,axis=0) + np.nansum(WVoffBase[np.newaxis]*WV_off,axis=0)\\n# # piece wise penalty function \\n# gradpen = lam[4]*np.sign(np.diff(xS[:,4]))\\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n# grad2[2:,4] = grad2[2:,4] + gradpen\\n# grad2[1:-1,4] = grad2[1:-1,4] - gradpen\\n \\n # O2 Common terms\\n grad2[1:,5] = np.nansum(O2onBase[np.newaxis]*O2_on,axis=0) + np.nansum(O2offBase[np.newaxis]*O2_off,axis=0)\\n# # piece wise penalty function \\n# gradpen = lam[5]*np.sign(np.diff(xS[:,5]))\\n# gradpen[np.nonzero(np.isnan(gradpen))] = 0\\n# grad2[2:,5] = grad2[2:,5] + gradpen\\n# grad2[1:-1,5] = grad2[1:-1,5] - gradpen\\n\\n grad2[0,0] = np.nansum(HSRLmolBase*HSRL_mol/xK[0])\\n grad2[0,1] = np.nansum(HSRLcombBase*HSRL_comb/xK[1])\\n grad2[0,2] = np.nansum(WVonBase*WV_on/xK[2])\\n grad2[0,3] = np.nansum(WVoffBase*WV_off/xK[3])\\n grad2[0,4] = np.nansum(O2onBase*O2_on/xK[4])\\n grad2[0,5] = np.nansum(O2offBase*O2_off/xK[5])\\n \\n# grad2[0,1] = np.nansum(WVbase*WVModel/xK[1])\\n# grad2[0,2] = np.nansum(O2base*O2Model/xK[2])\\n \\n# OptError = np.nansum(2*(HSRLModel-Prof['HSRL'])/ProfVar['HSRL']*) \\\\\\n# +np.nansum((WVModel-Prof['WV'])**2/ProfVar['WV']) \\\\\\n# +np.sum((O2Model-Prof['O2'])**2/ProfVar['O2'])\\n \\n return grad2.flatten()\",\n \"def change_variables((a,b,c,d), (n,r,m)): \\n return ( n*a**2 + r*a*b + m*b**2, 2*(n*a*c + m*b*d) + r*(a*d + c*b), \\\\\\n n*c**2 + r*c*d + m*d**2 )\",\n \"def f(self,y,psi):\\r\\n\\r\\n #1. check that number of params is consistent\\r\\n # assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'\\r\\n # assert psi.shape[1] == 4, 'inconsistent parameter dimensions'\\r\\n mpsi = psi.copy()\\r\\n d = psi[-1]\\r\\n mpsi = mpsi[:self.num_parameters-1].reshape(self.n_terms, 3)\\r\\n\\r\\n #3. transform data\\r\\n z = d*y.copy()\\r\\n for i in range(len(mpsi)):\\r\\n a,b,c = mpsi[i]\\r\\n z += a*np.tanh(b*(y+c))\\r\\n return z\",\n \"def F(cst, x):\\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\\n [u, v, g1, g2, g3] = x\\n a = g1*u1 - u0\\n b = g2*u2 - u0\\n c = g3*u - u0\\n l = g1*v1 - v0 \\n m = g2*v2 - v0\\n n = g3*v - v0\\n r = g1 - 1\\n s = g2 - 1\\n t = g3 - 1\\n return np.array([\\n coeffs[0]*(a**2-l**2) + 2*coeffs[1]*(a*b-l*m) + coeffs[2]*(b**2-m**2) + 2*coeffs[3]*(a*c-l*n) + 2*coeffs[4]*(b*c-m*n) + c**2 - n**2,\\n coeffs[0]*(l**2-r**2) + 2*coeffs[1]*(l*m-r*s) + coeffs[2]*(m**2-s**2) + 2*coeffs[3]*(l*n-r*t) + 2*coeffs[4]*(m*n-s*t) + n**2 - t**2,\\n coeffs[0]*a*l + coeffs[1]*(l*b+m*a) + coeffs[2]*m*b + coeffs[3]*(l*c+n*a) + coeffs[4]*(m*c+b*n) + c*n,\\n coeffs[0]*a*r + coeffs[1]*(r*b+s*a) + coeffs[2]*s*b + coeffs[3]*(r*c+t*a) + coeffs[4]*(s*c+b*t) + c*t,\\n coeffs[0]*r*l + coeffs[1]*(l*s+m*r) + coeffs[2]*m*s + coeffs[3]*(l*t+n*r) + coeffs[4]*(m*t+s*n) + t*n \\n ])\",\n \"def independent_variables(self, indep_vars):\\n self.set(independent_variables=indep_vars)\",\n \"def derivatives(self, increment_filter):\\n ######################################################################\\n # derivatives fluid and mass balance are static\\n k = self.num_nw_fluids * 2 + 2\\n\\n ######################################################################\\n # derivatives for energy balance equation\\n for i in range(2):\\n self.jacobian[k, i, 0] = (\\n self.outl[i].h.val_SI - self.inl[i].h.val_SI)\\n self.jacobian[k, i, 2] = -self.inl[i].m.val_SI\\n\\n self.jacobian[k, 2, 2] = self.inl[0].m.val_SI\\n self.jacobian[k, 3, 2] = self.inl[1].m.val_SI\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified heat transfer\\n if self.Q.is_set:\\n self.jacobian[k, 0, 0] = (\\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\\n self.jacobian[k, 0, 2] = -self.inl[0].m.val_SI\\n self.jacobian[k, 2, 2] = self.inl[0].m.val_SI\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified heat transfer coefficient\\n if self.kA.is_set:\\n f = self.kA_func\\n self.jacobian[k, 0, 0] = (\\n self.outl[0].h.val_SI - self.inl[0].h.val_SI)\\n for i in range(4):\\n if not increment_filter[i, 1]:\\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\\n if not increment_filter[i, 2]:\\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified heat transfer coefficient\\n if self.kA_char.is_set:\\n f = self.kA_char_func\\n if not increment_filter[0, 0]:\\n self.jacobian[k, 0, 0] = self.numeric_deriv(f, 'm', 0)\\n if not increment_filter[1, 0]:\\n self.jacobian[k, 1, 0] = self.numeric_deriv(f, 'm', 1)\\n for i in range(4):\\n if not increment_filter[i, 1]:\\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\\n if not increment_filter[i, 2]:\\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified upper terminal temperature difference\\n if self.ttd_u.is_set:\\n f = self.ttd_u_func\\n for i in [0, 3]:\\n if not increment_filter[i, 1]:\\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\\n if not increment_filter[i, 2]:\\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified lower terminal temperature difference\\n if self.ttd_l.is_set:\\n f = self.ttd_l_func\\n for i in [1, 2]:\\n if not increment_filter[i, 1]:\\n self.jacobian[k, i, 1] = self.numeric_deriv(f, 'p', i)\\n if not increment_filter[i, 2]:\\n self.jacobian[k, i, 2] = self.numeric_deriv(f, 'h', i)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified pressure ratio at hot side\\n if self.pr1.is_set:\\n self.jacobian[k, 0, 1] = self.pr1.val\\n self.jacobian[k, 2, 1] = -1\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified pressure ratio at cold side\\n if self.pr2.is_set:\\n self.jacobian[k, 1, 1] = self.pr2.val\\n self.jacobian[k, 3, 1] = -1\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified zeta at hot side\\n if self.zeta1.is_set:\\n f = self.zeta_func\\n if not increment_filter[0, 0]:\\n self.jacobian[k, 0, 0] = self.numeric_deriv(\\n f, 'm', 0, zeta='zeta1', inconn=0, outconn=0)\\n if not increment_filter[0, 1]:\\n self.jacobian[k, 0, 1] = self.numeric_deriv(\\n f, 'p', 0, zeta='zeta1', inconn=0, outconn=0)\\n if not increment_filter[0, 2]:\\n self.jacobian[k, 0, 2] = self.numeric_deriv(\\n f, 'h', 0, zeta='zeta1', inconn=0, outconn=0)\\n if not increment_filter[2, 1]:\\n self.jacobian[k, 2, 1] = self.numeric_deriv(\\n f, 'p', 2, zeta='zeta1', inconn=0, outconn=0)\\n if not increment_filter[2, 2]:\\n self.jacobian[k, 2, 2] = self.numeric_deriv(\\n f, 'h', 2, zeta='zeta1', inconn=0, outconn=0)\\n k += 1\\n\\n ######################################################################\\n # derivatives for specified zeta at cold side\\n if self.zeta2.is_set:\\n f = self.zeta_func\\n if not increment_filter[1, 0]:\\n self.jacobian[k, 1, 0] = self.numeric_deriv(\\n f, 'm', 1, zeta='zeta2', inconn=1, outconn=1)\\n if not increment_filter[1, 1]:\\n self.jacobian[k, 1, 1] = self.numeric_deriv(\\n f, 'p', 1, zeta='zeta2', inconn=1, outconn=1)\\n if not increment_filter[1, 2]:\\n self.jacobian[k, 1, 2] = self.numeric_deriv(\\n f, 'h', 1, zeta='zeta2', inconn=1, outconn=1)\\n if not increment_filter[3, 1]:\\n self.jacobian[k, 3, 1] = self.numeric_deriv(\\n f, 'p', 3, zeta='zeta2', inconn=1, outconn=1)\\n if not increment_filter[3, 2]:\\n self.jacobian[k, 3, 2] = self.numeric_deriv(\\n f, 'h', 3, zeta='zeta2', inconn=1, outconn=1)\\n k += 1\\n\\n ######################################################################\\n # derivatives for additional equations\\n self.additional_derivatives(increment_filter, k)\",\n \"def calc_equil(sst, ft_qv, use_NT=False):\\n \\n run_main(sst, ft_qv, use_NT)\\n \\n # grab csv file\\n with open('dumpmodel.csv','r') as f:\\n df_result=pd.read_csv(f)\\n\\n # last time step into named tupple\\n out=df_result.iloc[-1]\\n steady_state=make_tuple(out.to_dict())\\n steady_state\\n \\n # obtain steady-state values\\n dth=steady_state.deltheta\\n dqt=steady_state.delqv\\n thetal_m=steady_state.theta\\n qt_m=steady_state.qv\\n h=steady_state.h\\n press=tf.find_press(steady_state.h) #kPa\\n thetal_ft = steady_state.theta + dth\\n qt_ft = steady_state.qv + dqt\\n zb = steady_state.LCL\\n zi = steady_state.h\\n we = steady_state.went\\n \\n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\\n gamma = 6e-3 \\n thetal_3000 = thetal_ft + gamma*(3000-h)\\n LTS = thetal_3000 - steady_state.theta\\n \\n # calculate delta_Fr\\n delta_Frstar = 82.0 # Wm^-2\\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\\n\\n # calculate LWP\\n rho = 1.\\n LWP = 0.5*rho*(zi-zb)**2\\n \\n # put all required variables into output array\\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\\n \\n return out_array\",\n \"def DynamicsCo(x, t, T0, alpha, cost_co, cost_ch, K_co, K_ch, n, r):\\n y=np.zeros([np.size(x)])\\n D=np.zeros([2]) \\n #define fitnss\\n D[0]=dmax*x[0]**n/(K_co**n+x[0]**n) #cooperator\\n D[1]=dmax*x[0]**n/(K_ch**n+x[0]**n) #cheater \\n #degradation\\n deg=fmax*x[1]/(x[1]+Kd) \\n #ODE of eco-evo dynamics\\n y[0]=alpha*T0-deg*x[0]-alpha*x[0] #dt/dt\\n y[1]=x[1]*(r*(1-cost_co)*(1-x[1]-x[2])-D[0]-alpha)#d Co/dt\\n y[2]=x[2]*(r*(1-cost_ch)*(1-x[1]-x[2])-D[1]-alpha) #d Ch/dt\\n \\n return y\",\n \"def solve_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n y = params['y']\\n\\n unknowns['f_xy'] = (x-3.0)**2 + x*y + (y+4.0)**2 - 3.0\",\n \"def nonhomo_system_variation_of_parameters(xc: List[Symbol], gt, t: Symbol = Symbol('t', real=True)):\\n fund_matrix = eye(len(xc))\\n for i, x in enumerate(xc):\\n fund_matrix[:, i] = x\\n\\n procedure = Procedure()\\n procedure.text('Fundamental matrix ').latex('\\\\\\\\Psi', nl=True)\\\\\\n .eq(fund_matrix)\\n\\n gt = Matrix(gt)\\n fund_inv = fund_matrix**(-1)\\n procedure.text('Calculate the inverse of the fundamental matrix ').latex('\\\\\\\\Psi^{-1}', nl=True)\\\\\\n .latex('\\\\\\\\Psi^{-1} = ').eq(fund_inv)\\n\\n fund_inv_gt = expand(simplify(fund_inv * gt))\\n procedure.text('Compute ').latex('\\\\\\\\Psi^{-1} g(t)', nl=True)\\\\\\n .latex('\\\\\\\\Psi^{-1} g(t) = ').eq(fund_inv_gt)\\n\\n procedure.text('Compute the integral', nl=True)\\n fund_inv_gt_int = expand(simplify(integrate(fund_inv_gt)))\\n procedure.latex('\\\\\\\\int \\\\\\\\Psi^{-1} g(t) =').eq(fund_inv_gt_int)\\n\\n procedure.text('Finally, ').latex(\\n '\\\\\\\\vec{\\\\\\\\mathbf{x_p}} = \\\\\\\\Psi \\\\\\\\int \\\\\\\\Psi^{-1} g(t)', nl=True)\\n sol = expand(fund_matrix * fund_inv_gt_int)\\n procedure.latex('\\\\\\\\vec{\\\\\\\\mathbf{x_p}} =').eq(sol)\\n return sol, procedure\",\n \"def temp_func(self,var):\\n y = var['intercept']\\n for p,c in zip(var['powers'],var['coef']):\\n # Exp the 4 inputs to the power for that coef\\n #to plug them into the equation, un-scale them:\\n# a = np.multiply(self.ins,(1/self.scale_var))**p\\n a = self.ins**p\\n y += c* np.prod(a)\\n\\n #to fit this into the environment, re-scale:\\n y = y #* self.scale_var\\n\\n # Noise is a random number (positive or negative), scaled by self.noise\\n noise = random.randint(-1,1) * random.random() * self.noise * cfg['thresh1'] * self.scale_var #scales noise\\n y += noise\\n return y\",\n \"def efSolver2(self):\\n dx = self.dh[0] # dx\\n dy = self.dh[1] # dy\\n dz = self.dh[2] # dz\\n \\n \\\"\\\"\\\"\\n for i in np.arange(0, self.ni):\\n for j in np.arange(0, self.nj):\\n for k in np.arange(0, self.nk):\\n \\\"\\\"\\\"\\n\\n ##x-component#\\n #if i==0: \\n #x-component#\\n \\\"\\\"\\\"\\n if i==0: \\n # forward\\n self.ef[i][j][k][0] = -(-3*self.phi[i][j][k]+\\\\\\n 4*self.phi[i+1][j][k]-\\\\\\n self.phi[i+2][j][k])/(2*dx)\\n \\\"\\\"\\\"\\n \\n # forward\\n self.ef[0,0:self.nj,0:self.nk,0] = -(-3*self.phi[0,0:self.nj,0:self.nk]+\\\\\\n 4*self.phi[1,0:self.nj,0:self.nk]-\\\\\\n self.phi[2,0:self.nj,0:self.nk])/(2*dx)\\n \\n #elif i==self.ni-1: \\n \\\"\\\"\\\"\\n elif i==self.ni-1: \\n # backward\\n self.ef[i][j][k][0] = -(self.phi[i-2][j][k]-\\\\\\n 4*self.phi[i-1][j][k]+\\\\\\n 3*self.phi[i][j][k])/(2*dx)\\n \\\"\\\"\\\" \\n # backward\\n self.ef[self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[self.ni-3,0:self.nj,0:self.nk]-\\\\\\n 4*self.phi[self.ni-2,0:self.nj,0:self.nk]+\\\\\\n 3*self.phi[self.ni-1,0:self.nj,0:self.nk])/(2*dx)\\n \\\"\\\"\\\"\\n else: \\n #central\\n self.ef[i][j][k][0] = -(self.phi[i+1][j][k] - \\\\\\n self.phi[i-1][j][k])/(2*dx)\\n \\\"\\\"\\\" \\n #central\\n self.ef[1:self.ni-1,0:self.nj,0:self.nk,0] = -(self.phi[2:self.ni,0:self.nj,0:self.nk] - \\\\\\n self.phi[0:self.ni-2,0:self.nj,0:self.nk])/(2*dx)\\n\\n\\n #y-component\\n #if j==0:\\n \\\"\\\"\\\"\\n if j==0:\\n self.ef[i][j][k][1] = -(-3*self.phi[i][j][k] + \\\\\\n 4*self.phi[i][j+1][k]-\\\\\\n self.phi[i][j+2][k])/(2*dy)\\n \\n \\\"\\\"\\\"\\n self.ef[0:self.ni,0,0:self.nk,1] = -(-3*self.phi[0:self.ni,0,0:self.nk] + \\\\\\n 4*self.phi[0:self.ni,1,0:self.nk]-\\\\\\n self.phi[0:self.ni,2,0:self.nk])/(2*dy)\\n #elif j==self.nj-1:\\n \\\"\\\"\\\"\\n elif j==self.nj-1:\\n self.ef[i][j][k][1] = -(self.phi[i][j-2][k] - \\\\\\n 4*self.phi[i][j-1][k] +\\\\\\n 3*self.phi[i][j][k])/(2*dy)\\n \\n \\\"\\\"\\\"\\n self.ef[0:self.ni,self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,self.nj-3,0:self.nk] - \\\\\\n 4*self.phi[0:self.ni,self.nj-2,0:self.nk] +\\\\\\n 3*self.phi[0:self.ni,self.nj-1,0:self.nk])/(2*dy)\\n #else:\\n \\\"\\\"\\\"\\n else:\\n self.ef[i][j][k][1] = -(self.phi[i][j+1][k] - \\\\\\n self.phi[i][j-1][k])/(2*dy)\\n\\n \\\"\\\"\\\"\\n self.ef[0:self.ni,1:self.nj-1,0:self.nk,1] = -(self.phi[0:self.ni,2:self.nj,0:self.nk] - \\\\\\n self.phi[0:self.ni,0:self.nj-2,0:self.nk])/(2*dy)\\n\\n #z-component\\n '''\\n if k==0:\\n self.ef[i][j][k][2] = -(-3*self.phi[i][j][k] + \\\\\\n 4*self.phi[i][j][k+1]-\\n self.phi[i][j][k+2])/(2*dz)\\n \\n '''\\n #z-component\\n #if k==0:\\n self.ef[0:self.ni,0:self.nj,0,2] = -(-3*self.phi[0:self.ni,0:self.nj,0] + \\\\\\n 4*self.phi[0:self.ni,0:self.nj,1]-\\n self.phi[0:self.ni,0:self.nj,2])/(2*dz)\\n\\n \\\"\\\"\\\"\\n elif k==self.nk-1:\\n self.ef[i][j][k][2] = -(self.phi[i][j][k-2] - \\\\\\n 4*self.phi[i][j][k-1] + \\\\\\n 3*self.phi[i][j][k])/(2*dz)\\n \\\"\\\"\\\"\\n \\n #elif k==self.nk-1:\\n self.ef[0:self.ni,0:self.nj,self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,self.nk-3] - \\\\\\n 4*self.phi[0:self.ni,0:self.nj,self.nk-2] + \\\\\\n 3*self.phi[0:self.ni,0:self.nj,self.nk-1])/(2*dz) \\n \\\"\\\"\\\"\\n else:\\n self.ef[i][j][k][2] = -(self.phi[i][j][k+1] - \\\\\\n self.phi[i][j][k-1])/(2*dz)\\n \\\"\\\"\\\"\\n #else:\\n self.ef[0:self.ni,0:self.nj,1:self.nk-1,2] = -(self.phi[0:self.ni,0:self.nj,2:self.nk] - \\\\\\n self.phi[0:self.ni,0:self.nj,0:self.nk-2])/(2*dz)\",\n \"def function_PLD(coeffs, t, Pns, lc, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, PLD_params, eclipse = False):\\n new_flux = model_PLD(coeffs, t, Pns, coeffs_dict, coeffs_tuple, fix_coeffs, batman_params, PLD_params, eclipse = eclipse)\\n return lc - new_flux\",\n \"def __init__(self,\\n uDict,\\n phiDict,\\n testSpaceDict,\\n matType,\\n dofBoundaryConditionsDict,\\n dofBoundaryConditionsSetterDict,\\n coefficients,\\n elementQuadrature,\\n elementBoundaryQuadrature,\\n fluxBoundaryConditionsDict=None,\\n advectiveFluxBoundaryConditionsSetterDict=None,\\n diffusiveFluxBoundaryConditionsSetterDictDict=None,\\n stressTraceBoundaryConditionsSetterDict=None,\\n stabilization=None,\\n shockCapturing=None,\\n conservativeFluxDict=None,\\n numericalFluxType=None,\\n TimeIntegrationClass=None,\\n massLumping=False,\\n reactionLumping=False,\\n options=None,\\n name='defaultName',\\n reuse_trial_and_test_quadrature=True,\\n sd = True,\\n movingDomain=False):\\n #\\n #set the objects describing the method and boundary conditions\\n #\\n self.movingDomain=movingDomain\\n self.tLast_mesh=None\\n #\\n self.name=name\\n self.sd=sd\\n self.Hess=False\\n self.lowmem=True\\n self.timeTerm=True#allow turning off the time derivative\\n #self.lowmem=False\\n self.testIsTrial=True\\n self.phiTrialIsTrial=True\\n self.u = uDict\\n self.ua = {}#analytical solutions\\n self.phi = phiDict\\n self.dphi={}\\n for ck,phi in phiDict.iteritems():\\n if coefficients.potential.has_key(ck):\\n for cj in coefficients.potential[ck].keys():\\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\\n else:\\n self.dphi[(ck,ck)] = FiniteElementFunction(phi.femSpace)\\n #check for nonlinearities in the diffusion coefficient that don't match the potential\\n for ci,ckDict in coefficients.diffusion.iteritems():\\n #for ck,cjDict in coefficients.diffusion.iteritems(): #cek: bug?\\n for ck,cjDict in ckDict.iteritems():\\n for cj in cjDict.keys():\\n if not self.dphi.has_key((ck,cj)):\\n self.dphi[(ck,cj)] = FiniteElementFunction(phi.femSpace)\\n self.matType = matType\\n #try to reuse test and trial information across components if spaces are the same\\n self.reuse_test_trial_quadrature = reuse_trial_and_test_quadrature#True#False\\n if self.reuse_test_trial_quadrature:\\n for ci in range(1,coefficients.nc):\\n assert self.u[ci].femSpace.__class__.__name__ == self.u[0].femSpace.__class__.__name__, \\\"to reuse_test_trial_quad all femSpaces must be the same!\\\"\\n ## Simplicial Mesh\\n self.mesh = self.u[0].femSpace.mesh #assume the same mesh for all components for now\\n self.testSpace = testSpaceDict\\n self.dirichletConditions = dofBoundaryConditionsDict\\n self.dirichletNodeSetList=None #explicit Dirichlet conditions for now, no Dirichlet BC constraints\\n self.coefficients = coefficients\\n self.coefficients.initializeMesh(self.mesh)\\n self.nc = self.coefficients.nc\\n self.stabilization = stabilization\\n self.shockCapturing = shockCapturing\\n self.conservativeFlux = conservativeFluxDict #no velocity post-processing for now\\n self.fluxBoundaryConditions=fluxBoundaryConditionsDict\\n self.advectiveFluxBoundaryConditionsSetterDict=advectiveFluxBoundaryConditionsSetterDict\\n self.diffusiveFluxBoundaryConditionsSetterDictDict = diffusiveFluxBoundaryConditionsSetterDictDict\\n #determine whether the stabilization term is nonlinear\\n self.stabilizationIsNonlinear = False\\n #cek come back\\n if self.stabilization != None:\\n for ci in range(self.nc):\\n if coefficients.mass.has_key(ci):\\n for flag in coefficients.mass[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.advection.has_key(ci):\\n for flag in coefficients.advection[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.diffusion.has_key(ci):\\n for diffusionDict in coefficients.diffusion[ci].values():\\n for flag in diffusionDict.values():\\n if flag != 'constant':\\n self.stabilizationIsNonlinear=True\\n if coefficients.potential.has_key(ci):\\n for flag in coefficients.potential[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.reaction.has_key(ci):\\n for flag in coefficients.reaction[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n if coefficients.hamiltonian.has_key(ci):\\n for flag in coefficients.hamiltonian[ci].values():\\n if flag == 'nonlinear':\\n self.stabilizationIsNonlinear=True\\n #determine if we need element boundary storage\\n self.elementBoundaryIntegrals = {}\\n for ci in range(self.nc):\\n self.elementBoundaryIntegrals[ci] = ((self.conservativeFlux != None) or\\n (numericalFluxType != None) or\\n (self.fluxBoundaryConditions[ci] == 'outFlow') or\\n (self.fluxBoundaryConditions[ci] == 'mixedFlow') or\\n (self.fluxBoundaryConditions[ci] == 'setFlow'))\\n #\\n #calculate some dimensions\\n #\\n self.nSpace_global = self.u[0].femSpace.nSpace_global #assume same space dim for all variables\\n self.nDOF_trial_element = [u_j.femSpace.max_nDOF_element for u_j in self.u.values()]\\n self.nDOF_phi_trial_element = [phi_k.femSpace.max_nDOF_element for phi_k in self.phi.values()]\\n self.n_phi_ip_element = [phi_k.femSpace.referenceFiniteElement.interpolationConditions.nQuadraturePoints for phi_k in self.phi.values()]\\n self.nDOF_test_element = [femSpace.max_nDOF_element for femSpace in self.testSpace.values()]\\n self.nFreeDOF_global = [dc.nFreeDOF_global for dc in self.dirichletConditions.values()]\\n self.nVDOF_element = sum(self.nDOF_trial_element)\\n self.nFreeVDOF_global = sum(self.nFreeDOF_global)\\n #\\n NonlinearEquation.__init__(self,self.nFreeVDOF_global)\\n #\\n #build the quadrature point dictionaries from the input (this\\n #is just for convenience so that the input doesn't have to be\\n #complete)\\n #\\n elementQuadratureDict={}\\n elemQuadIsDict = isinstance(elementQuadrature,dict)\\n if elemQuadIsDict: #set terms manually\\n for I in self.coefficients.elementIntegralKeys:\\n if elementQuadrature.has_key(I):\\n elementQuadratureDict[I] = elementQuadrature[I]\\n else:\\n elementQuadratureDict[I] = elementQuadrature['default']\\n else:\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[I] = elementQuadrature\\n if self.stabilization != None:\\n for I in self.coefficients.elementIntegralKeys:\\n if elemQuadIsDict:\\n if elementQuadrature.has_key(I):\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature[I]\\n else:\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature['default']\\n else:\\n elementQuadratureDict[('stab',)+I[1:]] = elementQuadrature\\n if self.shockCapturing != None:\\n for ci in self.shockCapturing.components:\\n if elemQuadIsDict:\\n if elementQuadrature.has_key(('numDiff',ci,ci)):\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature[('numDiff',ci,ci)]\\n else:\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature['default']\\n else:\\n elementQuadratureDict[('numDiff',ci,ci)] = elementQuadrature\\n if massLumping:\\n for ci in self.coefficients.mass.keys():\\n elementQuadratureDict[('m',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n if reactionLumping:\\n for ci in self.coefficients.mass.keys():\\n elementQuadratureDict[('r',ci)] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n for I in self.coefficients.elementIntegralKeys:\\n elementQuadratureDict[('stab',)+I[1:]] = Quadrature.SimplexLobattoQuadrature(self.nSpace_global,1)\\n elementBoundaryQuadratureDict={}\\n if isinstance(elementBoundaryQuadrature,dict): #set terms manually\\n for I in self.coefficients.elementBoundaryIntegralKeys:\\n if elementBoundaryQuadrature.has_key(I):\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature[I]\\n else:\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature['default']\\n else:\\n for I in self.coefficients.elementBoundaryIntegralKeys:\\n elementBoundaryQuadratureDict[I] = elementBoundaryQuadrature\\n #\\n # find the union of all element quadrature points and\\n # build a quadrature rule for each integral that has a\\n # weight at each point in the union\\n #mwf include tag telling me which indices are which quadrature rule?\\n (self.elementQuadraturePoints,self.elementQuadratureWeights,\\n self.elementQuadratureRuleIndeces) = Quadrature.buildUnion(elementQuadratureDict)\\n self.nQuadraturePoints_element = self.elementQuadraturePoints.shape[0]\\n self.nQuadraturePoints_global = self.nQuadraturePoints_element*self.mesh.nElements_global\\n #\\n #Repeat the same thing for the element boundary quadrature\\n #\\n (self.elementBoundaryQuadraturePoints,\\n self.elementBoundaryQuadratureWeights,\\n self.elementBoundaryQuadratureRuleIndeces) = Quadrature.buildUnion(elementBoundaryQuadratureDict)\\n self.nElementBoundaryQuadraturePoints_elementBoundary = self.elementBoundaryQuadraturePoints.shape[0]\\n self.nElementBoundaryQuadraturePoints_global = (self.mesh.nElements_global*\\n self.mesh.nElementBoundaries_element*\\n self.nElementBoundaryQuadraturePoints_elementBoundary)\\n\\n #\\n #storage dictionaries\\n self.scalars_element = set()\\n #\\n #simplified allocations for test==trial and also check if space is mixed or not\\n #\\n self.q={}\\n self.ebq={}\\n self.ebq_global={}\\n self.ebqe={}\\n self.phi_ip={}\\n #mesh\\n self.q['x'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,3),'d')\\n self.q['det(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q['abs(det(J))'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q['J'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\\n self.q['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global,self.nSpace_global),'d')\\n self.ebqe['x'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['g'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n max(1,self.nSpace_global-1),\\n max(1,self.nSpace_global-1)),\\n 'd')\\n self.ebqe['inverse(J)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global,self.nSpace_global),'d')\\n self.ebqe['hat(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['bar(x)'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,3),'d')\\n self.ebqe['sqrt(det(g))'] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n self.ebqe[('n')] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n #shape\\n self.q[('v',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.q[('w',0)] = self.q[('v',0)]\\n self.q[('grad(v)',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.q[('grad(w)',0)] = self.q[('grad(v)',0)]\\n self.q[('grad(w)*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.q[('grad(w)*dV_f',0)] = self.q[('grad(w)*dV',0)]\\n #todo get rid of dV_{f,a}, etc\\n self.q[('w*dV',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.q[('w*dV_m',0)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n #assume all components are the same space for now\\n shapeKeysForAlias = ['v','w','grad(v)','grad(w)*dV','grad(w)*dV_f','w*dV','w*dV_m']\\n for ci in range(1,self.nc):\\n for key in shapeKeysForAlias:\\n key_ci = (key,ci)\\n key_0 = (key,0)\\n self.q[key_ci] = self.q[key_0]\\n #ELLAM weights stiffness, body integrals by dt\\n for ci in range(self.nc):\\n self.q[('dt*grad(w)*dV',ci)]= numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[ci],self.nSpace_global),'d')\\n #\\n self.ebqe[('v',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\\n self.ebqe[('w',0)] = self.ebqe[('v',0)]\\n self.ebqe[('grad(v)',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0],self.nSpace_global),'d')\\n self.ebqe[('w*dS_f',0)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nDOF_trial_element[0]),'d')\\n #assume all components are the same space for now\\n shapeKeysForAlias = ['v','w','grad(v)','w*dS_f']\\n for ci in range(1,self.nc):\\n for key in shapeKeysForAlias:\\n key_ci = (key,ci)\\n key_0 = (key,0)\\n self.ebqe[key_ci] = self.ebqe[key_0]\\n\\n for ci in range(self.nc):\\n self.q[('u',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('grad(u)',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n #f\\n for ci in self.coefficients.advection.keys():\\n self.q[('f',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n for cj in self.coefficients.advection[ci].keys():\\n self.q[('df',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nSpace_global),'d')\\n self.ebqe[('f',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n for cj in self.coefficients.advection[ci].keys():\\n self.ebqe[('df',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary,self.nSpace_global),'d')\\n\\n #a, linear dispersion single component\\n\\n for ci,ckDict in self.coefficients.diffusion.iteritems():\\n for ck,cjDict in ckDict.iteritems():\\n for flag in cjDict.values():\\n assert flag == 'constant', \\\"Error potential %s LADRellam does not handle diffusion = %s yet\\\" % (ck,flag)\\n\\n if self.coefficients.sdInfo != None and (ci,ck) in self.coefficients.sdInfo.keys():\\n self.q[('a',ci,ck)] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n for cj in cjDict.keys():\\n self.q[('da',ci,ck,cj)] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n self.ebqe[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n for cj in cjDict.keys():\\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.coefficients.sdInfo[(ci,ck)][0][self.nSpace_global]),\\n 'd')\\n\\n else:\\n self.q[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n for cj in cjDict.keys():\\n self.q[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nQuadraturePoints_element,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n self.ebqe[('a',ci,ck)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n for cj in cjDict.keys():\\n self.ebqe[('da',ci,ck,cj)]=numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),\\n 'd')\\n #dense storage\\n self.q[('grad(w)*dV_a',ci,ck)] = self.q[('grad(w)*dV_f',ci)]\\n self.q[('dt*grad(w)*dV_a',ci,ck)]= self.q[('dt*grad(w)*dV',ci)]\\n #ci,ckDict\\n #linear potential only for now, need to change for e.g., Buckley Leverett\\n for ck in self.phi.keys():\\n self.phi[ck].dof[:]=self.u[ck].dof\\n self.q[('grad(phi)',ck)] = self.q[('grad(u)',ck)]\\n for key in self.dphi.keys():\\n self.dphi[key].dof.fill(1.0)\\n self.q[('dphi',key[0],key[1])] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n\\n\\n\\n# if self.coefficients.diffusion.has_key(0):\\n# for ck,flag in self.coefficients.diffusion[0][0].iteritems():\\n# assert self.coefficients.diffusion[0][0][ck] == 'constant', \\\"Error potential %s LADRellam does not handle diffusion = %s yet\\\" % (ck,flag)\\n# if self.coefficients.sdInfo != None and (0,0) in self.coefficients.sdInfo.keys():\\n# self.q[('a',0,0)] = numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.q[('da',0,0,0)] = numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.ebqe[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.coefficients.sdInfo[(0,0)][0][self.nSpace_global]),\\n# 'd')\\n\\n# else:\\n# self.q[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.q[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nElements_global,\\n# self.nQuadraturePoints_element,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.ebqe[('a',0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# self.ebqe[('da',0,0,0)]=numpy.zeros(\\n# (self.mesh.nExteriorElementBoundaries_global,\\n# self.nElementBoundaryQuadraturePoints_elementBoundary,\\n# self.nSpace_global,\\n# self.nSpace_global),\\n# 'd')\\n# #\\n# self.phi[0].dof[:]=self.u[0].dof\\n# self.dphi[(0,0)].dof.fill(1.0)\\n# self.q[('grad(phi)',0)] = self.q[('grad(u)',0)]\\n# self.q[('dphi',0,0)] = numpy.ones((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n\\n# self.q[('grad(w)*dV_a',0,0)] = self.q[('grad(w)*dV_f',0)]\\n# self.q[('dt*grad(w)*dV_a',0,0)]= self.q[('dt*grad(w)*dV',0)]\\n\\n #r 'constant' ie not a function of solution but go ahead and include dr for now\\n for ci,cjDict in self.coefficients.reaction.iteritems():\\n self.q[('r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n for cj in cjDict.keys():\\n self.q[('dr',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('w*dV_r',ci)] = self.q[('w*dV',ci)]\\n self.q[('dt*w*dV_r',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element,self.nDOF_trial_element[0]),'d')\\n self.ebqe[('r',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n #m\\n for ci,cjDict in self.coefficients.mass.iteritems():\\n self.q[('m',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n for cj in cjDict.keys():\\n self.q[('dm',ci,cj)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('mt',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('m_last',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('m_tmp',ci)] = self.q[('m',ci)]\\n self.q[('cfl',ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.q[('numDiff',ci,ci)] = numpy.zeros((self.mesh.nElements_global,self.nQuadraturePoints_element),'d')\\n self.ebqe[('m',ci)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n for cj in cjDict.keys():\\n self.ebqe[('dm',ci,cj)] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n\\n ###ellam specific options with defauls here\\n self.ellamDiscretization = ELLAMtools.ELLAMdiscretization(self,options)\\n\\n #\\n self.needEBQ = options.needEBQ #could need for analytical velocity evaluation with RT0,BDM\\n\\n #beg normal stuff allocating things\\n self.points_elementBoundaryQuadrature= set()\\n self.scalars_elementBoundaryQuadrature= set([('u',ci) for ci in range(self.nc)])\\n self.vectors_elementBoundaryQuadrature= set()\\n self.tensors_elementBoundaryQuadrature= set()\\n\\n if self.needEBQ:\\n for k in ['x','hat(x)']:\\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n 3),'d')\\n self.ebq['n'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global),'d')\\n self.ebq['inverse(J)'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global,\\n self.nSpace_global),'d')\\n #allocate the metric tensor\\n self.ebq['g'] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n max(1,self.nSpace_global-1),\\n max(1,self.nSpace_global-1)),\\n 'd')\\n log(memory(\\\"element boundary quadrature\\\",\\\"LADRellam\\\"),level=4)\\n ebq_keys = ['sqrt(det(g))']\\n ebq_keys.extend([('u',ci) for ci in range(self.nc)])\\n for k in ebq_keys:\\n self.ebq[k] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n\\n #test and trial info\\n self.ebq[('w',0)] = numpy.zeros((self.mesh.nElements_global,\\n self.mesh.nElementBoundaries_element,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nDOF_trial_element[0]),'d')\\n for ci in range(1,self.nc):\\n self.ebq[('w',ci)] = self.ebq[('w',0)]\\n for ci in range(self.nc):\\n self.ebq[('v',ci)] = self.ebq[('w',0)]\\n\\n #ebq_global info\\n self.ebq_global['x'] = numpy.zeros((self.mesh.nElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n 3),'d')\\n self.ebq_global['n'] = numpy.zeros((self.mesh.nElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nSpace_global),'d')\\n #\\n # allocate residual and Jacobian storage\\n #\\n self.elementResidual = [numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci]),\\n 'd') for ci in range(self.nc)]\\n self.elementSpatialResidual = [numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci]),\\n 'd') for ci in range(self.nc)]\\n self.elementJacobian = {}\\n for ci in range(self.nc):\\n self.elementJacobian[ci]={}\\n for cj in range(self.nc):\\n if cj in self.coefficients.stencil[ci]:\\n self.elementJacobian[ci][cj] = numpy.zeros(\\n (self.mesh.nElements_global,\\n self.nDOF_test_element[ci],\\n self.nDOF_trial_element[cj]),\\n 'd')\\n #\\n self.fluxJacobian_exterior = {}\\n for ci in range(self.nc):\\n self.fluxJacobian_exterior[ci]={}\\n for cj in self.coefficients.stencil[ci]:\\n self.fluxJacobian_exterior[ci][cj] = numpy.zeros(\\n (self.mesh.nExteriorElementBoundaries_global,\\n self.nElementBoundaryQuadraturePoints_elementBoundary,\\n self.nDOF_trial_element[cj]),\\n 'd')\\n\\n #\\n #\\n #\\n #\\n log(memory(\\\"element and element boundary Jacobians\\\",\\\"OneLevelTransport\\\"),level=4)\\n self.inflowBoundaryBC = {}\\n self.inflowBoundaryBC_values = {}\\n self.inflowFlux = {}\\n for cj in range(self.nc):\\n self.inflowBoundaryBC[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,),'i')\\n self.inflowBoundaryBC_values[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nDOF_trial_element[cj]),'d')\\n self.inflowFlux[cj] = numpy.zeros((self.mesh.nExteriorElementBoundaries_global,self.nElementBoundaryQuadraturePoints_elementBoundary),'d')\\n self.internalNodes = set(range(self.mesh.nNodes_global))\\n #identify the internal nodes this is ought to be in mesh\\n ##\\\\todo move this to mesh\\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\\n eN_global = self.mesh.elementBoundaryElementsArray[ebN,0]\\n ebN_element = self.mesh.elementBoundaryLocalElementBoundariesArray[ebN,0]\\n for i in range(self.mesh.nNodes_element):\\n if i != ebN_element:\\n I = self.mesh.elementNodesArray[eN_global,i]\\n self.internalNodes -= set([I])\\n self.nNodes_internal = len(self.internalNodes)\\n self.internalNodesArray=numpy.zeros((self.nNodes_internal,),'i')\\n for nI,n in enumerate(self.internalNodes):\\n self.internalNodesArray[nI]=n\\n #\\n del self.internalNodes\\n self.internalNodes = None\\n log(\\\"Updating local to global mappings\\\",2)\\n self.updateLocal2Global()\\n log(\\\"Building time integration object\\\",2)\\n log(memory(\\\"inflowBC, internalNodes,updateLocal2Global\\\",\\\"OneLevelTransport\\\"),level=4)\\n #mwf for interpolating subgrid error for gradients etc\\n if self.stabilization and self.stabilization.usesGradientStabilization:\\n self.timeIntegration = TimeIntegrationClass(self,integrateInterpolationPoints=True)\\n else:\\n self.timeIntegration = TimeIntegrationClass(self)\\n\\n if options != None:\\n self.timeIntegration.setFromOptions(options)\\n log(memory(\\\"TimeIntegration\\\",\\\"OneLevelTransport\\\"),level=4)\\n log(\\\"Calculating numerical quadrature formulas\\\",2)\\n self.calculateQuadrature()\\n #lay out components/equations contiguously for now\\n self.offset = [0]\\n for ci in range(1,self.nc):\\n self.offset += [self.offset[ci-1]+self.nFreeDOF_global[ci-1]]\\n self.stride = [1 for ci in range(self.nc)]\\n #use contiguous layout of components for parallel, requires weak DBC's\\n comm = Comm.get()\\n self.comm=comm\\n if comm.size() > 1:\\n assert numericalFluxType != None and numericalFluxType.useWeakDirichletConditions,\\\"You must use a numerical flux to apply weak boundary conditions for parallel runs\\\"\\n self.offset = [0]\\n for ci in range(1,self.nc):\\n self.offset += [ci]\\n self.stride = [self.nc for ci in range(self.nc)]\\n #\\n log(memory(\\\"stride+offset\\\",\\\"OneLevelTransport\\\"),level=4)\\n if numericalFluxType != None:\\n if options == None or options.periodicDirichletConditions == None:\\n self.numericalFlux = numericalFluxType(self,\\n dofBoundaryConditionsSetterDict,\\n advectiveFluxBoundaryConditionsSetterDict,\\n diffusiveFluxBoundaryConditionsSetterDictDict)\\n else:\\n self.numericalFlux = numericalFluxType(self,\\n dofBoundaryConditionsSetterDict,\\n advectiveFluxBoundaryConditionsSetterDict,\\n diffusiveFluxBoundaryConditionsSetterDictDict,\\n options.periodicDirichletConditions)\\n else:\\n self.numericalFlux = None\\n #set penalty terms\\n #cek todo move into numerical flux initialization\\n if self.ebq_global.has_key('penalty'):\\n for ebN in range(self.mesh.nElementBoundaries_global):\\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\\n self.ebq_global['penalty'][ebN,k] = self.numericalFlux.penalty_constant/(self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power)\\n #penalty term\\n #cek move to Numerical flux initialization\\n if self.ebqe.has_key('penalty'):\\n for ebNE in range(self.mesh.nExteriorElementBoundaries_global):\\n ebN = self.mesh.exteriorElementBoundariesArray[ebNE]\\n for k in range(self.nElementBoundaryQuadraturePoints_elementBoundary):\\n self.ebqe['penalty'][ebNE,k] = self.numericalFlux.penalty_constant/self.mesh.elementBoundaryDiametersArray[ebN]**self.numericalFlux.penalty_power\\n log(memory(\\\"numericalFlux\\\",\\\"OneLevelTransport\\\"),level=4)\\n self.elementEffectiveDiametersArray = self.mesh.elementInnerDiametersArray\\n #use post processing tools to get conservative fluxes, None by default\\n import PostProcessingTools\\n self.velocityPostProcessor = PostProcessingTools.VelocityPostProcessingChooser(self)\\n log(memory(\\\"velocity postprocessor\\\",\\\"OneLevelTransport\\\"),level=4)\\n #helper for writing out data storage\\n import Archiver\\n self.elementQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n self.elementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n self.exteriorElementBoundaryQuadratureDictionaryWriter = Archiver.XdmfWriter()\\n #TODO get rid of this\\n for ci,fbcObject in self.fluxBoundaryConditionsObjectsDict.iteritems():\\n self.ebqe[('advectiveFlux_bc_flag',ci)] = numpy.zeros(self.ebqe[('advectiveFlux_bc',ci)].shape,'i')\\n for t,g in fbcObject.advectiveFluxBoundaryConditionsDict.iteritems():\\n if self.coefficients.advection.has_key(ci):\\n self.ebqe[('advectiveFlux_bc',ci)][t[0],t[1]] = g(self.ebqe[('x')][t[0],t[1]],self.timeIntegration.t)\\n self.ebqe[('advectiveFlux_bc_flag',ci)][t[0],t[1]] = 1\\n\\n if hasattr(self.numericalFlux,'setDirichletValues'):\\n self.numericalFlux.setDirichletValues(self.ebqe)\\n if not hasattr(self.numericalFlux,'isDOFBoundary'):\\n self.numericalFlux.isDOFBoundary = {}\\n for ci in range(self.nc):\\n self.numericalFlux.isDOFBoundary[ci]= numpy.zeros(self.ebqe[('u',ci)].shape,'i')\\n if not hasattr(self.numericalFlux,'ebqe'):\\n self.numericalFlux.ebqe = {}\\n for ci in range(self.nc):\\n self.numericalFlux.ebqe[('u',ci)]= numpy.zeros(self.ebqe[('u',ci)].shape,'d')\",\n \"def objective(trial):\\n # The parameters that we will calibrate the model for are shown here.\\n # Optuna trial i\\n BOD = trial.suggest_uniform(\\\"BOD\\\", 0, 1) #Review ranges here\\n k_r = trial.suggest_uniform(\\\"k_r\\\", 0, 1) #Review Ranges here \\n \\n def ChLa(t):\\n return 1 # Need to link to data\\n\\n def I(x):\\n return 1 # Need to link to data\\n\\n K_z = 2 * 10**(-5) # p.51\\n a = K_z\\n k_b = 0.1 # Table 5\\n th_b = 1.047 # Table 5\\n k_r = 0.1 # Table 5\\n YCHO2 = 0.0083 # Table 5\\n th_p = 1.036 # Table 5\\n th_s = 1.065 # Table 5\\n th_r = 1.047 # Table 5\\n\\n def Temp(t):\\n \\\"\\\"\\\"\\n Function that maps time to temperature\\n \\\"\\\"\\\"\\n return 20 # Need to link to data\\n\\n def P_max(t):\\n return 9.6 * 1.036 **(Temp(t) - 20) # Eq. 4\\n\\n def L_min(t):\\n I = 1 # Need to link to PAR data\\n K_1 = 0.687 * 1.086**(Temp(t) - 20)\\n K_2 = 15\\n return I * (1 + 2 * np.sqrt(K_1 / K_2)) / (I + K_1 + I**2 / K_2) # Eq. 5\\n \\n # f deals with sink and source terms \\n def f(x, t):\\n return -1 / YCHO2 * k_r * th_r**(Temp(t) - 20) * ChLa(t) + P_max(t) * L_min(t) * ChLa(t) - k_b * th_b**(Temp(t)-20) * BOD \\n\\n L = 200 # Length of domain\\n dt = 1 / 48 # Mesh spacing in t\\n F = a * dt # a * dt / dx**2\\n T = 100 # Simulation time stop\\n\\n # Solving the PDE\\n DO, x, t, _ = solver_FE_simple(I, a, f, L, dt, F, T)\\n \\n # Creating some bogus targets while database errors are happening\\n DO_data = DO + np.random.random(len(DO))\\n\\n # Using mean squared error as the measure of fit, where we want\\n # to minimize this number\\n return ((DO - DO_data)**2).mean()\",\n \"def fluid_reynolds(uu, param, grid, lnrho=list(), shock=list(), nghost=3,\\n lmix=True):\\n #viscous forces\\n th2 = 2./3\\n th1 = 1./3\\n fvisc = np.zeros_like(uu)\\n #molecular viscosity contribution\\n ldel2, lshock, lhyper3 = False, False, False\\n for ivisc in param.ivisc:\\n if not 'shock' in ivisc and not 'hyper' in ivisc\\\\\\n and not '\\\\n' in ivisc:\\n ldel2 = True\\n if 'shock' in ivisc:\\n lshock = True\\n if 'hyper3' in ivisc:\\n lhyper3 = True\\n \\n if ldel2:\\n if lhyper3:\\n lhyper3 = lhyper3==lmix\\n del2u = np.zeros_like(uu)\\n for j in range(0,3):\\n del2u[j] = del2(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n del2u[j, : nghost, nghost:-nghost, nghost:-nghost] = del2u[j,-2*nghost: -nghost, nghost: -nghost, nghost: -nghost]\\n del2u[j,-nghost: , nghost:-nghost, nghost:-nghost] = del2u[j, nghost:2*nghost, nghost: -nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost, : nghost, nghost:-nghost] = del2u[j, nghost: -nghost,-2*nghost: -nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost,-nghost: , nghost:-nghost] = del2u[j, nghost: -nghost, nghost:2*nghost, nghost: -nghost]\\n del2u[j, nghost:-nghost, nghost:-nghost, : nghost] = del2u[j, nghost: -nghost, nghost: -nghost,-2*nghost: -nghost]\\n del2u[j, nghost:-nghost, nghost:-nghost,-nghost: ] = del2u[j, nghost: -nghost, nghost: -nghost, nghost:2*nghost]\\n for ivisc in param.ivisc:\\n ivisc = str.strip(ivisc,'\\\\n')\\n if 'nu-const' not in ivisc and 'shock' not in ivisc\\\\\\n and 'hyper' not in ivisc and len(ivisc) > 0:\\n print('fluid_reynolds WARNING: '+ivisc+' not implemented\\\\n'+\\n 'terms may be missing from the standard rate of strain tensor')\\n fvisc = fvisc + param.nu*del2u\\n del(del2u)\\n tmp0 = grad(uu[0],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp0[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp0[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp0[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp0[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp0[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp0[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp0[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp0[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp0[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp0[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n tmp1 = grad(uu[1],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp1[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp1[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp1[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp1[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp1[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp1[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp1[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp1[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp1[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp1[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n tmp2 = grad(uu[2],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n tmp2[j, :nghost,nghost:-nghost,nghost:-nghost] = tmp2[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n tmp2[j,-nghost:,nghost:-nghost,nghost:-nghost] = tmp2[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost, :nghost,nghost:-nghost] = tmp2[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost,-nghost:,nghost:-nghost] = tmp2[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n tmp2[j,nghost:-nghost,nghost:-nghost, :nghost] = tmp2[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n tmp2[j,nghost:-nghost,nghost:-nghost,-nghost:] = tmp2[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n #effect of compressibility \\n if len(lnrho) > 0:\\n divu = div(uu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n divu[ :nghost,nghost:-nghost,nghost:-nghost] = divu[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n divu[-nghost:,nghost:-nghost,nghost:-nghost] = divu[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n divu[nghost:-nghost, :nghost,nghost:-nghost] = divu[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n divu[nghost:-nghost,-nghost:,nghost:-nghost] = divu[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n divu[nghost:-nghost,nghost:-nghost, :nghost] = divu[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n divu[nghost:-nghost,nghost:-nghost,-nghost:] = divu[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n gradlnrho = grad(lnrho,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n gradlnrho[j, :nghost,nghost:-nghost,nghost:-nghost] = gradlnrho[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradlnrho[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost, :nghost,nghost:-nghost] = gradlnrho[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradlnrho[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,nghost:-nghost, :nghost] = gradlnrho[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n gradlnrho[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradlnrho[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n Sglnrho = np.zeros_like(uu)\\n Sglnrho[0] = dot(tmp0,gradlnrho) +\\\\\\n (tmp0[0]+tmp1[0]+tmp2[0]-th2*divu)*gradlnrho[0] \\n Sglnrho[1] = dot(tmp1,gradlnrho) +\\\\\\n (tmp0[1]+tmp1[1]+tmp2[1]-th2*divu)*gradlnrho[1]\\n Sglnrho[2] = dot(tmp2,gradlnrho) +\\\\\\n (tmp0[2]+tmp1[2]+tmp2[2]-th2*divu)*gradlnrho[2]\\n graddivu = grad(divu,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n graddivu[j, :nghost,nghost:-nghost,nghost:-nghost] = graddivu[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n graddivu[j,-nghost:,nghost:-nghost,nghost:-nghost] = graddivu[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost, :nghost,nghost:-nghost] = graddivu[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost,-nghost:,nghost:-nghost] = graddivu[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n graddivu[j,nghost:-nghost,nghost:-nghost, :nghost] = graddivu[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n graddivu[j,nghost:-nghost,nghost:-nghost,-nghost:] = graddivu[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n fvisc = fvisc + param.nu*(th1*graddivu+Sglnrho)\\n del(Sglnrho)\\n elif param.ldensity:\\n print('fluid_reynolds WARNING: no lnrho provided\\\\n'+\\n 'rate of strain tensor likely incomplete')\\n #shock contribution\\n if lshock:\\n if len(shock) == 0:\\n print('fluid_reynolds WARNING: no shock provided\\\\n'+\\n 'rate of strain tensor likely incomplete')\\n else:\\n shock[ :nghost,nghost:-nghost,nghost:-nghost] = shock[-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n shock[-nghost:,nghost:-nghost,nghost:-nghost] = shock[ nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n shock[nghost:-nghost, :nghost,nghost:-nghost] = shock[nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n shock[nghost:-nghost,-nghost:,nghost:-nghost] = shock[nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n shock[nghost:-nghost,nghost:-nghost, :nghost] = shock[nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n shock[nghost:-nghost,nghost:-nghost,-nghost:] = shock[nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n divugradlnrho = np.zeros_like(uu)\\n gradshock = grad(shock,grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n coordinate_system=param.coord_system)\\n for j in range(0,3):\\n gradshock[j, :nghost,nghost:-nghost,nghost:-nghost] = gradshock[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n gradshock[j,-nghost:,nghost:-nghost,nghost:-nghost] = gradshock[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost, :nghost,nghost:-nghost] = gradshock[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost,-nghost:,nghost:-nghost] = gradshock[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n gradshock[j,nghost:-nghost,nghost:-nghost, :nghost] = gradshock[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n gradshock[j,nghost:-nghost,nghost:-nghost,-nghost:] = gradshock[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n for j in range(0,3):\\n divugradlnrho[j] = param.nu_shock*divu*gradshock[j] +\\\\\\n param.nu_shock*shock*(divu*gradlnrho[j] + graddivu[j])\\n del(divu,gradshock,gradlnrho,graddivu)\\n fvisc = fvisc + divugradlnrho\\n del(divugradlnrho)\\n if lhyper3:\\n #deluij5 = np.zeros_like([uu,uu,uu])\\n #uij5glnrho to be included\\n del6u = np.zeros_like(uu)\\n for j in range(0,3):\\n del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz)\\n del6u[j, :nghost,nghost:-nghost,nghost:-nghost] = del6u[j,-2*nghost:-nghost,nghost:-nghost,nghost:-nghost]\\n del6u[j,-nghost:,nghost:-nghost,nghost:-nghost] = del6u[j, nghost: 2*nghost,nghost:-nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost, :nghost,nghost:-nghost] = del6u[j,nghost:-nghost,-2*nghost:-nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost,-nghost:,nghost:-nghost] = del6u[j,nghost:-nghost, nghost: 2*nghost,nghost:-nghost]\\n del6u[j,nghost:-nghost,nghost:-nghost, :nghost] = del6u[j,nghost:-nghost,nghost:-nghost,-2*nghost:-nghost]\\n del6u[j,nghost:-nghost,nghost:-nghost,-nghost:] = del6u[j,nghost:-nghost,nghost:-nghost, nghost: 2*nghost]\\n #del6 for non-cartesian tba\\n #del6u[j] = del6(uu[j],grid.dx,grid.dy,grid.dz,x=grid.x,y=grid.y,\\n # coordinate_system=param.coord_system)\\n fvisc = fvisc + param.nu_hyper3*del6u\\n del(del6u)\\n fvisc2 = np.sqrt(dot2(fvisc))\\n #advective forces\\n advec = np.zeros_like(uu)\\n advec[0] = dot(uu,tmp0)\\n advec[1] = dot(uu,tmp1)\\n advec[0] = dot(uu,tmp2)\\n del(tmp0,tmp1,tmp2)\\n advec2 = np.sqrt(dot2(advec))\\n del(advec)\\n #avoid division by zero\\n if fvisc2.max() > 0:\\n fvisc2[np.where(fvisc2==0)] = fvisc2[np.where(fvisc2>0)].min()\\n Re = advec2/fvisc2\\n #set minimum floor to exclude zero-valued Re \\n Re[np.where(Re==0)] = Re[np.where(Re>0)].min()\\n else:\\n Re = advec2\\n print('Re undefined')\\n return Re\",\n \"def gather_derivatives(self):\\n self.xdot[0,0:self.n] = self.mdot[0:self.n] \\n self.xdot[1,0:self.n] = self.rdot[0:self.n,0]\\n self.xdot[2,0:self.n] = self.rdot[0:self.n,1]\\n self.xdot[3,0:self.n] = self.rdot[0:self.n,2]\\n self.xdot[4,0:self.n] = self.vdot[0:self.n,0]\\n self.xdot[5,0:self.n] = self.vdot[0:self.n,1]\\n self.xdot[6,0:self.n] = self.vdot[0:self.n,2]\\n self.xdot[7,0:self.n] = self.rhodot[0:self.n] \\n self.xdot[8,0:self.n] = 0\\n self.xdot[9,0:self.n] = 0\\n self.xdot[10,0:self.n] = self.udot[0:self.n]\\n return self.xdot\",\n \"def exocytosis_model(rate,twin,N_syn,\\n pars,N_var,\\n recompile=0,\\n model='spiking',\\n solver_options=None,\\n stimulus='poisson',spikes_pre=None):\\n\\n # First check that N_var is compatible\\n assert N_var<3 and N_var>=1, \\\"Number of variables of exocytosis model must be either 1 (x) or 2 (x,u)\\\"\\n\\n # Assures that twin is Numpy array for later handling\\n twin = np.asarray(twin,dtype=float)\\n\\n # Also convert make sure to recast N_eq in a way that is suitable for C\\n N_var = np.intc(N_var)\\n if not N_syn: N_syn = 0 # Dummy value of N_syn in the case of the mean-field model\\n N_syn = np.intc(N_syn)\\n\\n if model=='spiking':\\n # Create input_spikes\\n if twin.size == 2:\\n # One rate or multiple ones in the same interval\\n spikes = spg.input_spikes(N_syn, twin[1], rate, 0, stimulus=stimulus, spikes_pre=spikes_pre)\\n else:\\n # Multiple rates in different intervals\\n spikes = spg.input_spikes(N_syn, twin, rate, 0, stimulus=stimulus, spikes_pre=spikes_pre)\\n twin = np.r_[0, np.sum(twin)]\\n N_spk = int(np.shape(spikes)[1])\\n\\n # Check that ICs are of size N_var x N_syn\\n # NOTE: you will have to pass the whole vector of ICs to compute solutions from last point\\n if pars['ICs'].size != (N_var+1)*N_syn:\\n pars['ICs'] = np.tile(pars['ICs'][:N_var+1],(1,N_syn))[0]\\n\\n support_code = \\\"\\\"\\\"\\n #include \\\"gliotransmission_models.h\\\"\\n \\\"\\\"\\\"\\n source_files = [os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/pycapi_utils.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/solver_options.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/stochastic_solvers.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/code/gliotransmission_models.cpp')]\\n code = \\\"\\\"\\\"\\n // Version\\n double version = 1.0;\\n \\n // Define astrocyte model\\n release synapse(N_var,N_syn,N_spk,pars);\\n synapse.set_ics(pars);\\n \\n // Declare output structure\\n out_release out(N_spk,N_var);\\n \\n // Simulator\\n out = synapse.simulate(spikes.data());\\n \\n //Output \\n return_val = out.make_PyDict();\\n \\\"\\\"\\\"\\n libs = ['gsl', 'gslcblas', 'm']\\n dirs = [os.path.join(os.path.expanduser('~'), base_dir + '/code/'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers')]\\n vars = ['pars', 'spikes','N_spk','N_var','N_syn']\\n otm = weave.inline(code,\\n vars,\\n support_code=support_code,\\n sources=source_files,\\n libraries=libs,\\n library_dirs=dirs,\\n include_dirs=dirs,\\n runtime_library_dirs=dirs,\\n type_converters=converters.blitz,\\n compiler='gcc',\\n extra_compile_args=['-std=c++11'],\\n force=recompile)\\n # Post-stimulus processing\\n otm['spk'] = spikes[0] # Spike instants\\n otm['is'] = spikes[-1] # Synapse indexes in the spike train\\n otm['ICs'] = pars['ICs']\\n if N_var>1:\\n u_ = otm['u']\\n else:\\n u_ = None\\n otm['LCs'] = last_point(pars,otm['spk'],twin[-1],otm['x'],otm['y'],otm['is'],uval=u_)\\n elif model=='average':\\n # Check that rate is scalar\\n assert isscalar(rate), \\\"Mean-field rate must be scalar\\\"\\n\\n support_code = \\\"\\\"\\\"\\n #include \\\"gliotransmission_models.h\\\"\\n \\\"\\\"\\\"\\n source_files = [os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/pycapi_utils.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/solver_options.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers/stochastic_solvers.cpp'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/code/gliotransmission_models.cpp')]\\n code = \\\"\\\"\\\"\\n // Version\\n double version = 0.0;\\n\\n // Define astrocyte model\\n release_ave synapse(N_var);\\n\\n // Declare output structure\\n out_release out;\\n\\n // Simulator\\n out = synapse.simulate(rate,pars,solver_options);\\n\\n //Output \\n return_val = out.make_PyDict();\\n \\\"\\\"\\\"\\n libs = ['gsl', 'gslcblas', 'm']\\n dirs = [os.path.join(os.path.expanduser('~'), base_dir + '/code/'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules'),\\n os.path.join(os.path.expanduser('~'), base_dir + '/pycustommodules/solvers')]\\n vars = ['rate', 'pars', 'solver_options', 'N_var']\\n otm = weave.inline(code,\\n vars,\\n support_code=support_code,\\n sources=source_files,\\n libraries=libs,\\n library_dirs=dirs,\\n include_dirs=dirs,\\n runtime_library_dirs=dirs,\\n type_converters=converters.blitz,\\n compiler='gcc',\\n extra_compile_args=['-std=c++11'],\\n force=recompile)\\n\\n # Post-stimulus processing\\n otm['twin'] = twin # Simulate interval\\n # Add released resources\\n if (N_var < 2):\\n otm['r'] = pars['u0'] * otm['x']\\n else:\\n otm['r'] = np.multiply(otm['x'], otm['u'])\\n return otm\",\n \"def apply_nonlinear(self, params, unknowns, resids):\\n\\n x = params['x']\\n z = unknowns['z']\\n resids['z'] = x*z + z - 4.0\\n\\n # Output equations need to evaluate a residual just like an explicit comp.\\n resids['y'] = x + 2.0*z - unknowns['y']\\n #print(x, unknowns['y'], z, resids['z'], resids['y'])\",\n \"def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\\n self.A0 = A_phi[0]\\n self.phi0 = A_phi[1]\\n self.sim = sim\\n self.V0_frac = V0_frac\\n self.t = t\\n self.x_eq0 = self.sim.x_eq([0,0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\\n self.sol = integrate.odeint(sim, self.x0, t=t)\\n self.z = sim.zLI(self.sol, t)\\n self.phi = np.unwrap(np.angle(self.z))\\n self.t_filt = t_filt = t[15:] \\n self.i0 = i0 = np.argmin(abs(self.t_filt)) \\n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\\n self.t_wide = t_filt[::Ndec_response]\\n self.respRePts = self.sim.responseReVec(self.t_wide)\\n self.Ht = lambda t: np.interp(t, self.t_wide, self.respRePts)\\n \\n \\n \\n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act\",\n \"def pt_operators(N, L, J, theta, f, phi, band_idxs, deltaE, exact=False, qs=None, verbose=False, **kwargs):\\n if qs is None:\\n qs = range(N)\\n BandIdxs = band_idxs\\n DeltaE = deltaE\\n if 'theta0' in kwargs:\\n theta0 = kwargs['theta0']\\n else:\\n theta0 = theta\\n H0Op = pf.ParafermionicChainOp(N, L, J, theta0, 0.0, 0.0, q=0) # we get MPO object for full unperturbed Hamiltonian (note the same in each sector)\\n HfOps = []\\n for q in qs:\\n if 'exclude_side' in kwargs:\\n if kwargs['exclude_side'] == 'left':\\n fs = np.ones(L)*f\\n fs[0] = 0.0\\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\\n elif kwargs['exclude_side'] == 'right':\\n fs = np.ones(L)*f\\n fs[-1] = 0.0\\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\\n elif kwargs['exclude_side'] == 'neither':\\n fs = np.ones(L)*f\\n fs[0] = 0.0\\n fs[-1] = 0.0\\n Hf = pf.ParafermionicChainOp(N, L, J, theta, fs, phi, q=q)\\n else:\\n raise Exception('\\\\'exlude_side\\\\' argument should be either left or right')\\n else:\\n Hf = pf.ParafermionicChainOp(N, L, J, theta, f, phi, q=q)\\n Hf.add(H0Op, c1=1.0, c2=-1.0, inplace=True, compress=False)\\n HfOps.append(Hf)\\n [Partitions, H0Energies] = H0Op.get_bands_and_energies() # get all the partitions and energies of each\\n BandEnergy = H0Energies[BandIdxs[0]] # get the energy of the band we start from, this is E0\\n BandPartitions = list(map(lambda x: Partitions[x], BandIdxs)) # get the\\n FullBand = np.vstack(list(map(lambda x: pfcy.GetFullBandDW(BandPartitions[x]), range(len(BandIdxs)))))\\n FullBandDim = len(FullBand)\\n [NeighbouringBands,] = np.where(np.abs(H0Energies - BandEnergy) < DeltaE) # find other bands within deltaE in energy\\n FullSubspace = np.copy(FullBand)\\n for NeighbouringBand in NeighbouringBands:\\n if NeighbouringBand not in BandIdxs:\\n FullSubspace = np.vstack((FullSubspace, pfcy.GetFullBandDW(Partitions[NeighbouringBand])))\\n FullSubspaceDim = FullSubspace.shape[0]\\n if verbose: print('Full subspace dim: ' + str(FullSubspaceDim) + '.')\\n x = np.arange(FullSubspaceDim)\\n I = sps.diags(np.ones(FullSubspaceDim), 0)\\n P0 = sps.diags(np.piecewise(x, [x < FullBandDim, x >= FullBandDim], [1.0, 0.0]), 0)\\n Q0 = sps.diags(np.piecewise(x, [x < FullBandDim, x >= FullBandDim], [0.0, 1.0]), 0)\\n s = time.time()\\n H0 = H0Op.mats_subspace(FullSubspace)\\n e = time.time()\\n if verbose: print('Time taken to calculate H0 matrix: ' + str(e-s) + ' seconds.')\\n\\n s = time.time()\\n Hfs = list(map(lambda x : HfOps[x].mats_subspace(FullSubspace), qs))\\n e = time.time()\\n if verbose: print('Time taken to calculate V matrices: ' + str(e-s) + ' seconds.')\\n denominators = (BandEnergy - H0.diagonal()[FullBandDim:])\\n if len(np.where(denominators == 0)[0]) > 0:\\n return None\\n Q = sps.diags(np.hstack([np.zeros(FullBandDim),np.ones(FullSubspaceDim-FullBandDim)/denominators]), 0)\\n\\n if exact:\\n Offset = np.sum(map(lambda x: len(pfcy.GetFullBandDW(Partitions[x])), range(min(BandIdxs))))\\n # for debugging purposes, calculate some of full spectrum exactly, can be time consuming\\n FullEs = list(map(lambda x: pf.Diagonalise(N, L, J, theta, f, phi, q=x, k=Offset + FullBandDim), qs))\\n FullEs = list(map(lambda x: FullEs[x][0][Offset:(Offset+FullBandDim)], qs))\\n else:\\n FullEs = None\\n\\n return [P0, Q, H0, Hfs, FullBandDim, BandEnergy, FullEs]\",\n \"def variables(self, *args, **kwargs):\\n return self._optimizer.variables(*args, **kwargs)\",\n \"def f(self,y,psi):\\r\\n\\r\\n #1. check that number of params is consistent\\r\\n assert psi.shape[0] == self.n_terms, 'inconsistent parameter dimensions'\\r\\n assert psi.shape[1] == 3, 'inconsistent parameter dimensions'\\r\\n\\r\\n #2. exponentiate the a and b (positive!)\\r\\n mpsi = psi.copy()\\r\\n\\r\\n #3. transform data\\r\\n z = y.copy()\\r\\n for i in range(len(mpsi)):\\r\\n a,b,c = mpsi[i]\\r\\n z += a*np.tanh(b*(y+c))\\r\\n return z\",\n \"def set_coeffs(self, sol):\\n # TODO: look for bugs here!\\n self.log_debug(\\\"Set spline coefficients\\\")\\n\\n # task: find which of the free parameters (coeffs) belong to which spline object\\n sol_bak = sol.copy()\\n subs = dict()\\n\\n # iterate over the OrderedDict {'x1': [cx1_..., ...], 'u1': [cu1_...]}\\n for k, v in list(self.indep_vars.items()):\\n i = len(v)\\n # TODO: improve comment\\n subs[k] = sol[:i] # set numerical value to symbolical value\\n sol = sol[i:] ##:: sol = []\\n \\n if self._parameters['use_chains']:\\n for var in self.sys.states + self.sys.inputs:\\n for ic in self._chains:\\n if var in ic: ##:: ('x1','x2','u1') and ('x3','x4')\\n subs[var] = subs[ic.upper] ##:: elements in the same chain have the same coefficients (number, not symbol).\\n \\n # set numerical coefficients for each spline and derivative\\n # TODO: handle `!!`-comments after talking to yx \\n ##!! spline_key_plus_k = self.splines.keys().append('k')\\n for k in list(self.splines.keys()): ##:: ['x1','x3']\\n self.splines[k].set_coefficients(free_coeffs=subs[k])\\n ##:: self._indep_vars = free_coeffs (self.splines[k]._indep_coeffs=free_coeffs) makes symbols changing into numbers. {'x1': <Spline object>, 'x3': <Spline object>}, Spline._P[k] saves the polynomial.\\n \\n # yet another dictionary for solution and coeffs\\n# ##!! indep_vars['z_par'] = np.array([sp.symbols('k')])\\n# ##!! self.indep_vars = indep_vars\\n\\n coeffs_sol = OrderedDict()\\n\\n # used for indexing\\n i = 0\\n j = 0\\n\\n for k, v in list(self.indep_vars.items()):\\n ##:: ['x1': array([0.12,0.13,...,]), 'x3':...] symbols change into numbers\\n j += len(v)\\n coeffs_sol[k] = sol_bak[i:j]\\n i = j\\n\\n self.coeffs_sol = coeffs_sol\\n ##:: {'x1': array([ 25.94485709, 16.38313857, -35.65010072, ..., 2.28427004, 2.82974712, 1.88490863]), 'x3': array([-34.33884269, 45.13959025, 1.3272378 , -4.15546318,# 5.3863866 , -5.39286006, -8.86559812, -6.11620983, -2.95630206])}\\n\\n ##!! return self.coeffs_sol['z_par'].tolist()\",\n \"def compute_plume(self, full_nondim=False):\\n def compute_nondimensional(x):\\n \\\"\\\"\\\" both nondim melt rate and circulation \\\"\\\"\\\"\\n M = self.nondim_M(x)\\n M.attrs = {'long_name':'dimensionless meltrate; eqn. (26)'}\\n phi0 = self.phi0(x)\\n phi0.attrs = {'long_name':'dimensionless circulation; eqn. (25)'}\\n return M, phi0\\n\\n # calculations\\n self.dp['M'], self.dp['phi0'] = compute_nondimensional(self.dp.dgrl_)\\n\\n self.dp['m'] = self.dim_M().where(self.dp.mask==3)*3600*24*365 # [m/s] -> [m/yr]\\n self.dp.m.attrs = {'long_name':'dimensional meltrates; eqn. (28a)', 'units':'m/yr'}\\n \\n self.dp['Phi'] = self.Phi().where(self.dp.mask==3)\\n self.dp.Phi.attrs = {'long_name':'dimensional circulation; eqn. (29)', 'units':'m^3/s'}\\n\\n if full_nondim: # compute non-dimensional 1D melt curve for full [0,1] interval\\n self.dp = self.dp.assign_coords({'x_':np.linspace(0,1,51)})\\n self.dp.x_.attrs['long_name'] = 'non-dimensional coordinates in [0,1]'\\n self.dp['M_full'], self.dp['phi0_full'] = compute_nondimensional(self.dp.coords['x_'])\\n \\n return self.dp\",\n \"def dynstall_oye_dxdt(t,fs,u,p):\\n alpha = u['alpha'](t)\\n f_st = p['F_st'](alpha)\\n return 1/p['tau'] * (f_st - fs)\",\n \"def __init__(self, sim, A_phi, V0_frac, t, Ndec_response=4):\\n self.A0 = A_phi[0]\\n self.phi0 = A_phi[1]\\n self.sim = sim\\n self.V0_frac = V0_frac\\n self.t = t\\n self.x_eq0 = self.sim.x_eq([0, 0, V0_frac*self.sim.V(t[0])*self.sim.C], t[0]) # Given the initial charge...\\n self.sol = integrate.odeint(sim, self.x0, t=t)\\n self.z = sim.zLI(self.sol, t)\\n self.phi = np.unwrap(np.angle(self.z))\\n self.t_filt = t_filt = t[15:] \\n self.i0 = i0 = np.argmin(abs(self.t_filt)) \\n self.ip = ip = np.argmin(abs(self.t_filt-self.sim.V.tp))\\n self.phi_filt = phi_filt = np.convolve(self.phi, np.ones(16)/16.0, 'valid') # Dependent on using 16 samples / period\\n self.df_filt = df_filt = np.gradient(self.phi_filt)/np.gradient(self.t_filt)\\n self.t_wide = t_filt[::Ndec_response]\\n self.respRePts = self.sim.responseReVec(self.t_wide)\\n self.Ht = lambda tt: np.interp(tt, self.t_wide, self.respRePts)\\n \\n \\n \\n self.dphi_act = (phi_filt[ip] - phi_filt[i0])/ (2*np.pi)*1000\\n self.phi_filt_mcyc = (phi_filt - phi_filt[0])*1e3/(2*np.pi)\\n self.phi_est, self.dphi_est = estimate_dphi(self.df_python, self.i0, self.ip)\\n self.error = (self.dphi_est - self.dphi_act)/self.dphi_act\",\n \"def _redef_sp1_vars(self):\\r\\n\\r\\n if len(self.fq_list) == 0:\\r\\n no_rad = True\\r\\n lst_tmp = np.matrix(np.reshape(self.lst_tmp, \\r\\n (self.lst_tmp.size, 1)))\\r\\n else: no_rad = False\\r\\n # The practically constants...\\r\\n # Big Epsilon:\\r\\n if self.cond == True:\\r\\n self.Epsilon = self.d_T * self.thermal_conductivity\\r\\n else:\\r\\n self.Epsilon = (self.diff_scale ** 2) / \\\\\\r\\n (3.0 * self.absorb_coeffs[self.rad] ** 2)\\r\\n # Beta:\\r\\n if self.cond == True:\\r\\n self.Beta = (self.diff_scale * self.thermal_conductivity) / \\\\\\r\\n (self.convect_coeff)\\r\\n else:\\r\\n self.Beta = (1.0 + 3.0 * self.r2) * (2.0 * self.diff_scale) / \\\\\\r\\n ((1.0 - 2.0 * self.r1) * (\\r\\n 3.0 * self.absorb_coeffs[self.rad]))\\r\\n\\r\\n # The feild solutions at the last timestep.\\r\\n # The integral vF:\\r\\n if self.cond == True:\\r\\n # The horrifically complicated F:\\r\\n def F_func_cond(elem, eta):\\r\\n F = 0.0\\r\\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n F += Tn\\r\\n for k in range(0, len(self.fq_list)):\\r\\n vk = self.fq_list[k]\\r\\n try:\\r\\n vk_m = self.fq_list[k - 1]\\r\\n except:\\r\\n vk_m = self.v0_frequency\\r\\n absorbtion = self.absorb_coeffs[k]\\r\\n phi = elem.eval_elem(self.node_map, self.lst_rad[k],\\r\\n [eta])[0]\\r\\n inter1 = phi - 4.0 * sconst.pi * \\\\\\r\\n self.B_int_function(Tn, self.refr_idx_vol,\\r\\n vk, vk_m)\\r\\n inter2 = absorbtion * self.d_T / (self.diff_scale ** 2)\\r\\n F += inter2 * inter1\\r\\n return elem.funcs(eta) * F\\r\\n if not no_rad:\\r\\n # We're integrating something non-linear for SP1\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func_cond,\\r\\n self.node_map)\\r\\n else:\\r\\n # Or something easier if we're only looking at heat.\\r\\n self.vF_vect_vol = np.array(self.uv_vol * lst_tmp).reshape(-1)\\r\\n else:\\r\\n def F_func_radiative(elem, eta):\\r\\n T = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n vk = self.fq_list[self.rad]\\r\\n try:\\r\\n vk_minus = self.fq_list[self.rad - 1]\\r\\n except:\\r\\n vk_minus = self.v0_frequency\\r\\n n = self.refr_idx_vol\\r\\n F = 4.0 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\\r\\n return elem.funcs(eta) * F\\r\\n\\r\\n self.vF_vect_vol = et.elems_2_array(self.mesh,\\r\\n F_func_radiative,\\r\\n self.node_map)\\r\\n # The path integral vf:\\r\\n if self.cond == True:\\r\\n def f_func_cond(elem, eta):\\r\\n Tb = self.background_temperature\\r\\n Tn = elem.eval_elem(self.node_map, self.lst_tmp, [eta])[0]\\r\\n n = self.refr_idx_background\\r\\n vk = self.v0_frequency\\r\\n vk_minus = 0\\r\\n Bb0 = self.B_int_function(Tb, n, vk, vk_minus)\\r\\n Bn0 = self.B_int_function(Tn, n, vk, vk_minus)\\r\\n B_coeff = (self.alpha * sconst.pi) / self.convect_coeff\\r\\n f = Tb + B_coeff * (Bb0 - Bn0)\\r\\n return elem.funcs(eta) * f\\r\\n if not no_rad:\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func_cond,\\r\\n self.node_map)\\r\\n else:\\r\\n try:\\r\\n self.vf_vect_bound = self.cache_tb_integral_array\\r\\n except AttributeError:\\r\\n def elem_functor(elem, eta): return elem.funcs(eta)\\r\\n self.cache_tb_integral_array = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n elem_functor,\\r\\n self.node_map)\\r\\n self.cache_tb_integral_array *= self.background_temperature\\r\\n self.vf_vect_bound = self.cache_tb_integral_array\\r\\n \\r\\n else:\\r\\n # Radiation f = 4*pi*B^{(k)}(T_b, n_g)\\r\\n def f_func_radiative(elem, eta):\\r\\n T = self.background_temperature\\r\\n vk = self.fq_list[self.rad]\\r\\n try:\\r\\n vk_minus = self.fq_list[self.rad - 1]\\r\\n except:\\r\\n vk_minus = self.v0_frequency\\r\\n n = self.refr_idx_vol\\r\\n f = 4 * sconst.pi * self.B_int_function(T, n, vk, vk_minus)\\r\\n return elem.funcs(eta) * f\\r\\n\\r\\n self.vf_vect_bound = et.edge_2_array(self.mesh,\\r\\n \\\"Boundary\\\",\\r\\n f_func_radiative,\\r\\n self.node_map)\\r\\n assert (self.vF_vect_vol.size == self.vF_vect_vol.shape[0])\\r\\n assert (self.vf_vect_bound.size == self.vf_vect_bound.shape[0])\\r\\n assert (self.vf_vect_bound.shape[0] == \\\\\\r\\n self.vF_vect_vol.shape[0])\",\n \"def solve(self):\\n \\n # Definition of the parameters\\n Q_pc = self.parameters.getParam(\\\"Q_pc\\\")\\n V_c = self.parameters.getParam(\\\"V_c\\\")\\n V_p = self.parameters.getParam(\\\"V_p\\\")\\n CL = self.parameters.getParam(\\\"CL\\\")\\n initial_conditions = [\\n self.parameters.getParam(\\\"q_c0\\\"),\\n self.parameters.getParam(\\\"q_p0\\\"),\\n ]\\n t_eval = np.linspace(0, self.timespan, self.nsteps)\\n\\n # Definition of the model ODEs\\n def pk_iv_model(t, y, Q_pc, V_c, V_p, CL):\\n \\\"\\\"\\\"Defines the differential equations for the PK IV model.\\n\\n Parameters:\\n :param t: time (h)\\n :param y: list of the state variables of the ODEs system, in the\\n form [q_c, q_p]\\n :param Q_pc: transition rate between central and peripheral\\n compartments (mL/h)\\n :param V_c: volume of central compartment (mL)\\n :param V_p: volume of peripheral compartment (mL)\\n :param CL: clearance/elimination rate from the central\\n compartment (mL/h)\\n\\n The parameters (except for t and y) are extracted from the\\n Parameter class, using getParam method.\\n\\n Returns list containing the differential equations, in the form:\\n [dqc_dt, dqp_dt]\\n \\\"\\\"\\\"\\n q_c, q_p = y\\n transfer = Q_pc * (q_c / V_c - q_p / V_p)\\n dqc_dt = self.dosefunction(t) - q_c / V_c * CL - transfer\\n dqp_dt = transfer\\n return [dqc_dt, dqp_dt]\\n\\n # Solving the model\\n sol = scipy.integrate.solve_ivp(\\n fun=lambda t, y: pk_iv_model(t, y, Q_pc, V_c, V_p, CL),\\n t_span=[t_eval[0], t_eval[-1]],\\n y0=initial_conditions,\\n t_eval=t_eval,\\n )\\n\\n # Feeding the solution line by line to solution class\\n t = sol.t\\n y = sol.y\\n N = t.shape[0]\\n columnNames = [\\\"t\\\", \\\"dose\\\", \\\"q_c\\\", \\\"q_p\\\"]\\n self.solution.begin(columnNames, N)\\n for i in range(N):\\n arr = np.zeros((len(columnNames), 1))\\n arr[0] = t[i]\\n arr[1] = self.dosefunction(t[i])\\n arr[2:, 0] = y[:, i]\\n self.solution.report(arr)\",\n \"def variational_expectations(self, Fmu, Fvar, Y):\\n\\n gh_x, gh_w = hermgauss(self.num_gauss_hermite_points)\\n gh_x = gh_x.reshape(1, -1)\\n gh_w = gh_w.reshape(-1, 1) / np.sqrt(np.pi)\\n shape = tf.shape(Fmu)\\n Fmu, Fvar, Y = [tf.reshape(e, (-1, 1)) for e in (Fmu, Fvar, Y)]\\n X = gh_x * tf.sqrt(2.0 * Fvar) + Fmu\\n Y = tf.tile(Y, [1, self.num_gauss_hermite_points]) # broadcast Y to match X\\n\\n logp = self.logp(X, Y)\\n return tf.reshape(tf.matmul(logp, gh_w), shape)\",\n \"def test_var_exp(self):\\n with self.test_context() as session:\\n test_setups, F, feed = self.prepare()\\n for test_setup in test_setups:\\n l = test_setup.likelihood\\n y = test_setup.Y\\n l.compile()\\n r1 = session.run(l.logp(F, y), feed_dict=feed)\\n zero = F * 0.\\n r2 = session.run(\\n l.variational_expectations(F, zero, test_setup.Y), feed_dict=feed)\\n assert_allclose(r1, r2, atol=test_setup.tolerance, rtol=test_setup.tolerance)\",\n \"def f_obs(x, *args):\\n sslm, word_counts, totals, mean_deriv_mtx, word, deriv = args\\n # flag\\n init_mult = 1000\\n\\n T = len(x)\\n val = 0\\n term1 = 0\\n term2 = 0\\n\\n # term 3 and 4 for DIM\\n term3 = 0\\n term4 = 0\\n\\n sslm.obs[word] = x\\n sslm.mean[word], sslm.fwd_mean[word] = sslm.compute_post_mean(word, sslm.chain_variance)\\n\\n mean = sslm.mean[word]\\n variance = sslm.variance[word]\\n\\n # only used for DIM mode\\n # w_phi_l = sslm.w_phi_l[word]\\n # m_update_coeff = sslm.m_update_coeff[word]\\n\\n for t in range(1, T + 1):\\n mean_t = mean[t]\\n mean_t_prev = mean[t - 1]\\n\\n val = mean_t - mean_t_prev\\n term1 += val * val\\n term2 += word_counts[t - 1] * mean_t - totals[t - 1] * np.exp(mean_t + variance[t] / 2) / sslm.zeta[t - 1]\\n\\n model = \\\"DTM\\\"\\n if model == \\\"DIM\\\":\\n # stuff happens\\n pass\\n\\n if sslm.chain_variance > 0.0:\\n\\n term1 = - (term1 / (2 * sslm.chain_variance))\\n term1 = term1 - mean[0] * mean[0] / (2 * init_mult * sslm.chain_variance)\\n else:\\n term1 = 0.0\\n\\n final = -(term1 + term2 + term3 + term4)\\n\\n return final\",\n \"def tsne(x, no_dims=2, perplexity=30.0, max_iter=1000):\\n\\n # Check inputs\\n if isinstance(no_dims, float):\\n print(\\\"Error: array x should have type float.\\\")\\n return -1\\n\\n (n, d) = x.shape\\n\\n # 动量\\n initial_momentum = 0.5\\n final_momentum = 0.8\\n eta = 500\\n min_gain = 0.01\\n # 随机初始化Y\\n y = np.random.randn(n, no_dims)\\n # dy梯度\\n dy = np.zeros((n, no_dims))\\n # iy是什么\\n iy = np.zeros((n, no_dims))\\n\\n gains = np.ones((n, no_dims))\\n\\n # 对称化\\n P = seach_prob(x, 1e-5, perplexity)\\n P = P + np.transpose(P)\\n P = P / np.sum(P) #pij\\n # early exaggeration\\n # pi\\\\j，提前夸大\\n print (\\\"T-SNE DURING:%s\\\" % time.clock())\\n P = P * 4\\n P = np.maximum(P, 1e-12)\\n\\n # Run iterations\\n for iter in range(max_iter):\\n # Compute pairwise affinities\\n sum_y = np.sum(np.square(y), 1)\\n num = 1 / (1 + np.add(np.add(-2 * np.dot(y, y.T), sum_y).T, sum_y))\\n num[range(n), range(n)] = 0\\n Q = num / np.sum(num) #qij\\n Q = np.maximum(Q, 1e-12) #X与Y逐位比较取其大者\\n\\n # Compute gradient\\n # np.tile(A,N) 重复数组AN次 [1],5 [1,1,1,1,1]\\n # pij-qij\\n PQ = P - Q\\n # 梯度dy\\n for i in range(n):\\n dy[i,:] = np.sum(np.tile(PQ[:,i] * num[:,i], (no_dims, 1)).T * (y[i,:] - y), 0)\\n\\n # Perform the update\\n if iter < 20:\\n momentum = initial_momentum\\n else:\\n momentum = final_momentum\\n\\n gains = (gains + 0.2) * ((dy > 0) != (iy > 0)) + (gains * 0.8) * ((dy > 0) == (iy > 0))\\n gains[gains < min_gain] = min_gain\\n # 迭代\\n iy = momentum * iy - eta * (gains * dy)\\n y = y + iy\\n y = y - np.tile(np.mean(y, 0), (n, 1))\\n # Compute current value of cost function\\\\\\n if (iter + 1) % 100 == 0:\\n C = np.sum(P * np.log(P / Q))\\n print(\\\"Iteration \\\", (iter + 1), \\\": error is \\\", C)\\n if (iter+1) != 100:\\n ratio = C/oldC\\n print(\\\"ratio \\\", ratio)\\n if ratio >= 0.95:\\n break\\n oldC = C\\n # Stop lying about P-values\\n if iter == 100:\\n P = P / 4\\n print(\\\"finished training!\\\")\\n return y\",\n \"def forward(self,y,xt,D,h,param):\\n \\n \\n h_flip=h.flip(2,3)\\n a=conv(h,xt)-y \\n b=xt-self.learning_rate*conv(h_flip,a)\\n u=tor.matmul(D,tor.matmul(b,D.transpose(2,3)))\\n u_=rho(u,param)\\n xt1=tor.matmul(D.transpose(2,3),tor.matmul(u_,D))\\n \\n return xt1\",\n \"def den_evolve(self, delt, txp, src):\\n self.ne += (-txp.dfluxe + src.se)*delt\\n self.ni += (-txp.dfluxi + src.si)*delt\",\n \"def equations(self):\\n k = 0\\n ######################################################################\\n # equations for fluid balance\\n self.residual[k:k + self.num_nw_fluids] = self.fluid_func()\\n k += self.num_nw_fluids\\n\\n ######################################################################\\n # equations for mass flow balance\\n self.residual[k] = self.mass_flow_func()\\n k += 1\\n\\n ######################################################################\\n # equations for specified heta transfer\\n if self.Q.is_set:\\n self.residual[k] = self.inl[0].m.val_SI * (\\n self.outl[0].h.val_SI - self.inl[0].h.val_SI) - self.Q.val\\n k += 1\\n\\n ######################################################################\\n # equations for specified pressure ratio\\n if self.pr.is_set:\\n self.residual[k] = (\\n self.inl[0].p.val_SI * self.pr.val - self.outl[0].p.val_SI)\\n k += 1\\n\\n ######################################################################\\n # equations for specified zeta\\n if self.zeta.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n self.residual[k] = self.zeta_func(zeta='zeta')\\n k += 1\\n\\n ######################################################################\\n # equation for specified hydro-group paremeters\\n if self.hydro_group.is_set:\\n if np.absolute(self.residual[k]) > err ** 2 or self.it % 4 == 0:\\n # hazen williams equation\\n if self.hydro_group.method == 'HW':\\n func = self.hw_func\\n # darcy friction factor\\n else:\\n func = self.darcy_func\\n self.residual[k] = func()\\n k += 1\\n\\n ######################################################################\\n # additional equations\\n self.additional_equations(k)\",\n \"def cubic_evolve(self,nt=1):\\n #loop through time steps\\n for l in range(nt):\\n # temporary array\\n y_temp = np.zeros(self.y.shape[0])\\n # loop through array\\n for i in range(self.y.shape[0]):\\n # idx left to departure point\\n x_dep = self.x[i]-self.u[i]*self.dt\\n j = int(np.floor(x_dep/self.dx))\\n # alpha\\n a = (self.x[i]-self.u[i]*self.dt - j*self.dx)/self.dx\\n # calculate next time step\\n f = lambda x: x % self.y.shape[0] if x >= self.y.shape[0] else x\\n y_temp[i] = - a * (1-a)*(2-a)/6 * self.y[f(j-1)]\\n y_temp[i] += (1-a**2)*(2-a)/2 * self.y[f(j)]\\n y_temp[i] += a*(1+a)*(2-a)/2 * self.y[f(j+1)]\\n y_temp[i] -= a*(1-a**2)/6 * self.y[f(j+2)]\\n self.y = np.copy(y_temp)\\n return self.y\",\n \"def forward(self, inputs):\\n inputs = inputs.view(-1, self.nVar)\\n x, y, z = inputs[:, 0], inputs[:, 1], inputs[:, 2]\\n output = torch.zeros_like(inputs)\\n s = self.sigma\\n r = self.r\\n b = self.b\\n output[:, 0] = s * (y-x)\\n output[:, 1] = x * (r-z) - y\\n output[:, 2] = x*y - b*z\\n return output\",\n \"def _sesolve_func_td(H_func, psi0, tlist, e_ops, args, opt, progress_bar):\\n if debug:\\n print(inspect.stack()[0][3])\\n\\n #\\n # check initial state or oper\\n #\\n if psi0.isket:\\n oper_evo = False\\n elif psi0.isunitary:\\n oper_evo = True\\n else:\\n raise TypeError(\\\"The unitary solver requires psi0 to be\\\"\\n \\\" a ket as initial state\\\"\\n \\\" or a unitary as initial operator.\\\")\\n\\n if opt.moving_mode_indices is None:\\n n_modes = len(psi0.dims[0])\\n opt.moving_mode_indices = np.arange(n_modes)\\n\\n #\\n # setup integrator\\n #\\n new_args = None\\n\\n if type(args) is dict:\\n new_args = {}\\n for key in args:\\n if isinstance(args[key], Qobj):\\n new_args[key] = args[key].data\\n else:\\n new_args[key] = args[key]\\n\\n elif type(args) is list or type(args) is tuple:\\n new_args = []\\n for arg in args:\\n if isinstance(arg, Qobj):\\n new_args.append(arg.data)\\n else:\\n new_args.append(arg)\\n\\n if type(args) is tuple:\\n new_args = tuple(new_args)\\n else:\\n if isinstance(args, Qobj):\\n new_args = args.data\\n else:\\n new_args = args\\n\\n if oper_evo:\\n initial_vector = operator_to_vector(psi0).full().ravel()\\n # Check that function returns superoperator\\n if H_func(0, args).issuper:\\n L_func = H_func\\n else:\\n L_func = lambda t, args: spre(H_func(t, args))\\n\\n else:\\n new_psi0 = psi0\\n initial_vector = new_psi0.full().ravel()\\n L_func = H_func\\n if opt.moving_basis:\\n initial_mode_displacements = np.zeros(len(opt.moving_mode_indices), dtype=complex)\\n for mode_idx in opt.moving_mode_indices:\\n identities = [qeye(dim) for dim in psi0.dims[0]]\\n a_op_components = identities\\n a_op_components[mode_idx] = destroy(psi0.dims[0][mode_idx])\\n a_op = tensor(a_op_components)\\n a_expect = expect(a_op, psi0)\\n initial_mode_displacements[mode_idx] = a_expect\\n displacement_components = identities\\n displacement_components[mode_idx] = displace(psi0.dims[0][mode_idx], -a_expect)\\n displacement_op = tensor(displacement_components)\\n new_psi0 = displacement_op * new_psi0\\n initial_vector = np.hstack([initial_vector, initial_mode_displacements])\\n\\n if not opt.rhs_with_state:\\n print('Using cython function.')\\n r = scipy.integrate.ode(cy_ode_psi_func_td)\\n else:\\n if opt.moving_basis:\\n r = scipy.integrate.ode(_ode_psi_func_td_with_state_moving_basis)\\n else:\\n r = scipy.integrate.ode(_ode_psi_func_td_with_state)\\n\\n r.set_integrator('zvode', method=opt.method, order=opt.order,\\n atol=opt.atol, rtol=opt.rtol, nsteps=opt.nsteps,\\n first_step=opt.first_step, min_step=opt.min_step,\\n max_step=opt.max_step)\\n r.set_initial_value(initial_vector, tlist[0])\\n if opt.moving_basis:\\n r.set_f_params(L_func, new_args, opt, psi0.dims)\\n else:\\n r.set_f_params(L_func, new_args)\\n\\n #\\n # call generic ODE code\\n #\\n return _generic_ode_solve(r, psi0, tlist, e_ops, opt, progress_bar,\\n dims=psi0.dims)\",\n \"def gather_derivatives(self):\\n self.xdot[0,0:self.n] = self.mdot[0:self.n] \\n self.xdot[1,0:self.n] = self.rdot[0:self.n,0]\\n self.xdot[2,0:self.n] = self.rdot[0:self.n,1]\\n self.xdot[3,0:self.n] = self.rdot[0:self.n,2]\\n self.xdot[4,0:self.n] = self.vdot[0:self.n,0]\\n self.xdot[5,0:self.n] = self.vdot[0:self.n,1]\\n self.xdot[6,0:self.n] = self.vdot[0:self.n,2]\\n return self.xdot\",\n \"def obj_u_opt_N_opt(u, T, alpha, B, N, num_tumor_voxels, Td = 2):\\n x = T.dot(u)\\n alpha_tilde = alpha #np.repeat(N, num_tumor_voxels)*alpha\\n B_tilde = B #np.repeat(N, num_tumor_voxels)*B\\n #Note that all modalities must have the same number of tumor voxels:\\n return alpha_tilde.T.dot(x) - x.T.dot(B_tilde*x) + num_tumor_voxels*(np.sum(N)-1)*(np.log(2)/Td)\",\n \"def du(self):\\n du = setup_nonlinear_model_du() # Ux, Uy, theta_p\\n return du\",\n \"def compute_hessian(self, dw, trn_X, trn_y, epsilon: float = 0.01):\\n norm = torch.cat([w.view(-1) for w in dw]).norm()\\n eps = epsilon / norm\\n\\n dalpha_pos = self.finite_difference(dw, trn_X, trn_y, eps, wrt='alpha')\\n dalpha_neg = self.finite_difference(dw, trn_X, trn_y, -eps, wrt='alpha')\\n hessian = [(p - n) / (2. * eps) for p, n in zip(dalpha_pos, dalpha_neg)]\\n return hessian\",\n \"def J(cst, x):\\n [u0, v0, u1, v1, u2, v2, coeffs] = cst\\n [u, v, g1, g2, g3] = x\\n df1du = 2*u*g3**2 - 2*g3*u0 + 2*g3*coeffs[3]*(g1*u1-u0) + 2*g3*coeffs[4]*(g2*u2-u0)\\n df1dv = -2*v*g3**2 + 2*g3*v0 - 2*g3*coeffs[3]*(g1*v1-v0) - 2*g3*coeffs[4]*(g2*v2-v0)\\n df1dg1 = 2*g1*coeffs[0]*(u1**2-v1**2) + 2*(v1*v0-u1*u0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[3]*(u1*u-v1*v)\\n df1dg2 = 2*g2*coeffs[2]*(u2**2-v2**2) + 2*(v2*v0-u2*u0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(u1*u2-v1*v2) + 2*g3*coeffs[4]*(u2*u-v2*v)\\n df1dg3 = 2*g3*(u**2-v**2) + 2*(v*v0-u*u0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(u1*u-v1*v) + 2*g2*coeffs[4]*(u2*u-v2*v)\\n\\n df2du = 0\\n df2dv = 2*v*g3**2 + 2*g3*(-v0 + coeffs[3]*(g1*v1-v0) + coeffs[4]*(g2*v2-v0))\\n df2dg1 = 2*g1*coeffs[0]*(v1**2-1) + 2*(1-v1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + 2*g2*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[3]*(v1*v-1)\\n df2dg2 = 2*g2*coeffs[2]*(v2**2-1) + 2*(1-v2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + 2*g1*coeffs[1]*(v1*v2-1) + 2*g3*coeffs[4]*(v2*v-1)\\n df2dg3 = 2*g3*(v**2-1) + 2*(1-v*v0)*(coeffs[3]+coeffs[4]+1) + 2*g1*coeffs[3]*(v1*v-1) + 2*g2*coeffs[4]*(v2*v-1)\\n\\n df3du = g3*coeffs[3]*(g1*v1-v0) + g3*coeffs[4]*(g2*v2-v0) + g3*(g3*v-v0)\\n df3dv = g3*coeffs[3]*(g1*u1-u0) + g3*coeffs[4]*(g2*u2-u0) + g3*(g3*u-u0)\\n df3dg1 = 2*g1*coeffs[0]*u1*v1 - (v1*u0+u1*v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[3]*(v1*u+u1*v)\\n df3dg2 = 2*g2*coeffs[2]*u2*v2 - (v2*u0+u2*v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1*v2+v1*u2) + g3*coeffs[4]*(v2*u+u2*v)\\n df3dg3 = 2*g3*u*v - (u*v0+v*u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1*u+u1*v) + g2*coeffs[4]*(v2*u+u2*v)\\n\\n df4du = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\\n df4dv = 0\\n df4dg1 = 2*g1*coeffs[0]*u1 - (u0+u1)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(u1+u2) + g3*coeffs[3]*(u+u1)\\n df4dg2 = 2*g2*coeffs[2]*u2 - (u0+u2)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(u1+u2) + g3*coeffs[4]*(u+u2)\\n df4dg3 = 2*g3*u - (u+u0)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(u+u1) + g2*coeffs[4]*(u+u2)\\n\\n df5du = 0\\n df5dv = g3*coeffs[3]*(g1-1) + g3*coeffs[4]*(g2-1) + g3*(g3-1)\\n df5dg1 = 2*g1*coeffs[0]*v1 - (v1+v0)*(coeffs[0]+coeffs[1]+coeffs[3]) + g2*coeffs[1]*(v2+v1) + g3*coeffs[3]*(v1+v)\\n df5dg2 = 2*g2*coeffs[2]*v2 - (v2+v0)*(coeffs[1]+coeffs[2]+coeffs[4]) + g1*coeffs[1]*(v2+v1) + g3*coeffs[4]*(v2+v)\\n df5dg3 = 2*g3*v - (v0+v)*(coeffs[3]+coeffs[4]+1) + g1*coeffs[3]*(v1+v) + g2*coeffs[4]*(v2+v)\\n\\n return np.array([\\n [df1du, df1dv, df1dg1, df1dg2, df1dg3],\\n [df2du, df2dv, df2dg1, df2dg2, df2dg3],\\n [df3du, df3dv, df3dg1, df3dg2, df3dg3],\\n [df4du, df4dv, df4dg1, df4dg2, df4dg3],\\n [df5du, df5dv, df5dg1, df5dg2, df5dg3],\\n ])\",\n \"def LiftCoeff(h,Vc,Temp_m,W,S):\\n T,p,rho = isa(h)\\n return W*g0/(0.5*rho*VTrue(h,Vc,p,Temp_m)**2*S)\",\n \"def newtonian_profile(PSI):\\n\\n U = dot(MDX, PSI)\\n V = - dot(MDY, PSI)\\n VGRAD = dot(U,MDX) + dot(V,MDY)\\n\\n BPFEQNS = zeros((3*vecLen, 3*vecLen), dtype='D')\\n # Cxx eqn\\n # Cxx\\n BPFEQNS[0:vecLen, 0:vecLen] = Nu*MDX - VGRAD \\\\\\n + 2*tsm.c_prod_mat(dot(MDX,U)) - oneOverWi*II\\n # Cyy\\n BPFEQNS[0:vecLen, vecLen:2*vecLen] = 0\\n # Cxy\\n BPFEQNS[0:vecLen, 2*vecLen:3*vecLen] = 2*tsm.c_prod_mat(dot(MDY, U))\\n # Cyy eqn\\n # Cxx\\n BPFEQNS[vecLen:2*vecLen, 0:vecLen] = 0\\n # Cyy\\n BPFEQNS[vecLen:2*vecLen, vecLen:2*vecLen] = Nu*MDX - VGRAD - oneOverWi*II\\\\\\n + 2.*tsm.c_prod_mat(dot(MDY, V))\\n # Cxy\\n BPFEQNS[vecLen:2*vecLen, 2*vecLen:3*vecLen] = 2.*tsm.c_prod_mat(dot(MDX, V))\\n #Cxy eqn\\n # Cxx\\n BPFEQNS[2*vecLen:3*vecLen, 0:vecLen] = tsm.c_prod_mat(dot(MDX, V))\\n # Cyy \\n BPFEQNS[2*vecLen:3*vecLen, vecLen:2*vecLen] = tsm.c_prod_mat(dot(MDY, U))\\n # Cxy\\n BPFEQNS[2*vecLen:3*vecLen, 2*vecLen:3*vecLen] = Nu*MDX - VGRAD - oneOverWi*II \\n\\n RHS = zeros(3*vecLen, dtype='D')\\n RHS[0] = -oneOverWi\\n RHS[vecLen] = -oneOverWi\\n RHS[2*vecLen:3*vecLen] = 0\\n\\n soln = linalg.solve(BPFEQNS, RHS)\\n\\n Cxx = soln[0:vecLen]\\n Cyy = soln[vecLen:2*vecLen]\\n Cxy = soln[2*vecLen:3*vecLen]\\n\\n return Cxx, Cyy, Cxy\",\n \"def variational_objective(params, t, num_samples, beta=1.):\\n\\n # 1. draw samples from the variational posterior, eps ~ N(0,I)\\n zs, ldet_sums = draw_variational_samples(params, num_samples)\\n\\n # 1.5 negative entropy of z0 --- likely we need this for KL though\\n # not needed for optimization\\n\\n # 2. compute expected value of the sum of jacobian terms\\n E_ldet_sum = np.mean(ldet_sums)\\n\\n # 3. compute data term\\n lls = logprob(zs, t)\\n E_logprob = np.mean(lls)\\n\\n if debug_print:\\n print \\\"entropy term: \\\", E_ldet_sum\\n print \\\"data term : \\\", E_logprob, \\\" (+/- \\\", np.std(lls), \\\")\\\", \\\" min = \\\", np.min(lls)\\n\\n # return lower bound\\n beta = 1. if t >= len(beta_schedule) else beta_schedule[t]\\n lower_bound = beta * E_logprob + E_ldet_sum\\n return -lower_bound\",\n \"def dewT_2_q_magnus(ds, var):\\n A1, B1, C1 = 17.625, 243.04, 610.94\\n vpsl = C1 * np.exp(A1 * (ds[var['dew']] - 273.15) / (B1 + (ds[var['dew']] - 273.15)))\\n wsl = eps0 * vpsl / (ds[var['pressure']] - vpsl)\\n ds[var['spec_h']] = wsl / (1 + wsl)\\n return ds\",\n \"def ev(knotvec, coeffs, u):\\n assert len(coeffs) == knotvec.numdofs, 'Wrong size of coefficient vector'\\n return scipy.interpolate.splev(u, (knotvec.kv, coeffs, knotvec.p))\",\n \"def linear_evolve(self,nt=1):\\n for l in range(nt):\\n y_temp = np.empty(self.y.shape[0])\\n for i in range(self.y.shape[0]):\\n \\n # idx left to the departure point\\n j = int(np.floor((self.x[i]-self.u[i]*self.dt)/self.dx))\\n # idx right to the departure point\\n k = j+1\\n print i, j, k\\n # linear interpolation\\n alpha = (self.x[i]-self.u[i]*self.dt - j*self.dx)/self.dx\\n y_temp[i] = (1-alpha)*self.y[j] + alpha*self.y[k]\\n # copy array to current time\\n self.y = np.copy(y_temp)\\n stop\\n #return current varibale\\n return self.y\",\n \"def V_phi(self, x):\\n\\n x = self.featureExtractor.getFeatures(x)\\n\\n x = torch.tensor(x).float()\\n\\n x = F.relu(self.linear(x))\\n\\n v = self.linear_v(x)\\n\\n return v\",\n \"def none_model_vars():\\n obj_fun = None\\n time_sol = None\\n gap = None\\n s_pos = None\\n b_target = None\\n threads = None\\n\\n return obj_fun, time_sol, gap, s_pos, b_target, threads\",\n \"def update_variables(self):\\n self.dl21 = self.l21-self.l11; self.dl22 = self.l22-self.l12; self.dl23 = self.l23-self.l13;\\n self.kappa1, self.phi1, self.seg_len1 = self.configuration_space(self.l11, self.l12, self.l13, self.d, self.n)\\n self.kappa2, self.phi2, self.seg_len2 = self.configuration_space(self.dl21, self.dl22, self.dl23, self.d, self.n)\\n # aquire transformation matrices and tips for segment 1 and 2\\n self.T01_bishop = self.transformation_matrix_bishop(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_bishop = self.transformation_matrix_bishop(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_bishop = np.matmul(self.T01_bishop, self.T12_bishop)\\n self.T01_frenet = self.transformation_matrix_frenet(self.kappa1, self.phi1, self.seg_len1)\\n self.T12_frenet = self.transformation_matrix_frenet(self.kappa2, self.phi2, self.seg_len2)\\n self.T02_frenet = np.matmul(self.T01_frenet, self.T12_frenet)\\n self.tip_vec1 = np.matmul(self.T01_bishop, self.base)[0:3]\\n self.tip_vec2 = np.matmul(self.T02_bishop, self.base)[0:3]\\n # Frenet frames\\n self.normal_vec_frenet1 = self.T01_frenet[0:3, 0]\\n self.binormal_vec_frenet1 = self.T01_frenet[0:3, 1]\\n self.tangent_vec_frenet1 = self.T01_frenet[0:3, 2]\\n self.normal_vec_frenet2 = self.T02_frenet[0:3, 0]\\n self.binormal_vec_frenet2 = self.T02_frenet[0:3, 1]\\n self.tangent_vec_frenet2 = self.T02_frenet[0:3, 2]\\n # Bishop frames\\n self.normal_vec_bishop1 = self.T01_bishop[0:3, 0]\\n self.binormal_vec_bishop1 = self.T01_bishop[0:3, 1]\\n self.tangent_vec_bishop1 = self.T01_bishop[0:3, 2]\\n self.normal_vec_bishop2 = self.T02_bishop[0:3, 0]\\n self.binormal_vec_bishop2 = self.T02_bishop[0:3, 1]\\n self.tangent_vec_bishop2 = self.T02_bishop[0:3, 2]\",\n \"def get_dphi_dx_fast(self, sess, x_train):\\n dphi_dx = tf.gradients(self.featurizations, self.x)\\n feed_dict = {self.x: x_train}\\n dphi_dx_np = sess.run(dphi_dx, feed_dict = feed_dict)\\n return dphi_dx_np\",\n \"def get_dphi_dx(self, sess, x_test):\\n self.logger.debug(\\\"Finding dphi dx\\\")\\n n, d = x_test.shape\\n\\n #TODO: Remove the hardcoding from here\\n featurization_sliced = self.slice_tensor_one_d(self.featurizations, 32)\\n num_hidden_last_layer = len(featurization_sliced)\\n self.logger.debug(\\\"Completed slicing\\\")\\n\\n run_list = []\\n for i in range(len(featurization_sliced)):\\n dphi_i_dx = tf.gradients(featurization_sliced[i], self.x)\\n run_list.append(dphi_i_dx)\\n\\n self.logger.debug(\\\"Found run list\\\")\\n feed_dict = {self.x: x_test}\\n dphi_dx_np = sess.run(run_list, feed_dict = feed_dict)\\n dphi_dx_np = np.array(dphi_dx_np).squeeze()\\n\\n self.logger.debug(\\\"Found grads\\\")\\n #dphi_dx_np = dphi_dx_np.reshape((n, num_hidden_last_layer, d))\\n dphi_dx_np = dphi_dx_np.swapaxes(0, 1)\\n return dphi_dx_np\",\n \"def malthusiens(nb_init, t0, tf, eps, methode, gamma ) :\\n\\n f=lambda y, t : gamma*y\\n Y=meth_epsilon(nb_init, t0, tf, eps, f, methode)\\n return Y\",\n \"def additional_derivatives(self, increment_filter, k):\\n ######################################################################\\n # derivatives for saturated liquid at hot side outlet equation\\n if self.subcooling.val is False:\\n o1 = self.outl[0].to_flow()\\n self.jacobian[k, 2, 1] = -dh_mix_dpQ(o1, 0)\\n self.jacobian[k, 2, 2] = 1\\n k += 1\",\n \"def main():\\n\\n varList = {'beta': 6., 'convSpeed': 1.2, 'Mark': 0., 'axi': 1, 'acModes': 4, 'Nr': 801, 'Tf': 600., 'xf': 0.51}\\n\\n # Solve steady flame.\\n # BC1: I have the attachment BC at r = 1, always\\n # BC2: I need to set dF/dr = 0 at r = 0 iff Mark != 0\\n [qMean, r, FMean] = steady_flame_area_FD3(varList['Mark'], varList['beta'], varList['axi'], varList['Nr'])\\n r = r * varList['beta']\\n\\n # Calculate mean flame derivatives\\n dFMeanDr = derivsnew.FD1_CT2_D(FMean, r[1] - r[0])\\n d2FMeanDr2 = derivsnew.FD2_CT2_D(FMean, r[1] - r[0])\\n\\n #Apply BC smooth tip:\\n if(varList['Mark']!=0.0):\\n dFMeanDr[-1] = 0.0\\n\\n # Use correct number of points. Remember that the extrems need to be set depending on the BC!\\n # The attach BC (first point) is always assumed to be true and removed from the vector list\\n if(varList['Mark']==0):\\n Nr = varList['Nr'] / 2\\n dFMeanDr = dFMeanDr[1:]\\n d2FMeanDr2 = d2FMeanDr2[1:]\\n r = r[1:]\\n # The smooth BC holds only if Mark!=0 (second derivatives appear): remove also the last point\\n else:\\n Nr = varList['Nr'] / 2 - 1\\n dFMeanDr = dFMeanDr[1:-1]\\n d2FMeanDr2 = d2FMeanDr2[1:-1]\\n r = r[1:-1]\\n\\n # Calculate geometric values\\n den = 1 + varList['beta'] * varList['beta'] * dFMeanDr * dFMeanDr\\n dR = r[1] - r[0]\\n # Set Nx equal to Nr for now.\\n # The implementation is more complicated if they differ, and need to interpolate between values.\\n Nx = Nr\\n\\n # Nonuniform grid spacing along x!\\n # Nx = length(dx) has to hold.\\n dx = np.empty(len(FMean) - 1)\\n for ii in range(1, len(FMean)):\\n dx[ii - 1] = FMean[ii] - FMean[ii - 1]\\n\\n [A, B, C, tau] = loadAcoustics(varList['xf'], varList['Tf'], varList['acModes'], varList['beta'])\\n\\n Matrix = buildMatrix(Nr, dR, varList['beta'], den, r, FMean, dFMeanDr, d2FMeanDr2, varList['Mark'], varList['acModes'], A,\\n B, C, Nx, dx, tau, qMean, varList['convSpeed'])\\n\\n [d, W, V] = eigProblem.solveEigProb(Matrix)\\n [dnew, Wnew, Vnew] = eigProblem.selectUnstable(d, W, V)\\n\\n print dnew / (2. * np.pi)\",\n \"def set_solver_parameters(m, gamma, horizon, my_vars, timeout=30 * 60, pre_solve=-1):\\n h = ext.get_set_time(horizon)\\n beta = get_var(my_vars, 'beta')\\n m.setObjective(quicksum((gamma ** t) * beta[0, t] for t in h), GRB.MAXIMIZE)\\n\\n m.setParam('TimeLimit', timeout)\\n m.setParam('Threads', 8)\\n m.setParam('Presolve', pre_solve)\\n m.setParam('OutputFlag', 0)\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.80656624\",\n \"0.5865987\",\n \"0.5693037\",\n \"0.5615564\",\n \"0.55575585\",\n \"0.55294347\",\n \"0.55230105\",\n \"0.5521183\",\n \"0.5503675\",\n \"0.5450836\",\n \"0.540424\",\n \"0.53514105\",\n \"0.5325137\",\n \"0.53189623\",\n \"0.5317447\",\n \"0.5312849\",\n \"0.5308044\",\n \"0.5304588\",\n \"0.530239\",\n \"0.5301694\",\n \"0.5296928\",\n \"0.5287328\",\n \"0.52784234\",\n \"0.5275083\",\n \"0.52576554\",\n \"0.5250537\",\n \"0.5234691\",\n \"0.5217848\",\n \"0.5201104\",\n \"0.5196678\",\n \"0.5185753\",\n \"0.5178583\",\n \"0.51640326\",\n \"0.5152025\",\n \"0.5151244\",\n \"0.51207054\",\n \"0.5116701\",\n \"0.51075786\",\n \"0.5098229\",\n \"0.5094568\",\n \"0.509096\",\n \"0.50907564\",\n \"0.50852835\",\n \"0.508479\",\n \"0.5083289\",\n \"0.50821745\",\n \"0.5081304\",\n \"0.5077086\",\n \"0.50754094\",\n \"0.5074996\",\n \"0.5074658\",\n \"0.5070279\",\n \"0.50655884\",\n \"0.50642943\",\n \"0.5061261\",\n \"0.5057435\",\n \"0.50556505\",\n \"0.50451666\",\n \"0.50442165\",\n \"0.5028064\",\n \"0.5025042\",\n \"0.5021877\",\n \"0.50210303\",\n \"0.50141925\",\n \"0.5013818\",\n \"0.50130904\",\n \"0.50090134\",\n \"0.5006547\",\n \"0.50057006\",\n \"0.50003946\",\n \"0.49994954\",\n \"0.49975103\",\n \"0.49951848\",\n \"0.4994092\",\n \"0.49927926\",\n \"0.49904364\",\n \"0.4987641\",\n \"0.49866462\",\n \"0.49824142\",\n \"0.49808532\",\n \"0.4973733\",\n \"0.4969949\",\n \"0.4965457\",\n \"0.49650878\",\n \"0.49636203\",\n \"0.4962349\",\n \"0.4961448\",\n \"0.49608457\",\n \"0.4960218\",\n \"0.4957515\",\n \"0.49566132\",\n \"0.49551055\",\n \"0.4952623\",\n \"0.49514017\",\n \"0.49489293\",\n \"0.4947147\",\n \"0.4944185\",\n \"0.4943071\",\n \"0.49403766\",\n \"0.4939456\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73483014"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":8498,"cells":{"query":{"kind":"string","value":"find the lcl (in m) for a row in the dataframe"},"document":{"kind":"string","value":"def calc_lcl(row,psfc):\n Tdew = tf.tmr(row['qv'],psfc)\n LCL = tf.LCL(Tdew,row['theta'],psfc) #kPa\n #\n # rough approximation: 10 kPa = 1 km\n #\n delp=psfc - LCL\n lcl_h = delp*100.\n return lcl_h"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_index_lm(l, m):\n return (l+1)**2 -1 -l + m","def compute_kl(self, df):\n value_counts = [df[col].value_counts() for col in self.hist_cols]\n next_hists = self.value_counts_to_hists(value_counts)\n\n if self.prev_hists is None:\n self.prev_hists = next_hists\n return None\n\n output = []\n for prev_h, curr_h in zip(self.prev_hists, next_hists):\n for i in range(len(prev_h)):\n prev_h[i] = prev_h[i] if prev_h[i] != 0 else 1\n curr_h[i] = curr_h[i] if curr_h[i] != 0 else 1\n kl = entropy(prev_h, curr_h)\n output.append(kl)\n\n self.prev_hists = next_hists\n return output","def lfindwithin (data):\r\n\r\n numfact = len(data[0])-1\r\n withinvec = 0\r\n for col in range(1,numfact):\r\n examplelevel = pstats.unique(pstats.colex(data,col))[0]\r\n rows = pstats.linexand(data,col,examplelevel) # get 1 level of this factor\r\n factsubjs = pstats.unique(pstats.colex(rows,0))\r\n allsubjs = pstats.unique(pstats.colex(data,0))\r\n if len(factsubjs) == len(allsubjs): # fewer Ss than scores on this factor?\r\n withinvec = withinvec + (1 << col)\r\n return withinvec","def cal_L(self):\n # calculate the l matrix\n self.point_matrixs = self.point_matrix.reshape(\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\n self.point_matrixs = np.tile(self.point_matrixs,\n (self.attach_points.shape[0], 1))\n self.attach_points_matrix = np.matlib.repmat(\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\n self.attach_points_matrix = self.attach_points_matrix.reshape(\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\n self.L = np.subtract(self.attach_points_matrix,\n self.point_matrixs)\n # self.L[:,self.attach_points[:,3]==1,:] = \\\n # - self.L[:,self.attach_points[:,3]==1,:]\n # print(self.L)","def calculate_recovery_clifford(cl_in, desired_cl=0):\n row = list(clifford_lookuptable[cl_in])\n return row.index(desired_cl)","def calc_Lr(rho,mld,f,g=9.8,po=1027.):\n n2ml=np.ndarray(len(rho[1,:-1]))\n for i in range(len(rho[1,:-1])):\n n2ml[i]=-(g/po)*((rho[15,i]-rho[np.int8(mld[i])+15,i])/mld[i])\n Lr=(np.sqrt(n2ml)*mld[:-1])/f\n\n return Lr","def find_lcs(l1: str, l2: str, length1: int, length2: int):\n \"\"\" Theorem:{\n Initialize matrix with 0 for first row and colm\n If s1[i] = s2[j], update matrix[i][j] with value\n of matrix[i-1][j-1]+1\n Else update matrix[i][j] with max of value among\n matrix[i][j-1],matrix[i-1][j]\n Matrix[n][m] will be lcs\n }\n \"\"\"\n matrix = [[None]*(length1+1) for i in range(0, length2+1)]\n for i in range(0, length2+1):\n for j in range(0, length1+1):\n if i == 0 or j == 0:\n matrix[i][j] = 0\n elif l1[j-1] == l2[i-1]:\n matrix[i][j] = matrix[i-1][j-1] + 1\n else:\n matrix[i][j] = max(matrix[i][j-1], matrix[i-1][j])\n lcs = [None for i in range(0, matrix[length2][length1])]\n index = matrix[length2][length1]\n m = length2 \n n = length1\n while(m > -1 and n > -1):\n if l2[m-1] == l1[n-1]:\n lcs[index-1] = l2[m-1]\n index -= 1\n m -= 1\n n -= 1\n elif matrix[m-1][n] > matrix[m][n-1]:\n m -= 1\n else:\n n -= 1\n return lcs","def get_lims(data):\n return data[:, 0].min() - 1, data[:, 0].max() + 1, data[:, 1].min() - 1, data[:, 1].max() + 1","def _compute_lcs(source, target):\n table = _lcs_table(source, target)\n return _backtrack(table, source, target, len(source), len(target))","def getCL(self):\r\n return self.cL;","def calculate_LC(read):\n k_values = []\n for i in range(1,len(read)+1):\n k_values.append(i)\n observed_kmers = []\n for i in k_values:\n observed_kmers.append((count_kmers_observed(read, i)))\n possible_kmers = []\n for i in k_values:\n possible_kmers.append(count_kmers_possible(read, i))\n df = pd.DataFrame(list(zip(k_values, observed_kmers, possible_kmers)), columns = ['k','observed kmers','possible kmers'])\n df.at['Total', 'observed kmers'] = df['observed kmers'].sum()\n df.at['Total', 'possible kmers'] = df['possible kmers'].sum()\n x = int(df.at['Total', 'observed kmers'])\n y = int(df.at['Total', 'possible kmers'])\n LC = (x/y)\n return(LC)","def getLPos(self):\n c = 0\n while c <= ALIENS_IN_ROW-1:\n i = 0\n for a in range(ALIEN_ROWS):\n if self._aliens[a][c] != None:\n return self._aliens[a][c].x - ALIEN_WIDTH/2\n else:\n i +=1\n if i == ALIEN_ROWS:\n c +=1","def get_L(self, tolerance=None):\r\n index = self.data.index\r\n columns = self.data.columns\r\n\r\n # Obtain the eigenvalues and eigenvectors\r\n E, V = sandy.CategoryCov(self.data).get_eig(tolerance=tolerance)\r\n\r\n # need sparse because much faster for large matrices (2kx2k from J33 Pu9)\r\n # with a lot of zero eigs\r\n # this is exactly equivalent to V.values @ np.diag(np.sqrt(E.values))\r\n A = (sparse.csr_matrix(V.values) @ sparse.csr_matrix(np.diag(np.sqrt(E.values)))).todense()\r\n \r\n# u, s, vh = self.get_svd()\r\n# A = (sparse.csr_matrix(u) @ sparse.csr_matrix(np.diag(np.sqrt(s)))).todense()\r\n\r\n # QR decomposition\r\n Q, R = scipy.linalg.qr(A.T)\r\n L = R.T\r\n\r\n return pd.DataFrame(L, index=index, columns=columns)","def get_l(m):\n L = m.copy()\n for i in range(L.shape[0]):\n L[i, i] = 1\n L[i, i+1:] = 0\n return np.matrix(L)","def calcLnL(self, tree):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n return TreeLikelihoodBase.calcLnL(self, tree)","def lll(self, delta=0.75):\n if self.domain != ZZ:\n raise DMDomainError(\"ZZ expected, got %s\" % self.domain)\n elif self.rows > self.cols:\n raise DMShapeError(\"Matrix must not have more rows than columns.\")\n\n rep = self._lll(delta=delta)\n return self._new_rep(rep)","def generate_LLL_matrix(self, matrix):\n LLL_matrix = matrix.transpose().LLL().transpose()\n return LLL_matrix","def extract_hillslope_profile(node_matrix):\n ncols = numpy.size(node_matrix, 1)\n z = numpy.zeros(ncols)\n for col in range(ncols):\n dirt = numpy.where(node_matrix[:,col]!=0)[0]\n if len(dirt)>0:\n z[col] = numpy.amax(dirt)\n return z","def log_likelihoods(self):\n return self.__data_frame.loc[:, \"ll\":\"ll\"].values[:-1]","def recommend_L(self):\n\n min_eigenvalue = torch.min(torch.linalg.eigvalsh(self.Wc))\n lamBda = 1 / (1 + 4*torch.abs(min_eigenvalue - self.vars['q_init']))\n return lamBda","def getL(self):\r\n return self.L","def _getPMI(self, df, targetColname):\n pmi = 0\n search_term = df[targetColname]\n noofterms = len(search_term)\n startindex = 0\n pmiAccumulate = 0\n if(noofterms>1):\n for i in range(0,noofterms-1):\n pmi = self.computePMI(search_term[i],search_term[i+1])\n pmiAccumulate = pmiAccumulate+pmi\n pmiAccumulate = pmiAccumulate/noofterms\n pmi = pmiAccumulate\n return pmi","def Findlt(l,sp,rhs):\n m = sp.M(l)\n return (m / l**3) - rhs","def calcLnLFromNode(self, nd):\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\n return TreeLikelihoodBase.calcLnLFromNode(self, nd)","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def extract_ltri( m, context = FloatContext ):\n rows, cols = shape_mat(m)\n return [ row[:i+1] + [context.zero]*(cols - i - 1) \n for i, row in enumerate(m) ]","def get_number_of_rows_and_columns(m):\n\n r = int(np.sqrt(m))\n c = m // r if np.mod(m, r) == 0 else m // r + 1\n return r, c","def magma_cgels(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\n info = c_int_type()\n trans = _trans_conversion[trans]\n status = _libmagma.magma_cgels(trans, m, n, nrhs, int(A), lda,\n int(B), ldb, int(hwork), lwork,\n ctypes.byref(info))\n magmaCheckStatus(status)","def KL2kL(NL, KL, BL):\n # cycle through BL, finding matching inds in NL and thus KL\n # for row in BL, get KL value in row BL[i,0] and col where(NL[BL[i,0],:]==BL[i,1])[0]\n if (BL < 0).any():\n aBL = np.abs(BL)\n kL = np.array([KL[aBL[i, 0], np.where(NL[aBL[i, 0], :] == aBL[i, 1])[0]][0] for i in range(len(aBL))])\n kL2 = np.array([KL[aBL[i, 1], np.where(NL[aBL[i, 1], :] == aBL[i, 0])[0]][0] for i in range(len(aBL))])\n if np.abs(kL - kL2).any() > 1e-6:\n raise RuntimeError('KL is not properly symmetric! KL[i, j neighbor] != KL[j neighbor, i]')\n else:\n kL = np.array([KL[BL[i, 0], np.where(NL[BL[i, 0], :] == BL[i, 1])[0]][0] for i in range(len(BL))])\n return kL","def get_data_lcc(data):\n data_lcc = deepcopy(data)\n edge_index_sparse = edge_index_2_csr(data.edge_index, data.num_nodes)\n lcc = largest_connected_components(edge_index_sparse)\n edge_index_lcc_sparse = edge_index_sparse[lcc][:, lcc].tocoo()\n data_lcc.edge_index = torch.stack(list(to_Variable(edge_index_lcc_sparse.row, edge_index_lcc_sparse.col))).long()\n\n data_lcc.x = data.x[lcc]\n data_lcc.y = data.y[lcc]\n data_lcc.train_mask = data.train_mask[lcc]\n data_lcc.val_mask = data.val_mask[lcc]\n data_lcc.test_mask = data.test_mask[lcc]\n return data_lcc","def cov_l2cov_lmin(C_l) -> np.array:\n def isDiag(M):\n i, j = M.shape\n assert i == j \n test = M.reshape(-1)[:-1].reshape(i-1, j+1)\n return ~np.any(test[:, 1:])\n\n def invdiagmat(C):\n import copy\n ret = copy.deepcopy(C)\n row, col = np.diag_indices(ret.shape[0])\n ret[row, col] = 1/np.diag(ret)\n return ret\n\n elaw = np.ones(C_l.shape[-1])\n if isDiag(C_l):\n inv = invdiagmat(C_l)\n else:\n inv = np.linalg.inv(C_l)\n\n cov_minimal = elaw @ inv @ elaw\n return 1/cov_minimal","def gev_ll(loc,c,scale):\n \n def gev_logp(value):\n scaled = (value - loc) / scale\n logp = -(scale\n + ((c + 1) / c) * tt.log1p(c * scaled)\n + (1 + c * scaled) ** (-1/c))\n alpha = loc - scale / c\n \n # If the value is greater than zero, then check to see if \n # it is greater than alpha. Otherwise, check to see if it \n # is less than alpha.\n bounds = tt.switch(value > 0, value > alpha, value < alpha)\n \n # The returned array will have entries of -inf if the bound\n # is not satisfied. This condition says \"if c is zero or\n # value is less than alpha, return -inf and blow up \n # the log-likelihood.\n return bound(logp, bounds, c != 0)\n return gev_logp","def LC(f):\n return dmp_ground_LC(f.rep, f.lev, f.dom)","def get_k(df):\n\n return int(np.log(len(df.columns)))","def lcs_le(x: List, y: List) -> Tuple[Matrix, atrix]:\n m = len(x)\n n = len(y)\n lcs_matrix = [[None]*(n+1) for i in range(m+1)\n # each index is a optimal solution for each subproblem\n direction_matrix = [[None]*n for i in range(m)]\n # if either indecd is 0 then each element is 0\n for i n ranage(1, m+1):\n lcs_matrix[i][0] = 0\n for j in range(n+1):\n lcs_matrix[0][j] = 0\n for i in range(m):\n for j in range(n):\n if x[i] == y[j]:\n lcs_matrix[i+1][j+1] = lcs_matrix[i][j]+1\n direction_matrix[i][j] = Direction.UPPER_LEFT\n elif lcs_matrix[i][j+1] >= lcs_matrix[i+1][j]:\n lcs_matrix[i+1][j+1] = lcs_matrix[i][j+1]\n direction_matrix[i][j] = Direction.UP\n else:\n lcs_matrix[i+1][j+1] = lcs_matrix[i+1][j]\n direction_matrix[i][j] = Direction.LEFT\n return lcs_matrix, index_matrix","def glcm_stat_dissimilarity(glcm_matrix):\n it = np.nditer(glcm_matrix, flags=['multi_index'])\n accum = 0\n while (not it.finished):\n accum += glcm_matrix[it.multi_index] * abs(np.diff(it.multi_index))\n it.iternext()\n return accum","def llf(self):\n return self.model.loglike(self.params)","def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m = len(x), len(y)\n return table[n, m]","def _lcs(x, y):\n n, m = len(x), len(y)\n table = dict()\n for i in range(n + 1):\n for j in range(m + 1):\n if i == 0 or j == 0:\n table[i, j] = 0\n elif x[i - 1] == y[j - 1]:\n table[i, j] = table[i - 1, j - 1] + 1\n else:\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\n return table","def get_l_interface(n_v,n_c, neighbours, vs, CV_matrix,L):\n h_j = np.empty((n_v, 3, 2))\n for i in range(3):\n h_j[:, i] = vs\n h_jp1 = np.dstack((roll_reverse(neighbours[:,:,0]),roll_reverse(neighbours[:,:,1])))\n l = np.mod(h_j - h_jp1 + L/2,L) - L/2\n l = np.sqrt(l[:,:,0]**2 + l[:,:,1]**2)\n LI = np.zeros((n_c,n_c),dtype=np.float32)\n for i in range(3):\n LI+= np.asfortranarray(l[:,i]*CV_matrix[:,:,i])@np.asfortranarray(CV_matrix[:,:,np.mod(i+2,3)].T)\n return LI","def get_L(self):\n return self.get_par('XLAMBDA_0')","def estimate_L(da):\n from statsmodels.tsa.stattools import acf\n \n def acf_lag1(x):\n if np.sum(~np.isnan(x)) == 0: # if all NaNs\n return np.nan\n else:\n x = x[~np.isnan(x)]\n return acf(x, nlags=1)[-1]\n \n n = len(da.time.values)\n \n # DataArray of lag1 ACF coefficients\n rho_da = xr.apply_ufunc(acf_lag1, da, input_core_dims=[['time']], output_core_dims=[[]], vectorize=True, dask='allowed')\n \n # DataArray of effective sample size\n n_eff_da = n * ((1 - rho_da) / (1 + rho_da))\n \n # Initialise guess for block length\n Ls_da = xr.full_like(rho_da, 1)\n for i in range(10): # iterate to get estimate of L\n L_da = (n - Ls_da + 1) ** ( (2/3) * (1 - n_eff_da / n) )\n Ls_da = L_da\n \n return np.ceil(L_da) # round up to get block length","def lcr_matrix(H):\n if H.ndim != 2 or H.shape[0] != H.shape[1]:\n raise ValueError('H should be a square matrix')\n\n leverages = sqrt(1-H.diagonal())\n leverages = leverages[:, None]\n R = (eye(len(H)) - H) / leverages\n return R - R.mean(0)","def rel_matrix(df_long: pd.DataFrame) -> None:\n pass","def get_lthr_data(self):\n # FIXME one time point or whole data\n temp = self._data.copy()\n temp[temp < self._view_min] = 0\n return temp","def _f_lcs(llcs, m, n):\n r_lcs = llcs / m\n p_lcs = llcs / n\n beta = p_lcs / (r_lcs + 1e-12)\n num = (1 + (beta ** 2)) * r_lcs * p_lcs\n denom = r_lcs + ((beta ** 2) * p_lcs)\n f_lcs = num / (denom + 1e-12)\n return f_lcs","def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m = len(x), len(y)\n return table[n, m]","def _len_lcs(x, y):\n table = _lcs(x, y)\n n, m = len(x), len(y)\n return table[n, m]","def L(self) -> float:\n return self._L","def point_in_mbr(df, min_lat, max_lat, min_lon, max_lon):\n df = df[(df[\"lat\"] >= min_lat) &\n (df[\"lat\"] <= max_lat) &\n (df[\"lon\"] >= min_lon) &\n (df[\"lon\"] <= max_lon)\n ]\n return df","def test_lfc_pos_area_below_lcl():\n p = [902.1554, 897.9034, 893.6506, 889.4047, 883.063, 874.6284, 866.2387, 857.887,\n 849.5506, 841.2686, 833.0042, 824.7891, 812.5049, 796.2104, 776.0027, 751.9025,\n 727.9612, 704.1409, 680.4028, 656.7156, 629.077, 597.4286, 565.6315, 533.5961,\n 501.2452, 468.493, 435.2486, 401.4239, 366.9387, 331.7026, 295.6319, 258.6428,\n 220.9178, 182.9384, 144.959, 106.9778, 69.00213] * units.hPa\n t = [-3.039381, -3.703779, -4.15996, -4.562574, -5.131827, -5.856229, -6.568434,\n -7.276881, -7.985013, -8.670911, -8.958063, -7.631381, -6.05927, -5.083627,\n -5.11576, -5.687552, -5.453021, -4.981445, -5.236665, -6.324916, -8.434324,\n -11.58795, -14.99297, -18.45947, -21.92021, -25.40522, -28.914, -32.78637,\n -37.7179, -43.56836, -49.61077, -54.24449, -56.16666, -57.03775, -58.28041,\n -60.86264, -64.21677] * units.degC\n td = [-22.08774, -22.18181, -22.2508, -22.31323, -22.4024, -22.51582, -22.62526,\n -22.72919, -22.82095, -22.86173, -22.49489, -21.66936, -21.67332, -21.94054,\n -23.63561, -27.17466, -31.87395, -38.31725, -44.54717, -46.99218, -43.17544,\n -37.40019, -34.3351, -36.42896, -42.1396, -46.95909, -49.36232, -48.94634,\n -47.90178, -49.97902, -55.02753, -63.06276, -72.53742, -88.81377, -93.54573,\n -92.92464, -91.57479] * units.degC\n prof = parcel_profile(p, t[0], td[0]).to('degC')\n lfc_p, lfc_t = lfc(p, t, td, prof)\n assert_nan(lfc_p, p.units)\n assert_nan(lfc_t, t.units)","def ohodnotL(row, col, znak, prevx, prevy, pocet_ciest, hlbka, mx): # vlavo\r\n\r\n susedia = getSusedia_ohodnot(row, col, znak)\r\n\r\n if (values[row][col] != \"W\" and col != 0):\r\n hlbka += 1\r\n\r\n if col == 0:\r\n if values[row][col] == \"W\" and hlbka != 0:\r\n hlbka -= 1\r\n dlzkyL.append(hlbka)\r\n\r\n if (col != 0 and hlbka < mx):\r\n for sused in susedia:\r\n if (sused[1] == col - 1 or (sused[1] == col and (sused[0] != prevx or sused[1] != prevy))):\r\n if sused[1] == 0:\r\n pocet_ciest += 1\r\n pocet_ciest += ohodnotL(sused[0], sused[1], znak, row, col, 0, hlbka, mx)\r\n if (values[sused[0]][sused[1]] == \"W\") and col == 1: # nema zmysel sem umiestnovat - radsej inde\r\n pocet_ciest = 0\r\n return pocet_ciest","def correlate_rows(matrix):\n return np.dot(matrix, matrix.T) / (la.norm(matrix) ** 2)","def matlabels(df, rowlabel_fn):\n return df.index.to_frame().apply(rowlabel_fn, axis=1)","def lamPoint(numLams, lambdas, lam):\r\n \r\n help = [0.0 for i in range(numLams)]\r\n for i in range(numLams):\r\n\r\n help[i] = lambdas[i] - lam;\r\n help[i] = abs(help[i]);\r\n\r\n \r\n index = 0\r\n min = help[index]\r\n\r\n for i in range(1, numLams):\r\n\r\n if (help[i] < min): \r\n min = help[i]\r\n index = i\r\n\r\n return index","def keltner_channel_lband_indicator(high, low, close, n=10, fillna=False):\n lband = keltner_channel_lband(high, low, close, n)\n kc_lband_ind = np.clip(np.sign(lband-close), 0, 1)\n\n if fillna:\n kc_lband_ind = kc_lband_ind.replace([np.inf, -np.inf], np.nan).fillna(0)\n return pd.Series(kc_lband_ind, name='kc_lband_ind')","def lcms(argg: range) -> int:\n l = 1\n for arg in argg:\n l = lcm(l, arg)\n return l","def matchlines(nlines, wl, z, eml):\n lbdas = np.array(list(eml.keys()))\n a = (wl[:, np.newaxis] / (1 + z) - lbdas[np.newaxis, :]) ** 2.0\n jfound = np.argmin(a, axis=1)\n error = np.diag(a[:, jfound]).sum()\n error = np.sqrt(error / nlines)\n if((nlines >= 2)and(jfound[0] == jfound[1])):\n error = 15.\n return(error, jfound)","def Ls(GTn:torch.tensor, Mn:torch.tensor) -> torch.tensor:\n return (-(GTn * torch.log(Mn+1e-15) + (1- GTn) * torch.log((1- Mn)+1e-15))).sum()","def kldiv_lsm_ctc(logits, ylens):\n bs, _, vocab = logits.size()\n log_uniform = logits.new_zeros(logits.size()).fill_(math.log(1 / (vocab - 1)))\n probs = torch.softmax(logits, dim=-1)\n log_probs = torch.log_softmax(logits, dim=-1)\n loss = torch.mul(probs, log_probs - log_uniform)\n loss_mean = sum([loss[b, :ylens[b], :].sum() for b in range(bs)]) / ylens.sum()\n return loss_mean","def lam(freq):\n return C / freq","def calculateDistances(df):\n return","def calculate_L(self, n_t, n_n, l0, l1, dt, ctant):\n d_scl = np.sqrt((self.t_D.get_value() ** 2).sum(1).mean())\n\n a = n_t * n_n * l1 * dt * d_scl ** 2 * np.log(l1 / l0) ** 2\n # try:\n # self._b\n # except:\n # from scipy.linalg import eigh as largest_eigh\n # k=1\n # N = inv_cov.shape[0]\n # evals_large, _ = largest_eigh(inv_cov, eigvals=(N-k,N-1))\n b = 0.5 * self.QUAD_REG.max()\n # FIXME: not quite right for non-diagonal matrix, but is close enough\n # in practice\n\n # print a, b, b / a\n return ((a + b) * ctant).astype('float32')","def run_lmm(formula, df, reml=False, **kwargs):\n model = sm.MixedLM.from_formula(formula, df, **kwargs)\n return model.fit(reml=reml)","def CC_CLS_CL_TRS(Dataframe):\n\n Feature_DF = Dataframe.loc[:,['HNAME','CC_CLS_CL']]\n min_value = min(Feature_DF.loc[:,'CC_CLS_CL'])\n Feature_DF.loc[:,'CC_CLS_CL_TRS'] = Feature_DF.loc[:,'CC_CLS_CL'].apply(lambda x : (1+x-min_value)**(-9/8))\n Feature_DF = Feature_DF.loc[:,['HNAME','CC_CLS_CL_TRS']]\n\n return Feature_DF","def CC_REC_NUM_LT3_TRS(Dataframe):\n\n Feature_DF = Dataframe.loc[:,['HNAME','CC_REC_NUM_LT3']]\n min_value = min(Feature_DF.loc[:,'CC_REC_NUM_LT3'])\n Feature_DF.loc[:,'CC_REC_NUM_LT3_TRS'] = Feature_DF.loc[:,'CC_REC_NUM_LT3'].apply(lambda x : (1+x-min_value)**(-3/8))\n Feature_DF = Feature_DF.loc[:,['HNAME','CC_REC_NUM_LT3_TRS']]\n\n return Feature_DF","def LLE(data, n_components=2, n_neighbors=10):\n # Compute the nearest neighbors\n _, neighbors_idx = AffiinityMAt(data,n_neighbor=n_neighbors,kernel_method='Linear')\n\n n = data.shape[0]\n w = np.zeros((n, n))\n for i in range(n):\n # Center the neighbors matrix\n k_indexes = neighbors_idx[i, :]\n neighbors = data[k_indexes, :] - data[i, :]\n\n # Compute the corresponding gram matrix\n gram_inv = np.linalg.pinv(np.dot(neighbors, neighbors.T))\n\n # Setting the weight values according to the lagrangian\n lambda_par = 2/np.sum(gram_inv)\n w[i, k_indexes] = lambda_par*np.sum(gram_inv, axis=1)/2\n m = np.subtract(np.eye(n), w)\n values, u = np.linalg.eigh(np.dot(np.transpose(m), m))\n return u[:, 1:n_components+1]","def compute_log_likelihoods(df, error_rate=1e-3):\n df['log_likelihood_absent'] = df.apply(calculate_likelihood_absent, axis=1, args=(error_rate,))\n df['log_likelihood_present'] = df.apply(calculate_likelihood_present, axis=1, args=(error_rate,))\n\n return df","def get_rows(df):\n return df.shape[0]","def find_distances(frame, newPoint): \n distances = [] \n \n # iterate over all rows in the dataframe\n for index in range(frame.shape[0]):\n\n # get all columns of a row (except the label) \n point = frame.iloc[index,:-1] \n\n \t# compute the distance, then save distance and label \n # (use distance as first value)\n distance = euclidean_distance(point, newPoint)\n if distance == 0:\n distances.append((distance, frame.iloc[index,-1])) \n else:\n distances.append((sys.maxsize, frame.iloc[index,-1])) \n \n\n distances.sort() \n \n return distances","def find_densest(m, w_ncols, w_nrows):\n # Implement your solution here.\n pass","def _get_Mll(self):\n\t\t_M_ll = mll.get_mll(self.W_l, self.lmax)\n\t\treturn np.float64(_M_ll)","def laplacian(self, e, epos):\n s = int(e >= self._nelec[0])\n ao = np.real_if_close(\n self._mol.eval_gto(self.pbc_str + \"GTOval_sph_deriv2\", epos.configs)[\n [0, 4, 7, 9]\n ],\n tol=1e4,\n )\n molap = np.dot(\n [ao[0], ao[1:].sum(axis=0)], self.parameters[self._coefflookup[s]]\n )\n molap_vals = self._testrow(e, molap[1][:, self._det_occup[s]])\n testvalue = self._testrow(e, molap[0][:, self._det_occup[s]])\n\n return molap_vals / testvalue","def lil_cc(lil):\n n = len(lil)\n visited = np.zeros(n).astype(np.int_)\n label = - np.ones(n).astype(np.int_)\n k = 0\n while (visited == 0).any():\n front = [np.argmin(visited)]\n while len(front) > 0:\n pivot = front.pop(0)\n if visited[pivot] == 0:\n visited[pivot] = 1\n label[pivot] = k\n front += lil[pivot]\n k += 1\n return label","def lcmm(*args):\n\treturn reduce(lcm, args)","def lmin(self):\n return self._get_mean_and_samples_attribute('lmin')","def lla(self):\n return self._lla_shape","def _point_in_mbr(self, df):\n if df.empty:\n return df\n df = df[(df[\"lat\"] >= self._min_lat) &\n (df[\"lat\"] <= self._max_lat) &\n (df[\"lon\"] >= self._min_lon) &\n (df[\"lon\"] <= self._max_lon)\n ]\n return df","def lll(basis, delta=.75):\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n k = 1\n while k < len(basis):\n for j in range(k - 1, -1, -1):\n # Size condition\n if np.abs(mu[k][j]) > .5:\n basis[k] = basis[k] - np.rint(mu[k, j]) * basis[j]\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n # Lovasz condition\n if sq_norm(orthog[k]) >= (delta - mu[k][k - 1]**2) * sq_norm(orthog[k - 1]):\n k += 1\n else:\n temp = copy.deepcopy(basis[k])\n basis[k] = copy.deepcopy(basis[k - 1])\n basis[k - 1] = temp\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n k = max(k - 1, 1)\n return basis","def cl_interval(lnL, sigma=None, CL=0.68):\n import copy\n # Confidence limits\n if sigma is None:\n c0 = (1. - CL)/2.\n c1 = 1.-c0\n # Image dimensions\n shape = lnL.shape\n ndim = len(shape)\n slc = [slice(None)]*ndim\n # Find max\n norm_L = np.exp(np.maximum(lnL - np.max(lnL),-15.))\n # Find best indices\n indices = np.where(lnL == np.max(lnL))\n best_idx = [bi[0] for bi in indices]\n\n # Error intervals\n all_error = []\n for kk in range(ndim):\n # Collapse on this dimension\n slc = copy.deepcopy(best_idx)\n slc[kk] = slice(None)\n Lslice = norm_L[slc].flatten()\n # Interpolate and go\n cumul_area = np.cumsum(Lslice)\n f_area = interp1d(cumul_area/cumul_area[-1], np.arange(len(Lslice)))\n # Here we go\n idx0 = int(np.round(f_area(c0)))\n idx1 = int(np.round(f_area(c1)))\n all_error.append([idx0,idx1])\n\n # Return\n return best_idx, all_error","def compute_distance(df):\n pass","def lagrange(self,K, L):\n return lagrange(K, L)","def lll(basis, delta=.75):\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n k = 1\n while k < len(basis):\n for j in range(k - 1, -1, -1):\n # Size condition\n if np.abs(mu[k][j]) > .5:\n basis[k] = basis[k] - np.rint(mu[k, j]) * basis[j]\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n # Lovasz condition\n if sq_norm(orthog[k]) >= (delta - mu[k][k - 1]**2) * sq_norm(orthog[k - 1]):\n k += 1\n else:\n temp = copy.deepcopy(basis[k])\n basis[k] = copy.deepcopy(basis[k - 1])\n basis[k - 1] = temp\n orthog = gram_schmidt(basis)\n mu = get_mus(basis, orthog)\n k = max(k - 1, 1)\n return basis","def LCSubSeq(X, Y, m, n):\n LCSuff = [[0 for k in range (n+1)] for l in range(m+1)]\n result = 0\n\n for i in range(m+1):\n for j in range(n+1):\n if (i==0 or j==0):\n LCSuff[i][j] = 0\n elif (X[i-1] == Y[j - 1]):\n LCSuff[i][j] = LCSuff[i-1][j-1] + 1\n result = max(result, LCSuff[i][j])\n else:\n LCSuff[i][j] = 0\n return result","def get_cl(self, re, aa):\r\n return Turbine.v_fCL(re, np.degrees(aa))","def pos_conserved(df, conservation):\n nb_rows, nb_cols = df.shape\n\n value_counts = df.apply(pd.Series.value_counts, axis=0).max(axis=0).ge(conservation * nb_rows)\n\n ge = [i for i, x in enumerate(value_counts) if x]\n return ge","def get_maxmin_index_from_row(\n distance_matrix: np.ndarray,\n row: int,\n previous_indexes: List,\n type: str,\n )-> int:\n distance_matrix = distance_matrix.copy()\n arr = distance_matrix[row].astype(float)\n \n aux_list = range(arr.shape[0])\n aux_list_2 = []\n for i in aux_list:\n if i in previous_indexes:\n aux_list_2.append(True)\n else:\n aux_list_2.append(False)\n previous_indexes_bool = aux_list_2\n \n if type == 'max':\n arr[previous_indexes_bool] = -1\n target_index = np.argmax(arr)\n if type == 'min':\n arr[previous_indexes_bool] = np.Inf\n target_index = np.argmin(arr)\n \n return target_index","def cgid2llnr(self, cgID):\n cgID = cgID.replace('_240_37-ASM', '')\n possible = self.phenotypes[self.phenotypes.cgID == cgID].LLnr.values \n if len(possible) > 0:\n return possible[0]\n #fi\n return None","def _calculate_ll(self, x):\n observation_log_probs = self._observation_log_probs(x, mask=None)\n forward_log_probs = self._forward(observation_log_probs)\n log_likelihood = logsumexp(\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\n return log_likelihood","def labeling_func(df_clus):\n\n df_all_labeled = df_all_columns.copy()\n df_all_labeled['Clus_label'] = df_clus['Clus_label'].copy()\n df_all_labeled['Clus_label']= df_all_labeled['Clus_label'].astype(int)\n for i in range(0, clus_params['n_components']):\n df_all_labeled['Prob_L'+str(i)] = df_clus['Prob_L'+str(i)].copy()\n\n return df_all_labeled","def lcmm(*args): \r\n return reduce(lcm, args)","def norm_leg_coefs(self, cl):\n new_cl = np.zeros_like( cl )\n for i in range(cl.shape[1]):\n for j in range(cl.shape[2]):\n if (cl[:,i,j]).max() >0:\n new_cl[:,i,j] = cl[:,i,j]/(cl[:,i,j]).max()\n\n return new_cl","def _get_LFDP(self, segment):\n dists_to_fitting_line = self._get_dists_to_fitting_line(segment)\n return numpy.max(dists_to_fitting_line), numpy.argmax(dists_to_fitting_line)","def ml_kl_loss(self, simulation, c1 = 1.0, ndims = 2, ehigh=1e5, emax = 1e10, turnover=200):\n loss = MLKL(c1, simulation, ndims, ehigh, emax, turnover)\n return loss.lossFunction","def __isFarFromLevel(self, l):\n\n s = np.mean(self.df['high'] - self.df['low'])\n return np.sum([abs(l-x) < s for x in self.levels]) == 0","def lam2f(l):\n f=c['c']/l\n return f","def lap(self):\n\n gr = self.grid\n phi = gr.phi\n\n lapphi = gr.scratch_array()\n\n ib = gr.ilo\n ie = gr.ihi\n\n lapphi[ib:ie+1] = \\\n (phi[ib-1:ie] - 2.0*phi[ib:ie+1] + phi[ib+1:ie+2])/gr.dx**2\n\n return lapphi","def get_lca_in_bst(root, node_0, node_1):\n res = root\n s = node_0 if node_0.data < node_1.data else node_1\n b = node_1 if node_0.data < node_1.data else node_0\n while (res.data < s.data) or (res.data > b.data):\n while res.data < s.data:\n res = res.right\n while res.data > b.data:\n res = res.left\n return res","def lagrangePoints(mu):\n \n # define l = 1-mu\n l = 1 - mu\n \n # collinear points\n def eqL1(x):\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*l*mu+mu**2)*x**3 + (2*mu*l*(l-mu)+mu-l)*x**2 + (mu**2*l**2+2*(l**2+mu**2))*x + mu**3-l**3\n #fval = gamma**5 - (3-mu)*gamma**4 + (3-2*mu)*gamma**3 - mu*gamma**2 + 2*mu*gamma - mu\n return fval\n sol_l1 = optimize.root(eqL1, 0.5, method='hybr')\n l1 = np.array([sol_l1.x[0] , 0, 0])\n \n def eqL2(x):\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*l*mu+mu**2)*x**3 + (2*mu*l*(l-mu)-(mu+l))*x**2 + (mu**2*l**2+2*(l**2-mu**2))*x - (mu**3+l**3)\n #fval = gamma**5 + (3-mu)*gamma**4 + (3-2*mu)*gamma**3 - mu*gamma**2 - 2*mu*gamma - mu\n return fval\n sol_l2 = optimize.root(eqL2, 1.5, method='hybr')\n l2 = np.array([sol_l2.x[0] , 0, 0])\n \n def eqL3(x):\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*mu*l+mu**2)*x**3 + (2*mu*l*(l-mu)+(l+mu))*x**2 + (mu**2*l**2+2*(mu**2-l**2))*x + l**3+mu**3\n return fval\n sol_l3 = optimize.root(eqL3, -1, method='hybr')\n l3 = np.array([sol_l3.x[0] , 0, 0])\n \n # equilateral points\n # L4\n l4 = np.array([np.cos(np.pi/3) - mu , np.sin(np.pi/3), 0])\n # L5\n l5 = np.array([np.cos(np.pi/3) - mu , -np.sin(np.pi/3), 0])\n \n return _lagrangePointsReturn(l1,l2,l3,l4,l5)"],"string":"[\n \"def get_index_lm(l, m):\\n return (l+1)**2 -1 -l + m\",\n \"def compute_kl(self, df):\\n value_counts = [df[col].value_counts() for col in self.hist_cols]\\n next_hists = self.value_counts_to_hists(value_counts)\\n\\n if self.prev_hists is None:\\n self.prev_hists = next_hists\\n return None\\n\\n output = []\\n for prev_h, curr_h in zip(self.prev_hists, next_hists):\\n for i in range(len(prev_h)):\\n prev_h[i] = prev_h[i] if prev_h[i] != 0 else 1\\n curr_h[i] = curr_h[i] if curr_h[i] != 0 else 1\\n kl = entropy(prev_h, curr_h)\\n output.append(kl)\\n\\n self.prev_hists = next_hists\\n return output\",\n \"def lfindwithin (data):\\r\\n\\r\\n numfact = len(data[0])-1\\r\\n withinvec = 0\\r\\n for col in range(1,numfact):\\r\\n examplelevel = pstats.unique(pstats.colex(data,col))[0]\\r\\n rows = pstats.linexand(data,col,examplelevel) # get 1 level of this factor\\r\\n factsubjs = pstats.unique(pstats.colex(rows,0))\\r\\n allsubjs = pstats.unique(pstats.colex(data,0))\\r\\n if len(factsubjs) == len(allsubjs): # fewer Ss than scores on this factor?\\r\\n withinvec = withinvec + (1 << col)\\r\\n return withinvec\",\n \"def cal_L(self):\\n # calculate the l matrix\\n self.point_matrixs = self.point_matrix.reshape(\\n self.point_matrix.shape[0], 1, self.point_matrix.shape[1])\\n self.point_matrixs = np.tile(self.point_matrixs,\\n (self.attach_points.shape[0], 1))\\n self.attach_points_matrix = np.matlib.repmat(\\n self.attach_points[:, 0:3], self.point_matrix.shape[0], 1)\\n self.attach_points_matrix = self.attach_points_matrix.reshape(\\n self.point_matrix.shape[0], self.attach_points.shape[0], 3)\\n self.L = np.subtract(self.attach_points_matrix,\\n self.point_matrixs)\\n # self.L[:,self.attach_points[:,3]==1,:] = \\\\\\n # - self.L[:,self.attach_points[:,3]==1,:]\\n # print(self.L)\",\n \"def calculate_recovery_clifford(cl_in, desired_cl=0):\\n row = list(clifford_lookuptable[cl_in])\\n return row.index(desired_cl)\",\n \"def calc_Lr(rho,mld,f,g=9.8,po=1027.):\\n n2ml=np.ndarray(len(rho[1,:-1]))\\n for i in range(len(rho[1,:-1])):\\n n2ml[i]=-(g/po)*((rho[15,i]-rho[np.int8(mld[i])+15,i])/mld[i])\\n Lr=(np.sqrt(n2ml)*mld[:-1])/f\\n\\n return Lr\",\n \"def find_lcs(l1: str, l2: str, length1: int, length2: int):\\n \\\"\\\"\\\" Theorem:{\\n Initialize matrix with 0 for first row and colm\\n If s1[i] = s2[j], update matrix[i][j] with value\\n of matrix[i-1][j-1]+1\\n Else update matrix[i][j] with max of value among\\n matrix[i][j-1],matrix[i-1][j]\\n Matrix[n][m] will be lcs\\n }\\n \\\"\\\"\\\"\\n matrix = [[None]*(length1+1) for i in range(0, length2+1)]\\n for i in range(0, length2+1):\\n for j in range(0, length1+1):\\n if i == 0 or j == 0:\\n matrix[i][j] = 0\\n elif l1[j-1] == l2[i-1]:\\n matrix[i][j] = matrix[i-1][j-1] + 1\\n else:\\n matrix[i][j] = max(matrix[i][j-1], matrix[i-1][j])\\n lcs = [None for i in range(0, matrix[length2][length1])]\\n index = matrix[length2][length1]\\n m = length2 \\n n = length1\\n while(m > -1 and n > -1):\\n if l2[m-1] == l1[n-1]:\\n lcs[index-1] = l2[m-1]\\n index -= 1\\n m -= 1\\n n -= 1\\n elif matrix[m-1][n] > matrix[m][n-1]:\\n m -= 1\\n else:\\n n -= 1\\n return lcs\",\n \"def get_lims(data):\\n return data[:, 0].min() - 1, data[:, 0].max() + 1, data[:, 1].min() - 1, data[:, 1].max() + 1\",\n \"def _compute_lcs(source, target):\\n table = _lcs_table(source, target)\\n return _backtrack(table, source, target, len(source), len(target))\",\n \"def getCL(self):\\r\\n return self.cL;\",\n \"def calculate_LC(read):\\n k_values = []\\n for i in range(1,len(read)+1):\\n k_values.append(i)\\n observed_kmers = []\\n for i in k_values:\\n observed_kmers.append((count_kmers_observed(read, i)))\\n possible_kmers = []\\n for i in k_values:\\n possible_kmers.append(count_kmers_possible(read, i))\\n df = pd.DataFrame(list(zip(k_values, observed_kmers, possible_kmers)), columns = ['k','observed kmers','possible kmers'])\\n df.at['Total', 'observed kmers'] = df['observed kmers'].sum()\\n df.at['Total', 'possible kmers'] = df['possible kmers'].sum()\\n x = int(df.at['Total', 'observed kmers'])\\n y = int(df.at['Total', 'possible kmers'])\\n LC = (x/y)\\n return(LC)\",\n \"def getLPos(self):\\n c = 0\\n while c <= ALIENS_IN_ROW-1:\\n i = 0\\n for a in range(ALIEN_ROWS):\\n if self._aliens[a][c] != None:\\n return self._aliens[a][c].x - ALIEN_WIDTH/2\\n else:\\n i +=1\\n if i == ALIEN_ROWS:\\n c +=1\",\n \"def get_L(self, tolerance=None):\\r\\n index = self.data.index\\r\\n columns = self.data.columns\\r\\n\\r\\n # Obtain the eigenvalues and eigenvectors\\r\\n E, V = sandy.CategoryCov(self.data).get_eig(tolerance=tolerance)\\r\\n\\r\\n # need sparse because much faster for large matrices (2kx2k from J33 Pu9)\\r\\n # with a lot of zero eigs\\r\\n # this is exactly equivalent to V.values @ np.diag(np.sqrt(E.values))\\r\\n A = (sparse.csr_matrix(V.values) @ sparse.csr_matrix(np.diag(np.sqrt(E.values)))).todense()\\r\\n \\r\\n# u, s, vh = self.get_svd()\\r\\n# A = (sparse.csr_matrix(u) @ sparse.csr_matrix(np.diag(np.sqrt(s)))).todense()\\r\\n\\r\\n # QR decomposition\\r\\n Q, R = scipy.linalg.qr(A.T)\\r\\n L = R.T\\r\\n\\r\\n return pd.DataFrame(L, index=index, columns=columns)\",\n \"def get_l(m):\\n L = m.copy()\\n for i in range(L.shape[0]):\\n L[i, i] = 1\\n L[i, i+1:] = 0\\n return np.matrix(L)\",\n \"def calcLnL(self, tree):\\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\\n return TreeLikelihoodBase.calcLnL(self, tree)\",\n \"def lll(self, delta=0.75):\\n if self.domain != ZZ:\\n raise DMDomainError(\\\"ZZ expected, got %s\\\" % self.domain)\\n elif self.rows > self.cols:\\n raise DMShapeError(\\\"Matrix must not have more rows than columns.\\\")\\n\\n rep = self._lll(delta=delta)\\n return self._new_rep(rep)\",\n \"def generate_LLL_matrix(self, matrix):\\n LLL_matrix = matrix.transpose().LLL().transpose()\\n return LLL_matrix\",\n \"def extract_hillslope_profile(node_matrix):\\n ncols = numpy.size(node_matrix, 1)\\n z = numpy.zeros(ncols)\\n for col in range(ncols):\\n dirt = numpy.where(node_matrix[:,col]!=0)[0]\\n if len(dirt)>0:\\n z[col] = numpy.amax(dirt)\\n return z\",\n \"def log_likelihoods(self):\\n return self.__data_frame.loc[:, \\\"ll\\\":\\\"ll\\\"].values[:-1]\",\n \"def recommend_L(self):\\n\\n min_eigenvalue = torch.min(torch.linalg.eigvalsh(self.Wc))\\n lamBda = 1 / (1 + 4*torch.abs(min_eigenvalue - self.vars['q_init']))\\n return lamBda\",\n \"def getL(self):\\r\\n return self.L\",\n \"def _getPMI(self, df, targetColname):\\n pmi = 0\\n search_term = df[targetColname]\\n noofterms = len(search_term)\\n startindex = 0\\n pmiAccumulate = 0\\n if(noofterms>1):\\n for i in range(0,noofterms-1):\\n pmi = self.computePMI(search_term[i],search_term[i+1])\\n pmiAccumulate = pmiAccumulate+pmi\\n pmiAccumulate = pmiAccumulate/noofterms\\n pmi = pmiAccumulate\\n return pmi\",\n \"def Findlt(l,sp,rhs):\\n m = sp.M(l)\\n return (m / l**3) - rhs\",\n \"def calcLnLFromNode(self, nd):\\n #---+----|----+----|----+----|----+----|----+----|----+----|----+----|\\n return TreeLikelihoodBase.calcLnLFromNode(self, nd)\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def extract_ltri( m, context = FloatContext ):\\n rows, cols = shape_mat(m)\\n return [ row[:i+1] + [context.zero]*(cols - i - 1) \\n for i, row in enumerate(m) ]\",\n \"def get_number_of_rows_and_columns(m):\\n\\n r = int(np.sqrt(m))\\n c = m // r if np.mod(m, r) == 0 else m // r + 1\\n return r, c\",\n \"def magma_cgels(trans, m, n, nrhs, A, lda, B, ldb, hwork, lwork):\\n info = c_int_type()\\n trans = _trans_conversion[trans]\\n status = _libmagma.magma_cgels(trans, m, n, nrhs, int(A), lda,\\n int(B), ldb, int(hwork), lwork,\\n ctypes.byref(info))\\n magmaCheckStatus(status)\",\n \"def KL2kL(NL, KL, BL):\\n # cycle through BL, finding matching inds in NL and thus KL\\n # for row in BL, get KL value in row BL[i,0] and col where(NL[BL[i,0],:]==BL[i,1])[0]\\n if (BL < 0).any():\\n aBL = np.abs(BL)\\n kL = np.array([KL[aBL[i, 0], np.where(NL[aBL[i, 0], :] == aBL[i, 1])[0]][0] for i in range(len(aBL))])\\n kL2 = np.array([KL[aBL[i, 1], np.where(NL[aBL[i, 1], :] == aBL[i, 0])[0]][0] for i in range(len(aBL))])\\n if np.abs(kL - kL2).any() > 1e-6:\\n raise RuntimeError('KL is not properly symmetric! KL[i, j neighbor] != KL[j neighbor, i]')\\n else:\\n kL = np.array([KL[BL[i, 0], np.where(NL[BL[i, 0], :] == BL[i, 1])[0]][0] for i in range(len(BL))])\\n return kL\",\n \"def get_data_lcc(data):\\n data_lcc = deepcopy(data)\\n edge_index_sparse = edge_index_2_csr(data.edge_index, data.num_nodes)\\n lcc = largest_connected_components(edge_index_sparse)\\n edge_index_lcc_sparse = edge_index_sparse[lcc][:, lcc].tocoo()\\n data_lcc.edge_index = torch.stack(list(to_Variable(edge_index_lcc_sparse.row, edge_index_lcc_sparse.col))).long()\\n\\n data_lcc.x = data.x[lcc]\\n data_lcc.y = data.y[lcc]\\n data_lcc.train_mask = data.train_mask[lcc]\\n data_lcc.val_mask = data.val_mask[lcc]\\n data_lcc.test_mask = data.test_mask[lcc]\\n return data_lcc\",\n \"def cov_l2cov_lmin(C_l) -> np.array:\\n def isDiag(M):\\n i, j = M.shape\\n assert i == j \\n test = M.reshape(-1)[:-1].reshape(i-1, j+1)\\n return ~np.any(test[:, 1:])\\n\\n def invdiagmat(C):\\n import copy\\n ret = copy.deepcopy(C)\\n row, col = np.diag_indices(ret.shape[0])\\n ret[row, col] = 1/np.diag(ret)\\n return ret\\n\\n elaw = np.ones(C_l.shape[-1])\\n if isDiag(C_l):\\n inv = invdiagmat(C_l)\\n else:\\n inv = np.linalg.inv(C_l)\\n\\n cov_minimal = elaw @ inv @ elaw\\n return 1/cov_minimal\",\n \"def gev_ll(loc,c,scale):\\n \\n def gev_logp(value):\\n scaled = (value - loc) / scale\\n logp = -(scale\\n + ((c + 1) / c) * tt.log1p(c * scaled)\\n + (1 + c * scaled) ** (-1/c))\\n alpha = loc - scale / c\\n \\n # If the value is greater than zero, then check to see if \\n # it is greater than alpha. Otherwise, check to see if it \\n # is less than alpha.\\n bounds = tt.switch(value > 0, value > alpha, value < alpha)\\n \\n # The returned array will have entries of -inf if the bound\\n # is not satisfied. This condition says \\\"if c is zero or\\n # value is less than alpha, return -inf and blow up \\n # the log-likelihood.\\n return bound(logp, bounds, c != 0)\\n return gev_logp\",\n \"def LC(f):\\n return dmp_ground_LC(f.rep, f.lev, f.dom)\",\n \"def get_k(df):\\n\\n return int(np.log(len(df.columns)))\",\n \"def lcs_le(x: List, y: List) -> Tuple[Matrix, atrix]:\\n m = len(x)\\n n = len(y)\\n lcs_matrix = [[None]*(n+1) for i in range(m+1)\\n # each index is a optimal solution for each subproblem\\n direction_matrix = [[None]*n for i in range(m)]\\n # if either indecd is 0 then each element is 0\\n for i n ranage(1, m+1):\\n lcs_matrix[i][0] = 0\\n for j in range(n+1):\\n lcs_matrix[0][j] = 0\\n for i in range(m):\\n for j in range(n):\\n if x[i] == y[j]:\\n lcs_matrix[i+1][j+1] = lcs_matrix[i][j]+1\\n direction_matrix[i][j] = Direction.UPPER_LEFT\\n elif lcs_matrix[i][j+1] >= lcs_matrix[i+1][j]:\\n lcs_matrix[i+1][j+1] = lcs_matrix[i][j+1]\\n direction_matrix[i][j] = Direction.UP\\n else:\\n lcs_matrix[i+1][j+1] = lcs_matrix[i+1][j]\\n direction_matrix[i][j] = Direction.LEFT\\n return lcs_matrix, index_matrix\",\n \"def glcm_stat_dissimilarity(glcm_matrix):\\n it = np.nditer(glcm_matrix, flags=['multi_index'])\\n accum = 0\\n while (not it.finished):\\n accum += glcm_matrix[it.multi_index] * abs(np.diff(it.multi_index))\\n it.iternext()\\n return accum\",\n \"def llf(self):\\n return self.model.loglike(self.params)\",\n \"def _len_lcs(x, y):\\n table = _lcs(x, y)\\n n, m = len(x), len(y)\\n return table[n, m]\",\n \"def _lcs(x, y):\\n n, m = len(x), len(y)\\n table = dict()\\n for i in range(n + 1):\\n for j in range(m + 1):\\n if i == 0 or j == 0:\\n table[i, j] = 0\\n elif x[i - 1] == y[j - 1]:\\n table[i, j] = table[i - 1, j - 1] + 1\\n else:\\n table[i, j] = max(table[i - 1, j], table[i, j - 1])\\n return table\",\n \"def get_l_interface(n_v,n_c, neighbours, vs, CV_matrix,L):\\n h_j = np.empty((n_v, 3, 2))\\n for i in range(3):\\n h_j[:, i] = vs\\n h_jp1 = np.dstack((roll_reverse(neighbours[:,:,0]),roll_reverse(neighbours[:,:,1])))\\n l = np.mod(h_j - h_jp1 + L/2,L) - L/2\\n l = np.sqrt(l[:,:,0]**2 + l[:,:,1]**2)\\n LI = np.zeros((n_c,n_c),dtype=np.float32)\\n for i in range(3):\\n LI+= np.asfortranarray(l[:,i]*CV_matrix[:,:,i])@np.asfortranarray(CV_matrix[:,:,np.mod(i+2,3)].T)\\n return LI\",\n \"def get_L(self):\\n return self.get_par('XLAMBDA_0')\",\n \"def estimate_L(da):\\n from statsmodels.tsa.stattools import acf\\n \\n def acf_lag1(x):\\n if np.sum(~np.isnan(x)) == 0: # if all NaNs\\n return np.nan\\n else:\\n x = x[~np.isnan(x)]\\n return acf(x, nlags=1)[-1]\\n \\n n = len(da.time.values)\\n \\n # DataArray of lag1 ACF coefficients\\n rho_da = xr.apply_ufunc(acf_lag1, da, input_core_dims=[['time']], output_core_dims=[[]], vectorize=True, dask='allowed')\\n \\n # DataArray of effective sample size\\n n_eff_da = n * ((1 - rho_da) / (1 + rho_da))\\n \\n # Initialise guess for block length\\n Ls_da = xr.full_like(rho_da, 1)\\n for i in range(10): # iterate to get estimate of L\\n L_da = (n - Ls_da + 1) ** ( (2/3) * (1 - n_eff_da / n) )\\n Ls_da = L_da\\n \\n return np.ceil(L_da) # round up to get block length\",\n \"def lcr_matrix(H):\\n if H.ndim != 2 or H.shape[0] != H.shape[1]:\\n raise ValueError('H should be a square matrix')\\n\\n leverages = sqrt(1-H.diagonal())\\n leverages = leverages[:, None]\\n R = (eye(len(H)) - H) / leverages\\n return R - R.mean(0)\",\n \"def rel_matrix(df_long: pd.DataFrame) -> None:\\n pass\",\n \"def get_lthr_data(self):\\n # FIXME one time point or whole data\\n temp = self._data.copy()\\n temp[temp < self._view_min] = 0\\n return temp\",\n \"def _f_lcs(llcs, m, n):\\n r_lcs = llcs / m\\n p_lcs = llcs / n\\n beta = p_lcs / (r_lcs + 1e-12)\\n num = (1 + (beta ** 2)) * r_lcs * p_lcs\\n denom = r_lcs + ((beta ** 2) * p_lcs)\\n f_lcs = num / (denom + 1e-12)\\n return f_lcs\",\n \"def _len_lcs(x, y):\\n table = _lcs(x, y)\\n n, m = len(x), len(y)\\n return table[n, m]\",\n \"def _len_lcs(x, y):\\n table = _lcs(x, y)\\n n, m = len(x), len(y)\\n return table[n, m]\",\n \"def L(self) -> float:\\n return self._L\",\n \"def point_in_mbr(df, min_lat, max_lat, min_lon, max_lon):\\n df = df[(df[\\\"lat\\\"] >= min_lat) &\\n (df[\\\"lat\\\"] <= max_lat) &\\n (df[\\\"lon\\\"] >= min_lon) &\\n (df[\\\"lon\\\"] <= max_lon)\\n ]\\n return df\",\n \"def test_lfc_pos_area_below_lcl():\\n p = [902.1554, 897.9034, 893.6506, 889.4047, 883.063, 874.6284, 866.2387, 857.887,\\n 849.5506, 841.2686, 833.0042, 824.7891, 812.5049, 796.2104, 776.0027, 751.9025,\\n 727.9612, 704.1409, 680.4028, 656.7156, 629.077, 597.4286, 565.6315, 533.5961,\\n 501.2452, 468.493, 435.2486, 401.4239, 366.9387, 331.7026, 295.6319, 258.6428,\\n 220.9178, 182.9384, 144.959, 106.9778, 69.00213] * units.hPa\\n t = [-3.039381, -3.703779, -4.15996, -4.562574, -5.131827, -5.856229, -6.568434,\\n -7.276881, -7.985013, -8.670911, -8.958063, -7.631381, -6.05927, -5.083627,\\n -5.11576, -5.687552, -5.453021, -4.981445, -5.236665, -6.324916, -8.434324,\\n -11.58795, -14.99297, -18.45947, -21.92021, -25.40522, -28.914, -32.78637,\\n -37.7179, -43.56836, -49.61077, -54.24449, -56.16666, -57.03775, -58.28041,\\n -60.86264, -64.21677] * units.degC\\n td = [-22.08774, -22.18181, -22.2508, -22.31323, -22.4024, -22.51582, -22.62526,\\n -22.72919, -22.82095, -22.86173, -22.49489, -21.66936, -21.67332, -21.94054,\\n -23.63561, -27.17466, -31.87395, -38.31725, -44.54717, -46.99218, -43.17544,\\n -37.40019, -34.3351, -36.42896, -42.1396, -46.95909, -49.36232, -48.94634,\\n -47.90178, -49.97902, -55.02753, -63.06276, -72.53742, -88.81377, -93.54573,\\n -92.92464, -91.57479] * units.degC\\n prof = parcel_profile(p, t[0], td[0]).to('degC')\\n lfc_p, lfc_t = lfc(p, t, td, prof)\\n assert_nan(lfc_p, p.units)\\n assert_nan(lfc_t, t.units)\",\n \"def ohodnotL(row, col, znak, prevx, prevy, pocet_ciest, hlbka, mx): # vlavo\\r\\n\\r\\n susedia = getSusedia_ohodnot(row, col, znak)\\r\\n\\r\\n if (values[row][col] != \\\"W\\\" and col != 0):\\r\\n hlbka += 1\\r\\n\\r\\n if col == 0:\\r\\n if values[row][col] == \\\"W\\\" and hlbka != 0:\\r\\n hlbka -= 1\\r\\n dlzkyL.append(hlbka)\\r\\n\\r\\n if (col != 0 and hlbka < mx):\\r\\n for sused in susedia:\\r\\n if (sused[1] == col - 1 or (sused[1] == col and (sused[0] != prevx or sused[1] != prevy))):\\r\\n if sused[1] == 0:\\r\\n pocet_ciest += 1\\r\\n pocet_ciest += ohodnotL(sused[0], sused[1], znak, row, col, 0, hlbka, mx)\\r\\n if (values[sused[0]][sused[1]] == \\\"W\\\") and col == 1: # nema zmysel sem umiestnovat - radsej inde\\r\\n pocet_ciest = 0\\r\\n return pocet_ciest\",\n \"def correlate_rows(matrix):\\n return np.dot(matrix, matrix.T) / (la.norm(matrix) ** 2)\",\n \"def matlabels(df, rowlabel_fn):\\n return df.index.to_frame().apply(rowlabel_fn, axis=1)\",\n \"def lamPoint(numLams, lambdas, lam):\\r\\n \\r\\n help = [0.0 for i in range(numLams)]\\r\\n for i in range(numLams):\\r\\n\\r\\n help[i] = lambdas[i] - lam;\\r\\n help[i] = abs(help[i]);\\r\\n\\r\\n \\r\\n index = 0\\r\\n min = help[index]\\r\\n\\r\\n for i in range(1, numLams):\\r\\n\\r\\n if (help[i] < min): \\r\\n min = help[i]\\r\\n index = i\\r\\n\\r\\n return index\",\n \"def keltner_channel_lband_indicator(high, low, close, n=10, fillna=False):\\n lband = keltner_channel_lband(high, low, close, n)\\n kc_lband_ind = np.clip(np.sign(lband-close), 0, 1)\\n\\n if fillna:\\n kc_lband_ind = kc_lband_ind.replace([np.inf, -np.inf], np.nan).fillna(0)\\n return pd.Series(kc_lband_ind, name='kc_lband_ind')\",\n \"def lcms(argg: range) -> int:\\n l = 1\\n for arg in argg:\\n l = lcm(l, arg)\\n return l\",\n \"def matchlines(nlines, wl, z, eml):\\n lbdas = np.array(list(eml.keys()))\\n a = (wl[:, np.newaxis] / (1 + z) - lbdas[np.newaxis, :]) ** 2.0\\n jfound = np.argmin(a, axis=1)\\n error = np.diag(a[:, jfound]).sum()\\n error = np.sqrt(error / nlines)\\n if((nlines >= 2)and(jfound[0] == jfound[1])):\\n error = 15.\\n return(error, jfound)\",\n \"def Ls(GTn:torch.tensor, Mn:torch.tensor) -> torch.tensor:\\n return (-(GTn * torch.log(Mn+1e-15) + (1- GTn) * torch.log((1- Mn)+1e-15))).sum()\",\n \"def kldiv_lsm_ctc(logits, ylens):\\n bs, _, vocab = logits.size()\\n log_uniform = logits.new_zeros(logits.size()).fill_(math.log(1 / (vocab - 1)))\\n probs = torch.softmax(logits, dim=-1)\\n log_probs = torch.log_softmax(logits, dim=-1)\\n loss = torch.mul(probs, log_probs - log_uniform)\\n loss_mean = sum([loss[b, :ylens[b], :].sum() for b in range(bs)]) / ylens.sum()\\n return loss_mean\",\n \"def lam(freq):\\n return C / freq\",\n \"def calculateDistances(df):\\n return\",\n \"def calculate_L(self, n_t, n_n, l0, l1, dt, ctant):\\n d_scl = np.sqrt((self.t_D.get_value() ** 2).sum(1).mean())\\n\\n a = n_t * n_n * l1 * dt * d_scl ** 2 * np.log(l1 / l0) ** 2\\n # try:\\n # self._b\\n # except:\\n # from scipy.linalg import eigh as largest_eigh\\n # k=1\\n # N = inv_cov.shape[0]\\n # evals_large, _ = largest_eigh(inv_cov, eigvals=(N-k,N-1))\\n b = 0.5 * self.QUAD_REG.max()\\n # FIXME: not quite right for non-diagonal matrix, but is close enough\\n # in practice\\n\\n # print a, b, b / a\\n return ((a + b) * ctant).astype('float32')\",\n \"def run_lmm(formula, df, reml=False, **kwargs):\\n model = sm.MixedLM.from_formula(formula, df, **kwargs)\\n return model.fit(reml=reml)\",\n \"def CC_CLS_CL_TRS(Dataframe):\\n\\n Feature_DF = Dataframe.loc[:,['HNAME','CC_CLS_CL']]\\n min_value = min(Feature_DF.loc[:,'CC_CLS_CL'])\\n Feature_DF.loc[:,'CC_CLS_CL_TRS'] = Feature_DF.loc[:,'CC_CLS_CL'].apply(lambda x : (1+x-min_value)**(-9/8))\\n Feature_DF = Feature_DF.loc[:,['HNAME','CC_CLS_CL_TRS']]\\n\\n return Feature_DF\",\n \"def CC_REC_NUM_LT3_TRS(Dataframe):\\n\\n Feature_DF = Dataframe.loc[:,['HNAME','CC_REC_NUM_LT3']]\\n min_value = min(Feature_DF.loc[:,'CC_REC_NUM_LT3'])\\n Feature_DF.loc[:,'CC_REC_NUM_LT3_TRS'] = Feature_DF.loc[:,'CC_REC_NUM_LT3'].apply(lambda x : (1+x-min_value)**(-3/8))\\n Feature_DF = Feature_DF.loc[:,['HNAME','CC_REC_NUM_LT3_TRS']]\\n\\n return Feature_DF\",\n \"def LLE(data, n_components=2, n_neighbors=10):\\n # Compute the nearest neighbors\\n _, neighbors_idx = AffiinityMAt(data,n_neighbor=n_neighbors,kernel_method='Linear')\\n\\n n = data.shape[0]\\n w = np.zeros((n, n))\\n for i in range(n):\\n # Center the neighbors matrix\\n k_indexes = neighbors_idx[i, :]\\n neighbors = data[k_indexes, :] - data[i, :]\\n\\n # Compute the corresponding gram matrix\\n gram_inv = np.linalg.pinv(np.dot(neighbors, neighbors.T))\\n\\n # Setting the weight values according to the lagrangian\\n lambda_par = 2/np.sum(gram_inv)\\n w[i, k_indexes] = lambda_par*np.sum(gram_inv, axis=1)/2\\n m = np.subtract(np.eye(n), w)\\n values, u = np.linalg.eigh(np.dot(np.transpose(m), m))\\n return u[:, 1:n_components+1]\",\n \"def compute_log_likelihoods(df, error_rate=1e-3):\\n df['log_likelihood_absent'] = df.apply(calculate_likelihood_absent, axis=1, args=(error_rate,))\\n df['log_likelihood_present'] = df.apply(calculate_likelihood_present, axis=1, args=(error_rate,))\\n\\n return df\",\n \"def get_rows(df):\\n return df.shape[0]\",\n \"def find_distances(frame, newPoint): \\n distances = [] \\n \\n # iterate over all rows in the dataframe\\n for index in range(frame.shape[0]):\\n\\n # get all columns of a row (except the label) \\n point = frame.iloc[index,:-1] \\n\\n \\t# compute the distance, then save distance and label \\n # (use distance as first value)\\n distance = euclidean_distance(point, newPoint)\\n if distance == 0:\\n distances.append((distance, frame.iloc[index,-1])) \\n else:\\n distances.append((sys.maxsize, frame.iloc[index,-1])) \\n \\n\\n distances.sort() \\n \\n return distances\",\n \"def find_densest(m, w_ncols, w_nrows):\\n # Implement your solution here.\\n pass\",\n \"def _get_Mll(self):\\n\\t\\t_M_ll = mll.get_mll(self.W_l, self.lmax)\\n\\t\\treturn np.float64(_M_ll)\",\n \"def laplacian(self, e, epos):\\n s = int(e >= self._nelec[0])\\n ao = np.real_if_close(\\n self._mol.eval_gto(self.pbc_str + \\\"GTOval_sph_deriv2\\\", epos.configs)[\\n [0, 4, 7, 9]\\n ],\\n tol=1e4,\\n )\\n molap = np.dot(\\n [ao[0], ao[1:].sum(axis=0)], self.parameters[self._coefflookup[s]]\\n )\\n molap_vals = self._testrow(e, molap[1][:, self._det_occup[s]])\\n testvalue = self._testrow(e, molap[0][:, self._det_occup[s]])\\n\\n return molap_vals / testvalue\",\n \"def lil_cc(lil):\\n n = len(lil)\\n visited = np.zeros(n).astype(np.int_)\\n label = - np.ones(n).astype(np.int_)\\n k = 0\\n while (visited == 0).any():\\n front = [np.argmin(visited)]\\n while len(front) > 0:\\n pivot = front.pop(0)\\n if visited[pivot] == 0:\\n visited[pivot] = 1\\n label[pivot] = k\\n front += lil[pivot]\\n k += 1\\n return label\",\n \"def lcmm(*args):\\n\\treturn reduce(lcm, args)\",\n \"def lmin(self):\\n return self._get_mean_and_samples_attribute('lmin')\",\n \"def lla(self):\\n return self._lla_shape\",\n \"def _point_in_mbr(self, df):\\n if df.empty:\\n return df\\n df = df[(df[\\\"lat\\\"] >= self._min_lat) &\\n (df[\\\"lat\\\"] <= self._max_lat) &\\n (df[\\\"lon\\\"] >= self._min_lon) &\\n (df[\\\"lon\\\"] <= self._max_lon)\\n ]\\n return df\",\n \"def lll(basis, delta=.75):\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n k = 1\\n while k < len(basis):\\n for j in range(k - 1, -1, -1):\\n # Size condition\\n if np.abs(mu[k][j]) > .5:\\n basis[k] = basis[k] - np.rint(mu[k, j]) * basis[j]\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n # Lovasz condition\\n if sq_norm(orthog[k]) >= (delta - mu[k][k - 1]**2) * sq_norm(orthog[k - 1]):\\n k += 1\\n else:\\n temp = copy.deepcopy(basis[k])\\n basis[k] = copy.deepcopy(basis[k - 1])\\n basis[k - 1] = temp\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n k = max(k - 1, 1)\\n return basis\",\n \"def cl_interval(lnL, sigma=None, CL=0.68):\\n import copy\\n # Confidence limits\\n if sigma is None:\\n c0 = (1. - CL)/2.\\n c1 = 1.-c0\\n # Image dimensions\\n shape = lnL.shape\\n ndim = len(shape)\\n slc = [slice(None)]*ndim\\n # Find max\\n norm_L = np.exp(np.maximum(lnL - np.max(lnL),-15.))\\n # Find best indices\\n indices = np.where(lnL == np.max(lnL))\\n best_idx = [bi[0] for bi in indices]\\n\\n # Error intervals\\n all_error = []\\n for kk in range(ndim):\\n # Collapse on this dimension\\n slc = copy.deepcopy(best_idx)\\n slc[kk] = slice(None)\\n Lslice = norm_L[slc].flatten()\\n # Interpolate and go\\n cumul_area = np.cumsum(Lslice)\\n f_area = interp1d(cumul_area/cumul_area[-1], np.arange(len(Lslice)))\\n # Here we go\\n idx0 = int(np.round(f_area(c0)))\\n idx1 = int(np.round(f_area(c1)))\\n all_error.append([idx0,idx1])\\n\\n # Return\\n return best_idx, all_error\",\n \"def compute_distance(df):\\n pass\",\n \"def lagrange(self,K, L):\\n return lagrange(K, L)\",\n \"def lll(basis, delta=.75):\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n k = 1\\n while k < len(basis):\\n for j in range(k - 1, -1, -1):\\n # Size condition\\n if np.abs(mu[k][j]) > .5:\\n basis[k] = basis[k] - np.rint(mu[k, j]) * basis[j]\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n # Lovasz condition\\n if sq_norm(orthog[k]) >= (delta - mu[k][k - 1]**2) * sq_norm(orthog[k - 1]):\\n k += 1\\n else:\\n temp = copy.deepcopy(basis[k])\\n basis[k] = copy.deepcopy(basis[k - 1])\\n basis[k - 1] = temp\\n orthog = gram_schmidt(basis)\\n mu = get_mus(basis, orthog)\\n k = max(k - 1, 1)\\n return basis\",\n \"def LCSubSeq(X, Y, m, n):\\n LCSuff = [[0 for k in range (n+1)] for l in range(m+1)]\\n result = 0\\n\\n for i in range(m+1):\\n for j in range(n+1):\\n if (i==0 or j==0):\\n LCSuff[i][j] = 0\\n elif (X[i-1] == Y[j - 1]):\\n LCSuff[i][j] = LCSuff[i-1][j-1] + 1\\n result = max(result, LCSuff[i][j])\\n else:\\n LCSuff[i][j] = 0\\n return result\",\n \"def get_cl(self, re, aa):\\r\\n return Turbine.v_fCL(re, np.degrees(aa))\",\n \"def pos_conserved(df, conservation):\\n nb_rows, nb_cols = df.shape\\n\\n value_counts = df.apply(pd.Series.value_counts, axis=0).max(axis=0).ge(conservation * nb_rows)\\n\\n ge = [i for i, x in enumerate(value_counts) if x]\\n return ge\",\n \"def get_maxmin_index_from_row(\\n distance_matrix: np.ndarray,\\n row: int,\\n previous_indexes: List,\\n type: str,\\n )-> int:\\n distance_matrix = distance_matrix.copy()\\n arr = distance_matrix[row].astype(float)\\n \\n aux_list = range(arr.shape[0])\\n aux_list_2 = []\\n for i in aux_list:\\n if i in previous_indexes:\\n aux_list_2.append(True)\\n else:\\n aux_list_2.append(False)\\n previous_indexes_bool = aux_list_2\\n \\n if type == 'max':\\n arr[previous_indexes_bool] = -1\\n target_index = np.argmax(arr)\\n if type == 'min':\\n arr[previous_indexes_bool] = np.Inf\\n target_index = np.argmin(arr)\\n \\n return target_index\",\n \"def cgid2llnr(self, cgID):\\n cgID = cgID.replace('_240_37-ASM', '')\\n possible = self.phenotypes[self.phenotypes.cgID == cgID].LLnr.values \\n if len(possible) > 0:\\n return possible[0]\\n #fi\\n return None\",\n \"def _calculate_ll(self, x):\\n observation_log_probs = self._observation_log_probs(x, mask=None)\\n forward_log_probs = self._forward(observation_log_probs)\\n log_likelihood = logsumexp(\\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\\n return log_likelihood\",\n \"def labeling_func(df_clus):\\n\\n df_all_labeled = df_all_columns.copy()\\n df_all_labeled['Clus_label'] = df_clus['Clus_label'].copy()\\n df_all_labeled['Clus_label']= df_all_labeled['Clus_label'].astype(int)\\n for i in range(0, clus_params['n_components']):\\n df_all_labeled['Prob_L'+str(i)] = df_clus['Prob_L'+str(i)].copy()\\n\\n return df_all_labeled\",\n \"def lcmm(*args): \\r\\n return reduce(lcm, args)\",\n \"def norm_leg_coefs(self, cl):\\n new_cl = np.zeros_like( cl )\\n for i in range(cl.shape[1]):\\n for j in range(cl.shape[2]):\\n if (cl[:,i,j]).max() >0:\\n new_cl[:,i,j] = cl[:,i,j]/(cl[:,i,j]).max()\\n\\n return new_cl\",\n \"def _get_LFDP(self, segment):\\n dists_to_fitting_line = self._get_dists_to_fitting_line(segment)\\n return numpy.max(dists_to_fitting_line), numpy.argmax(dists_to_fitting_line)\",\n \"def ml_kl_loss(self, simulation, c1 = 1.0, ndims = 2, ehigh=1e5, emax = 1e10, turnover=200):\\n loss = MLKL(c1, simulation, ndims, ehigh, emax, turnover)\\n return loss.lossFunction\",\n \"def __isFarFromLevel(self, l):\\n\\n s = np.mean(self.df['high'] - self.df['low'])\\n return np.sum([abs(l-x) < s for x in self.levels]) == 0\",\n \"def lam2f(l):\\n f=c['c']/l\\n return f\",\n \"def lap(self):\\n\\n gr = self.grid\\n phi = gr.phi\\n\\n lapphi = gr.scratch_array()\\n\\n ib = gr.ilo\\n ie = gr.ihi\\n\\n lapphi[ib:ie+1] = \\\\\\n (phi[ib-1:ie] - 2.0*phi[ib:ie+1] + phi[ib+1:ie+2])/gr.dx**2\\n\\n return lapphi\",\n \"def get_lca_in_bst(root, node_0, node_1):\\n res = root\\n s = node_0 if node_0.data < node_1.data else node_1\\n b = node_1 if node_0.data < node_1.data else node_0\\n while (res.data < s.data) or (res.data > b.data):\\n while res.data < s.data:\\n res = res.right\\n while res.data > b.data:\\n res = res.left\\n return res\",\n \"def lagrangePoints(mu):\\n \\n # define l = 1-mu\\n l = 1 - mu\\n \\n # collinear points\\n def eqL1(x):\\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*l*mu+mu**2)*x**3 + (2*mu*l*(l-mu)+mu-l)*x**2 + (mu**2*l**2+2*(l**2+mu**2))*x + mu**3-l**3\\n #fval = gamma**5 - (3-mu)*gamma**4 + (3-2*mu)*gamma**3 - mu*gamma**2 + 2*mu*gamma - mu\\n return fval\\n sol_l1 = optimize.root(eqL1, 0.5, method='hybr')\\n l1 = np.array([sol_l1.x[0] , 0, 0])\\n \\n def eqL2(x):\\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*l*mu+mu**2)*x**3 + (2*mu*l*(l-mu)-(mu+l))*x**2 + (mu**2*l**2+2*(l**2-mu**2))*x - (mu**3+l**3)\\n #fval = gamma**5 + (3-mu)*gamma**4 + (3-2*mu)*gamma**3 - mu*gamma**2 - 2*mu*gamma - mu\\n return fval\\n sol_l2 = optimize.root(eqL2, 1.5, method='hybr')\\n l2 = np.array([sol_l2.x[0] , 0, 0])\\n \\n def eqL3(x):\\n fval = x**5 + 2*(mu-l)*x**4 + (l**2-4*mu*l+mu**2)*x**3 + (2*mu*l*(l-mu)+(l+mu))*x**2 + (mu**2*l**2+2*(mu**2-l**2))*x + l**3+mu**3\\n return fval\\n sol_l3 = optimize.root(eqL3, -1, method='hybr')\\n l3 = np.array([sol_l3.x[0] , 0, 0])\\n \\n # equilateral points\\n # L4\\n l4 = np.array([np.cos(np.pi/3) - mu , np.sin(np.pi/3), 0])\\n # L5\\n l5 = np.array([np.cos(np.pi/3) - mu , -np.sin(np.pi/3), 0])\\n \\n return _lagrangePointsReturn(l1,l2,l3,l4,l5)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59105355","0.57944614","0.5768694","0.5714691","0.5706475","0.5483352","0.5476161","0.5426626","0.53980875","0.5363862","0.53620285","0.5346637","0.530857","0.5301486","0.52655256","0.52437156","0.52226084","0.52145016","0.5176464","0.51718956","0.5150789","0.51381934","0.5129281","0.5109691","0.5087405","0.5087405","0.50779736","0.50730824","0.50631976","0.5058335","0.50577444","0.5046802","0.50406176","0.5040531","0.5038964","0.5037751","0.50369555","0.50353974","0.5031617","0.503026","0.50252753","0.5017688","0.50091106","0.50080657","0.50073093","0.50071096","0.50018156","0.4991105","0.4991105","0.49886194","0.49829197","0.49762926","0.49739558","0.4971177","0.49567407","0.49419445","0.49405253","0.49380735","0.49355355","0.49193478","0.49181682","0.491685","0.49057543","0.49018252","0.48970035","0.48878843","0.4885905","0.48795062","0.48748678","0.48742807","0.48701495","0.48689002","0.48659247","0.48632237","0.48608205","0.4858805","0.48533145","0.48276407","0.4821546","0.48213956","0.4819021","0.4817905","0.48175704","0.48149678","0.48112413","0.48082533","0.48070386","0.48062533","0.47967336","0.47933164","0.47917014","0.47833094","0.478048","0.47772712","0.4770565","0.47642025","0.47638798","0.47612715","0.47612226","0.47601923"],"string":"[\n \"0.59105355\",\n \"0.57944614\",\n \"0.5768694\",\n \"0.5714691\",\n \"0.5706475\",\n \"0.5483352\",\n \"0.5476161\",\n \"0.5426626\",\n \"0.53980875\",\n \"0.5363862\",\n \"0.53620285\",\n \"0.5346637\",\n \"0.530857\",\n \"0.5301486\",\n \"0.52655256\",\n \"0.52437156\",\n \"0.52226084\",\n \"0.52145016\",\n \"0.5176464\",\n \"0.51718956\",\n \"0.5150789\",\n \"0.51381934\",\n \"0.5129281\",\n \"0.5109691\",\n \"0.5087405\",\n \"0.5087405\",\n \"0.50779736\",\n \"0.50730824\",\n \"0.50631976\",\n \"0.5058335\",\n \"0.50577444\",\n \"0.5046802\",\n \"0.50406176\",\n \"0.5040531\",\n \"0.5038964\",\n \"0.5037751\",\n \"0.50369555\",\n \"0.50353974\",\n \"0.5031617\",\n \"0.503026\",\n \"0.50252753\",\n \"0.5017688\",\n \"0.50091106\",\n \"0.50080657\",\n \"0.50073093\",\n \"0.50071096\",\n \"0.50018156\",\n \"0.4991105\",\n \"0.4991105\",\n \"0.49886194\",\n \"0.49829197\",\n \"0.49762926\",\n \"0.49739558\",\n \"0.4971177\",\n \"0.49567407\",\n \"0.49419445\",\n \"0.49405253\",\n \"0.49380735\",\n \"0.49355355\",\n \"0.49193478\",\n \"0.49181682\",\n \"0.491685\",\n \"0.49057543\",\n \"0.49018252\",\n \"0.48970035\",\n \"0.48878843\",\n \"0.4885905\",\n \"0.48795062\",\n \"0.48748678\",\n \"0.48742807\",\n \"0.48701495\",\n \"0.48689002\",\n \"0.48659247\",\n \"0.48632237\",\n \"0.48608205\",\n \"0.4858805\",\n \"0.48533145\",\n \"0.48276407\",\n \"0.4821546\",\n \"0.48213956\",\n \"0.4819021\",\n \"0.4817905\",\n \"0.48175704\",\n \"0.48149678\",\n \"0.48112413\",\n \"0.48082533\",\n \"0.48070386\",\n \"0.48062533\",\n \"0.47967336\",\n \"0.47933164\",\n \"0.47917014\",\n \"0.47833094\",\n \"0.478048\",\n \"0.47772712\",\n \"0.4770565\",\n \"0.47642025\",\n \"0.47638798\",\n \"0.47612715\",\n \"0.47612226\",\n \"0.47601923\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.60421354"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":8499,"cells":{"query":{"kind":"string","value":"Adapted from interactive_vaporflux.ipynb sst, sea surface temperature (K) ft_qv, mixedlayer top qv (kg kg^1) use_NT, True or False outputs csv and json files with equilibrium values"},"document":{"kind":"string","value":"def run_main(sst, ft_qv, use_NT):\n\n dtout=10. #minutes\n end_time=8*24. #hours\n del_time=dtout*60. #seconds\n end_time=end_time*3600. #seconds\n #sst=297\n D=5.e-6 #s-1\n U=7 #m/s\n psfc=100. #kPa\n qsfc=tf.qs_tp(sst,psfc)\n ft_intercept = 292 #K\n ft_gamma = 6.e-3 #K/m\n #ft_qv = 2.e-3\n k=0.2 #entrainment efficiency\n Cd = 1.e-3 #drag coefficient\n tspan = np.arange(0.,end_time,del_time)\n vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start\n the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,\n qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT\n the_tup=make_tuple(the_tup,'coeffs')\n output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))\n result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])\n\n # save time/computation by only doing calculations for the last timestep (equilibrium)\n result['time']=tspan[-1]/3600./24. #days\n result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]\n result['delqv'] = ft_qv - result['qv'].iloc[-1]\n result['LCL'] = calc_lcl(result.iloc[-1], psfc)\n result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)\n result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)\n result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)\n \n # decide how to calculate entrainment\n the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]\n if use_NT:\n result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1], \n result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])\n else:\n result['went']=calc_went(result.iloc[-1],the_tup)\n\n result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)\n\n with open('dumpmodel.csv','w') as f:\n result.to_csv(f,index=False)\n \n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calc_equil(sst, ft_qv, use_NT=False):\n \n run_main(sst, ft_qv, use_NT)\n \n # grab csv file\n with open('dumpmodel.csv','r') as f:\n df_result=pd.read_csv(f)\n\n # last time step into named tupple\n out=df_result.iloc[-1]\n steady_state=make_tuple(out.to_dict())\n steady_state\n \n # obtain steady-state values\n dth=steady_state.deltheta\n dqt=steady_state.delqv\n thetal_m=steady_state.theta\n qt_m=steady_state.qv\n h=steady_state.h\n press=tf.find_press(steady_state.h) #kPa\n thetal_ft = steady_state.theta + dth\n qt_ft = steady_state.qv + dqt\n zb = steady_state.LCL\n zi = steady_state.h\n we = steady_state.went\n \n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\n gamma = 6e-3 \n thetal_3000 = thetal_ft + gamma*(3000-h)\n LTS = thetal_3000 - steady_state.theta\n \n # calculate delta_Fr\n delta_Frstar = 82.0 # Wm^-2\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\n\n # calculate LWP\n rho = 1.\n LWP = 0.5*rho*(zi-zb)**2\n \n # put all required variables into output array\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\n \n return out_array","def extractQuantities(path, run, t0, t1):\n data = pyLTR.Models.MIX(path, run)\n\n # hard-coded input for testing & debugging:\n #data = pyLTR.Models.LFM('/hao/aim2/schmitt/data/LTR-2_0_1b/r1432/March1995/LR/single', 'LRs')\n \n #Make sure variables are defined in the model.\n modelVars = data.getVarNames()\n for v in ['Grid X', 'Grid Y', \n 'Potential North [V]', 'Potential South [V]', \n 'FAC North [A/m^2]', 'FAC South [A/m^2]',\n 'Pedersen conductance North [S]', 'Pedersen conductance South [S]', \n 'Hall conductance North [S]', 'Hall conductance South [S]', \n 'Average energy North [keV]', 'Average energy South [keV]',\n 'Number flux North [1/cm^2 s]', 'Number flux South [1/cm^2 s]']:\n assert( v in modelVars )\n\n timeRange = data.getTimeRange()\n if len(timeRange) == 0:\n raise Exception(('No data files found. Are you pointing to the correct run directory?'))\n\n index0 = 0\n if t0:\n for i,t in enumerate(timeRange):\n if t0 >= t:\n index0 = i\n\n index1 = len(timeRange)-1\n if t1:\n for i,t in enumerate(timeRange):\n if t1 >= t:\n index1 = i \n\n print(( 'Extracting MIX quantities for time series over %d time steps.' % (index1-index0) ))\n \n # Output a status bar displaying how far along the computation is.\n progress = pyLTR.StatusBar(0, index1-index0)\n progress.start()\n\n t_doy = []\n cpcpNorth = []\n cpcpSouth = []\n hpNorth = []\n hpSouth = []\n ipfacNorth = []\n ipfacSouth = []\n\n # Pre-compute area of the grid.\n x = data.read('Grid X', timeRange[index0])\n y = data.read('Grid Y', timeRange[index0])\n # Fix singularity at the pole\n x[:,0] = 0.0\n y[:,0] = 0.0\n z = numpy.sqrt(1.0-x**2-y**2)\n ri = 6500.0e3 # Radius of ionosphere\n areaMixGrid = pyLTR.math.integrate.calcFaceAreas(x,y,z)*ri*ri\n\n for i,time in enumerate(timeRange[index0:index1]):\n try:\n # -- Day of Year\n tt = time.timetuple()\n t_doy.append(tt.tm_yday+tt.tm_hour/24.0+tt.tm_min/1440.0+tt.tm_sec/86400.0)\n\n # --- Cross Polar Cap Potential\n psiNorth = data.read('Potential North [V]', time) / 1000.0\n cpcpNorth.append(psiNorth.max() - psiNorth.min())\n\n psiSouth = data.read('Potential South [V]', time) / 1000.0\n cpcpSouth.append(psiSouth.max() - psiSouth.min())\n \n # --- Hemispheric Power\n energy = data.read('Average energy North [keV]', time)\n flux = data.read('Number flux North [1/cm^2 s]', time)\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\n # KeV/cm^2s to mW/m^2 to GW\n hpNorth.append(hp.sum() * 1.6e-21) \n\n energy = data.read('Average energy South [keV]', time)\n flux = data.read('Number flux South [1/cm^2 s]', time)\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\n # KeV/cm^2s to mW/m^2 to GW\n hpSouth.append(hp.sum() * 1.6e-21)\n\n # --- Positive current density\n fac = data.read('FAC North [A/m^2]', time)\n fac[fac <= 0] = 0.0\n pfac = areaMixGrid * fac[:-1,:-1]\n ipfacNorth.append(pfac.sum()/1.0e6)\n\n fac = data.read('FAC South [A/m^2]', time)\n fac[fac <= 0] = 0.0\n pfac = areaMixGrid * fac[:-1,:-1]\n ipfacSouth.append(pfac.sum()/1.0e6)\n\n progress.increment()\n except KeyboardInterrupt:\n # Exit when the user hits CTRL+C.\n progress.stop()\n progress.join() \n print('Exiting.')\n import sys\n sys.exit(0)\n except:\n # Cleanup progress bar if something bad happened.\n progress.stop()\n progress.join()\n raise\n progress.stop()\n progress.join()\n\n dataNorth = pyLTR.TimeSeries()\n dataSouth = pyLTR.TimeSeries()\n dataNorth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\n dataSouth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\n dataNorth.append('doy', 'Day of Year', '', t_doy)\n dataSouth.append('doy', 'Day of Year', '', t_doy)\n \n # \"N\" and \"S\" label subscripts are redundant here, potentially leading to\n # mis-labeling of plots\n #dataNorth.append('cpcp', r'$\\Phi_N$', 'kV', cpcpNorth)\n #dataSouth.append('cpcp', r'$\\Phi_S$', 'kV', cpcpSouth)\n #\n #dataNorth.append('hp', r'$HP_N$', 'GW', hpNorth)\n #dataSouth.append('hp', r'$HP_S$', 'GW', hpSouth)\n #\n #dataNorth.append('ipfac', r'$FAC_N$', 'MA', ipfacNorth)\n #dataSouth.append('ipfac', r'$FAC_S$', 'MA', ipfacSouth)\n \n dataNorth.append('cpcp', r'$\\Phi$', 'kV', cpcpNorth)\n dataSouth.append('cpcp', r'$\\Phi$', 'kV', cpcpSouth)\n \n dataNorth.append('hp', r'$HP$', 'GW', hpNorth)\n dataSouth.append('hp', r'$HP$', 'GW', hpSouth)\n \n dataNorth.append('ipfac', r'$FAC$', 'MA', ipfacNorth)\n dataSouth.append('ipfac', r'$FAC$', 'MA', ipfacSouth)\n\n return (dataNorth, dataSouth)","def water(fname):\n\n variables = [\"H2OICE\", \"QV_COLUMN\", \"QI_COLUMN\"]\n data = dict()\n for v in variables:\n data.update(common.zonal_mean_surface(fname, v))\n \n return data","def env_temperature(v3: \"float\", v4: \"float\") -> \"float\":","def get_iPTF14gqr(colorplt=False):\n z = 0.063\n # ebv = 0.082\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_exp = 56943.74 # \n t_max = 56950.26 # g band max light + 3\n \n tb = Table(fits.open('../data/otherSN/De2018/tables1.fit')[1].data)\n tb.rename_column('MJD' , 'mjd')\n tb['texp_rf'] = (tb['mjd'] - t_exp) / (1+z)\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n # tb = tb[tb[\"Filt\"]==\"g \"]\n tb = tb[~np.isnan(tb['e_mag'])]\n tb.rename_column('Filt' , 'filter')\n tb.rename_column('e_mag' , 'emag')\n tb.rename_column('mag' , 'mag0')\n \n ixg = tb['filter']==\"g \"\n ixB = tb['filter']==\"B \"\n ixV = tb['filter']==\"V \"\n ixr = tb['filter']==\"r \"\n ixi = tb['filter']==\"i \"\n ixUVW1 = tb['filter']==\"UVW1\"\n ixUVW2 = tb['filter']==\"UVW2\"\n \n tb['wave'] = np.zeros(len(tb))\n tb['wave'][ixUVW2] = 2079\n tb['wave'][ixUVW1] = 2614\n tb['wave'][ixB] = 4359\n tb['wave'][ixg] = 4814\n tb['wave'][ixV] = 5430\n tb['wave'][ixr] = 6422\n tb['wave'][ixi] = 7883\n \n tb['mag0_abs'] = tb['mag0'] - dis_mod\n \n tb = tb.to_pandas()\n tb[\"texp_rf\"] = tb[\"Phase\"]\n tb = tb.drop(columns=[\"recno\", \"Phase\", \"l_mag\"])\n \"\"\"\n ix = np.any([tb['Tel'].values==\"P60 \",\n tb[\"filter\"].values=='g '], axis=0)\n tb = tb[ix]\n \"\"\"\n tb = add_datecol(tb)\n tb = add_physcol(tb)\n tt = tb[\"tmax_rf\"].values\n epochs = [\" \" for x in range(len(tt))]\n epochs = np.array(epochs)\n \"\"\"\n ix = (tt>-5.6)&(tt<-5.55)\n epochs[ix] = \"epoch 01\"\n \"\"\"\n ix = (tt>-5.55)&(tt<-5.50)\n epochs[ix] = \"epoch 02\"\n \n ix = (tt>-5.50)&(tt<-5.45)\n epochs[ix] = \"epoch 03\"\n \n ix = (tt>-5.2)&(tt<-5.0)\n epochs[ix] = \"epoch 04\"\n ix = (tt>-5.0)&(tt<-4.7)\n epochs[ix] = \"epoch 05\"\n \n ix = (tt>-4.7)&(tt<-4.5)\n epochs[ix] = \"epoch 06\"\n ix = (tt>-4.5)&(tt<-3.5)\n epochs[ix] = \"epoch 07\"\n ix = (tt>-3.5)&(tt<-2.5)\n epochs[ix] = \"epoch 08\"\n ix = (tt>-1.5)&(tt<-1)\n epochs[ix] = \"epoch 09\"\n ix = (tt>-1)&(tt<-0.82)\n epochs[ix] = \"epoch 10\"\n ix = (tt>-0.82)&(tt<-0.6)\n epochs[ix] = \"epoch 11\"\n ix = (tt>-0.5)&(tt<0.5)\n epochs[ix] = \"epoch 12\"\n ix = (tt>0.5)&(tt<1.5)\n epochs[ix] = \"epoch 13\"\n ix = (tt>1.5)&(tt<2.5)\n epochs[ix] = \"epoch 14\"\n ix = (tt>3.5)&(tt<4.5)\n epochs[ix] = \"epoch 15\"\n ix = (tt>4.5)&(tt<5)\n epochs[ix] = \"epoch 16\"\n ix = (tt>5)&(tt<5.6)\n epochs[ix] = \"epoch 17\"\n ix = (tt>5.6)&(tt<5.8)\n epochs[ix] = \"epoch 18\"\n ix = (tt>6)&(tt<7)\n epochs[ix] = \"epoch 19\"\n ix = (tt>7)&(tt<8)\n epochs[ix] = \"epoch 20\"\n ix = (tt>8)&(tt<9)\n epochs[ix] = \"epoch 21\"\n tb[\"epoch\"] = epochs\n\n if colorplt==False:\n return tb\n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['g ', 'r ', 'i ']))\n tb = tb[ix]\n\n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r \" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g \"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r \"]\n itb = tbsub[tbsub[\"filter\"].values==\"i \"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def get_thermochem(file_set, results_dict, save_vibes, out_dir, tog_output_fname, qh_h_opt, write_mode):\n h = []\n qh_h = []\n gt = []\n qh_gt = []\n temps = []\n for index, file in enumerate(file_set):\n base_name = os.path.basename(file)\n if file == REACT_PROD_SEP:\n h.append(np.full([len(temps)], np.nan))\n qh_h.append(np.full([len(temps)], np.nan))\n gt.append(np.full([len(temps)], np.nan))\n qh_gt.append(np.full([len(temps)], np.nan))\n continue\n vibes_out = results_dict[base_name][GOODVIBES_OUT]\n found_structure = False\n skip_line = True\n h.append([])\n qh_h.append([])\n gt.append([])\n qh_gt.append([])\n # we know the last line should be dropped, and at least the first 10\n for line in vibes_out[10:-2]:\n if GOODVIBES_ERROR_PAT.match(line):\n raise InvalidDataError(\"See GoodVibes output: {}\".format(vibes_out))\n if not found_structure:\n if GOODVIBES_DATA_PAT.match(line):\n found_structure = True\n continue\n elif skip_line:\n skip_line = False\n continue\n else:\n vals = line.split()\n if index == 0:\n temps.append(float(vals[1]))\n h[index].append(float(vals[2]))\n if qh_h_opt:\n qh_h[index].append(float(vals[3]))\n gt[index].append(float(vals[-2]))\n qh_gt[index].append(float(vals[-1]))\n if save_vibes:\n vibes_out_fname = os.path.relpath(create_out_fname(file, suffix='_vibes', base_dir=out_dir, ext='.dat'))\n list_to_file(vibes_out, vibes_out_fname, print_message=False)\n print('Saved GoodVibes output as: {}'.format(vibes_out_fname))\n if tog_output_fname:\n list_to_file(vibes_out, tog_output_fname, mode=write_mode, print_message=False)\n if write_mode == 'w':\n print(\"Adding all GoodVibes output to: {}\".format(tog_output_fname))\n write_mode = \"a\"\n\n temps = np.asarray(temps)\n # for each molecule, multiply the array to convert to kcal/mol\n for index in range(len(gt)):\n h[index] = np.asarray(h[index]) * EHPART_TO_KCAL_MOL\n if qh_h_opt:\n qh_h[index] = np.asarray(qh_h[index]) * EHPART_TO_KCAL_MOL\n gt[index] = np.asarray(gt[index]) * EHPART_TO_KCAL_MOL\n qh_gt[index] = np.asarray(qh_gt[index]) * EHPART_TO_KCAL_MOL\n\n return temps, h, qh_h, gt, qh_gt","def main(folder, quiet=0):\n\n if quiet:\n output_stream = StringIO()\n else:\n output_stream = sys.stdout\n\n\n\n color1 = \"I4\" #filter system for first color of CMD\n color2 = \"M1\" #filter system for second color of CMD\n zeromagc1 = zero.zero_mag[color1]\n zeromagc2 = zero.zero_mag[color2]\n min_mag = 8. #minimal observation limit\n max_mag = 0. #maximal observation limit\n\n#getting file list\n files = sorted(os.listdir('%s/%s' % (os.getcwdu(), folder))) \n out = []\n\n for fil in files:\n#only using files created by the automated simulation\n if fil.startswith('sim_') and not 'settings' in fil.encode(\"ascii\"):\n print(\"%s/%s\" % (folder,fil.encode(\"ascii\")), file=output_stream)\n \n\n # Read in\n hdulist = fits.open('%s/%s' %(folder,fil))\n data = hdulist[1].data\n\n #calculating magnitudes from fluxes and converting to CMD-data\n x = -2.5*(np.log10(data['c%s' % color1]/zeromagc1) - np.log10(data['c%s' % color2]/zeromagc2))\n y = -2.5*(np.log10(data['c%s' % color2]/zeromagc2))\n\n \n sel = np.logical_and( (y > -10./3. * (x-1.) + 10.), np.logical_and(max_mag < y, y < min_mag))\n sel = np.logical_and(sel, y < -x + 12.)\n n = sum(sel)\n t = Table(hdulist[1].data)\n if 'sel' in t.columns:\n t.remove_column('sel')\n t.add_column(Column(name='sel', data=sel.astype('int')))\n \n hdulist[1].data = np.array(t)\n tmp, av, apera, age = fil.split('_')\n fits.update('%s/%s' %(folder,fil), np.array(t), ext = 1, clobber=True)\n out.append([av, apera, age, n])\n\n #writing obtained data to \"folder/__expected_number\"\n head = ['#', 'AV', 'Aperature_size', 'Age', 'Expected_number']\n f = open('%s/__expected_number' % folder, 'w')\n f.write(','.join(head)+'\\n' )\n np.savetxt(f, np.asarray(out).astype(int))\n f.close()\n \n print (\"Analysed %s files and saved output to %s\" % (len(out),'%s/__expected_number' % folder), file=output_stream)","def get_taux_variants(name):\n indicateurResult = get_empty_kpi()\n config = get_config(name)\n log.debug('Processing - '+name)\n\n indicateurResult['nom'] = config['nom']\n indicateurResult['unite'] = config['unite']\n indicateurResult['unite_short'] = config['unite_short']\n indicateurResult['trendType'] = config['trendType']\n indicateurResult['color'] = get_color_indicateur(config)\n\n if name == \"prop_variant_A\":\n colname = \"tx_A1\"\n elif name == \"prop_variant_B\":\n colname = \"tx_B1\"\n elif name == \"prop_variant_C\":\n colname = \"tx_C1\"\n elif name == \"prop_variant_D\":\n colname = \"tx_D1\"\n else:\n colname = \"tx_A0C0\"\n\n df = pd.read_csv(\n 'files_new/'+config['res_id_fra'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n df = enrich_dataframe(df, name)\n df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\n for country in tqdm(countries, desc=\"Processing National\"):\n res = process_stock(\n df,\n 'nat',\n 'fra',\n config['trendType'],\n colname\n )\n indicateurResult['france'].append(res)\n \n df = pd.read_csv(\n 'files_new/'+config['res_id_reg'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n df = df[~df['reg'].isnull()]\n df = enrich_dataframe(df, name)\n df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\n for reg in tqdm(df.reg.unique(), desc=\"Processing Régions\"):\n res = process_stock(\n df[df['reg'] == reg].copy(),\n 'reg',\n reg,\n config['trendType'],\n colname\n )\n indicateurResult['regions'].append(res)\n\n # df = pd.read_csv(\n # 'files_new/'+config['res_id_dep'],\n # sep=None,\n # engine='python',\n # dtype={'reg': str, 'dep': str}\n # )\n # df = enrich_dataframe(df, name)\n # df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\n # for dep in tqdm(df.dep.unique(), desc=\"Processing Départements\"):\n # res = process_stock(\n # df[df['dep'] == dep].copy(),\n # 'dep',\n # dep,\n # config['trendType'],\n # colname\n # )\n # indicateurResult['departements'].append(res)\n\n save_result(indicateurResult, name)","def ferry_data_QC(ferry, TH_abs, TH_u, TH_d):\n if type(ferry) is not xr.core.dataset.Dataset:\n raise ValueError('Ferry is not defined')\n # QC1: make nan all Absolute velocities that are greater than 6.5 m/s\n abs_u = ferry.eastward_absolute_water_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n\n abs_v = ferry.northward_absolute_water_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n\n abs_w = ferry.vertical_absolute_water_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n # Get bottom track velocity for reference\n # and also clean for TH in abs velocity\n east_btv = ferry.eastward_bottom_tracking_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n\n north_btv = ferry.northward_bottom_tracking_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n\n vert_btv = ferry.vertical_bottom_tracking_velocity.where(\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\n # Estimate u_true = abs_u + east_bt_v\n u_true = abs_u + east_btv\n v_true = abs_v + north_btv\n w_true = abs_w + vert_btv\n U = np.sqrt(u_true**2 + v_true**2)\n # QC2: check that u_true and v_true are less than 4 m/s\n u_true = u_true.where((u_true < TH_u) & (v_true < TH_u) & (U < TH_u))\n v_true = v_true.where((u_true < TH_u) & (v_true < TH_u) & (U < TH_u))\n w_true = w_true.where((u_true) < TH_u & (v_true < TH_u) & (U < TH_u))\n U = U.where((u_true) < TH_u & (v_true < TH_u) & (U < TH_u))\n # Add true velocity data to the dataset\n ferry['u_true'] = u_true\n ferry['v_true'] = v_true\n ferry['w_true'] = w_true\n ferry['Horizontal_speed'] = U\n # Remove first 5 rows of depth\n ferryQC = ferry.isel(depth=slice(TH_d, None))\n goodQC = True\n return(ferryQC, goodQC)","def read_elia_activated_energy_volumes(filename,status):\r\n df = pd.read_excel(filename,skiprows=2,parse_dates=False)\r\n df[\"Timestamp\"] = df[\"Date\"]+\" \"+df['Quarter'].map(lambda x: str(x)[:-9])\r\n pd.to_datetime(df[\"Timestamp\"])\r\n df.set_index(\"Timestamp\",inplace=True)\r\n if ((status == \"validated\") | (status == \"valid\")):\r\n df = df.drop(df[df.Status != \"Validated\"].index)\r\n df = df.drop([\"Date\",\"Quarter\",\"Status\"], axis=1)\r\n \r\n if ((len(df.columns)<13) & (len(df.columns)>11)) :\r\n df.columns.values[0:13] = [\"NRV in MW\", \"GUV in MW\", \"IGCC+ in MW\", \"R2+ in MW\", \"Bids+ in MW\", \"R3+ in MW\", \"R3DP+ in MW\", \"GDV in MW\", \"IGCC- in MW\", \"R2- in MW\", \"Bids- in MW\", \"R3- in MW\"]\r\n if len(df.columns)<= 11:\r\n df.columns.values[0:12] = [\"NRV in MW\", \"GUV in MW\", \"IGCC+ in MW\", \"R2+ in MW\", \"Bids+ in MW\", \"R3+ in MW\", \"GDV in MW\", \"IGCC- in MW\", \"R2- in MW\", \"Bids- in MW\", \"R3- in MW\"]\r\n if len(df.columns)>14:\r\n df.columns.values[0:16] = [\"NRV in MW\", \"SR in MW\",\"GUV in MW\", \"IGCC+ in MW\",\"R2+ in MW\",\"Bids+ in MW\",\"R3 std in MW\",\"R3 flex in MW\",\"ICH in MW\",\"inter TSO import in MW\",\"GDV in MW\",\"IGCC- in MW\",\"R2- in MW\",\"Bids- in MW\",\"inter TSO export in MW\"]\r\n \r\n return df","def main(daymet_dir,pickles,start_date='1980-10-01',end_date='2020-09-30',huc_col = 'huc8', **kwargs):\r\n\tprint(f'The huc col being processed is: {huc_col}')\r\n\t################################################################\r\n\t#first do the daymet data \r\n\t#read in all the files in this dir and combine them into one df\r\n\tearly=FormatData(glob.glob(daymet_dir+f'*_12_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\tmid=FormatData(glob.glob(daymet_dir+f'*_2_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\tlate=FormatData(glob.glob(daymet_dir+f'*_4_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\r\n\t################################################################\r\n\t#next do the snotel data \r\n\toutput=[]\r\n\r\n\t#read in some pickled objects, these look like a list of dfs with each being a station for the full time period \r\n\tfor item in ['PREC','TAVG','WTEQ']:\r\n\t\t#get the pickled objects for each parameter \r\n\t\tfiles = glob.glob(pickles+f'*{item}_{start_date}_{end_date}_snotel_data_list') #hardcoded currently\r\n\t\tdf=FormatData(files,drop_cols=['year','month','day']).read_in_pickles()\r\n\t\toutput.append(df) #the df here is 365 days x ~30 yrs x 237 stations so these are pretty big dfs\r\n\t\r\n\t#join the three enviro params \r\n\toutput_df = reduce(lambda left,right: pd.merge(left,right,how='inner',on=['date','id']), output)\r\n\t\r\n\t\r\n\t#convert the temp column from F to C \r\n\toutput_df['TAVG'] = (output_df['TAVG']-32)*(5/9) \r\n\t#there are a couple of erroneous temp values, remove those \r\n\toutput_df = output_df.loc[output_df['TAVG'] <= 50]\r\n\r\n\t#convert prec and swe cols from inches to cm \r\n\toutput_df['PREC'] = output_df['PREC']*2.54\r\n\toutput_df['WTEQ'] = output_df['WTEQ']*2.54\r\n\t\r\n\t#remove rows that have one of the data types missing- this might need to be amended because \r\n\t#it means that there are different numbers of records in some of the periods. \r\n\toutput_df=output_df.dropna()\r\n\t\r\n\t#cast the snotel id col to int to add the hucs \r\n\toutput_df['id'] = output_df['id'].astype('int')\r\n\r\n\t#add the as yet nonexistant hucs data to the outputs \r\n\thucs = kwargs.get('hucs')\r\n\toutput_df[huc_col] = output_df['id'].map(hucs)\r\n\r\n\t#there are multiple snotel stations in some of the basins, \r\n\t#combine those so there is just one number per basin like the \r\n\t#daymet and RS data. \r\n\r\n\toutput_df=output_df.groupby([huc_col,'date'])[['PREC','WTEQ','TAVG']].mean().reset_index()\r\n\r\n\tperiod_list = []\r\n\tfor p1,p2 in zip(['early','mid','late'],[early,mid,late]): \r\n\t\t\t#get snotel first\r\n\t\t#make a temporal chunk of data \r\n\t\tsnotel_chunk=FormatData(None,time_period=p1).split_yearly_data(output_df)\r\n\r\n\t\t##########working below here\r\n\t\t############################\r\n\t\t#calculate the snow droughts for that chunk \r\n\t\tif (p1 == 'mid') | (p1 == 'late'): \r\n\t\t\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',start_year=1991,sort_col=huc_col).prepare_df_cols()\r\n\t\t\t#print('snotel')\r\n\t\t\t#print(snotel_drought)\r\n\t\telse: \r\n\t\t\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',sort_col=huc_col).prepare_df_cols()\r\n\r\n\t\t#get cols of interest \r\n\t\t#snotel_drought=snotel_drought[['huc8','year','dry','warm','warm_dry']]\r\n\t\t#rename cols so they don't get confused when data are merged \r\n\t\t#snotel_drought.columns=['huc8','year']+['s_'+column for column in snotel_drought.columns if not (column =='huc8') | (column=='year')]\r\n\t\t\r\n\t\t#then do the same for daymet \r\n\t\tif (p1 == 'mid') | (p1 == 'late'): \r\n\t\t\tdaymet_drought=CalcSnowDroughts(p2,start_year=1991,sort_col=huc_col).prepare_df_cols()\r\n\t\telse: \r\n\t\t\tdaymet_drought=CalcSnowDroughts(p2,sort_col=huc_col).prepare_df_cols()\r\n\t\t#print('daymet',daymet_drought)\r\n\t\t#daymet_drought=daymet_drought[['huc8','year','dry','warm','warm_dry']]\r\n\t\t\r\n\t\t#daymet_drought.columns=['huc8','year']+['d_'+column for column in daymet_drought.columns if not (column =='huc8') | (column=='year')]\r\n\r\n\t##########################################\r\n\t\r\n\t\t#run the kmeans with drought types as intiilization conditions (centroids) for the clusters\r\n\t\t\r\n\t\t#these are all of the huc 4 basins in the study area \r\n\t\thuc4s = ['1708','1801','1710','1711','1709','1701','1702','1705','1703','1601','1707','1706','1712','1704']\r\n\t\ts_output = []\r\n\t\td_output = []\r\n\t\tfor huc4 in huc4s: \r\n\t\t\thuc4_s = sd.prep_clusters(snotel_drought,huc4,huc_col=huc_col) #get the subset of the snow drought data for a given huc4\r\n\t\t\thuc4_d = sd.prep_clusters(daymet_drought,huc4,huc_col=huc_col)\r\n\t\t\t#make the centroids that serve as the intialization for the kmeans clusters- these are like endmembers (ish)\r\n\t\t\ts_centroids = DefineClusterCenters(huc4_s,'WTEQ','PREC','TAVG').combine_centroids() #makes a numpy array with four centroids\r\n\t\t\td_centroids = DefineClusterCenters(huc4_d,'swe','prcp','tavg').combine_centroids() #makes a numpy array with four centroids\r\n\r\n\t\t\t#clusters should be like: {0:dry, 1:warm, 2:warm_dry, 3:no_drought} 6/8/2021 DOUBLE CHECK\r\n\t\t\t#run kmeans for the snotel data\r\n\t\t\ts_clusters = sd.run_kmeans(huc4_s[['WTEQ','PREC','TAVG']].to_numpy(),huc4_s['label'],s_centroids)\r\n\t\t\ts_clusters = sd.add_drought_cols_to_kmeans_output(s_clusters, huc_col=huc_col) #add a few cols needed for plotting \r\n\t\t\t#run kmeans for the daymet data \r\n\t\t\td_clusters = sd.run_kmeans(huc4_d[['swe','prcp','tavg']].to_numpy(),huc4_d['label'],d_centroids)\r\n\t\t\td_clusters = sd.add_drought_cols_to_kmeans_output(d_clusters, huc_col=huc_col) #add a few cols needed for plotting \r\n\r\n\t\t\ts_output.append(s_clusters)\r\n\t\t\td_output.append(d_clusters)\r\n\t\ts_plot = pd.concat(s_output)\r\n\r\n\t\t#select the cols of interest and rename so there's no confusion when dfs are merged \r\n\t\ts_plot=s_plot[[huc_col,'year','dry','warm','warm_dry']]\r\n\t\ts_plot.columns=[huc_col,'year']+['s_'+column for column in s_plot.columns if not (column == huc_col) | (column=='year')]\r\n\r\n\t\td_plot = pd.concat(d_output)\r\n\t\td_plot=d_plot[[huc_col,'year','dry','warm','warm_dry']]\r\n\t\td_plot.columns=[huc_col,'year']+['d_'+column for column in d_plot.columns if not (column == huc_col) | (column=='year')]\r\n\t\r\n\t\t#merge the two datasets into one df \r\n\t\tdfs = s_plot.merge(d_plot,on=[huc_col,'year'],how='inner')\r\n\t\t\r\n\t\t#deal with the scenario that there are basins with less than 30 years of data, remove those here\r\n\t\tdfs = sd.remove_short_dataset_stations(dfs,huc_col)\r\n\t\tperiod_list.append(dfs)\r\n\r\n\tplot_counts(period_list,kwargs.get('stats_dir'),huc_col=huc_col,**kwargs)","def show_trap_results():\n df_grid = pd.read_hdf('./temp_results.h5', '/optimize_grid')\n print(df_grid)\n \n print('Minimum fwhm:')\n print(df_grid[df_grid.fwhm_ovr_mean==df_grid.fwhm_ovr_mean.min()])\n \n plt.plot(df_grid.e_fit, df_grid.fwhm_ovr_mean, '.b')\n plt.show()","def get_ptf10iuv(colorplt = False):\n z = 0.0251485\n ebv = 0.0371 # SFD\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n print (\"adopt g band t_max estimated by myself\")\n t_max = 55357.387 \n tb = pd.read_csv('../data/otherSN/Kasliwal2012/PTF10iuv', sep='\\t')\n tb = tb.drop(columns=[\"Unnamed: 4\"])\n tb = tb.rename(columns={'Filter' : 'filter',\n 'MJD': 'mjd'})\n tb = tb[~np.array([x[0]=='>' for x in tb['Mag'].values])]\n tb['mag'] = np.array([float(x.split(\" +or-\")[0]) for x in tb['Mag'].values])\n tb['emag'] = np.array([float(x.split(\" +or-\")[1]) for x in tb['Mag'].values])\n tb = tb.drop(columns=[\"Mag\"])\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n ixz = tb['filter'].values == \"z\"\n ixB = tb['filter'].values == \"B\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixB] = 4359\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb['wave'].values[ixz] = 9670\n \n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n tb = tb.sort_values(by = \"mjd\")\n if colorplt==False:\n return tb\n \n else:\n tb = add_datecol(tb)\n ix = np.in1d(tb[\"filter\"].values, np.array(['g', 'r', 'i']))\n tb = tb[ix]\n tb = tb[tb.mjd > 55352.5]\n tb = tb[tb.mjd < 55593.5]\n \n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r\"]\n itb = tbsub[tbsub[\"filter\"].values==\"i\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def get_iPTF16asu():\n z = 0.187\n ebv = 0.0\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n \n tb = asci.read('../data/otherSN/Whitesides2017/table1.txt')\n tb = tb.to_pandas()\n tb = tb[tb[\"col4\"].values!=\">\"]\n \n tb = tb.rename(columns={'col1' : 'mjd',\n 'col2': 'tmax_rf',\n 'col3': 'filter',\n \"col4\": 'mag',\n 'col5': 'emag',\n 'col6': 'instrument'})\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb[\"mag\"] = np.array(tb[\"mag\"].values, dtype = np.float)\n #tb[\"emag\"] = np.array(tb[\"emag\"].values, dtype = np.float)\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb = tb[tb.wave!=0]\n return tb","def generate_experimental_condition(dir_output, file_name_prefix,list_temperature, partial_pressure_H2O=0.02,SinteringTemperature=1600,SinteringTime=24):\r\n\r\n print(\"Enter the host element occupying the A-site\")\r\n set_A1 = input (\"Ex: Ba\\n\")\r\n print(\"Enter the valence of the A-site host element\")\r\n set_A1_valence = input(\"Ex: 2\\n\")\r\n frac_A1 = '1'\r\n print(\"Enter the host element occupying the B-site\")\r\n set_B1 = input (\"Ex: Zr\\n\")\r\n print(\"Enter the valence of the B-site host element\")\r\n set_B1_valence = input(\"Ex:4\\n\")\r\n print(\"Enter the fraction that describes the composition of the B-site host element\")\r\n frac_B1 = str(format(float( input (\"Ex:0.8\\n\")), '.2f'))\r\n print(\"Enter the dopant element occupying the B-site\")\r\n set_B2 = input (\"Ex: Sc\\n\")\r\n print(\"Enter the valence of the B-dopant\")\r\n set_B2_valence = input(\"Ex: 3\\n\")\r\n frac_B2 = str(format((1 - float(frac_B1)), '.2f'))\r\n\r\n # generate dataframe for base\r\n CA = set_A1 + set_B1 + frac_B1 + set_B2 + frac_B2 + \"O3\"\r\n dic = {'Composition':CA,\r\n 'A1':set_A1, 'Valence A1':set_A1_valence, 'fraction A1':frac_A1,\r\n 'B1':set_B1, 'Valence B1':set_B1_valence, 'fraction B1':frac_B1,\r\n 'B2':set_B2, 'Valence B2':set_B2_valence, 'fraction B2':frac_B2}\r\n df = pd.DataFrame(dic,index=['i',])\r\n\r\n # add columns name\r\n columns_all = ['Composition','Temperature / C','pH2O / atm','CH',\r\n 'A1','Valence A1','fraction A1','A2','Valence A2','fraction A2',\r\n 'B1','Valence B1','fraction B1','B2','Valence B2','fraction B2',\r\n 'B3','Valence B3','fraction B3','X1','Valence X1','fraction X1','fraction total']\r\n for c in columns_all:\r\n if not(c in df.columns):\r\n df[c] = float(np.NaN)\r\n df = df[columns_all]\r\n\r\n # add another experimental conditions\r\n df['pH2O / atm'] = partial_pressure_H2O\r\n df['Sintering temperature/C'] = SinteringTemperature\r\n df['Sintering time / h'] = SinteringTime\r\n df['fraction A2']='0'\r\n df['fraction B3']='0'\r\n df['X1']='O'\r\n df['Valence X1']='-2'\r\n df['fraction X1']='0.2'\r\n df['fraction total']='1'\r\n\r\n for cnt, tmp in enumerate(list_temperature):\r\n df['Temperature / C'] = tmp\r\n if cnt==0:\r\n df_all = df.copy()\r\n else:\r\n df_all = pd.concat([df_all,df], ignore_index=True)\r\n file_name = os.path.join(dir_output,'{:}_all.csv'.format(file_name_prefix, tmp))\r\n df_all.to_csv(file_name, index=False)","def compute_surface_temperature(heat_flux):\n\n return 1.1e-4*heat_flux + 323","def main():\n std_template_file = os.path.join(os.environ['HOME'], 'prospect/py/prospect/data/std_templates.fits')\n if os.path.isfile(std_template_file):\n print('Error std template file already exists')\n\n #- Templates produced from 1st component of old (pre-Aug 2022) Redrock templates:\n template_dir = os.path.join(os.environ['DESICONDA'], '../code/redrock-templates/0.7.2')\n #std_templates = {'QSO': ('QSO',''), 'GALAXY': ('GALAXY',''), 'STAR': ('STAR','F') }\n std_templates = {'GALAXY': ('GALAXY',''), 'STAR': ('STAR','F') }\n delta_lambd_templates = 3\n\n rr_templts = load_redrock_templates(template_dir=template_dir)\n for key,rr_key in std_templates.items() :\n wave_array = np.arange(rr_templts[rr_key].wave[0], rr_templts[rr_key].wave[-1], delta_lambd_templates)\n flux_array = resample_flux(wave_array, rr_templts[rr_key].wave, rr_templts[rr_key].flux[0,:])\n table_templates = Table(data=[wave_array, flux_array], names=['wave_'+key, 'flux_'+key], meta={'name':key})\n table_templates.write(std_template_file, append=True)\n\n #- Case of QSO (Summer 2022): use new template provided by A. Brodzeller\n qsotemplate_file = os.environ['HOME'] + '/stdtemplate-qso.fits'\n hdul = fits.open(qsotemplate_file)\n qsowave = 10**(hdul[0].header['CRVAL1']+np.arange(hdul[0].header['NAXIS1'])*hdul[0].header['CDELT1'])\n qsoflux = hdul[0].data\n # Resample as previously:\n wave_array = np.arange(qsowave[0], qsowave[-1], delta_lambd_templates)\n flux_array = resample_flux(wave_array, qsowave, qsoflux)\n table_templates = Table(data=[wave_array, flux_array], names=['wave_QSO', 'flux_QSO'], meta={'name':'QSO'})\n table_templates.write(std_template_file, append=True)\n return 0","def produce_13TeV_template(tag_name=\"HKHI\"):\n num_rebin = 1\n file_name = \"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\"+tag_name+\"_Lin.root\"\n print \"Input: \", file_name\n fin = ROOT.TFile.Open(file_name, \"read\")\n h_nom = fin.Get(\"bkg_total_gg_full\").Clone(\"bkg_nominal_old\")\n h_nom.Rebin(num_rebin)\n fout = ROOT.TFile.Open(\"hists_input_\"+tag_name+\".root\", \"recreate\")\n\n h_purity_sys = fin.Get(\"bkg_purity_syst_gg_full\").Clone(\"bkg_purity_syst_gg\")\n h_reducible_sys = fin.Get(\"bkg_reducible_syst_gg_full\").Clone(\"bkg_reducible_syst_gg\")\n h_irreducible_sys = fin.Get(\"bkg_irreducible_syst_gg_full\").Clone(\"bkg_irreducible_syst_gg\")\n h_iso_sys = fin.Get(\"bkg_iso_syst_gg_full\").Clone(\"bkg_iso_syst_gg\")\n\n #file_iso = \"isolation_sys/hist.root\"\n #fin2 = ROOT.TFile.Open(file_iso, \"read\")\n #h_iso_sys = fin2.Get(\"bkg_isolation_syst_gg\")\n ## inflat irreducible uncertainty by factor of 10\n # so that it closes to stats uncertainty in data\n sf = 1\n if INFLATE_SYS:\n sf = 10\n\n # after rebinning systematic uncertainties, need to scale down,\n # otherwise the uncertainties are inflated.\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\n\n ## truncate the histograms to [200, 2000] GeV\n h_nom_new = truncate_hist(h_nom, \"bkg_nominal\")\n h_purity_sys_new = truncate_hist(h_purity_sys, \"h_purity_sys_new\")\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \"h_irreducible_sys_new\")\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \"h_reducible_sys_new\")\n h_iso_sys_new = truncate_hist(h_iso_sys, \"h_iso_sys_new\")\n\n #write down sys and nominal\n fout.cd()\n h_nom_new.Write()\n h_purity_sys_new.Write()\n h_reducible_sys_new.Write()\n h_irreducible_sys_new.Write()\n h_iso_sys_new.Write()\n\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \"purity_sys\")\n h_purity_up.Write()\n h_purity_down.Write()\n\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \"reducible_sys\")\n h_red_up.Write()\n h_red_down.Write()\n\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \"irreducible_sys\")\n h_irred_up.Write()\n h_irred_down.Write()\n\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \"isolation_sys\")\n h_iso_up.Write()\n h_iso_down.Write()\n\n fin.Close()\n fout.Close()","def analyzeViSDEMData(dict):\n \n if 'path_in' in dict:\n path_in = dict['path_in']\n else:\n print(\"Caution: No path for input folder containing the data has been defined. Please define path to folder by dict['path_in']=path_in\") \n return\n \n path_out_default = '../colordeficiency-data/' \n if 'path_out' in dict:\n path_out = dict['path_out']\n else:\n print(\"Caution: No path for output folder where the data should be stored has been defined. Using default output path instead: \"+str(path_out_default))\n path_out = path_out_default\n \n if 'round' in dict:\n round = dict['round']\n else:\n print(\"Error: You have to chose a round first.\")\n \n path = os.path.join(os.path.dirname(os.path.abspath(os.path.join(__file__,os.pardir))),'colordeficiency-data')\n \n # 0. Step: Get all the relevant information, i.e. motive_ids, obs_col_defs etc.\n if round == 1:\n visualsearch_ids = os.path.join(path,\"visualsearch_ids.csv\")\n elif round == 2:\n visualsearch_ids = os.path.join(path,\"visualsearch_ids_2.csv\")\n \n vs_ids_sheet = pandas.read_csv(visualsearch_ids,sep=\";\")\n \n # Get all the relevant information about the observers, i.e. obs_col_defs etc.\n observer_ids = os.path.join(path,\"observer_ids.csv\")\n obs_ids_sheet = pandas.read_csv(observer_ids,sep=\";\")\n \n # 1. Step: Read all the XLSX data in the path\n ext = 'xlsx'; xlsx_files = getAllXXXinPath(path_in,ext)\n dataArray = pandas.DataFrame()\n i=1\n for xlsx_file in xlsx_files:\n sys.stdout.write(xlsx_file)\n dataArray_tmp, testArray, extraDataDict = extractExperimentData(os.path.join(path_in,xlsx_file))\n \n newDataArray = dataArray_tmp[['dalt_id','coldef_type','resp.corr_raw','resp.rt_raw','stimFile']]\n \n if \"2. Session\" in extraDataDict:\n sessionID = int(extraDataDict['2. Session'])\n newDataArray['session_id'] = sessionID\n \n if 'group' in extraDataDict:\n obsGroup = str(extraDataDict['group'])\n newDataArray['obsGroup'] = obsGroup\n \n if '0. Participant ID' in extraDataDict:\n obsID = int(extraDataDict['0. Participant ID'])\n \n newDataArray['observer_id'] = obsID\n obs_coldef_type = obs_ids_sheet.loc[obs_ids_sheet['observer_id']==obsID,['observer_coldef_type']]\n newDataArray['observer_coldef_type'] = int(obs_coldef_type['observer_coldef_type'])\n \n dataArray = pandas.concat([dataArray, newDataArray])\n sys.stdout.write(' . ')\n if (i%5)==0: sys.stdout.write('\\n')\n i+=1\n sys.stdout.write('\\n')\n \n # 2. Step: Adapt values to programstandards\n for item in settings.colDefLong2ID:\n dataArray.loc[dataArray['coldef_type'] == item, ['coldef_type']] = settings.colDefLong2ID[item]\n \n for item in settings.dalt2ID:\n dataArray.loc[dataArray['dalt_id'] == item, ['dalt_id']] = settings.dalt2ID[item]\n \n dataArray.loc[dataArray['dalt_id'] == 'none', ['dalt_id']] = 0\n \n \n dataArray = dataArray.rename(columns={'dalt_id': 'dalt_id',\n 'coldef_type': 'coldef_type',\n 'resp.corr_raw': 'is_correct',\n 'resp.rt_raw': 'resp_time',\n 'stimFile': 'filepath'})\n dataArray = dataArray.reset_index()\n \n # Adding set_id, motive_id and variant_id to each file\n for index, row in dataArray.iterrows():\n path_tmp = row['filepath']\n filename = os.path.basename(path_tmp).split('.')[0]\n dict_tmp = getStatsFromFilename(filename)\n imgID_tmp = int(dict_tmp['img_id'])\n \n tempVSDataArray = vs_ids_sheet.loc[vs_ids_sheet['image_id']==imgID_tmp,['set_id','motive_id','variant_id']]\n \n dataArray.at[index,'image_id'] = imgID_tmp\n dataArray.ix[index,'set_id'] = int(tempVSDataArray['set_id'])\n dataArray.ix[index,'motive_id'] = int(tempVSDataArray['motive_id'])\n dataArray.ix[index,'variant_id'] = int(tempVSDataArray['variant_id'])\n\n dataArray.image_id = dataArray.image_id.astype(int)\n dataArray.set_id = dataArray.set_id.astype(int)\n dataArray.motive_id = dataArray.motive_id.astype(int)\n dataArray.variant_id = dataArray.variant_id.astype(int)\n dataArray.is_correct = dataArray.is_correct.astype(bool)\n \n dataArray = dataArray[['image_id','set_id','motive_id','variant_id','dalt_id','coldef_type','is_correct','resp_time','observer_id','observer_coldef_type','session_id','filepath','obsGroup']]\n \n # 3. Saving data to file\n try:\n dataArray.to_csv(os.path.join(path_out, 'visdem-data.csv'),sep=\";\")\n sys.stdout.write(\"Success: ViSDEM data successfully saved in '\"+str(path_out)+\"'.\\n\")\n except Exception as e:\n print(e)","def temperature(self):\n value = float(self._parent.query('R{}'.format(self._idx))[1:])\n return pq.Quantity(value, pq.Kelvin)","def vorticity(tsr,solidity):\n \n # Reading in csv file (vorticity database)\n basepath = path.join(path.dirname(path.realpath(__file__)),'data')\n fdata = basepath + path.sep + 'vortdatabase.csv'\n f = open(fdata)\n csv_f = csv.reader(f)\n \n i = 0\n sol_d = np.array([])\n for row in csv_f:\n if i == 0:\n raw = row\n raw = np.delete(raw,0)\n vortdat = raw\n tsr_d = raw # range of tip-speed ratios included\n if row[0] == 'solidity':\n sol_d = np.append(sol_d,float(row[1])) # range of solidities included\n elif row[0] != 'TSR' and row[0] != 'solidity':\n raw = row\n raw = np.delete(raw,0)\n vortdat = np.vstack([vortdat,raw]) # adding entry to vorticity database array\n i += 1\n f.close()\n \n vortdat = np.delete(vortdat,(0),axis=0) # eliminating first row used as a placeholder\n tsr_d = tsr_d.astype(np.float) # converting tip-speed ratio entries into floats\n vortdat = vortdat.astype(np.float) # converting vorticity database entries into floats\n \n # Creating arrays for each EMG parameter\n for i in range(np.size(sol_d)):\n sol = str(i+1)\n \n exec('s'+sol+'_loc1 = vortdat[i*10]\\ns'+sol+'_loc2 = vortdat[i*10+1]\\ns'+sol+'_loc3 = vortdat[i*10+2]\\ns'+sol+'_spr1 = vortdat[i*10+3]\\ns'+sol+'_spr2 = vortdat[i*10+4]\\ns'+sol+'_skw1 = vortdat[i*10+5]\\ns'+sol+'_skw2 = vortdat[i*10+6]\\ns'+sol+'_scl1 = vortdat[i*10+7]\\ns'+sol+'_scl2 = vortdat[i*10+8]\\ns'+sol+'_scl3 = vortdat[i*10+9]\\n')\n \n # BIVARIATE SPLINE FITTING\n \n iz = np.size(sol_d)\n jz = np.size(tsr_d)\n \n # Initializing rectangular matrices\n Z_loc1 = np.zeros((iz,jz))\n Z_loc2 = np.zeros((iz,jz))\n Z_loc3 = np.zeros((iz,jz))\n Z_spr1 = np.zeros((iz,jz))\n Z_spr2 = np.zeros((iz,jz))\n Z_skw1 = np.zeros((iz,jz))\n Z_skw2 = np.zeros((iz,jz))\n Z_scl1 = np.zeros((iz,jz))\n Z_scl2 = np.zeros((iz,jz))\n Z_scl3 = np.zeros((iz,jz))\n \n # Transferring raw data into rectangular matrices\n for i in range(iz):\n for j in range(jz):\n sol = str(i+1)\n exec('Z_loc1[i,j] = s'+sol+'_loc1[j]')\n exec('Z_loc2[i,j] = s'+sol+'_loc2[j]')\n exec('Z_loc3[i,j] = s'+sol+'_loc3[j]')\n exec('Z_spr1[i,j] = s'+sol+'_spr1[j]')\n exec('Z_spr2[i,j] = s'+sol+'_spr2[j]')\n exec('Z_skw1[i,j] = s'+sol+'_skw1[j]')\n exec('Z_skw2[i,j] = s'+sol+'_skw2[j]')\n exec('Z_scl1[i,j] = s'+sol+'_scl1[j]')\n exec('Z_scl2[i,j] = s'+sol+'_scl2[j]')\n exec('Z_scl3[i,j] = s'+sol+'_scl3[j]')\n \n # Creating a rectangular bivariate spline of the parameter data\n s_loc1 = RectBivariateSpline(sol_d,tsr_d,Z_loc1)\n s_loc2 = RectBivariateSpline(sol_d,tsr_d,Z_loc2)\n s_loc3 = RectBivariateSpline(sol_d,tsr_d,Z_loc3)\n s_spr1 = RectBivariateSpline(sol_d,tsr_d,Z_spr1)\n s_spr2 = RectBivariateSpline(sol_d,tsr_d,Z_spr2)\n s_skw1 = RectBivariateSpline(sol_d,tsr_d,Z_skw1)\n s_skw2 = RectBivariateSpline(sol_d,tsr_d,Z_skw2)\n s_scl1 = RectBivariateSpline(sol_d,tsr_d,Z_scl1)\n s_scl2 = RectBivariateSpline(sol_d,tsr_d,Z_scl2)\n s_scl3 = RectBivariateSpline(sol_d,tsr_d,Z_scl3)\n \n # Selecting the specific parameters to use for TSR and solidity\n loc1 = s_loc1(solidity,tsr)\n loc2 = s_loc2(solidity,tsr)\n loc3 = s_loc3(solidity,tsr)\n spr1 = s_spr1(solidity,tsr)\n spr2 = s_spr2(solidity,tsr)\n skw1 = s_skw1(solidity,tsr)\n skw2 = s_skw2(solidity,tsr)\n scl1 = s_scl1(solidity,tsr)\n scl2 = s_scl2(solidity,tsr)\n scl3 = s_scl3(solidity,tsr)\n \n # Creating arrays of the parameters\n loc = np.array([loc1[0,0],loc2[0,0],loc3[0,0]])\n spr = np.array([spr1[0,0],spr2[0,0]])\n skw = np.array([skw1[0,0],skw2[0,0]])\n scl = np.array([scl1[0,0],scl2[0,0],scl3[0,0]])\n \n return loc,spr,skw,scl","def process_weather(forecast_file):\n with open(forecast_file) as json_file:\n json_data = json.load(json_file)\n\n min_temp_store = {}\n max_temp_store = {}\n weather_results = str()\n header_results = str()\n\n for day_in_forecast in json_data['DailyForecasts']:\n day_date = day_in_forecast['Date']\n min_temp = day_in_forecast['Temperature']['Minimum'][\"Value\"]\n min_temp_c = convert_f_to_c(min_temp)\n min_temp_store[day_date] = min_temp_c\n max_temp = day_in_forecast['Temperature']['Maximum'][\"Value\"]\n max_temp_c = convert_f_to_c(max_temp)\n max_temp_store[day_date] = max_temp_c\n\n day_time_phrase = day_in_forecast['Day']['LongPhrase']\n rain_chance_day = day_in_forecast['Day']['RainProbability']\n night_time_phrase = day_in_forecast['Night']['LongPhrase']\n rain_chance_night = day_in_forecast['Night']['RainProbability']\n weather_results = weather_results + (f\"-------- {convert_date(day_date)} --------\\nMinimum Temperature: {format_temperature(round(min_temp_c,1))}\\nMaximum Temperature: {format_temperature(round(max_temp_c,1))}\\nDaytime: {day_time_phrase}\\n Chance of rain: {rain_chance_day}%\\nNighttime: {night_time_phrase}\\n Chance of rain: {rain_chance_night}%\\n\")+ \"\\n\"\n\n\n max_day = max(max_temp_store, key=max_temp_store.get)\n max_value = max_temp_store[max_day]\n min_day = min(min_temp_store, key=min_temp_store.get)\n min_value = min_temp_store[min_day]\n max_totals = (sum(max_temp_store.values()))\n min_totals = (sum(min_temp_store.values()))\n num_items = len(min_temp_store)\n mean_min = round(calculate_mean(min_totals,num_items),1)\n mean_max = round(calculate_mean(max_totals,num_items),1)\n\n save_header = (f\"{len(json_data['DailyForecasts'])} Day Overview\\n The lowest temperature will be {format_temperature(round((min_value),1))}, and will occur on {convert_date(min_day)}.\\n The highest temperature will be {format_temperature(round((max_value),1))}, and will occur on {convert_date(max_day)}.\\n The average low this week is {format_temperature(mean_min)}.\\n The average high this week is {format_temperature(mean_max)}.\\n\")\n\n header_results = save_header + \"\\n\"+ weather_results\n \n return(header_results)","def get_temp():\n epts = [\"cage_coldPlate_temp\", \"cage_pressure\"]\n # t_earlier_aug = '2019-10-02T00:00'\n # t_later_aug = datetime.utcnow().isoformat()\n t_earlier_aug = '2019-09-27T13:00'\n t_later_aug = '2019-09-28T19:49'\n dfs = pandas_db_query(epts, t_earlier_aug, t_later_aug)\n print(dfs[epts[0]].tail())\n\n exit()\n\n xv = dfs[epts[0]][\"timestamp\"]\n yv = dfs[epts[0]][epts[0]]\n plt.plot(xv, yv, '-b')\n plt.ylabel(epts[0], ha='right', y=1)\n\n p1a = plt.gca().twinx()\n xv = dfs[epts[1]][\"timestamp\"]\n yv = dfs[epts[1]][epts[1]]\n p1a.set_ylabel(epts[1], color='r', ha='right', y=1)\n p1a.tick_params('y', colors='r')\n p1a.semilogy(xv, yv, '-r')\n\n plt.gcf().autofmt_xdate()\n plt.tight_layout()\n plt.show()","def temperatures():\n hi_act= session.query(measurements.tobs,measurements.date,measurements.station).\\\n filter(measurements.station == 'USC00519281').\\\n filter(measurements.date >last_12).\\\n order_by(measurements.date).all()\n hi_act_df=pd.DataFrame(hi_act).set_index('date')\n hi_act_dict=hi_act_df.to_dict()\n return jsonify(hi_act_dict)","def run_get_tones_for_15k_subset():\n add_tone_columns_to_csv('politics_30_months_comments_cleaned_standardized_vader_flair_15k.csv',\n 'politics_30_months_comments_cleaned_standardized_vader_flair_15k_tones.csv')","def run_purity_plotting(input_tsvs, output_dir):\n # type: (list[str], str) -> None\n result_cols = [\"sensitivity\", \"sens_lo\", \"sens_hi\", \"sens_N\", \"precision\", \"prec_lo\", \"prec_hi\", \"prec_N\", \"purity\", \"pass\"]\n amp_results_df = DataFrame(columns=result_cols)\n del_results_df = DataFrame(columns=result_cols)\n\n min_sensitivity = 0.85\n min_precision = 0.8\n min_supported_purity = 0.39\n\n for i, input_tsv in enumerate(input_tsvs):\n purity = find_purity_from_filename(input_tsv)\n print(input_tsv + \" purity: \" + str(purity))\n\n if purity is None:\n print(\"The file \" + input_tsv + \" is unrecognized as being a HCC1143T purity file, so it is being skipped. Please see the src code here if you believe this is an error.\")\n continue\n\n sample = find_sample_from_filename(input_tsv)\n\n segs_df_tmp = pandas.read_csv(input_tsv, sep=\"\\t\", comment=\"@\")\n\n # Clean up by removing all locations where there was more than one ground truth value for copy number/ratio\n segs_df = segs_df_tmp[segs_df_tmp[GT_CN_COLUMN_NAME].apply(more_than_one_value)]\n tmp = segs_df[GT_CN_COLUMN_NAME]\n tmp = pandas.to_numeric(tmp, errors='coerce', downcast='integer')\n segs_df[GT_CN_COLUMN_NAME] = tmp\n\n cr_gt = 1 + (purity * ((segs_df[GT_CN_COLUMN_NAME] / ploidy) - 1))\n cr_gt.rename(GT_CR_COLUMN_NAME, inplace=True)\n\n if IS_LOG2_GUESS_CR:\n cr_guess = 2 ** segs_df[INPUT_GUESS_CR_COLUMN_NAME]\n else:\n cr_guess = segs_df[INPUT_GUESS_CR_COLUMN_NAME]\n\n cr_guess.rename(GUESS_CR_COLUMN_NAME, inplace=True)\n\n segs_df[GT_CR_COLUMN_NAME] = cr_gt\n segs_df[GUESS_CR_COLUMN_NAME] = cr_guess\n segs_gt_to_consider = segs_df[~segs_df[\"CALL\"].isnull() & (segs_df[\"CONTIG\"] != \"2\")]\n\n ## Amps\n tp = segs_gt_to_consider[(segs_gt_to_consider[\"CALL\"] == \"+\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] >= 5)]\n all_gt_amp = segs_gt_to_consider[segs_gt_to_consider[GT_CN_COLUMN_NAME] >= 5]\n sens_amps = float(len(tp)) / float(len(all_gt_amp))\n sens_amps_ci = clopper_pearson(len(tp), len(all_gt_amp))\n sens_amps_N = len(all_gt_amp)\n\n fp = segs_gt_to_consider[(segs_gt_to_consider[\"CALL\"] == \"+\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 4)]\n prec_amps = float(len(tp)) / float(len(tp) + len(fp))\n prec_amps_ci = clopper_pearson(len(tp), (len(tp) + len(fp)))\n prec_amps_N = len(tp) + len(fp)\n\n amp_result = Series(name=sample, data={result_cols[0]: sens_amps, result_cols[1]: sens_amps_ci[0],\n result_cols[2]: sens_amps_ci[1], result_cols[3]: sens_amps_N,\n result_cols[4]: prec_amps, result_cols[5]: prec_amps_ci[0],\n result_cols[6]: prec_amps_ci[1], result_cols[7]: prec_amps_N,\n result_cols[8]: purity,\n result_cols[9]: is_passing(sens_amps_ci[1], prec_amps_ci[1], purity, min_sensitivity, min_precision,\n min_supported_purity)})\n\n amp_results_df = amp_results_df.append(amp_result)\n amp_results_df.sort_values(result_cols[8], inplace=True)\n\n print(\"Amp sensitivity: \" + str(sens_amps) + \" \" + str(sens_amps_ci))\n print(\"Amp precision: \" + str(prec_amps) + \" \" + str(prec_amps_ci))\n\n ## Dels\n tp_del = segs_gt_to_consider[\n (segs_gt_to_consider[\"CALL\"] == \"-\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 2)]\n all_gt_del = segs_gt_to_consider[segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 2]\n sens_dels = float(len(tp_del)) / float(len(all_gt_del))\n sens_dels_ci = clopper_pearson(len(tp_del), len(all_gt_del))\n sens_dels_N = len(all_gt_del)\n\n fp_del = segs_gt_to_consider[\n (segs_gt_to_consider[\"CALL\"] == \"-\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] > 2)]\n prec_dels = float(len(tp_del)) / float(len(tp_del) + len(fp_del))\n prec_dels_ci = clopper_pearson(len(tp_del), (len(tp_del) + len(fp_del)))\n prec_dels_N = len(tp_del) + len(fp_del)\n\n del_result = Series(name=sample, data={result_cols[0]: sens_dels, result_cols[1]: sens_dels_ci[0],\n result_cols[2]: sens_dels_ci[1], result_cols[3]: sens_dels_N,\n result_cols[4]: prec_dels, result_cols[5]: prec_dels_ci[0],\n result_cols[6]: prec_dels_ci[1], result_cols[7]: prec_dels_N,\n result_cols[8]: purity,\n result_cols[9]: is_passing(sens_dels_ci[1], prec_dels_ci[1], purity,\n min_sensitivity, min_precision,\n min_supported_purity)})\n del_results_df = del_results_df.append(del_result)\n del_results_df.sort_values(result_cols[8], inplace=True)\n\n print(\"Del sensitivity: \" + str(sens_dels) + \" \" + str(sens_dels_ci))\n print(\"Del precision: \" + str(prec_dels) + \" \" + str(prec_dels_ci))\n\n if len(amp_results_df) > 0 and len(del_results_df) > 0:\n plot_purity_series(output_dir, amp_results_df, \"Amplifications\", min_sensitivity, min_precision, min_supported_purity)\n plot_purity_series(output_dir, del_results_df, \"Deletions\", min_sensitivity, min_precision, min_supported_purity)\n amp_results_df.to_csv(output_dir + \"Amplifications_table.tsv\", sep=\"\\t\")\n del_results_df.to_csv(output_dir + \"Deletions_table.tsv\", sep=\"\\t\")","def get_iPTF16hgs(colorplt = False):\n z = 0.017\n ebv = 0\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n \n tb = pd.read_csv('../data/otherSN/iPTF16hgs/table1.txt', sep=\"\\t\")\n tb = tb.drop(columns=[\"Unnamed: 5\"])\n tb = tb.rename(columns={'Filter' : 'filter',\n 'MJD': 'mjd'})\n tb = tb[~np.array([x[0]=='>' for x in tb['Magnitude'].values])]\n tb['mag'] = np.array([float(x.split(\" +or-\")[0]) for x in tb['Magnitude'].values])\n tb['emag'] = np.array([float(x.split(\" +or-\")[1]) for x in tb['Magnitude'].values])\n tb = tb.drop(columns=[\"Magnitude\"])\n \n ixg = tb['filter'].values == \"g\"\n ixr = tb['filter'].values == \"r\"\n ixi = tb['filter'].values == \"i\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'].values[ixg] = 4814\n tb['wave'].values[ixr] = 6422\n tb['wave'].values[ixi] = 7883\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n t_max = 57691.59 # from the paper\n tb['tmax_of'] = tb['mjd'] - t_max\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\n \"\"\"\n plt.errorbar(tb[\"tmax_rf\"].values[ixg], tb[\"mag\"].values[ixg], tb[\"emag\"].values[ixg], fmt=\".g\")\n plt.errorbar(tb[\"tmax_rf\"].values[ixr], tb[\"mag\"].values[ixr], tb[\"emag\"].values[ixr], fmt=\".r\")\n plt.errorbar(tb[\"tmax_rf\"].values[ixi], tb[\"mag\"].values[ixi], tb[\"emag\"].values[ixi], fmt=\".y\")\n \"\"\"\n tb = add_datecol(tb)\n tb = add_physcol(tb)\n #tb = tb.drop(columns=[\"datetime64\"])\n if colorplt==False:\n return tb\n else:\n #tb = tb[tb.mjd > 55352.5]\n #tb = tb[tb.mjd < 55593.5]\n \n dates = get_date_span(tb)\n datesave = []\n for i in range(len(dates)):\n x = dates[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n if len(tbsub)!=0:\n flts = tbsub['filter'].values\n if \"r\" in flts and np.sum(np.unique(flts))!=1:\n datesave.append(x)\n datesave = np.array(datesave)\n \n mcolor = []\n mcolor_unc = []\n mjds = []\n colorname = []\n for i in range(len(datesave)):\n x = datesave[i]\n ix = tb[\"date\"].values == x\n tbsub = tb[ix]\n gtb = tbsub[tbsub[\"filter\"].values==\"g\"]\n rtb = tbsub[tbsub[\"filter\"].values==\"r\"]\n itb = tbsub[tbsub[\"filter\"].values==\"i\"]\n if len(gtb)!=0:\n gmjds = gtb[\"mjd\"].values\n gmags = gtb[\"mag0\"].values\n gemags = gtb[\"emag\"].values\n gwtgs = 1/gemags**2\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\n gemag = 1/ np.sqrt(np.sum(gwtgs))\n if len(rtb)!=0:\n rmjds = rtb[\"mjd\"].values\n rmags = rtb[\"mag0\"].values\n remags = rtb[\"emag\"].values\n rwtgs = 1/remags**2\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\n remag = 1/ np.sqrt(np.sum(rwtgs))\n if len(itb)!=0:\n imjds = itb[\"mjd\"].values\n imags = itb[\"mag0\"].values\n iemags = itb[\"emag\"].values\n iwtgs = 1/iemags**2\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\n iemag = 1/ np.sqrt(np.sum(iwtgs))\n if len(gtb)!=0 and len(rtb)!=0:\n mcolor.append(gmag - rmag)\n mjds.append( 0.5 * (gmjd + rmjd) )\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\n colorname.append(\"gmr\")\n if len(rtb)!=0 and len(itb)!=0:\n mcolor.append(rmag - imag)\n mjds.append( 0.5 * (rmjd + imjd) )\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\n colorname.append(\"rmi\")\n \n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\n names = [\"mjd\", \"c\", \"ec\", \"cname\"])\n \n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\n ctb = ctb.to_pandas()\n return ctb","def main(temp, humid):\n user = 'root'\n password = 'root'\n dbname = 'iot'\n dbuser = 'raspberry'\n dbuser_password = 'password'\n query = 'select temp_value,humid_value from temp_humid;'\n json_body = [\n {\n \"measurement\": \"temp_humid\",\n \"fields\": {\n \"temp_value\": temp,\n \"humid_value\":humid \n\t}\n }\n ]\n\n client = InfluxDBClient('localhost', 8086, user, password, dbname)\n\n #client.create_database(dbname)\n\n print(\"Write points: {0}\".format(json_body))\n client.write_points(json_body)\n\n #print(\"Querying data: \" + query)\n #result = client.query(query)\n\n #print(\"Result: {0}\".format(result))\n\n #client.drop_database(dbname)","def get_taux(name):\n indicateurResult = get_empty_kpi()\n config = get_config(name)\n log.debug('Processing - '+name)\n\n indicateurResult['nom'] = config['nom']\n indicateurResult['unite'] = config['unite']\n indicateurResult['unite_short'] = config['unite_short']\n indicateurResult['trendType'] = config['trendType']\n indicateurResult['color'] = get_color_indicateur(config)\n\n df = pd.read_csv(\n 'files_new/'+config['res_id_fra'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n df = enrich_dataframe(df, name)\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\n for country in tqdm(countries, desc=\"Processing National\"):\n res = process_stock(\n df,\n 'nat',\n 'fra',\n config['trendType'],\n name\n )\n indicateurResult['france'].append(res)\n\n df = pd.read_csv(\n 'files_new/'+config['res_id_reg'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n df = enrich_dataframe(df, name)\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\n for reg in tqdm(df.reg.unique(), desc=\"Processing Régions\"):\n res = process_stock(\n df[df['reg'] == reg].copy(),\n 'reg',\n reg,\n config['trendType'],\n name\n )\n indicateurResult['regions'].append(res)\n\n df = pd.read_csv(\n 'files_new/'+config['res_id_dep'],\n sep=None,\n engine='python',\n dtype={'reg': str, 'dep': str}\n )\n df = enrich_dataframe(df, name)\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\n for dep in tqdm(df.dep.unique(), desc=\"Processing Départements\"):\n res = process_stock(\n df[df['dep'] == dep].copy(),\n 'dep',\n dep,\n config['trendType'],\n name\n )\n indicateurResult['departements'].append(res)\n\n save_result(indicateurResult, name)","def Q_flux(self):\n fields = self.read_vars(['x','y','z'])\n Z, Y, X = np.meshgrid(fields['z']/self.params['Lz'],\n fields['y']/self.params['Ly'] - 0.5,\n fields['x']/self.params['Lx'] - 0.5, indexing='ij')\n\n r = np.sqrt(X**2 + Y**2)\n r0 = 0.01\n msk = 0.5*(1.-np.tanh(r/r0))\n delta = 1/(self.params[\"global_nz\"])\n Q =1e-5*np.exp(-Z/delta)/delta*msk\n\n return Q","def amet_memoryWise(self):\r\n # set up logging files to monitor the calculation\r\n logging.basicConfig(filename = os.path.join(self.path,'history_amet_python.log'),\r\n filemode = 'w+', level = logging.DEBUG,\r\n format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\r\n # initialize the time span\r\n # define sigma level\r\n A, B = self.defineSigmaLevels()\r\n # use example input file to load the basic dimensions information\r\n datapath_var = os.path.join(self.path, 'MERRA2_400.inst3_3d_asm_Nv.20160101.nc4.nc')\r\n var_key = Dataset(datapath_var)\r\n lat = var_key.variables['lat'][:]\r\n lon = var_key.variables['lon'][:]\r\n # calculate the reference levels based on A & B and standard surface pressure\r\n half_level = A + B * 101325\r\n level = (half_level[1:] + half_level[:-1]) / 2\r\n # create space for the output\r\n # AMET in the entire column\r\n E = np.zeros((len(lat),len(lon)), dtype=float)\r\n cpT = np.zeros((len(lat),len(lon)), dtype=float)\r\n Lvq = np.zeros((len(lat),len(lon)), dtype=float)\r\n gz = np.zeros((len(lat),len(lon)), dtype=float)\r\n uv2 = np.zeros((len(lat),len(lon)), dtype=float)\r\n logging.info(\"Start retrieving variables T,q,u,v,sp\")\r\n # The shape of each variable is (8,72,361,576)\r\n T = var_key.variables['T'][:]\r\n q = var_key.variables['QV'][:]\r\n sp = var_key.variables['PS'][:] #(8,361,576)\r\n u = var_key.variables['U'][:]\r\n v = var_key.variables['V'][:]\r\n logging.info(\"Extracting variables successfully!\") \r\n # compute gz\r\n z_model = self.calc_gz(var_key)\r\n # get the basic shape\r\n tt, hh, yy, xx = q.shape\r\n AMET = amet.met()\r\n E, cpT, Lvq, gz, uv2 = AMET.calc_met(T, q, sp, u, v, z_model, A, B,\r\n tt, hh, len(lat), len(lon), lat, self.lat_unit)\r\n\r\n return np.mean(E)","def temperatures():\n\n return station_9281","def readqFile(self, PFC, t):\n if self.qFilePath[-1] != '/':\n base = self.qFilePath + '/'\n else:\n base = self.qFilePath\n\n f = base + '{:06d}/'.format(t) + PFC.name + '/' + self.qFileTag\n try:\n df = pd.read_csv(f, names=['X','Y','Z','HF'], skiprows=[0])\n if len(df['HF'].values) != len(PFC.centers):\n print('HF file mesh is not same length as STL file mesh.')\n print('Will not assign HF to mismatched mesh')\n print(\"qFile length: {:d}\".format(len(df['HF'].values)))\n print(\"PFC STL mesh length: {:d}\".format(len(PFC.centers)))\n val = -1\n else:\n PFC.qDiv = df['HF'].values\n PFC.powerFrac = self.getDivertorPowerFraction(PFC.DivCode)\n PFC.qOpticalList.append(PFC.qDiv)\n print(\"Loaded heat flux from file: \"+f)\n val = 0\n except:\n print(\"COULD NOT READ qFILE PATH: \"+f)\n print(\"Please point HEAT to a valid qFilePath and qFileTag,\")\n print(\"which should be a .csv file with (X,Y,Z,HF)\")\n val = -1\n\n return val","def test():\n temp_data = fetch_temp_data(\n (\"https://opendata-download-metobs.smhi.se/api/version/\" +\n \"latest/parameter/1/station/52350/period/latest-day/data.json\"))\n data = temp_series(temp_data)\n print(data)","def readtempfilt(MAIN_OUTPUT_FILE='photz', OUTPUT_DIRECTORY='./OUTPUT'):\n\n root = os.path.join(OUTPUT_DIRECTORY, MAIN_OUTPUT_FILE)\n \n ###### .tempfilt\n file_path = root+'.tempfilt'\n \n if os.path.exists(file_path) is False:\n raise ValueError('File, %s, not found.' %(file_path))\n\n with open(file_path,'rb') as f:\n # summary data\n s = np.fromfile(file=f,dtype=np.int32, count=4)\n NFILT=s[0] # number of filters\n NTEMP=s[1] # number of templates\n NZ=s[2] # number points on the redshift grid\n NOBJ=s[3] # number of objects\n # (?) template SED convolved with filter transmission at each redshift\n tempfilt = np.fromfile(file=f,dtype=np.double,count=NFILT*NTEMP*NZ).reshape((NZ,NTEMP,NFILT)).transpose()\n # filter pivot wavelengths\n lc = np.fromfile(file=f,dtype=np.double,count=NFILT)\n # redshift grid\n zgrid = np.fromfile(file=f,dtype=np.double,count=NZ)\n # observed flux\n fnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\n # (?) error in observed flux\n efnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\n \n tempfilt = {'NFILT':NFILT,'NTEMP':NTEMP,'NZ':NZ,'NOBJ':NOBJ,\\\n 'tempfilt':tempfilt,'lc':lc,'zgrid':zgrid,'fnu':fnu,'efnu':efnu}\n\n return tempfilt","def read_elia_activated_energy_prices(filename,status):\r\n \r\n df = pd.read_excel(filename,skiprows=2,parse_dates=False)\r\n df[\"Timestamp\"] = df[\"Date\"]+\" \"+df['Quarter'].map(lambda x: str(x)[:-9])\r\n pd.to_datetime(df[\"Timestamp\"])\r\n df.set_index(\"Timestamp\",inplace=True)\r\n if ((status == \"validated\") | (status == \"valid\")):\r\n df = df.drop(df[df.Status != \"Validated\"].index)\r\n df = df.drop([\"Date\",\"Quarter\",\"Status\"], axis=1)\r\n \r\n if len(df.columns)>14:\r\n df.columns.values[0:16] = [\"NRV in MW\",\"SR in euro/MWh\",\"MIP in euro/MWh\",\"IGGC+ in euro/MWh\", \"R2+ in euro/MWh\",\"Bids+ in euro/MWh\",\"R3 std in euro/MWh\", \"R3 flex in euro/MWh\", \"ICH in euro/MWh\", \"inter TSO import in euro/MWh\", \"MDP in euro/MWh\", \"IGCC- in euro/MWh\", \"R2- in euro/MWh\", \"Bids- in euro/MWh\", \"R3- in euro/MWh\"]\r\n\r\n if len(df.columns)<12:\r\n df.columns.values[0:12] = [\"NRV in MW\",\"MIP in euro/MWh\",\"IGGC+ in euro/MWh\", \"R2+ in euro/MWh\",\"Bids+ in euro/MWh\", \"R3+ in euro/MWh\", \"MDP in euro/MWh\", \"IGCC- in euro/MWh\", \"R2- in euro/MWh\", \"Bids- in euro/MWh\", \"R3- in euro/MWh\"]\r\n\r\n return df","def export_results_max():\n\n # initialise the list of generated files\n gen_files = []\n\n ######\n # Define allowed variable names and associated equations to generate values.\n ######\n # Note that mannings n (friction value) is taken as 0.01, as in the model\n # run density of water is 1000\n var_equations = {'stage': enm.export_newstage_max,\n 'oldstage': 'stage',\n 'momentum': '(xmomentum**2 + ymomentum**2)**0.5',\n 'olddepth': 'oldstage-elevation',\n 'depth': edm.export_depthonland_max,\n 'speed': '(xmomentum**2 + ymomentum**2)**0.5/(stage-elevation+1.e-6)',\n 'energy': '(((xmomentum/(stage-elevation+1.e-6))**2'\n ' + (ymomentum/(stage-elevation+1.e-6))**2)'\n '*0.5*1000*(stage-elevation+1.e-6))+(9.81*stage*1000)',\n 'bed_shear_stress': ('(((1/(stage-elevation+1.e-6)**(7./3.))*1000*9.81*0.01**2*(xmomentum/(stage-elevation+1.e-6))*((xmomentum/(stage-elevation+1.e-6))**2+(ymomentum/(stage-elevation+1.e-6))**2)**0.5)**2'\n '+ ((1/(stage-elevation+1.e-6)**(7./3.))*1000*9.81*0.01**2*(ymomentum/(stage-elevation+1.e-6))*((xmomentum/(stage-elevation+1.e-6))**2+(ymomentum/(stage-elevation+1.e-6))**2)**0.5)**2)**0.5'),\n 'elevation': 'elevation'}\n\n ######\n # Start script, running through variables, area, sww file\n ######\n\n for which_var in project.layers_list:\n which_var = which_var.lower()\n log.info(\"Exporting value: %s\" % which_var)\n\n if which_var not in var_equations:\n log.critical('Unrecognized variable name: %s' % which_var)\n break\n\n project.export_area = project.export_area.lower()\n if project.export_area == 'all':\n easting_min = None\n easting_max = None\n northing_min = None\n northing_max = None\n elif project.export_area == 'aoi':\n easting_min = project.xminAOI\n easting_max = project.xmaxAOI\n northing_min = project.yminAOI\n northing_max = project.ymaxAOI\n else:\n log.critical('Unrecognized area name: %s' % project.export_area)\n break\n\n name = os.path.join(project.output_folder, project.scenario)\n\n outname = name + '_' + project.export_area + '_' + which_var\n quantityname = var_equations[which_var]\n\n log.info('Generating output file: %s' % (outname+'.asc'))\n\n # assume 'quantityname' is a string, handle in the old way,\n # else call the handler function (same params as anuga.sww2dem)\n if isinstance(quantityname, basestring):\n export_func = anuga.sww2dem\n elif callable(quantityname):\n export_func = quantityname\n\n export_func(name+'.sww', outname+'.asc', quantity=quantityname,\n reduction=max, cellsize=project.cell_size,\n easting_min=easting_min, easting_max=easting_max,\n northing_min=northing_min, northing_max=northing_max,\n verbose=False)\n\n # add generated filename to result list\n gen_files.append(outname+'.asc')\n\n return gen_files","def cygx3IndFlux(self):\n # --------------------------------------------------------------------------------------------- #\n # Read data\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\n lcTab = Table.read(fitsNnam)\n detect = lcTab['ts'] >= self.tsmin\n lcTab = lcTab[detect] \n\n ind08 = (lcTab['mjd'] > 54700) & (lcTab['mjd'] < 54900) \n flux08 = lcTab['flux'][ind08]\n fluxerr08 = lcTab['fluxerr'][ind08]\n index08 = lcTab['index'][ind08]\n indexerr08 = lcTab['indexerr'][ind08]\n\n ind09 = (lcTab['mjd'] > 54900) & (lcTab['mjd'] < 55100) \n flux09 = lcTab['flux'][ind09]\n fluxerr09 = lcTab['fluxerr'][ind09]\n index09 = lcTab['index'][ind09]\n indexerr09 = lcTab['indexerr'][ind09]\n\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux08, flux09), axis=0) ) ))) \n\n # --------------------------------------------------------------------------------------------- #\n # Plot\n indplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\n indplt.figname = os.path.join(self.workpath, 'IndvsFlux.pdf')\n indplt.xlabel = r'Flux ($10^{%d}$ ph\\,cm$^{-2}$\\,s$^{-1}$)'%(int(np.log10(scale)))\n indplt.ylabel = r'Index'\n indplt.mksize = 2\n indplt.color = self.lblue\n indplt.label = r'2008'\n indplt.plot(x=flux08/scale, xerr=fluxerr08/scale, y=index08, yerr=indexerr08)\n indplt.color = self.loran\n indplt.label = r'2009'\n indplt.plot(x=flux09/scale, xerr=fluxerr09/scale, y=index09, yerr=indexerr09)\n indplt.save()\n\n print(\"\\t=== Figure '{}' created ===\".format(indplt.figname)) \n return","def test_plt_mag_time():\n\n ta = WATA()\n wata_data = define_testdata()\n ta.source = ColumnDataSource(data=wata_data)\n ta.add_time_column()\n ta.setup_date_range()\n\n # create the arrays per filter and readout pattern\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\n for i, val in enumerate(max_val_box):\n if '140' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(val)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(val)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif '110' in filter_used[i]:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(val)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(val)\n nrsrapidd6_clear.append(np.NaN)\n else:\n if readout[i].lower() == 'nrsrapid':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(val)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(np.NaN)\n elif readout[i].lower() == 'nrsrapidd6':\n nrsrapid_f140x.append(np.NaN)\n nrsrapid_f110w.append(np.NaN)\n nrsrapid_clear.append(np.NaN)\n nrsrapidd6_f140x.append(np.NaN)\n nrsrapidd6_f110w.append(np.NaN)\n nrsrapidd6_clear.append(val)\n # add to the bokeh data structure\n ta.source.data[\"nrsrapid_f140x\"] = nrsrapid_f140x\n ta.source.data[\"nrsrapid_f110w\"] = nrsrapid_f110w\n ta.source.data[\"nrsrapid_clear\"] = nrsrapid_clear\n ta.source.data[\"nrsrapidd6_f140x\"] = nrsrapidd6_f140x\n ta.source.data[\"nrsrapidd6_f110w\"] = nrsrapidd6_f110w\n ta.source.data[\"nrsrapidd6_clear\"] = nrsrapidd6_clear\n result = ta.plt_mag_time()\n\n assert bokeh_plot_type == type(result)","def plc_temp(coil_df):","def calc(main_system,dir_db,dir_def,xlfile,mu_limit,functional=\"GGA\",soc=False,logfile=None):\n \n ## set up logging\n if logfile:\n myLogger = logging.setup_logging(logfile)\n else:\n myLogger = logging.setup_logging()\n\n\n ## load list of dataframes from sheets from excel file \n df = pd.read_excel(os.path.join(dir_def,xlfile),sheet_name=None)\n \n \n ## find initdef.json file\n if osutils.check_file_exists(dir_def,\"initdef\") == True:\n for file in os.listdir(dir_def): \n if file.startswith(\"initdef\"):\n file_initdef = file\n ## get species i and ni from initdefect.json file \n with open(os.path.join(dir_def,file_initdef), 'r') as file:\n initdef = json.loads(file.read())\n species_list, ni_list = [],[]\n for defect in initdef:\n species, ni = get_i_ni(initdef[defect])\n species_list += species\n ni_list += ni\n myLogger.info(\"Atoms added/removed: \" + \\\n \", \".join([str(n)+\"*\"+i for n,i in zip(ni_list,species_list)]))\n\n \n for q in [qi for qi in df.keys()]:\n \n ## get the relevant chemical potentials\n found_mu = True\n for species in species_list:\n mu = \"mu_%s_%s\"%(species,mu_limit)\n \n ## check if the relevant database entry exists\n if osutils.check_file_exists(dir_db,\"%s.json\"%species) == True:\n dbentry_file = \"%s.json\"%species\n with open(os.path.join(dir_db, dbentry_file), 'r') as file:\n mater = json.loads(file.read())\n ## search for appropriate mu entry\n mu_key = \"mu\"\n for key in mater[functional].keys():\n if key.startswith(\"mu_%s\"%mu_limit):\n mu_key = key\n myLogger.info(\"Using chemical potential \" + mu_key + \" from \" + dbentry_file) \n ## input the corresponding mus into the dataframe\n df[q][mu] = mater[functional][mu_key]\n \n else:\n myLogger.info(\"Cannot find the database entry for \" + species)\n found_mu = False\n \n \n ## get the VBMs\n ## check if the relevant database entry exists\n if osutils.check_file_exists(dir_db,\"%s.json\"%main_system) == True:\n dbentry_file = \"%s.json\"%(main_system)\n with open(os.path.join(dir_db, dbentry_file), 'r') as file:\n mater = json.loads(file.read()) \n \n ## input the VBMs corresponding to each vacuum spacing into the dataframe\n for rowind in df[q].index.values:\n vac = df[q].loc[rowind].vacuum\n if vac in mater[functional].keys():\n df[q].at[rowind,'VBM'] = mater[functional][vac][\"VBM\"]\n else:\n myLogger.info(\"Cannot find the VBM entry for \" + vac) \n \n ## Finally, we can compute the uncorrected defect formation energy:\n ## Eform = Etot(def) - Etot(pristine) - sum(n_i*mu_i) + q*E_Fermi\n if found_mu:\n ## proceed if chemical potentials and VBMs have been correctly entered\n sum_mu = 0\n for n,species in zip(ni_list,species_list):\n mu = \"mu_%s_%s\"%(species,mu_limit)\n sum_mu += n * df[q][mu]\n if q == 'charge_0': \n colname = \"E_form_corr\"\n else:\n colname = \"E_form_uncorr\"\n df[q][colname] = df[q].loc[:,'E_def'] \\\n - df[q].loc[:,'E_bulk'] \\\n - sum_mu \\\n + int(q.split(\"_\")[-1]) * df[q].loc[:,'VBM'] \n \n else:\n myLogger.info(\"Cannot find the database entry for \" + main_system)\n\n\n ## write the updated excel file\n writer = pd.ExcelWriter(os.path.join(dir_def,xlfile))\n for q in df.keys(): \n df[q].to_excel(writer, q, index=False)\n writer.save()","def Thrustexe_data(h,Vc,Temp_m,FFl,FFr):\n T,p,rho = isa(h)\n M = Mach(h,Vc,p)\n Delta_T = Static_T(Temp_m,M)-T\n data = np.array([h,M,Delta_T,FFl,FFr]).T\n\n np.savetxt(\"matlab.dat\",data)\n return","def rainfall_series(self):\n\n # assign local temporal variables\n datatype = 'strds'\n increment = str(self.rain_interval)+\" minutes\"\n raster = 'raster'\n rain_excess = 'rain_excess'\n net_difference = 'net_difference'\n #iterations = sum(1 for row in precip)\n\n # create a raster space time dataset\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.elevation_timeseries,\n title=self.elevation_title,\n description=self.elevation_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.depth_timeseries,\n title=self.depth_title,\n description=self.depth_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.erdep_timeseries,\n title=self.erdep_title,\n description=self.erdep_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.flux_timeseries,\n title=self.flux_title,\n description=self.flux_description,\n overwrite=True)\n gscript.run_command(\n 't.create',\n type=datatype,\n temporaltype=self.temporaltype,\n output=self.difference_timeseries,\n title=self.difference_title,\n description=self.difference_description,\n overwrite=True)\n\n # register the initial digital elevation model\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=self.elevation,\n start=self.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # create evolution object\n evol = Evolution(\n elevation=self.elevation,\n precipitation=self.precipitation,\n start=self.start,\n rain_intensity=self.rain_intensity,\n rain_interval=self.rain_interval,\n walkers=self.walkers,\n runoff=self.runoff,\n mannings=self.mannings,\n detachment=self.detachment,\n transport=self.transport,\n shearstress=self.shearstress,\n density=self.density,\n mass=self.mass,\n grav_diffusion=self.grav_diffusion,\n erdepmin=self.erdepmin,\n erdepmax=self.erdepmax,\n k_factor=self.k_factor,\n c_factor=self.c_factor,\n m=self.m,\n n=self.n,\n threads=self.threads,\n fill_depressions=self.fill_depressions)\n\n # open txt file with precipitation data\n with open(evol.precipitation) as csvfile:\n\n # check for header\n has_header = csv.Sniffer().has_header(csvfile.read(1024))\n\n # rewind\n csvfile.seek(0)\n\n # skip header\n if has_header:\n next(csvfile)\n\n # parse time and precipitation\n precip = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\n\n # initial run\n initial = next(precip)\n evol.start = initial[0]\n evol.rain_intensity = 'rain_intensity'\n # compute rainfall intensity (mm/hr)\n # from rainfall observation (mm)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity}\"\n \"={rain_observation}\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_intensity=evol.rain_intensity,\n rain_observation=float(initial[1]),\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # determine mode and run model\n if self.mode == \"simwe_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps\n # from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # run the landscape evolution model for each rainfall record\n for row in precip:\n\n # update the elevation\n evol.elevation=evolved_elevation\n\n # update time\n evol.start=row[0]\n\n # compute rainfall intensity (mm/hr)\n # from rainfall observation (mm)\n rain_intensity = 'rain_intensity'\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity}\"\n \"={rain_observation}\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_intensity=rain_intensity,\n rain_observation=float(row[1]),\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\n # plus the depth (m) per rainfall interval (min)\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_excess}\"\n \"={rain_intensity}\"\n \"+{depth}\"\n \"/1000.\"\n \"/{rain_interval}\"\n \"*60.\".format(\n rain_excess=rain_excess,\n rain_intensity=rain_intensity,\n depth=depth,\n rain_interval=self.rain_interval),\n overwrite=True)\n\n # update excess rainfall\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{rain_intensity} = {rain_excess}\".format(\n rain_intensity='rain_intensity',\n rain_excess=rain_excess),\n overwrite=True)\n evol.rain_intensity = rain_intensity\n\n # determine mode and run model\n if self.mode == \"simwe_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.erosion_deposition()\n # remove relative timestamps\n # from r.sim.water and r.sim.sediment\n gscript.run_command(\n 'r.timestamp',\n map=depth,\n date='none')\n gscript.run_command(\n 'r.timestamp',\n map=erosion_deposition,\n date='none')\n\n elif self.mode == \"usped_mode\":\n (evolved_elevation, time, depth, erosion_deposition,\n difference) = evol.usped()\n\n elif self.mode == \"rusle_mode\":\n (evolved_elevation, time, depth, sediment_flux,\n difference) = evol.rusle()\n\n else:\n raise RuntimeError(\n '{mode} mode does not exist').format(mode=self.mode)\n\n # register the evolved maps\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.elevation_timeseries,\n maps=evolved_elevation,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.depth_timeseries,\n maps=depth,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.erdep_timeseries,\n maps=erosion_deposition,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n except (NameError, CalledModuleError):\n pass\n try:\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.flux_timeseries,\n maps=sediment_flux,\n start=evol.start,\n increment=increment,\n flags='i', overwrite=True)\n except (NameError, CalledModuleError):\n pass\n gscript.run_command(\n 't.register',\n type=raster,\n input=self.difference_timeseries,\n maps=difference,\n start=evol.start,\n increment=increment,\n flags='i',\n overwrite=True)\n\n # remove temporary maps\n gscript.run_command(\n 'g.remove',\n type='raster',\n name=['rain_excess'],\n flags='f')\n\n # compute net elevation change\n gscript.run_command(\n 'r.mapcalc',\n expression=\"{net_difference}\"\n \"= {evolved_elevation}-{elevation}\".format(\n net_difference=net_difference,\n elevation=self.elevation,\n evolved_elevation=evol.elevation),\n overwrite=True)\n gscript.write_command(\n 'r.colors',\n map=net_difference,\n rules='-',\n stdin=difference_colors)","def SAM_CSV_to_solar_data(filename):\n if not os.path.isfile(filename):\n raise FileNotFoundError(filename + \" does not exist.\")\n wfd = defaultdict(list)\n with open(filename) as file_in:\n info = []\n for i in range(2):\n info.append(file_in.readline().rstrip())\n info[i] = info[i].split(\",\")\n if \"Time Zone\" not in info[0]:\n raise ValueError(\"`Time Zone` field not found in solar resource file.\")\n latitude = info[1][info[0].index(\"Latitude\")]\n longitude = info[1][info[0].index(\"Longitude\")]\n tz = info[1][info[0].index(\"Time Zone\")]\n elev = info[1][info[0].index(\"Elevation\")]\n source = info[1][info[0].index(\"Source\")]\n reader = csv.DictReader(file_in)\n for row in reader:\n for col, dat in row.items():\n if len(col) > 0:\n wfd[col].append(float(dat))\n\n weather = dict()\n weather['tz'] = float(tz)\n weather['elev'] = float(elev)\n weather['lat'] = float(latitude)\n weather['lon'] = float(longitude)\n\n # Create dict with keys = keys passed to SAM and values = list of possible key versions found in resource files (NREL / NASA POWER)\n acceptable_keys = {\n 'year' : ['year', 'Year', 'yr'],\n 'month' : ['month', 'Month', 'mo'],\n 'day' : ['day', 'Day'],\n 'hour' : ['hour', 'Hour', 'hr'],\n 'minute' : ['minute', 'Minute', 'min'],\n 'dn' : ['dn', 'DNI','dni', 'beam', 'direct normal', 'direct normal irradiance'],\n 'df' : ['df', 'DHI', 'dhi', 'diffuse', 'diffuse horizontal', 'diffuse horizontal irradiance'],\n 'gh' : ['gh', 'GHI','ghi', 'global', 'global horizontal', 'global horizontal irradiance'],\n 'wspd' : ['wspd', 'Wind Speed', 'wind speed'],\n 'tdry' : ['tdry', 'Temperature', 'dry bulb', 'dry bulb temp', 'temperature', 'ambient', 'ambient temp'],\n 'wdir' : ['wdir', 'Wind Direction', 'wind direction'],\n 'pres' : ['pres', 'Pressure', 'pressure'],\n 'tdew' : ['tdew', 'Dew Point', 'Tdew', 'dew point', 'dew point temperature'],\n 'rhum' : ['rhum', 'Relative Humidity', 'rh', 'RH', 'relative humidity', 'humidity'],\n 'alb' : ['alb', 'Surface Albedo', 'albedo', 'surface albedo'],\n 'snow' : ['snow', 'Snow Depth', 'snow depth', 'snow cover']\n }\n \n # enumerates acceptable_keys, inserts key and values into weather dictionary if found in the resource file\n for key, list_of_keys in acceptable_keys.items():\n for good_key in list_of_keys:\n if good_key in wfd.keys():\n weather[key] = wfd.pop(good_key)\n break\n\n # handles averaged hourly data with no minute column provided by NASA POWER and removes 2/29 data for leap years\n # this is a workaround so PySAM/SAM processes as instantaneous data (not setup to handle no minute column)\n if source == 'NASA/POWER':\n weather['minute'] = [30] * len(weather['hour'])\n if len(weather['hour']) == 8784:\n for key in weather.keys():\n if key not in ['tz','elev','lat','lon']:\n del weather[key][1416:1440]\n\n\n return weather","def main():\n\n PARSER = argparse.ArgumentParser()\n PARSER.add_argument('-TIC', default=307210830, type=str)\n PARSER.add_argument('-from_file', default='any')\n PARSER.add_argument('-tic_id', default='TIC_ID')\n PARSER.add_argument('-path_plots', default='.')\n PARSER.add_argument('-interactive', action='store_true', default=False)\n PARSER.add_argument('-no_detrend', action='store_true', default=False)\n PARSER.add_argument('-window', default=1501, type=int)\n PARSER.add_argument('-GP', default=False, action='store_true')\n PARSER.add_argument('-from_fits', default=False, action='store_true')\n PARSER.add_argument('-path_fits', default=None)\n PARSER.add_argument('-noTLS', default=False, action='store_true')\n PARSER.add_argument('-tofile', default=False, action='store_true')\n PARSER.add_argument('-tojuliet', default=False, action='store_true')\n\n ARGS = PARSER.parse_args()\n\n if ARGS.from_file != 'any':\n TICs = []\n if os.path.isfile(ARGS.from_file):\n try:\n TICs = ascii.read(ARGS.from_file)[ARGS.tic_id]\n except KeyError:\n print('Column %s does not exist' % ARGS.tic_id)\n else:\n print('Filename does not exist')\n\n else:\n TICs = [float(i) for i in ARGS.TIC.split(',')]\n\n detrend = not ARGS.no_detrend\n path_plots = ARGS.path_plots\n interactive = ARGS.interactive\n window = ARGS.window\n gp = ARGS.GP\n from_fits = ARGS.from_fits\n path_fits = ARGS.path_fits\n noTLS = ARGS.noTLS\n tofile = ARGS.tofile\n tojuliet = ARGS.tojuliet\n\n if len(TICs) > 0:\n for i, TIC in enumerate(TICs):\n try:\n print('TIC %d (%d/%d)' % (TIC, i+1, len(TICs)))\n _ = get_lk(TIC, path_plots=path_plots, detrend=detrend, interactive=interactive,\n window=window, gp_detrend=gp, from_fits=from_fits, path_fits=path_fits,\n noTLS=noTLS, tofile=tofile, tojuliet=tojuliet)\n except Exception as e:\n print('Problem with object TIC %d' % TIC)\n _, _, exc_tb = sys.exc_info()\n print('line %d: %s' % (exc_tb.tb_lineno, e))","def get_sn2018kzr(colorplt = False):\n ebv = 0.113/3.1\n z = 0.053\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\n dis_mod = 5*np.log10(D / 10)\n t_max = 58480.422+0.1\n \n f = open('../data/otherSN/Mcbrien2019/table1.tex')\n lines = f.readlines()\n f.close()\n lines = lines[:-4]\n \n dates = [x.split(\"&\")[0] for x in lines]\n mjds = [float(x.split(\"&\")[1]) for x in lines]\n phases = [float(x.split(\"&\")[2].replace('$', '').replace('\\t', '')) for x in lines]\n gs = [x.split(\"&\")[3].replace('$', '') for x in lines]\n rs = [x.split(\"&\")[4].replace('$', '') for x in lines]\n iis = [x.split(\"&\")[5].replace('$', '') for x in lines]\n zs = [x.split(\"&\")[6].replace('$', '') for x in lines]\n insts = [x.split(\"&\")[7] for x in lines]\n \n dtg = digital_latex(mjds, phases, gs, insts)\n dtr = digital_latex(mjds, phases, rs, insts)\n dti = digital_latex(mjds, phases, iis, insts)\n \n filt = np.hstack([np.repeat(\"g\", len(dtg[0])),\n np.repeat(\"r\", len(dtr[0])),\n np.repeat(\"i\", len(dti[0]))])\n phase = np.hstack([dtg[1], dtr[1], dti[1]])\n mag = np.hstack([dtg[2], dtr[2], dti[2]])\n emag = np.hstack([dtg[3], dtr[3], dti[3]])\n mjd = np.hstack([dtg[0], dtr[0], dti[0]])\n \n tb = Table(data = [(mjd - t_max) / (1+z), mag, emag, filt],\n names = ['tmax_rf', 'mag', 'emag', 'filter'])\n \n ixr = tb['filter'] == \"r\"\n ixg = tb['filter'] == \"g\"\n ixi = tb['filter'] == \"i\"\n tb['wave'] = np.zeros(len(tb))\n tb['wave'][ixg] = 4814\n tb['wave'][ixr] = 6422\n tb['wave'][ixi] = 7883\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'], 3.1*ebv, 3.1)\n tb['mag0_abs'] = tb['mag0'] - dis_mod\n tb = tb.to_pandas()\n return tb","def main():\n # files\n summary_file = sys.argv[1]\n pwms_to_tfs_file = sys.argv[2]\n expressed_tfs_file = sys.argv[3] # TODO\n\n # TODO pull in num regions to resize things? but complicated with overlaps etc\n # TODO edit edges with type of interaction\n # TODO may want to color by trajectory, to demonstrate waves of trajectory\n \n # read in data\n summary = pd.read_csv(summary_file, sep=\"\\t\")\n pwms_to_tfs = pd.read_csv(pwms_to_tfs_file, sep=\"\\t\")\n pwms_to_tfs = pwms_to_tfs[pwms_to_tfs[\"expressed\"].notna()]\n pwms_to_filt_tfs = {} # dict: key - pwm names, vals - dict of ensembl ids to hgnc ids\n for line_idx in range(pwms_to_tfs.shape[0]):\n pwm_info = pwms_to_tfs.iloc[line_idx,:]\n pwm_name = pwm_info[\"hclust_model_name\"]\n pwm_to_tf = dict(zip(pwm_info[\"expressed\"].split(\";\"), pwm_info[\"expressed_hgnc\"].split(\";\")))\n pwms_to_filt_tfs[pwm_name] = pwm_to_tf\n\n \n # filter expressed hgncs for dynamic ones only\n tfs_filt = pd.read_csv(expressed_tfs_file, sep=\"\\t\", index_col=0)\n for pwm_name in pwms_to_filt_tfs.keys():\n tfs_tmp = pwms_to_filt_tfs[pwm_name]\n for ensembl_tf in tfs_tmp.keys():\n if ensembl_tf not in tfs_filt.index:\n del tfs_tmp[ensembl_tf]\n if len(tfs_tmp.keys()) == 0:\n del pwms_to_filt_tfs[pwm_name]\n pwms_to_filt_tfs[pwm_name] = tfs_tmp\n\n # add in tfs column\n tf1 = []\n for pwm in summary[\"pwm1\"]:\n tf_str = []\n for ensembl_id in pwms_to_filt_tfs[pwm]:\n tf_str.append(pwms_to_filt_tfs[pwm][ensembl_id])\n # TODO try add in max point\n expression = tfs_filt.loc[ensembl_id,:]\n max_idx = np.argmax(expression.values)\n tf_str.append(str(max_idx))\n tf_str = (\";\").join(tf_str)\n tf1.append(tf_str)\n summary[\"tf1\"] = tf1\n\n tf2 = []\n for pwm in summary[\"pwm2\"]:\n tf_str = []\n for ensembl_id in pwms_to_filt_tfs[pwm]:\n tf_str.append(pwms_to_filt_tfs[pwm][ensembl_id])\n expression = tfs_filt.loc[ensembl_id,:]\n max_idx = np.argmax(expression.values)\n tf_str.append(str(max_idx))\n tf_str = (\";\").join(tf_str)\n tf2.append(tf_str)\n summary[\"tf2\"] = tf2\n \n # remove failed rules\n summary = summary[~summary[\"interaction\"].str.contains(\"FAILED\")]\n \n # make graph\n graph = nx.from_pandas_edgelist(summary, \"tf1\", \"tf2\")\n\n # set up positions\n #pos = graphviz_layout(graph, prog=\"dot\")\n pos = graphviz_layout(graph, prog=\"neato\")\n scale_factor = 3\n for key in pos.keys():\n coords = pos[key]\n pos[key] = {\"x\": scale_factor*coords[0], \"y\": -scale_factor*coords[1]}\n nx.set_node_attributes(graph, pos, \"graphics\") # note this is diff from v1 to v2 in networkx\n \n # add graphics\n add_graphics_theme_to_nx_graph(graph)\n\n # write gml\n out_file = \"summary.gml\"\n nx.write_gml(stringize_nx_graph(graph), out_file, stringizer=str)\n\n # tfs: for each tf, get gene column\n \n \n return","def wem_market_value_all():\n __query = \"\"\"\n select\n date_trunc('month', wfs.trading_interval) AS trading_day,\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\n wf.fueltech_id\n from wem_facility_scada wfs\n left join wem_facility wf on wfs.facility_id = wf.code\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\n where\n wf.fueltech_id is not null\n group by 1, wf.fueltech_id\n order by 1 desc, 2 asc\n \"\"\"\n\n query = __query.format()\n\n json_envelope = {}\n\n with engine.connect() as c:\n rows = c.execute(query)\n\n current_tech = None\n\n for row in rows:\n\n current_tech = row[2]\n\n if current_tech not in json_envelope.keys():\n json_envelope[current_tech] = {\n \"id\": f\"wem.fuel_tech.{current_tech}.market_value\",\n \"fuel_tech\": current_tech,\n \"region\": \"wa\",\n \"type\": \"market_value\",\n \"units\": \"AUD\",\n \"history\": {\n \"interval\": \"1M\",\n \"start\": None,\n \"last\": None,\n \"data\": [],\n },\n }\n\n if (\n json_envelope[current_tech][\"history\"][\"start\"] == None\n or row[0] < json_envelope[current_tech][\"history\"][\"start\"]\n ):\n json_envelope[current_tech][\"history\"][\"start\"] = row[0]\n\n if (\n json_envelope[current_tech][\"history\"][\"last\"] == None\n or row[0] > json_envelope[current_tech][\"history\"][\"last\"]\n ):\n json_envelope[current_tech][\"history\"][\"last\"] = row[0]\n\n json_envelope[current_tech][\"history\"][\"data\"].append(row[1])\n\n return [json_envelope[i] for i in json_envelope.keys()]","def calculate_surface_heatflux(self, weather, spaces_dict, surface, temp_record, Coeff, space, h_surface, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, Aflag, terrain, areaDict, areaWinDict, shadowRatios, shadowRatioIndex):\r\n #print \"Reaching Surface function...\"\r\n\r\n # First get the As\r\n A_total = self.get_A(surface, areaDict, areaWinDict)\r\n if Aflag == 0:\r\n # If it is the first surface of the space, label the space ID in the log file:\r\n la = str(surface.obj_id)\r\n lb = str(surface.obj_type)\r\n #TM_user.info(\"%s,surface area,%s,%s\" % (la, A_total, lb))\r\n A_noWin = self.get_A_noWin(surface, areaDict, areaWinDict)\r\n A_noOp = self.get_A_noOp(surface, areaDict, areaWinDict)\r\n T_space = spaces_dict[space.obj_id][1]\r\n T1 = weather[\"t_outside\"]\r\n hc_external = float(self.get_hc_external(weather, surface, h_surface, terrain))\r\n transmitted_win = 0\r\n Q_flux = 0\r\n\r\n # need the surface related information, T_space, U, R3\r\n U = self.get_U_surface_e(A_total, A_noOp, surface, areaWinDict) # U = Infor_surface{11,i_surface}; Defined Below\r\n #print U\r\n R3 = 1/U\r\n # Using calculations from: self.surface.constr.layer.C # Infor_surface{10, i_surface} ; from gbXML\r\n C = self.get_C_surface(A_total, A_noOp, surface, Coeff, areaWinDict) # need to pass surface and opening ids\r\n #print C\r\n\r\n temperature = Temperature()\r\n\r\n #Sub-routines for each wall type based on the returned hc_external\r\n # This hc is different for each surface type so moved under this sub-routine area\r\n #hc = 3.076 sent this to the Temperature Object\r\n if surface.obj_type == \"ExteriorWall\":\r\n transmitted_win, Q_flux = temperature.exterior_wall(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\r\n #print Q_flux\r\n if surface.obj_type == \"Roof\":\r\n transmitted_win, Q_flux = temperature.roof(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\r\n #print Q_flux # Matches for Four Room\r\n if surface.obj_type == \"InteriorWall\":\r\n transmitted_win, Q_flux = temperature.interior_wall(surface, A_total, R3, C, spaces_dict, T_space, temp_record)\r\n #print Q_flux # Matches for Four Room\r\n if surface.obj_type == \"UndergroundWall\":\r\n transmitted_win, Q_flux = temperature.underground_wall(surface, A_total, R3, C, T_space, temp_record) # No instance of yet to test\r\n if surface.obj_type == \"RaisedFloor\":\r\n # This will eventually need some values when we start using raised floors\r\n transmitted_win, Q_flux = temperature.raised_floor(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record) # Not instance of yet to test\r\n\r\n return transmitted_win, Q_flux","def test_convert_azfp_01a_notemperature_notilt(azfp_path):\n azfp_01a_path = azfp_path / 'rutgers_glider_notemperature/22052500.01A'\n azfp_xml_path = azfp_path / 'rutgers_glider_notemperature/22052501.XML'\n\n echodata = open_raw(\n raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path\n )\n\n # Temperature variable is present in the Environment group and its values are all nan\n assert \"temperature\" in echodata[\"Environment\"]\n assert echodata[\"Environment\"][\"temperature\"].isnull().all()\n\n # Tilt variables are present in the Platform group and their values are all nan\n assert \"tilt_x\" in echodata[\"Platform\"]\n assert \"tilt_y\" in echodata[\"Platform\"]\n assert echodata[\"Platform\"][\"tilt_x\"].isnull().all()\n assert echodata[\"Platform\"][\"tilt_y\"].isnull().all()","def epflux_all(U, V, W, T, longitude, latitude, press, boa=None):\n pass","def do_qc(fn, df, year):\n (lon, lat) = fn2lonlat(fn)\n stage4 = compute_stage4(lon, lat, year)\n # Does the frame appear to have all dates?\n if len(df.index) != len(df.resample(\"D\").mean().index):\n print(\"ERROR: Appears to be missing dates!\")\n\n if open(fn).read()[-1] != \"\\n\":\n print(\"ERROR: File does not end with \\\\n\")\n\n print(\"--------- Summary stats from the .cli file\")\n print(\"YEAR | RAIN | MAXRATE | MAXACC | #DAYS | #>1RT | RAD/D\")\n print(\" --- | --- | --- | --- | --- | --- | ---\")\n for _year, gdf in df.groupby(by=df.index.year):\n print(\n (\"%s | %6.2f | %7.2f | %7.2f | %6i | %6i | %6.0f\")\n % (\n _year,\n mm2inch(gdf[\"pcpn\"].sum()),\n mm2inch(gdf[\"maxr\"].max()),\n mm2inch(gdf[\"pcpn\"].max()),\n len(gdf[gdf[\"pcpn\"] > 0].index),\n len(gdf[gdf[\"maxr\"] > 25.4].index),\n gdf[\"rad\"].mean(),\n )\n )\n\n print(\"---- Months with < 0.05 precipitation ----\")\n gdf = df.groupby(by=[df.index.year, df.index.month])[\"pcpn\"].sum()\n print(gdf[gdf < 1.0])\n\n print(\"----- Average high temperature -----\")\n print(\"YEAR | Avg High F | Avg Low F | Days > 100F\")\n print(\" --- | --- | --- | ---\")\n for _year, gdf in df.groupby(by=df.index.year):\n print(\n (\"%s | %6.2f | %6.2f | %3i\")\n % (\n _year,\n c2f(gdf[\"tmax\"].mean()),\n c2f(gdf[\"tmin\"].mean()),\n len(gdf[gdf[\"tmax\"] > 37.7].index),\n )\n )\n\n monthly = df[df.index.year == year][\"pcpn\"].resample(\"M\").sum().copy()\n monthly = pd.DataFrame(\n {\"dep\": mm2inch(monthly.values)}, index=range(1, 13)\n )\n\n # Get prism, for a bulk comparison\n prism = requests.get(\n (\n \"http://mesonet.agron.iastate.edu/json/prism/\"\n \"%.2f/%.2f/%s0101-%s1231\"\n )\n % (lon, lat, year, year)\n ).json()\n rows = []\n for entry in prism[\"data\"]:\n rows.append(\n {\n \"date\": datetime.datetime.strptime(\n entry[\"valid\"][:10], \"%Y-%m-%d\"\n ),\n \"precip\": entry[\"precip_in\"],\n }\n )\n prismdf = pd.DataFrame(rows)\n prismdf = prismdf.set_index(\"date\")\n monthly[\"prism\"] = prismdf[\"precip\"].resample(\"M\").sum().copy().values\n\n # Compare daily values\n iemjson = requests.get(\n (\n \"http://mesonet.agron.iastate.edu/iemre/multiday/\"\n \"%s-01-01/%s-12-31/%s/%s/json\"\n )\n % (year, year, lat, lon)\n ).json()\n rows = []\n for entry in iemjson[\"data\"]:\n rows.append(\n {\n \"date\": datetime.datetime.strptime(entry[\"date\"], \"%Y-%m-%d\"),\n \"precip\": entry[\"daily_precip_in\"],\n }\n )\n iemdf = pd.DataFrame(rows)\n iemdf = iemdf.set_index(\"date\")\n print(\"PRISM %s precip is: %.2f\" % (year, prismdf[\"precip\"].sum()))\n print(\"IEMRE sum precip is: %.2f\" % (iemdf[\"precip\"].sum(),))\n print(\"StageIV sum precip is: %.2f\" % (stage4[\"precip\"].sum(),))\n monthly[\"stage4\"] = stage4[\"precip\"].resample(\"M\").sum().copy().values\n monthly[\"iemre\"] = iemdf[\"precip\"].resample(\"M\").sum().copy().values\n monthly[\"prism-dep\"] = monthly[\"prism\"] - monthly[\"dep\"]\n monthly[\"iemre-dep\"] = monthly[\"iemre\"] - monthly[\"dep\"]\n\n print(\" --------- %s Monthly Totals --------\" % (year,))\n print(monthly)\n df.at[\n slice(datetime.date(year, 1, 1), datetime.date(year, 12, 31)),\n \"stage4_precip\",\n ] = stage4[\"precip\"].values\n df[\"iemre_precip\"] = iemdf[\"precip\"]\n df[\"diff_precip\"] = df[\"pcpn_in\"] - df[\"iemre_precip\"]\n df[\"diff_stage4\"] = df[\"pcpn_in\"] - df[\"stage4_precip\"]\n print(\" --- Top 5 Largest DEP > IEMRE ----\")\n print(\n df[\n [\n \"diff_precip\",\n \"pcpn_in\",\n \"iemre_precip\",\n \"stage4_precip\",\n \"diff_stage4\",\n ]\n ]\n .sort_values(by=\"diff_precip\", ascending=False)\n .head()\n )\n print(\" --- Top 5 Largest IEMRE > DEP ----\")\n print(\n df[\n [\n \"diff_precip\",\n \"pcpn_in\",\n \"iemre_precip\",\n \"stage4_precip\",\n \"diff_stage4\",\n ]\n ]\n .sort_values(by=\"diff_precip\", ascending=True)\n .head()\n )\n\n print(\" --- Top 10 Largest Stage4 > DEP ----\")\n print(\n df[\n [\n \"diff_precip\",\n \"pcpn_in\",\n \"iemre_precip\",\n \"stage4_precip\",\n \"diff_stage4\",\n ]\n ]\n .sort_values(by=\"diff_stage4\", ascending=True)\n .head(10)\n )\n print(\" vvv job listing based on the above vvv\")\n for dt in df.sort_values(by=\"diff_stage4\", ascending=True).head(10).index:\n print(\n \"python daily_clifile_editor.py 0 %s %s %s\"\n % (dt.year, dt.month, dt.day)\n )\n df2 = df.loc[slice(datetime.date(year, 1, 1), datetime.date(year, 1, 31))][\n [\"diff_precip\", \"pcpn_in\", \"iemre_precip\", \"stage4_precip\"]\n ].sort_values(by=\"diff_precip\")\n print(\" --- Daily values for month \" \"\")\n print(df2)","def get_ta_data(path_data):\n if file_exist(path_data) == 0:\n df = ts.pro_bar(ts_code=ts_code_str, adj='qfq', start_date=dt_baseDeltaValue_start_str,\n end_date=dt_now_str, freq=freq_label, ma=[3, 5, 55])\n # print(df.head())\n\n df['delta'] = df['ma3'] - df['ma5']\n writer = pd.ExcelWriter(path_data)\n # print(type(df).__name__)\n if type(df).__name__ == 'DataFrame':\n df.to_excel(writer, sheet_name='Data', index=False)\n writer.save()","def getData(self, indexSelect):\n csv_filename = '../ExpDataFiles.csv'\n fieldnames = ['index', 'filename', 'sample', 'shear']\n\n parameters = files_list_reduce(csv_filename, fieldnames)\n fileName, sample, shear = files_to_reduce(parameters, indexSelect)\n\n self.setName = '../2D_data_sub/' + fileName[0]\n\n self.expData_raw = np.loadtxt(self.setName, delimiter=\" \", skiprows=self.skipRows)\n bsq = np.sqrt(self.expData_raw[:, 0]**2 + self.expData_raw[:, 1]**2)\n self.beamStop = (bsq < self.qmin)\n self.expData_bs = self.expData_raw[~self.beamStop]\n\n # Removing detector shadow\n\n qx_neg_lower = -0.019\n qx_neg_upper = -0.012\n qx_pos_lower = 0.021\n qx_pos_upper = 0.029\n\n qy_lower = -0.014\n qy_upper = -0.011\n\n q_min = -0.062\n q_max = -0.0539\n\n qx_neg = np.logical_and(self.expData_bs[:, 0] <\n qx_neg_upper, self.expData_bs[:, 0] > qx_neg_lower)\n data_qx_neg = self.expData_bs[~qx_neg]\n\n qx_neg = np.logical_and(data_qx_neg[:, 0] <\n q_max, data_qx_neg[:, 0] > q_min)\n data_qx_neg = data_qx_neg[~qx_neg]\n\n qx_pos = np.logical_and(data_qx_neg[:, 0] < qx_pos_upper, data_qx_neg[:, 0] > qx_pos_lower)\n data_qx_pos = data_qx_neg[~qx_pos]\n\n qy_qxRange = np.logical_and(\n data_qx_pos[:, 0] < qx_pos_lower, data_qx_pos[:, 0] > qx_neg_upper)\n qyRange = np.logical_and(data_qx_pos[:, 1] < qy_upper, data_qx_pos[:, 1] > qy_lower)\n qy_mask = np.logical_and(qy_qxRange == True, qyRange == True)\n\n self.expData_masked = data_qx_pos[~qy_mask]\n\n # End removing detector shadow\n\n self.expData_sort = np.array(sorted(self.expData_masked, key=lambda col: (col[1], col[0])))\n\n self.qx_unique = np.unique(np.array(self.expData_sort[:, 0]))\n self.qy_unique = np.unique(np.array(self.expData_sort[:, 1]))\n\n# self.expData_sort = abs(self.expData_sort)\n\n # testing resolution\n self.expData = sasmodels.data.Data2D(x=self.expData_sort[:, 0], dx=0.1*abs(self.expData_sort[:, 0]), y=self.expData_sort[:, 1], dy=0.1*abs(\n self.expData_sort[:, 1]), z=self.expData_sort[:, 2], dz=self.expData_sort[:, 3])\n self.expData.sample = sample\n self.expData.shear = shear\n\n return","def optimize_trap(dg):\n f_peak = './temp_peak.lh5' # lh5\n f_results = './temp_results.h5' # pandas\n grp_data, grp_grid = '/optimize_data', '/optimize_grid'\n \n # epar, elo, ehi, epb = 'energy', 0, 1e7, 10000 # full range\n epar, elo, ehi, epb = 'energy', 3.88e6, 3.92e6, 500 # K40 peak\n \n show_movie = True\n write_output = True\n n_rows = None # default None\n \n with open('opt_trap.json') as f:\n dsp_config = json.load(f, object_pairs_hook=OrderedDict)\n \n # files to consider. fixme: right now only works with one file\n sto = lh5.Store()\n lh5_dir = os.path.expandvars(dg.config['lh5_dir'])\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\n f_raw = raw_list.values[0] \n tb_raw = 'ORSIS3302DecoderForEnergy/raw/'\n\n # quick check of the energy range\n # ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\n # plt.plot(bins[1:], hist, ds='steps')\n # plt.show()\n # exit()\n \n # set grid parameters\n # TODO: jason's suggestions, knowing the expected shape of the noise curve\n # e_rises = np.linspace(-1, 0, sqrt(sqrt(3))\n # e_rises # make another list which is 10^pwr of this list\n # np.linspace(log_tau_min, log_tau_max) # try this too\n e_rises = np.arange(1, 12, 1)\n e_flats = np.arange(1, 6, 1)\n # rc_consts = np.arange(54, 154, 10) # changing this here messes up DCR\n \n # -- create the grid search file the first time -- \n # NOTE: this makes a linear grid, and is editable by the arrays above.\n # jason also proposed a more active gradient-descent style search\n # like with Brent's method. (https://en.wikipedia.org/wiki/Brent%27s_method)\n \n if True:\n # if not os.path.exists(f_peak):\n print('Recreating grid search file')\n \n # create the grid file\n # NOTE: save it as an lh5 Table just as an example of writing/reading one\n lists = [e_rises, e_flats]#, rc_consts]\n prod = list(itertools.product(*lists)) # clint <3 stackoverflow\n df_grid = pd.DataFrame(prod, columns=['rise', 'flat'])#,'rc']) \n lh5_grid = {}\n for i, dfcol in df_grid.iteritems():\n lh5_grid[dfcol.name] = lh5.Array(dfcol.values)\n tb_grid = lh5.Table(col_dict=lh5_grid)\n sto.write_object(tb_grid, grp_grid, f_peak)\n \n # filter events by onboard energy\n ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\n # plt.plot(bins[1:], hist, ds='steps')\n # plt.show()\n if n_rows is not None:\n ene_raw = ene_raw[:n_rows]\n idx = np.where((ene_raw > elo) & (ene_raw < ehi))\n\n # create a filtered table with correct waveform and attrs\n # TODO: move this into a function in lh5.py which takes idx as an input\n tb_data, wf_tb_data = lh5.Table(), lh5.Table()\n\n # read non-wf cols (lh5 Arrays)\n data_raw = sto.read_object(tb_raw, f_raw, n_rows=n_rows)\n for col in data_raw.keys():\n if col=='waveform': continue\n newcol = lh5.Array(data_raw[col].nda[idx], attrs=data_raw[col].attrs)\n tb_data.add_field(col, newcol)\n \n # handle waveform column (lh5 Table)\n data_wfs = sto.read_object(tb_raw+'/waveform', f_raw, n_rows=n_rows)\n for col in data_wfs.keys():\n attrs = data_wfs[col].attrs\n if isinstance(data_wfs[col], lh5.ArrayOfEqualSizedArrays):\n # idk why i can't put the filtered array into the constructor\n aoesa = lh5.ArrayOfEqualSizedArrays(attrs=attrs, dims=[1,1])\n aoesa.nda = data_wfs[col].nda[idx]\n newcol = aoesa\n else:\n newcol = lh5.Array(data_wfs[col].nda[idx], attrs=attrs)\n wf_tb_data.add_field(col, newcol)\n tb_data.add_field('waveform', wf_tb_data)\n tb_data.attrs = data_raw.attrs\n sto.write_object(tb_data, grp_data, f_peak)\n\n else:\n print('Loading peak file. groups:', sto.ls(f_peak))\n tb_grid = sto.read_object(grp_grid, f_peak)\n tb_data = sto.read_object(grp_data, f_peak) # filtered file\n # tb_data = sto.read_object(tb_raw, f_raw) # orig file\n df_grid = tb_grid.get_dataframe()\n \n # check shape of input table\n print('input table attributes:')\n for key in tb_data.keys():\n obj = tb_data[key]\n if isinstance(obj, lh5.Table):\n for key2 in obj.keys():\n obj2 = obj[key2]\n print(' ', key, key2, obj2.nda.shape, obj2.attrs)\n else:\n print(' ', key, obj.nda.shape, obj.attrs)\n\n # clear new colums if they exist\n new_cols = ['e_fit', 'fwhm_fit', 'rchisq', 'xF_err', 'fwhm_ovr_mean']\n for col in new_cols:\n if col in df_grid.columns:\n df_grid.drop(col, axis=1, inplace=True)\n\n t_start = time.time()\n def run_dsp(dfrow):\n \"\"\"\n run dsp on the test file, editing the processor list\n alternate idea: generate a long list of processors with different names\n \"\"\"\n # adjust dsp config dictionary\n rise, flat = dfrow\n # dsp_config['processors']['wf_pz']['defaults']['db.pz.tau'] = f'{tau}*us'\n dsp_config['processors']['wf_trap']['args'][1] = f'{rise}*us'\n dsp_config['processors']['wf_trap']['args'][2] = f'{flat}*us'\n # pprint(dsp_config)\n \n # run dsp\n pc, tb_out = build_processing_chain(tb_data, dsp_config, verbosity=0)\n pc.execute()\n \n # analyze peak\n e_peak = 1460.\n etype = 'trapEmax'\n elo, ehi, epb = 4000, 4500, 3 # the peak moves around a bunch\n energy = tb_out[etype].nda\n \n # get histogram\n hE, bins, vE = pgh.get_hist(energy, range=(elo, ehi), dx=epb)\n xE = bins[1:]\n \n # should I center the max at 1460?\n\n # simple numerical width\n i_max = np.argmax(hE)\n h_max = hE[i_max]\n upr_half = xE[(xE > xE[i_max]) & (hE <= h_max/2)][0]\n bot_half = xE[(xE < xE[i_max]) & (hE >= h_max/2)][0]\n fwhm = upr_half - bot_half\n sig = fwhm / 2.355\n \n # fit to gaussian: amp, mu, sig, bkg\n fit_func = pgf.gauss_bkg\n amp = h_max * fwhm\n bg0 = np.mean(hE[:20])\n x0 = [amp, xE[i_max], sig, bg0]\n xF, xF_cov = pgf.fit_hist(fit_func, hE, bins, var=vE, guess=x0)\n\n # collect results\n e_fit = xF[0]\n xF_err = np.sqrt(np.diag(xF_cov))\n e_err = xF\n fwhm_fit = xF[1] * 2.355 * 1460. / e_fit\n \n fwhm_err = xF_err[2] * 2.355 * 1460. / e_fit\n \n chisq = []\n for i, h in enumerate(hE):\n model = fit_func(xE[i], *xF)\n diff = (model - h)**2 / model\n chisq.append(abs(diff))\n rchisq = sum(np.array(chisq) / len(hE))\n fwhm_ovr_mean = fwhm_fit / e_fit\n\n if show_movie:\n \n plt.plot(xE, hE, ds='steps', c='b', lw=2, label=f'{etype} {rise}--{flat}')\n\n # peak shape\n plt.plot(xE, fit_func(xE, *x0), '-', c='orange', alpha=0.5,\n label='init. guess')\n plt.plot(xE, fit_func(xE, *xF), '-r', alpha=0.8, label='peakshape fit')\n plt.plot(np.nan, np.nan, '-w', label=f'mu={e_fit:.1f}, fwhm={fwhm_fit:.2f}')\n\n plt.xlabel(etype, ha='right', x=1)\n plt.ylabel('Counts', ha='right', y=1)\n plt.legend(loc=2)\n\n # show a little movie\n plt.show(block=False)\n plt.pause(0.01)\n plt.cla()\n\n # return results\n return pd.Series({'e_fit':e_fit, 'fwhm_fit':fwhm_fit, 'rchisq':rchisq,\n 'fwhm_err':xF_err[0], 'fwhm_ovr_mean': fwhm_ovr_mean})\n \n # df_grid=df_grid[:10]\n df_tmp = df_grid.progress_apply(run_dsp, axis=1)\n df_grid[new_cols] = df_tmp\n # print(df_grid)\n \n if show_movie:\n plt.close()\n \n print('elapsed:', time.time() - t_start)\n if write_output:\n df_grid.to_hdf(f_results, key=grp_grid)\n print(f\"Wrote output file: {f_results}\")","def zonal_stats_workflow():\n save_as = \"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/summary/monthly_quickflow.csv\"\n scenario_dict = {\n 'pre-decline': \"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/pre_decline\",\n 'post-decline': \"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/post_decline\",\n }\n df_list = []\n for scenario in scenario_dict.iterkeys():\n results_dict = {\n 'scenario': [],\n 'month': [],\n 'sum_quickflow': [],\n }\n folder = scenario_dict[scenario]\n aoi_shp = os.path.join(folder, 'aggregated_results.shp')\n for month in xrange(1, 13):\n qf_raster = os.path.join(\n folder, 'intermediate_outputs', 'qf_{}.tif'.format(month))\n zonal_stats = pygeoprocessing.zonal_statistics(\n (qf_raster, 1), aoi_shp)\n sum_QF = zonal_stats[0]['sum']\n results_dict['scenario'].append(scenario)\n results_dict['month'].append(month)\n results_dict['sum_quickflow'].append(sum_QF)\n results_df = pandas.DataFrame(data=results_dict)\n df_list.append(results_df)\n combined_list = pandas.concat(df_list)\n combined_list.to_csv(save_as, index=False)","def getHFtableData(self, ep=None):\n HFdict = {}\n if self.hfMode == 'limiter':\n HFdict['Heat Flux Mode'] = 'Limiter'\n if self.lqCNmode == 'eich':\n HFdict[\"\\u03BB Near Mode\"] = 'Eich Regression #15'\n HFdict[\"Common Region Near Heat Flux Width (\\u03BBq CN) [mm]\"] = self.lqEich\n else:\n HFdict[\"\\u03BB Near Mode\"] = 'User Defined'\n HFdict[\"Common Region Near Heat Flux Width (\\u03BBq CN) [mm]\"] = self.lqCN\n if self.lqCFmode == 'horacek':\n HFdict[\"\\u03BB Far Mode\"] = 'Horacek Figure 6a'\n HFdict[\"Common Region Far Heat Flux Width (\\u03BBq CF) [mm]\"] = self.lqCF\n else:\n HFdict[\"\\u03BB Far Mode\"] = 'User Defined'\n HFdict[\"Common Region Far Heat Flux Width (\\u03BBq CF) [mm]\"] = self.lqCF\n\n HFdict[\"Common Region Near Power Fraction\"] = self.fracCN\n HFdict[\"Common Region Far Power Fraction\"] = self.fracCF\n\n elif self.hfMode == 'multiExp':\n HFdict['Heat Flux Mode'] = 'Multiple (4) Exponentials'\n if self.lqCNmode == 'eich':\n HFdict[\"\\u03BB Near Mode\"] = 'Eich Regression #15'\n HFdict[\"Common Region Near Heat Flux Width (\\u03BBq CN) [mm]\"] = self.lqEich\n else:\n HFdict[\"\\u03BB Near Mode\"] = 'User Defined'\n HFdict[\"Common Region Near Heat Flux Width (\\u03BBq CN) [mm]\"] = self.lqCN\n\n if self.lqCFmode == 'horacek':\n HFdict[\"\\u03BB Far Mode\"] = 'Horacek Figure 6a'\n else:\n HFdict[\"\\u03BB Far Mode\"] = 'User Defined'\n\n\n\n HFdict[\"Common Region Far Heat Flux Width (\\u03BBq CF) [mm]\"] = self.lqCF\n HFdict[\"Private Region Near Heat Flux Width (\\u03BBq PN) [mm]\"] = self.lqPN\n HFdict[\"Private Region Far Heat Flux Width (\\u03BBq PF) [mm]\"] = self.lqPF\n HFdict[\"Common Region Near Power Fraction\"] = self.fracCN\n HFdict[\"Common Region Far Power Fraction\"] = self.fracCF\n HFdict[\"Private Region Near Power Fraction\"] = self.fracPN\n HFdict[\"Private Region Far Power Fraction\"] = self.fracPF\n\n elif self.hfMode == 'qFile':\n HFdict[\"Heat Flux Mode\"] = 'Read HF from qFile'\n HFdict['qFilePath'] = self.qFilePath\n HFdict['qFileTag'] = self.qFileTag\n\n elif self.hfMode == 'eich':\n HFdict['Heat Flux Mode'] = 'Gaussian Spreading'\n if self.lqCNmode == 'eich':\n HFdict[\"\\u03BB Mode\"] = 'Eich Regression #15'\n HFdict[\"Heat Flux Width (\\u03BBq) [mm]\"] = self.lqEich\n else:\n HFdict[\"\\u03BB Mode\"] = 'User Defined'\n HFdict[\"Heat Flux Width (\\u03BBq) [mm]\"] = self.lqCN\n\n if self.SMode == 'makowski':\n HFdict['Greenwald Density Fraction'] = self.fG\n HFdict['Spreading (S) Mode'] = 'Makowski Figure 6'\n else:\n HFdict['Spreading (S) Mode'] = 'User Defined'\n HFdict['Greenwald Density Fraction'] = 'Only used for Makowski S Mode'\n HFdict['S [mm]'] = self.S\n HFdict['Background Heat Flux'] = self.qBG\n\n if self.hfMode != 'qFile':\n HFdict[\"Power Injected (Pinj) [MW]\"] = self.Pinj\n HFdict[\"Radiated Fraction of Injected Power\"] = self.coreRadFrac\n HFdict[\"Power Crossing Separatrix (Psol) [MW]\"] = self.Psol\n HFdict[\"Upper Inner Divertor Power Fraction\"] = self.fracUI\n HFdict[\"Upper Outer Divertor Power Fraction\"] = self.fracUO\n HFdict[\"Lower Inner Divertor Power Fraction\"] = self.fracLI\n HFdict[\"Lower Outer Divertor Power Fraction\"] = self.fracLO\n\n return HFdict","def get_data_iue(obsid, filt):\n\n # This error code will be used unless there's a problem reading any\n # of the FITS files in the list, or the FILTER value is not understood.\n errcode = 0\n\n # This defines a data point for a DataSeries object as a namedtuple.\n data_point = collections.namedtuple('DataPoint', ['x', 'y'])\n\n # For IUE, this defines the x-axis and y-axis units as a string.\n iue_xunit = \"Angstroms (vacuum, heliocentric)\"\n iue_yunit = \"ergs/cm^2/s/Angstrom\"\n\n # Parse the obsID string to determine the paths+files to read. Note:\n # this step will assign some of the error codes returned to the top level.\n if filt == ' ':\n filt = \"UNKNOWN\"\n if filt.upper() in [\"LOW_DISP\", \"HIGH_DISP\"] or filt == \"UNKNOWN\":\n parsed_files_result = parse_obsid_iue(obsid, filt.upper())\n errcode = parsed_files_result.errcode\n else:\n errcode = 4\n\n # In the case of low dispersion spectra, there can be two apertures for\n # a single obsID. In that case, we return a list of TWO DataSeries, one\n # for each aperture. In other words, we treat the single obsID as if it\n # were two different obsIDs in the case of a double-aperture.\n all_data_series = []\n\n # For each file, read in the contents and create a return JSON object.\n if errcode == 0:\n for sfile in parsed_files_result.specfiles:\n # Figure out if this is an mxhi or mxlo spectrum.\n if sfile[-7:] == \"mxlo.gz\":\n is_lo = True\n is_hi = False\n else:\n is_lo = False\n is_hi = True\n\n try:\n with fits.open(sfile) as hdulist:\n if is_lo:\n # Get the dispersion type from the primary header.\n dispersion = hdulist[0].header[\"disptype\"]\n # Get the aperture size(s) from the header.\n apertures = hdulist[1].data[\"aperture\"]\n n_apertures = len(apertures)\n # Number of spectral data points for each aperture size.\n n_wls = [int(x) for x in hdulist[1].data[\"npoints\"]]\n # Initial wavelength value(s).\n starting_wl = [float(x) for x in\n hdulist[1].data[\"wavelength\"]]\n # Step size(s) for each subsequent wavelength.\n delta_wl = [float(x) for x in hdulist[1].data[\"deltaw\"]]\n\n # Generate the full array of wavelength values, and get\n # full array of flux values, for each aperture.\n for aper in range(n_apertures):\n wls = [starting_wl[aper] +\n x*delta_wl[aper] for\n x in range(n_wls[aper])]\n fls = [float(x) for\n x in hdulist[1].data[\"flux\"][aper]]\n # Make sure wavelengths and fluxes are sorted\n # from smallest wavelength to largest.\n sort_indexes = [x[0] for x in\n sorted(enumerate(wls),\n key=itemgetter(1))]\n wls = [wls[x] for x in sort_indexes]\n fls = [fls[x] for x in sort_indexes]\n wlfls = [(x, y) for x, y in zip(wls, fls) if\n y != 0.]\n if wlfls != []:\n datapoints = [\n [data_point(x=float(\"{0:.8f}\".format(x)),\n y=float(\"{0:.8e}\".format(y)))\n for x, y in wlfls]]\n # Create the return DataSeries object.\n all_data_series.append(\n DataSeries('iue', obsid,\n datapoints,\n ['IUE_' + obsid + ' DISP:'\n + dispersion + ' APER:' +\n apertures[aper]],\n [iue_xunit], [iue_yunit],\n errcode))\n\n if is_hi:\n # Get the aperture from the primary header.\n aperture = hdulist[0].header[\"aperture\"].strip()\n # Get the dispersion type from the primary header.\n dispersion = hdulist[0].header[\"disptype\"].strip()\n # Get the camera used (SWP, LWP, LWR).\n camera = hdulist[0].header[\"camera\"].strip()\n # Get a list of spectral orders. Those that are beyond\n # the range defined in Solano are not considered.\n if camera == \"LWP\":\n max_order = 124\n elif camera == \"LWR\":\n max_order = 119\n else:\n max_order = 120\n orders = [int(x) for x in hdulist[1].data[\"order\"] if x\n <= max_order]\n n_orders = len(orders)\n # This lists will store each orders' spectral info.\n order_spectra = []\n\n # Loop over each order.\n for order in range(n_orders):\n # Number of fluxes for this order.\n n_p = int(hdulist[1].data[\"npoints\"][order])\n # Starting pixel within the array of 768 elements.\n s_pix = int(\n hdulist[1].data[\"startpix\"][order])\n # Wavelength corresponding to this start pixel.\n starting_wl = float(\n hdulist[1].data[\"wavelength\"][order])\n # Step size for each subsequent wavelength.\n delta_wl = float(\n hdulist[1].data[\"deltaw\"][order])\n # Generate the full array of wavelength values.\n wls = [starting_wl + x*delta_wl for x in\n range(n_p)]\n # Extract the fluxes that go along with these wls.\n all_fluxes = hdulist[1].data[\"abs_cal\"][order]\n fls = [float(x) for x in\n all_fluxes[(s_pix-1):(s_pix-1+n_p-1+1)]]\n # Extract the quality flags that go along with\n # these wls.\n all_qfs = hdulist[1].data[\"quality\"][order]\n qfs = [int(x) for x in all_qfs[(s_pix-1):(s_pix-1+\n n_p-1+1)]]\n # Only keep good Quality Flags, if the order is all\n # bad flags, don't add it.\n keep = [i for i, x in enumerate(qfs) if (qfs[i] >\n -16384)]\n if keep != [] and fls != [0.]*len(fls):\n wls = [wls[i] for i in keep]\n fls = [fls[i] for i in keep]\n # Create a dict that will store this order's\n # info.\n order_spec = {'order':orders[order],\n 'wls':numpy.asarray(wls),\n 'fls':numpy.asarray(fls)}\n order_spectra.append(order_spec)\n\n # Order-combine the spectra.\n comb_spec = order_combine(order_spectra, camera, False)\n\n # Resample onto an evenly-spaced wavelength scale.\n comb_spec_reb = resample_spectrum(comb_spec, camera,\n False)\n\n # Create the return DataSeries object.\n datapoints = [\n [data_point(x=float(\"{0:.8f}\".format(x)),\n y=float(\"{0:.8e}\".format(y)))\n for x, y in comb_spec_reb]]\n all_data_series.append(\n DataSeries('iue', obsid,\n datapoints,\n ['IUE_' + obsid + ' DISP:'\n + dispersion + ' APER:' +\n aperture],\n [iue_xunit], [iue_yunit],\n errcode))\n\n except IOError:\n errcode = 3\n all_data_series.append(\n DataSeries('iue', obsid, [], [''], [''], [''], errcode))\n\n else:\n # This is where an error DataSeries object would be returned.\n all_data_series.append(\n DataSeries('iue', obsid, [], [], [],\n [], errcode))\n\n # Return the DataSeries object back to the calling module.\n if len(all_data_series) == 1:\n return all_data_series[0]\n return all_data_series","def usefulquantities(dffin):\n dffin['log_length_box'] = np.log(dffin['length_box_um'])\n dffin['time_min']=dffin['time_sec']/60\n dffin['pred_length_box_um'] = np.exp(dffin['pred_log_length'])\n dffin['unique_id'] = dffin['cell']+dffin['time_sec'].apply(lambda x:str(x))\n dffin['cv_gr']= dffin.groupby('cell')['pred_growth_rate'].transform(lambda x:\\\n np.std(x)/np.mean(x))\n dffin['std_gr']= dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.std(x))\n dffin['mean_gr'] = dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.mean(x))\n dffin['mean_len'] = dffin.groupby('cell')['pred_length_box_um'].transform(lambda x: np.mean(x))\n dffin['norm_pred_growth_rate'] = (dffin['pred_growth_rate']-dffin.groupby('cell')['pred_growth_rate'].transform(lambda\\\n x: np.mean(x)))/dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.mean(x))\n dffin = rl.genalogy(dffin,'parent_cell') #Create genealogy\n dffin = rl.genalogy(dffin,'g_parent_cell')\n dffin = rl.genalogy(dffin,'g_g_parent_cell')\n dffin = dffin.set_index('unique_id')\n qq= dffin.groupby('cell').apply(lambda x: (x['pred_length_box_um']-x['pred_length_box_um'].iloc[0])/(x['pred_length_box_um'].iloc[-1]-x['pred_length_box_um'].iloc[0])).rename('add_len')\n jj= dffin.groupby('cell').apply(lambda x: (x['time_sec']-x['time_sec'].iloc[0])/(x['time_sec'].iloc[-1]-x['time_sec'].iloc[0])).rename('cell_cycle')\n return pd.concat([dffin, qq.reset_index().set_index('unique_id')['add_len'], jj.reset_index().set_index('unique_id')['cell_cycle']], axis=1, join='inner')","def add_derived_GEOSChem_specs2df(df):\n # Add temperature in deg C\n df['T'] = df['GMAO_TEMP'].copy()\n df['T'] = df['GMAO_TEMP'].values - 273.15\n # Inc. V nd U with same variable names as GEOS-CF\n df['V'] = df['GMAO_VWND'].copy()\n df['U'] = df['GMAO_UWND'].copy()\n # Add NOx as combined NO and NO2\n df['NOx'] = df['NO'].values + df['NO2'].values\n # Add NOy as defined in GEOS-CF\n # NOy = no_no2_hno3_hno4_hono_2xn2o5_pan_organicnitrates_aerosolnitrates\n vars2use = AC.GC_var('NOy-all')\n df['NOy'] = df['N2O5'].copy() # 2 N2O5 in NOy, so 2x via template\n for var in vars2use:\n try:\n df.loc[:, 'NOy'] = df['NOy'].values + df[var].values\n except KeyError:\n pass\n # Add a variable for gas-phase NOy (by subtracting aerosol nitrate)\n vars2use = AC.GC_var('NOy-gas')\n df['NOy-gas'] = df['N2O5'].copy() # 2 N2O5 in NOy, so 2x via template\n for var in vars2use:\n try:\n df.loc[:, 'NOy-gas'] = df['NOy-gas'].values + df[var].values\n except KeyError:\n pass\n # Include a variable of NOy where HNO3 is removed\n # NOy = no_no2_hno3_hno4_hono_2xn2o5_pan_organicnitrates_aerosolnitrates\n df['NOy-HNO3'] = df['NOy'].values - df['HNO3'].values\n # Include a variable of NOy where HNO3 is removed\n df['NOy-HNO3-PAN'] = df['NOy'].values - \\\n df['HNO3'].values - df['PAN'].values\n # gas-phase (exc. PAN, HNO3, HNO4, Org-NIT, N2O5)\n df['NOy-Limited'] = df['NO'].values + df['NO2'].values + \\\n df['HNO2'].values + df['NIT'].values + df['NITs'].values\n # Add an all sulfate tracer\n NewVar = 'SO4-all'\n vars2use = AC.GC_var(NewVar)\n df[NewVar] = df['NIT'].values\n for var2use in vars2use:\n try:\n df[NewVar] = df[NewVar].values + df[var2use].values\n except KeyError:\n pass\n # And a all nitrate tracer\n NewVar = 'NIT-all'\n vars2use = AC.GC_var(NewVar)\n df[NewVar] = df['NIT'].values\n for var2use in vars2use:\n try:\n df[NewVar] = df[NewVar].values + df[var2use].values\n except KeyError:\n pass\n # Uset the P-I variable as a model level variable\n df['model-lev'] = df['P-I'].copy()\n return df","def temperature() -> float:","def whc_tot(mukey, layers=''):\n #read appropriate soils.in content to a python list\n mukey = str(mukey)\n soil_path = \"/data/paustian/ernie/SSURGO_master_script/soil_test2/\"\n soil_fpath = soil_path+mukey[:-3]+\"/\"+mukey+\".in\"\n cont = [[]]\n data_input = open(soil_fpath, 'r')\n for line in data_input:\n cont.append(line.split())\n del cont[0]\n\n #convert all entries in the 2D list to float format where possible, or zero in the case\n #of very small numbers recorded in scientific notation\n for k in range(len(cont)):\n for l in range(len(cont[k])):\n cont[k][l] = float(cont[k][l])\n\n #loop through list and compute the water holding capacity increment represented in \n #each line\n min_h2o_evap = 0\n min_h2o = 0\n max_h2o = 0\n whc = 0\n for i in range(len(cont)):\n if not layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n else:\n if 1+i <= layers:\n depth = cont[i][1] - cont[i][0]\n FC = cont[i][3]\n WP = cont[i][4]\n WHC = FC - WP\n if i != 0:\n min_h2o_evap += depth*WP\n min_h2o += depth*WP\n max_h2o += depth*FC\n whc += depth*WHC\n if layers:\n if layers > len(cont):\n print \"NOTE: specified layer limit exceeds number of layers found in soils.in file\"\n\n return whc, min_h2o, max_h2o","def test_input_flux_file():\n # Generate an input file\n flux_input_file = tstutils.data_path('test.flux')\n if os.path.isfile(flux_input_file):\n os.remove(flux_input_file)\n\n cfg_lines = ['[fluxcalib]']\n cfg_lines += [' extinct_correct = False # Set to True if your SENSFUNC derived with the UVIS algorithm\\n']\n cfg_lines += ['# Please add your SENSFUNC file name below before running pypeit_flux_calib']\n\n # These files need to be in tests/files/\n data = Table()\n data['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data['sensfile'] = 'sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits'\n # \n paths = [tstutils.data_path('')]\n\n fluxFile = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data)\n # Write\n fluxFile.write(flux_input_file)\n\n # Read\n fluxFile2 = inputfiles.FluxFile.from_file(flux_input_file)\n assert np.all(fluxFile2.data['filename'] == data['filename'])\n\n # Test path\n assert fluxFile2.file_paths[0] == paths[0]\n assert fluxFile2.filenames[0] == os.path.join(paths[0], data['filename'][0])\n\n # #################\n # Tickle the other ways to do sensfiles\n data3 = Table()\n data3['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data3['sensfile'] = ['sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits',\n '']\n\n fluxFile3 = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data3)\n assert fluxFile3.sensfiles[1] == os.path.join(paths[0], data['sensfile'][0])\n \n data4 = Table()\n data4['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\n data4['sensfile'] = ''\n\n fluxFile4 = inputfiles.FluxFile(config=cfg_lines, \n file_paths=paths,\n data_table=data4)\n assert len(fluxFile4.sensfiles) == 0\n\n # Clean up\n os.remove(flux_input_file)","def addtonc(ncfout,key,vd,ofield,ftype=\"timeseries\"):\n nc_out=nc.Dataset(ncfout,'r+')\n if ftype==\"timeseries\":\n diml=['time','height','south_north','west_east'] # Tuple of Dimensions\n if vd['dims']==4:\n dimtup=tuple(diml)\n elif vd['dims']==3:\n dimtup = tuple([c for c in diml if c != \"height\"])\n elif vd['dims']==2:\n dimtup = tuple([c for c in diml if c not in [\"height\",\"time\"]])\n elif ftype==\"roughness\":\n diml=['south_north','west_east']\n dimtup=tuple(diml)\n elif ftype==\"tabfile\":\n diml=['south_north','west_east','sector','wind','stab']\n if vd['dims']==3:\n dimtup=tuple(diml.remove('wind').remove('stab'))\n if vd['dims']==2:\n dimtup=tuple(diml.remove('wind').remove('stab').remove('sector'))\n if key in (\"TKE\", \"ABLAT_CYL\", \"ACCRE_CYL\"):\n outv=nc_out.createVariable(key, 'f4', dimtup, zlib=True,\n complevel=9, fill_value=-999.)\n else:\n outv=nc_out.createVariable(key,'f4',dimtup,zlib=True,complevel=9)\n outv.units=vd['units']\n outv.long_name=vd['name']\n if vd['std_name'] is not None:\n outv.standard_name=vd['std_name']\n if key==\"PRECIP\":\n outv.cell_methods=\"time: sum\"\n outv.grid_mapping=\"crs\"\n outv.coordinates=\"XLAT XLON\"\n outv[:]=ofield[:]\n nc_out.close()\n return(None)","def load_zT(all_data=False):\n path = os.path.join(DATA_DIR, \"zT-citrination-165.csv\")\n df = pd.read_csv(path, index_col=None)\n if not all_data:\n df = df[[\"composition\", \"zT\"]]\n return df","def radiance_map(file, config, vmax=4200, levels=20, typ=''):\n \n # Select data from configuration \n azimuths = config['skymap'][:, 0] # +180 # azimuths\n zeniths = config['skymap'][:, 1] # zeniths\n\n if typ == 'sim':\n # look for wavelength index in array\n waves_sim = dataset.attrs['simulated_Columns'].split('nm')[0].split('[')[1].split(\n ']')[0].split(',')\n waves = np.asarray(list(map(int, waves_sim)))\n wave_indx = np.where(waves == wave)\n try:\n wave_indx = np.int(wave_indx[0][0])\n except:\n print(\"Wavelength is not in dataset\")\n z = dataset.simulated[:, wave_indx, time_indx]\n\n elif typ == 'meas':\n wave_indx = int((config['wavelength'] - 250 - config['wave_correction']) / 0.446)\n with h5py.File(file, 'r') as data:\n z = data['data'][:, wave_indx]\n else:\n print('Select a input data type(sim or meas)')\n\n # Add values in the origin to close the surface interpolation\n azimuths = np.append(azimuths, [270, 0, 0, 0, 0, 0, 0, 0])\n zeniths = np.append(zeniths, [0, 12, 24, 36, 48, 60, 72, 84])\n z = np.append(z, [z[0], z[3], z[9], z[19], z[33], z[51], z[73], z[99]])\n\n # Convert x to radians\n azimuths = np.radians(azimuths)\n zeniths = np.radians(zeniths)\n\n # Remove dead channels of the dataset\n azimuths = np.delete(azimuths, config['dead_fibre'])\n zeniths = np.delete(zeniths, config['dead_fibre'])\n z = np.delete(z, config['dead_fibre'])\n\n # Set up a regular grid of interpolation point\n thetai, ri = np.linspace(azimuths.min(), azimuths.max(),\n num=len(azimuths)), \\\n np.linspace(zeniths.min(), zeniths.max(), num=len(zeniths))\n\n ri, thetai = np.meshgrid(ri, thetai, indexing='ij')\n\n # zi = scipy.interpolate.griddata((azimuths, zeniths), z, (thetai, ri),\n # method='linear')\n\n rbf = scipy.interpolate.Rbf(azimuths, zeniths, z, fucntion='gaussian',\n epsilon=0.05)\n\n ZI = rbf(thetai, ri)\n\n if typ == 'sim':\n name = str(dataset.time[time_indx].values) # ''\n else:\n name = 'testing' #str(dataset.time[time_indx].values)\n\n # Create the directory to save the results\n # os.makedirs(os.path.dirname(config['path_note'] + '/figures/'),\n # exist_ok=True)\n if vmax == 'default':\n vmax = 4200\n else:\n vmax = vmax\n\n # Plot the dataset\n fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\n cmap = 'Spectral_r' # 'rainbow'\n a = plt.contourf(thetai, ri, ZI, levels, cmap=cmap, vmin=0,\n vmax=vmax) # , vmax=4932)\n plt.title('{} UTC {}nm'.format(name, config['wavelength']))\n plt.axis([0, 2*np.pi, 0, 1.48])\n\n plt.scatter(azimuths, zeniths, cmap=cmap, s=1)\n ax.grid(False)\n ax.set_theta_zero_location(\"N\") # Set the direction of polar plot\n ax.set_theta_direction(1) # Set the increase direction on azimuth angles\n # (-1 to clockwise, 1 counterclockwise)\n cbar = plt.colorbar(a)\n cbar.set_label(\"counts\", rotation=90)\n\n # if typ == 'sim':\n # plt.savefig(\n # 'figures/skymap/simulated/skymap{}nm_{}UTC_sim.jpeg'.format(wave,\n # name),\n # dpi=300)\n # plt.show();\n # else:\n # plt.savefig(\n # 'figures/skymap/measured/skymap{}nm_{}UTC_meas.jpeg'.format(wave,\n # name),\n # dpi=300)","def getTemperatureMeasurements(self):\n # self.board.readline()\n self.stop = False\n times = []\n temps = [[], [], []]\n \n # A synchronisation string containing the characters tx is sent before each set of measurements,\n # we ensure correct reading of the measurements by waiting for this string\n while str(self.board.readline()).strip('b\\'\\\\rn') != 'tx':\n pass\n \n while not self.stop:\n # A synchronisation string containing the characters tx is sent before each set of measurements\n tx = self.board.readline()\n if str(tx).strip('b\\'\\\\rn') == 'tx':\n rawData1 = self.board.readline()\n rawData2 = self.board.readline()\n rawData3 = self.board.readline()\n rawData4 = self.board.readline()\n \n \n timeStamp = str(rawData1).strip('b\\'\\\\rn')\n temp1 = str(rawData2).strip('b\\'\\\\rn')\n temp2 = str(rawData3).strip('b\\'\\\\rn')\n temp3 = str(rawData4).strip('b\\'\\\\rn')\n try:\n times.append(float(timeStamp) / 1000)\n temps[0].append(float(temp1) / 128)\n temps[1].append(float(temp2) / 128)\n temps[2].append(float(temp3) / 128)\n # print(f'\\rtime: {float(timeStamp) / 1000:.2f} s, Temperature measured on sensor 1: {float(temp1) / 128:.2f} °C,'\n # f'sensor 2: {float(temp2) / 128:.2f} °C, sensor 3: {float(temp3) / 128:.2f} °C', sep='', end='', flush=True)\n except:\n print(rawData1, rawData2, rawData3, rawData4)\n \n \n if self.stop:\n print('\\nMeasurement finished...')\n \n self.data_stack[self.fetch_kinds[0]] = times\n self.data_stack[self.fetch_kinds[1]] = temps[0]\n self.data_stack[self.fetch_kinds[2]] = temps[1]\n self.data_stack[self.fetch_kinds[3]] = temps[2]\n \n if (len(self.data_stack['Sensor 1 Temp']) != len(times) or len(self.data_stack['Sensor 2 Temp']) != len(times) or len(self.data_stack['Sensor 3 Temp']) != len(times)):\n print(\"Warning: There may be some missing values!\")","def read_spectral_k(filename=\"tc_dos_l.dat\"):\n # column headers for the data \n #tcdosl_labels = [\n # \"wavelength\",\n # \"k_xx_raw\",\"k_xx_smooth\",\n # \"k_yy_raw\",\"k_yy_smooth\",\n # \"k_zz_raw\",\"k_zz_smooth\"]\n\n tcdosl_labels = [\n \"wavelength\",\n \"k_xx_raw\",\"k_yy_raw\",\"k_zz_raw\",\n \"k_xx_smooth\",\"k_yy_smooth\",\"k_zz_smooth\"]\n\n def subselect_table_block(i_start,lines):\n i = i_start + 1\n\n table = []\n while(lines[i].strip() != \"\"):\n args = lines[i].split()\n args = [arg.strip() for arg in args]\n args = [float(arg) for arg in args]\n table.append(args)\n i += 1 \n return np.array(table)\n\n line = None # initialize\n with open(filename,'r') as f:\n lines = f.readlines()\n lines = [s.strip() for s in lines]\n\n temperatures = []\n tcdosl_dict = OrderedDict()\n\n for il,line in enumerate(lines):\n if line.startswith('# Temp:'):\n args = line.split(':')\n T = int(float(args[1].strip()))\n temperatures.append(T)\n tcdosl_dict[T] = subselect_table_block(il,lines)\n\n tcdosl_df_dict = OrderedDict()\n for temp in temperatures:\n tcdosl_df_dict[temp] = pd.DataFrame(\n copy.deepcopy(tcdosl_dict[temp]),\n columns=list(tcdosl_labels))\n\n return {k:v.copy() for k,v in tcdosl_df_dict.items()}","def get_ecmwf_forecast_statistics(params):\n blob_mapped_dir = \"/mnt/output\"\n\n path_to_rapid_output = blob_mapped_dir\n\n watershed_name = params[\"region\"].split(\"-\")[0]\n subbasin_name = params[\"region\"].split(\"-\")[1]\n river_id = params[\"reach_id\"]\n\n # units = params[\"units\"]\n # if (not units):\n # units = \"metric\"\n\n units = \"metric\"\n\n # forecast_folder = params[\"date\"]\n # if (not forecast_folder):\n # forecast_folder = 'most_recent'\n\n forecast_folder = 'most_recent'\n\n stat_type = params[\"stat\"]\n if (stat_type is None):\n stat_type = \"\"\n\n # find/check current output datasets\n path_to_output_files = \\\n os.path.join(path_to_rapid_output,\n \"{0}-{1}\".format(watershed_name, subbasin_name))\n forecast_nc_list, start_date = \\\n ecmwf_find_most_current_files(path_to_output_files, forecast_folder)\n if (not forecast_nc_list or not start_date):\n log.log_error('ECMWF forecast for %s (%s).' % (watershed_name, subbasin_name))\n\n # combine 52 ensembles\n qout_datasets = []\n ensemble_index_list = []\n for forecast_nc in forecast_nc_list:\n ensemble_index_list.append(\n int(os.path.basename(forecast_nc)[:-3].split(\"_\")[-1])\n )\n qout_datasets.append(\n xarray.open_dataset(forecast_nc, autoclose=True)\n .sel(rivid=river_id).Qout\n )\n\n merged_ds = xarray.concat(qout_datasets,\n pd.Index(ensemble_index_list, name='ensemble'))\n\n return_dict = {}\n if (stat_type == 'high_res' or not stat_type):\n # extract the high res ensemble & time\n try:\n return_dict['high_res'] = merged_ds.sel(ensemble=52).dropna('time')\n except:\n pass\n\n if (stat_type != 'high_res' or not stat_type):\n # analyze data to get statistic bands\n merged_ds = merged_ds.dropna('time')\n\n if (stat_type == 'mean' or 'std' in stat_type or not stat_type):\n return_dict['mean'] = merged_ds.mean(dim='ensemble')\n std_ar = merged_ds.std(dim='ensemble')\n if (stat_type == 'std_dev_range_upper' or not stat_type):\n return_dict['std_dev_range_upper'] = \\\n return_dict['mean'] + std_ar\n if (stat_type == 'std_dev_range_lower' or not stat_type):\n return_dict['std_dev_range_lower'] = \\\n return_dict['mean'] - std_ar\n if (stat_type == \"min\" or not stat_type):\n return_dict['min'] = merged_ds.min(dim='ensemble')\n if (stat_type == \"max\" or not stat_type):\n return_dict['max'] = merged_ds.max(dim='ensemble')\n\n for key in list(return_dict):\n if (units == 'english'):\n # convert m3/s to ft3/s\n return_dict[key] *= M3_TO_FT3\n # convert to pandas series\n return_dict[key] = return_dict[key].to_dataframe().Qout\n\n return return_dict, watershed_name, subbasin_name, river_id, units","def get_jhu(data_path: str, filter_: Union[dict, bool] = True) -> None:\n # Where JHU stores their data\n url_template = (\"https://raw.githubusercontent.com/CSSEGISandData/\"\n \"COVID-19/master/csse_covid_19_data/\"\n \"csse_covid_19_time_series/time_series_covid19_%s_%s.csv\")\n\n # Scrape the data\n dfs = {}\n for region in ['global', 'US']:\n dfs[region] = {}\n for kind in ['confirmed', 'deaths', 'recovered']:\n url = url_template % (kind, region) # Create the full data URL\n try:\n df = pd.read_csv(url) # Download the data into a dataframe\n except HTTPError:\n print(\"Could not download data for %s, %s\" % (kind, region))\n else:\n if region == 'global':\n has_no_province = df['Province/State'].isnull()\n # Whole countries only; use country name as index\n df1 = df[has_no_province].set_index('Country/Region')\n more_dfs = []\n for country in ['China', 'Canada', 'Australia']:\n if country == 'Canada' and kind in 'recovered':\n continue\n is_c = df['Country/Region'] == country\n df2 = df[is_c].sum(axis=0, skipna=False).to_frame().T\n df2['Country/Region'] = country\n df2 = df2.set_index('Country/Region')\n more_dfs.append(df2)\n df = pd.concat([df1] + more_dfs)\n elif region == 'US':\n # Use state name as index\n for k, v in us_state_abbrev.items(): # get US state abbrev\n if not us_state_abbrev[k].startswith('US_'):\n us_state_abbrev[k] = 'US_' + v # Add 'US_' to abbrev\n df.replace(us_state_abbrev, inplace=True)\n df = df.set_index('Province_State')\n df = df.groupby('Province_State').sum() # combine counties to create state level data\n\n df = df[[x for x in df if any(year in x for year in ['20', '21'])]] # Use only data columns\n # 20 or 21 signifies 2020 or 2021\n dfs[region][kind] = df # Add to dictionary of dataframes\n\n # Generate a list of countries that have \"good\" data,\n # according to these criteria:\n good_countries = get_countries(dfs['global'], filter_=filter_)\n\n # For each \"good\" country,\n # reformat and save that data in its own .csv file.\n source = dfs['global']\n for country in tqdm(good_countries, desc='Countries'): # For each country\n if country in ['Diamond Princess', 'MS Zaandam', 'Samoa',\n 'Vanuatu', 'Marshall Islands', 'US', 'Micronesia']:\n print(\"Skipping {}\".format(country))\n continue\n # If we have data in the downloaded JHU files for that country\n if country in source['confirmed'].index:\n df = pd.DataFrame(columns=['dates2', 'cum_cases', 'cum_deaths',\n 'cum_recover', 'new_cases',\n 'new_deaths', 'new_recover',\n 'new_uninfected'])\n df['dates2'] = source['confirmed'].columns\n df['dates2'] = df['dates2'].apply(fix_jhu_dates)\n df['cum_cases'] = source['confirmed'].loc[country].values\n df['cum_deaths'] = source['deaths'].loc[country].values\n df['cum_recover'] = source['recovered'].loc[country].values\n df[['new_cases', 'new_deaths', 'new_recover']] = \\\n df[['cum_cases', 'cum_deaths', 'cum_recover']].diff()\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\n\n\n try:\n population = get_population_count(data_path, country)\n df['population'] = population\n except:\n pass\n\n # Fill NaN with 0 and convert to int\n dfs[country] = df.set_index('dates2').fillna(0).astype(int)\n dfs[country].to_csv(data_path / ('covidtimeseries_%s.csv' % country))\n\n else:\n print(\"No data for %s\" % country)\n\n source = dfs['US']\n states = source['confirmed'].index.tolist()\n us_recovery_data = covid_tracking_recovery(data_path)\n for state in tqdm(states, desc='US States'): # For each country\n if state in ['Diamond Princess', 'Grand Princess', 'MS Zaandam', 'US_AS']:\n print(\"Skipping {}\".format(state))\n continue\n # If we have data in the downloaded JHU files for that country\n if state in source['confirmed'].index:\n df = pd.DataFrame(columns=['dates2', 'cum_cases', 'cum_deaths',\n 'new_cases','new_deaths','new_uninfected'])\n df['dates2'] = source['confirmed'].columns\n df['dates2'] = df['dates2'].apply(fix_jhu_dates)\n df['cum_cases'] = source['confirmed'].loc[state].values\n df['cum_deaths'] = source['deaths'].loc[state].values\n\n df[['new_cases', 'new_deaths']] = df[['cum_cases', 'cum_deaths']].diff()\n\n # add recovery data\n df.set_index('dates2', inplace=True)\n df = df.merge(us_recovery_data[state], on='dates2', how='left')\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\n\n try:\n population = get_population_count(data_path, state)\n df['population'] = population\n except:\n pass\n # Fill NaN with 0 and convert to int\n dfs[state] = df.fillna(0).astype(int)\n dfs[state].to_csv(data_path /\n ('covidtimeseries_%s.csv' % state))\n else:\n print(\"No data for %s\" % state)","def main():\n args = get_args()\n\n entries = []\n\n noe_dim = \"h1\" if args.hch else \"c1\" # save the name of the noe dimension\n\n with open(args.sparkylist) as lines:\n\n lines = lines.readlines()\n # lines = set(lines) # remove duplicate lines\n peak = 1\n for idx, line in enumerate(lines):\n idx = idx + 1\n\n try:\n label, c1, c2, h2, intensity, *rest = line.split()\n\n c1 = float(c1) # convert these to floats\n c2 = float(c2)\n h2 = float(h2)\n intensity = float(intensity)\n\n label = f\"peak{peak}\"\n peak += 1\n\n except ValueError:\n print(f\"invalid NOE definition on line {idx}\")\n continue\n\n dic = {\"label\": label, noe_dim: c1,\n \"c2\": c2, \"h2\": h2, \"intensity\": intensity}\n\n entries.append(dic)\n\n # create dataframe and write out\n csv = pd.DataFrame(entries)\n order = [\"label\", noe_dim, \"c2\", \"h2\", \"intensity\"]\n csv.to_csv(args.output, columns=order, index=False)","def read_core_temp(self) -> float:","def runqn3(h,steps):\n func = ['x7','x8','x9','x10','x11','x12','-(x1-x3)/(((x1-x3)**2+(x2-x4)**2)**(3.0/2))-(x1-x5)/(((x1-x5)**2+(x2-x6)**2)**(3.0/2))','-(x2-x4)/(((x1-x3)**2+(x2-x4)**2)**(3.0/2))-(x2-x6)/(((x1-x5)**2+(x2-x6)**2)**(3.0/2))','-(x3-x1)/(((x3-x1)**2+(x4-x2)**2)**(3.0/2))-(x3-x5)/(((x3-x5)**2+(x4-x6)**2)**(3.0/2))','-(x4-x2)/(((x3-x1)**2+(x4-x2)**2)**(3.0/2))-(x4-x6)/(((x3-x5)**2+(x4-x6)**2)**(3.0/2))','-(x5-x1)/(((x5-x1)**2+(x6-x2)**2)**(3.0/2))-(x5-x3)/(((x5-x3)**2+(x6-x4)**2)**(3.0/2))','-(x6-x2)/(((x5-x1)**2+(x6-x2)**2)**(3.0/2))-(x6-x4)/(((x5-x3)**2+(x6-x4)**2)**(3.0/2))']\n init = [0,0,0.97000436,-0.24308753,-0.97000436,0.24308753,-0.93240737,-0.86473146,0.466203685,0.43236573,0.466203685,0.43236573]\n rungeODE(func,init,0,h,steps)\n # extract data from CSV output file\n filename = open('RungeKuttaOutput.csv','rb')\n reader = csv.reader(filename,delimiter=',')\n x1=[]\n x2=[]\n x3=[]\n x4=[]\n x5=[]\n x6=[]\n for row in reader:\n x1 = x1 + [row[1]]\n x2 = x2 + [row[2]]\n x3 = x3 + [row[3]]\n x4 = x4 + [row[4]]\n x5 = x5 + [row[5]]\n x6 = x6 + [row[6]]\n x1out=[float(x1[i]) for i in range(1,len(x1))]\n x2out=[float(x2[i]) for i in range(1,len(x1))]\n x3out=[float(x3[i]) for i in range(1,len(x1))]\n x4out=[float(x4[i]) for i in range(1,len(x1))]\n x5out=[float(x5[i]) for i in range(1,len(x1))]\n x6out=[float(x6[i]) for i in range(1,len(x1))]\n plot(x1out,x2out,'r',x3out,x4out,'g',x5out,x6out,'b')\n show()","def useThibautsData(log, output, bcgr=72.2, sigma=0.75, iterations=4, loc=1900, galaxies=1000,\n datadir='/Users/smn2/EUCLID/CTItesting/uniform/',\n thibautCDM03=False, beta=False, serial=1, parallel=1):\n files = g.glob(datadir + '*.fits')\n #pick randomly\n files = np.random.choice(files, galaxies, replace=False)\n\n #trap parameters: parallel\n if thibautCDM03:\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_parallel.dat'\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_serial.dat'\n params = ThibautsCDM03params()\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\n else:\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_parallel.dat'\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_serial.dat'\n params = MSSLCDM03params()\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\n if beta:\n params.update(dict(beta_p=0.6, beta_s=0.6))\n\n print f1, f2\n\n #store shapes\n eclean = []\n e1clean = []\n e2clean = []\n R2clean = []\n xclean = []\n yclean = []\n eCTI = []\n e1CTI = []\n e2CTI = []\n R2CTI = []\n xCTI = []\n yCTI = []\n eCTIfixed = []\n e1CTIfixed = []\n e2CTIfixed = []\n R2CTIfixed = []\n xCTIfixed = []\n yCTIfixed = []\n\n fh = open(output.replace('.pk', '.csv'), 'w')\n fh.write('#files: %s and %s\\n' % (f1, f2))\n for key in params:\n print key, params[key]\n fh.write('# %s = %s\\n' % (key, str(params[key])))\n fh.write('#file, delta_e, delta_e1, delta_e2, delta_R2, delta_x, delta_y\\n')\n for f in files:\n print 'Processing: ', f\n\n #load data\n nocti = pf.getdata(f)\n\n #scale to SNR about 10 (average galaxy, a single exposure)\n nocti /= np.sum(nocti)\n nocti *= 1500.\n\n #place it on canvas\n tmp = np.zeros((2066, 2048))\n ysize, xsize = nocti.shape\n ysize /= 2\n xsize /= 2\n tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize] = nocti.copy()\n\n #add background\n tmp += bcgr\n\n #run CDM03\n c = CTI.CDM03bidir(params, [])\n tmp = c.applyRadiationDamage(tmp.copy().transpose()).transpose()\n\n #remove background and make a cutout\n CTIdata = tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize]\n CTIdata -= bcgr\n CTIdata[CTIdata < 0.] = 0.\n\n #write files\n #fileIO.writeFITS(nocti, f.replace('.fits', 'noCTI.fits'), int=False)\n #fileIO.writeFITS(CTI, f.replace('.fits', 'CTI.fits'), int=False)\n\n #reset settings\n settings = dict(sigma=sigma, iterations=iterations)\n\n #calculate shapes\n sh = shape.shapeMeasurement(nocti.copy(), log, **settings)\n results = sh.measureRefinedEllipticity()\n\n eclean.append(results['ellipticity'])\n e1clean.append(results['e1'])\n e2clean.append(results['e2'])\n R2clean.append(results['R2'])\n xclean.append(results['centreX'])\n yclean.append(results['centreY'])\n\n #CTI, fitted centroid\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\n results2 = sh.measureRefinedEllipticity()\n\n eCTI.append(results2['ellipticity'])\n e1CTI.append(results2['e1'])\n e2CTI.append(results2['e2'])\n R2CTI.append(results2['R2'])\n xCTI.append(results2['centreX'])\n yCTI.append(results2['centreY'])\n\n #fixed centroid\n settings['fixedPosition'] = True\n settings['fixedX'] = results['centreX']\n settings['fixedY'] = results['centreY']\n settings['iterations'] = 1\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\n results3 = sh.measureRefinedEllipticity()\n\n eCTIfixed.append(results3['ellipticity'])\n e1CTIfixed.append(results3['e1'])\n e2CTIfixed.append(results3['e2'])\n R2CTIfixed.append(results3['R2'])\n xCTIfixed.append(results3['centreX'])\n yCTIfixed.append(results3['centreY'])\n\n text = '%s,%e,%e,%e,%e,%e,%e\\n' % (f, results['ellipticity'] - results2['ellipticity'],\n results['e1'] - results2['e1'], results['e2'] - results2['e2'],\n results['R2'] - results2['R2'],\n results['centreX'] - results2['centreX'],\n results['centreY'] - results2['centreY'])\n fh.write(text)\n print text\n\n fh.close()\n\n results = {'eclean': np.asarray(eclean),\n 'e1clean': np.asarray(e1clean),\n 'e2clean': np.asarray(e2clean),\n 'R2clean': np.asarray(R2clean),\n 'xclean': np.asarray(xclean),\n 'yclean': np.asarray(yclean),\n 'eCTI': np.asarray(eCTI),\n 'e1CTI': np.asarray(e1CTI),\n 'e2CTI': np.asarray(e2CTI),\n 'R2CTI': np.asarray(R2CTI),\n 'xCTI': np.asarray(xCTI),\n 'yCTI': np.asarray(yCTI),\n 'eCTIfixed': np.asarray(eCTIfixed),\n 'e1CTIfixed': np.asarray(e1CTIfixed),\n 'e2CTIfixed': np.asarray(e2CTIfixed),\n 'R2CTIfixed': np.asarray(R2CTIfixed),\n 'xCTIfixed': np.asarray(xCTIfixed),\n 'yCTIfixed': np.asarray(yCTIfixed)}\n\n #save to a file\n fileIO.cPickleDumpDictionary(results, output)\n\n return results","def generate_summary(weather_data):\n# 5 Day Overview\n# The lowest temperature will be 9.4°C, and will occur on Friday 02 July 2021.\n# The highest temperature will be 20.0°C, and will occur on Saturday 03 July 2021.\n# The average low this week is 12.2°C.\n# The average high this week is 17.8°C.\n\n\n Number_of_days=0\n Min_Value=[]\n Date_value=[]\n Max_Value=[]\n\n\n for rows in weather_data: \n if len(rows) != 0:\n Number_of_days = Number_of_days + 1\n Min_Value.append(rows[1])\n Date_value.append(str(rows[0]))\n Max_Value.append(rows[2])\n \n min_temperature,min_position = find_min(Min_Value)\n min_tempe_celcius = convert_f_to_c(min_temperature)\n occur_date_min = convert_date(Date_value[min_position])\n max_temperature,max_position = find_max(Max_Value)\n max_tempe_celcius = convert_f_to_c(max_temperature)\n occur_date_max = convert_date(Date_value[max_position])\n mean_low = calculate_mean(Min_Value)\n mean_low__tempe_celcius = convert_f_to_c(mean_low)\n mean_high = calculate_mean(Max_Value)\n mean_high__tempe_celcius = convert_f_to_c(mean_high)\n\n summary=\"\"\n summary+=f\"{Number_of_days} Day Overview\\n\"\n summary+=f\" The lowest temperature will be {format_temperature(min_tempe_celcius)}, and will occur on {occur_date_min}.\\n\"\n summary+=f\" The highest temperature will be {format_temperature(max_tempe_celcius)}, and will occur on {occur_date_max}.\\n\"\n summary+=f\" The average low this week is {format_temperature(mean_low__tempe_celcius)}.\\n\"\n summary+=f\" The average high this week is {format_temperature(mean_high__tempe_celcius)}.\\n\"\n\n return summary","def prepare_and_save_data(df, index=None, output_path=None):\n assert index is not None\n assert output_path is not None\n\n df.columns.name = 'fundamental_variable'\n df = df.set_index(['Quarter end', 'symbol'])\n\n df = df.stack()\n df = df.unstack(level=1)\n\n # In the initial csv files, None is used to represent missing data\n df[df == 'None'] = np.nan\n\n df.columns.name = None\n\n #df.reset_index().to_csv(path.join(data_dir, 'fundamental_data_of_stocks_stacked.csv'), index=False)\n\n # split the large dataframe based on the fundamental_variables\n # now each DataFrame represent one fundamental variable about a bunch of stocks.\n df = df.reset_index()\n\n\n # convert Quarter end to datetime object\n df['Quarter end'] = pd.to_datetime(df['Quarter end'])\n df = df.sort(['Quarter end'], axis=0)\n # convert the strings to float\n df = df.set_index(['Quarter end', 'fundamental_variable']).astype(float).reset_index(level=1)\n\n\n def _reindex(df):\n # This function will be applied to each fundamental variable group.\n # It will reindex the DataFrame. It will add the target index to the existing index and sort the new index\n new_index = index.append(df.index).drop_duplicates().values\n new_index.sort()\n\n # fill NaN with forward fill method. We should use forward filling because we can only use historical data\n # not the data from the future.\n df = df.reindex(new_index).fillna(method='ffill')\n\n return df\n\n\n def _to_csv(df):\n var_name = df.iloc[0]['fundamental_variable']\n print(\"{:<30} {}\".format(\"preparing fundamental variable\", var_name))\n var_name = re.sub('[-\\s\\&\\/]', '_', var_name) # remove special characters\n var_name = re.sub('_+', '_', var_name) # remove repetitive underscores\n var_name = re.sub('_$', '', var_name) # remove the tailing underscore\n df.set_index(['Quarter end']).reindex(index).to_csv(path.join(output_path, var_name + '.csv'), index=True)\n\n\n\n # The following step is to reindex the DataFrame. Note that the reindex is done by each fundamental variable.\n # The reason for this is if we do the reindex without groupby, there will be NaN in the fundamental_variable after\n # reindex operation, which is problematic for the forward fill operation of the next step\n df_reindex = df.groupby(by=['fundamental_variable'], as_index=False).apply(_reindex).sort_index()\n df_reindex.index = df_reindex.index.droplevel(0)\n\n # save to csv file by each fundamental variable\n df_out = df_reindex.reset_index()\n df_out.groupby(by=['fundamental_variable']).apply(_to_csv)","def makeRooDataSet(type,infile_name,outfile_name,tree_name,nevents):\n\n\n\n\n inputfile = TFile.Open(infile_name,\"READ\")\n print \"Importing tree\"\n tree = TTree()\n inputfile.GetObject(tree_name, tree) #get the tree from the data file\n\n \n\n #define variables for the RooDataSet\n m_mumu = RooRealVar(\"m_mumu\", \"m_mumu\", 0.0, 4.0)\n y_mumu = RooRealVar(\"y_mumu\", \"y_mumu\", 0.0, 2.0 )\n pt_mumu = RooRealVar(\"pt_mumu\", \"pt_mumu\", 0.0, 260.0)\n eta_gamma = RooRealVar(\"eta_gamma\",\"eta_gamma\",-3.5, 3.5)\n pt_gamma = RooRealVar(\"pt_gamma\", \"pt_gamma\", 0.0, 100.0)\n m_gamma = RooRealVar(\"m_gamma\", \"m_gamma\", -0.1,0.1)\n \n m_chi_rf1S = RooRealVar(\"m_chi_rf1S\", \"m_chi_rf1S\", 0.0, 7.0)\n m_chi_rf2S = RooRealVar(\"m_chi_rf2S\", \"m_chi_rf2S\", -1.0, 1.0)\n #Qvalue = RooRealVar(\"Qvalue\",\"Q\", -15., 15.)\n s = RooRealVar(\"s\",\"s\", -10., 10.)\n \n ctpv = RooRealVar(\"ctpv\",\"ctpv\", -1.0, 3.5)\n ctpv_error = RooRealVar(\"ctpv_err\",\"ctpv_err\", -1.0, 1.0)\n pi0_abs_mass = RooRealVar(\"pi0_abs_mass\",\"pi0_abs_mass\", 0.0, 2.2)\n psi1S_nsigma = RooRealVar(\"psi1S_nsigma\",\"psi1S_nsigma\",0.0,1.0)\n psi2S_nsigma = RooRealVar(\"psi2S_nsigma\",\"psi2S_nsigma\",0.0,1.0)\n psi3S_nsigma = RooRealVar(\"psi3S_nsigma\",\"psi3S_nsigma\",0.0,1.0)\n rho_conv = RooRealVar(\"rho_conv\", \"rho_conv\", 0.0, 70.0)\n dz = RooRealVar(\"dz\",\"dz\", -1.0, 1.0)\n probFit1S = RooRealVar(\"probFit1S\",\"probFit1S\",0,1)\n probFit2S = RooRealVar(\"probFit2S\",\"probFit2S\",0,1)\n\n dataArgSet = RooArgSet(m_mumu,\n y_mumu,\n pt_mumu,\n eta_gamma,\n pt_gamma,\n m_gamma, \n m_chi_rf1S)\n \n dataArgSet.add( m_chi_rf2S )\n dataArgSet.add( s )\n dataArgSet.add( ctpv )\n dataArgSet.add( ctpv_error )\n dataArgSet.add( pi0_abs_mass )\n dataArgSet.add( psi1S_nsigma )\n dataArgSet.add( psi2S_nsigma )\n dataArgSet.add( rho_conv )\n dataArgSet.add( dz )\n dataArgSet.add( probFit1S )\n dataArgSet.add( probFit2S )\n\n\n print \"Creating DataSet\"\n dataSet = RooDataSet(\"chicds\",\"Chic RooDataSet\", dataArgSet)\n\n entries = tree.GetEntries()\n print entries\n\n if nevents is not 0:\n entries = nevents\n\n for ientry in range(0,entries):\n tree.GetEntry(ientry)\n\n # unfort ntuples are slightly different for chic and chib\n \n if type == 'chic':\n \n m_mumu.setVal(tree.dimuon_mass)\n y_mumu.setVal(tree.dimuon_rapidity) \n pt_mumu.setVal(tree.dimuon_pt)\n eta_gamma.setVal(tree.photon_eta)\n pt_gamma.setVal(tree.photon_pt)\n #m_gamma.setVal(tree.photon_p4.M())\n m_chi_rf1S.setVal(tree.rf1S_chic_mass)\n #m_chi_rf1S.setVal(tree.rf2S_chi_p4.M())\n #Qvalue.setVal((tree.chi_p4).M() - tree.dimuon_p4.M())\n #Qvalue.setVal((tree.chi_p4).M()**2 - tree.dimuon_p4.M()**2)\n #Qvalue.setVal((tree.rf1S_chic_mass**2 -tree.dimuon_mass**2)\n # / (3.5107**2 - 3.0969**2 ) -1)\n\n \n# this should be the correct one if the refitted variable was available\n# s.setVal((tree.rf1S_chic_mass**2 - tree.rf1S_dimuon_p4.M()**2)/ (3.5107**2 - 3.0969**2 ) -1)\n \n s.setVal((tree.rf1S_chic_mass**2 - 3.0969**2)/ (3.5107**2 - 3.0969**2 ) -1)\n\n\n \n psi1S_nsigma.setVal(tree.psi1S_nsigma)\n psi2S_nsigma.setVal(0) \n psi3S_nsigma.setVal(0)\n \n elif type == 'chib':\n\n m_mumu.setVal(tree.dimuon_mass)\n y_mumu.setVal(tree.dimuon_rapidity) \n pt_mumu.setVal(tree.dimuon_pt)\n eta_gamma.setVal(tree.photon_eta)\n pt_gamma.setVal(tree.photon_pt)\n m_chi_rf1S.setVal(tree.rf1S_chib_mass)\n m_chi_rf2S.setVal(tree.rf2S_chib_mass) \n Qvalue.setVal(tree.chib_mass - tree.dimuon_mass)\n psi1S_nsigma.setVal(tree.Y1S_nsigma)\n psi2S_nsigma.setVal(tree.Y2S_nsigma) \n psi3S_nsigma.setVal(tree.Y3S_nsigma)\n \n ctpv.setVal(tree.ct_pv)\n ctpv_error.setVal(tree.ct_pv_error)\n #pi0_abs_mass.setVal(tree.pi0_abs_mass)\n\n rho_conv.setVal(tree.Conv)\n dz.setVal(tree.Dz)\n probFit1S.setVal(tree.probfit1S)\n #probFit2S.setVal(tree.probFit2S)\n \n #if (tree.chic_pdgId == 20443):dataSet.add(dataArgSet)\n dataSet.add(dataArgSet)\n \n\n outfile = TFile(outfile_name,'recreate') \n dataSet.Write()","def result2json(ifilename, poiname, ofilename):\n nameMap = {\n \"SysWeight1\" : \"mc\",\n \"SysWeight2\" : \"FSR\",\n \"SysWeight3\" : \"bkg\",\n \"SysWeight4\" : \"tagpt\",\n \"SysWeight6\" : \"Prefire\",\n \"SysRecoil2\" : \"recoil_eta\",\n \"SysRecoil3\" : \"recoil_keys\",\n \"SysRecoil6\" : \"recoil_stat0\",\n \"SysRecoil7\" : \"recoil_stat1\",\n \"SysRecoil8\" : \"recoil_stat2\",\n \"SysRecoil9\" : \"recoil_stat3\",\n \"SysRecoil10\": \"recoil_stat4\",\n \"SysRecoil11\": \"recoil_stat5\",\n \"SysRecoil12\": \"recoil_stat6\",\n \"SysRecoil13\": \"recoil_stat7\",\n \"SysRecoil14\": \"recoil_stat8\",\n \"SysRecoil15\": \"recoil_stat9\",\n }\n\n def getNuisName(nuis):\n if nuis in nameMap.keys():\n return nameMap[nuis]\n elif bool(re.match(r\"\\w*bin\\d+shape\", nuis)):\n return \"QCD_\" + nuis\n else:\n return nuis\n\n ifile = ROOT.TFile(ifilename)\n himpact = ifile.Get(\"nuisance_impact_mu\")\n himpact_grouped = ifile.Get(\"nuisance_group_impact_mu\")\n tree = ifile.Get(\"fitresults\")\n tree.GetEntry(0)\n\n # find the POI bin for poiname\n ibinX = -1\n for binX in range(1, himpact.GetNbinsX()+1):\n poi = himpact.GetXaxis().GetBinLabel(binX)\n if poi == poiname:\n ibinX = binX\n continue\n assert ibinX >=0, \"Can not find the POI {} in the postfit file {}. Please check.\".format(poiname, ifilename)\n\n results = OrderedDict()\n results['POIs'] = []\n val = getattr(tree, poiname)\n err = abs(getattr(tree, poiname+\"_err\"))\n poi = OrderedDict()\n poi['fit'] = [val-err, val, val+err]\n poi['name'] = poiname\n results['POIs'].append(poi)\n\n results['method'] = 'default'\n results['params'] = []\n\n # dump impacts\n impacts = OrderedDict()\n for ibinY in range(1, himpact.GetNbinsY()+1):\n nuis = himpact.GetYaxis().GetBinLabel(ibinY)\n impacts[nuis] = himpact.GetBinContent(ibinX, ibinY)\n\n # add the grouped QCD and Recoil systematic\n groupnames = []\n for ibinY in range(1, himpact_grouped.GetNbinsY()+1):\n tmpY = himpact_grouped.GetYaxis().GetBinLabel(ibinY)\n if tmpY == 'stat':\n continue\n impacts[tmpY] = himpact_grouped.GetBinContent(ibinX, ibinY)\n groupnames.append(tmpY)\n\n # sort impacts, descending\n impacts = OrderedDict(sorted(impacts.items(), key=lambda x: abs(x[1]), reverse=True))\n\n pulls = OrderedDict()\n for nuis in impacts.keys():\n if nuis not in groupnames:\n val = getattr(tree, nuis)\n err = getattr(tree, nuis+\"_err\")\n err = abs(err)\n else:\n # manually set the postfit of the grouped sys to [-1,1], and pulled at 0,\n # since only the impacts are useful to us\n val = 0.\n err = 1.\n pulls[nuis] = [val - err, val, val + err]\n\n # save to results\n for nuis in impacts.keys():\n systematic = OrderedDict()\n systematic['fit'] = pulls[nuis]\n systematic['groups'] = []\n systematic['impact_' + poiname] = impacts[nuis]\n systematic['name'] = getNuisName(nuis)\n systematic['prefit'] = [-1.0, 0., 1.0]\n systematic[poiname] = [poi['fit'][1] - impacts[nuis], poi['fit'][1], poi['fit'][1] + impacts[nuis]]\n systematic['type'] = \"Gaussian\"\n print(getNuisName(nuis), pulls[nuis][1], pulls[nuis][1]-pulls[nuis][0], impacts[nuis])\n\n results['params'].append(systematic)\n\n with open(ofilename, 'w') as fp:\n json.dump(results, fp, indent=2)","def EORA(set_year=2016,save_output=True,print_output=True,in_dollars=False):\n\n # load mapping function for industries\n mapper_EORA = pd.read_excel(os.path.join(data_path,'other_sources','mappers.xlsx'),\n sheet_name=\"EORA_INDEC\")\n mapper_EORA = dict(zip(mapper_EORA['EORA'],mapper_EORA['INDEC']))\n\n ### Load supply and use table\n EORA_SUP = pd.read_excel(os.path.join(data_path,'EORA',\n 'EORA_IO_ARG_2015_BasicPrice.xlsx'),sheet_name='SUP',\n index_col=[0,1,2],header=[0,1,2])\n\n EORA_USE = pd.read_excel(os.path.join(data_path,'EORA',\n 'EORA_IO_ARG_2015_BasicPrice.xlsx'),sheet_name='USE',\n index_col=[0,1,2],header=[0,1,2]).fillna(0)\n\n # GET VARIABLES\n x = np.array(EORA_SUP.sum(axis=1)) # total production on industry level\n g = np.array(EORA_SUP.sum(axis=0)) # total production on product level\n F = EORA_USE.iloc[:196,125:].sum(axis=1)\n\n #Numpify\n Sup_array = np.asarray(EORA_SUP.iloc[:125,:196]) # numpy array of supply matrix\n Use_array = np.asarray(EORA_USE.iloc[:196,:125]) # numpy array of use matrix\n\n g_diag_inv = np.linalg.inv(np.diag(g)) # inverse of g (and diagolinized)\n x_diag_inv = np.linalg.inv(np.diag(x)) # inverse of x (and diagolinized)\n\n # Calculate the matrices\n B = np.dot(Use_array,x_diag_inv) # B matrix (U*x^-1)\n D = np.dot(Sup_array,g_diag_inv) # D matrix (V*g^-1)\n I_i = np.identity((len(x))) # Identity matrix for industry-to-industry\n\n # Inverse for industry-to-industry\n A_ii = np.dot(D,B)\n IDB_inv = np.linalg.inv((I_i-np.dot(D,B))) # (I-DB)^-1 \n\n # And canclulate sum of industries\n ind = np.dot(IDB_inv,np.dot(D,F)) # (I-DB)^-1 * DF\n\n # split FD in local, import and export\n LFD = np.dot(D,EORA_USE.iloc[:196,125:131].sum(axis=1) )\n Exp = np.dot(D,EORA_USE.iloc[:196,131])\n\n # combine all elements into one table\n EORA = pd.concat([pd.DataFrame(np.dot(A_ii,np.diag(ind))),pd.DataFrame(LFD),\n pd.DataFrame(Exp)],axis=1)\n EORA.columns = [x[2] for x in EORA_USE.columns[:125]]+['FinalD','Exports']\n EORA.index = [x[2] for x in EORA_USE.columns[:125]]\n\n VA = list(EORA_USE.iloc[196:202,:125].sum(axis=0))+[0,0]\n IMP = list(EORA_USE.iloc[202,:125])+[0,0]\n EORA.loc['ValueA'] = VA\n EORA.loc['Imports'] = IMP\n EORA[EORA < 1e-5] = 0\n\n # and map into the INDEC classes:\n EORA.columns = EORA.columns.map(mapper_EORA)\n EORA = EORA.groupby(level=0,axis=1).sum()\n EORA.index = EORA.index.map(mapper_EORA)\n EORA = EORA.groupby(level=0,axis=0).sum()\n\n EORA = EORA[[chr(i).upper() for i in range(ord('a'),ord('p')+1)]+['FinalD','Exports']]\n EORA = EORA.T[[chr(i).upper() for i in range(ord('a'),ord('p')+1)]+['ValueA','Imports']]\n EORA = EORA.T\n\n # convert to the desired year using INDEC time series\n Total_Prod = pd.read_excel(os.path.join(data_path,\n 'INDEC','sh_VBP_VAB_06_19.xls'),\n sheet_name='Cuadro 2',skiprows=3,index_col=[0])/1e3 \n\n rel_change = Total_Prod[set_year]/Total_Prod[2015] \n\n # and balance the table\n X0 = EORA.values[:,:]\n X0_sum = np.array(list(EORA.sum(axis=1)[:16]*rel_change)+[(EORA['FinalD'][:16]*rel_change).sum()]+[(EORA['Exports'][:16]*rel_change).sum()])\n\n # apply RAS method to rebalance the table\n new_IO = ras_method(X0,X0_sum,X0_sum,1e-5,print_out=False)\n\n if in_dollars:\n EORA.iloc[:,:] = new_IO/1e6\n else:\n EORA.iloc[:,:] = new_IO/1e6*dollar_pesos()[set_year]\n\n if save_output:\n EORA.to_csv(os.path.join(data_path,'national_tables','{}_EORA.csv'.format(set_year)))\n\n if print_output:\n print('NOTE : Standardized national table for Argentina for the year {} finished using EORA data'.format(set_year)) \n\n return EORA","def vibrational_analysis_summary(self):\n print(\"Summary of the vibrational analysis:\")\n #cols = [ \"eigvals [a.u.]\" , \"w [a.u.]\", \"w [THz]\", \"w [cm^-1]\", \"T [a.u.]\", \"T [ps]\",\"E [a.u.]\", \"n [a.u.]\"]\n df = pd.DataFrame()\n eigvals = self.eigvals.copy()\n eigvals [ eigvals == MicroStatePrivate.smallest_float ] = np.nan\n df[\"eigvals [a.u.]\"] = eigvals\n df[\"w [a.u.]\"] = [ np.sqrt(i) if i > 0. else None for i in eigvals ]\n df[\"w [THz]\"] = convert(df[\"w [a.u.]\"],\"frequency\",_from=\"atomic_unit\",_to=\"thz\")\n df[\"w [cm^-1]\"] = convert(df[\"w [a.u.]\"],\"frequency\",_from=\"atomic_unit\",_to=\"inversecm\")\n df[\"T [a.u.]\"] = 2*np.pi / df[\"w [a.u.]\"]\n df[\"T [ps]\"] = convert(df[\"T [a.u.]\"],\"time\",_from=\"atomic_unit\",_to=\"picosecond\")\n\n if hasattr(self,\"energy\"):\n df[\"E [a.u.]\"] = self.energy.mean(axis=0)\n\n if hasattr(self,\"occupations\"):\n df[\"n [a.u.]\"] = self.occupations.mean(axis=0)\n\n return df","def plot_uwrz_field(f=False,ax=False, oro='h500a7', vm=[-2,2],tinst=18,var='QVrz',ik=0,jk=0):\n\n tinst = int(tinst)\n \n # in case f is not passed, f and ax are created\n if not f:\n f,ax=plt.subplots()\n\n # Name the axes \n decorate_ax(ax,'',daytime(tinst),'')\n \n\n # specify full path of the data\n\n iensmean = 1\n \n if iensmean:\n srcpath = BASE3D + oro + '/' + sm +'/' + CIRCMEAN\n print srcpath\n else: # 1 single ensemble member first day\n srcpath = BASE3D + oro + '/' + sm +'/'\n seedname= filter(lambda x: x.startswith('circmean_seed'), os.listdir(srcpath))[0]\n srcpath +=seedname\n\n # Open the data in netcdf format and read it only \n ncfl = Dataset(srcpath,'r')\n \n # Load the fields of interest\n # COSMO fields dimensions are (time, z,rlat,rlon)\n #\n # Field dimensions in the following are (time,z,r)\n #\n # By specifing '::-1' the second timension is inverted\n urz = ncfl.variables['Urz'][tinst,::-1,:] # U component of wind\n wrz = ncfl.variables['Wrz'][tinst,::-1,:] # W component of wind\n\n speedrz = ncfl.variables['Speedrz'][tinst,::-1,:] # wind speed\n rhorz = ncfl.variables['RHOrz'][:,::-1,:] # density in rz coordinates\n TOTP = ncfl.variables['TOT_PRECr'][:,:]/2. # half-hourly output\n \n LCLr= ncfl.variables['LCL_MLr'][tinst,:]/1000. # LCL\n LFCr = ncfl.variables['LFC_MLr'][tinst,:]/1000. # LFC\n hPBLr = ncfl.variables['HPBLr'][tinst,:]/1000. \n \n CAPEr = ncfl.variables['CAPE_MLr'][tinst,:] # CAPE\n CINr = ncfl.variables['CIN_MLr'][tinst,:] # CIN\n \n TOTPsum = np.sum(TOTP[:tinst,:],axis=0)\n TOTPsum = TOTP[tinst,:]\n \n TPthres = 0.5\n rainyr=np.where(TOTPsum>TPthres)\n\n\n if var: #select a specific humidity var\n qvrz = ncfl.variables[var][:,::-1,:] # specficif humidity\n qcrz = ncfl.variables['QCrz'][:,::-1,:]\n else: # otherwise sum it all up:\n qxrzvars = filter(lambda x: x.startswith('Q'),ncfl.variables.keys())\n \n qvrz = np.zeros(ncfl.variables['QVrz'].shape)\n for var in qxrzvars:\n print var\n qvrz+=ncfl.variables[var][:,::-1,:]\n \n tref = 12 # 06:00\n ipltdiff= 0\n if ipltdiff:\n plotvar = rhorz[tinst,:]*qvrz[tinst,:]-rhorz[tref,:]*qvrz[tref,:]\n qcrz = rhorz[tinst,:]*qcrz[tinst,:]-rhorz[tref,:]*qcrz[tref,:]\n else:\n plotvar = rhorz[tinst,:]*qvrz[tinst,:]\n \n\n rhorz = rhorz[tinst,:]\n \n \n # Load the grid in r, z coordinate\n X = ncfl.variables['X'][:]\n \n \n # nasty and temporary workaround as Z is empty in single seed data - just\n # take it from the circmean field\n srcpath2 = BASE3D + oro + '/' + sm +'/' + CIRCMEAN\n ncfl2=Dataset(srcpath2)\n Z = ncfl2.variables['Z'][::-1,:]/1000. # highest index <-> ground\n ncfl2.close() \n \n # Add mountain contour\n r=X[0,:]\n ax.plot(r,Z[0,:],color='grey',linewidth=2)\n \n# ax.plot(r,hPBLr,'k-.',linewidth=1)\n# ax.plot(r,LCLr,'k-',linewidth=1) \n \n ax.fill_between(X[0,:],Z[0,:],0,color='lightgrey')\n \n #indicate rainy region\n \n ifillrainy = False\n if ifillrainy:\n ax.fill_between(X[0,rainyr][0],Z[0,rainyr][0],color='lightblue',alpha=0.8) \n ax.plot(X[0,rainyr][0],Z[0,rainyr][0],color='lightblue',linewidth=2)\n else:\n pass#ax.plot(X[0,rainyr][0],Z[0,rainyr][0],color='blue',linewidth=2,linestyle='dashed')\n \n # Velocity vectors with speed (length) smaller than speedthres are not \n # shown\n oroorig = oro\n if oro.endswith('bell') or oro.endswith('cos2'):\n \n oro = oro[:-5]\n print oro\n \n i,j=np.where(expnames==oro)\n\n if i==[]:\n print 'HH'\n \n oro = oro.split('_')[0]\n h,a = getha(oro)\n #h,a = hrange[i],arange[j]\n print h,a\n xrang = [0,40]\n yrang = [0,10.]\n delx = xrang[1]-xrang[0]\n dely = yrang[1]-yrang[0]\n \n speedthres = 0.1# m/s\n M = speedrz[:] < speedthres\n U = ma.masked_array(urz[:,:],mask=M)\n scalefac_w = 1. #delx/dely\n W = ma.masked_array(scalefac_w*wrz[:,:],mask=M) \n \n \n \n if 0: # REMOVE\n fact_agl = -dely/delx\n sin = np.arcsin(fact_agl)\n cos = np.arccos(fact_agl)\n U=cos*U-sin*W\n W=sin*U+cos*W\n # Plot radial velocity\n urzlevs = np.linspace(vm[0],vm[1],10)\n\n\n levs = np.array([2,4,6,8,10,12,14,16])\n #levs = np.array([0.25,0.5,1,2,4,8,16,32])\n #levs = np.array([0.5,1,2,4,8,16,32])\n if ipltdiff:\n levs=levs/8. #np.linspace(0.001,0.004,11)d\n #else:\n # levs=np.array([0.001,0.0015,0.002,0.0025,0.003,0.0035,0.004])*50\n #if var.startswith('QC'): levs=np.linspace(0.1,1.5,9)/1000.\n \n #cf = ax.contourf(X,Z,plotvar*1000., levels=levs*1000.,cmap=viridis_inv,extend='both')\n \n ipltmoistening = False \n if ipltmoistening:\n cf = ax.contourf(X,Z,plotvar*1000.,cmap=viridis_inv,extend='both',\n levels=levs)\n else:\n cf = 0\n \n #cfc = ax.contourf(X,Z,qvrz*1000.,cmap='Blues',extend='both', alpha=1,\n # levels=levs/4.) \n\n iplt_thermodynvar = False\n if iplt_thermodynvar:\n for var in [LFCr]:\n print var.shape\n print X.shape\n ax.plot(var)\n \n \n qstx = 2\n qstz = 2\n Q=ax.quiver(X[::qstz,::qstx],(Z+0.02)[::qstz,::qstx],U[::qstz,::qstx],W[::qstz,::qstx],\n units='inches',scale=10,angles='xy', color='red',pivot='mid',\n headwidth=4,width=0.009)\n #Q=ax.quiverkey(Q,a/2,h/2000.,1,r'$1\\frac{m}{s}$',labelpos='N',coordinates='data')\n\n if jk==2 or oroorig.endswith('cos2'):#(ik,jk)==(1,0):\n ax.quiverkey(Q,-2,0.05,1,r'$1 \\frac{m}{s}$',labelsep=0.05,\n labelpos='N',coordinates='data')\n\n\n ax.set_xticks(np.arange(0,80,20)) \n\n ax.set_xlim(xrang)\n ax.set_ylim(yrang)\n\n if ik==2:\n ax.set_xlabel('r in km')\n if jk==0:\n ax.set_ylabel('z in km') \n \n ax.grid(True)\n\n ncfl.close()\n \n return f,ax ,cf # plot_uwrz_field","def encircled_energy(uvotfilter, areapix):\n import os\n caldb = os.getenv(\"CALDB\")\n if uvotfilter == 'wh': uvotfilter = 'white'\n command=\"quzcif swift uvota - \"+uvotfilter.upper()+\\\n \" REEF 2009-10-30 12:00:00 - > quzcif.out\"\n print(command) \n if not os.system(command):\n print(\"not \" +command) \n f = open(\"quzcif.out\")\n reeffile, ext = f.read().split()\n ext = int(ext)\n f.close()\n os.system(\"rm -f quzcif.out\")\n print(reeffile, ext)\n f = fits.getdata(reeffile,ext)\n r = f['radius'] # in arc sec\n E = f['reef'] \n x = sqrt(old_div(areapix,pi))*0.502 # lookup radius\n f = interp1d(r,E)\n return f(x)","def potentials(path_to_units, path_to_eez, path_to_shared_coast,\n path_to_electricity_yield_pv_prio, path_to_electricity_yield_wind_prio,\n path_to_eligibility_categories, path_to_land_cover, path_to_protected_areas,\n path_to_result, scenario, config):\n with rasterio.open(path_to_eligibility_categories, \"r\") as src:\n eligibility_categories = src.read(1)\n with rasterio.open(path_to_electricity_yield_pv_prio, \"r\") as src:\n transform = src.transform\n electricity_yield_pv_prio = src.read(1)\n with rasterio.open(path_to_electricity_yield_wind_prio, \"r\") as src:\n electricity_yield_wind_prio = src.read(1)\n with rasterio.open(path_to_land_cover, \"r\") as src:\n land_cover = src.read(1)\n with rasterio.open(path_to_protected_areas, \"r\") as src:\n protected_areas = src.read(1)\n with fiona.open(path_to_units, \"r\") as src:\n unit_ids = [feature[\"properties\"][\"id\"] for feature in src]\n unit_geometries = [feature[\"geometry\"] for feature in src]\n with fiona.open(path_to_eez, \"r\") as src:\n eez_ids = [feature[\"properties\"][\"id\"] for feature in src]\n eez_geometries = [feature[\"geometry\"] for feature in src]\n shared_coasts = pd.read_csv(path_to_shared_coast, index_col=0)\n\n electricity_yield_pv_prio, electricity_yield_wind_prio = apply_scenario_config(\n potential_pv_prio=electricity_yield_pv_prio,\n potential_wind_prio=electricity_yield_wind_prio,\n categories=eligibility_categories,\n land_cover=land_cover,\n protected_areas=protected_areas,\n scenario_config=config[\"scenarios\"][scenario]\n )\n electricity_yield_pv_prio, electricity_yield_wind_prio = decide_between_pv_and_wind(\n potential_pv_prio=electricity_yield_pv_prio,\n potential_wind_prio=electricity_yield_wind_prio,\n electricity_yield_pv_prio=electricity_yield_pv_prio,\n electricity_yield_wind_prio=electricity_yield_wind_prio,\n eligibility_categories=eligibility_categories\n )\n\n onshore_potentials = pd.DataFrame(\n index=unit_ids,\n data={\n potential: potentials_per_shape(\n eligibilities=potential.eligible_on,\n potential_map=(electricity_yield_pv_prio if \"pv\" in str(potential).lower()\n else electricity_yield_wind_prio),\n eligibility_categories=eligibility_categories,\n shapes=unit_geometries,\n transform=transform\n )\n for potential in Potential.onshore()\n }\n )\n offshore_eez_potentials = pd.DataFrame(\n index=eez_ids,\n data={\n potential: potentials_per_shape(\n eligibilities=potential.eligible_on,\n potential_map=(electricity_yield_pv_prio if \"pv\" in str(potential).lower()\n else electricity_yield_wind_prio),\n eligibility_categories=eligibility_categories,\n shapes=eez_geometries,\n transform=transform\n )\n for potential in Potential.offshore()\n }\n )\n offshore_potentials = pd.DataFrame(\n data=shared_coasts.dot(offshore_eez_potentials),\n columns=Potential.offshore()\n )\n potentials = pd.concat([onshore_potentials, offshore_potentials], axis=1)\n potentials.index.name = \"id\"\n potentials.to_csv(\n path_to_result,\n header=True,\n index=True\n )","def initial_importer(initials, initialZMT=True):\n from .functions import cosd, lna\n ###filling the running variables with values depending on the systemconfiguration in rk4input###\n\n if Base.spatial_resolution == 0:\n dim = 0\n print('0D')\n Vars.T = initials['zmt']\n else:\n dim = 1\n # NS==True corresponds to southpole to northpole representation (180 Degrees)\n if Base.both_hemispheres == True:\n Latrange = 180\n\n # Checking if Temperature and Latitude is set on a latitudal circle (0°,10°,..if step=10)\n # or on a latitudinal belt and therefore between the boundaries (5°,15°,..if step=10)\n\n # circle==True and belt==False says on the latitudinal circle\n if Base.latitudinal_circle == True and Base.latitudinal_belt == False:\n Vars.Lat = np.linspace(-90 + Base.spatial_resolution, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution - 1))\n Vars.Lat2 = np.linspace(-90, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\n if initialZMT == True:\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution - 1))\n # Checking if the Temperature for each latitude starts with the same value or a\n # cosine shifted value range\n if initials['initial_temperature_cosine'] == True:\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\n\n # circle==False and belt==True say on the latitudinal belt\n if Base.latitudinal_circle == False and Base.latitudinal_belt == True:\n Vars.Lat2 = np.linspace(-90 + Base.spatial_resolution, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution - 1))\n Vars.Lat = np.linspace(-90, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\n if initialZMT == True:\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\n if initials['initial_temperature_cosine'] == True:\n if initials['initial_temperature_noise'] == True:\n z = [0] * len(Vars.Lat)\n for k in range(len(Vars.Lat)):\n z[k] = np.random.normal(0, initials['initial_temperature_noise_amplitude'])\n else:\n z = 0\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1) + lna(z)\n\n # Not from southpole to northpole rather equator to pole\n else:\n Latrange = 90\n if Base.latitudinal_circle == True and Base.latitudinal_belt == False:\n Vars.Lat = np.linspace(0, 90 - Base.spatial_resolution, int(Latrange / Base.spatial_resolution))\n Vars.Lat2 = np.linspace(0, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\n if initialZMT == True:\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\n if initials['initial_temperature_cosine'] == True:\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\n if Base.latitudinal_circle == False and Base.latitudinal_belt == True:\n Vars.Lat2 = np.linspace(0, 90 - Base.spatial_resolution, int(Latrange / Base.spatial_resolution))\n Vars.Lat = np.linspace(0, 90 - Base.spatial_resolution,\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\n if initialZMT == True:\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\n if initials['initial_temperature_cosine'] == True:\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\n\n Vars.t = initials['time']\n if Base.parallelization == True:\n if initialZMT == True:\n Vars.T = np.array([Vars.T] * Base.number_of_parallels)\n Vars.T_global = np.array([initials['gmt']] * Base.number_of_parallels)\n else:\n Vars.T_global = initials['gmt']","def ImportTemps(cls, mfg_enduse):\n\n def create_dict(file_dir, file):\n dict_out = dict(pd.read_excel(\n file_dir + file, _sheetname=0).iloc[:, (0, 2)].values\n )\n\n return dict_out\n\n ndict = {}\n\n for k, v in cls.nfiles.items():\n ndict[k[0:7]] = create_dict(cls.file_dir, v)\n\n temps = pd.read_excel(cls.file_dir + cls.temp_file, sheetname=0)\n\n temps.SIC.fillna(method='ffill', inplace=True)\n\n temps.loc[:, 'Temp_C'] = temps.Temp_C.apply(\n lambda x: int(np.around(x))\n )\n\n # Calculate energy fraction of each process by temperature\n temps = pd.DataFrame(temps.groupby(\n ['SIC', 'Unit_Process', 'Heat_type', 'Temp_C']\n )['E_Btu'].sum())\n\n e_totals = temps.reset_index()[\n temps.reset_index()['Unit_Process'] != 'Boiler'\n ].groupby(['SIC', 'Heat_type']).E_Btu.sum()\n\n for i in temps.index:\n if 'Boiler' in i:\n continue\n\n temps.loc[i, 'Fraction'] = \\\n temps.loc[i, 'E_Btu'] / e_totals.loc[(i[0], i[2])]\n\n temps.reset_index(inplace=True)\n\n temps.loc[:, 'SIC'] = temps.SIC.apply(lambda x: int(str(x)[0:4]))\n\n temps.loc[:, 'NAICS02'] = temps.SIC.map(ndict['sic_N02'])\n\n temps.loc[:, 'NAICS07'] = temps.NAICS02.map(ndict['N02_N07'])\n\n temps.loc[:, 'NAICS12'] = temps.NAICS07.map(ndict['N07_N12'])\n\n # Multiple entries for each SIC/NAICS; take simple mean. \n temps = temps.groupby(\n ['NAICS12', 'Unit_Process', 'Heat_type']\n )[['E_Btu', 'Temp_C', 'Fraction']].mean()\n\n # Create 4-, and 3-digit NAICS table for matching \n temps_NAICS = pd.DataFrame(index=temps.index.levels[0],\n columns=['N5', 'N4', 'N3']\n )\n\n for n in [5, 4, 3]:\n temps_NAICS.loc[:, 'N' + str(n)] = \\\n [float(str(x)[0:n]) for x in temps_NAICS.index.values]\n\n temps_NAICS.reset_index(inplace=True)\n\n eu_naics = pd.DataFrame(\n mfg_enduse.naics.drop_duplicates().sort_values(ascending=True),\n copy=True\n )\n\n eu_naics.reset_index(inplace=True, drop=True)\n\n eu_naics.rename(columns={'naics':'NAICS12'}, inplace=True)\n\n for n in [5, 4, 3]:\n eu_naics.loc[:, 'N' + str(n)] = \\\n [float(str(x)[0:n]) for x in eu_naics.NAICS12.values]\n\n # Match naics between end use data set and temperature info. \n nmatch = pd.DataFrame()\n for column in temps_NAICS.columns:\n nmatch = pd.concat([nmatch, pd.Series(\n [x in temps_NAICS[column].values for x in eu_naics[\n column\n ].values]\n )], axis=1)\n\n nmatch.columns = eu_naics.columns\n\n nmask = pd.DataFrame()\n\n for c in nmatch.columns:\n\n nmask = pd.concat(\n [nmask, eu_naics[c].multiply(nmatch[c])],\n axis=1\n )\n\n nmask.replace({0:np.nan}, inplace=True)\n\n # Values of 0 indicate no matching temperature data, even at 3-digit \n # level.\n nmask.N3.fillna(0, inplace=True)\n\n nmask.loc[:, 'TN_Match'] = nmask.apply(\n lambda x: int(list(x.dropna())[0]), axis=1\n )\n\n nmask.rename(columns={'NAICS12':'N6'}, inplace=True)\n\n nmask.loc[:, 'NAICS12'] = eu_naics.NAICS12\n\n # Merge matched NAICS values with end use energy data\n mfg_enduse = pd.merge(mfg_enduse,\n nmask[['NAICS12', 'TN_Match']], how='left',\n left_on='naics', right_on='NAICS12')\n\n mfg_enduse.drop('NAICS12', inplace=True, axis=1)\n\n # Merge temps and temps_NAICS for future operations by other NAICS\n temps.reset_index(inplace=True)\n\n temps = pd.merge(temps, temps_NAICS, left_on='NAICS12',\n right_on='NAICS12', how='left')\n\n for tb, tr in {'<100': (0, 99), '100-249': (100, 249),\n '250-399': (250, 399), '400-999': (400, 999),\n '>1000': (1000, 3000)}.items():\n\n ti = temps[temps.Temp_C.between(tr[0], tr[1])].index\n\n temps.loc[ti, 'Temp_Bucket'] = tb\n\n return mfg_enduse, temps","def test_all_false(directorio = str(), database = 'red_cod-db.pkl', \r\n local_function = 'fij_2.0_25_diccio'):\r\n df = pd.read_pickle(database)\r\n collection = pd.read_csv(directorio + '/compounds_collection.csv')\r\n \r\n cifs = [i for i in collection['cif']]\r\n maxsites = np.max(collection['sitios'])\r\n \r\n df = df[df['sitios'] > 0][df['sitios'] <= maxsites].reset_index(drop=True)\r\n df = df.loc[~df['cif'].isin(cifs)].reset_index(drop=True)\r\n \r\n x, _, df = inout_creator(df=df)\r\n \r\n x = compute_quotients(X=x)\r\n x, df = append_local_functions(X = x,df=df)\r\n \r\n busqueda = \"ls \" + directorio + \"/*.h5 > model_names.txt\"\r\n os.system(busqueda)\r\n \r\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\r\n \r\n X = (x - diccio['mean'])/diccio['std']\r\n x = np.reshape(X,(X.shape[0],X.shape[2]))\r\n \r\n with open('model_names.txt','r') as f:\r\n for line in f:\r\n modelo = models.load_model(line[:len(line)-1])\r\n nombre = line.split('/')[1]\r\n \r\n outpred = modelo.predict(x)\r\n prediction = outpred >= 0.5\r\n df['y_pred'] = np.ravel(prediction)\r\n \r\n with open(directorio+'/test_with_all_false.txt','a') as tr:\r\n tr.write(nombre + '\\n')\r\n \r\n for sitios in range(1, max(df['sitios']) + 1):\r\n \r\n acc = df[df['sitios'] == sitios][df['y_pred'] == False].shape[0]\r\n miniset = df[df['sitios'] == sitios].shape[0]\r\n percent = round(100*acc/miniset,2)\r\n \r\n \r\n tr.write('With '+ str(sitios) + ' sites:' + str(percent) +\\\r\n '(' + str(miniset) + ' samples)' + '\\n')\r\n tr.close()\r\n return","def data(dataset=\"bio_eventrelated_100hz\"):\n # TODO: one could further improve this function with like\n # selectors 'ecg=True, eda=True, restingstate=True' that would\n # find the most appropriate dataset\n\n dataset = dataset.lower()\n\n # TODO: change this path back to \"master\"\n path = \"https://raw.githubusercontent.com/neuropsychology/NeuroKit/dev/data/\"\n\n # Signals as vectors =======================\n if dataset in [\"eeg\", \"eeg_150hz\", \"eeg.txt\"]:\n return pd.read_csv(path + \"eeg.txt\").values[:, 0]\n\n if dataset in [\"rsp\", \"rsp_1000hz\", \"rsp_1000hz.txt\"]:\n return pd.read_csv(path + \"rsp_1000hz.txt\", header=None).values[:, 0]\n\n if dataset in [\"ecg\", \"ecg_1000hz\", \"ecg_1000hz.csv\"]:\n return pd.read_csv(path + \"ecg_1000hz.csv\")[\"ECG\"].values\n\n if dataset in [\"ecg_3000hz\", \"ecg_3000hz.csv\"]:\n return pd.read_csv(path + \"ecg_1000hz.csv\")[\"ECG\"].values\n\n if dataset in [\"eog\", \"veog\", \"eog_100hz\", \"eog_100hz.csv\"]:\n return pd.read_csv(path + \"eog_100hz.csv\")[\"vEOG\"].values\n\n # Dataframes ===============================\n if dataset == \"iris\":\n info = sklearn_datasets.load_iris()\n data = pd.DataFrame(\n info.data, columns=[\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\"]\n )\n data[\"Species\"] = info.target_names[info.target]\n return data\n\n if dataset in [\"eogs\", \"eogs_200hz\", \"eog_200hz\", \"eog_200hz.csv\"]:\n return pd.read_csv(path + \"eog_200hz.csv\")\n\n # Add extension\n if dataset in [\"bio_resting_8min_200hz\"]:\n dataset += \".json\"\n\n # Specific case for json file\n if dataset.endswith(\".json\"):\n if \"https\" not in dataset:\n data = pd.read_json(path + dataset, orient=\"index\")\n else:\n data = pd.read_json(dataset, orient=\"index\")\n df = {}\n for participant, row in data.iterrows():\n for _, data_string in row.items():\n data_list = json.loads(data_string)\n data_pd = pd.DataFrame(data_list)\n df[participant] = data_pd\n\n return df\n\n # TODO: Add more EEG (fif and edf datasets)\n if dataset in [\"eeg_1min_200hz\"]:\n\n return pickle.load(\n urllib.request.urlopen(\n \"https://github.com/neuropsychology/NeuroKit/blob/dev/data/eeg_1min_200hz.pickle?raw=true\"\n )\n )\n\n # General case\n file, ext = os.path.splitext(dataset) # pylint: disable=unused-variable\n if ext == \"\":\n df = pd.read_csv(path + dataset + \".csv\")\n else:\n if \"https\" not in dataset:\n df = pd.read_csv(path + dataset)\n else:\n df = pd.read_csv(dataset)\n return df","def summaryD5(self):\r\n\r\n if self.window.diff_tabs.tempruns_D45_set==3:\r\n\r\n dfD5_temp1_pos1= pd.read_csv('raw_text_D5_1_1.txt')\r\n dfD5_temp1_pos2= pd.read_csv('raw_text_D5_1_2.txt')\r\n \r\n D5summary_temp1=pd.concat([dfD5_temp1_pos1,dfD5_temp1_pos2],axis=1)\r\n D5summary_temp1.to_csv('D5summary_temp1.txt',index=False)\r\n\r\n dfD5_temp2_pos1= pd.read_csv('raw_text_D5_1_1.txt')\r\n dfD5_temp2_pos2= pd.read_csv('raw_text_D5_1_2.txt')\r\n \r\n\r\n D5summary_temp2=pd.concat([dfD5_temp2_pos1,dfD5_temp2_pos2],axis=1)\r\n D5summary_temp2.to_csv('D5summary_temp2.txt',index=False)\r\n\r\n dfD5_temp3_pos1= pd.read_csv('raw_text_D5_3_1.txt')\r\n dfD5_temp3_pos2= pd.read_csv('raw_text_D5_3_2.txt')\r\n \r\n\r\n D4summary_temp3=pd.concat([dfD5_temp3_pos1,dfD5_temp3_pos2],axis=1)\r\n D4summary_temp3.to_csv('D5summary_temp3.txt',index=False)","def ReadData( fileName ):\n \n # define column names\n colNames = ['Date','Precip','Max Temp', 'Min Temp','Wind Speed'] #NOTE: I changed the column names because .query() would not work when referencing column names with spaces\n global DataDF #added this line to make the dataframe visible in the variable explorer\n global ReplacedValuesDF #added this line to make the dataframe visible in the variable explorer\n # open and read the file\n DataDF = pd.read_csv(\"DataQualityChecking.txt\",header=None, names=colNames, \n delimiter=r\"\\s+\",parse_dates=[0])\n DataDF = DataDF.set_index('Date')\n \n # define and initialize the missing data dictionary\n ReplacedValuesDF = pd.DataFrame(0, index=[\"1. No Data\",\"2. Gross Error\",\"3. Swapped\",\"4. Range Fail\"], columns=colNames[1:]) #added additional indexed rows to make adding the values later easier\n \n return( DataDF, ReplacedValuesDF )","def readin (filename, hdu=0, non_std_fits=False,\n text_comments='#', text_skiprows=0, get_data=False, verbose=False,\n apply_WCS_rv=False):\n multi_order_txt = False\n use_naxis2='all'\n use_naxis3='all'\n \n \n preferred_wlsoln=None # !! need to fix this\n # !! should also be able to input wavelength solution?\n \n if preferred_wlsoln is not None: preferred_wlsoln = wlsolvefxn.get_func_name(preferred_wlsoln)\n \n #### check if file exists ####### #############\n if not os.path.exists(filename): raise IOError(\"File does not exist:'\"+filename+\"'\")\n\n\n #### check if file is text############# \n np_kwargs = {'comments':text_comments,\n 'skiprows':text_skiprows}\n is_text_file, txt_data = check_for_txt_format(filename,**np_kwargs)\n\n #### if it is a text file ######################\n if is_text_file:\n spec_obj = readin_txt(filename,txt_data,get_data) \n return spec_obj \n\n #### now check how it behaves as a fits file\n if non_std_fits: hdulist = pyfits.open(filename)\n else:\n # give standard pyfits readin a try\n try: hdulist = pyfits.open(filename)\n except: raise IOError(\"PYFITS DOES NOT LIKE THE FILE YOU GAVE ('\"+filename+\"'), TO SEE WHAT ERROR IT GIVES TRY: hdulist = pyfits.open('\"+filename+\"')\")\n\n\n #### open up fits file ##############################\n hdulist = pyfits.open(filename)\n\n # select which header unit ot use\n if len(hdulist) > 1: \n hdu = int(hdu)\n hdu = np.clip(hdu,0,len(hdulist)-1)\n else: hdu = 0\n\n # specify the current header unit\n header_unit = hdulist[hdu]\n prihdr = header_unit.header\n\n # can display some useful information \n if verbose: \n print \"=\"*60\n print (hdulist.info(),'\\n')\n if len(hdulist) > 1:\n print \"=\"*20+\" USING HEADER: \"+\"=\"*20\n print repr(hdulist[hdu])\n\n ##### fill in the data class\n # not get header info of relevance\n simple = query_fits_header(prihdr,'SIMPLE',noval=False)\n xtension = query_fits_header(prihdr,'XTENSION')\n if simple.found:\n if not simple.val: print \"HeadsUp: Header Keyword SIMPLE is False, you may encounter unexpected behavior\"\n else:\n if not xtension.found: print \"HeadsUp: No extension keyword found in headers, you may encounter unexpected behavior\"\n \n \n #### read in important information from header, if present\n ibits = query_fits_header(prihdr,'BITPIX') # how many bits per pixel in the data? Not currently necessary, numpy will adapt\n \n naxis = query_fits_header(prihdr,'NAXIS' ,noval=0) # how many dimenstions?\n naxis1 = query_fits_header(prihdr,'NAXIS1',noval=0) # number of points per order\n naxis2 = query_fits_header(prihdr,'NAXIS2',noval=0) # number of orders\n naxis3 = query_fits_header(prihdr,'NAXIS3',noval=0) # number of different spectra\n\n apformat = query_fits_header(prihdr,'APFORMAT')\n if apformat.found: print \"WARNING: I'M NOT SURE HOW TO DEAL WITH APFORMAT VALUES\" # !! though I think it's just the spec files\n\n if not naxis.found: raise IOError(\"ERROR: Keyword NAXIS not found\")\n\n bzero = query_fits_header(prihdr,\"BZERO\",noval=0)\n bscale = query_fits_header(prihdr,\"BSCALE\",noval=1)\n\n ###### read in data ##############################################\n data = header_unit.data\n\n if data is None:\n wl, data, inv_var = np.zeros(3).reshape((3,1))\n if get_data: return (wl,data,inv_var)\n else: return eyeSpec_spec(wl,data,inv_var,header_unit.header)\n else:\n # check that data matches up with at least one of the dimensions\n if data.ndim != naxis.val: raise ValueError(\"Dimension of data \"+str(data.ndim)+\" does not match keyword naxis \"+str(naxis.val))\n \n statement = 'Dimension does not match data.shape = '+str(data.shape)+\" fits file (naxis1, naxis2, naxis3) \"+str(tuple([naxis1.val,naxis2.val,naxis3.val]))\n if data.ndim == 1: \n assert data.shape == (naxis1.val,) , statement\n data = data.reshape((1,1,)+data.shape)\n \n elif data.ndim == 2: \n assert data.shape == (naxis2.val, naxis1.val), statement\n data = data.reshape((1,)+data.shape) \n \n elif data.ndim == 3: \n assert data.shape == (naxis3.val, naxis2.val, naxis1.val), statement\n \n ##### Determine the which data is useful \n # which orders to read in \n nband = np.arange(data.shape[0])+1\n nord = np.arange(data.shape[1])+1\n\n \n ##### Calculate the wavelengths for the data\n # set up wavelength and inverse_variance\n wl = np.ones(data.shape)\n \n # get the wavelength coefficients\n wlcoeff = wlsoln_coeff_from_header(header_unit.header, apply_WCS_rv, preferred_wlsoln)\n \n # the same wavelength solution is applied to all bands so just pick the first and broadcast\n band = 0\n priv_info = {}\n \n # go through all the orders\n do_progress = True\n progressive_pt = 1 # this will advance and be used when there is no wavelength solution\n for i in xrange(len(nord)):\n order_i = nord[i]\n\n # get the coefficients and function type \n equ_type = wlcoeff.get_equation_type()\n if equ_type in ['none',None,'no solution'] and do_progress: \n coeff = [progressive_pt,1]\n equ_type = 'pts'\n else: coeff = wlcoeff.get_coeffs(order_i)\n \n # pts[0] = 1 :: this was definitely the right thing to do for SPECTRE's 1-D output but may not be for other equations, may need pts[0]=0, this may be for bzero,bscale\n pts = np.arange(len(wl[0][i]))+1 \n # apply function\n wl[0][i] = wlsolvefxn(pts, coeff, equ_type) \n \n progressive_pt += len(pts)\n \n for j in xrange(len(nband)): \n band_j = nband[j]\n if (band_j,order_i) not in priv_info: priv_info[(band_j,order_i)] = {} \n # record the private information\n priv_info[(band_j,order_i)]['disp']= [coeff, equ_type]\n priv_info[(band_j,order_i)]['rv'] = [0] \n priv_info[(band_j,order_i)]['disp extr'] = deepcopy(wlcoeff.extra)\n \n # now propogate the solution to the other bands\n stdwl = wl[0]\n for i in xrange(1,len(nband)): wl[i] = stdwl \n \n inv_var = np.ones(data.shape)\n #=================================================================#\n # return the data .OR. go on and create the spec_obj\n if get_data: return (wl, data, inv_var)\n\n #=================================================================# \n spec_obj = eyeSpec_spec(wl,data,inv_var,header_unit.header)\n # set up private information\n priv_info['filename'] = filename\n spec_obj.filename = filename\n \n bands = np.array(np.arange(1,len(data)+1),dtype=str)\n band_info = {}\n i = -1\n for key in prihdr.keys():\n if key[:6] != 'BANDID': continue\n if i < len(bands):\n i+=1\n bands[i] = prihdr[key]\n band_info[key] = prihdr[key]\n else: raise IOError(\"MORE BANDID KEYWORDS IN HEADER THAN FIRST DIMENSION OF DATA\") \n\n # add band info if available:\n if len(band_info) != 0: priv_info['bandids'] = band_info\n else: priv_info['bandids'] = None\n \n # match up the private info created during read in to the spec_obj\n for key in priv_info: spec_obj._private_info[key] = priv_info[key]\n \n # map fits value => acutal index\n # spec_obj._bands = {}\n # spec_obj._orders = {}\n # for i in range(len(nspec)): spec_obj._bands[nspec[i]] = i\n # for i in range(len(nord)): spec_obj._orders[nord[i]] = i\n # \n \n if 7 in nband: spec_obj.set_band(6) # this is where Magellian data stores it's object data, i.e. BANDID7 which is index 6\n\n if len(hdulist) > 1: spec_obj.hdrlist = [h.header for h in hdulist]\n \n return spec_obj","def spectra_analysis(file_name, sky_file_name): \n\n # read file name and select out the id that we are dealing with\n curr_file_name = file_name.split('.')\n curr_file_name = curr_file_name[0].split('/')\n stk_f_n = curr_file_name[-1]\n cube_id = int(re.search(r'\\d+', stk_f_n).group())\n\n # read catalogue and obtain the HST redshift estimate\n #catalogue = np.load(\"data/matched_catalogue.npy\")\n catalogue = np.load(\"data/low_redshift_catalogue.npy\")\n cat_loc = np.where(catalogue[:,0] == cube_id)[0]\n cube_info = catalogue[cat_loc][0]\n \n hst_redshift = cube_info[7]\n\n # spectra and sky noise data\n spectra_data = spectrum_creator(file_name)\n wl_soln = wavelength_solution(file_name)\n sn_data = sky_noise(sky_file_name)\n\n galaxy_data = spectra_data['galaxy']\n\n # removing baseline from data\n base = peakutils.baseline(galaxy_data, 3)\n gd_mc = galaxy_data - base\n\n # scaling sky-noise to be similar to spectra data\n gd_max = np.amax(galaxy_data)\n sn_data_max = np.amax(sn_data)\n sn_scale = gd_max / sn_data_max\n\n sn_data = sn_data * sn_scale\n\n # spectra lines\n sl = {\n 'emis': {\n '[OII]': '3727',\n 'CaK': '3933',\n 'CaH': '3968',\n 'Hdelta': '4101', \n }, \n 'abs': {'K': '3934.777',\n }\n } \n\n # we can use the redshift from the HST catalogue to define the region to search for\n # the doublet in\n\n # lower and upper bound on wavelength range\n lower_lambda = (1+hst_redshift)*3600\n upper_lambda = (1+hst_redshift)*3850\n\n # x-axis data\n data_h_range = np.linspace(wl_soln['begin'], wl_soln['end'], wl_soln['steps'])\n mask = (lower_lambda < data_h_range) & (data_h_range < upper_lambda) \n\n lambda_data = data_h_range[mask]\n flux_data = gd_mc[mask] \n \n # Finding peaks with PeakUtils\n pu_peaks = peakutils.indexes(flux_data, thres=600, thres_abs=True)\n pu_peaks_x = peakutils.interpolate(lambda_data, flux_data, pu_peaks)\n\n pu_peaks_x = np.sort(pu_peaks_x)\n pu_peaks_x = pu_peaks_x[lower_lambda < pu_peaks_x]\n pu_peaks_x = pu_peaks_x[pu_peaks_x < upper_lambda]\n \n data_dir = 'cube_results/' + stk_f_n\n if not os.path.exists(data_dir):\n os.mkdir(data_dir)\n\n peaks_file = open(data_dir + '/' + stk_f_n + '_peaks.txt', 'w')\n peaks_file.write(\"Peaks found on \" + str(datetime.datetime.now()) + \"\\n\\n\")\n\n peaks_file.write(\"Number Wavelength \\n\")\n for i_peak in range(len(pu_peaks_x)):\n curr_peak = pu_peaks_x[i_peak]\n peaks_file.write(str(i_peak) + \" \" + str(curr_peak) + \"\\n\")\n\n # manually selecting which peak is the [OII] peak - given in wavelength\n if (pu_peaks_x.size != 0):\n otwo_wav = float(pu_peaks_x[0]) \n otwo_acc = float(sl['emis']['[OII]'])\n\n redshift = (otwo_wav / otwo_acc) - 1\n else:\n # accepting HST redshift if cannot find peak\n redshift = hst_redshift\n\n return {'gd_shifted': gd_mc, 'sky_noise': sn_data, 'spectra': sl, 'redshift': \n redshift, 'pu_peaks': pu_peaks_x}","def get_TESS_data(filename, fluxtype = 'PDCSAP_FLUX'):\n # Manipulate the fits file:\n data = fits.getdata(filename)\n\n # Identify zero-flux values to take them out of the data arrays:\n idx = np.where((data[fluxtype]!=0.)&(~np.isnan(data[fluxtype])))[0]\n\n # Return median-normalized flux:\n return data['TIME'][idx],data[fluxtype][idx]/np.median(data[fluxtype][idx]), \\\n data[fluxtype+'_ERR'][idx]/np.median(data[fluxtype][idx])","def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\n\n # def Q_gen(I, R):\n # w = I * I * R\n # return w\n\n # def Q_rad_in(I_sun, A_s, a_s):\n # w = I_sun * D * a_s\n # return w\n\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\n # w = htcoeff * A_s * (T_line - T_amb)\n # return w\n\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\n # return w\n\n def reynolds(V, D, v, Mair=1.103):\n r = V * D / v\n return r\n\n def nusselt(Re, Pr):\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\n c = (Re / 282000) ** (5.0/8.0)\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\n return n\n\n def air_prop(T_amb):\n # temp v k Pr\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\n [250, 11.44e-6, 22.3e-3, 0.720],\n [300, 15.89e-6, 26.3e-3, 0.707],\n [350, 20.92e-6, 30.0e-3, 0.700],\n [400, 26.41e-6, 33.8e-3, 0.690],\n [450, 32.39e-6, 37.3e-3, 0.686],\n [500, 38.79e-6, 40.7e-3, 0.684],\n [550, 45.57e-6, 43.9e-3, 0.683],\n [600, 52.69e-6, 46.9e-3, 0.685]])\n\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\n 0, air_prop[:,1:])\n return v, k, Pr\n\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\n if 273 <= T_line <= 323:\n R = ((R_lo + \n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\n *(T_line - T_range[0]))/N_cond)\n elif T_line > 323:\n R = ((R_mid + \n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\n *(T_line - T_range[1]))/N_cond)\n else:\n R = R_lo\n print('Out of bounds')\n return R\n\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\n temp_factor = 1\n wind_factor = 1\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\n\n T_amb = T_amb*temp_factor\n V = V*wind_factor\n\n v, k, Pr = air_prop(T_amb)\n Re = reynolds(V, D, v)\n htcoeff = nusselt(Re, Pr) * k / D\n\n def T_line(T_init):\n \n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\n print R\n\n C4 = e_s * sigma * D * math.pi\n C3 = 0.0\n C2 = 0.0\n C1 = htcoeff * D * math.pi\n C0 = - ( I ** 2 * R\n + I_sun * a_s * D\n + htcoeff * D * math.pi * T_amb\n + e_s * D * math.pi * sigma * (T_amb ** 4))\n\n return np.roots([C4, C3, C2, C1, C0])\n\n T_c = T_amb\n \n for i in range(n_iter):\n T_arr = T_line(T_c)\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\n print T_c\n\n return T_c","def temp_series(smhi_data):\n consumable_data = {\n \"station\": smhi_data[\"station\"][\"name\"],\n \"temp\": [],\n \"from\": smhi_data[\"value\"][0][\"date\"],\n \"to\": smhi_data[\"value\"][-1][\"date\"]\n }\n for temp_post in smhi_data[\"value\"]:\n consumable_data[\"temp\"].append(float(temp_post[\"value\"]))\n return consumable_data","def read_ep_data(path=None):\n if path is None:\n path = \"\"\"C:/Users/Brendan/Downloads/ExodusPoint - Assignment Data (Rates Quantitative Strategist) - New York.xlsx\"\"\"\n \n xls = pd.ExcelFile(path)\n legacy_sheets = ['TU - Com; NonCom','FV - Com;NonCom', 'TY - Com;NonCom', 'US - Com;NonCom ', 'WN - Com;NonCom']\n tff_sheets = ['TU - Sectorial','FV - Sectorial', 'TY - Sectorial', 'US - Sectorial', 'WN - Sectorial']\n \n a, b = {}, {}\n for s in legacy_sheets:\n d = pd.read_excel(xls, sheet_name=s, index_col=0, header=3, skipfooter=1, parse_dates=True)\n d = d.rename(rename_legacy_cols, axis=1).drop('Total', axis=1)\n ct = s[0:2]\n a[ct] = d\n legacy_raw = pd.concat(a, axis=1)\n \n for s in tff_sheets:\n d = pd.read_excel(xls, sheet_name=s, index_col=0, header=3, skipfooter=1, parse_dates=True)\n d = d.rename(rename_tff_cols, axis=1)\n ct = s[0:2]\n b[ct] = d\n tff_raw = pd.concat(b, axis=1)\n \n futures_raw = pd.read_excel(xls, sheet_name=\"Futures Price and Duration Data\",\n index_col=0, header=[3,4,5], skipfooter=1, parse_dates=True)\n futures_raw.columns = futures_raw.columns.droplevel(1)\n futures_raw.columns.set_levels([s[0:2] for s in futures_raw.columns.levels[0]], \n level=0, inplace=True)\n futures_raw.columns.set_names(['ct', 'field'], inplace=True)\n \n swaps_raw = pd.read_excel(xls, sheet_name=\"Swap Prices\",\n index_col=0, header=3, skipfooter=1, parse_dates=True)\n \n return legacy_raw, tff_raw, futures_raw, swaps_raw","def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\n \n dataType = 'wind_reconstruction_data'\n file_list = []\n \n\n # Retrieve data from folders in FTP\n \n possible_hours = [format(x,'02d') for x in range(24)]\n from_zone = tz.gettz('America/Mexico_City')\n to_zone = tz.gettz('UTC')\n \n for item in possible_hours:\n \n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\n local_time = local_time.replace(tzinfo=from_zone)\n \n utc_date = local_time.astimezone(to_zone)\n \n utc_day = utc_date.strftime(\"%Y-%m-%d\")\n \n utc_hour = utc_date.strftime(\"%H\")\n \n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\n \n creat_min = []\n try:\n\n ftp.dir(time_folder, creat_min.append)\n dbs = []\n for i in range(len(creat_min)):\n if creat_min[i].find('DBS') != -1:\n dbs.append(creat_min[i])\n\n if len(dbs) == 0:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n elif len(dbs) == 1:\n if dbs[0].find('-25-'):\n file_list.append(time_folder+dbs[0][55:])\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n elif dbs[0].find('-55-'):\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append(time_folder+dbs[0][55:])\n else:\n for i in range(len(dbs)):\n file_list.append(time_folder+dbs[i][55:])\n\n except:\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\n\n\n \n # Create output folder\n outputFolder = outputFolder+'/'+selectedDate\n try:\n mkdirp(outputFolder)\n except:\n print('warning: folder '+outputFolder+' already exists')\n \n # Make 'generic' outputfile\n outFile = outputFolder+'/'\n\n try:\n \n meas_time = []\n Usets = []\n Vsets = []\n Wsets = []\n \n for working_file in file_list:\n\n if working_file.find('missing_data') != -1:\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n meas_time.append(match)\n\n else: \n match = (re.search('\\d{4}-\\d{2}-\\d{2}_\\d{2}-\\d{2}',working_file)).group(0)\n #hour_min = re.search('_\\d{2}-\\d{2}-\\d{2}',working_file).group(0)\n meas_time.append(match)\n \n obj = DataContainer()\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\n 'CNR','ConfIdx']\n\n # Get the date from the file as a DataFrame\n data = obj.dataToArray(columns)\n data.drop(data[data.Range > ymax].index, inplace=True)\n data = data[['Range','Xwind','Ywind','Zwind']]\n # Replace abs values Xwind and Ywind greater than 50 with NaN\n \n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\n\n dataByRange = data.groupby('Range').mean() \n \n Xset = dataByRange[['Xwind']]\n Yset = dataByRange[['Ywind']]\n Zset = dataByRange[['Zwind']]\n \n Usets.append(Xset)\n Vsets.append(Yset)\n Wsets.append(Zset)\n \n df_U = pd.concat(Usets,axis=1)\n df_V = pd.concat(Vsets,axis=1)\n df_W = pd.concat(Wsets,axis=1)\n \n U = df_U.values;\n V = df_V.values;\n W = df_W.values;\n \n # Vertical wind grid\n x = np.linspace(0,48,49)\n y = np.linspace(100,2500,49)\n X, Y = np.meshgrid(x, y)\n \n # If the plot is going to have a variable pcolormesh uncomment next section\n# cmapLim = 0\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\n# cmapLim = abs(np.nanmax(W))\n# else:\n# cmapLim = abs(np.nanmin(W))\n \n # Change back the UTC times to local time\n \n timelabels = []\n to_zone = tz.gettz('America/Mexico_City')\n from_zone = tz.gettz('UTC')\n \n for entry in meas_time:\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\n utc_time = utc_time.replace(tzinfo=from_zone)\n \n local = utc_time.astimezone(to_zone)\n \n timelabels.append(local.strftime(\"%H:%M\"))\n \n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\n \n # Plot \n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\n fig, ax = plt.subplots(figsize=(20,8))\n \n # For variable pcolormesh uncomment next line\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\n \n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\n ax.set_title(title,fontsize = 24)\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\n ax.set_xticks(np.arange(2.5,48, step=4))\n ax.set_xticklabels(timelabels, rotation=45, size=12)\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\n ax.yaxis.set_tick_params(labelsize=12)\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\n \n # Colorbar\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \n cbar = fig.colorbar(cax, cax = cbaxes)\n cbar.set_label(r'Viento vertical [$m\\,s^{-1}$]', fontsize = 18)\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \n \n \n #Other a artists\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax.imshow(im)\n newax.axis('off')\n \n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\n newax1.imshow(im)\n newax1.axis('off')\n \n \n # Horizontal wind grid\n \n x_h = np.arange(0, len(meas_time), 1)\n Xh, Yh = np.meshgrid(x_h, y)\n \n # Plot arrows\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\,m\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\n \n \n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\n fig.savefig(finalOutputFile,bbox_inches='tight')\n plt.close()\n \n except Exception as e:\n print(\"Problema calculando Wind Time Series\", e)","def Uiso(data, logfile):\n printer.register_file(logfile, 'log', mode='a')\n # isofilters, isopartnerfilters = set_filter()\n #===========================================================================\n # keys=['cart_int','cart_ext','cart_sum','cart_meas','iso_meas']\n #===========================================================================\n use1, use2 = None, None\n try:\n use = config.arg('iso').split(':')\n use1 = use[0]\n use2 = use[1]\n\n except:\n use1 = 'cart_sum'\n use2 = 'cart_sum'\n # printer('\\nSelected filters:\\n\\n Attribute | Value | True | Function'\n # '\\n===================================================\\nAtom filter: | | |')\n # for isofilter in isofilters:\n # printer('{:15s} | {:12s} | {:5s} | {}'.format(isofilter[0], isofilter[1], isofilter[2], isofilter[3]))\n # printer('-----------------|--------------|-------|----------\\nPartner filter: | | |')\n # for isofilter in isopartnerfilters:\n # printer('{:15s} | {:12s} | {:5s} | {}'.format(isofilter[0], isofilter[1], isofilter[2], isofilter[3]))\n printer('\\nComparing {} of hydrogen atoms\\nwith {} of parent atoms.\\n'.format(use1, use2))\n printer(' Riding | Parent | U_rel | U_rel\\n atom | atom | geom | arith')\n printer(' ================================')\n geom_sum = []\n arit_sum = []\n for atom in data['exp'].atoms:\n # if apply_filter(atom, isofilters):\n for heavy_atom in cg.get_framework_neighbours(atom, useH=True):\n if not atom == heavy_atom and filter_atom_pair(config, atom, heavy_atom):\n U_rel_geom = cg.Uiso(atom.adp[use1]) / cg.Uiso(heavy_atom.adp[use2])\n geom_sum.append(U_rel_geom)\n U_rel_arith = cg.Uiso(atom.adp[use1], mean='arithmetic') / \\\n cg.Uiso(heavy_atom.adp[use2], mean='arithmetic')\n arit_sum.append(U_rel_arith)\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light=atom.name,\n heavy=heavy_atom,\n U=U_rel_geom,\n U2=U_rel_arith))\n printer(' -------|--------|-------|-------')\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light='mean',\n heavy='---',\n U=np.mean(geom_sum),\n U2=np.mean(arit_sum)))\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light='SD',\n heavy='---',\n U=np.std(geom_sum),\n U2=np.std(arit_sum)))\n\n printer('{temp:.1f} {U:5.3f} {Uer:5.3f} {U2:5.3f} {U2er:5.3f}'.format(temp=data.temperature,\n U=np.mean(geom_sum),\n Uer=np.std(geom_sum),\n U2=np.mean(arit_sum),\n U2er=np.std(arit_sum)),\n use=['log'])\n printer.spacer()","def test_get_engTemperature(self):\n for app_num, servo_type in app_nr.items():\n try:\n par = self.get_parameter(servo_type, app_num, ENG_TEMPERATURE_IDX, ENG_TEMPERATURE_SUB)\n param_obj = self.__dict__[servo_type]._get_engTemperature()\n acs_par, completion = param_obj.get_sync()\n if(completion.code):\n print \"\\nError code found in engTemperature...\"\n continue\n self.data_match(acs_par, par)\n except NackEx:\n continue","def data_vis():\n dataroot = 'solar_data.txt'\n debug = False \n diff = False\n X, y = read_data(dataroot, debug, diff)\n\n # First plot the original timeseries\n fig = plt.figure(figsize=(40,40))\n\n fig.add_subplot(3,3,1)\n plt.plot(y)\n plt.title('Avg Global PSP (vent/cor) [W/m^2]')\n # plt.show()\n\n fig.add_subplot(3,3,2)\n plt.plot(X[:,0])\n plt.title('Avg Zenith Angle [degrees]')\n # plt.show()\n\n fig.add_subplot(3,3,3)\n plt.plot(X[:,1])\n plt.title('Avg Azimuth Angle [degrees]')\n # plt.show()\n\n fig.add_subplot(3,3,4)\n plt.plot(X[:,2])\n plt.title('Avg Tower Dry Bulb Temp [deg C]')\n # plt.show()\n\n fig.add_subplot(3,3,5)\n plt.plot(X[:,3])\n plt.title('Avg Tower RH [%]')\n # plt.show()\n\n fig.add_subplot(3,3,6)\n plt.plot(X[:,4])\n plt.title('Avg Total Cloud Cover [%]')\n # plt.show()\n\n fig.add_subplot(3,3,7)\n plt.plot(X[:,5])\n plt.title('Avg Avg Wind Speed @ 6ft [m/s]')\n # plt.show()\n\n ##########################################################################################\n # Plotting the Fourier Transform of the signals\n\n freq = np.fft.fftfreq(len(y), 1*60*60)\n\n fig = plt.figure(figsize=(40,40))\n\n fig.add_subplot(3,3,1)\n plt.plot(freq, np.abs(np.fft.fft(y)))\n plt.title('Avg Global PSP (vent/cor) [W/m^2]')\n # plt.show()\n\n fig.add_subplot(3,3,2)\n plt.plot(freq, np.abs(np.fft.fft(X[:,0])))\n plt.title('Avg Zenith Angle [degrees]')\n # plt.show()\n\n fig.add_subplot(3,3,3)\n plt.plot(freq, np.abs(np.fft.fft(X[:,1])))\n plt.title('Avg Azimuth Angle [degrees]')\n # plt.show()\n\n fig.add_subplot(3,3,4)\n plt.plot(freq, np.abs(np.fft.fft(X[:,2])))\n plt.title('Avg Tower Dry Bulb Temp [deg C]')\n # plt.show()\n\n fig.add_subplot(3,3,5)\n plt.plot(freq, np.abs(np.fft.fft(X[:,3])))\n plt.title('Avg Tower RH [%]')\n # plt.show()\n\n fig.add_subplot(3,3,6)\n plt.plot(freq, np.abs(np.fft.fft(X[:,4])))\n plt.title('Avg Total Cloud Cover [%]')\n # plt.show()\n\n fig.add_subplot(3,3,7)\n plt.plot(freq, np.abs(np.fft.fft(X[:,5])))\n plt.title('Avg Avg Wind Speed @ 6ft [m/s]')\n # plt.show()\n\n ##################################################################################################\n # Print correlation matrix\n\n df = pd.DataFrame(np.c_[y, X])\n df.columns = ['Avg Global PSP (vent/cor) [W/m^2]','Avg Zenith Angle [degrees]','Avg Azimuth Angle [degrees]','Avg Tower Dry Bulb Temp [deg C]','Avg Tower RH [%]','Avg Total Cloud Cover [%]','Avg Avg Wind Speed @ 6ft [m/s]']\n f = plt.figure(figsize=(19, 15))\n plt.matshow(df.corr(), fignum=f.number)\n plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=20)\n plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\n cb = plt.colorbar()\n cb.ax.tick_params(labelsize=14)\n plt.title('Correlation Matrix', fontsize=16);\n plt.show()","def thermal(isatom, freq, scalfac,linnonlin,T):\n if isatom != \"true\":\n nfreq = len(freq)\n\n vib_temp = []\n for ifreq in range(nfreq):\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\n vib_temp.append(vib_temp_new)\n\n dE_vib = 0\n for ifreq in range(nfreq):\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\n\n dE_ZPE = 0.5 * sum(freq) * cmi2au\n\n if linnonlin == \"L\":\n dE_rot = kB * T * j2au\n elif linnonlin == \"NL\":\n dE_rot = kB * T * j2au * (3.0/2.0)\n else:\n with open(\"Thermochemistry.out\", \"a\") as ther_chem:\n ther_chem.write(\"ERROR: unknown entry for linear/nonlinear\")\n else:\n dE_ZPE = 0\n dE_vib = 0\n dE_rot = 0\n\n dE_tra = kB * T * j2au * (3.0/2.0)\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\n\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)"],"string":"[\n \"def calc_equil(sst, ft_qv, use_NT=False):\\n \\n run_main(sst, ft_qv, use_NT)\\n \\n # grab csv file\\n with open('dumpmodel.csv','r') as f:\\n df_result=pd.read_csv(f)\\n\\n # last time step into named tupple\\n out=df_result.iloc[-1]\\n steady_state=make_tuple(out.to_dict())\\n steady_state\\n \\n # obtain steady-state values\\n dth=steady_state.deltheta\\n dqt=steady_state.delqv\\n thetal_m=steady_state.theta\\n qt_m=steady_state.qv\\n h=steady_state.h\\n press=tf.find_press(steady_state.h) #kPa\\n thetal_ft = steady_state.theta + dth\\n qt_ft = steady_state.qv + dqt\\n zb = steady_state.LCL\\n zi = steady_state.h\\n we = steady_state.went\\n \\n # calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)\\n gamma = 6e-3 \\n thetal_3000 = thetal_ft + gamma*(3000-h)\\n LTS = thetal_3000 - steady_state.theta\\n \\n # calculate delta_Fr\\n delta_Frstar = 82.0 # Wm^-2\\n Frlambda = 7.9 # Wm^-2, using with CTL from Gesso\\n delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1\\n\\n # calculate LWP\\n rho = 1.\\n LWP = 0.5*rho*(zi-zb)**2\\n \\n # put all required variables into output array\\n out_array = np.array([thetal_m, qt_m, zi, zb, we, LWP, delta_Fr, LTS, dqt])\\n \\n return out_array\",\n \"def extractQuantities(path, run, t0, t1):\\n data = pyLTR.Models.MIX(path, run)\\n\\n # hard-coded input for testing & debugging:\\n #data = pyLTR.Models.LFM('/hao/aim2/schmitt/data/LTR-2_0_1b/r1432/March1995/LR/single', 'LRs')\\n \\n #Make sure variables are defined in the model.\\n modelVars = data.getVarNames()\\n for v in ['Grid X', 'Grid Y', \\n 'Potential North [V]', 'Potential South [V]', \\n 'FAC North [A/m^2]', 'FAC South [A/m^2]',\\n 'Pedersen conductance North [S]', 'Pedersen conductance South [S]', \\n 'Hall conductance North [S]', 'Hall conductance South [S]', \\n 'Average energy North [keV]', 'Average energy South [keV]',\\n 'Number flux North [1/cm^2 s]', 'Number flux South [1/cm^2 s]']:\\n assert( v in modelVars )\\n\\n timeRange = data.getTimeRange()\\n if len(timeRange) == 0:\\n raise Exception(('No data files found. Are you pointing to the correct run directory?'))\\n\\n index0 = 0\\n if t0:\\n for i,t in enumerate(timeRange):\\n if t0 >= t:\\n index0 = i\\n\\n index1 = len(timeRange)-1\\n if t1:\\n for i,t in enumerate(timeRange):\\n if t1 >= t:\\n index1 = i \\n\\n print(( 'Extracting MIX quantities for time series over %d time steps.' % (index1-index0) ))\\n \\n # Output a status bar displaying how far along the computation is.\\n progress = pyLTR.StatusBar(0, index1-index0)\\n progress.start()\\n\\n t_doy = []\\n cpcpNorth = []\\n cpcpSouth = []\\n hpNorth = []\\n hpSouth = []\\n ipfacNorth = []\\n ipfacSouth = []\\n\\n # Pre-compute area of the grid.\\n x = data.read('Grid X', timeRange[index0])\\n y = data.read('Grid Y', timeRange[index0])\\n # Fix singularity at the pole\\n x[:,0] = 0.0\\n y[:,0] = 0.0\\n z = numpy.sqrt(1.0-x**2-y**2)\\n ri = 6500.0e3 # Radius of ionosphere\\n areaMixGrid = pyLTR.math.integrate.calcFaceAreas(x,y,z)*ri*ri\\n\\n for i,time in enumerate(timeRange[index0:index1]):\\n try:\\n # -- Day of Year\\n tt = time.timetuple()\\n t_doy.append(tt.tm_yday+tt.tm_hour/24.0+tt.tm_min/1440.0+tt.tm_sec/86400.0)\\n\\n # --- Cross Polar Cap Potential\\n psiNorth = data.read('Potential North [V]', time) / 1000.0\\n cpcpNorth.append(psiNorth.max() - psiNorth.min())\\n\\n psiSouth = data.read('Potential South [V]', time) / 1000.0\\n cpcpSouth.append(psiSouth.max() - psiSouth.min())\\n \\n # --- Hemispheric Power\\n energy = data.read('Average energy North [keV]', time)\\n flux = data.read('Number flux North [1/cm^2 s]', time)\\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\\n # KeV/cm^2s to mW/m^2 to GW\\n hpNorth.append(hp.sum() * 1.6e-21) \\n\\n energy = data.read('Average energy South [keV]', time)\\n flux = data.read('Number flux South [1/cm^2 s]', time)\\n hp = areaMixGrid*energy[:-1,:-1] * flux[:-1,:-1]\\n # KeV/cm^2s to mW/m^2 to GW\\n hpSouth.append(hp.sum() * 1.6e-21)\\n\\n # --- Positive current density\\n fac = data.read('FAC North [A/m^2]', time)\\n fac[fac <= 0] = 0.0\\n pfac = areaMixGrid * fac[:-1,:-1]\\n ipfacNorth.append(pfac.sum()/1.0e6)\\n\\n fac = data.read('FAC South [A/m^2]', time)\\n fac[fac <= 0] = 0.0\\n pfac = areaMixGrid * fac[:-1,:-1]\\n ipfacSouth.append(pfac.sum()/1.0e6)\\n\\n progress.increment()\\n except KeyboardInterrupt:\\n # Exit when the user hits CTRL+C.\\n progress.stop()\\n progress.join() \\n print('Exiting.')\\n import sys\\n sys.exit(0)\\n except:\\n # Cleanup progress bar if something bad happened.\\n progress.stop()\\n progress.join()\\n raise\\n progress.stop()\\n progress.join()\\n\\n dataNorth = pyLTR.TimeSeries()\\n dataSouth = pyLTR.TimeSeries()\\n dataNorth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\\n dataSouth.append('datetime', 'Date & Time', '', timeRange[index0:index1])\\n dataNorth.append('doy', 'Day of Year', '', t_doy)\\n dataSouth.append('doy', 'Day of Year', '', t_doy)\\n \\n # \\\"N\\\" and \\\"S\\\" label subscripts are redundant here, potentially leading to\\n # mis-labeling of plots\\n #dataNorth.append('cpcp', r'$\\\\Phi_N$', 'kV', cpcpNorth)\\n #dataSouth.append('cpcp', r'$\\\\Phi_S$', 'kV', cpcpSouth)\\n #\\n #dataNorth.append('hp', r'$HP_N$', 'GW', hpNorth)\\n #dataSouth.append('hp', r'$HP_S$', 'GW', hpSouth)\\n #\\n #dataNorth.append('ipfac', r'$FAC_N$', 'MA', ipfacNorth)\\n #dataSouth.append('ipfac', r'$FAC_S$', 'MA', ipfacSouth)\\n \\n dataNorth.append('cpcp', r'$\\\\Phi$', 'kV', cpcpNorth)\\n dataSouth.append('cpcp', r'$\\\\Phi$', 'kV', cpcpSouth)\\n \\n dataNorth.append('hp', r'$HP$', 'GW', hpNorth)\\n dataSouth.append('hp', r'$HP$', 'GW', hpSouth)\\n \\n dataNorth.append('ipfac', r'$FAC$', 'MA', ipfacNorth)\\n dataSouth.append('ipfac', r'$FAC$', 'MA', ipfacSouth)\\n\\n return (dataNorth, dataSouth)\",\n \"def water(fname):\\n\\n variables = [\\\"H2OICE\\\", \\\"QV_COLUMN\\\", \\\"QI_COLUMN\\\"]\\n data = dict()\\n for v in variables:\\n data.update(common.zonal_mean_surface(fname, v))\\n \\n return data\",\n \"def env_temperature(v3: \\\"float\\\", v4: \\\"float\\\") -> \\\"float\\\":\",\n \"def get_iPTF14gqr(colorplt=False):\\n z = 0.063\\n # ebv = 0.082\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_exp = 56943.74 # \\n t_max = 56950.26 # g band max light + 3\\n \\n tb = Table(fits.open('../data/otherSN/De2018/tables1.fit')[1].data)\\n tb.rename_column('MJD' , 'mjd')\\n tb['texp_rf'] = (tb['mjd'] - t_exp) / (1+z)\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n # tb = tb[tb[\\\"Filt\\\"]==\\\"g \\\"]\\n tb = tb[~np.isnan(tb['e_mag'])]\\n tb.rename_column('Filt' , 'filter')\\n tb.rename_column('e_mag' , 'emag')\\n tb.rename_column('mag' , 'mag0')\\n \\n ixg = tb['filter']==\\\"g \\\"\\n ixB = tb['filter']==\\\"B \\\"\\n ixV = tb['filter']==\\\"V \\\"\\n ixr = tb['filter']==\\\"r \\\"\\n ixi = tb['filter']==\\\"i \\\"\\n ixUVW1 = tb['filter']==\\\"UVW1\\\"\\n ixUVW2 = tb['filter']==\\\"UVW2\\\"\\n \\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'][ixUVW2] = 2079\\n tb['wave'][ixUVW1] = 2614\\n tb['wave'][ixB] = 4359\\n tb['wave'][ixg] = 4814\\n tb['wave'][ixV] = 5430\\n tb['wave'][ixr] = 6422\\n tb['wave'][ixi] = 7883\\n \\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n \\n tb = tb.to_pandas()\\n tb[\\\"texp_rf\\\"] = tb[\\\"Phase\\\"]\\n tb = tb.drop(columns=[\\\"recno\\\", \\\"Phase\\\", \\\"l_mag\\\"])\\n \\\"\\\"\\\"\\n ix = np.any([tb['Tel'].values==\\\"P60 \\\",\\n tb[\\\"filter\\\"].values=='g '], axis=0)\\n tb = tb[ix]\\n \\\"\\\"\\\"\\n tb = add_datecol(tb)\\n tb = add_physcol(tb)\\n tt = tb[\\\"tmax_rf\\\"].values\\n epochs = [\\\" \\\" for x in range(len(tt))]\\n epochs = np.array(epochs)\\n \\\"\\\"\\\"\\n ix = (tt>-5.6)&(tt<-5.55)\\n epochs[ix] = \\\"epoch 01\\\"\\n \\\"\\\"\\\"\\n ix = (tt>-5.55)&(tt<-5.50)\\n epochs[ix] = \\\"epoch 02\\\"\\n \\n ix = (tt>-5.50)&(tt<-5.45)\\n epochs[ix] = \\\"epoch 03\\\"\\n \\n ix = (tt>-5.2)&(tt<-5.0)\\n epochs[ix] = \\\"epoch 04\\\"\\n ix = (tt>-5.0)&(tt<-4.7)\\n epochs[ix] = \\\"epoch 05\\\"\\n \\n ix = (tt>-4.7)&(tt<-4.5)\\n epochs[ix] = \\\"epoch 06\\\"\\n ix = (tt>-4.5)&(tt<-3.5)\\n epochs[ix] = \\\"epoch 07\\\"\\n ix = (tt>-3.5)&(tt<-2.5)\\n epochs[ix] = \\\"epoch 08\\\"\\n ix = (tt>-1.5)&(tt<-1)\\n epochs[ix] = \\\"epoch 09\\\"\\n ix = (tt>-1)&(tt<-0.82)\\n epochs[ix] = \\\"epoch 10\\\"\\n ix = (tt>-0.82)&(tt<-0.6)\\n epochs[ix] = \\\"epoch 11\\\"\\n ix = (tt>-0.5)&(tt<0.5)\\n epochs[ix] = \\\"epoch 12\\\"\\n ix = (tt>0.5)&(tt<1.5)\\n epochs[ix] = \\\"epoch 13\\\"\\n ix = (tt>1.5)&(tt<2.5)\\n epochs[ix] = \\\"epoch 14\\\"\\n ix = (tt>3.5)&(tt<4.5)\\n epochs[ix] = \\\"epoch 15\\\"\\n ix = (tt>4.5)&(tt<5)\\n epochs[ix] = \\\"epoch 16\\\"\\n ix = (tt>5)&(tt<5.6)\\n epochs[ix] = \\\"epoch 17\\\"\\n ix = (tt>5.6)&(tt<5.8)\\n epochs[ix] = \\\"epoch 18\\\"\\n ix = (tt>6)&(tt<7)\\n epochs[ix] = \\\"epoch 19\\\"\\n ix = (tt>7)&(tt<8)\\n epochs[ix] = \\\"epoch 20\\\"\\n ix = (tt>8)&(tt<9)\\n epochs[ix] = \\\"epoch 21\\\"\\n tb[\\\"epoch\\\"] = epochs\\n\\n if colorplt==False:\\n return tb\\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['g ', 'r ', 'i ']))\\n tb = tb[ix]\\n\\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r \\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g \\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r \\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i \\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def get_thermochem(file_set, results_dict, save_vibes, out_dir, tog_output_fname, qh_h_opt, write_mode):\\n h = []\\n qh_h = []\\n gt = []\\n qh_gt = []\\n temps = []\\n for index, file in enumerate(file_set):\\n base_name = os.path.basename(file)\\n if file == REACT_PROD_SEP:\\n h.append(np.full([len(temps)], np.nan))\\n qh_h.append(np.full([len(temps)], np.nan))\\n gt.append(np.full([len(temps)], np.nan))\\n qh_gt.append(np.full([len(temps)], np.nan))\\n continue\\n vibes_out = results_dict[base_name][GOODVIBES_OUT]\\n found_structure = False\\n skip_line = True\\n h.append([])\\n qh_h.append([])\\n gt.append([])\\n qh_gt.append([])\\n # we know the last line should be dropped, and at least the first 10\\n for line in vibes_out[10:-2]:\\n if GOODVIBES_ERROR_PAT.match(line):\\n raise InvalidDataError(\\\"See GoodVibes output: {}\\\".format(vibes_out))\\n if not found_structure:\\n if GOODVIBES_DATA_PAT.match(line):\\n found_structure = True\\n continue\\n elif skip_line:\\n skip_line = False\\n continue\\n else:\\n vals = line.split()\\n if index == 0:\\n temps.append(float(vals[1]))\\n h[index].append(float(vals[2]))\\n if qh_h_opt:\\n qh_h[index].append(float(vals[3]))\\n gt[index].append(float(vals[-2]))\\n qh_gt[index].append(float(vals[-1]))\\n if save_vibes:\\n vibes_out_fname = os.path.relpath(create_out_fname(file, suffix='_vibes', base_dir=out_dir, ext='.dat'))\\n list_to_file(vibes_out, vibes_out_fname, print_message=False)\\n print('Saved GoodVibes output as: {}'.format(vibes_out_fname))\\n if tog_output_fname:\\n list_to_file(vibes_out, tog_output_fname, mode=write_mode, print_message=False)\\n if write_mode == 'w':\\n print(\\\"Adding all GoodVibes output to: {}\\\".format(tog_output_fname))\\n write_mode = \\\"a\\\"\\n\\n temps = np.asarray(temps)\\n # for each molecule, multiply the array to convert to kcal/mol\\n for index in range(len(gt)):\\n h[index] = np.asarray(h[index]) * EHPART_TO_KCAL_MOL\\n if qh_h_opt:\\n qh_h[index] = np.asarray(qh_h[index]) * EHPART_TO_KCAL_MOL\\n gt[index] = np.asarray(gt[index]) * EHPART_TO_KCAL_MOL\\n qh_gt[index] = np.asarray(qh_gt[index]) * EHPART_TO_KCAL_MOL\\n\\n return temps, h, qh_h, gt, qh_gt\",\n \"def main(folder, quiet=0):\\n\\n if quiet:\\n output_stream = StringIO()\\n else:\\n output_stream = sys.stdout\\n\\n\\n\\n color1 = \\\"I4\\\" #filter system for first color of CMD\\n color2 = \\\"M1\\\" #filter system for second color of CMD\\n zeromagc1 = zero.zero_mag[color1]\\n zeromagc2 = zero.zero_mag[color2]\\n min_mag = 8. #minimal observation limit\\n max_mag = 0. #maximal observation limit\\n\\n#getting file list\\n files = sorted(os.listdir('%s/%s' % (os.getcwdu(), folder))) \\n out = []\\n\\n for fil in files:\\n#only using files created by the automated simulation\\n if fil.startswith('sim_') and not 'settings' in fil.encode(\\\"ascii\\\"):\\n print(\\\"%s/%s\\\" % (folder,fil.encode(\\\"ascii\\\")), file=output_stream)\\n \\n\\n # Read in\\n hdulist = fits.open('%s/%s' %(folder,fil))\\n data = hdulist[1].data\\n\\n #calculating magnitudes from fluxes and converting to CMD-data\\n x = -2.5*(np.log10(data['c%s' % color1]/zeromagc1) - np.log10(data['c%s' % color2]/zeromagc2))\\n y = -2.5*(np.log10(data['c%s' % color2]/zeromagc2))\\n\\n \\n sel = np.logical_and( (y > -10./3. * (x-1.) + 10.), np.logical_and(max_mag < y, y < min_mag))\\n sel = np.logical_and(sel, y < -x + 12.)\\n n = sum(sel)\\n t = Table(hdulist[1].data)\\n if 'sel' in t.columns:\\n t.remove_column('sel')\\n t.add_column(Column(name='sel', data=sel.astype('int')))\\n \\n hdulist[1].data = np.array(t)\\n tmp, av, apera, age = fil.split('_')\\n fits.update('%s/%s' %(folder,fil), np.array(t), ext = 1, clobber=True)\\n out.append([av, apera, age, n])\\n\\n #writing obtained data to \\\"folder/__expected_number\\\"\\n head = ['#', 'AV', 'Aperature_size', 'Age', 'Expected_number']\\n f = open('%s/__expected_number' % folder, 'w')\\n f.write(','.join(head)+'\\\\n' )\\n np.savetxt(f, np.asarray(out).astype(int))\\n f.close()\\n \\n print (\\\"Analysed %s files and saved output to %s\\\" % (len(out),'%s/__expected_number' % folder), file=output_stream)\",\n \"def get_taux_variants(name):\\n indicateurResult = get_empty_kpi()\\n config = get_config(name)\\n log.debug('Processing - '+name)\\n\\n indicateurResult['nom'] = config['nom']\\n indicateurResult['unite'] = config['unite']\\n indicateurResult['unite_short'] = config['unite_short']\\n indicateurResult['trendType'] = config['trendType']\\n indicateurResult['color'] = get_color_indicateur(config)\\n\\n if name == \\\"prop_variant_A\\\":\\n colname = \\\"tx_A1\\\"\\n elif name == \\\"prop_variant_B\\\":\\n colname = \\\"tx_B1\\\"\\n elif name == \\\"prop_variant_C\\\":\\n colname = \\\"tx_C1\\\"\\n elif name == \\\"prop_variant_D\\\":\\n colname = \\\"tx_D1\\\"\\n else:\\n colname = \\\"tx_A0C0\\\"\\n\\n df = pd.read_csv(\\n 'files_new/'+config['res_id_fra'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n df = enrich_dataframe(df, name)\\n df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\\n for country in tqdm(countries, desc=\\\"Processing National\\\"):\\n res = process_stock(\\n df,\\n 'nat',\\n 'fra',\\n config['trendType'],\\n colname\\n )\\n indicateurResult['france'].append(res)\\n \\n df = pd.read_csv(\\n 'files_new/'+config['res_id_reg'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n df = df[~df['reg'].isnull()]\\n df = enrich_dataframe(df, name)\\n df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\\n for reg in tqdm(df.reg.unique(), desc=\\\"Processing Régions\\\"):\\n res = process_stock(\\n df[df['reg'] == reg].copy(),\\n 'reg',\\n reg,\\n config['trendType'],\\n colname\\n )\\n indicateurResult['regions'].append(res)\\n\\n # df = pd.read_csv(\\n # 'files_new/'+config['res_id_dep'],\\n # sep=None,\\n # engine='python',\\n # dtype={'reg': str, 'dep': str}\\n # )\\n # df = enrich_dataframe(df, name)\\n # df['date'] = df['semaine'].apply(lambda x: str(x)[11:])\\n # for dep in tqdm(df.dep.unique(), desc=\\\"Processing Départements\\\"):\\n # res = process_stock(\\n # df[df['dep'] == dep].copy(),\\n # 'dep',\\n # dep,\\n # config['trendType'],\\n # colname\\n # )\\n # indicateurResult['departements'].append(res)\\n\\n save_result(indicateurResult, name)\",\n \"def ferry_data_QC(ferry, TH_abs, TH_u, TH_d):\\n if type(ferry) is not xr.core.dataset.Dataset:\\n raise ValueError('Ferry is not defined')\\n # QC1: make nan all Absolute velocities that are greater than 6.5 m/s\\n abs_u = ferry.eastward_absolute_water_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n\\n abs_v = ferry.northward_absolute_water_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n\\n abs_w = ferry.vertical_absolute_water_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n # Get bottom track velocity for reference\\n # and also clean for TH in abs velocity\\n east_btv = ferry.eastward_bottom_tracking_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n\\n north_btv = ferry.northward_bottom_tracking_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n\\n vert_btv = ferry.vertical_bottom_tracking_velocity.where(\\n (abs(ferry.eastward_absolute_water_velocity) < TH_abs) &\\n (abs(ferry.northward_absolute_water_velocity) < TH_abs))\\n # Estimate u_true = abs_u + east_bt_v\\n u_true = abs_u + east_btv\\n v_true = abs_v + north_btv\\n w_true = abs_w + vert_btv\\n U = np.sqrt(u_true**2 + v_true**2)\\n # QC2: check that u_true and v_true are less than 4 m/s\\n u_true = u_true.where((u_true < TH_u) & (v_true < TH_u) & (U < TH_u))\\n v_true = v_true.where((u_true < TH_u) & (v_true < TH_u) & (U < TH_u))\\n w_true = w_true.where((u_true) < TH_u & (v_true < TH_u) & (U < TH_u))\\n U = U.where((u_true) < TH_u & (v_true < TH_u) & (U < TH_u))\\n # Add true velocity data to the dataset\\n ferry['u_true'] = u_true\\n ferry['v_true'] = v_true\\n ferry['w_true'] = w_true\\n ferry['Horizontal_speed'] = U\\n # Remove first 5 rows of depth\\n ferryQC = ferry.isel(depth=slice(TH_d, None))\\n goodQC = True\\n return(ferryQC, goodQC)\",\n \"def read_elia_activated_energy_volumes(filename,status):\\r\\n df = pd.read_excel(filename,skiprows=2,parse_dates=False)\\r\\n df[\\\"Timestamp\\\"] = df[\\\"Date\\\"]+\\\" \\\"+df['Quarter'].map(lambda x: str(x)[:-9])\\r\\n pd.to_datetime(df[\\\"Timestamp\\\"])\\r\\n df.set_index(\\\"Timestamp\\\",inplace=True)\\r\\n if ((status == \\\"validated\\\") | (status == \\\"valid\\\")):\\r\\n df = df.drop(df[df.Status != \\\"Validated\\\"].index)\\r\\n df = df.drop([\\\"Date\\\",\\\"Quarter\\\",\\\"Status\\\"], axis=1)\\r\\n \\r\\n if ((len(df.columns)<13) & (len(df.columns)>11)) :\\r\\n df.columns.values[0:13] = [\\\"NRV in MW\\\", \\\"GUV in MW\\\", \\\"IGCC+ in MW\\\", \\\"R2+ in MW\\\", \\\"Bids+ in MW\\\", \\\"R3+ in MW\\\", \\\"R3DP+ in MW\\\", \\\"GDV in MW\\\", \\\"IGCC- in MW\\\", \\\"R2- in MW\\\", \\\"Bids- in MW\\\", \\\"R3- in MW\\\"]\\r\\n if len(df.columns)<= 11:\\r\\n df.columns.values[0:12] = [\\\"NRV in MW\\\", \\\"GUV in MW\\\", \\\"IGCC+ in MW\\\", \\\"R2+ in MW\\\", \\\"Bids+ in MW\\\", \\\"R3+ in MW\\\", \\\"GDV in MW\\\", \\\"IGCC- in MW\\\", \\\"R2- in MW\\\", \\\"Bids- in MW\\\", \\\"R3- in MW\\\"]\\r\\n if len(df.columns)>14:\\r\\n df.columns.values[0:16] = [\\\"NRV in MW\\\", \\\"SR in MW\\\",\\\"GUV in MW\\\", \\\"IGCC+ in MW\\\",\\\"R2+ in MW\\\",\\\"Bids+ in MW\\\",\\\"R3 std in MW\\\",\\\"R3 flex in MW\\\",\\\"ICH in MW\\\",\\\"inter TSO import in MW\\\",\\\"GDV in MW\\\",\\\"IGCC- in MW\\\",\\\"R2- in MW\\\",\\\"Bids- in MW\\\",\\\"inter TSO export in MW\\\"]\\r\\n \\r\\n return df\",\n \"def main(daymet_dir,pickles,start_date='1980-10-01',end_date='2020-09-30',huc_col = 'huc8', **kwargs):\\r\\n\\tprint(f'The huc col being processed is: {huc_col}')\\r\\n\\t################################################################\\r\\n\\t#first do the daymet data \\r\\n\\t#read in all the files in this dir and combine them into one df\\r\\n\\tearly=FormatData(glob.glob(daymet_dir+f'*_12_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\tmid=FormatData(glob.glob(daymet_dir+f'*_2_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\tlate=FormatData(glob.glob(daymet_dir+f'*_4_{huc_col}.csv'),drop_cols=['system:index','.geo','dayl','vp']).read_in_csvs()\\r\\n\\t################################################################\\r\\n\\t#next do the snotel data \\r\\n\\toutput=[]\\r\\n\\r\\n\\t#read in some pickled objects, these look like a list of dfs with each being a station for the full time period \\r\\n\\tfor item in ['PREC','TAVG','WTEQ']:\\r\\n\\t\\t#get the pickled objects for each parameter \\r\\n\\t\\tfiles = glob.glob(pickles+f'*{item}_{start_date}_{end_date}_snotel_data_list') #hardcoded currently\\r\\n\\t\\tdf=FormatData(files,drop_cols=['year','month','day']).read_in_pickles()\\r\\n\\t\\toutput.append(df) #the df here is 365 days x ~30 yrs x 237 stations so these are pretty big dfs\\r\\n\\t\\r\\n\\t#join the three enviro params \\r\\n\\toutput_df = reduce(lambda left,right: pd.merge(left,right,how='inner',on=['date','id']), output)\\r\\n\\t\\r\\n\\t\\r\\n\\t#convert the temp column from F to C \\r\\n\\toutput_df['TAVG'] = (output_df['TAVG']-32)*(5/9) \\r\\n\\t#there are a couple of erroneous temp values, remove those \\r\\n\\toutput_df = output_df.loc[output_df['TAVG'] <= 50]\\r\\n\\r\\n\\t#convert prec and swe cols from inches to cm \\r\\n\\toutput_df['PREC'] = output_df['PREC']*2.54\\r\\n\\toutput_df['WTEQ'] = output_df['WTEQ']*2.54\\r\\n\\t\\r\\n\\t#remove rows that have one of the data types missing- this might need to be amended because \\r\\n\\t#it means that there are different numbers of records in some of the periods. \\r\\n\\toutput_df=output_df.dropna()\\r\\n\\t\\r\\n\\t#cast the snotel id col to int to add the hucs \\r\\n\\toutput_df['id'] = output_df['id'].astype('int')\\r\\n\\r\\n\\t#add the as yet nonexistant hucs data to the outputs \\r\\n\\thucs = kwargs.get('hucs')\\r\\n\\toutput_df[huc_col] = output_df['id'].map(hucs)\\r\\n\\r\\n\\t#there are multiple snotel stations in some of the basins, \\r\\n\\t#combine those so there is just one number per basin like the \\r\\n\\t#daymet and RS data. \\r\\n\\r\\n\\toutput_df=output_df.groupby([huc_col,'date'])[['PREC','WTEQ','TAVG']].mean().reset_index()\\r\\n\\r\\n\\tperiod_list = []\\r\\n\\tfor p1,p2 in zip(['early','mid','late'],[early,mid,late]): \\r\\n\\t\\t\\t#get snotel first\\r\\n\\t\\t#make a temporal chunk of data \\r\\n\\t\\tsnotel_chunk=FormatData(None,time_period=p1).split_yearly_data(output_df)\\r\\n\\r\\n\\t\\t##########working below here\\r\\n\\t\\t############################\\r\\n\\t\\t#calculate the snow droughts for that chunk \\r\\n\\t\\tif (p1 == 'mid') | (p1 == 'late'): \\r\\n\\t\\t\\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',start_year=1991,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\t\\t#print('snotel')\\r\\n\\t\\t\\t#print(snotel_drought)\\r\\n\\t\\telse: \\r\\n\\t\\t\\tsnotel_drought=CalcSnowDroughts(snotel_chunk,swe_c='WTEQ',precip='PREC',temp='TAVG',sort_col=huc_col).prepare_df_cols()\\r\\n\\r\\n\\t\\t#get cols of interest \\r\\n\\t\\t#snotel_drought=snotel_drought[['huc8','year','dry','warm','warm_dry']]\\r\\n\\t\\t#rename cols so they don't get confused when data are merged \\r\\n\\t\\t#snotel_drought.columns=['huc8','year']+['s_'+column for column in snotel_drought.columns if not (column =='huc8') | (column=='year')]\\r\\n\\t\\t\\r\\n\\t\\t#then do the same for daymet \\r\\n\\t\\tif (p1 == 'mid') | (p1 == 'late'): \\r\\n\\t\\t\\tdaymet_drought=CalcSnowDroughts(p2,start_year=1991,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\telse: \\r\\n\\t\\t\\tdaymet_drought=CalcSnowDroughts(p2,sort_col=huc_col).prepare_df_cols()\\r\\n\\t\\t#print('daymet',daymet_drought)\\r\\n\\t\\t#daymet_drought=daymet_drought[['huc8','year','dry','warm','warm_dry']]\\r\\n\\t\\t\\r\\n\\t\\t#daymet_drought.columns=['huc8','year']+['d_'+column for column in daymet_drought.columns if not (column =='huc8') | (column=='year')]\\r\\n\\r\\n\\t##########################################\\r\\n\\t\\r\\n\\t\\t#run the kmeans with drought types as intiilization conditions (centroids) for the clusters\\r\\n\\t\\t\\r\\n\\t\\t#these are all of the huc 4 basins in the study area \\r\\n\\t\\thuc4s = ['1708','1801','1710','1711','1709','1701','1702','1705','1703','1601','1707','1706','1712','1704']\\r\\n\\t\\ts_output = []\\r\\n\\t\\td_output = []\\r\\n\\t\\tfor huc4 in huc4s: \\r\\n\\t\\t\\thuc4_s = sd.prep_clusters(snotel_drought,huc4,huc_col=huc_col) #get the subset of the snow drought data for a given huc4\\r\\n\\t\\t\\thuc4_d = sd.prep_clusters(daymet_drought,huc4,huc_col=huc_col)\\r\\n\\t\\t\\t#make the centroids that serve as the intialization for the kmeans clusters- these are like endmembers (ish)\\r\\n\\t\\t\\ts_centroids = DefineClusterCenters(huc4_s,'WTEQ','PREC','TAVG').combine_centroids() #makes a numpy array with four centroids\\r\\n\\t\\t\\td_centroids = DefineClusterCenters(huc4_d,'swe','prcp','tavg').combine_centroids() #makes a numpy array with four centroids\\r\\n\\r\\n\\t\\t\\t#clusters should be like: {0:dry, 1:warm, 2:warm_dry, 3:no_drought} 6/8/2021 DOUBLE CHECK\\r\\n\\t\\t\\t#run kmeans for the snotel data\\r\\n\\t\\t\\ts_clusters = sd.run_kmeans(huc4_s[['WTEQ','PREC','TAVG']].to_numpy(),huc4_s['label'],s_centroids)\\r\\n\\t\\t\\ts_clusters = sd.add_drought_cols_to_kmeans_output(s_clusters, huc_col=huc_col) #add a few cols needed for plotting \\r\\n\\t\\t\\t#run kmeans for the daymet data \\r\\n\\t\\t\\td_clusters = sd.run_kmeans(huc4_d[['swe','prcp','tavg']].to_numpy(),huc4_d['label'],d_centroids)\\r\\n\\t\\t\\td_clusters = sd.add_drought_cols_to_kmeans_output(d_clusters, huc_col=huc_col) #add a few cols needed for plotting \\r\\n\\r\\n\\t\\t\\ts_output.append(s_clusters)\\r\\n\\t\\t\\td_output.append(d_clusters)\\r\\n\\t\\ts_plot = pd.concat(s_output)\\r\\n\\r\\n\\t\\t#select the cols of interest and rename so there's no confusion when dfs are merged \\r\\n\\t\\ts_plot=s_plot[[huc_col,'year','dry','warm','warm_dry']]\\r\\n\\t\\ts_plot.columns=[huc_col,'year']+['s_'+column for column in s_plot.columns if not (column == huc_col) | (column=='year')]\\r\\n\\r\\n\\t\\td_plot = pd.concat(d_output)\\r\\n\\t\\td_plot=d_plot[[huc_col,'year','dry','warm','warm_dry']]\\r\\n\\t\\td_plot.columns=[huc_col,'year']+['d_'+column for column in d_plot.columns if not (column == huc_col) | (column=='year')]\\r\\n\\t\\r\\n\\t\\t#merge the two datasets into one df \\r\\n\\t\\tdfs = s_plot.merge(d_plot,on=[huc_col,'year'],how='inner')\\r\\n\\t\\t\\r\\n\\t\\t#deal with the scenario that there are basins with less than 30 years of data, remove those here\\r\\n\\t\\tdfs = sd.remove_short_dataset_stations(dfs,huc_col)\\r\\n\\t\\tperiod_list.append(dfs)\\r\\n\\r\\n\\tplot_counts(period_list,kwargs.get('stats_dir'),huc_col=huc_col,**kwargs)\",\n \"def show_trap_results():\\n df_grid = pd.read_hdf('./temp_results.h5', '/optimize_grid')\\n print(df_grid)\\n \\n print('Minimum fwhm:')\\n print(df_grid[df_grid.fwhm_ovr_mean==df_grid.fwhm_ovr_mean.min()])\\n \\n plt.plot(df_grid.e_fit, df_grid.fwhm_ovr_mean, '.b')\\n plt.show()\",\n \"def get_ptf10iuv(colorplt = False):\\n z = 0.0251485\\n ebv = 0.0371 # SFD\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n print (\\\"adopt g band t_max estimated by myself\\\")\\n t_max = 55357.387 \\n tb = pd.read_csv('../data/otherSN/Kasliwal2012/PTF10iuv', sep='\\\\t')\\n tb = tb.drop(columns=[\\\"Unnamed: 4\\\"])\\n tb = tb.rename(columns={'Filter' : 'filter',\\n 'MJD': 'mjd'})\\n tb = tb[~np.array([x[0]=='>' for x in tb['Mag'].values])]\\n tb['mag'] = np.array([float(x.split(\\\" +or-\\\")[0]) for x in tb['Mag'].values])\\n tb['emag'] = np.array([float(x.split(\\\" +or-\\\")[1]) for x in tb['Mag'].values])\\n tb = tb.drop(columns=[\\\"Mag\\\"])\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n ixz = tb['filter'].values == \\\"z\\\"\\n ixB = tb['filter'].values == \\\"B\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixB] = 4359\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb['wave'].values[ixz] = 9670\\n \\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n tb = tb.sort_values(by = \\\"mjd\\\")\\n if colorplt==False:\\n return tb\\n \\n else:\\n tb = add_datecol(tb)\\n ix = np.in1d(tb[\\\"filter\\\"].values, np.array(['g', 'r', 'i']))\\n tb = tb[ix]\\n tb = tb[tb.mjd > 55352.5]\\n tb = tb[tb.mjd < 55593.5]\\n \\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def get_iPTF16asu():\\n z = 0.187\\n ebv = 0.0\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n \\n tb = asci.read('../data/otherSN/Whitesides2017/table1.txt')\\n tb = tb.to_pandas()\\n tb = tb[tb[\\\"col4\\\"].values!=\\\">\\\"]\\n \\n tb = tb.rename(columns={'col1' : 'mjd',\\n 'col2': 'tmax_rf',\\n 'col3': 'filter',\\n \\\"col4\\\": 'mag',\\n 'col5': 'emag',\\n 'col6': 'instrument'})\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb[\\\"mag\\\"] = np.array(tb[\\\"mag\\\"].values, dtype = np.float)\\n #tb[\\\"emag\\\"] = np.array(tb[\\\"emag\\\"].values, dtype = np.float)\\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb = tb[tb.wave!=0]\\n return tb\",\n \"def generate_experimental_condition(dir_output, file_name_prefix,list_temperature, partial_pressure_H2O=0.02,SinteringTemperature=1600,SinteringTime=24):\\r\\n\\r\\n print(\\\"Enter the host element occupying the A-site\\\")\\r\\n set_A1 = input (\\\"Ex: Ba\\\\n\\\")\\r\\n print(\\\"Enter the valence of the A-site host element\\\")\\r\\n set_A1_valence = input(\\\"Ex: 2\\\\n\\\")\\r\\n frac_A1 = '1'\\r\\n print(\\\"Enter the host element occupying the B-site\\\")\\r\\n set_B1 = input (\\\"Ex: Zr\\\\n\\\")\\r\\n print(\\\"Enter the valence of the B-site host element\\\")\\r\\n set_B1_valence = input(\\\"Ex:4\\\\n\\\")\\r\\n print(\\\"Enter the fraction that describes the composition of the B-site host element\\\")\\r\\n frac_B1 = str(format(float( input (\\\"Ex:0.8\\\\n\\\")), '.2f'))\\r\\n print(\\\"Enter the dopant element occupying the B-site\\\")\\r\\n set_B2 = input (\\\"Ex: Sc\\\\n\\\")\\r\\n print(\\\"Enter the valence of the B-dopant\\\")\\r\\n set_B2_valence = input(\\\"Ex: 3\\\\n\\\")\\r\\n frac_B2 = str(format((1 - float(frac_B1)), '.2f'))\\r\\n\\r\\n # generate dataframe for base\\r\\n CA = set_A1 + set_B1 + frac_B1 + set_B2 + frac_B2 + \\\"O3\\\"\\r\\n dic = {'Composition':CA,\\r\\n 'A1':set_A1, 'Valence A1':set_A1_valence, 'fraction A1':frac_A1,\\r\\n 'B1':set_B1, 'Valence B1':set_B1_valence, 'fraction B1':frac_B1,\\r\\n 'B2':set_B2, 'Valence B2':set_B2_valence, 'fraction B2':frac_B2}\\r\\n df = pd.DataFrame(dic,index=['i',])\\r\\n\\r\\n # add columns name\\r\\n columns_all = ['Composition','Temperature / C','pH2O / atm','CH',\\r\\n 'A1','Valence A1','fraction A1','A2','Valence A2','fraction A2',\\r\\n 'B1','Valence B1','fraction B1','B2','Valence B2','fraction B2',\\r\\n 'B3','Valence B3','fraction B3','X1','Valence X1','fraction X1','fraction total']\\r\\n for c in columns_all:\\r\\n if not(c in df.columns):\\r\\n df[c] = float(np.NaN)\\r\\n df = df[columns_all]\\r\\n\\r\\n # add another experimental conditions\\r\\n df['pH2O / atm'] = partial_pressure_H2O\\r\\n df['Sintering temperature/C'] = SinteringTemperature\\r\\n df['Sintering time / h'] = SinteringTime\\r\\n df['fraction A2']='0'\\r\\n df['fraction B3']='0'\\r\\n df['X1']='O'\\r\\n df['Valence X1']='-2'\\r\\n df['fraction X1']='0.2'\\r\\n df['fraction total']='1'\\r\\n\\r\\n for cnt, tmp in enumerate(list_temperature):\\r\\n df['Temperature / C'] = tmp\\r\\n if cnt==0:\\r\\n df_all = df.copy()\\r\\n else:\\r\\n df_all = pd.concat([df_all,df], ignore_index=True)\\r\\n file_name = os.path.join(dir_output,'{:}_all.csv'.format(file_name_prefix, tmp))\\r\\n df_all.to_csv(file_name, index=False)\",\n \"def compute_surface_temperature(heat_flux):\\n\\n return 1.1e-4*heat_flux + 323\",\n \"def main():\\n std_template_file = os.path.join(os.environ['HOME'], 'prospect/py/prospect/data/std_templates.fits')\\n if os.path.isfile(std_template_file):\\n print('Error std template file already exists')\\n\\n #- Templates produced from 1st component of old (pre-Aug 2022) Redrock templates:\\n template_dir = os.path.join(os.environ['DESICONDA'], '../code/redrock-templates/0.7.2')\\n #std_templates = {'QSO': ('QSO',''), 'GALAXY': ('GALAXY',''), 'STAR': ('STAR','F') }\\n std_templates = {'GALAXY': ('GALAXY',''), 'STAR': ('STAR','F') }\\n delta_lambd_templates = 3\\n\\n rr_templts = load_redrock_templates(template_dir=template_dir)\\n for key,rr_key in std_templates.items() :\\n wave_array = np.arange(rr_templts[rr_key].wave[0], rr_templts[rr_key].wave[-1], delta_lambd_templates)\\n flux_array = resample_flux(wave_array, rr_templts[rr_key].wave, rr_templts[rr_key].flux[0,:])\\n table_templates = Table(data=[wave_array, flux_array], names=['wave_'+key, 'flux_'+key], meta={'name':key})\\n table_templates.write(std_template_file, append=True)\\n\\n #- Case of QSO (Summer 2022): use new template provided by A. Brodzeller\\n qsotemplate_file = os.environ['HOME'] + '/stdtemplate-qso.fits'\\n hdul = fits.open(qsotemplate_file)\\n qsowave = 10**(hdul[0].header['CRVAL1']+np.arange(hdul[0].header['NAXIS1'])*hdul[0].header['CDELT1'])\\n qsoflux = hdul[0].data\\n # Resample as previously:\\n wave_array = np.arange(qsowave[0], qsowave[-1], delta_lambd_templates)\\n flux_array = resample_flux(wave_array, qsowave, qsoflux)\\n table_templates = Table(data=[wave_array, flux_array], names=['wave_QSO', 'flux_QSO'], meta={'name':'QSO'})\\n table_templates.write(std_template_file, append=True)\\n return 0\",\n \"def produce_13TeV_template(tag_name=\\\"HKHI\\\"):\\n num_rebin = 1\\n file_name = \\\"inputs/BkgEstimation_Lin/BkgEstimation_NONE_TOPO_PTDEP_\\\"+tag_name+\\\"_Lin.root\\\"\\n print \\\"Input: \\\", file_name\\n fin = ROOT.TFile.Open(file_name, \\\"read\\\")\\n h_nom = fin.Get(\\\"bkg_total_gg_full\\\").Clone(\\\"bkg_nominal_old\\\")\\n h_nom.Rebin(num_rebin)\\n fout = ROOT.TFile.Open(\\\"hists_input_\\\"+tag_name+\\\".root\\\", \\\"recreate\\\")\\n\\n h_purity_sys = fin.Get(\\\"bkg_purity_syst_gg_full\\\").Clone(\\\"bkg_purity_syst_gg\\\")\\n h_reducible_sys = fin.Get(\\\"bkg_reducible_syst_gg_full\\\").Clone(\\\"bkg_reducible_syst_gg\\\")\\n h_irreducible_sys = fin.Get(\\\"bkg_irreducible_syst_gg_full\\\").Clone(\\\"bkg_irreducible_syst_gg\\\")\\n h_iso_sys = fin.Get(\\\"bkg_iso_syst_gg_full\\\").Clone(\\\"bkg_iso_syst_gg\\\")\\n\\n #file_iso = \\\"isolation_sys/hist.root\\\"\\n #fin2 = ROOT.TFile.Open(file_iso, \\\"read\\\")\\n #h_iso_sys = fin2.Get(\\\"bkg_isolation_syst_gg\\\")\\n ## inflat irreducible uncertainty by factor of 10\\n # so that it closes to stats uncertainty in data\\n sf = 1\\n if INFLATE_SYS:\\n sf = 10\\n\\n # after rebinning systematic uncertainties, need to scale down,\\n # otherwise the uncertainties are inflated.\\n h_purity_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_irreducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_reducible_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n h_iso_sys.Rebin(num_rebin).Scale(sf/num_rebin)\\n\\n ## truncate the histograms to [200, 2000] GeV\\n h_nom_new = truncate_hist(h_nom, \\\"bkg_nominal\\\")\\n h_purity_sys_new = truncate_hist(h_purity_sys, \\\"h_purity_sys_new\\\")\\n h_irreducible_sys_new = truncate_hist(h_irreducible_sys, \\\"h_irreducible_sys_new\\\")\\n h_reducible_sys_new = truncate_hist(h_reducible_sys, \\\"h_reducible_sys_new\\\")\\n h_iso_sys_new = truncate_hist(h_iso_sys, \\\"h_iso_sys_new\\\")\\n\\n #write down sys and nominal\\n fout.cd()\\n h_nom_new.Write()\\n h_purity_sys_new.Write()\\n h_reducible_sys_new.Write()\\n h_irreducible_sys_new.Write()\\n h_iso_sys_new.Write()\\n\\n h_purity_up, h_purity_down = create_sys_hist(h_nom_new, h_purity_sys_new, \\\"purity_sys\\\")\\n h_purity_up.Write()\\n h_purity_down.Write()\\n\\n h_red_up, h_red_down = create_sys_hist(h_nom_new, h_reducible_sys_new, \\\"reducible_sys\\\")\\n h_red_up.Write()\\n h_red_down.Write()\\n\\n h_irred_up, h_irred_down = create_sys_hist(h_nom_new, h_irreducible_sys_new, \\\"irreducible_sys\\\")\\n h_irred_up.Write()\\n h_irred_down.Write()\\n\\n h_iso_up, h_iso_down = create_sys_hist(h_nom_new, h_iso_sys, \\\"isolation_sys\\\")\\n h_iso_up.Write()\\n h_iso_down.Write()\\n\\n fin.Close()\\n fout.Close()\",\n \"def analyzeViSDEMData(dict):\\n \\n if 'path_in' in dict:\\n path_in = dict['path_in']\\n else:\\n print(\\\"Caution: No path for input folder containing the data has been defined. Please define path to folder by dict['path_in']=path_in\\\") \\n return\\n \\n path_out_default = '../colordeficiency-data/' \\n if 'path_out' in dict:\\n path_out = dict['path_out']\\n else:\\n print(\\\"Caution: No path for output folder where the data should be stored has been defined. Using default output path instead: \\\"+str(path_out_default))\\n path_out = path_out_default\\n \\n if 'round' in dict:\\n round = dict['round']\\n else:\\n print(\\\"Error: You have to chose a round first.\\\")\\n \\n path = os.path.join(os.path.dirname(os.path.abspath(os.path.join(__file__,os.pardir))),'colordeficiency-data')\\n \\n # 0. Step: Get all the relevant information, i.e. motive_ids, obs_col_defs etc.\\n if round == 1:\\n visualsearch_ids = os.path.join(path,\\\"visualsearch_ids.csv\\\")\\n elif round == 2:\\n visualsearch_ids = os.path.join(path,\\\"visualsearch_ids_2.csv\\\")\\n \\n vs_ids_sheet = pandas.read_csv(visualsearch_ids,sep=\\\";\\\")\\n \\n # Get all the relevant information about the observers, i.e. obs_col_defs etc.\\n observer_ids = os.path.join(path,\\\"observer_ids.csv\\\")\\n obs_ids_sheet = pandas.read_csv(observer_ids,sep=\\\";\\\")\\n \\n # 1. Step: Read all the XLSX data in the path\\n ext = 'xlsx'; xlsx_files = getAllXXXinPath(path_in,ext)\\n dataArray = pandas.DataFrame()\\n i=1\\n for xlsx_file in xlsx_files:\\n sys.stdout.write(xlsx_file)\\n dataArray_tmp, testArray, extraDataDict = extractExperimentData(os.path.join(path_in,xlsx_file))\\n \\n newDataArray = dataArray_tmp[['dalt_id','coldef_type','resp.corr_raw','resp.rt_raw','stimFile']]\\n \\n if \\\"2. Session\\\" in extraDataDict:\\n sessionID = int(extraDataDict['2. Session'])\\n newDataArray['session_id'] = sessionID\\n \\n if 'group' in extraDataDict:\\n obsGroup = str(extraDataDict['group'])\\n newDataArray['obsGroup'] = obsGroup\\n \\n if '0. Participant ID' in extraDataDict:\\n obsID = int(extraDataDict['0. Participant ID'])\\n \\n newDataArray['observer_id'] = obsID\\n obs_coldef_type = obs_ids_sheet.loc[obs_ids_sheet['observer_id']==obsID,['observer_coldef_type']]\\n newDataArray['observer_coldef_type'] = int(obs_coldef_type['observer_coldef_type'])\\n \\n dataArray = pandas.concat([dataArray, newDataArray])\\n sys.stdout.write(' . ')\\n if (i%5)==0: sys.stdout.write('\\\\n')\\n i+=1\\n sys.stdout.write('\\\\n')\\n \\n # 2. Step: Adapt values to programstandards\\n for item in settings.colDefLong2ID:\\n dataArray.loc[dataArray['coldef_type'] == item, ['coldef_type']] = settings.colDefLong2ID[item]\\n \\n for item in settings.dalt2ID:\\n dataArray.loc[dataArray['dalt_id'] == item, ['dalt_id']] = settings.dalt2ID[item]\\n \\n dataArray.loc[dataArray['dalt_id'] == 'none', ['dalt_id']] = 0\\n \\n \\n dataArray = dataArray.rename(columns={'dalt_id': 'dalt_id',\\n 'coldef_type': 'coldef_type',\\n 'resp.corr_raw': 'is_correct',\\n 'resp.rt_raw': 'resp_time',\\n 'stimFile': 'filepath'})\\n dataArray = dataArray.reset_index()\\n \\n # Adding set_id, motive_id and variant_id to each file\\n for index, row in dataArray.iterrows():\\n path_tmp = row['filepath']\\n filename = os.path.basename(path_tmp).split('.')[0]\\n dict_tmp = getStatsFromFilename(filename)\\n imgID_tmp = int(dict_tmp['img_id'])\\n \\n tempVSDataArray = vs_ids_sheet.loc[vs_ids_sheet['image_id']==imgID_tmp,['set_id','motive_id','variant_id']]\\n \\n dataArray.at[index,'image_id'] = imgID_tmp\\n dataArray.ix[index,'set_id'] = int(tempVSDataArray['set_id'])\\n dataArray.ix[index,'motive_id'] = int(tempVSDataArray['motive_id'])\\n dataArray.ix[index,'variant_id'] = int(tempVSDataArray['variant_id'])\\n\\n dataArray.image_id = dataArray.image_id.astype(int)\\n dataArray.set_id = dataArray.set_id.astype(int)\\n dataArray.motive_id = dataArray.motive_id.astype(int)\\n dataArray.variant_id = dataArray.variant_id.astype(int)\\n dataArray.is_correct = dataArray.is_correct.astype(bool)\\n \\n dataArray = dataArray[['image_id','set_id','motive_id','variant_id','dalt_id','coldef_type','is_correct','resp_time','observer_id','observer_coldef_type','session_id','filepath','obsGroup']]\\n \\n # 3. Saving data to file\\n try:\\n dataArray.to_csv(os.path.join(path_out, 'visdem-data.csv'),sep=\\\";\\\")\\n sys.stdout.write(\\\"Success: ViSDEM data successfully saved in '\\\"+str(path_out)+\\\"'.\\\\n\\\")\\n except Exception as e:\\n print(e)\",\n \"def temperature(self):\\n value = float(self._parent.query('R{}'.format(self._idx))[1:])\\n return pq.Quantity(value, pq.Kelvin)\",\n \"def vorticity(tsr,solidity):\\n \\n # Reading in csv file (vorticity database)\\n basepath = path.join(path.dirname(path.realpath(__file__)),'data')\\n fdata = basepath + path.sep + 'vortdatabase.csv'\\n f = open(fdata)\\n csv_f = csv.reader(f)\\n \\n i = 0\\n sol_d = np.array([])\\n for row in csv_f:\\n if i == 0:\\n raw = row\\n raw = np.delete(raw,0)\\n vortdat = raw\\n tsr_d = raw # range of tip-speed ratios included\\n if row[0] == 'solidity':\\n sol_d = np.append(sol_d,float(row[1])) # range of solidities included\\n elif row[0] != 'TSR' and row[0] != 'solidity':\\n raw = row\\n raw = np.delete(raw,0)\\n vortdat = np.vstack([vortdat,raw]) # adding entry to vorticity database array\\n i += 1\\n f.close()\\n \\n vortdat = np.delete(vortdat,(0),axis=0) # eliminating first row used as a placeholder\\n tsr_d = tsr_d.astype(np.float) # converting tip-speed ratio entries into floats\\n vortdat = vortdat.astype(np.float) # converting vorticity database entries into floats\\n \\n # Creating arrays for each EMG parameter\\n for i in range(np.size(sol_d)):\\n sol = str(i+1)\\n \\n exec('s'+sol+'_loc1 = vortdat[i*10]\\\\ns'+sol+'_loc2 = vortdat[i*10+1]\\\\ns'+sol+'_loc3 = vortdat[i*10+2]\\\\ns'+sol+'_spr1 = vortdat[i*10+3]\\\\ns'+sol+'_spr2 = vortdat[i*10+4]\\\\ns'+sol+'_skw1 = vortdat[i*10+5]\\\\ns'+sol+'_skw2 = vortdat[i*10+6]\\\\ns'+sol+'_scl1 = vortdat[i*10+7]\\\\ns'+sol+'_scl2 = vortdat[i*10+8]\\\\ns'+sol+'_scl3 = vortdat[i*10+9]\\\\n')\\n \\n # BIVARIATE SPLINE FITTING\\n \\n iz = np.size(sol_d)\\n jz = np.size(tsr_d)\\n \\n # Initializing rectangular matrices\\n Z_loc1 = np.zeros((iz,jz))\\n Z_loc2 = np.zeros((iz,jz))\\n Z_loc3 = np.zeros((iz,jz))\\n Z_spr1 = np.zeros((iz,jz))\\n Z_spr2 = np.zeros((iz,jz))\\n Z_skw1 = np.zeros((iz,jz))\\n Z_skw2 = np.zeros((iz,jz))\\n Z_scl1 = np.zeros((iz,jz))\\n Z_scl2 = np.zeros((iz,jz))\\n Z_scl3 = np.zeros((iz,jz))\\n \\n # Transferring raw data into rectangular matrices\\n for i in range(iz):\\n for j in range(jz):\\n sol = str(i+1)\\n exec('Z_loc1[i,j] = s'+sol+'_loc1[j]')\\n exec('Z_loc2[i,j] = s'+sol+'_loc2[j]')\\n exec('Z_loc3[i,j] = s'+sol+'_loc3[j]')\\n exec('Z_spr1[i,j] = s'+sol+'_spr1[j]')\\n exec('Z_spr2[i,j] = s'+sol+'_spr2[j]')\\n exec('Z_skw1[i,j] = s'+sol+'_skw1[j]')\\n exec('Z_skw2[i,j] = s'+sol+'_skw2[j]')\\n exec('Z_scl1[i,j] = s'+sol+'_scl1[j]')\\n exec('Z_scl2[i,j] = s'+sol+'_scl2[j]')\\n exec('Z_scl3[i,j] = s'+sol+'_scl3[j]')\\n \\n # Creating a rectangular bivariate spline of the parameter data\\n s_loc1 = RectBivariateSpline(sol_d,tsr_d,Z_loc1)\\n s_loc2 = RectBivariateSpline(sol_d,tsr_d,Z_loc2)\\n s_loc3 = RectBivariateSpline(sol_d,tsr_d,Z_loc3)\\n s_spr1 = RectBivariateSpline(sol_d,tsr_d,Z_spr1)\\n s_spr2 = RectBivariateSpline(sol_d,tsr_d,Z_spr2)\\n s_skw1 = RectBivariateSpline(sol_d,tsr_d,Z_skw1)\\n s_skw2 = RectBivariateSpline(sol_d,tsr_d,Z_skw2)\\n s_scl1 = RectBivariateSpline(sol_d,tsr_d,Z_scl1)\\n s_scl2 = RectBivariateSpline(sol_d,tsr_d,Z_scl2)\\n s_scl3 = RectBivariateSpline(sol_d,tsr_d,Z_scl3)\\n \\n # Selecting the specific parameters to use for TSR and solidity\\n loc1 = s_loc1(solidity,tsr)\\n loc2 = s_loc2(solidity,tsr)\\n loc3 = s_loc3(solidity,tsr)\\n spr1 = s_spr1(solidity,tsr)\\n spr2 = s_spr2(solidity,tsr)\\n skw1 = s_skw1(solidity,tsr)\\n skw2 = s_skw2(solidity,tsr)\\n scl1 = s_scl1(solidity,tsr)\\n scl2 = s_scl2(solidity,tsr)\\n scl3 = s_scl3(solidity,tsr)\\n \\n # Creating arrays of the parameters\\n loc = np.array([loc1[0,0],loc2[0,0],loc3[0,0]])\\n spr = np.array([spr1[0,0],spr2[0,0]])\\n skw = np.array([skw1[0,0],skw2[0,0]])\\n scl = np.array([scl1[0,0],scl2[0,0],scl3[0,0]])\\n \\n return loc,spr,skw,scl\",\n \"def process_weather(forecast_file):\\n with open(forecast_file) as json_file:\\n json_data = json.load(json_file)\\n\\n min_temp_store = {}\\n max_temp_store = {}\\n weather_results = str()\\n header_results = str()\\n\\n for day_in_forecast in json_data['DailyForecasts']:\\n day_date = day_in_forecast['Date']\\n min_temp = day_in_forecast['Temperature']['Minimum'][\\\"Value\\\"]\\n min_temp_c = convert_f_to_c(min_temp)\\n min_temp_store[day_date] = min_temp_c\\n max_temp = day_in_forecast['Temperature']['Maximum'][\\\"Value\\\"]\\n max_temp_c = convert_f_to_c(max_temp)\\n max_temp_store[day_date] = max_temp_c\\n\\n day_time_phrase = day_in_forecast['Day']['LongPhrase']\\n rain_chance_day = day_in_forecast['Day']['RainProbability']\\n night_time_phrase = day_in_forecast['Night']['LongPhrase']\\n rain_chance_night = day_in_forecast['Night']['RainProbability']\\n weather_results = weather_results + (f\\\"-------- {convert_date(day_date)} --------\\\\nMinimum Temperature: {format_temperature(round(min_temp_c,1))}\\\\nMaximum Temperature: {format_temperature(round(max_temp_c,1))}\\\\nDaytime: {day_time_phrase}\\\\n Chance of rain: {rain_chance_day}%\\\\nNighttime: {night_time_phrase}\\\\n Chance of rain: {rain_chance_night}%\\\\n\\\")+ \\\"\\\\n\\\"\\n\\n\\n max_day = max(max_temp_store, key=max_temp_store.get)\\n max_value = max_temp_store[max_day]\\n min_day = min(min_temp_store, key=min_temp_store.get)\\n min_value = min_temp_store[min_day]\\n max_totals = (sum(max_temp_store.values()))\\n min_totals = (sum(min_temp_store.values()))\\n num_items = len(min_temp_store)\\n mean_min = round(calculate_mean(min_totals,num_items),1)\\n mean_max = round(calculate_mean(max_totals,num_items),1)\\n\\n save_header = (f\\\"{len(json_data['DailyForecasts'])} Day Overview\\\\n The lowest temperature will be {format_temperature(round((min_value),1))}, and will occur on {convert_date(min_day)}.\\\\n The highest temperature will be {format_temperature(round((max_value),1))}, and will occur on {convert_date(max_day)}.\\\\n The average low this week is {format_temperature(mean_min)}.\\\\n The average high this week is {format_temperature(mean_max)}.\\\\n\\\")\\n\\n header_results = save_header + \\\"\\\\n\\\"+ weather_results\\n \\n return(header_results)\",\n \"def get_temp():\\n epts = [\\\"cage_coldPlate_temp\\\", \\\"cage_pressure\\\"]\\n # t_earlier_aug = '2019-10-02T00:00'\\n # t_later_aug = datetime.utcnow().isoformat()\\n t_earlier_aug = '2019-09-27T13:00'\\n t_later_aug = '2019-09-28T19:49'\\n dfs = pandas_db_query(epts, t_earlier_aug, t_later_aug)\\n print(dfs[epts[0]].tail())\\n\\n exit()\\n\\n xv = dfs[epts[0]][\\\"timestamp\\\"]\\n yv = dfs[epts[0]][epts[0]]\\n plt.plot(xv, yv, '-b')\\n plt.ylabel(epts[0], ha='right', y=1)\\n\\n p1a = plt.gca().twinx()\\n xv = dfs[epts[1]][\\\"timestamp\\\"]\\n yv = dfs[epts[1]][epts[1]]\\n p1a.set_ylabel(epts[1], color='r', ha='right', y=1)\\n p1a.tick_params('y', colors='r')\\n p1a.semilogy(xv, yv, '-r')\\n\\n plt.gcf().autofmt_xdate()\\n plt.tight_layout()\\n plt.show()\",\n \"def temperatures():\\n hi_act= session.query(measurements.tobs,measurements.date,measurements.station).\\\\\\n filter(measurements.station == 'USC00519281').\\\\\\n filter(measurements.date >last_12).\\\\\\n order_by(measurements.date).all()\\n hi_act_df=pd.DataFrame(hi_act).set_index('date')\\n hi_act_dict=hi_act_df.to_dict()\\n return jsonify(hi_act_dict)\",\n \"def run_get_tones_for_15k_subset():\\n add_tone_columns_to_csv('politics_30_months_comments_cleaned_standardized_vader_flair_15k.csv',\\n 'politics_30_months_comments_cleaned_standardized_vader_flair_15k_tones.csv')\",\n \"def run_purity_plotting(input_tsvs, output_dir):\\n # type: (list[str], str) -> None\\n result_cols = [\\\"sensitivity\\\", \\\"sens_lo\\\", \\\"sens_hi\\\", \\\"sens_N\\\", \\\"precision\\\", \\\"prec_lo\\\", \\\"prec_hi\\\", \\\"prec_N\\\", \\\"purity\\\", \\\"pass\\\"]\\n amp_results_df = DataFrame(columns=result_cols)\\n del_results_df = DataFrame(columns=result_cols)\\n\\n min_sensitivity = 0.85\\n min_precision = 0.8\\n min_supported_purity = 0.39\\n\\n for i, input_tsv in enumerate(input_tsvs):\\n purity = find_purity_from_filename(input_tsv)\\n print(input_tsv + \\\" purity: \\\" + str(purity))\\n\\n if purity is None:\\n print(\\\"The file \\\" + input_tsv + \\\" is unrecognized as being a HCC1143T purity file, so it is being skipped. Please see the src code here if you believe this is an error.\\\")\\n continue\\n\\n sample = find_sample_from_filename(input_tsv)\\n\\n segs_df_tmp = pandas.read_csv(input_tsv, sep=\\\"\\\\t\\\", comment=\\\"@\\\")\\n\\n # Clean up by removing all locations where there was more than one ground truth value for copy number/ratio\\n segs_df = segs_df_tmp[segs_df_tmp[GT_CN_COLUMN_NAME].apply(more_than_one_value)]\\n tmp = segs_df[GT_CN_COLUMN_NAME]\\n tmp = pandas.to_numeric(tmp, errors='coerce', downcast='integer')\\n segs_df[GT_CN_COLUMN_NAME] = tmp\\n\\n cr_gt = 1 + (purity * ((segs_df[GT_CN_COLUMN_NAME] / ploidy) - 1))\\n cr_gt.rename(GT_CR_COLUMN_NAME, inplace=True)\\n\\n if IS_LOG2_GUESS_CR:\\n cr_guess = 2 ** segs_df[INPUT_GUESS_CR_COLUMN_NAME]\\n else:\\n cr_guess = segs_df[INPUT_GUESS_CR_COLUMN_NAME]\\n\\n cr_guess.rename(GUESS_CR_COLUMN_NAME, inplace=True)\\n\\n segs_df[GT_CR_COLUMN_NAME] = cr_gt\\n segs_df[GUESS_CR_COLUMN_NAME] = cr_guess\\n segs_gt_to_consider = segs_df[~segs_df[\\\"CALL\\\"].isnull() & (segs_df[\\\"CONTIG\\\"] != \\\"2\\\")]\\n\\n ## Amps\\n tp = segs_gt_to_consider[(segs_gt_to_consider[\\\"CALL\\\"] == \\\"+\\\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] >= 5)]\\n all_gt_amp = segs_gt_to_consider[segs_gt_to_consider[GT_CN_COLUMN_NAME] >= 5]\\n sens_amps = float(len(tp)) / float(len(all_gt_amp))\\n sens_amps_ci = clopper_pearson(len(tp), len(all_gt_amp))\\n sens_amps_N = len(all_gt_amp)\\n\\n fp = segs_gt_to_consider[(segs_gt_to_consider[\\\"CALL\\\"] == \\\"+\\\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 4)]\\n prec_amps = float(len(tp)) / float(len(tp) + len(fp))\\n prec_amps_ci = clopper_pearson(len(tp), (len(tp) + len(fp)))\\n prec_amps_N = len(tp) + len(fp)\\n\\n amp_result = Series(name=sample, data={result_cols[0]: sens_amps, result_cols[1]: sens_amps_ci[0],\\n result_cols[2]: sens_amps_ci[1], result_cols[3]: sens_amps_N,\\n result_cols[4]: prec_amps, result_cols[5]: prec_amps_ci[0],\\n result_cols[6]: prec_amps_ci[1], result_cols[7]: prec_amps_N,\\n result_cols[8]: purity,\\n result_cols[9]: is_passing(sens_amps_ci[1], prec_amps_ci[1], purity, min_sensitivity, min_precision,\\n min_supported_purity)})\\n\\n amp_results_df = amp_results_df.append(amp_result)\\n amp_results_df.sort_values(result_cols[8], inplace=True)\\n\\n print(\\\"Amp sensitivity: \\\" + str(sens_amps) + \\\" \\\" + str(sens_amps_ci))\\n print(\\\"Amp precision: \\\" + str(prec_amps) + \\\" \\\" + str(prec_amps_ci))\\n\\n ## Dels\\n tp_del = segs_gt_to_consider[\\n (segs_gt_to_consider[\\\"CALL\\\"] == \\\"-\\\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 2)]\\n all_gt_del = segs_gt_to_consider[segs_gt_to_consider[GT_CN_COLUMN_NAME] <= 2]\\n sens_dels = float(len(tp_del)) / float(len(all_gt_del))\\n sens_dels_ci = clopper_pearson(len(tp_del), len(all_gt_del))\\n sens_dels_N = len(all_gt_del)\\n\\n fp_del = segs_gt_to_consider[\\n (segs_gt_to_consider[\\\"CALL\\\"] == \\\"-\\\") & (segs_gt_to_consider[GT_CN_COLUMN_NAME] > 2)]\\n prec_dels = float(len(tp_del)) / float(len(tp_del) + len(fp_del))\\n prec_dels_ci = clopper_pearson(len(tp_del), (len(tp_del) + len(fp_del)))\\n prec_dels_N = len(tp_del) + len(fp_del)\\n\\n del_result = Series(name=sample, data={result_cols[0]: sens_dels, result_cols[1]: sens_dels_ci[0],\\n result_cols[2]: sens_dels_ci[1], result_cols[3]: sens_dels_N,\\n result_cols[4]: prec_dels, result_cols[5]: prec_dels_ci[0],\\n result_cols[6]: prec_dels_ci[1], result_cols[7]: prec_dels_N,\\n result_cols[8]: purity,\\n result_cols[9]: is_passing(sens_dels_ci[1], prec_dels_ci[1], purity,\\n min_sensitivity, min_precision,\\n min_supported_purity)})\\n del_results_df = del_results_df.append(del_result)\\n del_results_df.sort_values(result_cols[8], inplace=True)\\n\\n print(\\\"Del sensitivity: \\\" + str(sens_dels) + \\\" \\\" + str(sens_dels_ci))\\n print(\\\"Del precision: \\\" + str(prec_dels) + \\\" \\\" + str(prec_dels_ci))\\n\\n if len(amp_results_df) > 0 and len(del_results_df) > 0:\\n plot_purity_series(output_dir, amp_results_df, \\\"Amplifications\\\", min_sensitivity, min_precision, min_supported_purity)\\n plot_purity_series(output_dir, del_results_df, \\\"Deletions\\\", min_sensitivity, min_precision, min_supported_purity)\\n amp_results_df.to_csv(output_dir + \\\"Amplifications_table.tsv\\\", sep=\\\"\\\\t\\\")\\n del_results_df.to_csv(output_dir + \\\"Deletions_table.tsv\\\", sep=\\\"\\\\t\\\")\",\n \"def get_iPTF16hgs(colorplt = False):\\n z = 0.017\\n ebv = 0\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n \\n tb = pd.read_csv('../data/otherSN/iPTF16hgs/table1.txt', sep=\\\"\\\\t\\\")\\n tb = tb.drop(columns=[\\\"Unnamed: 5\\\"])\\n tb = tb.rename(columns={'Filter' : 'filter',\\n 'MJD': 'mjd'})\\n tb = tb[~np.array([x[0]=='>' for x in tb['Magnitude'].values])]\\n tb['mag'] = np.array([float(x.split(\\\" +or-\\\")[0]) for x in tb['Magnitude'].values])\\n tb['emag'] = np.array([float(x.split(\\\" +or-\\\")[1]) for x in tb['Magnitude'].values])\\n tb = tb.drop(columns=[\\\"Magnitude\\\"])\\n \\n ixg = tb['filter'].values == \\\"g\\\"\\n ixr = tb['filter'].values == \\\"r\\\"\\n ixi = tb['filter'].values == \\\"i\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'].values[ixg] = 4814\\n tb['wave'].values[ixr] = 6422\\n tb['wave'].values[ixi] = 7883\\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'].values, 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n t_max = 57691.59 # from the paper\\n tb['tmax_of'] = tb['mjd'] - t_max\\n tb['tmax_rf'] = (tb['mjd'] - t_max) / (1+z)\\n \\\"\\\"\\\"\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixg], tb[\\\"mag\\\"].values[ixg], tb[\\\"emag\\\"].values[ixg], fmt=\\\".g\\\")\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixr], tb[\\\"mag\\\"].values[ixr], tb[\\\"emag\\\"].values[ixr], fmt=\\\".r\\\")\\n plt.errorbar(tb[\\\"tmax_rf\\\"].values[ixi], tb[\\\"mag\\\"].values[ixi], tb[\\\"emag\\\"].values[ixi], fmt=\\\".y\\\")\\n \\\"\\\"\\\"\\n tb = add_datecol(tb)\\n tb = add_physcol(tb)\\n #tb = tb.drop(columns=[\\\"datetime64\\\"])\\n if colorplt==False:\\n return tb\\n else:\\n #tb = tb[tb.mjd > 55352.5]\\n #tb = tb[tb.mjd < 55593.5]\\n \\n dates = get_date_span(tb)\\n datesave = []\\n for i in range(len(dates)):\\n x = dates[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n if len(tbsub)!=0:\\n flts = tbsub['filter'].values\\n if \\\"r\\\" in flts and np.sum(np.unique(flts))!=1:\\n datesave.append(x)\\n datesave = np.array(datesave)\\n \\n mcolor = []\\n mcolor_unc = []\\n mjds = []\\n colorname = []\\n for i in range(len(datesave)):\\n x = datesave[i]\\n ix = tb[\\\"date\\\"].values == x\\n tbsub = tb[ix]\\n gtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"g\\\"]\\n rtb = tbsub[tbsub[\\\"filter\\\"].values==\\\"r\\\"]\\n itb = tbsub[tbsub[\\\"filter\\\"].values==\\\"i\\\"]\\n if len(gtb)!=0:\\n gmjds = gtb[\\\"mjd\\\"].values\\n gmags = gtb[\\\"mag0\\\"].values\\n gemags = gtb[\\\"emag\\\"].values\\n gwtgs = 1/gemags**2\\n gmag = np.sum(gmags * gwtgs) / np.sum(gwtgs)\\n gmjd = np.sum(gmjds * gwtgs) / np.sum(gwtgs)\\n gemag = 1/ np.sqrt(np.sum(gwtgs))\\n if len(rtb)!=0:\\n rmjds = rtb[\\\"mjd\\\"].values\\n rmags = rtb[\\\"mag0\\\"].values\\n remags = rtb[\\\"emag\\\"].values\\n rwtgs = 1/remags**2\\n rmag = np.sum(rmags * rwtgs) / np.sum(rwtgs)\\n rmjd = np.sum(rmjds * rwtgs) / np.sum(rwtgs)\\n remag = 1/ np.sqrt(np.sum(rwtgs))\\n if len(itb)!=0:\\n imjds = itb[\\\"mjd\\\"].values\\n imags = itb[\\\"mag0\\\"].values\\n iemags = itb[\\\"emag\\\"].values\\n iwtgs = 1/iemags**2\\n imag = np.sum(imags * iwtgs) / np.sum(iwtgs)\\n imjd = np.sum(imjds * iwtgs) / np.sum(iwtgs)\\n iemag = 1/ np.sqrt(np.sum(iwtgs))\\n if len(gtb)!=0 and len(rtb)!=0:\\n mcolor.append(gmag - rmag)\\n mjds.append( 0.5 * (gmjd + rmjd) )\\n mcolor_unc.append( np.sqrt(gemag**2 + remag**2) )\\n colorname.append(\\\"gmr\\\")\\n if len(rtb)!=0 and len(itb)!=0:\\n mcolor.append(rmag - imag)\\n mjds.append( 0.5 * (rmjd + imjd) )\\n mcolor_unc.append( np.sqrt(remag**2 + iemag**2) )\\n colorname.append(\\\"rmi\\\")\\n \\n ctb = Table(data = [mjds, mcolor, mcolor_unc, colorname],\\n names = [\\\"mjd\\\", \\\"c\\\", \\\"ec\\\", \\\"cname\\\"])\\n \\n ctb['tmax_rf'] = (ctb['mjd'] - t_max) / (1+z)\\n ctb = ctb.to_pandas()\\n return ctb\",\n \"def main(temp, humid):\\n user = 'root'\\n password = 'root'\\n dbname = 'iot'\\n dbuser = 'raspberry'\\n dbuser_password = 'password'\\n query = 'select temp_value,humid_value from temp_humid;'\\n json_body = [\\n {\\n \\\"measurement\\\": \\\"temp_humid\\\",\\n \\\"fields\\\": {\\n \\\"temp_value\\\": temp,\\n \\\"humid_value\\\":humid \\n\\t}\\n }\\n ]\\n\\n client = InfluxDBClient('localhost', 8086, user, password, dbname)\\n\\n #client.create_database(dbname)\\n\\n print(\\\"Write points: {0}\\\".format(json_body))\\n client.write_points(json_body)\\n\\n #print(\\\"Querying data: \\\" + query)\\n #result = client.query(query)\\n\\n #print(\\\"Result: {0}\\\".format(result))\\n\\n #client.drop_database(dbname)\",\n \"def get_taux(name):\\n indicateurResult = get_empty_kpi()\\n config = get_config(name)\\n log.debug('Processing - '+name)\\n\\n indicateurResult['nom'] = config['nom']\\n indicateurResult['unite'] = config['unite']\\n indicateurResult['unite_short'] = config['unite_short']\\n indicateurResult['trendType'] = config['trendType']\\n indicateurResult['color'] = get_color_indicateur(config)\\n\\n df = pd.read_csv(\\n 'files_new/'+config['res_id_fra'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n df = enrich_dataframe(df, name)\\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\\n for country in tqdm(countries, desc=\\\"Processing National\\\"):\\n res = process_stock(\\n df,\\n 'nat',\\n 'fra',\\n config['trendType'],\\n name\\n )\\n indicateurResult['france'].append(res)\\n\\n df = pd.read_csv(\\n 'files_new/'+config['res_id_reg'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n df = enrich_dataframe(df, name)\\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\\n for reg in tqdm(df.reg.unique(), desc=\\\"Processing Régions\\\"):\\n res = process_stock(\\n df[df['reg'] == reg].copy(),\\n 'reg',\\n reg,\\n config['trendType'],\\n name\\n )\\n indicateurResult['regions'].append(res)\\n\\n df = pd.read_csv(\\n 'files_new/'+config['res_id_dep'],\\n sep=None,\\n engine='python',\\n dtype={'reg': str, 'dep': str}\\n )\\n df = enrich_dataframe(df, name)\\n df['date'] = df['semaine_glissante'].apply(lambda x: str(x)[11:])\\n for dep in tqdm(df.dep.unique(), desc=\\\"Processing Départements\\\"):\\n res = process_stock(\\n df[df['dep'] == dep].copy(),\\n 'dep',\\n dep,\\n config['trendType'],\\n name\\n )\\n indicateurResult['departements'].append(res)\\n\\n save_result(indicateurResult, name)\",\n \"def Q_flux(self):\\n fields = self.read_vars(['x','y','z'])\\n Z, Y, X = np.meshgrid(fields['z']/self.params['Lz'],\\n fields['y']/self.params['Ly'] - 0.5,\\n fields['x']/self.params['Lx'] - 0.5, indexing='ij')\\n\\n r = np.sqrt(X**2 + Y**2)\\n r0 = 0.01\\n msk = 0.5*(1.-np.tanh(r/r0))\\n delta = 1/(self.params[\\\"global_nz\\\"])\\n Q =1e-5*np.exp(-Z/delta)/delta*msk\\n\\n return Q\",\n \"def amet_memoryWise(self):\\r\\n # set up logging files to monitor the calculation\\r\\n logging.basicConfig(filename = os.path.join(self.path,'history_amet_python.log'),\\r\\n filemode = 'w+', level = logging.DEBUG,\\r\\n format = '%(asctime)s - %(name)s - %(levelname)s - %(message)s')\\r\\n # initialize the time span\\r\\n # define sigma level\\r\\n A, B = self.defineSigmaLevels()\\r\\n # use example input file to load the basic dimensions information\\r\\n datapath_var = os.path.join(self.path, 'MERRA2_400.inst3_3d_asm_Nv.20160101.nc4.nc')\\r\\n var_key = Dataset(datapath_var)\\r\\n lat = var_key.variables['lat'][:]\\r\\n lon = var_key.variables['lon'][:]\\r\\n # calculate the reference levels based on A & B and standard surface pressure\\r\\n half_level = A + B * 101325\\r\\n level = (half_level[1:] + half_level[:-1]) / 2\\r\\n # create space for the output\\r\\n # AMET in the entire column\\r\\n E = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n cpT = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n Lvq = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n gz = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n uv2 = np.zeros((len(lat),len(lon)), dtype=float)\\r\\n logging.info(\\\"Start retrieving variables T,q,u,v,sp\\\")\\r\\n # The shape of each variable is (8,72,361,576)\\r\\n T = var_key.variables['T'][:]\\r\\n q = var_key.variables['QV'][:]\\r\\n sp = var_key.variables['PS'][:] #(8,361,576)\\r\\n u = var_key.variables['U'][:]\\r\\n v = var_key.variables['V'][:]\\r\\n logging.info(\\\"Extracting variables successfully!\\\") \\r\\n # compute gz\\r\\n z_model = self.calc_gz(var_key)\\r\\n # get the basic shape\\r\\n tt, hh, yy, xx = q.shape\\r\\n AMET = amet.met()\\r\\n E, cpT, Lvq, gz, uv2 = AMET.calc_met(T, q, sp, u, v, z_model, A, B,\\r\\n tt, hh, len(lat), len(lon), lat, self.lat_unit)\\r\\n\\r\\n return np.mean(E)\",\n \"def temperatures():\\n\\n return station_9281\",\n \"def readqFile(self, PFC, t):\\n if self.qFilePath[-1] != '/':\\n base = self.qFilePath + '/'\\n else:\\n base = self.qFilePath\\n\\n f = base + '{:06d}/'.format(t) + PFC.name + '/' + self.qFileTag\\n try:\\n df = pd.read_csv(f, names=['X','Y','Z','HF'], skiprows=[0])\\n if len(df['HF'].values) != len(PFC.centers):\\n print('HF file mesh is not same length as STL file mesh.')\\n print('Will not assign HF to mismatched mesh')\\n print(\\\"qFile length: {:d}\\\".format(len(df['HF'].values)))\\n print(\\\"PFC STL mesh length: {:d}\\\".format(len(PFC.centers)))\\n val = -1\\n else:\\n PFC.qDiv = df['HF'].values\\n PFC.powerFrac = self.getDivertorPowerFraction(PFC.DivCode)\\n PFC.qOpticalList.append(PFC.qDiv)\\n print(\\\"Loaded heat flux from file: \\\"+f)\\n val = 0\\n except:\\n print(\\\"COULD NOT READ qFILE PATH: \\\"+f)\\n print(\\\"Please point HEAT to a valid qFilePath and qFileTag,\\\")\\n print(\\\"which should be a .csv file with (X,Y,Z,HF)\\\")\\n val = -1\\n\\n return val\",\n \"def test():\\n temp_data = fetch_temp_data(\\n (\\\"https://opendata-download-metobs.smhi.se/api/version/\\\" +\\n \\\"latest/parameter/1/station/52350/period/latest-day/data.json\\\"))\\n data = temp_series(temp_data)\\n print(data)\",\n \"def readtempfilt(MAIN_OUTPUT_FILE='photz', OUTPUT_DIRECTORY='./OUTPUT'):\\n\\n root = os.path.join(OUTPUT_DIRECTORY, MAIN_OUTPUT_FILE)\\n \\n ###### .tempfilt\\n file_path = root+'.tempfilt'\\n \\n if os.path.exists(file_path) is False:\\n raise ValueError('File, %s, not found.' %(file_path))\\n\\n with open(file_path,'rb') as f:\\n # summary data\\n s = np.fromfile(file=f,dtype=np.int32, count=4)\\n NFILT=s[0] # number of filters\\n NTEMP=s[1] # number of templates\\n NZ=s[2] # number points on the redshift grid\\n NOBJ=s[3] # number of objects\\n # (?) template SED convolved with filter transmission at each redshift\\n tempfilt = np.fromfile(file=f,dtype=np.double,count=NFILT*NTEMP*NZ).reshape((NZ,NTEMP,NFILT)).transpose()\\n # filter pivot wavelengths\\n lc = np.fromfile(file=f,dtype=np.double,count=NFILT)\\n # redshift grid\\n zgrid = np.fromfile(file=f,dtype=np.double,count=NZ)\\n # observed flux\\n fnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\\n # (?) error in observed flux\\n efnu = np.fromfile(file=f,dtype=np.double,count=NFILT*NOBJ).reshape((NOBJ,NFILT)).transpose()\\n \\n tempfilt = {'NFILT':NFILT,'NTEMP':NTEMP,'NZ':NZ,'NOBJ':NOBJ,\\\\\\n 'tempfilt':tempfilt,'lc':lc,'zgrid':zgrid,'fnu':fnu,'efnu':efnu}\\n\\n return tempfilt\",\n \"def read_elia_activated_energy_prices(filename,status):\\r\\n \\r\\n df = pd.read_excel(filename,skiprows=2,parse_dates=False)\\r\\n df[\\\"Timestamp\\\"] = df[\\\"Date\\\"]+\\\" \\\"+df['Quarter'].map(lambda x: str(x)[:-9])\\r\\n pd.to_datetime(df[\\\"Timestamp\\\"])\\r\\n df.set_index(\\\"Timestamp\\\",inplace=True)\\r\\n if ((status == \\\"validated\\\") | (status == \\\"valid\\\")):\\r\\n df = df.drop(df[df.Status != \\\"Validated\\\"].index)\\r\\n df = df.drop([\\\"Date\\\",\\\"Quarter\\\",\\\"Status\\\"], axis=1)\\r\\n \\r\\n if len(df.columns)>14:\\r\\n df.columns.values[0:16] = [\\\"NRV in MW\\\",\\\"SR in euro/MWh\\\",\\\"MIP in euro/MWh\\\",\\\"IGGC+ in euro/MWh\\\", \\\"R2+ in euro/MWh\\\",\\\"Bids+ in euro/MWh\\\",\\\"R3 std in euro/MWh\\\", \\\"R3 flex in euro/MWh\\\", \\\"ICH in euro/MWh\\\", \\\"inter TSO import in euro/MWh\\\", \\\"MDP in euro/MWh\\\", \\\"IGCC- in euro/MWh\\\", \\\"R2- in euro/MWh\\\", \\\"Bids- in euro/MWh\\\", \\\"R3- in euro/MWh\\\"]\\r\\n\\r\\n if len(df.columns)<12:\\r\\n df.columns.values[0:12] = [\\\"NRV in MW\\\",\\\"MIP in euro/MWh\\\",\\\"IGGC+ in euro/MWh\\\", \\\"R2+ in euro/MWh\\\",\\\"Bids+ in euro/MWh\\\", \\\"R3+ in euro/MWh\\\", \\\"MDP in euro/MWh\\\", \\\"IGCC- in euro/MWh\\\", \\\"R2- in euro/MWh\\\", \\\"Bids- in euro/MWh\\\", \\\"R3- in euro/MWh\\\"]\\r\\n\\r\\n return df\",\n \"def export_results_max():\\n\\n # initialise the list of generated files\\n gen_files = []\\n\\n ######\\n # Define allowed variable names and associated equations to generate values.\\n ######\\n # Note that mannings n (friction value) is taken as 0.01, as in the model\\n # run density of water is 1000\\n var_equations = {'stage': enm.export_newstage_max,\\n 'oldstage': 'stage',\\n 'momentum': '(xmomentum**2 + ymomentum**2)**0.5',\\n 'olddepth': 'oldstage-elevation',\\n 'depth': edm.export_depthonland_max,\\n 'speed': '(xmomentum**2 + ymomentum**2)**0.5/(stage-elevation+1.e-6)',\\n 'energy': '(((xmomentum/(stage-elevation+1.e-6))**2'\\n ' + (ymomentum/(stage-elevation+1.e-6))**2)'\\n '*0.5*1000*(stage-elevation+1.e-6))+(9.81*stage*1000)',\\n 'bed_shear_stress': ('(((1/(stage-elevation+1.e-6)**(7./3.))*1000*9.81*0.01**2*(xmomentum/(stage-elevation+1.e-6))*((xmomentum/(stage-elevation+1.e-6))**2+(ymomentum/(stage-elevation+1.e-6))**2)**0.5)**2'\\n '+ ((1/(stage-elevation+1.e-6)**(7./3.))*1000*9.81*0.01**2*(ymomentum/(stage-elevation+1.e-6))*((xmomentum/(stage-elevation+1.e-6))**2+(ymomentum/(stage-elevation+1.e-6))**2)**0.5)**2)**0.5'),\\n 'elevation': 'elevation'}\\n\\n ######\\n # Start script, running through variables, area, sww file\\n ######\\n\\n for which_var in project.layers_list:\\n which_var = which_var.lower()\\n log.info(\\\"Exporting value: %s\\\" % which_var)\\n\\n if which_var not in var_equations:\\n log.critical('Unrecognized variable name: %s' % which_var)\\n break\\n\\n project.export_area = project.export_area.lower()\\n if project.export_area == 'all':\\n easting_min = None\\n easting_max = None\\n northing_min = None\\n northing_max = None\\n elif project.export_area == 'aoi':\\n easting_min = project.xminAOI\\n easting_max = project.xmaxAOI\\n northing_min = project.yminAOI\\n northing_max = project.ymaxAOI\\n else:\\n log.critical('Unrecognized area name: %s' % project.export_area)\\n break\\n\\n name = os.path.join(project.output_folder, project.scenario)\\n\\n outname = name + '_' + project.export_area + '_' + which_var\\n quantityname = var_equations[which_var]\\n\\n log.info('Generating output file: %s' % (outname+'.asc'))\\n\\n # assume 'quantityname' is a string, handle in the old way,\\n # else call the handler function (same params as anuga.sww2dem)\\n if isinstance(quantityname, basestring):\\n export_func = anuga.sww2dem\\n elif callable(quantityname):\\n export_func = quantityname\\n\\n export_func(name+'.sww', outname+'.asc', quantity=quantityname,\\n reduction=max, cellsize=project.cell_size,\\n easting_min=easting_min, easting_max=easting_max,\\n northing_min=northing_min, northing_max=northing_max,\\n verbose=False)\\n\\n # add generated filename to result list\\n gen_files.append(outname+'.asc')\\n\\n return gen_files\",\n \"def cygx3IndFlux(self):\\n # --------------------------------------------------------------------------------------------- #\\n # Read data\\n fitsNnam = os.path.join(self.workpath, 'LCresults.fits')\\n lcTab = Table.read(fitsNnam)\\n detect = lcTab['ts'] >= self.tsmin\\n lcTab = lcTab[detect] \\n\\n ind08 = (lcTab['mjd'] > 54700) & (lcTab['mjd'] < 54900) \\n flux08 = lcTab['flux'][ind08]\\n fluxerr08 = lcTab['fluxerr'][ind08]\\n index08 = lcTab['index'][ind08]\\n indexerr08 = lcTab['indexerr'][ind08]\\n\\n ind09 = (lcTab['mjd'] > 54900) & (lcTab['mjd'] < 55100) \\n flux09 = lcTab['flux'][ind09]\\n fluxerr09 = lcTab['fluxerr'][ind09]\\n index09 = lcTab['index'][ind09]\\n indexerr09 = lcTab['indexerr'][ind09]\\n\\n scale = 10**int(np.floor(np.log10( np.mean( np.concatenate( (flux08, flux09), axis=0) ) ))) \\n\\n # --------------------------------------------------------------------------------------------- #\\n # Plot\\n indplt = FermiPlot(savepath='', xsize=8.5, ysize=6)\\n indplt.figname = os.path.join(self.workpath, 'IndvsFlux.pdf')\\n indplt.xlabel = r'Flux ($10^{%d}$ ph\\\\,cm$^{-2}$\\\\,s$^{-1}$)'%(int(np.log10(scale)))\\n indplt.ylabel = r'Index'\\n indplt.mksize = 2\\n indplt.color = self.lblue\\n indplt.label = r'2008'\\n indplt.plot(x=flux08/scale, xerr=fluxerr08/scale, y=index08, yerr=indexerr08)\\n indplt.color = self.loran\\n indplt.label = r'2009'\\n indplt.plot(x=flux09/scale, xerr=fluxerr09/scale, y=index09, yerr=indexerr09)\\n indplt.save()\\n\\n print(\\\"\\\\t=== Figure '{}' created ===\\\".format(indplt.figname)) \\n return\",\n \"def test_plt_mag_time():\\n\\n ta = WATA()\\n wata_data = define_testdata()\\n ta.source = ColumnDataSource(data=wata_data)\\n ta.add_time_column()\\n ta.setup_date_range()\\n\\n # create the arrays per filter and readout pattern\\n nrsrapid_f140x, nrsrapid_f110w, nrsrapid_clear = [], [], []\\n nrsrapidd6_f140x, nrsrapidd6_f110w, nrsrapidd6_clear = [], [], []\\n filter_used, readout = ta.source.data['tafilter'], ta.source.data['readout']\\n max_val_box, time_arr = ta.source.data['max_val_box'], ta.source.data['time_arr']\\n for i, val in enumerate(max_val_box):\\n if '140' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(val)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(val)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif '110' in filter_used[i]:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(val)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(val)\\n nrsrapidd6_clear.append(np.NaN)\\n else:\\n if readout[i].lower() == 'nrsrapid':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(val)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(np.NaN)\\n elif readout[i].lower() == 'nrsrapidd6':\\n nrsrapid_f140x.append(np.NaN)\\n nrsrapid_f110w.append(np.NaN)\\n nrsrapid_clear.append(np.NaN)\\n nrsrapidd6_f140x.append(np.NaN)\\n nrsrapidd6_f110w.append(np.NaN)\\n nrsrapidd6_clear.append(val)\\n # add to the bokeh data structure\\n ta.source.data[\\\"nrsrapid_f140x\\\"] = nrsrapid_f140x\\n ta.source.data[\\\"nrsrapid_f110w\\\"] = nrsrapid_f110w\\n ta.source.data[\\\"nrsrapid_clear\\\"] = nrsrapid_clear\\n ta.source.data[\\\"nrsrapidd6_f140x\\\"] = nrsrapidd6_f140x\\n ta.source.data[\\\"nrsrapidd6_f110w\\\"] = nrsrapidd6_f110w\\n ta.source.data[\\\"nrsrapidd6_clear\\\"] = nrsrapidd6_clear\\n result = ta.plt_mag_time()\\n\\n assert bokeh_plot_type == type(result)\",\n \"def plc_temp(coil_df):\",\n \"def calc(main_system,dir_db,dir_def,xlfile,mu_limit,functional=\\\"GGA\\\",soc=False,logfile=None):\\n \\n ## set up logging\\n if logfile:\\n myLogger = logging.setup_logging(logfile)\\n else:\\n myLogger = logging.setup_logging()\\n\\n\\n ## load list of dataframes from sheets from excel file \\n df = pd.read_excel(os.path.join(dir_def,xlfile),sheet_name=None)\\n \\n \\n ## find initdef.json file\\n if osutils.check_file_exists(dir_def,\\\"initdef\\\") == True:\\n for file in os.listdir(dir_def): \\n if file.startswith(\\\"initdef\\\"):\\n file_initdef = file\\n ## get species i and ni from initdefect.json file \\n with open(os.path.join(dir_def,file_initdef), 'r') as file:\\n initdef = json.loads(file.read())\\n species_list, ni_list = [],[]\\n for defect in initdef:\\n species, ni = get_i_ni(initdef[defect])\\n species_list += species\\n ni_list += ni\\n myLogger.info(\\\"Atoms added/removed: \\\" + \\\\\\n \\\", \\\".join([str(n)+\\\"*\\\"+i for n,i in zip(ni_list,species_list)]))\\n\\n \\n for q in [qi for qi in df.keys()]:\\n \\n ## get the relevant chemical potentials\\n found_mu = True\\n for species in species_list:\\n mu = \\\"mu_%s_%s\\\"%(species,mu_limit)\\n \\n ## check if the relevant database entry exists\\n if osutils.check_file_exists(dir_db,\\\"%s.json\\\"%species) == True:\\n dbentry_file = \\\"%s.json\\\"%species\\n with open(os.path.join(dir_db, dbentry_file), 'r') as file:\\n mater = json.loads(file.read())\\n ## search for appropriate mu entry\\n mu_key = \\\"mu\\\"\\n for key in mater[functional].keys():\\n if key.startswith(\\\"mu_%s\\\"%mu_limit):\\n mu_key = key\\n myLogger.info(\\\"Using chemical potential \\\" + mu_key + \\\" from \\\" + dbentry_file) \\n ## input the corresponding mus into the dataframe\\n df[q][mu] = mater[functional][mu_key]\\n \\n else:\\n myLogger.info(\\\"Cannot find the database entry for \\\" + species)\\n found_mu = False\\n \\n \\n ## get the VBMs\\n ## check if the relevant database entry exists\\n if osutils.check_file_exists(dir_db,\\\"%s.json\\\"%main_system) == True:\\n dbentry_file = \\\"%s.json\\\"%(main_system)\\n with open(os.path.join(dir_db, dbentry_file), 'r') as file:\\n mater = json.loads(file.read()) \\n \\n ## input the VBMs corresponding to each vacuum spacing into the dataframe\\n for rowind in df[q].index.values:\\n vac = df[q].loc[rowind].vacuum\\n if vac in mater[functional].keys():\\n df[q].at[rowind,'VBM'] = mater[functional][vac][\\\"VBM\\\"]\\n else:\\n myLogger.info(\\\"Cannot find the VBM entry for \\\" + vac) \\n \\n ## Finally, we can compute the uncorrected defect formation energy:\\n ## Eform = Etot(def) - Etot(pristine) - sum(n_i*mu_i) + q*E_Fermi\\n if found_mu:\\n ## proceed if chemical potentials and VBMs have been correctly entered\\n sum_mu = 0\\n for n,species in zip(ni_list,species_list):\\n mu = \\\"mu_%s_%s\\\"%(species,mu_limit)\\n sum_mu += n * df[q][mu]\\n if q == 'charge_0': \\n colname = \\\"E_form_corr\\\"\\n else:\\n colname = \\\"E_form_uncorr\\\"\\n df[q][colname] = df[q].loc[:,'E_def'] \\\\\\n - df[q].loc[:,'E_bulk'] \\\\\\n - sum_mu \\\\\\n + int(q.split(\\\"_\\\")[-1]) * df[q].loc[:,'VBM'] \\n \\n else:\\n myLogger.info(\\\"Cannot find the database entry for \\\" + main_system)\\n\\n\\n ## write the updated excel file\\n writer = pd.ExcelWriter(os.path.join(dir_def,xlfile))\\n for q in df.keys(): \\n df[q].to_excel(writer, q, index=False)\\n writer.save()\",\n \"def Thrustexe_data(h,Vc,Temp_m,FFl,FFr):\\n T,p,rho = isa(h)\\n M = Mach(h,Vc,p)\\n Delta_T = Static_T(Temp_m,M)-T\\n data = np.array([h,M,Delta_T,FFl,FFr]).T\\n\\n np.savetxt(\\\"matlab.dat\\\",data)\\n return\",\n \"def rainfall_series(self):\\n\\n # assign local temporal variables\\n datatype = 'strds'\\n increment = str(self.rain_interval)+\\\" minutes\\\"\\n raster = 'raster'\\n rain_excess = 'rain_excess'\\n net_difference = 'net_difference'\\n #iterations = sum(1 for row in precip)\\n\\n # create a raster space time dataset\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.elevation_timeseries,\\n title=self.elevation_title,\\n description=self.elevation_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.depth_timeseries,\\n title=self.depth_title,\\n description=self.depth_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.erdep_timeseries,\\n title=self.erdep_title,\\n description=self.erdep_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.flux_timeseries,\\n title=self.flux_title,\\n description=self.flux_description,\\n overwrite=True)\\n gscript.run_command(\\n 't.create',\\n type=datatype,\\n temporaltype=self.temporaltype,\\n output=self.difference_timeseries,\\n title=self.difference_title,\\n description=self.difference_description,\\n overwrite=True)\\n\\n # register the initial digital elevation model\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=self.elevation,\\n start=self.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # create evolution object\\n evol = Evolution(\\n elevation=self.elevation,\\n precipitation=self.precipitation,\\n start=self.start,\\n rain_intensity=self.rain_intensity,\\n rain_interval=self.rain_interval,\\n walkers=self.walkers,\\n runoff=self.runoff,\\n mannings=self.mannings,\\n detachment=self.detachment,\\n transport=self.transport,\\n shearstress=self.shearstress,\\n density=self.density,\\n mass=self.mass,\\n grav_diffusion=self.grav_diffusion,\\n erdepmin=self.erdepmin,\\n erdepmax=self.erdepmax,\\n k_factor=self.k_factor,\\n c_factor=self.c_factor,\\n m=self.m,\\n n=self.n,\\n threads=self.threads,\\n fill_depressions=self.fill_depressions)\\n\\n # open txt file with precipitation data\\n with open(evol.precipitation) as csvfile:\\n\\n # check for header\\n has_header = csv.Sniffer().has_header(csvfile.read(1024))\\n\\n # rewind\\n csvfile.seek(0)\\n\\n # skip header\\n if has_header:\\n next(csvfile)\\n\\n # parse time and precipitation\\n precip = csv.reader(csvfile, delimiter=',', skipinitialspace=True)\\n\\n # initial run\\n initial = next(precip)\\n evol.start = initial[0]\\n evol.rain_intensity = 'rain_intensity'\\n # compute rainfall intensity (mm/hr)\\n # from rainfall observation (mm)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity}\\\"\\n \\\"={rain_observation}\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_intensity=evol.rain_intensity,\\n rain_observation=float(initial[1]),\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # determine mode and run model\\n if self.mode == \\\"simwe_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps\\n # from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # run the landscape evolution model for each rainfall record\\n for row in precip:\\n\\n # update the elevation\\n evol.elevation=evolved_elevation\\n\\n # update time\\n evol.start=row[0]\\n\\n # compute rainfall intensity (mm/hr)\\n # from rainfall observation (mm)\\n rain_intensity = 'rain_intensity'\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity}\\\"\\n \\\"={rain_observation}\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_intensity=rain_intensity,\\n rain_observation=float(row[1]),\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # derive excess water (mm/hr) from rainfall rate (mm/hr)\\n # plus the depth (m) per rainfall interval (min)\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_excess}\\\"\\n \\\"={rain_intensity}\\\"\\n \\\"+{depth}\\\"\\n \\\"/1000.\\\"\\n \\\"/{rain_interval}\\\"\\n \\\"*60.\\\".format(\\n rain_excess=rain_excess,\\n rain_intensity=rain_intensity,\\n depth=depth,\\n rain_interval=self.rain_interval),\\n overwrite=True)\\n\\n # update excess rainfall\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{rain_intensity} = {rain_excess}\\\".format(\\n rain_intensity='rain_intensity',\\n rain_excess=rain_excess),\\n overwrite=True)\\n evol.rain_intensity = rain_intensity\\n\\n # determine mode and run model\\n if self.mode == \\\"simwe_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.erosion_deposition()\\n # remove relative timestamps\\n # from r.sim.water and r.sim.sediment\\n gscript.run_command(\\n 'r.timestamp',\\n map=depth,\\n date='none')\\n gscript.run_command(\\n 'r.timestamp',\\n map=erosion_deposition,\\n date='none')\\n\\n elif self.mode == \\\"usped_mode\\\":\\n (evolved_elevation, time, depth, erosion_deposition,\\n difference) = evol.usped()\\n\\n elif self.mode == \\\"rusle_mode\\\":\\n (evolved_elevation, time, depth, sediment_flux,\\n difference) = evol.rusle()\\n\\n else:\\n raise RuntimeError(\\n '{mode} mode does not exist').format(mode=self.mode)\\n\\n # register the evolved maps\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.elevation_timeseries,\\n maps=evolved_elevation,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.depth_timeseries,\\n maps=depth,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.erdep_timeseries,\\n maps=erosion_deposition,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n try:\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.flux_timeseries,\\n maps=sediment_flux,\\n start=evol.start,\\n increment=increment,\\n flags='i', overwrite=True)\\n except (NameError, CalledModuleError):\\n pass\\n gscript.run_command(\\n 't.register',\\n type=raster,\\n input=self.difference_timeseries,\\n maps=difference,\\n start=evol.start,\\n increment=increment,\\n flags='i',\\n overwrite=True)\\n\\n # remove temporary maps\\n gscript.run_command(\\n 'g.remove',\\n type='raster',\\n name=['rain_excess'],\\n flags='f')\\n\\n # compute net elevation change\\n gscript.run_command(\\n 'r.mapcalc',\\n expression=\\\"{net_difference}\\\"\\n \\\"= {evolved_elevation}-{elevation}\\\".format(\\n net_difference=net_difference,\\n elevation=self.elevation,\\n evolved_elevation=evol.elevation),\\n overwrite=True)\\n gscript.write_command(\\n 'r.colors',\\n map=net_difference,\\n rules='-',\\n stdin=difference_colors)\",\n \"def SAM_CSV_to_solar_data(filename):\\n if not os.path.isfile(filename):\\n raise FileNotFoundError(filename + \\\" does not exist.\\\")\\n wfd = defaultdict(list)\\n with open(filename) as file_in:\\n info = []\\n for i in range(2):\\n info.append(file_in.readline().rstrip())\\n info[i] = info[i].split(\\\",\\\")\\n if \\\"Time Zone\\\" not in info[0]:\\n raise ValueError(\\\"`Time Zone` field not found in solar resource file.\\\")\\n latitude = info[1][info[0].index(\\\"Latitude\\\")]\\n longitude = info[1][info[0].index(\\\"Longitude\\\")]\\n tz = info[1][info[0].index(\\\"Time Zone\\\")]\\n elev = info[1][info[0].index(\\\"Elevation\\\")]\\n source = info[1][info[0].index(\\\"Source\\\")]\\n reader = csv.DictReader(file_in)\\n for row in reader:\\n for col, dat in row.items():\\n if len(col) > 0:\\n wfd[col].append(float(dat))\\n\\n weather = dict()\\n weather['tz'] = float(tz)\\n weather['elev'] = float(elev)\\n weather['lat'] = float(latitude)\\n weather['lon'] = float(longitude)\\n\\n # Create dict with keys = keys passed to SAM and values = list of possible key versions found in resource files (NREL / NASA POWER)\\n acceptable_keys = {\\n 'year' : ['year', 'Year', 'yr'],\\n 'month' : ['month', 'Month', 'mo'],\\n 'day' : ['day', 'Day'],\\n 'hour' : ['hour', 'Hour', 'hr'],\\n 'minute' : ['minute', 'Minute', 'min'],\\n 'dn' : ['dn', 'DNI','dni', 'beam', 'direct normal', 'direct normal irradiance'],\\n 'df' : ['df', 'DHI', 'dhi', 'diffuse', 'diffuse horizontal', 'diffuse horizontal irradiance'],\\n 'gh' : ['gh', 'GHI','ghi', 'global', 'global horizontal', 'global horizontal irradiance'],\\n 'wspd' : ['wspd', 'Wind Speed', 'wind speed'],\\n 'tdry' : ['tdry', 'Temperature', 'dry bulb', 'dry bulb temp', 'temperature', 'ambient', 'ambient temp'],\\n 'wdir' : ['wdir', 'Wind Direction', 'wind direction'],\\n 'pres' : ['pres', 'Pressure', 'pressure'],\\n 'tdew' : ['tdew', 'Dew Point', 'Tdew', 'dew point', 'dew point temperature'],\\n 'rhum' : ['rhum', 'Relative Humidity', 'rh', 'RH', 'relative humidity', 'humidity'],\\n 'alb' : ['alb', 'Surface Albedo', 'albedo', 'surface albedo'],\\n 'snow' : ['snow', 'Snow Depth', 'snow depth', 'snow cover']\\n }\\n \\n # enumerates acceptable_keys, inserts key and values into weather dictionary if found in the resource file\\n for key, list_of_keys in acceptable_keys.items():\\n for good_key in list_of_keys:\\n if good_key in wfd.keys():\\n weather[key] = wfd.pop(good_key)\\n break\\n\\n # handles averaged hourly data with no minute column provided by NASA POWER and removes 2/29 data for leap years\\n # this is a workaround so PySAM/SAM processes as instantaneous data (not setup to handle no minute column)\\n if source == 'NASA/POWER':\\n weather['minute'] = [30] * len(weather['hour'])\\n if len(weather['hour']) == 8784:\\n for key in weather.keys():\\n if key not in ['tz','elev','lat','lon']:\\n del weather[key][1416:1440]\\n\\n\\n return weather\",\n \"def main():\\n\\n PARSER = argparse.ArgumentParser()\\n PARSER.add_argument('-TIC', default=307210830, type=str)\\n PARSER.add_argument('-from_file', default='any')\\n PARSER.add_argument('-tic_id', default='TIC_ID')\\n PARSER.add_argument('-path_plots', default='.')\\n PARSER.add_argument('-interactive', action='store_true', default=False)\\n PARSER.add_argument('-no_detrend', action='store_true', default=False)\\n PARSER.add_argument('-window', default=1501, type=int)\\n PARSER.add_argument('-GP', default=False, action='store_true')\\n PARSER.add_argument('-from_fits', default=False, action='store_true')\\n PARSER.add_argument('-path_fits', default=None)\\n PARSER.add_argument('-noTLS', default=False, action='store_true')\\n PARSER.add_argument('-tofile', default=False, action='store_true')\\n PARSER.add_argument('-tojuliet', default=False, action='store_true')\\n\\n ARGS = PARSER.parse_args()\\n\\n if ARGS.from_file != 'any':\\n TICs = []\\n if os.path.isfile(ARGS.from_file):\\n try:\\n TICs = ascii.read(ARGS.from_file)[ARGS.tic_id]\\n except KeyError:\\n print('Column %s does not exist' % ARGS.tic_id)\\n else:\\n print('Filename does not exist')\\n\\n else:\\n TICs = [float(i) for i in ARGS.TIC.split(',')]\\n\\n detrend = not ARGS.no_detrend\\n path_plots = ARGS.path_plots\\n interactive = ARGS.interactive\\n window = ARGS.window\\n gp = ARGS.GP\\n from_fits = ARGS.from_fits\\n path_fits = ARGS.path_fits\\n noTLS = ARGS.noTLS\\n tofile = ARGS.tofile\\n tojuliet = ARGS.tojuliet\\n\\n if len(TICs) > 0:\\n for i, TIC in enumerate(TICs):\\n try:\\n print('TIC %d (%d/%d)' % (TIC, i+1, len(TICs)))\\n _ = get_lk(TIC, path_plots=path_plots, detrend=detrend, interactive=interactive,\\n window=window, gp_detrend=gp, from_fits=from_fits, path_fits=path_fits,\\n noTLS=noTLS, tofile=tofile, tojuliet=tojuliet)\\n except Exception as e:\\n print('Problem with object TIC %d' % TIC)\\n _, _, exc_tb = sys.exc_info()\\n print('line %d: %s' % (exc_tb.tb_lineno, e))\",\n \"def get_sn2018kzr(colorplt = False):\\n ebv = 0.113/3.1\\n z = 0.053\\n D = cosmo.luminosity_distance([z])[0].value * 1e+6 # in pc\\n dis_mod = 5*np.log10(D / 10)\\n t_max = 58480.422+0.1\\n \\n f = open('../data/otherSN/Mcbrien2019/table1.tex')\\n lines = f.readlines()\\n f.close()\\n lines = lines[:-4]\\n \\n dates = [x.split(\\\"&\\\")[0] for x in lines]\\n mjds = [float(x.split(\\\"&\\\")[1]) for x in lines]\\n phases = [float(x.split(\\\"&\\\")[2].replace('$', '').replace('\\\\t', '')) for x in lines]\\n gs = [x.split(\\\"&\\\")[3].replace('$', '') for x in lines]\\n rs = [x.split(\\\"&\\\")[4].replace('$', '') for x in lines]\\n iis = [x.split(\\\"&\\\")[5].replace('$', '') for x in lines]\\n zs = [x.split(\\\"&\\\")[6].replace('$', '') for x in lines]\\n insts = [x.split(\\\"&\\\")[7] for x in lines]\\n \\n dtg = digital_latex(mjds, phases, gs, insts)\\n dtr = digital_latex(mjds, phases, rs, insts)\\n dti = digital_latex(mjds, phases, iis, insts)\\n \\n filt = np.hstack([np.repeat(\\\"g\\\", len(dtg[0])),\\n np.repeat(\\\"r\\\", len(dtr[0])),\\n np.repeat(\\\"i\\\", len(dti[0]))])\\n phase = np.hstack([dtg[1], dtr[1], dti[1]])\\n mag = np.hstack([dtg[2], dtr[2], dti[2]])\\n emag = np.hstack([dtg[3], dtr[3], dti[3]])\\n mjd = np.hstack([dtg[0], dtr[0], dti[0]])\\n \\n tb = Table(data = [(mjd - t_max) / (1+z), mag, emag, filt],\\n names = ['tmax_rf', 'mag', 'emag', 'filter'])\\n \\n ixr = tb['filter'] == \\\"r\\\"\\n ixg = tb['filter'] == \\\"g\\\"\\n ixi = tb['filter'] == \\\"i\\\"\\n tb['wave'] = np.zeros(len(tb))\\n tb['wave'][ixg] = 4814\\n tb['wave'][ixr] = 6422\\n tb['wave'][ixi] = 7883\\n tb['mag0'] = tb['mag'] - extinction.ccm89(tb['wave'], 3.1*ebv, 3.1)\\n tb['mag0_abs'] = tb['mag0'] - dis_mod\\n tb = tb.to_pandas()\\n return tb\",\n \"def main():\\n # files\\n summary_file = sys.argv[1]\\n pwms_to_tfs_file = sys.argv[2]\\n expressed_tfs_file = sys.argv[3] # TODO\\n\\n # TODO pull in num regions to resize things? but complicated with overlaps etc\\n # TODO edit edges with type of interaction\\n # TODO may want to color by trajectory, to demonstrate waves of trajectory\\n \\n # read in data\\n summary = pd.read_csv(summary_file, sep=\\\"\\\\t\\\")\\n pwms_to_tfs = pd.read_csv(pwms_to_tfs_file, sep=\\\"\\\\t\\\")\\n pwms_to_tfs = pwms_to_tfs[pwms_to_tfs[\\\"expressed\\\"].notna()]\\n pwms_to_filt_tfs = {} # dict: key - pwm names, vals - dict of ensembl ids to hgnc ids\\n for line_idx in range(pwms_to_tfs.shape[0]):\\n pwm_info = pwms_to_tfs.iloc[line_idx,:]\\n pwm_name = pwm_info[\\\"hclust_model_name\\\"]\\n pwm_to_tf = dict(zip(pwm_info[\\\"expressed\\\"].split(\\\";\\\"), pwm_info[\\\"expressed_hgnc\\\"].split(\\\";\\\")))\\n pwms_to_filt_tfs[pwm_name] = pwm_to_tf\\n\\n \\n # filter expressed hgncs for dynamic ones only\\n tfs_filt = pd.read_csv(expressed_tfs_file, sep=\\\"\\\\t\\\", index_col=0)\\n for pwm_name in pwms_to_filt_tfs.keys():\\n tfs_tmp = pwms_to_filt_tfs[pwm_name]\\n for ensembl_tf in tfs_tmp.keys():\\n if ensembl_tf not in tfs_filt.index:\\n del tfs_tmp[ensembl_tf]\\n if len(tfs_tmp.keys()) == 0:\\n del pwms_to_filt_tfs[pwm_name]\\n pwms_to_filt_tfs[pwm_name] = tfs_tmp\\n\\n # add in tfs column\\n tf1 = []\\n for pwm in summary[\\\"pwm1\\\"]:\\n tf_str = []\\n for ensembl_id in pwms_to_filt_tfs[pwm]:\\n tf_str.append(pwms_to_filt_tfs[pwm][ensembl_id])\\n # TODO try add in max point\\n expression = tfs_filt.loc[ensembl_id,:]\\n max_idx = np.argmax(expression.values)\\n tf_str.append(str(max_idx))\\n tf_str = (\\\";\\\").join(tf_str)\\n tf1.append(tf_str)\\n summary[\\\"tf1\\\"] = tf1\\n\\n tf2 = []\\n for pwm in summary[\\\"pwm2\\\"]:\\n tf_str = []\\n for ensembl_id in pwms_to_filt_tfs[pwm]:\\n tf_str.append(pwms_to_filt_tfs[pwm][ensembl_id])\\n expression = tfs_filt.loc[ensembl_id,:]\\n max_idx = np.argmax(expression.values)\\n tf_str.append(str(max_idx))\\n tf_str = (\\\";\\\").join(tf_str)\\n tf2.append(tf_str)\\n summary[\\\"tf2\\\"] = tf2\\n \\n # remove failed rules\\n summary = summary[~summary[\\\"interaction\\\"].str.contains(\\\"FAILED\\\")]\\n \\n # make graph\\n graph = nx.from_pandas_edgelist(summary, \\\"tf1\\\", \\\"tf2\\\")\\n\\n # set up positions\\n #pos = graphviz_layout(graph, prog=\\\"dot\\\")\\n pos = graphviz_layout(graph, prog=\\\"neato\\\")\\n scale_factor = 3\\n for key in pos.keys():\\n coords = pos[key]\\n pos[key] = {\\\"x\\\": scale_factor*coords[0], \\\"y\\\": -scale_factor*coords[1]}\\n nx.set_node_attributes(graph, pos, \\\"graphics\\\") # note this is diff from v1 to v2 in networkx\\n \\n # add graphics\\n add_graphics_theme_to_nx_graph(graph)\\n\\n # write gml\\n out_file = \\\"summary.gml\\\"\\n nx.write_gml(stringize_nx_graph(graph), out_file, stringizer=str)\\n\\n # tfs: for each tf, get gene column\\n \\n \\n return\",\n \"def wem_market_value_all():\\n __query = \\\"\\\"\\\"\\n select\\n date_trunc('month', wfs.trading_interval) AS trading_day,\\n sum(wfs.eoi_quantity * wbs.price) as energy_interval,\\n wf.fueltech_id\\n from wem_facility_scada wfs\\n left join wem_facility wf on wfs.facility_id = wf.code\\n join wem_balancing_summary wbs on wfs.trading_interval = wbs.trading_interval\\n where\\n wf.fueltech_id is not null\\n group by 1, wf.fueltech_id\\n order by 1 desc, 2 asc\\n \\\"\\\"\\\"\\n\\n query = __query.format()\\n\\n json_envelope = {}\\n\\n with engine.connect() as c:\\n rows = c.execute(query)\\n\\n current_tech = None\\n\\n for row in rows:\\n\\n current_tech = row[2]\\n\\n if current_tech not in json_envelope.keys():\\n json_envelope[current_tech] = {\\n \\\"id\\\": f\\\"wem.fuel_tech.{current_tech}.market_value\\\",\\n \\\"fuel_tech\\\": current_tech,\\n \\\"region\\\": \\\"wa\\\",\\n \\\"type\\\": \\\"market_value\\\",\\n \\\"units\\\": \\\"AUD\\\",\\n \\\"history\\\": {\\n \\\"interval\\\": \\\"1M\\\",\\n \\\"start\\\": None,\\n \\\"last\\\": None,\\n \\\"data\\\": [],\\n },\\n }\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] == None\\n or row[0] < json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"start\\\"] = row[0]\\n\\n if (\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] == None\\n or row[0] > json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"]\\n ):\\n json_envelope[current_tech][\\\"history\\\"][\\\"last\\\"] = row[0]\\n\\n json_envelope[current_tech][\\\"history\\\"][\\\"data\\\"].append(row[1])\\n\\n return [json_envelope[i] for i in json_envelope.keys()]\",\n \"def calculate_surface_heatflux(self, weather, spaces_dict, surface, temp_record, Coeff, space, h_surface, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, Aflag, terrain, areaDict, areaWinDict, shadowRatios, shadowRatioIndex):\\r\\n #print \\\"Reaching Surface function...\\\"\\r\\n\\r\\n # First get the As\\r\\n A_total = self.get_A(surface, areaDict, areaWinDict)\\r\\n if Aflag == 0:\\r\\n # If it is the first surface of the space, label the space ID in the log file:\\r\\n la = str(surface.obj_id)\\r\\n lb = str(surface.obj_type)\\r\\n #TM_user.info(\\\"%s,surface area,%s,%s\\\" % (la, A_total, lb))\\r\\n A_noWin = self.get_A_noWin(surface, areaDict, areaWinDict)\\r\\n A_noOp = self.get_A_noOp(surface, areaDict, areaWinDict)\\r\\n T_space = spaces_dict[space.obj_id][1]\\r\\n T1 = weather[\\\"t_outside\\\"]\\r\\n hc_external = float(self.get_hc_external(weather, surface, h_surface, terrain))\\r\\n transmitted_win = 0\\r\\n Q_flux = 0\\r\\n\\r\\n # need the surface related information, T_space, U, R3\\r\\n U = self.get_U_surface_e(A_total, A_noOp, surface, areaWinDict) # U = Infor_surface{11,i_surface}; Defined Below\\r\\n #print U\\r\\n R3 = 1/U\\r\\n # Using calculations from: self.surface.constr.layer.C # Infor_surface{10, i_surface} ; from gbXML\\r\\n C = self.get_C_surface(A_total, A_noOp, surface, Coeff, areaWinDict) # need to pass surface and opening ids\\r\\n #print C\\r\\n\\r\\n temperature = Temperature()\\r\\n\\r\\n #Sub-routines for each wall type based on the returned hc_external\\r\\n # This hc is different for each surface type so moved under this sub-routine area\\r\\n #hc = 3.076 sent this to the Temperature Object\\r\\n if surface.obj_type == \\\"ExteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.exterior_wall(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux\\r\\n if surface.obj_type == \\\"Roof\\\":\\r\\n transmitted_win, Q_flux = temperature.roof(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record, ShadowsFlag, ns, shadow_record, shade_surf_list, surfaces_dict, areaWinDict, shadowRatios, areaDict, shadowRatioIndex)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"InteriorWall\\\":\\r\\n transmitted_win, Q_flux = temperature.interior_wall(surface, A_total, R3, C, spaces_dict, T_space, temp_record)\\r\\n #print Q_flux # Matches for Four Room\\r\\n if surface.obj_type == \\\"UndergroundWall\\\":\\r\\n transmitted_win, Q_flux = temperature.underground_wall(surface, A_total, R3, C, T_space, temp_record) # No instance of yet to test\\r\\n if surface.obj_type == \\\"RaisedFloor\\\":\\r\\n # This will eventually need some values when we start using raised floors\\r\\n transmitted_win, Q_flux = temperature.raised_floor(surface, hc_external, T1, A_total, A_noWin, weather, R3, C, A_noOp, T_space, temp_record) # Not instance of yet to test\\r\\n\\r\\n return transmitted_win, Q_flux\",\n \"def test_convert_azfp_01a_notemperature_notilt(azfp_path):\\n azfp_01a_path = azfp_path / 'rutgers_glider_notemperature/22052500.01A'\\n azfp_xml_path = azfp_path / 'rutgers_glider_notemperature/22052501.XML'\\n\\n echodata = open_raw(\\n raw_file=azfp_01a_path, sonar_model='AZFP', xml_path=azfp_xml_path\\n )\\n\\n # Temperature variable is present in the Environment group and its values are all nan\\n assert \\\"temperature\\\" in echodata[\\\"Environment\\\"]\\n assert echodata[\\\"Environment\\\"][\\\"temperature\\\"].isnull().all()\\n\\n # Tilt variables are present in the Platform group and their values are all nan\\n assert \\\"tilt_x\\\" in echodata[\\\"Platform\\\"]\\n assert \\\"tilt_y\\\" in echodata[\\\"Platform\\\"]\\n assert echodata[\\\"Platform\\\"][\\\"tilt_x\\\"].isnull().all()\\n assert echodata[\\\"Platform\\\"][\\\"tilt_y\\\"].isnull().all()\",\n \"def epflux_all(U, V, W, T, longitude, latitude, press, boa=None):\\n pass\",\n \"def do_qc(fn, df, year):\\n (lon, lat) = fn2lonlat(fn)\\n stage4 = compute_stage4(lon, lat, year)\\n # Does the frame appear to have all dates?\\n if len(df.index) != len(df.resample(\\\"D\\\").mean().index):\\n print(\\\"ERROR: Appears to be missing dates!\\\")\\n\\n if open(fn).read()[-1] != \\\"\\\\n\\\":\\n print(\\\"ERROR: File does not end with \\\\\\\\n\\\")\\n\\n print(\\\"--------- Summary stats from the .cli file\\\")\\n print(\\\"YEAR | RAIN | MAXRATE | MAXACC | #DAYS | #>1RT | RAD/D\\\")\\n print(\\\" --- | --- | --- | --- | --- | --- | ---\\\")\\n for _year, gdf in df.groupby(by=df.index.year):\\n print(\\n (\\\"%s | %6.2f | %7.2f | %7.2f | %6i | %6i | %6.0f\\\")\\n % (\\n _year,\\n mm2inch(gdf[\\\"pcpn\\\"].sum()),\\n mm2inch(gdf[\\\"maxr\\\"].max()),\\n mm2inch(gdf[\\\"pcpn\\\"].max()),\\n len(gdf[gdf[\\\"pcpn\\\"] > 0].index),\\n len(gdf[gdf[\\\"maxr\\\"] > 25.4].index),\\n gdf[\\\"rad\\\"].mean(),\\n )\\n )\\n\\n print(\\\"---- Months with < 0.05 precipitation ----\\\")\\n gdf = df.groupby(by=[df.index.year, df.index.month])[\\\"pcpn\\\"].sum()\\n print(gdf[gdf < 1.0])\\n\\n print(\\\"----- Average high temperature -----\\\")\\n print(\\\"YEAR | Avg High F | Avg Low F | Days > 100F\\\")\\n print(\\\" --- | --- | --- | ---\\\")\\n for _year, gdf in df.groupby(by=df.index.year):\\n print(\\n (\\\"%s | %6.2f | %6.2f | %3i\\\")\\n % (\\n _year,\\n c2f(gdf[\\\"tmax\\\"].mean()),\\n c2f(gdf[\\\"tmin\\\"].mean()),\\n len(gdf[gdf[\\\"tmax\\\"] > 37.7].index),\\n )\\n )\\n\\n monthly = df[df.index.year == year][\\\"pcpn\\\"].resample(\\\"M\\\").sum().copy()\\n monthly = pd.DataFrame(\\n {\\\"dep\\\": mm2inch(monthly.values)}, index=range(1, 13)\\n )\\n\\n # Get prism, for a bulk comparison\\n prism = requests.get(\\n (\\n \\\"http://mesonet.agron.iastate.edu/json/prism/\\\"\\n \\\"%.2f/%.2f/%s0101-%s1231\\\"\\n )\\n % (lon, lat, year, year)\\n ).json()\\n rows = []\\n for entry in prism[\\\"data\\\"]:\\n rows.append(\\n {\\n \\\"date\\\": datetime.datetime.strptime(\\n entry[\\\"valid\\\"][:10], \\\"%Y-%m-%d\\\"\\n ),\\n \\\"precip\\\": entry[\\\"precip_in\\\"],\\n }\\n )\\n prismdf = pd.DataFrame(rows)\\n prismdf = prismdf.set_index(\\\"date\\\")\\n monthly[\\\"prism\\\"] = prismdf[\\\"precip\\\"].resample(\\\"M\\\").sum().copy().values\\n\\n # Compare daily values\\n iemjson = requests.get(\\n (\\n \\\"http://mesonet.agron.iastate.edu/iemre/multiday/\\\"\\n \\\"%s-01-01/%s-12-31/%s/%s/json\\\"\\n )\\n % (year, year, lat, lon)\\n ).json()\\n rows = []\\n for entry in iemjson[\\\"data\\\"]:\\n rows.append(\\n {\\n \\\"date\\\": datetime.datetime.strptime(entry[\\\"date\\\"], \\\"%Y-%m-%d\\\"),\\n \\\"precip\\\": entry[\\\"daily_precip_in\\\"],\\n }\\n )\\n iemdf = pd.DataFrame(rows)\\n iemdf = iemdf.set_index(\\\"date\\\")\\n print(\\\"PRISM %s precip is: %.2f\\\" % (year, prismdf[\\\"precip\\\"].sum()))\\n print(\\\"IEMRE sum precip is: %.2f\\\" % (iemdf[\\\"precip\\\"].sum(),))\\n print(\\\"StageIV sum precip is: %.2f\\\" % (stage4[\\\"precip\\\"].sum(),))\\n monthly[\\\"stage4\\\"] = stage4[\\\"precip\\\"].resample(\\\"M\\\").sum().copy().values\\n monthly[\\\"iemre\\\"] = iemdf[\\\"precip\\\"].resample(\\\"M\\\").sum().copy().values\\n monthly[\\\"prism-dep\\\"] = monthly[\\\"prism\\\"] - monthly[\\\"dep\\\"]\\n monthly[\\\"iemre-dep\\\"] = monthly[\\\"iemre\\\"] - monthly[\\\"dep\\\"]\\n\\n print(\\\" --------- %s Monthly Totals --------\\\" % (year,))\\n print(monthly)\\n df.at[\\n slice(datetime.date(year, 1, 1), datetime.date(year, 12, 31)),\\n \\\"stage4_precip\\\",\\n ] = stage4[\\\"precip\\\"].values\\n df[\\\"iemre_precip\\\"] = iemdf[\\\"precip\\\"]\\n df[\\\"diff_precip\\\"] = df[\\\"pcpn_in\\\"] - df[\\\"iemre_precip\\\"]\\n df[\\\"diff_stage4\\\"] = df[\\\"pcpn_in\\\"] - df[\\\"stage4_precip\\\"]\\n print(\\\" --- Top 5 Largest DEP > IEMRE ----\\\")\\n print(\\n df[\\n [\\n \\\"diff_precip\\\",\\n \\\"pcpn_in\\\",\\n \\\"iemre_precip\\\",\\n \\\"stage4_precip\\\",\\n \\\"diff_stage4\\\",\\n ]\\n ]\\n .sort_values(by=\\\"diff_precip\\\", ascending=False)\\n .head()\\n )\\n print(\\\" --- Top 5 Largest IEMRE > DEP ----\\\")\\n print(\\n df[\\n [\\n \\\"diff_precip\\\",\\n \\\"pcpn_in\\\",\\n \\\"iemre_precip\\\",\\n \\\"stage4_precip\\\",\\n \\\"diff_stage4\\\",\\n ]\\n ]\\n .sort_values(by=\\\"diff_precip\\\", ascending=True)\\n .head()\\n )\\n\\n print(\\\" --- Top 10 Largest Stage4 > DEP ----\\\")\\n print(\\n df[\\n [\\n \\\"diff_precip\\\",\\n \\\"pcpn_in\\\",\\n \\\"iemre_precip\\\",\\n \\\"stage4_precip\\\",\\n \\\"diff_stage4\\\",\\n ]\\n ]\\n .sort_values(by=\\\"diff_stage4\\\", ascending=True)\\n .head(10)\\n )\\n print(\\\" vvv job listing based on the above vvv\\\")\\n for dt in df.sort_values(by=\\\"diff_stage4\\\", ascending=True).head(10).index:\\n print(\\n \\\"python daily_clifile_editor.py 0 %s %s %s\\\"\\n % (dt.year, dt.month, dt.day)\\n )\\n df2 = df.loc[slice(datetime.date(year, 1, 1), datetime.date(year, 1, 31))][\\n [\\\"diff_precip\\\", \\\"pcpn_in\\\", \\\"iemre_precip\\\", \\\"stage4_precip\\\"]\\n ].sort_values(by=\\\"diff_precip\\\")\\n print(\\\" --- Daily values for month \\\" \\\"\\\")\\n print(df2)\",\n \"def get_ta_data(path_data):\\n if file_exist(path_data) == 0:\\n df = ts.pro_bar(ts_code=ts_code_str, adj='qfq', start_date=dt_baseDeltaValue_start_str,\\n end_date=dt_now_str, freq=freq_label, ma=[3, 5, 55])\\n # print(df.head())\\n\\n df['delta'] = df['ma3'] - df['ma5']\\n writer = pd.ExcelWriter(path_data)\\n # print(type(df).__name__)\\n if type(df).__name__ == 'DataFrame':\\n df.to_excel(writer, sheet_name='Data', index=False)\\n writer.save()\",\n \"def getData(self, indexSelect):\\n csv_filename = '../ExpDataFiles.csv'\\n fieldnames = ['index', 'filename', 'sample', 'shear']\\n\\n parameters = files_list_reduce(csv_filename, fieldnames)\\n fileName, sample, shear = files_to_reduce(parameters, indexSelect)\\n\\n self.setName = '../2D_data_sub/' + fileName[0]\\n\\n self.expData_raw = np.loadtxt(self.setName, delimiter=\\\" \\\", skiprows=self.skipRows)\\n bsq = np.sqrt(self.expData_raw[:, 0]**2 + self.expData_raw[:, 1]**2)\\n self.beamStop = (bsq < self.qmin)\\n self.expData_bs = self.expData_raw[~self.beamStop]\\n\\n # Removing detector shadow\\n\\n qx_neg_lower = -0.019\\n qx_neg_upper = -0.012\\n qx_pos_lower = 0.021\\n qx_pos_upper = 0.029\\n\\n qy_lower = -0.014\\n qy_upper = -0.011\\n\\n q_min = -0.062\\n q_max = -0.0539\\n\\n qx_neg = np.logical_and(self.expData_bs[:, 0] <\\n qx_neg_upper, self.expData_bs[:, 0] > qx_neg_lower)\\n data_qx_neg = self.expData_bs[~qx_neg]\\n\\n qx_neg = np.logical_and(data_qx_neg[:, 0] <\\n q_max, data_qx_neg[:, 0] > q_min)\\n data_qx_neg = data_qx_neg[~qx_neg]\\n\\n qx_pos = np.logical_and(data_qx_neg[:, 0] < qx_pos_upper, data_qx_neg[:, 0] > qx_pos_lower)\\n data_qx_pos = data_qx_neg[~qx_pos]\\n\\n qy_qxRange = np.logical_and(\\n data_qx_pos[:, 0] < qx_pos_lower, data_qx_pos[:, 0] > qx_neg_upper)\\n qyRange = np.logical_and(data_qx_pos[:, 1] < qy_upper, data_qx_pos[:, 1] > qy_lower)\\n qy_mask = np.logical_and(qy_qxRange == True, qyRange == True)\\n\\n self.expData_masked = data_qx_pos[~qy_mask]\\n\\n # End removing detector shadow\\n\\n self.expData_sort = np.array(sorted(self.expData_masked, key=lambda col: (col[1], col[0])))\\n\\n self.qx_unique = np.unique(np.array(self.expData_sort[:, 0]))\\n self.qy_unique = np.unique(np.array(self.expData_sort[:, 1]))\\n\\n# self.expData_sort = abs(self.expData_sort)\\n\\n # testing resolution\\n self.expData = sasmodels.data.Data2D(x=self.expData_sort[:, 0], dx=0.1*abs(self.expData_sort[:, 0]), y=self.expData_sort[:, 1], dy=0.1*abs(\\n self.expData_sort[:, 1]), z=self.expData_sort[:, 2], dz=self.expData_sort[:, 3])\\n self.expData.sample = sample\\n self.expData.shear = shear\\n\\n return\",\n \"def optimize_trap(dg):\\n f_peak = './temp_peak.lh5' # lh5\\n f_results = './temp_results.h5' # pandas\\n grp_data, grp_grid = '/optimize_data', '/optimize_grid'\\n \\n # epar, elo, ehi, epb = 'energy', 0, 1e7, 10000 # full range\\n epar, elo, ehi, epb = 'energy', 3.88e6, 3.92e6, 500 # K40 peak\\n \\n show_movie = True\\n write_output = True\\n n_rows = None # default None\\n \\n with open('opt_trap.json') as f:\\n dsp_config = json.load(f, object_pairs_hook=OrderedDict)\\n \\n # files to consider. fixme: right now only works with one file\\n sto = lh5.Store()\\n lh5_dir = os.path.expandvars(dg.config['lh5_dir'])\\n raw_list = lh5_dir + dg.fileDB['raw_path'] + '/' + dg.fileDB['raw_file']\\n f_raw = raw_list.values[0] \\n tb_raw = 'ORSIS3302DecoderForEnergy/raw/'\\n\\n # quick check of the energy range\\n # ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\\n # plt.plot(bins[1:], hist, ds='steps')\\n # plt.show()\\n # exit()\\n \\n # set grid parameters\\n # TODO: jason's suggestions, knowing the expected shape of the noise curve\\n # e_rises = np.linspace(-1, 0, sqrt(sqrt(3))\\n # e_rises # make another list which is 10^pwr of this list\\n # np.linspace(log_tau_min, log_tau_max) # try this too\\n e_rises = np.arange(1, 12, 1)\\n e_flats = np.arange(1, 6, 1)\\n # rc_consts = np.arange(54, 154, 10) # changing this here messes up DCR\\n \\n # -- create the grid search file the first time -- \\n # NOTE: this makes a linear grid, and is editable by the arrays above.\\n # jason also proposed a more active gradient-descent style search\\n # like with Brent's method. (https://en.wikipedia.org/wiki/Brent%27s_method)\\n \\n if True:\\n # if not os.path.exists(f_peak):\\n print('Recreating grid search file')\\n \\n # create the grid file\\n # NOTE: save it as an lh5 Table just as an example of writing/reading one\\n lists = [e_rises, e_flats]#, rc_consts]\\n prod = list(itertools.product(*lists)) # clint <3 stackoverflow\\n df_grid = pd.DataFrame(prod, columns=['rise', 'flat'])#,'rc']) \\n lh5_grid = {}\\n for i, dfcol in df_grid.iteritems():\\n lh5_grid[dfcol.name] = lh5.Array(dfcol.values)\\n tb_grid = lh5.Table(col_dict=lh5_grid)\\n sto.write_object(tb_grid, grp_grid, f_peak)\\n \\n # filter events by onboard energy\\n ene_raw = sto.read_object(tb_raw+'/'+epar, f_raw).nda\\n # hist, bins, var = pgh.get_hist(ene_raw, range=(elo, ehi), dx=epb)\\n # plt.plot(bins[1:], hist, ds='steps')\\n # plt.show()\\n if n_rows is not None:\\n ene_raw = ene_raw[:n_rows]\\n idx = np.where((ene_raw > elo) & (ene_raw < ehi))\\n\\n # create a filtered table with correct waveform and attrs\\n # TODO: move this into a function in lh5.py which takes idx as an input\\n tb_data, wf_tb_data = lh5.Table(), lh5.Table()\\n\\n # read non-wf cols (lh5 Arrays)\\n data_raw = sto.read_object(tb_raw, f_raw, n_rows=n_rows)\\n for col in data_raw.keys():\\n if col=='waveform': continue\\n newcol = lh5.Array(data_raw[col].nda[idx], attrs=data_raw[col].attrs)\\n tb_data.add_field(col, newcol)\\n \\n # handle waveform column (lh5 Table)\\n data_wfs = sto.read_object(tb_raw+'/waveform', f_raw, n_rows=n_rows)\\n for col in data_wfs.keys():\\n attrs = data_wfs[col].attrs\\n if isinstance(data_wfs[col], lh5.ArrayOfEqualSizedArrays):\\n # idk why i can't put the filtered array into the constructor\\n aoesa = lh5.ArrayOfEqualSizedArrays(attrs=attrs, dims=[1,1])\\n aoesa.nda = data_wfs[col].nda[idx]\\n newcol = aoesa\\n else:\\n newcol = lh5.Array(data_wfs[col].nda[idx], attrs=attrs)\\n wf_tb_data.add_field(col, newcol)\\n tb_data.add_field('waveform', wf_tb_data)\\n tb_data.attrs = data_raw.attrs\\n sto.write_object(tb_data, grp_data, f_peak)\\n\\n else:\\n print('Loading peak file. groups:', sto.ls(f_peak))\\n tb_grid = sto.read_object(grp_grid, f_peak)\\n tb_data = sto.read_object(grp_data, f_peak) # filtered file\\n # tb_data = sto.read_object(tb_raw, f_raw) # orig file\\n df_grid = tb_grid.get_dataframe()\\n \\n # check shape of input table\\n print('input table attributes:')\\n for key in tb_data.keys():\\n obj = tb_data[key]\\n if isinstance(obj, lh5.Table):\\n for key2 in obj.keys():\\n obj2 = obj[key2]\\n print(' ', key, key2, obj2.nda.shape, obj2.attrs)\\n else:\\n print(' ', key, obj.nda.shape, obj.attrs)\\n\\n # clear new colums if they exist\\n new_cols = ['e_fit', 'fwhm_fit', 'rchisq', 'xF_err', 'fwhm_ovr_mean']\\n for col in new_cols:\\n if col in df_grid.columns:\\n df_grid.drop(col, axis=1, inplace=True)\\n\\n t_start = time.time()\\n def run_dsp(dfrow):\\n \\\"\\\"\\\"\\n run dsp on the test file, editing the processor list\\n alternate idea: generate a long list of processors with different names\\n \\\"\\\"\\\"\\n # adjust dsp config dictionary\\n rise, flat = dfrow\\n # dsp_config['processors']['wf_pz']['defaults']['db.pz.tau'] = f'{tau}*us'\\n dsp_config['processors']['wf_trap']['args'][1] = f'{rise}*us'\\n dsp_config['processors']['wf_trap']['args'][2] = f'{flat}*us'\\n # pprint(dsp_config)\\n \\n # run dsp\\n pc, tb_out = build_processing_chain(tb_data, dsp_config, verbosity=0)\\n pc.execute()\\n \\n # analyze peak\\n e_peak = 1460.\\n etype = 'trapEmax'\\n elo, ehi, epb = 4000, 4500, 3 # the peak moves around a bunch\\n energy = tb_out[etype].nda\\n \\n # get histogram\\n hE, bins, vE = pgh.get_hist(energy, range=(elo, ehi), dx=epb)\\n xE = bins[1:]\\n \\n # should I center the max at 1460?\\n\\n # simple numerical width\\n i_max = np.argmax(hE)\\n h_max = hE[i_max]\\n upr_half = xE[(xE > xE[i_max]) & (hE <= h_max/2)][0]\\n bot_half = xE[(xE < xE[i_max]) & (hE >= h_max/2)][0]\\n fwhm = upr_half - bot_half\\n sig = fwhm / 2.355\\n \\n # fit to gaussian: amp, mu, sig, bkg\\n fit_func = pgf.gauss_bkg\\n amp = h_max * fwhm\\n bg0 = np.mean(hE[:20])\\n x0 = [amp, xE[i_max], sig, bg0]\\n xF, xF_cov = pgf.fit_hist(fit_func, hE, bins, var=vE, guess=x0)\\n\\n # collect results\\n e_fit = xF[0]\\n xF_err = np.sqrt(np.diag(xF_cov))\\n e_err = xF\\n fwhm_fit = xF[1] * 2.355 * 1460. / e_fit\\n \\n fwhm_err = xF_err[2] * 2.355 * 1460. / e_fit\\n \\n chisq = []\\n for i, h in enumerate(hE):\\n model = fit_func(xE[i], *xF)\\n diff = (model - h)**2 / model\\n chisq.append(abs(diff))\\n rchisq = sum(np.array(chisq) / len(hE))\\n fwhm_ovr_mean = fwhm_fit / e_fit\\n\\n if show_movie:\\n \\n plt.plot(xE, hE, ds='steps', c='b', lw=2, label=f'{etype} {rise}--{flat}')\\n\\n # peak shape\\n plt.plot(xE, fit_func(xE, *x0), '-', c='orange', alpha=0.5,\\n label='init. guess')\\n plt.plot(xE, fit_func(xE, *xF), '-r', alpha=0.8, label='peakshape fit')\\n plt.plot(np.nan, np.nan, '-w', label=f'mu={e_fit:.1f}, fwhm={fwhm_fit:.2f}')\\n\\n plt.xlabel(etype, ha='right', x=1)\\n plt.ylabel('Counts', ha='right', y=1)\\n plt.legend(loc=2)\\n\\n # show a little movie\\n plt.show(block=False)\\n plt.pause(0.01)\\n plt.cla()\\n\\n # return results\\n return pd.Series({'e_fit':e_fit, 'fwhm_fit':fwhm_fit, 'rchisq':rchisq,\\n 'fwhm_err':xF_err[0], 'fwhm_ovr_mean': fwhm_ovr_mean})\\n \\n # df_grid=df_grid[:10]\\n df_tmp = df_grid.progress_apply(run_dsp, axis=1)\\n df_grid[new_cols] = df_tmp\\n # print(df_grid)\\n \\n if show_movie:\\n plt.close()\\n \\n print('elapsed:', time.time() - t_start)\\n if write_output:\\n df_grid.to_hdf(f_results, key=grp_grid)\\n print(f\\\"Wrote output file: {f_results}\\\")\",\n \"def zonal_stats_workflow():\\n save_as = \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/summary/monthly_quickflow.csv\\\"\\n scenario_dict = {\\n 'pre-decline': \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/pre_decline\\\",\\n 'post-decline': \\\"C:/Users/ginge/Documents/NatCap/GIS_local/USFS/replicate_4th_draft_12.4.18/post_decline\\\",\\n }\\n df_list = []\\n for scenario in scenario_dict.iterkeys():\\n results_dict = {\\n 'scenario': [],\\n 'month': [],\\n 'sum_quickflow': [],\\n }\\n folder = scenario_dict[scenario]\\n aoi_shp = os.path.join(folder, 'aggregated_results.shp')\\n for month in xrange(1, 13):\\n qf_raster = os.path.join(\\n folder, 'intermediate_outputs', 'qf_{}.tif'.format(month))\\n zonal_stats = pygeoprocessing.zonal_statistics(\\n (qf_raster, 1), aoi_shp)\\n sum_QF = zonal_stats[0]['sum']\\n results_dict['scenario'].append(scenario)\\n results_dict['month'].append(month)\\n results_dict['sum_quickflow'].append(sum_QF)\\n results_df = pandas.DataFrame(data=results_dict)\\n df_list.append(results_df)\\n combined_list = pandas.concat(df_list)\\n combined_list.to_csv(save_as, index=False)\",\n \"def getHFtableData(self, ep=None):\\n HFdict = {}\\n if self.hfMode == 'limiter':\\n HFdict['Heat Flux Mode'] = 'Limiter'\\n if self.lqCNmode == 'eich':\\n HFdict[\\\"\\\\u03BB Near Mode\\\"] = 'Eich Regression #15'\\n HFdict[\\\"Common Region Near Heat Flux Width (\\\\u03BBq CN) [mm]\\\"] = self.lqEich\\n else:\\n HFdict[\\\"\\\\u03BB Near Mode\\\"] = 'User Defined'\\n HFdict[\\\"Common Region Near Heat Flux Width (\\\\u03BBq CN) [mm]\\\"] = self.lqCN\\n if self.lqCFmode == 'horacek':\\n HFdict[\\\"\\\\u03BB Far Mode\\\"] = 'Horacek Figure 6a'\\n HFdict[\\\"Common Region Far Heat Flux Width (\\\\u03BBq CF) [mm]\\\"] = self.lqCF\\n else:\\n HFdict[\\\"\\\\u03BB Far Mode\\\"] = 'User Defined'\\n HFdict[\\\"Common Region Far Heat Flux Width (\\\\u03BBq CF) [mm]\\\"] = self.lqCF\\n\\n HFdict[\\\"Common Region Near Power Fraction\\\"] = self.fracCN\\n HFdict[\\\"Common Region Far Power Fraction\\\"] = self.fracCF\\n\\n elif self.hfMode == 'multiExp':\\n HFdict['Heat Flux Mode'] = 'Multiple (4) Exponentials'\\n if self.lqCNmode == 'eich':\\n HFdict[\\\"\\\\u03BB Near Mode\\\"] = 'Eich Regression #15'\\n HFdict[\\\"Common Region Near Heat Flux Width (\\\\u03BBq CN) [mm]\\\"] = self.lqEich\\n else:\\n HFdict[\\\"\\\\u03BB Near Mode\\\"] = 'User Defined'\\n HFdict[\\\"Common Region Near Heat Flux Width (\\\\u03BBq CN) [mm]\\\"] = self.lqCN\\n\\n if self.lqCFmode == 'horacek':\\n HFdict[\\\"\\\\u03BB Far Mode\\\"] = 'Horacek Figure 6a'\\n else:\\n HFdict[\\\"\\\\u03BB Far Mode\\\"] = 'User Defined'\\n\\n\\n\\n HFdict[\\\"Common Region Far Heat Flux Width (\\\\u03BBq CF) [mm]\\\"] = self.lqCF\\n HFdict[\\\"Private Region Near Heat Flux Width (\\\\u03BBq PN) [mm]\\\"] = self.lqPN\\n HFdict[\\\"Private Region Far Heat Flux Width (\\\\u03BBq PF) [mm]\\\"] = self.lqPF\\n HFdict[\\\"Common Region Near Power Fraction\\\"] = self.fracCN\\n HFdict[\\\"Common Region Far Power Fraction\\\"] = self.fracCF\\n HFdict[\\\"Private Region Near Power Fraction\\\"] = self.fracPN\\n HFdict[\\\"Private Region Far Power Fraction\\\"] = self.fracPF\\n\\n elif self.hfMode == 'qFile':\\n HFdict[\\\"Heat Flux Mode\\\"] = 'Read HF from qFile'\\n HFdict['qFilePath'] = self.qFilePath\\n HFdict['qFileTag'] = self.qFileTag\\n\\n elif self.hfMode == 'eich':\\n HFdict['Heat Flux Mode'] = 'Gaussian Spreading'\\n if self.lqCNmode == 'eich':\\n HFdict[\\\"\\\\u03BB Mode\\\"] = 'Eich Regression #15'\\n HFdict[\\\"Heat Flux Width (\\\\u03BBq) [mm]\\\"] = self.lqEich\\n else:\\n HFdict[\\\"\\\\u03BB Mode\\\"] = 'User Defined'\\n HFdict[\\\"Heat Flux Width (\\\\u03BBq) [mm]\\\"] = self.lqCN\\n\\n if self.SMode == 'makowski':\\n HFdict['Greenwald Density Fraction'] = self.fG\\n HFdict['Spreading (S) Mode'] = 'Makowski Figure 6'\\n else:\\n HFdict['Spreading (S) Mode'] = 'User Defined'\\n HFdict['Greenwald Density Fraction'] = 'Only used for Makowski S Mode'\\n HFdict['S [mm]'] = self.S\\n HFdict['Background Heat Flux'] = self.qBG\\n\\n if self.hfMode != 'qFile':\\n HFdict[\\\"Power Injected (Pinj) [MW]\\\"] = self.Pinj\\n HFdict[\\\"Radiated Fraction of Injected Power\\\"] = self.coreRadFrac\\n HFdict[\\\"Power Crossing Separatrix (Psol) [MW]\\\"] = self.Psol\\n HFdict[\\\"Upper Inner Divertor Power Fraction\\\"] = self.fracUI\\n HFdict[\\\"Upper Outer Divertor Power Fraction\\\"] = self.fracUO\\n HFdict[\\\"Lower Inner Divertor Power Fraction\\\"] = self.fracLI\\n HFdict[\\\"Lower Outer Divertor Power Fraction\\\"] = self.fracLO\\n\\n return HFdict\",\n \"def get_data_iue(obsid, filt):\\n\\n # This error code will be used unless there's a problem reading any\\n # of the FITS files in the list, or the FILTER value is not understood.\\n errcode = 0\\n\\n # This defines a data point for a DataSeries object as a namedtuple.\\n data_point = collections.namedtuple('DataPoint', ['x', 'y'])\\n\\n # For IUE, this defines the x-axis and y-axis units as a string.\\n iue_xunit = \\\"Angstroms (vacuum, heliocentric)\\\"\\n iue_yunit = \\\"ergs/cm^2/s/Angstrom\\\"\\n\\n # Parse the obsID string to determine the paths+files to read. Note:\\n # this step will assign some of the error codes returned to the top level.\\n if filt == ' ':\\n filt = \\\"UNKNOWN\\\"\\n if filt.upper() in [\\\"LOW_DISP\\\", \\\"HIGH_DISP\\\"] or filt == \\\"UNKNOWN\\\":\\n parsed_files_result = parse_obsid_iue(obsid, filt.upper())\\n errcode = parsed_files_result.errcode\\n else:\\n errcode = 4\\n\\n # In the case of low dispersion spectra, there can be two apertures for\\n # a single obsID. In that case, we return a list of TWO DataSeries, one\\n # for each aperture. In other words, we treat the single obsID as if it\\n # were two different obsIDs in the case of a double-aperture.\\n all_data_series = []\\n\\n # For each file, read in the contents and create a return JSON object.\\n if errcode == 0:\\n for sfile in parsed_files_result.specfiles:\\n # Figure out if this is an mxhi or mxlo spectrum.\\n if sfile[-7:] == \\\"mxlo.gz\\\":\\n is_lo = True\\n is_hi = False\\n else:\\n is_lo = False\\n is_hi = True\\n\\n try:\\n with fits.open(sfile) as hdulist:\\n if is_lo:\\n # Get the dispersion type from the primary header.\\n dispersion = hdulist[0].header[\\\"disptype\\\"]\\n # Get the aperture size(s) from the header.\\n apertures = hdulist[1].data[\\\"aperture\\\"]\\n n_apertures = len(apertures)\\n # Number of spectral data points for each aperture size.\\n n_wls = [int(x) for x in hdulist[1].data[\\\"npoints\\\"]]\\n # Initial wavelength value(s).\\n starting_wl = [float(x) for x in\\n hdulist[1].data[\\\"wavelength\\\"]]\\n # Step size(s) for each subsequent wavelength.\\n delta_wl = [float(x) for x in hdulist[1].data[\\\"deltaw\\\"]]\\n\\n # Generate the full array of wavelength values, and get\\n # full array of flux values, for each aperture.\\n for aper in range(n_apertures):\\n wls = [starting_wl[aper] +\\n x*delta_wl[aper] for\\n x in range(n_wls[aper])]\\n fls = [float(x) for\\n x in hdulist[1].data[\\\"flux\\\"][aper]]\\n # Make sure wavelengths and fluxes are sorted\\n # from smallest wavelength to largest.\\n sort_indexes = [x[0] for x in\\n sorted(enumerate(wls),\\n key=itemgetter(1))]\\n wls = [wls[x] for x in sort_indexes]\\n fls = [fls[x] for x in sort_indexes]\\n wlfls = [(x, y) for x, y in zip(wls, fls) if\\n y != 0.]\\n if wlfls != []:\\n datapoints = [\\n [data_point(x=float(\\\"{0:.8f}\\\".format(x)),\\n y=float(\\\"{0:.8e}\\\".format(y)))\\n for x, y in wlfls]]\\n # Create the return DataSeries object.\\n all_data_series.append(\\n DataSeries('iue', obsid,\\n datapoints,\\n ['IUE_' + obsid + ' DISP:'\\n + dispersion + ' APER:' +\\n apertures[aper]],\\n [iue_xunit], [iue_yunit],\\n errcode))\\n\\n if is_hi:\\n # Get the aperture from the primary header.\\n aperture = hdulist[0].header[\\\"aperture\\\"].strip()\\n # Get the dispersion type from the primary header.\\n dispersion = hdulist[0].header[\\\"disptype\\\"].strip()\\n # Get the camera used (SWP, LWP, LWR).\\n camera = hdulist[0].header[\\\"camera\\\"].strip()\\n # Get a list of spectral orders. Those that are beyond\\n # the range defined in Solano are not considered.\\n if camera == \\\"LWP\\\":\\n max_order = 124\\n elif camera == \\\"LWR\\\":\\n max_order = 119\\n else:\\n max_order = 120\\n orders = [int(x) for x in hdulist[1].data[\\\"order\\\"] if x\\n <= max_order]\\n n_orders = len(orders)\\n # This lists will store each orders' spectral info.\\n order_spectra = []\\n\\n # Loop over each order.\\n for order in range(n_orders):\\n # Number of fluxes for this order.\\n n_p = int(hdulist[1].data[\\\"npoints\\\"][order])\\n # Starting pixel within the array of 768 elements.\\n s_pix = int(\\n hdulist[1].data[\\\"startpix\\\"][order])\\n # Wavelength corresponding to this start pixel.\\n starting_wl = float(\\n hdulist[1].data[\\\"wavelength\\\"][order])\\n # Step size for each subsequent wavelength.\\n delta_wl = float(\\n hdulist[1].data[\\\"deltaw\\\"][order])\\n # Generate the full array of wavelength values.\\n wls = [starting_wl + x*delta_wl for x in\\n range(n_p)]\\n # Extract the fluxes that go along with these wls.\\n all_fluxes = hdulist[1].data[\\\"abs_cal\\\"][order]\\n fls = [float(x) for x in\\n all_fluxes[(s_pix-1):(s_pix-1+n_p-1+1)]]\\n # Extract the quality flags that go along with\\n # these wls.\\n all_qfs = hdulist[1].data[\\\"quality\\\"][order]\\n qfs = [int(x) for x in all_qfs[(s_pix-1):(s_pix-1+\\n n_p-1+1)]]\\n # Only keep good Quality Flags, if the order is all\\n # bad flags, don't add it.\\n keep = [i for i, x in enumerate(qfs) if (qfs[i] >\\n -16384)]\\n if keep != [] and fls != [0.]*len(fls):\\n wls = [wls[i] for i in keep]\\n fls = [fls[i] for i in keep]\\n # Create a dict that will store this order's\\n # info.\\n order_spec = {'order':orders[order],\\n 'wls':numpy.asarray(wls),\\n 'fls':numpy.asarray(fls)}\\n order_spectra.append(order_spec)\\n\\n # Order-combine the spectra.\\n comb_spec = order_combine(order_spectra, camera, False)\\n\\n # Resample onto an evenly-spaced wavelength scale.\\n comb_spec_reb = resample_spectrum(comb_spec, camera,\\n False)\\n\\n # Create the return DataSeries object.\\n datapoints = [\\n [data_point(x=float(\\\"{0:.8f}\\\".format(x)),\\n y=float(\\\"{0:.8e}\\\".format(y)))\\n for x, y in comb_spec_reb]]\\n all_data_series.append(\\n DataSeries('iue', obsid,\\n datapoints,\\n ['IUE_' + obsid + ' DISP:'\\n + dispersion + ' APER:' +\\n aperture],\\n [iue_xunit], [iue_yunit],\\n errcode))\\n\\n except IOError:\\n errcode = 3\\n all_data_series.append(\\n DataSeries('iue', obsid, [], [''], [''], [''], errcode))\\n\\n else:\\n # This is where an error DataSeries object would be returned.\\n all_data_series.append(\\n DataSeries('iue', obsid, [], [], [],\\n [], errcode))\\n\\n # Return the DataSeries object back to the calling module.\\n if len(all_data_series) == 1:\\n return all_data_series[0]\\n return all_data_series\",\n \"def usefulquantities(dffin):\\n dffin['log_length_box'] = np.log(dffin['length_box_um'])\\n dffin['time_min']=dffin['time_sec']/60\\n dffin['pred_length_box_um'] = np.exp(dffin['pred_log_length'])\\n dffin['unique_id'] = dffin['cell']+dffin['time_sec'].apply(lambda x:str(x))\\n dffin['cv_gr']= dffin.groupby('cell')['pred_growth_rate'].transform(lambda x:\\\\\\n np.std(x)/np.mean(x))\\n dffin['std_gr']= dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.std(x))\\n dffin['mean_gr'] = dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.mean(x))\\n dffin['mean_len'] = dffin.groupby('cell')['pred_length_box_um'].transform(lambda x: np.mean(x))\\n dffin['norm_pred_growth_rate'] = (dffin['pred_growth_rate']-dffin.groupby('cell')['pred_growth_rate'].transform(lambda\\\\\\n x: np.mean(x)))/dffin.groupby('cell')['pred_growth_rate'].transform(lambda x: np.mean(x))\\n dffin = rl.genalogy(dffin,'parent_cell') #Create genealogy\\n dffin = rl.genalogy(dffin,'g_parent_cell')\\n dffin = rl.genalogy(dffin,'g_g_parent_cell')\\n dffin = dffin.set_index('unique_id')\\n qq= dffin.groupby('cell').apply(lambda x: (x['pred_length_box_um']-x['pred_length_box_um'].iloc[0])/(x['pred_length_box_um'].iloc[-1]-x['pred_length_box_um'].iloc[0])).rename('add_len')\\n jj= dffin.groupby('cell').apply(lambda x: (x['time_sec']-x['time_sec'].iloc[0])/(x['time_sec'].iloc[-1]-x['time_sec'].iloc[0])).rename('cell_cycle')\\n return pd.concat([dffin, qq.reset_index().set_index('unique_id')['add_len'], jj.reset_index().set_index('unique_id')['cell_cycle']], axis=1, join='inner')\",\n \"def add_derived_GEOSChem_specs2df(df):\\n # Add temperature in deg C\\n df['T'] = df['GMAO_TEMP'].copy()\\n df['T'] = df['GMAO_TEMP'].values - 273.15\\n # Inc. V nd U with same variable names as GEOS-CF\\n df['V'] = df['GMAO_VWND'].copy()\\n df['U'] = df['GMAO_UWND'].copy()\\n # Add NOx as combined NO and NO2\\n df['NOx'] = df['NO'].values + df['NO2'].values\\n # Add NOy as defined in GEOS-CF\\n # NOy = no_no2_hno3_hno4_hono_2xn2o5_pan_organicnitrates_aerosolnitrates\\n vars2use = AC.GC_var('NOy-all')\\n df['NOy'] = df['N2O5'].copy() # 2 N2O5 in NOy, so 2x via template\\n for var in vars2use:\\n try:\\n df.loc[:, 'NOy'] = df['NOy'].values + df[var].values\\n except KeyError:\\n pass\\n # Add a variable for gas-phase NOy (by subtracting aerosol nitrate)\\n vars2use = AC.GC_var('NOy-gas')\\n df['NOy-gas'] = df['N2O5'].copy() # 2 N2O5 in NOy, so 2x via template\\n for var in vars2use:\\n try:\\n df.loc[:, 'NOy-gas'] = df['NOy-gas'].values + df[var].values\\n except KeyError:\\n pass\\n # Include a variable of NOy where HNO3 is removed\\n # NOy = no_no2_hno3_hno4_hono_2xn2o5_pan_organicnitrates_aerosolnitrates\\n df['NOy-HNO3'] = df['NOy'].values - df['HNO3'].values\\n # Include a variable of NOy where HNO3 is removed\\n df['NOy-HNO3-PAN'] = df['NOy'].values - \\\\\\n df['HNO3'].values - df['PAN'].values\\n # gas-phase (exc. PAN, HNO3, HNO4, Org-NIT, N2O5)\\n df['NOy-Limited'] = df['NO'].values + df['NO2'].values + \\\\\\n df['HNO2'].values + df['NIT'].values + df['NITs'].values\\n # Add an all sulfate tracer\\n NewVar = 'SO4-all'\\n vars2use = AC.GC_var(NewVar)\\n df[NewVar] = df['NIT'].values\\n for var2use in vars2use:\\n try:\\n df[NewVar] = df[NewVar].values + df[var2use].values\\n except KeyError:\\n pass\\n # And a all nitrate tracer\\n NewVar = 'NIT-all'\\n vars2use = AC.GC_var(NewVar)\\n df[NewVar] = df['NIT'].values\\n for var2use in vars2use:\\n try:\\n df[NewVar] = df[NewVar].values + df[var2use].values\\n except KeyError:\\n pass\\n # Uset the P-I variable as a model level variable\\n df['model-lev'] = df['P-I'].copy()\\n return df\",\n \"def temperature() -> float:\",\n \"def whc_tot(mukey, layers=''):\\n #read appropriate soils.in content to a python list\\n mukey = str(mukey)\\n soil_path = \\\"/data/paustian/ernie/SSURGO_master_script/soil_test2/\\\"\\n soil_fpath = soil_path+mukey[:-3]+\\\"/\\\"+mukey+\\\".in\\\"\\n cont = [[]]\\n data_input = open(soil_fpath, 'r')\\n for line in data_input:\\n cont.append(line.split())\\n del cont[0]\\n\\n #convert all entries in the 2D list to float format where possible, or zero in the case\\n #of very small numbers recorded in scientific notation\\n for k in range(len(cont)):\\n for l in range(len(cont[k])):\\n cont[k][l] = float(cont[k][l])\\n\\n #loop through list and compute the water holding capacity increment represented in \\n #each line\\n min_h2o_evap = 0\\n min_h2o = 0\\n max_h2o = 0\\n whc = 0\\n for i in range(len(cont)):\\n if not layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n else:\\n if 1+i <= layers:\\n depth = cont[i][1] - cont[i][0]\\n FC = cont[i][3]\\n WP = cont[i][4]\\n WHC = FC - WP\\n if i != 0:\\n min_h2o_evap += depth*WP\\n min_h2o += depth*WP\\n max_h2o += depth*FC\\n whc += depth*WHC\\n if layers:\\n if layers > len(cont):\\n print \\\"NOTE: specified layer limit exceeds number of layers found in soils.in file\\\"\\n\\n return whc, min_h2o, max_h2o\",\n \"def test_input_flux_file():\\n # Generate an input file\\n flux_input_file = tstutils.data_path('test.flux')\\n if os.path.isfile(flux_input_file):\\n os.remove(flux_input_file)\\n\\n cfg_lines = ['[fluxcalib]']\\n cfg_lines += [' extinct_correct = False # Set to True if your SENSFUNC derived with the UVIS algorithm\\\\n']\\n cfg_lines += ['# Please add your SENSFUNC file name below before running pypeit_flux_calib']\\n\\n # These files need to be in tests/files/\\n data = Table()\\n data['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data['sensfile'] = 'sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits'\\n # \\n paths = [tstutils.data_path('')]\\n\\n fluxFile = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data)\\n # Write\\n fluxFile.write(flux_input_file)\\n\\n # Read\\n fluxFile2 = inputfiles.FluxFile.from_file(flux_input_file)\\n assert np.all(fluxFile2.data['filename'] == data['filename'])\\n\\n # Test path\\n assert fluxFile2.file_paths[0] == paths[0]\\n assert fluxFile2.filenames[0] == os.path.join(paths[0], data['filename'][0])\\n\\n # #################\\n # Tickle the other ways to do sensfiles\\n data3 = Table()\\n data3['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data3['sensfile'] = ['sens_cN20170331S0206-HIP62745_GNIRS_20170331T083351.681.fits',\\n '']\\n\\n fluxFile3 = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data3)\\n assert fluxFile3.sensfiles[1] == os.path.join(paths[0], data['sensfile'][0])\\n \\n data4 = Table()\\n data4['filename'] = ['spec1d_cN20170331S0216-pisco_GNIRS_20170331T085412.181.fits',\\n 'spec1d_cN20170331S0217-pisco_GNIRS_20170331T085933.097.fits']\\n data4['sensfile'] = ''\\n\\n fluxFile4 = inputfiles.FluxFile(config=cfg_lines, \\n file_paths=paths,\\n data_table=data4)\\n assert len(fluxFile4.sensfiles) == 0\\n\\n # Clean up\\n os.remove(flux_input_file)\",\n \"def addtonc(ncfout,key,vd,ofield,ftype=\\\"timeseries\\\"):\\n nc_out=nc.Dataset(ncfout,'r+')\\n if ftype==\\\"timeseries\\\":\\n diml=['time','height','south_north','west_east'] # Tuple of Dimensions\\n if vd['dims']==4:\\n dimtup=tuple(diml)\\n elif vd['dims']==3:\\n dimtup = tuple([c for c in diml if c != \\\"height\\\"])\\n elif vd['dims']==2:\\n dimtup = tuple([c for c in diml if c not in [\\\"height\\\",\\\"time\\\"]])\\n elif ftype==\\\"roughness\\\":\\n diml=['south_north','west_east']\\n dimtup=tuple(diml)\\n elif ftype==\\\"tabfile\\\":\\n diml=['south_north','west_east','sector','wind','stab']\\n if vd['dims']==3:\\n dimtup=tuple(diml.remove('wind').remove('stab'))\\n if vd['dims']==2:\\n dimtup=tuple(diml.remove('wind').remove('stab').remove('sector'))\\n if key in (\\\"TKE\\\", \\\"ABLAT_CYL\\\", \\\"ACCRE_CYL\\\"):\\n outv=nc_out.createVariable(key, 'f4', dimtup, zlib=True,\\n complevel=9, fill_value=-999.)\\n else:\\n outv=nc_out.createVariable(key,'f4',dimtup,zlib=True,complevel=9)\\n outv.units=vd['units']\\n outv.long_name=vd['name']\\n if vd['std_name'] is not None:\\n outv.standard_name=vd['std_name']\\n if key==\\\"PRECIP\\\":\\n outv.cell_methods=\\\"time: sum\\\"\\n outv.grid_mapping=\\\"crs\\\"\\n outv.coordinates=\\\"XLAT XLON\\\"\\n outv[:]=ofield[:]\\n nc_out.close()\\n return(None)\",\n \"def load_zT(all_data=False):\\n path = os.path.join(DATA_DIR, \\\"zT-citrination-165.csv\\\")\\n df = pd.read_csv(path, index_col=None)\\n if not all_data:\\n df = df[[\\\"composition\\\", \\\"zT\\\"]]\\n return df\",\n \"def radiance_map(file, config, vmax=4200, levels=20, typ=''):\\n \\n # Select data from configuration \\n azimuths = config['skymap'][:, 0] # +180 # azimuths\\n zeniths = config['skymap'][:, 1] # zeniths\\n\\n if typ == 'sim':\\n # look for wavelength index in array\\n waves_sim = dataset.attrs['simulated_Columns'].split('nm')[0].split('[')[1].split(\\n ']')[0].split(',')\\n waves = np.asarray(list(map(int, waves_sim)))\\n wave_indx = np.where(waves == wave)\\n try:\\n wave_indx = np.int(wave_indx[0][0])\\n except:\\n print(\\\"Wavelength is not in dataset\\\")\\n z = dataset.simulated[:, wave_indx, time_indx]\\n\\n elif typ == 'meas':\\n wave_indx = int((config['wavelength'] - 250 - config['wave_correction']) / 0.446)\\n with h5py.File(file, 'r') as data:\\n z = data['data'][:, wave_indx]\\n else:\\n print('Select a input data type(sim or meas)')\\n\\n # Add values in the origin to close the surface interpolation\\n azimuths = np.append(azimuths, [270, 0, 0, 0, 0, 0, 0, 0])\\n zeniths = np.append(zeniths, [0, 12, 24, 36, 48, 60, 72, 84])\\n z = np.append(z, [z[0], z[3], z[9], z[19], z[33], z[51], z[73], z[99]])\\n\\n # Convert x to radians\\n azimuths = np.radians(azimuths)\\n zeniths = np.radians(zeniths)\\n\\n # Remove dead channels of the dataset\\n azimuths = np.delete(azimuths, config['dead_fibre'])\\n zeniths = np.delete(zeniths, config['dead_fibre'])\\n z = np.delete(z, config['dead_fibre'])\\n\\n # Set up a regular grid of interpolation point\\n thetai, ri = np.linspace(azimuths.min(), azimuths.max(),\\n num=len(azimuths)), \\\\\\n np.linspace(zeniths.min(), zeniths.max(), num=len(zeniths))\\n\\n ri, thetai = np.meshgrid(ri, thetai, indexing='ij')\\n\\n # zi = scipy.interpolate.griddata((azimuths, zeniths), z, (thetai, ri),\\n # method='linear')\\n\\n rbf = scipy.interpolate.Rbf(azimuths, zeniths, z, fucntion='gaussian',\\n epsilon=0.05)\\n\\n ZI = rbf(thetai, ri)\\n\\n if typ == 'sim':\\n name = str(dataset.time[time_indx].values) # ''\\n else:\\n name = 'testing' #str(dataset.time[time_indx].values)\\n\\n # Create the directory to save the results\\n # os.makedirs(os.path.dirname(config['path_note'] + '/figures/'),\\n # exist_ok=True)\\n if vmax == 'default':\\n vmax = 4200\\n else:\\n vmax = vmax\\n\\n # Plot the dataset\\n fig, ax = plt.subplots(subplot_kw=dict(projection='polar'))\\n cmap = 'Spectral_r' # 'rainbow'\\n a = plt.contourf(thetai, ri, ZI, levels, cmap=cmap, vmin=0,\\n vmax=vmax) # , vmax=4932)\\n plt.title('{} UTC {}nm'.format(name, config['wavelength']))\\n plt.axis([0, 2*np.pi, 0, 1.48])\\n\\n plt.scatter(azimuths, zeniths, cmap=cmap, s=1)\\n ax.grid(False)\\n ax.set_theta_zero_location(\\\"N\\\") # Set the direction of polar plot\\n ax.set_theta_direction(1) # Set the increase direction on azimuth angles\\n # (-1 to clockwise, 1 counterclockwise)\\n cbar = plt.colorbar(a)\\n cbar.set_label(\\\"counts\\\", rotation=90)\\n\\n # if typ == 'sim':\\n # plt.savefig(\\n # 'figures/skymap/simulated/skymap{}nm_{}UTC_sim.jpeg'.format(wave,\\n # name),\\n # dpi=300)\\n # plt.show();\\n # else:\\n # plt.savefig(\\n # 'figures/skymap/measured/skymap{}nm_{}UTC_meas.jpeg'.format(wave,\\n # name),\\n # dpi=300)\",\n \"def getTemperatureMeasurements(self):\\n # self.board.readline()\\n self.stop = False\\n times = []\\n temps = [[], [], []]\\n \\n # A synchronisation string containing the characters tx is sent before each set of measurements,\\n # we ensure correct reading of the measurements by waiting for this string\\n while str(self.board.readline()).strip('b\\\\'\\\\\\\\rn') != 'tx':\\n pass\\n \\n while not self.stop:\\n # A synchronisation string containing the characters tx is sent before each set of measurements\\n tx = self.board.readline()\\n if str(tx).strip('b\\\\'\\\\\\\\rn') == 'tx':\\n rawData1 = self.board.readline()\\n rawData2 = self.board.readline()\\n rawData3 = self.board.readline()\\n rawData4 = self.board.readline()\\n \\n \\n timeStamp = str(rawData1).strip('b\\\\'\\\\\\\\rn')\\n temp1 = str(rawData2).strip('b\\\\'\\\\\\\\rn')\\n temp2 = str(rawData3).strip('b\\\\'\\\\\\\\rn')\\n temp3 = str(rawData4).strip('b\\\\'\\\\\\\\rn')\\n try:\\n times.append(float(timeStamp) / 1000)\\n temps[0].append(float(temp1) / 128)\\n temps[1].append(float(temp2) / 128)\\n temps[2].append(float(temp3) / 128)\\n # print(f'\\\\rtime: {float(timeStamp) / 1000:.2f} s, Temperature measured on sensor 1: {float(temp1) / 128:.2f} °C,'\\n # f'sensor 2: {float(temp2) / 128:.2f} °C, sensor 3: {float(temp3) / 128:.2f} °C', sep='', end='', flush=True)\\n except:\\n print(rawData1, rawData2, rawData3, rawData4)\\n \\n \\n if self.stop:\\n print('\\\\nMeasurement finished...')\\n \\n self.data_stack[self.fetch_kinds[0]] = times\\n self.data_stack[self.fetch_kinds[1]] = temps[0]\\n self.data_stack[self.fetch_kinds[2]] = temps[1]\\n self.data_stack[self.fetch_kinds[3]] = temps[2]\\n \\n if (len(self.data_stack['Sensor 1 Temp']) != len(times) or len(self.data_stack['Sensor 2 Temp']) != len(times) or len(self.data_stack['Sensor 3 Temp']) != len(times)):\\n print(\\\"Warning: There may be some missing values!\\\")\",\n \"def read_spectral_k(filename=\\\"tc_dos_l.dat\\\"):\\n # column headers for the data \\n #tcdosl_labels = [\\n # \\\"wavelength\\\",\\n # \\\"k_xx_raw\\\",\\\"k_xx_smooth\\\",\\n # \\\"k_yy_raw\\\",\\\"k_yy_smooth\\\",\\n # \\\"k_zz_raw\\\",\\\"k_zz_smooth\\\"]\\n\\n tcdosl_labels = [\\n \\\"wavelength\\\",\\n \\\"k_xx_raw\\\",\\\"k_yy_raw\\\",\\\"k_zz_raw\\\",\\n \\\"k_xx_smooth\\\",\\\"k_yy_smooth\\\",\\\"k_zz_smooth\\\"]\\n\\n def subselect_table_block(i_start,lines):\\n i = i_start + 1\\n\\n table = []\\n while(lines[i].strip() != \\\"\\\"):\\n args = lines[i].split()\\n args = [arg.strip() for arg in args]\\n args = [float(arg) for arg in args]\\n table.append(args)\\n i += 1 \\n return np.array(table)\\n\\n line = None # initialize\\n with open(filename,'r') as f:\\n lines = f.readlines()\\n lines = [s.strip() for s in lines]\\n\\n temperatures = []\\n tcdosl_dict = OrderedDict()\\n\\n for il,line in enumerate(lines):\\n if line.startswith('# Temp:'):\\n args = line.split(':')\\n T = int(float(args[1].strip()))\\n temperatures.append(T)\\n tcdosl_dict[T] = subselect_table_block(il,lines)\\n\\n tcdosl_df_dict = OrderedDict()\\n for temp in temperatures:\\n tcdosl_df_dict[temp] = pd.DataFrame(\\n copy.deepcopy(tcdosl_dict[temp]),\\n columns=list(tcdosl_labels))\\n\\n return {k:v.copy() for k,v in tcdosl_df_dict.items()}\",\n \"def get_ecmwf_forecast_statistics(params):\\n blob_mapped_dir = \\\"/mnt/output\\\"\\n\\n path_to_rapid_output = blob_mapped_dir\\n\\n watershed_name = params[\\\"region\\\"].split(\\\"-\\\")[0]\\n subbasin_name = params[\\\"region\\\"].split(\\\"-\\\")[1]\\n river_id = params[\\\"reach_id\\\"]\\n\\n # units = params[\\\"units\\\"]\\n # if (not units):\\n # units = \\\"metric\\\"\\n\\n units = \\\"metric\\\"\\n\\n # forecast_folder = params[\\\"date\\\"]\\n # if (not forecast_folder):\\n # forecast_folder = 'most_recent'\\n\\n forecast_folder = 'most_recent'\\n\\n stat_type = params[\\\"stat\\\"]\\n if (stat_type is None):\\n stat_type = \\\"\\\"\\n\\n # find/check current output datasets\\n path_to_output_files = \\\\\\n os.path.join(path_to_rapid_output,\\n \\\"{0}-{1}\\\".format(watershed_name, subbasin_name))\\n forecast_nc_list, start_date = \\\\\\n ecmwf_find_most_current_files(path_to_output_files, forecast_folder)\\n if (not forecast_nc_list or not start_date):\\n log.log_error('ECMWF forecast for %s (%s).' % (watershed_name, subbasin_name))\\n\\n # combine 52 ensembles\\n qout_datasets = []\\n ensemble_index_list = []\\n for forecast_nc in forecast_nc_list:\\n ensemble_index_list.append(\\n int(os.path.basename(forecast_nc)[:-3].split(\\\"_\\\")[-1])\\n )\\n qout_datasets.append(\\n xarray.open_dataset(forecast_nc, autoclose=True)\\n .sel(rivid=river_id).Qout\\n )\\n\\n merged_ds = xarray.concat(qout_datasets,\\n pd.Index(ensemble_index_list, name='ensemble'))\\n\\n return_dict = {}\\n if (stat_type == 'high_res' or not stat_type):\\n # extract the high res ensemble & time\\n try:\\n return_dict['high_res'] = merged_ds.sel(ensemble=52).dropna('time')\\n except:\\n pass\\n\\n if (stat_type != 'high_res' or not stat_type):\\n # analyze data to get statistic bands\\n merged_ds = merged_ds.dropna('time')\\n\\n if (stat_type == 'mean' or 'std' in stat_type or not stat_type):\\n return_dict['mean'] = merged_ds.mean(dim='ensemble')\\n std_ar = merged_ds.std(dim='ensemble')\\n if (stat_type == 'std_dev_range_upper' or not stat_type):\\n return_dict['std_dev_range_upper'] = \\\\\\n return_dict['mean'] + std_ar\\n if (stat_type == 'std_dev_range_lower' or not stat_type):\\n return_dict['std_dev_range_lower'] = \\\\\\n return_dict['mean'] - std_ar\\n if (stat_type == \\\"min\\\" or not stat_type):\\n return_dict['min'] = merged_ds.min(dim='ensemble')\\n if (stat_type == \\\"max\\\" or not stat_type):\\n return_dict['max'] = merged_ds.max(dim='ensemble')\\n\\n for key in list(return_dict):\\n if (units == 'english'):\\n # convert m3/s to ft3/s\\n return_dict[key] *= M3_TO_FT3\\n # convert to pandas series\\n return_dict[key] = return_dict[key].to_dataframe().Qout\\n\\n return return_dict, watershed_name, subbasin_name, river_id, units\",\n \"def get_jhu(data_path: str, filter_: Union[dict, bool] = True) -> None:\\n # Where JHU stores their data\\n url_template = (\\\"https://raw.githubusercontent.com/CSSEGISandData/\\\"\\n \\\"COVID-19/master/csse_covid_19_data/\\\"\\n \\\"csse_covid_19_time_series/time_series_covid19_%s_%s.csv\\\")\\n\\n # Scrape the data\\n dfs = {}\\n for region in ['global', 'US']:\\n dfs[region] = {}\\n for kind in ['confirmed', 'deaths', 'recovered']:\\n url = url_template % (kind, region) # Create the full data URL\\n try:\\n df = pd.read_csv(url) # Download the data into a dataframe\\n except HTTPError:\\n print(\\\"Could not download data for %s, %s\\\" % (kind, region))\\n else:\\n if region == 'global':\\n has_no_province = df['Province/State'].isnull()\\n # Whole countries only; use country name as index\\n df1 = df[has_no_province].set_index('Country/Region')\\n more_dfs = []\\n for country in ['China', 'Canada', 'Australia']:\\n if country == 'Canada' and kind in 'recovered':\\n continue\\n is_c = df['Country/Region'] == country\\n df2 = df[is_c].sum(axis=0, skipna=False).to_frame().T\\n df2['Country/Region'] = country\\n df2 = df2.set_index('Country/Region')\\n more_dfs.append(df2)\\n df = pd.concat([df1] + more_dfs)\\n elif region == 'US':\\n # Use state name as index\\n for k, v in us_state_abbrev.items(): # get US state abbrev\\n if not us_state_abbrev[k].startswith('US_'):\\n us_state_abbrev[k] = 'US_' + v # Add 'US_' to abbrev\\n df.replace(us_state_abbrev, inplace=True)\\n df = df.set_index('Province_State')\\n df = df.groupby('Province_State').sum() # combine counties to create state level data\\n\\n df = df[[x for x in df if any(year in x for year in ['20', '21'])]] # Use only data columns\\n # 20 or 21 signifies 2020 or 2021\\n dfs[region][kind] = df # Add to dictionary of dataframes\\n\\n # Generate a list of countries that have \\\"good\\\" data,\\n # according to these criteria:\\n good_countries = get_countries(dfs['global'], filter_=filter_)\\n\\n # For each \\\"good\\\" country,\\n # reformat and save that data in its own .csv file.\\n source = dfs['global']\\n for country in tqdm(good_countries, desc='Countries'): # For each country\\n if country in ['Diamond Princess', 'MS Zaandam', 'Samoa',\\n 'Vanuatu', 'Marshall Islands', 'US', 'Micronesia']:\\n print(\\\"Skipping {}\\\".format(country))\\n continue\\n # If we have data in the downloaded JHU files for that country\\n if country in source['confirmed'].index:\\n df = pd.DataFrame(columns=['dates2', 'cum_cases', 'cum_deaths',\\n 'cum_recover', 'new_cases',\\n 'new_deaths', 'new_recover',\\n 'new_uninfected'])\\n df['dates2'] = source['confirmed'].columns\\n df['dates2'] = df['dates2'].apply(fix_jhu_dates)\\n df['cum_cases'] = source['confirmed'].loc[country].values\\n df['cum_deaths'] = source['deaths'].loc[country].values\\n df['cum_recover'] = source['recovered'].loc[country].values\\n df[['new_cases', 'new_deaths', 'new_recover']] = \\\\\\n df[['cum_cases', 'cum_deaths', 'cum_recover']].diff()\\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\\n\\n\\n try:\\n population = get_population_count(data_path, country)\\n df['population'] = population\\n except:\\n pass\\n\\n # Fill NaN with 0 and convert to int\\n dfs[country] = df.set_index('dates2').fillna(0).astype(int)\\n dfs[country].to_csv(data_path / ('covidtimeseries_%s.csv' % country))\\n\\n else:\\n print(\\\"No data for %s\\\" % country)\\n\\n source = dfs['US']\\n states = source['confirmed'].index.tolist()\\n us_recovery_data = covid_tracking_recovery(data_path)\\n for state in tqdm(states, desc='US States'): # For each country\\n if state in ['Diamond Princess', 'Grand Princess', 'MS Zaandam', 'US_AS']:\\n print(\\\"Skipping {}\\\".format(state))\\n continue\\n # If we have data in the downloaded JHU files for that country\\n if state in source['confirmed'].index:\\n df = pd.DataFrame(columns=['dates2', 'cum_cases', 'cum_deaths',\\n 'new_cases','new_deaths','new_uninfected'])\\n df['dates2'] = source['confirmed'].columns\\n df['dates2'] = df['dates2'].apply(fix_jhu_dates)\\n df['cum_cases'] = source['confirmed'].loc[state].values\\n df['cum_deaths'] = source['deaths'].loc[state].values\\n\\n df[['new_cases', 'new_deaths']] = df[['cum_cases', 'cum_deaths']].diff()\\n\\n # add recovery data\\n df.set_index('dates2', inplace=True)\\n df = df.merge(us_recovery_data[state], on='dates2', how='left')\\n df['new_uninfected'] = df['new_recover'] + df['new_deaths']\\n\\n try:\\n population = get_population_count(data_path, state)\\n df['population'] = population\\n except:\\n pass\\n # Fill NaN with 0 and convert to int\\n dfs[state] = df.fillna(0).astype(int)\\n dfs[state].to_csv(data_path /\\n ('covidtimeseries_%s.csv' % state))\\n else:\\n print(\\\"No data for %s\\\" % state)\",\n \"def main():\\n args = get_args()\\n\\n entries = []\\n\\n noe_dim = \\\"h1\\\" if args.hch else \\\"c1\\\" # save the name of the noe dimension\\n\\n with open(args.sparkylist) as lines:\\n\\n lines = lines.readlines()\\n # lines = set(lines) # remove duplicate lines\\n peak = 1\\n for idx, line in enumerate(lines):\\n idx = idx + 1\\n\\n try:\\n label, c1, c2, h2, intensity, *rest = line.split()\\n\\n c1 = float(c1) # convert these to floats\\n c2 = float(c2)\\n h2 = float(h2)\\n intensity = float(intensity)\\n\\n label = f\\\"peak{peak}\\\"\\n peak += 1\\n\\n except ValueError:\\n print(f\\\"invalid NOE definition on line {idx}\\\")\\n continue\\n\\n dic = {\\\"label\\\": label, noe_dim: c1,\\n \\\"c2\\\": c2, \\\"h2\\\": h2, \\\"intensity\\\": intensity}\\n\\n entries.append(dic)\\n\\n # create dataframe and write out\\n csv = pd.DataFrame(entries)\\n order = [\\\"label\\\", noe_dim, \\\"c2\\\", \\\"h2\\\", \\\"intensity\\\"]\\n csv.to_csv(args.output, columns=order, index=False)\",\n \"def read_core_temp(self) -> float:\",\n \"def runqn3(h,steps):\\n func = ['x7','x8','x9','x10','x11','x12','-(x1-x3)/(((x1-x3)**2+(x2-x4)**2)**(3.0/2))-(x1-x5)/(((x1-x5)**2+(x2-x6)**2)**(3.0/2))','-(x2-x4)/(((x1-x3)**2+(x2-x4)**2)**(3.0/2))-(x2-x6)/(((x1-x5)**2+(x2-x6)**2)**(3.0/2))','-(x3-x1)/(((x3-x1)**2+(x4-x2)**2)**(3.0/2))-(x3-x5)/(((x3-x5)**2+(x4-x6)**2)**(3.0/2))','-(x4-x2)/(((x3-x1)**2+(x4-x2)**2)**(3.0/2))-(x4-x6)/(((x3-x5)**2+(x4-x6)**2)**(3.0/2))','-(x5-x1)/(((x5-x1)**2+(x6-x2)**2)**(3.0/2))-(x5-x3)/(((x5-x3)**2+(x6-x4)**2)**(3.0/2))','-(x6-x2)/(((x5-x1)**2+(x6-x2)**2)**(3.0/2))-(x6-x4)/(((x5-x3)**2+(x6-x4)**2)**(3.0/2))']\\n init = [0,0,0.97000436,-0.24308753,-0.97000436,0.24308753,-0.93240737,-0.86473146,0.466203685,0.43236573,0.466203685,0.43236573]\\n rungeODE(func,init,0,h,steps)\\n # extract data from CSV output file\\n filename = open('RungeKuttaOutput.csv','rb')\\n reader = csv.reader(filename,delimiter=',')\\n x1=[]\\n x2=[]\\n x3=[]\\n x4=[]\\n x5=[]\\n x6=[]\\n for row in reader:\\n x1 = x1 + [row[1]]\\n x2 = x2 + [row[2]]\\n x3 = x3 + [row[3]]\\n x4 = x4 + [row[4]]\\n x5 = x5 + [row[5]]\\n x6 = x6 + [row[6]]\\n x1out=[float(x1[i]) for i in range(1,len(x1))]\\n x2out=[float(x2[i]) for i in range(1,len(x1))]\\n x3out=[float(x3[i]) for i in range(1,len(x1))]\\n x4out=[float(x4[i]) for i in range(1,len(x1))]\\n x5out=[float(x5[i]) for i in range(1,len(x1))]\\n x6out=[float(x6[i]) for i in range(1,len(x1))]\\n plot(x1out,x2out,'r',x3out,x4out,'g',x5out,x6out,'b')\\n show()\",\n \"def useThibautsData(log, output, bcgr=72.2, sigma=0.75, iterations=4, loc=1900, galaxies=1000,\\n datadir='/Users/smn2/EUCLID/CTItesting/uniform/',\\n thibautCDM03=False, beta=False, serial=1, parallel=1):\\n files = g.glob(datadir + '*.fits')\\n #pick randomly\\n files = np.random.choice(files, galaxies, replace=False)\\n\\n #trap parameters: parallel\\n if thibautCDM03:\\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_parallel.dat'\\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_thibaut_serial.dat'\\n params = ThibautsCDM03params()\\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\\n else:\\n f1 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_parallel.dat'\\n f2 = '/Users/smn2/EUCLID/vissim-python/data/cdm_euclid_serial.dat'\\n params = MSSLCDM03params()\\n params.update(dict(parallelTrapfile=f1, serialTrapfile=f2, rdose=8.0e9, serial=serial, parallel=parallel))\\n if beta:\\n params.update(dict(beta_p=0.6, beta_s=0.6))\\n\\n print f1, f2\\n\\n #store shapes\\n eclean = []\\n e1clean = []\\n e2clean = []\\n R2clean = []\\n xclean = []\\n yclean = []\\n eCTI = []\\n e1CTI = []\\n e2CTI = []\\n R2CTI = []\\n xCTI = []\\n yCTI = []\\n eCTIfixed = []\\n e1CTIfixed = []\\n e2CTIfixed = []\\n R2CTIfixed = []\\n xCTIfixed = []\\n yCTIfixed = []\\n\\n fh = open(output.replace('.pk', '.csv'), 'w')\\n fh.write('#files: %s and %s\\\\n' % (f1, f2))\\n for key in params:\\n print key, params[key]\\n fh.write('# %s = %s\\\\n' % (key, str(params[key])))\\n fh.write('#file, delta_e, delta_e1, delta_e2, delta_R2, delta_x, delta_y\\\\n')\\n for f in files:\\n print 'Processing: ', f\\n\\n #load data\\n nocti = pf.getdata(f)\\n\\n #scale to SNR about 10 (average galaxy, a single exposure)\\n nocti /= np.sum(nocti)\\n nocti *= 1500.\\n\\n #place it on canvas\\n tmp = np.zeros((2066, 2048))\\n ysize, xsize = nocti.shape\\n ysize /= 2\\n xsize /= 2\\n tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize] = nocti.copy()\\n\\n #add background\\n tmp += bcgr\\n\\n #run CDM03\\n c = CTI.CDM03bidir(params, [])\\n tmp = c.applyRadiationDamage(tmp.copy().transpose()).transpose()\\n\\n #remove background and make a cutout\\n CTIdata = tmp[loc-ysize:loc+ysize, loc-xsize:loc+xsize]\\n CTIdata -= bcgr\\n CTIdata[CTIdata < 0.] = 0.\\n\\n #write files\\n #fileIO.writeFITS(nocti, f.replace('.fits', 'noCTI.fits'), int=False)\\n #fileIO.writeFITS(CTI, f.replace('.fits', 'CTI.fits'), int=False)\\n\\n #reset settings\\n settings = dict(sigma=sigma, iterations=iterations)\\n\\n #calculate shapes\\n sh = shape.shapeMeasurement(nocti.copy(), log, **settings)\\n results = sh.measureRefinedEllipticity()\\n\\n eclean.append(results['ellipticity'])\\n e1clean.append(results['e1'])\\n e2clean.append(results['e2'])\\n R2clean.append(results['R2'])\\n xclean.append(results['centreX'])\\n yclean.append(results['centreY'])\\n\\n #CTI, fitted centroid\\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\\n results2 = sh.measureRefinedEllipticity()\\n\\n eCTI.append(results2['ellipticity'])\\n e1CTI.append(results2['e1'])\\n e2CTI.append(results2['e2'])\\n R2CTI.append(results2['R2'])\\n xCTI.append(results2['centreX'])\\n yCTI.append(results2['centreY'])\\n\\n #fixed centroid\\n settings['fixedPosition'] = True\\n settings['fixedX'] = results['centreX']\\n settings['fixedY'] = results['centreY']\\n settings['iterations'] = 1\\n sh = shape.shapeMeasurement(CTIdata.copy(), log, **settings)\\n results3 = sh.measureRefinedEllipticity()\\n\\n eCTIfixed.append(results3['ellipticity'])\\n e1CTIfixed.append(results3['e1'])\\n e2CTIfixed.append(results3['e2'])\\n R2CTIfixed.append(results3['R2'])\\n xCTIfixed.append(results3['centreX'])\\n yCTIfixed.append(results3['centreY'])\\n\\n text = '%s,%e,%e,%e,%e,%e,%e\\\\n' % (f, results['ellipticity'] - results2['ellipticity'],\\n results['e1'] - results2['e1'], results['e2'] - results2['e2'],\\n results['R2'] - results2['R2'],\\n results['centreX'] - results2['centreX'],\\n results['centreY'] - results2['centreY'])\\n fh.write(text)\\n print text\\n\\n fh.close()\\n\\n results = {'eclean': np.asarray(eclean),\\n 'e1clean': np.asarray(e1clean),\\n 'e2clean': np.asarray(e2clean),\\n 'R2clean': np.asarray(R2clean),\\n 'xclean': np.asarray(xclean),\\n 'yclean': np.asarray(yclean),\\n 'eCTI': np.asarray(eCTI),\\n 'e1CTI': np.asarray(e1CTI),\\n 'e2CTI': np.asarray(e2CTI),\\n 'R2CTI': np.asarray(R2CTI),\\n 'xCTI': np.asarray(xCTI),\\n 'yCTI': np.asarray(yCTI),\\n 'eCTIfixed': np.asarray(eCTIfixed),\\n 'e1CTIfixed': np.asarray(e1CTIfixed),\\n 'e2CTIfixed': np.asarray(e2CTIfixed),\\n 'R2CTIfixed': np.asarray(R2CTIfixed),\\n 'xCTIfixed': np.asarray(xCTIfixed),\\n 'yCTIfixed': np.asarray(yCTIfixed)}\\n\\n #save to a file\\n fileIO.cPickleDumpDictionary(results, output)\\n\\n return results\",\n \"def generate_summary(weather_data):\\n# 5 Day Overview\\n# The lowest temperature will be 9.4°C, and will occur on Friday 02 July 2021.\\n# The highest temperature will be 20.0°C, and will occur on Saturday 03 July 2021.\\n# The average low this week is 12.2°C.\\n# The average high this week is 17.8°C.\\n\\n\\n Number_of_days=0\\n Min_Value=[]\\n Date_value=[]\\n Max_Value=[]\\n\\n\\n for rows in weather_data: \\n if len(rows) != 0:\\n Number_of_days = Number_of_days + 1\\n Min_Value.append(rows[1])\\n Date_value.append(str(rows[0]))\\n Max_Value.append(rows[2])\\n \\n min_temperature,min_position = find_min(Min_Value)\\n min_tempe_celcius = convert_f_to_c(min_temperature)\\n occur_date_min = convert_date(Date_value[min_position])\\n max_temperature,max_position = find_max(Max_Value)\\n max_tempe_celcius = convert_f_to_c(max_temperature)\\n occur_date_max = convert_date(Date_value[max_position])\\n mean_low = calculate_mean(Min_Value)\\n mean_low__tempe_celcius = convert_f_to_c(mean_low)\\n mean_high = calculate_mean(Max_Value)\\n mean_high__tempe_celcius = convert_f_to_c(mean_high)\\n\\n summary=\\\"\\\"\\n summary+=f\\\"{Number_of_days} Day Overview\\\\n\\\"\\n summary+=f\\\" The lowest temperature will be {format_temperature(min_tempe_celcius)}, and will occur on {occur_date_min}.\\\\n\\\"\\n summary+=f\\\" The highest temperature will be {format_temperature(max_tempe_celcius)}, and will occur on {occur_date_max}.\\\\n\\\"\\n summary+=f\\\" The average low this week is {format_temperature(mean_low__tempe_celcius)}.\\\\n\\\"\\n summary+=f\\\" The average high this week is {format_temperature(mean_high__tempe_celcius)}.\\\\n\\\"\\n\\n return summary\",\n \"def prepare_and_save_data(df, index=None, output_path=None):\\n assert index is not None\\n assert output_path is not None\\n\\n df.columns.name = 'fundamental_variable'\\n df = df.set_index(['Quarter end', 'symbol'])\\n\\n df = df.stack()\\n df = df.unstack(level=1)\\n\\n # In the initial csv files, None is used to represent missing data\\n df[df == 'None'] = np.nan\\n\\n df.columns.name = None\\n\\n #df.reset_index().to_csv(path.join(data_dir, 'fundamental_data_of_stocks_stacked.csv'), index=False)\\n\\n # split the large dataframe based on the fundamental_variables\\n # now each DataFrame represent one fundamental variable about a bunch of stocks.\\n df = df.reset_index()\\n\\n\\n # convert Quarter end to datetime object\\n df['Quarter end'] = pd.to_datetime(df['Quarter end'])\\n df = df.sort(['Quarter end'], axis=0)\\n # convert the strings to float\\n df = df.set_index(['Quarter end', 'fundamental_variable']).astype(float).reset_index(level=1)\\n\\n\\n def _reindex(df):\\n # This function will be applied to each fundamental variable group.\\n # It will reindex the DataFrame. It will add the target index to the existing index and sort the new index\\n new_index = index.append(df.index).drop_duplicates().values\\n new_index.sort()\\n\\n # fill NaN with forward fill method. We should use forward filling because we can only use historical data\\n # not the data from the future.\\n df = df.reindex(new_index).fillna(method='ffill')\\n\\n return df\\n\\n\\n def _to_csv(df):\\n var_name = df.iloc[0]['fundamental_variable']\\n print(\\\"{:<30} {}\\\".format(\\\"preparing fundamental variable\\\", var_name))\\n var_name = re.sub('[-\\\\s\\\\&\\\\/]', '_', var_name) # remove special characters\\n var_name = re.sub('_+', '_', var_name) # remove repetitive underscores\\n var_name = re.sub('_$', '', var_name) # remove the tailing underscore\\n df.set_index(['Quarter end']).reindex(index).to_csv(path.join(output_path, var_name + '.csv'), index=True)\\n\\n\\n\\n # The following step is to reindex the DataFrame. Note that the reindex is done by each fundamental variable.\\n # The reason for this is if we do the reindex without groupby, there will be NaN in the fundamental_variable after\\n # reindex operation, which is problematic for the forward fill operation of the next step\\n df_reindex = df.groupby(by=['fundamental_variable'], as_index=False).apply(_reindex).sort_index()\\n df_reindex.index = df_reindex.index.droplevel(0)\\n\\n # save to csv file by each fundamental variable\\n df_out = df_reindex.reset_index()\\n df_out.groupby(by=['fundamental_variable']).apply(_to_csv)\",\n \"def makeRooDataSet(type,infile_name,outfile_name,tree_name,nevents):\\n\\n\\n\\n\\n inputfile = TFile.Open(infile_name,\\\"READ\\\")\\n print \\\"Importing tree\\\"\\n tree = TTree()\\n inputfile.GetObject(tree_name, tree) #get the tree from the data file\\n\\n \\n\\n #define variables for the RooDataSet\\n m_mumu = RooRealVar(\\\"m_mumu\\\", \\\"m_mumu\\\", 0.0, 4.0)\\n y_mumu = RooRealVar(\\\"y_mumu\\\", \\\"y_mumu\\\", 0.0, 2.0 )\\n pt_mumu = RooRealVar(\\\"pt_mumu\\\", \\\"pt_mumu\\\", 0.0, 260.0)\\n eta_gamma = RooRealVar(\\\"eta_gamma\\\",\\\"eta_gamma\\\",-3.5, 3.5)\\n pt_gamma = RooRealVar(\\\"pt_gamma\\\", \\\"pt_gamma\\\", 0.0, 100.0)\\n m_gamma = RooRealVar(\\\"m_gamma\\\", \\\"m_gamma\\\", -0.1,0.1)\\n \\n m_chi_rf1S = RooRealVar(\\\"m_chi_rf1S\\\", \\\"m_chi_rf1S\\\", 0.0, 7.0)\\n m_chi_rf2S = RooRealVar(\\\"m_chi_rf2S\\\", \\\"m_chi_rf2S\\\", -1.0, 1.0)\\n #Qvalue = RooRealVar(\\\"Qvalue\\\",\\\"Q\\\", -15., 15.)\\n s = RooRealVar(\\\"s\\\",\\\"s\\\", -10., 10.)\\n \\n ctpv = RooRealVar(\\\"ctpv\\\",\\\"ctpv\\\", -1.0, 3.5)\\n ctpv_error = RooRealVar(\\\"ctpv_err\\\",\\\"ctpv_err\\\", -1.0, 1.0)\\n pi0_abs_mass = RooRealVar(\\\"pi0_abs_mass\\\",\\\"pi0_abs_mass\\\", 0.0, 2.2)\\n psi1S_nsigma = RooRealVar(\\\"psi1S_nsigma\\\",\\\"psi1S_nsigma\\\",0.0,1.0)\\n psi2S_nsigma = RooRealVar(\\\"psi2S_nsigma\\\",\\\"psi2S_nsigma\\\",0.0,1.0)\\n psi3S_nsigma = RooRealVar(\\\"psi3S_nsigma\\\",\\\"psi3S_nsigma\\\",0.0,1.0)\\n rho_conv = RooRealVar(\\\"rho_conv\\\", \\\"rho_conv\\\", 0.0, 70.0)\\n dz = RooRealVar(\\\"dz\\\",\\\"dz\\\", -1.0, 1.0)\\n probFit1S = RooRealVar(\\\"probFit1S\\\",\\\"probFit1S\\\",0,1)\\n probFit2S = RooRealVar(\\\"probFit2S\\\",\\\"probFit2S\\\",0,1)\\n\\n dataArgSet = RooArgSet(m_mumu,\\n y_mumu,\\n pt_mumu,\\n eta_gamma,\\n pt_gamma,\\n m_gamma, \\n m_chi_rf1S)\\n \\n dataArgSet.add( m_chi_rf2S )\\n dataArgSet.add( s )\\n dataArgSet.add( ctpv )\\n dataArgSet.add( ctpv_error )\\n dataArgSet.add( pi0_abs_mass )\\n dataArgSet.add( psi1S_nsigma )\\n dataArgSet.add( psi2S_nsigma )\\n dataArgSet.add( rho_conv )\\n dataArgSet.add( dz )\\n dataArgSet.add( probFit1S )\\n dataArgSet.add( probFit2S )\\n\\n\\n print \\\"Creating DataSet\\\"\\n dataSet = RooDataSet(\\\"chicds\\\",\\\"Chic RooDataSet\\\", dataArgSet)\\n\\n entries = tree.GetEntries()\\n print entries\\n\\n if nevents is not 0:\\n entries = nevents\\n\\n for ientry in range(0,entries):\\n tree.GetEntry(ientry)\\n\\n # unfort ntuples are slightly different for chic and chib\\n \\n if type == 'chic':\\n \\n m_mumu.setVal(tree.dimuon_mass)\\n y_mumu.setVal(tree.dimuon_rapidity) \\n pt_mumu.setVal(tree.dimuon_pt)\\n eta_gamma.setVal(tree.photon_eta)\\n pt_gamma.setVal(tree.photon_pt)\\n #m_gamma.setVal(tree.photon_p4.M())\\n m_chi_rf1S.setVal(tree.rf1S_chic_mass)\\n #m_chi_rf1S.setVal(tree.rf2S_chi_p4.M())\\n #Qvalue.setVal((tree.chi_p4).M() - tree.dimuon_p4.M())\\n #Qvalue.setVal((tree.chi_p4).M()**2 - tree.dimuon_p4.M()**2)\\n #Qvalue.setVal((tree.rf1S_chic_mass**2 -tree.dimuon_mass**2)\\n # / (3.5107**2 - 3.0969**2 ) -1)\\n\\n \\n# this should be the correct one if the refitted variable was available\\n# s.setVal((tree.rf1S_chic_mass**2 - tree.rf1S_dimuon_p4.M()**2)/ (3.5107**2 - 3.0969**2 ) -1)\\n \\n s.setVal((tree.rf1S_chic_mass**2 - 3.0969**2)/ (3.5107**2 - 3.0969**2 ) -1)\\n\\n\\n \\n psi1S_nsigma.setVal(tree.psi1S_nsigma)\\n psi2S_nsigma.setVal(0) \\n psi3S_nsigma.setVal(0)\\n \\n elif type == 'chib':\\n\\n m_mumu.setVal(tree.dimuon_mass)\\n y_mumu.setVal(tree.dimuon_rapidity) \\n pt_mumu.setVal(tree.dimuon_pt)\\n eta_gamma.setVal(tree.photon_eta)\\n pt_gamma.setVal(tree.photon_pt)\\n m_chi_rf1S.setVal(tree.rf1S_chib_mass)\\n m_chi_rf2S.setVal(tree.rf2S_chib_mass) \\n Qvalue.setVal(tree.chib_mass - tree.dimuon_mass)\\n psi1S_nsigma.setVal(tree.Y1S_nsigma)\\n psi2S_nsigma.setVal(tree.Y2S_nsigma) \\n psi3S_nsigma.setVal(tree.Y3S_nsigma)\\n \\n ctpv.setVal(tree.ct_pv)\\n ctpv_error.setVal(tree.ct_pv_error)\\n #pi0_abs_mass.setVal(tree.pi0_abs_mass)\\n\\n rho_conv.setVal(tree.Conv)\\n dz.setVal(tree.Dz)\\n probFit1S.setVal(tree.probfit1S)\\n #probFit2S.setVal(tree.probFit2S)\\n \\n #if (tree.chic_pdgId == 20443):dataSet.add(dataArgSet)\\n dataSet.add(dataArgSet)\\n \\n\\n outfile = TFile(outfile_name,'recreate') \\n dataSet.Write()\",\n \"def result2json(ifilename, poiname, ofilename):\\n nameMap = {\\n \\\"SysWeight1\\\" : \\\"mc\\\",\\n \\\"SysWeight2\\\" : \\\"FSR\\\",\\n \\\"SysWeight3\\\" : \\\"bkg\\\",\\n \\\"SysWeight4\\\" : \\\"tagpt\\\",\\n \\\"SysWeight6\\\" : \\\"Prefire\\\",\\n \\\"SysRecoil2\\\" : \\\"recoil_eta\\\",\\n \\\"SysRecoil3\\\" : \\\"recoil_keys\\\",\\n \\\"SysRecoil6\\\" : \\\"recoil_stat0\\\",\\n \\\"SysRecoil7\\\" : \\\"recoil_stat1\\\",\\n \\\"SysRecoil8\\\" : \\\"recoil_stat2\\\",\\n \\\"SysRecoil9\\\" : \\\"recoil_stat3\\\",\\n \\\"SysRecoil10\\\": \\\"recoil_stat4\\\",\\n \\\"SysRecoil11\\\": \\\"recoil_stat5\\\",\\n \\\"SysRecoil12\\\": \\\"recoil_stat6\\\",\\n \\\"SysRecoil13\\\": \\\"recoil_stat7\\\",\\n \\\"SysRecoil14\\\": \\\"recoil_stat8\\\",\\n \\\"SysRecoil15\\\": \\\"recoil_stat9\\\",\\n }\\n\\n def getNuisName(nuis):\\n if nuis in nameMap.keys():\\n return nameMap[nuis]\\n elif bool(re.match(r\\\"\\\\w*bin\\\\d+shape\\\", nuis)):\\n return \\\"QCD_\\\" + nuis\\n else:\\n return nuis\\n\\n ifile = ROOT.TFile(ifilename)\\n himpact = ifile.Get(\\\"nuisance_impact_mu\\\")\\n himpact_grouped = ifile.Get(\\\"nuisance_group_impact_mu\\\")\\n tree = ifile.Get(\\\"fitresults\\\")\\n tree.GetEntry(0)\\n\\n # find the POI bin for poiname\\n ibinX = -1\\n for binX in range(1, himpact.GetNbinsX()+1):\\n poi = himpact.GetXaxis().GetBinLabel(binX)\\n if poi == poiname:\\n ibinX = binX\\n continue\\n assert ibinX >=0, \\\"Can not find the POI {} in the postfit file {}. Please check.\\\".format(poiname, ifilename)\\n\\n results = OrderedDict()\\n results['POIs'] = []\\n val = getattr(tree, poiname)\\n err = abs(getattr(tree, poiname+\\\"_err\\\"))\\n poi = OrderedDict()\\n poi['fit'] = [val-err, val, val+err]\\n poi['name'] = poiname\\n results['POIs'].append(poi)\\n\\n results['method'] = 'default'\\n results['params'] = []\\n\\n # dump impacts\\n impacts = OrderedDict()\\n for ibinY in range(1, himpact.GetNbinsY()+1):\\n nuis = himpact.GetYaxis().GetBinLabel(ibinY)\\n impacts[nuis] = himpact.GetBinContent(ibinX, ibinY)\\n\\n # add the grouped QCD and Recoil systematic\\n groupnames = []\\n for ibinY in range(1, himpact_grouped.GetNbinsY()+1):\\n tmpY = himpact_grouped.GetYaxis().GetBinLabel(ibinY)\\n if tmpY == 'stat':\\n continue\\n impacts[tmpY] = himpact_grouped.GetBinContent(ibinX, ibinY)\\n groupnames.append(tmpY)\\n\\n # sort impacts, descending\\n impacts = OrderedDict(sorted(impacts.items(), key=lambda x: abs(x[1]), reverse=True))\\n\\n pulls = OrderedDict()\\n for nuis in impacts.keys():\\n if nuis not in groupnames:\\n val = getattr(tree, nuis)\\n err = getattr(tree, nuis+\\\"_err\\\")\\n err = abs(err)\\n else:\\n # manually set the postfit of the grouped sys to [-1,1], and pulled at 0,\\n # since only the impacts are useful to us\\n val = 0.\\n err = 1.\\n pulls[nuis] = [val - err, val, val + err]\\n\\n # save to results\\n for nuis in impacts.keys():\\n systematic = OrderedDict()\\n systematic['fit'] = pulls[nuis]\\n systematic['groups'] = []\\n systematic['impact_' + poiname] = impacts[nuis]\\n systematic['name'] = getNuisName(nuis)\\n systematic['prefit'] = [-1.0, 0., 1.0]\\n systematic[poiname] = [poi['fit'][1] - impacts[nuis], poi['fit'][1], poi['fit'][1] + impacts[nuis]]\\n systematic['type'] = \\\"Gaussian\\\"\\n print(getNuisName(nuis), pulls[nuis][1], pulls[nuis][1]-pulls[nuis][0], impacts[nuis])\\n\\n results['params'].append(systematic)\\n\\n with open(ofilename, 'w') as fp:\\n json.dump(results, fp, indent=2)\",\n \"def EORA(set_year=2016,save_output=True,print_output=True,in_dollars=False):\\n\\n # load mapping function for industries\\n mapper_EORA = pd.read_excel(os.path.join(data_path,'other_sources','mappers.xlsx'),\\n sheet_name=\\\"EORA_INDEC\\\")\\n mapper_EORA = dict(zip(mapper_EORA['EORA'],mapper_EORA['INDEC']))\\n\\n ### Load supply and use table\\n EORA_SUP = pd.read_excel(os.path.join(data_path,'EORA',\\n 'EORA_IO_ARG_2015_BasicPrice.xlsx'),sheet_name='SUP',\\n index_col=[0,1,2],header=[0,1,2])\\n\\n EORA_USE = pd.read_excel(os.path.join(data_path,'EORA',\\n 'EORA_IO_ARG_2015_BasicPrice.xlsx'),sheet_name='USE',\\n index_col=[0,1,2],header=[0,1,2]).fillna(0)\\n\\n # GET VARIABLES\\n x = np.array(EORA_SUP.sum(axis=1)) # total production on industry level\\n g = np.array(EORA_SUP.sum(axis=0)) # total production on product level\\n F = EORA_USE.iloc[:196,125:].sum(axis=1)\\n\\n #Numpify\\n Sup_array = np.asarray(EORA_SUP.iloc[:125,:196]) # numpy array of supply matrix\\n Use_array = np.asarray(EORA_USE.iloc[:196,:125]) # numpy array of use matrix\\n\\n g_diag_inv = np.linalg.inv(np.diag(g)) # inverse of g (and diagolinized)\\n x_diag_inv = np.linalg.inv(np.diag(x)) # inverse of x (and diagolinized)\\n\\n # Calculate the matrices\\n B = np.dot(Use_array,x_diag_inv) # B matrix (U*x^-1)\\n D = np.dot(Sup_array,g_diag_inv) # D matrix (V*g^-1)\\n I_i = np.identity((len(x))) # Identity matrix for industry-to-industry\\n\\n # Inverse for industry-to-industry\\n A_ii = np.dot(D,B)\\n IDB_inv = np.linalg.inv((I_i-np.dot(D,B))) # (I-DB)^-1 \\n\\n # And canclulate sum of industries\\n ind = np.dot(IDB_inv,np.dot(D,F)) # (I-DB)^-1 * DF\\n\\n # split FD in local, import and export\\n LFD = np.dot(D,EORA_USE.iloc[:196,125:131].sum(axis=1) )\\n Exp = np.dot(D,EORA_USE.iloc[:196,131])\\n\\n # combine all elements into one table\\n EORA = pd.concat([pd.DataFrame(np.dot(A_ii,np.diag(ind))),pd.DataFrame(LFD),\\n pd.DataFrame(Exp)],axis=1)\\n EORA.columns = [x[2] for x in EORA_USE.columns[:125]]+['FinalD','Exports']\\n EORA.index = [x[2] for x in EORA_USE.columns[:125]]\\n\\n VA = list(EORA_USE.iloc[196:202,:125].sum(axis=0))+[0,0]\\n IMP = list(EORA_USE.iloc[202,:125])+[0,0]\\n EORA.loc['ValueA'] = VA\\n EORA.loc['Imports'] = IMP\\n EORA[EORA < 1e-5] = 0\\n\\n # and map into the INDEC classes:\\n EORA.columns = EORA.columns.map(mapper_EORA)\\n EORA = EORA.groupby(level=0,axis=1).sum()\\n EORA.index = EORA.index.map(mapper_EORA)\\n EORA = EORA.groupby(level=0,axis=0).sum()\\n\\n EORA = EORA[[chr(i).upper() for i in range(ord('a'),ord('p')+1)]+['FinalD','Exports']]\\n EORA = EORA.T[[chr(i).upper() for i in range(ord('a'),ord('p')+1)]+['ValueA','Imports']]\\n EORA = EORA.T\\n\\n # convert to the desired year using INDEC time series\\n Total_Prod = pd.read_excel(os.path.join(data_path,\\n 'INDEC','sh_VBP_VAB_06_19.xls'),\\n sheet_name='Cuadro 2',skiprows=3,index_col=[0])/1e3 \\n\\n rel_change = Total_Prod[set_year]/Total_Prod[2015] \\n\\n # and balance the table\\n X0 = EORA.values[:,:]\\n X0_sum = np.array(list(EORA.sum(axis=1)[:16]*rel_change)+[(EORA['FinalD'][:16]*rel_change).sum()]+[(EORA['Exports'][:16]*rel_change).sum()])\\n\\n # apply RAS method to rebalance the table\\n new_IO = ras_method(X0,X0_sum,X0_sum,1e-5,print_out=False)\\n\\n if in_dollars:\\n EORA.iloc[:,:] = new_IO/1e6\\n else:\\n EORA.iloc[:,:] = new_IO/1e6*dollar_pesos()[set_year]\\n\\n if save_output:\\n EORA.to_csv(os.path.join(data_path,'national_tables','{}_EORA.csv'.format(set_year)))\\n\\n if print_output:\\n print('NOTE : Standardized national table for Argentina for the year {} finished using EORA data'.format(set_year)) \\n\\n return EORA\",\n \"def vibrational_analysis_summary(self):\\n print(\\\"Summary of the vibrational analysis:\\\")\\n #cols = [ \\\"eigvals [a.u.]\\\" , \\\"w [a.u.]\\\", \\\"w [THz]\\\", \\\"w [cm^-1]\\\", \\\"T [a.u.]\\\", \\\"T [ps]\\\",\\\"E [a.u.]\\\", \\\"n [a.u.]\\\"]\\n df = pd.DataFrame()\\n eigvals = self.eigvals.copy()\\n eigvals [ eigvals == MicroStatePrivate.smallest_float ] = np.nan\\n df[\\\"eigvals [a.u.]\\\"] = eigvals\\n df[\\\"w [a.u.]\\\"] = [ np.sqrt(i) if i > 0. else None for i in eigvals ]\\n df[\\\"w [THz]\\\"] = convert(df[\\\"w [a.u.]\\\"],\\\"frequency\\\",_from=\\\"atomic_unit\\\",_to=\\\"thz\\\")\\n df[\\\"w [cm^-1]\\\"] = convert(df[\\\"w [a.u.]\\\"],\\\"frequency\\\",_from=\\\"atomic_unit\\\",_to=\\\"inversecm\\\")\\n df[\\\"T [a.u.]\\\"] = 2*np.pi / df[\\\"w [a.u.]\\\"]\\n df[\\\"T [ps]\\\"] = convert(df[\\\"T [a.u.]\\\"],\\\"time\\\",_from=\\\"atomic_unit\\\",_to=\\\"picosecond\\\")\\n\\n if hasattr(self,\\\"energy\\\"):\\n df[\\\"E [a.u.]\\\"] = self.energy.mean(axis=0)\\n\\n if hasattr(self,\\\"occupations\\\"):\\n df[\\\"n [a.u.]\\\"] = self.occupations.mean(axis=0)\\n\\n return df\",\n \"def plot_uwrz_field(f=False,ax=False, oro='h500a7', vm=[-2,2],tinst=18,var='QVrz',ik=0,jk=0):\\n\\n tinst = int(tinst)\\n \\n # in case f is not passed, f and ax are created\\n if not f:\\n f,ax=plt.subplots()\\n\\n # Name the axes \\n decorate_ax(ax,'',daytime(tinst),'')\\n \\n\\n # specify full path of the data\\n\\n iensmean = 1\\n \\n if iensmean:\\n srcpath = BASE3D + oro + '/' + sm +'/' + CIRCMEAN\\n print srcpath\\n else: # 1 single ensemble member first day\\n srcpath = BASE3D + oro + '/' + sm +'/'\\n seedname= filter(lambda x: x.startswith('circmean_seed'), os.listdir(srcpath))[0]\\n srcpath +=seedname\\n\\n # Open the data in netcdf format and read it only \\n ncfl = Dataset(srcpath,'r')\\n \\n # Load the fields of interest\\n # COSMO fields dimensions are (time, z,rlat,rlon)\\n #\\n # Field dimensions in the following are (time,z,r)\\n #\\n # By specifing '::-1' the second timension is inverted\\n urz = ncfl.variables['Urz'][tinst,::-1,:] # U component of wind\\n wrz = ncfl.variables['Wrz'][tinst,::-1,:] # W component of wind\\n\\n speedrz = ncfl.variables['Speedrz'][tinst,::-1,:] # wind speed\\n rhorz = ncfl.variables['RHOrz'][:,::-1,:] # density in rz coordinates\\n TOTP = ncfl.variables['TOT_PRECr'][:,:]/2. # half-hourly output\\n \\n LCLr= ncfl.variables['LCL_MLr'][tinst,:]/1000. # LCL\\n LFCr = ncfl.variables['LFC_MLr'][tinst,:]/1000. # LFC\\n hPBLr = ncfl.variables['HPBLr'][tinst,:]/1000. \\n \\n CAPEr = ncfl.variables['CAPE_MLr'][tinst,:] # CAPE\\n CINr = ncfl.variables['CIN_MLr'][tinst,:] # CIN\\n \\n TOTPsum = np.sum(TOTP[:tinst,:],axis=0)\\n TOTPsum = TOTP[tinst,:]\\n \\n TPthres = 0.5\\n rainyr=np.where(TOTPsum>TPthres)\\n\\n\\n if var: #select a specific humidity var\\n qvrz = ncfl.variables[var][:,::-1,:] # specficif humidity\\n qcrz = ncfl.variables['QCrz'][:,::-1,:]\\n else: # otherwise sum it all up:\\n qxrzvars = filter(lambda x: x.startswith('Q'),ncfl.variables.keys())\\n \\n qvrz = np.zeros(ncfl.variables['QVrz'].shape)\\n for var in qxrzvars:\\n print var\\n qvrz+=ncfl.variables[var][:,::-1,:]\\n \\n tref = 12 # 06:00\\n ipltdiff= 0\\n if ipltdiff:\\n plotvar = rhorz[tinst,:]*qvrz[tinst,:]-rhorz[tref,:]*qvrz[tref,:]\\n qcrz = rhorz[tinst,:]*qcrz[tinst,:]-rhorz[tref,:]*qcrz[tref,:]\\n else:\\n plotvar = rhorz[tinst,:]*qvrz[tinst,:]\\n \\n\\n rhorz = rhorz[tinst,:]\\n \\n \\n # Load the grid in r, z coordinate\\n X = ncfl.variables['X'][:]\\n \\n \\n # nasty and temporary workaround as Z is empty in single seed data - just\\n # take it from the circmean field\\n srcpath2 = BASE3D + oro + '/' + sm +'/' + CIRCMEAN\\n ncfl2=Dataset(srcpath2)\\n Z = ncfl2.variables['Z'][::-1,:]/1000. # highest index <-> ground\\n ncfl2.close() \\n \\n # Add mountain contour\\n r=X[0,:]\\n ax.plot(r,Z[0,:],color='grey',linewidth=2)\\n \\n# ax.plot(r,hPBLr,'k-.',linewidth=1)\\n# ax.plot(r,LCLr,'k-',linewidth=1) \\n \\n ax.fill_between(X[0,:],Z[0,:],0,color='lightgrey')\\n \\n #indicate rainy region\\n \\n ifillrainy = False\\n if ifillrainy:\\n ax.fill_between(X[0,rainyr][0],Z[0,rainyr][0],color='lightblue',alpha=0.8) \\n ax.plot(X[0,rainyr][0],Z[0,rainyr][0],color='lightblue',linewidth=2)\\n else:\\n pass#ax.plot(X[0,rainyr][0],Z[0,rainyr][0],color='blue',linewidth=2,linestyle='dashed')\\n \\n # Velocity vectors with speed (length) smaller than speedthres are not \\n # shown\\n oroorig = oro\\n if oro.endswith('bell') or oro.endswith('cos2'):\\n \\n oro = oro[:-5]\\n print oro\\n \\n i,j=np.where(expnames==oro)\\n\\n if i==[]:\\n print 'HH'\\n \\n oro = oro.split('_')[0]\\n h,a = getha(oro)\\n #h,a = hrange[i],arange[j]\\n print h,a\\n xrang = [0,40]\\n yrang = [0,10.]\\n delx = xrang[1]-xrang[0]\\n dely = yrang[1]-yrang[0]\\n \\n speedthres = 0.1# m/s\\n M = speedrz[:] < speedthres\\n U = ma.masked_array(urz[:,:],mask=M)\\n scalefac_w = 1. #delx/dely\\n W = ma.masked_array(scalefac_w*wrz[:,:],mask=M) \\n \\n \\n \\n if 0: # REMOVE\\n fact_agl = -dely/delx\\n sin = np.arcsin(fact_agl)\\n cos = np.arccos(fact_agl)\\n U=cos*U-sin*W\\n W=sin*U+cos*W\\n # Plot radial velocity\\n urzlevs = np.linspace(vm[0],vm[1],10)\\n\\n\\n levs = np.array([2,4,6,8,10,12,14,16])\\n #levs = np.array([0.25,0.5,1,2,4,8,16,32])\\n #levs = np.array([0.5,1,2,4,8,16,32])\\n if ipltdiff:\\n levs=levs/8. #np.linspace(0.001,0.004,11)d\\n #else:\\n # levs=np.array([0.001,0.0015,0.002,0.0025,0.003,0.0035,0.004])*50\\n #if var.startswith('QC'): levs=np.linspace(0.1,1.5,9)/1000.\\n \\n #cf = ax.contourf(X,Z,plotvar*1000., levels=levs*1000.,cmap=viridis_inv,extend='both')\\n \\n ipltmoistening = False \\n if ipltmoistening:\\n cf = ax.contourf(X,Z,plotvar*1000.,cmap=viridis_inv,extend='both',\\n levels=levs)\\n else:\\n cf = 0\\n \\n #cfc = ax.contourf(X,Z,qvrz*1000.,cmap='Blues',extend='both', alpha=1,\\n # levels=levs/4.) \\n\\n iplt_thermodynvar = False\\n if iplt_thermodynvar:\\n for var in [LFCr]:\\n print var.shape\\n print X.shape\\n ax.plot(var)\\n \\n \\n qstx = 2\\n qstz = 2\\n Q=ax.quiver(X[::qstz,::qstx],(Z+0.02)[::qstz,::qstx],U[::qstz,::qstx],W[::qstz,::qstx],\\n units='inches',scale=10,angles='xy', color='red',pivot='mid',\\n headwidth=4,width=0.009)\\n #Q=ax.quiverkey(Q,a/2,h/2000.,1,r'$1\\\\frac{m}{s}$',labelpos='N',coordinates='data')\\n\\n if jk==2 or oroorig.endswith('cos2'):#(ik,jk)==(1,0):\\n ax.quiverkey(Q,-2,0.05,1,r'$1 \\\\frac{m}{s}$',labelsep=0.05,\\n labelpos='N',coordinates='data')\\n\\n\\n ax.set_xticks(np.arange(0,80,20)) \\n\\n ax.set_xlim(xrang)\\n ax.set_ylim(yrang)\\n\\n if ik==2:\\n ax.set_xlabel('r in km')\\n if jk==0:\\n ax.set_ylabel('z in km') \\n \\n ax.grid(True)\\n\\n ncfl.close()\\n \\n return f,ax ,cf # plot_uwrz_field\",\n \"def encircled_energy(uvotfilter, areapix):\\n import os\\n caldb = os.getenv(\\\"CALDB\\\")\\n if uvotfilter == 'wh': uvotfilter = 'white'\\n command=\\\"quzcif swift uvota - \\\"+uvotfilter.upper()+\\\\\\n \\\" REEF 2009-10-30 12:00:00 - > quzcif.out\\\"\\n print(command) \\n if not os.system(command):\\n print(\\\"not \\\" +command) \\n f = open(\\\"quzcif.out\\\")\\n reeffile, ext = f.read().split()\\n ext = int(ext)\\n f.close()\\n os.system(\\\"rm -f quzcif.out\\\")\\n print(reeffile, ext)\\n f = fits.getdata(reeffile,ext)\\n r = f['radius'] # in arc sec\\n E = f['reef'] \\n x = sqrt(old_div(areapix,pi))*0.502 # lookup radius\\n f = interp1d(r,E)\\n return f(x)\",\n \"def potentials(path_to_units, path_to_eez, path_to_shared_coast,\\n path_to_electricity_yield_pv_prio, path_to_electricity_yield_wind_prio,\\n path_to_eligibility_categories, path_to_land_cover, path_to_protected_areas,\\n path_to_result, scenario, config):\\n with rasterio.open(path_to_eligibility_categories, \\\"r\\\") as src:\\n eligibility_categories = src.read(1)\\n with rasterio.open(path_to_electricity_yield_pv_prio, \\\"r\\\") as src:\\n transform = src.transform\\n electricity_yield_pv_prio = src.read(1)\\n with rasterio.open(path_to_electricity_yield_wind_prio, \\\"r\\\") as src:\\n electricity_yield_wind_prio = src.read(1)\\n with rasterio.open(path_to_land_cover, \\\"r\\\") as src:\\n land_cover = src.read(1)\\n with rasterio.open(path_to_protected_areas, \\\"r\\\") as src:\\n protected_areas = src.read(1)\\n with fiona.open(path_to_units, \\\"r\\\") as src:\\n unit_ids = [feature[\\\"properties\\\"][\\\"id\\\"] for feature in src]\\n unit_geometries = [feature[\\\"geometry\\\"] for feature in src]\\n with fiona.open(path_to_eez, \\\"r\\\") as src:\\n eez_ids = [feature[\\\"properties\\\"][\\\"id\\\"] for feature in src]\\n eez_geometries = [feature[\\\"geometry\\\"] for feature in src]\\n shared_coasts = pd.read_csv(path_to_shared_coast, index_col=0)\\n\\n electricity_yield_pv_prio, electricity_yield_wind_prio = apply_scenario_config(\\n potential_pv_prio=electricity_yield_pv_prio,\\n potential_wind_prio=electricity_yield_wind_prio,\\n categories=eligibility_categories,\\n land_cover=land_cover,\\n protected_areas=protected_areas,\\n scenario_config=config[\\\"scenarios\\\"][scenario]\\n )\\n electricity_yield_pv_prio, electricity_yield_wind_prio = decide_between_pv_and_wind(\\n potential_pv_prio=electricity_yield_pv_prio,\\n potential_wind_prio=electricity_yield_wind_prio,\\n electricity_yield_pv_prio=electricity_yield_pv_prio,\\n electricity_yield_wind_prio=electricity_yield_wind_prio,\\n eligibility_categories=eligibility_categories\\n )\\n\\n onshore_potentials = pd.DataFrame(\\n index=unit_ids,\\n data={\\n potential: potentials_per_shape(\\n eligibilities=potential.eligible_on,\\n potential_map=(electricity_yield_pv_prio if \\\"pv\\\" in str(potential).lower()\\n else electricity_yield_wind_prio),\\n eligibility_categories=eligibility_categories,\\n shapes=unit_geometries,\\n transform=transform\\n )\\n for potential in Potential.onshore()\\n }\\n )\\n offshore_eez_potentials = pd.DataFrame(\\n index=eez_ids,\\n data={\\n potential: potentials_per_shape(\\n eligibilities=potential.eligible_on,\\n potential_map=(electricity_yield_pv_prio if \\\"pv\\\" in str(potential).lower()\\n else electricity_yield_wind_prio),\\n eligibility_categories=eligibility_categories,\\n shapes=eez_geometries,\\n transform=transform\\n )\\n for potential in Potential.offshore()\\n }\\n )\\n offshore_potentials = pd.DataFrame(\\n data=shared_coasts.dot(offshore_eez_potentials),\\n columns=Potential.offshore()\\n )\\n potentials = pd.concat([onshore_potentials, offshore_potentials], axis=1)\\n potentials.index.name = \\\"id\\\"\\n potentials.to_csv(\\n path_to_result,\\n header=True,\\n index=True\\n )\",\n \"def initial_importer(initials, initialZMT=True):\\n from .functions import cosd, lna\\n ###filling the running variables with values depending on the systemconfiguration in rk4input###\\n\\n if Base.spatial_resolution == 0:\\n dim = 0\\n print('0D')\\n Vars.T = initials['zmt']\\n else:\\n dim = 1\\n # NS==True corresponds to southpole to northpole representation (180 Degrees)\\n if Base.both_hemispheres == True:\\n Latrange = 180\\n\\n # Checking if Temperature and Latitude is set on a latitudal circle (0°,10°,..if step=10)\\n # or on a latitudinal belt and therefore between the boundaries (5°,15°,..if step=10)\\n\\n # circle==True and belt==False says on the latitudinal circle\\n if Base.latitudinal_circle == True and Base.latitudinal_belt == False:\\n Vars.Lat = np.linspace(-90 + Base.spatial_resolution, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution - 1))\\n Vars.Lat2 = np.linspace(-90, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\\n if initialZMT == True:\\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution - 1))\\n # Checking if the Temperature for each latitude starts with the same value or a\\n # cosine shifted value range\\n if initials['initial_temperature_cosine'] == True:\\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\\n\\n # circle==False and belt==True say on the latitudinal belt\\n if Base.latitudinal_circle == False and Base.latitudinal_belt == True:\\n Vars.Lat2 = np.linspace(-90 + Base.spatial_resolution, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution - 1))\\n Vars.Lat = np.linspace(-90, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\\n if initialZMT == True:\\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\\n if initials['initial_temperature_cosine'] == True:\\n if initials['initial_temperature_noise'] == True:\\n z = [0] * len(Vars.Lat)\\n for k in range(len(Vars.Lat)):\\n z[k] = np.random.normal(0, initials['initial_temperature_noise_amplitude'])\\n else:\\n z = 0\\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1) + lna(z)\\n\\n # Not from southpole to northpole rather equator to pole\\n else:\\n Latrange = 90\\n if Base.latitudinal_circle == True and Base.latitudinal_belt == False:\\n Vars.Lat = np.linspace(0, 90 - Base.spatial_resolution, int(Latrange / Base.spatial_resolution))\\n Vars.Lat2 = np.linspace(0, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\\n if initialZMT == True:\\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\\n if initials['initial_temperature_cosine'] == True:\\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\\n if Base.latitudinal_circle == False and Base.latitudinal_belt == True:\\n Vars.Lat2 = np.linspace(0, 90 - Base.spatial_resolution, int(Latrange / Base.spatial_resolution))\\n Vars.Lat = np.linspace(0, 90 - Base.spatial_resolution,\\n int(Latrange / Base.spatial_resolution)) + Base.spatial_resolution / 2\\n if initialZMT == True:\\n Vars.T = np.array([initials['zmt']] * int(Latrange / Base.spatial_resolution))\\n if initials['initial_temperature_cosine'] == True:\\n Vars.T = Vars.T + initials['initial_temperature_amplitude'] * (cosd(Vars.Lat) - 1)\\n\\n Vars.t = initials['time']\\n if Base.parallelization == True:\\n if initialZMT == True:\\n Vars.T = np.array([Vars.T] * Base.number_of_parallels)\\n Vars.T_global = np.array([initials['gmt']] * Base.number_of_parallels)\\n else:\\n Vars.T_global = initials['gmt']\",\n \"def ImportTemps(cls, mfg_enduse):\\n\\n def create_dict(file_dir, file):\\n dict_out = dict(pd.read_excel(\\n file_dir + file, _sheetname=0).iloc[:, (0, 2)].values\\n )\\n\\n return dict_out\\n\\n ndict = {}\\n\\n for k, v in cls.nfiles.items():\\n ndict[k[0:7]] = create_dict(cls.file_dir, v)\\n\\n temps = pd.read_excel(cls.file_dir + cls.temp_file, sheetname=0)\\n\\n temps.SIC.fillna(method='ffill', inplace=True)\\n\\n temps.loc[:, 'Temp_C'] = temps.Temp_C.apply(\\n lambda x: int(np.around(x))\\n )\\n\\n # Calculate energy fraction of each process by temperature\\n temps = pd.DataFrame(temps.groupby(\\n ['SIC', 'Unit_Process', 'Heat_type', 'Temp_C']\\n )['E_Btu'].sum())\\n\\n e_totals = temps.reset_index()[\\n temps.reset_index()['Unit_Process'] != 'Boiler'\\n ].groupby(['SIC', 'Heat_type']).E_Btu.sum()\\n\\n for i in temps.index:\\n if 'Boiler' in i:\\n continue\\n\\n temps.loc[i, 'Fraction'] = \\\\\\n temps.loc[i, 'E_Btu'] / e_totals.loc[(i[0], i[2])]\\n\\n temps.reset_index(inplace=True)\\n\\n temps.loc[:, 'SIC'] = temps.SIC.apply(lambda x: int(str(x)[0:4]))\\n\\n temps.loc[:, 'NAICS02'] = temps.SIC.map(ndict['sic_N02'])\\n\\n temps.loc[:, 'NAICS07'] = temps.NAICS02.map(ndict['N02_N07'])\\n\\n temps.loc[:, 'NAICS12'] = temps.NAICS07.map(ndict['N07_N12'])\\n\\n # Multiple entries for each SIC/NAICS; take simple mean. \\n temps = temps.groupby(\\n ['NAICS12', 'Unit_Process', 'Heat_type']\\n )[['E_Btu', 'Temp_C', 'Fraction']].mean()\\n\\n # Create 4-, and 3-digit NAICS table for matching \\n temps_NAICS = pd.DataFrame(index=temps.index.levels[0],\\n columns=['N5', 'N4', 'N3']\\n )\\n\\n for n in [5, 4, 3]:\\n temps_NAICS.loc[:, 'N' + str(n)] = \\\\\\n [float(str(x)[0:n]) for x in temps_NAICS.index.values]\\n\\n temps_NAICS.reset_index(inplace=True)\\n\\n eu_naics = pd.DataFrame(\\n mfg_enduse.naics.drop_duplicates().sort_values(ascending=True),\\n copy=True\\n )\\n\\n eu_naics.reset_index(inplace=True, drop=True)\\n\\n eu_naics.rename(columns={'naics':'NAICS12'}, inplace=True)\\n\\n for n in [5, 4, 3]:\\n eu_naics.loc[:, 'N' + str(n)] = \\\\\\n [float(str(x)[0:n]) for x in eu_naics.NAICS12.values]\\n\\n # Match naics between end use data set and temperature info. \\n nmatch = pd.DataFrame()\\n for column in temps_NAICS.columns:\\n nmatch = pd.concat([nmatch, pd.Series(\\n [x in temps_NAICS[column].values for x in eu_naics[\\n column\\n ].values]\\n )], axis=1)\\n\\n nmatch.columns = eu_naics.columns\\n\\n nmask = pd.DataFrame()\\n\\n for c in nmatch.columns:\\n\\n nmask = pd.concat(\\n [nmask, eu_naics[c].multiply(nmatch[c])],\\n axis=1\\n )\\n\\n nmask.replace({0:np.nan}, inplace=True)\\n\\n # Values of 0 indicate no matching temperature data, even at 3-digit \\n # level.\\n nmask.N3.fillna(0, inplace=True)\\n\\n nmask.loc[:, 'TN_Match'] = nmask.apply(\\n lambda x: int(list(x.dropna())[0]), axis=1\\n )\\n\\n nmask.rename(columns={'NAICS12':'N6'}, inplace=True)\\n\\n nmask.loc[:, 'NAICS12'] = eu_naics.NAICS12\\n\\n # Merge matched NAICS values with end use energy data\\n mfg_enduse = pd.merge(mfg_enduse,\\n nmask[['NAICS12', 'TN_Match']], how='left',\\n left_on='naics', right_on='NAICS12')\\n\\n mfg_enduse.drop('NAICS12', inplace=True, axis=1)\\n\\n # Merge temps and temps_NAICS for future operations by other NAICS\\n temps.reset_index(inplace=True)\\n\\n temps = pd.merge(temps, temps_NAICS, left_on='NAICS12',\\n right_on='NAICS12', how='left')\\n\\n for tb, tr in {'<100': (0, 99), '100-249': (100, 249),\\n '250-399': (250, 399), '400-999': (400, 999),\\n '>1000': (1000, 3000)}.items():\\n\\n ti = temps[temps.Temp_C.between(tr[0], tr[1])].index\\n\\n temps.loc[ti, 'Temp_Bucket'] = tb\\n\\n return mfg_enduse, temps\",\n \"def test_all_false(directorio = str(), database = 'red_cod-db.pkl', \\r\\n local_function = 'fij_2.0_25_diccio'):\\r\\n df = pd.read_pickle(database)\\r\\n collection = pd.read_csv(directorio + '/compounds_collection.csv')\\r\\n \\r\\n cifs = [i for i in collection['cif']]\\r\\n maxsites = np.max(collection['sitios'])\\r\\n \\r\\n df = df[df['sitios'] > 0][df['sitios'] <= maxsites].reset_index(drop=True)\\r\\n df = df.loc[~df['cif'].isin(cifs)].reset_index(drop=True)\\r\\n \\r\\n x, _, df = inout_creator(df=df)\\r\\n \\r\\n x = compute_quotients(X=x)\\r\\n x, df = append_local_functions(X = x,df=df)\\r\\n \\r\\n busqueda = \\\"ls \\\" + directorio + \\\"/*.h5 > model_names.txt\\\"\\r\\n os.system(busqueda)\\r\\n \\r\\n diccio = np.load(directorio + '/feature_standarisation.npy').item()\\r\\n \\r\\n X = (x - diccio['mean'])/diccio['std']\\r\\n x = np.reshape(X,(X.shape[0],X.shape[2]))\\r\\n \\r\\n with open('model_names.txt','r') as f:\\r\\n for line in f:\\r\\n modelo = models.load_model(line[:len(line)-1])\\r\\n nombre = line.split('/')[1]\\r\\n \\r\\n outpred = modelo.predict(x)\\r\\n prediction = outpred >= 0.5\\r\\n df['y_pred'] = np.ravel(prediction)\\r\\n \\r\\n with open(directorio+'/test_with_all_false.txt','a') as tr:\\r\\n tr.write(nombre + '\\\\n')\\r\\n \\r\\n for sitios in range(1, max(df['sitios']) + 1):\\r\\n \\r\\n acc = df[df['sitios'] == sitios][df['y_pred'] == False].shape[0]\\r\\n miniset = df[df['sitios'] == sitios].shape[0]\\r\\n percent = round(100*acc/miniset,2)\\r\\n \\r\\n \\r\\n tr.write('With '+ str(sitios) + ' sites:' + str(percent) +\\\\\\r\\n '(' + str(miniset) + ' samples)' + '\\\\n')\\r\\n tr.close()\\r\\n return\",\n \"def data(dataset=\\\"bio_eventrelated_100hz\\\"):\\n # TODO: one could further improve this function with like\\n # selectors 'ecg=True, eda=True, restingstate=True' that would\\n # find the most appropriate dataset\\n\\n dataset = dataset.lower()\\n\\n # TODO: change this path back to \\\"master\\\"\\n path = \\\"https://raw.githubusercontent.com/neuropsychology/NeuroKit/dev/data/\\\"\\n\\n # Signals as vectors =======================\\n if dataset in [\\\"eeg\\\", \\\"eeg_150hz\\\", \\\"eeg.txt\\\"]:\\n return pd.read_csv(path + \\\"eeg.txt\\\").values[:, 0]\\n\\n if dataset in [\\\"rsp\\\", \\\"rsp_1000hz\\\", \\\"rsp_1000hz.txt\\\"]:\\n return pd.read_csv(path + \\\"rsp_1000hz.txt\\\", header=None).values[:, 0]\\n\\n if dataset in [\\\"ecg\\\", \\\"ecg_1000hz\\\", \\\"ecg_1000hz.csv\\\"]:\\n return pd.read_csv(path + \\\"ecg_1000hz.csv\\\")[\\\"ECG\\\"].values\\n\\n if dataset in [\\\"ecg_3000hz\\\", \\\"ecg_3000hz.csv\\\"]:\\n return pd.read_csv(path + \\\"ecg_1000hz.csv\\\")[\\\"ECG\\\"].values\\n\\n if dataset in [\\\"eog\\\", \\\"veog\\\", \\\"eog_100hz\\\", \\\"eog_100hz.csv\\\"]:\\n return pd.read_csv(path + \\\"eog_100hz.csv\\\")[\\\"vEOG\\\"].values\\n\\n # Dataframes ===============================\\n if dataset == \\\"iris\\\":\\n info = sklearn_datasets.load_iris()\\n data = pd.DataFrame(\\n info.data, columns=[\\\"Sepal.Length\\\", \\\"Sepal.Width\\\", \\\"Petal.Length\\\", \\\"Petal.Width\\\"]\\n )\\n data[\\\"Species\\\"] = info.target_names[info.target]\\n return data\\n\\n if dataset in [\\\"eogs\\\", \\\"eogs_200hz\\\", \\\"eog_200hz\\\", \\\"eog_200hz.csv\\\"]:\\n return pd.read_csv(path + \\\"eog_200hz.csv\\\")\\n\\n # Add extension\\n if dataset in [\\\"bio_resting_8min_200hz\\\"]:\\n dataset += \\\".json\\\"\\n\\n # Specific case for json file\\n if dataset.endswith(\\\".json\\\"):\\n if \\\"https\\\" not in dataset:\\n data = pd.read_json(path + dataset, orient=\\\"index\\\")\\n else:\\n data = pd.read_json(dataset, orient=\\\"index\\\")\\n df = {}\\n for participant, row in data.iterrows():\\n for _, data_string in row.items():\\n data_list = json.loads(data_string)\\n data_pd = pd.DataFrame(data_list)\\n df[participant] = data_pd\\n\\n return df\\n\\n # TODO: Add more EEG (fif and edf datasets)\\n if dataset in [\\\"eeg_1min_200hz\\\"]:\\n\\n return pickle.load(\\n urllib.request.urlopen(\\n \\\"https://github.com/neuropsychology/NeuroKit/blob/dev/data/eeg_1min_200hz.pickle?raw=true\\\"\\n )\\n )\\n\\n # General case\\n file, ext = os.path.splitext(dataset) # pylint: disable=unused-variable\\n if ext == \\\"\\\":\\n df = pd.read_csv(path + dataset + \\\".csv\\\")\\n else:\\n if \\\"https\\\" not in dataset:\\n df = pd.read_csv(path + dataset)\\n else:\\n df = pd.read_csv(dataset)\\n return df\",\n \"def summaryD5(self):\\r\\n\\r\\n if self.window.diff_tabs.tempruns_D45_set==3:\\r\\n\\r\\n dfD5_temp1_pos1= pd.read_csv('raw_text_D5_1_1.txt')\\r\\n dfD5_temp1_pos2= pd.read_csv('raw_text_D5_1_2.txt')\\r\\n \\r\\n D5summary_temp1=pd.concat([dfD5_temp1_pos1,dfD5_temp1_pos2],axis=1)\\r\\n D5summary_temp1.to_csv('D5summary_temp1.txt',index=False)\\r\\n\\r\\n dfD5_temp2_pos1= pd.read_csv('raw_text_D5_1_1.txt')\\r\\n dfD5_temp2_pos2= pd.read_csv('raw_text_D5_1_2.txt')\\r\\n \\r\\n\\r\\n D5summary_temp2=pd.concat([dfD5_temp2_pos1,dfD5_temp2_pos2],axis=1)\\r\\n D5summary_temp2.to_csv('D5summary_temp2.txt',index=False)\\r\\n\\r\\n dfD5_temp3_pos1= pd.read_csv('raw_text_D5_3_1.txt')\\r\\n dfD5_temp3_pos2= pd.read_csv('raw_text_D5_3_2.txt')\\r\\n \\r\\n\\r\\n D4summary_temp3=pd.concat([dfD5_temp3_pos1,dfD5_temp3_pos2],axis=1)\\r\\n D4summary_temp3.to_csv('D5summary_temp3.txt',index=False)\",\n \"def ReadData( fileName ):\\n \\n # define column names\\n colNames = ['Date','Precip','Max Temp', 'Min Temp','Wind Speed'] #NOTE: I changed the column names because .query() would not work when referencing column names with spaces\\n global DataDF #added this line to make the dataframe visible in the variable explorer\\n global ReplacedValuesDF #added this line to make the dataframe visible in the variable explorer\\n # open and read the file\\n DataDF = pd.read_csv(\\\"DataQualityChecking.txt\\\",header=None, names=colNames, \\n delimiter=r\\\"\\\\s+\\\",parse_dates=[0])\\n DataDF = DataDF.set_index('Date')\\n \\n # define and initialize the missing data dictionary\\n ReplacedValuesDF = pd.DataFrame(0, index=[\\\"1. No Data\\\",\\\"2. Gross Error\\\",\\\"3. Swapped\\\",\\\"4. Range Fail\\\"], columns=colNames[1:]) #added additional indexed rows to make adding the values later easier\\n \\n return( DataDF, ReplacedValuesDF )\",\n \"def readin (filename, hdu=0, non_std_fits=False,\\n text_comments='#', text_skiprows=0, get_data=False, verbose=False,\\n apply_WCS_rv=False):\\n multi_order_txt = False\\n use_naxis2='all'\\n use_naxis3='all'\\n \\n \\n preferred_wlsoln=None # !! need to fix this\\n # !! should also be able to input wavelength solution?\\n \\n if preferred_wlsoln is not None: preferred_wlsoln = wlsolvefxn.get_func_name(preferred_wlsoln)\\n \\n #### check if file exists ####### #############\\n if not os.path.exists(filename): raise IOError(\\\"File does not exist:'\\\"+filename+\\\"'\\\")\\n\\n\\n #### check if file is text############# \\n np_kwargs = {'comments':text_comments,\\n 'skiprows':text_skiprows}\\n is_text_file, txt_data = check_for_txt_format(filename,**np_kwargs)\\n\\n #### if it is a text file ######################\\n if is_text_file:\\n spec_obj = readin_txt(filename,txt_data,get_data) \\n return spec_obj \\n\\n #### now check how it behaves as a fits file\\n if non_std_fits: hdulist = pyfits.open(filename)\\n else:\\n # give standard pyfits readin a try\\n try: hdulist = pyfits.open(filename)\\n except: raise IOError(\\\"PYFITS DOES NOT LIKE THE FILE YOU GAVE ('\\\"+filename+\\\"'), TO SEE WHAT ERROR IT GIVES TRY: hdulist = pyfits.open('\\\"+filename+\\\"')\\\")\\n\\n\\n #### open up fits file ##############################\\n hdulist = pyfits.open(filename)\\n\\n # select which header unit ot use\\n if len(hdulist) > 1: \\n hdu = int(hdu)\\n hdu = np.clip(hdu,0,len(hdulist)-1)\\n else: hdu = 0\\n\\n # specify the current header unit\\n header_unit = hdulist[hdu]\\n prihdr = header_unit.header\\n\\n # can display some useful information \\n if verbose: \\n print \\\"=\\\"*60\\n print (hdulist.info(),'\\\\n')\\n if len(hdulist) > 1:\\n print \\\"=\\\"*20+\\\" USING HEADER: \\\"+\\\"=\\\"*20\\n print repr(hdulist[hdu])\\n\\n ##### fill in the data class\\n # not get header info of relevance\\n simple = query_fits_header(prihdr,'SIMPLE',noval=False)\\n xtension = query_fits_header(prihdr,'XTENSION')\\n if simple.found:\\n if not simple.val: print \\\"HeadsUp: Header Keyword SIMPLE is False, you may encounter unexpected behavior\\\"\\n else:\\n if not xtension.found: print \\\"HeadsUp: No extension keyword found in headers, you may encounter unexpected behavior\\\"\\n \\n \\n #### read in important information from header, if present\\n ibits = query_fits_header(prihdr,'BITPIX') # how many bits per pixel in the data? Not currently necessary, numpy will adapt\\n \\n naxis = query_fits_header(prihdr,'NAXIS' ,noval=0) # how many dimenstions?\\n naxis1 = query_fits_header(prihdr,'NAXIS1',noval=0) # number of points per order\\n naxis2 = query_fits_header(prihdr,'NAXIS2',noval=0) # number of orders\\n naxis3 = query_fits_header(prihdr,'NAXIS3',noval=0) # number of different spectra\\n\\n apformat = query_fits_header(prihdr,'APFORMAT')\\n if apformat.found: print \\\"WARNING: I'M NOT SURE HOW TO DEAL WITH APFORMAT VALUES\\\" # !! though I think it's just the spec files\\n\\n if not naxis.found: raise IOError(\\\"ERROR: Keyword NAXIS not found\\\")\\n\\n bzero = query_fits_header(prihdr,\\\"BZERO\\\",noval=0)\\n bscale = query_fits_header(prihdr,\\\"BSCALE\\\",noval=1)\\n\\n ###### read in data ##############################################\\n data = header_unit.data\\n\\n if data is None:\\n wl, data, inv_var = np.zeros(3).reshape((3,1))\\n if get_data: return (wl,data,inv_var)\\n else: return eyeSpec_spec(wl,data,inv_var,header_unit.header)\\n else:\\n # check that data matches up with at least one of the dimensions\\n if data.ndim != naxis.val: raise ValueError(\\\"Dimension of data \\\"+str(data.ndim)+\\\" does not match keyword naxis \\\"+str(naxis.val))\\n \\n statement = 'Dimension does not match data.shape = '+str(data.shape)+\\\" fits file (naxis1, naxis2, naxis3) \\\"+str(tuple([naxis1.val,naxis2.val,naxis3.val]))\\n if data.ndim == 1: \\n assert data.shape == (naxis1.val,) , statement\\n data = data.reshape((1,1,)+data.shape)\\n \\n elif data.ndim == 2: \\n assert data.shape == (naxis2.val, naxis1.val), statement\\n data = data.reshape((1,)+data.shape) \\n \\n elif data.ndim == 3: \\n assert data.shape == (naxis3.val, naxis2.val, naxis1.val), statement\\n \\n ##### Determine the which data is useful \\n # which orders to read in \\n nband = np.arange(data.shape[0])+1\\n nord = np.arange(data.shape[1])+1\\n\\n \\n ##### Calculate the wavelengths for the data\\n # set up wavelength and inverse_variance\\n wl = np.ones(data.shape)\\n \\n # get the wavelength coefficients\\n wlcoeff = wlsoln_coeff_from_header(header_unit.header, apply_WCS_rv, preferred_wlsoln)\\n \\n # the same wavelength solution is applied to all bands so just pick the first and broadcast\\n band = 0\\n priv_info = {}\\n \\n # go through all the orders\\n do_progress = True\\n progressive_pt = 1 # this will advance and be used when there is no wavelength solution\\n for i in xrange(len(nord)):\\n order_i = nord[i]\\n\\n # get the coefficients and function type \\n equ_type = wlcoeff.get_equation_type()\\n if equ_type in ['none',None,'no solution'] and do_progress: \\n coeff = [progressive_pt,1]\\n equ_type = 'pts'\\n else: coeff = wlcoeff.get_coeffs(order_i)\\n \\n # pts[0] = 1 :: this was definitely the right thing to do for SPECTRE's 1-D output but may not be for other equations, may need pts[0]=0, this may be for bzero,bscale\\n pts = np.arange(len(wl[0][i]))+1 \\n # apply function\\n wl[0][i] = wlsolvefxn(pts, coeff, equ_type) \\n \\n progressive_pt += len(pts)\\n \\n for j in xrange(len(nband)): \\n band_j = nband[j]\\n if (band_j,order_i) not in priv_info: priv_info[(band_j,order_i)] = {} \\n # record the private information\\n priv_info[(band_j,order_i)]['disp']= [coeff, equ_type]\\n priv_info[(band_j,order_i)]['rv'] = [0] \\n priv_info[(band_j,order_i)]['disp extr'] = deepcopy(wlcoeff.extra)\\n \\n # now propogate the solution to the other bands\\n stdwl = wl[0]\\n for i in xrange(1,len(nband)): wl[i] = stdwl \\n \\n inv_var = np.ones(data.shape)\\n #=================================================================#\\n # return the data .OR. go on and create the spec_obj\\n if get_data: return (wl, data, inv_var)\\n\\n #=================================================================# \\n spec_obj = eyeSpec_spec(wl,data,inv_var,header_unit.header)\\n # set up private information\\n priv_info['filename'] = filename\\n spec_obj.filename = filename\\n \\n bands = np.array(np.arange(1,len(data)+1),dtype=str)\\n band_info = {}\\n i = -1\\n for key in prihdr.keys():\\n if key[:6] != 'BANDID': continue\\n if i < len(bands):\\n i+=1\\n bands[i] = prihdr[key]\\n band_info[key] = prihdr[key]\\n else: raise IOError(\\\"MORE BANDID KEYWORDS IN HEADER THAN FIRST DIMENSION OF DATA\\\") \\n\\n # add band info if available:\\n if len(band_info) != 0: priv_info['bandids'] = band_info\\n else: priv_info['bandids'] = None\\n \\n # match up the private info created during read in to the spec_obj\\n for key in priv_info: spec_obj._private_info[key] = priv_info[key]\\n \\n # map fits value => acutal index\\n # spec_obj._bands = {}\\n # spec_obj._orders = {}\\n # for i in range(len(nspec)): spec_obj._bands[nspec[i]] = i\\n # for i in range(len(nord)): spec_obj._orders[nord[i]] = i\\n # \\n \\n if 7 in nband: spec_obj.set_band(6) # this is where Magellian data stores it's object data, i.e. BANDID7 which is index 6\\n\\n if len(hdulist) > 1: spec_obj.hdrlist = [h.header for h in hdulist]\\n \\n return spec_obj\",\n \"def spectra_analysis(file_name, sky_file_name): \\n\\n # read file name and select out the id that we are dealing with\\n curr_file_name = file_name.split('.')\\n curr_file_name = curr_file_name[0].split('/')\\n stk_f_n = curr_file_name[-1]\\n cube_id = int(re.search(r'\\\\d+', stk_f_n).group())\\n\\n # read catalogue and obtain the HST redshift estimate\\n #catalogue = np.load(\\\"data/matched_catalogue.npy\\\")\\n catalogue = np.load(\\\"data/low_redshift_catalogue.npy\\\")\\n cat_loc = np.where(catalogue[:,0] == cube_id)[0]\\n cube_info = catalogue[cat_loc][0]\\n \\n hst_redshift = cube_info[7]\\n\\n # spectra and sky noise data\\n spectra_data = spectrum_creator(file_name)\\n wl_soln = wavelength_solution(file_name)\\n sn_data = sky_noise(sky_file_name)\\n\\n galaxy_data = spectra_data['galaxy']\\n\\n # removing baseline from data\\n base = peakutils.baseline(galaxy_data, 3)\\n gd_mc = galaxy_data - base\\n\\n # scaling sky-noise to be similar to spectra data\\n gd_max = np.amax(galaxy_data)\\n sn_data_max = np.amax(sn_data)\\n sn_scale = gd_max / sn_data_max\\n\\n sn_data = sn_data * sn_scale\\n\\n # spectra lines\\n sl = {\\n 'emis': {\\n '[OII]': '3727',\\n 'CaK': '3933',\\n 'CaH': '3968',\\n 'Hdelta': '4101', \\n }, \\n 'abs': {'K': '3934.777',\\n }\\n } \\n\\n # we can use the redshift from the HST catalogue to define the region to search for\\n # the doublet in\\n\\n # lower and upper bound on wavelength range\\n lower_lambda = (1+hst_redshift)*3600\\n upper_lambda = (1+hst_redshift)*3850\\n\\n # x-axis data\\n data_h_range = np.linspace(wl_soln['begin'], wl_soln['end'], wl_soln['steps'])\\n mask = (lower_lambda < data_h_range) & (data_h_range < upper_lambda) \\n\\n lambda_data = data_h_range[mask]\\n flux_data = gd_mc[mask] \\n \\n # Finding peaks with PeakUtils\\n pu_peaks = peakutils.indexes(flux_data, thres=600, thres_abs=True)\\n pu_peaks_x = peakutils.interpolate(lambda_data, flux_data, pu_peaks)\\n\\n pu_peaks_x = np.sort(pu_peaks_x)\\n pu_peaks_x = pu_peaks_x[lower_lambda < pu_peaks_x]\\n pu_peaks_x = pu_peaks_x[pu_peaks_x < upper_lambda]\\n \\n data_dir = 'cube_results/' + stk_f_n\\n if not os.path.exists(data_dir):\\n os.mkdir(data_dir)\\n\\n peaks_file = open(data_dir + '/' + stk_f_n + '_peaks.txt', 'w')\\n peaks_file.write(\\\"Peaks found on \\\" + str(datetime.datetime.now()) + \\\"\\\\n\\\\n\\\")\\n\\n peaks_file.write(\\\"Number Wavelength \\\\n\\\")\\n for i_peak in range(len(pu_peaks_x)):\\n curr_peak = pu_peaks_x[i_peak]\\n peaks_file.write(str(i_peak) + \\\" \\\" + str(curr_peak) + \\\"\\\\n\\\")\\n\\n # manually selecting which peak is the [OII] peak - given in wavelength\\n if (pu_peaks_x.size != 0):\\n otwo_wav = float(pu_peaks_x[0]) \\n otwo_acc = float(sl['emis']['[OII]'])\\n\\n redshift = (otwo_wav / otwo_acc) - 1\\n else:\\n # accepting HST redshift if cannot find peak\\n redshift = hst_redshift\\n\\n return {'gd_shifted': gd_mc, 'sky_noise': sn_data, 'spectra': sl, 'redshift': \\n redshift, 'pu_peaks': pu_peaks_x}\",\n \"def get_TESS_data(filename, fluxtype = 'PDCSAP_FLUX'):\\n # Manipulate the fits file:\\n data = fits.getdata(filename)\\n\\n # Identify zero-flux values to take them out of the data arrays:\\n idx = np.where((data[fluxtype]!=0.)&(~np.isnan(data[fluxtype])))[0]\\n\\n # Return median-normalized flux:\\n return data['TIME'][idx],data[fluxtype][idx]/np.median(data[fluxtype][idx]), \\\\\\n data[fluxtype+'_ERR'][idx]/np.median(data[fluxtype][idx])\",\n \"def T_c(I, T_amb, V, D, R_list, N_cond=1, T_range=[298,323,348], a_s=0.9, e_s=0.9, I_sun=900.0, temp_factor=1, wind_factor=1, n_iter=10):\\n\\n # def Q_gen(I, R):\\n # w = I * I * R\\n # return w\\n\\n # def Q_rad_in(I_sun, A_s, a_s):\\n # w = I_sun * D * a_s\\n # return w\\n\\n # def Q_conv(htcoeff, A_s, T_lin, T_amb):\\n # w = htcoeff * A_s * (T_line - T_amb)\\n # return w\\n\\n # def Q_rad_out(e_s, A_s, sigma, T_line, T_amb):\\n # w = e_s * D * sigma * (T_line**4 - T_amb**4)\\n # return w\\n\\n def reynolds(V, D, v, Mair=1.103):\\n r = V * D / v\\n return r\\n\\n def nusselt(Re, Pr):\\n a = 0.62 * ( (Re) ** (1.0/2.0) ) * ( Pr ** (1.0/3.0) )\\n b = (1 + (0.4/(Pr**(2.0/3.0) ) ) ) ** (1.0/4.0)\\n c = (Re / 282000) ** (5.0/8.0)\\n n = 0.3 + (a/b) * ( (1 + c) ** (4.0/5.0) )\\n return n\\n\\n def air_prop(T_amb):\\n # temp v k Pr\\n air_prop = np.array([[200, 7.59e-6, 18.1e-3, 0.737],\\n [250, 11.44e-6, 22.3e-3, 0.720],\\n [300, 15.89e-6, 26.3e-3, 0.707],\\n [350, 20.92e-6, 30.0e-3, 0.700],\\n [400, 26.41e-6, 33.8e-3, 0.690],\\n [450, 32.39e-6, 37.3e-3, 0.686],\\n [500, 38.79e-6, 40.7e-3, 0.684],\\n [550, 45.57e-6, 43.9e-3, 0.683],\\n [600, 52.69e-6, 46.9e-3, 0.685]])\\n\\n v, k, Pr = np.apply_along_axis(lambda x: np.interp(T_amb, air_prop[:,0], x),\\n 0, air_prop[:,1:])\\n return v, k, Pr\\n\\n def R_T(R_lo, R_mid, R_hi, T_line, N_cond, T_range=T_range):\\n if 273 <= T_line <= 323:\\n R = ((R_lo + \\n ((R_lo - R_mid)/(T_range[0] - T_range[1]))\\n *(T_line - T_range[0]))/N_cond)\\n elif T_line > 323:\\n R = ((R_mid + \\n ((R_mid - R_hi)/(T_range[1] - T_range[2]))\\n *(T_line - T_range[1]))/N_cond)\\n else:\\n R = R_lo\\n print('Out of bounds')\\n return R\\n\\n R_lo, R_mid, R_hi = R_list[0], R_list[1], R_list[2]\\n temp_factor = 1\\n wind_factor = 1\\n sigma = 5.6703e-8 # Stefan-Boltzmann constant\\n\\n T_amb = T_amb*temp_factor\\n V = V*wind_factor\\n\\n v, k, Pr = air_prop(T_amb)\\n Re = reynolds(V, D, v)\\n htcoeff = nusselt(Re, Pr) * k / D\\n\\n def T_line(T_init):\\n \\n R = R_T(R_lo, R_mid, R_hi, T_init, N_cond)\\n print R\\n\\n C4 = e_s * sigma * D * math.pi\\n C3 = 0.0\\n C2 = 0.0\\n C1 = htcoeff * D * math.pi\\n C0 = - ( I ** 2 * R\\n + I_sun * a_s * D\\n + htcoeff * D * math.pi * T_amb\\n + e_s * D * math.pi * sigma * (T_amb ** 4))\\n\\n return np.roots([C4, C3, C2, C1, C0])\\n\\n T_c = T_amb\\n \\n for i in range(n_iter):\\n T_arr = T_line(T_c)\\n T_c = np.real(T_arr[np.where((np.real(T_arr) > 0) & ~(np.iscomplex(T_arr)))]).mean()\\n print T_c\\n\\n return T_c\",\n \"def temp_series(smhi_data):\\n consumable_data = {\\n \\\"station\\\": smhi_data[\\\"station\\\"][\\\"name\\\"],\\n \\\"temp\\\": [],\\n \\\"from\\\": smhi_data[\\\"value\\\"][0][\\\"date\\\"],\\n \\\"to\\\": smhi_data[\\\"value\\\"][-1][\\\"date\\\"]\\n }\\n for temp_post in smhi_data[\\\"value\\\"]:\\n consumable_data[\\\"temp\\\"].append(float(temp_post[\\\"value\\\"]))\\n return consumable_data\",\n \"def read_ep_data(path=None):\\n if path is None:\\n path = \\\"\\\"\\\"C:/Users/Brendan/Downloads/ExodusPoint - Assignment Data (Rates Quantitative Strategist) - New York.xlsx\\\"\\\"\\\"\\n \\n xls = pd.ExcelFile(path)\\n legacy_sheets = ['TU - Com; NonCom','FV - Com;NonCom', 'TY - Com;NonCom', 'US - Com;NonCom ', 'WN - Com;NonCom']\\n tff_sheets = ['TU - Sectorial','FV - Sectorial', 'TY - Sectorial', 'US - Sectorial', 'WN - Sectorial']\\n \\n a, b = {}, {}\\n for s in legacy_sheets:\\n d = pd.read_excel(xls, sheet_name=s, index_col=0, header=3, skipfooter=1, parse_dates=True)\\n d = d.rename(rename_legacy_cols, axis=1).drop('Total', axis=1)\\n ct = s[0:2]\\n a[ct] = d\\n legacy_raw = pd.concat(a, axis=1)\\n \\n for s in tff_sheets:\\n d = pd.read_excel(xls, sheet_name=s, index_col=0, header=3, skipfooter=1, parse_dates=True)\\n d = d.rename(rename_tff_cols, axis=1)\\n ct = s[0:2]\\n b[ct] = d\\n tff_raw = pd.concat(b, axis=1)\\n \\n futures_raw = pd.read_excel(xls, sheet_name=\\\"Futures Price and Duration Data\\\",\\n index_col=0, header=[3,4,5], skipfooter=1, parse_dates=True)\\n futures_raw.columns = futures_raw.columns.droplevel(1)\\n futures_raw.columns.set_levels([s[0:2] for s in futures_raw.columns.levels[0]], \\n level=0, inplace=True)\\n futures_raw.columns.set_names(['ct', 'field'], inplace=True)\\n \\n swaps_raw = pd.read_excel(xls, sheet_name=\\\"Swap Prices\\\",\\n index_col=0, header=3, skipfooter=1, parse_dates=True)\\n \\n return legacy_raw, tff_raw, futures_raw, swaps_raw\",\n \"def windTimeSeries(ftp, rootFolder, selectedDate, outputFolder, ymax):\\n \\n dataType = 'wind_reconstruction_data'\\n file_list = []\\n \\n\\n # Retrieve data from folders in FTP\\n \\n possible_hours = [format(x,'02d') for x in range(24)]\\n from_zone = tz.gettz('America/Mexico_City')\\n to_zone = tz.gettz('UTC')\\n \\n for item in possible_hours:\\n \\n local_time = datetime.strptime(selectedDate+item, '%Y-%m-%d%H')\\n local_time = local_time.replace(tzinfo=from_zone)\\n \\n utc_date = local_time.astimezone(to_zone)\\n \\n utc_day = utc_date.strftime(\\\"%Y-%m-%d\\\")\\n \\n utc_hour = utc_date.strftime(\\\"%H\\\")\\n \\n time_folder = rootFolder+utc_day+'/'+dataType+'/'+utc_hour+'-00/'\\n \\n creat_min = []\\n try:\\n\\n ftp.dir(time_folder, creat_min.append)\\n dbs = []\\n for i in range(len(creat_min)):\\n if creat_min[i].find('DBS') != -1:\\n dbs.append(creat_min[i])\\n\\n if len(dbs) == 0:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n elif len(dbs) == 1:\\n if dbs[0].find('-25-'):\\n file_list.append(time_folder+dbs[0][55:])\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n elif dbs[0].find('-55-'):\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append(time_folder+dbs[0][55:])\\n else:\\n for i in range(len(dbs)):\\n file_list.append(time_folder+dbs[i][55:])\\n\\n except:\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-25')\\n file_list.append('missing_data_'+utc_day+'_'+utc_hour+'-55')\\n\\n\\n \\n # Create output folder\\n outputFolder = outputFolder+'/'+selectedDate\\n try:\\n mkdirp(outputFolder)\\n except:\\n print('warning: folder '+outputFolder+' already exists')\\n \\n # Make 'generic' outputfile\\n outFile = outputFolder+'/'\\n\\n try:\\n \\n meas_time = []\\n Usets = []\\n Vsets = []\\n Wsets = []\\n \\n for working_file in file_list:\\n\\n if working_file.find('missing_data') != -1:\\n Xset = pd.DataFrame(np.nan,columns=['Xwind'],index=range(100,2550,50))\\n Yset = pd.DataFrame(np.nan,columns=['Ywind'],index=range(100,2550,50))\\n Zset = pd.DataFrame(np.nan,columns=['Zwind'],index=range(100,2550,50))\\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n meas_time.append(match)\\n\\n else: \\n match = (re.search('\\\\d{4}-\\\\d{2}-\\\\d{2}_\\\\d{2}-\\\\d{2}',working_file)).group(0)\\n #hour_min = re.search('_\\\\d{2}-\\\\d{2}-\\\\d{2}',working_file).group(0)\\n meas_time.append(match)\\n \\n obj = DataContainer()\\n ftp.retrbinary('RETR %s' % working_file, obj.readFromFTP)\\n columns = ['Timestamp', 'Azimuth', 'Elevation','Range','Xwind','Ywind','Zwind',\\\\\\n 'CNR','ConfIdx']\\n\\n # Get the date from the file as a DataFrame\\n data = obj.dataToArray(columns)\\n data.drop(data[data.Range > ymax].index, inplace=True)\\n data = data[['Range','Xwind','Ywind','Zwind']]\\n # Replace abs values Xwind and Ywind greater than 50 with NaN\\n \\n data.Xwind.where((data.Xwind < 50) | (data.Xwind > -50),np.nan,inplace=True)\\n data.Ywind.where((data.Ywind > 50) | (data.Ywind > -50),np.nan,inplace=True)\\n\\n dataByRange = data.groupby('Range').mean() \\n \\n Xset = dataByRange[['Xwind']]\\n Yset = dataByRange[['Ywind']]\\n Zset = dataByRange[['Zwind']]\\n \\n Usets.append(Xset)\\n Vsets.append(Yset)\\n Wsets.append(Zset)\\n \\n df_U = pd.concat(Usets,axis=1)\\n df_V = pd.concat(Vsets,axis=1)\\n df_W = pd.concat(Wsets,axis=1)\\n \\n U = df_U.values;\\n V = df_V.values;\\n W = df_W.values;\\n \\n # Vertical wind grid\\n x = np.linspace(0,48,49)\\n y = np.linspace(100,2500,49)\\n X, Y = np.meshgrid(x, y)\\n \\n # If the plot is going to have a variable pcolormesh uncomment next section\\n# cmapLim = 0\\n# if abs(np.nanmax(W)) > abs(np.nanmin(W)):\\n# cmapLim = abs(np.nanmax(W))\\n# else:\\n# cmapLim = abs(np.nanmin(W))\\n \\n # Change back the UTC times to local time\\n \\n timelabels = []\\n to_zone = tz.gettz('America/Mexico_City')\\n from_zone = tz.gettz('UTC')\\n \\n for entry in meas_time:\\n utc_time = datetime.strptime(entry, '%Y-%m-%d_%H-%M')\\n utc_time = utc_time.replace(tzinfo=from_zone)\\n \\n local = utc_time.astimezone(to_zone)\\n \\n timelabels.append(local.strftime(\\\"%H:%M\\\"))\\n \\n timelabels = [i.replace('25','00') for i in timelabels[2:48:4]]\\n \\n # Plot \\n title = ('Perfil vertical del viento, %s (serie de tiempo)' % selectedDate)\\n fig, ax = plt.subplots(figsize=(20,8))\\n \\n # For variable pcolormesh uncomment next line\\n #cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= (-cmapLim), vmax=cmapLim)\\n \\n cax = ax.pcolormesh(X, Y, W, cmap='seismic', vmin= -3, vmax=3)\\n ax.set_title(title,fontsize = 24)\\n ax.set_xlabel('Hora Local [hh:mm]', fontsize = 18, labelpad = 14)\\n ax.set_xticks(np.arange(2.5,48, step=4))\\n ax.set_xticklabels(timelabels, rotation=45, size=12)\\n ax.set_ylabel('Altura [m]', fontsize = 18, labelpad = 18)\\n ax.yaxis.set_tick_params(labelsize=12)\\n ax.text(0.18,-0.08,'Datos de perfilador LIDAR Leosphere Windcube 100s. Lat 19.3262, Lon -99.1761, Alt 2280 msnm',transform=plt.gcf().transFigure, fontdict={'size': 18})\\n \\n # Colorbar\\n cbaxes = fig.add_axes([0.91, 0.125, 0.02, 0.4]) \\n cbar = fig.colorbar(cax, cax = cbaxes)\\n cbar.set_label(r'Viento vertical [$m\\\\,s^{-1}$]', fontsize = 18)\\n cbaxes.text(-0.3,1.07,'(Hacia arriba)')\\n cbaxes.text(-0.3,-0.07,'(Hacia abajo)') \\n \\n \\n #Other a artists\\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/time_adv.png')\\n newax = fig.add_axes([0.6, -.03, 0.06, 0.06], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax.imshow(im)\\n newax.axis('off')\\n \\n im = mpimg.imread('/home/perfilador/perfila/drozanes/resources/directions.png')\\n newax1 = fig.add_axes([0.90, 0.75, 0.15, 0.15], anchor='NW', zorder=0) #[xo,yo,width,height]\\n newax1.imshow(im)\\n newax1.axis('off')\\n \\n \\n # Horizontal wind grid\\n \\n x_h = np.arange(0, len(meas_time), 1)\\n Xh, Yh = np.meshgrid(x_h, y)\\n \\n # Plot arrows\\n Q = ax.quiver(Xh[::2], Yh[::2], U[::2], V[::2], pivot = 'mid',alpha=0.75)\\n ax.quiverkey(Q, 0.93, 0.68, 10, r'$10\\\\,m\\\\,s^{-1}$',coordinates = 'figure', labelpos='N' )\\n \\n \\n finalOutputFile = outFile+'timeseries_daily_'+selectedDate\\n fig.savefig(finalOutputFile,bbox_inches='tight')\\n plt.close()\\n \\n except Exception as e:\\n print(\\\"Problema calculando Wind Time Series\\\", e)\",\n \"def Uiso(data, logfile):\\n printer.register_file(logfile, 'log', mode='a')\\n # isofilters, isopartnerfilters = set_filter()\\n #===========================================================================\\n # keys=['cart_int','cart_ext','cart_sum','cart_meas','iso_meas']\\n #===========================================================================\\n use1, use2 = None, None\\n try:\\n use = config.arg('iso').split(':')\\n use1 = use[0]\\n use2 = use[1]\\n\\n except:\\n use1 = 'cart_sum'\\n use2 = 'cart_sum'\\n # printer('\\\\nSelected filters:\\\\n\\\\n Attribute | Value | True | Function'\\n # '\\\\n===================================================\\\\nAtom filter: | | |')\\n # for isofilter in isofilters:\\n # printer('{:15s} | {:12s} | {:5s} | {}'.format(isofilter[0], isofilter[1], isofilter[2], isofilter[3]))\\n # printer('-----------------|--------------|-------|----------\\\\nPartner filter: | | |')\\n # for isofilter in isopartnerfilters:\\n # printer('{:15s} | {:12s} | {:5s} | {}'.format(isofilter[0], isofilter[1], isofilter[2], isofilter[3]))\\n printer('\\\\nComparing {} of hydrogen atoms\\\\nwith {} of parent atoms.\\\\n'.format(use1, use2))\\n printer(' Riding | Parent | U_rel | U_rel\\\\n atom | atom | geom | arith')\\n printer(' ================================')\\n geom_sum = []\\n arit_sum = []\\n for atom in data['exp'].atoms:\\n # if apply_filter(atom, isofilters):\\n for heavy_atom in cg.get_framework_neighbours(atom, useH=True):\\n if not atom == heavy_atom and filter_atom_pair(config, atom, heavy_atom):\\n U_rel_geom = cg.Uiso(atom.adp[use1]) / cg.Uiso(heavy_atom.adp[use2])\\n geom_sum.append(U_rel_geom)\\n U_rel_arith = cg.Uiso(atom.adp[use1], mean='arithmetic') / \\\\\\n cg.Uiso(heavy_atom.adp[use2], mean='arithmetic')\\n arit_sum.append(U_rel_arith)\\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light=atom.name,\\n heavy=heavy_atom,\\n U=U_rel_geom,\\n U2=U_rel_arith))\\n printer(' -------|--------|-------|-------')\\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light='mean',\\n heavy='---',\\n U=np.mean(geom_sum),\\n U2=np.mean(arit_sum)))\\n printer(' {light:5s} | {heavy:5s} | {U:5.3f} | {U2:5.3f}'.format(light='SD',\\n heavy='---',\\n U=np.std(geom_sum),\\n U2=np.std(arit_sum)))\\n\\n printer('{temp:.1f} {U:5.3f} {Uer:5.3f} {U2:5.3f} {U2er:5.3f}'.format(temp=data.temperature,\\n U=np.mean(geom_sum),\\n Uer=np.std(geom_sum),\\n U2=np.mean(arit_sum),\\n U2er=np.std(arit_sum)),\\n use=['log'])\\n printer.spacer()\",\n \"def test_get_engTemperature(self):\\n for app_num, servo_type in app_nr.items():\\n try:\\n par = self.get_parameter(servo_type, app_num, ENG_TEMPERATURE_IDX, ENG_TEMPERATURE_SUB)\\n param_obj = self.__dict__[servo_type]._get_engTemperature()\\n acs_par, completion = param_obj.get_sync()\\n if(completion.code):\\n print \\\"\\\\nError code found in engTemperature...\\\"\\n continue\\n self.data_match(acs_par, par)\\n except NackEx:\\n continue\",\n \"def data_vis():\\n dataroot = 'solar_data.txt'\\n debug = False \\n diff = False\\n X, y = read_data(dataroot, debug, diff)\\n\\n # First plot the original timeseries\\n fig = plt.figure(figsize=(40,40))\\n\\n fig.add_subplot(3,3,1)\\n plt.plot(y)\\n plt.title('Avg Global PSP (vent/cor) [W/m^2]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,2)\\n plt.plot(X[:,0])\\n plt.title('Avg Zenith Angle [degrees]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,3)\\n plt.plot(X[:,1])\\n plt.title('Avg Azimuth Angle [degrees]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,4)\\n plt.plot(X[:,2])\\n plt.title('Avg Tower Dry Bulb Temp [deg C]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,5)\\n plt.plot(X[:,3])\\n plt.title('Avg Tower RH [%]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,6)\\n plt.plot(X[:,4])\\n plt.title('Avg Total Cloud Cover [%]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,7)\\n plt.plot(X[:,5])\\n plt.title('Avg Avg Wind Speed @ 6ft [m/s]')\\n # plt.show()\\n\\n ##########################################################################################\\n # Plotting the Fourier Transform of the signals\\n\\n freq = np.fft.fftfreq(len(y), 1*60*60)\\n\\n fig = plt.figure(figsize=(40,40))\\n\\n fig.add_subplot(3,3,1)\\n plt.plot(freq, np.abs(np.fft.fft(y)))\\n plt.title('Avg Global PSP (vent/cor) [W/m^2]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,2)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,0])))\\n plt.title('Avg Zenith Angle [degrees]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,3)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,1])))\\n plt.title('Avg Azimuth Angle [degrees]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,4)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,2])))\\n plt.title('Avg Tower Dry Bulb Temp [deg C]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,5)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,3])))\\n plt.title('Avg Tower RH [%]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,6)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,4])))\\n plt.title('Avg Total Cloud Cover [%]')\\n # plt.show()\\n\\n fig.add_subplot(3,3,7)\\n plt.plot(freq, np.abs(np.fft.fft(X[:,5])))\\n plt.title('Avg Avg Wind Speed @ 6ft [m/s]')\\n # plt.show()\\n\\n ##################################################################################################\\n # Print correlation matrix\\n\\n df = pd.DataFrame(np.c_[y, X])\\n df.columns = ['Avg Global PSP (vent/cor) [W/m^2]','Avg Zenith Angle [degrees]','Avg Azimuth Angle [degrees]','Avg Tower Dry Bulb Temp [deg C]','Avg Tower RH [%]','Avg Total Cloud Cover [%]','Avg Avg Wind Speed @ 6ft [m/s]']\\n f = plt.figure(figsize=(19, 15))\\n plt.matshow(df.corr(), fignum=f.number)\\n plt.xticks(range(df.shape[1]), df.columns, fontsize=14, rotation=20)\\n plt.yticks(range(df.shape[1]), df.columns, fontsize=14)\\n cb = plt.colorbar()\\n cb.ax.tick_params(labelsize=14)\\n plt.title('Correlation Matrix', fontsize=16);\\n plt.show()\",\n \"def thermal(isatom, freq, scalfac,linnonlin,T):\\n if isatom != \\\"true\\\":\\n nfreq = len(freq)\\n\\n vib_temp = []\\n for ifreq in range(nfreq):\\n freq[ifreq] = float(freq[ifreq]) * float(scalfac)\\n vib_temp_new = c * 100.0 * h * float(freq[ifreq]) / kB\\n vib_temp.append(vib_temp_new)\\n\\n dE_vib = 0\\n for ifreq in range(nfreq):\\n dE_vib = dE_vib + kB * vib_temp[ifreq] * j2au * ( 0.5 + 1 / ( np.exp(vib_temp[ifreq]/T) - 1) )\\n\\n dE_ZPE = 0.5 * sum(freq) * cmi2au\\n\\n if linnonlin == \\\"L\\\":\\n dE_rot = kB * T * j2au\\n elif linnonlin == \\\"NL\\\":\\n dE_rot = kB * T * j2au * (3.0/2.0)\\n else:\\n with open(\\\"Thermochemistry.out\\\", \\\"a\\\") as ther_chem:\\n ther_chem.write(\\\"ERROR: unknown entry for linear/nonlinear\\\")\\n else:\\n dE_ZPE = 0\\n dE_vib = 0\\n dE_rot = 0\\n\\n dE_tra = kB * T * j2au * (3.0/2.0)\\n dE_thermal = (dE_vib - dE_ZPE) + dE_rot + dE_tra\\n\\n return(dE_ZPE, dE_vib, dE_rot, dE_tra, dE_thermal)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6243293","0.5701022","0.5552244","0.5491569","0.5487227","0.5479191","0.54358137","0.54272795","0.5394585","0.5392419","0.53615534","0.533306","0.53179353","0.5315923","0.5295476","0.5265205","0.52420974","0.5239961","0.52259064","0.52226466","0.52215695","0.52029717","0.51637083","0.5157517","0.5152349","0.51520777","0.5150459","0.51497936","0.5144993","0.51448935","0.5137341","0.5125648","0.5116353","0.51061916","0.5106179","0.5091296","0.50789547","0.5066447","0.50615686","0.50563544","0.5051718","0.5051292","0.5037253","0.50225985","0.5020266","0.50184566","0.50047016","0.50023496","0.50021005","0.499682","0.49967542","0.4985171","0.49824747","0.4971986","0.49593222","0.49538475","0.49490976","0.49468535","0.49437168","0.4935829","0.49346888","0.49257395","0.49223068","0.49219185","0.49171853","0.49164367","0.4911596","0.49089813","0.49074277","0.4906587","0.49046436","0.49044317","0.49020398","0.48814997","0.48804653","0.48803282","0.48737067","0.48729736","0.48681065","0.48654687","0.4861055","0.48598596","0.48589188","0.4856761","0.48551357","0.4854716","0.48511732","0.48396713","0.48359758","0.48335093","0.48299232","0.48297882","0.48276323","0.4827087","0.48234984","0.48183045","0.48145908","0.48119092","0.48098204","0.4809589"],"string":"[\n \"0.6243293\",\n \"0.5701022\",\n \"0.5552244\",\n \"0.5491569\",\n \"0.5487227\",\n \"0.5479191\",\n \"0.54358137\",\n \"0.54272795\",\n \"0.5394585\",\n \"0.5392419\",\n \"0.53615534\",\n \"0.533306\",\n \"0.53179353\",\n \"0.5315923\",\n \"0.5295476\",\n \"0.5265205\",\n \"0.52420974\",\n \"0.5239961\",\n \"0.52259064\",\n \"0.52226466\",\n \"0.52215695\",\n \"0.52029717\",\n \"0.51637083\",\n \"0.5157517\",\n \"0.5152349\",\n \"0.51520777\",\n \"0.5150459\",\n \"0.51497936\",\n \"0.5144993\",\n \"0.51448935\",\n \"0.5137341\",\n \"0.5125648\",\n \"0.5116353\",\n \"0.51061916\",\n \"0.5106179\",\n \"0.5091296\",\n \"0.50789547\",\n \"0.5066447\",\n \"0.50615686\",\n \"0.50563544\",\n \"0.5051718\",\n \"0.5051292\",\n \"0.5037253\",\n \"0.50225985\",\n \"0.5020266\",\n \"0.50184566\",\n \"0.50047016\",\n \"0.50023496\",\n \"0.50021005\",\n \"0.499682\",\n \"0.49967542\",\n \"0.4985171\",\n \"0.49824747\",\n \"0.4971986\",\n \"0.49593222\",\n \"0.49538475\",\n \"0.49490976\",\n \"0.49468535\",\n \"0.49437168\",\n \"0.4935829\",\n \"0.49346888\",\n \"0.49257395\",\n \"0.49223068\",\n \"0.49219185\",\n \"0.49171853\",\n \"0.49164367\",\n \"0.4911596\",\n \"0.49089813\",\n \"0.49074277\",\n \"0.4906587\",\n \"0.49046436\",\n \"0.49044317\",\n \"0.49020398\",\n \"0.48814997\",\n \"0.48804653\",\n \"0.48803282\",\n \"0.48737067\",\n \"0.48729736\",\n \"0.48681065\",\n \"0.48654687\",\n \"0.4861055\",\n \"0.48598596\",\n \"0.48589188\",\n \"0.4856761\",\n \"0.48551357\",\n \"0.4854716\",\n \"0.48511732\",\n \"0.48396713\",\n \"0.48359758\",\n \"0.48335093\",\n \"0.48299232\",\n \"0.48297882\",\n \"0.48276323\",\n \"0.4827087\",\n \"0.48234984\",\n \"0.48183045\",\n \"0.48145908\",\n \"0.48119092\",\n \"0.48098204\",\n \"0.4809589\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64122236"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":84,"numItemsPerPage":100,"numTotalItems":95772,"offset":8400,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NDI2OTczNywic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3NzQyNzMzMzcsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.7gd4hMeI_tyJR_cGTnL5wUcLQNQyl2x6W7u1hyqut_ZVZornRJbksPr5eHVCudZ1RWd8taKKSwtLnAnJy5caCA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Update parameters using one step of gradient descent | def update_parameters_with_gd(parameters, grads, learning_rate):
L = len(parameters) // 2 # number of layers in the neural networks
# Update rule for each parameter
for l in range(L):
parameters["W" + str(l+1)] = parameters["W" + str(l+1)] - learning_rate * grads['dW' + str(l+1)]
parameters["b" + str(l+1)] = parameters["b" + str(l+1)] - learning_rate * grads['db' + str(l+1)]
return parameters | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _update_parameters(self, loss):\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()",
"def updateParams(self,gradients):\n for i in xrange(len(self.params)):\n self.params[i].set_value(self.params[i].get_value()-gradients[i]/(1/self.learning_rate+self... | [
"0.7269349",
"0.7062195",
"0.7061314",
"0.7021435",
"0.6942396",
"0.693245",
"0.69282365",
"0.6900024",
"0.68978393",
"0.68952733",
"0.6880095",
"0.6862448",
"0.68579733",
"0.6851211",
"0.6844264",
"0.68293726",
"0.6826274",
"0.67916423",
"0.67859596",
"0.67605203",
"0.675119... | 0.0 | -1 |
Creates a list of random minibatches from (X, Y) | def random_mini_batches(X, Y, mini_batch_size = 64, seed = 0):
np.random.seed(seed) # To make your "random" minibatches the same as ours
m = X.shape[1] # number of training examples
mini_batches = []
# Step 1: Shuffle (X, Y)
permutation = list(np.random.permutation(m))
shuffled_X = X[:, permutation]
shuffled_Y = Y[:, permutation].reshape((1,m))
# Step 2: Partition (shuffled_X, shuffled_Y). Minus the end case.
num_complete_minibatches = math.floor(m/mini_batch_size) # number of mini batches of size mini_batch_size in your partitionning
for k in range(0, num_complete_minibatches):
mini_batch_X = shuffled_X[:, k*mini_batch_size:(k+1)*mini_batch_size]
mini_batch_Y = shuffled_Y[:, k*mini_batch_size:(k+1)*mini_batch_size]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
# Handling the end case (last mini-batch < mini_batch_size)
if m % mini_batch_size != 0:
mini_batch_X = shuffled_X[:, num_complete_minibatches*mini_batch_size:]
mini_batch_Y = shuffled_X[:, num_complete_minibatches*mini_batch_size:]
mini_batch = (mini_batch_X, mini_batch_Y)
mini_batches.append(mini_batch)
return mini_batches | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def random_mini_batches(X, Y, mini_batch_size = 64):\n\n\n\tm = X.shape[1]\n\tmini_batches = []\n\n\t#Shuffling around the data randomly according to the 'permutation' list\n\tpermutation = list(np.random.permutation(m))\n\tshuffled_X = X[:, permutation]\n\tshuffled_Y = Y[:, permutation].reshape((1,m))\n\n\tcomple... | [
"0.65385675",
"0.6229042",
"0.6218775",
"0.62119395",
"0.62032425",
"0.61461645",
"0.6137679",
"0.60998034",
"0.6097758",
"0.60959524",
"0.60723096",
"0.60708946",
"0.6014316",
"0.5990937",
"0.5940285",
"0.5884059",
"0.5871586",
"0.5844728",
"0.58423215",
"0.5820612",
"0.5779... | 0.6306558 | 1 |
Yield successive nsized chunks from l. | def chunks(l, n):
for i in range(0, len(l), n):
yield l[i:i+n] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _chunk(self, l, n):\n for i in range(0, len(l) + 1, n):\n yield l[i:i + n]",
"def chunks(self, l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def __chunks(l, n):\n for i in range(0, len(l), n):\n yield l[i:i + n]",
"def get_chunks(self, ... | [
"0.8038813",
"0.79248375",
"0.7923423",
"0.7885103",
"0.78773195",
"0.7815877",
"0.77655786",
"0.77556044",
"0.77441615",
"0.7731815",
"0.77288336",
"0.772473",
"0.77028215",
"0.76889825",
"0.76889825",
"0.7664208",
"0.76570904",
"0.7655856",
"0.7655856",
"0.7635831",
"0.7547... | 0.76458347 | 21 |
Tests that Predictor instances are not serializable. | def test_serialization():
# Class is serializable.
ray.put(DummyPredictor)
# Instance is not serializable.
predictor = DummyPredictor()
with pytest.raises(PredictorNotSerializableException):
ray.put(predictor) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_deserialize_bad_data(self):\n data = \"this is not a dictionary\"\n recommendation = Recommendation()\n self.assertRaises(DataValidationError, recommendation.deserialize, data)",
"def test_valid_serialization_unfit_model(self):\n instance = GammaUnivariate()\n result =... | [
"0.58206624",
"0.58120334",
"0.5762754",
"0.57272524",
"0.56799954",
"0.56695384",
"0.56055886",
"0.5602577",
"0.5546996",
"0.5540443",
"0.5539814",
"0.55207306",
"0.54866004",
"0.5472597",
"0.5461503",
"0.5412684",
"0.53859967",
"0.537395",
"0.535977",
"0.53534746",
"0.53437... | 0.7470269 | 0 |
Fit model to current data batch + previous data batch | def fit(self, x, y, logger):
history = self.model1.fit(x=x, y=y, batch_size=self.batch_size, epochs=self.epochs)
logger.log({'ValFuncLoss': history.history['loss'][-1]}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fit(self, data):\n\n \"\"\"YOUR CODE HERE \"\"\"\n # unormalized data\n un_st = np.concatenate([datum[\"observations\"] for datum in data])\n un_stp1 = np.concatenate([datum[\"next_observations\"] for datum in data])\n un_at = np.concatenate([datum[\"actions\"] for datum in d... | [
"0.7298221",
"0.7222674",
"0.71828973",
"0.7077382",
"0.7039742",
"0.7018279",
"0.69872963",
"0.6833547",
"0.68039864",
"0.6773409",
"0.6749072",
"0.6726586",
"0.6716468",
"0.6715232",
"0.6707798",
"0.6707481",
"0.6704683",
"0.668996",
"0.668996",
"0.668996",
"0.668996",
"0... | 0.6526704 | 53 |
Adds player calendar to team_cal and returns filled teams | def match_with_player(self, name, player_cal):
updated_team_cal = self.team_cal.copy()
filled_team_keys = []
for loc in player_cal.stack().index:
current_player_count = self.team_cal.at[loc]
if self.price_cal.at[loc] <= player_cal.at[loc]:
if current_player_count < self.team_size * 2:
updated_team_cal.at[loc] += 1
self.team_dict[f'{loc[1]}-{loc[0]}'].append(name)
if current_player_count == self.team_size * 2 - 1:
filled_team_keys.append(f'{loc[1]}-{loc[0]}')
else:
continue # team is filled
self.team_cal = updated_team_cal
return filled_team_keys | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_teams():",
"def _create_teams(self):\n\t\tself.teamsDict = {}\n\t\tself.teamNamesList = []\n\t\tfor team in range(self.numberOfTeams):\n\t\t\tname = 'TEAM_'+str(team+1)\n\t\t\tself.teamNamesList.append(name)\n\t\t\tself.teamsDict[name] = app.game.team.Team(sport_type=self.gameData['sportType'])",
"def ... | [
"0.6536402",
"0.59650755",
"0.5887284",
"0.58515286",
"0.5829555",
"0.579289",
"0.5745127",
"0.57173425",
"0.57036144",
"0.56878537",
"0.56028706",
"0.55922323",
"0.5529244",
"0.5521643",
"0.54728687",
"0.54638255",
"0.54560107",
"0.5452628",
"0.5441508",
"0.5441441",
"0.5440... | 0.6215539 | 1 |
Sort players by score and alternate team picks | def make_teams(players, timeslot):
player_list_with_scores = []
for name in players:
player = pickle.loads(playersdb.get(name))
# while we have player object loaded, set game timeslot for player
player['games'].append(timeslot)
playersdb.set(name, pickle.dumps(player))
player_list_with_scores.append((name, player['score']))
player_list_with_scores.sort(key=lambda tup: tup[1], reverse=True) # sort by score
teamA = [p[0] for p in player_list_with_scores[::2]]
teamB = [p[0] for p in player_list_with_scores[1::2]]
return teamA, teamB | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort(self):\r\n\t\tif ScoreOpt.isGroupVassals():\r\n\t\t\tself._playerScores.sort(lambda x, y: cmp(x.sortKey(), y.sortKey()))\r\n\t\t\tself._playerScores.reverse()\r\n\t\tmaxPlayers = ScoreOpt.getMaxPlayers()\r\n\t\tif maxPlayers > 0 and len(self._playerScores) > maxPlayers:\r\n\t\t\tself._playerScores = self.... | [
"0.77570707",
"0.6806551",
"0.6747068",
"0.6733204",
"0.66566753",
"0.6630154",
"0.65957487",
"0.6527466",
"0.64854187",
"0.64108056",
"0.64096063",
"0.6390266",
"0.6356324",
"0.63238096",
"0.6280292",
"0.6257902",
"0.6251791",
"0.6247992",
"0.62457335",
"0.62404174",
"0.6236... | 0.5901569 | 39 |
Sends message to RabbitMQ exchange | def send_message(msg, exchange, key=None):
print(msg)
connection = pika.BlockingConnection(pika.ConnectionParameters(host='rabbitmq'))
channel = connection.channel()
exchange_type = 'direct' if exchange == 'other' else 'topic'
channel.exchange_declare(exchange=exchange, exchange_type=exchange_type)
if key is not None and exchange == 'logs':
routing_key = f'scheduler.{key}'
else:
routing_key = ''
channel.basic_publish(exchange=exchange, routing_key=routing_key, body=msg)
connection.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send_rabbit_message (params ):\n print \"sending message to rabbitmq exchange\"\n logging.basicConfig()\n rabbitmq_host = params.get( 'host' )\n rabbitmq_port = params.get( 'port' )\n rabbitmq_username = params.get( 'user-name' )\n rabbitmq_password = params.get( 'password' )\n exchange_na... | [
"0.7955153",
"0.73282826",
"0.69862974",
"0.6899452",
"0.68242073",
"0.676501",
"0.6764902",
"0.6729278",
"0.669033",
"0.668374",
"0.66331697",
"0.66054446",
"0.65912455",
"0.6582951",
"0.6516775",
"0.64865685",
"0.6484264",
"0.64665216",
"0.64554477",
"0.6451518",
"0.643939"... | 0.75768226 | 1 |
The Slack Real Time Messaging API is an events firehose. this parsing function returns None unless a message is directed at the Bot, based on its ID. | def parse_slack_output(slack_rtm_output, bot):
output_list = slack_rtm_output
if output_list and len(output_list) > 0:
for output in output_list:
at_bot = "<@%s>" % bot['bot_id']
if output and 'text' in output and at_bot in output['text'] and 'channel' in output:
# return text after the @ mention, whitespace removed
return output['text'].split(at_bot)[1].strip(), \
output['channel']
return None, None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_bot_commands(slack_events):\n\n for event in slack_events:\n if event[\"type\"] == \"message\" and not \"subtype\" in event:\n print event[\"text\"]\n user_id, message = parse_direct_mention(event[\"text\"])\n if user_id == starterbot_id:\n return... | [
"0.66511947",
"0.66095793",
"0.6603378",
"0.6577621",
"0.6566526",
"0.6535629",
"0.6535629",
"0.6535629",
"0.6493531",
"0.6481132",
"0.6459471",
"0.6458375",
"0.6428089",
"0.63861305",
"0.63535863",
"0.63366276",
"0.61743027",
"0.6160538",
"0.6147883",
"0.6101927",
"0.6010351... | 0.56720054 | 28 |
Import, overwrite fixtures from `[app]/fixtures` | def sync_dashboards(app=None):
if not cint(frappe.db.get_single_value('System Settings', 'setup_complete')):
return
if app:
apps = [app]
else:
apps = frappe.get_installed_apps()
for app_name in apps:
print("Updating Dashboard for {app}".format(app=app_name))
for module_name in frappe.local.app_modules.get(app_name) or []:
frappe.flags.in_import = True
make_records_in_module(app_name, module_name)
frappe.flags.in_import = False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fixture_example_data():\n import_example_data()",
"def fixtures():",
"def setUp(self):\n self.fixtures_path = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), \"fixtures/\"\n )",
"def populate_fixtures():\n languages()\n words()",
"def load_fixtures(self):\n... | [
"0.7610767",
"0.71214706",
"0.70407146",
"0.6833973",
"0.6788867",
"0.6743615",
"0.6529511",
"0.650409",
"0.64089555",
"0.6364059",
"0.6342745",
"0.6301757",
"0.6232977",
"0.6109557",
"0.61017966",
"0.6088769",
"0.58966875",
"0.5851127",
"0.5831919",
"0.58071595",
"0.57650894... | 0.0 | -1 |
Used with optparser for multiple arguments of the same type. | def key_callback(option,opt_str,value,parser):
if "--epi-key" in opt_str:
parser.values.epi_keys.append(value)
elif "--exclude" in opt_str:
parser.values.exclude_paths.append(value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Args(parser):",
"def consume_options(cls, data, hittype, args):\n opt_position = 0\n data[\"t\"] = hittype # integrate hit type parameter\n if hittype in cls.option_sequence:\n for expected_type, optname in cls.option_sequence[hittype]:\n if opt_position < len(... | [
"0.66007906",
"0.6587666",
"0.6514029",
"0.6472643",
"0.64082843",
"0.6338237",
"0.6190322",
"0.6178333",
"0.61771864",
"0.61676735",
"0.61545366",
"0.6126562",
"0.61167157",
"0.60940254",
"0.60646427",
"0.605548",
"0.60479504",
"0.60187566",
"0.5961234",
"0.59607315",
"0.594... | 0.0 | -1 |
Email summary of results to user. | def EmailResults(recipient, error_mesg, topdir, dumpfile, logfile, motcor_summary):
#*********************************************************************************
if recipient is None:
return
elif 'noname' in recipient:
return
sender = 'preprocess'
if 'Abnormal' in error_mesg > 0:
subject = 'Problem while preprocessing %s' % topdir
else:
subject = 'Preprocessing complete for %s' % topdir
mssg = error_mesg
if logfile is not None and isinstance(logfile, str):
f = open(logfile, 'r')
lines = f.readlines()
f.close()
logged_errors = ''
for i in xrange(len(lines)):
if 'rror' in lines[i]:
mssg += ''.join(lines[i-1:])
break
mssg += motcor_summary
if dumpfile is not None:
f = open(dumpfile,'r')
mssg += '\nSummary of processing:\n'
mssg += f.read()
f.close()
send_email(recipient, subject, mssg, sender) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _show_summary(self):\n print 'Summary:'\n print ' Reports downloaded successfully: %d' % self.counts\n print ' Reports not downloaded: %d\\n' % self.failed",
"def publish_summary(self, jobs):\n pass",
"def report(self, results):\n self.notice(\"Test Report\\n\")\n\n ... | [
"0.6340017",
"0.6205421",
"0.6195181",
"0.61352056",
"0.60605377",
"0.60505825",
"0.5933892",
"0.5929879",
"0.5912047",
"0.57762545",
"0.57740766",
"0.57343775",
"0.56606674",
"0.56550497",
"0.5646071",
"0.56351393",
"0.5634105",
"0.5610652",
"0.5604074",
"0.56035924",
"0.559... | 0.648858 | 0 |
Walk through directories and categorize the data. This method builds the "info" attribute a dictionary that characterizes each data series and defines the options and input/output filenames for each stage of processing. | def FindStuffToDo(self):
while self.topdir.endswith('/'):
self.topdir = self.topdir[:-1]
if hasattr(self, 'LogProcess'):
self.LogProcess()
# Look for data to process.
self.WalkPath(self.topdir)
if os.path.islink('%s/anatomicals' % self.topdir):
# os.walk won't follow links, so do this one manually.
if not os.path.exists('%s/dicoms' % self.topdir):
# Don't do a duplicate search.
pathname = os.readlink('%s/anatomicals' % self.topdir)
self.WalkPath(pathname)
# Pair-up fieldmaps with EPI's
self._SetFmapInfo()
# Pair fieldmaps with strucural images.
self._SetAnatTgts()
# Assocate a ref.dat file with each EPI.
self._GetRefdat()
self._MakeEpiScratchDir()
# Order the EPIs so the names are correct.
self._GetEpiOrder()
# Associate each EPI with an anatomical, determine if it was
# acquired before or after the epi
self._SetBaseEpi()
self.motcor_summary = self.SummarizeMotionTargets()
f = open('%s/motion_corr.txt' % self.logdir, 'w')
f.write(self.motcor_summary)
f.close()
if self.verbose:
print self.motcor_summary | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_data_dir(self, data_dir):\n categories = os.listdir(data_dir)\n for folder_name in categories:\n all_fnames_list_fname = os.path.join(data_dir, folder_name,\n folder_name + \".bmf\")\n if not os.path.isfile(all_fnames_li... | [
"0.589833",
"0.58914953",
"0.58123064",
"0.58081996",
"0.57288224",
"0.56694525",
"0.56223905",
"0.56043434",
"0.5600391",
"0.55908597",
"0.5513597",
"0.5498663",
"0.54749286",
"0.5472291",
"0.54717815",
"0.54624385",
"0.54597956",
"0.5453633",
"0.544727",
"0.54446405",
"0.54... | 0.0 | -1 |
Create directory or exit on error. | def MakeDir(self, dirname):
if os.path.exists(dirname):
return
try:
os.umask(UMASK_DIR)
os.makedirs(dirname)
except OSError:
self.errors = True
errstr = '\nCould not create directory: %s ... ' % dirname
self.LogErrors(errstr)
raise OSError(errstr)
os.umask(UMASK_FILE) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_dir(self):\n if not os.path.exists(self.d):\n try:\n os.mkdir(self.d)\n except OSError, e:\n if e.errno != 17:\n raise\n pass",
"def make_dir(path=None):\n\n if not os.path.exists(path):\n try:\n ... | [
"0.7861626",
"0.77017003",
"0.7609223",
"0.7593223",
"0.75566745",
"0.75102687",
"0.75102687",
"0.7497127",
"0.7463986",
"0.74487466",
"0.74413145",
"0.74352056",
"0.74352056",
"0.74352056",
"0.74352056",
"0.742612",
"0.74215883",
"0.7420352",
"0.74127334",
"0.74027",
"0.7396... | 0.0 | -1 |
Fill in a heirarcy of template files. The default template file is first loaded. Then entries are overwritten by entries in the studylevel template (in the directory containing each subjectlevel directories). Finally, entries in the subjectlevel template are loaded. | def _FindTemplateFile(self, topdir):
if topdir.endswith('..'):
topdir = '/'.join(topdir.split('/')[:-2])
fnames = os.listdir(topdir)
for fname in fnames:
filename = '%s/%s' % (topdir, fname)
if filename.endswith('.yaml') and not os.path.isdir(filename) and \
os.path.exists(filename):
f = open(filename, 'r')
magic_code = f.read(22)
f.close()
if '#!fmri_file_template' in magic_code:
return filename
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __fill_all_templates__(self,configs):\n template_dir = configs['system'].get('Common_directories','template')\n sample_template = os.path.join(template_dir,configs['pipeline'].get('Template_files','sample'))\n system_template = os.path.join(template_dir,configs['pipeline'].get('Template_fi... | [
"0.68409294",
"0.644731",
"0.6447111",
"0.6338145",
"0.62998015",
"0.6060812",
"0.5989269",
"0.58991736",
"0.5831137",
"0.5817373",
"0.57764006",
"0.57094055",
"0.56525546",
"0.5633865",
"0.56115997",
"0.5558664",
"0.5556968",
"0.5553622",
"0.5548341",
"0.55309093",
"0.550637... | 0.0 | -1 |
Read a single template file and return the resulting dict object. | def _LoadTemplate(self,fname):
f = open(fname, 'r')
lines = f.readlines()
data = ''
for line in lines:
if not line.startswith('---'):
data += line
data = data.replace('\t',' ')
if '\t' in data:
errstr = \
'Illegal tabs encountered in template file. Use spaces instead.'
raise ScannerError(errstr)
proc.LogErrors(errstr)
tmplt = yaml.load(data)
f.close()
return tmplt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read_template_file(self):\n try:\n return json.loads(open(self.TEMPLATE_FILE,'r').read())\n except FileNotFoundError:\n sys.stdout.write(\"Template-file does not exist.\\n\")\n except OSError as e:\n sys.stdout.write(\n \"Error: \\'{}\\' occ... | [
"0.7782369",
"0.76745313",
"0.7547481",
"0.74933225",
"0.74301606",
"0.738433",
"0.7244087",
"0.7218751",
"0.71575886",
"0.6929455",
"0.69022644",
"0.6875117",
"0.68383133",
"0.67652184",
"0.659829",
"0.65947074",
"0.65571237",
"0.6519493",
"0.64934295",
"0.649163",
"0.648591... | 0.64322484 | 23 |
Load the hierarchy of templates. | def _GetTemplate(self):
# First read default template.
tmplt = self._LoadTemplate(c.preproc_template_default)
tmplt['proc'] = self.topdir
self.template_type = 'default'
self.templates = []
if self.template_file is not None:
tmplt.update(self._LoadTemplate(self.template_file))
self.template_type = 'command-line'
self.templates.append(os.path.abspath(self.template_file))
found_template = True
else:
# Find a study specific template file.
study_template_file = self._FindTemplateFile('%s/..' % self.topdir)
if study_template_file is not None:
# Merge study template into default, study template has precedence.
if self.verbose:
print "Using study template at " + study_template_file
tmplt.update(self._LoadTemplate(study_template_file))
self.template_type = 'study-specific'
self.templates.append(os.path.abspath(study_template_file))
found_template = True
else:
found_template = False
# Now look for a subject-specific template file.
subject_template_file = self._FindTemplateFile('%s' % self.topdir)
if subject_template_file is not None:
# Merge subject template, subject template has precedence.
if self.verbose:
print "Using subject-specific template at %s" % \
subject_template_file
tmplt.update(self._LoadTemplate(subject_template_file))
self.template_type = 'study-specific'
self.templates.append(os.path.abspath(subject_template_file))
found_template = True
if not found_template:
raise RuntimeError('Could not find template file.')
if tmplt.get('subject','same') == 'same':
# Default subdirectory is same as data directory.
tmplt['subject'] = self.topdir.split('/')[-1]
else:
if not isinstance(tmplt['subject'],str):
errstr = 'preprocess: Invalid subject number. Be sure to ' + \
'enclose the subject number item with double quotes.'
raise RuntimeError(errstr)
# Keys that apply to all EPIs.
self.fsl_flip = tmplt.get('fsl_flip', False)
if self.fsl_flip:
self.flip_opts = '-LT'
else:
self.flip_opts = ''
# Replace strings with python types.
for key in tmplt.keys():
if tmplt[key] == 'None':
tmplt[key] = None
elif key == 'True':
tmplt[key] = True
elif key == 'False':
tmplt[key] = False
return tmplt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_templates(cls):\n if cls._raw_templates is None:\n cls._raw_templates = fetch_rrlyrae_templates()",
"def load_templates(self):\n TemplateHandler.templates = []\n for template in os.listdir(TemplateHandler.templates_path):\n template_config = self.load_template... | [
"0.727779",
"0.7196964",
"0.65432745",
"0.64201707",
"0.6378715",
"0.6374151",
"0.6279858",
"0.6175564",
"0.6173083",
"0.60840946",
"0.60698014",
"0.5997303",
"0.5987318",
"0.5981226",
"0.59653664",
"0.5951422",
"0.58668286",
"0.58246344",
"0.58026916",
"0.576884",
"0.5767016... | 0.5642072 | 24 |
Process the data in the templates and set attributes accordingly. | def _ProcessTemplate(self,topdir):
self.dicomdir = "%s/anatomicals" % self.topdir
self.rawdir = "%s/raw" % topdir
self.rawdirs = {}
tmplt = self._GetTemplate()
if self.opts.outdir is not None:
# Override template output directory.
tmplt['top_outdir'] = self.opts.outdir
self.tmplt = tmplt
if len(tmplt['top_outdir']) == 0:
tmplt['top_outdir'] = os.path.realpath(self.topdir)
raise RuntimeError('Template file must specify an output directory.')
tmplt['top_outdir'] = os.path.realpath(tmplt['top_outdir'])
if '/home' in tmplt['top_outdir'][:7]:
raise RuntimeError('Image data cannot be stored in the /home partition. Change the "top_outdir" entry in the template file: %s.' % (' '.join(self.templates)))
# tmplt['subject'] = 'orig'
self.procdir = os.path.abspath("%s/%s" % \
(tmplt['top_outdir'],tmplt['subject']))
target = os.path.abspath('%s/../..' % tmplt['top_outdir'])
if not ismounted(target):
raise RuntimeError('Could not access partition at %s' % target)
self.anatdir = "%s/anat" % self.procdir
self.fmapdir = "%s/%s" % (self.procdir,tmplt['fmap']['outdir'])
self.dtidir = "%s/%s" % (self.procdir,tmplt['dti']['outdir'])
self.logdir = "%s/%s" % (self.procdir,tmplt['logdir'])
self.skip = tmplt.get('skip', DEFAULT_SKIP)
self.acq_tr = tmplt.get('acq_tr',None)
self.episetup_dir = "%s/%s" % (self.procdir,tmplt['first_epi'])
self.fsl_cmpblty = tmplt.get('fsl_compatibility',False)
self.epi_file_format = self.tmplt['epi_file_format']
self.censor_thresh = tmplt.get('censor_threshold', 2.)
self.censor_interleave = tmplt.get('censor_interleave', True)
# self.server_userid = self.tmplt.get('server_userid','default')
# Overide flags for aligning EPIs and skull-stripping with command-
# line options.
if self.opts.align_fmaps:
self.align_fmaps = True
else:
self.align_fmaps = self.tmplt.get('epi_align', False)
if self.opts.no_align_fmaps:
self.no_align_fmaps = True
else:
self.no_align_fmaps = self.tmplt.get('no_epi_align', False)
if self.opts.skull_strip:
self.skull_strip = True
else:
self.skull_strip = self.tmplt.get('skull_strip', False)
# Create log file now so it can be used immediately.
if not os.path.exists(self.logdir):
if self.verbose:
print 'mkdir %s' % self.logdir
if not self.opts.fake_opts:
self.MakeDir(self.logdir)
self._ProcessTemplateEpiInfo() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mk_data(self):\n self.data = self.DEFAULTS.copy()\n\n for template in self.raw_data.get('extends', []):\n template_data = self.load_template(template)\n self.data.update(template_data)\n\n self.data.update(self.raw_data)\n\n str_replace(self.data)\n\n if... | [
"0.6795622",
"0.62453735",
"0.6222564",
"0.6101384",
"0.6039472",
"0.5970306",
"0.5819297",
"0.57768494",
"0.57472324",
"0.5730784",
"0.5694763",
"0.5607646",
"0.5585788",
"0.5584792",
"0.5528987",
"0.5528372",
"0.5523228",
"0.54737645",
"0.5458935",
"0.5447084",
"0.5441327",... | 0.5548209 | 14 |
Synthesize yaml header filename from directory name. | def _yaml_filename(self, path):
fullpath = os.path.abspath(path)
if not os.path.isdir(fullpath):
dirname = os.path.dirname(fullpath)
else:
dirname = path
if dirname.endswith('/'):
dirname = dirname[:-1]
fname = dirname.split('/')[-1] + '.yaml'
return dirname, fname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_file_name(name: types.TSeedName) -> str:\n return f\"{name}.yml\"",
"def format_filename(title: str, id: Any, ext: str = \".\", dirFormat=None):\r\n ...",
"def file_title(self):\n basename = os.path.basename(self.__path)\n index_dot = basename.rfind(\".\")\n if index_dot... | [
"0.63299215",
"0.6177597",
"0.6154436",
"0.6145138",
"0.61193216",
"0.6117771",
"0.60900265",
"0.6084294",
"0.59812474",
"0.59652424",
"0.5949886",
"0.59462756",
"0.5918559",
"0.59006333",
"0.5891024",
"0.58728975",
"0.5864215",
"0.5860745",
"0.5852123",
"0.58312166",
"0.5803... | 0.6754749 | 0 |
Get T1 and T2 weighted structural image info. | def _AnatInfo(self, info, path):
if info['data_filetype'] == 'ge_data':
return ERROR
outdir = '%s/%s' % (self.procdir, self.tmplt['anat']['outdir'])
info['InversionTime'] = self.hdr['native_header']['InversionTime']
if info['psdname'] == 'efgre3d' or info['psdname'] == 'bravo':
# Structural scans are 3d inversion-recovery.
if self.hdr['native_header']['InversionTime'] < 1.:
# Only inversion recovery used for anatomy. Must be calibration.
return None
elif self.hdr['zsize'] > 1.25:
# Only one slab acquired. Assume thick slices.
name = 'T1Low_%d' % self.n_t1low
self.n_t1low += 1
else:
if self.n_t1high == 0:
name = 'T1High'
else:
name = 'T1High_%d' % self.n_t1high
self.n_t1high += 1
else:
psdname = info['psdname']
name = self.imgtype.get(psdname, info['psdname'])
if self.ntype.has_key(psdname):
self.ntype[psdname] += 1
name = '%s_%0d' % (name, self.ntype[psdname])
else:
self.ntype[psdname] = 1
info['norm_src'] = False
info['outdir'] = outdir
info['filetype'] = self.tmplt['anat']['format']
info['imgfile'] = '%s/%s' % (info['outdir'], name)
self.entry_map['anat'].append(self.current_entry)
return OK | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scalarInfo(img, cnt):\n\tm = cntInfo(img, cnt)\n\td = {\"perimeter\":m[\"perimeter\"], \"oreientation\":m[\"orientation\"], \"solidity\":m[\"solidity\"],\"height\":m[\"height\"], \"extent\":m[\"extent\"], \"aspect ratio\":m[\"aspect ratio\"], \"area\":m[\"area\"], \"sum intensity\":m[\"sum intensity\"], \"widt... | [
"0.56604415",
"0.56434953",
"0.5608303",
"0.55392164",
"0.55089027",
"0.5473448",
"0.5417464",
"0.5324782",
"0.5314561",
"0.5309392",
"0.5288557",
"0.5276307",
"0.524422",
"0.52250624",
"0.52200425",
"0.52154565",
"0.5210282",
"0.5189751",
"0.51866674",
"0.515786",
"0.5154455... | 0.0 | -1 |
Create list of epis in pfile format (epi_series) and of epis in dicom format (epirt_paths) | def _EpiInfo(self, info, path):
epi_vals = {'tdim':self.hdr['tdim'], 'plane':self.hdr['plane'], \
'SeriesNumber':self.hdr['subhdr']['SeriesNumber']}
for key in self.epi_keys.keys():
if self.epi_keys[key] != str(epi_vals[key]):
# Return None, which will cause these data to be ignored.
return None
# Early versions of the EPIC software saved p-files for the setup epis.
# Don't process these (or any epi with fewer than eight useable frames).
if self.hdr['tdim'] < (8 + self.skip):
return None
info['slice_order'] = self.shdr.get('SliceOrder', 'altplus')
if self.shdr['EffEchoSpacing'] is not None:
info['echo_spacing'] = self.shdr['EffEchoSpacing']/1000.
else:
info['echo_spacing'] = 0.
if info['data_filetype'] == 'dicom':
# Entry is name of dirctory for dicom images.
if not os.path.isdir(path):
entry = os.path.dirname(path)
else:
entry = path
else:
# Otherwise it is the name of a directory containing p-files.
entry = path
if info['data_filetype'] == 'ge_data' and info['type'] is not None:
# Found a pfile. Add it to the list.
if entry not in self.pfiles and info['tdim'] > 2:
self.pfiles.append(entry)
self.entry_map['epi'].append(entry)
if info['series'] not in self.epi_series:
self.epi_series.append(info['series'])
elif info['data_filetype'] == 'dicom' and \
info['psdname'] == 'epibold':
# This is the initial EPI done during setup.
info['outdir'] = self.episetup_dir
info['type'] = 'first_epi'
self.entry_map['first_epi'].append(entry)
info['imgfile'] = '%s/first_epi_%d' % \
(self.episetup_dir, len(self.entry_map['first_epi']))
elif ('epirt' in info['psdname'] or info['psdname'] == 'epi' or \
info['psdname'] == '*epfid2d1_64') and info['tdim'] > 2:
# This is an epi reconstructed on the scanner.
self.epi_series.append(info['series'])
self.entry_map['epi'].append(entry)
if not os.path.isdir(path):
tmp_path = os.path.dirname(path)
else:
tmp_path = path
self.epirt_paths.append(tmp_path)
if self.fsl_flip:
info['filetype'] = 'brik'
else:
info['filetype'] = self.tmplt['epi_file_format']
info['TR'] = self.hdr['tsize']
if self.tmplt['acq_tr'] is None:
info['acq_tr'] = float(info['TR'])
else:
info['acq_tr'] = float(self.tmplt['acq_tr'])
return OK | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AssignEpiNames(self):\n# Sort each run in the series by its acquisition time.\n epi_sort = self.epi_times.keys()\n epi_sort.sort()\n# Rewrite pfiles as an ordered list of p-files to be reconstructed.\n for idx in xrange(len(epi_sort)):\n entry = self.epi_times[epi_so... | [
"0.6314016",
"0.59568083",
"0.5881365",
"0.5844795",
"0.5802068",
"0.5752258",
"0.56392115",
"0.54253495",
"0.5387592",
"0.5376296",
"0.53625387",
"0.5316459",
"0.52880996",
"0.5277681",
"0.52515364",
"0.52128714",
"0.5204417",
"0.5186836",
"0.5180629",
"0.5144857",
"0.514438... | 0.63749075 | 0 |
Read the header from the raw data specified by "path" and use this information combined with the template information to generate the "info" dict object. This object defines the options and paths for each operation. | def _GetImageInfo(self,path):
hd = Header(path, scan=True)
hdr = hd.hdr
self.hdr = hdr
if hdr is None:
# Either a ref.dat file or it isn't an imaging file.
if 'ref' in path and 'dat' in path:
self.refdats[os.path.realpath(path)] = True
info = {'type':'refdat'}
return info
else:
return None
elif hdr['filetype'] == 'dicom' and not path.endswith('.yaml'):
# Write a yaml file to the raw data directory if possible.
dirname, outfile = self._yaml_filename(path)
yaml_name = '%s/%s' % (dirname, outfile)
if not os.path.exists(yaml_name):
# Create yaml file using dirname,
# e.g., ../anatomicals/S2_EFGRE3D/s2_efgre3d.yaml
try:
hd.write_hdr_to_yaml('%s/%s' % (dirname,outfile))
except IOError:
# This is a nonessential function, so ignore exceptions
# such as access violations.
pass
elif hdr['filetype'] == 'dicom' or hdr['filetype'] == 'ge_ifile':
if not os.path.isdir(path):
path = os.path.dirname(path)
shdr = hdr['subhdr']
nhdr = hdr['native_header']
self.shdr = shdr
if 'dti' in shdr.get('PulseSequenceName','').lower() \
or 'dti' in nhdr.get('PulseSequenceFile',''):
psdname = 'dti'
else:
psdname = os.path.basename((shdr.get('PulseSequenceName','').strip()).lower())
info = {'psdname':psdname, \
'acqtime':shdr['AcqTime'], \
'series':int(shdr['SeriesNumber']), \
'plane':hdr['plane'].strip(), \
'type':self.imgtype.get(psdname,None), \
'plane':hdr['plane'], \
'acqtime':shdr['SeriesTime'], \
# 'fmapdir':None, \
'refdat':None, \
'imgfile':None, \
'base':None, \
'tdim':int(hdr['tdim']), \
'echo_spacing':None, \
'filetype':'brik', \
'suffix':self.suffix.get(hdr['filetype'], 'brik'), \
'data_filetype':hdr['filetype']}
if info['type'] == 'localizer':
# Don't process the localizer.
return info
if isinstance(info['acqtime'], int):
info['acquisition_time'] = time.ctime(info['acqtime'])
if nhdr.get('ImageFormat',('unknown'))[0] == 'DERIVED' and info['type'] == 'epi':
# Sometimes screenshots are defined as epis.
info['type'] = None
# Call the method appropriate to the type of scan in this series.
stat = apply( self.GetInfoMethods.get(info['type'], self._NoInfo), \
[info, path])
if stat:
info = {'type':'break'}
return info
info['suffix'] = self.suffix.get(info['filetype'], 'brik')
return info | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _main_header(self, hdr):\n d = {}\n # Called readDefAnalysis in OpenMIMS\n d['sample type'], d['data included'], d['sample x'], d['sample y'], \\\n d['analysis type'], d['user name'], d['sample z'], date, time = \\\n unpack(self._bo + '4i 32s 16s i 12x 16s 16s', hdr.r... | [
"0.58803326",
"0.57795227",
"0.5498735",
"0.5484519",
"0.5375694",
"0.5355326",
"0.5330148",
"0.5242671",
"0.5242115",
"0.52344966",
"0.52192587",
"0.52183825",
"0.51776946",
"0.51531774",
"0.51515484",
"0.5147676",
"0.5141665",
"0.50517124",
"0.5044997",
"0.5039923",
"0.5027... | 0.56649214 | 2 |
Pair up each epi with a fieldmap. | def _SetFmapInfo(self):
for epi in self.pfiles + self.epirt_paths:
self.info[epi]['fmapname'] = None
self.info[epi]['fmap_entry'] = None
for entry in self.entry_map['fmap']:
fmap_name = self.info[entry]['imgfile'] + self.info[entry]['suffix']
if self.info[entry]['plane'] == self.info[epi]['plane']:
# Use the fieldmap acquired at the same plane.
self.info[epi]['fmapname'] = fmap_name
self.info[epi]['fmap_entry'] = entry
break
else:
# for fmap in self.fmaps.keys():
for entry in self.entry_map['fmap']:
# No fmap at same orientation, look for fmaps in other planes.
# There won't be more than one, so it isn't much of a choice.
fmap_name = self.info[entry]['imgfile'] + \
self.info[entry]['suffix']
if self.info[entry]['plane'] == 'sagittal':
self.info[epi]['fmapname'] = fmap_name
self.info[epi]['fmap_entry'] = entry
break
elif self.info[entry]['plane'] == 'axial':
self.info[epi]['fmapname'] = fmap_name
self.info[epi]['fmap_entry'] = entry
break
elif self.info[entry]['plane'] == 'coronal':
self.info[epi]['fmapname'] = fmap_name
self.info[epi]['fmap_entry'] = entry
break
elif self.info[entry]['plane'] == 'oblique':
self.info[epi]['fmapname'] = fmap_name
self.info[epi]['fmap_entry'] = entry
self.info[epi]['plane'] = 'oblique'
break | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def MakeFieldmaps(self):\n if self.verbose:\n print 'Compute fieldmaps.'\n for entry in self.info:\n if self.info[entry]['type'] == 'fmap':\n if self.info[entry]['imgfile'] == None:\n# Fieldmap data not found.\n return\n# ... | [
"0.5794583",
"0.56985533",
"0.56411123",
"0.5450113",
"0.5425905",
"0.5395057",
"0.53146863",
"0.5253061",
"0.52433765",
"0.5232632",
"0.5187637",
"0.51365435",
"0.511374",
"0.5107384",
"0.51051044",
"0.51040184",
"0.50853544",
"0.50818384",
"0.50797653",
"0.5053735",
"0.5050... | 0.6167625 | 0 |
Find the hires structural image that was acquired nearest to "acqtime" | def _FindNearestAnat(self, acqtime):
tdiff_min = 1e6
for anat in self.entry_map['anat']:
if self.info[anat]['type'] == 'T1High' and \
self.info[anat]['InversionTime'] > 0.:
tdiff = abs(acqtime - self.info[anat]['acqtime'])
if tdiff < tdiff_min:
tdiff_min = tdiff
anat_min = anat
return anat_min | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def closest_in_time(images, target):\n\n tgt_mjd = fits.getheader(target, ext=1)['mjd-obs']\n mjds = np.array([fits.getheader(i, ext=1)['mjd-obs'] for i in images])\n\n return images[abs(mjds - tgt_mjd).argsort()[0]]",
"def find(image):\n keypoint, description = describe(image)\n # load keypoints, des... | [
"0.61136955",
"0.5495255",
"0.53449804",
"0.53088015",
"0.5267729",
"0.5264703",
"0.52540445",
"0.51978594",
"0.51893973",
"0.5173132",
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"0.50616145",
"0.5060293",
"0.5051561",
"0.5042053",
"0.5037389",
"0.50302... | 0.58393806 | 1 |
Create structures defining acquisition time for fieldmaps and anatomicals. First find the fieldmap (or hires structural if no fieldmap was collected) nearest (on average) to the epis. Then define this series as the one that should be in register with the epis. | def _SetAnatTgts(self):
anat_candidates = {}
fmap_candidates = {}
for entry in self.entry_map['anat']:
if self.info[entry]['type'] == 'T1High':
anat_candidates[entry] = self.info[entry]['acqtime']
# Find the valid anatomical acquired nearest to fieldmap.
tdiff_min = 1e6
if len(self.entry_map['fmap']) > 0:
for entry in self.entry_map['fmap']:
anat_tgt = self. _FindNearestAnat(self.info[entry]['acqtime'])
self.info[entry]['anat_ref'] = anat_tgt
else:
# No fieldmaps were collected. Find the structural nearest the
# beginning of the EPIs.
if len(self.entry_map['anat']) == 1:
anat_tgt = self.entry_map['anat'][0]
else:
epi_start = []
tmin = 1e6
for anat in self.entry_map['anat']:
if self.info[anat]['type'] != 'T1High':
continue
tsum1 = 0; tsum2 = 0;
for epi in self.entry_map['epi']:
# Difference from start of structural and first epi
tsum1 += abs(self.info[anat]['acqtime'] - \
self.info[epi]['acqtime'])
# Difference from start of structural and last epi
tsum2 += abs(self.info[anat]['acqtime'] - \
(self.info[epi]['acqtime'] +\
self.info[epi]['TR']*self.info[epi]['tdim']))
if tsum1 < tmin or tsum2 < tmin:
tmin = min(tsum1, tsum2)
anat_tgt = anat
# Resolve anatomical names and links.
self._SetAnatNames(anat_tgt)
# Set appropriate attributes in the entry for each EPI.
for epi in self.entry_map['epi']:
if len(self.entry_map['fmap']) > 0 and not self.no_fmapcorr:
fmap_entry = self.info[epi]['fmap_entry']
anat_ref = self.info[fmap_entry]['anat_ref']
self.info[epi]['anat_tgt'] = fmap_entry
self.info[epi]['anat_matfile'] = self.info[fmap_entry]['matfile']
if self.align_fmaps or (not self.no_align_fmaps and \
self._SetCatMotionFmapMats(fmap_entry, anat_ref)):
# Concatenate motion-correction matrices with tranform from
# fieldmap to structural. Use the registered fieldmap.
self.info[epi]['catmats'] = True
fmap_info = self.info[self.info[epi]['fmap_entry']]
self.info[epi]['fmapname'] = \
fmap_info['imgfile_r'] + fmap_info['suffix']
else:
# Assume fieldmap is in register with the structural.
self.info[epi]['catmats'] = False
else:
self.info[epi]['anat_tgt'] = anat_tgt
self.info[epi]['anat_matfile'] = None
self.info[epi]['catmats'] = False
self.info[epi]['anat_link'] = self.info[anat_tgt]['imgfile'] + \
self.info[anat_tgt]['suffix'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetEpiAcqTimes(self, series):\n# Find minimum and maximum start times for each acquistion in series.\n self.epi_times = {}\n for entry in self.entry_map['epi']:\n# Loop through each file in this series.\n if self.info[entry]['series'] == series and \\\n ... | [
"0.62284875",
"0.5733199",
"0.54121506",
"0.53654045",
"0.5362718",
"0.53480995",
"0.53380924",
"0.5298414",
"0.5262806",
"0.52612984",
"0.52312875",
"0.5173023",
"0.5139651",
"0.5129809",
"0.5126058",
"0.5113684",
"0.51000977",
"0.5095595",
"0.5085868",
"0.5080458",
"0.50771... | 0.59192055 | 1 |
Resolve anatomical names and links. | def _SetAnatNames(self, anat_tgt):
# Define links to structural image in each output directory.
for entry in self.entry_map['epi'] + self.entry_map['fmap'] + \
self.entry_map['dti'] + self.entry_map['asl']:
self.info[entry]['anat_link'] = anat_tgt
# Name the normalization source image T1High. Number the rest.
anat_entries = self.entry_map['anat'][:]
anat_entries.remove(anat_tgt)
n_t1high = 1
for entry in anat_entries:
if self.info[entry]['type'] == 'T1High':
# High res T1-weighted, not normalization target. Rename it.
fname = 'T1High_%d' % n_t1high
fullname = '%s/%s' % (self.info[entry]['outdir'], fname)
self.info[entry]['imgfile'] = fullname
self.info[entry]['imgfile_skstrip'] = '%s_skstrip' % fullname
self.info[entry]['matfile'] = '%s_matfile.aff12.1D' % fullname
self.info[anat_tgt]['norm_src'] = False
n_t1high += 1
fname = 'T1High'
fullname = '%s/%s' % (self.info[anat_tgt]['outdir'], fname)
self.info[anat_tgt]['imgfile'] = fullname
self.info[anat_tgt]['imgfile_skstrip'] = '%s_skstrip' % fullname
self.info[anat_tgt]['matfile'] = '%s_matfile.aff12.1D' % fullname
self.info[anat_tgt]['norm_src'] = True
self.anatomical = '%s%s' % (self.info[anat_tgt]['imgfile'], \
self.info[anat_tgt]['suffix'])
# The target for motin correction is the source for spatial normalization.
self.norm_src = anat_tgt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resolveNames(self):\n client.resolveNames(self)\n # TODO: Do any name resolutions here.\n # The names of other objects this object refers to, either intrinsically or in its parameters, should be checked here.",
"def resolve_references(self):\n self.specs = self._resolve_partial(self.p... | [
"0.6231827",
"0.6040455",
"0.6001876",
"0.5930197",
"0.5827227",
"0.5807689",
"0.56926876",
"0.562395",
"0.5538968",
"0.55118114",
"0.54805756",
"0.54794437",
"0.54488856",
"0.54369795",
"0.5422763",
"0.5406316",
"0.53882045",
"0.53638905",
"0.53599083",
"0.5340648",
"0.53325... | 0.0 | -1 |
Determine whether to (1) motioncorrect to frame nearest T1High and assume that T1High and the fieldmap are in register or (2) catenate transformations to the base epi with a transformation from the base epi to T1High. | def _SetCatMotionFmapMats(self, fmap, anat):
if abs(self.info[fmap]['series'] - self.info[anat]['series']) == 1:
# Adjacent series, use them.
return False
elif abs(self.info[fmap]['acqtime'] - self.info[anat]['acqtime']) < 180:
return False
else:
sernos = []
min_series = min(self.info[fmap]['series'], self.info[anat]['series'])
max_series = max(self.info[fmap]['series'], self.info[anat]['series'])
gap_series = range(min_series+1, max_series, 1)
for entry in self.info.keys():
if self.info[entry]['type'] != 'null':
sernos.append(self.info[entry]['series'])
for series in gap_series:
if series in sernos:
# Fieldmap is separated from structural by one "full" series,
# where a full series is any series that was worth processing
# by this progroam, i.e, not a HOS, an asset cal scan, a
# b1 cal scan or any other very sort calibration scan.
return True
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.... | [
"0.53131735",
"0.5240588",
"0.5225582",
"0.52202463",
"0.5204122",
"0.51401055",
"0.51253384",
"0.51180834",
"0.51146686",
"0.50602734",
"0.5022961",
"0.49833623",
"0.49649775",
"0.4957402",
"0.4932199",
"0.49016243",
"0.48917276",
"0.4890399",
"0.48004982",
"0.47914508",
"0.... | 0.44811964 | 70 |
Define the series and frame for the target epi for motion correction. This is done by first creating a dictionary indexed by the timedelay | def _SetBaseEpi(self):
tinfo = {}
for entry in self.entry_map['epi']:
info = self.info[entry]
if self.info[entry]['fmap_entry'] is None:
tgt = info['anat_tgt']
else:
tgt = info['fmap_entry']
tgt_time = self.info[tgt]['acqtime']
plane = info['plane']
if not tinfo.has_key(plane):
tinfo[plane] = {}
tdiff = abs(info['acqtime'] - tgt_time)
tinfo[plane][tdiff] = (entry, 'start')
tdiff = abs(info['acqtime'] + info['TR']*info['tdim']/1000 - tgt_time)
tinfo[plane][tdiff] = (entry, 'end')
bases = {}
for plane in tinfo.keys():
tdiffs = tinfo[plane].keys()
tdiffs.sort()
bases[plane] = tinfo[plane][tdiffs[0]]
for epi in self.entry_map['epi']:
plane = self.info[epi]['plane']
base_entry, base = bases[plane]
self.info[epi]['base_entry'] = base_entry
self.info[epi]['base'] = base
self.info[epi]['basefile'] = '%s'%(self.info[base_entry]['imgfile']) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _animation_step(self, par_dict):\n\n t0 = time.time()\n dt = par_dict[\"dt\"]\n controller = par_dict[\"controller\"]\n integrator = par_dict[\"integrator\"]\n if controller is not None:\n _, _, tau = controller.get_control_output(\n meas_pos=self.x[... | [
"0.55629534",
"0.52799535",
"0.5275234",
"0.52682",
"0.5261582",
"0.52376616",
"0.5227446",
"0.52257586",
"0.5189562",
"0.51574963",
"0.5152989",
"0.5131383",
"0.51249796",
"0.5111892",
"0.5099293",
"0.5094038",
"0.50885504",
"0.5081776",
"0.5076241",
"0.50693774",
"0.5060204... | 0.0 | -1 |
Strip of leading directory names to make a pretty path for display. | def GetBase(self, fname, suffix):
wds = fname.split('/')
suff = suffix.replace('.BRIK','')
suff = suff.replace('.HEAD','')
if len(wds) > 1:
return '.../%s' % '/'.join(wds[-2:]) + suff
else:
return fname + suff | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prettify_path(path, leading=None):\r\n leading = (leading or os.getcwd()).replace(os.altsep, os.sep)\r\n s = os.path.splitext(path.replace(os.altsep, os.sep))[0]\r\n if s.startswith(leading):\r\n s = s.replace(leading, '')\r\n return s.strip(os.sep)",
"def clean_directory_path(path):\n ... | [
"0.7440191",
"0.67219174",
"0.67075866",
"0.6677584",
"0.6655997",
"0.6626382",
"0.65079343",
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"0.6372779",
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"0.63264537",
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"0.6200279",
"0.619991",
"0.61735916",
"0.61682737",
"0.6166878",
"0.61517286... | 0.0 | -1 |
Create a text string summarizing how the motion correction was done. | def SummarizeMotionTargets(self):
text = '\nSummary of motion-correction: \n'
for epi in self.entry_map['epi']:
info = self.info[epi]
text += self.GetBase(epi, '')
base = self.GetBase(info['base_entry'], '')
text += ' ->3dvolreg-> %s[%s]' % (base, info['base'])
if info['fmap_entry'] is not None:
fmap = info['fmap_entry']
text += ' ->assume-registered-> %s' % self.GetBase(fmap, '')
anat = self.info[fmap]['anat_ref']
if info['catmats']:
text += ' ->3dAllineate-> %s' % \
self.GetBase(anat, '')
else:
text += ' ->assume-registered-> %s' % self.GetBase(anat, '')
else:
anat = info['anat_tgt']
text += ' ->assume-registered-> %s' % self.GetBase(anat, '')
text += '\nEPIs should be in register with %s\n' % \
self.GetBase(self.anatomical, '')
return text | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prescription(self):\n prescription = \"\\n{0:>10}\\t{1:>10}\\t{2:>10}\\t{3:>10}\\n\".format(\"R\",\"Material\",\"d\",\"diameter\")\n for surface in self.lensSurfaces():\n prescription += \"{0:>10.2f}\\t{1:>10}\\t{2:>10.2f}\\t{3:>10.2f}\\n\".format(surface.R, str(surface.mat), surface.s... | [
"0.6384936",
"0.634679",
"0.63305056",
"0.63101876",
"0.62891483",
"0.62351215",
"0.62246543",
"0.6216952",
"0.61375284",
"0.6118015",
"0.6031679",
"0.597089",
"0.59577096",
"0.59560966",
"0.5955112",
"0.59385556",
"0.5877444",
"0.58648694",
"0.58440596",
"0.58404136",
"0.583... | 0.69408065 | 0 |
Find the correct ref.dat file for each pfile. | def _GetRefdat(self):
for rfile in self.refdats.keys():
# Get times for ref.dat files with a time-stamp.
words = rfile.replace('.','_').split('_')
if len(words) == 6 and words[-2].count(':') == 20:
# This file was time-stamped by the sequence. Get the
# date and time. file name format:
# ref_Sep_9_2007_11:28:32.dat
rtime[rfile] = hms_to_secs(words[-2])
for pfile in self.pfiles:
min_difftime = 1.e20
self.info[pfile]['refdat'] = None
for rfile in self.refdats.keys():
if rfile[:3] == 'ref' and 'dat' in rfile:
# This is a reference data file. First see if the orientation is
# appended. If the file has neither a time-stamp nor a plane and
# there is more than one ref.dat, the epi reconstruction will
# be aborted.
rinfo = {}
ref_file = None
if 'sag' in rfile and self.info[pfile]['plane'] == 'sagittal':
# self.info[pfile]['refdat'] = rfile
ref_file = rfile
break
elif 'cor' in rfile and self.info[pfile]['plane'] == 'coronal':
# self.info[pfile]['refdat'] = rfile
ref_file = rfile
break
elif 'axial' in rfile and self.info[pfile]['plane'] == 'axial':
# self.info[pfile]['refdat'] = rfile
ref_file = rfile
break
elif len(self.refdats.keys()) == 1:
# Use the only one if that is all there is.
ref_file = rfile
epi_time = hms_to_secs(self.info[pfile]['acqtime'].split()[-2])
if epi_time - rtime[rfile] < min_difftime and \
rftime[rfile] > epi_time:
# Use the reference file that acquired nearest to the EPI
# but before it.
min_difftime = epi_time - rtime[rfile]
# self.info[pfile]['refdat'] = rfile
ref_file = rfile
if ref_file:
# Found a candidate.
if not self.info[pfile]['refdat']:
# Haven't found one yet, use it.
self.info[pfile]['refdat'] = ref_file
else:
# Found two. Choose one in the same directory.
oldpath = os.path.dirname(self.info[pfile]['refdat'])
newpath = os.path.dirname(ref_file)
pfile_path = os.path.dirname(pfile)
if oldpath == newpath:
# Same path, use the old one.
self.info[pfile]['refdat'] = ref_file
elif newpath == pfile_path:
self.info[pfile]['refdat'] = ref_file
# else Do nothing, use existing choice.
elif not os.path.exists(rfile):
self.info[pfile]['refdat'] = None
elif os.stat(rfile).st_size > 0:
# This path is taken if no info is encoded in the file name.
# Don't use empty ref.dat files.
self.info[pfile]['refdat'] = rfile | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_reference_data():\n return {f:read_local_file(f) for f in os.listdir(DATA_DIR)}",
"def get_input_file(self, *args, refsep='$', docopy=True):\n # filename = self.get_data(*args, docopy=docopy)\n filename = args[1]\n ref_files = ref_from_image(filename, ['IDCTAB', 'OFFTAB', 'NPOLFI... | [
"0.5827411",
"0.55954766",
"0.5543815",
"0.5515996",
"0.5477217",
"0.5447455",
"0.5424407",
"0.54009223",
"0.5372652",
"0.5370227",
"0.5364059",
"0.5320857",
"0.5294063",
"0.52870816",
"0.5261717",
"0.5260605",
"0.5242285",
"0.52146846",
"0.51936823",
"0.51909316",
"0.5184307... | 0.6289985 | 0 |
Order the epis and assign names defined in the template files. | def _GetEpiOrder(self):
self.epi_series.sort()
for series in self.epi_series:
self.GetEpiAcqTimes(series)
self.AssignEpiNames() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AssignEpiNames(self):\n# Sort each run in the series by its acquisition time.\n epi_sort = self.epi_times.keys()\n epi_sort.sort()\n# Rewrite pfiles as an ordered list of p-files to be reconstructed.\n for idx in xrange(len(epi_sort)):\n entry = self.epi_times[epi_so... | [
"0.6324153",
"0.5177771",
"0.5155023",
"0.5115554",
"0.5099046",
"0.50792223",
"0.50491947",
"0.5045727",
"0.49775788",
"0.49729112",
"0.4915597",
"0.49100342",
"0.48777694",
"0.48749626",
"0.48587674",
"0.48527986",
"0.48417845",
"0.48114392",
"0.47574326",
"0.47461927",
"0.... | 0.50911015 | 5 |
Fill structure for sorting acquisition times. | def GetEpiAcqTimes(self, series):
# Find minimum and maximum start times for each acquistion in series.
self.epi_times = {}
for entry in self.entry_map['epi']:
# Loop through each file in this series.
if self.info[entry]['series'] == series and \
self.info[entry]['tdim'] > 2:
# Relate each entry to its time of acquisition.
self.epi_times[self.info[entry]['acqtime']] = entry | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _sort_time(self):\n time = np.copy(self.data[\"time\"][:])\n ind_sorted = np.argsort(time)\n ind_valid: list[int] = []\n for ind in ind_sorted:\n if time[ind] not in time[ind_valid]:\n ind_valid.append(ind)\n n_time = len(time)\n for key, arra... | [
"0.6283316",
"0.6228001",
"0.6228001",
"0.620398",
"0.57670397",
"0.57652324",
"0.57647645",
"0.5753199",
"0.57444894",
"0.5722699",
"0.57000864",
"0.5646933",
"0.55846196",
"0.55585563",
"0.5551203",
"0.5549234",
"0.55354136",
"0.5506358",
"0.54945",
"0.54808986",
"0.5460458... | 0.5688327 | 11 |
Assign names to each epi file based on information in the template. | def AssignEpiNames(self):
# Sort each run in the series by its acquisition time.
epi_sort = self.epi_times.keys()
epi_sort.sort()
# Rewrite pfiles as an ordered list of p-files to be reconstructed.
for idx in xrange(len(epi_sort)):
entry = self.epi_times[epi_sort[idx]]
info = self.info[entry]
if info['data_filetype'] == 'ge_data':
self.pfiles_recon.append(entry)
info['run'] = '%0d' % (self.n_epi)
self.n_epi = self.n_epi + 1
plane = info['plane']
if not self.epinames.has_key(plane):
plane = 'any'
n_epi = self.epinames[plane]['n_epi']
if n_epi > len(self.epinames[plane]['names'])-1:
if self.epinames.has_key('any') and \
n_epi < len(self.epinames['any']):
plane = 'any'
n_epi = self.epinames[plane]['n_epi']
else:
self.DumpInfo()
errstr = 'Not enough EPI names in template file'
raise RuntimeError(errstr)
# epiname = self.epinames[plane]['names'][n_epi]
filebase = os.path.basename(self.epinames[plane]['names'][n_epi])
epi_mf_outdir = os.path.dirname(\
self.epinames[plane]['names'][n_epi])
epi_base = self.epinames[plane]['subdir'][n_epi]
tmp_outdir = '%s/%s' % (self.tmpdir, epi_base)
# Get output directory for raw epis.
if self.no_motcorr:
epi_r_outdir = epi_mf_outdir
elif self.keep_epi_raw:
epi_r_outdir = self.epi_scratch_space
else:
epi_r_outdir = tmp_outdir
# Get output directory for motion-corrected epis.
if self.keep_epi_mot:
epi_m_outdir = self.epi_scratch_space
else:
epi_m_outdir = tmp_outdir
info['outdir'] = epi_mf_outdir
if n_epi < len(self.epinames[plane]['names']):
epiname = self.epinames[plane]['names'][n_epi]
info['imgfile'] = '%s/%s' % (epi_r_outdir, filebase)
else:
info['imgfile'] = '%s/s%0d_epi_run%0d' % \
(epi_r_outdir, n_epi, idx+1)
self.epinames[plane]['n_epi'] += 1
info['mot_file'] = '%s/%s_mtn.txt' % (epi_mf_outdir, filebase)
info['censor_prefix'] = '%s/%s' % (epi_mf_outdir, filebase)
info['imgfile_t'] = '%s/%s_t' % (epi_m_outdir, filebase)
if self.no_motcorr:
info['imgfile_m'] = None
info['imgfile_mf'] = None
info['imgfile_final'] = info['imgfile']
else:
info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)
if self.no_fmapcorr or info['fmap_entry'] is None:
info['imgfile_m'] = '%s/%s_m' % (epi_mf_outdir, filebase)
info['imgfile_mf'] = None
info['imgfile_final'] = info['imgfile_m']
else:
info['imgfile_m'] = '%s/%s_m' % (epi_m_outdir, filebase)
info['imgfile_mf'] = '%s/%s_mf' % (epi_mf_outdir, filebase)
info['imgfile_final'] = info['imgfile_mf']
info['skip'] = self.skip
info['motion_ref_frame'] = self.tmplt['motion_ref_frame']
info['motion_interp'] = self.tmplt['epi_motion_interp']
if not info['motion_interp'].startswith('-'):
info['motion_interp'] = '-%s' % info['motion_interp']
info['filetype'] = self.tmplt['epi_file_format']
info['valid'] = True
self.info[entry] = info
if not self.no_motcorr:
epi_base = os.path.basename(info['imgfile_m'])
info['matfile_m'] = '%s/%s.aff12.1D' % (info['outdir'], epi_base)
info['matfile_mcat'] = '%s/%scat.aff12.1D' % (info['outdir'], epi_base) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_filename_metadata(self, extra_metadata): \n \n # Make sure product_info section exists\n extra_metadata.setdefault('product_info', {})\n \n file_name = os.path.basename(self.fname)\n fn_comps = file_name.split(\"_\")\n \n if self.__class__ == SA... | [
"0.5765641",
"0.5753263",
"0.5717375",
"0.5547444",
"0.55394816",
"0.54884017",
"0.54623926",
"0.53843206",
"0.5353627",
"0.531927",
"0.52712166",
"0.5261715",
"0.523186",
"0.52143687",
"0.51853824",
"0.51815045",
"0.51764005",
"0.5170443",
"0.5149673",
"0.51465887",
"0.51442... | 0.72918445 | 0 |
Dump the info object to a yaml file. | def DumpInfo(self):
if self.logdir is None:
return
self.dumpfile = '%s/preprocess_info.yaml' % (self.logdir)
try:
f = open(self.dumpfile,'w')
f.write(yaml.dump(self.info,default_flow_style=False, indent=4))
f.close()
except IOError:
self.errors = True
errstr = 'Error accessing %s' % self.dumpfile
raise IOError(errstr)
self.LogErrors(errstr) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, file=sys.stdout):\n d = self.to_dict()\n if d:\n yaml.dump([d], file, default_flow_style=False)",
"def __exit__(self, *_):\n with self._info_yaml_file_path.open(\"w\") as info:\n self._yml.dump(self._info, info)",
"def write(self):\n self.f.writ... | [
"0.7140269",
"0.6745914",
"0.67231625",
"0.6591301",
"0.6522555",
"0.6498975",
"0.64982736",
"0.6444522",
"0.63701254",
"0.62643826",
"0.62501603",
"0.61706054",
"0.60966444",
"0.60667735",
"0.6060812",
"0.6049239",
"0.6019757",
"0.60015565",
"0.60012335",
"0.59733576",
"0.59... | 0.7883224 | 0 |
Load the info dictionary from a yaml file. | def UnDumpInfo(self):
filename = '%s/preprocess_info.yaml' % self.logdir
f = open(filename,'r')
self.info = yaml.load(f.read())
f.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_datas(self) -> tp.Dict[str, dict]:\n with open(self._file, \"r\") as stream:\n try:\n load: tp.Dict[str, dict] = yaml.safe_load(stream)\n logger.info(\"YAML imported\")\n return load\n except yaml.YAMLError as exc:\n ... | [
"0.73581326",
"0.70676595",
"0.70332026",
"0.6870221",
"0.68692476",
"0.6855705",
"0.68462366",
"0.680534",
"0.6785473",
"0.6768446",
"0.6765261",
"0.6741415",
"0.6676443",
"0.6671067",
"0.66030633",
"0.6585844",
"0.6558528",
"0.65580547",
"0.6556143",
"0.65504235",
"0.654742... | 0.0 | -1 |
Ensure all epi files are recomputed by verifying that all output prefixes either don't exist or are deleted. | def CleanEpi(self):
for entry in self.info.keys():
info = self.info[entry]
if info['psdname'] == 'epi':
for tag in ('imgfile', 'imgfile_m', 'imgfile_mf', 'imgfile_t'):
if info.has_key(tag) and info[tag] is not None and \
os.path.exists(info[tag]):
print 'Deleting %s*' % (info[tag], info['suffix'])
cmd = '/bin/rm %s%s*' % (info[tag], info['suffix'])
self.ExecCmd(cmd)
if '.BRIK' in info['suffix']:
cmd = '/bin/rm %s%s*' % (info[tag], \
info['suffix'].replace('.BRIK','.HEAD'))
self.ExecCmd(cmd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanupAthenaMP(workdir, outputfiles=[]):\n\n for ampdir in glob('%s/athenaMP-workers-*' % (workdir)):\n for (p, d, f) in os.walk(ampdir):\n for filename in f:\n if 'core' in filename or 'tmp.' in filename:\n path = os.path.join(p, filename)\n ... | [
"0.5984496",
"0.5982936",
"0.59657407",
"0.59632736",
"0.5948575",
"0.5879957",
"0.5869022",
"0.57011503",
"0.5682905",
"0.56816006",
"0.56665385",
"0.5654999",
"0.5644077",
"0.5643207",
"0.5622836",
"0.5610936",
"0.56048816",
"0.56018406",
"0.5580712",
"0.55641234",
"0.55630... | 0.5448935 | 33 |
Create output directories if they don't already exist. | def CreateDirs(self):
# First, create a list of directories.
dnames = []
tags = ['', '_m', '_mf']
for entry in self.info.keys():
if self.info[entry]['type'] == 'epi':
for tag in tags:
fname = self.info[entry].get('imgfile%s' % tag, None)
if fname is not None:
dnames.append(os.path.dirname(fname))
else:
if self.info[entry].get('outdir',None) is not None:
dnames.append(self.info[entry]['outdir'])
# Create them if they don't already exist.
for dname in dnames:
if not os.path.exists(dname):
self.MakeDir(dname)
if self.verbose:
print 'mkdir %s' % dname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _make_output_dir(self):\n out_dir = os.path.dirname(self._out_format)\n if not os.path.exists(out_dir):\n os.makedirs(out_dir)\n LOG.info('Created output directory: %s', out_dir)",
"def setup_outdir():\n try:\n shutil.rmtree(OUTDIR)\n except FileNotFoundError:\n ... | [
"0.8051411",
"0.7945233",
"0.7910225",
"0.78630805",
"0.78603286",
"0.78426844",
"0.78248453",
"0.77685827",
"0.77487713",
"0.759815",
"0.7533603",
"0.75111306",
"0.74982744",
"0.745719",
"0.7449486",
"0.7433374",
"0.7404734",
"0.7333325",
"0.72971153",
"0.7263825",
"0.723374... | 0.6967413 | 37 |
Execute a bash command. This method is obsolete now. At one time it called a library function that worked around a deadlock bug in popen2 | def ExecCmd(self, cmd, halt_on_error=True):
self.f_bash.write("%s\n"%cmd)
self.f_bash.flush()
if not self.dry_run:
try:
execCmd(cmd, self.f_log, self.f_crash, self.verbose)
self.f_log.flush()
except RuntimeError, errstr:
if halt_on_error:
raise RuntimeError(errstr)
else:
self.LogErrors('%s' % errstr)
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bash_command(cmd):\n subprocess.Popen(['/bin/bash', '-c', cmd])",
"def bash(cmd):\n subprocess.run(cmd, shell=True, executable='/bin/bash') # ,",
"def bash_command(cmd):\n cmd = str(cmd)\n chain = cmd.split(\"|\")\n n_pipes = len(chain)\n\n for i in range(n_pipes):\n if i == 0:\n ... | [
"0.75032",
"0.7133294",
"0.70825005",
"0.7043764",
"0.70271105",
"0.6944504",
"0.6944504",
"0.6944504",
"0.6891018",
"0.6809242",
"0.67647636",
"0.66959184",
"0.66665363",
"0.66484356",
"0.6620994",
"0.65703243",
"0.65675676",
"0.65449697",
"0.6537749",
"0.65313303",
"0.65089... | 0.0 | -1 |
Convert anatomical images from dicom or ifiles to briks or niftis. | def ConvertAnat(self):
if self.verbose:
print 'Convert T1 and T2 images...'
for entry in self.info:
info = self.info[entry]
if self.info[entry]['imgfile'] is None:
continue
if self.info[entry]['type'] in self.anat_types:
key = self.info[entry]['type']
imgfile = self.info[entry]['imgfile']
cmd = 'convert_file %s %s %s %s' % (self.flip_opts, entry, \
imgfile, self.info[entry]['filetype'])
checkfile = '%s%s' % (imgfile, self.info[entry]['suffix'])
self.CheckExec(cmd, [checkfile])
if self.info[entry]['norm_src'] and self.skull_strip:
cmd = "3dSkullStrip -input %s -prefix %s" % \
(checkfile, self.info[entry]['imgfile_skstrip'])
checkfile = '%s+orig.BRIK' % \
(self.info[entry]['imgfile_skstrip'])
self.CheckExec(cmd, [checkfile]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(input_folder, output_images_folder, output_files_folder, bb_file,\n archive_folder, name_mapping):\n\n output_images_folder = Path(output_images_folder)\n output_files_folder = Path(output_files_folder)\n archive_folder = Path(archive_folder)\n output_images_folder.mkdir(exist_ok=True)... | [
"0.5878483",
"0.5656656",
"0.5646914",
"0.5597715",
"0.5594963",
"0.55353117",
"0.5508064",
"0.54495406",
"0.54446286",
"0.5424856",
"0.5355759",
"0.5332224",
"0.5326884",
"0.53148586",
"0.52936196",
"0.5291717",
"0.52617556",
"0.5258072",
"0.5257662",
"0.525628",
"0.52407",
... | 0.5975682 | 0 |
Register the magnitude image from the fieldmap data to the hires structural. Save the matrices for later use in motion correction. | def AlignFieldmaps(self):
for entry in self.entry_map['fmap']:
info = self.info[entry]
# Register the magnitude image at the shortest TR to the T1-IR
# structural image.
target = self.info[self.norm_src]['imgfile'] + \
self.info[self.norm_src]['suffix']
source = info['magfile'] + info['suffix']
matfile = info['matfile']
fmt = '3dAllineate -prefix NULL -1Dmatrix_save %s -base %s ' + \
'-source %s -cost mi -warp shift_rotate'
cmd = fmt % (info['matfile'], target, source)
self.CheckExec(cmd, [info['matfile']])
# Convert to unitary matrix (remove scaling component.)
cmd = 'cat_matvec -ONELINE %s -P > %s' % \
(info['matfile'], info['matfile_unitary'])
self.CheckExec(cmd, [info['matfile_unitary']])
# Rotate the magnitude image to the new grid.
fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'
cmd = fmt % (info['magfile_r']+info['suffix'], \
info['matfile_unitary'], info['magfile'] + info['suffix'])
self.CheckExec(cmd, [info['magfile_r']+info['suffix']])
# Rotate the fieldmap to the new grid.
fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s %s'
cmd = fmt % (info['imgfile_r']+info['suffix'], \
info['matfile_unitary'], info['imgfile'] + info['suffix'])
self.CheckExec(cmd, [info['imgfile_r']+info['suffix']]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recon(self, spirec):\n tmpdir = tempfile.mkdtemp()\n basename = 'recon'\n basepath = os.path.join(tmpdir, basename)\n pfilename = os.path.abspath(self.pfilename)\n\n # run spirec to get the mag file and the fieldmap file\n cmd = spirec + ' -l --rotate -90 --magfile --s... | [
"0.61536443",
"0.5817665",
"0.5757287",
"0.5740727",
"0.5657208",
"0.56416994",
"0.5542085",
"0.54642224",
"0.54503155",
"0.54379785",
"0.5347235",
"0.52391076",
"0.5095052",
"0.5094995",
"0.5089604",
"0.50434417",
"0.5038968",
"0.5036496",
"0.5012753",
"0.497948",
"0.4972110... | 0.49835664 | 19 |
Convert anatomical images to briks. | def ProcessDTI(self):
for entry in self.info:
if self.info[entry]['type'] == 'dti':
if self.verbose:
print 'Processing DTI data in %s' % os.path.basename(entry)
# dtiname = '%s/s%s_dti' % \
# (self.info[entry]['outdir'],self.info[entry]['series'])
cmd = 'convert_file %s %s %s' % (entry, \
self.info[entry]['imgfile'], self.info[entry]['filetype'])
fname = '%s%s' % \
(self.info[entry]['imgfile'], self.info[entry]['suffix'])
self.CheckExec(cmd, [fname]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bayer_images(self) -> typing.List[np.ndarray]:\n return [rbg_to_bayer_bg(c.get_image()) for c in self.cameras]",
"def ConvertAnat(self):\n if self.verbose:\n print 'Convert T1 and T2 images...'\n for entry in self.info:\n info = self.info[entry]\n if ... | [
"0.60772157",
"0.58175284",
"0.5723036",
"0.5568555",
"0.5511439",
"0.5510956",
"0.5482435",
"0.5474333",
"0.5450582",
"0.53886837",
"0.53840804",
"0.5379313",
"0.5322583",
"0.53046983",
"0.52473575",
"0.5239021",
"0.52281713",
"0.5225414",
"0.5208012",
"0.5204904",
"0.515314... | 0.0 | -1 |
Convert ASL images to nifti. | def ProcessAsl(self):
for entry in self.info:
if self.info[entry]['type'] == 'asl':
if self.verbose:
print 'Processing ASL data in %s' % os.path.basename(entry)
cmd = 'convert_file %s %s %s' % (entry, \
self.info[entry]['imgfile'], self.info[entry]['filetype'])
fname = '%s%s' % \
(self.info[entry]['imgfile'], self.info[entry]['suffix'])
self.CheckExec(cmd, [fname]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_to_nifti(log, brain):\n log.info('Doing convert_to_nifti')\n cmdargs = split('3dAFNItoNIFTI {}'.format(brain))\n proc = Popen(cmdargs, stdout=PIPE, stderr=STDOUT)\n log.info(proc.stdout.read())",
"def ConvertAnat(self):\n if self.verbose:\n print 'Convert T1 and T2 image... | [
"0.6358437",
"0.6124715",
"0.5904855",
"0.57210785",
"0.57022226",
"0.5685979",
"0.5665494",
"0.5628516",
"0.5568676",
"0.5515679",
"0.5492054",
"0.5473638",
"0.5446226",
"0.5407872",
"0.54046696",
"0.5352408",
"0.53269494",
"0.5309064",
"0.53084403",
"0.5288916",
"0.52865076... | 0.55716157 | 8 |
Create the fieldmap(s) and the corresponding magnitude images. | def MakeFieldmaps(self):
if self.verbose:
print 'Compute fieldmaps.'
for entry in self.info:
if self.info[entry]['type'] == 'fmap':
if self.info[entry]['imgfile'] == None:
# Fieldmap data not found.
return
# Make a magnitude image for use in checking registration.
cmd = 'convert_file -f0 -m0 %s %s nii' % \
(entry, self.info[entry]['magfile'])
self.CheckExec(cmd, [self.info[entry]['magfile'] + '.nii'])
# Make fieldmap. Use separate loop in case make_fmap aborts.
for entry in self.info:
if self.info[entry]['type'] == 'fmap':
fmapname = self.info[entry]['imgfile']
if not os.path.exists('%s.nii' % fmapname) or self.redo:
# Couldn't find or existing fmap, compute a new one.
if self.verbose:
extra_args = '-v'
else:
extra_args = ''
if self.info[entry]['correct_fmap_phase'] == 'force':
extra_args += ' --force-slicecorr'
elif self.info[entry]['correct_fmap_phase'] == 'omit':
extra_args += ' --omit-slicecorr'
cmd = 'make_fmap %s %s %s' % (extra_args, entry, fmapname)
# error = self.ExecCmd(cmd, halt_on_error=False)
if self.no_fmapcorr:
halt_on_error = False
else:
halt_on_error = True
error = self.CheckExec(cmd, ['%s.nii' % fmapname], \
halt_on_error=halt_on_error)
if error:
self.info[entry]['valid'] = False
del self.fmaps[entry] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AlignFieldmaps(self):\n for entry in self.entry_map['fmap']:\n info = self.info[entry]\n\n# Register the magnitude image at the shortest TR to the T1-IR\n# structural image.\n target = self.info[self.norm_src]['imgfile'] + \\\n ... | [
"0.677067",
"0.64014757",
"0.62887424",
"0.6082136",
"0.58927894",
"0.58386827",
"0.58132875",
"0.57924163",
"0.57924163",
"0.57452226",
"0.5743414",
"0.57319504",
"0.57206607",
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"0.563697",
"0.5628693",
"0.56157184",
"0.5559617",
"0.55475587",
"0.5537074",
"0.55... | 0.7716374 | 0 |
Create link to structural image if it doesn't already exist. | def LinkAnat(self):
if self.anatomical is None:
return
for entry in self.info.keys():
info = self.info[entry]
if info.has_key('anat_link'):
self.LinkFiles(info['outdir'], self.anatomical) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_HTML_a_img(link_url, image_url):\n img = '<img src=\"' + image_url + '\">'\n linked_image = create_HTML_a(link_url, img)\n return linked_image",
"def make_image(self, path):\n\t\treturn None",
"def image(self, link, title, alt):\n if not link.startswith(('http://', 'https://')):\n ... | [
"0.6233887",
"0.60792506",
"0.60782003",
"0.5984427",
"0.58839905",
"0.58724886",
"0.5832461",
"0.58206445",
"0.580554",
"0.5745005",
"0.5738044",
"0.5731455",
"0.5714038",
"0.5617503",
"0.560202",
"0.5583627",
"0.55811983",
"0.5542545",
"0.55367535",
"0.5529259",
"0.5487097"... | 0.0 | -1 |
Create links to BRIK, HEAD, and .nii files. | def LinkFiles(self, srcdir, target):
if '+orig' in target:
tgt_prefix = target.replace('.BRIK','')
tgt_prefix = tgt_prefix.replace('.HEAD','')
linkfiles = ['%s.HEAD'%tgt_prefix, '%s.BRIK' %tgt_prefix]
else:
linkfiles = [target]
for linkfile in linkfiles:
linkname = '%s/%s' % (srcdir, os.path.basename(linkfile))
rel_linkdir = abspath_to_relpath(os.path.dirname(target), srcdir)
rel_linkfile = '%s/%s' % (rel_linkdir, os.path.basename(linkfile))
if not os.path.exists(linkname) and not os.path.islink(linkname):
cmd = 'cd %s && ln -s %s %s' % (srcdir, rel_linkfile, linkname)
self.ExecCmd(cmd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _create_links(self):\n for line in self.iter_files_to_install():\n arcname, link = line.split()\n if link == 'False':\n continue\n self.files.append(create_link(arcname, link, self.prefix))",
"def makeLinks(self):\n self.deleteIndexFileIfExists()\... | [
"0.667778",
"0.59682673",
"0.5959984",
"0.567664",
"0.5635121",
"0.55980814",
"0.553239",
"0.5522011",
"0.54892784",
"0.54057527",
"0.5380412",
"0.5370104",
"0.53354234",
"0.5332412",
"0.5303601",
"0.5300853",
"0.52821183",
"0.5264791",
"0.5261789",
"0.5261702",
"0.5261481",
... | 0.6162017 | 1 |
Extract the initial EPIs stored in dicom format. | def ExtractFirstEpi(self):
for entry in self.info:
if self.info[entry]['type'] == 'first_epi':
epiname = self.info[entry]['imgfile']
cmd = 'convert_file %s -f0 %s %s %s' % \
(self.flip_opts, entry,epiname, self.info[entry]['filetype'])
fname = '%s%s' % (epiname, self.info[entry]['suffix'])
self.CheckExec(cmd, [fname])
self.info[entry]['imgfile'] = fname | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dicom_load():\n # Identify folders with EPI data\n dirs = [i for i in os.listdir(dcm_dir) if os.path.isdir(os.path.join(dcm_dir, i))]\n d_cnt = 0\n for d in dirs:\n dcm_file = os.path.join(dcm_dir,d,os.listdir(os.path.join(dcm_dir,d))[0])\n try:\n dcm_data = pydicom.dcmread... | [
"0.6132044",
"0.59762484",
"0.5642774",
"0.53444785",
"0.52427983",
"0.52217543",
"0.5216209",
"0.51387924",
"0.5125599",
"0.50237185",
"0.5018066",
"0.5001039",
"0.49862388",
"0.49604324",
"0.4950634",
"0.49402615",
"0.49259216",
"0.49019468",
"0.48623866",
"0.4825966",
"0.4... | 0.6886132 | 0 |
Reconstruct the EPIs from pfiles. | def ReconEpis(self):
run = zeros(100)
if self.verbose:
print 'Reconstruct EPIs'
for pfile in self.pfiles_recon:
if self.info[pfile]['refdat'] is None:
# Find the ref.dat file later.
continue
if self.info[pfile]['compression'] is not None:
# Data are compressed, copy to tmp.
compression = self.info[pfile]['compression']
pfile_decomp = '%s/%s' % (self.tmpdir, \
os.path.basename(self.info[pfile]['pfile_decomp']))
if os.path.exists(pfile_decomp):
errstr = 'Attempting to overwrite existing p-file (%s)' % pfile_decomp + \
' in ReconEpis'
cmd = '%s %s > %s' % \
(decompress_cmds[compression], pfile, pfile_decomp)
self.ExecCmd(cmd)
else:
# Create a link on /tmp to the pfile so the link to ref.dat will also
# be on /tmp, (which is always writeable.)
pfile_decomp = '%s/%s' % (self.tmpdir, os.path.basename(pfile))
if not os.path.exists(pfile_decomp):
os.symlink(pfile, pfile_decomp)
refname, refcmpress = self.CheckCompression( \
self.info[pfile]['refdat'])
if refcmpress is not None:
refdat_decomp = '%s/%s' % (self.tmpdir, os.path.basename(refname))
cmd = '%s %s > %s' % \
(decompress_cmds[refcmpress], \
self.info[pfile]['refdat'], refdat_decomp)
self.ExecCmd(cmd)
else:
refdat_decomp = self.info[pfile]['refdat']
if refdat_decomp is not None:
if refdat_decomp != 'ref.dat':
# Create link bearing the file name epirecon_ex expects.
refdat_link = '%s/ref.dat' % self.tmpdir
if not os.path.exists(refdat_link):
if self.verbose:
print 'ln -s %s %s' % (refdat_decomp, refdat_link)
if os.path.islink(refdat_link):
# ref.dat is a broken symbolic link.
if self.verbose:
print 'rm %s' % ref_file
os.remove(refdat_link)
try:
os.symlink(refdat_decomp, refdat_link)
except OSError:
self.errors = True
pfile_link = '%s/%s' % (self.tmpdir, os.path.basename(pfile_decomp))
os.symlink(pfile_decomp, pfile_link)
os.symlink(refdat_decomp, '%s/ref.dat' % self.tmpdir)
series = int(self.info[pfile]['series'])
run[series] = run[series] + 1
epiname = self.info[pfile]['imgfile']
cmd = 'epirecon_ex -F -f %s -NAME %s -fmt brik -skip %d' % \
(pfile_decomp, epiname, self.skip)
fname = '%s+orig.BRIK' % epiname
self.CheckExec(cmd, [fname])
# self.epi_prefixes[pfile] = self.info[pfile]['imgfile']
else:
errstr = '*******************************************\n' + \
'No ref.dat file exists for %s\n' % pfile + \
'*******************************************\n'
self.error_log = self.error_log + errstr
self.f_crash.write(errstr) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def AssignEpiNames(self):\n# Sort each run in the series by its acquisition time.\n epi_sort = self.epi_times.keys()\n epi_sort.sort()\n# Rewrite pfiles as an ordered list of p-files to be reconstructed.\n for idx in xrange(len(epi_sort)):\n entry = self.epi_times[epi_so... | [
"0.64974564",
"0.61578393",
"0.57928175",
"0.57109356",
"0.5541757",
"0.5491878",
"0.5438996",
"0.54297066",
"0.5325947",
"0.5219759",
"0.52026135",
"0.5192286",
"0.5152777",
"0.51420754",
"0.5114544",
"0.5093096",
"0.50334144",
"0.5020539",
"0.5005299",
"0.49603912",
"0.4959... | 0.64618903 | 1 |
Eliminate entries in epi recon table that have already been reconstructed. I don't remember why this is here but I know that at one time it was important. | def PruneEpiEntries(self):
pruned = {}
basefiles = []
baseentries = {}
for entry in self.entry_map['epi']:
if baseentries.has_key(self.info[entry]['basefile']):
baseentries[self.info[entry]['basefile']].append(entry)
else:
baseentries[self.info[entry]['basefile']] = [entry]
for entry in self.entry_map['epi']:
targets = []
if self.no_motcorr:
target = self.info[entry]['imgfile']
elif self.info[entry]['fmapname'] is None or self.no_fmapcorr:
target = self.info[entry]['imgfile_m']
else:
target = self.info[entry]['imgfile_mf']
targets.append(target + self.info[entry]['suffix'])
targets.append('%s%s' % (self.info[entry]['censor_prefix'], '_censor.1D'))
pruned[entry] = [True, baseentries[self.info[entry]['basefile']]]
for target in targets:
pruned[entry] = \
[False, baseentries[self.info[entry]['basefile']]]
for key in pruned.keys():
if not pruned[key][0]:
for entry in pruned[key][1]:
pruned[entry][0] = False
tmp = new_map = []
for entry in self.entry_map['epi']:
if pruned[entry][0]:
if self.verbose:
print 'Skipping %s: Already reconstructed.' % targets[0]
if entry in self.pfiles_recon:
self.pfiles_recon.remove(entry)
else:
new_map.append(entry)
self.entry_map['epi'] = new_map | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def elimination_ofconc(a2_data):\n for data in a2_data.values():\n data.pop('conc')\n return a2_data",
"def nonphysicalxs_remotion(a2_data,res_nufi_removal):\n for i in a2_data['I'].keys():\n if i=='MACR' and res_nufi_removal==True:\n if 'nufi' in a2_data['I'][i]['R'].keys():\n ... | [
"0.6324132",
"0.59182566",
"0.5790216",
"0.57542723",
"0.57150894",
"0.5654134",
"0.55529314",
"0.5501398",
"0.54495615",
"0.5417837",
"0.5416808",
"0.5410812",
"0.53797144",
"0.53654283",
"0.5352083",
"0.5328368",
"0.53084546",
"0.530087",
"0.5300535",
"0.5298115",
"0.529656... | 0.6166888 | 1 |
Convert epis reconstructed on the scanner. | def ConvertRtEpis(self):
if self.verbose:
print 'Convert EPIs to brik'
for entry in self.entry_map['epi']:
if ('epirt' in self.info[entry]['psdname'] or \
self.info[entry]['psdname'] == 'epi' or \
self.info[entry]['psdname'] == '*epfid2d1_64') and \
self.info[entry]['data_filetype'] == 'dicom':
series = self.info[entry]['series']
if self.info[entry]['skip'] > 0:
skip = '--skip=%s' % self.info[entry]['skip']
else:
skip = ''
cmd = 'convert_file %s %s %s brik' % \
(skip, entry, self.info[entry]['imgfile'])
checkname = '%s+orig.BRIK' % (self.info[entry]['imgfile'])
self.CheckExec(cmd, [checkname]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ReconEpis(self):\n run = zeros(100)\n if self.verbose:\n print 'Reconstruct EPIs'\n for pfile in self.pfiles_recon:\n if self.info[pfile]['refdat'] is None:\n# Find the ref.dat file later.\n continue\n if self.info[pfile]['compr... | [
"0.5776446",
"0.5423936",
"0.5320594",
"0.5170984",
"0.51104087",
"0.5029466",
"0.5025242",
"0.50205046",
"0.48759243",
"0.4801463",
"0.47459334",
"0.47000486",
"0.46995685",
"0.4694864",
"0.46835348",
"0.467961",
"0.4648427",
"0.46379155",
"0.46379155",
"0.4625023",
"0.45730... | 0.6517192 | 0 |
Correct for motion and call SliceTimeCorrect. | def CorrectMotion(self):
if self.verbose:
print "Correct for motion"
for entry in self.entry_map['epi']:
info = self.info[entry]
if os.path.exists(info['imgfile_m'] + info['suffix']):
return
# Always use brik for 3dDeconvolve.
suffix = '+orig'
epifile = '%s%s' % (info['imgfile'], suffix)
prefix = info['imgfile_m']
base_entry = info['base_entry']
if info['base'] == 'start':
# Use the first frame specified in template file. Defaults
# to zero.
base = info['motion_ref_frame']
else:
# Use the last frame.
base = self.info[base_entry]['tdim'] - info['skip']-1
base = ('%d' % base).replace(' ','')
# Correct for slice-timing.
self.SliceTimeCorrect(info, epifile)
plane = info['plane']
anat_tgt = info['anat_tgt']
# anat_entry = self.anat_entry[plane]
if info['catmats']:
# Include additonal transformation in motion correction such
# that final image is in register with the fieldmap, which has
# been registered to the structural image that will be used for
# spatial normalization.
self.MotcorCatenate(info, base, anat_tgt)
else:
# Assume fieldmap is in register with the structural.
self.Motcor(info, base)
if info.get('fmapname', None) is None:
# No fieldmap correction.
if self.fsl_flip:
# Flip the way fslview likes it.
self.FSLFlip(info['imgfile_m'], info['imgfile_final'])
elif info['suffix'] == '.nii':
# Copy motion-corrected images from /tmp to output directory
outfile = info['imgfile_final'] + info['suffix']
cmd = '3dcopy %s+orig %s' % (info['imgfile_m'], outfile)
self.CheckExec(cmd, [outfile], force=True)
cmd = '/bin/rm %s+orig*' % info['imgfile_m']
self.CheckExec(cmd, [], force=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare_drift_correction(self, pos):\n\n\t\tprint(\"function not supported yet\")",
"def refframe_correct(self, ra, dec, obstime, sobjs=None):\n # Correct Telescope's motion\n refframe = self.par['calibrations']['wavelengths']['refframe']\n if refframe in ['heliocentric', 'barycentric'] ... | [
"0.60318804",
"0.54270923",
"0.53947157",
"0.53792375",
"0.5303855",
"0.5262295",
"0.5260591",
"0.52238935",
"0.5190531",
"0.5141736",
"0.5131086",
"0.50906426",
"0.5085911",
"0.5074954",
"0.50716",
"0.506681",
"0.50448614",
"0.50436366",
"0.50436366",
"0.50377357",
"0.503135... | 0.6062027 | 0 |
Compute motioncorrection transformation matrices, catenate with transform from fieldmap to structural, then inteprolate the data to the final grid. | def MotcorCatenate(self, info, base, anat_tgt):
# First compute the transformation matrices due to epi-to-epi motion.
fmt = '3dvolreg -prefix NULL -1Dmatrix_save %s -twopass ' + \
'-verbose -base %s+orig[%s] -dfile %s %s+orig'
cmd = fmt % (info['matfile_m'], info['basefile'], base, \
info['mot_file'], info['imgfile_t'])
self.CheckExec(cmd, [info['matfile_m']])
# Catenate with transformation from epi base image to the anatomical.
cmd = 'cat_matvec -ONELINE %s -P %s -P > %s' % \
(self.info[anat_tgt]['matfile'], info['matfile_m'], \
info['matfile_mcat'])
self.CheckExec(cmd, [info['matfile_mcat']])
# Interpolate the data to the new grid.
fmt = '3dAllineate -prefix %s -interp cubic -1Dmatrix_apply %s ' + \
'-warp shift_rotate -base %s+orig[%s] %s+orig'
cmd = fmt % (info['imgfile_m'], info['matfile_mcat'], info['basefile'], \
base, info['imgfile_t'])
self.CheckExec(cmd, ['%s+orig.BRIK'%info['imgfile_m'], \
'%s+orig.HEAD'%info['imgfile_m']]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.... | [
"0.62273926",
"0.6207363",
"0.6104549",
"0.5850489",
"0.58403313",
"0.58327806",
"0.5827466",
"0.5725734",
"0.5681582",
"0.56439",
"0.5603615",
"0.5602365",
"0.5596903",
"0.5577693",
"0.555407",
"0.5526825",
"0.5518143",
"0.5506637",
"0.5487023",
"0.5475069",
"0.54521763",
... | 0.5767431 | 7 |
Motion correct using 3dvolreg. No slicetime correction. | def Motcor(self, info, base):
fmt = '3dvolreg -prefix %s -twopass %s -verbose -base %s+orig[%s] ' + \
'-dfile %s %s+orig'
cmd = fmt % (info['imgfile_m'], info['motion_interp'], \
info['basefile'], base, info['mot_file'], info['imgfile_t'])
self.CheckExec(cmd, ['%s+orig.BRIK' % info['imgfile_m'], \
'%s+orig.HEAD' % info['imgfile_m']]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def CorrectMotion(self):\n if self.verbose:\n print \"Correct for motion\"\n for entry in self.entry_map['epi']:\n info = self.info[entry]\n\n if os.path.exists(info['imgfile_m'] + info['suffix']):\n return\n# Always use brik for 3dDeconvolve.... | [
"0.65022635",
"0.5807314",
"0.5593249",
"0.55692303",
"0.5540268",
"0.5456648",
"0.5415684",
"0.539106",
"0.5349843",
"0.5269265",
"0.52284133",
"0.52205676",
"0.5212929",
"0.5210599",
"0.52055985",
"0.52035886",
"0.5195111",
"0.51901066",
"0.5163019",
"0.5134201",
"0.5127333... | 0.47712728 | 92 |
Call the jump_censor program to characterize the degree of motion. | def JumpCensor(self):
if self.verbose:
print 'Computing censor files.'
for entry in self.entry_map['epi']:
if self.censor_interleave:
input_file = '%s+orig' % self.info[entry]['imgfile']
interleave = '--interleave'
else:
interleave = ''
if os.path.exists(self.info[entry]['mot_file']):
input_file = self.info[entry]['mot_file']
else:
input_file = '%s+orig' % self.info[entry]['imgfile']
cmd = \
"jump_censor -v --prefix=%s %s --store-plot --threshold=%f %s" % \
(self.info[entry]['censor_prefix'],
interleave,
self.censor_thresh,
input_file)
try:
self.CheckExec(cmd, ['%s_censor.1D' %
self.info[entry]['censor_prefix']],
force=False)
except:
print 'Error computing censor files.' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def censoring_fcn(self, q):\n return 1.0",
"def jump(self):\n\t\tself.vel = -10\n\t\tself.tick_count = 0\n\t\tself.height = self.y",
"def on_jump_press(self) -> None:\r\n if not self.node:\r\n return\r\n if don.jumpFly:\r\n self.node.handlemessage(\"impulse\",self.nod... | [
"0.59280413",
"0.541607",
"0.5290688",
"0.52872306",
"0.5283519",
"0.51918846",
"0.5162083",
"0.5111304",
"0.50946474",
"0.50678855",
"0.5063725",
"0.50507736",
"0.50380087",
"0.5027665",
"0.50178564",
"0.49831063",
"0.4976855",
"0.4957412",
"0.49509645",
"0.49407175",
"0.491... | 0.7094361 | 0 |
Check if output file exists, then execute commmand. If there is more than one output file, the command will be executed if at least one is missing. | def CheckExec(self, cmd, checknames, force=False, halt_on_error=True):
gone = False
names = []
for name in checknames:
if '+orig' in name:
if name.endswith('+orig'):
names.append('%s.HEAD' % name)
names.append('%s.BRIK' % name)
elif name.endswith('HEAD'):
names.append(name)
newname = name[:-4] + 'BRIK'
if newname not in checknames:
names.append(newname)
elif name.endswith('BRIK'):
newname = name[:-4] + 'HEAD'
if newname not in checknames:
names.append(newname)
names.append(name)
else:
names.append(name)
for name in names:
if not os.path.exists(name) and not os.path.exists('%s.gz'%name):
gone = True
elif self.redo or force or gone:
os.remove(name)
gone = True
if self.redo or gone:
self.ExecCmd(cmd, halt_on_error=halt_on_error)
if '+orig.' in names[0]:
name = names[0].replace('.BRIK','')
name = name.replace('.HEAD','')
append_history_note(name, cmd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_preexisting_output_file(output_file_path):\n if path.exists(f\"{output_file_path}\"):\n print(\"Output file at specified save location file path already exists!\")\n print(\"Aborting operation!\")\n sys.exit()",
"def exec_command_string_one_file(command_str,output):\n pri... | [
"0.6835208",
"0.6785431",
"0.667483",
"0.6603357",
"0.6349663",
"0.606884",
"0.60539275",
"0.5998481",
"0.59256774",
"0.58965987",
"0.5828717",
"0.57747966",
"0.5732897",
"0.56906545",
"0.56636125",
"0.5657635",
"0.56451756",
"0.5630182",
"0.56291837",
"0.5592528",
"0.5535338... | 0.0 | -1 |
Compute the temporal SNR for each epi, save in a nifti file, and store a summmary in a png file. | def ComputeSNR(self):
for epi in self.entry_map['epi']:
epifile = self.info[epi]['imgfile_final'] + self.info[epi]['suffix']
prefix = self.info[epi]['imgfile_final'] + '_snr'
if not os.path.exists('%s_snr.png' % prefix):
if self.verbose:
print 'TemporalSnr(epifile=%s, prefix=%s)' % \
(epifile, prefix)
try:
TemporalSnr(epifile=epifile, prefix=prefix)()
except:
print("Error computing temporal SNR") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_SNR(snid: int, photo_data: pd.DataFrame, \n head_data: pd.DataFrame, code_zenodo: int, \n snana_file_index: int, code_snana: int):\n \n types_names = {90: 'Ia', 62: 'Ibc', 42: 'II', 67: '91bg', 52: 'Iax',\n 64:'KN', 95: 'SLSN', 994: 'PISN', 99... | [
"0.5856596",
"0.58353704",
"0.58029324",
"0.5760767",
"0.57194346",
"0.5685501",
"0.565635",
"0.5610267",
"0.5596072",
"0.5580801",
"0.5550373",
"0.55146116",
"0.55122143",
"0.54595554",
"0.53850317",
"0.53831476",
"0.53568536",
"0.5345135",
"0.53432506",
"0.5322765",
"0.5319... | 0.78557074 | 0 |
Flip axes to orientation fslview expects. | def FSLFlip(self, infile, prefix):
cmd = '3dresample -orient LPI -prefix %s.nii -inset %s+orig' % \
(prefix, infile)
self.CheckExec(cmd, ['%s.nii' % prefix])
fname = '%s+orig.BRIK' % infile
if os.path.exists(fname):
os.remove(fname)
fname = '%s+orig.HEAD' % infile
if os.path.exists(fname):
os.remove(fname) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def flip(self, axes=None):\n axes = self._make_axes_as_num(axes)\n vals = self.values\n if 0 in axes:\n vals = vals[::-1, :]\n if 1 in axes:\n vals = vals[:, ::-1]\n return Signal2D(vals, index=self.index, columns=self.columns)",
"def flip_axes(input_file,... | [
"0.7156261",
"0.7035609",
"0.7033058",
"0.6533171",
"0.64487714",
"0.63454676",
"0.6332849",
"0.6305558",
"0.63048905",
"0.62795544",
"0.61534804",
"0.6124441",
"0.6114931",
"0.60523576",
"0.6050565",
"0.5979607",
"0.5953482",
"0.5949251",
"0.5904345",
"0.5891284",
"0.58893",... | 0.0 | -1 |
Change ownership to group read read/write. | def Chown(self):
cmd = 'chmod -R 0775 %s' % self.procdir
self.ExecCmd(cmd) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def changeOwnership(self, document):\n document.changeOwnership(getSecurityManager().getUser(), False)",
"def chown(self, user, group, rec=0):\n uid = getuserid(user)\n gid = getgroupid(group)\n if rec:\n for x in self.visit(rec=lambda x: x.check(link=0)):\n ... | [
"0.66892415",
"0.62186366",
"0.6200737",
"0.6195593",
"0.6075884",
"0.6041262",
"0.599601",
"0.59780955",
"0.5939623",
"0.5920451",
"0.58722764",
"0.5808822",
"0.5740759",
"0.565018",
"0.56067586",
"0.55544484",
"0.5519573",
"0.5503808",
"0.5484997",
"0.5481513",
"0.54399353"... | 0.56320053 | 14 |
Store some useful information in the log file. | def LogProcess(self):
time = datetime.today().strftime('%a %Y%b%d %X')
# Get user name.
f = os.popen("whoami","r")
user = f.read().strip()
f.close()
entry = '%s\t%s\t%s\t%s\n' % (time, self.topdir, user, self.version)
if ismounted(c.exams_file):
# Append info to the exams file.
try:
f = open(c.exams_file,'a+')
f.seek(0, 2)
f.write(entry)
f.close()
except:
# Not a huge problem if this doesn't work.
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_data(self):\n\n self.check_dir()\n with open(self.log_file, \"a\") as logger_file:\n logger_file.write(\"{}, {}\\n\".format(self.time, self.msg))",
"def log(self):\n f = open(self.log_dir + 'parsed.log', 'a')\n try:\n # Write: local time | CurrentCost \"t... | [
"0.7579466",
"0.7374402",
"0.7347465",
"0.70612556",
"0.69241893",
"0.69229144",
"0.69018483",
"0.6860829",
"0.68271667",
"0.67728823",
"0.6762465",
"0.6749048",
"0.6685187",
"0.66748893",
"0.6671111",
"0.66622746",
"0.6618556",
"0.6599946",
"0.6599946",
"0.6597023",
"0.65964... | 0.64186484 | 36 |
Delete temporary files, close log files and email results. | def CleanUp(self):
if (not self.keep_epi_raw or not self.keep_epi_mot) \
and not self.opts.debug_tmp:
self.tmp.Clean()
overall_msg = self.SummaryErrorMessage()
if self.tmplt and not self.no_email:
EmailResults(self.tmplt['email'], overall_msg, \
self.topdir, self.dumpfile, self.logfile, self.motcor_summary)
# Write the error message to the log file.
if self.f_log is None:
# Log file not opened yet, do it now.
if self.logdir is not None:
logfile = '%s/preprocess.log' % self.logdir
f_log = open(logfile,'w')
f_log.write('\n%s\n' % overall_msg)
f_log.close()
else:
self.f_log.write('\n%s\n' % overall_msg)
sys.exit() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def teardown():\n for filename in files_to_delete:\n delete_file(filename)",
"def cleanup(self):\n if self.log_fo:\n self.log_fo.close()",
"def classCleanup(cls):\n cls.RemoveTempFile(\"child_send1.txt\")\n cls.RemoveTempFile(\"child_read1.txt\")\n cls.R... | [
"0.68277276",
"0.67437613",
"0.660802",
"0.6526464",
"0.6455161",
"0.6444915",
"0.64396834",
"0.64194924",
"0.6357929",
"0.635103",
"0.6348235",
"0.6337223",
"0.631956",
"0.62995",
"0.6263754",
"0.6261455",
"0.6259881",
"0.6235108",
"0.6226701",
"0.62038773",
"0.6193479",
"... | 0.58914703 | 56 |
Create summary message for email. | def SummaryErrorMessage(self, error_log=None):
if error_log is None:
error_log = self.error_log
# server = socket.gethostname().split('.')[0]
mssg = '\nPreprocessing script complete for data in %s\n\nServer: %s\n'\
% (self.topdir, self.server)
# Log time.
ms = time.time()
ms = int(1000*(ms - int(ms)))
mssg += '\nTime: %s:%03d\n' % \
(datetime.today().strftime('%a %b %d, %Y; %X'), ms)
if len(error_log) > 0:
mssg += 'Command: %s\n\nSummary:\n' % (' '.join(sys.argv))
lines = error_log.split('\n')
for line in lines:
if line.startswith('Description:'):
mssg += line[12:]
mssg += '\n\nDetails:' + error_log
else:
mssg += '\nNo problems detected (this does NOT imply that everything was computed.).\n\n'
return mssg | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def composeSummaryEmail(self):\r\n message = \"\"\"From: Douglas Gregor <dgregor@osl.iu.edu>\r\nTo: boost@lists.boost.org\r\nReply-To: boost@lists.boost.org\r\nSubject: [Report] \"\"\"\r\n message += str(self.numFailures()) + \" failures on \" + branch\r\n if branch != 'trunk':\r\n ... | [
"0.63322425",
"0.61821836",
"0.61753136",
"0.6175085",
"0.61733943",
"0.6131707",
"0.61256206",
"0.60573125",
"0.6047461",
"0.6034194",
"0.60252714",
"0.6003194",
"0.5889488",
"0.5864201",
"0.584809",
"0.58364767",
"0.58144146",
"0.5763619",
"0.57625306",
"0.5760647",
"0.5760... | 0.5509327 | 39 |
Bear in mind some simulations cannot contain neither peds nor objs | def __init__(self, no_peds=0, peds_topics=[], num_s_samples=1, objs=None):
self.global_ts = -1
self.PEDESTRIAN_TOPICS = peds_topics
self.drone = DroneModel(num_s_samples)
self.subject = SubjectModel(num_s_samples)
if no_peds > 0:
self.peds = {
str(i): PedestrianModel(num_s_samples) for i in range(no_peds)
}
else:
self.peds = None
self.objs = objs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_simulations(self):\n pass",
"def simulation():\n\n return {\n \"type\": \"class\",\n \"base\": \"iso.process_step\",\n \"is_abstract\": False,\n \"is_document\": True,\n \"pstr\": (\"({}/{}/{})\", (\"used\", \"ran_for_experiments\", \"ensemble_id\")),\n ... | [
"0.62294304",
"0.5881555",
"0.5799523",
"0.5603874",
"0.5549049",
"0.55471927",
"0.5523414",
"0.55085677",
"0.5462701",
"0.54552025",
"0.54473364",
"0.5418987",
"0.5409605",
"0.54048645",
"0.5397913",
"0.5395056",
"0.53824604",
"0.53729665",
"0.53604305",
"0.535905",
"0.53540... | 0.53972495 | 15 |
Check if the given data is not filled already in the bag | def is_coord_empty(self, data):
check = False
if data["topic"] in DRONE_POS_TOPICS:
check = self.drone.check_if_pos(data["coord"])
elif data["topic"] in DRONE_VEL_TOPICS:
check = self.drone.check_if_vel(data["coord"])
elif data["topic"] in DRONE_ACC_TOPICS:
check = self.drone.check_if_acc(data["coord"])
elif data["topic"] in SUBJECT_TOPICS:
check = self.subject.check_if_pos(data["coord"])
elif data["topic"] in self.PEDESTRIAN_TOPICS:
check = self.peds[data["pid"]].check_if_pos(data["coord"])
return check | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def has_data(self):\n return ([0] != self.__contexts) and ([0] != self.__weights)",
"def empty(self):\n return 0 >= len(self.__data)",
"def verify_if_basket_is_empty(self):\n self._basket.verify_if_basket_is_empty()",
"def not_empty(entry):\n gt_boxes = entry['boxes']\n ret... | [
"0.65717185",
"0.63385725",
"0.6331197",
"0.62527806",
"0.61652166",
"0.61505044",
"0.608145",
"0.608145",
"0.60609126",
"0.60432065",
"0.60016364",
"0.5989013",
"0.59870905",
"0.5972411",
"0.59252816",
"0.59170127",
"0.59170127",
"0.59170127",
"0.59170127",
"0.59170127",
"0.... | 0.0 | -1 |
Stores the given data | def add(self, data):
if data["topic"] in DRONE_POS_TOPICS:
self.drone.set_pos_val(data["ts"], data["coord"], data["value"])
elif data["topic"] in DRONE_VEL_TOPICS:
self.drone.set_vel_val(data["ts"], data["coord"], data["value"])
elif data["topic"] in DRONE_ACC_TOPICS:
self.drone.set_acc_val(data["ts"], data["coord"], data["value"])
elif data["topic"] in SUBJECT_TOPICS:
self.subject.set_val(data["ts"], data["coord"], data["value"])
elif data["topic"] in self.PEDESTRIAN_TOPICS:
self.peds[data["pid"]].set_val(data["ts"], data["coord"], data["value"]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def store_data(self, data):\n self.data.append(data)",
"def store_data(self, store_data):\n self._store_data = store_data",
"def saveData(self):\n pass",
"def save_data(self):\n pass",
"def save(self, data):\n\t\tif self.value:\n\t\t\tdata['value'] = self.value",
"def store_da... | [
"0.82004386",
"0.788964",
"0.7607003",
"0.75672203",
"0.753746",
"0.7510255",
"0.74823844",
"0.74805313",
"0.7291415",
"0.7250676",
"0.71713775",
"0.7075155",
"0.7072606",
"0.7048692",
"0.69521874",
"0.69172645",
"0.6839893",
"0.68263394",
"0.68209374",
"0.6810317",
"0.678245... | 0.0 | -1 |
Check if all models stored are complete for the given timestamp. | def is_full(self):
core_full = self.drone.complete() and self.subject.complete()
if self.peds is None:
return core_full
else:
return core_full and all([p.complete() for p in self.peds.values()]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complete(self, verbose=False):\r\n if (self.__num_tasks == 0 or\r\n self.__arrival_time == 0 or\r\n self.__num_tasks != len(self.__tasks)):\r\n #\r\n if verbose:\r\n print (\"Request %s incomplete. %d expected tasks, %d recorded tasks, \"\r\n ... | [
"0.617297",
"0.6029285",
"0.59201694",
"0.5905041",
"0.5833025",
"0.5806174",
"0.58009607",
"0.5789535",
"0.5736804",
"0.56675947",
"0.566142",
"0.56097996",
"0.55602163",
"0.55594164",
"0.555323",
"0.55530936",
"0.55497646",
"0.55493504",
"0.5537272",
"0.5535819",
"0.5528456... | 0.57210994 | 9 |
Empties the models within the bag | def empty_bag(self):
if self.peds is not None:
for _, model in self.peds.items():
model.reset()
self.drone.reset()
self.subject.reset() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear():\n\t\tModel.counter = 0",
"def ClearModels(self):\n self._modelFileNames = []\n self._models = []\n self.Modified(readAgain=True)",
"def clearmodels(self):\n \n dbpath, config = self._start() \n ModelDescriptionTable(dbpath).empty()\n... | [
"0.77507436",
"0.76478666",
"0.7637113",
"0.74050826",
"0.7388874",
"0.73118776",
"0.7252325",
"0.709604",
"0.7079502",
"0.6967123",
"0.69053054",
"0.6871042",
"0.68186563",
"0.6815605",
"0.68100023",
"0.68016225",
"0.6794553",
"0.6774137",
"0.67709094",
"0.67597353",
"0.6744... | 0.8674077 | 0 |
Flushes the stored data and empties the bag. Use the drone ts | def get_data(self):
data = {
"ts": self.drone.pos[0][0],
"drone": self.drone,
"subject": self.subject,
"peds": self.peds, # can be None
"objs": self.objs # can be None
}
self.empty_bag()
return data | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_data():\n redis_db.flushdb()",
"def _flush(self):\n self._d = {}",
"def flush(self):\n self.cur_stocks = self.cur_stocks.drop(self.cur_stocks.index)\n # add history that flushed whole stocks",
"def reset(self):\n self.temp_data.clear()",
"def empty_bag(self):\n ... | [
"0.7044794",
"0.6967814",
"0.68903756",
"0.6873136",
"0.6852722",
"0.68504673",
"0.68156374",
"0.67802024",
"0.6756149",
"0.6756149",
"0.67459303",
"0.67278725",
"0.6683704",
"0.6683704",
"0.6683704",
"0.66800135",
"0.6664512",
"0.66624105",
"0.66586465",
"0.66405725",
"0.659... | 0.0 | -1 |
Store data samples sent in multiple batches | def store_in_bag(self, data):
# timestamp is (s, nanos): data["ts"], data["tnanos"]
self.bag.add(data)
# Ensure that all data have the same timestamp and are not None
# Also there can't be more than a sample per second.
if self.bag.is_full():
if random() > 0.99999:
print("Telemetry data: ", data["topic"])
print("Bag data: ", self.bag.print_data())
# Then flush the data to process it and empty the bag
data = self.bag.get_data()
self.on_full(data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self, num_batches: int):",
"def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:",
"def set_batch_data():\r\n if not os.path.exists(filepath):\r\n download_data()\r\n for n in range(0,6):\r\n d = read(filepath + flist[n])\r\n metadata = read(filepath + ... | [
"0.67769766",
"0.6642692",
"0.66230625",
"0.6600419",
"0.6592506",
"0.64439774",
"0.64208335",
"0.63826704",
"0.62283033",
"0.619053",
"0.61626637",
"0.6160936",
"0.61458206",
"0.6139684",
"0.61335284",
"0.61245483",
"0.61134374",
"0.6109564",
"0.60928875",
"0.60751015",
"0.6... | 0.58928144 | 53 |
Do computations when multiple samples with equal timestamp are received | def on_full(self, bag_data):
# bag_data is a dict {ts, drone, subject, peds, objs}
# Bear in mind some simulations cannot contain neither peds nor objs
dr_pos, dr_vel, dr_acc = bag_data["drone"].get_data()
subj_pos = bag_data["subject"].get_pos()
peds_poses = []
if bag_data["peds"] is not None:
peds_poses = [p.get_pos() for p in bag_data["peds"].values()]
objs_poses = []
if bag_data["objs"] is not None:
objs_poses = [m.pose for m in bag_data["objs"]]
# Bear in mind some simulations cannot contain neither peds nor objs
# (ped and obj force would be 0.0)
forces = [0,0,0]
if not self.file.closed:
rowdata = [
bag_data["ts"], dr_pos, dr_vel, dr_acc, subj_pos, forces[0], forces[1], forces[2]
] + peds_poses + objs_poses
# print("\n\nROWDATA: ", rowdata, "\n\n")
self.csv_writer.writerow(rowdata) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def audioEpochFeats(cur,uid,timestamp):\n\tuidA = uid +'audio'\n\n\tvar_stats = []\n\tstd_stats = []\n\tnoise = []\n\tvoiceToSilenceRatio = []\n\n\tfor i in range(1,24):\n\t\ths_timestamp = timestamp-86400+(i-1)*hour\n\t\the_timestamp = timestamp-86400+i*hour\n\t\t# Determining if start/end time of given hour is i... | [
"0.60448503",
"0.5995645",
"0.58302253",
"0.57981944",
"0.5796712",
"0.57483286",
"0.57325566",
"0.57073534",
"0.56920207",
"0.5675369",
"0.56446433",
"0.5632045",
"0.5621465",
"0.5616494",
"0.559965",
"0.5543054",
"0.55349135",
"0.55306476",
"0.5527352",
"0.55071265",
"0.547... | 0.0 | -1 |
Get the list of pedestrian topics | def get_ped_topics(n_peds):
ls = []
for n in range(n_peds):
ls += [coord.format(n) for coord in PED_TEMPL_TOPICS]
return ls | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_topics(self):\r\n return [x[0] for x in get_published_topics()]",
"def topics(ctx):\n pass",
"def topics(self):\r\n return topics.Topics(self)",
"def topics(self, project: str) -> list:\n assert self.exists(project), f'Project {project} inesistente'\n\n cursor = self.pr... | [
"0.7867339",
"0.73566186",
"0.733255",
"0.73222786",
"0.72298735",
"0.71772873",
"0.7127868",
"0.7058027",
"0.7025698",
"0.6943055",
"0.68688285",
"0.6814466",
"0.67832524",
"0.6769526",
"0.6753593",
"0.6670809",
"0.660663",
"0.6604779",
"0.6540701",
"0.65293837",
"0.650491",... | 0.68354625 | 11 |
Stop timer when user enter a nonescape command. | def stop(self):
command = input("Enter anything to finish (or 'exit' to cancel)>>>")
return command != 'exit' | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handleKeyboardInterupt():\n System.stopExecution(TERMINATED_BY_USER)",
"def on_KeyboardInterrupt(player):\n print(\"paused by KeyboardInterrupt\")\n player.edit()",
"def term():\n curses.endwin()\n unicornhathd.off()",
"def stop_timer(self):\r\n self.countdownTimer.stop()",
"def cb_... | [
"0.61809236",
"0.59877855",
"0.590018",
"0.5879762",
"0.58237314",
"0.5802075",
"0.57889146",
"0.57686067",
"0.5766843",
"0.57374257",
"0.5713238",
"0.5673947",
"0.5642621",
"0.564084",
"0.5605514",
"0.5603989",
"0.55528235",
"0.55528235",
"0.55528235",
"0.55528235",
"0.55464... | 0.5726188 | 10 |
Logs an entry for the homework log using a timer | def do_start(self, input):
course_name = course.course_name(input)
if course_name in config.current_courses:
timer = Timer(course_name)
timer.start()
if self.stop():
timer.stop() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def timer(work_log):\n start = time.time()\n print '\\nyou started working at %s\\n' % time.ctime(int(start))\n\n input = raw_input(\"\\ntype 'stop' to stop timer...\\n\")\n while (input != 'stop'):\n input = raw_input(\"\\ntype 'stop' to stop timer...\\n\")\n work = raw_input(\"\\nwhat'd you... | [
"0.70600265",
"0.64849997",
"0.64838594",
"0.6340308",
"0.626186",
"0.62565213",
"0.6244451",
"0.61845344",
"0.6172261",
"0.61576736",
"0.61439645",
"0.6137266",
"0.61355144",
"0.61291295",
"0.60836816",
"0.60411006",
"0.60167503",
"0.6007121",
"0.59954494",
"0.5931569",
"0.5... | 0.0 | -1 |
Determines if an input is a float. | def is_float(self, input):
try:
float(input)
return True
except ValueError:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_float(x):\r\n try:\r\n float(x)\r\n except ValueError:\r\n return False\r\n return True",
"def isfloat(value):\r\n try:\r\n float(value)\r\n return True\r\n except ValueError:\r\n return False",
"def isFloat(value): \n try:\n float(value... | [
"0.82458997",
"0.8190943",
"0.81595534",
"0.81558645",
"0.81085587",
"0.8093256",
"0.807079",
"0.807079",
"0.80703425",
"0.8061959",
"0.8060793",
"0.8011374",
"0.7838578",
"0.7823464",
"0.77867234",
"0.77559304",
"0.7752695",
"0.7747797",
"0.7746936",
"0.77243704",
"0.7696662... | 0.884747 | 0 |
Takes a valid float from the user or the escape command | def hours_studied(self):
value = input("Enter value (or 'exit')>>>")
while not self.is_float(value):
value = input("Enter value (or 'exit')>>>")
# Escape command
if value == 'exit':
return value
return float(value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prompt_float_input(prompt_name: str, get_user_input: GetInputFunc) -> float:\n try:\n return float(get_user_input(f\"{prompt_name}:\"))\n except (ValueError, IndexError) as e:\n raise InvalidInput(str(e))",
"def get_float(self, prompt=\"> \"):\n\t\twhile True:\n\t\t\tans = raw_input(promp... | [
"0.71381927",
"0.7047657",
"0.7025061",
"0.69696033",
"0.67903113",
"0.6774239",
"0.67697823",
"0.66692364",
"0.6616977",
"0.6591584",
"0.656603",
"0.65500516",
"0.6470003",
"0.64432317",
"0.64170504",
"0.6386314",
"0.6362336",
"0.6322216",
"0.6319651",
"0.63095284",
"0.62830... | 0.6430425 | 14 |
Logs an entry for the homework log using a userentered value | def do_record(self, input):
course_name = course.course_name(input)
if course_name in config.current_courses:
hours_studied = self.hours_studied()
# Checks for escape command
if hours_studied != 'exit':
write.to_csv(course_name, hours_studied) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log(exercise):\n global logfile\n msg = raw_input(\"Enter your message. \")\n logfile.write(exercise + \" >>> \" + msg + \"\\n\")",
"def logentry(jobid, label, typ, content=None, path=None):\n ud = str(uuid.uuid4())\n db.logs.save({\"uuid\":ud, \"jobid\":jobid, \"label\":label, \"type\":typ, \... | [
"0.68945855",
"0.6559437",
"0.6485974",
"0.62908894",
"0.62812376",
"0.6237484",
"0.6155005",
"0.60995555",
"0.60548156",
"0.6035026",
"0.60128146",
"0.59510344",
"0.5913017",
"0.59052706",
"0.5875756",
"0.58669865",
"0.5864817",
"0.5801178",
"0.5774821",
"0.57367194",
"0.569... | 0.54031944 | 54 |
Given a latitude and longitude calculate distance to airplane including altitude, return kilometers | def distance(self, lat: float, long: float) -> float:
# Initial euclidian formula below
# diff_lat = self.lat - lat
# diff_long = self.long - long
# euclidian = math.sqrt((diff_lat ** 2 + diff_long ** 2 + self.altitude ** 2))
return self._haversine(lat, long) + self.altitude / 1000 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_distance_meters(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aLocation2.lon - aLocation1.lon\n return math.sqrt((dlat*dlat) + (dlong*dlong)) * 1.113195e5",
"def get_distance_metres(aLocation1, aLocation2):\n dlat = aLocation2.lat - aLocation1.lat\n dlong = aL... | [
"0.70389205",
"0.6926336",
"0.6884272",
"0.6884272",
"0.6884272",
"0.6884272",
"0.6884272",
"0.68767065",
"0.68557805",
"0.67505145",
"0.67494184",
"0.67259705",
"0.6697738",
"0.6696931",
"0.66856134",
"0.66700953",
"0.65975",
"0.6567814",
"0.6560275",
"0.6525618",
"0.6513142... | 0.6852247 | 9 |
Both paths should be full. | def _put(self, src_fname, dst_fname):
logging.info('Transferring file %s to %s', src_fname, self._ip_addr)
sftp_cli = self._get_sftp_client()
if sftp_cli is None:
raise Exception('Not supported without ssh.')
return sftp_cli.put(src_fname, dst_fname) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __combine_path(self, other):\n self.path = other.path + self.path",
"def join(self, path, *paths):",
"def aix_path_join(path_one, path_two):\n if path_one.endswith('/'):\n path_one = path_one.rstrip('/')\n\n if path_two.startswith('/'):\n path_two = path_two.lstrip('/')\n\n fi... | [
"0.645393",
"0.6182501",
"0.61075705",
"0.60068315",
"0.60003424",
"0.5978063",
"0.5929083",
"0.592156",
"0.59077317",
"0.59016716",
"0.58891624",
"0.58888334",
"0.584172",
"0.5833566",
"0.5822319",
"0.579614",
"0.57439196",
"0.5732762",
"0.57041806",
"0.5696847",
"0.5693155"... | 0.0 | -1 |
dst_fname should be full path. Creates directories if required. | def put_file(self, src_fname, dst_fname):
dst_fname = os.path.normpath(dst_fname)
self.mkdirs(os.path.dirname(dst_fname))
self._put(src_fname, dst_fname) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _prepare_dst_dir(self, dst, src=None, perm=None, **kwargs):\n if self.isdir(dst):\n full_dst = os.path.join(dst, os.path.basename(src)) if src else dst\n\n elif self.isfile(dst):\n full_dst = dst\n\n else:\n # interpret dst as a file name, create missing di... | [
"0.7712215",
"0.7621111",
"0.6710671",
"0.6514752",
"0.63302606",
"0.62029976",
"0.6054697",
"0.6051175",
"0.6038802",
"0.5964814",
"0.5951201",
"0.59485644",
"0.5948254",
"0.5946226",
"0.59095293",
"0.58884376",
"0.58736056",
"0.58708906",
"0.5866591",
"0.5849726",
"0.584694... | 0.71607935 | 2 |
gpu_model_to_scale is a dict from model string to scale. | def avail_gpu_compute(self, gpu_model_to_scale):
self._check_spy_stats_available()
l = []
for u, model in zip(self._util.gpu_compute, self._capacity.gpu_model):
found = False
for k, scale in gpu_model_to_scale.items():
if k in model:
found = True
break
if found:
l.append(scale * (1 - u))
else:
raise Exception('Unknown GPU model %s found on host %s' %
(model, self.name))
return l | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scale_model(model, scale):\n params = model.named_parameters()\n dict_params = dict(params)\n with torch.no_grad():\n for name, param in dict_params.items():\n dict_params[name].set_(dict_params[name].data * scale)",
"def scale_model(model,scaleparname='A',scaleval=1):\n model =... | [
"0.67240673",
"0.6272403",
"0.5994706",
"0.5941544",
"0.5833461",
"0.5681746",
"0.5676147",
"0.5382901",
"0.5348",
"0.53371793",
"0.5301205",
"0.52537143",
"0.52262044",
"0.51800525",
"0.51736313",
"0.5139863",
"0.512118",
"0.5083435",
"0.50683033",
"0.50677323",
"0.50584626"... | 0.6472911 | 1 |
From all the data, it takes the columns TopicID and Question and for each topic, count the number of+ different SubTopic/Question | def get_data_frame_count_type_of_topic(data_frame: DataFrame) -> pb.DataFrame:
try:
data_frame = data_frame \
.select("TopicID", "Question") \
.distinct() \
.groupBy("TopicID") \
.count() \
.sort("TopicID")
except Py4JError:
raise AnalysisException('One columns is incorrect')
print("The following table represent the number of the type of each topic")
data_frame.show()
data_frame_pandas = data_frame.toPandas()
return data_frame_pandas | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def topic_count():\n # get the number topics and their counts as tuples: ('Topic', 123)\n query = peewee.RawQuery(Post, \"select topic, count(topic) from post group by topic\").tuples()\n\n # turn the result of the query object into a list of tuples\n tuple_result = []\n for each_tuple in query:\n ... | [
"0.6717123",
"0.63702446",
"0.61713773",
"0.5966875",
"0.5961095",
"0.5927321",
"0.5897049",
"0.579996",
"0.5752333",
"0.5736918",
"0.5653994",
"0.5621641",
"0.55750763",
"0.54705304",
"0.54477894",
"0.54441655",
"0.5436638",
"0.5420717",
"0.5412772",
"0.53985864",
"0.5367074... | 0.62771106 | 2 |
Take an specific list from rdd spark, which is formed as list of tuples (Topic, Question) | def get_rdd_count_type_of_topy(rdd: list) -> pb.DataFrame:
data_frame_pandas = pb.DataFrame(rdd, columns=['Topic', 'Question'])
print(data_frame_pandas)
return data_frame_pandas | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_questions_of_topic(topic):\n\n dynamodb = boto3.resource(\"dynamodb\", region_name=\"eu-central-1\")\n question_table = dynamodb.Table(\"Questions\")\n\n fe = Attr(\"TopicId\").eq(topic.get(\"TopicId\"))\n response = question_table.scan(FilterExpression=fe)\n questions = ... | [
"0.57467985",
"0.5711735",
"0.55085766",
"0.5506645",
"0.53411525",
"0.5329091",
"0.53050333",
"0.52999526",
"0.524876",
"0.5236957",
"0.5155814",
"0.51508963",
"0.514394",
"0.51368946",
"0.5046468",
"0.50216925",
"0.5004249",
"0.4996361",
"0.49733895",
"0.49623284",
"0.49622... | 0.5424828 | 4 |
From all the data, it takes the columns TopicID, and count the topic based on the gender | def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:
data_frame_topic = data_frame \
.filter(data_frame["Stratification1"].contains("Male")) \
.distinct() \
.groupBy("TopicID") \
.count() \
.sort("TopicID")
print("The following table represent the number of men group by the topic: ")
data_frame_topic.show()
data_frame_pandas = data_frame.toPandas()
return data_frame_pandas | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_male_female_topicsDF(data_dict, gender):\n dataDF = pd.DataFrame.from_dict(data_dict[gender], orient='index')\n outlet_gender_topicsDF = pd.json_normalize(dataDF['topic_mean'])\n outlet_gender_topicsDF.index = dataDF.index\n outlet_gender_topicsDF = outlet_gender_topicsDF.sort_index()\n outl... | [
"0.6474987",
"0.63504976",
"0.6320572",
"0.62746453",
"0.627228",
"0.6258495",
"0.61637425",
"0.6114557",
"0.5977991",
"0.58644605",
"0.5836324",
"0.5831244",
"0.5673213",
"0.5647648",
"0.5610889",
"0.5574155",
"0.5492381",
"0.54264843",
"0.53719985",
"0.53717935",
"0.5295619... | 0.80123305 | 0 |
From all the data, it takes the columns TopicID, and count the topic based on the ethnicity | def get_data_frame_count_black_ethnicity_by_topic(data_frame: DataFrame) -> pb.DataFrame:
data_frame_topic = data_frame \
.filter(data_frame["Stratification1"].contains("Black, non-Hispanic")) \
.distinct() \
.groupBy("TopicID") \
.count() \
.sort("TopicID")
print("The following table represent the number of black ethnicity people group by the topic: ")
data_frame_topic.show()
data_frame_pandas = data_frame.toPandas()
return data_frame_pandas | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_data_frame_count_male_gender_by_topic(data_frame: DataFrame) -> pb.DataFrame:\n data_frame_topic = data_frame \\\n .filter(data_frame[\"Stratification1\"].contains(\"Male\")) \\\n .distinct() \\\n .groupBy(\"TopicID\") \\\n .count() \\\n .sort(\"TopicID\")\n\n print... | [
"0.670414",
"0.6682198",
"0.6473323",
"0.63052845",
"0.6284073",
"0.5883223",
"0.58540165",
"0.5768975",
"0.56314296",
"0.5595589",
"0.55580306",
"0.5541051",
"0.5484164",
"0.54825205",
"0.5471465",
"0.5443077",
"0.53968257",
"0.5374345",
"0.5362987",
"0.5333066",
"0.5320533"... | 0.7382751 | 0 |
Plot a data frame with bar type | def plot_type_of_topic(data_frame: pb.DataFrame) -> None:
plt.interactive(False)
plt.figure()
data_frame.plot(kind='bar', x= data_frame['TopicID'])
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compte(df):\n\n df.value_counts()[:100].plot(kind='bar')\n plt.show()",
"def bar_plot(df, data_pt):\n \n x=df.loc[data_pt]\n y= df.columns.tolist()\n sorte=x.tolist()\n a=sorted(zip(sorte, y))[-10:]\n y=[y for _, y in a]\n ## soru burda yapıp altı ona göre duzeliyecegim birde\n ... | [
"0.72341543",
"0.7051117",
"0.7032588",
"0.702026",
"0.6910697",
"0.6906154",
"0.68671536",
"0.67882943",
"0.6782081",
"0.6735545",
"0.6699668",
"0.6685477",
"0.66498506",
"0.663799",
"0.6616477",
"0.6604195",
"0.6479067",
"0.6469803",
"0.64694345",
"0.64263964",
"0.6424513",... | 0.7253609 | 0 |
Plot a data frame with bar type | def plot_type_of_two_topic(data_frame1: pb.DataFrame, data_frame2: pb.DataFrame) -> None:
plt.interactive(False)
plt.figure()
data_frame1.plot(kind='bar', x= data_frame['TopicID'])
data_frame2.plot(kind='bar', x= data_frame['TopicID'])
plt.show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_type_of_topic(data_frame: pb.DataFrame) -> None:\n plt.interactive(False)\n plt.figure()\n data_frame.plot(kind='bar', x= data_frame['TopicID'])\n plt.show()",
"def compte(df):\n\n df.value_counts()[:100].plot(kind='bar')\n plt.show()",
"def bar_plot(df, data_pt):\n \n x=df.loc... | [
"0.7253609",
"0.72341543",
"0.7051117",
"0.7032588",
"0.702026",
"0.6910697",
"0.6906154",
"0.68671536",
"0.67882943",
"0.6782081",
"0.6735545",
"0.6699668",
"0.6685477",
"0.66498506",
"0.663799",
"0.6616477",
"0.6604195",
"0.6479067",
"0.6469803",
"0.64694345",
"0.64263964",... | 0.60265684 | 56 |
Return the project name when printed. | def __str__(self):
return self.project_name | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project_name(self):\n pass",
"def getProjectName():",
"def full_name(self):\n if not self.project_id:\n raise ValueError('Missing project ID.')\n return 'projects/%s' % (self.project_id)",
"def get_project_name(self):\n return self.line_edit.text()",
"def project(... | [
"0.8392448",
"0.82995224",
"0.7878728",
"0.78781843",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.7737",
"0.76813203",
"0.76813203",
"0.76813203",
"0.76813203",
"0.76813203",
"0.76813203",
"0... | 0.77389073 | 5 |
Return the skill name. | def __str__(self):
return self.skill | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def skill(self):\n return self._get(\"skill\")",
"def test_get_skill_name(self):\n result = self.runner.invoke(\n cli,\n [*CLI_LOG_OPTION, \"config\", \"get\", \"skills.dummy.name\"],\n standalone_mode=False,\n )\n assert result.exit_code == 0\n ... | [
"0.77862394",
"0.7630785",
"0.7034226",
"0.6951422",
"0.67984796",
"0.6626635",
"0.66073817",
"0.6603298",
"0.6590914",
"0.6590914",
"0.6590914",
"0.6585181",
"0.6584061",
"0.6573996",
"0.6573996",
"0.6573996",
"0.6573996",
"0.6573996",
"0.6573996",
"0.6573996",
"0.6573996",
... | 0.702125 | 3 |
Returns the account for the given client. If it does not exist a new one is created and returned | def get_account(self, client: int):
try:
return self.accounts[client]
except KeyError:
return self._create_account(client) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_client(self, clientname):\n client = self.dbsession.query(Client).filter_by(clientname=clientname).all()\n if not client:\n return self.create_client({'clientname': clientname})\n else:\n return client[0]",
"def get_client(self, user_id: int, client_name: str) -... | [
"0.66133344",
"0.6345957",
"0.6296915",
"0.62183553",
"0.6195077",
"0.61447585",
"0.6143067",
"0.61124474",
"0.6101005",
"0.6082482",
"0.6080354",
"0.60321856",
"0.60321856",
"0.60195756",
"0.5999535",
"0.59913605",
"0.5953246",
"0.5937958",
"0.5926711",
"0.59228164",
"0.5893... | 0.9063625 | 0 |
Write a volume to a file path. | def write(img, path):
create_directories_for_file_name(path)
writer = sitk.ImageFileWriter()
writer.Execute(img, path, True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write(self, path, **kwargs):\n client = self.connect(VAULT_TOKEN)\n client.write(path, **kwargs)",
"def write_to_path(self, path):\n assert not path.exists()\n fout = path.open(\"wb\")\n fout.write(self.to_string())\n assert not fout.close()\n path.setdata()",... | [
"0.6251699",
"0.60698074",
"0.60018075",
"0.5975603",
"0.5835609",
"0.5832426",
"0.5821404",
"0.5796797",
"0.5752071",
"0.5750823",
"0.57148486",
"0.5701186",
"0.5690606",
"0.56724447",
"0.565024",
"0.56472427",
"0.5638502",
"0.56315726",
"0.56258756",
"0.5602037",
"0.5573271... | 0.5269131 | 62 |
r""" Calculates precipitable water (cm) from ambient air temperature (C) and relatively humidity (%) using an empirical model. The accuracy of this method is approximately 20% for moderate PW (13 cm) and less accurate otherwise. | def gueymard94_pw(temp_air, relative_humidity):
T = temp_air + 273.15 # Convert to Kelvin # noqa: N806
RH = relative_humidity # noqa: N806
theta = T / 273.15
# Eq. 1 from Keogh and Blakers
pw = (
0.1 *
(0.4976 + 1.5265*theta + np.exp(13.6897*theta - 14.9188*(theta)**3)) *
(216.7*RH/(100*T)*np.exp(22.330 - 49.140*(100/T) -
10.922*(100/T)**2 - 0.39015*T/100)))
pw = np.maximum(pw, 0.1)
return pw | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_compensated_temperature() -> float:\n comp_factor = 2.25\n cpu_temp = get_cpu_temperature()\n raw_temp = bme280.get_temperature()\n comp_temp = raw_temp - ((cpu_temp - raw_temp) / comp_factor)\n # print(\"\"\"\n # Compensated_Temperature: {:05.2f} *C\n # Pressure: {:05.2f} hPa\n # R... | [
"0.6271697",
"0.61329305",
"0.61147255",
"0.60481554",
"0.5957173",
"0.59200716",
"0.58812517",
"0.5873447",
"0.5873447",
"0.58698595",
"0.58185816",
"0.57858187",
"0.5785377",
"0.572641",
"0.56684095",
"0.56592894",
"0.56545794",
"0.56407183",
"0.5634677",
"0.56337875",
"0.5... | 0.542909 | 35 |
Approximate broadband aerosol optical depth. Bird and Hulstrom developed a correlation for broadband aerosol optical depth (AOD) using two wavelengths, 380 nm and 500 nm. | def bird_hulstrom80_aod_bb(aod380, aod500):
# approximate broadband AOD using (Bird-Hulstrom 1980)
return 0.27583 * aod380 + 0.35 * aod500 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_blue_haze_TOA(d,sza,L):\n rho_blue_TOA=np.pi*(d**2)*L[:,5,:]/(2.16*np.cos(sza))\n return rho_blue_TOA",
"def ead(o2, depth):\n fraction_o2 = percentage_to_fraction(o2)\n fraction_n2 = 1.0 - fraction_o2\n return math.ceil(((depth + 10.0) * (fraction_n2 / 0.79)) - 10.0)",
"def optica... | [
"0.5567054",
"0.55453414",
"0.5525464",
"0.5403252",
"0.539342",
"0.53296256",
"0.53177613",
"0.52844834",
"0.52782506",
"0.524908",
"0.52308244",
"0.52085507",
"0.5208206",
"0.5199287",
"0.5178122",
"0.51560545",
"0.51540154",
"0.51226914",
"0.51127833",
"0.5078825",
"0.5057... | 0.53029495 | 7 |
Calculate Linke turbidity using Kasten pyrheliometric formula. Note that broadband aerosol optical depth (AOD) can be approximated by AOD measured at 700 nm according to Molineaux [4] . Bird and Hulstrom offer an alternate approximation using AOD measured at 380 nm and 500 nm. Based on original implementation by Armel Oumbe. | def kasten96_lt(airmass_absolute, precipitable_water, aod_bb):
# "From numerically integrated spectral simulations done with Modtran
# (Berk, 1989), Molineaux (1998) obtained for the broadband optical depth
# of a clean and dry atmospshere (fictitious atmosphere that comprises only
# the effects of Rayleigh scattering and absorption by the atmosphere gases
# other than the water vapor) the following expression"
# - P. Ineichen (2008)
delta_cda = -0.101 + 0.235 * airmass_absolute ** (-0.16)
# "and the broadband water vapor optical depth where pwat is the integrated
# precipitable water vapor content of the atmosphere expressed in cm and am
# the optical air mass. The precision of these fits is better than 1% when
# compared with Modtran simulations in the range 1 < am < 5 and
# 0 < pwat < 5 cm at sea level" - P. Ineichen (2008)
delta_w = 0.112 * airmass_absolute ** (-0.55) * precipitable_water ** 0.34
# broadband AOD
delta_a = aod_bb
# "Then using the Kasten pyrheliometric formula (1980, 1996), the Linke
# turbidity at am = 2 can be written. The extension of the Linke turbidity
# coefficient to other values of air mass was published by Ineichen and
# Perez (2002)" - P. Ineichen (2008)
lt = -(9.4 + 0.9 * airmass_absolute) * np.log(
np.exp(-airmass_absolute * (delta_cda + delta_w + delta_a))
) / airmass_absolute
# filter out of extrapolated values
return lt | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def estimate_lwdown(tairK, rh):\n zeroC = 273.15\n\n sat_vapress = 611.2 * np.exp(17.67 * ((tairK - zeroC) / (tairK - 29.65)))\n vapress = np.maximum(5.0, rh) / 100. * sat_vapress\n lw_down = 2.648 * tairK + 0.0346 * vapress - 474.0\n\n return lw_down",
"def derive_RiekeLebofsky(wavelength):\n ... | [
"0.6074756",
"0.60489684",
"0.6021683",
"0.58620036",
"0.5819184",
"0.5817391",
"0.5701168",
"0.56542647",
"0.5622918",
"0.5620226",
"0.5618999",
"0.55917037",
"0.55595964",
"0.5550017",
"0.5537136",
"0.5530169",
"0.5526478",
"0.5487843",
"0.5477624",
"0.5460396",
"0.54503703... | 0.64853966 | 0 |
r""" Get AOD at specified wavelength using Angstrom turbidity model. | def angstrom_aod_at_lambda(aod0, lambda0, alpha=1.14, lambda1=700.0):
return aod0 * ((lambda1 / lambda0) ** (-alpha)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_experimental_spectra(mol):\n\n data = pd.read_csv(mol, sep=',')\n wavelength = data.values[:, 0]\n\n absorption = data.values[:, 1]\n\n func = interp1d(wavelength, absorption, kind='quadratic')\n wavelength_new = 1. / np.linspace(1. / wavelength.max(), 1. / wavelength.min(), 100)\n absorp... | [
"0.5712585",
"0.5581919",
"0.5486108",
"0.53831595",
"0.53533727",
"0.53258014",
"0.5305268",
"0.526969",
"0.52618587",
"0.52508855",
"0.52357584",
"0.51796144",
"0.516192",
"0.5157981",
"0.5145479",
"0.5129892",
"0.50970304",
"0.50838137",
"0.5080797",
"0.5045471",
"0.504416... | 0.0 | -1 |
r""" Calculate Angstrom alpha exponent. | def angstrom_alpha(aod1, lambda1, aod2, lambda2):
return - np.log(aod1 / aod2) / np.log(lambda1 / lambda2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_exponent():\n pass",
"def powerlaw(E,alpha,A):\n\n\treturn A*E**alpha",
"def calc_alpha(epsilon): \n return float(0.5 * np.log((1-epsilon)/epsilon))",
"def encode_exponent(e: int) -> int:\n assert 0 <= e <= MAX_EXPONENT\n return DECODING_TABLE[e]",
"def test_exp_decay(self, alp... | [
"0.71915054",
"0.6592303",
"0.64282143",
"0.6347429",
"0.63466847",
"0.63178813",
"0.63117254",
"0.6303169",
"0.6258958",
"0.62512463",
"0.6246484",
"0.62283045",
"0.62211376",
"0.62090594",
"0.61820316",
"0.6104411",
"0.61022353",
"0.608179",
"0.60706884",
"0.60041946",
"0.5... | 0.56222945 | 50 |
Given a file name for baby.html, returns a list starting with the year string followed by the namerank strings in alphabetical order. ['2006', 'Aaliyah 91', Aaron 57', 'Abagail 895', ' ...] | def extract_names(filename):
f = open(filename,'rU')
name_data = f.read()
year_data= re.search(r'Popularity\sin\s(\d\d\d\d)', name_data)
if not year_data :
print ' no year found '
sys.exit(1)
name_year=year_data.group(1)
#print 'year :'
#print name_year
tuples=re.findall(r'<td>(\d+)</td><td>(\w+)</td><td>(\w+)</td>',name_data)
#print 'tuples'
#print tuples
dict_name = {}
for a,b,c in tuples :
#print a + ' boy name: ' + b + ' , girl name : ' + c
if b not in dict_name :
dict_name[b] = a
if c not in dict_name :
dict_name[c] = a
#print dict_name
lst_names = sorted(dict_name.keys())
result_names_sorted = []
result_names_sorted.append(name_year)
for name in lst_names :
#print name + " : " + dict_name[name]
result_names_sorted.append(name + ' ' + dict_name[name])
#print result_names_sorted
return result_names_sorted | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_names(filename):\n\n # Extracting the year\n year_match = re.search(r'\\d\\d\\d\\d', filename)\n if not year_match:\n sys.stderr.write('Could not find a year!\\n')\n sys.exit()\n year = year_match.group()\n\n # Opening the file\n try:\n with open(filename) as file... | [
"0.7748594",
"0.74819154",
"0.73469853",
"0.7098531",
"0.70823675",
"0.6961224",
"0.67496955",
"0.6549014",
"0.6204802",
"0.6009291",
"0.5994891",
"0.59557843",
"0.59324557",
"0.591772",
"0.5879463",
"0.5868835",
"0.5803098",
"0.5757769",
"0.57187426",
"0.57022166",
"0.569899... | 0.7415442 | 2 |
input h (meters) and the coefficients for the linear profile for the free troposphere theta (ft_intercept (K) and slope gamma (K/m)) return the free tropospher theta at height h | def theta_ft(h,ft_intercept,gamma):
theta_top = ft_intercept + h*gamma
return theta_top | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_theta_surf_hex_h(theta_hs_in_h: float, theta_hs_out_h: float, v_hs: float) -> float:\n\n c = get_specific_heat()\n rho = get_air_density()\n\n # sensible heating capacity of heat source for heating, W\n q_hs_h = (theta_hs_out_h - theta_hs_in_h) * c * rho * v_hs / 3600\n\n # sensible heat tra... | [
"0.5862929",
"0.5850773",
"0.57733494",
"0.5686483",
"0.56282175",
"0.5592743",
"0.55332625",
"0.55067706",
"0.55043215",
"0.5491298",
"0.5484844",
"0.5470747",
"0.5449616",
"0.5431975",
"0.5429191",
"0.5417069",
"0.54054326",
"0.5379685",
"0.53791565",
"0.5375629",
"0.536189... | 0.75179046 | 0 |
the_vars[0]= thetabar the_vars[1] = h the_vars[2] = qv surface flux from drag law with subsidence and diagnosed deltheta | def dmixed_vars(the_vars,tstep,coeffs):
deltheta = theta_ft(the_vars[1],coeffs.ft_intercept,coeffs.ft_gamma) - the_vars[0]
F0 = coeffs.U*coeffs.Cd*(coeffs.sst - the_vars[0]) #surface heat flux
Fqv0 = coeffs.U*coeffs.Cd*(coeffs.qsfc - the_vars[2]) #surface vapor flux
Fint = -coeffs.k*F0 #entrainment heat flux
if coeffs.use_NT: # use NT parameterization by calculating we using function
went = calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0) # Nicholls-Turton parameterization
else: # use simple we parameterization
went = -Fint/deltheta #simple entrainment parameterization
# calculate delta_Fr
delta_Frstar = 82.0 # Wm^-2
Frlambda = 7.9 # Wm^-2, using with CTL from Gesso
delta_Fr = delta_Frstar - Frlambda*coeffs.ft_qv*1000 # convert qt_ft to g kg^-1
Fqvent = -went*( coeffs.ft_qv - the_vars[2])
wsubs = -coeffs.D*the_vars[1]
rho=1.
cp=1004.
derivs=np.empty_like(the_vars)
# higher delta_Fr from drier air at mixed-layer top...hence cloudy air results in less radiative cooling
derivs[0]=(F0 - Fint)/(the_vars[1]*rho) - delta_Fr/1004./the_vars[1]
derivs[1] = went + wsubs
derivs[2] = (Fqv0 - Fqvent)/the_vars[1]
return derivs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ha(env, cstate=0):\n T1 = 10\n T2 = 10\n thM = 20\n thm = 5\n vr = 10.5\n v1 = -1.3\n v2 = -2.7\n assert(T1 == T2)\n\n delta = None # None to cause failure\n # The continous variables used in this ha\n x = T1 # clock1 variable\n y = T2 ... | [
"0.6345493",
"0.6289429",
"0.6155934",
"0.6074104",
"0.60056967",
"0.5953673",
"0.59231526",
"0.5861008",
"0.5852607",
"0.5842901",
"0.58290935",
"0.58032346",
"0.57665646",
"0.576412",
"0.57412446",
"0.57161164",
"0.5708992",
"0.5680603",
"0.56741995",
"0.5667289",
"0.565103... | 0.7249938 | 0 |
NichollsTurton entrainment parameterization the_vars and coeffs are inputs into dmixed_vars deltheta, F0, Fqv0 are calculated in dmixed_vars | def calc_went_NT(the_vars, coeffs, deltheta, F0, Fqv0):
thetal_m = the_vars[0]
qt_m = the_vars[2]
zi = the_vars[1]
dth = deltheta
thetal_ft = thetal_m + dth
qt_ft = coeffs.ft_qv
dqt = qt_ft - qt_m
# calculate thetal at z = 3000 m (take qt(z = 3000m) = qt(z = h), so delta_qt = dqt)
gamma = 6e-3
thetal_3000 = thetal_ft + gamma*(3000-zi)
LTS = thetal_3000 - coeffs.sst # lower tropospheric stability
# calculate coefficients
press=tf.find_press(zi)
Ad,Bd,issat = tf.calc_ABcoeffs(thetal_ft,qt_ft,press)
Aw,Bw,issat = tf.calc_ABcoeffs(thetal_m,qt_m,press)
invert= tf.t_uos_thetal(thetal_m,qt_m,press)
T_0 = invert.temp
lv=tf.L_t(invert.temp)
Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)
del_thv_dry = Ad * dth + Bd * dqt
del_thv_sat = Aw * dth + Bw * dqt
# account for evaporative cooling (increases we)
ql_max = invert.ql
Cl = (Ad*lv/tc.CPD - T_0/tc.EPS)
Del_thv = del_thv_dry - Cl * ql_max
# calculate buoyancy integral terms
rho = 1.
lcl_press=tf.LCL_thetal(thetal_m,qt_m)
zb=tf.find_height(lcl_press)
T1 = zb/zi
T2 = 0.5 * zb**2 / zi**2
T3 = (zi-zb)/zi
T4 = 0.5 * (zi**2 - zb**2) / zi**2
# calculate delta_Fr
delta_Frstar = 82.0 # Wm^-2
Frlambda = 7.9 # Wm^-2, using with CTL from Gesso
delta_Fr = delta_Frstar - Frlambda*qt_ft*1000 # convert qt_ft to g kg^-1
wtl_0=F0
wqt_0=Fqv0
Del_F = delta_Fr/(tc.CPD*rho) # use sensitivity to radiation a la Gesso Fig. 3
term1 = wtl_0 * (Ad * (T1-T2) + Aw * (T3-T4))
term2 = wqt_0 * (Bd * (T1-T2) + Bw * (T3-T4))
term3 = Del_F * (Ad * T2 + Aw * T4)
Theta_NE = term1 + term2 + term3
# calculate w*
wstar=(2.5*9.8/T_0*zi*Theta_NE)**(1/3.)
# calculate chi*
chi_star = Cl * ql_max / (del_thv_dry - del_thv_sat)
# calculate del_m
Del_m = del_thv_dry + chi_star * (2. - chi_star) * (del_thv_sat - del_thv_dry)
# calculate we
a2=15.
Del_thv_NT = Del_thv / (1. + a2 * (1. - Del_m/Del_thv))
A_NT = 0.2
fac_NT = 2.5
term4 = Del_thv_NT
term5 = A_NT * fac_NT * (T2 * del_thv_dry + T4 * del_thv_sat)
denominator = term4 + term5
we = A_NT * fac_NT * Theta_NE / denominator
return we | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dmixed_vars(the_vars,tstep,coeffs):\n\n deltheta = theta_ft(the_vars[1],coeffs.ft_intercept,coeffs.ft_gamma) - the_vars[0]\n F0 = coeffs.U*coeffs.Cd*(coeffs.sst - the_vars[0]) #surface heat flux\n Fqv0 = coeffs.U*coeffs.Cd*(coeffs.qsfc - the_vars[2]) #surface vapor flux\n Fint = -coeffs.k*F0 #en... | [
"0.80656624",
"0.5865987",
"0.5693037",
"0.5615564",
"0.55575585",
"0.55294347",
"0.55230105",
"0.5521183",
"0.5503675",
"0.5450836",
"0.540424",
"0.53514105",
"0.5325137",
"0.53189623",
"0.5317447",
"0.5312849",
"0.5308044",
"0.5304588",
"0.530239",
"0.5301694",
"0.5296928",... | 0.73483014 | 1 |
find the lcl (in m) for a row in the dataframe | def calc_lcl(row,psfc):
Tdew = tf.tmr(row['qv'],psfc)
LCL = tf.LCL(Tdew,row['theta'],psfc) #kPa
#
# rough approximation: 10 kPa = 1 km
#
delp=psfc - LCL
lcl_h = delp*100.
return lcl_h | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_index_lm(l, m):\n return (l+1)**2 -1 -l + m",
"def compute_kl(self, df):\n value_counts = [df[col].value_counts() for col in self.hist_cols]\n next_hists = self.value_counts_to_hists(value_counts)\n\n if self.prev_hists is None:\n self.prev_hists = next_hists\n ... | [
"0.59105355",
"0.57944614",
"0.5768694",
"0.5714691",
"0.5706475",
"0.5483352",
"0.5476161",
"0.5426626",
"0.53980875",
"0.5363862",
"0.53620285",
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"0.52226084",
"0.52145016",
"0.5176464",
"0.51718956",
"0.51507... | 0.60421354 | 0 |
Adapted from interactive_vaporflux.ipynb sst, sea surface temperature (K) ft_qv, mixedlayer top qv (kg kg^1) use_NT, True or False outputs csv and json files with equilibrium values | def run_main(sst, ft_qv, use_NT):
dtout=10. #minutes
end_time=8*24. #hours
del_time=dtout*60. #seconds
end_time=end_time*3600. #seconds
#sst=297
D=5.e-6 #s-1
U=7 #m/s
psfc=100. #kPa
qsfc=tf.qs_tp(sst,psfc)
ft_intercept = 292 #K
ft_gamma = 6.e-3 #K/m
#ft_qv = 2.e-3
k=0.2 #entrainment efficiency
Cd = 1.e-3 #drag coefficient
tspan = np.arange(0.,end_time,del_time)
vars_init=[285.,400.,8.e-3] #theta (K), height (m) qv (kg/kg) to start
the_tup=dict(D=D,U=U,sst=sst,ft_intercept=ft_intercept,ft_gamma=ft_gamma,
qsfc=qsfc,ft_qv=ft_qv,k=k,Cd=Cd,radcool=30.,use_NT=use_NT) # include use_NT
the_tup=make_tuple(the_tup,'coeffs')
output=integrate.odeint(dmixed_vars, vars_init, tspan,(the_tup,))
result=pd.DataFrame.from_records(output,columns=['theta','h','qv'])
# save time/computation by only doing calculations for the last timestep (equilibrium)
result['time']=tspan[-1]/3600./24. #days
result['deltheta'] = theta_ft(result['h'].values[-1],ft_intercept,ft_gamma) - result['theta'].iloc[-1]
result['delqv'] = ft_qv - result['qv'].iloc[-1]
result['LCL'] = calc_lcl(result.iloc[-1], psfc)
result['q_flux_0']=calc_sfc_qvap_flux(result.iloc[-1],the_tup)
result['T_flux_0']=calc_sfc_theta_flux(result.iloc[-1],the_tup)
result['entflux_theta']=calc_entflux_theta(result.iloc[-1],the_tup)
# decide how to calculate entrainment
the_vars = [result['theta'].iloc[-1],result['h'].iloc[-1],result['qv'].iloc[-1]]
if use_NT:
result['went']=calc_went_NT(the_vars, the_tup, result['deltheta'].iloc[-1],
result['T_flux_0'].iloc[-1], result['q_flux_0'].iloc[-1])
else:
result['went']=calc_went(result.iloc[-1],the_tup)
result['entflux_qv']=calc_entflux_qv(result.iloc[-1],the_tup)
with open('dumpmodel.csv','w') as f:
result.to_csv(f,index=False)
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_equil(sst, ft_qv, use_NT=False):\n \n run_main(sst, ft_qv, use_NT)\n \n # grab csv file\n with open('dumpmodel.csv','r') as f:\n df_result=pd.read_csv(f)\n\n # last time step into named tupple\n out=df_result.iloc[-1]\n steady_state=make_tuple(out.to_dict())\n steady_stat... | [
"0.6243293",
"0.5701022",
"0.5552244",
"0.5491569",
"0.5487227",
"0.5479191",
"0.54358137",
"0.54272795",
"0.5394585",
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"0.5295476",
"0.5265205",
"0.52420974",
"0.5239961",
"0.52259064",
"0.52226466",
"0.5221569... | 0.64122236 | 0 |
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